Publications of Erwan Monier - Associate Professor at UC Davis

List of my scientific publications along with links to the articles, abstract summaries and downloadable references:

Journal Articles
46.	JD East, E Monier, RK Saari, F Garcia-Menendez Projecting Changes in the Frequency and Magnitude of Ozone Pollution Events Under Uncertain Climate Sensitivity Journal Article Earth’s Future, 12 (6), pp. e2023EF003941, 2024. Abstract \| Links \| BibTeX @article{East2024projecting, title = {Projecting Changes in the Frequency and Magnitude of Ozone Pollution Events Under Uncertain Climate Sensitivity}, author = {JD East and E Monier and RK Saari and F Garcia-Menendez}, url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EF003941}, doi = {10.1029/2023EF003941}, year = {2024}, date = {2024-06-03}, journal = {Earth’s Future}, volume = {12}, number = {6}, pages = {e2023EF003941}, abstract = {Climate change is projected to worsen ozone pollution over many populated regions, with larger impacts at higher concentrations. More intense and frequent ozone episodes risk setbacks to human health and environmental policy achievements. However, assessing these changes is complicated by uncertain climate sensitivity, closely related to climate model response, and internal variability in simulations projecting climate's influence on air quality. Here, leveraging a global modeling framework that one-way couples a human activity model, an Earth system model of intermediate complexity, and an atmospheric chemistry model, we investigate the role of climate sensitivity in climate-induced changes to high ozone pollution episodes in the United States using multiple greenhouse gas emissions scenarios, representations of climate sensitivity, and initial condition members. We bias correct and evaluate historical model simulations, identifying modeled and observed O3 episodes using extreme value theory, and extend the approach to projections of mid- and end-century climate impacts. Results show that the influence of climate sensitivity can be as significant as that of greenhouse gas emissions scenario absent precursor emissions changes. Climate change is projected to increase the magnitude of the highest annually occurring O3 concentrations by over 2.3 ppb on average across the U.S. at mid-century under a high climate sensitivity and moderate emissions scenario, but the increase is limited to less than 0.3 ppb under lower climate sensitivity. Further, we show that areas in the U.S. currently meeting air quality standards risk being pushed into non-compliance due to a climate-induced increase in frequency of high ozone days.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Climate change is projected to worsen ozone pollution over many populated regions, with larger impacts at higher concentrations. More intense and frequent ozone episodes risk setbacks to human health and environmental policy achievements. However, assessing these changes is complicated by uncertain climate sensitivity, closely related to climate model response, and internal variability in simulations projecting climate's influence on air quality. Here, leveraging a global modeling framework that one-way couples a human activity model, an Earth system model of intermediate complexity, and an atmospheric chemistry model, we investigate the role of climate sensitivity in climate-induced changes to high ozone pollution episodes in the United States using multiple greenhouse gas emissions scenarios, representations of climate sensitivity, and initial condition members. We bias correct and evaluate historical model simulations, identifying modeled and observed O3 episodes using extreme value theory, and extend the approach to projections of mid- and end-century climate impacts. Results show that the influence of climate sensitivity can be as significant as that of greenhouse gas emissions scenario absent precursor emissions changes. Climate change is projected to increase the magnitude of the highest annually occurring O3 concentrations by over 2.3 ppb on average across the U.S. at mid-century under a high climate sensitivity and moderate emissions scenario, but the increase is limited to less than 0.3 ppb under lower climate sensitivity. Further, we show that areas in the U.S. currently meeting air quality standards risk being pushed into non-compliance due to a climate-induced increase in frequency of high ozone days. Close https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EF003941 doi:10.1029/2023EF003941 Close
45.	MS Sparks, I Farahbakhsh, M Anand, CT Bauch, KC Conlon, JD East, T Li, M Lickley, F Garcia-Menendez, E Monier, RK Saari Health and equity implications of individual adaptation to air pollution in a changing climate Journal Article Proceedings of the National Academy of Sciences, 21 (5), pp. e2215685121, 2024. Abstract \| Links \| BibTeX @article{Sparks2024health, title = {Health and equity implications of individual adaptation to air pollution in a changing climate}, author = {MS Sparks and I Farahbakhsh and M Anand and CT Bauch and KC Conlon and JD East and T Li and M Lickley and F Garcia-Menendez and E Monier and RK Saari}, url = {https://www.pnas.org/doi/abs/10.1073/pnas.2215685121}, doi = {10.1073/pnas.2215685121}, year = {2024}, date = {2024-01-16}, journal = {Proceedings of the National Academy of Sciences}, volume = {21}, number = {5}, pages = {e2215685121}, abstract = {Future climate change can cause more days with poor air quality. This could trigger more alerts telling people to stay inside to protect themselves, with potential consequences for health and health equity. Here, we study the change in US air quality alerts over this century due to fine particulate matter (PM2.5), who they may affect, and how they may respond. We find air quality alerts increase by over 1 mo per year in the eastern United States by 2100 and quadruple on average. They predominantly affect areas with high Black populations and leakier homes, exacerbating existing inequalities and impacting those less able to adapt. Reducing emissions can offer significant annual health benefits ($5,400 per person) by mitigating the effect of climate change on air pollution and its associated risks of early death. Relying on people to adapt, instead, would require them to stay inside, with doors and windows closed, for an extra 142 d per year, at an average cost of $11,000 per person. It appears likelier, however, that people will achieve minimal protection without policy to increase adaptation rates. Boosting adaptation can offer net benefits, even alongside deep emission cuts. New adaptation policies could, for example: reduce adaptation costs; reduce infiltration and improve indoor air quality; increase awareness of alerts and adaptation; and provide measures for those working or living outdoors. Reducing emissions, conversely, lowers everyone’s need to adapt, and protects those who cannot adapt. Equitably protecting human health from air pollution under climate change requires both mitigation and adaptation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Future climate change can cause more days with poor air quality. This could trigger more alerts telling people to stay inside to protect themselves, with potential consequences for health and health equity. Here, we study the change in US air quality alerts over this century due to fine particulate matter (PM2.5), who they may affect, and how they may respond. We find air quality alerts increase by over 1 mo per year in the eastern United States by 2100 and quadruple on average. They predominantly affect areas with high Black populations and leakier homes, exacerbating existing inequalities and impacting those less able to adapt. Reducing emissions can offer significant annual health benefits ($5,400 per person) by mitigating the effect of climate change on air pollution and its associated risks of early death. Relying on people to adapt, instead, would require them to stay inside, with doors and windows closed, for an extra 142 d per year, at an average cost of $11,000 per person. It appears likelier, however, that people will achieve minimal protection without policy to increase adaptation rates. Boosting adaptation can offer net benefits, even alongside deep emission cuts. New adaptation policies could, for example: reduce adaptation costs; reduce infiltration and improve indoor air quality; increase awareness of alerts and adaptation; and provide measures for those working or living outdoors. Reducing emissions, conversely, lowers everyone’s need to adapt, and protects those who cannot adapt. Equitably protecting human health from air pollution under climate change requires both mitigation and adaptation. Close https://www.pnas.org/doi/abs/10.1073/pnas.2215685121 doi:10.1073/pnas.2215685121 Close
44.	D Rastogi, J Trok, N Depsky, E Monier, AD Jones Historical evaluation and future projections of compound heatwave and drought extremes over the conterminous United States in CMIP6 Journal Article Environmental Research Letters, 19 , pp. 014039, 2024. Abstract \| Links \| BibTeX @article{Rastogi2024historical, title = {Historical evaluation and future projections of compound heatwave and drought extremes over the conterminous United States in CMIP6}, author = {D Rastogi and J Trok and N Depsky and E Monier and AD Jones}, doi = {10.1088/1748-9326/ad0efe}, year = {2024}, date = {2024-01-01}, journal = {Environmental Research Letters}, volume = {19}, pages = {014039}, abstract = {Independently, both droughts and heatwaves can induce severe impacts on human and natural systems. However, when these two climate extremes occur concurrently in a given region, their compound impacts are often more pronounced. With the improvement in both the spatiotemporal resolution and representation of complex climate processes in the global climate models (GCMs), they are increasingly used to study future changes in these extremes and associated regional impacts. However, GCM selection for such impact assessments is generally based on historical performance and/or future mean changes, without considering individual or compound extremes. In contrast, this study evaluates historical performance and projected changes in heatwaves, droughts, and compound heatwave-droughts using an ensemble of GCMs from the latest Phase 6 of Coupled Models Intercomparison Project at a regional scale across the conterminous United States. Additionally, we explore the inter-model differences in the projected changes that are associated with various characteristics of extremes and the choice of drought indices. Our analysis reveals considerable variation among the GCMs, as well as substantial differences in the projected changes based on the choice of drought indices and region of interest. For example, the projected increases in both the frequency and intensity of drought and associated compound extreme days, based on the standardized precipitation evapotranspiration index far exceed those derived from the standard precipitation index. Further, the largest changes in the frequency of compound extremes are projected over the Southwest, South Central, and parts of the Southeast while the smallest changes are projected over the Northeast. Overall, this study provides important insights for the interpretation and selection of GCMs for future assessment studies that are crucial for the development of regional adaptation strategies in the face of climate change.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Independently, both droughts and heatwaves can induce severe impacts on human and natural systems. However, when these two climate extremes occur concurrently in a given region, their compound impacts are often more pronounced. With the improvement in both the spatiotemporal resolution and representation of complex climate processes in the global climate models (GCMs), they are increasingly used to study future changes in these extremes and associated regional impacts. However, GCM selection for such impact assessments is generally based on historical performance and/or future mean changes, without considering individual or compound extremes. In contrast, this study evaluates historical performance and projected changes in heatwaves, droughts, and compound heatwave-droughts using an ensemble of GCMs from the latest Phase 6 of Coupled Models Intercomparison Project at a regional scale across the conterminous United States. Additionally, we explore the inter-model differences in the projected changes that are associated with various characteristics of extremes and the choice of drought indices. Our analysis reveals considerable variation among the GCMs, as well as substantial differences in the projected changes based on the choice of drought indices and region of interest. For example, the projected increases in both the frequency and intensity of drought and associated compound extreme days, based on the standardized precipitation evapotranspiration index far exceed those derived from the standard precipitation index. Further, the largest changes in the frequency of compound extremes are projected over the Southwest, South Central, and parts of the Southeast while the smallest changes are projected over the Northeast. Overall, this study provides important insights for the interpretation and selection of GCMs for future assessment studies that are crucial for the development of regional adaptation strategies in the face of climate change. Close doi:10.1088/1748-9326/ad0efe Close
43.	SD Eastham, E Monier, D Rothenberg, S Paltsev, NE Selin Rapid Estimation of Climate−Air Quality Interactions in Integrated Assessment Using a Response Surface Model Journal Article ACS Environmental Au, 2023. Abstract \| Links \| BibTeX @article{Eastham2023rapid, title = {Rapid Estimation of Climate−Air Quality Interactions in Integrated Assessment Using a Response Surface Model}, author = {SD Eastham and E Monier and D Rothenberg and S Paltsev and NE Selin}, doi = {10.1021/acsenvironau.2c00054}, year = {2023}, date = {2023-02-01}, journal = {ACS Environmental Au}, abstract = {Air quality and climate change are substantial and linked sustainability challenges, and there is a need for improved tools to assess the implications of addressing these challenges together. Due to the high computational cost of accurately assessing these challenges, integrated assessment models (IAMs) used in policy development often use global- or regional-scale marginal response factors to calculate air quality impacts of climate scenarios. We bridge the gap between IAMs and high-fidelity simulation by developing a computationally efficient approach to quantify how combined climate and air quality interventions affect air quality outcomes, including capturing spatial heterogeneity and complex atmospheric chemistry. We fit individual response surfaces to high- fidelity model simulation output for 1525 locations worldwide under a variety of perturbation scenarios. Our approach captures known differences in atmospheric chemical regimes and can be straightforwardly implemented in IAMs, enabling researchers to rapidly estimate how air quality in different locations and related equity-based metrics will respond to large-scale changes in emission policy. We find that the sensitivity of air quality to climate change and air pollutant emission reductions differs in sign and magnitude by region, suggesting that calculations of “co-benefits” of climate policy that do not account for the existence of simultaneous air quality interventions can lead to inaccurate conclusions. Although reductions in global mean temperature are effective in improving air quality in many locations and sometimes yield compounding benefits, we show that the air quality impact of climate policy depends on air quality precursor emission stringency. Our approach can be extended to include results from higher-resolution modeling and also to incorporate other interventions toward sustainable development that interact with climate action and have spatially distributed equity dimensions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Air quality and climate change are substantial and linked sustainability challenges, and there is a need for improved tools to assess the implications of addressing these challenges together. Due to the high computational cost of accurately assessing these challenges, integrated assessment models (IAMs) used in policy development often use global- or regional-scale marginal response factors to calculate air quality impacts of climate scenarios. We bridge the gap between IAMs and high-fidelity simulation by developing a computationally efficient approach to quantify how combined climate and air quality interventions affect air quality outcomes, including capturing spatial heterogeneity and complex atmospheric chemistry. We fit individual response surfaces to high- fidelity model simulation output for 1525 locations worldwide under a variety of perturbation scenarios. Our approach captures known differences in atmospheric chemical regimes and can be straightforwardly implemented in IAMs, enabling researchers to rapidly estimate how air quality in different locations and related equity-based metrics will respond to large-scale changes in emission policy. We find that the sensitivity of air quality to climate change and air pollutant emission reductions differs in sign and magnitude by region, suggesting that calculations of “co-benefits” of climate policy that do not account for the existence of simultaneous air quality interventions can lead to inaccurate conclusions. Although reductions in global mean temperature are effective in improving air quality in many locations and sometimes yield compounding benefits, we show that the air quality impact of climate policy depends on air quality precursor emission stringency. Our approach can be extended to include results from higher-resolution modeling and also to incorporate other interventions toward sustainable development that interact with climate action and have spatially distributed equity dimensions. Close doi:10.1021/acsenvironau.2c00054 Close
42.	JD East, E Monier, F Garcia-Menendez Characterizing and quantifying uncertainty in projections of climate change impacts on air quality Journal Article Environmental Research Letters, 17 (9), pp. 094042, 2022. Abstract \| Links \| BibTeX @article{East2022characterizing, title = {Characterizing and quantifying uncertainty in projections of climate change impacts on air quality}, author = {JD East and E Monier and F Garcia-Menendez}, doi = {10.1088/1748-9326/ac8d17}, year = {2022}, date = {2022-09-12}, journal = {Environmental Research Letters}, volume = {17}, number = {9}, pages = {094042}, abstract = {Climate change can aggravate air pollution, with important public health and environmental consequences. While major sources of uncertainty in climate change projections—greenhouse gas (GHG) emissions scenario, model response, and internal variability—have been investigated extensively, their propagation to estimates of air quality impacts has not been systematically assessed. Here, we compare these uncertainties using a coupled modeling framework that includes a human activity model, an Earth system model of intermediate complexity, and a global atmospheric chemistry model. Uncertainties in projections of U.S. air quality under 21st century climate change are quantified based on a climate-chemistry ensemble that includes multiple initializations, representations of climate sensitivity, and climate policy scenarios, under constant air pollution emissions. We find that climate-related uncertainties are comparable at mid-century, making it difficult to distinguish the impact of variations in GHG emissions on ozone and particulate matter pollution. While GHG emissions scenario eventually becomes the dominant uncertainty based on the scenarios considered, all sources of uncertainty are significant through the end of the century. The results provide insights into intrinsically different uncertainties in projections of air pollution impacts and the potential for large ensembles to better capture them.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Climate change can aggravate air pollution, with important public health and environmental consequences. While major sources of uncertainty in climate change projections—greenhouse gas (GHG) emissions scenario, model response, and internal variability—have been investigated extensively, their propagation to estimates of air quality impacts has not been systematically assessed. Here, we compare these uncertainties using a coupled modeling framework that includes a human activity model, an Earth system model of intermediate complexity, and a global atmospheric chemistry model. Uncertainties in projections of U.S. air quality under 21st century climate change are quantified based on a climate-chemistry ensemble that includes multiple initializations, representations of climate sensitivity, and climate policy scenarios, under constant air pollution emissions. We find that climate-related uncertainties are comparable at mid-century, making it difficult to distinguish the impact of variations in GHG emissions on ozone and particulate matter pollution. While GHG emissions scenario eventually becomes the dominant uncertainty based on the scenarios considered, all sources of uncertainty are significant through the end of the century. The results provide insights into intrinsically different uncertainties in projections of air pollution impacts and the potential for large ensembles to better capture them. Close doi:10.1088/1748-9326/ac8d17 Close
41.	PM Reed, A Hadjimichael, RH Moss, C Brelsford, C Burleyson, S Cohen, A Dyreson, D Gold, R Gupta, K Keller, M Konar, J Macknick, E Monier, J Morris, V Srikrishnan, N Voisin, J Yoon MultiSector Dynamics: Advancing the Science of Complex Adaptive Human‐Earth Systems Journal Article Earth's Future, 10 (3), pp. e2021EF002621, 2022. Links \| BibTeX @article{Reed2022MSDb, title = {MultiSector Dynamics: Advancing the Science of Complex Adaptive Human‐Earth Systems}, author = {PM Reed and A Hadjimichael and RH Moss and C Brelsford and C Burleyson and S Cohen and A Dyreson and D Gold and R Gupta and K Keller and M Konar and J Macknick and E Monier and J Morris and V Srikrishnan and N Voisin and J Yoon}, doi = {10.1029/2021EF002621}, year = {2022}, date = {2022-03-01}, journal = {Earth's Future}, volume = {10}, number = {3}, pages = {e2021EF002621}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close doi:10.1029/2021EF002621 Close
40.	P Wu, S Dutkiewicz, E Monier, Y Zhang Bottom-Heavy Trophic Pyramids Impair Methylmercury Biomagnification in the Marine Plankton Ecosystems Journal Article Environmental Science & Technology, 55 (22), pp. 15476–15483, 2021. Abstract \| Links \| BibTeX @article{wu2021bottom, title = {Bottom-Heavy Trophic Pyramids Impair Methylmercury Biomagnification in the Marine Plankton Ecosystems}, author = {P Wu and S Dutkiewicz and E Monier and Y Zhang}, doi = {10.1021/acs.est.1c04083}, year = {2021}, date = {2021-11-05}, journal = {Environmental Science & Technology}, volume = {55}, number = {22}, pages = {15476–15483}, abstract = {Methylmercury (CH3Hg+, MMHg) in the phytoplankton and zooplankton, which form the bottom of marine food webs, is a good predictor of MMHg in top predators, including humans. Therefore, evaluating the potential exposure of MMHg to higher trophic levels (TLs) requires a better understanding of relationships between MMHg biomagnification and plankton dynamics. In this study, a coupled ecological/physical model with 366 plankton types of different sizes, biogeochemical functions, and temperature tolerance is used to simulate the relationships between MMHg biomagnification and the ecosystem structure. The study shows that the MMHg biomagnification becomes more significant with increasing TLs. Trophic magnification factors (TMFs) in the lowest two TLs show the opposite spatial pattern to TMFs in higher TLs. The low TMFs are usually associated with a short food-chain length. The less bottom-heavy trophic pyramids in the oligotrophic oceans enhance the MMHg trophic transfer. The global average TMF is increased from 2.3 to 2.8 in the warmer future with a medium climate sensitivity of 2.5 °C. Our study suggests that if there are no mitigation measures for Hg emission, MMHg in the high-trophic-level plankton is increased more dramatically in the warming future, indicating greater MMHg exposure for top predators such as humans.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Methylmercury (CH3Hg+, MMHg) in the phytoplankton and zooplankton, which form the bottom of marine food webs, is a good predictor of MMHg in top predators, including humans. Therefore, evaluating the potential exposure of MMHg to higher trophic levels (TLs) requires a better understanding of relationships between MMHg biomagnification and plankton dynamics. In this study, a coupled ecological/physical model with 366 plankton types of different sizes, biogeochemical functions, and temperature tolerance is used to simulate the relationships between MMHg biomagnification and the ecosystem structure. The study shows that the MMHg biomagnification becomes more significant with increasing TLs. Trophic magnification factors (TMFs) in the lowest two TLs show the opposite spatial pattern to TMFs in higher TLs. The low TMFs are usually associated with a short food-chain length. The less bottom-heavy trophic pyramids in the oligotrophic oceans enhance the MMHg trophic transfer. The global average TMF is increased from 2.3 to 2.8 in the warmer future with a medium climate sensitivity of 2.5 °C. Our study suggests that if there are no mitigation measures for Hg emission, MMHg in the high-trophic-level plankton is increased more dramatically in the warming future, indicating greater MMHg exposure for top predators such as humans. Close doi:10.1021/acs.est.1c04083 Close
39.	WJ Gutowski Jr, P Ullrich, A Hall, R Leung, T O'Brien, C Patricola, RW Arritt, M Bukovsky, KV Calvin, Z Feng, AD Jones, GJ Kooperman, E Monier, MS Pritchard, S Pryor, Y Qian, AM Rhoades, AF Roberts, K Sakaguchi, N Urban, C Zarzycki The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information Journal Article Bulletin of the American Meteorological Society, 101 (5), pp. E664–E683, 2020. Abstract \| Links \| BibTeX @article{gutowski2020ongoing, title = {The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information}, author = {WJ Gutowski Jr and P Ullrich and A Hall and R Leung and T O'Brien and C Patricola and RW Arritt and M Bukovsky and KV Calvin and Z Feng and AD Jones and GJ Kooperman and E Monier and MS Pritchard and S Pryor and Y Qian and AM Rhoades and AF Roberts and K Sakaguchi and N Urban and C Zarzycki}, doi = {10.1175/BAMS-D-19-0113.1}, year = {2020}, date = {2020-05-29}, journal = {Bulletin of the American Meteorological Society}, volume = {101}, number = {5}, pages = {E664--E683}, abstract = {Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions. Close doi:10.1175/BAMS-D-19-0113.1 Close
38.	IC Dedoussi, SD Eastham, E Monier, SRH Barrett Premature mortality related to United States cross-state air pollution Journal Article Nature, 578 , pp. 261–265, 2020. Abstract \| Links \| BibTeX @article{dedoussi2020premature, title = {Premature mortality related to United States cross-state air pollution}, author = {IC Dedoussi and SD Eastham and E Monier and SRH Barrett}, doi = {10.1038/s41586-020-1983-8}, year = {2020}, date = {2020-02-12}, journal = {Nature}, volume = {578}, pages = {261--265}, abstract = {Outdoor air pollution adversely affects human health and is estimated to be responsible for five to ten per cent of the total annual premature mortality in the contiguous United States. Combustion emissions from a variety of sources, such as power generation or road traffic, make a large contribution to harmful air pollutants such as ozone and fine particulate matter (PM2.5). Efforts to mitigate air pollution have focused mainly on the relationship between local emission sources and local air quality. Air quality can also be affected by distant emission sources, however, including emissions from neighbouring federal states. This cross-state exchange of pollution poses additional regulatory challenges. Here we quantify the exchange of air pollution among the contiguous United States, and assess its impact on premature mortality that is linked to increased human exposure to PM2.5 and ozone from seven emission sectors for 2005 to 2018. On average, we find that 41 to 53 per cent of air-quality-related premature mortality resulting from a state’s emissions occurs outside that state. We also find variations in the cross-state contributions of different emission sectors and chemical species to premature mortality, and changes in these variations over time. Emissions from electric power generation have the greatest cross-state impacts as a fraction of their total impacts, whereas commercial/residential emissions have the smallest. However, reductions in emissions from electric power generation since 2005 have meant that, by 2018, cross-state premature mortality associated with the commercial/residential sector was twice that associated with power generation. In terms of the chemical species emitted, nitrogen oxides and sulfur dioxide emissions caused the most cross-state premature deaths in 2005, but by 2018 primary PM2.5 emissions led to cross-state premature deaths equal to three times those associated with sulfur dioxide emissions. These reported shifts in emission sectors and emission species that contribute to premature mortality may help to guide improvements to air quality in the contiguous United States.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Outdoor air pollution adversely affects human health and is estimated to be responsible for five to ten per cent of the total annual premature mortality in the contiguous United States. Combustion emissions from a variety of sources, such as power generation or road traffic, make a large contribution to harmful air pollutants such as ozone and fine particulate matter (PM2.5). Efforts to mitigate air pollution have focused mainly on the relationship between local emission sources and local air quality. Air quality can also be affected by distant emission sources, however, including emissions from neighbouring federal states. This cross-state exchange of pollution poses additional regulatory challenges. Here we quantify the exchange of air pollution among the contiguous United States, and assess its impact on premature mortality that is linked to increased human exposure to PM2.5 and ozone from seven emission sectors for 2005 to 2018. On average, we find that 41 to 53 per cent of air-quality-related premature mortality resulting from a state’s emissions occurs outside that state. We also find variations in the cross-state contributions of different emission sectors and chemical species to premature mortality, and changes in these variations over time. Emissions from electric power generation have the greatest cross-state impacts as a fraction of their total impacts, whereas commercial/residential emissions have the smallest. However, reductions in emissions from electric power generation since 2005 have meant that, by 2018, cross-state premature mortality associated with the commercial/residential sector was twice that associated with power generation. In terms of the chemical species emitted, nitrogen oxides and sulfur dioxide emissions caused the most cross-state premature deaths in 2005, but by 2018 primary PM2.5 emissions led to cross-state premature deaths equal to three times those associated with sulfur dioxide emissions. These reported shifts in emission sectors and emission species that contribute to premature mortality may help to guide improvements to air quality in the contiguous United States. Close doi:10.1038/s41586-020-1983-8 Close
37.	A Libardoni, CE Forest, AP Sokolov, E Monier Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties Journal Article Geophysical Research Letters, 46 (16), pp. 10000–100007, 2019. Abstract \| Links \| BibTeX @article{libardoni2019underestimating, title = {Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties}, author = {A Libardoni and CE Forest and AP Sokolov and E Monier}, doi = {10.1029/2019GL082442}, year = {2019}, date = {2019-08-28}, journal = {Geophysical Research Letters}, volume = {46}, number = {16}, pages = {10000--100007}, abstract = {Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability. In this study, we investigate the sensitivity of probability distributions for climate system properties in the Massachusetts Institute of Technology Earth System Model to the internal variability estimate. In particular, we derive probability distributions using the internal variability extracted from 25 different Coupled Model Intercomparison Project Phase 5 models. We further test the sensitivity by pooling variability estimates from models with similar characteristics. We find the distributions to be highly sensitive when estimating the internal variability from a single model. When merging the variability estimates across multiple models, the distributions tend to converge to a wider distribution for all properties. This suggests that using a single model to approximate the internal climate variability produces distributions that are too narrow and do not fully represent the uncertainty in the climate system property estimates.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability. In this study, we investigate the sensitivity of probability distributions for climate system properties in the Massachusetts Institute of Technology Earth System Model to the internal variability estimate. In particular, we derive probability distributions using the internal variability extracted from 25 different Coupled Model Intercomparison Project Phase 5 models. We further test the sensitivity by pooling variability estimates from models with similar characteristics. We find the distributions to be highly sensitive when estimating the internal variability from a single model. When merging the variability estimates across multiple models, the distributions tend to converge to a wider distribution for all properties. This suggests that using a single model to approximate the internal climate variability produces distributions that are too narrow and do not fully represent the uncertainty in the climate system property estimates. Close doi:10.1029/2019GL082442 Close
36.	DW Kicklighter, JM Melillo, E Monier, AP Sokolov, Q Zhuang Future N availability and its effect on C sequestration in Northern Eurasia Journal Article Nature Communications, 10 (1), pp. 3024, 2019. Abstract \| Links \| BibTeX @article{kicklighter2019future, title = {Future N availability and its effect on C sequestration in Northern Eurasia}, author = {DW Kicklighter and JM Melillo and E Monier and AP Sokolov and Q Zhuang}, doi = {10.1038/s41467-019-10944-0}, year = {2019}, date = {2019-07-09}, journal = {Nature Communications}, volume = {10}, number = {1}, pages = {3024}, abstract = {Nitrogen (N) availability exerts strong control on carbon storage in the forests of Northern Eurasia. Here, using a process-based model, we explore how three factors that alter N availability—permafrost degradation, atmospheric N deposition, and the abandonment of agricultural land to forest regrowth (land-use legacy)—affect carbon storage in the region’s forest vegetation over the 21st century within the context of two IPCC global-change scenarios (RCPs 4.5 and 8.5). For RCP4.5, enhanced N availability results in increased tree carbon storage of 27.8 Pg C, with land-use legacy being the most important factor. For RCP8.5, enhanced N availability results in increased carbon storage in trees of 13.4 Pg C, with permafrost degradation being the most important factor. Our analysis reveals complex spatial and temporal patterns of regional carbon storage. This study underscores the importance of considering carbon-nitrogen interactions when assessing regional and sub-regional impacts of global change policies.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Nitrogen (N) availability exerts strong control on carbon storage in the forests of Northern Eurasia. Here, using a process-based model, we explore how three factors that alter N availability—permafrost degradation, atmospheric N deposition, and the abandonment of agricultural land to forest regrowth (land-use legacy)—affect carbon storage in the region’s forest vegetation over the 21st century within the context of two IPCC global-change scenarios (RCPs 4.5 and 8.5). For RCP4.5, enhanced N availability results in increased tree carbon storage of 27.8 Pg C, with land-use legacy being the most important factor. For RCP8.5, enhanced N availability results in increased carbon storage in trees of 13.4 Pg C, with permafrost degradation being the most important factor. Our analysis reveals complex spatial and temporal patterns of regional carbon storage. This study underscores the importance of considering carbon-nitrogen interactions when assessing regional and sub-regional impacts of global change policies. Close doi:10.1038/s41467-019-10944-0 Close
35.	B Pienkosz, RK Saari, E Monier, F Garcia-Menendez Natural variability in projections of climate change impacts on fine particulate matter pollution Journal Article Earth's Future, 7 , pp. 762–770, 2019. Abstract \| Links \| BibTeX @article{pienkosz2019natural, title = {Natural variability in projections of climate change impacts on fine particulate matter pollution}, author = {B Pienkosz and RK Saari and E Monier and F Garcia-Menendez}, doi = {10.1029/2019EF001195}, year = {2019}, date = {2019-07-01}, journal = {Earth's Future}, volume = {7}, pages = {762--770}, abstract = {Variations in meteorology associated with climate change can impact fine particulate matter (PM2.5) pollution by affecting natural emissions, atmospheric chemistry, and pollutant transport. However, substantial discrepancies exist among model‐based projections of PM2.5 impacts driven by anthropogenic climate change. Natural variability can significantly contribute to the uncertainty in these estimates. Using a large ensemble of climate and atmospheric chemistry simulations, we evaluate the influence of natural variability on projections of climate change impacts on PM2.5 pollution in the United States. We find that natural variability in simulated PM2.5 can be comparable or larger than reported estimates of anthropogenic‐induced climate impacts. Relative to mean concentrations, the variability in projected PM2.5 climate impacts can also exceed that of ozone impacts. Based on our projections, we recommend that analyses aiming to isolate the effect climate change on PM2.5 use 10 years or more of modeling to capture the internal variability in air quality and increase confidence that the anthropogenic‐forced effect is differentiated from the noise introduced by natural variability. Projections at a regional scale or under greenhouse gas mitigation scenarios can require additional modeling to attribute impacts to climate change. Adequately considering natural variability can be an important step toward explaining the inconsistencies in estimates of climate‐induced impacts on PM2.5. Improved treatment of natural variability through extended modeling lengths or initial condition ensembles can reduce uncertainty in air quality projections and improve assessments of climate policy risks and benefits.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Variations in meteorology associated with climate change can impact fine particulate matter (PM2.5) pollution by affecting natural emissions, atmospheric chemistry, and pollutant transport. However, substantial discrepancies exist among model‐based projections of PM2.5 impacts driven by anthropogenic climate change. Natural variability can significantly contribute to the uncertainty in these estimates. Using a large ensemble of climate and atmospheric chemistry simulations, we evaluate the influence of natural variability on projections of climate change impacts on PM2.5 pollution in the United States. We find that natural variability in simulated PM2.5 can be comparable or larger than reported estimates of anthropogenic‐induced climate impacts. Relative to mean concentrations, the variability in projected PM2.5 climate impacts can also exceed that of ozone impacts. Based on our projections, we recommend that analyses aiming to isolate the effect climate change on PM2.5 use 10 years or more of modeling to capture the internal variability in air quality and increase confidence that the anthropogenic‐forced effect is differentiated from the noise introduced by natural variability. Projections at a regional scale or under greenhouse gas mitigation scenarios can require additional modeling to attribute impacts to climate change. Adequately considering natural variability can be an important step toward explaining the inconsistencies in estimates of climate‐induced impacts on PM2.5. Improved treatment of natural variability through extended modeling lengths or initial condition ensembles can reduce uncertainty in air quality projections and improve assessments of climate policy risks and benefits. Close doi:10.1029/2019EF001195 Close
34.	RK Saari, Y Mei, E Monier, F Garcia-Menendez Effect of Health-related Uncertainty and Natural Variability on Health Impacts and Co-Benefits of Climate Policy Journal Article Environmental Science & Technology, 53 (3), pp. 1098–1108, 2019. Abstract \| Links \| BibTeX @article{saari2019future, title = {Effect of Health-related Uncertainty and Natural Variability on Health Impacts and Co-Benefits of Climate Policy}, author = {RK Saari and Y Mei and E Monier and F Garcia-Menendez}, doi = {10.1021/acs.est.8b05094}, year = {2019}, date = {2019-02-05}, journal = {Environmental Science & Technology}, volume = {53}, number = {3}, pages = {1098--1108}, abstract = {Climate policy can mitigate health risks attributed to intensifying air pollution under climate change. However, few studies quantify risks of illness and death, examine their contribution to climate policy benefits, or assess their robustness in light of natural climate variability. We employ an integrated modeling framework of the economy, climate, air quality, and human health to quantify the effect of natural variability on U.S. air pollution impacts under future climate and two global policies (2 and 2.5 °C stabilization scenarios) using 150 year ensemble simulations for each scenario in 2050 and 2100. Climate change yields annual premature deaths related to fine particulate matter and ozone (95CI: 25 000–120 000), heart attacks (900–9400), and lost work days (3.6M-4.9M) in 2100. It raises air pollution health risks by 20%, while policies avert these outcomes by 40–50% in 2050 and 70–88% in 2100. Natural variability introduces “climate noise”, yielding some annual estimates with negative cobenefits, and others that reach 100% of annual policy costs. This “noise” is three times the magnitude of uncertainty (95CI) in health and economic responses in 2050. Averaging five annual simulations reduces this factor to two, which is still substantially larger than health-related uncertainty. This study quantifies the potential for inaccuracy in climate impacts projected using too few annual simulations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Climate policy can mitigate health risks attributed to intensifying air pollution under climate change. However, few studies quantify risks of illness and death, examine their contribution to climate policy benefits, or assess their robustness in light of natural climate variability. We employ an integrated modeling framework of the economy, climate, air quality, and human health to quantify the effect of natural variability on U.S. air pollution impacts under future climate and two global policies (2 and 2.5 °C stabilization scenarios) using 150 year ensemble simulations for each scenario in 2050 and 2100. Climate change yields annual premature deaths related to fine particulate matter and ozone (95CI: 25 000–120 000), heart attacks (900–9400), and lost work days (3.6M-4.9M) in 2100. It raises air pollution health risks by 20%, while policies avert these outcomes by 40–50% in 2050 and 70–88% in 2100. Natural variability introduces “climate noise”, yielding some annual estimates with negative cobenefits, and others that reach 100% of annual policy costs. This “noise” is three times the magnitude of uncertainty (95CI) in health and economic responses in 2050. Averaging five annual simulations reduces this factor to two, which is still substantially larger than health-related uncertainty. This study quantifies the potential for inaccuracy in climate impacts projected using too few annual simulations. Close doi:10.1021/acs.est.8b05094 Close
33.	S Dutkiewicz, A Hickman, O Jahn, S Henson, C Beaulieu, E Monier Ocean Colour Signature of Climate Change Journal Article Nature Communications, 10 (1), pp. 578, 2019. Abstract \| Links \| BibTeX @article{dutkiewicz2019ocean, title = {Ocean Colour Signature of Climate Change}, author = {S Dutkiewicz and A Hickman and O Jahn and S Henson and C Beaulieu and E Monier}, doi = {10.1038/s41467-019-08457-x}, year = {2019}, date = {2019-02-04}, journal = {Nature Communications}, volume = {10}, number = {1}, pages = {578}, abstract = {Monitoring changes in marine phytoplankton is important as they form the foundation of the marine food web and are crucial in the carbon cycle. Often Chlorophyll-a (Chl-a) is used to track changes in phytoplankton, since there are global, regular satellite-derived estimates. However, satellite sensors do not measure Chl-a directly. Instead, Chl-a is estimated from remote sensing reflectance (RRS): the ratio of upwelling radiance to the downwelling irradiance at the ocean’s surface. Using a model, we show that RRS in the blue-green spectrum is likely to have a stronger and earlier climate-change-driven signal than Chl-a. This is because RRS has lower natural variability and integrates not only changes to in-water Chl-a, but also alterations in other optically important constituents. Phytoplankton community structure, which strongly affects ocean optics, is likely to show one of the clearest and most rapid signatures of changes to the base of the marine ecosystem.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Monitoring changes in marine phytoplankton is important as they form the foundation of the marine food web and are crucial in the carbon cycle. Often Chlorophyll-a (Chl-a) is used to track changes in phytoplankton, since there are global, regular satellite-derived estimates. However, satellite sensors do not measure Chl-a directly. Instead, Chl-a is estimated from remote sensing reflectance (RRS): the ratio of upwelling radiance to the downwelling irradiance at the ocean’s surface. Using a model, we show that RRS in the blue-green spectrum is likely to have a stronger and earlier climate-change-driven signal than Chl-a. This is because RRS has lower natural variability and integrates not only changes to in-water Chl-a, but also alterations in other optically important constituents. Phytoplankton community structure, which strongly affects ocean optics, is likely to show one of the clearest and most rapid signatures of changes to the base of the marine ecosystem. Close doi:10.1038/s41467-019-08457-x Close
32.	A Libardoni, CE Forest, AP Sokolov, E Monier Estimates of Climate System Properties Incorporating Recent Climate Change Journal Article Advances in Statistical Climatology, Meteorology and Oceanography, 4 (1-2), pp. 19–36, 2018. Abstract \| Links \| BibTeX @article{libardoni2018estimates, title = {Estimates of Climate System Properties Incorporating Recent Climate Change}, author = {A Libardoni and CE Forest and AP Sokolov and E Monier}, doi = {10.5194/ascmo-4-19-2018}, year = {2018}, date = {2018-11-30}, journal = {Advances in Statistical Climatology, Meteorology and Oceanography}, volume = {4}, number = {1-2}, pages = {19--36}, abstract = {Historical time series of surface temperature and ocean heat content changes are commonly used metrics to diagnose climate change and estimate properties of the climate system. We show that recent trends, namely the slowing of surface temperature rise at the beginning of the 21st century and the acceleration of heat stored in the deep ocean, have a substantial impact on these estimates. Using the Massachusetts Institute of Technology Earth System Model (MESM), we vary three model parameters that influence the behavior of the climate system: effective climate sensitivity (ECS), the effective ocean diffusivity of heat anomalies by all mixing processes (Kv), and the net anthropogenic aerosol forcing scaling factor. Each model run is compared to observed changes in decadal mean surface temperature anomalies and the trend in global mean ocean heat content change to derive a joint probability distribution function for the model parameters. Marginal distributions for individual parameters are found by integrating over the other two parameters. To investigate how the inclusion of recent temperature changes affects our estimates, we systematically include additional data by choosing periods that end in 1990, 2000, and 2010. We find that estimates of ECS increase in response to rising global surface temperatures when data beyond 1990 are included, but due to the slowdown of surface temperature rise in the early 21st century, estimates when using data up to 2000 are greater than when data up to 2010 are used. We also show that estimates of Kv increase in response to the acceleration of heat stored in the ocean as data beyond 1990 are included. Further, we highlight how including spatial patterns of surface temperature change modifies the estimates. We show that including latitudinal structure in the climate change signal impacts properties with spatial dependence, namely the aerosol forcing pattern, more than properties defined for the global mean, climate sensitivity, and ocean diffusivity.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Historical time series of surface temperature and ocean heat content changes are commonly used metrics to diagnose climate change and estimate properties of the climate system. We show that recent trends, namely the slowing of surface temperature rise at the beginning of the 21st century and the acceleration of heat stored in the deep ocean, have a substantial impact on these estimates. Using the Massachusetts Institute of Technology Earth System Model (MESM), we vary three model parameters that influence the behavior of the climate system: effective climate sensitivity (ECS), the effective ocean diffusivity of heat anomalies by all mixing processes (Kv), and the net anthropogenic aerosol forcing scaling factor. Each model run is compared to observed changes in decadal mean surface temperature anomalies and the trend in global mean ocean heat content change to derive a joint probability distribution function for the model parameters. Marginal distributions for individual parameters are found by integrating over the other two parameters. To investigate how the inclusion of recent temperature changes affects our estimates, we systematically include additional data by choosing periods that end in 1990, 2000, and 2010. We find that estimates of ECS increase in response to rising global surface temperatures when data beyond 1990 are included, but due to the slowdown of surface temperature rise in the early 21st century, estimates when using data up to 2000 are greater than when data up to 2010 are used. We also show that estimates of Kv increase in response to the acceleration of heat stored in the ocean as data beyond 1990 are included. Further, we highlight how including spatial patterns of surface temperature change modifies the estimates. We show that including latitudinal structure in the climate change signal impacts properties with spatial dependence, namely the aerosol forcing pattern, more than properties defined for the global mean, climate sensitivity, and ocean diffusivity. Close doi:10.5194/ascmo-4-19-2018 Close
31.	A Libardoni, CE Forest, AP Sokolov, E Monier Baseline Evaluation of Model Parameter Estimates in the Updated MIT Earth System Model Journal Article Geoscientific Model Development, 11 , pp. 3313–3325, 2018. Abstract \| Links \| BibTeX @article{libardoni2018baseline, title = {Baseline Evaluation of Model Parameter Estimates in the Updated MIT Earth System Model}, author = {A Libardoni and CE Forest and AP Sokolov and E Monier}, doi = {10.5194/gmd-11-3313-2018}, year = {2018}, date = {2018-08-21}, journal = {Geoscientific Model Development}, volume = {11}, pages = {3313--3325}, abstract = {For over 20 years, the Massachusetts Institute of Technology Earth System Model (MESM) has been used extensively for climate change research. The model is under continuous development with components being added and updated. To provide transparency in the model development, we perform a baseline evaluation by comparing model behavior and properties in the newest version to the previous model version. In particular, changes resulting from updates to the land surface model component and the input forcings used in historical simulations of climate change are investigated. We run an 1800-member ensemble of MESM historical climate simulations where the model parameters that set climate sensitivity, the rate of ocean heat uptake, and the net anthropogenic aerosol forcing are systematically varied. By comparing model output to observed patterns of surface temperature changes and the linear trend in the increase in ocean heat content, we derive probability distributions for the three model parameters. Furthermore, we run a 372-member ensemble of transient climate simulations where all model forcings are fixed and carbon dioxide concentrations are increased at the rate of 1 %/year. From these runs, we derive response surfaces for transient climate response and thermosteric sea level rise as a function of climate sensitivity and ocean heat uptake. We show that the probability distributions shift towards higher climate sensitivities and weaker aerosol forcing when using the new model and that the climate response surfaces are relatively unchanged between model versions. Because the response surfaces are independent of the changes to the model forcings and similar between model versions with different land surface models, we suggest that the change in land surface model has limited impact on the temperature evolution in the model. Thus, we attribute the shifts in parameter estimates to the updated model forcings.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close For over 20 years, the Massachusetts Institute of Technology Earth System Model (MESM) has been used extensively for climate change research. The model is under continuous development with components being added and updated. To provide transparency in the model development, we perform a baseline evaluation by comparing model behavior and properties in the newest version to the previous model version. In particular, changes resulting from updates to the land surface model component and the input forcings used in historical simulations of climate change are investigated. We run an 1800-member ensemble of MESM historical climate simulations where the model parameters that set climate sensitivity, the rate of ocean heat uptake, and the net anthropogenic aerosol forcing are systematically varied. By comparing model output to observed patterns of surface temperature changes and the linear trend in the increase in ocean heat content, we derive probability distributions for the three model parameters. Furthermore, we run a 372-member ensemble of transient climate simulations where all model forcings are fixed and carbon dioxide concentrations are increased at the rate of 1 %/year. From these runs, we derive response surfaces for transient climate response and thermosteric sea level rise as a function of climate sensitivity and ocean heat uptake. We show that the probability distributions shift towards higher climate sensitivities and weaker aerosol forcing when using the new model and that the climate response surfaces are relatively unchanged between model versions. Because the response surfaces are independent of the changes to the model forcings and similar between model versions with different land surface models, we suggest that the change in land surface model has limited impact on the temperature evolution in the model. Thus, we attribute the shifts in parameter estimates to the updated model forcings. Close doi:10.5194/gmd-11-3313-2018 Close
30.	A Sokolov, D Kicklighter, A Schlosser, C Wang, E Monier, B Brown-Steiner, R Prinn, C Forest, X Gao, A Libardoni, S Eastham Description and Evaluation of the MIT Earth System Model (MESM) Journal Article Journal of Advances in Modeling Earth Systems, 10 , pp. 1759–1789, 2018. Abstract \| Links \| BibTeX @article{sokolov2018description, title = {Description and Evaluation of the MIT Earth System Model (MESM)}, author = {A Sokolov and D Kicklighter and A Schlosser and C Wang and E Monier and B Brown-Steiner and R Prinn and C Forest and X Gao and A Libardoni and S Eastham}, doi = {10.1029/2018MS001277}, year = {2018}, date = {2018-08-01}, journal = {Journal of Advances in Modeling Earth Systems}, volume = {10}, pages = {1759--1789}, abstract = {The Massachusetts Institute of Technology Integrated Global System Model (IGSM) is designed for analyzing the global environmental changes that may result from anthropogenic causes, quantifying the uncertainties associated with the projected changes, and assessing the costs and environmental effectiveness of proposed policies to mitigate climate risk. The IGSM consists of the Massachusetts Institute of Technology Earth System Model (MESM) of intermediate complexity and the Economic Projections and Policy Analysis model. This paper documents the current version of the MESM, which includes a two‐dimensional (zonally averaged) atmospheric model with interactive chemistry coupled to the zonally averaged version of Global Land System model and an anomaly‐diffusing ocean model.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close The Massachusetts Institute of Technology Integrated Global System Model (IGSM) is designed for analyzing the global environmental changes that may result from anthropogenic causes, quantifying the uncertainties associated with the projected changes, and assessing the costs and environmental effectiveness of proposed policies to mitigate climate risk. The IGSM consists of the Massachusetts Institute of Technology Earth System Model (MESM) of intermediate complexity and the Economic Projections and Policy Analysis model. This paper documents the current version of the MESM, which includes a two‐dimensional (zonally averaged) atmospheric model with interactive chemistry coupled to the zonally averaged version of Global Land System model and an anomaly‐diffusing ocean model. Close doi:10.1029/2018MS001277 Close
29.	B Brown-Steiner, NE Selin, RG Prinn, E Monier, S Tilmes, L Emmons, F Garcia-Menendez Maximizing Ozone Signals Among Chemical, Meteorological, and Climatological Variability Journal Article Atmospheric Chemistry and Physics, 18 , pp. 8373–8388, 2018. Abstract \| Links \| BibTeX @article{brown-steiner2018maximizing, title = {Maximizing Ozone Signals Among Chemical, Meteorological, and Climatological Variability}, author = {B Brown-Steiner and NE Selin and RG Prinn and E Monier and S Tilmes and L Emmons and F Garcia-Menendez}, doi = {10.5194/acp-18-8373-2018}, year = {2018}, date = {2018-06-15}, journal = {Atmospheric Chemistry and Physics}, volume = {18}, pages = {8373--8388}, abstract = {The detection of meteorological, chemical, or other signals in modeled or observed air quality data – such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season – is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical, meteorological, and climatological variabilities (and their interactions) that exist both in space and in time, and which include variability in emissions and surface processes. This can present difficulties for both policymakers and researchers as they attempt to identify the influence or signal of climate trends (e.g., any pauses in warming trends), the impact of enacted emission reductions policies (e.g., United States NOx State Implementation Plans), or an estimate of the mean state of highly variable data (e.g., summertime ozone over the northeastern United States). Here we examine the scale dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the continental US. For signals that are large compared to the meteorological variability (e.g., strong emissions reductions), shorter averaging periods and smaller spatial averaging regions may be sufficient, but for many signals that are smaller than or comparable in magnitude to the underlying meteorological variability, we recommend temporal averaging of 10–15 years combined with some level of spatial averaging (up to several hundred kilometers). If this level of averaging is not practical (e.g., the signal being examined is at a local scale), we recommend some exploration of the spatial and temporal variability to provide context and confidence in the robustness of the result. These results are consistent between simulated and observed data, as well as within a single model with different sets of parameters. The strategies selected in this study are not limited to surface ozone data and could potentially maximize signal detection capabilities within a broad array of climate and chemical observations or model output.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close The detection of meteorological, chemical, or other signals in modeled or observed air quality data – such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season – is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical, meteorological, and climatological variabilities (and their interactions) that exist both in space and in time, and which include variability in emissions and surface processes. This can present difficulties for both policymakers and researchers as they attempt to identify the influence or signal of climate trends (e.g., any pauses in warming trends), the impact of enacted emission reductions policies (e.g., United States NOx State Implementation Plans), or an estimate of the mean state of highly variable data (e.g., summertime ozone over the northeastern United States). Here we examine the scale dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the continental US. For signals that are large compared to the meteorological variability (e.g., strong emissions reductions), shorter averaging periods and smaller spatial averaging regions may be sufficient, but for many signals that are smaller than or comparable in magnitude to the underlying meteorological variability, we recommend temporal averaging of 10–15 years combined with some level of spatial averaging (up to several hundred kilometers). If this level of averaging is not practical (e.g., the signal being examined is at a local scale), we recommend some exploration of the spatial and temporal variability to provide context and confidence in the robustness of the result. These results are consistent between simulated and observed data, as well as within a single model with different sets of parameters. The strategies selected in this study are not limited to surface ozone data and could potentially maximize signal detection capabilities within a broad array of climate and chemical observations or model output. Close doi:10.5194/acp-18-8373-2018 Close
28.	E Monier, S Paltsev, A Sokolov, H Chen, X Gao, Q Ejaz, E Couzo, CA Schlosser, S Dutkiewicz, C Fant, J Scott, D Kicklighter, J Morris, H Jacoby, R Prinn, M Haigh Toward a consistent modeling framework to assess multi-sectoral climate impacts Journal Article Nature Communications, 9 , pp. 660, 2018. Abstract \| Links \| BibTeX @article{monier2018toward, title = {Toward a consistent modeling framework to assess multi-sectoral climate impacts}, author = {E Monier and S Paltsev and A Sokolov and H Chen and X Gao and Q Ejaz and E Couzo and CA Schlosser and S Dutkiewicz and C Fant and J Scott and D Kicklighter and J Morris and H Jacoby and R Prinn and M Haigh}, doi = {10.1038/s41467-018-02984-9}, year = {2018}, date = {2018-02-13}, journal = {Nature Communications}, volume = {9}, pages = {660}, abstract = {Efforts to estimate the physical and economic impacts of future climate change face substantial challenges. To enrich the currently popular approaches to impact analysis—which involve evaluation of a damage function or multi-model comparisons based on a limited number of standardized scenarios—we propose integrating a geospatially resolved physical representation of impacts into a coupled human-Earth system modeling framework. Large internationally coordinated exercises cannot easily respond to new policy targets and the implementation of standard scenarios across models, institutions and research communities can yield inconsistent estimates. Here, we argue for a shift toward the use of a self-consistent integrated modeling framework to assess climate impacts, and discuss ways the integrated assessment modeling community can move in this direction. We then demonstrate the capabilities of such a modeling framework by conducting a multi-sectoral assessment of climate impacts under a range of consistent and integrated economic and climate scenarios that are responsive to new policies and business expectations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Efforts to estimate the physical and economic impacts of future climate change face substantial challenges. To enrich the currently popular approaches to impact analysis—which involve evaluation of a damage function or multi-model comparisons based on a limited number of standardized scenarios—we propose integrating a geospatially resolved physical representation of impacts into a coupled human-Earth system modeling framework. Large internationally coordinated exercises cannot easily respond to new policy targets and the implementation of standard scenarios across models, institutions and research communities can yield inconsistent estimates. Here, we argue for a shift toward the use of a self-consistent integrated modeling framework to assess climate impacts, and discuss ways the integrated assessment modeling community can move in this direction. We then demonstrate the capabilities of such a modeling framework by conducting a multi-sectoral assessment of climate impacts under a range of consistent and integrated economic and climate scenarios that are responsive to new policies and business expectations. Close doi:10.1038/s41467-018-02984-9 Close
27.	P Groisman, H Shugart, D Kicklighter, G Henebry, N Tchebakova, S Maksyutov, E Monier, G Gutman, S Gulev, J Qi, A Prishchepov, E Kukavskaya, B Porfiriev, A Shiklomanov, T Loboda, N Shiklomanov, S Nghiem, K Bergen, J Albrechtová, J Chen, M Shahdedanova, A Shvidenko, N Speranskaya, A Soja, K deBeurs, O Bulygina, J McCarty, Q Zhuang, O Zolina Northern Eurasia Future Initiative (NEFI): Facing the Challenges and Pathways of Global Change in the 21st Century Journal Article Progress in Earth and Planetary Science, 4 , pp. 41, 2017. Abstract \| Links \| BibTeX @article{groisman2017nefi, title = {Northern Eurasia Future Initiative (NEFI): Facing the Challenges and Pathways of Global Change in the 21st Century}, author = {P Groisman and H Shugart and D Kicklighter and G Henebry and N Tchebakova and S Maksyutov and E Monier and G Gutman and S Gulev and J Qi and A Prishchepov and E Kukavskaya and B Porfiriev and A Shiklomanov and T Loboda and N Shiklomanov and S Nghiem and K Bergen and J Albrechtová and J Chen and M Shahdedanova and A Shvidenko and N Speranskaya and A Soja and K deBeurs and O Bulygina and J McCarty and Q Zhuang and O Zolina}, doi = {10.1186/s40645-017-0154-5}, year = {2017}, date = {2017-12-27}, journal = {Progress in Earth and Planetary Science}, volume = {4}, pages = {41}, abstract = {During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision-makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia’s role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large-scale water withdrawals, land use, and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that integrated assessment models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close During the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision-makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia’s role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large-scale water withdrawals, land use, and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that integrated assessment models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts. Close doi:10.1186/s40645-017-0154-5 Close
26.	L Xu, RD Pyles, KT Paw U, RL Snyder, E Monier, M Falk, SH Chen Impact of Canopy Representations on Regional Modeling of Evapotranspiration using the WRF-ACASA Coupled Model Journal Article Agricultural and Forest Meteorology, 247 , pp. 79–92, 2017. Abstract \| Links \| BibTeX @article{xu2017impact, title = {Impact of Canopy Representations on Regional Modeling of Evapotranspiration using the WRF-ACASA Coupled Model}, author = {L Xu and RD Pyles and KT Paw U and RL Snyder and E Monier and M Falk and SH Chen}, doi = {10.1016/j.agrformet.2017.07.003}, year = {2017}, date = {2017-12-15}, journal = {Agricultural and Forest Meteorology}, volume = {247}, pages = {79--92}, abstract = {In this study, we couple the Weather Research and Forecasting Model (WRF) with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model, to investigate the impact of canopy representation on regional evapotranspiration. The WRF-ACASA model uses a multilayer structure to represent the canopy, consequently allowing microenvironmental variables such as leaf area index (LAI), air and canopy temperature, wind speed and humidity to vary both horizontally and vertically. The improvement in canopy representation and canopy-atmosphere interaction allow for more realistic simulation of evapotranspiration on both regional and local scales. The coupled WRF-ACASA model is compared with the widely used intermediate complexity Noah land surface model in WRF (WRF-Noah) for both potential (ETo) and actual evapotranspiration (ETa). Two LAI datasets (USGS and MODIS) are used to study the model responses to surface conditions. Model evaluations over a diverse surface stations from the CIMIS and AmeriFlux networks show that an increase surface representations increase the model accuracy in ETa more so than ETo. Overall, while the high complexity of WRF-ACASA increases the realism of plant physiological processes, the model sensitivity to surface representation in input data such as LAI also increases.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close In this study, we couple the Weather Research and Forecasting Model (WRF) with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model, to investigate the impact of canopy representation on regional evapotranspiration. The WRF-ACASA model uses a multilayer structure to represent the canopy, consequently allowing microenvironmental variables such as leaf area index (LAI), air and canopy temperature, wind speed and humidity to vary both horizontally and vertically. The improvement in canopy representation and canopy-atmosphere interaction allow for more realistic simulation of evapotranspiration on both regional and local scales. The coupled WRF-ACASA model is compared with the widely used intermediate complexity Noah land surface model in WRF (WRF-Noah) for both potential (ETo) and actual evapotranspiration (ETa). Two LAI datasets (USGS and MODIS) are used to study the model responses to surface conditions. Model evaluations over a diverse surface stations from the CIMIS and AmeriFlux networks show that an increase surface representations increase the model accuracy in ETa more so than ETo. Overall, while the high complexity of WRF-ACASA increases the realism of plant physiological processes, the model sensitivity to surface representation in input data such as LAI also increases. Close doi:10.1016/j.agrformet.2017.07.003 Close
25.	E Monier, DW Kicklighter, AP Sokolov, Q Zhuang, IN Sokolik, R Lawford, M Kappas, SV Paltsev, PYa Groisman A Review of and Perspectives on Global Change Modeling for Northern Eurasia Journal Article Environmental Research Letters, 12 (8), pp. 083001, 2017. Abstract \| Links \| BibTeX @article{monier2017review, title = {A Review of and Perspectives on Global Change Modeling for Northern Eurasia}, author = {E Monier and DW Kicklighter and AP Sokolov and Q Zhuang and IN Sokolik and R Lawford and M Kappas and SV Paltsev and PYa Groisman}, doi = {10.1088/1748-9326/aa7aae}, year = {2017}, date = {2017-08-08}, journal = {Environmental Research Letters}, volume = {12}, number = {8}, pages = {083001}, abstract = {Northern Eurasia is made up of a complex and diverse set of physical, ecological, climatic and human systems, which provide important ecosystem services including the storage of substantial stocks of carbon in its terrestrial ecosystems. At the same time, the region has experienced dramatic climate change, natural disturbances and changes in land management practices over the past century. For these reasons, Northern Eurasia is both a critical region to understand and a complex system with substantial challenges for the modeling community. This review is designed to highlight the state of past and ongoing efforts of the research community to understand and model these environmental, socioeconomic, and climatic changes. We further aim to provide perspectives on the future direction of global change modeling to improve our understanding of the role of Northern Eurasia in the coupled human–Earth system. Modeling efforts have shown that environmental and socioeconomic changes in Northern Eurasia can have major impacts on biodiversity, ecosystems services, environmental sustainability, and the carbon cycle of the region, and beyond. These impacts have the potential to feedback onto and alter the global Earth system. We find that past and ongoing studies have largely focused on specific components of Earth system dynamics and have not systematically examined their feedbacks to the global Earth system and to society. We identify the crucial role of Earth system models in advancing our understanding of feedbacks within the region and with the global system. We further argue for the need for integrated assessment models (IAMs), a suite of models that couple human activity models to Earth system models, which are key to address many emerging issues that require a representation of the coupled human–Earth system.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Northern Eurasia is made up of a complex and diverse set of physical, ecological, climatic and human systems, which provide important ecosystem services including the storage of substantial stocks of carbon in its terrestrial ecosystems. At the same time, the region has experienced dramatic climate change, natural disturbances and changes in land management practices over the past century. For these reasons, Northern Eurasia is both a critical region to understand and a complex system with substantial challenges for the modeling community. This review is designed to highlight the state of past and ongoing efforts of the research community to understand and model these environmental, socioeconomic, and climatic changes. We further aim to provide perspectives on the future direction of global change modeling to improve our understanding of the role of Northern Eurasia in the coupled human–Earth system. Modeling efforts have shown that environmental and socioeconomic changes in Northern Eurasia can have major impacts on biodiversity, ecosystems services, environmental sustainability, and the carbon cycle of the region, and beyond. These impacts have the potential to feedback onto and alter the global Earth system. We find that past and ongoing studies have largely focused on specific components of Earth system dynamics and have not systematically examined their feedbacks to the global Earth system and to society. We identify the crucial role of Earth system models in advancing our understanding of feedbacks within the region and with the global system. We further argue for the need for integrated assessment models (IAMs), a suite of models that couple human activity models to Earth system models, which are key to address many emerging issues that require a representation of the coupled human–Earth system. Close doi:10.1088/1748-9326/aa7aae Close
24.	E Blanc, J Caron, C Fant, E Monier Is current irrigation sustainable in the United States? An integrated assessment of climate change impacts on water resources and irrigated crop yields Journal Article Earth's Future, 5 , pp. 877–892, 2017. Abstract \| Links \| BibTeX @article{blanc2017current, title = {Is current irrigation sustainable in the United States? An integrated assessment of climate change impacts on water resources and irrigated crop yields}, author = {E Blanc and J Caron and C Fant and E Monier}, doi = {10.1002/2016EF000473}, year = {2017}, date = {2017-08-01}, journal = {Earth's Future}, volume = {5}, pages = {877--892}, abstract = {While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climate change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water‐stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO2 fertilization effect compared to an unconstrained GHG emission scenario.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close While climate change impacts on crop yields has been extensively studied, estimating the impact of water shortages on irrigated crop yields is challenging because the water resources management system is complex. To investigate this issue, we integrate a crop yield reduction module and a water resources model into the MIT Integrated Global System Modeling framework, an integrated assessment model linking a global economic model to an Earth system model. We assess the effects of climate and socioeconomic changes on water availability for irrigation in the U.S. as well as subsequent impacts on crop yields by 2050, while accounting for climate change projection uncertainty. We find that climate and socioeconomic changes will increase water shortages and strongly reduce irrigated yields for specific crops (i.e., cotton and forage), or in specific regions (i.e., the Southwest) where irrigation is not sustainable. Crop modeling studies that do not represent changes in irrigation availability can thus be misleading. Yet, since the most water‐stressed basins represent a relatively small share of U.S. irrigated areas, the overall reduction in U.S. crop yields is small. The response of crop yields to climate change and water stress also suggests that some level of adaptation will be feasible, like relocating croplands to regions with sustainable irrigation or switching to less irrigation intensive crops. Finally, additional simulations show that greenhouse gas (GHG) mitigation can alleviate the effect of water stress on irrigated crop yields, enough to offset the reduced CO2 fertilization effect compared to an unconstrained GHG emission scenario. Close doi:10.1002/2016EF000473 Close
23.	X Gao, CA Schlosser, P O'Gorman, E Monier, D Entekhabi Twenty-First-Century Changes in U.S. Regional Heavy Precipitation Frequency Based on Resolved Atmospheric Patterns Journal Article Journal of Climate, 30 (7), pp. 2501–2521, 2017. Abstract \| Links \| BibTeX @article{gao2017twenty-first-century, title = {Twenty-First-Century Changes in U.S. Regional Heavy Precipitation Frequency Based on Resolved Atmospheric Patterns}, author = {X Gao and CA Schlosser and P O'Gorman and E Monier and D Entekhabi}, doi = {10.1175/JCLI-D-16-0544.1}, year = {2017}, date = {2017-04-01}, journal = {Journal of Climate}, volume = {30}, number = {7}, pages = {2501--2521}, abstract = {Precipitation-gauge observations and atmospheric reanalysis are combined to develop an analogue method for detecting heavy precipitation events based on prevailing large-scale atmospheric conditions. Combinations of atmospheric variables for circulation (geopotential height and wind vector) and moisture (surface specific humidity, column and up to 500-hPa precipitable water) are examined to construct analogue schemes for the winter [December–February (DJF)] of the “Pacific Coast California” (PCCA) region and the summer [June–August (JJA)] of the Midwestern United States (MWST). The detection diagnostics of analogue schemes are calibrated with 1979–2005 and validated with 2006–14 NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). All analogue schemes are found to significantly improve upon MERRA precipitation in characterizing the occurrence and interannual variations of observed heavy precipitation events in the MWST. When evaluated with the late twentieth-century climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5), all analogue schemes produce model medians of heavy precipitation frequency that are more consistent with observations and have smaller intermodel discrepancies than model-based precipitation. Under the representative concentration pathways (RCP) 4.5 and 8.5 scenarios, the CMIP5-based analogue schemes produce trends in heavy precipitation occurrence through the twenty-first century that are consistent with model-based precipitation, but with smaller intermodel disparity. The median trends in heavy precipitation frequency are positive for DJF over PCCA but are slightly negative for JJA over MWST. Overall, the analyses highlight the potential of the analogue as a powerful diagnostic tool for model deficiencies and its complementarity to an evaluation of heavy precipitation frequency based on model precipitation alone.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Precipitation-gauge observations and atmospheric reanalysis are combined to develop an analogue method for detecting heavy precipitation events based on prevailing large-scale atmospheric conditions. Combinations of atmospheric variables for circulation (geopotential height and wind vector) and moisture (surface specific humidity, column and up to 500-hPa precipitable water) are examined to construct analogue schemes for the winter [December–February (DJF)] of the “Pacific Coast California” (PCCA) region and the summer [June–August (JJA)] of the Midwestern United States (MWST). The detection diagnostics of analogue schemes are calibrated with 1979–2005 and validated with 2006–14 NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). All analogue schemes are found to significantly improve upon MERRA precipitation in characterizing the occurrence and interannual variations of observed heavy precipitation events in the MWST. When evaluated with the late twentieth-century climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5), all analogue schemes produce model medians of heavy precipitation frequency that are more consistent with observations and have smaller intermodel discrepancies than model-based precipitation. Under the representative concentration pathways (RCP) 4.5 and 8.5 scenarios, the CMIP5-based analogue schemes produce trends in heavy precipitation occurrence through the twenty-first century that are consistent with model-based precipitation, but with smaller intermodel disparity. The median trends in heavy precipitation frequency are positive for DJF over PCCA but are slightly negative for JJA over MWST. Overall, the analyses highlight the potential of the analogue as a powerful diagnostic tool for model deficiencies and its complementarity to an evaluation of heavy precipitation frequency based on model precipitation alone. Close doi:10.1175/JCLI-D-16-0544.1 Close
22.	J Kim, E Monier, B Sohngen, G Pitts, R Drapek, S Ohrel, J Cole, J McFarland Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios Journal Article Environmental Research Letters, 12 (4), pp. 045001, 2017. Abstract \| Links \| BibTeX @article{kim2017assessing, title = {Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios}, author = {J Kim and E Monier and B Sohngen and G Pitts and R Drapek and S Ohrel and J Cole and J McFarland}, doi = {10.1088/1748-9326/aa63fc}, year = {2017}, date = {2017-03-28}, journal = {Environmental Research Letters}, volume = {12}, number = {4}, pages = {045001}, abstract = {We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomes of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world's forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO2 fertilization effects may considerably reduce the range of projections.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomes of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world's forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO2 fertilization effects may considerably reduce the range of projections. Close doi:10.1088/1748-9326/aa63fc Close
21.	F Garcia-Menendez, E Monier, NE Selin The role of natural variability in projections of climate change impacts on U.S. ozone pollution Journal Article Geophysical Research Letters, 44 (6), pp. 2911–2921, 2017. Abstract \| Links \| BibTeX @article{garcia-menendez2017role, title = {The role of natural variability in projections of climate change impacts on U.S. ozone pollution}, author = {F Garcia-Menendez and E Monier and NE Selin}, doi = {10.1002/2016GL071565}, year = {2017}, date = {2017-03-28}, journal = {Geophysical Research Letters}, volume = {44}, number = {6}, pages = {2911--2921}, abstract = {Climate change can impact air quality by altering the atmospheric conditions that determine pollutant concentrations. Over large regions of the U.S., projected changes in climate are expected to favor formation of ground‐level ozone and aggravate associated health effects. However, modeling studies exploring air quality‐climate interactions have often overlooked the role of natural variability, a major source of uncertainty in projections. Here we use the largest ensemble simulation of climate‐induced changes in air quality generated to date to assess its influence on estimates of climate change impacts on U.S. ozone. We find that natural variability can significantly alter the robustness of projections of the future climate's effect on ozone pollution. In this study, a 15 year simulation length minimum is required to identify a distinct anthropogenic‐forced signal. Therefore, we suggest that studies assessing air quality impacts use multidecadal simulations or initial condition ensembles. With natural variability, impacts attributable to climate may be difficult to discern before midcentury or under stabilization scenarios.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Climate change can impact air quality by altering the atmospheric conditions that determine pollutant concentrations. Over large regions of the U.S., projected changes in climate are expected to favor formation of ground‐level ozone and aggravate associated health effects. However, modeling studies exploring air quality‐climate interactions have often overlooked the role of natural variability, a major source of uncertainty in projections. Here we use the largest ensemble simulation of climate‐induced changes in air quality generated to date to assess its influence on estimates of climate change impacts on U.S. ozone. We find that natural variability can significantly alter the robustness of projections of the future climate's effect on ozone pollution. In this study, a 15 year simulation length minimum is required to identify a distinct anthropogenic‐forced signal. Therefore, we suggest that studies assessing air quality impacts use multidecadal simulations or initial condition ensembles. With natural variability, impacts attributable to climate may be difficult to discern before midcentury or under stabilization scenarios. Close doi:10.1002/2016GL071565 Close
20.	E Monier, L Xu, RL Snyder Uncertainty in Future Agro-Climate Projections in the United States and Benefits of Greenhouse Gas Mitigation Journal Article Environmental Research Letters, 11 , pp. 055001, 2016. Abstract \| Links \| BibTeX @article{monier2016uncertainty, title = {Uncertainty in Future Agro-Climate Projections in the United States and Benefits of Greenhouse Gas Mitigation}, author = {E Monier and L Xu and RL Snyder}, doi = {10.1088/1748-9326/11/5/055001}, year = {2016}, date = {2016-04-26}, journal = {Environmental Research Letters}, volume = {11}, pages = {055001}, abstract = {Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios, climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations—although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Finally, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios, climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations—although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Finally, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions. Close doi:10.1088/1748-9326/11/5/055001 Close
19.	I Sue Wing, E Monier, A Stern, A Mundra US major crops' uncertain climate change risks and greenhouse gas mitigation benefits Journal Article Environmental Research Letters, 10 (11), pp. 115002, 2015. Abstract \| Links \| BibTeX @article{suewing2015us, title = {US major crops' uncertain climate change risks and greenhouse gas mitigation benefits}, author = {I Sue Wing and E Monier and A Stern and A Mundra}, doi = {10.1088/1748-9326/10/11/115002}, year = {2015}, date = {2015-10-28}, journal = {Environmental Research Letters}, volume = {10}, number = {11}, pages = {115002}, abstract = {We estimate the costs of climate change to US agriculture, and associated potential benefits of abating greenhouse gas emissions. Five major crops' yield responses to climatic variation are modeled empirically, and the results combined with climate projections for a no-policy, high-warming future, as well as moderate and stringent mitigation scenarios. Unabated warming reduces yields of wheat and soybeans by 2050, and cotton by 2100, but moderate warming increases yields of all crops except wheat. Yield changes are monetized using the results of economic simulations within an integrated climate-economy modeling framework. Uncontrolled warming's economic effects on major crops are slightly positive—annual benefits <$4 B. These are amplified by emission reductions, but subject to diminishing returns—by 2100 reaching $17 B under moderate mitigation, but only $7 B with stringent mitigation. Costs and benefits are sensitive to irreducible uncertainty about the fertilization effects of elevated atmospheric carbon dioxide, without which unabated warming incurs net costs of up to $18 B, generating benefits to moderate (stringent) mitigation as large as $26 B ($20 B).}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close We estimate the costs of climate change to US agriculture, and associated potential benefits of abating greenhouse gas emissions. Five major crops' yield responses to climatic variation are modeled empirically, and the results combined with climate projections for a no-policy, high-warming future, as well as moderate and stringent mitigation scenarios. Unabated warming reduces yields of wheat and soybeans by 2050, and cotton by 2100, but moderate warming increases yields of all crops except wheat. Yield changes are monetized using the results of economic simulations within an integrated climate-economy modeling framework. Uncontrolled warming's economic effects on major crops are slightly positive—annual benefits <$4 B. These are amplified by emission reductions, but subject to diminishing returns—by 2100 reaching $17 B under moderate mitigation, but only $7 B with stringent mitigation. Costs and benefits are sensitive to irreducible uncertainty about the fertilization effects of elevated atmospheric carbon dioxide, without which unabated warming incurs net costs of up to $18 B, generating benefits to moderate (stringent) mitigation as large as $26 B ($20 B). Close doi:10.1088/1748-9326/10/11/115002 Close
18.	F Garcia-Menendez, RK Saari, E Monier, NE Selin U.S. air quality and health benefits from avoided climate change under greenhouse gas mitigation Journal Article Environmental Science & Technology, 49 (13), pp. 7580–7588, 2015. Abstract \| Links \| BibTeX @article{garcia-menendez2015us, title = {U.S. air quality and health benefits from avoided climate change under greenhouse gas mitigation}, author = {F Garcia-Menendez and RK Saari and E Monier and NE Selin}, doi = {10.1021/acs.est.5b01324}, year = {2015}, date = {2015-07-07}, journal = {Environmental Science & Technology}, volume = {49}, number = {13}, pages = {7580--7588}, abstract = {We evaluate the impact of climate change on U.S. air quality and health in 2050 and 2100 using a global modeling framework and integrated economic, climate, and air pollution projections. Three internally consistent socioeconomic scenarios are used to value health benefits of greenhouse gas mitigation policies specifically derived from slowing climate change. Our projections suggest that climate change, exclusive of changes in air pollutant emissions, can significantly impact ozone (O3) and fine particulate matter (PM2.5) pollution across the U.S. and increase associated health effects. Climate policy can substantially reduce these impacts, and climate-related air pollution health benefits alone can offset a significant fraction of mitigation costs. We find that in contrast to cobenefits from reductions to coemitted pollutants, the climate-induced air quality benefits of policy increase with time and are largest between 2050 and 2100. Our projections also suggest that increasing climate policy stringency beyond a certain degree may lead to diminishing returns relative to its cost. However, our results indicate that the air quality impacts of climate change are substantial and should be considered by cost-benefit climate policy analyses.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close We evaluate the impact of climate change on U.S. air quality and health in 2050 and 2100 using a global modeling framework and integrated economic, climate, and air pollution projections. Three internally consistent socioeconomic scenarios are used to value health benefits of greenhouse gas mitigation policies specifically derived from slowing climate change. Our projections suggest that climate change, exclusive of changes in air pollutant emissions, can significantly impact ozone (O3) and fine particulate matter (PM2.5) pollution across the U.S. and increase associated health effects. Climate policy can substantially reduce these impacts, and climate-related air pollution health benefits alone can offset a significant fraction of mitigation costs. We find that in contrast to cobenefits from reductions to coemitted pollutants, the climate-induced air quality benefits of policy increase with time and are largest between 2050 and 2100. Our projections also suggest that increasing climate policy stringency beyond a certain degree may lead to diminishing returns relative to its cost. However, our results indicate that the air quality impacts of climate change are substantial and should be considered by cost-benefit climate policy analyses. Close doi:10.1021/acs.est.5b01324 Close
17.	K Strzepek, Smith J, J Martinich, J Neumann, B Boehlert, M Hejazi, J Henderson, C Wobus, K Calvin, D Johnson, R Jones, E Monier, J Strzepek, J Yoon Benefits of Greenhouse Gas Mitigation on the Supply, Management, and Use of Water Resources in the United States Journal Article Climatic Change, 131 (1), pp. 127–141, 2015. Abstract \| Links \| BibTeX @article{strzepek2015benefits, title = {Benefits of Greenhouse Gas Mitigation on the Supply, Management, and Use of Water Resources in the United States}, author = {K Strzepek and Smith J and J Martinich and J Neumann and B Boehlert and M Hejazi and J Henderson and C Wobus and K Calvin and D Johnson and R Jones and E Monier and J Strzepek and J Yoon}, doi = {10.1007/s10584-014-1279-9}, year = {2015}, date = {2015-07-01}, journal = {Climatic Change}, volume = {131}, number = {1}, pages = {127--141}, abstract = {Climate change impacts on water resources in the United States are likely to be far-reaching and substantial because the water is integral to climate, and the water sector spans many parts of the economy. This paper estimates impacts and damages from five water resource-related models addressing runoff, drought risk, economics of water supply/demand, water stress, and flooding damages. The models differ in the water system assessed, spatial scale, and unit of assessment, but together provide a quantitative and descriptive richness in characterizing water sector effects that no single model can capture. The results, driven by a consistent set of greenhouse gas (GHG) emission and climate scenarios, examine uncertainty from emissions, climate sensitivity, and climate model selection. While calculating the net impact of climate change on the water sector as a whole may be impractical, broad conclusions can be drawn regarding patterns of change and benefits of GHG mitigation. Four key findings emerge: 1) GHG mitigation substantially reduces hydro-climatic impacts on the water sector; 2) GHG mitigation provides substantial national economic benefits in water resources related sectors; 3) the models show a strong signal of wetting for the Eastern US and a strong signal of drying in the Southwest; and 4) unmanaged hydrologic systems impacts show strong correlation with the change in magnitude and direction of precipitation and temperature from climate models, but managed water resource systems and regional economic systems show lower correlation with changes in climate variables due to non-linearities created by water infrastructure and the socio-economic changes in non-climate driven water demand.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Climate change impacts on water resources in the United States are likely to be far-reaching and substantial because the water is integral to climate, and the water sector spans many parts of the economy. This paper estimates impacts and damages from five water resource-related models addressing runoff, drought risk, economics of water supply/demand, water stress, and flooding damages. The models differ in the water system assessed, spatial scale, and unit of assessment, but together provide a quantitative and descriptive richness in characterizing water sector effects that no single model can capture. The results, driven by a consistent set of greenhouse gas (GHG) emission and climate scenarios, examine uncertainty from emissions, climate sensitivity, and climate model selection. While calculating the net impact of climate change on the water sector as a whole may be impractical, broad conclusions can be drawn regarding patterns of change and benefits of GHG mitigation. Four key findings emerge: 1) GHG mitigation substantially reduces hydro-climatic impacts on the water sector; 2) GHG mitigation provides substantial national economic benefits in water resources related sectors; 3) the models show a strong signal of wetting for the Eastern US and a strong signal of drying in the Southwest; and 4) unmanaged hydrologic systems impacts show strong correlation with the change in magnitude and direction of precipitation and temperature from climate models, but managed water resource systems and regional economic systems show lower correlation with changes in climate variables due to non-linearities created by water infrastructure and the socio-economic changes in non-climate driven water demand. Close doi:10.1007/s10584-014-1279-9 Close
16.	S Paltsev, E Monier, J Scott, A Sokolov, J Reilly Integrated economic and climate projections for impact assessment Journal Article Climatic Change, 131 (1), pp. 21–33, 2015. Abstract \| Links \| BibTeX @article{paltsev2015integrated, title = {Integrated economic and climate projections for impact assessment}, author = {S Paltsev and E Monier and J Scott and A Sokolov and J Reilly}, doi = {10.1007/s10584-013-0892-3}, year = {2015}, date = {2015-07-01}, journal = {Climatic Change}, volume = {131}, number = {1}, pages = {21--33}, abstract = {We designed scenarios for impact assessment that explicitly address policy choices and uncertainty in climate response. Economic projections and the resulting greenhouse gas emissions for the “no climate policy” scenario and two stabilization scenarios: at 4.5 W/m2 and 3.7 W/m2 by 2100 are provided. They can be used for a broader climate impact assessment for the US and other regions, with the goal of making it possible to provide a more consistent picture of climate impacts, and how those impacts depend on uncertainty in climate system response and policy choices. The long-term risks, beyond 2050, of climate change can be strongly influenced by policy choices. In the nearer term, the climate we will observe is hard to influence with policy, and what we actually see will be strongly influenced by natural variability and the earth system response to existing greenhouse gases. In the end, the nature of the system is that a strong effect of policy, especially directed toward long-lived GHGs, will lag by 30 to 40 years its implementation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close We designed scenarios for impact assessment that explicitly address policy choices and uncertainty in climate response. Economic projections and the resulting greenhouse gas emissions for the “no climate policy” scenario and two stabilization scenarios: at 4.5 W/m2 and 3.7 W/m2 by 2100 are provided. They can be used for a broader climate impact assessment for the US and other regions, with the goal of making it possible to provide a more consistent picture of climate impacts, and how those impacts depend on uncertainty in climate system response and policy choices. The long-term risks, beyond 2050, of climate change can be strongly influenced by policy choices. In the nearer term, the climate we will observe is hard to influence with policy, and what we actually see will be strongly influenced by natural variability and the earth system response to existing greenhouse gases. In the end, the nature of the system is that a strong effect of policy, especially directed toward long-lived GHGs, will lag by 30 to 40 years its implementation. Close doi:10.1007/s10584-013-0892-3 Close
15.	E Monier, X Gao, JR Scott, AP Sokolov, CA Schlosser A framework for modeling uncertainty in regional climate change Journal Article Climatic Change, 131 (1), pp. 51–66, 2015. Abstract \| Links \| BibTeX @article{monier2015framework, title = {A framework for modeling uncertainty in regional climate change}, author = {E Monier and X Gao and JR Scott and AP Sokolov and CA Schlosser}, doi = {10.1007/s10584-014-1112-5}, year = {2015}, date = {2015-07-01}, journal = {Climatic Change}, volume = {131}, number = {1}, pages = {51--66}, abstract = {In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSM-CAM framework, which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSM-CAM framework, which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US. Close doi:10.1007/s10584-014-1112-5 Close
14.	E Monier, X Gao Climate change impacts on extreme events in the United States: an uncertainty analysis Journal Article Climatic Change, 131 (1), pp. 67–81, 2015. Abstract \| Links \| BibTeX @article{monier2015climate, title = {Climate change impacts on extreme events in the United States: an uncertainty analysis}, author = {E Monier and X Gao}, doi = {10.1007/s10584-013-1048-1}, year = {2015}, date = {2015-07-01}, journal = {Climatic Change}, volume = {131}, number = {1}, pages = {67--81}, abstract = {In this study, we analyze changes in extreme temperature and precipitation over the US in a 60-member ensemble simulation of the 21st century with the Massachusetts Institute of Technology (MIT) Integrated Global System Model–Community Atmosphere Model (IGSM-CAM). Four values of climate sensitivity, three emissions scenarios and five initial conditions are considered. The results show a general intensification and an increase in the frequency of extreme hot temperatures and extreme precipitation events over most of the US. Extreme cold temperatures are projected to decrease in intensity and frequency, especially over the northern parts of the US. This study displays a wide range of future changes in extreme events in the US, even simulated by a single climate model. Results clearly show that the choice of policy is the largest source of uncertainty in the magnitude of the changes. The impact of the climate sensitivity is largest for the unconstrained emissions scenario and the implementation of a stabilization scenario drastically reduces the changes in extremes, even for the highest climate sensitivity considered. Finally, simulations with different initial conditions show conspicuously different patterns and magnitudes of changes in extreme events, underlining the role of natural variability in projections of changes in extreme events.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close In this study, we analyze changes in extreme temperature and precipitation over the US in a 60-member ensemble simulation of the 21st century with the Massachusetts Institute of Technology (MIT) Integrated Global System Model–Community Atmosphere Model (IGSM-CAM). Four values of climate sensitivity, three emissions scenarios and five initial conditions are considered. The results show a general intensification and an increase in the frequency of extreme hot temperatures and extreme precipitation events over most of the US. Extreme cold temperatures are projected to decrease in intensity and frequency, especially over the northern parts of the US. This study displays a wide range of future changes in extreme events in the US, even simulated by a single climate model. Results clearly show that the choice of policy is the largest source of uncertainty in the magnitude of the changes. The impact of the climate sensitivity is largest for the unconstrained emissions scenario and the implementation of a stabilization scenario drastically reduces the changes in extremes, even for the highest climate sensitivity considered. Finally, simulations with different initial conditions show conspicuously different patterns and magnitudes of changes in extreme events, underlining the role of natural variability in projections of changes in extreme events. Close doi:10.1007/s10584-013-1048-1 Close
13.	D Mills, R Jones, K Carney, A St Juliana, R Ready, A Crimmins, J Martinich, K Shouse, B DeAngelo, E Monier Quantifying and Monetizing Potential Climate Change Policy Impacts on Terrestrial Ecosystem Carbon Storage and Wildfires in the United States Journal Article Climatic Change, 131 (1), pp. 163–178, 2015. Abstract \| Links \| BibTeX @article{mills2015quantifying, title = {Quantifying and Monetizing Potential Climate Change Policy Impacts on Terrestrial Ecosystem Carbon Storage and Wildfires in the United States}, author = {D Mills and R Jones and K Carney and A St Juliana and R Ready and A Crimmins and J Martinich and K Shouse and B DeAngelo and E Monier}, doi = {10.1007/s10584-014-1118-z}, year = {2015}, date = {2015-07-01}, journal = {Climatic Change}, volume = {131}, number = {1}, pages = {163--178}, abstract = {This paper develops and applies methods to quantify and monetize projected impacts on terrestrial ecosystem carbon storage and areas burned by wildfires in the contiguous United States under scenarios with and without global greenhouse gas mitigation. The MC1 dynamic global vegetation model is used to develop physical impact projections using three climate models that project a range of future conditions. We also investigate the sensitivity of future climates to different initial conditions of the climate model. Our analysis reveals that mitigation, where global radiative forcing is stabilized at 3.7 W/m2 in 2100, would consistently reduce areas burned from 2001 to 2100 by tens of millions of hectares. Monetized, these impacts are equivalent to potentially avoiding billions of dollars (discounted) in wildfire response costs. Impacts to terrestrial ecosystem carbon storage are less uniform, but changes are on the order of billions of tons over this time period. The equivalent social value of these changes in carbon storage ranges from hundreds of billions to trillions of dollars (discounted). The magnitude of these results highlights their importance when evaluating climate policy options. However, our results also show national outcomes are driven by a few regions and results are not uniform across regions, time periods, or models. Differences in the results based on the modeling approach and across initializing conditions also raise important questions about how variability in projected climates is accounted for, especially when considering impacts where extreme or threshold conditions are important.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close This paper develops and applies methods to quantify and monetize projected impacts on terrestrial ecosystem carbon storage and areas burned by wildfires in the contiguous United States under scenarios with and without global greenhouse gas mitigation. The MC1 dynamic global vegetation model is used to develop physical impact projections using three climate models that project a range of future conditions. We also investigate the sensitivity of future climates to different initial conditions of the climate model. Our analysis reveals that mitigation, where global radiative forcing is stabilized at 3.7 W/m2 in 2100, would consistently reduce areas burned from 2001 to 2100 by tens of millions of hectares. Monetized, these impacts are equivalent to potentially avoiding billions of dollars (discounted) in wildfire response costs. Impacts to terrestrial ecosystem carbon storage are less uniform, but changes are on the order of billions of tons over this time period. The equivalent social value of these changes in carbon storage ranges from hundreds of billions to trillions of dollars (discounted). The magnitude of these results highlights their importance when evaluating climate policy options. However, our results also show national outcomes are driven by a few regions and results are not uniform across regions, time periods, or models. Differences in the results based on the modeling approach and across initializing conditions also raise important questions about how variability in projected climates is accounted for, especially when considering impacts where extreme or threshold conditions are important. Close doi:10.1007/s10584-014-1118-z Close
12.	L Xu, RD Pyles, KT Paw U, SH Chen, E Monier Coupling the high-complexity land surface model ACASA to the mesoscale model WRF Journal Article Geoscientific Model Development, 7 , pp. 2917–2932, 2014. Abstract \| Links \| BibTeX @article{xu2014coupling, title = {Coupling the high-complexity land surface model ACASA to the mesoscale model WRF}, author = {L Xu and RD Pyles and KT Paw U and SH Chen and E Monier}, doi = {10.5194/gmd-7-2917-2014}, year = {2014}, date = {2014-12-10}, journal = {Geoscientific Model Development}, volume = {7}, pages = {2917--2932}, abstract = {In this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high-complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF, such as the popular NOAH model, are simple and lack the capability of representing the canopy structure. In contrast, ACASA is a complex multilayer land surface model with interactive canopy physiology and high-order turbulence closure that allows for an accurate representation of heat, momentum, water, and carbon dioxide fluxes between the land surface and the atmosphere. It allows for microenvironmental variables such as surface air temperature, wind speed, humidity, and carbon dioxide concentration to vary vertically within and above the canopy. Surface meteorological conditions, including air temperature, dew point temperature, and relative humidity, simulated by WRF-ACASA and WRF-NOAH are compared and evaluated with observations from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy but also properly accounts for the dominant biological and physical processes describing ecosystem–atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF-ACASA and WRF-NOAH models also highlight the impact of different land surface models on atmospheric and surface conditions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close In this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high-complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF, such as the popular NOAH model, are simple and lack the capability of representing the canopy structure. In contrast, ACASA is a complex multilayer land surface model with interactive canopy physiology and high-order turbulence closure that allows for an accurate representation of heat, momentum, water, and carbon dioxide fluxes between the land surface and the atmosphere. It allows for microenvironmental variables such as surface air temperature, wind speed, humidity, and carbon dioxide concentration to vary vertically within and above the canopy. Surface meteorological conditions, including air temperature, dew point temperature, and relative humidity, simulated by WRF-ACASA and WRF-NOAH are compared and evaluated with observations from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF-ACASA model not only maintains model accuracy but also properly accounts for the dominant biological and physical processes describing ecosystem–atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF-ACASA and WRF-NOAH models also highlight the impact of different land surface models on atmospheric and surface conditions. Close doi:10.5194/gmd-7-2917-2014 Close
11.	X Gao, CA Schlosser, P Xie, E Monier, D Entekhabi An Analogue Approach to Identify Heavy Precipitation Events: Evaluation and Application to CMIP5 Climate Models in the United States Journal Article Journal of Climate, 27 , pp. 5941–5963, 2014. Abstract \| Links \| BibTeX @article{gao2014analogue, title = {An Analogue Approach to Identify Heavy Precipitation Events: Evaluation and Application to CMIP5 Climate Models in the United States}, author = {X Gao and CA Schlosser and P Xie and E Monier and D Entekhabi}, doi = {10.1175/JCLI-D-13-00598.1}, year = {2014}, date = {2014-08-01}, journal = {Journal of Climate}, volume = {27}, pages = {5941--5963}, abstract = {An analogue method is presented to detect the occurrence of heavy precipitation events without relying on modeled precipitation. The approach is based on using composites to identify distinct large-scale atmospheric conditions associated with widespread heavy precipitation events across local scales. These composites, exemplified in the south-central, midwestern, and western United States, are derived through the analysis of 27-yr (1979–2005) Climate Prediction Center (CPC) gridded station data and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). Circulation features and moisture plumes associated with heavy precipitation events are examined. The analogues are evaluated against the relevant daily meteorological fields from the MERRA reanalysis and achieve a success rate of around 80% in detecting observed heavy events within one or two days. The method also captures the observed interannual variations of seasonal heavy events with higher correlation and smaller RMSE than MERRA precipitation. When applied to the same 27-yr twentieth-century climate model simulations from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), the analogue method produces a more consistent and less uncertain number of seasonal heavy precipitation events with observation as opposed to using model-simulated precipitation. The analogue method also performs better than model-based precipitation in characterizing the statistics (minimum, lower and upper quartile, median, and maximum) of year-to-year seasonal heavy precipitation days. These results indicate the capability of CMIP5 models to realistically simulate large-scale atmospheric conditions associated with widespread local-scale heavy precipitation events with a credible frequency. Overall, the presented analyses highlight the improved diagnoses of the analogue method against an evaluation that considers modeled precipitation alone to assess heavy precipitation frequency.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close An analogue method is presented to detect the occurrence of heavy precipitation events without relying on modeled precipitation. The approach is based on using composites to identify distinct large-scale atmospheric conditions associated with widespread heavy precipitation events across local scales. These composites, exemplified in the south-central, midwestern, and western United States, are derived through the analysis of 27-yr (1979–2005) Climate Prediction Center (CPC) gridded station data and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). Circulation features and moisture plumes associated with heavy precipitation events are examined. The analogues are evaluated against the relevant daily meteorological fields from the MERRA reanalysis and achieve a success rate of around 80% in detecting observed heavy events within one or two days. The method also captures the observed interannual variations of seasonal heavy events with higher correlation and smaller RMSE than MERRA precipitation. When applied to the same 27-yr twentieth-century climate model simulations from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), the analogue method produces a more consistent and less uncertain number of seasonal heavy precipitation events with observation as opposed to using model-simulated precipitation. The analogue method also performs better than model-based precipitation in characterizing the statistics (minimum, lower and upper quartile, median, and maximum) of year-to-year seasonal heavy precipitation days. These results indicate the capability of CMIP5 models to realistically simulate large-scale atmospheric conditions associated with widespread local-scale heavy precipitation events with a credible frequency. Overall, the presented analyses highlight the improved diagnoses of the analogue method against an evaluation that considers modeled precipitation alone to assess heavy precipitation frequency. Close doi:10.1175/JCLI-D-13-00598.1 Close
10.	E Monier, JR Scott, AP Sokolov, CE Forest, CA Schlosser An integrated assessment modelling framework for uncertainty studies in global and regional climate change: the MIT IGSM-CAM (version 1.0) Journal Article Geoscientific Model Development, 6 (6), pp. 2063–2085, 2013. Abstract \| Links \| BibTeX @article{monier2013integrated, title = {An integrated assessment modelling framework for uncertainty studies in global and regional climate change: the MIT IGSM-CAM (version 1.0)}, author = {E Monier and JR Scott and AP Sokolov and CE Forest and CA Schlosser}, doi = {10.5194/gmd-6-2063-2013}, year = {2013}, date = {2013-12-04}, journal = {Geoscientific Model Development}, volume = {6}, number = {6}, pages = {2063--2085}, abstract = {This paper describes a computationally efficient framework for uncertainty studies in global and regional climate change. In this framework, the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity to a human activity model, is linked to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Since the MIT IGSM-CAM framework (version 1.0) incorporates a human activity model, it is possible to analyze uncertainties in emissions resulting from both uncertainties in the underlying socio-economic characteristics of the economic model and in the choice of climate-related policies. Another major feature is the flexibility to vary key climate parameters controlling the climate system response to changes in greenhouse gases and aerosols concentrations, e.g., climate sensitivity, ocean heat uptake rate, and strength of the aerosol forcing. The IGSM-CAM is not only able to realistically simulate the present-day mean climate and the observed trends at the global and continental scale, but it also simulates ENSO variability with realistic time scales, seasonality and patterns of SST anomalies, albeit with stronger magnitudes than observed. The IGSM-CAM shares the same general strengths and limitations as the Coupled Model Intercomparison Project Phase 3 (CMIP3) models in simulating present-day annual mean surface temperature and precipitation. Over land, the IGSM-CAM shows similar biases to the NCAR Community Climate System Model (CCSM) version 3, which shares the same atmospheric model. This study also presents 21st century simulations based on two emissions scenarios (unconstrained scenario and stabilization scenario at 660 ppm CO2-equivalent) similar to, respectively, the Representative Concentration Pathways RCP8.5 and RCP4.5 scenarios, and three sets of climate parameters. Results of the simulations with the chosen climate parameters provide a good approximation for the median, and the 5th and 95th percentiles of the probability distribution of 21st century changes in global mean surface air temperature from previous work with the IGSM. Because the IGSM-CAM framework only considers one particular climate model, it cannot be used to assess the structural modeling uncertainty arising from differences in the parameterization suites of climate models. However, comparison of the IGSM-CAM projections with simulations of 31 CMIP5 models under the RCP4.5 and RCP8.5 scenarios show that the range of warming at the continental scale shows very good agreement between the two ensemble simulations, except over Antarctica, where the IGSM-CAM overestimates the warming. This demonstrates that by sampling the climate system response, the IGSM-CAM, even though it relies on one single climate model, can essentially reproduce the range of future continental warming simulated by more than 30 different models. Precipitation changes projected in the IGSM-CAM simulations and the CMIP5 multi-model ensemble both display a large uncertainty at the continental scale. The two ensemble simulations show good agreement over Asia and Europe. However, the ranges of precipitation changes do not overlap – but display similar size – over Africa and South America, two continents where models generally show little agreement in the sign of precipitation changes and where CCSM3 tends to be an outlier. Overall, the IGSM-CAM provides an efficient and consistent framework to explore the large uncertainty in future projections of global and regional climate change associated with uncertainty in the climate response and projected emissions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close This paper describes a computationally efficient framework for uncertainty studies in global and regional climate change. In this framework, the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity to a human activity model, is linked to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Since the MIT IGSM-CAM framework (version 1.0) incorporates a human activity model, it is possible to analyze uncertainties in emissions resulting from both uncertainties in the underlying socio-economic characteristics of the economic model and in the choice of climate-related policies. Another major feature is the flexibility to vary key climate parameters controlling the climate system response to changes in greenhouse gases and aerosols concentrations, e.g., climate sensitivity, ocean heat uptake rate, and strength of the aerosol forcing. The IGSM-CAM is not only able to realistically simulate the present-day mean climate and the observed trends at the global and continental scale, but it also simulates ENSO variability with realistic time scales, seasonality and patterns of SST anomalies, albeit with stronger magnitudes than observed. The IGSM-CAM shares the same general strengths and limitations as the Coupled Model Intercomparison Project Phase 3 (CMIP3) models in simulating present-day annual mean surface temperature and precipitation. Over land, the IGSM-CAM shows similar biases to the NCAR Community Climate System Model (CCSM) version 3, which shares the same atmospheric model. This study also presents 21st century simulations based on two emissions scenarios (unconstrained scenario and stabilization scenario at 660 ppm CO2-equivalent) similar to, respectively, the Representative Concentration Pathways RCP8.5 and RCP4.5 scenarios, and three sets of climate parameters. Results of the simulations with the chosen climate parameters provide a good approximation for the median, and the 5th and 95th percentiles of the probability distribution of 21st century changes in global mean surface air temperature from previous work with the IGSM. Because the IGSM-CAM framework only considers one particular climate model, it cannot be used to assess the structural modeling uncertainty arising from differences in the parameterization suites of climate models. However, comparison of the IGSM-CAM projections with simulations of 31 CMIP5 models under the RCP4.5 and RCP8.5 scenarios show that the range of warming at the continental scale shows very good agreement between the two ensemble simulations, except over Antarctica, where the IGSM-CAM overestimates the warming. This demonstrates that by sampling the climate system response, the IGSM-CAM, even though it relies on one single climate model, can essentially reproduce the range of future continental warming simulated by more than 30 different models. Precipitation changes projected in the IGSM-CAM simulations and the CMIP5 multi-model ensemble both display a large uncertainty at the continental scale. The two ensemble simulations show good agreement over Asia and Europe. However, the ranges of precipitation changes do not overlap – but display similar size – over Africa and South America, two continents where models generally show little agreement in the sign of precipitation changes and where CCSM3 tends to be an outlier. Overall, the IGSM-CAM provides an efficient and consistent framework to explore the large uncertainty in future projections of global and regional climate change associated with uncertainty in the climate response and projected emissions. Close doi:10.5194/gmd-6-2063-2013 Close
9.	E Monier, A Sokolov, A Schlosser, J Scott, X Gao Probabilistic projections of 21st century climate change over Northern Eurasia Journal Article Environmental Research Letters, 8 , pp. 045008, 2013. Abstract \| Links \| BibTeX @article{monier2013probabilistic, title = {Probabilistic projections of 21st century climate change over Northern Eurasia}, author = {E Monier and A Sokolov and A Schlosser and J Scott and X Gao}, doi = {10.1088/1748-9326/8/4/045008}, year = {2013}, date = {2013-10-13}, journal = {Environmental Research Letters}, volume = {8}, pages = {045008}, abstract = {We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity with a two-dimensional zonal-mean atmosphere to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three-dimensional atmospheric model, and a statistical downscaling, where a pattern scaling algorithm uses climate change patterns from 17 climate models. This framework allows for four major sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections, climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate), natural variability, and structural uncertainty. The results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also find that different initial conditions lead to differences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider these sources of uncertainty when modeling climate impacts over Northern Eurasia.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity with a two-dimensional zonal-mean atmosphere to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three-dimensional atmospheric model, and a statistical downscaling, where a pattern scaling algorithm uses climate change patterns from 17 climate models. This framework allows for four major sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections, climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate), natural variability, and structural uncertainty. The results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also find that different initial conditions lead to differences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider these sources of uncertainty when modeling climate impacts over Northern Eurasia. Close doi:10.1088/1748-9326/8/4/045008 Close
8.	K Zickfeld, M Eby, AJ Weaver, K Alexander, E Crespin, NR Edwards, AV Eliseev, G Feulner, T Fichefet, CE Forest, P Friedlingstein, H Goose, PB Holden, F Joos, M Kawamiya, D Kicklighter, H Kienert, K Matsumoto, II Mokhov, E Monier, SM Olsen, JOP Pedersen, M Perrette, G Philippon-Berthier, A Ridgwell, A Schlosser, Schneider T von Deimling, G Shaffer, A Sokolov, R Spahni, M Steinacher, K Tachiiri, KS Tokos, M Yoshimori, N Zeng, F Zhao Long-Term Climate Change Commitment and Reversibility: An EMIC Intercomparison Journal Article Journal of Climate, 26 (16), pp. 5782–5809, 2013. Abstract \| Links \| BibTeX @article{zickfeld2013long, title = {Long-Term Climate Change Commitment and Reversibility: An EMIC Intercomparison}, author = {K Zickfeld and M Eby and AJ Weaver and K Alexander and E Crespin and NR Edwards and AV Eliseev and G Feulner and T Fichefet and CE Forest and P Friedlingstein and H Goose and PB Holden and F Joos and M Kawamiya and D Kicklighter and H Kienert and K Matsumoto and II Mokhov and E Monier and SM Olsen and JOP Pedersen and M Perrette and G Philippon-Berthier and A Ridgwell and A Schlosser and Schneider T von Deimling and G Shaffer and A Sokolov and R Spahni and M Steinacher and K Tachiiri and KS Tokos and M Yoshimori and N Zeng and F Zhao}, doi = {10.1175/JCLI-D-12-00584.1}, year = {2013}, date = {2013-08-15}, journal = {Journal of Climate}, volume = {26}, number = {16}, pages = {5782--5809}, abstract = {This paper summarizes the results of an intercomparison project with Earth System Models of Intermediate Complexity (EMICs) undertaken in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). The focus is on long-term climate projections designed to 1) quantify the climate change commitment of different radiative forcing trajectories and 2) explore the extent to which climate change is reversible on human time scales. All commitment simulations follow the four representative concentration pathways (RCPs) and their extensions to year 2300. Most EMICs simulate substantial surface air temperature and thermosteric sea level rise commitment following stabilization of the atmospheric composition at year-2300 levels. The meridional overturning circulation (MOC) is weakened temporarily and recovers to near-preindustrial values in most models for RCPs 2.6–6.0. The MOC weakening is more persistent for RCP8.5. Elimination of anthropogenic CO2 emissions after 2300 results in slowly decreasing atmospheric CO2 concentrations. At year 3000 atmospheric CO2 is still at more than half its year-2300 level in all EMICs for RCPs 4.5–8.5. Surface air temperature remains constant or decreases slightly and thermosteric sea level rise continues for centuries after elimination of CO2 emissions in all EMICs. Restoration of atmospheric CO2 from RCP to preindustrial levels over 100–1000 years requires large artificial removal of CO2 from the atmosphere and does not result in the simultaneous return to preindustrial climate conditions, as surface air temperature and sea level response exhibit a substantial time lag relative to atmospheric CO2.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close This paper summarizes the results of an intercomparison project with Earth System Models of Intermediate Complexity (EMICs) undertaken in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). The focus is on long-term climate projections designed to 1) quantify the climate change commitment of different radiative forcing trajectories and 2) explore the extent to which climate change is reversible on human time scales. All commitment simulations follow the four representative concentration pathways (RCPs) and their extensions to year 2300. Most EMICs simulate substantial surface air temperature and thermosteric sea level rise commitment following stabilization of the atmospheric composition at year-2300 levels. The meridional overturning circulation (MOC) is weakened temporarily and recovers to near-preindustrial values in most models for RCPs 2.6–6.0. The MOC weakening is more persistent for RCP8.5. Elimination of anthropogenic CO2 emissions after 2300 results in slowly decreasing atmospheric CO2 concentrations. At year 3000 atmospheric CO2 is still at more than half its year-2300 level in all EMICs for RCPs 4.5–8.5. Surface air temperature remains constant or decreases slightly and thermosteric sea level rise continues for centuries after elimination of CO2 emissions in all EMICs. Restoration of atmospheric CO2 from RCP to preindustrial levels over 100–1000 years requires large artificial removal of CO2 from the atmosphere and does not result in the simultaneous return to preindustrial climate conditions, as surface air temperature and sea level response exhibit a substantial time lag relative to atmospheric CO2. Close doi:10.1175/JCLI-D-12-00584.1 Close
7.	M Eby, AJ Weaver, K Alexander, K Zickfeld, A Abe-Ouchi, AA Cimatoribus, E Crespin, SS Drijfhout, NR Edwards, AV Eliseev, G Feulner, T Fichefet, CE Forest, H Goosse, PB Holden, F Joos, M Kawamiya, D Kicklighter, H Kienert, K Matsumoto, II Mokhov, E Monier, SM Olsen, JOP Pedersen, M Perrette, G Philippon-Berthier, A Ridgwell, A Schlosser, Schneider T von Deimling, G Shaffer, RS Smith, R Spahni, AP Sokolov, M Steinacher, K Tachiiri, K Tokos, M Yoshimori, N Zeng, F Zhao Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity Journal Article Climate of the Past, 9 , pp. 1111–1140, 2013. Abstract \| Links \| BibTeX @article{eby2013historical, title = {Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity}, author = {M Eby and AJ Weaver and K Alexander and K Zickfeld and A Abe-Ouchi and AA Cimatoribus and E Crespin and SS Drijfhout and NR Edwards and AV Eliseev and G Feulner and T Fichefet and CE Forest and H Goosse and PB Holden and F Joos and M Kawamiya and D Kicklighter and H Kienert and K Matsumoto and II Mokhov and E Monier and SM Olsen and JOP Pedersen and M Perrette and G Philippon-Berthier and A Ridgwell and A Schlosser and Schneider T von Deimling and G Shaffer and RS Smith and R Spahni and AP Sokolov and M Steinacher and K Tachiiri and K Tokos and M Yoshimori and N Zeng and F Zhao}, doi = {10.5194/cp-9-1111-2013}, year = {2013}, date = {2013-05-16}, journal = {Climate of the Past}, volume = {9}, pages = {1111--1140}, abstract = {Both historical and idealized climate model experiments are performed with a variety of Earth system models of intermediate complexity (EMICs) as part of a community contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report. Historical simulations start at 850 CE and continue through to 2005. The standard simulations include changes in forcing from solar luminosity, Earth's orbital configuration, CO2, additional greenhouse gases, land use, and sulphate and volcanic aerosols. In spite of very different modelled pre-industrial global surface air temperatures, overall 20th century trends in surface air temperature and carbon uptake are reasonably well simulated when compared to observed trends. Land carbon fluxes show much more variation between models than ocean carbon fluxes, and recent land fluxes appear to be slightly underestimated. It is possible that recent modelled climate trends or climate–carbon feedbacks are overestimated resulting in too much land carbon loss or that carbon uptake due to CO2 and/or nitrogen fertilization is underestimated. Several one thousand year long, idealized, 2 × and 4 × CO2 experiments are used to quantify standard model characteristics, including transient and equilibrium climate sensitivities, and climate–carbon feedbacks. The values from EMICs generally fall within the range given by general circulation models. Seven additional historical simulations, each including a single specified forcing, are used to assess the contributions of different climate forcings to the overall climate and carbon cycle response. The response of surface air temperature is the linear sum of the individual forcings, while the carbon cycle response shows a non-linear interaction between land-use change and CO2 forcings for some models. Finally, the preindustrial portions of the last millennium simulations are used to assess historical model carbon-climate feedbacks. Given the specified forcing, there is a tendency for the EMICs to underestimate the drop in surface air temperature and CO2 between the Medieval Climate Anomaly and the Little Ice Age estimated from palaeoclimate reconstructions. This in turn could be a result of unforced variability within the climate system, uncertainty in the reconstructions of temperature and CO2, errors in the reconstructions of forcing used to drive the models, or the incomplete representation of certain processes within the models. Given the forcing datasets used in this study, the models calculate significant land-use emissions over the pre-industrial period. This implies that land-use emissions might need to be taken into account, when making estimates of climate–carbon feedbacks from palaeoclimate reconstructions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Both historical and idealized climate model experiments are performed with a variety of Earth system models of intermediate complexity (EMICs) as part of a community contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report. Historical simulations start at 850 CE and continue through to 2005. The standard simulations include changes in forcing from solar luminosity, Earth's orbital configuration, CO2, additional greenhouse gases, land use, and sulphate and volcanic aerosols. In spite of very different modelled pre-industrial global surface air temperatures, overall 20th century trends in surface air temperature and carbon uptake are reasonably well simulated when compared to observed trends. Land carbon fluxes show much more variation between models than ocean carbon fluxes, and recent land fluxes appear to be slightly underestimated. It is possible that recent modelled climate trends or climate–carbon feedbacks are overestimated resulting in too much land carbon loss or that carbon uptake due to CO2 and/or nitrogen fertilization is underestimated. Several one thousand year long, idealized, 2 × and 4 × CO2 experiments are used to quantify standard model characteristics, including transient and equilibrium climate sensitivities, and climate–carbon feedbacks. The values from EMICs generally fall within the range given by general circulation models. Seven additional historical simulations, each including a single specified forcing, are used to assess the contributions of different climate forcings to the overall climate and carbon cycle response. The response of surface air temperature is the linear sum of the individual forcings, while the carbon cycle response shows a non-linear interaction between land-use change and CO2 forcings for some models. Finally, the preindustrial portions of the last millennium simulations are used to assess historical model carbon-climate feedbacks. Given the specified forcing, there is a tendency for the EMICs to underestimate the drop in surface air temperature and CO2 between the Medieval Climate Anomaly and the Little Ice Age estimated from palaeoclimate reconstructions. This in turn could be a result of unforced variability within the climate system, uncertainty in the reconstructions of temperature and CO2, errors in the reconstructions of forcing used to drive the models, or the incomplete representation of certain processes within the models. Given the forcing datasets used in this study, the models calculate significant land-use emissions over the pre-industrial period. This implies that land-use emissions might need to be taken into account, when making estimates of climate–carbon feedbacks from palaeoclimate reconstructions. Close doi:10.5194/cp-9-1111-2013 Close
6.	W Hallgren, CA Schlosser, E Monier, D Kicklighter, A Sokolov, J Melillo Climate Impacts of a Large-Scale Biofuels Expansion Journal Article Geophysical Research Letters, 40 , pp. 1624–1630, 2013. Abstract \| Links \| BibTeX @article{hallgren2013climate, title = {Climate Impacts of a Large-Scale Biofuels Expansion}, author = {W Hallgren and CA Schlosser and E Monier and D Kicklighter and A Sokolov and J Melillo}, doi = {10.1002/grl.50352}, year = {2013}, date = {2013-04-28}, journal = {Geophysical Research Letters}, volume = {40}, pages = {1624--1630}, abstract = {A global biofuels program will potentially lead to intense pressures on land supply and cause widespread transformations in land use. These transformations can alter the Earth climate system by increasing greenhouse gas (GHG) emissions from land use changes and by changing the reflective and energy exchange characteristics of land ecosystems. Using an integrated assessment model that links an economic model with climate, terrestrial biogeochemistry, and biogeophysics models, we examined the biogeochemical and biogeophysical effects of possible land use changes from an expanded global second‐generation bioenergy program on surface temperatures over the first half of the 21st century. Our integrated assessment model shows that land clearing, especially forest clearing, has two concurrent effects—increased GHG emissions, resulting in surface air warming; and large changes in the land's reflective and energy exchange characteristics, resulting in surface air warming in the tropics but cooling in temperate and polar regions. Overall, these biogeochemical and biogeophysical effects will only have a small impact on global mean surface temperature. However, the model projects regional patterns of enhanced surface air warming in the Amazon Basin and the eastern part of the Congo Basin. Therefore, global land use strategies that protect tropical forests could dramatically reduce air warming projected in these regions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close A global biofuels program will potentially lead to intense pressures on land supply and cause widespread transformations in land use. These transformations can alter the Earth climate system by increasing greenhouse gas (GHG) emissions from land use changes and by changing the reflective and energy exchange characteristics of land ecosystems. Using an integrated assessment model that links an economic model with climate, terrestrial biogeochemistry, and biogeophysics models, we examined the biogeochemical and biogeophysical effects of possible land use changes from an expanded global second‐generation bioenergy program on surface temperatures over the first half of the 21st century. Our integrated assessment model shows that land clearing, especially forest clearing, has two concurrent effects—increased GHG emissions, resulting in surface air warming; and large changes in the land's reflective and energy exchange characteristics, resulting in surface air warming in the tropics but cooling in temperate and polar regions. Overall, these biogeochemical and biogeophysical effects will only have a small impact on global mean surface temperature. However, the model projects regional patterns of enhanced surface air warming in the Amazon Basin and the eastern part of the Congo Basin. Therefore, global land use strategies that protect tropical forests could dramatically reduce air warming projected in these regions. Close doi:10.1002/grl.50352 Close
5.	J Reilly, S Paltsev, K Strzepek, NE Selin, Y Cai, K-M Nam, E Monier, S Dutkiewicz, J Scott, M Webster, A Sokolov Valuing climate impacts in integrated assessment models: the MIT IGSM Journal Article Climatic Change, 117 (3), pp. 561–573, 2013. Abstract \| Links \| BibTeX @article{reilly2013valuing, title = {Valuing climate impacts in integrated assessment models: the MIT IGSM}, author = {J Reilly and S Paltsev and K Strzepek and NE Selin and Y Cai and K-M Nam and E Monier and S Dutkiewicz and J Scott and M Webster and A Sokolov}, doi = {10.1007/s10584-012-0635-x}, year = {2013}, date = {2013-04-01}, journal = {Climatic Change}, volume = {117}, number = {3}, pages = {561--573}, abstract = {We discuss a strategy for investigating the impacts of climate change on Earth’s physical, biological and human resources and links to their socio-economic consequences. As examples, we consider effects on agriculture and human health. Progress requires a careful understanding of the chain of physical changes—global and regional temperature, precipitation, ocean acidification, polar ice melting. We relate those changes to other physical and biological variables that help people understand risks to factors relevant to their daily lives—crop yield, food prices, premature death, flooding or drought events, land use change. Finally, we investigate how societies may adapt, or not, to these changes and how the combination of measures to adapt or to live with losses will affect the economy. Valuation and assessment of market impacts can play an important role, but we must recognize the limits of efforts to value impacts where deep uncertainty does not allow a description of the causal chain of effects that can be described, much less assigned a likelihood. A mixed approach of valuing impacts, evaluating physical and biological effects, and working to better describe uncertainties in the earth system can contribute to the social dialogue needed to achieve consensus on the level and type of mitigation and adaptation actions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close We discuss a strategy for investigating the impacts of climate change on Earth’s physical, biological and human resources and links to their socio-economic consequences. As examples, we consider effects on agriculture and human health. Progress requires a careful understanding of the chain of physical changes—global and regional temperature, precipitation, ocean acidification, polar ice melting. We relate those changes to other physical and biological variables that help people understand risks to factors relevant to their daily lives—crop yield, food prices, premature death, flooding or drought events, land use change. Finally, we investigate how societies may adapt, or not, to these changes and how the combination of measures to adapt or to live with losses will affect the economy. Valuation and assessment of market impacts can play an important role, but we must recognize the limits of efforts to value impacts where deep uncertainty does not allow a description of the causal chain of effects that can be described, much less assigned a likelihood. A mixed approach of valuing impacts, evaluating physical and biological effects, and working to better describe uncertainties in the earth system can contribute to the social dialogue needed to achieve consensus on the level and type of mitigation and adaptation actions. Close doi:10.1007/s10584-012-0635-x Close
4.	AP Sokolov, E Monier Changing the Climate Sensitivity of an Atmospheric General Circulation Model through Cloud Radiative Adjustment Journal Article Journal of Climate, 25 (19), pp. 6567–6584, 2012. Abstract \| Links \| BibTeX @article{sokolov2012changing, title = {Changing the Climate Sensitivity of an Atmospheric General Circulation Model through Cloud Radiative Adjustment}, author = {AP Sokolov and E Monier}, doi = {10.1175/JCLI-D-11-00590.1}, year = {2012}, date = {2012-04-09}, journal = {Journal of Climate}, volume = {25}, number = {19}, pages = {6567--6584}, abstract = {Conducting probabilistic climate projections with a particular climate model requires the ability to vary the model’s characteristics, such as its climate sensitivity. In this study, the authors implement and validate a method to change the climate sensitivity of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 3 (CAM3), through cloud radiative adjustment. Results show that the cloud radiative adjustment method does not lead to physically unrealistic changes in the model’s response to an external forcing, such as doubling CO2 concentrations or increasing sulfate aerosol concentrations. Furthermore, this method has some advantages compared to the traditional perturbed physics approach. In particular, the cloud radiative adjustment method can produce any value of climate sensitivity within the wide range of uncertainty based on the observed twentieth century climate change. As a consequence, this method allows Monte Carlo–type probabilistic climate forecasts to be conducted where values of uncertain parameters not only cover the whole uncertainty range, but cover it homogeneously. Unlike the perturbed physics approach that can produce several versions of a model with the same climate sensitivity but with very different regional patterns of change, the cloud radiative adjustment method can only produce one version of the model with a specific climate sensitivity. As such, a limitation of this method is that it cannot cover the full uncertainty in regional patterns of climate change.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Conducting probabilistic climate projections with a particular climate model requires the ability to vary the model’s characteristics, such as its climate sensitivity. In this study, the authors implement and validate a method to change the climate sensitivity of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 3 (CAM3), through cloud radiative adjustment. Results show that the cloud radiative adjustment method does not lead to physically unrealistic changes in the model’s response to an external forcing, such as doubling CO2 concentrations or increasing sulfate aerosol concentrations. Furthermore, this method has some advantages compared to the traditional perturbed physics approach. In particular, the cloud radiative adjustment method can produce any value of climate sensitivity within the wide range of uncertainty based on the observed twentieth century climate change. As a consequence, this method allows Monte Carlo–type probabilistic climate forecasts to be conducted where values of uncertain parameters not only cover the whole uncertainty range, but cover it homogeneously. Unlike the perturbed physics approach that can produce several versions of a model with the same climate sensitivity but with very different regional patterns of change, the cloud radiative adjustment method can only produce one version of the model with a specific climate sensitivity. As such, a limitation of this method is that it cannot cover the full uncertainty in regional patterns of climate change. Close doi:10.1175/JCLI-D-11-00590.1 Close
3.	E Monier, BC Weare Climatology and trends in the forcing of the stratospheric zonal-mean flow Journal Article Atmospheric Chemistry and Physics, 11 , pp. 12751–12771, 2011. Abstract \| Links \| BibTeX @article{monier2011climatology2, title = {Climatology and trends in the forcing of the stratospheric zonal-mean flow}, author = {E Monier and BC Weare}, doi = {10.5194/acp-11-12751-2011}, year = {2011}, date = {2011-12-16}, journal = {Atmospheric Chemistry and Physics}, volume = {11}, pages = {12751--12771}, abstract = {The momentum budget of the Transformed Eulerian-Mean (TEM) equation is calculated using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-40) and the National Centers for Environmental Prediction (NCEP) Reanalysis 2 (R-2). This study outlines the considerable contribution of unresolved waves, deduced to be gravity waves, to the forcing of the zonal-mean flow. A trend analysis, from 1980 to 2001, shows that the onset and break down of the Northern Hemisphere (NH) stratospheric polar night jet has a tendency to occur later in the season in the more recent years. This temporal shift follows long-term changes in planetary wave activity that are mainly due to synoptic waves, with a lag of one month. In the Southern Hemisphere (SH), the polar vortex shows a tendency to persist further into the SH summertime. This also follows a statistically significant decrease in the intensity of the stationary EP flux divergence over the 1980–2001 period. Ozone depletion is well known for strengthening the polar vortex through the thermal wind balance. However, the results of this work show that the SH polar vortex does not experience any significant long-term changes until the month of December, even though the intensification of the ozone hole occurs mainly between September and November. This study suggests that the decrease in planetary wave activity in November provides an important feedback to the zonal wind as it delays the breakdown of the polar vortex. In addition, the absence of strong eddy feedback before November explains the lack of significant trends in the polar vortex in the SH early spring. A long-term weakening in the Brewer-Dobson (B-D) circulation in the polar region is identified in the NH winter and early spring and during the SH late spring and is likely driven by the decrease in planetary wave activity previously mentioned. During the rest of the year, there are large discrepancies in the representation of the B-D circulation and the unresolved waves between the two reanalyses, making trend analyses unreliable.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close The momentum budget of the Transformed Eulerian-Mean (TEM) equation is calculated using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-40) and the National Centers for Environmental Prediction (NCEP) Reanalysis 2 (R-2). This study outlines the considerable contribution of unresolved waves, deduced to be gravity waves, to the forcing of the zonal-mean flow. A trend analysis, from 1980 to 2001, shows that the onset and break down of the Northern Hemisphere (NH) stratospheric polar night jet has a tendency to occur later in the season in the more recent years. This temporal shift follows long-term changes in planetary wave activity that are mainly due to synoptic waves, with a lag of one month. In the Southern Hemisphere (SH), the polar vortex shows a tendency to persist further into the SH summertime. This also follows a statistically significant decrease in the intensity of the stationary EP flux divergence over the 1980–2001 period. Ozone depletion is well known for strengthening the polar vortex through the thermal wind balance. However, the results of this work show that the SH polar vortex does not experience any significant long-term changes until the month of December, even though the intensification of the ozone hole occurs mainly between September and November. This study suggests that the decrease in planetary wave activity in November provides an important feedback to the zonal wind as it delays the breakdown of the polar vortex. In addition, the absence of strong eddy feedback before November explains the lack of significant trends in the polar vortex in the SH early spring. A long-term weakening in the Brewer-Dobson (B-D) circulation in the polar region is identified in the NH winter and early spring and during the SH late spring and is likely driven by the decrease in planetary wave activity previously mentioned. During the rest of the year, there are large discrepancies in the representation of the B-D circulation and the unresolved waves between the two reanalyses, making trend analyses unreliable. Close doi:10.5194/acp-11-12751-2011 Close
2.	E Monier, BC Weare Climatology and trends in the forcing of the stratospheric ozone transport Journal Article Atmospheric Chemistry and Physics, 11 , pp. 6311–6323, 2011. Abstract \| Links \| BibTeX @article{monier2011climatologyb, title = {Climatology and trends in the forcing of the stratospheric ozone transport}, author = {E Monier and BC Weare}, doi = {10.5194/acp-11-6311-2011}, year = {2011}, date = {2011-07-04}, journal = {Atmospheric Chemistry and Physics}, volume = {11}, pages = {6311--6323}, abstract = {A thorough analysis of the ozone transport was carried out using the Transformed-Mean Eulerian (TEM) tracer continuity equation and the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). In this budget analysis, the chemical net production term, which is calculated as the residual of the other terms, displays the correct features of a chemical sink and source term, including location and seasonality, and shows good agreement in magnitude compared to other methods of calculating ozone loss rates. This study provides further insight into the role of the eddy ozone transport and underlines its fundamental role in the recovery of the ozone hole during spring. The trend analysis reveals that the ozone hole intensification over the 1980–2001 period is not solely related to the trend in chemical losses, but more specifically to the balance between the trends in chemical losses and ozone transport. That is because, in the Southern Hemisphere from October to December, the large increase in the chemical destruction of ozone is balanced by an equally large trend in the eddy transport, associated with a small increase in the mean transport. This study shows that the increase in the eddy transport is characterized by more poleward ozone eddy flux by transient waves in the midlatitudes and by stationary waves in the polar region. Overall, this study makes clearer the close interaction between the trends in ozone chemistry and ozone transport. It reveals that the eddy ozone transport and its long-term changes are an important natural mitigation mechanism for the ozone hole. This work also underlines the need for diagnostics of the eddy transport in chemical transport models used to investigate future ozone recovery.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close A thorough analysis of the ozone transport was carried out using the Transformed-Mean Eulerian (TEM) tracer continuity equation and the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). In this budget analysis, the chemical net production term, which is calculated as the residual of the other terms, displays the correct features of a chemical sink and source term, including location and seasonality, and shows good agreement in magnitude compared to other methods of calculating ozone loss rates. This study provides further insight into the role of the eddy ozone transport and underlines its fundamental role in the recovery of the ozone hole during spring. The trend analysis reveals that the ozone hole intensification over the 1980–2001 period is not solely related to the trend in chemical losses, but more specifically to the balance between the trends in chemical losses and ozone transport. That is because, in the Southern Hemisphere from October to December, the large increase in the chemical destruction of ozone is balanced by an equally large trend in the eddy transport, associated with a small increase in the mean transport. This study shows that the increase in the eddy transport is characterized by more poleward ozone eddy flux by transient waves in the midlatitudes and by stationary waves in the polar region. Overall, this study makes clearer the close interaction between the trends in ozone chemistry and ozone transport. It reveals that the eddy ozone transport and its long-term changes are an important natural mitigation mechanism for the ozone hole. This work also underlines the need for diagnostics of the eddy transport in chemical transport models used to investigate future ozone recovery. Close doi:10.5194/acp-11-6311-2011 Close
1.	E Monier, BC Weare, WI Gustafson The Madden-Julian oscillation wind-convection coupling and the role of moisture processes in the MM5 model Journal Article Climate Dynamics, 35 (2-3), pp. 435–447, 2010. Abstract \| Links \| BibTeX @article{monier2010madden, title = {The Madden-Julian oscillation wind-convection coupling and the role of moisture processes in the MM5 model}, author = {E Monier and BC Weare and WI Gustafson}, doi = {10.1007/s00382-009-0626-4}, year = {2010}, date = {2010-07-24}, journal = {Climate Dynamics}, volume = {35}, number = {2-3}, pages = {435--447}, publisher = {Springer}, abstract = {The Madden–Julian oscillation (MJO) produced by a mesoscale model is investigated using standardized statistical diagnostics. Results show that upper- and lower-level zonal winds display the correct MJO structure, phase speed (8 m/s) and space–time power spectrum. However, the simulated free atmosphere moisture, outgoing longwave radiation and precipitation do not exhibit any clear MJO signal. Yet, the boundary layer moisture, moist static energy and atmospheric instability, measured using a moist static energy instability index, have clear MJO signals. A significant finding is the ability of the model to simulate a realistic MJO phase speed in the winds without reproducing the MJO wind-convection coupling or a realistic propagation in the free atmosphere water vapor. This study suggests that the convergence of boundary layer moisture and the discharge and recharge of the moist static energy and atmospheric instability may be responsible for controlling the speed of propagation of the MJO circulation.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close The Madden–Julian oscillation (MJO) produced by a mesoscale model is investigated using standardized statistical diagnostics. Results show that upper- and lower-level zonal winds display the correct MJO structure, phase speed (8 m/s) and space–time power spectrum. However, the simulated free atmosphere moisture, outgoing longwave radiation and precipitation do not exhibit any clear MJO signal. Yet, the boundary layer moisture, moist static energy and atmospheric instability, measured using a moist static energy instability index, have clear MJO signals. A significant finding is the ability of the model to simulate a realistic MJO phase speed in the winds without reproducing the MJO wind-convection coupling or a realistic propagation in the free atmosphere water vapor. This study suggests that the convergence of boundary layer moisture and the discharge and recharge of the moist static energy and atmospheric instability may be responsible for controlling the speed of propagation of the MJO circulation. Close doi:10.1007/s00382-009-0626-4 Close

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