In this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Cano... more 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.
ABSTRACT This paper summarizes the results of an intercomparison project with Earth System Models... more 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: (i) quantify the climate change commitment of different radiative forcing trajectories, and (ii) explore the extent to which climate change is reversible on human timescales. All commitment simulations follow the four Representative Concentration Pathways (RCPs) and their extensions to 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 pre-industrial values in most models for RCPs 2.6–6.0. The MOC weakening is more persistent for RCP 8.5. 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 pre-industrial levels over 100–1000 years requires large artificial removal of CO2 from the atmosphere and does not result in the simultaneous return to pre-industrial climate conditions, as surface air temperature and sea level response exhibit a substantial time lag relative to atmospheric CO2.
The MIT IGSM is used for a study of the climate response to various historical and projected forc... more The MIT IGSM is used for a study of the climate response to various historical and projected forcings over the period 850-4000 AD. The MIT IGSM includes a zonally-averaged atmospheric model coupled to land and ocean models. Both land and ocean models simulate carbon cycle. Two configurations of the IGSM were used in the simulations; one with the MIT 3D OGCM and other with anomaly diffusing ocean model. Over the period 850-2005, a historical run with all time-varying natural and anthropogenic forcings is compared to a set of runs where only a single component of the forcing time series is varied. Over 2005-3000, climate projections as forced by four different Representation Concentration Pathways are compared. These projections are extended by decreasing forcings back to pre-industrial levels over years 3000-4000. In addition to changes in surface air temperature, carbon uptake in the ocean and land systems and changes in the oceans’ large-scale circulation are a focus in analyses of...
Simulations of climate change that support work by impact modelers must take into account multipl... more Simulations of climate change that support work by impact modelers must take into account multiple dimensions of uncertainty, from uncertainty in emissions scenarios to uncertainty in the climate response and including structural uncertainty arising from differences in climate models. In order to investigate uncertainty in climate change over the United States, the Massachusetts Institute of Technology (MIT) Joint Program on the Science and Policy of Global Change has implemented a two-pronged approach that revolves around the Integrated Global System Model (IGSM) framework, an integrated assessment model that couples an earth system model of intermediate complexity (with a 2D zonal-mean atmosphere) to a human activity model. Since the IGSM includes a human activity model, it is possible to analyze uncertainties in emissions resulting from uncertainties intrinsic to the economic model, from parametric uncertainty to uncertainty in future climate policies. Another major feature is th...
Climate change is expected to significantly influence meteorology and may therefore impact future... more Climate change is expected to significantly influence meteorology and may therefore impact future air quality. Several studies have attempted to estimate the effect of climate change on air pollution by using meteorological fields derived from general circulation model simulations to drive atmospheric chemistry and transport models and project future pollutant concentrations. However, large uncertainties are associated with climate simulations and may propagate into predictions of future air quality. Beyond uncertainty in emissions and model response, climate projections are significantly influenced by natural variability. However, little attention has been given to the role of natural climate fluctuations in modeling analyses aimed at quantifying the effect of climate change on air pollution. As internal variability intrinsically limits the ability of models to predict climate on time scales smaller than a decade, multiyear or multidecadal mean data may be necessary to adequately c...
ABSTRACT This study investigates the complex terrestrial ecosystems response to extreme weather e... more ABSTRACT This study investigates the complex terrestrial ecosystems response to extreme weather events using three different land surface models. Previous studies have showed that extreme weather events can have serious and damaging impacts on human and natural systems and they are most evident on regional and local scales. Under climate change, extreme weather events are likely to increase in both magnitude and frequency, making realistic simulation of ecosystems response to extreme events more essential than ever in assessing the potential damaging impacts. Three different land surface models are used to explore the impacts of extreme events on regional to continental ecosystem responses. The Terrestrial Ecosystem Model (TEM) is a process-based ecosystem model that uses spatially referenced information on climate, elevation, soils, vegetation and water availability to make monthly estimates of vegetation and soil carbon and nitrogen fluxes and pool sizes. The Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) is a multi-layered land surface model based on eddy-covariance theory to calculate the biosphere-atmosphere exchanges of carbon dioxide, water, and momentums. The Community Land Model (CLM) is a community-based model widely used in global-scale land data assimilation research. The study focuses on the complex interactions and feedbacks between the terrestrial ecosystem and the atmosphere such as water cycle, carbon and nitrogen budgets, and environmental conditions. The model simulations and performances are evaluated using the biogeophysical and micrometeorological observation data from the AmeriFlux sites across the continental US. This study compares and evaluates the ability of different models and their key components to capture terrestrial response to extreme weather events.
ABSTRACT An important question for climate change science is possible shifts in the extremes of r... more ABSTRACT An important question for climate change science is possible shifts in the extremes of regional water cycle, especially changes in patterns, intensity and/or frequency of extreme precipitation events. In this study, an analogue method is developed to help detect extreme precipitation events and their potential changes under future climate regimes without relying on the highly uncertain modeled precipitation. Our approach is based on the use of composite maps to identify the distinct synoptic and large-scale atmospheric conditions that lead to extreme precipitation events at local scales. The analysis of extreme daily precipitation events, exemplified in the south-central United States, is carried out using 62-yr (1948-2010) CPC gridded station data and NASA’s Modern Era Retrospective-analysis for Research and Applications (MERRA). Various aspects of the daily extremes are examined, including their historical ranking, associated common circulation features at upper and lower levels of the atmosphere, and moisture plumes. The scheme is first evaluated for the multiple climate model simulations of the 20th century from Coupled Model Intercomparison Project Phase 5 (CMIP5) archive to determine whether the statistical nature of modeled precipitation events (i.e. the numbers of occurrences over each season) could well correspond to that of the observed. Further, the approach will be applied to the CMIP5 multi-model projections of various climate change scenarios (i.e. Representative Concentration Pathways (RCP) scenarios) in the next century to assess the potential changes in the probability of extreme precipitation events. The research results from this study should be of particular significance to help society develop adaptive strategies and prevent catastrophic losses.
ABSTRACT In this study, we investigate possible climate change over Northern Eurasia and its impa... more ABSTRACT In this study, we investigate possible climate change over Northern Eurasia and its impact on hydrological and carbon cycles. Northern Eurasia is a major player in the global carbon budget because of boreal forests and wetlands. Permafrost degradation associated with climate change could result in wetlands releasing large amounts of carbon dioxide and methane. Changes in the frequency and magnitude of extreme events, such as extreme precipitation, are likely to have substantial impacts on Northern Eurasia ecosystems. For this reason, it is very important to quantify the possible climate change over Northern Eurasia under different emissions scenarios, while accounting for the uncertainty in the climate response.
In this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Cano... more 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.
ABSTRACT This paper summarizes the results of an intercomparison project with Earth System Models... more 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: (i) quantify the climate change commitment of different radiative forcing trajectories, and (ii) explore the extent to which climate change is reversible on human timescales. All commitment simulations follow the four Representative Concentration Pathways (RCPs) and their extensions to 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 pre-industrial values in most models for RCPs 2.6–6.0. The MOC weakening is more persistent for RCP 8.5. 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 pre-industrial levels over 100–1000 years requires large artificial removal of CO2 from the atmosphere and does not result in the simultaneous return to pre-industrial climate conditions, as surface air temperature and sea level response exhibit a substantial time lag relative to atmospheric CO2.
The MIT IGSM is used for a study of the climate response to various historical and projected forc... more The MIT IGSM is used for a study of the climate response to various historical and projected forcings over the period 850-4000 AD. The MIT IGSM includes a zonally-averaged atmospheric model coupled to land and ocean models. Both land and ocean models simulate carbon cycle. Two configurations of the IGSM were used in the simulations; one with the MIT 3D OGCM and other with anomaly diffusing ocean model. Over the period 850-2005, a historical run with all time-varying natural and anthropogenic forcings is compared to a set of runs where only a single component of the forcing time series is varied. Over 2005-3000, climate projections as forced by four different Representation Concentration Pathways are compared. These projections are extended by decreasing forcings back to pre-industrial levels over years 3000-4000. In addition to changes in surface air temperature, carbon uptake in the ocean and land systems and changes in the oceans’ large-scale circulation are a focus in analyses of...
Simulations of climate change that support work by impact modelers must take into account multipl... more Simulations of climate change that support work by impact modelers must take into account multiple dimensions of uncertainty, from uncertainty in emissions scenarios to uncertainty in the climate response and including structural uncertainty arising from differences in climate models. In order to investigate uncertainty in climate change over the United States, the Massachusetts Institute of Technology (MIT) Joint Program on the Science and Policy of Global Change has implemented a two-pronged approach that revolves around the Integrated Global System Model (IGSM) framework, an integrated assessment model that couples an earth system model of intermediate complexity (with a 2D zonal-mean atmosphere) to a human activity model. Since the IGSM includes a human activity model, it is possible to analyze uncertainties in emissions resulting from uncertainties intrinsic to the economic model, from parametric uncertainty to uncertainty in future climate policies. Another major feature is th...
Climate change is expected to significantly influence meteorology and may therefore impact future... more Climate change is expected to significantly influence meteorology and may therefore impact future air quality. Several studies have attempted to estimate the effect of climate change on air pollution by using meteorological fields derived from general circulation model simulations to drive atmospheric chemistry and transport models and project future pollutant concentrations. However, large uncertainties are associated with climate simulations and may propagate into predictions of future air quality. Beyond uncertainty in emissions and model response, climate projections are significantly influenced by natural variability. However, little attention has been given to the role of natural climate fluctuations in modeling analyses aimed at quantifying the effect of climate change on air pollution. As internal variability intrinsically limits the ability of models to predict climate on time scales smaller than a decade, multiyear or multidecadal mean data may be necessary to adequately c...
ABSTRACT This study investigates the complex terrestrial ecosystems response to extreme weather e... more ABSTRACT This study investigates the complex terrestrial ecosystems response to extreme weather events using three different land surface models. Previous studies have showed that extreme weather events can have serious and damaging impacts on human and natural systems and they are most evident on regional and local scales. Under climate change, extreme weather events are likely to increase in both magnitude and frequency, making realistic simulation of ecosystems response to extreme events more essential than ever in assessing the potential damaging impacts. Three different land surface models are used to explore the impacts of extreme events on regional to continental ecosystem responses. The Terrestrial Ecosystem Model (TEM) is a process-based ecosystem model that uses spatially referenced information on climate, elevation, soils, vegetation and water availability to make monthly estimates of vegetation and soil carbon and nitrogen fluxes and pool sizes. The Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) is a multi-layered land surface model based on eddy-covariance theory to calculate the biosphere-atmosphere exchanges of carbon dioxide, water, and momentums. The Community Land Model (CLM) is a community-based model widely used in global-scale land data assimilation research. The study focuses on the complex interactions and feedbacks between the terrestrial ecosystem and the atmosphere such as water cycle, carbon and nitrogen budgets, and environmental conditions. The model simulations and performances are evaluated using the biogeophysical and micrometeorological observation data from the AmeriFlux sites across the continental US. This study compares and evaluates the ability of different models and their key components to capture terrestrial response to extreme weather events.
ABSTRACT An important question for climate change science is possible shifts in the extremes of r... more ABSTRACT An important question for climate change science is possible shifts in the extremes of regional water cycle, especially changes in patterns, intensity and/or frequency of extreme precipitation events. In this study, an analogue method is developed to help detect extreme precipitation events and their potential changes under future climate regimes without relying on the highly uncertain modeled precipitation. Our approach is based on the use of composite maps to identify the distinct synoptic and large-scale atmospheric conditions that lead to extreme precipitation events at local scales. The analysis of extreme daily precipitation events, exemplified in the south-central United States, is carried out using 62-yr (1948-2010) CPC gridded station data and NASA’s Modern Era Retrospective-analysis for Research and Applications (MERRA). Various aspects of the daily extremes are examined, including their historical ranking, associated common circulation features at upper and lower levels of the atmosphere, and moisture plumes. The scheme is first evaluated for the multiple climate model simulations of the 20th century from Coupled Model Intercomparison Project Phase 5 (CMIP5) archive to determine whether the statistical nature of modeled precipitation events (i.e. the numbers of occurrences over each season) could well correspond to that of the observed. Further, the approach will be applied to the CMIP5 multi-model projections of various climate change scenarios (i.e. Representative Concentration Pathways (RCP) scenarios) in the next century to assess the potential changes in the probability of extreme precipitation events. The research results from this study should be of particular significance to help society develop adaptive strategies and prevent catastrophic losses.
ABSTRACT In this study, we investigate possible climate change over Northern Eurasia and its impa... more ABSTRACT In this study, we investigate possible climate change over Northern Eurasia and its impact on hydrological and carbon cycles. Northern Eurasia is a major player in the global carbon budget because of boreal forests and wetlands. Permafrost degradation associated with climate change could result in wetlands releasing large amounts of carbon dioxide and methane. Changes in the frequency and magnitude of extreme events, such as extreme precipitation, are likely to have substantial impacts on Northern Eurasia ecosystems. For this reason, it is very important to quantify the possible climate change over Northern Eurasia under different emissions scenarios, while accounting for the uncertainty in the climate response.
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Papers by Erwan Monier