Erwan Monier

Long-Term Climate Change Commitment and Reversibility: An EMIC Intercomparison

AbstractThis 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 resu...

A framework for modeling uncertainty in regional climate change

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.

Climate change impacts on extreme events in the United States: an uncertainty analysis

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.

An Analogue Approach to Identify Heavy Precipitation Events: Evaluation and Application to CMIP5 Climate Models in the United States

AbstractAn 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 RMS...

Valuing climate impacts in integrated assessment models: the MIT IGSM

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.

U.S. Air Quality and Health Benefits from Avoided Climate Change under Greenhouse Gas Mitigation.

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.

Probabilistic Projections of 21st Century Climate Change over Northern Eurasia

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.

Changing the Climate Sensitivity of an Atmospheric General Circulation Model through Cloud Radiative Adjustment

AbstractConducting 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...

Uncertainty in Future Agro-Climate Projections in the United States and Benefits of Greenhouse Gas Mitigation

This work was partially funded by the US Environmental Protection Agency’s Climate Change Division, under Cooperative Agreement #XA-83600001, by the US Department of Energy, Office of Biological and Environmental Research, under grant DE-FG02-94ER61937, and by the National Science Foundation Macrosystems Biology Program Grant #EF1137306. The Joint Program on the Science and Policy of Global Change is funded by a number of federal agencies and a consortium of 40 industrial and foundation sponsors. (For the complete list see http://globalchange.mit.edu/sponsors/current.html). This research used the Evergreen computing cluster at the Pacific Northwest National Laboratory. Evergreen is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-76RL01830.

Benefits of Greenhouse Gas Mitigation on the Supply, Management, and Use of Water Resources in the United States

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.