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A framework for modeling uncertainty in regional climate change
Erwan Monier, Xiang Gao, Jeff Scott, Andrei Sokolov and Adam Schlosser
Climate Modeling DOE PI Meeting May 14, 2014
h"p://globalchange.mit.edu/
Objec,ves Investigate the contributions of major sources of uncertainty to regional climate projections and provide guidance for climate impact studies.
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Objec,ves Investigate the contributions of major sources of uncertainty to regional climate projections and provide guidance for climate impact studies. Examples of sources of uncertainty: • Emissions forecasting
o Assumptions on economic growth o Implementation of climate policies
• Climate system response o Climate sensitivity o Strength of aerosol forcing o Ocean heat uptake rate
• Natural variability o Chaotic nature of the climate system
• Model structural uncertainty o Differences in parameterizations, resolution…
h"p://globalchange.mit.edu/
The MIT Integrated Global System Model Integrated assessment model: couples an earth system model of intermediate complexity to a human activity model.
Trace gas
and policy constraints VOCs, BC, etc.
Hydrology/ water resources
Agriculture, forestry, bio-energy, ecosystem productivity
Human health
Earth System
Emissions Prediction and Policy Analysis (EPPA)National and/or Regional Economic Development,
Emissions & Land Use
Human System
Land use change
Atmosphere2-Dimensional Dynamical,
Physical & Chemical Processes
Ocean2- or 3-Dimensional
Dynamical, Biological, Chemical & Ice Processes
Urban AirshedAir Pollution Processes
LandWater & Energy Budgets (CLM)
Biogeochemical Processes (TEM & NEM)
Coupled Ocean, Atmosphere, and Land
Sea level change
Climate/ energy demand
Solar forcing
Volcanic forcing
h"p://globalchange.mit.edu/
The MIT Integrated Global System Model Integrated assessment model: couples an earth system model of intermediate complexity to a human activity model. Representation of uncertainty: • Emissions • Climate system response • Greenhouse gas cycle
Trace gas
and policy constraints VOCs, BC, etc.
Hydrology/ water resources
Agriculture, forestry, bio-energy, ecosystem productivity
Human health
Earth System
Emissions Prediction and Policy Analysis (EPPA)National and/or Regional Economic Development,
Emissions & Land Use
Human System
Land use change
Atmosphere2-Dimensional Dynamical,
Physical & Chemical Processes
Ocean2- or 3-Dimensional
Dynamical, Biological, Chemical & Ice Processes
Urban AirshedAir Pollution Processes
LandWater & Energy Budgets (CLM)
Biogeochemical Processes (TEM & NEM)
Coupled Ocean, Atmosphere, and Land
Sea level change
Climate/ energy demand
Solar forcing
Volcanic forcing
h"p://globalchange.mit.edu/
The MIT Integrated Global System Model Integrated assessment model: couples an earth system model of intermediate complexity to a human activity model. Representation of uncertainty: • Emissions • Climate system response • Greenhouse gas cycle Representation of climate policies (global carbon tax)
Trace gas
and policy constraints VOCs, BC, etc.
Hydrology/ water resources
Agriculture, forestry, bio-energy, ecosystem productivity
Human health
Earth System
Emissions Prediction and Policy Analysis (EPPA)National and/or Regional Economic Development,
Emissions & Land Use
Human System
Land use change
Atmosphere2-Dimensional Dynamical,
Physical & Chemical Processes
Ocean2- or 3-Dimensional
Dynamical, Biological, Chemical & Ice Processes
Urban AirshedAir Pollution Processes
LandWater & Energy Budgets (CLM)
Biogeochemical Processes (TEM & NEM)
Coupled Ocean, Atmosphere, and Land
Sea level change
Climate/ energy demand
Solar forcing
Volcanic forcing
h"p://globalchange.mit.edu/
The MIT Integrated Global System Model Integrated assessment model: couples an earth system model of intermediate complexity to a human activity model. Representation of uncertainty: • Emissions • Climate system response • Greenhouse gas cycle Representation of climate policies (global carbon tax) Computationally efficient framework that allows ensemble simulations with number of members in the 100s/1000s
Trace gas
and policy constraints VOCs, BC, etc.
Hydrology/ water resources
Agriculture, forestry, bio-energy, ecosystem productivity
Human health
Earth System
Emissions Prediction and Policy Analysis (EPPA)National and/or Regional Economic Development,
Emissions & Land Use
Human System
Land use change
Atmosphere2-Dimensional Dynamical,
Physical & Chemical Processes
Ocean2- or 3-Dimensional
Dynamical, Biological, Chemical & Ice Processes
Urban AirshedAir Pollution Processes
LandWater & Energy Budgets (CLM)
Biogeochemical Processes (TEM & NEM)
Coupled Ocean, Atmosphere, and Land
Sea level change
Climate/ energy demand
Solar forcing
Volcanic forcing
h"p://globalchange.mit.edu/
Regional Climate Modeling Framework The IGSM has a 2D zonal-mean atmosphere, so we use a two-pronged approach to obtain regional changes:
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Regional Climate Modeling Framework The IGSM has a 2D zonal-mean atmosphere, so we use a two-pronged approach to obtain regional changes:
• Dynamical downscaling: The IGSM-CAM framework, which links the IGSM to the NCAR Community Atmosphere Model (CAM). - low, median and high climate sensitivity based on its PDF - net aerosol forcing that best reproduces historical climate change - 5-member ensemble with different representation of natural variability
h"p://globalchange.mit.edu/
Regional Climate Modeling Framework The IGSM has a 2D zonal-mean atmosphere, so we use a two-pronged approach to obtain regional changes:
• Dynamical downscaling: The IGSM-CAM framework, which links the IGSM to the NCAR Community Atmosphere Model (CAM). - low, median and high climate sensitivity based on its PDF - net aerosol forcing that best reproduces historical climate change - 5-member ensemble with different representation of natural variability
• Statistical downscaling: A pattern scaling method that extends the IGSM 2D zonal-mean atmosphere using patterns from observations and climate models. - 400-member ensemble of IGSM, with Latin Hypercube sampling of climate parameters (climate sensitivity, strength of aerosol forcing, ocean heat uptake rate) based on their PDFs - Pattern scaling based on 17 CMIP3 models
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NORTHERN EURASIA
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Northern Eurasia Projec,ons No Policy
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Northern Eurasia Projec,ons Stabiliza,on at 660 ppm CO2-‐eq
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Northern Eurasia Projec,ons
IMPACT OF POLICY AND CLIMATE RESPONSE
HIGH_UCE
MED_UCE
LOW_UCE LOW_LS2
MED_LS2
HIGH_LS2
2081-‐2100 MEAN MINUS 1981-‐2000 MEAN
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Northern Eurasia Projec,ons
IMPACT OF POLICY AND CLIMATE RESPONSE
HIGH_UCE
MED_UCE
LOW_UCE LOW_LS2
MED_LS2
HIGH_LS2 MED_LS2 MED_LS2
MED_LS2 MED_LS2
MED_LS2
MEMBER #1 MEMBER #2
MEMBER #3 MEMBER #4
MEMBER #5
IMPACT OF NATURAL VARIABILITY
2081-‐2100 MEAN MINUS 1981-‐2000 MEAN
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CONTIGUOUS UNITED STATES
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Uncertainty in US Temperature Projec,ons
0
1
2
3
4
5
2020 2040 2060 2080 2100
TEM
PE
RAT
UR
E (°
C)
YEAR
CLIMATE SENSITIVITY CHOICE OF POLICY
NATURAL VARIABILITY CHOICE OF MODEL
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Uncertainty in US Precipita,on Projec,ons
0
0.1
0.2
0.3
2020 2040 2060 2080 2100
PR
EC
IPIT
ATIO
N (m
m/d
ay)
YEAR
CLIMATE SENSITIVITY CHOICE OF POLICY
NATURAL VARIABILITY CHOICE OF MODEL
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Conclusions Wide range of temperature and precipitation changes over Northern Eurasia and the contiguous US, even using one single climate model. ⇒ need to sample the global climate system response within each model.
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Conclusions Wide range of temperature and precipitation changes over Northern Eurasia and the contiguous US, even using one single climate model. ⇒ need to sample the global climate system response within each model. Each source of uncertainty considered contributes differently to the range of temperature and precipitation changes. • For temperature:
- choice of policy and climate sensitivity • For precipitation:
- natural variability dominates until 2050 - all four sources contribute more equally by 2100
h"p://globalchange.mit.edu/
Conclusions Wide range of temperature and precipitation changes over Northern Eurasia and the contiguous US, even using one single climate model. ⇒ need to sample the global climate system response within each model. Each source of uncertainty considered contributes differently to the range of temperature and precipitation changes. • For temperature:
- choice of policy and climate sensitivity • For precipitation:
- natural variability dominates until 2050 - all four sources contribute more equally by 2100
What does this mean for climate impacts? Relying on a small ensemble of climate simulations or not accounting for the major sources of uncertainty in climate projections would likely underestimate climate impacts.
h"p://globalchange.mit.edu/
IGSM-‐CAM
Urban AirshedAir Pollution Processes
Atmosphere2-Dimensional Dynamical,
Physical & Chemical Processes
Ocean3-Dimensional Dynamical,
Biological, Chemical, and Ice Processes (MITgcm)
LandWater & Energy Budgets (CLM)
Biogeochemical Processes (TEM & NEM)
Trace gas fluxes (CO2, CH4, N2O) and policy constraints
CO2, CH4, CO, N2O, NOx, SOx, NH3, CFCs, HFCs, PFCs, SF6, VOCs, BC, etc.
Climate/ energy demand
Hydrology/ water resources
Sea level change
Agriculture, forestry, bio-energy, ecosystem productivity
Human health effects
Coupled Ocean, Atmosphere, and Land
Implementation of feedbacks is under development
Exchanges utilized in targeted studies
Exchanges represented in standard runs of the system
Earth System
Emissions Prediction and Policy Analysis (EPPA)National and/or Regional Economic Development,
Emissions & Land Use
Human System
Land use change
Wind stress
Solar forcing
Volcanic forcing
Coupled Land and Atmosphere
CAM3
Atmosphere3-Dimensional Dynamical
& Physical Processes
LandWater & Energy Budgets (CLM)
Solar forcing
Volcanic forcing
SSTs andsea ice cover
Land use change
CO2, CH4, N2O, CFCs, HFCs, PFCs, SF6, SOx, BC and O3
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IGSM-‐pa"ern scaling
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