Studying different sources of uncertainty in the projection of the future climate change over Northern Eurasia

Transcription

1 Studying different sources of uncertainty in the projection of the future climate change over Northern Eurasia Andrei Sokolov, Erwan Monier, Adam Schlosser, Jeffery Scott and Xiang Gao NEESPI/SIRC Workshop September 3-5, 2013 Petrozavodsk Russia

2 Objec,ve To investigate the uncertainty in future projections of climate change at the regional level, with a focus on the NEESPI region. Modeling framework is based on: integrated economic and climate projections a consistent framework for uncertainty in regional climate change Our focus is on 4 sources of uncertainty in climate projections: Emissions scenarios (policy) Climate system response to forcing (climate parameters) Natural variability (initial conditions) Structural uncertainty/regional patterns of change (multiple models)

3 Integrated Assessment Modeling Human activity Mitigation Adaptation Climate Impacts INTEGRATED ASSESSMENT MODELING Anthropogenic emissions Carbon capture Climate change Atmospheric concentrations Geo-engineering

4 Uncertainty in emissions projec,ons van Vuuren et al. (2011) Emissions (GtC) CO 2 Emissions (Tg CH 4 ) CH 4 Emissions (Tg N) N 2 O RCP2.6 RCP4.5 RCP6 RCP Emissions of main greenhouse gases across the RCPs. Grey area indicates the 98th and 90th percen,les (light/dark grey) of the literature

5 Uncertainty in climate sensi,vity From Knutti and Hegerl (2008) Most likely Likely Very likely Instrumental period Volcanic eruptions Data Models Last Glacial Maximum Current mean climate state Proxy data from millions of years ago General circulation models Last millennium Combining different lines of evidence Climate sensitivity (ºC) Climate sensitivity (ºC)

6 Structural uncertainty Annual Mean Surface Air Temperature Response Annual Mean Precipita,on Response IPCC AR4 (2007)

7 Uncertainty in natural variability From Deser et al. (2012) Average W armest Coolest Temperature trend ( C per 55 y ears) 5 6

8 The MIT Integrated Global System Model Human System Emissions Prediction and Policy Analysis (EPPA) National and/or Regional Economic Development, Emissions & Land Use The IGSM is an integrated assessment model that couples an earth system model of intermediate complexity to a human activity model. Hydrology/ water resources Land use change Agriculture, forestry, bio-energy, ecosystem productivity Earth System Trace gas and policy constraints VOCs, BC, etc. Human health Climate/ energy demand Sea level change Flexibility to vary climate parameters (climate sensitivity, ocean heat uptake rate and net aerosol forcing). Volcanic forcing Solar forcing Atmosphere 2-Dimensional Dynamical, Physical & Chemical Processes Ocean 2- or 3-Dimensional Dynamical, Biological, Chemical & Ice Processes Coupled Ocean, Atmosphere, and Land Urban Airshed Air Pollution Processes Land Water & Energy Budgets (CLM) Biogeochemical Processes (TEM & NEM) Flexibility to analyze uncertainties in emissions resulting from uncertainties intrinsic to the economic model, from parametric uncertainty to uncertainty in future climate policies.

9 Regional climate modeling framework Because the IGSM has a 2D zonal-mean atmosphere, we use a two-pronged approach to obtain regional changes: The MIT IGSM-CAM framework links the IGSM to the NCAR CAM model, with new modules in CAM to allow climate parameters to be changed to match those of the IGSM. The climate sensitivity of CAM is changed through cloud radiative adjustment method. A pattern scaling method extends the latitudinal projections of the IGSM 2D zonal-mean atmosphere by applying longitudinally resolved patterns from observations and from IPCC AR4 climate models.

10 IGSM- CAM Human System Emissions Prediction and Policy Analysis (EPPA) National and/or Regional Economic Development, Emissions & Land Use Hydrology/ water resources Land use change Agriculture, forestry, bio-energy, ecosystem productivity Trace gas and policy constraints VOCs, BC, etc. Human health Climate/ energy demand Sea level change Volcanic forcing Earth System Atmosphere 2-Dimensional Dynamical, Physical & Chemical Processes Urban Airshed Air Pollution Processes SSTs and sea ice cover CAM3 Atmosphere 3-Dimensional Dynamical & Physical Processes Volcanic forcing Solar forcing Coupled Ocean, Atmosphere, and Land Coupled Land and Atmosphere Wind stress Ocean 3-Dimensional Dynamical, Biological, Chemical & Ice Processes (MITgcm) Land Water & Energy Budgets (CLM) Biogeochemical Processes (TEM & NEM) Land use change Land Water & Energy Budgets (CLM) Solar forcing

11 Pa"ern scaling method

12 Comparison of IGSM- CAM and IGSM- pa"ern scaling Surface air temperature and precipita,on for NEESPI region

13 Comparison of IGSM- CAM and IGSM- pa"ern scaling Surface air temperature and precipita,on for NEESPI region

14 Descrip,on of the simula,ons 9 core simulations with IGSM: 3 emissions scenarios (reference, stabilization at 5.2 and 3.7 W/m 2 ) 3 climate sensitivities (2.0, 3.0, 4.5 C) 45 IGSM-CAM simulations: 5 different initial conditions for each set of policy/climate parameters 36 pattern scaling of IGSM core simulations 4 IPCC AR4 GCM patterns: - NCAR CCSM3.0 - BCCR BCM2.0 - MIROC3.2 MEDRES - multi-model mean

15 Descrip,on of the simula,ons INITIAL CONDITIONS CLIMATE SENSITIVITY EMISSIONS SCENARIO EMISSIONS SCENARIO CLIMATE SENSITIVITY MODEL PATTERNS INIC1 MIROC2.3 medres INIC2 CS2.0 REF REF CS2.0 NCAR CCSM3.0 INIC3 CS3.0 POL5.2 IGSM-CAM IGSM IGSM pattern scaling POL5.2 CS3.0 BCCR BCM2.0 INIC4 CS4.5 POL3.7 POL3.7 CS4.5 Multi-model mean INIC5 45 IGSM-CAM SIMULATIONS 48 IGSM-PATTERN SCALING SIMULATIONS TOTAL OF 93 SIMULATIONS

16 Economic scenarios 3 economic scenarios: REF: a reference scenario with unconstrained emissions after 2012, with a GHG radiative forcing of 9.7W/m 2 by 2100 POL5.2: a stabilization scenario, with a GHG radiative forcing of 5.2W/m 2 by 2100 POL3.7: a more stringent stabilization scenario, with a GHG radiative forcing of 3.7W/m 2 by The policies are obtained through a simple policy design: A uniform global carbon tax, constant in net present value terms, with CO 2 emissions from land use change excluded from the tax.

17 Changes in global mean temperature

18 Changes in global mean precipita,on

19 Changes in NEESPI mean temperature

20 Changes in NEESPI mean temperature

21 Changes in NEESPI mean temperature

22 Changes in NEESPI mean precipita,on

23 Changes in NEESPI mean precipita,on

24 Changes in NEESPI mean precipita,on

25 Maps of temperature changes for rela,ve to mean

26 Maps of temperature changes for rela,ve to mean

27 Maps of temperature changes for rela,ve to mean

28 Maps of temperature changes for rela,ve to mean

29 Maps of precipita,on changes for rela,ve to mean

30 Maps of precipita,on changes for rela,ve to mean

31 Maps of precipita,on changes for rela,ve to mean

32 Maps of precipita,on changes for rela,ve to mean

33 Range of change by source of uncertainty for NEESPI region

34 Range of change by source of uncertainty for USA

35 Impact of source of uncertainty a) NEESPI MEAN RANGE OF TEMPERATURE CHANGE FROM C Policy Climate Sensivity Initial Conditions Models b) NEESPI MEAN RANGE OF PRECIPITATION CHANGE FROM mm/day

36 Impact of source of uncertainty a) USA MEAN RANGE OF TEMPERATURE CHANGE FROM C Policy Climate Sensivity Initial Conditions Models b) USA MEAN RANGE OF PRECIPITATION CHANGE FROM mm/day

37 Conclusions The modeling framework presented allows multiple sources of uncertainty in regional climate change to be explored: Emissions scenarios (policy) Global climate response (climate parameters) Natural variability (initial conditions) Structural uncertainty/regional patterns of change (multiple models) The simulations shows a very large range of future climate change over the NEESPI region, in terms of future warming and with different patterns of drying and moistening. This has very important consequences for climate impact studies (agriculture, energy ). The choice of policy is the largest source of uncertainty in future projections of climate change. It is also, the only source that society has control over.