Climate Change Risk Assessment Chris E. Forest, K. Keller, A. - PowerPoint PPT Presentation

Climate Change Risk Assessment Chris E. Forest, K. Keller, A. Libardoni, J. Tsai, W. Li, R. Nicholas, R. Sriver (U. Illinois), A. Sokolov (MIT) and many SCRIM Collaborators. ! Workshop on Uncertainty Quantification in Climate Modeling and Projection ! 2015 July 13-17

an NSF-sponsored research network for Sustainable Climate Risk Management What are sustainable, scientifically sound, technologically feasible, economically efficient, and ethically defensible strategies for managing the risks associated with climate change? http://scrimhub.org 2

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Earth System Modeling Computational Methods Uncertainty Quantification & Analysis & Cyber Tools Bayesian data-model fusion model emulation simple climate models, ice sheet high-performance computation, parameter estimation models, GCMs, Earth system models sensitivity analysis, optimization Our workshop has focused on these three issues with attention being given to Technology Assessment Trade-Off Analysis Integrated Assessment these other parts of the decision making process. fossil fuels, renewables, cost-benefit/expected utility, robust DICE, RICE, FUND, Phoenix, GCAM, carbon sequestration, geoengineering, decisionmaking, precautionary iESM, and new models adaptation principle, scenario discovery Coupled Ethical- Stakeholders & Epistemic Analysis Decisionmakers ethical analysis, justice, value objectives, preferences, judgments in science and society constraints, mental models Research Interactions provides INPUT to provides METHODS for provides INSIGHTS for refinement 4

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This brings us back to some of the tools and techniques being discussed here…

Fundamental uncertainties exist for 
 projections of future climate The uncertainties fall into a few broad categories: - observational uncertainty - forcing or scenario uncertainty - model uncertainty ! structural, parametric (i.e., right physics, right settings) - natural/internal variability Goals 1. Can we separate uncertainties between Global v. Regional response? 2. How do we compare ensemble approaches? MME v. PPE v. ICE 3. How does structural uncertainty in regional changes assessed? 9

global mean temperature projections in CMIP3 and CMIP5 10

global mean temperature projections in CMIP3 and CMIP5 At global scales, IPCC archives provide a set of possible futures with specific models and forcing scenarios. These can be used for regional impacts & adaptation. 11

The Pattern Scaling approach: A Method for Regional Model Predictions P ( x, y, z, t ) = T ( t ) p ( x, y, z ) RCP: 2.6 4.5 8.5 Tebaldi & Arblaster (2014, Climatic Change)

The Problem: Model Predictions have multiple sources of uncertainty… " Epistemic uncertainty: getting the model right (the right model physics & right structure) Example Work at Penn State: Developing Global Teleconnection Operators (GTOs) to estimate structural uncertainty in regional response to global Sea Surface Temperature patterns Li, Forest, & Barsugli (2012, J. Geophys. Res.) Comparing two methods to estimate the sensitivity of regional climate simulations to tropical SST anomalies Tsai, Forest, & Wagener (2014, Clim. Dynamics) Estimating the regional climate responses over river basins to changes in tropical sea surface temperature patterns

What matters for long-term climate prediction? ! Controls on: ! Uncertainties in: " Long-term warming ! " Climate Sensitivity ! " Delay by ocean " Rate of Ocean Heat . ! Uptake ! " Net forcing ! " Forcing by: Aerosols, Carbon-cycle, Land- use, Natural GHG Emissions, etc. 14

Climate Sensitivity is the equilibrium response of the climate to a constant forcing Green: EBM response Red: GFDL R15a response T 2x = ECS = Equilibrium Climate Sensitivity TCR = Transient Climate Response Figure 9.1 from IPCC AR3 (2001) 15

Major Climate Projection Uncertainties Consider the energy balance equation for the change in global-mean surface temperature ( ! T) from equilibrium: Flux of heat Change in global Future Net into deep- mean heat content Forcings Feedbacks ocean ! = 1/S Conceptually: This is a good framework for organizing where the uncertainty exists. In practice: For state-of-the-art models, each uncertainty is an aggregate quantity and cannot be identified with any one specific model component or process. 16

Uncertainty in Atmospheric Model Feedbacks (IPCC WG1 AR5 Figure 9.43) " Uncertainty in P lanck, W ater V apor, L apse R ate, C loud, A lbedo, and ALL Combined 17

Uncertainty in Equilibrium Climate Sensitivity " IPCC Range: 1.5-4.5 ºC ! " Box 12.2 Figure 1 ! " Multiple approaches based on di # erent methodologies and observational data/ proxies. Work by our groups: 18

Climate Sensitivity p(S eff ): Extending Diagnostic 0.8 ! How are the marginal PDFs 0.6 impacted? 0.4 Density 0.2 0.0 -0.2 ! Extending model 0 2 4 6 8 10 Effective Climate Sensitivity (K) diagnostics with the new Ocean Diffusivity p(K v ): Extending Diagnostic 0.8 model 0.6 0.4 Density 0.2 All Diagnostics start in 1941 0.0 -0.2 Diagnostics end in 1990 0 2 4 6 8 SQRT( Effective Oceanic Diffusion ) (Sqrt(cm 2 /s)) Diagnostics end in 2000 Aerosol Forcing p(F aer ): Extending Diagnostic 0.8 Diagnostics end in 2010 0.6 0.4 Density 0.2 0.0 Libardoni, Forest, & Sokolov (In prep) -0.2 19 -1.5 -1.0 -0.5 0.0 0.5 Net Aerosol Forcing (W/m 2 )

Climate Sensitivity p(S eff ): Paramater Probability Distribution 1.0 ! How are the marginal PDFs 0.8 0.6 impacted? Density 0.4 0.2 0.0 ! Changing the model and -0.2 0 2 4 6 8 Effective Climate Sensitivity (K) forcings Ocean Diffusivity p(K v ): Paramater Probability Distribution 1.0 0.8 0.6 Density 0.4 New Model (MESM) 0.2 Old Model (IGSM) 0.0 (Libardoni & Forest, 2013) -0.2 0 2 4 6 8 SQRT( Effective Oceanic Diffusion ) (Sqrt(cm 2 /s)) Aerosol Forcing p(F aer ): Paramater Probability Distribution Both use Same 1.0 0.8 Diagnostics 0.6 Density 0.4 0.2 Libardoni, Forest, & Sokolov (In prep) 0.0 -0.2 20 -1.5 -1.0 -0.5 0.0 0.5 Net Aerosol Forcing (W/m 2 )

So this begins to address global scale uncertainties… " How do we start assessing uncertainties at regional scales? 21