11A.4
Studying regional climate change based on uncertainty in climate parameters using CAM3

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Thursday, 27 January 2011: 9:15 AM
Studying regional climate change based on uncertainty in climate parameters using CAM3
608 (Washington State Convention Center)
Erwan Monier, MIT, Cambridge, MA; and A. Sokolov, J. Scott, A. Schlosser, and C. E. Forest

The MIT Integrated Global Systems Model (IGSM) version 2.3 is an intermediate complexity model that couples a zonally-averaged statistical dynamical atmospheric model with a full 3D ocean GCM and, therefore, simulates feedbacks associated with changes in ocean circulation. A fundamental feature of the IGSM2.3 is the ability to modify its climate sensitivity (through cloud adjustment), net aerosol forcing and ocean heat uptake rate (via the diapycnal diffusion coefficient). As such, the IGSM2.3 provides an efficient tool for generating probabilistic distribution functions of climate parameters (climate sensitivity, aerosol forcing and ocean heat uptake rate) using optimal fingerprint diagnostics. Probabilistic distributions of sea surface temperature (SST) and sea ice cover (SIC) changes for the 21st century can then be obtained using Latin-Hypercube sampling of climate parameters under various emissions scenarios. The emissions scenarios used in this study are based on the MIT Emissions Predictions and Policy Analysis (EPPA) model and include a no policy case where emissions of long-lived GHGs are uncertain, and a range of stabilization scenarios from stringent policy to milder policy.

In order to investigate future regional climate impacts, the MIT IGSM2.3 is coupled to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model version 3 (CAM3). For linkages between the IGSM2.3 and CAM3, the 3-D atmospheric model is driven by the IGSM2.3 SST anomalies with a climatological annual cycle taken from an observed dataset, instead of the full IGSM2.3 SSTs, to provide a better SST annual cycle and more realistic features between the ocean and atmospheric components. This approach yields a consistent regional distribution and climate change over the 20th century as compared to observational datasets. For each emissions scenario, an ensemble member of the IGSM2.3 SST/SIC probabilistic distribution drives CAM3 to span the multi-dimensional space of uncertainty in climate parameters. For consistency, for each set of IGSM2.3/CAM3 runs, the trace gas concentrations calculated by the atmospheric chemistry component of the IGSM2.3 is used to force CAM3. The cloud adjustment scheme used in the IGSM2.3 was implemented in CAM3, which allows modifying its climate sensitivity to match that of the IGSM2.3 setup that generates the SST field used to drive CAM3.

With this approach, regional climate impacts can be assessed under various emissions scenarios based on probability distributions of climate parameters. In this paper, preliminary results from these ensemble simulations are presented. A particular focus is placed on the distribution of extreme events. For example, the frequency, duration and intensity of extreme events such as heat waves, floods and droughts, precipitation and storm activities can be investigated, as well as other dynamical features such as jet stream modulation.