Assessing uncertainty of regional climate change from global climate models

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Monday, 24 January 2011: 5:00 PM
Assessing uncertainty of regional climate change from global climate models
609 (Washington State Convention Center)
Chris E. Forest, Penn State University, University Park, PA; and W. Li and J. Barsugli

In this work, we present the sensitivity of regional climate change to model structural differences using ensemble experiments from three versions of the NCAR Community Atmospheric Model (CAM3.1, CAM3.5 and CAM4.0). Taking SST anomaly as an example, we estimate the global teleconnection operator (GTO) as a metric to assess the uncertainty of regional climate information resulting from model structural differences. A random perturbation method (RPM) is used to generate a set of ensemble experiments that will provide similar sensitivity information as compared to the “patch” experiments developed by Barsugli and Sardeshmukh (2002, J. Climate). We compare the distribution of the GTO (i.e. sensitivity map) to assess and explore the sensitivity of regional information to different models. Seasonal responses at global, hemispheric, and regional scales are examined.

The ability to simulate and accurately predict changes in regional climate is a significant challenge for current climate models. By quantifying and exploring the uncertainty of regional climate change, we aim to assess the factors that cause the uncertainty and this should eventually lead to model improvement. On global scales, the community uses the standard 1%/year increasing CO2 concentration scenario and the transient climate response (TCR) provides one metric for comparing the response of an AOGCM with others. At this time, such metrics are not being used in the regional climate modeling community to explore the sensitivity of regional information to model differences. This is one goal of this research effort and we encourage other modeling groups to run similar ensembles with these scenarios.

Initial results indicate that significant differences exist between models and depend on location and season. In general, the global scale response is less dependent on model structure than the regional scales. In addition to hemispheric and global scales, we have analyzed the model response for the 21 "Giorgi" regions to changes in the SST anomalies. Also, it appears that CAM3.5 results (specifically, CAM3.5.42) are more similar to CAM3.1 than to CAM4.0.