School of Meteorology

Understanding decadal rainfall variability in West Africa using CMIP5 models

Dr. Elinor R. Martin
Department of Atmospheric and Environmental Sciences, University at Albany (SUNY)

30 January 2014, 4:00 PM

National Weather Center, Room 1313
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

Precipitation in the Sahel region of Africa is well known to have undergone large decadal variability over the past century, with wet conditions in the 1940s and 1950s, a switch to dry conditions in the 1960s and a recovery to near average conditions since 2000. With decadal and interannual variability similar in magnitude in the Sahel, understanding and simulating these observed decadal variations is a vital step in making decadal and longer predictions for the Sahel in order to prepare and adapt to changes in water availability in the future.
Output from models in the Coupled Model Intercomparison Project phase 5 (CMIP5) is used to investigate and evaluate decadal variability in Sahel rainfall and the associated teleconnections with global sea surface temperatures (SSTs). Multiple simulation types, including historical, control, atmosphere only, and decadal hindcasts are used to extract information about the simulation of rainfall variability.
Decadal variability of Sahel rainfall is lower than observed in historical CMIP5 simulations and the impact of SSTs on this variability is examined. Teleconnections with the Indian Ocean are negative in observations but the majority of CMIP5 historical simulations have a large and positive correlation. In the North Atlantic, correlations with SSTs (or the Atlantic Multidecadal Oscillation, AMO) are positive in the simulations (the same sign as observations). To investigate why some models produce larger Sahel decadal variability than others despite having similar AMO decadal variability, two groups of models (good and poor) are selected. It is evident that the poor models are failing to capture the teleconnection between the AMO and Sahel rainfall because the distribution of SST variability across the North Atlantic is incorrect. The lack of signal in the tropical North Atlantic reduces the interhemispheric SST gradient and through circulation changes, the rainfall variability in the Sahel. This reduced tropical North Atlantic SST variability is also evident in control simulations and was attributed to a combination of poorly simulated cloud amounts and feedbacks in the stratocumulus region, dust-SST-rainfall feedbacks and sulfate aerosol interactions with clouds.
By understanding the deficits and successes of the CMIP5 historical simulations, future projections and decadal hindcasts for the region can be examined with additional confidence.

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