A43J-03
A-Train Based Observational Metrics for Model Evaluation in Extratropical Cyclones

Thursday, 17 December 2015: 14:10
3010 (Moscone West)
Catherine M Naud, Columbia University, Applied Physics and Applied Mathematics, New York, NY, United States, James F Booth, CUNY City College of New York, New York, NY, United States, Anthony D Del Genio, NASA Goddard Institute for Space Studies, New York, NY, United States, Derek J Posselt, University of Michigan Ann Arbor, Ann Arbor, MI, United States and Susan C van den Heever, Colorado State University, Atmospheric Science, Fort Collins, CO, United States
Abstract:
Extratropical cyclones contribute most of the precipitation in the midlatitudes, i.e. up to 70% during winter in the northern hemisphere, and can generate flooding, extreme winds, blizzards and have large socio-economic impacts. As such, it is important that general circulation models (GCMs) accurately represent these systems so their evolution in a warming climate can be understood. However, there are still uncertainties on whether warming will increase their frequency of occurrence, their intensity and how much rain or snow they bring. Part of the issue is that models have trouble representing their strength, but models also have biases in the amount of clouds and precipitation they produce. This is caused by potential issues in various aspects of the models: convection, boundary layer, and cloud scheme to only mention a few.

In order to pinpoint which aspects of the models need improvement for a better representation of extratropical cyclone precipitation and cloudiness, we will present A-train based observational metrics: cyclone-centered, warm and cold frontal composites of cloud amount and type, precipitation rate and frequency of occurrence. Using the same method to extract similar fields from the model, we will present an evaluation of the GISS-ModelE2 and the IPSL-LMDZ-5B models, based on their AR5 and more recent versions. The AR5 version of the GISS model underestimates cloud cover in extratropical cyclones while the IPSL AR5 version overestimates it.

In addition, we will show how the observed CloudSat-CALIPSO cloud vertical distribution across cold fronts changes with moisture amount and cyclone strength, and test if the two models successfully represent these changes. We will also show how CloudSat-CALIPSO derived cloud type (i.e. convective vs. stratiform) evolves across warm fronts as cyclones age, and again how this is represented in the models. Our third process-based analysis concerns cumulus clouds in the post-cold frontal region and how their amount relates to the stability of the boundary layer. This test uses Aqua cloud and vertical atmospheric profiles and when applied to the model output can help assess the accuracy of the convection, boundary layer and cloud scheme.

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