How Well Are Clouds Simulated over Greenland in Climate Models? Consequences for the Surface Cloud Radiative Effect over the Ice Sheet

Author:

Lacour A.1,Chepfer H.1,Miller N. B.23,Shupe M. D.23,Noel V.4,Fettweis X.5,Gallee H.6,Kay J. E.2,Guzman R.7,Cole J.8

Affiliation:

1. Sorbonne Université, Université Pierre et Marie Curie, Laboratoire de Météorologie Dynamique, Institut Pierre Simon Laplace Ecole Polytechnique, Palaiseau, France

2. Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado

3. NOAA/Earth System Research Laboratory, Boulder, Colorado

4. CNRS/INSU, Laboratoire d’Aérologie, Toulouse, France

5. Department of Geography, University of Liege, Liege, Belgium

6. Laboratoire de Glaciologie et Geophysique de l’Environnement, Grenoble, France

7. CNRS, Laboratoire de Météorologie Dynamique, Institut Pierre Simon Laplace Ecole Polytechnique, Palaiseau, France

8. Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada

Abstract

Using lidar and radiative flux observations from space and ground, and a lidar simulator, we evaluate clouds simulated by climate models over the Greenland ice sheet, including predicted cloud cover, cloud fraction profile, cloud opacity, and surface cloud radiative effects. The representation of clouds over Greenland is a central concern for the models because clouds impact ice sheet surface melt. We find that over Greenland, most of the models have insufficient cloud cover during summer. In addition, all models create too few nonopaque, liquid-containing clouds optically thin enough to let direct solar radiation reach the surface (−1% to −3.5% at the ground level). Some models create too few opaque clouds. In most climate models, the cloud properties biases identified over all Greenland also apply at Summit, Greenland, proving the value of the ground observatory in model evaluation. At Summit, climate models underestimate cloud radiative effect (CRE) at the surface, especially in summer. The primary driver of the summer CRE biases compared to observations is the underestimation of the cloud cover in summer (−46% to −21%), which leads to an underestimated longwave radiative warming effect (CRELW = −35.7 to −13.6 W m−2 compared to the ground observations) and an underestimated shortwave cooling effect (CRESW = +1.5 to +10.5 W m−2 compared to the ground observations). Overall, the simulated clouds do not radiatively warm the surface as much as observed.

Funder

National Aeronautics and Space Administration

National Science Foundation

Centre National d’Etudes Spatiales

Publisher

American Meteorological Society

Subject

Atmospheric Science

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