Arctic Cloud Fraction and Radiative Fluxes in Atmospheric Reanalyses

Abstract

Abstract Arctic radiative fluxes, cloud fraction, and cloud radiative forcing are evaluated from four currently available reanalysis models using data from the North Slope of Alaska (NSA) Barrow site of the Atmospheric Radiation Measurement Program (ARM). A primary objective of the ARM–NSA program is to provide a high-resolution dataset of direct measurements of Arctic clouds and radiation so that global climate models can better parameterize high-latitude cloud radiative processes. The four reanalysis models used in this study are the 1) NCEP–NCAR global reanalysis, 2) 40-yr ECMWF Re-Analysis (ERA-40), 3) NCEP–NCAR North American Regional Reanalysis (NARR), and 4) Japan Meteorological Agency and Central Research Institute of Electric Power Industry 25-yr Reanalysis (JRA25). The reanalysis models simulate the radiative fluxes well if/when the cloud fraction is simulated correctly. However, the systematic errors of climatological reanalysis cloud fractions are substantial. Cloud fraction and radiation biases show considerable scatter, both in the annual mean and over a seasonal cycle, when compared to those observed at the ARM–NSA. Large seasonal cloud fraction biases have significant impacts on the surface energy budget. Detailed comparisons of ARM and reanalysis products reveal that the persistent low-level cloud fraction in summer is particularly difficult for the reanalysis models to capture creating biases in the shortwave radiation flux that can exceed 160 W m−2. ERA-40 is the best performer in both shortwave and longwave flux seasonal representations at Barrow, largely because its simulation of the cloud coverage is the most realistic of the four reanalyses. Only two reanalyses (ERA-40 and NARR) capture the observed transition from positive to negative surface net cloud radiative forcing during a 2–3-month period in summer, while the remaining reanalyses indicate a net warming impact of Arctic clouds on the surface energy budget throughout the entire year. The authors present a variable cloud radiative forcing metric to diagnose the erroneous impact of reanalysis cloud fraction on the surface energy balance. The misrepresentations of cloud radiative forcing in some of the reanalyses are attributable to errors in both simulated cloud amounts and the models’ radiative response to partly cloudy conditions.