Arctic Cloud Fraction and Radiative Fluxes in Atmospheric Reanalyses

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.

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

“…The surface radiative fluxes are highly dependent on the model parameterization of cloud processes, so large differences are expected among the reanalyses as reported by Walsh et al (2009). The surface fluxes are not assimilated by any of the models.…”

Near-surface meteorology during the Arctic Summer Cloud Ocean Study (ASCOS): evaluation of reanalyses and global climate models

“…In particular, the representation of cloud and radiation processes has been demonstrated to be problematic at high latitudes. Walsh et al (2008) used measurements from the Atmospheric Radiation Measurement (ARM) program's north slope of Alaska site at Barrow (71.3 • N, 156.6 • W) to evaluate cloud and radiation fields in four different atmospheric reanalyses. These included the National Center for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) reanalysis (hereafter R-1, Kalnay et al, 1996), the European Centre for MediumRange Weather Forecasting 40 yr reanalysis (ERA-40, Uppala et al, 2005), the NCEP-NCAR North American Reanalysis (NARR, Mesinger et al, 2006) and the Japan Meteorological Agency and Central Research Institute of Electric Power Industry 25 yr reanalysis (JRA-25, Onogi et al, 2007).…”

Strong wind events across Greenland�s coast and their influence on the ice sheet, sea ice and ocean

“…Depending on which snow class I use, the final heat content difference between the warm and the cold winters ranges between 4.5 · 10 6 J m −2 for 'Tundra' days. I note that there is a large uncertainty associated with the radiative heat fluxes in ERA-I [Chaudhuri et al, 2014], despite the fact that ERA-I is one of the better performers when different reanalyses are compared [Walsh et al, 2009]. Thus, the time period of 17 to 20 days should be considered as an estimate only.…”

“…is the sensible heat flux, is the latent heat flux, is the heat flux due to the net shortwave radiation and is due to the net longwave radiation. All of these variables are obtained from [Walsh et al, 2009, Chaudhuri et al, 2014, but it is likely that they play a minor role during DWE compared to the turbulent fluxes.…”