A Comparison of Atmospheric Reanalysis Products for the Arctic Ocean and Implications for Uncertainties in Air–Sea Fluxes

Abstract

The uncertainties related to atmospheric fields in the Arctic Ocean from commonly used and recently available reanalysis products are investigated. Fields from the 1) ECMWF Interim Re-Analysis (ERA-Interim), 2) Common Ocean–Ice Reference Experiment version 2 (CORE2), 3) Japanese 25-yr Reanalysis Project (JRA-25), 4) NCEP–NCAR reanalysis, 5) NCEP Climate Forecast System Reanalysis (CFSR), and 6) Modern-Era Retrospective Analysis for Research and Applications (MERRA) are evaluated against satellite-derived and in situ observations for zonal and meridional winds, precipitation, specific humidity, surface air temperature, and downwelling longwave and shortwave radiation fluxes. Comparison to reference observations shows that for variables such as air temperature and humidity, all reanalysis products have similar solutions. However, other variables such as winds, precipitation, and radiation show large spreads. The magnitude of uncertainties in all fields is large when compared to the signal. Biases in Arctic cloud parameterizations and predicted temperature and humidity profiles in reanalyses as discussed in other studies are likely common sources of error that affect surface downwelling radiation, air temperature, humidity, and precipitation.

Heat Transport Pathways into the Arctic and their Connections to Surface Air Temperatures

“…ERA Interim was chosen as it represents the temperature in the Arctic well (Chaudhuri et al, 2014;Simmons and 5 Paul, 2015). Daily means were calculated from 6-hourly output.…”

International Arctic Systems for Observing the Atmosphere: An International Polar Year Legacy Consortium

“…6). Clouds, and particularly mixed-phase and high-latitude clouds, induce some of the largest uncertainties in numerical modeling (Chaudhuri et al 2014;de Boer et al 2012). Some of the challenges with simulation of Arctic clouds have come from obstacles involved with validating the model performance through the comparison of grid-boxaverage cloud properties to temporal averages derived at observatories using upward-looking sensors (e.g., Cox et al 2014).…”

A seamless approach to understanding and predicting Arctic sea ice in Met Office modelling systems

“…Analysis of the atmospheric energy budget in HadGEM2-ES shows that relative to ERA-40 the model reproduces the main features of the seasonal cycle of the heat budget in the Arctic ( figure 3) [28] noting that the uncertainty in radiation fluxes is large compared with the signal [29]. The main model issues are that the surface and top of atmosphere fluxes are slightly too strongly downward in May/June, whereas the surface flux is too strongly upward in late autumn.…”