Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

Abstract

Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice-ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981-2010: National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP-U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice-ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R 5 0.82). The trend in the total ice volume in September is greatest with MERRA (24.1 3 10 3 km 3 decade 21 ) and least with CFSR (22.7 3 10 3 km 3 decade 21 ).

Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas

“…Trace precipitation amounts for solid precipitation were assigned values from 0.03 to 0.07 mm that varied inversely with latitude following Mekis and Vincent (2011). CFSR, MERRA and ERA-Interim monthly mean temperature and precipitation data were previously evaluated over the Canadian Arctic by Rapaić et al (2015) and over the entire Arctic by Lindsay et al (2014). Both papers indicate that MERRA and ERA-Interim have relatively small warm and wet biases compared to other reanalyses, while CFSR was found to have particularly large positive precipitation biases.…”

“…As a consequence, climate model evaluations over such regions are often carried out using station observations or reanalyses (e.g. Lindsay et al 2014;Glisan and Gutowski 2014a, b;Matthes et al 2010Matthes et al , 2015Wilson et al 2012). In this study, we use surface observations of mean, minimum and maximum daily temperature (Tmean, Tmin and Tmax, respectively) and daily precipitation (Pr) from the climate stations included in the National Climate Data and Information Archive at Environment and Climate Change Canada (http://ccds-dscc.…”

Investigating the local-scale influence of sea ice on Greenland surface melt

“…MERRA is run on a 1/2 • latitude by 2/3 • longitude global grid with 72 hybrid-sigma vertical levels to produce analyses from 1979 to present. MERRA has been evaluated extensively since its release (Cullather and Bosilovich, 2011b;Kennedy et al, 2011; and has compared favorably with other reanalysis products in the Arctic (Zib et al, 2012;Cullather and Bosilovich, 2011;Lindsay et al, 2014).…”

Seasonal cycle and long-term trend of solar energy fluxes through Arctic sea ice

“…Downward surface solar radiation data were obtained four times per day from the European Centre for Medium-Range Weather Forecast (ECMWF) EraInterim re-analyses (Dee et al, 2011;Lindsay et al, 2014). The data (four values per day) were averaged to daily means and have been available since 1979.…”