Validation of atmospheric reanalyses over the central Arctic Ocean

Abstract

Atmospheric reanalyses were validated against tethersonde sounding data on air temperature, air humidity and wind speed, collected during the drifting ice station Tara in the central Arctic in April–August 2007. The data were not assimilated into the reanalyses, providing a rare possibility for their independent validation, which was here made for the lowermost 890 m layer. The following reanalyses were included in the study: the European ERA‐Interim, the Japanese JCDAS, and the U.S. NCEP‐CFSR, NCEP‐DOE, and NASA‐MERRA. All reanalyses included large errors. ERA‐Interim was ranked first; it outperformed the other reanalyses in the bias and root‐mean‐square‐error (RMSE) for air temperature as well as in the bias, RMSE, and correlation coefficient for the wind speed. ERA‐Interim suffered, however, from a warm bias of up to 2°C in the lowermost 400 m layer and a moist bias of 0.3 to 0.5 g kg−1throughout the 890 m layer. The NCEP‐CFSR, NCEP‐DOE, and NASA‐MERRA reanalyses outperformed the other reanalyses with respect to 2‐m air temperature and specific humidity and 10‐m wind speed, which makes them, especially NCEP‐CSFR, better in providing turbulent flux forcing for sea ice models. Considering the whole vertical profile, however, the older NCEP‐DOE got the second highest overall ranking, being better than the new NCEP‐CFSR. Considering the whole group of reanalyses, the largest air temperature errors surprisingly occurred during higher‐than‐average wind speeds. The observed biases in temperature, humidity, and wind speed were in many cases comparable or even larger than the climatological trends during the latest decades.

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

“…Currently, Arctic temperature and humidity inversions are not realistically captured with respect to strength, depth, and base height by operational weather forecasting models (Lammert et al, 2010), climate models (Medeiros et al, 2011), high-resolution mesoscale models (Kilpeläinen et al, 2012), or even reanalyses (Lüpkes et al, 2010;Jakobson et al, 2012;Serreze et al, 2012). In particular, it is the nature of the Arctic atmosphere to contain multiple inversion layers and this is not reproduced in the models (Kilpeläinen et al, 2012).…”

“…Tjernström et al (2008) showed that the radiation errors were strongly related to errors in cloud occurrence, heights, and properties (such as water and ice content and their vertical distribution). In an evaluation of the latest atmospheric reanalyses against independent tethersonde sounding data from the central Arctic sea ice, Jakobson et al (2012) showed that all five reanalyses included in the evaluation had large systematic errors. Even the best one (ERA-Interim of the ECMWF, 2012; Dee et al, 2011) suffered from a warm bias of up to 2 • C in the lowermost 400 m layer and a significant moist bias throughout the lowermost 900 m. The observed biases in temperature, humidity, and wind speed were in many cases comparable to or even larger than the climatological trends during the latest decades.…”

Time and space variability of freshwater content, heat content and seasonal ice melt in the Arctic Ocean from 1991 to 2011

“…From the ERA-Interim Reanalysis project cumulative fluxes at the air-sea interface with 12 h time step are used. Although the ERA-Interim is known to perform better especially in the high latitudes (Jakobson et al, 2012), the large errors involved with all reanalysed products should be kept in mind.…”

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

“…Radiative flux densities, while marginally better at Barrow, are still demonstrated to be problematic, with R-2 demonstrating the largest biases. Most recently, Jakobson et al (2012) evaluated the performance of several reanalysis products over the central Arctic Ocean. Using measurements from the Tara drifting ice station (Gascard et al, 2008;Vihma et al, 2008) the authors demonstrate that the ERAInterim reanalysis outperforms several others, including R-2 and MERRA, using a ranking system.…”