An evaluation of surface meteorology and fluxes over the Iceland and Greenland Seas in ERA5 reanalysis: The impact of sea ice distribution
Journal article, Peer reviewed
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Original versionQuarterly Journal of the Royal Meteorological Society. 2021, 147 (734), 691-712. 10.1002/qj.3941
The Iceland and Greenland Seas are a crucial region for the climate system, being the headwaters of the lower limb of the Atlantic Meridional Overturning Circulation. Investigating the atmosphere–ocean–ice processes in this region often necessitates the use of meteorological reanalyses—a representation of the atmospheric state based on the assimilation of observations into a numerical weather prediction system. Knowing the quality of reanalysis products is vital for their proper use. Here we evaluate the surface-layer meteorology and surface turbulent fluxes in winter and spring for the latest reanalysis from the European Centre for Medium-Range Weather Forecasts, i.e., ERA5. In situ observations from a meteorological buoy, a research vessel, and a research aircraft during the Iceland–Greenland Seas Project provide unparalleled coverage of this climatically important region. The observations are independent of ERA5. They allow a comprehensive evaluation of the surface meteorology and fluxes of these subpolar seas and, for the first time, a specific focus on the marginal ice zone. Over the ice-free ocean, ERA5 generally compares well to the observations of surface-layer meteorology and turbulent fluxes. However, over the marginal ice zone, the correspondence is noticeably less accurate: for example, the root-mean-square errors are significantly higher for surface temperature, wind speed, and surface sensible heat flux. The primary reason for the difference in reanalysis quality is an overly smooth sea-ice distribution in the surface boundary conditions used in ERA5. Particularly over the marginal ice zone, unrepresented variability and uncertainties in how to parameterize surface exchange compromise the quality of the reanalyses. A parallel evaluation of higher-resolution forecast fields from the Met Office's Unified Model corroborates these findings.