Central Arctic weather forecasting: Confronting the ECMWF IFS with observations from the Arctic Ocean 2018 expedition

Forecasts with the European Centre for Medium-Range Weather Forecasts' numerical weather prediction model are evaluated using an extensive set of observations from the Arctic Ocean 2018 expedition on the Swedish icebreaker Oden. The atmospheric model (Cy45r1) is similar to that used for the ERA5 reanalysis (Cy41r2). The evaluation covers 1 month, with the icebreaker moored to drifting sea ice near the North Pole; a total of 125 forecasts issued four times per day were used. Standard surface observations and 6-hourly soundings were assimilated to ensure that the initial model error is small. Model errors can be divided into two groups. First, variables related to dynamics feature errors that grow with forecast length; error spread also grows with time. Initial errors are small, facilitating a robust evaluation of the second group; thermodynamic variables. These feature fast error growth for 6-12 hr, after which errors saturates; error spread is roughly constant. Both surface and near-surface air temperatures are too warm in the model. During the summer both are typically above zero in spite of the ongoing melt; however, the warm bias increases as the surface freezes. The warm bias is due to a too warm atmosphere; errors in surface sensible heat flux transfer additional heat from the atmosphere to the surface. The lower troposphere temperature error has a distinct vertical structure: a substantial warm bias in the lowest few 100 m and a large cold bias around 1 km; this structure features a significant diurnal cycle and is tightly coupled to errors in the modelled clouds. Clouds appear too often and in a too deep layer of the lower atmosphere; the lowest clouds essentially never break up. The largest error in cloud presence is aligned with the largest cold bias at around 1 km.

Automated summary

Forecasts from the European Centre for Medium-Range Weather Forecasts' numerical weather prediction model were evaluated using observations from the 2018 Arctic Ocean expedition. The study found that errors in the model are categorized into two groups: dynamics-related variables that grow with forecast length, and thermodynamic variables that exhibit fast error growth and a warm bias in surface temperatures. The errors in surface temperature are attributed to the atmosphere being too warm and a transfer of additional heat from the atmosphere to the surface.