Abstract

AbstractForecasts 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 icebreakerOden. 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.

Highlights

  • Weather forecasting for the Arctic Ocean is becoming increasingly important (Jung et al, 2016)

  • Deep high-humidity events associated with weather systems and deep frontal clouds agree well with observations (Figure 2e,f); see for example around Day of the Year (DoY) 233 and 240, and several systems that appear during DoY 245–253

  • We evaluate operational European Centre for Medium-Range Weather Forecasts (ECMWF)/Integrated Forecasting System (IFS) high-resolution deterministic (HRES) forecasts using an extensive observational dataset from the Arctic Ocean 2018 expedition, deployed on the Swedish icebreaker Oden

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Summary

Introduction

Weather forecasting for the Arctic Ocean is becoming increasingly important (Jung et al, 2016). The most obvious manifestation is the rapid reduction in sea ice extent (Onarheim et al, 2018), thickness, and age (Ricker et al, 2017; Kwok, 2018) This opens up the Arctic Ocean for increased shipping (Smith and Stephenson, 2013), creating opportunities for resource extraction and tourism, and economic growth as well as environmental risks. It changes living conditions for indigenous peoples, who may no longer be able to trust traditional knowledge, with extreme weather occurring more often as sea ice becomes more vulnerable (Holland and Stroeve, 2011). Their quality is limited by both that of the numerical models used to progress information forward in time, and by the quality and availability of the observations constraining the analyses

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