The winter central Arctic surface energy budget: A model evaluation using observations from the MOSAiC campaign

This study evaluates the simulation of wintertime (15 October, 2019, to 15 March, 2020) statistics of the central Arctic near-surface atmosphere and surface energy budget observed during the MOSAiC campaign with short-term forecasts from 7 state-of-the-art operational and experimental forecast systems. Five of these systems are fully coupled ocean-sea ice-atmosphere models. Forecast systems need to simultaneously simulate the impact of radiative effects, turbulence, and precipitation processes on the surface energy budget and near-surface atmospheric conditions in order to produce useful forecasts of the Arctic system. This study focuses on processes unique to the Arctic, such as, the representation of liquid-bearing clouds at cold temperatures and the representation of a persistent stable boundary layer. It is found that contemporary models still struggle to maintain liquid water in clouds at cold temperatures. Given the simple balance between net longwave radiation, sensible heat flux, and conductive ground flux in the wintertime Arctic surface energy balance, a bias in one of these components manifests as a compensating bias in other terms. This study highlights the different manifestations of model bias and the potential implications on other terms. Three general types of challenges are found within the models evaluated: representing the radiative impact of clouds, representing the interaction of atmospheric heat fluxes with sub-surface fluxes (i.e., snow and ice properties), and representing the relationship between stability and turbulent heat fluxes.

[1]  J. Cassano,et al.  Meteorological conditions during the MOSAiC expedition , 2021, Elementa: Science of the Anthropocene.

[2]  L. Magnusson,et al.  Central Arctic weather forecasting: Confronting the ECMWF IFS with observations from the Arctic Ocean 2018 expedition , 2021, Quarterly Journal of the Royal Meteorological Society.

[3]  Malte Müller,et al.  On the warm bias in atmospheric reanalyses induced by the missing snow over Arctic sea-ice , 2019, Nature Communications.

[4]  Ekaterina Kourzeneva,et al.  Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1 , 2018 .

[5]  Rainer Knust,et al.  Polar Research and Supply Vessel POLARSTERN Operated by the Alfred-Wegener-Institute , 2017 .

[6]  M. Buchhold,et al.  Parameterisation of sea and lake ice in numerical weather prediction models of the German Weather Service , 2012 .

[7]  E. Girard,et al.  An Evaluation of Arctic Cloud and Radiation Processes Simulated by the Limited-Area Version of the Global Multiscale Environmental Model (GEM-LAM) , 2011 .

[8]  M. Zagar,et al.  An evaluation of Arctic cloud and radiation processes during the SHEBA year: simulation results from eight Arctic regional climate models , 2008 .

[9]  M. J. Shaw,et al.  Evaluation of an ensemble of Arctic regional climate models: spatiotemporal fields during the SHEBA year , 2006 .

[10]  Klaus Wyser,et al.  ‘Modelling the Arctic Boundary Layer: An Evaluation of Six Arcmip Regional-Scale Models using Data from the Sheba Project’ , 2005 .

[11]  D. Lawrence,et al.  Overview of the MOSAiC expedition—Atmosphere , 2022, Elementa: Science of the Anthropocene.

[12]  G. Zängl,et al.  The ICON (ICOsahedral Non‐hydrostatic) modelling framework of DWD and MPI‐M: Description of the non‐hydrostatic dynamical core , 2015 .