Preprints
https://doi.org/10.5194/acp-2021-662
https://doi.org/10.5194/acp-2021-662

  09 Sep 2021

09 Sep 2021

Review status: this preprint is currently under review for the journal ACP.

Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System

Gillian Young1, Jutta Vüllers1, Peggy Achtert2, Paul Field1,3, Jonathan J. Day4, Richard Forbes4, Ruth Price1, Ewan O'Connor5, Michael Tjernström6, John Prytherch6, Ryan Neely III1,7, and Ian M. Brooks1 Gillian Young et al.
  • 1Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK
  • 2Meteorological Observatory Hohenpeißenberg, German Weather Service, Germany
  • 3Met Office, Exeter, UK
  • 4European Centre for Medium-Range Weather Forecasts, Reading, UK
  • 5Finnish Meteorological Institute, Helsinki, Finland
  • 6Department of Meteorology, Stockholm University, Sweden
  • 7National Centre for Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK

Abstract. By synthesising remote-sensing measurements made in the central Arctic into a model-gridded Cloudnet cloud product, we evaluate how well the Met Office Unified Model (UM) and European Centre for Medium-Range Weather Forecasting Integrated Forecasting System (IFS) capture Arctic clouds and their associated interactions with the surface energy balance and the thermodynamic structure of the lower troposphere. This evaluation was conducted using a four-week observation period from the Arctic Ocean 2018 expedition, where the transition from sea ice melting to freezing conditions was measured. Three different cloud schemes were tested within a nested limited area model (LAM) configuration of the UM – two regionally-operational single-moment schemes (UM_RA2M and UM_RA2T), and one novel double-moment scheme (UM_CASIM-100) – while one global simulation was conducted with the IFS, utilising its default cloud scheme (ECMWF_IFS).

Consistent weaknesses were identified across both models, with both the UM and IFS overestimating cloud occurrence below 3 km. This overestimation was also consistent across the three cloud configurations used within the UM framework, with > 90 % mean cloud occurrence simulated between 0.15 and 1 km in all model simulations. However, the cloud microphysical structure, on average, was modelled reasonably well in each simulation, with the cloud liquid water content (LWC) and ice water content (IWC) comparing well with observations over much of the vertical profile. The key microphysical discrepancy between the models and observations was in the LWC between 1 and 3 km, where most simulations (all except UM_RA2T) overestimated the observed LWC.

Despite this reasonable performance in cloud physical structure, both models failed to adequately capture cloud-free episodes: this consistency in cloud cover likely contributes to the ever-present near-surface temperature bias simulated in every simulation. Both models also consistently exhibited temperature and moisture biases below 3 km, with particularly strong cold biases coinciding with the overabundant modelled cloud layers. These biases are likely due to too much cloud top radiative cooling from these persistent modelled cloud layers and were interestingly consistent across the three UM configurations tested, despite differences in their parameterisations of cloud on a sub-grid-scale. Alarmingly, our findings suggest that these biases in the regional model were inherited from the driving model, thus triggering too much cloud formation within the lower troposphere. Using representative cloud condensation nuclei concentrations in our double-moment UM configuration, while improving cloud microphysical structure, does little to alleviate these biases; therefore, no matter how comprehensive we make the cloud physics in the nested LAM configuration used here, its cloud and thermodynamic structure will continue to be overwhelmingly biased by the meteorological conditions of its driving model.

Gillian Young et al.

Status: open (until 21 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Gillian Young et al.

Gillian Young et al.

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Short summary
In this study, we show that recent versions of two atmospheric models – the Unified Model and Integrated Forecasting System – overestimate Arctic cloud fraction within the lower troposphere by comparing with recent remote-sensing measurements made during the Arctic Ocean 2018 expedition. The overabundance of cloud influences the modelled thermodynamic structure, with strong negative temperature biases coincident with these overestimated cloud layers.
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