Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System
Authors: McCusker Gillian Young , Vüllers Jutta , Achtert Peggy , Field Paul , Day Jonathan J. , Forbes Richard , Price Ruth , O'Connor Ewan , Tjernström Michael , Prytherch John , Neely III Ryan , Brooks Ian M. 
Published: April 2023

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 the European Centre for Medium-Range Weather Forecasting (ECMWF) 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 4-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 the 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
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 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 global model, driving a cause–effect relationship between the excessive low-altitude cloudiness and the coincident cold bias.
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.