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

Abstract

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.

Thermodynamic and cloud evolution in a cold air outbreak during HALO-(AC)3: Quasi-Lagrangian observations compared to the ERA5 and CARRA reanalyses

“…In all MCAO stages, ERA5 significantly over-estimates the amount of liquid water present in the clouds. A similar enhanced abundance of liquid-bearing clouds especially over sea ice has been reported for the IFS, the model behind ERA5 (Tjernström et al, 2021;McCusker et al, 2023). In the MCAO case here, this is in contrast to CARRA (Figure 9c).…”

“…The ERA5 clouds systematically precipitate stronger over the MIZ and ocean than clouds in CARRA (Figure A8e). The significance of our findings is reinforced by McCusker et al (2023), who showed that issues such as an over-abundance of low, liquid-bearing clouds can propagate into higher-resolution models through large-scale forcings.…”

“…Many authors reported on similar warm biases of skin and near-surface air temperatures in ERA5 (Batrak and Müller, 2019;Wang et al, 2019;Tjernström et al, 2021;McCusker et al, 2023). The skin temperatures are generally considered too warm as an insulating layer of snow is missing atop the floating ice, which can introduce surplus heat into the lower atmosphere (Batrak and Müller, 2019;Wang et al, 2019).…”

The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica

“…For example, errors with respect to the vertical distribution of liquid and ice, and especially the representation of thin supercooled liquid layers within mixed-phase clouds, can induce radiative biases (Gilbert et al, 2020;Vignon et al, 2021;Inoue et al, 2021). In addition to microphysics, model cloud macrophysical parameterisations, especially relating to cloud fraction, may impact cloud radiative effects (Van Weverberg et al, 2023;McCusker et al, 2023).…”

Large-eddy simulation of a two-layer boundary-layer cloud system from the Arctic Ocean 2018 expedition

“…Models often cannot reproduce cloud-free conditions when the aerosol particle (and thus CCN) concentrations are low in the region Stevens et al, 2018). Many models have simplified microphysical schemes and, for example, assume constant droplet number or aerosol concentrations (Wesslén et al, 2014;Bulatovic et al, 2019;McCusker et al, 2023).…”