Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System
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.
Gillian Young et al.
Gillian Young et al.
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This paper compares NWP model output from the UM (using several different cloud schemes) and the IFS against ground-based observations collected in the central Arctic during a summer time cruise that sampled both sea ice melting conditions and refreezing conditions. The results demonstrated that both model frameworks overestimated cloud occurrence, but that using a total water content method, which takes into account the insensitivity of the ground-based remote sensors to very small amounts of cloud water content (or very small particle size) provided better agreement in cloud occurrence. Even still, the models tended to overestimate the LWC relative to the observations, which was hypothesized to result in too much cloud top radiative cooling which had deleterious impacts on the simulated temperature profile. However, they also showed that the UM models, which were all limited area models, sensitive to the forcing conditions from the global model used to drive these simulations. They also showed that a more accurate treatment of aerosols in the UM-LAM with the most complex cloud microphysics did change the profile of LWC in the lowest levels, but had virtually no other effect on the cloud lifetime, precipitation amount, or the biases in the thermodynamic profiles.
I found this paper very interesting, well-motivated, and well written.
My main comment is associated with the 4th point raised in the conclusions:
I think that the sensitivity of the results to the forcing dataset used to drive the UM models casts a lot of questions on this analysis. In section 3.4 (and later sections), the authors work hard to connect errors in clouds to errors in thermodynamic profiles (it seemed like a cause-effect implication). However, I don’t think the authors have done enough to convince me that the errors in the clouds are causing the rest of the issues. I think this could be addressed reasonably simply by showing the biases in the thermodynamic profiles over the region from the forcing dataset itself (e.g., in Fig 13 and subsequent figures). I realize that the UM models are providing 12-36 h forecasts that start from the forcing dataset, but I think adding these bias profiles would still be useful in making their case.
In a very similar and related comment: it looks like the biases in the LWC profiles from the three UM models are quite different, but the biases in the temperature and moisture profiles are essentially identical. If errors in the cloud properties are truly the driver (via radiation) of the biases in the temperature profiles, then I would have hypothesized we would see differences in the biases in the temperature profiles from the three models. Why don’t we?