Comment on acp-2021-662

doi:10.5194/acp-2021-662-rc1

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.

Highlights

Here, we evaluate the performance of recent revisions of both the Unified Model (UM) and Integrated Forecasting System (IFS) focusing on their ability to capture clouds and the thermodynamic structure of the boundary layer (BL), highlighting common process relationships between the models which may explain differences from observations
LWnet biases do not exceed +5.5 W m-2 over periods 4—6; biases are greater due to the models’ inability to reproduce cloud-free conditions. This relationship with cloud cover influences the surface downwelling longwave (LW↓) biases: with the exception of the standard UM configurations during period 5, all LW↓ biases are positive (Table 4). Combining these radiative components, we find that Rnet is overestimated by all simulations during the melt, largely driven by too much surface SWnet when cloud is present in reality, indicating that the model surface albedo is too low and does not reflect enough SW↓
3.3.1 Influence of the UM driving model To investigate how much the large-scale forcing is influencing the UM biases, an additional test was performed over a subset of the drift (31 Aug to 5 Sep) using ERA-Interim to initialise the UM global model. This test was designed to evaluate whether the initial conditions of the global driving model, and the associated data assimilation (DA) systems used to derive the operational analyses used for initialisation, are largely responsible for the limited area model (LAM) thermodynamic biases we have found in this study

Summary

Introduction

The Arctic is warming at more than twice the global average rate (Serreze and Barry, 2011; Cohen et al, 2014), with recent evidence suggesting the rate of warming could be up to three times the global average (AMAP 2021). With accelerating Arctic warming, we need to build suitable numerical models to confidently predict how the atmosphere will change both on short weather prediction and longer climate time scales (Jung et al, 2016) Models such as the Met Office Unified Model (UM) and European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) are commonly used for assessing future Arctic change; recent work has shown that, like other large-scale models, both exhibit surface energy balance discrepancies with comparison to high Arctic observations. In both the UM and the IFS, these biases have largely been attributed to incorrect cloud cover (Birch et al, 2012; Sotiropoulou et al, 2016; Tjernström et al, 2021)

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Conclusion