Surface melting on Antarctic Peninsula ice shelves can influence ice shelf mass balance, and consequently sea level rise. We show that summertime cloud phase on the Larsen C ice shelf on the Antarctic Peninsula strongly influences the amount of radiation received at the surface and can determine whether or not melting occurs. While previous work has separately evaluated cloud phase and the surface energy balance (SEB) during summertime over Larsen C, no previous studies have examined this relationship quantitatively. Furthermore, regional climate models frequently produce surface radiation biases related to cloud ice and liquid water content. This study uses a high‐resolution regional configuration of the UK Met Office Unified Model (MetUM) to assess the influence of cloud ice and liquid properties on the SEB, and consequently melting, over the Larsen C ice shelf. Results from a case‐study show that simulations producing a vertical cloud phase structure more comparable to aircraft observations exhibit smaller surface radiative biases. A configuration of the MetUM adapted to improve the simulation of cloud phase reproduces the observed surface melt most closely. During a five‐week simulation of summertime conditions, model melt biases are reduced to <2 W·m−2: a four‐fold improvement on a previous study that used default MetUM settings. This demonstrates the importance of cloud phase in determining summertime melt rates on Larsen C.
The Kinematic Driver-Aerosol (KiD-A) intercomparison was established to test the hypothesis that detailed warm microphysical schemes provide a benchmark for lower-complexity bulk microphysics schemes. KiD-A is the first intercomparison to compare multiple Lagrangian cloud models (LCMs), size bin-resolved schemes, and double-moment bulk microphysics schemes in a consistent 1D dynamic framework and box cases. In the absence of sedimentation and collision-coalescence, the drop size distributions (DSDs) from the LCMs exhibit similar evolution with expected physical behaviors and good inter-scheme agreement, with the volume mean diameter (Dvol ) from the LCMs within 1 to 5% of each other. In contrast, the bin schemes exhibit non-physical broadening with condensational growth. These results further strengthen the case that LCMs are an appropriate numerical benchmark for DSD evolution under condensational growth. When precipitation processes are included, however, the simulated liquid water path, precipitation rates, and response to modified cloud drop/aerosol number concentrations from the LCMs vary substantially, while the bin and bulk schemes are relatively more consistent with each other. The lack of consistency in the LCM results stems from both the collision-coalescence process and the sedimentation process, limiting their application as a numerical benchmark for precipitation processes. Reassuringly, however, precipitation from bulk schemes, which are the basis for cloud microphysics in weather and climate prediction, is within the spread of precipitation from the detailed schemes (LCMs and bin). Overall, this intercomparison identifies the need for focused effort on the comparison of collision-coalescence methods and sedimentation in detailed microphysics schemes, especially LCMs.
Abstract. Convection-permitting simulations are used to understand the effects of cloud–aerosol interactions in a case of heavy rainfall over southern China. The simulations are evaluated using radar observations from the Southern China Monsoon Rainfall Experiment (SCMREX) and remotely sensed estimates of precipitation, clouds and radiation. We focus on the effects of complexity in cloud–aerosol interactions, especially the depletion and transport of aerosol material by clouds. In particular, simulations with aerosol concentrations held constant are compared with a fully cloud–aerosol-interacting system to investigate the effects of two-way coupling between aerosols and clouds on a line of organised deep convection. It is shown that the cloud processing of aerosols can change the vertical structure of the storm by using up aerosols within the core of line, thereby maintaining a relatively clean environment which propagates with the heaviest rainfall. This induces changes in the statistics of surface rainfall, with a cleaner environment being associated with less-intense but more-frequent rainfall. These effects are shown to be related to a shortening of the timescale for converting cloud droplets to rain as the aerosol number concentration is decreased. The simulations are compared to satellite-derived estimates of surface rainfall, a condensed-water path and the outgoing flux of short-wave radiation. Simulations for fewer aerosol particles outperform the more polluted simulations for surface rainfall but give poorer representations of top-of-atmosphere (TOA) radiation.
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.
<p>State-of-the-art numerical models such as the UK Met Office Unified Model and European Centre for Medium-Range Weather Forecasting Integrated Forecasting System are crucial tools for forecasting future Arctic warming. However, their ability to reproduce clouds and boundary layer meteorology in the high Arctic has not been thoroughly evaluated following significant model developments over the last 10 years. Model evaluation is key to understanding where remaining process weaknesses lie, thus informing further parametrization developments to improve the simulated surface energy budget.</p><p>Here, we evaluate model performance with comparison to observations made during the Arctic Ocean 2018 expedition, where a suite of remote-sensing instrumentation was active aboard the Swedish icebreaker <em>Oden </em>measuring summertime Arctic cloud and boundary layer properties. We find that both models do not reproduce cloud fractions well at altitude (up to 8 km) and overestimate the occurrence of low (<1 km) clouds during the sea ice melt period of the expedition. Low cloud agreement with observations improves when the sea ice begins to refreeze; however, the underestimation of cloud aloft remains consistent regardless of sea ice conditions. In this presentation, we will indicate which model processes need to be improved to capture these summertime Arctic clouds more effectively.</p>
Abstract. We use convective-scale simulations of monsoonal clouds to reveal a self-similar probability density function that underpins surface rainfall statistics. This density is independent of cloud-droplet number concentration and is unchanged by aerosol perturbations. It therefore represents an invariant property of our model with respect to cloud–aerosol interactions. For a given aerosol concentration, if the dependence of at least one moment of the rainfall distribution on cloud-droplet number is a known input parameter, then the self-similar density can be used to reconstruct the entire rainfall distribution to a useful degree of accuracy. In particular, we present both single-moment and double-moment reconstructions that are able to predict the responses of the rainfall distributions to changes in aerosol concentration. In doing so, we show that the seemingly high-dimensional space of possible aerosol-induced rainfall-distribution transformations can be parameterised by surprisingly few (at most 3) independent “degrees of freedom”: the self-similar density and auxiliary information about two moments of the rainfall distribution. Comparisons to convection-permitting forecasts of mid-latitude weather and atmosphere-only global simulations show that the self-similar density is also independent of model physics and background meteorology. A theoretical explanation for this invariance is given, based on numerical results from a stochastic rainfall simulator. This suggests that, although aerosol indirect effects on any specific hydro-meteorological system may be multifarious in terms of rainfall changes and physical mechanisms, there may, nevertheless, be a universal constraint on the number of independent degrees of freedom needed to represent the dependencies of rainfall on aerosols.
<p>Shallow cumulus clouds interact with their environment in myriad significant ways, and yet their behavour is still poorly understood, and is responsible for much uncertainty in climate models. Improving our understanding of these clouds is therefore an important part of improving our understanding of the climate system as a whole.</p><p>Modelling studies of shallow convection have traditionally made use of highly idealised simulations using large-eddy models, which allow for high resolution, detailed simulations. However, this idealised nature, with periodic boundaries and constant forcing, and the quasi-equilibrium cloud fields produced, means that they do not capture the effect of transient forcing and conditions found in the real atmosphere, which contains shallow cumulus cloud fields unlikely to be in equilibrium.<span>&#160;</span></p><p>Simulations with more realistic nested domains and forcings have previously been shown to have significant persistent responses differently to aerosol perturbations, in contrast to many large eddy simulations in which perturbed runs tend to reach a similar quasi-equilibrium.<span>&#160;</span></p><p>Here, we further this investigation by using a single model to present a comparison of familiar idealised simulations of trade wind cumuli in periodic domains, and simulations with a nested domain, whose boundary conditions are provided by a global driving model, able to simulate transient synoptic conditions.<span>&#160;</span></p><p>The simulations are carried out using the Met Office Unified Model (UM), and are based on a case study from the Rain In Cumulus over the Ocean (RICO) field campaign. Large domains of 500km are chosen in order to capture large scale cloud field behaviour. A double-moment interactive microphysics scheme is used, along with prescribed aerosol profiles based on RICO observations, which are then perturbed.</p><p>We find that the choice between realistic nested domains with transient forcing and idealised periodic domains with constant forcing does indeed affect the nature of the response to aerosol perturbations, with the realistic simulations displaying much larger persistent changes in domain mean fields such as liquid water path and precipitation rate.<span>&#160;</span></p>
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