A highly scalable Met Office NERC Cloud model

Large Eddy Simulation is a critical modelling tool for scien- tists investigating atmospheric flows, turbulence and cloud microphysics. Within the UK, the principal LES model used by the atmospheric research community is the Met Office Large Eddy Model (LEM). The LEM was originally devel- oped in the late 1980s using computational techniques and assumptions of the time, which means that the it does not scale beyond 512 cores. In this paper we present the Met Office NERC Cloud model, MONC, which is a re-write of the existing LEM. We discuss the software engineering and architectural decisions made in order to develop a flexible, extensible model which the community can easily customise for their own needs. The scalability of MONC is evaluated, along with numerous additional customisations made to fur- ther improve performance at large core counts. The result of this work is a model which delivers to the community signifi- cant new scientific modelling capability that takes advantage of the current and future generation HPC machine

Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere

We demonstrate that LFRic-Atmosphere, a model built using the Met Office's GungHo dynamical core, is able to reproduce idealised large-scale atmospheric circulation patterns specified by several widely-used benchmark recipes. This is motivated by the rapid rate of exoplanet discovery and the ever-growing need for numerical modelling and characterisation of their atmospheres. Here we present LFRic-Atmosphere's results for the idealised tests imitating circulation regimes commonly used in the exoplanet modelling community. The benchmarks include three analytic forcing cases: the standard Held-Suarez test, the Menou-Rauscher Earth-like test, and the Merlis-Schneider Tidally Locked Earth test. Qualitatively, LFRic-Atmosphere agrees well with other numerical models and shows excellent conservation properties in terms of total mass, angular momentum and kinetic energy. We then use LFRic-Atmosphere with a more realistic representation of physical processes (radiation, subgrid-scale mixing, convection, clouds) by configuring it for the four TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) scenarios. This is the first application of LFRic-Atmosphere to a possible climate of a confirmed terrestrial exoplanet. LFRic-Atmosphere reproduces the THAI scenarios within the spread of the existing models across a range of key climatic variables. Our work shows that LFRic-Atmosphere performs well in the seven benchmark tests for terrestrial atmospheres, justifying its use in future exoplanet climate studies.Comment: 34 pages, 9(12) figures; Submitted to Geoscientific Model Development; Comments are welcome (see Discussion tab on the journal's website: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-647

Progress towards accelerating the unified model on hybrid multi-core systems

he cloud microphysics scheme, CASIM, and the radiation scheme, SOCRATES, are two computationally intensive parts within the Met Office’s Unified Model (UM). This study enables CASIM and SOCRATES to use accelerated multi-core systems for optimal com- putational performance of the UM. Using profiling to guide our efforts, we refactored the code for optimal threading and kernel arrangement and implemented OpenACC directives manually or through the CLAW source-to-source translator. Initial porting re- sults achieved 10.02x and 9.25x speedup in CASIM and SOCRATES respectively on 1 GPU compared with 1 CPU core. A granular per- formance analysis of the strategy and bottlenecks are discussed. These improvements will enable UM to run on heterogeneous com- puters and a path forward for further improvements is provided

Cloud feedbacks in extratopical cyclones: insight from long-term satellite data and high-resolution global simulations

A negative extratropical shortwave cloud feedback driven by changes in cloud optical depth is a feature of global climate models (GCMs). A robust positive trend in observed liquid water path (LWP) over the last two decades across the warming Southern Ocean supports the negative shortwave cloud feedback predicted by GCMs. This feature has been proposed to be due to transitions from ice to liquid with warming. To gain insight into the shortwave cloud feedback we examine extratropical cyclone variability and the response of extratropical cyclones to transient warming in GCM simulations. Multi-Sensor Advanced Climatology Liquid Water Path (MAC-LWP) microwave observations of cyclone properties from the period 1992–2015 are contrasted with GCM simulations, with horizontal resolutions ranging from 7 km to hundreds of kilometers. We find that inter-cyclone variability in LWP in both observations and models is strongly driven by the moisture flux along the cyclone's warm conveyor belt (WCB). Stronger WCB moisture flux enhances the LWP within cyclones. This relationship is replicated in GCMs, although its strength varies substantially across models. It is found that more than 80 % of the enhancement in Southern Hemisphere (SH) extratropical cyclone LWP in GCMs in response to a transient 4 K warming can be predicted based on the relationship between the WCB moisture flux and cyclone LWP in the historical climate and their change in moisture flux between the historical and warmed climates. Further, it is found that that the robust trend in cyclone LWP over the Southern Ocean in observations and GCMs is consistent with changes in the moisture flux. We propose two cloud feedbacks acting within extratropical cyclones: a negative feedback driven by Clausius–Clapeyron increasing water vapor path (WVP), which enhances the amount of water vapor available to be fluxed into the cyclone, and a feedback moderated by changes in the life cycle and vorticity of cyclones under warming, which changes the rate at which existing moisture is imported into the cyclone. Both terms contribute to increasing LWP within the cyclone. While changes in moisture flux predict cyclone LWP trends in the current climate and the majority of changes in LWP in transient warming simulations, a portion of the LWP increase in response to climate change that is unexplained by increasing moisture fluxes may be due to phase transitions. The variability in LWP within cyclone composites is examined to understand what cyclonic regimes the mixed-phase cloud feedback is relevant to. At a fixed WCB moisture flux cyclone LWP increases with increasing sea surface temperature (SST) in the half of the composite poleward of the low and decreases in the half equatorward of the low in both GCMs and observations. Cloud-top phase partitioning observed by the Atmospheric Infrared Sounder (AIRS) indicates that phase transitions may be driving increases in LWP in the poleward half of cyclones

Precipitation sensitivity to autoconversion rate in a numerical weather-prediction model

Aerosols are known to significantly affect cloud and precipitation patterns and intensity, but these interactions are ignored or very simplistically handled in climate and numerical weather-prediction (NWP) models. A suite of one-way nested Met Office Unified Model (UM) runs, with a single-moment bulk microphysics scheme was used to study two convective cases with contrasting characteristics observed in southern England. The autoconversion process that converts cloud water to rain is directly controlled by the assumed droplet number. The impact of changing cloud droplet number concentration (CDNC) on cloud and precipitation evolution can be inferred through changes to the autoconversion rate. This was done for a range of resolutions ranging from regional NWP (1 km) to high resolution (up to 100 m grid spacing) to evaluate the uncertainties due to changing CDNC as a function of horizontal grid resolution. The first case is characterised by moderately intense convective showers forming below an upper-level potential vorticity anomaly, with a low freezing level. The second case, characterised by one persistent stronger storm, is warmer with a deeper boundary layer. The colder case is almost insensitive to even large changes in CDNC, while in the warmer case a change of a factor of 3 in assumed CDNC affects total surface rain rate by ~17%. In both cases the sensitivity to CDNC is similar at all grid spacings <1 km. The contrasting sensitivities of these cases are induced by their contrasting ice-phase proportion. The ice processes in this model damp the precipitation sensitivity to CDNC. For this model the convection is sensitive to CDNC when the accretion process is more significant than the melting process and vice versa

Intercomparison of Large-Eddy Simulations of Arctic Mixed-Phase Clouds: Importance of Ice Size Distribution Assumptions

Large-eddy simulations of mixed-phase Arctic clouds by 11 different models are analyzed with the goal of improving understanding and model representation of processes controlling the evolution of these clouds. In a case based on observations from the Indirect and Semi-Direct Aerosol Campaign (ISDAC), it is found that ice number concentration, Ni, exerts significant influence on the cloud structure. Increasing Ni leads to a substantial reduction in liquid water path (LWP), in agreement with earlier studies. In contrast to previous intercomparison studies, all models here use the same ice particle properties (i.e., mass-size, mass-fall speed, and mass-capacitance relationships) and a common radiation parameterization. The constrained setup exposes the importance of ice particle size distributions (PSDs) in influencing cloud evolution. A clear separation in LWP and IWP predicted by models with bin and bulk microphysical treatments is documented and attributed primarily to the assumed shape of ice PSD used in bulk schemes. Compared to the bin schemes that explicitly predict the PSD, schemes assuming exponential ice PSD underestimate ice growth by vapor deposition and overestimate mass-weighted fall speed leading to an underprediction of IWP by a factor of two in the considered case. Sensitivity tests indicate LWP and IWP are much closer to the bin model simulations when a modified shape factor which is similar to that predicted by bin model simulation is used in bulk scheme. These results demonstrate the importance of representation of ice PSD in determining the partitioning of liquid and ice and the longevity of mixed-phase clouds

Intercomparison of cloud model simulations of Arctic mixed‐phase boundary layer clouds observed during SHEBA/FIRE‐ACE

An intercomparison of six cloud‐resolving and large‐eddy simulation models is presented. This case study is based on observations of a persistent mixed‐phase boundary layer cloud gathered on 7 May, 1998 from the Surface Heat Budget of Arctic Ocean (SHEBA) and First ISCCP Regional Experiment ‐ Arctic Cloud Experiment (FIRE‐ACE). Ice nucleation is constrained in the simulations in a way that holds the ice crystal concentration approximately fixed, with two sets of sensitivity runs in addition to the baseline simulations utilizing different specified ice nucleus (IN) concentrations. All of the baseline and sensitivity simulations group into two distinct quasi‐steady states associated with either persistent mixed‐phase clouds or all‐ice clouds after the first few hours of integration, implying the existence of multiple states for this case. These two states are associated with distinctly different microphysical, thermodynamic, and radiative characteristics. Most but not all of the models produce a persistent mixed‐phase cloud qualitatively similar to observations using the baseline IN/crystal concentration, while small increases in the IN/crystal concentration generally lead to rapid glaciation and conversion to the all‐ice state. Budget analysis indicates that larger ice deposition rates associated with increased IN/crystal concentrations have a limited direct impact on dissipation of liquid in these simulations. However, the impact of increased ice deposition is greatly enhanced by several interaction pathways that lead to an increased surface precipitation flux, weaker cloud top radiative cooling and cloud dynamics, and reduced vertical mixing, promoting rapid glaciation of the mixed‐phase cloud for deposition rates in the cloud layer greater than about 1 − 2 × 10−5 g kg−1 s−1 for this case. These results indicate the critical importance of precipitation‐radiative‐dynamical interactions in simulating cloud phase, which have been neglected in previous fixed‐dynamical parcel studies of the cloud phase parameter space. Large sensitivity to the IN/crystal concentration also suggests the need for improved understanding of ice nucleation and its parameterization in models