Search

This study utilizes the wealth of observational data collected during the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift experiment to constrain and evaluate close to two‐hundred daily Large‐Eddy Simulations (LES) of Arctic boundary layers and clouds at high resolutions. A standardized approach is adopted to tightly integrate field measurements into the experimental configuration. Covering the full drift represents a step forward from single‐case LES studies, and allows for a robust assessment of model performance against independent data under a range of atmospheric conditions. A homogeneously forced domain is simulated in a Lagrangian frame of reference, initialized with radiosonde and value‐added cloud profiles. Prescribed boundary conditions include various measured surface characteristics. Time‐constant composite forcing is applied, primarily consisting of subsidence rates sampled from reanalysis data. The simulations run for 3 hours, allowing turbulence and clouds to spin up while still facilitating direct comparison to MOSAiC data. Key aspects such as the vertical thermodynamic structure, cloud properties, and surface energy fluxes are well reproduced and maintained. The model captures the bimodal distribution of atmospheric states that is typical of Arctic climate. Selected days are investigated more closely to assess the model's skill in maintaining the observed boundary layer structure. The sensitivity to various aspects of the experimental configuration and model physics is tested. The model input and output are available to the scientific community, supplementing the MOSAiC data archive. The close agreement with observed meteorology justifies the use of LES for gaining further insight into Arctic boundary layer processes and their role in Arctic climate change. Plain Language Summary: The Arctic is one of the regions most affected by global climate change, warming up to four times as fast as the rest of the globe. It is also a particularly inaccessible region to conduct measurements. Fortunately, between 2019 and 2020 the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign collected an unprecedented amount of data in the Arctic. In this study, numerous of these measurements are incorporated into high‐resolution computer simulations of the lowest part of the Arctic atmosphere. This simulation data complements and contextualizes the observations and enables insight into complex physical processes, for example, cloud formation, cloud ice production, or turbulent mixing. The Arctic is an extreme place, and models often struggle to represent the atmosphere accurately. Therefore, the main achievement of this study is to successfully simulate 190 atmospheric situations as measured during the campaign. The generated data set performs well when compared to independent observations. Single cases deliver information about individual atmospheric conditions, and the collection gives insight into how key climate variables behaved throughout the MOSAiC year. Key Points: A standardized LES setup based on campaign data is developed with an aim to supplement the local measurements during the Multidisciplinary drifting Observatory for the Study of Arctic Climate driftIndependent drift‐long statistics on key aspects of the surface energy budget, thermodynamic structure, and clouds are reproducedSensitivity tests indicate microphysics, ice‐radiation interaction and surface representation are critical for successful daily simulations [ABSTRACT FROM AUTHOR]

Abstract This study utilizes the wealth of observational data collected during the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift experiment to constrain and evaluate close to two‐hundred daily Large‐Eddy Simulations (LES) of Arctic boundary layers and clouds at high resolutions. A standardized approach is adopted to tightly integrate field measurements into the experimental configuration. Covering the full drift represents a step forward from single‐case LES studies, and allows for a robust assessment of model performance against independent data under a range of atmospheric conditions. A homogeneously forced domain is simulated in a Lagrangian frame of reference, initialized with radiosonde and value‐added cloud profiles. Prescribed boundary conditions include various measured surface characteristics. Time‐constant composite forcing is applied, primarily consisting of subsidence rates sampled from reanalysis data. The simulations run for 3 hours, allowing turbulence and clouds to spin up while still facilitating direct comparison to MOSAiC data. Key aspects such as the vertical thermodynamic structure, cloud properties, and surface energy fluxes are well reproduced and maintained. The model captures the bimodal distribution of atmospheric states that is typical of Arctic climate. Selected days are investigated more closely to assess the model's skill in maintaining the observed boundary layer structure. The sensitivity to various aspects of the experimental configuration and model physics is tested. The model input and output are available to the scientific community, supplementing the MOSAiC data archive. The close agreement with observed meteorology justifies the use of LES for gaining further insight into Arctic boundary layer processes and their role in Arctic climate change.

Abstract The Advanced Microphysics Prediction System (AMPS), which adopts a two‐moment hybrid‐bin habit‐predicting scheme, was previously developed to study cloud microphysical processes that depend on ice habit; however, only one particular atmospheric model, the University of Wisconsin‐Nonhydrostatic Modeling System, has been used to test the AMPS. In this study, AMPS is implemented into the Scalable Computing for Advanced Library and Environment (SCALE) large‐eddy simulation model. The AMPS Eulerian advection scheme for non‐mass characteristic variables of ice particles, such as axis lengths, is refined to minimize numerical artifacts. The resulting SCALE‐AMPS model successfully reproduces features of mixed‐phase clouds observed during the Surface Heat Budget of the Arctic campaign, including liquid water path (LWP), ice particle size distributions, and ice habits, when ice particle number concentrations (Nice) are reproduced. Sensitivity studies show that increases in Nice result in reductions of LWP that are generally consistent with previous results. Interestingly, LWP reductions lead to changes in ice habits through increases in cloud temperature due to weaker cloud top radiative cooling. Furthermore, aspect ratios of precipitating particles also change following LWP reductions, because in Bigg's immersion freezing scheme, adopted in this study, the aspect ratios depend on the initial size of the ice particles and freezing rates depend on both temperature and droplet size. Because habits of ice particles affect their growth rates, fall speeds, and collision rates, the results obtained in this study reveal possible feedback processes of Arctic mixed‐phase clouds operating through ice habits.

Advances in high‐performance computing have enabled large‐eddy simulations (LES) of turbulence, convection, and clouds. However, their potential to improve parameterizations in global climate models (GCMs) is only beginning to be harnessed, with relatively few canonical LES available so far. The purpose of this paper is to begin creating a public LES library that expands the training data available for calibrating and evaluating GCM parameterizations. To do so, we use an experimental setup in which LES are driven by large‐scale forcings from GCMs, which in principle can be used at any location, any time of year, and in any climate state. We use this setup to create a library of LES of clouds across the tropics and subtropics, in the present and in a warmer climate, with a focus on the transition from stratocumulus to shallow cumulus over the East Pacific. The LES results are relatively insensitive to the choice of host GCM driving the LES. Driven with large‐scale forcing under global warming, the LES simulate a positive but weak shortwave cloud feedback, adding to the accumulating evidence that low clouds amplify global warming. Plain Language Summary: Clouds remain one of the largest uncertainties in our understanding and in predictions of climate change because it is challenging to represent their small‐scale dynamics in climate models. However, high‐resolution simulations can provide faithful simulations of clouds and turbulence in limited areas, and these can be used to calibrate climate models. So far, only a limited set of simulations has been used for calibration of climate models, with focus on a few specific locations. This study presents an experimental setup that allows the high‐resolution simulations to be run anywhere on the globe, in any climate state, driven by output from different climate models. The setup is used to create a library of high‐resolution simulations of clouds across the tropics and subtropics in both the current and a warmer climate. The library substantially expands the data set available for the calibration and evaluation of climate models. Key Points: A library of high‐resolution simulations of clouds is created with large‐eddy simulations (LES) driven by a few global climate models (GCMs)Clouds simulated by LES driven by different GCMs are more similar to one another than the corresponding GCM‐simulated cloudsUnder global warming, LES‐simulated low clouds exhibit a weak but positive feedback on warming [ABSTRACT FROM AUTHOR]

Abstract Advances in high‐performance computing have enabled large‐eddy simulations (LES) of turbulence, convection, and clouds. However, their potential to improve parameterizations in global climate models (GCMs) is only beginning to be harnessed, with relatively few canonical LES available so far. The purpose of this paper is to begin creating a public LES library that expands the training data available for calibrating and evaluating GCM parameterizations. To do so, we use an experimental setup in which LES are driven by large‐scale forcings from GCMs, which in principle can be used at any location, any time of year, and in any climate state. We use this setup to create a library of LES of clouds across the tropics and subtropics, in the present and in a warmer climate, with a focus on the transition from stratocumulus to shallow cumulus over the East Pacific. The LES results are relatively insensitive to the choice of host GCM driving the LES. Driven with large‐scale forcing under global warming, the LES simulate a positive but weak shortwave cloud feedback, adding to the accumulating evidence that low clouds amplify global warming.

Abstract Interactions between clouds and radiation are at the root of many difficulties in numerically predicting future weather and climate and in retrieving the state of the atmosphere from remote sensing observations. The broad range of issues related to these interactions, and to three‐dimensional interactions in particular, has motivated the development of accurate radiative tools able to compute all types of radiative metrics, from monochromatic, local, and directional observables to integrated energetic quantities. Building on this community effort, we present here an open‐source library for general use in Monte Carlo algorithms. This library is devoted to the acceleration of ray tracing in complex data, typically high‐resolution large‐domain grounds and clouds. The main algorithmic advances embedded in the library are related to the construction and traversal of hierarchical grids accelerating the tracing of paths through heterogeneous fields in null‐collision (maximum cross‐section) algorithms. We show that with these hierarchical grids, the computing time is only weakly sensitive to the refinement of the volumetric data. The library is tested with a rendering algorithm that produces synthetic images of cloud radiances. Other examples of implementation are provided to demonstrate potential uses of the library in the context of 3‐D radiation studies and parameterization development, evaluation, and tuning.

Abstract It is well known that subtropical shallow convection transports heat and water vapor upwards from the surface. It is less clear if it also transports horizontal momentum upwards to significantly affect the trade winds in which it is embedded. We utilize unique multiday large‐eddy simulations run over the tropical Atlantic with ICON‐LEM to investigate the character of shallow convective momentum transport (CMT). For a typical trade‐wind profile during boreal winter, CMT acts as an apparent friction to decelerate the north‐easterly flow. This effect maximizes below the cloud base while in the cloud layer, friction is very small, although present over a relatively deep layer. In the cloud layer, the zonal component of the momentum flux is counter‐gradient and penetrates deeper than reported in traditional shallow cumulus LES cases. The transport through conditionally sampled convective updrafts and downdrafts explains a weak friction effect, but not the counter‐gradient flux near the cloud tops. The analysis of the momentum flux budget reveals that, in the cloud layer, the counter‐gradient flux is driven by convectively triggered nonhydrostatic pressure‐gradients and horizontal circulations surrounding the clouds. A model set‐up with large domain size and realistic boundary conditions is necessary to resolve these effects.

Abstract Process‐scale development, evaluation, and calibration of physically based parameterizations of clouds and radiation are powerful levers for improving weather and climate models. In a series of papers, we propose a strategy for process‐based calibration of climate models that uses machine learning techniques. It relies on systematic comparisons of single‐column versions of climate models with explicit simulations of boundary‐layer dynamics and clouds (Large‐Eddy Simulations [LES]). This paper focuses on the calibration of cloud geometry parameters (vertical overlap, horizontal heterogeneity, and cloud size) that appear in the parameterization of radiation. The solar component of a radiative transfer (RT) scheme that includes a parameterization for 3D radiative effects of clouds (SPARTACUS) is run in offline single‐column mode on an ensemble of input cloud profiles synthesized from LES outputs. The space of cloud geometry parameter values is efficiently explored by sampling a large number of parameter sets (configurations) from which radiative metrics are computed using fast surrogate models that emulate the SPARTACUS solver. The sampled configurations are evaluated by comparing these radiative metrics to reference values provided by a 3D RT Monte Carlo model. The best calibrated configurations yield better predictions of TOA and surface fluxes than the one that uses parameter values computed from the 3D cloud fields: The root‐mean‐square errors averaged over cumulus cloud fields and solar angles are reduced from ∼10 Wm−2 with LES‐derived parameters to ∼5 Wm−2 with adjusted parameters. However, the calibration of cloud geometry fails to reduce the errors on absorption, which remain around 2–4 Wm−2.

It is well known that subtropical shallow convection transports heat and water vapor upwards from the surface. It is less clear if it also transports horizontal momentum upwards to significantly affect the trade winds in which it is embedded. We utilize unique multiday large‐eddy simulations run over the tropical Atlantic with ICON‐LEM to investigate the character of shallow convective momentum transport (CMT). For a typical trade‐wind profile during boreal winter, CMT acts as an apparent friction to decelerate the north‐easterly flow. This effect maximizes below the cloud base while in the cloud layer, friction is very small, although present over a relatively deep layer. In the cloud layer, the zonal component of the momentum flux is counter‐gradient and penetrates deeper than reported in traditional shallow cumulus LES cases. The transport through conditionally sampled convective updrafts and downdrafts explains a weak friction effect, but not the counter‐gradient flux near the cloud tops. The analysis of the momentum flux budget reveals that, in the cloud layer, the counter‐gradient flux is driven by convectively triggered nonhydrostatic pressure‐gradients and horizontal circulations surrounding the clouds. A model set‐up with large domain size and realistic boundary conditions is necessary to resolve these effects. Plain Language Summary: The vertical profile of temperature and moisture is strongly controlled by atmospheric moist convection as it mixes heat and water vapor upwards from the surface. It is less clear if it also mixes horizontal momentum upwards to significantly affect the vertical profile of winds. Past studies have found that the subtropical‐shallow convection mainly transports momentum down‐gradient so as to reduce the vertical wind shear. We utilize unique multiday large‐eddy simulations run over the tropical Atlantic under the German HD(CP)2 project to quantify the convective momentum transport. We find that for a typical trade wind profile, convection acts like a friction on the surrounding flow below cloud base while near cloud tops it transports momentum so as to enhance the vertical shear in the mean wind. Detailed analysis of momentum fluxes indicates that the convectively driven turbulent circulations around the clouds facilitate this transport. This mechanism of momentum transport is typically not included in most climate models and may have fundamental implications for simulations of the trade winds. Key Points: Shallow convective momentum transport decelerates northeasterly trade winds below cloud base and favors nonlocal, counter‐gradient momentum flux near cloud‐topsThe counter‐gradient momentum transport is arbitrated by horizontal circulations surrounding the clouds driven by cross‐cloud pressure gradientsAnalysis of conditional sampling through clouds confirm their small contribution to counter‐gradient fluxes and a so‐called "cumulus friction" [ABSTRACT FROM AUTHOR]

Process‐scale development, evaluation, and calibration of physically based parameterizations of clouds and radiation are powerful levers for improving weather and climate models. In a series of papers, we propose a strategy for process‐based calibration of climate models that uses machine learning techniques. It relies on systematic comparisons of single‐column versions of climate models with explicit simulations of boundary‐layer dynamics and clouds (Large‐Eddy Simulations [LES]). This paper focuses on the calibration of cloud geometry parameters (vertical overlap, horizontal heterogeneity, and cloud size) that appear in the parameterization of radiation. The solar component of a radiative transfer (RT) scheme that includes a parameterization for 3D radiative effects of clouds (SPARTACUS) is run in offline single‐column mode on an ensemble of input cloud profiles synthesized from LES outputs. The space of cloud geometry parameter values is efficiently explored by sampling a large number of parameter sets (configurations) from which radiative metrics are computed using fast surrogate models that emulate the SPARTACUS solver. The sampled configurations are evaluated by comparing these radiative metrics to reference values provided by a 3D RT Monte Carlo model. The best calibrated configurations yield better predictions of TOA and surface fluxes than the one that uses parameter values computed from the 3D cloud fields: The root‐mean‐square errors averaged over cumulus cloud fields and solar angles are reduced from ∼10 Wm−2 with LES‐derived parameters to ∼5 Wm−2 with adjusted parameters. However, the calibration of cloud geometry fails to reduce the errors on absorption, which remain around 2–4 Wm−2. Plain Language Summary: A way to improve the accuracy of climate models is to improve the physical formulations that represent the effects of small‐scale processes on the evolution of atmospheric state. Processes that involve clouds and radiation are particularly important due to their key role on climate. Choosing values for the parameters inherent to these formulations is a difficult task. This series of papers presents a rigorous strategy for calibrating models. It is based on comparisons between high‐resolution models that accurately represent clouds and single‐column versions of a climate model, on the basis of process‐oriented metrics such as cloud height. A set of acceptable parameters is efficiently found using machine learning techniques. In this third part, the parameters that control the radiative effects of cloud geometry are calibrated. A recent radiation model that includes realistic representation of the radiative effect of cloud heterogeneity, cloud vertical structure and cloud size is evaluated and calibrated using references that are provided by a ray‐tracing algorithm that tracks virtual photons in virtual cloud fields produced by high‐resolution models (Large‐Eddy Simulations [LES]). Calibration improves the model with respect to using parameters diagnosed in the LES. Good agreement is found only when interception of sunlight by cloud sides is represented. Key Points: A novel calibration approach is applied to an offline radiation scheme to disentangle sources of uncertainty in cloud radiative effectsThe SPARTACUS solver is run on cloud profiles derived from Large‐Eddy Simulations (LES) cumulus fields and compared to Monte Carlo 3D radiative transfer computationsAdjusting SPARTACUS cloud geometry parameters provides effective values that improve surface and TOA fluxes compared to LES‐derived values [ABSTRACT FROM AUTHOR]

Abstract The development of parameterizations is a major task in the development of weather and climate models. Model improvement has been slow in the past decades, due to the difficulty of encompassing key physical processes into parameterizations, but also of calibrating or “tuning” the many free parameters involved in their formulation. Machine learning techniques have been recently used for speeding up the development process. While some studies propose to replace parameterizations by data‐driven neural networks, we rather advocate that keeping physical parameterizations is key for the reliability of climate projections. In this paper we propose to harness machine learning to improve physical parameterizations. In particular, we use Gaussian process‐based methods from uncertainty quantification to calibrate the model free parameters at a process level. To achieve this, we focus on the comparison of single‐column simulations and reference large‐eddy simulations over multiple boundary‐layer cases. Our method returns all values of the free parameters consistent with the references and any structural uncertainties, allowing a reduced domain of acceptable values to be considered when tuning the three‐dimensional (3D) global model. This tool allows to disentangle deficiencies due to poor parameter calibration from intrinsic limits rooted in the parameterization formulations. This paper describes the tool and the philosophy of tuning in single‐column mode. Part 2 shows how the results from our process‐based tuning can help in the 3D global model tuning.

The development of parameterizations is a major task in the development of weather and climate models. Model improvement has been slow in the past decades, due to the difficulty of encompassing key physical processes into parameterizations, but also of calibrating or "tuning" the many free parameters involved in their formulation. Machine learning techniques have been recently used for speeding up the development process. While some studies propose to replace parameterizations by data‐driven neural networks, we rather advocate that keeping physical parameterizations is key for the reliability of climate projections. In this paper we propose to harness machine learning to improve physical parameterizations. In particular, we use Gaussian process‐based methods from uncertainty quantification to calibrate the model free parameters at a process level. To achieve this, we focus on the comparison of single‐column simulations and reference large‐eddy simulations over multiple boundary‐layer cases. Our method returns all values of the free parameters consistent with the references and any structural uncertainties, allowing a reduced domain of acceptable values to be considered when tuning the three‐dimensional (3D) global model. This tool allows to disentangle deficiencies due to poor parameter calibration from intrinsic limits rooted in the parameterization formulations. This paper describes the tool and the philosophy of tuning in single‐column mode. Part 2 shows how the results from our process‐based tuning can help in the 3D global model tuning. Key Points: We apply uncertainty quantification to single‐column model/large‐eddy simulation comparison to calibrate free parametersWe revisit model development strategy with an emphasis on processes for model calibrationThe proposed tuning tool allows to formalize the complementary use of multicases with various metrics [ABSTRACT FROM AUTHOR]

Interactions between clouds and radiation are at the root of many difficulties in numerically predicting future weather and climate and in retrieving the state of the atmosphere from remote sensing observations. The broad range of issues related to these interactions, and to three‐dimensional interactions in particular, has motivated the development of accurate radiative tools able to compute all types of radiative metrics, from monochromatic, local, and directional observables to integrated energetic quantities. Building on this community effort, we present here an open‐source library for general use in Monte Carlo algorithms. This library is devoted to the acceleration of ray tracing in complex data, typically high‐resolution large‐domain grounds and clouds. The main algorithmic advances embedded in the library are related to the construction and traversal of hierarchical grids accelerating the tracing of paths through heterogeneous fields in null‐collision (maximum cross‐section) algorithms. We show that with these hierarchical grids, the computing time is only weakly sensitive to the refinement of the volumetric data. The library is tested with a rendering algorithm that produces synthetic images of cloud radiances. Other examples of implementation are provided to demonstrate potential uses of the library in the context of 3‐D radiation studies and parameterization development, evaluation, and tuning. Key Points: A path‐tracing library is distributed for flexible implementation of Monte Carlo algorithms in cloudy atmospheresNull‐collision algorithms and hierarchical grids are combined to accelerate ray tracing in large volumetric dataInsensitivity of radiative transfer computational cost to surface and volume complexity is achieved [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]