Nonhydrostatic ICosahedral Research Articles

Toward the Direct Simulation of the Quasi‐Biennial Oscillation in a Global Storm‐Resolving Model

AbstractThis study presents the first attempt to simulate a full cycle of the quasi‐biennial oscillation (QBO) in a global storm‐resolving model (GSRM) that explicitly simulates deep convection and gravity waves instead of parameterizing them. Using the Icosahedral Nonhydrostatic (ICON) model with horizontal and vertical resolutions of about and , respectively, we show that an untuned state‐of‐the‐art GSRM is already on the verge of simulating a QBO‐like oscillation of the zonal wind in the tropical stratosphere for the right reasons. ICON shows overall good fidelity in simulating the QBO momentum budget and the downward propagation of the QBO jets in the upper QBO domain (25–35 km). In the lowermost stratosphere, however, ICON does not simulate the downward propagation of the QBO jets to the tropopause. This is the result of a pronounced lack of QBO wave forcing, mainly on planetary scales. The lack of planetary‐scale wave forcing in the lowermost stratosphere is caused by an underestimation of planetary‐scale wave momentum fluxes entering the stratosphere. We attribute this lack of planetary‐scale wave momentum fluxes to a substantial lack of convectively coupled equatorial waves (CCEWs) in the tropical troposphere. Therefore, we conclude that in ICON, simulating a realistic spatio‐temporal variability of tropical deep convection, in particular CCEWs, is currently the main roadblock toward simulating a reasonable QBO. To overcome this intermediate situation, we propose to aim at an improved explicit simulation of tropical deep convection by retuning the remaining parameterizations of cloud microphysics and vertical diffusion, and by increasing the horizontal resolution.

Changes of Intense Extratropical Cyclone Deepening Mechanisms in a Warmer Climate in Idealized Simulations

Abstract To understand how extratropical cyclones (ETCs) may change in a warmer climate, we conduct idealized baroclinic life cycle simulations using the Icosahedral Nonhydrostatic (ICON)–NWP model with varied initial conditions. With respect to a present-day climate, two experiments are highlighted: a 4-K uniform warming and a more realistic late twenty-first-century warming pattern projected by a Coupled Model Intercomparison Project, version 6 (CMIP6), model. Different ETC deepening mechanisms, especially by diabatic processes, are quantified via the pressure tendency equation analysis, and the horizontal model resolution dependency is examined by contrasting coarse-grid (80 km) and convection-permitting (2.5 km) simulations. While our simulated ETCs are primarily baroclinically driven, dominated by the horizontal warm-air advection in the air column above the surface low, such an effect only strengthens by 10% in both warming experiments. However, the direct contribution of diabatic heating to surface pressure drop almost doubles, which likely feeds back positively to horizontal warm-air advection. Although their combined response to warming is pronounced, it is largely offset by the strengthened adiabatic cooling (17%) due to enhanced upward motions in warmer and moister ETCs, leading to a marginal ETC deepening at maturity (lowers by ∼1.5–4 hPa). Nevertheless, the near-surface impacts strongly increase, particularly the local extreme precipitation (up to 56%). The convection-permitting and the coarse-grid simulations show qualitatively consistent ETC responses to global warming. We suggest that the systematically weaker ETCs (with higher central pressure) in 2.5-km compared to 80-km simulations might be primarily caused by model uncertainty in representing the convective-diabatic heating over the warm front near the cyclone core.

An Evaluation of Cloud‐Precipitation Structures in Mixed‐Phase Stratocumuli Over the Southern Ocean in Kilometer‐Scale ICON Simulations During CAPRICORN

AbstractA persistent shortwave radiative bias of Southern Ocean (SO) clouds in climate models is strongly associated with incorrect cloud phase representation, which impacts precipitation. Measurements characterizing precipitation in low‐level mixed‐phase clouds, which frequently form over the SO, are rare, and our understanding of precipitation efficacy within these clouds remains limited. The simulated surface precipitation bias has an indirect effect on determining global climate sensitivity and a direct impact on the hydrological cycle. This study investigates the representation of low clouds, cloud variability, and precipitation statistics over the SO in real‐case Icosahedral Nonhydrostatic (ICON) simulations at the kilometer scale. The simulations are contrasted with 48 hr of continuous shipborne observations of open and closed‐cell stratocumuli, south of Tasmania. Our simulations show the significance of heavily rimed particle formation, their in‐cloud growth, and subcloud melting to capture the observed cloud‐precipitation vertical structure. In addition, supercooled drizzle formation impacts the vertical structure and precipitation statistics. ICON captures the observed intermittency of precipitation even at a standard vertical resolution of 200 m in the boundary layer but only captures the observed sparse distribution of intense precipitation (>1 mm hr−1) when the maximum vertical resolution is reduced to 100 m. However, the simulations of the 2‐day accumulated precipitation and the radiative effect are largely insensitive to the vertical resolution. The cloud reflectivity of the broken cloud deck is underestimated due to negative biases in cloud optical depth.

The Impact of the Weather Forecast Model on Improving AI-Based Power Generation Predictions through BiLSTM Networks

This study aims to comprehensively analyze five weather forecasting models obtained from the Open-Meteo historical data repository, with a specific emphasis on evaluating their impact in predicting wind power generation. Given the increasing focus on renewable energy, namely, wind power, accurate weather forecasting plays a crucial role in optimizing energy generation and ensuring the stability of the power system. The analysis conducted in this study incorporates a range of models, namely, ICOsahedral Nonhydrostatic (ICON), the Global Environmental Multiscale Model (GEM Global), Meteo France, the Global Forecast System (GSF Global), and the Best Match technique. The Best Match approach is a distinctive solution available from the weather forecast provider that combines the data from all available models to generate the most precise forecast for a particular area. The performance of these models was evaluated using various important metrics, including the mean squared error, the root mean squared error, the mean absolute error, the mean absolute percentage error, the coefficient of determination, and the normalized mean absolute error. The weather forecast model output was used as an essential input for the power generation prediction models during the evaluation process. This method was confirmed by comparing the predictions of these models with actual data on wind power generation. The ICON model, for example, outscored others with a root mean squared error of 1.7565, which is a tiny but essential improvement over Best Match, which had a root mean squared error of 1.7604. GEM Global and Gsf Global showed more dramatic changes, with root mean squared errors (RMSEs) of 2.0086 and 2.0242, respectively, indicating a loss in prediction accuracy of around 24% to 31% compared to ICON. Our findings reveal significant disparities in the precision of the various models used, and certain models exhibited significantly higher predictive precision.

Investigating the sign of stratocumulus adjustments to aerosols in the ICON global storm-resolving model

Abstract. Aerosols can cause brightening of stratocumulus clouds, thereby cooling the climate. Observations and models disagree on the magnitude of this cooling, partly because of the aerosol-induced liquid water path (LWP) adjustment, with climate models predicting an increase in the LWP and satellites observing a weak decrease in response to increasing aerosols. With higher-resolution global climate models, which allow the simulation of mesoscale circulations in which stratocumulus clouds are embedded, there is hope to start bridging this gap. In this study, we present boreal summertime simulations conducted with the ICOsahedral Non-hydrostatic (ICON) global storm-resolving model (GSRM). Compared to geostationary satellite data, ICON produces realistic cloud coverage in the stratocumulus regions; however, these clouds look cumuliform, and the sign of LWP adjustments disagrees with observations. We investigate this disagreement with a causal approach, which combines time series with knowledge of cloud processes, allowing us to diagnose the sources of observation–model discrepancies. The positive ICON LWP adjustment results from a superposition of processes, with an overestimated positive response due to (1) precipitation suppression, (2) a lack of wet scavenging, and (3) cloud deepening under a weak inversion, despite (4) small negative influences from cloud-top entrainment enhancement. We also find that precipitation suppression and entrainment enhancement occur at different intensities during the day and the night, implying that daytime satellite studies suffer from selection bias. This causal methodology can guide modelers on how to modify model parameterizations and setups to reconcile conflicting studies concerning the sign and magnitude of LWP adjustments across different data sources.

Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)

Abstract. The calculation of the radiative transfer is a key component of global circulation models. In this article, we describe the most recent updates of the radiation infrastructure in the Modular Earth Submodel System (MESSy). These updates include the implementation of the PSrad radiation scheme within the RAD submodel. Furthermore, the radiation-related submodels CLOUDOPT (for the calculation of cloud optical properties) and AEROPT (for the calculation of aerosol optical properties) have been updated and are now more flexible in order to deal with different sets of shortwave and longwave bands of radiation schemes. In the wake of these updates, a new submodel (ALBEDO), which features solar-zenith-angle-dependent albedos and a new satellite-based background (white sky) albedo, was created. All of these developments are backward compatible, and previous features of the MESSy radiation infrastructure remain available. Moreover, these developments mark an important step in the use of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model, as the update of the radiation scheme was a key aspect in the development of the sixth generation of the European Centre for Medium-Range Weather Forecasts – HAMburg (ECHAM6) model from ECHAM5. The developments presented here are also aimed towards using the MESSy infrastructure with the ICOsahedral Non-hydrostatic (ICON) model as a base model. The improved infrastructure will also aid in the implementation of additional radiation schemes once this should be needed. We have optimized the set of free parameters for two general circulation model-type (GCM-type) setups for pre-industrial and present-day conditions: one with the radiation scheme that was used to date (i.e. the radiation scheme of ECHAM5) and one with the newly implemented PSrad radiation scheme. After this parameter optimization, we performed four model simulations and evaluated the corresponding model results using reanalysis and observational data. The most apparent improvements related to the updated radiation scheme are the reduced cold biases in the tropical upper troposphere and lower stratosphere and the extratropical lower stratosphere and a strengthened polar vortex. The former is also related to improved stratospheric humidity and its variability if the new radiation scheme is employed. Using the multiple radiation call capability of MESSy, we have applied the two model configurations to calculate instantaneous and stratospheric-adjusted radiative forcings related to changes in greenhouse gases. Overall, we find that for many forcing experiments the simulations with the new radiation scheme show improved radiative forcing values. This is in particular the case for methane radiative forcings, which are considerably higher when assessed with the new radiation scheme and thus in better agreement with reference values.

The impact of mesh size, turbulence parameterization, and land‐surface‐exchange scheme on simulations of the mountain boundary layer in the hectometric range

AbstractThe horizontal grid spacing of numerical weather prediction models keeps decreasing towards the hectometric range. We perform limited‐area simulations with the Icosahedral Nonhydrostatic (ICON) model across horizontal grid spacings (1 km, 500 m, 250 m, 125 m) in the Inn Valley, Austria, and evaluate the model with observations from the Cross‐Valley Flow in the Inn Valley Investigated by Dual‐Doppler LIDAR Measurements (CROSSINN) measurement campaign. This allows us to investigate whether increasing the horizontal resolution automatically improves the representation of the flow structure, surface exchange, and common meteorological variables. Increasing the horizontal resolution results in an improved simulation of the thermally induced circulation. However, the model still faces challenges with scale interactions and the evening transition of the up‐valley flow. Differences between two turbulence schemes (1D turbulence kinetic energy (TKE) and 3D Smagorinsky) emerge due to their different surface transfer formulations, yielding a delayed evening transition in the 3D Smagorinsky scheme. Generally speaking, the correct simulation of the mountain boundary layer depends mostly on the representation of model topography and surface exchange, and the choice of turbulence parameterization is secondary.

MS-GWaM: A Three-Dimensional Transient Gravity Wave Parametrization for Atmospheric Models

Abstract Parameterizations for internal gravity waves in atmospheric models are traditionally subject to a number of simplifications. Most notably, they rely on both neglecting wave propagation and advection in the horizontal direction (single-column assumption) and an instantaneous balance in the vertical direction (steady-state assumption). While these simplifications are well justified to cover some essential dynamic effects and keep the computational effort small, it has been shown that both mechanisms are potentially significant. In particular, the recently introduced Multiscale Gravity Wave Model (MS-GWaM) successfully applied ray-tracing methods in a novel type of transient but columnar internal gravity wave parameterization (MS-GWaM-1D). We extend this concept to a three-dimensional version of the parameterization (MS-GWaM-3D) to simulate subgrid-scale nonorographic internal gravity waves. The resulting global wave model—implemented into the weather forecast and climate code Icosahedral Nonhydrostatic (ICON)—contains three-dimensional transient propagation with accurate flux calculations, a latitude-dependent background source, and convectively generated waves. MS-GWaM-3D helps reproduce expected temperature and wind patterns in the mesopause region in the climatological zonal mean state and thus proves a viable internal gravity wave (IGW) parameterization. Analyzing the global wave action budget, we find that horizontal wave propagation is as important as vertical wave propagation. The corresponding wave refraction includes previously missing but well-known effects such as wave refraction into the polar jet streams. On a global scale, three-dimensional wave refraction leads to a horizontal flow-dependent redistribution of waves such that the structures of the zonal mean wave drag and consequently the zonal mean winds are modified.

Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)

Abstract. Machine learning (ML) algorithms can be used in Earth system models (ESMs) to emulate sub-grid-scale processes. Due to the statistical nature of ML algorithms and the high complexity of ESMs, these hybrid ML ESMs require careful validation. Simulation stability needs to be monitored in fully coupled simulations, and the plausibility of results needs to be evaluated in suitable experiments. We present the coupling of SuperdropNet, a machine learning model for emulating warm-rain processes in cloud microphysics, with ICON (Icosahedral Nonhydrostatic) model v2.6.5. SuperdropNet is trained on computationally expensive droplet-based simulations and can serve as an inexpensive proxy within weather prediction models. SuperdropNet emulates the collision–coalescence of rain and cloud droplets in a warm-rain scenario and replaces the collision–coalescence process in the two-moment cloud microphysics scheme. We address the technical challenge of integrating SuperdropNet, developed in Python and PyTorch, into ICON, written in Fortran, by implementing three different coupling strategies: embedded Python via the C foreign function interface (CFFI), pipes, and coupling of program components via Yet Another Coupler (YAC). We validate the emulator in the warm-bubble scenario and find that SuperdropNet runs stably within the experiment. By comparing experiment outcomes of the two-moment bulk scheme with SuperdropNet, we find that the results are physically consistent and discuss differences that are observed in several diagnostic variables. In addition, we provide a quantitative and qualitative computational benchmark for three different coupling strategies – embedded Python, coupler YAC, and pipes – and find that embedded Python is a useful software tool for validating hybrid ML ESMs.

Quantifying uncertainty in simulations of the West African monsoon with the use of surrogate models

Abstract. Simulating the West African monsoon (WAM) system using numerical weather and climate models suffers from large uncertainties, which are difficult to assess due to nonlinear interactions between different components of the WAM. Here we present a fundamentally new approach to the problem by approximating the behavior of a numerical model – here the Icosahedral Nonhydrostatic (ICON) model – through a statistical surrogate model based on universal kriging, a general form of Gaussian process regression, which allows for a comprehensive global sensitivity analysis. The main steps of our analysis are as follows: (i) identify the most important uncertain model parameters and their probability density functions, for which we employ a new strategy dealing with non-uniformity in the kriging process. (ii) Define quantities of interest (QoIs) that represent general meteorological fields, such as temperature, pressure, cloud cover and precipitation, as well as the prominent WAM features, namely the tropical easterly jet, African easterly jet, Saharan heat low (SHL) and intertropical discontinuity. (iii) Apply a sampling strategy with regard to the kriging method to identify model parameter combinations which are used for numerical modeling experiments. (iv) Conduct ICON model runs for identified model parameter combinations over a nested limited-area domain from 28° W to 34° E and from 10° S to 34° N. The simulations are run for August in 4 different years (2016 to 2019) to capture the peak northward penetration of rainfall into West Africa, and QoIs are computed based on the mean response over the whole month in all years. (v) Quantify sensitivity of QoIs to uncertain model parameters in an integrated and a local analysis. The results show that simple isolated relationships between single model parameters and WAM QoIs rarely exist. Changing individual parameters affects multiple QoIs simultaneously, reflecting the physical links between them and the complexity of the WAM system. The entrainment rate in the convection scheme and the terminal fall velocity of ice particles show the greatest effects on the QoIs. Larger values of these two parameters reduce cloud cover and precipitation and intensify the SHL. The entrainment rate primarily affects 2 m temperature and 2 m dew point temperature and causes latitudinal shifts, whereas the terminal fall velocity of ice mostly affects cloud cover. Furthermore, the parameter that controls the evaporative soil surface has a major effect on 2 m temperature, 2 m dew point temperature and cloud cover. The results highlight the usefulness of surrogate models for the analysis of model uncertainty and open up new opportunities to better constrain model parameters through a comparison of the model output with selected observations.

Convection‐Permitting ICON‐LAM Simulations for Renewable Energy Potential Estimates Over Southern Africa

AbstractRenewable energy is recognized in Africa as a means for climate change mitigation, but also to provide access to electricity in sub‐Saharan Africa, where three‐quarters of the global population without electricity resides. Reliable and highly resolved renewable energy potential (REP) information is indispensable to support power plants expansion. Existing atmospheric data sets over Africa that are used for REP estimates are often characterized by data gaps, or coarse resolution. With the aim to overcome these challenges, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON‐NWP) model in its Limited Area Mode (ICON‐LAM) is implemented and run over southern Africa in a hindcast dynamical downscaling setup at a convection‐permitting 3.3 km horizontal resolution. The simulation time span covers contrasting solar and wind weather years from 2017 to 2019. To assess the suitability of the novel simulations for REP estimates, the simulated hourly 10 m wind speed (sfcWind) and hourly surface solar irradiance (rsds) are extensively evaluated against a large compilation of in situ observations, satellite, and composite data products. ICON‐LAM reproduces the spatial patterns, temporal evolution, the variability, and absolute values of sfcWind sufficiently well, albeit with a slight overestimation and a mean bias (mean error (ME)) of 1.12 m s−1 over land. Likewise the simulated rsds with an ME of 50 W m−2 well resembles the observations. This new ICON simulation data product will be the basis for ensuing REP estimates that will be compared with existing lower resolution data sets.

Cloud-Resolving ICON Simulations of Secondary Ice Production in Arctic Mixed-Phase Stratocumuli Observed during M-PACE

Abstract Field measurements and modeling studies suggest that secondary ice production (SIP) may close the gap between observed Arctic ice nucleating particle (INP) concentrations and ice crystal number concentrations ni. Here, we explore sensitivities with respect to the complexity of different INP parameterizations under the premise that ni is governed by SIP. Idealized, cloud-resolving simulations are performed for the marine cold air outbreak cloud deck sampled during the Mixed-Phase Arctic Cloud Experiment (M-PACE) with the Icosahedral Nonhydrostatic (ICON) model. The impact of the droplet shattering (DS) of raindrops and collisional breakup (BR) in addition to the existing Hallet–Mossop rime splintering mechanism were investigated. Overall, 12 different model experiments (12-h runs) were performed and analyzed. Despite the considerable amount of uncertainty remaining with regard to SIP mechanisms and their process representation in numerical models, we conclude from these experiments that (i) only simulations where DS dominates the SIP signal (potentially amplified by BR) capture observed ice-phase and liquid-phase cloud properties, and (ii) SIP events cluster around the convective outflow region and are structurally linked to mesoscale cloud organization. In addition, interactions with primary nucleation parameterizations of varied complexity were investigated. Here, our simulations show that (i) a stable long-lived mixed-phase cloud (MPC) can be maintained in the absence of primary nucleation once SIP is established, (ii) experiments using a computationally more efficient relaxation-based parameterization of primary nucleation are statistically invariant from simulations considering prognostic INP, and (iii) primary nucleation at cloud-top controls the areal extent of the mixed-phase cloud region, and reduces SIP efficacy via DS due to increased depletion of cloud liquid throughout the entire cloud column. Significance Statement Secondary ice production (SIP) remains a key challenge in our understanding of boundary layer mixed-phase clouds. Here, we use sensitivity experiments performed with the ICON model at the cloud-resolving scale to explore potential interactions between primary nucleation, SIP, and mesoscale cloud organization. We simulate an Arctic single-layer cold air outbreak stratocumulus deck that was sampled during the M-PACE campaign. We find that once established, SIP alone is sufficient to maintain the mixed-phase cloud state until the end of the simulation. Our sensitivity analysis also shows that numerically more efficient treatments of immersion freezing are statistically invariant from simulations with a full prognostic INP budget.

How Can We Improve the Seamless Representation of Climatological Statistics and Weather Toward Reliable Global K‐Scale Climate Simulations?

AbstractToward the achievement of reliable global kilometer‐scale (k‐scale) climate simulations, we improve the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) by focusing on moist physical processes. A goal of the model improvement is to establish a configuration that can simulate realistic fields seamlessly from the daily‐scale variability to the climatological statistics. Referring to the two representative configurations of the present NICAM, each of which has been used for climate‐scale and sub‐seasonal‐scale experiments, we try to find the appropriate partitioning of fast/local and slow/global‐scale circulations. In a series of sensitivity experiments at 14‐km horizontal resolution, we test (a) the tuning of terminal velocities of rain, snow, and cloud ice, (b) the implementation of turbulent diffusion by the Leonard term, and (c) enhanced vertical resolution. These tests yield reasonable convection triggering and convection‐induced tropospheric moistening, and result in better performance than in previous NICAM climate simulations. In the mean state, double Intertropical Convergence Zone bias disappears, and the zonal contrast of equatorial precipitation, top‐of‐atmosphere radiation balance, vertical temperature profile, and position/strength of subtropical jet are reproduced dramatically better. Variability such as equatorial waves and the Madden–Julian oscillation (MJO) is spontaneously realized with appropriate spectral power balance, and the Asian summer monsoon, boreal‐summer MJO, and tropical cyclone (TC) activities are more realistically simulated especially around the western Pacific. Meanwhile, biases still exist in the representation of low‐cloud fraction, TC intensity, and precipitation diurnal cycle, suggesting that both higher spatial resolutions and further model development are warranted.

Impacts of a double-moment bulk cloud microphysics scheme (NDW6-G23) on aerosol fields in NICAM.19 with a global 14 km grid resolution

Abstract. In accordance with progression in current capabilities towards high-resolution approaches, applying a convective-permitting resolution to global aerosol models helps comprehend how complex cloud–precipitation systems interact with aerosols. This study investigates the impacts of a double-moment bulk cloud microphysics scheme, i.e., NICAM Double-moment bulk Water 6 developed in this study (NDW6-G23), on the spatiotemporal distribution of aerosols in the Nonhydrostatic ICosahedral Atmospheric Model as part of the version-19 series (NICAM.19) with 14 km grid spacing. The mass concentrations and optical thickness of the NICAM-simulated aerosols are generally comparable to those obtained from in situ measurements. However, for some aerosol species, especially dust and sulfate, the differences between experiments of NDW6 and of the NICAM single-moment bulk module with six water categories (NSW6) were larger than those between experiments with different horizontal resolutions (14 and 56 km grid spacing), as shown in a previous study. The simulated aerosol burdens using NDW6 are generally lower than those using NSW6; the net instantaneous radiative forcing due to aerosol–radiation interaction (IRFari) is estimated to be −1.36 W m−2 (NDW6) and −1.62 W m−2 (NSW6) in the global annual mean values at the top of the atmosphere (TOA). The net effective radiative forcing due to anthropogenic aerosol–radiation interaction (ERFari) is estimated to be −0.19 W m−2 (NDW6) and −0.23 W m−2 (NSW6) in the global annual mean values at the TOA. This difference among the experiments using different cloud microphysics modules, i.e., 0.26 W m−2 or 16 % difference in IRFari values and 0.04 W m−2 or 16 % difference in ERFari values, is attributed to a different ratio of column precipitation to the sum of the column precipitation and column liquid cloud water, which strongly determines the magnitude of wet deposition in the simulated aerosols. Since the simulated ratios in the NDW6 experiment are larger than those of the NSW6 result, the scavenging effect of the simulated aerosols in the NDW6 experiment is larger than that in the NSW6 experiment. A large difference between the experiments is also found in the aerosol indirect effect (AIE), i.e., the net effective radiative forcing due to aerosol–cloud interaction (ERFaci) from the present to preindustrial days, which is estimated to be −1.28 W m−2 (NDW6) and −0.73 W m−2 (NSW6) in global annual mean values. The magnitude of the ERFaci value in the NDW6 experiment is larger than that in the NSW6 result due to the differences in both the Twomey effect and the susceptibility of the simulated cloud water to the simulated aerosols between NDW6 and NSW6. Therefore, this study shows the importance of the impacts of the cloud microphysics module on aerosol distributions through both aerosol wet deposition and the AIE.

Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc

Abstract. The confrontation of complex Earth system model (ESM) codes with novel supercomputing architectures poses challenges to efficient modeling and job submission strategies. The modular setup of these models naturally fits a modular supercomputing architecture (MSA), which tightly integrates heterogeneous hardware resources into a larger and more flexible high-performance computing (HPC) system. While parts of the ESM codes can easily take advantage of the increased parallelism and communication capabilities of modern GPUs, others lag behind due to the long development cycles or are better suited to run on classical CPUs due to their communication and memory usage patterns. To better cope with these imbalances between the development of the model components, we performed benchmark campaigns on the Jülich Wizard for European Leadership Science (JUWELS) modular HPC system. We enabled the weather and climate model Icosahedral Nonhydrostatic (ICON) to run in a coupled atmosphere–ocean setup, where the ocean and the model I/O is running on the CPU Cluster, while the atmosphere is simulated simultaneously on the GPUs of JUWELS Booster (ICON-MSA). Both atmosphere and ocean are running globally with a resolution of 5 km. In our test case, an optimal configuration in terms of model performance (core hours per simulation day) was found for the combination of 84 GPU nodes on the JUWELS Booster module to simulate the atmosphere and 80 CPU nodes on the JUWELS Cluster module, of which 63 nodes were used for the ocean simulation and the remaining 17 nodes were reserved for I/O. With this configuration the waiting times of the coupler were minimized. Compared to a simulation performed on CPUs only, the MSA approach reduces energy consumption by 45 % with comparable runtimes. ICON-MSA is able to scale up to a significant portion of the JUWELS system, making best use of the available computing resources. A maximum throughput of 170 simulation days per day (SDPD) was achieved when running ICON on 335 JUWELS Booster nodes and 268 Cluster nodes.

Understanding the dependence of mean precipitation on convective treatment and horizontal resolution in tropical aquachannel experiments

Abstract. The Intertropical Convergence Zone (ITCZ) is a key circulation and precipitation feature in the tropics. There has been a large spread in the representation of the ITCZ in global weather and climate models for a long time, the reasons for which remain unclear. This paper presents a novel approach with which we disentangle different physical processes responsible for the changeable behavior of the ITCZ in numerical models. The diagnostic tool is based on a conceptual framework developed by Emanuel (2019) and allows for physically consistent estimates of convective mass flux and precipitation efficiency for simulations with explicit and parameterized convection. We apply our diagnostic tool to a set of tropical aquachannel experiments using the ICOsahedral Nonhydrostatic (ICON) model with horizontal grid spacings of 13 and 5 km and with various representations of deep and shallow convection. The channel length corresponds to the Earth's circumference and has rigid walls at 30∘ N/S. Zonally symmetric sea surface temperatures are prescribed. All experiments simulate an ITCZ at the Equator coinciding with the ascending branch of the Hadley circulation and descending branches at 15∘ N/S with subtropical jets and easterly trade wind belts straddling the ITCZ. With explicit deep convection, however, rainfall in the ITCZ increases and the Hadley circulation becomes stronger. Increasing horizontal resolution substantially reduces the rainfall maximum in the ITCZ, while the strength of the Hadley circulation changes only marginally. Our diagnostic framework reveals that boundary-layer quasi-equilibrium (BLQE) is a key to physically understanding those differences. At 13 km, enhanced surface enthalpy fluxes with explicit deep convection are balanced by increased convective downdrafts. As precipitation efficiency is hardly affected, convective updrafts and rainfall increase. The surface enthalpy fluxes are mainly controlled by mean surface winds, closely linked to the Hadley circulation. These links also help understand rainfall differences between different resolutions. At 5 km, the wind–surface-fluxes–convection relation holds, but additionally explicit convection dries the mid-troposphere, which increases the import of air with lower moist static energy into the boundary layer, thereby enhancing surface fluxes. Overall, the different model configurations create little variations in precipitation efficiency and radiative cooling, the effects of which are compensated for by changes in dry stability. The results highlight the utility of our diagnostic tool to pinpoint processes important for rainfall differences between models, suggesting applicability for climate model intercomparison projects.

Impact of Convection‐Permitting and Model Resolution on the Simulation of Mesoscale Convective System Properties Over East Asia

AbstractIn this study, two limited‐area convection‐permitting models (ICON‐CPMs) and convection‐parameterized models (ICON‐CParMs) covering the Asia monsoon region from ICOsahedral Nonhydrostatic model are conducted to investigate the sensitivities of Mesoscale Convective Systems (MCSs) to convection configurations and horizontal resolutions. We find that ICON‐CPMs outperform ICON‐CParMs and ERA5 in number, lifetime, size, shape, orientation, effective speed, and MCS‐mean precipitation. Compared to the satellite observation, MCSs in ICON‐CParMs and ERA5 are oversized (30%–66%) and have longer (21%–29%) lifetimes but occur less (37%–46%) frequently. When turning off the convection scheme, performances are improved to a large extent. The underestimation of MCS‐mean precipitation rates in ICON‐CParMs and ERA5 are improved in ICON‐CPMs. Moisture budgets show that low‐level winds rather than moisture determine the strength of MCS‐mean precipitation rate. The strong low‐level convergence produces strong updrafts in ICON‐CPMs and eventually results in strong MCS precipitation. The overly intense convective precipitation of MCSs in CPMs is an inherent problem since the scale of convection triggered at the gray zone grid spacing is larger than that in the real world. Although the MCS‐mean precipitation rate is closer to the satellite observation in ICON‐CPMs, it is actually a compensation for overly strong convective precipitation and overly weak stratiform precipitation. Upgrading resolution from 8 to 4 km in ICON‐CPMs has benefits in simulating MCS lifetime, size, shape, orientation, and precipitation but not in MCS number.

Sensitivity of cloud-phase distribution to cloud microphysics and thermodynamics in simulated deep convective clouds and SEVIRI retrievals

Abstract. The formation of ice in clouds is an important process in mixed-phase clouds, and the radiative properties and dynamical developments of clouds strongly depend on their partitioning between the liquid and ice phases. In this study, we investigated the sensitivities of the cloud phase to the ice-nucleating particle (INP) concentration and thermodynamics. Moreover, passive satellite retrieval algorithms and cloud products were evaluated to identify whether they could detect cloud microphysical and thermodynamical perturbations. Experiments were conducted using the ICOsahedral Nonhydrostatic (ICON) model at the convection-permitting resolution of about 1.2 km on a domain covering significant parts of central Europe, and they were compared to two different retrieval products based on Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements. We selected a day with multiple isolated deep convective clouds, reaching a homogeneous freezing temperature at the cloud top. The simulated cloud liquid pixel fractions were found to decrease with increasing INP concentration, both within clouds and at the cloud top. The decrease in the cloud liquid pixel fraction was not monotonic and was stronger in high-INP cases. Cloud-top glaciation temperatures shifted toward warmer temperatures with an increasing INP concentration by as much as 8 ∘C. Moreover, the impact of the INP concentration on cloud-phase partitioning was more pronounced at the cloud top than within the cloud. Furthermore, initial and lateral boundary temperature fields were perturbed with increasing and decreasing temperature increments from 0 to ±3 and ±5 K between 3 and 12 km, respectively. Perturbing the initial thermodynamic state was also found to systematically affect the cloud-phase distribution. However, the simulated cloud-top liquid pixel fraction, diagnosed using radiative transfer simulations as input to a satellite forward operator and two different satellite remote-sensing retrieval algorithms, deviated from one of the satellite products regardless of perturbations in the INP concentration or the initial thermodynamic state for warmer subzero temperatures while agreeing with the other retrieval scheme much better, in particular for the high-INP and high-CAPE (convective available potential energy) scenarios. Perturbing the initial thermodynamic state, which artificially increases the instability of the mid- and upper-troposphere, brought the simulated cloud-top liquid pixel fraction closer to the satellite observations, especially in the warmer mixed-phase temperature range.

The Sensitivity of the Icosahedral Non-Hydrostatic Numerical Weather Prediction Model over Greece in Reference to Observations as a Basis towards Model Tuning

The ICON (icosahedral non-hydrostatic) numerical weather prediction model (NWP)’s sensitivity is evaluated for the geographical area of Greece. As ICON model has recently been endorsed operationally by the Members of the COSMO (Consortium for Small-scale Modeling); this attempt is in line with the further understanding of the model features, especially in the considered domain, characterized by a complex orography as well as an almost equally partitioned land–sea surface area. An extraordinary number of 24 model parameters have been tested for the whole 2020 year in reference to 88 Greek meteorological stations, with regard to the standard synoptic meteorological variables of 2 m temperature, 2 m minimum and maximum temperatures, dew-point temperature, 10 m wind intensity and 12 h accumulated precipitation. For these variables, the model sensitivity is given in terms of the annual average of all stations for the fifth lead day of the model runs when the sensitivity is expected to reach its peak. It was found that there is a considerable impact regarding the minimum and maximum values for many of the examined parameters in reference to their default values, and consideration is given to a heuristic recommendation on the selection of the most sensitive parameters.

Comparative study of strongly and weakly coupled data assimilation with a global land–atmosphere coupled model

Abstract. This study explores coupled land–atmosphere data assimilation (DA) for improving weather and hydrological forecasts by assimilating soil moisture (SM) data. This study integrates a land DA component into a global atmospheric DA system of the Nonhydrostatic ICosahedral Atmospheric Model and the local ensemble transform Kalman filter (NICAM-LETKF) and performs both strongly and weakly coupled land–atmosphere DA experiments. We explore various types of coupled DA experiments by assimilating atmospheric observations and SM data simultaneously. The results show that analyzing atmospheric variables by assimilating SM data improves the SM analysis and forecasts and mitigates a warm bias in the lower troposphere where a dry SM bias exists. On the other hand, updating SM by assimilating atmospheric observations has detrimental impacts due to spurious error correlations between the atmospheric observations and land model variables. We also find that assimilating SM by strongly coupled DA is beneficial in the Sahel and equatorial Africa from May to October. These regions are characterized by seasonal variations in the precipitation patterns and benefit from updates in the atmospheric variables through SM DA during periods of increased precipitation. Additionally, these regions coincide with those identified in the previous studies, where a global initialization of SM would enhance the prediction skill of seasonal precipitation.