Global nonhydrostatic models that cover the globe with a kilometer (km)-scale mesh have been developed by various organizations worldwide and are expected to be next-generation models that can explicitly calculate deep convective clouds. However, it is known that convective upward motions are not sufficiently represented at the km-scale resolution, and the mesh size of O(100m) is required to obtain convergence of upward motions. To understand the limitation of global km-scale models, we investigate the representation of cloud, precipitation, and circulation with the resolution in the global simulations between km-scale to sub-km-scales.

We conduct the global atmospheric simulations by the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for the mesh size of 3.5 km, 1.7 km, 870 m, 440 m, and 220 m using the Supercomputer "Fugaku."  The 3.5 km experiment started on August 1, 2016, the same day as DYAMOND-summer, and the next higher resolution was run using the lower resolution simulation results as initial conditions. We analyzed data on August 5, 2016. We conducted the global 220m simulation for 8 hours.

The resolution dependence of cloud, precipitation, and convection was investigated. Lower clouds decrease with increasing resolution. High cloud increased or decreased with respect to resolution depending on the turbulence scheme. The precipitation distribution and zonal mean humidity do not change significantly, but the precipitation intensity changes with resolution. For the grid spacing of less than km, it eliminates overconcentration of precipitation, and the rain area widens as the resolution becomes finer. The coarse-grained rainfall distribution is smoother in the sub-km scale model than in the km scale model. A finer scale convection core is reproduced in the sub-km scale model. Vertical wind speed at grid point scales increases with increasing resolution. However, when horizontally averaged over a few-degree grid, the vertical wind speed decreases, and the circulation becomes weaker with higher resolution. We found that the km-scale model may be creating large strong convection. Uncertainties resulting from the turbulence scheme also appear to be large in the km/sub-km models.

How to cite: Matsugishi, S., Ohno, T., and Satoh, M.: Differences in the cloud, precipitation, and convection representation between the global sub-km mesh simulation and km simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14676, https://doi.org/10.5194/egusphere-egu24-14676, 2024.

Tropical precipitation is the result of a complex interplay of processes across a wide range of atmospheric scales and is highly variable from place to place. A particularly interesting geographical pattern is obtained for the lag 1 autocorrelation of daily precipitation. Generally, this metric displays a relatively uniform distribution of positive values throughout the tropics. However, certain land regions, such as the Sahel, stand out due to exceptionally low autocorrelation values. These low values correspond to a dominance of high frequency precipitation events in the power spectrum.

In accordance with previous work, we show that CMIP6 climate models struggle to create a similar autocorrelation pattern. Global kilometer-scale models circumvent many of the shortcomings of the conventional coarse models, by resolving deep convection. We find that the two global kilometer-scale models developed as part of the nextGEMS project produce an autocorrelation pattern that is quite similar to the observations. These models also provide an opportunity to study the processes associated with the autocorrelation pattern.

We compare simulations with deep convection parameterization turned on and off to investigate how the parameterization scheme affects the autocorrelation pattern and the underlying power spectrum. Additionally, we perform a precipitation variance analysis based on filtering of convectively coupled equatorial waves to study the genesis of the autocorrelation pattern.

How to cite: Spät, D., Voigt, A., Biasutti, M., and Schuhbauer, D.: Autocorrelation – A Simple Diagnostic for Tropical Precipitation in Global Kilometer-Scale Climate Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9221, https://doi.org/10.5194/egusphere-egu24-9221, 2024.

The amplitude of precipitation extremes across Europe is expected to increase through the 21st century under most climate change scenarios. Current CMIP-style global climate models broadly project increased flooding and drought extremes; however, they often rely on parametrization schemes or downscaling methods for inferring about potential future extreme events. These methods often introduce errors leading to high levels of uncertainty for policymakers and infrastructure planning. The need for accurate extreme event projections became further evident after the July 2021 floods and summer 2022 record-breaking heatwaves and droughts across Western Europe.

The ongoing H2020 Next Generation Earth Modelling Systems (nextGEMS) project aims to address these issues with the development of storm-resolving, fully-coupled, Earth-system models. Using the latest Cycle 3 runs from the Integrated Forecast System from ECMWF and ICON from MPI-M, we examine the dynamical representation of extreme precipitation events across Europe and compare it against a suite of observations (station and satellite based), reanalysis datasets, and CESM2 simulations. Focusing on tail-end extremes, the results focus on the realism of high precipitation extremes, value of upscaling to CMIP6 resolution, representation of precipitation drivers, and dry extremes (dry day percentages and consecutive dry days). Overall, both ICON and IFS perform reasonably well in representing high precipitation extremes although issues related to the ICON non-parameterized, deep convection causes overly frequent precipitation events.

How to cite: Wille, J. and Fischer, E.: Representation of extreme precipitation events in storm-resolving global climate models within the nextGEMS project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11797, https://doi.org/10.5194/egusphere-egu24-11797, 2024.

River discharge has direct influence on the water-food-energy-environment nexus and can have devastating impacts during extreme events with rapid onsets such as floods. Floods often occur after extreme precipitation events, which are challenging to forecast accurately, both in time and space. Unresolved small-scale processes and features, including convection and orography, have a detrimental effect on precipitation and consequently hydrological forecast skill. This calls for a spatial resolution increase in Numerical Weather Prediction (NWP) models, including their land component.

The Destination Earth programme of the European Commission addresses this with globally coupled forecasts at spatial resolutions down to the km-scale with lead times of 5 days: the Digital Twin on Weather-Induced Extremes (EDT). These meteorological forecasts are used to force ECMWF’s Land Surface Modelling System (ECLand), the land component of the Integrated Forecasting System (IFS), to generate runoff. Subsequently, the river-routing scheme CaMa-Flood, effectively 1-way coupled to the IFS, is used to route runoff in rivers and to produce hydrological simulations. Essentially, CaMa-Flood will be part of the continuous component of the EDT, which in phase 2 of Destination Earth will provide daily high-resolution forecasts to monitor extreme events, such as floods, in real time. As river discharge acts as a natural integrator of the water cycle, CaMa-Flood can be used as a diagnostic tool to assess the hydrological response to increases in spatial resolution of the forcing and the river-routing network.

In this study, two data products are derived: i) long-term hydrological simulations forced by atmospheric analysis data (e.g. ERA5 or ECMWF operational analysis) and ii) hydrological forecasts (daily forecasts in June - July 2021 and January - February 2022 as well as selected flood cases). To assess their quality, these data are validated with point-observed river-discharge time series. Analysis shows that the long-term hydrological simulations benefit from spatial resolution increases in the meteorological forcing and to a lesser extent from spatial resolution increases in the river-routing network. This is evidenced by higher Kling-Gupta Efficiency (KGE), higher correlations and lower biases across 876 river stations in Europe. Further, hydrological forecasts also benefit from higher spatial resolution meteorological forcing, evidenced both by higher correlations of the continuous summer/winter forecasts against river discharge observations from 798 river stations across Europe and by more pronounced flood peak magnitude for selected flood cases. These results highlight the added value of high resolution for hydrological forecast accuracy.

How to cite: Denissen, J., Arduini, G., Zsoter, E., Mazzetti, C., Prudhomme, C., Harrigan, S., Balsamo, G., Ayan-Miguez, I., Dueben, P., Sandu, I., and Vanniere, B.: Towards a global km-scale flood forecasting system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15657, https://doi.org/10.5194/egusphere-egu24-15657, 2024.

Clouds are one of the largest sources of uncertainty in climate predictions. Emerging next-generation km-scale climate models need to simulate clouds and precipitation accurately to reliably predict future climates. To isolate issues in their representation of clouds, and thereby facilitate their improvement, km-scale models need to be thoroughly evaluated via comparisons with observations.

Traditionally, climate models are evaluated using spatio-temporally averaged observations. However, aggregated evaluation loses crucial information about underlying physical processes, such as convective updrafts, and the resulting cloud macrophysical structures. We postulate that a novel spatio-temporal evaluation strategy using satellite observations provides direct constraints on physical processes.

Here, we introduce multifractal analysis as a method for evaluating km-scale simulations. We apply it to top-of-atmosphere outgoing longwave radiation (OLR) fields to investigate structural differences between observed and simulated clouds in the tropics. For this purpose, we compute structure functions from OLR fields to which we fit scaling exponents. We then parameterise the scaling exponents to compute scaling parameters. The parameters compactly characterise OLR variability and can be compared across simulations and observations. We use this method to evaluate the ICON-Sapphire and IFS-FESOM simulations run for cycle 3 of the nextGEMS project via comparison with data from the geostationary satellite GOES-16.

We find that clouds in both models exhibit multifractal scaling from 50 to 1000km. However, the scaling parameters are significantly different between ICON and IFS, and neither match observations. In the ICON model, multifractal scaling exponents are lower than in observations whereas in IFS, they are larger. The observed differences indicate how the modelling approaches in ICON and IFS impact the organisation of clouds. More specifically, the deep convection scheme in ICON is switched off completely whereas it is still active in IFS, which could explain the difference in scaling behaviour we observed.

Our results show that spatio-temporal analysis is a promising new way to constrain global km-scale models. It can provide key insights into model performance and shed light on issues in the representation of clouds.

How to cite: Freischem, L., Weiss, P., Christensen, H., and Stier, P.: Multifractal analysis for evaluating the representation of clouds in global km-scale models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17906, https://doi.org/10.5194/egusphere-egu24-17906, 2024.

Clouds crucially impact Earth’s climate. The distribution of clouds, horizontally and vertically, influences the radiative transfer through the atmosphere. Hence, to correctly compute the radiative transfer, it is important to understand the horizontal and vertical distribution of clouds.  Km-scale earth system models enable to resolve convection explicitly and offer the potential to represent cloud patterns more realistically. We investigate simulations of the earth system model ICON with a horizontal resolution of 5 km performed within the project nextGEMS. We identify cloud objects using connected component labelling. The method is applied to the vertically integrated cloud field as well as to the global three-dimensional cloud field. We analyse the distribution of cloud objects, their water and ice content as well as their fractal dimension on a global and regional scale. The choice of the threshold for identifying cloud objects strongly influences the analysis of the objects.

How to cite: Rieger, V., Splechtna, P., and Voigt, A.: Identifying cloud objects in the km-scale earth system model ICON, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5731, https://doi.org/10.5194/egusphere-egu24-5731, 2024.

Clouds are an important regulator of earth’s radiation balance. Therefore, future changes in clouds and corresponding feedbacks are likely to influence global climate sensitivity. How clouds respond to greenhouse warming on global and regional scales is still not well understood. Here we present first results from a km-scale, cloud-permitting greenhouse warming simulation conducted with the coupled OpenIFS-FESOM2 model (AWI-CM3) with ~9 km atmosphere resolution, 137 vertical levels and  4-15 km variable ocean resolution. Our analysis is based on a  set of 10-year time-slice simulations, which branched off from a lower-resolution (31 km) SSP585 transient scenario run with relatively high climate sensitivity. We will quantify the effect of atmosphere resolution and cloud granularity on cloud radiative feedbacks. We will further present results from the calculation of radiative kernels to determine the role of high cloud feedbacks in polar amplification. 

How to cite: Moon, J.-Y., Lee, S.-S., Timmermann, A., Streffing, J., Semmler, T., and Jung, T.: Cloud-feedbacks in global km-scale earth system model simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8603, https://doi.org/10.5194/egusphere-egu24-8603, 2024.

Cloud microphysics are a prime example of processes that remain unresolved in atmospheric modelling with storm-resolving resolution. In this study, we explore how uncertainties in the representation of microphysical processes affect the tropical energy budget in a global storm-resolving model (SRM). We use the global SRM ICON with a one-moment or a two-moment microphysics schemes and do several sensitivity runs, where we vary one parameter of the applied microphysics scheme in its range of uncertainty. We find that the two microphysics schemes have distinct signatures, e.g., in how condensate is distributed among the different hydrometeor categories or in the intensity distribution of precipitation, but their tropical mean cloud fraction and total condensate profiles are rather robust. Precipitation efficiency sets the amount of condensate in the atmosphere and thereby links microphysical processes to the radiative properties of the atmosphere. Uncertainties in the representation of microphysical processes cause substantial spread in the top-of-the-atmosphere (TOA) energy balance. In agreement with the robustness of the cloud fraction, changes in the radiative balance at TOA are dominated by changes in the radiative properties of cloudy points. A shift towards higher cloud-ice concentrations in simulations with the two-moment microphysics scheme leads to more reflected shortwave radiation that is not fully compensated by less outgoing longwave radiation and results in a slight cooling of the atmospheric column. Overall, microphysical sensitivities at storm-resolving resolution are substantial and resemble part of the inter-model spread of a multi-model ensemble.

How to cite: Naumann, A. K., Esch, M., and Stevens, B.: How the representation of microphysical processes affects the tropical energy budget in a global storm-resolving model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16801, https://doi.org/10.5194/egusphere-egu24-16801, 2024.

We propose a protocol for observational intensive intercomparison experiments of global storm-resolving models, targeting for evaluation by the EarthCARE satellite, the new satellite scheduled to be launched in May 2024. Previously, a month-long or 40-day simulation of an intercomparison of global storm-resolving models was conducted under the DYAMOND (the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains) project. Global storm-resolving models can simulate meso-scale systems in the global domain, and it has been shown that the month-long simulations under the DYAMOND project reproduce the evolution of meso-scale convective systems comparable to nature in many aspects. As a next step of the feasibility of the global storm-resolving models, two directions of the intercomparison experiments are considered. One is to extend the simulation time to cover a longer period, such as a one-year experiment with a seasonal march (Takasuka et al. 2024, in preparation). The other is to evaluate with intensive observations. Here, we propose a possible protocol for the short-term (a few weeks to a month) intercomparison experiment to evaluate GSRM results with observation by the EarthCARE satellite and the coordinated grand observation campaign called ORCESTRA.

The EarthCARE satellite will enable the world's first observations of Doppler velocities from space using radar. This groundbreaking capability allows for the observational understanding of global snow and raindrop falling velocities. In numerical climate and weather forecasting models, falling velocities of snow and raindrops have traditionally relied on empirical formulas based on fragmented observations, lacking comprehensive validation through global observations. These falling velocities have frequently been used as tuning parameters for numerical models. The falling velocity of upper-level clouds directly impacts radiation balance through variations in cloud amount. In contrast, the raindrop velocity influences the formation of cold pools and the organization of convective clouds. After obtaining Doppler velocity observations from the EarthCARE satellite, reliance on these falling velocities as tuning parameters becomes obsolete, introducing observational constraints. Conversely, altering these falling velocities from traditional prescribed values in numerical models leads to deviations in model climatology and equilibrium states from observations, necessitating refinement of other processes, which require the resolution of new compensatory errors. This presentation analyzes the characteristics of Doppler velocities using the global non-hydrostatic model NICAM and discusses the impact of snow and raindrops falling velocities. Specifically, utilizing the EarthCARE-like simulated data based on a global 220m mesh NICAM simulation, we aim to comprehend the global view of falling velocity characteristics and gain insights to analyze the EarthCARE satellite observational data.

How to cite: Satoh, M., Roh, W., and Matsugishi, S.: Proposal for an Intensive Short-term Intercomparison Experiment of Global Storm Resolution Models for Evaluation by EarthCARE Satellite Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3359, https://doi.org/10.5194/egusphere-egu24-3359, 2024.

Assessing the future risk of natural disasters, securing sustainable energy and water resources, and developing strategies for adapting to climate change remain challenging due to the large uncertainties in regional-scale climate projections. Recent efforts to address this issue include km-scale coupled climate model simulations that resolve mesoscale processes in the atmosphere and ocean, as well as their interactions with the large-scale environment and small-scale topographic features. Our presentation shows the first results from a series of global 9 km-scale greenhouse warming simulations using the AWI Climate Model Version 3 which is based on the OpenIFS atmosphere model at TCO1279 resolution and 137 vertical levels and the FESOM2 ocean model at 4-15 km resolution. By comparing a set of consecutive 10-year time-slice simulations forced by the CMIP6 SSP585 scenario with a transient simulation at a lower-resolution (31 km in the OpenIFS), we identify key differences in weather and climate-related phenomena, including tropical cyclones, ENSO, and regional climate change features that can be attributed to km-scale dynamics in clouds and atmospheric circulation patterns. The findings from our cloud-permitting climate simulations provide valuable insights into the role of small-scale processes in the sensitivity of the regional and global climate.

How to cite: Lee, S.-S., Moon, J.-Y., Timmermann, A., Streffing, J., Semmler, T., and Jung, T.: Projections of future climate changes from the cloud-permitting greenhouse warming simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7170, https://doi.org/10.5194/egusphere-egu24-7170, 2024.

The nextGEMS project is dedicated to develop global coupled earth-system models for multidecadal climate projections at a kilometre-scale resolution. By harnessing the strengths of high spatial resolution, the project seeks to improve the representation of physical processes and provide climate information at spatial scales that align with real-world measurements. Preparing for 30-year production runs, nextGEMS has achieved significant milestones, including the successful completion of five-year global coupled runs with a 5 km spatial resolution by two different Earth-System models: ICON, and ECMWF’s Integrated Forecasting System (IFS) coupled to the sea ice-ocean model FESOM. In this work we focus on the km-scale IFS-FESOM configuration, along with a comparable set of coarser IFS simulations coupled to either FESOM or NEMO ocean models.

We first provide a brief overview of the most relevant scientific modifications on IFS and FESOM through the development cycles needed to perform multi-annual simulations: a reduction of the global water and energy imbalance by orders of magnitude, as well as the modification in cloud physics parameters to provide a stable climate, improved coupling of ocean surface currents and fluxes, and the addition of improved high-resolution land use and land cover maps.

We further investigate the impact that the new refined surface maps have on the representation of climate at the surface and near-surface. We first explore the spatio-temporal surface-atmosphere coupling in these km-scale simulations. We then focus on more local phenomena: In particular, we pioneer the study of urban climate via coupled global multiannual simulations and explore surface-atmosphere interactions over urbanized areas, by combining refined land use/land cover maps with the active urban scheme in IFS. We find a more realistic spatial distribution of surface temperature in both urban and rural areas, especially noticeable at spatial resolutions of 9km and finer. By showing that the diurnal cycle of urban heat island intensity exhibits improved accuracy in numerous large European urban areas, our global simulations can provide local granularity at the scale of individual cities The enhancements in representing urban climate features are quantified through reduced bias, root-mean square error, and increased correlation with successively increasing model resolution.

How to cite: Pedruzo-Bagazgoitia, X., Becker, T., Milinski, S., Rackow, T., Sandu, I., Boussetta, S., Dutra, E., Hadade, I., Martins, J., McNorton, J., Sützl, B., and Wedi, N.: Demonstrating the potential of km-scale multi-annual coupled global simulations in nextGEMS: a (urban) surface perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8254, https://doi.org/10.5194/egusphere-egu24-8254, 2024.

Atmospheric blocking and its associated extreme phenomena, such as hot and cold spells represent a risk to society. Current climate models struggle to simulate the atmospheric blocking properties, making it difficult to understand the underlying physical processes and raising uncertainty about their evolution under warming. Today, several climate models attempt to better resolve small-scale processes and have demonstrated their ability to convincingly simulate them; however, few studies have evaluated the impact of these tunings on large-scale flow.

Here, we investigate the representation of Atmospheric blocking characteristics in the two new generations of storm-resolving Earth-system models (nextGEMS), consisting of the Icosahedral Nonhydrostatic Weather and Climate Model (ICON) and the ECMWF Integrated Forecasting System (hereafter only IFS). These models are run at high horizontal resolution, ICON at 5 km (convective parameterization off) and IFS at 4.4 km and 28 km (convective parameterization on). Both models are fully coupled models with eddy-resolving ocean models. The five years of simulations are compared with the reanalysis ERA5 and one CMIP6 model (MPI-ESM1-2-LR). Atmospheric blockings are identified and tracked using a Lagrangian approach based on the geopotential height anomaly at 500 hPa. Properties such as intensity, size, and zonal speed are evaluated.

The nextGEMS showed an increased skill in reproducing atmospheric blocking at the system scale. Firstly, the Atmospheric blocking intensity, spatial extension, and zonal speed are closer to the ERA5 than the CMIP6 model. However, the block intensity and size in the IFS model are simulated better than in the ICON model, and its improvement increases at the finest resolution, 4.4 km. This improvement at higher resolution coincides with more precipitation upstream to the block center than at lower resolution during the onset phase. The latter is consistent with recent studies, indicating that increased moist processes contribute to stronger and bigger blocks. Thus, we provide insights into how the large-scale flow can benefit from the storm-resolving climate models by increasing their skill to simulate atmospheric blocking characteristics and the diabatic processes at higher resolution in a fully coupled system. A more comprehensive evaluation of the large-scale flow in the nextGEMS models will be performed with longer runs.

How to cite: Dolores Tesillos, E. and Martius, O.: Improved northern hemispheric atmospheric blocking properties in two storm-resolving climate models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6596, https://doi.org/10.5194/egusphere-egu24-6596, 2024.

Efforts to enhance climate model simulations by achieving higher resolutions to explicitly capture sub-grid scale processes constitute a central objective in contemporary climate modeling. In this pursuit, our focus is on resolving a pivotal element of the climate system—the ocean meso-scale eddies. At the Alfred-Wegener-Institute, we are working towards this objective by employing the ocean-sea ice model FESOM at approximately 5km horizontal resolution (NG5), coupled with the atmospheric model IFS at a 9km horizontal resolution (tco1279).

This presentation showcases preliminary results from the control simulations of IFS-FESOM under 1950 radiative conditions. Furthermore, we provide an initial glimpse into results from a historical simulation starting in 1950 with the same model configuration. Our analysis illuminates how ocean eddy-rich regions are portrayed in our simulations relative to observations. We delineate the changes and improvements in key climate components, encompassing North Atlantic/Southern Ocean temperatures, NAO, atmospheric blocking, midlatitude storm tracks, ENSO, Monsoon, ITCZ, Hadley/Walker Cells, MJO, meridional overturning, gyre circulations, as well as Arctic/Antarctic Sea ice dynamics under such high resolution.

Moreover, we endeavor to demonstrate how regional high-frequency weather and climate processes can be accurately represented in such simulations, including capturing the nature of regional extremes. In essence, our goal is to illustrate how advancing model resolution to resolve ocean eddies contributes to a more comprehensive representation of the climate system.

How to cite: Ghosh, R., Cheedela, S. K., Koldunov, N., John, A., Streffing, J., Müller, V., Beyer, S., Rackow, T., Sidorenko, D., Danilov, S., and Jung, T.: Ocean Eddy-rich Climate Simulation with IFS-FESOM, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8565, https://doi.org/10.5194/egusphere-egu24-8565, 2024.

Models resolving km-scale processes, such as deep convection, improve the representation of precipitation associated to several processes at sub-synoptic scales, e.g. diurnal cycle, mesoscale convective systems or tropical cyclones. These models generally improve extremes and add value to hazard forecasting, in particular over the tropics. However, these models have been unaffordable to run on a pseudo-global scale until recently and thus their impact in large scale processes is not well known.

Aiming to develop the next generation of Met Office weather and climate prediction systems, the UK K-scale project has been established to evaluate the technical challenges, the scientific improvements and the predictability benefits of km-scale models. The first step of the program is the development of a K-scale “model hierarchy”, a family of simulations across several resolutions and two scientific configurations under the same MetOffice Unified Modelling framework (MetUM). Such hierarchy comprises a generic global model at 12km resolution, realizations at different resolutions of the Cyclic Tropical Channel (CTC), which is a global model in the zonal with north and south boundaries at 26N and 44S respectively, and limited area models (LAMS) over several locations at 2.2km. The two scientific configurations are (i) a global-like aimed at global resolutions above 10km, which includes a parametrization of shallow and mid-level convection, and (ii) a regional-like aimed to km- and sub-km-scale LAM which does not parametrize convection at any level.

Our results from simulations of the 40-day DYAMOND summer and winter periods show than differences between global-like and regional-like configurations at the same resolution can be as large as differences between models at 12km and 4.4km resolution with the same configuration. When all convective processes are not parametrized in the whole tropics at km-scale resolution, the PDF of precipitation shift towards higher intensities, the diurnal cycle improves in several regions, and the wet and dry biases around the E-W boundaries of LAMs are reduced.

The African tropical easterly jet is represented differently across the simulations; with a stronger jet in global-like configurations with convective parametrization. A significant change in mean-state upper wind over the Indian Ocean has potential implications on both subsidence over East Africa, and wind shear over West Africa. These are both tied to widespread rainfall patterns over Africa.

Regional-like configurations at km-scale resolution capture the kinetic energy spectra slope -5/3, poorly represented by the global-like model at 12km. The uncertainty growth across the kscale hierarchy is explored with the use of a twin experiment methodology, and in particular the role of equatorial waves in the error growth across resolutions and science configurations.

How to cite: Sanchez, C., Lewis, H., Jones, R., Warner, J., and Shchepanovska, D.: Evaluation of the K-scale model hierarchy across MetOffice models., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1430, https://doi.org/10.5194/egusphere-egu24-1430, 2024.

In recent years, great efforts have been made to reduce the horizontal grid spacing of atmospheric models to a few kilometers to build so-called Global Storm-Resolving Models (GSRMs). However, the vertical grid spacings used in these models are generally of the same order of magnitude as those used in classical climate models with horizontal grid spacings of a few hundred kilometers. From previous sensitivity experiments with a variety of model types, from direct numerical simulations to these classical climate models, it is known that especially the simulation of clouds can strongly depend on the vertical model resolution. To test the importance of the vertical grid spacing in GSRMs we have performed simulations with the ICON atmospheric model at 5 km horizontal grid spacing and with between 55 and 540 vertical layers, corresponding to maximum tropospheric vertical grid spacings between 800 and 50 m.  

Here we present results of these simulations. They results show that for most of the variables considered, halving the vertical grid spacing by half has a less pronounced impact than halving the horizontal grid spacing, but the effect is not negligible. For example, for each halving of the vertical grid spacing, coupled with necessary reductions in the time step length, cloud liquid water increases globally by approximately 7%, while it decreases by roughly 16% when halving the horizontal grid spacing. Both the grid spacing and the time step contribute to these effects. Comparison of selected climate variables with observations shows that model biases are only in some cases reduced by higher vertical resolution, because of the dominance of model biases with other origins.

How to cite: Schmidt, H.: Exploring the impact of the vertical grid spacing for the climate simulated in a global storm-resolving model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18060, https://doi.org/10.5194/egusphere-egu24-18060, 2024.

With increasing horizontal resolution in global models, we may expect an increasingly more realistic representation of cloud development over land as both large-scale circulations and local surface heterogeneities, such as orography and land use type, are better resolved. As clouds are a dominant contributor to inter- and intra-diurnal variations in both solar and thermal surface irradiance, the spatiotemporal irradiance variability should then be better represented than in conventional climate models. Here, we use the 5-year coupled atmosphere-ocean global simulations performed in Cycle 3 of the nextGEMS project to evaluate the surface irradiance variability over land. These 5-year simulations were performed at different resolution, from 4.4 to 28 km, and with two different global models, the Integrated Forecasting System (IFS) and the Icosahedral Nonhydrostatic model (ICON), allowing us to separate the impacts of horizontal resolution and of implementation choices concerning model physics. We select a couple of representative locations with varying climate and land surface characteristics where high-quality irradiance observations from the Baseline Surface Radiation Network (BSRN) are available. While first results show some benefits of increased horizontal resolution, higher resolutions simulations do not consistently produce more accurate surface irradiances than simulations at lower resolution. Furthermore, differences between the IFS and ICON models are often larger than differences between the IFS simulations at varying resolutions. These results suggest that if realistic surface irradiance predictions are concerned, e.g. for solar energy applications, the road to model improvement by increasing horizontal resolution is not straightforward. 

How to cite: Veerman, M. and van Heerwaarden, C.: Surface irradiance variability over land in storm-resolving models., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19072, https://doi.org/10.5194/egusphere-egu24-19072, 2024.

Destination Earth (DestinE) is a major initiative by the European Commission aiming to create a highly accurate global digital twin of Earth. The Climate Adaptation Digital Twin in DestinE is an ambitious project of several different climate simulations at the km-scale producing a large amount of heavy dataset, difficult to access and analyse with standard data processing  pipelines. Each project and each model produces data that may differ in format (NetCDF, GRIB, Zarr), structure and metadata, leading to the necessity of tweaks and complex pipelines in order to prepare data for analysis.

We thus introduce AQUA, an Application for Quality assessment and Uncertainty quAntification. AQUA is composed of a core engine facilitating data access, combined with a series of modular and independent diagnostics to be run continuously to monitor and evaluate climate simulations. In this contribution we present the core engine and its features. 

Though many available suites already exist to analyse data from global climate models, AQUA has been specifically developed to deal with large km-scale datasets, with the goal of unifying and simplifying climate data access for all users. AQUA responds to the need for users to have the focus on the development of their data analysis, while datasets are found, retrieved and homogenised by an external tool to which they can connect their pipeline. 

Developed in Python, leveraging the power of Dask and Xarray libraries, AQUA prioritises efficiency through lazy data access. Noteworthy is the utilisation of cdo for one-time weight computation, enhancing performances in regridding and averaging operations. A key strength lies in its ability to handle high-resolution, high-frequency data, loading into memory only when necessary. AQUA not only unifies and simplifies climate data access for users but also addresses the crucial need for responsive feedback to climate model developers.

How to cite: Nurisso, M., von Hardenberg, J., Caprioli, S., Ghosh, S., Koldunov, N., P. Kinoshita, B., Nazarova, N., Ghinassi, P., and Davini, P.: AQUA: a novel quality assessment tool for km-scale simulations in the Destination Earth Climate Digital Twin - the core framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10935, https://doi.org/10.5194/egusphere-egu24-10935, 2024.

Destination Earth (DestinE) is a major initiative by the European Commission aiming to create a highly accurate global digital twin of Earth. This model, supported by advanced high-performance computing and artificial intelligence, will monitor and simulate interactions between natural phenomena and human activities with unprecedented accuracy. Developed within the Climate Adaptation Digital Twin of the Destination Earth project, AQUA (Application for Quality assessment and Uncertainty quAntification) is a specialized model evaluation framework for running climate data diagnostics.

While existing diagnostic suites for global climate model data are already available, AQUA stands out by specifically addressing extensive kilometer-scale datasets, to simplify climate data access for all possible users. AQUA features two diagnostic families:

• "state-of-the-art” diagnostics, which compare low-resolution data with observations to assess general model performance and to identify biases and drifts (performance indices, radiation budget, atmospheric global mean time series and biases, teleconnection indices, ocean circulation evaluation, tropical cyclones detection, tracking and zoom-in)
• “frontier” diagnostics, which exploit new high-resolution (i.e., km-scale hourly) climate data to provide insight at climatological scales of physical/dynamical processes that could not be investigated before (sea surface height variability, tropical rainfall) 

Beyond offering a flexible and efficient framework for processing and analyzing large volumes of climate data, AQUA’s modular design offers the possibility of seamless integration of new diagnostic tools, with plans for further expansion in the future phases of the project.
In this contribution, we will introduce the current suite of AQUA diagnostics and outline its planned future developments.

How to cite: Caprioli, S., von Hardenberg, J., Ghinassi, P., Ghosh, S., Kluft, L., Koldunov, N., Massonnet, F., Nazarova, N., Nurisso, M., Ortega, P., Sayed, S., Sharma, T., and Davini, P.: AQUA: a novel quality assessment tool for km-scale simulations in the Destination Earth Climate Digital Twin - the diagnostics suite, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11230, https://doi.org/10.5194/egusphere-egu24-11230, 2024.