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The long-existing double-ITCZ problem in GCMs affects not only the models' ability in simulating the current climate, but also implies limitations regarding the assessment of climate sensitivity and global climate change. Using a regional climate model (RCM) with explicit convection at a horizontal grid spacing of 12 km in a large computational domain covering the tropical and sub-tropical Atlantic, we develop a bias-correction downscaling methodology to remove the biases of a driving GCM. The methodology is related to the pseudo-global warming (PGW) approach. Normally this method is used to impress the climate-change signal to a reanalysis-driven RCM simulation, but it can also be used to modulate the lateral-boundary conditions of a GCM, such as to remove the large-scale biases. We show that the double ITCZ problem persists with classical dynamical downscaling (i.e. when driving the RCM directly by the GCM output), but with our bias-corrected downscaling the double ITCZ problem can be removed. Detailed analysis reveals that the main cause of the double ITCZ problem can be attributed to the GCMs' SST bias. Compared to the GCMs' AMIP simulations, RCMs with higher resolution allow explicit deep convection and enable a better simulation of tropical convection and clouds. By improving the corresponding radiative forcing, vertical motion is better simulated. Subsidence stronger to the south of the ITCZ pushes the ITCZ more north in the boreal spring, which is consistent with the observation of the ITCZ. The developed methodology provides an opportunity for better constraining climate sensitivity by removing double-ITCZ biases.

Currently major efforts are underway toward refining the horizontal grid spacing of climate models to about 1 km, using both global and regional models. Such resolutions have been used for about a decade in limited-area numerical weather prediction applications and have demonstrated significant improvements in the representation of convective precipitation events (thunderstorms and rain showers). There is the well-founded hope that these benefits carry over to climate models, as the approach enables replacing the parameterizations of moist convection and gravity-wave drag by explicit treatments.In this presentation, we will review three areas of km-resolution climate modeling. First, consideration will be given to an ensemble of km-resolution simulations from the CORDEX-FPS program on convection-permitting climate modelling, with a computational domain covering the greater Alpine region. This addresses the occurrence of short-term heavy precipitation events including their impacts such as flash floods, hail, and lightning. Results demonstrate the benefits of high computational resolution, in particular for the representation of short-term heavy events of severe weather. Second, we will present recent results from the projects trCLIM / CONSTRAIN carried out over the tropical and subtropical Atlantic, with the goal to assess the potential of the methodology to constrain estimates of the equilibrium climate sensitivity. It will be argued that km-resolution is a highly promising approach for constraining uncertainties in global climate change projections, due to improvements in the representation of tropical and subtropical clouds that goes along with an improved representation of the intertropical convergence zone (ITCZ).Third, technical aspects of developing km-resolution global models will be addressed. Developing this approach requires a concerted effort between climate and computer sciences. Key challenges are the exploitation of the next generation hardware architectures using accelerators (e.g. graphics processing units, GPUs), the development of suitable approaches to overcome the output avalanche, and the consistent maintenance of the rapidly-developing model source codes on a number of different compute architectures. Despite these challenges, it will be argued that km-resolution GCMs with a capacity to run at 1 SYPD (simulated year per day), might be much closer than commonly believed. However, as the computational load of CMIP-style simulations is tremendous, alternative ways to exploit these models will be needed.

The north and south of the Alps, as well as the eastern shores of the Adriatic Sea, are hot spots of severe weather events, including hail and lightning associated with deep convection. With advancements in computing power, it has become feasible to simulate deep convection explicitly in climate models by decreasing the horizontal grid spacing to less than 4 km. These so-called kilometer-scale or convection-resolving models improve the representation of orography and reduce uncertainties associated with the use of deep convection parameterizations. In this study, we perform convection-resolving simulations for eight observed cases of severe convective events (seven with and one without observed hail) over the Alpine-Adriatic region. The simulations are performed with COSMO-crCLIM, a climate version of the Consortium for Small-scale Modeling (COSMO) regional model that runs on Graphics Processing Units (GPUs) at a horizontal grid spacing of 2.2 km. For analyzing hail and lightning, we have explored the hail growth model (HAILCAST) and lightning potential index (LPI) diagnostics integrated with the COSMO-crCLIM model. Comparison with available high-resolution observations reveals good performance of the model in simulating heavy precipitation, hail, and lightning. By performing a detailed analysis of three of the case studies, we identified the importance of significant meteorological factors for heavy thunderstorms that were reproduced by the model. Among these are the moist unstable boundary layer and dry mid-level air, the topographic barrier, as well as an approaching upper-level trough and cold front. Although COSMO HAILCAST tends to underestimate the hail size on the ground, the results indicate that both HAILCAST and LPI are promising candidates for future climate research.

Estimates of the tropical cloud feedback of global climate models (GCMs) show a large inter-model spread due to the small-scale nature of convective cloud processes. Estimates from large-eddy simulations (LES) are more consistent among themselves, but difficult to scale to the climate system. Here we consider a compromise between GCMs and LES, and study how tropical clouds over the Atlantic respond to a realistic climate perturbation in a kilometer-resolution model.We perform two 4-year-long simulations at 3.3 km horizontal grid spacing with the limited-area model COSMO on a 9000x7000 km2 domain covering the tropical Atlantic: a control simulation and a climate change simulation using the pseudo-global warming approach. In a previous publication we have demonstrated that this approach yields a credible representation of the tropical climate without the double-ITCZ bias commonly seen in GCMs.Here we address the cloud feedback and find a reduction of ITCZ high-cloud cover resulting in a negative longwave cloud feedback slightly stronger than in the analyzed CMIP6 GCMs. We find a reduction of stratocumulus clouds, which we argue is primarily a thermodynamic response. More surprisingly, the shallow cumulus clouds over the West Atlantic increase which we argue is due to increased stability resulting in weaker entrainment and a more humid boundary layer. The mean shortwave cloud feedback over the tropical Atlantic is positive, comparable to the analyzed GCMs. The emerging estimate of the total cloud-radiative feedback over the Atlantic is slightly negative, consistent with CMIP6 GCMs, but less negative than that of the driving GCM.

Clouds over tropical oceans are an important factor in the Earth's response to increased greenhouse gas concentrations, but their representation in climate models is challenging due to the small-scale nature of the involved convective processes. We perform two 4-year-long simulations at kilometer-resolution (3.3 km horizontal grid spacing) with the limited-area model COSMO over the tropical Atlantic on a 9,000 × 7,000 km2 domain: A control simulation under current climate conditions driven by the ERA5 reanalysis, and a climate change scenario simulation using the Pseudo-Global Warming approach. We compare these results to the changes projected in the CMIP6 scenario ensemble. Validation shows a good representation of the annual cycle of albedo, in particular for trade-wind clouds, even compared to the ERA5 reanalysis. Also, the vertical structure and annual cycle of the intertropical convergence zone (ITCZ) is accurately simulated, and the simulation does not suffer from the double ITCZ problem commonly present in global climate models (GCMs). The response to global warming differs between the COSMO simulation and the analyzed GCMs. While both exhibit an overall weakening of the Hadley circulation, the narrowing of the ITCZ (known as the deep-tropics squeeze) is not so pronounced in the kilometer-resolution simulation, likely due to the absence of a double ITCZ bias. Also, there is a more pronounced intensification of the ITCZ at the equator in the kilometer-resolution COSMO simulation, and a stronger associated increase in the anvil cloud fraction. ISSN:0148-0227 ISSN:2169-897X

An adequate representation of the interaction between the land surface and the atmosphere is critical for both numerical weather prediction and climate models. The surface energy and mass balances are tightly coupled to the terrestrial water cycle, mainly through the state of soil moisture. An inadequate representation of the terrestrial water cycle will deteriorate the state of the land surface model and introduce biases to the atmospheric model. The validation of land surface models is challenging, as there are very few observations and the soil is highly heterogeneous. In this paper, a validation framework for land surface schemes based on catchment mass balances is presented. The main focus of our development lies in the application to kilometer-resolution numerical weather prediction and climate models, although the approach is scalable in both space and time. The methodology combines information from multiple observation-based datasets. Observational uncertainties are estimated by using independent sets of observations. It is shown that the combination of observation-based datasets and river discharge measurements close the water balance fairly well for the chosen catchments. As a showcase application, the framework is then applied to compare and validate four different versions of TERRA ML, the land surface scheme of the COSMO numerical weather prediction and climate model over five mesoscale catchments in Switzerland ranging from 105 to 1713 km2. Despite large observational uncertainties, validation results clearly suggest that errors in terrestrial storage changes are closely linked to errors in runoff generation and emphasize the crucial role of infiltration processes.

Atmospheric vortex streets are a widely studied dynamical effect of isolated mountainous islands. Observational evidence comes from case studies and satellite imagery, but the climatology and annual cycle of vortex shedding are often poorly understood. Using the non-hydrostatic limited-area COSMO model driven by the ERA-Interim reanalysis, we conducted a 10-year-long simulation over a mesoscale domain covering the Madeira and Canary archipelagos at high spatial (grid spacing of 1 km) and temporal resolutions. Basic properties of vortex streets were analysed and validated through a 6 d long case study in the lee of Madeira Island. The simulation compares well with satellite and aerial observations and with existing literature on idealised simulations. Our results show a strong dependency of vortex shedding on local and synoptic-flow conditions, which are to a large extent governed by the location, shape and strength of the Azores high. As part of the case study, we developed a vortex identification algorithm. The algorithm is based on a set of criteria and enabled us to develop a climatology of vortex shedding from Madeira Island for the 10-year simulation period. The analysis shows a pronounced annual cycle with an increasing vortex-shedding rate from April to August and a sudden decrease in September. This cycle is consistent with mesoscale wind conditions and local inversion height patterns., Weather and Climate Dynamics, 4 (1), ISSN:2698-4016, ISSN:2698-4008

Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state of the art for these projections, as they resolve convective processes that are key to short-duration extremes. Recent observational studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This "reverse orographic effect"could be related to processes which are subgrid even for CPMs. To quantify the reliability of future projections of extreme short-duration precipitation in mountainous regions, it is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands however, CPM simulations are still too short for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value: SMEV) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We analyze an ERA-Interim-driven Consortium for Small-Scale Modeling (COSMO-crCLIM, convection-resolving Climate Modelling) simulation (2000-2009; 2.2 km resolution), and we use hourly precipitation from 174 rain gauges in an orographically complex area in northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes, as compared to observational data. We focus on extremes as high as the 20-year return levels. While overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation, implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias-correction approaches should account for orography. SMEV's capability of estimating reliable rare extremes from short periods promises further applications on short-time-period CPM projections and model ensembles., Hydrology and Earth System Sciences, 27 (5), ISSN:1027-5606, ISSN:1607-7938

Heatwaves constitute a major threat to human health and ecosystems. Projected increases in heatwave frequency and severity thus lead to the need for prediction to enhance preparedness and minimize adverse impacts. In this Review, we document current capabilities for heatwave prediction at daily to decadal timescales and outline projected changes under anthropogenic warming. Various local and remote drivers and feedbacks influence heatwave development. On daily timescales, extratropical atmospheric blocking and global land–atmosphere coupling are most pertinent, and on subseasonal to seasonal timescales, soil moisture and ocean surface anomalies contribute. Knowledge of these drivers allows heatwaves to be skilfully predicted at daily to weekly lead times. Predictions are challenging beyond timescales of a few weeks, but tendencies for above-average temperatures can be estimated. Further into the future, heatwaves are anticipated to become more frequent, persistent and intense in nearly all inhabited regions, with trends amplified by soil drying in some areas, especially the mid-latitudes. There is also an increased occurrence of humid heatwaves, especially in southern Asia. A better understanding of the relevant drivers and their model representation, including atmospheric dynamics, atmospheric and soil moisture, and surface cover should be prioritized to improve heatwave prediction and projection. ISSN:2662-138X

Hail is a significant convective weather hazard, often causing considerable crop and property damage across the world. Although extremely damaging, hail still remains a challenging phenomenon to model and forecast, given the limited computational resolution and the gaps in understanding the processes involved in hail formation. Here, eight hailstorms occurring over the Alpine-Adriatic region are analyzed using Weather Research and Forecasting (WRF) and Consortium for Small Scale Modeling (COSMO) simulations, with embedded HAILCAST and Lightning Potential Index (LPI) diagnostics at kilometer-scale grid spacing (~2.2 km). In addition, a model intercomparison study is performed to investigate the ability of the different modeling systems in reproducing such convective extremes, and to further assess the uncertainties associated with simulations of such localized phenomena. The results are verified by hailpad observations over Croatia, radar estimates of hail over Switzerland and lightning measurements from the LINET network. The analysis revealed that both HAILCAST and LPI are able to reproduce the areas and intensities affected by hail and lightning. Moreover, the hail and lightning fields produced by both models are similar, although a slight tendency of WRF to produce smaller hail swaths with larger hailstones and higher LPI compared to COSMO is visible. It is found that these differences can be explained by systematic differences in vertical profiles of microphysical properties and updraft strength between the models. Overall, the promising results indicate that both HAILCAST and LPI could be valuable tools for real-time forecasting and climatological assessment of hail and lightning in current and changing climates.

Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state of the art for these projections, as they resolve convective processes that are key to short-duration extremes. Recent observational studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This “reverse orographic effect” could be related to processes which are subgrid even for CPMs. To quantify the reliability of future projections of extreme short-duration precipitation in mountainous regions, it is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands however, CPM simulations are still too short for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value: SMEV) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We analyze an ERA-Interim-driven Consortium for Small-Scale Modeling (COSMO-crCLIM, convection-resolving Climate Modelling) simulation (2000–2009; 2.2 km resolution), and we use hourly precipitation from 174 rain gauges in an orographically complex area in northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes, as compared to observational data. We focus on extremes as high as the 20-year return levels. While overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation, implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias-correction approaches should account for orography. SMEV's capability of estimating reliable rare extremes from short periods promises further applications on short-time-period CPM projections and model ensembles.

Clouds over tropical oceans are an important factor in the Earth's response to increased greenhouse gas concentrations, but their representation in climate models is challenging due to the small-scale nature of the involved convective processes. We perform two 4-year-long simulations at kilometer-resolution (3.3 km horizontal grid spacing) with the limited-area model COSMO over the tropical Atlantic on a 9,000 × 7,000 km2 domain: A control simulation under current climate conditions driven by the ERA5 reanalysis, and a climate change scenario simulation using the Pseudo-Global Warming approach. We compare these results to the changes projected in the CMIP6 scenario ensemble. Validation shows a good representation of the annual cycle of albedo, in particular for trade-wind clouds, even compared to the ERA5 reanalysis. Also, the vertical structure and annual cycle of the intertropical convergence zone (ITCZ) is accurately simulated, and the simulation does not suffer from the double ITCZ problem commonly present in global climate models (GCMs). The response to global warming differs between the COSMO simulation and the analyzed GCMs. While both exhibit an overall weakening of the Hadley circulation, the narrowing of the ITCZ (known as the deep-tropics squeeze) is not so pronounced in the kilometer-resolution simulation, likely due to the absence of a double ITCZ bias. Also, there is a more pronounced intensification of the ITCZ at the equator in the kilometer-resolution COSMO simulation, and a stronger associated increase in the anvil cloud fraction., Journal of Geophysical Research: Atmospheres, 128 (5), ISSN:0148-0227, ISSN:2169-897X

Atmospheric vortex streets are a widely studied dynamical effect of isolated mountainous islands. Observational evidence comes from case studies and satellite imagery, but the climatology and annual cycle of vortex shedding are often poorly understood. Using the non-hydrostatic limited-area COSMO model driven by the ERA-Interim reanalysis, we conducted a ten-year-long simulation over a mesoscale domain covering Madeira and Canary Archipelagos at high spatial (grid spacing 1 km) and temporal resolutions. Basic properties of vortex streets were analyzed and validated through a 6-day-long case study in the lee of Madeira Island. The simulation compares well with satellite and aerial observations and with existing literature on idealized simulations. Our results show a strong dependency of vortex shedding on local and synoptic flow conditions, which are to a large extent governed by the location, shape, and strength of the Azores high. As part of the case study, we developed a vortex identification algorithm. The algorithm is based on a set of objective criteria and enabled us to develop a climatology of vortex shedding from Madeira Island for the 10-year simulation period. The analysis shows a pronounced annual cycle with an increasing vortex shedding rate from April to August and a sudden decrease in September. This cycle is consistent with mesoscale wind conditions and local inversion height patterns.

Soilmoisture-atmosphere interactions are key elements of the regional climate system. There is a well-founded hope that a more accurate representation of the soil moisture-precipitation feedback would improve the simulation of summer precipitation on daily to seasonal, to climate time scales. However, uncertainties have persistently remained as the simulated feedback is strongly sensitive to the model representation of deep convection. Here we assess the feedback representation using a GPU-accelerated version of the regional climate model COSMO.We simulate and compare the impact of continentalscale springtime soil moisture anomalies on summer precipitation at convection-resolving (2.2 km) and convectionparameterizing resolution (12 km). We conduct reanalysis-driven simulations of 10 summer seasons (1999-2008) in continental Europe.While both simulations qualitatively agree on a positive sign of soil moisture-induced precipitation, they strongly differ in precipitation frequency: when convection is parameterized, wetter soil predominantly leads tomore frequent precipitation events, and when convection is treated explicitly, they primarily become more intense. The results indicate that the sensitivity to soil moisture is stronger with parameterized convection, suggesting that the land surface-atmosphere coupling may be overestimated. In addition, the feedback's sensitivity in complex terrain is assessed for soil perturbations of different horizontal scales. The convection-resolving simulations confirm a negative feedback for subcontinental soil moisture anomalies, which manifests itself in a local decrease of wet-hour frequency. However, the intensity feedback reinforces precipitation events at the same time (positive feedback). The two processes are represented differently in simulations with explicit and parameterized convection, explaining much of the difference between the two simulations., Journal of Climate, 34 (13), ISSN:0894-8755, ISSN:1520-0442

The term pseudo-global warming (PGW) refers to a simulation strategy in regional climate modeling. The strategy consists of directly imposing large-scale changes in the climate system on a control regional climate simulation (usually representing current conditions) by modifying the boundary conditions. This differs from the traditional dynamic downscaling technique where output from a global climate model (GCM) is used to drive regional climate models (RCM). The PGW climate changes are usually derived from a transient global climate model (GCM) simulation. The PGW approach offers several benefits such as lowering computational requirements, flexibility in the simulation design, and avoiding biases from global climate models. Yet, implementing a PGW simulation is non-trivial and care must be taken not to deteriorate the physics of the regional climate model when modifying the boundary conditions. To simplify the preparation of PGW simulations, we present a detailed description of the methodology and provide the companion software PGW4ERA5 facilitating the preparation of PGW simulations. In describing the methodology, particular attention is devoted to the adjustment of the pressure and geopotential fields. Such an adjustment is required when ensuring consistency between thermodynamical (temperature and humidity) changes on the one hand, and dynamical changes on the other hand. It is demonstrated that this adjustment is important in the extratropics, and highly essential in tropical and sub-tropical regions. We show that climate projections of PGW simulations prepared using the presented methodology are closely comparable to traditional dynamic downscaling for most climatological variables.

Non-hydrostatic km-scale weather and climate models show significant improvements in simulating clouds and precipitation, especially of convective nature. However, even km-scale models need to parameterize some physical processes and are thus subject to the corresponding parameter uncertainty. Systematic calibration has the advantage of improving model performance with transparency and reproducibility, thus benefiting model intercomparison projects, process studies, and climate-change scenario simulations. In this paper, the regional atmospheric climate model COSMO v6 is systematically calibrated over the Tropical South Atlantic. First, the parameters' sensitivities are evaluated with respect to a set of validation fields. Five of the most sensitive parameters are chosen for calibration. The objective calibration then closely follows a methodology previously used for regional climate simulations. This includes simulations considering the interaction of all pairs of parameters, and the exploitation of a quadratic-form metamodel to emulate the simulations. In the current set-up with 5 parameters, 51 simulations are required to build the metamodel. The model is calibrated for the year 2016 and validated in two different years using slightly different model setups (domain and resolution). Both years demonstrate significant improvements, in particular for outgoing shortwave radiation, with reductions of the bias by a factor of 3–4. The results thus show that parameter calibration is a useful and efficient tool for model improvement. Calibrating over a larger domain might help to further improve the overall performance, but could potentially also lead to compromises among different regions and variables, and require more computational resources., Journal of Geophysical Research: Atmospheres, 127 (23), ISSN:0148-0227, ISSN:2169-897X

We analyze a multi-model ensemble at a convection-resolving resolution based on the DYAMOND models and a resolution ensemble based on the limited-area model COSMO over 40 days to study how tropical and subtropical marine low clouds are represented at a kilometer-scale resolution. The analyzed simulations produce low cloud fields that look in general realistic in comparison with satellite images. The evaluation of the radiative balance, however, reveals substantial inter-model differences and an under estimated low cloud cover in most models. Models that simulate increased low cloud cover are found to have a deeper marine boundary layer (MBL), stronger entrainment, and an enhanced latent heat flux. These findings demonstrate that some of the fundamental relations of the MBL are systematically represented by the model ensemble, which implies that the relevant dynamical processes start to become resolved on the model grid at a kilometer-scale resolution. A sensitivity experiment with the COSMO model suggests that differences in the strength of turbulent vertical mixing may contribute to the inter-model spread in cloud cover. ISSN:0026-1165

Currently, major efforts are under way to refine the horizontal resolution of weather and climate models to kilometer-scale grid spacing (Dx). Besides refining the representation of the atmospheric dynamics and enabling the use of explicit convection, this will also provide higher resolution in the representation of orography. This study investigates the influence of these resolution increments on the simulation of orographic moist convection. Nine days of fair-weather thermally driven flow over the Alps are analyzed. Two sets of simulations with the COSMO model are compared, each consisting of three runs at Dx of 4.4, 2.2, and 1.1 km: one set using a fixed representation of orography at a resolution of 8.8 km, and one with varying representation at the resolution of the computational mesh. The spatial distribution of precipitation during daytime is only marginally affected by the orographic details, but nighttime convection to the south of the Alps-triggered by cold-air outflow from the valleys-is very sensitive to orography and precipitation is enhanced if more detailed orography is provided. During daytime, the onset of precipitation is delayed. The amplitude of the diurnal cycle of precipitation is reduced, even though more moisture converges toward the Alpine region during the afternoon. The hereby accumulated moisture sustains precipitation during the evening and nighttime over the surrounding plains. For these differences, the effects of changes in orographic detail are more important than changes in grid spacing. In addition, the individual convective cells are weaker, but their number increases with higher resolved orography. © 2020 American Meteorological Society ISSN:1520-0493 ISSN:0027-0644

The “gray zone” of convection is defined as the range of horizontal grid-space resolutions at which convective processes are partially but not fully resolved explicitly by the model dynamics (typically estimated from a few kilometers to a few hundred meters). The representation of convection at these scales is challenging, as both parameterizing convective processes or relying on the model dynamics to resolve them might cause systematic model biases. Here, a regional climate model over a large European domain is used to study model biases when either using parameterizations of deep and shallow convection or representing convection explicitly. For this purpose, year-long simulations at horizontal resolutions between 50- and 2.2-km grid spacing are performed and evaluated with datasets of precipitation, surface temperature, and top-of-the-atmosphere radiation over Europe. While simulations with parameterized convection seem more favorable than using explicit convection at around 50-km resolution, at higher resolutions (grid spacing ≤ 25 km) models tend to perform similarly or even better for certain model skills when deep convection is turned off. At these finer scales, the representation of deep convection has a larger effect in model performance than changes in resolution when looking at hourly precipitation statistics and the representation of the diurnal cycle, especially over nonorographic regions. The shortwave net radiative balance at the top of the atmosphere is the variable most strongly affected by resolution changes, due to the better representation of cloud dynamical processes at higher resolutions. These results suggest that an explicit representation of convection may be beneficial in representing some aspects of climate over Europe at much coarser resolutions than previously thought, thereby reducing some of the uncertainties derived from parameterizing deep convection.

Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather prediction and climate research. As such, they are subject to continuous changes, thanks to advances in computer systems, numerical methods, more and better observations, and the ever-increasing knowledge about the atmosphere of earth. Many of the changes in today's models relate to seemingly innocuous modifications associated with minor code rearrangements, changes in hardware infrastructure, or software updates. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere – any small change, even rounding errors, can have a significant impact on individual simulations. Overall, this represents a serious challenge to a consistent model development and maintenance framework. Here we propose a new methodology for quantifying and verifying the impacts of minor changes in the atmospheric model or its underlying hardware/software system by using ensemble simulations in combination with a statistical hypothesis test for instantaneous or hourly values of output variables at the grid-cell level. The methodology can assess the effects of model changes on almost any output variable over time and can be used with different underlying statistical hypothesis tests. We present the first applications of the methodology with the regional weather and climate model COSMO. While providing very robust results, the methodology shows a great sensitivity even to very small changes. Specific changes considered include applying a tiny amount of explicit diffusion, the switch from double to single precision, and a major system update of the underlying supercomputer. Results show that changes are often only detectable during the first hours, suggesting that short-term ensemble simulations (days to months) are best suited for the methodology, even when addressing long-term climate simulations. Furthermore, we show that spatial averaging – as opposed to testing at all grid points – reduces the test's sensitivity for small-scale features such as diffusion. We also show that the choice of the underlying statistical hypothesis test is not essential and that the methodology already works well for coarse resolutions, making it computationally inexpensive and therefore an ideal candidate for automated testing., Geoscientific Model Development, 15 (8), ISSN:1991-9603, ISSN:1991-959X

Terrain parameters like topographic horizon and sky view factor (SVF) are used in numerous fields and applications. In atmospheric and climate modelling, such parameters are utilized to parameterise the effect of terrain geometry on radiation exchanges between the surface and the atmosphere. Ideally, these parameters are derived from a high-resolution digital elevation model (DEM), because inferring them from coarser elevation data induces a smoothing effect. Computing topographic horizon with conventional algorithms is however slow, because large amounts of non-local terrain data have to be processes. We propose a new and more efficient method, which is based on a high-performance ray tracing library. By applying terrain simplification to remote topography, this allows the application of the new algorithms also with very high-resolution (< 5 m) DEM data, which otherwise would induce an excessive memory footprint. The topographic horizon algorithm is accompanied by a SVF algorithm, which was verified to work accurately for all terrain – even very steep and complex one. We compare the computational performance and accuracy of the new horizon algorithm with two reference methods from literature and illustrate its benefits. Finally, we illustrate how sub-grid SVF values can be efficiently computed with the newly derived horizon algorithm for a wide range of target grid resolutions (1–25 km).

Even though the complexity and resolution of global climate models has increased over the last decades, the inter-model spread in equilibrium climate sensitivity has not narrowed. The representation of tropical and subtropical marine clouds remains a major source of uncertainty in climate models. Going to higher model resolution to explicitly resolve a larger fraction of the underlying convective circulations is the most direct way towards reducing the uncertainty associated with these clouds. Convection-resolving models (CRMs) are therefore an attractive complementary tool to study tropical cloud feedbacks. Even though decade-long global CRM simulations are not yet computationally feasible, CRMs can be used in climate applications for selected limited-area domains and periods.Here we run 3-year-long CRM simulations with the COSMO model over the tropical-to-subtropical Atlantic. We run a control simulation to evaluate the model’s capability of representing the clouds and the radiative balance. We also run a climate change scenario simulation using the pseudo-global warming (PGW) approach to study cloud-radiative feedbacks at convection-resolving resolution.We find a good agreement between the simulated and observed annual cycle in the top-of-the-atmosphere radiative fluxes, despite a mean bias that should be possible to reduce through model calibration. There are pronounced improvements in the CRM simulation compared to the CMIP6 models over the ITCZ and the trade-wind cumulus region, while the representation of stratocumulus clouds remains challenging also in the CRM simulation. The simulated cloud-radiative feedback is at the upper end of what the CMIP6 models predict due to a pronounced positive longwave feedback at the ITCZ caused by an increase in high clouds. The shortwave cloud-radiative feedback is moderately positive and lies well within the range of the CMIP6 models with a reduction in the low-level cloud fraction over the subtropics and a partly compensating increase in the cloud fraction at the ITCZ.

In mountainous regions, incoming surface radiation is strongly influenced by surrounding and local terrain. The direct beam part of incoming shortwave radiation depends both on local slope angle and azimuth as well as on neighbouring terrain, which can induce topographic shading. Shortwave radiation can be reflected (multiple times) by terrain, which leads to enhanced incoming diffuse shortwave radiation for locations with a reduced sky view factor (SVF) – particularly under snow-covered conditions when surface reflectivity is high. Finally, incoming longwave radiation can also be modulated by neighbouring terrain due to radiation exchange between facing slopes.Considering these effects in spatially distributed land surface models – either stand-alone or embedded in weather and climate models – typically requires the following topographic quantities: slope angle, slope aspect, terrain horizon and SVF. The first two quantities can be computed rapidly because they only depend on local terrain. The computation of the latter two quantities is however expensive, particularly for high-resolution (~30 m) digital elevation models (DEMs), because a large quantity of non-local DEM information has to be processed. We developed a new efficient algorithm for terrain horizon computation, which is based on a high-performance ray-tracing library. A benchmark against conventional algorithms confirmed its high performance – particularly for DEMs with very high resolution and for large terrain horizon search distances. Furthermore, due to the smooth representation of terrain by a triangle mesh, the new algorithm does not reveal artefacts in the computed horizon line in cases where the horizon is formed by proximal terrain. Finally, we demonstrate that the new algorithm is also eligible to compute sub-grid SVF for large spatial domains in a very efficient way. Sub-grid SVF is a useful quantity to parameterise above-mentioned topographic effects on surface radiation in weather and climate models applied on regional or even global scales.

Compared to standard regional climate models (RCMs), convection-permitting models (CPMs) provide an improved representation of sub-daily precipitation statistics and extremes thanks mainly to the possibility to switch off the deep convection parameterisation, a known source of model error and uncertainties. The more realistic representation of local processes in CPMs leads to a greater confidence in their projections of future changes in short-duration precipitation extremes.The quantification of uncertainties on future changes at this resolution has been barely touched. Using the first-ever ensemble of CPMs run within the UK Climate Projections project, Fosser et al. (2020) found that the climate change signal for extreme summer precipitation may converge in CPMs in contrast to RCMs, thanks to a more realistic representation of the local storm dynamics.Here we use the first multi-model CPMs ensemble over the greater Alpine region, run under the auspices of the World Climate Research Programme’s (WCRP) Coordinated Regional Downscaling Experiment Flagship Pilot Study on Convective phenomena at high resolution over Europe and the Mediterranean (Coppola et al. 2020). Several statistics are used to determine the uncertainties in the climate change signal trying to disentangle model uncertainties from natural variability. We found that the contribution of model to the total uncertainties is substantially reduced in CPMs compared to the driving models in summer. This is likely linked to the removal of the uncertainties associated with the convective parameterisation and to a more realistic representation of convective and local dynamical processes in the CPMs. Fosser G, Kendon EJ, Stephenson D, Tucker S (2020) Convection‐Permitting Models Offer Promise of More Certain Extreme Rainfall Projections. Geophys Res Lett 47:0–2. doi: 10.1029/2020GL088151Coppola, E., Sobolowski, S., Pichelli, E. et al.A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean. Clim Dyn55, 3–34 (2020). https://doi.org/10.1007/s00382-018-4521-8

The Hengudan Mountains, located at the south-eastern fringe of the Tibetan Plateau, reveal exceptionally high biodiversity. It is believed that this feature is linked to past complex interactions between climate, land surface dynamics and plate tectonics in this region. Contemporary topography was formed by plate tectonics, causing surface uplift, and spatially heterogeneous erosion, which shaped the deep river valleys. The non-hydrostatic regional climate model COSMO is applied to study the impact of surface uplift and river incision. Decades-long simulations at horizontal resolutions of 12 and 4.4 km grid spacing are performed. To study the impact of local topography on climate, we consider two idealized experiments with terrains deviating from the present-day topography: In the first experiment, we reduce the topography in a spatially non-uniform way. This altered topography reflects a past potential state of the Hengduan Mountains. In the second experiment, we remove the deep valleys by applying an envelope topography to quantify the effects of deep valleys on the local climate. Both experiments assume that that the large-scale (continental) climate did not change, i.e., the experiments are driven by large-scale reanalysis data. Preliminary results from the coarse-resolution 12 km COSMO simulation indicate that the uplift of the Hengduan Mountains has a strong impact on the summer monsoon over South Asia caused by circulation changes around the uplifted region. The uplift of the Hengduan Mountains strengthens the westerly wind anomalies from the ocean in South Asia and markedly intensifies the precipitation in Indochina and southwestern China. Besides, the cyclonic circulation in the Bay of Bengal extends eastward, indicating an intensification of the East Asian summer monsoon. The diabatic heating in the eastern Tibetan Plateau increases in response to the regional uplift and it is coupled with the increased precipitation in summer through moist processes. On the contrary, the uplift has little impact on the strengthening of the winter monsoon. In the next stage, we will conduct the same simulations at a higher horizontal resolution of 4.4 km, which captures local terrain more accurate. These simulations will use explicit rather than parameterized convection, thereby providing more realistic estimates of heavy precipitation and erosion. Subsequently, we will run the same experiments for the envelop topography. We expect to relate the changes in the frequency and intensity of extreme precipitation to the changes in the local moisture transport and vertical movement in the high-resolution perspective. In the future, the two different topographies along with the modern topography will be used for simulations of two time periods in the past (i.e., the Last Glacial Maximum (21,000 years ago) and a phase in the Late Miocene (∼7 Ma)) and the future climate (2070–2100).

Subdaily extreme precipitation may trigger fast hydro-geomorphic responses, such as flash floods and debris flows, which cause numerous fatalities and large damage. Compared to coarser resolution models, high-resolution models, called convection-permitting (CPMs), more realistically represent convective processes that are key for the correct representation of subdaily extremes, and thus provide higher confidence in the future extreme estimates. However, due to the high computational demands, the existing CPM simulations are only available for relatively short time periods (10–20 years at most), too short for deriving precipitation frequency analyses with conventional approaches. Recent extreme value analysis methods, based on all “ordinary” observations rather than on just yearly maxima or a few values over a high threshold, offer an opportunity for exploiting these short data records to reliably estimate return levels associated with long return periods. Here, we examine subdaily precipitation extremes from three 10-year time slices (historical 1996-2005, near-future 2041-2050, and far future 2090-2099 – under the RCP8.5 scenario) of COSMO-crCLIM model simulations at 2.2 km resolution. We focus on the Eastern Alpine transect characterised by a complex orography, where significant changes in subdaily annual maxima have been already observed. We find that, although the storms' frequency will decrease in the region, the mean annual maxima will increase continuously in the near and far future, especially at shorter durations. Investigation of extreme return levels shows a similar trend, with larger changes in the far future. A shift in the seasonality is also reported, with extremes moving from late summer-autumn (historical), to autumn (near future), and autumn-winter (far future).

Hail is a significant convective weather hazard, often causing considerable crop and property damage across the world. Although extremely damaging, hail still remains a challenging phenomenon to model and forecast given the gaps in understanding the processes involved in hail formation. Recently, a physically-based one-dimensional hail model called HAILCAST was developed. HAILCAST forecasts the maximum expected hail diameter at the ground using a vertical profile of the updraft, temperature, liquid and ice water content and can be embedded within a convection-permitting model (CPM). Furthermore, lightning activity is a characteristic phenomenon that often accompanies severe weather, and especially hailstorms, as well as a damaging phenomenon in itself. One of the ways to diagnose the areas prone to lighting activity is by using a Lightning Potential Index (LPI). LPI is a measure of the potential for charge generation and separation inside a thundercloud, which results in lightning flashes during convective thunderstorms. Therefore, LPI maps the area with the potential for electrical activity based on the model’s dynamical and microphysical fields. Here, eight hailstorms occurring over the Alpine-Adriatic region are analyzed using Weather Research and Forecasting (WRF) and Consortium for Small Scale Modeling in Climate Mode (COSMO) simulations with embedded HAILCAST and LPI at convection-permitting resolution (~2.2 km). In addition, a model intercomparison study is performed to investigate the ability of different modelling systems in reproducing such convective extremes and to further assess the uncertainties associated with simulations of such local-area phenomena. The results are verified by direct hail observations from Croatia (hailpad network), radar estimates of hail from Switzerland (probability of hail, maximum expected severe hail size) and lightning measurements from the LINET network. The analysis revealed that both HAILCAST and LPI are able to reproduce the observed hail and lightning activity. Namely, both models are able to capture the areas affected by hail and lightning as well as its intensity. Moreover, the fields produced by both models are remarkably similar, although, a slight tendency of WRF to produce smaller hail swaths with larger hailstone diameters and larger LPI values seems to be present. Overall, the analysis revealed promising results and indicates that both HAILCAST and LPI could be valuable tools for real-time forecasting and climatological assessment of hail and lightning occurrence in current and possibly changing climate.

The Alpine and Adriatic regions are hotspots of frequent hail and lightning. Hail and lightning are associated with severe convective storms that happen under the large-scale forcing of surface fronts, upper-level fronts, convergence zones, or local thermal-topographic forcing. Convection-resolving models are run at the km-scale resolution, which improves the representation of topography. Moreover, they can explicitly resolve deep convection, thus reducing the uncertainties related to the use of deep convection parameterization in lower resolution models. Both aspects are beneficial for studying processes that drive severe convective storms over mountainous regions.In this study, we analyze convection-resolving simulations of 8 heavy convective events performed with the COSMO-crCLIM model (GPU version of the Consortium for Small-scale Modeling) at 2.2 km horizontal grid spacing over the Alpine-Adriatic region. The cases are selected according to their impacts (size of hailstones, number of lightning strikes and damages), and the simulations are driven by the ERA5 reanalysis. For the simulation of hail and lightning, we use the one-dimensional hail growth model HAILCAST and the lightning potential index (LPI) implemented into the COSMO model, and compare results with observed hail properties and lightning flashes. In addition, we look into key variables for hail formation, including temperature, humidity, CAPE and CIN, and bulk wind shear. By performing a detailed analysis, we identify several environments that are favorable for strong convection and associated hail and lightning, such as a "loaded-gun" sounding, conditionally unstable layer and intrusion of dry air aloft. Evaluation of model simulations with available observations demonstrates overall very good performance of the model for the simulation of precipitation, hail and lightning. However, results depend upon the predictability of the cases, with lower predictability for deep convection events driven by local thermal-topographic forcing. Our findings indicate that HAILCAST and LPI can diagnose hail and lightning associated with severe weather events. Recently, we have started to assess changes in the occurrence and severity of such events with multi-seasonal simulations under current and future climate conditions using the pseudo-global warming (PGW) approach.

Currently major efforts are underway toward refining the horizontal grid spacing of climate models to about 1 km, using both global and regional climate models. There is the well-founded hope that this increase in resolution will improve climate models, as it enables replacing the parameterizations of moist convection and gravity-wave drag by explicit treatments. Results suggest that this approach has a high potential in improving the representation of the water cycle and extreme events, and in reducing uncertainties in climate change projections. The presentation will provide examples of these developments in the areas of heavy precipitation and severe weather events over Europe. In addition, it will be argued that km-resolution is a promising approach toward constraining uncertainties in global climate change projections, due to improvements in the representation of tropical and subtropical clouds. Work in the latter area has only recently started and results are highly encouraging. For a few years there have also been attempts to make km-resolution available in global climate models for decade-long simulations. Developing this approach requires a concerted effort. Key challenges include the exploitation of the next generation hardware architectures using accelerators (e.g. graphics processing units, GPUs), the development of suitable approaches to overcome the output avalanche, and the maintenance of the rapidly-developing model source codes on a number of different compute architectures. Despite these challenges, it will be argued that km-resolution GCMs with a capacity to run at 1 SYPD (simulated year per day), might be much closer than commonly believed. The presentation is largely based on a recent collaborative paper (Schär et al., 2020, BAMS, https://doi.org/10.1175/BAMS-D-18-0167.1) and ongoing studies. It will also present aspects of a recent Swiss project in this area (EXCLAIM, https://exclaim.ethz.ch/).

Over the last decade kilometer-scale weather predictions and climate projections have become established. Thereby both the representation of atmospheric processes, as well as land-surface processes need adaptions to the higher-resolution. Soil moisture is a critical variable for determining the exchange of water and energy between the atmosphere and the land surface on hourly to seasonal time scales, and a poor representation of soil processes will eventually feed back on the simulation quality of the atmosphere. Especially the partitioning between infiltration and surface runoff will feed back on the hydrological cycle. Several aspects of the coupled system are affected by a shift to kilometer-scale, convection-permitting models. First of all, the precipitation-intensity distribution changes to more intense events. Second, the time-step of the numerical integration becomes smaller. The aim of this study is to investigate the numerical convergence of the one-dimensional Richards Equation with respect to the soil hydraulic model, vertical layer thickness and time-step during the infiltration process. Both regular and non-regular (unequally spaced) grids typical in land surface modelling are considered, using a conventional semi-implicit vertical discretization. For regular grids, results from a highly idealized experiment on the infiltration process show poor numerical convergence for layer thicknesses larger than approximately 5 cm and for time steps greater than 40 s, irrespective of the soil hydraulic model. The velocity of the wetting front decreases systematically with increasing time step and decreasing vertical resolution. For non-regular grids, a new discretization based on a coordinate transform is introduced. In contrast to simpler vertical discretizations, it is able to represent the solution second-order accurate. The results for non-regular grids are qualitatively similar, as a fast increase in layer thickness with depth is equivalent to a lower vertical resolution. It is argued that the sharp gradients in soil moisture around the propagating wetting front must be resolved properly in order to achieve an acceptable numerical convergence of the Richards Equation. Furthermore, it is shown that the observed poor numerical convergence translates directly into a poor convergence of infiltration-runoff partitioning for precipitation time series characteristic of weather and climate models. As a consequence, soil simulations with low resolution in space and time may produce almost twice the amount of surface runoff within 24 hours than their high-resolution counterparts. Our analysis indicates that the problem is particularly pronounced for kilometer-resolution models.

Greenhouse gas-driven global temperature change projections exhibit spatial variations, meaning that certain land areas will experience substantially enhanced or reduced surface warming. It is vital to understand enhanced regional warming anomalies as they locally increase heat-related risks to human health and ecosystems. We argue that tropospheric lapse-rate changes play a key role in shaping the future summer warming pattern around the globe in mid-latitudes and the tropics. We present multiple lines of evidence supporting this finding based on idealized simulations over Europe, as well as regional and global climate model ensembles. All simulations consistently show that the vertical distribution of tropospheric summer warming is different in regions characterized by enhanced or reduced surface warming. Enhanced warming is projected where lapse-rate changes are small, implying that the surface and the upper troposphere experience similar warming. On the other hand, strong lapse-rate changes cause a concentration of warming in the upper troposphere and reduced warming near the surface. The varying magnitude of lapse-rate changes is governed by the temperature dependence of the moist-adiabatic lapse rate and the available tropospheric humidity. We conclude that tropospheric temperature changes should be considered along with surface processes when assessing the causes of surface warming patterns.