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A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius–Clapeyron (CC) rate (6–7% K −1 ), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.

Atmospheric vortex streets are one of the widely studied dynamical effects of isolated islands. However, the study of vortex shedding is still limited by the availability of observational wind fields of high spatial and temporal resolutions. Although the geometry, kinematics, and dynamics of vortex streets have been intensively investigated in numerous theoretical, numerical, and observational studies, our understanding of vortex shedding in the real atmosphere and atmospheric models is still insufficient.Using the non-hydrostatic limited-area COSMO model driven by the ERA-Interim reanalysis, we simulated a mesoscale domain in high spatial (grid spacing 1 km) and temporal resolutions over one decade. This enabled us to investigate vortex streets within the planetary boundary layer despite limited observations. The basic properties of vortex streets are analyzed and validated through a 6-day-long case study in the lee of the Madeira island. The simulation compares well with satellite and aerial observations, and with the 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 magnitude of the Azores high, which represents one pole of the North Atlantic Oscillation. As part of the case study, we have developed a vortex identification algorithm, consisting of a wavelet analysis using a set of objective criteria. The algorithm shows good performance in terms of false-positive rate and enables us to develop a climatology of vortex shedding in this region for the 10-year simulation period. Based on the long term analysis, we can identify an increasing vortex shedding rate from April to August and a sudden decrease in September, which can be well explained by the large-scale wind conditions.

In mountainous regions, atmospheric and surface conditions (like snow coverage) are strongly modulated by complex terrain. One relevant process is the topographic effect on incoming/outgoing surface short- and longwave radiation by surrounding terrain. Radiation in weather and climate models is typically represented by the two-stream approximation, which only allows for vertical radiation exchange and thus no lateral interaction with terrain. In reality, surface radiation can be modulated through various processes: the direct-beam part of the incoming shortwave radiation depends on local surface inclination and on shading from the neighbouring terrain. Incoming diffuse shortwave radiation is modified by partial sky-obstruction and terrain reflection. Outgoing longwave radiation is reduced by interception from neighbouring terrain.In this study, we develop a parameterisation which considers the above-mentioned processes on a sub-grid scale, and implement the scheme in the Regional Climate Model COSMO (Consortium for Small-scale Modeling). On the grid scale, such a parameterisation is already available and has been applied in the numerical weather prediction mode of COSMO. Applying this parameterisation in the climate mode of COSMO has revealed that biases like the over-/underestimation of snow cover duration at south-/north-facing slopes can be improved. However, the associated radiation correction appears to be too weak because only terrain effects on the resolved scales are considered. We therefore parameterise these effects on a sub-grid scale.The (current) surface radiation correction scheme requires consideration of topographic parameters like the elevation of the horizon and the sky-view factor. The computation of these parameters on the sub-grid scale is very expensive, because non-local information of a large high-resolution Digital Elevation Model (DEM) needs to be processed. We developed a new algorithm, which allows for horizon computations from a high-resolution DEM in a fast and flexible way. We furthermore found that existing sky-view factor algorithms might yield inaccurate results for locations with very steep terrain and subsequently developed an improved method. Output of these new algorithms will be used for the new sub-grid radiation parameterisation scheme.

It has long been recognized that the Mediterranean is a ‘hot-spot’ of climate change. The model-projected year-round precipitation decline and amplified summer warming are among the leading causes of the vulnerability of the Mediterranean to greenhouse gas-driven warming. We investigate large-scale drivers influencing both the Mediterranean drying and summer warming in regional climate simulations. To isolate the influence of multiple large-scale drivers, we sequentially add the respective drivers from global models to regional climate model simulations. Additionally, we confirm the robustness of our results across multiple ensembles of global and regional climate simulations. We will present in detail how changes in the atmospheric stratification are key in causing the amplified Mediterranean summer warming. Together with the land-ocean warming contrast, stratification changes also drive the summer precipitation decline. Summer circulation changes generally have a surprisingly small influence on the changing Mediterranean summer climate. In contrast, changes in the circulation are the primary driver for the projected winter precipitation decline. Since land-ocean contrast and stratification changes are more robust in global climate simulations than circulation changes, we argue that the uncertainty associated with the projected climate change patterns should be considered smaller in summer than in winter. References: Brogli, R., S. L. Sørland, N. Kröner, and C. Schär, 2019: Causes of future Mediterranean precipitation decline depend on the season. Environmental Research Letters, 14, 114017, doi:10.1088/1748-9326/ab4438. Brogli, R., N. Kröner, S. L. Sørland, D. Lüthi and C. Schär, 2019: The Role of Hadley Circulation and Lapse-Rate Changes for the Future European Summer Climate. Journal of Climate, 32, 385-404, doi:10.1175/JCLI-D-18-0431.1

Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. 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 big 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 atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.

In the last decade, the Climate Limited-area Modeling (CLM) Community has contributed to the Coordinated Re- gional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM community model, ERA-Interim reanalysis and eight Global Climate Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44° (∼50 km), 0.22° (∼25 km) and 0.11° (∼12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Fed- eration (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modelling communities is needed to increase the reliability of the GCM-RCM modelling chain.

Six strategies to couple the dynamical core with physical parameterizations in atmospheric models are analyzed from a numerical perspective. Thanks to a suitably designed theoretical framework featuring a high level of abstraction, the truncation error analysis and the linear stability study are carried out under weak assumptions. Indeed, second-order conditions are derived which are not influenced either by the specific formulation of the governing equations, nor by the number of parameterizations, nor by the structural design and implementation details of the time-stepping methods. The theoretical findings are verified on two idealized test beds. Particularly, a hydrostatic model in isentropic coordinates is used for vertical slice simulations of a moist airflow past an isolated mountain. Self-convergence tests show that the sensitivity of the prognostic variables to the coupling scheme may vary. For those variables (e.g., momentum) whose evolution is mainly driven by the dry dynamics, the truncation error associated with the dynamical core dominates and hides the error due to the coupling. In contrast, the coupling error of moist variables (e.g., the precipitation rate) emerges gradually as the spatio-temporal resolution increases. Eventually, each coupling scheme tends toward the formal order of accuracy, upon a careful treatment of the grid cell condensation. Indeed, the well-established saturation adjustment may cap the convergence rate to first order. A prognostic formulation of the condensation and evaporation process is derived from first principles. This solution is shown effective to alleviate the convergence issues in our experiments. Potential implications for a complete forecasting system are discussed. ISSN:1942-2466

N. Ban et al., Here we present the first multi-model ensemble of regional climate simulations at kilometer-scale horizontal grid spacing over a decade long period. A total of 23 simulations run with a horizontal grid spacing of ∼3 km, driven by ERA-Interim reanalysis, and performed by 22 European research groups are analysed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution (∼ 12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons. The results show that kilometer-scale models produce a more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. On average, the multi-model mean shows a reduction of bias from ∼ −40% at 12 km to ∼ −3% at 3 km for heavy hourly precipitation in summer. Furthermore, the uncertainty ranges i.e. the variability between the models for wet hour frequency is reduced by half with the use of kilometer-scale models. Although differences between the model simulations at the kilometer-scale and observations still exist, it is evident that these simulations are superior to the coarse-resolution RCM simulations in the representing precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales., Open access funding provided by University of Innsbruck and Medical University of Innsbruck., The ETH team, MZ, CNRM IPSL, ICTP, SMHI, Met-Office, DMI, CMCC, HZG, KNMI acknowledge funding from the HORIZON 2020 EUCP (European Climate Prediction System) project (https://www.eucp-project.eu, grant agreement No. 776613). AL-G acknowledges support by the Spanish government through grant BES-2016-078158 and MINECO/FEDER co-funded project MULTI-SDM (CGL2015-66583-R).

This study presents a detailed analysis of the climatological distribution of precipitation in relation to cyclones and fronts over Europe for the 9-year period 2000–2008. The analysis uses hourly output of a COSMO (Consortium for Small-scale Modeling) model simulation with 2.2 km grid spacing and resolved deep convection. Cyclones and fronts are identified as two-dimensional features in 850 hPa geopotential, equivalent potential temperature, and wind fields and subsequently tracked over time based on feature overlap and size. Thermal heat lows and local thermal fronts are removed based on track properties. This dataset then serves to define seven mutually exclusive precipitation components: cyclonic (near cyclone center), cold-frontal, warm-frontal, collocated (e.g., occlusion area), far-frontal, high-pressure (e.g., summer convection), and residual. The approach is illustrated with two case studies with contrasting precipitation characteristics. The climatological analysis for the 9-year period shows that frontal precipitation peaks in winter and fall over the eastern North Atlantic and the Alps (> 70 % in winter), where cold frontal precipitation is also crucial year-round; cyclonic precipitation is largest over the North Atlantic (especially in summer with > 40 %) and in the northern Mediterranean (widespread > 40 %); high-pressure precipitation occurs almost exclusively over land and primarily in summer (widespread 30 %–60 %, locally >80 %); and the residual contributions uniformly amount to about 20 % in all seasons. Considering heavy precipitation events (defined based on the local 99.9th all-hour percentile) reveals that high-pressure precipitation dominates in summer over the continent (50 %–70 %, locally >80 %); cold fronts produce much more heavy precipitation than warm fronts; and cyclones contribute substantially (50 %–70 %), especially in the Mediterranean in fall through spring and in northern Europe in summer.

We investigate the effect of using convection-permitting models (CPMs) spanning a pan-European domain on the representation of precipitation distribution at a climatic scale. In particular we compare two 2.2 km models with two 12 km models run by ETH Zurich (ETH-12 km and ETH-2.2 km) and the Met-Office (UKMO-12 km and UKMO-2.2 km). The two CPMs yield qualitatively similar differences to the precipitation climatology compared to the 12 km models, despite using different dynamical cores and different parameterization packages. A quantitative analysis confirms that the CPMs give the largest differences compared to 12 km models in the hourly precipitation distribution in regions and seasons where convection is a key process: in summer across the whole of Europe and in autumn over the Mediterranean Sea and coasts. Mean precipitation is increased over high orography, with an increased amplitude of the diurnal cycle. We highlight that both CPMs show an increased number of moderate to intense short-lasting events and a decreased number of longer-lasting low-intensity events everywhere, correcting (and often over-correcting) biases in the 12 km models. The overall hourly distribution and the intensity of the most intense events is improved in Switzerland and to a lesser extent in the UK but deteriorates in Germany. The timing of the peak in the diurnal cycle of precipitation is improved. At the daily time-scale, differences in the precipitation distribution are less clear but the greater Alpine region stands out with the largest differences. Also, Mediterranean autumnal intense events are better represented at the daily time-scale in both 2.2 km models, due to improved representation of mesoscale processes.

We present an analysis of extreme precipitation events in convection-resolving climate simulations. The simulations are performed with the COSMO-CLM model at 2.2 km resolution across an extended Alpine region and its larger-scale surrounding. Generalized extreme value theory (GEV) is applied to address projections of 5-day, daily and hourly extreme precipitation events in all seasons. Validation using ERA-Interim driven simulations reveals significant improvements with the 2.2 km resolution. In comparison to its driving 12 km model, high resolution improves the simulation of precipitation on most investigated timescales and seasons. The climate change signal is analyzed in 10-year long control and scenario simulations (1991–2000 and 2081–2090) driven by a CMIP5 coupled climate model (MPI-ESM-LR) under an RCP8.5 greenhouse gas scenario. Analysis shows negligible differences between the two resolutions for winter precipitation on all time scales, while in the other seasons the 2.2 km model shows smaller changes in extreme hourly precipitation, and yields narrower uncertainty estimates. Changes in extreme summer precipitation qualitatively scale with the Clausius–Clapeyron rate, i.e., 6–7% per degree warming, and are consistent with previous percentile based analysis. In winter, changes exceed the Clausius–Clapeyron rate. Some interpretations of this result are provided.

Thermally driven upslope flows in mountainous areas provide favorable conditions for diurnal deep moist convection especially during episodes of weak synoptic forcing. The present study investigates the response of deep convection to axisymmetric orography as a function of orographic width and height by running ensembles of idealized convection-resolving simulations with a horizontal grid spacing of Δx = 1 km, full-physics parameterizations, and an interactive land surface. Deep convection is explicitly resolved and not parameterized. To cover a wide range of orographic scales, simulations are conducted with heights between 250 and 4000 m and widths between 5 and 30 km. The mountain slope strongly affects upslope wind speed characteristics, the timing and intensity of local updrafts, and local rain intensity. Although the day-to-day variability is substantial, the statistical-mean rain amount extracted by the mountain scales almost linearly with the mountain volume. Simulations with alternative mountain geometries, multiple peaks, and large-scale flow suggest that the linear scaling is valid for a surprisingly large portion of the parameter space. The scaling breaks down in the limit of relatively strong large-scale flows, sufficiently tall mountains, or elongated mountains. The existence of the simple linear scaling over such a wide range of configurations suggests that the response of thermally driven orographic deep convection over many mountainous areas is strongly affected by mountain volume. As a consequence, the rain amount is disproportionally dominated by the large horizontal scales of orography, as they contribute mostly to the mountain volume.

Currently major efforts are underway toward refining the horizontal resolution (or grid spacing) of climate models to about 1 km, using both global and regional climate models (GCMs and RCMs). Several groups have succeeded in conducting kilometer-scale multiweek GCM simulations and decadelong continental-scale RCM simulations. There is the well-founded hope that this increase in resolution represents a quantum jump in climate modeling, as it enables replacing the parameterization of moist convection by an explicit treatment. It is expected that this will improve the simulation of the water cycle and extreme events and reduce uncertainties in climate change projections. While kilometer-scale resolution is commonly employed in limited-area numerical weather prediction, enabling it on global scales for extended climate simulations requires a concerted effort. In this paper, we exploit an RCM that runs entirely on graphics processing units (GPUs) and show examples that highlight the prospects of this approach. A particular challenge addressed in this paper relates to the growth in output volumes. It is argued that the data avalanche of high-resolution simulations will make it impractical or impossible to store the data. Rather, repeating the simulation and conducting online analysis will become more efficient. A prototype of this methodology is presented. It makes use of a bit-reproducible model version that ensures reproducible simulations across hardware architectures, in conjunction with a data virtualization layer as a common interface for output analyses. An assessment of the potential of these novel approaches will be provided.

We present a roadmap towards exascale computing based on true application performance goals. It is based on two state-of-the art European numerical weather prediction models (IFS from ECMWF and COSMO from MeteoSwiss) and their current performance when run at very high spatial resolution on present-day supercomputers. We conclude that these models execute about 100–250 times too slow for operational throughput rates at a horizontal resolution of 1 km, even when executed on a full petascale system with nearly 5000 state-of-the-art hybrid GPU-CPU nodes. Our analysis of the performance in terms of a metric that assesses the efficiency of memory use shows a path to improve the performance of hardware and software in order to meet operational requirements early next decade.

Land-Atmosphere coupling is a fundamental process of the earth system. From the perspective of atmospheric sciences, a quantitative understanding of the surface energy and mass balances is of vital importance for both numerical weather prediction and climate modeling and needs to be represented adequately in land surface schemes. The partitioning of net radiation into sensible, latent and ground heat fluxes is dependent on the state of the land surface with soil moisture being an important state variable, introducing memory effects of monthly to annual timescales to the coupling. An inadequate representation of the terrestrial water cycle will therefore degrade forecasts and introduce biases in climate simulations. While there exist reasonable estimates for the terrestrial water cycle on subcontinental scales and various observations for evapotranspiration on the point-scale, the validation of land-surface-schemes on the kilometer-scale remains challenging. The fundamental unit on which one can validate all terms of the water balance is the catchment. Here, we present a validation framework based on water balances of mesoscale catchments. The framework incorporates observations of precipitation and streamflow as well as estimates for evapotranspiration from remote sensing data.The methodology is applied to five mesoscale catchments in Switzerland ranging from 105 km2 to 1713 km2 for the years 2010-2012. Observations include MeteoSwiss operational analyses, hourly discharge measurements provided by the federal office for the environment and the MODIS MOD16A2 evapotranspiration product. While relying on observations, these datasets are subject to substantial uncertainties. We aim to quantify the major part of this observational uncertainty by using data from FLUXNET sites in the Alpine region and a rain-gauge based precipitation dataset from MeteoSwiss (RhiresM).As a showcase application, the validation framework is used in order to compare and validate two different parameterizations for soil/ groundwater hydrology in TERRA, the land surface scheme of the COSMO numerical weather prediction and regional climate model. While both versions are based on Richard’s equation, one is implemented with a free drainage boundary condition, while the other one is implemented with a simple parameterization for groundwater. Results from TERRA standalone runs forced with COSMO analysis fields suggest that errors in terrestrial storage change are mostly driven by errors in runoff. In turn, runoff is very sensitive to the parameterization of infiltration and to soil hydraulic parameter. We show that despite large uncertainties in the observations at hand, it is possible to identify respective shortcomings of the two different groundwater formulations and to improve the simulated water balance by introducing a mathematically sound formulation for infiltration and by tuning key parameter associated to ground water discharge.

Secondary disturbances spawning frontal waves along the fronts of mature midlatitude low-pressure systems were identified decades ago from satellite images. While their development has been studied using analytical models, field campaigns (e.g. FASTEX) and re-analysis datasets, simulation of the phenomenon in state-of-the-art global weather and climate models so far remained unattainable.Today's flagship supercomputers allow performing simulations at kilometer-scale resolution on computational domains covering the entire lifecycle of synoptic-scale systems and thus enable explicit representation of small-scale disturbances embedded in large-scale circulations. We demonstrate these capabilities in two different types of kilometer-scale simulations. The first is a 10-day-long near-global simulation of an idealized moist baroclinic wave, performed at 1 km grid spacing and employing 16,001 × 36,006 × 60 grid points. The second is a real-case simulation of an extratropical low-pressure system, driven by the European Centre for Medium-Range Weather Forecasts's operational analysis. At kilometer-scale resolution, both simulations display clear evidence of embedded mesoscale vortices spawning along frontal systems of mature extratropical cyclones. The vortices appearing in the real-case simulation can also be identified in satellite imagery of the system. The simulated developments are due to a barotropic instability mechanism and driven by strong low-level horizontal wind shear. While the simulation of the frontal systems is amenable at model resolutions around 10–50 km, the instability mechanism itself relies on the representation of a narrow shear zone, requiring about 5 times finer resolution. Results suggest that the flow regimes suppressing or fostering barotropic vortices can coexist in the same synoptic system. Far away from the cyclone core, the instability is suppressed by deformation associated with the large-scale flow, while close to the mature cyclone core, the narrow frontal structure becomes unstable.Leutwyler, D. and C. Schär (2019): Barotropic instability of a cyclone core at kilometer-scale resolution, J. Adv. Model. Earth Sy., 11.

Even though the complexity and resolution of global climate models (GCMs) has increased over the last decades, the inter-model spread of equilibrium climate sensitivity has not narrowed. The representation of subtropical low-level clouds and their associated radiative feedbacks in climate models still poses a major challenge. A fundamental problem underlying the simulation of such clouds is their multiscale nature. On the one hand, current GCMs allow to capture the large-scale processes but are too coarse to represent the mesoscale and microscale dynamical processes governing their formation and dissipation. On the other hand, large eddy simulations (LES) resolving the micro scale are bound to small domains and thus lack a robust representation of the large-scale flow and the mesoscale organisation of the clouds. Convection-resolving models (CRMs) are an attractive compromise between the former two since they allow for simulations at much higher resolution than in conventional GCMs and on larger domains than in LES.Here we analyse how CRMs simulate stratocumulus decks and investigate causes for inter-model differences. We consider a set of ten CRMs (nine GCMs that are run at convection-resolving resolution during a short time period as part of the pioneering DYAMOND initiative, and the limited area model COSMO run by ourselves) used to simulate stratocumulus clouds over the South-East Atlantic during a 40 day period. The simulations cover a range of horizontal grid spacings between 5 and 1 km.We find pronounced differences in the mean cloud cover among the analysed CRMs. In comparison to observed radiation (CERES), most of them underestimate cloud cover, in particular the low-lying stratus decks close to the African coast. Nevertheless, the simulated mesoscale cloud organisation is realistic and similar in the set of CRMs, with few exceptions showing organisation on larger scales than in the other models. In general, the simulated cloud field appears to be more sensitive to the model choice than to the horizontal resolution.Despite the differences in the cloud cover, most models capture the subtropical inversion and its spatial structure relatively well. Therefore, differences in the inversion strengths do not suffice to explain variability in the simulated cloud cover fraction between models. However, we find a relation between the mean height of the stratocumulus layer (or inversion layer) and its cloud cover fraction: Models with higher inversions tend to simulate a higher cloud cover fraction, bringing them closer to observations. Similarly, stronger vertical mixing within the boundary layer and enhanced surface latent heat fluxes appear to be related to higher cloud cover. Such relations may help to determine the physical processes responsible for the differences among CRMs in the simulated stratocumulus field.

We present a multi-model ensemble of regional climate model scenario simulations run at scales allowing for explicit treatment of convective processes (2-3km) over historical and end of century time slices, providing an overview of future precipitation changes over the Alpine domain within the convection-permitting CORDEX-FPS initiative. The 12 simulations of the ensemble have been performed by different research groups around Europe. The simulations are compared with high resolution observations to assess the performance over the historical period and the ensemble of 12 to 25 km resolution driving models is used as a benchmark.An improvement of the representation of fine scale details of the analyzed fields on a seasonal scale is found, as well as of the onset and peak of the summer diurnal convection. An enhancement of the projected patterns of change and modifications of its sign for the daily precipitation intensity and heavy precipitation over some regions are found with respect to coarse resolution ensemble. A change of the amplitude of the diurnal cycle for precipitation intensity and frequency is also shown, as well also a larger positive change for high to extreme events for daily and hourly precipitation distributions. The results are challenging and promising for further assessment of the local impacts of climate change.

The increasing availability of computing power allows the use of kilometer-scale convection-resolving weather and climate models for operational forecasts. One of the open questions at these scales concerns the validity of the hydrostatic approximation, which assumes that vertical accelerations are small compared to the balancing forces of gravity and the vertical pressure gradient. This assumption is valid as long as the ratio of vertical to horizontal length scales of motion is small. Results from previous studies suggest that the horizontal resolution at which the hydrostatic approximation becomes invalid is highly dependent on the particular model, model configuration, and case setup. While most of the previous studies have been conducted with an idealized setup, this work will concentrate on a real-world case. To this end, a few summer days with strong convection over complex terrain in Europe are simulated with the nonhydrostatic regional Consortium for Small-scale Modelling (COSMO) model with horizontal resolutions ranging from 12 km to 275 m. To assess the validity of the hydrostatic approximation, we developed a hydrostatic reconstruction technique and diagnose the vertical wind using the hydrostatic set of equations. The diagnosed values are then compared to the actual nonhydrostatic up- and downdrafts with a statistical analysis of vertical wind speed frequencies for the different resolutions. Results suggest that the diagnosed hydrostatic vertical velocities are very similar to the nonhydrostatic vertical velocities up to horizontal resolutions of 1 km and thus the use of the hydrostatic approximations at these scales still seems to be a valid option. Furthermore, the study contains an intercomparison of precipitation and vertical winds produced by the nonhydrostatic COSMO model and the hydrostatic Integrated Forecast System (IFS) from ECMWF for the same case. The intercomparison supports the previous findings that at resolutions of ∼2 km and ∼4 km the effect of the hydrostatic approximation is negligible. The results also show that a small enough timestep size is essential in order to properly resolve the high vertical velocities associated with convection.

Weather and climate in alpine areas are strongly modulated by complex topography. Besides its influence on atmospheric flow and thermodynamics (such as orographic precipitation and foehn winds), topography also affects incoming surface radiation in various ways. Direct shortwave radiation might be blocked due to shading effects from neighbouring terrain. Diffuse shortwave radiation can be altered by a reduced sky view factor and reflectance of radiation from surrounding terrain. Similar, the net longwave radiation is affected by emissions from neighbouring terrain.Radiation in virtually all state-of-the-art weather and climate models is only computed in the vertical direction using the column approximation, and the above-mentioned effects are usually not represented. Still, a few models consider topographic effects by correcting incoming radiation fluxes based on topographic parameters like slope aspect and angle, elevation of horizon, and sky view factor. The Consortium for Small-scale Modeling (COSMO) model includes such a scheme, which is currently only used in the Numerical Weather Prediction mode of the model.In this study, we apply the surface radiation correction scheme in the climate mode of COSMO. To study its impacts in detail, we force COSMO’s land-surface model (TERRA) offline with output from a COSMO simulation, which was run without radiation correction at a horizontal resolution of 2.2 km and for a domain covering the Alps. A useful proxy to study the impact of the correction scheme is snow cover duration (SCD), because snow cover length is, amongst other factors, strongly controlled by incoming surface radiation that drives ablation. A comparison of SCD simulated by COSMO with satellite-derived snow cover data (MODIS and AVHRR) reveals a distinctive bias, where SCD is overestimated for south-facing grid cells and underestimated for north-facing cells. Applying the radiation correction in the offline TERRA simulation shows only a moderate reduction of the bias. One reason for this minor improvement is the fact that the topographic parameters are computed from a smoothed digital elevation model (DEM) – thus the impact of the radiation correction scheme is damped. If topographic parameters are computed from unsmoothed DEM, biases in SCD are further reduced. Currently, further sensitivity experiments are conducted to investigate the effect of computing the topographic parameters from a sub-grid DEM and to assess the energy conservation of the radiation correction scheme.

In this study, we perform an evaluation of PRIMAVERA high-resolution (25–50 km) Global Climate Models (GCMs) relative to CORDEX Regional Climate Models (RCMs) over Europe (12–50 km resolutions). It is the first time such assessment is performed for regional climate information using ensembles of GCMs and RCMs at similar horizontal resolutions. We perform this exercise for the distribution of daily precipitation contributions to rainfall bins over Europe under current climate conditions. Both ensembles are evaluated against high quality national gridded observations in terms of resolution and station density. We show that PRIMAVERA GCMs simulate very similar distribution to CORDEX RCMs that CMIP5 cannot because of their coarse resolutions. PRIMAVERA and CORDEX ensembles generally show similar strengths and weaknesses. They are of good quality in summer and autumn in most European regions, but tend to overestimate precipitation in winter and spring. PRIMAVERA show improvements in the latter bias by reducing mid-rain rate biases in Central and Eastern Europe. Moreover, CORDEX simulate less light rainfall than PRIMAVERA in most regions and seasons, which improves this common GCM bias. Finally, PRIMAVERA simulate less heavy precipitation than CORDEX in most regions and seasons, especially in summer. PRIMAVERA appear to be closer to observations. However, when we apply an averaged precipitation undercatch error of 20 %, CORDEX become closer to these synthetic datasets. Considering 50 km resolution GCM or RCM datasets over Europe results in large benefits compared to CMIP5 models for impact studies at the regional scale. The effect of increasing resolution from 50 km to 12 km in CORDEX simulations is, in comparison, small in most regions and seasons outside mountainous regions (due to the importance of orography) and coastal regions (mostly depending on the resolution of the land-sea contrast). Now that GCMs are able to reach the level of information provided by CORDEX RCMs run at similar resolutions, there is an opportunity to better coordinate GCM and RCM simulations for future model intercomparison projects.

By the end of the century, climate projections for southern Europe exhibit an enhanced near-surface summer warming in response to greenhouse gas emissions, which is known as the Mediterranean amplification. Possible causes for this amplified warming signal include a poleward Hadley cell expansion as well as tropospheric lapse-rate changes. In this work, regional climate model (RCM) simulations driven by three different global climate models (GCMs) are performed, representing the RCP8.5 emission scenario. For every downscaled GCM, the climate change signal over Europe is separated into five contributions by modifying the lateral boundary conditions of the RCM. This simulation strategy is related to the pseudo–global warming method. The results show that a poleward expansion of the Hadley cell is of minor importance for the Mediterranean amplification. During summer, the simulated Hadley circulation is weak, and projections show no distinct expansion in the European sector. The north–south contrast in lapse-rate changes is suggested as the most important factor causing the Mediterranean amplification. Lapse-rate changes are projected throughout Europe, but are weaker over the Mediterranean than over northern Europe (around 0.15 vs 0.3 K km−1 by the end of the century). The weaker lapse-rate changes result in a strong near-surface summer warming over the Mediterranean, since the upper-tropospheric warming is of similar magnitude throughout Europe. The differing lapse-rate changes can be understood as a thermodynamic response to lower-tropospheric humidity contrasts.

Convection-resolving models (CRMs) can explicitly simulate deep convection and resolve interactions between convective updrafts. They are thus increasingly used in numerous weather and climate applications. However, the truncation of the continuous energy cascade at scales of $$\mathcal {O}$$ (1 km) poses a serious challenge, as in kilometer-scale simulations the size and properties of the simulated convective cells are often determined by the horizontal grid spacing ( $$\Delta x$$ ).In this study, idealized simulations of deep moist convection over land are performed to assess the convergence behavior of a CRM at $$\Delta x =$$ 8, 4, 2, 1 km and 500 m. Two types of convergence estimates are investigated: bulk convergence addressing domain-averaged and integrated variables related to the water and energy budgets, and structural convergence addressing the statistics and scales of individual clouds and updrafts. Results show that bulk convergence generally begins at $$\Delta x =4$$ km, while structural convergence is not yet fully achieved at the kilometer scale, despite some evidence that the resolution sensitivity of updraft velocities and convective mass fluxes decreases at finer resolution. In particular, at finer grid spacings the maximum updraft velocity generally increases, and the size of the smallest clouds is mostly determined by $$\Delta x$$ . A number of different experiments are conducted, and it is found that the presence of orography and environmental vertical wind shear yields more energetic structures at scales much larger than $$\Delta x$$ , sometimes reducing the resolution sensitivity. Overall the results lend support to the use of kilometer-scale resolutions in CRMs, despite the inability of these models to fully resolve the associated cloud field.

Over the past few decades the horizontal resolution of regional climate models (RCMs) has steadily increased, leading to a better representation of small-scale topographic features and more details in simulating dynamical aspects, especially in coastal regions and over complex terrain. Due to its complex terrain, the broader Adriatic region represents a major challenge to state-of-the- art RCMs in simulating local wind systems realistically. The objective of this study is to identify the added value in near-surface wind due to the refined grid spacing of RCMs. For this purpose, we use a multi-model ensemble composed of CORDEX regional climate simulations at 0.11° and 0.44° grid spacing, forced by the ERA-Interim reanalysis, a COSMO convection- parameterizing simulation at 0.11° and a COSMO convection-resolving simulation at 0.02° grid spacing. Surface station observations from this region and satellite QuikSCAT data over the Adriatic Sea have been compared against daily output obtained from the available simulations. Both day-to-day wind and its frequency distribution are examined. The results indicate that the 0.44° RCMs rarely outperform ERA- Interim reanalysis, while the performance of the high- resolution simulations surpasses that of ERA- Interim. We also disclose that refining the grid spacing to a few km is needed to properly capture the small-scale wind systems. Finally, we show that the simulations frequently yield the accurate angle of local wind regimes, such as for the Bora flow, but overestimate the associated wind magnitude. Finally, spectral analysis shows good agreement between measurements and simulations, indicating the correct temporal variability of the wind speed.

The Global Energy Balance Archive (GEBA) is a database for the central storage of the worldwide measured energy fluxes at the Earth's surface, maintained at ETH Zurich (Switzerland). This paper documents the status of the GEBA version 2017 dataset, presents the new web interface and user access, and reviews the scientific impact that GEBA data had in various applications. GEBA has continuously been expanded and updated and contains in its 2017 version around 500 000 monthly mean entries of various surface energy balance components measured at 2500 locations. The database contains observations from 15 surface energy flux components, with the most widely measured quantity available in GEBA being the shortwave radiation incident at the Earth's surface (global radiation). Many of the historic records extend over several decades. GEBA contains monthly data from a variety of sources, namely from the World Radiation Data Centre (WRDC) in St. Petersburg, from national weather services, from different research networks (BSRN, ARM, SURFRAD), from peer-reviewed publications, project and data reports, and from personal communications. Quality checks are applied to test for gross errors in the dataset. GEBA has played a key role in various research applications, such as in the quantification of the global energy balance, in the discussion of the anomalous atmospheric shortwave absorption, and in the detection of multi-decadal variations in global radiation, known as global dimming and brightening. GEBA is further extensively used for the evaluation of climate models and satellite-derived surface flux products. On a more applied level, GEBA provides the basis for engineering applications in the context of solar power generation, water management, agricultural production and tourism. GEBA is publicly accessible through the internet via http://www.geba.ethz.ch. Supplementary data are available at https://doi.org/10.1594/PANGAEA.873078.

Convection-resolving models allow to explicitly resolve deep convection at horizontal grid spacings of O(1 km). On current supercomputers, refining the grid spacing to the kilometer scale is computationally still extremely demanding, and therefore climate simulations at this resolution have so far largely been limited to sub-continental computational domains. However, new supercomputers that mix conventional multi-core CPUs and accelerators possess properties beneficial for climate codes. Exploiting these capabilities allows expansion of the size of the computational domains to continental scales. Here we present such a convection-resolving climate simulation, using a version of the COSMO model, capable of exploiting GPU accelerators. The simulation has a grid spacing of 2.2 km, 1536×1536×60 grid points, covers the period 1999-2008, and is driven by the ERA-Interim reanalysis. An assessment of the 10-year-long simulation is conducted using a wide range of data sets, including several rain-gauge networks, energy balance stations, and a remotely-sensed lightning data set. Substantial improvements are found for the 2 km simulation in terms of the diurnal cycles of precipitation. This confirms results found in studies using smaller computational domains. However, the continental-scale simulations also reveal deficiencies such as substantial performance differences between regions with and without strong orographic forcing. Analysis of the statistical distribution of updrafts and downdrafts shows an increase of the amplitude in seasons with convection, and a pronounced asymmetry between updrafts and downdrafts. Furthermore, the analysis of lightning data shows that the convection-resolving simulation is able to reproduce important features of the annual cycle of deep convection in Europe.

Models are attractive tools to deepen the understanding of atmospheric and climate processes. In practice, such investigations often involve numerical experiments that switch on or off individual factors (such as latent heating, nonlinear coupling, or some climate forcing). However, as in general many factors can be considered, the analysis of these experiments is far from straightforward. In particular, as pointed out in an influential study on factor separation by Stein and Alpert, the analysis will often require the consideration of nonlinear interaction terms. In the current paper an alternative factor separation methodology is proposed and analyzed. Unlike the classical method, sequential factor separation (SFS) does not involve the derivation of the interaction terms but, rather, provides some uncertainty measure that addresses the quality of the separation. The main advantage of the proposed methodology is that in the case of n factors it merely requires 2n simulations (rather than 2n for the classical analysis). The paper provides an outline of the methodology, a detailed mathematical analysis, and a theoretical intercomparison against the classical methodology. In addition, an example and an intercomparison using regional climate model experiments with n = 3 factors are presented. The results relate to the Mediterranean amplification and demonstrate that—at least in the particular example considered—the two methodologies yield almost identical results and that the SFS is rather insensitive with respect to design choices.

This paper is a contribution to the special issue on Advances in Convection-Permitting Climate Modeling, consisting of papers that focus on the evaluation, climate change assessment, and feedback processes in kilometer-scale simulations and observations., A recently launched project under the auspices of the World Climate Research Program’s (WCRP) Coordinated Regional Downscaling Experiments Flagship Pilot Studies program (CORDEX-FPS) is presented. This initiative aims to build first-of-its-kind ensemble climate experiments of convection permitting models to investigate present and future convective processes and related extremes over Europe and the Mediterranean. In this manuscript the rationale, scientific aims and approaches are presented along with some preliminary results from the testing phase of the project. Three test cases were selected in order to obtain a first look at the ensemble performance. The test cases covered a summertime extreme precipitation event over Austria, a fall Foehn event over the Swiss Alps and an intensively documented fall event along the Mediterranean coast. The test cases were run in both “weather-like” (WL, initialized just before the event in question) and “climate” (CM, initialized 1 month before the event) modes. Ensembles of 18–21 members, representing six different modeling systems with different physics and modelling chain options, was generated for the test cases (27 modeling teams have committed to perform the longer climate simulations). Results indicate that, when run in WL mode, the ensemble captures all three events quite well with ensemble correlation skill scores of 0.67, 0.82 and 0.91. They suggest that the more the event is driven by large-scale conditions, the closer the agreement between the ensemble members. Even in climate mode the large-scale driven events over the Swiss Alps and the Mediterranean coasts are still captured (ensemble correlation skill scores of 0.90 and 0.62, respectively), but the inter-model spread increases as expected. In the case over Mediterranean the effects of local-scale interactions between flow and orography and land–ocean contrasts are readily apparent. However, there is a much larger, though not surprising, increase in the spread for the Austrian event, which was weakly forced by the large-scale flow. Though the ensemble correlation skill score is still quite high (0.80). The preliminary results illustrate both the promise and the challenges that convection permitting modeling faces and make a strong argument for an ensemble-based approach to investigating high impact convective processes., IG and LS have been supported by the Croatian Science Foundation (HrZZ) project CARE (no. 2831). JF acknowledges support by the Spanish R + D programme through MINECO/FEDER co-funded project INSIGNIA (CGL2016-79210-R). AL-G is supported by the Spanish government though grant BES-2016-078158 and MINECO/FEDER co-funded project MULTI-SDM (CGL2015-66583-R). JM and KW-S gratefully acknowledge the support by the German Science Foundation (DFG) through project FOR 1695. DM, MP, and HT gratefully acknowledge the support received via the Austrian Science Fund (FWF) project NHCM-2 (no. P24758-N29) and the projects HighEnd:Extremes and EASICLIM, funded by the Austrian Climate Research Programme (ACRP) of the Klima- und Energiefonds (nos. KR13AC6K10981 and KR16AC0K13160, respectively). SS and TL acknowledge the support of NOTUR project no. NN9280K and the Research Council of Norway and its basic institute support of their strategic project on Climate Services. RMC and PMMS gratefully acknowledge the support of the SOLAR project (PTDC/GEOMET/7078/2014) financed by the Portuguese Foundation for Science and Technology. EJ Kendon gratefully acknowledges funding from the Joint Department of Energy and Climate Change (DECC) and Department for Environment Food and Rural Affairs (Defra) Met Office Hadley Centre Climate Programme (GA01101). S. Khodayar research is supported by the Bundesministerium für Bildung und Forschung (BMBF; German Federal Ministry of Education and Research). Support of Ministry of Education, Youth and Sports of the Czech Republic for funding the participation in Euro-CORDEX activities via the scheme INTER-TRANSFER under the Grant no. LTT17007. Funding from the Research Council of Norway through the SUPER (Grant no. 250573) and HYPRE (Grant no. 243942) projects.

The latest Swiss Climate Scenarios (CH2018), released in November 2018, consist of several datasets derived through various methods that provide robust and relevant information on climate change in Switzerland. The scenarios build upon the regional climate model projections for Europe produced through the internationally coordinated downscaling effort EURO-CORDEX. The simulations from EURO-CORDEX consist of simulations at two spatial horizontal resolutions, several global climate models, and three different emission scenarios. Even with this unique dataset of regional climate scenarios, a number of practical challenges regarding a consistent interpretation of the model ensemble arise. Here we present the methodological chain employed in CH2018 in order to generate a multi-model ensemble that is consistent across scenarios and is used as a basis for deriving the CH2018 products. The different steps involve a thorough evaluation of the full EURO-CORDEX model ensemble, the removal of doubtful and potentially erroneous simulations, a time-shift approach to account for an equal number of simulations for each emission scenario, and the multi-model combination of simulations with different spatial resolutions. Each component of this cascade of processing steps is associated with an uncertainty that eventually contributes to the overall scientific uncertainty of the derived scenario products. We present a comparison and an assessment of the uncertainties from these individual effects and relate them to probabilistic projections. It is shown that the CH2018 scenarios are generally supported by the results from other sources. Thus, the CH2018 scenarios currently provide the best available dataset of future climate change estimates in Switzerland., Climate Services, 20, ISSN:2405-8807

Potential vorticity is among the most important scalar quantities in atmospheric dynamics. For instance, potential vorticity plays a key role in particularly strong wind peaks in extratropical cyclones and it is able to explain the occurrence of frontal rain bands. Potential vorticity combines the key quantities of atmospheric dynamics, namely rotation and stratification. Under suitable wind conditions elongated banners of potential vorticity appear in the lee of mountains. Their role in atmospheric dynamics has recently raised considerable interest in the meteorological community for instance due to their influence in aviation wind hazards and maritime transport. In order to support meteorologists and climatologists in the analysis of these structures, we developed an extraction algorithm and a visual exploration framework consisting of multiple linked views. For the extraction we apply a predictor-corrector algorithm that follows streamlines and realigns them with extremal lines of potential vorticity. Using the agglomerative hierarchical clustering algorithm, we group banners from different sources based on their proximity. To visually analyze the time-dependent banner geometry, we provide interactive overviews and enable the query for detail on demand, including the analysis of different time steps, potentially correlated scalar quantities, and the wind vector field. In particular, we study the relationship between relative humidity and the banners for their potential in indicating the development of precipitation. Working with our method, the collaborating meteorologists gained a deeper understanding of the three-dimensional processes, which may spur follow-up research in the future.

This review paper explores the field of mesoscale to microscale modeling over complex terrain as it traverses multiple so-called gray zones. In an attempt to bridge the gap between previous large-scale and small-scale modeling efforts, atmospheric simulations are being run at an unprecedented range of resolutions. The gray zone is the range of grid resolutions where particular features are neither subgrid nor fully resolved, but rather are partially resolved. The definition of a gray zone depends strongly on the feature being represented and its relationship to the model resolution. This paper explores three gray zones relevant to simulations over complex terrain: turbulence, convection, and topography. Taken together, these may be referred to as the gray continuum. The focus is on horizontal grid resolutions from ∼10 km to ∼10 m. In each case, the challenges are presented together with recent progress in the literature. A common theme is to address cross-scale interaction and scale-awareness in parameterization schemes. How numerical models are designed to cross these gray zones is critical to complex terrain applications in numerical weather prediction, wind resource forecasting, and regional climate modeling, among others., Atmosphere, 10 (5), ISSN:2073-4433

ISSN:0148-0227 ISSN:2169-897X

In recent studies we quantified the global mean Earth energy balance based on direct observations from surface and space. Here we infer complementary reference estimates for its components specifically under cloud-free conditions. While the clear-sky fluxes at the top of atmosphere (TOA) are accurately known from satellite measurements, the corresponding fluxes at the Earth’s surface are not equally well established, as they cannot be directly measured from space. This is also evident in 38 global climate models from CMIP5, which are shown to greatly vary in their clear-sky surface radiation budgets. To better constrain the latter, we established new clear-sky reference climatologies of surface downward shortwave and longwave radiative fluxes from worldwide distributed Baseline Surface Radiation Network sites. 33 out of the 38 CMIP5 models overestimate the clear-sky downward shortwave reference climatologies, whereas both substantial overestimations and underestimations are found in the longwave counterparts in some of the models. From the bias structure of the CMIP5 models we infer best estimates for the global mean surface downward clear-sky shortwave and longwave radiation, at 247 and 314 Wm−2, respectively. With a global mean surface albedo of 13.5% and net shortwave clear-sky flux of 287 Wm−2 at the TOA this results in a global mean clear-sky surface and atmospheric shortwave absorption of 214 and 73 Wm−2, respectively. From the newly-established diagrams of the global energy balance under clear-sky and all-sky conditions, we quantify the cloud radiative effects not only at the TOA, but also within the atmosphere and at the surface. ISSN:0930-7575 ISSN:1432-0894

Diurnal moist convection is an important element of summer precipitation over Central Europe and the Alps. It is poorly represented in models using parameterized convection. In this study, we investigate the diurnal cycle of convection during 11 days in June 2007 using the COSMO model. The numerical simulations are compared with satellite measurements of GERB and SEVIRI, AVHRR satellite-based cloud properties and ground-based precipitation and temperature measurements. The simulations use horizontal resolutions of 12 km (convection-parameterizing model, CPM) and 2 km (convection-resolving model, CRM) and either a one-moment microphysics scheme (1M) or a two-moment microphysics scheme (2M).They are conducted for a computational domain that covers an extended Alpine area from Northern Italy to Northern Germany. The CPM with 1M exhibits a significant overestimation of high cloud cover. This results in a compensation effect in the top of the atmosphere energy budget due to an underestimation of outgoing longwave radiation (OLR) and an overestimation of reflected solar radiation (RSR). The CRM reduces high cloud cover and improves the OLR bias from a domain mean of −20.1 to −2.6 W/m2. When using 2M with ice sedimentation in the CRM, high cloud cover is further reduced. The stronger diurnal cycle of high cloud cover and associated convection over the Alps, compared to less mountainous regions, is well represented by the CRM but underestimated by the CPM. Despite substantial differences in high cloud cover, the use of a 2M has no significant impact on the diurnal cycle of precipitation. Furthermore, a negative mid-level cloud bias is found for all simulations., Meteorologische Zeitschrift, 25 (2), ISSN:0941-2948, ISSN:1610-1227

The effect of the snow-albedo feedback (SAF) on 2m temperatures and their future changes in the European Alps is investigated in the ENSEMBLES regional climate models (RCMs) with a focus on the spring season. A total of 14 re-analysis-driven RCM experiments covering the period 1961–2000 and 10 GCM-driven transient climate change projections for 1950–2099 are analysed. A positive springtime SAF is found in all RCMs, but the range of the diagnosed SAF is large. Results are compared against an observation-based SAF estimate. For some RCMs, values very close to this estimate are found; other models show a considerable overestimation of the SAF. Net shortwave radiation has the largest influence of all components of the energy balance on the diagnosed SAF and can partly explain its spatial variability. Model deficiencies in reproducing 2m temperatures above snow and ice and associated cold temperature biases at high elevations seem to contribute to a SAF overestimation in several RCMs. The diagnosed SAF in the observational period strongly influences the estimated SAF contribution to twenty first century temperature changes in the European Alps. This contribution is subject to a clear elevation dependency that is governed by the elevation-dependent change in the number of snow days. Elevations of maximum SAF contribution range from 1500 to 2000 m in spring and are found above 2000 m in summer. Here, a SAF contribution to the total simulated temperature change between 0 and 0.5 °C until 2099 (multi-model mean in spring: 0.26 °C) or 0 and 14 % (multi-model mean in spring: 8 %) is obtained for models showing a realistic SAF. These numbers represent a well-funded but only approximate estimate of the SAF contribution to future warming, and a remaining contribution of model-specific SAF misrepresentations cannot be ruled out.

Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions., Hydrology and Earth System Sciences, 23 (1), ISSN:1027-5606, ISSN:1607-7938

Climatic Change, 81 (S1), ISSN:0165-0009, ISSN:1573-1480

Climate Dynamics, 41 (3-4), ISSN:0930-7575, ISSN:1432-0894

Climate Dynamics, 33 (6), ISSN:0930-7575, ISSN:1432-0894

In the framework of the global energy balance, the radiative energy exchanges between Sun, Earth and space are now accurately quantified from new satellite missions. Much less is known about the magnitude of the energy flows within the climate system and at the Earth surface, which cannot be directly measured by satellites. In addition to satellite observations, here we make extensive use of the growing number of surface observations to constrain the global energy balance not only from space, but also from the surface. We combine these observations with the latest modeling efforts performed for the 5th IPCC assessment report to infer best estimates for the global mean surface radiative components. Our analyses favor global mean downward surface solar and thermal radiation values near 185 and 342 Wm−2, respectively, which are most compatible with surface observations. Combined with an estimated surface absorbed solar radiation and thermal emission of 161 and 397 Wm−2, respectively, this leaves 106 Wm−2 of surface net radiation available globally for distribution amongst the non-radiative surface energy balance components. The climate models overestimate the downward solar and underestimate the downward thermal radiation, thereby simulating nevertheless an adequate global mean surface net radiation by error compensation. This also suggests that, globally, the simulated surface sensible and latent heat fluxes, around 20 and 85 Wm−2 on average, state realistic values. The findings of this study are compiled into a new global energy balance diagram, which may be able to reconcile currently disputed inconsistencies between energy and water cycle estimates., Climate Dynamics, 40, ISSN:0930-7575, ISSN:1432-0894

Climatic Change, 101 (3-4), ISSN:0165-0009, ISSN:1573-1480

Climate impact studies constitute the basis for the formulation of adaptation strategies. Usually such assessments apply statistically postprocessed output of climate model projections to force impact models. Increasingly, time series with daily resolution are used, which require high consistency, for instance with respect to transition probabilities (TPs) between wet and dry days and spell durations. However, both climate models and commonly applied statistical tools have considerable uncertainties and drawbacks. This paper compares the ability of 1) raw regional climate model (RCM) output, 2) bias-corrected RCM output, and 3) a conventional weather generator (WG) that has been calibrated to match observed TPs to simulate the sequence of dry, wet, and very wet days at a set of long-term weather stations across Switzerland. The study finds systematic biases in TPs and spell lengths for raw RCM output, but a substantial improvement after bias correction using the deterministic quantile mapping technique. For the region considered, bias-corrected climate model output agrees well with observations in terms of TPs as well as dry and wet spell durations. For the majority of cases (models and stations) bias-corrected climate model output is similar in skill to a simple Markov chain stochastic weather generator. There is strong evidence that bias-corrected climate model simulations capture the atmospheric event sequence more realistically than a simple WG.