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Flash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, radar rainfall nowcasting can provide an alternative. Because this observation‐based method quickly loses skill after the first 2 hr of the forecast, it needs to be combined with NWP forecasts to extend the skillful lead time of short‐term rainfall forecasts, which should increase decision‐making times. We implemented an adaptive scale‐dependent ensemble blending method in the open‐source pysteps library, based on the Short‐Term Ensemble Prediction System scheme. In this implementation, the extrapolation (ensemble) nowcast, (ensemble) NWP, and noise components are combined with skill‐dependent weights that vary per spatial scale level. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy. We describe the implementation details and evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments. We benchmark the results of the 48‐member blended forecasts against the Belgian NWP forecast, a 48‐member nowcast, and a simple 48‐member linear blending approach. Both on the radar domain and catchment scale, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event‐averaged continuous ranked probability score and critical success index values) and adds value compared with NWP for the first hours of the forecast, although the difference, particularly with the linear blending method, reduces when we focus on catchment‐average cumulative rainfall sums instead of instantaneous rainfall rates. By properly combining observations and NWP forecasts, blending methods such as these are a crucial component of seamless prediction systems. [ABSTRACT FROM AUTHOR]

This paper describes a reduced-order quasigeostrophic coupled ocean-atmosphere model that allows for an arbitrary number of atmospheric and oceanic modes to be retained in the spectral decomposition. The modularity of this new model allows one to easily modify the model physics. Using this new model, coined the "Modular Arbitrary-Order Ocean-Atmosphere Model" (MAOOAM), we analyse the dependence of the model dynamics on the truncation level of the spectral expansion, and unveil spurious behaviour that may exist at low resolution by a comparison with the higher-resolution configurations. In particular, we assess the robustness of the coupled low-frequency variability when the number of modes is increased. An "optimal" configuration is proposed for which the ocean resolution is sufficiently high, while the total number of modes is small enough to allow for a tractable and extensive analysis of the dynamics. [ABSTRACT FROM AUTHOR]

This paper describes a reduced-order quasi-geostrophic coupled ocean-atmosphere model that allows for an arbitrary number of atmospheric and oceanic modes to be retained in the spectral decomposition. The modularity of this new model allows one to easily modify the model physics. Using this new model, coined "Modular Arbitrary-Order Ocean-Atmosphere Model" (MAOOAM), we analyse the dependence of the model dynamics on the truncation level of the spectral expansion, and unveil spurious behaviour that may exist at low resolution by a comparison with the higher resolution versions. In particular, we assess the robustness of the coupled low-frequency variability when the number of modes is increased. An "optimal" version is proposed for which the ocean resolution is sufficiently high while the total number of modes is small enough to allow for a tractable and extensive analysis of the dynamics. [ABSTRACT FROM AUTHOR]

Model output from climate projections often requires bias‐adjustment to compensate for systematic model errors. A bias‐adjustment method for extreme precipitation intensity is proposed that preserves the scaling equation for different accumulation levels from hourly to daily, using intensity‐duration‐frequency (IDF) modeling. A validation is performed within a pseudo‐reality setting, based on hourly precipitation from 28 regional climate model projections of the EURO‐CORDEX ensemble over Belgium. The scaling‐based adjustment methods improve upon previous methods, an optimal method is identified, and, analytical quantile mapping methods must be avoided due to three identified problems. The ensemble mean of the adjusted extreme precipitation intensity obeys the above‐mentioned scale‐invariance property, which is consistent with observed extreme intensities. We thus show that IDF modeling provides added value in the context of bias‐adjustment, and, that the particular IDF model proposed balances well between accuracy and the preservation of desired properties such as scale invariance and consistency among rainfall durations. Plain Language Summary: Heavy precipitation is known to have major impacts on society, including crop damage, soil erosion, landslides, and increased flood risk. In recent years, there is a growing body of evidence that human‐induced climate change has intensified extreme precipitation on the global scale. Both regional and global climate models predict that this will continue as the climate warms. Nevertheless, trend analysis or climate‐change attribution of local rainfall extremes remains particularly difficult for many reasons including high variability in space and time. Therefore it is essential to use statistical methods that make use of all available information. Climate model simulations are known to exhibit systematic biases. Here, for the first time, we use a well‐established technique in hydrology (intensity‐duration‐frequency modeling) to adjust extreme precipitation intensity as a function of the rainfall duration and event probability. Validation results with a large multi‐model ensemble illustrate that the new method improves upon existing approaches. As an illustration, we present a bias‐adjusted multi‐model climate ensemble for future projections of heavy rainfall statistics over Belgium. Key Points: New bias‐adjustment methods for subdaily extreme precipitation consistent across different precipitation durationsPseudo‐reality cross‐validation shows higher skill than existing bias‐adjustment methods for subdaily extremesApplication to regional climate model projections over Belgium shows, despite large uncertainties, consistent future increase in subdaily precipitation extremes [ABSTRACT FROM AUTHOR]

We address the issue of unbiased model selection and propose a methodology based on Bayesian inference to extract physical information from kaon photoproduction γ p → K+Λ data. We use the single-channel Regge-plus-resonance (RPR) framework for γ p → K+Λ to illustrate the proposed strategy. The Bayesian evidence Z is a quantitative measure for the model's fitness given the world's data. We present a numerical method for performing the multidimensional integrals in the expression for the Bayesian evidence. We use the γ p → K+Λ data with an invariant energy W > 2.6 GeV in order to constrain the background contributions in the RPR framework with Bayesian inference. Next, the resonance information is extracted from the analysis of differential cross sections and single- and double-polarization observables. This background and resonance content constitutes the basis of a model, which is coined RPR-2011. It is shown that RPR-2011 yields a comprehensive account of the kaon photoproduction data and provides reasonable predictions for e + p → e' + K+ + Λ observables. [ABSTRACT FROM AUTHOR]

We address the issue of consistent interactions for off-shell fermion fields of arbitrary spin. These interactions play a crucial role in the quantum hadrodynamical description of high-spin baryon resonances in hadronic processes. The Rarita-Schwinger (R-S) description of high-spin fermion fields involves unphysical degrees of freedom associated with their lower-spin content. These enter the interaction if not eliminated outright. The invariance condition of the interaction under the unconstrained R-S gauge removes the lower-spin content of the fermion propagator and leads to a consistent description of the interaction. We develop the most general consistent interaction structure for high-spin fermions. We find that the power of the momentum dependence of a consistent interaction rises with the spin of the fermion field. This leads to unphysical structures in the energy dependence of the computed tree-level cross sections when the short-distance physics is cut off with standard hadronic form factors. A spin-dependent hadronic form factor is proposed that suppresses the unphysical artifacts. [ABSTRACT FROM AUTHOR]

The predictability of the atmosphere at short and long time scales, associated with the coupling to the ocean, is explored in a new version of the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM). This version features a new ocean basin geometry with periodic boundary conditions in the zonal direction. The analysis presented in this work considers a low-order version of the model with 40 dynamical variables.First the increase of surface friction (and the associated heat flux) with the ocean can either induce chaos when the aspect ratio between the meridional and zonal directions of the domain of integration is small, or suppress chaos when it is large. This reflects the potentially counter-intuitive role that the ocean can play in the coupled dynamics.Second, and perhaps more importantly, the emergence of long-term predictability within the atmosphere for specific values of the friction coefficient occurs through intermittent excursions in the vicinity of a (long-period) unstable periodic solution. Once close to this solution the system is predictable for long times, i.e. a few years. The intermittent transition close to this orbit is, however, erratic and probably hard to predict. This new route to long-term predictability contrasts with the one found in the closed ocean-basin low-order version of MAOOAM, in which the chaotic solution is permanently wandering in the vicinity of an unstable periodic orbit for specific values of the friction coefficient. The model solution is thus at any time influenced by the unstable periodic orbit and inherits from its long-term predictability. [ABSTRACT FROM AUTHOR]

To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22 ∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified. [ABSTRACT FROM AUTHOR]

To allow for climate impact studies on human and natural systems high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. This publication aims at addressing one of these regional gaps by presenting an evaluation study for two regional climate models (RCMs) (REMO and ALARO-0) at a horizontal resolution of 0.22° (25 km) over Central Asia. The output of the ERA-Interim driven RCMs is compared with different observational datasets over the 1980-2017 period. The choice of the observational dataset has an impact on the scores but in general one can conclude that both models reproduce reasonably well the spatial patterns for temperature and precipitation. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. More detailed studies of the annual cycle over subregions should be carried out to reveal whether this is due to an incorrect simulation in cloud cover, atmospheric circulation or heat and moisture fluxes. In general, the REMO model scores better for temperature whereas the ALARO-0 model prevails for precipitation. This publication demonstrates that the REMO and ALARO-0 RCMs can be used to perform climate projections over Central Asia and that the produced climate data can be applied in impact modelling. [ABSTRACT FROM AUTHOR]

The predictability of the atmosphere at short and long time‐scales associated with coupling to the ocean is explored in a new version of the Modular Arbitrary‐Order Ocean–Atmosphere Model (MAOOAM), based on a two‐layer quasigeostrophic atmosphere and a one‐layer reduced‐gravity quasigeostrophic ocean. This version features a new ocean basin geometry with periodic boundary conditions in the zonal direction. The analysis presented in this article considers a low‐order version of the model with 40 dynamical variables. First, the increase of surface friction (and the associated heat flux) due to the ocean can either induce chaos when the aspect ratio between the meridional and zonal directions of the domain of integration is small or suppress chaos when it is large. This reflects the potentially counterintuitive role that the ocean can play in the coupled dynamics. Second, and perhaps more importantly, the emergence of long‐term predictability within the atmosphere for specific values of the friction coefficient occurs through intermittent excursions in the vicinity of a (long‐period) unstable periodic solution. Once close to this solution, the system is predictable for long times, i.e. a few years. The intermittent transition close to this orbit is, however, erratic and probably hard to predict. This new route to long‐term predictability contrasts with the one found in the closed ocean‐basin low‐order version of MAOOAM, in which the chaotic solution is permanently wandering in the vicinity of an unstable periodic orbit for specific values of the friction coefficient. The model solution is thus influenced by the unstable periodic orbit at any time, and inherits from its long‐term predictability. [ABSTRACT FROM AUTHOR]

We formulate and study a low-order nonlinear coupled ocean–atmosphere model with an emphasis on the impact of radiative and heat fluxes and of the frictional coupling between the two components. This model version extends a previous 24-variable version by adding a dynamical equation for the passive advection of temperature in the ocean, together with an energy balance model. The bifurcation analysis and the numerical integration of the model reveal the presence of low-frequency variability (LFV) concentrated on and near a long-periodic, attracting orbit. This orbit combines atmospheric and oceanic modes, and it arises for large values of the meridional gradient of radiative input and of frictional coupling. Chaotic behavior develops around this orbit as it loses its stability; this behavior is still dominated by the LFV on decadal and multi-decadal time scales that is typical of oceanic processes. Atmospheric diagnostics also reveals the presence of predominant low- and high-pressure zones, as well as of a subtropical jet; these features recall realistic climatological properties of the oceanic atmosphere. Finally, a predictability analysis is performed. Once the decadal-scale periodic orbits develop, the coupled system’s short-term instabilities–as measured by its Lyapunov exponents–are drastically reduced, indicating the ocean’s stabilizing role on the atmospheric dynamics. On decadal time scales, the recurrence of the solution in a certain region of the invariant subspace associated with slow modes displays some extended predictability, as reflected by the oscillatory behavior of the error for the atmospheric variables at long lead times. [ABSTRACT FROM AUTHOR]

We explore a methodology to statistically downscale snowfall – the primary driver of surface mass balance in Antarctica – from an ensemble of historical (1850–present day) simulations performed with an earth system model over the coastal region of Dronning Maud Land (East Antarctica). This approach consists of associating daily snowfall simulations from a polar-oriented regional atmospheric climate model at 5.5 km spatial resolution with specific weather patterns observed over 1979–2010 CE with the atmospheric reanalyses ERA-Interim and ERA5. This association is then used to generate the spatial distribution of snowfall for the period from 1850 to present day for an ensemble of 10 members from the Community Earth System Model (CESM2). The new dataset of daily and yearly snowfall accumulation based on this methodology is presented in this paper (MASS2ANT dataset; 10.5281/zenodo.4287517;). Based on a comparison with available ice cores and spatial reconstructions, our results show that the spatio-temporal distribution of snowfall is improved in the downscaled dataset compared with the CESM2 simulations. This dataset thus provides information that may be useful in identifying the large-scale patterns associated with the local precipitation conditions and their changes over the past century. [ABSTRACT FROM AUTHOR]

Subdaily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration extreme rainfall in a warmer future climate. Traditionally, regional climate models (RCMs) are run at a coarse resolution using deep-convection parameterization for these extreme events. As computational resources are continuously ramping up, these models are run at convection-permitting resolution, thereby partly resolving the small-scale precipitation events explicitly. To date, a comprehensive evaluation of convection-permitting models is still missing. We propose an evaluation strategy for simulated subdaily rainfall extremes that summarizes the overall RCM performance. More specifically, the following metrics are addressed: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling, and spatiotemporal clustering. The aim of this paper is as follows: (i) to provide a statistical modeling framework for some of the metrics, based on extreme value analysis, (ii) to apply the evaluation metrics to a microensemble of convection-permitting RCM simulations over Belgium against high-frequency observations, and (iii) to investigate the added value of convection-permitting scales with respect to coarser 12-km resolution. We find that convection-permitting models improved precipitation extremes on shorter time scales (i.e., hourly or 2 hourly), but not on 6-24-h time scales. Some metrics such as the diurnal cycle or the Clausius-Clapeyron rate are improved by convection-permitting models, whereas the seasonal cycle appears to be robust across spatial scales. On the other hand, the spatial dependence is poorly represented at both convection-permitting scales and coarser scales. Our framework provides perspectives for improving high-resolution atmospheric numerical modeling and datasets for hydrological applications. [ABSTRACT FROM AUTHOR]

Statistical postprocessing techniques are nowadays key components of the forecasting suites in many national meteorological services (NMS), with, for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on in this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS toward running ensemble numerical weather prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations, the necessity to preserve space--time correlation of high-dimensional corrected fields, the need to reduce the impact of model changes affecting the parameters of the corrections, the necessity for techniques to merge different types of forecasts and ensembles with different behaviors, and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues are also discussed. [ABSTRACT FROM AUTHOR]

The surface mass balance (SMB) over the Antarctic Ice Sheet displays large temporal and spatial variations. Due to the complex Antarctic topography, modelling the climate at high resolution is crucial to accurately represent the dynamics of SMB. While ice core records provide a means to infer the SMB over centuries, the view is very spatially constrained. General circulation models (GCMs) estimate its spatial distribution over centuries, but with a resolution that is too coarse to capture the large variations due to local orographic effects. We have therefore explored a methodology to statistically downscale snowfall accumulation, the primary driver of SMB, from climate model historical simulations (1850-present day) over the coastal region of Dronning Maud Land. An analog method is set up over a period of 30 years with the ERA-Interim and ERA5 reanalyses (1979-2010 AD) and associated with snowfall daily accumulation forecasts from the Regional Atmospheric Climate Model (RACMO2.3) at 5.5 km spatial resolution over Dronning Maud in East Antarctica. The same method is then applied to the period from 1850 to present day using an ensemble of ten members from the CESM2 model. This method enables to derive a spatial distribution of the accumulation of snowfall, the principal driver of the SMB variability over the region. A new dataset of daily and yearly snowfall accumulation based on this methodology is presented in this paper (MASS2ANT dataset, http://doi.org:10.5281/zenodo.4287517, Ghilain et al. (2021)), along with comparisons with ice core data and available spatial reconstructions. It offers a more detailed spatio-temporal view of the changes over the past 150 years compared to other available datasets, allowing a possible connection with the ice core records, and provides information that may be useful in identifying the large-scale patterns associated to the local precipitation conditions and their changes over the past century. [ABSTRACT FROM AUTHOR]

Convection-permitting models (CPMs) have been proven successful in simulating extreme precipitation statistics. However, when such models are used to study climate change, contrasting sensitivities with respect to resolution (CPM vs. models with parameterized convection) are found for different parts of the world. In this study, we explore to which extent this contrasting sensitivity is due to the specific characteristics of the model or due to the characteristics of the region. Therefore, we examine the results of 360 years of climate model data from two different climate models (COSMO-CLM driven by EC-EARTH and ALARO-0 driven by CNRM ARPEGE) both at convection-permitting scale (CPS, ~ 3 km resolution) and non-convection-permitting scale (non-CPS, 12.5 km resolution) over two distinct regions (flatland vs. hilly region) in Belgium. We found that both models show an overall consistent scale-dependency of the future increase in hourly extreme precipitation for day-time. More specifically, both models yield a larger discrepancy in the day-time climate change signal between CPS and non-CPS for extreme precipitation over flatland (Flanders) than for orographically induced extreme precipitation (Ardennes). This result is interesting, since both RCMs are very different (e.g., in terms of model physics and driving GCM) and use very different ways to represent deep convection processes. Despite those model differences, the scale-dependency of projected precipitation extremes is surprisingly similar in both models, suggesting that the this scale-dependency is more dependent on the characteristics of the region, than on the model used. [ABSTRACT FROM AUTHOR]

Using the regional climate model ALARO-0, the Royal Meteorological Institute of Belgium and Ghent University have performed two simulations of the past observed climate within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The ERAInterim reanalysis was used to drive the model for the period 1979-2010 on the EURO-CORDEX domain with two horizontal resolutions, 0.11 and 0.44°. ALARO-0 is characterised by the new microphysics scheme 3MT, which allows for a better representation of convective precipitation. In Kotlarski et al. (2014) several metrics assessing the performance in representing seasonal mean near-surface air temperature and precipitation are defined and the corresponding scores are calculated for an ensemble of models for different regions and seasons for the period 1989--2008. Of special interest within this ensemble is the ARPEGE model by the Centre National de Recherches Météorologiques (CNRM), which shares a large amount of core code with ALARO-0. Results show that ALARO-0 is capable of representing the European climate in an acceptable way as most of the ALARO-0 scores lie within the existing ensemble. However, for near-surface air temperature, some large biases, which are often also found in the ARPEGE results, persist. For precipitation, on the other hand, the ALARO-0 model produces some of the best scores within the ensemble and no clear resemblance to ARPEGE is found, which is attributed to the inclusion of 3MT. Additionally, a jackknife procedure is applied to the ALARO-0 results in order to test whether the scores are robust, meaning independent of the period used to calculate them. Periods of 20 years are sampled from the 32-year simulation and used to construct the 95% confidence interval for each score. For most scores, these intervals are very small compared to the total ensemble spread, implying that model differences in the scores are significant. [ABSTRACT FROM AUTHOR]

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics. [ABSTRACT FROM AUTHOR]

The interdisciplinary research project AFTER, funded by the ERA.Net RUS PLUS program, started in 2018. The aim of AFTER is to investigate the "Impacts of climate change and climate extremes on Agriculture and Forestry in the Europe-Russia-Turkey Region". To meet this goal, regional climate information at high resolution is essential. Although various high-resolution climate experiments are currently available for Europe and Turkey (e.g. EURO-CORDEX or Med-CORDEX), Russia and Central Asia are covered by only a few and low-resolution simulations. There is a need for a consistent set of climate projections over Europe, Russia and Turkey to complete the research objectives of AFTER. Therefore, the CORDEX Central Asia (CAS-CORDEX) domain, which encompasses the region of interest, is selected. To enlarge the available climate data over the CAS-CORDEX domain we follow the CORDEX-CORE protocol and run two RCMs at a resolution from 25 km up to 12.5 km: ALARO-0 (RMIB-UGent) and REMO (HZG-GERICS). Moreover, for both models we carry out ERA-Interim as well as coupled model intercomparison project 5 (CMIP5) driven simulations using the representative concentration pathways (RCPs) 2.6, 4.5 and 8.5. The first results on the evaluation of the ALARO-0 (RMIB-UGent) and REMO (HZG-GERICS) models will be presented. More specifically, inter-model comparison results with respect to high-quality observational station data for Latvia, Russia, and Turkey will be given. This allows for an accurate estimation of local climate information together with their uncertainties, that in turn will be used as input to study the impact of climate change on forest ecosystems, phenological processes of the crops and quality and quantity of the crop yields. Finally, in collaboration with stakeholders of the agriculture and forestry sectors for the selected regions, the best available climate data will be translated into tailored information required for the formulation of adaptation and mitigation strategies. The forthcoming scientifically based assessment with multiple risks of observed and future climate change for agriculture and forestry that will result from this research project is crucial since the effects of climate change in both sectors are already felt in this region. [ABSTRACT FROM AUTHOR]