Explore the vast range of titles available.

Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that finescale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.

Taking advantage of a large ensemble of Convection Permitting‐Regional Climate Models on a pan‐Alpine domain and of an object‐oriented dedicated analysis, this study aims to investigate future changes in high‐impact fall Mediterranean Heavy Precipitation Events at high warming levels. We identify a robust multi‐model agreement for an increased frequency from central Italy to the northern Balkans combined with a substantial extension of the affected areas, for a dominant influence of the driving Global Climate Models for projecting changes in the frequency, and for an increase in intensity, area, volume and severity over the French Mediterranean. However, large quantitative uncertainties persist despite the use of convection‐permitting models, with no clear agreement in frequency changes over southeastern France and a large range of plausible changes in events' properties, including for the most intense events. Model diversity and international coordination are still needed to provide policy‐relevant climate information regarding precipitation extremes. Plain Language Summary Despite growing computational resources and multiple model developments, projecting future changes in the high‐impact Mediterranean Heavy Precipitation Events remains both a numerical and scientific challenge. The present study takes advantage of the recent availability of a relatively large ensemble of high resolution Regional Climate Models (2–3 km), which represent a step change in the simulation of precipitation extremes, and of an object‐oriented approach, allowing us to track the convective precipitating systems on an hourly basis. Looking at future changes in fall Mediterranean Heavy Precipitation Events at high warming levels, we identify a robust multi‐model agreement for an increased frequency from central Italy to the northern Balkans combined with a substantial expansion of the affected areas, and an increase in intensity, area, volume and severity over the French Mediterranean. However, considerable uncertainties remain in terms of frequency over parts of the domain arising from uncertainty in changes in large scale weather patterns, and in terms of degree of intensification for the most intense events. It suggests the need for model diversity and for more coordinated high resolution climate projections with careful selection of different driving global models in order to provide policy‐relevant climate information regarding precipitation extremes. Key Points High‐resolution ensemble and object‐oriented approach offer a unique opportunity to study changes in Mediterranean extreme precipitation Robust agreement is found for an increase in intensity, volume and severity for future French Mediterranean Heavy Precipitation Events Even at convection‐permitting scale, considerable uncertainty remains regarding the degree of intensification of the most extreme events

Extreme ocean warming events, known as marine heatwaves (MHWs), have been observed to perturb significantly marine ecosystems and fisheries around the world. Here, we propose a detection method for long-lasting and large-scale summer MHWs, using a local, climatological 99th percentile threshold, based on present-climate (1976–2005) daily SST. To assess their future evolution in the Mediterranean Sea we use, for the first time, a dedicated ensemble of fully-coupled Regional Climate System Models from the Med-CORDEX initiative and a multi-scenario approach. The models appear to simulate well MHW properties during historical period, despite biases in mean and extreme SST. In response to increasing greenhouse gas forcing, the events become stronger and more intense under RCP4.5 and RCP8.5 than RCP2.6. By 2100 and under RCP8.5, simulations project at least one long-lasting MHW every year, up to three months longer, about 4 times more intense and 42 times more severe than present-day events. They are expected to occur from June-October and to affect at peak the entire basin. Their evolution is found to occur mainly due to an increase in the mean SST, but increased daily SST variability also plays a noticeable role. Until the mid-21st century, MHW characteristics rise independently of the choice of the emission scenario, the influence of which becomes more evident by the end of the period. Further analysis reveals different climate change responses in certain configurations, more likely linked to their driving global climate model rather than to the individual model biases.

Computational advances have made atmospheric modeling at convection‐permitting (≤4 km) grid spacings increasingly feasible. These simulations hold great promise in the projection of climate change impacts including rainfall and flood extremes. The relatively short model runs that are currently feasible, however, inhibit the assessment of the upper tail of rainfall and flood quantiles using conventional statistical methods. Stochastic storm transposition (SST) and process‐based flood frequency analysis are two approaches that together can help to mitigate this limitation. SST generates large numbers of extreme rainfall scenarios by temporal resampling and geospatial transposition of rainfall fields from relatively short data sets. Coupling SST with process‐based flood frequency analysis enables exploration of flood behavior at a range of spatial and temporal scales. We apply these approaches with outputs of 13‐year simulations of regional climate to examine changes in extreme rainfall and flood quantiles up to the 500‐year recurrence interval in a medium‐sized watershed in the Midwestern United States. Intensification of extreme precipitation across a range of spatial and temporal scales is identified in future climate; changes in flood magnitudes depend on watershed area, with small watersheds exhibiting the greatest increases due to their limited capacity to attenuate flood peaks. Flood seasonality and snowmelt are predicted to be earlier in the year under projected warming, while the most extreme floods continue to occur in early summer. Findings highlight both the potential and limitations of convection‐resolving climate models to help understand possible changes in rainfall and flood frequency across watershed scales. Plain Language Summary High‐resolution “convection‐permitting” regional climate model simulations hold great promise in projection of climate change impacts including extreme rainfall and flooding. The relatively short (~10‐year) model runs that are currently feasible, however, are insufficient for examining very rare events like 100‐year storms and floods. Meanwhile, existing rainfall and flood data sets have a number of shortcomings that make it difficult to understand how floods have and will continue to change. In this study, we use several novel computer modeling methods to help mitigate these limitations. We apply these methods together with detailed simulations of flood hydrology and high‐resolution regional climate simulation results to examine current and future extreme rainfall and flooding in an agricultural watershed in northeastern Iowa, in the Midwestern United States. Floods there are projected to become more severe, driven by complex seasonal changes in rainfall, temperature, and snow. The magnitude of these changes depends on upstream watershed area. This work demonstrates how cutting‐edge climate and hydrology simulations and methods, together with flood theory and data, can help to predict future changes in flooding. Key Points Process‐based frequency analysis framework is coupled with convection‐permitting RCM outputs to study rainfall and flood nonstationarity We examine current and future rainfall and flood quantiles up to the 500‐year recurrence interval in a medium‐sized watershed Extreme rainfall enhancement is identified across scales, while changes in flood hazards are highly dependent on scale and magnitude

Two different bias correction methods, the quantile mapping (QM) and quantile delta mapping (QDM), are applied to simulated daily temperature and precipitation over China from a set of 21st century regional climate model (the ICTP RegCM4) projections. The RegCM4 is driven by five different general circulation models (GCMs) under the representative concentration pathway RCP4.5 at a grid spacing of 25 km using the CORDEX East Asia domain. The focus is on mean temperature and precipitation in December–January–February (DJF) and June–July–August (JJA). The impacts of the two methods on the present day biases and future change signals are investigated. Results show that both the QM and QDM methods are effective in removing the systematic model biases during the validation period. For the future changes, the QDM preserves the temperature change signals well, in both magnitude and spatial distribution, while the QM artificially modifies the change signal by decreasing the warming and modifying the patterns of change. For precipitation, both methods preserve the change signals well but they produce greater magnitude of the projected increase, especially the QDM. We also show that the effects of bias correction are variable- and season-dependent. Our results show that different bias correction methods can affect in different way the simulated change signals, and therefore care has to be taken in carrying out the bias correction process.

The traditional dynamical downscaling (TDD) method employs continuous integration of regional climate models (RCM) with the general circulation model (GCM) providing the initial and lateral boundary conditions. Dynamical downscaling simulations are constrained by physical principles and can generate a full set of climate information, providing one of the important approaches to projecting fine spatial-scale future climate information. However, the systematic biases of climate models often degrade the TDD simulations and hinder the application of dynamical downscaling in the climate-change related studies. New methods developed over past decades improve the performance of dynamical downscaling simulations. These methods can be divided into four groups: the TDD method, the pseudo global warming method, dynamical downscaling with GCM bias corrections, and dynamical downscaling with both GCM and RCM bias corrections. These dynamical downscaling methods are reviewed and compared in this paper. The merits and limitations of each dynamical downscaling method are also discussed. In addition, the challenges and potential directions in progressing dynamical downscaling methods are stated.

Extreme precipitation events have been extensively applied to the design of social infra structures. Thus, a method to more scientifically estimate the extreme event is required. This paper suggests a method to estimate the extreme precipitation in Korea using a regional climate model. First, several historical extreme events are identified and the most extreme event of Typhoon Rusa (2002) is selected. Second, the selected event is reconstructed through the Weather Research and Forecasting (WRF) model, one of the Regional Climate Models (RCMs). Third, the reconstructed event is maximized by adjusting initial and boundary conditions. Finally, the Probable Maximum Precipitation (PMP) is obtained. The WRF could successfully simulate the observed precipitation in terms of spatial and temporal distribution (R2 = 0.81). The combination of the WRF Single-Moment (WSM 6-class graupel scheme (of microphysics), the Betts-Miller-Janjic scheme (of cumulus parameterization) and the Mellor-Yamada-Janjic Turbulent Kinetic Energy (TKE) scheme (of planetary boundary layer) was determined to be the best combination to reconstruct Typhoon Rusa. The estimated PMP (RCM_PMP) was compared with the existing PMP. The RCM_PMP was generally in good agreement with the PMP. The suggested methodology is expected to provide assessments of the existing PMP and to provide a new alternative for estimating PMP.

Here, we analyze future projections of cyclone activity in the Mediterranean region at the end of the twenty-first century based on an ensemble of state-of-the-art fully-coupled Regional Climate System Models (RCSMs) from the Med-CORDEX initiative under the Representative Concentration Pathway (RCP) 8.5. Despite some noticeable biases, all the RCSMs capture spatial patterns and cyclone activity key characteristics in the region and thus all of them can be considered as plausible representations of the future evolution of Mediterranean cyclones. In general, the RCSMs show at the end of the twenty-first century a decrease in the number and an overall weakening of cyclones moving across the Mediterranean. Five out of seven RCSMs simulate also a decrease of the mean size of the systems. Moreover, in agreement with what already observed in CMIP5 projections for the area, the models suggest an increase in the Central part of the Mediterranean region and a decrease in the South-eastern part of the region in the cyclone-related wind speed and precipitation rate. These rather two opposite tendencies observed in the precipitation should compensate and amplify, respectively, the effect of the overall reduction of the frequency of cyclones on the water budget over the Central and South-eastern part of the region. A pronounced inter-model spread among the RCSMs emerges for the projected changes in the cyclone adjusted deepening rate, seasonal cycle occurrence and associated precipitation and wind patterns over some areas of the basin such as Ionian Sea and Iberian Peninsula. The differences observed appear to be determined by the driving Global Circulation Model (GCM) and influenced by the RCSM physics and internal variability. These results point to the importance of (1) better characterizing the range of plausible futures by relying on ensembles of models that explore well the existing diversity of GCMs and RCSMs as well as the climate natural variability and (2) better understanding the driving mechanisms of the future evolution of Mediterranean cyclones properties.

Modelling the rare but high-impact Mediterranean Heavy Precipitation Events (HPEs) at climate scale remains a largely open scientific challenge. The issue is adressed here by running a 38-year-long continuous simulation of the CNRM-AROME Convection-Permitting Regional Climate Model (CP-RCM) at a 2.5 km horizontal resolution and over a large pan-Alpine domain. First, the simulation is evaluated through a basic Eulerian statistical approach via a comparison with selected high spatial and temporal resolution observational datasets. Northwestern Mediterranean fall extreme precipitation is correctly represented by CNRM-AROME at a daily scale and even better at an hourly scale, in terms of location, intensity, frequency and interannual variability, despite an underestimation of daily and hourly highest intensities above 200 mm/day and 40 mm/h, respectively. A comparison of the CP-RCM with its forcing convection-parameterised 12.5 km Regional Climate Model (RCM) demonstrates a clear added value for the CP-RCM, confirming previous studies. Secondly, an object-oriented Lagrangian approach is proposed with the implementation of a precipitating system detection and tracking algorithm, applied to the model and the reference COMEPHORE precipitation dataset for twenty fall seasons. Using French Mediterranean HPEs as objects, CNRM-AROME’s ability to represent the main characteristics of fall convective systems and tracks is highlighted in terms of number, intensity, area, duration, velocity and severity. Further, the model is able to simulate long-lasting and severe extreme fall events similar to observations. However, it fails to reproduce the precipitating systems and tracks with the highest intensities (maximum intensities above 40 mm/h) well, and the model’s tendency to overestimate the cell size increases with intensity.

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets.