Use Of Regional Climate Models Research Articles

Validation of CORDEX Regional climate models to simulate climate trends and variability across various agro-ecological zones of North Shewa, Ethiopia

Climate variability significantly affects the overall agricultural production and productivity over space and time. To quantify the effect of climate variability on natural systems on Earth in the future, the use of regional climate models (RCMs) as a tool is mandatory. Thus, the use of well-validated RCM provides efficient climate data; and predicts future climate variability over agroecological zones (AEZs). Eleven RCMs from CORDEX-Ethiopia were evaluated to simulate maximum temperature (Tmax), minimum temperature (Tmin) and rainfall variability over space and time, to validate their performance and to select the best-fit models. The temporal variability Tmax and rainfall was smaller with a CV of 10 %, indicating uniform distribution over stations. However, CV for Tmin over stations shows satisfactory distributions. Most of the evaluated RCMs have a better capability to capture the spatial variability of rainfall,Tmax, and Tmin in some stations. However, some of the RCMs can handle the spatial variability of Tmax while others are not. None of the RCMs can handle the spatial variability of Tmin in Debre Birhan, Majete, and Kewat AEZs. In general, most RCMs can capture the spatial and temporal variability of rainfall and temperature. It might be better to use the ensemble average of the output from these RCMs for precise estimation of data than specific RCMs. Therefore, this work could suggest the use of high-resolution RCMs for the projection of temperature and rainfall over different AEZs.

Performance evaluation of multiple regional climate models to simulate rainfall in the Central Rift Valley Lakes Basin of Ethiopia and their selection criteria for the best climate model.

The historical datasets of five regional climate models (RCMs) available in the Coordinated Regional Downscaling Experiment (CORDEX)-Africa database are evaluated against ground-based observed rainfall in the Central Rift Valley Lakes Basin of Ethiopia. The evaluation is aimed at determining how well the RCMs reproduce monthly, seasonal, and annual cycles of rainfall and quantify the uncertainty between the RCMs in downscaling the same global climate model outputs. Root mean square, bias, and correlation coefficient are used to evaluate the ability of the RCM output. The multicriteria decision method of compromise programming was used to choose the best climate models for the climate condition of the Central Rift Valley Lakes subbasin. The Rossby Center Regional Atmospheric Model (RCA4) has downscaled ten global climate models (GCMs) and reproduces the monthly rainfall with a complex spatial distribution of bias and root mean square errors. The monthly bias varies in the range of - 35.8 to 189%. The summer (wet), spring, winter (dry), and annual rainfall varied within the range of 1.44 to 23.66%, - 7.08 to 20.04%, - 7.35 to 57%, and - 3.11 to 16.5%, respectively. To find the source of uncertainty, the same GCMs but downscaled by different RCMs were analyzed. The test results showed that each RCM differently downscaled the same GCM, and there was no single RCM model that consistently simulated the climate conditions over the stations in the study regions. However, the evaluation finds reasonable model skill in representing the temporal cycles of rainfall and suggests the use of RCMs where climate data is scarce after bias correction.

Consistency of the regional response to global warming levels from CMIP5 and CORDEX projections

Assessing the regional responses to different Global Warming Levels (GWLs; e.g. + 1.5, 2, 3 and 4 ºC) is one of the most important challenges in climate change sciences since the Paris Agreement goal of keeping global temperature increase well below 2 °C with respect to the pre-industrial period. Regional responses to global warming were typically analyzed using global projections from Global Climate Models (GCMs) and, more recently, using higher resolution Regional Climate Models (RCMs) over limited regions. For instance, the IPCC AR6 WGI Atlas provides results of the regional response to different GWLs for several climate variables from both GCMs and RCMs. These results are calculated under the assumption that the regional signal to global warming is consistent between the GCMs and the nested RCMs. In the present study we investigate the above assumption by evaluating the consistency of regional responses to global warming from global (CMIP5) and regional (CORDEX) projections. The dataset aggregated over the new IPCC reference regions, available from the IPCC AR6 WGI Atlas repository, is analyzed here for temperature and precipitation. The existing relationships between the regional climate change signals and global warming are compared for both CMIP5 and CORDEX. Our results show significant linear scaling relationships between regional changes and global warming for most of the regions. CORDEX and CMIP5 show remarkably similar scaling relationships and similar robustness in the emergence of the climate change signal for most of the regions. These results support the use of regional climate models in the context of global warming level studies.

Performance Evaluation of Bias Correction Methods and Projection of Future Precipitation Changes Using Regional Climate Model over Thanjavur, Tamil Nadu, India

The use of regional climate models in climate change risk assessments is uncertain because of the threat of biases. Prior to using simulations, some corrections must be made. The aim of this study is to evaluate the effectiveness of simple and complex correction approaches on CORDEX (Coordinated Regional Climate Downscaling Experiment) precipitation datasets over Thanjavur in Tamil Nadu, India. The various bias correction approaches were assessed using the two-sample cross-validation technique, and the best method was chosen using statistical metrics such as the correlation coefficient, root mean square error, and percent bias for correcting the future scenario datasets. According to the findings, all methods significantly improved raw simulated estimations after correction. The Delta Change method was used to adjust the RCP2.6 and RCP8.5 scenarios datasets for this study region because it produced good agreement (0.98) and low wet bias (1%) against observed precipitation. In addition, this study found that the annual mean precipitation projections showed an increase in precipitation intensity of between 4.2 and 6.8% under the RCP2.6 scenario and between 14 and 20.1% under the RCP8.5 scenario, compared to observed data (1976–2005). Furthermore, the analysis showed that most years in the twenty-first century will experience heavy precipitation, resulting in floods. This research has offered reliable knowledge about future precipitation to environmentalists, urban administrators, and policy-makers, enable them to make plausible strategies to mitigate the detrimental effects of climate change.

Evaluation of statistical downscaling methods for climate change projections over Spain: Future conditions with pseudo reality (transferability experiment)

AbstractThe Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC‐2) and this is the last of three papers aimed to evaluate and intercompare five empirical/statistical downscaling (ESD) methods developed at AEMET: (a) Analog, (b) Regression, (c) Artificial Neural Networks, (d) Support Vector Machines and (e) Kernel Ridge Regression, in order to decide which methods and under what configurations are more suitable for that purpose. Following the framework established by the EU COST Action VALUE, in this experiment we test the transferability of these methods to future climate conditions with the use of regional climate models (RCMs) as pseudo observations. We evaluate the marginal aspects of the distributions of daily maximum/minimum temperatures and daily accumulated precipitation, over mainland Spain and the Balearic Islands, analysed by season. For maximum/minimum temperatures all methods display certain transferability issues, being remarkable for Support Vector Machines and Kernel Ridge Regression. For precipitation all methods appear to suffer from transferability difficulties as well, although conclusions are not as clear as for temperature, probably due to the fact that precipitation does not present such a marked signal of change. This study has revealed how an analysis over a historical period is not enough to fully evaluate ESD methods, so we propose that some type of analysis of transferability should be added in a standard procedure of a complete evaluation.

Evaluation of the Large EURO‐CORDEX Regional Climate Model Ensemble

AbstractThe use of regional climate model (RCM)‐based projections for providing regional climate information in a research and climate service contexts is currently expanding very fast. This has been possible thanks to a considerable effort in developing comprehensive ensembles of RCM projections, especially for Europe, in the EURO‐CORDEX community (Jacob et al., 2014, 2020). As of end of 2019, EURO‐CORDEX has developed a set of 55 historical and scenario projections (RCP8.5) using 8 driving global climate models (GCMs) and 11 RCMs. This article presents the ensemble including its design. We target the analysis to better characterize the quality of the RCMs by providing an evaluation of these RCM simulations over a number of classical climate variables and extreme and impact‐oriented indices for the period 1981–2010. For the main variables, the model simulations generally agree with observations and reanalyses. However, several systematic biases are found as well, with shared responsibilities among RCMs and GCMs: Simulations are overall too cold, too wet, and too windy compared to available observations or reanalyses. Some simulations show strong systematic biases on temperature, others on precipitation or dynamical variables, but none of the models/simulations can be defined as the best or the worst on all criteria. The article aims at supporting a proper use of these simulations within a climate services context.

Simulated and observed air temperature trends in the eastern Adriatic

AbstractClimate predictions of air temperature in coastal regions represent a great challenge due to the complex interactions among the atmosphere, sea, and land. With approximately 1,200 islands, the Adriatic is a region with a strong land‐sea contrast, land‐atmosphere feedback, and intense air‐sea interaction. Because the Mediterranean has been regarded as a “hot spot” for climate change, regional climate models can be used to provide insight into a more realistic representation of small‐scale weather and climate structure and variability. This advantage is due to the better representation of complex topography, developed coastlines, and land‐sea contrasts, which are important for investigating air temperature trends. The use of regional climate models together with high‐resolution reanalyses and observations in assessments of climate variability and climate change is highly valuable for understanding climate processes at regional scales. The present study focused on air temperature and its trends calculated from measurements and simulated by eight regional climate models from the EURO‐CORDEX database; these data were represented by UERRA reanalyses and E‐OBS gridded data. In the evaluation period (1989–2008), the models' RMSEs were fairly small, in the range 0.5–1.5°C, compared to the historical period (1961–2005), with RMSEs greater than 1.75°C. However, the models showed small absolute trend differences (up to 0.12°C·decade−1 for the historical period). The ensemble means in both periods showed an accuracy improvement of 15–20% compared to the individual models. The models exhibited more success in terms of representing the main statistics and variability of the air temperature structure than in reproducing the temperature trends over 45 years, especially in the northern Adriatic, where there is complex coastal topography and significant seasonal variability in the wind regime. The reanalyses well represented temperature structure but showed less success in explaining the temperature trends than the results from the measured data.

Assessing Future Impacts of Climate Change on Water Supply System Performance: Application to the Pozzillo Reservoir in Sicily, Italy

Climate change induced by greenhouse gas emissions is expected to alter the natural availability of water, affecting domestic, agricultural and industrial uses. This work aims at assessing the possible future impacts of climate change on precipitation, temperature and runoff, and to simulate the effects on reservoir demand–performance curves. To this aim, a modeling chain is set up, based on the combined use of regional climate models (RCMs) and water supply system simulation models. The methodology is applied to the Pozzillo reservoir, located in Sicily (Italy), which has experienced several droughts in the past. We use an RCM model that, based on a previous study, has proved to be the most reliable in the area, among those of the EURO-CORDEX initiative. RCM precipitation and temperature monthly time series are used to generate future reservoir inflow data, according to two representative concentration pathways, RCP4.5 (intermediate emissions scenario) and RCP8.5 (high emissions scenario) and a two-step bias correction procedure. Simulation of the reservoir indicated that, due to reservoir inflow reduction induced by climate change, performances of the Pozzillo reservoir are predicted to decrease significantly in the future, with impacts of RCP8.5 generally higher than RCP4.5.

Evaluation of regional climate model simulated rainfall over Indonesia and its application for downscaling future climate projections

ABSTRACTThe need for good quality information on climate and its future changes has become increasingly important for society. Of particular interest are analysis and predictions of rainfall at pertinent spatial scales. An option is the use of regional climate models (RCMs) as a physics‐based downscaling tool to retrieve higher spatial resolution information from coarser present and future climate datasets. In order to verify if simulations provide added values that result in an appropriate representation of the state of climate at finer spatial resolutions, the adoption of RCMs requires prior optimization. The performance of the Weather Research and Forecasting Model RCM in simulating precipitation over Indonesia is examined by a series of sensitivity experiments using different parameterized convective physics. Among four tested schemes, the best performance is provided by the Betts‐Miller‐Janjic (BMJ) parameterization. RCM multiannual seasonal rainfall bias outperforms or matches the reanalysis. Modeled results provide added value in simulating rainfall‐related climate indices but show low skill in recreating the annual rainfall pattern at monthly resolution. RCM precipitation exhibits complex spatial response to different ENSO phases, with El Niño conditions resulting in a general loss of model skill during the southern hemisphere spring. A series of regional climate simulations using the BMJ convective scheme forced by a future climate projection dataset show changes in rainfall aligned with previous studies.

Application of Regional Climate Models for Updating Intensity-duration-frequency Curves under Climate Change

Global Climate Models (GCMs) are currently the most powerful tools for accessing changes in the hydrological regime at the watershed scale due to climate change and variability. GCMs, however, have limitations due to their coarse spatial and temporal resolutions. Regional Climate Models (RCMs) are often referred to as suitable alternatives due to their higher resolution of the long-term climate projections. It is expected that RCMs are better for simulating extreme conditions than the GCMs. This present work, investigate the difference in updated IDF (Intensity-Duration-Frequency) relationships developed using GCMs and RCMs. The IDF updating method implemented with the IDF_CC tool for Canada has been used for comparison. The analyses are conducted using 369 selected Environment and Climate Change Canada hydro-meteorological stations from the IDF_CC tool database with record length longer than 20 years. Results for the future period (2020-2100), are based on multi-model ensembles of (i) the RCMs from the NA-CORDEX (North-American Coordinated Regional Climate Downscaling Experiment) project (ensemble 1) (ii) a sub-set of six GCMs from the GCMs available in the IDF_CC tool used as drivers for the RCMs (ensemble 2) and (iii) all 24 GCMs from the IDF_CC tool database (ensemble 3). One representative concentration pathway (RCP), RCP 8.5, is used in the analysis. The RCMs from the NA-CORDEX project selected for this study use six GCMs as drivers to produce the future predictions for the North American continent, including Canada. Two metrics are applied for the comparison of results: (i) the difference in projected precipitation using the multi-model ensemble median; and (ii) the difference in uncertainty range. The uncertainty range is defined in this study as the percentage projected change in future, 25 to 75 quantiles obtained using the RCMs a GCMs ensembles. The regional models from the NA-CORDEX project generated lower extreme precipitation projections than the GCMs for the stations located in the Canadian prairies (provinces of Alberta, Saskatchewan, Manitoba). Stations located at the East and West coasts of Canada show a smaller difference in the projected extremes obtained using GCMs and RCMs. The use of RCMs shows increase in uncertainty when compared to GCMs. This result indicates that even when using regional climate models, it’s advisable to extend the analyses and include as many as possible models from different climate centers.

Thirty Years of Regional Climate Modeling: Where Are We and Where Are We Going next?

AbstractThe year 2019 marks the thirtieth anniversary of the development of the first regional climate model (RCM), and here an overview is provided of the progress in regional modeling research and of the main challenges lying ahead. RCMs were primarily developed to provide fine‐scale climate information for impact studies, but they have evolved into general and multipurpose modeling tools. Among the main achievements in RCM research the focus is on: the development of community RCMs applicable to a wide variety of studies and regional contexts; the increase of model simulation length up to centennial scales and spatial resolutions up to convection‐permitting scales (few kilometers), leading to a better understanding of regional to local climate change signals; the development of fully coupled Regional Earth System Models; the inception of intercomparison projects culminating in the international Coordinated Regional Climate Downscaling Experiment; the extensive use of RCM simulations for impact assessments; and the involvement of the scientific community from developing countries in climate modeling research. Among the outstanding issues in need of more attention are the Added Value of using this downscaling technique; various technical aspects concerning RCM simulations; and uncertainties in RCM‐based climate projections. Future directions in RCM research are discussed, with highlight on: transition to convection‐permitting modeling systems; further development of Regional Earth System Models including the human component; next phase of the Coordinated Regional Climate Downscaling Experiment project; and use of RCMs in the distillation of actionable information for contribution to climate service activities. A brief historical overview of regional climate modeling is also presented.

Modelling and evaluating the impacts of climate change on three major crops in south-eastern Australia using regional climate model simulations

The use of regional climate models (RCMs) to localise results from coarse-resolution global climate models has recently attracted more interest in agricultural impact studies. Theoretically, it has advantages over global climate models in terms of realisation of future climate projections. However, there are few studies that have used dynamical downscaling results to assess climate change impacts on Australian cropping systems. In this study, we used post-processing bias-corrected climate data from the NSW/ACT Regional Climate Modelling (NARCliM) project to drive the Agricultural Production Systems SIMulator (APSIM) for modelling and evaluating the response of three major crops (wheat, canola and lupin) to projected climate conditions under various farm management practices in south-eastern Australia. Our results showed that historical crop yields from APSIM simulations forced by RCM output tended to underestimate yields from simulations forced by observations due to large biases in the NARCliM simulations. Therefore, bias correction was used to correct the APSIM outputs before conducting future impact analysis. The bias-corrected results showed that ensemble-mean yields based on 12 RCMs were projected to increase over the study area under the A2 emission scenario. However, the magnitude of yield increase depended on the time periods, crop type and location. Multiple linear regression models showed that the changes of radiation, rainfall and temperature and elevated CO2 concentration could explain 60–78% of crop yield changes. It is interesting to note that residue incorporation and nitrogen application had a large effect on percentage yield increases for wheat due to future climate change, but had limited effects on the response of lupin and canola yields. Our study suggests that sufficient bias-correction method is needed when using NARCliM outputs in crop models. Although uncertainties (e.g. the choice of emission scenarios and RCMs) still exist in our study, the results are of central importance in the development of high-yield adaptive strategies for local farmers and policy makers in south-eastern Australia.

High-resolution climate projections for South Asia to inform climate impacts and adaptation studies in the Ganges-Brahmaputra-Meghna and Mahanadi deltas

Climate impacts and adaptation studies often use output from impact models that require data representing future climates at a resolution greater than can be provided by Global Climate Models (GCMs). This paper describes the use of Regional Climate Model (RCM) simulations to generate high-resolution future climate information for assessing climate impacts in the Ganges-Brahmaputra-Meghna (GBM) and Mahanadi deltas as part of the DECCMA project. In this study, three different GCMs (HadGEM2-ES, CNRM-CM5 and GFDL-CM3), all using a single scenario for future greenhouse forcing of the atmosphere (RCP 8.5), were downscaled to a horizontal resolution of 25 km over south Asia using the HadRM3P RCM. These three GCMs were selected based on ability to represent key climate processes over south Asia and ability to sample a range of regional climate change responses to greenhouse gas forcing. RCM simulations of temperature, precipitation, and lower level (850 hPa) atmospheric circulation in the monsoon season (June, July, August, September – JJAS) were compared with observational datasets and their respective driving GCMs to ensure large-scale consistency. Although there are some biases in the RCM simulations, these comparisons indicate that the RCMs are able to simulate realistically aspects of the observed climate of South Asia, such as the monsoon circulation and associated precipitation that are key for informing downstream impacts and adaptation studies. Simulated future temperature and precipitation changes on seasonal and daily timescales suggest increases in both temperature and precipitation across all three models during the monsoon season, with an increase in the amount of extremely heavy precipitation over the GBM and Mahanadi basins. Despite different driving conditions, these results are consistent across all three RCM simulations, providing a level of confidence in the magnitudes and spatial characteristics of temperature and precipitation projections for South Asia.

Global Radiative Flux and Cloudiness Variability for the Period 1959–2010 in Belgium: A Comparison between Reanalyses and the Regional Climate Model MAR

The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (Eg↓) that are found in reanalysis products and global models, as they allow for a finer spatial resolution and a finer parametrisation of surface and atmospheric processes. In this study, we assess the ability of the MAR («Modèle Atmosphérique Régional») RCM to reproduce observed changes in Eg↓, and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizontal resolution of 5 km for the period 1959–2010 by forcing MAR with different reanalysis products: ERA40/ERA-interim, NCEP/NCAR-v1, ERA-20C, and 20CRV2C. Measurements of Eg↓ from the Global Energy Balance Archive (GEBA) and from the Royal Meteorological Institute of Belgium (RMIB), as well as cloud cover observations from Belgocontrol and RMIB, were used for the evaluation of the MAR model and the forcing reanalyses. Results show that MAR enables largely reducing the mean biases that are present in the reanalyses. The trend analysis shows that only MAR forced by ERA40/ERA-interim shows historical trends, which is probably because the ERA40/ERA-interim has a better horizontal resolution and assimilates more observations than the other reanalyses that are used in this study. The results suggest that the solar brightening observed since the 1980s in Belgium has mainly been due to decreasing cloud cover.

Using raw regional climate model outputs for quantifying climate change impacts on hydrology

AbstractGeneral circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.

Application of a regional model to astronomical site testing in western Antarctica

The quality of ground-based astronomical observations is significantly affected by local atmospheric conditions, and the search for the best sites has led to the construction of observatories at increasingly remote locations, including recent initiatives on the high plateaus of East Antarctica where the calm, dry, and cloud-free conditions during winter are recognized as amongst the best in the world. Site selection is an important phase of any observatory development project, and candidate sites must be tested in the field with specialized equipment, a process both time consuming and costly. A potential means of screening site locations before embarking on field testing is through the use of regional climate models (RCMs). In this study, we describe the application of the Polar version of the Weather Research and Forecasting (WRF) model to the preliminary site suitability assessment of a hitherto unstudied region in West Antarctica. Numerical simulations with WRF were carried out for the winter (MJJA) of 2011 at 3- and 1-km spatial resolution over a region centered on the Ellsworth mountain range. Comparison with observations of surface wind speed and direction, temperature, and specific humidity at nine automatic weather stations indicates that the model performed well in capturing the mean values and time variability of these variables. Credible features revealed by the model includes zones of high winds over the southernmost part of the Ellsworth Mountains, a deep thermal inversion over the Ronne-Fincher Ice Shelf, and strong west to east moisture gradient across the entire study area. Comparison of simulated cloud fraction with a CALIPSO spacebourne Lidar climatology indicates that the model may underestimate cloud occurrence, a problem that has been noted in previous studies. A simple scoring system was applied to reveal the most promising locations. The results of this study indicate that the WRF model is capable of providing useful guidance during the initial site selection stage of project development.

Use of Regional Climate Models for Proxy Data over Transboundary Regions

AbstractThe sharing of water resources across transboundary regions between countries has long been a political problem. Sharing of data among countries is also a significant impediment for both planning and research purposes. In the context of climate change, it is necessary to know about possible future climatic changes and their potential impacts that would be especially crucial to water resources management. This requires data for hydrological modeling and data management, as in the case of river basins. This paper presents a study over the course of the Da River, which flows from China (upstream) to Vietnam (downstream), and it is assumed that rainfall data from China are not available. To overcome data limitation, regional climate model outputs are used as proxy data for this upstream region to study changes over the downstream Da River. The Weather Research and Forecasting (WRF) model was used as the regional climate model, driven by the European reanalysis data for the baseline period of 1961–1987...

Tropical cyclones in climate models

In this review, we provide a historical overview of the simulation of tropical cyclones (TCs) in climate models, from the first attempts in the 1970s to the current state‐of‐the‐art models. We discuss the status of TC simulation across multiple time scales, from intraseasonal, seasonal, and decadal, to climate change. One of the limitations on the simulation of TCs in climate models has been, and continues to be, balancing the high resolution necessary to accurately simulate TCs themselves with the need to run simulations for many years and using many ensemble members. Several approaches to inferring TC activity indirectly, rather than relying on the models own under‐resolved TCs, are reviewed, including the use of TC genesis indices based on the large‐scale environment and downscaling methods such as the use of regional climate models and statistical–dynamical techniques. We also provide an update on the status of climate change projections from the current class of models, where it is feasible to directly track the model's TCs. While there has been great progress in the capability of climate models to simulate TCs and provide useful forecasts and projections across multiple time scales, there remains much work to be done. We list some of the sources of uncertainty and model sensitivity, describe where improvements are necessary, and provide a few suggestions for promising research directions. WIREs Clim Change 2016, 7:211–237. doi: 10.1002/wcc.373This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models

Regional Dynamical Downscaling and the CORDEX Initiative

We review the challenges and future perspectives of regional climate model (RCM), or dynamical downscaling, activities. Among the main technical issues in need of better understanding are those of selection and sensitivity to the model domain and resolution, techniques for providing lateral boundary conditions, and RCM internal variability. The added value (AV) obtained with the use of RCMs remains a central issue, which needs more rigorous and comprehensive analysis strategies. Within the context of regional climate projections, large ensembles of simulations are needed to better understand the models and characterize uncertainties. This has provided an impetus for the development of the Coordinated Regional Downscaling Experiment (CORDEX), the first international program offering a common protocol for downscaling experiments, and we discuss how CORDEX can address the key scientific challenges in downscaling research. Among the main future developments in RCM research, we highlight the development of coupled regional Earth system models and the transition to very high-resolution, cloud-resolving models.

Use of RegCM gridded dataset for thunderstorm favorable conditions analysis over Poland\u2014climatological approach

The paper analyzes equivalent data for a low density meteorological station network (spatially discontinuous data) and poor temporal homogeneity of thunderstorm observational data. Due to that, a Regional Climate Model (RegCM) dataset was tested. The Most Unstable Convective Available Potential Energy index value (MUCAPE) above the 200 J kg−1 threshold was selected as a predictor describing favorable conditions for the occurrence of thunderstorms. The quality of the dataset was examined through a comparison between model results and soundings from several aerological stations in Central Europe. Good, statistically significant (0.05 significance level) results were obtained through correlation analysis; the value of Pearson’s correlation coefficient was above 0.8 in every single case. Then, using methods associated with gridded climatology, data series for 44 weather stations were derived and an analysis of correlation between RegCM modeled data and in situ thunderstorm observations was conducted with coefficients in the range of 0.75–0.90. The possibility of employing the dataset in thunderstorm climatology analysis was checked via a few examples by mapping monthly, seasonal, and annual means. Moreover, long-term variability and trend analysis along with modeled MUCAPE data were tested. As a result, the RegCM modeled MUCAPE gridded dataset was proposed as an easily available, suitable, and valuable predictor for thunderstorm climatology analysis and mapping. Finally, some limitations are discussed and recommendations for further improvements are given.