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Dynamical downscaling provides physics-based high-resolution climate change projections across regional and local scales. This is particularly important for island nations characterized by complex terrain, where the coarse resolution of global climate model (GCM) output often prohibits direct use. One of the main motivations for dynamical downscaling is to reduce biases relative to the host GCM at the local scale, which can be quantified through assessing 'added value'. However, added value from downscaling is not guaranteed; quantifying this can help users make informed decisions about how best to use available climate projection data. Here we describe the experiment design of the updated national climate projections for New Zealand based on dynamical downscaling. The global non-hydrostatic Conformal Cubic Atmospheric Model (CCAM) is primarily used for downscaling, with a global stretched grid targeting high resolution over New Zealand (12-km) and the wider South Pacific region (12–35-km). Focusing on the historical simulations, we assess added value for a range of metrics, climatological fields, extreme indices, and tropical cyclones. The main strengths of the downscaling include generally large improvements relative to the host GCM for temperature and orographic precipitation. Inter-annual variability in temperature is well captured across New Zealand, and several temperature and precipitation-based extreme indices show large improvements. The representation of tropical cyclones reaching at least category 2 intensity is generally improved relative to the large consistent under-representation in the host GCMs. The remaining biases are explored and discussed forming the basis for ongoing bias-correction work. [ABSTRACT FROM AUTHOR]

This study presents an assessment of the added value of downscaling utilizing Regional Climate Models (RCMs) compared to Global Climate Models (GCMs) in the high mountain region of the Pyrenees, characterized by complex topography. The EURO-CORDEX ensemble was investigated, employing a gridded high-resolution observational database as a reference. A recently proposed method is applied to quantify the performance gains or losses associated with dynamic downscaling. Our analysis focuses on calculating the added value by exploring the extremes of the Probability Density Function (PDF), spatial distribution patterns, and its relationship with elevation. Overall, our findings reveal significant improvements in the representation and general characterization of precipitation, minimum temperature, and maximum temperature in the Pyrenean region. Furthermore, RCMs demonstrate enhanced performance in capturing maximum precipitation events; however, they struggle to represent low precipitation rates, particularly in the Mediterranean area of the mountain range. Regarding temperature extremes, dynamical downscaling exhibits improvements in capturing maximum events. Nevertheless, deficiencies are observed in the RCMs' representation of minimum temperature events for both minimum and maximum temperature variables, as well as in representing near-freezing temperatures. [ABSTRACT FROM AUTHOR]

The advancement of computational resources has allowed researchers to run convection-permitting regional climate model (CPRCM) simulations. A pioneering effort promoting a multimodel ensemble of such simulations is the CORDEX Flagship Pilot Studies (FPS) on "Convective Phenomena over Europe and the Mediterranean" over an extended Alps region. In this study, the Distribution Added Value metric is used to determine the improvement of the representation of all available FPS hindcast simulations for the daily mean near-surface wind speed. The analysis is performed on normalized empirical probability distributions and considers station observation data as the reference. The use of a normalized metric allows for spatial comparison among the different regions (coast and inland), altitudes and seasons. This approach permits a direct assessment of the added value between the CPRCM simulations against their global driving reanalysis (ERA-Interim) and respective coarser resolution regional model counterparts. In general, the results show that CPRCMs add value to their global driving reanalysis or forcing regional model, due to better-resolved topography or through better representation of ocean-land contrasts. However, the nature and magnitude of the improvement in the wind speed representation vary depending on the model, the season, the altitude, or the region. Among seasons, the improvement is usually larger in summer than winter. CPRCMs generally display gains at low and medium-range altitudes. In addition, despite some shortcomings in comparison to ERA-Interim, which can be attributed to the assimilation of wind observations on the coast, the CPRCMs outperform the coarser regional climate models, both along the coast and inland. [ABSTRACT FROM AUTHOR]

Since a decade, convection-permitting regional climate models (CPRCM) have emerged showing promising results, especially in improving the simulation of precipitation extremes. In this article, the CPRCM CNRM-AROME developed at the Centre National de Recherches Météorologiques (CNRM) since a few years is described and evaluated using a 2.5-km 19-year long hindcast simulation over a large northwestern European domain using different observations through an added-value analysis in which a comparison with its driving 12-km RCM CNRM-ALADIN is performed. The evaluation is challenging due to the lack of high-quality observations at both high temporal and spatial resolutions. Thus, a high spatio-temporal observed gridded precipitation dataset was built from the collection of seven national datasets that helped the identification of added value in CNRM-AROME. The evaluation is based on a series of standard climatic features that include long-term means and mean annual cycles of precipitation and near-surface temperature where CNRM-AROME shows little improvements compared to CNRM-ALADIN. Additional indicators such as the summer diurnal cycle and indices of extreme precipitation show, on the contrary, a more realistic behaviour of the CNRM-AROME model. Moreover, the analysis of snow cover shows a clear added-value in the CNRM-AROME simulation, principally due to the improved description of the orography with the CPRCM high resolution. Additional analyses include the evaluation of incoming shortwave radiation, and cloud cover using satellite estimates. Overall, despite some systematic biases, the evaluation indicates that CNRM-AROME is a suitable CPRCM that is superior in many aspects to the RCM CNRM-ALADIN. [ABSTRACT FROM AUTHOR]

The increase in computational resources has enabled the emergence of multi-model ensembles of convection-permitting regional climate model (CPRCM) simulations at very high horizontal resolutions. An example is the CORDEX Flagship Pilot Study on "Convective phenomena at high resolution over Europe and the Mediterranean", a set of kilometre-scale simulations over an extended Alpine domain. This first-of-its-kind multi-model ensemble, forced by the ERA-Interim reanalysis, can be considered a benchmark dataset. This study uses a recently proposed metric to determine the added value of all the available Flagship Pilot Study hindcast kilometre-scale simulations for maximum and minimum temperature. The analysis is performed using state-of-the-art gridded and station observations as ground truth. This approach directly assesses the added value between the high-resolution CPRCMs against their driving global simulations and coarser resolution RCM counterparts. Overall, models display some modest gains, but also considerable shortcomings are exhibited. In part, these deficiencies can be attributed to the assimilation of temperature observations into ERA-Interim. Although the gains for the use of kilometre-scale resolution for temperature are limited, the improvement of the spatial representation of local atmospheric circulations and land–atmosphere interactions can ultimately lead to gains, particularly in coastal areas. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is an important component of the global climate system, while the characteristics of its climate are poorly represented in most regional climate models at coarse resolutions. In this study, a 20-year (2000–2019) dynamical downscaling simulation at the gray-zone resolution (9 km) using the WRF model driven by the ERA5 reanalysis is conducted over the TP. Based on comparisons against in-situ observations and the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite precipitation product, the assessment of basic climate variables, such as near-surface air temperature (T2m) and precipitation, is performed to evaluate the model's performance and understand its added value better. Results show that both WRF and ERA5 can successfully reproduce the spatial patterns of annual mean and seasonal mean surface air temperature. However, significant cold and wet biases are found especially over the southeastern TP in ERA5, which are greatly improved in WRF with reduced RMSEs. Not only the climatological characteristics, but also the inter-annual variability and seasonal variation of T2m and precipitation are well captured by WRF which reduces the cold and wet biases especially in winter and summer compared to ERA5, respectively. Besides, at daily scale, the overestimation of precipitation in WRF and ERA5 is mainly caused by the overestimated precipitation frequency when precipitation intensity changed slightly. Furthermore, WRF outperforms ERA5 in capturing the diurnal variation of precipitation with more realistic peak time in all sub-regions over the TP. Further investigation into the mechanism of model bias reveals that less simulated snow cover fraction plays a crucial role in increasing the surface net energy by affecting surface albedo over the southeastern TP in WRF, leading to higher T2m. In addition, less water vapor transport from the southern boundary of TP leads to reduced wet bias in WRF, indicating that the added value in dynamical downscaling at gray-zone resolution is obtained by representing water vapor transport more realistically. [ABSTRACT FROM AUTHOR]

A hybrid statistical dynamical downscaling method intended to emulate regional climate models is described and applied to Western Europe. The method is based on a constructed analogues algorithm, already used for statistical downscaling. For emulation, the statistical downscaling relationship is not derived from observations but from climate projections at low and high resolution. The hybrid approach therefore does not rely on the stationarity assumption inherent to conventional statistical downscaling. Within a perfect model framework, and using a large number of regional projections, the hybrid method is shown to reproduce climate change signals very well and to outperform a conventional statistical downscaling method also based on constructed analogues. The hybrid approach remains skillful even when applied to very low resolution climate data. In practice, two emulation modes exist. In the GCM/RCM mode, the downscaling relationship is built between a RCM and its forcing GCM. In the RCM/RCM mode, the relationship is built between a RCM and the same RCM after aggregation of its results to a low resolution grid. The large-scale climate change signal of the downscaled GCM is generally retained with the RCM/RCM mode, but not with the GCM /RCM mode. Additionally, the choice of the GCM/RCM pair used for learning leads to large differences in downscaling results at large scale (i.e. at low resolution) with the GCM /RCM mode, but not with the RCM/RCM mode. These results are explained by the differences that generally exist at large scale between projected changes by current RCMs and their forcing GCMs. Whether these differences are a testimony of a real added value of RCMs at large scale in the climate change context, or whether they have other causes, is therefore a crucial question. [ABSTRACT FROM AUTHOR]

The issue of the added value (AV) of high resolution regional climate models is complex and still strongly debated. Here, we approach AV in a perfect model framework within a 16-member single model initial condition ensemble with the regional climate model RACMO2 embedded in the global climate model EC-Earth2.3. In addition, we also used an ensemble produced by a pseudo global warming (PGW) approach. Results for winter temperature and precipitation are investigated from two different perspectives: (1) a signal-to-noise perspective analysing the systematic response to changing emission forcings versus internal climate variability, and (2) a prediction perspective aimed at predicting a 30-year future climate state. Systematic changes in winter temperature and precipitation contain fine-scale response patterns, but in particular for precipitation these patterns are small compared to internal variability. Therefore, single members of the ensemble provide only limited information on these systematic patterns. However, they can be estimated more reliably from PGW members because of the stronger constraints on internal variability. From the prediction perspective, we analysed AV of fine-scale information by comparing three prediction pairs. This analysis shows that there is AV in the fine-scale information for temperature, yet for precipitation adding fine-scale changes generally deteriorates the predictions. Using only the large-scale change (without fine scales) from a single ensemble member as a delta change on top of the present-day climate state, already provides a robust estimate of the future climate state and therefore can be used as a simple benchmark to measure added value. [ABSTRACT FROM AUTHOR]

This study demonstrated the influence of downscaling using the regional climate model (RCM) driven by Era-Interim reanalysis (EIN) in simulating different aspects of the Indian summer monsoon (ISM). It is also examined, whether increasing the horizontal resolution of RCM will inevitably be capable of adding more information to ISM characteristics and its spatio-temporal variability. In this regard, two RCM (at 50 km: Reg50 and 25 km: Reg25) simulations were conducted for six years from 2000 to 2005 for the South Asia Coordinated Regional Downscaling Experiment (CORDEX) domain. The added value (AV) is found to be strongly dependent on region and considered metrics. A slight improvement towards increasing spatial resolution is observed in the simulation of the mean ISM characteristics, while considerable improvements are noticed for the frequency distribution of extremes. The notable improvement in the daily climatology of precipitation is observed over the region of northeast India (~ 35%) and the Hilly region (~ 32%) and the lowest improvement over north-central India (~ 8%). The reduction of anomalously strong northeasterly flow over the southeastern Arabian Sea and strengthening of the moisture leaden southeasterly wind flow from the Bay of Bengal in Reg25 compared to Reg50 is consistent with the reduction of dry bias over India in Reg25. The robust improvements are noticed for the heavy precipitation events (probability density function: PDF tails) and mean precipitation due to extreme precipitation events, particularly over the areas characterized by complex topographical features (e.g., the Western Ghats, Indo-Gangetic plains, and northeast India and Hilly regions) as well as over the areas having substantial bias (e.g., central India), indicating its strong sensitivity towards model resolution. The increasing latent heat flux in Reg25 contributes to increasing the moisture and hence rainfall over India. Both simulations apparently simulate many of the ISM characteristics better than the EIN, thereby emphasizing the usefulness of finer resolutions in the better simulation of the Indian monsoon, especially for heavy rainfall. However, the RegCM bias is comparable to or even greater in some places than the EIN bias. This suggests that high-resolution models are important for improving performance; however, it does not necessarily mean that they can have AV for every aspect and all places. Apart from this, the substantial difference in the AV over different regions or aspects highlights the importance of carefully selecting AV matrices for the different areas and characteristics being investigated. RegCM exhibits some systematic biases in precipitation despite substantial improvement due to misrepresentation of dynamical and thermodynamical processes, including northward and eastward propagating convective bands. [ABSTRACT FROM AUTHOR]

In this study, we have assessed the added value on the spatio-temporal distribution of the precipitation of convection-permitting simulation (3 km) compared to the parent coarse-scale parameterized convection simulation (20 km) with the high-resolution observational datasets i.e., SPREAD (5 km) and IBERIA01 (10 km) over the Iberian Peninsula (IP) in all four seasons during 2000–2009. Both simulations are evaluation runs based on ERA-Interim reanalysis and performed with the RegIPSL regional earth system model in the frame of the European Climate Prediction system (EUCP) H2020 project and COordinated Regional climate Downscaling Experiment (CORDEX). We have not found significant improvement in the convection-permitting simulation compared to the parent coarse-scale simulation for the seasonal mean precipitation of the IP except the spatial variation over mountainous peaks. The kilometer-scale simulation significantly underestimates the observed seasonal mean precipitation over the western parts of the IP compared to the coarse-scale simulation, which may be attributed to a change of local dynamics in the kilometer-scale simulation with a weakening and southward shift of the moisture-laden westerly winds approaching from the Atlantic Ocean over the IP (i.e., a decline in atmospheric moisture transport from the Atlantic Ocean towards the IP). However, the added value of kilometer-scale simulation over the driving coarse-scale simulation is obtained for various indices; in the representation of the spatio-temporal distribution of the wet-day precipitation frequency and intensity, and the extreme/heavy precipitation events for each season at both resolutions i.e., downscaled and upscaled. It has also been noted that the spatio-temporal distribution of precipitation for all metrics used varies between the two observational datasets for all seasons. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study presents near future (2020–2044) temperature and precipitation changes over the Antarctic Peninsula under the high-emission scenario (RCP8.5). We make use of historical and projected simulations from 19 global climate models (GCMs) participating in Coupled Model Intercomparison Project phase 5 (CMIP5). We compare and contrast GCMs projections with two groups of regional climate model simulations (RCMs): (1) high resolution (15-km) simulations performed with Polar-WRF model forced with bias-corrected NCAR-CESM1 (NC-CORR) over the Antarctic Peninsula, (2) medium resolution (50-km) simulations of KNMI-RACMO21P forced with EC-EARTH (EC) obtained from the CORDEX-Antarctica. A further comparison of historical simulations (1981–2005) with respect to ERA5 reanalysis is also included for circulation patterns and near-surface temperature climatology. In general, both RCM boundary conditions represent well the main circulation patterns of the historical period. Nonetheless, there are important differences in projections such as a notable deepening and weakening of the Amundsen Sea Low in EC and NC-CORR, respectively. Mean annual near-surface temperatures are projected to increase by about 0.5–1.5 ∘ C across the entire peninsula. Temperature increase is more substantial in autumn and winter (∼ 2 ∘ C). Following opposite circulation pattern changes, both EC and NC-CORR exhibit different warming rates, indicating a possible continuation of natural decadal variability. Although generally showing similar temperature changes, RCM projections show less warming and a smaller increase in melt days in the Larsen Ice Shelf compared to their respective driving fields. Regarding precipitation, there is a broad agreement among the simulations, indicating an increase in mean annual precipitation (∼ 5 to 10%). However, RCMs show some notable differences over the Larsen Ice Shelf where total precipitation decreases (for RACMO) and shows a small increase in rain frequency. We conclude that it seems still difficult to get consistent projections from GCMs for the Antarctic Peninsula as depicted in both RCM boundary conditions. In addition, dominant and common changes from the boundary conditions are largely evident in the RCM simulations. We argue that added value of RCM projections is driven by processes shaped by finer local details and different physics schemes that are introduced by RCMs, particularly over the Larsen Ice Shelf. [ABSTRACT FROM AUTHOR]

The potential of the RegCM4–CLM4.5 model for value addition over the MIROC5 GCM for Indian Summer Monsson (ISM) simulation for the present climate (1975–2005) has been examined. The added value has been investigated in terms of the added value index (AV) and the Modified Brier Skill Score (MBSS). In general, a strong dry (wet) bias > 5 mm/day in the RegCM4 (MIROC5) dataset was prevalent over different parts of India. Additional precipitation statistics such as the area-averaged mean, standard deviation, correlation coefficient, and percentage bias over the homogeneous region of precipitation suggest that the RegCM4 occasionally improves over the MIROC5, depending upon the region and the metric under consideration. Further, a comparable tropospheric temperature gradient and the vertical wind shear have been found for both the dataset. However, this does not hold for the large-scale moisture transport as the RegCM4 shows an advantage in representing the magnitude and the location of the low-level jet. The dominant unrealistic moisture transport and associated stronger Bay of Bengal branch of circulation causes heavy precipitation (> 12 mm/day), thus leading to improved precipitation statistics for MIROC5. A quantifiable added value over the GCM in terms of the positive indices have been found in the RegCM4. The AV (MBSS) suggests positive values i.e. 0.6–0.8 (0.2–0.5) over major parts of India, signifying added information and/or the improvement upon the downscaling. The experiment certainly provides additional information in the precipitation simulation over the major part of India however the two indices do not necessarily agree over certain areas. [ABSTRACT FROM AUTHOR]

South-East France is a region often affected by heavy precipitating events the characteristics of which are likely to be significantly impacted in the future climate. In this study, cnrm-arome, a Convection-Permitting Regional Climate Model with a 2.5 km horizontal resolution is compared to its forcing model, the Regional Climate Model aladin-climate at a horizontal resolution of 12.5 km, self-driven by the era-interim reanalysis. An hourly observation dataset with a resolution of 1 km, comephore, is used in order to assess simulated surface precipitation from a seasonal to hourly scale. The representation of the spatial pattern of fall precipitation climatology is improved by cnrm-arome. It also shows a clear added value with respect to aladin-climate through the improvement of the localization and intensity of extreme rainfall on a daily and hourly time scale on both fine and coarse spatial scales (2.5, 12.5 and 50 km). cnrm-arome in particular is able to simulate intense rainfall on lowlands and makes sub-daily rainfall events more intense than aladin-climate. cnrm-arome still underestimates very extreme precipitation from above 30 mm/h or 230 mm/day. [ABSTRACT FROM AUTHOR]

We perform simulations with the WRF regional climate model at 12 and 3 km grid resolution for the current and future climates over Central Europe and evaluate their added value with a focus on the daily cycle and frequency distribution of rainfall and the relation between extreme precipitation and air temperature. First, a 9 year period of ERA-Interim driven simulations is evaluated against observations; then global climate model runs (MPI-ESM-LR RCP4.5 scenario) are downscaled and analyzed for three 12-year periods: a control, a mid-of-century and an end-of-century projection. The higher resolution simulations reproduce both the diurnal cycle and the hourly intensity distribution of precipitation more realistically compared to the 12 km simulation. Moreover, the observed increase of the temperature–extreme precipitation scaling from the Clausius–Clapeyron (C–C) scaling rate of ~ 7% K−1 to a super-adiabatic scaling rate for temperatures above 11 °C is reproduced only by the 3 km simulation. The drop of the scaling rates at high temperatures under moisture limited conditions differs between sub-regions. For both future scenario time spans both simulations suggest a slight decrease in mean summer precipitation and an increase in hourly heavy and extreme precipitation. This increase is stronger in the 3 km runs. Temperature–extreme precipitation scaling curves in the future climate are projected to shift along the 7% K−1 trajectory to higher peak extreme precipitation values at higher temperatures. The curves keep their typical shape of C–C scaling followed by super-adiabatic scaling and a drop-off at higher temperatures due to moisture limitation. [ABSTRACT FROM AUTHOR]

This paper aims to identify those regions within the South American continent where the Regional Climate Models (RCMs) have the potential to add value (PAV) compared to their coarser-resolution global forcing. For this, we used a spatial-scale filtering method based on the wavelet theory to distinguish the regional climatic signal present in atmospheric surface fields from observed data (CPC and TRMM) and 6 RCM simulations belonging to the CORDEX Project. The wavelet used for filtering was Haar wavelet, but a comparative analysis with Daubechies 4 wavelet indicated that meteorological fields or regional indices were not very sensitive to the wavelet selected. Once the longer wavelengths were filtered, we focused on analyzing the spatial variability of extreme rainfall and the spatiotemporal variability of maximum and minimum surface air temperature on a daily basis. The results obtained suggest essential differences in the spatial distribution of the small-scale signal of extreme precipitation between TRMM and regional models, together with a large dispersion between models. While TRMM and CPC register a large signal throughout the continent, the RCMs place it over the Andes Cordillera and some over tropical South America. PAV signal for surface air temperature was found over the Andes Cordillera and the Brazilian Highlands, which are regions characterized by complex topography, and also on the coasts of the continent. The signal came specially from the small-scale stationary component. The transient part is much smaller than the stationary one, except over la Plata Basin where they are of the same order of magnitude. Also, the RCMs and CPC showed a large spread between them in representing this transient variability. The results confirm that RCMs have the potential to add value in the representation of extreme precipitation and the mean surface temperature in South America. However, this condition is not applicable throughout the whole continent but is particularly relevant in those terrestrial regions where the surface forcing is strong, such as the Andes Cordillera or the coasts of the continent. [ABSTRACT FROM AUTHOR]

This study investigates the added value of very high resolution in long-term climate simulations over South Korea using the Weather Research and Forecasting (WRF) model. A one-way double-nested modeling system consisting of a mother domain (20-km resolution) and nested domain (5-km resolution) is customized for simulating the distinct climatological patterns in Korea, where the region-specific climate is largely influenced by the area's complex geographical features. The ERA-Interim reanalysis data are used for the initial and boundary conditions, and the simulation spans the period from December 1, 1985 to December 31, 2005 (20-year analysis period with 1-month spin-up). Simulations from both the mother and the nested domain show reasonable performance in capturing the general characteristics of summer temperature and precipitation in terms of temporally and spatially averaged quantities. However, the added value from the nested domain with its higher resolution is apparently found in the reproduction of the intensity and frequency of extreme events and in the physical realism related to the partitioning of convective and large-scale precipitation. The nested domain not only better resolves the sharp gradients of temperature variation over short distances but also substantially reduces the systematic cold bias seen in temperature extremes produced by the mother domain. Furthermore, the nested domain is better able to simulate the upper tail of precipitation distributions and thus of extreme events. The higher resolution also improves the simulation of partitioning between convective and large-scale precipitation, leading to a plausible relationship between extreme precipitation and temperature and showing good agreement with in situ observation. Given the different behaviors of convective and large-scale precipitation in response to temperature changes, their realistic partitioning in the model has important potential for enhancing the reliability of precipitation projection under global warming. [ABSTRACT FROM AUTHOR]

We employ a large ensemble of Regional Climate Models (RCMs) from the COordinated Regional-climate Downscaling EXperiment to explore two questions: (1) what can we know about the future precipitation characteristics over Africa? and (2) does this information differ from that derived from the driving Global Climate Models (GCMs)? By taking into account both the statistical significance of the change and the models' agreement on its sign, we identify regions where the projected climate change signal is robust, suggesting confidence that the precipitation characteristics will change, and those where changes in the precipitation statistics are non-significant. Results show that, when spatially averaged, the RCMs median change is usually in agreement with that of the GCMs ensemble: even though the change in seasonal mean precipitation may differ, in some cases, other precipitation characteristics (e.g., intensity, frequency, and duration of dry and wet spells) show the same tendency. When the robust change (i.e., the value of the change averaged only over the land points where it is robust) is compared between the GCMs and RCMs, similarities are striking, indicating that, although with some uncertainty on the geographical extent, GCMs and RCMs project a consistent future. Potential added value of downscaling future climate projections (i.e., non-negligible fine-scale information that is absent in the lower resolution simulations) is found for instance over the Ethiopian highlands, where the RCM ensemble shows a robust decrease in mean precipitation in contrast with the GCMs results. This discrepancy may be associated with the better representation of topographical details that are missing in the large scale GCMs. The impact of the heterogeneity of the GCM–RCM matrix on the results has been also investigated; we found that, for most regions and indices, where results are robust or non-significant, they are so independently on the choice of the RCM or GCM. However, there are cases, especially over Central Africa and parts of West Africa, where results are uncertain, i.e. most of the RCMs project a statistically significant change but they do not agree on its sign. In these cases, especially where results are clearly clustered according to the RCM, there is not a simple way of subsampling the model ensemble in order to reduce the uncertainty or to infer a more robust result. [ABSTRACT FROM AUTHOR]

Change to precipitation in a warming climate holds many implications for water management into the future, and an enhancement of a precipitation decrease or increase on or around mountains would have numerous impacts. Here, an intermediate resolution regional climate model (RCM) ensemble projects enhanced precipitation decrease on the windward slopes of over many mid-latitude mountains in winter, consistent with theory and model studies of idealised mountain ranges. This ensemble projects that an increase in convective rainfall determines the sign of total rainfall change in many regions in summer, only some of which are on or near mountains such as the European Alps. These same projected changes are present in inland slopes of the Australian Alps compared to surrounding regions as simulated by three RCM ensembles (the intermediate resolution and two high resolution ensembles), which agree on an enhanced precipitation decrease on the windward slopes in winter and spring, as well as an enhanced precipitation increase in summer driven by an increase in convective rainfall. The ensembles disagree on an enhanced precipitation decrease in autumn. The results represent regional-scale added value in the climate change signal of projections from high resolution models in cooler seasons, but suggest that the specific model components such as convection schemes strongly influence projections of summer rainfall change. Confidence in the simulation of change in convective rainfall, or convection-permitting modelling may be needed to raise confidence in summer rainfall projections over mountains. [ABSTRACT FROM AUTHOR]

This study is aimed to assess the added value of Regional Climate Models (RCMs) with respect to their driving Global Climate Models (GCMs) focused on the behavior of precipitation over South America (SA) at different temporal scales. RCMs from the CORDEX experiments available for the South American domain at 0.44° resolution were used together with their driving GCMs from the Coupled Models Intercomparison Project (CMIP5) dataset for the period 1979–2005. Observed data from the CPC-Global Unified Gauge-Based Analysis of Daily Precipitation was used to evaluate the simulations. Precipitation data were first filtered to retain the variability at the interannual, intraseasonal and synoptic timescales. Statistics of the daily precipitation data were also evaluated, including percentiles of light, moderate, heavy and extreme precipitation events. The added value was quantified in terms of two metrics accounting for the spatial distribution of the particular precipitation feature and the M-skill score, representing a measure of normalized square errors. Added value is strongly dependent on the RCM-GCM pair evaluated. RCMs with an overall good performance add value over their driving GCM for all metrics analyzed, from the climatological seasonal mean to the intensity of extreme events. The added value arises more clearly as long as the smaller the scale of the precipitation feature assessed is. [ABSTRACT FROM AUTHOR]

A new set of CORDEX simulations over South America, together with their coarser-resolution driving Global Climate Models (GCMs) are used to investigate added value of Regional Climate Models (RCMs) in reproducing mean climate conditions over the continent. There are two types of simulations with different lateral boundary conditions: five hindcast simulations use re-analysis as boundary conditions, and five other historical simulations use GCMs outputs. Multi-model ensemble means and individual simulations are evaluated against two or three observation-based gridded datasets for 2-m surface air temperature and total precipitation. The analysis is performed for summer and winter, over a common period from 1990 to 2004. Results indicate that added value of RCMs is dependent on driving fields, surface properties of the area, season and variable considered. A robust added value for RCMs driven by ERA-Interim is obtained in reproducing the summer climatology of surface air temperature over tropical and subtropical latitudes. Mixed results can be seen, however, for summer precipitation climatology in both hindcast and historical experiments. For winter, there is no noticeable improvement by the RCMs for the large-scale precipitation and surface air temperature climatology. To further understand the added value of RCMs, models deviations from observation are decomposed according to different terms that reflect the observational uncertainty, the representativeness error, the interpolation error, and the actual performance of the model. Regions where these errors are not negligible, such as in complex terrain regions, among others, can be identified. There is a clear need for complementary assessment to understand better the real value added by RCMs. [ABSTRACT FROM AUTHOR]

The climate in Mexico and Central America is influenced by the Pacific and the Atlantic oceanic basins and atmospheric conditions over continental North and South America. These factors and important ocean–atmosphere coupled processes make the region's climate a great challenge for global and regional climate modeling. We explore the benefits that coupled regional climate models may introduce in the representation of the regional climate with a set of coupled and uncoupled simulations forced by reanalysis and global model data. Uncoupled simulations tend to stay close to the large-scale patterns of the driving fields, particularly over the ocean, while over land they are modified by the regional atmospheric model physics and the improved orography representation. The regional coupled model adds to the reanalysis forcing the air–sea interaction, which is also better resolved than in the global model. Simulated fields are modified over the ocean, improving the representation of the key regional structures such as the Intertropical Convergence Zone and the Caribbean Low Level Jet. Higher resolution leads to improvements over land and in regions of intense air–sea interaction, e.g., off the coast of California. The coupled downscaling improves the representation of the Mid Summer Drought and the meridional rainfall distribution in southernmost Central America. Over the regions of humid climate, the coupling corrects the wet bias of the uncoupled runs and alleviates the dry bias of the driving model, yielding a rainfall seasonal cycle similar to that in the reanalysis-driven experiments. [ABSTRACT FROM AUTHOR]

A number of studies have shown that added value is obtained by increasing the horizontal resolution of a regional climate model to capture additional fine-scale weather processes. However, the mechanisms leading to this added value are different over areas with complicated orographic features, such as the Tibetan Plateau (TP). To determine the role that horizontal resolution plays over the TP, a detailed comparison was made between the results from the REMO regional climate model at resolutions of 25 and 50 km for the period 1980-2007. The model was driven at the lateral boundaries by the European Centre for Medium-Range Weather Forecasts Interim Reanalysis data. The experiments differ only in representation of topography, all other land parameters (e.g., vegetation characteristics, soil texture) are the same. The results show that the high-resolution topography affects the regional air circulation near the ground surface around the edge of the TP, which leads to a redistribution of the transport of atmospheric water vapor, especially over the Brahmaputra and Irrawaddy valleys—the main water vapor paths for the southern TP—increasing the amount of atmospheric water vapor transported onto the TP by about 5%. This, in turn, significantly decreases the temperature at 2 m by > 1.5 °C in winter in the high-resolution simulation of the southern TP. The impact of topography on the 2 m temperature over the TP is therefore by influencing the transport of atmospheric water vapor in the main water vapor paths. [ABSTRACT FROM AUTHOR]

The Etesians are among the most persistent regional scale wind systems in the lower troposphere that prevail over the eastern Mediterranean during the extended summer season. The performance of five high-resolution EURO-CORDEX regional climate models (RCMs) in simulating the Etesian climatology as well as the associated large-scale atmospheric circulation is investigated. The model outputs are validated against reanalysis datasets (ERA-Interim, 20CR-v2c and ERA20-C) and daily station observations covering the period May to September 1989-2004. Results show that most RCMs coherently reproduce the number of observed Etesian days, the duration of their episodes and the wind field over the Aegean Sea. The majority of RCMs better reproduce in situ wind speed than the driving model, especially over the central and southwestern Aegean Sea. All models represent very well the mean state of the large-scale atmospheric circulation associated with Etesians both at the surface and at mid to upper troposphere, compared to reanalyses. Statistically significant differences vary depending on the subperiod, generally with a better performance for September. The performance of the models improves significantly with decreasing pressure gradient over the Aegean. Finally, results highlight the ability of EURO-CORDEX RCMs in simulating the Etesians over the Aegean, especially the DMI, SMHI and IPSL, which makes them efficient tools for wind energy applications. [ABSTRACT FROM AUTHOR]

This study compares the direct and semi-direct aerosol effects of different annual cycles of tropospheric aerosol loads for Europe from 1950 to 2009 using the regional climate model COSMO-CLM, which is laterally forced by reanalysis data and run using prescribed, climatological aerosol optical properties. These properties differ with respect to the analysis strategy and the time window, and are then used for the same multi-decadal period. Five simulations with different aerosol loads and one control simulation without any tropospheric aerosols are integrated and compared. Two common limitations of our simulation strategy, to fully assess direct and semi-direct aerosol effects, are the applied observed sea surface temperatures and sea ice conditions, and the lack of short-term variations in the aerosol load. Nevertheless, the impact of different aerosol climatologies on common regional climate model simulations can be assessed. The results of all aerosol-including simulations show a distinct reduction in solar irradiance at the surface compared with that in the control simulation. This reduction is strongest in the summer season and is balanced primarily by a weakening of turbulent heat fluxes and to a lesser extent by a decrease in longwave emissions. Consequently, the seasonal mean surface cooling is modest. The temperature profile responses are characterized by a shallow near-surface cooling and a dominant warming up to the mid-troposphere caused by aerosol absorption. The resulting stabilization of stratification leads to reduced cloud cover and less precipitation. A decrease in cloud water and ice content over Central Europe in summer possibly reinforce aerosol absorption and thus strengthen the vertical warming. The resulting radiative forcings are positive. The robustness of the results was demonstrated by performing a simulation with very strong aerosol forcing, which lead to qualitatively similar results. A distinct added value over the default aerosol setup of Tanré et al. (1984) was found in the simulations with more recent aerosol data sets for solar irradiance. The improvements are largest under low cloud conditions, while overestimated cloud cover in all setups causes a common underestimation of low and medium values of solar irradiance. In addition, the prevalent cold bias in the COSMO-CLM is reduced in winter and spring when using updated aerosol data. Our results emphasize the importance of semi-direct aerosol effects, especially over Central Europe in terms of changes in turbulent fluxes and changes in cloud properties. We also suggest to replace the default Tanré et al. (1984) aerosol climatology with more recent and realistic data sets. Thereby, a better model performance in comparison to observations can be achieved, or the masking of model shortcomings due to a too strong direct aerosol forcing thus far is prevented. [ABSTRACT FROM AUTHOR]

Extratropical Cyclone (EC) characteristics depend on a combination of large-scale factors and regional processes. However, the latter are considered to be poorly represented in global climate models (GCMs), partly because their resolution is too coarse. This paper describes a framework using possibilities given by regional climate models (RCMs) to gain insight into storm activity during winter over North America (NA). Recent past climate period (1981-2005) is considered to assess EC activity over NA using the NCEP regional reanalysis (NARR) as a reference, along with the European reanalysis ERA-Interim (ERAI) and two CMIP5 GCMs used to drive the Canadian Regional Climate Model—version 5 (CRCM5) and the corresponding regional-scale simulations. While ERAI and GCM simulations show basic agreement with NARR in terms of climatological storm track patterns, detailed bias analyses show that, on the one hand, ERAI presents statistically significant positive biases in terms of EC genesis and therefore occurrence while capturing their intensity fairly well. On the other hand, GCMs present large negative intensity biases in the overall NA domain and particularly over NA eastern coast. In addition, storm occurrence over the northwestern topographic regions is highly overestimated. When the CRCM5 is driven by ERAI, no significant skill deterioration arises and, more importantly, all storm characteristics near areas with marked relief and over regions with large water masses are significantly improved with respect to ERAI. Conversely, in GCM-driven simulations, the added value contributed by CRCM5 is less prominent and systematic, except over western NA areas with high topography and over the Western Atlantic coastlines where the most frequent and intense ECs are located. Despite this significant added-value on seasonal-mean characteristics, a caveat is raised on the RCM ability to handle storm temporal ‘seriality’, as a measure of their temporal variability at a given location. In fact, the driving models induce some significant footprints on the RCM skill to reproduce the intra-seasonal pattern of storm activity. [ABSTRACT FROM AUTHOR]

In this paper, the regional climate model REMO is used to investigate the added value of downscaling low resolutions global climate models (GCMs) and the climate change projections over Central Africa. REMO was forced by two GCMs (EC-Earth and MPI-ESM), for the period from 1950 to 2100 under the Representative Concentration Pathway 8.5 scenario. The performance of the REMO simulations for current climate is compared first with REMO simulation driven by ERA-Interim reanalysis, then by the corresponding GCMs in order to determine whether REMO outputs are able to effectively lead to added value at local scale. We found that REMO is generally able to better represent some aspects of the rainfall inter-annual variability, the daily rainfall intensity distribution as well as the intra-seasonal variability of the Central African monsoon, though few biases are still evident. It is also found that the boundary conditions strongly influences the spatial distribution of seasonal 2-m temperature and rainfall. From the analysis of the climate change signal from the present period 1976-2005 to the future 2066-2095, we found that all models project a warming at the end of the twenty-first century although the details of the climate change differ between REMO and the driving GCMs, specifically in REMO where we observe a general decrease in rainfall. This rainfall decrease is associated with delayed onset and anticipated recession of the Central African monsoon and a shortening of the rainy season. Small-scales variability of the climate change signal for 2-m temperature are usually smaller than that of the large-scales climate change part. For rainfall however, small-scales induce change of about 70% compared to the present climate statistics. [ABSTRACT FROM AUTHOR]

Commonly termed 'added value', the additional regional details gained by high-resolution regional climate models (RCMs) over the coarser resolution reanalysis driving data are often indistinguishable at the 0.44° grid mesh computationally affordable large CORDEX domains. In an attempt to highlight the benefits of finer resolutions to study the RCM added value, five North American weather phenomena are evaluated in RCM hindcast simulations using grid meshes of 0.44°, 0.22° and 0.11° with available observations. The results show that the orographic precipitation on the west coast of North America is enhanced and more realistic, with two distinct rain bands in the finer resolution simulation. The spatial distribution of precipitation in August and the high frequency of summer precipitation extremes over southwestern United States reveal that the North American monsoon is improved with increasing resolution. Only the finer RCM simulation shows skill at producing snowbelts around the Great Lakes by capturing lake-effect snow. A comparison of wind roses in the St. Lawrence River Valley indicates that only the finer RCM simulation is able to reproduce wind channeling by resolving complex orography. Finally, the simulation of the summer land-sea breezes by the RCM simulations leads to added value in the diurnal cycle of precipitation over the Florida peninsula and the Caribbean islands. Overall, the almost systematic improvements of the finer resolution simulations suggest that higher resolutions, only computationally affordable over smaller domains, might get a higher priority to promote RCM added value. [ABSTRACT FROM AUTHOR]

Dynamic Regional Climate Models (RCMs) work at fine resolution for a limited region and hence they are presumed to simulate regional climate better than General Circulation Models (GCMs). Simulations by RCMs are used for impacts assessment, often without any evaluation. There is a growing debate on the added value made by the regional models to the projections of GCMs specifically for the regions like, United States and Europe. Evaluation of RCMs for Indian Summer Monsoon Rainfall (ISMR) has been overlooked in literature, though there are few disjoint studies on Indian monsoon extremes and biases. Here we present a comprehensive study on the evaluations of RCMs for the ISMR with all its important characteristics such as northward and eastward propagation, onset, seasonal rainfall patterns, intra-seasonal oscillations, spatial variability and patterns of extremes. We evaluate nine regional simulations from Coordinated Regional Climate Downscaling Experiment and compare them with their host Coupled Model Intercomparison Project-5 GCM projections. We do not find any consistent improvement in the RCM simulations with respect to their host GCMs for any of the characteristics of Indian monsoon except the spatial variation. We also find that the simulations of the ISMR characteristics by a good number of RCMs, are worse than those of their host GCMs. No consistent added value is observed in the RCM simulations of changes in ISMR characteristics over recent periods, compared to past; though there are few exceptions. These results highlight the need for proper evaluation before utilizing regional models for impacts assessment and subsequent policy making for sustainable climate change adaptation. [ABSTRACT FROM AUTHOR]

Daily precipitation statistics as simulated by the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble are evaluated over two distinct regions of the European continent, namely the European Alps and Spain. The potential added value of the high-resolution 12 km experiments with respect to their 50 km resolution counterparts is investigated. The statistics considered consist of wet-day intensity and precipitation frequency as a measure of mean precipitation, and three precipitation-derived indicators (90th percentile on wet days-90pWET, contribution of the very wet days to total precipitation-R95pTOT and number of consecutive dry days-CDD). As reference for model evaluation high resolution gridded observational data over continental Spain (Spain011/044) and the Alpine region (EURO4M-APGD) are used. The assessment and comparison of the two resolutions is accomplished not only on their original horizontal grids (approximately 12 and 50 km), but the high-resolution RCMs are additionally regridded onto the coarse 50 km grid by grid cell aggregation for the direct comparison with the low resolution simulations. The direct application of RCMs e.g. in many impact modelling studies is hampered by model biases. Therefore bias correction (BC) techniques are needed at both resolutions to ensure a better agreement between models and observations. In this work, the added value of the high resolution (before and after the bias correction) is assessed and the suitability of these BC methods is also discussed. Three basic BC methods are applied to isolate the effect of biases in mean precipitation, wet-day intensity and wet-day frequency on the derived indicators. Daily precipitation percentiles are strongly affected by biases in the wet-day intensity, whereas the dry spells are better represented when the simulated precipitation frequency is adjusted to the observed one. This confirms that there is no single optimal way to correct for RCM biases, since correcting some distributional features typically leads to an improvement of some aspects but to a deterioration of others. Regarding mean seasonal biases before the BC, we find only limited evidence for an added value of the higher resolution in the precipitation intensity and frequency or in the derived indicators. Thereby, evaluation results considerably depend on the RCM, season and indicator considered. High resolution simulations better reproduce the indicators' spatial patterns, especially in terms of spatial correlation. However, this improvement is not statistically significant after applying specific BC methods. [ABSTRACT FROM AUTHOR]

In the framework of the EURO-CORDEX initiative an ensemble of European-wide high-resolution regional climate simulations on a $$0.11^{\circ }\,({\sim}12.5\,\hbox {km})$$ grid has been generated. This study investigates whether the fine-gridded regional climate models are found to add value to the simulated mean and extreme daily and sub-daily precipitation compared to their coarser-gridded $$0.44^{\circ }\,({\sim}50\,\hbox {km})$$ counterparts. Therefore, pairs of fine- and coarse-gridded simulations of eight reanalysis-driven models are compared to fine-gridded observations in the Alps, Germany, Sweden, Norway, France, the Carpathians, and Spain. A clear result is that the $$0.11^{\circ }$$ simulations are found to better reproduce mean and extreme precipitation for almost all regions and seasons, even on the scale of the coarser-gridded simulations (50 km). This is primarily caused by the improved representation of orography in the $$0.11^{\circ }$$ simulations and therefore largest improvements can be found in regions with substantial orographic features. Improvements in reproducing precipitation in the summer season appear also due to the fact that in the fine-gridded simulations the larger scales of convection are captured by the resolved-scale dynamics . The $$0.11^{\circ }$$ simulations reduce biases in large areas of the investigated regions, have an improved representation of spatial precipitation patterns, and precipitation distributions are improved for daily and in particular for 3 hourly precipitation sums in Switzerland. When the evaluation is conducted on the fine (12.5 km) grid, the added value of the $$0.11^{\circ }$$ models becomes even more obvious. [ABSTRACT FROM AUTHOR]

Important discrepancies in the large-scale summer climate change projections were recently detected between the global and regional climate models (RCM/GCM) in the EURO-CORDEX ensemble for several variables including surface temperature, total precipitation, and surface solar radiation. In this study, we use a new experimental framework inspired by the Big-Brother–Little-Brother protocol to explore the mechanisms responsible for generating large-scale discrepancies in future projections between GCM/RCM pairs over Europe in summer. Starting from past and future simulations with a perfect GCM/RCM pair (same resolution, same physics, same forcings), we then disentangle the role of potential sources of GCM/RCM inconsistency by carrying out targeted sensitivity studies. We show that by following such a perfect approach, it is possible to obtain a GCM/RCM pair without statistically significant inconsistencies in projected climate change. Such discrepancies are mainly generated by differences in aerosols representation and atmospheric physics. The role of plant physiology is limited and unlikely to be the dominant factor in the detected discrepancies. Finally, it is unlikely that the discrepancies in the EURO-CORDEX ensemble projections are a result of the upscaled added value, as we show that the effect of increased resolution is not strong enough and mostly limited to areas with complex topography. These findings raise important questions about the current practices in regional climate modelling. In the short term, implementing RCM external forcings consistent with the driving GCM can significantly improve the situation at low cost. In the long term, adopting a seamless strategy in developing the GCM/RCM models should be questioned. [ABSTRACT FROM AUTHOR]

In this work we present the results of the application of the consortium for small-scale modeling (COSMO) regional climate model (COSMO-CLM, hereafter, CCLM) over Africa in the context of the coordinated regional climate downscaling experiment. An ensemble of climate change projections has been created by downscaling the simulations of four global climate models (GCM), namely: MPI-ESM-LR, HadGEM2-ES, CNRM-CM5, and EC-Earth. Here we compare the results of CCLM to those of the driving GCMs over the present climate, in order to investigate whether RCMs are effectively able to add value, at regional scale, to the performances of GCMs. It is found that, in general, the geographical distribution of mean sea level pressure, surface temperature and seasonal precipitation is strongly affected by the boundary conditions (i.e. driving GCMs), and seasonal statistics are not always improved by the downscaling. However, CCLM is generally able to better represent the annual cycle of precipitation, in particular over Southern Africa and the West Africa monsoon (WAM) area. By performing a singular spectrum analysis it is found that CCLM is able to reproduce satisfactorily the annual and sub-annual principal components of the precipitation time series over the Guinea Gulf, whereas the GCMs are in general not able to simulate the bimodal distribution due to the passage of the WAM and show a unimodal precipitation annual cycle. Furthermore, it is shown that CCLM is able to better reproduce the probability distribution function of precipitation and some impact-relevant indices such as the number of consecutive wet and dry days, and the frequency of heavy rain events. [ABSTRACT FROM AUTHOR]

This study evaluates the capability of regional climate models (RCMs) in simulating the link between El Niño Southern Oscillation (ENSO) and Southern African droughts. It uses the Standardized Precipitation-Evapotranspiration Index (SPEI, computed using rainfall and temperature data) to identify 3-month drought over Southern Africa, and compares the observed and simulated correlation between ENSO and SPEI. The observation data are from the Climate Research Unit, while the simulation data are from ten RCMs (ARPEGE, CCLM, HIRHAM, RACMO, REMO, PRECIS, RegCM3, RCA, WRF, and CRCM) that participated in the regional climate downscaling experiment (CORDEX) project. The study analysed the rainy season (December-February) data for 19 years (1989-2008). The results show a strong link between ENSO and droughts (SPEI) over Southern Africa. The link is owing to the influence of ENSO on both rainfall and temperature fields, but the correlation between ENSO and temperature is stronger than the correlation between ENSO and rainfall. Hence, using only rainfall to monitor droughts in Southern Africa may underestimate the influence of ENSO on the droughts. Only few CORDEX RCMs simulate the influence of ENSO on Southern African drought as observed. In this regard, the ARPEGE model shows the best simulation, while CRCM shows the worst. The different in the performance may be due to their lateral boundary conditions. The RCA-simulated link between ENSO and Southern African droughts is sensitive to the global dataset used as the lateral boundary conditions. In some cases, using RCA to downscale global circulation models (GCM) simulations adds value to the simulated link between ENSO and the droughts, but in other cases the downscaling adds no value to the link. The added value of RCA to the simulated link decreases as the capability of the GCM to simulate the link increases. This study suggests that downscaling GCM simulations with RCMs over Southern Africa may improve or depreciate the simulated ENSO-drought link over the region. [ABSTRACT FROM AUTHOR]

In this study the added value of a ensemble of convection permitting climate simulations (CPCSs) compared to coarser gridded simulations is investigated. The ensemble consists of three non hydrostatic regional climate models providing five simulations with ~10 and ~3 km (CPCS) horizontal grid spacing each. The simulated temperature, precipitation, relative humidity, and global radiation fields are evaluated within two seasons (JJA 2007 and DJF 2007-2008) in the eastern part of the European Alps. Spatial variability, diurnal cycles, temporal correlations, and distributions with focus on extreme events are analyzed and specific methods (FSS and SAL) are used for in-depth analysis of precipitation fields. The most important added value of CPCSs are found in the diurnal cycle improved timing of summer convective precipitation, the intensity of most extreme precipitation, and the size and shape of precipitation objects. These improvements are not caused by the higher resolved orography but by the explicit treatment of deep convection and the more realistic model dynamics. In contrary improvements in summer temperature fields can be fully attributed to the higher resolved orography. Generally, added value of CPCSs is predominantly found in summer, in complex terrain, on small spatial and temporal scales, and for high precipitation intensities. [ABSTRACT FROM AUTHOR]

Nested Limited-Area Models require driving data to define their lateral boundary conditions (LBC). The optimal choice of domain size and the repercussions of LBC errors on Regional Climate Model (RCM) simulations are important issues in dynamical downscaling work. The main objective of this paper is to investigate the effect of domain size, particularly on the larger scales, and to question whether an RCM, when run over very large domains, can actually improve the large scales compared to those of the driving data. This study is performed with a detailed atmospheric model in its global and regional configurations, using the 'Imperfect Big-Brother' (IBB) protocol. The ERA-Interim reanalyses and five global simulations are used to drive RCM simulations for five winter seasons, on four domain sizes centred over the North American continent. Three variables are investigated: precipitation, specific humidity and zonal wind component. The results following the IBB protocol show that, when an RCM is driven by perfect LBC, its skill at reproducing the large scales decreases with increasing the domain of integration, but the errors remain small even for very large domains. On the other hand, when driven by LBC that contain errors, RCMs can bring some reduction of errors in large scales when very large domains are used. The improvement is found especially in the amplitude of patterns of both the stationary and the intra-seasonal transient components. When large errors are present in the LBC, however, these are only partly corrected by the RCM. Although results showed that an RCM can have some skill at improving imperfect large scales supplied as driving LBC, the main added value of an RCM is provided by its small scales and its skill to simulate extreme events, particularly for precipitation. Under the IBB protocol all RCM simulations were fairly skilful at reproducing small scales statistics, although the skill decreased with increasing LBC errors. Coarse-resolution model simulations have difficulties in simulating heavy precipitation events, and as a result their precipitation distributions are systematically shifted toward smaller intensity. Under the IBB protocol, all RCM simulations have distributions very similar to the reference field, being little affected by LBC errors, and no significant differences were found between the small scales statistics and the precipitation distributions obtained over different RCM domains. [ABSTRACT FROM AUTHOR]

Regional Climate Models (RCMs) constitute the most often used method to perform affordable high-resolution regional climate simulations. The key issue in the evaluation of nested regional models is to determine whether RCM simulations improve the representation of climatic statistics compared to the driving data, that is, whether RCMs add value. In this study we examine a necessary condition that some climate statistics derived from the precipitation field must satisfy in order that the RCM technique can generate some added value: we focus on whether the climate statistics of interest contain some fine spatial-scale variability that would be absent on a coarser grid. The presence and magnitude of fine-scale precipitation variance required to adequately describe a given climate statistics will then be used to quantify the potential added value (PAV) of RCMs. Our results show that the PAV of RCMs is much higher for short temporal scales (e.g., 3-hourly data) than for long temporal scales (16-day average data) due to the filtering resulting from the time-averaging process. PAV is higher in warm season compared to cold season due to the higher proportion of precipitation falling from small-scale weather systems in the warm season. In regions of complex topography, the orographic forcing induces an extra component of PAV, no matter the season or the temporal scale considered. The PAV is also estimated using high-resolution datasets based on observations allowing the evaluation of the sensitivity of changing resolution in the real climate system. The results show that RCMs tend to reproduce relatively well the PAV compared to observations although showing an overestimation of the PAV in warm season and mountainous regions. [ABSTRACT FROM AUTHOR]

Through its various radiative effects and latent heat release, water plays a major role in the maintenance of climate. Therefore a better understanding of climate and climate changes requires a better understanding of the hydrological cycle. In this study we investigate the scale-decomposed atmospheric water budget over North America as simulated by the Canadian Regional Climate Model (CRCM) driven by the Canadian Coupled Global Climate Model (CGCM) under current conditions for 1961-1990 and the SRES A2 scenario for 2041-2070. A discrete cosine transform is applied to the atmospheric water budget variables in order to separate small scales that are resolved exclusively by the high-resolution CRCM, from larger scales resolved by both the CRCM and low-resolution driving CGCM. The moisture flux divergence is alternatively decomposed in terms of three scales of wind and humidity to provide nine interaction terms. Statistics of these fields are calculated for winter and summer seasons, and the local statistical significance of climate-change projections is tested. The contributions of each scale band to the water budget current climatology and to its evolution in a warmer climate are investigated, addressing the issue of the potential added value of smaller scales. Results show a time variability larger than the time mean for all variables, and a significant small-scale contribution to time variability, which is even dominant in summer, both in the current and future climates. Future climate exhibits an overall intensification of the hydrological cycle in winter, and more mixed changes in summer. Relative changes in the time mean and time variability appear comparable, and the contribution of each scale band to variability changes remains overall very consistent with their contribution to current climate variability. [ABSTRACT FROM AUTHOR]