Evaluation Of Regional Climate Models Research Articles

Evaluation of snow depth from multiple observation-based, reanalysis, and regional climate model datasets over a low-altitude Central European region

This study evaluates snow depth (SD) from several data sources: a combined satellite-based and in situ snow water equivalent product from the Copernicus Global Land Service (CGLS), a dataset constructed from temperature, precipitation, and relative humidity using a snow model (CARPATCLIM), two state-of-the-art reanalyses by ECMWF (ERA5 and ERA5-Land), and Weather Research and Forecasting (WRF) regional climate simulations at grid spacings of 50 km and 10 km. SD observations from weather stations are used as a reference for the pointwise comparison. The study area covers the Pannonian Basin region (part of Central and Eastern Europe). Results are presented for the 2006–2010 and 1985–2010 periods. All datasets adequately reproduce the average day-to-day variation of SD but with different error magnitudes. The ERA5 reanalysis and the CGLS product represent SD remarkably well, with correlation coefficients above 0.9 and mean errors close to zero. On the other hand, ERA5-Land and CARPATCLIM overestimate daily mean SD by 2–3 cm for some stations and display lower correlations (0.7–0.9) during the 26-year time span. The WRF simulations significantly overestimate SD in the melting period (February–March). Reduction of the grid spacing from 50 to 10 km does not improve the results. The excessive snow cover might negatively impact land–atmosphere interactions in the model and lead to biases like temperature underestimation found in previous regional climate model evaluation studies. The results indicate that even in regions where snow is not a major climatic factor, SD errors can be substantial and should be considered in model evaluation and adaptation. Over the Carpathian Mountain ranges, SD from the different data sources diverges to the extent that the sign of the monthly mean model bias changes depending on the choice of the reference dataset.

Quantifying uncertainties related to observational datasets used as reference for regional climate model evaluation over complex topography — a case study for the wettest year 2010 in the Carpathian region

Gridded observational datasets are often used for the evaluation of regional climate model (RCM) simulations. However, the uncertainty of observations affects the evaluation. This work introduces a novel method to quantify the uncertainties in the observational datasets and how these uncertainties affect the evaluation of RCM simulations. Besides precipitation and temperature, our method uses geographic variables (e.g. elevation, variability of elevation, effect of station), which are considered as uncertainty sources. To assess these uncertainties, a complex analysis based on various statistical tools, e.g. correlation analysis and permutation test, was carried out. Furthermore, we used a special metric, the reduction of error (RE) to identify where the RCM shows improvement compared to the lateral boundary conditions (LBCs). We focused on the Carpathian region, because of its unique orographic and climatic conditions. The method is applied to two observational datasets (CarpatClim and E-OBS) and to RegCM simulations for 2010, the wettest year in this region since 1901.The results show that CarpatClim is wetter than E-OBS, while temperature is similar over the lowland; however, E-OBS is significantly warmer than CarpatClim over the mountains. By the RE metric, RegCM has improvement against the LBCs over mountains for temperature and areas with dense station network for precipitation. Nevertheless, there are significant differences in the results depending on which observational dataset was used concerning precipitation. The evaluation method can be applied to other datasets, different time periods and areas. It is also suitable to find dataset errors, which is also exemplified in this paper.

Evaluation of regional climate models and future wave characteristics in an enclosed sea: A case study of the Black Sea

The main objective of this study is to evaluate a possible climate change impact on wave climate in an enclosed sea (Black Sea) until the end of the 21st century. To achieve the goal, the wave data are generated from a third-generation wave model forced with the widely used reanalysis data (Climate Forecast System Reanalysis), and the hindcast wind fields simulated by a Regional Climate Model, namely Rossby Centre regional atmospheric model (version RCA4). The pattern of change in the wave climate is discussed by comparing the historical (1970–2005) with the future for periods 2021–2060 and 2061–2100 under two climate scenarios. The projected changes in the mean wave characteristics are characterized by an increase in the near future for both RCP scenarios that is more prominent under RCP8.5 scenario in the eastern basin. The middle future under RCP8.5 scenario suggests more significant changes in annual mean values than those obtained under RCP4.5 scenario. In the context of climate change, the possible effects are more prominent on extreme wave heights. For example, the increases in the wave heights for the 100-year return period reach the value of 5% and 25% in the western and eastern Black Sea, respectively.

Complementing ERA5 and E-OBS with high-resolution river discharge over Europe

The 0.5° resolution of many global observational or quasi-observational datasets is not sufficient for the evaluation of current state-of-the-art regional climate models or the forcing of ocean model simulations over Europe. While higher resolved products are available for meteorological data, e.g. ERA5 reanalysis and the E-OBS vs 22 (EOBS22) datasets, they lack crucial information at the land-ocean boundary. ERA5 is frequently used to force regional climate models (RCMs) or ocean models and both datasets are commonly used as reference datasets for the evaluation of RCMs. Therefore, we extended both datasets with high-resolution river discharge for the period 1979–2018. On the one hand, our discharge data close the water cycle at the land-ocean interface so that the discharges can be used as lateral freshwater input for ocean models applied in the European region. On the other hand, the data can be used to identify trends in discharge that are induced by recent climate change as ERA5 and EOBS22 are rather independent datasets. The experimental setup to generate the discharges was chosen in a way that it could be easily adapted in a climate or Earth system modelling framework. Consequently, the recently developed 5 Min. horizontal resolution version of the hydrological discharge (HD) model was used to simulate discharge. It has already been applied in multiple climate modelling studies and is coupled within several global and regional Earth system models. As the HD model currently does not regard direct human impacts of the river runoff, it is well suited to investigate climate change-related discharge trends. In order to calculate the necessary gridded input fields for the HD model from ERA5 and EOBS22 data, we used the HydroPy global hydrological model. For both experiments, we found that the general behavior of discharge is captured well for many European rivers, which is consistent to earlier results. For the EOBS22 based discharges, a widespread low bias in simulated discharge occurs, which is likely caused by the missing undercatch correction in the underlying precipitation data. The analysis of trends over Southeastern Europe was hampered by missing data in EOBS22 after 2004. Using both experiments, we identified consistent trend patterns in various discharge statistics, with increases in low flow characteristics over Northern Europe and general drying trends over Central and Southern Europe. In summary, we introduced an experimental setup that is useful to generate high-resolution river runoff data consistent with the meteorological forcing for historical periods and future scenarios from any climate model data instead of having to rely on observed time series.

Evaluation of Regional Climate Models (RCMs) Using Precipitation and Temperature-Based Climatic Indices: A Case Study of Florida, USA

The overarching objective of this study was to evaluate the performance of nine precipitation-based and twelve temperature-based climatic indices derived from four regional climate models (CRCM5-UQUAM, CanRCM4, RCA4 and HIRHAM5) driven by three global circulation models (CanESM2, EC-EARTH and MPI-ESM-LR) and their ensemble mean for the reference period of 31 years (1975–2005). The absolute biases, pattern correlation, the reduction of variance (RV) and the Standardized Precipitation Evapotranspiration Index (SPEI at 3-, 6- and 12-month aggregate periods) techniques were used to evaluate the climate model simulations. The result, in general, shows each climate model has a skill in reproducing at least one of the climatic indices considered in this study. Based on the pattern correlation result, however, EC-EARTH.HIRHAM5 and MPI-ESM-LR.CRCM5-UQAM RCMs showed a relatively good skill in reproducing the observed climatic indices as compared to the other climate model simulations. EC-EARTH.RCA4, CanESM2.RCA4 and MPI-ESM-LR.CRCM5-UQAM RCMs showed a good skill when evaluated using the reduction of variance. The ensemble mean of the RCMs showed relatively better skill in reproducing the observed temperature-based climatic indices as compared to the precipitation-based climatic indices. There were no exceptional differences observed among the performance of the climate models compared to the SPEI, but CanESM2.CRCM5-UQAM, EC-EARTH.RCA4 and the ensemble mean of the RCMs performed relatively good in comparison to the other climate models. The good performance of some of the RCMs has good implications for their potential application for climate change impact studies and future trend analysis of extreme events. They could help in developing an early warning system to mitigate and prepare for possible future impacts of climate extremes (e.g., drought) and vulnerability to climate change across Florida.

Evaluation of regional climate models and future wind characteristics in the Black Sea

AbstractThe main objective of the present study is to provide a comprehensive image of the expected wind patterns along the 21st century in the Black Sea using the wind fields simulated by Regional Climate Model (RCM), namely Rossby Centre regional atmospheric model (version RCA4). Long‐term averages, long‐term variability, and long‐term trends in wind characteristics over the Black Sea are evaluated in two future time slices (near future 2021–2060 and middle future 2061–2100) under the two climate scenarios (RCP4.5 and 8.5). Detailed analysis of wind (annual mean and 99th percentile wind speeds) and storm characteristics (number and duration of storms) are conducted for three different locations selected on the same meridional section to assess the variability on the wind characteristics from west to east. Spatiotemporal variability on the wind characteristics indicates that the eastern Black Sea will be characterized by lower mean wind speed, stronger 99th percentile wind speed, and higher variability. Considering the global indicators of climate change (e.g., increasing frequency and intensity of extreme events), the eastern Black Sea, characterized by complex atmospheric circulation due to surrounding mountains, is more likely to be subjected to the impacts of climate change as the occurrence of extreme wind speeds in future periods is more prominent in this region. In the future periods, the 100‐year return period wind speeds increase approximately 7% and 27% in the western and eastern Black Sea, respectively, compared to the historical period. The strong and relatively stable winds with low variability in the western Black Sea indicate a reliable, persistent, and sustainable wind energy potential in this region in the future.

Evaluation of Regional Climate Models (RCMs) Performance in Simulating Seasonal Precipitation over Mountainous Central Pindus (Greece)

During the last few years, there is a growing concern about climate change and its negative effects on water availability. This study aims to evaluate the performance of regional climate models (RCMs) in simulating seasonal precipitation over the mountainous range of Central Pindus (Greece). To this end, observed precipitation data from ground-based rain gauge stations were compared with RCMs grid point’s simulations for the baseline period 1974–2000. Statistical indexes such as root mean square error (RMSE), mean absolute error (MAE), Pearson correlation coefficient, and standard deviation (SD) were used in order to evaluate the model’s performance. The results demonstrated that RCMs fail to represent the temporal variability of precipitation time series with exception of REMO. Although, concerning the model’s prediction accuracy, it was found that better performance was achieved by the RegCM3 model in the study area. In addition, regarding a future projection (2074–2100), it was highlighted that precipitation will significantly decrease by the end of the 21st century, especially in spring (−30%). Therefore, adaption of mountainous catchment management to climate change is crucial to avoid water scarcity.

The Impact of Climate Change on Material Degradation Criteria in Heritage over Iran: Regional Climate Model Evaluation

Understanding how climate change accelerates or slows down the process of material deterioration is the first step towards assessing adaptive approaches for the preservation of historical heritage. Analysis of the climate change effects on the degradation risk assessment parameters like salt crystallization cycles is of crucial importance when considering mitigating actions. Due to the vulnerability of cultural heritage in Iran to climate change, the impact of this phenomenon on basic parameters plus variables more critical to building damage like salt crystallization index needs to be analyzed. Regional climate modelling projections can be used to asses the impact of climate change effects on heritage. The output of two different regional climate models, the ALARO-0 model (Ghent University-RMI, Belgium) and the REMO model (HZG-GERICS, Germany), is analyzed to find out which model is more adapted to the region. So the focus of this research is mainly on the evaluation to determine the reliability of both models over the region. For model validation, a comparison between model data and observations was performed in 4 different climate zones for 30 years to find out how reliable these models are in the field of building pathology.

Evaluation of Regional Climate Models Using Regionally Optimized GRACE Mascons in the Amery and Getz Ice Shelves Basins, Antarctica

AbstractWe develop regionally optimized Gravity Recovery and Climate Experiment (GRACE) solutions to evaluate the mass balance of the drainage basins of Amery Ice Shelf, East Antarctica, and Getz Ice Shelf, West Antarctica. We find that the Amery region is near balance, while the Getz region is rapidly losing mass. We compare the results with the mass budget method (MBM) combining ice discharge along the periphery with surface mass balance derived from three regional climate models: (1) Regional Atmospheric Climate Model (RACMO) 2.3p1 and (2) 2.3p2, and (3) Modèle Atmosphérique Régional 3.6.41. For Amery, MBM/RACMO2.3p1 agrees with GRACE, while MBM/RACMO2.3p2 and MBM/MAR3.6.41 suggest a positive mass balance. For Getz, all estimates agree with a mass loss and the GRACE results are robust to uncertainties in glacial isostatic adjustment derived from an ensemble 128,000 forward models. Over the period April 2002 to November 2015, the mass loss of the Getz drainage basin is 22.9 ± 10.9 Gt/yr with an acceleration of 1.6 ± 0.9 Gt/yr2.

Impact of Reference Dataset Selection on RCM Evaluation, Bias Correction, and Resulting Climate Change Signals of Precipitation

Abstract Gridded datasets of precipitation are of great importance to evaluate recent climate models and are frequently applied to select a subset of available models. As climate models are still prone to biases on the regional scale, gridded datasets are also essential to correct or adjust these biases. Various studies revealed considerable differences, that is, observational uncertainty, in the available gridded datasets of precipitation, especially over complex terrain. This study focuses on the impacts of observational uncertainty on the evaluation, selection, and bias correction of 15 regional climate model (RCM) simulations provided through the EURO-CORDEX initiative over the alpine Adige catchment located in northern Italy. Nine reference datasets originating from observations, reanalysis, and remote sensing are applied to evaluate the performance of RCMs and select a subset based on validity. These reference datasets are then applied to bias correct the RCM ensemble using a standard quantile mapping method, and the resulting changes in the projections are assessed. The presented results show a selection of similar RCMs, indicating that observational uncertainty is lower than model uncertainty. The influence of the choice of the reference dataset on bias correction is negligible for the climate change signals. Small differences in projected change signals can be attributed to model selection. As expected, the choice of the reference dataset strongly influences future projections of precipitation even more pronounced for the extremes. The findings of this study highlight the need to account for observational uncertainty for bias correction of RCM simulations for impact modeling studies.

Climate Model Evaluation in the Presence of Observational Uncertainty: Precipitation Indices over the Contiguous United States

Abstract Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large divergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.

Evaluating reanalysis-driven CORDEX regional climate models over Australia: model performance and errors

The ability of regional climate models (RCMs) to accurately simulate current and future climate is increasingly important for impact assessment. This is the first evaluation of all reanalysis-driven RCMs within the CORDEX Australasia framework [four configurations of the Weather Forecasting and Research (WRF) model, and single configurations of COSMO-CLM (CCLM) and the Conformal-Cubic Atmospheric Model (CCAM)] to simulate the historical climate of Australia (1981–2010) at 50 km resolution. Simulations of near-surface maximum and minimum temperature and precipitation were compared with gridded observations at annual, seasonal, and daily time scales. The spatial extent, sign, and statistical significance of biases varied markedly between the RCMs. However, all RCMs showed widespread, statistically significant cold biases in maximum temperature which were the largest during winter. This bias exceeded − 5 K for some WRF configurations, and was the lowest for CCLM at ± 2 K. Most WRF configurations and CCAM simulated minimum temperatures more accurately than maximum temperatures, with biases in the range of ± 1.5 K. RCMs overestimated precipitation, especially over Australia’s populous eastern seaboard. Strong negative correlations between mean monthly biases in precipitation and maximum temperature suggest that the maximum temperature cold bias is linked to precipitation overestimation. This analysis shows that the CORDEX Australasia ensemble is a valuable dataset for future impact studies, but improving the representation of land surface processes, and subsequently of surface temperatures, will improve RCM performance. The varying RCM capabilities identified here serve as a foundation for the development of future regional climate projections and impact assessments for Australia.

Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here, we present a user-oriented document describing the latest version of RCMES, its development process, and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in situ observations, as well as satellite and reanalysis data products. The main components of RCMES are (1) a database of observations widely used for climate model evaluation, (2) various data loaders to import climate models and observations on local file systems and Earth System Grid Federation (ESGF) nodes, (3) a versatile processor to subset and regrid the loaded datasets, (4) performance metrics designed to assess and quantify model skill, (5) plotting routines to visualize the performance metrics, (6) a toolkit for statistically downscaling climate model simulations, and (7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with a brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

Extreme temperature events on Greenland in observations and the MAR regional climate model

Abstract. Meltwater from the Greenland Ice Sheet contributed 1.7–6.12 mm to global sea level between 1993 and 2010 and is expected to contribute 20–110 mm to future sea level rise by 2100. These estimates were produced by regional climate models (RCMs) which are known to be robust at the ice sheet scale but occasionally miss regional- and local-scale climate variability (e.g. Leeson et al., 2017; Medley et al., 2013). To date, the fidelity of these models in the context of short-period variability in time (i.e. intra-seasonal) has not been fully assessed, for example their ability to simulate extreme temperature events. We use an event identification algorithm commonly used in extreme value analysis, together with observations from the Greenland Climate Network (GC-Net), to assess the ability of the MAR (Modèle Atmosphérique Régional) RCM to reproduce observed extreme positive-temperature events at 14 sites around Greenland. We find that MAR is able to accurately simulate the frequency and duration of these events but underestimates their magnitude by more than half a degree Celsius/kelvin, although this bias is much smaller than that exhibited by coarse-scale Era-Interim reanalysis data. As a result, melt energy in MAR output is underestimated by between 16 and 41 % depending on global forcing applied. Further work is needed to precisely determine the drivers of extreme temperature events, and why the model underperforms in this area, but our findings suggest that biases are passed into MAR from boundary forcing data. This is important because these forcings are common between RCMs and their range of predictions of past and future ice sheet melting. We propose that examining extreme events should become a routine part of global and regional climate model evaluation and that addressing shortcomings in this area should be a priority for model development.

Evaluation of Precipitation From EURO‐CORDEX Regional Climate Simulations in a Small‐Scale Mediterranean Site

AbstractThe evaluation of regional climate models' (RCMs') ability to reproduce the present‐day climate is critical to support their utility in impact studies under future climatic scenarios. This study evaluates the skill of an ensemble of state‐of‐the‐art regional climate simulations from the European Coordinated Regional Downscaling Experiment initiative in reproducing the precipitation (P) properties in Sardinia, a Mediterranean island of ~24,000 km2. The ensemble includes simulations at 0.44° and 0.11° grid spacing of the “Historical” experiment from 1950 to 2005. Precipitation records from a high‐density network of gauges are used as reference data set. The interannual and seasonal climatology of P, presence of trend, and orographic effect are assessed at the original RCM grid spacings and different spatial scales of aggregation. Most models capture the observed positive relation between annual P and elevation, with better performance at 0.11° spacing. The simulated spatial patterns of the climatological annual and seasonal means are well correlated with the observation at both spacings, but their spatial variability is overestimated. Positive and negative bias of up to ±60% are found in the simulation of annual mean and interannual variability. While the majority of the models reproduce the phase of the seasonal cycle, they underestimate (overestimate) winter (summer) P. The RCMs exhibit different deficiencies in capturing the negative annual and seasonal observed trends. In general, models' skill degrades when analyses are conducted at smaller aggregation scales. Results of this study reveal insight on RCM performances in small‐scale regions, which are often targeted by impact studies and have so far received less attention.

Evaluation of regional COSMO‐CLM climate simulations over the Eastern Mediterranean for the period 1979–2011

ABSTRACTThe Regional Climate Model (RCM) COSMO‐CLM capability to reproduce the climate characteristics, including extreme values, over the Eastern Mediterranean (EM) was tested. Model configuration has been chosen based on a previously performed sensitivity analysis, aimed to ascertain the accuracy of model performances over Israel. Three simulations driven by ERA Interim reanalysis data for 1979–2011 have been performed using the 0.44°, 0.22° and 0.0715° horizontal resolutions equivalent to about 50, 25 and 8 km, respectively. The CORDEX‐MENA domain has been employed for the simulation at resolutions 0.44° and 0.22°. Nested in the 0.22° domain the highest resolution of 0.0715° is performed over Israel. The model response was analysed for daily precipitation, 2 m average temperature, maximum temperature, minimum temperature and a subset of climate indicators defined by the Expert Team on Climate Change Detection and Indices for temperature and precipitation. Results were inter‐compared and evaluated against observations. The increased resolution was found to improve precipitation and temperature results. Extreme precipitation indices were well reproduced compared with observations, with a 13% averaged percentage bias. COSMO‐CLM was able to reproduce the EM precipitation gradients, with mostly overestimations in the coastal plains and underestimations in the mountains. Extreme temperature indices related to maximum temperatures were reproduced relatively well with an averaged percentage bias of 5.7%. The ability of the model to reproduce minimum temperature observational values was found to be highly dependent on station location with respect to topography. The results in this study are considered a substantial improvement from earlier RCM evaluation studies.

Observational uncertainty and regional climate model evaluation: A pan‐European perspective

The influence of uncertainties in gridded observational reference data on regional climate model (RCM) evaluation is quantified on a pan‐European scale. Three different reference data sets are considered: the coarse‐resolved E‐OBS data set, a compilation of regional high‐resolution gridded products (HR) and the European‐scale MESAN reanalysis. Five high‐resolution ERA‐Interim‐driven RCM experiments of the EURO‐CORDEX initiative are evaluated against each of these references over eight European sub‐regions and considering a range of performance metrics for mean daily temperature and daily precipitation. The spatial scale of the evaluation is 0.22°, that is, the grid spacing of the coarsest data set in the exercise (E‐OBS). While the three reference grids agree on the overall mean climatology, differences can be pronounced over individual regions. These differences partly translate into RCM evaluation uncertainty. For most cases observational uncertainty is smaller than RCM uncertainty. Nevertheless, for individual sub‐regions and performance metrics observational uncertainty can dominate. This is especially true for precipitation and for metrics targeting the wet‐day frequency, the pattern correlation and the distributional similarity. In some cases the spatially averaged mean bias can also be considerably affected. An illustrative ranking exercise highlights the overall effect of observational uncertainty on RCM ranking. Over individual sub‐domains, the choice of a specific reference can modify RCM ranks by up to four levels (out of five RCMs). For most cases, however, RCM ranks are stable irrespective of the reference. These results provide a twofold picture: model uncertainty dominates for most regions and for most performance metrics considered, and observational uncertainty plays a minor role. For individual cases, however, observational uncertainty can be pronounced and needs to be definitely taken into account. Results can, to some extent, also depend on the treatment of precipitation undercatch in the observational reference.

The MSG-SEVIRI-based cloud property data record CLAAS-2

Abstract. Clouds play a central role in the Earth's atmosphere, and satellite observations are crucial for monitoring clouds and understanding their impact on the energy budget and water cycle. Within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF), a new cloud property data record was derived from geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements for the time frame 2004–2015. The resulting CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) data record is publicly available via the CM SAF website (https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002). In this paper we present an extensive evaluation of the CLAAS-2 cloud products, which include cloud fractional coverage, thermodynamic phase, cloud top properties, liquid/ice cloud water path and corresponding optical thickness and particle effective radius. Data validation and comparisons were performed on both level 2 (native SEVIRI grid and repeat cycle) and level 3 (daily and monthly averages and histograms) with reference datasets derived from lidar, microwave and passive imager measurements. The evaluation results show very good overall agreement with matching spatial distributions and temporal variability and small biases attributed mainly to differences in sensor characteristics, retrieval approaches, spatial and temporal samplings and viewing geometries. No major discrepancies were found. Underpinned by the good evaluation results, CLAAS-2 demonstrates that it is fit for the envisaged applications, such as process studies of the diurnal cycle of clouds and the evaluation of regional climate models. The data record is planned to be extended and updated in the future.

Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas

This study focuses on the evaluation of daily precipitation and temperature climate indices and extremes simulated by an ensemble of 12 Regional Climate Model (RCM) simulations from the ARCTIC-CORDEX experiment with surface observations in the Canadian Arctic from the Adjusted Historical Canadian Climate Dataset. Five global reanalyses products (ERA-Interim, JRA55, MERRA, CFSR and GMFD) are also included in the evaluation to assess their potential for RCM evaluation in data sparse regions. The study evaluated the means and annual anomaly distributions of indices over the 1980–2004 dataset overlap period. The results showed that RCM and reanalysis performance varied with the climate variables being evaluated. Most RCMs and reanalyses were able to simulate well climate indices related to mean air temperature and hot extremes over most of the Canadian Arctic, with the exception of the Yukon region where models displayed the largest biases related to topographic effects. Overall performance was generally poor for indices related to cold extremes. Likewise, only a few RCM simulations and reanalyses were able to provide realistic simulations of precipitation extreme indicators. The multi-reanalysis ensemble provided superior results to individual datasets for climate indicators related to mean air temperature and hot extremes, but not for other indicators. These results support the use of reanalyses as reference datasets for the evaluation of RCM mean air temperature and hot extremes over northern Canada, but not for cold extremes and precipitation indices.

Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as “field” or “global” significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Monthly temperature climatology for the 1990–2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.