https://archive-ouverte.unige.ch/unige:30039/ATTACHMENT01

21st century climate change in the European Alps--a review.

. EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatiotemporal patterns of the European climate for the period 1989–2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS data set as observational reference. The ensemble consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight subdomains of the European continent. Results are compared to those for the ERA40-driven ENSEMBLES simulations. The analysis confirms the ability of RCMs to capture the basic features of the European climate, including its variability in space and time. But it also identifies nonnegligible deficiencies of the simulations for selected metrics, regions and seasons. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 °C, while precipitation biases are typically located in the ±40% range. Some bias characteristics, such as a predominant cold and wet bias in most seasons and over most parts of Europe and a warm and dry summer bias over southern and southeastern Europe reflect common model biases. For seasonal mean quantities averaged over large European subdomains, no clear benefit of an increased spatial resolution (12 vs. 50 km) can be identified. The bias ranges of the EURO-CORDEX ensemble mostly correspond to those of the ENSEMBLES simulations, but some improvements in model performance can be identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different configurations of one individual model can be of a similar magnitude as the spread across different models, demonstrating a strong influence of the specific choices in physical parameterizations and experimental setup on model performance. Based on a number of simply reproducible metrics, the present study quantifies the currently achievable accuracy of RCMs used for regional climate simulations over Europe and provides a quality standard for future model developments.

/pdf/regional-climate-modeling-on-european-scales-a-joint-3rq3kb8508.pdf

Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble

Although regional climate models (RCMs) are powerful tools for describing regional and even smaller scale climate conditions, they still feature severe systematic errors. In order to provide optimized climate scenarios for climate change impact research, this study merges linear and nonlinear empirical-statistical downscaling techniques with bias correction methods and investigates their ability for reducing RCM error characteristics. An ensemble of seven empirical-statistical downscaling and error correction methods (DECMs) is applied to post-process daily precipitation sums of a high-resolution regional climate hindcast simulation over the Alpine region, their error characteristics are analysed and compared to the raw RCM results.

Drastic reductions in error characteristics due to application of DECMs are demonstrated. Direct point-wise methods like quantile mapping and local intensity scaling as well as indirect spatial methods as nonlinear analogue methods yield systematic improvements in median, variance, frequency, intensity and extremes of daily precipitation. Multiple linear regression methods, even if optimized by predictor selection, transformation and randomization, exhibit significant shortcomings for modelling daily precipitation due to their linear framework. Comparing the well-performing methods to each other, quantile mapping shows the best performance, particularly at high quantiles, which is advantageous for applications related to extreme precipitation events. The improvements are obtained regardless of season and region, which indicates the potential transferability of these methods to other regions. Copyright © 2010 Royal Meteorological Society

http://leg.ufpr.br/~eder/Artigos/Bias/Jakob_2010.pdf

Empirical‐statistical downscaling and error correction of daily precipitation from regional climate models

Realizing the error characteristics of regional climate models (RCMs) and the consequent limitations in their direct utilization in climate change impact research, this study analyzes a quantile-based empirical-statistical error correction method (quantile mapping, QM) for RCMs in the context of climate change. In particular the success of QM in mitigating systematic RCM errors, its ability to generate “new extremes” (values outside the calibration range), and its impact on the climate change signal (CCS) are investigated. In a cross-validation framework based on a RCM control simulation over Europe, QM reduces the bias of daily mean, minimum, and maximum temperature, precipitation amount, and derived indices of extremes by about one order of magnitude and strongly improves the shapes of the related frequency distributions. In addition, a simple extrapolation of the error correction function enables QM to reproduce “new extremes” without deterioration and mostly with improvement of the original RCM quality. QM only moderately modifies the CCS of the corrected parameters. The changes are related to trends in the scenarios and magnitude-dependent error characteristics. Additionally, QM has a large impact on CCSs of non-linearly derived indices of extremes, such as threshold indices.

http://www.value-cost.eu/sites/default/files/themessl_etal-2012-ClimChange-ErrorCorrScenarios-online.pdf

Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal

Climate change impact research needs regional climate scenarios of mul- tiple meteorological variables. Those variables are available from regional climate models (RCMs), but affected by considerable biases. We evaluate the application of an empirical-statistical error correction method, quantile mapping (QM), for a small ensemble of RCMs and six meteorological variables. Annual and monthly biases are reduced to close to zero by QM for all variables in most cases. Exceptions are found, if non-stationarity of the model's error characteristics occur. Even in the worst cases of non-stationarity, QM clearly improves the biases of raw RCMs. In addition, QM successfully adjusts the distributions of the analysed variables. To approach the question whether time series and inter-variable relationships are still plausible after correction, we evaluate the root-mean-square error (RMSE), autocorrelation and inter-variable correlation. We found improvement or no clear effect in RMSE and autocorrelation, and no clear effect on the correlation between meteorological variables. These results demonstrate that QM retains the quality of the temporal structure in time series and the inter-variable dependencies of RCMs. It has to be emphasised that this cannot be interpreted as an improvement and that deficiencies of the RCMs in those features are retained as well. Our results give some indication for the performance of QM applied to future scenarios, since our evaluation relies

/pdf/multi-variable-error-correction-of-regional-climate-models-42w0xcsj3c.pdf

Multi-variable error correction of regional climate models

This study describes typical error ranges of high resolution regional climate models operated over complex orography and investigates the scale-dependence of these error ranges. The results are valid primarily for the European Alpine region, but to some extent they can also be transferred to other orographically complex regions of the world. We investigate the model errors by evaluating a set of 62 one-year hindcast experiments for the year 1999 with four different regional climate models. The analysis is conducted for the parameters mean sea level pressure, air temperature (mean, minimum and maximum) and precipitation (mean, frequency and intensity), both as an area average over the whole modeled domain (the “Greater Alpine Region”, GAR) and in six subregions. The subregional seasonal error ranges, defined as the interval between the 2.5th percentile and the 97.5th percentile, lie between −3.2 and +2.0 K for temperature and between −2.0 and +3.1 mm/day (−45.7 and +94.7%) for precipitation, respectively. While the temperature error ranges are hardly broadened at smaller scales, the precipitation error ranges increase by 28%. These results demonstrate that high resolution RCMs are applicable in relatively small scale climate impact studies with a comparable quality as on well investigated larger scales as far as temperature is concerned. For precipitation, which is a much more demanding parameter, the quality is moderately degraded on smaller scales.

/pdf/error-characteristics-of-high-resolution-regional-climate-44eovrz0zm.pdf

Error characteristics of high resolution regional climate models over the Alpine area

The core of climate services is to provide high quality climate-related information and data that are beneficial for the users. Between the provision of data and the application of climate services, a chain of providers and subsequent users exists. It is an ongoing challenge for providers to conclusively define what users perceive as beneficial regarding the quality of climate model output. This study aims (1) to understand the needs of users with regard to the quality of climate data and information, and (2) to enable providers to assess the quality of climate data input and derived products. From a large-scale survey, we distilled three main user groups: (i) Donna data (data user/product provider), (ii) Pete product (product user/product provider) and (iii) Nick non (potential-user). The survey results show that all three user groups struggle—amongst other things—with identifying reliable climate model output, that is relevant to their needs. They also desire guidance on how to evaluate the quality of climate model data to determine the suitability of the selected dataset for their purpose. Addressing this central need is breaking new ground. The evaluation of quality in the field of climate services in terms of climate model output is of high relevance to both climate model data users and providers of tailored climate information and not restricted to scientific standards and technical quality. We present a customized and tested tool (“QUACK”) as one of the first hands-on, scientifically-based and at the same time user-oriented guidelines on how to assure data quality and to self-evaluate the processing of the data.

Evaluation of Climate Services: Enabling Users to Assess the Quality of Multi-model Climate Projections and Derived Products

As a response to the IPCC needs for coordinated high resolution simulations, the WCRP Initiative on CORDEX designed a framework to perform an ensemble of regional climate models covering major land-masses of the globe at a high spatial resolution of 25 km. This CORDEX Coordinated Output for Regional Evaluation (CORE) framework will produce a baseline set of homogeneous high-resolution dynamically downscaled projections forced by selected global climate models and two representative concentration pathways scenarios (lowand high-end scenario).

High resolution climate change simulations over ten CORDEX-CORE Domains using the regional climate model REMO

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Error characteristics of high resolution regional climate models over the Alpine area

Evaluation of Climate Services: Enabling Users to Assess the Quality of Multi-model Climate Projections and Derived Products

High resolution climate change simulations over ten CORDEX-CORE Domains using the regional climate model REMO