Fidelity of CORDEX Evaluation runs under Non-stationary climate

2020 ◽  
Author(s):  
Swati Singh ◽  
Kaustubh Salvi ◽  
Subimal Ghosh ◽  
Subhankar Karmakar
Keyword(s):  
Boundary Conditions ◽  
Statistical Methods ◽  
Statistical Downscaling ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Reanalysis Data ◽  
Observation Data ◽  
The Difference

<p>The downscaling approaches: Statistical and Dynamic, developed for regional climate predictions, have both advantages and limitations. The statistical downscaling is computationally inexpensive but suffers from the violation of the assumption of stationarity in statistical (predictor-predictand) relationship. The dynamical downscaling is assumed to take care of stationarity but suffers from the biases associated with various sources.  Here we propose a joint approach of both the methods by applying statistical methods: bias correction & statistical downscaling to <strong>Coordinated Regional Climate Downscaling Experiment (</strong>CORDEX) evaluation runs. The evaluation runs are considered as perfect simulations of CORDEX Regional Climate Models (RCMs) with the boundary conditions by ERA-Interim reanalysis data. The statistical methods are also applied to ERA-Interim reanalysis data and compared with observation data for Indian Summer Monsoon characteristics. We evaluate the ability of statistical methods under the non-stationary environment by taking the difference of years close to extreme future runs (RCP8.5) as warmer years and preindustrial runs as cooler years. We find statistical downscaling of CORDEX evaluation runs shows skill in reproducing the signal of non-stationarity. The study can be extended methods by applying statistical downscaling to CORDEX RCMs with the CMIP5 boundary conditions. </p>

Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling

2018 ◽  
Author(s):  
Aristita Busuioc ◽  
Alexandru Dumitrescu
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Local Climate

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.

scholarly journals Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

2021 ◽  
Vol 21 (11) ◽  
pp. 3573-3598
Author(s):  
Benjamin Poschlod
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Regional Climate ◽  
Daily Rainfall ◽  
Regional Climate Models ◽  
Reanalysis Data ◽  
Extreme Value ◽  
Return Level ◽  
Gev Distribution ◽  
Return Levels

Abstract. Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes is based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10- and 100-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at a 12 km spatial resolution and the Weather and Forecasting Research (WRF) model at a 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. WRF at a 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the generalized extreme value (GEV) distribution, the CRCM5 setup can reproduce the observational 10-year return levels with an areal average bias of +6.6 % and a spatial Spearman rank correlation of ρ=0.72. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (+4.7 %) and spatial correlation (ρ=0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no further improvement in the spatial correlation (ρ=0.82) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three further approaches. Applied to the WRF-ERA5 data, the GEV distributions with a fixed shape parameter (bias is +2.5 %; ρ=0.79) and the generalized Pareto (GP) distributions (bias is +2.9 %; ρ=0.81) show almost equivalent results for the 10-year return period, whereas the metastatistical extreme value (MEV) distribution leads to a slight underestimation (bias is −7.8 %; ρ=0.84). For the 100-year return level, however, the MEV distribution (bias is +2.7 %; ρ=0.73) outperforms the GEV distribution (bias is +13.3 %; ρ=0.66), the GEV distribution with fixed shape parameter (bias is +12.9 %; ρ=0.70), and the GP distribution (bias is +11.9 %; ρ=0.63). Hence, for applications where the return period is extrapolated, the MEV framework is recommended. From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate-change-induced alterations in rainfall return levels at regional to local scales. This can allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

scholarly journals Reassessing Model Uncertainty for Regional Projections of Precipitation with an Ensemble of Statistical Downscaling Methods

2017 ◽  
Vol 30 (1) ◽  
pp. 203-223 ◽  
Author(s):  
D. San-Martín ◽  
R. Manzanas ◽  
S. Brands ◽  
S. Herrera ◽  
J. M. Gutiérrez
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Companion Paper ◽  
Climate Projections ◽  
Interaction Terms ◽  
Weather Typing ◽  
Ensembles Project ◽  
Families First

This is the second in a pair of papers in which the performance of statistical downscaling methods (SDMs) is critically reassessed with respect to their robust applicability in climate change studies. Whereas the companion paper focused on temperatures, the present manuscript deals with precipitation and considers an ensemble of 12 SDMs from the analog, weather typing, and regression families. First, the performance of the methods is cross-validated considering reanalysis predictors, screening different geographical domains and predictor sets. Standard accuracy and distributional similarity scores and a test for extrapolation capability are considered. The results are highly dependent on the predictor sets, with optimum configurations including information from midtropospheric humidity. Second, a reduced ensemble of well-performing SDMs is applied to four GCMs to properly assess the uncertainty of downscaled future climate projections. The results are compared with an ensemble of regional climate models (RCMs) produced in the ENSEMBLES project. Generally, the mean signal is similar with both methodologies (with the exception of summer, which is drier for the RCMs) but the uncertainty (spread) is larger for the SDM ensemble. Finally, the spread contribution of the GCM- and SDM-derived components is assessed using a simple analysis of variance previously applied to the RCMs, obtaining larger interaction terms. Results show that the main contributor to the spread is the choice of the GCM, although the SDM dominates the uncertainty in some cases during autumn and summer due to the diverging projections from different families.

Finding homogeneous regions of extreme rainfall in reanalysis data

2020 ◽  
Author(s):  
Marjanne Zander ◽  
Frederiek Sperna Weiland ◽  
Albrecht Weerts
Keyword(s):  
Climate Models ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Extreme Rainfall ◽  
Regional Climate Models ◽  
Rain Gauge ◽  
Reanalysis Data ◽  
Regional Frequency Analysis ◽  
Homogeneous Regions ◽  
Rainfall Statistics

<p>In this study a methodology is developed and tested to delineate homogeneous regions of extreme rainfall around a city of interest using meteorological indices from reanalysis data.</p><p>Scenarios of future climate change established with numerical climate models are well-established tools to help inform climate adaptation policy. The latest generation of regional climate models is now employed at a grid resolution of 2 to 3 kilometers. This enables the simulation of convection; whereby intensive convective rainfall is better represented (Kendon et al., 2017). However, the associated large computational burden limits the simulation length, which poses a challenge for estimating future rainfall statistics.</p><p>Rainfall return periods are a commonly used indicator in the planning, design and evaluation of urban water systems and urban water management. In order to estimate potential future rainfall for return periods larger than the length of the simulation length, regional frequency analysis (RFA) can be applied (Li et al., 2017).  For applying RFA, time series from nearby locations are pooled, the locations considered should fall within the same hydroclimatic climate. This is a region which can be assumed statistically homogeneous for extreme rainfall (Hosking & Wallis, 2009).</p><p>Traditionally, these homogeneous regions are defined on geographical region characteristics and rain gauge statistics (Hosking & Wallis, 2009).  To make the methodology less dependent on rain gauge record availability, Gabriele & Chiaravalloti (2013) used meteorological indices derived from reanalysis data to delineate the homogeneous regions.</p><p>Here we evaluate the methodology to delineate homogeneous regions around cities. Meteorological indices are calculated from the ERA-5 reanalysis dataset (Hersbach et al., 2018) for days with extreme rainfall. The variation herein is used as a measure of homogeneity. The derived homogeneous regions will in future work be used for data pooling of convection-permitting regional climate model simulations datasets to enable the derivation of future extreme rainfall statistics.</p><p>This study is embedded in the EU H2020 project EUCP (EUropean Climate Prediction system) (https://www.eucp-project.eu/), which aims to develop a regional climate prediction and projection system based on high-resolution climate models for Europe, to support climate adaptation and mitigation decisions for the coming decades.</p><p>References:</p><p>Gabriele, S., & Chiaravalloti, F. (2013). “Searching regional rainfall homogeneity using atmospheric fields”. Advances in Water Resources, 53, 163–174. https://doi.org/https://doi.org/10.1016/j.advwatres.2012.11.002</p><p>Hersbach, H., de Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C., …, Zuo, H. (2018). “Operational global reanalysis: progress, future directions and synergies with NWP”, ECMWF.</p><p>Hosking, J. R. M., & Wallis, J. R. (2009). “Regional Frequency Analysis: An Approach Based on L-Moments”. The Edinburgh Building, Cambridge CB2 2RU, UK: Cambridge University Press. ISBN: 9780511529443.</p><p>Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., … Wilkinson, J. M. (2017). “Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?” BAMS, 98(1), 79–93. https://doi.org/10.1175/BAMS-D-15-0004.1</p><p> Li, J., Evans, J., Johnson, F., & Sharma, A. (2017). “A comparison of methods for estimating climate change impact on design rainfall using a high-resolution RCM.” Journal of Hydrology, 547(Supplement C), 413–427. https://doi.org/https://doi.org/10.1016/j.jhydrol.2017.02.019</p>

scholarly journals On the Use of Reanalysis Data for Downscaling

2012 ◽  
Vol 25 (7) ◽  
pp. 2517-2526 ◽  
Author(s):  
S. Brands ◽  
J. M. Gutiérrez ◽  
S. Herrera ◽  
A. S. Cofiño
Keyword(s):  
Climate Models ◽  
Pearson Correlation ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Reanalysis Data ◽  
Specific Humidity ◽  
Weather Forecasts ◽  
Kolmogorov Smirnov ◽  
Similarity Maps ◽  
Daily Time

Abstract In this study, a worldwide overview on the expected sensitivity of downscaling studies to reanalysis choice is provided. To this end, the similarity of middle-tropospheric variables—which are important for the development of both dynamical and statistical downscaling schemes—from 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and NCEP–NCAR reanalysis data on a daily time scale is assessed. For estimating the distributional similarity, two comparable scores are used: the two-sample Kolmogorov–Smirnov statistic and the probability density function (PDF) score. In addition, the similarity of the day-to-day sequences is evaluated with the Pearson correlation coefficient. As the most important results demonstrated, the PDF score is found to be inappropriate if the underlying data follow a mixed distribution. By providing global similarity maps for each variable under study, regions where reanalysis data should not assumed to be “perfect” are detected. In contrast to the geopotential and temperature, significant distributional dissimilarities for specific humidity are found in almost every region of the world. Moreover, for the latter these differences not only occur in the mean, but also in higher-order moments. However, when considering standardized anomalies, distributional and serial dissimilarities are negligible over most extratropical land areas. Since transformed reanalysis data are not appropriate for regional climate models—in opposition to statistical approaches—their results are expected to be more sensitive to reanalysis choice.

scholarly journals Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

2011 ◽  
Vol 92 (9) ◽  
pp. 1181-1192 ◽  
Author(s):  
Frauke Feser ◽  
Burkhardt Rockel ◽  
Hans von Storch ◽  
Jörg Winterfeldt ◽  
Matthias Zahn
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Small Scale ◽  
Model Data ◽  
Polar Lows

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

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