Swen Brands

How well do CMIP5 Earth System Models simulate present climate conditions in Europe and Africa

The present study assesses the ability of seven Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 5 to reproduce present climate conditions in Europe and Africa. This is done from a downscaling perspective, taking into account the requirements of both statistical and dynamical approaches. ECMWF’s ERA-Interim reanalysis is used as reference for an evaluation of circulation, temperature and humidity variables on daily timescale, which is based on distributional similarity scores. To additionally obtain an estimate of reanalysis uncertainty, ERA-Interim’s deviation from the Japanese Meteorological Agency JRA-25 reanalysis is calculated. Areas with considerable differences between both reanalyses do not allow for a proper assessment, since ESM performance is sensitive to the choice of reanalysis. For use in statistical downscaling studies, ESM performance is computed on the grid-box scale and mapped over a large spatial domain covering Europe and Africa, additionally highlighting those regions where significant distributional differences remain even for the centered/zero-mean time series. For use in dynamical downscaling studies, performance is specifically assessed along the lateral boundaries of the three CORDEX domains defined for Europe, the Mediterranean Basin and Africa.

Reassessing Statistical Downscaling Techniques for Their Robust Application under Climate Change Conditions

AbstractThe performance of statistical downscaling (SD) techniques is critically reassessed with respect to their robust applicability in climate change studies. To this end, in addition to standard accuracy measures and distributional similarity scores, the authors estimate the robustness of the methods under warming climate conditions working with anomalous warm historical periods. This validation framework is applied to intercompare the performances of 12 different SD methods (from the analog, weather typing, and regression families) for downscaling minimum and maximum temperatures in Spain. First, a calibration of these methods is performed in terms of both geographical domains and predictor sets; the results are highly dependent on the latter, with optimum predictor sets including near-surface temperature data (in particular 2-m temperature), which appropriately discriminate cold episodes related to temperature inversion in the lower troposphere.Although regression methods perform best in terms of co...

On the Use of Reanalysis Data for Downscaling

AbstractIn 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 ...

Seasonal Predictability of Wintertime Precipitation in Europe Using the Snow Advance Index

AbstractThis study tests the applicability of Eurasian snow cover increase in October, as described by the recently published snow advance index (SAI), for forecasting December–February precipitation totals in Europe. On the basis of a classical correlation analysis, global significance was obtained and locally significant correlation coefficients of up to 0.89 and −0.78 were found for the Iberian Peninsula and southern Norway, respectively. For a more robust assessment of these results, a linear regression approach is followed to hindcast the precipitation sums in a 1-yr-out cross-validation framework, using the SAI as the only predictor variable. With this simple empirical approach, local-scale precipitation could be reproduced with a correlation of up to 0.84 and 0.71 for the Iberian Peninsula and southern Norway, respectively, while catchment aggregations on the Iberian Peninsula could be hindcast with a correlation of up to 0.73. These findings are confirmed when repeating the hindcast approach to a ...

Seasonal variations in North Atlantic atmospheric river activity and associations with anomalous precipitation over the Iberian Atlantic Margin

This work was funded by the Ministerio Espanol de Economia y Competitividad (CGL2013-45932-R) and the European Commission FP7 project EartH2Observe. S.B. would additionally like to thank the CSIC JAE-PREDOC programme for financial support.

Statistical Downscaling in the Tropics Can Be Sensitive to Reanalysis Choice: A Case Study for Precipitation in the Philippines

AbstractThis work shows that local-scale climate projections obtained by means of statistical downscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a generalized linear model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested separately with two distinct reanalyses (ERA-Interim and JRA-25) using a cross-validation scheme over the period 1981–2000. When the observed and downscaled time series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal–bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a global climate model (MPI-ECHAM5) in o...

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

AbstractThis 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 ...

Skillful Seasonal Predictions of Boreal Winter Accumulated Heating Degree-Days and Relevance for the Weather Derivative Market

AbstractIt is demonstrated that boreal winter accumulated heating degree-days, a weather derivative product that is frequently demanded by energy suppliers (among others), can be skillfully predicted with a lead time of 1 month, that is, at the beginning of the previous November, for many regions of the Northern Hemisphere extratropics. This finding contradicts the assumption of poor seasonal predictability for this variable. This short paper is meant to properly inform the participants of the weather derivative market to assure market transparency and to foster a scientific discussion on how to disseminate this formerly unknown expert knowledge.

Present and future climate conditions for winegrowing in Spain

This study deals with the question of how winegrowing in Spain may be altered by anthropogenic climate change. The present state and expected future development of three bioclimatic indices relevant for winegrowing were assessed by observation, and four regional climate models from the EU-ENSEMBLES project were investigated. When comparing the 2061–2090 scenario period to the 1961–1990 reference period, the models unanimously indicate a significant increase in the mean of the two considered thermal indices over the entire study region. However, for the index based on temperature and precipitation, the models are heavily biased when verified against observations and generally disagree on the size of the projected future change. For this index, unanimous model agreement was only found for northwestern Spain where all models indicated a significant decrease in the mean. From these results, regional climate change is expected to negatively affect the quality of wine in the growing regions of central and southern Spain, and the Ebro valley, whereas positive effects should be expected in the northwest. No significant changes in the risk of mildew infestation are to be expected except for the northwest, where this risk is projected to decrease.

Predicting average wintertime wind and wave conditions in the North Atlantic sector from Eurasian snow cover in?October

The present study assesses the lead‐lag teleconnection between Eurasian snow cover in October and the December-to-February mean boreal winter climate with respect to the predictability of 10 m wind speed and significant wave heights in the North Atlantic and adjacent seas. Lead‐lag correlations exceeding a magnitude of 0.8 are found for the short time period of 1997/98‐2012/13 (nD 16) for which daily satellite-sensed snow cover data is available to date. The respective cross-validated hindcast skill obtained from using linear regression as a statistical forecasting technique is similarly large in magnitude. When using a longer but degraded time series of weekly snow cover data for calculating the predictor variable (1979/80‐2011/12, nD 34), hindcast skill decreases but yet remains significant over a large fraction of the study area. In addition, Monte-Carlo field significance tests reveal that the patterns of skill are globally significant. The proposed method might be used to make forecast decisions for wind and wave energy generation, seafaring, fishery and offshore drilling. To exemplify its potential suitability for the latter sector, it is additionally applied to DJF frequencies of significant wave heights exceeding 2 m, a threshold value above which mooring conditions at oil platforms are no longer optimal.