Seasonal predictability of wintertime precipitation in Europe using the snow advance index

This 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 20.78 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 degraded but much longer version of the SAI. With the recommendation to monitor the robustness of these results as the sample size of the SAI increases, the authors encourage its use for the purpose of seasonal forecasting in southern Norway and the Iberian Peninsula, where general circulation models are known to perform poorly for the variable in question.SB, RM, and JMG acknowledge funding from the CICYT Project CGL2010-21869 and from QWeCI (EU Grant 243964) and the CSIC JAE-PREDOC program. JC is supported by the National Science Foundation Grants ARC-0909459 and ARC-0909457, and NOAA Grant NA10OAR4310163. The authors are thankful for the helpful comments of the three anonymous reviewers and acknowledge the E-OBS dataset from the ENSEMBLES project (http://ensembles-eu.metoffice.com/) as well the ECA&D(http:// eca.knmi.nl/) and AEMET station datasets

Reassessing statistical downscaling techniques for their robust application under climate change conditions

The 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 correlation, analog and weather generator approaches are more appropriate for reproducing the observed distributions, especially in case of wintertime minimum temperature. However, the latter two families significantly underestimate the temperature anomalies of the warm periods considered in this work. This underestimation is found to be critical when considering the warming signal in the late twenty-first century as given by a global climate model [the ECHAM5-Max Planck Institute (MPI) model]. In this case, the different downscaling methods provide warming values with differences in the range of 1°C, in agreement with the robustness significance values. Therefore, the proposed test is a promising technique for detecting lack of robustness in statistical downscaling methods applied in climate change studies.Thiswork has been funded by the Spanish I1D1i 2008-11 Program: A strategic action for energy and climate change (ESTCENA, code 200800050084078) and the project CGL2010-21869 (EXTREMBLES). S.B. was supported by a JAE PREDOC grant (CSIC, Spain). The authors would like to especially thank the three anonymous reviewers who helped to considerably improve this manuscript

Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods

This is the second in a pair of papers in which the performance of Statistical Downscaling Methods (SDMs) is critically re-assessed with respect to their robust applicability in climate change studies. Whereas Part I focused on temperatures (Gutierrez et al., 2013), the present manuscript deals with precipitation and considers an ensemble of twelve SDMs from the analog, weather typing, and regression (GLM) families. In the first part, we assess the performance of the methods with perfect (reanalysis) predictors, screening different geographical domains and predictor sets. To this aim, standard accuracy and distributional similarity scores, and a test for extrapolation capability based on dry observed historical periods are considered. As in Part I, the results are highly dependent on the predictor sets, with optimum configurations including information of middle tropospheric humidity (in particular Q850). As a result of this analysis, deficient SDMs are discarded in order to properly assess the spread (uncertainty) of future climate projections, avoiding the noise introduced by unsuitable models. In the second part, the resulting ensemble of SDMs is applied to four Global Circulation Models (GCMs) from the ENSEMBLES (CMIP3) project to obtain historical (1961-2000, 20C3M scenario) and future (2001-2100, A1B) regional projections. The obtained results are compared with those produced by an ensemble of Regional Climate Models (RCMs) driven by almost the same GCMs in the ENSEMBLES project. In general, the mean signal is similar with both methodologies (with the exception of Summer, where the RCMs project drier conditions) but the spread is larger for the SDM results. Finally, the contribution of the GCM and SDM-derived components to the total spread is assessed using a simple analysis of variance previously applied to the ENSEMBLES RCM ensemble. Results show that the main contributor to the spread is the choice of the GCM, except for the autumn results in the Atlantic sub-region of Spain and the Autumn and Summer results in the Mediterranean sub-region, where the choice of the SDM dominates the uncertainty during the second half of the 21st century due mainly to the different projections obtained from different families of SDM techniques. The most noticeable difference with the RCMs is the magnitude of the interaction terms, which is larger in all cases in the present study.This work has been funded by the strategic action for energy and climate change by the Spanish R&D 2008–2011 program ‘‘Programa coordinado para la generación de escenarios regionalizados de cambio climático: Regionalización Estadística (esTcena),’’ code 200800050084078, and the project CGL2015-66583-R (MINECO/FEDER). The RCM simulations used in this study were obtained from the European Union–funded FP6 Integrated Project ENSEMBLES (Contract 505539)

A global climate model performance atlas for the southern hemisphere extratropics based on regional atmospheric circulation patterns

The performance of 61 global climate models participating in CMIP5 and 6 is evaluated for the Southern Hemisphere extratropics in terms of typical regional-scale atmospheric circulation patterns. These patterns are known to be linked with a number of key variables in atmospheric physics and chemistry and provide an overarching concept for model evaluation. First, hemispheric-wide error and ranking maps are provided for each model and regional details are described. Then, the results are compared with those obtained in a companion study for the Northern Hemisphere. For most models, the average error magnitude and ranking position is similar on both hemispheres, ruling out systematic tuning toward either of the two. CMIP6 models perform better on average than CMIP5 models and the interactive simulation of more climate system components does not deteriorate the results for most model families. Better performance is associated with higher resolution in the atmosphere, following a non-linear relationship.This research work was funded by the European Commission – Next-GenerationEU (Regulation EU 2020/2094), through CSIC's Interdis-ciplinary Thematic Platform Clima (PTI Clima)/Development of Oper-ational Climate Services and by the I+D+i project CORDyS (PID2020-116595RB-I00), funded by MCIN/AEI/10.13039/501100011033. J.A.F. acknowledges support from project ATLAS (PID2019-111481RB-I00), grant PRE2020-094728 funded by MCIN/AEI/10.13039/501100011033 and ESF investing in your future. A.C. acknowl-edges support from Project COMPOUND (TED2021-131334A-I00) funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGener-ationEU/PRTR. S.B. would like to thank CESGA and AMTEGA for providing computational resources. The authors acknowledge the public availability of the CMIP datasets via the ESGF data portals, as well the free distribution of the ECMWF and JMA reanalysis products

Exploring the limits of the Jenkinson–Collison weather types classification scheme: a global assessment based on various reanalyses

The Jenkinson-Collison weather typing scheme (JC-WT) is an automated method used to classify regional sea-level pressure into a reduced number of typical recurrent patterns. Originally developed for the British Isles in the early 1970´s on the basis of expert knowledge, the method since then has seen many applications. Encouraged by the premise that the JC-WT approach can in principle be applied to any mid-to-high latitude region, the present study explores its global extra-tropical applicability, including the Southern Hemisphere. To this aim, JC-WT is applied at each grid-box of a global 2.5º regular grid excluding the inner tropics (± 5º band). Thereby, 6-hourly JC-WT catalogues are obtained for 5 distinct reanalyses, covering the period 1979-2005, which are then applied to explore (1) the limits of method applicability and (2) observational uncertainties inherent to the reanalysis datasets. Using evaluation criteria, such as the diversity of occurring circulation types and the frequency of unclassified situations, we extract empirically derived applicability thresholds which suggest that JC-WT can be generally used anywhere polewards of 23.5º, with some exceptions. Seasonal fluctuations compromise this finding along the equatorward limits of the domain. Furthermore, unreliable reanalysis sea-level pressure estimates in elevated areas with complex orography (such as the Tibetan Plateau, the Andes, Greenland and Antarctica) prevent the application of the method in these regions. In some other regions, the JC-WT classifications obtained from the distinct reanalyses substantially differ from each other, which may bring additional uncertainties when the method is used in model evaluation experiments.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This paper is part of the R+D+i projects CORDyS (PID2020-116595RB-I00) and ATLAS (PID2019-111481RBI00), funded by MCIN/AEI/10.13039/501100011033. J.A.F. has received research support from grant PRE2020-094728 funded by MCIN/AEI/10.13039/501100011033. J.B. and A.C. received research support from the project INDECIS, part of the European Research Area for Climate Services Consortium (ERA4CS) with co-funding by the European Union (grant no. 690462)

Statistical downscaling in the tropics can be sensitive to reanalysis choice: A case study for precipitation in the Philippines

This 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 order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to “delta-change” estimates differing by up to 35% (on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is an important contributor to the uncertainty of local-scale climate change projections and, consequently, should be treated with as much care as other better-known sources of uncertainty (e.g., the choice of the GCM and/or downscaling method). Implications of the results for the entire tropics, as well as for the model output statistics downscaling approach are also briefly discussed.The authors are grateful to the free distribution of the ECMWF ERA-Interim (http://www.ecmwf.int/en/research/climate-reanalysis/era-interim), JMA JRA-25 (http://jra.kishou.go.jp/JRA-25/index_en.html), and MPI-ECHAM5 data (http://cera-www.dkrz.de/WDCC/ui/Compact.jsp?acronym=ENSEMBLES_MPEH5_SRA1B_3_D) and acknowledge PAGASA for the observational data provided. This study was supported by the EU projects QWeCI and SPECS, funded by the European Commission through the Seventh Framework Programme for Research under Grant Agreements 243964 and 308378, respectively. RM also acknowledges the EU project EUPORIAS, funded by the European Commission through the Seventh Framework Programme for Research under Grant Agreement 308291. SB is grateful to the CSIC-JAE-Predoc Program for financial support

Statistical downscaling of daily temperatures in the NW Iberian Peninsula from global climate models: validation and future scenarios

14 páginas, 9 figuras, 3 tablas.-- Artículo Open Access.We used the analogue method to generate ensemble projections of local daily mean, maximum and minimum air temperatures in the NW Iberian Peninsula until the middle of this century. A 3-step method was followed. (1) The error of the analogue method under optimal conditions was estimated, using air temperatures at 850 hPa and mean sea level pressure from reanalysis data as predictor variables. (2) The method's error under suboptimal conditions was assessed by taking these predictors from control runs of a multi-model, multi-initial-conditions ensemble of global climate models. Neither the predictor data nor the downscaled series were corrected. Under these suboptimal conditions, none of the individual downscaled series could robustly reproduce the cumulative distribution function (CDF) of the observations in any season of the year. However, when the single downscaled series were combined into a multi-model series, CDFs were reliably reconstructed for summer and autumn. (3) Temperature series were downscaled from the ensemble’s scenario runs and compared to observations in the reference period to detect local climate change. In addition to the mean relative warming, it can be shown that the less frequent the event in the reference period, the higher its frequency increase and the broader its uncertainty interval in the scenario period. This tendency is more pronounced for daytime than for night-time heat/warm events, leading to a tripling to quadrupling of the former in summer. The local projections’ uncertainty intervals are dominated by model errors rather than by forcing or initial-conditions uncertainties.Peer reviewe