An RCM multi-physics ensemble over Europe: Multi-variable evaluation to avoid error compensation

ABSTRACT:Regional Climate Models (RCMs) are widely used tools to add detail to the coarse resolution of global simulations. However, these are known to be affected by biases. Usually, published model evaluations use a reduced number of variables, frequently precipitation and temperature. Due to the complexity of the models, this may not be enough to assess their physical realism (e.g. to enable a fair comparison when weighting ensemble members). Furthermore, looking at only a few variables makes difficult to trace model errors. Thus, in many previous studies, these biases are de- scribed but their underlying causes and mechanisms are often left unknown. In this work the ability of a multi-physics ensemble in reproducing the observed climatologies of any variables over Europe is analysed. These are temperature, precipitation, cloud cover, ra- diative fluxes and total soil moisture content. It is found that, during winter, the model suffers a significant cold bias over snow covered regions. This is shown to be re- lated with a poor representation of the snow-atmosphere interaction, and is amplified by an albedo feedback. It is shown how two members of the ensemble are able to alleviate this bias, but by generating a too large cloud cover. During summer, a large sensitivity to the cumulus parameterization is found, related to large differences in the cloud cover and short wave radiation flux. Results also show that small errors in one variable are sometimes a result of error compensation, so the high dimensionality of the model evaluation problem cannot be disregarded.This work was partially supported by Projects EXTREMBLES (CGL2010-21869) and CORWES (CGL2010-22158-C02), funded by the Spanish R&D Programme. WRF4G (CGL2011-28864) provided the framework to run the model; this Spanish R&D project is co-funded by the European Regional Development Fund (ERDF). Partial support from the 7th European Framework Programme (FP7) through Grant 308291 (EUPORIAS) is also acknowledged

Spatio-temporal error growth in the multi-scale Lorenz’96 model

The influence of multiple spatio-temporal scales on the error growth and predictability of atmospheric flows is analyzed throughout the paper. To this aim, we consider the two-scale Lorenz’96 model and study the interplay of the slow and fast variables on the error growth dynamics. It is shown that when the coupling between slow and fast variables is weak the slow variables dominate the evolution of fluctuations whereas in the case of strong coupling the fast variables impose a non-trivial complex error growth pattern on the slow variables with two different regimes, before and after saturation of fast variables. This complex behavior is analyzed using the recently introduced Mean-Variance Logarithmic (MVL) diagram

Spatiotemporal characterization of Ensemble Prediction Systems – the Mean-Variance of Logarithms (MVL) diagram

We present a novel approach to characterize and graphically represent the spatiotemporal evolution of ensembles using a simple diagram. To this aim we analyze the fluctuations obtained as differences between each member of the ensemble and the control. The lognormal character of these fluctuations suggests a characterization in terms of the first two moments of the logarithmic transformed values. On one hand, the mean is associated with the exponential growth in time. On the other hand, the variance accounts for the spatial correlation and localization of fluctuations. In this paper we introduce the MVL (Mean-Variance of Logarithms) diagram to intuitively represent the interplay and evolution of these two quantities. We show that this diagram uncovers useful information about the spatiotemporal dynamics of the ensemble. Some universal features of the diagram are also described, associated either with the nonlinear system or with the ensemble method and illustrated using both toy models and numerical weather prediction systems

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)

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

Evaluation and projection of daily temperature percentiles from statistical and dynamical downscaling methods

ABSTRACT. The study of extreme events has become of great interest in recent years due to their direct impact on society. Extremes are usually evaluated by using extreme indicators, based on order statistics on the tail of the probability distribution function (typically percentiles). In this study, we focus on the tail of the distribution of daily maximum and minimum temperatures. For this purpose, we analyse high (95th) and low (5th) percentiles in daily maximum and minimum temperatures on the Iberian Peninsula, respectively, derived from different downscaling methods (statistical and dynamical). First, we analyse the performance of reanalysisdriven downscaling methods in present climate conditions. The comparison among the different methods is performed in terms of the bias of seasonal percentiles, considering as observations the public gridded data sets E-OBS and Spain02, and obtaining an estimation of both the mean and spatial percentile errors. Secondly, we analyse the increments of future percentile projections under the SRES A1B scenario and compare them with those corresponding to the mean temperature, showing that their relative importance depends on the method, and stressing the need to consider an ensemble of methodologies

Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44

Deep learning (DL) has recently emerged as an innovative tool to downscale climate variables from large-scale atmospheric fields under the perfect-prognosis (PP) approach. Different convolutional neural networks (CNNs) have been applied under present-day conditions with promising results, but little is known about their suitability for extrapolating future climate change conditions. Here, we analyze this problem from a multi-model perspective, developing and evaluating an ensemble of CNN-based downscaled projections (hereafter DeepESD) for temperature and precipitation over the European EUR-44i (0.5º) domain, based on eight global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). To our knowledge, this is the first time that CNNs have been used to produce downscaled multi-model ensembles based on the perfect-prognosis approach, allowing us to quantify inter-model uncertainty in climate change signals. The results are compared with those corresponding to an EUR-44 ensemble of regional climate models (RCMs) showing that DeepESD reduces distributional biases in the historical period. Moreover, the resulting climate change signals are broadly comparable to those obtained with the RCMs, with similar spatial structures. As for the uncertainty of the climate change signal (measured on the basis of inter-model spread), DeepESD preserves the uncertainty for temperature and results in a reduced uncertainty for precipitation. To facilitate further studies of this downscaling approach, we follow FAIR principles and make publicly available the code (a Jupyter notebook) and the DeepESD dataset. In particular, DeepESD is published at the Earth System Grid Federation (ESGF), as the first continental-wide PP dataset contributing to CORDEX (EUR-44).This research has been supported by the Spanish Government (MCIN/AEI /10.13039/501100011033) through project CORDyS (grant no. PID2020-116595RB-I00)

Evaluation and projection of extreme temperature percentiles by means of statistical and dynamical downscaling methods

ABSTRACT: The study of extreme events has become of great interest in the recent years due to their direct impact on society. Extremes can be evaluated by using either extreme value statistics or extreme indicators, the latter being based in order statistics on the tail of the probability distribution (typically percentiles). In this study we analyze the highest (95p) and the lowest (5p) percentiles in maximum and minimum temperatures, respectively, derived from different downscaling methods (statistical and dynamical) in the Iberian Peninsula. In particular, we analyze the results of the esTcena and ESCENA projects, two strategic actions of Plan Nacional de I+D+i 2008-2011 funded by the Spanish government, which contributed to the new version of the regional climate change scenarios program Escenarios-PNACC 2012 within Plan Nacional de Adaptación al Cambio Climático. First, the skill of the downscaling methods to reproduce extreme percentiles is tested in present climate conditions, using reanalysis-driven simulations. The comparison among the different methods is performed in terms of the seasonal bias, considering the public gridded dataset Spain02, a new regular (approximately 20km) daily gridded precipitation and temperature dataset covering continental Spain and Balearic Islands. Secondly, we analyze future projections in different climate change scenarios to check the increments and the uncertainty of the results up to the mid of the century. We also study the effect of nesting the methods to different Global Circulation Models (GCMs), using the 20C3M historical scenario as reference. By analyzing these changes, we are able to extract differences due to the downscaling method and to the driving GCM.RESUMEN: El estudio de eventos extremos se ha convertido recientemente en un tema de gran interés debido a su impacto directo en la sociedad. Los extremos pueden ser evaluados por medio de la Teoría de Valores Extremos o mediante indicadores de extremos, estos últimos basados en los estadísticos de la cola de la distribución de probabilidad (típicamente percentiles). En este estudio analizamos uno de los percentiles más altos en temperatura máxima (95) y de los más bajos en la mínima (5) obtenidos a partir de diferentes métodos de regionalización (estadísticos y dinámicos) en la Península Ibérica. En particular, hemos analizado los resultados de los proyectos esTcena y ESCENA, dos acciones estratégicas del Plan Nacional de I+D+i 2008-2011 financiado por el Gobierno de España, que contribuyen a la nueva versión del programa de escenarios regionales de cambio climático Escenarios-PNACC 2012 dentro del Plan Nacional de Adaptación al Cambio Climático. En primer lugar, se ha probado la habilidad de los métodos de regionalización a la hora de reproducir los percentiles extremos en clima presente, usando simulaciones anidadas a datos de reanálisis. La comparación entre los distintos métodos se ha realizado en términos del bias estacional, considerando la nueva rejilla pública Spain02, una rejilla regular (de aproximadamente 20km) de precipitación y temperatura que cubre España continental y las Islas Baleares. A continuación, se han analizado proyecciones de futuro en distintos escenarios de cambio climático para conocer los incrementos e incertidumbre de los resultados a mediados del siglo XXI. También se ha estudiado el efecto de anidar los métodos a diferentes Modelos de Circulación General (GCMs), usando el escenario 20C3M como referencia. Analizando esos cambios, somos capaces de atribuir esas diferencias al método de regionalización o al GCM