Evaluación de métodos de regionalización estadística para la generación de proyecciones climáticas en el marco del PNACC-2 2021-2030

La Agencia Estatal de Meteorología (AEMET) es responsable de la elaboración de las proyecciones climáticas regionalizadas sobre el territorio de España según lo establecido en el segundo Plan Nacional de Adaptación al Cambio Climático (PNACC-2). Las proyecciones climáticas regionalizadas suponen un aumento de la resolución espacial con respecto a las proyecciones climáticas proporcionadas por los modelos climáticos globales. AEMET elabora las proyecciones climáticas regionalizadas sobre España aplicando métodos de regionalización empírico-estadísticos a las proyecciones climáticas de un conjunto de modelos climáticos globales. Las proyecciones climáticas regionalizadas son necesarias para la realización de estudios de impacto y vulnerabilidad que requieren datos en alta resolución espacial. La presente nota técnica tiene dos objetivos: 1) evaluar y seleccionar un conjunto de modelos climáticos globales cuyas proyecciones serán regionalizadas; 2) comparar diferentes métodos empírico-estadísticos de regionalización a fin de elegir un método para regionalizar los datos diarios de las siguientes variables climáticas de interés (temperatura máxima, temperatura mínima y precipitación acumulada). En cuanto al primer objetivo, los resultados han permitido seleccionar un conjunto de once modelos climáticos globales que serán regionalizados. Respecto al segundo objetivo, a partir de la comparativa de métodos se establece que la temperatura máxima y la temperatura mínima se regionalizarán utilizando el método híbrido de regresión-análogos (MLR-ANA), mientras que la precipitación acumulada se regionalizará aplicando el método eXtreme Gradient Boost (XGB). Además, tras la regionalización, tanto en temperaturas como en precipitación, se establece la conveniencia de aplicar una corrección de sesgos a posteriori mediante Quantile Delta Mapping (QDM) para mejorar los índices de comparación

Evaluation of statistical downscaling methods for climate change projections over Spain: future conditions with pseudo reality (transferability experiment)

The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC-2) and this is the last of three papers aimed to evaluate and intercompare five empirical/statistical downscaling (ESD) methods developed at AEMET: (a) Analog, (b) Regression, (c) Artificial Neural Networks, (d) Support Vector Machines and (e) Kernel Ridge Regression, in order to decide which methods and under what configurations are more suitable for that purpose. Following the framework established by the EU COST Action VALUE, in this experiment we test the transferability of these methods to future climate conditions with the use of regional climate models (RCMs) as pseudo observations. We evaluate the marginal aspects of the distributions of daily maximum/minimum temperatures and daily accumulated precipitation, over mainland Spain and the Balearic Islands, analysed by season. For maximum/minimum temperatures all methods display certain transferability issues, being remarkable for Support Vector Machines and Kernel Ridge Regression. For precipitation all methods appear to suffer from transferability difficulties as well, although conclusions are not as clear as for temperature, probably due to the fact that precipitation does not present such a marked signal of change. This study has revealed how an analysis over a historical period is not enough to fully evaluate ESD methods, so we propose that some type of analysis of transferability should be added in a standard procedure of a complete evaluation.Marta Domínguez has received funding from the MEDSCOPE project co-funded by the European Commission as part of ERA4CS, an ERA-NET initiated by JPI Climate, grant agreement 690462. MEDSCOPE, Grant/Award Number: 69046

A critical view on the suitability of machine learning techniques to downscale climate change projections : illustration for temperature with a toy experiment

Machine learning is a growing field of research with many applications. It provides a series of techniques able to solve complex nonlinear problems, and that has promoted their application for statistical downscaling. Intercomparison exercises with other classical methods have so far shown promising results. Nevertheless, many evaluation studies of statistical downscaling methods neglect the analysis of their extrapolation capability. In this study, we aim to make a wakeup call to the community about the potential risks of using machine learning for statistical downscaling of climate change projections. We present a set of three toy experiments, applying three commonly used machine learning algorithms, two different implementations of artificial neural networks and a support vector machine, to downscale daily maximum temperature, and comparing them with the classical multiple linear regression. We have tested the four methods in and out of their calibration range, and have found how the three machine learning techniques can perform poorly under extrapolation. Additionally, we have analysed the impact of this extrapolation issue depending on the degree of overlapping between the training and testing datasets, and we have found very different sensitivities for each method and specific implementation

Statistical Downscaling in the Tropics and Midlatitudes: A Comparative Assessment over Two Representative Regions

Statistical downscaling (SD) of climate change projections is a key piece for impact and adaptation studies due to its low computational expense compared to dynamical downscaling, which allows exploration of uncertainties through the generation of large ensembles. SD has been extensively evaluated and applied in the extratropics, but few examples exist in tropical regions. In this study, several state-of-the-art methods belonging to different families have been evaluated for maximum/minimum daily temperature and daily accumulated precipitation (both from the ERA5 at 0.258) in two regions with very different climates: Spain (midlatitudes) and Central America (tropics). Some key assumptions of SD have been tested: the strength of the predictor–predictand links, the skill of different approaches, and the extrapolation capability of each method. It has been found that relevant predictors are different in both regions, as is the behavior of statistical methods. For temperature, most methods perform significantly better in Spain than in Central America, where transfer function (TF) methods present important extrapolation problems, probably due to the low variability of the training sample (present climate). In both regions, model output statistics (MOS) methods have achieved the best results for temperature. In Central America, TF methods have achieved better results than MOS methods in the evaluation in the present climate, but they do not preserve trends in the future. For precipitation, MOS methods and the extreme gradient boost machine learning method have achieved the best results in both regions. In addition, it has been found that, although the use of humidity indices as predictors improves results for the downscaling of precipitation, future trends given by statistical methods are very sensitive to the use of one or another index. Three indices have been compared: relative humidity, specific humidity, and dewpoint depression. The use of the specific humidity has been found to lead to trends given by the downscaled projections that deviate seriously from those given by raw global climate models in both regions

The Nowcasting SAF (NWC SAF): examples of operational use of its geostationary products in nowcasting tasks

Presentación realizada en la 2nd European Nowcasting Conference, celebrada en Deutches Wetterdienst, Offenbach (Alemania) del 3 al 5 de mayo de 2017

Evaluation of statistical downscaling methods for climate change projections over Spain: Present conditions with perfect predictors

The Spanish Meteorological Agency (AEMET) is responsible for the elaboration of downscaled climate projections over Spain to feed the Second National Plan of Adaptation to Climate Change (PNACC-2). The main objective of this article is to establish a comparison among five statistical downscaling methods developed at AEMET: (1) Analog, (2) Regression, (3) Artificial Neural Networks, (4) Support Vector Machines and (5) Kernel Ridge Regression. This comparison has been carried out under present conditions and with perfect predictors, based on the framework established by the VALUE network, in particular, on its perfect predictor experiment. In this experiment, we evaluate the marginal aspects of the distributions of daily maximum/minimum temperatures and daily accumulated precipitation analysed by seasons, on a high resolution observational grid (0.05°) over mainland Spain and the Balearic Islands. This is the first of a set of three experiments aimed to allow us to decide which methods, and under what configuration, is more appropriate for the generation of downscaled climate projections over our region. For maximum/minimum temperatures, all methods display a similar behaviour. They capture very satisfactorily the mean values although slight biases are detected on the extremes. In general, results for maximum temperature appear to be more accurate than for minimum temperature, and the nonlinear methods display certain added value. For precipitation, remarkable differences are found among all methods. Most of the methods are capable of reproducing the total precipitation amount quite satisfactorily, whereas other aspects such as intense precipitations and the precipitation occurrence are captured with more accuracy by the Analog method.Funding from the MEDSCOPE project co-funded by the European Commission as part of ERA4CS, an ERA-NET initiated by JPI Climate, grant agreement 690462

On the limitations of deep learning for statistical downscaling of climate change projections : the transferability and the extrapolation issues

Convolutional neural networks (CNNs) have become one of the state-of-the-art techniques for downscaling climate projections. They are being applied under Perfect-Prognosis (trained in a historical period with observations) and hybrid approaches (as Regional Climate Models (RCMs) emulators), with satisfactory results. Nevertheless, two important aspects have not been, to our knowledge, properly assessed yet: (1) their performance as emulators for other Earth System Models (ESMs) different to the one used for training, and (2) their performance under extrapolation, that is, when applied outside of their calibration range. In this study, we use UNET, a popular CNN, to assess these two aspects through two pseudo-reality experiments, and we compare it with simpler emulators: an interpolation and a linear regression. The RCA4 regional model, with 0.11° resolution over a complex domain centered in the Pyrenees, and driven by the CNRM-CM5 global model is used to train the emulators. Two frameworks are followed for the training: predictors are taken (1) from the upscaled RCM and (2) from the ESM. In both frameworks, the performance of the UNET when applied for other ESMs different to the one used for training is considerably worse, indicating poor generalization. For the linear method a similar deterioration is seen, so this limitation does not seem method specific but inherent to the task. For the second experiment, the emulators are trained in present and evaluated in future, under extrapolation. While averaged aspects such as the mean values are well simulated in future, significant biases (up to 5°C) appear when assessing warm extremes. These biases are larger by UNET than those produced by the linear method. This limitation suggests that, for variables such as temperature, with a marked signal of change and a strong linear relationship with predictors, simple linear methods might be more appropriate than the sophisticated deep learning techniques

High resolution climate change projections for the Pyrenees region

Número monográfico dedicado al "19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019".The Pyrenees, located in the transition zone of Atlantic and Mediterranean climates, constitute a paradigmatic example of mountains undergoing rapid changes in environmental conditions, with potential impact on the availability of water resources, mainly for downstream populations. High-resolution probabilistic climate change projections for precipitation and temperature are a crucial element for stakeholders to make well-informed decisions on adaptation to new climate conditions. In this line, we have generated high–resolution climate projections for 21st century by applying two statistical downscaling methods (regression for max and min temperatures, and analogue for precipitation) over the Pyrenees region in the frame of the CLIMPY project over a new high-resolution (5 km × 5 km) observational grid using 24 climate models from CMIP5. The application of statistical downscaling to such a high resolution observational grid instead of station data partially circumvent the problems associated to the non-uniform distribution of observational in situ data. This new high resolution projections database based on statistical algorithms complements the widely used EUROCORDEX data based on dynamical downscaling and allows to identify features that are dependent on the particular downscaling method. In our analysis, we not only focus on maximum and minimum temperatures and precipitation changes but also on changes in some relevant extreme indexes, being 1986–2005 the reference period. Although climate models predict a general increase in temperature extremes for the end of the 21st century, the exact spatial distribution of changes in temperature and much more in precipitation remains uncertain as they are strongly model dependent. Besides, for precipitation, the uncertainty associated to models can mask – depending on the zones- the signal of change. However, the large number of downscaled models and the high resolution of the used grid allow us to provide differential information at least at massif level. The impact of the RCP becomes significant for the second half of the 21st century, with changes – differentiated by massifs – of extreme temperatures and analysed associated extreme indexes for RCP8.5 at the end of the century.The CLIMPY project (EFA081/15) is under the umbrella of the Pyrenees Climate Change Observatory (OPCCCTP), and it has a 65 % funding by the European Regional Development Fund (FEDER) through the Interreg Programme V-A SpainFrance-Andorra (POCTEFA 2014–2020). More information: https: //www.opcc-ctp.org/en/climpy (last access: 26 September 2020)

The NWCSAF/GEO software package for the MSG/IODC satellite service

Póster presentado en: EUMETSAT Meteorological Satellite Conference celebrada del 2 al 6 de Octubre de 2017 en Roma