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

Abstract

<p>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 work 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. All the five methods have been applied with a Perfect Prog approach to downscale daily maximum/minimum temperatures and daily precipitation on a high resolution observational grid (0.05<sup>o</sup>) over mainland Spain and the Balearic Islands, a region particularly challenging due to its large regional spatio-temporal variabilities. The 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. The evaluation here performed is focused on marginal aspects, through an analysis of the four seasonal distributions of each variable. In order to enable a manageable comparison among all methods three indexes commonly used for climate change adaptation and impact studies have been used: the mean value and the 10th and 90th percentiles for daily maximum/minimum temperatures and the total precipitation amount (PRCPTOT), the total precipitation on very wet days (R95p) and the number of wet days (R01) for precipitation. For maximum/minimum temperatures, all methods display a similar behavior. 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 non-linear methods display certain added value. For precipitation, remarkable differences are found among all methods: most of them are capable of reproducing the total precipitation amount quite satisfactorily, while other aspects such as intense precipitations and the precipitation occurrence are captured with more accuracy by the Analog method.</p><p> </p>

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

“…This lack of transferability has been found to be very remarkable in the cases of SVM and KRR. Experiments 1 and 2 (Hernanz et al ., 2021, 2021b) showed how these two methods, which are able to reproduce complex nonlinear relationships and are based on different types of Support Vector Machines, could achieve fairly good results under present conditions, overcoming the linear method REG both with perfect and imperfect predictors, but this study has revealed some important transferability issues in them. This relates with the well‐known problem of machine learning algorithms to deal with new situations to which they have not been trained, and calls in question their suitability for downscaling climate projections, as pointed out by Hsieh (2009).…”

“…The five ESD methods and their different configurations (see Table 5) are briefly presented here. For a more detailed description see Hernanz et al ., 2021. (a) Analog (ANA) methods (Lorenz, 1969; Zorita and von Storch, 1999) are based on the assumption of similar local conditions under similar synoptic situations, and one of their major drawbacks is their limitation to predict values outside of the observed range (Imbert and Benestad, 2005).…”

“…The methodology adopted for the complete evaluation follows the guidelines proposed by Vrac et al . (2007) and the EU COST Action VALUE (Maraun et al ., 2015) and consists of a set of three experiments: “Experiment 1: perfect predictor,” “Experiment 2: global climate model (GCM) predictor” and “Experiment 3: pseudo reality.” The first experiment (Hernanz et al ., 2021) showed good results by the five methods both for the mean values and the tails of maximum/minimum temperatures. As for precipitation, most methods were able to capture the total precipitation amount, but only a few methods and configurations seemed suitable for extreme events and for the precipitation occurrence.…”

“…As for precipitation, most methods were able to capture the total precipitation amount, but only a few methods and configurations seemed suitable for extreme events and for the precipitation occurrence. The second experiment (Hernanz et al ., 2021) showed how ESD methods presented a significant sensitivity to the use of imperfect predictors from GCMs and in this third paper we study the transferability of those methods to future conditions with the use of pseudo observations from regional climate models (RCMs) as predictands. Pseudo reality experiments are aimed to reveal whether predictors/predictand relationships found by ESD methods at a historical period are transferable to future climate conditions and whether predictors necessary to simulate the response to climate change have not been taken into account (Maraun et al ., 2015; Maraun and Widmann, 2018).…”

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

“…Nonlinear methods do not add much value here because of the high linear correlation among predictor and predictand (Figure 1). Nevertheless, these very same methods have proved to overcome MLR when using a greater set of predictors (Hernanz et al, 2021).…”