On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections

نویسندگان

چکیده

Abstract In a recent paper, Baño-Medina et al. (Configuration and Intercomparison of deep learning neural models for statistical downscaling. preprint, 2019) assessed the suitability convolutional networks (CNNs) downscaling temperature precipitation over Europe using large-scale ‘perfect’ reanalysis predictors. They compared results provided by CNNs with those obtained from set standard methods which have been traditionally used purposes (linear generalized linear models), concluding that are well suited continental-wide applications. That analysis is extended here assessing future climate change projections Global Climate Model (GCM) outputs as This particularly relevant this type “black-box” models, whose cannot be easily explained based on physical reasons could potentially lead to implausible downscaled due uncontrolled extrapolation artifacts. Based premise, we analyze in work two key assumptions made perfect prognosis downscaling: (1) predictors chosen build model should reproduced GCMs (2) able reliably extrapolate out sample (climate change) conditions. As first step test these latter assumption analyzing how affect raw GCM signal (defined difference, or delta, between historical climate). Our show that, well-established (GLMs), yield smaller departures end century, resulting more plausible Moreover, consequence automatic treatment spatial features, also found provide spatially homogeneous patterns than GLMs.

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ژورنال

عنوان ژورنال: Climate Dynamics

سال: 2021

ISSN: ['0930-7575', '1432-0894']

DOI: https://doi.org/10.1007/s00382-021-05847-0