Reder Alfredo, Fedele Giusy, Manco Ilenia, Mercogliano Paola
CMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, Italy.
Physics and Astronomy Department, University of Bologna, Bologna, Italy.
Sci Rep. 2025 Jan 3;15(1):621. doi: 10.1038/s41598-024-84527-5.
The increasing availability of coarse-scale climate simulations and the need for ready-to-use high-resolution variables drive the climate community to the challenge of reducing computational resources and time for downscaling purposes. To this end, statistical downscaling is gaining interest as a potential strategy for integrating high-resolution climate information obtained through dynamical downscaling over limited years, providing a clear understanding of the gains and losses in combining dynamical and statistical downscaling. In this regard, several questions can be raised: (i) what is the performance of statistical downscaling, assuming dynamical downscaling as a reference over a shared time window; (ii) how much the performance of statistical downscaling is affected by changes in the number of years available for training; (iii) how does the climate normal considered for the training affect the predictions. This study addresses these issues by applying a statistical downscaling procedure based on the empirical quantile mapping bias adjustment, obtaining finer-resolution climate variables. This procedure was adopted in order to downscale temperature and precipitation from ERA5 climate reanalysis, having as reference both for training and validation, the respective variables obtained through the dynamical downscaling of ERA5 over Italy for about 30 years. The availability of such a long simulation allows us to define several long time windows, used to calibrate the statistical relationships and evaluate the performance of statistical downscaling versus dynamical downscaling over a shared blind prediction period, taking advantage of a set of spatial and temporal metrics. The study shows that (i) the statistical downscaling successfully represents mean values and extremes of temperature and precipitation; (ii) its performance remains satisfactory regardless of the number of years used as training; (iii) the shorter is the time window considered for the training, the higher is the sensitivity to changes in the time interval due to the inter-annual variability. Nevertheless, the performance deviations are somehow not so remarkable.
粗尺度气候模拟的可得性不断提高，以及对即用型高分辨率变量的需求，促使气候学界面临着为降尺度目的减少计算资源和时间的挑战。为此，统计降尺度作为一种潜在策略正受到关注，该策略用于整合通过有限年份的动力降尺度获得的高分辨率气候信息，从而清晰地了解将动力降尺度和统计降尺度相结合的得失。在这方面，可以提出几个问题：（i）假设在共享时间窗口内以动力降尺度为参考，统计降尺度的性能如何；（ii）可用于训练的年份数量变化对统计降尺度的性能有多大影响；（iii）训练所考虑的气候正常值如何影响预测。本研究通过应用基于经验分位数映射偏差调整的统计降尺度程序来解决这些问题，从而获得更高分辨率的气候变量。采用该程序是为了对ERA5气候再分析的温度和降水进行降尺度，将通过对意大利约30年的ERA5动力降尺度获得的各自变量用作训练和验证的参考。如此长时间模拟的可得性使我们能够定义几个长时间窗口，用于校准统计关系，并利用一组空间和时间指标在共享的盲预测期内评估统计降尺度与动力降尺度相比的性能。研究表明：（i）统计降尺度成功地再现了温度和降水的平均值及极值；（ii）无论用作训练的年份数量如何，其性能都保持令人满意；（iii）用于训练的时间窗口越短，由于年际变率，对时间间隔变化的敏感性就越高。然而，性能偏差在某种程度上并不那么显著。
