Statistical downscaling of daily precipitation over southeastern South America: Assessing the performance in extreme events,International Journal of Climatology

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Statistical downscaling of daily precipitation over southeastern South America: Assessing the performance in extreme events
International Journal of Climatology ( IF 3.5 ) Pub Date : 2021-07-17 , DOI: 10.1002/joc.7303
Olmo Matías Ezequiel _{1,

2,

3} , Bettolli María Laura _{1,

2,

3}

Affiliation

The performance of multiple empirical statistical downscaling (ESD) methods was assessed for simulating daily precipitation during 1979–2017 over southeastern South America (SESA), a region where extremes are remarkable. Meteorological stations were used as reference and three gridded precipitation products were included to account for observational uncertainty. The set of ESD models involved different configurations of the analogue method (ANs), deterministic and stochastic versions of neural networks (NNs) and generalized linear models (GLMs) and circulation-conditioned GLMs (GLM_WTs). The years with the largest number of extreme events (wet years) were calibrated separately. The spatio-temporal variability of extremes was assessed by analysing their intensity, spatial extent, frequency and interannual variability. An overall good performance of the ESD models was found for several aspects of daily precipitation. ESD performance dispersion was usually contained in the observational spread. No particular model configuration was found to perform best in all aspects, indicating the advantage of considering a multi-model ensemble. The ANs tended to follow the stations, satisfactorily simulating daily precipitation and its extremes. The deterministic GLMs strongly underestimated precipitation estimates and were not able to represent extreme frequencies and intensities, but this was alleviated by employing a stochastic version of the method. Furthermore, the use of weather types to condition the GLMs (GLM_WTs) considerably improved model performance, particularly for the annual cycle and the spatial structure of extreme precipitation. The NNs adequately reproduced the spatial behaviour and intra-annual variability of extreme precipitation, although they underestimated its intensity in their deterministic version. An analysis of the regional time series of extremes showed consistency among datasets and evidenced the influence of the ENSO teleconnection on the wet years, which were commonly well-simulated by the statistical models. ESD models presented good skills in simulating a wetter climate over SESA, which is of particular importance in a climate change scenario.

中文翻译：

南美洲东南部每日降水的统计缩减：评估极端事件中的表现

评估了多种经验统计降尺度 (ESD) 方法的性能，以模拟 1979 年至 2017 年期间南美洲东南部 (SESA) 的每日降水，该地区极端事件非常显着。气象站被用作参考，并包括三个网格降水产品来解释观测的不确定性。ESD 模型集涉及模拟方法 (AN)、神经网络 (NN) 的确定性和随机版本以及广义线性模型 (GLM) 和循环条件 GLM (GLM_WT) 的不同配置。极端事件（雨季）最多的年份分别进行了校准。通过分析极端事件的强度、空间范围、频率和年际变异性来评估极端事件的时空变异性。发现 ESD 模型在日常降水的几个方面总体表现良好。ESD 性能偏差通常包含在观察分布中。没有发现特定的模型配置在所有方面都表现最好，这表明考虑多模型集合的优势。AN 倾向于跟随台站，令人满意地模拟每日降水及其极端情况。确定性 GLM 严重低估了降水估计值，并且无法表示极端频率和强度，但通过采用该方法的随机版本可以缓解这种情况。此外，使用天气类型来调节 GLM（GLM_WTs）显着提高了模型性能，特别是对于年周期和极端降水的空间结构。NN 充分再现了极端降水的空间行为和年内变化，尽管他们在确定性版本中低估了极端降水的强度。对区域性极值时间序列的分析显示了数据集之间的一致性，并证明了 ENSO 遥相关对潮湿年份的影响，这通常被统计模型很好地模拟。ESD 模型在模拟 SESA 上的潮湿气候方面表现出良好的技能，这在气候变化情景中尤为重要。这通常被统计模型很好地模拟。ESD 模型在模拟 SESA 上的潮湿气候方面表现出良好的技能，这在气候变化情景中尤为重要。这通常被统计模型很好地模拟。ESD 模型在模拟 SESA 上的潮湿气候方面表现出良好的技能，这在气候变化情景中尤为重要。

更新日期：2021-07-17

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