评估 2000 至 2022 年尼日利亚海面温度(SST)变化与降水类型之间的关系

Tertsea Igbawua , Fanan Ujoh , Solomon Kwaghfan Mkighirga , Grace Adagba
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引用次数: 0

摘要

本研究调查了与尼日利亚不同气候区降雨量变化相关的环流模式(2000-2022 年)。数据来源于 MERRA2、美国国家海洋和大气管理局(NOAA)提供的网格 ERSST5 以及气候研究单位(CRU)提供的观测数据记录。采用主成分分析(PCA)对不同降水类型进行比较,同时采用多项式逻辑回归模型来检验 SSTa 对降水的影响,并给出回归系数,显著性水平设定为 p <0.05。利用降水数据划分了五个不同的标准降水指数(SPI)等级:极干、干、正常、湿和极湿。分析在 R-studio 中进行,包括数据准备、模型训练和评估,重点是解释系数,以辨别 SST 异常对每个特定等级降水的影响。结果表明,TOP、AVP、LSP 和 CNP 各不相同:从空间上看,北部地区从大西洋获得的水汽预算较低,而不同气候带降水的时间分布表明这些气候带的降水变化很大。西太平洋区域的平均 SSTa 主要为正值(0.5 和 1)。全球最低(最高)的 SST 值主要出现在 DJF(JJA)季节,而 WAf 地区 SSTa 的月度分布则显示出中性(2000-2016 年)和厄尔尼诺(2016-2022 年)现象。对 NIF 值的分析表明,与 nino3.4 SST 与降水类型相比,WAf SST 异常与降水类型之间的关系各不相同,但总体上更为密切。作为预测尼日利亚不同气候带降水的季节和空间分布的信号,该成果可支持粮食安全、水和生物多样性保护以及适应和减缓气候变化的规划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of relationship between sea surface temperature (SST) changes and precipitation types in Nigeria from 2000 to 2022

This study investigated the circulation patterns associated with rainfall variations across Nigeria’s different climatic zones (2000–2022). Data was acquired from MERRA2, gridded ERSST5 provided by the NOAA, and observation data records obtained from the Climatic Research Unit (CRU). Principal Component Analysis (PCA) was employed to compare different precipitation types, while the Multinomial Logistic Regression Model was employed to examine the influence of SSTa on precipitation presenting regression coefficients with a significance level set at p < 0.05. Five distint standard precipitation index (SPI) classes: very dry, dry, normal, wet and very wet were categorised using prcipitation data. Analysis was done in R-studio and involved data preparation, model training, and evaluation, with emphasis on interpreting the coefficients to discern the impact of SST anomalies on precipitation for each specified level. The results show that TOP, AVP, LSP, and CNP varied: spatially, the northern region received low moisture budget from the Atlantic Ocean while the temporal distribution of precipitation across different climatic zones indicate high variability in precipitation across these zones. The mean SSTa in the WAf region were predominantly positive (0.5 and 1). The lowest (highest) global SST values were prevalent during the DJF (JJA) season(s) whereas, the monthly distribution of SSTa for the WAf region reveal neutral (2000–2016) and El Niño (2016–2022) episodes. The analysis of NIF values indicates a varied but generally stronger relationship between WAf SST anomalies and precipitation types compared to nino3.4 SST versus precipitation types. As a signal for prediction of seasonal and spatial distribution of precipitation across Nigeria’s different climatic zones, this outcome can support planning for food security, water and biodiversity conservation, and climate change adaptation and mitigation.

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