The Relationship between Extreme Precipitation Events in East Africa during the Short Rainy Season and Indian Ocean Sea Surface Temperature

The East African short rainy season (October-November-December) is one of the major flood seasons in the East African region. The amount of rainfall during the short rainy season is closely related to the lives of the people and the socio-economic development of the area. By using precipitation data and sea surface temperature data, this study reveals the spatial and temporal variation patterns of extreme precipitation during the East African short rainy season. Key findings include significant rainfall variability, with Tanzania experiencing the highest amounts in December due to the southward shift of the Intertropical Convergence Zone (ITCZ), while other regions receive less than 100 mm. Extreme rainfall events (90th percentiles) are evenly distributed, averaging 2 to 10 days annually. Historical data shows maximum seasonal rainfall often peaks at 15 mm, with frequent occurrences of daily rainfall exceeding 10 mm during OND. Additionally, a positive correlation (0.48) between OND precipitation extremes and Indian Ocean Dipole (IOD) anomalies is statistically significant. These findings highlight the climatic variability and potential trends in extreme rainfall events in East Africa, providing valuable insights for regional climate adaptation strategies.

Assessment of the Evolution and Socio-Economic Impacts of Extreme Rainfall Events in October 2019 over the East Africa

This study aimed at establishing and quantifying the evolution and socio-economic impacts of extreme rainfall events in October 2019. The study also focused on ascertaining the extent to which the Indian Ocean Dipole (IOD) and the El Ni?o Southern Oscillation (ENSO) influenced anomalous rainfall over East Africa (EA) in October 2019. It employed Singular Value Decomposition (SVD) methods to analyze inter-annual variability of EA rainfall and the Sea Surface Temperature Anomalies (SSTA) over the Indian and Pacific Ocean with a focus on October to December 2019 rainfall season. The SVD analysis enabled the exploration of the leading modes from the mean monthly rainfall and SSTs leading to the determination of the likely influence of the IOD and ENSO respectively. The first SVD coupled modes, which dominate the co-variability between the October rainfall over the EA domain, and SSTA over the Indian and Pacific Oceans based on 1981 to 2010 climatology indicate the monopole positive co-variability with rainfall over the entire EA domain. The corresponding spatial pattern for the SSTA over the Indian Ocean (IO) recaptures the positive IOD event while the central equatorial Pacific Ocean (i.e., over Ni?o 3.4 region) reveals a monopole positive loading, a typical signal for the warm phase of ENSO. The positive rainfall anomaly over the EA during October is found to be associated with either the IOD event or ENSO condition events independently or in phase. However, the inter-annual variability between October rainfall over EA and ENSO reveals a moderate relationship (r = 0.4212) while a robust association (r = 0.7084) is revealed with IOD. Comparatively, the October 2019 rainfall anomaly peaks the highest in history over the EA and was found to be coupled with highest positive IOD event in record. Unlikely, the 1997 October rainfall (which peaked the second in history), was associated with the co-occurrence of the positive phase of ENSO and IOD events. The findings of this study suggest that the positive IOD coupled mode had large impact on the distribution and variability of the October 2019 rainfall over the EA region.

中国冬季大范围极端冷、暖日的时空变化特征

利用1961—2020年中国逐日地表气温观测资料,综合考虑其概率密度分布与空间范围,定义了全国一致冷日、一致暖日与全国反位相型冷暖日3种全国性极端冷、暖日,并分析了各自的变化特征。结果表明,中国冬季全国性极端冷、暖日共960 d。其中一致冷日358 d,一致暖日271 d,反位相型冷暖日331 d。在21世纪前,一致冷日数与累计强度随时间逐渐下降,冬季与2月的一致冷日数均在20世纪80年代出现显著突变减少;一致暖日则随时间呈上升趋势,其中只有12月的一致暖日数在70年代末突变增加。21世纪前冬季一致冷(暖)日的下降(上升)受2月的影响最大。进入21世纪后,1月一致冷日的增加使冬季一致冷日出现小幅度上升,而2月一致暖日迅速减少导致冬季一致暖日在21世纪初有小幅度下降,之后1月一致暖日的迅速增加使得冬季一致暖日继续上升。冬季、1月、2月的反位相型冷暖日的天数均在20世纪70—80年代突变减少,21世纪10年代前,冬季反位相型冷暖日主要受到2月变化的影响,之后1月的贡献更大。

中国东部夏季极端高温的空间分布特征及其环流型

利用1961-2008年中国751个地面观测站气温资料插值而成的CN05格点资料,首先分析了中国东部夏季极端高温日数变化特征。结果表明,中国东部整体夏季极端高温日数有显著增多趋势,以东北、内蒙古中东部和东南沿海增多趋势最明显,而江淮黄淮地区增多趋势最不明显甚至出现减少趋势。极端高温日数年际波动周期以2.5~3.0年为主,江淮地区以5年为主。对极端高温日数进行EOF分解的结果表明,第1典型场主要表现了极端高温日数的线性变化趋势,第2、3典型场分别表现为江淮和东北、华南和华北极端高温日数的两种南北＂偶极型＂分布特征。合成分析表明,第2典型场正＂偶极型＂对应江淮地区500 hPa位势高度正异常和东北高度负异常以及西太平洋副热带高压（简称副高）偏北偏强,负＂偶极型＂则相反;第3典型场正＂偶极型＂对应华南500 hPa位势高度正异常和华北高度负异常以及西太平洋副高偏南偏强,负＂偶极型＂则相反。偏相关分析表明,鄂霍次克海阻高与东北和东南沿海极端高温日数正相关显著,与黄淮部分地区呈显著负相关;副高脊线与华北和东北极端高温日数呈显著正相关;副高强度与东南沿海呈显著正相关。

东北夏季极端低温天气事件的定义及其冷空气路径分析

采用东北三省150个测站1961～2010年的逐日温度资料和同期美国环境预报中心（NCEP）以及国家大气研究中心（NCAR）2．5°×2．5°分辨率的全球再分析资料，定义了东北夏季极端低温天气事件（extremelow—tempera—tureevent以下简称ELTE事件），统计给出了ELTE事件的发生、峰值和持续时间的日历表。根据该日历分析，发现了影响东北地区夏季ELTE事件’酌最主要的2条冷空气路径（西北路径和东北路径），1961～2010年期间，影响东北地区的ELTE事件最主要是受西北路径的冷空气的影响，其次是东北路径。同时也发现，ELTE事件20世纪80年代发生频率最高，90年代后开始减少，但是东北路径的ELTE事件在90年代以后开始明显的增加。还发现，ELTE事件发生的前4d，贝加尔湖地区若是被低压槽覆盖，易发生西北路径的ELTE事件，若该区域被一个东北．西南走向的高压斜脊控制，同时鄂霍次克海区域存在一个低压中心，形成西北“＋”东南“一”的跷跷板形态，易发生东北路径的ELTE事件。