摘要
In recent years, extreme high temperature events occurred more frequently in Northern Africa (NA) posing significant impacts on ecological systems and socioeconomic development. However, the physical origin of these extreme high temperatures remains unexplored. To address this issue, Empirical Orthogonal Function (EOF) analysis technics is employed to investigate the key physical factors influencing the spatial patterns of extreme high temperature days (EHDs) over NA. Three major modes of EHDs (EOF1, EOF2 and EOF3) accounting for 43%, 11% and 8% of the total variance were identified in this study. EOF1 features uniform distribution associated with positive geopotential heights and anticyclonic flows, while EOF2 is characterized by a meridional dipole pattern. Using reanalysis datasets, these modes are further linked to ocean – land – atmosphere interactions to reveal underlying physical mechanism. EOF1 is influenced by tropical and subtropical positive SSTA associated by mid tropospheric heights which triggers heat wave transport and subsidence. This mode is also influenced by weakening of west African monsoon system which suppresses moisture transport towards NA. EOF2 is influenced by combination of tropical Indian ocean and western Pacific wave trains leading subsidence over NA. EOF3 captures more the transient or regional scale influences on EHDs due to it weak association with large-scale teleconnections. Generally, this study classifies the factors influencing summer patterns of EHDs over NA as 1) tropical and subtropical SST warming, 2) decaying of Monsoon circulation, and 3) Strengthened upper-level subsidence. Gaining an understanding of these processes is essential for improving climate prediction and setting strategies for early warning and mitigation of the impacts from extreme heat events.
In recent years, extreme high temperature events occurred more frequently in Northern Africa (NA) posing significant impacts on ecological systems and socioeconomic development. However, the physical origin of these extreme high temperatures remains unexplored. To address this issue, Empirical Orthogonal Function (EOF) analysis technics is employed to investigate the key physical factors influencing the spatial patterns of extreme high temperature days (EHDs) over NA. Three major modes of EHDs (EOF1, EOF2 and EOF3) accounting for 43%, 11% and 8% of the total variance were identified in this study. EOF1 features uniform distribution associated with positive geopotential heights and anticyclonic flows, while EOF2 is characterized by a meridional dipole pattern. Using reanalysis datasets, these modes are further linked to ocean – land – atmosphere interactions to reveal underlying physical mechanism. EOF1 is influenced by tropical and subtropical positive SSTA associated by mid tropospheric heights which triggers heat wave transport and subsidence. This mode is also influenced by weakening of west African monsoon system which suppresses moisture transport towards NA. EOF2 is influenced by combination of tropical Indian ocean and western Pacific wave trains leading subsidence over NA. EOF3 captures more the transient or regional scale influences on EHDs due to it weak association with large-scale teleconnections. Generally, this study classifies the factors influencing summer patterns of EHDs over NA as 1) tropical and subtropical SST warming, 2) decaying of Monsoon circulation, and 3) Strengthened upper-level subsidence. Gaining an understanding of these processes is essential for improving climate prediction and setting strategies for early warning and mitigation of the impacts from extreme heat events.
作者
Nestory Silvestry Mosha
Daniel Stephano Semgomba
Charles Yusuph Ntigwaza
Daniel Jonathan Masunga
Nestory Silvestry Mosha;Daniel Stephano Semgomba;Charles Yusuph Ntigwaza;Daniel Jonathan Masunga(State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China;Tanzania Meteorological Authority, Dodoma, Tanzania;National Meteorological Training Centre, Kigoma, Tanzania)
