Extreme precipitation events are becoming less frequent but more intense over Jeddah, Saudi Arabia. Are shifting weather regimes the cause?

Abstract

This study analyses the connection between extreme rainfall events in Jeddah, Saudi Arabia, and synoptic-scale weather patterns over the Arabian Peninsula.Mean rainfall follows a decreasing trend; however, the number of rainy days has increased. Interestingly, extreme rainfall is becoming less frequent but shows an increased intensity. Here we utilize self-organizing maps (SOMs) to identify the weather patterns of the most intense rainy days and the synoptic systems causing extreme rainfall in the Jeddah region. Three main weather patterns that cause heavy rainfall events over Jeddah during the cooler months (November-April) are identified, all reflect tropical-extratropical interactions. Extreme events in the early period (1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998) are characterized by a stronger tropical influence and local precipitation patterns, while a stronger extratropical forcing and higher extreme rainfall amounts are spotted in the late period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018). Our results suggest that in recent decades, the mechanism causing extreme rainfall over the city of Jeddah has shifted toward a weather regime with stronger extratropical influence.

Self‐organizing map classification of the boundary layer profile: A refinement of Eastern Mediterranean winter synoptic regimes

“…The average precipitation under active RSTs is shown at nodes 16 and 17. Unlike the typical Cyprus cyclone activity, significant convective heavy rainfall is obtained over Jordan and Saudi Arabia (Luong et al ., 2020), following a marked moist anomaly over Egypt and Sinai, steered by the PV anomaly. Precipitation is therefore distributed anomalously during this profile regime throughout the EM and southern Levant.…”

“…This method enables objective classification of the BL profile according to the 3D variability of the meteorological variables, rather than the subjective (e.g., Dayan et al ., 1988) or semi‐objective (Alpert et al ., 2004) classifications which consider the variability next to the surface, without considering the 3D variability and the effect of humidity. Luong et al ., 2020 presented SOM classification of reanalysis data related to extreme flood events over Jeddah and Saudi Arabia, and demonstrated the ability of SOM to elucidate fine differences of the large scale patterns. SOM classification of surface wind regimes over Israel and their relation to synoptic variables was presented by Berkovic (2017, 2018).…”

S-SOM v1.0: a structural self-organizing map algorithm for weather typing

“…The original MSLP data at a 0.75 • resolution on a regular grid were interpolated to an equal-area scalable Earth-type grid at a spatial resolution of 100 km. This interpolation method has been commonly applied in high-latitude regions (Lynch et al, 2016;Gibson et al, 2017). The data are divided according to the four seasons: winter (December, January, February; DJF), spring (March, April, May; MAM), summer (June, July, August; JJA), autumn (September, October, November; SON).…”

“…For example, with a SOM, Horton et al (2015) found that changes in the frequency of geopotential height patterns since the 1980s have modified extreme temperature trends in some Northern Hemisphere regions. Also, SOMs have been used to discover the association between rainfall changes and shifts in large-scale circulation patterns (e.g., Alexander et al, 2010;Lennard and Hegerl, 2015;Swales et al, 2016;Nguyen-Le and Yamada, 2019;Luong et al, 2020). SOMs are also used as a statistical downscaling method for the future climate by associating the changes in the frequency of synoptic occurrences with surface variables (e.g., Gibson et al, 2016;Ohba and Sugimoto, 2019).…”

A structural self-organizing map (S-SOM) algorithm for weather typing

“…in some northern hemisphere regions. Also, SOMs have been used to discover the association between changes in rainfall and shifts in large-scale circulation patterns (e.g., Alexander et al, 2010;Lennard and Hegerl (2015), Swales et al, 2016;Nguyen-Le et al, 2019;Luong et al, 2020). In addition, SOMs are also used as a statistical downscaling method for the future climate by associating the changes in the frequency of synoptic occurrences with surface variables (e.g., Gibson et al 2016.…”