Synoptic classification of the summer season for the Levant using an ‘environment to climate’ approach

Abstract

The Levant region is dominated in the summer by the persistent Persian Trough. Thus, the current synoptic classifications lack the ability to reflect inter‐diurnal variations in the local weather conditions. This study aims to overcome this weakness and has two objectives: to develop operative prediction equations for the local weather conditions based on synoptic‐scale data, and to propose a new synoptic classification, based on the environment to climate approach, to better reflect the local climatic stress. Prediction equations were derived for the national heat‐stress (NHS) and the height of the persistent marine inversion (inversion base, IB). The potential predictors are comprised of atmospheric variables found correlated with these weather attributes together with indices representing synoptic to large‐scale features. The indices were derived through a composite maps analysis of days with extreme values of these attributes. The Climatic Stress Index (CSI) developed is a combination of the heat‐stress and the inversion height, for which a separate prediction equation was derived. The spectrum of the calculated daily CSI values was divided into three parts of equal size, used as the basis of the new synoptic types, defined as: ‘comfort’, ‘medium’, and ‘discomfort’. The attribution of a certain day to one of these types is determined according to its calculated CSI. Our proposed classification was found to better delineate the NHS and the height of the IB in Israel. The downscaling schemes and the new synoptic classification are applicable for both operative weather prediction and climate prediction. The latter enables the assessment of future trends in the climatic stress, in general, and heat‐stress, in particular. This is due to being compatible with the output of operative weather prediction models as well as for the format of the disseminated output of climate models.

Natural and Anthropogenic Particulate Matter Haifa Metropolitan Area, Israel and its Relation to the Synoptic Conditions and the Summer Climatic Stress

“…This period is entitled hereafter, the 'synoptic summer'. The classification of the summer days is based on the climatic stress index (CSI), developed by Saaroni et al (2017). This index is calculated based on a prediction equation (i.e., a regression model) that combines the normalized and standardized national heat stress and the height of the persistent marine inversion.…”

A New View of Heat Wave Dynamics and Predictability over the Eastern Mediterranean

“…The CSI is comprised of the national heat stress index, 125 used operationally by the Israel Meteorological Service, and the boundary layer height, which is a major factor influencing the summer weather conditions over the Eastern Mediterranean (Ziv et al, 2004). Saaroni et al (2017)…”

“…Trough occurs on more than nine out of eleven days. For additional details on the computation of the CSI index and its evaluation, the reader is referred to Saaroni et al (2017).…”

A new view of heat wave dynamics and predictability over the eastern Mediterranean

“…Still, episodes when the temperature drops to below normal values do occur, some of which are accompanied by summer rains (Saaroni and Ziv, 2000). Saaroni et al (2017) detected weaknesses in the ability of earlier synoptic classifications (Alpert et al, 2004a;Dayan et al, 2012) to describe local weather conditions during the eastern Mediterranean summer season. The authors proposed a "climatic stress index" (CSI), which is a combination of the national heat stress index, used operationally by the Israeli Meteorological Service, and the height of the marine inversion base height (see Sect.…”

“…In this framework, the temperature of an air parcel increases by (i) adiabatic warming related to descent and (ii) diabatic heating including latent and sensible heat fluxes, shortwave radiation and long-wave radiation (Holton, 2004). Recent studies have revealed that extreme temperatures during heat waves are most often a combination of adiabatic warming related to descent and diabatic heating near the surface (e.g., Black et al, 2004;Bieli et al, 2015;Santos et al, 2015;Quinting and Reeder, 2017;Zschenderlein et al, 2019). The adiabatic warming is typically associated with upper-level ridges, which promote subsidence.…”