Weather regimes and analogues downscaling of seasonal precipitation for the 21st century: A case study over Israel

Abstract

Global climate models provide only partial information on local‐scale phenomenon, such as precipitation, primarily due to their coarse resolution. In this study, statistical downscaling algorithms, based on both weather regimes and past analogues, are operated for 18 Israeli rain gauges with an altitude ranging between −200 and ~1,000 m above sea level (ASL). To project seasonal precipitation over Israel and its hydrologic basins, the algorithms are applied to six Coupled Model Inter‐comparison Project Phase 5 (CMIP5) models for the end of the 21st century, according to the RCP4.5 and RCP8.5 scenarios. The downscaled models can capture quite well the seasonal precipitation distribution, though with underestimation in winter and overestimation in spring. All models display a significant reduction of seasonal precipitation for the 21st century according to both scenarios. The winter reductions for the end of the century and the RCP8.5 scenario are found to be ~22 and ~37% according to the weather regimes and the analogues downscaling methods, respectively. Spring reductions are found to be ~10–20% larger than winter reductions. It is shown that the projected reduction results from a decrease in the frequency of the rain‐bearing systems, as well as a decrease in the average daily precipitation intensity. The areas with the largest reductions in seasonal precipitation are found over the central mountains, the Mediterranean coastal area, and the Sea of Galilee hydrologic basins, which are the main fresh‐water aquifers and reservoirs of Israel. The statistical downscaling methods applied in this study can be easily transferred to other regions where long‐term data sets of observed precipitation are available. This study and others may serve as a basis for priority and policy setting toward better climate adaptation with associated uncertainties related to the methods used and nonstationary of the climate system.

Assessing the Dynamics of Plant Species Invasion in Eastern-Mediterranean Coastal Dunes Using Cellular Automata Modeling and Satellite Time-Series Analyses

“…Additionally, future expansion of the focal species will affect landscape heterogeneity as the diversity of the land cover diminishes with time (Figure 6). Although we have not directly tested in this study the heterogeneity-diversity relationships [66,67], nor the projected climate change projections, particularly on rainfall distribution changes [68], we can plausibly assume that the expansion of H. subaxillaris and A. saligna may alter the quality of the landscape matrix and reduce the potential of the area to support the current biodiversity of flora and fauna.…”

Changes in the characteristics of ‘wet’ and ‘dry’ Red Sea Trough over the Eastern Mediterranean in CMIP5 climate projections

“…In particular, one drawback of the analogs' method is that it cannot simulate rain quantities that have not occurred in the past (e.g., Maraun et al 2010). Still, the analogs' method shows advantages with respect to, e.g., weather regime downscaling techniques, as it provides additional information on the tails of the rainfall distribution (Hochman et al 2020b). With this aim, the algorithm identifies the closest analogs as past intense/weaker events, and these analogs are then chosen more often by the algorithm.…”

“…Here, we use a statistical downscaling algorithm (Rostkier-Edelstein et al 2016), based on past analogs, a concept first proposed by Lorenz (1969). This technique enables a good reproduction of the observed mean amounts, their inter-annual, and spatial variability of seasonal rainfall over the main ground water basins of Israel (Rostkier-Edelstein et al 2016;Hochman et al 2020b).…”

Development of a climate‐informed analog downscaling method for Asian regions

“…Governments and decision‐makers across a range of sectors, including agriculture, transportation, energy and hydrology, utilize climate data (Mach and Field, 2017; Kirchhoff et al ., 2019; Wang et al ., 2020). Climate data are increasingly important in hydrology because extreme hydroclimatic conditions are used to design water resource infrastructures, predict water disasters and manage water resources (Werner and Cannon, 2016; Hochman et al ., 2020). Climate data are constructed as location‐based and gridded data.…”