Spatio‐temporal analysis for extreme temperature indices over the Levant region

Salameh, Ala A. M.; Gámiz‐Fortis, Sonia Raquel; Castro‐Díez, Yolanda; Hammad, Ahmad Abu; Esteban‐Parra, María Jesús

doi:10.1002/joc.6171

Cited by 26 publications

(21 citation statements)

References 67 publications

(122 reference statements)

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“…Positive trends have been detected in air temperature over Israel, over the past several decades [25,26]. Over a limited area of the Dead Sea valley, the regional atmospheric warming is uniform.…”

Section: Decrease In Spatial Non-uniformity Of Dead Sea Sst During Thmentioning

confidence: 99%

Spatial Non-Uniformity of Surface Temperature of the Dead Sea and Adjacent Land Areas

et al. 2019

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Pronounced spatial non-uniformity has been obtained of daytime sea surface temperature (SST) of the Dead Sea and of land surface temperature (LST) over areas adjacent to the Dead Sea. This non-uniformity was observed in the summer months, under uniform solar radiation. Our findings are based on Moderate Resolution Imaging Spectroradiometer (MODIS) data (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) on board the Terra and Aqua satellites. MODIS data showed that, on average for the 15-year study period, daytime SST over the eastern part of the lake (Te) exceeded by 5 • C that over the western part (Tw). This SST non-uniformity (observed in the absence of surface heat flow from land to sea at the eastern side) was accompanied by spatial non-uniform distribution of land surface temperature (LST) over areas adjacent to the Dead Sea. Specifically, LST over areas adjacent to the eastern side exceeded by 10 • C that over areas adjacent to the western side. Our findings of spatial non-uniformity of SST/LST based on MODIS data were supported by Meteosat Second Generation LST records. Regional atmospheric warming led to a decrease in spatial non-uniformity of SST during the study period. Temperature difference between Te and Tw steadily decreased at the rate of 0.32 • C decade −1 , based on MODIS/Terra data, and 0.54 • C decade −1 , based on MODIS/Aqua data. Our simulations of monthly skin temperature distribution over the Dead Sea by the Weather Forecast and Research (WRF) model contradict satellite observations. The application to modeling of the observed SST/LST spatial non-uniformity will advance our knowledge of atmospheric dynamics over hypersaline lakes. the daytime. Little is known about the patterns of Dead Sea surface temperature. Nehorai et al. [3] discussed that the SST field is heterogeneous in the daytime and homogeneous in the nighttime. Kishcha et al. [7] showed that, in the summer months, the spatial distribution of daytime Dead Sea surface temperature was non-uniform with maximal SST observed near the coastline, while minimal SST was observed in the middle of the Dead Sea. Further investigation of spatial non-uniformity of SST will advance the general understanding of SST-related atmospheric processes not only over the Dead Sea but also over other hypersaline lakes.Buoy measurements of temperature vertical profiles in the Dead Sea provide information about water temperature at different depths [3,[8][9][10][11]. There are several studies on the Dead Sea SST [3,7,[12][13][14]. Kishcha et al. [7] showed that increasing warming of steadily shrinking Dead Sea surface water was observed during the period of 2000-2016. They found that a positive feedback loop between steady shrinking of the Dead Sea and positive sea surface temperature trends causes the acceleration of Dead Sea evaporation and water level drop [7].Available remote sensing observations of skin surface temperature from MODIS on board the two satellites-Terra and Aqua [15,16]-provided us with an opportun...

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Section: Decrease In Spatial Non-uniformity Of Dead Sea Sst During Thmentioning

confidence: 99%

Spatial Non-Uniformity of Surface Temperature of the Dead Sea and Adjacent Land Areas

et al. 2019

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“…The spline method predicts the spatial coverage of temperature accurately -especially for an agricultural region [26], while geostatistical tools of ArcGIS software were used to perform spatial interpolation over temperature data [27]. Regression and moving average methods were employed to investigate the temporal behavior of temperature [28,29]. The moving average method is well suited for handling statistical series of meteorological data [30].…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Variability of Temperature and Its Extremes Over an Agro-Ecological Region of Tamil Nadu, India

Ganeshkumar¹,

Krishna²

2020

Pol. J. Environ. Stud.

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Temperature data from 14 stations were used to explore the spatiotemporal variability of temperature and its extremes for the period 1960-2018 in the Cauvery River Delta (CRD) area, an agro-ecological region of Tamil Nadu State, India. This investigation was carried out by using spline interpolation method, linear regression and Mann-Kendall test with a Sen's slope estimator. Results indicated that the spatial patterns and temporal trends of annual and seasonal temperature showed an upward trend in most parts of the CRD region. The spatial changes of mean temperature at annual and seasonal scale were similar and showed an upward trend. Extreme temperature indices showed an increasing trend throughout the area. This investigation was carried out for a period of six decades from the year 1960. The CRD region exhibited a warming trend after the 1980s. Temperature variability over this region was witnessed by the global and regional level climate warming. These variability conditions would influence the growth and life cycle of agricultural crops. The results of this investigation could support decision makers and authorities to activate adequate irrigation and crop-changing strategies in order to protect vulnerable ecosystems and environmental conditions of the CRD region.

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“…(2019) reported a significant increasing trend of 8.03 days per decade in the WSDI (warm spell duration index) over the period 1988–2017, whereas Salameh et al . (2019) reported a much smaller increase of only 0.63 days per decade over the nearly identical period of 1987–2016. Since both studies used similar weather stations and almost the same period of interest, this large discrepancy should be attributed mainly to the selected base period.…”

Section: Introductionmentioning

confidence: 93%

“…Recently, Salameh et al . (2019) published a study of the changes in climate extreme indices (utilizing the ETCCDI indices) across Israel in the period 1987–2016. Their database was homogenized by using absolute homogenization methods.…”

Section: Introductionmentioning

confidence: 99%

“…(2019), who similarly studied changes in climate extreme indices across Israel, had analysed a longer period 1950–2017, compared it to the 1988–2017 period, and applied a more robust homogenization procedure using relative homogenization methods in conjunction with metadata. A comparison of the spatial and the averaged regional trends of the percentile‐based indices (e.g., warm/cold spells duration, warm/cold days and nights) in the two studies (i.e., Salameh et al ., 2019; Yosef et al ., 2019) reveals different results that cannot be attributed to the use of different homogenization methods. For instance, Yosef et al .…”

Section: Introductionmentioning

confidence: 99%

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Is it possible to fit extreme climate change indices together seamlessly in the era of accelerated warming?

Yosef

Aguilar

Alpert

2020

Intl Journal of Climatology

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This study examines the problematic impact of selecting a different base period (colder 1961-1990 vs. warmer 1988-2017), on the trend magnitude of widely used percentile-based extreme temperature indices (e.g., warm/cold spells, warm/cold days and nights). The percentile-based indices are part of a core set of indices (27 in total) that have become a common standard for monitoring climate change, as recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices were designed to be comparable across regions provided that similar analyses are employed. Unfortunately, the use of different base periods and periods of interest to explore local and global climate change undermines the comparability of findings across regions. When utilizing "day-count" indices with fixed thresholds, the use of different base/ reference periods changes the intercept without influencing the slope (for a given period length). However, this assertion does not hold with percentilebased indices. Our analyses show that percentile-based temperature indices (e.g., days with temperature below the 10 th or above the 90 th percentiles) are particularly susceptible to the problematic use of different base periods. Hence, using percentile-based indices may have adverse effects on researchers' conclusions. The current paper reports the results of a comparative study that used different base periods for the most commonly used percentile-based extreme temperature indices. It was found that the (negative) trend magnitude of the cold percentile-based indices (frequency of cold days and nights and cold spells) is strongly amplified while the (positive) trend magnitude of the warm indices (frequency of warm days and nights and warm spells) is dramatically diminished when percentiles were derived from a base period that included records from the last two decades (e.g., 1981-2010, 1988-2017). These features are even more pronounced when the study period covers only the last 30-40 years. K E Y W O R D S base period, climate change, climate extreme indices, percentile-based indices, warming trends

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Spatio‐temporal analysis for extreme temperature indices over the Levant region

Cited by 26 publications

References 67 publications

Spatial Non-Uniformity of Surface Temperature of the Dead Sea and Adjacent Land Areas

Spatial Non-Uniformity of Surface Temperature of the Dead Sea and Adjacent Land Areas

Spatiotemporal Variability of Temperature and Its Extremes Over an Agro-Ecological Region of Tamil Nadu, India

Is it possible to fit extreme climate change indices together seamlessly in the era of accelerated warming?

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