scholarly journals Enhanced cold-season warming in semi-arid regions

2012 ◽  
Vol 12 (12) ◽  
pp. 5391-5398 ◽  
Author(s):  
J. Huang ◽  
X. Guan ◽  
F. Ji

Abstract. This study examined surface air temperature trends over global land from 1901–2009. It is found that the warming trend was particularly enhanced, in the boreal cold season (November to March) over semi-arid regions (with precipitation of 200–600 mm yr−1) showing a temperature increase of 1.53 °C as compared to the global annual mean temperature increase of 1.13 °C over land. In mid-latitude semi-arid areas of Europe, Asia, and North America, temperatures in the cold season increased by 1.41, 2.42, and 1.5 °C, respectively. The semi-arid regions contribute 44.46% to global annual-mean land-surface temperature trend. The mid-latitude semi-arid regions in the Northern Hemisphere contribute by 27.0% of the total, with the mid-latitude semi-arid areas in Europe, Asia, and North America accounting for 6.29%, 13.81%, and 6.85%, respectively. Such enhanced semi-arid warming (ESAW) imply drier and warmer trend of these regions.

2012 ◽  
Vol 12 (2) ◽  
pp. 4627-4653 ◽  
Author(s):  
J. Huang ◽  
X. Guan ◽  
F. Ji

Abstract. This study examined surface air temperature trends over global land from 1901–2009. It is found that the warming trend was particularly enhanced, in the boreal cold season (November to March) over semi-arid regions (with precipitation of 200–600 mm yr−1), showing a temperature increase of 1.53 °C as compared to the global annual mean temperature increase of 1.13 °C over land. In mid-latitude semi-arid areas of Europe, Asia, and North America, temperatures in the cold season increased by 1.41, 2.42, and 1.5 °C, respectively. The semi-arid regions contribute 44.46% to global annual-mean land-surface temperature trend. The mid-latitude semi-arid regions in the Northern Hemisphere accounting contribute by 27.0% of the total, with the mid-latitude semi-arid areas in Europe, Asia, and North America accounting for 6.29%, 13.81%, and 6.85%, respectively. Such enhanced semi-arid warming (ESAW) may cause these regions to become drier and warmer.


2021 ◽  
Vol 56 (1-2) ◽  
pp. 635-650 ◽  
Author(s):  
Qingxiang Li ◽  
Wenbin Sun ◽  
Xiang Yun ◽  
Boyin Huang ◽  
Wenjie Dong ◽  
...  

2019 ◽  
Vol 11 (20) ◽  
pp. 2369 ◽  
Author(s):  
Ahmed M. El Kenawy ◽  
Mohamed E. Hereher ◽  
Sayed M. Robaa

Space-based data have provided important advances in understanding climate systems and processes in arid and semi-arid regions, which are hot-spot regions in terms of climate change and variability. This study assessed the performance of land surface temperatures (LSTs), retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua platform, over Egypt. Eight-day composites of daytime and nighttime LST data were aggregated and validated against near-surface seasonal and annual observational maximum and minimum air temperatures using data from 34 meteorological stations spanning the period from July 2002 to June 2015. A variety of accuracy metrics were employed to evaluate the performance of LST, including the bias, normalized root-mean-square error (nRMSE), Yule–Kendall (YK) skewness measure, and Spearman’s rho coefficient. The ability of LST to reproduce the seasonal cycle, anomalies, temporal variability, and the distribution of warm and cold tails of observational temperatures was also evaluated. Overall, the results indicate better performance of the nighttime LSTs compared to the daytime LSTs. Specifically, while nighttime LST tended to underestimate the minimum air temperature during winter, spring, and autumn on the order of −1.3, −1.2, and −1.4 °C, respectively, daytime LST markedly overestimated the maximum air temperature in all seasons, with values mostly above 5 °C. Importantly, the results indicate that the performance of LST over Egypt varies considerably as a function of season, lithology, and land use. LST performs better during transitional seasons (i.e., spring and autumn) compared to solstices (i.e., winter and summer). The varying interactions and feedbacks between the land surface and the atmosphere, especially the differences between sensible and latent heat fluxes, contribute largely to these seasonal variations. Spatially, LST performs better in areas with sandstone formations and quaternary sediments and, conversely, shows lower accuracy in regions with limestone, igneous, and metamorphic rocks. This behavior can be expected in hybrid arid and semi-arid regions like Egypt, where bare rocks contribute to the majority of the Egyptian territory, with a lack of vegetation cover. The low surface albedo of igneous and limestone rocks may explain the remarkable overestimation of daytime temperature in these regions, compared to the bright formations of higher surface albedo (i.e., sandy deserts and quaternary rocks). Overall, recalling the limited coverage of meteorological stations in Egypt, this study demonstrates that LST obtained from the MODIS product can be trustworthily employed as a surrogate for or a supplementary source to near-surface measurements, particularly for minimum air temperature. On the other hand, some bias correction techniques should be applied to daytime LSTs. In general, the fine space-based climatic information provided by MODIS LST can be used for a detailed spatial assessment of climate variability in Egypt, with important applications in several disciplines such as water resource management, hydrological modeling, agricultural management and planning, urban climate, biodiversity, and energy consumption, amongst others. Also, this study can contribute to a better understanding of the applications of remote sensing technology in assessing climatic feedbacks and interactions in arid and semi-arid regions, opening new avenues for developing innovative algorithms and applications specifically addressing issues related to these regions.


Author(s):  
Neda Esfandiari ◽  
Hassan Lashkari

Abstract Atmospheric rivers (ARs) as massive and concentrated water vapour paths can have a critical impact on extreme events in arid and semi-arid areas. This study investigated the effect of ARs on heavy precipitation events during the cold, rainy months (November–April) in Iran for 11 years. The results showed that 107 ARs had an influence on heavy precipitation, which providing partial moisture for Iran's precipitation. On average, 11 heavy precipitation days were linked to the presence of ARs in the six cold months of each year. During the study period, ARs accounted for almost 20–50% of the country's total heavy precipitation monthly. Although most ARs entered the country from the south through coastal areas, the western part of Iran, especially elevated stations along the western slope of the Zagros Mountains, received the highest heavy precipitation. Accordingly, about 66% of ARs directly originated from the Red Sea and the Gulf of Aden. Moreover, December experienced the highest frequency of ARs linked to heavy precipitation during the statistical period.


2011 ◽  
Vol 356-360 ◽  
pp. 2307-2311
Author(s):  
Qin Li ◽  
Xi Chen ◽  
An Ming Bao

A typical arid area in China is the province of Xinjiang. The objective of this paper is to present a description of a method based on the energy balance and evaporative fraction, which is obtained by broadband albedo and land surface temperature, to estimate evapotranspiration (ET) in Arid Areas. In Arid areas, the ET always fluctuates from 0 to 2mm in the most part of region in 2005, especially the Tarim, Junggar and Turpan basins. In the mountain especially Tian-shan mountain and crop land of oasis, ET values rises 6mm.


2003 ◽  
Vol 20 (4) ◽  
pp. 530-539 ◽  
Author(s):  
Ma Yaoming ◽  
Wang Jiemin ◽  
Huang Ronghui ◽  
Wei Guoan ◽  
Massimo Menenti ◽  
...  

2017 ◽  
Vol 30 (19) ◽  
pp. 7585-7598 ◽  
Author(s):  
Karen A. McKinnon ◽  
Andrew Poppick ◽  
Etienne Dunn-Sigouin ◽  
Clara Deser

Abstract Estimates of the climate response to anthropogenic forcing contain irreducible uncertainty due to the presence of internal variability. Accurate quantification of this uncertainty is critical for both contextualizing historical trends and determining the spread of climate projections. The contribution of internal variability to uncertainty in trends can be estimated in models as the spread across an initial condition ensemble. However, internal variability simulated by a model may be inconsistent with observations due to model biases. Here, statistical resampling methods are applied to observations in order to quantify uncertainty in historical 50-yr (1966–2015) winter near-surface air temperature trends over North America related to incomplete sampling of internal variability. This estimate is compared with the simulated trend uncertainty in the NCAR CESM1 Large Ensemble (LENS). The comparison suggests that uncertainty in trends due to internal variability is largely overestimated in LENS, which has an average amplification of variability of 32% across North America. The amplification of variability is greatest in the western United States and Alaska. The observationally derived estimate of trend uncertainty is combined with the forced signal from LENS to produce an “Observational Large Ensemble” (OLENS). The members of OLENS indicate the range of observationally constrained, spatially consistent temperature trends that could have been observed over the past 50 years if a different sequence of internal variability had unfolded. The smaller trend uncertainty in OLENS suggests that is easier to detect the historical climate change signal in observations than in any given member of LENS.


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