Does elevation dependent warming exist in high mountain Asia?

Li, Baofu; Chen, Yaning; Shi, Xun

doi:10.1088/1748-9326/ab6d7f

Cited by 48 publications

(39 citation statements)

References 60 publications

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“…However, EDW is not present in all sub-regions. Li et al (2020) similarly suggested that EDW only occurs in certain areas, which is consistent with our results (Figure 6). The most pronounced EDW and elevation-dependent variations of snow depth simultaneously occur in the southeastern TP, where the largest 5) difference in elevation from 2,000 to 6,000 m is located.…”

Section: Discussionsupporting

confidence: 93%

“…There is consensus regarding the overall enhanced warming of the TP, but the exact elevation profile of EDW is still controversial (You et al, 2008;Rangwala et al, 2009;Kang et al, 2010;You et al, 2010;Guo and Wang, 2012;Gao et al, 2018). For example, Li et al (2020) suggested that EDW has only occurred in specific mountainous ranges on the TP. It is expected that such regional EDW differences across the plateau could significantly impact the spatiotemporal characteristics of snow depth.…”

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confidence: 99%

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Changes in snow depth under elevation‐dependent warming over the Tibetan Plateau

Shen

Zhang

Ullah

et al. 2021

Atmospheric Science Letters

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Snow plays an essential role in regulating climate change, the hydrological cycle, and various biological processes. Passive microwave snow depth data and gridded data from the Climate Research Unit (CRU_TS4.04) are utilized in this study to investigate spatiotemporal variations of snow depth over the Tibetan Plateau (TP), with special focus on the vertical dimension. The response of snow to elevationdependent warming (EDW) is determined accordingly. High mountains experience more rapid warming than lower elevations. During 1980-2014, the total snow depth over the TP decreased; areas with the most significant decreasing trends are mainly concentrated in the northwestern and southwestern parts of the TP. The plateauwide decrease in snow depth (À0.24 cm/decade) is mainly affected by increasing temperature (0.30 C/decade). The reduction in snow depth trend intensifies as subregional mean elevation increases from 3,332 m (IID2) to 5,074 m (ID1). A stronger snow depth decrease in high-elevation sub-regions generally corresponds to higher warming rates, which demonstrates EDW. The most pronounced correlation between snow depth decrease rate and elevation occurs in the southeastern TP, which covers the largest elevation range on the plateau (from 2,000 to 6,000 m).

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Section: Discussionsupporting

confidence: 93%

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confidence: 99%

Changes in snow depth under elevation‐dependent warming over the Tibetan Plateau

Shen

Zhang

Ullah

et al. 2021

Atmospheric Science Letters

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“…Many studies 14,17,29,30 have shown that the transport of heat by atmospheric circulations reduces the temperature heterogeneity at the Earth's surface and this process also affects the regional climate. To determine which circulation has the greatest influence on the temperature in the three regions, we first analyze the correlation between the air temperature and 23 major atmospheric circulations (Supplementary Note 1) and 2 sea ice indexes (Supplementary Table 1a).…”

Section: Resultsmentioning

confidence: 99%

“…We use the Mann–Kendall statistical method to assess the significance of the mean temperature trends in the study area. The Mann–Kendall statistical test has been commonly used to assess the significance of monotonic trends in climatic series 30 . The Monte Carlo method 18 – 20 is used to estimate the field significance of the temperature cooling trend in the CEU.…”

Section: Methodsmentioning

confidence: 99%

Recent fall Eurasian cooling linked to North Pacific sea surface temperatures and a strengthening Siberian high

Chen

et al. 2020

Nat Commun

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Winter Eurasian cooling after the mid-1990s has been verified by numerous studies, although in recent decades, the mid-latitudes of the Northern Hemisphere have been rapidly warming globally. Because the cooling is not uniform at different spatial and temporal scales, over time, this change may not truly reflect the nature of climate fluctuations. Here, by using three types of data (reanalysis, weather station, and remote sensing image data) to assess variations in Eurasian seasonal cooling, we examine the causes of these changes. During a 30-year climatology (1989–2018), we show that a significant (P < 0.05) abrupt change in the autumn Eurasian air temperature trend occurred in 2003. Our results suggest that from 2004–2018, the autumn Eurasian temperature reveals a significant cooling trend (P < 0.05). We demonstrate that the autumn cooling in Eurasia is likely influenced by the Pacific Decadal Oscillation (PDO) and Siberian high (SH). Since 2004, the strengthening of the PDO and SH explains approximately 54% and 18% of the autumn cooling in Eurasia, respectively. We also find that the cooling in autumn is stronger than that in winter.

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“…At the regional scale, geomorphology, topography and land cover are known to affect the climate patterns to some extent (Pepin et al 2015;Bright et al 2017;Minder et al 2018;Aguilar-Lome et al 2019). Many studies have shown that regions of higher elevations are more susceptible to global climate change than regions of lower elevations (Pepin et al 2015;Li et al 2020aLi et al , 2020bYou et al 2020); with increases in elevation, the annual mean temperature and precipitation generally become more variable (You et al 2020;Li et al 2020aLi et al , 2020cAguilar-Lome et al 2019;Minder et al 2018;Pepin et al 2015). Topography may affect climate through interference to lower atmospheric circulation and air-flow dynamics (Jiang 2003;Barros et al 2006;Yang and Zhang 2019;He et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Spatially differentiated changes in regional climate and underlying drivers in southwestern China

2021

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The climate in Southwest China are predominantly under the influences of three contrasting climate systems, namely the East Asian monsoon, the South Asian monsoon, and the westerlies. However, it is unclear if the diversified climate systems, in combination with the complex terrain and varying vegetation types, would result in contrasting patterns of changes in climate across the region. Based on the CRU TS data for the period 1901−2017, we examined the spatiotemporal characteristics of the regional climate, and identified types of climate change patterns and drivers. Overall, the region experienced significant increases in annual mean temperature during 1901−2017, with occurrence of a significant turning point in 1954 for a more pronounced warming (0.16 °C/10 a). The annual precipitation fluctuated greatly over the study period without apparent trend, albeit the occurrence of a significant turning point in 1928 for a slight increase in the later period (1.19 mm/10 a). Spatially the multi-year averages of selective climate variables during 1901–2017 displayed a trend of decreases from southeast to northwest, but with increasing variability. We identified five major climate change types across the study region, including warmer (T+), drier (P−), warmer-drier (T+P−), warmer-wetter (T+P+), and no significant changes (NSC). The type T+P+ mainly occurred in the western parts over the plateau sub-frigid semiarid ecozone (77.0%) and the plateau sub-frigid semihumid ecozone (19.9%). The central parts of the region are characterized by the type T+, corresponding to six ecozones, including the mid-subtropical humid ecozone (33.1%), the plateau temperate humid-semihumid ecozone (28.8%), the plateau sub-rigid semihumid ecozone (9.5%), the southern subtropical humid ecozone (8.1%), the plateau sub-frigid arid ecozone (7.3%), and the plateau temperate semiarid ecozone (6.6%). No significant change in climate was detected for the eastern parts over the mid-subtropical humid ecozone (67.3%), the plateau temperate humid and semihumid ecozone (19.5%) and the plateau sub-frigid semihumid ecozone (8.8%). The types P− and T+P− together accounted for less than 5% of the entire study region, which predominantly occurred in central Yunnan-Guizhou Plateau and south of the southeastern Xizang, corresponding predominantly to the mid-subtropical humid ecozone. Across the region and within the zonal climate change types, vegetation and topography both played a significant role in determining the climate variability and magnitude of changes. Our results suggest that the southwestern China experienced intensified influences of the southeasterly monsoon and the southerly monsoon in the regional climate, while the westerly alpine influences subsided; topography and vegetation affected the magnitudes of the directional changes in climate at a local scale.

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Does elevation dependent warming exist in high mountain Asia?

Cited by 48 publications

References 60 publications

Changes in snow depth under elevation‐dependent warming over the Tibetan Plateau

Changes in snow depth under elevation‐dependent warming over the Tibetan Plateau

Recent fall Eurasian cooling linked to North Pacific sea surface temperatures and a strengthening Siberian high

Spatially differentiated changes in regional climate and underlying drivers in southwestern China

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