Near-surface air temperature lapse rates in Xinjiang, northwestern China

Du, Mingxia; Zhang, Mingjun; Wang, Shengjie; Zhu, Xiaofan; Che, Yue

doi:10.1007/s00704-017-2040-x

Cited by 14 publications

(15 citation statements)

References 46 publications

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“…As for the Xinjiang Autonomous Region located in Northwest China, temperature vertical gradient inversion has also been observed, in winter, in previous studies [34,38,53]. In Northeast China, however, the results of our analysis were different from some of the previous studies [38,40], which did not observe the phenomenon of temperature vertical gradient inversion, but found a steep TLR in winter.…”

Section: Discussioncontrasting

confidence: 99%

“…A temperature inversion is frequently found in winter [23,31,32]. The annual cycle of the TLR shows different seasonal patterns, with some steeper in summer and shallower in winter [21,23,33,34], some steeper in winter and shallower in summer [11], and others that are steeper in spring and shallower in later summer or autumn [35,36].…”

Section: Introductionmentioning

confidence: 99%

“…Linear regression is widely used to calculate the TLR of a small region based on observational data of meteorological stations [24,37]. For a large region, stations are usually divided into several groups and the TLR in each group is calculated separately [22,34,38], or aggregated through a moving spatial window, thus each station has a local TLR [39,40]. Regarding the grid data, linear regression [41,42] and moving window regression [20] have also been adopted, based on grid values.…”

Section: Introductionmentioning

confidence: 99%

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A New Methodology for Estimating the Surface Temperature Lapse Rate Based on Grid Data and Its Application in China

et al. 2018

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Land surface temperature (LST) is an important parameter in the study of the physical processes of land surface. Understanding the surface temperature lapse rate (TLR) can help to reveal the characteristics of mountainous climates and regional climate change. A methodology was developed to calculate and analyze land-surface TLR in China based on grid datasets of MODIS LST and digital elevation model (DEM), with a formula derived on the basis of the analysis of the temperature field and the height field, an image enhancement technique used to calculate gradient, and the fuzzy c-means (FCM) clustering applied to identify the seasonal pattern of the TLR. The results of the analysis through the methodology showed that surface temperature vertical gradient inversion widely occurred in Northeast, Northwest, and North China in winter, especially in the Xinjiang Autonomous Region, the northern and the western parts of the Greater Khingan Mountains, the Lesser Khingan Mountains, and the northern area of Northwest and North China. Summer generally witnessed the steepest TLR among the four seasons. The eastern Tibetan Plateau showed a distinctive seasonal pattern, where the steepest TLR happened in winter and spring, with a shallower TLR in summer. Large seasonal variations of TLR could be seen in Northeast China, where there was a steep TLR in spring and summer and a strong surface temperature vertical gradient inversion in winter. The smallest seasonal variation of TLR happened in Central and Southwest China, especially in the Ta-pa Mountains and the Qinling Mountains. The TLR at very high altitudes (>5 km) was usually steeper than at low altitudes, in all months of the year.

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Section: Discussioncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Methodology for Estimating the Surface Temperature Lapse Rate Based on Grid Data and Its Application in China

et al. 2018

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“…The significant differences in LRs as a function of the subregion and season are relevant. The spatial uncertainty and spatial distribution of LRs have already been evident in several studies, coming mainly from environmental factors such as specific humidity (Bolstad et al ., 1998; Du et al ., 2017), topography, continentality (Lewkowicz and Bonnaventure, 2011), distribution and representativeness of weather stations (Navarro‐Serrano et al ., 2018).…”

Section: Resultsmentioning

confidence: 99%

Maximum and minimum air temperature lapse rates in the Andean region of Ecuador and Peru

Navarro-Serrano

López‐Moreno

Domínguez‐Castro

et al. 2020

Intl Journal of Climatology

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To know the vertical distribution of air temperature is complex, and this is necessary for different applications. The main explanatory variable of air temperature is elevation above sea level, whose relationship with air temperature is measured by air temperature lapse rates (LRs). LRs can vary considerably spatiotemporally due to a wide spectrum of geographical, environmental, and other atmospheric factors. Our study presents the first comprehensive assessment of spatiotemporal changes of LRs over the Tropical Andes. Our study is focused on the Peruvian and Ecuadorian Andes, divided in two subregions by the parallel 9.5° (i.e., north and south). Maximum and minimum air temperatures were employed from 115 quality checked weather stations, for the period 1994–2011. Maximum (LRmax) and minimum (LRmin) air temperature lapse rates have been calculated for the whole study period. The effects of seasonality, humidity content and ENSO on the variability of LRs have been analysed. Results show that LRs have large spatiotemporal variability, since references values of LRmax range from −3.57 (South, dry season) to −4.77 (North, wet season), and LRmin range from −3.78 (North, dry season) to −4.93°C·km−1 (South, dry season), in function of season and subregion. Results indicate that the ENSO phases contribute significantly to the variability of southern subregion LRs. This study also presents that minimum air temperatures were more unpredictable than maximum air temperatures in terms of error and uncertainty, as a consequence of the larger spatial variability of nocturnal air temperatures, mainly influenced by local topography. In conclusion, this work goes deeper into the need to obtain precise LRs adapted to the study region, and shows that the use of standard LR values can cause significant failures in modelling air temperature in regions of complex terrain, such as the Andes.

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“…The weather station network used in this study included a large number of weather stations at elevation ranges from 0 to 1,500 m a.s.l, but with few number of stations above 1,500 m a.s.l. The lack of high‐elevation stations is a common problem in many previous studies (e.g., Blöschl, ; Rolland, ; Benavides et al, ; Du et al, ). Thus, our results are robust for interpolating air temperatures at low and medium elevations, whereas lapse rate values for high‐elevation mountain areas have to be interpreted with caution.…”

Section: Discussionmentioning

confidence: 99%

Estimation of near‐surface air temperature lapse rates over continental Spain and its mountain areas

Navarro-Serrano

López‐Moreno

Azorín-Molina

et al. 2018

Intl Journal of Climatology

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Although the mean environmental lapse rate (MELR) value (a linear decrease of −6.5 °C/km) is the most widely used, near‐surface (i.e., non‐free atmosphere) air temperature lapse rates (NSLRs; measured at ~1.5 m height) are variable in space and time because of their dependence on topography and meteorological conditions. In this study we conducted the first analysis of the spatial and temporal variability of NSLRs for continental Spain and their relationship to synoptic atmospheric circulation (circulation weather types [CWTs]), focusing on major mountain areas including the Pyrenees, Cantabrian, Central, Baetic, and Iberian ranges. The results showed that the NSLR varied markedly at spatial and seasonal scales and depended on the dominant atmospheric conditions. The median NSLR values were weaker (less negative) than the MELR for the mountain areas (Pyrenees −5.17 °C/km; Cantabrian range −5.22 °C/km; Central range −5.78 °C/km; Baetic range −4.83 °C/km; Iberian range −5.79 °C/km) and for the entire continental Spain (−5.28 °C/km). For the entire continental Spain the steepest NSLR values were found in April (−5.80 °C/km), May (−5.58 °C/km), and October (−5.54 °C/km) because of the dominance of northerly and westerly advections of cold air. The weakest NSLR values were found in July (−4.67 °C/km) and August (−4.78 °C/km) because of the inland heating, and in winter because of the occurrence of thermal inversions. As the use of the MELR involves the assumption of large errors, we propose 1 zonal, 12 monthly, 11 CWTs, and 132 hybrid monthly–CWTs NSLRs for each of the mountain ranges and for the entire continental Spain. More regional studies are urgently needed to accurately assess the NSLR as a function of atmospheric circulation conditions.

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Near-surface air temperature lapse rates in Xinjiang, northwestern China

Cited by 14 publications

References 46 publications

A New Methodology for Estimating the Surface Temperature Lapse Rate Based on Grid Data and Its Application in China

A New Methodology for Estimating the Surface Temperature Lapse Rate Based on Grid Data and Its Application in China

Maximum and minimum air temperature lapse rates in the Andean region of Ecuador and Peru

Estimation of near‐surface air temperature lapse rates over continental Spain and its mountain areas

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