Temperature–topographic elevation relationship for high mountain terrain: an example from the southeastern Tibetan Plateau

Abstract

This study quantifies the spatial–temporal distribution of the near‐surface temperature lapse rate (TLR) on the southeastern Tibetan Plateau (SETP) on the northern slopes of the eastern Himalayas, using the jackknife regression model. A 20‐year (1985–2004) climatic data set of 16 stations between the elevation range of 3553–4801 m asl was used for this investigation. Controls for the spatial–temporal variation on TLRs and causes of higher mean square error (MSE) from the jackknife regressions were also analysed. Dry convection due to increasing sensible heating at lower elevations associated with clear skies, the effect of cold air surges, cloud/fog, as well as pronounced long‐wave radiation loss due to snow cover at higher elevation, results in the super‐dry adiabatic TLR in the dry winter. In response to the effects of high rainfall and humidity, intense latent heating at higher elevation due to moist adiabatic cooling causes TLRs to decrease significantly in summer. The variation in net radiation due to differences in moisture, rainfall, cloud cover and air mass between higher (fewer) and lower (higher) elevations further contributes to reducing TLRs values in this season. Observed steeper values of TLRs at higher elevations and more shallow values at lower elevations are due to the thermal contrast between snow‐free ground and snow‐covered mountain terrain, as well as the effect of variations in air mass and moisture. Based on derived values, this article also estimates near‐surface temperature at higher elevations and analyses the precision of estimated values. Measurement of the lowest discrepancy (bias) between observed and estimated values from this study suggests that the estimation skills of the model are good enough. More precisely, including the smallest MSE from the jackknife regression and biases of the estimated values can be made for summer, perhaps due to the moisture controlled throughout the SETP, i.e. associated with the Indian monsoon. Higher MSE and biases are observed during winter and are due to the substantial effects of westerlies, as well as the station's geographical coordinate, local topography and microclimate. The MSE and the estimated biases are lowest at high‐elevation stations and are associated with the lower contaminating effects of the surface. This estimation model, using derived values from this investigation, could be useful for glacier‐hydro‐climatic and ecological modelling in this region.

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

“…The weakening of LRs during the dry season when the atmosphere is rather stable has been reported in previous works (Richardson et al ., 1999; Pepin and Losleben, 2002; Harlow et al ., 2004; Marshall et al ., 2007), although this is not always valid, as Kattel et al . (2015) and Kattel and Yao (2018) show in their Himalayan works; and Córdova et al . (2016) in a specific Ecuadorian valley for a 12‐month period.…”

“…In contrast, during the stable dry season, unexpectedly LR min is steeper than LR max in the southern subregion. It is widely accepted that LR min are weaker than LR max , since they are generally impacted by several processes (e.g., cold air pool formations, thermal inversions in lowlands [Kattel and Yao, 2018]), which can weaken LR, as pointed out by many authors (Bonnardot et al ., 2012; Hanna et al ., 2017). However, the specific pattern found in the southern subregion could be attributed to some local factors of the database.…”

Water Balance and Soil Moisture Deficit of Different Vegetation Units under Semiarid Conditions in the Andes of Southern Ecuador

“…The lower threshold for T (80%) compared to P (90%) was set, because T does not show high spatiotemporal variability and can be correlated with geographic (e.g., latitude) or topographic (e.g., altitude) parameters [69,70]. Therefore, to interpolate the T data, Kriging with detrended raw data was applied [41], for which, first, specific mean monthly and annual altitudinal gradients for the study area were deviated.…”

Near-Surface Air Temperature Dependence on Elevation and Geographical Coordinates Over Tropical Desert Land Surfaces

“…Local topography can also significantly alter deviations from the linear dependence of near surface air temperature on elevation, as well as its relationship with geographical coordinates (Lookingbill and Urban, 2003;Kattel et al, 2019). Furthermore, the presence of mountains, surface characteristics, synoptic climatic regime, vegetation cover, wind speed, proximity to the ocean, pooling of cold air, and inversion effects in low-lying areas all have an impact on their relationship (Mahrt, 2006;Kattel et al, 2017;Kattel and Yao, 2018;Kattel et al, 2019).…”