scholarly journals Temporal and spatial changes in estimated near-surface air temperature lapse rates on Tibetan Plateau

2018 ◽  
Vol 38 (7) ◽  
pp. 2907-2921 ◽  
Author(s):  
Yuanwei Wang ◽  
Lei Wang ◽  
Xiuping Li ◽  
Deliang Chen
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Spatial Changes ◽  
Temporal And Spatial ◽  
Lapse Rates

Recent Trends in Land Surface Temperature on the Tibetan Plateau

2006 ◽  
Vol 19 (12) ◽  
pp. 2995-3003 ◽  
Author(s):  
Yuichiro Oku ◽  
Hirohiko Ishikawa ◽  
Shigenori Haginoya ◽  
Yaoming Ma
Keyword(s):  
Tibetan Plateau ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Precipitation Amount ◽  
Surface Air Temperature ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Diurnal Range

Abstract The diurnal, seasonal, and interannual variations in land surface temperature (LST) on the Tibetan Plateau from 1996 to 2002 are analyzed using the hourly LST dataset obtained by Japanese Geostationary Meteorological Satellite 5 (GMS-5) observations. Comparing LST retrieved from GMS-5 with independent precipitation amount data demonstrates the consistent and complementary relationship between them. The results indicate an increase in the LST over this period. The daily minimum has risen faster than the daily maximum, resulting in a narrowing of the diurnal range of LST. This is in agreement with the observed trends in both global and plateau near-surface air temperature. Since the near-surface air temperature is mainly controlled by LST, this result ensures a warming trend in near-surface air temperature.

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

2017 ◽  
Vol 131 (3-4) ◽  
pp. 1221-1234 ◽  
Author(s):  
Mingxia Du ◽  
Mingjun Zhang ◽  
Shengjie Wang ◽  
Xiaofan Zhu ◽  
Yanjun Che
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Northwestern China ◽  
Near Surface ◽  
Lapse Rates

scholarly journals Modelling spatial patterns of near-surface air temperature over a decade of melt seasons on McCall Glacier, Alaska

2020 ◽  
Vol 66 (257) ◽  
pp. 386-400
Author(s):  
Patrick Troxler ◽  
Álvaro Ayala ◽  
Thomas E. Shaw ◽  
Matt Nolan ◽  
Ben W. Brock ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Air Temperature ◽  
Flow Line ◽  
Surface Air Temperature ◽  
Strong Relationship ◽  
Near Surface ◽  
Elevation Difference ◽  
Dependent Model ◽  
Glacier Flow ◽  
Lapse Rates

AbstractWe examine the spatial patterns of near-surface air temperature (Ta) over a melting glacier using a multi-annual dataset from McCall Glacier, Alaska. The dataset consists of a 10-year (2005–2014) meteorological record along the glacier centreline up to an upper glacier cirque, spanning an elevation difference of 900 m. We test the validity of on-glacier linear lapse rates, and a model that calculates Ta based on the influence of katabatic winds and other heat sources along the glacier flow line. During the coldest hours of each summer (10% of time), average lapse rates across the entire glacier range from −4.7 to −6.7°C km−1, with a strong relationship between Ta and elevation (R2 > 0.7). During warm conditions, Ta shows more complex, non-linear patterns that are better explained by the flow line-dependent model, reducing errors by up to 0.5°C compared with linear lapse rates, although more uncertainty might be associated with these observations due to occasionally poor sensor ventilation. We conclude that Ta spatial distribution can vary significantly from year to year, and from one glacier section to another. Importantly, extrapolations using linear lapse rates from the ablation zone might lead to large underestimations of Ta on the upper glacier areas.

Seasonal and Synoptic Variations in Near-Surface Air Temperature Lapse Rates in a Mountainous Basin

2008 ◽  
Vol 47 (1) ◽  
pp. 249-261 ◽  
Author(s):  
Troy R. Blandford ◽  
Karen S. Humes ◽  
Brian J. Harshburger ◽  
Brandon C. Moore ◽  
Von P. Walden ◽  
...  
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Lapse Rate ◽  
Maximum Temperature ◽  
Weather Types ◽  
Near Surface ◽  
Air Masses ◽  
Synoptic Weather ◽  
South Central ◽  
Lapse Rates

Abstract To accurately estimate near-surface (2 m) air temperatures in a mountainous region for hydrologic prediction models and other investigations of environmental processes, the authors evaluated daily and seasonal variations (with the consideration of different weather types) of surface air temperature lapse rates at a spatial scale of 10 000 km2 in south-central Idaho. Near-surface air temperature data (Tmax, Tmin, and Tavg) from 14 meteorological stations were used to compute daily lapse rates from January 1989 to December 2004 for a medium-elevation study area in south-central Idaho. Daily lapse rates were grouped by month, synoptic weather type, and a combination of both (seasonal–synoptic). Daily air temperature lapse rates show high variability at both daily and seasonal time scales. Daily Tmax lapse rates show a distinct seasonal trend, with steeper lapse rates (greater decrease in temperature with height) occurring in summer and shallower rates (lesser decrease in temperature with height) occurring in winter. Daily Tmin and Tavg lapse rates are more variable and tend to be steepest in spring and shallowest in midsummer. Different synoptic weather types also influence lapse rates, although differences are tenuous. In general, warmer air masses tend to be associated with steeper lapse rates for maximum temperature, and drier air masses have shallower lapse rates for minimum temperature. The largest diurnal range is produced by dry tropical conditions (clear skies, high solar input). Cross-validation results indicate that the commonly used environmental lapse rate [typically assumed to be −0.65°C (100 m)−1] is solely applicable to maximum temperature and often grossly overestimates Tmin and Tavg lapse rates. Regional lapse rates perform better than the environmental lapse rate for Tmin and Tavg, although for some months rates can be predicted more accurately by using monthly lapse rates. Lapse rates computed for different months, synoptic types, and seasonal–synoptic categories all perform similarly. Therefore, the use of monthly lapse rates is recommended as a practical combination of effective performance and ease of implementation.

scholarly journals Analysis On The Regional Features of Future Warming Using High-Resolution Downscaled Multi-Model Climate Scenarios In China

2021 ◽  
Author(s):  
Lei Zhang ◽  
Yinlong Xu ◽  
Chunchun Meng ◽  
Yuncheng Zhao ◽  
Changgui Wang
Keyword(s):  
Heat Stress ◽  
High Resolution ◽  
Tibetan Plateau ◽  
Air Temperature ◽  
South China ◽  
Surface Air Temperature ◽  
Northern China ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Future Warming

Abstract The frequency and magnitude of global warming events varies greatly across different regions and countries. The climatic diversity for China and future warming features are projected across twelve climatic zones based on the ensemble of the five well-performing high resolution downscaled climate models for each zone. There are warming patterns for the mean near surface air temperature (Tm), maximum near surface air temperature (Tmax), minimum near surface air temperature (Tmin) as well as heat stress and frost events. Under RCP4.5 and RCP8.5 scenarios, the three indices (i.e., Tm, Tmax and Tmin) countrywide are likely to increase at respective rates of 0.30-0.31 and 0.64-0.67 oC per decade. The extent of freezing-event extent (FE) are projected to decrease at a rate of -1912 and -4442 day·km2 per decade while the extent of heat-stress event (HE) increase at 1116 and 3557 day·km2 per decade. A higher increment in temperatures as well as a decreasing trend in the diurnal temperature range (DTR) and frost days and FE are present on the Tibetan Plateau and northern China including Xinjiang, Northeast China, the eastern part of northwest China, Inner Mongolia and North China. These trends are opposite to those projected for southern China including Huanghuai, Jianghuai, Jianghan, the south Yangzi River, South China and Southwestern China. The warming occur faster in the current colder zones (northern China and the Tibetan Plateau) while heat stress is more intense and severe in Jianghuai, Jianghan, the south Yangzi River, South China and Xinjiang. These potential changes indicate that adaption and mitigation strategies are necessary in response to future warming.

scholarly journals Near-surface air temperature lapse rates in the mainland China during 1962-2011

2013 ◽  
Vol 118 (14) ◽  
pp. 7505-7515 ◽  
Author(s):  
Xiuping Li ◽  
Lei Wang ◽  
Deliang Chen ◽  
Kun Yang ◽  
Baolin Xue ◽  
...  
Keyword(s):  
Air Temperature ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Lapse Rates

scholarly journals Estimation of near-surface air temperature lapse rates over continental Spain and its mountain areas

2018 ◽  
Vol 38 (8) ◽  
pp. 3233-3249 ◽  
Author(s):  
F. Navarro-Serrano ◽  
J. I. López-Moreno ◽  
C. Azorin-Molina ◽  
E. Alonso-González ◽  
M. Tomás-Burguera ◽  
...  
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Mountain Areas ◽  
Near Surface ◽  
Lapse Rates

scholarly journals Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau

2020 ◽  
Vol 12 (11) ◽  
pp. 1722
Author(s):  
Mingxi Zhang ◽  
Bin Wang ◽  
James Cleverly ◽  
De Li Liu ◽  
Puyu Feng ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Vegetation Indices ◽  
Surface Air Temperature ◽  
The Tibetan Plateau ◽  
Global Studies ◽  
Near Surface ◽  
Automated Mapping ◽  
Land Surface Temperatures

The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002–present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9–0.95 and 1–2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000–3000 m and 4000–5000 m, whereas the elevation interval at 6000–7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming.

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