scholarly journals A climatology of surface–air temperature difference over the Tibetan Plateau: Results from multi‐source reanalyses

2020 ◽  
Vol 40 (14) ◽  
pp. 6080-6094
Author(s):  
Xuejia Wang ◽  
Deliang Chen ◽  
Guojin Pang ◽  
Tinghai Ou ◽  
Meixue Yang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Temperature Difference ◽  
Surface Air Temperature ◽  
The Tibetan Plateau

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.

Dominant Interannual and Decadal Variability of Winter Surface Air Temperature over Asia and the Surrounding Oceans

2008 ◽  
Vol 21 (6) ◽  
pp. 1371-1386 ◽  
Author(s):  
Chihiro Miyazaki ◽  
Tetsuzo Yasunari
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Silk Road ◽  
Western Pacific ◽  
The Tibetan Plateau ◽  
The Third ◽  
The North ◽  
Atmospheric Wave ◽  
Fourth Mode

Abstract To clarify the interannual variability of winter surface air temperature (SAT) over Asia and the surrounding oceans, the authors applied principal component analysis to normalized monthly SATs. The first mode represents the Asian north–south dipole pattern with a node over the Tibetan Plateau. This component has close relationships to the Arctic Oscillation and cold surge variability around Southeast Asia, showing decadal oscillation with signal changes in 1988 and 1997. The second mode is the inner-Asian mode with a center to the north of the Tibetan Plateau. This component connects to fluctuations of not only the western Siberian high but also the Icelandic low, which is associated with the pattern of the polar vortex over Eurasia. A recent warming trend and possible relationship to solar activity are also shown. The modes of Asian SAT variability associated with ENSO are extracted as the north–south dipole mode over the tropical western Pacific and Japan (the third mode) and Silk Road mode (the fourth mode). The two independent modes appear to be caused by different sea surface temperature (SST) anomalies over the western Pacific and Indian Ocean and their associated atmospheric Rossby wave responses: the atmospheric wave trains over both the north and south of the Tibetan Plateau in the third mode, and the atmospheric wave train that propagates toward the Silk Road via Greenland in the fourth mode.

The AMV phase-dependence of the connection between February NAO and March surface air temperature over the Tibetan Plateau

2021 ◽  
Author(s):  
Jingyi Li ◽  
Fei Li ◽  
Shengping He ◽  
Huijun Wang ◽  
Yvan J Orsolini
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Rossby Wave ◽  
North Atlantic ◽  
Wave Train ◽  
Surface Air Temperature ◽  
The Tibetan Plateau ◽  
The North ◽  
Rossby Wave Train ◽  
The North Atlantic

<p>The Tibetan Plateau (TP), referred to as the “Asian water tower”, contains one of the largest land ice masses on Earth. The local glacier shrinkage and frozen-water storage are strongly affected by variations in surface air temperature over the TP (TPSAT), especially in springtime. This study reveals a distinct out-of-phase connection between the February North Atlantic Oscillation (NAO) and March TPSAT, which is non-stationary and regulated by the warm phase of the Atlantic Multidecadal Variability (AMV+). The results show that during the AMV+, the negative phase of the NAO persists from February to March, and is accompanied by a quasi-stationary Rossby wave train trapped along a northward-shifted subtropical westerly jet stream across Eurasia, inducing an anomalous adiabatic descent that warms the TP. However, during the cold phase of the AMV, the negative NAO does not persist into March. The Rossby wave train propagates along the well-separated polar and subtropical westerly jets, and the NAO−TPSAT connection is broken. Further investigation suggests that the enhanced synoptic eddy and low-frequency flow (SELF) interaction over the North Atlantic in February and March during the AMV+, caused by the enhanced and southward-shifted storm track, help maintain the NAO anomaly pattern via positive eddy feedback. This study provides a new detailed perspective on the decadal variability of the North Atlantic−TP connections in late winter−early spring.</p>

scholarly journals Trends of Sampling Error in Surface Air Temperature on the Tibetan Plateau during Recent Decades

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Wei Hua ◽  
Tianci Huang ◽  
Zouxin Lin ◽  
Lihua Zhu ◽  
Xin Wang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Sampling Error ◽  
Surface Air Temperature ◽  
The Tibetan Plateau ◽  
Statistical Inferences ◽  
Increasing Trends ◽  
Climate Studies ◽  
Monthly Variations

Understanding errors in surface air temperature (SAT) data and related uncertainties is crucial for climate studies because of their impact on the accuracy of statistical inferences in scientific conclusions. In recent decades, considerable research has focused on the trends and evolution of SAT on the Tibetan Plateau (TP). However, assessment of the uncertainties in SAT change on the TP has not been done adequately, which is of considerable importance for climate research. Using station-observed SAT data from the TP, this study estimates long-term variations and trends of sampling error variances in gridded monthly SAT data over recent decades. Results revealed large sampling error variances in northern and western parts of the TP but small variances in eastern, southern, and central areas. The sampling error variances also exhibited strong monthly variations with maximum errors in winter and minimum values in summer. Furthermore, spatial distributions of the trends of seasonal and annual mean sampling error variances were found distributed unevenly with decreasing trends found mainly in central and southern parts of the TP and increasing trends in northeastern, southeastern, and northwestern areas. Additionally, differences were also found in the trends of seasonal and annual mean sampling error variances on various timescales.

scholarly journals Systematic bias of Tibetan Plateau snow cover in subseasonal-to-seasonal models

2020 ◽  
Author(s):  
Shuzhen Hu ◽  
Wenkai Li
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Air Temperature ◽  
Lead Time ◽  
Surface Air Temperature ◽  
The Tibetan Plateau ◽  
Systematic Bias ◽  
Forecast Lead Time ◽  
Systematic Biases ◽  
Tibetan Plateau Snow Cover

Abstract. Accurate subseasonal-to-seasonal (S2S) atmospheric forecasts and hydrological forecasts have considerable socioeconomic value. This study conducts a multimodel comparison of the Tibetan Plateau snow cover (TPSC) prediction skill using three models (ECMWF, NCEP and CMA) selected from the S2S project database to understand their performance in capturing TPSC variability. S2S models can skilfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Compared with the observational snow cover analysis, all three models tend to overestimate the area of TPSC, especially during winter. Another remarkable issue regarding the TPSC forecast is the increasing TPSC with forecast lead time, which further increases the systematic positive biases of TPSC in the S2S models at longer forecast lead times. The underestimation of TPSC dissipation induces an increase in TPSC with forecast lead time in the models. Such systematic biases of TPSC influence the forecasted surface air temperature in the S2S models. The surface air temperature over the Tibetan Plateau becomes colder with increasing forecast lead time in the S2S models.

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.

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