scholarly journals Genetic adaptation of Tibetan poplar ( Populus szechuanica var. tibetica ) to high altitudes on the Qinghai–Tibetan Plateau

2020 ◽  
Vol 10 (20) ◽  
pp. 10974-10985
Author(s):  
Chenfei Zheng ◽  
Lizhi Tan ◽  
Mengmeng Sang ◽  
Meixia Ye ◽  
Rongling Wu

Authorea ◽  
2020 ◽  
Author(s):  
Chenfei Zheng ◽  
Lizhi Tan ◽  
mengmeng Sang ◽  
Meixia Ye ◽  
Rongling Wu


2018 ◽  
Vol 16 ◽  
pp. e00455 ◽  
Author(s):  
Geraldine Werhahn ◽  
Helen Senn ◽  
Muhammad Ghazali ◽  
Dibesh Karmacharya ◽  
Adarsh Man Sherchan ◽  
...  


2018 ◽  
Vol 48 (6) ◽  
pp. 671-683 ◽  
Author(s):  
MinQiang ZHOU ◽  
QiLin ZHANG ◽  
XingZhuo YANG ◽  
MingLong Yuan ◽  
ChengLin JIA ◽  
...  


2020 ◽  
Vol 40 (12) ◽  
pp. 5114-5127 ◽  
Author(s):  
Duo Li ◽  
Kun Yang ◽  
Wenjun Tang ◽  
Xin Li ◽  
Xu Zhou ◽  
...  


2019 ◽  
Vol 13 (8) ◽  
pp. 2221-2239 ◽  
Author(s):  
Yvan Orsolini ◽  
Martin Wegmann ◽  
Emanuel Dutra ◽  
Boqi Liu ◽  
Gianpaolo Balsamo ◽  
...  

Abstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole, is the world's highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impact on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric reanalyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global reanalyses with satellite and in situ data. Yet, snow in reanalyses provides critical surface information for forecast systems from the medium to sub-seasonal timescales. Here, snow depth and snow cover from four recent global reanalysis products, namely the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 and ERA-Interim reanalyses, the Japanese 55-year Reanalysis (JRA-55) and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2), are inter-compared over the TP region. The reanalyses are evaluated against a set of 33 in situ station observations, as well as against the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in situ observations provides confidence in the station data despite the relative paucity of in situ measurement sites and the harsh operating conditions. While several reanalyses show a systematic overestimation of the snow depth or snow cover, the reanalyses that assimilate local in situ observations or IMS snow cover are better capable of representing the shallow, transient snowpack over the TP region. The latter point is clearly demonstrated by examining the family of reanalyses from the ECMWF, of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. We further tested the sensitivity of the ERA5-Land model in offline experiments, assessing the impact of blown snow sublimation, snow cover to snow depth conversion and, more importantly, excessive snowfall. These results suggest that excessive snowfall might be the primary factor for the large overestimation of snow depth and cover in ERA5 reanalysis. Pending a solution for this common model precipitation bias over the Himalayas and the TP, future snow reanalyses that optimally combine the use of satellite snow cover and in situ snow depth observations in the assimilation and analysis cycles have the potential to improve medium-range to sub-seasonal forecasts for water resources applications.





2019 ◽  
Author(s):  
Yvan Orsolini ◽  
Martin Wegmann ◽  
Emanuel Dutra ◽  
Boqi Liu ◽  
Gianpaolo Balsamo ◽  
...  

Abstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole and, is the world highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impacts on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric re-analyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global re-analyses with satellite and in-situ data. Yet, snow in re-analyses provides critical surface information for forecast systems from the medium to sub-seasonal time scales. Here, snow depth and snow cover from 5 recent global reanalysis products are inter-compared over the TP region, and evaluated against a set of 33 in-situ station observations, as well as against the Interactive Multi-sensor Snow and Ice Mapping System (or IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in-situ observations provides confidence in the station data despite the relative paucity of in-situ measurement sites and the harsh operating conditions. While several re-analyses show a systematic over-estimation of the snow depth or snow cover, the reanalyses that assimilate local in-situ observations or IMS snow-cover are better capable of representing the shallow, transient snowpack over the TP region. The later point is clearly demonstrated by examining the family of re-analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF), of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. One missing process in the re-analyses is the blown snow sublimation, which seems important in the dry, windy and cold conditions of the TP. By incorporating a simple parametrisation of this process in the ECMWF land re-analysis, the positive snow bias is somewhat alleviated. Future snow reanalyses that optimally combine the use of satellite snow cover and in-situ snow-depth observations over the Tibetan Plateau region in the assimilation and analysis cycles, along with improved representation of snow processes, have the potential to substantially improve weather and climate prediction and water resources applications.



2010 ◽  
Vol 3 (1) ◽  
pp. 1-7 ◽  
Author(s):  
A. Shimono ◽  
H. Zhou ◽  
H. Shen ◽  
M. Hirota ◽  
T. Ohtsuka ◽  
...  


2017 ◽  
Vol 7 (4) ◽  
pp. 1267-1276 ◽  
Author(s):  
Dongsheng Zhang ◽  
Mengchao Yu ◽  
Peng Hu ◽  
Sihua Peng ◽  
Yimeng Liu ◽  
...  


2013 ◽  
Vol 4 (1) ◽  
Author(s):  
Yanhua Qu ◽  
Hongwei Zhao ◽  
Naijian Han ◽  
Guangyu Zhou ◽  
Gang Song ◽  
...  


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