Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-2725-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-2725-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
12 Nov 2020
Data description paper |
| 12 Nov 2020
| 12 Nov 2020
Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017
Yi Zheng
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Ruoque Shen
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Yawen Wang
Key Laboratory of Physical Oceanography, College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China
Xiangqian Li
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Shuguang Liu
College of Life Science and Technology, Central South University of
Forestry and Technology (CSUFT), Changsha 410004, Hunan, China
Shunlin Liang
Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
Jing M. Chen
Department of Geography, University of Toronto, Toronto, M5G 3G3 Canada
College of Geographical Science, Fujian Normal University, Fuzhou 3500007, Fujian, China
Weimin Ju
International Institute for Earth System Sciences, Nanjing University,
Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical
Information Resource Development and Application, Nanjing, China
Li Zhang
Key Laboratory of Digital Earth Science, Aerospace Information
Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Wenping Yuan
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, Guangdong, China
Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519000, Guangdong, China
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The Köppen-Geiger climate classification has been widely applied in climate change and ecology studies to characterize climatic conditions. We present a new 1-km global dataset of Köppen-Geiger climate classification and bioclimatic variables for historical and future climates. The new climate maps offer higher classification accuracy, correspond well with distributions of vegetation and topographic features, and demonstrate the ability to identify recent and future changes in climate zones.
Xiongxin Xiao, Shunlin Liang, Tao He, Daiqiang Wu, Congyuan Pei, and Jianya Gong
The Cryosphere, 15, 835–861, https://doi.org/10.5194/tc-15-835-2021, https://doi.org/10.5194/tc-15-835-2021, 2021
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Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Fei Jiang, Hengmao Wang, Jing M. Chen, Weimin Ju, Xiangjun Tian, Shuzhuang Feng, Guicai Li, Zhuoqi Chen, Shupeng Zhang, Xuehe Lu, Jane Liu, Haikun Wang, Jun Wang, Wei He, and Mousong Wu
Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, https://doi.org/10.5194/acp-21-1963-2021, 2021
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We present a 6-year inversion from 2010 to 2015 for the global and regional carbon fluxes using only the GOSAT XCO2 retrievals. We find that the XCO2 retrievals could significantly improve the modeling of atmospheric CO2 concentrations and that the inferred interannual variations in the terrestrial carbon fluxes in most land regions have a better relationship with the changes in severe drought area or leaf area index, or are more consistent with the previous estimates about drought impact.
Liangjun Zhu, Shuguang Liu, Haifeng Zhu, David J. Cooper, Danyang Yuan, Yu Zhu, Zongshan Li, Yuandong Zhang, Hanxue Liang, Xu Zhang, Wenqi Song, and Xiaochun Wang
Clim. Past Discuss., https://doi.org/10.5194/cp-2021-2, https://doi.org/10.5194/cp-2021-2, 2021
Manuscript not accepted for further review
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In this study, we take the temperature reconstruction for Changbai Mountains in northeast China as an example to illustrate a novel tree-species mixing reconstruction method, which clearly improve the accuracy of tree-ring-based reconstructions in areas with unstable growth-climate relationships. Our reconstruction is more accurate than previous temperature reconstructions developed from a single species. The AMO plays a key role in modulating temperature in the northern Changbai Mountains.
Jin Ma, Ji Zhou, Frank-Michael Göttsche, Shunlin Liang, Shaofei Wang, and Mingsong Li
Earth Syst. Sci. Data, 12, 3247–3268, https://doi.org/10.5194/essd-12-3247-2020, https://doi.org/10.5194/essd-12-3247-2020, 2020
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Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from 20 years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
Jie Dong, Yangyang Fu, Jingjing Wang, Haifeng Tian, Shan Fu, Zheng Niu, Wei Han, Yi Zheng, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 3081–3095, https://doi.org/10.5194/essd-12-3081-2020, https://doi.org/10.5194/essd-12-3081-2020, 2020
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For the first time, we produced a 30 m winter wheat distribution map in China for 3 years during 2016–2018. Validated with 33 776 survey samples, the map had perfect performance with an overall accuracy of 89.88 %. Moreover, the method can identify planting areas of winter wheat 3 months prior to harvest; that is valuable information for production predictions and is urgently necessary for policymakers to reduce economic loss and assess food security.
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Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Accurately reproducing the interannual variations in vegetation gross primary production (GPP)...
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