Articles | Volume 13, issue 12
https://doi.org/10.5194/bg-13-3665-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-13-3665-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Preface: Impacts of extreme climate events and disturbances on carbon dynamics
Jingfeng Xiao
CORRESPONDING AUTHOR
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Shuguang Liu
U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA
Paul C. Stoy
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717, USA
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In this study, carbon flux and auxiliary meteorological data are post-processed to create an analysis-ready dataset for 34 sites across six ecosystems in the Heihe River basin. Overall, 18 sites have multi-year observations, while 16 were observed only during the 2012 growing season, totaling 1513 site months. This dataset can be used to explore carbon exchange, assess ecosystem responses to climate change, support upscaling studies, and evaluate carbon cycle models.
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Short summary
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In 2022, Europe's forests faced unprecedented dry conditions. Our study aimed to understand how different forest types respond to extreme drought. Using meteorological data and satellite imagery, we compared 2022 with two previous extreme years, 2003 and 2018. Despite less severe drought in 2022, forests showed a 30 % greater decline in photosynthesis compared to 2018 and 60 % more than 2003. This suggests an alarming level of vulnerability of forests across Europe to more frequent droughts.
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Short summary
Short summary
Based on various existing datasets, we comprehensively considered spatiotemporal differences in land surfaces and CO2 effects on plant stomatal resistance to parameterize the Shuttleworth–Wallace model, and we generated a global 5 km ensemble mean monthly potential evapotranspiration (PET) dataset (including potential transpiration PT and soil evaporation PE) during 1982–2015. The new dataset may be used by academic communities and various agencies to conduct various studies.
Sinan Li, Li Zhang, Jingfeng Xiao, Rui Ma, Xiangjun Tian, and Min Yan
Hydrol. Earth Syst. Sci., 26, 6311–6337, https://doi.org/10.5194/hess-26-6311-2022, https://doi.org/10.5194/hess-26-6311-2022, 2022
Short summary
Short summary
Accurate estimation for global GPP and ET is important in climate change studies. In this study, the GLASS LAI, SMOS, and SMAP datasets were assimilated jointly and separately in a coupled model. The results show that the performance of joint assimilation for GPP and ET is better than that of separate assimilation. The joint assimilation in water-limited regions performed better than in humid regions, and the global assimilation results had higher accuracy than other products.
Brendan Byrne, Junjie Liu, Yonghong Yi, Abhishek Chatterjee, Sourish Basu, Rui Cheng, Russell Doughty, Frédéric Chevallier, Kevin W. Bowman, Nicholas C. Parazoo, David Crisp, Xing Li, Jingfeng Xiao, Stephen Sitch, Bertrand Guenet, Feng Deng, Matthew S. Johnson, Sajeev Philip, Patrick C. McGuire, and Charles E. Miller
Biogeosciences, 19, 4779–4799, https://doi.org/10.5194/bg-19-4779-2022, https://doi.org/10.5194/bg-19-4779-2022, 2022
Short summary
Short summary
Plants draw CO2 from the atmosphere during the growing season, while respiration releases CO2 to the atmosphere throughout the year, driving seasonal variations in atmospheric CO2 that can be observed by satellites, such as the Orbiting Carbon Observatory 2 (OCO-2). Using OCO-2 XCO2 data and space-based constraints on plant growth, we show that permafrost-rich northeast Eurasia has a strong seasonal release of CO2 during the autumn, hinting at an unexpectedly large respiration signal from soils.
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev., 15, 6637–6657, https://doi.org/10.5194/gmd-15-6637-2022, https://doi.org/10.5194/gmd-15-6637-2022, 2022
Short summary
Short summary
Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Jing Fang, Xing Li, Jingfeng Xiao, Xiaodong Yan, Bolun Li, and Feng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-452, https://doi.org/10.5194/essd-2021-452, 2022
Revised manuscript not accepted
Short summary
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The dataset provided the vegetation photosynthetic phenology instead of traditional phenology to represent plant seasonal activities. This dataset had the latest period (2001–2020) and a fine spatial resolution (0.05 degree). Our phenology metrics revealed the spatial-temporal patterns of the multiple growing seasons in the Northern Hemisphere. The dataset will facilitate various research such as developing models, evaluating phenology shifts, and monitoring climate change worldwide.
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Short summary
This special issue showcases recent advancements on the impacts of disturbances and extreme events on the carbon (C) cycle. Notable advancements include quantifying harvest impacts on forest structure, recovery, and carbon stocks; observed dissolved organic C and methane increases in thermokarst lakes following summer warming; disentangling the roles of herbivores and fire on forest carbon dioxide flux; and improved atmospheric inversion of regional C flux by incorporating disturbances.
This special issue showcases recent advancements on the impacts of disturbances and extreme...
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