Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6637-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6637-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
02 Sep 2022
Development and technical paper |
| 02 Sep 2022
| 02 Sep 2022
Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data
Rui Ma
CORRESPONDING AUTHOR
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Jingfeng Xiao
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
Department of Geography, University of Hong Kong, Hong Kong SAR 999077,
China
Department of Geography, University of Hong Kong, Hong Kong SAR 999077,
China
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
College of Resources and Environment, University of Chinese Academy
of Sciences, Beijing 100049, China
Xiaobang Liu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Haibo Lu
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai
519082, China
Related authors
Xinyan Liu, Tao He, Shunlin Liang, Ruibo Li, Xiongxin Xiao, Rui Ma, and Yichuan Ma
Earth Syst. Sci. Data, 15, 3641–3671, https://doi.org/10.5194/essd-15-3641-2023, https://doi.org/10.5194/essd-15-3641-2023, 2023
Short summary
Short summary
We proposed a data fusion strategy that combines the complementary features of multiple-satellite cloud fraction (CF) datasets and generated a continuous monthly 1° daytime cloud fraction product covering the entire Arctic during the sunlit months in 2000–2020. This study has positive significance for reducing the uncertainties for the assessment of surface radiation fluxes and improving the accuracy of research related to climate change and energy budgets, both regionally and globally.
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.
Liang Feng, Paul Palmer, Luke Smallman, Jingfeng Xiao, Paulo Cristofanelli, Ove Hermansen, John Lee, Casper Labuschagne, Simonetta Montaguti, Steffen Noe, Stephen Platt, Xinrong Ren, Martin Steinbacher, and Irene Xueref-Remy
EGUsphere, https://doi.org/10.5194/egusphere-2025-1793, https://doi.org/10.5194/egusphere-2025-1793, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
2023 saw an unexpectedly high global atmospheric CO2 growth. Satellite data reveal a role for increased emissions over the tropics. Larger emissions over eastern Brazil can be explained by warmer temperatures, while changes in rainfall and soil moisture play more of a role in emission increases elsewhere in the tropics.
Xufeng Wang, Tao Che, Jingfeng Xiao, Tonghong Wang, Junlei Tan, Yang Zhang, Zhiguo Ren, Liying Geng, Haibo Wang, Ziwei Xu, Shaomin Liu, and Xin Li
Earth Syst. Sci. Data, 17, 1329–1346, https://doi.org/10.5194/essd-17-1329-2025, https://doi.org/10.5194/essd-17-1329-2025, 2025
Short summary
Short summary
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.
Hui Liang, Shunlin Liang, Bo Jiang, Tao He, Feng Tian, Jianglei Xu, Wenyuan Li, Fengjiao Zhang, and Husheng Fang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-136, https://doi.org/10.5194/essd-2025-136, 2025
Preprint under review for ESSD
Short summary
Short summary
This paper describes 1 km daily mean land surface sensible heat flux (H) and land surface – air temperature difference (Tsa) datasets on the global scale during 2000–2020. The datasets were developed using a data-driven approach and rigorously validated against in situ observations and existing H and Tsa datasets, demonstrating both high accuracy and exceptional spatial resolution.
Yi Liu, Jingfeng Xiao, Xing Li, and Yue Li
Hydrol. Earth Syst. Sci., 29, 1241–1258, https://doi.org/10.5194/hess-29-1241-2025, https://doi.org/10.5194/hess-29-1241-2025, 2025
Short summary
Short summary
This work demonstrates that multi-source satellite-based water and carbon fluxes can capture critical soil moisture at a large spatial scale. In particular, grassland and clay with critical soil moisture higher than average soil moisture may be in a state of water limitation for long periods. Increased water demand could expose western grassland to more vulnerability.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Feng Tian, Guodong Zhang, and Jianglei Xu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-553, https://doi.org/10.5194/essd-2024-553, 2025
Preprint under review for ESSD
Short summary
Short summary
Soil moisture (SM) plays a vital role in climate, agriculture, and hydrology, yet reliable long-term seamless global datasets remain scarce. To fill this gap, we developed a four-decade seamless global daily 5 km SM product using multi-source datasets and deep learning techniques. This product has long-term coverage, spatial and temporal integrity, and high accuracy, making it a valuable tool for applications like SM trend analysis, drought monitoring, and assessing vegetation responses.
Mana Gharun, Ankit Shekhar, Jingfeng Xiao, Xing Li, and Nina Buchmann
Biogeosciences, 21, 5481–5494, https://doi.org/10.5194/bg-21-5481-2024, https://doi.org/10.5194/bg-21-5481-2024, 2024
Short summary
Short summary
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.
Xinyan Liu, Tao He, Qingxin Wang, Xiongxin Xiao, Yichuan Ma, Yanyan Wang, Shanjun Luo, Lei Du, and Zhaocong Wu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-458, https://doi.org/10.5194/essd-2024-458, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This study addresses the challenge of how clouds affect the Earth's energy balance, which is vital for understanding climate change. We developed a new method to create long-term cloud radiative kernels to improve the accuracy of sunlight reaching the surface, which significantly reduces errors. Findings suggest that prior estimates of cloud cooling effects may have been overstated, emphasizing the need for better strategies to manage climate change impacts in the Arctic.
Bing Li, Shunlin Liang, Han Ma, Guanpeng Dong, Xiaobang Liu, Tao He, and Yufang Zhang
Earth Syst. Sci. Data, 16, 3795–3819, https://doi.org/10.5194/essd-16-3795-2024, https://doi.org/10.5194/essd-16-3795-2024, 2024
Short summary
Short summary
This study describes 1 km all-weather instantaneous and daily mean land surface temperature (LST) datasets on the global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long global-scale time series. The generated datasets were evaluated by the observations from in situ stations and other LST datasets, and the evaluation indicated that the dataset is sufficiently reliable.
Shanlei Sun, Zaoying Bi, Jingfeng Xiao, Yi Liu, Ge Sun, Weimin Ju, Chunwei Liu, Mengyuan Mu, Jinjian Li, Yang Zhou, Xiaoyuan Li, Yibo Liu, and Haishan Chen
Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, https://doi.org/10.5194/essd-15-4849-2023, 2023
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.
Xinyan Liu, Tao He, Shunlin Liang, Ruibo Li, Xiongxin Xiao, Rui Ma, and Yichuan Ma
Earth Syst. Sci. Data, 15, 3641–3671, https://doi.org/10.5194/essd-15-3641-2023, https://doi.org/10.5194/essd-15-3641-2023, 2023
Short summary
Short summary
We proposed a data fusion strategy that combines the complementary features of multiple-satellite cloud fraction (CF) datasets and generated a continuous monthly 1° daytime cloud fraction product covering the entire Arctic during the sunlit months in 2000–2020. This study has positive significance for reducing the uncertainties for the assessment of surface radiation fluxes and improving the accuracy of research related to climate change and energy budgets, both regionally and globally.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, Bing Li, Jianglei Xu, Guodong Zhang, Xiaobang Liu, and Changhao Xiong
Earth Syst. Sci. Data, 15, 2055–2079, https://doi.org/10.5194/essd-15-2055-2023, https://doi.org/10.5194/essd-15-2055-2023, 2023
Short summary
Short summary
Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.
Aolin Jia, Shunlin Liang, Dongdong Wang, Lei Ma, Zhihao Wang, and Shuo Xu
Earth Syst. Sci. Data, 15, 869–895, https://doi.org/10.5194/essd-15-869-2023, https://doi.org/10.5194/essd-15-869-2023, 2023
Short summary
Short summary
Satellites are now producing multiple global land surface temperature (LST) products; however, they suffer from data gaps caused by cloud cover, seriously restricting the applications, and few products provide gap-free global hourly LST. We produced global hourly, 5 km, all-sky LST data from 2011 to 2021 using geostationary and polar-orbiting satellite data. Based on the assessment, it has high accuracy and can be used to estimate evapotranspiration, drought, etc.
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.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
Short summary
Short summary
The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing 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.
Jianglei Xu, Shunlin Liang, and Bo Jiang
Earth Syst. Sci. Data, 14, 2315–2341, https://doi.org/10.5194/essd-14-2315-2022, https://doi.org/10.5194/essd-14-2315-2022, 2022
Short summary
Short summary
Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current products have drawbacks of coarse resolutions, large uncertainty, and short time spans. A deep learning method was used to obtain global surface Rn. A long-term Rn product was generated from 1981 to 2019 using AVHRR data. The product has the highest accuracy and a reasonable spatiotemporal variation compared to three other products. Our product will play an important role in long-term climate change.
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
Short summary
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.
Xueyuan Gao, Shunlin Liang, Dongdong Wang, Yan Li, Bin He, and Aolin Jia
Earth Syst. Dynam., 13, 219–230, https://doi.org/10.5194/esd-13-219-2022, https://doi.org/10.5194/esd-13-219-2022, 2022
Short summary
Short summary
Numerical experiments with a coupled Earth system model show that large-scale nighttime artificial lighting in tropical forests will significantly increase carbon sink, local temperature, and precipitation, and it requires less energy than direct air carbon capture for capturing 1 t of carbon, suggesting that it could be a powerful climate mitigation option. Side effects include CO2 outgassing after the termination of the nighttime lighting and impacts on local wildlife.
Xiaona Chen, Shunlin Liang, Lian He, Yaping Yang, and Cong Yin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-279, https://doi.org/10.5194/essd-2021-279, 2021
Preprint withdrawn
Short summary
Short summary
The present study developed a 39 year consistent 8-day 0.05 degree gap-free SCE dataset over the NH for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite based on the NOAA AVHRR-SR CDR and several contributory datasets. Compared with published SCE datasets, GLASS SCE has several advantages in snow cover studies, including long time series, finer spatial resolution (especially for years before 2000), and complete spatial coverage.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data, 13, 5087–5114, https://doi.org/10.5194/essd-13-5087-2021, https://doi.org/10.5194/essd-13-5087-2021, 2021
Short summary
Short summary
Large portions of the Earth's surface are expected to experience changes in climatic conditions. The rearrangement of climate distributions can lead to serious impacts on ecological and social systems. Major climate zones are distributed in a predictable pattern and are largely defined following the Köppen climate classification. This creates an urgent need to compile a series of Köppen climate classification maps with finer spatial and temporal resolutions and improved accuracy.
Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang
Earth Syst. Sci. Data, 13, 4241–4261, https://doi.org/10.5194/essd-13-4241-2021, https://doi.org/10.5194/essd-13-4241-2021, 2021
Short summary
Short summary
This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (Ta) using a machine learning method, and developed an all-sky 1 km daily mean land Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-53, https://doi.org/10.5194/essd-2021-53, 2021
Preprint withdrawn
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
Short summary
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.
Han Liu, Peng Gong, Jie Wang, Nicholas Clinton, Yuqi Bai, and Shunlin Liang
Earth Syst. Sci. Data, 12, 1217–1243, https://doi.org/10.5194/essd-12-1217-2020, https://doi.org/10.5194/essd-12-1217-2020, 2020
Short summary
Short summary
We built the first set of 5 km resolution CDRs to record the annual dynamics of global land cover (GLASS-GLC) from 1982 to 2015. The average overall accuracy is 82 %. By conducting long-term change analysis, significant land cover changes and spatiotemporal patterns at various scales were found, which can improve our understanding of global environmental change and help achieve sustainable development goals. This will be further applied in Earth system modeling to facilitate relevant studies.
Aolin Jia, Shunlin Liang, Dongdong Wang, Bo Jiang, and Xiaotong Zhang
Atmos. Chem. Phys., 20, 881–899, https://doi.org/10.5194/acp-20-881-2020, https://doi.org/10.5194/acp-20-881-2020, 2020
Short summary
Short summary
The Tibetan Plateau (TP) plays a vital role in regional and global climate change due to its location and orography. After generating a long-term surface radiation (SR) dataset, we characterized the SR spatiotemporal variation along with temperature. Evidence from multiple data sources indicated that the TP dimming was primarily driven by increased aerosols from human activities, and the cooling effect of aerosol loading offsets TP surface warming, revealing the human impact on regional warming.
Xu Yue, Nadine Unger, Kandice Harper, Xiangao Xia, Hong Liao, Tong Zhu, Jingfeng Xiao, Zhaozhong Feng, and Jing Li
Atmos. Chem. Phys., 17, 6073–6089, https://doi.org/10.5194/acp-17-6073-2017, https://doi.org/10.5194/acp-17-6073-2017, 2017
Short summary
Short summary
While it is widely recognized that air pollutants adversely affect human health and climate change, their impacts on the regional carbon balance are less well understood. We apply an Earth system model to quantify the combined effects of ozone and aerosol particles on net primary production in China. Ozone vegetation damage dominates over the aerosol effects, leading to a substantial net suppression of land carbon uptake in the present and future worlds.
Jingfeng Xiao, Shuguang Liu, and Paul C. Stoy
Biogeosciences, 13, 3665–3675, https://doi.org/10.5194/bg-13-3665-2016, https://doi.org/10.5194/bg-13-3665-2016, 2016
Short summary
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.
W. Wang, J. Xiao, S. V. Ollinger, A. R. Desai, J. Chen, and A. Noormets
Biogeosciences, 11, 6667–6682, https://doi.org/10.5194/bg-11-6667-2014, https://doi.org/10.5194/bg-11-6667-2014, 2014
X. Xie, S. Meng, S. Liang, and Y. Yao
Hydrol. Earth Syst. Sci., 18, 3923–3936, https://doi.org/10.5194/hess-18-3923-2014, https://doi.org/10.5194/hess-18-3923-2014, 2014
Q. Shi and S. Liang
Atmos. Chem. Phys., 14, 5659–5677, https://doi.org/10.5194/acp-14-5659-2014, https://doi.org/10.5194/acp-14-5659-2014, 2014
F. Deng, J. M. Chen, Y. Pan, W. Peters, R. Birdsey, K. McCullough, and J. Xiao
Biogeosciences, 10, 5335–5348, https://doi.org/10.5194/bg-10-5335-2013, https://doi.org/10.5194/bg-10-5335-2013, 2013
N. F. Liu, Q. Liu, L. Z. Wang, S. L. Liang, J. G. Wen, Y. Qu, and S. H. Liu
Hydrol. Earth Syst. Sci., 17, 2121–2129, https://doi.org/10.5194/hess-17-2121-2013, https://doi.org/10.5194/hess-17-2121-2013, 2013
T. R. Xu, S. M. Liu, Z. W. Xu, S. Liang, and L. Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-3927-2013, https://doi.org/10.5194/hessd-10-3927-2013, 2013
Preprint withdrawn
Related subject area
Integrated assessment modeling
Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1
emIAM v1.0: an emulator for integrated assessment models using marginal abatement cost curves
pathways-ensemble-analysis v1.1.0: an open-source library for systematic and robust analysis of pathway ensembles
MESSAGEix-Materials v1.1.0: representation of material flows and stocks in an integrated assessment model
InsNet-CRAFTY v1.0: Integrating institutional network dynamics powered by large language models with land use change simulation
GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model
CLASH – Climate-responsive Land Allocation model with carbon Storage and Harvests
Carbon Monitor Power-Simulators (CMP-SIM v1.0) across countries: a data-driven approach to simulate daily power generation
Intercomparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1–CMAQ v5.3.1, WRF–Chem v4.1.1, and WRF v3.7.1–CHIMERE v2020r1) in eastern China
MESSAGEix-GLOBIOM nexus module: integrating water sector and climate impacts
Minimum-variance-based outlier detection method using forward-search model error in geodetic networks
Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)
Bidirectional coupling of the long-term integrated assessment model REgional Model of INvestments and Development (REMIND) v3.0.0 with the hourly power sector model Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) v1.0.2
GCAM-CDR v1.0: enhancing the representation of carbon dioxide removal technologies and policies in an integrated assessment model
The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures
Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with Coupled Model Intercomparison Project (CMIP) climate data
A tool for air pollution scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies
Improved CASA model based on satellite remote sensing data: simulating net primary productivity of Qinghai Lake basin alpine grassland
Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information
TIM: modelling pathways to meet Ireland's long-term energy system challenges with the TIMES-Ireland Model (v1.0)
ANEMI_Yangtze v1.0: a coupled human–natural systems model for the Yangtze Economic Belt – model description
Nested leave-two-out cross-validation for the optimal crop yield model selection
GCAM-USA v5.3_water_dispatch: integrated modeling of subnational US energy, water, and land systems within a global framework
GOBLIN version 1.0: a land balance model to identify national agriculture and land use pathways to climate neutrality via backcasting
Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2
REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits
Parallel gridded simulation framework for DSSAT-CSM (version 4.7.5.21) using MPI and NetCDF
Estimating global land system impacts of timber plantations using MAgPIE 4.3.5
Hassan Niazi, Stephen B. Ferencz, Neal T. Graham, Jim Yoon, Thomas B. Wild, Mohamad Hejazi, David J. Watson, and Chris R. Vernon
Geosci. Model Dev., 18, 1737–1767, https://doi.org/10.5194/gmd-18-1737-2025, https://doi.org/10.5194/gmd-18-1737-2025, 2025
Short summary
Short summary
Superwell is a physics-based hydro-economic model that estimates the production costs and availability of groundwater worldwide. It calculates how much groundwater can be extracted and at what cost, using detailed maps and data of the Earth's properties. Through these estimates, and by using them with other models, Superwell facilitates exploration of coupled human–environmental system challenges, such as future water supply sustainability or multi-sectoral energy–water–land feedbacks.
Weiwei Xiong, Katsumasa Tanaka, Philippe Ciais, Daniel J. A. Johansson, and Mariliis Lehtveer
Geosci. Model Dev., 18, 1575–1612, https://doi.org/10.5194/gmd-18-1575-2025, https://doi.org/10.5194/gmd-18-1575-2025, 2025
Short summary
Short summary
emIAM v1.0 is a new emulator for integrated assessment models (IAMs) using global and regional time-independent marginal abatement cost (MAC) curves for multiple greenhouse gases, optionally with time-dependent MAC curves for enhanced accuracy. Combined with climate emulators, emIAM enables development of cost-effective, multi-IAM emission pathways directly linked to temperature targets at low computational cost.
Lara Welder, Neil Grant, and Matthew J. Gidden
Geosci. Model Dev., 18, 239–252, https://doi.org/10.5194/gmd-18-239-2025, https://doi.org/10.5194/gmd-18-239-2025, 2025
Short summary
Short summary
Pathways investigating the link between emissions and global warming have been continuously used to inform climate policy. We have developed a tool that can facilitate the systematic and robust analysis of ensembles of such pathways. We describe the structure of this tool and then show an illustrative application of it. The application indicates the usefulness of the tool to the research community and shows how it can be used to establish best practices.
Gamze Ünlü, Florian Maczek, Jihoon Min, Stefan Frank, Fridolin Glatter, Paul Natsuo Kishimoto, Jan Streeck, Nina Eisenmenger, Dominik Wiedenhofer, and Volker Krey
Geosci. Model Dev., 17, 8321–8352, https://doi.org/10.5194/gmd-17-8321-2024, https://doi.org/10.5194/gmd-17-8321-2024, 2024
Short summary
Short summary
Extraction and processing of raw materials constitute a significant source of CO2 emissions in industry and so are contributors to climate change. We develop an open-source tool to assess different industry decarbonization pathways in integrated assessment models (IAMs) with a representation of material flows and stocks. We highlight the importance of expanding the scope of climate change mitigation options to include circular-economy and material efficiency measures in IAM scenario analysis.
Yongchao Zeng, Calum Brown, Mohamed Byari, Joanna Raymond, Thomas Schmitt, and Mark Rounsevell
EGUsphere, https://doi.org/10.5194/egusphere-2024-2661, https://doi.org/10.5194/egusphere-2024-2661, 2024
Short summary
Short summary
Understanding environmental policy interventions is challenging due to complex institutional actor interactions. Large language models (LLMs) offer new solutions by mimicking the actors. We present InsNet-CRAFTY v1.0, a multi-LLM-agent model coupled with a land system model, simulating competing policy priorities. The model shows how LLM agents can simulate decision-making in institutional networks, highlighting both their potential and limitations in advancing land system modelling.
Mengqi Zhao, Thomas B. Wild, Neal T. Graham, Son H. Kim, Matthew Binsted, A. F. M. Kamal Chowdhury, Siwa Msangi, Pralit L. Patel, Chris R. Vernon, Hassan Niazi, Hong-Yi Li, and Guta W. Abeshu
Geosci. Model Dev., 17, 5587–5617, https://doi.org/10.5194/gmd-17-5587-2024, https://doi.org/10.5194/gmd-17-5587-2024, 2024
Short summary
Short summary
The Global Change Analysis Model (GCAM) simulates the world’s climate–land–energy–water system interactions , but its reservoir representation is limited. We developed the GLObal Reservoir Yield (GLORY) model to provide GCAM with information on the cost of supplying water based on reservoir construction costs, climate and demand conditions, and reservoir expansion potential. GLORY enhances our understanding of future reservoir capacity needs to meet human demands in a changing climate.
Tommi Ekholm, Nadine-Cyra Freistetter, Aapo Rautiainen, and Laura Thölix
Geosci. Model Dev., 17, 3041–3062, https://doi.org/10.5194/gmd-17-3041-2024, https://doi.org/10.5194/gmd-17-3041-2024, 2024
Short summary
Short summary
CLASH is a numerical model that portrays land allocation between different uses, land carbon stocks, and agricultural and forestry production globally. CLASH can help in examining the role of land use in mitigating climate change, providing food and biogenic raw materials for the economy, and conserving primary ecosystems. Our demonstration with CLASH confirms that reduction of animal-based food, shifting croplands and storing carbon in forests are effective ways to mitigate climate change.
Léna Gurriaran, Yannig Goude, Katsumasa Tanaka, Biqing Zhu, Zhu Deng, Xuanren Song, and Philippe Ciais
Geosci. Model Dev., 17, 2663–2682, https://doi.org/10.5194/gmd-17-2663-2024, https://doi.org/10.5194/gmd-17-2663-2024, 2024
Short summary
Short summary
We developed a data-driven model simulating daily regional power demand based on climate and socioeconomic variables. Our model was applied to eight countries or regions (Australia, Brazil, China, EU, India, Russia, South Africa, US), identifying influential factors and their relationship with power demand. Our findings highlight the significance of economic indicators in addition to temperature, showcasing country-specific variations. This research aids energy planning and emission reduction.
Chao Gao, Xuelei Zhang, Aijun Xiu, Qingqing Tong, Hongmei Zhao, Shichun Zhang, Guangyi Yang, Mengduo Zhang, and Shengjin Xie
Geosci. Model Dev., 17, 2471–2492, https://doi.org/10.5194/gmd-17-2471-2024, https://doi.org/10.5194/gmd-17-2471-2024, 2024
Short summary
Short summary
A comprehensive comparison study is conducted targeting the performances of three two-way coupled meteorology and air quality models (WRF-CMAQ, WRF-Chem, and WRF-CHIMERE) for eastern China during 2017. The impacts of aerosol–radiation–cloud interactions on these models’ results are evaluated against satellite and surface observations. Further improvements to the calculation of aerosol–cloud interactions in these models are crucial to ensure more accurate and timely air quality forecasts.
Muhammad Awais, Adriano Vinca, Edward Byers, Stefan Frank, Oliver Fricko, Esther Boere, Peter Burek, Miguel Poblete Cazenave, Paul Natsuo Kishimoto, Alessio Mastrucci, Yusuke Satoh, Amanda Palazzo, Madeleine McPherson, Keywan Riahi, and Volker Krey
Geosci. Model Dev., 17, 2447–2469, https://doi.org/10.5194/gmd-17-2447-2024, https://doi.org/10.5194/gmd-17-2447-2024, 2024
Short summary
Short summary
Climate change, population growth, and depletion of natural resources all pose complex and interconnected challenges. Our research offers a novel model that can help in understanding the interplay of these aspects, providing policymakers with a more robust tool for making informed future decisions. The study highlights the significance of incorporating climate impacts within large-scale global integrated assessments, which can help us in generating more climate-resilient scenarios.
Utkan M. Durdağ
Geosci. Model Dev., 17, 2187–2196, https://doi.org/10.5194/gmd-17-2187-2024, https://doi.org/10.5194/gmd-17-2187-2024, 2024
Short summary
Short summary
This study introduces a novel approach to outlier detection in geodetic networks, challenging conventional and robust methods. By treating outliers as unknown parameters within the Gauss–Markov model and exploring numerous outlier combinations, this approach prioritizes minimal variance and eliminates iteration dependencies. The mean success rate (MSR) comparisons highlight its effectiveness, improving the MSR by 40–45 % for multiple outliers.
Michaja Pehl, Felix Schreyer, and Gunnar Luderer
Geosci. Model Dev., 17, 2015–2038, https://doi.org/10.5194/gmd-17-2015-2024, https://doi.org/10.5194/gmd-17-2015-2024, 2024
Short summary
Short summary
We extend the REMIND model (used to investigate climate mitigation strategies) by an industry module that represents cement, chemical, steel, and other industries. We also present a method for deriving scenarios of industry subsector activity and energy demand, consistent with established socioeconomic scenarios, allowing us to investigate the different climate change mitigation challenges and strategies in industry subsectors in the context of the entire energy–economy–climate system.
Chen Chris Gong, Falko Ueckerdt, Robert Pietzcker, Adrian Odenweller, Wolf-Peter Schill, Martin Kittel, and Gunnar Luderer
Geosci. Model Dev., 16, 4977–5033, https://doi.org/10.5194/gmd-16-4977-2023, https://doi.org/10.5194/gmd-16-4977-2023, 2023
Short summary
Short summary
To mitigate climate change, the global economy must drastically reduce its greenhouse gas emissions, for which the power sector plays a key role. Until now, long-term models which simulate this transformation cannot always accurately depict the power sector due to a lack of resolution. Our work bridges this gap by linking a long-term model to an hourly model. The result is an almost full harmonization of the models in generating a power sector mix until 2100 with hourly resolution.
David R. Morrow, Raphael Apeaning, and Garrett Guard
Geosci. Model Dev., 16, 1105–1118, https://doi.org/10.5194/gmd-16-1105-2023, https://doi.org/10.5194/gmd-16-1105-2023, 2023
Short summary
Short summary
GCAM-CDR is a variant of the Global Change Analysis Model that makes it easier to study the roles that carbon dioxide removal (CDR) might play in climate policy. Building on GCAM 5.4, GCAM-CDR adds several extra technologies to permanently remove carbon dioxide from the air and enables users to simulate a wider range of CDR-related policies and controls.
Jarmo S. Kikstra, Zebedee R. J. Nicholls, Christopher J. Smith, Jared Lewis, Robin D. Lamboll, Edward Byers, Marit Sandstad, Malte Meinshausen, Matthew J. Gidden, Joeri Rogelj, Elmar Kriegler, Glen P. Peters, Jan S. Fuglestvedt, Ragnhild B. Skeie, Bjørn H. Samset, Laura Wienpahl, Detlef P. van Vuuren, Kaj-Ivar van der Wijst, Alaa Al Khourdajie, Piers M. Forster, Andy Reisinger, Roberto Schaeffer, and Keywan Riahi
Geosci. Model Dev., 15, 9075–9109, https://doi.org/10.5194/gmd-15-9075-2022, https://doi.org/10.5194/gmd-15-9075-2022, 2022
Short summary
Short summary
Assessing hundreds or thousands of emission scenarios in terms of their global mean temperature implications requires standardised procedures of infilling, harmonisation, and probabilistic temperature assessments. We here present the open-source
climate-assessmentworkflow that was used in the IPCC AR6 Working Group III report. The paper provides key insight for anyone wishing to understand the assessment of climate outcomes of mitigation pathways in the context of the Paris Agreement.
Théo Le Guenedal, Philippe Drobinski, and Peter Tankov
Geosci. Model Dev., 15, 8001–8039, https://doi.org/10.5194/gmd-15-8001-2022, https://doi.org/10.5194/gmd-15-8001-2022, 2022
Short summary
Short summary
The CATHERINA model produces simulations of cyclone-related annualized damage costs at a country level from climate data and open-source socioeconomic indicators. The framework couples statistical and physical modeling of tropical cyclones to bridge the gap between general circulation and integrated assessment models providing a precise description of tropical-cyclone-related damages.
William Atkinson, Sebastian D. Eastham, Y.-H. Henry Chen, Jennifer Morris, Sergey Paltsev, C. Adam Schlosser, and Noelle E. Selin
Geosci. Model Dev., 15, 7767–7789, https://doi.org/10.5194/gmd-15-7767-2022, https://doi.org/10.5194/gmd-15-7767-2022, 2022
Short summary
Short summary
Understanding policy effects on human-caused air pollutant emissions is key for assessing related health impacts. We develop a flexible scenario tool that combines updated emissions data sets, long-term economic modeling, and comprehensive technology pathways to clarify the impacts of climate and air quality policies. Results show the importance of both policy levers in the future to prevent long-term emission increases from offsetting near-term air quality improvements from existing policies.
Chengyong Wu, Kelong Chen, Chongyi E, Xiaoni You, Dongcai He, Liangbai Hu, Baokang Liu, Runke Wang, Yaya Shi, Chengxiu Li, and Fumei Liu
Geosci. Model Dev., 15, 6919–6933, https://doi.org/10.5194/gmd-15-6919-2022, https://doi.org/10.5194/gmd-15-6919-2022, 2022
Short summary
Short summary
The traditional Carnegie–Ames–Stanford Approach (CASA) model driven by multisource data such as meteorology, soil, and remote sensing (RS) has notable disadvantages. We drove the CASA using RS data and conducted a case study of the Qinghai Lake basin alpine grassland. The simulated result is similar to published and measured net primary productivity (NPP). It may provide a reference for simulating vegetation NPP to satisfy the requirements of accounting carbon stocks and other applications.
Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert Van Schaeybroeck, Llorenç Lledó, Nicolau Manubens, Emmanuel Roulin, M. Carmen Alvarez-Castro, Lauriane Batté, Pierre-Antoine Bretonnière, Susana Corti, Carlos Delgado-Torres, Marta Domínguez, Federico Fabiano, Ignazio Giuntoli, Jost von Hardenberg, Eroteida Sánchez-García, Verónica Torralba, and Deborah Verfaillie
Geosci. Model Dev., 15, 6115–6142, https://doi.org/10.5194/gmd-15-6115-2022, https://doi.org/10.5194/gmd-15-6115-2022, 2022
Short summary
Short summary
CSTools (short for Climate Service Tools) is an R package that contains process-based methods for climate forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. In addition to describing the structure and methods in the package, we also present three use cases to illustrate the seasonal climate forecast post-processing for specific purposes.
Olexandr Balyk, James Glynn, Vahid Aryanpur, Ankita Gaur, Jason McGuire, Andrew Smith, Xiufeng Yue, and Hannah Daly
Geosci. Model Dev., 15, 4991–5019, https://doi.org/10.5194/gmd-15-4991-2022, https://doi.org/10.5194/gmd-15-4991-2022, 2022
Short summary
Short summary
Ireland has significantly increased its climate mitigation ambition, with a recent commitment to reduce greenhouse gases by an average of 7 % yr-1 in the period to 2030 and a net-zero target for 2050. This article describes the TIMES-Ireland model (TIM) developed to inform Ireland's energy system decarbonisation challenge. The paper also outlines a priority list of future model developments to better meet the challenge, taking into account equity, cost-effectiveness, and technical feasibility.
Haiyan Jiang, Slobodan P. Simonovic, and Zhongbo Yu
Geosci. Model Dev., 15, 4503–4528, https://doi.org/10.5194/gmd-15-4503-2022, https://doi.org/10.5194/gmd-15-4503-2022, 2022
Short summary
Short summary
The Yangtze Economic Belt is one of the most dynamic regions of China. The fast urbanization and strong economic growth in the region pose severe challenges for its sustainable development. To improve our understanding of the interactions among coupled human–natural systems in the Belt and to provide the foundation for science-based policy-making for the sustainable development of the Belt, we developed an integrated system-dynamics-based simulation model (ANEMI_Yangtze) for the Belt.
Thi Lan Anh Dinh and Filipe Aires
Geosci. Model Dev., 15, 3519–3535, https://doi.org/10.5194/gmd-15-3519-2022, https://doi.org/10.5194/gmd-15-3519-2022, 2022
Short summary
Short summary
We proposed the leave-two-out method (i.e. one particular implementation of the nested cross-validation) to determine the optimal statistical crop model (using the validation dataset) and estimate its true generalization ability (using the testing dataset). This approach is applied to two examples (robusta coffee in Cu M'gar and grain maize in France). The results suggested that the simple models are more suitable in crop modelling where a limited number of samples is available.
Matthew Binsted, Gokul Iyer, Pralit Patel, Neal T. Graham, Yang Ou, Zarrar Khan, Nazar Kholod, Kanishka Narayan, Mohamad Hejazi, Son Kim, Katherine Calvin, and Marshall Wise
Geosci. Model Dev., 15, 2533–2559, https://doi.org/10.5194/gmd-15-2533-2022, https://doi.org/10.5194/gmd-15-2533-2022, 2022
Short summary
Short summary
GCAM-USA v5.3_water_dispatch is an open-source model that represents key interactions across economic, energy, water, and land systems in a global framework, with subnational detail in the United States. GCAM-USA can be used to explore future changes in demand for (and production of) energy, water, and crops at the state and regional level in the US. This paper describes GCAM-USA and provides four illustrative scenarios to demonstrate the model's capabilities and potential applications.
Colm Duffy, Remi Prudhomme, Brian Duffy, James Gibbons, Cathal O'Donoghue, Mary Ryan, and David Styles
Geosci. Model Dev., 15, 2239–2264, https://doi.org/10.5194/gmd-15-2239-2022, https://doi.org/10.5194/gmd-15-2239-2022, 2022
Short summary
Short summary
The GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) model is a new high-resolution integrated
bottom-upbiophysical land use model capable of identifying broad pathways towards climate neutrality in the agriculture, forestry, and other land use (AFOLU) sector. The model is intended to bridge the gap between hindsight representations of national emissions and much larger globally integrated assessment models.
Samuel Lüthi, Gabriela Aznar-Siguan, Christopher Fairless, and David N. Bresch
Geosci. Model Dev., 14, 7175–7187, https://doi.org/10.5194/gmd-14-7175-2021, https://doi.org/10.5194/gmd-14-7175-2021, 2021
Short summary
Short summary
In light of the dramatic increase in economic impacts due to wildfires, the need for modelling impacts of wildfire damage is ever increasing. Insurance companies, households, humanitarian organisations and governmental authorities are worried by climate risks. In this study we present an approach to modelling wildfire impacts using the open-source modelling platform CLIMADA. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South.
Lavinia Baumstark, Nico Bauer, Falk Benke, Christoph Bertram, Stephen Bi, Chen Chris Gong, Jan Philipp Dietrich, Alois Dirnaichner, Anastasis Giannousakis, Jérôme Hilaire, David Klein, Johannes Koch, Marian Leimbach, Antoine Levesque, Silvia Madeddu, Aman Malik, Anne Merfort, Leon Merfort, Adrian Odenweller, Michaja Pehl, Robert C. Pietzcker, Franziska Piontek, Sebastian Rauner, Renato Rodrigues, Marianna Rottoli, Felix Schreyer, Anselm Schultes, Bjoern Soergel, Dominika Soergel, Jessica Strefler, Falko Ueckerdt, Elmar Kriegler, and Gunnar Luderer
Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, https://doi.org/10.5194/gmd-14-6571-2021, 2021
Short summary
Short summary
This paper presents the new and open-source version 2.1 of the REgional Model of INvestments and Development (REMIND) with the aim of improving code documentation and transparency. REMIND is an integrated assessment model (IAM) of the energy-economic system. By answering questions like
Can the world keep global warming below 2 °C?and, if so,
Under what socio-economic conditions and applying what technological options?, it is the goal of REMIND to explore consistent transformation pathways.
Phillip D. Alderman
Geosci. Model Dev., 14, 6541–6569, https://doi.org/10.5194/gmd-14-6541-2021, https://doi.org/10.5194/gmd-14-6541-2021, 2021
Short summary
Short summary
This paper documents a framework for accessing crop model input data directly from spatially referenced file formats and running simulations in parallel across a geographic region using the Decision Support System for Agrotechnology Transfer Cropping Systems Model (a widely used crop model system). The framework greatly reduced the execution time when compared to running the standard version of the model.
Abhijeet Mishra, Florian Humpenöder, Jan Philipp Dietrich, Benjamin Leon Bodirsky, Brent Sohngen, Christopher P. O. Reyer, Hermann Lotze-Campen, and Alexander Popp
Geosci. Model Dev., 14, 6467–6494, https://doi.org/10.5194/gmd-14-6467-2021, https://doi.org/10.5194/gmd-14-6467-2021, 2021
Short summary
Short summary
Timber plantations are an increasingly important source of roundwood production, next to harvest from natural forests. However, timber plantations are currently underrepresented in global land-use models. Here, we include timber production and plantations in the MAgPIE modeling framework. This allows one to capture the competition for land between agriculture and forestry. We show that increasing timber plantations in the coming decades partly compete with cropland for limited land resources.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for
ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413,
2013.
Alton, P. B.: From site-level to global simulation: Reconciling carbon,
water and energy fluxes over different spatial scales using a process-based
ecophysiological land-surface model, Agr. Forest Meteorol., 176, 111–124,
https://doi.org/10.1016/j.agrformet.2013.03.010, 2013.
Bacour, C., Peylin, P., MacBean, N., Rayner, P. J., Delage, F., Chevallier,
F., Weiss, M., Demarty, J., Santaren, D., Baret, F., Berveiller, D.,
Dufrêne, E., and Prunet, P.: Joint assimilation of eddy covariance flux
measurements and FAPAR products over temperate forests within a
process-oriented biosphere model, J. Geophys. Res.-Biogeo., 120,
1839–1857, https://doi.org/10.1002/2015jg002966, 2015.
Barman, R., Jain, A. K., and Liang, M.: Climate-driven uncertainties in
modeling terrestrial gross primary production: a site level to global-scale
analysis, Glob. Change Biol., 20, 1394–1411, https://doi.org/10.1111/gcb.12474, 2014.
Bloom, A. A., Exbrayat, J.-F., van der Velde, I. R., Feng, L., and Williams, M.: The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times, P. Natl. Acad. Sci. USA, 113, 1285–1290, https://doi.org/10.1073/pnas.1515160113, 2016.
Bonan, G. B., Lawrence, P. J., Oleson, K. W., Levis, S., Jung, M., Reichstein, M., Lawrence, D. M., Swenson, S. C., Bonan, C., Lawrence, P. J., Oleson, K. W., Levis, S., Jung, M., Reichstein, M., Lawrence, D. M., and Swenson, S. C.: Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data, J. Geophys. Res.-Biogeo., 116, G02014, https://doi.org/10.1029/2010JG001593, 2011.
Cao, X., Zhou, Z., Chen, X., Shao, W., and Wang, Z.: Improving leaf area
index simulation of IBIS model and its effect on water carbon and energy – A
case study in Changbai Mountain broadleaved forest of China, Ecol. Model.,
303, 97–104, https://doi.org/10.1016/j.ecolmodel.2015.02.012, 2015.
Chaney, N. W., Herman, J. D., Ek, M. B., and Wood, E. F.: Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning, J. Geophys. Res.-Atmos., 121, 13–218, https://doi.org/10.1002/2016JD024821, 2016.
Chen, J. M., Ju, W., Ciais, P., Viovy, N., Liu, R., Liu, Y., and Lu, X.: Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink, Nat. Commun., 10, 4259, https://doi.org/10.1038/s41467-019-12257-8, 2019.
Chuter, A. M., Aston, P. J., Skeldon, A. C., and Roulstone, I.: A dynamical systems analysis of the data assimilation linked ecosystem carbon (DALEC) models, Chaos, 25, 036401, https://doi.org/10.1063/1.4897912, 2015.
Croft, H., Chen, J. M., Luo, X., Bartlett, P., Chen, B., and Staebler, R.
M.: Leaf chlorophyll content as a proxy for leaf photosynthetic capacity,
Global Change Biol., 23, 3513–3524, https://doi.org/10.1111/gcb.13599, 2017.
Cunha, A. P. M. A., Alvalá, R. C. S., Sampaio, G., Shimizu, M. H., and
Costa, M. H.: Calibration and Validation of the Integrated Biosphere
Simulator (IBIS) for a Brazilian Semiarid Region, J. Appl. Meteorol.
Climatol., 52, 2753–2770, https://doi.org/10.1175/jamc-d-12-0190.1, 2013.
Dagon, K., Sanderson, B. M., Fisher, R. A., and Lawrence, D. M.: A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, 2020.
Famiglietti, C. A., Smallman, T. L., Levine, P. A., Flack-Prain, S., Quetin, G. R., Meyer, V., Parazoo, N. C., Stettz, S. G., Yang, Y., Bonal, D., Bloom, A. A., Williams, M., and Konings, A. G.: Optimal model complexity for terrestrial carbon cycle prediction, Biogeosciences, 18, 2727–2754, https://doi.org/10.5194/bg-18-2727-2021, 2021.
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of
photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149,
78–90, https://doi.org/10.1007/BF00386231, 1980.
Fer, I., Kelly, R., Moorcroft, P. R., Richardson, A. D., Cowdery, E. M., and Dietze, M. C.: Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation, Biogeosciences, 15, 5801–5830, https://doi.org/10.5194/bg-15-5801-2018, 2018.
Fernández-Martínez, M., Sardans, J., Chevallier, F., Ciais, P.,
Obersteiner, M., Vicca, S., Canadell, J. G., Bastos, A., Friedlingstein, P.,
Sitch, S., Piao, S. L., Janssens, I. A., and Peñuelas, J.: Global trends
in carbon sinks and their relationships with CO2 and temperature, Nat.
Clim. Change, 9, 73–79, https://doi.org/10.1038/s41558-018-0367-7, 2018.
Foley, J. A., Prentice, I. C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S., and Haxeltine, A.: An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics, Global Biogeochem. Cy., 10, 603–628, https://doi.org/10.1029/96GB02692, 1996.
Forkel, M., Carvalhais, N., Schaphoff, S., v. Bloh, W., Migliavacca, M., Thurner, M., and Thonicke, K.: Identifying environmental controls on vegetation greenness phenology through model–data integration, Biogeosciences, 11, 7025–7050, https://doi.org/10.5194/bg-11-7025-2014, 2014.
Gong, W. and Duan, Q. Y.: An adaptive surrogate modelingbased sampling strategy for parameter optimization and distribution estimation (ASMO-PODE), Environ. Model. Softw., 95, 61–75, https://doi.org/10.1016/j.envsoft.2017.05.005, 2017.
Homer, C., Dewitz, J., Jin, S., Xian, G., Costello, C., Danielson, P., Gass,
L., Funk, M., Wickham, J., Stehman, S., Auch, R., and Riitters, K.:
Conterminous United States land cover change patterns 2001–2016 from the
2016 National Land Cover Database, ISPRS J. Photogramm. Remote Sens., 162,
184–199, https://doi.org/10.1016/j.isprsjprs.2020.02.019, 2020.
Hu, Z., Chen, X., Zhou, Q., Chen, D., and Li, J.: DISO: A rethink of Taylor
diagram, Int. J. Climatol., 39, 2825–2832, https://doi.org/10.1002/joc.5972, 2019.
Keenan, T. F., Davidson, E., Moffat, A. M., Munger, W., and Richardson, A.
D.: Using model-data fusion to interpret past trends, and quantify
uncertainties in future projections, of terrestrial ecosystem carbon
cycling, Glob. Change Biol., 18, 2555–2569,
10.1111/j.1365-2486.2012.02684.x, 2012.
Kucharik, C. J., Foley, J. A., Delire, C., Fisher, V. A., Coe, M. T., Lenters, J. D., Young-Molling, C., Ramankutty, N., Norman, J. M., and Gower, S. T.: Testing the performance of a dynamic global ecosystem model: water balance, carbon balance, and vegetation structure, Global Biogeochem. Cy., 14, 795–825, https://doi.org/10.1029/1999GB001138, 2000.
Kucharik, C. J., Barford, C. C., Maayar, M. E., Wofsy, S. C., Monson, R. K.,
and Baldocchi, D. D.: A multiyear evaluation of a Dynamic Global Vegetation
Model at three AmeriFlux forest sites: Vegetation structure, phenology, soil
temperature, and CO2 and H2O vapor exchange, Ecol. Model., 196,
1–31, https://doi.org/10.1016/j.ecolmodel.2005.11.031, 2006.
Kumar, S. V., Holmes, T. R., Bindlish, R., de Jeu, R., and Peters-Lidard, C.: Assimilation of vegetation optical depth retrievals from passive microwave radiometry, Hydrol. Earth Syst. Sci., 24, 3431–3450, https://doi.org/10.5194/hess-24-3431-2020, 2020.
Kuppel, S., Peylin, P., Maignan, F., Chevallier, F., Kiely, G., Montagnani, L., and Cescatti, A.: Model–data fusion across ecosystems: from multisite optimizations to global simulations, Geosci. Model Dev., 7, 2581–2597, https://doi.org/10.5194/gmd-7-2581-2014, 2014.
LDAS: NLDAS-2 Forcing Dataset, Land Data Assimilation Systems [data set], https://ldas.gsfc.nasa.gov/nldas/NLDAS2forcing.php (last access: 9 January 2021), 2016.
Li, J., Duan, Q., Wang, Y.-P., Gong, W., Gan, Y., and Wang, C.: Parameter
optimization for carbon and water fluxes in two global land surface models
based on surrogate modelling, Int. J. Climatol., 38, e1016–e1031,
https://doi.org/10.1002/joc.5428, 2018.
Li, Y., Zhou, L., Wang, S., Chi, Y., and Chen, J.: Leaf Temperature and
Vapour Pressure Deficit (VPD) Driving Stomatal Conductance and Biochemical
Processes of Leaf Photosynthetic Rate in a Subtropical Evergreen Coniferous
Plantation, Sustainability, 10, 4063, https://doi.org/10.3390/su10114063, 2018.
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan,
W., Zhang, X., Zhao, X., and Zhou, J.: The Global Land Surface Satellite
(GLASS) Product Suite, B. Am. Meteorol. Soc. 102, E323–E337,
https://doi.org/10.1175/bams-d-18-0341.1, 2021.
Liu, D., Cai, W., Xia, J., Dong, W., Zhou, G., Chen, Y., Zhang, H., and
Yuan, W.: Global validation of a process-based model on vegetation gross
primary production using eddy covariance observations, PLoS One, 9, e110407,
https://doi.org/10.1371/journal.pone.0110407, 2014.
Liu, M., He, H., Ren, X., Sun, X., Yu, G., Han, S., Wang, H., and Zhou, G.:
The effects of constraining variables on parameter optimization in carbon
and water flux modeling over different forest ecosystems, Ecol. Model., 303,
30–41, https://doi.org/10.1016/j.ecolmodel.2015.01.027, 2015.
Liu, X., Liang, S., Li, B., Ma, H., and He, T.: Mapping 30 m Fractional Forest Cover over China’s Three-North Region from Landsat-8 Data Using Ensemble Machine Learning Methods, Remote Sens., 13, 2592, https://doi.org/10.3390/rs13132592, 2021.
Lu, D., Ricciuto, D., Walker, A., Safta, C., and Munger, W.: Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods, Biogeosciences, 14, 4295–4314, https://doi.org/10.5194/bg-14-4295-2017, 2017.
Ma, H. and Liang, S. L.: Development of the GLASS 250-m Leaf Area Index Product (Version 6) from MODIS data using the bidirectional LSTM deep learning model, Remote Sens. Environ., 273, 112985, https://doi.org/10.1016/j.rse.2022.112985, 2022.
Ma, R., Xiao, J., Liang, S., Ma, H., He, T., Guo, D., Liu, X., and Lu, H.: Modified adaptive surrogate modeling (MASM) (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.6953354, 2022.
MacBean, N., Peylin, P., Chevallier, F., Scholze, M., and Schürmann, G.: Consistent assimilation of multiple data streams in a carbon cycle data assimilation system, Geosci. Model Dev., 9, 3569–3588, https://doi.org/10.5194/gmd-9-3569-2016, 2016.
Mäkelä, J., Knauer, J., Aurela, M., Black, A., Heimann, M., Kobayashi, H., Lohila, A., Mammarella, I., Margolis, H., Markkanen, T., Susiluoto, J., Thum, T., Viskari, T., Zaehle, S., and Aalto, T.: Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH, Geosci. Model Dev., 12, 4075–4098, https://doi.org/10.5194/gmd-12-4075-2019, 2019.
Morris, M. D.: Factorial Sampling Plans for Preliminary Computational
Experiments, Technometrics, 33, 161–174, 1991.
NCEP: National Centers for Environmental Prediction (NCEP) North American
Regional Reanalysis (NARR), in: Research Data Archive at the
National Center for Atmospheric Research, Computational and Information
Systems Laboratory, Boulder, CO [data set],
https://psl.noaa.gov/data/gridded/data.narr.html (last access: 19 January 2021), 2005.
Owen, A. B.: Controlling Correlations in Latin Hypercube Samples, J. Am. Stat. Assoc., 89, 1517–1522,
https://doi.org/10.1080/01621459.1994.10476891, 1994.
Pastorello, G., Trotta, C., and Canfora, E., et al.: The FLUXNET2015 dataset
and the ONEFlux processing pipeline for eddy covariance data, Sci. Data, 7,
225, https://doi.org/10.1038/s41597-020-0534-3, 2020.
Peaucelle, M., Bacour, C., Ciais, P., Vuichard, N., Kuppel, S., Peñuelas, J., Marchesini, L. B., Blanken, P. D., Buchmann, N., Chen, J., Delpierre, N., Desai, A. R., Dufrene, E., Gianelle, D., Gimeno-Colera, C., Gruening, C., Helfter, C., Hörtnagl, L., Ibrom, A., Joffre, R., Kato, T., Kolb, T. E., Law, B., Lindroth, A., Mammarella, I., Merbold, L., Minerbi, S., Montagnani, L., Šigut, L., Sutton, M., Varlagin, A., Vesala, T., Wohlfahrt, G., Wolf, S., Yakir, D., and Viovy, N.: Covariations between plant functional traits emerge from constraining parameterization of a terrestrial biosphere model, Glob. Ecol. Biogeogr., 28, 1351–1365, https://doi.org/10.1111/geb.12937, 2019.
Piao, S., Wang, X., Wang, K., Li, X., Bastos, A., Canadell, J. G., Ciais,
P., Friedlingstein, P., and Sitch, S.: Interannual variation of terrestrial
carbon cycle: Issues and perspectives, Global Change Biol., 26, 300–318,
https://doi.org/10.1111/gcb.14884, 2020.
Pilaš, I., Gašparović, M., Novkinić, A., and Klobučar,
D.: Mapping of the Canopy Openings in Mixed Beech–Fir Forest at Sentinel-2
Subpixel Level Using UAV and Machine Learning Approach, Remote Sens., 12,
3925, https://doi.org/10.3390/rs12233925, 2020.
Raoult, N. M., Jupp, T. E., Cox, P. M., and Luke, C. M.: Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0, Geosci. Model Dev., 9, 2833–2852, https://doi.org/10.5194/gmd-9-2833-2016, 2016.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019.
Rogers, A.: The use and misuse of Vc,max in Earth System Models, Photosyn.
Res., 119, 15–29, https://doi.org/10.1007/s11120-013-9818-1, 2014.
Gelman, A. and Rubin, D. B.: Inference from Iterative Simulation Using Multiple Sequences, Stat. Sci., 7, 457–472, https://doi.org/10.1214/ss/1177011136, 1992.
Ryu, Y., Jiang, C., Kobayashi, H., and Detto, M.: MODIS-derived global land
products of shortwave radiation and diffuse and total photosynthetically
active radiation at 5 km resolution from 2000, Remote Sens. Environ., 204,
812–825, https://doi.org/10.1016/j.rse.2017.09.021, 2018.
Safta, C., Ricciuto, D. M., Sargsyan, K., Debusschere, B., Najm, H. N., Williams, M., and Thornton, P. E.: Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model, Geosci. Model Dev., 8, 1899–1918, https://doi.org/10.5194/gmd-8-1899-2015, 2015.
Sawada, Y. and Koike, T.: Simultaneous estimation of both hydrological and ecological parameters in an ecohydrological model by assimilating microwave signal, J. Geophys. Res.-Atmos., 119, 8839–8857, https://doi.org/10.1002/2014JD021536, 2014.
Scholze, M., Kaminski, T., Knorr, W., Blessing, S., Vossbeck, M., Grant, J.
P., and Scipal, K.: Simultaneous assimilation of SMOS soil moisture and
atmospheric CO2 in-situ observations to constrain the global
terrestrial carbon cycle, Remote Sens. Environ., 180, 334–345,
https://doi.org/10.1016/j.rse.2016.02.058, 2016.
Schürmann, G. J., Kaminski, T., Köstler, C., Carvalhais, N., Voßbeck, M., Kattge, J., Giering, R., Rödenbeck, C., Heimann, M., and Zaehle, S.: Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0, Geosci. Model Dev., 9, 2999–3026, https://doi.org/10.5194/gmd-9-2999-2016, 2016.
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: A global soil data
set for earth system modeling, J. Adv. Model. Earth Syst., 6, 249–263,
https://doi.org/10.1002/2013ms000293, 2014.
Tao, F., Zhou, Z., Huang, Y., Li, Q., Lu, X., Ma, S., Huang, X., Liang, Y., Hugelius, G., Jiang, L., Doughty, R., Ren, Z., and Luo, Y.: Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States, Front. Big Data, 3, 17, https://doi.org/10.3389/fdata.2020.00017, 2020.
Thoning, K. W., Tans, P. P., and Komhyr, W. D.: Atmospheric carbon dioxide
at Mauna Loa Observatory: 2. Analysis of the NOAA GMCC data, 1974–1985, J.
Geophys. Res.-Atmospheres, 94, 8549–8565, 1989.
Thornton, M. M., Shrestha, R., Thornton, P. E., Kao, S., Wei, Y., and
Wilson, B. E.: Wilson: Daymet Version 4 Monthly Latency: Daily Surface
Weather Data. ORNL DAAC, Oak Ridge, Tennessee, USA [dataset],
https://doi.org/10.3334/ORNLDAAC/1904, 2021.
Varejão, C. G., Costa, M. H., and Camargos, C. C. S.: A multi-objective
hierarchical calibration procedure for land surface/ecosystem models,
Inverse Probl. Sci. Eng., 21, 357–386, https://doi.org/10.1080/17415977.2011.639453, 2013.
Walker, A. P., Beckerman, A. P., Gu, L., Kattge, J., Cernusak, L. A.,
Domingues, T. F., Scales, J. C., Wohlfahrt, G., Wullschleger, S. D., and
Woodward, F. I.: The relationship of leaf photosynthetic traits – Vcmax and Jmax – to leaf nitrogen, leaf phosphorus, and specific leaf area: a
meta-analysis and modeling study, Ecol. Evol., 4, 3218–3235,
https://doi.org/10.1002/ece3.1173, 2014.
Wang, C., Duan, Q., Gong, W., Ye, A., Di, Z., and Miao, C.: An evaluation of adaptive surrogate modeling based optimization with two benchmark problems, Environ. Modell. Softw., 60, 167–179, https://doi.org/10.1016/j.envsoft.2014.05.026, 2014.
Wang, J., Sun, J., Xia, J., He, N., Li, M., Niu, S., and Luo, Y.: Soil and
vegetation carbon turnover times from tropical to boreal forests, Funct.
Ecol., 32, 71–82, https://doi.org/10.1111/1365-2435.12914, 2017.
Wutzler, T. and Carvalhais, N.: Balancing multiple constraints in model-data
integration: Weights and the parameter block approach, J. Geophys. Res.-Biogeo., 119, 2112–2129, https://doi.org/10.1002/2014jg002650, 2014.
Xiao, J., Davis, K. J., Urban, N. M., and Keller, K.: Uncertainty in model
parameters and regional carbon fluxes: A model-data fusion approach, Agr.
Forest Meteorol., 189–190, 175–186, https://doi.org/10.1016/j.agrformet.2014.01.022, 2014.
Xu, H., Zhang, T., Luo, Y., Huang, X., and Xue, W.: Parameter calibration in global soil carbon models using surrogate-based optimization, Geosci. Model Dev., 11, 3027–3044, https://doi.org/10.5194/gmd-11-3027-2018, 2018.
Yuan, W., Liang, S., Liu, S., Weng, E., Luo, Y., Hollinger, D., and Zhang,
H.: Improving model parameter estimation using coupling relationships
between vegetation production and ecosystem respiration, Ecol. Model., 240,
29–40, https://doi.org/10.1016/j.ecolmodel.2012.04.027, 2012.
Yuan, W., Liu, D., Dong, W., Liu, S., Zhou, G., Yu, G., Zhao, T., Feng, J.,
Ma, Z., Chen, J., Chen, Y., Chen, S., Han, S., Huang, J., Li, L., Liu, H.,
Liu, S., Ma, M., Wang, Y., Xia, J., Xu, W., Zhang, Q., Zhao, X., and Zhao,
L.: Multiyear precipitation reduction strongly decreases carbon uptake over
northern China, J. Geophys. Res.-Biogeo., 119, 881–896,
https://doi.org/10.1002/2014jg002608, 2014.
Zhang, Q. R., Shi, L. S., Holzman, M., Ye, M., Wang, Y. K., Carmona, F., and Zha, Y. Y.: A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation, Adv. Water Resour., 132, 103407, https://doi.org/10.1016/j.advwatres.2019.103407, 2019.
Zheng, Y., Shen, R., Wang, Y., Li, X., Liu, S., Liang, S., Chen, J. M., Ju, W., Zhang, L., and Yuan, W.: Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017, Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, 2020.
Zhong, L., Hu, L., and Zhou, H.: Deep learning based multi-temporal crop
classification, Remote Sens. Environ., 221, 430–443,
https://doi.org/10.1016/j.rse.2018.11.032, 2019.
Zhou, Q., Chen, D., Hu, Z., and Chen, X.: Decompositions of Taylor diagram
and DISO performance criteria, Int. J. Climatol., 41, 5726–5732,
https://doi.org/10.1002/joc.7149, 2021.
Zhou, Y., Williams, C. A., Lauvaux, T., Davis, K. J., Feng, S., Baker, I., Denning, S., and Wei, Y.: A multiyear gridded data ensemble of surface biogenic carbon fluxes for North America: evaluation and analysis of results, J. Geophys. Res.-Biogeo., 125, e2019JG005314, https://doi.org/10.1029/2019jg005314, 2020.
Zobitz, J. M., Moore, D. J. P., Quaife, T., Braswell, B. H., Bergeson, A.,
Anthony, J. A., and Monson, R. K.: Joint data assimilation of satellite
reflectance and net ecosystem exchange data constrains ecosystem carbon
fluxes at a high-elevation subalpine forest, Agr. Forest Meteorol., 195–196,
73–88, https://doi.org/10.1016/j.agrformet.2014.04.011, 2014.
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.
Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted...
