Articles | Volume 12, issue 7
https://doi.org/10.5194/gmd-12-3119-2019
© Author(s) 2019. 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-12-3119-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Jul 2019
Development and technical paper |
| 22 Jul 2019
| 22 Jul 2019
Comparison of different sequential assimilation algorithms for satellite-derived leaf area index using the Data Assimilation Research Testbed (version Lanai)
Xiao-Lu Ling
Institute for Climate and Global Change Research and School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences of Ministry of Education, Nanjing 210023, China
Department of Geological Sciences, John A. and Katherine G. Jackson School of Geosciences, University of Texas at Austin, Austin, TX 78705, USA
Cong-Bin Fu
CORRESPONDING AUTHOR
Institute for Climate and Global Change Research and School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences of Ministry of Education, Nanjing 210023, China
Department of Geological Sciences, John A. and Katherine G. Jackson School of Geosciences, University of Texas at Austin, Austin, TX 78705, USA
Wei-Dong Guo
Institute for Climate and Global Change Research and School of
Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Joint International Research Laboratory of Atmospheric and Earth
System Sciences of Ministry of Education, Nanjing 210023, China
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Jinbo Wang, Jiaping Wang, Yuxuan Zhang, Tengyu Liu, Xuguang Chi, Xin Huang, Dafeng Ge, Shiyi Lai, Caijun Zhu, Lei Wang, Qiaozhi Zha, Ximeng Qi, Wei Nie, Congbin Fu, and Aijun Ding
EGUsphere, https://doi.org/10.5194/egusphere-2024-879, https://doi.org/10.5194/egusphere-2024-879, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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In this study, we found large spatial discrepancies in the physical and chemical properties of black carbon over the Tibetan Plateau (TP). Elevated anthropogenic emissions from low-altitude regions can significantly change the mass concentration, mixing state and chemical composition of black carbon -containing aerosol in TP region, further altering its light absorption ability. Our study emphasizes the vulnerability of remote plateau regions to intense anthropogenic influences.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
EGUsphere, https://doi.org/10.5194/egusphere-2023-2634, https://doi.org/10.5194/egusphere-2023-2634, 2023
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A process-based plant Carbon (C)-Nitrogen (N) interface coupling framework has been developed, which mainly focuses on the plant resistance and N limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C/N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem-biogeochemical model and testing results show a general improvement in simulating plant properties with this framework.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
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Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Hui Zheng, Wenli Fei, Zong-Liang Yang, Jiangfeng Wei, Long Zhao, Lingcheng Li, and Shu Wang
Earth Syst. Sci. Data, 15, 2755–2780, https://doi.org/10.5194/essd-15-2755-2023, https://doi.org/10.5194/essd-15-2755-2023, 2023
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An ensemble of evapotranspiration, runoff, and water storage is estimated here using the Noah-MP land surface model by perturbing model parameterization schemes. The data could be beneficial for monitoring and understanding the variability of water resources. Model developers could also gain insights by intercomparing the ensemble members.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
EGUsphere, https://doi.org/10.5194/egusphere-2022-1111, https://doi.org/10.5194/egusphere-2022-1111, 2022
Preprint archived
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A process-based plant Carbon (C)-Nitrogen (N) interface coupling framework has been developed, which mainly focuses on the plant resistance and N limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem-biogeochemical model and testing results show a general improvement in simulating plant properties with this framework.
Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022, https://doi.org/10.5194/hess-26-5163-2022, 2022
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High-resolution river modeling is of great interest to local governments and stakeholders for flood-hazard mitigation. This work presents a physics-guided, machine learning (ML) framework for combining the strengths of high-resolution process-based river network models with a graph-based ML model capable of modeling spatiotemporal processes. Results show that the ML model can approximate the dynamics of the process model with high fidelity, and data fusion further improves the forecasting skill.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Weidong Guo, Sanaa Hobeichi, and Peter R. Briggs
Earth Syst. Dynam., 12, 919–938, https://doi.org/10.5194/esd-12-919-2021, https://doi.org/10.5194/esd-12-919-2021, 2021
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Groundwater can buffer the impacts of drought and heatwaves on ecosystems, which is often neglected in model studies. Using a land surface model with groundwater, we explained how groundwater sustains transpiration and eases heat pressure on plants in heatwaves during multi-year droughts. Our results showed the groundwater’s influences diminish as drought extends and are regulated by plant physiology. We suggest neglecting groundwater in models may overstate projected future heatwave intensity.
Xiaolu Ling, Ying Huang, Weidong Guo, Yixin Wang, Chaorong Chen, Bo Qiu, Jun Ge, Kai Qin, Yong Xue, and Jian Peng
Hydrol. Earth Syst. Sci., 25, 4209–4229, https://doi.org/10.5194/hess-25-4209-2021, https://doi.org/10.5194/hess-25-4209-2021, 2021
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Soil moisture (SM) plays a critical role in the water and energy cycles of the Earth system, for which a long-term SM product with high quality is urgently needed. In situ observations are generally treated as the true value to systematically evaluate five SM products, including one remote sensing product and four reanalysis data sets during 1981–2013. This long-term intercomparison study provides clues for SM product enhancement and further hydrological applications.
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
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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.
Meng-Zhuo Zhang, Zhongfeng Xu, Ying Han, and Weidong Guo
Geosci. Model Dev., 14, 3079–3094, https://doi.org/10.5194/gmd-14-3079-2021, https://doi.org/10.5194/gmd-14-3079-2021, 2021
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The Multivariable Integrated Evaluation Tool (MVIETool) is a simple-to-use and straightforward tool designed for evaluation and intercomparison of climate models in terms of vector fields or multiple fields. The tool incorporates some new improvements in vector field evaluation (VFE) and multivariable integrated evaluation (MVIE) methods, which are introduced in this paper.
Wenkai Li, Shuzhen Hu, Pang-Chi Hsu, Weidong Guo, and Jiangfeng Wei
The Cryosphere, 14, 3565–3579, https://doi.org/10.5194/tc-14-3565-2020, https://doi.org/10.5194/tc-14-3565-2020, 2020
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Understanding the forecasting skills of the subseasonal-to-seasonal (S2S) model on Tibetan Plateau snow cover (TPSC) is the first step to applying the S2S model to hydrological forecasts over the Tibetan Plateau. This study conducted a multimodel comparison of the TPSC prediction skill to learn about their performance in capturing TPSC variability. S2S models can skillfully forecast TPSC within a lead time of 2 weeks but show limited skill beyond 3 weeks. Systematic biases of TPSC were found.
Jun Ge, Andrew J. Pitman, Weidong Guo, Beilei Zan, and Congbin Fu
Hydrol. Earth Syst. Sci., 24, 515–533, https://doi.org/10.5194/hess-24-515-2020, https://doi.org/10.5194/hess-24-515-2020, 2020
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We investigate the impact of revegetation on the hydrology of the Loess Plateau based on high-resolution simulations using the Weather Research and Forecasting (WRF) model. We find that past revegetation has caused decreased runoff and soil moisture with increased evapotranspiration as well as little response from rainfall. WRF suggests that further revegetation could aggravate this water imbalance. We caution that further revegetation might be unsustainable in this region.
Aijun Ding, Xin Huang, Wei Nie, Xuguang Chi, Zheng Xu, Longfei Zheng, Zhengning Xu, Yuning Xie, Ximeng Qi, Yicheng Shen, Peng Sun, Jiaping Wang, Lei Wang, Jianning Sun, Xiu-Qun Yang, Wei Qin, Xiangzhi Zhang, Wei Cheng, Weijing Liu, Liangbao Pan, and Congbin Fu
Atmos. Chem. Phys., 19, 11791–11801, https://doi.org/10.5194/acp-19-11791-2019, https://doi.org/10.5194/acp-19-11791-2019, 2019
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Based on continuous measurement at the SORPES statin in Nanjing, eastern China, we report the trend of PM2.5 and relevant chemical species there during 2011–2018. We found significant reduction of PM2.5 in both winter and early summer due to emission reduction of fossil-fuel combustion and open biomass burning, respectively. Reduction of fossil-fuel combustions contributed to 76 % of the wintertime PM2.5 decrease, with the remaining 24 % being caused by the change of meteorology.
Qiuji Ding, Jianning Sun, Xin Huang, Aijun Ding, Jun Zou, Xiuqun Yang, and Congbin Fu
Atmos. Chem. Phys., 19, 7759–7774, https://doi.org/10.5194/acp-19-7759-2019, https://doi.org/10.5194/acp-19-7759-2019, 2019
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Aerosol plays an important role in advection–radiation fog formation in eastern China though stabilizing atmospheric stratification and enhancing onshore flow. For the fog–haze episode in December 2013, the effect of aerosol–radiation interaction overwhelmed that of aerosol–cloud interaction. Light-absorbing aerosol like black carbon was more crucial than scattering aerosols. This paper highlights the importance of interaction among aerosol, regional circulation and boundary layer.
Derong Zhou, Ke Ding, Xin Huang, Lixia Liu, Qiang Liu, Zhengning Xu, Fei Jiang, Congbin Fu, and Aijun Ding
Atmos. Chem. Phys., 18, 16345–16361, https://doi.org/10.5194/acp-18-16345-2018, https://doi.org/10.5194/acp-18-16345-2018, 2018
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We investigate the vertical distribution, transport characteristics, source contribution and meteorological feedback of dust, biomass burning and fossil fuel combustion aerosols for a unique pollution episode that occurred in late March 2015 in eastern Asia, based on various measurement data and modeling methods. We found that cold front played an important role in the long-range transport of different pollutants and caused a three-layer vertical structure of pollutants over eastern China.
Yicheng Shen, Aki Virkkula, Aijun Ding, Jiaping Wang, Xuguang Chi, Wei Nie, Ximeng Qi, Xin Huang, Qiang Liu, Longfei Zheng, Zheng Xu, Tuukka Petäjä, Pasi P. Aalto, Congbin Fu, and Markku Kulmala
Atmos. Chem. Phys., 18, 5265–5292, https://doi.org/10.5194/acp-18-5265-2018, https://doi.org/10.5194/acp-18-5265-2018, 2018
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Aerosol optical properties (AOPs) were measured at SORPES, a regional background station in Nanjing, China from June 2013 to May 2015. The aerosol was highly scattering. The single-scattering albedo in Nanjing appears to be slightly higher than at several other sites. The data do not suggest any significant contribution to absorption by brown carbon. The sources of high values are mainly in eastern China. During pollution episodes, pollutant concentrations increased gradually but decreased fast.
Zhongfeng Xu, Ying Han, and Congbin Fu
Geosci. Model Dev., 10, 3805–3820, https://doi.org/10.5194/gmd-10-3805-2017, https://doi.org/10.5194/gmd-10-3805-2017, 2017
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The paper develops a multivariable integrated evaluation (MVIE) method for evaluating the overall performance of a climate model in simulating multiple fields. MVIE takes multiple statistics of multiple variables into account and is expected to provide a more accurate and comprehensive evaluation of model performance. Moreover, a multivariable integrated evaluation index (MIEI) is also developed to concisely summarize model performance and facilitate multi-model intercomparison and ranking.
Xueqian Wang, Weidong Guo, Bo Qiu, Ye Liu, Jianning Sun, and Aijun Ding
Atmos. Chem. Phys., 17, 4989–4996, https://doi.org/10.5194/acp-17-4989-2017, https://doi.org/10.5194/acp-17-4989-2017, 2017
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Land use or cover change is a fundamental anthropogenic forcing for climate change. Based on field observations, we quantified the contributions of different factors to surface temperature change and deepened the understanding of its mechanisms. We found evaporative cooling plays the most important role in the temperature change, while radiative forcing, which is traditionally emphasized, is not significant. This study provided firsthand evidence to verify the model results in IPCC AR5.
Bingliang Zhuang, Tijian Wang, Jane Liu, Shu Li, Min Xie, Yong Han, Pulong Chen, Qiduo Hu, Xiu-qun Yang, Congbin Fu, and Jialei Zhu
Atmos. Chem. Phys., 17, 1143–1160, https://doi.org/10.5194/acp-17-1143-2017, https://doi.org/10.5194/acp-17-1143-2017, 2017
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The observed near-surface aerosol optical properties in urban Nanjing are analysed from March 2014 to February 2016. Substantial analysis in the key optical properties of the surface aerosol fill the gaps in the study on aerosols in Nanjing, even in the Yangtze River Delta (YRD). Relationships between the aerosol extinction coefficient (single scattering albedo) and atmospheric visibility are also carried out in different seasons to figure out the effect of aerosol on the visibility in Nanjing, YRD.
Zhongfeng Xu, Zhaolu Hou, Ying Han, and Weidong Guo
Geosci. Model Dev., 9, 4365–4380, https://doi.org/10.5194/gmd-9-4365-2016, https://doi.org/10.5194/gmd-9-4365-2016, 2016
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This paper devises a new diagram called the vector field evaluation (VFE) diagram. The VFE diagram is a generalized Taylor diagram and is able to provide a concise evaluation of model performance in simulating vector fields (e.g., vector winds) in terms of three statistical variables. The VFE diagram can be applied to the evaluation of full vector fields or anomaly fields as needed. Some potential applications of the VFE diagram in model evaluation are also presented in the paper.
Xin Huang, Aijun Ding, Lixia Liu, Qiang Liu, Ke Ding, Xiaorui Niu, Wei Nie, Zheng Xu, Xuguang Chi, Minghuai Wang, Jianning Sun, Weidong Guo, and Congbin Fu
Atmos. Chem. Phys., 16, 10063–10082, https://doi.org/10.5194/acp-16-10063-2016, https://doi.org/10.5194/acp-16-10063-2016, 2016
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We conducted a comprehensive modelling work to understand the impact of biomass burning on synoptic weather during agricultural burning season in East China. We demonstrated that the numerical model with fire emission, chemical processes, and aerosol–meteorology online coupled could reproduce the change of air temperature and precipitation induced by air pollution during this event. This study highlights the importance of including human activities in numerical-model-based weather forecast.
Weidong Guo, Xueqian Wang, Jianning Sun, Aijun Ding, and Jun Zou
Atmos. Chem. Phys., 16, 9875–9890, https://doi.org/10.5194/acp-16-9875-2016, https://doi.org/10.5194/acp-16-9875-2016, 2016
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Basic characteristics of land–atmosphere interactions at four neighboring sites with different underlying surfaces in southern China, a typical monsoon region, are analyzed systematically. Despite the same climate background, the differences in land surface characteristics like albedo and aerodynamic roughness length due to land use/cover change exert distinct influences on the surface radiative budget and energy allocation and result in differences of near-surface micrometeorological elements.
X. Cai, Z.-L. Yang, J. B. Fisher, X. Zhang, M. Barlage, and F. Chen
Geosci. Model Dev., 9, 1–15, https://doi.org/10.5194/gmd-9-1-2016, https://doi.org/10.5194/gmd-9-1-2016, 2016
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A terrestrial nitrogen dynamics model is integrated into Noah-MP. The new model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). The addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. This improvement advances the capability of Noah-MP to simultaneously predict weather and water quality.
Q. Jin, J. Wei, Z.-L. Yang, B. Pu, and J. Huang
Atmos. Chem. Phys., 15, 9897–9915, https://doi.org/10.5194/acp-15-9897-2015, https://doi.org/10.5194/acp-15-9897-2015, 2015
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Satellite data show that Indian summer monsoon (ISM) rainfall is closely associated with Middle East dust aerosols. Numerical modeling shows that the increased ISM rainfall is related to the enhanced southwesterly flow and moisture transport from the Arabian Sea to the Indian subcontinent, associated with the development of an anomalous low-pressure system over the Iranian Plateau and the Arabian Sea due to dust-induced atmospheric heating.
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Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
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Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
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Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
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Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
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This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
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Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
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Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
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This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
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The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
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Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
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Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
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This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
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This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
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Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
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Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
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Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Baiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
EGUsphere, https://doi.org/10.5194/egusphere-2023-1733, https://doi.org/10.5194/egusphere-2023-1733, 2023
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For the first time, we coupled a regional climate chemistry model RegCM-Chem with a dynamic vegetation model YIBs to create a regional climate-chemistry-ecology model RegCM-Chem-YIBs. We applied it to simulate climatic, chemical and ecological parameters in East Asia and fully validated it on a variety of observational data. The research results show that RegCM-Chem-YIBs model is a valuable tool for studying terrestrial carbon cycle, atmospheric chemistry, and climate change in regional scale.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
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We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
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This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
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We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-223, https://doi.org/10.5194/gmd-2023-223, 2023
Revised manuscript accepted for GMD
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion by either uniform erosion processes where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea level history, material properties, and the relative influence of different erosional processes.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
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In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Cited articles
Albergel, C., Munier, S., Leroux, D. J., Dewaele, H., Fairbairn, D., Barbu,
A. L., Gelati, E., Dorigo, W., Faroux, S., Meurey, C., Moigne, P. L.,
Decharme, B., Mahfouf, J. F., and Calvet, J. C.: Sequential assimilation of
satellite-derived vegetation and soil moisture products using
SURFEX_v8.0: LDAS-Monde assessment over the
Euro-Mediterranean area, Geosci. Model Develop., 10, 3889–3912,
https://doi.org/10.5194/gmd-10-3889-2017, 2017.
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation,
Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001.
Anderson, J. L.: A local least squares framework for ensemble filtering,
Mon. Weather Rev., 131, 634–642, https://doi.org/10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2, 2003.
Anderson, J. L.: An adaptive covariance inflation error correction algorithm
for ensemble filters, Tellus, 59, 210–224,
https://doi.org/10.1111/j.1600-0870.2006.00216.x, 2007.
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev, 127, 2741–2758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2, 1999.
Anderson, J. L., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and
Arellano, A.: The data assimilation research testbed: A community facility,
B. Am. Meteorol. Soc., 90, 1283–1296,
https://doi.org/10.1175/2009BAMS2618.1, 2009.
Bannister, R. N.: A review of operational methods of variational and
ensemble variational data assimilation, Q. J. Roy. Meteorol. Soc., 143,
607–633, https://doi.org/10.1002/qj.2982, 2017.
Barbu, A. L., Calvet, J.-C., Mahfouf, J.-F., Albergel, C., and Lafont, S.: Assimilation of Soil Wetness Index and Leaf Area Index into the ISBA-A-gs land surface model: grassland case study, Biogeosciences, 8, 1971–1986, https://doi.org/10.5194/bg-8-1971-2011, 2011.
Bonan, G. B.: Land atmospheric interactions for climate system models:
Coupling biophysical, biogeochemical and ecosystem dynamical processes,
Remote Sens. Environ., 51, 57–73, https://doi.org/10.1016/0034-4257(94)00065-U, 1995.
Bonan, G. B., Pollard, D., and Thompson, S. L.: Effects of boreal forest
vegetation on global climate, Nature, 359, 716–718,
https://doi.org/10.1038/359716a0, 1992.
Burgers, G., van Leeuwen, P. J., and Evensen, G.: Analysis scheme in the
ensemble Kalman filter, Mon. Weather Rev., 126, 1719–1724,
https://doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2, 1998.
Carrera, M. L., Belair, S., and Bilodeau, B.: The Canadian Land Data
Assimilation System (CaLDAS): Description and Synthetic Evaluation Study, J.
Hydrometeorol., 16, 1293–1314, https://doi.org/10.1175/JHM-D-14-0089.1, 2015.
Dai, Y. J., Zeng, X. B., and Dickinson, R. E.: The common land model (CLM),
B. Am. Meteor. Soc., 84, 1013–1023,
https://doi.org/10.1175/BAMS-84-8-1013, 2003.
Danabasoglu, G., Bates, S., Briegleb, B. P., Jayne, S. R., Jochum, M.,
Large, W. G., Peacock, S., and Yeager, S. G.: The CCSM4 Ocean Component, J.
Climate, 25, 1361–1389, https://doi.org/10.1175/JCLI-D-11-00091.1, 2012.
CESM: CESM Models and Supported Releases, available at: http://www.cesm.ucar.edu/index.html, last access: 18 June 2019.
DART: DART Classic Documentation, available at: https://www.image.ucar.edu/DAReS/DART_classic/DART_Overview.html#explanation, last access: 1 July 2019.
Dimet F. X. L. and Talagrand, O.: Variational algorithms for analysis and
assimilation of meteorological observations: theoretical aspects, Tellus,
38A, 97–110, https://doi.org/10.3402/tellusa.v38i2.11706, 1986.
Doucet, A., Godsill, S., and Andrieu, C.: On sequential Monte Carlo sampling
methods for Bayesian filtering, Stat. Comput., 10, 197–208,
https://doi.org/10.1023/A:1008935410038, 2000.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using monte-carlo methods to forecast error statistics, J. Geophys.
Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94jc00572, 1994.
Evensen, G.: The Ensemble Kalman Filter: Theoretical Formulation and
Practical Implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003.
Evensen, G.: Data Assimilation, the Ensemble Kalman Filter, Springer, p. 279, 2007.
Fertig, E. J., Harlim, J., and Hunt, B. R.: A comparative study of 4D-VAR
and a 4D ensemble filter: Perfect model simulations with Lorenz-96, Tellus,
59A, 96–100, https://doi.org/10.1111/j.1600-0870.2006.00205.x, 2007.
Fox, A. M., Hoar, T. J., Anderson, J. L., Arellano, A. F., Smith, W. K.,
Litvak, M. E., MacBean, N., Schimel, D. S., and Moore, D. J. P.: Evaluation of a data assimilation system for land
surface models using CLM4.5, J. Adv. Model. Earth Syst.,
10, 2471–2494, https://doi.org/10.1007/BF02900602, 2018.
Gordon, N. J., Salmond, D. J., and Smith, A. F.: Novel approach to
nonlinear/non-Gaussian Bayesian state estimation, IEE Proc., 140, 107–113,
https://doi.org/10.1049/ip-f-2.1993.0015, 1993.
Han, X. J. and Li, X.: An evaluation of the nonlinear/non-Gaussian filters
for the sequential data assimilation, Remote Sens. Environ., 112,
1434–1449, https://doi.org/10.1016/j.rse.2007.07.008, 2008.
Houtekamer, P. L. and Mitchell, H.: Data assimilation using an ensemble
Kalman filter technique, Mon. Weather Rev., 126, 796–811,
https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2, 1998.
Houtekamer, P. L., Mitchell, H. L., Pellerin, G., Buehner, M., Charron, M.,
Spacek, L., and Hansen, B.: Atmospheric data assimilation with an ensemble
Kalman filter: Results with real observations, Mon. Weather Rev., 133, 604–620,
https://doi.org/10.1175/MWR-2864.1, 2005.
Hu, S. J., Qiu, C. Y., Zhang, L. Y., Huang, Q. C., Yu, H. P., and Chou, J.
F.: An approach to estimating and extrapolating model error based on inverse
problem methods: towards accurate numerical weather prediction, Chin. Phys.
B, 23, 089201, https://doi.org/10.1088/1674-1056/23/8/089201, 2014.
Huang, C. L. and Li, X.: A Review of Land Data Assimilation System, Remote
Sens. Techn. Appl., 19, 424–024, https://doi.org/10.1016/j.csr.2003.12.006,
2004.
Hunt, B. R., Kalnay, E., Kostelich, E. J., Ott, E., Patil, D. J., and Sauer,
T.: Four-dimensional ensemble Kalman filtering, Tellus, 56A, 273–277,
https://doi.org/10.1111/j.1600-0870.2004.00066.x, 2004.
Ines, A. V. M., Das, N. N., Hansen, J. P., and Njoku, E. G.: Assimilation of
remotely sensed soil moisture and vegetation with a crop simulation model
for maize yield prediction, Remote Sens. Environ., 138, 149–164,
https://doi.org/10.1016/j.rse.2013.07.018, 2013.
Jacobs, C. M. J., Moors, E. J., Maat, H. W. Ter., Teuling, A. J., Balsamo,
G., Bergaoui, K., Ettema, J., Lange, M., Hurk, B. J. J. M. Van Den, Viterbo,
P., and Wergen, W.: Evaluation of European Land Data Assimilation System
(ELDAS) products using in situ observations, Tellus, 60A, 1023–1037,
https://doi.org/10.1111/j.1600-0870.2008.00351.x, 2008.
Jin, X., Kumar, L., Li, Z., Xu, X., Yang, G., and Wang, J.: A review of data
assimilation of remote sensing and crop models, Eur. J. Agron., 92, 141–152,
https://doi.org/10.1016/j.eja.2017.11.002, 2018.
Kalman, R. E.: A new approach to linear filtering and prediction problems.
Trans. ASME J. Basic Eng., 82, 35–45, https://doi.org/10.1115/1.3662552,
1960.
Kalnay, E.: Amospheric Modeling, Data Assimilation and Predictability,
Cambridge University Press, p. 512, 2003.
Kalnay, E., Li, H., Miyoshi, T., Yang, S. -C., and Ballabrera-Poy, J.:
4-D-Var or ensemble Kalman filter?, Tellus, 59A, 758–773,
https://doi.org/10.1111/j.1600-0870.2007.00261.x, 2007.
Knyazikhin, Y., Martonchik, J. V., Myneni, R. B., Diner, D. J., and Running,
S. W.: Synergistic algorithm for estimating vegetation canopy leaf area
index and fraction of absorbed photosynthetically active radiation from
MODIS and MISR data, J. Geophys. Res., 103, 32257–32275,
https://doi.org/10.1029/98jd02462, 1998.
Kwon, Y., Yang, Z. L., Zhao, L., Hoar, T. J., Toure, A. M., and Rodell, M.:
Estimating snow water storage in North America using CLM4, DART, and Snow
Radiance Data Assimilation, J. Hydrometeorol., 17, 2853–2874,
https://doi.org/10.1175/JHM-D-16-0028.1, 2016.
Lahoz, W. A. and De Lannoy, G. J. M.: Closing the Gaps in Our Knowledge of
the Hydrological Cycle over Land: Conceptual Problems, Surv. Geophys., 35,
623–660, https://doi.org/10.1007/s10712-013-9221-7, 2014.
Lawrence, P. J. and Chase, T. N.: Representing a new MODIS consistent land
surface in the Community Land Model (CLM 3.0), J. Geophys. Res., 112,
G01023, https://doi.org/10.1029/2006JG000168, 2007.
Leng, H. Z. and Song, J. Q.: Hybrid three-dimensional variation and
particle filtering for nonlinear systems, Chin. Phys. B, 22, 030505,
https://doi.org/10.1088/1674-1056/22/3/030505, 2013.
Levis, S., Bonan, G. B., Vertenstein, M., and Oleson, K. W.: The Community
Land Model's Dynamic Global Vegetation Model (CLM-DGVM): Technical
Description and User's Guide, Boulder, Colorado: National Center for
Atmospheric Research, NCAR/TN-459+IA, 2004.
Li, X. J., Xiao, Z. Q., Wang, J. D., Qu, Y., and Jin, H. A.: Dual Ensemble
Kalman Filter assimilation method for estimating time series LAI, J. Remote.
Sens., 18, 27–44, https://doi.org/10.11834/jrs.20133036, 2014.
Li, Y., Zhao, M. S., Motesharrei, S., Mu, Q. Z., Kalnay, E., and Li, S. C.:
Local cooling and warming effects of forests based on satellite
observations, Nat. Commun., 6, 6603, https://doi.org/10.1038/ncomms7603,
2015.
Lin, P. R., Wei, J. F., Yang, Z. L., Zhang, Y. F., and Zhang, K.: Snow data
assimilation-constrained land initialization improves seasonal temperature
prediction, Geophy. Res. Lett., 43, 11423,
https://doi.org/10.1002/2016GL070966, 2016.
Lindgren, F., Geladi, P., and Wold, S.: The kernel algorithm for PLS, J.
Chemometrics, 7, 45–59, https://doi.org/10.1002/cem.1180070104, 1993.
Liu, Q., Gu, L., Dickinson, R. E., Tian, Y., Zhou, L., and Post, W. M.: Assimilation of satellite
reflectance data into a dynamical leaf model to infer seasonally varying
leaf areas for climate and carbon models, J. Geophy. Res., 113, D19113,
https://doi.org/10.1029/2007JD009645, 2008.
Luo, L. F., Robock, A., Mitchell, K. E., Houser, P. R., Wood, E. F.,
Schaake, J. C., Lohmann, D., Cosgrove, B., Wen, F. H., Sheffield, J., Duan,
Q. Y., Higgins, R. W., Pinker, R. T., and Tarpldy, D.: Validation of the
North American Land Data Assimilation System (NLDAS) retrospective forcing
over the southern Great Plains, J. Geophys. Res.-Atmos., 108, 8843,
https://doi.org/10.1029/2002JD003246, 2003.
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J. C.,
Robock, A., Cosgrove, B. A., Sheffield, J., Duan, Q. Y., Luo, L. F.,
Higgins, R. W., Pinker, R. T., Tarpley, J. D., Lettenmaier, D. P., Marchall,
C. H., Entin, J. K., Pan, M. Koren, V., Meng, J., Ramsay, B. H., and Bailey,
A. A.: The multi-institution North American Land Data Assimilation System
(NLDAS): Utilizing multiple GCIP products and partners in a continental
distributed hydrological modeling system, J. Geophys. Res., 109, D07S90,
https://doi.org/10.1029/2003JD003823, 2004.
Mokhtari, A., Noory, H., and Vazifedoust, M.: Improving crop yield
estimation by assimilating LAI and inputting satellitebased surface incoming
solar radiation into SWAP model, Agr. Forest Meteorol., 250–251, 159–170,
https://doi.org/10.1016/j.agrformet.2017.12.250, 2018.
Montzka, C., Pauwels, V. R. N., Franssen, H.-J. H., Han, X.-J., and
Vereecken, H.: Multivariate and multiscale data assimilation in terrestrial
systems: a review, Sensors, 12, 16291–16333,
https://doi.org/10.3390/s121216291, 2012.
Moradkhani, H., Hsu, K. L., Gupta, H., and Sorooshian, S.: Uncertainty
assessment of hydrologic model states and parameters: Sequential data
assimilation using the particle filter, Water Resour. Res., 41, W05012,
https://doi.org/10.1029/2004WR003604, 2005.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E.,
Lawrence, P. J., Levis, S., Swenson, S. C., Thornton, P. E., Dai, A. G.,
Decker, M., Dickinson, R., Feddema, J., Heald, C. L., Hoffman, F., Lamarque,
J. -F., Mahowald, N., Niu, G. Y., Qian, T. T., Randerson, J., Running, S.,
Sakaguchi, K., Slater, A., Stöckli, R., Wang, A. H., Yang, Z. L., Zeng,
X. D., and Zeng, X. B.: Technical Description of Version 4.0 of the
Community Land Model, NCAR Tech Note (NCAR/TN-478 + STR), 257 pp., 2010.
Pitman, A. J.: The evolution of, and revolution in, land surface schemes
designed for climate models, Int. J. Climatol., 23, 479–510,
https://doi.org/10.1002/joc.893, 2003.
Pitman, A. J., Noblet-Ducoudré, N. de, Cruz, F. T., Davin, E. L., Bonan,
G. B., Brovkin, V., Claussen, M., Delire, C., Ganzeveld, L., Gayler, V., van
den Hurk, B. J. J. M., Lawrence, P. J., van der Molen, M. K., Müller, C.,
Reick, C. H., Seneviratne, S. I., Strengers, B. J., and Voldoire, A.:
Uncertainties in climate responses to past land cover change: First results
from the LUCID intercomparison study, Geophys. Res. Lett., 36, L14814,
https://doi.org/10.1029/2009GL039076, 2009.
Pitman, A. J., de Noblet-Ducoudré, N., Avila, F. B., Alexander, L. V., Boisier, J.-P., Brovkin, V., Delire, C., Cruz, F., Donat, M. G., Gayler, V., van den Hurk, B., Reick, C., and Voldoire, A.: Effects of land cover change on temperature and rainfall extremes in multi-model ensemble simulations, Earth Syst. Dynam., 3, 213–231, https://doi.org/10.5194/esd-3-213-2012, 2012.
Qian, T. T., Dai, A. G., Trenberth, K. E., and Oleson, K. W.: Simulation of
global land surface conditions from 1948 to 2004: Part I: Forcing data and
evaluations, J. Hydrometeor., 7, 953–975,
https://doi.org/10.1175/JHM540.1, 2006.
Raeder, K., Anderson, J. L., Collins, N., Hoar, T. J., Kay, J. E.,
Lauritzen, P. H., and Pincus, R., DART/CAM: An Ensemble Data Assimilation
System for CESM Atmospheric Models, J. Climate, 25, 6304–6317,
https://doi.org/10.1175/JCLI-D-11-00395.1, 2012.
Reichle, R. H., De Lannoy, G. J. M., Forman, B. A., Draper, C. S., and Liu,
Q.: Connecting Satellite Observations with Water Cycle Variables Through
Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS, Surv. Geophys.,
35, 577–606, https://doi.org/10.1007/s10712-013-9220-8, 2014.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The global land data
assimilation system, B. Am. Meteororl. Soc., 85, 381–394,
https://doi.org/10.1175/BAMS-85-3-381, 2004.
Sabater, J. M., Rüdiger, C., Calvet, J.-C., Fritz, N., Jarlan, L., and
Kerr, Y.: Joint assimilation of surface soil moisture and LAI observations
into a land surface model, Agr. Forest Meteorol., 148, 1362–1373,
https://doi.org/10.1016/j.agrformet.2008.04.003, 2008.
Sawada, Y.: Quantifying Drought Propagation from Soil Moisture to Vegetation
Dynamics Using a Newly Developed Ecohydrological Land Reanalysis, Remote
Sens., 10, 1197, https://doi.org/10.3390/rs10081197, 2018.
Sawada, Y., Koike, T., and Walker, J. P.: A land data assimilation system for
simultaneous simulation of soil moisture and vegetation dynamics. J.
Geophys. Res.-Atmos., 120, 5910–5930, https://doi.org/10.1002/2014JD022895,
2015.
Shi, M. J., Yang, Z. L., Lawrence, D. M., Dickinson, R. E., and Subin, Z.
M.: Spin-up processes in the Community Land Model version 4 with explicit
carbon and nitrogen components, Ecol. Modell., 263, 308–325,
https://doi.org/10.1016/j.ecolmodel.2013.04.008, 2013.
Thornton, P. E. and Zimmermann, N. E.: An improved canopy integration scheme
for a land surface model with prognostic canopy structure, J. Climate, 20,
3092–3923, https://doi.org/10.1175/JCLI4222.1, 2007.
Verger, A., Baret, F., and Weiss, M.: Near real-time vegetation monitoring at
global scale, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 7, 3473–3481,
https://doi.org/10.1109/JSTARS.2014.2328632, 2014.
Vetra-Carvalho, S., van Leeuwen, P. J., Nerger, L., Barth, A., Altaf, M. U.,
Brasseur, P., Kirchgessner, P., and Beckers, J.-M.: State-of-the-art
stochastic data assimilation methods for high-dimensional non-Gaussian
problems, Tellus A, 70, 1445364,
https://doi.org/10.1080/16000870.2018.1445364, 2018.
Viskari, T., Hardiman, B., Deasi, A. R., and Dietz, M. C.: Model-data
assimilation of multiple phenological observations to constrain and predict
leaf area index, Ecol. Appl., 25, 546–558,
https://doi.org/10.1890/14-0497.1, 2015.
Wan, L. Y., Zhu, J., Wang, H., Yan, C. X., and Bertino, L.: A “dressed”
ensemble Kalman filter using the hybrid coordinate ocean model in the
pacific, Adv. Atmos. Sci., 26, 1042–1052,
https://doi.org/10.1007/s00376-009-7208-6, 2009.
Wang, X. G., Hamill, T. M., Whitaker, J. S., and Bishop, C. H.: A Comparison
of Hybrid Ensemble Transform Kalman Filter-Optimum Interpolation and
Ensemble Square Root Filter Analysis Schemes, Mon. Weather Rev., 135,
1055–1076, https://doi.org/10.1175/MWR3307.1, 2007.
Whitaker, J. S. and Hamill, T. M.: Ensemble data assimilation without
perturbed observations, Mon. Weather Rev., 130, 1913–1924,
https://doi.org/10.1175/MWR3156.1, 2002.
Whitaker, J. S., Hamill, T. M., Wei, X., Song, Y., and Toth, Z.: Ensemble
data assimilation with the NCEP global forecasting system, Mon. Weather Rev.,
136, 463–482, https://doi.org/10.1175/2007MWR2018.1, 2008.
Wu, X. R., Han, G. J., Li, D., and Li, W.: A hybrid ensemble filter and 3D
variational analysis scheme, J. Trop. Oceanogr., 30, 24–30,
2011 (in Chinese).
Xia, Y. L., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L. F.,
Alonge, C., Wei, H., Meng, J., Livneh, B., Duan, Q. Y., and Lohmann, D.:
Continental-scale water and energy flux analysis and validation for North
American Land Data Assimilation System project phase 2 (NLDAS-2): 2.
Validation of model-simulated streamflow, J. Geophys. Res., 117, D03109,
https://doi.org/10.1029/2011JD016051, 2012.
Xiao, Z. Q., Liang, S. L., Wang, J. D., and Wu, X. Y.: Use of an ensemble
Kalman Filter for real-time inversion of Leaf Area Index from MODIS time
series data, IEEE Trans. Geosci. Remote Sens., 4, 73–76,
https://doi.org/10.1109/TGRS.2013.2237780, 2009.
Zhang, F. Q., Zhang, M., and Hansen, J. A.: Coupling Ensemble Kalman Filter
with Four-dimensional Variational Data Assimilation, Adv. Atmos. Sci., 26,
1–8, https://doi.org/10.1007/s00376-009-0001-8, 2009.
Zhang, Y. F., Hoar, T. J., Yang, Z. L., Anderson, J. L., Toure, A. M., and
Rodell, M.: Assimilation of MODIS snow cover through the Data Assimilation
Research Testbed and the Community Land Model version 4, J. Geophys. Res.-Atmos., 119, 7091–7103, https://doi.org/10.1002/2013JD021329, 2014.
Zhao, L. and Yang, Z. L.: Multi-sensor land data assimilation: Toward a
robust global soil moisture and snow estimation, Remote Sens. Environ., 216,
13–27, https://doi.org/10.1016/j.rse.2018.06.033, 2018.
Zhao, L., Yang, Z. L., and Hoar, T. J.: Global Soil Moisture Estimation by
Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4-RTM-DART
System, J. Hydrometeorol., 17, 2431–2454,
https://doi.org/10.1175/JHM-D-15-0218.1, 2016.
Zhao, X., Liang, S. L., Liu, S. H., Yuan, W. P., Xiao, Z. Q., Liu, Q., Cheng, J., Zhang, X. T., Tang, H. R., Zhang, X., Liu, Q., Zhou, G. Q., and Yu, K.: The Global Land Surface Satellite (GLASS) Remote Sensing Data
Processing System and Products, Remote Sens., 5, 2436–2450,
https://doi.org/10.3390/rs5052436, 2013.
Zupanski, M.: Maximum likelihood ensemble filter: Theoretical aspects, Mon. Weather Rev., 133, 1710–1726, https://doi.org/10.1175/MWR2946.1, 2005.
Short summary
Observation and simulation can provide the temporal and spatial variation of vegetation characteristics, while they are not satisfactory for understanding the mechanism of the exchange between ecosystems and atmosphere. Data assimilation (DA) can combine the observation and models via mathematical statistical analysis. Results show that the ensemble adjust Kalman filter (EAKF) is the optimal algorithm. In addition, models perform better when the DA accepts a higher proportion of observations.
Observation and simulation can provide the temporal and spatial variation of vegetation...
