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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Le Wang, Xin Miao, Xinyun Hu, Yizhuo Li, Bo Qiu, Jun Ge, and Weidong Guo
EGUsphere, https://doi.org/10.5194/egusphere-2024-3431, https://doi.org/10.5194/egusphere-2024-3431, 2024
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Snow phenology is a crucial indicator for assessing seasonal changes in snow. In this work, we find that snow phenology is significantly impacted by datasets and methods used, and current methods often overlook the spatial and temporal variability in snow across the Northern Hemisphere. To address this, we develop a dynamic threshold method, which contributes to better representing the seasonal changes of snow cover across the Northern Hemisphere, especially on the Tibetan Plateau.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-168, https://doi.org/10.5194/gmd-2024-168, 2024
Preprint under review for GMD
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We present a new hydrological model based on the popular Noah-MP. It was developed by translating the FORTRAN version of Noah-MP to C# code. A river routing model was integrated. It can run in parallel on Windows systems using today's PCs. The NMP-Hydro code has been tested to ensure it produces the same results as the original WRF-Hydro. Maps and changes in variables show consistent results with the original model. We think it is a reliable replacement for Noah-MP in WRF-Hydro 3.0.
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
Atmos. Chem. Phys., 24, 11063–11080, https://doi.org/10.5194/acp-24-11063-2024, https://doi.org/10.5194/acp-24-11063-2024, 2024
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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 the 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
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
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A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on 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
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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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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
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We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
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This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
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Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
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The Icosahedral Nonhydrostatic (ICON) Model Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++ and Python) and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
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In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
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Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
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This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
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We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
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This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
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The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
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This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged between WRF and any other model (e.g. an ocean model) using the coupling library Ocean-Atmosphere-Sea-Ice-Soil – Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
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A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on 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.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
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We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
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A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
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Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
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Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
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We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
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Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
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Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
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The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
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We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
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Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-119, https://doi.org/10.5194/gmd-2024-119, 2024
Revised manuscript accepted for GMD
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10-15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100-km and a 25-km grid. The three models are compared with observations to study the improvements thanks to the increased in the resolution.
Daniel Francis James Gunning, Kerim Hestnes Nisancioglu, Emilie Capron, and Roderik van de Wal
EGUsphere, https://doi.org/10.5194/egusphere-2024-1384, https://doi.org/10.5194/egusphere-2024-1384, 2024
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This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth’s orbit. We demonstrate ZEMBA reproduces many features of the Earth’s climate for both the pre-industrial period and the Earth’s most recent cold extreme- the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
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Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
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Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
K. Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
EGUsphere, https://doi.org/10.5194/egusphere-2024-1431, https://doi.org/10.5194/egusphere-2024-1431, 2024
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The study aimed to improve the representation of spring wheat and rice in the CLM5. The modified CLM5 model performed significantly better than the default model in simulating crop phenology, yield, carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific parameters for accurately simulating vegetation processes and land surface processes.
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
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Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
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The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
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Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
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We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
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The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
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...
