Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-3025-2024
© Author(s) 2024. 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-17-3025-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Pengfei Shi
CORRESPONDING AUTHOR
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Bin Wang
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Kai Zhang
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Samson M. Hagos
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
Shixuan Zhang
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA
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Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
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Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
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Atmos. Chem. Phys., 23, 13911–13940, https://doi.org/10.5194/acp-23-13911-2023, https://doi.org/10.5194/acp-23-13911-2023, 2023
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Hydrol. Earth Syst. Sci., 27, 3911–3934, https://doi.org/10.5194/hess-27-3911-2023, https://doi.org/10.5194/hess-27-3911-2023, 2023
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Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
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Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850, https://doi.org/10.5194/gmd-16-2833-2023, https://doi.org/10.5194/gmd-16-2833-2023, 2023
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C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Aishwarya Raman, Thomas Hill, Paul J. DeMott, Balwinder Singh, Kai Zhang, Po-Lun Ma, Mingxuan Wu, Hailong Wang, Simon P. Alexander, and Susannah M. Burrows
Atmos. Chem. Phys., 23, 5735–5762, https://doi.org/10.5194/acp-23-5735-2023, https://doi.org/10.5194/acp-23-5735-2023, 2023
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Ice-nucleating particles (INPs) play an important role in cloud processes and associated precipitation. Yet, INPs are not accurately represented in climate models. This study attempts to uncover these gaps by comparing model-simulated INP concentrations against field campaign measurements in the SO for an entire year, 2017–2018. Differences in INP concentrations and variability between the model and observations have major implications for modeling cloud properties in high latitudes.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
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PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
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We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
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We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Chandan Sarangi, Yun Qian, L. Ruby Leung, Yang Zhang, Yufei Zou, and Yuhang Wang
Atmos. Chem. Phys., 23, 1769–1783, https://doi.org/10.5194/acp-23-1769-2023, https://doi.org/10.5194/acp-23-1769-2023, 2023
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We show that for air quality, the densely populated eastern US may see even larger impacts of wildfires due to long-distance smoke transport and associated positive climatic impacts, partially compensating the improvements from regulations on anthropogenic emissions. This study highlights the tension between natural and anthropogenic contributions and the non-local nature of air pollution that complicate regulatory strategies for improving future regional air quality for human health.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
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Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Ming Yin, Yilun Han, Yong Wang, Wenqi Sun, Jianbo Deng, Daoming Wei, Ying Kong, and Bin Wang
Geosci. Model Dev., 16, 135–156, https://doi.org/10.5194/gmd-16-135-2023, https://doi.org/10.5194/gmd-16-135-2023, 2023
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All global climate models (GCMs) use the grid-averaged surface heat fluxes to drive the atmosphere, and thus their horizontal variations within the grid cell are averaged out. In this regard, a novel scheme considering the variation and partitioning of the surface heat fluxes within the grid cell is developed. The scheme reduces the long-standing rainfall biases on the southern and eastern margins of the Tibetan Plateau. The performance of key variables at the global scale is also evaluated.
Dongyu Feng, Zeli Tan, Darren Engwirda, Chang Liao, Donghui Xu, Gautam Bisht, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, https://doi.org/10.5194/hess-26-5473-2022, 2022
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Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
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We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Shixuan Zhang, Kai Zhang, Hui Wan, and Jian Sun
Geosci. Model Dev., 15, 6787–6816, https://doi.org/10.5194/gmd-15-6787-2022, https://doi.org/10.5194/gmd-15-6787-2022, 2022
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This study investigates the nudging implementation in the EAMv1 model. We find that (1) revising the sequence of calculations and using higher-frequency constraining data to improve the performance of a simulation nudged to EAMv1’s own meteorology, (2) using the relocated nudging tendency and 3-hourly ERA5 reanalysis to obtain a better agreement between nudged simulations and observations, and (3) using wind-only nudging are recommended for the estimates of global mean aerosol effects.
Qirui Zhong, Nick Schutgens, Guido van der Werf, Twan van Noije, Kostas Tsigaridis, Susanne E. Bauer, Tero Mielonen, Alf Kirkevåg, Øyvind Seland, Harri Kokkola, Ramiro Checa-Garcia, David Neubauer, Zak Kipling, Hitoshi Matsui, Paul Ginoux, Toshihiko Takemura, Philippe Le Sager, Samuel Rémy, Huisheng Bian, Mian Chin, Kai Zhang, Jialei Zhu, Svetlana G. Tsyro, Gabriele Curci, Anna Protonotariou, Ben Johnson, Joyce E. Penner, Nicolas Bellouin, Ragnhild B. Skeie, and Gunnar Myhre
Atmos. Chem. Phys., 22, 11009–11032, https://doi.org/10.5194/acp-22-11009-2022, https://doi.org/10.5194/acp-22-11009-2022, 2022
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Aerosol optical depth (AOD) errors for biomass burning aerosol (BBA) are evaluated in 18 global models against satellite datasets. Notwithstanding biases in satellite products, they allow model evaluations. We observe large and diverse model biases due to errors in BBA. Further interpretations of AOD diversities suggest large biases exist in key processes for BBA which require better constraining. These results can contribute to further model improvement and development.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
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The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
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Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Sol Kim, L. Ruby Leung, Bin Guan, and John C. H. Chiang
Geosci. Model Dev., 15, 5461–5480, https://doi.org/10.5194/gmd-15-5461-2022, https://doi.org/10.5194/gmd-15-5461-2022, 2022
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The Energy Exascale Earth System Model (E3SM) project is a state-of-the-science Earth system model developed by the US Department of Energy (DOE). Understanding how the water cycle behaves in this model is of particular importance to the DOE’s mission. Atmospheric rivers (ARs) – which are crucial to the global water cycle – move vast amounts of water vapor through the sky and produce rain and snow. We find that this model reliably represents atmospheric rivers around the world.
Lingcheng Li, Gautam Bisht, and L. Ruby Leung
Geosci. Model Dev., 15, 5489–5510, https://doi.org/10.5194/gmd-15-5489-2022, https://doi.org/10.5194/gmd-15-5489-2022, 2022
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Land surface heterogeneity plays a critical role in the terrestrial water, energy, and biogeochemical cycles. Our study systematically quantified the effects of four dominant heterogeneity sources on water and energy partitioning via Sobol' indices. We found that atmospheric forcing and land use land cover are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning. Our findings can help prioritize the future development of land surface models.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
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Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Donghui Xu, Gautam Bisht, Khachik Sargsyan, Chang Liao, and L. Ruby Leung
Geosci. Model Dev., 15, 5021–5043, https://doi.org/10.5194/gmd-15-5021-2022, https://doi.org/10.5194/gmd-15-5021-2022, 2022
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The runoff outputs in Earth system model simulations involve high uncertainty, which needs to be constrained by parameter calibration. In this work, we used a surrogate-assisted Bayesian framework to efficiently calibrate the runoff-generation processes in the Energy Exascale Earth System Model v1 at a global scale. The model performance was improved compared to the default parameter after calibration, and the associated parametric uncertainty was significantly constrained.
Shuaiqi Tang, Jerome D. Fast, Kai Zhang, Joseph C. Hardin, Adam C. Varble, John E. Shilling, Fan Mei, Maria A. Zawadowicz, and Po-Lun Ma
Geosci. Model Dev., 15, 4055–4076, https://doi.org/10.5194/gmd-15-4055-2022, https://doi.org/10.5194/gmd-15-4055-2022, 2022
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We developed an Earth system model (ESM) diagnostics package to compare various types of aerosol properties simulated in ESMs with aircraft, ship, and surface measurements from six field campaigns across spatial scales. The diagnostics package is coded and organized to be flexible and modular for future extension to other field campaign datasets and adapted to higher-resolution model simulations. Future releases will include comprehensive cloud and aerosol–cloud interaction diagnostics.
Yun Lin, Jiwen Fan, Pengfei Li, Lai-yung Ruby Leung, Paul J. DeMott, Lexie Goldberger, Jennifer Comstock, Ying Liu, Jong-Hoon Jeong, and Jason Tomlinson
Atmos. Chem. Phys., 22, 6749–6771, https://doi.org/10.5194/acp-22-6749-2022, https://doi.org/10.5194/acp-22-6749-2022, 2022
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How sea spray aerosols may affect cloud and precipitation over the region by acting as ice-nucleating particles (INPs) is unknown. We explored the effects of INPs from marine aerosols on orographic cloud and precipitation for an atmospheric river event observed during the 2015 ACAPEX field campaign. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds. This work suggests models need to consider the impacts of marine INPs.
Susannah M. Burrows, Richard C. Easter, Xiaohong Liu, Po-Lun Ma, Hailong Wang, Scott M. Elliott, Balwinder Singh, Kai Zhang, and Philip J. Rasch
Atmos. Chem. Phys., 22, 5223–5251, https://doi.org/10.5194/acp-22-5223-2022, https://doi.org/10.5194/acp-22-5223-2022, 2022
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Sea spray particles are composed of a mixture of salts and organic substances from oceanic microorganisms. In prior work, our team developed an approach connecting sea spray chemistry to ocean biology, called OCEANFILMS. Here we describe its implementation within an Earth system model, E3SM. We show that simulated sea spray chemistry is consistent with observed seasonal cycles and that sunlight reflected by simulated Southern Ocean clouds increases, consistent with analysis of satellite data.
Hui Wan, Kai Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, Shixuan Zhang, and Ross Dixon
Geosci. Model Dev., 15, 3205–3231, https://doi.org/10.5194/gmd-15-3205-2022, https://doi.org/10.5194/gmd-15-3205-2022, 2022
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This paper describes a tool embedded in a global climate model for sampling atmospheric conditions and monitoring physical processes as a numerical simulation is being carried out. The tool facilitates process-level model evaluation by allowing the users to select a wide range of quantities and processes to monitor at run time without having to do tedious ad hoc coding.
Pinya Wang, Yang Yang, Huimin Li, Lei Chen, Ruijun Dang, Daokai Xue, Baojie Li, Jianping Tang, L. Ruby Leung, and Hong Liao
Atmos. Chem. Phys., 22, 4705–4719, https://doi.org/10.5194/acp-22-4705-2022, https://doi.org/10.5194/acp-22-4705-2022, 2022
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China is now suffering from both severe ozone (O3) pollution and heat events. We highlight that North China Plain is the hot spot of the co-occurrences of extremes in O3 and high temperatures in China. Such coupled extremes exhibit an increasing trend during 2014–2019 and will continue to increase until the middle of this century. And the coupled extremes impose more severe health impacts to human than O3 pollution occurring alone because of elevated O3 levels and temperatures.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
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An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Sally S.-C. Wang, Yun Qian, L. Ruby Leung, and Yang Zhang
Atmos. Chem. Phys., 22, 3445–3468, https://doi.org/10.5194/acp-22-3445-2022, https://doi.org/10.5194/acp-22-3445-2022, 2022
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This study develops an interpretable machine learning (ML) model predicting monthly PM2.5 fire emission over the contiguous US at 0.25° resolution and compares the prediction skills of the ML and process-based models. The comparison facilitates attributions of model biases and better understanding of the strengths and uncertainties in the two types of models at regional scales, for informing future model development and their applications in fire emission projection.
Guta Wakbulcho Abeshu, Hong-Yi Li, Zhenduo Zhu, Zeli Tan, and L. Ruby Leung
Earth Syst. Sci. Data, 14, 929–942, https://doi.org/10.5194/essd-14-929-2022, https://doi.org/10.5194/essd-14-929-2022, 2022
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Existing riverbed sediment particle size data are sparsely available at individual sites. We develop a continuous map of median riverbed sediment particle size over the contiguous US corresponding to millions of river segments based on the existing observations and machine learning methods. This map is useful for research in large-scale river sediment using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc.
Hong-Yi Li, Zeli Tan, Hongbo Ma, Zhenduo Zhu, Guta Wakbulcho Abeshu, Senlin Zhu, Sagy Cohen, Tian Zhou, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 665–688, https://doi.org/10.5194/hess-26-665-2022, https://doi.org/10.5194/hess-26-665-2022, 2022
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We introduce a new multi-process river sediment module for Earth system models. Application and validation over the contiguous US indicate a satisfactory model performance over large river systems, including those heavily regulated by reservoirs. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.
Claudia Tebaldi, Kalyn Dorheim, Michael Wehner, and Ruby Leung
Earth Syst. Dynam., 12, 1427–1501, https://doi.org/10.5194/esd-12-1427-2021, https://doi.org/10.5194/esd-12-1427-2021, 2021
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We address the question of how large an initial condition ensemble of climate model simulations should be if we are concerned with accurately projecting future changes in temperature and precipitation extremes. We find that for most cases (and both models considered), an ensemble of 20–25 members is sufficient for many extreme metrics, spatial scales and time horizons. This may leave computational resources to tackle other uncertainties in climate model simulations with our ensembles.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
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Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
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.
Hui Wan, Shixuan Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, and Huiping Yan
Geosci. Model Dev., 14, 1921–1948, https://doi.org/10.5194/gmd-14-1921-2021, https://doi.org/10.5194/gmd-14-1921-2021, 2021
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Numerical models used in weather and climate research and prediction unavoidably contain numerical errors resulting from temporal discretization, and the impact of such errors can be substantial. Complex process interactions often make it difficult to pinpoint the exact sources of such errors. This study uses a series of sensitivity experiments to identify components in a global atmosphere model that are responsible for time step sensitivities in various cloud regimes.
Jianfeng Li, Zhe Feng, Yun Qian, and L. Ruby Leung
Earth Syst. Sci. Data, 13, 827–856, https://doi.org/10.5194/essd-13-827-2021, https://doi.org/10.5194/essd-13-827-2021, 2021
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Deep convection has different properties at different scales. We develop a 4 km h−1 observational data product of mesoscale convective systems and isolated deep convection in the United States from 2004–2017. We find that both types of convective systems contribute significantly to precipitation east of the Rocky Mountains but with distinct spatiotemporal characteristics. The data product will be useful for observational analyses and model evaluations of convection events at different scales.
Bo Zhang, Hongyu Liu, James H. Crawford, Gao Chen, T. Duncan Fairlie, Scott Chambers, Chang-Hee Kang, Alastair G. Williams, Kai Zhang, David B. Considine, Melissa P. Sulprizio, and Robert M. Yantosca
Atmos. Chem. Phys., 21, 1861–1887, https://doi.org/10.5194/acp-21-1861-2021, https://doi.org/10.5194/acp-21-1861-2021, 2021
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We simulate atmospheric 222Rn using the GEOS-Chem model to improve understanding of 222Rn emissions and characterize convective transport in the model. We demonstrate the potential of a customized global 222Rn emission scenario to improve simulated surface 222Rn concentrations and seasonality. We assess convective transport using observed 222Rn vertical profiles. Results have important implications for using chemical transport models to interpret the transport of trace gases and aerosols.
Jingyu Wang, Jiwen Fan, Robert A. Houze Jr., Stella R. Brodzik, Kai Zhang, Guang J. Zhang, and Po-Lun Ma
Geosci. Model Dev., 14, 719–734, https://doi.org/10.5194/gmd-14-719-2021, https://doi.org/10.5194/gmd-14-719-2021, 2021
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This paper presents an evaluation of the E3SM model against NEXRAD radar observations for the warm seasons during 2014–2016. The COSP forward simulator package is implemented in the model to generate radar reflectivity, and the NEXRAD observations are coarsened to the model resolution for comparison. The model severely underestimates the reflectivity above 4 km. Sensitivity tests on the parameters from cumulus parameterization and cloud microphysics do not improve this model bias.
María A. Burgos, Elisabeth Andrews, Gloria Titos, Angela Benedetti, Huisheng Bian, Virginie Buchard, Gabriele Curci, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Anton Laakso, Julie Letertre-Danczak, Marianne T. Lund, Hitoshi Matsui, Gunnar Myhre, Cynthia Randles, Michael Schulz, Twan van Noije, Kai Zhang, Lucas Alados-Arboledas, Urs Baltensperger, Anne Jefferson, James Sherman, Junying Sun, Ernest Weingartner, and Paul Zieger
Atmos. Chem. Phys., 20, 10231–10258, https://doi.org/10.5194/acp-20-10231-2020, https://doi.org/10.5194/acp-20-10231-2020, 2020
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We investigate how well models represent the enhancement in scattering coefficients due to particle water uptake, and perform an evaluation of several implementation schemes used in ten Earth system models. Our results show the importance of the parameterization of hygroscopicity and model chemistry as drivers of some of the observed diversity amongst model estimates. The definition of dry conditions and the phenomena taking place in this relative humidity range also impact the model evaluation.
Cited articles
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and the hydrologic cycle, Nature, 419, 224–232, https://doi.org/10.1038/nature01092, 2002.
Ardilouze, C., Batté, L., Bunzel, F., Decremer, D., Déqué, M., Doblas-Reyes, F. J., Douville, H., Fereday, D., Guemas, V., MacLachlan, C., and Müller, W.: Multi-model assessment of the impact of soil moisture initialization on mid-latitude summer predictability, Clim. Dynam., 49, 3959–3974, https://doi.org/10.1007/s00382-017-3555-7, 2017.
Balmaseda, M. A., Alves, O. J., Arribas, A., Awaji, T., Behringer, D. W., Ferry, N., Fujii, Y., Lee, T., Rienecker, M., Rosati, T., and Stammer, D.: Ocean initialization for seasonal forecasts, Oceanography, 22, 154–159, https://doi.org/10.5670/oceanog.2009.73, 2009.
Bellucci, A., Gualdi, S., Masina, S., Storto, A., Scoccimarro, E., Cagnazzo, C., Fogli, P., Manzini, E., and Navarra, A.: Decadal climate predictions with a coupled OAGCM initialized with oceanic reanalyses, Clim. Dynam., 40, 1483–1497, https://doi.org/10.1007/s00382-012-1468-z, 2013.
Boer, G. J., Smith, D. M., Cassou, C., Doblas-Reyes, F., Danabasoglu, G., Kirtman, B., Kushnir, Y., Kimoto, M., Meehl, G. A., Msadek, R., Mueller, W. A., Taylor, K. E., Zwiers, F., Rixen, M., Ruprich-Robert, Y., and Eade, R.: The Decadal Climate Prediction Project (DCPP) contribution to CMIP6, Geosci. Model Dev., 9, 3751–3777, https://doi.org/10.5194/gmd-9-3751-2016, 2016.
Browne, P. A., De Rosnay, P., Zuo, H., Bennett, A., and Dawson, A.: Weakly coupled ocean-atmosphere data assimilation in the ECMWF NWP system, Remote Sens., 11, 234, https://doi.org/10.3390/rs11030234, 2019.
Buehner, M., Du, P., and Bédard, J.: A new approach for estimating the observation impact in ensemble–variational data assimilation, Mon. Weather Rev., 146, 447-465, https://doi.org/10.1175/MWR-D-17-0252.1, 2018.
Carrassi, A., Weber, R. J. T., Guemas, V., Doblas-Reyes, F. J., Asif, M., and Volpi, D.: Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations, Nonlin. Processes Geophys., 21, 521–537, https://doi.org/10.5194/npg-21-521-2014, 2014.
Chen, Z., Zeng, Y., Shen, G., Xiao, C., Xu, L., and Chen, N. C.: Spatiotemporal characteristics and estimates of extreme precipitation in the Yangtze River Basin using GLDAS data, Int. J. Climatol., 41, 1812–1830, https://doi.org/10.1002/joc.6813, 2021.
Collins, M. and Allen, M. R.: Assessing the relative roles of initial and boundary conditions in interannual to decadal climate predictability, J. Climate, 15, 3104–3109, https://doi.org/10.1175/1520-0442(2002)015<3104:ATRROI>2.0.CO;2, 2002.
Conil, S., Douville, H., and Tyteca, S.: The relative influence of soil moisture and SST in climate predictability explored within ensembles of AMIP type experiments, Clim. Dynam., 28, 125–145, https://doi.org/10.1007/s00382-006-0172-2, 2007.
Courtier, P., Thépaut, J. M., and Hollingsworth, A.: A strategy for operational implementation of 4D-Var, using an incremental approach, Q. J. Roy. Meteor. Soc., 120, 1367–1387, https://doi.org/10.1002/qj.49712051912, 1994.
Craig, A. P., Vertenstein, M., and Jacob, R.: A new flexible coupler for Earth system modeling developed for CCSM4 and CESM1, Int. J. High Perform. C., 26, 31–42, https://doi.org/10.1177/1094342011428141, 2012.
Dee, D. P., Balmaseda, M., Balsamo, G., Engelen, R., Simmons, A. J., and Thépaut, J. N.: Toward a consistent reanalysis of the climate system, B. Am. Meteorol. Soc., 95, 1235–1248, https://doi.org/10.1175/BAMS-D-13-00043.1, 2014.
Dennis, J. M., Edwards, J., Loy, R., Jacob, R., Mirin, A. A., Craig, A. P., and Vertenstein, M.: An application-level parallel I/O library for Earth system models, Int. J. High Perform. C., 26, 43–53, https://doi.org/10.1177/1094342011428143, 2012.
Du, H., Doblas-Reyes, F. J., García-Serrano, J., Guemas, V., Soufflet, Y., and Wouters, B.: Sensitivity of decadal predictions to the initial atmospheric and oceanic perturbations, Clim. Dynam., 39, 2013–2023, https://doi.org/10.1007/s00382-011-1285-9, 2012.
Du, J., Kimball, J. S., Velicogna, I., Zhao, M., Jones, L. A., Watts, J. D., and Kim, Y.: Multicomponent satellite assessment of drought severity in the contiguous United States from 2002 to 2017 using AMSR-E and AMSR2, Water Resour. Res., 55, 5394–5412, https://doi.org/10.1029/2018WR024633, 2019.
Edwards, A. C., Scalenghe, R., and Freppaz, M.: Changes in the seasonal snow cover of alpine regions and its effect on soil processes: a review, Quaternary Int., 162, 172–181, https://doi.org/10.1016/j.quaint.2006.10.027, 2007.
European Centre for Medium-Range Weather Forecasts: ERA-Interim Project. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6CR5RD9, 2009.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Fowler, A. M. and Lawless, A. S.: An idealized study of coupled atmosphere–ocean 4D-Var in the presence of model error, Mon. Weather Rev., 144, 4007–4030, https://doi.org/10.1175/MWR-D-15-0420.1, 2016.
Fujii, Y., Nakaegawa, T., Matsumoto, S., Yasuda, T., Yamanaka, G., and Kamachi, M.: Coupled climate simulation by constraining ocean fields in a coupled model with ocean data, J. Climate, 22, 5541–5557, https://doi.org/10.1175/2009JCLI2814.1, 2009.
Gao, F., Hilker, T., Zhu, X., Anderson, M., Masek, J., Wang, P., and Yang, Y.: Fusing Landsat and MODIS data for vegetation monitoring, IEEE Geosci. Remote Sens., 3, 47–60, https://doi.org/10.1109/MGRS.2015.2434351, 2015.
GES DISC Northern Eurasian Earth Science Partnership Initiative Project: MODIS/Terra Monthly mean Day-Time Land Surface Temperature at 1x1 degree V005, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/4SI45J6G6BW5, 2001.
Girotto, M., De Lannoy, G. J., Reichle, R. H., and Rodell, M.: Assimilation of gridded terrestrial water storage observations from GRACE into a land surface model, Water Resour. Res., 52, 4164–4183, https://doi.org/10.1002/2015WR018417, 2016.
Golaz, J. C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H. Y., Lin, W., Lipscomb, W. H., Ma, P. L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/https://doi.org/10.1029/2018MS001603, 2019.
Golaz, J. C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W. Y., Bradley, A. M., Tang, Q., Maltrud, M. E., Forsyth, R. M., Zhang, C. Z., Zhou, T., Zhang, K., Zender, C. S., Wu, M. X., Wang, H. L., Turner, A. K., Singh, B., Richter, J. H., Qin, Y., Petersen, M. R., Mametjanov, A., Ma, P., Larson, V. E., Krishna, J., Keen, N. D., Jeffery, N., Hunke, E. C., Hannah, W. M., Guba, O., Griffin, B. M., Feng, Y., Engwirda, D., Vittorio, A. V., Cheng, D., Conlon, L. M., Chen, C., Brunke, M. A., Bisht, G., Benedict, J. J., Asay-Davis, X. S., Zhang, Y. Y., Zhang, M., Zeng, X. B., Xie, S. C., Wolfram, P. J., Vo, T., Veneziani, M., Tesfa, T. K., Sreepathi, S., Salinger, A. G., Jack Reeves Eyre, J. E., Prather, M. J., Mahajan, S., Li, Q., Jones, P. W., Jacob, R. L., Huebler, G. W., Huang, X. L., Hillman, B. R., Harrop, B. E., Foucar, J. G., Fang, Y. L., Comeau, D. S., Caldwell, P. M., Bartoletti, T., Balaguru, K., Taylor, M. A., McCoy, R. B., Leung, L. R., and Bader, D. C.: The DOE E3SM Model version 2: Overview of the physical model and initial model evaluation, J. Adv. Model. Earth Sy., 14, e2022MS003156, https://doi.org/10.1029/2022MS003156, 2022.
Guo, Z., Dirmeyer, P. A., Delsole, T., and Koster, R. D.: Rebound in atmospheric predictability and the role of the land surface, J. Climate, 25, 4744–4749, https://doi.org/10.1175/JCLI-D-11-00651.1, 2012.
He, Y., Wang, B., Liu, M., Liu, L., Yu, Y., Liu, J., Li, R., Zhang, C., Xu, S., Huang, W., Liu, Q., Wang, Y., and Li, F.: Reduction of initial shock in decadal predictions using a new initialization strategy, Geophys. Res. Lett., 44, 8538–8547, https://doi.org/10.1002/2017GL074028, 2017.
He, Y., Wang, B., Liu, L., Huang, W., Xu, S., Liu, J., Wang, Y., Li, L., Huang, X., Peng, Y., Lin, Y., and Yu, Y.: A DRP-4DVar-based coupled data assimilation system with a simplified off-line localization technique for decadal predictions, J. Adv. Model. Earth Sy., 12, e2019MS001768, https://doi.org/10.1029/2019MS001768, 2020a.
He, Y., Wang, B., Huang, W., Xu, S., Wang, Y., Liu, L., Li, L., Liu, J., Yu, Y., Lin, Y., Huang, X., and Peng, Y.: A new DRP-4DVar-based coupled data assimilation system for decadal predictions using a fast online localization technique, Clim. Dynam., 54, 3541–3559, https://doi.org/10.1007/s00382-020-05190-w, 2020b.
Hoke, J. E. and Anthes, R. A.: The initialization of numerical models by a dynamic-initialization technique, Mon. Weather Rev., 104, 1551–1556, https://doi.org/10.1175/1520-0493(1976)104<1551:TIONMB>2.0.CO;2, 1976.
Houborg, R., Rodell, M., Li, B., Reichle, R., and Zaitchik, B. F.: Drought indicators based on model-assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations, Water Resour. Res., 48, W07525, https://doi.org/10.1029/2011WR011291, 2012.
Hu, S., Zhou, T., and Wu, B.: Improved ENSO prediction skill resulting from reduced climate drift in IAP-DecPreS: A comparison of full-field and anomaly initializations, J. Adv. Model. Earth Sy., 12, e2019MS001759, https://doi.org/10.1029/2019MS001759, 2020.
Ireson, A. M., Van Der Kamp, G., Ferguson, G., Nachshon, U., and Wheater, H. S.: Hydrogeological processes in seasonally frozen northern latitudes: understanding, gaps and challenges, Hydrogeol. J., 21, 53–66, https://doi.org/10.1007/s10040-012-0916-5, 2013.
Justice, C. O., Townshend, J. R. G., Vermote, E. F., Masuoka, E., Wolfe, R. E., Saleous, N., Roy, D. P., and Morisette, J. T.: An overview of MODIS Land data processing and product status, Remote Sens. Environ., 83, 3–15, https://doi.org/10.1016/S0034-4257(02)00084-6, 2002.
Kimmritz, M., Counillon, F., Bitz, C. M., Massonnet, F., Bethke, I., and Gao, Y.: Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model, Tellus, 70A, 1435945, https://doi.org/10.1080/16000870.2018.1435945, 2018.
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.
Lea, D. J., Mirouze, I., Martin, M. J., King, R. R., Hines, A., Walters, D., and Thurlow, M.: Assessing a new coupled data assimilation system based on the Met Office coupled atmosphere–land–ocean–sea ice model, Mon. Weather Rev., 143, 4678–4694, https://doi.org/10.1175/MWR-D-15-0174.1, 2015.
Lei, L. L. and Hacker, J. P.: Nudging, ensemble, and nudging ensembles for data assimilation in the presence of model error, Mon. Weather Rev., 143, 2600–2610, https://doi.org/10.1175/MWR-D-14-00295.1, 2015.
Leung, L. R., Bader, D. C., Taylor, M. A., and McCoy, R. B.: An introduction to the E3SM special collection: Goals, science drivers, development, and analysis, J. Adv. Model. Earth Sy., 12, e2019MS001821, https://doi.org/10.1029/2019MS001821, 2020.
Li, F., Wang, B., He, Y., Huang, W., Xu, S., Liu, L., Liu, J. and Li, L.: Important role of North Atlantic air–sea coupling in the interannual predictability of summer precipitation over the eastern Tibetan Plateau, Clim. Dynam., 56, 1433–1448, https://doi.org/10.1007/s00382-020-05542-6, 2021.
Li, H. Y., Wigmosta, M. S., Wu, H., Huang, M., Ke, Y., Coleman, A. M., and Leung, L. R.: A physically based runoff routing model for land surface and Earth system models, J. Hydrometeorol., 14, 808–828, https://doi.org/10.1175/JHM-D-12-015.1, 2013.
Lin, L. F., Ebtehaj, A. M., Wang, J., and Bras, R. L.: Soil moisture background error covariance and data assimilation in a coupled land-atmosphere model, Water Resour. Res., 53, 1309–1335, https://doi.org/10.1002/2015WR017548, 2017.
Lin, P., Yang, Z. L., Wei, J., Dickinson, R. E., Zhang, Y., and Zhao, L.: Assimilating multi-satellite snow data in ungauged Eurasia improves the simulation accuracy of Asian monsoon seasonal anomalies, Environ. Res. Lett., 15, 064033, https://doi.org/10.1088/1748-9326/ab80ef, 2020.
Liu, D. and Mishra, A. K.: Performance of AMSR_E soil moisture data assimilation in CLM4.5 model for monitoring hydrologic fluxes at global scale, J. Hydrometeorol., 547, 67–79, https://doi.org/10.1016/j.jhydrol.2017.01.036, 2017.
Liu, J. J., Wang, B., and Xiao, Q. N.: An evaluation study of the DRP-4-DVar approach with the Lorenz-96 model, Tellus A, 63, 256–262, https://doi.org/10.1111/j.1600-0870.2010.00487.x, 2011.
Lorenc, A. C., Bowler, N. E., Clayton, A. M., Pring, S. R., and Fairbairn, D.: Comparison of hybrid-4DEnVar and hybrid-4DVar data assimilation methods for global NWP, Mon. Weather Rev., 143, 212–229, https://doi.org/10.1175/MWR-D-14-00195.1, 2015.
McCabe, M. F., Wood, E. F., Wójcik, R., Pan, M., Sheffield, J., Gao, H., and Su, H.: Hydrological consistency using multi-sensor remote sensing data for water and energy cycle studies, Remote Sens. Environ., 112, 430–444, https://doi.org/10.1016/j.rse.2007.03.027, 2008.
Mertes, C. M., Schneider, A., Sulla-Menashe, D., Tatem, A. J., and Tan, B.: Detecting change in urban areas at continental scales with MODIS data, Remote Sens. Environ., 158, 331–347, https://doi.org/10.1016/j.rse.2014.09.023, 2015.
Mochizuki, T., Masuda, S., Ishikawa, Y., and Awaji, T.: Multiyear climate prediction with initialization based on 4D-Var data assimilation, Geophys. Res. Lett., 43, 3903–3910, https://doi.org/10.1002/2016GL067895, 2016.
NASA Global Land Data Assimilation System: GLDAS CLM Land Surface Model monthly 1.0 x 1.0 degree [data set], https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS (last access: June 2007), 2007.
Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K., and Nghiem, S. V.: Soil moisture retrieval from AMSR-E, IEEE T. Geosci. Remote, 41, 215–229, https://doi.org/10.1109/TGRS.2002.808243, 2003.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C., Thornton, P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E., Lamarque, J. F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S., Ricciuto, D. M., Sacks, W., Sun, Y., Tang, J., and Yang, Z. L.: Technical description of version 4.5 of the Community Land Model (CLM) (Tech. Rep. NCAR/TN-503+STR). Boulder, Colorado, USA: National Center for Atmospheric Research, https://doi.org/10.5065/D6RR1W7M, 2013.
Penny, S. G. and Hamill, T. M.: Coupled data assimilation for integrated earth system analysis and prediction, B. Am. Meteorol. Soc., 98, ES169–ES172, https://doi.org/10.1175/BAMS-D-17-0036.1, 2017.
Penny, S. G., Bach, E., Bhargava, K., Chang, C. C., Da, C., Sun, L., and Yoshida, T.: Strongly coupled data assimilation in multiscale media: Experiments using a quasi-geostrophic coupled model, J. Adv. Model. Earth Sy., 11, 1803–1829, https://doi.org/10.1029/2019MS001652, 2019.
Petersen, M., Asay-Davis, X. S., Jacobsen, D., Maltrud, M., Ringler, T., Van Roekel, L., and Wolfram, P.: MPAS ocean user's guide V6, Zenodo, https://doi.org/10.5281/zenodo.1246893, 2018.
Polkova, I., Köhl, A., and Stammer, D.: Climate-mode initialization for decadal climate predictions, Clim. Dynam., 53, 7097–7111, https://doi.org/10.1007/s00382-019-04975-y, 2019.
Reckinger, S. M., Petersen, M. R., and Reckinger, S. J.: A study of overflow simulations using MPAS-Ocean: Vertical grids, resolution, and viscosity, Ocean Model., 96, 291–313, https://doi.org/10.1016/j.ocemod.2015.09.006, 2015.
Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A., Martins, J. V., Li, R. R., Ichoku, C., Levy, R. C., Kleidman, R. G., Eck, T. F., Vermote, E., and Holben, B. N.: The MODIS aerosol algorithm, products, and validation, J. Atmos. Sci., 62, 947–973, https://doi.org/10.1175/JAS3385.1, 2005.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M. and Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The global land data assimilation system, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004.
Sakaguchi, K., Zeng, X., and Brunke, M. A.: The hindcast skill of the CMIP ensembles for the surface air temperature trend, J. Geophys. Res.-Atmos., 117, D1611, https://doi.org/10.1029/2012JD017765, 2012.
Shi, P.: E3SMv2 source codes, Zenodo [code], https://doi.org/10.5281/zenodo.8194050, 2023a.
Shi, P.: Data for the analyses, Zenodo [data set] https://doi.org/10.5281/zenodo.8148737, 2023b.
Shi, P. F., Wang, B., He, Y., Lu, H., Yang, K., Xu, S. M., Huang, W. Y., Liu, L., Liu, J. J., Li, L. J., and Wang, Y.: Contributions of weakly coupled data assimilation–based land initialization to interannual predictability of summer climate over Europe, J. Climate, 35, 517–535, https://doi.org/10.1175/JCLI-D-20-0506.1, 2022.
Simmons, A. J. and Hollingsworth, A.: Some aspects of the improvement in skill of numerical weather prediction, Q. J. Roy. Meteorol. Soc., 128, 647–677, https://doi.org/10.1256/003590002321042135, 2002.
Sluka, T. C., Penny, S. G., Kalnay, E., and Miyoshi, T.: Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation, Geophys. Res. Lett., 43, 752-759, https://doi.org/10.1002/2015GL067238, 2016.
Smith, D. M., Eade, R., and Pohlmann, H.: A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction, Clim. Dynam., 41, 3325–3338, https://doi.org/10.1007/s00382-013-1683-2, 2013.
Smith, P. J., Fowler, A. M., and Lawless, A. S.: Exploring strategies for coupled 4DVar data assimilation using an idealised atmosphere–ocean model, Tellus A, 67, 27025, https://doi.org/10.3402/tellusa.v67.27025, 2015.
Sugiura, N., Awaji, T., Masuda, S., Mochizuki, T., Toyoda, T., Miyama, T., Igarashi, H., and Ishikawa, Y.: Development of a four-dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations, J. Geophys. Res.-Oceans, 113, C10017, https://doi.org/10.1029/2008JC004741, 2008.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Taylor, M. A., Guba, O., Steyer, A., Ullrich, P. A., Hall, D. M., and Eldrid, C.: An energy consistent discretization of the nonhydrostatic equations in primitive variables, J. Adv. Model. Earth Sy., 12, e2019MS001783, https://doi.org/10.1029/2019MS001783, 2020.
Volpi, D., Guemas, V., and Doblas-Reyes, F. J.: Comparison of full field and anomaly initialisation for decadal climate prediction: towards an optimal consistency between the ocean and sea-ice anomaly initialisation state, Clim. Dynam., 49, 1181–1195, https://doi.org/10.1007/s00382-016-3373-3, 2017.
Wang, B., Liu, J., Wang, S., Cheng, W., Liu, J., Liu, C., Xiao, Q., and Kuo, Y. H.: An economical approach to four-dimensional variational data assimilation, Adv. Atmos. Sci., 27, 715–727, https://doi.org/10.1007/s00376-009-9122-3, 2010.
Wang, B., Liu, J., Liu, L., Xu, S., and Huang, W.: An approach to localization for ensemble-based data assimilation, PloS one, 13, e0191088, https://doi.org/10.1371/journal.pone.0191088, 2018.
Wang, G., Dolman, A. J., Blender, R., and Fraedrich, K.: Fluctuation regimes of soil moisture in ERA-40 reanalysis data, Theor. Appl. Climatol., 99, 1–8, https://doi.org/10.1007/s00704-009-0111-3, 2010.
Yao, P. and Lu, H.: A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002–2022), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Soil.tpdc.270960, 2020.
Yao, Y., Luo, Y., Huang, J., and Ma, J.: Improving the downscaled springtime temperature in Central Asia through assimilating meteorological and snow cover observations, Atmos. Res., 258, 105619, https://doi.org/10.1016/j.atmosres.2021.105619, 2021.
Yin, J., Zhan, X., Zheng, Y., Liu, J., Hain, C. R., and Fang, L.: Impact of quality control of satellite soil moisture data on their assimilation into land surface model, Geophys. Res. Lett., 41, 7159–7166, https://doi.org/10.1002/2014GL060659, 2014.
Yoshida, T. and Kalnay, E.: Correlation-cutoff method for covariance localization in strongly coupled data assimilation, Mon. Weather Rev., 146, 2881–2889, https://doi.org/10.1175/MWR-D-17-0365.1, 2018.
Zeng, X. and Decker, M.: Improving the numerical solution of soil moisture–based Richards equation for land models with a deep or shallow water table, J. Hydrometeorol., 10, 308–319, https://doi.org/10.1175/2008JHM1011.1, 2009.
Zhang, H., Zhang, L. L., Li, J., An, R. D., and Deng, Y.: Climate and Hydrological Change Characteristics and Applicability of GLDAS Data in the Yarlung Zangbo River Basin, China, Water, 10, 254, https://doi.org/10.3390/w10030254, 2018.
Zhang, S., Harrison, M. J., Wittenberg, A. T., Rosati, A., Anderson, J. L., and Balaji, V.: Initialization of an ENSO forecast system using a parallelized ensemble filter, Mon. Weather Rev., 133, 3176–3201, https://doi.org/10.1175/MWR3024.1, 2005.
Zhang, S., Harrison, M. J., Rosati, A., and Wittenberg, A.: System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies, Mon. Weather Rev., 135, 3541–3564, https://doi.org/10.1175/MWR3466.1, 2007.
Zhang, S., Liu, Z., Zhang, X., Wu, X., Han, G., Zhao, Y., Yu, X., Liu, C., Liu, Y., Wu, S., Lu, F., Li, M., and Deng, X.: Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review, Clim. Dynam., 54, 5127–5144, https://doi.org/10.1007/s00382-020-05275-6, 2020.
Zhou, J., Yang, K., Crow, W. T., Dong, J., Zhao, L., Feng, H., Zou, M., Lu, H., Tang, R. and Jiang, Y.: Potential of remote sensing surface temperature-and evapotranspiration-based land-atmosphere coupling metrics for land surface model calibration, Remote Sens. Environ., 291, 113557, https://doi.org/10.1016/j.rse.2023.113557, 2023.
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Improving climate predictions have profound socio-economic impacts. This study introduces a new...