Articles | Volume 15, issue 16
https://doi.org/10.5194/gmd-15-6371-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6371-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
29 Aug 2022
Development and technical paper |
| 29 Aug 2022
| 29 Aug 2022
Representing surface heterogeneity in land–atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertime
Pacific Northwest National Laboratory, Richland, WA, USA
Po-Lun Ma
Pacific Northwest National Laboratory, Richland, WA, USA
Nathaniel W. Chaney
Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA
Dalei Hao
Pacific Northwest National Laboratory, Richland, WA, USA
Gautam Bisht
Pacific Northwest National Laboratory, Richland, WA, USA
Megan D. Fowler
National Center for Atmospheric Research, Boulder, CO, USA
Vincent E. Larson
Pacific Northwest National Laboratory, Richland, WA, USA
Department of Mathematical Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
L. Ruby Leung
Pacific Northwest National Laboratory, Richland, WA, USA
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Calvin Howes, Pablo E. Saide, Hugh Coe, Amie Dobracki, Steffen Freitag, Jim M. Haywood, Steven G. Howell, Siddhant Gupta, Janek Uin, Mary Kacarab, Chongai Kuang, L. Ruby Leung, Athanasios Nenes, Greg M. McFarquhar, James Podolske, Jens Redemann, Arthur J. Sedlacek, Kenneth L. Thornhill, Jenny P. S. Wong, Robert Wood, Huihui Wu, Yang Zhang, Jianhao Zhang, and Paquita Zuidema
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Dongyu Feng, Zeli Tan, Donghui Xu, and L. Ruby Leung
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Charlotte M. Beall, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Adam Varble, Kentaroh Suzuki, and Takuro Michibata
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Adam C. Varble, Po-Lun Ma, Matthew W. Christensen, Johannes Mülmenstädt, Shuaiqi Tang, and Jerome Fast
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Hydrol. Earth Syst. Sci., 27, 2787–2805, https://doi.org/10.5194/hess-27-2787-2023, https://doi.org/10.5194/hess-27-2787-2023, 2023
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Lingcheng Li, Gautam Bisht, Dalei Hao, and Lai-Yung Ruby Leung
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-242, https://doi.org/10.5194/essd-2023-242, 2023
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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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High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
EGUsphere, https://doi.org/10.5194/egusphere-2023-1055, https://doi.org/10.5194/egusphere-2023-1055, 2023
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Anthropogenic aerosol emissions are essential part of the global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
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Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
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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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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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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Enrico Zorzetto, Sergey Malyshev, Nathaniel Chaney, David Paynter, Raymond Menzel, and Elena Shevliakova
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In this paper we develop a methodology to model the spatial distribution of solar radiation received by land over mountainous terrain. The approach is designed to be used in Earth system models, where coarse grid cells hinder the description of fine-scale land–atmosphere interactions. We adopt a clustering algorithm to partition the land domain into a set of homogeneous sub-grid
tiles, and for each tile we evaluate solar radiation received by land based on terrain properties.
Matthew W. Christensen, Po-Lun Ma, Peng Wu, Adam C. Varble, Johannes Mülmenstädt, and Jerome D. Fast
Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023, https://doi.org/10.5194/acp-23-2789-2023, 2023
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An increase in aerosol concentration (tiny airborne particles) is shown to suppress rainfall and increase the abundance of droplets in clouds passing over Graciosa Island in the Azores. Cloud drops remain affected by aerosol for several days across thousands of kilometers in satellite data. Simulations from an Earth system model show good agreement, but differences in the amount of cloud water and its extent remain despite modifications to model parameters that control the warm-rain process.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
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Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
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.
Han Qiu, Dalei Hao, Yelu Zeng, Xuesong Zhang, and Min Chen
Earth Syst. Dynam., 14, 1–16, https://doi.org/10.5194/esd-14-1-2023, https://doi.org/10.5194/esd-14-1-2023, 2023
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The carbon cycling in terrestrial ecosystems is complex. In our analyses, we found that both the global and the northern-high-latitude (NHL) ecosystems will continue to have positive net ecosystem production (NEP) in the next few decades under four global change scenarios but with large uncertainties. NHL ecosystems will experience faster climate warming but steadily contribute a small fraction of the global NEP. However, the relative uncertainty of NHL NEP is much larger than the global values.
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.
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.
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.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, Rodrigo Guzman, Cyprien Gindre, Po-Lun Ma, and Marjolaine Chiriaco
Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, https://doi.org/10.5194/amt-15-1055-2022, 2022
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Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).
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.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
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Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
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.
Zhonghua Zheng, Matthew West, Lei Zhao, Po-Lun Ma, Xiaohong Liu, and Nicole Riemer
Atmos. Chem. Phys., 21, 17727–17741, https://doi.org/10.5194/acp-21-17727-2021, https://doi.org/10.5194/acp-21-17727-2021, 2021
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Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. We present a framework for evaluating the error in aerosol mixing state induced by aerosol representation assumptions, which is one of the important contributors to structural uncertainty in aerosol models. Our study provides insights into potential improvements to model process representation for aerosols.
Nathaniel W. Chaney, Laura Torres-Rojas, Noemi Vergopolan, and Colby K. Fisher
Geosci. Model Dev., 14, 6813–6832, https://doi.org/10.5194/gmd-14-6813-2021, https://doi.org/10.5194/gmd-14-6813-2021, 2021
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Although there have been significant advances in river routing and sub-grid heterogeneity (i.e., tiling) schemes in Earth system models over the past decades, there has yet to be a concerted effort to couple these two concepts. This paper aims to bridge this gap through the development of a two-way coupling between tiling schemes and river networks in the HydroBlocks land surface model. The scheme is implemented and tested over a 1 arc degree domain in Oklahoma, United States.
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.
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
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Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, and Daniel Rothenberg
Geosci. Model Dev., 14, 3067–3077, https://doi.org/10.5194/gmd-14-3067-2021, https://doi.org/10.5194/gmd-14-3067-2021, 2021
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The activation of aerosol into cloud droplets is an important but uncertain process in the Earth system. The physical and chemical interactions that govern this process are too computationally expensive to explicitly resolve in modern Earth system models. Here, we demonstrate how hybrid machine learning approaches can provide a potential path forward, enabling the representation of the more detailed physics and chemistry at a reduced computational cost while still retaining physical information.
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.
Noemi Vergopolan, Sitian Xiong, Lyndon Estes, Niko Wanders, Nathaniel W. Chaney, Eric F. Wood, Megan Konar, Kelly Caylor, Hylke E. Beck, Nicolas Gatti, Tom Evans, and Justin Sheffield
Hydrol. Earth Syst. Sci., 25, 1827–1847, https://doi.org/10.5194/hess-25-1827-2021, https://doi.org/10.5194/hess-25-1827-2021, 2021
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Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.
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.
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.
Johannes Quaas, Antti Arola, Brian Cairns, Matthew Christensen, Hartwig Deneke, Annica M. L. Ekman, Graham Feingold, Ann Fridlind, Edward Gryspeerdt, Otto Hasekamp, Zhanqing Li, Antti Lipponen, Po-Lun Ma, Johannes Mülmenstädt, Athanasios Nenes, Joyce E. Penner, Daniel Rosenfeld, Roland Schrödner, Kenneth Sinclair, Odran Sourdeval, Philip Stier, Matthias Tesche, Bastiaan van Diedenhoven, and Manfred Wendisch
Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, https://doi.org/10.5194/acp-20-15079-2020, 2020
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Anthropogenic pollution particles – aerosols – serve as cloud condensation nuclei and thus increase cloud droplet concentration and the clouds' reflection of sunlight (a cooling effect on climate). This Twomey effect is poorly constrained by models and requires satellite data for better quantification. The review summarizes the challenges in properly doing so and outlines avenues for progress towards a better use of aerosol retrievals and better retrievals of droplet concentrations.
Dalei Hao, Ghassem R. Asrar, Yelu Zeng, Qing Zhu, Jianguang Wen, Qing Xiao, and Min Chen
Earth Syst. Sci. Data, 12, 2209–2221, https://doi.org/10.5194/essd-12-2209-2020, https://doi.org/10.5194/essd-12-2209-2020, 2020
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We adopted machine-learning models to generate the first global land products of SW–PAR based on DSCOVR/EPIC data. Our products are consistent with ground-based observations, capture the spatiotemporal patterns well and accurately track substantial diurnal, monthly and seasonal variations in SW–PAR. Our products provide a valuable alternative for solar photovoltaic applications and can be used to improve our understanding of the diurnal cycles of terrestrial water, carbon and energy fluxes.
Kurt C. Solander, Brent D. Newman, Alessandro Carioca de Araujo, Holly R. Barnard, Z. Carter Berry, Damien Bonal, Mario Bretfeld, Benoit Burban, Luiz Antonio Candido, Rolando Célleri, Jeffery Q. Chambers, Bradley O. Christoffersen, Matteo Detto, Wouter A. Dorigo, Brent E. Ewers, Savio José Filgueiras Ferreira, Alexander Knohl, L. Ruby Leung, Nate G. McDowell, Gretchen R. Miller, Maria Terezinha Ferreira Monteiro, Georgianne W. Moore, Robinson Negron-Juarez, Scott R. Saleska, Christian Stiegler, Javier Tomasella, and Chonggang Xu
Hydrol. Earth Syst. Sci., 24, 2303–2322, https://doi.org/10.5194/hess-24-2303-2020, https://doi.org/10.5194/hess-24-2303-2020, 2020
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We evaluate the soil moisture response in the humid tropics to El Niño during the three most recent super El Niño events. Our estimates are compared to in situ soil moisture estimates that span five continents. We find the strongest and most consistent soil moisture decreases in the Amazon and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. Our results can be used to improve estimates of soil moisture in tropical ecohydrology models at multiple scales.
Johannes Mülmenstädt, Edward Gryspeerdt, Marc Salzmann, Po-Lun Ma, Sudhakar Dipu, and Johannes Quaas
Atmos. Chem. Phys., 19, 15415–15429, https://doi.org/10.5194/acp-19-15415-2019, https://doi.org/10.5194/acp-19-15415-2019, 2019
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The effect of aerosol–cloud interactions (ACIs) on Earth's energy budget continues to be highly uncertain. We decompose the effective radiative forcing by ACIs (ERFaci) into the instantaneous forcing due to anthropogenic increases in the number of cloud droplets and fast responses of cloud properties to the droplet number perturbation in the ECHAM–HAMMOZ aerosol–climate model. This decomposition maps onto the IPCC's Fifth Assessment Report analysis of ERFaci more directly than previous work.
Zhiyuan Hu, Jianping Huang, Chun Zhao, Yuanyuan Ma, Qinjian Jin, Yun Qian, L. Ruby Leung, Jianrong Bi, and Jianmin Ma
Atmos. Chem. Phys., 19, 12709–12730, https://doi.org/10.5194/acp-19-12709-2019, https://doi.org/10.5194/acp-19-12709-2019, 2019
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This study investigates aerosol chemical compositions and relative contributions to total aerosols in the western US. The results show that trans-Pacific aerosols have a maximum concentration in the boreal spring, with the greatest contribution from dust. Over western North America, the trans-Pacific aerosols dominate the column-integrated aerosol mass and number concentration. However, near the surface, aerosols mainly originated from local emissions.
Mingchen Ma, Yang Gao, Yuhang Wang, Shaoqing Zhang, L. Ruby Leung, Cheng Liu, Shuxiao Wang, Bin Zhao, Xing Chang, Hang Su, Tianqi Zhang, Lifang Sheng, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 19, 12195–12207, https://doi.org/10.5194/acp-19-12195-2019, https://doi.org/10.5194/acp-19-12195-2019, 2019
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Ozone pollution has become severe in China, and extremely high ozone episodes occurred in summer 2017 over the North China Plain. While meteorology impacts are clear, we find that enhanced biogenic emissions, previously ignored by the community, driven by high vapor pressure deficit, land cover change and urban landscape contribute substantially to ozone formation. This study has significant implications for ozone pollution control with more frequent heat waves and urbanization growth in future.
Chun Zhao, Mingyue Xu, Yu Wang, Meixin Zhang, Jianping Guo, Zhiyuan Hu, L. Ruby Leung, Michael Duda, and William Skamarock
Geosci. Model Dev., 12, 2707–2726, https://doi.org/10.5194/gmd-12-2707-2019, https://doi.org/10.5194/gmd-12-2707-2019, 2019
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Simulations at global uniform and variable resolutions share similar characteristics of precipitation and wind in the refined region. The experiments reveal the significant impacts of resolution on simulating the distribution and intensity of precipitation and updrafts. This study provides evidence supporting the use of convection-permitting global variable-resolution simulations to study extreme precipitation.
Zhibo Zhang, Hua Song, Po-Lun Ma, Vincent E. Larson, Minghuai Wang, Xiquan Dong, and Jianwu Wang
Atmos. Chem. Phys., 19, 1077–1096, https://doi.org/10.5194/acp-19-1077-2019, https://doi.org/10.5194/acp-19-1077-2019, 2019
Junxi Zhang, Yang Gao, L. Ruby Leung, Kun Luo, Huan Liu, Jean-Francois Lamarque, Jianren Fan, Xiaohong Yao, Huiwang Gao, and Tatsuya Nagashima
Atmos. Chem. Phys., 19, 887–900, https://doi.org/10.5194/acp-19-887-2019, https://doi.org/10.5194/acp-19-887-2019, 2019
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ACCMIP simulations were used to study NOy deposition over East Asia in the future. Both dry and wet NOy deposition show significant decreases in the 2100s under RCP4.5 and RCP8.5 due to large anthropogenic emission reduction. The changes in climate only significantly affect the wet deposition primarily linked to changes in precipitation. Over the coastal seas of China, weaker transport of NOy from land due to emission reduction infers a larger impact from shipping and lightning emissions.
Ge Zhang, Yang Gao, Wenju Cai, L. Ruby Leung, Shuxiao Wang, Bin Zhao, Minghuai Wang, Huayao Shan, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 19, 565–576, https://doi.org/10.5194/acp-19-565-2019, https://doi.org/10.5194/acp-19-565-2019, 2019
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Based on observed data, this study reveals a distinct seesaw feature of abnormally high and low PM2.5 concentrations in December 2015 and January 2016 over North China. The mechanism of the seesaw pattern was found to be linked to a super El Niño and the Arctic Oscillation (AO). During the mature phase of El Niño in December 2015, the weakened East Asian winter monsoon favors strong haze formation; however, the circulation pattern was reversed in the next month due to the phase change of the AO.
Gautam Bisht, William J. Riley, Glenn E. Hammond, and David M. Lorenzetti
Geosci. Model Dev., 11, 4085–4102, https://doi.org/10.5194/gmd-11-4085-2018, https://doi.org/10.5194/gmd-11-4085-2018, 2018
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Most existing global land surface models used to study impacts of climate change on water resources routinely use different models for near-surface unsaturated soil and the deeper groundwater table. We developed a model that uses a unified treatment of soil hydrologic processes throughout the entire soil column. Using a calibrated drainage parameter, the new model is able to correctly predict deep water table depth as reported in an observationally constrained global dataset.
Hua Song, Zhibo Zhang, Po-Lun Ma, Steven Ghan, and Minghuai Wang
Geosci. Model Dev., 11, 3147–3158, https://doi.org/10.5194/gmd-11-3147-2018, https://doi.org/10.5194/gmd-11-3147-2018, 2018
Junxi Zhang, Yang Gao, Kun Luo, L. Ruby Leung, Yang Zhang, Kai Wang, and Jianren Fan
Atmos. Chem. Phys., 18, 9861–9877, https://doi.org/10.5194/acp-18-9861-2018, https://doi.org/10.5194/acp-18-9861-2018, 2018
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We used a regional model to investigate the impact of atmosphere with high temperature and low wind speed on ozone concentration. When these compound events (heat waves and stagnant weather) occur simultaneously, a striking ozone enhancement is revealed. This type of compound event is projected to increase more dominantly compared to single events in the future over the US, Europe, and China, implying the importance of reducing emissions in order to alleviate the impact from the compound events.
Christine A. Shields, Jonathan J. Rutz, Lai-Yung Leung, F. Martin Ralph, Michael Wehner, Brian Kawzenuk, Juan M. Lora, Elizabeth McClenny, Tashiana Osborne, Ashley E. Payne, Paul Ullrich, Alexander Gershunov, Naomi Goldenson, Bin Guan, Yun Qian, Alexandre M. Ramos, Chandan Sarangi, Scott Sellars, Irina Gorodetskaya, Karthik Kashinath, Vitaliy Kurlin, Kelly Mahoney, Grzegorz Muszynski, Roger Pierce, Aneesh C. Subramanian, Ricardo Tome, Duane Waliser, Daniel Walton, Gary Wick, Anna Wilson, David Lavers, Prabhat, Allison Collow, Harinarayan Krishnan, Gudrun Magnusdottir, and Phu Nguyen
Geosci. Model Dev., 11, 2455–2474, https://doi.org/10.5194/gmd-11-2455-2018, https://doi.org/10.5194/gmd-11-2455-2018, 2018
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ARTMIP (Atmospheric River Tracking Method Intercomparison Project) is a community effort with the explicit goal of understanding the uncertainties, and the implications of those uncertainties, in atmospheric river science solely due to detection algorithm. ARTMIP strives to quantify these differences and provide guidance on appropriate algorithmic choices for the science question posed. Project goals, experimental design, and preliminary results are provided.
Kai Zhang, Philip J. Rasch, Mark A. Taylor, Hui Wan, Ruby Leung, Po-Lun Ma, Jean-Christophe Golaz, Jon Wolfe, Wuyin Lin, Balwinder Singh, Susannah Burrows, Jin-Ho Yoon, Hailong Wang, Yun Qian, Qi Tang, Peter Caldwell, and Shaocheng Xie
Geosci. Model Dev., 11, 1971–1988, https://doi.org/10.5194/gmd-11-1971-2018, https://doi.org/10.5194/gmd-11-1971-2018, 2018
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The conservation of total water is an important numerical feature for global Earth system models. Even small conservation problems in the water budget can lead to systematic errors in century-long simulations for sea level rise projection. This study quantifies and reduces various sources of water conservation error in the atmosphere component of the Energy Exascale Earth System Model.
Tianyi Fan, Xiaohong Liu, Po-Lun Ma, Qiang Zhang, Zhanqing Li, Yiquan Jiang, Fang Zhang, Chuanfeng Zhao, Xin Yang, Fang Wu, and Yuying Wang
Atmos. Chem. Phys., 18, 1395–1417, https://doi.org/10.5194/acp-18-1395-2018, https://doi.org/10.5194/acp-18-1395-2018, 2018
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We found that 22–28 % of the low AOD bias in eastern China simulated by the Community Atmosphere Model version 5 can be improved by using a new emission inventory. The concentrations of primary aerosols are closely related to the emission, while the seasonal variations of secondary aerosols depend more on atmospheric processes. This study highlights the importance of improving both the emission and atmospheric processes in modeling the atmospheric aerosols and their radiative effects.
Gautam Bisht, William J. Riley, Haruko M. Wainwright, Baptiste Dafflon, Fengming Yuan, and Vladimir E. Romanovsky
Geosci. Model Dev., 11, 61–76, https://doi.org/10.5194/gmd-11-61-2018, https://doi.org/10.5194/gmd-11-61-2018, 2018
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The land model integrated into the Energy Exascale Earth System Model was extended to include snow redistribution (SR) and lateral subsurface hydrologic and thermal processes. Simulation results at a polygonal tundra site near Barrow, Alaska, showed that inclusion of SR resulted in a better agreement with observations. Excluding lateral subsurface processes had a small impact on mean states but caused a large overestimation of spatial variability in soil moisture and temperature.
Gautam Bisht, Maoyi Huang, Tian Zhou, Xingyuan Chen, Heng Dai, Glenn E. Hammond, William J. Riley, Janelle L. Downs, Ying Liu, and John M. Zachara
Geosci. Model Dev., 10, 4539–4562, https://doi.org/10.5194/gmd-10-4539-2017, https://doi.org/10.5194/gmd-10-4539-2017, 2017
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A fully coupled three-dimensional surface and subsurface land model, CP v1.0, was developed to simulate three-way interactions among river water, groundwater, and land surface processes. The coupled model can be used for improving mechanistic understanding of ecosystem functioning and biogeochemical cycling along river corridors under historical and future hydroclimatic changes. The dataset presented in this study can also serve as a good benchmarking case for testing other integrated models.
Randal D. Koster, Alan K. Betts, Paul A. Dirmeyer, Marc Bierkens, Katrina E. Bennett, Stephen J. Déry, Jason P. Evans, Rong Fu, Felipe Hernandez, L. Ruby Leung, Xu Liang, Muhammad Masood, Hubert Savenije, Guiling Wang, and Xing Yuan
Hydrol. Earth Syst. Sci., 21, 3777–3798, https://doi.org/10.5194/hess-21-3777-2017, https://doi.org/10.5194/hess-21-3777-2017, 2017
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Large-scale hydrological variability can affect society in profound ways; floods and droughts, for example, often cause major damage and hardship. A recent gathering of hydrologists at a symposium to honor the career of Professor Eric Wood motivates the present survey of recent research on this variability. The surveyed literature and the illustrative examples provided in the paper show that research into hydrological variability continues to be strong, vibrant, and multifaceted.
Yang Yang, Hailong Wang, Steven J. Smith, Richard Easter, Po-Lun Ma, Yun Qian, Hongbin Yu, Can Li, and Philip J. Rasch
Atmos. Chem. Phys., 17, 8903–8922, https://doi.org/10.5194/acp-17-8903-2017, https://doi.org/10.5194/acp-17-8903-2017, 2017
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Sulfate has significant impacts on air quality and climate. Local sulfate pollution could result from remote influences, making domestic mitigation efforts inefficient. Using CESM with a sulfur source-tagging technique, we found that, over regions with relatively low emissions, sulfate concentrations are primarily attributed to non-local sources and sulfate indirect radiative forcing over the Southern Hemisphere is more sensitive to emission perturbation than the polluted Northern Hemisphere.
Shi Zhong, Yun Qian, Chun Zhao, Ruby Leung, Hailong Wang, Ben Yang, Jiwen Fan, Huiping Yan, Xiu-Qun Yang, and Dongqing Liu
Atmos. Chem. Phys., 17, 5439–5457, https://doi.org/10.5194/acp-17-5439-2017, https://doi.org/10.5194/acp-17-5439-2017, 2017
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An online climate–chemistry coupled model (WRF-Chem) is integrated for 5 years at cloud-permitting scale to quantify the impacts of urbanization-induced changes in land cover and pollutants emission on regional climate in the Yangtze River Delta region in eastern China. Urbanization over this region increases the frequency of extreme precipitation and heat wave in summer. The results could help China government in making policies in mitigating the environmental impact of urbanization.
Yang Yang, Hailong Wang, Steven J. Smith, Po-Lun Ma, and Philip J. Rasch
Atmos. Chem. Phys., 17, 4319–4336, https://doi.org/10.5194/acp-17-4319-2017, https://doi.org/10.5194/acp-17-4319-2017, 2017
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The source attributions of black carbon (BC) in China are quantified using the Community Earth System Model by source tagging. BC impacts neighboring regions greatly. Transport is important in increasing BC during regional polluted days. Emissions outside China contribute 35 % of BC direct radiative forcing in China. Efficiency analysis shows that reduction in BC emissions over eastern China could have a greater benefit for regional air quality in China, especially in the winter haze season.
Xiangyu Luo, Hong-Yi Li, L. Ruby Leung, Teklu K. Tesfa, Augusto Getirana, Fabrice Papa, and Laura L. Hess
Geosci. Model Dev., 10, 1233–1259, https://doi.org/10.5194/gmd-10-1233-2017, https://doi.org/10.5194/gmd-10-1233-2017, 2017
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This study shows that alleviating vegetation-caused biases in DEM data, refining channel cross-sectional geometry and Manning roughness coefficients, as well as accounting for backwater effects can effectively improve the modeling of streamflow, river stages and flood extent in the Amazon Basin. The obtained understanding could be helpful to hydrological modeling in basins with evident inundation, which has important implications for improving land–atmosphere interactions in Earth system models.
Teklu K. Tesfa and Lai-Yung Ruby Leung
Geosci. Model Dev., 10, 873–888, https://doi.org/10.5194/gmd-10-873-2017, https://doi.org/10.5194/gmd-10-873-2017, 2017
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Motivated by the significant topographic influence on land surface processes, this study explored two methods to discretize watersheds into two types of subgrid structures to capture spatial heterogeneity for land surface models. Adopting geomorphologic concepts in watershed discretization yields improved capability in capturing subgrid topographic heterogeneity, which also allowed climatic and land cover variability to be better represented with a nominal increase in computational requirements.
Jiwen Fan, L. Ruby Leung, Daniel Rosenfeld, and Paul J. DeMott
Atmos. Chem. Phys., 17, 1017–1035, https://doi.org/10.5194/acp-17-1017-2017, https://doi.org/10.5194/acp-17-1017-2017, 2017
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How orographic mixed-phase clouds respond to changes in cloud condensation nuclei (CCN) and ice nucleating particles (INPs) is highly uncertain. We conducted this study to improve understanding of these processes. We found a new mechanism through which CCN can invigorate orographic mixed-phase clouds and drastically intensify snow precipitation when CCN concentrations are high. Our findings have very important implications for orographic precipitation in polluted regions.
Brian M. Griffin and Vincent E. Larson
Geosci. Model Dev., 9, 4273–4295, https://doi.org/10.5194/gmd-9-4273-2016, https://doi.org/10.5194/gmd-9-4273-2016, 2016
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Microphysical process rates, such as the formation, growth, and evaporation of precipitation, affect the variances, covariances, and fluxes of moisture and heat content. These effects appear as covariance terms within the Reynolds-averaged predictive equations for the scalar (co)variances and fluxes. Using a multivariate probability density function (PDF) and a simple warm-rain microphysics scheme, these microphysical covariance terms can be obtained by analytic integration over the PDF.
Reindert J. Haarsma, Malcolm J. Roberts, Pier Luigi Vidale, Catherine A. Senior, Alessio Bellucci, Qing Bao, Ping Chang, Susanna Corti, Neven S. Fučkar, Virginie Guemas, Jost von Hardenberg, Wilco Hazeleger, Chihiro Kodama, Torben Koenigk, L. Ruby Leung, Jian Lu, Jing-Jia Luo, Jiafu Mao, Matthew S. Mizielinski, Ryo Mizuta, Paulo Nobre, Masaki Satoh, Enrico Scoccimarro, Tido Semmler, Justin Small, and Jin-Song von Storch
Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, https://doi.org/10.5194/gmd-9-4185-2016, 2016
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Recent progress in computing power has enabled climate models to simulate more processes in detail and on a smaller scale. Here we present a common protocol for these high-resolution runs that will foster the analysis and understanding of the impact of model resolution on the simulated climate. These runs will also serve as a more reliable source for assessing climate risks that are associated with small-scale weather phenomena such as tropical cyclones.
Jitendra Kumar, Nathan Collier, Gautam Bisht, Richard T. Mills, Peter E. Thornton, Colleen M. Iversen, and Vladimir Romanovsky
The Cryosphere, 10, 2241–2274, https://doi.org/10.5194/tc-10-2241-2016, https://doi.org/10.5194/tc-10-2241-2016, 2016
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Microtopography of the low-gradient polygonal tundra plays a critical role in these ecosystem; however, patterns and drivers are poorly understood. A modeling-based approach was developed in this study to characterize and represent the permafrost soils in the model and simulate the thermal dynamics using a mechanistic high-resolution model. Results shows the ability of the model to simulate the patterns and variability of thermal regimes and improve our understanding of polygonal tundra.
Brian M. Griffin and Vincent E. Larson
Geosci. Model Dev., 9, 2031–2053, https://doi.org/10.5194/gmd-9-2031-2016, https://doi.org/10.5194/gmd-9-2031-2016, 2016
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A multivariate probability density function (PDF) can be used to represent the subgrid (below grid-box size) variability of atmospheric fields. The PDF was previously extended to include hydrometeor fields, such as rain water mixing ratio. Now, the PDF of hydrometeor fields is altered to account for precipitating and precipitation-less regions of the subgrid domain. Accounting for these regions allowed the hydrometeor PDF to produce an improved match to results from large-eddy simulations.
Bin Zhao, Kuo-Nan Liou, Yu Gu, Cenlin He, Wee-Liang Lee, Xing Chang, Qinbin Li, Shuxiao Wang, Hsien-Liang R. Tseng, Lai-Yung R. Leung, and Jiming Hao
Atmos. Chem. Phys., 16, 5841–5852, https://doi.org/10.5194/acp-16-5841-2016, https://doi.org/10.5194/acp-16-5841-2016, 2016
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We examine the impact of buildings on surface solar fluxes in Beijing by accounting for their 3-D structures. We find that inclusion of buildings changes surface solar fluxes by within ±1 W m−2, ±1–10 W m−2, and up to ±100 W m−2 at grid resolutions of 4 km, 800 m, and 90 m, respectively. We can resolve pairs of positive-negative flux deviations on different sides of buildings at ≤ 800 m resolutions. We should treat building-effect on solar fluxes differently in models with different resolutions.
Zhiyuan Hu, Chun Zhao, Jianping Huang, L. Ruby Leung, Yun Qian, Hongbin Yu, Lei Huang, and Olga V. Kalashnikova
Geosci. Model Dev., 9, 1725–1746, https://doi.org/10.5194/gmd-9-1725-2016, https://doi.org/10.5194/gmd-9-1725-2016, 2016
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This study conducts the simulation of WRF-Chem with the quasi-global configuration for 2010–2014, and evaluates the simulation with multiple observation datasets for the first time. This study demonstrates that the WRF-Chem quasi-global simulation can be used for investigating trans-Pacific transport of aerosols and providing reasonable inflow chemical boundaries for the western USA to further understand the impact of transported pollutants on the regional air quality and climate.
N. I. Kristiansen, A. Stohl, D. J. L. Olivié, B. Croft, O. A. Søvde, H. Klein, T. Christoudias, D. Kunkel, S. J. Leadbetter, Y. H. Lee, K. Zhang, K. Tsigaridis, T. Bergman, N. Evangeliou, H. Wang, P.-L. Ma, R. C. Easter, P. J. Rasch, X. Liu, G. Pitari, G. Di Genova, S. Y. Zhao, Y. Balkanski, S. E. Bauer, G. S. Faluvegi, H. Kokkola, R. V. Martin, J. R. Pierce, M. Schulz, D. Shindell, H. Tost, and H. Zhang
Atmos. Chem. Phys., 16, 3525–3561, https://doi.org/10.5194/acp-16-3525-2016, https://doi.org/10.5194/acp-16-3525-2016, 2016
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Processes affecting aerosol removal from the atmosphere are not fully understood. In this study we investigate to what extent atmospheric transport models can reproduce observed loss of aerosols. We compare measurements of radioactive isotopes, that attached to ambient sulfate aerosols during the 2011 Fukushima nuclear accident, to 19 models using identical emissions. Results indicate aerosol removal that is too fast in most models, and apply to aerosols that have undergone long-range transport.
Guoping Tang, Fengming Yuan, Gautam Bisht, Glenn E. Hammond, Peter C. Lichtner, Jitendra Kumar, Richard T. Mills, Xiaofeng Xu, Ben Andre, Forrest M. Hoffman, Scott L. Painter, and Peter E. Thornton
Geosci. Model Dev., 9, 927–946, https://doi.org/10.5194/gmd-9-927-2016, https://doi.org/10.5194/gmd-9-927-2016, 2016
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We demonstrate that CLM-PFLOTRAN predictions are consistent with CLM4.5 for Arctic, temperate, and tropical sites. A tight relative tolerance may be needed to avoid false convergence when scaling, clipping, or log transformation is used to avoid negative concentration in implicit time stepping and Newton-Raphson methods. The log transformation method is accurate and robust while relaxing relative tolerance or using the clipping or scaling method can result in efficient solutions.
X. Liu, P.-L. Ma, H. Wang, S. Tilmes, B. Singh, R. C. Easter, S. J. Ghan, and P. J. Rasch
Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, https://doi.org/10.5194/gmd-9-505-2016, 2016
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In this study, we describe and evaluate a new four-mode version of the Modal Aerosol Module (MAM4) in the Community Atmosphere Model version 5 (CAM5). Compared to the current three-mode version of MAM in CAM5, MAM4 significantly improves the simulation of seasonal variation of BC concentrations in the polar regions, by increasing the BC concentrations in all seasons and particularly in cold seasons.
E. K. Raut and V. E. Larson
Geosci. Model Dev., 9, 413–429, https://doi.org/10.5194/gmd-9-413-2016, https://doi.org/10.5194/gmd-9-413-2016, 2016
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Numerical models of weather and climate can estimate grid-box-averaged rates of physical processes such as microphysics using Monte Carlo integration. Monte Carlo integration is simple and general but requires many evaluations of the physical process rate. To reduce the number of function evaluations, this paper describes a new, flexible method of importance sampling. It divides the domain into categories, and allows the modeler to prescribe the sampling density in each category.
K. Thayer-Calder, A. Gettelman, C. Craig, S. Goldhaber, P. A. Bogenschutz, C.-C. Chen, H. Morrison, J. Höft, E. Raut, B. M. Griffin, J. K. Weber, V. E. Larson, M. C. Wyant, M. Wang, Z. Guo, and S. J. Ghan
Geosci. Model Dev., 8, 3801–3821, https://doi.org/10.5194/gmd-8-3801-2015, https://doi.org/10.5194/gmd-8-3801-2015, 2015
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This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that is implemented in CAM v5.3. We show mean climate and tropical variability results from global simulations. The model has a degradation in precipitation skill but improvements in shortwave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation. We also show estimation of computational expense and sensitivity to number of subcolumns.
R. Zhang, H. Wang, D. A. Hegg, Y. Qian, S. J. Doherty, C. Dang, P.-L. Ma, P. J. Rasch, and Q. Fu
Atmos. Chem. Phys., 15, 12805–12822, https://doi.org/10.5194/acp-15-12805-2015, https://doi.org/10.5194/acp-15-12805-2015, 2015
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We use a global climate model with an explicit source tagging technique to quantify contributions of emissions from various geographical regions and sectors to BC in North America. Model results are evaluated against measurements of near-surface and in-snow BC. We found strong spatial variations of BC and its radiative forcing that can be quantitatively attributed to the various source origins, and also identified a significant source of BC in snow that is likely missing in most climate models.
X. Shi, P. E. Thornton, D. M. Ricciuto, P. J. Hanson, J. Mao, S. D. Sebestyen, N. A. Griffiths, and G. Bisht
Biogeosciences, 12, 6463–6477, https://doi.org/10.5194/bg-12-6463-2015, https://doi.org/10.5194/bg-12-6463-2015, 2015
C. He, K.-N. Liou, Y. Takano, R. Zhang, M. Levy Zamora, P. Yang, Q. Li, and L. R. Leung
Atmos. Chem. Phys., 15, 11967–11980, https://doi.org/10.5194/acp-15-11967-2015, https://doi.org/10.5194/acp-15-11967-2015, 2015
R. Zhang, H. Wang, Y. Qian, P. J. Rasch, R. C. Easter, P.-L. Ma, B. Singh, J. Huang, and Q. Fu
Atmos. Chem. Phys., 15, 6205–6223, https://doi.org/10.5194/acp-15-6205-2015, https://doi.org/10.5194/acp-15-6205-2015, 2015
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We use the CAM5 model with a novel source-tagging technique to characterize the fate of BC particles emitted from various geographical regions and sectors and their transport pathways to the Himalayas and Tibetan Plateau (HTP). We show a comprehensive picture of the seasonal and regional dependence of BC source attributions, and find strong seasonal and spatial variations in BC-in-snow radiative forcing in the HTP that can be quantitatively attributed to the various regional/sectoral sources.
W.-L. Lee, Y. Gu, K. N. Liou, L. R. Leung, and H.-H. Hsu
Atmos. Chem. Phys., 15, 5405–5413, https://doi.org/10.5194/acp-15-5405-2015, https://doi.org/10.5194/acp-15-5405-2015, 2015
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This paper investigates 3-D mountain effects on solar flux distributions and their impact on surface hydrology over the western United States, specifically the Rocky Mountains and the Sierra Nevada, using the global CCSM4 (CAM4/CLM4) with a 0.23°×0.31° resolution for simulations over 6 years. We show that deviations in the net surface fluxes are not only affected by 3-D mountains but also influenced by feedbacks of cloud and snow in association with the long-term simulations.
S. Tilmes, J.-F. Lamarque, L. K. Emmons, D. E. Kinnison, P.-L. Ma, X. Liu, S. Ghan, C. Bardeen, S. Arnold, M. Deeter, F. Vitt, T. Ryerson, J. W. Elkins, F. Moore, J. R. Spackman, and M. Val Martin
Geosci. Model Dev., 8, 1395–1426, https://doi.org/10.5194/gmd-8-1395-2015, https://doi.org/10.5194/gmd-8-1395-2015, 2015
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The Community Atmosphere Model (CAM), version 5, is now coupled to extensive tropospheric and stratospheric chemistry, called CAM5-chem, and is available in addition to CAM4-chem in the Community Earth System Model (CESM) version 1.2. Both configurations are well suited as tools for atmospheric chemistry modeling studies in the troposphere and lower stratosphere.
Y. Fang, C. Liu, and L. R. Leung
Geosci. Model Dev., 8, 781–789, https://doi.org/10.5194/gmd-8-781-2015, https://doi.org/10.5194/gmd-8-781-2015, 2015
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1. A gradient projection method was used to reduce the computation time of carbon-nitrogen spin-up processes in CLM4.
2. Point-scale simulations showed that the cyclic stability of total carbon for some cases differs from that of the periodic atmospheric forcing, and some cases even showed instability.
3. The instability issue is resolved after the hydrology scheme in CLM4 is replaced with a flow model for variably saturated porous media.
R. L. Storer, B. M. Griffin, J. Höft, J. K. Weber, E. Raut, V. E. Larson, M. Wang, and P. J. Rasch
Geosci. Model Dev., 8, 1–19, https://doi.org/10.5194/gmd-8-1-2015, https://doi.org/10.5194/gmd-8-1-2015, 2015
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Representing clouds in climate models is a challenging problem. It is particularly difficult to represent deep convective clouds and, historically, deep convective parameterization is separate from the representation of other cloud types. Here we use a single-column cloud model to simulate three deep convective cases, and two shallow cloud cases. The results look reasonable, demonstrating that it may be possible to use one parameterization within a climate model for all cloud types.
G. Bisht and W. J. Riley
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-12833-2014, https://doi.org/10.5194/hessd-11-12833-2014, 2014
Revised manuscript has not been submitted
C. Zhao, Z. Hu, Y. Qian, L. Ruby Leung, J. Huang, M. Huang, J. Jin, M. G. Flanner, R. Zhang, H. Wang, H. Yan, Z. Lu, and D. G. Streets
Atmos. Chem. Phys., 14, 11475–11491, https://doi.org/10.5194/acp-14-11475-2014, https://doi.org/10.5194/acp-14-11475-2014, 2014
G. S. H. Pau, G. Bisht, and W. J. Riley
Geosci. Model Dev., 7, 2091–2105, https://doi.org/10.5194/gmd-7-2091-2014, https://doi.org/10.5194/gmd-7-2091-2014, 2014
K. Zhang, H. Wan, X. Liu, S. J. Ghan, G. J. Kooperman, P.-L. Ma, P. J. Rasch, D. Neubauer, and U. Lohmann
Atmos. Chem. Phys., 14, 8631–8645, https://doi.org/10.5194/acp-14-8631-2014, https://doi.org/10.5194/acp-14-8631-2014, 2014
T. K. Tesfa, H.-Y. Li, L. R. Leung, M. Huang, Y. Ke, Y. Sun, and Y. Liu
Geosci. Model Dev., 7, 947–963, https://doi.org/10.5194/gmd-7-947-2014, https://doi.org/10.5194/gmd-7-947-2014, 2014
P.-L. Ma, P. J. Rasch, J. D. Fast, R. C. Easter, W. I. Gustafson Jr., X. Liu, S. J. Ghan, and B. Singh
Geosci. Model Dev., 7, 755–778, https://doi.org/10.5194/gmd-7-755-2014, https://doi.org/10.5194/gmd-7-755-2014, 2014
J. Fan, L. R. Leung, P. J. DeMott, J. M. Comstock, B. Singh, D. Rosenfeld, J. M. Tomlinson, A. White, K. A. Prather, P. Minnis, J. K. Ayers, and Q. Min
Atmos. Chem. Phys., 14, 81–101, https://doi.org/10.5194/acp-14-81-2014, https://doi.org/10.5194/acp-14-81-2014, 2014
Y. Sun, Z. Hou, M. Huang, F. Tian, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 17, 4995–5011, https://doi.org/10.5194/hess-17-4995-2013, https://doi.org/10.5194/hess-17-4995-2013, 2013
K. N. Liou, Y. Gu, L. R. Leung, W. L. Lee, and R. G. Fovell
Atmos. Chem. Phys., 13, 11709–11721, https://doi.org/10.5194/acp-13-11709-2013, https://doi.org/10.5194/acp-13-11709-2013, 2013
N. Voisin, L. Liu, M. Hejazi, T. Tesfa, H. Li, M. Huang, Y. Liu, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 4555–4575, https://doi.org/10.5194/hess-17-4555-2013, https://doi.org/10.5194/hess-17-4555-2013, 2013
Y. Fang, M. Huang, C. Liu, H. Li, and L. R. Leung
Geosci. Model Dev., 6, 1977–1988, https://doi.org/10.5194/gmd-6-1977-2013, https://doi.org/10.5194/gmd-6-1977-2013, 2013
C. Zhao, X. Liu, Y. Qian, J. Yoon, Z. Hou, G. Lin, S. McFarlane, H. Wang, B. Yang, P.-L. Ma, H. Yan, and J. Bao
Atmos. Chem. Phys., 13, 10969–10987, https://doi.org/10.5194/acp-13-10969-2013, https://doi.org/10.5194/acp-13-10969-2013, 2013
C. Zhao, S. Chen, L. R. Leung, Y. Qian, J. F. Kok, R. A. Zaveri, and J. Huang
Atmos. Chem. Phys., 13, 10733–10753, https://doi.org/10.5194/acp-13-10733-2013, https://doi.org/10.5194/acp-13-10733-2013, 2013
V. E. Larson and D. P. Schanen
Geosci. Model Dev., 6, 1813–1829, https://doi.org/10.5194/gmd-6-1813-2013, https://doi.org/10.5194/gmd-6-1813-2013, 2013
N. Voisin, H. Li, D. Ward, M. Huang, M. Wigmosta, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 3605–3622, https://doi.org/10.5194/hess-17-3605-2013, https://doi.org/10.5194/hess-17-3605-2013, 2013
Y. Ke, L. R. Leung, M. Huang, and H. Li
Geosci. Model Dev., 6, 1609–1622, https://doi.org/10.5194/gmd-6-1609-2013, https://doi.org/10.5194/gmd-6-1609-2013, 2013
H. Wan, P. J. Rasch, K. Zhang, J. Kazil, and L. R. Leung
Geosci. Model Dev., 6, 861–874, https://doi.org/10.5194/gmd-6-861-2013, https://doi.org/10.5194/gmd-6-861-2013, 2013
H. Wang, R. C. Easter, P. J. Rasch, M. Wang, X. Liu, S. J. Ghan, Y. Qian, J.-H. Yoon, P.-L. Ma, and V. Vinoj
Geosci. Model Dev., 6, 765–782, https://doi.org/10.5194/gmd-6-765-2013, https://doi.org/10.5194/gmd-6-765-2013, 2013
Related subject area
Climate and Earth system modeling
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Towards the definition of a solar forcing dataset for CMIP7
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region
Constraining the carbon cycle in JULES-ES-1.0
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data
The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation
Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1
Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations
Exploring the ocean mesoscale at reduced computational cost with FESOM 2.5: efficient modeling strategies applied to the Southern Ocean
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid-ammonia nucleation
Truly conserving with conservative remapping methods
High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia
Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc
Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts
Understanding changes in cloud simulations from E3SM version 1 to version 2
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Getting the leaves right matters for estimating temperature extremes
The Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative: scientific objectives and experimental design
Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
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Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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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.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
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This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
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Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
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Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
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This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
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The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
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Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Carl Svenhag, Moa Kristina Sporre, Tinja Olenius, Daniel Yazgi, Sara Marie Blichner, Lars Peter Nieradzik, and Pontus Roldin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2665, https://doi.org/10.5194/egusphere-2023-2665, 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 showed that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacted the number of particles in the air and clouds. It also changed the radiation balance in the same magnitude as man-made greenhouse emissions.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
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Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
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This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
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This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
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Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
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Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
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Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
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Short summary
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.
The land surface in one grid cell may be diverse in character. This study uses an explicit way...
