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2024

  1. Abeshu, G. W., Li, H.-Y., Shi, M., Brookshire, J., Tang, J., Xu, C., et al. (2024). Generalized Relationship Linking Water Balance and Vegetation Productivity across Site-to-Regional Scales. Journal of Hydrologic Engineering, 29(5), 04024030. https://doi.org/10.1061/JHYEFF.HEENG-6163
  2. Balaguru, K., Wang, S. S. ‐C., Leung, L. R., Hagos, S., Harrop, B., Chang, C., et al. (2024). Influence of Eastern Pacific Hurricanes on the Southwest US Wildfire Environment. Geophysical Research Letters, 51(4), e2023GL106774. https://doi.org/10.1029/2023GL106774
  3. Bisht, G., Riley, W. J., & Mills, R. T. (2024). Development of a multi-layer canopy model for E3SM Land Model with support for heterogeneous computing. Journal of Computational Science, 81, 102366. https://doi.org/10.1016/j.jocs.2024.102366
  4. Donahue, A. S., Caldwell, P. M., Bertagna, L., Beydoun, H., Bogenschutz, P. A., Bradley, A. M., et al. (2024). To Exascale and Beyond—The Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM), a Performance Portable Global Atmosphere Model for Cloud‐Resolving Scales. Journal of Advances in Modeling Earth Systems, 16(7), e2024MS004314. https://doi.org/10.1029/2024MS004314
  5. Guba, O., Taylor, M. A., Bosler, P. A., Eldred, C., & Lauritzen, P. H. (2024). Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models. Geoscientific Model Development, 17(3), 1429–1442. https://doi.org/10.5194/gmd-17-1429-2024
  6. Hassan, T., Zhang, K., Li, J., Singh, B., Zhang, S., Wang, H., & Ma, P.-L. (2024). Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model. Geoscientific Model Development, 17(8), 3507–3532. https://doi.org/10.5194/gmd-17-3507-2024
  7. La Fuente, S., Jennings, E., Lenters, J. D., Verburg, P., Tan, Z., Perroud, M., et al. (2024). Ensemble modeling of global lake evaporation under climate change. Journal of Hydrology, 631, 130647. https://doi.org/10.1016/j.jhydrol.2024.130647
  8. Lee, H.-H., Tang, Q., & Prather, M. (2024, January 19). E3SM Chemistry Diagnostics Package (ChemDyg) Version 0.1.4. https://doi.org/10.5194/gmd-2023-203
  9. Mahfouz, N., Mülmenstädt, J., & Burrows, S. (2024, February 9). Present-day correlations insufficient to constrain cloud albedo change by anthropogenic aerosols in E3SM v2. https://doi.org/10.5194/egusphere-2024-366
  10. Qin, Y., Tang, Q., Xue, Y., Liu, Y., & Lin, Y. (2024). Improved subseasonal-to-seasonal precipitation prediction of climate models with nudging approach for better initialization of Tibetan Plateau-Rocky Mountain Circumglobal wave train and land surface conditions. Climate Dynamics. https://doi.org/10.1007/s00382-023-07082-1
  11. Qin, Y., Zheng, X., Klein, S. A., Zelinka, M. D., Ma, P., Golaz, J., & Xie, S. (2024). Causes of Reduced Climate Sensitivity in E3SM From Version 1 to Version 2. Journal of Advances in Modeling Earth Systems, 16(1), e2023MS003875. https://doi.org/10.1029/2023MS003875
  12. Shan, Y., Fan, J., Zhang, K., Shpund, J., Terai, C., Zhang, G. J., et al. (2024). Improving Aerosol Radiative Forcing and Climate in E3SM: Impacts of New Cloud Microphysics and Improved Wet Removal Treatments. Journal of Advances in Modeling Earth Systems, 16(8), e2023MS004059. https://doi.org/10.1029/2023MS004059
  13. Shi, X., Wang, Y., Mao, J., Thornton, P. E., Riccuito, D. M., Hoffman, F. M., & Hao, Y. (2024). Quantifying the long-term changes of terrestrial water storage and their driving factors. Journal of Hydrology, 635, 131096. https://doi.org/10.1016/j.jhydrol.2024.131096
  14. Tan, Z., Yao, H., Melack, J., Grossart, H., Jansen, J., Balathandayuthabani, S., et al. (2024). A Lake Biogeochemistry Model for Global Methane Emissions: Model Development, Site‐Level Validation, and Global Applicability. Journal of Advances in Modeling Earth Systems, 16(10), e2024MS004275. https://doi.org/10.1029/2024MS004275
  15. Tesfa, T. K., Leung, L. R., Thornton, P. E., Brunke, M. A., & Duan, Z. (2024). Impacts of Topography‐Based Subgrid Scheme and Downscaling of Atmospheric Forcing on Modeling Land Surface Processes in the Conterminous US. Journal of Advances in Modeling Earth Systems, 16(8), e2023MS004064. https://doi.org/10.1029/2023MS004064
  16. Tolento, J. P., Zender, C. S., & Whicker‐Clarke, C. A. (2024). Surface and Atmospheric Heating Responses to Spectrally Resolved Albedo of Frozen and Liquid Water Surfaces. Journal of Geophysical Research: Atmospheres, 129(9), e2023JD039824. https://doi.org/10.1029/2023JD039824
  17. Vo, T., Po-Chedley, S., Boutte, J., Lee, J., & Zhang, C. (2024). xCDAT: A Python Package for Simple and Robust Analysisof Climate Data. Journal of Open Source Software, 9(98), 6426. https://doi.org/10.21105/joss.06426
  18. Wan, H., Zhang, K., Vogl, C. J., Woodward, C. S., Easter, R. C., Rasch, P. J., et al. (2024). Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 1: Dust budget analyses and the impacts of a revised coupling scheme. Geoscientific Model Development, 17(3), 1387–1407. https://doi.org/10.5194/gmd-17-1387-2024
  19. Whicker‐Clarke, C. A., Antwerpen, R., Flanner, M. G., Schneider, A., Tedesco, M., & Zender, C. S. (2024). The Effect of Physically Based Ice Radiative Processes on Greenland Ice Sheet Albedo and Surface Mass Balance in E3SM. Journal of Geophysical Research: Atmospheres, 129(7), e2023JD040241. https://doi.org/10.1029/2023JD040241
  20. Yarger, D., Wagman, B. M., Chowdhary, K., & Shand, L. (2024). Autocalibration of the E3SM Version 2 Atmosphere Model Using a PCA‐Based Surrogate for Spatial Fields. Journal of Advances in Modeling Earth Systems, 16(4), e2023MS003961. https://doi.org/10.1029/2023MS003961
  21. Zhang, M., Xie, S., Feng, Z., Terai, C. R., Lin, W., Tao, C., et al. (2024). Mesoscale Convective Systems Represented in High Resolution E3SMv2 and Impact of New Cloud and Convection Parameterizations. Journal of Geophysical Research: Atmospheres, 129(18), e2024JD040828. https://doi.org/10.1029/2024JD040828
  22. Zhang, X., Fang, Y., Niu, G., Troch, P. A., Guo, B., Leung, L. R., et al. (2024). Impacts of Topography‐Driven Water Redistribution on Terrestrial Water Storage Change in California Through Ecosystem Responses. Water Resources Research, 60(2), e2023WR035572. https://doi.org/10.1029/2023WR035572
  23. Zhang, Y., Xie, S., Qin, Y., Lin, W., Golaz, J.-C., Zheng, X., et al. (2024). Understanding changes in cloud simulations from E3SM version 1 to version 2. Geoscientific Model Development, 17(1), 169–189. https://doi.org/10.5194/gmd-17-169-2024
  24. Zhu, Q., Riley, W. J., Tang, J., & Bouskill, N. J. (2024). Plant responses to elevated CO 2 under competing hypotheses of nitrogen and phosphorus limitations. Ecological Applications, 34(3), e2967. https://doi.org/10.1002/eap.2967

2023

 

  1. Abeshu, G. W., Li, H.-Y., Shi, M., Brookshire, J., Tang, J., Xu, C., et al. (2023, May 25). A generalized relationship linking water balance and vegetation carbon uptake across site-to-regional scales. https://doi.org/10.22541/essoar.168500385.58575293/v1
  2. Bogenschutz, P. A., Eldred, C., & Caldwell, P. M. (2023). Horizontal Resolution Sensitivity of the Simple Convection‐Permitting E3SM Atmosphere Model in a Doubly‐Periodic Configuration. Journal of Advances in Modeling Earth Systems, 15(7), e2022MS003466. https://doi.org/10.1029/2022MS003466
  3. Fang, Y., & Leung, L. R. (2023). Northern Hemisphere Snow Drought in Earth System Model Simulations and ERA5‐Land Data in 1980–2014. Journal of Geophysical Research: Atmospheres, 128(23), e2023JD039308. https://doi.org/10.1029/2023JD039308
  4. Feng, Z., Leung, L. R., Hardin, J., Terai, C. R., Song, F., & Caldwell, P. (2023). Mesoscale Convective Systems in DYAMOND Global Convection‐Permitting Simulations. Geophysical Research Letters, 50(4), e2022GL102603. https://doi.org/10.1029/2022GL102603
  5. Ghimire, G. R., Hansen, C., Gangrade, S., Kao, S., Thornton, P. E., & Singh, D. (2023). Insights From Dayflow: A Historical Streamflow Reanalysis Dataset for the Conterminous United States. Water Resources Research, 59(2). https://doi.org/10.1029/2022WR032312
  6. Hao, D., Bisht, G., Wang, H., Xu, D., Huang, H., Qian, Y., & Leung, L. R. (2023). A cleaner snow future mitigates Northern Hemisphere snowpack loss from warming. Nature Communications, 14(1), 6074. https://doi.org/10.1038/s41467-023-41732-6
  7. Harrop, B. E., Balaguru, K., Golaz, J., Leung, L. R., Mahajan, S., Rhoades, A. M., et al. (2023). Evaluating the Water Cycle Over CONUS at the Watershed Scale for the Energy Exascale Earth System Model Version 1 (E3SMv1) Across Resolutions. Journal of Advances in Modeling Earth Systems, 15(11), e2022MS003490. https://doi.org/10.1029/2022MS003490
  8. Ikuyajolu, O. J., Van Roekel, L., Brus, S. R., Thomas, E. E., Deng, Y., & Sreepathi, S. (2023). Porting the WAVEWATCH III (v6.07) wave action source terms to GPU. Geoscientific Model Development, 16(4), 1445–1458. https://doi.org/10.5194/gmd-16-1445-2023
  9. Jeong, H., Turner, A. K., Roberts, A. F., Veneziani, M., Price, S. F., Asay-Davis, X. S., et al. (2023). Southern Ocean polynyas and dense water formation in a high-resolution, coupled Earth system model. The Cryosphere, 17(7), 2681–2700. https://doi.org/10.5194/tc-17-2681-2023
  10. Lee, J. M., Tao, C., Hannah, W. M., Xie, S., & Bader, D. C. (2023). Assessment of Warm and Dry Bias over ARM SGP Site in E3SMv2 and E3SM-MMF. Journal of the Atmospheric Sciences, 80(10), 2545–2556. https://doi.org/10.1175/JAS-D-23-0062.1
  11. Mahajan, S., Passarella, L. S., Tang, Q., Keen, N. D., Caldwell, P. M., Van Roekel, L. P., & Golaz, J. (2023). ENSO Diversity and the Simulation of Its Teleconnections to Winter Precipitation Extremes Over the US in High Resolution Earth System Models. Geophysical Research Letters, 50(11), e2022GL102657. https://doi.org/10.1029/2022GL102657
  12. Manzo, L., Zender, C. S., Tolento, J. P., & Whicker, C. A. (2023, December 27). Spectrally Resolved Longwave Surface Emissivity Reduces Atmospheric Heating Biases. https://doi.org/10.22541/essoar.170365370.01968576/v1
  13. Martin, Z. K., Simpson, I. R., Lin, P., Orbe, C., Tang, Q., Caron, J. M., et al. (2023). The Lack of a QBO‐MJO Connection in Climate Models With a Nudged Stratosphere. Journal of Geophysical Research: Atmospheres, 128(17), e2023JD038722. https://doi.org/10.1029/2023JD038722
  14. Passarella, L. S., & Mahajan, S. (2023). Assessing Tropical Pacific-induced Predictability of Southern California Precipitation Using a Novel Multi-input Multi-output Autoencoder. Artificial Intelligence for the Earth Systems, 1–30. https://doi.org/10.1175/AIES-D-23-0003.1
  15. Qian, Y., Guo, Z., Larson, V. E., Leung, L. R., Lin, W., Ma, P.-L., et al. (2023). Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model. Climate Dynamics. https://doi.org/10.1007/s00382-023-06977-3
  16. Schneider, A., Zender, C., Loeb, N., & Price, S. (2023). Use of Shallow Ice Core Measurements to Evaluate and Constrain 1980–1990 Global Reanalyses of Ice Sheet Precipitation Rates. Geophysical Research Letters, 50(19), e2023GL103943. https://doi.org/10.1029/2023GL103943
  17. Schwartz, P., Wang, D., Yuan, F., & Thornton, P. (2023). Developing Ultrahigh-Resolution E3SM Land Model for GPU Systems. In O. Gervasi, B. Murgante, D. Taniar, B. O. Apduhan, A. C. Braga, C. Garau, & A. Stratigea (Eds.), Computational Science and Its Applications – ICCSA 2023 (Vol. 13956, pp. 277–290). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-36805-9_19
  18. Shan et al. (2023). Improving Aerosol Radiative Forcing and Climate in E3SM: Impacts of New Cloud Microphysics and Improved Wet Removal Treatments. https://doi.org/10.5281/ZENODO.8417139
  19. Silva, S. J., Burrows, S. M., Calvin, K., Cameron‐Smith, P. J., Shi, X., & Zhou, T. (2023). Contrasting the Biophysical and Radiative Effects of Rising CO 2 Concentrations on Ozone Dry Deposition Fluxes. Journal of Geophysical Research: Atmospheres, 128(6), e2022JD037668. https://doi.org/10.1029/2022JD037668
  20. Sinha, E., Calvin, K. V., Bond‐Lamberty, B., Drewniak, B. A., Ricciuto, D. M., Sargsyan, K., et al. (2023). Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2). Journal of Advances in Modeling Earth Systems, 15(1), e2022MS003171. https://doi.org/10.1029/2022MS003171
  21. Sinha, E., Bond‐Lamberty, B., Calvin, K. V., Drewniak, B. A., Bisht, G., Bernacchi, C., et al. (2023). The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2). Journal of Geophysical Research: Biogeosciences, 128(3), e2022JG007187. https://doi.org/10.1029/2022JG007187
  22. Song, X., Zhang, G., Wan, H., & Xie, S. (2023). Incorporating the Effect of Large‐Scale Vertical Motion on Convection Through Convective Mass Flux Adjustment in E3SMv2. Journal of Advances in Modeling Earth Systems, 15(10), e2022MS003553. https://doi.org/10.1029/2022MS003553
  23. Strauss, R. R., Bishnu, S., & Petersen, M. R. (2023). Julia for Geophysical Fluid Dynamics: Performance Comparisons between CPU, GPU, and Fortran-MPI (preprint). Preprints. https://doi.org/10.22541/essoar.167390522.28476012/v1
  24. Tang, Q., Golaz, J.-C., Van Roekel, L. P., Taylor, M. A., Lin, W., Hillman, B. R., et al. (2023). The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results. Geoscientific Model Development, 16(13), 3953–3995. https://doi.org/10.5194/gmd-16-3953-2023
  25. Taylor, Mark A. (SNL), Peter M. Caldwell (LLNL), Luca Bertagna (SNL), Conrad Clevenger (SNL), Aaron S. Donahue (LLNL), James G. Foucar (SNL), Oksana Guba (SNL), Benjamin R. Hillman (SNL), Noel Keen (LBNL), Jayesh Krishna (ANL), Matthew R. Norman (ORNL), Sarat Sreepathi (ORNL), Christopher R. Terai (LLNL), James B. White III (HPE), Danqing Wu (ANL), Andrew G. Salinger (SNL), Renata B. McCoy (LLNL), L. Ruby Leung (PNNL), David C. Bader (LLNL), The Simple Cloud-Resolving E3SM Atmosphere Model Running on the Frontier Exascale System, Submission to the ACM Gordon Bell Prize for Climate Modeling, 2023. https://doi.org/10.1145/3581784.3627044
  26. Tebaldi, C., Wehner, M., Leung, R., & Lawrence, D. (2023). Is land use producing robust signals in future projections from Earth system models, all else being equal? Environmental Research Letters, 18(8), 084009. https://doi.org/10.1088/1748-9326/ace3da
  27. Wan, H., Zhang, K., Vogl, C. J., Woodward, C. S., Easter, R. C., Rasch, P. J., et al. (2023a). Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1), part I: dust budget analyses and the impacts of a revised coupling scheme (Version 3). https://doi.org/10.48550/ARXIV.2306.05377
  28. Yang, X., Thornton, P., Ricciuto, D., Wang, Y., & Hoffman, F. (2023). Global evaluation of terrestrial biogeochemistry in the Energy Exascale Earth System Model (E3SM) and the role of the phosphorus cycle in the historical terrestrial carbon balance. Biogeosciences, 20(14), 2813–2836. https://doi.org/10.5194/bg-20-2813-2023
  29. Yuan, F., Wang, D., Kao, S.-C., Thornton, M., Ricciuto, D., Salmon, V., et al. (2023). An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska. Journal of Computational Science, 73, 102145. https://doi.org/10.1016/j.jocs.2023.102145
  30. Zhang, M., Xie, S., Liu, X., Zhang, D., Lin, W., Zhang, K., et al. (2023). Evaluating EAMv2 Simulated High Latitude Clouds Using ARM Measurements in the Northern and Southern Hemispheres. Journal of Geophysical Research: Atmospheres, 128(15), e2022JD038364. https://doi.org/10.1029/2022JD038364
  31. Zhang, Y., Xie, S., Qin, Y., Lin, W., Golaz, J.-C., Zheng, X., et al. (2023). Understanding Changes in Cloud Simulations from E3SM Version 1 to Version 2 (preprint). Climate and Earth system modeling. https://doi.org/10.5194/egusphere-2023-1263
  32. Zhu, Q., Riley, W., Tang, J., Burrows, S., Harrop, B., Shi, X., et al. (2023). Present and Future Changes in Land‐Atmosphere Coupling of Water and Energy Over Extratropical Forest Regions. Journal of Geophysical Research: Atmospheres, 128(8), e2022JD037887. https://doi.org/10.1029/2022JD037887
  33. Zhuang, Q., Guo, M., Melack, J. M., Lan, X., Tan, Z., Oh, Y., & Leung, L. R. (2023). Current and Future Global Lake Methane Emissions: A Process‐Based Modeling Analysis. Journal of Geophysical Research: Biogeosciences, 128(3), e2022JG007137. https://doi.org/10.1029/2022JG007137

2022

 

  1. Abeshu, G. W., Li, H.-Y., Zhu, Z., Tan, Z., & Leung, L. R. (2022). Median bed-material sediment particle size across rivers in the contiguous US. Earth System Science Data, 14(2), 929–942. https://doi.org/10.5194/essd-14-929-2022
  2. Balaguru, K., Foltz, G. R., Leung, L. R., & Hagos, S. M. (2022). Impact of Rainfall on Tropical Cyclone‐Induced Sea Surface Cooling. Geophysical Research Letters, 49(10). https://doi.org/10.1029/2022GL098187
  3. Begeman, C. B., Asay-Davis, X., & Van Roekel, L. (2022). Ice-shelf ocean boundary layer dynamics from large-eddy simulations. The Cryosphere, 16(1), 277–295. https://doi.org/10.5194/tc-16-277-2022
  4. Bishnu, S., Petersen, M. R., Quaife, B., & Schoonover, J. A. (2022). A Verification Suite of Test Cases for the Barotropic Solver of Ocean Models (preprint). Preprints. https://doi.org/10.22541/essoar.167100170.03833124/v1
  5. Bogenschutz, P. A., Bogenschutz, P. A., Eldred, C., & Caldwell, P. M. (2022). Horizontal Resolution Sensitivity of the Simple Convection-Permitting E3SM Atmosphere Model in a Doubly-Periodic Configuration (preprint). Preprints. https://doi.org/10.22541/essoar.167252708.80272066/v1
  6. Bradley, A. M., Bosler, P. A., & Guba, O. (2022). Islet: interpolation semi-Lagrangian element-based transport. Geoscientific Model Development, 15(16), 6285–6310. https://doi.org/10.5194/gmd-15-6285-2022
  7. Brown, H., Wang, H., Flanner, M., Liu, X., Singh, B., Zhang, R., et al. (2022). Brown Carbon Fuel and Emission Source Attributions to Global Snow Darkening Effect. Journal of Advances in Modeling Earth Systems, 14(4). https://doi.org/10.1029/2021MS002768
  8. Burrows, S. M., Easter, R. C., Liu, X., Ma, P.-L., Wang, H., Elliott, S. M., et al. (2022). OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) sea spray organic aerosol emissions – implementation in a global climate model and impacts on clouds. Atmospheric Chemistry and Physics, 22(8), 5223–5251. https://doi.org/10.5194/acp-22-5223-2022
  9. Calandrini, S., Jones, P. W., Petersen, M. R., (2022). An Exponential Time Differencing Time-Stepping Scheme for the Tracer Equations in MPAS-Ocean.
    International Journal of Numerical Analysis and Modeling. 19 (2-3). 175-193. https://global-sci.org/intro/article_detail/ijnam/20476.html
  10. Comeau, D., Asay‐Davis, X. S., Begeman, C. B., Hoffman, M. J., Lin, W., Petersen, M. R., et al. (2022). The DOE E3SM v1.2 Cryosphere Configuration: Description and Simulated Antarctic Ice‐Shelf Basal Melting. Journal of Advances in Modeling Earth Systems, 14(2). https://doi.org/10.1029/2021MS002468
  11. Fang, Y., Leung, R., Knox, R., Koven, C., & Bond-Lamberty, B. (2022). Impact of numerical solution approach of a plant hydrodynamic model on vegetation dynamics (preprint). Numerical methods. https://doi.org/10.5194/gmd-2022-105
  12. Fang, Y., & Leung, L. R. (2022). Relative Controls of Vapor Pressure Deficit and Soil Water Stress on Canopy Conductance in Global Simulations by an Earth System Model. Earth’s Future, 10(9). https://doi.org/10.1029/2022EF002810
  13. Fang, Y., Leung, L. R., Knox, R., Koven, C., & Bond-Lamberty, B. (2022). Impact of the numerical solution approach of a plant hydrodynamic model (v0.1) on vegetation dynamics. Geoscientific Model Development, 15(16), 6385–6398. https://doi.org/10.5194/gmd-15-6385-2022
  14. Feng, Y., Wang, H., Rasch, P. J., Zhang, K., Lin, W., Tang, Q., et al. (2022). Global Dust Cycle and Direct Radiative Effect in E3SM Version 1: Impact of Increasing Model Resolution. Journal of Advances in Modeling Earth Systems, 14(7). https://doi.org/10.1029/2021MS002909
  15. Fung, K. M., Heald, C. L., Kroll, J. H., Wang, S., Jo, D. S., Gettelman, A., et al. (2022). Exploring dimethyl sulfide (DMS) oxidation and implications for global aerosol radiative forcing. Atmospheric Chemistry and Physics, 22(2), 1549–1573. https://doi.org/10.5194/acp-22-1549-2022
  16. Golaz, J.-C., Van Roekel, L. P., Zheng, X., Roberts, A., Wolfe, J. D., Lin, W., et al. (2022). The DOE E3SM Model Version 2: Overview of the physical model (preprint). Climatology (Global Change). https://doi.org/10.1002/essoar.10511174.1
  17. Golaz, J., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., et al. (2022). The DOE E3SM Model Version 2: Overview of the Physical Model and Initial Model Evaluation. Journal of Advances in Modeling Earth Systems, 14(12). https://doi.org/10.1029/2022MS003156
  18. Golaz, J.-C., Van Roekel, L. P., Zheng, X., Roberts, A., Wolfe, J. D., Lin, W., et al. (2022). The DOE E3SM Model Version 2: Overview of the physical model and initial model evaluation (preprint). Climatology (Global Change). https://doi.org/10.1002/essoar.10511174.2
  19. Hannah, W. (2022). E3SMv2 branch used for checkerboard signal analysis. Zenodo. https://doi.org/10.5281/ZENODO.6407199
  20. Hannah, W., Pressel, K., Ovchinnikov, M., & Elsaesser, G. (2022). Checkerboard patterns in E3SMv2 and E3SM-MMFv2. Geoscientific Model Development, 15(15), 6243–6257. https://doi.org/10.5194/gmd-15-6243-2022
  21. Harrop, B. E., Balaguru, K., Golaz, J.-C., Leung, L. R., Mahajan, S., Rhoades, A. M., et al. (2022). Evaluating the water cycle over CONUS at the watershed scale for the Energy Exascale Earth System Model version 1 (E3SMv1) across resolutions (preprint). Climatology (Global Change). https://doi.org/10.1002/essoar.10512849.1
  22. Harrop, B. E., Burrows, S. M., Calvin, K., Kooperman, G. J., Leung, L. R., Maltrud, M. E., et al. (2022). Diurnal rainfall response to the physiological and radiative effects of CO 2 in tropical forests in the Energy Exascale Earth System Model v1. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2021JD036148
  23. Hickmon, Nicki L., Varadharajan, Charuleka, Hoffman, Forrest M., Collis, Scott, and Wainwright, Haruko M. Artificial Intelligence for Earth System Predictability (AI4ESP) Workshop Report. United States: N. p., 2022. Web. doi:10.2172/1888810.
  24. Holland, M., Hunke, E., & National Center for Atmospheric Research. (2022). A Review of Arctic Sea Ice Climate Predictability in Large-Scale Earth System Models. Oceanography. https://doi.org/10.5670/oceanog.2022.113
  25. Huang, M., Ma, P.-L., Chaney, N. W., Hao, D., Bisht, G., Fowler, M. D., et al. (2022). Representing surface heterogeneity in land–atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertime. Geoscientific Model Development, 15(16), 6371–6384. https://doi.org/10.5194/gmd-15-6371-2022
  26. Kim, S., Leung, L. R., Guan, B., & Chiang, J. C. H. (2022). Atmospheric river representation in the Energy Exascale Earth System Model (E3SM) version 1.0. Geoscientific Model Development, 15(14), 5461–5480. https://doi.org/10.5194/gmd-15-5461-2022
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  50. Wang, J., Fan, J., Feng, Z., Zhang, K., Roesler, E., Hillman, B., et al. (2021). Impact of a New Cloud Microphysics Parameterization on the Simulations of Mesoscale Convective Systems in E3SM. Journal of Advances in Modeling Earth Systems, 13(11). https://doi.org/10.1029/2021MS002628
  51. Wang, J., Fan, J., Houze Jr., R. A., Brodzik, S. R., Zhang, K., Zhang, G. J., & Ma, P.-L. (2021). Using radar observations to evaluate 3-D radar echo structure simulated by the Energy Exascale Earth System Model (E3SM) version 1. Geoscientific Model Development, 14(2), 719–734. https://doi.org/10.5194/gmd-14-719-2021
  52. Wang, M., Xu, B., Wang, H., Zhang, R., Yang, Y., Gao, S., et al. (2021). Black carbon deposited in Hariqin Glacier of the Central Tibetan Plateau record changes in the emission from Eurasia. Environmental Pollution, 273, 115778. https://doi.org/10.1016/j.envpol.2020.115778
  53. Wang, W., Zender, C. S., van As, D., Fausto, R. S., & Laffin, M. K. (2021). Greenland Surface Melt Dominated by Solar and Sensible Heating. Geophysical Research Letters, 48(7). https://doi.org/10.1029/2020GL090653
  54. Wang, Yong (2021). Disproportionate control on aerosol burden by light rain. Nature Geoscience.  https://doi.org/10.1038/s41561-020-00675-z
  55. Wang, Y., Zhang, G. J., Xie, S., Lin, W., Craig, G. C., Tang, Q., & Ma, H.-Y. (2021). Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model. Geoscientific Model Development, 14(3), 1575–1593. https://doi.org/10.5194/gmd-14-1575-2021
  56. Woolway, R. I., Denfeld, B., Tan, Z., Jansen, J., Weyhenmeyer, G. A., & La Fuente, S. (2021). Winter inverse lake stratification under historic and future climate change. Limnology and Oceanography Letters, lol2.10231. https://doi.org/10.1002/lol2.10231
  57. Woolway, R. I., Sharma, S., Weyhenmeyer, G. A., Debolskiy, A., Golub, M., Mercado-Bettín, D., et al. (2021). Phenological shifts in lake stratification under climate change. Nature Communications, 12(1), 2318. https://doi.org/10.1038/s41467-021-22657-4
  58. Xu, L., Zhu, Q., Riley, W. J., Chen, Y., Wang, H., Ma, P.-L., & Randerson, J. T. (2021). The influence of fire aerosols on surface climate and gross primary production in the Energy Exascale Earth System Model (E3SM). Journal of Climate, 1–60. https://doi.org/10.1175/JCLI-D-21-0193.1
  59. Xue, Y., Yao, T., Boone, A. A., Diallo, I., Liu, Y., Zeng, X., et al. (2021). Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase I (LS4P-I): organization and experimental design. Geoscientific Model Development, 14(7), 4465–4494. https://doi.org/10.5194/gmd-14-4465-2021
  60. Yang, C. A., Diao, M., Gettelman, A., Zhang, K., Sun, J., McFarquhar, G., & Wu, W. (2021). Ice and Supercooled Liquid Water Distributions Over the Southern Ocean Based on In Situ Observations and Climate Model Simulations. Journal of Geophysical Research: Atmospheres, 126(24). https://doi.org/10.1029/2021JD036045
  61. Yang, Y., Zhou, Y., Li, K., Wang, H., Ren, L., Zeng, L., et al. (2021). Atmospheric Circulation Patterns Conducive to Severe Haze in Eastern China Have Shifted Under Climate Change. Geophysical Research Letters, 48(23). https://doi.org/10.1029/2021GL095011
  62. Yuan, F., Wang, Y., Ricciuto, D. M., Shi, X., Yuan, F., Hanson, P. J., et al. (2021). An Integrative Model for Soil Biogeochemistry and Methane Processes. II: Warming and Elevated CO 2 Effects on Peatland CH 4 Emissions. Journal of Geophysical Research: Biogeosciences, 126(8). https://doi.org/10.1029/2020JG005963
  63. Yuan, F., Wang, Y., Ricciuto, D. M., Shi, X., Yuan, F., Brehme, T., et al. (2021). Hydrological feedbacks on peatland CH4 emission under warming and elevated CO2: A modeling study. Journal of Hydrology, 603, 127137. https://doi.org/10.1016/j.jhydrol.2021.127137
  64. Zampieri, L., Kauker, F., Fröhle, J., Sumata, H., Hunke, E. C., & Goessling, H. F. (2021). Impact of Sea‐Ice Model Complexity on the Performance of an Unstructured‐Mesh Sea‐Ice/Ocean Model under Different Atmospheric Forcings. Journal of Advances in Modeling Earth Systems, 13(5). https://doi.org/10.1029/2020MS002438
  65. Zaveri, R. A., Easter, R. C., Singh, B., Wang, H., Lu, Z., Tilmes, S., et al. (2021). Development and Evaluation of Chemistry‐Aerosol‐Climate Model CAM5‐Chem‐MAM7‐MOSAIC: Global Atmospheric Distribution and Radiative Effects of Nitrate Aerosol. Journal of Advances in Modeling Earth Systems, 13(4). https://doi.org/10.1029/2020MS002346
  66. Zeng, L., Yang, Y., Wang, H., Wang, J., Li, J., Ren, L., et al. (2021). Intensified modulation of winter aerosol pollution in China by El Niño with short duration. Atmospheric Chemistry and Physics, 21(13), 10745–10761. https://doi.org/10.5194/acp-21-10745-2021
  67. Zeng, X., Reeves Eyre, J. E. J., Dixon, R. D., & Arevalo, J. (2021). Quantifying the Occurrence of Record Hot Years Through Normalized Warming Trends. Geophysical Research Letters, 48(10). https://doi.org/10.1029/2020GL091626
  68. Zhang, T., Lin, W., Vogelmann, A. M., Zhang, M., Xie, S., Qin, Y., & Golaz, J. (2021). Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning. Journal of Advances in Modeling Earth Systems, 13(5). https://doi.org/10.1029/2020MS002365

2020

 

  1. Bailey, D. A., Holland, M. M., DuVivier, A. K., Hunke, E. C., & Turner, A. K. (2020). Impact of a New Sea Ice Thermodynamic Formulation in the CESM2 Sea Ice Component. Journal of Advances in Modeling Earth Systems, 12(11). https://doi.org/10.1029/2020MS002154
  2. Balaguru, K., Leung, L. R., Van Roekel, L., Golaz, J.-C., Ullrich, P., Caldwell, P. M., et al. (2020). Characterizing Tropical Cyclones in the Energy Exascale Earth System Model version 1. Journal of Advances in Modeling Earth Systems, n/a(n/a), e2019MS002024. https://doi.org/10.1029/2019MS002024
  3. Balaguru, K., Patricola, C. M., Hagos, S. M., Leung, L. R., & Dong, L. (2020). Enhanced predictability of eastern North Pacific tropical cyclone activity using the ENSO Longitude Index. Geophysical Research Letters, n/a(n/a), e2020GL088849. https://doi.org/10.1029/2020GL088849
  4. Bogenschutz, P. A., Tang, S., Caldwell, P. M., Xie, S., Lin, W., & Chen, Y. (2020). The E3SM version 1 Single Column Model. Geoscientific Model Development Discussions, 1–26. https://doi.org/10.5194/gmd-2020-27
  5. Burrows, S.M., Maltrud, M.E., Yang, X., Zhu, Q., Jeffery, N., Shi, X., & Ricciuto, D.M., et al. 2020. The DOE E3SM coupled model v1.1 biogeochemistry configuration: overview and evaluation of coupled carbon-climate experiments. Journal of Advances in Modeling Earth Systems. https://doi.org/10.1029/2019MS001766
  6. Butler, E. E., Chen, M., Ricciuto, D., Flores‐Moreno, H., Wythers, K. R., Kattge, J., et al. (2020). Seeing the Canopy for the Branches: Improved Within Canopy Scaling of Leaf Nitrogen. Journal of Advances in Modeling Earth Systems, 12(10). https://doi.org/10.1029/2020MS002237
  7. Chen, Y., Huang, X., Yang, P., Kuo, C., & Chen, X. (2020). Seasonal Dependent Impact of Ice Cloud Longwave Scattering on the Polar Climate. Geophysical Research Letters, 47(23). https://doi.org/10.1029/2020GL090534
  8. DeSantis, D., Wolfram, P. J., Bennett, K., & Alexandrov, B. (2020). Coarse-Grain Cluster Analysis of Tensors with Application to Climate Biome Identification. ArXiv:2001.07827 [Cs, Stat]. Retrieved from http://arxiv.org/abs/2001.07827
  9. Di Vittorio, A. V., Shi, X., Bond‐Lamberty, B., Calvin, K., & Jones, A. (2020). Initial Land Use/Cover Distribution Substantially Affects Global Carbon and Local Temperature Projections in the Integrated Earth System Model. Global Biogeochemical Cycles, 34(5), e2019GB006383. https://doi.org/10.1029/2019GB006383
  10. Di Vittorio, Alan V., Vernon, C. R., & Shu, S. (2020). Moirai Version 3: A Data Processing System to Generate Recent Historical Land Inputs for Global Modeling Applications at Various Scales. Journal of Open Research Software, 8(1), 1. https://doi.org/10.5334/jors.266
  11. Dunne, J. P., Winton, M., Bacmeister, J., Danabasoglu, G., Gettelman, A., Golaz, J., et al. (2020). Comparison of Equilibrium Climate Sensitivity Estimates From Slab Ocean, 150‐Year, and Longer Simulations. Geophysical Research Letters, 47(16). https://doi.org/10.1029/2020GL088852
  12. Golden, K. M., Bennetts, L. G., Cherkaev, E., Eisenman, I., Feltham, D., Horvat, C., et al. (2020). Modeling Sea Ice. Notices of the American Mathematical Society, 67(10), 1. . https://doi.org/10.1090/noti2171
  13. Gryspeerdt, E., Mülmenstädt, J., Gettelman, A., Malavelle, F. F., Morrison, H., Neubauer, D., et al. (2020). Surprising similarities in model and observational aerosol radiative forcing estimates. Atmospheric Chemistry and Physics, 20(1), 613–623. https://doi.org/10.5194/acp-20-613-2020
  14. Guba, O., Taylor, M. A., Bradley, A. M., Bosler, P. A., & Steyer, A. (2020). A framework to evaluate IMEX schemes for atmospheric models. Geoscientific Model Development, 13(12), 6467–6480. https://doi.org/10.5194/gmd-13-6467-2020
  15. Guo, M., Zhuang, Q., Tan, Z., Shurpali, N., Juutinen, S., Kortelainen, P., & Martikainen, P. J. (2020). Rising methane emissions from boreal lakes due to increasing ice-free days. Environmental Research Letters, 15(6), 064008. https://doi.org/10.1088/1748-9326/ab8254
  16. Guo, M., Q. Zhuang, H. Yao, M. Golub, L. R. Leung, D. Pierson, and Z. Tan (2020). Validation and sensitivity analysis of a 1‐D lake model across global lakes. Journal of Geophysical Research: Atmospheres, 125, e2020JD033417. https:doi.org/10.1029/2020JD033417
  17. Guseva, S., Bleninger, T., Jöhnk, K., Polli, B. A., Tan, Z., Thiery, W., et al. (2020). Multimodel simulation of vertical gas transfer in a temperate lake. Hydrology and Earth System Sciences, 24(2), 697–715. https://doi.org/10.5194/hess-24-697-2020
  18. Hannah, W. M., Jones, C. R., Hillman, B. R., Norman, M. R., Bader, D. C., Taylor, M. A., et al. (2020). Initial Results From the Super-Parameterized E3SM. Journal of Advances in Modeling Earth Systems, 12(1), e2019MS001863. https://doi.org/10.1029/2019MS001863
  19. Hoch, K. E., Petersen, M. R., Brus, S. R., Engwirda, D., Roberts, A. F., Rosa, K. L., & Wolfram, P. J. (2020). MPAS-Ocean Simulation Quality for Variable-Resolution North American Coastal Meshes. Journal of Advances in Modeling Earth Systems, 12(3), e2019MS001848. https://doi.org/10.1029/2019MS001848
  20. Holm, J. A., Knox, R. G., Zhu, Q., Fisher, R. A., Koven, C. D., Lima, A. J. N., et al. (2020). The Central Amazon Biomass Sink Under Current and Future Atmospheric CO2: Predictions From Big-Leaf and Demographic Vegetation Models. Journal of Geophysical Research: Biogeosciences, 125(3), e2019JG005500. https://doi.org/10.1029/2019JG005500
  21. Hsu, J. C., & Prather, M. J. (2020). Assessing Uncertainties and Approximations in Solar Heating of the Climate System. Journal of Advances in Modeling Earth Systems, 12, e2020MS002131. https://doi.org/10.1029/2020MS002131
  22. Hu, A., Van Roekel, L., Weijer, W., Garuba, O. A., Cheng, W., & Nadiga, B. T. (2020). Role of AMOC in Transient Climate Response to Greenhouse Gas Forcing in Two Coupled Models. Journal of Climate, 33(14), 5845–5859. https://doi.org/10.1175/JCLI-D-19-1027.1
  23. Hunke, E., Allard, R., Blain, P., Blockley, E., Feltham, D., Fichefet, T., et al. (2020). Should Sea-Ice Modeling Tools Designed for Climate Research Be Used for Short-Term Forecasting? Current Climate Change Reports. https://doi.org/10.1007/s40641-020-00162-y
  24. Jeffery, N., Maltrud, M. E., Hunke, E. C., Wang, S., Wolfe, J., Turner, A. K., et al. (2020). Investigating controls on sea ice algal production using E3SMv1.1-BGC. Annals of Glaciology, 1–22. https://doi.org/10.1017/aog.2020.7
  25. Jeong, H., Asay-Davis, X. S., Turner, A. K., Comeau, D. S., Price, S. F., Abernathey, R. P., et al. (2020). Impacts of Ice-Shelf Melting on Water-Mass Transformation in the Southern Ocean from E3SM Simulations. Journal of Climate, 33(13), 5787–5807. https://doi.org/10.1175/JCLI-D-19-0683.1
  26. Jiang, J., Zhou, T., Wang, H., Qian, Y., Noone, D., & Man, W. (2020). Tracking Moisture Sources of Precipitation over Central Asia: A Study Based on the Water-Source-Tagging Method. Journal of Climate, 33(23), 10339–10355. https://doi.org/10.1175/JCLI-D-20-0169.1
  27. Lee, D. Y., Lin, W., & Petersen, M. R. (2020). Wintertime Arctic Oscillation and North Atlantic Oscillation and their impacts on the Northern Hemisphere climate in E3SM. Climate Dynamics. https://doi.org/10.1007/s00382-020-05316-0
  28. Leung, L. R., Bader, D. C., Taylor, M. A., & McCoy, R. B. (2020). An Introduction to the E3SM Special Collection: Goals, Science Drivers, Development, and Analysis. Journal of Advances in Modeling Earth Systems, 12(11), e2019MS001821. https://doi.org/10.1029/2019MS001821
  29. Liao, C., Tesfa, T., Duan, Z., & Leung, L. R. (2020). Watershed delineation on a hexagonal mesh grid. Environmental Modelling & Software, 128, 104702. https://doi.org/10.1016/j.envsoft.2020.104702
  30. Lou, Sijia, Manish Shrivastava*, Richard C Easter, Yang Yang, Po-Lun Ma, Hailong Wang, Michael J Cubison, Pedro Campuzano-Jost, Jose L Jimenez, Qi Zhang, Philip J Rasch, John E Shilling, Alla Zelenyuk, Manvendra Dubey, Philip Cameron-Smith, Scot T Martin, Johannes Schneider, Christiane Schulz (2020). New SOA treatments within the Energy Exascale Earth System Model (E3SM): Strong production and sinks govern atmospheric SOA distributions and radiative forcing. Journal of Advances in Modeling Earth Systems, 12, e2020MS002266.  https://doi.org/10.1029/2020MS002266
  31. MacDonald, M., Kurowski, M. J., & Teixeira, J. (2020). Direct Numerical Simulation of the Moist Stably Stratified Surface Layer: Turbulence and Fog Formation. Boundary-Layer Meteorology, 175(3), 343–368. https://doi.org/10.1007/s10546-020-00511-2
  32. Matheou, G., Davis, A. B., & Teixeira, J. (2020). The Spiderweb Structure of Stratocumulus Clouds. Atmosphere, 11(7), 730. https://doi.org/10.3390/atmos11070730
  33. Meng, L., Mao, J., Zhou, Y., Richardson, A. D., Lee, X., Thornton, P. E., et al. (2020). Urban warming advances spring phenology but reduces the response of phenology to temperature in the conterminous United States. Proceedings of the National Academy of Sciences, 117(8), 4228–4233. https://doi.org/10.1073/pnas.1911117117
  34. Metzler, H., Zhu, Q., Riley, W., Hoyt, A., Müller, M., & Sierra, C. A. (2020). Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From C Dynamics. Journal of Advances in Modeling Earth Systems, 12(1), e2019MS001776. https://doi.org/10.1029/2019MS001776
  35. Myhre, G., Samset, B. H., Mohr, C. W., Alterskjær, K., Balkanski, Y., Bellouin, N., et al. (2020). Cloudy-sky contributions to the direct aerosol effect. Atmospheric Chemistry and Physics, 20(14), 8855–8865. https://doi.org/10.5194/acp-20-8855-2020
  36. Niu, H., Kang, S., Wang, H., Du, J., Pu, T., Zhang, G., et al. (2020). Light-absorbing impurities accelerating glacial melting in southeastern Tibetan Plateau. Environmental Pollution, 257, 113541. https://doi.org/10.1016/j.envpol.2019.113541
  37. Orbe, C., Van Roekel, L., Adames, Á. F., Dezfuli, A., Fasullo, J., Gleckler, P. J., et al. (2020). Representation of Modes of Variability in 6 U.S. Climate Models. Journal of Climate. https://doi.org/10.1175/JCLI-D-19-0956.1
  38. Palviainen, M., Laurén, A., Pumpanen, J., Bergeron, Y., Bond‐Lamberty, B., Larjavaara, M., et al. (2020). Decadal‐Scale Recovery of Carbon Stocks After Wildfires Throughout the Boreal Forests. Global Biogeochemical Cycles, 34(8). https://doi.org/10.1029/2020GB006612
  39. Rieger, L. A., Cole, J. N. S., Fyfe, J. C., Po-Chedley, S., Cameron-Smith, P. J., Durack, P. J., et al. (2020). Quantifying CanESM5 and EAMv1 sensitivities to Mt. Pinatubo volcanic forcing for the CMIP6 historical experiment. Geoscientific Model Development, 13(10), 4831–4843. https://doi.org/10.5194/gmd-13-4831-2020
  40. Sharma, A., Wuebbles, D. J., Kotamarthi, R., Calvin, K., Drewniak, B., Catlett, C. E., & Jacob, R. (2020). Urban-Scale Processes in High-Spatial-Resolution Earth System Models. Bulletin of the American Meteorological Society, 101(9), E1555–E1561. https://doi.org/10.1175/BAMS-D-20-0114.1
  41. Shi, Z., Allison, S. D., He, Y., Levine, P. A., Hoyt, A. M., Beem-Miller, J., et al. (2020). The age distribution of global soil carbon inferred from radiocarbon measurements. Nature Geoscience. https://doi.org/10.1038/s41561-020-0596-z
  42. Skeie, R. B., Myhre, G., Hodnebrog, Ø., Cameron-Smith, P. J., Deushi, M., Hegglin, M. I., et al. (2020). Historical total ozone radiative forcing derived from CMIP6 simulations. Npj Climate and Atmospheric Science, 3(1).  https://doi.org/10.1038/s41612-020-00131-0
  43. Sockwell, K. C., Peterson, K., Kuberry, P., Bochev, P., & Trask, N. (2020). Interface Flux Recovery coupling method for the ocean–atmosphere system. Results in Applied Mathematics, 8, 100110. https://doi.org/10.1016/j.rinam.2020.100110
  44. Sun, S., Pattyn, F., Simon, E. G., Albrecht, T., Cornford, S., Calov, R., et al. (2020). Antarctic ice sheet response to sudden and sustained ice-shelf collapse (ABUMIP). Journal of Glaciology, 1–14. https://doi.org/10.1017/jog.2020.67
  45. Tan, Z., Leung, L. R., Li, H.-Y., Tesfa, T., Zhu, Q., & Huang, M. (2020). A substantial role of soil erosion in the land carbon sink and its future changes. Global Change Biology, 26(4), 2642–2655. https://doi.org/10.1111/gcb.14982
  46. Taylor, M. A., Guba, O., Steyer, A., Ullrich, P. A., Hall, D. M., & Eldrid, C. (2020). An Energy Consistent Discretization of the Nonhydrostatic Equations in Primitive Variables. Journal of Advances in Modeling Earth Systems, 12(1), e2019MS001783. https://doi.org/10.1029/2019MS001783
  47. Tesfa, T. K., Leung, L. R., & Ghan, S. J. (2020). Exploring Topography-Based Methods for Downscaling Subgrid Precipitation for Use in Earth System Models. Journal of Geophysical Research: Atmospheres, 125(5), e2019JD031456. https://doi.org/10.1029/2019JD031456
  48. Thomas, H. J. D., Bjorkman, A. D., Myers-Smith, I. H., Elmendorf, S. C., Kattge, J., Diaz, S., et al. (2020). Global plant trait relationships extend to the climatic extremes of the tundra biome. Nature Communications, 11(1), 1351. https://doi.org/10.1038/s41467-020-15014-4
  49. Vanderkelen, I., Lipzig, N. P. M. van, Lawrence, D. M., Droppers, B., Golub, M., Gosling, S. N., et al. (2020). Global Heat Uptake by Inland Waters. Geophysical Research Letters, 47(12), e2020GL087867. https://doi.org/10.1029/2020GL087867
  50. Wan, H., Woodward, C. S., Zhang, S., Vogl, C. J., Stinis, P., Gardner, D. J., et al. (2020). Improving Time Step Convergence in an Atmosphere Model With Simplified Physics: The Impacts of Closure Assumption and Process Coupling. Journal of Advances in Modeling Earth Systems, 12(10), e2019MS001982. https://doi.org/10.1029/2019MS001982
  51. Wang, H., Easter, R. C., Zhang, R., Ma, P.-L., Singh, B., Zhang, K., et al. (2020). Aerosols in the E3SM Version 1: New Developments and Their Impacts on Radiative Forcing. Journal of Advances in Modeling Earth Systems, 12(1), e2019MS001851. https://doi.org/10.1029/2019MS001851
  52. Wang, Y.-C., Xie, S., Tang, S., & Lin, W. (2020). Evaluation of an Improved Convective Triggering Function: Observational Evidence and SCM Tests. Journal of Geophysical Research: Atmospheres, 125(11), e2019JD031651. https://doi.org/10.1029/2019JD031651
  53. Wu, E., Yang, H., Kleissl, J., Suselj, K., Kurowski, M. J., & Teixeira, J. (2020). On the Parameterization of Convective Downdrafts for Marine Stratocumulus Clouds. Monthly Weather Review, 148(5), 1931–1950. https://doi.org/10.1175/MWR-D-19-0292.1
  54. Wu, M., Liu, X., Yu, H., Wang, H., Shi, Y., Yang, K., et al. (2020). Understanding Processes that Control Dust Spatial Distributions with Global Climate Models and Satellite Observations. Atmospheric Chemistry and Physics Discussions, 1–52. https://doi.org/10.5194/acp-2020-160
  55. Yang, Y., Lou, S., Wang, H., Wang, P., & Liao, H. (2020). Trends and source apportionment of aerosols in Europe during 1980–2018. Atmospheric Chemistry and Physics, 20(4), 2579–2590. https://doi.org/10.5194/acp-20-2579-2020
  56. Yang, Y., Ren, L., Li, H., Wang, H., Wang, P., Chen, L., et al. (2020). Fast Climate Responses to Aerosol Emission Reductions During the COVID‐19 Pandemic. Geophysical Research Letters, 47(19). https://doi.org/10.1029/2020GL089788
  57. Yu, Y., Mao, J., Thornton, P. E., Notaro, M., Wullschleger, S. D., Shi, X., et al. (2020). Quantifying the drivers and predictability of seasonal changes in African fire. Nature Communications, 11(1), 2893. https://doi.org/10.1038/s41467-020-16692-w
  58. Zhang, M., Xie, S., Liu, X., Lin, W., Zhang, K., Ma, H.-Y., et al. (2020). Toward Understanding the Simulated Phase Partitioning of Arctic Single-Layer Mixed-Phase Clouds in E3SM. Earth and Space Science, n/a(n/a), e2020EA001125. https://doi.org/10.1029/2020EA001125
  59. Zheng, X., Klein, S. A., Ghate, V. P., Santos, S., McGibbon, J., Caldwell, P., et al. (2020). Assessment of Precipitating Marine Stratocumulus Clouds in the E3SMv1 Atmosphere Model: A Case Study from the ARM MAGIC Field Campaign. Monthly Weather Review, 148(8), 3341–3359. https://doi.org/10.1175/MWR-D-19-0349.1
  60. Zhou, T., Leung, L. R., Leng, G., Voisin, N., Li, H.-Y., Craig, A. P., et al. (2020). Global Irrigation Characteristics and Effects Simulated by Fully Coupled Land Surface, River, and Water Management Models in E3SM. Journal of Advances in Modeling Earth Systems, 12(10), e2020MS002069. https://doi.org/10.1029/2020MS002069
  61. Zhu, Q., Riley, W. J., Iversen, C. M., & Kattge, J. (2020). Assessing Impacts of Plant Stoichiometric Traits on Terrestrial Ecosystem Carbon Accumulation Using the E3SM Land Model. Journal of Advances in Modeling Earth Systems, 12(4), e2019MS001841. https://doi.org/10.1029/2019MS001841

2019

 

  1. Banesh, D., Petersen, M., Wendelberger, J., Ahrens, J., & Hamann, B. (2019). Comparison of piecewise linear change point detection with traditional analytical methods for ocean and climate data. Environmental Earth Sciences, 78(21), 623. https://doi.org/10.1007/s12665-019-8636-y
  2. Bertagna, L., Deakin, M., Guba, O., Sunderland, D., Bradley, A. M., Tezaur, I. K., et al. (2019). HOMMEXX 1.0: a performance-portable atmospheric dynamical core for the Energy Exascale Earth System Model. Geoscientific Model Development, 12(4), 1423–1441. https://doi.org/10.5194/gmd-12-1423-2019
  3. Bisht, G., & Riley, W. J. (2019). Development and Verification of a Numerical Library for Solving Global Terrestrial Multiphysics Problems. Journal of Advances in Modeling Earth Systems, 11(6), 1516–1542. https://doi.org/10.1029/2018MS001560
  4. Bosler, P. A., Bradley, A. M., & Taylor, M. A. (2019). Conservative Multimoment Transport along Characteristics for Discontinuous Galerkin Methods. SIAM Journal on Scientific Computing, 41(4), B870–B902. https://doi.org/10.1137/18M1165943
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  7. Cai, X., Riley, W. J., Zhu, Q., Tang, J., Zeng, Z., Bisht, G., & Randerson, J. T. (2019). Improving Representation of Deforestation Effects on Evapotranspiration in the E3SM Land Model. Journal of Advances in Modeling Earth Systems, 11(8), 2412–2427. https://doi.org/10.1029/2018MS001551
  8. Caldwell, P. M., Mametjanov, A., Tang, Q., Van Roekel, L. P., Golaz, J.-C., Lin, W., et al. (2019). The DOE E3SM Coupled Model Version 1: Description and Results at High Resolution. Journal of Advances in Modeling Earth Systems, 11(12), 4095–4146. https://doi.org/10.1029/2019MS001870
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  13. Drewniak, B. A. (2019). Simulating Dynamic Roots in the Energy Exascale Earth System Land Model. Journal of Advances in Modeling Earth Systems, 11(1), 338–359. https://doi.org/10.1029/2018MS001334
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  20. Harrop, B. E., Ma, P.-L., Rasch, P. J., Qian, Y., Lin, G., & Hannay, C. (2019). Understanding Monsoonal Water Cycle Changes in a Warmer Climate in E3SMv1 Using a Normalized Gross Moist Stability Framework. Journal of Geophysical Research: Atmospheres, 124(20), 10826–10843. https://doi.org/10.1029/2019JD031443
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  23. Jiang, T., Evans, K., Branstetter, M., Caldwell, P., Neale, R., Rasch, P. J., et al. (2019). Northern Hemisphere Blocking in ∼25-km-Resolution E3SM v0.3 Atmosphere-Land Simulations. Journal of Geophysical Research: Atmospheres, 124(5), 2465–2482. https://doi.org/10.1029/2018JD028892
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  26. Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., et al. (2019). The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty. Journal of Advances in Modeling Earth Systems, 11(12), 4245–4287. https://doi.org/10.1029/2018MS001583
  27. Lee, D. Y., Petersen, M. R., & Lin, W. (2019). The Southern Annular Mode and Southern Ocean Surface Westerly Winds in E3SM. Earth and Space Science, 6(12), 2624–2643. https://doi.org/10.1029/2019EA000663
  28. Levine, P. A., Randerson, J. T., Chen, Y., Pritchard, M. S., Xu, M., & Hoffman, F. M. (2019). Soil Moisture Variability Intensifies and Prolongs Eastern Amazon Temperature and Carbon Cycle Response to El Niño–Southern Oscillation. Journal of Climate, 32(4), 1273–1292. https://doi.org/10.1175/JCLI-D-18-0150.1
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  34. Pal, A., Mahajan, S., & Norman, M. R. (2019). Using Deep Neural Networks as Cost-Effective Surrogate Models for Super-Parameterized E3SM Radiative Transfer. Geophysical Research Letters, 46(11), 6069–6079. https://doi.org/10.1029/2018GL081646
  35. Paukert, M., Fan, J., Rasch, P. J., Morrison, H., Milbrandt, J. A., Shpund, J., & Khain, A. (2019). Three-Moment Representation of Rain in a Bulk Microphysics Model. Journal of Advances in Modeling Earth Systems, 11(1), 257–277. https://doi.org/10.1029/2018MS001512
  36. Petersen, M. R., Asay‐Davis, X. S., Berres, A. S., Chen, Q., Feige, N., Hoffman, M. J., et al. (2019). An Evaluation of the Ocean and Sea Ice Climate of E3SM Using MPAS and Interannual CORE-II Forcing. Journal of Advances in Modeling Earth Systems, 11(5), 1438–1458. https://doi.org/10.1029/2018MS001373
  37. Pouchard, L., Baldwin, S., Elsethagen, T., Jha, S., Raju, B., Stephan, E., et al. (2019). Computational reproducibility of scientific workflows at extreme scales: The International Journal of High Performance Computing Applications. https://doi.org/10.1177/1094342019839124
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  42. Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., et al. (2019). The Global Methane Budget 2000-2017. Earth System Science Data Discussions, 1–136. https://doi.org/10.5194/essd-2019-128
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  44. Sun, J., Zhang, K., Wan, H., Ma, P.-L., Tang, Q., & Zhang, S. (2019). Impact of Nudging Strategy on the Climate Representativeness and Hindcast Skill of Constrained EAMv1 Simulations. Journal of Advances in Modeling Earth Systems, 11(12), 3911–3933. https://doi.org/10.1029/2019MS001831
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  48. Wang, W., Zender, C. S., As, D. van, & Miller, N. B. (2019). Spatial Distribution of Melt Season Cloud Radiative Effects Over Greenland: Evaluating Satellite Observations, Reanalyses, and Model Simulations Against In Situ Measurements. Journal of Geophysical Research: Atmospheres, 124(1), 57–71. https://doi.org/10.1029/2018JD028919
  49. Wu, M., Liu, X., Yang, K., Luo, T., Wang, Z., Wu, C., et al. (2019). Modeling Dust in East Asia by CESM and Sources of Biases. Journal of Geophysical Research: Atmospheres, 124(14), 8043–8064. https://doi.org/10.1029/2019JD030799
  50. Xie, S., Wang, Y.-C., Lin, W., Ma, H.-Y., Tang, Q., Tang, S., et al. (2019). Improved Diurnal Cycle of Precipitation in E3SM With a Revised Convective Triggering Function. Journal of Advances in Modeling Earth Systems, 11(7), 2290–2310. https://doi.org/10.1029/2019MS001702
  51. Xu, X., Jain, A. K., & Calvin, K. V. (2019). Quantifying the biophysical and socioeconomic drivers of changes in forest and agricultural land in South and Southeast Asia. Global Change Biology, 25(6), 2137–2151. https://doi.org/10.1111/gcb.14611
  52. Yang, X., Ricciuto, D. M., Thornton, P. E., Shi, X., Xu, M., Hoffman, F., & Norby, R. J. (2019). The Effects of Phosphorus Cycle Dynamics on Carbon Sources and Sinks in the Amazon Region: A Modeling Study Using ELM v1. Journal of Geophysical Research: Biogeosciences, 124(12), 3686–3698. https://doi.org/10.1029/2019JG005082
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2018

 

  1. Bisht, G., Riley, W. J., Hammond, G. E., & Lorenzetti, D. M. (2018). Development and evaluation of a variably saturated flow model in the global E3SM Land Model (ELM) version 1.0. Geoscientific Model Development, 11(10), 4085–4102. https://doi.org/10.5194/gmd-11-4085-2018
  2. Bisht, G., Riley, W. J., Wainwright, H. M., Dafflon, B., Yuan, F., & Romanovsky, V. E. (2018). Impacts of microtopographic snow redistribution and lateral subsurface processes on hydrologic and thermal states in an Arctic polygonal ground ecosystem: a case study using ELM-3D v1.0. Geoscientific Model Development, 11(1), 61–76. https://doi.org/10.5194/gmd-11-61-2018
  3. Bjorkman, A. D., Myers-Smith, I. H., Elmendorf, S. C., Normand, S., Rüger, N., Beck, P. S. A., et al. (2018). Plant functional trait change across a warming tundra biome. Nature, 562(7725), 57–62. https://doi.org/10.1038/s41586-018-0563-7
  4. Brown, H., Liu, X., Feng, Y., Jiang, Y., Wu, M., Lu, Z., et al. (2018). Radiative effect and climate impacts of brown carbon with the Community Atmosphere Model (CAM5). Atmospheric Chemistry and Physics, 18(24), 17745–17768. https://doi.org/10.5194/acp-18-17745-2018
  5. Chen, Y., Wang, H., Singh, B., Ma, P.-L., Rasch, P. J., & Bond, T. C. (2018). Investigating the Linear Dependence of Direct and Indirect Radiative Forcing on Emission of Carbonaceous Aerosols in a Global Climate Model. Journal of Geophysical Research: Atmospheres, 123(3), 1657–1672. https://doi.org/10.1002/2017JD027244
  6. Collalti, A., Trotta, C., Keenan, T. F., Ibrom, A., Bond‐Lamberty, B., Grote, R., et al. (2018). Thinning Can Reduce Losses in Carbon Use Efficiency and Carbon Stocks in Managed Forests Under Warmer Climate. Journal of Advances in Modeling Earth Systems, 10(10), 2427–2452. https://doi.org/10.1029/2018MS001275
  7. Delman, A. S., McClean, J. L., Sprintall, J., Talley, L. D., & Bryan, F. O. (2018). Process‐Specific Contributions to Anomalous Java Mixed Layer Cooling During Positive IOD Events. Journal of Geophysical Research: Oceans, 123(6), 4153–4176. https://doi.org/10.1029/2017JC013749
  8. Fyke, J., Sergienko, O., Löfverström, M., Price, S., & Lenaerts, J. T. M. (2018). An Overview of Interactions and Feedbacks Between Ice Sheets and the Earth System. Reviews of Geophysics, 56(2), 361–408. https://doi.org/10.1029/2018RG000600
  9. Gorris, M. E., Cat, L. A., Zender, C. S., Treseder, K. K., & Randerson, J. T. (2018). Coccidioidomycosis Dynamics in Relation to Climate in the Southwestern United States: Cocci. and climate in the SW U.S. GeoHealth, 2(1), 6–24. https://doi.org/10.1002/2017GH000095
  10. Harrop, B. E., Ma, P.-L., Rasch, P. J., Neale, R. B., & Hannay, C. (2018). The Role of Convective Gustiness in Reducing Seasonal Precipitation Biases in the Tropical West Pacific. Journal of Advances in Modeling Earth Systems, 10(4), 961–970. https://doi.org/10.1002/2017MS001157
  11. Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T., Jacobsen, D., et al. (2018). MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids. Geoscientific Model Development, 11(9), 3747–3780. https://doi.org/10.5194/gmd-11-3747-2018
  12. Jones, A. D., Calvin, K. V., Shi, X., Vittorio, A. V. D., Bond‐Lamberty, B., Thornton, P. E., & Collins, W. D. (2018). Quantifying Human-Mediated Carbon Cycle Feedbacks. Geophysical Research Letters, 45(20), 11,370-11,379. https://doi.org/10.1029/2018GL079350
  13. Kuipers Munneke, P., Luckman, A. J., Bevan, S. L., Smeets, C. J. P. P., Gilbert, E., van den Broeke, M. R., et al. (2018). Intense Winter Surface Melt on an Antarctic Ice Shelf. Geophysical Research Letters, 45(15), 7615–7623. https://doi.org/10.1029/2018GL077899
  14. Larios, A., Petersen, M. R., Titi, E. S., & Wingate, B. (2018). A computational investigation of the finite-time blow-up of the 3D incompressible Euler equations based on the Voigt regularization. Theoretical and Computational Fluid Dynamics, 32(1), 23–34. https://doi.org/10.1007/s00162-017-0434-0
  15. Lee, D., Palha, A., & Gerritsma, M. (2018). Discrete conservation properties for shallow water flows using mixed mimetic spectral elements. Journal of Computational Physics, 357, 282–304. https://doi.org/10.1016/j.jcp.2017.12.022
  16. Li, F., Lawrence, D. M., & Bond-Lamberty, B. (2018). Human impacts on 20th century fire dynamics and implications for global carbon and water trajectories. Global and Planetary Change, 162, 18–27. https://doi.org/10.1016/j.gloplacha.2018.01.002
  17. Ma, P.-L., Rasch, P. J., Chepfer, H., Winker, D. M., & Ghan, S. J. (2018). Observational constraint on cloud susceptibility weakened by aerosol retrieval limitations. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-05028-4
  18. MacDonald, A. B., Dadashazar, H., Chuang, P. Y., Crosbie, E., Wang, H., Wang, Z., et al. (2018). Characteristic Vertical Profiles of Cloud Water Composition in Marine Stratocumulus Clouds and Relationships With Precipitation. Journal of Geophysical Research: Atmospheres, 123(7), 3704–3723. https://doi.org/10.1002/2017JD027900
  19. Mahajan, S., Evans, K. J., Branstetter, M. L., & Tang, Q. (2018). Model Resolution Sensitivity of the Simulation of North Atlantic Oscillation Teleconnections to Precipitation Extremes. Journal of Geophysical Research: Atmospheres, 123(20), 11,392-11,409. https://doi.org/10.1029/2018JD028594
  20. Niu, H., Kang, S., Wang, H., Zhang, R., Lu, X., Qian, Y., et al. (2018). Seasonal variation and light absorption property of carbonaceous aerosol in a typical glacier region of the southeastern Tibetan Plateau. Atmospheric Chemistry and Physics, 18(9), 6441–6460. https://doi.org/10.5194/acp-18-6441-2018
  21. Qian, Y., Wan, H., Yang, B., Golaz, J.-C., Harrop, B., Hou, Z., et al. (2018). Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations. Journal of Geophysical Research: Atmospheres, 123(23), 13,046-13,073. https://doi.org/10.1029/2018JD028927
  22. Ricciuto, D., Sargsyan, K., & Thornton, P. (2018). The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model. Journal of Advances in Modeling Earth Systems, 10(2), 297–319. https://doi.org/10.1002/2017MS000962
  23. Riley, W. J., Zhu, Q., & Tang, J. Y. (2018). Weaker land–climate feedbacks from nutrient uptake during photosynthesis-inactive periods. Nature Climate Change, 8(11), 1002–1006. https://doi.org/10.1038/s41558-018-0325-4
  24. Roberts, A. F., Hunke, E. C., Allard, R., Bailey, D. A., Craig, A. P., Lemieux, J.-F., & Turner, M. D. (2018). Quality control for community-based sea-ice model development. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2129), 20170344. https://doi.org/10.1098/rsta.2017.0344
  25. Saravia, L. A., Doyle, S. R., & Bond-Lamberty, B. (2018). Power laws and critical fragmentation in global forests. Scientific Reports, 8(1), 17766. https://doi.org/10.1038/s41598-018-36120-w
  26. van Sebille, E., Griffies, S. M., Abernathey, R., Adams, T. P., Berloff, P., Biastoch, A., et al. (2018). Lagrangian ocean analysis: Fundamentals and practices. Ocean Modelling, 121, 49–75. https://doi.org/10.1016/j.ocemod.2017.11.008
  27. Tan, Z., Leung, L. R., Li, H.-Y., & Tesfa, T. (2018). Modeling Sediment Yield in Land Surface and Earth System Models: Model Comparison, Development, and Evaluation. Journal of Advances in Modeling Earth Systems, 10(9), 2192–2213. https://doi.org/10.1029/2017MS001270
  28. Tang, J., & Riley, W. J. (2018). Predicted Land Carbon Dynamics Are Strongly Dependent on the Numerical Coupling of Nitrogen Mobilizing and Immobilizing Processes: A Demonstration with the E3SM Land Model. Earth Interactions, 22(11), 1–18. https://doi.org/10.1175/EI-D-17-0023.1 
  29. Terai, C. R., Caldwell, P. M., Klein, S. A., Tang, Q., & Branstetter, M. L. (2018). The atmospheric hydrologic cycle in the ACME v0.3 model. Climate Dynamics, 50(9–10), 3251–3279. https://doi.org/10.1007/s00382-017-3803-x
  30. Turner, A., Lipscomb, W., Hunke, E., Jacobsen, D., Jeffery, N., Ringler, T., & Wolfe, J. (2018). Mpas-Seaice: A New Variable Resolution Sea-Ice Model. https://doi.org/10.5281/ZENODO.1194373
  31. Van Roekel, L., Adcroft, A. J., Danabasoglu, G., Griffies, S. M., Kauffman, B., Large, W., et al. (2018). The KPP Boundary Layer Scheme for the Ocean: Revisiting Its Formulation and Benchmarking One-Dimensional Simulations Relative to LES. Journal of Advances in Modeling Earth Systems, 10(11), 2647–2685. https://doi.org/10.1029/2018MS001336
  32. Wang, H., Burleyson, C. D., Ma, P.-L., Fast, J. D., & Rasch, P. J. (2018). Using the Atmospheric Radiation Measurement (ARM) Datasets to Evaluate Climate Models in Simulating Diurnal and Seasonal Variations of Tropical Clouds. Journal of Climate, 31(8), 3301–3325. https://doi.org/10.1175/JCLI-D-17-0362.1
  33. Wang, W., Zender, C. S., van As, D., & Miller, N. B. (2018). Spatial distribution of melt‐season cloud radiative effects over Greenland: Evaluating satellite observations, reanalyses, and model simulations against in situ measurements. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2018JD028919
  34. Wang, W., Zender, C. S., & van As, D. (2018). Temporal Characteristics of Cloud Radiative Effects on the Greenland Ice Sheet: Discoveries From Multiyear Automatic Weather Station Measurements. Journal of Geophysical Research: Atmospheres, 123(20), 11,348-11,361. https://doi.org/10.1029/2018JD028540
  35. Xie, S., Lin, W., Rasch, P. J., Ma, P.-L., Neale, R., Larson, V. E., et al. (2018). Understanding Cloud and Convective Characteristics in Version 1 of the E3SM Atmosphere Model. Journal of Advances in Modeling Earth Systems, 10(10), 2618–2644. https://doi.org/10.1029/2018MS001350
  36. Zhang, K., Rasch, P. J., Taylor, M. A., Wan, H., Leung, R., Ma, P.-L., et al. (2018). Impact of numerical choices on water conservation in the E3SM Atmosphere Model version 1 (EAMv1). Geoscientific Model Development, 11(5), 1971–1988. https://doi.org/10.5194/gmd-11-1971-2018

2017

 

  1. Asay-Davis, X. S., Jourdain, N. C., & Nakayama, Y. (2017). Developments in Simulating and Parameterizing Interactions Between the Southern Ocean and the Antarctic Ice Sheet. Current Climate Change Reports, 3(4), 316–329. https://doi.org/10.1007/s40641-017-0071-0
  2. Berres, A. S., Turton, T. L., Petersen, M., Rogers, D. H., & Ahrens, J. P. (2017). Video Compression for Ocean Simulation Image Databases. Workshop on Visualisation in Environmental Sciences (EnvirVis), 5 pages. https://doi.org/10.2312/ENVIRVIS.20171104
  3. Butler, E. E., Datta, A., Flores-Moreno, H., Chen, M., Wythers, K. R., Fazayeli, F., et al. (2017). Mapping local and global variability in plant trait distributions. Proceedings of the National Academy of Sciences, 114(51), E10937–E10946. https://doi.org/10.1073/pnas.1708984114
  4. Centurioni, L., Hormann, V., Talley, L., Arzeno, I., Scripps Institution of Oceanography, Beal, L., et al. (2017). Northern Arabian Sea Circulation-Autonomous Research (NASCar): A Research Initiative Based on Autonomous Sensors. Oceanography, 30(2), 74–87. https://doi.org/10.5670/oceanog.2017.224
  5. Chen, R., Gille, S. T., & McClean, J. L. (2017). Isopycnal eddy mixing across the Kuroshio Extension: Stable versus unstable states in an eddying model: MIXING ACROSS KUROSHIO EXTENSION. Journal of Geophysical Research: Oceans, 122(5), 4329–4345. https://doi.org/10.1002/2016JC012164
  6. Duarte, H. F., Raczka, B. M., Ricciuto, D. M., Lin, J. C., Koven, C. D., Thornton, P. E., et al. (2017). Evaluating the Community Land Model (CLM4.5) at a coniferous forest site in northwestern United States using flux and carbon-isotope measurements. Biogeosciences, 14(18), 4315–4340. https://doi.org/10.5194/bg-14-4315-2017
  7. Fang, Y., Leung, L. R., Duan, Z., Wigmosta, M. S., Maxwell, R. M., Chambers, J. Q., & Tomasella, J. (2017). Influence of landscape heterogeneity on water available to tropical forests in an Amazonian catchment and implications for modeling drought response. Journal of Geophysical Research: Atmospheres, 122(16), 8410–8426. https://doi.org/10.1002/2017JD027066
  8. Hewitt, H. T., Bell, M. J., Chassignet, E. P., Czaja, A., Ferreira, D., Griffies, S. M., et al. (2017). Will high-resolution global ocean models benefit coupled predictions on short-range to climate timescales? Ocean Modelling, 120, 120–136. https://doi.org/10.1016/j.ocemod.2017.11.002
  9. Leng, G., Leung, L. R., & Huang, M. (2017). Significant impacts of irrigation water sources and methods on modeling irrigation effects in the ACME Land Model. Journal of Advances in Modeling Earth Systems, 9(3), 1665–1683. https://doi.org/10.1002/2016MS000885
  10. Liu, S., Bond-Lamberty, B., Boysen, L. R., Ford, J. D., Fox, A., Gallo, K., et al. (2017). Grand Challenges in Understanding the Interplay of Climate and Land Changes. Earth Interactions, 21(2), 1–43. https://doi.org/10.1175/EI-D-16-0012.1
  11. Luo, X., Li, H.-Y., Leung, L. R., Tesfa, T. K., Getirana, A., Papa, F., & Hess, L. L. (2017). Modeling surface water dynamics in the Amazon Basin using MOSART-Inundation v1.0: impacts of geomorphological parameters and river flow representation. Geoscientific Model Development, 10(3), 1233–1259. https://doi.org/10.5194/gmd-10-1233-2017
  12. Mahajan, S., Gaddis, A. L., Evans, K. J., & Norman, M. R. (2017). Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale. Procedia Computer Science, 108, 735–744. https://doi.org/10.1016/j.procs.2017.05.259
  13. Metcalfe, D. B., Ricciuto, D., Palmroth, S., Campbell, C., Hurry, V., Mao, J., et al. (2017). Informing climate models with rapid chamber measurements of forest carbon uptake. Global Change Biology, 23(5), 2130–2139. https://doi.org/10.1111/gcb.13451
  14. Parajuli, S. P., & Zender, C. S. (2017). Connecting geomorphology to dust emission through high-resolution mapping of global land cover and sediment supply. Aeolian Research, 27, 47–65. https://doi.org/10.1016/j.aeolia.2017.06.002
  15. Ringler, T., Saenz, J. A., Wolfram, P. J., & Van Roekel, L. (2017). A Thickness-Weighted Average Perspective of Force Balance in an Idealized Circumpolar Current. Journal of Physical Oceanography, 47(2), 285–302. https://doi.org/10.1175/JPO-D-16-0096.1
  16. Silver, J. D., & Zender, C. S. (2017). The compression–error trade-off for large gridded data sets. Geoscientific Model Development, 10(1), 413–423. https://doi.org/10.5194/gmd-10-413-2017
  17. Stephan, E., Raju, B., Elsethagen, T., Pouchard, L., & Gamboa, C. (2017). A scientific data provenance harvester for distributed applications. In 2017 New York Scientific Data Summit (NYSDS) (pp. 1–9). New York, NY, USA: IEEE. https://doi.org/10.1109/NYSDS.2017.8085041
  18. Sun, Y., Peng, S., Goll, D. S., Ciais, P., Guenet, B., Guimberteau, M., et al. (2017). Diagnosing phosphorus limitations in natural terrestrial ecosystems in carbon cycle models. Earth’s Future, 5(7), 730–749. https://doi.org/10.1002/2016EF000472
  19. Tan, Z., Leung, L. R., Li, H., Tesfa, T., Vanmaercke, M., Poesen, J., et al. (2017). A Global Data Analysis for Representing Sediment and Particulate Organic Carbon Yield in Earth System Models: A GLOBAL DATA ANALYSIS OF SEDIMENT YIELD. Water Resources Research, 53(12), 10674–10700. https://doi.org/10.1002/2017WR020806
  20. Tang, J.-Y., & Riley, W. J. (2017). SUPECA kinetics for scaling redox reactions in networks of mixed substrates and consumers and an example application to aerobic soil respiration. Geoscientific Model Development, 10(9), 3277–3295. https://doi.org/10.5194/gmd-10-3277-2017
  21. Tesfa, T. K., & Leung, L.-Y. R. (2017). Exploring new topography-based subgrid spatial structures for improving land surface modeling. Geoscientific Model Development, 10(2), 873–888. https://doi.org/10.5194/gmd-10-873-2017
  22. Thornton, P. E., Calvin, K., Jones, A. D., Di Vittorio, A. V., Bond-Lamberty, B., Chini, L., et al. (2017). Biospheric feedback effects in a synchronously coupled model of human and Earth systems. Nature Climate Change, 7(7), 496–500. https://doi.org/10.1038/nclimate3310
  23. Urrego-Blanco, J. R., Hunke, E. C., Urban, N. M., Jeffery, N., Turner, A. K., Langenbrunner, J. R., & Booker, J. M. (2017). Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION. Journal of Geophysical Research: Oceans, 122(4), 2923–2944. https://doi.org/10.1002/2016JC012602
  24. Voisin, N., Hejazi, M. I., Leung, L. R., Liu, L., Huang, M., Li, H.-Y., & Tesfa, T. (2017). Effects of spatially distributed sectoral water management on the redistribution of water resources in an integrated water model. Water Resources Research, 53(5), 4253–4270. https://doi.org/10.1002/2016WR019767
  25. Wan, H., Zhang, K., Rasch, P. J., Singh, B., Chen, X., & Edwards, J. (2017). A new and inexpensive non-bit-for-bit solution reproducibility test based on time step convergence (TSC1.0). Geoscientific Model Development, 10(2), 537–552. https://doi.org/10.5194/gmd-10-537-2017
  26. Wan, W., Zhao, J., Li, H.-Y., Mishra, A., Leung, L. R., Hejazi, M., et al. (2017). Hydrological Drought in the Anthropocene: Impacts of Local Water Extraction and Reservoir Regulation in the U.S. Journal of Geophysical Research: Atmospheres, 122(21), 11,313-11,328. https://doi.org/10.1002/2017JD026899
  27. Wang, D., Luo, X., Yuan, F., & Podhorszki, N. (2017). A Data Analysis Framework for Earth System Simulation within an In-Situ Infrastructure. Journal of Computer and Communications, 05(14), 76–85. https://doi.org/10.4236/jcc.2017.514007
  28. Wang, Dali, Pei, Y., Hernandez, O., Wu, W., Yao, Z., Kim, Y., et al. (2017). Compiler technologies for understanding legacy scientific code: A case study on an ACME land module. Procedia Computer Science, 108, 2418–2422. https://doi.org/10.1016/j.procs.2017.05.264
  29. Williams, D. N. (2017). 6th Annual Earth System Grid Federation Face to Face Conference Report (No. LLNL–TR-726011, 1369382). https://doi.org/10.2172/1369382
  30. Wolfram, P. J., & Ringler, T. D. (2017a). Computing eddy-driven effective diffusivity using Lagrangian particles. Ocean Modelling, 118, 94–106. https://doi.org/10.1016/j.ocemod.2017.08.008
  31. Wolfram, P. J., & Ringler, T. D. (2017b). Quantifying Residual, Eddy, and Mean Flow Effects on Mixing in an Idealized Circumpolar Current. Journal of Physical Oceanography, 47(8), 1897–1920. https://doi.org/10.1175/JPO-D-16-0101.1
  32. Xu, Y., Wang, D., Janjusic, T., Wu, W., Pei, Y., & Yao, Z. (2017). A Web-based Visual Analytic Framework for Understanding Large-scale Environmental Models: A Use Case for The Community Land Model. Procedia Computer Science, 108, 1731–1740. https://doi.org/10.1016/j.procs.2017.05.181
  33. Zhu, Q., Riley, W. J., & Tang, J. (2017). A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions. Ecological Applications, 27(3), 875–886. https://doi.org/10.1002/eap.1490

2016

 

  1. Asay-Davis, X. S., Cornford, S. L., Durand, G., Galton-Fenzi, B. K., Gladstone, R. M., Gudmundsson, G. H., et al. (2016). Experimental design for three interrelated marine ice sheet and ocean model intercomparison projects: MISMIP v. 3 (MISMIP +), ISOMIP v. 2 (ISOMIP +) and MISOMIP v. 1 (MISOMIP1). Geoscientific Model Development, 9(7), 2471–2497. https://doi.org/10.5194/gmd-9-2471-2016
  2. Brunke, M. A., Broxton, P., Pelletier, J., Gochis, D., Hazenberg, P., Lawrence, D. M., et al. (2016). Implementing and Evaluating Variable Soil Thickness in the Community Land Model, Version 4.5 (CLM4.5). Journal of Climate, 29(9), 3441–3461. https://doi.org/10.1175/JCLI-D-15-0307.1
  3. Burrows, S. M., Gobrogge, E., Fu, L., Link, K., Elliott, S. M., Wang, H., & Walker, R. (2016). OCEANFILMS-2: Representing coadsorption of saccharides in marine films and potential impacts on modeled marine aerosol chemistry: SACCHARIDE COADSORPTION IN MARINE FILMS. Geophysical Research Letters, 43(15), 8306–8313. https://doi.org/10.1002/2016GL069070
  4. Center for Coastal Physical Oceanography, Old Dominion University, Dinniman, M., Asay-Davis, X., Galton-Fenzi, B., Holland, P., Jenkins, A., & Timmermann, R. (2016). Modeling Ice Shelf/Ocean Interaction in Antarctica: A Review. Oceanography, 29(4), 144–153. https://doi.org/10.5670/oceanog.2016.106
  5. Ghimire, B., Riley, W. J., Koven, C. D., Mu, M., & Randerson, J. T. (2016). Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions: LEAF AND ROOT TRAITS IN CLM. Journal of Advances in Modeling Earth Systems, 8(2), 598–613. https://doi.org/10.1002/2015MS000538
  6. Gleckler, P., Doutriaux, C., Durack, P., Taylor, K., Zhang, Y., Williams, D., et al. (2016). A More Powerful Reality Test for Climate Models. Eos, 97. https://doi.org/10.1029/2016EO051663
  7. He, H., Wang, D., Xu, Y., & Tan, J. (2016). Data synthesis in the Community Land Model for ecosystem simulation. Journal of Computational Science, 13, 83–95. https://doi.org/10.1016/j.jocs.2016.01.005
  8. Mao, J., Ricciuto, D. M., Thornton, P. E., Warren, J. M., King, A. W., Shi, X., et al. (2016). Evaluating the Community Land Model in a pine stand with shading manipulations and 13 CO 2 Biogeosciences, 13(3), 641–657. https://doi.org/10.5194/bg-13-641-2016
  9. McEnerney, J., Ames, S., Christensen, C., Doutriaux, C., Hoang, T., Painter, J., et al. (2016). Parallelization of Diagnostics for Climate Model Development. Journal of Software Engineering and Applications, 09(05), 199–207. https://doi.org/10.4236/jsea.2016.95016
  10. Medlyn, B. E., De Kauwe, M. G., Zaehle, S., Walker, A. P., Duursma, R. A., Luus, K., et al. (2016). Using models to guide field experiments: a priori predictions for the CO 2 response of a nutrient- and water-limited native Eucalypt woodland. Global Change Biology, 22(8), 2834–2851. https://doi.org/10.1111/gcb.13268
  11. Raczka, B., Duarte, H. F., Koven, C. D., Ricciuto, D., Thornton, P. E., Lin, J. C., & Bowling, D. R. (2016). An observational constraint on stomatal function in forests: evaluating coupled carbon and water vapor exchange with carbon isotopes in the Community Land Model (CLM4.5). Biogeosciences, 13(18), 5183–5204. https://doi.org/10.5194/bg-13-5183-2016
  12. Ren, H., Hou, Z., Huang, M., Bao, J., Sun, Y., Tesfa, T., & Ruby Leung, L. (2016). Classification of hydrological parameter sensitivity and evaluation of parameter transferability across 431 US MOPEX basins. Journal of Hydrology, 536, 92–108. https://doi.org/10.1016/j.jhydrol.2016.02.042
  13. Sun, Y., Piao, S., Huang, M., Ciais, P., Zeng, Z., Cheng, L., et al. (2016). Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models: Global patterns of ecosystem WUE. Global Ecology and Biogeography, 25(3), 311–323. https://doi.org/10.1111/geb.12411
  14. Tang, G., Zheng, J., Xu, X., Yang, Z., Graham, D. E., Gu, B., et al. (2016). Biogeochemical modeling of CO 2 and CH 4 production in anoxic Arctic soil microcosms. Biogeosciences, 13(17), 5021–5041. https://doi.org/10.5194/bg-13-5021-2016
  15. Wang, W., Zender, C. S., van As, D., Smeets, P. C. J. P., & van den Broeke, M. R. (2016). A Retrospective, Iterative, Geometry-Based (RIGB) tilt-correction method for radiation observed by automatic weather stations on snow-covered surfaces: application to Greenland. The Cryosphere, 10(2), 727–741. https://doi.org/10.5194/tc-10-727-2016
  16. Ware, C., Rogers, D., Petersen, M., Ahrens, J., & Aygar, E. (2016). Optimizing for Visual Cognition in High Performance Scientific Computing. Electronic Imaging, 2016(16), 1–9. https://doi.org/10.2352/ISSN.2470-1173.2016.16.HVEI-130
  17. Williams, D. (2016). Better Tools to Build Better Climate Models. Eos, 97. https://doi.org/10.1029/2016EO045055
  18. Williams, D. N. (2016). 5th Annual Earth System Grid Federation (No. LLNL-TR–689917, 1253685). https://doi.org/10.2172/1253685
  19. Williams, D. N., Balaji, V., Cinquini, L., Denvil, S., Duffy, D., Evans, B., et al. (2016). A Global Repository for Planet-Sized Experiments and Observations. Bulletin of the American Meteorological Society, 97(5), 803–816. https://doi.org/10.1175/BAMS-D-15-00132.1
  20. Williams, D. N., Doutriaux, C., Aashish Chaudhary, Fries, S., Lipsa, D., Sankhesh Jhaveri, et al. (2016). Uvcdat V2.4.0. Zenodo. https://doi.org/10.5281/ZENODO.45136
  21. Woodring, J., Petersen, M., Schmeiber, A., Patchett, J., Ahrens, J., & Hagen, H. (2016). In Situ Eddy Analysis in a High-Resolution Ocean Climate Model. IEEE Transactions on Visualization and Computer Graphics, 22(1), 857–866. https://doi.org/10.1109/TVCG.2015.2467411
  22. Xu, L., Cameron-Smith, P., Russell, L. M., Ghan, S. J., Liu, Y., Elliott, S., et al. (2016). DMS role in ENSO cycle in the tropics: DMS Role in ENSO Cycle in Tropics. Journal of Geophysical Research: Atmospheres, 121(22), 13,537-13,558. https://doi.org/10.1002/2016JD025333
  23. Yao, Z., Jia, Y., Wang, D., Steed, C., & Atchley, S. (2016). In Situ Data Infrastructure for Scientific Unit Testing Platform 1. Procedia Computer Science, 80, 587–598. https://doi.org/10.1016/j.procs.2016.05.344
  24. Zender, C. S. (2016). Bit Grooming: statistically accurate precision-preserving quantization with compression, evaluated in the netCDF Operators (NCO, v4.4.8+). Geoscientific Model Development, 9(9), 3199–3211. https://doi.org/10.5194/gmd-9-3199-2016
  25. Zhang, L., Mao, J., Shi, X., Ricciuto, D., He, H., Thornton, P., et al. (2016). Evaluation of the Community Land Model simulated carbon and water fluxes against observations over ChinaFLUX sites. Agricultural and Forest Meteorology, 226–227, 174–185. https://doi.org/10.1016/j.agrformet.2016.05.018
  26. Zhu, Q., Riley, W. J., Tang, J., & Koven, C. D. (2016). Multiple soil nutrient competition between plants, microbes, and mineral surfaces: model development, parameterization, and example applications in several tropical forests. Biogeosciences, 13(1), 341–363. https://doi.org/10.5194/bg-13-341-2016

2015

 

  1. Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis, D. J., et al. (2015). Improving the representation of hydrologic processes in Earth System Models: REPRESENTING HYDROLOGIC PROCESSES IN EARTH SYSTEM MODELS. Water Resources Research, 51(8), 5929–5956. https://doi.org/10.1002/2015WR017096
  2. Collins, W. D., Craig, A. P., Truesdale, J. E., Di Vittorio, A. V., Jones, A. D., Bond-Lamberty, B., et al. (2015). The integrated Earth system model version 1: formulation and functionality. Geoscientific Model Development, 8(7), 2203–2219. https://doi.org/10.5194/gmd-8-2203-2015
  3. Jones, A. D., Calvin, K. V., Collins, W. D., & Edmonds, J. (2015). Accounting for radiative forcing from albedo change in future global land-use scenarios. Climatic Change, 131(4), 691–703. https://doi.org/10.1007/s10584-015-1411-5
  4. Leng, G., Huang, M., Tang, Q., & Leung, L. R. (2015). A modeling study of irrigation effects on global surface water and groundwater resources under a changing climate: IRRIGATION EFFECTS ON WATER RESOURCES. Journal of Advances in Modeling Earth Systems, 7(3), 1285–1304. https://doi.org/10.1002/2015MS000437
  5. Li, H.-Y., Leung, L. R., Getirana, A., Huang, M., Wu, H., Xu, Y., et al. (2015). Evaluating Global Streamflow Simulations by a Physically Based Routing Model Coupled with the Community Land Model. Journal of Hydrometeorology, 16(2), 948–971. https://doi.org/10.1175/JHM-D-14-0079.1
  6. Li, H.-Y., Leung, L. R., Tesfa, T., Voisin, N., Hejazi, M., Liu, L., et al. (2015). Modeling stream temperature in the Anthropocene: An earth system modeling approach. Journal of Advances in Modeling Earth Systems, 7(4), 1661–1679. https://doi.org/10.1002/2015MS000471
  7. Petersen, M. R., Jacobsen, D. W., Ringler, T. D., Hecht, M. W., & Maltrud, M. E. (2015). Evaluation of the arbitrary Lagrangian–Eulerian vertical coordinate method in the MPAS-Ocean model. Ocean Modelling, 86, 93–113. https://doi.org/10.1016/j.ocemod.2014.12.004
  8. Reckinger, S. M., Petersen, M. R., & Reckinger, S. J. (2015). A study of overflow simulations using MPAS-Ocean: Vertical grids, resolution, and viscosity. Ocean Modelling, 96, 291–313. https://doi.org/10.1016/j.ocemod.2015.09.006
  9. Reed, S. C., Yang, X., & Thornton, P. E. (2015). Incorporating phosphorus cycling into global modeling efforts: a worthwhile, tractable endeavor. New Phytologist, 208(2), 324–329. https://doi.org/10.1111/nph.13521
  10. Safta, C., Ricciuto, D. M., Sargsyan, K., Debusschere, B., Najm, H. N., Williams, M., & Thornton, P. E. (2015). Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model. Geoscientific Model Development, 8(7), 1899–1918. https://doi.org/10.5194/gmd-8-1899-2015
  11. Samsel, F., Petersen, M., Geld, T., Abram, G., Wendelberger, J., & Ahrens, J. (2015). Colormaps that Improve Perception of High-Resolution Ocean Data. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems – CHI EA ’15 (pp. 703–710). Seoul, Republic of Korea: ACM Press. https://doi.org/10.1145/2702613.2702975
  12. Tang, J. Y. (2015). On the relationships between the Michaelis–Menten kinetics, reverse Michaelis–Menten kinetics, equilibrium chemistry approximation kinetics, and quadratic kinetics. Geoscientific Model Development, 8(12), 3823–3835. https://doi.org/10.5194/gmd-8-3823-2015
  13. Wang, S., Elliott, S., Maltrud, M., & Cameron‐Smith, P. (2015). Influence of explicit Phaeocystis parameterizations on the global distribution of marine dimethyl sulfide. Journal of Geophysical Research: Biogeosciences, 120(11), 2158–2177. https://doi.org/10.1002/2015JG003017
  14. Williams, D. N. (2015). 2014 Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Conference Report (No. LLNL-TR–666753, 1182238). https://doi.org/10.2172/1182238
  15. Wolfram, P. J., Ringler, T. D., Maltrud, M. E., Jacobsen, D. W., & Petersen, M. R. (2015). Diagnosing Isopycnal Diffusivity in an Eddying, Idealized Midlatitude Ocean Basin via Lagrangian, in Situ, Global, High-Performance Particle Tracking (LIGHT). Journal of Physical Oceanography, 45(8), 2114–2133. https://doi.org/10.1175/JPO-D-14-0260.1
  16. Xu, X., Hui, D., King, A. W., Song, X., Thornton, P. E., & Zhang, L. (2015). Convergence of microbial assimilations of soil carbon, nitrogen, phosphorus and sulfur in terrestrial ecosystems. Scientific Reports, 5(1). https://doi.org/10.1038/srep17445
  17. Zhu, Q., & Riley, W. J. (2015). Improved modelling of soil nitrogen losses. Nature Climate Change, 5(8), 705–706. https://doi.org/10.1038/nclimate2696

 

2014

 

  1. Ali, M., Ye, S., Li, H., Huang, M., Leung, L. R., Fiori, A., & Sivapalan, M. (2014). Regionalization of subsurface stormflow parameters of hydrologic models: Up-scaling from physically based numerical simulations at hillslope scale. Journal of Hydrology, 519, 683–698. https://doi.org/10.1016/j.jhydrol.2014.07.018
  2. Cai, X., Yang, Z.-L., Xia, Y., Huang, M., Wei, H., Leung, L. R., & Ek, M. B. (2014). Assessment of simulated water balance from Noah, Noah-MP, CLM, and VIC over CONUS using the NLDAS test bed. Journal of Geophysical Research: Atmospheres, 119(24), 13,751-13,770. https://doi.org/10.1002/2014JD022113
  3. Steed, C. A., Evans, K. J., Harney, J. F., Jewell, B. C., Shipman, G., Smith, B. E., et al. (2014). Web-based visual analytics for extreme scale climate science. In 2014 IEEE International Conference on Big Data (Big Data) (pp. 383–392). https://doi.org/10.1109/BigData.2014.7004255
  4. Ye, S., Li, H.-Y., Huang, M., Ali, M., Leng, G., Leung, L. R., et al. (2014). Regionalization of subsurface stormflow parameters of hydrologic models: Derivation from regional analysis of streamflow recession curves. Journal of Hydrology, 519, 670–682. https://doi.org/10.1016/j.jhydrol.2014.07.017
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