Journal Publications

2021

Published

2020

Published

Accepted

1. Ragnhild Bieltvedt Skeie, Gunnar Myhre1, Øivind Hodnebrog, Philip J. Cameron-Smith, Makoto Deushi, Michaela I. Hegglin, Larry W. Horowitz, Ryan J. Kramer, Martine Michou, Michael J. Mills, Dirk Jan Leo Olivie, Fiona M. O’Connor, David Paynter, Alistair Sellar, Drew Shindell, Toshihiko Takemura, Simone Tilmes, Tongwen Wu, “Historical total ozone radiative forcing derived from CMIP6 simulations”, npj Climate and Atmospheric Science (accepted)
2. Skeie, R. B., Myhre, G., Hodnebrog, Ø., Cameron-Smith, P. J., Deushi, M., Hegglin, M. I., Horowitz, L. W., Kramer, R. J., Michou, M., Mills, M. J., Olivie, D. J. L., O’Connor, F. M., Paynter, D., Sellar, A., Shindell, D., Takemura, T., Tilmes, S., Wu, T., Historical total ozone radiative forcing derived from CMIP6 simulations. npj Climate and Atmospheric Science.
3. Tang, Q., Prather, M. J., Hsu, J., Ruiz, D. J., Cameron-Smith, P. J., Xie, S., and Golaz, J.-C. (2020). Evaluation of the interactive stratospheric ozone (O3v2) module in the E3SM version 1 Earth System Model. Accepted to Geosci. Model Dev.

2019

Published

2018

Published

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 E3SMLand Model (ELM) Version 1.0 (preprint). Climate and Earth System Modeling. https://doi.org/10.5194/gmd-2018-44
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

2017

Published

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. 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
5. 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
6. 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
7. 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
8. 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
9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. Scripps Institution of Oceanography, Centurioni, L., Hormann, V., Talley, L., Arzeno, I., 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
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 <i>In-Situ</i> 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. Zhang, K., Rasch, P. J., Taylor, M. A., Wan, H., Leung, L.-Y. R., Ma, P.-L., et al. (2017). Impact of numerical choices on water conservation in the E3SM Atmosphere Model Version 1 (EAM V1) (preprint). Atmospheric Sciences. https://doi.org/10.5194/gmd-2017-293
34. 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

Published

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

Published

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

Published

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

As of July 2020, we are reorganizing and updating the publications page.  The content below contains citations from the old publications page.  Eventually, all the publications below that have been published will be added to the list above, if they are not there already.  Thank you for your patience while we improve this publications page.

Published or In Press

1. Bisht, Gautam, Riley, William J. (2019).  Development and Verification of a Numerical Library for Solving Global Terrestrial Multiphysics Problems.  Journal of Advances in Modeling Earth Systems.  https://doi.org/10.1029/2018ms001560.
2. 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. https://doi.org/10.1029/2019MS001870.
3. Golaz, J.-C., P. M. Caldwell, L. P. Van Roekel, M. R. Petersen, Q. Tang, J. D. Wolfe and co-authors (2019).  The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution. Journal of Advances in Modeling Earth Systems.  https://doi.org/10.1029/2018MS001603.
4. Harrop et al. (2019).  Understanding Monsoonal Water Cycle Changes in a Warmer Climate in E3SMv1 Using a Normalized Gross Moist Stability Framework.  JGR Atmospheres.  https://doi.org/10.1029/2019JD031443.
5. 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, 2465–2482.  https://doi.org/10.1029/2018JD028892.
6. Reeves Eyre, J.E.J, L.P Van Roekel, X. Zeng, M.A. Brunke, and J.-C. Golaz, (2019). Ocean barrier layers in the Energy Exascale Earth System Model. GRL.  https://doi.org/10.1029/2019GL083591.
7. Tang, Q., Klein, S. A., Xie, S., Lin, W., Golaz, J.-C., Roesler, E. L., Taylor, M. A., Rasch, P. J., Bader, D. C., Berg, L. K., Caldwell, P., Giangrande, S. E., Neale, R. B., Qian, Y., Riihimaki, L. D., Zender, C. S., Zhang, Y., and Zheng, X. (2019).  Regionally refined test bed in E3SM atmosphere model version 1 (EAMv1) and applications for high-resolution modeling.  Geosci. Model Dev., 12, 2679-2706.  https://doi.org/10.5194/gmd-12-2679-2019.
8. Zheng et al. (2019). The Summertime Precipitation Bias in E3SM Atmosphere Model Version 1 (EAMv1) over the Central United States. JGR Atmosheres.  https://doi.org/10.1029/2019JD030662.
9. 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, 13046 – 13073.  https://doi.org/10.1029/2018JD028927.
10. 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, 2618 – 2644.  https://doi.org/10.1029/2018MS001350.

Submitted

1. Balaguru et al., Tropical Cyclones in E3SMv1, In final coauthor review before resubmission to JAMES.
2. Brunke, Michael (10 Jan 2019).  E3SMv1 Stratocumulus Deck Evaluation, submitted to JGR-Atmospheres.
3. Hoch, K. M. Petersen, et al., Analysis and characterization of variable resolution meshes for ocean, accepted JAMES.
4. Hu, A., L. Van Roekel et al.  AMOC influence on transient climate variability in CESM2 and E3SMv1, accepted at J. Climate.
5. Lee,D.Y., M. Petersen, et al., Analysis of wintertime modes of variability in E3SMv1, submitted.
6. Orbe, C, L. Van Roekel, et al.  Modes of Variability in Six US Climate models, resubmitted to J. Climate.
7. Richter et al. (2019). Improved Simulation of the QBO in E3SMv1. JGR, submitted to JAMES.
8. Zhou et al. (2019). Global irrigation characteristics and effects simulated by fully coupled land surface, river, and water management models in E3SM, JAMES.
9. Bisht, Gautam (26 Nov 2018). Verification of Subsurface Physics, in progress, submitted to JAMES.
10. Neale et al. (2018). Sub-Seasonal Tropical Variability in the Energy Exascale Earth System Model (E3SM) version 1, to be submitted to JAMES.
11. Zhang, Yuying (14 Nov 2018). Ealuation of EAMv1 using COSP, submitted to JAMES.

To be Submitted:

1. Balaguru, Karthik, TC Track Climatology, in progress.
2. Burrows, Susannah, Organic Sea Spray, in progress.
3. Feng, Yan, Dust Life cycle analysis, in progress.
4. Roesler, Erika, EAMv0 RRM, in revision mode.
5. Roberts, Andrew, Paper on Sea Ice Satellite Emulator, in progress.
6. Zhang, Kai, EAMv1 Ice Cloud Evaluation, Finalizing Draft, target Journal is JAMES 10 Jan 2019.
7. Zhou, Tian, Water Management Paper, in progress, finalizing draft, target journal is JAMEs 20 Jan 2019.
8. Van Roekel, Luke and Veneziani, Milena, Ocean analysis of broader ocean climate and select modes, nearly complete.
9. Van Roekel, Luke, Veneziani, Milena and Maltrud, Mathew, Comparison to v0.1 HR, in progress.
10. Van Roekel, Luke, Analysis of muted AMOC strength and variability, in progress.

Published or In Press

1. 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, e2019GB006383.  https://doi.org/10.1029/2019GB006383.
2. Di Vittorio, A, C Vernon, and S Shu. (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. https://doi.org/10.5334/jors.266.
3. Holm, J. A., Knox, R. G., Zhu, Q., Fisher, R. A., Koven, C. D., Nogueira Lima, A. J., 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, e2019JG005500. https://doi.org/10.1029/2019JG005500.
4. Metzler, H., Zhu, Q., Riley, W. J., Hoyt, A. M., 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, e2019MS001776.  https://doi.org/10.1029/2019MS001776.
5. Saunois, M., et.al. (2020). The Global Methane Budget 2000-2017.  Earth System Science Data. https://www.earth-syst-sci-data-discuss.net/essd-2019-128/.
6. 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, e2019JD031456. https://doi.org/10.1029/2019JD031456.
7. 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, e2019MS001841. https://doi.org/10.1029/2019MS001841.
8. Burrows, S.M., M.E. Maltrud, X. Yang, Q. Zhu, N. Jeffery, X. Shi, and D.M. Ricciuto, et al. (2019).  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://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/488136.
9. Cai, X., Riley, W. J., Zhu, Q., Tang, J., Zeng, Z., Bisht, G., & Randerson, J. T. (2019).  Improving the representation of deforestation effects on evapotranspiration in the E3SM Earth system model. JAMES, 11, 2412.  https://doi.org/10.1029/2018MS001551.
10. Calvin, K. V., Bond-Lamberty B, Jones A D, Shi X, Di Vittorio A, Thornton P E. (2019).  Characteristics of human-climate feedbacks differ at different radiative forcing levels.  Global Planetary Change.  https://doi.org/10.1016/j.gloplacha.2019.06.003.
11. Chen, J, Q Zhu, W Riley, Y He, J Randerson, and S Trumbore (2019).  Comparison with Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model.  Journal of Geophysical Research: Biogeosciences.  http://doi.org/10.1029/2018jg004795.
12. Di Vittorio, A., X. Shi, B. Bond-Lamberty, K.V. Calvin, and A.D. Jones (2019).  Land use/cover distribution is a primary determinant of global carbon and local temperature projections.  Geophysical Research Letters.
13. Drewniak, B. A. (2019).  Simulating dynamic roots in the Energy Exascale Earth System Land Model.  Journal of Advances in Modeling Earth Systems.  https://doi.org/1029/2018MS001334.
14. Fleischer, K., Rammig, A., De Kauwe, M.G. et al. (2019).  Amazon forest response to CO₂ fertilization dependent on plant phosphorus acquisition. Nat. Geosci. 12, 736–741. https://doi.org/10.1038/s41561-019-0404-9.
15. Mao, Y., Zhou, T., Leung, L. R., Tesfa, T. K., Li, H.‐Y., Wang, K., et al. ( 2019). Flood inundation generation mechanisms and their changes in 1953–2004 in global major river basins. Journal of Geophysical Research: Atmospheres, 124, 11672– 11692. https://doi.org/10.1029/2019JD031381.
16. Bjorkman,  A.D., I.H. Myers-Smith, S.C. Elmendorf, H. Thomas, J. Kattge, S. Diaz, and M. Vellend, et al. (2018).  Change in plant functional traits across a warming tundra biome.  Nature562, no. 7725:57-62.  https://doi.org/1038/s41586-018-0563-7.
17. Collalti, A., C. Trotta, T. Keenan, A. Ibrom, B. Medlyn, C. Reyer, and R. Grote, 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, no. 10:2427-2452. PNNL-SA-124209.  https://doi.org/1029/2018MS001275.
18. Jones, A.D., K.V. Calvin, X. Shi, A. Di Vittorio, B. Bond-Lamberty, P. Thornton, and W.D. Collins. (2018).  Quantifying Human-Mediated Carbon Cycle Feedbacks. Geophysical Research Letters 45, no. 20:11370-11379.   https://doi.org/1029/2018GL079350.
19. Leung, L., T.K. Tesfa, Z. Tan, and H. Li. (2018).  Modeling sediment yield in land surface and Earth system models: Model comparison, development and evaluation.  Journal of Advances in Modeling Earth Systems 10, no. 9:2192-2213.  https://doi.org/1029/2017MS001270.
20. Riley, W. J., Q. Zhu, and J. Y. Tang (2018).  Weaker land–climate feedbacks from nutrient uptake during photosynthesis-inactive periods.  Nat. Clim. Chang., 8, 1002–1006.  https://doi.org/1038/s41558-018-0325-4.
21. Saravia, L., S. Doyle, and B. Bond-Lamberty (2018).  Power laws and critical fragmentation in global forests. Scientific Reports 8, no. 1:17766.  https://doi.org/1038/s41598-018-36120-w.

Submitted

1. Bond-Lamberty, B., Di Vittorio A , Jones A D, Shi X, Calvin K V.  Quantifying the variability of an integrated assessment model driven by a wide variety of earth system and agricultural models. Climatic Change.
2. Yang, X. et al. ELM v1 simulations over the Amazon.

To be Submitted

1. Drewniak et al. “Representing dynamic planting dates in ELM”
2. Jeffrey et al. “MPAS-O in the CBGC v1 simulations”
3. Ricciuto et al. “Comparing ELM results to litter decomposition data”
4. Shi, X et al. “Investigating the CO2 radiative and physiological effects and human intervention on water cycle”
5. Thornton, P et al. “Implications of phosphorous on the carbon cycle”
6. Yang, X et al. “Global ELM v1 simulations”
7. Zhu et al. “Nutrient limitations on the carbon cycle”

Published or In Press

1. Dang, C., Zender, C. S., and Flanner, M. G. (2019).  Inter-comparison and improvement of two-stream shortwave radiative transfer models in ESMs for a unified treatment of cryospheric surfaces.  The Cryosphere.  https://www.the-cryosphere.net/13/2325/2019.
2. Hoch, Kristin, Petersen, Mark, Brus, Steven, Engwirda, Darren, Roberts, Andrew, Rosa, Kevin, & Wolfram, Phillip. (2020).  MPAS-Ocean Simulation Quality for Variable-Resolution North American Coastal Meshes.  JAMES, 12, e2019MS001848.  https://doi.org/10.1029/2019MS001848.
3. Jeong, H., X. S. Asay-Davis, A. K. Turner, D. S. Comeau, S. F. Price, R. P. Abernathey, M. Veneziani, M. R. Petersen, M. J. Hoffman, M. R. Mazloff, and T. D. Ringler (2019).  Impacts of ice-shelf melting on water mass transformation in the Southern Ocean from E3SM simulations. J. Climate.  https://doi.org/10.5281/zenodo.3406735.
4. 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.  https://doi.org/10.1029/2019EA000663.
5. Petersen, Mark R.; Asay-Davis, Xylar S.; Berres, Anne S.; Chen, Qingshan; Feige, Nils; Hoffman, Matthew J.; Jacobsen, Douglas W.; Jones, Philip W.; Maltrud, Mathew E.; Price, Stephen F.; Ringler, Todd D.; Streletz, Gregory J.; Turner, Adrian K.; Van Roekel, Luke P.; Veneziani, Milena; Wolfe, Jonathan D.; Wolfram, Phillip J.; Woodring, Jonathan L. (2019).  An evaluation of the ocean and sea ice climate of E3SM using MPAS and interannual CORE-II forcing. JAMES.  https://doi.org/10.1029/2018MS001373.
6. Hoffman, M. J., M. Perego, S. F. Price, W. H. Lipscomb, T. Zhang, D. Jacobsen, I. Tezaur, A. G. Salinger, R. Tuminaro, and L. Bertagna (2018).  MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids.  Model Dev., 11, 3747–3780. https://doi.org/10.5194/gmd-11-3747-2018.
7. 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

Submitted

1. Hoffman, M. J., X. Asay-Davis, S. F. Price, J. G. Fyke, M. Perego, Effects of ice shelf melt variability on the evolution of Thwaites Glacier, West Antarctica. JGR Earth Surface (in re-review).
2. 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, Clim. Dyn. (in re-review following first revision) (joint with Water Cycle).
3. Zhang, M., S. Xie, X. Liu, W. Lin, K. Zhang, H.-Y. Ma, X. Zheng, Y. Zhang (2020). Toward Understanding the Characteristics of Mixed-Phase Clouds Simulated in E3SM with ARM M-PACE observations, Earth and Space Science (in review).

To be Submitted

1. Comeau, D., Turner, A., and Hunke, E.. An Eulerian Iceberg Model in the Energy Exascale Earth System Model (E3SM). Target journal: The Cryosphere.
2. Comeau, D., X. Asay-Davis, M. Hoffman, M. Petersen, S. Price, M. Veneziani, J.Wolfe. Ice-shelf basal melt rates from a global Earth system model (https://www.overleaf.com/5897735775srsnpvjjntrz) [Jira epic link].
3. Conlon, L.M., L.P. Van Roekel, A.F. Roberts, Q. Li, K. Hoch, K. Rosa, J. Wolfe, and S.R. Brus. Mitigating Labrador Sea Ice Bias in the standard-resolution variant of E3SM, JAMES (in prep).
4. Hoffman, M.J., D. Comeau, X. Asay-Davis, C. Begeman, S. Price, K. Hoch, M. Veneziani, J. Wolfe, M. Maltrud. Ice-shelf melt teleconnections in the Weddell Sea. JGR-Oceans (in prep).
5. Jeffery, N., Hunke, E., Turner A. K., and Wolfe, J.D. How does enhanced complexity in snow-on-seaice modeling influence global simulations? (in prep) Ocean modeling or IGS. [Jira epic link]
6. Jeong, H., A. K. Turner, A. F. Roberts, M. Veneziani, L. P. Van Roekel, and J. D. Wolfe (2019). Antarctic dense water formation in coastal and open-ocean polynyas from E3SM high-resolution, coupled simulation, J. Climate, (in prep).
7. Lee, D. Y., Lin, W, Petersen, M.R., (2020).  Impact of the Pacific-North American Pattern on North American weather.  J. Climate (in prep).
8. Lee, D.Y., Lin, W., Petersen, M.R. (2020).  Wintertime Northern Hemisphere climate modes and their impacts in E3SM, (in prep).
9. Roberts, A., A. Turner, J. Wolfe, E. Hunke (LANL), DuVivier (NCAR), Maslowski (NPS), Farrell (U.Maryland) (2019).  Evaluating sea ice thickness in Earth System Models using altimetric emulators. Expected submission April 2019 to Atmospheric Ocean. Technol.  
10. Rosa, K.L., Petersen, M.R., Brus, S.R., Engwirda, D., Hoch, K.E., Maltrud, M.E., Van Roekel, L.P., Wolfram, P.J. Boundary current impacts of coastal refinement in the E3SM unstructured-mesh ocean model MPAS-Ocean, (in prep).
11. Turner, A. K., William H. Lipscomb, Elizabeth C. Hunke, Douglas W. Jacobsen, Nicole Jeffery, Todd D. Ringler, and Jonathan D. Wolfe. MPAS-Seaice: a new variable resolution sea-ice model. GMD
12. Veneziani, M., X. Asay-Davis, S. Price, W. Lin, D. Comeau, H. Jeong, et al. The Antarctic Slope Front in a coupled high-resolution E3SMv1 simulation. JGR-Oceans or JCLI.

Published or In Press

1. Prather and Hsu (2019).  A Round Earth for Climate Models. PNAS.  https://doi.org/10.1073/pnas.1908198116.
2. Rasch, Xie et al. (2019).  An overview of Version 1 of DOE E3SM Atmosphere Model (EAMV1).  JAMES special issue, in press. Article ID: jame20932, Article DOI: 10.1029/2019MS001629.  EAM-Atmosphere-overview_accepted.pdf.
3. Richter et al. (2019).  Improved Simulation of the QBO in E3SMv1. JAMES.  https://doi.org/10.1029/2019MS001763.
4. 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.  https://doi.org/10.1029/2019MS001702.
5. Zhang, Y., Xie, S., Lin, W., Klein, S. A., Zelinka, M., Ma, P.-L., Rasch P., Qian Y., Tang Q., Ma, H.-Y. (2019).  Evaluation of Clouds in Version 1 of the E3SM Atmosphere Model with Satellite Simulators. Journal of Advances in Modeling Earth Systems.  https://doi.org/10.1029/2018ms001562.
6. Brown, H., X. Liu, Y. Feng, Y. Jiang, M. Wu, Z. Lu, C. Wu, S. Murphy, and R. Pokhrel (2018).  Radiative forcing and climate impacts of Brown Carbon with the Community Atmosphere Model (CAM5).  Atmos. Chem. Phys., 18, 17745-17768. https://doi.org/10.5194/acp-18-17745-2018.
7. Paukert, M., Fan, J., Rasch, P. J., Morrison, H., Milbrandt, J. A., Shpund, J., & Khain, A. (2018).  Three-Moment Representation of Rain in a Bulk Microphysics Model.  Journal of Advances in Modeling Earth Systems.  https://doi.org/10.1029/2018ms001512.

Submitted

1. Ito, A., S. Myriokefalitakis, M. Kanakidou, N. M. Mahowald, R. A. Scanza, D. S. Hamilton, A. R. Baker, T. Jickells, M. Sarin, S. Bikkina, Y. Gao, R. U. Shelley, C. S. Buck, W. M. Landing, A. R. Bowie, M. G. Perron, C. Guieu, N Meskhidze, M S. Johnson, Y. Feng, J. F. Kok, A. Nenes, R. A. Duce (2018).  Constraints on attribution of labile iron in aerosols to combustion and mineral dust sources from observations and models, submitted to Science Advances.
2. Meskhidze, N., C. Voelker, H. Al-Abadleh, K. Barbeau, M. Bressac, C. Buck, R. Bundy, P. Croot, Y. Feng, A. Ito, A. M. Johansen, W. Landing, J. Mao, S. Myriokefalitakis, Daniel Ohnemus, Benoît Pasquier, and Y. Ye, (2018).  Perspective on Identifying and Characterizing the Processes Controlling Iron Speciation and Residence Time at the Atmosphere-Ocean Interface, submitted to Marine Chemistry.

To be Submitted

1. Cameron-Smith et al. (2019).  Analysis of E3SMv1 analysis of the stratosphere in the E3SM DECK simulations, in prep for JAMES
2. Feng, Y., H. Wang, R. Easter, P. Rasch, W. Lin, K. Zhang, P.-L. Ma, S. Xie, D. Hamilton, N. Mahowald, J. Kok, and H. Yu.  Dust life cycle and direct radiative effects in the E3SM: Impact of increasing model resolution. To be submitted to JAMES.
3. Lou, S., M. Shrivastava, R.C. Easter, Y. Yang, P.-L. Ma, H. Wang, P.J. Rasch, V. Ghate, J.L. Jimenez, Q. Zhang, J.E. Shilling, S.T. Martin.  Impact of oxidation reaction, cloud-aerosol interaction, and vertical resolution on organic aerosols, in the Energy Exascale Earth System Model (E3SM).  In preparation
4. Wang, H., et al. Aerosols and their radiative forcing in the Energy Exascale Earth System Model version 1 (E3SMv1). In preparation for JAMES.
5. Prather et al. Solar-J v7.6 with uncertainty Quantification.  In prep.
6. Zaveri R., et al., Global atmospheric distribution and radiative effect of nitrate aerosols. In preparation.

Published or In Press

1. Mahajan, S., Evans, K.J., Kennedy, J.H., Xu, M. and Norman, M.R. (2019).  A Multivariate Approach to Ensure Statistical Reproducibility of Climate Model Simulations. In Proceedings of the Platform for Advanced Scientific Computing Conference (p. 14).  ACM.  https://doi.org/10.1145/3324989.3325724.
2. Mahajan, S., K Evans, J Kennedy, M Xu, M Norman, and M Branstetter (2019).  Ongoing Solution Reproducibility of Earth System Models as They Progress Toward Exascale Computing.  The International Journal of High Performance Computing Applications 109434201983734.  https://doi.org/10.1177/1094342019837341.

Submitted

1. Steyer, A., Vogl, C.J., Taylor, M., and Guba, O.  Efficient IMEX Runge-Kutta methods for nonhydrostatic dynamics.

To be Submitted

Published or In Press 

1. Hewitt, H.T., M.J. Bell, E.P. Chassignet, A. Czaja, D. Ferreira, S.M. Griffies, P. Hyder, J.L. McClean, A.L. New, M.J. Roberts (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.

To be submitted

1. Ivanova, D.P., J.L. McClean, J. Sprintall, and R. Chen, Predicting rainfall over the Maritime and Australian continents using high-resolution fully-coupled E3SMv0.  Journal of Geophysical Research: Oceans
2. McClean, J.L., D.C. Bader, M.E. Maltrud, K. J. Evans, D. P. Ivanova, M.A. Taylor, Q.Tang, C. Veneziani, C. Papadopoulos, J. Ritchie, M. Branstetter, and S. Mahajan, Initialization of high-resolution, fully-coupled transient climate simulations. JAMES

1. Evans et al. (2019).  Northern Hemisphere blocking in simulations at 25km resolution E3SM atmosphere-land simulations.  JGR Atmospheres.  https://doi.org/10.1029/2018JD028892.
2. 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, 1657–1672.  https://doi.org/10.1002/2017JD027244.
3. Harrop, B.E., P.-L. Ma, P.J. Rasch, R.B. Neale, C. Hannay (2018).  The Role of Convective Gustiness in Reducing Seasonal Precipitation Biases in the Tropical West Pacific. JAMES.  https://doi.org/10.1002/2017MS001157.
4. MacDonald, A. B., H. Dadashazar, P.Y. Chuang, E. Crosbie, H. Wang, Z. Wang, et al. (2018).  Characteristic vertical profiles of cloud water composition in marine stratocumulus clouds and relationships with precipitation.  Journal of Geophysical Research: Atmospheres, 123, 3704–3723.  https://doi.org/10.1002/2017JD027900.
5. Mahajan, S. et al. (2018).  Model Resolution-sensitivity of the Simulation of North Atlantic Oscillation Teleconnections to Precipitation Extremes.  JGR. Atmosphere.  https://doi.org/10.1029/2018JD028594.
6. Niu, H., S. Kang, H. Wang, R. Zhang, X. Lu, Y. Qian, R. Paudyal, S. Wang, X. Shi, and X. Yan (2018).  Seasonal variation and light absorption property of carbonaceous aerosol in a typical glacier region of the southeastern Tibetan Plateau. Atmos. Chem. Phys., 18, 6441-6460.  https://doi.org/10.5194/acp-18-6441-2018.
7. Qian, Y., H. Wan, B. Yang, J. C. Golaz, B. Harrop, Z. Hou, V. E. Larson, L. R. Leung, G. Lin, W. Lin, P. Ma, H. Ma, P. Rasch, B. Singh, H. Wang, S. Xie, and K. Zhang (2018).  Parametric sensitivity and uncertainty quantification in the version 1 of E3SM Atmosphere Model based on short Perturbed Parameters Ensemble simulations. Journal of Geophysical Research-Atmospheres.  https://doi.org/10.1029/2018JD028927.
8. Wang, H., C.D. Burleyson, P.-L. Ma, J.D. Fast, and P.J. Rasch (2018).  Using the Atmospheric Radiation Measurement (ARM) Datasets to Evaluate Climate Models in Simulating Diurnal and Seasonal Variations of Tropical Clouds. J. Climate. https://doi.org/10.1175/JCLI-D-17-0362.1.
9. Ma, P-L., P. J. Rasch, H. Chepfer, D. M. Winker, and S. J. Ghan (2018).  Observational Constraint on Cloud Susceptibility Weakened by Aerosol Retrieval Limitations. Nature Communications. Accepted.
10. Xie et al. (2018).  Understanding Cloud and Convective Characteristics in Version 1 of the E3SM Atmosphere Model.  JAMES.  https://doi.org/10.1029/2018MS001350.
11. Mahajan, Salil, Abby Gaddis, Kate Evans, Matt Norman (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.
12. Terai, C. R., P. M. Caldwell, S. A. Klein, Q. Tang, and M. L. Branstetter (2017).  The atmospheric hydrologic cycle in the ACME v0.3 model. Clim. Dyn.  https://doi.org/10.1007/s00382-017-3803-x.
13. Wan, H., Zhang, K., Rasch, P. J., Singh, B., Chen, X., and Edwards, J. (2017).  A new and inexpensive non-bit-for-bit solution reproducibility test based on time step convergence (TSC1.0).  Geosci. Model Dev., 10, 537-552. https://doi.org/10.5194/gmd-10-537-2017.
14. Zhang, K., Rasch, P. J., Taylor, M. A., Wan, H., Leung, L.-Y. R., Ma, P.-L., Golaz, J.-C., Wolfe, J., Lin, W., Singh, B., Burrows, S., Yoon, J.-H., Wang, H., Qian, Y., Tang, Q., Caldwell, P., and Xie, S. (2017).  Impact of numerical choices on water conservation in the E3SM Atmosphere Model Version 1 (EAM V1).  Geosci. Model Dev.  https://doi.org/10.5194/gmd-2017-293.
15. Zhang, Y., S. Xie, et al. (2017).  ARM Cloud Radar Simulator for Global Climate Models – A New Tool for Bridging Field Data and Climate Models.  BAMS,.  https://doi.org/10.1175/BAMS-D-16-0258.1.

1. Anderson-Teixeira, Wang, McGarvey, Herrmann, Tepley, Bond-Lamberty, and LeBauer (2018).  ForC: A global database of forest carbon stocks and fluxes. Ecology. http://doi.org/10.1002/ecy.2229.
2. Bisht, G., W. J. Riley, H. Wainwright, B. Dafflon, F. Yuan, and V. E. Romanovsky (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, 61-76.  http://doi.org/10.5194/gmd-11-61-2018.
3. Bisht, G., Riley, W. J., Hammond, G. E., and Lorenzetti, D. M. (2018).  Development and evaluation of a variably saturated flow model in the global E3SM Land Model (ELM) Version 1.0.  Geosci. Model Dev. Discuss.  https://doi.org/10.5194/gmd-2018-44. 
4. Di Vittorio, A. V., Mao, J., Shi, X., Chini, L., Hurtt, G., & Collins, W. D. (2018).  Quantifying the effects of historical land cover conversion uncertainty on global carbon and climate estimates. Geophysical Research Letters, 45.  http://doi.org/10.1002/2017GL075124
5. Li, Lawrence, and Bond-Lamberty (2018).  Human impacts on 20th century fire dynamics and implications for global carbon and water trajectories. Global and Planetary Change.  http://doi.org/10.1016/j.gloplacha.2018.01.002
6. Ricciuto, D., K. Sargsyan and P. Thornton (2018).  The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model. Journal of Advances in Modeling Earth Systems 10.  https://doi.org/10.1002/2017MS000962.
7. Tan, Z., L.R. Leung, H.-Y. Li, and T. Tesfa (2018).  Modeling Sediment Yield in Land Surface and Earth System Models: Model Comparison, Development and Evaluation.  JAMES, 10.  https://doi.org/10.1029/2017MS001270.
8. Tang, J. Y., and Riley, W. J. (2018).  Divergent global carbon cycle predictions resulting from ambiguous numerical interpretation of nitrogen limitation. Earth Interactions.  https://doi.org/10.1175/EI-D-17-0023.1.
9. Zheng, J., P. E. Thornton, S. L. Painter, B. Gu, S. D. Wullschleger and D. E. Graham (2018).  Modeling anaerobic soil organic carbon decomposition in Arctic polygon tundra: insights into soil geochemical influences on carbon mineralization. Biogeosciences Discuss. 2018: 1-31 doi: https://doi.org/10.5194/bg-2018-63.
10. Butler, Datta, et al. (50 co-authors, including Bond-Lamberty) (2017).  Mapping local and global variability in plant trait distributions. Proceedings of the National Academy of Science 114(51):E10937-E10946. http://doi.org/10.1073/pnas.1708984114.
11. Duarte, H. F., B. M. Raczka, D. M. Ricciuto, J. C. Lin, C. D. Koven, P. E. Thornton, D. R. Bowling, C. T. Lai, K. J. Bible and J. R. Ehleringer (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.  http://doi.org/10.5194/bg-14-4315-2017.
12. Fang, Y., L.R. Leung, Z. Duan, M.S. Wigmosta, R. Maxwell, J. Chambers, and J. Tomasella (2017).  Influence of Landscape Heterogeneity on Water Available to Tropical Forests in an Amazonian Catchment and Implications for Modeling Drought Response.  J. Geophys. Res., 122, 8410-8426.  http://doi.org/10.1002/2017JD027066.
13. Holm, J. A., S. J. Van Bloem, G. R. Larocque, and H. H. Shugart (2017).  Shifts in biomass and productivity for a subtropical dry forest in response to simulated elevated hurricane disturbances.  Environ Res Lett, 12.  http://doi.org/10.1088/1748-9326/aa583c/pdf.
14. Leng, G., L.R. Leung, and M. Huang (2017).  Significant Impacts of Irrigation Water Sources and Methods on Modeling Irrigation Effects in the ACME Land Model.  J. Adv. Mod. Earth Syst., 9. http://doi.org/10.1002/2016MS000885.
15. Li ,F., D. M. Lawrence, and B. Bond-Lamberty (2017).  Impact of Fire on Global Land Surface Air Temperature and Energy Budget for the 20th Century due to Changes within Ecosystems.  Environmental Research Letters. 12(4). 44014.  http://doi.org/10.1088/1748-9326/aa6685/meta.
16. Liu S., B. Bond-Lamberty, L. R. Boysen, J. D. Ford, A. Fox, K. Gallo, J. Hatfield, G. M. Henebry, T. G. Huntington, Z. Liu, T. R. Loveland, R. J. Norby, T. Sohl, A. L. Steiner, W. Yuan, Z. Zhang, and S. Zhao (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.
17. Luo, X., H. Li, L.R. Leung, T. Tesfa, A.C.V. Getirana, F. Papa, and L.L. Hess (2017).  Modeling Surface Water Dynamics in MOSART-Inundation-v1.0: Impacts of Geomorphological Parameters and River Flow Representation in the Amazon Basin. Geosci. Model Dev., 10, 1233-1259.  https://doi.org/10.5194/gmd-10-1233-2017.
18. Metcalfe, D. B., D. Ricciuto, S. Palmroth, C. Campbell, V. Hurry, J. Mao, S. G. Keel, S. Linder, X. Shi, T. Näsholm, K. E. A. Ohlsson, M. Blackburn, P. E. Thornton and R. Oren (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.
19. Sun, Y., Peng, S., Goll, D. S., Ciais, P., Guenet, B., Guimberteau, M., Hinsinger, P., Janssens, I. A., Peñuelas, J., Piao, S., Poulter, B., Violette, A., Yang, X., Yin, Y. and Zeng, H. (2017).  Diagnosing phosphorus limitations in natural terrestrial ecosystems in carbon cycle models. Earth’s Future, 5: 730–749.  https://doi.org/10.1002/2016EF000472.
20. Tan, Z., L.R. Leung, H. Li, T. Tesfa, M. Vanmaercke, J. Poesen, X. Zhang, H. Lu, and J. Hartmann (2017).  A Global Data Analysis for Representing Sediment and Particulate Organic Carbon Yield in Earth System Models. Water Resour. Res., 53.  https://doi.org/10.1002/2017WR020806.
21. Tang, J. Y., and W. J. Riley (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, 3277-3295.   https://doi.org/10-5194/gmd-10-3277-2017.
22. Tesfa, T., and L.R. Leung (2017).  Exploring New Topography-based Subgrid Spatial Structures for Improving Land Surface Modeling. Geosci. Model Dev., 10, 873-888.  https://doi.org/10.5194/gmd-10-873-2017.
23. Thornton, P. E., K. Calvin, A. D. Jones, A. V. D. Vittorio, B. Bond-Lamberty, L. Chini, X. Shi, J. Mao, W. D. Collins, J. Edmonds, A. Thomson, J. Truesdale, A. Craig, M. L. Branstetter and G. Hurtt (2017).  Biospheric feedback effects in a synchronously coupled model of human and Earth systems. Nature Clim. Change 7: 496-500.  https://doi.org/10.1038/nclimate3310.
24. Voisin, N., M. Hejazi, L.R. Leung, L. Liu, M. Huang, H.-Y. Li, and T. Tesfa (2017).  Effects of Spatially Distributed Sector Water Management on the Redistribution of Water Resources in an Integrated Water Model.  Water Resour. Res., 53, 4253–4270.  https://doi.org/10.1002/2016WR019767.
25. Wan, W., H.-Y. Li, J. Zhao, A. Mishra, L.R. Leung, M. Hejazi, W. Wei, H. Lu, Z. Deng, and Y. Demissie (2017).  Hydrological Drought in the Anthropocene: Impacts of Local Water Extraction and Reservoir Regulation in the US.  J. Geophys. Res., 122.  https://doi/org/10.1002/2017JD026899.
26. Wang, D., X. Luo, F. Yuan and N. Podhorszki (2017).  A Data Analysis Framework for Earth System Simulation within an In-Situ Infrastructure.  Journal of Computer and Communications Vol.05 No.14: 10.  https://doi.org/10.4236/jcc.2017.514007.
27. Wang, D., Y. Pei, O. Hernandez, W. Wu, Z. Yao, Y. Kim, M. Wolfe and R. Kitchen (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.
28. Xu, Y., D. Wang, T. Janjusic, W. Wu, Y. Pei and Z. Yao (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
29. Zeng, Z., S. Piao, L. Li, L. Zhou, P. Ciais, Y. Li, X. Lian, P. Friedlingstein, J. Mao, R. Myneni, S. Peng, X. Shi, S. Seneviratne, T. Wang and Y. Wang (2017).  Climate mitigation from vegetation biophysical feedbacks during the past three decades. Nature Climate Change.  https://www.nature.com/articles/nclimate3299.
30. Zhu, Q., W. J. Riley, and J. Y. Tang (2017).  A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions, Ecol Appl, 27, WOS:000398577200013, 875-886. doi: https://doi.org/10.1002/eap.1490.
31. Brunke, M.A., P. Broxton, J. Pelletier, D. Gochis, P. Hazenberg, D. Lawrence, L.R. Leung, G.-Y. Niu, P. Troch, and X. Zeng (2016).  Implementing and Testing Variable Soil Thickness in the Community Land Model Version 4.5. , J. Clim., 29, 3441-3461. http://doi.org/10.1175/JCLI-D-15-0307.1.
32. Ghimire, B., W. J. Riley, C. D. Koven, M. Mu, and J. T. Randerson (2016).  Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions.  JAMES.  http://doi.org/10.1002/2015MS000538
33. He, H., D. Wang, Y. Xu and J. Tan (2016).  Data synthesis in the Community Land Model for ecosystem simulation. Journal of Computational Science 13: 83-95.  http://doi.org/10.1016/j.jocs.2016.01.005.
34. Mao, J., D. M. Ricciuto, P. E. Thornton, J. M. Warren, A. W. King, X. Shi, C. M. Iversen and R. J. Norby (2016).  Evaluating the Community Land Model in a pine stand with shading manipulations and 13CO2 labeling. Biogeosciences 13(3): 641-657.  https://doi.org/10.5194/bg-13/641/2016.
35. Mao, J., A. Ribes, B. Yan, X. Shi, P. E. Thornton, R. Séférian, P. Ciais, R. B. Myneni, H. Douville, S. Piao, Z. Zhu, R. E. Dickinson, Y. Dai, D. M. Ricciuto, M. Jin, F. M. Hoffman, B. Wang, M. Huang and X. Lian (2016).  Human-induced greening of the northern extratropical land surface. Nature Climate Change 6: 959.  https://doi.org/10.1038/nclimate3056.
36. Medlyn, B. E., M. G. De Kauwe, S. Zaehle, A. P. Walker, R. A. Duursma, K. Luus, M. Mishurov, B. Pak, B. Smith, Y.-P. Wang, X. Yang, K. Y. Crous, J. E. Drake, T. E. Gimeno, C. A. Macdonald, R. J. Norby, S. A. Power, M. G. Tjoelker and D. S. Ellsworth (2016).  Using models to guide field experiments: a priori predictions for the CO2 response of a nutrient- and water-limited native Eucalypt woodland. Global Change Biology 22(8): 2834-2851.  https://doi.org/10.1111/gcb.13268.
37. Raczka, B., H. F. Duarte, C. D. Koven, D. Ricciuto, P. E. Thornton, J. C. Lin and D. R. Bowling (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.
38. Ren, H., Z. Hou, M. Huang, J. Bao, Y. Sun, T. Tesfa, and L.R. Leung (2016).  Classification of Hydrological Parameter Sensitivity and Evaluation of Parameter Transferability Across 431 US MOPEX Basins. J. Hydrol., 536, 92-108, doi:  https://doi.org/10.1016/j.jhydrol.2016.02.042.
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