Journal Publications

2021

Published

2020

Published

Accepted/In Review

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 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

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 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

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. Neale et al. (2018). Sub-Seasonal Tropical Variability in the Energy Exascale Earth System Model (E3SM) version 1, to be submitted to JAMES.

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.

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

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.

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.

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.

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., & 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
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., Thornton, P. E., Painter, S. L., Gu, B., Wullschleger, S. D., & Graham, D. E. (2019). Modeling anaerobic soil organic carbon decomposition in Arctic polygon tundra: insights into soil geochemical influences on carbon mineralization. Biogeosciences, 16(3), 663–680. https://doi.org/10.5194/bg-16-663-2019
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.
39. Sun, Y., S. Piao, M. Huang, P. Ciais, Z. Zeng, L. Cheng, X. Li, X. Zhang, J. Mao, S. Peng, B. Poulter, X. Shi, X. Wang, Y.-P. Wang and H. Zeng (2016).  Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models. Global Ecology and Biogeography 25(3): 311-323.  https://doi.org/10.1111/geb.12411.
40. Tang, G., F. Yuan, G. Bisht, G. E. Hammond, P. C. Lichtner, J. Kumar, R. T. Mills, X. Xu, B. Andre, F. M. Hoffman, S. L. Painter and P. E. Thornton (2016).  Addressing numerical challenges in introducing a reactive transport code into a land surface model: a biogeochemical modeling proof-of-concept with CLM–PFLOTRAN 1.0. Geosci. Model Dev. 9(3): 927-946.  https://doi.org/10.5194/gmd-9-927-2016.
41. Tang, G., J. Zheng, X. Xu, Z. Yang, D. E. Graham, B. Gu, S. L. Painter and P. E. Thornton (2016).  Biogeochemical modeling of CO2 and CH4 production in anoxic Arctic soil microcosms. Biogeosciences 13(17): 5021-5041. https://doi.org/10.5194/bg-13-5021-2016.
42. Tang, J. Y., and W. J. Riley (2016).  Technical Note: A generic law-of-the-minimum flux limiter for simulating substrate limitation in biogeochemical models. Biogeosciences, 13, 723-735.  https://doi.org/10.5194/bg-13/723/2016/.
43. Wang, D., Jens Domke, Jiafu Mao, Xiaoying Shi, Daniel Ricciuto (2016).  A Scalable Framework for the Global Offline Community Land Model Ensemble Simulation. International Journal of Computational Science and Engineering, pp73-85, Vol. 12, No. 1.
44. Yao, Z., Y. Jia, D. Wang, C. Steed and S. Atchley (2016).  In Situ Data Infrastructure for Scientific Unit Testing Platform. Procedia Computer Science 80: 587-598.  https://doi.org/10.1016/j.procs.2016.05.344.
45. Zhang, L., J. Mao, X. Shi, D. Ricciuto, H. He, P. Thornton, G. Yu, P. Li, M. Liu, X. Ren, S. Han, Y. Li, J. Yan, Y. Hao and H. Wang (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.
46. Zhu, Q., W. J. Riley, J. Y. Tang, and C. D. Koven (2016).  Multiple soil nutrient competition between plants, microbes, and mineral surfaces: Model development, parameterization, and example applications in several tropical forests, Biogeosciences, 13, 341-363.  https://doi.org/10.5194/bg-13-341-2016.
47. Clark, M. P., Y. Fan, D.M. Lawrence, J.C. Adam, D. Bolster, D.J. Gochis, R.P. Hooper, M. Kumar, L.R. Leung, D.S. Mackay, R.M. Maxwell, C. Shen, S.C. Swenson, and X. Zeng (2015).  Improving the Representation of Hydrologic Processes in Earth System Models. Water Resour. Res., 51, 5929-5956, http://doi.org/10.1002/2015WR017096.
48. Collins, W. D., A. P. Craig, J. E. Truesdale, A. V. Di Vittorio, A. D. Jones, B. Bond-Lamberty, K.V. Calvin, et al. (2015).  The Integrated Earth System Model Version 1: Formulation and Functionality.  Geoscientific Model Development 8 (7): 2203–19.  http://doi.org/10.5194/gmd-8-2203-2015.
49. Jones, Andrew D., Katherine V. Calvin, William D. Collins, and James Edmonds (2015).  Accounting for Radiative Forcing From Albedo Change in Future Global Land-Use Scenarios. Climatic Change, April.  http://doi.org/10.1007/s10584-015-1411-5.
50. Leng, G., M. Huang, Q. Tang, and L.R. Leung (2015).  A Modeling Study of Irrigation Effects on Global Surface and Groundwater Resources Under a Changing Climate.  J. Adv. Model. Earth Sys., 7, 1285-1304,  http://doi.org/10.1002/2015MS000437.
51. Li, H.-Y., L.R. Leung, A. Getirana, M. Huang, H. Wu, Y. Xu, J. Guo, and N. Voisin (2015).  Evaluating Global Streamflow Simulations by a Physically-Based Routing Model Coupled With the Community Land Model.,  J. Hydrometeor., 16, 948-971.  https://doi.org/10.1175/JHM-D-14-0079.1
52. Li, H.-Y., L.R. Leung, T. Tesfa, N. Voisin, M. Hejazi, L. Liu, Y. Liu, J. Rice, H. Wu, and X. Yang (2015).  Modeling Stream Temperature in the Anthropocene – An Earth System Modeling Approach.  J. Adv. Mod. Earth Sys., 7, 1661-1679.  https://doi.org/10.1002/2015MS000471.
53. Reed, S. C., X. Yang and P. E. Thornton (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.
54. Safta, C., D. M. Ricciuto, K. Sargsyan, B. Debusschere, H. N. Najm, M. Williams and P. E. Thornton (2015).  Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model. Geosci. Model Dev. 8(7): 1899-1918.  https://doi.org/10.5194/gmd-8-1899-2015.
55. 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, WOS.  https://doi.org/10.5194/gmd-8-3823-2015.
56. Xu, X., D. Hui, A. W. King, X. Song, P. E. Thornton and L. Zhang (2015).  Convergence of microbial assimilations of soil carbon, nitrogen, phosphorus, and sulfur in terrestrial ecosystems. Scientific Reports 5: 17445.  https://doi.org/10.1038/srep17445.
57. Zhu, Q., and W. J. Riley (2015).  Improved modeling of soil nitrogen losses, Nature Climate Change, 5, 705-706.  https://doi.org/10.1038/nclimate2696.
58. Ali, M., S. Ye, H.-Y. Li, M. Huang, L.R. Leung, A. Fiori, and M. Sivapalan. (2014).  Regionalization of Subsurface Stormflow Parameters of Hydrologic Models: Upscaling From Physically Based Numerical Simulations at Hillslope Scale. J. Hydrol., 519, 683-698.  http://doi.org/10.1016/j.jhydrol.2014.07.018.
59. Cai, X., Z.-L. Yang, Y. Xia, M. Huang, H. Wei, L.R. Leung, and M. Ek. (2014).  Benchmarking Land Models: Evaluation of Noah, Noah-MP, CLM, and VIC Over CONUS Using the NLDAS Testbed.  J. Geophys. Res., 119, 13,751-13,770,  http://doi.org/10.1002/2014JD022113.
60. Ye, S., H.-Y. Li, M. Huang, M. Ali, G. Leng, L.R. Leung, S.-W. Wang, and M. Sivapalan (2014).  Regionalization of Subsurface Stormflow Parameters of Hydrologic Models: Derivation From Regional Analysis of Streamflow Recession Curves. J. Hydrol., 519, 670-682.  https://doi.org/10.1016/j.jhydrol.2014.07.017.

1. Hoffman, M., X Asay-Davis, S Price, J Fyke, and M Perego  (2019).  Effect of Subshelf Melt Variability on Sea Level Rise Contribution From Thwaites Glacier, Antarctica.  Journal of Geophysical Research: Earth Surface.   https://doi.org/10.1029/2019jf005155.
2. Lee, D., M.R. Petersen, and W. Lin.  (2019).  The Southern Annular Mode and Southern Ocean Surface Westerly Winds in E3SM.  Earth and Space Science 6: 1-20.   https://doi.org/10.1029/2019ea000663.
3. Delman, A.S., J.L. McClean, J. Sprintall, L.D. Talley, F.O. Bryan (2018).  Process-specific contributions to anomalous Java mixed layer cooling during positive IOD events. Journal of Geophysical Research- Oceans.  https://doi.org/10.1029/2017CJ013749.
4. Fyke, J., O. Sergienko, J. Lenaerts, M. Loverstrom, S. Price (2018).  An overview of interactions and feedbacks between ice sheets and the Earth system. Reviews of Geophysics.  https://doi.org/10.1029/2018RG000600 (in press).
5. Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Jacobsen, D., Tezaur, I., Salinger, A. G., Tuminaro, R. and Zhang, T. (2018).  MPAS-Albany Land Ice (MALI): A variable resolution ice sheet model for Earth system modeling using Voronoi grids, Geosci. Model Dev. Discuss., 1–47. https://doi.org/10.5194/gmd-11-3747-2018.
6. Larios, A., Petersen, M.R., Titi, E.S. et al. (2018).  A computational investigation of the finite-time blow-up of the 3D incompressible Euler equations based on the Voigt regularization. Theor. Comput. Fluid Dyn. 32: 23.  https://doi.org/10.1007/s00162-017-0434-0.
7. Turner, A. K, Lipscomb, W. H., Hunke, E. C., Jacobsen, D. W., Jeffery, N., Ringler, T. D., Wolfe, J. D. (2018).  MPAS-Seaice: a new variable resolution sea-ice model. Journal of Advances in Modeling Earth Systems.   https://doi.org/10.5281/zenodo.1194373.
8. Van Roekel, L., A. J. Adcroft, G. Danabasoglu, S. M. Griffies, B. Kauffman, W. Large, M. Levy, B. G. Reichl, T. Ringler, and M. Schmidt (2018).  The KPP boundary layer scheme for the ocean: Revisiting its formulation and benchmarking one-dimensional simulations relative to LES.  JAMES, 10(11), 2647–2685.  https://doi.org/10.1029/2018MS001336.
9. 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. Curr Clim Change Rep 3, 316–329.  https://doi.org/10.1007/s40641-017-0071-0.
10. Berres, Anne S., Terece L. Turton, Mark Petersen, David H. Rogers, and James P. Ahrens (2017).  Video Compression for Ocean Simulation Image Databases. In Karsten Rink, Ariane Middel, Dirk Zeckzer, and Roxana Bujack, editors, Workshop on Visualisation in Environmental Sciences (EnvirVis). The Eurographics Association.  https://doi.org/10.2312/envirvis.20171104.
11. Centurioni, LR, V. Hormann, L.D. Talley, I. Arzeno, L. Beal, M. Caruso, P. Conroy, R. Echols, H.J.S Fernando, S.N. Giddings, A. Gordon, H. Graber, R. Harcourt, S.R. Jayne, T.G. Jensen, C.M. Lee, P.F.J. Lermusiaux, L’Hegaret, A.J. Lucas, A. Mahadevan, J.L. McClean, G. Pawlak, L. Rainville, S. Riser, H. Seo, A. Y. Shcherbina, E. Skyllingstad, J. Sprintall, B. Subrahmanyan, E. Terrill, R.E. Todd, C. Trott, H.N. Ulloa, H. Wang (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.
12. Chen, R., S. T. Gille, and J.L. McClean (2017).  Isopycnal eddy mixing across the Kuroshio Extension: Stable versus unstable states in an eddying model, Journal of Geophysical Research: Oceans, 122, 4329–4345. https://doi.org/10.1002/2016JC012164.
13. Lee, D., Palha, A. and Gerritsma, M. (2017).  Discrete conservation properties for shallow water flows using mixed mimetic spectral elements. Journal of Computational Physics.  http://doi.org/10.1016/j.jcp.2017.12.022.
14. Ringler, T., Saenz, J.A., Wolfram, P.J. and Van Roekel, L. (2017).  A thickness-weighted average perspective of force balance in an idealized circumpolar current. Journal of Physical Oceanography, 47(2), pp.285-302.  https://doi.org/10.1175/JPO-D-16-0096.1.
15. Urrego‐Blanco, J.R., Hunke, E.C., Urban, N.M., Jeffery, N., Turner, A.K., Langenbrunner, J.R. and Booker, J.M. (2017).  Validation of sea ice models using an uncertainty‐based distance metric for multiple model variables. Journal of Geophysical Research: Oceans, 122(4), pp.2923-2944.  https://doi.org/10.1002/2016JC012602.
16. Van Sebille, Erik, et al. (2017).  Lagrangian ocean analysis: fundamentals and practices. Ocean Modelling.  https://doi.org/10.1016/j.ocemod.2017.11.008.
17. Wang, S., S. Elliott, M. Maltrud, and P. Cameron-Smith (2017).  Influence of dimethyl sulfide on marine ecosystems and the global carbon cycle. Journal of Geophysical Research, Biogeosciences.  https://doi.org/10.1002/2015JG003017.
18. Wolfram, P.J. and Ringler, T.D. (2017).  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.
19. Wolfram, P.J. and Ringler, T.D. (2017).  Computing eddy-driven effective diffusivity using Lagrangian particles. Ocean Modelling, 118, pp.94-106.  https://doi.org/10.1016/j.ocemod.2017.08.008.
20. Asay-Davis, X.S., Cornford, S.L., Galton-Fenzi, B.K., Gladstone, R.M., Gudmundsson, G.H., Holland, D.M., Holland, P.R. and Martin, D.F. (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), p.2471.  https://doi.org/10.5194/gmd-9-2471-2016.
21. Burrows, S.M., Gobrogge, E., Fu, L., Link, K., Elliott, S.M., Wang, H. and Walker, R. (2016).  OCEANFILMS‐2: Representing coadsorption of saccharides in marine films and potential impacts on modeled marine aerosol chemistry. Geophysical Research Letters, 43(15), pp.8306-8313.  https://doi.org/10.1002/2016GL069070.
22. Dinniman, M.S., Asay-Davis, X.S., Galton-Fenzi, B.K., Holland, P.R., Jenkins, A. and Timmermann, R. (2016).  Modeling ice shelf/ocean interaction in Antarctica: A review. Oceanography, 29(4), pp.144-153.  https://doi.org/10.5670/oceanog.2016.106.
23. Ware, C., Rogers, D., Petersen, M., Ahrens, J., and Aygar, E. (2016).  Optimizing for Visual Cognition in High Performance Scientific Computing. Electronic Imaging 2016.16, pp. 1–9.  https://doi.org/10.2352/ISSN.2470-1173.2016.16.HVEI-130.
24. Woodring, J., Petersen, M., Schmeisser, A., Patchett, J., Ahrens, J., Hagen, H. (2016).  In Situ Eddy Analysis in a High-Resolution Ocean Climate Model.  Visualization and Computer Graphics, IEEE Transactions on , vol.22, no.1, pp.857-866.  https://doi.org/10.1109/TVCG.2015.2467411.
25. Xu, L., Cameron‐Smith, P., Russell, L.M., Ghan, S.J., Liu, Y., Elliott, S., Yang, Y., Lou, S., Lamjiri, M.A. and Manizza, M. (2016).  DMS role in ENSO cycle in the tropics. Journal of Geophysical Research: Atmospheres, 121(22). doi: https://doi.org/10.1002/2016JD025333.
26. Woodring, J., Petersen, M., Schmeisser, A., Patchett, J., Ahrens, J., Hagen, H. (2016).  In Situ Eddy Analysis in a High-Resolution Ocean Climate Model.  Visualization and Computer Graphics, IEEE Transactions on , vol.22, no.1, pp.857-866.  https://doi.org/10.1109/TVCG.2015.2467411.
27. Xu, L., Cameron‐Smith, P., Russell, L.M., Ghan, S.J., Liu, Y., Elliott, S., Yang, Y., Lou, S., Lamjiri, M.A. and Manizza, M. (2016).  DMS role in ENSO cycle in the tropics. Journal of Geophysical Research: Atmospheres, 121(22). doi: https://doi.org/10.1002/2016JD025333.
28. Petersen, M.R., D.W. Jacobsen, T.D. Ringler, M.W. Hecht, M.E. Maltrud (2015).  Evaluation of the arbitrary Lagrangian–Eulerian vertical coordinate method in the MPAS-Ocean model. Ocean Modelling, Volume 86, February 2015, Pages 93-113, ISSN 1463-5003.  https://doi.org.10.1016/j.ocemod.2014.12.004.
29. Reckinger, S., Petersen, M.R., Reckinger, S.J. (2015).  A study of overflow simulations using MPAS-Ocean: Vertical grids, resolution, and viscosity. Ocean Modelling, Volume 96, Part 2, 291-313, ISSN 1463-5003.  https://doi.og/10.1016/j.ocemod.2015.09.006.
30. Samsel F., M. Petersen, G. Abram, T. L. Turton, D. Rogers, and J. Ahrens (2015).  Visualization of ocean currents and eddies in a high-resolution global ocean-climate model.  Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis.  https://doi.org/10.1002/2015JG003017.
31. Samsel F., Mark Petersen, Terece Geld, Greg Abram, Joanne Wendelberger, James Ahrens (2015).  Colormaps That Improve Perception of High-Resolution Ocean Data.  https://doi.org/10.1145/2702613.2702975.
32. Wolfram, Phillip J., et al. (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.

1.  Wang, W., C. S. Zender, D. van As, and N. B. Miller (2019).  Spatial distribution of melt-season cloud radiative effects over Greenland: Evaluating satellite observations, reanalyses, and model simulations against in situ measurements,  J. Geophys. Res. Atm., 124, doi:10.1029/2018JD028919.
2. Wang, W., C. S. Zender, and D. van As (2018).  Temporal Characteristics of Cloud Radiative Effects on the Greenland Ice Sheet: Discoveries from Multiyear Automatic Weather Station Measurements, J. Geophys. Res. Atm., 123, doi:10.1029/2018JD028540.
3. Gorris, M. E., L. A. Cat, C. S. Zender, K. K. Treseder, and J. T. Randerson (2018).  Coccidioidomycosis dynamics in relation to climate in the southwestern United States. AGU,2(1), 6-24.  https://doi.org/10.1002/2017GH000095 .
4. Munneke, P. K., A. J. Luckman, S. L. Bevan, C. J. P. P. Smeets, E. Gilbert, M. R. van den Broeke, W. Wang, C. S. Zender, B. Hubbard, D. Ashmore, A. Orr, and J. C. King (2018).  Intense winter surface melt on an Antarctic ice shelf. Geophys. Res. Lett.  https://doi.org/10.1029/2018GL077899.
5. Parajuli, S. P., and C. S. Zender (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.
6. Silver, J. D. and Zender, C. S. (2017).  The compression-error trade-off for large gridded datasets. Geosci. Model Dev., 10, 413-423.  https://doi.org/10.5194/gmd-10-413-2017.
7. Stephan E., B. Raju, T. Elsethagen, L. Pouchard and C. Gamboa (2017).  A scientific data provenance harvester for distributed applications. 2017 New York Scientific Data Summit (NYSDS), New York, NY, 2017, pp. 1-9.  https://doi.org/10.1109/NYSDS.2017.8085041.
8. Williams, Dean N., et al. (2017).  U.S. DOE. 6th Annual Earth System Grid Federation Face-to-Face Conference Report. DOE/SC-0188. U.S. Department of Energy Office of Science, March 2017.  https://doi.org/10.2172/1369382.
9. Elsethagen, T. O., Stephan, E. G., Raju, B., Schram, M., Macduff, M. C., Kerbyson, D. J., Kleese-Van Dam, K., Singh, A., Altintas, I.  (2016).  Data Provenance Hybridization Supporting Extreme-Scale Scientific WorkflowApplications.  NYSDS 2016 – Data Driven Discovery.
10. Gleckler, P. J., C. Doutriaux, P. J. Durack, K. E. Taylor, Y. Zhang, and D. N. Williams, E. Mason, and J. Servonnat (2016).  A more powerful reality test for climate models. Eos, 97.  https://doi.org/10.1029/2016EO051663.
11. Harris, Matthew B., Sam B. Fries, Dean N. Williams, Sterling A. Baldwin, James W. Crean, Bryce J. Sampson, Edward M. Brown, Anna Paula M. Pawlicka (2016).  The Legend of CDAT: A Link to the Past.  http://www.iaeng.org/publication/WCECS2016/WCECS2016_pp181-184.pdf.
12. McEnerney, J. , Ames, S. , Christensen, C. , Doutriaux, C. , Hoang, T. , Painter, J. , Smith, B. , Shaheen, Z. and Williams, D. (2016).  Parallelization of Diagnostics for Climate Model Development. Journal of Software Engineering and Applications, 9, 199-207.  https://doi.org/10.4236/jsea.2016.95016.
13. Raju,B., Elsethagen, T. O., Stephan, E. G., Kleese van Dam, K. (2016).  A Scientific Data Provenance API for Distributed Applications.  The 6th International Workshop on Semantic Technologies for Information-Integrated Collaboration.
14. Sterling A. Baldwin, Matthew B. Harris, Samuel B. Fries (2016). Science as a Service,  Proceedings of The World Congress on Engineering and Computer Science (2016).  Vol. I, WCECS 2016, 21-23 October, 2016, San Francisco, USA, pp123-126.   http://www.iaeng.org/publication/WCECS2015/ ISBN: 978-988-19253-6-7.
15. Thomas, M., Laskin, J., Raju, B., Stephan, E. G., Elsethagen, T. O., Van, N.Y.S, Nguyen, S. N. (2016).  Enabling Re-executable Workflows with Near-real-time Visualization, Provenance Capture and Advanced Querying for Mass Spectrometry Data.  NYSDS 2016 – Data-Driven Discovery.
16. U.S. DOE. 2016. Working Group on Virtual Data Integration (2016).  A Report from the August 13–14, 2015, Workshop. DOE/SC-0180. U.S. Department of Energy Office of Science.  https://doi.org/10.2172/1227017.
17. Wang, W., Zender, C. S., van As, D., Smeets, P. C. J. P., and 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, 727-741.  https://doi.org/10.5194/tc-10-727-2016.
18. Williams, D. N. (2016).  Better tools to build better climate models. Cover of Eos, 97.  https://doi.org/10.1029/2016EO045055.
19. Williams, Dean N., V. Balaji, Luca Cinquini, Sébastien Denvil, Daniel Duffy, Ben Evans, Robert Ferraro, Rose Hansen, Michael Lautenschlager, and Claire Trenham (2016).  A Global Repository for Planet-Sized Experiments and Observations. Bulletin of the American Meteorological Society, June 2016.  https://doi.org/10.1175/BAMS-D-15-00132.1
20. Williams, Dean N., Charles Doutriaux, Aashish Chaudhary, Sam Fries, Dan Lipsa, Sankhesh Jhaveri, Paul J. Durack, Jeffrey Painter, Denis Nadeau, Thomas Maxwell, Matthew Harris and Jonathan Beezley (2016).  UV-CDAT, 2016.  https://doi.org/10.5281/zenodo.45136.
21. Williams, Dean N. (2016).  5th Annual Earth System Grid Federation and Observations. LLNL Technical Report #LLNL-TR-689917, April 2016, https://e-reports-ext.llnl.gov/pdf/806992.pdf.   https://doi.org.10.2172/1182238.
22. Williams, Dean N., et al. (2016).  U.S. DOE. 2016. 5th Annual Earth System Grid Federation Face-to-Face Conference Report. DOE/SC-0181. U.S. Department of Energy Office of Science., March 2016.  https://doi.org.10.2172/1253685.
23. Zender, C. S. (2016).  Bit Grooming: statistically accurate precision-preserving quantization with compression, evaluated in the netCDF Operators (NCO, v4.4.8+). Geosci. Model Dev, 9, 3199-3211. https://doi.org.10.5194/gmd-9-3199-2016.
24. Steed, Chad A., Katherine J. Evans, John F. Harney, Brian C. Jewell, Galen Shipman, Brian E. Smith, Peter E. Thornton, and Dean N. Williams (2014).  Web-based Visual Analytics for Extreme Scale Climate ScienceBig Data (Big Data), 2014. IEEE International Conference on 2014 pp 383 – 392.  https://doi.org/10.1109/BigData.2014.7004255.