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Journal Publications

Coupled Group Publications

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. doi: 10.1016/j.ocemod.2017.11.002.

To be submitted

  1. Golaz, C., et al. 2018. The DOE E3SM coupled model v1: overview and evaluation at standard resolution. JAMES
  2. Caldwell, P., et al. 2018. Evaluation of the coupled E3SM model at high horizontal resolution. JAMES
  3. Burrows, S., et al. 2018. Description of the E3SM coupled biogeochemistry model. JAMES
  4. 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
  5. 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

Atmosphere Group Publications

Published or In Press

  1. 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. doi: 10.1002/2017JD027244
  2. 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. Accepted.  doi: 10.1002/2017MS001157
  3. 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. doi: 10.1002/2017JD027900
  4. 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, doi: 10.1016/j.procs.2017.05.259
  5. Mahajan, S. et al., 2018: “Model Resolution-sensitivity of the Simulation of North Atlantic Oscillation Teleconnections to Precipitation Extremes,  JGR. Atmosphere, doi :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, doi: 10.5194/acp-18-6441-2018.
  7. 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., doi:10.1007/s00382-017-3803-x.
  8. 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, doi: 10.5194/gmd-10-537-2017.
  9. 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, doi: 10.1175/JCLI-D-17-0362.1
  10. 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., doi: 10.5194/gmd-2017-293.
  11. 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, accepted. doi: 10.1175/BAMS-D-16-0258.1
  12. 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.
  13. Xie et al. 2018, Understanding Cloud and Convective Characteristics in Version 1 of the E3SM Atmosphere Model, JAMES, in press,  doi:10.1029/2018MS001350

 

In review

  1. Burrows Susannah, R. Easter, X. Liu, P.-L. Ma, H. Wang, S. M. Elliott, B. Singh, K. Zhang, P. J. Rasch, 2017: “OCEANFILMS organic sea spray emissions – Part 1: implementation and impacts on clouds”, Submitted to Atmos. Chem. Phys..
  2. Evans et al. 2018:  Northern Hemisphere blocking in simulations at 25km resolution E3SM atmosphere-land simulations, to be resubmitted to JGR-Atmosphere.
  3. Feng, Y., J.E. Penner, S. Smith and S. Tilmes, 2018: Understanding Short Lived Climate Forcers: Progress and Uncertainties, submitted to Annual Review of Environment and Resources.
  4. Qian et al. 2018, Parametric sensitivity and uncertainty quantification in the version 1 of E3SM Atmosphere Model based on short Perturbed Parameters Ensemble simulations, submitted to JGR-Atmosphere
  5. Roesler et al., 2018: Climatology of the Exascale Energy Earth System Model’s Atmospheric Model, version 0, Configured with Variable Resolution over the Contiguous United States, submitted to Theoretical and Applied Climatology.
  6. Soize, C., R. Ghanem, C. Safta, X. Huan, Z. P. Vane, J. Oefelein, G. Lacaze, H. N. Najm, Q. Tang, and X. Chen, 2018: Entropy-based closure for probabilistic learning on manifolds. Computational Physics, In review.
  7. Tang et al. 2018: How well does a regionally refined model represent the globally uniform high-resolution E3SM Atmosphere Model Version 1 (EAM1) over the contiguous United States?, submitted to JAMES.

 

To be submitted

  1. Cameron-Smith, et al., 2018: Interactive Linearized Stratospheric Ozone for the E3SM Version 1 Climate Model, to be submitted to JAMES.
  2. Feng et al., 2018: Global life cycle and direct radiative effect of dust in the Energy Exascale Earth System Model (E3SM) version 1, to be submitted to JAMES.
  3. Harrop et al., 2018: Impacts of parametric uncertainties on monsoon systems, to be submitted to JAMES.
  4. Lin, et al., 2018: A strategy for tuning high-resolution climate model using short-term simulations, to be submitted to JAMES
  5. Neale et al., 2018: Sub-Seasonal Tropical Variability in the Energy Exascale Earth System Model (E3SM) version 1, to be submitted to JAMES.
  6. Ma et al. 2018, Warm cloud response to aerosol and temperature perturbations attributed to model characterization of liquid water variability, entrainment efficiency, and aerosol loading, to be submitted to J. of Climate.
  7. Rasch et al. 2018, An overview of Version 1 of DOE E3SM Atmosphere Model (EAMV1). to be submitted to JAMES.
  8. Terai, C.R., P.M. Caldwell, and S.A. Klein, 2018: Why Do Climate Models Drizzle Too Much and What Impact Does This Have?, to be submitted to JAMES
  9. Zhang et al. 2018: Evaluation of EAMv1 simulated clouds and their sensitivity to model resolution with satellite and ground-based simulators, to be submitted to JAMES.
  10. Zhang et al. 2018: Representation of ice cloud microphysics and its interaction with aerosols in the E3SM Atmosphere Model (EAM) V1, in preparation for JAMES

Land Group Publications

Published or In Press

  1. 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, doi: 10.1016/j.jhydrol.2014.07.018.
  2. Anderson-Teixeira, Wang, McGarvey, Herrmann, Tepley, Bond-Lamberty, and LeBauer (2018). ForC: A global database of forest carbon stocks and fluxes, Ecology. (In press.) doi:  10.1002/ecy.2229
  3. Bisht, G., W. J. Riley, H. Wainwright, B. Dafflon, F. Yuan, and V. E. Romanovsky (2018b), 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. doi: 10.5194/gmd-11-61-2018
  4. 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, doi: 10.1073/pnas.1708984114.
  5. 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, doi: 10.1175/JCLI-D-15-0307.1.
  6. 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, doi: 10.1002/2014JD022113.
  7. 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, doi: 10.1002/2015WR017096.
  8. 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. doi:10.5194/gmd-8-2203-2015.
  9. 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. doi: 10.1002/2017GL075124
  10. 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 doi: 10.5194/bg-14-4315-2017.
  11. 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. doi: 10.1002/2015MS000538
  12. 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 doi: 10.1016/j.jocs.2016.01.005.
  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, doi:  10.1088/1748-9326/aa583c/pdf
  14. 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. doi: 10.1007/s10584-015-1411-5..
  15. 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, doi: 10.1002/2015MS000437.
  16. 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, doi: 10.1002/2016MS000885.
  17. Li F, DM 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. doi: http://iopscience.iop.org/article/10.1088/1748-9326/aa6685/meta
  18. 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, doi: 10.1016/j.gloplacha.2018.01.002 (In press.)
  19. 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, doi: 10.1175/JHM-D-14-0079.1
  20. 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, doi: 10.1002/2015MS000471.
  21. Liu S, B Bond-Lamberty, LR Boysen, JD Ford, A Fox, K Gallo, J Hatfield, GM Henebry, TG Huntington, Z Liu, TR Loveland, RJ Norby, T Sohl, AL 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. doi: 10.1175/EI-D-16-0012.1
  22. 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, doi: 10.5194/gmd-10-1233-2017
  23. 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. Biogeosciences13(3): 641-657 doi:  10.5194/bg-13/641/2016
  24. 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 doi:  10.1038/nclimate3056
  25. 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 doi: 10.1111/gcb.13268
  26. 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 doi: 10.1111/gcb.13451.
  27. 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 doi: 10.5194/bg-13-5183-2016.
  28. 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 doi: 10.1111/nph.13521
  29. 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:  10.1016/j.jhydrol.2016.02.042
  30. 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 doi:  10.1002/2017MS000962
  31. 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. doi: 10.5194/gmd-8-1899-2015
  32. 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. doi: 10.1002/2016EF000472
  33. 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 doi: 10.1111/geb.12411
  34. 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, doi:  10.1002/2017WR020806
  35. 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 doi:  10.5194/gmd-9-927-2016
  36. 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 doi:  10.5194/bg-13-5021-2016
  37. 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: doi: 10.5194/gmd-8-3823-2015
  38. 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.doi: 10.5194/bg-13/723/2016/
  39. 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. doi:  10-5194/gmd-10-3277-2017
  40. Tang, J. Y., and Riley, W. J. (2018), Divergent global carbon cycle predictions resulting from ambiguous numerical interpretation of nitrogen limitation, Earth Interactions, doi: 10.1175/EI-D-17-0023.1.
  41. 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, doi: 10.5194/gmd-10-873-2017
  42. 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. doi: 10.1038/nclimate3310
  43. 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, doi:  10.1002/2016WR019767
  44. 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, doi: 10.1002/2017JD026899.
  45. 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
  46. 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 doi: 10.4236/jcc.2017.514007.
  47. 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 doi: 10.1016/j.procs.2017.05.264.
  48. 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 doi: 10.1038/srep17445
  49. 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 Science108: 1731-1740 doi: 10.1016/j.procs.2017.05.181.
  50. Yang, X., P. E. Thornton, D. M. Ricciuto and F. M. Hoffman 2016. Phosphorus feedbacks constraining tropical ecosystem responses to changes in atmospheric CO2 and climate. Geophysical Research Letters 43(13): 7205-7214 doi: 10.1002/2016GL069241.
  51. 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 doi: 10.1016/j.procs.2016.05.344.
  52. 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, doi: 10.1016/j.jhydrol.2014.07.017.
  53. 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, doi: https://www.nature.com/articles/nclimate3299
  54. 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 doi: 10.1016/j.agrformet.2016.05.018.
  55. Zhu, Q., and W. J. Riley (2015), Improved modeling of soil nitrogen losses, Nature Climate Change, 5, 705-706. doi: 10.1038/nclimate2696
  56. 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. doi: 10.5194/bg-13-341-2016
  57. 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: 10.1002/eap.1490

 

In review

  1. Bisht, G., Riley, W. J., Hammond, G. E., and Lorenzetti, D. M.: 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, in review, 2018
  2. Bond-Lamberty B, Di Vittorio A, Jones A, Shi X, Calvin K (in review). “Quantifying the variability of an integrated assessment model driven by a wide variety of earth system and agricultural models.” Climatic Change.
  3. Calvin, K and B Bond-Lamberty (in review). “Integrated human-earth system modeling–state of the science and future directions.” Environmental Research Letters.
  4. Calvin, K, B Bond-Lamberty, A Jones, X Shi, A Di Vittorio, P Thornton (in review). Characteristics of human-climate feedbacks differ at different radiative forcing levels. Global and Planetary Change.
  5. Chen, J., Q. Zhu, W. J. Riley, Y. He, and J. T. Randerson (2018), Towards improved predictions of global radiocarbon (14C) through comparison between site observations and climate model outputs, in review Global Change Biology.
  6. Forbes, W., J. Mao, D.M. Ricciuto, P.E. Thornton, F. Hoffman, et al. in review. Contribution of climatic and non-climatic forcings to US runoff changes for the period 1950-2010. Environmental Res. Letters, in review.
  7. Holm, J. A., R. G. Knox, Q. Zhu, C. D. Koven, R. A. Fisher, A. J. N. Lima, W. J. Riley, M. Longo, R. I. Negron-Juarez, A. C. d. Araujo, L. M. Kueppers, P. R. Moorcroft, N. Higuchi, and J. Q. Chambers (2018), The Central Amazon forest sink under current and future atmospheric CO2: Predictions from big-leaf and demographic vegetation models, in review JGR-Biogeosciences.
  8. Jones, Calvin, Shi, DiVittorio, Bond-Lamberty, Thornton, Collins (submitted). Quantifying and partitioning the strength of human-mediated carbon cycle feedbacks. Nature Climate Change.
  9. Leng, G., and L.R. Leung. 2018. “Recent Increase in US Flood Risk and Damage: Regional Contributions and Drivers of Change.” Environ. Res. Lett., submitted.
  10. Liang, J., G. Wang, D. Ricciuto, L. Gu, P. J. Hanson, J. Wood, and M. Mayes, In review: Evaluating the E3SM Land model at a temperate forest site using flux and soil water measurements. Geosci Model Dev, in review.
  11. Lu, D., D. Ricciuto, M. Stoyanov, and L. Gu, In review: Calibration of a land model using surrogate based global optimization. Journal of Advances in Modeling Earth Systems, in review.
  12. Riley, W. J., Zhu, Q., and Tang, J. Y., In review: Weaker C-Climate feedbacks from nutrient acquisition during photosynthesis-inactive periods, in review Nature Climate Change.
  13. 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.” J. Adv. Mod. Earth Syst., revised.
  14. Tang, J. Y., and W. J. Riley (2018), Divergent global carbon cycle predictions resulting from ambiguous numerical interpretation of nitrogen limitation, in review Earth Interactions.
  15. Zheng, J., P. E. Thornton, S. L. Painter, B. Gu, S. D. Wullschleger and D. E. Graham In review. Modeling anaerobic soil organic carbon decomposition in Arctic polygon tundra: insights into soil geochemical influences on carbon mineralization. Biogeosciences Discuss. 2018: 1-31 DOI: 10.5194/bg-2018-63.
  16. Zhu, Q., W. J. Riley, J. Tang, N. Collier, F. M. Hoffman, X. Yang, G. Bisht In review. Representing carbon, nitrogen, and phosphorus interaction in the E3SM Land Model v1: Model development and global benchmarking, in review JAMES.

 

To be submitted

  1. Bond-Lamberty et al. 2018. Evaluation and comparison of two approaches for representing terrestrial biogeochemistry in E3SM. JGR-Biogeosciences.
  2. Di Vittorio et al. (in prep). Land use and land cover distribution is a primary determinant of global carbon cycle projections and regional temperature projections.
  3. Drewniak, B. A.: Simulating dynamic roots in the Energy Exascale Earth System Land Model (in prep; to be submitted to JAMES)
  4. Drewniak, B. A.: Climate-driven crop planting date in the Energy Exascale Earth System Land Model (in prep).
  5. Hoffman, F. M., M. Xu, and others In Prep. A tropical ecological forecasting strategy for ENSO based on a global Earth system modeling framework.
  6. Holm, J. A., R. G. Knox, C. D. Koven, W. J. Riley, L. M. Kueppers, and R. Fisher (in prep), How is vegetation size class distribution represented in demography models? Lessons learned and paths forward, Geoscientific Model Development.
  7. Levine, P. A., M. Xu, F. M. Hoffman, Y. Chen, M. S. Pritchard, J. T. Randerson to be submitted. Soil moisture variability intensifies and prolongs Amazon temperature and carbon cycle response to El Niño sea surface temperature anomalies.
  8. Li, H., et al. 2018. A physically based representation of sediment processes in land surface and earth system models, JAMES
  9. Ricciuto, D.M., X. Yang, P.E. Thornton in prep. Evaluation of a converging trophic cascade decomposition model using results from multi-site, multi-species litter decomposition experiments.
  10. Ricciuto, D.M., X. Yang., F. Hoffman, N. Collier, P.E. Thornton in prep. Evaluation of biogeochemical and biophysical predictions from the ELM-CTC-CNP land model against multiple independent observational constraints.
  11. Shi et al. (in prep). Investigating the Effects of Co2 and Human Intervention on the Water Cycle.
  12. Thornton, P.E., D.M. Ricciuto, X. Yang in prep. The influence of plant carbon and nutrient storage mechanisms on modeled global-scale net ecosystem carbon flux.
  13. Xu, M., F. M. Hoffman, and others In Prep. Tropical PFT response to ENSO-induced droughts.
  14. Yang, X., D.M. Ricciuto, P.E. Thornton in prep. Evaluation of ELM-CTC-CNP productivity, mortality, and biomass accumulation in tropical forests, and implications for ecosystem response to future climate change.
  15. Yang, X., X. Shi, D.M. Ricciuto, P.E. Thornton, and other E3SM coupled simulation group participants to be submitted. The influence of prognostic phosphorus cycle in land ecosystems on biogeochemistry-climate feedbacks in a fully coupled Earth system model.
  16. Zhang, X., et al. 2018. Global stream temperature modeling in the Energy Exascale Earth System Model (E3SM), JAMES
  17. Zhu, Q., W. J. Riley, and Others (in prep-a), A global synthesis of plant CNP Stoichiometry: Implications for land carbon projections over the 21st century.
  18. Zhu, Q., W. J. Riley, and Others (in prep-b), Plant nitrogen and phosphorus co-limitation: Confronting models with fertilization experiments at a global scale, Journal of Geophsical Research – B.

Ocean/Ice Group Publications

Published or In Press

  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. Curr Clim Change Rep 3, 316–329. doi: 10.1007/s40641-017-0071-0
  2. 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. doi: 10.5194/gmd-9-2471-2016
  3. Berres Anne S., Terece L. Turton, Mark Petersen, David H. Rogers, and James P. Ahrens. 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, 2017. doi: 10.2312/envirvis.20171104
  4. 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. doi: 10.1002/2016GL069070
  5. 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, 10.5670/oceanog.2017.224.
  6. 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, doi:10.1002/2016JC012164.
  7. Delman, AS, 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, doi: 10.1029/2017CJ013749
  8. 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. doi: 10.5670/oceanog.2016.106
  9. 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, doi.org/10.1029/2018RG000600 (in press)
  10. Larios, A., Petersen, M.R., Titi, E.S. et al. Theor. Comput. Fluid Dyn. (2018) 32: 23. doi: 10.1007/s00162-017-0434-0
  11. Lee, D., Lowrie, R., Petersen, M., Ringler, T. and Hecht, M. 2016. A High Order Characteristic Discontinuous Galerkin Scheme for Advection on Unstructured Meshes. Journal of Computational Physics, 324 pp.289. doi: 10.1016/j.jcp.2016.08.010
  12. Lee, D., Palha, A. and Gerritsma, M. 2017. Discrete conservation properties for shallow water flows using mixed mimetic spectral elements. Journal of Computational Physics (in press) doi: doi: 10.1016/j.jcp.2017.12.022
  13. Petersen, M.R., D.W. Jacobsen, T.D. Ringler, M.W. Hecht, M.E. Maltrud, 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, doi: 10.1016/j.ocemod.2014.12.004.
  14. Reckinger, S, Petersen, MR, Reckinger, SJ, A study of overflow simulations using MPAS-Ocean: Vertical grids, resolution, and viscosity, Ocean Modelling, Volume 96, Part 2, December 2015, Pages 291-313, ISSN 1463-5003 doi: 10.1016/j.ocemod.2015.09.006
  15. 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. doi: 10.1175/JPO-D-16-0096.1
  16. Samsel F., M. Petersen, G. Abram, T. L. Turton, D. Rogers, and J. Ahrens. Visualization of ocean currents and eddies in a high-resolution global ocean-climate model. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis 2015, November 2015.
    https://www.researchgate.net/publication/286450916_Visualization_of_Ocean_Currents_and_Eddies_in_a_High-Resolution_Global_Ocean-Climate_Model
  17. Samsel F.,Mark Petersen, Terece Geld, Greg Abram, Joanne Wendelberger, James Ahrens, 2015. Colormaps That Improve Perception of High-Resolution Ocean Data, doi: 10.1145/2702613.2702975
  18. 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. doi: 10.1002/2016JC012602
  19. Van Roekel Luke P, Alistair J. Adcroft, Gokhan Danabasoglu, Stephen M. Griffies, Brian Kauffman, William Large, Michael Levy, Brandon Reichl, Todd Ringler, Martin Schmidt, The KPP boundary layer scheme: revisiting its formulation and  benchmarking one-dimensional ocean simulations relative to LES, JAMES,  doi:10.1029/2018MS001336
  20. Van Sebille, Erik, et al. “Lagrangian ocean analysis: fundamentals and practices.” Ocean Modelling (2017). doi: 10.1016/j.ocemod.2017.11.008
  21. 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 (in press).
  22. Ware, C., Rogers, D., Petersen, M., Ahrens, J., and Aygar, E. “Optimizing for Visual Cognition in High Performance Scientific Computing”. In: Electronic Imaging 2016.16 (2016), pp. 1–9. doi: 10.2352/ISSN.2470-1173.2016.16.HVEI-130
  23. 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. doi: 10.1175/JPO-D-16-0101.1
  24. Wolfram, P.J. and Ringler, T.D., 2017. Computing eddy-driven effective diffusivity using Lagrangian particles. Ocean Modelling, 118, pp.94-106. doi: 10.1016/j.ocemod.2017.08.008
  25. Wolfram, Phillip J., et al. “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 (2015): 2114-2133. doi: 10.1175/JPO-D-14-0260.1
  26. Woodring, J., Petersen, M., Schmeisser, A., Patchett, J., Ahrens, J., Hagen, H., “In Situ Eddy Analysis in a High-Resolution Ocean Climate Model,” in Visualization and Computer Graphics, IEEE Transactions on , vol.22, no.1, pp.857-866, Jan. 31 2016. doi: 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: 10.1002/2016JD025333

In review

  1. Kurtakoti Prajvala, Veneziani, M., Stoessel, A. and Weijer, W. Preconditioning and formation of Maud Rise polynyas in a high-resolution Earth System Model. Submitted to J. of Climate.
  2. Lee, D., Petersen, M., Lowrie, R. and Ringler, T. 2017. Tracer Transport within an Unstructured Grid Ocean Model using Characteristic Discontinuous Galerkin Advection. Submitted to Computers and Mathematics with Applications.
  3. Petersen, M., Asay-Davis, X., Berres, A., Comeau, D., Feige, N., Jacobsen, D., Jones, P., Maltrud, M., Ringler, T., Streletz, G., Turner, A., Van Roekel, L., Veneziani, M., Wolfe, J., Wolfram, P., Woodring, J. 2018. An evaluation of the ocean and sea ice climate of E3SM using MPAS and interannual CORE-II forcing.  Journal of Advances in Modeling Earth Systems. https://doi.org/10.5281/zenodo.1194911
  4. Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Jacobsen, D., Tezaur, I., Salinger, A. G., Tuminaro, R. and Zhang, T.: MPAS-Albany Land Ice (MALI): A variable resolution ice sheet model for Earth system modeling using Voronoi grids, Geosci. Model Dev. Discuss., in review, 1–47, doi:10.5194/gmd-2018-78, 2018.
  5. 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. in review with Journal of Advances in Modeling Earth Systems.   DOI: 10.5281/zenodo.1194373
  6. Wang, H., J.L. McClean, L.D. Talley, and S. Yeager, 2018: Seasonal cycle and annual reversal of the Somali Current in an eddy-resolving global ocean model. Journal of Geophysical Research: Oceans. In revision.

 

To be submitted

  1. Hoffman, M., J. Fyke, S. Price, X. Asay-Davis, and M. Perego (in prep.): Effects of ice shelf melt variability on the evolution of Thwaites Glacier, West Antarctica. Geophys. Res. Lett.
  2. Jeffery, N, S. Elliott, S. Wang, E. Hunke, M. Maltrud, J. Wolfe, A. Turner, and W. Lipscomb (in prep): Understanding polar contrasts in sea ice and ocean production using the E3SM. Biogeosciences.
  3. Jeong, H., A. Turner, Ringler and R. Abernathey: Water-mass transformation by the sea-ice component of E3SM climate simulations over the Southern Ocean. Submitting to Journal of Geophysical Research (Ocean)                         

Planned

  1. K. Turner, Fidelit y of variable resolution simulations with MPAS-Seaice
  2. X. S. Asay-Davis, others, Overview paper of submarine ocean circulation physics implementation and testing in MPAS-Ocean.
  3. Venezian, M. and others, A Study of the Antarctic Slope Front: Structure, Variability and Evolution in a Changing Southern Ocean.
  4. Van Roekel, LP and others, Tracer ventilation of Southern Ocean waters: mixed layer contributions
  5. Wolfram, PJ and others, A global, three-dimensional estimate of mesoscale eddy mixing from a strongly eddying simulation.

Workflow Group Publications

Published or In Press

  1. 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
  2. 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, doi: 10.1029/2016EO051663
  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, 2(1), 6-24, doi: 10.1002/2017GH000095 
  4. 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
  5. 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. doi: 10.4236/jsea.2016.95016.
  6. 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., DOI: 10.1029/2018GL077899 
  7. 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, doi: 10.1016/j.aeolia.2017.06.002.
  8. 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
  9. Silver, J. D. and Zender, C. S.: The compression-error trade-off for large gridded datasets, Geosci. Model Dev., 10, 413-423, doi: 10.5194/gmd-10-413-2017.
  10. 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, DOI:10.1109/BigData.2014.7004255
  11. Stephan E., B. Raju, T. Elsethagen, L. Pouchard and C. Gamboa, “A scientific data provenance harvester for distributed applications,” 2017 New York Scientific Data Summit (NYSDS), New York, NY, 2017, pp. 1-9. doi: 10.1109/NYSDS.2017.8085041
  12. Sterling A. Baldwin, Matthew B. Harris, Samuel B. Fries 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
  13. Thomas M ,Laskin J ,Raju B ,Stephan E G,Elsethagen T O,Van NYS ,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
  14. U.S. DOE. 2016. Working Group on Virtual Data Integration: A Report from the August 13–14, 2015,
    Workshop. DOE/SC-0180. U.S. Department of Energy Office of Science. DOI: 10.2172/1227017.
  15. Wang, W., Zender, C. S., van As, D., Smeets, P. C. J. P., and van den Broeke, M. R.: 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, doi: 10.5194/tc-10-727-2016
  16. Williams, D. N., “Better tools to build better climate models”, Cover of Eos, 97, DOI: 10.1029/2016EO045055. Published on 9 February 2016
  17. Williams Dean N., V. Balaji, Luca Cinquini, Sébastien Denvil, Daniel Duffy, Ben Evans, Robert Ferraro, Rose Hansen, Michael Lautenschlager, and Claire Trenham, “A Global Repository for Planet-Sized Experiments and Observations”, Bulletin of the American Meteorological Society, June 2016, doi: 10.1175/BAMS-D-15-00132.1
  18. WilliamsDean N., et al. 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, DOI: 10.2172/1369382.
  19. Williams Dean N.; Charles Doutriaux; Aashish Chaudhary; Sam Fries; Dan Lipsa; Sankhesh Jhaveri; Paul J. Durack; Jeffrey Painter; Denis Nadeau; Thomas Maxwell; Matthew Harris; Jonathan Beezley, UV-CDAT, 2016, 10.5281/zenodo.45136
  20. Williams Dean N., “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   doi: 10.2172/1182238
  21. Williams Dean N., et al., 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, DOI:  10.2172/1253685.
  22. Zender, C. S.: Bit Grooming: statistically accurate precision-preserving quantization with compression, evaluated in the netCDF Operators (NCO, v4.4.8+), Geosci. Model Dev., 9, 3199-3211, doi:10.5194/gmd-9-3199-2016, 2016.

 

In review

  1. Parajuli, S. P., and C. S. Zender, Projected changes in dust emissions and regional air quality due to the shrinking Salton Sea, Aeolian Research, submitted
  2. Wang, W., C. S. Zender, and D. van As, Temporal Characteristics of Cloud Radiative Effects on Greenland’s Surface Energy Budget During Melt Season: Discoveries from Multi-year Automatic Weather Station Measurements, J. Geophys. Res. Atm., submitted
  3. Wang, W., C. S. Zender, D. van As, and N. B. Miller, 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., submitted

 

To be submitted

 

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