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E3SM Phase 2 Publications

Watercycle Group Publications

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, accepted. https://doi.org/10.1029/2018MS001603.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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. Brunke, Michael (10 Jan 2019). E3SMv1 Stratocumulus Deck Evaluation, submitted to JGR-Atmospheres.
  2. Harrop et al. (2019). Understanding Monsoonal Water Cycle Changes in a Warmer Climate in E3SMv1 Using a Normalized Gross Moist Stability Framework, submitted to JGR Atmospheres.
  3. Richter et al. (2019). Improved Simulation of the QBO in E3SMv1. JGR, submitted to JAMES.
  4. Zheng et al. (2019). The Summertime Precipitation Bias in E3SM Atmosphere Model Version 1 (EAMv1) over the Central United States. JGR, submitted.
  5. Zhou et al. (2019). Global irrigation characteristics and effects simulated by fully coupled land surface, river, and water management models in E3SM, JAMES.
  6. Bisht, Gautam (26 Nov 2018). Verification of Subsurface Physics, in progress, submitted to JAMES.
  7. Neale et al. (2018). Sub-Seasonal Tropical Variability in the Energy Exascale Earth System Model (E3SM) version 1, to be submitted to JAMES.
  8. Zhang, Yuying (14 Nov 2018). Ealuation of EAMv1 using COSP, submitted to JAMES.

To be Submitted:

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

Biogeochemical Cycles Group Publications

Published or In Press

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. Saravia, L., S. Doyle, and B. Bond-Lamberty (2018). Power laws and critical fragmentation in global forests. Scientific Reports 8, no. 1:17766.  https://doi.org/1038/s41598-018-36120-w.

Submitted

  1. Bond-Lamberty, B., Di Vittorio A , Jones A D, Shi X, Calvin K V.  Quantifying the variability of an integrated assessment model driven by a wide variety of earth system and agricultural models. Climatic Change.
  2. 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.
  3. 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.
  4. Yang, X. et al. ELM v1 simulations over the Amazon.

To be Submitted

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

Cryosphere Group Publications

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. (2019).  MPAS-Ocean Simulation Quality for Variable-Resolution North American Coastal Meshes.  JAMES.  https://doi.org/10.5281/zenodo.3560182.
  3. 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.
  4. 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.

Submitted

  1. Hoffman, M. J., X. Asay-Davis, S. F. Price, J. G. Fyke, M. Perego, Effects of ice shelf melt variability on the evolution of Thwaites Glacier, West Antarctica. JGR Earth Surface (in re-review).
  2. 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 (in revision)  https://doi.org/10.5281/zenodo.3406735.
  3. Lee, D.Y., Petersen, M.R., Lin, W. The Southern Annular Mode and Southern Ocean Surface Westerly Winds in E3SM. Earth and Space Science (in review).

To be Submitted

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

NGD Atmosphere Physics Publications

Published or In Press

 

  1. 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
  2. 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.
  3. 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.
  4. 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.
  5. Paukert, M., Fan, J., Rasch, P. J., Morrison, H., Milbrandt, J. A., Shpund, J., & Khain, A. (2018). Three-Moment Representation of Rain in a Bulk Microphysics Model.  Journal of Advances in Modeling Earth Systems.  https://doi.org/10.1029/2018ms001512.

 

Submitted

 

  1. Prather and Hsu (2019). A Round Earth for Climate Models, is being revised for PNAS and is likely to be accepted.
  2. Richter et al. (2019). Improved Simulation of the QBO in E3SMv1. JGR, submitted to JAMES.
  3. Ito, A., S. Myriokefalitakis, M. Kanakidou, N. M. Mahowald, R. A. Scanza, D. S. Hamilton, A. R. Baker, T. Jickells, M. Sarin, S. Bikkina, Y. Gao, R. U. Shelley, C. S. Buck, W. M. Landing, A. R. Bowie, M. G. Perron, C. Guieu, N Meskhidze, M S. Johnson, Y. Feng, J. F. Kok, A. Nenes, R. A. Duce (2018). Constraints on attribution of labile iron in aerosols to combustion and mineral dust sources from observations and models, submitted to Science Advances.
  4. Meskhidze, N., C. Voelker, H. Al-Abadleh, K. Barbeau, M. Bressac, C. Buck, R. Bundy, P. Croot, Y. Feng, A. Ito, A. M. Johansen, W. Landing, J. Mao, S. Myriokefalitakis, Daniel Ohnemus, Benoît Pasquier, and Y. Ye, (2018). Perspective on Identifying and Characterizing the Processes Controlling Iron Speciation and Residence Time at the Atmosphere-Ocean Interface, submitted to Marine Chemistry.

To be Submitted

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

NGD Software and Algorithms Publications

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.
  2. Mahajan, S., K Evans, J Kennedy, M Xu, M Norman, and M Branstetter (2019).  Ongoing Solution Reproducibility of Earth System Models as They Progress Toward Exascale Computing.  The International Journal of High Performance Computing Applications 109434201983734.  https://doi.org/10.1177/1094342019837341.

Submitted

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

To be Submitted

 

E3SM Phase 1 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.  https://doi.org/10.1016/j.ocemod.2017.11.002.

To be submitted

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

Atmosphere Group Publications

Published or In Press

  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. Accepted.  doi: 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, in press.  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, accepted.  https://doi.org/10.1175/BAMS-D-16-0258.1.

 

In review

 

 

  1. Xie et al. (2019). Improved Diurnal Cycle of Precipitation in E3SM with a Revised Convective Triggering Function, submitted to JAMES.
  2. 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.
  3. 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.
  4. Rasch et al. (2018).  An overview of Version 1 of DOE E3SM Atmosphere Model (EAMV1). to be submitted to JAMES.
  5. 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.
  6. 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.
  7. 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.
  8. 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..

 

To be submitted

 

  1. Lee et al. (2019).  The southern annular mode and southern ocean surface westerly winds in E3SM, to be submitted to JAMES.
  2. Vogl et al. (2019). Evaluation of Implicit-Explicit Runge-Kutta Integrators for the HOMME-NH Dynamical Core, to be submitted to JAMES.
  3. Cameron-Smith, et al. (2018).  Interactive Linearized Stratospheric Ozone for the E3SM Version 1 Climate Model, to be submitted to JAMES.
  4. 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.
  5. Harrop et al. (2018).  Impacts of parametric uncertainties on monsoon systems, to be submitted to JAMES.
  6. Lin, et al. (2018).  A strategy for tuning high-resolution climate model using short-term simulations, to be submitted to JAMES.
  7. Neale et al. (2018).  Sub-Seasonal Tropical Variability in the Energy Exascale Earth System Model (E3SM) version 1, to be submitted to JAMES.
  8. 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.
  9. 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.
  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. Metzler, H., Q. Zhu, W. Riley, A. Hoyt, M. Maller, and C. Sierra (2020).  Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From C Dynamics.  Journal of Advances in Modeling Earth Systems 12(1).   https://doi.org/10.1029/2019ms001776.
  2. 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.
  3. Anderson-Teixeira, Wang, McGarvey, Herrmann, Tepley, Bond-Lamberty, and LeBauer (2018).  ForC: A global database of forest carbon stocks and fluxes. Ecology. (In press.)  http://doi.org/10.1002/ecy.2229.
  4. 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.
  5. 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
  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. 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 (In press.)
  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. 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.
  59. 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.

Submitted

  1. Yang, X., D.R. Ricciuto, P.E. Thornton, X. Shi, M. Xu, F.M. Hoffman, R.J. Norby (2019). The effects of phosphorus cycle dynamics on carbon sources and sinks in the Amazon region: a modeling study using ELM v1. Global Biogeochemical Cycles
  2. Zhou et al. (2019). Global irrigation characteristics and effects simulated by fully coupled land surface, river, and water management models in E3SM.  JAMES
  3. Tan et al. (2019). A substantial role of soil erosion in the land carbon sink and its future changes.  GRL
  4. Chen, J., Zhu, Q., Riley, W. J., He, Y., and Randerson, J. T. (2018). Towards improved predictions of global radiocarbon (14C) through comparison between site observations and climate model outputs, in review JGR-Biogeosciences.
  5. Holm, J. A., Knox, Q. Zhu, R. G., Lima, A. J. N., Koven, C. D., Longo, M., Riley, W. J., Negron-Juarez, R. I., Araujo, A., Kueppers, L. M., Moorcroft, P. R., Higuchi, N., and Chambers, J. Q. (2018).  The Central Amazon forest sink under current and future atmospheric CO2: Predictions from big-leaf and demographic vegetation models. In review JGR-Biogeosciences.
  6. Zhu, Q., Riley, W. J., Tang, J. Y., Randerson, J. R., Hoffman, F. M., and Mu, M. (2018).  Representing carbon, nitrogen, and phosphorus interaction in the E3SM Land Model v1: Model development and global benchmarking, in review JAMES.
  7. Cai, X., Riley, W. J., Zhu, Q., Tang, J., Zeng, Z., Bisht, G., & Randerson, J. T. (2018). Improving the representation of deforestation effects on evapotranspiration in the E3SM Earth system model.  Journal of Advances in Modeling Earth Systems.
  8. Bisht, G., and Riley, W. J. (2018). Development and verification of a numerical library for solving global terrestrial multi-physics problems.  Journal of Advances in Modeling Earth Systems.
  9. 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.

In review

  1. Bisht, G., Riley, W. J., Hammond, G. E., and Lorenzetti, D. M. (2018).  Development and evaluation of a variably saturated flow model in the global E3SM Land Model (ELM) Version 1.0.  Geosci. Model Dev. Discuss.  https://doi.org/10.5194/gmd-2018-44.  In review.
  2. Bond-Lamberty, B., Di Vittorio, A., Jones, A., Shi, X., Calvin, K.  Quantifying the variability of an integrated assessment model driven by a wide variety of earth system and agricultural models. Climatic Change. In review.
  3. Calvin, K. and B. Bond-Lamberty.  Integrated human-earth system modeling–state of the science and future directions. Environmental Research Letters. In review.
  4. Calvin, K., B. Bond-Lamberty, A. Jones, X. Shi, A. Di Vittorio, P. Thornton. Characteristics of human-climate feedbacks differ at different radiative forcing levels. Global and Planetary Change. In review.
  5. Forbes, W., J. Mao, D.M. Ricciuto, P.E. Thornton, F. Hoffman, et al. Contribution of climatic and non-climatic forcings to US runoff changes for the period 1950-2010. Environmental Res. Letters.  In review.
  6. Jones, Calvin, Shi, DiVittorio, Bond-Lamberty, Thornton, Collins. Quantifying and partitioning the strength of human-mediated carbon cycle feedbacks. Nature Climate Change.  Submitted.
  7. 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.
  8. Liang, J., G. Wang, D. Ricciuto, L. Gu, P. J. Hanson, J. Wood, and M. Mayes.  Evaluating the E3SM Land model at a temperate forest site using flux and soil water measurements. Geosci Model Dev. In review.
  9. Lu, D., D. Ricciuto, M. Stoyanov, and L. Gu. Calibration of a land model using surrogate based global optimization. Journal of Advances in Modeling Earth Systems. In review.
  10. Riley, W. J., Zhu, Q., and Tang, J. Y. Weaker. C-Climate feedbacks from nutrient acquisition during photosynthesis-inactive periods.  Nature Climate Change. In review.
  11. Zheng, J., P. E. Thornton, S. L. Painter, B. Gu, S. D. Wullschleger and D. E. Graham (2018). Modeling anaerobic soil organic carbon decomposition in Arctic polygon tundra: insights into soil geochemical influences on carbon mineralization. Biogeosciences Discuss. 2018: 1-31 doi: https://doi.org/10.5194/bg-2018-63. In review
  12. Zhu, Q., W. J. Riley, J. Tang, N. Collier, F. M. Hoffman, X. Yang, G. Bisht. Representing carbon, nitrogen, and phosphorus interaction in the E3SM Land Model v1: Model development and global benchmarking.  JAMES. In review.

 

To be submitted

  1. Bond-Lamberty et al. (2018). Evaluation and comparison of two approaches for representing terrestrial biogeochemistry in E3SM. JGR-Biogeosciences.
  2. Drewniak, B. A.: Simulating dynamic roots in the Energy Exascale Earth System Land Model. JAMES.  In prep; to be submitted.
  3. Drewniak, B. A.: Climate-driven crop planting date in the Energy Exascale Earth System Land Model.  In prep.
  4. Hoffman, F. M., M. Xu, and others.  A tropical ecological forecasting strategy for ENSO based on a global Earth system modeling framework. In prep.
  5. Holm, J. A., R. G. Knox, C. D. Koven, W. J. Riley, L. M. Kueppers, and R. Fisher. How is vegetation size class distribution represented in demography models? Lessons learned and paths forward, Geoscientific Model Development. In prep.
  6. Levine, P. A., M. Xu, F. M. Hoffman, Y. Chen, M. S. Pritchard, J. T. Randerson. Soil moisture variability intensifies and prolongs Amazon temperature and carbon cycle response to El Niño sea surface temperature anomalies. To be submitted.
  7. Li, H., et al. (2018). A physically based representation of sediment processes in land surface and earth system models.  JAMES.
  8. Li et al. (2019). Representing suspended sediment in Earth System Models. JAMES.
  9. Ricciuto, D.M., X. Yang, P.E. Thornton. Evaluation of a converging trophic cascade decomposition model using results from multi-site, multi-species litter decomposition experiments. In prep.
  10. Ricciuto, D.M., X. Yang., F. Hoffman, N. Collier, P.E. Thornton. Evaluation of biogeochemical and biophysical predictions from the ELM-CTC-CNP land model against multiple independent observational constraints. In prep.
  11. Shi et al.  Investigating the Effects of Co2 and Human Intervention on the Water Cycle. In prep.
  12. Thornton, P.E., D.M. Ricciuto, X. Yang. The influence of plant carbon and nutrient storage mechanisms on modeled global-scale net ecosystem carbon flux. In prep.
  13. Xu, M., F. M. Hoffman, and others. Tropical PFT response to ENSO-induced droughts. In prep.
  14. Yang, X., D.M. Ricciuto, P.E. Thornton. Evaluation of ELM-CTC-CNP productivity, mortality, and biomass accumulation in tropical forests, and implications for ecosystem response to future climate change. In prep.
  15. Yang, X., X. Shi, D.M. Ricciuto, P.E. Thornton, and other E3SM coupled simulation group participants . The influence of prognostic phosphorus cycle in land ecosystems on biogeochemistry-climate feedbacks in a fully coupled Earth system model. To be submitted.
  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.  A global synthesis of plant CNP Stoichiometry: Implications for land carbon projections over the 21st century. In prep-a.
  18. Zhu, Q., W. J. Riley, and Others. Plant nitrogen and phosphorus co-limitation: Confronting models with fertilization experiments at a global scale, Journal of Geophsical Research – B. In prep-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.  http://doi.org/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.  http://doi.org/10.5194/gmd-9-2471-2016.
  3. 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.  http://doi.org/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: http://doi.org/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.  http://doi.org/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. http://doi.org/10.1002/2016JC012164.
  7. 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, 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: 10.1029/2018RG000600 (in press)
  10. 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-11-3747-2018
  11. 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.  doi: 10.1029/2019jf005155.
  12. Larios, A., Petersen, M.R., Titi, E.S. et al. Theor. Comput. Fluid Dyn. (2018) 32: 23. doi: 10.1007/s00162-017-0434-0
  13. 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
  14. 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.  doi: 10.1029/2019ea000663.
  15. 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: 10.1016/j.jcp.2017.12.022
  16. 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.
  17. Reckinger, S., Petersen, M.R., Reckinger, S.J., 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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. doi: 10.1029/2018MS001336
  24. Van Sebille, Erik, et al. “Lagrangian ocean analysis: fundamentals and practices.” Ocean Modelling (2017). doi: 10.1016/j.ocemod.2017.11.008
  25. 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).
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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. 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, Fidelity 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, 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
  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. Williams, Dean 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 and Jonathan Beezley, UV-CDAT, 2016, doi: 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

 

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