Lawrence Livermore National Laboratory
7000 East Avenue, L-103
Livermore, CA 94551
Ph.D., 1998, State University of New York at Stony Brook. New York, USA.
M.S., 1988, Chinese Academy of Meteorological Sciences. Beijing, China.
B.S., 1985, Nanjing Institute of Meteorology, Nanjing, China.
Lawrence Livermore National Laboratory, Livermore, California, USA (1998 – present).
School of Marine and Atmospheric Sciences, SUNY at Stony Brook. New York, USA. (1993 - 1998)
National Meteorological Center, Beijing, China (1988 - 1993).
My research covers weather and climate modeling, climate model diagnosis and validation, atmospheric physical parameterization developments, in particular, convection parameterizations, and observational analysis and uncertainty quantification. I work with the DOE Energy Exascale Earth System Model (E3SM) and the NSF/DOE Community Atmospheric Model (CAM). I use a hierarchy process modeling approach, including single-column model (SCM), cloud-resolving model (CRM), and the short-range hindcast approach (e.g., the Cloud-Associated Parameterizations Testbed (CAPT)), to test existing and new parameterizations with both field data and satellite observations.
As lead of the E3SM Atmospheric Group, I led/co-led the development of the first generation of E3SM Atmosphere Model (EAMv1) and its the next generation development of atmospheric physics for EAMv3. I am currently leading the development of EAMv3 with the specific emphasis on integrating and assessing various improvements in atmospheric chemistry, aerosol, clouds, and convection in E3SM.
For climate model diagnosis and validation, I co-led a team that utilized the short-range weather forecast technique in diagnosing climate model errors particularly associated with cloud-related processes. I also systematically explored the correspondence between short- and long-timescale systematic errors in current climate models and clarify over what timescales model systematic errors develop with the goal to provide essential clue to the origins of these errors.
My research on physical parameterization developments is mainly focused on convective trigger/closure for deep convection. Specifically, I would like to understand what physical processes and large-scale dynamic and thermodynamic control of cumulus convection and how these processes can be appropriately parameterized in climate models. By performing observational analyses, I demonstrated how cumulus clouds are related to environmental conditions, which resulted in the development of a physically based convective triggering function that is used in several current weather and climate models. I currently emphasize on improving the diurnal cycle of model precipitation, particularly the nocturnal elevated convection over lands, through improving convective processes in climate models. I am leading a GEWEX Global Atmospheric System Studies (GASS) project on modeling diurnal and semi-diurnal cycle of precipitation in different climate regimes.
For the field data analysis, the goal is to develop necessary data products with advanced data analysis methods and quantify their uncertainty in support of cloud modeling study and climate model development. I have been heavily involved in the DOE Atmospheric Radiation Measurement (ARM) program since 1998. One of my responsibilities is to transform the detailed ARM observations into a form that can be easily used by the climate modeling community. Some widely used data products from my ARM science infrastructure group include the ARM best estimate dataset (ARMBE) and the variational analysis derived large-scale forcing data for SCM/CRM/LES studies. We also develop tools to promote ARM data particularly to the climate modeling community, which includes the ongoing effort to develop an ARM radar simulator and an ARM-oriented process diagnostics package for GCMs.
In recent years, I have started to explore the machine learning approach to help climate model developments and address data quality issues with ARM observations.
Before I came to the United States, I had worked for 5 years at the National Meteorological Center of China. My research was in the development of numerical weather forecast model for medium-range weather forecasts, which included the development of numerical methods for calculating large-scale advection terms and testing various parameterizations of cumulus convection. I was the major project leader for the development of the second generation of the Chinese Medium-range Weather Forecast Model.
Climate Modeling and Evaluation
Leader of the Atmosphere Group of the US DOE Energy Exascale Earth System Model (E3SM) (2015 - present), managing 10 multi-lab/institution E3SM task teams consisting of 40-50 technical staff to develop E3SM atmosphere model v1 and its next generation atmospheric physics.
E3SM Council member (2018 - present)
Co-Lead of the DOE multi-lab & institution Climate Model Development and Validation (CMDV) - RRM project (2016 - 2019)
Co-PI for the DOE Cloud-Associated Parameterizations Testbed (CAPT) project at LLNL, which uses the weather forecast technique to diagnose and understand climate model errors (2010 –2016)
Atmospheric Radiation Measurement (ARM) and Atmospheric System Research (ASR)
Leader of the DOE ARM data infrastructure project at LLNL for community modeling data support (2008 – )
Modeling Lead of the DOE ARM Architecture and Services Strategy Team (ASST) (2012 – )
Member of the DOE ARM-ASR Coordination Team (AACT), a constituent group that includes members from both ARM and ASR leadership (2019 – 2021)
Lead ARM Translator (liaisons between the scientific community and ARM infrastructure staff members) (2019 – 2021)
Led/co-led the development of the E3SM atmosphere model version 1 (2018) and the next generation of atmospheric physics for E3SM version 3 (2022).
Co-led the development of the Cloud-Associated Parameterizations Testbed (CAPT) that facilitated broad utilization of weather-forecast techniques to evaluate cloud-related processes in climate models.
Developed a physically based convective trigger function that improved simulation of precipitation and its diurnal cycle in weather and climate models. The scheme and its variants have been used in E3SM since 2021 and the operational weather prediction model of the Japanese Meteorological Agency (JMA) since 2008. Relevant studies were highlighted in Eos research spot, “Our Changing Planet - The U.S. Climate Change Science Program for Fiscal Year 2006” and the “DOE ARM Notable Research Findings for the Past Five Years”.
Led the development of the ARM Best Estimate (ARMBE) dataset, which was highlighted by the 2010 DOE BER Advisory Committee (BERAC) 10-year review for ARM. The idea of creating ARMBE-like dataset has been considered as a significant product to bridge the gap between climate models and field observations and adopted by major field programs in US and Europe.
Led the development of the continuous large-scale forcing datasets for SCMs/CRMs, which was highlighted in the “DOE ARM Notable Research Finds for the Past Five Years”.
Developed the 2nd generation of Chinese medium-range weather forecast model (1993) as the technical lead and major model developer. The work received the first-place award of the Chinese Meteorological Administration (CMA) Science and Technology Award in 1994, the most prestigious award in CMA, China.
Built an SCM based on the NCAR CCM3.
DOE Energy Exascale Earth System Model (E3SM) Award for “Leadership and dedication to the Phase E3SM project as Phase 1 Atmosphere Group Leaders”, DOE, 2018.
Physical and Life Sciences Directorate Award for “Leadership and dedication to the DOE E3SM version 1 development”, LLNL, 2019
LLNL Deputy Director for Science and Technology Excellence in Publication Awards “The role of surface energy budget errors to the warm surface air temperature error over the central United States”. LLNL, September 2019.
Physical and Life Sciences Directorate Award for Excellence in Publication “For improving our ability to model one of climate’s most challenging aspects: precipitation”, LLNL, 2020.
Physical and Life Sciences Directorate Award for Excellence in Publication “For improving our understanding of the role of clouds, radiation, and precipitation processes in contributing to surface temperature biases”, LLNL, 2018.
Physical and Life Sciences Directorate Mentor Award for “Outstanding Mentorship of Postdoctoral Staff”, Lawrence Livermore National Laboratory, 2016.
Energy and Environment Directorate Program Award for “developing a new convective triggering function for NCAR CAM2 to improve the predicted precipitation over land”, Lawrence Livermore National Laboratory, 2004.
Chinese Meteorological Administration (CMA) Science and Technology Award (1st place) for “developing the second generation of Chinese medium-range weather forecast model”, CMA, China, 1994. This is a team award.
Beijing Meteorological Society Young Scientist Best Science Paper Award (3rd place) for the paper “A new positive definite advection scheme and its application to the moisture equation”, 1992.
Chinese National Meteorological Center Young Scientist Award (1st place) for “developing a new positive definite advection scheme and its implementation in removing negative moisture in the Beijing Limited-Area Model”, National Meteorological Center, 1988.
Professional Activities and Leadership Roles (Selected)
Tao, C., Xie, S., Tang, S. et al. Diurnal cycle of precipitation over global monsoon systems in CMIP6 simulations. Clim Dyn (2022). https://doi.org/10.1007/s00382-022-06546-0
Zhang, M., Xie, S., Liu, X., Lin, W., Zheng, X., Golaz, J.-C., & Zhang, Y. (2022). Cloud phase simulation at high latitudes in EAMv2: Evaluation using CALIPSO observations and comparison with EAMv1. Journal of Geophysical Research: Atmospheres, 127, e2022JD037100. https://doi.org/10.1029/2022JD037100
Zhang, M., Xie, S., et al. (2022). Evaluating EAMv2 simulated stratiform mixed-phase cloud properties at Northern and Southern high latitudes against ARM measurements. Submitted to JGR.
Tang, S., Xie, S., Guo, Z., Hong, S.-Y., Khouider, B., Klocke, D., et al. (2021) Long-term single-column model intercomparison of diurnal cycle of precipitation over midlatitude and tropical land. Q J R Meteorol Soc, 1– 29. Available from: https://doi.org/10.1002/qj.4222.120.
Tang, S., P. Gleckler, S. Xie, et al. (2021): Evaluating the Diurnal and Semidiurnal Cycle of Precipitation in CMIP6 Models Using Satellite- and Ground-Based Observations, Journal of Climate, 34(8), 3189-3210. https://doi.org/10.1175/JCLI-D-20-0639.1. (S. Xie was the corresponding author)
Lin, W. and S. Xie (2020): Chapter on Frameworks for Testing and Evaluating Fast Physics Parameterizations in Climate and Weather Forecasting Models (accepted), in the book Fast Physics in Large Scale Atmospheric Models: Parameterization, Evaluation, and Observations, edited by Y. Liu, P. Kollias and L. J. Donner.
Zhang, M., S. Xie, X. Liu, et al. (2020). Toward Understanding the Simulated Phase Partitioning of Arctic Single-Layer Mixed-Phase Clouds in E3SM. Earth and Space Science, https://doi.org/10.1002/essoar.10502164.1.
Xie et al. (2020) “Improving the Simulation of Diurnal Precipitation over Monsoon Regimes. Invited article for 2020 quarter 4 GEWEX QUARTERLY. https://www.gewex.org/gewex-content/uploads/2020/12/Q42020.pdf.
Wang, Y.‐C., Xie, S., Tang, S., & Lin, W. (2020). Evaluation of an improved convective triggering function: Observational evidence and SCM tests. Journal of Geophysical Research: Atmospheres, 125, 2019JD031651. https://doi.org/10.1029/2019JD031651.
Tang, S., S. Xie, M. Zhang, and S. Endo, 2020: Improvement of Atmospheric Objective Analysis over Sloping Terrain and Its Impact on Shallow‐Cumulus Clouds in Large‐Eddy Simulations. Journal of Geophysical Research: Atmospheres, https://doi.org/10.1029/2020JD032492.
Zhang, C., S. Xie, C. Tao, S. Tang, T. Emmeneger, J. Neelin, K. Schiro, and W. Lin (2020): The ARM Data-oriented Metrics and Diagnostics Package for Climate Models - A New Tool for Evaluating Climate Models with Field Data. Bull. Amer. Meteor. Soc., doi: https://doi.org/10.1175/BAMS-D-19-0282.1.
Xie, 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.
Rasch, P. J., S. Xie, P. Ma, W. Lin, et al. (2019): An Overview of the Atmospheric Component of the Energy Exascale Earth System Model. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2019MS001629.
Tang, S., Xie, S., Zhang, M., Tang, Q., Zhang, Y., Klein, S. A., et al (2019). Differences in Eddy‐Correlation and Energy‐Balance Surface Turbulent Heat Flux Measurements and Their Impacts on the Large‐scale Forcing Fields at the ARM SGP Site. Journal of Geophysical Research: Atmospheres, 124. https://doi.org/10.1029/2018JD029689.
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.
Zhang, Y. S. Xie, et al. 2018: Evaluation of clouds in version 1 of the E3SM atmosphere model with satellite simulators. Journal of Advances in Modeling Earth Systems, 11. https://doi.org/10.1029/2018MS001562.
Tang, Q., S. Xie, Y. Zhang, T. Phillips, J. Santanello, D. Cook, L. Riihimaki, and K. Gaustad, 2018: Heterogeneity in Warm-Season Land-Atmosphere Coupling over the U.S. Southern Great Plains. J. Geophys. Res., (Atmospheres), https://doi.org/10.1029/2018JD028463.
Zhang, Y., S. Xie, et al., 2018: The ARM cloud radar simulator for global climate models: Bridging field data and climate models. Bull. Amer. Meteor. Soc., 99, 21–26, https://doi.org/10.1175/BAMS-D-16-0258.1.
Zhang, C., S. Xie, S. A. Klein, H. Ma, S. Tang, K. Van Weverbery, C. J. Morcrette, and J. Petch, 2018: CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site. J. Geophys. Res. Atmos., 123, 2968–2992. DOI: 10.1002/2017JD027200.
Tang, S., S. Xie, et al. 2016: Large-Scale Vertical Velocity, Diabatic Heating and Drying Profiles Associated with Seasonal and Diurnal Variations of Convective Systems Observed in the GoAmazon2014/5 Experiment, Atmos. Chem. Phys. Discuss., 2016, 1-39, doi: 10.5194/acp-2016-644.
Xie, S., Y. Zhang, S. E. Giangrande, M. P. Jensen, R. McCoy, and M. Zhang, 2014: Interactions between Cumulus Convection and Its Environment as Revealed by the MC3E Sounding Array. J. Geophys. Res. Atmos., 119, 11784–11808, doi: 10.1002/2014JD022011.
Ma, H., S. Xie, S. Klein, et al. 2014: On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models. J. Climate. 27, 1781-1798. doi: 10.1175/JCLI-D-13-00474.1.
Zhao, C., S. Xie, X. Chen, M. Jensen, and M. Dunn, 2014: Quantifying uncertainties of cloud microphysical property retrievals with a perturbation method. J. Geophys. Res. Atmos., 119, 5375–5385, doi:10.1002/2013JD021112.
Xie, S., X. Liu, C. Zhao, and Y. Zhang, 2013: Sensitivity of CAM5 Simulated Arctic Clouds and Radiation to Ice Nucleation, J. Clim. 26, 5981-5999, DOI: 10.1175/JCLI-D-12-00517.1.
Ma, H., S. Xie, J. Boyle, S. Klein, and Y. Zhang, 2013: Development of Metrics and Diagnostics for CAM Climate Model Short-range Forecasts. J. Climate. 26, 1516-1534. doi: 10.1175/JCLI-D-12-00235.1.
Zhang, Y., S. Xie, C. Covey, D. D. Lucas, P. Gleckler, S. Klein, J. Tannahill, C. Doutriaux,and R. Klein, 2012: Regional assessment of the parameter-dependent performance of CAM4 in simulating tropical clouds. Geophys. Res. Lett., 39, L14708, doi:10.1029/2012GL052184.
Xie, S., H. Ma, J. Boyle, S. Klein, and Y. Zhang, 2012: On the Correspondence between Short- and Long- Timescale Systematic Errors in CAM4/CAM5 for the Years of Tropical Convection. J. Clim. 25, 7937–7955. doi: 10.1175/JCLI-D-12-00134.1.
Liu, X., S. Xie, J. Boyle, S. A. Klein, X. Shi, Z. Wang, W. Lin, S. J. Ghan, M. Earle, P. S. K. Liu, and A. Zelenyuk1, 2011: Testing Cloud Microphysics Parameterizations in NCAR CAM5 with ISDAC and M-PACE Observations. J. Geophys. Res., 116, D00T11, doi:10.1029/2011JD015889.
Xie, S., and 16-coauthors, 2010: ARM climate modeling best estimate data, Bull. Amer. Meteor. Soc, 91, 13–20 , doi:10.1175/2009BAMS2891.1 .
Xie, S., T. Hume, C. Jakob, S. Klein, R. McCoy, and M. Zhang, 2010: Observed large-scale structures and diabatic heating and drying profiles during TWP-ICE, J. Climate, 23, 57-79, doi:10.1175/2009JCLI3071.1< .
Xie, S., J. Boyle, S. A. Klein, X. Liu and S. Ghan, 2008: Simulations of Arctic Mixed-Phase Clouds in Forecasts with CAM3 and AM2 for M-PACE, Journal of Geophysical Research, 113, D04211, doi:10.1029/2007JD009225.
Liu, X., S. Xie, and S. J. Ghan, 2007: Evaluation of a new mixed-Phase cloud microphysics parameterization with the NCAR single column climate model (SCAM) and ARM M-PACE observations, Geophysical Research Letter, 34, L23712, doi:10.1029/2007GL031446.
Xie, S., S. Klein, M. Zhang, J. Yio, R. Cederall, and R. McCoy, 2006: Developing large-scale forcing data for single-column and cloud-resolving models from the Mixed-Phase Arctic Cloud Experiment. J. Geophys. Res., 111, D19104, doi:10.1029/2005JD006950 .
Xie, S., et al., 2006: An assessment of ECMWF analyses and model forecasts over the North Slope of Alaska using observations from the ARM Mixed-Phase Arctic Cloud Experiment. J. Geophys. Res., 111, D05107, doi:10.1029/2005JD006509 .
Xie, S., and 24 co-authors, 2005: Simulations of midlatitude frontal clouds by SCMs and CSRMs during the ARM March 2000 Cloud IOP. J. Geophys. Res., 110, D15S03, doi:10.1029/2004JD005119
Xie, S., M. H. Zhang, J. S. Boyle, R. T. Cederwall, G. L. Potter, and W. Y. Lin, 2004: Impact of a revised convective triggering mechanism on CAM2 model simulations: results from short-range weather forecasts. J. Geophys. Res. 109, D14102, doi:10.1029/2004JD004692.
Xie, S., R. T. Cederwall, and M. H. Zhang, 2004: Developing long-term single-column model/cloud system-resolving model forcing using numerical weather prediction products constrained by surface and top of the atmosphere observations. J. Geophys. Res., 109, D01104, doi:10.1029/2003JD004045
Xie, S., R. T. Cederwall, M. H. Zhang, and J. J. Yio, 2003: Comparison of SCM and CSRM Forcing Data Derived from the ECMWF Model and From Objective Analysis at the ARM SGP Site. J. Geophys. Res., 108 (D16), 4499, doi:10.1029/2003JD003541.
Xie, S., et al., 2002: Intercomparison and Evaluation of Cumulus Parameterization under Summertime Midlatitude Continental Conditions. Q. J. R. Meteorol. Soc., 128, 1095-1136, DOI: 10.1256/003590002320373229
Xie, S., and M. H. Zhang, 2000: Impact of the Convection triggering Function on Single-Column Model Simulations. J. Geophys. Res., 105, 14983-14996, DOI: 10.1029/2000JD900170
Xie, S. C., 1991: A New Positive Definite Advection Scheme and its Application to the Moisture Equation. ACTA METEOROLOGICA SINICA, Vol.49, 11-20.
Tian Y., and S. C. Xie, 1987: Test of Explicit Integration Schemes for the Barotropic Primitive Equation Model. Journal of Nanjing Institute of Meteorology, Vol.10, 95-102.
Dong, X., Zheng, X., Xi, B., & Xie, S. (2023). A Climatology of Midlatitude Maritime Cloud Fraction and Radiative Effect Derived from the ARM ENA Ground-Based Observations, Journal of Climate, 36(2), 531-546. Retrieved Jan 25, 2023, from https://journals.ametsoc.org/view/journals/clim/36/2/JCLI-D-22-0290.1.xml.
Golaz, J.-C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., et al. (2022). The DOE E3SM Model version 2: Overview of the physical model and initial model evaluation. Journal of Advances in Modeling Earth Systems, 14, e2022MS003156. https://doi.org/10.1029/2022MS003156
Zhang, C. et al. (2022). The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation. Geosci. Model Dev., 15, 9031–9056, 2022. https://doi.org/10.5194/gmd-15-9031-2022.
Wu, M., Wang, H., Easter, R. C., Lu, Z., Liu, X., Singh, B., et al. (2022). Development and evaluation of E3SM-MOSAIC: Spatial distributions and radiative effects of nitrate aerosol. Journal of Advances in Modeling Earth Systems, 14, e2022MS003157. https://doi.org/10.1029/2022MS003157
Xue, Y., Diallo, I., Boone, A. A., Yao, T., Zhang, Y., Zeng, X., Neelin, J. D., et al. (2022). Spring Land Temperature in Tibetan Plateau and Global-Scale Summer Precipitation: Initialization and Improved Prediction, Bulletin of the American Meteorological Society, 103(12), E2756-E2767. Retrieved Jan 25, 2023, from https://journals.ametsoc.org/view/journals/bams/103/12/BAMS-D-21-0270.1.xml.
Leung, L. R., et al. (including S. Xie) (2022): Exploratory precipitation metrics: spatiotemporal characteristics, process-oriented, and phenomena-based, Journal of Climate. https://journals.ametsoc.org/view/journals/clim/aop/JCLI-D-21-0590.1/JCLI-D-21-0590.1.xml.
Feng, Y., Wang, H., Rasch, P. J., Zhang, K., Lin, W., Tang, Q., et al. (2022). Global dust cycle and direct radiative effect in E3SM version 1: Impact of increasing model resolution. Journal of Advances in Modeling Earth Systems, 14, e2021MS002909. https://doi.org/10.1029/2021MS002909
Zhang, K.,et al. (including S. Xie) (2022): Effective radiative forcing of anthropogenic aerosols in E3SMv1: historical changes, causality, decomposition, and parameterization sensitivities, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-1087, 2022
Voldoire, A., Roehrig, R., Giordani, H., Waldman, R., Zhang, Y., Xie, S., and Bouin, M.-N.(2022) : Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration, Geosci. Model Dev., 15, 3347–3370, https://doi.org/10.5194/gmd-15-3347-2022, 2022.
Emmenegger, T., Kuo, Y., Xie, S., Zhang, C., Tao, C., & Neelin, J. D. (2022). Evaluating Tropical Precipitation Relations in CMIP6 Models with ARM Data, Journal of Climate, 35(19), 2743-2760. Retrieved Jan 25, 2023, from https://journals.ametsoc.org/view/journals/clim/35/19/JCLI-D-21-0386.1.xml
Ma, P.-L., et al. (including S. Xie) (2022): Better calibration of cloud parameterizations and subgrid effects increases the fidelity of the E3SM Atmosphere Model version 1, Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, 2022.
Ma, H.-Y., Zhang, K., Tang, S., Xie, S., & Fu, R. (2021). Evaluation of the causes of wet-season dry biases over Amazonia in CAM5. Journal of Geophysical Research: Atmospheres, 126, e2020JD033859. https://doi.org/10.1029/2020JD033859.
Wang, J, J Fan, Z Feng, K Zhang, E Roesler, B Hillman, J Shpund, W Lin, and S Xie. 2021. “Impact of a New Cloud Microphysics Parameterization on the Simulations of Mesoscale Convective Systems in E3SM.” Journal of Advances in Modeling Earth Systems 13(11). https://doi.org/10.1029/2021ms002628.
Chen, H., Jin, F.-F., Zhao, S., Wittenbert, A. T., and Xie, S., (2021): ENSO Dynamics in the E3SM-1-0, CESM2, and GFDL-CM4 Climate Models. J. Climate, 34(23), 9365-9384. https://doi.org/10.1175/JCLI-D-21-0355.1.
Zhang, T., Lin, W., Vogelmann, A. M., Zhang, M., Xie, S., Qin, Y., & Golaz, J.-C. (2021). Improving convection trigger functions in deep convective parameterization schemes using machine learning. Journal of Advances in Modeling Earth Systems, 13, e2020MS002365. https://doi.org/10.1029/2020MS002365
Ciesielski, P. E., Johnson, R. H., Tang, S., Zhang, Y., & Xie, S. (2021). Comparison of conventional and constrained variational methods for computing large-scale budgets and forcing fields. Journal of Geophysical Research: Atmospheres, 126, e2021JD035183. https://doi.org/10.1029/2021JD035183
Xue, Y., et al. (including S. Xie) (2021): Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase I (LS4P-I): Organization and Experimental design, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-329, Accepted.
Chen, C.-C., et al. (including S. Xie) (2021). Effects of organized convection parameterization on the MJO and precipitation in E3SMv1. Part I: Mesoscale heating. Journal of Advances in Modeling Earth Systems, 13, e2020MS002401. https://doi.org/10.1029/2020MS002401.
Cui, Z., G. Zhang, Y. Wang, and S. Xie (2021): Understanding the Roles of Convective Trigger Functions in the Diurnal Cycle of Precipitation in the NCAR CAM5. J. Climate. https://doi.org/10.1175/JCLI-D-20-0699.1.
Tang, Q., M. J. Prather, J. Hsu, D. Ruiz, P. Cameron-Smith, S. Xie, and J. C. Golaz (2021): Evaluation of the interactive stratospheric ozone (O3v2) module in the E3SM version 1 Earth system model, Geosci. Model Dev., 14, 1219–1236, https://doi.org/10.5194/gmd-14-1219-2021, 2021.
Wang Y., W. Xia, X. Liu, S. Xie, et al. (2021): Disproportionate control on aerosol burden by light rain. Nat. Geosci. 14, 72–76 (2021). https://doi.org/10.1038/s41561-020-00675-z.
Wang Y., G. Zhang, S. Xie, W. Lin, et al. (2021): Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model, Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021, 2021.
Tao, C., et al. (including S. Xie), 2021: Land–Atmosphere Coupling at the U.S. Southern Great Plains: A Comparison on Local Convective Regimes between ARM Observations, Reanalysis, and Climate Model Simulations, Journal of Hydrometeorology, 22(2), 463-481. https://doi.org/10.1175/JHM-D-20-0078.1
Ma, H.-Y., A. C. Siongco, S. A. Klein, S. Xie, A. R. Karspeck, K. Raeder, J. L. Anderson, J. Lee, B. P. Kirtman, W. J. Merryfield, H. Murakami, and J. J. Tribbia, 2021: On the Correspondence between Seasonal Forecast Biases and Long-Term Climate Biases in Sea Surface Temperature, Journal of Climate, 34(1), 427-446. https://doi.org/10.1175/JCLI-D-20-0338.1.
Ma, H.-Y., C. Zhou, Y. Zhang, S. A. Klein, M. D. Zelinka, X. Zheng, S. Xie, W.-T. Chen, and C.-M. Wu, 2021: Evaluating climate model moist processes from diurnal to interannual time scales using a multi-year ensemble of short-range hindcasts. Geosci. Model Dev., 14, 73–90, 2021. https://doi.org/10.5194/gmd-14-73-2021.
Wang, H., Easter, R. C., Zhang, R., Ma, P.‐L., Singh, B., Zhang, K., et al. ( 2020). Aerosols in the E3SM Version 1: New developments and their impacts on radiative forcing. Journal of Advances in Modeling Earth Systems, 12, e2019MS001851. https://doi.org/10.1029/2019MS001851.
Neale, R. B., W. Lin, S. Xie, C. Hannay, J. Bacmeister, 2020: Sub-Seasonal Tropical Variability in the DOE EarthEnergy Exascale System Model (E3SM) version 1. Submitted to JAMES.
Bogenschutz, P. A., Tang, S., Caldwell, P. M., Xie, S., Lin, W., and Chen, Y.-S.: The E3SM version 1 single-column model, Geosci. Model Dev., 13, 4443–4458, https://doi.org/10.5194/gmd-13-4443-2020, 2020.
Siongco, A.C., H. Ma, S.A. Klein, S. Xie, A.R. Karspeck, K. Raeder, and J.L. Anderson, 2020: A Hindcast Approach to Diagnosing the Equatorial Pacific Cold Tongue SST Bias in CESM1. J. Climate, 33, 1437–1453, https://doi.org/10.1175/JCLI-D-19-0513.1.
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Zhang, K., P. J. Rasch, M. A. Taylor, H. Wan, R. Leung, P.-L. Ma, J.-C. Golaz, J. Wolfe, W. Lin, B. Singh, S. Burrows, J.-H. Yoon, H.Wang, Y. Qian, Q. Tang, P. Caldwell, and S. Xie (2018), Impact of numerical choices on water conservation in the E3SM Atmosphere Model version 1 (EAMv1), Geosci. Model Dev., 11(5), 1971–1988, doi:10.5194/gmd-11-1971-2018.
Phillips, T. J., S. A. Klein, H. Ma, Q. Tang, S. Xie, I. N. Williams, J. A. Santanello, D. R. Cook, and M. S. Torn, 2017: Using ARM observations to evaluate climate model simulations of land-atmosphere coupling on the U.S. Southern Great Plains. J. Geophys. Res., 122, 11,524–11,548. Doi:10.1002/2017JD027141
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Tang, S., M. Zhang, and S. Xie, 2017: Investigating the Scale Dependence of SCM Simulated Precipitation and Clouds by Using Gridded Forcing Data at SGP. J. Geophys. Res., 122, 8724–8738, https://doi.org/10.1002/2017JD026565
Zhang, Y., S. A. Klein, J. Fan, A. Chundra, P. Kollias, S. Xie and S. Tang, 2017: Large-eddy simulation of shallow cumulus clouds over land: A composite case based on ARM long-term observations at its Southern Great Plains site. J. Atmos. Sci., 74, 3229-3251, DOI: 10.1175/JAS-D-16-0317.1.
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Jensen, M. P., Toto, T., Troyan, D., Ciesielski, P. E., Holdridge, D., Kyrouac, J., Schatz, J., Zhang, Y., and Xie, S.: The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis, Atmos. Meas. Tech., 8, 421-434, doi:10.5194/amt-8-421-2015, 2015.
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