Tying in High Resolution E3SM with ARM Data (THREAD)

Abstract

The emergence of nonhydrostatic Global Storm Resolving Models (GSRMs) with kilometer-scale horizontal resolutions signifies the advent of an exciting new generation of weather and climate models. By explicitly simulating the kilometer-scale motions associated with deep convection and other mesoscale circulations, GSRMs circumvent a critical weakness associated with the deep convection parameterization in traditional lower-resolution climate models. At this frontier, DOE’s Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM, referring to the cloud-resolving configurations of the current EAMxx) has successfully completed its debut 40-day global simulation at 3.25 km resolution and published its findings (Caldwell et al. 2021). Despite impressive improvements when compared to conventional climate models, GSRMs like SCREAM still need to represent the effect of processes at scales smaller than 3.25 km with imperfect parameterizations of sub-grid turbulence and the microphysics of aerosols, clouds, and precipitation. Yet we also lack a systematic understanding of the interactions and feedbacks between resolved and parameterized processes across different spatio-temporal scales in GSRMs such as SCREAM, particularly at the interface between the mesoscale and the convective scale.

Fortunately, observations available from DOE’s Atmospheric Radiation Measurement (ARM) program are ideally suited to assess processes in SCREAM and facilitate the development of improved process representations. In the last decade, the ARM program has collected data from many of the world’s most important cloud and convection regimes. Furthermore, advances in instrumentation provide detailed continuous profiling and scanning measurements of sub-cloud turbulence, clouds, aerosols, and precipitation. Moreover, GSRMs with their kilometer-scale resolution break down the scale gap that exists between the lower resolution of traditional climate models and the point nature of ARM observations making ARM data even more relevant to model diagnosis and improvement.

Given this context, THREAD as a new ASR Science Focus Area (SFA) project (funded in October 2022) will integrate these two prominent capabilities of DOE from a process-oriented perspective and generate new understanding of the processes essential to the successful simulation of clouds and convection by GSRMs.

Objectives and Hypotheses 

THREAD’s general science foci are boundary layer clouds and convection, their interaction with the underlying surface, and the physical mechanisms driving the spatio-temporal variabilities in cloud and precipitation morphologies, which are crucial for cloud radiative effects and the hydrologic cycle. THREAD aims to answer three science questions:

1. How can we effectively diagnose a model’s strengths and weaknesses and transfer process-level understanding based on observations into improvements in global storm resolving models?
2. How well can SCREAM represent the interactions between the explicitly resolved mesoscale variability and the parameterized sub-grid scale turbulence, cloud microphysics, and aerosol processes?
3. How well can SCREAM represent the fast interactive physics of local land-atmosphere coupling at short-time scales and the feedbacks to longer time scales?

The overarching hypothesis of THREAD is that process-level understanding and diagnosis can be used to integrate the recent advancements in DOE ARM observational data with the DOE kilometer scale global model SCREAM leading to a pronounced reduction of model uncertainties in aerosols, clouds and precipitation processes, and radiative effects.

Modeling Tools and Experimental Approaches 

To bring ARM data into the improvement of SCREAM, THREAD will heavily employ three modeling tools that allow one efficiently to zoom into ARM sites. These include a regionally refined version of SCREAM, called RRM-SCREAM, which has 3.25 km resolution only over the ARM site of interest. Even more efficient is the single point doubly-periodic version of SCREAM (DP-SCREAM), which is analogous to a cloud-resolving model. Finally, we will also assist the evaluation of the traditional lower-resolution version of E3SM via a single-column model that uses the parameterized physics of the lower-resolution configuration of E3SM.

To achieve our science goals, THREAD will adopt three approaches. First, THREAD will perform initial surveys of model performance through assessments of global SCREAM simulations and regional simulations of DP-SCREAM and RRM-SCREAM against the available ARM field campaign and satellite data. Second, the model strengths and deficiencies identified in the first approach will be the subject of in-depth process-oriented critiques where we trace back the sources of model biases and explore possible solutions using DP-SCREAM or RRM-SCREAM. Third, we will explore machine learning methods for parametric calibration of the turbulence parameterization and its coupling with microphysics. Finally, we envision a close relationship with SCREAM developers involving rapid iteration on potential solutions (developed either by THREAD team members or SCREAM developers) to problems identified by THREAD.

Impact

By integrating process-level ARM data with high-resolution E3SM modeling, THREAD will interact with the ARM/ASR and E3SM communities by establishing and sharing (1) a comprehensive protocol for sub-grid scale parameterization validation via convection case libraries featuring process-level ARM data, (2) a hierarchy of modeling tools including regionally refined configurations of SCREAM around ARM sites, and (3) a workflow to calibrate SCREAM sub-grid scale physics with ARM data using machine learning. THREAD will propel SCREAM to develop in physically sound ways rooted in the fundamental understanding of cloud, precipitation, and aerosol processes achieved by DOE ARM observations.

Related Publications

Su T and Y Zhang. 2024. “Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains.” Geosci. Model Dev., 17, 6319–6336. 10.5194/gmd-17-6319-2024

Su T and Y Zhang. 2024. “Deep-learning-derived planetary boundary layer height from conventional meteorological measurements.” Atmos. Chem. Phys., 24, 6477–6493. 10.5194/acp-24-6477-2024

Tian J, Y Zhang, SA Klein, CR.Terai, PM Caldwell, H Beydoun, P Bogenschutz, H-Y Ma, AS Donahue. 2024. “How well does the DOE global storm resolving model simulate clouds and precipitation over the Amazon?” Geophysical Research Letters, 51, e2023GL108113. 10.1029/2023GL108113

Zheng X, Y Zhang, SA Klein, M Zhang, Z Zhang, M Deng, J Tian, CR Terai, B Geerts, P Caldwell, PA Bogenschutz. 2024. “Using satellite and ARM observations to evaluate cold air outbreak cloud transitions in E3SM global storm-resolving simulations.” Geophysical Research Letters, 51, e2024GL109175. 10.1029/2024GL109175

Donahue AS, PM Caldwell, L Bertagna, H Beydoun, PA Bogenschutz, AM Bradley, TC Clevenger, J Foucar, C Golaz, O Guba, W Hannah, BR Hillman, JN Johnson, N Keen, W Lin, B Singh, MA Taylor, J Tian, CR Terai, PA Ullrich, X Yuan, Y Zhang. 2024. “To exascale and beyond—The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), a performance portable global atmosphere model for cloud-resolving scales.” Journal of Advances in Modeling Earth Systems, 16, e2024MS004314. 10.1029/2024MS004314

Su T, Z Li, Y Zhang, Y Zheng, and H Zhang. 2024. “Observation and reanalysis derived relationships between cloud and land surface fluxes across cumulus and stratiform coupling over the Southern Great Plains.” Geophysical Research Letters, 51, e2023GL108090. 10.1029/2023GL108090

Faiola CL, M Helbig, Y Zhang, ER Beamesderfer, ZM Sanchez-Mejia, AM Yáñez-Serrano, and AD Richardson. 2024. “AFM Special Issue Summary-Integrating Surface Flux with Boundary Layer Measurements.” Agricultural and Forest Meteorology, 346, p.109872. 10.1016/j.agrformet.2023.109872

Tao C, S Xie, H-Y Ma, P Bechtold, Z Cui, PA Vaillancourt, et al. 2024. “Diurnal cycle of precipitation over the tropics and central United States: intercomparison of general circulation models.” Quarterly Journal of the Royal Meteorological Society, 150(759), 911(759), 911–936.  10.1002/qj.4629

Qin H, SA Klein, H-Y Ma, K Van Weverberg, Z Feng, X Chen, et al. 2023. “Summertime near-surface temperature biases over the central United States in convection-permitting simulations.” Journal of Geophysical Research: Atmospheres, 128, e2023JD038624. 10.1029/2023JD038624

Pressel KG, T Heus, Y Zhang, C van Heerwaarden, A Jaruga, G Matheou, M Witte, L Fierce, K Lundquist, V Ghate, F Yang, C Chiu, and D Richter. 2023. “Department of Energy’s Atmospheric System Research (ASR) Program’s Workshop on the Future of Atmospheric Large Eddy Simulation (LES): Workshop Report.” United States: N. p., 10.2172/2004980.

Tang Q and co-authors including Y Zhang.  2023. “The fully coupled regionally refined model of E3SM version 2: Overview of the Atmosphere, Land and River.” Geosci. Model Dev. 16, 3953-3995. 10.5194/gmd-2022-262.

Ma H, S Klein, J Lee, M Ahn, C Tao, and P Gleckler. 2022. “Superior daily and sub-daily precipitation statistics for intense and long-lived storms in global storm-resolving models.” Geophysical Research Letters, 49(8), e2021GL096759. 10.1029/2021GL096759

Tian J, Y Zhang, S Klein, R Ӧktem, and L Wang. 2022. “How does land cover and its heterogeneity length scales affect the formation of summertime shallow cumulus clouds in observations from the US Southern Great Plains?” Geophysical Research Letters, 49(7), e2021GL097070, 10.1029/2021GL097070.

Tian Y, Y Zhang, and S Klein. 2022. “What determines the number and the timing of pulses in afternoon precipitation in the Green Ocean Amazon (GoAmazon) observations?” Geophysical Research Letters, 49(2), e2021GL096075, 10.1029/2021GL096075.

Bogenschutz P, C Eldred and P Caldwell. 2022. “The horizontal resolution sensitivity of the Simple Convection-Permitting E3SM Atmosphere Model in a Doubly-Periodic Configuration”, in review, Journal of Advances in Modeling Earth Systems, 10.1002/essoar.10512691.1

Beamesderfer E, C Buechner, C Faiola, M Helbig, Z Sanchez‐Mejia, A Yáñez‐Serrano, Y Zhang, and A Richardson. 2022. “Advancing Cross‐Disciplinary Understanding of Land‐Atmosphere Interactions.” Journal of Geophysical Research: Biogeosciences, 127(2), e2021JG006707, 10.1029/2021JG006707.

Tian J, Y Zhang, S Klein, L Wang, R Öktem, and D Romps. 2021. “Summertime Continental Shallow Cumulus Cloud Detection Using GOES-16 Satellite and Ground-Based Stereo Cameras at the DOE ARM Southern Great Plains Site.” Remote Sensing, 13(12), 10.3390/rs13122309.

Ma H, K Zhang, S Tang, S Xie, and R Fu. 2021. “Evaluation of the causes of wet‐season dry biases over Amazonia in CAM5.” Journal of Geophysical Research: Atmospheres, 126(11), e2020JD033859, 10.1029/2020JD033859.

Tao C, Y Zhang, Q Tang, H Ma, V Ghate, S Tang, S Xie, and J Santanello. 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), 10.1175/JHM-D-20-0078.1.

Tian Y, Y Zhang, S Klein, and C Schumacher. 2021. “Interpreting the diurnal cycle of clouds and precipitation in the ARM GoAmazon observations: Shallow to deep convection transition.” Journal of Geophysical Research: Atmospheres, 126(5), 2020JD033766, 10.1029/2020JD033766.

Ma H, C Zhou, Y Zhang, S Klein, M Zelinka, X Zheng, S Xie, W Chen, and C Wu. 2021. “A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales.” Geoscientific Model Development, 14(1), 10.5194/gmd-14-73-2021.

Caldwell P, C Terai, B Hillman, N Keen, P Bogenschutz, W Lin, H Beydoun, M Taylor, L Bertagna, A Bradley, T Clevenger, A Donahue, C Eldred, J Foucar, J Golaz, O Guba, R Jacob, J Johnson, J Krishna, W Liu, K Pressel, A Salinger, B Singh, A Steyer, P Ullrich, D Wu, X Yuan, J Shpund, H Ma, and C Zender. 2021. “Convection‐Permitting Simulations with the E3SM Global Atmosphere Model.” Journal of Advances in Modeling Earth Systems, 13(11), e2021MS002544, 10.1029/2021MS002544.

Tang S, S Xie, Z Guo, S Hong, B Khouider, D Klocke, M Köhler, M Koo, P Krishna, V Larson, S Park, P Vaillancourt, Y Wang, J Yang, C Daleu, C Homeyer, T Jones, N Malap, R Neggers, T Prabhakaran, E Ramirez, C Schumacher, C Tao, P Bechtold, H Ma, J Neelin, and X Zeng. 2021. “Long-Term Single-Model Intercomparison of Diurnal Cycle of Precipitation over Midlatitude and Tropical Land.” Quarterly Journal of the Royal Meteorological Society, 148(743), 10.1002/qj.4222.

Pan B, G Anderson, A Goncalves, D Lucas, C Bonfils, J Lee, Y Tian, and H Ma. 2021. “Learning to Correct Climate Projection Biases.” Journal of Advances in Modeling Earth Systems, 13(10), e2021MS002509, 10.1029/2021MS002509.

Bogenschutz P, S Tang, P Caldwell, S Xie, W Lin, and Y-S Chen. 2020. “The E3SM version 1 single-column model.” Geoscientific Model Development, 13, 4443–4458, 10.5194/gmd-13-4443-2020

Zhang M, S Xie, X Liu, W Lin, K Zhang, H Ma, X Zheng, and Y Zhang. 2020. “Toward Understanding the Simulated Phase Partitioning of Arctic Single‐Layer Mixed‐Phase Clouds in E3SM.” Earth and Space Science, 7(7), e2020EA001125, 10.1029/2020EA001125.

Zheng X, S Klein, V Ghate, S Santos, J McGibbon, P Caldwell, P Bogenschutz, W Lin, and M Cadeddu. 2020. “Assessment of Precipitating Marine Stratocumulus Clouds in the E3SMv1 Atmosphere Model: A Case Study from the ARM MAGIC Field Campaign.” Monthly Weather Review, 148(8), 10.1175/MWR-D-19-0349.1.

Caldwell P, A Mametjanov, Q Tang, L Van Roekel, J Golaz, W Lin, D Bader, N Keen, Y Feng, R Jacob, M Maltrud, A Roberts, M Taylor, M Veneziani, H Wang, J Wolfe, K Balaguru, P Cameron‐Smith, L Dong, S Klein, L Leung, H Li, Q Li, X Liu, R Neale, M Pinheiro, Y Qian, P Ullrich, S Xie, Y Yang, Y Zhang, K Zhang, and T Zhou. 2019. “The DOE E3SM Coupled Model Version 1: Description and Results at High Resolution.” Journal of Advances in Modeling Earth Systems, 11(12), 10.1029/2019MS001870.

Siongco A, H Ma, S Klein, S Xie, A Karspeck, K Raeder, and J Anderson. 2019. “A hindcast approach to diagnosing the equatorial Pacific cold tongue SST bias in CESM1.” Journal of Climate, 33(4), 10.1175/JCLI-D-19-0513.1.

Tao C, Y Zhang, S Tang, Q Tang, H Ma, S Xie, and M Zhang. 2019. “Regional Moisture Budget and Land‐Atmosphere Coupling over the U.S. Southern Great Plains Inferred from the ARM Long‐Term Observations.” Journal of Geophysical Research: Atmospheres, 124(17-18), 10.1029/2019JD030585.

Zhang Y, S Xie, W Lin, S Klein, M Zelinka, P Ma, P Rasch, Y Qian, Q Tang, and H Ma. 2019. “Evaluation of Clouds in Version 1 of the E3SM Atmosphere Model With Satellite Simulators.” Journal of Advances in Modeling Earth Systems, 11(5), 10.1029/2018MS001562.

Tang S, S Xie, M Zhang, Q Tang, Y Zhang, S Klein, D Cook, and R Sullivan. 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(6), 10.1029/2018JD029689.

Terai C, Y Zhang, S Klein, M Zelinka, J Chiu, and Q Min. 2019. “Mechanisms behind the extratropical stratiform low‐cloud optical depth response to temperature in ARM site observations.” Journal of Geophysical Research: Atmospheres, 124(4), doi:10.1029/2018JD029359.

Lee J, Y Zhang, and S Klein. 2018. “The effect of land surface heterogeneity and background wind on shallow cumulus clouds and the transition to deeper convection.” Journal of the Atmospheric Sciences, 76(2), 10.1175/JAS-D-18-0196.1.

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.” Journal of Geophysical Research: Atmospheres, 123(15), 10.1029/2018JD028463.

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Lareau N, Y Zhang, and S Klein. 2018. “Observed Boundary Layer Controls on Shallow Cumulus at the ARM Southern Great Plains Site.” Journal of the Atmospheric Sciences, 75(7), 10.1175/JAS-D-17-0244.1.

Morcrette C, K Van Weverberg, H Ma, M Ahlgrimm, E Bazile, L Berg, A Cheng, F Cheruy, J Cole, R Forbes, W Gustafson, M Huang, W Lee, Y Liu, L Mellul, W Merryfield, Y Qian, R Roehrig, Y Wang, S Xie, K Xu, C Zhang, S Klein, and J Petch. 2018. “Introduction to CAUSES: Description of Weather and Climate Models and Their Near-Surface Temperature Errors in 5 day Hindcasts Near the Southern Great Plains.” Journal of Geophysical Research: Atmospheres, 123(5), doi:10.1002/2017JD027199.

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Zhang Y, S Xie, S Klein, R Marchand, P Kollias, E Clothiaux, W Lin, K Johnson, D Swales, A Bodas-Salcedo, S Tang, J Haynes, S Collis, M Jensen, N Bharadwaj, J Hardin, and B Isom. 2018. “The ARM Cloud Radar Simulator for Global Climate Models: A New Tool for Bridging Field Data and Climate Models.” Bulletin of the American Meteorological Society, 99(1), 10.1175/BAMS-D-16-0258.1.

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Zhang Y, S Klein, J Fan, A Chandra, P Kollias, S Xie, and S Tang. 2017. “Large-eddy simulation of shallow cumulus over land: A composite case based on ARM long-term observations at its Southern Great Plains site.” Journal of the Atmospheric Sciences, 74(10), 10.1175/JAS-D-16-0317.1.

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Ma H, S Xie, SA Klein, KD Williams, JS Boyle, S Bony, H Douville, S Fermepin, B Medeiros, S Tyteca, M Watanabe, and DL Williamson. 2014. “On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models.” Journal of Climate, 27(4), 10.1175/jcli-d-13-00474.1.

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Zhang Y and SA Klein. 2013. “Factors Controlling the Vertical Extent of Fair-Weather Shallow Cumulus Clouds over Land: Investigation of Diurnal-Cycle Observations Collected at the ARM Southern Great Plains Site.” Journal of the Atmospheric Sciences, 70(4), 10.1175/jas-d-12-0131.1.

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