Regional Superparametrization of OpenIFS by 3D LES Gijs van den Oord (NLeSC), Fredrik Jansson (CWI), Inti Pelupessy (NLeSC), Pier Siebesma (TU Delft, KNMI), Daan Crommelin (CWI) ICCS2019: MMS
Motivation DNS LES LAM GCM Resolved convection & Physics Grey Zone clouds parametrizations ICCS2019: MMS
Superparametrizing OpenIFS by DALES Dutch Atmospheric Large Eddy Simulation (DALES) ICCS2019: MMS
Coupling method: Grabowski scheme Forcing upon local LES models due to large-scale dynamics Tendencies within the GCM due to locally resolved physics ICCS2019: MMS
Regional superparametrization ● Beware not to double-count physics tendencies ● Extremely unbalanced cost between grid point physics ● Surface fmuxes and roughness lengths transferred from GCM→ LES ICCS2019: MMS
Implementation in OMUSE ICCS2019: MMS
Cabauw case ➔ OpenIFS: ● T511 (~40 km), ● 91 layers ➔ DALES: ● 42 models ● 200 m, 200 x 200 ● 160 layers, up to 4 km ICCS2019: MMS
Cabauw case ICCS2019: MMS
Cabauw case Liquid water profjles difger signifjcantly from original OpenIFS and ERA-5. Advected liquid water converted to vapour near edges of SP-region ICCS2019: MMS
Cabauw case Development of cumulus is better captured by the LES ICCS2019: MMS
Cabauw case: cloud cover bias Cloud cover underestimated in SP domain: advecting liquid water directly is impossible! ICCS2019: MMS
Liquid water problem DALES OpenIFS Total humidity prognostic, liquid water and Liquid water and water vapor separately water vapor diagostic: prognostic, total humidity diagnostic Grabowski scheme ‘smears out’ humidity fmuctuations, converting liquid water to water vapour ICCS2019: MMS
Performance ● Load balancing remains a problem due to adaptive DALES time step & thermodynamics ● Speedup can be achieved by smaller LES domains and mean-state acceleration ICCS2019: MMS
Summary ● Superparametrization of OpenIFS by 3D LES technically working. No feedback yet of rain and surface and radiation still in GCM. ● Cumulus evolution improved in testcase above Cabauw. Evolution of BL height seems in line with observations. ● Our SP setup blocks advection of liquid water: developing strategies to stimulation variance. More validation and verifjcation needed. ● Computational burden high and unbalanced due to DALES adaptive time step → technically hard problem to solve... ICCS2019: MMS
Aknowledgements ● ECMWF for support on OpenIFS, ERA-5, and use of compute infrastructure. ● DALES community for support on DALES. ● Cloudnet and Cesar observatory for observational data. ICCS2019: MMS
Superparameterization Grabowski, W. W.: An Improved Framework for Superparameterization, J. Atmos. Sci., 61, 1940– References 1952, 2004 Khairoutdinov, M., et al: Simulations of the F . Jansson et al: Regional superparameterization in a Atmospheric General Circulation Using a Cloud- global circulation model using large-eddy simulations, Resolving Model as a Superparameterization of submitted to JAMES, 2019 Physical Processes, J. Atmos. Sci, 62, 2136–2154, Code on GitHub 2005. https://github.com/CloudResolvingClimateModeling/sp- Narenpitak, P ., et al: Cloud and circulation feedbacks coupler in a near-global aquaplanet cloud-resolving model, JAMES, 9, 1069–1090, 2017. OMUSE Acceleration Pelupessy, I., et al: The Oceanographic Multipurpose Software Environment (OMUSE v1.0), GMD, 10, 3167– Parishani, H., et al: T oward low-cloud-permitting cloud 3187, 2017. superparameterization with explicit boundary layer turbulence, JAMES, 9, 1542–1571, 2017. DALES Xing, Y ., et al: New Effjcient Sparse Space-Time Heus, T., et al: Formulation of the Dutch Atmospheric Algorithms for Superparameterization on Mesoscales, Large-Eddy Simulation (DALES) and overview of its Monthly Weather Review, 137, 4307–4324, 2009. applications, GMD, 3, 415–444, 2010. Jones, C. R., et al: Mean-state acceleration of cloud- resolving models and large eddy simulations, JAMES, 7, 1643–1660, 2015
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