Regional Superparametrization of OpenIFS by 3D LES Gijs van den - - PowerPoint PPT Presentation

Regional Superparametrization

• f OpenIFS by 3D LES

Gijs van den Oord (NLeSC), Fredrik Jansson (CWI), Inti Pelupessy (NLeSC), Pier Siebesma (TU Delft, KNMI), Daan Crommelin (CWI)

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Motivation

DNS LES LAM GCM Grey Zone Resolved convection & clouds Physics parametrizations

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Superparametrizing OpenIFS by DALES

Dutch Atmospheric Large Eddy Simulation (DALES)

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Coupling method: Grabowski scheme

Forcing upon local LES models due to large-scale dynamics Tendencies within the GCM due to locally resolved physics

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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

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Implementation in OMUSE

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Cabauw case

➔ OpenIFS:

• T511 (~40 km),
• 91 layers

➔ DALES:

• 42 models
• 200 m, 200 x 200
• 160 layers, up to 4

km

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Cabauw case

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Cabauw case

Liquid water profjles difger signifjcantly from

• riginal OpenIFS and

ERA-5. Advected liquid water converted to vapour near edges of SP-region

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Cabauw case

Development of cumulus is better captured by the LES

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Cabauw case: cloud cover bias

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Cloud cover underestimated in SP domain: advecting liquid water directly is impossible!

Liquid water problem

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DALES Total humidity prognostic, liquid water and water vapor diagostic: OpenIFS Liquid water and water vapor separately prognostic, total humidity diagnostic Grabowski scheme ‘smears out’ humidity fmuctuations, converting liquid water to water vapour

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

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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...

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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.

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References

Superparameterization Grabowski, W. W.: An Improved Framework for Superparameterization, J. Atmos. Sci., 61, 1940– 1952, 2004 Khairoutdinov, M., et al: Simulations of the Atmospheric General Circulation Using a Cloud- Resolving Model as a Superparameterization of Physical Processes, J. Atmos. Sci, 62, 2136–2154, 2005. Narenpitak, P ., et al: Cloud and circulation feedbacks in a near-global aquaplanet cloud-resolving model, JAMES, 9, 1069–1090, 2017. Acceleration Parishani, H., et al: T

• ward low-cloud-permitting cloud

superparameterization with explicit boundary layer turbulence, JAMES, 9, 1542–1571, 2017. Xing, Y ., et al: New Effjcient Sparse Space-Time Algorithms for Superparameterization on Mesoscales, Monthly Weather Review, 137, 4307–4324, 2009. Jones, C. R., et al: Mean-state acceleration of cloud- resolving models and large eddy simulations, JAMES, 7, 1643–1660, 2015 F . Jansson et al: Regional superparameterization in a global circulation model using large-eddy simulations, submitted to JAMES, 2019 Code on GitHub https://github.com/CloudResolvingClimateModeling/sp- coupler OMUSE Pelupessy, I., et al: The Oceanographic Multipurpose Software Environment (OMUSE v1.0), GMD, 10, 3167– 3187, 2017. DALES Heus, T., et al: Formulation of the Dutch Atmospheric Large-Eddy Simulation (DALES) and overview of its applications, GMD, 3, 415–444, 2010.