ICON Physics: General Overview Martin Köhler and ICON team ICON physics

The standard Reynolds decomposition and averaging, leads to co-variances that need “closure” or “parametrization”. Radiation absorbed, scattered and emitted by molecules, aerosols and cloud droplets plays an important role in the atmosphere and needs parametrization. Cloud microphysical processes need “parametrization”. Parametrization schemes express the effect of sub-grid processes on resolved variables. Model variables are U,V,T,q, (l,i,r,s,a) What is parametrization and why is it needed

1 hour100 hours0.01 hour microscale turbulence Diffusive transport in the atmosphere is dominated by turbulence. Time scale of turbulence varies from seconds to half hour. Length scale varies from mm for dissipative eddies to 100 m for transporting eddies. The largest eddies are the most efficient ones for transport. spectral gap diurnal cycle cyclones data: 1957 Space and Time Scales

courtesy to Anton Beljaars Space and time scales

Parametrized processes courtesy to Anton Beljaars

Basic equations mom. equ.’s continuity

Reynolds decomposition Substitute, apply averaging operator, Boussinesq approximation (density in buoyancy terms only) and hydrostatic approximation (vertical acceleration << buoyancy). Averaging (overbar) is over grid box, i.e. sub-grid turbulent motion is averaged out. Property of averaging operator:

Reynolds equations Boundary layer approximation (horizontal scales >> vertical scales), e.g. : High Reynolds number approximation (molecular diffusion << turbulent transports), e.g.: Reynolds Stress

Shear productionTurbulent transport Buoyancy Mean flow TKE advection Turbulent Kinetic Energy equation local TKE: Derive equation for E by combining equations of total velocity components and mean velocity components: Dissipation Storage mean TKE: Pressure correlation

Simple closures Mass-flux method: K-diffusion method: analogy to molecular diffusion mass flux (needs M closure) entraining plume model

ProcessAuthorsSchemeOrigin Radiation Mlawer et al. (1997) Barker et al. (2002) RRTM (later with McICA & McSI)ECHAM6/IFS Ritter and Geleyn (1992)δ two-streamGME/COSMO Non-orographic gravity wave drag Scinocca (2003) Orr, Bechtold et al. (2010) wave dissipation at critical levelIFS Sub-grid scale orographic drag Lott and Miller (1997)blocking, GWDIFS Cloud cover Doms and Schättler (2004)sub-grid diagnosticGME/COSMO Köhler et al. (new development)diagnostic (later prognostic) PDFICON Microphysics Doms and Schättler (2004) Seiffert (2010) prognostic: water vapor, cloud water, cloud ice, rain and snow GME/COSMO Convection Bechthold et al. (2008)mass-flux shallow and deepIFS Plant, Craig (2008)stochastic based on Kain-FritschLMU, Munich Turbulent transfer Raschendorfer (2001)prognostic TKECOSMO Mironov, Mayuskava (new)prognostic TKE and scalar var.ECHAM6 Neggers, Köhler, Beljaars (2010)EDMF-DUALMIFS Land Heise and Schrodin (2002), Helmert, Mironov (2008, lake) tiled TERRA + FLAKE + multi-layer snow GME/COSMO Raddatz, Knorr, SchnurJSBACHECHAM6 Physics in ICON

ICON dynamics-physics cycling Slow Physics Non-Orographic Gravity Wave Drag Non-Orographic Gravity Wave Drag Sub-Grid-Scale Orographic Drag Land/Lake/Sea-Ice dtime dt_gwd dt_sso dt_conv dt_rad dtime * iadv_rcf dt_conv Fast Physics Output Tracer Advection Dynamics Turbulent Diffusion Microphysics Satur. Adjustment Radiation Cloud Cover Convection „dt_output“ Tendencies

T-tendencies due to solar radiation scheme [K/day] Jan. 2012

T-tendencies due to terrestrial radiation scheme [K/day] Jan. 2012

T-tendencies due to turbulence scheme Jan [K/day]

T-tendencies due to convection scheme [K/day] Jan. 2012

T-tendencies due to SSO+GWD schemes [K/day] Jan. 2012

T-tendencies due to microphysics / sat.adj. scheme [K/day] Jan microphysicssaturation adjustment Jan. 2012

JSBACH Land Surface Model Schnur, Knurr, Raddatz, MPI Hamburg JSBACH is the land surface parametrization within the ECHAM physics in the MPI Earth System Model. Physical processes: Energy and moisture balance at the surface (implicit coupling within vertical diffusion scheme of atmosphere) 5-layer soil temperatures and hydrology Snow, glaciers Hydrologic discharge (coupling to ocean) Bio-geochemical processes: Vegetation characteristics represented by Plant Functional Types Phenology Photosynthesis Carbon cycle Nutrient limitation (nitrogen and phosphorus cycles) Dynamic vegetation Land use change

EDMF-DUALM turbulence scheme in ICON Goals:  turbulence option to ICON that is scientifically and operationally appealing  reference for default TKE scheme  reserach (HeRZ and HD(CP) 2 )  potential for climate Martin Köhler and ICON team

DUALM concept: multiple updrafts with flexible area partitioning

preVOCA: VOCALS at Oct 2006 – Low Cloud

Daniel Klocke‘s Jülich 100m ICON LES run: qc+qi

GCSS process: GEWEX Cloud System Study ( ) Randall et al, 2003

extra slides

Maike Ahlgrimm: CALIPSO trade cumulus TiedtkeDUALM

call tree EDMF  3 parcel updrafts (test, sub-cloud, cloud)  mass-flux closure  z0 calculation  exchange coefficients  call TERRA to get land fluxes  ocean cold skin, warm layer description  TOFD, drag from 5m-5km orography  EDMF solvers for qt/T, u/v, tracer (e.g. aerosol)  multiple diagnostics including T2m, gusts

JSBACH in ICON Schnur, Knurr, Raddatz, MPI Hamburg New development of unified JSBACH code that works with the ICON and ECHAM6 (MPI-ESM1) models. Has its own svn repository ( and is pulled into the ICON code on svn checkout/update via svn:externals propertyhttps://svn.zmaw.de/svn/jsbach Self-contained model; ICON code itself only contains calls to JSBACH for initialization and surface updating at each time step (src/atm_phy_echam/mo_surface.f90) Currently, only the physical processes have been implemented in the new JSBACH code; bio-geochemical process to be ported to new code in the coming months New structures for memory and sub-surface types (tiles) that allow a more flexible handling of surface characteristics and processes: PFTs, bare soil, lakes, glaciers, wetlands, forest management, urban surfaces, etc.

ICON physics upgrades and tunings 2013 Aug-Dec Non-orographic gravity wave tuning Marine surface latent heat flux in TKE scheme - rat_sea Land surface physics Exponential roots Moisture dependent heat conductivity Cloud cover scheme Tiedtke/Bechtold convection parameters Bechtold diurnal cycle upgrade Horizontal diffusion new TURBDIFF code

non-orographic gravity wave tuning launch amplitude x10 -3 Pa IFS analysis URAP observation July 1992 (Kristina Fröhlich) 3.75, default U bias

non-orographic gravity wave drag tuning 1.0 launch amplitude x10 -3 Pa U bias

In TERRA plant roots are a sink constant to a depth dependent on vegetation type. Now: the uptake of moisture is described exponentially as a function of depth. The default setting soil level 1-4 are moister than the IFS soil and the levels below 5-8 are dryer after 10 days simulation in July. The new formulation exactly counter acts those IFS/ICON differences with 1-4 becoming dryer and 5-8 becoming moister. So more moisture is left lower down and more is taken out near the top of the soil. ICON: exponential roots

ICON: moisture dependent soil heat conductivity Moisture dependent formulation based on Johansen (1975) as described in Peters-Lidard et al (1998, JAS). The impact is most prominent in the Sahara, which has virtually no soil moisture, because the previous constant formulation was tuned to moist soils. The cooling in the Sahara in the top most soil level and a warming in the lowest dynamic soil level after 24 hours at 00UTC is shown. This night-time near-surface cooling is a signal of a larger diurnal cycle resulting from a smaller ground heat flux.. default level 2 moisture dependency level 2 default level 7 moisture dependency level 7