Cloud Related Physics Parameterizations for the GEOS-5 AGCM Description, tuning, plans CMAI meeting 4/20/06 Goal(s) Develop model physics for assimilation, forecasting, climate (coupled and uncoupled) applications GEOS-5 tags released Cerebus (spring 05), Daedalus (fall 05). Both had severe cloud/moist physics related biases. Eros (spring 06 to be used in MERRA) Goals for Eros tag: Acceptable LWP vs SSMI Acceptable SWCF vs ERBE Near cancellation of SWCF and LWCF in convective zones Global mean precip not too high (~ or < 3.2 mm/d all year) Moist Physics for GEOS-5 Moist physics consists of RAS convection and large-scale cloud condensate scheme. Two condensate phases: liquid and ice Two condensate families: convective source, statistical source Moist Physics State Variables (vapor, 4 condensates, 2 fractions qs and fs) RAS outputsdetrained mass and cloud condensates, precipitating condensate profiles, updated T,q,u,v, tracers Prognostic Cloud scheme inputs T, u, v, qs, fs, convective mass and condensate profiles outputsupdated T, qs, fs, rain rates (post-processed for radiation) f tot, q vap, q ice, q liq, R ice, R liq Effective radii Prognostic cloud scheme Two families - 1) convective source and 2) RH-based, large-scale source equivalent to simple, bi-modal prognostic PDF of total water: q sat q* tot,an Constraints on PDF are provided by prognostic values of q l,ls, q l,an, q i,ls q i,an and f an Can also be thought of as union of Tiedtke-like scheme for anvil clouds, with statistical large-scale scheme. RAS with enhanced microphysics, loosely following Sud and Walker (1999) Each call invokes spectrum of plumes Choose cloud-base, cloud-top. Find necessary plume entrainment Calculate mass fluxes Now estimate a steady-state updraft velocity from Cloud and environmental profiles. This provides time scale - z/w for auto- conversion of cloud to precip Currently in-plume cloud condensate not included in radiation calculation, but could be with little difficulty Cloud detrainment levels can be sequential or random. Cloud base can be fixed or variable, e.g. LCL, PBL top. cloud precip vapor In-cloud total water profile zz Convection scheme Stable situations w/ no or weakly-cooling PBL cloud still use Louis et al st order scheme Strongly cooling PBL cloud. Unstable surface layer (shown here added to cloud top -driven K zz.) Lock et al turbulence scheme. Invoked in unstable or cloud-topped PBLs. (obtained from S.A. Klein) GEOS-5 extension: unstable surface parcel calculation includes moist heating and entrainment. Non-locally determined K-profiles via test parcels PBL Scheme Physics tuning for Eros tag Goals: Acceptable LWP vs SSMI Acceptable SWCF vs ERBE Near cancellation of SWCF and LWCF in convective zones Global mean precip not too high (~ or < 3.2 mm/d all year) Ratio of ERBE SWCF (W m -2 ) to SSMI LWP (g m -2 ) (DJF) effective clouds - layers? Ratio of ERBE SWCF (W m -2 ) to SSMI LWP (g m -2 ) (DJF) Less effective -low-fraction cumulus? Physics tuning for Eros tag Goals: Acceptable LWP vs SSMI Acceptable SWCF vs ERBE Near cancellation of SWCF and LWCF in convective zones Global mean precip not too high (~ or < 3.2 mm/d all year) Tweak autoconversion rates, fractions passed to radiation, PBL mixing strength (surface vs entrainment??) Physics tuning for Eros tag Goals: Acceptable LWP vs SSMI Acceptable SWCF vs ERBE Near cancellation of SWCF and LWCF in convective zones Global mean precip not too high (~ or < 3.2 mm/d all year) Tweak effective radii for anvil ice, ice cloud formation, Physics tuning for Eros tag Goals: Acceptable LWP vs SSMI Acceptable SWCF vs ERBE Near cancellation of SWCF and LWCF in convective zones Global mean precip not too high (~ or < 3.2 mm/d all year) Adjust PBL mixing strength GEOS-5 LWP and SWCF (DJF) LWP SWCF model Obs. Diff. LWCF vs SWCF for DJF over SST>301K (black=model, red=ERBE) Cross section of simulated fraction at 15S (June 1995) longitude Andes pressure (hPa) Dateline ~50% GEOS-5 (Eros tag) precipitation model Obs. Diff. DJF JJA Future Plans for Model Physics Single column model framework (SCM) will be used as platform for development and testing GEOS-5 SCM and full AGCM are the same code ARM, SCSMEX forcings implemented SCSMEX runs to be compared with GCE CRM (with W. K. Tao) SCM driven with parameterized dynamics rather than observed dynamical tendencies (proposed to MAP w/ Adam Sobel, Brian Mapes) SCM driven with CAM3-derived forcings (through CPT with M. Zhang) comparison with +2K,-2K SST AGCM runs Parameterization Swaps, e.g., McRAS (Sud and Walker) - already implemented and run both in SCM and 3D SCM is available to MAP investigators (support is manpower limited). Running at MSFC (Pete Robertson and co.) Current picture: Successive clouds w/ variable entrainment rates. No direct effect on each others environment. Deepest clouds => most weakly entraining clouds Alternative picture: Successive clouds w/ large entrainment rates. Environment for later clouds incorporates in-cloud profiles from earlier clouds. Deepest clouds => later and luckier clouds Environments to be constructed by sampling cloud PDFs. Most of the important details, e.g. vertical correlation lengths, still need to be worked out (proposed to MAP). CRM results (Tao) could provide guidance. Using cloud information to construct convective environments (with Brian Mapes) Improved gravity wave parameterization will include estimates of wave induced temperature and w perturbations (w/ Byron Boville, Steve Eckermann) Lee wave clouds over Wales, ~ 1000 m orography Overall importance in ERB not clear, but conceptually simple to incorporate. Non-orographic GW also considered. What we would like * User friendly data sets describing collocated TOA radiative fluxes, cloud condensates (at least paths - separately for ice and liquid), cloud fractions Information on subgrid distribution (climate grid ~50 to 100 km) and vertical structure of cloud quantities -from CRMs -from high resolution satellite measurements Collaboration/input from observationalists on best ways to compare model output with measurements, e.g., ISCCP simulator *No doubt much or some of this already exists. Appendices Shorter term plans for Moist Physics Adopt more realistic PDF description - replace boxcar with more realistic shape - spread not tied to RH, but to turbulence, shear etc.. - introduce subgrid temperature and vertical motion variability - more realistic treatment of cloud overlaps for precipitation and radiation (e.g., independent column approximation w/ Lazaros Oreopoulos) Update microphysics - replace Sundquist autoconversion formulations - improve w-model for convective updrafts - incorporate aerosol physics into cloud microphysics - improve ice and mixed phase microphysics Modify convection parameterization - use cloud information in specifying environment for convection (w/ Brian Mapes) Future Plans for Turbulence and Model Physics in General No immediate plans for major changes to turbulence scheme, but current scheme will probably require additional tuning. Parameter estimation using data assimilation techniques (w/ Peter Norris and A. DaSilva) Prognostic cloud scheme SOURCES: Total new LS condensate at each step from a single ramped RH-based PDF Convective mass and condensate fluxes Newly-formed ice and liquid phase condensate partitioned according to same polynomial ramp in T, for both LS and conv source. SINKS: Autoconversion with Sundquist-type formulas for loss rate (also used in RAS) Settling/Sedimentation of ice-phase condensate with terminal velocities from Lawrence and Crutzen (1998) Scavenging/Accretion by precipitation All precipitation is disposed of in prognostic cloud scheme. Three separate showers considered: 1) updraft-core, 2) anvil stratiform and 3) large-scale. Same but separately tunable re-evaporation formulation used for all 3. Prognostic cloud scheme Two phases Newly-formed condensate initially partitioned ~(T-T 0 ) 3 between -40 o C and 0 o C. RH calculated based on Freezing continues as long as T