270 likes | 295 Views
Boundary Layer parametrisation in the climate model ECHAM5-HAM. Colombe Siegenthaler - Le Drian , Peter Spichtinger, Ulrike Lohmann. CFMIP/GCSS Boundary Layer WG Workshop, 8 th -12 th June, 2009. Layout. Aim:.
E N D
Boundary Layer parametrisation in the climate model ECHAM5-HAM Colombe Siegenthaler - Le Drian, Peter Spichtinger, Ulrike Lohmann CFMIP/GCSS Boundary Layer WG Workshop, 8th-12th June, 2009
Layout Aim: Improve the representation of the boundary layer in order to study the interaction aerosols – clouds and its impact on the future climate in the GCM ECHAM5-HAM Introduction ECHAM today – problems - solution diffusion on moist conserved variables explicit entrainment parametrisation
LES study N N LWP drizzle drizzle Sensitivity of LWP with CCN related with the entrainment rate, and relative humidity above the cloud A. Ackermann et al., 2004, ‘The impact of humidity above stratiform clouds on indirect aerosol climate forcing’, Nature)
ECHAM5-HAM : Standard • Turbulent diffusion on non-conserved variables • T63 x L31, Δt = 15 min • TKE model with prognostic TKE • Moist Richardson number, modified Blackadar mixing length (Brinkop, 1995) • Statistical cloud cover scheme (Tompkins, 2002) • 2 moment cloud microphysical scheme coupled with double moment aerosol scheme (Lohmann, 2008)
Diffusion on conserved variables Standard version Diffusion on moist conserved variables ql qv ql qt
Retrieve the condensate from the PDF Statistical cloud scheme (Tompkins, 2002) Grid box variables qt PDF liquid _ __ corresponds qt , qc , cc , ... vapour does not correspond a b qsat(T) Variance threshold :C a b a b <C =C standard version : 10 % C : “tuning parameter” moist version : 0.1 %
Total cloud cover ISCCP cloud cover [%] reduced minimal width PDF (RED) , C =0.1 % standard (C =10 %)
Total cloud cover - C = 0.1 % reduced minimal width PDF (RED) diffusion on moist conserved variable (CMO) cloud cover [%] difference CMO-RED difference cloud cover [%]
TKE scheme robust on resolution ? 3 hours simulation of a nocturnal Stratocumulus, idealisation based on ASTEX campaign TKE [m2s-2] very high resolution (LES) SCM resolution L31 In ECHAM5 In the low resolution experiment, the structure of the TKE is not well represented, particularly at the cloud top Lenderink et al.,1999,‘Evaluation of the Kinetic Energy Approach for modeling turbulent fluxes in stratocumulus’)
= Criterion for onset explicit entrainment S. Klein & D. Harmann, 1993, J. Climate) explicit entrainment if: positive subsidence region cloud is present cloud top below 700hPa low tropospheric stability () > 15 K 20 K frequency of occurrence of the onset criterion over one year [%]:
___c ___d ω'χ' ω'χ' 11 Mean fluxes above cloud/clear sky • buoyancy flux:
12 Entrainment parametrisation Replace at the top of the boundary layer (i=interface) Turton and Nicholls (1987) entrainment parametrisation: Buoyancy production into the TKE equation :
Resulting profiles - SCM SCM version of ECHAM5, averaged over 1 hour of simulations for the ASTEX case 13
Effect of entrainment on LWP, SCM First results with NT87 entrainment parametrisation promising! Same figure as Ackermann (2004), but with SCM version of ECHAM5, averaged over 1 hour of simulations for the ASTEX case
Diurnal cycle, SCM TKE [m/s] Cloud cover [%] Cloud liquid [kg/kg¨] STD , CMO Dec06/nbhbv1 Dec06/nbhbv1 Dec06/nbhbv1 we NT Local Time Local Time Local Time
Outlook Turbulent diffusion done on moist conserved variables. With a reduction of the minimal width of the PDF, the turbulent diffusion on moist variables shows an improvement in Scu regions. Our TKE model with coarse resolution do not represent entrainment satisfactory Turton and Nicholls (1987) explicit entrainment rate has been implemented only over cloudy grid box, we first observe a reduction of the LWP in SCM study. Diurnal cycle of and structure of TKE and cloud cover better represented in a SCM study because of the addition and competition of LW radiative cooling and entrainment in the buoyancy flux in SCM study.
Cross-section-Very Preliminary results we NT CMO Radiative cooling contribution in TKE only when explicit entrainment active Cloud cover [%], JJA 2000 we NT Radiative cooling contribution in TKE once a cloud is present
LES study Sensitivity of LWP with CCN related to the decoupling induced by the interaction between drizzle inhibition-SW warming-reduced sensible heat flux and entrainment rate I. Sandu et al., 2008, ‘Aerosol Impacts on the Diurnal Cycle of Marine Stratocumulus’, AMS)
drizzle LWP LWP 144 Number of aerosols U. Lohmann, 2007, ACP drizzle , entrainment ( ) Now in ECHAM (without entrainment parametrisation) In nature
Mesured entrainments ~ 10-3 [ms-1] The actual scheme produce equivalent entrainment rates 3 orders of magnitude too small Caldwell et al., 2005,” Mixed-Layer Budget Analysis of the Diurnal Cycle of Entrainment in Southeast Pacific Stratocumulus”, J. Atm. Sci.
Entrainment parametrisation - future improvements Replace at the top of the boundary layer (i=interface) Several entrainment parametrisations form: Turton & Nicholls (1987) Adaptation of Bretherton (2007) Lock (1998)
In ECHAM5... 144 (MODIS) Too strong sensitivity of the increasing LWP with increasing AOD U. Lohmann, 2007, ‘Global anthropogenic aerosol effects on convective clouds in ECHAM5-HAM’,ACP)
Eddy diffusivities – standard/equivalent Version with entrainment :
What does ECHAM do? ECHAM5-strat ECHAM5-conv ECHAM5-apc (zonal mean) ISCCP surf. obs. The stratocumulus are not well resolved in ECHAM U. Lohmann, 2007, ‘Global anthropogenic aerosol effects on convective clouds in ECHAM5-HAM’,ACPD)
Diffusion on conserved variables - 3D Sundqvist scheme Difference of annual mean total cloud cover (MOIST-REF)[%]
TKE scheme..... 1.5 order closure introduction of a TKE equation m states for momentum, h for heat : turbulent kinetic energy (TKE) computed prognostically for each time step (conservation equation) : mixing length, depends on the stability, vertical position ~“ measure of the average distance a parcel moves in the mixing process that generates flux”
Vertical cloud cover vertical cloud cover (%) averaged over jja Method seems to find clouds in the good locations even without perfect profiles