Boundary layer parameterization and climate. Chris Bretherton. University of Washington

Transcription

1 Boundary layer parameterization and climate Chris Bretherton University of Washington

2 Some PBL-related climate modeling issues PBL cloud feedbacks on tropical circulations, climate sensitivity and aerosol indirect effect in all latitudes. Wintertime land surface temperature biases stable boundary layer cloud biases role of land surface vs. PBL PBL interactions with deep convection gustiness, enhanced surface flux over warm oceans cumulus cloud base properties diurnal cycle over land Surface wind stress ocean (warm pool, coasts, cold side of ocean fronts) complex terrain Optimal resolution tradeoff z and t vs. x- y.

3 PBL params improving (with largescale feedbacks); biases remain. z (km) EPIC2001 ECMWF NCEP AM2.10 CAM2.0 Bretherton et al. (2004) LWC (g/m 3 ) Yu and Mechoso (2001)

4 PBL cloud feedbacks on climate sensitivity Is every geographical region different? Lack of published physical mechanisms: (-) In warmer climate, adiabatic dlwc/dz larger, so PBL clouds of given thickness are more reflective (Somerville and Remer 1984). but not much evidence for such trends with T. Tselioudis and Rossow (1994)

5 (-) In warmer climate, steeper moist adiabat raises lower tropospheric stability, increasing low cloud cover (Miller 1997 based on Klein and Hartmann 1993). but how to apply Klein line to changed climate (or even its robustness in current climate) is questionable. Observed TOA net CRF moderately correlated to LTS. In CAM3, LTS increases ~2 K in 2xCO2 climate with little NCRF change.

6 Alternative LTS measures more climate-stable? Estimated Inversion Strength (Rob Wood) EIS = LTS - (z 700 -LCL)*(dθ/dz) moistad (700 mb) 700 LCL 1000 LTS = θ 700 θ 1000 EIS = θ + LCL,ma θ 1000 CF (Klein and Hartmann 1993) Pac N P Pac Atl P Cal N Pac Atl Atl Ch Cal Ch A P N A Cal Cal A P A Ch Ca CF Ch Ch Ca Pac Atl N Atl N P A Cal ACal A P A Pac N P PCal Pac Atl N Atl CalCh 20 Ch Ca Ca Ca 20 Ch Ca Ca Ca LTS [K] EIS [K] Collapses midlatitude vs. low-lat. Sc regimes better. EIS less sensitive than LTS to steeper moist adiabat predicts less low cloud feedback on climate sensitivity.

7 Anthropogenic aerosol-ctbl feedbacks Major IPCC uncertainty (current F TOA = 0-2 W m -2 ). CTBLs likely a major contributor. Manifest in POCs (pockets of open cells; Stevens et al. 2005)? Limited understanding of aerosol feedbacks on PBL cloud thickness, lifetime thru drizzle, radiative feedbacks. POCs Bretherton et al (2004)

8 Rob Wood Sandy Yuter POCS, cloud droplet size, and drizzle

9 35N GOES-10 Channel 1 Reflectance DYCOMS RF02 nocturnal drizzling stratocumulus 30N 125W 120W N d ~ cm -3 (Stevens et al. 2003; vanzanten et al. 2005)

10 GCSS RF02 SCM/LES intercomparison LES Extremely diverse dependence of Sc drizzle on LWP

11 High-latitude wintertime PBL AGCMs often have large high-latitude surface temperature biases. These can feed back on snowalbedo feedback and biogeochemistry. CAM3 DJF T2m bias Low cloud bias

12 Inadequate attention has been paid to understanding the role of stable BL turbulence vs. clouds vs. land-surface in these biases. Often addressed by large-ri tuning of stability functions, which may compensate other errors. NWP-mode testing of climate models would help! Sensitive to underresolution of PBL depth & terrain. Enhanced hi-ri mixing M-Y stability fns

13 PBL interactions with deep convection The PBL under precipitating deep convection is highly inhomogeneous, enhancing surface fluxes and complicating Cu parameterization. Kuang and Bretherton (2005)

14 Cu base properties Cold pool tail (Kuang and Bretherton 2005) Narrow range (±0.1K, 0.3 g kg -1 ) of cloudy updraft properties Moist (but not most buoyant) tail of PBL air forms Cu bases. Cold pools affect near-surface (but not upper PBL) air properties. Suggests prediction of PBL T/q PDF would help Cu parameterization.

15 Diurnal cycle biases (Yang and Slingo 2001) Satellite UKMO Unified Model Local time of peak precipitation Satellite shows early evening peak over land, early morning peak over ocean ITCZ. Models show late morning peak over land, midnight peak over ocean. Mainly Cu param, but maybe also PBL parameterization issue?

16 Wind stress in EPIC W cross-equatorial flow Raymond et al. (2004) aircraft obs. NCEP GDAS Shear in SBL Surface jet in CBL Observed dτ y /dy absent GCM resolution badly smears wind stress gradients

17 PBL wind stress errors II Momentum transport problems in stable warm-advection PBLs (Brown et al. 2005). u g EC ctrl M-O stab sonde

18 Resolution Vertical resolution is a concern for both stable and cloudtopped boundary layers. In GCMs, we typically screw up our climate when changing vertical resolution. The role of PBL processes in this is hard to disentangle, but likely significant. More GABLS/GCSS-style benchmark LES/SCM/regional model simulations (including katabatic flows over undulating terrain) are needed for us to better appreciate our discretization errors.

19 Some perspectives on these problems In the U.S. we have great field expts and data, but almost no full-time PBL parameterization developers and closely connected to major GCMs. This is a social crisis. Broad PBL issues (e. g. high latitude low cloud/land temperature biases) are going almost unstudied in the U.S. climate community. Cross-talk with NWP models and NWP-mode simulations are valuable strategies. These biases assume increasing importance as we couple more systems (e.g. plants, ice) into our models. We often explore horizontal resolution, but we must also routinely push GCMs to much higher vertical and time resolution to understand how well converged their PBL climatology is, even if we are afraid of the answers.