Testing and Improving Pacific NW PBL forecasts

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1 Testing and Improving Pacific NW PBL forecasts Chris Bretherton and Matt Wyant University of Washington Eric Grimit 3Tier NASA MODIS Image

2 Testing and Improving Pacific NW PBL forecasts PBL-related forecast deficiencies in the real-time MM5 forecast system: Mean biases - Winds too strong and geostrophic, especially at night. - Temperatures too cool at night. Forecast busts due to excessive vertical mixing? - Fog episodes / forecasts dissipated fog too readily - Shallow winter cold layers/freezing rain episodes Note: PBL biases can be influenced by many physical parameterizations (land surface, radiation, clouds, deep convection), the integration of parameterizations in software, and model resolution, not just PBL/turbulence schemes.

3 Improving cloud representation in PBL and elsewhere could improve PNW forecasts Shallow cumulus treatment is critical in diurnal cycle simulation, for example recent improvements in ECMWF forecast model, Neggers et al. (2007). Stratocumulus physics strongly affects PBL depth and structure in part of the PNW domain. Other indirect effects

4 Background and interest in this issue Chris Bretherton s group at UW developed moist turbulence and shallow Cu schemes designed to improve the simulation of marine cloud-topped boundary layers and their radiative effects in global climate models. These schemes were first tested in MM5 using regional simulations with encouraging results. NE and SE Pacific (McCaa and Bretherton 2004 MWR) Oklahoma ARM site diurnal cycle (Zhu and Bretherton 2004 MWR) Implemented but not rigorously tested in WRF.

5 Background (continued) Our focus has been on their implementation in NCAR s CAM climate model, where they are just about to replace the current Holtslag-Boville and Hack schemes. In a CAM framework, these schemes produced reasonable temperature and wind profiles in the recent GABLS Arctic stable-pbl intercomparison (Cuxart et al. 2006). Goal: Use PNW as a regional modeling testbed to refine these parameterizations to work well both in CAM and WRF.

6 UW schemes Grenier-Bretherton (2001 MWR) moist turbulence scheme. TKE-predicting scheme; TKE transport in convective layers Mellor-Yamada-like downgradient diffusive transport of moistconserved variables θ l and q t Explicit entrainment closure w e = Ae 3/2 /λ b for convective layers. Diffusivity K e = w e z. Multiple turbulent layers allowed. Bretherton-McCaa-Grenier (2004 MWR) ShCu scheme Buoyancy-sorting bulk entraining-detraining plume (Kain-Fritsch-like). Cu-base mass flux closed on convective inhibition M eexp( c CIN / e) b 2

7 UW PBL testing in CAM

8 MM5 over NE Pacific (McCaa & Bretherton 2004 MWR) JJA 1987 Forced at boundary with time-varying ECMWF analyses 28 σ-levels (11 with σ > 0.8), x = 60 km. CCM2 radiation scheme (Dudhia unsatisfactory). Compared UW TKE+ShCu schemes with existing MM5 PBL schemes (no Grell ShCu, only KF deep convection).

9 JJA shortwave cloud forcing UW scheme decent. Other MM5 PBL schemes over-predicted NE Pacific cloud albedo. ERBE

10 Zhu and Bretherton (2005 MWR): MM5 with UW schemes vs. other PBLs at ARM SGP site. ECMWF boundary forcing for July levels (17 with σ > 0.8), x = 40 km Focus on mean diurnal cycle. Mainly p.m. shallow Cu. What about over land?

11 PNW MM5 forecast system Eric Grimit implemented UW schemes in Cliff Mass s MM5 in a PBL parameterization bakeoff based on 8 Aug-8 Nov 2004 using 12 km domain. UW wind speed best of all tested schemes (marginally better than control MRF scheme, much better than YSU) Temperature has ~1.5 K nighttime cold bias (not as good as YSU). No scheme yet decisively outperforms the control MRF scheme.

12 Issues We need to migrate systematic PBL testing in PNW regional forecast system into WRF. Detailed vertical profile information is required to understand biases. Eric Grimit also implemented YSU and UW schemes in 12 km WRF runs for Nov.-Dec Recent analysis of these runs suggests a WRF implementation bug in UW turbulence scheme. Here we compare the YSU runs with Hanford tower data

13 Hanford 400 tower Winds 20, 50, 200 and 400 Temperature 3, 30, 50, 100, 200, 250, 300, 400 Soil temperature -0.5, -15, -36 Hanford Tower

14 YSU temps are not bad except cold when model incorrectly fogs 12Z (0400LT)

15 Mean YSU vertical temperature profiles are also good (a little too diffused at night)

16 YSU winds have high bias at 20 m, but not at 120 m

17 Ways forward for Stable PBL Vertical profile data is essential. Hanford data has shown YSU skillful in winter and should be useful test for debugged WRF-UW too. Integrated data from SHEBA (Arctic basin) is also useful for testing. LES experiments could inform stable-pbl parameterization development. There is some encouraging work in stable sub-grid-scale parameterization for LES. The recent GABLS LES and SCM intercomparison (Beare et al. 2006, Cuxart et al. 2006) involved weakly stable arctic PBL. Operational models tended to do worse than research models. Prognostic-TKE schemes performed more similarly to LES.

18 Moving forward National PBL improvement effort, especially parameterization development, currently has little funding or manpower. There is interest from the WRF community (e.g. June 2007 PBL workshop). We need a unified approach to PBL treatment in forecast models. Improvements made in a regional model based on forecast skill scores may not work in a different regional climate. Chris Bretherton will shortly submit to NSF a proposal for unified PBL scheme development for CAM and WRF using the PNW forecast system as a test bed. We may try to adapt ideas from YSU scheme into UW moist turbulence scheme for stable conditions.

19 Moving forward (continued) Integrated vertical profile and surface energy balance information (in addition to surface met.) are key for PBL testing. However, new observational platforms need to be accompanied by comparable resources for data analysis. Examples and case studies where the PBL scheme is clearly implicated as the primary problem would be very helpful. These could be used to test new PBL schemes. Developing good tests for model components in isolation is critical, especially in the case of PBL schemes.