Testing and Improving Pacific NW PBL...
Transcript of Testing and Improving Pacific NW PBL...
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Testing and Improving Pacific NWPBL forecasts
Chris Bretherton and Matt WyantUniversity of Washington
Eric Grimit3Tier
NASA MODIS Image
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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.
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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
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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.
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Background (continued)
• Our focus has been on their implementation in NCAR’sCAM 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.
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UW schemesGrenier-Bretherton (2001 MWR) moist turbulence scheme.• TKE-predicting scheme; TKE transport in convective layers• Mellor-Yamada-like downgradient diffusive transport of moist-
conserved variables θl and qt• Explicit entrainment closure we = Ae3/2/λ∆b for convective
layers. Diffusivity Ke = we∆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
2exp( / )bM e c CIN e∝ −
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UW PBL testing in CAM
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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 GrellShCu, only KF deep convection).
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JJA shortwave cloud forcing
• UW scheme decent.
• Other MM5 PBL schemes over-predicted NE Pacific cloud albedo.
ERBE
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What about over land?• Zhu and Bretherton
(2005 MWR): MM5 with UW schemes vs. other PBLs at ARM SGP site.– ECMWF boundary
forcing for July 1997– 38 levels (17 with σ
> 0.8), ∆x = 40 km– Focus on mean
diurnal cycle. Mainly p.m. shallow Cu.
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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.
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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. 2005. Recent analysis of these runs suggests a WRF implementation bug in UW turbulence scheme.
Here we compare the YSU runs with Hanford tower data…
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Hanford 400’ tower
HanfordTower
• Winds– 20, 50, 200 and 400’
• Temperature– 3, 30, 50, 100, 200,
250, 300, 400’• Soil temperature
– -0.5, -15, -36”
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YSU temps are not bad except cold when model incorrectly ‘fogs’12Z (0400LT)
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Mean YSU vertical temperature profiles are also good(a little too diffused at night)
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YSU winds have high bias at 20 m, but not at 120 m
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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.
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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.
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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.