Abl activities

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<ul><li> 1. Atmospheric Boundary Layer Activities in ESRL Wayne M. AngevineWith help from:the Surface and PBL theme teamSara Tucker Bob BantaOla PerssonJim WilczakChris Fairall </li></ul> <p> 2. Atmospheric Boundary LayerDiurnal Variation(a severe simplification) 2000Inversion 1500 Height (meters) Convective 1000Mixed Layer Residual LayerResidual Layer 500 Stable (nocturnal) LayerStable (nocturnal) Layer 0Sunrise NoonSunsetSunrise Adapted from Introduction to Boundary Layer Meteorology -R.B. Stull, 1988 3. Coastal Boundary Layer- warm to cold One of many2000other types of boundary1500 Inversionlayers Height (meters) Polar BLs areConvectiveMixed Layer stable for Layer1000 Residual longIntermediate Layer periods Some oceanic 500BLs are continuously 0Stable marine boundary Layer Stable (nocturnal) layerweakly convective Sunrise 4. Why do we care aboutthe boundary layer? Controls transport of pollutants and climate-forcing constituents Source of heat, water, and turbulence Vital for weather and climate prediction Location of aerosols, clouds, and pollutants Context for other measurementsAlmost any measurement or predictioninvolves the boundary layer 5. Critical scientific gapsGrand challenges Cloud formation and transitions within the boundary layer Concentration and transport of pollutants in realistically variable boundary layer structures Factors affecting the vertical transport of heat and matter during the boundary layer daily cycle Partitioning of various causes of climate changes in the polar regions Interaction between the boundary layer and atmosphere above (free troposphere) 6. Recent findingsCloud formation and transitions within the boundary layer Cloud effects on pollutant concentration and transport Aerosol effects on clouds Concentration and transport of pollutants in realistically variable boundary layer structures Stable boundary layer characterization Mixing depths over water Factors affecting the vertical transport of heat and matter during the boundary layer daily cycle Coastal pollutant transport Parameterizations for mesoscale models validation and development Automated BL height detection Partitioning of various causes of climate changes in the polar regions Arctic boundary layer characterization Interaction between the boundary layer and atmosphere above (free troposphere) Transitional boundary layers BL-top entrainment Links to larger scales 7. Mixing depths over the Gulf of Mexico and Galveston Bay Measurements from High Resolution Doppler Lidar on the Ronald H. Brown Reliability enhanced by multiple parameters aerosol backscatter turbulence intensity wind shear Key finding: BL over water is weakly convective shallow but not very shallow no diurnal cycle Backed up by sea surface flux and temperature measurements Key to interpretation of in-situ chemistry and aerosol measurements 8. Validation of diurnal cycle inforecast modelsUsing BL heights derived by an automated algorithm for 5 radar wind profilers in Texas Average over 52 days 9. Stable boundary layer structure Two types lead to different chemical and physical behavior Weak LLJ Stronger LLJ Closely tied to strength of low-level jet: Weak LLJ (&lt; 3 m s-1) - O3 0 6-7 m s-1 or more - [O3] stays &gt; 20 ppbSimilar effects on temperature and water vapor profiles 10. Stable boundary layer structure Decoupling of flow aloft from surface friction acceleration after sunset Happens every night to some degree Doppler lidar provides highly resolved wind and turbulence information A challenge to model parameterizations, and a framework for improving them 11. Arctic climate trends driven by boundarylayer processes Magnitude and sign oftemperature trends can varysignificantly over small spatialdistances - Red = positive - Blue = negative - size of dot proportional to trend magnitude Spatial variability of near-surface trends is caused byboundary-layer phenomenalinked to complex terrain and thepresence of coastlines 12. Arctic climate trends5Alert Base driven by4060214-060225T (deg C)boundary layer processes-343 Boundary layer structure during 2 downslope wind events at Alert (from rawinsonde data)1 Drastically different from mid-latitude 04648 5052 5456Year Day continental BL structure Descent of temperature and mixing ratio-17 MAM, 205-275(&gt;4.5 m/s), 1985-2006-17inversion top causes low-level warming S=+0.21; r2 = 0.35-19-19 and moistening-21-21Events descend from 2-4 km heights-23-23Relatively few events have a big impact-25-25 on annual averages-27-27 Will climate change affect the number of 0 510 152025 these events?Frequency of Wind Events (% time) 13. Cloud-aerosol feedbacks: Stratocumulus cloud form variability off Chile Most climate models do not make reasonable marine stratus clouds Present PBL models do not capture solid-broken transition Major effect on radiative input to sea surface and BL structure One Hypothesis: Aerosol drizzle coupling 14. Cloud-aerosol feedbacks Manifest in POCs (pockets of open cells)? POCsBretherton et al (2004) 15. What are we doing?Research to understand BL structure and its interactions with clouds, aerosols, chemistry, and larger-scale processes in maritime, mid-latitude continental, coastal, and polar environments to address climate, air quality, and weather prediction 16. Problems Issues OpportunitiesFaster progress through better coordination in parameterization development Model users may get ahead of us in some areas Making best use of past and future field projects and data Extending BL expertise to others within ESRL 17. ESRLs capabilities ObservationsChemistryAnalysis andPhysicsModels 18. How?Collect data during small and large field projects that last for a few weeks to many years Frequently utilize internally-developed, unique instrumentation Develop and test model capabilities Use observations and models to understand processes and interactions to plan future observations, instruments, and models 19. Validation of WRF BL and land surfaceschemes NoahRUC Looking at two BL schemes withtwo land surface schemes Nighttime Y Approx. 2 months in Oklahoma S Mean sensible heat flux agrees Uwith observations Lots of variation not captured bythe model Both sensible and latent heatfluxes overestimated in daytime (not Mshown) Y Jred thin obs, brown bold mean obs, blue thin models, black bold mean models 20. Stratocumulus cloud observations Much more variable than they look! EPIC 2001, , Ceilometer base (red), MMCR Top (black)4000 3203500 3153000 3102500 Height (m) 3052000 1500 3001000 295500 290010/1010/15 10/201025 Day Time height mapping of potential temperature ,ceilometer cloud base (red) and MMCR cloud top(black). The temporal resolution of the cloudboundaries is 10 min 21. Stratocumulus cloud form variability off Chile </p>