Characterizing the response of simulated atmospheric boundary layers to stochastic cloud radiative forcing Robert Tardif, Josh Hacker (NCAR Research Applications Lab.) Howard Bondell, Justin Shows Montserrat Fuentes and Brian Reich (SAMSI)

Outline Part 1 (NCAR): Part 2 (SAMSI): Scientific problem – link between cloud & boundary layer variability General approach – ensemble simulations of idealized boundary layers Toolbox Numerical model (single column model) Observations (cloud properties) Statistical models Part 2 (SAMSI): Evolving cloud properties … a statistical perspective Stochastic model design From the simplified to the comprehensive

Scientific problem - concepts Atmospheric boundary layer Definition: “Part of the atmosphere directly influenced by the presence of the surface over “short” time scales” turbulent fluxes to/from atmosphere turbulence vertical transport of mean quantities e.g. momentum roughness net energy water Diurnal cycle (day) (night) Height free atmosphere residual layer mixed layer entrainment zone capping inversion stable boundary layer

Scientific problem - concepts Clouds & boundary layer variability Clouds interact with radiation Reflection/absorption of sunlight (shortwave radiation) - daytime Emission of longwave radiation – nighttime Height free atmosphere residual layer mixed layer stable boundary layer variability in boundary layer diurnal evolution → modulation of surface radiation (net energy) → modification of boundary layer turbulent energy transfer

Scientific problem - concepts Clouds show considerable variability Present or not Height Optical properties (dependent on macro- and microphysical cloud structure) Large uncertainties on cloud parameters in numerical weather forecasts Clouds greatly influenced by small(subgrid)-scale processes Not completely accessible from observations Not fully resolved in models stochastic cloud variability boundary layer variability

Scientific problem - goals Theme: Investigate the factors that limit the skill of current boundary layer predictions Questions: How strong is the link between cloud variability & variability in simulated boundary layer structure (focus on wind)? Any enhanced sensitivity to timing of cloudy periods? (w.r.t. boundary layer transitions) Any dependence on time scales characterizing cloud variability? Better understanding of variability in low-level jet characteristics Step toward characterization of model errors Reproduced from Hoxit (1975) Wind speed

Scientific problem - strategy How to characterize link between cloud & boundary layer variability? Complex system (non-linear interactions) Going beyond simple sensitivity experiments Numerical modeling / ensemble simulations Single-column model (SCM) w/ WRF parameterizations Surface-atmosphere interactions Boundary layer turbulence Radiative transfer radiation turbulence surface-atmosphere interactions

Scientific problem - strategy Numerical modeling / ensemble simulations Ensembles Members defined by realizations of stochastic model Wide spectrum of cloud variability Statistical characterization of output (sensitivity) Work within the Data Assimilation Research Testbed Ensemble simulation capabilities Analysis tools Poised for data assimilation experiments

Scientific problem - strategy Cloud variability defined with: Cloud presence Cloud height Liquid water path (LWP) z ql zt zb cloud water Obs. clouds cloud base height LWP Stochastic model Single column boundary layer model Radiation Wind solar thermal

Dataset (stochastic model development) Observations from DOE ARM/SGP/CART in Oklahoma Integrated ground-based instrumentation for measuring cloud properties State-of-the-art instrumentation Ceilometer (cloud detection, cloud base height) Microwave radiometer or MWR (liquid water path) Complemented by co-located: Upper-air soundings Cloud radar (MMCR) Surface obs. (T, Td, precipitation) soundings radiometer ceilometer cloud radar sfc. obs.

Dataset Most “up-to-date” MWR dataset (Turner et al.,2007) From April 2001 to December 2005 Temporal resolution: 20 sec. Focus on “fair-weather” days absence of precip. & deep convective clouds Divide dataset into: “coupled” (to surface) clouds (within boundary layer) “de-coupled” (from surface) (free atmospheric) clouds cloud base height LWP “coupled” Zi LCL night “de-coupled”

Experiments Start simple(“er”)… “de-coupled clouds” (no feedback) How is diurnal cycle of BL structure (temperature, wind) affected by: Cloud presence @ times w.r.t. “key times” in BL evolution → (morning transition, afternoon, evening transition …) Characteristic of cloud field (duration “ℓ” of cloudy “periods”) Cloud height variability - within cloudy period (nighttime) Cloud thickness variability - within cloudy period (daytime) Various combinations thereof… Large “parameter space”! Need large ensembles Feasible with SCM approach ℓ sunrise sunset height

Experiments LWP ~ 40 kg m-2 solar radiative flux @ surface day night spin-up LWP ~ 40 kg m-2

Experiments Sensitivity to cloud presence!

Now to Howard …

What’s next … Implement ARMA stochastic models into SCM Validate implementation w/ comparison of time series generated w/ SAMSI and NCAR codes Run ensembles of SCM simulations w/ stochastic uncoupled clouds Analyze results Statistical properties of ensemble output Characterize relationship to cloud statistics Link results to boundary layer predictability Go to more comprehensive stochastic models Impact? Coupled clouds…