Exploiting the role of the Convective PBL for Local Land-Atmosphere Coupling Studies Joseph A. Santanello, Jr. ESSIC – University of Maryland & NASA/GSFC Hydrological Sciences Branch 21 September 2005

Outline Background: The CBL in LoCo Coupling Diagnostics Remote sensing of CBL properties Incorporating CBL observations into LoCo

Overarching goal: Accurately understand, model and predict the role of local land – atmosphere coupling in the evolution of land-atmosphere fluxes and state variables, including clouds. Science Questions: Are the results of PILPS, GSWP, or data assimilation experiments affected by the lack of land-atmosphere coupling? Can we explain the physical mechanisms leading to the coupling strength differences found in GLACE or other coupled NWP/climate experiments? Are there observable diagnostics that quantify the role of local land- atmosphere coupling? LoCo Motivation

NASA model and observation products for the study of land-atmosphere coupling and its impact on water and energy cycles C. D. Peters-Lidard, W-K. Tao, M. Bosilovich, M. Rodell, and W. Lau (NASA/GSFC), J. Santanello (UMDCP/ESSIC), J. Chern (UMBC/GEST) Objectives ( ): –Study factors controlling Land-Atmosphere Coupling (LAC) and the effect of this coupling on efforts to assimilate NASA observations into water and energy cycle prediction systems. –Develop a suite of modeling and observational products to study the strength of local LAC, and provide these products to the developing GLASS “Local Coupled Action” (LoCo) project community and the NEWS science team. Community Deliverables (2007): –Boundary and initial conditions for LoCo sites extracted from the Goddard fvGCM/MMF, used to drive a Single Column Model (SCM) coupled to a suite of land surface models. –Source code for the coupled Land Information System (LIS), with the combined Weather Research and Forecasting Model (WRF)/Goddard Cumulus Ensemble (GCE) models. –Data assimilation modules, which have been under development at NASA by Houser, Zhan, Reichle et al. (soil moisture), Rodell et al. (snow), Bosilovich et al. (skin temperature). NASA Funding for LoCo Development

Exploiting CBL Diagnostics for LoCo PBL Height 12 UTC 20 UTC Mixed Layer Free Atmosphere Residual Layer 17 14

CBL Budget Terms h 12 UTC Entrainment Surface sensible heat flux Advection RFD

CBL Diagnostics –Maximum CBL Height (h) ·responds directly to the flux of heat into the PBL –Atmospheric Stability (  ) ·d  /dz: Initial (morning) change in pot. temperature with height –Soil Water Content (w) [or Evaporative Fraction (EF)] ·controls partitioning of surface fluxes –Change in 2-meter potential temperature (  2m ) ·sensitive to heat input and PBL height  h  i  f w → EF

Outline Background: The CBL in LoCo Coupling diagnostics –Observed (ARM-SGP) –Modeled (OSU-1D PBL) –Feedbacks (equilibrium vs. extreme conditions) Remote sensing of CBL properties Incorporating CBL observations into LoCo

ARM-SGP Central Facility (Lamont, OK) –Atmospheric data ·Radiosondes: 6:30am, 9:30am, 12:30pm, 3:30pm ·Profiles of temperature, humidity, pressure, and wind –Land surface data ·EBBR flow towers ·Surface meteorological data ·5 soil moisture probes (0-5 cm) ▪ Average of 3 surrounding sites + Lamont (~100 km) –132 days from JJA of 1997, 1999, and 2001

w (% vol)  (K m -1 )  2m (K)  q 2m (g kg -1 ) h(m) Stability Soil water content 2m Pot. temp 2m Sp. humidity ARM-SGP CBL Diagnostics Santanello, J. A., M. A. Friedl, and W. P. Kustas 2005: An Empirical Investigation of Convective Planetary Boundary Layer Evolution and Its Relationship with the Land Surface. J. Applied Meteorol. 44,

ARM-SGP CBL Diagnostics w (% vol)  (K m -1 ) R 2 = 0.85 Predictions of CBL height from observations of soil moisture and initial stability Santanello, J. A., M. A. Friedl, and W. P. Kustas 2005: An Empirical Investigation of Convective Planetary Boundary Layer Evolution and Its Relationship with the Land Surface. J. Applied Meteorol. 44,

Observed and Modeled CBL Diagnostics ARM-SGP (previous slide) OSU Simulations A coupled SCM (OSU; Mahrt and Pan 1984) can be used to simulate these relationships → 100 simulations covering full range of observed soil moisture and stability w (% vol) Predictions of CBL Height from OSU simulations agree well with observed (R 2 = 0.83)

Simulated CBL Diagnostics vs. LAI  (K m -1 ) w (m 3 m -3 ) Leaf Area Index = 0.5 Leaf Area Index = 6.0 h (m) How does vegetation cover affect the principle controls on CBL height? We can identify similar relationships for soils….

Feedbacks: Fluxes vs. Soil Moisture The main influence of CBL coupling is seen through changes in the atmospheric demand for ET…. A) For moist soils, a negative feedback on ET occurs due to CBL coupling.

Atmosphere-Limited Feedback w  q ml   HsHs HiHi hh  ml  q ml   Saturated Soil Begins Evaporating PBL growth maintains evaporative rate…..

Feedbacks: Fluxes vs. Soil Moisture B) For dry soils, a positive feedback on ET and drought occurs… Are offline LSM’s and data assimilation affected…?

ww  HsHs HiHi hh  ml  q ml  ,w   RL  Soil Water Decreases Below Dry Threshold Soil-Limited Feedback Dry soils promote the existence and persistence of a residual layer that acts to enhance PBL growth and soil desiccation

CBL height versus residual layer depth ·Existence of a residual layer supports significant CBL growth on the following day ·There is predictive ability and information in the structure of the CBL that determines flux equilibrium at the land surface Soil-Limited Feedback nw  hMin H s Max H s Days with RL Days without RL

Simulated ‘Vegetation Stress’ -For high vegetation cover, water can be transported from deeper soil layers and evaporation remains atmosphere-limited. -Evaporation becomes soil-limited more quickly for bare soils. OSU simulations of soil moisture-flux relationships with varying LAI

Outline Background: The CBL in LoCo Coupling Diagnostics Remote sensing of PBL properties –MODIS/AIRS temperature profiles –AIRS radiances Incorporating PBL observations into LoCo

Temperature Profile Retrievals in the CBL Remote Sensing now offers the ability to monitor conditions in the lower troposphere on diurnal timescales with unprecedented spatial and spectral resolution. MODIS: –Aboard Terra and Aqua –7 vertical levels below 600mb (36 bands) –5 km horizontal resolution –10:30am, 1:30pm local overpasses –High horizontal resolution but weak weighting functions in the PBL AIRS: –Aboard Aqua –8 levels below 600mb from (2085 channels) –~50 km horizontal resolution –1:30am/pm local overpasses –True ‘profiler’ of the troposphere Evaluation ● 44 clear days at SGP in JJA ● Temperature profile retrievals (L2) and cloud-cleared (L1B) radiances ● 2 complete soil dry-downs ● Daily CBL ranges from m.

Synthetic MODIS/AIRS Profile Retrievals Generated from nearest vertical levels of radiosonde observations to specific standard retrieval levels of each sensor (23 June 2001)

MOD-T 1130 UTC 2330 UTC 5 July UTC (12:55pm) 10 July UTC (11:35am) MODIS-Terra Evaluation MOD-T 1130 UTC 2330 UTC - MODIS simulates the free atmosphere and ‘mean’ lapse rate of temperature well. - Near-surface profiles are very similar to 1130 UTC soundings and no variation in the CBL. Theta (K) h (m) Profiles of potential temperature retrieved from MODIS compared with co-located radiosonde measurements at the ARM-SGP Central Facility

MOD-A 1130 UTC 2330 UTC MOD-A 1130 UTC 2330 UTC 5 July UTC (1:30pm) 10 July UTC (1:45pm) MODIS-Aqua Evaluation - MODIS responds to the heating of the mixed-layer (bend point ~4 km). - Spectral resolution is not sharp enough to capture mixed-layer structure of CBL height. Theta (K) h (m) Profiles of potential temperature retrieved from MODIS compared with co-located radiosonde measurements at the ARM-SGP Central Facility

AIRS-n 1130 UTC 2330 UTC AIRS-n 1130 UTC 2330 UTC 4 June UTC (4:40am) 27 July UTC (3:45am) AIRS-Night Evaluation - Overall initial lapse rate is captured well by AIRS. - Responds to stability in the mixed-layer (and presence of a residual layer). Profiles of potential temperature retrieved from AIRS compared with co-located radiosonde measurements at the ARM-SGP Central Facility

AIRS-d 1130 UTC 2330 UTC AIRS-d 1130 UTC 2330 UTC 15 June UTC (1:30pm) 28 July UTC (1:30pm) AIRS-Day Evaluation - AIRS has a negative temperature bias between 700 and 850 mb. - As a result, AIRS-Day cannot accurately represent the mixed-layer or CBL height. x x x x x Profiles of potential temperature retrieved from MODIS compared with co-located radiosonde measurements at the ARM-SGP Central Facility

Diurnal Evaluation of MODIS/AIRS - Bias correction for AIRS-Day is not uniform and depends on CBL depth and strength - There still is a signal of CBL structure and evolution in the retrievals. MOD-T MOD-A AIRS-d AIRS-n AIRS-d Pot. Temp (K) P (mb) MODIS/AIRS 1130 UTC 2330 UTC AIRS 1130 UTC 2330 UTC

12-hour changes in AIRS radiances (day - night) for three days in 2003 h w H s  Storage 7 July June June Diurnal Evaluation of AIRS

12-h changes in AIRS Radiances AIRS-d Radiances ● Correlations of the 3 main PC’s computed from 2085 AIRS channel radiance measurements and the and R 2 values of the multiple regression on CBL and LS properties. ● Following Diak et al. (1990’s) synthetic study using HIRS channel specifications to infer surface quantities from radiances. AIRS Level 1B Channel Radiances

AIRS-Day radiances in selected channels R 2 values and wavenumbers of ≤ 5 AIRS-d channel radiances used in linear multiple regressions with PBL and land surface variables AIRS Level 1B Channel Radiances Three channels in the ‘window’ region + 15  m CO 2 channels that peak near 2 and 14 mb (tropopause-level)

Observations of the CBL - Summary Results demonstrate how the CBL is strongly linked and feeds back upon the land surface. Initial remote sensing analyses indicate that: a) the structure of the CBL (profiles) b) the integrated diurnal cycle of surface fluxes (day – night) c) information on seasonal surface drying and warming of the troposphere …..can all be obtained via remote sensing → CBL information obtained from remote sensing using update, initialization, or assimilation (T, q) techniques may add information and improve models over a range of scales.

Outline Background: The CBL in LoCo Coupling Diagnostics Remote sensing of PBL properties Incorporating PBL observations into LoCo –Assimilation possibilities ?

Assimilation Activities in LoCo Land Surface assimilation –MODIS Snow Cover (M. Rodell and P. Houser), SWE –AMSR-E/TMI/Hydros Soil Moisture (R. Reichle and R. Koster) –Skin Temperature (M. Bosilovich) Screen-level assimilation –Incorporate relationships shown here (2m-temp change) –Examine relationship to profile data and assimilation PBL profile and radiance data –AIRS (day + night overpasses) –Bias in PBL temp, merged water vapor – (E. Fetzer; AIRS team) –Profiles will only improve going forward

Use retrieved profiles to initialize a SCM or regional coupled model (WRF) Preliminary Results: - Use AIRS-Night profiles to initialize the OSU 1-D model OSU simulations of potential temperature initialized using AIRS data compared with radiosonde measurements. Towards Assimilation of PBL Data

GrADS/DODS Server WRF GCE LSM Ensemble Noah, CLM2, Mosaic, HYSSiB, VIC LIS ESMF Coupled Uncoupled Global, Regional (Re-)Analyses or Forecasts Station Data Satellite Products NASA’s Land Information System ESMF Code and Documentation at Kumar et al., 2005, EMS, in press

LIS “Plugin” Design LIS driver Domain-plugin LSM-plugin CLM Noah VIC Mosaic HySSIB SSIB Forcing- plugin Lat/lon Gaussian UTM GEOS GDAS AGRM NLDAS ECMWF CMAP Persiann Huffman CMORPH Parameter-plugin soils LAI land cover elevation

Thank You ! GLASS – GABLS Panel NASA Energy and Water Cycle Study (NEWS) NASA Earth System Science Fellowship

Discussion Topics Issues: Radiation and Precipitation external forcing of SCM (RFD?) WRF not mentioned specifically in revised proposal (fvGCM) Common ‘coupler’ being established within ALMA initiative of GLASS? ELDAS: Coupling TESSEL + ISBA models to common SCM + assimilation procedure…status?

Radiative Transfer Model STREAMER (J. Key 2002) –2+ stream model, Discrete ordinates –20 cm -1 longwave spectral resolution; 24 shortwave bands –Flexible cloud, aerosol, and gas specifications Output: –Radiances or fluxes –Brightness temperatures/albedos –Up to 100 atmospheric levels ·Long and shortwave fluxes, R n, Heating/Cooling rates

Radiative Flux Divergence (RFD): Describes the net amount of heat added to or removed from the PBL through radiation processes (R n =SW  + LW  + SW  + LW  ) Previously assumed to be negligible for budget studies.  Use a Radiative Transfer Model to simulated RFD in the PBL for a range of conditions.

Results ·Large diurnal cycle ·RFD is up to 60% of total heat storage in the PBL ·RFD is larger for dry conditions RFD can be a significant contributor to the heat budget of the PBL RFD simulated by STREAMER for 5 select days in 1997 at Lamont, OK.

Empirical Approaches MODIS Brightness Temperatures ·Weighting functions for bands 27, 28, and peak at different vertical levels. ·Differences between brightness temperatures at the surface (31,32) and these levels are related to PBL structure. ·PBL height is correlated to changes in band differentials