Graduate Course: Advanced Remote Sensing Data Analysis and Application Satellite-Based Tropical Warm Pool Surface Heat Budgets Shu-Hsien Chou Department of Atmospheric Science National Taiwan University Objectives: Study spatial distributions of surface heat budgets, related parameters, and SST tendency (dT S /dt) over tropical eastern Indian and western Pacific ocean during Oct 1997-Sep 2000 Study seasonal correlation between dT S /dt and surface heat budgets Chou, S.-H., M.-D. Chou, P.-K. Chan, P.-H. Lin, and K.-H Wang, 2004: Tropical warm pool surface heat budgets and temperature: Contrasts between 1997/98 El Nino and 1998/99 La Nina. J. Climate, 17,

Outlines: Motivations GSSTF2 data Goddard Satellite-retrieved Surface Radiation Budget (GSSRB) Data Set GSSRB Derivation Validation of Solar Radiative Flux Validation of LHF, U a, and Q a Spatial Distributions of 3-yr Annual Mean Surface Heat Budgets and Related Parameters over Tropical Warm Pool Spatial Distributions of 3-yr Seasonal Mean Net Surface Heating and SST Tendency over Tropical Warm Pool Spatial Distributions of Seasonal correlation between SST Tendency and Net Surface Heating, Solar Heating, and Evaporative Heating over Tropical Warm Pool 1-D Ocean Mixed Layer Heat Budget Spatial Distributions of Ocean Mixed Layer Depth and Solar Radiation Penetration through Ocean Mixed Layer Bottom

Motivations: Tropical Indian and western Pacific warm pool is a climatically important region; characterized by warmest SST, frequent heavy rainfall, strong atmospheric heating and weak mean winds with highly intermittent westerly wind bursts Heating drives global climate and plays a key role in ENSO and Asian-Australian monsoon (Webster et al. 1998) Small changes in SST of Pacific warm pool associated with eastward shift of warm pool during ENSO events have been shown to affect the global climate (Palmer and Mansfield 1984) TOGA/COARE was conducted with the aim to better understand various physical processes responsible for SST variation in the western Pacific warm pool

Version 2 Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF2; Chou et al. 2003) (1)* Latent heat flux (9) Total column water vapor (2)* Zonal wind stress (10) SST (3)* Meridional wind stress (11) 2-m temperature (4)* Sensible heat flux (12) SLP (5)* 10-m specific humidity (6)* 500-m bottom layer water vapor (7)* 10-m wind speed (8)* Sea-air humidity difference Duration: July 1987–Dec 2000 Spatial resolution: 1 o x 1 o lat-long Temporal resolutions: one day, and one month (Combine DMSP F8, F10, F11, F13, F14 satellites) Climatology: monthly- and annual-mean ( , combine all satellites) *Archive at NASA/GSFC DAAC: _gsstf2.0.html Chou, S.-H., E. Nelkin, J. Ardizzone, R. M. Atlas, and C.-L. Shie, 2003: Surface turbulent heat and momentum fluxes over global oceans based on the Goddard satellite retrievals, version 2 (GSSTF2). J. Climate, 16,

RETRIEVAL OF GSSTF2: (Chou et al. 2003) wind stress  =  C D (U–U s ) 2 sensible heat flux F SH =  C p C H (U–U s ) (  s –  ) latent heat flux F LH =  L v C E (U–U s ) (Q s –Q) U -- daily SSM/I-v4 10-m wind (Wentz 1997)  s -- daily SST (NCEP reanalysis) Q s x e s /P s (salinity, cool skin effect) Q -- daily SSM/I-v4 10-m specific humidity (Chou et al. 1995, 1997)  -- daily 2-m potential temp (NCEP reanalysis) stress direction -- SSM/I-v4 10-m wind direction (Atlas, et al. 1996) C D, C H, C E depend on U, (  s –  ) & (Q s –Q) (surface layer similarity theory) Chou, S.-H., E. Nelkin, J. Ardizzone, R. M. Atlas, and C.-L. Shie, 2003: Surface turbulent heat and momentum fluxes over global oceans based on the Goddard satellite retrievals, version 2 (GSSTF2). J. Climate, 16,

Latent Heat Flux (F LH ) F LH =  Lv C E (U–Us) (Qs–Q)F LH =  Lv C E (U–Us) (Qs–Q) C E depends on U, (  s –  ), and (Q s –Q)C E depends on U, (  s –  ), and (Q s –Q)

GSSTF2 BULK FLUX MODEL: (Chou 1993; Chou et al. 2003) * C D and C E depend on U, (  s–  ), & (Qs–Q) (Monin-Obukhov similarity theory) C D = k 2 /[ln( Z / Z O ) –  u ( Z/L )] 2 C E = C D 1/2 k/[ln( Z / Z O q ) –  q ( Z/L )] Momentum roughness length: Z o = u * 2 / g  /u * Humidity roughness length: Z oq =  /u * [a 2 ( Z O u * /  ) b2 ]

GSSTF2 BULK FLUX MODEL: (Chou 1993; Chou et al. 2003) wind stress  =  u * 2 sensible heat flux F SH = –  Cp u *  * latent heat flux F LH = –  Lv u * q * Flux –Profile Relationship in Atmospheric Surface Layer: (U – Us)/u * = [ln(Z/Z o ) –  u (Z/L)]/k (  –  s )/  * = [ln(Z/Z oT ) –  T (Z/L)]/k (Q – Qs)/q * = [ln(Z/Z o q ) –  q (Z/L)]/k  =∫(1 –  ) d ln(Z/L), L =  v u * 2 /(g k  v* ) Unstable:  u = (1 – 16 Z/L)  0.25,  T =  q = (1 – 16 Z/L )  0.5 Stable:  u =  T =  q = Z/L k = 0.40

1913-hourly fluxes calculated from ship data using GSSTF2 bulk flux model vs observed latent heat fluxes determined by covariance method of 10 field experiments. C: COARE F: FASTEX X: other experiments

GSSTF2 daily (a) latent heat fluxes, (b) surface winds, and (c) surface air specific humidity vs those of nine field experiments. C: COARE F: FASTEX X: other experiments

Goddard Satellite-retrieved Surface Radiation Budget (GSSRB; Chou et al. 2001) Surface net solar (SW) radiative flux Surface IR (LW) radiative flux Data source: GMS-5 radiances Domain: 40 o S-40 o N, 90 o E-170 o W Duration: Oct 1997-Dec 2000 Spatial resolution: 0.5 o x 0.5 o lat-lon Temporal resolution: one day Archive at NASA/GSFC DAAC: /hd_gssrb.html Chou, M.-D., P.-K. Chan, and M. M.-H. Yan, 2001: A sea surface radiation dataset for climate applications in the tropical western Pacific and South China Sea. J. Geophy. Res., 106,

Retrieval of GSSRB: (Chou et al. 2001) Surface net solar (shortwave) radiative flux F SW = (1-  sfc ) S sfc S o : Solar constant  o : Cosine of solar zenith angle   : Atmospheric transmittance  vis : GMS-5 albedo  sfc : Sea surface albedo (0.05) Surface IR (longwave) radiative flux F LW =  T s 4 -  F sfc F sfc = F o ( T s / T o ) 4 F o = T B W WT B T s : Sea surface temperature (SST) T o : Mean SST (302K) W : SSM/I-total column water vapor T B : GMS-5 IR brightness temp (11-  m)   : Stefan-Boltzmann constant  Emissivity of sea surface (0.97) Chou, M.-D., P.-K. Chan, and M. M.-H. Yan, 2001: A sea surface radiation dataset for climate applications in the tropical western Pacific and South China Sea. J. Geophy. Res., 106, = S o  o  (  vis,  o ) S sfc

Daily variations of downward surface SW flux measured at the ARM Manus site (2.06°S, °E) and retrieved GSSRB from GMS-5 albedo measurements. Comparison is shown only for the period Dec 1999–Dec Units of flux are W m -2. bias = 6.7 W m -2 sde = 28.4 w m -2

HEAT BUDGET OF OCEAN MIXED LAYER*: h  C P (∂T S /∂t) = F NET - f(h) F SW f(h) =  e -  h + (1-  ) e -  h (Paulson and Simpson 1977) (∂T S /∂t): SST tendency (K s -1 ) h:Ocean mixed-layer depth (m)  :Density of sea water (10 3 kg m -3 ) C P : Heat capacity of sea water (3.94 x10 3 J kg -1 K -1 ) F NET :Net surface heating (W m -2 ) F SW :Net surface solar heating (W m -2 ) f (h):Fraction of F SW penetrating h  :Weight for visible region (0.38) (1-  ):Weight for near infrared region  :Absorption coefficient of sea water for visible region (0.05 m -1 )  :Absorption coefficient of sea water for near infrared region (1.67 m -1 ) *Neglect horizontal advection of heat (due to small SST gradient and weak current) and entrainment of cold water from thermocline (barrier layer)

CONCLUSIONS: Magnitude of solar heating (F SW ; Wm -2 ) is larger than evaporative cooling (F LH ; Wm -2 ) Spatial variation is larger for F LH than for F SW ; thus the variability of net surface heating (F NET ) is dominated by F LH Ocean gains heat within ~10 o of equator of the warm pool, but loses heat poleward of 10 o Seasonal variations of F NET and SST tendency (dT S /dt) are correlated significantly (~0.7–0.9), except equatorial western Pacific due to large solar radiation penetration through shallow ocean mixed layer associated with weak winds