1. Graduate Course: Advanced Remote Sensing Data Analysis and Application Satellite-Based Tropical Warm Pool Surface Heat Budgets: Contrasts Between 1997/98 El Nino and 1998/99 La Nina Shu-Hsien Chou Department of Atmospheric Science National Taiwan University Objectives: Compare surface heat budgets, SST tendency (dT S /dt), deep convection (OLR), and zonal wind stress over tropical eastern Indian and western Pacific oceans between 1997/98 El Nino and 1998/99 La Nina 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, 1845-1858.

2. Outlines: Motivations GSSTF2 data Goddard Satellite-retrieved Surface Radiation Budget (GSSRB) Data Set GSSRB Derivation Validation of Solar Radiative Flux Validation of LHF, Ua, and Qa Spatial Distributions of 1997/98 El Nino – 1998/99 La Nina Differences of SST, OLR, Zonal Wind Stress, and Wind Speed over Tropical Warm Pool for October-March Spatial Distributions of 1997/98 El Nino – 1998/99 La Nina Differences of Net Solar Heating, Evaporative Cooling, Net Surface Heating, and SST Tendency over Tropical Warm Pool for October-March 1-D Ocean Mixed Layer Heat Budget Spatial Distributions of 1997/98 El Nino – 1998/99 La Nina Differences of Ocean Mixed Layer Depth and Solar Radiation Penetration through Ocean Mixed Layer Bottom over Tropical Warm Pool for October-March

3. Motivations: 1997/98 El Nino is one of strongest ENSO warm events took place in the tropical Pacific Ocean and a record-breaking SST warming occurred in the entire Indian Ocean (Yu and Rienecker 2000; Bell et al. 1999). During July–December 1997, SST had a negative (positive) anomaly in eastern (western) equatorial Indian Ocean; called Indian Ocean dipole mode. SST anomalies were large enough to reverse climatological equatorial SST gradient. Strong 1997/98 El Nino, which was followed by moderate 1998/2000 La Nina, has rapid onset and decay (McPhaden 1999, Bell et al. 1999, 2000). Unusual nature of both events have motivated many studies to investigate the physical processes responsible for the events, the relation of ENSO and Indian Ocean climate system, and various ENSO theories (e.g., Webster et al. 1999; Saji et al. 1999; Yu and Rienecker 2000, Wang and McPhaden 2001).

4. Version 2 Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF2; Chou et al. 2003) (1)* Latent heat flux (9) Total column water vapo (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 (1988-2000, combine all satellites) *Archive at NASA/GSFC DAAC: http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/hydrology/hd _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, 3256-3273.

5. 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 -- 0.98 x 0.622 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, 3256-3273.

6. 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)

7. 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 = 0.0144 u * 2 / g + 0.11  /u * Humidity roughness length: Z oq =  /u * [a 2 ( Z O u * /  ) b2 ]

8. 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 = 1 + 7 Z/L k = 0.40

11. 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

13. 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

14. 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: http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/hydrology /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, 7219-7228.

15. 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 = 502 - 0.464 T B - 6.75 W + 0.0565 WT B T s : Sea surface temperature (SST, K) T o : Mean SST (302K) W : SSM/I-total column water vapor (g cm -2 ) T B : GMS-5 IR brightness temp (11-  m, K)   : 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, 7219-7228. = S o  o  (  vis,  o ) S sfc

16. Daily variations of downward surface SW flux measured at the ARM Manus site (2.06°S, 147.43°E) and retrieved GSSRB from GMS-5 albedo measurements. Comparison is shown only for the period Dec 1999–Dec 2000. Units of flux are W m -2. bias = 6.7 W m -2 sde = 28.4 w m -2

20. 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)

22. CONCLUSIONS: 97/98 El Nino - 98/99 La Nina relative changes: Tropical eastern Indian Ocean: reduced F LH (weakened winds) during 97/98 El Nino is associated with enhanced F SW (reduced cloudiness); leading to enhanced interannual variability of F NET Tropical western Pacific: reduced F LH (weakened winds) is generally associated with reduced F SW (increased cloudiness), and vice versa; leading to reduced interannual variability of F NET Interannual variations of F NET and dT S /dt are weakly correlated, due to wind stress induced changes in ocean dynamics variations : 1.Reduce heat transport from Pacific to Indian Ocean via Indonesian throughflow during El Nino 2.Enhance heat transport from South to North Indian Ocean during El Nino 3.Change ocean mixed layer depth and solar radiation penetration 4.Change upwelling/downwelling strength near equator