An evaluation of a hybrid satellite and NWP- based turbulent fluxes with TAO buoys ChuanLi Jiang, Kathryn A. Kelly, and LuAnne Thompson University of Washington Meghan Cronin NOAA/PMEL

Outline 1. Motivation 2. Scatterometer “raises the bar” 3. TAO buoy comparisons 4. Heat flux map comparisons 5. Applications

Motivation  Intra-seasonal heat budget important in ENSO and climate change (McPhaden, 2002; Kessler et al. 1995; Zhang, 2001)  Need accurate air-sea fluxes to force an ocean model  NWP winds and heat flux products have systematic errors  Satellite measurements provide accurate inputs for both momentum and turbulent heat fluxes  Can QuikSCAT winds and microwave SST improve turbulent heat flux products? Jiang, Cronin, Kelly and Thompson, under revision for JGR Jiang, Cronin, Kelly and Thompson, under revision for JGR

Scatterometer “Raises the Bar” on Vector Wind Measurements  Scatterometers revealed systematic 7 o direction error in TAO buoys  Difference between scatterometer winds and anemometer winds is ocean currents  Scatterometer comparisons show importance of using a scalar average for wind speed

Scatterometer winds wind vector relative to ocean surface

TAO - QuikSCAT winds = currents (ADCP) Kelly, Dickinson, McPhaden, and Johnson, GRL, 2001

Scalar Averaging for Wind Speed For LHF and SHF QuikSCAT winds converted to speed and then scalar averaged For LHF and SHF QuikSCAT winds converted to speed and then scalar averaged TAO 10-minute winds vector averaged to obtain “daily” winds (for ARGOS transmission) TAO 10-minute winds vector averaged to obtain “daily” winds (for ARGOS transmission) Comparisons with TAO10-minute winds show 4-day scalar average of QuikSCAT is more accurate than 4-day average of “daily” TAO wind Comparisons with TAO10-minute winds show 4-day scalar average of QuikSCAT is more accurate than 4-day average of “daily” TAO wind SCALAR average winds for fluxes (i.e., compute wind speed from observations and then average or map) SCALAR average winds for fluxes (i.e., compute wind speed from observations and then average or map)

Passive Microwave SST (TMI) microwave can see through clouds microwave can see through clouds 25km resolution 25km resolution 40S-40N 40S-40N MW/OI from Remote Sensing Systems MW/OI from Remote Sensing Systems

Method Bulk algorithm:  State variables used in COARE v3.0 algorithm  Most accurate state variables determined by comparison with TAO buoys  Turbulent heat flux products compared with TAO variables in COARE v3.0 algorithm

State variable evaluation  Relative wind speed  Sea surface temperature  Sea surface temperature SST  Air specific humidity  Air temperature

State Variable Sources State Variables State Variable Sources“Truth” QuikSCATMW/OINCEP1NCEP2ERA40TAO buoy 4-day 1 degree 6 hourly Gaussian 6 hourly Gaussian 6 hourly 2.5 degree hourly SST 3-day.25 degree daily 2 years: 2000 – 2001 Average all variables to 4-day resolution (QuikSCAT mapping) Scalar-average relative wind speed: ocean current from altimeter ( Kelly et al ) TAO Bulk SST (skin SST not available)

TAO buoys used in comparisons 38 buoys for 64 buoys for +

Wind speed NCEP1 too weak NCEP2 better than NCEP1 ERA40 better than NCEP QuikSCAT best, Higher along 165E & 8N Bias = Product -TAO

Histograms of wind speed in eastern Pacific best match NWP lacks high winds

Histograms of wind speed near ITCZ best match lacks low wind speed weak zonal currents or rain contamination

SST comparison Bias = product - TAO SDD = STD(product - TAO) NWP SST: warm in the cold tongue; cold off the equator MW/OI: consistently cold (but may be correct)

Air specific humidity ERA40 has best humidity Dry along 165E NCEP2 worse than NCEP1 Better along 8N NCEP too dry in the east too wet in the west

State variable evaluation summary Sources BiasSDDBiasSDDBiasSDDBiasSDD NCEP NCEP ERA MW/OI QuikSCAT.0.5 MW/OIERA40 QuikSCATHybrid

Sensitivity of LHF to state variable LHF(var(i) + all other TAO) - LHF(all TAO variables)

Summary of sensitivity of LHF to state variables LHF errors: 1) humidity 2) wind 3) SST All errors in W/m QuikSCAT MW/OI ERA NCEP NCEP1 SDDBiasSDDBiasSDDBiasSDDBias Products 32 1

Latent Heat Flux Products for evaluation against TAO/COARE Productsalgorithm NCEP1C NCEP1COARE NCEP2C NCEP2COARE ERA40C ERA40COARE Hybrid MW/OIERA40 QuikSCATCOARE

LHF comparison NCEP1C underestimates NCEP2C overestimates ERA40C good Hybrid best in the east overestimates along 165E,8N

LHF bias along 165E ERA40: low humidity compensates for weak winds  smaller bias Hybrid: low humidity + stronger winds  too strong LHF biasfrom

How do NWP products compare with using their state variables in the COARE algorithm?

LHF SDDBiasSDDBiasSDDBias ERA40NCEP2 NCEP1 NWP products LHF SDDBiasSDDBiasSDDBiasSDDBias Hybrid ERA40CNCEP2CNCEP1C Using COARE Difference: Algorithm + State variables + Temporal resolution of input variables Summary of LHF comparison Algorithm tuned to weak winds Same algorithm as NCEP1 COARE decreases bias

Map comparisons in the tropical Pacific

Wind speed map comparison NWP winds are weaker than QuikSCAT

NWP SST warmer in the cold tongue colder off the equator SST map comparison

LHF map comparison Hybrid LHF Larger than NWP/COARE Hybrid LHF is similar to NCEP1 off the equator

GOAL Role of downwelling Kelvin wave in ENSO variability. Method MODELHIM Turbulent Heat flux Hybrid product Momentum flux QuikSCAT Solar radiation Corrected ISCCP Application of Hybrid product to intra-seasonal heat budget in ocean circulation model in ocean circulation model

Summary QuikSCAT accuracy improves turbulent heat fluxes (scalar average)QuikSCAT accuracy improves turbulent heat fluxes (scalar average) LHF sensitive to specific humidity, wind speed, and SSTLHF sensitive to specific humidity, wind speed, and SST Differences in products from both state variables and bulk algorithm (NCEP1 vs. NCEP2)Differences in products from both state variables and bulk algorithm (NCEP1 vs. NCEP2) Improvement in LHF from wind speed offset by error in air specific humidityImprovement in LHF from wind speed offset by error in air specific humidity Problem areas for hybrid fluxes: ITCZ and warm poolProblem areas for hybrid fluxes: ITCZ and warm pool