Air-sea flux distributions from satellite and models across

the global oceans

Carol Anne Clayson

Woods Hole Oceanographic Institution

Earth Observation for Ocean-Atmosphere Interactions Science 2014

Frascati, Italy29 October 2014

Overall goal of researchAir-sea interaction through surface fluxes of heat

and moisture, combined with other weather properties, across variety of spatial and temporal scales

Seeking to understand:The variability and extremes in air-sea fluxes of heat

and moisture in context of water and energy cycleHow distribution of fluxes varies of time, location,

differing weather and climate states

Using satellite data sets (ISCCP, SRB, SeaFlux, GSSTF, TMPA, HOAPS, GPROF) and MERRA

Current Satellite/Blended Datasets

• Goddard product: GSSTF3 Daily, 0.25°, input variables and turbulent fluxes;

satellite plus Ta from reanalysis 1988- 2008; global oceans No current plans to update again – funding issues

• IFREMER version 3 Daily, 0.25°, input variables, turbulent fluxes; satellites plus Ta from reanalysis Currently available: 1992 (1999 with QuikSCAT) – November 2009; global oceans

• Japanese Ocean Flux datasets: J-OFURO2v2 – Input variables, fluxes, radiation, satellites plus Ta from reanalysis

Daily, 1°, 1988 – 2005; global oceans Satellites, JMA model analyses

• HOAPS3.2 6-hourly, 0.5°, global oceans; input variables, precipitation; satellites July 1987 - December 2008

• OAFlux Daily, 1°, global oceans; blended using reanalysis, in situ, satellites July 1985 – current (monthly available back to 1958)

Near-surface air temperature and humidityRoberts et al. (2010) neural net techniqueSSM/I only from CSU brightness

temperatures (thus only covers 1997 - 2006)

Gap-filling methodology -- use of MERRA variability – 3 hour

WindsUses CCMP winds (cross-calibrated SSM/I,

AMSR-E, TMI, QuikSCAT, SeaWinds)Gap-filling methodology -- use of MERRA

variability – 3 hour SST

Pre-dawn based on Reynolds OISSTDiurnal curve from new parameterizationNeeds peak solar, precip

Uses neural net version of COARE Available at http://seaflux.org

1999 Latent Heat Flux

1999 Sensible Heat Flux

SeaFlux Data Set Version 1.0

Average effect of diurnal SST on fluxes

Clayson and Bogdanoff (2013)

Example distributions

Latent heat flux 1999

95th Percentile

Extremes in LHF

Extremes in winds

Extremes in Qs-Qa

Trends . . .

1998 2007

Difference between 2007 and 1998

Latent Heat Flux 95th Percentile Values

Changes in extremes in latent heat flux

Weather Regimes Example Use of ISCCP cluster weather

states (Jakob and Tselioudis 2003) Tropical convection and MJO

(Tromeur and Rossow, 2010; Chen and Del Genio, 2009)

Datasets: ISCCP Extratropical Cloud

Clusters (35N/S, 2.5°x2.5° 1985-2007, 3-hr)

SEAFLUX (1998-2007,0.25°x0.25° 3-hr), LHF/SHF/Surface Variables

Product Homogenization: Fluxes regridded and

resampled to ISCCP 2.5x2.5 ISCCP 3-hr used to assign a

daily class based on the most frequent cluster

More convection

Less convection

Decomposition of surface fluxes by weather state

Weather regimes result in distributions of fluxes with different mean and extreme characteristics

Associated with changes in means

Both wind speed and near-surface humidity gradients are particularly well stratified, though the latent heat flux means are less so Indicates potential

compensations

LHF SHF

Qs-Qa Ts-Ta

Wind Speed

Tropics

Tropics

Mid-Latitude North

Mid-Latitude North

Compositing methodology Conditionally sample data using weather state classification (WS1-

WS8; most convective to least convective)

Further sampled based on compositing index to evaluate low-frequency coupled variability

Use NOAA Climate Prediction Center (CPC) indices for ENSO and MJO

Examining diff erences in means can be decomposed as changes in class mean (A), changes in RFO (B), and covariant changes (C)

A B C

MJO Composites – Decomposition into Weather states

Decompose LHF into weather state means and relative frequency of occurrence (RFO)

Systematic variations of both weather state means and RFO with MJO index

Both variations contribute to total impact of a given weather state on mean energy exchange associated with MJO evolution

Example: Climate regimes

Composite MJO based on index strength not time-lagging

All three regions typically show increased evaporation during convective phase and decreased evaporation during suppressed phase

The Indo-Pacific region changes more wind-driven Eastern Pacific changes more near-surface moisture gradient changes But: EIO more coherent near-

surface moisture changes than WP

Convective

Neutral Suppressed

EIO70E-90E

WP130E-150E

EP130W-110W

ENSO Composites by strength

West Pacific (130E-150E) latent heating anomalies primarily driven by QSQA anomalies For MJO, near-surface wind

speed was also anti-correlated but it was stronger than QSQA

The East Pacific (130W-110W) LHF acts to damp the existing SST anomalies Unlike on MJO time scales,

wind speed and QSQA are positively correlated

EIO

WP

EP

El Nino

La Nina

MLD and surface flux effects on SST tendencies

The mixed layer depth is an important contributor to the observed surface heat flux tendency pattern.

EIO and WP: deeper ML in convective; EP: slightly deeper ML in suppressed

WP: LHF variability has roughly same effect on SST tendency throughout MJO. EP: LHF much higher effect on variability during convective phase

EIO: Even shallower ML in suppressed phase, but still large LHF due to Qs-Qa difference: LHF variability strongest effect during suppressed phase

Convective

Neutral Suppressed

EIO70E-90E

WP130E-150E

EP130W-110W

Deeper MLShallower ML

Deeper MLShallower ML

LHF variability roughly same effect

LHF variability very different effect

Summary Cloud-based weather states can be used to provide improved

understanding of surface energy flux variability

• Tropics: main contributor to latent heating is the trade cumulus regime: nearly highest mean LHF, most frequent weather state (clear sky has highest mean latent heat flux)

• Midlatitudes: main contributor to latent heating is the shallow BL cumulus (highest frequency and mean)

• Fair/foul weather: foul weather tends to have sharper peaks, fair broader distributions

MJO variability is particularly well decomposed using ISCCP weather regimes from convective to neutral and suppressed states

Different regions in the tropics show MJO and ENSO variability being driven by different processes

Even when total LHF differences are equivalent, if winds versus Qs-Qa effects (with resulting MLD differences) occur, changes in LHF different effects on temperature tendency For example: fair weather cirrus vs. marine stratus vs. clear

skies during suppressed conditionsBoth the weather state and the ML state affect resulting impacts on SST

Latent Heat Flux

Specific Humidity

Comparisons with CMIP4 models

Sea Surface Temperature

Winds

Winds

Correlation of variability: satellites, CMIP4

LHF and wind

LHF and humidity

LHF and SST With Natassa Romanou

Extremes in LHF

Extremes in winds

Extremes in Qs-Qa

Intercomparing products by weather state While there are systematic mean differences in products, the

anomalous changes between products (here, SeaFlux & OAFlux) are more closely aligned.

The differences here can be related to specific types of weather regimes OAFlux shows a slight increase in the latent heat flux associated with deep

convective conditions while SeaFlux shows a slight decrease. In broken stratocumulus conditions, SeaFlux indicates about a 20% change, nearly

2x that of OAFlux, again primarily from differences in near-surface moisture gradients