Chelle L. Gentemann & Peter J. Minnett

Introduction to the upper ocean thermal structure

Diurnal modelsM-AERI dataExamples of diurnal warmingConclusions

A physics based empirical model of diurnal warming

in the skin layer

What is a daily SST?

Foundation SST

Sunrise

3 K @ 2PM

Diurnal warming aliased onto climate time series

0530

0730

13300830

POES

AQUATRMM

1.5 K

In situ observations of diurnal warming in the skin layer

• Blending satellite SST observations taken at different local times necessitates a model of diurnal warming valid at infrared and microwave retrieval depths

• Validation using buoys or blending buoy and satellite data requires a model to couple the two depths together

• Few measurements of diurnal warming with skin temperatures exist

• Most research / model development use in situ observations extrapolated from 0.5m or 1.0 m to skin layer

Lukas

Lukas (1991). “The diurnal cycle of sea surface temperature in the western equatorial Pacific.” TOGA notes.

1.75 14.67

2

cloud fractionf

Cf

SSTLukas u

C

Webster Clayson

daily average wind speed

with Q=0 and P = 0 the equation reduces to:

SST (Q ,u,P ) aQ bP cln(u ) dQ ln( u ) eu fWebster max max max

Q insolation ; u

SST (Q ,u,P ) c ln( u ) eu fWebster max

Webster, P. J., C. A. Clayson, et al. (1996). “Clouds, radiation, and the diurnal cycle of sea surface temperature in the tropical western Pacific.” J. Climate 9: 1712-1730.

Kawai2 2

daily peak insolation daily average wind speed

with Q=0 the equation reduces to:

SST (Q ,u ) aQ bln( u ) cQ ln( u ) dKawai max max max

Q ; u

SST (Q ,u ) b ln( u ) dKawai max

Kawai, Y., and H. Kawamura, Evaluation of the diurnal warming of sea surface temperature using satellite-derived marine meteorological data, J. Oceanogr. 58, 805-814, 2002.

CG

0

4 2 0 449 632 10 1

insolation local time wind speed ; truncated Fourier Series

as wind increases, equation approaches 0

Q <Q equation = 0

t t . uSST ( t ,Q,u ) f ( t )[(Q Q ) . (Q Q ) ]etmi o o

Q ; t ; u f ( t )

,

Gentemann, C. L., C. J. Donlon, et al. (2003). “Diurnal signals in satellite sea surface temperature measurements.” Geophysical Research Letters 30(3): 1140.

Mean STD

NO correction Day – Reynolds +0.06 0.638

Night – Reynolds -0.12 0.625

Shape correction Day – Diurnal - Reynolds -0.11 0.621

Night – Diurnal - Reynolds -0.12 0.625

Inst. Insol correction

Day – Diurnal_New - Reynolds -0.12 0.618

Night – Diurnal_New - Reynolds -0.12 0.625

ASM

Stuart-Menteth, A., I. Robinson, C.J. Donlon, (2006) Sensitivity of the diurnal warm layer to meteorological fluctuations. Part 2: a new parameterization for diurnal warming

ASM

Stuart-Menteth, A., I. Robinson, C.J. Donlon, (2006) Sensitivity of the diurnal warm layer to meteorological fluctuations. Part 2: a new parameterization for diurnal warming

ASM

Stuart-Menteth, A., I. Robinson, C.J. Donlon, (2006) Sensitivity of the diurnal warm layer to meteorological fluctuations. Part 2: a new parameterization for diurnal warming

2min

1 min min2min

( )( ) * ( ), (0 )

(0 )D

t tT t cgrad t t t

t

min

2min

2 12 min2min

( )( ) ( ), ( 12)

(12 )D t

t tT t T t t

t

min max 12

2max

3 12 2max

( )( ) ( ), (12 )

(12 )D t t t pm

t tT t T T t t

t

min

min

24 24

4 24 2

( ( ) )( 24)( ) ( ), ( 24)

(24 )D pm t

D t pmpm

T t T tT t T t t

t

ASM

Stuart-Menteth, A., I. Robinson, C.J. Donlon, (2006) Sensitivity of the diurnal warm layer to meteorological fluctuations. Part 2: a new parameterization for diurnal warming

ASM

PWP Physical Model

1.51 2

4

1

1.52

2 2; ;

( * )

i p lp

l w w

sw sw

lw sb lw

r lw sensible latent

S ac sw r

ac ac

T

S

Sw

R c a gCd Cd c

a g R

Q q

Q T q

Q Q Q Q RF

I q fxp Q Q t

I t

Cd ID

I

Cd IT

I

The simplified PWP developed by the TOGA-COARE group was utilized. (No seasonal entrainment of cool ML water).

The main modification to their code was to change the reset of all variables from midnight to 6AM.

Accumulated wind stress, radiative forcing, and warming were all reset to zero at midnight, since warming may persist well beyond midnight, I changed this to 6AM.

Model assumes instantaneous mixing

PWP Physical Model

For the sensitivity studies each model run used constant wind speed throughout the run, and short wave radiation was realistically varied throughout the day using geometrically calculated insolation.

PWP Physical Model

Model run at different latitudes on Jan 1. Running the model from -80 to 80 latitude with geometrically calculated insolation encompasses all lengths of day.

PWP Physical Model

Skin observations of diurnal warming at high latitudes, allowing examination of how the changing length of day will affect the shape and amplitude of diurnal warming don’t exist.

The objective of this research is to test the PWP, possibly improve PWP using data, then extend our knowledge of diurnal warming at high latitudes using PWP to create an empirical model based on length of day, hours from dawn, wind speed, and insolation.

Explorer of the Seas

M-AERI

• Measures sky, sea, reflected radiance

• IFOV = 1.3deg (few square meters at sea surface)

• Observation of skin SST every 10 minutes

• Very accurate (<0.1K), traceable to NIST standards

Cruise tracks

• Weekly cruises on alternating tracks

• Most daytime spent in port, but to-from destination provides open ocean daytime observations

• 2001 - present

Lag-correlations• The insolation is

positively correlated, with a peak lag-correlation at 50 minutes, correlation rapidly diminishes after 100 minutes

• Wind is negatively correlated with a peak lag-correlation 30-40 minutes, correlation diminishes after 120 minutes

Comparison of models

PWP testing

PWP2

• Changed solar absorption to 9-band model

• added function (cos) to account for angle of sun during day

PWP3

• 6 equations, weighting shifts between EQ based on wind speed

Ward SkinDeEP profiler data

PWP3

• 6 equations, weighting shifts between EQ based on wind speed

PWP testing

Statistics

Comparison Mean Bias (K) STD (K)

Number Obs

PWP-MAERI -0.09 0.41 3199

PWP_2-MAERI -0.02 0.40 3199

PWP_3-MAERI -0.03 0.35 3199

CG-MAERI -0.07 0.36 3199

ASM_bulk – MAERI 0.03 0.48 3199

ASM_skin – MAERI 0.09 0.48 3199

Conclusions

• PWP with new absorption profile and better temperature profile is able to match both the empirical TMI model and the Explorer data

• Retains heat too long in afternoon and responds too slowly to changes in wind/insolation

• Accurate enough to use in development of new empirical model taking into account length of day

Future work

• Develop new model – problems with constant wind in sensitivity models, force with realistic wind patterns(?) to develop model?

• Since PWP can return DV at any depth, explore through comparisons to buoy data the 1-m temperature, the new profiles will affect this observation

What to do ?

• Suggestions of using models to calculate l4 DV, Fairall, PWP, K-T ?

• Cloud?

• Sensitivity to errors in input parameters!