Chelle L. Gentemann & Peter J. Minnett Introduction to the upper ocean thermal structure Diurnal models M-AERI data Examples of diurnal warming Conclusions A physics based empirical model of diurnal warming in the skin layer

What is a daily SST? Foundation SST Sunrise 3 2PM Diurnal warming aliased onto climate time series POES AQUA TRMM 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.

Webster Clayson 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:

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

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

MeanSTD NO correctionDay – Reynolds Night – Reynolds Shape correctionDay – Diurnal - Reynolds Night – Diurnal - Reynolds Inst. Insol correction Day – Diurnal_New - Reynolds Night – Diurnal_New - Reynolds

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

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 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 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 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 ComparisonMean Bias (K)STD (K) Number Obs PWP-MAERI PWP_2-MAERI PWP_3-MAERI CG-MAERI ASM_bulk – MAERI ASM_skin – MAERI

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!