This paper describes the various physical processes relating near-surface atmospheric and oceanographic bulk variables ; their relationship to the surface fluxes of momentum, sensible heat, and latent heat ; and their expression in a bulk flux algorithm. The algorithm follows the standard Monin-Obukhov similarity approach for near-surface meteorological measurements but includes separate models for the ocean's cool skin and the diurnal warm layer, which are used to derive true skin temperature from the bulk temperature measured at some depth near the surface. The basic structure is an outgrowth of the Liu-Katsaros-Businger [Liu et al., 1979] method, with modifications to include a different specification of the roughness/stress relationship, a gustiness velocity to account for the additional flux induced by boundary layer scale variability, and profile functions obeying the convective limit. Additionally, we have considered the contributions of the sensible heat carried by precipitation and the requirement that the net dry mass flux be zero (the so-called Webb correction [Webb et al., 1980]). The algorithm has been tuned to fit measurements made on the R/V Moana Wave in the three different cruise legs made during the Coupled Ocean-Atmosphere Response Experiment. These measurements yielded 1622 fifty-min averages of fluxes and bulk variables in the wind speed range from 0.5 to 10 m s -1 . The analysis gives statistically reliable values for the Charnock [1955] constant (a = 0.011) and the gustiness parameter (β = 1.25). An overall mean value for the latent heat flux, neutral bulk-transfer coefficient was 1.11 x 10 -3 , declining slightly with increasing wind speed. Mean values for the sensible and latent heat fluxes were 9.1 and 103.5 W m -2 ; mean values for the Webb and rain heat fluxes were 2.5 and 4.5 W m -2 . Accounting for all factors, the net surface heat transfer to the ocean was 17.9 ± 10 W m -2 .

/pdf/bulk-parameterization-of-air-sea-fluxes-for-tropical-ocean-1mceyr1e2a.pdf

Bulk parameterization of air‐sea fluxes for Tropical Ocean‐Global Atmosphere Coupled‐Ocean Atmosphere Response Experiment

It has been over 10 years since the phenomenon of extensive coral bleaching was first described. In most cases bleaching has been attributed to elevated temperature, but other instances involving high solar irradiance, and sometimes disease, have also been documented. It is timely, in view of our concern about worldwide reef condition, to review knowledge of physical and biological factors involved in bleaching, the mechanisms of zooxanthellae and pigment loss, and the ecological consequences for coral communities. Here we evaluate recently acquired data on temperature and irradiance-induced bleaching, including long-term data sets which suggest that repeated bleaching events may be the consequence of a steadily rising background sea temperature that will in the future expose corals to an increasingly hostile environment. Cellular mechanisms of bleaching involve a variety of processes that include the degeneration of zooxanthellae in situ, release of zooxanthellae from mesenterial filaments and release of algae within host cells which become detached from the endoderm. Photo-protective defences (particularly carotenoid pigments) in zooxanthellae are likely to play an important role in limiting the bleaching response which is probably elicited by a combination of elevated temperature and irradiance in the field. The ability of corals to respond adaptively to recurrent bleaching episodes is not known, but preliminary evidence suggests that phenotypic responses of both corals and zooxanthellae may be significant.

/pdf/coral-bleaching-causes-and-consequences-5ewp5zv1gs.pdf

Coral bleaching: causes and consequences

The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system

I describe an algorithm for retrieving geophysical parameters over the ocean from special sensor microwave/imager (SSM/I) observations. This algorithm is based on a model for the brightness temperature T(sub B) of the ocean and intervening atmosphere. The retrieved parameters are the near-surface wind speed W, the columnar water vapor V, the columnar cloud liquid water L, and the line-of-sight wind W(sub LS). I restrict my analysis to ocean scenes free of rain, and when the algorithm detects rain, the retrievals are discarded. The model and algorithm are precisely calibrated using a very large in situ database containing 37,650 SSM/I overpasses of buoys and 35,108 overpasses of radiosonde sites. A detailed error analysis indicates that the T(sub B) model rms accuracy is between 0.5 and 1 K and that the rms retrieval accuracies for wind, vapor, and cloud are 0.9 m/s, 1.2 mm, and 0.025 mm, respectively. The error in specifying the cloud temperature will introduce an additional 10% error in the cloud water retrieval. The spatial resolution for these accuracies is 50 km. The systematic errors in the retrievals are smaller than the rms errors, being about 0.3 m/s, 0.6 mm, and 0.005 mm for W, V, and L, respectively. The one exception is the systematic error in wind speed of -1.0 m/s that occurs for observations within +/-20 deg of upwind. The inclusion of the line-of-sight wind W(sub LS) in the retrieval significantly reduces the error in wind speed due to wind direction variations. The wind error for upwind observations is reduced from -3.0 to -1.0 m/s. Finally, I find a small signal in the 19-GHz, horizontal polarization (h(sub pol) T(sub B) residual DeltaT(sub BH) that is related to the effective air pressure of the water vapor profile. This information may be of some use in specifying the vertical distribution of water vapor.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/96JC01751

A well‐calibrated ocean algorithm for special sensor microwave / imager

To obtain bulk surface flux estimates approaching the ±10 W m−2 accuracy desired for the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (COARE) program, bulk water temperature data from ships and buoys must be corrected for cool-skin and diurnal warm-layer effects. In this paper we describe two simple scaling models to estimate these corrections. The cool-skin model is based on the standard Saunders [1967] treatment, including the effects of solar radiation absorption, modified to include both shear-driven and convectively driven turbulence through their relative contributions to the near-surface turbulent kinetic energy dissipation rate. Shear and convective effects are comparable at a wind speed of about 2.5 m s−1. For the R/V Moana Wave COARE data collected in the tropical western Pacific, the model gives an average cool skin of 0.30 K at night and an average local noon value of 0.18 K. The warm-layer model is based on a single-layer scaling version of a model by Price et al. [1986]. In this model, once solar heating of the ocean exceeds the combined cooling by turbulent scalar heat transfer and net longwave radiation, then the main body of the mixed layer is cut off from its source of turbulence. Thereafter, surface inputs of heat and momentum are confined to a depth DT that is determined by the subsequent integrals of the heat and momentum. The model assumes linear profiles of temperature-induced and surface-stress-induced current in this “warm layer.” The model is shown to describe the peak afternoon warming and diurnal cycle of the warming quite accurately, on average, with a choice of a critical Richardson number of 0.65. For a clear day with a 10-m wind speed of 1 m s−1, the peak afternoon warming is about 3.8 K with a warm-layer depth of 0.7 m, decreasing to about 0.2 K and 19 m at a wind speed of 7 m s−1. For an average over 70 days sampled during COARE, the cool skin increases the average atmospheric heat input to the ocean by about 11 W m−2; the warm layer decreases it by about 4 W m−2 (but the effect can be 50 W m−2 at midday).

Cool-skin and warm-layer effects on sea surface temperature

Satellite infrared sensors only observe the temperature of the skin of the ocean rather than the bulk sea surface temperature (SST) traditionally measured from ships and buoys. In order to examine the differences and similarities between skin and bulk temperatures, radiometric measurements of skin temperature were made in the North Atlantic Ocean from a research vessel along with coincident measurements of subsurface bulk temperatures, radiative fluxes, and meteorological variables. Over the entire 6-week data set the bulk-skin temperature differences (ΔT) range between −1.0 and 1.0 K with mean differences of 0.1 to 0.2 K depending on wind and surface heat flux conditions. The bulk-skin temperature difference varied between day and night (mean differences 0.11 and 0.30 K, respectively) as well as with different cloud conditions, which can mask the horizontal variability of SST in regions of weak horizontal temperature gradients. A coherency analysis reveals strong correlations between skin and bulk temperatures at longer length scales in regions with relatively weak horizontal temperature gradients. The skin-bulk temperature difference is parameterized in terms of heat and momentum fluxes (or their related variables) with a resulting accuracy of 0.11 K and 0.17 K for night and daytime. A recommendation is made to calibrate satellite derived SST's during night with buoy measurements and the additional aid of meteorological variables to properly handle ΔT variations.

/pdf/on-the-bulk-skin-temperature-difference-and-its-impact-on-4hsu97ehol.pdf

On the bulk-skin temperature difference and its impact on satellite remote sensing of sea surface temperature

A radiative-transport model has been developed to study the emitted radiation from variable cloud and rain systems at the frequencies of the special sensor microwave imager (SSM/I). The model is based on the matrix operator method and takes into account multiple scattering as well as polarization. The simulations are based on a multivariate approach using globally distributed atmospheric profiles of pressure, temperature, humidity, and different cloud types in multiple layers. The inhomogeneous vertical distributions of water and ice in clouds and rain are modeled by bulk analytical formulae. Fractional coverages of cloud and rain clusters are incorporated into the analysis to account for the low spatial resolution of microwave radiometers, which decreases the correlations between the brightness temperatures and the microphysical parameters. Retrieval algorithms are developed for the water vapor, total water, and ice paths as well as for surface rain rates. By the exclusion of strong convective conditions, the retrieval of ice paths is found to be problematic owing to the strong gradient of scattering efficiency with sizes. The water vapor paths are also derived for cloudy and raining conditions with limited spatial coverages. The theoretical standard errors of the retrievals lie in the range of 0.1–0.2 g/cm2 for vapor paths of 0–7 g/cm2, 0.02–0.03 g/cm2 for total water paths of 0–1.2 g/cm2, 0.015 g/cm2 for ice paths of 0–0.25 g/cm2, and 1.3 mm/h for rain rates of 0–30 mm/h. The algorithms are applied to global sets of SSM/I data for August 1987. The resulting fields of monthly rain amounts are compared to those of satellite infrared measurements, a surface climatology, and a general circulation model (GCM) experiment. The results are found to be mainly consistent with the climatology, whereas the IR data and the GCM experiment produce questionable inconsistencies. The water vapor path estimations are compared to colocated radiosonde measurements showing deviations below 0.78 g/cm2 including cloudy and rainy situations, whereas our total water path estimations show the best results for inhomogeneous scenes compared to those calculated by algorithms taken from the literature.

Rainfall, total water, ice water, and water vapor over sea from polarized microwave simulations and Special Sensor Microwave/Imager data

A statistical retrieval technique is developed to derive the atmospheric water vapor column content from the Special Sensor Microwave/Imager (SSM/I) measurements. The radiometer signals are simulated by means of radiative-transfer calculations for a large set of atmospheric/oceanic situations. These simulated responses are subsequently summarized by multivariate analyses, giving water-vapor coefficients and error estimates. Radiative-transfer calculations show that the SSM/I microwave imager can detect atmospheric water vapor structures with an accuracy from 0.145 to 0.17 g/sq cm. The accuracy of the method is confirmed by globally distributed match-ups with radiosonde measurements.

Atmospheric water vapour over oceans from SSM/I measurements

The microwave sensor SSM/I (Special Sensor Microwave/Imager) on board of the DMSP satellite can be used to develop a retrieval method for the water vapour content in the atmospheric boundary layer close to the sea surface, by means of radiative transfer calculations. It is found that the SSM/ I measurements are sufficiently sensitive to the water vapour w1 in the lowermost 500 m of the atmosphere to allow a retrieval of w, from the 19, 22, 37 GHz vertical and 19 GHz horizontal polarization measurements with an accuracy of 0-06 g cm−The technique is validated with globally distributed radiosonde measurements located together with satellite soundings during the months July and August 1987. A linear relationship is established statistically to determine the near-surface specific humidity from w, with an accuracy of l-2gKg−1

Water vapour in the atmospheric boundary layer over oceans from SSM/I measurements

Sea surface temperatures (SSTs), computed from sensor systems on the National Oceanographic and Atmospheric Administration (NOAA) polar-orbiting satellites, are compared with surface skin temperatures (from an infrared radiometer mounted on a ship) and subsurface temperature measurements. Three split window retrieval methods using channels 4 and 5 of the NOAA 7 advanced very high resolution radiometer (AVHRR) sensor were investigated. These methods were (1) using AVHRR alone, (2) using AVHRR with atmospheric temperature and water vapor profiles from the TIROS operational vertical sounder (TOVS), and (3) using AVHRR and data from the high-resolution infrared sounder (HIRS). TOVS sensors (including HIRS) are carried by the same satellite as the AVHRR and provide simultaneous corrections for the AVHRR-based SST estimates. The importance of scan angle correction to define the correct atmospheric path is discussed, and the improvement of SST retrievals using sensor combinations is demonstrated with satellite versus ship skin temperature mean differences ranging from 0.55° to 0.73°C for AVHRR alone, from −0.39° to 0.71°C for AVHRR plus TOVS, and from 0.22° to 0.33°C for AVHRR plus HIRS. The improved SST accuracy by AVHRR plus HIRS is due to additional correction for the atmospheric water vapor and temperature structures, made possible with some of the HIRS channels. Significant differences between ship skin and subsurface temperatures were observed, with the mean deviation being 0.2°C for a range of temperature differences between −0.25° and 0.6°C.

/pdf/comparison-of-satellite-derived-sea-surface-temperatures-43blespq37.pdf

Peter Schluessel

Papers

On the bulk-skin temperature difference and its impact on satellite remote sensing of sea surface temperature

Rainfall, total water, ice water, and water vapor over sea from polarized microwave simulations and Special Sensor Microwave/Imager data

Atmospheric water vapour over oceans from SSM/I measurements

Water vapour in the atmospheric boundary layer over oceans from SSM/I measurements

Comparison of satellite‐derived sea surface temperatures with in situ skin measurements