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Abstract
Six global and two regional Pacific monthly sea surface temperature climatologies were compared. The climatologies were based on either surface marine observations or oceanographic cast (surface plus subsurface temperatures) observations. Although the cast data is more accurate than the surface marine, the data density of the cast observations is much more sparse. In this study, the surface marine climatologies were generally found to be superior to the cast climatologies. The individual differences between the climatologies are described and evaluated.
Abstract
Six global and two regional Pacific monthly sea surface temperature climatologies were compared. The climatologies were based on either surface marine observations or oceanographic cast (surface plus subsurface temperatures) observations. Although the cast data is more accurate than the surface marine, the data density of the cast observations is much more sparse. In this study, the surface marine climatologies were generally found to be superior to the cast climatologies. The individual differences between the climatologies are described and evaluated.
Abstract
The June 1991 eruptions of Mount Pinatubo produced new stratospheric aerosols that were greater than the aerosols from the 1982 eruptions of El Chichón. These new aerosols strongly affected the advanced very high resolution radiometer (AVHRR) retrievals of sea surface temperature in the tropics where negative biases occurred with magnitudes greater than 1°C. The time dependence of these biases are shown. In addition, a method to correct these biases is discussed and integrated into the National Meteorological Center's optimum interpolation sea surface temperature analysis.
Abstract
The June 1991 eruptions of Mount Pinatubo produced new stratospheric aerosols that were greater than the aerosols from the 1982 eruptions of El Chichón. These new aerosols strongly affected the advanced very high resolution radiometer (AVHRR) retrievals of sea surface temperature in the tropics where negative biases occurred with magnitudes greater than 1°C. The time dependence of these biases are shown. In addition, a method to correct these biases is discussed and integrated into the National Meteorological Center's optimum interpolation sea surface temperature analysis.
Abstract
A global monthly sea surface temperature analysis is described which uses real-lime in situ (ship and buoy) and satellite data. The method combines the advantages of both types of data: the ground truth of in situ data and the improved coverage of satellite data. The technique also effectively eliminates most of the bias differences between the in situ and satellite data. Examples of the method are shown to illustrate these points.
Sea surface temperature (SST) data from quality-controlled drifting buoys are used to develop error statistics for a 24-month period from January 1985 through December 1986. The average rms monthly error is 0.78°C; the modulus of the monthly blasts (i.e., the average of the absolute value of the monthly biases) is 0.09°C.
Abstract
A global monthly sea surface temperature analysis is described which uses real-lime in situ (ship and buoy) and satellite data. The method combines the advantages of both types of data: the ground truth of in situ data and the improved coverage of satellite data. The technique also effectively eliminates most of the bias differences between the in situ and satellite data. Examples of the method are shown to illustrate these points.
Sea surface temperature (SST) data from quality-controlled drifting buoys are used to develop error statistics for a 24-month period from January 1985 through December 1986. The average rms monthly error is 0.78°C; the modulus of the monthly blasts (i.e., the average of the absolute value of the monthly biases) is 0.09°C.
Abstract
A numerical finite-difference model using the Laplace tidal equations on an f-plane was developed to predict how tidal motion is disturbed by an elliptic ridge. With the use of an open-ocean matching condition the model was used to study the effects of several generalized types of elliptic bottom topographies and to study the particular case of the Hawaiian Ridge.
Abstract
A numerical finite-difference model using the Laplace tidal equations on an f-plane was developed to predict how tidal motion is disturbed by an elliptic ridge. With the use of an open-ocean matching condition the model was used to study the effects of several generalized types of elliptic bottom topographies and to study the particular case of the Hawaiian Ridge.
Abstract
An extended reconstruction of monthly mean oceanic historical sea level pressure (SLP) based on Comprehensive Ocean–Atmosphere Data Set (COADS) release-2 observations is produced for the 1854–1997 period. The COADS data are first screened using an adaptive quality-control procedure. Land SLP data from coastal and island stations are used to supplement the COADS data. The SLP anomalies are analyzed monthly to a 2° grid using statistics based on 20 yr of assimilated atmospheric reanalysis. A first-order correction is applied to the reconstruction to minimize variations associated with spurious long-term changes in the atmospheric mass over the oceans.
In the nineteenth century, the reconstruction appears to underestimate the SLP-anomaly amplitudes, and error estimates for the reconstruction are largest. After 1900 the reconstruction variance is stronger, although there are periods in the first half of the twentieth century when sampling is poor and the variance decreases. Spatial correlations between the reconstruction and several comparison analyses are highest in the second half of the twentieth century, suggesting greater reconstruction reliability after 1950.
Abstract
An extended reconstruction of monthly mean oceanic historical sea level pressure (SLP) based on Comprehensive Ocean–Atmosphere Data Set (COADS) release-2 observations is produced for the 1854–1997 period. The COADS data are first screened using an adaptive quality-control procedure. Land SLP data from coastal and island stations are used to supplement the COADS data. The SLP anomalies are analyzed monthly to a 2° grid using statistics based on 20 yr of assimilated atmospheric reanalysis. A first-order correction is applied to the reconstruction to minimize variations associated with spurious long-term changes in the atmospheric mass over the oceans.
In the nineteenth century, the reconstruction appears to underestimate the SLP-anomaly amplitudes, and error estimates for the reconstruction are largest. After 1900 the reconstruction variance is stronger, although there are periods in the first half of the twentieth century when sampling is poor and the variance decreases. Spatial correlations between the reconstruction and several comparison analyses are highest in the second half of the twentieth century, suggesting greater reconstruction reliability after 1950.
Abstract
This paper describes daytime sea surface temperature (SST) climate analyses derived from 16 years (1985–2000) of reprocessed Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres (PATMOS) multichannel radiometric data. Two satellite bias correction methods are employed: the first being an aerosol correction, the second being an in situ correction of satellite biases. The aerosol bias correction is derived from observed statistical relationships between the slant-path aerosol optical depth and AVHRR multichannel SST (MCSST) depressions for elevated levels of tropospheric and stratospheric aerosol. Weekly analyses of SST are produced on a 1° equal-angle grid using optimum interpolation (OI) methodology. Four separate OI analyses are derived based on 1) MCSST without satellite bias correction, 2) MCSST with aerosol satellite bias correction, 3) MCSST with in situ correction of satellite biases, and 4) MCSST with both aerosol and in situ corrections of satellite biases. These analyses are compared against the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager OI SST, along with the extended reconstruction SST in situ analysis product. The OI analysis 1 exhibits significant negative and positive biases. Analysis 2, derived exclusively from satellite data, reduces globally the negative bias associated with elevated atmospheric aerosol, and subsequently reveals pronounced variations in diurnal warming consistent with recently published works. Analyses 3 and 4, derived from in situ correction of satellite biases, alleviate biases (positive and negative) associated with both aerosol and diurnal warming, and also reduce the dispersion. The PATMOS OISST 1985–2000 daytime climate analyses presented here provide a high-resolution (1° weekly) empirical database for studying seasonal and interannual climate processes.
Abstract
This paper describes daytime sea surface temperature (SST) climate analyses derived from 16 years (1985–2000) of reprocessed Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres (PATMOS) multichannel radiometric data. Two satellite bias correction methods are employed: the first being an aerosol correction, the second being an in situ correction of satellite biases. The aerosol bias correction is derived from observed statistical relationships between the slant-path aerosol optical depth and AVHRR multichannel SST (MCSST) depressions for elevated levels of tropospheric and stratospheric aerosol. Weekly analyses of SST are produced on a 1° equal-angle grid using optimum interpolation (OI) methodology. Four separate OI analyses are derived based on 1) MCSST without satellite bias correction, 2) MCSST with aerosol satellite bias correction, 3) MCSST with in situ correction of satellite biases, and 4) MCSST with both aerosol and in situ corrections of satellite biases. These analyses are compared against the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager OI SST, along with the extended reconstruction SST in situ analysis product. The OI analysis 1 exhibits significant negative and positive biases. Analysis 2, derived exclusively from satellite data, reduces globally the negative bias associated with elevated atmospheric aerosol, and subsequently reveals pronounced variations in diurnal warming consistent with recently published works. Analyses 3 and 4, derived from in situ correction of satellite biases, alleviate biases (positive and negative) associated with both aerosol and diurnal warming, and also reduce the dispersion. The PATMOS OISST 1985–2000 daytime climate analyses presented here provide a high-resolution (1° weekly) empirical database for studying seasonal and interannual climate processes.
Abstract
The monthly global sea surface temperature (SST) analysis of Reynolds using real-time in situ and satellite SST data has now been improved by using sea ice data to simulate SSTs in ice-covered regions. The simulated SSTs now become the external boundary condition for the analysis solution. This technique eliminates any high-latitude satellite biases and extends the analysis to the ice edge. The analysis with the ice data has been computed for the period January 1982 to present.
Abstract
The monthly global sea surface temperature (SST) analysis of Reynolds using real-time in situ and satellite SST data has now been improved by using sea ice data to simulate SSTs in ice-covered regions. The simulated SSTs now become the external boundary condition for the analysis solution. This technique eliminates any high-latitude satellite biases and extends the analysis to the ice edge. The analysis with the ice data has been computed for the period January 1982 to present.
Abstract
The new NOAA operational global sea surface temperature (SST) analysis is described. The analyses use 7 days of in situ (ship and buoy) and satellite SST. These analyses are produced weekly and daily using optimum interpolation (OI) on a 1° grid. The OI technique requires the specification of data and analysis error statistics. These statistics are derived and show that the SST rms data errors from ships are almost twice as large as the data errors from buoys or satellites. In addition, the average e-folding spatial error scales have been found to be 850 km in the zonal direction and 615 km in the meridional direction.
The analysis also includes a preliminary step that corrects any satellite biases relative to the in situ data using Poisson's equation. The importance of this correction is demonstrated using recent data following the 1991 eruptions of Mt. Pinatubo. The OI analysis has been computed using the in situ and bias-corrected satellite data for the period 1985 to present.
Abstract
The new NOAA operational global sea surface temperature (SST) analysis is described. The analyses use 7 days of in situ (ship and buoy) and satellite SST. These analyses are produced weekly and daily using optimum interpolation (OI) on a 1° grid. The OI technique requires the specification of data and analysis error statistics. These statistics are derived and show that the SST rms data errors from ships are almost twice as large as the data errors from buoys or satellites. In addition, the average e-folding spatial error scales have been found to be 850 km in the zonal direction and 615 km in the meridional direction.
The analysis also includes a preliminary step that corrects any satellite biases relative to the in situ data using Poisson's equation. The importance of this correction is demonstrated using recent data following the 1991 eruptions of Mt. Pinatubo. The OI analysis has been computed using the in situ and bias-corrected satellite data for the period 1985 to present.
Abstract
Six different SST analyses are compared with each other and with buoy data for the period 2007–08. All analyses used different combinations of satellite data [for example, infrared Advanced Very High Resolution Radiometer (AVHRR) and microwave Advanced Microwave Scanning Radiometer (AMSR) instruments] with different algorithms, spatial resolution, etc. The analyses considered are the National Climatic Data Center (NCDC) AVHRR-only and AMSR+AVHRR, the Navy Coupled Ocean Data Assimilation (NCODA), the Remote Sensing Systems (RSS), the Real-Time Global High-Resolution (RTG-HR), and the Operational SST and Sea Ice Analysis (OSTIA); the spatial grid sizes were 
To help quantify SST analysis differences, wavenumber spectra were computed at several locations. These results suggested that the RSS is much noisier and that the RTG-HR analysis is much smoother than the other analyses. Further comparisons made using collocated buoys showed that RSS was especially noisy in the tropics and that RTG-HR had winter biases near the Aleutians region during January and February 2007. The correlation results show that NCODA and, to a somewhat lesser extent, OSTIA are strongly tuned locally to buoy data. The results also show that grid spacing does not always correlate with analysis resolution.
The AVHRR-only analysis is useful for climate studies because it is the only daily SST analysis that extends back to September 1981. Furthermore, comparisons of the AVHRR-only analysis and the AMSR+AVHRR analysis show that AMSR data can degrade the combined AMSR and AVHRR resolution in cloud-free regions while AMSR otherwise improves the resolution. These results indicate that changes in satellite instruments over time can impact SST analysis resolution.
Abstract
Six different SST analyses are compared with each other and with buoy data for the period 2007–08. All analyses used different combinations of satellite data [for example, infrared Advanced Very High Resolution Radiometer (AVHRR) and microwave Advanced Microwave Scanning Radiometer (AMSR) instruments] with different algorithms, spatial resolution, etc. The analyses considered are the National Climatic Data Center (NCDC) AVHRR-only and AMSR+AVHRR, the Navy Coupled Ocean Data Assimilation (NCODA), the Remote Sensing Systems (RSS), the Real-Time Global High-Resolution (RTG-HR), and the Operational SST and Sea Ice Analysis (OSTIA); the spatial grid sizes were
To help quantify SST analysis differences, wavenumber spectra were computed at several locations. These results suggested that the RSS is much noisier and that the RTG-HR analysis is much smoother than the other analyses. Further comparisons made using collocated buoys showed that RSS was especially noisy in the tropics and that RTG-HR had winter biases near the Aleutians region during January and February 2007. The correlation results show that NCODA and, to a somewhat lesser extent, OSTIA are strongly tuned locally to buoy data. The results also show that grid spacing does not always correlate with analysis resolution.
The AVHRR-only analysis is useful for climate studies because it is the only daily SST analysis that extends back to September 1981. Furthermore, comparisons of the AVHRR-only analysis and the AMSR+AVHRR analysis show that AMSR data can degrade the combined AMSR and AVHRR resolution in cloud-free regions while AMSR otherwise improves the resolution. These results indicate that changes in satellite instruments over time can impact SST analysis resolution.
Abstract
The NOAA ¼° daily optimum interpolation sea surface temperature analysis (DOISST) is available either as a 31-yr (from 1981 onward) time series based on Advanced Very High Resolution Radiometer (AVHRR) observations or as a 9-yr (2002–11) time series that incorporates additional data from the Advanced Microwave Scanning Radiometer (AMSR) on the Earth Observing System (EOS) platform. In October 2011, AVHRR+AMSR DOISST production was discontinued when the AMSR instrument lost its capability to collect daily, global-coverage data. Sea surface temperatures from the follow-up AMSR2 instrument will not be available until mid-2013. To ensure an uninterrupted consistent long-term microwave-based DOISST time series, this study tested the feasibility of bridging the gap between AMSR and AMSR2 using WindSat Polarimetric Radiometer (WSAT) data. Comparison of WSAT and AMSR SST data during their period of overlap from 2004 to 2011 showed temporal gaps were common for WSAT, especially before 2009. The WSAT daily spatial coverage was slightly inferior to AMSR but still far superior to AVHRR. When satellite SSTs were bias adjusted with respect to in situ data, the resulting AMSR- and WSAT-only analyses were very similar. Monthly zonal averages agreed to within 0.1 K, except when WSAT data were continuously missing for over three weeks. However, if satellite SSTs were not bias adjusted prior to analysis, seasonally varying zonal average differences were observed, with maxima of ~0.3 K occurring at midlatitudes. Thus, WSAT can be used to continue the microwave-based DOISST time series because the methodology compensates for the different equatorial crossing times of the two satellites.
Abstract
The NOAA ¼° daily optimum interpolation sea surface temperature analysis (DOISST) is available either as a 31-yr (from 1981 onward) time series based on Advanced Very High Resolution Radiometer (AVHRR) observations or as a 9-yr (2002–11) time series that incorporates additional data from the Advanced Microwave Scanning Radiometer (AMSR) on the Earth Observing System (EOS) platform. In October 2011, AVHRR+AMSR DOISST production was discontinued when the AMSR instrument lost its capability to collect daily, global-coverage data. Sea surface temperatures from the follow-up AMSR2 instrument will not be available until mid-2013. To ensure an uninterrupted consistent long-term microwave-based DOISST time series, this study tested the feasibility of bridging the gap between AMSR and AMSR2 using WindSat Polarimetric Radiometer (WSAT) data. Comparison of WSAT and AMSR SST data during their period of overlap from 2004 to 2011 showed temporal gaps were common for WSAT, especially before 2009. The WSAT daily spatial coverage was slightly inferior to AMSR but still far superior to AVHRR. When satellite SSTs were bias adjusted with respect to in situ data, the resulting AMSR- and WSAT-only analyses were very similar. Monthly zonal averages agreed to within 0.1 K, except when WSAT data were continuously missing for over three weeks. However, if satellite SSTs were not bias adjusted prior to analysis, seasonally varying zonal average differences were observed, with maxima of ~0.3 K occurring at midlatitudes. Thus, WSAT can be used to continue the microwave-based DOISST time series because the methodology compensates for the different equatorial crossing times of the two satellites.
