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Abstract
A new global 2°×2° monthly sea surface temperature (SST) climatology, primarily derived from a 1950–1979-based SST climatology from the Climate Analysis Center (CAC), is presented and described. The CAC climatology has been modified by using data from the Comprehensive Ocean-Atmosphere Data Set to improve the SST estimates in the regions of the Kuroshio and the Gulf Stream. This results in considerably larger and more realistic SST gradients in these regions. This modified climatology is smoothed in time using a truncated Fourier series to eliminate mean annual cycle fluctuations of three months or less, and finally, some spatial smoothing is applied over the high-latitude southern oceans.
This new SST climatology, which we call the Shea-Trenberth-Reynolds (STR) climatology, is compared with the Alexander and Mobley (AM) SST climatology often used as a lower boundary condition by general circulation models. Significant differences are noted. Generally, the STR climatology is warmer in the Northern Hemisphere and in the subtropics of the Southern Hemisphere during the northern winter. It is often colder south of 45°S in all months. The largest differences are more than 5°C in the Kuroshio and Gulf Stream regions, and in the mid- to high-latitude southern oceans, the SSTs are often more than 2°C lower. In addition, the STR climatology is temporally and spatially less noisy than the AM SST climatology.
Global SST anomalies spanning the period 1982 to 1990 are discussed. The largest anomalies are associated with the El Niño (1982–83 and 1986–87) and La Niña (1988) events in the tropical Pacific. However, because of differences in procedures in producing the 1982–1990 SSTs compared with the CAC climatology, the anomalies in certain regions are really compensating for deficiencies in the climatology and should not be interpreted as true climate anomalies.
Abstract
A new global 2°×2° monthly sea surface temperature (SST) climatology, primarily derived from a 1950–1979-based SST climatology from the Climate Analysis Center (CAC), is presented and described. The CAC climatology has been modified by using data from the Comprehensive Ocean-Atmosphere Data Set to improve the SST estimates in the regions of the Kuroshio and the Gulf Stream. This results in considerably larger and more realistic SST gradients in these regions. This modified climatology is smoothed in time using a truncated Fourier series to eliminate mean annual cycle fluctuations of three months or less, and finally, some spatial smoothing is applied over the high-latitude southern oceans.
This new SST climatology, which we call the Shea-Trenberth-Reynolds (STR) climatology, is compared with the Alexander and Mobley (AM) SST climatology often used as a lower boundary condition by general circulation models. Significant differences are noted. Generally, the STR climatology is warmer in the Northern Hemisphere and in the subtropics of the Southern Hemisphere during the northern winter. It is often colder south of 45°S in all months. The largest differences are more than 5°C in the Kuroshio and Gulf Stream regions, and in the mid- to high-latitude southern oceans, the SSTs are often more than 2°C lower. In addition, the STR climatology is temporally and spatially less noisy than the AM SST climatology.
Global SST anomalies spanning the period 1982 to 1990 are discussed. The largest anomalies are associated with the El Niño (1982–83 and 1986–87) and La Niña (1988) events in the tropical Pacific. However, because of differences in procedures in producing the 1982–1990 SSTs compared with the CAC climatology, the anomalies in certain regions are really compensating for deficiencies in the climatology and should not be interpreted as true climate anomalies.
Abstract
Optimal averaging (OA) is used to compute the area-average seasonal sea surface temperature (SST) for a variety of areas from 1860 to 1989. The OA gives statistically improved averages and the objective assignment of confidence intervals to these averages. The ability to assign confidence intervals is the main advantage of this method. Confidence intervals reflect how densely and uniformly an area is sampled during the averaging season. For the global average, the early part of the record (1860–1890) and the times of the two world wars have largest uncertainties. Analysis of OA-based uncertainty estimates shows that before 1930 sampling in the Southern Hemisphere was as good as it was in the Northern Hemisphere. From about 1930 to 1950, uncertainties decreased in both hemispheres, but the magnitude of the Northern Hemisphere uncertainties reduced more and remained smaller. After the early 1950s uncertainties were relatively constant in both hemispheres, indicating that sampling was relatively consistent over the period. During the two world wars, increased uncertainties reflected the sampling decreases over all the oceans, with the biggest decreases south of 40°S. The OA global SST anomalies are virtually identical to estimates of global SST anomalies computed using simpler methods, when the same data corrections are applied. When data are plentiful over an area there is no clear advantage of the OA over simpler methods. The major advantage of the OA over the simpler methods is the accompanying error estimates.
The OA analysis suggests that SST anomalies were not significantly different from 0 from 1860 to 1900. This result is heavily influenced by the choice of the data corrections applied before the 1950s. Global anomalies are also near zero from 1940 until the mid-1970s. The OA analysis suggests that negative anomalies dominated the period from the early 1900s through the 1930s although the uncertainties are quite large during and immediately following World War I. Finally, the OA analysis shows significant positive global SST anomalies beginning in the late 1970s. The SST anomalies in the Indian Ocean and Southern Ocean poleward of 20°S make the strongest contributions to the positive global anomalies observed since the late 1970s. In contrast to the more recent period, the SST anomalies in the period from the early 1900s through 1940 were dominated by the anomalies in the Northern Hemisphere poleward of 20°N.
Abstract
Optimal averaging (OA) is used to compute the area-average seasonal sea surface temperature (SST) for a variety of areas from 1860 to 1989. The OA gives statistically improved averages and the objective assignment of confidence intervals to these averages. The ability to assign confidence intervals is the main advantage of this method. Confidence intervals reflect how densely and uniformly an area is sampled during the averaging season. For the global average, the early part of the record (1860–1890) and the times of the two world wars have largest uncertainties. Analysis of OA-based uncertainty estimates shows that before 1930 sampling in the Southern Hemisphere was as good as it was in the Northern Hemisphere. From about 1930 to 1950, uncertainties decreased in both hemispheres, but the magnitude of the Northern Hemisphere uncertainties reduced more and remained smaller. After the early 1950s uncertainties were relatively constant in both hemispheres, indicating that sampling was relatively consistent over the period. During the two world wars, increased uncertainties reflected the sampling decreases over all the oceans, with the biggest decreases south of 40°S. The OA global SST anomalies are virtually identical to estimates of global SST anomalies computed using simpler methods, when the same data corrections are applied. When data are plentiful over an area there is no clear advantage of the OA over simpler methods. The major advantage of the OA over the simpler methods is the accompanying error estimates.
The OA analysis suggests that SST anomalies were not significantly different from 0 from 1860 to 1900. This result is heavily influenced by the choice of the data corrections applied before the 1950s. Global anomalies are also near zero from 1940 until the mid-1970s. The OA analysis suggests that negative anomalies dominated the period from the early 1900s through the 1930s although the uncertainties are quite large during and immediately following World War I. Finally, the OA analysis shows significant positive global SST anomalies beginning in the late 1970s. The SST anomalies in the Indian Ocean and Southern Ocean poleward of 20°S make the strongest contributions to the positive global anomalies observed since the late 1970s. In contrast to the more recent period, the SST anomalies in the period from the early 1900s through 1940 were dominated by the anomalies in the Northern Hemisphere poleward of 20°N.
Abstract
A new sea surface temperature (SST) climatological mean was constructed using the first 30 years (1982–2011) of the NOAA daily optimum interpolation (OI) SST. The daily analysis blends in situ and satellite data on a ¼° (~25 km) spatial grid. Use of an analysis allows computation of a climatological value for all ocean grid points, even those without observations. Comparisons were made with a monthly, 1°-spatial-resolution climatology produced by the National Centers for Environmental Prediction, computed primarily from the NOAA weekly OISST. Both climatologies were found to provide a good representation of major oceanic features and the annual temperature cycle. However, the daily climatology showed tighter gradients along western boundary currents and better resolution along coastlines. The two climatologies differed by over 0.6°C in high-SST-gradient regions because of resolution differences. The two climatologies also differed at very high latitudes, where the sea ice processing differed between the OISST products. In persistently cloudy areas, the new climatology was generally cooler by approximately 0.4°C, probably reflecting differences between the input satellite SSTs to the two analyses. Since the new climatology represents mean conditions at scales that match the daily analysis, it would be more appropriate for computing the corresponding daily anomalies.
Abstract
A new sea surface temperature (SST) climatological mean was constructed using the first 30 years (1982–2011) of the NOAA daily optimum interpolation (OI) SST. The daily analysis blends in situ and satellite data on a ¼° (~25 km) spatial grid. Use of an analysis allows computation of a climatological value for all ocean grid points, even those without observations. Comparisons were made with a monthly, 1°-spatial-resolution climatology produced by the National Centers for Environmental Prediction, computed primarily from the NOAA weekly OISST. Both climatologies were found to provide a good representation of major oceanic features and the annual temperature cycle. However, the daily climatology showed tighter gradients along western boundary currents and better resolution along coastlines. The two climatologies differed by over 0.6°C in high-SST-gradient regions because of resolution differences. The two climatologies also differed at very high latitudes, where the sea ice processing differed between the OISST products. In persistently cloudy areas, the new climatology was generally cooler by approximately 0.4°C, probably reflecting differences between the input satellite SSTs to the two analyses. Since the new climatology represents mean conditions at scales that match the daily analysis, it would be more appropriate for computing the corresponding daily anomalies.
Abstract
Equatorial Pacific current and temperature fields were simulated with and without assimilation of subsurface temperature measurements for April 1992–March 1995 and compared with moored buoy and research vessel current measurements. Data assimilation intensified the mean east–west slope of the thermocline along the equator in the eastern Pacific, shifted eastward the longitude of the mean Equatorial Undercurrent (EUC) maximum speed 800 km to 125°W, and produced a 25% stronger mean EUC core speed in the eastern Pacific. In the eastern Pacific the mean EUC core speed simulated with data assimilation was slightly more representative of observations compared to that computed without data assimilated; in the western Pacific the data assimilation had no impact on mean EUC simulations.
Data assimilation intensified the north–south slope of the thermocline south of the equator in the western Pacific to produce a thicker and more intense westward-flowing South Equatorial Current (SEC) in the western Pacific. In the western Pacific the mean SEC transport per unit width simulated with data assimilation was more representative of observations compared to that computed without data assimilation. However, large differences remained between the observed SEC transport per unit width and that simulated with data assimilation. In the eastern Pacific, the data assimilation had no impact on mean SEC simulations.
The temporal variability of monthly mean EUC core speeds and SEC transports per unit width were increased significantly by data assimilation. It also increased the representativeness of monthly mean SEC transports per unit width to the observations. However, the data representativeness of monthly mean EUC core speeds was decreased. Results could be explained by the coupling between zonal gradient of temperature and EUC and between meridional gradient of temperature and SEC. Longitudinal variations along the Pacific equator of the impact of data assimilation on the EUC and SEC precludes the choice of a single site to evaluate the effectiveness of data assimilation schemes.
Abstract
Equatorial Pacific current and temperature fields were simulated with and without assimilation of subsurface temperature measurements for April 1992–March 1995 and compared with moored buoy and research vessel current measurements. Data assimilation intensified the mean east–west slope of the thermocline along the equator in the eastern Pacific, shifted eastward the longitude of the mean Equatorial Undercurrent (EUC) maximum speed 800 km to 125°W, and produced a 25% stronger mean EUC core speed in the eastern Pacific. In the eastern Pacific the mean EUC core speed simulated with data assimilation was slightly more representative of observations compared to that computed without data assimilated; in the western Pacific the data assimilation had no impact on mean EUC simulations.
Data assimilation intensified the north–south slope of the thermocline south of the equator in the western Pacific to produce a thicker and more intense westward-flowing South Equatorial Current (SEC) in the western Pacific. In the western Pacific the mean SEC transport per unit width simulated with data assimilation was more representative of observations compared to that computed without data assimilation. However, large differences remained between the observed SEC transport per unit width and that simulated with data assimilation. In the eastern Pacific, the data assimilation had no impact on mean SEC simulations.
The temporal variability of monthly mean EUC core speeds and SEC transports per unit width were increased significantly by data assimilation. It also increased the representativeness of monthly mean SEC transports per unit width to the observations. However, the data representativeness of monthly mean EUC core speeds was decreased. Results could be explained by the coupling between zonal gradient of temperature and EUC and between meridional gradient of temperature and SEC. Longitudinal variations along the Pacific equator of the impact of data assimilation on the EUC and SEC precludes the choice of a single site to evaluate the effectiveness of data assimilation schemes.
Abstract
Empirical orthogonal functions of the combined variability of temperature and salinity have been used as basis functions for the indirect reconstruction of salinity from observations of temperature alone. The method employs a weighted least squares procedure that minimizes the misfit between the reconstructed temperature and the observed temperature, but also constrains the variability of the reconstructed salinity to remain within specified bounds.
The method has been tested by fitting to temperature profiles from the Tropical Atmosphere Ocean array along 165°E in the western equatorial Pacific Ocean (8°N–8°S) for the 1986–97 period. Comparisons of the reconstructed salinity field with sea surface salinity and conductivity–temperature–depth data and of the reconstructed dynamic height with TOPEX/Poseidon observations of sea level demonstrate the reliability of the method. The reconstructed data successfully capture the upper-ocean variability at annual to ENSO timescales. The impact of neglecting salinity variability on the dynamic height anomaly in the western tropical Pacific Ocean is addressed.
Abstract
Empirical orthogonal functions of the combined variability of temperature and salinity have been used as basis functions for the indirect reconstruction of salinity from observations of temperature alone. The method employs a weighted least squares procedure that minimizes the misfit between the reconstructed temperature and the observed temperature, but also constrains the variability of the reconstructed salinity to remain within specified bounds.
The method has been tested by fitting to temperature profiles from the Tropical Atmosphere Ocean array along 165°E in the western equatorial Pacific Ocean (8°N–8°S) for the 1986–97 period. Comparisons of the reconstructed salinity field with sea surface salinity and conductivity–temperature–depth data and of the reconstructed dynamic height with TOPEX/Poseidon observations of sea level demonstrate the reliability of the method. The reconstructed data successfully capture the upper-ocean variability at annual to ENSO timescales. The impact of neglecting salinity variability on the dynamic height anomaly in the western tropical Pacific Ocean is addressed.
Abstract
The equatorial sea level analysis of the National Centers for Environmental Predictions deviates by as much as 8 cm from independent TOPEX/Poseidon (T/P) observations. This may be due to the model’s underestimation of salinity variability. Therefore, methods are developed to improve the model’s salinity field through T/P data assimilation and use of sea surface salinity (SSS) observations.
In regions where temperature is well known, salinity estimates are made with the use of climatological temperature–salinity (T–S) correlations. These estimates are improved by combining T–S with SSS observations and corrected with dynamic height, which provides information on salinity variability. Tests with independent conductivity temperature depth data show that the combination of T–S with SSS significantly improves salinity estimates. In the western Pacific, the maximum root-mean-square (rms) estimation error of 0.55 psu is reduced to 0.42 psu by the use of SSS in the salinity estimate. Correction with dynamic height reduces this rms to 0.22 psu. Also in other parts of the tropical Pacific Ocean the salinity estimation errors are reduced by a factor of 2 by combination of the T–S estimate with SSS and dynamic height. This study provides the first step toward an assimilation scheme in which salinity is corrected with the use of T/P sea level observations.
Abstract
The equatorial sea level analysis of the National Centers for Environmental Predictions deviates by as much as 8 cm from independent TOPEX/Poseidon (T/P) observations. This may be due to the model’s underestimation of salinity variability. Therefore, methods are developed to improve the model’s salinity field through T/P data assimilation and use of sea surface salinity (SSS) observations.
In regions where temperature is well known, salinity estimates are made with the use of climatological temperature–salinity (T–S) correlations. These estimates are improved by combining T–S with SSS observations and corrected with dynamic height, which provides information on salinity variability. Tests with independent conductivity temperature depth data show that the combination of T–S with SSS significantly improves salinity estimates. In the western Pacific, the maximum root-mean-square (rms) estimation error of 0.55 psu is reduced to 0.42 psu by the use of SSS in the salinity estimate. Correction with dynamic height reduces this rms to 0.22 psu. Also in other parts of the tropical Pacific Ocean the salinity estimation errors are reduced by a factor of 2 by combination of the T–S estimate with SSS and dynamic height. This study provides the first step toward an assimilation scheme in which salinity is corrected with the use of T/P sea level observations.
Abstract
A method is presented to evaluate the adequacy of the recent in situ network for climate sea surface temperature (SST) analyses using both in situ and satellite observations. Satellite observations provide superior spatiotemporal coverage, but with biases; in situ data are needed to correct the satellite biases. Recent NOAA/U.S. Navy operational Advanced Very High Resolution Radiometer (AVHRR) satellite SST biases were analyzed to extract typical bias patterns and scales. Occasional biases of 2°C were found during large volcano eruptions and near the end of the satellite instruments’ lifetime. Because future biases could not be predicted, the in situ network was designed to reduce the large biases that have occurred to a required accuracy. Simulations with different buoy density were used to examine their ability to correct the satellite biases and to define the residual bias as a potential satellite bias error (PSBE).
The PSBE and buoy density (BD) relationship was found to be nearly exponential, resulting in an optimal BD range of 2–3 per 10° × 10° box for efficient PSBE reduction. A BD of two buoys per 10° × 10° box reduces a 2°C maximum bias to below 0.5°C and reduces a 1°C maximum bias to about 0.3°C. The present in situ SST observing system was evaluated to define an equivalent buoy density (EBD), allowing ships to be used along with buoys according to their random errors. Seasonally averaged monthly EBD maps were computed to determine where additional buoys are needed for future deployments. Additionally, a PSBE was computed from the present EBD to assess the in situ system’s adequacy to remove potential future satellite biases.
Abstract
A method is presented to evaluate the adequacy of the recent in situ network for climate sea surface temperature (SST) analyses using both in situ and satellite observations. Satellite observations provide superior spatiotemporal coverage, but with biases; in situ data are needed to correct the satellite biases. Recent NOAA/U.S. Navy operational Advanced Very High Resolution Radiometer (AVHRR) satellite SST biases were analyzed to extract typical bias patterns and scales. Occasional biases of 2°C were found during large volcano eruptions and near the end of the satellite instruments’ lifetime. Because future biases could not be predicted, the in situ network was designed to reduce the large biases that have occurred to a required accuracy. Simulations with different buoy density were used to examine their ability to correct the satellite biases and to define the residual bias as a potential satellite bias error (PSBE).
The PSBE and buoy density (BD) relationship was found to be nearly exponential, resulting in an optimal BD range of 2–3 per 10° × 10° box for efficient PSBE reduction. A BD of two buoys per 10° × 10° box reduces a 2°C maximum bias to below 0.5°C and reduces a 1°C maximum bias to about 0.3°C. The present in situ SST observing system was evaluated to define an equivalent buoy density (EBD), allowing ships to be used along with buoys according to their random errors. Seasonally averaged monthly EBD maps were computed to determine where additional buoys are needed for future deployments. Additionally, a PSBE was computed from the present EBD to assess the in situ system’s adequacy to remove potential future satellite biases.
Abstract
SST predictions are usually issued in terms of anomalies and standardized anomalies relative to a 30-yr normal: climatological mean (CM) and standard deviation (SD). The World Meteorological Organization (WMO) suggests updating the 30-yr normal every 10 yr. In complying with the WMO's suggestion, a new 30-yr normal for the 1971–2000 base period is constructed. To put the new 30-yr normal in historical perspective, all the 30-yr normals since 1871 are investigated, starting from the beginning of each decade (1871–1900, 1881–1910, … , 1971–2000). Using the extended reconstructed sea surface temperature (ERSST) on a 2° grid for 1854–2000 and the Hadley Centre Sea Ice and SST dataset (HadISST) on a 1° grid for 1870–1999, eleven 30-yr normals are calculated, and the interdecadal changes of seasonal CM, seasonal SD, and seasonal persistence (P) are discussed. The interdecadal changes of seasonal CM are prominent (0.3°–0.6°) in the tropical Indian Ocean, the midlatitude North Pacific, the midlatitude North Atlantic, most of the South Atlantic, and the sub-Antarctic front. Four SST indices are used to represent the key regions of the interdecadal changes: the Indian Ocean (“INDIAN”; 10°S–25°N, 45°–100°E), the Pacific decadal oscillation (PDO; 35°–45°N, 160°E–160°W), the North Atlantic Oscillation (NAO; 40°–60°N, 20°–60°W), and the South Atlantic (SATL; 22°S–2°N, 35°W–10°E). Both INDIAN and SATL show a warming trend that is consistent between ERSST and HadISST. Both PDO and NAO show a multidecadal oscillation that is consistent between ERSST and HadISST except that HadISST is biased toward warm in summer and cold in winter relative to ERSST. The interdecadal changes in Niño-3 (5°S–5°N, 90°–150°W) are small (0.2°) and are inconsistent between ERSST and HadISST. The seasonal SD is prominent in the eastern equatorial Pacific, the North Pacific, and North Atlantic. The seasonal SD in Niño-3 varies interdecadally: intermediate during 1885–1910, small during 1910–65, and large during 1965–2000. These interdecadal changes of ENSO variance are further verified by the Darwin sea level pressure. The seasonality of ENSO variance (smallest in spring and largest in winter) also varies interdecadally: moderate during 1885–1910, weak during 1910–65, and strong during 1965–2000. The interdecadal changes of the seasonal SD of other indices are weak and cannot be determined well by the datasets. The seasonal P, measured by the autocorrelation of seasonal anomalies at a two-season lag, is largest in the eastern equatorial Pacific, the tropical Indian, and the tropical North and South Atlantic Oceans. It is also seasonally dependent. The “spring barrier” of P in Niño-3 (largest in summer and smallest in winter) varies interdecadally: relatively weak during 1885–1910, moderate during 1910–55, strong during 1955–75, and moderate during 1975–2000. The interdecadal changes of SD and P not only have important implications for SST forecasts but also have significant scientific values to be explored.
Abstract
SST predictions are usually issued in terms of anomalies and standardized anomalies relative to a 30-yr normal: climatological mean (CM) and standard deviation (SD). The World Meteorological Organization (WMO) suggests updating the 30-yr normal every 10 yr. In complying with the WMO's suggestion, a new 30-yr normal for the 1971–2000 base period is constructed. To put the new 30-yr normal in historical perspective, all the 30-yr normals since 1871 are investigated, starting from the beginning of each decade (1871–1900, 1881–1910, … , 1971–2000). Using the extended reconstructed sea surface temperature (ERSST) on a 2° grid for 1854–2000 and the Hadley Centre Sea Ice and SST dataset (HadISST) on a 1° grid for 1870–1999, eleven 30-yr normals are calculated, and the interdecadal changes of seasonal CM, seasonal SD, and seasonal persistence (P) are discussed. The interdecadal changes of seasonal CM are prominent (0.3°–0.6°) in the tropical Indian Ocean, the midlatitude North Pacific, the midlatitude North Atlantic, most of the South Atlantic, and the sub-Antarctic front. Four SST indices are used to represent the key regions of the interdecadal changes: the Indian Ocean (“INDIAN”; 10°S–25°N, 45°–100°E), the Pacific decadal oscillation (PDO; 35°–45°N, 160°E–160°W), the North Atlantic Oscillation (NAO; 40°–60°N, 20°–60°W), and the South Atlantic (SATL; 22°S–2°N, 35°W–10°E). Both INDIAN and SATL show a warming trend that is consistent between ERSST and HadISST. Both PDO and NAO show a multidecadal oscillation that is consistent between ERSST and HadISST except that HadISST is biased toward warm in summer and cold in winter relative to ERSST. The interdecadal changes in Niño-3 (5°S–5°N, 90°–150°W) are small (0.2°) and are inconsistent between ERSST and HadISST. The seasonal SD is prominent in the eastern equatorial Pacific, the North Pacific, and North Atlantic. The seasonal SD in Niño-3 varies interdecadally: intermediate during 1885–1910, small during 1910–65, and large during 1965–2000. These interdecadal changes of ENSO variance are further verified by the Darwin sea level pressure. The seasonality of ENSO variance (smallest in spring and largest in winter) also varies interdecadally: moderate during 1885–1910, weak during 1910–65, and strong during 1965–2000. The interdecadal changes of the seasonal SD of other indices are weak and cannot be determined well by the datasets. The seasonal P, measured by the autocorrelation of seasonal anomalies at a two-season lag, is largest in the eastern equatorial Pacific, the tropical Indian, and the tropical North and South Atlantic Oceans. It is also seasonally dependent. The “spring barrier” of P in Niño-3 (largest in summer and smallest in winter) varies interdecadally: relatively weak during 1885–1910, moderate during 1910–55, strong during 1955–75, and moderate during 1975–2000. The interdecadal changes of SD and P not only have important implications for SST forecasts but also have significant scientific values to be explored.
Abstract
Prior efforts have produced a sea surface temperature (SST) optimum interpolation (OI) analysis that is widely used, especially for climate purposes. The analysis uses in situ (ship and buoy) and infrared (IR) satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Beginning in December 1997, “microwave” SSTs became available from the Tropical Rainfall Measuring Mission (TRMM) satellite Microwave Imager (TMI). Microwave SSTs have a significant coverage advantage over “IR” SSTs because microwave SSTs can be retrieved in cloud-covered regions while IR SSTs cannot. However, microwave SSTs are at a much lower spatial resolution than the IR SSTs.
In this study, the impact of SSTs derived from TMI was tested from the perspective of the OI analysis. Six different versions of the OI were produced weekly from 10 December 1997 to 1 January 2003 using different combinations of AVHRR and TMI data and including versions with and without a bias correction of the satellite data. To make the results more objective, 20% of the buoys were randomly selected and the SSTs from these buoys were withheld from the OI for independent verification.
The results of the intercomparisons show that both AVHRR and TMI data have biases that must be corrected for climate studies. These biases change with time as physical properties of the atmosphere change and as satellite instruments and the orbits of the satellites, themselves, change. It is critical to monitor differences between satellite and other products to quickly diagnose any of these changes.
For the OI analyses with bias correction, it is difficult using the withheld buoys to clearly demonstrate that there is a significant advantage in adding TMI data. The advantage of TMI data is clearly shown in the OI analyses without bias correction. Because IR and microwave satellite algorithms are affected by different sources of error, biases may tend to cancel when both TMI and AVHRR data are used in the OI. Bias corrections cannot be made in regions where there are no in situ data. In these regions, the results of the analyses without bias corrections apply. Because there are areas of the ocean with limited in situ data and restricted AVHRR coverage due to cloud cover, the use of both TMI and AVHRR should improve the accuracy of the analysis in these regions. In addition, the use of more than one satellite product is helpful in diagnosing problems in these products.
Abstract
Prior efforts have produced a sea surface temperature (SST) optimum interpolation (OI) analysis that is widely used, especially for climate purposes. The analysis uses in situ (ship and buoy) and infrared (IR) satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Beginning in December 1997, “microwave” SSTs became available from the Tropical Rainfall Measuring Mission (TRMM) satellite Microwave Imager (TMI). Microwave SSTs have a significant coverage advantage over “IR” SSTs because microwave SSTs can be retrieved in cloud-covered regions while IR SSTs cannot. However, microwave SSTs are at a much lower spatial resolution than the IR SSTs.
In this study, the impact of SSTs derived from TMI was tested from the perspective of the OI analysis. Six different versions of the OI were produced weekly from 10 December 1997 to 1 January 2003 using different combinations of AVHRR and TMI data and including versions with and without a bias correction of the satellite data. To make the results more objective, 20% of the buoys were randomly selected and the SSTs from these buoys were withheld from the OI for independent verification.
The results of the intercomparisons show that both AVHRR and TMI data have biases that must be corrected for climate studies. These biases change with time as physical properties of the atmosphere change and as satellite instruments and the orbits of the satellites, themselves, change. It is critical to monitor differences between satellite and other products to quickly diagnose any of these changes.
For the OI analyses with bias correction, it is difficult using the withheld buoys to clearly demonstrate that there is a significant advantage in adding TMI data. The advantage of TMI data is clearly shown in the OI analyses without bias correction. Because IR and microwave satellite algorithms are affected by different sources of error, biases may tend to cancel when both TMI and AVHRR data are used in the OI. Bias corrections cannot be made in regions where there are no in situ data. In these regions, the results of the analyses without bias corrections apply. Because there are areas of the ocean with limited in situ data and restricted AVHRR coverage due to cloud cover, the use of both TMI and AVHRR should improve the accuracy of the analysis in these regions. In addition, the use of more than one satellite product is helpful in diagnosing problems in these products.
Abstract
Surface wind analyses from three data assimilation systems are compared with independent wind observations from six buoys located in the Pacific within 8 deg of the equator. The period of comparison is 6 months (February to July 1987), with daily sampling.
The agreement between the assimilation systems and the independent buoy data is disappointing. The longterm mean differences between the buoy and the assimilated zonal and meridional winds are as large as 3.1 m s−1, which is comparable to the size of the means themselves. The zonal and meridional daily wind correlations range between 0.66 and 0.17. The wind field agreement was actually better among the different systems than between any system and the buoys. However, the agreement among the analysis products was usually better for the zonal winds than for the meridional winds. For the time period and locations presented, the comparisons with the independent data show that no assimilation system is clearly superior to any of the others.
Abstract
Surface wind analyses from three data assimilation systems are compared with independent wind observations from six buoys located in the Pacific within 8 deg of the equator. The period of comparison is 6 months (February to July 1987), with daily sampling.
The agreement between the assimilation systems and the independent buoy data is disappointing. The longterm mean differences between the buoy and the assimilated zonal and meridional winds are as large as 3.1 m s−1, which is comparable to the size of the means themselves. The zonal and meridional daily wind correlations range between 0.66 and 0.17. The wind field agreement was actually better among the different systems than between any system and the buoys. However, the agreement among the analysis products was usually better for the zonal winds than for the meridional winds. For the time period and locations presented, the comparisons with the independent data show that no assimilation system is clearly superior to any of the others.
