ALBERT

Description: A merged land–air–sea surface temperature reconstruction analysis is developed for monthly anomalies. The reconstruction is global and spatially complete. Reconstructed anomalies damp toward zero in regions with insufficient sampling. Error estimates account for the damping associated with sparse sampling, and also for bias uncertainty in both the land and sea observations. Averages of the reconstruction are similar to simple averages of the unanalyzed data for most of the analysis period. For the nineteenth century, when sampling is most sparse and the error estimates are largest, the differences between the averaged reconstruction and the simple averages are largest. Sampling is always sparse poleward of 60° latitude, and historic reconstructions for the polar regions should be used with caution.

Description: Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses. The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR–AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.

Description: 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.

Description: A monthly extended reconstruction of global SST (ERSST) is produced based on Comprehensive Ocean–Atmosphere Data Set (COADS) release 2 observations from the 1854–1997 period. Improvements come from the use of updated COADS observations with new quality control procedures and from improved reconstruction methods. In addition error estimates are computed, which include uncertainty from both sampling and analysis errors. Using this method, little global variance can be reconstructed before the 1880s because data are too sparse to resolve enough modes for that period. Error estimates indicate that except in the North Atlantic ERSST is of limited value before 1880, when the uncertainty of the near-global average is almost as large as the signal. In most regions, the uncertainty decreases through most of the period and is smallest after 1950. The large-scale variations of ERSST are broadly consistent with those associated with the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) reconstruction produced by the Met Office. There are differences due to both the use of different historical bias corrections as well as different data and analysis procedures, but these differences do not change the overall character of the SST variations. Procedures used here produce a smoother analysis compared to HadISST. The smoother ERSST has the advantage of filtering out more noise at the possible cost of filtering out some real variations when sampling is sparse. A rotated EOF analysis of the ERSST anomalies shows that the dominant modes of variation include ENSO and modes associated with trends. Projection of the HadISST data onto the rotated eigenvectors produces time series similar to those for ERSST, indicating that the dominant modes of variation are consistent in both.

Description: 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 , respectively. In addition, all analyses except RSS used in situ data. Most analysis procedures and weighting functions differed. Thus, differences among analyses could be large in high-gradient and data-sparse regions. An example off the coast of South Carolina showed winter SST differences that exceeded 5°C. 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.

Description: Considerable effort is presently being devoted to producing high-resolution sea surface temperature (SST) analyses with a goal of spatial grid resolutions as low as 1 km. Because grid resolution is not the same as feature resolution, a method is needed to objectively determine the resolution capability and accuracy of SST analysis products. Ocean model SST fields are used in this study as simulated “true” SST data and subsampled based on actual infrared and microwave satellite data coverage. The subsampled data are used to simulate sampling errors due to missing data. Two different SST analyses are considered and run using both the full and the subsampled model SST fields, with and without additional noise. The results are compared as a function of spatial scales of variability using wavenumber auto- and cross-spectral analysis. The spectral variance at high wavenumbers (smallest wavelengths) is shown to be attenuated relative to the true SST because of smoothing that is inherent to both analysis procedures. Comparisons of the two analyses (both having grid sizes of roughly ) show important differences. One analysis tends to reproduce small-scale features more accurately when the high-resolution data coverage is good but produces more spurious small-scale noise when the high-resolution data coverage is poor. Analysis procedures can thus generate small-scale features with and without data, but the small-scale features in an SST analysis may be just noise when high-resolution data are sparse. Users must therefore be skeptical of high-resolution SST products, especially in regions where high-resolution (~5 km) infrared satellite data are limited because of cloud cover.