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Thomas M. Smith

Abstract

Historical reconstructions of climate fields, such as sea surface temperature (SST), are important for climate studies and monitoring. Reconstructions use statistics from a well-sampled base period to analyze a sparsely sampled historical period. Here a method is shown for adjusting the base-period statistics using the available historical data so that statistics better represent historical variations. The method is demonstrated using annual SST anomalies from a coupled GCM historical run, 1861–2005, forced by greenhouse gases and aerosols. Simulated data are constructed from the model’s SST using observed historical SST sampling with error estimates added. Reconstructions are performed using the simulated data, and the results are compared to the full model SST without added errors. The results from applying other reconstruction methods to the simulated data are compared. The tests show that the method improves annual SST reconstructions, especially in the early years, when sampling is most sparse and in the extratropics. In particular, the 1881–1900 correlation averaged over 30°–60°S and over 30°–60°N improves from about 0.4 using noniterative reconstruction to about 0.6 using iterative reconstruction. The correlations of annual values in the tropics are about 0.7 with both methods. Incorporating those improvements into an SST reconstruction could better represent extratropical climate variations in the nineteenth and early twentieth centuries, and improve the value of the reconstruction for long-period climate studies and for validating climate models.

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Thomas M. Smith

Abstract

Monthly sea level in the Pacific Ocean (30°S–30°N) is analyzed for the 1948–98 period. It is shown that most sea level variations in this region have large scales, and so the available network of tide gauge stations is sufficient for analysis over this period. Analysis is done using statistically optimal interpolation and the full covariance structure defined by a more recent well-sampled period. The analysis reflects major variations in the station data, including warm and cool episodes in the tropical Pacific and an increase in variance in the second half of the analysis period. At the tide gauge stations, the analysis correlation with observations generally exceeds 0.9. Cross-validation tests show that errors in the tropical Pacific sea level analysis are typically less than 4.5 cm throughout the analysis period and less than 4 cm in the best-sampled most recent period. This result compares well with the TOPEX/Poseidon (Ocean Topography Experiment) satellite error estimate of 2 cm.

Rotated EOF analysis of the 1948–98 sea level anomalies shows the dominance of ENSO variations. Taken together, the first three modes account for most of the variance and represent different phases of the ENSO cycle, with two buildup modes and a mature phase mode. One of the buildup modes is associated with a western Pacific warm pool centered north of the equator and is negatively correlated with variations in the east-equatorial Pacific. The other buildup mode is associated with a western Pacific warm pool centered south of the equator, which gives a strong indication of east-equatorial Pacific variations a year in advance. An increasing trend in the sea level over the record period may be related to increasing variance in the sea level, which is reflected by stronger and more frequent ENSO variations in the second half of the record.

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Ming Ji and Thomas M. Smith

Abstract

Two 11-yr Pacific Ocean simulations using an ocean general circulation model are compared with corresponding ocean analyses and with in situ observations from moorings and island tide gauges. The ocean simulations were forced by combining the climatological wind stress of Hellerman and Rosenstein with wind stress anomalies obtained from (a) The Florida State University surface wind analysis and (b) a two-member ensemble from an atmospheric model simulation. The ocean analyses were obtained by assimilating observed surface and subsurface temperatures into an ocean GCM, forced with the same wind stress anomaly fields used in the simulations.

The difference in thermocline depth between simulation and analysis using the same wind stress forcing is large in the off-equatorial regions near the North Equatorial Counter Current trough and in the South Pacific, suggesting that the mean climatological stress fields may be in error. The simulation results using the atmospheric GCM stress anomalies failed to show anomalous interannual sea level responses in the eastern equatorial Pacific, indicating that there are significant errors in the AGCM stress anomalies due to errors in the atmospheric model. The analyses show significant improvement over the comparable simulations when compared with the tide gauge data, indicating that assimilation of subsurface oceanic thermal data can compensate for stress-forcing errors and model errors on interannual timescales. However, the more accurate stress-forcing field leads to a better ocean analysis, indicating that the present density of temperature data is not sufficient to determine the ocean state.

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Thomas M. Smith and Muthuvel Chelliah

Abstract

An analysis of the tropical Pacific Ocean from January 1983 to December 1992 is used to describe the annual cycle, with the main focus on subsurface temperature variations. Some analysis of ocean-current variations are also considered. Monthly mean fields are generated by assimilation of surface and subsurface temperature data into an ocean general circulation model. Data used in the analysis include satellite sea surface temperature observations and surface and subsurface temperature observations from ships and buoys. Comparisons with observations show that the analysis reasonably describes large-scale ocean thermal variations. Ocean currents are not assimilated and do not compare as well with observations. However, the ocean-current variations in the analysis are qualitatively similar to the known variations given by others. The authors use harmonic analysis to separate the mean annual cycle and estimate its contribution to total variance.

The analysis shows that in most regions the annual cycle of subsurface thermal variations is larger than surface variations and that these variations are associated with changes in the depth of the thermocline. The annual cycle accounts for most of the total surface variance poleward of about 10° latitude but accounts for much less surface and subsurface total variance near the equator. Large subsurface annual cycles occur near 10°N associated with shifts of the intertropical convergence zone and along the equator associated with the annual cycle of equatorial wind stress. The hemispherically asymmetric depths of the 20°C isotherms indicate that the large Southern Hemisphere warm pool, which extends to near the equator, may play an important role in thermal variations on the equator.

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Tosiyuki Nakaegawa, Masao Kanamitsu, and Thomas M. Smith

Abstract

This study addresses the interdecadal trend in potential skill score as estimated from the 500-hPa height temporal correlation coefficient (TCC), based on a 50-yr 10-member ensemble GCM integration with observed SST. The skill scores are based on the perfect model assumption, in which one of the members of the ensemble is assumed to be true. A distinct decadal positive trend in the TCC in boreal winter (December–January–February) was found. This trend is shown to be consistent with the positive trend in the interdecadal time-scale temporal variance of SST. The geographical pattern of the differences of the TCC between each decade and the 50-yr period resembles the Matsuno–Gill pattern, suggesting that the increase in the TCC is due to the Rossby wave excitation induced by the anomalous diabatic heating caused by the anomalous SST. Similar interdecadal trends in the variance of the Southern Oscillation index and Pacific–North American pattern were found in both the observation and the simulation. The interdecadal trend in the variance of 500-hPa geopotential height over the continental United States, however, existed only in the simulation.

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Richap W. Reynolds and Thomas M. Smith

Abstract

In response to the development of a new higher-resolution sea surface temperature (SST) analysis at the National Meteorological Center (NMC), a new monthly 1° global sea surface temperature climatology was constructed from two intermediate climatologies: the 2° SST climatology presently used at NMC and a 1° SST climatology derived from the new analysis. The 2° SST climatology used a 30-yr 1950–1979 base period between roughly 40°S and 60°N based on in situ (ship and buoy) SST data supplemented by four years (1982–1985) of satellite SST retrievals. The 1° SST climatology was based on monthly analyses using in situ SST data, satellite SST retrievals, and sea-ice coverage data over a 12-yr period (1982–1993). The final climatology was combined from these two products so that a 1° resolution was maintained and the base period was adjusted to the 1950–1979 period wherever possible (approximately between 40°S and 60°N). Compared to the 2° climatology, the 1° climatology resolves equatorial upwelling and fronts much better. This leads to a better matching of the scales of the new analysis and climatology. In addition, because the magnitudes of large-scale features are consistently maintained in both the older 2° and the new 1° climatologies, climate monitoring of large-scale anomalies will be minimally affected by the analysis change. The use of 12 years of satellite SST retrievals makes this new climatology useful for many additional purposes because its effective resolution actually approaches 1° everywhere over the global ocean and because the mean SST values are more accurate south of 40°S than climatologies without these data.

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Thomas M. Smith and Richard W. Reynolds

Abstract

An improved SST reconstruction for the 1854–1997 period is developed. Compared to the version 1 analysis, in the western tropical Pacific, the tropical Atlantic, and Indian Oceans, more variance is resolved in the new analysis. This improved analysis also uses sea ice concentrations to improve the high-latitude SST analysis and a modified historical bias correction for the 1939–41 period. In addition, the new analysis includes an improved error estimate. Analysis uncertainty is largest in the nineteenth century and during the two world wars due to sparse sampling. The near-global average SST in the new analysis is consistent with the version 1 reconstruction. The 95% confidence uncertainty for the near-global average is 0.4°C or more in the nineteenth century, near 0.2°C for the first half of the twentieth century, and 0.1°C or less after 1950.

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Thomas M. Smith and Richard W. Reynolds

Abstract

In an earlier study (Reynolds and Smith), a monthly 1° SST climatology was produced for the 1950–79 base period. This climatology was constructed from two intermediate climatologies: a 2° SST climatology developed from in situ data for the period 1950–79, and a 1° SST climatology for the period 1982–93 derived from an optimum interpolation SST analysis that uses in situ and satellite data. Since then a new 2° spatial resolution near-global SST analysis has been developed, which can produce a similar high-resolution climatology for any base period within the analysis period (1950–92). In this note the procedure is utilized to change the base period to 1961–90, which is the climatological base period suggested by the World Meteorological Organization. The method is nearly identical to that used in the earlier study except for the formation of the 2° climatology from new analyses. The results show that the change in the climatology is generally small with absolute differences usually less than 0.2°C. As with the earlier climatology, in regions where insufficient in situ observations are available prior to 1982 there is no adjustment. In those regions, which include the Southern Ocean, the climatology base period is 1982–96.

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Thomas M. Smith and Richard W. Reynolds

Abstract

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.

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Thomas M. Smith and Richard W. Reynolds

Abstract

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.

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