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Reconstructions Improvements Using Iteratively Adjusted Statistics, Demonstrated Using Model-Output Annual SST Anomalies and Historical SamplingAbstract
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.
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.
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.
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.
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.
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.
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.
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.
Abstract
This paper is an extension of a study by C. Ropelewski and M. Halpert, which examines observed precipitation relationships with the Southern Oscillation. Here, the authors repeat their analysis using atmospheric general circulation model precipitation from the average of a 13-run ensemble. The GCM is the atmospheric component of the coupled model used for seasonal prediction at the National Centers for Environmental Prediction, except that in this study, the observed sea surface temperatures were specified for the ensemble runs. Results are compared and contrasted with the observed Southern Oscillation–related precipitation behavior. These comparisons show that the multiple ensemble simulations compare favorably to the observations for most areas in the Tropics and subtropics. However, outside of the deep Tropics, the model simulations show large shifts or biases in the location of the Southern Oscillation–related anomalies. In particular, anomalies shown by the observations to occur in the southeastern United States are shifted westward in the simulation.
Abstract
This paper is an extension of a study by C. Ropelewski and M. Halpert, which examines observed precipitation relationships with the Southern Oscillation. Here, the authors repeat their analysis using atmospheric general circulation model precipitation from the average of a 13-run ensemble. The GCM is the atmospheric component of the coupled model used for seasonal prediction at the National Centers for Environmental Prediction, except that in this study, the observed sea surface temperatures were specified for the ensemble runs. Results are compared and contrasted with the observed Southern Oscillation–related precipitation behavior. These comparisons show that the multiple ensemble simulations compare favorably to the observations for most areas in the Tropics and subtropics. However, outside of the deep Tropics, the model simulations show large shifts or biases in the location of the Southern Oscillation–related anomalies. In particular, anomalies shown by the observations to occur in the southeastern United States are shifted westward in the simulation.
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.
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.
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.
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.
Abstract
Because of changes in SST sampling methods in the 1940s and earlier, there are biases in the earlier period SSTs relative to the most recent 50 years. Published results from the Met Office have shown the need for historic bias correction and have developed several correction techniques. An independent bias-correction method is developed here from an analysis using nighttime marine air temperatures and SST observations from the Comprehensive Ocean–Atmosphere Data Set (COADS). Because this method is independent from methods proposed by the Met Office, the differences indicate uncertainties and similarities indicate where users may have more confidence in the bias correction.
The new method gives results that are broadly consistent with the latest Met Office bias estimates. However, this bias estimate has a stronger annual cycle of bias in the Northern Hemisphere in comparison with the Met Office estimate. Both estimates have midlatitude annual cycles, with the greatest bias in the cold season, and both have a small annual cycle in the Tropics. From the 1850s into the early twentieth century both bias estimates increase with time, although this estimate increases slightly less than the Met Office estimate over that period. Near-global average temperatures are not greatly affected by the choice of bias correction. However, the need for a bias correction in some periods may introduce greater uncertainty in the global averages. Differences in the bias corrections suggest that this bias-induced uncertainty in the near-global average may be 0.1°C in the nineteenth century, with less uncertainty in the early twentieth century.
Abstract
Because of changes in SST sampling methods in the 1940s and earlier, there are biases in the earlier period SSTs relative to the most recent 50 years. Published results from the Met Office have shown the need for historic bias correction and have developed several correction techniques. An independent bias-correction method is developed here from an analysis using nighttime marine air temperatures and SST observations from the Comprehensive Ocean–Atmosphere Data Set (COADS). Because this method is independent from methods proposed by the Met Office, the differences indicate uncertainties and similarities indicate where users may have more confidence in the bias correction.
The new method gives results that are broadly consistent with the latest Met Office bias estimates. However, this bias estimate has a stronger annual cycle of bias in the Northern Hemisphere in comparison with the Met Office estimate. Both estimates have midlatitude annual cycles, with the greatest bias in the cold season, and both have a small annual cycle in the Tropics. From the 1850s into the early twentieth century both bias estimates increase with time, although this estimate increases slightly less than the Met Office estimate over that period. Near-global average temperatures are not greatly affected by the choice of bias correction. However, the need for a bias correction in some periods may introduce greater uncertainty in the global averages. Differences in the bias corrections suggest that this bias-induced uncertainty in the near-global average may be 0.1°C in the nineteenth century, with less uncertainty in the early twentieth century.
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
A reconstructed sea surface temperature (SST) dataset is used to examine relationships between SST and seasonal mean surface temperature (T) and total precipitation (P) over most of the global continents for the 1950–92 period. Both specification (i.e., simultaneous) and predictive relations are studied.
Canonical correlation analysis (CCA) is used to describe the relationships and to provide information aiding in physical interpretation. A sequence of four consecutive 3-month periods of global SST anomalies is related to T and P anomalies during the fourth period for the specification analyses, and to 3-month periods ranging from one to four seasons later for the predictive analyses. Dynamical specifications of the National Centers for Environmental Prediction (NCEP) atmospheric model, using observed SST anomalies as boundary conditions, are also examined for confirmation of and comparison with the statistical specification relationships suggested by the CCA.
Specification and predictive cross-validated skill is modest except for certain regions and/or times of the year having correlations of 0.5 and greater. Seasonal T is generally specified/predicted with greater skill than P. Some regions have seasonally in their specificability/predictability, where skill varies more strongly as a function of the target season than lead time for T, P, or both. In these cases, such as Sahel African rainfall in northern summer or northeastern Australian rainfall in May through July, the skill of specification is not substantially higher than the skills of short or even moderately long lead prediction.
Specifications and predictions are skillful in areas affected by the ENSO, including the tropical Pacific islands for all seasons, and during specific seasons in northern and eastern Australia, and parts of Africa and North and South America. Skill is lowest in Europe and midlatitude Asia where ENSO's direct influence is lacking. However, non-ENSO predictive skill sources also contribute substantially to final skill; these exist both in regions strongly and minimally influenced by ENSO. The most important of these is an interdecadal trend from the 1950s to the 1980s–90s defined by a warming in the Indian and South Atlantic Oceans paralleling a cooling in the North Pacific and Atlantic basins. Another controlling SST dipole with a less obvious trend includes mainly the tropical SST of all three ocean basins versus the extratropical (especially Northern Hemisphere) SST. Still other, more localized, SST patterns are suggested as critical.
Some of the regions that show modest but usable seasonal predictive potential have no prior specificative or predictive history because they are not directly influenced by ENSO and/or have marginal data quality or density. This is encouraging, since the statistical skill realized here should be reproducible, and hopefully surpassable, using dynamical models.
Abstract
A reconstructed sea surface temperature (SST) dataset is used to examine relationships between SST and seasonal mean surface temperature (T) and total precipitation (P) over most of the global continents for the 1950–92 period. Both specification (i.e., simultaneous) and predictive relations are studied.
Canonical correlation analysis (CCA) is used to describe the relationships and to provide information aiding in physical interpretation. A sequence of four consecutive 3-month periods of global SST anomalies is related to T and P anomalies during the fourth period for the specification analyses, and to 3-month periods ranging from one to four seasons later for the predictive analyses. Dynamical specifications of the National Centers for Environmental Prediction (NCEP) atmospheric model, using observed SST anomalies as boundary conditions, are also examined for confirmation of and comparison with the statistical specification relationships suggested by the CCA.
Specification and predictive cross-validated skill is modest except for certain regions and/or times of the year having correlations of 0.5 and greater. Seasonal T is generally specified/predicted with greater skill than P. Some regions have seasonally in their specificability/predictability, where skill varies more strongly as a function of the target season than lead time for T, P, or both. In these cases, such as Sahel African rainfall in northern summer or northeastern Australian rainfall in May through July, the skill of specification is not substantially higher than the skills of short or even moderately long lead prediction.
Specifications and predictions are skillful in areas affected by the ENSO, including the tropical Pacific islands for all seasons, and during specific seasons in northern and eastern Australia, and parts of Africa and North and South America. Skill is lowest in Europe and midlatitude Asia where ENSO's direct influence is lacking. However, non-ENSO predictive skill sources also contribute substantially to final skill; these exist both in regions strongly and minimally influenced by ENSO. The most important of these is an interdecadal trend from the 1950s to the 1980s–90s defined by a warming in the Indian and South Atlantic Oceans paralleling a cooling in the North Pacific and Atlantic basins. Another controlling SST dipole with a less obvious trend includes mainly the tropical SST of all three ocean basins versus the extratropical (especially Northern Hemisphere) SST. Still other, more localized, SST patterns are suggested as critical.
Some of the regions that show modest but usable seasonal predictive potential have no prior specificative or predictive history because they are not directly influenced by ENSO and/or have marginal data quality or density. This is encouraging, since the statistical skill realized here should be reproducible, and hopefully surpassable, using dynamical models.
