While adequate for many purposes, the SST data sets all contain problems of one sort or another. Noise is evident in the GISST data and realistic temporal persistence of SST anomalies after 1981 is lacking. Trends in recent years are quite different between the GISST and NCEP analyses, and this can be partially traced to differences in the processing of in situ data and an increasing cold bias in the NCEP OI data arising from incompletely-corrected satellite data. Significant discrepancies also exist in centennial trends from the LDEO and GISST data sets, and these likely reflect the separate treatment of the very low frequency signal in the GISST analysis and questionable assumptions about the stationarity of statistics in the LDEO method.
Ensembles of integrations with an atmospheric general circulation model (AGCM) are used with three of the SST data sets as lower boundary conditions to show that the differences among them imply physically important differences in the atmospheric circulation. Over the tropics, where masking by internal atmospheric variability is small, SST differences affect moist convection and systematically produce strong responses in the local divergent circulation. A case study shows that analyzed SST differences in the tropical Pacific can be as large as for a moderate El Niño. Such large discrepancies induce local rainfall anomalies up to 8 mm/day -1 and, in addition to the tropical circulation anomalies, are associated with global teleconnections that influence temperatures and precipitation around the world. Results also show the limitations to using AGCMs when forced by specified SSTs.
The likely sources of the problems evident in the different SST products are identified and discussed. Several of the problems are being addressed by current efforts to reprocess the SST data, which is strongly recommended, but remaining problems demand further attention and attempts to resolve them should continue. The choice among SST analyses used for AGCM simulations, for the atmospheric reanalysis projects, for identifiying climate signals, and for monitoring climate is important, as known flaws in the analyses can compromise the results.
The need for correct SSTs in coupled simulations stems directly from several feedback processes that would be seriously distorted by inaccurate surface temperatures. Errors at high latitudes, for instance, can greatly impact the sea ice extent resulting in too much or too little with resultant ice-albedo feedbacks potentially exacerbating the original SST errors. Errors in tropical SSTs can greatly impact moist convection and the hydrological cycle, thereby affecting the water vapor feedback and global teleconnections such as those observed during El Niño events.
Given the critical need for correct SSTs, it is important to document and understand how well SSTs are known and whether or not the errors matter. These are the key questions addressed in this paper. We show that, while new methods of interpolating and extrapolating into areas devoid of observations likely have improved the historical SST analyses, important flaws remain in the global SST fields which are used for driving and validating models and in monitoring climate. Moreover, some of these are sufficiently serious that they compromise the results of model simulations and analyses produced with four-dimensional data assimilation (4DDA). Some problems with temporal continuity can be partially ameliorated with smoothing. However, other flaws or uncertainties, such as in trends, are not easily reduced.
We have firstly performed comprehensive comparisons among four monthly SST data sets: from NCEP both the optimal interpolation (OI) SST analysis of Reynolds and Smith (1994) and the empirical orthogonal function (EOF) reconstructed SST analysis of Smith et al. (1996), from the United Kingdom Meteorological Office (UKMO) version 2.3b of the Global Sea-Ice and SST Data Set (GISST) of Rayner et al. (1996), and from the Lamont Doherty Earth Observatory (LDEO) the optimal smoothing (OS) SST analysis of Kaplan et al. (1997; 1998). As well as significant differences between the long-term mean climatologies of the NCEP and GISST products, large differences exist in the monthly time series and are dramatically revealed by the autocorrelations of each product. The different SST data sets have been used as lower boundary conditions for ensembles of runs with the NCAR AGCM (version 3 of the Community Climate Model, CCM3), which enables us to assess the climatic significance of the analyzed SST differences. As we will show, displaced convection in the tropical Pacific because of errors in SSTs has a strong and direct impact on the tropical circulation, as well as on teleconnections into middle latitudes and elsewhere.
Another key issue is the long-term trends in the data sets. New methods for analyzing SSTs use a recent well observed base period to define spatial structures (modes) and statistics, such as how much a given observation projects onto each mode, that are then used to interpolate in space and time. This procedure has the advantage of more reliably projecting the SST anomaly patterns that exist based on limited observations, but it depends critically on the assumption of stationarity of the statistics. In particular, the presence of trends, such as those expected with climate change, seriously violates the assumptions of stationarity. For instance, given the presence of a linear trend in a long record, the standard deviation of that trend in a subsample of the record is proportional to the length of the subsample. Statistics based on the subsample will, therefore, necessarily underestimate the component that projects onto the long term trend. Different treatments of the very low frequency signal can give greatly different results. Both the GISST and LDEO data sets extend over a century and feature quite different trends. The LDEO data set, for example, indicates less warming that the GISST data set at most locations, including a cooling in the tropical eastern Pacific since the turn of the century (Cane et al. 1997).
Other recent comparisons of SST data sets have been carried out recently by Trenberth et al. (1992) (henceforth TCH) and Folland et al. (1993). A more complete summary of the results of TCH on errors in SSTs and their origins is given is Section 6 along with a discussion of the problems in defining SST and the impacts of satellite retrievals. TCH compared the reproducibility of SSTs in analyses from the UKMO and the U.S. Climate Prediction Center (CPC) which revealed monthly anomaly correlations on a 5° grid exceeding 0.9 over the northern oceans but less than 0.6 in the central tropical Pacific and south of about 35°S. Root mean square differences between CPC and UKMO monthly SST anomalies exceed 0.6°C in the regions where the correlation is lower than about 0.6. Similar results are reported here, and the dependence on the number of in situ observations is clear.
The data sets compared and evaluated are described in Section 2, and in Section 3 the model experiments used to assess the importance of the discrepancies in the SST fields are outlined. The results of the comparisons are presented in Section 4, and the results of the model experiments are given in Section 5. The results are discussed in the context of other sources of information relating to the reasons why discrepancies exist in Section 6, and conclusions are drawn in Section 7.
With the OI product, the high resolution of the satellite data is better preserved and the analysis is done weekly (and daily for operations). The first step is to use the blending technique to provide a preliminary large-scale time-dependent correction of satellite biases. The in situ and bias-corrected satellite SST data are then analyzed using OI on a 1° latitude and longitude grid. Optimal interpolation produces an interpolated value from a weighted sum of the data. Weights are computed using estimates of local spatial covariance and data error variance. The first guess is the previous analysis of the anomalies which, therefore, persists the anomalies in the absence of new information. The technique does not otherwise utilize information from earlier or later times.
The NCEP OI product is global and is therefore very useful for both climate monitoring and as a lower boundary condition for AGCM simulations; however, because of its reliance on SST retrievals from the AVHRR instruments, its period of coverage extends only from November 1981 onward.
The EOF-based SST analyses have a southern limit of 45°S because of the relative lack of in situ SST observations at high southern latitudes, and their northern limit (~ 65°N) is determined by regions of sea ice which are not well-represented by the EOFs. Gridpoints not reconstructed are assigned values from the 1982-1993 OI climatology of Reynolds and Smith (1995). The period of coverage is from January 1950 to the present. Overall, Smith et al. (1996) concluded that their EOF-based interpolation method (or eigenvector projection method, see Kaplan et al. 1997, 1998) results in an improved SST analysis which more realistically represents the large-scale SST structure in sparsely-sampled regions than more traditional analysis techniques. In regions where in situ sampling is dense, the EOF-based reconstruction does not have such a clear advantage.
Several different steps have been used to construct the GISST analyses.
(i) GISS 2.1. The analyzed fields are monthly from January 1982 onward and make use of the Poisson blending technique of Reynolds (1988) to incorporate bias-corrected satellite-derived SST data from the AVHRR instruments with in situ data from the quality-controlled Meteorological Office historical SST data set (MOHSST version 6, Bottomley et al. 1990; Parker et al. 1994; Parker et al. 1995a) and COADS, sea-ice data and a statistically-based ice-zone SST specification (Rayner et al. 1996). The analysis is done using an anomaly resolution of 2°, and total SST fields are obtained by adding back the 1° resolution 1961-1990 climatology of Parker et al. (1995b).
(ii) GISS 1.2. Spatial gaps in 5° gridded monthly MOHSST (version 5) anomalies for 1903-1948 are infilled using coarse resolution (~ 10°) ocean basin EOFs based on seasonal data from 1901-1990. For data poor areas where EOFs cannot be adequately defined, SST values are estimated using the Poisson equation technique with assumptions about SST near the ice edge as in Parker et al. (1995c). The anomalies used in the 1° resolution climatology added back are with respect to 1951-1980.
(iii) GISST 2.2. Over the period 1949-1981 SST fields are based on reconstructions using eigenvectors of in situ SST anomalies with a 2° spatial resolution in a fashion similar to that employed in the NCEP EOF analyses of Smith et al. (1996). Details differ, however, in the length of the SST records used to construct the EOFs, in the areas over which the EOFs are computed, and in the methods used to deal with the trend component. The first step of the analysis is to remove a global multidecadal SST trend signal represented by the first global EOF of low-pass filtered coarse resolution data (Fig. 3 in Rayner et al. 1996; see also Parker and Folland 1991). Next, 2° resolution regional EOFs over four ocean basins are calculated using MOHSST (version 6) anomalies over 1951-1990 interpolated using a background field reconstructed from 20 coarse resolution global EOFs. As for the NCEP EOF analyses, the areas covered by the regional EOFs overlap slightly (Rayner et al. 1996), so a near-global SST reconstruction is made by averaging across the overlapping domains before adding back the low-frequency-trend EOF. Where data coverage is too sparse to determine EOFs (e.g., parts of the Southern Ocean and the southeastern Pacific), SSTs are infilled using Laplacian interpolation similar to the scheme used in GISST 1.2. As for GISST 2.1, ice-zone SSTs are specified statistically, and the 1° resolution 1961-1990 climatology of Parker et al. (1995b) is added back to obtain the total SST fields.
The combination of the EOF-reconstructed SSTs over 1949-1981, GISST 1.2 and GISST 2.1 is termed GISST 2.2. Rayner et al. (1996) discuss improvements relative to GISST 1.1 (Parker et al. 1995c): in particular, after 1948 GISST 2.2 contains more accurate sea-ice data, a better representation of near-ice SST, an improved background climatology and higher resolution SST analyses.
(iv) GISST 2.3. The SST analyses over 1903-1948 are improved and extended back to 1871 by reanalyzing the data in a similar way to the EOF-reconstructed SSTs discussed above. In particular, MOHSST (version 6) data are used throughout, a global trend EOF and 4° resolution ocean basin EOFs are utilized in the reconstructions, and the anomaly analysis is done on a 4° grid.
At high latitudes, marginal ice zone SSTs in GISST 2.3 are obtained through simple regression relations between SST and sea-ice concentration in areas where both exist. The sea ice concentration data are from Walsh (1995) over the Arctic and various climatologies and other sources over the Antarctic and inland seas (see Rayner et al. 1996 for details). Observed monthly mean SSTs from in situ sources for 1961-1990 over the NH and satellite-derived SSTs for 1982-1994 over the Antarctic are regressed against the sea-ice concentration data. The resulting equations depend on season and longitude over the Arctic, but only season over the Antarctic.
Satellite data used in the construction of the GISST 2.3 analysis were erroneously biased from 1982 onward in the initial release of the data (GISST 2.3a). The correction of this error led to the release of GISST 2.3b, which is the data set examined here.
The OS method and its differences from other statistically-based analysis methods such as OI and the eigenvector projection technique of Smith et al. (1996) is described in detail by Kaplan et al. (1997), who also demonstrate the differences in the analyses over the relatively data rich Atlantic north of approximately 30°S. The techniques are then applied and compared over the global oceans by Kaplan et al. (1998).
The OS technique combines data reduction and least squares optimal estimation. The data reduction involves computing EOFs of the MOHSST data over the period 1951-1991, then using a subset (80 global EOFs) as a basis for the analyzed solution. Unlike the approaches used in the construction of the NCEP EOF and the GISST analyses, the trend component is not analyzed separately in the OS technique, so unless the base period contains the whole trend, it is likely to be underestimated.
It is the determination of the stationary spatial covariance of the SST field that is perhaps the most novel feature of the OS analysis. Because of data gaps and observational error in the covariance field, Kaplan et al. (1997) smooth it in each spatial direction in such a way as to preserve the large scale relations in the original covariance while eliminating the small scale variations, which are presumed to be dominated by observational error. The variance of the original SST data removed by this procedure is then recovered by inflating the variance in the smoothed spatial covariance. The EOFs to be used as a basis set are then calculated and are also used for fitting a first-order linear autoregression model of time transitions. Thus, the OS technique provides a best estimate of SST based on available observations at all space points, but it also utilizes information from all times (preceding, during, and after the analyses time), in contrast to the OI and projection methods.
Over periods of relatively good data coverage, Kaplan et al. (1997; 1998) find that the OS, OI and projection methods give comparable results; however, at times of especially poor coverage the use of information from other times appears to give the OS method an advantage. As in the Smith et al. (1996) SST analyses, the OS SST product is not global: in extremely data sparse areas no attempt is made to estimate the spatial covariance field.
The other 1° resolution climatology we examine is the GISST 2.2 product described by Parker et al. (1995b), which improves upon earlier UKMO climatologies (e.g., Bottomley et al. 1990) especially in data sparse regions because of the utilization of satellite data. A globally complete background SST field is first created from blended satellite and MOHSST data over 1982-1994 (GISST 1.1, Parker et al. 1995c). Worldwide in situ SSTs over the years 1961-1990, combined with statistically-based estimates of SSTs in sea-ice zones, are then blended with the background SST field as outlined in Parker et al. (1995b), and the resulting monthly SSTs are averaged to form a new monthly 1° resolution climatology representative of the WMO standard reference period. This monthly climatology is then interpolated to daily resolution, and these values are used in the quality-control of the latest version of MOHSST (version 6) which, in turn, is used in the development of the monthly GISST 2.2 analyses described above. The final step is to average the resulting GISST 2.2 analyses to form a new GISST 2.2 1961-1990 monthly climatology.
Here we examine the impact of differences in analyzed SSTs on a model-simulated climate with a recent version of the NCAR AGCM, CCM3, described in detail by Kiehl et al. (1998). The standard model configuration uses a triangular wavenumber 42 (T42) horizontal spectral resolution (approximately a 2.8° by 2.8° transform grid) with 18 unequally-spaced vertical (hybrid) levels. Fifteen integrations are analyzed. One five-member ensemble is forced with the monthly NCEP EOF-reconstructed SST analyses over 1950-1997, while another five-member ensemble is forced with those SSTs through 1981 and the NCEP OI SST analyses over 1982-1997. The final five runs are forced with the GISST 2.2 SST analyses over the period 1903-1994. Analyzed monthly SSTs are assigned to the mid-month date and updated every time step at each ocean grid point using linear interpolation.
Other large differences relate to the ability of the NCEP adjusted OI climatology to resolve real small-scale structures and sharp SST gradients. This is particularly evident in the equatorial Pacific upwelling region, where the GISST climatology is warmer by almost 0.5°C, and in areas where SST gradients are large. Absolute SST differences exceed 1°C, for instance, in the retroflection region south of Africa and near the Peru, Falkland and Benguela currents. Even more striking are the large differences in the Kuroshio extension in the North Pacific and in the Gulf Stream of the North Atlantic. In the latter area, a narrow Gulf Stream is well defined in the NCEP climatology, but not in the GISST, which leads to a dipole structure in the differences with absolute values exceeding 1°C (Fig. 1).
Higher resolution SST data sets, such as the NCEP OI and GISST 2.3b products, are directly compared; however, they are area-averaged onto coarser resolution grids for comparisons with the NCEP EOF-reconstructed and LDEO SST data sets. For brevity, we first focus primarily on the NCEP OI and GISST 2.3b SST analyses from 1982 onward. Both incorporate in situ and satellite SST data over this period and, because they have complete global coverage, they are commonly used to force AGCMs.
The standard deviation of monthly SST anomalies from both the NCEP OI and GISST analyses is largest along the equatorial tropical Pacific and South American coast where interannual variability associated with the El Niño/Southern Oscillation (ENSO) phenomenon is most pronounced (Fig. 2). Large month-to-month variability is also evident in the SSTs over the North Pacific and over the North Atlantic associated with the Kuroshio extension and the position of the Gulf Stream. In all of these areas the variance is larger in the NCEP OI analyses than in the GISST product partially reflecting the coarser resolution of the latter. Over much of the rest of the global oceans, however, monthly SST variability is greater in GISST. This is especially true over data sparse regions where GISST relies heavily on locally interpolated in situ observations and sampling uncertainty is large. Smith et al. (1996) have also compared these two analyses to their EOF-reconstructed SSTs and our findings (figures not shown) are consistent. In general, the NCEP reanalyzed SSTs retain most of the variance of the OI product, with very good agreement over the northern oceans and slightly less variance over the tropical Pacific.
Global maps of correlation coefficients and rms differences between the NCEP OI and GISST 1° global SST monthly anomalies (Fig. 3) reveal that over the northern oceans and the eastern tropical Pacific correlation coefficients are highest, mostly exceeding 0.9. Values are generally lower than 0.75 south of about 10°N and are much lower locally over the western tropical Pacific and the southern oceans, both regions where the number of in situ observations drops off considerably. Root-mean-square differences between the SST analyses increase from less than 0.2°C over the central North Atlantic to over 0.6°C over the eastern tropical Pacific, in the eastern Pacific south of 10°S, and generally south of 35°S except near New Zealand (Fig. 3b). In the two latter areas the correlations are less than ~ 0.6. As shown in TCH, there is a striking resemblance between both the pattern of correlations and rms differences and the numbers of in situ observations available. Because essentially the same in situ and satellite observations are used in both the GISST and NCEP OI products, the correlations and rms differences reflect differences in the quality control and analysis methods and it is apparent that there is uncertainty in the true anomalies related to the rms differences (see discussion in Section 6).
One reason for the differences is the coarser resolution used in the GISST analysis. A coarser-grid analysis may be more realistic for the pre-satellite record, and especially before 1950 when sampling is often poor. But it is costly when high-resolution data are available. For example, GISST can not resolve equatorial upwelling as well as the NCEP OI (Fig. 1). In addition, the GISST analysis relies more heavily on in situ data which are noisier than satellite data (e.g., Reynolds and Smith 1994).
Another reason for the differences in correlation relates to the size and persistence of the climate signal, and some insight into this is given by maps of the standard deviation of the monthly anomalies (Fig. 2). This quantity squared depicts the sum of the actual signal plus the noise variance. Although details differ, large signals are hard to miss and are captured in both analyses. This is true, for instance, over the eastern tropical Pacific where the El Niño signal is large. In contrast, over much of the rest of the tropical oceans, the signal is small and the influence of noise is greater.
The low lag-1 month autocorrelations and their pattern in GISST 2.3b from 1982 onward relates primarily to the use of the Poisson blending technique (Rayner, 1998, personal communication). From 1982-1994 in situ data were infilled using the Laplacian of the AVHRR SST field (after 1995 the NCEP OI SSTs were used for this purpose), and the result is that areas of poor in situ coverage are temporally incoherent. As expected, this problem can be partially alleviated through smoothing. When the lag-1 autocorrelations are computed from running 3-month mean anomalies, for instance, the agreement between the NCEP OI and GISST products improves considerably, with autocorrelations exceeding 0.7 at all ocean points in both data sets, although autocorrelations remain slightly lower in the GISST product (not shown).
The lack of temporal continuity in the monthly GISST analyses after 1981 is not, however, evident when earlier times are examined. Over the period when the GISST fields are based mainly on reconstructions using eigenvectors of in situ SST anomalies, for example, lag-1 month autocorrelations are much higher (Fig. 5). In fact they seem to be too high in the Kuroshio extension and Gulf Stream regions (cf. the NCEP OI in Fig. 4). Low values are still found locally over middle and high latitudes of the Southern Hemisphere (SH), where there is no reason physically for the values to be lower than comparable NH regions where a Sverdrup balance dominates. This again reveals that the data coverage is a factor in the analyses.
Also shown in Fig. 5 is the autocorrelation from the LDEO analyses. For direct comparison with the GISST results, the lag-one month autocorrelations are shown for the period 1950-1981. The results are consistent with those from other periods as well and are very smooth, which reflects the very coarse 5° resolution. Also the coarse resolution reduces the variability of small scale eddies, such as in the Gulf Stream, and thus increases the autocorrelation. Otherwise the values are reasonably consistent with those of the NCEP OI analyses.
While the GISST results are not guaranteed to be correct, the trend is analyzed separately, as is desirable to take into account the non-stationary effects trends produce in any analysis of variance. The low frequency signal is also removed in the NCEP analyses of Smith et al. (1996, 1998) before gridding of the residual signal, in order to avoid this problem, and the low-frequency variance is only added back on after gridding. In contrast, it is clear that the trend will not be correctly projected using the OS technique, unless the base period of 1951-1991 contains the entire trend, which is clearly does not.
Over the NH extratropics (20°N-60°N, Fig. 7) monthly SST fluctuations are highly correlated between the GISST and both NCEP analyses (0.87) over the period since 1950, and the largest differences tend to appear in individual months rather than over extended periods. Monthly differences become smaller after the mid 1970s except toward the end of the record (e.g., May 1995 and August 1996) because of spiky behavior in the GISST analyses. Of note are the differences in warming over the past five years, with NCEP EOF SSTs showing distinctly less warming.
In order to examine the extent of the agreement and discrepancies in more detail, 5-yr running means of cross correlations, autocorrelations, standard deviations and rms differences were computed. One noticeable feature this revealed over the NH was very low correlations (~ 0.2) between the GISST and NCEP EOF SSTs during the early 1960s and during the mid 1970s, and these are primarily due to very different trends over those short periods. Centered on January 1961, for instance, the GISST analyses cool at a rate of -0.16°C per 5-yr while the linear trend in the NCEP data is 0.11°C per 5-yr (Fig. 7). The NCEP EOF SSTs exhibit slightly less monthly variance than either the GISST or the OI products, consistent with the findings of Smith et al. (1996), although this characteristic is more noticeable in regions where the in situ coverage is worse such as the tropics and the SH.
The best agreement in all statistical measures among the three data sets occurs when the SST anomalies are averaged over the tropics (Fig. 7). Over this portion of the globe (20°S-20°N), the large interannual variability associated with ENSO is well-captured in each analysis, although absolute differences of up to 0.2°C are evident between the GISST and NCEP reconstructed data prior to the 1980s. Clearly seen in all three products is the post 1976 jump to higher SSTs (Trenberth and Hoar 1996, 1997). Over the past 15 years, differences are small but show a slight warming in the GISST data relative to both NCEP products.
The agreement between monthly SST anomalies is worst over the SH extratropics (45°S-20°S) where absolute differences are large (up to 0.5°C between the GISST and NCEP OI analyses which correlate at 0.52 since 1982) and spiky in character (Fig. 7). The warming of the GISST relative to both NCEP products is most pronounced in the SH which is also where there is the least amount of in situ data. As discussed in Section 6, it is likely that the NCEP OI products is biased cold, and even more so in the 1990s, because of the satellite data. The NCEP EOF analyses do not use satellite data, however, so the differences with GISST must be due to the processing of in situ data (Reynolds, 1999 personal communication).
The temporal evolution of SST anomalies over the large 1982-1983 El Niño event is depicted differently in the NCEP OI analyses relative to the GISST analyses (Fig. 8). Monthly tropical Pacific SST anomalies, referenced to a 1950-1979 base period, are shown as a function of time and longitude averaged between 5°S and 5°N. The lack of temporal consistency in the monthly GISST data is clearly evident: relatively large SST anomalies of one sign are often followed 1 or 2 months later by equally large SST anomalies of opposite sign, in contrast to the relatively smooth temporal behavior of the NCEP OI product. Differences between the two total SST fields (Fig. 9) further reveal the noisier structure in GISST. Some of this can be removed by smoothing (e.g., 3- or 5-month running averages), but systematic differences remain.
We have examined the impact of differences between the NCEP OI, the NCEP EOF reconstructed and the GISST 2.2 SST data sets on 5-member ensemble simulations performed with CCM3. Differences between the ensemble-mean monthly total precipitation averaged over 5°S-5°N (Fig. 9) are as large as 5 mm day-1, especially west of dateline, and the noisier structure from the GISST analysis is apparent. The relationship between differences in total precipitation and SST is, however, nonlinear. Over the eastern tropical Pacific, for instance, differences between two SST analyses as large as those in Fig. 9 do not usually translate into significant precipitation differences except during warm events. This is seen by the lack of rainfall differences after mid 1983 when the SSTs returned to below normal in this region (Fig. 9). Thus over cold upwelling equatorial waters it does not rain in the model (or in nature) even if one SST analysis is 1-2°C warmer or colder than another. The same is not true over the warm pool region, however, where ensemble-mean monthly total precipitation differences over 1982-1984 (and all other periods as well) are more frequently (but not always) of the same sign as the SST differences, and this is also true when the El Niño warming spreads into the eastern Pacific (as in 1982-early 1983) (Fig. 9).
How realistic is the precipitation response in CCM3 to differences in analyzed SSTs? The correlation coefficients from 1979 to 1995 between monthly SST anomalies from Smith et al. (1996) and monthly precipitation anomalies from Xie and Arkin (1996) reveal that the highest values (>0.45) are observed over the tropical Pacific, with considerably lower values elsewhere but with large-scale structure (Fig. 10). In comparison, the gridpoint correlations between the same SST data set and the ensemble-mean precipitation anomalies from CCM3 reveal higher correlations that are positive almost everywhere (Fig. 10). Similar results have been found for other AGCMs as well, including those used in the reanalysis of atmospheric data (Masutani 1997). This most likely indicates a shortcoming of most state-of-the-art AGCM experiments driven with specified SSTs. In such integrations heat fluxes from the ocean into the atmosphere do not cool the SSTs (Saravanan 1998), whereas, over the extratropics, it is established that the atmosphere typically drives the ocean rather than the other way round (Trenberth et al. 1998). Because there are substantial uncertainties in the precipitation data set (Xie and Arkin 1996), correlations may be lower in the ``observed'' panel in Fig. 10 than in reality. Nevertheless, it is likely that these kinds of AMIP runs are only pertinent for SST anomalies in the tropics where the ocean drives the atmosphere and has a large upper ocean heat content support. A salient point here is that most AGCMs, including CCM3, do respond relatively strongly and realistically to changes (or differences) in monthly SST in low latitudes.
We found many examples of this using CCM3, and we illustrate one in Fig. 11. Averaged over the 3-months December-January-February (DJF) 1954/55, SSTs in GISST near and just west of the dateline along the equator are ~ 1°C warmer than they are in the NCEP EOF-reconstructed SST analyses. Differences in CCM3 ensemble-mean total precipitation over this area reach up to 8 mm day-1 (with the GISST-forced precipitation rate greater), and such large changes in the modeled latent heating also imply changes in atmospheric responses and teleconnections.
Also shown in Fig. 11 are the differences between the ensemble-mean 200 mb divergent wind component and the 200 mb streamfunction averaged over DJF 1954/55. The response in the local divergent wind is clear and is as expected: over the region where the GISST SSTs are warmer and the CCM3 simulated precipitation rate is greater, the divergent outflow is stronger in the GISST-forced runs, and this is true in each individual member of the ensemble as well. Moreover, differences in the ensemble-mean subtropical and extratropical rotational flow are consistent with the changes in the tropical heating and outflow (Trenberth et al. 1998). In addition to twin anticyclonic centers just poleward of the region of the largest differences in SST, strong wavetrains are evident over the extratropical Pacific and downstream over both hemispheres with geopotential height anomalies exceeding 100 m in places. The magnitudes of the streamfunction anomalies are similar to those observed during warm or cold ENSO events (e.g., see Fig. 39 in Hurrell et al. 1998).
More generally, however, we often found it difficult to identify an unequivocal extratropical response to analyzed SST differences using CCM3, as would be expected given the large amount of internal atmospheric variability in middle and high latitudes. This is especially true of periods when large differences in tropical SSTs persisted for only 1 or 2 months, although such cases undoubtedly add spurious variance to the simulated climate. In several cases when tropical SST differences persist for several months or longer (such as DJF 1954/55), however, extratropical responses very consistent with the tropical heating anomalies that occur in nature are identifiable. This has important implications for the interpretation of climate variability and predictability from AGCM runs forced with analyzed SSTs.
Using the COADS, TCH analyzed sources of errors for in situ SSTs. By assessing the variability within 2° longitude by 2° latitude boxes within each month for 1979, TCH found that individual SST measurements are representative of the monthly mean to within a standard error of 1.0°C in the tropics and 1.2 to 1.4°C in the extratropics. The standard error is larger in the North Pacific than in the North Atlantic and it is much larger in regions of strong SST gradient, such as within the vicinity of the Gulf Stream. This is because both within-month temporal variability and the within-2° box spatial variability are enhanced. The total standard error of the monthly mean in each box is reduced approximately by the square root of the number of observations available. The overall noise in SSTs ranges from less than 0.1°C over the North Atlantic to over 0.5°C over the oceans south of about 35°S.
An additional problem with SST is that it is not as well defined as is desirable. Historically, SST has referred to a bulk near-surface ocean temperature measured by tossing a bucket over the side of a ship in order to obtain a water sample. The design and insulation of the buckets has changed with time, however, so that corrections must be applied (Folland and Parker 1995). During World War II, moreover, there was a switch from bucket measurements to measuring the temperature of water taken on to cool the ship's engines. These temperatures depend on the depth (3 to 7 or more) and size (10 to 51 cm in diameter) of the ship's intake, the lading of the ship, the configuration of the engine room and the point where the measurement is taken. Such differences are responsible for some of the noise in the SST measurements, but biases also arise because heat from the engine room more than offsets any cold bias from the depth of the intake. Overall, the differences between engine intake and bucket temperatures is typically 0.3°C (see TCH for a more complete review).
With satellite remote sensing of SSTs has come additional problems related to the skin (radiometric) temperature and the difference between the near-surface and bulk temperatures. While infrared satellite measurements of SST in principle give the skin temperature, many algorithms convert the skin temperature into a bulk SST measurement using a form of regression with selected buoy observations (e.g., Reynolds and Marsico 1993, Reynolds and Smith 1994). In the tropical western Pacific warm pool, Webster et al. (1996) compared the values of skin temperature versus the bulk temperatures at 1 cm, 0.5 m and 5 m depth, where the latter corresponds to the typical depth of measurements from buoys and ship intakes. Skin temperatures are lower than the bulk 1 cm depth SST by typically 0.2°C. In well mixed windy conditions the three bulk SSTs are about the same, while in light wind conditions with high surface insolation strong near-surface warming occurs, and 1 cm temperatures are warmer than the 5 m depth SSTs by as much as 3°C. This gives rise to a significant diurnal cycle in SSTs on days with light winds. Webster et al. (1996) point out that a 1°C error in SSTs typically results in errors of 27 W m-2 in surface energy balance. This would be expected to have a significant impact on convection, as we have shown in the previous section.
As shown in Section 4, in all regions there is a recent warming in GISST relative to the NCEP OI data, and GISST is systematically warmer relative to the other analyses after 1982, especially in the tropics and SH. These features are most likely related to differences in the processing of in situ data, and the cold bias in the NCEP OI product arises also from incompletely bias-corrected satellite data and is worse in the 1990s than in the 1980s (Reynolds, 1998, personal communication). Reynolds (1988) showed that the satellite data were causing biases in the analyses, and the biases were different for daytime and nighttime retrievals. Folland et al. (1993) showed that the CPC satellite SST data were biased cold, typically for example by 0.5°C in the tropics and SH, where in situ data used for bias correction are few. Satellite based SSTs are not available in cloudy regions and, while they allow for water vapor attenuation, they are adversely affected by aerosols. The 1991 Mount Pinatubo eruption, for instance, produced a cold bias in the retrieved SSTs (Reynolds and Smith 1994). During the 1980s, in situ data from COADS were used to correct biases in the satellite-derived SSTs, so that roughly 30 thousand ship observations per week globally were used. During the 1990s, however, only GTS in situ data have been used, which translates into approximately 15 thousand observations per week (Reynolds, 1998, personal communication). Because the bias correction is underestimated where in situ observations are sparse, the difference in the number of ship observations is a likely cause of the cold bias in the NCEP OI analyses relative to GISST, which is especially evident over the SH middle and high latitudes during the 1990s. Therefore, although global SST analyses are likely to be more complete when satellite retrieved SSTs are included, they may be subjected to biases that vary in time.
Clearly there is scope for a more physical treatment of satellite measurements. It is desirable, for instance, to explicitly recognize the different nature of skin and bulk SST measurements and parameterize the diurnal cycle in satellite SSTs estimates, especially in light wind areas (Webster et al. 1996).
Near the sea ice zones large differences exist in the SST analyses which need to be resolved. Observations are few and far between in these regions but there is information that should allow improved empirical models to be developed and used to make the analyses of SST more reliable. In particular, it is important that the SSTs should be consistent with the observed ice cover. One of the most important problems is uncertainty in the sea ice concentrations. During northern summer, for instance, climatological sea ice concentrations over the Arctic based on objective analyses of microwave satellite observations differ by 20% or more in some regions from a subjective analysis (Knight 1984) of in situ and satellite data (Reynolds 1999, personal communication). The collocated data used to define the constants in the GISST regressions (section 2c and Rayner et al. 1996) primarily occur at low concentrations near the ice margins. The statistically generated SSTs at higher latitudes, however, are far from most of these data and are thus sensitive to the choice of the sea ice concentration data set. Careful comparisons and evaluations of different sea ice analyses are therefore critical.
The problem of sparse or non-existent data in the more distant past will remain. Quality control is also more difficult with few observations, and redundancy is essential for cross checking. Our experience indicates that at least three observations are desirable before a monthly anomaly can be reliably defined. However, exploiting the full information contained in each observation and their time sequence is a problem that is only beginning to be addressed. The OS technique of Kaplan et al. (1998) takes full advantage of the spatial and temporal structure expected in the SSTs. The noise evident in the GISST analyses appears to come partly from shortcomings in quality control which should otherwise catch the problems seen in Fig. 8. However, the lack of continuity in the GISST analyses after 1982 (Fig. 4) also shows the need to exploit the temporal persistence better.
As noted in the Introduction and in section 4d, the treatment of trends is difficult and different approaches yield greatly different results. There is a need to recognize that a warming trend is present, for whatever reason, in the SSTs in most places. Moreover, a warming trend is expected from human activities and the increases in carbon dioxide and other greenhouse gases in the atmosphere. Consequently, the climate should not be assumed stationary. The use of EOFs onto which to project an observation based upon relationships in a recent well observed period assumes stationarity and is therefore invalid. The standard deviation of a linear trend is proportional to the length of the sample record. Therefore any trend pattern will be underestimated in a sub-sample of the entire record. This accounts for the differences seen in Fig. 6, in which the LDEO values are more suspect. The GISST approach and that of Smith et al. (1996, 1998) separates out the trend before carrying out the remaining analysis and it is clear that such special treatment is essential.
In GISST 2.3b there is evidence that the quality control of observations could be improved and there are major deficiencies in the temporal continuity of SST anomalies after 1981. If exisiting GISST analyses are used for driving AGCMs, we strongly recommend that a 3- or 5-month running mean filter should be applied to the monthly means after 1981. Satellite data usage in analyses has the advantage of better defining spatial patterns of SST and regions of strong gradient, but more attention needs to be paid to possible biases in retrievals, especially associated with volcanic aerosol, and to physical differences between skin and bulk temperature. Such biases can easily affect trends, and this seems to be the case for the NCEP OI SSTs. Other differences between the SST analyses relate to differences in the processing of in situ data. Differences between the processed in situ data used at NCEP and the MOHSST (version 6) data, for instance, are larger than differences in the unprocessed in situ data from the two centers (Reynolds 1999, personal communication), so a careful comparison of the in situ processing methods is warranted.
New methods of interpolating and extrapolating into areas devoid of observations likely improves the SST analyses but caution is called for in what such methods may imply for trends or other non-stationary features of time series. Consequently the long-term trends in the LDEO SST analyses are especially suspect. However, the optimal smoothing analysis technique has much to recommend it, and it is hoped that something like it could be applied to higher resolution data sets with special measures taken to deal with the trends.
In the course of this study, we have examined ensembles of runs with the CCM3 forced by specified SST sequences from the different analyses. As shown in Fig. 10, there appear to be significant differences between the modeled local precipitation response to the SST anomalies and the estimates from observations. This result has to be tempered by the uncertainty in the observed precipitation estimates and also in the SSTs, as shown here, but it provides a strong indication that such model experiments may not be well posed physically. Implicit in these AGCM experiments is the assumption that the atmosphere responds locally to the SSTs and that the SSTs evolve in a realistic fashion. There is a good basis for the first assumption in the tropics and subtropics, such as with El Niño events, as the atmosphere responds locally and fairly deterministically (e.g., Shukla 1998, Trenberth et al. 1998). But in the extratropics this is not the case, as more typically it seems that the atmosphere drives the SST changes and the atmospheric response is unlikely to be primarily local (Trenberth et al. 1998). In addition, as chaotic aspects dominate the flow it is unlikely that the SST evolution matches that implied by the surface fluxes in the AGCM simulation. These points add to those of Saravanan (1998) who found that the relationship between the surface heat flux is opposite in sign in AMIP-type integrations versus fully coupled ocean-atmosphere models, so there are limitations to using AGCMs with specified SSTs.
We used the AGCM model results to show that the tropical SST differences which exist in the data sets are important, not only in the region they occur but also globally through teleconnections. Differences can be as large as those from modest El Niño events, including the implied differences in temperatures and precipitation. Therefore the SST data set chosen for AMIP-type integrations, for attempting to sort out and detect climate signals (e.g., Folland et al. 1998), for monitoring climate and for global reanalyses matters and should be a factor in evaluating results. We note that in evaluation of the AMIP integrations (Gates et al. 1999), no mention is made of SST uncertainties at all.
Nevertheless, many of the problems can be readily addressed in reprocessing of the SSTs. For GISST, a later version (GISST 3.0) has many of the same characteristics as shown here. But ongoing efforts, for instance, have eliminated the post-1981 lack of temporal continuity by reanalyzing the recent record using an OI technique in place of the Poisson blending technique (Rayner, 1999, personal communication). The result is that the lag-1 month autocorrelations in the reprocessed GISST data (GISST 4.0, yet to be determined) are in much closer agreement with the NCEP OI results shown in Fig. 4.
Other discussions have already occurred among the groups involved with producing the SST analyses, and moves are underway to reanalyze the data in ways that should produce a much-improved historical SST record. New analysis techniques at NCEP are reducing (but not entirely eliminating) the cold bias evident in the OI product relative to GISST (Fig. 7), and there appears to be a convergence toward adoption of the UKMO statistical techniques for deriving marginal ice-zone SSTs, although results need to be tested using independent data. Clearly, as the SST analyses evolve, it will be important to continue critical evaluations and comparisons of the data sets.
The authors wish to thank Chris Folland and John Walsh for their helpful comments and suggestions as referees. The comments of one anonymous referee also helped to improve the paper. The authors also appreciated useful comments provided by Mark Cane, Alexey Kaplan, David Parker, Nick Rayner and Richard Reynolds. We also thank David Stepaniak for his help in producing some of the figures. This research is partly sponsored by a joint NOAA/NASA grant NA56GP0576.
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