Tropical Sea Surface Temperature Variability Research Articles

Sea Surface Temperature of the Bay of Bengal Derived from the TRMM Microwave Imager*,+

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) with the capability of measuring sea surface temperature (SST) in the presence of clouds, has been providing an unprecedented view of tropical basin-scale SST variability. In this paper, an assessment of the accuracy of the SST derived from TMI over the Bay of Benga using in situ data collected from moored buoys and research ships, is presented. The authors find that TMI captures the evolution of the SST of the bay on seasonal time scales with reasonable accuracy. The mean difference between the SST from TMI and buoys is less than 0.1$^{\mathrm{0}}C$,and the rms difference is about 0.6$^{\mathrm{0}}C$. The time scales of the intraseasonal variation of the TMI SST are realistic. However, the amplitude of the SST variation on the intraseasonal scale is overestimated by a factor of about 1.3 when compared to buoy data. It is observed that the SST derived from TMI tends to be lower during periods with deep convection or winds stronger than 10$m s^{\mathrm{-1}}$, or both. There is better agreement during weak conditions of convection/wind. This leads to a cold bias during convectively active periods when running average SST time series are constructed from SSTs retrieved from the TMI.

Variability of seasonal-mean fields arising from intraseasonal variability. part 2, application to nh winter circulations

A study has been made, using the National Centers for Environmental Prediction and National Center for Atmospheric Research re-analysis 500 hPa geopotential height data, to determine how intraseasonal variability influences, or can generate, coherent patterns of interannual variability in the extratropical summer and winter Southern Hemisphere atmospheric circulation. In addition, by separating this intraseasonal component of interannual variability, we also consider how slowly varying external forcings and slowly varying (interannual and longer) internal dynamics might influence the interannual variability of the Southern Hemisphere circulation. This slow component of interannual variation is more likely to be potentially predictable. How sea surface temperatures are related to the slow components is also considered. The four dominant intraseasonal modes of interannual variability have horizontal structures similar to those seen in both well-known intraseasonal dynamical modes and statistical modes of intraseasonal variability. In particular, they reflect intraseasonal variability in the high latitudes associated with the Southern Annular Mode, and wavenumber 4 (summer) and wavenumber 3 (winter) patterns associated with south Pacific regions of persistent anomalies and blocking, and possibly variability related to the Madden-Julian Oscillation (MJO). The four dominant slow components of interannual variability, in both seasons, are related to high latitude variability associated with the Southern Annular Mode, El Nino Southern Oscillation (ENSO) variability, and South Pacific Wave variability associated with Indian Ocean SSTs. In both seasons, there are strong linear trends in the first slow mode of high latitude variability and these are shown to be related to similar trends in the Indian Ocean. Once these are taken into account there is no significant sea surface temperature forcing of these high latitude modes. The second and third ENSO related slow modes, in each season, have high correlations with tropical sea surface temperature variability in the Pacific and Indian Oceans, both contemporaneously and at one season lag. The fourth slow mode has a characteristic South Pacific wave structure of either a wavenumber 4 (summer) or wavenumber 3 (winter) pattern, with strongest loadings in the South Pacific sector, and an association simultaneously with a dipole SST temperature gradient in the subtropical Indian Ocean.

Independent component analysis of multivariate time series: Application to the tropical SST variability

With the aim of identifying the physical causes of variability of a given dynamical system, the geophysical community has made an extensive use of classical component extraction techniques such as principal component analysis (PCA) or rotational techniques (RT). We introduce a recently developed algorithm based on information theory: independent component analysis (ICA). This new technique presents two major advantages over classical methods. First, it aims at extracting statistically independent components where classical techniques search for decorrelated components (i.e., a weaker constraint). Second, the linear hypothesis for the mixture of components is not required. In this paper, after having briefly summarized the essentials of classical techniques, we present the new method in the context of geophysical time series analysis. We then illustrate the ICA algorithm by applying it to the study of the variability of the tropical sea surface temperature (SST), with a particular emphasis on the analysis of the links between El Niño Southern Oscillation (ENSO) and Atlantic SST variability. The new algorithm appears to be particularly efficient in describing the complexity of the phenomena and their various sources of variability in space and time.

Tropical‐extratropical connection in the Atlantic atmosphere‐ocean variability

A question how sea surface temperature (SST) fluctuations in the tropical Atlantic affect the North Atlantic atmosphere‐ocean system is addressed using an atmospheric general circulation model coupled to a 50‐m‐deep slab ocean (MGCM), and a linear baroclinic model. A comparison of a 60‐yr integration, in which the tropical SSTs are prescribed to observations (TOGA‐ML), with the control MGCM run shows that the tropical SST variations selectively enhance variance of the North Atlantic Oscillation (NAO) by about 80%. This is partly due to extratropical height anomalies associated with two dominant modes, monopole and dipole, of tropical Atlantic SST variability because they both have a projection on to the NAO in the TOGA‐ML run. The simulated midlatitude height anomalies accompany characteristic SST anomaly patterns beneath, and they are consistent with observations. Analyses of steady linear responses to the heating in the tropical Atlantic and transient eddy feedback in midlatitude reveal that the substantial part of height anomalies is generated by the latter rather than the former. The anomalous eddy forcing is, however, organized not only by the heating in the tropical Atlantic but also by the tropical Pacific SST variations through the teleconnection over North America. It is suggested that the tropical Atlantic SST variability does affect the North Atlantic atmosphere and ocean, but the dynamical connection is tenuous in comparison with the Pacific.

Prediction of tropical Atlantic sea surface temperature

The variability of sea surface temperature (SST) in the tropical Atlantic Ocean has a significant influence on the rainfall over Northeast Brazil and the Sahel. The predictability of the SST fluctuations in this region has, therefore, been a central concern for Atlantic climate studies. Here we show that the north tropical Atlantic SST variability has significant predictability using statistical models at short lead times (≤ 1 year), and using a coupled ocean‐atmosphere model for longer‐range forecasts (lead times up to about 3 years). While the remote influence of the IndoPacific, primarily due to El Niño, has been found to enhance the predictability of SST in the north Atlantic at short lead times, local ocean‐atmosphere interactions dominate the predictable dynamics farther south, and at longer time scales.

Decadal Climate Variability in the North Pacific during the Recent Decades

Abstract Decadal wintertime variability in the North Pacific climate system observed over the last few decades is documented. The decadal sea surface temperature (SST) variability is found to be concentrated around two major oceanic fronts. The variability around the subtropical front, accompanied by the anomalous subtropical high, exhibits strong negative simultaneous correlation with the tropical SST variability, but that around the subarctic front does not. In fact, cooling around the subarctic front in the mid-1970s cannot be attributed to the influence through the atmosphere of tropical warming that occurred about two years later. During the coolest period around the subarctic front in the mid-1980s, the enhanced surface westerlies associated with the intensified Aleutian low seemed to reinforce the underlying SST anomalies. The westerlies tended to be substantially weaker during the warmest period around 1970. These findings are suggestive of self-maintaining mechanisms inherent to the northern Nort...

Greenhouse Warming, Decadal Variability, or El Niño? An Attempt to Understand the Anomalous 1990s

The dominant variability modes in the Tropics are investigated and contrasted with the anomalous situation observed during the last few years. The prime quantity analyzed is anomalous sea surface temperature (SST) in the region 308S-608N. Additionally, observed tropical surface wind stress fields were investigated. Further tropical atmospheric information was derived from a multidecadal run with an atmospheric general circulation model that was forced by the same SSTs. The tropical SST variability can be characterized by three modes: an interannual mode (the El Nino-Southern Oscillation (ENSO)), a decadal mode, and a trend or unresolved ultra- low-frequency variability. The dominant mode of SST variability is the ENSO mode. It is strongest in the eastern equatorial Pacific, but influences also the SSTs in other regions through atmospheric teleconnections, such as the Indian and North Pacific Oceans. The ENSO mode was strong during the 1980s, but it existed with very weak amplitude and short period after 1991. The second most energetic mode is characterized by considerable decadal variability. This decadal mode is connected with SST anomalies of the same sign in all three tropical oceans. The tropical Pacific signature of the decadal mode resembles closely that observed during the last few years and can be characterized by a horseshoe pattern, with strongest SST anomalies in the western equatorial Pacific, extending to the northeast and southeast into the subtropics. It is distinct from the ENSO mode, since it is not connected with any significant SST anomalies in the eastern equatorial Pacific, which is the ENSO key region. However, the impact of the decadal mode on the tropical climate resembles in many respects that of ENSO. In particular, the decadal mode is strongly linked to decadal rainfall fluctuations over northeastern Australia in the observations. It is shown that the anomalous 1990s were dominated by the decadal mode. Considerable SST variability can be attributed also to a linear trend or unresolved ultra-low-frequency vari- ability. This trend that might be related to greenhouse warming is rather strong and positive in the Indian Ocean and western equatorial Pacific where it accounts for up to 30% of the total SST variability. Consistent with the increase of SST in the warm pool region, the trends over the tropical Pacific derived from both the observations and the model indicate a strengthening of the trade winds. This is inconsistent with the conditions observed during the 1990s. If the wind trends reflect greenhouse warming, it must be concluded that the anomalous 1990s are not caused by greenhouse warming. Finally, hybrid coupled ocean-atmosphere model experiments were conducted in order to investigate the sensistivity of ENSO to the low-frequency changes induced by the decadal mode and the trend. The results indicate that ENSO is rather sensitive to these changes in the background conditions.

Short-Term Climatic Variability in the Thermal Structure of the Pacific Ocean during 1979–82

Abstract Short-term climatic variability in both sea surface temperature (SST) and vertically averaged temperature over the upper 400 m of ocean (Tav) is mapped over the Pacific from 20°S to 50°N each bimonth for four years from 1979 to 1982, leading up to the 1982–83 ENSO (El Nino–Southern Oscillation) event. This mapping was made possible by the collection of approximately 85 000 temperature/depth observations in the Pacific Ocean by volunteer observing ships. Anomalies of SST and Tav were approximately the same magnitude at midlatitude as in the tropics, with the exception of large changes occurring in the tropics during the 1982–83 ENSO event. During the ENSO event, (Tav variability was largest in the western tropical North Pacific and SST variability was largest in the eastern equatorial Pacific. Both parameters had spatial patterns which were of opposite phase on either side of the ocean, indicating an eastward shift of warm waters during the ENSO event. Correlation studies determined that on averag...