Monthly Sea Surface Temperature Anomalies Research Articles

Contribution of El Niño Southern Oscillation (ENSO) Diversity to Low‐Frequency Changes in ENSO Variance

AbstractEl Niño Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical Pacific, as Central Pacific events are typically weaker than Eastern Pacific events. SSTA pattern and intensity undergo low‐frequency modulations, affecting ENSO prediction skill and remote impacts, and resulting in low‐frequency changes in ENSO variance. Yet, how different ENSO types contribute to these decadal variance changes remains unclear. Here, we decompose the low‐frequency changes of ENSO variance into contributions from ENSO diversity categories. We propose a fuzzy clustering of monthly SSTA to allow for non‐binary event category memberships, where each event can belong to different clusters. Our approach identifies two La Niña and three El Niño categories and shows that the major shift of ENSO variance in the mid‐1970s was associated with an increasing likelihood of strong La Niña and extreme El Niño events.

ENSO Dynamics, Trends, and Prediction Using Machine Learning

AbstractEl Niño–Southern Oscillation (ENSO) has global effects on the hydrological cycle, agriculture, ecosystems, health, and society. We present a novel nonhomogeneous hidden Markov model (NHMM) for studying the underlying dynamics of sea surface temperature anomalies (SSTA) over the region 15°N–15°S, 150°E–80°W from January 1856 to December 2019, using the monthly SSTA data from the Kaplan extended SST v2 product. This nonparametric machine learning scheme dynamically simulates and predicts the spatiotemporal evolution of ENSO patterns, including their asymmetry, long-term trends, persistence, and seasonal evolution. The model identifies five hidden states whose spatial SSTA patterns are similar to the so-called ENSO flavors in the literature. From the fitted NHMM, the model shows that there are systematic trends in the frequency and persistence of the regimes over the last 160 years that may be related to changes in the mean state of basin temperature and/or global warming. We evaluated the ability of NHMM to make out-of-sample probabilistic predictions of the spatial structure of temperature anomalies for the period 1995–2016 using a training period from January 1856 to December 1994. The results show that NHMMs can simulate the behavior of the Niño-3.4 and Niño-1.2 regions quite well. The NHMM results over this period are comparable or superior to the commonly available ENSO prediction models, with the additional advantage of directly providing insights as to the space patterns, seasonal, and longer-term trends of the SSTA in the equatorial Pacific region.

Indian Ocean tripole mode and its associated atmospheric and oceanic processes

Differing from the Indian Ocean dipole (IOD) that has sea surface temperature anomalies (SSTAs) of opposing signs over the tropical southeastern and western Indian Ocean, a tripole pattern, characterized by positive (negative) SSTAs over the tropical central (southeastern and western) Indian Ocean, is observed and named the Indian Ocean tripole (IOT). This study proposes the concept of the IOT and further investigates the associated atmospheric and oceanic processes. Using empirical orthogonal function (EOF) analysis, the IOT (IOD) is represented by the third (second) leading mode of the monthly SSTAs in the tropical Indian Ocean, explaining about 8.2% (10.3%) of the total variance. The IOT peaks in boreal summer, while the IOD has its mature phase in boreal fall. The spatio-temporal differences, together with the significant separation of these two EOF patterns, illustrate that the IOT is independent of the IOD. Heat budget diagnoses indicate that the anomalous temperature over the southeastern and western Indian Ocean is mainly generated by the net heat flux during April–May and by the total ocean advection in June–August. In contrast, the anomalous temperature is mainly driven by the advection of the mean temperature by the anomalous current in April and the residual term in May–June over the central Indian Ocean, which is replaced by advection of the anomalous temperature by the mean zonal current in July.

Contrasting features of hydroclimatic teleconnections and the predictability of seasonal rainfall over east and west Japan

AbstractHydroclimatic teleconnections between global sea surface temperature (SST) anomaly fields and monthly rainfall over east and west Japan (divided along 138° E longitude) are identified for summer (June–August) and winter (December–February) using the concept of global climate pattern (GCP). The analysis indicates that the hydroclimatic teleconnections over both regions vary at both intra‐ and inter‐seasonal time scales. In addition, the teleconnections over the two regions have differing origins. The teleconnection features associated with rainfall anomalies over west Japan have origins in the tropical Pacific and Indian oceans, whereas those over east Japan are associated with high‐latitude SST anomalies. The early summer (winter) rainfall over west Japan is linked to the El Niño Modoki (La Niña Modoki) phenomena, whereas the early summer and winter rainfall anomalies over east Japan are associated with the SST anomaly over the eastern subtropical Pacific and South Pacific oceans, respectively. Having identified the teleconnections, prediction model approaches—a machine‐learning approach, namely support vector regression (SVR), and a hybrid graphical modelling/C‐Vine copula (GM‐Copula)—were developed to forecast the rainfall over both east and west Japan. The predictors were derived from the monthly SST anomalies at different lags (1–6 months), and whereas the hidden, nonlinear relationship was well captured by the SVR approach, the complex association was decidedly better captured by the GM‐Copula approach. Hence, it is recommended for forecasting the rainfall over east and west Japan.

The “Ocean Stabilization Machine” May Represent a Primary Factor Underlying the Effect of “Global Warming on Climate Change”

Contemporary references to global warming pertain to the dramatic increase in monthly global land surface temperature (GLST) anomalies since 1976. In this paper,we argue that recent global warming is primarily a result of natural causes; we have established three steps that support this viewpoint. The first is to identify periodic functions that perfectly match all of the monthly anomaly data for GLST; the second is to identify monthly sea surface temperature (SST) anomalies that are located within different ocean basin domains and highly correlated with the monthly GLST anomalies; and the third is to determine whether the dramatically increasing (or dramatically decreasing) K-line diagram signals that coincide with GLST anomalies occurred in El Ni&#241o years (or La Ni&#241a years). We have identified 15,295 periodic functions that perfectly fit the monthly GLST anomalies from 1880 to 2013 and show that the monthly SST anomalies in six domains in different oceans are highly correlated with the monthly GLST anomalies. In addition,most of the annual dramatically increasing GLST anomalies occur in El Ni&#241o years; and most of the annual dramatically decreasing GLST anomalies occur in La Ni&#241a years. These findings indicate that the “ocean stabilization machine” might represent a primary factor underlying the effect of “global warming on climate change”.

Downscaling and extrapolating dynamic seasonal marine forecasts for coastal ocean users

Marine weather and climate forecasts are essential in planning strategies and activities on a range of temporal and spatial scales. However, seasonal dynamical forecast models, that provide forecasts in monthly scale, often have low offshore resolution and limited information for inshore coastal areas. Hence, there is increasing demand for methods capable of fine scale seasonal forecasts covering coastal waters. Here, we have developed a method to combine observational data with dynamical forecasts from POAMA (Predictive Ocean Atmosphere Model for Australia; Australian Bureau of Meteorology) in order to produce seasonal downscaled, corrected forecasts, extrapolated to include inshore regions that POAMA does not cover. We demonstrate the method in forecasting the monthly sea surface temperature anomalies in the Great Australian Bight (GAB) region. The resolution of POAMA in the GAB is approximately 2°×1° (lon.×lat.) and the resolution of our downscaled forecast is approximately 1°×0.25°. We use data and model hindcasts for the period 1994–2010 for forecast validation. The predictive performance of our statistical downscaling model improves on the original POAMA forecast. Additionally, this statistical downscaling model extrapolates forecasts to coastal regions not covered by POAMA and its forecasts are probabilistic which allows straightforward assessment of uncertainty in downscaling and prediction. A range of marine users will benefit from access to downscaled and nearshore forecasts at seasonal timescales.

Interannual variability in chlorophyll-a on the southern Queensland continental shelf and its relationship to ENSO

Coastal phytoplankton blooms can result from upwelling of colder nutrient-rich water, seasonal fluvial or anthropogenic point sources of nutrient. Here we analyze 15-year time series of monthly mean and 8-day satellite-derived chlorophyll-a (Chl-a) and sea surface temperature (SST) on the southern Queensland continental shelf (24.25–28.25°S) from March 2000 to February 2015. We examine the interannual variability in these parameters and its relationship to algal bloom dynamics. Seasonal climatological means are computed and analyzed. Empirical orthogonal function (EOF) analysis is applied to these time series. Cross-correlation and spatial correlation analyses are used to investigate the relationship between the multivariate ENSO index (MEI), Chl-a and SST. Computed eigenvectors of the time series of Chl-a and SST present a strong seasonal variability on the first EOF modes. Thus, the seasonal variability was removed by computing monthly and 8-day Chl-a and SST anomalies. The EOF analysis was then applied to the anomaly time series. Correlation analysis results show a positive correlation between MEI and the eigenvector of the first EOF of the monthly Chl-a anomaly with time lag of three to four months. We find a negative correlation between MEI and the eigenvector of the second EOF of the monthly Chl-a anomaly with time lag of three to four months. There is no correlation between MEI and eigenvectors of the monthly SST anomaly. There are significant correlations between eigenvectors of the first and second EOF modes of 8-day Chl-a and the first and second EOF modes of 8-day SST respectively. Negative correlation coefficients between 8-day anomalies of Chl-a and SST are found on the continental shelf to the east of Fraser Island and Stradbroke Island. Analysis of a particular algal bloom event indicates a negative SST anomaly and negative curl of wind stress in the waters to the southeast of Fraser Island suggesting that wind stress is possibly a secondary but significant physical driver of the upwelling system. Our results suggest that variability in phytoplankton biomass on the southern Queensland continental shelf is driven by upwelling of nutrients during the spring dry season and fluvial nutrient loads during the summer and autumn wet seasons. Seasonal change is the primary factor controlling the variability of Chl-a and SST while climate variability (ENSO) and upwelling are secondary but significant factors.

The interannual variability of size at maturity of the brown crab Callinectes bellicosus Stimpson, 1859 (Brachyura, Portunidae) in the Gulf of California

This study examines how the size at maturity (CW50%) of Callinectes bellicosus Stimpson, 1859 is affected by environmental variability and by the source of the data. The samples were obtained from a coastal lagoon in the eastern Gulf of California between 2000 and 2003, and in 2011. To explore for interannual variation in environmental conditions, an extended negative power-law model was used, with SSTa as the annual sum of monthly sea surface temperature anomalies and Chl-a as the monthly chlorophyll a mean in a year. The interannual variability in the CW50% of C. bellicosus was also calculated. We found that the percent of mature female drives inter-annual variability in CW50% and that the addition of Chl-a to the model does not contribute further to the explanation of this phenomenon. The hypothesis that environmental variations affect the reproductive dynamics of the stocks needs to be tested in this species in another area.

Preliminary results of a new global ocean reanalysis

Using a new global ocean reanalysis of the second generation Global Ocean Data Assimilation System of the Beijing Climate Center (BCC_GODAS2.0) spanning the period 1990–2009, we firstly quantify the accuracy of BCC_GODAS2.0 in representing the temperature and salinity by comparing with OISST and SODA data. The results show that the assimilation system may effectively improve the estimations of temperature and salinity by assimilating all kinds of observations, especially in the equatorial eastern Pacific. Moreover, the root mean square errors of monthly temperature and salinity are respectively reduced by 0.53°C and 0.28 psu, compared with the model control simulation results. Then, the applicability of this ocean reanalysis for sea surface temperature (SST) anomaly variability in the tropical Pacific is evaluated with the observational HadISST data. The NINO3 index of the new reanalysis shows a good agreement with that of HadISST, with a correlation of 93.6%. Variations in SST from BCC_GODAS2.0 are similar to those obtained from HadISST data along the equator, showing the major large zonal-scale features such as the strong magnitude of seasonal cycle. The amplitude of SST anomaly standard deviation in the equatorial eastern Pacific is also closer to observations (HadISST) than NCEP GODAS does. Besides, the first two leading empirical orthogonal function (EOF) modes of the monthly SST anomalies over the tropical Pacific region are explored. The EOF1 pattern of BCC_GODAS2.0 captures a traditional El Nino pattern, which improves magnitudes of the positive SST anomaly in the cold tongue of the eastern Pacific. The EOF2 pattern exhibits a El Nino Modoki pattern. Comparatively, the EOF2 pattern of BCC_GODAS2.0 extends more strongly toward the subtropics. It also overcomes the problem that negative loadings are confined in the narrow equatorial eastern Pacific. Consequently, the magnitude and spatial distribution of the leading EOF patterns of BCC_GODAS2.0 are well consistent with those of HadISST.

Influence of ENSO on Pacific Decadal Variability: An Analysis Based on the NCEP Climate Forecast System

Abstract The influence of El Niño–Southern Oscillation (ENSO) on Pacific decadal variability (PDV) is investigated by comparing two 500-yr simulations with the National Centers for Environmental Prediction (NCEP) Climate Forecast System coupled model. One simulation is a no-ENSO run, in which model daily sea surface temperature (SST) in the tropical Pacific Ocean is relaxed to the observed climatology. The other simulation is a fully coupled run and retains ENSO variability. The PDV considered in this study is the first two empirical orthogonal functions of monthly SST anomalies in the North Pacific: the Pacific decadal oscillation (PDO) and the North Pacific Gyre Oscillation (NPGO). The PDO in the no-ENSO run can be clearly identified. Without ENSO, the PDO displays relatively higher variance at the decadal time scale and no spectral peak at the interannual time scale. In the ENSO run, the PDO variability increases slightly. ENSO not only enhances the variability of the PDO at the interannual time scale, but also shifts the PDO to longer time scales—both consistent with observations. ENSO modulates the Aleutian low and associated surface wind over the North Pacific. The latter, in turn, helps establish a more persistent PDO in the ENSO run. The results also indicate a PDO modulation of global ENSO impacts and the linearity in the superposition of the ENSO-forced and PDO-related atmospheric anomalies. Compared to observations, the NPGO in both simulations lacks power at the time scale longer than 30 yr. On the decadal time scale, the variability of the NPGO is weaker in the ENSO run than in the no-ENSO run.

<b>PREDICTION OF PLANTING DATE AND GROWING PERIOD USING SEA SURFACE TEMPERATURE (SST) ANOMALIES IN NINO 3.4 FOR INDRAMAYU DISTRICT </b>

<div>Agriculture is a very important sector in Indramayu’s economy. Approximately 53.52% of Indramayu residents are involved in agriculture. Indramayu is a center of rice production in Indonesia. Most of the </div><div>farmers use a traditional cropping method called Pranata Mangsa, which is based on periodic natural events, but not consider climate variability well. Climate variability has become a major obstacle to achieving a successful harvest, because it can affect the timing of planting and length of the growing season, which leads to drought and flood vulnerability. The planting date and growing season predicted by using monthly sea surface temperature anomalies (SSTa) in Nino 3.4. The August SSTa can describe the planting date better than the growing season, which are demonstrated best in Lohbener with R</div><div>2</div><div> = 45% with forecast skill reach = 84% and 92% for advanced and delayed planting dates. Knowing the planting date and growing season length produce a more effective cropping calendar, which includes details such as when to prepare the land, plant seeds, and harvest. This cropping calendar is expected to reduce the impacts of climate variability by providing a more efficient cropping pattern and avoiding potential harvest failures.Agriculture is a very important sector in Indramayu's economy. Approximately 53.52% of Indramayu residents are involved in agriculture. Indramayu is a center of rice production in Indonesia. Most of the farmers use a traditional cropping method called Pranata Mangsa, which is based on periodic natural events, but not consider climate variability well. Climate variability has become a major obstacle to achieving a successful harvest, because it can affect the timing of planting and length of the growing season, which leads to drought and flood vulnerability. The planting date and growing season predicted by using monthly sea surface temperature anomalies (SSTa) in Nino 3.4. The August SSTa can describe the planting date better than the growing season, which are demonstrated best in Lohbener with R2 = 45% with forecast skill reach = 84% and 92% for advanced and delayed planting dates. Knowing the planting date and growing season length produce a more effective cropping calendar, which includes details such as when to prepare the land, plant seeds, and harvest. This cropping calendar is expected to reduce the impacts of climate variability by providing a more efficient cropping pattern and avoiding potential harvest failures.</div>

Long-Term Water Temperature Variations in Daya Bay, China Using Satellite and In Situ Observations

Daya Bay is a shallow, semi-enclosed bay in the northern section of the South China Sea. The present study analyzed variations of water temperature in Daya Bay over the past 21 years (1985 - 2005) using Advanced Very High Resolution Radiometer (AVHRR) satellite remote sensing data and in situ observations. Results showed that AVHRR readings of sea surface temperature (SST) increased by 0.07 degrees C y(-1). Linear regression analysis for monthly SST anomalies (SSTA) showed a shift from negative to positive from 1995 - 1996, when the Daya Bay nuclear power station commenced operations in 1994. The slope of linear regression analysis for SSTA nearly doubled from 0.05 (1985 - 1993) to 0.09 (1994 - 2005). Monthly AVHRR images showed a thermal plume from the power station and revealed the increase of SST over 21 years. In situ observations in water temperature also showed an increasing trend for the same period (1985 - 2005). Variations in water temperature in Daya Bay were connected with climatic perturbations and increasing human activity including thermal discharge from nuclear power stations and the rapid economic development around the bay area.

Long-Range Niño-3.4 Predictions Using Pairwise Dynamic Combinations of Multiple Models

Abstract The interest in climate prediction has seen a rise in the number of modeling alternatives in recent years. One way to reduce the predictive uncertainty from any such modeling procedure is to combine or average the modeled outputs. Multiple model results can be combined such that the combination weights may either be static or vary over time. This research develops a methodology for combining forecasts from multiple models in a dynamic setting. The authors mix models on a pairwise basis using importance weights that vary in time, reflecting the persistence of individual model skills. Such an approach is referred to here as a dynamic pairwise combination tree and is presented as an improvement over the case where the importance weights are static or constant over time. The pairwise importance weight is modeled as a product of a “mixture ratio” and a “bias direction,” the former representing the fraction of the absolute residual error associated with each of the paired models, and the latter representing an indicator of the sign of the two residual errors. The mixture ratio is modeled using a generalized autoregressive model and the bias direction using ordered logistic regression. The method is applied to combine three climate models, the variables of interest being the monthly sea surface temperature anomalies averaged over the Niño-3.4 region from 1956 to 2001. The authors test the combined model skill using a “leave ± 6 months out cross-validation” approach along with validation in 10-yr blocks. This study attained a small but consistent improvement of the predictive skill of the dynamically combined models compared to the existing practice of static weight combination.

ATLANTIC SEA SURFACE TEMPERATURES AND NEW ENGLAND SNOWFALL

This paper examines the possibility of an association between winter (December-March) snowfall in New England and sea surface temperature anomalies (SSTAs) in the adjacent Atlantic Ocean. Regional snowfall indices for southern and northern New England were obtained by rotated principal components analysis (PCA). Composite maps of winter Atlantic SSTAs and 700-mb geopotential height anomalies were generated for cases of above and below average winter snowfall totals for southern and northern New England, respectively. A monthly index of SSTAs around the coast of New England was obtained from rotated PCA of SSTAs in the western Atlantic Ocean and compared for high snow and low snow cases. In northern New England, no direct association between snowfall and SSTAs is indicated by either the composite maps or the monthly SSTA index - high or low snowfall totals can be attributed primarily to anomalies in the 700-mb circulation. In southern New England, high (low) snowfall totals are associated with negative (positive) SSTAs off the Atlantic coast, and these anomalies are often already evident in December, suggesting Atlantic sea surface temperatures may be of utility in long-range winter forecasts for coastal regions.

Lag Relationships Involving Tropical Sea Surface Temperatures

A long historical record (∼100 years) of monthly sea surface temperature anomalies from the Comprehensive Ocean–Atmosphere Data Set was used to examined the lag relationships between different locations in the global Tropics. Application of complex principal component (CPC) analysis revealed that the leading mode captures ENSO-related quasi-cyclical warming and cooling in the tropical Pacific Ocean. The dominant features of this mode indicate that SST anomalies in the eastern Pacific lead those of the central Pacific. However, a somewhat weaker aspect of this mode also indicates that SST anomalies in the tropical Indian and western tropical North Atlantic Oceans vary roughly in concert with each other but lag behind those in the central and eastern Pacific. The stability of these lag relationships is indicated by the fact that the leading mode is quite similar in three different 30-year time periods. In order to further examine these relationships some simple indexes were formed as the average over several grid points in each of the four key areas suggested by the CPC analyses. Several different types of analyses including lag correlation, checking the correspondence between extrema, and visual examination of time series plots were used to confirm the relationships implied by the CPC spatial patterns. By aggregating the lag correlations over the three 30-year time periods and performing a Monte Carlo simulation the relationships were found to be statistically significant at the 1% level. Reasonable agreement in the pattern of lag correlations was found using a different SST dataset. Without aggregation of the lag correlations (i.e., considering each 30-year period separately) the area in the Pacific and Indian were consistently well related, but those involving the North Atlantic were more variable. The weaker correlations involving the Atlantic Ocean underscore the more tenuous nature of this remote relationship. While major ENSO-related swings in tropical Pacific SST are often followed by like variations in a portion of the Atlantic, there are times when there is either no obvious association or one of opposite sign. It may be that while ENSO variability tends to have an impact in the Atlantic, more localized factors can override this tendency. This may explain some of the contradictory statements found in the literature regarding such remote associations. In comparing the findings of this project with some studies that utilize very recent data (since about 1982) some discrepancies were noted. In particular, some studies have reported evidence of 1) an inverse relationship between SST anomalies in the tropical Pacific and those in the eastern tropical South Atlantic and 2) the appearance of ENSO-related SST anomalies in the central tropical Pacific prior to those in the eastern tropical Pacific. From a historical perspective both of these characteristics are unusual. Thus, the recent time period may merit special attention. However, it is important to stress that caution should be exercised in generalizing findings based only on this recent time period.

南部アフリカ半乾燥地域の降水、高度場、海水面温度の関係

An observational study has been conducted of the southern summer rainfall variability over the semi-arid region of southern Africa (SASA) since 1958, as related to the tropical African rainbelt, tropical atmospheric circulation, and sea surface temperature (SST) patterns. A rotated empirical orthogonal function (R-EOF) analysis was applied to the global monthly SST anomalies. On a year-to-year basis, the first R-EOF mode, related to the El Nino/Southern Oscillation (ENSO) phenomenon, was most strongly correlated with the southern African rainfall and two rainbelt indices ; the latitude of its center of gravity and the total rainfall that occurs over the entire rainbelt zone. The anomalous warming of the central-eastern equatorial Pacific, identified as the first R-EOF mode, was associated with increased 700 hPa heights throughout the tropics except for the eastern Pacific, especially marked over southern Africa and the eastern Indian Ocean-central Pacific region. Simultaneous intensification of the 700 hPa subtropical high over southern Africa was found to cause a northward displacement in the rainbelt's center of gravity and a decrease in total rainfall. These changes in the rainbelt parameters resulted in decreased rainfall for the SASA region, located south of the main rainbelt. On the other hand, the anomalous cooling of the eastern Pacific was related to a weakening of the southern African sub tropical high. This leads to a simultaneous southward advance and enhanced convection of the rainbelt, which ultimately result in increased SASA rainfall. On the decadal time scale, the third R-EOF mode, characterized by a dominant signal over the South Atlantic, is most strongly correlated with trends in the SASA rainfall and rainbelt parameters. The warming of the South Atlantic and simultaneous increases in the 700 hPa heights over southern Africa result in decreased rainfall ; cooling and decreased 700 hPa heights lead to increased rainfall.

South Atlantic sea surface temperature anomalies and air-sea interactions: stochastic models

Abstract. Data on the South Atlantic monthly sea surface temperature anomalies (SSTA) are analysed using the maximum-entropy method. It is shown that the Markov first-order process can describe, to a first approximation, SSTA series. The region of maximum SSTA values coincides with the zone of maximum residual white noise values (sub-Antarctic hydrological front). The theory of dynamic-stochastic climate models is applied to estimate the variability of South Atlantic SSTA and air-sea interactions. The Adem model is used as a deterministic block of the dynamic-stochastic model. Experiments show satisfactorily the SSTA intensification in the sub-Antarctic front zone, with appropriate standard deviations, and demonstrate the leading role of the abnormal drift currents in these processes.

South Atlantic sea surface temperature anomalies and air-sea interactions: stochastic models

<strong class="journal-contentHeaderColor">Abstract.</strong> Data on the South Atlantic monthly sea surface temperature anomalies (SSTA) are analysed using the maximum-entropy method. It is shown that the Markov first-order process can describe, to a first approximation, SSTA series. The region of maximum SSTA values coincides with the zone of maximum residual white noise values (sub-Antarctic hydrological front). The theory of dynamic-stochastic climate models is applied to estimate the variability of South Atlantic SSTA and air-sea interactions. The Adem model is used as a deterministic block of the dynamic-stochastic model. Experiments show satisfactorily the SSTA intensification in the sub-Antarctic front zone, with appropriate standard deviations, and demonstrate the leading role of the abnormal drift currents in these processes.

Latent and Sensible Heat Flux Anomalies over the Northern Oceans: Driving the Sea Surface Temperature

Abstract A part of the large-scale thermodynamic forcing of the upper ocean is examined by relating monthly anomalous latent and sensible heat flux to changes in sea surface temperature (SST) anomalies over the North Atlantic and North Pacific. The fluxes are estimated using bulk formulas from a set of about four decades of marine observations from the COADS dataset from 1946 to 1986. Monthly anomalies are constructed by removing the long-term monthly means. The latent and sensible flux anomalies are strongly correlated over most of the ocean, so they are considered together as a sum. The heat flux estimates contain large spatial-scale anomalies consistent with both atmospheric circulation anomalies and with month-to-month changes (tendencies) in monthly SST anomalies. The monthly flux anomalies and the SST anomaly tendency are significantly correlated over much of the oceans, with anomalous positive/negative fluxes associated with anomalous cooling/warming. The connection between the flux and the SST ten...

The regional feature of the sea surface temperature anomalies in the Tropical West Pacific

The complex empirical orthogonal function is employed to analyse the monthly sea surface temperature anomalies (SSTA) in the Tropical West Pacific (TWP) for the period from 1951 to 1986. The results show that the SSTA in the area appear in three patterns: a quasi-stationary mode, a latitudinally propagating mode and a longitudinally propagating mode. The three modes contribute almost equally to the total variance of SST. Digital filtering, time-lag correlation, etc., are applied to analyse the quasi-period low frequency oscillations of the modes and their relations to the southern oscillation (SO) and the West Pacific Subtropical High (WPSH).