Seasonal Sea Surface Temperature Anomalies Research Articles

NGHIÊN CỨU XU THẾ BIẾN ĐỘNG TRƯỜNG NHIỆT ĐỘ BỀ MẶT BIỂN VÀ ẢNH HƯỞNG CỦA ENSO ĐẾN VÙNG BIỂN TÂY NAM VIỆT NAM SỬ DỤNG DỮ LIỆU VIỄN THÁM VÀ GIS

In this article, the sea surface temperature trends and the influence of ENSO on the southwest sea of Vietnam were analyzed using the continuous satellite-acquired data sequence of SST in the period of 2002–2018. GIS and average statistical methods were applied to calculate the average monthly and seasonal sea surface temperature, the seasonal sea surface temperature anomalies for each year and for the whole study period. Subsequently, the changing trends of sea surface temperature in the northeast and southwest monsoon seasons were estimated using linear regression analysis. Research results indicated that the sea surface temperature changed significantly throughout the calendar year, in which the maximum and minimum sea surface temperature are 31oC in May and 26oC in January respectively. Sea surface temperature trends range from 0oC/year to 0.05oC/year during the Northeast monsoon season and from 0.025oC/year to 0.055oC/year during the southwest monsoon season. Results based on the Oceanic Niño Index (ONI) analysis also show that the sea surface temperature in the study area and adjacent areas is strongly influenced and significantly fluctuates during El Niño and La Niña episodes.

Extratropical-cyclone-induced sea surface temperature anomalies in the 2013–2014 winter

Abstract. The 2013–2014 winter averaged sea surface temperature (SST) was anomalously cool in the mid-North Atlantic region. This season was also unusually stormy, with extratropical cyclones passing over the mid-North Atlantic every 3 d. However, the processes by which cyclones contribute towards seasonal SST anomalies are not fully quantified. In this paper a cyclone identification and tracking method is combined with European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere and ocean reanalysis fields to calculate cyclone-relative net surface heat flux anomalies and resulting SST changes. Anomalously large negative heat flux is located behind the cyclones' cold front, resulting in anomalous cooling up to 0.2 K d−1 when the cyclones are at maximum intensity. This extratropical-cyclone-induced “cold wake” extends along the cyclones' cold front but is small compared to climatological variability in the SSTs. To investigate the potential cumulative effect of the passage of multiple cyclone-induced SST cooling in the same location, we calculate Earth-relative net surface heat flux anomalies and resulting SST changes for the 2013–2014 winter period. Anomalously large winter averaged negative heat flux occurs in a zonally orientated band extending across the North Atlantic between 40 and 60∘ N. The 2013–2014 winter SST cooling anomaly associated with air–sea interactions (ASIs; anomalous heat flux, mixed layer depth and entrainment at the base of the ocean mixed layer) is estimated to be −0.67 K in the mid-North Atlantic (68 % of the total cooling anomaly). The role of cyclones is estimated using a cyclone-masking technique which encompasses each cyclone centre and its cold wake. The environmental flow anomaly in 2013–2014 sets the overall tripole pattern of heat flux anomalies over the North Atlantic. However, the presence of cyclones doubles the magnitude of the negative heat flux anomaly in the mid-North Atlantic. Similarly, the environmental flow anomaly determines the location of the SST cooling anomaly, but the presence of cyclones enhances the SST cooling anomaly. Thus air–sea interactions play a major part in determining the extreme 2013–2014 winter season SST cooling anomaly. The environmental flow anomaly determines where anomalous heat flux and associated SST changes occur, and the presence of cyclones influences the magnitude of those anomalies.

Seasonal Prediction from Arctic Sea Surface Temperatures: Opportunities and Pitfalls

Arctic sea ice extent and sea surface temperature (SST) anomalies have been shown to be skillful predictors of weather anomalies in the midlatitudes on the seasonal time scale. In particular, below-normal sea ice extent in the Barents Sea in fall has sometimes preceded cold winters in parts of Eurasia. Here we explore the potential for predicting seasonal surface air temperature (SAT) anomalies in Europe from seasonal SST anomalies in the Nordic seas throughout the year. First, we show that fall SST anomalies not just in the Barents Sea but also in the Norwegian Sea have the potential to predict wintertime SAT anomalies in Europe. Norwegian Sea SST anomalies in spring are also significant predictors of European SAT anomalies in summer. Second, we demonstrate that the potential for prediction is sensitive to trends in the data. In particular, the lagged correlation between Norwegian Sea SST anomalies in spring and European SAT anomalies in summer is considerably higher for raw data than linearly detrended data, largely due to warming SST and SAT trends in recent decades. Third, we show that the potential for prediction has not been stationary in time. One key result is that, according to two twentieth-century reanalyses, the strength of the negative lagged correlation between Barents Sea SST anomalies in fall and European SAT anomalies in winter after 1979 is unprecedented since 1900.

Global and regional sea surface temperature trends during Marine Isotope Stage 11

Abstract. The Marine Isotope Stage (MIS) 11 (424–374 ka) was characterized by a protracted deglaciation and an unusually long climatic optimum. It remains unclear to what degree the climate development during this interglacial reflects the unusually weak orbital forcing or greenhouse gas trends. Previously, arguments about the duration and timing of the MIS11 climatic optimum and about the pace of the deglacial warming were based on a small number of key records, which appear to show regional differences. In order to obtain a global signal of climate evolution during MIS11, we compiled a database of 78 sea surface temperature (SST) records from 57 sites spanning MIS11, aligned these individually on the basis of benthic (N = 28) or planktonic (N = 31) stable oxygen isotope curves to a common time frame and subjected 48 of them to an empirical orthogonal function (EOF) analysis. The analysis revealed a high commonality among all records, with the principal SST trend explaining almost 49% of the variability. This trend indicates that on the global scale, the surface ocean underwent rapid deglacial warming during Termination V, in pace with carbon dioxide rise, followed by a broad SST optimum centered at ~410 kyr. The second EOF, which explained ~18% of the variability, revealed the existence of a different SST trend, characterized by a delayed onset of the temperature optimum during MIS11 at ~398 kyr, followed by a prolonged warm period lasting beyond 380 kyr. This trend is most consistently manifested in the mid-latitude North Atlantic and Mediterranean Sea and is here attributed to the strength of the Atlantic meridional overturning circulation. A sensitivity analysis indicates that these results are robust to record selection and to age-model uncertainties of up to 3–6 kyr, but more sensitive to SST seasonal attribution and SST uncertainties >1 °C. In order to validate the CCSM3 (Community Climate System Model, version 3) predictive potential, the annual and seasonal SST anomalies recorded in a total of 74 proxy records were compared with runs for three time slices representing orbital configuration extremes during the peak interglacial of MIS11. The modeled SST anomalies are characterized by a significantly lower variance compared to the reconstructions. Nevertheless, significant correlations between proxy and model data are found in comparisons on the seasonal basis, indicating that the model captures part of the long-term variability induced by astronomical forcing, which appears to have left a detectable signature in SST trends.

Using Artificial Neural Networks to Forecast Monthly and Seasonal Sea Surface Temperature Anomalies in the Western Indian Ocean

A study implementing Nonlinear Autoregressive with Exogenous Input (NARX) neural network has been undertaken to predict monthly and seasonal SST anomalies in the western Indian Ocean. The study involves a coastal site located along the eastern African seashore, and an oceanic site that lies precisely within the western pole of the Indian Ocean Dipole. Performance of the network is measured by a series of statistical indicators during testing phase (1981–2010), and results are compared with outputs from three other neural networks and a linear system, the Autoregressive Integrated Moving Average with Exogenous Input (ARIMAX) model. The NARX network has provided the best overall performance, but the other four models have also given sufficiently good predictions. The monthly predictions are on average within an error of ±0.09°C for the first 50% and 90% within ±0.22°C. The corresponding errors for the seasonal predictions are ±0.04°C and ±0.09°C, respectively. The RMSE between observations and predictions is about 0.13°C and 0.06°C for the monthly and seasonal SST anomalies, while the average correlation coefficient is about 0.88 and 0.98, respectively.

Potential predictability of an Iberian river flow based on its relationship with previous winter global SST

We examine the potential predictability of an important Iberian river (Douro) based on the coupling of a time series approach (ARMA) and previous seasonal sea surface temperature (SST) anomalies. A comprehensive search for predictors has identified only two key regions where spring streamflow anomalies are stably correlated with previous winter SST anomalies during the whole period under study (1956–2006); (1) central North Atlantic Ocean and (2) south-western Atlantic. A modelling scheme (the SST_model), based on linear regression, is developed and applied to simulate streamflow anomalies from these key SST regions.

An atmosphere–ocean regional climate model for the Mediterranean area: assessment of a present climate simulation

We present an atmosphere-ocean regional climate model for the Mediterranean basin, called the PROTHEUS system, composed by the regional climate model RegCM3 as the atmospheric component and by a regional configuration of the MITgcm model as the oceanic component. The model is applied to an area encompassing the Mediterranean Sea and compared to a stand-alone version of its atmospheric component. An assessment of the model performances is done by using available observational datasets. Despite a persistent bias, the PROTHEUS system is able to capture the inter-annual variability of seasonal sea surface temperature (SST) and also the fine scale spatio-temporal evolution of observed SST anomalies, with spatial correlation as high as 0.7 during summer. The close inspection of a 10-day strong wind event during the summer of 2000 proves the capability of the PROTHEUS system to correctly describe the daily evolution of SST under strong air-sea interaction conditions. As a consequence of the model's skill in reproducing observed SST and wind fields, we expect a reliable estimation of air-sea fluxes. The model skill in reproducing climatological land surface fields is in line with that of state of the art regional climate models.

Seasonal Prediction of Sea Surface Temperature Anomalies Using a Suite of 13 Coupled Atmosphere–Ocean Models

Abstract Improved seasonal prediction of sea surface temperature (SST) anomalies over the global oceans is the theme of this paper. Using 13 state-of-the-art coupled global atmosphere–ocean models and 13 yr of seasonal forecasts, the performance of individual models, the ensemble mean, the bias-removed ensemble mean, and the Florida State University (FSU) superensemble are compared. A total of 23 400 seasonal forecasts based on 1-month lead times were available for this study. Evaluation metrics include both deterministic and probabilistic skill measures, such as verification of anomalies based on model and observed climatology, time series of specific climate indices, standard deterministic ensemble mean scores including anomaly correlations, root-mean-square (RMS) errors, and probabilistic skill measures such as equitable threat scores for seasonal SST forecasts. This study also illustrates the Niño-3.4 SST forecast skill for the equatorial Pacific Ocean and for the dipole index for the Indian Ocean. The relative skills of total SST fields and of the SST anomalies from the 13 coupled atmosphere–ocean models are presented. Comparisons of superensemble-based seasonal forecasts with recent studies on SST anomaly forecasts are also shown. Overall it is found that the multimodel superensemble forecasts are characterized by considerable RMS error reductions and increased accuracy in the spatial distribution of SST. Superensemble SST skill also persists for El Niño and La Niña forecasts since the large comparative skill of the superensemble is retained across such years. Real-time forecasts of seasonal sea surface temperature anomalies appear to be possible.

Empirical prediction of Indian summer monsoon rainfall with different lead periods based on global SST anomalies

The main objective of this study was to develop empirical models with different seasonal lead time periods for the long range prediction of seasonal (June to September) Indian summer monsoon rainfall (ISMR). For this purpose, 13 predictors having significant and stable relationships with ISMR were derived by the correlation analysis of global grid point seasonal Sea-Surface Temperature (SST) anomalies and the tendency in the SST anomalies. The time lags of the seasonal SST anomalies were varied from 1 season to 4 years behind the reference monsoon season. The basic SST data set used was the monthly NOAA Extended Reconstructed Global SST (ERSST) data at 2° × 2° spatial grid for the period 1951–2003. The time lags of the 13 predictors derived from various areas of all three tropical ocean basins (Indian, Pacific and Atlantic Oceans) varied from 1 season to 3 years. Based on these inter-correlated predictors, 3 predictor sub sets A, B and C were formed with prediction lead time periods of 0, 1 and 2 seasons, respectively, from the beginning of the monsoon season. The selected principal components (PCs) of these predictor sets were used as the input parameters for the models A, B and C, respectively. The model development period was 1955–1984. The correct model size was derived using all-possible regressions procedure and Mallow’s “Cp” statistics.

The relationship between monthly and seasonal South‐west England rainfall anomalies and concurrent North Atlantic sea surface temperatures

AbstractThis paper assesses the relationship between a regional index of rainfall (SWER) over Devon and Cornwall, South‐west England, and concurrent gridded (5° × 5°) sea‐surface temperature anomalies (SSTAs) for the North Atlantic–European domain (10–70°N, 80°W–20°E) over the period 1950–97. Monthly and seasonal SSTA : SWER correlation fields are derived, and stepwise regression models are then constructed to specify SWER through SSTA variations. In particular, this paper emphasizes the importance of assessing all correlation matrices for field significance, an analysis stage that is often ignored by many researchers. The most temporally reproducible signal is found between depressed SSTs to the west of the British Isles and above‐average rainfall over Devon and Cornwall, although this eastern Atlantic signal is by no means constant over time in terms of its location and spatial extent. Another distinctive feature of the anomaly correlation maps is for locally significant positive SSTA : SWER associations to emerge at high latitudes (60–70°N) in summer and early autumn, e.g. Davis Strait, Norwegian Sea. Field significance testing reveals that seasonal SSTA : SWER correlation fields are statistically more robust than their monthly counterparts. This is demonstrated by the fact that the seasonal fields (excluding summer) all achieve field significance at the 0.1 level, whereas only five of the 12 monthly fields (January, May, July, October and December) do so, suggesting that some of the locally significant SSTA : SWER correlations presented in this paper could have been obtained by merely correlating SWER with a random number series. Copyright © 2002 Royal Meteorological Society.

Climatic Indices, Principal Components, and the Gauss-Markov Theorem

If indices are to be used as the variables predicted by linear statistical models, it is important to be able to recover as much local information as possible from the values forecast for the indices. Here it is shown that the indices that encapsulate the most information about the local climatic state are determined by a generalized (two-matrix) eigenvalue problem that is equivalent to the usual (one-matrix) eigenvalue problem involving the sample correlation matrix. Thus, the best indices in the sense of providing the most location-specific information are familiar principal-component indices. Regarding the indices as predictors in linear statistical models similar to those routinely used for estimating meteorological fields from observations reveals the role of the Gauss-Markov theorem in EOF analyses. From this perspective each index can he characterized by two EOF-like maps: the first illustrating the linear combinations of the data used to define the index, and the second displaying the Gauss-Markov weights for the index to predict local variables, both of which are related to the eigenvectors of the sample correlation matrix. Other maps can be used to display information about sampling errors: one to characterize the uncertainty of the weights; another to display the skill with which the index accounts for the training data; and a third to show how well it explains independent data. Such maps are illustrated within the context of 43 years of North Atlantic seasonal sea surface temperature anomalies. The analysis presented here underlines two additional points. First, any linear combination of the indices would result in an equivalent model yielding exactly the same forecast. Consequently, it may be desirable to use indices that are easier to interpret physically. Second, when indices are regarded as being variables of a linear statistical model, the analysis of sampling error can be formulated in terms of the uncertainty of the Gauss-Markov weights inferred from a limited training set rather than in terms of the sample-to-sample variability of eigenvalues and eigenvectors.