Historical Sea Surface Temperature Data Research Articles

MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST

Sea Surface Temperature (SST) prediction is a hot topic that has received tremendous popularity in recent years. Existing methods for SST prediction usually select one sea area of interest and conduct SST prediction by learning the spatial and temporal dependencies and patterns in historical SST data. However, global SST is a unified system of high regionality, and the SST in different sea areas shows different changing patterns due to the influence of various factors, e.g., geographic location, ocean currents and sea depth. Without a good understanding of such regionality of SST, we cannot quantitatively integrate the regionality information of SST into SST prediction models to make them adaptive to different SST patterns around the world and improve the prediction accuracy. To address this issue, we proposed the Multi-Stage Spatio–Temporal Clustering (MuSTC) method to quantitatively identify sea areas with similar SST patterns. First, MuSTC sequentially learns the representation of long-term SST with a deep temporal encoder and calculates the spatial correlation scores between grid ocean regions with self-attention. Then, MuSTC clusters grid ocean regions based on the original SST data, encoded long-term SST representation and spatial correlation scores, respectively, to obtain the sea areas with similar SST patterns from different perspectives. According to the experiments in three ocean areas, i.e., the North Pacific Ocean (NPO), the South Atlantic Ocean (SAO) and the North Atlantic Ocean (NAO), the clustering results generally match the distribution of ocean currents, which demonstrates the effectiveness of our MuSTC method. In addition, we integrate the clustering results into two representative spatio–temporal prediction models, i.e., Spatio–Temporal Graph Convolutional Networks (STGCN) and Adaptive Graph Convolutional Recurrent Network (AGCRN), to conduct SST prediction. According to the results of experiments, the integration of regionality information leads to the reduction of Root Mean Square Error (RMSE) by 1.95%, 1.39% and 1.28% in NPO, SAO and NAO, respectively, using the STGCN model, and the reduction of RMSE by 4.94%, 0.74% and 1.43% by using the AGCRN model. Such results indicate that the integration of regionality information could notably improve the prediction accuracy of SST.

MUST: A Multi-source Spatio-Temporal data fusion Model for short-term sea surface temperature prediction

Sea surface temperature (SST) plays an important role in various oceanic applications, including climate prediction, ocean environment monitoring and marine disaster warning. Although many approaches have been developed for predicting SST, most of them conduct the prediction only based on historical SST data. However, SST is essentially affected by many external factors, e.g., the short-wave radiation from the sun and the long-wave radiation from the atmosphere and ground, which are ignored by existing approaches. In this work, we proposed a Multi-source Spatio-Temporal data fusion model (MUST) to fuse multi-source data, including SST data and external factors, to improve the accuracy of short-term SST prediction. Concretely, MUST first introduces Bicubic Convolutional Interpolation (BCI) to address the issue of inconsistent spatial resolutions of multi-source data, then employs the Spatio-Temporal Dilated ConvLSTM (ST-DC) to learn the spatio-temporal features of SST and external factors, and finally fuses the learned features from multi-source data to predict SST with a Cross Data Fusion (CDF) component. As validated on two real datasets, MUST achieves much better performance than existing SST prediction approaches.

RSST-ARGM: a data-driven approach to long-term sea surface temperature prediction

For the purpose of exploring the long-term variation of regional sea surface temperature (SST), this paper studies the historical SST in regional sea areas and the emission pattern of greenhouse gases, proposing a Grey model of regional SST atmospheric reflection which can be used to predict SST variation in a long time span. By studying the Grey systematic relationship between historical SST data, the model obtains the development law of temperature variation, and further introduces different greenhouse gas emission scenarios in the future as the indexes coefficient to determine the corresponding changing results of seawater temperature in the next 50 years. Taking the North Atlantic Ocean as an example, the cosine similarity test method is used to verify the model proposed in this paper. The accuracy of the model is as high as 0.99984. The model predicts that the regional SST could reach a maximum of 15.3,^{circ }{mathrm {C}} by 2070. This model is easy to calculate, with advantages of the high accuracy and good robustness.

A Multi-Channel LSTM Model for Sea Surface Temperature Prediction

Sea Surface Temperature (SST) plays an important role in marine ecology. SST prediction raises considerable attention in ocean-related fields. Recently, deep learning models are widely used in SST prediction, but it is not easy to obtain optimal prediction results using historical observation data directly. More than temporal information, SST data also contains other features, such as trend, periodicity and disturbance. In this paper, we proposed a Multi-Channel LSTM (MC-LSTM) model to improve SST prediction. Firstly, a wavelet transform is used to decompose time-sequence data into multiple sequences representing trend, period and disturbance respectively. Secondly, we use multiple LSTM channels to train these data in parallel, and then obtain the combined prediction results. MC-LSTM can predict Sea surface temperature only by historical SST data, without the help of other spatial and climatic information, so it is easy to get the data. Compared with the direct use of LSTM for prediction, MC-LSTM can improve the prediction accuracy by 26%.

A Novel Method for Sea Surface Temperature Prediction Based on Deep Learning

Sea surface temperature (SST) forecasting is the task of predicting future values of a given sequence using historical SST data, which is beneficial for observing and studying hydroclimatic variability. Most previous studies ignore the spatial information in SST prediction and the forecasting models have limitations to process the large-scale SST data. A novel model of SST prediction integrated Deep Gated Recurrent Unit and Convolutional Neural Network (DGCnetwork) is proposed in this paper. The DGCnetwork has a compact structure and focuses on learning deep long-term dependencies in SST time series. Temporal information and spatial information are all included in our procedure. Differential Evolution algorithm is applied in order to configure DGCnetwork’s optimum architecture. Optimum Interpolation Sea Surface Temperature (OISST) data is selected to conduct experiments in this paper, which has good temporal homogeneity and feature resolution. The experiments demonstrate that the DGCnetwork significantly obtains excellent forecasting result, predicting SST by different lengths flexibly and accurately. On the East China Sea dataset and the Yellow Sea dataset, the accuracy of the prediction results is above 98% on the whole and all mean absolute error (MAE) values are lower than 0.33°C. Compared with the other models, root mean square error (RMSE), root mean square percentage error (RMSPE), and mean absolute percentage Error (MAPE) of the proposed approach reduce at least 0.1154, 0.2594, and 0.3938. The experiments of SST time series show that the DGCnetwork model maintains good prediction results, better performance, and stronger stability, which has reached the most advanced level internationally.

Global and regional evolution of sea surface temperature under climate change

Sea surface temperature (SST) has important local and remote influence on global climate through the distribution and transport of heat and moisture, which in turn affect precipitation and air temperature patterns across the globe. Thus, this work aims to assess the worldwide regionalization of the SST evolution for the 21 st century under the influence of climate change by means of: 1) division of the worldwide SST data in regions applying a K-means cluster procedure and validation of the most suitable CMIP5 models by a comparative analysis with SST data from Era-Interim reanalysis; 2) calculation of SST trends along the 21 st century and assessment of future SST differences between RCP 4.5 and RCP 8.5 greenhouse gas emission scenarios and historical SST data. Global climate model (GCM) projections show a SST warming worldwide although with different intensity depending on the region. Northern Hemisphere regions present a higher seasonal thermal amplitude comparing with equivalent regions in the Southern Hemisphere. Northern Hemisphere has also a higher annual SST increment than clusters in the Southern Hemisphere, independently of the future time period and climate scenario. The Northern Sub-Tropical cluster (STRN) shows the highest significant increment (4.34 °C on RCP 8.5). Under the RCP 4.5 climate scenario, SST trends from 1975 to 2100 range from 0.075 °C dec −1 at South Polar cluster (PRS) to 0.21 °C dec −1 at STRN cluster. Relatively to RCP 8.5 climatic scenario, SST trends change from 0.13 °C dec −1 at PRS cluster to 0.36 °C dec −1 at the North Polar cluster (PRN). • Identification of CMIP5 models with best SST performance. • The optimal worldwide SST division is eight clusters. • Northern Hemisphere clusters will present a higher SST increment.

Comment on “The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity”

AbstractIn 2018, Lewis and Curry presented a method for estimating the transient climate response (TCR) of the climate system from the temperature change between two time windows: an early baseline period in the nineteenth century and a modern period primarily in the twenty-first century. The results suggest a lower value of TCR than estimates from climate model simulations. Previous studies have identified uncertainty in the historical forcings, the impact of the time evolution of the forcing on temperature response, and observational issues as contributory factors to this disagreement. We investigate a further factor: uncertainty in the bias corrections applied to historical sea surface temperature data. This uncertainty can particularly affect the estimation of variables on decadal time scales and therefore affect the estimation of TCR using the window method as well as estimates of internal variability. We demonstrate that use of the whole historical record can mitigate the impacts of working with short time windows to some extent, particularly with respect to the early part of the record.

Optimal Configuration Method of Sampling Points Based on Variability of Sea Surface Temperature

In situ observation is one of the most direct and efficient ways to understand the ocean, but it is usually limited in terms of spatial and temporal coverage. The determination of optimal sampling strategies that effectively utilize available resources to maximize the information content of the collected ocean data is becoming an open problem. The historical sea surface temperature (SST) dataset contains the spatial variability information of SST, and this prior knowledge can be used to optimize the configuration of sampling points. Here, a configuration method of sampling points based on the variability of SST is studied. Firstly, in order to get the spatial variability of SST in the ocean field to be sampled, the historical SST data of the field is analyzed. Then, K-means algorithm is used to cluster the subsampled fields to make the configuration of sampling points more suitable. Finally, to evaluate the sampling performance of the new configuration method of sampling points, the SST field is reconstructed by the method based on compression sensing algorithm. Results show that the proposed optimal configuration method of sampling points significantly outperforms the traditional random sampling points distribution method in terms of reconstruction accuracy. These results provide a new method for configuring sampling points of ocean in situ observation with limited resources.

Identification of global marine hotspots: sentinels for change and vanguards for adaptation action

Major changes consistent with the fingerprint of global warming have been reported for nearly every ecosystem on earth. Recently, studies have moved beyond correlation-based inference to demonstrate mechanistic links between warming and biological responses, particularly in regions experiencing rapid change. However, the assessment of climate change impacts and development of adaptation options that humans can undertake are at the earliest stages, particularly for marine systems. Here, we use trends in ocean temperature to characterize regions that can act as natural laboratories or focal points for early learning. These discrete marine ‘hotspots’, where ocean warming is fastest, were identified based on 50 years of historical sea surface temperature data. Persistence of these hotspots into the future was evaluated using global climate models. This analysis provides insights and a starting point for scientists aiming to identify key regions of concern with regard to ocean warming, and illustrates a potential approach for considering additional physical drivers of change such as ocean pH or oxygenation. We found that some hotspot regions were of particular concern due to other non-climate stressors. For instance, many of the marine hotspots occur where human dependence on marine resources is greatest, such as south-east Asia and western Africa, and are therefore of critical consideration in the context of food security. Intensive study and development of comprehensive inter-disciplinary networks based on the hotspot regions identified here will allow earliest testing of management and adaptation pathways, facilitating rapid global learning and implementation of adaptation options to cope with future change.

Improving sea level reconstructions using non‐sea level measurements

We present a new method for reconstructing sea level involving cyclostationary empirical orthogonal functions (CSEOFs). While we show results from a CSEOF reconstruction using basis functions computed from satellite altimetry and subsequently fit to tide gauge data, our focus is on how other ocean observations such as sea surface temperature can be leveraged to create an improved reconstructed sea level data set spanning the time period from 1900 to present. Basis functions are computed using satellite measurements of sea surface temperature, and using a simple regression technique, these basis functions are transformed to represent a similar temporal evolution to corresponding satellite altimeter‐derived sea level basis functions. The resulting sea level and sea surface temperature basis functions are fit to tide gauge data and historical sea surface temperature data, respectively, to produce a reconstructed sea level data set spanning the period from 1900 to present. We demonstrate the use of this reconstructed data set for climate monitoring, focusing primarily on climate signals in the Pacific Ocean. The CSEOF reconstruction technique can be used to create indices computed solely from sea level measurements for monitoring signals such as the eastern Pacific (EP) El Niño–Southern Oscillation (ENSO), central Pacific (CP) ENSO, and Pacific Decadal Oscillation (PDO). The EP ENSO, CP ENSO, and PDO signals are all well represented in the CSEOF reconstruction relying solely on sea level measurements from 1950 to present; however, significant improvement can be made in reconstructing these signals during the first half of the twentieth century by including sea surface temperature measurements in the sea level reconstruction procedure.

Turning up the Heat: Increasing Temperature and Coral Bleaching at the High Latitude Coral Reefs of the Houtman Abrolhos Islands

BackgroundCoral reefs face increasing pressures particularly when on the edge of their distributions. The Houtman Abrolhos Islands (Abrolhos) are the southernmost coral reef system in the Indian Ocean, and one of the highest latitude reefs in the world. These reefs have a unique mix of tropical and temperate marine fauna and flora and support 184 species of coral, dominated by Acropora species. A significant La Niña event during 2011 produced anomalous conditions of increased temperature along the whole Western Australian coastline, producing the first-recorded widespread bleaching of corals at the Abrolhos.Methodology/ Principal FindingsWe examined long term trends in the marine climate at the Abrolhos using historical sea surface temperature data (HadISST data set) from 1900–2011. In addition in situ water temperature data for the Abrolhos (from data loggers installed in 2008, across four island groups) were used to determine temperature exposure profiles. Coupled with the results of coral cover surveys conducted annually since 2007; we calculated bleaching thresholds for monitoring sites across the four Abrolhos groups.Conclusions/ Significance In situ temperature data revealed maximum daily water temperatures reached 29.54°C in March 2011 which is 4.2°C above mean maximum daily temperatures (2008–2010). The level of bleaching varied across sites with an average of ∼12% of corals bleached. Mortality was high, with a mean ∼50% following the 2011 bleaching event. Prior to 2011, summer temperatures reached a mean (across all monitoring sites) of 25.1°C for 2.5 days. However, in 2011 temperatures reached a mean of 28.1°C for 3.3 days. Longer term trends (1900–2011) showed mean annual sea surface temperatures increase by 0.01°C per annum. Long-term temperature data along with short-term peaks in 2011, outline the potential for corals to be exposed to more frequent bleaching risk with consequences for this high latitude coral reef system at the edge of its distribution.

An abrupt drop in Northern Hemisphere sea surface temperature around 1970

The twentieth-century trend in global-mean surface temperature was not monotonic: temperatures rose from the start of the century to the 1940s, fell slightly during the middle part of the century, and rose rapidly from the mid-1970s onwards. The warming-cooling-warming pattern of twentieth-century temperatures is typically interpreted as the superposition of long-term warming due to increasing greenhouse gases and either cooling due to a mid-twentieth century increase of sulphate aerosols in the troposphere, or changes in the climate of the world's oceans that evolve over decades (oscillatory multidecadal variability). Loadings of sulphate aerosol in the troposphere are thought to have had a particularly important role in the differences in temperature trends between the Northern and Southern hemispheres during the decades following the Second World War. Here we show that the hemispheric differences in temperature trends in the middle of the twentieth century stem largely from a rapid drop in Northern Hemisphere sea surface temperatures of about 0.3 °C between about 1968 and 1972. The timescale of the drop is shorter than that associated with either tropospheric aerosol loadings or previous characterizations of oscillatory multidecadal variability. The drop is evident in all available historical sea surface temperature data sets, is not traceable to changes in the attendant metadata, and is not linked to any known biases in surface temperature measurements. The drop is not concentrated in any discrete region of the Northern Hemisphere oceans, but its amplitude is largest over the northern North Atlantic.

Coastal upwelling along the north coast of Papua New Guinea and SST cooling over the pacific warm pool: A case study for the 2002/03 El Niño event

We investigate an overlooked mechanism—coastal upwelling—for sea surface temperature (SST) cooling in the western side of the mean location of the Pacific warm pool (WSWP: 5°S–5°N, 140°E–150°E) prior to El Nino onset. We analyze various observed data such as the TRIangle Trans-Ocean buoy Network (TRITON) moored buoy data, Conductivity-Temperature-Depth (CTD) data, satellite data and a hindcast experiment output by a high-resolution ocean general circulation model (OGCM). We focus on the precondition of the 2002/03 El Nino event, for which many datasets are available. Relatively cool water upwelled along the north coast of Papua New Guinea (PNG) during December 2001, prior to the onset of the 2002/03 El Nino event, and then spread out over a wider area to the northeast. Simultaneously, strong west-northerly surface winds occur along the north coast. Heat budget analysis of TRITON buoy data in the WSWP reveals that negative zonal heat advection due to eastward current is the main factor for cooling the mixed layer in the WSWP in contrast to the warming effect of the surface heat flux during the period. This cooling requires a source of colder water to the west. Similar analysis of OGCM outputs also suggests that the upwelled relatively cool water along the PNG north coast, and its northeastward extension to the equatorial region, contributes to cooling of the surface water over the WSWP mainly via negative zonal heat advection. Similar mechanisms are confirmed also for the 1982/83 and 1997/98 El Nino events by analyses of OGCM outputs and historical SST data. The low SST in the WSWP generated a positive zonal SST gradient together with high SST east of the WSWP. It may contribute to enhancement of the westerly surface wind in this region, leading to the onset of the 2002/03 El Nino event.

Assimilation of historical SST data for long-term ENSO retrospective forecasts

In this study, the assimilation of historic SST (sea surface temperature) data was performed for long-term ENSO hindcasts. The emphasis was placed on the design of background error covariance (BEC) that dominates the transfer of SST information to the subsurface. Four different data-assimilation schemes, based on Optimal Interpolation (OI) algorithm, were proposed, and compared in terms of ENSO simulation and prediction skills for the period from 1876 to 2000. It was found that the data-assimilation scheme that has a three-dimensional BEC constructed from model simulations forced by observed wind stress can effectively correct the second-layer temperature in the SST assimilation and lead to the best ENSO prediction skill. Further analysis for the long-term hindcasts shows that the prediction skills have a striking decadal/interdecadal variability similar to that found in other models. These results provide a fundamental basis for the further study of ENSO predictability.

Climatological context for large-scale coral bleaching

Large-scale coral bleaching was first observed in 1979 and has occurred throughout virtually all of the tropics since that time. Severe bleaching may result in the loss of live coral and in a decline of the integrity of the impacted coral reef ecosystem. Despite the extensive scientific research and increased public awareness of coral bleaching, uncertainties remain about the past and future of large-scale coral bleaching. In order to reduce these uncertainties and place large-scale coral bleaching in the longer-term climatological context, specific criteria and methods for using historical sea surface temperature (SST) data to examine coral bleaching-related thermal conditions are proposed by analyzing three, 132 year SST reconstructions: ERSST, HadISST1, and GISST2.3b. These methodologies are applied to case studies at Discovery Bay, Jamaica (77.27°W, 18.45°N), Sombrero Reef, Florida, USA (81.11°W, 24.63°N), Academy Bay, Galapagos, Ecuador (90.31°W, 0.74°S), Pearl and Hermes Reef, Northwest Hawaiian Islands, USA (175.83°W, 27.83°N), Midway Island, Northwest Hawaiian Islands, USA (177.37°W, 28.25°N), Davies Reef, Australia (147.68°E, 18.83°S), and North Male Atoll, Maldives (73.35°E, 4.70°N). The results of this study show that (1) The historical SST data provide a useful long-term record of thermal conditions in reef ecosystems, giving important insight into the thermal history of coral reefs and (2) While coral bleaching and anomalously warm SSTs have occurred over much of the world in recent decades, case studies in the Caribbean, Northwest Hawaiian Islands, and parts of other regions such as the Great Barrier Reef exhibited SST conditions and cumulative thermal stress prior to 1979 that were comparable to those conditions observed during the strong, frequent coral bleaching events since 1979. This climatological context and knowledge of past environmental conditions in reef ecosystems may foster a better understanding of how coral reefs will respond in future, ocean warming scenarios.

Effect of subjective choices on the objective analysis of sea surface temperature data in the tropical Atlantic and Pacific oceans

Many subjective choices are required to perform an objective interpolation (OI) analysis of environmental variables. Herein, we consider the effects on the statistical analysis of sea surface temperature (SST) using (1) a structure function or covariance analysis, (2) different analytical expressions to represent the statistics of the raw data, and (3) different historical SST data sets. The historical data sets are the well-sampled Comprehensive Ocean–Atmospheric Data Set (COADS) and the poorly sampled historical expendable bathythermograph (XBT) data set. Results from these analyses are used to generate error maps for a poorly-sampled, two month XBT array and a proposed well-sampled profiling float array. For the relatively data-rich COADS analysis, decorrelation scales are the same using either the structure function or covariance analyses. Results differ for the data-poor XBT analysis. Representative decorrelation scales in the Pacific (Atlantic) are about 11–14 (6–10) degrees in the zonal direction and 4–7 (3–6) degrees in the meridional direction. As COADS SST data are less precise than XBT SST data, error and signal variances are greater for the former. The choice of analytical fit to the raw data (needed to generate error maps) has a dramatic effect on the resulting uncertainty fields. Gaussian fits, because of their parabolic shape near the origin, result in smaller errors than exponential fits for the same observing array. Finally, the proposed float array can achieve the accuracies needed to resolve satisfactory upper layer heat content changes over larger areas than the present XBT network. © 2000 Ifremer/CNRS/IRD/Éditions scientifiques et médicales Elsevier SAS objective analysis / sea surface temperature / structure function / statistical analysis / objective interpolation

Analyses of global sea surface temperature 1856–1991

Global analyses of monthly sea surface temperature (SST) anomalies from 1856 to 1991 are produced using three statistically based methods: optimal smoothing (OS), the Kaiman filter (KF) and optimal interpolation (OI). Each of these is accompanied by estimates of the error covariance of the analyzed fields. The spatial covariance function these methods require is estimated from the available data; the timemarching model is a first‐order autoregressive model again estimated from data. The data input for the analyses are monthly anomalies from the United Kingdom Meteorological Office historical sea surface temperature data set (MOHSST5) [Parker et al., 1994] of the Global Ocean Surface Temperature Atlas (GOSTA) [Bottomley et al., 1990].These analyses are compared with each other, with GOSTA, and with an analysis generated by projection (P) onto a set of empirical orthogonal functions (as in Smith et al. [1996]). In theory, the quality of the analyses should rank in the order OS, KF, OI, P, and GOSTA. It is found that the first four give comparable results in the data‐rich periods (1951–1991), but at times when data is sparse the first three differ significantly from P and GOSTA. At these times the latter two often have extreme and fluctuating values, prima facie evidence of error. The statistical schemes are also verified against data not used in any of the analyses (proxy records derived from corals and air temperature records from coastal and island stations). We also present evidence that the analysis error estimates are indeed indicative of the quality of the products. At most times the OS and KF products are close to the OI product, but at times of especially poor coverage their use of information from other times is advantageous.The methods appear to reconstruct the major features of the global SST field from very sparse data. Comparison with other indications of the El Niño‐Southern Oscillation cycle show that the analyses provide usable information on interannual variability as far back as the 1860s.

Reduced space optimal analysis for historical data sets: 136 years of Atlantic sea surface temperatures

A computationally efficient method for analyzing meteorological and oceanographic historical data sets has been developed. The method combines data reduction and least squares optimal estimation. The data reduction involves computing empirical orthogonal functions (EOFs) of the data based on their recent, high‐quality portion and using a leading EOF subset as a basis for the analyzed solution and for fitting a first‐order linear model of time transitions. We then formulate optimal estimation problems in terms of the EOF projection of the analyzed field to obtain reduced space analogues of the optimal smoother, the Kalman filter, and optimal interpolation techniques. All reduced space algorithms are far cheaper computationally than their full grid prototypes, while their solutions are not necessarily inferior since the sparsity and error in available data often make estimation of small‐scale features meaningless. Where covariance patterns can be estimated from the available data, the analysis methods fill gaps, correct sampling errors, and produce spatially and temporally coherent analyzed data sets. As with classical least squares estimation, the reduced space versions also provide theoretical error estimates for analyzed values. The methods are demonstrated on Atlantic monthly sea surface temperature (SST) anomalies for 1856–1991 from the United Kingdom Meteorological Office historical sea surface temperature data set (version MOHSST5). Choice of a reduced space dimension of 30 is shown to be adequate. The analyses are tested by withholding a significant part of the data and prove to be robust and in agreement with their own error estimates; they are also consistent with a partially independent optimal interpolation (OI) analysis by Reynolds and Smith [1994] produced in the National Centers for Environmental Prediction (NCEP)(known as the NCEP OI analysis). A simple statistical model is used to depict the month‐to‐month SST evolution in the optimal smoother algorithm. Results are somewhat superior to both the Kalman filter, which relies less on the model, and the optimal interpolation, which does not use it at all. The method generalizes a few recent works on using a reduced space for data set analyses. Difficulties of methods which simply fit EOF patterns to observed data are pointed out, and the more complete analysis procedures developed here are suggested as a remedy.

Sahel rainfall and worldwide sea temperatures, 1901–85

Using the comprehensively quality-controlled Meteorological Office Historical Sea Surface Temperature data set (MOHSST)1,2 we show for the first time that persistently wet and dry periods in the Sahel region of Africa are strongly related to contrasting patterns of sea-surface temperature (SST) anomalies on a near-global scale. The anomalies include relative changes in SST between the hemispheres, on timescales of years to tens of years, which are most pronounced in the Atlantic. Experiments with an 11-level global atmospheric general circulation model (AGCM) support the idea that the worldwide SST anomalies modulate summer Sahel rainfall through changes in tropical atmospheric circulation3–6. El Nino events may also play a part. We do not discount the effects of soil moisture and albedo changes in the Sahel7,8, although Courel et al.9 have questioned the importance of albedo changes, but we do suggest that worldwide SST anomalies may have a more fundamental influence on Sahel rainfall.