Changes in Decadally Averaged Sea Surface Temperature over the World 1861–1980

1984 ◽  
pp. 721-727 ◽  
Author(s):  
C. Folland ◽  
F. Kates
2016 ◽  
Vol 42 ◽  
pp. 73-81
Author(s):  
Miguel Tasambay-Salazar ◽  
María José OrtizBeviá ◽  
Antonio RuizdeElvira ◽  
Francisco José Alvarez-García

Abstract. The El Niño-Southern Oscillation (ENSO) phenomenon is the main source of the predictability skill in many regions of the world for seasonal and interannual timescales. Longer lead predictability experiments of Niño3.4 Index using simple statistical linear models have shown an important skill loss at longer lead times when the targeted season is summer or autumn. We develop different versions of the model substituting some its variables with others that contain tropical or extratropical information, produce a number of hindcasts with these models using two different predictions schemes and cross validate them. We have identified different sets of tropical or extratropical predictors, which can provide useful values of potential skill. We try to find out the sources of the predictability by comparing the sea surface temperature (SST) and heat content (HC) anomalous fields produced by the successful predictors for the 1980–2012 period. We observe that where tropical predictors are used the prediction reproduces only the equatorial characteristics of the warming (cooling). However, where extratropical predictors are included, the predictions are able to simulate the absorbed warming in the South Pacific Convergence Zone (SPCZ).


2010 ◽  
Vol 23 (3) ◽  
pp. 485-503 ◽  
Author(s):  
Kirsten L. Findell ◽  
Thomas L. Delworth

Abstract Climate model simulations run as part of the Climate Variability and Predictability (CLIVAR) Drought Working Group initiative were analyzed to determine the impact of three patterns of sea surface temperature (SST) anomalies on drought and pluvial frequency and intensity around the world. The three SST forcing patterns include a global pattern similar to the background warming trend, a pattern in the Pacific, and a pattern in the Atlantic. Five different global atmospheric models were forced by fixed SSTs to test the impact of these SST anomalies on droughts and pluvials relative to a climatologically forced control run. The five models generally yield similar results in the locations of drought and pluvial frequency changes throughout the annual cycle in response to each given SST pattern. In all of the simulations, areas with an increase in the mean drought (pluvial) conditions tend to also show an increase in the frequency of drought (pluvial) events. Additionally, areas with more frequent extreme events also tend to show higher intensity extremes. The cold Pacific anomaly increases drought occurrence in the United States and southern South America and increases pluvials in Central America and northern and central South America. The cold Atlantic anomaly increases drought occurrence in southern Central America, northern South America, and central Africa and increases pluvials in central South America. The warm Pacific and Atlantic anomalies generally lead to reversals of the drought and pluvial increases described with the corresponding cold anomalies. More modest impacts are seen in other parts of the world. The impact of the trend pattern is generally more modest than that of the two other anomaly patterns.


2020 ◽  
Author(s):  
Benjamin Martinez-Lopez

<p>Sea surface temperature (SST) is the only oceanic parameter on which depend heat fluxes between ocean and atmosphere and, therefore, SST is one of the key factors that influence climate and its variability. Over the twentieth century, SSTs have significantly increased around the global ocean, warming that has been attributed to anthropogenic climate change, although it is not yet clear how much of it is related to natural causes and how much is due to human activities. A considerable part of available literature regarding climate change has been built based on the global or hemispheric analysis of surface temperature trends. There are, however, some key open questions that need to be answered and for this task estimates of long-term SST trend patterns represent a source of valuable information. Unfortunately, long-term SST trend patterns have large uncertainties and although SST constitutes one of the most-measured ocean variables of our historic records, their poor spatial and temporal sampling, as well as inhomogeneous measurements technics, hinder an accurate determination of long-term SST trends, which increases their uncertainty and, therefore, limit their physical interpretation as well as their use in the verification of climate simulations.<br>Most of the long-term SST trend patterns have been built using linear techniques, which are very usefull when they are used to extract information of measurements satisfying two key assumptions: linearity and stationarity. The global warming resulting of our economic activities, however, affect the state of the World Ocean and the atmosphere inducing changes in the climate that may result in oscillatory modes of variability of different frequencies, which may undergo non-stationary and non-linear evolutions. In this work, we construct long-term SST trend patterns by using non-linear techniques to extract non-linear, long-term trends in each grid-point of two available global SST datasets: the National Oceanic and Atmospheric Administration Extended Reconstructed SST (ERSST) and from the Hadley Centre sea ice and SST (HadISST). The used non-linear technique makes a good job even if the SST data are non-linear and non-stationary. Additionally, the nonlinearity of the extracted trends allows the use of the first and second derivative to get more information about the global, long-term evolution of the SST fields, favoring thus a deeper understanding and interpretation of the observed changes in SST. Particularly, our results clearly show, in both ERSST and HadISST datasets, the non-uniform warming observed in the tropical Pacific, which seems to be related to the enhanced vertical heat flux in the eastern equatorial Pacific and the strengthening of the warm pool in the western Pacific. By using the second derivative of the nonlinear SST trends, emerges an interesting pattern delimiting several zones in the Pacific Ocean which have been responded in a different way to the impose warming of the last century.</p>


2021 ◽  
Author(s):  
Nicola Scafetta

AbstractThe 0.6 °C warming observed in global temperature datasets from 1940 to 1960 to 2000–2020 can be partially due to urban heat island (UHI) and other non-climatic biases in the underlying data, although several previous studies have argued to the contrary. Here we identify land regions where such biases could be present by locally evaluating their diurnal temperature range (DTR = TMax − TMin trends between the decades 1945–1954 and 2005–2014 and between the decades 1951–1960 and 1991–2000 versus their synthetic hindcasts produced by the CMIP5 models. Vast regions of Asia (in particular Russia and China) and North America, a significant part of Europe, part of Oceania, and relatively small parts of South America (in particular Colombia and Venezuela) and Africa show DTR reductions up to 0.5–1.5 °C larger than the hindcasted ones, mostly where fast urbanization has occurred, such as in central-east China. Besides, it is found: (1) from May to October, TMax globally warmed 40% less than the hindcast; (2) in Greenland, which appears nearly free of any non-climatic contamination, TMean warmed about 50% less than the hindcast; (3) the world macro-regions with, on average, the lowest DTR reductions and with low urbanization (60S-30N:120 W–90 E and 60 S–10 N:90 E–180 E: Central and South America, Africa, and Oceania) warmed about 20–30% less than the models’ hindcast. Yet, the world macro-region with, on average, the largest DTR reductions and with high urbanization (30 N–80 N:180 W–180 E: most of North America, Europe, and Central Asia) warmed just a little bit more (5%) than the hindcast, which indicates that the models well agree only with potentially problematic temperature records. Indeed, also tree-based proxy temperature reconstructions covering the 30°N–70°N land area produce significantly less warming than the correspondent instrumentally-based temperature record since 1980. Finally, we compare land and sea surface temperature data versus their CMIP5 simulations and find that 25–45% of the 1 °C land warming from 1940–1960 to 2000–2020 could be due to non-climatic biases. By merging the sea surface temperature record (assumed to be correct) and an adjusted land temperature record based on the model prediction, the global warming during the same period is found to be 15–25% lower than reported. The corrected warming is compatible with that shown by the satellite UAH MSU v6.0 low troposphere global temperature record since 1979. Implications for climate model evaluation and future global warming estimates are briefly addressed.


2010 ◽  
Vol 23 (5) ◽  
pp. 1047-1059 ◽  
Author(s):  
Jorge Vázquez-Cuervo ◽  
Edward M. Armstrong ◽  
Kenneth S. Casey ◽  
Robert Evans ◽  
Katherine Kilpatrick

Abstract Two Pathfinder sea surface temperature (SST) datasets—version 5.0 (V50) and version 4.1 (V41)—were compared in two test areas: 1) the Gulf Stream (GS) between 35° and 43°N, 75° and 60°W and 2) the California coast (CC) between 30° and 45°N, 130° and 120°W. Using a nearest-neighbor approach, V50 data were regridded to the lower resolution V41 9-km data. The V50 and V41 versions were also independently compared with data from the World Ocean Database (WOD). Climatological monthly rms differences between V50 and V41 were calculated as well as seasonal differences between V50, V41, and the WOD. Maximum rms differences of 0.8°C between the V50 and V41 were seen in June for the GS. In the CC maximum differences of 0.4°C were seen in July. Significant seasonal trends were evident in rms differences between V41 and the WOD, with a maximum of 1.5°C occurring in the GS in June and in the CC in July. No seasonal peaks occurred in the rms differences between V50 and the WOD. SST gradients were calculated using both V50 and V41 datasets. Maximum climatological SST gradients were seen in the June time frame for the GS and July for the CC, consistent with the largest rms differences compared to the WOD. Results indicate the importance of projects such as the Group for High-Resolution Sea Surface Temperature (GHRSST) and the creation of high-resolution SST datasets for resolving air–sea interactions, specifically in areas of strong SST gradients.


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