Response of El Niño sea surface temperature variability to greenhouse warming

We satellite-tracked five Humboldt penguins during the strong 1997/98 El Nino Southern Oscillation (ENSO) from their breeding island Pan de Azucar (26 degrees 09′S, 70 degrees 40′W) in Northern Chile and related their activities at sea to satellite-derived information on sea surface temperature (SST), sea surface temperature anomaly (SSTA), wind direction and speed, chlorophyll a concentrations and statistical data on fishery landings. We found that Humboldt penguins migrated by up to 895 km as marine productivity decreased. The total daily dive duration was highly correlated with SSTA, ranging from 3.1 to 12.5 h when the water was at its warmest (+4 degrees C). Birds travelled between 2 and 116 km every day, travelling further when SSTA was highest. Diving depths (maximum 54 m), however, were not increased with respect to previous years. Two penguins migrated south and, independently of each other, located an area of high chlorophyll a concentration 150 km off the coast. Humboldt penguins seem to use day length, temperature gradients, wind direction and olfaction to adapt to changing environmental conditions and to find suitable feeding grounds. This makes Humboldt penguins biological in situ detectors of highly productive marine areas, with a potential use in the verification of trends detected by remote sensors on board satellites.

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Seasonal Covariability of Dryness or Wetness in China and Global Sea Surface Temperature

Journal of Climate ◽

10.1175/jcli-d-19-0250.1 ◽

2020 ◽

Vol 33 (2) ◽

pp. 727-747

Author(s):

Chunxiang Li ◽

Chunzai Wang ◽

Tianbao Zhao

Keyword(s):

Indian Ocean ◽

Surface Temperature ◽

Sea Surface Temperature ◽

El Niño ◽

El Nino ◽

Sea Surface ◽

Interdecadal Variability ◽

Coupled Modes ◽

Indian Ocean Basin Mode ◽

Global Sea Surface Temperature

AbstractSeasonal covariability of the dryness/wetness in China and global sea surface temperature (SST) is investigated by using the monthly self-calibrated Palmer drought severity index (PDSI) data and other data from 1950 to 2014. The singular value decomposition (SVD) analysis shows two recurring PDSI–SST coupled modes. The first SVD mode of PDSI is associated with the warm phases of the eastern Pacific–type El Niño–Southern Oscillation (ENSO), the interdecadal Pacific oscillation (IPO) or Pacific decadal oscillation (PDO), the Indian Ocean basin mode (IOBM) in the autumn and winter, and the cold phase of the IOBM in the spring. Meanwhile, the Atlantic multidecadal oscillation (AMO) pattern appears in every season except the autumn. The second SVD mode of PDSI is accompanied by a central Pacific–type El Niño developing from the winter to autumn over the tropical Pacific and a positive phase of IPO or PDO from the winter to summer. Moreover, an AMO pattern is observed in all seasons except the summer, whereas the SST over the tropical Indian Ocean shows negligible variations. The further analyses suggest that AMO remote forcing may be a primary factor influencing interdecadal variability of PDSI in China, and interannual to interdecadal variability of PDSI seems to be closely associated with the ENSO-related events. Meanwhile, the IOBM may be a crucial factor in interannual variability of PDSI during its mature phase in the spring. In general, the SST-related dryness/wetness anomalies can be explained by the associated atmospheric circulation changes.

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Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle

Ocean Science ◽

10.5194/os-14-301-2018 ◽

2018 ◽

Vol 14 (2) ◽

pp. 301-320 ◽

Cited By ~ 2

Author(s):

Mei Hong ◽

Xi Chen ◽

Ren Zhang ◽

Dong Wang ◽

Shuanghe Shen ◽

...

Keyword(s):

Statistical Model ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Correlation Coefficient ◽

El Niño ◽

Temperature Anomaly ◽

El Nino ◽

Sea Surface ◽

Sea Surface Temperature Anomaly

Abstract. With the objective of tackling the problem of inaccurate long-term El Niño–Southern Oscillation (ENSO) forecasts, this paper develops a new dynamical–statistical forecast model of the sea surface temperature anomaly (SSTA) field. To avoid single initial prediction values, a self-memorization principle is introduced to improve the dynamical reconstruction model, thus making the model more appropriate for describing such chaotic systems as ENSO events. The improved dynamical–statistical model of the SSTA field is used to predict SSTA in the equatorial eastern Pacific and during El Niño and La Niña events. The long-term step-by-step forecast results and cross-validated retroactive hindcast results of time series T1 and T2 are found to be satisfactory, with a Pearson correlation coefficient of approximately 0.80 and a mean absolute percentage error (MAPE) of less than 15 %. The corresponding forecast SSTA field is accurate in that not only is the forecast shape similar to the actual field but also the contour lines are essentially the same. This model can also be used to forecast the ENSO index. The temporal correlation coefficient is 0.8062, and the MAPE value of 19.55 % is small. The difference between forecast results in spring and those in autumn is not high, indicating that the improved model can overcome the spring predictability barrier to some extent. Compared with six mature models published previously, the present model has an advantage in prediction precision and length, and is a novel exploration of the ENSO forecast method.

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