Investigation of Long Term Trend of Spatio-Temporal changes of Sea Surface Temperature in Oman Sea

Abstract. There is growing understanding that recent deterioration of the Black Sea ecosystem was partly due to changes in the marine physical environment. This study uses high resolution 0.25° climatology to analyze sea surface temperature variability over the 20th century in two contrasting regions of the sea. Results show that the deep Black Sea was cooling during the first three quarters of the century and was warming in the last 15–20 years; on aggregate there was a statistically significant cooling trend. The SST variability over the Western shelf was more volatile and it does not show statistically significant trends. The cooling of the deep Black Sea is at variance with the general trend in the North Atlantic and may be related to the decrease of westerly winds over the Black Sea, and a greater influence of the Siberian anticyclone. The timing of the changeover from cooling to warming coincides with the regime shift in the Black Sea ecosystem.

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Climatology comparison and long-term variations of sea surface temperature over the tropical Atlantic Ocean

Journal of Geophysical Research Atmospheres ◽

10.1029/jc095ic06p09421 ◽

1990 ◽

Vol 95 (C6) ◽

pp. 9421 ◽

Cited By ~ 11

Author(s):

Jacques Servain ◽

Marc Séva ◽

Pierre Rual

Keyword(s):

Atlantic Ocean ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Surface ◽

Tropical Atlantic ◽

Tropical Atlantic Ocean ◽

Long Term Variations

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Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

Ocean Science ◽

10.5194/os-5-403-2009 ◽

2009 ◽

Vol 5 (4) ◽

pp. 403-419 ◽

Cited By ~ 11

Author(s):

C. Skandrani ◽

J.-M. Brankart ◽

N. Ferry ◽

J. Verron ◽

P. Brasseur ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Reanalysis Data ◽

Ocean Model ◽

Sea Surface ◽

Global Ocean ◽

Atmospheric Forcing ◽

Ocean Reanalysis ◽

Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

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Spatio-Temporal Variability and Trend of Rainfall and Its Association with Pacific Ocean Sea Surface Temperature in West Harerge Zone, Eastern Ethiopia

10.21203/rs.3.rs-104059/v1 ◽

2020 ◽

Author(s):

Getachew Bayable Tiruneh ◽

Gedamu Amare ◽

Getnet Alemu ◽

Temesgen Gashaw

Keyword(s):

Pacific Ocean ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Temporal Variability ◽

Rainfall Variability ◽

Sea Surface ◽

Annual Rainfall ◽

Eastern Ethiopia ◽

Spatio Temporal ◽

Annual Time

Abstract Background: Rainfall variability is a common characteristic in Ethiopia and it exceedingly affects agriculture particularly in the eastern parts of the country where rainfall is relatively scarce. Hence, understanding the spatio-temporal variability of rainfall is indispensable for planning mitigation measures during high and low rainfall seasons. This study examined the spatio-temporal variability and trends of rainfall in the West Harerge Zone, eastern Ethiopia.Method: The coefficient of variation (CV) and standardized anomaly index (SAI) was employed to analyze rainfall variability while Mann-Kendall (MK) trend test and Sen’s slop estimator were employed to examine the trend and magnitude of the rainfall changes, respectively. The association between rainfall and Pacific Ocean Sea Surface Temperature (SST) was also evaluated by the Pearson correlation coefficient (r).Results: The annual rainfall CV ranges from 12-19.36% while the seasonal rainfall CV extends from 15-28.49%, 24-35.58%, and 38-75.9% for average Kiremt (June-September), Belg (February-May), and Bega (October-January) seasons, respectively (1983-2019). On the monthly basis, the trends of rainfall decreased in all months except in July, October, and November. However, the trends of rainfall were not statistically significant (α = 0.05), unlike November. The annual rainfall trends showed a non-significant decreasing trend. On a seasonal basis, the trend of mean Kiremt and Belg seasons rainfall was decreased. But, it increased in Bega season although it was not statistically significant. Moreover, the correlation between rainfall and Pacific Ocean SST was negative for Kiremt while positive for Belg and Bega seasons. Besides, the correlation between rainfall and Pacific Ocean SST was negative at annual time scales.Conclusions: High spatial and temporal rainfall variability on monthly, seasonal, and annual time scales was observed in the study area. Seasonal rainfall has high inter-annual variability in the dry season (Bega) than other seasons. The trends in rainfall were decreased in most of the months. Besides, the trend of rainfall was increased annually and in the Bega season rather than other seasons. Generally, the occurrence of droughts in the study area was associated with ENSO events like most other parts of Ethiopia and East Africa.

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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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