Tidal and atmospheric forcing of the upper ocean in the Gulf of California: 1. Sea surface temperature variability

1991 ◽  
Vol 96 (C10) ◽  
pp. 18337 ◽  
Author(s):  
Cynthia A. Paden ◽  
Mark R. Abbott ◽  
Clinton D. Winant
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Gulf Of California ◽  
Temperature Variability ◽  
Sea Surface ◽  
Upper Ocean ◽  
Atmospheric Forcing

Structure and function of the southeastern Gulf of California ecosystem during low and high sea surface temperature variability

2021 ◽  
Vol 43 ◽  
pp. 101686
Author(s):  
Juan Carlos Hernández-Padilla ◽  
Manuel J. Zetina-Rejón ◽  
F. Arreguín-Sánchez ◽  
Pablo del Monte-Luna ◽  
José T. Nieto-Navarro ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Gulf Of California ◽  
Structure And Function ◽  
Temperature Variability ◽  
Sea Surface ◽  
And Function

Seasonal and Interannual Sea Surface Temperature Variability in the Coastal Cities of Arabian Sea and Bay of Bengal

2004 ◽  
Vol 31 (2) ◽  
pp. 549-560 ◽  
Author(s):  
Tariq Masood Ali Khan ◽  
Dewan Abdul Quadir ◽  
Tad S. Murty ◽  
Majajul Alam Sarker
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Temperature Variability ◽  
Sea Surface ◽  
Coastal Cities

scholarly journals Sea-surface temperature records of Termination 1 in the Gulf of California: Challenges for seasonal and interannual analogues of tropical Pacific climate change

2012 ◽  
Vol 27 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Erin L. McClymont ◽  
Raja S. Ganeshram ◽  
Laetitia E. Pichevin ◽  
Helen M. Talbot ◽  
Bart E. van Dongen ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Gulf Of California ◽  
Tropical Pacific ◽  
Sea Surface ◽  
Temperature Records

Niño 4 West (Niño‐4W) Sea Surface Temperature Variability

2021 ◽  
Vol 126 (9) ◽  
Author(s):  
Ming Feng ◽  
Ying Zhang ◽  
Harry H. Hendon ◽  
Michael J. McPhaden ◽  
Andrew G. Marshall
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Variability ◽  
Sea Surface

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

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 403-419 ◽  
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.

scholarly journals Sea surface temperature variability of the Iceland-Faeroe front: Correction

1994 ◽  
Vol 99 (C8) ◽  
pp. 16409
Author(s):  
Alex Warn-Varnas ◽  
Pearn P. Niiler ◽  
Malcolm Zahorodny ◽  
Steve Piacsek ◽  
Lucas Neuberg
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Variability ◽  
Sea Surface
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