scholarly journals Which surface atmospheric variable drives the seasonal cycle of sea surface temperature over the global ocean?

2009 ◽  
Vol 114 (D5) ◽  
Author(s):  
A. B. Kara ◽  
A. J. Wallcraft ◽  
H. E. Hurlburt ◽  
W.-Y. Loh
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Seasonal Cycle ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Variable

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 North Australian Sea Surface Temperatures and the El Niño–Southern Oscillation in Observations and Models

2012 ◽  
Vol 25 (14) ◽  
pp. 5011-5029 ◽  
Author(s):  
Jennifer L. Catto ◽  
Neville Nicholls ◽  
Christian Jakob
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Seasonal Cycle ◽  
Temporal Evolution ◽  
Sea Surface ◽  
Austral Spring ◽  
The North ◽  
Australian Climate ◽  
The Relationship ◽  
Sst Anomalies

Abstract Interannual variations in the sea surface temperature (SST) to the north of Australia are strongly linked to variations in Australian climate, including winter rainfall and tropical cyclone numbers. The north Australian SSTs are also closely linked to ENSO and tropical Pacific SSTs, with the relationship exhibiting a strong seasonal cycle. Credible predictions of Australian climate change therefore depend on climate models being able to represent ENSO and its connection to north Australian SSTs, the topic of this study. First, the observational datasets of the Met Office Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) and the NOAA Extended Reconstructed Sea Surface Temperature (ERSST) are used to document the links between the Niño-3.4 index and a north Australian SST index, and the temporal evolution of north Australian SSTs during ENSO events. During austral autumn, the correlation between Niño-3.4 SST and north Australian SST is positive, while in austral spring it is strongly negative. During El Niño events, the north Australian SST anomalies become negative in the austral spring preceding the development of the positive Niño-3.4 SST anomalies. The coupled models participating in the Coupled Model Intercomparison Project phase 3 (CMIP3) are evaluated in terms of this temporal evolution of Niño-3.4 SST and the relationship to north Australian SST for the twentieth-century simulations. Some of the models perform very well, while some do not capture the seasonal cycle of correlations at all. The way in which these relationships may change in the future is examined using the A2 emissions scenario in those models that do a reasonable job of capturing the present-day observed relationship, and very little change is found.

Climatic trends of sea surface temperature and sea ice concentration in the Barents Sea

2021 ◽  
Author(s):  
Bayoumy Mohamed ◽  
Frank Nilsen ◽  
Ragnheid Skogseth
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Seasonal Cycle ◽  
Barents Sea ◽  
Warming Trend ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
The Barents Sea ◽  
Ice Concentration

<p>Sea ice loss in the Arctic region is an important indicator for climate change. Especially in the Barents Sea, which is expected to be free of ice by the mid of this century (Onarheim et al., 2018). Here, we analyze 38 years (1982-2019) of daily gridded sea surface temperature (SST) and sea ice concentration (SIC) from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) project. These data sets have been used to investigate the seasonal cycle and linear trends of SST and SIC, and their spatial distribution in the Barents Sea. From the SST seasonal cycle analysis, we have found that most of the years that have temperatures above the climatic mean (1982-2019) were recorded after 2000. This confirms the warm transition that has taken place in the Barents Sea over the last two decades. The year 2016 was the warmest year in both winter and summer during the study period.   </p><p>Results from the linear trend analysis reveal an overall statistically significant warming trend for the whole Barents Sea of about 0.33±0.03 °C/decade, associated with a sea ice reduction rate of about -4.9±0.6 %/decade. However, the SST trend show a high spatial variability over the Barents Sea. The highest SST trend was found over the eastern part of the Barents Sea and south of Svalbard (Storfjordrenna Trough), while the Northern Barents Sea shows less distinct and non-significant trends. The largest negative trend of sea ice was observed between Novaya Zemlya and Franz Josef Land. Over the last two decades (2000-2019), the data show an amplified warming trend in the Barents Sea where the SST warming trend has increased dramatically (0.46±0.09 °C/decade) and the SIC is here decreasing with rate of about -6.4±1.5 %/decade.  Considering the current development of SST, if this trend persists, the Barents Sea annual mean SST will rise by around 1.4 °C by the end of 2050, which will have a drastic impact on the loss of sea ice in the Barents Sea.   </p><p> </p><p>Keywords: Sea surface temperature; Sea ice concentration; Trend analysis; Barents Sea</p>

scholarly journals Influence of tropical cyclones on sea surface temperature seasonal cycle and ocean heat transport

2012 ◽  
Vol 41 (7-8) ◽  
pp. 2019-2038 ◽  
Author(s):  
Emmanuel M. Vincent ◽  
Gurvan Madec ◽  
Matthieu Lengaigne ◽  
Jérôme Vialard ◽  
Ariane Koch-Larrouy
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Heat Transport ◽  
Tropical Cyclones ◽  
Seasonal Cycle ◽  
Sea Surface ◽  
Ocean Heat Transport
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