scholarly journals Time–Space SST Variability in the Atlantic during 2013: Seasonal Cycle

2015 ◽  
Vol 32 (9) ◽  
pp. 1689-1705 ◽  
Author(s):  
Liyan Liu ◽  
Carlos Lozano ◽  
Dan Iredell
Keyword(s):  
Seasonal Cycle ◽  
Temporal Distribution ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Model Output ◽  
Orthogonal Functions ◽  
Cooling Period ◽  
Sst Variability ◽  
Time Space ◽  
Orthogonal Components

AbstractA 2-yr-long daily gridded field of sea surface temperature (SST) in the Atlantic centered for the year 2013 is projected onto orthogonal components: its mean, six harmonics of the year cycle, the slow-varying contribution, and the fast-varying contribution. The periodic function defined by the year harmonics, referred to here as the seasonal harmonic, contains most of the year variability in 2013. The seasonal harmonic is examined in its spatial and temporal distribution by describing the amplitude and phase of its maxima and minima, and other associated parameters. In the seasonal harmonic, the ratio of the duration of warming period to cooling period ranges from 0.2 to 2.0. There are also differences in the spatial patterns and dominance of the year harmonics—in general associated with regions with different insolation, oceanic, and atmospheric regimes. Empirical orthogonal functions (EOFs) of the seasonal harmonic allow for a succinct description of the seasonal evolution for the Atlantic and its subdomains. The decomposition can be applied to model output, allowing for a more incisive model validation and data assimilation. The decorrelation time scale of the rapidly varying signal is found to be nearly independent of the time of the year once four or more harmonics are used. The decomposition algorithm, here implemented for a single year cycle, can be applied to obtain a multiyear average of the seasonal harmonic.

Gaps Filling in HF Radar Sea Surface Current Data Using Complex Empirical Orthogonal Functions

2020 ◽  
Vol 177 (12) ◽  
pp. 5969-5992
Author(s):  
Siva Srinivas Kolukula ◽  
Balaji Baduru ◽  
P. L. N. Murty ◽  
J. Pavan Kumar ◽  
E. Pattabhi Rama Rao ◽  
...  
Keyword(s):  
Surface Current ◽  
Current Data ◽  
Empirical Orthogonal Functions ◽  
Hf Radar ◽  
Sea Surface ◽  
Orthogonal Functions

scholarly journals DINEOF reconstruction of clouded images including error maps – application to the Sea-Surface Temperature around Corsican Island

Ocean Science ◽  
2006 ◽  
Vol 2 (2) ◽  
pp. 183-199 ◽  
Author(s):  
J.-M. Beckers ◽  
A. Barth ◽  
A. Alvera-Azcárate
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Optimal Interpolation ◽  
Orthogonal Functions ◽  
Ligurian Sea ◽  
Additional Information ◽  
Error Covariance ◽  
Data Coverage

Abstract. We present an extension to the Data INterpolating Empirical Orthogonal Functions (DINEOF) technique which allows not only to fill in clouded images but also to provide an estimation of the error covariance of the reconstruction. This additional information is obtained by an analogy with optimal interpolation. It is shown that the error fields can be obtained with a clever rearrangement of calculations at a cost comparable to that of the interpolation itself. The method is presented on the reconstruction of sea-surface temperature in the Ligurian Sea and around the Corsican Island (Mediterranean Sea), including the calculation of inter-annual variability of average surface values and their expected errors. The application shows that the error fields are not only able to reflect the data-coverage structure but also the covariances of the physical fields.

Filling in missing sea-surface temperature satellite data over the Eastern Mediterranean Sea using the DINEOF algorithm

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Andreas Nikolaidis ◽  
Georgios Georgiou ◽  
Diofantos Hadjimitsis ◽  
Evangelos Akylas
Keyword(s):  
Missing Data ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Eastern Mediterranean ◽  
Western Mediterranean ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Temperature Fields ◽  
Special Technique ◽  
Orthogonal Functions

AbstractThe Data Interpolating Empirical Orthogonal Functions method is a special technique based on Empirical Orthogonal Functions and developed to reconstruct missing data from satellite images, which is especially useful for filling in missing data from geophysical fields. Successful experiments in the Western Mediterranean encouraged extension of the application eastwards using a similar experimental implementation. The present study summarizes the experimental work done, the implementation of the method and its ability to reconstruct the sea-surface temperature fields over the Eastern Mediterranean basin, and specifically in the Levantine Sea. L3 type Satellite Sea-surface Temperature data has been used and reprocessed in order to recover missing information from cloudy images. Data reconstruction with this method proved to be extremely effective, even when using a relatively small number of time steps, and markedly accelerated the procedure. A detailed comparison with the two oceanographic models proves the accuracy of the method and the validity of the reconstructed fields.

The Forcing of Southwestern Asia Teleconnections by Low-Frequency Sea Surface Temperature Variability during Boreal Winter

2015 ◽  
Vol 28 (4) ◽  
pp. 1511-1526 ◽  
Author(s):  
Andrew Hoell ◽  
Chris Funk ◽  
Mathew Barlow
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Land Surface ◽  
Climate Models ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Annual Rainfall ◽  
Semiarid Region ◽  
Boreal Winter ◽  
Sst Variability

Abstract Southwestern Asia, defined here as the domain bounded by 20°–40°N and 40°–70°E, which includes the nations of Iraq, Iran, Afghanistan, and Pakistan, is a water-stressed and semiarid region that receives roughly 75% of its annual rainfall during November–April. The November–April climate of southwestern Asia is strongly influenced by tropical Indo-Pacific variability on intraseasonal and interannual time scales, much of which can be attributed to sea surface temperature (SST) variations. The influences of lower-frequency SST variability on southwestern Asia climate during November–April Pacific decadal SST (PDSST) variability and the long-term trend in SST (LTSST) is examined. The U.S. Climate Variability and Predictability Program (CLIVAR) Drought Working Group forced global atmospheric climate models with PDSST and LTSST patterns, identified using empirical orthogonal functions, to show the steady atmospheric response to these modes of decadal to multidecadal SST variability. During November–April, LTSST forces an anticyclone over southwestern Asia, which results in reduced precipitation and increases in surface temperature. The precipitation and tropospheric circulation influences of LTSST are corroborated by independent observed precipitation and circulation datasets during 1901–2004. The decadal variations of southwestern Asia precipitation may be forced by PDSST variability, with two of the three models indicating that the cold phase of PDSST forces an anticyclone and precipitation reductions. However, there are intermodel circulation variations to PDSST that influence subregional precipitation patterns over the Middle East, southwestern Asia, and subtropical Asia. Changes in wintertime temperature and precipitation over southwestern Asia forced by LTSST and PDSST imply important changes to the land surface hydrology during the spring and summer.

scholarly journals Spatial–Temporal Variability of the Calculated Characteristics of the Ocean in the Arctic Zone of Russia by Using the NEMO Model with Altimetry Data Assimilation

2020 ◽  
Vol 8 (10) ◽  
pp. 753
Author(s):  
Konstantin Belyaev ◽  
Andrey Kuleshov ◽  
Ilya Smirnov
Keyword(s):  
Data Assimilation ◽  
Temporal Variability ◽  
Satellite Observation ◽  
Sea Surface Height ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
The Arctic ◽  
Observation Data ◽  
Arctic Zone ◽  
Orthogonal Functions

The spatial–temporal variability of the calculated characteristics of the ocean in the Arctic zone of Russia is studied. In this study, the known hydrodynamic model of the ocean Nucleus for European Modelling of the Ocean (NEMO) is used with assimilation of observation data on the sea surface height taken from the Archiving, Validating and Interpolation Satellite Observation (AVISO) archive. We use the Generalized Kalman filter (GKF) method, developed earlier by the authors of this study, in conjunction with the method of decomposition of symmetric matrices into empirical orthogonal functions (EOF, Karhunen–Loeve decomposition). The investigations are focused mostly on the northern seas of Russia. The main characteristics of the ocean, such as the current velocity, sea surface height, and sea surface temperature are calculated with data assimilation (DA) and without DA (the control calculation). The calculation results are analyzed and their spatial–temporal variability over a time period of 14 days is studied. It is shown that the main spatial variability of characteristics after DA is in good agreement with the localization of currents in the North Atlantic and in the Arctic zone of Russia. The contribution of each of the eigenvectors and eigenvalues of the covariation matrix to the spatial–temporal variability of the calculated characteristics is shown by using the EOF analysis.

scholarly journals Testing the validity of regional detail in global analyses of sea surface temperature – the case of Chinese coastal waters

Ocean Science ◽  
2019 ◽  
Vol 15 (6) ◽  
pp. 1455-1467
Author(s):  
Yan Li ◽  
Hans von Storch ◽  
Qingyuan Wang ◽  
Qingliang Zhou ◽  
Shengquan Tang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Careful Examination ◽  
Local Data ◽  
High Quality ◽  
Orthogonal Functions ◽  
Local Quality ◽  
Break Points

Abstract. We have designed a method for testing the quality of multidecadal analyses of sea surface temperature (SST) in regional seas by using a set of high-quality local SST observations. In recognizing that local data may reflect local effects, we focus on the dominant empirical orthogonal functions (EOFs) of the local data and of the localized data of the gridded SST analyses. We examine the patterns, variability, and trends of the principal components. This method is applied to examine three different SST analyses, i.e., HadISST1, ERSST, and COBE SST. They have been assessed using a newly constructed high-quality dataset of SST at 26 coastal stations along the Chinese coast in 1960–2015, which underwent careful examination with respect to quality and a number of corrections for inhomogeneities. The three gridded analyses perform generally well from 1960 to 2015, in particular since 1980. However, for the pre-satellite period prior to the 1980s, the analyses differ among each other and show some inconsistencies with the local data, such as artificial break points, periods of bias, and differences in trends. We conclude that gridded SST analyses need improvement in the pre-satellite period (prior to the 1980s) by reexamining in detail archives of local quality-controlled SST data in many data-sparse regions of the world.

The Leading Mode of Observed and CMIP5 ENSO-Residual Sea Surface Temperatures and Associated Changes in Indo-Pacific Climate*

2015 ◽  
Vol 28 (11) ◽  
pp. 4309-4329 ◽  
Author(s):  
Chris C. Funk ◽  
Andrew Hoell
Keyword(s):  
Middle East ◽  
East Africa ◽  
Radiative Forcing ◽  
Decadal Variability ◽  
Walker Circulation ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Eastern Pacific ◽  
Spring Precipitation ◽  
Orthogonal Functions

Abstract SSTs in the western Pacific Ocean have tracked closely with CMIP5 simulations despite recent hiatus cooling in the eastern Pacific. This paper quantifies these similarities and associated circulation and precipitation variations using the first global 1900–2012 ENSO-residual empirical orthogonal functions (EOFs) of 35 variables: observed SSTs; 28 CMIP5 SST simulations; Simple Ocean Data Assimilation (SODA) 25-, 70-, and 171-m ocean temperatures and sea surface heights (SSHs); and Twentieth Century Reanalysis, version 2 (20CRv2), surface winds and precipitation. While estimated independently, these leading EOFs across all variables fit together in a meaningful way, and the authors refer to them jointly as the west Pacific warming mode (WPWM). WPWM SST EOFs correspond closely in space and time. Their spatial patterns form a “western V” extending from the Maritime Continent into the extratropical Pacific. Their temporal principal components (PCs) have increased rapidly since 1990; this increase has been primarily due to radiative forcing and not natural decadal variability. WPWM circulation changes appear consistent with a Matsuno–Gill-like atmospheric response associated with an ocean–atmosphere dipole structure contrasting increased (decreased) western (eastern) Pacific precipitation, SSHs, and ocean temperatures. These changes have enhanced the Walker circulation and modulated weather on a global scale. An AGCM experiment and the WPWM of global boreal spring precipitation indicate significant drying across parts of East Africa, the Middle East, the southwestern United States, southern South America, and Asia. Changes in the WPWM have tracked closely with precipitation and the increase in drought frequency over the semiarid and water-insecure areas of East Africa, the Middle East, and southwest Asia.

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