Search Results

You are looking at 1 - 10 of 39,134 items for :

  • Sea surface temperature x
  • Refine by Access: All Content x
Clear All
Susana M. Barbosa

-defined parametric statistical tests are applied in order to evaluate the trend-stationary assumption in global sea surface temperature (SST). 2. Methods Parametric statistical tests have been developed in econometrics for discriminating between difference-stationary and trend-stationary time series. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test ( Kwiatkowski et al. 1992 ) tests the null hypothesis of a trend-stationary process against a difference-stationary alternative. Rejection of the null hypothesis

Full access
Ming Luo, Yee Leung, Yu Zhou, and Wei Zhang

1. Introduction Sea surface temperature (SST) is one of the most important parameters for the understanding of climate dynamics and climate change. Though SST can be easily measured, it is not always simple to analyze because of its irregular and nonlinear evolution across the temporal and spatial scales ( Gan et al. 2007 ). Characterizing the variability of SST at all relevant temporal and spatial scales is a challenge to the research community. While intense studies have focused on the low

Full access
R. Kipp Shearman and Steven J. Lentz

, several long-running sea surface temperature (SST) observations have been made at tide stations ( U.S. Coast and Geodetic Survey 1955 ) and independent institutions, such as the Woods Hole Oceanographic Institution ( Nixon et al. 2004 ). The merger of these historical and recent observations presents a unique opportunity to evaluate the long-term temperature variability over the continental shelf along the entire U.S. East Coast. 2. Background a. Circulation along the U.S. East Coast The coastal ocean

Full access
Shahadat Chowdhury and Ashish Sharma

a , hereafter CS2009) . Such weights are termed dynamic model combination weights [or dynamic weights (DW)]. The improvement resulting from dynamic weight for 3-month-ahead forecasts of the Niño-3.4 index in contrast to temporally invariant weights [or static weights (SW)] has been documented in CS2009 . While the CS2009 study was limited to the prediction of a univariate response (Niño-3.4), this paper extends the method for prediction of gridded global sea surface temperature anomalies

Full access
G. Reverdin, S. Morisset, H. Bellenger, J. Boutin, N. Martin, P. Blouch, J. Rolland, F. Gaillard, P. Bouruet-Aubertot, and B. Ward

1. Introduction Diurnal warm surface layers impact the estimates of air–sea flux exchanges ( Fairall et al. 1996 ; Ward 2006 ). They need to be taken into account to estimate average sea surface temperature (SST) and its variability, from intraseasonal to interannual and even on climate variability scales ( Shinoda 2005 ; Bernie et al. 2005 ; Bellenger et Duvel 2009 ). The SST daily cycles are very variable in the ocean, to a large extent in relation to insolation and wind intensity

Full access
Mingyue Chen, Wanqiu Wang, and Arun Kumar

1. Introduction The short- to medium-range numerical prediction of day-to-day weather relies on atmospheric initialization: the accurate specification of atmospheric pressures, temperatures, winds, and humidity at the beginning of the forecast. For the time scales of seasonal or longer, on the other hand, a primary source of atmospheric prediction skill is the lower boundary conditions such as the variability associated with the tropical Pacific sea surface temperatures (SSTs) related to El

Full access
Zhengzhao Johnny Luo, Dieter Kley, Richard H. Johnson, G. Y. Liu, Susanne Nawrath, and Herman G. J. Smit

, whereby air from close to the surface is lifted to the upper troposphere. Outside of the deep convective region air generally experiences gentle subsidence due to the radiative cooling of the atmosphere, so the upper troposphere of the nonconvective regions does not feel much of an influence from the surface immediately below. A number of previous studies have investigated how upper-tropospheric temperature and humidity are affected by the variation of the sea surface temperature, which is often taken

Full access
James W. Hurrell, James J. Hack, Dennis Shea, Julie M. Caron, and James Rosinski

.0) was released in June 2004, and the release included complete collections of component model source code, documentation, and input data, as well as model output from several experiments. The purpose of this note is to document the global sea surface temperature (SST) and sea ice concentration (SIC) boundary dataset that has been developed specifically for uncoupled simulations with present and future versions of CAM. Perhaps the most important field in climate system modeling is SST. A significant

Full access
Somkiat Apipattanavis, Gregory J. McCabe, Balaji Rajagopalan, and Subhrendu Gangopadhyay

value decomposition (SVD) to examine primary modes of global PDSI and sea surface temperature (SST) variability on decadal to multidecadal (D2M) time scales. Results indicated two principal modes of D2M variability. The first mode of D2M variability is related to the Pacific decadal oscillation (PDO), Indian Ocean SSTs, and an index of ENSO, while the second mode is related to the Atlantic multidecadal oscillation (AMO). Mann and Park (1996) performed a frequency analysis of the joint variability

Full access
Yaru Guo, Yuanlong Li, Fan Wang, Yuntao Wei, and Zengrui Rong

1. Introduction A sea surface temperature (SST) warming of ~5 K was observed near the west coast of Australia in the southeast Indian Ocean (SEIO) during the austral summer of 2010–11 ( Feng et al. 2013 ; Pearce and Feng 2013 ; Kataoka et al. 2014 ; Marshall et al. 2015 ). Such strong marine heat waves occurring in the SEIO ( Wernberg et al. 2012 ; Zinke et al. 2014 ) are named the Ningaloo Niño ( Feng et al. 2013 ) in analog to the Benguela Niño in the Atlantic ( Shannon et al. 1986

Free access