Sentences with phrase «sea surface temperature anomalies on»

The sequence shows a superposition of sea surface temperature anomalies on anomalies of the sea surface elevation.

Findell, Kirsten L., and Thomas L Delworth, February 2010: Impact of common sea surface temperature anomalies on global drought and pluvial frequency.

The impact of midlatitude sea surface temperature anomalies on the annular modes is thought to be small, at least on intraseasonal suggest interannual timescales.

Observing sea surface temperature anomalies on the NOAA website, the first thing that stands out is the weak La Nina in the equatorial Pacific.

Not exact matches

Sea surface temperature anomalies (SSTAs) will continue to be on the warm side into early May.

He finds that the past three months score a very strong 2.31 on the oceanic Niño index, one of the primary measures of anomalies in sea surface temperatures.

(c) The PDO is actually inversely related to the sea surface temperature anomalies of that portion of the North Pacific on decadal timescales.

«On May 22nd, 2014, global sea surface temperature anomalies spiked to an amazing +1.25 degrees Celsius above the, already warmer than normal, 1979 to 2000 average.

Canadian Ice Service, 4.7, Multiple Methods As with CIS contributions in June 2009, 2010, and 2011, the 2012 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter arctic ice thicknesses and extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoSea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model, which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice predictors.

Canadian Ice Service, 4.7 (+ / - 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoSea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice predictors.

Air temperatures at 925 millibar (about 3,000 ft above the surface) were mostly above average over the Arctic Ocean, with positive anomalies of 4 to 6º Celsius over the Chukchi and Bering seas on the Pacific side of the Arctic, and over the East Greenland Sea on the Atlantic side.

The improved simulation of ENSO amplitude is mainly due to the reasonable representation of the thermocline and thermodynamic feedbacks: On the one hand, the deeper mean thermocline results in a weakened thermocline response to the zonal wind stress anomaly, and the looser vertical stratification of mean temperature leads to a weakened response of anomalous subsurface temperature to anomalous thermocline depth, both of which cause the reduced thermocline feedback in g2; on the other hand, the alleviated cold bias of mean sea surface temperature leads to more reasonable thermodynamic feedback in gOn the one hand, the deeper mean thermocline results in a weakened thermocline response to the zonal wind stress anomaly, and the looser vertical stratification of mean temperature leads to a weakened response of anomalous subsurface temperature to anomalous thermocline depth, both of which cause the reduced thermocline feedback in g2; on the other hand, the alleviated cold bias of mean sea surface temperature leads to more reasonable thermodynamic feedback in gon the other hand, the alleviated cold bias of mean sea surface temperature leads to more reasonable thermodynamic feedback in g2.

The National Center for Atmospheric Research («NCAR») reports on a newly substantiated teleconnection between positive sea surface temperature anomalies («SSTA») in the Pacific and the temperatures over the continental United States («CONUS») 50 days later.

The regression - based forecast submitted by Tivy for the Greenland Sea (Figure 7) also shows a reduction in September sea ice area compared to 2009 based on fall (Sep - Oct - Nov) sea surface temperature anomalies in the North AtlantSea (Figure 7) also shows a reduction in September sea ice area compared to 2009 based on fall (Sep - Oct - Nov) sea surface temperature anomalies in the North Atlantsea ice area compared to 2009 based on fall (Sep - Oct - Nov) sea surface temperature anomalies in the North Atlantsea surface temperature anomalies in the North Atlantic.

A statistical forecast based on canonical correlation analysis with fall sea surface temperature anomalies as the main predictor was submitted by Tivy (Figure 3).

Canadian Ice Service; 5.0; Statistical As with Canadian Ice Service (CIS) contributions in June 2009 and June 2010, the 2011 forecast was derived using a combination of three methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic Multi-Year Ice (MYI) extents, as well as an examination of Surface Air Temperature (SAT), Sea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictoSea Level Pressure (SLP) and vector wind anomaly patterns and trends; 2) an experimental Optimal Filtering Based (OFB) Model which uses an optimal linear data filter to extrapolate NSIDC's September Arctic Ice Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictosea ice predictors.

The fact: correlations between NINO3.4 SST anomalies and global sea surface temperatures are basically the same for El Niño and La Niña events; that is, El Niño and La Niña events have similar effects on regional sea surface temperatures; they are simply the opposite sign.

''... worked with two sediment cores they extracted from the seabed of the eastern Norwegian Sea, developing a 1000 - year proxy temperature record «based on measurements of δ18O in Neogloboquadrina pachyderma, a planktonic foraminifer that calcifies at relatively shallow depths within the Atlantic waters of the eastern Norwegian Sea during late summer,» which they compared with the temporal histories of various proxies of concomitant solar activity... This work revealed, as the seven scientists describe it, that «the lowest isotope values (highest temperatures) of the last millennium are seen ~ 1100 - 1300 A.D., during the Medieval Climate Anomaly, and again after ~ 1950 A.D.» In between these two warm intervals, of course, were the colder temperatures of the Little Ice Age, when oscillatory thermal minima occurred at the times of the Dalton, Maunder, Sporer and Wolf solar minima, such that the δ18O proxy record of near - surface water temperature was found to be «robustly and near - synchronously correlated with various proxies of solar variability spanning the last millennium,» with decade - to century - scale temperature variability of 1 to 2 °C magnitude.»

Pokrovsky predicts a further acceleration of melting of the thin ice and in general greater ice loss compared to his June prediction; this change is based on the increase in the sea surface temperature (SST) anomalies in the North Atlantic and the presence of hot air masses over Siberia and the Russian Arctic.

Spring (March to June) sea surface temperature anomalies based on the NOAA Optimum Interpolation (OI) SST data set.

Using a statistical model based on canonical correlation analysis with fall sea surface temperature anomalies in the North Atlantic as the main predictor, Tivy shows below - normal ice concentrations throughout most of the region (Figure 12), which suggests an earlier - than - normal opening of the shipping season.

But if as Kevin Trenberth argues that for every «1 degree Celsius sea surface temperature anomalies gives 10 to 15 percent increase in rainfall», then the science is correct about AGW and the sceptics are just raving on.

Any discussion on that webpage you linked... https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php... regarding their preference for anomalies has to do with land surface, not sea surface, temperatures, which is why their land surface temperature data and consequently their combined land + ocean data are presented as anomalies.

To estimate heat stress on corals, NOAA scientists multiply the sea surface temperature anomaly (how many degrees Celsius the temperature is above the area's normal summertime maximum) times the number of weeks the anomaly lasts.

Decadal variations in the North Pacific Gyre Oscillation are characterized by a pattern of sea surface temperature anomalies that resemble the central Pacific El Niño, a dominant mode of interannual variability with far - reaching effects on global climate patterns5, 6, 7.

Canadian Ice Service, 4.7 (± 0.2), Heuristic / Statistical (same as June) The 2015 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness extents, as well as winter Surface Air Temperature, Sea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoSea Level Pressure and vector wind anomaly patterns and trends; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice predictors.

Doing this on a year - to - year basis shows NO apparent correlation with the absolute «globally and annually averaged land and sea surface temperature anomaly» (i.e. HadCRUT3), but does show a weak correlation with the CHANGE in temperature from the previous year, for example:

All these «bets» on what is going to happen with the adjusted, variance - corrected and otherwise manipulated «globally and annually averaged land and sea surface temperature anomaly» are interesting but slightly silly IMO.

However, for changes over time, only anomalies, as departures from a climatology, are used, most commonly based on the area - weighted global average of the sea surface temperature anomaly and land surface air temperature anomaly.

The anomaly map on the left is a product of a merged land surface temperature (Global Historical Climatology Network, GHCN) and sea surface temperature (ERSST.v4) anomaly analysis as described in Huang et al. (2016).

The JMA Index defines El Niño events based on the sea surface temperature anomalies in the region 4 ° N to 4 ° S and 150 ° W to 90 ° W.