Sentences with phrase «ocean sea surface temperature anomalies»

The best way to envision the relation between ENSO and precipitation over East Africa is to regard the Indian Ocean as a mirror of the Pacific Ocean sea surface temperature anomalies [much like the Western Hemisphere Warm Pool creates such a SST mirror with the Atlantic Ocean too]: during a La Niña episode, waters in the eastern Pacific are relatively cool as strong trade winds blow the tropically Sun - warmed waters far towards the west.

Not exact matches

«The data showed that both greenhouse gases and sea surface temperature anomalies contributed strongly to the risk of snow drought in Oregon and Washington,» said Mote, a professor in OSU's College of Earth, Ocean, and Atmospheric Sciences.

This image shows the sea surface temperature anomaly in the Pacific Ocean from April 14 — 21, 2008.

(1) The warm sea surface temperatures are not just some short - term anomaly but are part of a long - term observed warming trend, in which ocean temperatures off the US east coast are warming faster than global average temperatures.

During El Nino events the ocean circulation changes in such a way as to cause a large and temporary positive sea surface temperature anomaly in the tropical Pacific.

The 2005 Jan - Sep land data (which is adjusted for urban biases) is higher than the previously warmest year (0.76 °C compared to the 1998 anomaly of 0.75 °C for the same months, and a 0.71 °C anomaly for the whole year), while the land - ocean temperature index (which includes sea surface temperature data) is trailing slightly behind (0.58 °C compared to 0.60 °C Jan - Sep, 0.56 °C for the whole of 1998).

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.

Third, note how the sea surface temperature anomalies in the Western Pacific (and East Indian Ocean) continue to rise as the La Niña event strengthens.

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.

Key factors expected to influence the regional climate during the OND 2016 season include the evolution of Sea Surface Temperature (SST) anomalies over the tropical Oceans.

El Niño Watch... Positive equatorial sea surface temperature (SST) anomalies continue across the Pacific Ocean.

The graphic below (Roemmich & Gilson [2011]- The Global Ocean Imprint of ENSO) is derived from ARGO subsurface temperature observations for the region 60 ° N - 60 ° S, the red line denotes the positive / negative phases of ENSO, and the black line is the sea surface temperature anomaly.

southern oscillation a large - scale atmospheric and hydrospheric fluctuation centered in the equatorial Pacific Ocean; exhibits a nearly annual pressure anomaly, alternatively high over the Indian Ocean and high over the South Pacific; its period is slightly variable, averaging 2.33 years; the variation in pressure is accompanied by variations in wind strengths, ocean currents, sea - surface temperatures, and precipitation in the surrounding Ocean; exhibits a nearly annual pressure anomaly, alternatively high over the Indian Ocean and high over the South Pacific; its period is slightly variable, averaging 2.33 years; the variation in pressure is accompanied by variations in wind strengths, ocean currents, sea - surface temperatures, and precipitation in the surrounding Ocean and high over the South Pacific; its period is slightly variable, averaging 2.33 years; the variation in pressure is accompanied by variations in wind strengths, ocean currents, sea - surface temperatures, and precipitation in the surrounding ocean currents, sea - surface temperatures, and precipitation in the surrounding areas

Given the context of this highly anomalous and extremely persistent atmospheric ridging over the northeastern Pacific Ocean, it's very interesting to note that there has also been a region of strongly positive sea surface temperature anomalies in same the general vicinity for the past 10 - 11 months.

Index Profile of the Stadium Wave: ■ Atlantic Multidecadal Oscillation (AMO)-- a monopolar pattern of sea - surface - temperature (SST) anomalies in theNorth Atlantic Ocean.

Some processes arise through interactions with other parts of the climate system such as the ocean (for example as manifested through sea surface temperature anomalies), sea ice anomalies, snow cover anomalies as well as through coupling to the circulation in the stratosphere.

In July, sea surface temperatures anomalies were already at 1.0 °C above normal in the central equatorial Pacific Ocean, and in excess of 2.0 °C above normal across the eastern Pacific — and still rising.

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.

Regardless of whether or not the oceans integrate ENSO and portray it in sea surface temperature anomalies, the West Pacific and East Indian Oceans warm in response to both El Nino and La Nina events, so there is a cumulative response to ENSO by a major portion of the global ooceans integrate ENSO and portray it in sea surface temperature anomalies, the West Pacific and East Indian Oceans warm in response to both El Nino and La Nina events, so there is a cumulative response to ENSO by a major portion of the global oOceans warm in response to both El Nino and La Nina events, so there is a cumulative response to ENSO by a major portion of the global oceansoceans.

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.

For central India and its west coast, rainfall in the early (15 May - 20 June) and late (15 September - 20 October) monsoon season correlates with Pacific Ocean sea - surface temperature (SST) anomalies in the preceding month (April and August, respectively) sufficiently well, that those SST anomalies...

Strong, localized sea surface temperature anomalies may reveal that an ocean current, such as the Gulf Stream Current off the east coast of the United States, has veered off its usual path for a time or is stronger or weaker than usual.

Here we use an ensemble of simulations with a coupled ocean — atmosphere model to show that the sea surface temperature anomalies associated with central Pacific El Niño force changes in the extra-tropical atmospheric circulation.

Over ocean stretches with a positive SST anomaly air convection is higher (as the temperature difference between the warm sea surface and the cool air higher up in the troposphere is greater), so a higher likelihood for the formation of depressions exists and more precipitation is to be expected.

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.

To help show those multiyear effects, I've animated sea surface temperature, sea level, TLT, cloud amount, ocean currents, ocean heat content, precipitation, equatorial Pacific subsurface temperature anomaly cross-sections.

The large interannual to decadal hydroclimatic variability in winter precipitation is highly influenced by sea surface temperature (SST) anomalies in the tropical Pacific Ocean and associated changes in large - scale atmospheric circulation patterns [16].

Lamont's Ryan Abernathey and Richard Seager are studying how changes in the ocean cause sea surface temperature to vary, and how these anomalies drive changes in atmospheric circulation to create extreme weather events.

The 1997/98 El Nino shifted Sea Surface Temperature anomalies upward in this area of the global oceans, too.

To a large extent the probability forecasts in Figure 11 resemble the surface air temperature anomaly of the last two months in Figure 7 in the high latitudes, illustrating the persistence of weak climate anomalies over the sea ice and ocean covered regions throughout the summer months.

Strongly positive sea surface temperature (SST) anomalies have prevailed in the Northeast Pacific Ocean since 2013.

However, in the polar sea ice zones, GISTEMP extrapolates the land surface air temperature anomalies over the oceans to a radial distance of 1,200 km (Hansen et al. 2010).

(1) The warm sea surface temperatures are not just some short - term anomaly but are part of a long - term observed warming trend, in which ocean temperatures off the US east coast are warming faster than global average temperatures.

The topic of discussion is the sea surface temperature anomalies of the Pacific Ocean (60S - 65N, 120E - 80W) for the past 19 years, using the Reynolds OI.v2 sea surface temperature data.

The topic of discussion is the sea surface temperature anomalies of the Pacific Ocean, since 1994, not the global oceans minus the polar oceans.

Over the last month or so warm sea - surface temperature [SST] and upper - ocean heat content anomalies have increased in the near - equatorial central Pacific, while the SST cool tongue in the near - equatorial far - eastern Pacific has weakened, with warm anomalies now evident there.

Climate change indicators: Global Mean Temperature (GMT); Hemispheric Temperature Variance; Greenhouse gases; Arctic, Antarctic Ice Extent and Volume; Ocean Oscillations; Sea Level Rise (SLR); Solar Cycle Data; Sea Surface Temperatures and Anomalies; Global Fire Activity, Drought.

Here we quantitatively relate the impacts of warm (and cold) sea surface temperature anomalies in the eastern tropical Pacific Ocean to the number of hurricanes making landfall in the United States.

Unlike the UAH land and ocean TLT anomalies, sea surface and land surface temperature data are not measured the same way, and there are boundaries between them.

Current climate conditions trends and averages: Oceans: Sea Level Rise (SLR), Sea Surface Temperature (SST), Sea Surface Temperature Anomalies (SSTA), Sea Surface Height (SSH), Sea Surface Salinity (SSS).