Sentences with phrase «sea level pressure anomalies in»

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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.

The change in May is explained by the sea level pressure (SLP) and air temperature anomaly field for May (Figure 8, top).

In general, indices of the annular modes are based on either 1) the leading principal component (PC) time series of gridded geopotential height anomalies at a given pressure level or 2) approximations of the leading PC time series of geopotential height anomalies using differences between sea level pressure anomalies at stations in middle and high latitudeIn general, indices of the annular modes are based on either 1) the leading principal component (PC) time series of gridded geopotential height anomalies at a given pressure level or 2) approximations of the leading PC time series of geopotential height anomalies using differences between sea level pressure anomalies at stations in middle and high latitudein middle and high latitudes.

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.

A comparison of detrended North Atlantic SST anomalies and scaled NAO (inverted) and NINO3.4 SST anomalies shows that a change in Sea Level Pressure preceded the 2001/02 change in the North Atlantic SST anomalies.

The response pattern associated with GCR consisted of a negative temperature anomaly that was limited to parts of eastern Europe, and a weak anomaly in the sea - level pressure (SLP), but coincided with higher pressure over the Norwegian Ssea - level pressure (SLP), but coincided with higher pressure over the Norwegian SeaSea.

It is manifested as strong anomalous easterly trade winds, distinctive sea - level pressure patterns, and large rainfall anomalies in the Pacific, which resemble the Pacific Decadal Oscillation (PDO).

These graphs show sea level pressure anomalies or differences from average sea level pressure in the Northern Hemisphere for April, May, June, and July 2016.

The plot above shows July 2016 Arctic air temperature anomalies at the 925 hPa level in degrees Celsius and sea level pressure anomalies.

The first principal component is significantly correlated with the SAM index (the first principal component of sea - level - pressure or 500 - hPa geopotential heights for 20u S — 90u S), and the second principal component reflects the zonal wave - 3 pattern, which contributes to the Antarctic dipole pattern of sea - ice anomalies in the Ross Sea and Weddell Sea sectosea - level - pressure or 500 - hPa geopotential heights for 20u S — 90u S), and the second principal component reflects the zonal wave - 3 pattern, which contributes to the Antarctic dipole pattern of sea - ice anomalies in the Ross Sea and Weddell Sea sectosea - ice anomalies in the Ross Sea and Weddell Sea sectoSea and Weddell Sea sectoSea sectors.

In July, the Arctic Dipole Anomaly (DA) pattern that was dominant in June (which promotes clear skies, warm air temperatures, and winds that push ice away from coastal areas and encourages melt) was replaced by low sea level pressure (SLP) over the Arctic Ocean, leading to ice divergence (ice extent «spreading out») and cooler temperatureIn July, the Arctic Dipole Anomaly (DA) pattern that was dominant in June (which promotes clear skies, warm air temperatures, and winds that push ice away from coastal areas and encourages melt) was replaced by low sea level pressure (SLP) over the Arctic Ocean, leading to ice divergence (ice extent «spreading out») and cooler temperaturein June (which promotes clear skies, warm air temperatures, and winds that push ice away from coastal areas and encourages melt) was replaced by low sea level pressure (SLP) over the Arctic Ocean, leading to ice divergence (ice extent «spreading out») and cooler temperatures.