Sentences with phrase «september sea ice trend»

Not exact matches

«We found that in years when the sea ice extent departed strongly from the trend, such as in 2012 and 2013, predictions failed regardless of the method used to forecast the September sea ice extent,» said Julienne Stroeve, a senior scientist at NSIDC and professor at University of College London.

Since at least 1979, Arctic sea ice has generally been on a downward slope, trending 4.5 percent lower per decade overall and 13.7 percent lower per decade during the September summer minimum.

However, given the shortness of the available sea ice extent time series, the apparent steepening of the downward September trend may not be sustained.

Cawley, 4.35 (+ / - 1.16), Statistical This is a purely statistical method (related to Krigging) to estimate the long term trend from previous observations of September Arctic sea ice extent.

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 predictoIce 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 predictoice 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 predictoIce 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 predictoice 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 predictoIce 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 predictoice 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 predictoice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice predictoice predictors.

Figure 4: September Arctic Sea Ice Extent (thin, light blue) with long term trend (thick, dark blue).

The September 2010 sea ice extent nevertheless ended up as third lowest in the satellite record, behind 2007 and barely above 2008, reinforcing the long - term downward trend.»

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 predictoIce 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 predictoIce 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 predictoIce (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 predictoIce 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 predictoice predictors.

The spread of Outlook contributions suggests about a 29 % chance of reaching a new September sea ice minimum in 2010 and only an 18 % chance of an extent greater than the 2009 minimum (or a return to the long - term trend for summer sea ice loss).

53 % of the Outlook contributions suggest the September minimum will remain below 5 million square kilometers, representing a continued trend of declining sea ice extent.

2012's sea ice area and extent were already trending low this year, but damage done to the thin and low concentration of ice by this storm almost ensures that 2012 will eclipse 2007 in all categories as the lowest sea ice on record by the time the September low is set.

Looking at AR5, these seem to be the take away messages: «Comparing trends from the CCSM4 ensemble to observed trends suggests that internal variability could account for approximately half of the observed 1979 — 2005 September Arctic sea ice extent loss.»

Stern: My estimate for September average sea ice extent (4.67 million square kilometers) was simply based on extrapolation of the 10 - year trend (1989 - 2008).

Unless these nine models share common systematic biases, it is thus expected that the average 2014 September Arctic sea ice extent will be in the range 3.95 - 5.6 million km ², and likely above the trend line (5.1 million km ²), a situation similar to 2013.

Cawley, 4.35 (± 1.16), Statistical (Same as July) This is a purely statistical method (related to Krigging) to estimate the long term trend from previous observations of September Arctic sea ice extent.

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 predictoIce 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 predictoice 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 predictoice extent timeseries into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictosea ice predictoice predictors.

The outlook for the pan-arctic sea ice extent in September 2008 indicates a continuation of the recent trend of sea ice loss.

Sea ice extents in September 2007 and 2008 were well below the long - term trend.

«In fact, the September sea ice volume is already down 75 % with a trend to zero by September 2016, suggests that the Arctic is heading for complete meltdown, which would be a planetary catastrophe,» Ibid.

Cawley, 4.27 (± 1.15), Statistical (Same as June) This is a purely statistical method (Gaussian Process, related to Kriging) to estimate the long - term trend from previous observations of September Arctic sea ice extent.

The departures of September sea ice from the trend line are averaged for those five years, and that average is our forecast (departure from trend line) for 2016.

In Fig. 5b, the July — August — September (JAS) winds are overlaid on the same SON sea ice and continental air temperature trends.

For example, additional evidence of a warming trend can be found in the dramatic decrease in the extent of Arctic sea ice at its summer minimum (which occurs in September), decrease in spring snow cover in the Northern Hemisphere, increases in the global average upper ocean (upper 700 m or 2300 feet) heat content (shown relative to the 1955 — 2006 average), and in sea - level rise.

Figure 1: September sea ice extent during the satellite era, according to HadISST1.2, with linear trend indicated.

The linear trend in September sea ice from 1979 - 2012 was a loss of 13 percent per decade relative to the 1979 - 2000 mean (Fetterer et al., 2012; Stroeve et al., 2012a).

Figure 5: Arctic sea ice extent for September 2017 according to HadISST1.2, compared to an extrapolation from the long - term linear trend and predictions submitted to the three SIPN reports.

Cohen published a study in September that found this Arctic paradox pattern has become common in years with low fall sea ice cover and rapidly advancing fall snow cover across parts of Asia, and that there is a likely link between the trends.

Two contributors forecast a September minimum below that of 2007 at 4.0 million square kilometers and 3 contributors suggest a return to the long term downward linear trend for September sea ice loss (5.5 to 5.6 million square kilometers).

Lett., 2011, doi: 10.1029 / 2011GL048008) evaluated the NCAR CCSM4 model arctic sea ice trends and found that on time - scales less than 10 years, it's equally possible for the September sea ice to increase or decrease even into the 21st century.

Late - summer sea ice followed its long - term downward trend and scientific predictions of September extent averaged out close to the true final values.