Sentences with phrase «sea ice trend data»

Not exact matches

A new University of Washington study, with funding and satellite data from NASA and other agencies, finds a trend toward earlier sea ice melt in the spring and later ice growth in the fall across all 19 polar bear populations, which can negatively impact the feeding and breeding capabilities of the bears.

Scientists at the National Snow and Ice Data Center (NSIDC), University College London, University of New Hampshire and University of Washington analyzed 300 summer Arctic sea ice forecasts from 2008 to 2013 and found that forecasts are quite accurate when sea ice conditions are close to the downward trend that has been observed in Arctic sea ice for the last 30 yeaIce Data Center (NSIDC), University College London, University of New Hampshire and University of Washington analyzed 300 summer Arctic sea ice forecasts from 2008 to 2013 and found that forecasts are quite accurate when sea ice conditions are close to the downward trend that has been observed in Arctic sea ice for the last 30 yeaice forecasts from 2008 to 2013 and found that forecasts are quite accurate when sea ice conditions are close to the downward trend that has been observed in Arctic sea ice for the last 30 yeaice conditions are close to the downward trend that has been observed in Arctic sea ice for the last 30 yeaice for the last 30 years.

The trends revealed by the data were clear: The average albedo in the northern area of the Arctic Ocean, including open water and sea ice, is declining in all summer months (May - August).

Comiso and other climate scientists reject the suggestion that his data set may overestimate the recent trend in Antarctic sea - ice growth — by as much as two - thirds, according to Eisenman's analysis.

The authors of a new study reviewing the volume data, detailed on Monday in the journal Nature Geoscience, are quick to caution, though, that one single year of rebound doesn't suggest any sea ice recovery, as the overall trend is still downward.

Further signs of this warming trend can be seen in the Northern Hemisphere Sea Ice Extent from the National Snow and Ice Data Center.

Clearly, the sea ice volume data plot is the single most important topic of discussion, yet in the article it is shown in Figure 1 with a poor vertical scale and amongst linear trend lines which mislead and make the curve appear to be linear and reach the zero point far out in the future.

If you plot the average Arctic Sea Ice extent for 20 years, the you should also plot the monthly maximum and minimum values on the same figure so that we can get some perspective on where the 2007 and 2008 data falls in the context of annual variability, or examine for trends.

Comiso and other climate scientists reject the suggestion that his data set may overestimate the recent trend in Antarctic sea - ice growth — by as much as two - thirds, according to Eisenman's analysis.

Updated, July 23, 1:40 p.m. A new study of methods used to track Antarctic sea ice trends has raised important questions about whether recent increases in ice there are, to a significant extent, an illusion created by flawed analysis of data collected by a series of satellites.

Another NASA sea - ice data set, processed using the other standard algorithm, shows a growth trend similar to that in Comiso's current data.

Figure 3: National Snow and Ice Data Center (NSIDC) Antarctic, Arctic, and global (sum of the two) sea ice extents with linear trenIce Data Center (NSIDC) Antarctic, Arctic, and global (sum of the two) sea ice extents with linear trenice extents with linear trends.

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.

Thus, the data indicate a continued trend towards flushing of old multiyear ice out of Canadian Basin into the Beaufort and Chukchi Seas.

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.

When sceptics look at statistical data, whether it is recent ice melt, deep sea temperatures, current trend in global surface temperatures, troposphere temperatures, ice core records etc. they look at the data as it is without any pre-conceptions and describe what it says.

However, currently both modern and paleo data - model intercomparisons display large differences in sea - ice extent and trends.

Updating this analysis using observational data through 2011 (not even including the 2012 record low sea ice extent), the 32 - year trend (1979 - 2011) is -530 thousand square km per decade, and the 20 - year trend is -700 thousand square km per decade.

Since current ice melt data could indicate variable climate trends and aren't necessarily part of an accelerating trend, the study warned that predictions of future sea - level rise should not be based on measurements of glacial loss» Daily Mail.

[3] An implementation of the diff - of - gaussian filter is presented here: https://climategrog.wordpress.com/2016/09/18/diff-of-gaussian-filter/ [4] The sea - ice area data used in the decadal trend analysis are provided by Cryosphere Today team at U. Illinois.

This is an important article, Climategrog, because it shows from a different type of data (date of minimum extent) that something happened around 2007 to Arctic sea ice that interrupted a 35 year 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.

Actually Fielding's use of that graph is quite informative of how denialist arguments are framed — the selected bit of a selected graph (and don't mention the fastest warming region on the planet being left out of that data set), or the complete passing over of short term variability vs longer term trends, or the other measures and indicators of climate change from ocean heat content and sea levels to changes in ice sheets and minimum sea ice levels, or the passing over of issues like lag time between emissions and effects on temperatures... etc..

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.

Sea ice extent data, however, has become skewed due to the strong downward trend in ice extent, with a wider spread of values and more values falling at the low end of the range.

The area of Arctic sea ice was nearly 30 % greater in August than a year ago, according to recent satellite data, though projections based on longer - term trends suggest the sea ice will continue its decline over time.

If the sea ice around Antarctica is growing (on a decadal trend basis)-- which any schoolboy analysis of freely available basic data shows...... what does this tell you about the certainty of global warming?

Note that we computed sea ice — temperature regression coefficients with detrended data and then multiplied these by the trend of the sea ice area to obtain the congruent temperature change.

The downward trend is quite clear, according to Julienne Stroeve, a sea ice expert at the National Snow and Ice Data Center, who is currently based at University College Londice expert at the National Snow and Ice Data Center, who is currently based at University College LondIce Data Center, who is currently based at University College London.

«Global Sea ice trend by year only (barely) crosses 95 % significance when the first two months of satellite data is included for the entire record.»

Here, the sea ice data are used to illuminate how the trends in sea ice concentration relate spatially and temporally to the trends in land temperatures.

We showed that it is the combination of the second and third PCs of Z850 data in the high - latitude SH that best capture the trend in Z850 and best explain the trends in sea ice and temperature.

If you strip the scientific context of a globally rising temperature trend, you could argue that observed melting of sea ice is just some noise in the data, part of natural variation.

If we look at the data of seasonal variation of sea ice year by year at Arctic [North Pole] and Antarctic [South Pole] the year to year variations of seasonal trends are above the average at Antarctica and below the average at Arctic in the last decade.

I believe that there is a downward trend in Arctic sea ice because the data since 1979 points towards one.

Figure 1: Coverage of sea ice in both the Arctic (Top) and Antarctica (Bottom) for both summer minimums and winter maximums Source: National Snow and Ice Data Center One must also be careful how you interpret trends in Antarctic sea iice in both the Arctic (Top) and Antarctica (Bottom) for both summer minimums and winter maximums Source: National Snow and Ice Data Center One must also be careful how you interpret trends in Antarctic sea iIce Data Center One must also be careful how you interpret trends in Antarctic sea iceice.

Tamino compares and analyses the long term trends in sea ice data from the Northern and Southern Hemisphere in Sea Ice, North and South, Then and Nsea ice data from the Northern and Southern Hemisphere in Sea Ice, North and South, Then and Nice data from the Northern and Southern Hemisphere in Sea Ice, North and South, Then and NSea Ice, North and South, Then and NIce, North and South, Then and Now.