Sentences with phrase «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.

«These assessments of ice shelves need to be done regularly» to build up a time series of data — and ultimately to be able to separate a trend signal from the noise.

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).

Now the trend may be going in the other direction with new data about ice sheets, although a lot more research is needed, he said.

To project that trend forward, the team then used models recently developed to analyze Antarctic ice sheet collapse, plus large global data sets to tailor specific Atlantic tropical cyclone data and create «synthetic» storms to simulate future weather patterns.

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.

first, given all the talk on that «michael's graph» thread, i have to laugh at the fact that all those trend lines are drawn starting at 1998... [do we have any «proxy data» from which to infer attitudes going back to the little ice age?

BPL: The warming trend is not twenty years old, but 165 years old by direct measurements and perhaps 250 years old if you include ice - core data.

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

Still, the scientists, at the National Snow and Ice Data Center in Boulder, Colo., said that the extent of the ice in the Arctic this summer was 33 percent smaller than the average extent tracked since satellites started monitoring the region in 1979, and that the long - term trend is toward an ice - free summer in the Arctic Ocean within a few decadIce Data Center in Boulder, Colo., said that the extent of the ice in the Arctic this summer was 33 percent smaller than the average extent tracked since satellites started monitoring the region in 1979, and that the long - term trend is toward an ice - free summer in the Arctic Ocean within a few decadice in the Arctic this summer was 33 percent smaller than the average extent tracked since satellites started monitoring the region in 1979, and that the long - term trend is toward an ice - free summer in the Arctic Ocean within a few decadice - free summer in the Arctic Ocean within a few decades.

The same issues have dogged other attempts by climate scientists to glean clues on climate trends from bodies of data collected by satellites and weather balloons for other reasons (not to mention ongoing attempts to discern climate patterns in tree rings, ice layers, and other natural substitutes for thermometers; remember the «hockey stick» debate?).

Still not sure if their method is the best way to visualize this ice - out data, but I like that you can see the trends of many lakes at once.

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.

The next implication is that those who try to predict ice volume using past ice volume data are largely picking up the artifical trend that is built into the data.

The horror story is not what the scientists present as data, but what the media make of a (too) short trend, including the complete melting of the Greenland ice sheet.

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.

The overall global glacier mass balance trend is shown on the National Snow and Ice Data Center (NDIS) graph here.

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.

Study of Greenland ice provides a 2500 year perspective indicating we headed into a mini-ice-age as happened from the mid 1300's to the mid 1800's; and the data from the 1200 or so floating buoys around the world's oceans are likewise showing a cooling trend, and have been for some time.

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 predictoIce Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictoice predictors.

The application of trends to climate data began in the 1970s with the prediction of a coming ice age as temperatures declined from 1940.

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

People observed this trend of earlier ice - out dates by comparing to their own recollections, and all one has to do is look at the aggregated data which has been collected over the years.

Our recently published Nature paper (King et al, 2012), used GRACE gravity data to infer Antarctic ice mass trends as in previous work, but with an updated estimate of the GIA correction.

The antarctic ice winter max decreased by about one third during this period, and the HadCRUT temp data base for that region during that period does show a substantial surface air temperature warming trend.

«To summarize - Using the 60 and 1000 year quasi repetitive patterns in conjunction with the solar data leads straightforwardly to the following reasonable predictions for Global SSTs 1 Continued modest cooling until a more significant temperature drop at about 2016 - 17 2 Possible unusual cold snap 2021 - 22 3 Built in cooling trend until at least 2024 4 Temperature Hadsst3 moving average anomaly 2035 — 0.15 5Temperature Hadsst3 moving average anomaly 2100 — 0.5 6 General Conclusion — by 2100 all the 20th century temperature rise will have been reversed, 7 By 2650 earth could possibly be back to the depths of the little ice age.

More importantly, we must wonder what the satellites would have observed happening in the Antarctic when Roald Amundsen sailed through the Arctic in 1903 - 1905 on the small wooden ship Gjøa when the Northwest Passage was open to sailing vessels, and again in 1940 - 42 and 1944 (St. Roch), it is possible the reduction in Arctic ice is not an indicator of warming, since it was balanced by record high Antarctic ice levels and a rising trend line for the data set since 1979.

The only thing that maybe correct in the analysis is the oscillation, because the warming trend from the mini ice age is in the data too.

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.

Independent non-thermometer data (so - called proxies, like tree rings, ice cores, ocean sediments, stalagmites, etc.) also show no warming trend between 1978 and 2000.

Also, the Greenland ice core data do agree pretty good with sulfate emissions estimates, but Greenland is located downwind of the US and Canada and does not represent global trends impacted by developing countries.

Figure 3 (Data sources here and here) There is a secular warming trend that has persisted since the end of the Little Ice Age in the 19th century.

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.

The study found it had almost enough data to conclude Antarctica's ice sheets are melting as part of an increasing trend with a «reasonable level of confidence.

Decadal trends also differ greatly between POLES and TOVS primarily owing to the discontinuation of ice station data in the POLES dataset after 1991.

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.

Yet if we look at the earlier data as well, it just shows an overall warming trend (natural) from the 1650's from a period known as a «little ice age».

ie, a look at the actual temperature in the central england data set from the 1600's, would give a null hypothesis for any significant observable human AGW signature (ie a low % of AGW) as there only appear to be a gradual warming trend from a period known as the «little ice age».

[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 predictoIce Extent time series into the future; and 3) an experimental Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere, and sea ice predictoice predictors.

This is one reason why GISS does not currently use SST data in the seasonal ice region above 75N, even when this data is seasonally available (as is increasingly the case due to diminishing trends in ice extent and better coverage due to satellite data).

B. Martín - Español et al 2016 - Spatial and temporal Antarctic Ice Sheet mass trends, glacio - isostatic adjustment, and surface processes from a joint inversion of satellite altimeter, gravity, and GPS data