Sentences with phrase «observed sea ice trends»

Clearly, therefore, something must be fundamentally wrong with the climate models, for their predictions to be so far off from the observed sea ice trends.»

Not exact matches

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.

A comparison of observed sea ice decline with the model ensemble spread can tell us only how likely an observed trend is relative to that ensemble.

Here are some possible choices — in order of increasing sophistication: * All (or most) scientists agree (the principal Gore argument) * The 20th century is the warmest in 1000 years (the «hockeystick» argument) * Glaciers are melting, sea ice is shrinking, polar bears are in danger, etc * Correlation — both CO2 and temperature are increasing * Sea levels are rising * Models using both natural and human forcing accurately reproduce the detailed behavior of 20th century global temperature * Modeled and observed PATTERNS of temperature trends («fingerprints») of the past 30 years agsea ice is shrinking, polar bears are in danger, etc * Correlation — both CO2 and temperature are increasing * Sea levels are rising * Models using both natural and human forcing accurately reproduce the detailed behavior of 20th century global temperature * Modeled and observed PATTERNS of temperature trends («fingerprints») of the past 30 years agSea levels are rising * Models using both natural and human forcing accurately reproduce the detailed behavior of 20th century global temperature * Modeled and observed PATTERNS of temperature trends («fingerprints») of the past 30 years agree

We find that the available observations are sufficient to virtually exclude internal variability and self - acceleration as an explanation for the observed long - term trend, clustering, and magnitude of recent sea - ice minima.

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.

(08/20/2013) As sea ice levels continue to decline in the northern hemisphere, scientists are observing an unsettling trend in harp seal young mortalities regardless of juvenile fitness.

We interpret the split of 2013 Outlooks above and below the 4.1 level to different interpretations of the guiding physics: those who considered that observed sea ice extent in 2012 being well below the 4.1 level indicates a shift in arctic conditions, especially with regard to reduced sea ice thickness and increased sea ice mobility; and those who have estimates above 4.1 who support a return to the longer - term downward trend line (1979 - 2007).

We interpret the split of 2013 Outlooks above and below the 4.1 median to different interpretations of the guiding physics: those who considered that observed sea ice extent in 2012 being well below the 4.1 level indicates a shift in arctic conditions, especially with regard to reduced sea ice thickness and increased sea ice mobility; and those with estimates above 4.1 who support a return to the longer - term downward trend line (1979 - 2007).

This model has been proven skillful in reproducing the monthly arctic (and Antarctic) sea ice extent anomalies over the last 30 years, as well as the observed long - term downward trend.

«the available observations are sufficient to virtually exclude internal variability and self - acceleration as an explanation for the observed long - term trend, clustering, and magnitude of recent sea - ice minima.

The authors used very long control runs of both the Geophysical Fluid Dynamics Laboratory (GFDL) and Hadley Centre climate models (5,000 years for the GFDL model) to assess the probability that the observed and model - predicted trends in Arctic sea ice extent occur by chance as the result of natural climate variability.

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.

Taken together, these changes suggest that at least part of the thinning of sea ice recently observed over the Arctic Ocean can be attributed to the trend in the AO toward the high - index polarity.

Conclusions Recently observed decadal trends in Arctic winter sea ice extent are not well explained by external forcing alone.

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.»

... observations suggested the bears drowned in rough seas and high winds and «suggest that drowning - related deaths of polar bears may increase in the future if the observed trend of regression of pack ice and / or longer open water periods continues.»

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.

There sea ice was not reduced and surface temperatures average 5 to 10 ° cooler, and the steep winter warming trend was not observed.

Of course, we all know that Arctic sea ice has been on a downward trend since satellite records started in 1979, reversing a growing trend from the 1940s to the 1970s, which was observed by other means by mostly Russian records.

While it is tempting to attribute the unexplained sea ice trends to other factors such as increased upwelling of relatively warm circumpolar deepwater (Thoma et al. 2008), an intensification of the hydrological cycle and increased ocean stratification (Liu and Curry 2010), or eastward propagation of sea ice anomalies (Holland et al. 2005), the observed northerly wind trends (Fig. 5a) are qualitatively consistent with the decrease in sea ice in the 30 ° W — 60 ° W sector.

After 90 days, the Antarctic Peninsula anomaly can locally exceed 0.5 °C, and within a few years can explain the observed warming trends at the base of the ice shelves in the Amundsen and Bellingshausen seas.

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.

Past climate models, as judged by the performance of the majority of Coupled Model Intercomparison Project 3 (CMIP3) simulations used in the IPCC Fourth Assessment Report, underestimated the observed linear trend in Arctic sea ice loss (Stroeve et al., 2007).

The newer CMIP5 simulations that are being used in the upcoming IPCC Fifth Assessment Report are in better agreement with the observed sea ice loss (Stroeve et al., 2012a; Massonnet et al., 2012), but the reasons for the differences in sea ice trends between the CMIP3 and CMIP5 models remain unclear.

Global climate model projections for sea ice trends around Antarctica are at odds with what is being observed.

For example, while all of the global climate models participating in the most recent Intergovernmental Panel on Climate Change report show a decline in Arctic sea ice over the period of available observations, none of them match the severity of the trends we actually observe.

Unfortunately, the majority of GCMs, including those participating in the IPCC AR4, have not been able to adequately reproduce observed multi-decadal sea ice variability and trends in the pan-Arctic region.

«Pretending that extrapolating an observed trend or that CMIP5 simulations will produce a useful decadal prediction of sea ice is pointless (well there is a potential point but it is to mislead).»

Observed blocking trends are diagnosed to test the hypothesis that recent Arctic warming and sea ice loss has increased the likelihood of blocking over the Northern Hemisphere.