Sentences with phrase «ice anomalies on»

G. Magnusdottir, C. Deser and R. Saravanan, 2004: The effects of North Atlantic SST and sea - ice anomalies on the winter circulation in CCM3.

Deser, C, G. Magnusdottir, R. Saravanan and A. Philips, 2004: The effects of the North Atlantic SST and sea - ice anomalies on the winter circulation in CCM3.

The effects of North Atlantic SST and sea ice anomalies on the winter circulation in CCM3.

Not exact matches

Using all available geologic, tectonic and geothermal heat flux data for Greenland — along with geothermal heat flux data from around the globe — the team deployed a machine learning approach that predicts geothermal heat flux values under the ice sheet throughout Greenland based on 22 geologic variables such as bedrock topography, crustal thickness, magnetic anomalies, rock types and proximity to features like trenches, ridges, young rifts, volcanoes and hot spots.

On Tuesday, the team successfully executed the last of seven daring orbit correction maneuvers that kept MESSENGER aloft long enough for the spacecraft's instruments to collect critical information on Mercury's crustal magnetic anomalies and ice - filled polar craters, among other featureOn Tuesday, the team successfully executed the last of seven daring orbit correction maneuvers that kept MESSENGER aloft long enough for the spacecraft's instruments to collect critical information on Mercury's crustal magnetic anomalies and ice - filled polar craters, among other featureon Mercury's crustal magnetic anomalies and ice - filled polar craters, among other features.

- The melting days anomaly is greater on the West of the Ice Sheet.

Based on the comparison between reconstructions and simulations, there is high confidence that not only external orbital, solar and volcanic forcing, but also internal variability, contributed substantially to the spatial pattern and timing of surface temperature changes between the Medieval Climate Anomaly and the Little Ice Age (1450 to 1850).

Due to the cold spring in Alaska last year, the ice break up date was the latest since 1917, consistent with the spring temperature anomaly state - wide being one of the coldest on record (unsurprisingly the Nenana break up date is quite closely correlated to spring Alaskan temperatures).

This gravitational discussion should come closer to home, there is a strange tidal anomaly which occurs uniquely during the full or new moon on Arctic Ocean ice.

Given the standard deviation in the residuals (about 10 days), the 30 + day earlier ice out was a massive anomaly (more than 3) and was noticed and commented on at the time.

Regarding the «global ice at 1980 levels», here is the canned response we wrote in rebuttal to the astonishingly twisted piece in Daily Tech: What the graph shows is that the global sea ice area for early January 2009 is on the long term average (zero anomaly).

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.

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.

Along with the negative AO index, we've seen an increased frequency of the the Arctic Dipole Anomaly, whereby the deep polar closed low that normally keeps the Arctic air contained is split into a pressure zones on both sides of the pole (i.e. a dipole) creating zonal winds across the Arctic shunt both cold air (and ice) more vigorously out of the Arctic.

At least relative to my questions above, what struck me was the possibility of starting with your reduction and analysis of the snow cover / fall anomaly data to come up with a research project based on some quite complicated but fascinating calculations on net TOA energy balance as a result of your conclusion about the relation of Arctic sea ice loss to NH snow cover / amount anomaly.

Summer atmospheric circulation anomalies over the Arctic Ocean and their influences on September sea ice extent: A cautionary tale.

The high altitude camping on Devon Island allowed them to correct their data sets to take into account anomalies like the radar - based GRACE penetrating the soft snow and bouncing off harder ice below, or ICESat's laser - based system bouncing off clouds

You can see in section four and related figures that the progression continues, next including the Pacific and ice in the East Eurasian Arctic in stage three, and then anomaly trends come to a close in stage four, with cumulative effects on ice, heat flux, atmospheric response, etc..

The reference temperature anomaly T9 is based on Hadley Centre Ice and SST version 1 (HadISST1, http://www.metoffice.gov.uk/hadobs/hadisst/; ref.

The regression - based forecast submitted by Tivy for the Greenland Sea (Figure 7) also shows a reduction in September sea ice area compared to 2009 based on fall (Sep - Oct - Nov) sea surface temperature anomalies in the North Atlantic.

The September ice area is predicted to be comparable to 2009 based on winter (Jan - Feb - Mar) sea level pressure anomalies over the Kara and Laptev Seas.

Now, since 2007, at the height of the global warming scare tactics about arctic sea ice, the antarctic sea ice extents anomaly CONTINUOUSLY exceeds 1.25 Mkm ^ 2 for 3 years straight now, and is larger than 1.5 Mkm ^ 2 so often for such long times that it is not even newsworthy on a skeptic site.

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.

''... worked with two sediment cores they extracted from the seabed of the eastern Norwegian Sea, developing a 1000 - year proxy temperature record «based on measurements of δ18O in Neogloboquadrina pachyderma, a planktonic foraminifer that calcifies at relatively shallow depths within the Atlantic waters of the eastern Norwegian Sea during late summer,» which they compared with the temporal histories of various proxies of concomitant solar activity... This work revealed, as the seven scientists describe it, that «the lowest isotope values (highest temperatures) of the last millennium are seen ~ 1100 - 1300 A.D., during the Medieval Climate Anomaly, and again after ~ 1950 A.D.» In between these two warm intervals, of course, were the colder temperatures of the Little Ice Age, when oscillatory thermal minima occurred at the times of the Dalton, Maunder, Sporer and Wolf solar minima, such that the δ18O proxy record of near - surface water temperature was found to be «robustly and near - synchronously correlated with various proxies of solar variability spanning the last millennium,» with decade - to century - scale temperature variability of 1 to 2 °C magnitude.»

A persistence forecast based on February ice concentration anomalies is generated using CCA.

Pokrovsky predicts a further acceleration of melting of the thin ice and in general greater ice loss compared to his June prediction; this change is based on the increase in the sea surface temperature (SST) anomalies in the North Atlantic and the presence of hot air masses over Siberia and the Russian Arctic.

Using a statistical model based on canonical correlation analysis with fall sea surface temperature anomalies in the North Atlantic as the main predictor, Tivy shows below - normal ice concentrations throughout most of the region (Figure 12), which suggests an earlier - than - normal opening of the shipping season.

I'm very convinced that the physical process of global warming is continuing, which appears as a statistically significant increase of the global surface and tropospheric temperature anomaly over a time scale of about 20 years and longer and also as trends in other climate variables (e.g., global ocean heat content increase, Arctic and Antarctic ice decrease, mountain glacier decrease on average and others), and I don't see any scientific evidence according to which this trend has been broken, recently.

Anomalies in ice albedo or downwelling shortwave radiation at the start of the melt season have more than a five-fold larger impact on solar heating of ice than those at the end of the melt season (Perovich et al., J. Geophys.

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.

Another way of stating the question is whether below normal multi-year ice fractions account for a persistence in ice extent anomalies on interannual time scales, or whether the ice pack is now back in a mode with no interannual correlation between extent anomalies (Bitz, personal communication).

Global solar irradiance reconstruction [48 — 50] and ice - core based sulfate (SO4) influx in the Northern Hemisphere [51] from volcanic activity (a); mean annual temperature (MAT) reconstructions for the Northern Hemisphere [52], North America [29], and the American Southwest * expressed as anomalies based on 1961 — 1990 temperature averages (b); changes in ENSO - related variability based on El Junco diatom record [41], oxygen isotopes records from Palmyra [42], and the unified ENSO proxy [UEP; 23](c); changes in PDSI variability for the American Southwest (d), and changes in winter precipitation variability as simulated by CESM model ensembles 2 to 5 [43].

The mean ice concentration anomaly for June 2013 is 0.9 x 106 square kilometers greater than June 2012, however Arctic sea ice thicknesses and volumes continue to remain the lowest on record.

A new paper Global Signatures and Dynamical Origins of the Little Ice Age and Medieval Climate Anomaly (Mann et al 2009)(see here for press release) addresses this question, focusing on regional temperature change during the Medieval Warm Period and Little Ice Age.

On the other hand, over the course of the entire year, the last 4 years of ever - larger «excess» sea ice anomalies around Antarctic are reflecting 1.7 times as much energy from the planet as is absorbed up north.

Once the ice age is over and all the ice is gone, the planet returns to its equilibrium temperature of 25ºC, on the anomaly scale shown.

Top row (a — c): Regressions of the leading detrended Z850 PC timeseries with anomalies in continental Antarctic temperature from M10 (colors on Antarctic land), sea ice concentration (colors over ocean; (note the sea ice colorscale is reversed with respect to the temperature colorscale), and geopotential height (contours).

It is well established that large - scale modes of the extratropical SH atmospheric circulation have strong impacts on Antarctic sea ice (e.g. Yuan and Li 2008; Stammerjohn et al. 2008; Holland and Raphael 2006; Liu et al. 2004; Kwok and Comiso 2002; Yuan and Martinson 2000) and on continental temperature anomalies (e.g. Marshall 2007; van den Broeke and van Lipzig 2004; Schneider et al. 2004; Kwok and Comiso 2002; Turner 2004; Bromwich et al. 2004; Thompson and Solomon 2002).

Some authors have assessed how anomalies in one season could reappear in the next or even a year later (Gloersen and White 2001; Stammerjohn et al. 2008), while others have focused on the eastward propagation of sea ice anomalies (e.g. White and Peterson 1996).

For the purposes of argument, I'll acknowledge that it likely did so, but as the person who brought it up, the onus is on you to demonstrate that the amount the ice sheet receded during the Medieval Climate Anomaly (MCA) is similar to the amount it has receded in modern times.

«Marked periods like the warming anomaly in the Middle Ages or the small ice age come up on a regional level but don't show a single global picture,» said Heinz Wanner, the study's lead researcher.

It includes inter alia information on global temperatures during 2007, regional temperatures anomalies, droughts, storms and flooding, cyclones, and Artic sea ice.

You can see that the Medieval Climate Anomaly (renamed from the Medieval Warm Period) and the Little Ice Age are clearly marked on the graph.

«Global Signatures and Dynamical Origins of the Little Ice Age and Medieval Climate Anomaly» goes into considerable detail on both topics with numerous graphics.

there is a clear change (eyeballing) in the anomaly pattern after 1998 autumn (before it the autumns were quite normal) but doing a linear reg one on weekly intervals might still give some means to guess the 1st winter without full ice cover.

The up - to - date Southern Hemisphere Sea Ice Anomaly chart is based on the era of satellite data, beginning in 1975.

Despite temperatures at the beginning of 2017 not being record - breaking the sea ice area remained much lower than average during the first three months of the year, with January showing the lowest negative anomaly on record - 600,000 square kilometres below the 1981 - 2010 average for January.

Wang and Bai (National Oceanic and Atmospheric Administration); 4.9 Million Square Kilometers; Statistical Prediction is based on regression of September ice area to the summer Dipole Anomaly (DA) index.