Sentences with phrase «sea level trends based»

Table 11.9 of the TAR listed several estimates for global and regional 20th - century sea level trends based on the Permanent Service for Mean Sea Level (PSMSL) data set (Woodworth and Player, 2003).

The map of regional mean sea level trends provides an overview of variations in the rates of relative local mean sea level observed at long - term tide stations (based on a minimum of 30 years of data in order to account for long - term sea level variations and reduce errors in computing sea level trends based on monthly mean sea level).

Not exact matches

After over a year of sideways and downward movement from late 2015 through early 2017, the most recent NASA report shows that over the past year an acceleration in sea level rise has become visible on the NASA graph, even with just a quick glance (then again, while the long term trend is consistently upward, the annual trend is so variable, that it's likely foolish on my part to suggest a change in trend based on the most recent periods of increase which have only been occurring for less than 12 months).

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 predicbased 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 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 predicBased (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 ice 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 predicbased 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 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 predicBased 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 predictosea ice predictors.

Raw satellite - based trends in global mean sea levels over the period 1992 - 2000, according to Morner, 2004.

John Church, a top IPCC author at the Commonwealth Scientific and Industrial Research Organization in Australia, told Reuters he did not expect any impact on the IPCC's core sea level projections, which are not based on past trends.

The Harvard - led study said the new findings might affect projections of the future pace of sea level rise, especially those based on historical trends.

We also compared how the observed number of nuisance flood days in 2014 compared to what would be expected based on sea level rise trends in each location.

Finally, we project the number of nuisance flood days that we would expect from May 2015 - April 2016 based on sea level trends trends alone and with the added influence of El Niño.

Combine the satellite trend with the surface observations and the umpteen non-temperature based records that reflect temperature change (from glaciers to phenology to lake freeze dates to snow - cover extent in spring & fall to sea level rise to stratospheric temps) and the evidence for recent gradual warming is, well, unequivocal.

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.

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 predicbased 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 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 predicBased (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 ice predictors.

IPCC synthesis reports offer conservative projections of sea level increase based on assumptions about future behavior of ice sheets and glaciers, leading to estimates of sea level roughly following a linear upward trend mimicking that of recent decades.

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 predicbased 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 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 predicBased 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 predictosea ice predictors.

It remains possible that the data base is insufficient to compute mean sea level trends with the accuracy necessary to discuss the impact of global warming — as disappointing as this conclusion may be.

The increase in the rate of sea level rise at Stockholm (the longest record that extends past 1900) has been based on differencing 100 - year trends from 1774 — 1884 and 1885 — 1985.

Although the calculations of 18 - year rates of GMSL rise based on the different reconstruction methods disagree by as much as 2 mm mm yr - 1 before 1950 and on details of the variability (Figure 3.14), all do indicate 18 - year trends that were significantly higher than the 20th century average at certain times (1920 — 1950, 1990 — present) and lower at other periods (1910 — 1920, 1955 — 1980), likely related to multidecadal variability.The IPCC AR5 found that it is likely that a sea level rise rate comparable to that since 1993 occurred between 1920 and 1950.

Notes: Excel was used to calculate and plot the moving sea level per century curves and fitted trends (Excel slope function produced trends based on moving 360 - month periods for each month in the dataset; then converted to per century trends (inches) for each month).

In this context, we develop national projections of the urban and non-urban coastal population on the basis of four environmental and socio - economic scenarios which account for sea - level rise (for the flood plain analysis), population distribution, trends in urbanisation and coastal population growth.

See E.W. Leuliette, R.S. Nerem, and G.T. Mitchum, «Results of TOPEX / Poseidon and Jason - 1 calibration to construct a continuous record of mean sea level,» Marine Geodesy 27:79 - 94, 2004, and B.D. Beckley, F.G. Lemoine, S.B. Luthcke, R.D. Ray, and N.P. Zelensky, «A reassessment of global and regional mean sea level trends from TOPEX and Jason - 1 altimetry based on revised reference frame and orbits,» Geophysical Research Letters 34 (14): L14608, 2007.

Yes, the first Table (Recent short - term sea level trends in the Project area based upon SEAFRAME data through September 2006) lists trends of 2.7 to 17 mm / yr.

This expansion, combined with the melting of land - based ice, has caused global average sea level to rise by roughly 7 - 8 inches since 1900 — a trend that is expected to accelerate over coming decades.