Sentences with phrase «multiple ocean surface temperature»

It is the net impact of multiple ocean surface temperature changes, rather than a single ocean basin change, that plays a main driver for the multi-decadal global warming accelerations and slowdowns.

The new finding of the importance of multiple ocean surface temperature changes to the multi-decadal global warming accelerations and slowdowns is supported by a set of computer modeling experiments, in which observed sea surface temperature changes are specified in individual ocean basins, separately.

The multi-decadal global warming rate changes are primarily attributed to multiple ocean surface temperature changes, according to research by Institute of Atmospheric Physics and Australian Bureau of Meteorology.

Not exact matches

Forest et al. 2006 compares observations of multiple surface, upper air and deep - ocean temperature changes with simulations thereof by the MIT 2D climate model run at many climate parameter settings.

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 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 predictors.

More generally, we are using multiple sensor & associated data sets (low frequency microwave radiometers, ocean color, sea surface temperature, wind, wave, altimeter products, model and in situ data..)

«Estimating changes in global temperature since the pre-industrial period» «A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets» «Deducing Multidecadal Anthropogenic Global Warming Trends Using Multiple Regression Analysis» «Early onset of industrial - era warming across the oceans and continents»

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 predictors.

However, the situation is complicated by multiple other factors that include delay in the response of global temperature to regional ENSO phenomena, and the triggering of feedbacks that can heat or cool the ocean in the same direction as surface trends rather than opposing them.

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 predictors.

The new research uses multiple runs of a coupled ocean - atmosphere computer model to simulate global temperature changes in response to climate forcing when the sea surface temperature (SST) in the el Niño region follows its historically observed values.

For example, reductions in seasonal sea ice cover and higher surface temperatures may open up new habitat in polar regions for some important fish species, such as cod, herring, and pollock.128 However, continued presence of cold bottom - water temperatures on the Alaskan continental shelf could limit northward migration into the northern Bering Sea and Chukchi Sea off northwestern Alaska.129, 130 In addition, warming may cause reductions in the abundance of some species, such as pollock, in their current ranges in the Bering Sea131and reduce the health of juvenile sockeye salmon, potentially resulting in decreased overwinter survival.132 If ocean warming continues, it is unlikely that current fishing pressure on pollock can be sustained.133 Higher temperatures are also likely to increase the frequency of early Chinook salmon migrations, making management of the fishery by multiple user groups more challenging.134

As with previous CIS contributions, the 2016 forecast was derived by considering a combination of methods: 1) a qualitative heuristic method based on observed end - of - winter Arctic ice thickness / extent, as well as winter surface air temperature, spring ice conditions and the summer temperature forecast; 2) a simple statistical method, Optimal Filtering Based Model (OFBM), that uses an optimal linear data filter to extrapolate the September sea ice extent time - series into the future and 3) a Multiple Linear Regression (MLR) prediction system that tests ocean, atmosphere and sea ice predictors.

Remember that the temperature near the surface need not be a true reflection of total system energy content if there is variability at multiple levels from ocean depths to top of atmosphere.

Girma Orssengo rightly demonstrates that one can not determine climate sensitivity empirically from observed changes in CO2 concentration and in global mean surface temperature unless one either studies periods that are multiples of ~ 60 years to cancel the transient effects of the warming and cooling phases of the Pacific and related ocean oscillations or studies periods centered on a phase - transition in the ocean oscillations.