Sentences with phrase «modeled ocean temperature pattern»

Regional trends are notoriously problematic for models, and seems more likely to me that the underprediction of European warming has to do with either the modeled ocean temperature pattern, the modelled atmospheric response to this pattern, or some problem related to the local hydrological cycle and boundary layer moisture dynamics.

Not exact matches

«By prescribing the effects of human - made climate change and observed global ocean temperatures, our model can reproduce the observed shifts in weather patterns and wildfire occurrences.»

Climate models show the absence of a global atmospheric circulation pattern which bolsters high ocean temperatures key to coral bleaching

For the change in annual mean surface air temperature in the various cases, the model experiments show the familiar pattern documented in the SAR with a maximum warming in the high latitudes of the Northern Hemisphere and a minimum in the Southern Ocean (due to ocean heat uptakOcean (due to ocean heat uptakocean heat uptake)(2)

This seems to be associated with particular patterns of change in sea surface temperature in the Atlantic and Pacific oceans, a teleconnection which is well - captured in climate models on seasonal timescales.

A new paper closely examining ocean temperatures throws a twist into understanding of the pattern of global warming seen in the 20th century, but does it throw established concepts and climate models into question?

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.

Abstract: «The patterns of time / space changes in near - surface temperature due to the separate forcing components are simulated with a coupled atmosphere — ocean general circulation model»

«By prescribing the effects of man - made climate change and observed global ocean temperatures, our model can reproduce the observed shifts in weather patterns and wildfire occurrences.»

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.

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.

To estimate the uncertainty range (2σ) for mean tropical SST cooling, we consider the error contributions from (a) large - scale patterns in the ocean data temperature field, which hamper a direct comparison with a coarse - resolution model, and (b) the statistical error for each reconstructed paleo - temperature value.

In general, the pattern of change in return values for 20 - year extreme temperature events from an equilibrium simulation for doubled CO2 with a global atmospheric model coupled to a non-dynamic slab ocean shows moderate increases over oceans and larger increases over land masses (Zwiers and Kharin, 1998; Figure 9.29).

To answer this question, large ensemble simulations of regional climate models will be carried out for an East Asian domain for two worlds: (1) Real world condition for which the observed sea surface temperatures will be prescribed and (2) Counter-factual world condition for which we will use adjusted sea surface temperatures obtained by removing human - induced ocean warming patterns.

The models (and there are many) have numerous common behaviours — they all cool following a big volcanic eruption, like that at Mount Pinatubo in 1991; they all warm as levels of greenhouse gases are increased; they show the same relationships connecting water vapour and temperature that we see in observations; and they can quantify how the giant lakes left over from the Ice Age may have caused a rapid cooling across the North Atlantic as they drained and changed ocean circulation patterns.

For the change in annual mean surface air temperature in the various cases, the model experiments show the familiar pattern documented in the SAR with a maximum warming in the high latitudes of the Northern Hemisphere and a minimum in the Southern Ocean (due to ocean heat uptake) evident in the zonal mean for the CMIP2 models (Figure 9.8) and the geographical patterns for all categories of models (Figure 9Ocean (due to ocean heat uptake) evident in the zonal mean for the CMIP2 models (Figure 9.8) and the geographical patterns for all categories of models (Figure 9ocean heat uptake) evident in the zonal mean for the CMIP2 models (Figure 9.8) and the geographical patterns for all categories of models (Figure 9.10).