Sentences with phrase «observed air temperature trends»

Kevin Trenberth is now arguing that the reason observed air temperature trends don't match modeled trends is because of «missing heat» in the oceans.

Not exact matches

411 SG Bolstrom, I am observing a particular trend unlike the recent past, whereas the Arctic air profiles are leaning more adiabatically during winter, this means a whole lot of confusion with respect to temperature trends, namely the high Upper Air should cool as the surface warms, and the reverse, the Upper air warms when heat from the lower atmosphere is transferred upwarair profiles are leaning more adiabatically during winter, this means a whole lot of confusion with respect to temperature trends, namely the high Upper Air should cool as the surface warms, and the reverse, the Upper air warms when heat from the lower atmosphere is transferred upwarAir should cool as the surface warms, and the reverse, the Upper air warms when heat from the lower atmosphere is transferred upwarair warms when heat from the lower atmosphere is transferred upwards.

The above model results have significant implications because the observed air - temperature trend over the elevated Himalayas has accelerated to between 0.15 — 0.3 K per decade18 during the past several decades.

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.

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.

Despite the many unresolved issues touched on in this chapter and discussed in more detail in chapters 5 — 9, the progress that has been achieved over the past few years provides a basis for drawing some tentative conclusions concerning the nature of the observed differences between surface and upper air temperature trends, and their implications for the detection and attribution of global climate change.break

Map of trends (°C / century) of annual (July — June) mean surface air temperature for observing stations west of 116 ° W. Triangles mark statistically significant (p < 0.05) trends (red, upward pointing: positive; blue, downward pointing: negative).

My attempts to determine the ratios and differences between the observed ocean air versus ocean SST temperature trends to compare with the model results were limited by the sparseness of the observed data.

As a result the most those papers can do is attempt to quantify the effects on measurements such as model TCR and model trends using air temperatures for land and ocean and comparisons with the observed using blended temperatures.

The thing is that as regards the sequence of observed events leading to changes in tropospheric temperature trends and the cyclical poleward and equatorward shifts in the air circulation systems the NCM is pretty robust.