Computer
Modelling Average Annual Temperature in the Ground Layer of Air for the South of Western Siberia (Russia)
Not exact matches
Unlike previous Pliocene
models, this «no ice» version returned
temperatures 18 to 27 F warmer than today's
average annual temperatures for the Canadian Arctic and Greenland, coming closer to what the historical data pulled from the ground said.
Mora's
models do show only
average annual temperature — collapsing seasonal extremes into one number for the year.
Similarly, all climate
models used in this assessment agree that the
average annual temperature in Montana will increase over the next century.
The scientists, using computer
models, compared their results with observations and concluded that global
average annual temperatures have been lower than they would otherwise have been because of the oscillation.
The inverted
model, though, outputs
annual temperatures (strictly, an
average temperature for each year's grape - growing season).
Global climate
models project an increase in
annual average temperature of almost 3 °C in our region by the 2050s.
Over the last decade or so, the
models have not shown an ability to predict the lack (or very muted) change in the
annual average global surface
temperature trend.
And, of course, we do not need to global climate
models to run impact
models with an
annual average increase in the mean surface air
temperature of +1 C and +2 C prescribed for the Netherlands.
Instead of plotting individual year datapoints for observed
temperatures, plotted 3 - year (36 - month
averages ending in December): this reflects an expectation that
models can't predict accurately every
annual period, but over longer 3 - year periods the
model and observation trends should better match.
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].
All of these characteristics (except for the ocean
temperature) have been used in SAR and TAR IPCC (Houghton et al. 1996; 2001) reports for
model - data inter-comparison: we considered as tolerable the following intervals for the
annual means of the following climate characteristics which encompass corresponding empirical estimates: global SAT 13.1 — 14.1 °C (Jones et al. 1999); area of sea ice in the Northern Hemisphere 6 — 14 mil km2 and in the Southern Hemisphere 6 — 18 mil km2 (Cavalieri et al. 2003); total precipitation rate 2.45 — 3.05 mm / day (Legates 1995); maximum Atlantic northward heat transport 0.5 — 1.5 PW (Ganachaud and Wunsch 2003); maximum of North Atlantic meridional overturning stream function 15 — 25 Sv (Talley et al. 2003), volume
averaged ocean
temperature 3 — 5 °C (Levitus 1982).
ECS is the increase in the global
annual mean surface
temperature caused by an instantaneous doubling of the atmospheric concentration of CO2 relative to the pre-industrial level after the
model relaxes to radiative equilibrium, while the TCR is the
temperature increase
averaged over 20 years centered on the time of doubling at a 1 % per year compounded increase.
This research required Dr Marohasy to compile long
temperature series for different locations as arrays for a neural network
model, in the process she became interested in the methodology used by the Bureau of Meteorology in the compilation of an
annual average temperature for Australia.