Secondly, the spread around the model - mean value is calculated after the anomalies are taken which has the visual effect of minimizing the range
of modeled temperatures.
All these complications have traditionally rendered attempts at modeling rainfall — which is much harder than
modeling temperature changes — futile.
If one were to
model the temperature rise in the years 1974 - 1999 it would be very easy to overestimate aerosol cooling and so to overestimate the value of CO2 climate sensitivity.
The chart specifically compares state - of - the - art climate
model temperature output for the U.S. corn belt region versus NOAA's climate network system (USHCN).
Probabilistic projections with the box model allowed consideration of all major uncertainties, such
as modeled temperature sensitivities to CO2 concentrations, Greenland Ice Sheet melt sensitivities to temperature changes, and AMOC sensitivities to both temperature and Greenland Ice Sheet melt changes.
The paper then compares the global surface temperature data (with these three influences both included and removed) to the envelope of climate
model temperature projections in both the 2001 and 2007 IPCC reports (Figure 2).
A paper led by James Risbey (2014) in Nature Climate Change takes a clever approach to evaluating how accurate climate
model temperature predictions have been while getting around the noise caused by natural cycles.
It doesn't pass a sniff test: if we actually inserted this range of cloud forcing into the GCMs, will we ever
get model temperatures that are below absolute zero or hotter than the Sun?
We can say that the GCM results (using the same input forcings and the
same modeled temperature outputs) can be matched with a zero - dimensional model with low effective climate sensitivity.
As you can clearly see, the 60 - month UAH alignment shifts the entire record down, artificially offsetting the satellite temps and making surface and
model temperatures seem much higher.
In their study, Meehl et al. show that natural forcings can not account for the increase in global temperatures in the second half of the 20th century, and that models using both natural and anthropogenic
forcings model the temperature change over the 20th century most accurately.
The uncertainty in the regions affected by high - end climate change during DJF (the Arctic) is mostly due to differences in
modelled temperature increases, which are caused by biases in simulated ice sheet extent and snow cover.
Within this context, it would be helpful to
model the temperature rise through to say 2200, 2300, 2400 and 2500 so that we get a sense of whether we are on a path to extinguish life on earth in a historically speaking, a short period of time.
The modelled temperature of the lava indicated it had barely had time to cool, suggesting that the event was dominated by lava fountains.
Also puzzling in their paper is their plot which shows
no modeled temperature increase due to anthropogenic forcings prior to 1970, during which time atmospheric CO2 concentrations increased to 325 ppm.
Johannessen OM, Bengtson L, Miles MW, Kuzmina SI, Semenov VA, Alekseev GV, Nagurnyi AP, Zakharov VF, Bobylev LP, Pettersson H, Hasselmann K, Cattle HP (2004) Arctic climate change: observed and
modeled temperature and sea - ice variability.