To foster closer links between
the palaeoclimate modelling and data communities.
The goal of PAGES» LandCover6k Working Group is to use pollen, archaeological and historical data to provide information on past land cover and land use change that can be used to evaluate and improve Anthropogenic Land - Cover Change (ALCC) scenarios for
palaeoclimate modelling and the study of land - use as a climate forcing.
Not exact matches
These changes coincide with the different
palaeoclimate zones proposed in the
models, so the faunal distribution was probably climate - related,» comments Josep Fortuny, an ICP researcher who took part in the study.
You have to design
models that are highly computationally efficient to study
palaeoclimate.
In
models run with the GISS forcing data, the «natural + anthropogenic» temperature evolution matches observations very well for a climate sensitivity of 0.75 °C / W / m ², which agrees with the value derived from
palaeoclimate data.
In all this, why don't you pay attention to the physical
models we have: the climate over the last few decades, and
palaeoclimate.
Your point 4: «The early onset of sustained, significant warming in
palaeoclimate records and
model simulations suggests that greenhouse forcing of industrial - era warming commenced as early as the mid-nineteenth century and included an enhanced equatorial ocean response mechanism.
Co-author Gerrit Lohmann, who leads the Wegener Institute's
palaeoclimate dynamics group, said: «Using the simulations performed with our climate
model, we were able to demonstrate that the climate system can respond to small changes with abrupt climate swings.
I'm not eliding weather
modeling and decadal forcing processes that interact with the hydrosphere —
palaeoclimate reflects everything, radiative forcing included, but it remains arguably worse understood, and hence harder to backcast than recent climate.
The evidence from surface temperature observations is strong: The observed warming is highly significant relative to estimates of internal climate variability which, while obtained from
models, are consistent with estimates obtained from both instrumental data and
palaeoclimate reconstructions.
In using AOGCM output in this way, it is important not only to demonstrate that these unforced simulations do not drift significantly (Osborn, 1996), but also to evaluate the extent to which
model estimates of low - frequency variability are comparable to those estimated from measured climates (Osborn et al., 2000) or reconstructed
palaeoclimates (Jones et al., 1998).
A combination of numerical
models,
palaeoclimate data and modern observations indicated that ocean saltiness was key to understanding it.
Estimates of low (< 1C) sensitivity are not supported by observational,
palaeoclimate or
modelling studies.]
Sohl, L.E., and M.A. Chandler, 2007: Reconstructing Neoproterozoic
palaeoclimates using a combined data /
modelling approach.