[Marxism] climate modeling and local observations

Les Schaffer schaffer at optonline.net
Sat Aug 11 19:11:46 MDT 2007

ok, one last post for the day on this subject, then off to a faux-Dead
concert, where Buddy Guy is playing!!!!!

as our man Dyson has said, climate modeling is nothing more than fluid
mechanics, radiation transfer, thermodynamics, blah blah, blah. Dyson
believes, so far as i know, in all of these principles.

however, what does it take to run simulations of complex phenomena using
these simple principles?

answer: blood, sweat, and tears, otherwise known as complex computer

these models often break down into three areas: coding the actual
physical model for the evolutionary dynamics, presenting and
interpreting the often huge datasets to see what hath the model spoketh,
and gathering  user input of initial and boundary conditions for the
simulation to take place (simulation parameters come in here as well). i
am often surprised how the latter two areas greatly outweigh the actual
dynamical model in terms of raw lines-of-code effort.

On Thursday, a paper was published in Science online that dealt with
gathering boundary conditions for climate change simulations from local
(in time) observations: the muddy messy stuff our beloved savior Dyson
has spoke about. they worked out a scheme for getting data on the yucky
stuff into the model: the initial state of the atmosphere and ocean,
local (in time) projected changes in solar irradiance, actually existing
levels of volcanic aerosols. messy stuff (Dyson, my hero). they ran
their model backwards in time for a several handfuls of years to make
sure it wasn't spitting out pure nonsense and looked something like
planet earth seen from outside the air-condition offices (thats for
Dyson again), then ran the models forward for about a decade. they feel
this is the first model to accurately make short time predictions. their

    "Our system predicts that internal variability will partially offset
    the anthropogenic global warming signal for the next few years.
    However, climate will continue to warm, with at least half of the
    years after 2009 predicted to exceed the warmest year currently on

[how earlier models worked and why they do not conflict with this one
we'll leave for another time.]


Science 10 August 2007:
Vol. 317. no. 5839, pp. 796 - 799
DOI: 10.1126/science.1139540

Improved Surface Temperature Prediction for the Coming Decade from a
Global Climate Model
Doug M. Smith,* Stephen Cusack, Andrew W. Colman, Chris K. Folland, Glen
R. Harris, James M. Murphy

Previous climate model projections of climate change accounted for
external forcing from natural and anthropogenic sources but did not
attempt to predict internally generated natural variability. We present
a new modeling system that predicts both internal variability and
externally forced changes and hence forecasts surface temperature with
substantially improved skill throughout a decade, both globally and in
many regions. Our system predicts that internal variability will
partially offset the anthropogenic global warming signal for the next
few years. However, climate will continue to warm, with at least half of
the years after 2009 predicted to exceed the warmest year currently on

Met office Hadley Centre, FitzRoy Road, Exeter, Ex1 3PB, UK.

It is very likely that the climate will warm over the coming century in
response to changes in radiative forcing arising from anthropogenic
emissions of greenhouse gases and aerosols (1). There is, however,
particular interest in the coming decade, which represents a key
planning horizon for infrastructure upgrades, insurance, energy policy,
and business development. On this time scale, climate could be dominated
by internal variability (2) arising from unforced natural changes in the
climate system such as El Niño, fluctuations in the thermohaline
circulation, and anomalies of ocean heat content. This could lead to
short-term changes, especially regionally, that are quite different from
the mean warming (3–5) expected over the next century in response to
anthropogenic forcing. Idealized studies (6–12) show that some aspects
of internal variability could be predictable several years in advance,
but actual predictive skill assessed against real observations has not
previously been reported beyond a few seasons (13). Global climate
models have been used to make predictions of climate change on decadal
(14, 15) or longer time scales (4, 5, 16), but these only accounted for
projections of external forcing, neglecting initial condition
information needed to predict internal variability. We examined the
potential skill of decadal predictions using the newly developed Decadal
Climate Prediction System (DePreSys), based on the Hadley Centre Coupled
Model, version 3 (HadCM3) (17), a dynamical global climate model (GCM).
DePreSys (18) takes into account the observed state of the atmosphere
and ocean in order to predict internal variability, together with
plausible changes in anthropogenic sources of greenhouse gases and
aerosol concentrations (19) and projected changes in solar irradiance
and volcanic aerosol (20).

We assessed the accuracy of DePreSys in a set of 10-year hindcasts (21),
starting from the first of March, June, September, and December from
1982 to 2001 (22) inclusive (80 start dates in total, although those
that project into the future cannot be assessed at all lead times). We
also assessed the impact of initial condition information by comparing
DePreSys against an additional hindcast set (hereafter referred to as
NoAssim), which is identical to DePreSys but does not assimilate the
observed state of the atmosphere or ocean. Each NoAssim hindcast
consists of four ensemble members, with initial conditions at the same
80 start dates as the DePreSys hindcasts taken from four independent
transient integrations (3) of HadCM3, which covered the period from 1860
to 2001 (18). The NoAssim hindcasts sampled a range of initial states of
the atmosphere and ocean that were consistent with the internal
variability of HadCM3 but were independent of the observed state. In
contrast, the DePreSys hindcasts were initialized by assimilating
atmosphere and ocean observations into one of the transient integrations
(18). In order to sample the effects of error growth arising from
imperfect knowledge of the observed state, four DePreSys ensemble
members were initialized from consecutive days preceding and including
each hindcast start date (23). Fig. S1 summarizes our experimental

More information about the Marxism mailing list