Dynamically complex systems such as climate are theoretically
determinant but few would argue that we have sufficient understanding
of the fine details of the evolution of climate sub-system
interactions and, in particular, theshold phenomenon (or tipping
points) that push climate into new states on decadal scales. Or
indeed that we have the observational network for sufficiently
detailed initialisation of physically realistic numerical models.
The current generation of GCM are impossibly unrealistic and the
claims to be able to predict climate to 2100, based on an artificial
distinction between weather and climate, are fantastically
improbable.
Hurrell et al (quoted below from a December 2009 article in BAMS) are
part of a climate modelling community push for vastly expanded
computing power (2000 times more) to address climate as an initial
value problem. This may eventually (subject to filling in significant
knowledge gaps) be useful for global and regional climate prediction
over a decade at most - much as and for the same reasons - that
weather can be predicted only a week or so out.
A UNIFIED MODELING APPROACH TO CLIMATE SYSTEM PREDICTION
by James Hurrell, Gerald A. Meehl, David Bader, Thomas L. Delworth ,
Ben Kirtman, and Bruce Wielicki: BAMS December 2009 | 1819: DOI:
10.1175/2009BAMS2752.1
The global coupled atmosphere–ocean–land–cryosphere system exhibits a
wide range of physical and dynamical phenomena with associated
physical, biological, and chemical feedbacks that collectively result
in a continuum of temporal and spatial variability. The traditional
boundaries between weather and climate are, therefore, somewhat
artificial.
The large-scale climate, for instance, determines the environment for
microscale (1 km or less) and mesoscale (from several kilometers to
several hundred kilometers) processes that govern weather and local
climate, and these small-scale processes likely have significant
impacts on the evolution of the large-scale circulation.
The accurate representation of this continuum of variability in
numerical models is, consequently, a challenging but essential goal.
Fundamental barriers to advancing weather and climate prediction on
time scales from days to years, as well as longstanding systematic
errors in weather and climate models, are partly attributable to our
limited understanding of and capability for simulating the complex,
multiscale interactions intrinsic to atmospheric, oceanic, and
cryospheric fluid motions.
The purpose of this paper is to identify some of the research
questions and challenges that are raised by the movement toward a more
unified modeling framework that provides for the hierarchical
treatment of forecast and climate phenomena that span a wide range of
space and time scales. This has sometimes been referred to as the
“seamless prediction” of weather and climate (WCRP 2005; Palmer et al.
2008; Shapiro et al. 2009, manuscript submitted to BAMS; Brunet et al.
2009, manuscript submitted to BAMS). The central unifying theme is
that all climate system predictions, regardless of time scale, share
processes and mechanisms that consequently could benefit from the
initialization of coupled general circulation models with best
estimates of the observed state of the climate (e.g., Smith et al.
2007; Keenlyside et al.2008; Pohlmann et al. 2009).
However, what is the best method of initialization, given the biases
in models that make observations possibly incompatible with the model
climate state, and how can predictions best be performed and verified?
Hurricane prediction, for example, has traditionally been regarded as
a short-term weather prediction from an initialized atmospheric model.
However, hurricanes generate a cold wake as they churn up the ocean
and not only extract considerable amounts of heat through evaporative
cooling but also mix heat down into the thermocline (e.g., Emanuel
2001, 2006; Trenberth and Fasullo 2007; Korty et al. 2008). Feedback
from the cold wake is now thought to be important to improving the
forecast accuracy of intensity and track, and the heat and freshwater
fluxes could contribute to multidecadal variability in the Atlantic
Ocean climate system (e.g., Hu and Meehl 2009). Hence, hurricane
forecasting is a short-term coupled problem as well as a longer-term
climate problem
requiring not only an initialized atmospheric model but also the
initialization of a model of the ocean and its heat content.
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