But we also speak of «forcing» and «feedback» when emulating GCMs
with simple energy balance models.
The former goes out on a limb; the latter should be easy to demonstrate as a statistical exercise
with a simple energy balance model.
In the response by raypierre - I agree about the problems
with simple energy balance model and its lack of spatial representation, but it's tough to fault the authors for the lack of cloud detail, since the science is not up to the task of solving that problem (and doing so would be outside the scope of the paper; very few paleoclimate papers that tackle the sensitivity issue do much with clouds).
The former goes out on a limb; the latter should be easy to demonstrate as a statistical exercise
with a simple energy balance model.
Given that those projections were done
with a simple energy balance model though, it would be trivial to add Pinatubo in as an extra forcing and see what difference it made.
Not exact matches
Studies that point towards the lower end also rely on
simple energy -
balance models with constant feedbacks for all forcings — and forcing quantifications that are derived from various
modeling exercises.
To get an idea of why this is, we can start
with the
simplest 1D
energy balance equilibrium climate
model:
With reference to the «simple Energy Balance Model»; Why is there no arrow showing the reflection of the incoming insolation at the boundary with
With reference to the «
simple Energy Balance Model»; Why is there no arrow showing the reflection of the incoming insolation at the boundary
withwith Ta?
A vast array of thought has been brought to bear on this problem, beginning
with Arrhenius»
simple energy balance calculation, continuing through Manabe's one - dimensional radiative - convective
models in the 1960's, and culminating in today's comprehensive atmosphere - ocean general circulation
models.
Of course, how much we warm in reality will vary
with latitude and be much more complex than a
simple energy balance model can possibly indicate.
They then estimated the heat flux into the thermocline using a standard (accepted)
model,
with a thermocline eddy diffusion coefficient of 1.2E - 5 m ^ 2 / s from Ledwell: We estimate s by using this slope along
with k = 1.2x10 - 5 m2 / s (the eddy diffusion coefficient in the thermocline [Ledwell et al., 1998]-RRB- So if they are wrong, either their basic
model is wrong (which seems unlikely - it is just a
simple energy balance model after all), or their choice of eddy diffusion coefficient is wrong.
So, that is what we came up
with — A few very
simple models, such as the one that involves 3 objects: one object A producing thermal
energy and radiating
energy at a fixed rate, two other objects B and C whose temperature is determined via radiative
balance with object A and empty space,
with a geometry such that the temperature of object B is higher than that of object C. And, what we wanted to illustrate is that the object C «warms» B in the colloquial sense of the word... i.e., that the presence of object C causes B to be at a higher temperature than if C is absent.
Currently, there are several EMICs in operation such as: two - dimensional, zonally averaged ocean
models coupled to a
simple atmospheric module (e.g., Stocker et al., 1992; Marchal et al., 1998) or geostrophic two - dimensional (e.g., Gallee et al., 1991) or statistical - dynamical (e.g., Petoukhov et al., 2000) atmospheric modules; three - dimensional
models with a statistical - dynamical atmospheric and oceanic modules (Petoukhov et al., 1998; Handorf et al., 1999); reduced - form comprehensive
models (e.g., Opsteegh et al., 1998) and those that involve an
energy - moisture
balance model coupled to an OGCM and a sea - ice
model (e.g., Fanning and Weaver, 1996).
Consistent results are found using both a three - dimensional ocean circulation
model coupled to an
energy balance atmospheric
model and
with a much
simpler ocean box
model.