date: Mon, 07 Jun 2004 10:15:34 -0600
from: Tom Wigley <wigleyatXYZxyz.ucar.edu>
to: Sarah Raper <sraperatXYZxyz-bremerhaven.de>
So the issue is whether RLO(t) *should* be constant in time for step
that, if RLO(t) is constant then sensitivity is constant). [I use RLO(t)
distinguish the RLO that we input from the backed-out transient land/ocean
It seems from the Gregory plot that you think RLO(t) *is* constant in
time in at least
one AOGCM -- although what you say is 'pattern of warming is constant'
more general. Am I interpreting you right here?
But some GCMs have non-constant sensitivity. So, presumably, these would
have non-constant RLO(t) ????
My physical intuition says that, in general, RLO(t) should *not* be
constant in this
type of experiment. The processes that affect RLO(t) are the input RLO,
XKLO (and to a lesser extent XKNS). Greater RLO-in can be offset by greater
XKLO. One can easily quantify these effects in a 2-box model. However, we do
not know these things a priori so we cannot a priori say whether RLO-in wins
or XKLO wins or they balance. What we can say is that there is no a
why they should balance since they involve separate and largely independent
For the PCM case that you show the XKLO that gives RLO(t) constant is 16,
which seems to be a very high number. This is for RLO-in = 1.4. If you
smaller RLO you would get a smaller (and more realistic?) XKLO to give
RLO(t). So perhaps the PCM RLO we use is wrong? I am not sure how we can
'fix' this -- i.e., how we can back out XKLO and RLO independently. I
that if we are just concerned with global-mean temperature it doesn't
there must be a whole range of XKLO/RLO-in combinations that will work. One
can't use ocean heat flux (or expansion) because this seems to be
independent of the XKLO/RLO(t) choice.
My guess is that fitting both land and ocean temperatures won't solve
but I could be wrong. Perhaps SO4 forcing cases may help, given the big
differences in forcing.
Have you looked at all the CMIP 1% CO2 model runs to see how RLO(t) varies?
I think you only had those 20-year average results when you did this
before, and it
would be better to use full time series. I can easily do this for the