cc: <willmott@udel.edu>
date: Fri, 30 Jan 2009 15:36:30 -0500
from: "Scott Robeson" <srobeson@indiana.edu>
subject: RE: More Thoughts
to: <P.Jones@uea.ac.uk>

Phil,

Thanks for the helpful comments.  Interesting week here -- 30+ cm of snow,
which is not typical for southern Indiana. 

I have picked up the new datasets and run them through 2008.

- The v version showed some differences from the previous results -- the
difference in the trends between the percentiles actually seems to be larger
in the v version.  Please see the attachment.  Note that these are for the
HadCRUT data (previous results were for CRUTEM).  I also did a time series
of 90th minus 10th percentile time series and the post-war discontinuity in
SSTs seems evident here.

- Yes, you're right about the high latitude areas (more variable regions)
driving the high and low percentiles, but data from all over the globe still
contribute somewhat.  So, in a sense the trends in the percentiles are most
representative of these high-latitude regions.  I could start to do some
regional analyses, but I'd like to keep the focus on how spatial variability
is changing across large spatial scales.  Perhaps a hemispheric analysis
might be useful along those lines and at least it would ensure that
something like having all the 10th percentiles from the SH and 90th
percentiles from the NH isn't happening.  

- Fig. 2 is more erratic since the 1970s as the trends are calculated over
increasingly shorter time periods.  The last several points on that graph
are only for about 30 years while the first ones are for the whole 1881-2008
period. The trend analysis still uses the monthly data, but I just
calculated one trend per year (Jan 1881 to Dec 2008, then Jan 1882 to Dec
2008, etc.).  So, the original figure caption was misleading in that it
didn't mention the months used.

- I had thought of a fixed grid analysis too -- then we would know if the
changes are due to the inclusion of a larger number of more-variable regions
later in the record or to "real" changes in the structure of the thermal
anomalies.  When you say 90% complete time series, do you mean that a grid
point is included if it has 90% data available for a given time period
(i.e., excluded if it has more than 10% missing)?  

Thanks,
Scott

-----Original Message-----
From: P.Jones@uea.ac.uk [mailto:P.Jones@uea.ac.uk] 
Sent: Wednesday, January 28, 2009 11:28 AM
To: srobeson@indiana.edu
Cc: willmott@udel.edu
Subject: More Thoughts

Scott,
   I picked up a copy of your docs/pics etc in Norwich on
 Monday. I've now had a read through, so here's some thoughts
 from Switzerland.
   I think running the v version through would be worthwhile,
 as a sensitivity test. If it shows little difference, then you
 have something that is quite robust.
   I don't think there are many data issues, just coverage changes.
 Can you run with a fixed grid - say 90% complete time series
 over the 1901-2007 period?
   I'm still wondering where the big increase in 90th percentiles
 is coming from? I can see how you calculate it, but spatially to
 my mind this would be dominated by the more variable regions. Maybe
 if you split into two groups - north of 30N and south of 30N.
   Seems like your Fig 2 is more erratic since the 1970s. This
 is an annual whereas Fig 1 is all months. To get annual do you
 do things annually or average the months.

   By the way 2008 is complete now.

 Cheers
 Phil




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