date: Fri Oct 24 17:00:26 2008
from: Keith Briffa <k.briffa@uea.ac.uk>
subject: Re: Question on climate reconstructions, and a query on model
to: "Richard Baldwin" <rich.baldwin@gmail.com>

   see you then - cheers
   At 16:55 24/10/2008, you wrote:

     Hi Keith,
     Thanks for the replies.
     There are elements of standardisation that I'm still not quite sure of, so post-reading
     and armed with questions I shall drop by one day next week when I'm in Norwich (I live
     in London...) to go through some of this further.
     Thanks again.
     Richard.
     2008/10/24 Keith Briffa <[1]k.briffa@uea.ac.uk>

          Hi Richard
          sorry for delayed response - things a bit manic here
          At 15:09 22/10/2008, you wrote:

                Hi Keith,
                In further query of your comments on issues with standardising climate data,
                and in a way hopefully simplifying the process to myself, is the (simply
                expressed) issue with the divergence between current instrumental climate data
                and observed tree ring density that the change has been too rapid to allow a
                'smoothing' of the high frequency variability to occur?

          No
           merely that the divergence is only apparent (at the NH average scale) in the
          smoothed i.e. lower-frequency domain. You need to smooth the tree-ring records and
          the temperature to see it. However, the divergence is largely an artifact of using
          curve fitting (i.e. based on least-squares fitted regression lines or functions ) to
          estimate the unwanted (biological) growth trend in the tree-ring data. These fits
          are influenced by climate warming signals in the recent data , and this signal is
          inadvertently removed in the standardising process. When non-curve fitting methods
          are used (such as RCS) this problem is largely removed. I attach a recent papers
          that goes into this - though you DO NOT NEED TO UNDERSTAND ALL THE DETAILS OF THIS.

                 Or is there something else lurking in there still? I must admit that having
                read some of the reading list papers I'm still a bit unclear on some of these
                points...

          If you wish to discuss this further after looking at the papers - come and chat

                And secondly - with so much of the SH being ocean, is it the case that a
                certain number of grid cells in GCMs will necessarily be made up of 'best
                estimate' parameters due to a lack of a) direct observations and b) proxy
                data,

          GCMs are based only on physical equations  - no observational data or proxy data
          really affect the formulation of the models. OK some reworking of model parameters
          does in reality take place to get the model to better simulate observed conditions
          - but only at a gross average scale . That is why we can justifiably compare
          simulated model output with observations , where the models are forced with
          realistic estimates of the net effects of processes that produce climate changes ie
          volcanic activity, solar radiation changes , changes in atmospheric constituents  -
          all of which directly or indirectly produce the radiative forcing that ultimately
          produces changes in regional and global climates.

                and if so will this have an effect on the uncertainty levels of predictions
                based on the model output? Or as it is all ocean can more assumptions on
                parameter values be made along similar uniformitarian principles as with the
                tree ring data?

                The design of models is based on our physical understanding of the climate
                system - true though that some components of the system are ignored or very
                simplified  - as was interactive  vegetation until very recentlty. In as much
                as the values ascribed to these processes may be poorly understood and may not
                be valid on some timescales or through time the uniformitarianism principle
                can be considered as applying here also - but not in the sense of
                regression-based interpretations of proxy data (when these are merely
                regressed against instrumental data with no consideration (often unavoidably)
                for the underlying  influence of other processes that may obscure, mask or
                bias the apparent relationships thus established , and that are used to
                retrodict climate over periods when the "other" processes may not act in the
                same way.

                Thanks.
                Richard.
                2008/10/7 Keith Briffa <<[2]mailto:k.briffa@uea.ac.uk>[3]k.briffa@uea.ac.uk>
                Richard
                happy to chat about this after tomorrow's lecture if you wish - but in the
                meantime ;
                the distinction I make in the chapter is between empirical signal on the one
                hand and theoretical signal on the other.
                This of course is a frame of reference invented for convenience. The
                theoretical signal in this chapter should be taken to be a measure of the
                representation, within the chronology or chronologies , of the specific
                climate variability with which we are concerned. This could be , for example
                the average of June,July and August temperature as measured by some
                instrumental record for the region.
                What I mean by the statement is that if I am interested in reconstructing the
                past variability of this specific climate variability at long time scales -
                i.e. how mean JJA temperature changes on time scales of a century or more, the
                chronologies must be processed (i.e. standardised) in such a way as the
                expressed empirical signal (i.e. the expression of the common variability
                actually contained within the trees we have sampled) is at least potentially
                preserved at this same long time scale. This is not to say that preserving the
                long time scale information will ensure a good representation of the
                theoretical signal as it is expressed by the chronology. Rather that, even if
                tree growth in an area is influenced by summer temperatures at this long time
                scale, if we process the measured ring-width data in such a way that the long
                time scale variance is removed (effectively high-pass filtering the
                chronology) no evidence of long time scale temperature variability can
                possibly be recovered from these standardised data. In fact , in some
                situations, it is better to sacrifice this "potential" information in the
                chronologies in order to ensure the reliability of the preserved (higher
                frequency) variance. In doing this we can often get a more reliable
                reconstruction , although of only the high-frequency part of the variance
                spectrum. This is because in some situations preserving the low-frequency
                involves accepting low reliability of this information in the chronology , or
                because the low-frequency information preserved in the trees is simply not
                well correlated with the low-frequency evidence of measured temperatures in
                the area. You will see in the later lecture that , depending on the approach
                we use to scale (calibrate) the tree-ring variability against the the climate
                series we seek to reconstruct, it can be better to throw away the
                low-frequency information and scale directly against only the equivelent time
                scale climate information. We can discuss this in more detail later.
                For now , hope this answers your question - we need to make this point clear
                because it has wide relevance in the use of various proxy interpretations.
                cheers
                Keith
                At 13:48 07/10/2008, you wrote:
                Keith,
                I'm reading through your Ch5. and have a query regarding the following
                phrase in section 5.5.2:
                "If the required theoretical signal involves long-timescale variability, a
                very conservative approach must be adopted when standarizing..."
                In that context, what is meant by 'theoretical signal'? Or can I removed
                'theoretical' from it and simply think of it in terms of signal and noise
                as was discussed in the lecture?
                Thanks.
                Richard.
                --
                Professor Keith Briffa,
                Climatic Research Unit
                University of East Anglia
                Norwich, NR4 7TJ, U.K.
                Phone: +44-1603-593909
                Fax: +44-1603-507784
                <[4]http://www.cru.uea.ac.uk/cru/people/briffa/>[5]http://www.cru.uea.ac.uk/cr
                u/people/briffa/
                --
                Richard Baldwin
                07878 37 49 64
                <[6]mailto:rich.baldwin@gmail.com>[7]rich.baldwin@gmail.com

          --
          Professor Keith Briffa,
          Climatic Research Unit
          University of East Anglia
          Norwich, NR4 7TJ, U.K.
          Phone: +44-1603-593909
          Fax: +44-1603-507784
          [8]http://www.cru.uea.ac.uk/cru/people/briffa/

     --
     Richard Baldwin
     07878 37 49 64
     [9]rich.baldwin@gmail.com

   --
   Professor Keith Briffa,
   Climatic Research Unit
   University of East Anglia
   Norwich, NR4 7TJ, U.K.

   Phone: +44-1603-593909
   Fax: +44-1603-507784
   [10]http://www.cru.uea.ac.uk/cru/people/briffa/

