date: Fri Oct 24 17:00:26 2008 from: Keith Briffa subject: Re: Question on climate reconstructions, and a query on model to: "Richard Baldwin" 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/