date: Wed Dec 20 16:44:41 2006 from: Tim Osborn subject: Re: Choice of model selection for expert elicitation to: Saffron O'Neill looks like a suitable choice to me. Tim At 14:54 18/12/2006, you wrote: Mike (and Tim) I've decided which model to use for the time series component of the elicitation. Tim and I worked this through and after spotting a few - now corrected! - errors, we have come up with the following plot (see the second page of the attachment 'seaice_anncyc')). This provides a more thorough method than simply estimating the model to use 'by eye' - several models (orange, red and pink triangles, light blue and dark purple circle) appear in the bottom left as showing the least deviation from the observed and model mean data. Taking this information and looking at the 'change' graph on page 1, the dotted orange line appears closest in shape to the model mean change for the seasonal cycle pattern (especially in the shoulder seasons of melt and freeze up, when the calculations for the [50%] time series plot to be used will be most affected). I've checked the timeseries for Hudson Bay, as an region I know a little about now from the polar bear lit, and the present day data appears reasonable. Therefore, I will be using the Max Plank echam5 model (unless either of you object to this). As echam5 has more than one model run time series, I will use the first model run for each region for consistency. (I've also attached a pdf showing 3 of the regions, scroll down to the bottom of page 5 for echam5 timeseries). Saffron Tim Osborn wrote: Some further comments: At 17:43 29/11/2006, Mike Hulme wrote: My thoughts on this below .... At 16:20 29/11/2006, Saffron O'Neill wrote: Tim and Mike I'm sorry, I did get my wires crossed on that one. To make things clear, I'll put the maps I need in order as per Mike's email: (a) model mean spatial map of change in [50%] 'ice-free' length in months i.e., (max = 12, min = -12) but this means mostly positive values, i.e., general increases in 'ice-free' length. yes, -12 to +12, but in fact most values look to be between -1 and +6 months (b) The time series - one per key region but preserve IAV - Mike, you have suggested I use the one closest to the model mean here, in which case I need to have figures for the mean for each of these time series sets and perhaps perform some kind of analysis on this to pick the closest model to the mean. Or, as I think Tim is suggesting, should I pick a model by eye that is closest for each regional set to the median value for that region? I would have thought choose the model that in its mean seasonal cycle response (when averaged across the whole Artic) is closest to the model-mean, i.e., choose one model and stick with it (one could choose different models for different regions, but this seems too complicated). I would concur with Mike, that seems simpler to choose one model for all 6 time series. You now have the data for the annual cycle of changes in total sea-ice area over the Arctic from me, so you could use that to select a model that is (a) fairly close to the observed annual cycle in the present-day period; and (b) fairly close to the model-mean change in the annual cycle. I would just use criterion (b) if it results in choosing a model that has the target change, but starts from a poor present-day simulation. (c) Instead of relying on the [50%] value (although this is backed up by the lit. I think it would be good to provide a little more information here, otherwise as you say Tim, the same judgment will be given to a 52% to 47% change as an 80% to 15% change). So instead, a model mean map for Sept and for March of change in sea ice concentration (NOT sea ice extent). If I understand it the scale would be from (near) infinity through zero to -100 with most values on the negative side (i.e., change in concentration in a cell in March from 87% to 63% = -24%, where a change in concentration from 1% to 4% = 400%). Fine, but the colour scale and legend may need skilful choosing. Mike -- you are right about choosing the colour scale/levels carefully if the change is expressed as a percentage of the initial value, i.e.: %change = 100% * (future - present) / present because if present is small then %change can be very large. But if this is simply: change = future - present then the values will be in a more reasonable range. Saffron -- when I've had time to get the individual model changes regridded onto a common grid, ready for averaging into a multi-model mean, perhaps you might want to come over and we can try various plotting options on screen so you can choose what you think conveys the necessary information in the simplest/clearest way for your purposes. It'll probably be Monday or Tuesday next week. Sound ok? Cheers Tim Dr Timothy J Osborn, Academic Fellow Climatic Research Unit School of Environmental Sciences University of East Anglia Norwich NR4 7TJ, UK e-mail: t.osborn@uea.ac.uk phone: +44 1603 592089 fax: +44 1603 507784 web: [1]http://www.cru.uea.ac.uk/~timo/ sunclock: [2]http://www.cru.uea.ac.uk/~timo/sunclock.htm -- --------------------------------------------------------------- Saffron O'Neill (PhD Researcher) Tyndall Centre for Climate Change Research Zuckerman Institute for Connective Environmental Research School of Environmental Sciences University of East Anglia Norwich, NR4 7TJ United Kingdom T: +44 (0) 1603 593 911 F: +44 (0) 1603 593 901 E-mail: s.o-neill@uea.ac.uk Web: [3]http://www.tyndall.ac.uk