Abstract
Abstract. We investigate the identifiability of the climate by limited proxy data. We test a data assimilation approach through perfect model pseudoproxy experiments, using a simple likelihood-based weighting based on the particle filtering process. Our experimental set-up enables us to create a massive 10 000-member ensemble at modest computational cost, thus enabling us to generate statistically robust results. We find that the method works well when data are sparse and imprecise, but in this case the reconstruction has a rather low accuracy as indicated by residual RMS errors. Conversely, when data are relatively plentiful and accurate, the estimate tracks the target closely, at least when considering the hemispheric mean. However, in this case, our prior ensemble size of 10 000 appears to be inadequate to correctly represent the true posterior, and the regional performance is poor. Using correlations to assess performance gives a more encouraging picture, with significant correlations ranging from about 0.3 when data are sparse to values over 0.7 when data are plentiful, but the residual RMS errors are substantial in all cases. Our results imply that caution is required in interpreting climate reconstructions, especially when considering the regional scale, as skill on this basis is markedly lower than on the large scale of hemispheric mean temperature.
Highlights
Reconstructions of climate variation over recent centuries make an important contribution to our understanding of climate change, and in particular help us to place the recent anthropogenically forced changes in the context of natural variability
Numerous reconstructions have been presented for the mean temperature of the Northern Hemisphere, where proxies are most numerous (e.g. Jansen et al, 2007, Fig.6.10), and rather less commonly for global temperature
As in that work, we focus on the Northern Hemisphere where the proxy data are most plentiful
Summary
Reconstructions of climate variation over recent centuries make an important contribution to our understanding of climate change, and in particular help us to place the recent anthropogenically forced changes in the context of natural variability. An alternative approach to climate reconstruction has been developed, in which the proxy data are assimilated into a climate model, generating what is generally referred to as a “reanalysis” of the climate state (Goosse et al, 2006; Widmann et al, 2010) In principle, such an approach could have several notable advantages over a purely statistical method. An additional benefit arises from the temporal relationships embodied in the model: an estimate of the climate state at a given time can be enhanced by data observed both before the synoptic time (as in filtering methods) and even from data observed after this time Such an approach is known as smoothing, but note that. We adopt an identical twin paradigm, in which pseudoproxy observations are generated from a model run (Smerdon, 2012), so as to focus on the methodological aspects and theoretical performance limits
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