Testing a Particle Filter to Reconstruct Climate Changes Over the Past Centuries

Dubinkina, Svetlana; Goosse, Hugues; Sallaz-Damaz, Yoann; Crespin, Elisabeth; Crucifix, Michel

doi:10.1142/s0218127411030763

Cited by 62 publications

(76 citation statements)

References 21 publications

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“…Instead of choosing the single simulation with the best fit to the data, however, we calculate a weight for each member of the full ensemble, which generates a probabilistic posterior distribution. While Goosse et al (2006Goosse et al ( , 2010) also used a degenerate particle filtering approach in which the best simulation was selected, Dubinkina et al (2011) implemented a probabilistic approach more similar to ours, but using a far more dense network of recent observations. In contrast to the latter experiments, we make no allowance for model error, since it is absent in our identical twin experiments.…”

Section: Methodsmentioning

confidence: 99%

“…Further, we also consider the viability of particle-based methods (in particular, in respect of the required ensemble size) to undertake this task. Our investigations are complementary to those of Dubinkina et al (2011) who used a more extensive data set based on the recent observational period. We adopt an identical twin paradigm, in which pseudoproxy observations are generated from a model run (Smerdon, 2012), so as to focus specifically on the methodological aspects and theoretical performance limits.…”

Section: Introductionmentioning

confidence: 90%

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Identification of climatic state with limited proxy data

Annan

Hargreaves

2012

Clim. Past

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

Identification of climatic state with limited proxy data

Annan

Hargreaves

2012

Clim. Past

View full text Add to dashboard Cite

show abstract

“…So far, several very diverse paleo-DA schemes have been investigated, including pattern nudging (von Storch et al, 2000), forcing singular vectors (Barkmeijer et al, 2003;van der Schrier and Barkmeijer, 2005), 4D-Var (Paul and Schäfer-Neth, 2005;Kurahashi-Nakamura et al, 2014), particle filters (Annan and Hargreaves, 2012;Dubinkina et al, 2011;Dubinkina and Goosse, 2013;Mathiot et al, 2013;Matsikaris et al, 2015) and ensemble Kalman filter techniques (EnKF; Huntley and Hakim, 2010;Bhend et al, 2012;Pendergrass et al, 2012;Steiger et al, 2014; see Hughes and Ammann, 2009;Widmann et al, 2010;Hakim et al, 2013 for further references).…”

Section: Introductionmentioning

confidence: 99%

Assimilation of pseudo-tree-ring-width observations into an atmospheric general circulation model

et al. 2017

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Abstract. Paleoclimate data assimilation (DA) is a promising technique to systematically combine the information from climate model simulations and proxy records. Here, we investigate the assimilation of tree-ring-width (TRW) chronologies into an atmospheric global climate model using ensemble Kalman filter (EnKF) techniques and a processbased tree-growth forward model as an observation operator. Our results, within a perfect-model experiment setting, indicate that the "online DA" approach did not outperform the "off-line" one, despite its considerable additional implementation complexity. On the other hand, it was observed that the nonlinear response of tree growth to surface temperature and soil moisture does deteriorate the operation of the timeaveraged EnKF methodology. Moreover, for the first time we show that this skill loss appears significantly sensitive to the structure of the growth rate function, used to represent the principle of limiting factors (PLF) within the forward model. In general, our experiments showed that the error reduction achieved by assimilating pseudo-TRW chronologies is modulated by the magnitude of the yearly internal variability in the model. This result might help the dendrochronology community to optimize their sampling efforts.

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“…LOVECLIM results have been constrained to follow a proxybased sea ice reconstruction through a process of assimilation, using a particle filter with resampling (van Leeuwen, et al (2012) 2009; Dubinkina et al, 2011), in a similar manner as in several recent studies (e.g. Goosse et al, 2012;Mathiot et al, 2013;Mairesse et al, 2013).…”

Section: Data Assimilation Methodsmentioning

confidence: 99%

Model–data comparison and data assimilation of mid-Holocene Arctic sea ice concentration

et al. 2014

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Abstract.The consistency between new quantitative reconstructions of Arctic sea ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The response of the models mainly follows the increase in summer insolation, modulated to a limited extent by changes in atmospheric circulation. This leads to differences between regions in the models that are smaller than in the reconstruction. It is, however, impossible to precisely assess the models' skills because the sea ice concentration changes at the mid-Holocene are small in both the reconstructions and the models and of the same order of magnitude as the reconstruction uncertainty. Performing simulations with data assimilation using the model LOVECLIM amplifies the regional differences and improves the modeldata agreement as expected. This is mainly achieved through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin, inducing an increase in the ice concentration in the Barents and Chukchi seas. This underlines the potential role of atmospheric circulation in explaining the reconstructed changes during the Holocene.

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Testing a Particle Filter to Reconstruct Climate Changes Over the Past Centuries

Cited by 62 publications

References 21 publications

Identification of climatic state with limited proxy data

Identification of climatic state with limited proxy data

Assimilation of pseudo-tree-ring-width observations into an atmospheric general circulation model

Model–data comparison and data assimilation of mid-Holocene Arctic sea ice concentration

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