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End date: 2019 Ă— Start date: 2011 Ă— Subject: climate variation Ă— Subject: uncertainty analysis Ă— WGL Institute: PIK Ă—

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Now showing 1 - 3 of 3
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    Similarity estimators for irregular and age-uncertain time series
    (MĂĽnchen : European Geopyhsical Union, 2014) Rehfeld, K.; Kurths, J.
    Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60–55% (in the linear case) to 53–42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity contributes less, particularly for the adapted Gaussian-kernel-based estimators and the event synchronization function. The introduced link strength concept summarizes the hypothesis test results and balances the individual strengths of the estimators: while gXCF is particularly suitable for short and irregular time series, gMI and the ESF can identify nonlinear dependencies. ESF could, in particular, be suitable to study extreme event dynamics in paleoclimate records. Programs to analyze paleoclimatic time series for significant dependencies are included in a freely available software toolbox.
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    Estimation of sedimentary proxy records together with associated uncertainty
    (Göttingen : Copernicus GmbH, 2015) Goswami, B.; Heitzig, J.; Rehfeld, K.; Marwan, N.; Anoop, A.; Prasad, S.; Kurths, J.
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    The PMIP4 contribution to CMIP6 - Part 3: The last millennium, scientific objective, and experimental design for the PMIP4 past1000 simulations
    (Göttingen : Copernicus GmbH, 2017) Jungclaus, J.H.; Bard, E.; Baroni, M.; Braconnot, P.; Cao, J.; Chini, L.P.; Egorova, T.; Evans, M.; Fidel González-Rouco, J.; Goosse, H.; Hurtt, G.C.; Joos, F.; Kaplan, J.O.; Khodri, M.; Klein Goldewijk, K.; Krivova, N.; Legrande, A.N.; Lorenz, S.J.; Luterbacher, J.; Man, W.; Maycock, A.C.; Meinshausen, M.; Moberg, A.; Muscheler, R.; Nehrbass-Ahles, C.; Otto-Bliesner, B.I.; Phipps, S.J.; Pongratz, J.; Rozanov, E.; Schmidt, G.A.; Schmidt, H.; Schmutz, W.; Schurer, A.; Shapiro, A.I.; Sigl, M.; Smerdon, J.E.; Solanki, S.K.; Timmreck, C.; Toohey, M.; Usoskin, I.G.; Wagner, S.; Wu, C.-J.; Leng Yeo, K.; Zanchettin, D.; Zhang, Q.; Zorita, E.
    The pre-industrial millennium is among the periods selected by the Paleoclimate Model Intercomparison Project (PMIP) for experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and the fourth phase of the PMIP (PMIP4). The past1000 transient simulations serve to investigate the response to (mainly) natural forcing under background conditions not too different from today, and to discriminate between forced and internally generated variability on interannual to centennial timescales. This paper describes the motivation and the experimental set-ups for the PMIP4-CMIP6 past1000 simulations, and discusses the forcing agents orbital, solar, volcanic, and land use/land cover changes, and variations in greenhouse gas concentrations. The past1000 simulations covering the pre-industrial millennium from 850 Common Era (CE) to 1849 CE have to be complemented by historical simulations (1850 to 2014 CE) following the CMIP6 protocol. The external forcings for the past1000 experiments have been adapted to provide a seamless transition across these time periods. Protocols for the past1000 simulations have been divided into three tiers. A default forcing data set has been defined for the Tier 1 (the CMIP6 past1000) experiment. However, the PMIP community has maintained the flexibility to conduct coordinated sensitivity experiments to explore uncertainty in forcing reconstructions as well as parameter uncertainty in dedicated Tier 2 simulations. Additional experiments (Tier 3) are defined to foster collaborative model experiments focusing on the early instrumental period and to extend the temporal range and the scope of the simulations. This paper outlines current and future research foci and common analyses for collaborative work between the PMIP and the observational communities (reconstructions, instrumental data).