Sedproxy: a forward model for sediment archived climate proxies

Dolman, Andrew M.; Laepple, Thomas

doi:10.5194/cp-2018-13

Cited by 29 publications

(41 citation statements)

References 48 publications

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“…Simulation based forward modelling approaches such as sedproxy (Dolman and Laepple, 2018) and PRYSM (Dee et al, 2015) could also be used to estimate these quantities by generating and summarizing over many simulated pseudo-proxy records. The advantages of PSEM are that it provides an analytic understanding of the timescale dependence of error components while retaining the mechanistic understanding of the proxy generating process and can make these uncertainty estimates rapidly for large sets of parameters.…”

Section: Discussionmentioning

confidence: 99%

“…The PSEM version proposed here includes the sediment proxy processes described earlier for the proxy forward model sedproxy (Dolman and Laepple, 2018) and represents a trade-off between complexity and completeness. For example, the interaction of seasonality in the recording and climate signal is the only slowly varying process so far included.…”

Section: Discussionmentioning

confidence: 99%

“…In this section we illustrate the error spectrum framework for the specific example of temperature related proxies in the shells of planktic foraminifera recovered from marine sediment cores. The major processes contributing uncertainty to these proxy records have been explored using physically motivated proxy forward models that simulate pseudo-proxies from an assumed true input climate (Laepple and Huybers, 2013;Dolman and Laepple, 2018). These processes include seasonality in the creation of proxy signal carriers (e.g.…”

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confidence: 99%

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Estimating the timescale-dependent uncertainty of paleoclimate records – a spectral approach. Part II: Application and interpretation

Dolman

Kunz

Groeneveld

et al. 2020

Preprint

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Abstract. Proxy climate records are an invaluable source of information about the earth’s climate prior to the instrumental record. The temporal- and spatial-coverage of records continues to increase, however, these records of past climate are associated with significant uncertainties due to non-climate processes that influence the recorded and measured proxy values. Generally, these uncertainties are timescale-dependent and correlated in time. Accounting for structure in the errors is essential to providing realistic error estimates for smoothed or stacked records, detection of anomalies and identifying trend, but this structure is seldom accounted for. In the first of these companion articles we outlined a theoretical framework for handling proxy uncertainties by deriving the power spectrum of proxy error components from which it is possible to obtain timescale-dependent error estimates. Here in part II, we demonstrate the practical application of this theoretical framework using the example of marine sediment cores. We consider how to obtain estimates for the required parameters and give examples of the application of this approach for typical marine sediment proxy records. Our new approach of estimating and providing timescale-dependent proxy errors overcomes the limitations of simplistic single value error estimates. We aim to provide the conceptual basis for a more quantitative use of paleo-records for applications such as model-data comparison, regional and global synthesis of past climate states and data assimilation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Estimating the timescale-dependent uncertainty of paleoclimate records – a spectral approach. Part II: Application and interpretation

Dolman

Kunz

Groeneveld

et al. 2020

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“…To investigate for the presence of such artefacts, we employed the Δ 14 C-enabled, single-specimen SEdiment AccuMUlation Simulator (SEAMUS) (Lougheed, 2019). This model uses a similar understanding of bioturbation as included in existing bioturbation models (Trauth, 2013;Dolman and Laepple, 2018), but differs in that it explicitly simulates the accumulation and bioturbation of single foraminifera, each with individually assigned 14 C activities, to create a synthetic sediment archive history. Subsequently, current https://doi.org/10.5194/gchron-2019-10 Preprint.…”

Section: Introductionmentioning

confidence: 99%

Re-evaluating 14C dating accuracy in deep-sea sediment archives

Lougheed¹,

Ascough

Dolman

et al. 2019

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Abstract. The current geochronological state-of-the-art for applying the radiocarbon (14C) method to deep-sea sediment archives lacks key information on sediment bioturbation. Here, we apply a sediment accumulation model that simulates the sedimentation and bioturbation of millions of foraminifera, whereby realistic 14C activities (i.e. from a 14C calibration curve) are assigned to each single foraminifera based on its simulation timestep. We find that the normal distribution of 14C age typically used to represent discrete-depth sediment intervals (based on the reported laboratory 14C age and measurement error) is unlikely to be a faithful reflection of the actual 14C age distribution for a specific depth interval. We also find that this deviation from the actual 14C age distribution is greatly amplified during the calibration process. We find a systematic underestimation of total geochronological error in many cases (by up to thousands of years), as well as the generation of age-depth artefacts in downcore calibrated median age. Specifically, we find that even in the case of <q>perfect</q> simulated sediment archive scenarios, whereby sediment accumulation rate (SAR), bioturbation depth, reservoir age and species abundance are all kept constant, the 14C dating and calibration process generates temporally dynamic median age-depth artefacts, on the order of hundreds of years &#8211; even in the case of high SAR scenarios of 40&#8201;cm&#8201;ka&#8722;1 and 60&#8201;cm&#8201;ka&#8722;1. Such age-depth artefacts can be especially pronounced during periods corresponding to dynamic changes in the Earth's &#916;14C, where single foraminifera of varying 14C activity can be incorporated into single discrete-depth sediment intervals. In certain SAR scenarios, a discrete depth&#8217;s true median age can consistently fall outside the 95.45&#8201;% calibrated age range predicted by the 14C dating and calibration process. Our findings suggest the possibility of 14C-derived age-depth artefacts in the literature: since age-depth artefacts are likely to coincide with large-scale changes in global &#916;14C, which themselves can coincide with large-scale changes in global climate (such as the last deglaciation), 14C-derived age-depth artefacts may have been previously been (partially) misinterpreted as due to changes in global climate. Our study highlights the need for the development of improved deep-sea sediment 14C calibration techniques that include an a priori representation of bioturbation for multi-specimen samples.

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“…Paleoclimate records can have errors arising from many different sources: biological effects (e.g., Elderfield et al, 2002;Adkins et al, 2003), aliasing onto seasonal cycles (Wunsch, 2000;Fairchild et al, 2006;Dolman and Laepple, 2018), spatial representativeness (Van Sebille et al, 2015), proxy-climate calibrations (e.g., Tierney and Tingley, 2014), and instrument errors, to name a few. This paper focuses on errors from temporal representativeness (TR), which we define as the degree to which a measurement averaging over one time interval can be used to represent a second, target time interval.…”

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confidence: 99%

How large are temporal representativeness errors in paleoclimatology?

Amrhein¹

2019

Preprint

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Abstract. Ongoing work in paleoclimate reconstruction prioritizes understanding the origins and magnitudes of errors that arise when comparing models and data. One class of such errors arises from assumptions of proxy temporal representativeness – broadly, the time scales over which paleoclimate proxy measurements are associated with climate variables. In the case of estimating time mean values over an interval, errors can arise when the time interval over which data are averaged and the interval that is being studied have different lengths, or if those intervals are offset from one another in time. Because it is challenging to tailor proxy measurements to precise time intervals, such errors are expected to be common in model-data and data-data comparisons, but how large and prevalent they are is unclear. The goal of this work is to provide a framework for first-order quantification of temporal representativity errors and to study the interacting effects of sampling error, archive smoothing (e.g. by bioturbation in sediment cores), chronological offsets and errors (e.g. arising from radiocarbon dating), and the spectral character of the climate process being sampled. In some cases, particularly for small values of target intervals τx relative to sample intervals τy, errors can be large relative to signals of interest. Errors from mismatches in τx and τy can have magnitudes comparable to those from chronological uncertainty. Archive smoothing can reduce sampling errors by acting as an anti-aliasing filter, but destroys high-frequency climate information. An extension of the approach to paleoclimate time series, which are sequences of time-average values, shows that measurement intervals shorter than the spacing between samples lead to errors, absent compensating effects from archive smoothing. Including these sources of uncertainty will improve accuracy in model-data comparisons and data comparisions and syntheses. Moreover, because sampling procedures emerge as important parameters in uncertainty quantification, reporting salient information about how records are processed and assessments of archive smoothing and chronological uncertainties alongside published data is important to be able to use records to their maximum potential in paleoclimate reconstruction and data assimilation.

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Sedproxy: a forward model for sediment archived climate proxies

Cited by 29 publications

References 48 publications

Estimating the timescale-dependent uncertainty of paleoclimate records – a spectral approach. Part II: Application and interpretation

Estimating the timescale-dependent uncertainty of paleoclimate records – a spectral approach. Part II: Application and interpretation

Re-evaluating <sup>14</sup>C dating accuracy in deep-sea sediment archives

How large are temporal representativeness errors in paleoclimatology?

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Sedproxy: a forward model for sediment archived climate proxies

Cited by 29 publications

References 48 publications

Estimating the timescale-dependent uncertainty of paleoclimate records – a spectral approach. Part II: Application and interpretation

Estimating the timescale-dependent uncertainty of paleoclimate records – a spectral approach. Part II: Application and interpretation

Re-evaluating &lt;sup&gt;14&lt;/sup&gt;C dating accuracy in deep-sea sediment archives

How large are temporal representativeness errors in paleoclimatology?

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Re-evaluating <sup>14</sup>C dating accuracy in deep-sea sediment archives