ALBERT

Description: The progress of science is tied to the standardization of measurements, instruments, and data. This is especially true in the Big Data age, where analyzing large data volumes critically hinges on the data being standardized. Accordingly, the lack of community‐sanctioned data standards in paleoclimatology has largely precluded the benefits of Big Data advances in the field. Building upon recent efforts to standardize the format and terminology of paleoclimate data, this article describes the Paleoclimate Community reporTing Standard (PaCTS), a crowdsourced reporting standard for such data. PaCTS captures which information should be included when reporting paleoclimate data, with the goal of maximizing the reuse value of paleoclimate data sets, particularly for synthesis work and comparison to climate model simulations. Initiated by the LinkedEarth project, the process to elicit a reporting standard involved an international workshop in 2016, various forms of digital community engagement over the next few years, and grassroots working groups. Participants in this process identified important properties across paleoclimate archives, in addition to the reporting of uncertainties and chronologies; they also identified archive‐specific properties and distinguished reporting standards for new versus legacy data sets. This work shows that at least 135 respondents overwhelmingly support a drastic increase in the amount of metadata accompanying paleoclimate data sets. Since such goals are at odds with present practices, we discuss a transparent path toward implementing or revising these recommendations in the near future, using both bottom‐up and top‐down approaches.

Description: Several methods currently exist to quantitatively reconstruct palaeoclimatic variables from fossil botanical data. Of these, pdf-based (probability density functions) methods have proven valuable as they can be applied to a wide range of plants assemblages. Most commonly applied to fossil pollen data, their performance, however, can be limited by the taxonomic resolution of the pollen data, as many species may belong to a given pollen-type. Consequently, the climate information associated with different species cannot sometimes not be precisely identified, resulting less accurate reconstructions. This can become particularly problematic in regions of high biodiversity. In this paper, we propose a novel pdf-based method that takes into account the different climatic requirements of each species constituting the broader pollen-type. Pdfs are fitted in two successive steps, with parametric pdfs fitted first for each species, and then a combination of those individual species pdfs into a broader single pdf to represent the pollen-type as a unit. A climate value for the pollen assemblage is estimated from the likelihood function obtained after the multiplication of the pollen-type pdfs, with each being weighted according to its pollen percentage. To test the robustness of the method, we have applied the method to southern Africa as a regional case study, and reconstructed a suite of climatic variables based on extensive botanical data derived from herbarium collections. The reconstructions proved to be accurate for both temperature and precipitation. Predictable exceptions were areas that experience conditions at the extremes of the regional climatic spectra. Importantly, the accuracy of the reconstructed values is independent from the vegetation type where the method is applied or the number of species used. The method used in this study is publicly available in a software package entitled CREST (Climate REconstruction SofTware) and will provide the opportunity to reconstruct reliable quantitative estimates of climatic variables even in areas with high geographical and botanical diversity.

Description: Several methods currently exist to quantitatively reconstruct palaeoclimatic variables from fossil botanical data. Of these, probability density function (PDF)-based methods have proven valuable as they can be applied to a wide range of plant assemblages. Most commonly applied to fossil pollen data, their performance, however, can be limited by the taxonomic resolution of the pollen data, as many species may belong to a given pollen type. Consequently, the climate information associated with different species cannot always be precisely identified, resulting in less-accurate reconstructions. This can become particularly problematic in regions of high biodiversity. In this paper, we propose a novel PDF-based method that takes into account the different climatic requirements of each species constituting the broader pollen type. PDFs are fitted in two successive steps, with parametric PDFs fitted first for each species and then a combination of those individual species PDFs into a broader single PDF to represent the pollen type as a unit. A climate value for the pollen assemblage is estimated from the likelihood function obtained after the multiplication of the pollen-type PDFs, with each being weighted according to its pollen percentage. To test its performance, we have applied the method to southern Africa as a regional case study and reconstructed a suite of climatic variables (e.g. winter and summer temperature and precipitation, mean annual aridity, rainfall seasonality). The reconstructions are shown to be accurate for both temperature and precipitation. Predictable exceptions were areas that experience conditions at the extremes of the regional climatic spectra. Importantly, the accuracy of the reconstructed values is independent of the vegetation type where the method is applied or the number of species used. The method used in this study is publicly available in a software package entitled CREST (Climate REconstruction SofTware) and will provide the opportunity to reconstruct quantitative estimates of climatic variables even in areas with high geographical and botanical diversity.