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Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

Chronological uncertainty is a hallmark of the paleoenvironmental sciences and geosciences. While many tools have been made available to researchers to quantify age uncertainties suitable for various settings and assumptions, disparate tools and output formats often discourage integrative approaches. In addition, associated tasks like propagating age-model uncertainties to subsequent analyses, and visualizing the results, have received comparatively little attention in the literature and available software. Here, we describe geoChronR, an open-source R package to facilitate these tasks. geoChronR is built around an emerging data standard (Linked PaleoData, or LiPD) and offers access to four popular age-modeling techniques (Bacon, BChron, OxCal, BAM). The output of these models is used to conduct ensemble data analysis, quantifying the impact of chronological uncertainties on common analyses like correlation, regression, principal component, and spectral analyses by repeating the analysis across a large collection of plausible age models. We present five real-world use cases to illustrate how geoChronR may be used to facilitate these tasks, visualize the results in intuitive ways, and store the results for further analysis, promoting transparency and reusability.

A comprehensive database of paleoclimate records is needed to place recent warming into the longer-term context of natural climate variability. We present a global compilation of quality-controlled, published, temperature-sensitive proxy records extending back 12,000 years through the Holocene. Data were compiled from 679 sites where time series cover at least 4000 years, are resolved at sub-millennial scale (median spacing of 400 years or finer) and have at least one age control point every 3000 years, with cut-off values slackened in data-sparse regions. The data derive from lake sediment (51%), marine sediment (31%), peat (11%), glacier ice (3%), and other natural archives. The database contains 1319 records, including 157 from the Southern Hemisphere. The multi-proxy database comprises paleotemperature time series based on ecological assemblages, as well as biophysical and geochemical indicators that reflect mean annual or seasonal temperatures, as encoded in the database. This database can be used to reconstruct the spatiotemporal evolution of Holocene temperature at global to regional scales, and is publicly available in Linked Paleo Data (LiPD) format. Fil: Kaufman, Darrell. Northern Arizona University.; Estados Unidos Fil: McKay, Nicholas. Northern Arizona University.; Estados Unidos Fil: Routson, Cody. Northern Arizona University.; Estados Unidos Fil: Erb, Michael. Northern Arizona University.; Estados Unidos Fil: Davis, Basil. University Of Lausanne; Suiza Fil: Heiri, Oliver. University Of Basel; Suiza Fil: Jaccard, Samuel. University Of Bern; Suiza Fil: Tierney, Jessica. University of Arizona; Estados Unidos Fil: Dätwyler, Christoph. University Of Bern; Suiza Fil: Axford, Yarrow. Northwestern University; Estados Unidos Fil: Brussel, Thomas. University of Utah; Estados Unidos Fil: Cartapanis, Olivier. University Of Bern; Suiza Fil: Chase, Brian. Universite de Montpellier; Francia Fil: Dawson, Andria. Mount Royal University; Canadá Fil: de Vernal, Anne. Université du Québec a Montreal; Canadá Fil: Engels, Stefan. University of London; Reino Unido Fil: Jonkers, Lukas. University Of Bremen; Alemania Fil: Marsicek, Jeremiah. University of Wisconsin-Madison; Estados Unidos Fil: Moffa Sánchez, Paola. University of Durham; Reino Unido Fil: Morrill, Carrie. University of Colorado; Estados Unidos Fil: Orsi, Anais. Université Paris-Saclay; Francia Fil: Rehfeld, Kira. Heidelberg University; Alemania Fil: Saunders, Krystyna. Australian Nuclear Science And Technology Organisation; Australia Fil: Sommer, Philipp. University Of Lausanne; Suiza Fil: Thomas, Elizabeth. University At Buffalo; Estados Unidos Fil: Tonello, Marcela Sandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Tóth, Mónika. Balaton Limnological Institute; Hungría Fil: Vachula, Richard. Brown University; Estados Unidos Fil: Andreev, Andrei. Alfred Wegener Institut Helmholtz Centre for Polar and Marine Research; Alemania Fil: Bertrand, Sebastien. Ghent University; Bélgica Fil: Massaferro, Julieta. Administración de Parques Nacionales. Parque Nacional "Nahuel Huapi"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

Chronological uncertainty is a hallmark of the paleosciences. While many tools have been made available to researchers to quantify age uncertainties suitable for various settings and assumptions, disparate tools and output formats often discourage integrative approaches. In addition, associated tasks like propagating age model uncertainties to subsequent analyses, and visualizing the results, have received comparatively little attention in the literature and available software. Here we describe GeoChronR, an open-source R package to facilitate these tasks. GeoChronR is built around emerging data standards for the paleosciences (Linked PaleoData, or LiPD), and offers access to four popular age modeling techniques (Bacon, BChron, Oxcal, BAM). The output of these models is used to support ensemble-aware analyses, quantifying the impact of chronological uncertainties on common analyses like age-uncertain correlation, regression, principal component, and spectral analyses. We present five real-world use cases to illustrate how GeoChronR may be used to facilitate these tasks, to visualize the results in intuitive ways, and to store the results for further analysis, promoting transparency and reusability.

A comprehensive database of paleoclimate records is needed to place recent warming into the longer-term context of natural climate variability. We present a global compilation of quality-controlled, published, temperature-sensitive proxy records extending back 12,000 years through the Holocene. Data were compiled from 679 sites where time series cover at least 4000 years, are resolved at sub-millennial scale (median spacing of 400 years or finer) and have at least one age control point every 3000 years, with cut-off values slackened in data-sparse regions. The data derive from lake sediment (51%), marine sediment (31%), peat (11%), glacier ice (3%), and other natural archives. The database contains 1319 records, including 157 from the Southern Hemisphere. The multi-proxy database comprises paleotemperature time series based on ecological assemblages, as well as biophysical and geochemical indicators that reflect mean annual or seasonal temperatures, as encoded in the database. This database can be used to reconstruct the spatiotemporal evolution of Holocene temperature at global to regional scales, and is publicly available in Linked Paleo Data (LiPD) format., Measurement(s)climateTechnology Type(s)digital curationFactor Type(s)temporal interval • geographic location • proxy typeSample Characteristic - Environmentclimate systemSample Characteristic - LocationEarth (planet) Machine-accessible metadata file describing the reported data: https://www.ncdc.noaa.gov/paleo/study/27330

Understanding the mechanisms behind any changes in the climate system often requires establishing the timing of events imprinted on the geological record. However, these proxy records are prone to large uncertainties, which may preclude meaningful conclusions about the relative timing of events. In this study, we put forth a framework to estimate the uncertainty in phase relationships inferred from marine sedimentary records. The novelty of our method lies in the accounting of the various sources of uncertainty inherent to paleoclimate reconstruction and timing analysis. Specifically, we use a Monte-Carlo process allowing sampling of possible realizations of the time series as functions of uncertainties in time, the climate proxy, and the identification of the termination timing. We then apply this technique to 15 published sea surface temperature records from the equatorial Pacific to evaluate whether we observed any significant changes in the termination timing between the East and the West. We find that the uncertainty on the relative timing estimates is on the order of several thousand years, and mainly stems from age model uncertainty (90%). However, even small differences in mean termination timings can be detected with a sufficiently large number of samples. Improvements in the dating of sediment records provide an opportunity to reduce uncertainty in studies of this kind.

Scientific software is crucial for understanding, reusing and reproducing results in computational sciences. Software is often stored in code repositories, which may contain human readable instructions necessary to use it and set it up. However, a significant amount of time is usually required to understand how to invoke a software component, prepare data in the format it requires, and use it in combination with other software. In this paper we introduce OKG-Soft, an open knowledge graph that describes scientific software in a machine readable manner. OKG-Soft includes: 1) an ontology designed to describe software and the specific data formats it uses; 2) an approach to publish software metadata as an open knowledge graph, linked to other Web of Data objects; and 3) a framework to annotate, query, explore and curate scientific software metadata. OKG-Soft supports the FAIR principles of findability, accessibility, interoperability, and reuse for software. We demonstrate the benefits of OKG-Soft with two applications: a browser for understanding scientific models in the environmental and social sciences, and a portal to combine climate, hydrology, agriculture, and economic software models.

Climate records exhibit scaling behavior with large exponents, resulting in larger fluctuations at longer timescales. It is unclear whether climate models are capable of simulating these fluctuations, which draws into question their ability to simulate such variability in the coming decades and centuries. Using the latest simulations and data syntheses, we find agreement for spectra derived from observations and models on timescales ranging from interannual to multimillennial. Our results confirm the existence of a scaling break between orbital and annual peaks, occurring around millennial periodicities. That both simple and comprehensive ocean-atmosphere models can reproduce these features suggests that long-range persistence is a consequence of the oceanic integration of both gradual and abrupt climate forcings. This result implies that Holocene low-frequency variability is partly a consequence of the climate system's integrated memory of orbital forcing. We conclude that climate models appear to contain the essential physics to correctly simulate the spectral continuum of global-mean temperature; however, regional discrepancies remain unresolved. A critical element of successfully simulating suborbital climate variability involves, we hypothesize, initial conditions of the deep ocean state that are consistent with observations of the recent past.

Understanding the interactions between natural processes and human activities poses major challenges as it requires the integration of models and data across disparate disciplines. It typically takes many months and even years to create valid end-to-end simulations as different models need to be configured in consistent ways and generate data that is usable by other models. MINT is a novel framework for model integration that captures extensive knowledge about models and data and aims to automatically compose them together. MINT guides a user to pose a well-formed modeling question, select and configure appropriate models, find and prepare appropriate datasets, compose data and models into end-to-end workflows, run the simulations, and visualize the results. MINT currently includes hydrology, agriculture, and socioeconomic models.

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. ©2019. American Geophysical Union. All Rights Reserved., Code and data to reproduce the figures of this article are available on GitHub and released on Zenodo (doi:10.5281/zenodo.3165019). Definition of properties and recommendations are summarized here: http://wiki.linked.earth/PaCTS_v1.0 . This work was supported by the National Science Foundation through the EarthCube Program with Grant ICER‐1541029. Feedback solicitation on the standard was facilitated by the Past Global Changes (PAGES) organization. The 2016 workshop on Paleoclimate Data Standards was hosted by the World Data Service for Paleoclimatology (WDS/NOAA‐Paleo), and the participation of international attendees was made possible by a PAGES travel grant. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Oxygen minimum zones (OMZs), located below highly productive marine regions, are sites of microbially mediated denitrification and biogeochemical cycling that have global significance. The intensity of OMZs fluctuates naturally; however, the degree of these fluctuations and a comprehensive understanding of the factors that drive these fluctuations on decadal to centennial time scales is lacking. Our high-resolution (near-annual) record of δ15Nsed from laminated sediments at the Pescadero Slope in the Gulf of California (eastern tropical North Pacific) fluctuates between maximum values of 10.5‰ and minimum values of 8.0‰ over the past 1200 years. An analysis of the relationship between δ15NO3− and [O2] in the water column suggests that the observed range of δ15Nsed values is equivalent to an approximately 8 µM fluctuation in O2 content and that these changes can occur in less than 25 years. Our findings show that the OMZ typically intensifies quickly and contracts gradually; the average rate of OMZ intensification (−0.24 µM O2/yr) is twice as fast as the rate of OMZ reoxygenation. Spectral analyses of the δ15Nsed record and Br/Cl counts, with the latter are used as a proxy for organic carbon preservation, suggest that the Pacific Decadal Oscillation and the Suess (deVries) solar cycle (solar irradiance) may influence the intensity of the OMZ and carbon production/export during the late Holocene. Coherence between δ15Nsed and weight percent organic carbon also suggests that similar mechanisms influence both OMZ fluctuations and variation in organic carbon production/export.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Traditional approaches to ontology development have a large lapse between the time when a user using the ontology has found a need to extend it and the time when it does get extended. For scientists, this delay can be weeks or months and can be a significant barrier for adoption. We present a new approach to ontology development and data annotation enabling users to add new metadata properties on the fly as they describe their datasets, creating terms that can be immediately adopted by others and eventually become standardized. This approach combines a traditional, consensus-based approach to ontology development, and a crowdsourced approach where expert users (the crowd) can dynamically add terms as needed to support their work. We have implemented this approach as a socio-technical system that includes: (1) a crowdsourcing platform to support metadata annotation and addition of new terms, (2) a range of social editorial processes to make standardization decisions for those new terms, and (3) a framework for ontology revision and updates to the metadata created with the previous version of the ontology. We present a prototype implementation for the Paleoclimate community, the Linked Earth Framework, currently containing 700 datasets and engaging over 50 active contributors. Users exploit the platform to do science while extending the metadata vocabulary, thereby producing useful and practical metadata.

The assessment of age uncertainty in stratigraphically aligned records is a pressing need in paleoceanographic research. The alignment of ocean sediment cores is used to develop mutually consistent age models for climate proxies and is often based on the δ18O of calcite from benthic foraminifera, which records a global ice volume and deep water temperature signal. To date, δ18O alignment has been performed by manual, qualitative comparison or by deterministic algorithms. Here we present a hidden Markov model (HMM) probabilistic algorithm to find 95% confidence bands for δ18O alignment. This model considers the probability of every possible alignment based on its fit to the δ18O data and transition probabilities for sedimentation rate changes obtained from radiocarbon-based estimates for 37 cores. Uncertainty is assessed using a stochastic back trace recursion to sample alignments in exact proportion to their probability. We applied the algorithm to align 35 late Pleistocene records to a global benthic δ18O stack and found that the mean width of 95% confidence intervals varies between 3 and 23 kyr depending on the resolution and noisiness of the record's δ18O signal. Confidence bands within individual cores also vary greatly, ranging from ~0 to >40 kyr. These alignment uncertainty estimates will allow researchers to examine the robustness of their conclusions, including the statistical evaluation of lead-lag relationships between events observed in different cores. Key Points Hidden Markov model estimates uncertainty in benthic oxygen isotope alignmentsRadiocarbon-based estimates of sedimentation rate variabilityBayesian statistics: alignment confidence band, tests of lead-lag relationships

evaluate the relative strength/frequency of El Nino and La Nina events. In contrast to previous studies, we use robust and resistant statistics to quantify the spread and symmetry of the d 18 O distributions; an approach motivated by the relatively small sample size and the presence of outliers. Furthermore, we use a pseudo‐proxy approach to investigate the effects of the different paleo‐environmental factors on the statistics of the d 18 O distributions, which could bias the paleo‐ENSO reconstruction. We find no systematic difference in the magnitude/strength of ENSO during the Northern Hemisphere MCA or LIA. However, our results suggest that ENSO during the MCA was skewed toward stronger/more frequent La Nina than El Nino, an observation consistent with the medieval megadroughts documented from sites in western North America.

[1] An absolute dated speleothem oxygen isotope (δ 18 O) record from northeastern Peru documents monsoon precipitation variability over northern South America during the past that 1000 years and indicates the annual precipitation in the 15th through the 18th centuries, the so-called Little Ice Age (LIA), was on average ∼10% higher than during the 20th century. Over the 20th century recurrent modes of seasonal rainfall variability across northern South America were associated with discrete sea surface temperature anomaly patterns within the Atlantic and Pacific Oceans. Calling upon these SST-rainfall teleconnectivity patterns, and paleo-SST reconstructions that span the past 8 centuries, higher annual rainfall across northern South America during the LIA is attributed to cooler boreal spring SSTs in the tropical North Atlantic. Weaker co-variance between north Atlantic SSTs and the South American Monsoon System (SAMS) rainfall during the 20th century suggests that ENSO has become a more dominant influence than it was during the LIA.