Preface: Advances in paleoclimate data synthesis and analysis of associated uncertainty: towards data–model integration to understand the climate

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks in our ability to process information have reduced our capacity to fully exploit the growing volume and variety of data. Here, we take a critical look at the information infrastructure that connects modeling and measurement efforts, and propose a roadmap that accelerates production of new knowledge. We propose that community cyberinfrastructure tools can help mend the divisions between empirical research and modeling, and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, transparent tools that integrate the expertise of the whole community, not just a clique of ‘modelers’. This roadmap focuses on five key opportunities for community tools: the underlying backbone to community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is key to meeting the pressing needs of science and society in the 21st century.

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LGM hosing approach to Heinrich Event 1: results and perspectives from data–model integration using water isotopes

Quaternary Science Reviews ◽

10.1016/j.quascirev.2014.07.020 ◽

2014 ◽

Vol 106 ◽

pp. 247-261 ◽

Cited By ~ 17

Author(s):

Didier M. Roche ◽

Didier Paillard ◽

Thibaut Caley ◽

Claire Waelbroeck

Keyword(s):

Data Model ◽

Water Isotopes ◽

Model Integration ◽

Heinrich Event ◽

Heinrich Event 1

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Querying Multidimensional Data

Encyclopedia of Information Science and Technology, First Edition ◽

10.4018/978-1-59140-553-5.ch423 ◽

2005 ◽

pp. 2392-2396

Author(s):

Leonardo Tininini

Keyword(s):

Data Model ◽

Data Cube ◽

Level Of Detail ◽

Multidimensional Data ◽

Model Integration ◽

Integration Policy ◽

Multidimensional Database ◽

The Core ◽

Core Concepts ◽

Logical Data

A powerful, easy-to-use querying environment is without doubt one of the most important components in a multidimensional database. Its effectiveness is influenced by many aspects, both logical (data model, integration, policy of view materialization, etc.) and physical (multidimensional or relational storage, indexes, etc.). Multidimensional querying is often based on the core concepts of multidimensional data modeling, namely the metaphor of the data cube and the concepts of facts, measures and dimensions (Agrawal, Gupta, & Sarawagi, 1997; Gyssens & Lakshmanan, 1997). In contrast to conventional transactional environments, multidimensional querying is often an exploratory process, performed by navigating along dimensions and measures, increasing/decreasing the level of detail and focusing on specific subparts of the cube that appear “promising” for the required information.

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Paleocene-Eocene Data Model Integration

Eos ◽

10.1029/2007eo350007 ◽

2007 ◽

Vol 88 (35) ◽

pp. 344

Author(s):

Cindy Shellito ◽

Jean-Francois Lamarque ◽

J. Kiehl

Keyword(s):

Data Model ◽

Model Integration

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Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques

Geoscientific Model Development ◽

10.5194/gmd-12-1791-2019 ◽

2019 ◽

Vol 12 (5) ◽

pp. 1791-1807 ◽

Cited By ~ 7

Author(s):

Dan Lu ◽

Daniel Ricciuto

Keyword(s):

Machine Learning ◽

Data Model ◽

Large Scale ◽

Surrogate Modeling ◽

Optimization Techniques ◽

Bayesian Optimization ◽

Model Integration ◽

Model Parameters ◽

Earth System ◽

Surrogate System

Abstract. Improving predictive understanding of Earth system variability and change requires data–model integration. Efficient data–model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a large-scale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations. In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fast-to-evaluate surrogate system of model outputs over large spatial and temporal domains. We first use singular value decomposition to reduce the output dimensions and then use Bayesian optimization techniques to generate an accurate neural network surrogate model based on limited ESM simulation samples. Our machine-learning-based surrogate methods can build and evaluate a large surrogate system of many variables quickly. Thus, whenever the quantities of interest change, such as a different objective function, a new site, and a longer simulation time, we can simply extract the information of interest from the surrogate system without rebuilding new surrogates, which significantly reduces computational efforts. We apply the proposed method to a regional ecosystem model to approximate the relationship between eight model parameters and 42 660 carbon flux outputs. Results indicate that using only 20 model simulations, we can build an accurate surrogate system of the 42 660 variables, wherein the consistency between the surrogate prediction and actual model simulation is 0.93 and the mean squared error is 0.02. This highly accurate and fast-to-evaluate surrogate system will greatly enhance the computational efficiency of data–model integration to improve predictions and advance our understanding of the Earth system.

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