Dataset Information

A data integration framework for spatial interpolation of temperature observations using climate model data.

ABSTRACT: Meteorological station measurements are an important source of information for understanding the weather and its association with risk, and are vital in quantifying climate change. However, such data tend to lack spatial coverage and are often plagued with flaws such as erroneous outliers and missing values. Alternative meteorological data exist in the form of climate model output that have better spatial coverage, at the expense of bias. We propose a probabilistic framework to integrate temperature measurements with climate model (reanalysis) data, in a way that allows for biases and erroneous outliers, while enabling prediction at any spatial resolution. The approach is Bayesian which facilitates uncertainty quantification and simulation based inference, as illustrated by application to two countries from the Middle East and North Africa region, an important climate change hotspot. We demonstrate the use of the model in: identifying outliers, imputing missing values, non-linear bias correction, downscaling and aggregation to any given spatial configuration.

SUBMITTER: Economou T

PROVIDER: S-EPMC9838203 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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A data integration framework for spatial interpolation of temperature observations using climate model data.

Economou Theo T Lazoglou Georgia G Tzyrkalli Anna A Constantinidou Katiana K Lelieveld Jos J

PeerJ 20230110

Meteorological station measurements are an important source of information for understanding the weather and its association with risk, and are vital in quantifying climate change. However, such data tend to lack spatial coverage and are often plagued with flaws such as erroneous outliers and missing values. Alternative meteorological data exist in the form of climate model output that have better spatial coverage, at the expense of bias. We propose a probabilistic framework to integrate tempera ...[more]

PMID: 36643648

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Project description:BackgroundSystems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights.ResultsIn order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified.ConclusionsOur SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.

Project description:BackgroundThe Global Calculator is an open-source model of the world's energy, land, and food systems. It is a pioneering online calculator to project the impact of interventions to mitigate climate change on global temperature. A few studies have been conducted to evaluate the health co-benefits of climate change mitigation, though they are still fragmentary.ObjectivesOur objectives are to identify which sectors could yield the greatest results in terms of climate change mitigation and suggest whether existing evidence could be used to weight mitigation actions based on their ancillary impacts on human health or health co-benefits.MethodsUsing the International Energy Agency (IEA) 4DS scenario as a referent (i.e., the "4-degree Celsius increase scenario"), we simulated changes in different policy "levers" (encompassing 43 potential technological and behavioral interventions, grouped by 14 sectors) and assessed the relative importance of each lever in terms of changes in annual greenhouse gas emissions in 2050 and cumulative emissions by 2100. In addition, we examined existing estimates for the health co-benefits associated with different interventions, using evidence from the Lancet Pathfinder and four other tools.DiscussionOur simulations suggest that-after accounting for demographic change-transition from fossil fuels to renewables and changes in agriculture, forestry, land use, and food production are key sectors for climate change mitigation. The role of interventions in other sectors, like carbon capture and storage (CCS) or nuclear power, is more modest. Our work also identifies mitigation actions that are likely to have large health co-benefits, including shifts to renewable energy and changes in land use as well as dietary and travel behaviors. In conclusion, some of the sectors/interventions which have been at the center of policy debate (e.g., CCS or nuclear power) are likely to be far less important than changes in areas such as dietary habits or forestry practices by 2050. https://doi.org/10.1289/EHP14906.

Dataset Information

A data integration framework for spatial interpolation of temperature observations using climate model data.

Publications

A data integration framework for spatial interpolation of temperature observations using climate model data.

Similar Datasets

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