The Climate Change Knowledge Graph: Supporting Climate Services
- URL: http://arxiv.org/abs/2602.19786v1
- Date: Mon, 23 Feb 2026 12:42:05 GMT
- Title: The Climate Change Knowledge Graph: Supporting Climate Services
- Authors: Miguel Ceriani, Fiorela Ciroku, Alessandro Russo, Massimiliano Schembri, Fai Fung, Neha Mittal, Vito Trianni, Andrea Giovanni Nuzzolese,
- Abstract summary: The Climate Change Knowledge Graph is designed to integrate diverse data sources related to climate simulations into a coherent knowledge graph.<n>This innovative resource allows for executing complex queries involving climate models, simulations, variables, configurations,temporal domains, and granularities.
- Score: 33.331299436929946
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Climate change impacts a broad spectrum of human resources and activities, necessitating the use of climate models to project long-term effects and inform mitigation and adaptation strategies. These models generate multiple datasets by running simulations across various scenarios and configurations, thereby covering a range of potential future outcomes. Currently, researchers rely on traditional search interfaces and APIs to retrieve such datasets, often piecing together information from metadata and community vocabularies. The Climate Change Knowledge Graph is designed to address these challenges by integrating diverse data sources related to climate simulations into a coherent and interoperable knowledge graph. This innovative resource allows for executing complex queries involving climate models, simulations, variables, spatio-temporal domains, and granularities. Developed with input from domain experts, the knowledge graph and its underlying ontology are published with open access license and provide a comprehensive framework that enhances the exploration of climate data, facilitating more informed decision-making in addressing climate change issues.
Related papers
- Querying Climate Knowledge: Semantic Retrieval for Scientific Discovery [18.553569226042374]
This paper introduces a domain-specific Knowledge Graph (KG) built from climate publications and broader scientific texts.<n>Unlike keyword based search, our KG supports structured, semantic queries that help researchers discover precise connections.
arXiv Detail & Related papers (2025-09-12T09:28:29Z) - ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method [61.76389719956301]
We contribute a multi-modal climate benchmark, i.e., ClimateBench-M, which aligns time series climate data from ERA5, extreme weather events data from NOAA, and satellite image data from NASA.<n>Under each data modality, we also propose a simple but strong generative method that could produce competitive performance in weather forecasting, thunderstorm alerts, and crop segmentation tasks.
arXiv Detail & Related papers (2025-04-10T02:22:23Z) - Analyzing Regional Impacts of Climate Change using Natural Language
Processing Techniques [0.9387233631570752]
We use BERT (Bidirectional Representations from Transformers) for Named Entity Recognition (NER) to identify specific geographies within the climate literature.
We conduct region-specific climate trend analyses to pinpoint the predominant themes or concerns related to climate change within a particular area.
These in-depth examinations of location-specific climate data enable the creation of more customized policy-making, adaptation, and mitigation strategies.
arXiv Detail & Related papers (2024-01-11T16:44:59Z) - Comparing Data-Driven and Mechanistic Models for Predicting Phenology in
Deciduous Broadleaf Forests [47.285748922842444]
We train a deep neural network to predict a phenological index from meteorological time series.
We find that this approach outperforms traditional process-based models.
arXiv Detail & Related papers (2024-01-08T15:29:23Z) - Foundation Models for Weather and Climate Data Understanding: A
Comprehensive Survey [39.08108001903514]
We offer an exhaustive, timely overview of state-of-the-art AI methodologies specifically engineered for weather and climate data.
Our primary coverage encompasses four critical aspects: types of weather and climate data, principal model, model scopes and applications, and datasets for weather and climate.
arXiv Detail & Related papers (2023-12-05T01:10:54Z) - FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems [56.0640340392818]
We introduce a framework, FREE, that enables the use of varying features and available information to train a universal model.<n>The core idea is to map available environmental data into a text space and then convert the traditional predictive modeling task in environmental science to a semantic recognition problem.<n>Our evaluation on two societally important real-world applications, stream water temperature prediction and crop yield prediction, demonstrates the superiority of FREE over multiple baselines.
arXiv Detail & Related papers (2023-11-17T00:53:09Z) - ClimateLearn: Benchmarking Machine Learning for Weather and Climate
Modeling [20.63843548201849]
ClimateLearn is an open-source library that vastly simplifies the training and evaluation of machine learning models for data-driven climate science.
It is the first large-scale, open-source effort for bridging research in weather and climate modeling with modern machine learning systems.
arXiv Detail & Related papers (2023-07-04T20:36:01Z) - Federated Prompt Learning for Weather Foundation Models on Devices [37.88417074427373]
On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing.
This paper propose Federated Prompt Learning for Weather Foundation Models on Devices (FedPoD)
FedPoD enables devices to obtain highly customized models while maintaining communication efficiency.
arXiv Detail & Related papers (2023-05-23T16:59:20Z) - ClimaX: A foundation model for weather and climate [51.208269971019504]
ClimaX is a deep learning model for weather and climate science.
It can be pre-trained with a self-supervised learning objective on climate datasets.
It can be fine-tuned to address a breadth of climate and weather tasks.
arXiv Detail & Related papers (2023-01-24T23:19:01Z) - CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims [4.574830585715129]
We introduce CLIMATE-FEVER, a new dataset for verification of climate change-related claims.
We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet.
We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the textscfever framework.
arXiv Detail & Related papers (2020-12-01T16:32:54Z) - Analyzing Sustainability Reports Using Natural Language Processing [68.8204255655161]
In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context.
This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG)
We present this tool and the methodology that we used to develop it in the present article.
arXiv Detail & Related papers (2020-11-03T21:22:42Z) - Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
We find a third intermediate stable state in one of the two climate models we consider.
The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
arXiv Detail & Related papers (2020-10-20T15:31:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.