A climate index collection based on model data.

Landt-Hayen, Marco, Rath, Willi , Wahl, Sebastian , Niebaum, Nils, Claus, Martin and Kröger, Peer (2023) A climate index collection based on model data. Open Access Environmental Data Science, 2 . Art.Nr. e9. DOI 10.1017/eds.2023.5.

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Machine learning (ML) and in particular deep learning (DL) methods push state-of-the-art solutions for many hard problems, for example, image classification, speech recognition, or time series forecasting. In the domain of climate science, ML and DL are known to be effective for identifying causally linked modes of climate variability as key to understand the climate system and to improve the predictive skills of forecast systems. To attribute climate events in a data-driven way, we need sufficient training data, which is often limited for real-world measurements. The data science community provides standard data sets for many applications. As a new data set, we introduce a consistent and comprehensive collection of climate indices typically used to describe Earth System dynamics. Therefore, we use 1000-year control simulations from Earth System Models. The data set is provided as an open-source framework that can be extended and customized to individual needs. It allows users to develop new ML methodologies and to compare results to existing methods and models as benchmark. For example, we use the data set to predict rainfall in the African Sahel region and El Niño Southern Oscillation with various ML models. Our aim is to build a bridge between the data science community and researchers and practitioners from the domain of climate science to jointly improve our understanding of the climate system.

Document Type: Article
Keywords: Climate events; data mining; deep learning; machine learning; time series forecasting
Research affiliation: OceanRep > GEOMAR > FB1 Ocean Circulation and Climate Dynamics > FB1-OD Ocean Dynamics
OceanRep > GEOMAR > FB1 Ocean Circulation and Climate Dynamics > FB1-ME Maritime Meteorology
Kiel University
Main POF Topic: PT2: Ocean and Cryosphere
Refereed: Yes
Open Access Journal?: Yes
Related URLs:
Projects: MarDATA
Date Deposited: 06 Jun 2023 08:10
Last Modified: 07 Feb 2024 15:46
URI: https://oceanrep.geomar.de/id/eprint/58622

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