An expert system‐based approach to prediction of year‐to‐year climatic variations in the North Atlantic region

Rodionov, Sergei; Martin, J.H.

doi:10.1002/(sici)1097-0088(199907)19:9<951::aid-joc372>3.3.co;2-a

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…[6] With abrupt changes we mean a sudden stepwise change in the time series. We used three methods to detect abrupt changes in long-term time series, the Excel add-in ''Sequential Regime Shift Detection version 3.2'' (SRSD) (http://www.beringclimate.noaa.gov, see also Rodionov and Overland [2005], Rodionov and Martin [1999], and Rodionov [2004), the software Change-Point Analyzer (version 2.3, Taylor Enterprises Inc, http://www.variation.com) and the manually performed CUSUM/Pettit-test [Buishand, 1982;Lanzante, 1996;Pettitt, 1979]. Since all three methods revealed similar results we focused on the first method.…”

Section: Abrupt Change Detectionmentioning

confidence: 99%

Abrupt changes in air temperature and precipitation: Do they matter for water chemistry?

Temnerud

Weyhenmeyer

2008

Global Biogeochemical Cycles

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We analyzed 120 years long time series of air temperature and precipitation from 29 respective 44 sites distributed all over Sweden and determined abrupt changes by using three methods. For air temperature we found significant changes in 1930 and 1989 and for precipitation in 1920, 1979, and 1998. Analyzing more than 30 yearlong time series of ice cover (333 sites), discharge and watercourses chemistry (87 sites), we observed abrupt changes in 1977, 1989, and 1998 for discharge but first in 1998 for watercourses chemistry, most pronounced for organic matter and sulfate concentrations. We suggest that the abrupt increase in air temperature in 1989 liberated more easily mobilized organic matter in the catchments, which, for water chemistry, was first detected in 1998 as a consequence of increased discharge. We conclude that increases in air temperatures can make ecosystems more sensitive to further changes in precipitation.

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Section: Abrupt Change Detectionmentioning

confidence: 99%

Abrupt changes in air temperature and precipitation: Do they matter for water chemistry?

Temnerud

Weyhenmeyer

2008

Global Biogeochemical Cycles

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Enhancing climate forecasting with AI: Current state and future prospect

Kumar,

Goel,

Sidana

et al. 2024

F1000Res

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Background The escalating impact of climate change underscores the critical need for advanced and sustainable climate forecasting techniques. This review examines the current state and future prospects of leveraging Artificial Intelligence (AI) for climate forecasting, focusing on enhancing accuracy and identifying complex patterns in large datasets. Methods A systematic bibliometric methodology was employed, analyzing peer-reviewed literature from the past two decades. The study screened 455 articles from Scopus and Web of Science databases using specific keywords related to AI and weather forecasting. After removing duplicates and irrelevant studies, 218 articles were selected for detailed analysis. Bibliometric analysis was conducted using RStudio software to examine publication trends, co-word co-occurrence, and thematic evolution. Results The findings indicate significant growth in AI applications for climate forecasting, particularly from 2014 to 2023. AI techniques such as machine learning, artificial neural networks, and deep learning have shown promise in improving the accuracy of weather forecasts and early warning systems. The thematic analysis identified key themes like numerical weather prediction, feature selection, and neural networks as fundamental areas of research. Additionally, AI-based early warning systems for extreme weather events were highlighted as a crucial application. Below Figure 1. shows the graphical abstract of research Conclusions AI has the potential to significantly enhance climate forecasting by analyzing vast amounts of data and identifying complex patterns. Future research should focus on developing universal AI models, increasing model accuracy with explainable AI techniques, and integrating region-specific forecasts to aid decision-making in various sectors. Addressing ethical concerns and ensuring sustainable AI applications are essential for the responsible deployment of AI in climate forecasting.

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An expert system‐based approach to prediction of year‐to‐year climatic variations in the North Atlantic region

Cited by 2 publications

References 55 publications

Abrupt changes in air temperature and precipitation: Do they matter for water chemistry?

Abrupt changes in air temperature and precipitation: Do they matter for water chemistry?

Enhancing climate forecasting with AI: Current state and future prospect

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