A Novel Information Inferencing Fusion for Global Warming Agents

Information & Security: An International Journal, 1999

6.1. Classification of physiological condition for advance warning 6.2. Early warning through classification of security situations 6.3. Computational intelligence on the implementation stage 6.4. Computational intelligence on the design stage VII. Conclusion Acknowledgment References PETYA IVANOVA holds a M.Sc. degree in bioengineering from the Technical University of Sofia, Bulgaria (1992), and a M.Sc. degree in Decision Support Systems from the University of Sunderland, U.K. (1994). She has more than thirty publications, mainly in the application of neural network and evolutionary methods for the purposes of classification and forecasting, and is currently finalizing her Ph.D. dissertation on "Designing Nonlinear Predictors: Chaos Theory and Evolutionary Approaches". Ms Ivanova is currently with the

Meteorologische …, 2004

In this paper we would like to put forward another view of climate change signal analysis, namely a data based decision or selection process of specific scenarios (or hypotheses in statistical language). Given at least two scenarios (1) a control one (CTL) from unforced climate model simulations and (2) a set of climate model realizations under an identical external climate forcing, a climate change signal analysis can also be viewed as a decision or selection process of the scenario which is most probable in view of the observations. It is shown that our approach includes the classical fingerprint method. The approach is based on Bayesian decision theory. The selection of hypothesis/scenario uses the Bayes factors. It is exemplified with NCEP/NCAR reanalysis data from 1979 to 1999 as observation, a greenhouse-gas forced scenario (G) with four realizations, and a greenhouse-gas plus sulfate aerosol forced scenario (GS) with two realizations generated by the ECHAM3/LSG from 1880 to 2049. The CTL scenario is obtained from an unforced control run of ECHAM3/LSG of 1400 years length. To provide a vivid example, Northern Hemisphere extratropics area-averaged and 13-month running mean 2 m and 70 hPa temperature anomalies are selected as analysis variables. The Northern Hemisphere extratropics from 1979-1999 are considered as the most reliable area of NCEP/NCAR reanalysis data and the two dimensional setting provides valuable insight into the results. The decision test between the CTL and G scenarios shows that observations of near surface and lower stratosphere temperatures provide evidence against the CTL scenario since the late 1990s even if the prior probability for the G scenario is a third of that of the CTL scenario. The second decision experiment with a three-scenario case shows that both climate change scenarios (G and GS) have higher evidence in view of the observations of the late 1990s than the CTL scenario. A sensitivity analysis with respect to the strength of the natural variance in observations indicates that the specification of the level of the observed natural variability is a crucial factor in the Bayesian decision. In both decision experiments some of the decisions are based upon observations which have very small likelihood value given any of the scenarios, which might be due to the omission of stratospheric ozone forcing in climate change simulations or volcanic/solar forcing in the no change scenario simulation. Zusammenfassung In diesem Artikel wird ein alternativer Ansatz zur Analyse von Klimaänderungssignalen vorgestellt, der auf einem datenbasierten Entscheidungs-oder Selektionsprozess für bestimmte Szenarien, oder statistisch ausgedrückt Hypothesen, beruht. Wenn zumindest zwei Szenarien gegeben sind, zum Beispiel ein Kontrollszenario aus Klimamodellsimulationen ohne externen Antrieb und ein Satz von Klimamodellrealisierungen mit identischem externen Antrieb, kann eine Klimaänderungsanalyse auch als Entscheidungs-oder Selektionsprozess verstanden werden, der das Szenario bestimmt, welches im Hinblick auf die Beobachtungsdaten am wahrscheinlichsten ist. Dieser Ansatz beinhaltet die klassische Fingerprint-Methode und basiert auf dem Entscheidungstheorem von Bayes. Die Auswahl der Hypothese oder des Szenarios beruht auf dem Bayes-Faktor. Beispielhaft wird die Methode durchgespielt für die NCEP/NCAR-Reanalysen als Beobachtungen, ein Szenario mit steigenden Treibhausgaskonzentrationen (G), welches aus vier Ensemblerealisierungen des ECHAM3/LSG-Modelles besteht, sowie zwei Ensembleläufe des gleichen Modelles mit zusätzlichem Konzentrationsanstieg der Sulfataerosole (GS). Alle Szenarioläufe umfassen den Zeitraum 1880 bis 2049. Das Kontrollszenario (CTL) wird aus einem 1400-jährigen Kontrolllauf des ECHAM3/LSG abgeleitet. Untersucht werden die nordhemisphärisch gemittelten undüber 13 Monate tiefpassgefilterten Temperaturanomalien in zwei Meter Höhe und in 70 hPa. Die NCEP/NCAR-Reanalysen gelten in den nordhemisphärischen Außertropen im Zeitraum 1979 bis 1999 als besonders zuverlässig. Darüber hinaus stellt die zweidimensionale Betrachtung der bodennahen und stratosphärischen Temperaturen einen aussagekräftigen Fingerprint dar. Der Entscheidungstest zwischen den Szenarien CTL und G zeigt, dass die Beobachtungen der bodennahen und niederstratosphärischen Temperatur seit den späten 1990er Jahren nicht mit dem CTL-Szenario vereinbar sind. Das gilt auch, wenn die a priori-Wahrscheinlichkeit für das G-Szenario nur ein Drittel des Wertes für das CTL-Szenario beträgt. Der zweite Entscheidungstest bezieht sich auf drei Szenarien und zeigt, dass beide Klimaänderungsszenarien (G und GS) im Hinblick auf die Beobachtungsdaten am Ende des 20. Jahrhunderts wahrscheinlicher sind als das CTL-Szenario. Eine Sensitivitätsstudie bezüglich der Abschätzung der natürlichen Varianz der Beobachtungsdaten verdeutlicht die große Bedeutung dieses Parameters für das Ergebnis des Entscheidungsprozesses nach Bayes. In beiden Anwendungen treten Fälle auf, in denen die Beobachtungsdaten nur eine geringeÄhnlichkeit (Likelihood) zu allen drei Szenarios besitzen. Das könnte darin begründet liegen, dass insbesondere die Kontrollexperimente relevante Prozesse im Klimasystem wie die stratosphärische Ozondynamik, die Vulkantätigkeit oder die solaren Schwankungen nicht berücksichtigen.

International Journal on Science and Technology (IJSAT), 2025

The phenomenon of climate change emerges as a significant concern of the 21st century, wielding substantial implications for ecosystems, human populations, and economic stability. Effective monitoring plays an indispensable role in addressing these challenges by facilitating the identification of trends and providing the necessary data to inform mitigation and adaptation strategies. Traditional climate monitoring methodologies often exhibit limitations, including high costs, slow data processing, and insufficient resolution, which hinder timely responses to rapid environmental changes. In this context, Artificial Intelligence (AI) has assumed a crucial role in overcoming these challenges. The capabilities of AI in processing extensive datasets, recognizing patterns, and making predictions enhance the accuracy and efficiency of climate change surveillance at unprecedented rates. This study evaluates various AI technologies that support climate change monitoring, including machine learning, deep learning, natural language processing, and big data analytics. The analysis encompasses applications in climate forecasting, satellite observation, wildfire detection, energy management, and carbon emission tracking. The study further discusses the benefits of AI, such as improved data processing, real-time monitoring, and predictive abilities, while acknowledging the challenges of data integrity, integration, scalability, and ethical considerations. The paper concludes by speculating on future advancements in AI, including ecosystem modelling, AI-enhanced climate policy formulation, and collaborative AI networks, which hold the potential to significantly bolster global initiatives aimed at promoting climate resilience and sustainability.

Despite climate mitigation efforts to keep global warming below 1.5°C, many experts expect the world to warm by 3.5°C by the turn of the next century. This ever-warming world has brought floods and wildfires and the loss of life with more disruption expected in the decades to come. Between 3.3 and 3.6 billion people live in areas at high risk of climate change, areas where we already see or will see a significant increase in natural disasters and this is likely to increase as the climate crisis is exacerbated. When modelling extreme weather events, a large number of variables must be included and AI is well situated to model for this complexity due to its ability to gather, complete and analyse large datasets. It can be leveraged for early-warning systems and long-term predictive modelling of local climate events, empowering stakeholders to take a more data-driven approach to climate adaptation. Destination Earth, led by the European Space Agency, for example, aims to create an AI-based model of the Earth to monitor and predict the interaction between climate phenomena, such as droughts and human activities. As the average annual global cost of wildfires is about $50 billion, this should be welcomed as AI can make combatting wildfires more efficient and cost-effective. To support this, the World Economic Forum has started FireAId, which is working towards building real AI models and piloting them in countries such as Turkey.