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Related papers: Breaching 1.5{\deg}C: Give me the odds

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Projections of future climate change rely heavily on climate models, and combining climate models through a multi-model ensemble is both more accurate than a single climate model and valuable for uncertainty quantification. However,…

Applications · Statistics 2020-02-27 Huang Huang , Dorit Hammerling , Bo Li , Richard Smith

The climate change attribution problem is addressed using empirical decomposition. Cycles in solar motion and activity of 60 and 20 years were used to develop an empirical model of Earth temperature variations. The model was fit to the…

Geophysics · Physics 2012-06-27 Craig Loehle , Nicola Scafetta

In recent years, climate change repercussions have increasingly captured public interest. Consequently, corporations are emphasizing their environmental efforts in sustainability reports to bolster their public image. Yet, the absence of…

Computation and Language · Computer Science 2024-11-26 Avalon Vinella , Margaret Capetz , Rebecca Pattichis , Christina Chance , Reshmi Ghosh , Kai-Wei Chang

Dam breach models are commonly used to predict outflow hydrographs of potentially failing dams and are key ingredients for evaluating flood risk. In this paper a new dam breach modeling framework is introduced that shall improve the…

Computation · Statistics 2018-06-14 S. J. Peter , A. Siviglia , J. Nagel , S. Marelli , R. M. Boes , D. Vetsch , B. Sudret

Extreme weather events, rare yet profoundly impactful, are often accompanied by severe conditions. Increasing global temperatures are poised to exacerbate these events, resulting in greater human casualties, economic losses, and ecological…

Atmospheric and Oceanic Physics · Physics 2025-04-17 Yuhao Feng , Jun Meng , Jingfang Fan

In settings ranging from weather forecasts to political prognostications to financial projections, probability estimates of future binary outcomes often evolve over time. For example, the estimated likelihood of rain on a specific day…

Machine Learning · Computer Science 2021-11-05 Zhiyuan Jerry Lin , Hao Sheng , Sharad Goel

In this paper, we assess and develop a climate service focused on the production of seasonal predictions for summer wildfires in a Mediterranean region through a participatory approach with end-users. We start by building a data-driven…

Atmospheric and Oceanic Physics · Physics 2019-05-06 Marco Turco , Raul Marcos-Matamoros , Xavier Castro , Esteve Canyameras , Maria Carmen Llasat

Forecasting techniques for assessing the power of future experiments to discriminate between theories or discover new laws of nature are of great interest in many areas of science. In this paper, we introduce a Bayesian forecasting method…

Data Analysis, Statistics and Probability · Physics 2024-09-24 Mohammad Hossein Namjoo

Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. Whilst…

Applications · Statistics 2023-12-22 Lachlan Astfalck , Daniel Williamson , Niall Gandy , Lauren Gregoire , Ruza Ivanovic

Roy and Bhattacharya (2020) provided Bayesian characterization of infinite series, and their most important application, namely, to the Dirichlet series characterizing the (in)famous Riemann Hypothesis, revealed insights that are not in…

Probability · Mathematics 2020-09-15 Sucharita Roy , Sourabh Bhattacharya

According to the second law of thermodynamics, for every transformation performed on a system which is in contact with an environment of fixed temperature, the extracted work is bounded by the decrease of the free energy of the system.…

Quantum Physics · Physics 2016-07-15 Vasco Cavina , Andrea Mari , Vittorio Giovannetti

Global temperature is a fundamental climate metric highly correlated with sea level, which implies that keeping shorelines near their present location requires keeping global temperature within or close to its preindustrial Holocene range.…

Artificial intelligence (AI) - and specifically machine learning (ML) - applications for climate prediction across timescales are proliferating quickly. The emergence of these methods prompts a revisit to the impact of data preprocessing, a…

A measure of privacy infringement for agents (or participants) travelling across a transportation network in participatory-sensing schemes for traffic estimation is introduced. The measure is defined to be the conditional probability that…

Optimization and Control · Mathematics 2016-09-06 Farhad Farokhi , Iman Shames

The ultimate climate emergency is a "runaway greenhouse": a hot and water vapour rich atmosphere limits the emission of thermal radiation to space, causing runaway warming. Warming ceases only once the surface reaches ~1400K and emits…

Atmospheric and Oceanic Physics · Physics 2015-06-03 Colin Goldblatt , Andrew J. Watson

Decadal temperature prediction provides crucial information for quantifying the expected effects of future climate changes and thus informs strategic planning and decision-making in various domains. However, such long-term predictions are…

Machine Learning · Computer Science 2023-04-20 Jinfu Ren , Yang Liu , Jiming Liu

The development of robust Early Warning Signals (EWS) is necessary to quantify the risk of crossing tipping points in the present-day climate change. Classically, EWS are statistical measures based on time series of climate state variables,…

Atmospheric and Oceanic Physics · Physics 2025-03-25 Laure Moinat , Jérôme Kasparian , Maura Brunetti

We propose a non-steady state model of the global temperature change. The model describes Earth's surface temperature dynamics under main climate forcing. The equations were derived from basic physical relationships and detailed assessment…

Various subsystems of the Earth system may undergo critical transitions by passing a so-called tipping point, under sustained changes to forcing. For example, the Atlantic Meridional Overturning Circulation (AMOC) is of particular…

Atmospheric and Oceanic Physics · Physics 2021-11-01 Kerstin Lux , Peter Ashwin , Richard Wood , Christian Kuehn

Significant wave height forecasting is a key problem in ocean data analytics. This task affects several maritime operations, such as managing the passage of vessels or estimating the energy production from waves. In this work, we focus on…

Machine Learning · Statistics 2024-05-07 Vitor Cerqueira , Luis Torgo
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