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Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More…

Rapid retreat of ice in the Amundsen Sea sector of West Antarctica may cause drastic sea level rise, posing significant risks to populations in low-lying coastal regions. Calibration of computer models representing the behavior of the West…

Applications · Statistics 2016-05-23 Won Chang , Murali Haran , Patrick Applegate , David Pollard

Human-induced climate change may cause significant ice volume loss from the West Antarctic Ice Sheet (WAIS). Projections of ice volume change from ice-sheet models and corresponding future sea-level rise have large uncertainties due to…

Applications · Statistics 2018-05-08 Won Chang , Murali Haran , Patrick Applegate , David Pollard

We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past…

Applications · Statistics 2019-08-20 Ben Seiyon Lee , Murali Haran , Robert Fuller , David Pollard , Klaus Keller

Bayesian calibration of computer models tunes unknown input parameters by comparing outputs with observations. For model outputs that are distributed over space, this becomes computationally expensive because of the output size. To overcome…

Methodology · Statistics 2018-10-05 Kai-Lan Chang , Serge Guillas

Computer models are used to model complex processes in various disciplines. Often, a key source of uncertainty in the behavior of complex computer models is uncertainty due to unknown model input parameters. Statistical computer model…

Methodology · Statistics 2013-08-02 Won Chang , Murali Haran , Roman Olson , Klaus Keller

In the context of computer models, calibration is the process of estimating unknown simulator parameters from observational data. Calibration is variously referred to as model fitting, parameter estimation/inference, an inverse problem, and…

Methodology · Statistics 2023-10-16 Richard D. Wilkinson , Christopher W. Lanyon

Models of complex dynamical systems like the Earth's climate often involve large numbers of uncertain parameters. Comprehensive exploration of the parameter space is typically prohibitive due to excessive computational costs. Systematic…

Atmospheric and Oceanic Physics · Physics 2026-03-27 Daniel Pals , Sebastian Bathiany , Richard Wood , Joel Kuettel , Niklas Boers

Ice sheet models are used to study the deglaciation of North America at the end of the last ice age (past 21,000 years), so that we might understand whether and how existing ice sheets may reduce or disappear under climate change. Though…

Applications · Statistics 2022-10-03 James M. Salter , Daniel B. Williamson , Lauren J. Gregoire , Tamsin L. Edwards

Computer model calibration involves using partial and imperfect observations of the real world to learn which values of a model's input parameters lead to outputs that are consistent with real-world observations. When calibrating models…

Methodology · Statistics 2023-10-31 Wenzhe Xu , Daniel B. Williamson , Frederic Hourdin , Romain Roehrig

Computer models are commonly used to represent a wide range of real systems, but they often involve some unknown parameters. Estimating the parameters by collecting physical data becomes essential in many scientific fields, ranging from…

Applications · Statistics 2020-05-27 Chih-Li Sung , Beau David Barber , Berkley J. Walker

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal…

Atmospheric and Oceanic Physics · Physics 2023-10-25 Subhankar Ghosh , Shuai An , Arun Sharma , Jayant Gupta , Shashi Shekhar , Aneesh Subramanian

Seasonal forecast of Arctic sea ice concentration is key to mitigate the negative impact and assess potential opportunities posed by the rapid decline of sea ice coverage. Seasonal prediction systems based on climate models often show…

Machine Learning · Computer Science 2026-02-10 Parsa Gooya , Reinel Sospedra-Alfonso

Standard methods in computer model calibration treat the calibration parameters as constant throughout the domain of control inputs. In many applications, systematic variation may cause the best values for the calibration parameters to…

Methodology · Statistics 2017-02-09 D. Andrew Brown , Sez Atamturktur

Numerical climate models are used to project future climate change due to both anthropogenic and natural causes. Differences between projections from different climate models are a major source of uncertainty about future climate. Emergent…

Applications · Statistics 2020-02-06 Philip G. Sansom , David B. Stephenson , Thomas J. Bracegirdle

Seasonal forecasting is a crucial task when it comes to detecting the extreme heat and colds that occur due to climate change. Confidence in the predictions should be reliable since a small increase in the temperatures in a year has a big…

Machine Learning · Computer Science 2024-04-05 Busra Asan , Abdullah Akgül , Alper Unal , Melih Kandemir , Gozde Unal

In this paper, we present a semiparametric model for describing the effect of temperature on Antarctic ice accumulation on a paleoclimatic time scale. The model is motivated by sharp ups and downs in the rate of ice accumulation apparent…

Methodology · Statistics 2025-07-29 Radhendushka Srivastava , Debasis Sengupta

Calibration refers to the estimation of unknown parameters which are present in computer experiments but not available in physical experiments. An accurate estimation of these parameters is important because it provides a scientific…

Methodology · Statistics 2019-03-21 Chih-Li Sung , Ying Hung , William Rittase , Cheng Zhu , C. F. Jeff Wu

This paper considers the computer model calibration problem and provides a general frequentist solution. Under the proposed framework, the data model is semi-parametric with a nonparametric discrepancy function which accounts for any…

Methodology · Statistics 2015-09-14 Raymond K. W. Wong , Curtis B. Storlie , Thomas C. M. Lee

How will the climate system respond to anthropogenic forcings? One approach to this question relies on climate model projections. Current climate projections are considerably uncertain. Characterizing and, if possible, reducing this…

Applications · Statistics 2014-08-01 Won Chang , Murali Haran , Roman Olson , Klaus Keller
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