Ifølge Ole Henrik Ellestads innlegg i Nordlys 20. juli er alle modeller som ikke stemmer overens med observasjoner gale. Det kan man godt si, men det betyr at alle modeller er gale. En modell er en forenklet versjon av virkeligheten, og vil alltid avvike noe fra observasjonene. Man lærer likevel mye av å bruke modeller.Klimamodellene er gode på å forutsi forventet temperaturrespons på eksterne ...

A test for isotropy of images modeled as stationary or intrinsically stationary random fields on a lattice is developed. The test is based on wavelet theory, and can operate on the horizontal and vertical scale of choice, or on any combination of scales. Scale is introduced through the wavelet variances (sometimes referred to as the wavelet power spectrum), which decompose the variance over ...

Consider the Earth’s global mean surface temperature time series (GMST) as a realization of a stochastic process. Based on a number of studies, a long-range memory (LRM) stochastic process seems to describe the GMST better than a shortrange memory model, such as the AR(1)-process. We want to study the persistence in climate model simulations, to find out if simulated temperature data exhibit the ...

Climate change poses challenges for decision makers across society, not just in preparing for the climate of the future but even when planning for the climate of the present day. When making climate sensitive decisions, policy makers and adaptation planners would benefit from information on local scales and for user-specific quantiles (e.g. the hottest/coldest 5% of days) and thresholds (e.g. ...

The paper explores the hypothesis that the temporal global temperature response can be modeled as a long-range memory (LRM) stochastic process characterized by a Hurst exponent 0.5 < H≲1.0 on time scales from months to decades. The LRM is a mathematical representation of the multitude of response times associated with the various subsystems. By analysis of instrumental and reconstructed temperature ...

The paper proposes an approach to assessment of timescale errors in proxy-based series with chronological uncertainties. The method relies on approximation of the physical process(es) forming a proxy archive by a random Gamma process. Parameters of the process are partly data-driven and partly determined from prior assumptions. For a particular case of a linear accumulation model and absolutely dated ...

Kernel density estimation and kernel regression are useful ways to visualize and assess the structure of data. Using these techniques we define a temporal scale space as the vector space spanned by bandwidth and a temporal variable. In this space significance regions that reflect a significant derivative in the kernel smooth similar to those of SiZer (Significant Zero-crossings of derivatives) are ...

This paper develops methodology that provides a toolbox for routinely fitting complex models to realistic spatial point pattern data. We consider models that are based on log-Gaussian Cox processes and include local interaction in these by considering constructed covariates. This enables us to use integrated nested Laplace approximation and to considerably speed up the inferential task. In addition, ...