Related papers: Are temperature reconstructions regionally biased?
Recent work has observed that one can outperform exact inference in Bayesian neural networks by tuning the "temperature" of the posterior on a validation set (the "cold posterior" effect). To help interpret this phenomenon, we argue that…
Predicting the ancestral sequences of a group of homologous sequences related by a phylogenetic tree has been the subject of many studies, and numerous methods have been proposed to this purpose. Theoretical results are available that show…
The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but…
Trees have long been used as a graphical representation of species relationships. However complex evolutionary events, such as genetic reassortments or hybrid speciations which occur commonly in viruses, bacteria and plants, do not fit into…
The complexity of atmospheric retrieval models is largely data-driven and one-dimensional models have generally been considered adequate with current data quality. However, recent studies have suggested that using 1D models in retrievals…
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…
Recent work has provided ample evidence that nonlinear methods of time series analysis potentially allow for detecting periods of anomalous dynamics in paleoclimate proxy records that are otherwise hidden to classical statis- tical…
Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve the ability of a machine learning model to classify a person's…
Temperature scaling is a popular technique for tuning the sharpness of a model distribution. It is used extensively for sampling likely generations and calibrating model uncertainty, and even features as a controllable parameter to many…
Analyzing data from paleoclimate archives such as tree rings or lake sediments offers the opportunity of inferring information on past climate variability. Often, such data sets are univariate and a proper reconstruction of the system's…
Theoretical studies of the physical processes guiding the formation and evolution of galaxies and galaxy clusters in the X-ray are mainly based on the results of numerical hydrodynamical N-body simulations, which in turn are often directly…
Global Climate Models (GCM) are very useful tools to study theoretically the general dynamics and specific phenomena in planetary atmospheres. In the case of Venus, several GCMs succeeded in reproducing the atmosphere's superrotation and…
The present work deals with the derivation of corrector estimates for the two-scale homogenization of a thermo-diffusion model with weak thermal coupling posed in a heterogeneous medium endowed with periodically arranged high-contrast…
The specification of the upper atmosphere strongly relies on solar proxies that can properly reproduce the solar energetic input in the UV. Whilst the microwave flux at 10.7 cm (also called F10.7 index) has been routinely used as a solar…
Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leading cause of uncertainty in climate projections. A promising…
This paper contributes to the limited literature on the temperature sensitivity of residential energy demand on a global scale. Using a Bayesian Partial Pooling model, we estimate country-specific intercepts and slopes, focusing on…
This paper is a continuation of a study by Douglass and Clader. We extend the analysis through December 2003 using the latest updates of the observational temperature and solar irradiance data sets in addition to a new volcano proxy data…
Motivated by the questions of risk assessment in climatology (temperature change in North America) and medicine (impact of statin usage and COVID-19 on hospitalized patients), we address the problem of estimating the set in the domain of a…
Recently, there has been a surge of research on data-driven weather forecasting systems, especially applications based on convolutional neural networks (CNNs). These are usually trained on atmospheric data represented on regular…
Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth system models can provide detailed insight into these…