Prépublication
· 2026 arXiv
EN
The Global Ocean Observing System is key to understanding oceanic variability and climate change. The ocean is vast and often sparsely and irregularly sampled in time and space by various instruments and systems. Statistical models, especially spatio-temporal...
Mikael Kuusela, Sofia C. Olhede et al.
#stat.AP
Prépublication
· 2026 arXiv
EN
Atmospheric warming raises evaporative demand, but actual evapotranspiration (ETa) may track demand or decouple under water limitation. Here, we quantified demand-tracking ability from regression slopes relating deseasonalized ETa and reference evapotranspirat...
Yan Zhang, Veronica J. Berrocal et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
A combination of remote sensing techniques, open space data and field testing was developed to derive astronomical and meteorological parameters and assess Dark Sky brightness of the hitherto unstudied Schist Villages region in the mountainous Interior Center...
Domingos Barbosa, Dalmiro Maia et al.
#astro-ph.IM
Prépublication
· 2026 arXiv
EN
Dissolved oxygen is important for the ocean's ecosystems and biogeochemical cycles. Yet, Earth System Models (ESMs) vary in their simulations of the present-day and future ocean oxygen inventory. To narrow down the uncertainty in estimates of ocean oxygen cont...
Linus Vogt, Laure Zanna
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
The production of adipic acid generates nitrous oxide as a by-product, which, if emitted, very effectively warms the climate and depletes the stratospheric ozone layer. There exists cost-effective technology to mitigate these nitrous oxide emissions from adipi...
Nicholas Balasus, Daniel H. Cusworth
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Data assimilation (DA) is the process of combining forecasts from a model with observations in order to optimally estimate the state of a system. This is critical for chaotic systems, such as the atmosphere, since if observations are not continually assimilate...
Eviatar Bach
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
Deep learning has revolutionised weather forecasting in recent years, especially through atmospheric foundation models, which offer competitive skill for a fraction of the computational costs of classic physics-based models. However, most existing foundation m...
Eliot Walt, Miltiadis Kofinas et al.
#cs.LG
Prépublication
· 2026 arXiv
EN
Despite substantial progress in short-to-medium-range weather forecasting, predicting high-impact events such as flash droughts remains a key challenge for both early warning operations and physically-based subseasonal-to-seasonal (S2S) prediction systems. Her...
Noelia Otero, Atahan Özer et al.
#cs.LG
Prépublication
· 2026 arXiv
EN
We develop structure-preserving methods for the compressible Euler equations with gravity in vector-invariant form and potential temperature as a prognostic variable within the flux-differencing discontinuous Galerkin spectral element method (DGSEM) framework....
Marco Artiano, Kieran Ricardo et al.
#math.NA
Prépublication
· 2026 arXiv
EN
Analytical greenhouse models traditionally rely on the gray approximation, simplifying radiative transfer but treating optical depth as an unconstrained parameter that yields unrealistic surface temperatures. We derive an exact, closed-form solution directly f...
Carmine Serio
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-century regional temperature and precipitation changes. The protocol is designed to evaluate any physi...
Duncan Watson-Parris, Willa Tobin et al.
#physics.ao-ph
Prépublication
· 2026 arXiv
EN
The Gálvez Davison Index (GDI) is a thermodynamic diagnostic designed to summarize convective favorability in the tropical regions, yet its transferability across complex terrain and heterogeneous hydroclimates remains uncertain. Here we assess the climatologi...
Mauricio López-Reyes, Carlos Calvo-Sancho et al.
#physics.ao-ph