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Open access
Aug 2026
Estimating present temperature climate in a warming world: probabilistic verification of a model-based approach for years 2008–2025
When climate changes, statistics derived from past observations become unrepresentative of the true present climate. In 2008, Räisänen and Ruokolainen proposed a method for alleviating this bias, combining global mean temperature change with model-based regression coefficients that translate the global warming to chang...
J. Räisänen, M. Rantanen, Antti Toropainen
· Climate Dynamics · 1 citation
Preprint
Sep 2026
Hybrid Models for Short-Term Sea-Level Forecasting
Accurate tide forecasts are essential for coastal management, navigation, flood-risk reduction, and infrastructure protection. Observed sea level can be decomposed into astronomical and non-astronomical components, the latter mainly driven by meteorological effects. This study investigates a hybrid framework for hourly...
Pierdomenico Duttilo, Francesco Lisi
· 0 citations
Open access
Sep 2026
Evaluation of a stochastically perturbed parametrisations scheme for sea ice in sub‐seasonal forecasts
Within the European Centre for Medium‐Range Weather Forecasts (ECMWF) sub‐seasonal ensemble prediction system, sea ice forecasts exhibit pronounced underdispersion, with ensemble spread being systematically smaller than the mean squared error of the ensemble mean. A contributing source of this underdispersion is that...
J. Spaeth, Steffen Tietsche, Kristian Strommen et al.
· Quarterly Journal of the Roy... · 0 citations
Kilometer-Scale AI Downscaling of Atlantic Hurricanes with Generative Ensembles
This study presents an AI-based dynamical downscaling system for Tropical Cyclones (TCs). The system incorporates an AI-based limited-area model that downscales 3-hourly low-resolution boundary forcings into hourly high-resolution fields autoregressively, and a diffusion model that converts the outputs into ensembles o...
Ying-Kai Sha, T. Mayo, Ethan D. Gutmann et al.
· 0 citations
Preprint
Aug 2026
DySCo: Dynamically consistent data-driven downscaling of extremes in climate projections
Results show DySCo achieves superior dynamical consistency with the coarse GCM trajectories, essentially applying a minimal, causal correction to the GCM, preserving top statistical performance comparable to state-of-the-art unsupervised models.
S. Stamatelopoulos, M. Wang, I. Lopez-Gomez et al.
· 0 citations
Open access
Aug 2026
Forecasting Repeated-Measures Trajectories Using Nonlinear Mixed-Effects Models: A Comparison of Population-Averaged, Subject-Specific, and Autocorrelation-Based Predictions
Nonlinear mixed-effects models (NLMMs) provide a flexible framework for modeling repeated-measures trajectories. However, how best to forecast future observations, especially at ages well beyond those represented in the data, remains relatively underexamined. In this study, we develop a Chapman–Richards NLMM with a spa...
S. Ahmed, V. LeMay, Andrew Robinson et al.
· Mathematics · 0 citations