Work

Probabilistic modelling of atmosphere–surface coupling with a copula Bayesian network

Year: 2026

Type: article

Abstract: Land-atmosphere coupling is an important process for correctly modelling near-surface temperature profiles, but it involves various uncertainties due to subgrid-scale processes, such as turbulent fluxes or unresolved surface heterogeneities, suggesting a probabilistic modelling approach. We develop a copula Bayesian network (CBN) to interpolate temperature profiles, acting as alternative to T2m-diagnostics used in numerical weather prediction (NWP) systems. The new CBN results in (1) a reduction of the warm bias inherent to NWP predictions of wintertime stable boundary layers allowing cold temperature extremes to be better represented, and (2) consideration of uncertainty associated with subgrid-scale spatial variability. The use of CBNs combines the advantages of uncertainty propagation inherent to Bayesian networks with the ability to model complex dependence structures between random variables through copulas. By combining insights from copula modelling and information entropy, criteria for the applicability of CBNs in the further development of parameterizations in NWP models are derived. • Copula entropy is used to quantify land-atmosphere coupling uncertainty. • A copula Bayesian network (CBN) is constructed to predict near-surface temperatures. • The CBN reduces the warm bias inherent to NWP forecast under stable stratification. • The CBN represents uncertainty that scales with subgrid-scale spatial variability.

Source: Environmental Modelling & Software

DOI: 10.1016/j.envsoft.2026.107003

Authors: Laura Mack, Marvin Kähnert, Norbert Pirk

Institutions: University of Oslo, Norwegian Meteorological Institute

Language: English

Topic: Meteorological Phenomena and Simulations

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $3,500