Work

Reassessing Global Atmospheric Flux of Dissolved Inorganic Phosphorus With Machine Learning and Earth System Model Outputs

Year: 2026

Type: article

Abstract: Atmospheric deposition of dissolved inorganic phosphorus (DIP) is crucial to nutrient cycling but remains poorly constrained in chemical transport models (CTMs). Large uncertainties in phosphorus emissions and aerosol dissolution processes challenge the reliable estimates of DIP fluxes. Here, we developed a two‐stage random forest residual learning framework integrating observational DIP data sets with CTM simulation outputs. Compared to the original CTM results, our machine learning model substantially narrows the gap of DIP deposition fluxes across the globe between the observations and model estimates, with improved Pearson correlation from 0.10 to 0.85. The optimized global DIP deposition is estimated of 2.02 Tg DIP yr −1 , with a 95% confidence interval of 1.98–2.59 Tg DIP yr −1 . Our estimate is 60%–460% higher than the CTM‐based estimates from previous studies. In particular, 1.35 Tg DIP yr −1 (67%) is deposited over land and 0.67 Tg DIP yr −1 (33%) over ocean. The source appointment analysis suggests dust‐ related predictor variables (including dust aerosols and dust‐sulfate interactions) and black carbon as the major contributors to correcting CTM‐observation bias in DIP deposition. Overall, our approach helps to better understand the atmospheric phosphorus cycling and enhance Earth system models' capabilities by refining ecosystem response simulations.

Source: Journal of Geophysical Research Machine Learning and Computation

DOI: 10.1029/2025jh000987

Authors: Hongxuan Jiang, Mingxu Liu, Orfeas Karathanasopoulos, Stelios Myriokefalitakis

Institutions: Peking University, National Observatory of Athens

Language: English

Topic: Atmospheric chemistry and aerosols

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,500