Nitrous oxide (N2O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N2O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high-emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on ~12,000 N2O chamber measurements at 17 US Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R2= 0.84, RMSE = 16.4 g N ha−1d−1) and held-out testing sites (R2= 0.84, RMSE = 6.2 g N ha−1d−1). Analyses identified six dominant N2O drivers: soil organic carbon (SOC), NH4+, NO3-, water-filled pore space, temperature, and aboveground biomass production. Wet, warm soils produced large N2O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N2O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.
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The Modeled Seasonal Cycles of Surface N 2 O Fluxes and Atmospheric N 2 O
Abstract Nitrous oxide (N2O) is a greenhouse gas and stratospheric ozone‐depleting substance with large and growing anthropogenic emissions. Previous studies identified the influx of N2O‐depleted air from the stratosphere to partly cause the seasonality in tropospheric N2O (aN2O), but other contributions remain unclear. Here, we combine surface fluxes from eight land and four ocean models from phase 2 of the Nitrogen/N2O Model Intercomparison Project with tropospheric transport modeling to simulate aN2O at eight remote air sampling sites for modern and pre‐industrial periods. Models show general agreement on the seasonal phasing of zonal‐average N2O fluxes for most sites, but seasonal peak‐to‐peak amplitudes differ several‐fold across models. The modeled seasonal amplitude of surface aN2O ranges from 0.25 to 0.80 ppb (interquartile ranges 21%–52% of median) for land, 0.14–0.25 ppb (17%–68%) for ocean, and 0.28–0.77 ppb (23%–52%) for combined flux contributions. The observed seasonal amplitude ranges from 0.34 to 1.08 ppb for these sites. The stratospheric contributions to aN2O, inferred by the difference between the surface‐troposphere model and observations, show 16%–126% larger amplitudes and minima delayed by ∼1 month compared to Northern Hemisphere site observations. Land fluxes and their seasonal amplitude have increased since the pre‐industrial era and are projected to grow further under anthropogenic activities. Our results demonstrate the increasing importance of land fluxes for aN2O seasonality. Considering the large model spread, in situ aN2O observations and atmospheric transport‐chemistry models will provide opportunities for constraining terrestrial and oceanic biosphere models, critical for projecting carbon‐nitrogen cycles under ongoing global warming.
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- Award ID(s):
- 2135749
- PAR ID:
- 10569666
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- AGU Wiley
- Date Published:
- Journal Name:
- Global Biogeochemical Cycles
- Volume:
- 38
- Issue:
- 7
- ISSN:
- 0886-6236
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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