Biogenic CO2 flux uncertainty: numerical experiments and validation over south-eastern South America
Nahuel E. Bautista
A B * , Juan J. Ruiz B C D , Paola V. Salio B C D , Lucas J. Burgos E and María I. Gassmann A B
A
B
C
D
E
Abstract
Understanding carbon dioxide (CO2) surface fluxes is essential in the context of a changing climate. In particular, agriculture significantly contributes to CO2 fluxes. Recently, some studies have focused on understanding how synoptic-scale variability modulates CO2 fluxes associated with vegetation and agriculture, finding that frontal passages and precipitation events exert a strong influence on these fluxes. This variability is particularly relevant in the Argentinean Pampas, where large CO2 fluxes associated with extensive agriculture combine with strong synoptic variability. Numerical modelling provides a valuable tool for investigating surface CO2 fluxes and their variability, despite the uncertainties associated with the model’s limitations. In this work, we investigate simulated CO2 fluxes in the Argentinean Pampas using the Weather Research and Forecasting Model (WRF) coupled with the Vegetation, Respiration and Photosynthesis Model (VPRM) over three case studies representing different synoptic-scale conditions. In addition, we estimate the uncertainty in the simulations by comparing simulated CO2 fluxes using various WRF configurations and the ERA5 reanalysis. We found that the synoptic-scale conditions have a considerable impact on the magnitude of fluxes as well as the simulation uncertainty. Uncertainties in simulated CO2 fluxes can be as high as 60%, being larger at sunrise and sunset. Also, the largest uncertainty is associated with a case with a cold frontal passage and widespread precipitation. These results allow a more accurate estimation of CO2 flux uncertainty, which is key to understanding the effects of climate change.
Keywords: carbon dioxide, ecosystem respiration, ERA5 reanalysis, frontal passages, gross primary productivity, model validation, model uncertainties, net ecosystem exchange, numerical modelling, sensitivity analysis, Weather Research and Forecasting, WRF.
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