Climate data induced uncertainties in simulated carbon fluxes under corn and soybean systems

Abstract. Net carbon balance on croplands depends on numerous factors (e.g., crop type, soil, climate and management practices) and their interactions. Agroecosystem models are generally used to assess cropland carbon fluxes under various agricultural land use and land management practices because of their ability to capture the complex interactive effects of factors influencing carbon balance. For regional carbon flux simulations, generally gridded climate data sets are used because they offer data for each grid cell of the region of interest. However, studies consistently report large uncertainties in gridded climate datasets, which will affect the accuracy of carbon flux simulations. This study investigates the uncertainties in daily weather variables of commonly used high resolution gridded climate datasets in the U.S (NARR, NLDAS, Prism and Daymet), and their impact on the accuracy of simulated Net Ecosystem Exchange (NEE) under irrigated and non-irrigated corn and soybeans using the Environmental Policy Integrated Climate (EPIC) agroecosystem model and observational data at four flux tower cropland sites in the U.S Midwest region. Further, the relative significance of each weather variable in influencing the uncertainty in flux estimates was evaluated. Results suggest that daily weather variables in all gridded climate datasets display some degree of bias, leading to considerable uncertainty in simulated NEE fluxes. The gridded climate datasets produced based on interpolation techniques (i.e. Daymet and Prism) were shown to have less uncertainties, and resulted in NEE estimates with relatively higher accuracy, likely due to their higher spatial resolution and higher dependency on meteorological station observations. The Mean Absolute Percentage Errors (MAPE) values of average growing season NEE estimates for Dayment, Prism, NLDAS and NARR include 22.53 %, 23.45 %, 62.52 % and 66.18 %, respectively. The NEE under irrigation management (MAPE = 53.15 %) tends to be more sensitive to uncertainties compared to the fluxes under non-irrigation (MAPE = 34.19 %). Further, this study highlights that NEE fluxes respond differently to the individual climate variables, and responses vary with management practices. Under irrigation management, NEE fluxes are more sensitive to shortwave radiation and temperature. Conversely, under non-irrigation management, precipitation is the most dominant climate factor influencing uncertainty in simulated NEE fluxes. These findings demonstrate that careful consideration is necessary when selecting climate data to mitigate uncertainties in simulated NEE fluxes. Further, alternative approaches such as integration of remote sensing data products may help reduce the models' dependency on climate datasets and improve the accuracy in the simulated CO2 fluxes.