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Sensitivity of spring phenology simulations to the selection of model structure and driving meteorological data

David, R. A., Barcza, Z., Kern, A., Kristof, E., Hollos, R., Kis, A., Lukac, M. ORCID: https://orcid.org/0000-0002-8535-6334 and Fodor, N. (2021) Sensitivity of spring phenology simulations to the selection of model structure and driving meteorological data. Atmosphere, 12 (8). 963. ISSN 2073-4433

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To link to this item DOI: 10.3390/atmos12080963

Abstract/Summary

Accurate estimation of the timing of intensive spring leaf growth initiation at mid and high latitudes is crucial for improving the predictive capacity of biogeochemical and Earth system models. In this study, we focus on the modeling of climatological onset of spring leaf growth in Central Europe and use three spring phenology models driven by three meteorological datasets. The MODIS-adjusted NDVI3g dataset was used as a reference for the period between 1982 and 2010, enabling us to study the long-term mean leaf onset timing and its interannual variability (IAV). The performance of all phenology model–meteorology database combinations was evaluated with one another, and against the reference dataset. We found that none of the constructed model–database combinations could reproduce the observed start of season (SOS) climatology within the study region. The models typically overestimated IAV of the leaf onset, where spatial median SOS dates were best simulated by the models based on heat accumulation. When aggregated for the whole study area, the complex, bioclimatic index-based model driven by the CarpatClim database could capture the observed overall SOS trend. Our results indicate that the simulated timing of leaf onset primarily depends on the choice of model structure, with a secondary contribution from the choice of the driving meteorological dataset.

Item Type:Article
Refereed:Yes
Divisions:Life Sciences > School of Agriculture, Policy and Development > Biodiversity, Crops and Agroecosystems Division > Centre for Agri-environmental Research (CAER)
ID Code:99928
Publisher:MDPI

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