Comparison of Phenology Models for Predicting the Onset of Growing Season over the Northern Hemisphere

Abstract

Vegetation phenology models are important for examining the impact of climate change on the length of the growing season and carbon cycles in terrestrial ecosystems. However, large uncertainties in present phenology models make accurate assessment of the beginning of the growing season (BGS) a challenge. In this study, based on the satellite-based phenology product (i.e. the V005 MODIS Land Cover Dynamics (MCD12Q2) product), we calibrated four phenology models, compared their relative strength to predict vegetation phenology; and assessed the spatial pattern and interannual variability of BGS in the Northern Hemisphere. The results indicated that parameter calibration significantly influences the models' accuracy. All models showed good performance in cool regions but poor performance in warm regions. On average, they explained about 67% (the Growing Degree Day model), 79% (the Biome-BGC phenology model), 73% (the Number of Growing Days model) and 68% (the Number of Chilling Days-Growing Degree Day model) of the BGS variations over the Northern Hemisphere. There were substantial differences in BGS simulations among the four phenology models. Overall, the Biome-BGC phenology model performed best in predicting the BGS, and showed low biases in most boreal and cool regions. Compared with the other three models, the two-phase phenology model (NCD-GDD) showed the lowest correlation and largest biases with the MODIS phenology product, although it could catch the interannual variations well for some vegetation types. Our study highlights the need for further improvements by integrating the effects of water availability, especially for plants growing in low latitudes, and the physiological adaptation of plants into phenology models.

Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: 1. Implementation in the Simple Biosphere Model (SiB4)

“…Plant phenology, which incorporates visible seasonal events while integrating underlying biological functionality, can be easily seen and understood, making it a powerful indicator of climate change; however, since phenology provides a holistic view of biological mechanisms, it is also a powerful tool for scientists. Since there is a critical link between phenology and terrestrial biochemical, biophysical, and spectral properties, studying phenology not only leads to further understanding of ecosystem processes but also can be used to monitor and understand biospheric responses to global climate change (Fu et al, ; Gu et al, ; Richardson et al, ).…”

Influence of dynamic vegetation on carbon-nitrogen cycle feedback in the Community Land Model (CLM4)

“…Although the result here is based on a single land model, the relevant model processes-phenology parameterizations and closely related soil hydrologyplant interactions, poorly represented competition between tree and grass PFTs, and the strength of nitrogen constraint on plant growth-are known to be difficult for other models as well (Christoffersen et al 2014, Fu et al 2014, Baudena et al 2015. Hence we believe that the outcome of this study is useful for other modelling groups and encourage more systematic evaluations of land carbon sensitivities in ESMs with varying assumptions in their land cover representations.…”

Update and evaluation of the ozone dry deposition in Oslo CTM3 v1.0

“…The simplest assumption is that greening starts after 5 consecutive days with a daily average temperature above 5 • C, and vice versa for EGS. Other estimates use growing degree days (Levis and Bonan, 2004;Fu et al, 2014a), include soil moisture (Fu et al, 2014b), or rely on satellite observations. A comprehensive evaluation of different techniques is given by Anav et al (2017).…”