Leaf onset in the northern hemisphere triggered by daytime temperature

Abstract

Recent warming significantly advanced leaf onset in the northern hemisphere. This signal cannot be accurately reproduced by current models parameterized by daily mean temperature (Tmean). Here using in situ observations of leaf unfolding dates (LUDs) in Europe and the United States, we show that the interannual anomalies of LUD during 1982–2011 are triggered by daytime (Tmax) more than by nighttime temperature (Tmin). Furthermore, an increase of 1 °C in Tmax would advance LUD by 4.7 days in Europe and 4.3 days in the United States, more than the conventional temperature sensitivity estimated from Tmean. The triggering role of Tmax, rather than the Tmin or Tmean variable, is also supported by analysis of the large-scale patterns of satellite-derived vegetation green-up in spring in the northern hemisphere (>30°N). Our results suggest a new conceptual framework of leaf onset using daytime temperature to improve the performance of phenology modules in current Earth system models.

Temporal Changes in Coupled Vegetation Phenology and Productivity are Biome-Specific in the Northern Hemisphere

“…Vegetation metrics like phenology (i.e., the timing of start/end of vegetation growing season and its duration) and photosynthetic activity (commonly measured by the seasonally integrated "greenness" of vegetation) are critical biological characteristics of vegetation, and are sensitive to climate change [5][6][7][8][9][10]. Moreover, even small changes in vegetation photosynthesis could have large effects on the role of terrestrial ecosystems as a carbon sink [11][12][13][14].…”

Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

“…Ground data are commonly recorded at the species level and used for conducting species-specific phenology analysis at local scales [10][11][12][13]. Meanwhile, satellite data are mainly employed to define the growing season characteristics of entire landscapes at regional to global scales [14][15][16][17][18][19]. Various satellite-derived vegetation indices have been developed to extract vegetation phenological metrics, such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) [20][21][22][23].…”

Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

“…MOD09A1 was chosen over MCD43A4 to guarantee more available data, because MCD43A4 was generated based on MOD09A1, and the lack of valid MODIS BRDF parameters [24] during the rainy season might reduce the data availability of MCD43A4. NDVI was selected to extract SOS because NDVI was recognized as a good proxy for indicating the phenology response to the climate change [25,26]. Additionally, compared with the simple ratio (SR), the wide range dynamic vegetation index (WRDVI), the enhanced vegetation index 2 (EVI2), the global environmental monitoring index (GEMI), and the soil-adjusted vegetation index (SAVI), NDVI had shown the highest correlation between the SOS derived from MODIS data and in-situ observations from FLUXNET sites [27].…”