Temperature sensitivity of vegetation phenology in spring in mid- to high-latitude regions of Northern Hemisphere during the recent three decades

doi:10.17521/cjpe.2021.0188

Abstract

Abstract:

Aims Under the current global warming, there are abundant evidence that the phenological events of vegetation in spring have advanced. Advancement of the phenological events in Northern Hemisphere under a gradual warming is considered a process of acclimation rather than an instantaneous feedback. Moreover, the occurrence of spring phenological advancement also varies across ecoregions. Following up on our previous studies, here we aim to determine the temproal scale that temperature has the most influential effect on changes in spring phenology. We further explore how the local spring temperature affects the temperature sensitivity of the spring phenology and the underlying mechanism.

Methods We extracted the dates for spring phenological events by five different methods derived from the GIMMS3g normalized difference vegetation index dataset during 1982-2009. We also employed gridded climatic datasets to calculate the temperature sensitivity of the spring phenology of vegetation, and analyzed the relationship between the temperature sensitivity of phenological events of natural vegetation and environmental variables.

Important findings The spring phenological events of vegetation were mainly regulated by the early spring temperature over the mid- to high-latitude regions in the Northern Hemisphere. Specifically, we found that the maximum temperature in the month of the green-up onset or in the preceding month played the dominant role in affecting the shifts in spring phenology over 54% of the pixels for the study regions; over 91.3% of the pixels displayed phenological shifts by early spring temperature. Interestingly, across the study regions, the standard deviation in interannual temperature, accumulative precipitation and short-wave radiation contrasted in their effects, and differentially or synergistically regulated the temperature sensitivity of spring vegetation phenology.

Key words: temperature sensitivity, vegetation spring phenology, remote sensing, climate change

CONG Nan, ZHANG Yang-Jian, ZHU Jun-Tao. Temperature sensitivity of vegetation phenology in spring in mid- to high-latitude regions of Northern Hemisphere during the recent three decades[J]. Chin J Plant Ecol, 2022, 46(2): 125-135.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2021.0188

https://www.plant-ecology.com/EN/Y2022/V46/I2/125

Figures/Tables 8

Table 1 Core formulas for extraction of vegetation spring phenology

方法名称 Name of method	滤波核心公式 Core formula for filtering	阈值确定方式 Threshold algorithm
Gauss-Midpoint	$NDVI(t)=a+b\times {{\text{e}}^{-{{(({{x}_{t}}-c)/d)}^{2}}}}$	\[NDV{{I}_{\text{ratio}}}(t)=\frac{NDV{{I}_{t}}-NDV{{I}_{\min }}}{NDV{{I}_{\max }}-NDV{{I}_{\min }}}\]
Spline-Midpoint	$NDVI(t)={{a}_{t}}{{t}^{3}}+{{b}_{t}}{{t}^{2}}+{{c}_{t}}t+{{d}_{t}}$	\[NDV{{I}_{\text{ratio}}}(t)=\frac{NDV{{I}_{t}}-NDV{{I}_{\min }}}{NDV{{I}_{\max }}-NDV{{I}_{\min }}}\]
HANTS-Maximum	$NDVI(t)={{a}_{0}}+\underset{i=1}{\overset{n}{\mathop \sum }}\,{{a}_{i}}\cos ({{\omega }_{i}}t-{{\varphi }_{i}})$	\[NDV{{I}_{\text{ratio}}}(t)=\frac{NDVI(t+1)-NDVI(t)}{NDVI(t)}\]
Polyfit-Maximum	$NDVI(t)={{a}_{0}}+{{a}_{1}}t+{{a}_{2}}{{t}^{2}}+{{a}_{3}}{{t}^{3}}+\cdots {{a}_{n}}{{t}^{n}}$	\[NDV{{I}_{\text{ratio}}}(t)=\frac{NDVI(t+1)-NDVI(t)}{NDVI(t)}\]
Timesat-SG	$NDVI(t)=\frac{\sum\limits_{i=-m}^{i=m}{{{C}_{i}}NDV{{I}_{j+i}}}}{N}$	\[\text{Threshold}=20%\times (NDV{{I}_{\max }}-NDV{{I}_{\min }})\]

Fig. 1 Correlation coefficients of spring green-up dates with temperature and precipitation for different vegetation types in different periods in mid- to high-latitude regions of Northern Hemisphere (mean ± SD). The calculation was made backward by one-month starting from May as the average start season. E.g., the value 1 on the x-axis indicates the start month of phenological event (May in this study), and 2 the preceding month (April in this study). All way backward to November of the previous year (7).

Fig. 2 The “lag effect” of mean temperature on spring green-up onset in mid- to high-latitude regions of Northern Hemisphere. The values for period show the month(s) precede the average start of season. E.g. value 1 represents the month of start of spring, and 2 the preceding month before the start of month, calculated as the average temperature between the month of phenological event and the preceding month.

Fig. 3 Correlations of green-up with temperature (T), precipitation (P) and radiation (R) in mid- to high-latitude regions of Northern Hemisphere.

Fig. 4 Spatial pattern of temperature sensitivity for dates of vegetation spring green-up in mid- to high-latitude regions of Northern Hemisphere.

Fig. 5 Distribution of temperature sensitivity (mean ± SD) in vegetation spring phenology along gradient of standard deviation for temperature in mid- to high-latitude regions of Northern Hemisphere.

Fig. 6 Distribution of temperature sensitivity of vegetation phenology along precipitation gradient in mid- to high-latitude regions of Northern Hemisphere (mean ± SD).

Fig. 7 Distribution of temperature sensitivity of vegetation phenology along radiation gradient in mid- to high-latitude regions of Northern Hemisphere (mean ± SD).

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