Global climate change substantially influences vegetation spring phenology, that is, green-up date (GUD), in the northern permafrost region. Changes in GUD regulate ecosystem carbon uptake, further feeding back to local and regional climate systems. Extant studies mainly focused on the direct effects of climate factors, such as temperature, precipitation, and insolation; however, the responses of GUD to permafrost degradation caused by warming (i.e., indirect effects) remain elusive yet. In this study, we examined the impacts of permafrost degradation on GUD by analyzing the long-term trend of satellite-based GUD in relation to permafrost degradation measured by the start of thaw (SOT) and active layer thickness (ALT). We found significant trends of advancing GUD, SOT, and thickening ALT (p < 0.05), with a spatially averaged slope of −2.1 days decade⁻¹, −4.1 days decade⁻¹, and +1.1 cm decade⁻¹, respectively. Using partial correlation analyses, we found more than half of the regions with significantly negative correlations between spring temperature and GUD became nonsignificant after considering permafrost degradation. GUD exhibits dominant-positive (37.6% vs. 0.6%) and dominant-negative (1.8% vs. 35.1%) responses to SOT and ALT, respectively. Earlier SOT and thicker ALT would enhance soil water availability, thus alleviating water stress for vegetation green-up. Based on sensitivity analyses, permafrost degradation was the dominant factor controlling GUD variations in 41.7% of the regions, whereas only 19.6% of the regions were dominated by other climatic factors (i.e., temperature, precipitation, and insolation). Our results indicate that GUDs were more sensitive to permafrost degradation than direct climate change in spring among different vegetation types, especially in high latitudes. This study reveals the significant impacts of permafrost degradation on vegetation GUD and highlights the importance of permafrost status in better understanding spring phenological responses to future climate change.

1 INTRODUCTION

Global climate change has greatly impacted vegetation spring phenology, that is, green-up date (GUD), in the northern permafrost region during the past few decades (de Beurs & Henebry, 2005; Piao et al., 2015; Yang et al., 2017; Yu et al., 2010; Zhang et al., 2013). Changes in GUD not only regulate terrestrial vegetation growth and carbon dynamics (Piao et al., 2019) but also influence the vegetation feedbacks to the climate systems (Bonan, 2008; Richardson et al., 2013). Many studies have examined how climatic forcings, such as temperature (Fu et al., 2015a; Yang et al., 2017), precipitation (Wipf et al., 2009; Yun et al., 2018), and insolation (Fu et al., 2015b), drive the interannual variability of GUD (i.e., direct effects). High-latitude warming accelerates permafrost degradation with the change of permafrost characteristics, such as the increases in temperature and thickness and decrease in extent, further impacting regional hydrological dynamics and ecosystem structure, however, the impacts of permafrost degradation on GUD (i.e., indirect effects) remain elusive yet. These impacts may help reduce the large uncertainty in GUD prediction and improve the modeling of carbon balance in permafrost ecosystems (McGuire et al., 2016). Therefore, it is crucial to improve our understanding of GUD responses to permafrost degradation under warming.

Previous studies emphasized the direct effects of climate change on GUD at local, regional, and global scales (Badeck et al., 2004; Fu et al., 2015a; Richardson et al., 2013; Vitasse et al., 2018), but considerable uncertainties exist regarding the impact of temperature, precipitation, and insolation. For example, analysis of tree-ring data revealed an advancing trend of GUD in the Qinghai-Tibet Plateau (QTP) due to temperature increase (Yang et al., 2017). However, satellite observations showed no significant trend of GUD in the QTP during 2000–2011 (Shen et al., 2014), possibly due to the winter warming that decreases chilling accumulation for vegetation greening up (Wang et al., 2015, 2019a), resulting in uncertainties of warming effects. Increased precipitation could have a delaying effect on GUD (Fu et al., 2014; Wipf et al., 2009; Yun et al., 2018) owing to increased snowmelt heat flux and reduced insolation but may also advance GUD by alleviating water stress for vegetation growth, especially for arid and semi-arid regions (Shen et al., 2015). Higher insolation, closely related to temperature, would increase heat accumulation, leading to earlier vegetation green-up (Fu et al., 2015a). Contrastingly, enhanced insolation could accelerate evapotranspiration (Anderson et al., 2012), possibly leading to deferred GUD. These discrepancies suggest climate factors alone are not enough to explain the complex responses of spring phenological events to environmental cues and thus call for more investigations on other potential drivers.

Permafrost, defined as the ground that remains at or below 0°C for at least two consecutive years, underlies 24% of the land in the Northern Hemisphere (Chadburn et al., 2017). With a rapid increase of temperature in the northern permafrost region (~0.6°C decade⁻¹), the normally frozen ground has thawed and released more carbon, further amplifying global climate change (Schuur et al., 2015). Unlike other ecosystems with relatively stable ground status, permafrost is degrading due to high sensitivity to climate warming, with a substantial decrease of permafrost extent, and increases of soil temperature, annual thawing duration (i.e., earlier start of thaw [SOT] and later end of thaw), and active layer thickness (ALT) (Biskaborn et al., 2019; Box et al., 2017). For example, Wang et al. (2019b) reported that the permafrost extent declined significantly in QTP regions, with a rate of 6.6 × 10⁴ km² decade⁻¹ from 1980 to 2010. Based on field measurements of permafrost soil temperature from the Global Terrestrial Network, Biskaborn et al. (2019) found a global warming trend of 0.29 ± 0.12°C in permafrost regions. In addition, a 14 cm year⁻¹ trend of ALT was observed in the East Siberian Arctic Shelf during the last three decades (Shakhova et al., 2017). All these changes not only alter the spatial extent and thermal state of permafrost, ALT, freeze/thaw status (F/T), and snow depth (Park et al., 2016; Schuur et al., 2009) but also regulate vegetation growth and ecosystem carbon uptake via changes in soil hydrology and nutrients (Chen et al., 2012; Jin et al., 2021). For example, earlier thaw and increased ALT are expected to amplify the sensitivity of vegetation to permafrost hydrological dynamics (Walvoord & Kurylyk, 2016), associated with soil nutrient availability (Keuper et al., 2012). However, due to the paucity of field measurements and ground, sub-ground observations, our understanding of how permafrost degradation impacts local vegetation activities is still inadequate (Frey & McClelland, 2009). Given the critical role of permafrost in maintaining global carbon balance and climate stability (Myneni et al., 1997; Schuur et al., 2015), understanding the biogeochemical implications of permafrost degradation, particularly its effect on vegetation spring phenology (i.e., GUD), is needed for a better interpretation of carbon cycle and projection of climate change.

Permafrost acts as a barrier to vertical water flow, and its degradation could increase soil moisture and soil nutrients availability under warming (Lawrence et al., 2015), further impacting vegetation growth in spring. Consequently, permafrost degradation could be an essential but neglected factor that controls the interannual variability of GUD. To test this, we quantitatively investigated the effects of permafrost degradation on GUD. The objectives of the study were (a) to analyze the impacts of permafrost degradation on GUD across the northern permafrost region and (b) to quantify the sensitivities of GUD to climate change and permafrost degradation.

2 MATERIAL AND METHODS

2.1 Study area

Our study focused on the permafrost regions in Northern Hemisphere, mainly located in Siberia, the Tibetan Plateau, Alaska, the Canadian Arctic, and other high mountain regions (Figure 1, Gruber, 2012). High-latitude permafrost regions, with the high possibility of forming permafrost measured by permafrost zonation index (PZI, Figure 1), are mainly covered by Open Shrublands (OS), Evergreen Needleleaf Forests (ENF), and Deciduous Needleleaf Forests (DNF). High-mountain and mid-latitude regions, with relatively lower PZI, are dominated by Grasslands (GRA) and Mixed Forests (MF). Other vegetation types include Savannas (SAV) and Permanent Wetlands (WET). Based on a land cover data set (MODIS/Terra + Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG, MCD12C1), we removed all croplands in the study area to exclude the effects of human activity on agricultural ecosystems. We also eliminated areas with sparse vegetation by removing pixels with mean annual NDVI <0.1, reducing the noises from non-vegetation areas (Shen et al., 2014).

Details are in the caption following the image — **FIGURE 1**
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Spatial distribution of vegetation types from MCD12C1 v006 across the northern permafrost region. The permafrost map is delineated by Brown et al. (1997). The subfigure is the Permafrost Zonation Index (PZI) that represents to what degree permafrost exists (Gruber, 2012)

2.2 Data sets

2.2.1 Satellite NDVI data set

We used the third-generation GIMMS NDVI (NDVI3g, 1982–2015) time series, with a spatial resolution of 1/12° and a temporal resolution of 15 days, to extract satellite-based vegetation GUD. The NDVI3g time series (http://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1), as the longest continuous observation of vegetation greenness, has been widely applied for monitoring vegetation dynamics at regional and global scales (Fensholt & Proud, 2012; de Jong et al., 2011; Kim et al., 2012; Kong et al., 2017). Studies using the NDVI3g to extract vegetation GUD have demonstrated the reliability of this data set in spring phenological research (Barichivich et al., 2013; Buitenwerf et al., 2015; Jeong et al., 2011; Piao et al., 2015; Zhang et al., 2013).

2.2.2 Landscape surface freeze/thaw status

We used the earth system data record of daily landscape freeze/thaw status (FT-ESDR) at a spatial resolution of 25 km from the Numerical Terradynamic Simulation Group (NTSG), the University of Montana, to determine permafrost SOT (Kim et al., 2017) (http://files.ntsg.umt.edu/data/FT_ESDR/). The FT-ESDR is derived using daily radiometric brightness temperature (Tb) measurement time series at 37GHz (V-pol) frequency from the Scanning Multichannel Microwave Radiometer (SMMR, 1979–1986) and the Special Sensor Microwave Imager (SSM/I, 1987–2020). A detailed description of the FT-ESDR product, methods development, and validation scheme are provided by Kim et al. (2012). To obtain long-term observation of SOT, here we only used SSM/I-based daily (morning overpass) FT data to extract permafrost SOT (1987–2017).

2.2.3 Water indicators

We collected monthly volumetric soil moisture (0.25° × 0.25°, 1987–2015) that represents the content of liquid water in a surface soil layer of 2–5 cm depth (m³ m⁻³), derived from the Climate Data Store (CDS, https://cds.climate.copernicus.eu). This data set is based on ESA Climate Change Initiative soil moisture version 03.3, providing soil moisture estimates over the globe from a large set of satellite sensors. To quantify water availability, we also acquired the Standardized Precipitation–Evapotranspiration Index (SPEI, 0.5° × 0.5°, 1987–2015) from SPEIbase v.2.5 at Consejo Superior de Investigaciones Científicas (CSIC) (http://spei.csic.es/database.html). As a widely used drought index, the SPEI is determined by the difference between precipitation and potential evapotranspiration, which is standardized following a log-logistic distribution. In this study, we applied 3-month SPEI that denotes the cumulative water balance for the previous three months (Peng et al., 2019).

2.2.4 Multiscalar climate data sets

Spring (March–May) monthly climate data sets, including temperature, precipitation, and cloud cover (a proxy of insolation), were derived from the Climatic Research Unit Time Series (CRU-TS 4.00, Harris et al., 2014) at a spatial resolution of 0.5° (https://sites.uea.ac.uk/). Several studies used these data sets to explore the relationships of plant phenological changes with climate (Fu et al., 2015; Piao et al., 2014, 2015; Wu et al., 2021). In addition, we used daily mean temperature at 0.5° spatial resolution from the Climate Prediction Center (CPC) (https://psl.noaa.gov/) to estimate permafrost ALT.

2.3 Determination of vegetation green-up date

Based on the time overlapping of NDVI3g (1982–2015) and FT-ESDR (1987–2017), our study focused on the period of 1987–2015. We first used a modified Savitzky–Golay filter to exclude abnormal NDVI data (Chen et al., 2004) and replaced snow-affected data with the most recent acceptable quality data (Shen et al., 2014). We then applied two independent methods to extract GUD from the NDVI time series and used their averaged results as final GUD (Wu et al., 2021) to reduce uncertainty from one single method (Figure 2). The first method is the dynamic threshold approach (White et al., 1997). For each pixel, we calculated the NDVI ratio annually as follows:

$urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0001$ (1)

where NDVI, NDVI_min and NDVI_max are the daily NDVI, the annual maximum and minimum of the NDVI curve, respectively. Vegetation GUD was determined as the day of the year when NDVI_ratio increased to 0.5.

The second method is using the double logistic function (Elmore et al., 2012). We divided the annual NDVI curve for each pixel into two sections by the maximum of NDVI, and applied a piecewise logistic function to fit each section:

$urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0002$ (2)

where $urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0003$ is time in days, y(t) is the NDVI value at time $urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0004$ , and $urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0005$ are fitting parameters. Specifically, $urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0006$ is the background NDVI; $urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0007$ is the difference between the background and the amplitude of the late summer and autumn plateau NDVI units; $urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0008$ and $urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0009$ are the midpoints of the transitions for green-up and senescence/abscission, respectively; $urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0010$ and $urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0011$ are the transitions curvature parameters (normalized slope coefficients); and $urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0012$ is the summer green-down parameter. Vegetation GUD was determined as the time when the change rate of fitted NDVI reached its first local maximum value.

2.4 Determination of permafrost start of thaw

We used daily FT records derived from FT-ESDR to estimate permafrost SOT for 1987–2015. The FT-ESDR identifies frozen status and thawed status with a state code of 0 and 1, respectively (Kim et al., 2017). To remove regions with the all-the-year stable ground, we first excluded pixels with yearly consistent frozen or thawed status. We also applied FT-ESDR annual quality assurance map to remove regions of low quality (<70% agreement with station observations). The SOT is determined by two conditions: (a) the date with a transition of state code from 0 to 1 (i.e., from frozen to thawed) and (b) the state code equals 1 (i.e., thawed state) at least seven consecutive days. For the threshold selection, we used 3, 5, 7, 9, and 11 days as thresholds and got similar results, indicating that the choice of the threshold of consecutive days does not affect the interannual variability of SOT, but higher or lower thresholds will lead to slightly later or earlier mean SOT.

2.5 Estimation of permafrost ALT

We applied the Stefan equation to estimate permafrost ALT using daily mean temperature. This approach has been widely used to predict thaw depth, especially in remote regions with limited ground measurements (Anisimov et al., 2002; Zhang et al., 2005). The simplified equation is expressed as (Shiklomanov & Nelson, 2002):

$urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0013$ (3)

where ALT (m) is estimated using degree-days of thaw (DDT), which is an index of thawing (^oC-day) calculated as the sum of the daily mean temperature above 0°C; E is an edaphic factor that is calculated as:

$urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0014$ (4)

where λ is the thermal conductivity of thawing soil (W m⁻¹ °C⁻¹); s = 86,400 (s day⁻¹) is a time conversion factor; Q is the volumetric latent heat of fusion (J m⁻³); n is the n-factor calculated as the ratio of degree–day sums of the ground surface to air temperatures when both are above 0°C. In this study, we used the static site-based edaphic factor, grouped by vegetation type, to estimate ALT (Zhang et al., 2005). The mean edaphic factor of forests, open shrublands, savannas, grasslands, and permafrost wetlands is 0.046, 0.050, 0.047, 0.033, and 0.064, respectively. Detailed descriptions of the edaphic factor are provided in Zhang et al. (2005).

2.6 Statistical analysis

In this study, we used partial correlation analyses to examine the responses of vegetation GUD to permafrost degradation in terms of SOT and ALT. With this approach, the confounding effects of spring temperature, precipitation, and insolation on vegetation GUD can be, to the greatest degree, excluded. Statistical significance was set at p < 0.05, with an R threshold of ±0.367 for 29-year analysis according to the correlation significance table. Before performing statistical analysis, we resampled climatic data sets, SOT, and ALT into 1/12° to match GUD derived from NDVI3g data. We used the Theil–Sen slope estimator, a nonparametric and median-based slope estimator, to investigate the temporal trends of vegetation GUD, permafrost SOT, and ALT. All trends were evaluated by the Mann–Kendall trend test at a significance level of 0.05.

To further quantify and compare the responses of GUD to climatic and environmental change, we applied multiple linear regression to determine and compare the sensitivities of GUD to climatic variables (i.e., temperature, precipitation, insolation) and permafrost degradation (i.e., SOT and ALT) (Fu et al., 2015b; Piao et al., 2015; Wu et al., 2021). Temporal anomaly of GUD (GUD_ANL) was set as the dependent variable and temporal anomaly of spring mean temperature (T_ANL), accumulated precipitation (P_ANL), insolation (I_ANL), SOT (SOT_ANL), and ALT (ALT_ANL) were set as independent variables (all variables non-detrended). To identify the dominant factor with the greatest control on GUD variations for a location, we calculated the standardized regression coefficients as the sensitivities of each variable (i.e., Sen_T, Sen_P, Sen_I, Sen_SOT, and Sen_ALT). For pixels under the significance test of regression (t-test, p < 0.05), the independent variable with the highest absolute value of sensitivity represents the dominant factor controlling GUD variations. For example, the dominant factor to GUD_ANL variations of each pixel can be determined as:

$urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0015$ (5)

$urn:x-wiley:13541013:media:gcb16011:gcb16011-math-0016$ (6)

where β_T-GUD (days/°C), β_P-GUD (days/mm), β_I-GUD (days/%), β_SOT-GUD (days/day), and β_ALT-GUD (days/cm) represent regression coefficients of spring temperature, precipitation, insolation, SOT and ALT in Equation (5), respectively. RES is the residual. Sen_VAR is the sensitivity of variable determined by the standardized regression coefficient. SD (VAR_ANL) and SD (GUD_ANL) represent standard deviation of anomalies of variables and GUD, respectively. The dominant factor of GUD_ANL variations is defined as the factor with the highest absolute value of Sen_VAR. To compare the responses of GUD_ANL and SOT_ANL to temperature variations, we also calculated the regression coefficients of SOT_ANL to T_ANL (β_T_-SOT), P_ANL (β_P_-SOT), I_ANL (β_I_-SOT), and ALT_ANL (β_ALT-SOT) using the same approach.

Given the uncertainty of gridded climate factors, especially temperature from CRU and CPC data sets in high latitudes, some cautions are needed when interpreting the SOT and ALT estimation, partial correlations, and sensitivity analyses.

3 RESULTS

3.1 Temporal trends of green-up date, start of thaw, and active layer thickness

We found that, apart from high-altitude regions (QTP) with high GUD, multiyear averaged GUD generally increased along with the latitude gradient, with a mean value of DOY 138 in the northern permafrost region during 1987–2015 (Figure 3a). Using the Theil–Sen slope estimator, we found 51.3% of the areas experienced significantly earlier GUD (p < 0.05), three times more than regions with delaying trends (15.1%, p < 0.05), with a mean slope of −2.1 day decade⁻¹ (Figure 3b). The largest advancing slope was observed in regions with relatively high PFI (0.8–1) (Figure 3c), with a mean value of −2.9 ± 1.8 day decade⁻¹. Grouping slope into different vegetation types confirms dominantly advancing trends among most ecosystems (Figure 3d), with a mean value of −2.2 ± 1.9, −1.9 ± 1.1, −1.7 ± 2.1, −1.7 ± 2.0 day decade⁻¹ for OS, MF, ENF, and SAV, respectively.

Multiyear averaged SOT is spatially similar to GUD that higher SOT was observed in high latitudes and QTP regions, with a mean value of DOY 124 (Figure 4a). Nearly 37% of the areas showed significantly advanced SOT (p < 0.05), with a mean value of −4.1 days decade⁻¹ (Figure 4c). Compared with the regions with low PZI (0–0.2), high-PZI permafrost showed a strong advancing trend (Figure 4e). Generally, higher ALT was observed in comparatively lower latitude and high mountains, such as forests and QTP grasslands (Figure 4b), with a mean of 47.3 cm. We found that ALT significantly increased (p < 0.05) in 49.1% of the regions with a mean slope of 1.1 cm decade⁻¹ (Figure 4d). For areas with high PZI (0.8–1), ALT was observed with the highest increasing trend, with a mean value of +1 cm decade⁻¹ (Figure 4f).

3.2 Responses of vegetation green-up date to permafrost degradation

Using partial correlation analysis to exclude the effects of precipitation and insolation, we found GUD was negatively correlated with temperature for 47.9% of the regions (p < 0.05, Figure 5a). After further excluding the effects of SOT and ALT, the percentage of negative correlations decreased to 19.1% (p < 0.05, Figure 5b). Based on variations of averaged R along the latitudinal gradient, we observed a substantial increase of R when SOT and ALT are excluded, with a significant slope of 0.011 per degree (p = 0.003, Figure 5c). In addition, the frequency of significantly negative correlations decreased, especially for regions with high PZI (0.8–1, Figure 5d).

GUD showed significant positive partial correlations with SOT in 37.6% of the regions (p < 0.05, Figure 6a), where OS, SAV, and DNF were the dominant vegetation types (Figure 6c). In contrast, GUD was partially and negatively correlated with ALT in 35.1% of the regions (p < 0.05, Figure 6b), and 1.8% of the areas with positive partial correlations were mainly distributed in the OS and GRA (p < 0.05, Figure 6d).

3.3 Relationship between permafrost degradation and soil water stress

By excluding the effects of temperature, precipitation, and insolation, we found significant partial correlations between SOT and soil moisture in 17% of the regions (p < 0.05), among which nearly 95% were negatively correlated (Figure 7a). In addition, ALT was significantly and positively correlated with soil moisture in 13.6% of the regions (p < 0.05), six times more than significantly negative correlations (2.2%, Figure 7b). Spring SPEI generally showed an increasing trend during 1987–2015 over permafrost regions with a mean slope of 0.3 decade⁻¹, and the highest increasing trends were observed in the areas with low PZI (Figure 7c,d).

3.4 Sensitivities of green-up date to climate change and permafrost degradation

Spatially averaged sensitivities of GUD to temperature, precipitation, insolation, SOT, and ALT were −0.09 ± 0.12, 0.05 ± 0.11, −0.01 ± 0.08, 0.29 ± 0.14, and −0.31 ± 0.13, respectively (Figure 8). We determined the dominant factor for pixels under significance test of regression (t-test, p < 0.05) using the absolute value of sensitivity. Overall, climatic factors dominated 19.6% of the regions (temperature: 8.9%, precipitation: 5.8%, insolation: 4.9%); SOT and ALT dominated 20.8% and 20.9% of the regions, respectively (Figure 9a). Along the latitudinal gradient, we found an overall decrease of climatic factors and an increase of SOT and ALT as the dominant factor, with a balance point at ~51^oN where the portion of climate factors equals the sum of SOT and ALT (Figure 9b). Grouping regions into different vegetation types shows that more than 40% of WET, ENF, and DNF were significantly regressed, while the percentage decreased to ~20% for MF and GRA (Figure 9c). Overall, permafrost degradation dominated more regions than climate change for each vegetation type, and the ratios of permafrost degradation to climate change were higher than or close to four for OS, SAV, and DNF. The ratio of permafrost degradation to climate change in the regions with high PZI (>0.8) was nearly four times more than that with lower PZI (Figure 9d).

3.5 Warming effects on green-up date and start of thaw

We found that the regression coefficient of GUD_ANL-T_ANL (β_T-GUD) was overall lower than the regression coefficient of SOT_ANL-T_ANL (β_T-SOT), with a mean value of −0.6 and −0.9 days ^oC⁻¹, respectively (Figure 10a and b). For pixels under the significance test of regression (t-test, p < 0.05), 65% of β_T-GUD was negative, but it increased by 11% for β_T-SOT. The multi-year average difference of GUD and SOT (DIF_GUD-SOT) varied along with latitude, with a slope of −1.1 days per degree (p < 0.001). Negative DIF_GUD-SOT was mainly located in Northeastern Russia and Alaska (Figure 10c and d). Temporally, DIF_GUD-SOT showed an increasing trend in 21.9% of the regions (p < 0.05), with a mean slope of 3.4 days decade⁻¹. No significant trend of DIF_GUD-SOT slope was found along the latitudinal gradient (Figure 10e and f).

4 DISCUSSION

4.1 Effects of permafrost degradation on green-up date

Northern permafrost regions are warming more rapidly than lower latitudes and altitudes due to climate amplification caused by albedo-temperature feedback (Chapin et al., 2005). Permafrost ecosystems are, thus, predicted to respond more rapidly to climate change than other terrestrial ecosystems (Schuur et al., 2015). However, large discrepancies remain in findings from extant studies based on the direct climate effects, calling for more investigation on other environmental cues. Our results showed that permafrost degradation under warming plays an important but neglected role in controlling the interannual variability of GUD during 1987–2015, which is highly related to soil water availability. In line with previous research (Yang et al., 2017; Yu et al., 2010; Zhang et al., 2013), we found dominantly advanced GUD across the northern permafrost region, with a mean of −2.1 days decade⁻¹, along with earlier SOT and thicker ALT. In this study, more than half of the areas with significant partial correlations between temperature and GUD become nonsignificant if the effects of permafrost degradation are excluded, especially in high latitudes (Figure 5), indicating that permafrost degradation might control the warming impact on GUD. Partial correlation analyses also revealed that GUD had a predominantly positive correlation with SOT and a negative correlation with ALT (Figure 6) after excluding the effects of temperature, precipitation, and insolation. The direct linkage between GUD and permafrost might be related to the hydrological impacts of permafrost degradation. Given the transition from frozen to thaw could directly regulate soil water dynamics and nutrient availability, this transition timing would have biochemical impacts on vegetation growth (Jin et al., 2021). For example, a review of the hydrologic effects of permafrost degradation emphasized that earlier thawing and increased ALT would amplify the sensitivity of vegetation to permafrost thaw-induced changes in hydrology (Walvoord & Kurylyk, 2016). Additionally, Keuper et al. (2012) reported that thawing permafrost increases plant-available nitrogen, stimulating vegetation productivity. Our results confirmed that advanced SOT and thicker ALT could improve soil water availability (Figure 7a,b), further leading to earlier GUD. As a key indicator of drought, SPEI has been used to examine the effects of drought on vegetation phenology (Peng et al., 2019). In this study, we observed a significantly increasing trend of SPEI in spring, indicating alleviated water stress that advances GUD. Due to physiological adaptations and functional strategies, permafrost regions with limited precipitation can respond quickly to thawing-induced soil water change (Yang et al., 2018). It should be noted that interannual variabilities of GUD, SOT, and ALT are mainly driven by temperature directly or indirectly. In our study, SOT and ALT estimations are fully based on brightness temperature and air mean temperature, which could bring uncertainties regarding the impacts of permafrost degradation. Moreover, we only focused on two indicators, that is, SOT and ALT, which may fail to accurately quantify permafrost thawing and degradation, especially without enough validation by field observations. For example, permafrost extent, setting as fixed in this study, has been significantly decreasing under warming. Thus, additional manipulation experiments are needed to understand better how spring phenology responds to permafrost degradation. In addition, permafrost degradation-induced vegetation successions should be considered as a crucial factor of greenness-based phenology changes. The impacts of permafrost degradation on GUD could be more complex, especially including the direct climate effects (i.e., temperature, precipitation, and insolation), in need of more future investigations on the climate–permafrost–vegetation interactions.

4.2 Comparison of the sensitivities of green-up date to climate change and permafrost degradation

Piao et al. (2015) reported relatively high temperature sensitivity to GUD in high latitudes, indicating a stronger response of GUD to warming in permafrost regions. Interestingly, by adding SOT and ALT into the regression model, we found nearly 35% of the areas, mainly located in high latitudes, showed significantly positive temperature sensitivity (Figure 9a and b). This result suggested that temperature increases might directly have a neutral or even delaying effect on GUD due to insufficient chilling accumulation (Piao et al., 2019), consistent with previous conclusions (Laube et al., 2013; Yu et al., 2010). The advancement of local GUD might be attributed to the indirect climate effect, here namely warming-induced permafrost degradation. For example, we found stronger temperature sensitivity of SOT than that of GUD, with a mean of −0.9 and −0.6 days ^oC⁻¹, respectively (Figure 9b,a). Thus, advanced SOT might better alleviate soil water stress (Figure 7a,c) and further offset the delaying effect of temperature. With a uniform temperature increase, SOT is expected to advance faster than GUD. This discrepancy of temperature sensitivities explained the difference of GUD and SOT, which overall had an increasing trend during 1987–2015 (Figure 10e,f).

Compared with climate factors, permafrost degradation indicators, that is, SOT and ALT, dominated all vegetation types across the northern permafrost region (Figure 9c). Stronger dominance was observed in herbaceous species, such as shrublands and savanna, than forest woody species. This divergence might be associated with vegetation biophysical characteristics. Previous studies illustrated that phenological transitions of herbaceous species could be highly regulated by soil and plant water deficits, resulting in a close relationship between phenology and soil water dynamics (Gómez-Giráldez et al., 2020; Richardson et al., 2013). Several studies also illustrated the importance of the timing of snowmelt in driving shifts in GUD in subalpine meadows and the Arctic and alpine tundra (Høye et al., 2007; Richardson et al., 2013; Steltzer et al., 2009), involving the exposure to cold air temperatures and soil water stress. Compared with woody species, herbaceous species with shorter roots are more easily affected by the permafrost temperature and water availability. Meanwhile, the dominant factor ratio of permafrost degradation to climate change in the regions with high PZI (>0.8) was higher than that with lower PZI (Figure 9d), also indicating the close link between GUD and permafrost existences.

5 CONCLUSIONS

Using 29-year (1987–2015) continuous satellite observations across the northern permafrost region, we found that permafrost degradation has substantial effects on vegetation GUD. Based on partial correlation analyses, SOT and ALT showed predominantly positive and negative correlation with GUD, respectively. Our results indicate that these correlations could be largely explained by soil water availability: an advancement of SOT and an increase of ALT would enhance soil water availability, further leading to earlier GUD. We applied multiple linear regression to quantify and compare the direct and indirect climatic sensitivities. We found that GUD is more sensitive to permafrost degradation than direct climate effects, indicating the importance of permafrost status in modeling GUD. The discrepancy of temperature sensitivities (GUD vs. SOT) also explained the increasing trend of the difference between GUD and SOT. Overall, our results emphasized the importance of warming-induced permafrost degradation in explaining plant spring phenology variations, which is enlightening for improved projections.

ACKNOWLEDGMENTS

This study was funded by the National Science Foundation (#1724786). The authors would like to thank three anonymous reviewers for their comments and suggestions, which greatly helped to improve the quality of the manuscript. We appreciate all public data sets PIs in providing their valuable data for our analyses.

AUTHOR CONTRIBUTIONS

Jian Wang and Desheng Liu designed the study. Jian Wang carried out all the analyses, constructed the figures and tables, and wrote the first draft. All authors contributed to the interpretation of the results and the text.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available in Zenodo at https://doi.org/10.5281/zenodo.5728302.

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Citing Literature

Volume28, Issue4

February 2022

Pages 1569-1582

This article also appears in:

Climate Change and Extreme Events are Changing the Biology of Polar Regions

Details

Research funding

National Science Foundation. Grant Number: 1724786

Keywords

Publication History

Issue Online: 16 January 2022
Version of Record online: 09 December 2021
Accepted manuscript online: 01 December 2021
Manuscript accepted: 22 November 2021
Manuscript revised: 04 November 2021
Manuscript received: 09 August 2021

Vegetation green-up date is more sensitive to permafrost degradation than climate change in spring across the northern permafrost region

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Abstract