Distribution and Attribution of Terrestrial Snow Cover Phenology Changes over the Northern Hemisphere during 2001–2020

Chen, Xiaona; Yang, Yibo; Ma, Yingzhao; Li, Huan

doi:10.3390/rs13091843

Cited by 22 publications

(12 citation statements)

References 55 publications

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“…where, C T is the relative contributions of T s to SOS anomalies, C P is the relative contributions of P t to SOS anomalies, and C S is the relative contributions of S c to SOS anomalies. This approach was used to quantify the contributions from climate variables to alpine vegetation green-up on the roof of the world [44], snow-cover phenology anomalies over the NH [61], and GPP anomalies in the Three North region of China [62].…”

Section: Attribution Analysismentioning

confidence: 99%

Distribution and Attribution of Earlier Start of the Growing Season over the Northern Hemisphere from 2001–2018

Chen

Yang

2022

Remote Sensing

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The start of the growing season (SOS) is a vital ecological indicator for climate change and the terrestrial ecosystem. Previous studies have reported that the SOS over the Northern Hemisphere (NH) has experienced remarkable changes in the past few decades. However, because of the different spatial and temporal coverages of existing SOS studies, a coherent and robust account for SOS changes in the NH has been lacking. Using satellite-retrieved vegetation-phenology datasets, ground observations, and several auxiliary datasets, this study evaluated the performance of the latest MODIS vegetation-dynamics dataset (MCD12Q2-C6) and explored the distribution and attribution of the SOS to climate change over the NH for the period 2001–2018. The validation results using the Chinese Ecosystem Research Network (CERN) and Lilac-leafing observations (Lilac) displayed that the MCD12Q2-C6 has a good performance in SOS monitoring over the NH mid-latitudes. Meanwhile, evidence from MCD12Q2-C6 pointed out that the SOS was advanced by 2.08 days on average over the NH during 2001–2018, especially for Europe, China, and Alaska, United States. In addition, detailed-sensitivity analysis showed that the increased surface air temperature (Ts) (−1.21 ± 0.34 days °C−1) and reduced snow-cover fraction (Sc) (0.62 ± 0.29 days%−1) were the key driving factors of the observed SOS changes over the NH during 2001–2018. Compared with Ts and Sc, the role of total precipitation (Pt) was minor in dominating the spring vegetation-phenology changes at the same period. The findings of this study contribute to our understanding of the responses of SOS to the competing changes of Ts, Pt, and Sc over the NH.

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Section: Attribution Analysismentioning

confidence: 99%

Distribution and Attribution of Earlier Start of the Growing Season over the Northern Hemisphere from 2001–2018

Chen

Yang

2022

Remote Sensing

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“…This warming has been particularly severe in the Arctic (Collins et al, 2013; Houghton et al, 2001; Kåresdotter et al, 2021; Serreze & Barry, 2011). It has led to advanced snowmelt (Chen et al, 2021; Zhong et al, 2021) and earlier emergence of insects (Saalfeld et al, 2021), forcing animals such as shorebirds to breed and migrate earlier (Lameris et al, 2017), with one cost being lower adult survival (Rakhimberdiev et al, 2018). In general, many organisms have advanced their timing of reproduction to counter the adverse effects of the warming climate (Jenni & Kéry, 2003; Root et al, 2003; Walther et al, 2002); the taxa that have advanced the least have shown the most substantial population declines (Lameris et al, 2018; Ross et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Current breeding distributions and predicted range shifts under climate change in two subspecies of Black‐tailed Godwits in Asia

Zhu

Verhoeven

Velasco

et al. 2022

Global Change Biology

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Current breeding distributions and predicted range shifts under climate change in two subspecies of Black-tailed Godwits in Asia This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as

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“…The attribution of SCP change over the past few decades has also received extensive attention [17,18]. Several studies have shown that the distribution of and changes in SCP are susceptible to surface temperature and precipitation [19][20][21] and are greatly affected by topography [22]. However, in China, most snow cover studies are on small areas.…”

Section: Introductionmentioning

confidence: 99%

Snow Cover Phenology Change and Response to Climate in China during 2000–2020

et al. 2022

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Snow cover phenology (SCP) is critical to the climate system. China has the most comprehensive snow cover distribution in the middle and low latitudes and has shown dramatic changes over the past few decades. However, the spatiotemporal characteristics of SCP parameters and their sensitivity to meteorological factors (temperature and precipitation) under different conditions (altitude, snow cover classification, or season) in China are insufficiently studied. Therefore, using improved daily MODIS cloud-gap-filled (CGF) snow-cover-extent (SCE) products, the spatiotemporal characteristics (distribution and variation) and respond to climate of snow cover area (SCA), snow cover start (SCS), snow cover melt (SCM), and snow cover days (SCD) are explored from 2000 to 2020. The results show that in the past 20 years, snow cover in China has demonstrated a trend of decreasing SCA, decreasing SCD, advancing SCS, and advancing SCM, with SCM advancing faster than SCS. The greatest snowfall occurs in January, mainly in northeastern China, northern Xinjiang, and the Tibet Plateau. Spatially, the slope of SCP was mainly within ±0.5 day/year (d/y) Statistics indicated that the area proportion where SCD is significantly reduced is greater than increased; SCD, SCS, and SCM are shortened or advanced in three snow-covered area classifications. Moreover, compared with precipitation, the significantly correlated regions (6–47.2% more than precipitation) and correlation degree (1.23–8.33 times precipitation in significantly correlated snow cover classification) between temperature and SCP in different seasons are larger. For stable snow-covered areas (SSA), SCD are mainly affected by spring temperature below 1500 m and mainly by autumn temperature above 1500 m; the precipitation is more affected in autumn. The correlation of SCP with temperature and precipitation has obvious spatial and seasonal differences and shows characteristic variation with altitude. These results can provide important data support for climate prediction, hydrological research, and disaster warning.

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Distribution and Attribution of Terrestrial Snow Cover Phenology Changes over the Northern Hemisphere during 2001–2020

Cited by 22 publications

References 55 publications

Distribution and Attribution of Earlier Start of the Growing Season over the Northern Hemisphere from 2001–2018

Distribution and Attribution of Earlier Start of the Growing Season over the Northern Hemisphere from 2001–2018

Current breeding distributions and predicted range shifts under climate change in two subspecies of Black‐tailed Godwits in Asia

Snow Cover Phenology Change and Response to Climate in China during 2000–2020

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