Effects of temperature variability and extremes on spring phenology across the contiguous United States from 1982 to 2016

Abstract

Warming climate and its impact on vegetation phenological trends have been widely investigated. However, interannual variability in temperature is considerably large in recent decades, which is expected to trigger an increasing trend of variation in vegetation phenology. To explore the interannual phenological variation across the contiguous United States (CONUS), we first detected the onset of vegetation greenup using the time series of the daily two-band Enhanced Vegetation Index (EVI2) observed from the AVHRR Long-Term Data Record (1982–1999) and the MODIS Climate Modeling Grid (2000–2016). We then calculated the interannual variation in greenup onset during four decadal periods: 1982–1989, 1990–1999, 2000–2009 and 2010–2016. Further, the trend of interannual variation in greenup onset from 1982 to 2016 was analyzed at pixel and state levels. Extreme phenological events were also determined using a greenup onset anomaly for each pixel. Similar approaches were applied to spring temperatures to detect extreme years and to the temporal trend of interannual variation to explain the phenological variation. The results revealed that 62% of pixels show an increasing interannual variation in greenup onset, and in 44% of pixels, this variation could be explained by the temperature. Although extreme phenology occurred locally in different years, three nationwide extreme phenological years were distinguished. The extreme warm spring that occurred in 2012 resulted in the occurrence of greenup onset as much as 20 days earlier than normal in large parts of the CONUS. In contrast, greenup onset was much later (up to 30 days) in 1983 and 1996 due to cool spring temperatures. These findings suggest that interannual variation in spring phenology could be much stronger in the future in response to climate variation, which could have more significant impacts on terrestrial ecosystems than the regular long-term phenological trend.

Natural and Political Determinants of Ecological Vulnerability in the Qinghai–Tibet Plateau: A Case Study of Shannan, China

“…Ecological environmental problems, such as climate anomalies [1], vegetation reduction [2], soil erosion [3], and land desertification [4], frequently occur all over the world and have led to a vulnerable environment in recent decades [5]. Ecological vulnerability is an ecosystem-inherent property, and it occurs when the ecological environment is gradually degraded or deteriorated by external interference on a specific space-time scale, including the sensitivity of the system to external interference and the assessment and estimation of system changes [6,7].…”

Disorder or a new order: how climate change affects phenological variability

“…If spring temperatures become more variable between years, spring phenology should also become more variable. But the expectation of more climatic variability may not be borne out broadly in observations, as changes in inter-annual climate variance have been geographically heterogeneous (Liu et al, 2020), and we found an overall slight reduction in interannual temperature variance at the sites represented in this study (Figure S6.5). The observation that climate change leads to more extreme weather events within seasons does not necessarily mean that we should expect more extreme years when the overall, mean trends of climate change are accounted for (Ummenhofer et al, 2017).…”

“…Most studies have assumed constant inter-annual variance, and some have checked and accounted for heteroscedasticity in time-series residuals (e.g., Bartomeus et al, 2011;Wadgymar et al, 2018) but have not made it a focus of study. There is reason to think that inter-annual variance in phenology might be changing, as there have been recent, geographically heterogeneous changes in inter-annual temperature variance (Liu et al, 2020). Further, decreased sensitive to temperature variation (Mulder et al, 2017), chilling requirements in plants (Fu et al, 2015), and physiological development time requirements between phenophases (Ettinger et al, 2018;Primack, 1987) may produce patterns of phenological variance that are different from the variance of their cues.…”

“…Further, decreased sensitive to temperature variation (Mulder et al, 2017), chilling requirements in plants (Fu et al, 2015), and physiological development time requirements between phenophases (Ettinger et al, 2018;Primack, 1987) may produce patterns of phenological variance that are different from the variance of their cues. To our knowledge, the studies that have examined inter-annual phenology variance on a broad scale, using citizen science data (Pearse et al, 2017) and satellite imagery (Liu et al, 2020), have found increases in variance over time, though such changes might also be influenced by changes in monitoring schemes or community composition over time (de Keyzer et al, 2017).…”

Testing the match–mismatch hypothesis in bighorn sheep in the context of climate change

“…We first compared temporal variation of spring and autumn phenology to test whether the mismatch between parturition and spring green-up dates changed over time and if so, whether mismatch was better explained by earlier conception because of warmer autumn weather, or by earlier green-up. We focused on autumn and spring temperatures, since autumn conditions affect median parturition date (Renaud et al, 2019, and see below) and spring temperature affects onset of green-up (Liu & Zhang, 2020). We used generalized additive mixed models ('GAMMs') to identify temporal trends in environmental variables, mismatch and parturition date.…”