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Contrasting Responses of Autumn-Leaf Senescence to Daytime and Night-Time Warming

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Contrasting Responses of Autumn-Leaf Senescence to Daytime and Night-Time Warming LETTERS https://doi.org/10.1038/s41558-018-0346-z Contrasting responses of autumn-leaf senescence to daytime and night-time warming Chaoyang Wu 1,2*, Xiaoyue Wang1,2, Huanjiong Wang 1,2*, Philippe Ciais 3, Josep Peñuelas 4,5, Ranga B. Myneni6, Ankur R. Desai 7, Christopher M. Gough8, Alemu Gonsamo 9, Andrew T. Black1, Rachhpal S. Jassal10, Weimin Ju11, Wenping Yuan12, Yongshuo Fu13, Miaogen Shen14, Shihua Li15, Ronggao Liu16, Jing M. Chen9 and Quansheng Ge 1,2* Plant phenology is a sensitive indicator of climate change1–4 be as important as spring in regulating the interannual variability and plays an important role in regulating carbon uptake by of carbon balance7. plants5–7. Previous studies have focused on spring leaf-out by LSD has been occurring later in most regions over the past few daytime temperature and the onset of snow-melt time8,9, but decades18, but providing an explanation for this change is difficult9. the drivers controlling leaf senescence date (LSD) in autumn An increase in global temperature is assumed to be a driver of LSD remain largely unknown10–12. Using long-term ground pheno- trends19, but studies have indicated that the contribution of tem- logical records (14,536 time series since the 1900s) and satel- perature to LSD variability is low, especially compared with spring lite greenness observations dating back to the 1980s, we show phenology20,21. We argue that ignoring the asymmetric effects22 of that rising pre-season maximum daytime (Tday) and minimum Tday versus Tnight and their differing impacts on LSD contributes to night-time (Tnight) temperatures had contrasting effects on the the reported overall low contribution of temperature to LSD vari- timing of autumn LSD in the Northern Hemisphere (> 20° N). ability. To test this, we used measured and gridded pre-season If higher Tday leads to an earlier or later LSD, an increase in Tnight (defined as months from June to LSD) Tday and Tnight values in the systematically drives LSD to occur oppositely. Contrasting Northern Hemisphere, together with LSD data from three different impacts of daytime and night-time warming on drought stress datasets: (1) long-term phenological observations at ground sites may be the underlying mechanism. Our LSD model consider- from 14,536 time series since the 1900s (Supplementary Fig. 1), ing these opposite effects improved autumn phenology mod- (2) the latest third generation of the normalized difference vegeta- elling and predicted an overall earlier autumn LSD by the end tion index (NDVI; Global Inventory Modeling and Mapping Studies of this century compared with traditional projections. These NDVI3g version 1) for 1982–2015 and (3) NDVI and enhanced veg- results challenge the notion of prolonged growth under higher etation index (EVI) values from the Moderate-Resolution Imaging autumn temperatures, suggesting instead that leaf senes- Spectroradiometer (MODIS) products for 2001–2015. cence in the Northern Hemisphere will begin earlier than Pre-season forcing had a better predictive strength on LSD than currently expected, causing a positive climate feedback. either summer or autumn climate forcing alone (Supplementary Climate change over the past several decades has modified Fig. 2). Because pre-season Tday and Tnight were highly correlated, we the dates of plant flowering, leaf emergence, growth stages and used a partial correlation to remove the effects of Tnight and of precip- 13 senescence, collectively termed phenology , with substantial itation and radiation (similarly for Tnight) to investigate the response 4 + + ecological and environmental consequences . Both observations of LSD to Tday. Correlations were classified into four types, Tday Tnight − − + − − + and model simulations have found that air temperature has a (type A), Tday Tnight (type B), Tday Tnight (type C) and Tday Tnight (type D), positive influence on the onset of plant growth in the Northern where T+ and T− represent the positive and negative partial correla- Hemisphere; for example, higher spring temperature triggers tion coefficient, R, respectively, of temperature T with LSD. earlier leaf-out and flowering dates and hence extends the grow- Overall, all three datasets suggest that the onset of autumn LSD 8,14,15 ing season . In contrast to those extensive research efforts on responded oppositely to Tday and Tnight. The proportions of ground spring phenology, autumn phenology, particularly LSD, is more sites of types A and B were significantly lower than those of types challenging to understand and has not received sufficient atten- C and D (Fig. 1a). More significant R values for both Tday and Tnight tion16,17, while also serving as an important indicator of changing were found within types C and D, with only two and one records foliar physiological properties. However, autumn phenology may out of 2,231 time series having significant R values within types 1The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. 2University of the Chinese Academy of Sciences, Beijing, China. 3Laboratoire des Sciences du Climat et de l’Environnement, IPSL-LSCE CEA CNRS UVSQ, Gif-sur-Yvette, France. 4CSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain. 5CREAF, Barcelona, Spain. 6Department of Earth and Environment, Boston University, Boston, MA, USA. 7Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, WI, USA. 8Department of Biology, Virginia Commonwealth University, Richmond, VA, USA. 9Department of Geography and Planning, University of Toronto, Toronto, Ontario, Canada. 10Faculty of Land and Food Systems, University of British Columbia, Vancouver, British Columbia, Canada. 11International Institute for Earth System Science, Nanjing University, Nanjing, China. 12School of Atmospheric Sciences, Center for Monsoon and Environment Research, Sun Yat-Sen University, Guangzhou, China. 13College of Water Sciences, Beijing Normal University, Beijing, China. 14CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China. 15School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, China. 16State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China. *e-mail: [email protected]; [email protected]; [email protected] 1092 NatURE CLImatE CHANGE | VOL 8 | DECEMBER 2018 | 1092–1096 | www.nature.com/natureclimatechange NATURE CLIMATE CHANGE LETTERS a Ground sites The satellite greenness products also allowed us to evaluate spa- 50 tial patterns of LSD changes in response to variations in Tday and P day > 0.05 and P night > 0.05 Tnight (Fig. 2). For the NDVI3g data, higher Tday was associated with 40 P day > 0.05 and P night > 0.05 a delayed LSD for 10.7% of the pixels (mostly boreal regions) and ) with an earlier LSD for 7.5% of the pixels (central North America, P > 0.05 and P > 0.05 30 day night borders of Eurasia and central China). Tnight had evident opposite influences on LSD than Tday. The patterns of opposite effects from P day > 0.05 and P night > 0.05 20 T and T on LSD were highly spatially consistent in all regions Frequency (% day night where Tday and Tnight were significantly correlated with LSD. Similar 10 results were obtained with MODIS observations (Fig. 2b,d). LSD for approximately 20% of all pixels was significantly correlated with Tday, of which 60.1% and 39.9% were negatively and positively correlated, 0 respectively. The area where LSD was positively correlated with Tnight T + T + T – T – T + T – T – T + day night day night day night day night was larger (9.4%) than the area with negative correlations (6.5%). Correlation type Vegetation grouped into Köppen–Geiger zones showed con- b NDVI3g trasting patterns between the effects of Tday and Tnight on LSD 50 (Fig. 2e,f). Type D was more widely distributed, while type C was more common for continental climates. Monsoon-influenced but 40 not extremely cold regions and mild climates also had higher pro- portions of type C. Grouping these correlation types by vegetation 30 type led to similar results (Fig. 2g,h). In theory, we would expect to find type C more in wet vegetation types and type D in dry types. The 20 real world seems to show the same thing, but there could be many Frequency (%) locations that do not neatly fall into that continuum, suggesting addi- tional mechanisms may be at work, probably species adaptation. 10 Our results suggest that ecological trade-offs, particularly those driven by regional differences in water stress, may underlie the con- 0 trasting relationships between LSD and Tday and Tnight. Type C was T + T + T – T – T + T – T – T + day night day night day night day night mostly found in humid regions where water is a less limiting factor Correlation type for plant growth. In these cases, a higher Tday, in the likely absence of severe water stress, benefits photosynthesis, while elevated T c night MODIS increases night-time leaf respiration. 50 Explanations for the prevalence of type D relations in dry regions are more complicated. The standardized precipitation evapotrans- 40 piration index (SPEI)23, an indicator of drought stress, accounted ) for the contrasting effects of increases in Tday and Tnight on LSD for 30 type D (Fig. 3). Partial correlation data indicate that increased Tday is negatively correlated with the SPEI (Fig. 3a), a stronger sensitiv- 20 ity to drought in dry regions that negatively affects plant growth Frequency (% and consequently leads to an earlier LSD. In contrast, we found that 10 an increase in Tnight is associated with a higher SPEI, that is, wetter conditions and, arguably, reduced water stress, which could extend the duration of photosynthesis and lead to delayed LSD (Fig. 3b). 0 + + – – + – – + The latter mechanism is consistent with the generally positive par- T day T night T day T night T day T night T day T night tial correlation values between evapotranspiration and Tnight, that is, Correlation type more soil moisture being available for evapotranspiration in the late season, sustaining delayed LSD (Fig.
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