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Urban−rural gradients reveal joint control of elevated CO2 and temperature on extended photosynthetic seasons

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Abstract

Photosynthetic phenology has large effects on the land–atmosphere carbon exchange. Due to limited experimental assessments, a comprehensive understanding of the variations of photosynthetic phenology under future climate and its associated controlling factors is still missing, despite its high sensitivities to climate. Here, we develop an approach that uses cities as natural laboratories, since plants in urban areas are often exposed to higher temperatures and carbon dioxide (CO2) concentrations, which reflect expected future environmental conditions. Using more than 880 urban–rural gradients across the Northern Hemisphere (≥30° N), combined with concurrent satellite retrievals of Sun-induced chlorophyll fluorescence (SIF) and atmospheric CO2, we investigated the combined impacts of elevated CO2 and temperature on photosynthetic phenology at the large scale. The results showed that, under urban conditions of elevated CO2 and temperature, vegetation photosynthetic activity began earlier (−5.6 ± 0.7 d), peaked earlier (−4.9  ± 0.9 d) and ended later (4.6 ± 0.8 d) than in neighbouring rural areas, with a striking two- to fourfold higher climate sensitivity than greenness phenology. The earlier start and peak of season were sensitive to both the enhancements of CO2 and temperature, whereas the delayed end of season was mainly attributed to CO2 enrichments. We used these sensitivities to project phenology shifts under four Representative Concentration Pathway climate scenarios, predicting that vegetation will have prolonged photosynthetic seasons in the coming two decades. This observation-driven study indicates that realistic urban environments, together with SIF observations, provide a promising method for studying vegetation physiology under future climate change.

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Data availability

OCO-2 SIF and XCO2 data are available at https://disc.gsfc.nasa.gov/. MODIS EVI and LST data are available at https://ladsweb.modaps.eosdis.nasa.gov/. Precipitation data can be obtained from https://pmm.nasa.gov. Global Historical Climatology Network (version 2) and Climate Anomaly Monitoring System air temperature data are available at https://www.esrl.noaa.gov. Climate Change Initiative land cover data are available at http://maps.elie.ucl.ac.be/CCI/viewer/download.php. GTOPO30 digital elevation model data are available at https://earthexplorer.usgs.gov/. NO2 and O3 data are available at http://www.temis.nl/index.php. Projected CO2 concentrations can be obtained from the RCP Database (http://www.iiasa.ac.at/web-apps/tnt/RcpDb). Model results and the urban clusters are available at https://drive.google.com/drive/folders/1yzcoRAjjubiLDqlg6zbLUCfE_m1mHAsL?usp=sharing.

Code availability

The codes used to estimate the phenological indicators in this study are available at https://drive.google.com/drive/folders/1yzcoRAjjubiLDqlg6zbLUCfE_m1mHAsL?usp=sharing.

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Competing interests

The authors declare no competing interests.

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Acknowledgements

This research was financially supported by the National Key R&D Program of China (2016YFA0600202), Strategic Priority Research Program of the Chinese Academy of Sciences (under grant XDA19040500), Jiangsu Provincial Natural Science Fund for Distinguished Young Scholars of China (BK20170018), International Cooperation and Exchange Programs between NSFC and DFG (41761134082) and General Program of National Science Foundation of China (41671421). J.P. acknowledges financial support from the European Research Council Synergy grant ERC-SyG-2013-610028 IMBALANCE-P. A.H. acknowledges financial support from Australian Research Council Discovery Program grant DP170101630. S.H.W. was supported by the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX18_0037) and the Key Research Program of the Chinese Academy of Sciences (grant number KFZD-SW-310).

Author information

Y. Zhang designed the research. S.W. performed the analysis. S.W., Y. Zhang and W.J. drafted the paper. J.P. and A.C. contributed to interpreting the results and writing the paper. A.H., Y. Zhou and Y.F. contributed to writing the paper. Y. Zhou and M.L. provided the data.

Competing interests

The authors declare no competing interests.

Correspondence to Yongguang Zhang.

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Fig. 1: Mean urban–rural phenological differences based on SIF and EVI.
Fig. 2: Spatial distributions of the four urban–rural phenological differences based on SIF.
Fig. 3: Controlling factors of urban–rural phenological differences.
Fig. 4: Temperature and CO2 associations with the phenological gradients.
Fig. 5: Future projections of photosynthetic phenology shifts.