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Diminishing carryover benefits of earlier spring vegetation growth

Nature Ecology & Evolution volume 8, pages 218–228 (2024)Cite this article

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Abstract

Spring vegetation growth can benefit summer growth by increasing foliage area and carbon sequestration potential, or impair it by consuming additional resources needed for sustaining subsequent growth. However, the prevalent driving mechanism and its temporal changes remain unknown. Using satellite observations and long-term atmospheric CO2 records, here we show a weakening trend of the linkage between spring and summer vegetation growth/productivity in the Northern Hemisphere during 1982–2021. This weakening is driven by warmer and more extreme hot weather that becomes unfavourable for peak-season growth, shifting peak plant functioning away from earlier periods. This is further exacerbated by seasonally growing ecosystem water stress due to reduced water supply and enhanced water demand. Our finding suggests that beneficial carryover effects of spring growth on summer growth are diminishing or even reversing, acting as an early warning sign of the ongoing shift of climatic effects from stimulating to suppressing plant photosynthesis during the early to peak seasons.

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Fig. 1: Attenuated linkage between spring and summer vegetation status.
Fig. 2: GLS changes controlled by climate.
Fig. 3: NH ecosystem responses to hot extremes.
Fig. 4: Seasonal imbalance of the demand versus supply of water for four representative regions.

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Data availability

All observational data that support the findings of this study are available as follows. The LCREF product is available at https://doi.org/10.5281/zenodo.7920380. The LCSIF product is available at: https://doi.org/10.5281/zenodo.7916851 and https://doi.org/10.5281/zenodo.7916879. The LTDR AVH09C1 product is available at https://ltdr.modaps.eosdis.nasa.gov/. The atmospheric CO2 records at Barrow is available at https://gml.noaa.gov/aftp/data/trace_gases/co2/in-situ/surface/. The climatic variables from the CRU v4.0.1 data are available at https://crudata.uea.ac.uk/cru/data/hrg/. The GLEAM v3.5b datasets are available at https://www.gleam.eu/. The AVHRR GIMMS NDVI3g data are available at https://poles.tpdc.ac.cn/en/data/9775f2b4-7370-4e5e-a537-3482c9a83d88/. The LT.SIFc product is available at https://figshare.com/articles/dataset/Temporally_corrected_long-term_satellite_solar-induced_chlorophyll_fluorescence_SIF_dataset_1995-2018_/21546066/1?file=38191833.

Code availability

The processing MATLAB codes are available from the corresponding author upon request.

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Acknowledgements

X.L., Y.R., J.F., T.F.K. and P.G. acknowledge support from the LEMONTREE (Land Ecosystem Models based on New Theory, obseRvations and ExperimEnts) project, funded through the generosity of E. and W. Schmidt by recommendation of the Schmidt Futures programme. A.C. was supported by grants from the US Department of Energy (DE-SC0022074) and the US Geological Survey (G22AC00431-00). T.F.K. acknowledges support from NASA Awards 80NSSC21K1705 and 80NSSC20K1801.

Author information

Authors and Affiliations

  1. Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA

    Xu Lian, Jianing Fang & Pierre Gentine

  2. CREAF, Barcelona, Spain

    Josep Peñuelas

  3. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain

    Josep Peñuelas

  4. Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South Korea

    Youngryel Ryu

  5. Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Shilong Piao & Yao Zhang

  6. State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Shilong Piao

  7. Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Trevor F. Keenan

  8. Department of Environmental Science Policy and Management, UC Berkeley, Berkeley, CA, USA

    Trevor F. Keenan

  9. Department of Ecology & Evolutionary Biology, High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA

    Kailiang Yu

  10. Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA

    Anping Chen

  11. Center for Learning the Earth with Artificial intelligence and Physics (LEAP), Columbia University, New York, NY, USA

    Pierre Gentine

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Contributions

X.L. designed the research, performed analysis and drafted the paper. X.L., J.P., Y.R., S.P., T.F.K., J.F., K.Y., A.C., Y.Z. and P.G. contributed to the interpretation of the results and to the text.

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Correspondence to Xu Lian.

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Extended data

Extended Data Fig. 1 Schematic of the inter-seasonal linkage of plant growth and climate responses.

a, Processes contributing to variability of seasonal vegetation growth. Arrows and adjacent texts denote the influence of the driver on the response. The black boxes represent processes involved in ecosystem responses to past (left) and current (right) climate variability. The red boxes represent processes involved in ecosystem responses to past vegetation status. b, Seasonal trajectories of vegetation growth/productivity under scenarios of normal springs/summers, ‘greener springs, greener summers (GSGS)’ and ‘greener springs, browner summers (GSBS)’. Arrows denote changes of phenological timing or growth relatively to the climatology. T air temperature, P precipitation, SM soil moisture, SOS start of the growing season, POS peak of the growing season.

Extended Data Fig. 2 LCSIF and kNDVI reconstructed from reflectance data in comparison to TROPOMI SIF.

a, Latitudinal variations of the mean dates of the start of the growing season (SOS, before July) and the end of the growing season (EOS, after July) inferred from different data and metrics. b, Spatial patterns of the correlation between LCSIF and TROPOMI minus that between kNDVI and TROPOMI SIF, all calculated on a monthly basis during 2018 − 2021.

Extended Data Fig. 3 Robustness tests for the attenuated linkage between spring and summer vegetation status.

These plots are the same as Fig. 1a, except (a) using an independent GIMMS kNDVI product; (b) using an independent SIF product (LT_SIFc) based on a multi-sensor fusion approach. Asterisks denote years with statistically significant correlation (p < 0.05) using two-sided t test.

Extended Data Fig. 4 Observed changes in spring−summer coupling of vegetation status.

a, b, Linear trends of the Pearson correlation between detrended MAM and JJA time series during 1982 − 2021, based on satellite-observed SIF and kNDVI, respectively. c, d, Linear trends of the growth linkage strength (GLS) during 1982 − 2021, based on SIF and kNDVI, respectively. Stipples indicate statistically significant (p < 0.05, based on two-sided t test) trends.

Extended Data Fig. 5 Changes of the spring-summer growth linkage grouped by eight vegetation types.

Bars indicate the linear trends of \({r}_{{\rm{JJA}}}^{{\rm{MAM}}}\) during 1982 − 2021 calculated for eight major vegetation types. Error bars centred at the estimated trends denote their 95th confidence intervals. The \({r}_{{\rm{JJA}}}^{{\rm{MAM}}}\) denotes the Pearson correlation between MAM and MAM anomalies of SIF or kNDVI, calculated using 15-year moving windows. Significance level (*** p < 0.01, ** p < 0.05, * p < 0.1, × p > 0.1) of correlation is based on two-sided t test. The inset map shows the distribution of the eight major vegetation types based on the MODIS International Geosphere Biosphere Programme classification map. Sparsely vegetated or cropland-dominated areas in grey are masked out. EBF evergreen broadleaf forest, DBF deciduous broadleaf forest, ENF evergreen needleleaf forest, MF mixed forest, SAV savanna, SHR shrubland, GRA grassland and WET wetland.

Extended Data Fig. 6 Seasonal trends of climatic anomalies.

a-c, Linear trends of temperature (T) and precipitation (P) for DJF (a), MAM (b) and JJA (c) during 1982 − 2021. d-f, Linear trends of T and P anomalies during DJF (d), MAM (e) and JJA (f) for years with positive kNDVI anomalies during 1982 − 2021.

Extended Data Fig. 7 Contribution of temperature (T) and precipitation (P) to the GLS changes.

a, Linear trends of the GLS (mean of the SIF- and kNDVI-based estimates) for years during 1982 − 2021 with positive MAM NDVI anomalies. b, PCR (principal component regression)-predicted trend of GLS during 1982 − 2021, driven by detrended anomalies of T and P. c, d, PCR-predicted trends of GLS during 1982 − 2021, driven by concurrent trends of T and P, respectively (see Methods). The combined effect of T and P changes is presented in Fig. 2b.

Extended Data Fig. 8 Comparison between observed temperature and optimal temperature for productivity (Topt).

a, Difference between spring mean temperature (TMAM) and Topt. b, Difference between summer mean temperature (TJJA) and Topt. c, d, Conceptual illustration of how air warming affects the MAM−JJA coupling of vegetation growth, for mid- and high- latitudes, respectively. The blue curves are the hump-shaped dependence of growth (or productivity) on temperature, where Topt corresponds to the maximum rate of growth/photosynthesis. dT represents the observed warming trend, and the thick arrows represent the responses of spring and summer growth to warming.

Extended Data Fig. 9 Effects of mean climate change (climatic extreme excluded) on the spring-summer growth linkage.

This is the same as Fig. 1a, except that years with dry/hot extremes are excluded from the analysis. Asterisks denote years with statistically significant correlation (p < 0.05) using two-sided t test. The extreme years are defined as those with the standardized precipitation evapotranspiration index (SPEI of at least one spring/summer months falling 1.5 standard deviation below its climatology. We used a 3-month SPEI dataset based on the CRU climatic data to capture short-term water deficits (https://spei.csic.es/database.html).

Extended Data Fig. 10 NH ecosystem responses to hot extremes.

a, b, Same as Fig. 3a, but showing results based on SIF and kNDVI, respectively. c, d, Same as Fig. 3b, but showing results based on SIF and kNDVI, respectively. The dots and plus symbols on the maps indicate lower and higher mean anomalies, respectively, under hot extremes than under all conditions.

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Lian, X., Peñuelas, J., Ryu, Y. et al. Diminishing carryover benefits of earlier spring vegetation growth. Nat Ecol Evol 8, 218–228 (2024). https://doi.org/10.1038/s41559-023-02272-w

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