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Light limitation regulates the response of autumn terrestrial carbon uptake to warming

Nature Climate Change volume 10, pages 739–743 (2020)Cite this article

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Abstract

Global warming is projected to shift the phenology and increase the productivity of northern ecosystems1,2,3,4,5,6. Both changes will further feed back to climate through biophysical and biogeochemical processes and are critical for future prediction7,8. However, it remains unclear whether warming and the extended growing season, especially in autumn, would lead to increased net ecosystem carbon uptake9,10. Here we analyse satellite observations, field measurements and model simulations and show a prevalent radiation limitation on carbon uptake in northern ecosystems, especially in autumn. By comparing the start and end of the growing season estimated from vegetation indices and from solar-induced chlorophyll fluorescence (a proxy for gross primary production11,12 (GPP)), we find a greater change in greenness-based start and end of season than that from GPP, mostly caused by the radiation limitation on photosynthesis. This radiation limitation explains the contrasting responses of autumn net carbon exchanges to warming, using both eddy covariance measurements and model simulations from Coupled Model Intercomparison Project Phase 5. Regions with weak radiation limitation benefit more from warming and enhanced vegetation greenness in autumn, where GPP increases can outweigh the warming-induced respiration carbon losses. With continued warming, radiation limitation will increase and exert a strong upper bound on northern ecosystems to act as carbon sinks.

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Fig. 1: Trends of spring growth onset and autumn dormancy onset in 2001–2017.
Fig. 2: Phenological sensitivity (γ) between GPP and greenness for spring and autumn.
Fig. 3: Relationship between temperature anomaly and NEE for spring and autumn at 28 eddy covariance flux tower sites.
Fig. 4: Latitudinal comparison of radiation limitations derived from CSIF and CMIP5 models.
Fig. 5: Future radiation limitation simulated by CMIP5 models under the RCP8.5 scenario.

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Data availability

The MODIS VI and land surface temperature data are from https://lpdaac.usgs.gov/node/841 and https://lpdaac.usgs.gov/node/834, the CSIF data are from https://doi.org/10.17605/OSF.IO/8XQY6, the FLUXNET2015 dataset is from http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/ and the CMIP5 model simulations are from https://esgf-node.llnl.gov/search/cmip5/. Raw data for Figs. 1–5 are available via FigShare at https://doi.org/10.6084/m9.figshare.11986764.

Code availability

The code used for the phenology retrieval and data analysis are available from Y.Z. upon request.

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Acknowledgements

This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization were carried out by the European Fluxes Database Cluster, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices. Y.Z. acknowledges funding from NASA ROSES Advanced Information System Technology (grant no. 80NSSC17K0285). P.G. acknowledges support from NASA ROSES Terrestrial Hydrology (grant no. 80NSSC18K0998), NOAA MAPP (grant no. NA17OAR4310127) and European Research Council (grant no. CU18-3746). A.P.W. acknowledges support from the NASA Modeling, Analysis, and Prediction (MAP) programme (grant no. NASA 80NSSC17K0265).

Author information

Authors and Affiliations

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

    Yao Zhang, Sha Zhou & Pierre Gentine

  2. Lamont–Doherty Earth Observatory of Columbia University, Palisades, NY, USA

    Róisín Commane, Sha Zhou & A. Park Williams

  3. Earth Institute, Columbia University, New York, NY, USA

    Sha Zhou & Pierre Gentine

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Contributions

Y.Z. and P.G. conceived and designed the study. Y.Z. performed the data analysis. Y.Z., S.Z., R.C., A.P.W. and P.G. contributed to the interpretation of the results. Y.Z. drafted the manuscript. All authors participated in discussions and the editing of the manuscript.

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Correspondence to Yao Zhang.

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Peer review information Nature Climate Change thanks Su-Jong Jeong and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison between CSIF OCO-2 SIF and GPP at multiple scales.

a Comparison between OCO-2 SIF (SIFOCO-2), CSIF, NDVI and EVI for the northern ecosystems (>30°N). The error bands associated with SIFOCO-2 and CSIF represent the standard deviation of all observations in northern ecosystems, with the solid lines for the mean. For readability, error bands for NDVI and EVI are not shown. b-g Comparison between the spring growth onset and autumn dormancy onset derived from CSIF and flux tower estimated GPP at 40 sites from the FLUXNET2015 tier 1 dataset (Supplementary Table 3). b Locations of the 40 sites. c Comparison between eddy-covariance (EC) estimated GPP and CSIF at 4-day temporal resolution. d, e Comparison between SOS and EOS derived from EC estimated GPP and CSIF for all site-years. f, g Comparison between the interannual anomaly of SOS and EOS (ΔSOS and ΔEOS) for each site. The anomalies were calculated as the phenological dates for each year minus the multi-year average. Only sites with at least 5 years of observations were used for this interannual comparison.

Extended Data Fig. 2 Average growth onset and dormancy onset for 2001–2017 for four vegetation indicators.

a, b NDVI, c, d EVI and e, f CSIF, g, h GOME-2 SIF. The median value from four methods (wSpline-Thr, wHANTS-MR, wPolyfit-MR, wDLogistic) were shown. The growth onset and dormancy onset for GOME-2 SIF were derived from a mean seasonal cycle between 2007-2017, while others were derived for each year and averaged over the entire period.

Extended Data Fig. 3 MODIS VI processing schemes for phenology retrieval.

The nighttime LST (1:30 am local time) from Aqua satellite were used as it is close to the minimum LST.

Extended Data Fig. 4 A schematic diagram showing the extension of fPAR based growing season and its impact on GPP based growing season.

Left panels show how γ can be calculated. b shows a zoom in of autumn dormancy from schematic seasonal cycles a. Solid blue line in b represents the original fPAR×LUE and dashed-lines shows that with one day delay (corresponding to a one-day delay of greenness-based EOS). As a result, GPP based EOS delayed by γ day, from EOS0 to EOS1. Open circles and dots in b indicate the fPAR×LUE and PAR values for EOS0 and EOS1, respectively. At this new EOS date, fPAR×LUE increases by Δ(fPAR×LUE) and PAR decreases by ΔPAR. Note that Δ(fPAR×LUE) does not equal to ΔPAR, but rather \(\left[ {1 + \frac{{\Delta \left( {{\mathrm{fPAR}} \times {\mathrm{LUE}}} \right)}}{{{\mathrm{fPAR}} \times {\mathrm{LUE}}}}} \right]\left[ {1 - \frac{{\Delta {\mathrm{PAR}}}}{{{\mathrm{PAR}}}}} \right] = 1\) (see Methods). The right panels show two examples of how latitude and plant species (evergreen or deciduous) may affect γ values. Both PAR and fPAR×LUE in c and d were normalized by the PAR and fPAR×LUE values at EOS0, respectively. With increases in latitude, γ decreases, suggesting an increase in radiation limitation. Evergreen forests also exhibit relatively smaller γ values compared to deciduous species because of the relatively smaller percentage decrease of fPAR×LUE.

Extended Data Fig. 5 Comparison between radiation limitation derived from CSIF and that derived from GOME-2 SIF.

Left column is for spring and right column is for autumn.

Extended Data Fig. 6 Comparison between the radiation limitation using different threshold values to retrieve SOS and EOS.

Three thresholds for wSplineThr method to retrieve SOS and EOS were compared here. a,b threshold is minimum +30% of the seasonal magnitude, c,d threshold is minimum +25%, of the seasonal magnitude, e,f threshold is minimum +35% of the seasonal magintude. The difference in radiation limitation between the 35% and 25% thresholds is 0.9% (95% confidence interval: -2.1% to 14.0%).

Extended Data Fig. 7 Trend in the change of radiation limitation (1-γ) during 2001-2017.

Pie charts shows the areal percentages with decreasing trend (‘–‘) or increasing trend (‘+’).

Extended Data Fig. 8 Change of normalized derivative of PAR (parameter b in Equations. 6 or 9) along latitude and season.

Thick solid lines represent the median value of multi-year average SOS and EOS along latitude. Thin dashed and dotted lines represent the timings when b reaches maximum or minimum for each latitude bins, respectively. In the spring, advancing of the SOS would lead to increase of b and thus a greater radiation limitation. In the autumn, delaying of the EOS would lead to decrease of b until the minimum, also leading to a greater radiation limitation.

Extended Data Fig. 9 Correlation between temperature sensitivity of autumn NEE (\({\upgamma}_{\mathrm{T}}^{{\mathrm{NEE}}}\)) and radiation limitation.

Positive \({\upgamma}_{\mathrm{T}}^{{\mathrm{NEE}}}\) values indicate that ecosystem will release carbon (postive NEE) when temperature increases. Shaded area indicate 95% confidence interval of the regression.

Extended Data Fig. 10 Radiation limitation and temperature responses of autumn NEE for CMIP5 models.

Net ecosystem exchange (NEE) is calculated as the summation of autotropic and heterotopic respiration minus gross primary production, with a negative value indicates a carbon sink. Similar with inset in Fig. 3b, autumn NEE sensitivity is categorized into increase (blue) or decrease (red) carbon uptake with autumn warming. Only the gridcells that are limited by temperature is used for analysis (GPP based EOS delays when autumn temperature increases). Autumn is defined as 60 days interval around the GPP determined multi-year average EOS date. GPP, ecosystem respiration (Ra+Rh) and air temperature were interpolated from monthly to daily values to get the autumn average. Upper and lower boundary of the boxes represent the 25 and 75 percentiles, with the white lines in the middle the median values. The horizontal dashed green dashed line indicates the threshold to separate the negative and positive temperature sensitivity to ecosystem autumn NEE based on flux tower analysis. The grey shaded area represents the standard deviation of the threshold estimated by bootstrapping (n=2000). *** represent significance level at 0.001.

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Supplementary Figs. 1–6, Tables 1–4 and references.

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Zhang, Y., Commane, R., Zhou, S. et al. Light limitation regulates the response of autumn terrestrial carbon uptake to warming. Nat. Clim. Chang. 10, 739–743 (2020). https://doi.org/10.1038/s41558-020-0806-0

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