Weakening temperature control on the interannual variations of spring carbon uptake across northern lands

Transcription

1 In the format provided by the authors and unedited. SUPPLEMENTARY INFORMATION DOI: /NCLIMATE3277 Weakening temperature control on the interannual variations of spring carbon uptake across northern lands Shilong Piao 1,2,3 *, Zhuo Liu 2, Tao Wang 1,3, Shushi Peng 2, Philippe Ciais 4, Mengtian Huang 2, Anders Ahlstrom 5, John F. Burkhart 6, Frédéric Chevallier 4, Ivan A. Janssens 7, Su-Jong Jeong 8, Xin Lin 4, Jiafu Mao 9, John Miller 10,11, Anwar Mohammat 12, Ranga B. Myneni 13, Josep Peñuelas 14,15, Xiaoying Shi 9, Andreas Stohl 16, Yitong Yao 2, Zaichun Zhu 2 and Pieter P. Tans 10 Ongoing spring warming allows the growing season to begin (hereafter referred to as Barrow) atmospheric measurement station 1 Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing , China. 2 Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing , China. 3 Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing , China. 4 Laboratoire des Sciences du Climat et de l Environnement, CEA CNRS UVSQ, Gif-sur-Yvette 91191, France. 5 School of Earth, Energy and Environmental Sciences, Stanford University, Stanford, California , USA. 6 Department of Geosciences, University of Oslo, PO Box 1047 Blindem, 0316 Oslo, Norway. 7 Department of Biology, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium. 8 School of Environmental Science and Engineering, South University of Science and Technology of China, Shenzhen , China. 9 Climate Change Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA. 10 National Oceanic and Atmospheric Administration Earth Systems Research Laboratory (NOAA/ESRL), 325 Broadway, Boulder, Colorado 80305, USA. 11 Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder 80309, USA. 12 Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi , Xinjiang, China. 13 Department of Earth and Environment, Boston University, 675 Commonwealth Avenue, Boston, Massachusetts 02215, USA. 14 CREAF, Cerdanyola del Valles, Barcelona 08193, Catalonia, Spain. 15 CSIC, Global Ecology Unit CREAF-CEAB-CSIC-UAB, Cerdanyola del Valles, Barcelona 08193, Catalonia, Spain. 16 NILU Norwegian Institute for Air Research, PO Box 100, 2027 Kjeller, Norway. * slpiao@pku.edu.cn NATURE CLIMATE CHANGE Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

2 Figure S1 A schematic to describe the terms for characterizing spring carbon uptake. We use a smoothed detrended annual cycle of CO2 (the black solid line) for the year 1979 at Barrow. The horizontal dashed line represents the detrended mean CO2 concentration. The two vertical dashed lines indicate the start and end of the spring carbon uptake period (May-June). Spring zero crossing date (SZC) is defined as the day of the year when CO2 crosses down its annual mean level (marked in blue). Spring carbon capture (SCC) is calculated as the seasonal magnitude of the observed CO2 decrease between the first week of May and the last week of June (marked in red).

3 Figure S2 Temporal evolution of observed spring zero crossing date (SZC), spring carbon capture (SCC) and the average spring (March-June) temperature over the vegetated region north of 50 o N (ST). a, SZC, b, SCC and, c, ST, from 1979 to 2012.

4 Figure S3 The partial correlation coefficient of a, SZC (RSZC) and b, SCC (RSCC) with preseason temperature using different preseason periods. The preseason is defined as the period before 30 June with different start months varying from November to June. All variables are detrended before the partial correlation analysis. ** indicates statistically significant at the 5% level and * statistically significant at the 10% level.

5 Figure S4 Same as Figure 2, but showing the frequency distributions of the partial correlation coefficients of observed SZC (RSZC) and SCC (RSCC) at Barrow with spring (March-June) cloudiness (a, b) and precipitation (c, d) during the first 17 years ( ) and the last 17 years ( ).

6 Figure S5 Frequency distributions of the temperature sensitivity of (a) SZC (γszc) and (b) SCC (γscc) at Barrow during the first 17 years ( ; blue) and during the second, more recent 17 years ( ; red). The temperature sensitivity of SZC (SCC) is calculated as the slope of temperature in a multiple regression of SZC (SCC) against temperature, cloud cover and precipitation during March-June over the vegetated lands north of 50 o N. The frequency distributions of temperature sensitivity are calculated by randomly selecting 14 years during and All variables are detrended for each study period before multiple linear regression analysis. Abbreviations of transport simulations are defined in Table 1.

7 Figure S6 Mean spring footprint for Barrow derived from two different approaches during three time periods. In the left panel, the footprint was derived from the adjoint code of the LMDZ model. In the right panel, the footprint was derived from the Lagrangian particle dispersion model FLEXPART. Note that the FLEXPART simulations are only available from 1985 to 2009.

8 Figure S7 Frequency distributions of the partial correlation coefficient of observed SZC (RSZC) and SCC (RSCC) at Barrow with spring (March-June) temperature during the first period ( ) and during the second, more recent period ( ). Here climate variables (temperature, precipitation and cloud cover) were computed as the spatial average weighted by the sensitivities (flux sensitivity from LMDZ and potential emission sensitivity from FLEXPART) over the vegetated land area within the mean spring footprint. In a-d, we used the mean spring footprint during the whole study period ( for LMDZ and for FLEXPART, see Fig. S6 c and f). In e-h, we used the mean spring footprint during the two time periods for the first 17 years and the last 17 years (Fig. S6 a, b, d and e).

9 Figure S8 Same as Figure 2, but for frequency distributions of the partial correlation coefficient of observed SZC (RSZC) and SCC (RSCC) at Barrow with spring (March-June) temperature based on three different climate data sets during the first period ( ) and during the second, more recent period ( ). The climate data sets include Climate Research Unit (CRU, a and b), WATCH Forcing Data methodology applied to ERA-Interim data (WFDEI, c and d) and Climatic Research Unit-National Centers for Environmental Prediction (CRU-NCEP, e and f). The partial correlation coefficient between SZC (SCC) and ST is calculated by statistically controlling for interannual variation in precipitation and radiation (here approximated by cloudiness for CRU) during the period from March to June.

10 Figure S9 Same as Figure 2, but for frequency distributions of the partial correlation coefficient of observed SZC (RSZC) and SCC (RSCC) at Barrow with preseason temperature during the first period ( ) and during the second, more recent period ( ). We calculate frequency distributions of RSZC and RSCC through randomly selecting 14 years during both and For each randomly selected period, the preseason is defined as the period (with 1 month steps) before June for which the negative correlation between SZC and temperature (positive correlation for SCC) was highest (see Methods). Here SZC and SCC were calculated from daily atmospheric CO2 concentration records derived from surface in situ continuous measurements based on three outlier rejection criteria (2.5, 3.0 and 5.0 standard deviations) and two FWHM averaging filters (1.5 and 1.0 month). Frequency distributions of (a) RSZC and (b) RSCC based on the outlier rejection criterion of 5.0 standard deviations and the 1.5 month FWHM averaging filter. Frequency distributions of (c) RSZC and (d) RSCC based on the outlier rejection criterion of 5.0 standard deviations and the 1.0 month FWHM averaging filter. Frequency distributions of RSZC (e) and RSCC (f) based on the outlier rejection criterion of 3.0 standard deviations and the 1.0 month FWHM averaging filter. Frequency distributions of (g) RSZC and (h) RSCC based on the outlier rejection criterion of 2.5 standard deviations and the 1.0 month FWHM averaging filter.

11 Figure S10 Same as Figure 2, but for frequency distributions of partial correlation coefficient of observed SZC (RSZC) and SCC (RSCC) at Barrow with preseason temperature based on weekly atmospheric CO2 concentration records during the first period ( ) and during the second, more recent period ( ). Here the weekly atmospheric CO2 concentration records were derived from (a, b) surface in situ continuous measurements of SZC and SCC respectively, using the 1.5 month FWHM averaging filter; and (c, d) surface flask samples of SZC and SCC respectively, using the 1.5 month FWHM averaging filter. We calculate frequency distributions of RSZC and RSCC through randomly selecting 14 years during both and For each randomly selected period, the preseason is defined as the period (with 1 month steps) before June for which the negative correlation between SZC and temperature (positive correlation for SCC) was highest (see Methods).

12 Figure S11 Frequency distributions of P value for the partial correlation coefficients of observed SZC (PSZC) and SCC (PSCC) at Barrow with spring (March-June) temperature considering the co-variation in snow water equivalent and previous winter temperature during the first period ( ) and during the second, more recent period ( ). Frequency distributions of P value of the partial correlation coefficient of (a) SZC (PSZC) and (b) SCC (PSCC) with spring temperature after statistically controlling for spring precipitation, spring cloud cover and maximum snow water equivalent from November to June. Frequency distributions of P value of the partial correlation coefficient of (c) SZC (PSZC) and (d) SCC (PSCC) with spring temperature after statistically controlling for spring precipitation, spring cloud cover and winter (November to February) temperature. We calculate frequency distributions of PSZC and PSCC through randomly selecting 14 years during both and Note that snow water equivalent data is only available from September Thus the maximum snow water equivalent from November 1978 to June 1979 was replaced by one value (null) when calculating P value of partial correlation coefficient. Statistically significant P values are marked by the dotted line (magenta: P < 0.05 and brown: P < 0.1). The positive and negative sign indicate the corresponding positive and negative partial correlation coefficients, respectively.

13 Figure S12 Frequency distributions of P value for the partial correlation coefficients of observed SZC (PSZC) and SCC (PSCC) at Cold Bay (CBA) and Ocean Station M (STM) with preseason temperature over the vegetated lands north of 50 N during the first period ( ) and during the second, more recent period ( ). We calculated frequency distributions of PSZC and PSCC using the 1.5 month FWHM averaging filter for (a, b) CBA and (c, d) STM, respectively. The CO2 data at CBA and STM stations are based on surface flasks sampled on a weekly basis. We calculate frequency distributions of PSZC and PSCC through randomly selecting 14 years during and For each randomly selected period, the preseason is defined as the period (with 1 month steps) before June for which the negative correlation between SZC and temperature (positive correlation for SCC) was highest (see Methods). Note that for STM, weekly CO2 records are only available from 1981 to Thus all missing values were replaced by one value (null) when calculating P value of partial correlation coefficient.

14 Figure S13 Changes in the partial correlation coefficients of a) SZC (RSZC) and (b) SCC (RSCC) at Barrow with preseason (March-June) temperature over the vegetated lands north of 50 o N during after applying 15-year moving windows. The partial correlation coefficient RSZC (RSCC) is computed as the correlation between the residuals calculated after regressing SZC (SCC) on precipitation and cloud cover and those after regressing ST on precipitation and cloud cover. Year on the horizontal axis is the central year of the 15-year moving window (e.g., 1986 indicates a moving window from ). Solid circles indicate statistically significant partial correlation (P < 0.05), solid squares indicate statistically marginally significant partial correlation (P < 0.1), and hollow circles indicate insignificant partial correlation (P > 0.1). All variables are detrended for each study period before partial correlation analysis. Abbreviations of transport simulations are defined in Table 1.

15 Figure S14 Anomalies of observed and transport model simulated (a) SZC and (b) SCC at Barrow from 1979 to Abbreviations of transport simulations are as defined in Table 1. TFTT: simulation with transient global NEE and transient transport; CFTT: simulation with global NEE of year 1979 but transient transport (indicating the effect of wind change on SZC and SCC variability); TFCT: the difference between TFTT and CFTT (indicating the effect of global land carbon flux change on SZC and SCC variability); TFCT-B: the difference between TFTT-B and CFTT (indicating the effect of boreal land carbon flux change on SZC and SCC variability); TFCT-TE: the difference between TFTT-TE and CFTT (indicating the effect of land carbon flux change over temperate regions defined as o N on SZC and SCC variability). The coefficient of determination (R 2 ) between observed and transport model simulated SZC (SCC) is given. R 2 = 0.11 and R 2 = 0.08 correspond to the 0.05 and 0.1 significance levels, respectively. All variables are detrended.

16 Figure S15 Frequency distributions of the partial correlation coefficient of (a) SZC (RSZC) and (b) SCC (RSCC) with March-June temperature during the first 17 years ( ) and the second more recent 17 years ( ). Here SZC and SCC were calculated from the difference of the transport simulation CFTT-Ocean and CFTT (see Methods), by which the impact of ocean flux on RSZC and RSCC can be estimated. Frequency distributions of RSZC and RSCC were calculated as for Fig. 2 in the main text. Statistically significant partial correlation coefficients are indicated as the dashed lines (magenta: P < 0.05 and brown: P < 0.1).

17 Figure S16 Detrended anomalies of net ecosystem productivity (NEP), net primary productivity (NPP) and heterotrophic respiration (HR) in boreal regions derived from ORCHIDEE simulations (a) S3 and (b) S1. In S3, atmospheric CO2 and all historical climate factors were changed. In S1, only historical temperature was changed. The coefficient of determination (R 2 ) between NEP and NPP/HR is given.

18 Figure S17 The consistency in the direction of change in the partial correlation coefficient of spring NPP and NDVI with temperature ( R) between NPP and NDVI shown in Fig. 3. The direction of R is shown on the horizontal axis, with the first symbol for NDVI and the second for NPP under different scenarios (CO2+climate/ Only T/ Only T during dormancy period). (+ +) and (- -) indicate a consistent direction of R between NDVI and NPP, whereas (+ -) and (- +) indicate an opposing direction of R. The percentage of all grids over the boreal vegetated area is given on the vertical axis.

19 Figure S18 Difference in (a) extreme hot days and (b) frost days between and The extreme hot days were calculated as the sum of days when daily temperature exceeded the 90th percentile of the temperature during from 1 March to 30 June. The frost days were calculated as the sum of days when daily minimum air temperature was below 0 from the start of the growing season (SOS) to the summer solstice. Here we determined SOS by taking the ensemble mean of the results from four SOS estimation methods (HANTS-Maximum, Polyfit-Maximum, double logistic and piecewise logistic, see Methods) applied to satellite NDVI data. The dots indicate regions with statistically significant differences (P < 0.05).

20 Figure S19 Spatial distribution of (a and b) the mean green-up onset dates and (c and d) trends in vegetation green-up dates during The ORCHIDEE model-derived results are shown in a and c, and the satellite-derived results are shown in b and d. The modelled spring green-up date was estimated based on the seasonal cycle of simulated leaf area index (LAI) following the approach developed by ref 31. The observed spring green-up date was obtained by taking the ensemble mean of the four estimation methods (HANTS-Maximum, Polyfit-Maximum, double logistic and piecewise logistic, see Methods) applied to satellite NDVI data. In the ORCHIDEE simulation, atmospheric CO2 and all historical climate factors are changed. Note that the satellite data are only available from 1982 to 2011.

21 Figure S20 Same as Figure 2, but with no variables detrended.

22 Figure S21 The standard deviation (sd) of (a) SZC, (b) SCC, (c) spring temperature (ST), andthe partial correlation coefficient of (d) SZC and (e) SCC with ST during the first 17 years ( ), the second 17 years ( ) and the first 17 years excluding year All variables are detrended before the sd calculation and partial correlation analysis. The uncertainty is given using 500 bootstrap estimates.