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Increased atmospheric vapor pressure deficit reduces global vegetation growth

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Four global climate datasets were used to investigate the long-term changes of atmospheric VPD, including CRU, ERA-Interim, HadISDH, and MERRA. Monthly gridded CRU and HadISDH datasets were based on climate observations from global meteorological stations (31, 32). ERA-Interim and MERRA datasets were reanalysis products based on Integrated Forecast System of European Centre for Medium-Range Weather Forecasts (ECMWF-IFS) (33) and the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5) (34), respectively. VPD was calculated on the basis of different variables of four datasets (35)

CRU:$VPD=SVP−AVP$(1)

ERA-Interim:$AVP=6.112×fw×e17.67TdTd+243.5$(2)$VPD=SVP−AVP$(3)

HadISDH and MERRA:$AVP=RH100×SVP$(4)$VPD=SVP−AVP$(5)where SVP and AVP are saturated vapor pressure and actual vapor pressure (kPa), respectively. Td is the dew point temperature (°C). RH is the land relative humidity (%).$SVP=6.112×fw×e17.67TaTa+243.5$(6)$fw=1+7×10−4+3.46×10−6Pmst$(7)$Pmst=Pmsl((Ta+273.16)(Ta+273.16)+0.0065×Z)5.625$(8)where Ta is the land air temperature (°C). Z is the altitude (m). Pmst is the air pressure (hPa), and Pmsl is the air pressure at mean sea level (1013.25 hPa). In addition, the OAFlux dataset was used to examine the variability of oceanic evaporation (table S1) (20).

We used the newest release of the advanced very high resolution radiometer (AVHRR) NDVI to indicate vegetation growth from 1982 to 2015. The AVHRR is a nonstationary NDVI version 3 dataset made available by NASA’s Global Inventory Modeling and Monitoring Study third-generation dataset (GIMMS3g) group (36). GIMMS3g contains global NDVI observations at approximately 8-km spatial resolution and bimonthly temporal resolution, derived from AVHRR channels 1 and 2, corresponding to red (0.58 to 0.68 μm) and infrared (0.73 to 1.1 μm) wavelengths, respectively. Each 15-day data value is the result of maximum value compositing, a process that aims to minimize the influence of atmospheric contamination from aerosols and clouds. Moreover, this study analyzed long-term trends of LAI based on four global satellite LAI products (table S1): GLASS (version 4) (37), GLOBMap (38), LAI3g (39), and the TCDR (40).

We calculated the annual growing season mean NDVI and LAI by averaging monthly NDVI and LAI values with monthly mean temperatures above 0°C. We also calculated multiyear averaged monthly mean temperatures from the CRU dataset to ensure that the same growing season land mask was used over the entire period (1982–2015). The global mean NDVI and LAI values were calculated by the average of the annual growing season mean NDVI and LAI, excluding unvegetated regions. The MODIS land cover type product (MCD12Q1) was used to identify the vegetated regions.

We calculated the LUE (g C m−2 MJ−1) based on EC measurements from the FLUXNET2015 dataset (www.fluxdata.org) to examine the correlation between LUE and VPD (table S4)$LUE=GPPfPAR×PAR$(9)where GPP indicates the estimated GPP values from EC measurements, PAR is photosynthetically active radiation (MJ m−2), and fPAR is the fraction of PAR absorbed by the vegetation canopy calculated by GIMMS3g NDVI (41).

The tree-ring width measurements around the world were used from the International Tree-Ring Data Bank (ITRDB) (42). The wood samples were taken and processed following standard protocols and taking two radial cores per tree at 1.3 m. Tree-ring width measurements were detrended and standardized by the scientists who contributed the chronologies to the ITRDB. Each local chronology represents the average growth of several trees (typically more than 10) of the same species growing at the same site. The temporal span of the tree-ring data series selected began at 1982, lasting at least until 2005. Eventually, 171 sites were analyzed and each chronology of the sites is a representation of annual tree-ring width.

This study conducts the partial correlation analysis between VPD and tree-ring width by excluding the impacts of air temperature, radiation, and atmospheric CO2 concentration. Air temperature and PAR from MERRA dataset were used. For atmospheric CO2 concentration, this study used the GLOBALVIEW-CO2 product, which provides observations of atmospheric CO2 concentration at 7-day intervals over 313 global air-sampling sites (43). If missing 7-day data accounted for >20% of all data for an entire year, then the value for that year was indicated as “missing.” For a site to be included in this study, it had to have at least 10 years of observations. Eventually, 77 sites were included equally in the calculation of global monthly mean CO2 concentration without any weighting of individual sites.

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