Research ArticleENVIRONMENTAL STUDIES

Summer soil drying exacerbated by earlier spring greening of northern vegetation

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Science Advances  03 Jan 2020:
Vol. 6, no. 1, eaax0255
DOI: 10.1126/sciadv.aax0255
  • Fig. 1 Coupling between observed spring LAI and summer SWC during 1982–2011.

    Spatial pattern of partial correlation coefficients between Global Inventory Monitoring and Modeling Studies (GIMMS) spring LAI and (A) GLEAM summer SWC or (B) GRACE-REC summer TWS for 1982–2011. Black stipples indicate regions with a statistically significant correlation (P < 0.05). Heterogeneous regression maps of (C and E) GIMMS spring LAI and (D and F) GLEAM summer SWC (or GRACE-REC summer TWS), associated with the first MCA mode for 1982–2011. The squared fractional covariance (SFC) explained by the first MCA mode is 46.3 and 58.9% for the GLEAM and GRACE-REC datasets, respectively.

  • Fig. 2 IPSL-simulated changes in summer soil moisture induced by spring LAI changes.

    (A to C) Interannual anomalies of the area-weighted average of spring LAI (green dotted lines) and summer δSWC (blue solid lines) for (A) all northern latitudes (25° to 90°N), (B) regions with positive LAI trends (i.e., greening), and (C) regions with negative LAI trends (i.e., browning). The red lines indicate the least-squares linear regression of GCM-based δSWC (straight lines) against time and the 95% confidence intervals (curves). Note that the right axes are reversed, so higher LAI values are toward the bottom of the plots. (D to F) Interannual trends in mean spring LAI and resultant changes in the spring and summer hydrological variables of ET, precipitation (P), runoff (Q), and SWC. The subplot maps at the bottom of each main panel display the corresponding averaged areas as gray [corresponding to (A) to (C)]. ***P < 0.01; **P < 0.05; *P < 0.1; n.s., P > 0.1.

  • Fig. 3 Schematic of the effect of earlier greening on summer soil moisture.

    Earlier spring greening influences spring soil moisture by altering land-atmosphere water exchanges (via ET, P, and Q) and by the redistribution of atmospheric water vapor by atmospheric circulation. This spring soil moisture anomaly persists later into the following summer due to the carryover effects of soil moisture. The magnitude of this cross-seasonal vegetation feedback and the role of atmospheric circulation, however, vary geographically. Three typical examples of the circulation-modulated vegetation feedback (Europe, Siberia, and eastern China) are displayed at the bottom of the schematic.

  • Fig. 4 Spatial patterns of IPSL-simulated trends in soil moisture induced by spring LAI changes.

    Spatial patterns of the linear trends in spring (left) and summer (right) δSWC changes are shown. This change in soil moisture induced by earlier spring greening, δSWC, is obtained as the difference between the two simulations with and without spring LAI changes. (i.e., LAIobsMAMLAIclimMAM). Black stipples indicate regions with a statistically significant linear trend (P < 0.05).

  • Fig. 5 IPSL-simulated changes in summer extreme hot temperature indices related to δSWC changes.

    The average linear trend of δTXx and δTX90p due to earlier greening for different summer δSWC trends binned into 0.4 (×10−4 m3 m−3 year−1) intervals. The δTXx and δTX90p values are obtained as the difference between the two simulations with and without spring LAI changes (LAIobsMAMLAIclimMAM). The insets are scatterplots of trends in δTXx (left) and δTX90p (right) versus trends in δSWC for 1982–2011. Each colored dot in the scatterplots represents a grid cell in the pattern of trends in δTXx or δTX90p (fig. S10).

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/1/eaax0255/DC1

    Supplementary Text

    Section S1. Earlier spring vegetation greening revealed by remote sensing

    Section S2. Mathematical basis of the MCA analysis

    Section S3. Model evaluation using available observations

    Fig. S1. Spatial patterns of the trend in spring vegetation changes for 1982–2011.

    Fig. S2. Grassland partial correlations between ESA-CCI summer SWC and GIMMS spring LAI during 1982–2011.

    Fig. S3. Partial correlations between spring LAI and either summer SWC or summer TWS, as affected by agricultural extent.

    Fig. S4. Time series (or expansion coefficients) of the leading first MCA patterns.

    Fig. S5. Validation of IPSL-simulated hydrological variables.

    Fig. S6. Robustness test of the IPSL-simulated relationship between spring LAI and summer SWC.

    Fig. S7. Comparison of trends of observation-based and IPSL-simulated ET.

    Fig. S8. Spatial patterns of IPSL-simulated trends in key hydrological variables induced by spring LAI changes.

    Fig. S9. IPSL-simulated average global prevailing wind at 10-m height.

    Fig. S10. Spatial patterns of IPSL-simulated trends in summer extreme hot temperature indices induced by spring LAI changes.

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Text
    • Section S1. Earlier spring vegetation greening revealed by remote sensing
    • Section S2. Mathematical basis of the MCA analysis
    • Section S3. Model evaluation using available observations
    • Fig. S1. Spatial patterns of the trend in spring vegetation changes for 1982–2011.
    • Fig. S2. Grassland partial correlations between ESA-CCI summer SWC and GIMMS spring LAI during 1982–2011.
    • Fig. S3. Partial correlations between spring LAI and either summer SWC or summer TWS, as affected by agricultural extent.
    • Fig. S4. Time series (or expansion coefficients) of the leading first MCA patterns.
    • Fig. S5. Validation of IPSL-simulated hydrological variables.
    • Fig. S6. Robustness test of the IPSL-simulated relationship between spring LAI and summer SWC.
    • Fig. S7. Comparison of trends of observation-based and IPSL-simulated ET.
    • Fig. S8. Spatial patterns of IPSL-simulated trends in key hydrological variables induced by spring LAI changes.
    • Fig. S9. IPSL-simulated average global prevailing wind at 10-m height.
    • Fig. S10. Spatial patterns of IPSL-simulated trends in summer extreme hot temperature indices induced by spring LAI changes.
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