Figure 1 (C to F) presents the leading first MCA mode characterizing spatial patterns of the coupling between spring LAI and summer SWC. This mode, in particular, includes potential nonlocal impacts of LAI-induced moisture transport anomalies on SWC at downwind regions. This mode pair explains 46 and 59% of the total squared covariance between the two variables for GLEAM SWC and GRACE-REC TWS, respectively. For both SWC-related products, the first spring LAI mode shows widespread positive anomalies (i.e., earlier greening) in the majority of the Eurasia (>80%) and eastern United States, while negative anomalies (i.e., delayed greening, also known as browning) cover vast areas of central and western North America ( Fig. 1,C and E ). The patterns of the leading LAI mode generally resemble the trend pattern of satellite-observed spring LAI (fig. S1). As a consequence, the leading summer SWC (or TWS) mode ( Fig. 1, D and F ) coupled to the spring LAI patterns demonstrates a direct response pattern of summer SWC to the emerging spring greening. The MCA-based annual time series linked to the spring LAI and summer SWC (or TWS) pattern are also highly correlated (r = 0.86 for GLEAM SWC and r = 0.80 for GRACE-REC TWS, P < 0.05 for both; fig. S4). This reiterates the strong coupling between the two coupled patterns shown in Fig. 1 (C to F). In general, negative summer SWC anomalies (i.e., soil drying) are preceded by spring greening, whereas positive SWC anomalies (i.e., soil wetting) dominate the western North America with spring browning ( Fig. 1, D and F versus C and E). The dominance of negative LAI-SWC coupling, as also seen by partial correlation patterns, implies an impact of earlier spring onset on local summer soil drying. The cropland-dominated central Europe has wetter summers during years with greener springs ( Fig. 1, C to F ), which is in agreement with the positive correlation between spring greenness and summer soil moisture ( Fig. 1, A and B ). A notable exception occurs in the Siberia region, where summer SWC increases despite the presence of a clear positive spring LAI anomaly and limited human interventions. This is also in sharp contrast to the negative partial correlations in central Siberia after removing covarying effects of precipitation ( Fig. 1, A and B ). Such difference implies that altered precipitation patterns driven by spring LAI changes cause extra moisture to be brought into this area.

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.

Partial correlations between spring greenness and subsequent summer SWC show a widespread negative pattern across most northern lands (64% of land area for GLEAM SWC and 56% for GRACE-REC TWS) ( Fig. 1, A and B ), including significantly greening regions of Europe, eastern United States, East Asia, and Siberia (fig. S1). For temperate and semi-arid grasslands where LAI is coupled to shallow moisture, this dominance of negative correlation between spring LAI and summer moisture is additionally supported by using microwave-derived summer top-layer soil moisture (fig. S2). The widespread negative correlation indicates that, when effects of interannual variations of precipitation are factored out, earlier greening in spring generally results in local soil drying in subsequent summers. On the other side, positive correlation is also apparent over regions including the North China Plain, central and southeastern Europe, and the U.S. Great Plains, especially for TWS that additionally captures storage variations in canopy, snow, streams, lakes, and groundwater ( Fig. 1, A and B ). Many such regions are influenced by intensive agricultural activities, which potentially decouples the relationship between spring LAI and summer SWC observed in natural ecosystems. By averaging land surface undisturbed by cropland management, we find a negative correlation between spring LAI and summer SWC that is statistically significant (−0.44 for GLEAM SWC and −0.40 for GRACE-REC TWS, P < 0.05) (fig. S3). However, an increasing fraction of land areas affected by croplands leads to a weakening of this negative correlation, which shifts robustly to being positive when croplands occupy over 50% of local land areas (fig. S3).

To examine spatiotemporal connections between recent spring phenological changes (section S1 and fig. S1) and soil water content (SWC) dynamics in the following summer, we first analyze long-term satellite data of spring leaf area index (LAI; as a proxy for vegetation phenology and growth; Materials and Methods) ( 20 ) and summer values of two observation-driven SWC-related datasets for the period of 1982–2011. The two SWC-related data include root-zone soil moisture from the Global Land Evaporation Amsterdam Model (GLEAM; Materials and Methods) ( 21 ) and the long-term reconstruction of terrestrial water storage (TWS) anomalies based on Gravity Recovery and Climate Experiment satellites (GRACE-REC) ( 22 ). Our study domain only covers the middle to high latitudes of the Northern Hemisphere (25° to 90°N) because of their distinctive vegetation seasonality and particularly pronounced recent changes in springtime LAI phenology (fig. S1). We define spring across the domain as the period from March to May (MAM) and summer as the period from June to August (JJA). We calculate partial correlations between spring LAI and summer SWC (or TWS), whereby covarying effects of summer LAI and climatic variables (i.e., temperature, precipitation, and solar radiation) are controlled for (Materials and Methods). As potential nonlocal precipitation feedbacks modulate the local relationship between spring LAI and summer SWC ( 14 , 19 ), we also implement a lagged maximum covariance analysis (MCA; Materials and Methods, section S2) ( 23 ). This statistical approach isolates pairs of coupled spatial patterns that explain a maximum fraction of the covariance between two space-time data fields (in our case, spring LAI and summer SWC). Hence, the MCA method has obvious advantages over routine correlation analysis that only partially describes covariability between two fields (i.e., spatial correlation using geographical points only or temporal correlation using time series only).

Simulating the spring-to-summer vegetation feedbacks

Statistical analyses of observation-based data show that summertime soil moisture anomalies negatively co-vary with the preceding spring greening patterns. However, the detection of systematic legacy effects of spring LAI on summer soil moisture within data is partially affected by noise from weather variability, potential nonlinearity in soil moisture responses, different rooting depth and moisture access across ecosystems, and slowly varying drivers of both LAI and soil moisture trends such as vapor pressure deficit and rising atmospheric CO 2 . To confirm a causal link between spring greening and summer soil drying, and to better understand the local and nonlocal mechanisms, we perform further analyses using the IPSL-CM4 coupled land-atmosphere global climate model (GCM; Materials and Methods) (24). Coupled land-atmosphere models explicitly describe biophysical interactions between the land surface and the atmosphere. This includes local exchanges of water and energy fluxes and large-scale changes of atmospheric circulation and moisture convergence. Paired time-evolving (i.e., transient) numerical experiments are run with the land surface component of IPSL-CM4, ORCHIDEE (Organizing Carbon and Hydrology In Dynamic Ecosystems; Materials and Methods), forced with two contrasting spring LAI conditions. One set of simulations (labeled as “ LAI obs MAM ”) is prescribed with annually varying LAI from satellite observations (20) during all seasons, including MAM. The second set of simulations (labeled as “ LAI clim MAM ”) is also prescribed with annually varying LAI during all seasons, except in MAM when it is fixed to the climatological mean LAI conditions observed during 1982–2011 (Materials and Methods). Apart from the spring LAI differences, the two model experiments are forced identically with observed boundary conditions of sea-surface temperature (SST), sea-ice fraction (SIF), and atmospheric CO 2 concentration. This enables us to account for the vegetation biophysical feedbacks stemming from interactions with changes in oceanic processes or CO 2 radiative/physiological forcings. Multimember paired ensembles are generated for each coupled model experiment by performing 30 repeated runs but with different initial conditions (Materials and Methods).

Model evaluation against global observational datasets supports the capacity of the IPSL-CM4 GCM to capture the annual and seasonal features of key hydrological variables, including ET and precipitation (fig. S5, A and B; further details in section S3). To avoid systematic biases from the diverging magnitude of modeled and observation-based SWC, SWC changes are instead compared as the fraction of the present-day climatological mean (1982–2011) (25). At the scale of the Northern Hemisphere (25° to 90°N), the IPSL-simulated SWC shows a negative trend, whereas GLEAM SWC shows a weaker positive trend (fig. S5C). While this sign difference is possibly a concern for global mean changes, we find zonally coherent patterns of the IPSL-simulated SWC trend compared to GLEAM SWC (fig. S5, D to I). The IPSL-simulated SWC broadly captures regions where soil wetting or drying occurs, at both annual and seasonal time scales (fig. S5, D to I). This regional consistency strengthens our confidence in examining both the observed zonally different fingerprint of spring greening on summer soil moisture and the ability of our GCM to explain such changes.

The difference between the two factorial simulations with variable and fixed spring LAI ( LAI obs MAM − LAI clim MAM ) allows us to isolate the fingerprint of observed spring LAI changes on summer soil moisture (δSWC) and other important hydrological variables. The symbol “δ” hereafter denotes the influence of spring LAI changes on other target variables of interest. Our factorial model simulations confirm a strong coupling between spring LAI and summer δSWC anomalies (r = −0.51, P < 0.01) (Fig. 2A). Hence, on a year-to-year basis, the observed higher spring LAI values prescribed to our GCM correspond to lower summer SWC estimates. We then examine whether there is a robust overall trend of decreasing δSWC, driven by the significantly increasing spring LAI (greening). This effect is more difficult to detect because the interannual trend of LAI over the past 30 years is smaller than concurrent climate-driven variabilities. Average summer δSWC across all the northern lands (25° to 90°N) tends to decrease at a rate of −0.11 ± 0.09 × 10−4 m3 m−3 year−1 (linear trend: mean ± 1 SD, P = 0.22) due to the spring LAI changes. This overall summer δSWC trend is negative, in line with our hypothesis that higher green foliage cover is transpiring more in spring and leaving soils drier in summer. The calculated trend is not statistically significant, partly due to the presence of contrasting effects on summer SWC in regions with spring greening (i.e., positive LAI trends) versus spring browning (i.e., negative LAI trends). When instead considering only regions where spring LAI generally increases, a statistically significant and much stronger summer drying trend in summer δSWC (−0.17 ± 0.07 × 10−4 m3 m−3 year−1, P < 0.05) is detected (Fig. 2B). On average, including areas with spring browning, this decreasing trend of summer SWC associated with spring LAI changes contributes an additional 16% to the simulated decrease in summer SWC directly caused by climate change during 1982–2011 (overall decrease, −0.68 × 10−4 m3 m−3 year−1 from the LAI obs MAM simulation).

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.

We also perform the MCA analysis on observed spring LAI- and IPSL-simulated summer SWC (from simulation LAI obs MAM ). The coupling patterns between spring LAI and summer SWC for the first mode (fig. S6, A and B) show high accordance with those purely based on observations (Fig. 1, C to F), confirming the ability of our GCM to simulate the interseasonal vegetation–soil moisture feedbacks. We further test whether insights based on our GCM-based factorial simulations could be overly influenced by water-stressed regions at low latitudes. In doing so, we calculate spring LAI and summer δSWC anomalies weighted by both area and annual mean soil moisture values. Again, a significant negative correlation is found between spring LAI and summer δSWC (fig. S6, C and D). This spring LAI-induced drying trend of summer SWC (−0.09 ± 0.06 × 10−4 m3 m−3 year−1) is consistent with that without assigning a lower weight to drier regions (Fig. 2A), partly because earlier greening mainly occurs in regions with favorable moisture conditions. Therefore, our GCM simulations provide robust process-based evidence that the observed overall earlier greening of northern land causes extra decreases in summer soil moisture.

Intra- and interseasonal soil moisture anomalies are determined by the balance between changes in ET, precipitation, and runoff (schematic, Fig. 3). To gain more insights into how spring vegetation changes feedback on soil moisture, we further use the paired GCM experiments to examine changes in these major hydrological fluxes. Higher spring LAI levels tend to accelerate water recycling from the land surface to the atmosphere through enhanced plant transpiration. As transpiration dominates total terrestrial ET (average transpiration/ET ratio of IPSL-CM4: 0.47 for spring and 0.76 for summer) (26), a higher spring transpiration directly leads to higher modeled spring ET (+0.28 ± 0.06 mm year−2, P < 0.01) (Fig. 2D). We also note that the overall trend of IPSL-simulated spring ET is not statistically significant (P > 0.05), which is confirmed by two observation-based ET products (fig. S5A). This is mainly because the significant ET increase in regions with spring greening is compensated by the ET decrease in regions with spring browning when integrated to the hemispheric scale (fig. S7). For this greening-enhanced ET, however, only a proportion (71%) is subsequently recycled as additional land precipitation during MAM (0.20 ± 0.09 mm year−2, P < 0.01) (Fig. 2D). During MAM, this imbalance between increasing precipitation and ET due to earlier phenology and LAI development results in a decreasing trend in both soil moisture (−0.11 ± 0.09 × 10−4 m3 m−3 year−1) and surface runoff (−0.08 ± 0.09 mm year−2). The larger increase in ET than in precipitation is much more pronounced when considering only regions with spring greening, where it causes a strong signal of soil drying (Fig. 2, B and E). However, in regions with spring browning, there is a stronger decrease in ET than in precipitation, thus causing soil wetting (Fig. 2, C and F). These contrasting responses, where ET changes do not balance precipitation changes, suggest a geographical redistribution of the extra atmospheric water vapor from regions with earlier greening, toward regions with browning, within the springtime.

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.

Our factorial model simulations allow further investigation of the soil moisture legacy effects into the summer following a spring with altered phenology and hydrology. We find that the spring soil moisture deficit caused by earlier greening is carried over into the summer months (Fig. 2D, blue bars). The interseasonal soil moisture memory propagates this moisture deficit forward, and this also involves additional contribution of soil moisture–atmosphere biophysical feedbacks. Given the short memory of days to weeks of the atmosphere, the atmospheric moisture anomaly from earlier greening is dissipated mainly in the springtime through extra spring rainfall (Fig. 2D). The lower soil moisture levels in late spring due to a prolonged period of stronger decrease and insufficient rainfall resupply become less accessible for plant root uptake and soil evaporation (27), particularly in climatologically water-stressed regimes. Hence, summer δET decreases substantially across the entire study area (−0.10 ± 0.05 mm year−2), and summer δP (precipitation) decreases even more (−0.19 ± 0.08 mm year−2), leading to a slightly strengthened decreasing trend in summer SWC (−0.11 ± 0.09 × 10−4 m3 m−3 year−1) (Fig. 2D). This summer drying signal is larger and statistically significant in areas with extra spring greening (Fig. 2E). The significantly decreasing spring LAI (P < 0.05) in areas with spring browning do not, however, generate significant changes in any of the hydrological fluxes in summer (i.e., ET, P, runoff, and SWC; all P > 0.1) (blue bars in Fig. 2F). In these browning regions, soil moisture anomalies in spring have little impact on subsequent summer water recycling (δET ≈ 0) and hence summer rainfall feedbacks. It is noteworthy that both spring greening and browning tend to decrease the amount of summer precipitation (Fig. 2, E and F). This implies that the current spring LAI levels may have evolved to be an optimum that maximizes the precipitation retained on continents in the subsequent summer season.

We further investigated the spatial pattern of the GCM-derived soil moisture trend in δSWC (Fig. 4; other hydrological fluxes in fig. S8). In general, the modeled summer δSWC pattern and observed summer SWC anomalies associated with the first MCA mode (Fig. 1, D and F) are highly similar, providing mechanistic support for our observation-based findings. Besides, the summer δSWC pattern closely resembles the spring δSWC pattern (Fig. 4B versus Fig. 4A), implying the dominant role of soil moisture memory in carrying this greening-induced water deficit into the summer. Areas with the highest rates of summer drying (>0.1 × 10−3 m3 m−3 year−1) occur mainly in Europe, eastern Asia, western Asia, and eastern North America (Fig. 4B), where pronounced advances of vegetation greening occur during spring (fig. S1). Conversely, a summer wetting trend of soil moisture dominates regions with spring browning, such as central and northwestern North America (Fig. 4B versus fig. S1). One notable exception is again the central Siberian regions, where a strong modeled soil wetting trend occurs in both spring and summer (>0.05 × 10−3 m3 m−3 year−1) (Fig. 4), despite the presence of a clear spring greening tendency there that generally lowers summer soil moisture elsewhere (Fig. 4 and fig. S1). The GCM simulations show that this trend of regional wetting in Siberia is caused by different mechanisms in the two seasons. In spring, the extra water vapor recycled into the atmosphere in upstream regions (Europe) with strong greening is carried to Siberia by westerly winds (fig. S9A). This imported extra precipitation moistens local soils and offsets the soil moisture deficit originated from local spring greening (schematic, Fig. 3). During the following summer, when the prevailing westerly winds become weaker (fig. S9B), the Siberian region operates as a more closed system with intensified local water recycling (fig. S8). Consequently, the imported extra soil moisture in springtime helps to sustain local positive feedbacks on summer precipitation and is ultimately retained in the Siberian soils throughout the boreal summer. This additional summer wetting from the earlier greening of local and remote regions, in total, accounts for over 90% of the overall summer soil wetting trend derived from LAI obs MAM , showing its much more important role for Siberia than the ongoing climate change.