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Regional asymmetry in the response of global vegetation growth to springtime compound climate events

Illustration of the compound event indices

Building on earlier studies24,25, we develop two univariate indices to model concurrent climate conditions, i.e., a CWD index that varies from compound cold-wet conditions to CWD conditions, and a CCD index that varies from compound warm-wet conditions to CCD conditions (see “Methods”). The two indices incorporate the dependence between temperature and precipitation and are a measure of how warm/cold and dry a point is relative to the distribution of climate conditions at a given location. We illustrate the two indices on two grid points that have strong but opposite temperature-precipitation correlation. In the case where temperature and precipitation are strongly negatively correlated, the CWD index is well aligned with the primary axis of the bivariate distribution (Fig. 1a). In the case where temperature and precipitation are strongly positively correlated, the same holds for the CCD index (Fig. 1d). As illustrated for several concurrent hot-dry and cold-dry events that occurred around the globe, the two indices well capture these events (Supplementary Figs. 1 and 2).

Fig. 1: The relationship between precipitation and temperature and compound indices.

a Scatter plot of summer precipitation and temperature anomalies (z-score) with corresponding CWD index in color (see “Methods”). The location is at 97.25°W and 33.75°N from 1901 to 2018. b The same as a but for spring at 84.75°E and 66.75°N. c Same distribution as in a but colored based on the CCD index. d Same distribution as in b but colored based on the CCD index.

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Notably, in the case where precipitation and temperature are strongly positively correlated, the CWD index indicates the relative anomalies of bivariate joint distribution, and some counterintuitive situations might occur relative to the univariate marginals (Fig. 1b). For instance, points might be labeled as strong CWD events (CWD index > 1.5) even though temperature is anomalously cold (temperature anomalies < 0, red dots in lower left quadrant of Fig. 1b). The CCD index exhibits similar behavior (Fig. 1c). This indicates an interesting property of the compound indices to identify strong compound conditions relative to bivariate distribution that are not necessarily extreme from a univariate perspective3,24,26,27.

Widespread direct and lagged impacts of springtime compound climate conditions

To evaluate the lagged summer vegetation responses to spring compound climate conditions, we compute partial correlation between CWD (CCD) spring and subsequent summer vegetation variation by controlling for the influence of summer compound climate conditions on these correlations (see “Methods”). Results show widespread negative associations between CWD spring and subsequent summer vegetation in the mid-latitudes (<50°N; 19% of the total study area, p < 0.05), indicating that CWD spring inhibits summertime vegetation growth. In contrast, positive associations are found in the high latitudes (>50°N; 8%, Fig. 2a). When considering CCD events, CCD spring primarily exhibits negative lagged effects (18%) whereas positive lagged effects are rare (1%, Fig. 2b). Notably, in line with the fact that vegetation is often impacted by both temperature and precipitation28, our results indicate that considering springtime temperature and precipitation concurrently can slightly better explain summertime vegetation variability than considering temperature or precipitation in isolation. Specifically, CWD climate conditions in spring are significantly correlated (p < 0.05) with summer vegetation growth in 27% of the total study area compared to 15% for temperature only and 24% for precipitation only (Fig. 2a and Supplementary Fig. 3).

Fig. 2: Summer vegetation lagged responses to spring compound climate conditions.

a Grid-based partial correlations between yearly spring CWD climate conditions and summer NDVI (a proxy for vegetation growth) during the period 1981–2015, computed based on partial correlation analysis (see “Methods”). b The same as a, but for spring CCD climate conditions. Absolute values of the partial correlation coefficients (r) correspond to significance levels of p = 0.2 (r = 0.22), p = 0.1 (r = 0.28), or p = 0.05 (r = 0.33). For each map, frequency histograms show the areal coverage corresponding to positive and negative correlations. Light gray areas, i.e. cropland, urban, barren, and permanent snow, and ice, are not included in the analysis.

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To investigate the direct responses, we then calculate correlations between CWD (CCD) indices and vegetation growth in spring and summer separately (Supplementary Fig. 4). There are widespread direct positive responses to CWD climate conditions in spring (52% of the study area, p < 0.05) and summer (30%) in the Northern hemisphere, while negative responses for spring (10%) and summer (22%) are distributed in mid-latitudes (Supplementary Fig. 4a, c). CCD climate conditions predominantly show negative correlations in spring (25%) and summer (20%, Supplementary Fig. 4b, d). We further compare the direct effects of compound climate conditions with that of individual temperature or precipitation and find that CWD climate conditions can well incorporate the direct effects of both temperature and precipitation (Supplementary Fig. 4a, c and Fig. 5).

Process-based dynamic global vegetation models are commonly used tools to simulate the responses of vegetation growth and ecosystem productivity to climate extremes8,29. We thus assess the ability of the current set of state-of-the-art vegetation models (TRENDYv6)30 to capture the lagged responses to spring compound climate conditions (Supplementary Fig. 6). Overall, we find that the models tend to overestimate the areal extent with positive lagged effects to CWD springs, and the areas with negative lagged effects to CCD springs (Supplementary Fig. 6a, d). In contrast, the models and observation-based products generally show good agreement on the areal extent that experience negative lagged effects to CWD springs (Supplementary Fig. 6b, c). When considering LAI, the results are similar (Supplementary Fig. 6e–h).

We finally assess the ability of TRENDYv6 models to replicate direct response to compound climate conditions. Overall, the models overestimate the coverage that is significantly negatively correlated to CCD climate conditions (Supplementary Fig. 7a–d). In contrast, the areal extents with significant correlations to CWD climate conditions are generally well captured by vegetation models (Supplementary Fig. 7e–h). The results are similar when employing LAI (Supplementary Fig. 7i–p).

Regional asymmetries in direct and legacy effects of CWD event

Our results above indicate widespread positive vegetation responses to CWD climate conditions in high latitudes (>50°N), and a negative response in mid-latitudes (23.5–50°N/S). CCD climate conditions mostly exhibit negative effects in mid-to-high latitudes. To understand the effects of CWD and CCD events in more detail and the corresponding physical mechanisms, we perform a regional analysis, focusing on the areas in Fig. 2a where summer vegetation responds positively (r ≥ 0.22) and negatively (r ≤ −0.22) to spring CWD climate conditions, and the areas in Fig. 2b where summer vegetation responds negatively (r ≤ −0.22) to spring CCD climate conditions. Across the focus areas, we investigate the composites of average anomalies in GPP, LAI, soil moisture, runoff, and terrestrial water storage (TWS) (see “Methods”). Notably, over these areas, we find that the direction of direct and lagged productivity responses between the models and observation-based products overall shows good agreement, despite the difference in response magnitude (Figs. 3–5).

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Fig. 3: Seasonal evolution of vegetation productivity and hydrological variables during CWD events in high latitudes (>50°N).

ac The average standardized anomalies (z-score) of GPP during CWD spring but subsequent non-CWD summer (a), non-CWD spring but subsequent CWD summer (b), and consecutive CWD spring and summer (c) for areas in Fig. 2a where summer vegetation responds positively (r ≥ 0.22) to spring CWD climate conditions. df The same as ac, but for soil moisture. gi The same as ac, but for runoff. The bar plots with dash lines (without dash line) indicate the average anomalies of multiple observation-based (model-based) products, and the circles indicate the average anomalies of each product. GLASS, LUE, NIRv, Flux-CRU, and Flux-ERA5 are observation-based GPP products, while model simulations are taken from TRENDYv6. GLEAM is observation-based soil moisture. GRUN represents observation-based runoff. GLDAS-VIC, GLDAS-Noah, GLDAS-Catchment, and FLDAS indicate assimilatory soil moisture and runoff that incorporate satellite- and ground-based observational products.

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Fig. 4: The responses of vegetation productivity and hydrological variables to CWD events in mid-latitudes (23.5–50°N/S).

ac The average standardized anomalies (z-score) of GPP during CWD spring but subsequent non-CWD summer (a), non-CWD spring but subsequent CWD summer (b), and consecutive CWD spring and summer (c) for areas in Fig. 2a where summer vegetation responds negatively (r ≤ −0.22) to spring CWD climate conditions. df The same as ac, but for soil moisture. gi The same as ac, but for runoff. The bar plots with dash lines (without dash line) indicate the average anomalies of multiple observation-based (model-based) products, and the circles indicate the average anomalies of each product. For details on data see Fig. 3.

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Fig. 5: The effects of CCD events on vegetation productivity and hydrological variables in mid-to-high latitudes.

ac The average standardized anomalies (z-score) of GPP during CCD spring but subsequent non-CCD summer (a), non-CCD spring but subsequent CCD summer (b), and consecutive CCD spring and summer (c) for areas in Fig. 2b where summer vegetation responds negatively (r ≤ −0.22) to spring CCD climate conditions. df The same as ac, but for soil moisture. gi The same as ac, but for runoff. The bar plots with dash lines (without dash line) indicate the average anomalies of multiple observation-based (model-based) products, and the circles indicate the average anomalies of each product. For details on data see Fig. 3.

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CWD events increase vegetation productivity in high latitudes

We first analyze the direct responses of productivity to springtime and summertime CWD events across high latitudes (>50°N, Fig. 3). Productivity increases during CWD spring and summer (Fig. 3a–c), which is consistent with vegetation responses (Supplementary Fig. 8a–c). Despite elevated spring greenness, spring water overall shows positive anomalies during CWD spring (Fig. 3d, f, g, i). This result indicates that spring greenness during CWD conditions is not associated with dry spring across high latitudes, which is further confirmed by similar anomalies in springtime TWS (Supplementary Fig. 8d, f). In contrast, severe water reduction is found in CWD summer (Fig. 3e, f, h, i). This suggests that despite the beneficial effects of CWD events on productivity in summer, they are associated with summer water deficit.

Next, to analyze the lagged effects of springtime CWD events, we investigate the productivity anomalies in summer under three cases, namely CWD spring but non-CWD summer, non-CWD spring but CWD summer, and consecutive CWD spring and summer. Our results indicate that springtime CWD events have positive lagged effects on summer productivity across high latitudes (Fig. 3). Specifically, we find that during non-CWD summer (that is not favorable for summer vegetation growth) preceded by CWD spring, positive anomalies are still found in summer productivity (Fig. 3a). In contrast, during CWD summer (preceded by non-CWD spring), some models and observation-based products exhibit a reduction in summer productivity (Fig. 3b). We further find that summer productivity highly increases during consecutive events (Fig. 3c). Vegetation anomalies show similar behaviors (Supplementary Fig. 8a–c). Regarding the lagged responses of hydrological variables, CWD springs followed by non-CWD summers do not lead to water dryness, despite increased vegetation greenness (Fig. 3d, g). The magnitude of summer water deficit is similar for both cases that include CWD summer (Fig. 3e, f, h, i) and is consistent with summer TWS anomalies (Supplementary Fig. 8e, f). These results imply that in high latitudes, summer water reductions characterized by TWS, soil moisture, and runoff are not associated with increased spring greenness but are primarily caused by summer precipitation deficit.

The productivity responses to compound climate conditions may be stronger than that to individual events through the synergistic effects of temperature and precipitation28. To investigate this, we compute the average anomalies in GPP and soil moisture associated with univariate events across the focus areas, which are then compared with the effects of CWD and CCD events in high latitudes (see “Methods”). Warm events can not only directly increase productivity but also show positive lagged effects (Supplementary Fig. 9a, b). In contrast, dry events reduce productivity (Supplementary Fig. 9e, f). This indicates that the direct and lagged positive effects of CWD events across high latitudes are mainly dominated by the warm component, while dry conditions have negative effects. Therefore, the warm-induced increase in productivity slightly exceeds that associated with CWD events (Supplementary Fig. 9b). Soil moisture under warm springs shows positive anomalies (Supplementary Fig. 9c, d), while they slightly decline during dry spring (Supplementary Fig. 9g, h). This suggests that the positive anomalies in soil water during CWD spring are driven by the warm component.

CWD events reduce vegetation productivity in mid-latitudes

Here, we first investigate the direct effects of springtime and summertime CWD events across mid-latitudes (23.5–50°N/S). Springtime productivity exhibits little changes during CWD spring (Fig. 4a, c), despite dry spring (Fig. 4d, f, g, i). When considering the direct effects of CWD events in summer, the results are similar, whereas the negative magnitude of productivity in summer is larger than that in spring (Fig. 4b, c). This difference suggests CWD conditions in summer show more adverse effects on productivity than that in spring in mid-latitudes. The anomalies in vegetation and TWS are consistent (Supplementary Fig. 10).

Next, the lagged effects of springtime CWD events in mid-latitudes are assessed. In cases with CWD spring but non-CWD summer, summer productivity exhibits slight anomalies (Fig. 4a), with slightly decreased summer water (Fig. 4d, g). Summer productivity and water show much higher reductions in case with consecutive events (Fig. 4c, f, i) than for the case with only CWD summer (Fig. 4b, e, h). These results are supported by the responses of vegetation indices and TWS (Supplementary Fig. 10), revealing that springtime CWD events in mid-latitudes have negative lagged effects on summer productivity and water availability.

The direct and lagged effects of individual events are finally compared with that of CWD events in mid-latitudes. Dry conditions in spring and summer directly decrease productivity and cause soil water dryness (Supplementary Fig. 11a–d). Moreover, dry spring depletes soil moisture earlier, which, in turn, causes dry summer and reduction in productivity during non-dry summer (Supplementary Fig. 11a, c). This indicates that dry springs have negative lagged effects on summer productivity. In contrast, productivity and soil water show positive anomalies during warm springs, while they show negative anomalies in summer (Supplementary Fig. 11e–h). These results suggest that the direct and lagged negative effects of CWD springs are dominated by the dry component in mid-latitudes, while the warm component mitigates the negative effects of the dry component in spring. Accordingly, the decline in productivity due to dry conditions thus exceeds that triggered by CWD events (Supplementary Fig. 11b).

Decreased vegetation productivity due to the negative synergistic effects of CCD events

Here, we first investigate the direct effects of CCD events across mid-to-high latitudes. Productivity reductions are found during springtime and summertime CCD events (Fig. 5a–c) concurrent with water reductions (Fig. 5). Vegetation and TWS show similar behaviors during CCD spring and summer (Supplementary Fig. 12). These results reveal that CCD events in spring and summer can impose direct adverse impacts on productivity and soil water across mid-to-high latitudes. The productivity reductions in spring and summer are similar in magnitude (Fig. 5a, b), indicating that CCD events between spring and summer can cause similar damage to productivity.

We then analyze the lagged effects of springtime CCD events. Our results indicate that springtime CCD events show negative lagged effects on summer productivity and cause summer water reductions in mid-to-high latitudes (Fig. 5). Specifically, we find that in cases with CCD spring but non-CCD summer, summer productivity and water exhibit strongly negative anomalies (Fig. 5a, d, g). Moreover, summer anomalies are higher during consecutive events (Fig. 5c, f, i) than the cases including only CCD summer (Fig. 5b, e, h). Vegetation indices and TWS show similar responses (Supplementary Fig. 12). Our results further indicate that CCD spring has more severe negative lagged effects on productivity than CWD spring. That is, we find that in comparison to cases with preceding CWD spring and consecutive CWD events, summer productivity shows higher reduction in cases with preceding CCD spring and consecutive CCD events (Fig. 4a, c versus Fig. 5a, c). Moreover, in cases with CCD spring but non-CCD summer (Fig. 5a, d, g), summer anomalies are close to those in scenarios with non-CCD spring but CCD summer (Fig. 5b, e, h). The vegetation and TWS anomalies further confirm this situation (Supplementary Fig. 12a, b, d, e). These results suggest that the lagged effects of CCD spring can be of similar magnitude as their direct adverse effects.

We finally compare the direct and lagged effects of individual events with that of CCD events in mid-to-high latitudes. Cold conditions in spring and summer directly reduce productivity but show weak effects on soil moisture (Supplementary Fig. 13a–d), and cold spring shows negative lagged effects on summer productivity (Supplementary Fig. 13a). Dry events show direct and lagged negative effects on productivity and soil moisture (Supplementary Fig. 13e–h). These results imply that the negative lagged effects of CCD springs are dominated by both cold and dry components. The effects of CCD events on productivity mostly exceeds the individual dry or cold impacts (Supplementary Fig. 13a, b, e, f).


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