the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Apr 2026
Amplification of ENSO-driven vegetation variability at decadal and longer timescales
Abstract. The El Niño-Southern Oscillation (ENSO) is recognized as the dominant driver of global vegetation variability on interannual timescales. Here, we examine how ENSO affects decadal to multi-decadal vegetation variability. We address this with partial spectral and mediation analysis applied to multi-centennial pre-industrial control simulations from 11 CMIP6 models with dynamic leaf area index (LAI). We find a spectral reddening of ENSO-driven vegetation variability, with a 20–25 % amplification of the LAI signal at multi-decadal timescales and a 25–65 % reduction at interannual timescales. The coherence between ENSO and LAI on multi-decadal timescales is governed by a direct causal impact of ENSO on LAI (88 %), while the Pacific Decadal Oscillation (PDO) acts as a weak mediator (12 %). Mechanistically, persistence in vegetation originates from ENSO-induced changes in near-surface soil moisture, which is subsequently amplified by vegetation dynamics. This ENSO-related memory also manifests in Gross Primary Production (GPP), but it is suppressed in Net Primary Production (NPP) by a compensatory increase in autotrophic respiration. Our results illustrate how terrestrial persistence acts as a predictable, non-oceanic source of decadal variability, which could help extend the skill of climate predictions and improve hydrological risk management.
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RC1: 'Comment on egusphere-2026-1961', Anonymous Referee #1, 22 Apr 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-1961/egusphere-2026-1961-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2026-1961-RC1 -
RC2: 'Comment on egusphere-2026-1961', Anonymous Referee #2, 12 May 2026
Review of “Amplification of ENSO-driven vegetation variability at decadal and longer timescales” by Fahrenbach and Wills
The manuscript explores a foundational question in Earth system dynamics: the capacity of the terrestrial biosphere to modulate and amplify oceanic climate signals across decadal frequencies. By employing a multi-model ensemble long equilibrium PI simulation of 11 CMIP6 models with dynamic vegetation, the authors identify a spectral reddening of the vegetation response to ENSO, specifically manifesting as a 20-25% amplification of LAI variability at multi-decadal timescales. This synthesis of partial spectral analysis and mediation frameworks provides insights for the causal chain from SST anomalies to vegetation and terrestrial carbon flux dynamics. The authors show that near-surface soil moisture acts as a hydrological integrator, subsequently amplified by internal vegetation dynamics and propose a theory for the case of non-oceanic sources of decadal climate/ecosystem predictability.
The manuscript is clearly written and easy to read. While the methodology itself is mathematically rigorous and the multi-model agreement lends significant weight to the conclusions, several critical areas require deeper theoretical and technical elaboration. I therefore recommend major revisions before the manuscript is considered for publication.
Major comments
1. The current analytical framework focuses almost exclusively on ENSO and the PDO as the primary drivers of decadal variability. However, the Earth system contains additional important ocean–atmosphere oscillations operating on similar timescales, such as the Interdecadal Pacific Oscillation (IPO) and the Atlantic Multidecadal Oscillation (AMO). Importantly, these modes can also be influenced by ENSO variability (e.g. Dong et al. 2006; Timmermann et al. 2018). Therefore, the real-world impacts may be substantially broader and more complex than represented in the current framework. For example, ENSO may influence LAI indirectly through its modulation of IPO or AMO, while IPO and AMO themselves may also exert independent influences on LAI that are distinct from ENSO effects. The potential contributions of these additional climate modes should be evaluated and discussed. If they are currently treated as residual variability, the manuscript should quantify the magnitude of their contributions and clarify the associated uncertainties.
2. A core weakness of the manuscript is the lack of a clear physical interpretation for "ENSO-driven decadal variability". While the statistical methods identify low-frequency power, the biological and climate-forcing features of this signal remain opaque. Based on the methodology, my understanding is that this variability (Nino 3.4 index with a 10-yr low-pass filter) primarily reflects modulation in the amplitude and/or frequency of ENSO-related SST anomalies on longer (decadal) timescales, rather than representing an independent climate mode. However, this conceptual link is not explicitly articulated in the text.
I suggest that the authors provide a clearer mechanistic framework describing how decadal-scale SST anomalies emerge from ENSO variability (e.g., through changes in ENSO amplitude, frequency, or phase asymmetry, etc.). It would also be helpful to explicitly relate these SST anomalies to the underlying climate forcing pathways that generate them.
Furthermore, the connection between these decadal SST variations and vegetation dynamics (e.g., LAI responses) should be more clearly explained. In particular, the manuscript would benefit from a clearer description of how large-scale SST variability is translated into regional hydroclimatic anomalies, and subsequently into vegetation responses, rather than treating these links implicitly.
3. The concept of "memory" in soil moisture longer than its typical seasonal timescale is novel. As the authors extend this concept to decadal timescales, this leap that requires a more robust physical explanation. It is quite surprising that the process is governed by the interplay between near-surface soil water content and vegetation processes, rather than deeper soil water. Because in traditional hydrology, the deep soil and groundwater are the primary candidates for multi-year or longer memory, acting as low-pass filters that integrate high-frequency surface pulses. How this land surface coupling effectively stabilizes the anomalies, preventing their rapid dissipation and allowing the "memory" to persist across the decadal threshold should be more explicitly discussed.
4. The authors completely ignore temperature influence in their analytical framework, while only considering radiation and water stress as mediators (L355). Although light and water are primary limiting factors in the tropics, temperature (thermal energy) can be the dominant control over vegetation growth in mid-latitude and high-latitude biomes. For instance, regional warming has been the primary driver of enhanced ecosystem productivity and advanced growing season phenology across the northern extratropics (e.g. Pan et al. 2015).
Crucially, ENSO systematically impacts land surface air temperature on a global scale through atmospheric teleconnections. Studies have shown that El Niño events contribute to a global-scale warming in the subsequent summer, with a strong coupling between interannual variations in the CO2 growth rate and tropical surface temperature (Wang et al. 2013). The use of a global pattern-projection index (Eq 2) to characterize amplification is potentially "misleading" if it overlooks the temperature pathway in the 33% of the Earth's vegetated surface where temperature is the primary limiting factor (Nemani et al. 2003). If the authors intend to limit their mechanistic analysis to radiation and water stress, they should consider limiting the spatial extent of the Eq. 2 analysis strictly to the tropics (e.g. between 24S and 24N). Otherwise, the manuscript must include temperature in the mediation/partial spectral framework to justify the "global" scope of the reported decadal memory.Minor comments
L99 please elaborate on the two-way feedbacks commonly included in the models.
Please specify whether this data was obtained from a single unique realization per model, or if multiple initial-condition ensemble members of 1 model were used to robustly characterize internal variability.
L131 (Eq 2 ): The pattern-projection index Tv represents global vegetation anomalies as a single collapsed scalar time series. While this isolates the temporal evolution of a specific mode, it risks obscuring regional features due to spatial cancellation—where out-of-phase land anomalies (such as tropical drying counteracted by high-latitude greening) smooth out the global signal. To evaluate how representative Tv truly is of decentralized global dynamics, it would be highly valuable to loop the coherence and gain calculations across individual grid cells. Plotting global gridcell-level maps of spectral coherence and gain directly against the oceanic index would provide a more robust, straightforward, and geographically interpretable picture of terrestrial memory hotspots.Refs
Dong, B., Sutton, R. T., & Scaife, A. A. (2006). Multidecadal modulation of El Niño–Southern Oscillation (ENSO) variance by Atlantic Ocean sea surface temperatures. Geophysical Research Letters, 33(8).
Timmermann, A., An, S. I., Kug, J. S., Jin, F. F., Cai, W., Capotondi, A., ... & Zhang, X. (2018). El Niño–southern oscillation complexity. Nature, 559(7715), 535-545.Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C., Tucker, C. J., ... & Running, S. W. (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. science, 300(5625), 1560-1563.
Pan, S., Tian, H., Dangal, S. R., Ouyang, Z., Lu, C., Yang, J., ... & Zhang, B. (2015). Impacts of climate variability and extremes on global net primary production in the first decade of the 21st century. Journal of Geographical Sciences, 25(9), 1027-1044.
Wang, W., Ciais, P., Nemani, R. R., Canadell, J. G., Piao, S., Sitch, S., ... & Myneni, R. B. (2013). Variations in atmospheric CO2 growth rates coupled with tropical temperature. Proceedings of the National Academy of Sciences, 110(32), 13061-13066.
Citation: https://doi.org/10.5194/egusphere-2026-1961-RC2
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