SIRENE

Summary

The SIRENE (Satellite Information for Resilience Monitoring and Early warning of Ecosystem tipping points) project aims to enhance understanding of terrestrial ecosystems and in particular the detection and prediction of their climate-driven tipping points.

The project is developing improved and uncertainty-aware methods for resilience monitoring. In SIRENE, we will use combine AI-driven analyses with advanced Earth Observation (EO) techniques and will benchmark results with ground data - such as CO₂ fluxes or tree diameter. With a focus on vulnerable regions like tropical rainforests, boreal forests, and drylands, this will help to identify early warning signals of potential ecosystem collapse.

The ultimate goal is to create a robust framework for tracking tipping points, supporting climate risk assessments, and informing global reports like those from the IPCC and IPBES.

Background

From a policy perspective, the lack of reliable early warning systems prevents timely intervention. Due to uncertainties in climate risk assessments, reports from organizations like the IPCC and IPBES may underestimate ecosystem vulnerability. Furthermore, there still exists a disconnect between science and policy, as complex resilience indicators are difficult to translate into actionable measures.

In particular, a critical challenge is the uncertainty in defining intervention thresholds. Since tipping elements develop gradually before collapse, policymakers struggle to determine when action is necessary to prevent irreversible ecosystem loss. Additionally, regional variations in climate impacts mean that policies must be localized rather than generalised. Moreover, few best-practices exist, making the standardization of resilience metrics difficult and thus, hindering the comparison of resilience results.

One major scientific challenge is the complexity and nonlinearity of tipping points, which make them not only difficult to predict but sometimes irreversible. Current Earth system models (ESMs) struggle to incorporate abrupt ecological changes such as large-scale tree mortality, fire regimes, and ecosystem shifts. Additionally, data uncertainty remains a challenge, as satellite observations are affected by sensor biases, unrelated environmental conditions such as cloud cover, and variations in data processing methods (e.g., cloud correction, vegetation indices). This introduces high uncertainty to estimates of resilience and resilience changes.

Statistical limitations further complicate predictions. Metrics like Critical Slowing Down (CSD), used to identify early warning signals, may be biased by measurement noise or external influences. Additionally, ecosystems respond differently to stressors such as drought, fire, and land-use change, making it difficult to generalize resilience indicators. Lastly, while AI and machine learning could enhance monitoring, their potential remains largely unexploited in ecosystem resilience studies.

Aims and objectives

The SIRENE project aims to enhance the detection and prediction of climate-driven tipping points in terrestrial ecosystems by developing more reliable resilience metrics and improving early warning systems.

One major focus is improving ecosystem resilience assessments. Current resilience metrics, such as those based on Critical Slowing Down (CSD), are limited by the noise, short time-series data, and external influences. This project seeks to refine these resilience estimation of terrestrial ecosystems by incorporating multi-sensor satellite data, enhanced and validated with ground-based data, long-term climate records, and robust resilience estimators. We will employ machine learning for detecting patterns and improving prediction accuracy. The further integration of AI-driven analysis and Earth system modeling will provide a more holistic understanding of ecosystem resilience.

To make the results more relevant for policymakers, we will quantify and propagate uncertainties from data and resilience estimation into the final results. This will allow clearer risk assessments. With this, the SIRENE project enables more effective climate adaptation strategies, ecosystem conservation efforts, and land-use planning decisions. We also seek to develop region-specific resilience assessments, recognizing that ecosystems respond differently to climate stressors such as drought, fire, and deforestation. Understanding how different types of ecosystems respond to climate extremes will help guide restoration efforts and improve ecosystem management.

The SIRENE project envisions a future where climate-driven tipping points can be predicted with greater certainty, allowing for timely policy interventions and conservation actions. Furthermore, by standardizing resilience metrics and ensuring they are accessible to policymakers, the project aims to foster stronger science-policy collaboration. Ultimately, this research will contribute to global efforts to mitigate climate change impacts and protect vulnerable and valuable ecosystems.