The Satellite and Model Data to Inform Solar Radiation Modification Techniques (STATISTICS) project aims to contribute to the integration of climate modelling with Earth observation to inform potential future Solar Radiation Modification assessment, policy and governance.

Current climate policies are expected to lead to a 2.7°C rise in temperature by 2100, which goes beyond the main goal of the Paris Agreement. While reducing greenhouse gas emissions is the top priority, other approaches—like carbon dioxide removal (CDR) and Solar Radiation Modification (SRM) - are receiving growing attention as ways to help limit global warming.

Solar Radiation Modification methods, such as stratospheric aerosol injection, marine cloud brightening, and cirrus cloud thinning, may offer benefits but also carry potential risks. This makes further research critical. To date, most SRM studies rely on climate modelling (e.g. GeoMIP) and make limited use of real-world observations. However, high-resolution satellite data and AI-powered tools could improve our understanding of natural analogues such as volcanic eruptions

Background

Solar Radiation Modification (SRM) has emerged as a potential, yet highly contentious, climate intervention strategy aimed at mitigating global warming. While SRM techniques, such as stratospheric aerosol injection (SAI) and marine cloud brightening (MCB), have been explored primarily through climate modelling, significant gaps remain in understanding their feasibility, risks, and long-term impacts.

The STATISTICS project seeks to bridge these gaps by better integrating satellite-based Earth observations with advanced modelling techniques to improve the accuracy and reliability of SRM assessments. Both scientific and policy-related challenges are addressed to ensure a comprehensive and responsible approach.

One of the primary scientific hurdles is the limited availability of observational constraints for SRM techniques. While climate models provide valuable insights, high-resolution observations to validate key processes is often lacking, especially at injection sites or in regions where SRM effects are expected to be most pronounced. Current observational datasets, such as those derived from ESA Earth Observations missions are underutilised in SRM research.

This project aims to harness these resources to refine models and improve our understanding, while generating new datasets that can reduce uncertainties in climate response simulations.

Currently, no clear governance framework for SRM research exists raising concerns about equity, ethical considerations, and geopolitical risks. This project acknowledges the importance of engaging with international organisations - including the European Commission (EC), UNEP, WCRP - to ensure that research efforts align with broader environmental and governance frameworks.

Furthermore, anticipating future observational needs is essential, especially in the event of unauthoried or uncoordinated SRM deployment. Our project will address the detectability question using radiative transfer calculations and a future ESA mission as an example.

Aims and objectives

The STATISTICS project will conduct targeted investigations across five key areas:

1 Stratospheric Aerosol Injection (SAI) Model Intercomparison and Evaluation

• Comparative analysis of aerosol microphysics and radiative impacts using multiple climate models and satellite data.
• Assess the ability of aerosol models to simulate stratospheric sulphur injections, using moderate volcanic eruptions (e.g., Raikoke and Ulawun, 2019) as analogues.
• Examine key knowledge gaps, such as aerosol particle size distribution (PSD), radiative impacts, and discrepancies in climate model heating rates at injection sites.

2 Marine Cloud Brightening (MCB) and Aerosol-Cloud Interactions

• Investigate aerosol-cloud interactions using natural analogues, such as from a volcanic eruption, to better understand the climatic impacts of MCB.
• Use advanced retrieval techniques (e.g., GRASP algorithm) to analyse aerosol changes from satellite and ground-based observations.

3 Cirrus Cloud Thinning (CCT) and Mixed-Phase Cloud Thinning (MCT)

• Reassess the viability of CCT by reconciling observational studies and climate model results regarding cirrus cloud susceptibility to ice nucleating particle (INP) seeding.
• Investigate potential interactions between SAI particles (e.g., sulphate, alumina, calcite) and cirrus clouds.
• Conduct exploratory modelling studies of MCT, a newly proposed SRM technique targeting mixed-phase clouds.

4 Impact of SAI on Solar Energy Resources

• Analyse how SAI-induced changes in aerosol properties affect surface solar radiation and photovoltaic (PV) energy potential.
• Use satellite and ground-based data to quantify changes in total, diffuse, and spectral solar radiation.
• Assess mitigation strategies, such as optimising PV farm design, to minimise energy losses due to increased atmospheric scattering.

5 Detectability of SRM Field Experiments and Deployment

• Evaluate the technical limitations of current observational capabilities for detecting SRM, based on natural analogues.
• Perform Observing System Simulation Experiments (OSSE) to assess the feasibility of SRM monitoring.
• Contribute to ongoing international efforts aimed at mapping SRM monitoring needs for policy discussions.

Project plan

1. Desktop Research – Conduct a literature review to assess the current state of SRM research, identify gaps, and align with IPCC and policy studies.
2. Liaison with Ongoing Projects – Engage with CCI and Horizon Europe projects (CERTAINTY, CleanCloud, Co-CREATE) to ensure alignment and maximise synergies.
3. Research & Monitoring Gap Analysis – Identify key gaps in knowledge and monitoring based on existing studies and international assessments (e.g., IPCC, UNEP).
4. Bridging Modeling & Earth Observation (EO) – Integrate EO data with climate models to refine SRM impact assessments and guide future monitoring.
5. Natural & Anthropogenic Analogues – Use data from natural (volcanic eruptions) and anthropogenic sources (industrial emissions) to improve SRM understanding.
6. Workshop Organisation – Host a mid-project SRM workshop in June to validate progress, foster collaboration, and refine research.
7. Compilation of Existing Datasets – Create a centralised list of SRM-related models and datasets (e.g., GeoMIP, CCI, EUMETSAT).
8. Targeted Simulations & Satellite Retrievals – Conduct climate model simulations and EO retrievals to fill critical data gaps.
9. Impact & Detectability Assessments – Analyse the impact of SAI on PV energy production, and explore strategies to mitigate negative impacts. Assess SRM detectability using EO instruments (e.g., 3MI, GAPMAP, CAIRT, AOS).
10. Synthesis – Summarise findings and exploring AI-driven approaches for merging models and observations and developing climate system digital twins.