STATISTIC’s overview

The Paris Agreement set ambitious climate targets, yet current policies project a 2.7°C temperature rise by 2100, exceeding these goals. While greenhouse gas emissions reduction remains crucial, carbon dioxide removal (CDR) and Solar Radiation Modification (SRM) are gaining attention as complementary strategies. SRM techniques, such as stratospheric aerosol injection, marine cloud brightening and cirrus cloud thinning, show some potential but also pose new risks, making research essential.

Most SRM studies focus on modeling (e.g. GeoMIP), with limited observational data utilization. High-resolution satellite data and AI-driven approaches could enhance our understanding of natural analogues such as volcanic eruptions. This project seeks to assess existing knowledge, identify gaps, and explore new research avenues. Given SRM’s governance challenges, future observational needs ought to be anticipated. The project thus aims to contribute to the integration of climate modeling with Earth observation to inform potential SRM assessments and policies.

STATISTICS’s Description

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 modeling, 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 modeling 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, we often lack high-resolution data to validate key processes, 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’s observations missions- are underutilized in SRM research. Our 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.

SRM remains a polarizing topic in international climate policy. There is no clear governance framework for SRM research, let alone deployment, raising concerns about equity, ethical considerations, and geopolitical risks. Our project acknowledges the importance of engaging with international organizations -including ESA, the European Commission (EC), UNEP, WCRP, and other science-policy groups- to ensure that research efforts align with broader environmental and governance frameworks.

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

STATISTIC’s objectives

The STATISTICS project aims to advance the understanding of Solar Radiation Modification (SRM) by conducting targeted investigations into five key areas:

  1. Stratospheric Aerosol Injection (SAI) Model Intercomparison and Evaluation
  • Conduct a 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 in 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.
  1. 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 analyze aerosol changes from satellite and ground-based observations.
  1. 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 modeling studies of MCT, a newly proposed SRM technique targeting mixed-phase clouds.
  1. Impact of SAI on Solar Energy Resources
  • Analyze 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 optimizing PV farm design, to minimize energy losses due to increased atmospheric scattering.
  1. 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.
STATISTICS’s approach

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 maximize 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 Organization
Host a mid-project SRM workshop in June to validate progress, foster collaboration, and refine research.

7. Compilation of Existing Datasets
Create a centralized 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
Analyze 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
Summarize findings and exploring AI-driven approaches for merging models and observations and developing climate system digital twins.

STATISTICS’s Consortium

 

GRASP SAS (Generalized Retrieval of Aerosol and Surface Properties), France, is the project leader, responsible for the scientific coordination and the technical project management of the project, relations with ESA and communications with relevant scientific communities. GRASP is also leading the atmospheric and surface satellite retrievals and contributing to the study on aerosol-cloud interactions, particularly the MCB within the project.

CNRS-LOA (Laboratoire d’Optique Atmosphérique), France, is co-leading the Earth Observation Science Team. Furthermore, LOA is together with GRASP responsible for the aerosol retrievals using both satellite and ground-based measurements.

CNRS-IPSL (Institut Pierre-Simon Laplace), France, is leading the Climate Modelling Team. IPSL is responsible for aerosol and climate modelling, as well as modelling of climate intervention techniques. It also contributes to the assessment of detectability using Earth Observing Systems.

 MPI-M (Max-Planck Institute for Meteorology), Germany, is contributing to the research on aerosol-cloud interaction, climate modelling of SAI, and the validation and testing against observations. 

UiO (University of Oslo), Norway, is contributing to the study of aerosol-cloud interactions, climate modelling related to CCT and MCT, and the validation and testing against observations.  

PMOD (Physikalisch-Meteorologisches Observatorium Davos), Switzerland, is contributing to aerosol retrievals from ground-based observations, as well as studies related to PV potential under SAI scenarios. 

PCR (Perspectives Climate Research), Switzerland, is leading the Climate Policy Advisory and Research Team, and is responsible for the study of international climate policy and governance aspects of SRM.

Data

The STATISTICS project will generate new modelling and observational dasasets. In particular, it is anticipated new climate model simulations of natural analogues, benchmark radiative transfer model calculations, and satellite retrievals of aerosols and clouds on key regions of interest. The data will be available soon.