Ongoing impact modeling

Predicting how SAI will change surface climate — including temperature and precipitation — remains uncertain. Those changes would, in turn, affect human and natural systems across many domains, ranging from food security and human health to ecosystem, infrastructure, and economic effects. Analyzing these effects often requires integrating the outputs of Earth System Models (ESMs) into more detailed models of specific systems of interest, such as fisheries or crop yields.

The ability to predict, and attribute, SAI's effects on a diverse set of impacts will develop both as the impact models themselves improve and as the climate models used to drive them improve (for example, by incorporating data from aerosol microphysics and aerosol evolution and transport experiments). Additionally, decision makers will require assessments of how different injection strategies (varying location, magnitude, timing, and deployment duration) affect specific climate impacts, and these scenarios will likely evolve with the learning expected under experiments and any early deployment. Continuous climate impact modeling is therefore necessary through Phases 2 and 3, across all relevant domains, to ensure that decision makers have the most current and credible estimates of the changes SAI has caused — both aimed for and inadvertent — under any deployment.

This activity encompasses ongoing impact model development, validation, and runs, as well as the analysis of model outputs, across a range of SAI scenarios as simulated in Earth System Models. It includes all climate impacts relevant to SAI, such as agricultural and food security impacts, water availability and global hydrological responses, sea-level rise and its impacts on infrastructure, ecosystem responses, human health under temperature extremes, and human health impacts of air quality and radiation changes, among others.

The work includes:

1. Incorporating new climate model simulations — systematically incorporating new climate model simulations into impact models.
2. Model intercomparison and validation — conducting model intercomparison and validation studies to identify systematic biases and areas of disagreement.
3. Running and analyzing updated scenarios — running impact models driven by updated SAI scenarios and reporting, analyzing, and comparing outputs to inform assessments of SAI's attributable impacts.
4. Cross-domain synthesis — synthesizing modeling results across domains to evaluate potential trade-offs and benefits of different strategies.

This is a distributed, ongoing research effort involving teams of climate scientists across multiple institutions and impact domains, extending from pre-deployment phases throughout any deployment, with no fixed end date. Success requires maintaining sustained capacity for climate modeling research and ensuring regular synthesis and communication of findings to support adaptive management of SAI operations.