Improving Models for Solar Climate Intervention Research

By Sebastian Eastham, Sarah Doherty, David Keith, Jadwiga H. Richter, and Lili Xia, EOS.org

Modern climate models were designed to simulate natural systems and changes mainly due to atmospheric carbon dioxide, rather than to predict effects of deliberate climate interventions.

Solar climate intervention, also known as solar radiation modification, is an approach intended to mitigate the impacts of climate change by reducing the amount of solar energy that the Earth system traps. It sits alongside three other plausible responses to climate risk: emission cuts and decarbonization, atmospheric carbon dioxide (CO2) removal, and adaptation to a changing climate.

Unlike the other approaches, solar climate intervention (SCI), which comprises various techniques, aims to modify Earth’s radiation budget—the amounts and balance of solar energy that Earth absorbs and reflects—directly. Implementing SCI means either decreasing inbound solar (shortwave) radiation by reflecting it back into space before it is absorbed or increasing the amount of outbound terrestrial (longwave) radiation.

Chelsea Thompson, University of Colorado/CIRES and NOAA Chemical Sciences Laboratory
Some of the most widely discussed forms of solar climate intervention increase the quantity of solar radiation reflected back into space, including surface albedo enhancement, marine cloud brightening (MCB), stratospheric aerosol injection (SAI), and space-based methods. Cirrus cloud thinning (CCT) instead involves the removal of cirrus clouds to increase the amount of terrestrial radiation “lost” from the system. All of these methods would alter fluxes of both longwave (red) and shortwave (yellow) light.

Potential methods of SCI include stratospheric aerosol injection (SAI), marine cloud brightening, cirrus cloud thinning, surface albedo modification, and space-based methods involving, for example, mirrors (Figure 1). At present, the potential efficacy and risks of implementing these approaches to reduce climate change are highly uncertain and likely depend on how they are implemented.

The Geoengineering Modeling Research Consortium (GMRC) was founded to coordinate SCI modeling research and to identify and resolve relevant issues with physical models, especially where existing climate research is unlikely to do so. Here we synthesize 2 years of GMRC meetings and research, and we offer specific recommendations for future model development.

Current Climate Models and SCI Simulations

SCI research is an engineering application of Earth system science focused on developing the capacity to reduce the magnitude of climate change. SCI strategies are commonly investigated using global climate models (GCMs). However, these models are most frequently used to study the consequences of anthropogenic emissions (e.g., of CO2), and they are not optimized to represent SCI’s deployment and effects.

Thus, critical processes in SCI simulation, including fundamental physical, chemical, and biogeophysical processes that occur at subgrid scales (i.e., finer than the resolution of GCMs), are missing or poorly represented by these models (Figure 2). For example, most models used to study stratospheric aerosol intervention cannot resolve the physics and chemistry of an injected aerosol plume in the stratosphere, or its subsequent interactions with cloud processes.

Broader uncertainties also come into play: Different climate models estimate different radiative forcings (human-induced changes in the radiative budget), resulting in different simulated climate impacts. These differences persist even when the models use a common, crude representation of the effects of SCI, such as a uniform increase in stratospheric aerosol optical depth (i.e., a quantifiable decrease in the “transparency” of the stratosphere).

Meanwhile, in some cases (e.g., cirrus cloud thinning), significant uncertainties remain concerning even the fundamental physics of relevant processes. Because of these uncertainties, even agreement among multiple models does not imply scientific confidence in the result. Targeted and coordinated modeling efforts are needed to quantify and reduce these uncertainties.

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