Discussion of Feasibility of Albedo Modification Technologies
Assessing an albedo modification strategy’s feasibility (ignoring the extremely important need for appropriate governance issues dealt with elsewhere in this document) hinges upon
- Developing a theoretical and conceptual framework for a particular strategy for producing an albedo modification and
- Identifying system components and means that are critical to testing the scientific and physical concepts important to the strategy, and the technology necessary for implementing those strategies.
It is worth noting that the implementation details, and costs needed to test the underlying concept, would differ significantly from those that would be employed if the strategy were to be used at a larger scale. Assessing the conceptual feasibility of a strategy need not initially use the same implementation methods that would be considered feasible for a larger-scale implementation. So it is necessary to distinguish between assessing the “scientific feasibility” of a strategy (e.g., what calculations, instrument developments, laboratory and field experiments are needed to demonstrate an understanding of underlying physics to produce an intended perturbation to albedo in a particular region and time) and the “practical feasibility” issues associated with a larger deployment (e.g., Is it possible? And what would the cost be for a deployment intended to affect the planetary albedo sufficiently to counter some fraction the radiative forcing arising from increasing greenhouse gases?).
Understanding both types of feasibility studies is important and they can be considered in parallel. The scientific feasibility studies would provide better information for more realistic estimates of costs and practical strategies to produce a measurable effect on the climate processes. These studies would also examine local impacts to radiative forcing, quantify the intended changes, and assess whether models are capable (or not) of simulating and predicting the statistical characteristics of those changes to the climate processes to demonstrate some physical understanding of the climate process being manipulated. The process is necessarily iterative. The first step
uses theory, existing analogues in the real world (e.g., volcanoes and ship tracks), and both process and climate models to provide a “zero-order guess” at the amplitude of the induced perturbation to component processes and the “fast” response of the climate system (the so-called “adjusted radiative forcing”). These modeling studies and analyses of existing analogues provide basic estimates of relevant forcing, as well as the local responses guiding estimates of costs, and implementation details, but there is a limit to their utility. There can easily be flaws in physical understanding expressed in models or overlooked issues that were not considered. At some point more stringent assessments would require that laboratory and field experiments would be needed to make sure that initial estimates are realistic and robust across location, climate regimes, and seasons.
If exploratory field experiments were successful in producing the desired effect on the component behavior, they would (a) provide information needed to characterize the potential for a particular strategy (perhaps for only a subset of important regimes or seasons) to produce a significant radiative perturbation; (b) provide a mechanism for estimating the cost of inducing such a change; and (c) identify the immediate, local impact of those changes on that component of the climate system. Exploration of albedo modification to other regimes, locations, and season might then be considered to identify their potential to produce radiative forcing, and eventually consideration of slower feedbacks, and consequences to the climate system become important considerations.
Feasibility estimates should thus be contingent upon (1) first-guess estimates based on models and measured analogues found in our current environment; (2) staged series of laboratory and de minimus field experiments designed to test basic understanding and components important to the strategy, and the overall robustness, of the models; (3) updated estimates of feasibility produced by improved knowledge from the de minimus field experiments; and (4) testing of the robustness of the mechanisms as the amplitude of forcing and temporal and areal extent are increased, where nonlinearities become important. Eventually, as the amplitude of the forcing is increased, assessing the feasibility of the strategy becomes primarily a signature detection problem—that of teasing out a signal (the climate response to a perturbation) in the presence of the background “noise” of natural climate variability (MacMynowski et al., 2011).
