Regional Climate Responses To Planetary-Scale Geoengineering Activities, as Modeled Using climateprediction.net/HadCM3L

Concerns that climate mitigation is occurring too slowly, or that there may be a rapid "climate surprise," have lead to renewed dialogue within the scientific community about cooling the planet through geoengineering, specifically stratospheric albedo modification (SAM). There is little consensus about regional hydrological effects of such activities despite a recent spate of climate modeling studies looking at its potential impacts. Here we present the results from one large-ensemble experiment that used Hadley Centre Coupled Model, version 3 with reduced resolution over the ocean (HadCM3L), implemented through climateprediction.net. The analysis examines 54 globally-uniform stratospheric optical depth modification scenarios designed to stabilize global temperatures under SRES A1B. We present normalized regional temperature anomalies versus normalized regional precipitation and subsurface runoff anomalies (for example, see Figure 1) and the results of regression analyses to quantify the relationships between level of stratospheric optical property modification (i.e., geoengineering) and regional hydrology. Results show that while such shortwave compensations for longwave anthropogenic forcings does generally return regional climates to closer to their baseline climate states than the no-geoengineering, business-as-usual scenarios, the magnitudes and sensitivities of regional responses to this type of activity, as modeled in HadCM3L, are highly variable. Regions, such as Eastern China and India, migrate away from their baseline climate states in different ways, illustrating the impossibility of simultaneous stabilization of regional climates. The linearity of the effect of incrementally increasing stratospheric optical depth also varies regionally. Figure 1: Normalized regional temperature and precipitation anomalies (<2020s>-<1990s> and <2070s>-<1990s>) in units of baseline standard deviations for each region). Each grayscale point in-series near the origin represents data from 60 simulations including 6 geoenginering (SAM) scenarios, for a total of 540 simulations/54 scenarios per series. Each dashed line shows a least-squares fit for SAM scenarios point series. Each light gray point to the right represents data from 30 no-SAM simulations (i.e. SRES A1B with stratospheric optical depth equivalent to mean natural volcanic activity). Baseline dataset is compiled from 54 simulations. Regions are: solid boxes - East Canada; solid diamonds - Central US; stars - Amazonia; solid triangles - S. Europe/N.Africa; cirlcles - Northern Asia; open diamonds - East Asia; inverted triangles - Southern Asia; crosses - SE Asia.