The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei

Abstract

Aerosol–cloud interaction effects are a major source of uncertainty in climate models so it is important to quantify the sources of uncertainty and thereby direct research efforts. However, the computational expense of global aerosol models has prevented a full statistical analysis of their outputs. Here we perform a variance-based analysis of a global 3-D aerosol microphysics model to quantify the magnitude and leading causes of parametric uncertainty in model-estimated present-day concentrations of cloud condensation nuclei (CCN). Twenty-eight model parameters covering essentially all important aerosol processes, emissions and representation of aerosol size distributions were defined based on expert elicitation. An uncertainty analysis was then performed based on a Monte Carlo-type sampling of an emulator built for each model grid cell. The standard deviation around the mean CCN varies globally between about ±30% over some marine regions to ±40–100% over most land areas and high latitudes, implying that aerosol processes and emissions are likely to be a significant source of uncertainty in model simulations of aerosol–cloud effects on climate. Among the most important contributors to CCN uncertainty are the sizes of emitted primary particles, including carbonaceous combustion particles from wildfires, biomass burning and fossil fuel use, as well as sulfate particles formed on sub-grid scales. Emissions of carbonaceous combustion particles affect CCN uncertainty more than sulfur emissions. Aerosol emission-related parameters dominate the uncertainty close to sources, while uncertainty in aerosol microphysical processes becomes increasingly important in remote regions, being dominated by deposition and aerosol sulfate formation during cloud-processing. The results lead to several recommendations for research that would result in improved modelling of cloud–active aerosol on a global scale

Aged boreal biomass-burning aerosol size distributions from BORTAS 2011

“…Biomass-burning (BB) parti-cles may also act as cloud condensation nuclei (CCN) and affect climate and radiation through modifying cloud albedo and lifetime (Pierce et al, 2007;Spracklen et al, 2011) (indirect aerosol effects). Globally, the direct and indirect climate effects represent the largest uncertainties in radiative forcing as quantified by the recent IPCC report (Myhre et al, 2013), and biomass-burning emissions represent significant contributions to each of the effects globally (Alonso-Blanco, 2014; Lee et al, 2013).…”

Tropospheric Ozone Assessment Report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends

“…While useful snapshots of the state-of-the-science, such experiments are not designed to explore uncertainty systematically, and the inter-model spread likely underestimates the true structural uncertainty. Ongoing research is exploiting advanced statistical techniques (after Carslaw et al, 2013;Lee et al, 2013) to more fully quantify the drivers of uncertainty in modeled tropospheric chemistry. Such work is important to better target our limited scientific resources onto the most important uncertainties, to improve the models and increase the reliability of the projections they make.…”

Improving the simulation of global aerosol with size-segregated anthropogenic number emissions

“…However, the modeled GAINS accumulation mode particle number concentrations agree with observation significantly better than AeroCom. This, based on the sensitivity analysis by Lee et al (2013), suggests that the GAINS implementation is likely to estimate CCN concentrations better than AeroCom. In any case, further studies are needed to address the tangible contribution of the GAINS model in improving modeled CCN concentration.…”

“…The effects of these physical processes on future aerosol climate forcing requires application of detailed aerosol microphysical schemes in global climate models. Furthermore, the global uncertainty in CCN is highly sensitive to the assumed emission size distribution (Lee et al, 2013).…”