Climate processes

Abstract:

<jats:p>Abstract. It is often implicitly assumed that over suitably long periods the mean of observations and models should be comparable, even if they have different temporal sampling. We assess the errors incurred due to ignoring temporal sampling and show they are of similar magnitude as (but smaller than) actual model errors (20–60 %). Using temporal sampling from remote sensing datasets (the satellite imager MODIS and the ground-based sun photometer network AERONET) and three different global aerosol models, we compare annual and monthly averages of full model data to sampled model data. Our results show that sampling errors as large as 100 % in AOT (Aerosol Optical Thickness), 0.4 in AE (Ångström Exponent) and 0.05 in SSA (Single Scattering Albedo) are possible. Even in daily averages, sampling errors can be significant. More-over these sampling errors are often correlated over long distances giving rise to artificial contrasts between pristine and polluted events and regions. Additionally, we provide evidence that suggests that models will underestimate these errors. To prevent sampling errors, model data should be temporally collocated to the observations before any analysis is made. We also discuss how this work has consequences for in-situ measurements (e.g. aircraft campaigns or surface measurements) in model evaluation. </jats:p>

Abstract:

<jats:p>Abstract. The global distribution of cloud condensation nuclei (CCN) is the fundamental quantity that determines how changes in aerosols affect climate through changes in cloud drop concentrations, cloud albedo and precipitation. 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 CCN concentrations. 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 monthly-mean model grid cell from an ensemble of 168 one-year model simulations covering the uncertainty space of the 28 parameters. The standard deviation around the mean CCN varies globally between about ±30% of the mean over some marine regions to ±40–100% over most land areas and high latitudes. The results imply that aerosol processes and emissions are likely to be a significant source of uncertainty in model simulations of aerosol-cloud effects on climate. Variance decomposition enables the importance of the parameters for CCN uncertainty to be quantified and ranked from local to global scales. 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 sulphate particles formed on sub-grid scales. Emissions of carbonaceous combustion particles affect CCN uncertainty more than sulphur 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 sulphate formation during cloud-processing. Most of the 28 parameters are important for CCN uncertainty somewhere on the globe. The results lead to several recommendations for research that would result in improved modelling of cloud-active aerosol on a global scale. </jats:p>