Robust observational constraint of uncertain aerosol processes and emissions in a climate model and the effect on aerosol radiative forcing

Abstract

Abstract. The effect of observational constraint on the ranges of uncertain physical and chemical process parameters was explored in a global aerosol–climate model. The study uses 1 million variants of the HadGEM3-UKCA climate model that sample 26 sources of uncertainty, together with over 9000 monthly aggregated grid-box measurements of aerosol optical depth, PM2.5, particle number concentrations, sulphate and organic mass concentrations. Despite many compensating effects in the model, the procedure constrains the probability distributions of parameters related to secondary organic aerosol, anthropogenic SO2 emissions, residential emissions, sea spray emissions, dry deposition rates of SO2 and aerosols, new particle formation, cloud droplet pH and the diameter of primary combustion particles. Observational constraint rules out nearly 98 % of the model variants. On constraint, the ± 1σ (standard deviation) range of global annual mean direct radiative forcing, RFari, is reduced by 33 % to −0.14 to −0.26 W m−2, and the 95 % credible interval (CI) is reduced by 34 % to −0.1 to −0.32 W m−2. For the global annual mean aerosol–cloud radiative forcing, RFaci, the ± 1σ range is reduced by 7 % to −1.66 to −2.48 W m−2, and the 95 % CI by 6 % to −1.28 to −2.88 W m−2. The tightness of the constraint is limited by parameter cancellation effects (model equifinality) as well as the large and poorly defined representativeness error associated with comparing point measurements with a global model. The constraint could also be narrowed if model structural errors that prevent simultaneous agreement with different measurement types in multiple locations and seasons could be improved. For example, constraints using either sulphate or PM2.5 measurements individually result in RFari ± 1σ ranges that only just overlap, which shows that emergent constraints based on one measurement type may be over-confident.

The value of remote marine aerosol measurements for constraining radiative forcing uncertainty

“…The PPEs were created by systematically perturbing numerous model parameters related to natural and anthropogenic emissions and physical processes. The simulated aerosol forcings have uncertainty ranges that exceed those of multi-model ensembles (Yoshioka et al, 2019;Johnson et al, 2019). Instantaneous radiative forcing (RF) is quantified using the 26-parameter AER PPE in which just aerosol-related parameters were varied, and the effective radiative forcing (ERF) is quantified using the 27-parameter AER-ATM PPE in which aerosol and physical atmosphere parameters were varied (Yoshioka et al, 2019).…”

“…The simulated aerosol forcings have uncertainty ranges that exceed those of multi-model ensembles (Yoshioka et al, 2019;Johnson et al, 2019). Instantaneous radiative forcing (RF) is quantified using the 26-parameter AER PPE in which just aerosol-related parameters were varied, and the effective radiative forcing (ERF) is quantified using the 27-parameter AER-ATM PPE in which aerosol and physical atmosphere parameters were varied (Yoshioka et al, 2019). We use these PPEs to quantify how the constraint provided by pristine aerosol measurements affects the spread of aerosol forcings simulated by the ensembles.…”

“…For example, Regayre et al, (2018) showed that top-of-the-atmosphere shortwave radiation flux measurements reduce ERFaci uncertainty by only around 10%, despite the fluxes in the present-day and early-industrial environments sharing multiple causes of uncertainty. Johnson et al (2019) showed that a much larger dataset of over 9000 (predominantly Northern Hemisphere) aerosol measurements constrained the global, annual mean aerosol RF uncertainty by only around 8%. These measurements reduce the uncertainty in a small number of parameters related to anthropogenic emissions and aerosol processing in polluted environments.…”

“…Model variants that were judged to be observationally implausible against the measurements were rejected, resulting in a set of plausible variants from which the uncertainty in aerosol forcing could be computed (see SI Methods). In the results shown below, we retained approximately 3% of model variants (following Johnson et al, 2019) that best match all four measured aerosol properties.…”

“…The complementary nature of these constraints means that the combined constraint marginal parameter pdfs (Fig. 5) The Johnson et al (2019) constraint reduced the RFaci uncertainty by around 6% and our ACE-SPACE measurement constraint reduced the uncertainty by around 9%. However, the RFaci uncertainty is reduced by around 21% (Fig.…”