Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF

Leighton A Regayre,Masaru Yoshioka,Kirsty J Pringle,Kenneth S Carslaw,David M H Sexton,Jill S Johnson,Lindsay A Lee,Ben B B Booth,Nicolas Bellouin

doi:10.5194/acp-18-9975-2018

Leighton A Regayre, Masaru Yoshioka + Show 7 more

Open Access

https://doi.org/10.5194/acp-18-9975-2018

Copy DOI

Abstract

Abstract. Changes in aerosols cause a change in net top-of-the-atmosphere (ToA) short-wave and long-wave radiative fluxes; rapid adjustments in clouds, water vapour and temperature; and an effective radiative forcing (ERF) of the planetary energy budget. The diverse sources of model uncertainty and the computational cost of running climate models make it difficult to isolate the main causes of aerosol ERF uncertainty and to understand how observations can be used to constrain it. We explore the aerosol ERF uncertainty by using fast model emulators to generate a very large set of aerosol–climate model variants that span the model uncertainty due to 27 parameters related to atmospheric and aerosol processes. Sensitivity analyses shows that the uncertainty in the ToA flux is dominated (around 80 %) by uncertainties in the physical atmosphere model, particularly parameters that affect cloud reflectivity. However, uncertainty in the change in ToA flux caused by aerosol emissions over the industrial period (the aerosol ERF) is controlled by a combination of uncertainties in aerosol (around 60 %) and physical atmosphere (around 40 %) parameters. Four atmospheric and aerosol parameters account for around 80 % of the uncertainty in short-wave ToA flux (mostly parameters that directly scale cloud reflectivity, cloud water content or cloud droplet concentrations), and these parameters also account for around 60 % of the aerosol ERF uncertainty. The common causes of uncertainty mean that constraining the modelled planetary brightness to tightly match satellite observations changes the lower 95 % credible aerosol ERF value from −2.65 to −2.37 W m−2. This suggests the strongest forcings (below around −2.4 W m−2) are inconsistent with observations. These results show that, regardless of the fact that the ToA flux is 2 orders of magnitude larger than the aerosol ERF, the observed flux can constrain the uncertainty in ERF because their values are connected by constrainable process parameters. The key to reducing the aerosol ERF uncertainty further will be to identify observations that can additionally constrain individual parameter ranges and/or combined parameter effects, which can be achieved through sensitivity analysis of perturbed parameter ensembles.

Highlights

We account for above-cloud aerosols (Ghan, 2013) in our calculation of ERFACI and ERF from aerosol–radiation interactions (ERFARI), which affects the balance between these two components of aerosol effective radiative forcing (ERF) (Yoshioka et al, 2018)
We explore the extent to which present-day measurements of global mean ToA reflected short-wave radiation (RSR) could in principle help to constrain the change in flux between two time periods, which was previously explored by Lohmann and Ferrachat (2010), who perturbed four physical atmosphere parameters
We sampled the uncertainty in 18 aerosol and 9 atmospheric parameters within a single global climate model, identified the important causes of aerosol ERF uncertainty and constrained this uncertainty using ToA radiative flux measurements

Summary

Introduction

Large aerosol radiative forcing uncertainty has persisted through all Intergovernmental Panel on Climate Change assessment reports since 1996 despite substantial developments in climate model complexity (Flato et al, 2013, Sect. 9.1.3), numerous intercomparison projects (Randles et al, 2013; Tsigaridis et al, 2014; Kim et al, 2014; Mann et al, 2014; Pan et al, 2015; Lacagnina et al, 2015; Kipling et al, 2016; Ghan et al, 2016; Koffi et al, 2016) and enormous investments in observing systems (Khain et al, 2000; Lacagnina et al, 2015; Seinfeld et al, 2016; Reddington et al, 2017). Large aerosol radiative forcing uncertainty has persisted through all Intergovernmental Panel on Climate Change assessment reports since 1996 despite substantial developments in climate model complexity Reducing aerosol forcing uncertainty has proven to be one of the most challenging and persistent problems in atmospheric science. L. Regayre et al.: Aerosol ERF uncertainty and constraint would improve climate change projections (Andreae et al, 2005; Myhre et al, 2013; Collins et al, 2013; Tett et al, 2013; Seinfeld et al, 2016). Satellite-derived observations of present-day (PD) aerosol–cloud relationships have the potential to constrain the aerosol ERF uncertainty but require an improved understanding of aerosol changes over the industrial period (Gryspeerdt et al, 2017). Important sources of uncertainty are known to be aerosol emission fluxes (Granier et al, 2011), representations of complex sub-grid processes such as clouds (Haerter et al, 2009; Lohmann and Ferrachat, 2010; Guo et al, 2013; Gettleman et al, 2013; Golaz et al, 2013; Neubauer et al, 2014; Lohmann, 2017), precipitation responses (Tost et al, 2010; Croft et al, 2012; Michibata and Takemura, 2015), aerosol processes (Croft et al, 2012; Textor et al, 2006, 2007; Storelvmo et al, 2009; Kasoar et al, 2016), radiation calculations (Stier et al, 2013; Wilcox et al, 2015) and subsequent feedbacks on atmospheric dynamics (Booth et al, 2012; Bollasina et al, 2013; Kirtman et al, 2013; Villarini and Vecchi, 2013; Allen et al, 2014) and surface temperatures (Golaz et al, 2013)

Methods

Results

Conclusion