The Climatic Importance of Uncertainties in Regional Aerosol–Cloud Radiative Forcings over Recent Decades

Regayre, Leighton A.; Pringle, K. J.; Lee, Lindsay; Rap, A.; Browse, Jo; Mann, G. W.; Reddington, Carly L.; Carslaw, K. S.; Booth, Ben B. B.; Woodhouse, M. T.

doi:10.1175/jcli-d-15-0127.1

Cited by 23 publications

(42 citation statements)

References 94 publications

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“…Emulators have been used in several studies to analyze complex models in multiple scientific fields including tsunami modeling (Sarri et al, 2012), aerosol and cloud modeling (e.g., Carslaw et al, 2013;Hamilton et al, 2014;Johnson et al, 2015;Lee et al, 2013;Regayre et al, 2014Regayre et al, , 2015, and galaxy formation (Rodrigues et al, 2017;Vernon et al, 2010). A recent study by Harvey et al (2018) also successfully applied this approach to volcanic ash modeling of the 2010 Eyjafjallajökull eruption to understand the influence of eruption source parameters and internal model parameters on the simulated output.…”

Section: Emulation and Sensitivity Analysismentioning

confidence: 99%

Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation

et al. 2019

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The radiative forcing caused by a volcanic eruption is dependent on several eruption source parameters such as the mass of sulfur dioxide (SO2) emitted, the eruption column height, and the eruption latitude. General circulation models with prognostic aerosol and chemistry schemes can be used to investigate how each parameter influences the volcanic forcing. However, the range of multidimensional parameter space that can be explored is restricted because such simulations are computationally expensive. Here we use statistical emulation to explore the radiative impact of eruptions over a wide covarying range of SO2 emission magnitudes, injection heights, and eruption latitudes based on only 30 simulations. We use the emulators to build response surfaces to visualize and predict the sulfate aerosol e‐folding decay time, the stratospheric aerosol optical depth, and net radiative forcing of thousands of different eruptions. We find that the volcanic stratospheric aerosol optical depth and net radiative forcing are primarily determined by the mass of SO2 emitted, but eruption latitude is the most important parameter in determining the sulfate aerosol e‐folding decay time. The response surfaces reveal joint effects of the eruption source parameters in influencing the net radiative forcing, such as a stronger influence of injection height for tropical eruptions than high‐latitude eruptions. We also demonstrate how the emulated response surfaces can be used to find all combinations of eruption source parameters that produce a particular volcanic response, often revealing multiple solutions.

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Section: Emulation and Sensitivity Analysismentioning

confidence: 99%

Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation

et al. 2019

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“…Filter Table 3 and highlighted in Fig. 10. gions where cloud droplet concentrations are updraft-limited (Reutter et al, 2009;Sullivan et al, 2016). Anth_SO2 makes its greatest contribution to aerosol ERF uncertainty in tropical marine regions (R6 and R10) by causing uncertainty in ERF ARI .…”

Section: Region Descriptionmentioning

confidence: 99%

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

et al. 2018

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Changes in aerosols cause a change in net topof-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 aerosolclimate 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.

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“…output outside of an observation's estimated uncertainty range) are rejected and, likewise, the forcings that they calculate are also rejected. A similar constraint methodology has been applied to environmental models (Salter and Williamson, 2016), hydrological models (Liu and Gupta, 2007), galaxy formation (Rodrigues et al, 2017), disease transmission (Andrianakis et al, 2017), climate models (Murphy et al, 2004;Regayre et al, 2018;Sexton et al, 2011;Williamson et al, 2013) and aerosol models (Lee et al, 2011(Lee et al, , 2013Reddington et al, 2017;Regayre et al, 2018Regayre et al, , 2014Regayre et al, , 2015. In this paper the observations comprise aerosol and cloud state variables and trends, but the approach could readily be extended to include any observations, such as of aerosol-cloud-radiation relationships.…”

Section: Introductionmentioning

confidence: 99%

The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing

et al. 2018

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Abstract. Observational constraint of simulated aerosol and cloud properties is an essential part of building trustworthy climate models for calculating aerosol radiative forcing. Models are usually tuned to achieve good agreement with observations, but tuning produces just one of many potential variants of a model, so the model uncertainty cannot be determined. Here we estimate the uncertainty in aerosol effective radiative forcing (ERF) in a tuned climate model by constraining 4 million variants of the HadGEM3-UKCA aerosol–climate model to match nine common observations (top-of-atmosphere shortwave flux, aerosol optical depth, PM2.5, cloud condensation nuclei at 0.2 % supersaturation (CCN0.2), and concentrations of sulfate, black carbon and organic carbon, as well as decadal trends in aerosol optical depth and surface shortwave radiation.) The model uncertainty is calculated by using a perturbed parameter ensemble that samples 27 uncertainties in both the aerosol model and the physical climate model, and we use synthetic observations generated from the model itself to determine the potential of each observational type to constrain this uncertainty. Focusing over Europe in July, we show that the aerosol ERF uncertainty can be reduced by about 30 % by constraining it to the nine observations, demonstrating that producing climate models with an observationally plausible “base state” can contribute to narrowing the uncertainty in aerosol ERF. However, the uncertainty in the aerosol ERF after observational constraint is large compared to the typical spread of a multi-model ensemble. Our results therefore raise questions about whether the underlying multi-model uncertainty would be larger if similar approaches as adopted here were applied more widely. The approach presented in this study could be used to identify the most effective observations for model constraint. It is hoped that aerosol ERF uncertainty can be further reduced by introducing process-related constraints; however, any such results will be robust only if the enormous number of potential model variants is explored.

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The Climatic Importance of Uncertainties in Regional Aerosol–Cloud Radiative Forcings over Recent Decades

Cited by 23 publications

References 94 publications

Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation

Exploring How Eruption Source Parameters Affect Volcanic Radiative Forcing Using Statistical Emulation

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

The importance of comprehensive parameter sampling and multiple observations for robust constraint of aerosol radiative forcing

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