Uncertainty in aerosol effective radiative forcing from anthropogenic and natural aerosol parameters in ECHAM6.3-HAM2.3

Abstract. Interactions between aerosols, clouds, and radiation remain a major source of uncertainty in effective radiative forcing (ERF), limiting the accuracy of climate projections. This study aims to quantify parametric uncertainties in aerosol–cloud and aerosol–radiation interactions using a perturbed parameter ensemble (PPE) of 221 simulations with the ECHAM6.3-HAM2.3 climate model, varying 23 aerosol-related parameters that control emissions, removal, chemistry, and microphysics. The resulting global mean aerosol ERF is −1.24 W m−2 (5–95 percentile: −1.56 to −0.89 W m−2). Uncertainty in ERF is dominated by sulfate-related processes, biomass burning, aerosol size, and natural emissions. For aerosol-cloud interactions, dimethyl sulfide (DMS) and biomass burning emissions are key drivers, whereas sulfate chemistry and dry deposition exert the strongest influence on aerosol-radiation interactions. Structural uncertainty is difficult to characterize, and this study focuses primarily on evaluating parametric uncertainty. The leading sources of ERF parametric uncertainty identified here are consistent with those found in other PPE studies, highlighting common sensitivities across climate models. Comparison with POLDER-3/PARASOL satellite retrievals reveals persistent model biases in aerosol optical depth (AOD), Ångström exponent (AE), and single-scattering albedo (SSA), many of which fall within the parametric uncertainty range. Sulfate-related processes account for over 40 % of AOD uncertainty, while AE and SSA are most sensitive to DMS, sea salt, and black carbon parameters. Correlation analysis between key parameters and observables indicates that several biases may be reduced by tuning through physically consistent parameter adjustments for bias reduction. Our results highlight the need for combined efforts in parameter optimization and structural model development to improve confidence in aerosol-forcing estimates and future climate projections.

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Journal
Atmospheric chemistry and physics
Published
2026-01-07
DOI
https://doi.org/10.5194/acp-26-269-2026
Primary Topic
Atmospheric chemistry and aerosols
Type
article
Field-Weighted Citation Impact
0.00

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article

Uncertainty in aerosol effective radiative forcing from anthropogenic and natural aerosol parameters in ECHAM6.3-HAM2.3

Athanasios Nenes, Otto Hasekamp, Yusuf Bhatti, Duncan Watson‐Parris et al.
Atmospheric chemistry and physics
Atmospheric chemistry and aerosols
article

Uncertainty in aerosol effective radiative forcing from anthropogenic and natural aerosol parameters in ECHAM6.3-HAM2.3

Athanasios Nenes, Otto Hasekamp, Yusuf Bhatti, Duncan Watson‐Parris, David Neubauer, Bastiaan van Diedenhoven, Leighton A. Regayre, Athanasios Tsikerdekis, Carl Svenhag, Ulaş İm, Guangliang Fu, Hailing Jia, Nick Schutgens, Muhammed Irfan, Ardit Arifi
article en

Abstract

Abstract. Interactions between aerosols, clouds, and radiation remain a major source of uncertainty in effective radiative forcing (ERF), limiting the accuracy of climate projections. This study aims to quantify parametric uncertainties in aerosol–cloud and aerosol–radiation interactions using a perturbed parameter ensemble (PPE) of 221 simulations with the ECHAM6.3-HAM2.3 climate model, varying 23 aerosol-related parameters that control emissions, removal, chemistry, and microphysics. The resulting global mean aerosol ERF is −1.24 W m−2 (5–95 percentile: −1.56 to −0.89 W m−2). Uncertainty in ERF is dominated by sulfate-related processes, biomass burning, aerosol size, and natural emissions. For aerosol-cloud interactions, dimethyl sulfide (DMS) and biomass burning emissions are key drivers, whereas sulfate chemistry and dry deposition exert the strongest influence on aerosol-radiation interactions. Structural uncertainty is difficult to characterize, and this study focuses primarily on evaluating parametric uncertainty. The leading sources of ERF parametric uncertainty identified here are consistent with those found in other PPE studies, highlighting common sensitivities across climate models. Comparison with POLDER-3/PARASOL satellite retrievals reveals persistent model biases in aerosol optical depth (AOD), Ångström exponent (AE), and single-scattering albedo (SSA), many of which fall within the parametric uncertainty range. Sulfate-related processes account for over 40 % of AOD uncertainty, while AE and SSA are most sensitive to DMS, sea salt, and black carbon parameters. Correlation analysis between key parameters and observables indicates that several biases may be reduced by tuning through physically consistent parameter adjustments for bias reduction. Our results highlight the need for combined efforts in parameter optimization and structural model development to improve confidence in aerosol-forcing estimates and future climate projections.

Atmospheric chemistry and physicsVol. 26(1)
Met Office (GB), University of Leeds (GB), Scripps Institution of Oceanography (US), University of Eastern Finland (FI), Aarhus University (DK), ETH Zurich (CH), University of California San Diego (US), Nederlandse Vereniging voor Kindergeneeskunde (NL), FORTH Institute of Chemical Engineering Sciences (GR), Space Research Organisation Netherlands (NL), École Polytechnique Fédérale de Lausanne (CH), Vrije Universiteit Amsterdam (NL)
European Commission, HORIZON EUROPE Framework Programme
Climate action
Openalex Percentile: Top 97%
Atmospheric chemistry and aerosols
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