Publications by authors named "T P C van Noije"

Article Synopsis
  • Biomass burning (BB) emits aerosols that significantly influence climate factors like radiation balance and cloudiness in tropical areas, but there's a lot of uncertainty in assessments due to reliance on global models.
  • By using observations from both satellite and ground sources, researchers constrained the aerosol absorption optical depth (AAOD) specifically in the Amazon and Africa, identifying major error sources for each region.
  • The study found that correcting these errors can reduce differences in aerosol radiative effects among models by threefold, suggesting a stronger potential for improving the understanding of radiative forcing from biomass burning aerosols.
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Biomass burning (BB) is a major source of aerosols that remain the most uncertain components of the global radiative forcing. Current global models have great difficulty matching observed aerosol optical depth (AOD) over BB regions. A common solution to address modelled AOD biases is scaling BB emissions.

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The pandemic in 2020 caused an abrupt change in the emission of anthropogenic aerosols and their precursors. We estimate the associated change in the aerosol radiative forcing at the top of the atmosphere and the surface. To that end, we perform new simulations with the CMIP6 global climate model EC-Earth3.

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Many nations responded to the corona virus disease-2019 (COVID-19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty.

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A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties.

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