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16 pages, 5 figures, 1 table, supplement https://doi.org/10.5194/bg-20-1813-2023-supplement.-- Data availability: DMS and ancillary in situ data (SST and salinity) are sourced from the global surface seawater DMS database (GSSDD; https://saga.pmel.noaa.gov/dms/) and supplied by authors Rafel Simó, Martí Galí, Anoop S. Mahajan (Malaspina Expedition in 2010–2011, M10), Thomas G. Bell (North Atlantic Aerosol and Marine Ecosystem Study in 2015–2018, NAAMES; https://doi.org/10.5067/SeaBASS/NAAMES/DATA001, Behrenfeld et al., 2018), and George Manville (Southern oCean SeAsonaL Experiment in 2019, SCALE). Requests for access to M10 and SCALE DMS data can be sent to the corresponding authors (George Manville and Thomas G. Bell). Satellite data are available in online NASA repositories for chlorophyll a (https://doi.org/10.5067/AQUA/MODIS/L3M/CHL/2018, NASA Goddard Space Flight Center, 2018) and sea surface height anomalies (https://doi.org/10.5067/SLREF-CDRV2, Zlotnicki et al., 2019), Dimethylsulfide (DMS) emitted from the ocean makes a significant global contribution to natural marine aerosol and cloud condensation nuclei and, therefore, our planet's climate. Oceanic DMS concentrations show large spatiotemporal variability, but observations are sparse, so products describing global DMS distribution rely on interpolation or modelling. Understanding the mechanisms driving DMS variability, especially at local scales, is required to reduce uncertainty in large-scale DMS estimates. We present a study of mesoscale and submesoscale (, This research has been supported by the Natural Environment Research Council (grant nos. NE/R007586/1 and NE/W009277/1), Met Office Hadley Centre Climate Programme funded by BEIS, the Horizon 2020 (grant nos. 101003536 and ERC-2018-ADG-834162), the Spanish National Plan for Scientific and Technical Research and Innovation (grant nos. CTM2016-81008-R, 202230I123, and CEX2019-000928-S), and the Indian Institute of Tropical Meteorology (IITM)

Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill at making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy-balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties in the model, limit the effectiveness of observational constraint of the uncertain physical processes. We sample uncertainty in 37 model parameters related to aerosols, clouds and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate 1 million model variants (different parameter settings from Gaussian Process emulators) against satellite-derived observations over several cloudy regions. We show that it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 %, constraining it to a range in close agreement with energy-balance constraints (around −1.3 to −0.1 W m−2). However, our analysis of a very large set of model variants exposes model internal inconsistencies that would not be apparent in a small set of model simulations. Incorporating observations associated with these inconsistencies weakens the forcing constraint because they require a wider range of parameter values to accommodate conflicting information. Our estimated aerosol forcing range is the maximum feasible constraint using our structurally imperfect model and the chosen observations. Structural model developments targeted at the identified inconsistencies would enable a larger set of observations to be used for constraint, which would then narrow the uncertainty further. Such an approach provides a rigorous pathway to improved model realism and reduced uncertainty that has so far not been achieved through the normal model development approach.

Despite a large number of studies, the effect of aerosols has the largest uncertainty in global climate model radiative forcing estimates. There have been studies of aerosol optical properties in climate models, but the effects of particle number size distribution need a more thorough inspection. We investigated the trends and seasonality of particle number concentrations in different sizes in total of for 21 measurement sites in Europe and Arctic. For 13 of those, with longer measurement time series, we compared the field observations with the results from five climate models, namely EC-Earth3, ECHAM-M7, ECHAM-SALSA, NorESM1.2, and UKESM1. This is the first extensive comparison of detailed aerosol size distribution trends between in-situ observations from Europe and five earth system models (ESM). We found that the trends of particle number concentrations were mostly consistent and decreasing in both, measurements and models. However, for many sites, climate models showed weaker decreasing trends than the measurements. Seasonal variability in measured number concentrations, quantified by the ratio between maximum and minimum monthly number concentration, were typically stronger in northern measurement sites compared to other locations. Models had large differences in their seasonal representation, and they can be roughly divided into two categories. For EC-Earth and NorESM, the seasonal cycle was relatively similar for all sites, for others, the pattern of seasonality varied between northern and southern sites. In addition, the variability in concentrations across sites varied between models, some having relatively similar concentrations for all sites, whereas others showing clear differences in concentrations between remote and urban sites. To conclude, although anthropogenic mass emissions are harmonized in models, trends in different sized particles vary among the model due to assumptions in emission sizes and differences in how models treat size dependent aerosol processes. The inter-model variability was largest in the accumulation mode, i.e. sizes which have implications for aerosol-cloud interactions.

Ammonium nitrate is a major aerosol constituent over many land regions and contributes to air pollution episodes, ecosystem destruction, regional haze, and aerosol-induced climate forcing. Many climate models that represent ammonium nitrate assume that the ammonium-sulphate-nitrate chemistry reaches thermodynamic equilibrium instantaneously without considering kinetic limitations on condensation rates. The Met Office's Unified Model (UM) is employed to investigate the sensitivity of ammonium nitrate concentrations to the nitric acid uptake coefficient (γ) in a newly-developed nitrate scheme in which first order condensation theory is utilised to limit the rate at which thermodynamic equilibrium is attained. Two values of γ representing fast (γ = 0.193) and slow (γ = 0.001) uptake rates are tested in 20-year global UM integrations. The global burden of nitrate associated with ammonium in the “fast” simulation (0.11 Tg[N]) is twice as great as in the “slow” simulation (0.05 Tg[N]), while the top-of-the-atmosphere radiative impact of representing nitrate is −0.19 Wm−2 in the “fast” simulation and −0.07 Wm−2 in the “slow” simulation. In general, the “fast” simulation exhibits better spatial correlation with observed nitrate concentrations while the “slow” simulation better resolves the magnitude of concentrations. Local near-surface nitrate concentrations are found to be highly correlated with seasonal ammonia emissions suggesting that ammonia is the predominant limiting factor controlling nitrate prevalence. This study highlights the high sensitivity of ammonium nitrate concentrations to nitric acid uptake rates and provides a mechanism for reducing nitrate concentration biases in climate model simulations. The new UM nitrate scheme represents a step-change in aerosol modelling capability in the UK across weather and climate timescales.

Quantifying forcings from anthropogenic perturbations to the Earth System (ES) is important for understanding changes in climate since the pre-industrial period. In this paper, we quantify and analyse a wide range of present-day (PD) anthropogenic climate forcings with the UK's Earth System Model (ESM), UKESM1, following the protocols defined by the Radiative Forcing Model Intercomparison Project (RFMIP) and the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). In particular, by quantifying effective radiative forcings (ERFs) that include rapid adjustments within a full ESM, it enables the role of various climate-chemistry-aerosol-cloud feedbacks to be quantified. Global mean ERFs are 1.83, 0.13, −0.33, and 0.93 W m−2 at the PD (Year 2014) relative to the pre-industrial (PI; Year 1850) for carbon dioxide, nitrous oxide, ozone-depleting substances, and methane, respectively. The PD total greenhouse gas ERF is 2.89 W m−2, larger than the sum of the individual GHG ERFs. UKESM1 has an aerosol forcing of −1.13 W m−2. A relatively strong negative forcing from aerosol-cloud interactions and a small negative instantaneous forcing from aerosol-radiation interactions are partially offset by a substantial forcing from black carbon absorption. Internal mixing and chemical interactions mean that neither the forcing from aerosol-radiation interactions nor aerosol-cloud interactions are linear, making the total aerosol ERF less than the sum of the individual speciated aerosol ERFs. Tropospheric ozone precursors, in addition to exerting a positive forcing due to ozone, lead to oxidant changes which in turn cause an indirect aerosol ERF, altering the sign of the net ERF from nitrogen oxide emissions. Together, aerosol and tropospheric ozone precursors (near-term climate forcers, NTCFs) exert a global mean ERF of −1.12 W m−2, mainly due to changes in the cloud radiative effect. There is also a negative PD ERF from land use (−0.32 W m−2). It is outside the range of previous estimates, and is most likely due to too strong an albedo response. In combination, the net anthropogenic ERF is potentially biased low (1.61 W m−2) relative to other estimates, due to the inclusion of non-linear feedbacks and ES interactions. By including feedbacks between greenhouse gases, stratospheric and tropospheric ozone, aerosols, and clouds, some of which act non-linearly, this work demonstrates the importance of ES interactions when quantifying climate forcing. It also suggests that rapid adjustments need to include chemical as well as physical adjustments to fully account for complex ES interactions.