Uncertainties in effective radiative forcings through aerosol–cloud
interactions (ERFaci, also called aerosol indirect effects)
contribute strongly to the uncertainty in the total
...preindustrial-to-present-day anthropogenic forcing. Some forcing estimates of
the total aerosol indirect effect are so negative that they even offset the
greenhouse gas forcing. This study highlights the role of oxidants in
modeling of preindustrial-to-present-day aerosol indirect effects. We argue
that the aerosol precursor gases should be exposed to oxidants of its era to
get a more correct representation of secondary aerosol formation. Our model
simulations show that the total aerosol indirect effect changes from
−1.32 to −1.07 W m−2 when the precursor gases in the preindustrial
simulation are exposed to preindustrial instead of present-day oxidants. This
happens because of a brightening of the clouds in the preindustrial
simulation, mainly due to large changes in the nitrate
radical (NO3). The weaker oxidative power of the preindustrial
atmosphere extends the lifetime of the precursor gases, enabling them to be
transported higher up in the atmosphere and towards more remote areas where
the susceptibility of the cloud albedo to aerosol changes is high. The
oxidation changes also shift the importance of different chemical reactions
and produce more condensate, thus increasing the size of the aerosols and
making it easier for them to activate as cloud condensation nuclei.
We document model updates and present and discuss modeling and validation results from a further developed production-tagged aerosol module, OsloAero5.3, for use in Earth system models. The aerosol ...module has in this study been implemented and applied in CAM5.3-Oslo. This model is based on CAM5.3–CESM1.2 and its own predecessor model version CAM4-Oslo. OsloAero5.3 has improved treatment of emissions, aerosol chemistry, particle life cycle, and aerosol–cloud interactions compared to its predecessor OsloAero4.0 in CAM4-Oslo. The main new features consist of improved aerosol sources; the module now explicitly accounts for aerosol particle nucleation and secondary organic aerosol production, with new emissions schemes also for sea salt, dimethyl sulfide (DMS), and marine primary organics. Mineral dust emissions are updated as well, adopting the formulation of CESM1.2. The improved model representation of aerosol–cloud interactions now resolves heterogeneous ice nucleation based on black carbon (BC) and mineral dust calculated by the model and treats the activation of cloud condensation nuclei (CCN) as in CAM5.3. Compared to OsloAero4.0 in CAM4-Oslo, the black carbon (BC) mass concentrations are less excessive aloft, with a better fit to observations. Near-surface mass concentrations of BC and sea salt aerosols are also less biased, while sulfate and mineral dust are slightly more biased. Although appearing quite similar for CAM5.3-Oslo and CAM4-Oslo, the validation results for organic matter (OM) are inconclusive, since both of the respective versions of OsloAero are equipped with a limited number of OM tracers for the sake of computational efficiency. Any information about the assumed mass ratios of OM to organic carbon (OC) for different types of OM sources is lost in the transport module. Assuming that observed OC concentrations scaled by 1.4 are representative for the modeled OM concentrations, CAM5.3-Oslo with OsloAero5.3 is slightly inferior for the very sparsely available observation data. Comparing clear-sky column-integrated optical properties with data from ground-based remote sensing, we find a negative bias in optical depth globally; however, it is not as strong as in CAM4-Oslo, but has positive biases in some areas typically dominated by mineral dust emissions. Aerosol absorption has a larger negative bias than the optical depth globally. This is reflected in a lower positive bias in areas where mineral dust is the main contributor to absorption. Globally, the low bias in absorption is smaller than in CAM4-Oslo. The Ångström parameter exhibits small biases both globally and regionally, suggesting that the aerosol particle sizes are reasonably well represented. Cloud-top droplet number concentrations over oceans are generally underestimated compared to satellite retrievals, but seem to be overestimated downwind of major emissions of dust and biomass burning sources. Finally, we find small changes in direct radiative forcing at the top of the atmosphere, while the cloud radiative forcing due to anthropogenic aerosols is now more negative than in CAM4-Oslo, being on the strong side compared to the multi-model estimate in IPCC AR5. Although not all validation results in this study show improvement for the present CAM5.3-Oslo version, the extended and updated aerosol module OsloAero5.3 is more advanced and applicable than its predecessor OsloAero4.0, as it includes new parameterizations that more readily facilitate sensitivity and process studies and use in climate and Earth system model studies in general.
Large volcanic eruptions have strong impacts on both atmospheric and ocean dynamics that can last for decades. Numerical models have attempted to reproduce the effects of major volcanic eruptions on ...climate; however, there are remarkable inter-model disagreements related to both short-term dynamical response to volcanic forcing and long-term oceanic evolution. The lack of robust simulated behaviour is related to various aspects from model formulation to simulated background internal variability to the eruption details. Here, we use the Norwegian Earth System Model version 1 to calculate interactively the volcanic aerosol loading resulting from SO
2
emissions of the second largest high-latitude volcanic eruption in historical time (the Laki eruption of 1783). We use two different approaches commonly used interchangeably in the literature to generate ensembles. The ensembles start from different background initial states, and we show that the two approaches are not identical on short-time scales (<1 yr) in discerning the volcanic effects on climate, depending on the background initial state in which the simulated eruption occurred. Our results also show that volcanic eruptions alter surface climate variability (in general increasing it) when aerosols are allowed to realistically interact with circulation: Simulations with fixed volcanic aerosol show no significant change in surface climate variability. Our simulations also highlight that the change in climate variability is not a linear function of the amount of the volcanic aerosol injected. We then provide a tentative estimation of the ensemble size needed to discern a given volcanic signal on surface temperature from the natural internal variability on regional scale: At least 20-25 members are necessary to significantly detect seasonally averaged anomalies of 0.5°C; however, when focusing on North America and in winter, a higher number of ensemble members (35-40) is necessary.
Several studies have shown the importance of aerosols in the Earth's radiative balance. The radiative effect of dust has earlier been quantified in global and regional climate models. Fewer studies ...have included prediction of aerosols online in numerical weather prediction (NWP) models. Predicting climate effect of aerosols is different from including aerosols actively in weather prediction because climate models give average responses over long timescales, whereas the purpose of weather prediction models is to calculate the right weather on short timescales. In this paper, we run a mesoscale NWP with four different assumptions on dust aerosols: (1) no dust, (2) climatological data set for dust aerosols, (3) forecasted dust using the Dust Entrainment and Deposition model (DEAD) and (4) same as assumption 3 but with decreased single scattering albedo. We show that the assumption on dust is important for predicting ground temperature and convective precipitation in the model. Dust changes the model physics through changing the radiative fluxes. We interpret the changes through analyzing the energy budgets for four zones close to the major dust sources. The zones include both convective and nonconvective areas. In convective areas over ocean, dust can decrease convective activity. The vertical gradient of the aerosols and their single scattering albedo determine how efficient they are. Over land, dust also influences the surface energy budget so that decreased latent heat fluxes result if dust plumes pass over areas where water evaporates from the surface.
Recent field observations demonstrate that a significant discrepancy exists between models and measurements of large dust aerosol particles at remote sites. We assess the fraction of this bias ...explained by assumptions involving four different dust production processes. These include dust source size distribution (constant or dynamically changing according to saltation and sandblasting theory), wind speed distributions (using mean wind or a probability density function (PDF)), parent soil aggregate size distribution, and the discretization (number of bins) in the dust size distribution. The Dust Entrainment and Deposition global model is used to simulate the measurements from the Puerto Rican Dust Experiment (PRIDE) (2000). Using wind speed PDFs from observed National Centers for Environmental Prediction winds results in small changes in downwind size distribution for the production which neglects sandblasting, but it results in significant changes when production includes sandblasting. Saltation‐sandblasting generally produces more large dust particles than schemes which neglect sandblasting. Parent soil aggregate size distribution is an important factor when calculating size‐distributed dust emissions. Changing from a soil with large grains to a soil with smaller grains increases by 50% the fraction of large aerosols (D >5 μm) modeled at Puerto Rico. Assuming that the coarse medium sand typical of West Africa dominates all source regions produces the best agreement with PRIDE observations.
Global atmospheric dust is simulated using the Dust Entrainment and Deposition (DEAD) model in combination with the global‐scale Oslo chemical transport model CTM2 using meteorological data for 1996. ...Dust sources are calculated using both mean wind speeds with model resolution T63 and subgrid wind speeds. Different data sets are used to describe soil erodibility. We explain how the different assumptions about dust production affect atmospheric dust burden and deposition. Some aspects of the annual dust cycle, such as the east Asian dust emissions, are largely dependent on the data used to determine soil erodibility. Other aspects, such as the timing of the maximum in the African plume at Northern Hemisphere summer, are well modeled with all data sets applied here. We show that the daily variation in optical depth at Cape Verde on the west coast of Africa is well simulated when we assume that erodibility is correlated with surface reflectivity from Moderate‐Resolution Imaging Spetroradiometer (MODIS) satellite data. Using a subgrid probability density function of wind speed to drive the dust sources facilitates dust emissions in areas with low wind speeds. Dust concentrations in remote areas are sensitive to the parameterization of wet deposition. Our results point out the need for a detailed soil erodibility data set for global dust modeling, and they suggest that surface reflectivity is potentially valuable for producing or evaluating such data sets.
The Oslo chemical transport model (Oslo CTM2) is driven by meteorological data to model mineral dust during the Saharan Dust Experiment (SHADE) campaign in September 2000. Model calculations of the ...optical properties and radiative transfer codes are used to assess the direct radiative impact in the solar and terrestrial regions of the spectrum. The model calculations are compared to a wide range of measurements (satellite, ground‐based, and aircraft) during the campaign. The model reproduces the main features during the SHADE campaign, including a large mineral dust storm. The optical properties and the vertical profiles are in reasonable agreement with the measurements. There is a very good agreement between the modeled radiative impact and observations. The strongest local solar radiative impact we model is around −115 Wm−2. On a global scale the radiative effect of mineral dust from Sahara exerts a significant negative net radiative effect.
This paper describes the aerosol model Organic Inorganic Lognormal Aerosol Model including Secondary Organic Aerosol (ORILAM‐SOA) which is an extension of the lognormal aerosol dynamics model ORILAM. ...ORILAM‐SOA consists of the original aerosol dynamics routines, a photochemical scheme able to predict SOA precursors, and an equilibrium scheme able to predict partitioning of the precursors between the gas and aerosol phases. We show that ORILAM‐SOA is computationally efficient enough to be run in three‐dimensional (3‐D) atmospheric models. ORILAM‐SOA is based on existing models. We use a numerical reduction technique to reduce the Caltech Atmospheric Chemistry Mechanism (CACM) and a new, fast, convergent iteration technique to increase the speed of the Model to Predict the Multiphase Partitioning of Organics (MPMPO). We compare the ORILAM‐SOA to its parent models in terms of gas concentrations, aerosol concentrations, and CPU time spent during the computations. For illustrative purposes we include a 3‐D simulation of SOA over southern France.
A global three-dimensional chemical transport model (CTM) is used to model the yearly cycle of sea salt. Sea salt particles are produced by wind acting on the sea surface, and they are removed by wet ...and dry deposition. In this study, forecast meteorological data are taken from the ECMWF. The modeled concentrations are compared to measured concentrations at sea level, and both absolute values and monthly variations compare well with measurements. Radiation calculations have been performed using the same meteorological input data as the CTM calculations. The global, yearly average burden of sea salt is found to be 12 mg m−2. This is within the range of earlier estimates that vary between 11 and 22 mg m−2. The radiative impact of sea salt is calculated to be −1.1 W m−2. The total, yearly flux of sea salt is estimated to be 6500 Tg yr−1.
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. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24% and -35% for particles with dry diameters > 50 and > 120nm, as well as -36% and -34% for CCN at supersaturations of 0.2% and 1.0%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (< 0.1%) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN(0.2)) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer.
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