Uncertainty analysis for estimates of the first indirect aerosol effect

Abstract. The IPCC has stressed the importance of producing unbiased estimates of the uncertainty in indirect aerosol forcing, in order to give policy makers as well as research managers an understanding of the most important aspects of climate change that require refinement. In this study, we use 3-D meteorological fields together with a radiative transfer model to examine the spatially-resolved uncertainty in estimates of the first indirect aerosol forcing. Uncertainties in the indirect forcing associated with aerosol and aerosol precursor emissions, aerosol mass concentrations from different chemical transport models, aerosol size distributions, the cloud droplet parameterization, the representation of the in-cloud updraft velocity, the relationship between effective radius and volume mean radius, cloud liquid water content, cloud fraction, and the change in the cloud drop single scattering albedo due to the presence of black carbon are calculated. The cloud fraction is found to be the most important source of uncertainty and causes an overestimation of the indirect forcing by almost 0.8 Wm−2 in the reference case. Uncertainties associated with aerosol and aerosol precursor emissions are the next most important uncertainty followed closely by uncertainties in the calculation of aerosol burden by chemical transport models and uncertainties in the representation of the aerosol size distribution (including the representation of the pre-industrial size distribution). There are significant regional differences in the uncertainty associated with the first indirect forcing with largest uncertainties in regions associated with the major biomass burning regions followed by uncertainties in Asia and Europe.

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The relationship between aerosol and cloud drop number concentrations in a global aerosol microphysics model

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-9-3207-2009 ◽

2009 ◽

Vol 9 (1) ◽

pp. 3207-3241 ◽

Cited By ~ 1

Author(s):

K. J. Pringle ◽

K. S. Carslaw ◽

D. V. Spracklen ◽

G. M. Mann ◽

M. P. Chipperfield

Keyword(s):

Size Distribution ◽

Cloud Droplet ◽

Global Scale ◽

Rate Of Change ◽

The Arctic ◽

Aerosol Size Distribution ◽

Percentage Error ◽

Empirical Relationships ◽

Wide Range ◽

Global Mean

Abstract. Empirical relationships that link cloud droplet number (CDN) to aerosol number or mass are commonly used to calculate global fields of CDN for climate forcing assessments. In this work we use a sectional global model of sulfate and sea-salt aerosol coupled to a mechanistic aerosol activation scheme to explore the limitations of this approach. We find that a given aerosol number concentration produces a wide range of CDN concentrations due to variations in the shape of the aerosol size distribution. On a global scale, the dependence of CDN on the size distribution results in regional biases in predicted CDN (for a given aerosol number). Empirical relationships between aerosol number and CDN are often derived from regional data but applied to the entire globe. In an analogous process, we derive regional "correlation-relations" between aerosol number and CDN and apply these regional relations to calculations of CDN on the global scale. The global mean percentage error in CDN caused by using regionally derived CDN-aerosol relations is 20 to 26%, which is about half the global mean percentage change in CDN caused by doubling the updraft velocity. However, the error is as much as 25–75% in the Southern Ocean, the Arctic and regions of persistent stratocumulus when an aerosol-CDN correlation relation from the North Atlantic is used. These regions produce much higher CDN concentrations (for a given aerosol number) than predicted by the globally uniform empirical relations. CDN-aerosol number relations from different regions also show very different sensitivity to changing aerosol. The magnitude of the rate of change of CDN with particle number, a measure of the aerosol efficacy, varies by a factor 4. CDN in cloud processed regions of persistent stratocumulus is particularly sensitive to changing aerosol number. It is therefore likely that the indirect effect will be underestimated in these important regions.

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The relationship between aerosol and cloud drop number concentrations in a global aerosol microphysics model

Atmospheric Chemistry and Physics ◽

10.5194/acp-9-4131-2009 ◽

2009 ◽

Vol 9 (12) ◽

pp. 4131-4144 ◽

Cited By ~ 54

Author(s):

K. J. Pringle ◽

K. S. Carslaw ◽

D. V. Spracklen ◽

G. M. Mann ◽

M. P. Chipperfield

Keyword(s):

Size Distribution ◽

Cloud Droplet ◽

Global Scale ◽

Rate Of Change ◽

The Arctic ◽

Aerosol Size Distribution ◽

Percentage Error ◽

Empirical Relationships ◽

Wide Range ◽

Global Mean

Abstract. Empirical relationships that link cloud droplet number (CDN) to aerosol number or mass are commonly used to calculate global fields of CDN for climate forcing assessments. In this work we use a sectional global model of sulfate and sea-salt aerosol coupled to a mechanistic aerosol activation scheme to explore the limitations of this approach. We find that a given aerosol number concentration produces a wide range of CDN concentrations due to variations in the shape of the aerosol size distribution. On a global scale, the dependence of CDN on the size distribution results in regional biases in predicted CDN (for a given aerosol number). Empirical relationships between aerosol number and CDN are often derived from regional data but applied to the entire globe. In an analogous process, we derive regional "correlation-relations" between aerosol number and CDN and apply these regional relations to calculations of CDN on the global scale. The global mean percentage error in CDN caused by using regionally derived CDN-aerosol relations is 20 to 26%, which is about half the global mean percentage change in CDN caused by doubling the updraft velocity. However, the error is as much as 25–75% in the Southern Ocean, the Arctic and regions of persistent stratocumulus when an aerosol-CDN correlation relation from the North Atlantic is used. These regions produce much higher CDN concentrations (for a given aerosol number) than predicted by the globally uniform empirical relations. CDN-aerosol number relations from different regions also show very different sensitivity to changing aerosol. The magnitude of the rate of change of CDN with particle number, a measure of the aerosol efficacy, varies by a factor 4. CDN in cloud processed regions of persistent stratocumulus is particularly sensitive to changing aerosol number. It is therefore likely that the indirect effect will be underestimated in these important regions.

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Air quality forecasting system based on chemical transport models

Hydrometeorological research and forecasting ◽

10.37162/2618-9631-2019-4-203-218 ◽

2019 ◽

Vol 4 ◽

pp. 203-218

Author(s):

I.N. Kusnetsova ◽

◽

I.U. Shalygina ◽

M.I. Nahaev ◽

U.V. Tkacheva ◽

...

Keyword(s):

Air Quality ◽

Chemical Transport ◽

Transport Models ◽

Chemical Transport Models ◽

Forecasting System ◽

Air Quality Forecasting

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Machine learning based bias correction for numerical chemical transport models

Atmospheric Environment ◽

10.1016/j.atmosenv.2020.118022 ◽

2021 ◽

Vol 248 ◽

pp. 118022

Author(s):

Min Xu ◽

Jianbing Jin ◽

Guoqiang Wang ◽

Arjo Segers ◽

Tuo Deng ◽

...

Keyword(s):

Machine Learning ◽

Bias Correction ◽

Chemical Transport ◽

Transport Models ◽

Chemical Transport Models

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Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

10.20944/preprints201811.0607.v1 ◽

2018 ◽

Author(s):

Scott D. Chambers ◽

Elise-Andree Guérette ◽

Khalia Monk ◽

Alan D. Griffiths ◽

Yang Zhang ◽

...

Keyword(s):

Air Quality ◽

Diurnal Cycle ◽

Transport Model ◽

Chemical Transport ◽

Chemical Transport Model ◽

Transport Models ◽

Potential Health ◽

Meteorological Model ◽

Model Skill ◽

Chemical Transport Models

We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

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The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO<sub>2</sub> from nadir-viewing satellites by weighted convolution

Atmospheric Measurement Techniques ◽

10.5194/amt-9-2753-2016 ◽

2016 ◽

Vol 9 (7) ◽

pp. 2753-2779 ◽

Cited By ~ 13

Author(s):

Steffen Beirle ◽

Christoph Hörmann ◽

Patrick Jöckel ◽

Song Liu ◽

Marloes Penning de Vries ◽

...

Keyword(s):

Input Data ◽

Polar Vortex ◽

Chemical Transport ◽

Estimation Algorithm ◽

Satellite Observations ◽

Satellite Measurements ◽

Transport Models ◽

Verification Algorithm ◽

Chemical Transport Models ◽

Total Column

Abstract. The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1–0.2 × 1015 molecules cm−2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study.

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