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5 results for “Chemistry - transport model”
Atmospheric data used for calibrating the tropopause in global chemistry-climate or chemistry-transport models
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Global sensitivity and uncertainty analysis of an atmospheric chemistry transport model: the FRAME model (version 9.15.0) as a case study
<p>Atmospheric chemistry transport models (ACTMs) are widely used to underpin policy decisions associated with the impact of potential changes in emissions on future pollutant concentrations and deposition. It is therefore essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input pollutant emissions. ACTMs incorporate complex and non-linear descriptions of chemical and physical processes which means that interactions and non-linearities in input–output relationships may not be revealed through the local one-at-a-time sensitivity analysis typically used. The aim of this work is to demonstrate a global sensitivity and uncertainty analysis approach for an ACTM, using as an example the FRAME model, which is extensively employed in the UK to generate source-receptor matrices for the UK Integrated Assessment Model and to estimate critical load exceedances. An optimised Latin hypercube sampling design was used to construct model runs within ± 40 % variation range for the UK emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub>, from which regression coefficients for each input-output combination and each model grid (>10,000 across the UK) were calculated. Surface concentrations of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (and of deposition of S and N) were found to be predominantly sensitive to the emissions of the respective pollutant, while sensitivities of secondary species such as HNO<sub>3</sub> and particulate SO<sub>4</sub><sup>2-</sup>, NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup> to pollutant emissions were more complex and geographically variable. The uncertainties in model output variables were propagated from the uncertainty ranges reported by the UK National Atmospheric Emissions Inventory for the emissions of SO<sub>2</sub>, NO<sub>x</sub> and NH<sub>3</sub> (± 4 %, ± 10 % and ± 20 % respectively). The uncertainties in the surface concentrations of NH<sub>3</sub> and NO<sub>x</sub> and the depositions of NH<sub>x</sub> and NO<sub>y</sub> were dominated by the uncertainties in emissions of NH<sub>3</sub>, and NO<sub>x</sub> respectively, whilst concentrations of SO<sub>2</sub> and deposition of SO<sub>y</sub> were affected by the uncertainties in both SO<sub>2</sub> and NH<sub>3</sub> emissions. Likewise, the relative uncertainties in the modelled surface concentrations of each of the secondary pollutant variables (NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup> and HNO<sub>3</sub>) were due to uncertainties in at least two input variables. In all cases the spatial distribution of relative uncertainty was found to be geographically heterogeneous. The global methods used here can be applied to conduct sensitivity and uncertainty analyses of other ACTMs.</p> <p>The dataset contains model outputs used for the sensitivity and uncertainty analyses.</p>
Snow particle number, aerosol concentration and 10 meter windspeed data from MOSAiC, N-ICE, Weddel Sea expeditions and chemistry transport model data (p-TOMCAT) .
<p>The folder contains data for the MOSAiC, N-ICE and Weddell sea expedition for Snow particle counter measurements. Coarse aerosol measurements from the MOSAiC expedition are also included. Simulation data from a chemistry transport model (p-TOMCAT) is also available. This version includes both .mat and .nc files</p>
Advanced methods for uncertainty assessment and global sensitivity analysis of a Eulerian atmospheric chemistry transport model
<p>Atmospheric chemistry transport models (ACTMs) are extensively used to provide scientific support for the development of policies to mitigate against the detrimental effects of air pollution on human health and ecosystems. Therefore, it is essential to quantitatively assess the level of model uncertainty and to identify the model input parameters that contribute the most to the uncertainty. For complex process-based models, such as ACTMs, uncertainty and global sensitivity analyses are still challenging and are often limited by computational constraints due to the requirement of a large number of model runs. In this work, we demonstrate an emulator-based approach to uncertainty quantification and variance-based sensitivity analysis for the EMEP4UK model (regional application of the European Monitoring and Evaluation Programme Meteorological Synthesizing Centre-West). A separate Gaussian process emulator was used to estimate model predictions at unsampled points in the space of the uncertain model inputs for every modelled grid cell. The training points for the emulator were chosen using an optimised Latin hypercube sampling design. The uncertainties in surface concentrations of O<sub>3</sub>, NO<sub>2</sub>, and PM<sub>2.5</sub> were propagated from the uncertainties in the anthropogenic emissions of NO<sub>x</sub>, SO<sub>2</sub>, NH<sub>3</sub>, VOC, and primary PM<sub>2.5</sub> reported by the UK National Atmospheric Emissions Inventory. The results of the EMEP4UK uncertainty analysis for the annually averaged model predictions indicate that modelled surface concentrations of O<sub>3</sub>, NO<sub>2</sub>, and PM<sub>2.5</sub> have the highest level of uncertainty in the grid cells comprising urban areas (up to ± 7%, ± 9%, and ± 9% respectively). The uncertainty in the surface concentrations of O<sub>3 </sub>and NO<sub>2</sub> were dominated by uncertainties in NO<sub>x</sub> emissions combined from non-dominant sectors (i.e. all sectors excluding energy production and road transport) and shipping emissions. Additionally, uncertainty in O<sub>3</sub> was driven by uncertainty VOC emissions combined from sectors excluding solvent use. Uncertainties in the modelled PM<sub>2.5</sub> concentrations were mainly driven by uncertainties in primary PM<sub>2.5</sub> emissions and NH<sub>3</sub> emissions from the agricultural sector. Uncertainty and sensitivity analyses were also performed for five selected grid sells for monthly averaged model predictions to illustrate the seasonal change in the magnitude of uncertainty and change in the contribution of different model inputs to the overall uncertainty. Our study demonstrates the viability of a Gaussian process emulator-based approach for uncertainty and global sensitivity analyses, which can be applied to other ACTMs. Conducting these analyses helps to increase the confidence in model predictions. Additionally, the emulators created for these analyses can be used to predict the ACTM response for any other combination of perturbed input emissions within the ranges set for the original Latin hypercube sampling design without the need to re-run the ACTM, thus allowing fast exploratory assessments at significantly reduced computational costs.</p> <p>The upload contains the uncertainty and sensitivity data together with the analysis scripts.</p>
High Mountain Asia 12 km Modeled Estimates of Aerosol Transport, Chemistry, and Deposition Reanalysis, 2003-2019 V001
This data set contains a 12 km resolution, simulated reanalysis of aerosol transport, chemistry, and deposition over the High Mountain Asia (HMA) region for 1 January 2003 through 31 August 2019. Two-dimensional surface data are provided at one hour intervals. Three-dimensional atmospheric data are provided at three-hour intervals for 35 sigma levels extending from the surface to 50 hPa. Also known as the Model for Atmospheric Transport and Chemistry in Asia (MATCHA), the data comprise a wide range of variables intended to help assess the impacts of aerosols on the cryosphere in the HMA region, including: concentrations of black/brown carbon and other light absorbing particles (LAPs), broken out by source region; longwave/shortwave heating rates due to LAPs; wet/dry deposition of LAPs; precipitation and hydrological data; and meteorological state variables. The simulation was generated using a fully coupled, regional chemistry-climate model (WRF-Chem-CLM-SNICAR), constrained by aerosol optical depth (AOD) and carbon monoxide (CO) satellite observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Measurements Of Pollution In The Troposphere (MOPITT) instruments, respectively.
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