scholarly journals Development of a reduced-complexity plant canopy physics surrogate model for use in chemical transport models: a case study with GEOS-Chem v12.3.0

2020 ◽  
Vol 13 (6) ◽  
pp. 2569-2585
Author(s):  
Sam J. Silva ◽  
Colette L. Heald ◽  
Alex B. Guenther
Keyword(s):  
Surrogate Model ◽  
Large Scale ◽  
Surface Ozone ◽  
Chemical Transport ◽  
Spatiotemporal Variability ◽  
Computationally Efficient ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Leaf Level ◽  
Canopy Model

Abstract. Biosphere–atmosphere interactions strongly influence the chemical composition of the atmosphere. Simulating these interactions at a detailed process-based level has traditionally been computationally intensive and resource prohibitive, commonly due to complexities in calculating radiation and light at the leaf level within plant canopies. Here we describe a surrogate canopy physics model based on the MEGAN3 detailed canopy model parameterized using a statistical learning technique. This surrogate canopy model is specifically designed to rapidly calculate leaf-level temperature and photosynthetically active radiative (PAR) for use in large-scale chemical transport models (CTMs). Our surrogate model can reproduce the dominant spatiotemporal variability of the more detailed MEGAN3 canopy model to within 10 % across the globe. Implementation of this surrogate model into the GEOS-Chem CTM leads to small local changes in ozone dry deposition velocities of less than 5 % and larger local changes in isoprene emissions of up to ∼40 %, though annual global isoprene emissions remain largely consistent (within 5 %). These changes to surface–atmosphere exchange lead to small changes in surface ozone concentrations of ±1 ppbv, modestly reducing the northern hemispheric ozone bias, which is common to many CTMs, here from 8 to 7 ppbv. The use of this computationally efficient surrogate canopy model drives emissions of isoprene and concentrations of surface ozone closer to observationally constrained values. Additionally, this surrogate model allows for the further development and implementation of leaf-level emission factors in the calculation of biogenic emissions in the GEOS-Chem CTM. Though not the focus of this work, this ultimately enables a complete implementation of the MEGAN3 emissions framework within GEOS-Chem, which produces 570 Tg yr−1 of isoprene for 2012.

scholarly journals Development of a Reduced Complexity Plant Canopy Physics Surrogate Model for use in Chemical Transport Models: A Case Study with GEOS-Chem v12.3.0

2020 ◽  
Author(s):  
Sam J. Silva ◽  
Colette L. Heald ◽  
Alex B. Guenther
Keyword(s):  
Surrogate Model ◽  
Large Scale ◽  
Surface Ozone ◽  
Chemical Transport ◽  
Spatiotemporal Variability ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Leaf Level ◽  
Computationally Intensive ◽  
Canopy Model

Abstract. Biosphere-atmosphere interactions strongly influence the chemical composition of the atmosphere. Simulating these interactions at a detailed process-based level has traditionally been computationally intensive and resource prohibitive, commonly due to complexities in calculating radiation and light at the leaf level within plant canopies. Here we describe a surrogate canopy physics model based on the MEGAN3 detailed canopy model parameterized using a statistical learning technique. This surrogate canopy model is designed specifically to rapidly calculate leaf-level temperature and photosynthetically active radiative (PAR) for use in large-scale chemical transport models (CTMs). Our surrogate model can reproduce the dominant spatiotemporal variability of the more detailed MEGAN3 canopy model to within 10 % across the globe. Implementation of this surrogate model into the GEOS-Chem CTM leads to small local changes in ozone dry deposition velocities of less than 5 %, and larger local changes in isoprene emissions of up to ∼40 %, though annual global isoprene emissions remain largely consistent (within 5 %). These changes to surface-atmosphere exchange lead to modest changes in surface ozone concentrations of ± 1 ppbv. The use of this surrogate canopy model drives emissions of isoprene and concentrations of surface ozone closer to observationally constrained values, without any noticeable impact on computational demand. Additionally, this surrogate model allows for the further development and implementation of leaf-level emission factors in the calculation of biogenic emissions in the GEOS-Chem CTM. Though not the focus of this work, this ultimately enables a complete implementation of the MEGAN3 emissions framework within GEOS-Chem, which produces 570 Tg yr−1 of isoprene in 2012.

scholarly journals Modeling Emissions from Concentrated Sources into Large-Scale Models: Theory and apriori Testing

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 863
Author(s):  
Roberto Paoli
Keyword(s):  
Large Scale ◽  
General Procedure ◽  
Chemical Transport ◽  
Reaction Rates ◽  
Small Scale ◽  
Background Concentrations ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Scale Models ◽  
Effective Reaction

This paper presents a general procedure to incorporate the effects emissions from localized sources, such as aircraft or ship engines, into chemical transport models (CTM). In this procedure, the species concentrations in each grid box of a CTM are split into plume or small-scale concentrations and background concentrations, respectively, and the corresponding conservation equations are derived. The plume concentrations can be interpreted as subgrid contributions for the CTM grid-box averaged concentrations. The chemical reactions occurring inside the plume are parameterized by introducing suitable “effective” reaction rates rather than modifying the emission indices of the species inside the plume. Various methods for implementation into large-scale models are discussed that differ by the accuracy of the description of plume process. The mathematical consistency of the method is verified on simple idealized setting consisting of a reactive plume in homogeneous turbulence.

scholarly journals Systematic bias in evaluating chemical transport models with maximum daily 8 h average (MDA8) surface ozone for air quality applications: a case study with GEOS-Chem v9.02

2019 ◽  
Vol 12 (8) ◽  
pp. 3641-3648 ◽  
Author(s):  
Katherine R. Travis ◽  
Daniel J. Jacob
Keyword(s):  
Air Quality ◽  
Mixed Layer ◽  
Diurnal Cycle ◽  
Surface Ozone ◽  
Chemical Transport ◽  
Systematic Bias ◽  
Transport Models ◽  
Ozone Deposition ◽  
Chemical Transport Models

Abstract. Chemical transport models frequently evaluate their simulation of surface ozone with observations of the maximum daily 8 h average (MDA8) concentration, which is the standard air quality policy metric. This requires successful simulation of the surface ozone diurnal cycle including nighttime depletion, but models often have difficulty simulating this diurnal cycle for a number of reasons, including (1) vertical grid structure in the surface layer, (2) timing of changes in mixed layer dynamics and ozone deposition velocity across the day–night transition, (3) poor representation of nighttime stratification, and (4) uncertainties in ozone nighttime deposition. We analyze the problem with the GEOS-Chem model, taking as a representative case study the Southeast US during the NASA SEAC4RS aircraft campaign in August–September 2013. The model is unbiased relative to the daytime mixed layer aircraft observations but has a mean +8 ppb bias at its lowest level (65 m) relative to MDA8 surface ozone observations. The bias can be corrected to +5 ppb by implicit sampling of the model at the 10 m altitude of the surface observations. The model does not capture frequent observed occurrences of <20 ppb MDA8 surface ozone on rainy days, possibly because of enhanced ozone deposition to wet surfaces that is unaccounted for. Restricting the surface ozone evaluation to dry days still shows inconsistencies with MDA8 ozone because of model errors in the ozone diurnal cycle. Restricting the evaluation to afternoon ozone completely removes the bias. We conclude that better representation of diurnal variations in mixed layer dynamics and ozone deposition velocities is needed in models to properly describe the diurnal cycle of ozone.

scholarly journals Systematic bias in evaluating chemical transport models with maximum daily 8-hour average (MDA8) surface ozone for air quality applications

2019 ◽  
Author(s):  
Katherine R. Travis ◽  
Daniel J. Jacob
Keyword(s):  
Air Quality ◽  
Diurnal Cycle ◽  
Surface Ozone ◽  
Chemical Transport ◽  
Systematic Bias ◽  
Transport Models ◽  
Chemical Transport Models ◽  
Aircraft Observations ◽  
Southeast Us ◽  
Quality Policy

Abstract. Chemical transport models typically evaluate their simulation of surface ozone with observations of the maximum daily 8-hour average (MDA8) concentration, which is the standard air quality policy metric. This requires successful simulation of the surface ozone diurnal cycle including nighttime depletion, but models are generally biased high at night because of difficulty in resolving the stratified conditions near the surface. We quantify the problem with the GEOS-Chem model for the Southeast US during the NASA SEAC4RS aircraft campaign in August–September 2013. The model is unbiased relative to the daytime mixed layer aircraft observations but has a +5 ppb bias relative to MDA8 surface ozone observations. The model also does not capture observed occurrences of

Air quality forecasting system based on chemical transport models

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

Machine learning based bias correction for numerical chemical transport models

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

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

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 (&lt;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 &lsquo;natural complicating factors&rsquo; (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 &ldquo;rush hour&rdquo; 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.

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

2016 ◽  
Vol 9 (7) ◽  
pp. 2753-2779 ◽  
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.

scholarly journals Comparing atmospheric transport models for future regional inversions over Europe – Part 1: mapping the atmospheric CO<sub>2</sub> signals

2007 ◽  
Vol 7 (13) ◽  
pp. 3461-3479 ◽  
Author(s):  
C. Geels ◽  
M. Gloor ◽  
P. Ciais ◽  
P. Bousquet ◽  
P. Peylin ◽  
...  
Keyword(s):  
High Resolution ◽  
High Altitude ◽  
Large Scale ◽  
Fossil Fuel ◽  
Atmospheric Transport ◽  
Inverse Methods ◽  
Spatiotemporal Variability ◽  
Transport Models ◽  
Sources And Sinks ◽  
Low Altitude

Abstract. The CO2 source and sink distribution across Europe can be estimated in principle through inverse methods by combining CO2 observations and atmospheric transport models. Uncertainties of such estimates are mainly due to insufficient spatiotemporal coverage of CO2 observations and biases of the models. In order to assess the biases related to the use of different models the CO2 concentration field over Europe has been simulated with five different Eulerian atmospheric transport models as part of the EU-funded AEROCARB project, which has the main goal to estimate the carbon balance of Europe. In contrast to previous comparisons, here both global coarse-resolution and regional higher-resolution models are included. Continuous CO2 observations from continental, coastal and mountain sites as well as flasks sampled on aircrafts are used to evaluate the models' ability to capture the spatiotemporal variability and distribution of lower troposphere CO2 across Europe. 14CO2 is used in addition to evaluate separately fossil fuel signal predictions. The simulated concentrations show a large range of variation, with up to ~10 ppm higher surface concentrations over Western and Central Europe in the regional models with highest (mesoscale) spatial resolution. The simulation – data comparison reveals that generally high-resolution models are more successful than coarse models in capturing the amplitude and phasing of the observed short-term variability. At high-altitude stations the magnitude of the differences between observations and models and in between models is less pronounced, but the timing of the diurnal cycle is not well captured by the models. The data comparisons show also that the timing of the observed variability on hourly to daily time scales at low-altitude stations is generally well captured by all models. However, the amplitude of the variability tends to be underestimated. While daytime values are quite well predicted, nighttime values are generally underpredicted. This is a reflection of the different mixing regimes during day and night combined with different vertical resolution between models. In line with this finding, the agreement among models is increased when sampling in the afternoon hours only and when sampling the mixed portion of the PBL, which amounts to sampling at a few hundred meters above ground. The main recommendations resulting from the study for constraining land carbon sources and sinks using high-resolution concentration data and state-of-the art transport models through inverse methods are given in the following: 1) Low altitude stations are presently preferable in inverse studies. If high altitude stations are used then the model level that represents the specific sites should be applied, 2) at low altitude sites only the afternoon values of concentrations can be represented sufficiently well by current models and therefore afternoon values are more appropriate for constraining large-scale sources and sinks in combination with transport models, 3) even when using only afternoon values it is clear that data sampled several hundred meters above ground can be represented substantially more robustly in models than surface station records, which emphasize the use of tower data in inverse studies and finally 4) traditional large scale transport models seem not sufficient to resolve fine-scale features associated with fossil fuel emissions, as well as larger-scale features like the concentration distribution above the south-western Europe. It is therefore recommended to use higher resolution models for interpretation of continental data in future studies.

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