The application of mean averaging kernels to mean trace gas distributions

Clarmann, T. von; Glatthor, N.

doi:10.5194/amt-12-5155-2019

Cited by 8 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although potential issues with using monthly mean, rather than averaging kernels for individual profiles, can arise for certain species and instruments (von Clarmann and Glatthor, 2019), agreement between model and observations was found to be improved substantially through application of monthly mean averaging kernels in this analysis, in agreement with previous work (Aghedo et al, 2011;Williams et al, 2019).…”

Section: Data Processingsupporting

confidence: 82%

Seasonal, interannual and decal variability of Tropospheric Ozone in the North Atlantic: Comparison of UM-UKCA and remote sensing observations for 2005–2018

Russo

Kerridge

Abraham

et al. 2022

Preprint

View full text Add to dashboard Cite

Abstract. Tropospheric ozone is an important component of the Earth System as it can affect both climate and air quality. In this work we use observed tropospheric column ozone derived from the Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) OMI-MLS, in addition to OMI ozone retrieved in discrete vertical layers, and compare it to tropospheric ozone from UM-UKCA simulations (which utilise the Unified Model, UM, coupled to UK Chemistry and Aerosol, UKCA). Our aim is to investigate recent changes (2005–2018) in tropospheric ozone in the North Atlantic region, and specifically its seasonal, interannual and decadal variability and to understand what factors are driving such changes. Through sensitivity experiments, timeseries correlation and comparison with the LIS-OTD lightning flash dataset, the model positive bias in the Tropics is attributed to shortcomings in the convection and lightning parameterisations. Use of OMI data, for which vertical averaging kernels and a priori information are available, suggests that the model negative bias at mid latitudes relative to OMI-MLS tropospheric column could be the result of vertical sampling. Ozone in the North Atlantic peaks in spring and early summer, with generally good agreement between the modelled and observed seasonal cycle. Recent trends in tropospheric ozone were investigated and the discrepancy between model and observations was linked to possible differences in lower stratospheric ozone trends and associated stratosphere to troposphere transport. Modelled tropospheric ozone interannual variability (IAV) is driven by IAV of tropical emissions of NOx from lightning and IAV of ozone transport from the stratosphere; however, the modelled and observed IAV differ. To understand the IAV discrepancy we investigated how modelled ozone and its drivers respond to large scale modes of variability. Using OMI height-resolved data and model idealised tracers, we were able to identify stratospheric transport of ozone into the troposphere as the main driver of the dynamical response of North Atlantic ozone to the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO). Finally, the ozone response to El Nino Southern Oscillation (ENSO) and its impact on observed and modelled north Atlantic ozone variability was analysed.

show abstract

Section: Data Processingsupporting

confidence: 82%

Seasonal, interannual and decal variability of Tropospheric Ozone in the North Atlantic: Comparison of UM-UKCA and remote sensing observations for 2005–2018

Russo

Kerridge

Abraham

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For comparison with OMI data (2010–2017) in the surface‐450 hPa and 450–170 hPa layers, we use monthly, gridded averaging kernels and a priori information to minimize vertical sampling differences between OMI and UKCA. Although potential issues with using monthly mean rather than individual averaging kernels can arise for certain species and instruments (von Clarmann & Glatthor, 2019), agreement between model and observation are found to be improved substantially through application of monthly mean averaging kernels in this analysis.…”

Section: Resultsmentioning

confidence: 95%

The Common Representative Intermediates Mechanism Version 2 in the United Kingdom Chemistry and Aerosols Model

Archer‐Nicholls

Abraham

Shin

et al. 2021

J Adv Model Earth Syst

View full text Add to dashboard Cite

We document the implementation of the Common Representative Intermediates Mechanism version 2, reduction 5 into the United Kingdom Chemistry and Aerosol model (UKCA) version 10.9. The mechanism is merged with the stratospheric chemistry already used by the StratTrop mechanism, as used in UKCA and the UK Earth System Model, to create a new CRI-Strat mechanism. CRI-Strat simulates a more comprehensive treatment of non-methane volatile organic compounds (NMVOCs) and provides traceability with the Master Chemical Mechanism. In total, CRI-Strat simulates the chemistry of 233 species competing in 613 reactions (compared to 87 species and 305 reactions in the existing StratTrop mechanism). However, while more than twice as complex than StratTrop, the new mechanism is only 75% more computationally expensive. CRI-Strat is evaluated against an array of in situ and remote sensing observations and simulations using the StratTrop mechanism in the UKCA model. It is found to increase production of ozone near the surface, leading to higher ozone concentrations compared to surface observations. However, ozone loss is also greater in CRI-Strat, leading to less ozone away from emission sources and a similar tropospheric ozone burden compared to StratTrop. CRI-Strat also produces more carbon monoxide than StratTrop, particularly downwind of biogenic VOC emission sources, but has lower burdens of nitrogen oxides as more is converted into reservoir species. The changes to tropospheric ozone and nitrogen budgets are sensitive to the treatment of NMVOC emissions, highlighting the need to reduce uncertainty in these emissions to improve representation of tropospheric chemical composition. Plain Language SummaryTo understand the climate and predict how it will change in the future, we need to understand its chemical composition-the trace gases and small particles that exist in tiny quantities in the atmosphere. A key tool we use to do this are computer models which simulate the atmosphere and processes within it. Key processes include the formation of ozone, a harmful pollutant and greenhouse gas in the lower atmosphere. However, the chemistry involved in forming ozone is very complicated, so computer simulations of the atmosphere must greatly simplify the chemistry. These simple schemes may introduce errors in the model. We also have much more complex chemical mechanisms which simulate our best understanding of all chemical reactions, but these complex schemes require too much computational power to be used when simulating the whole atmosphere. In this paper, we describe the implementation of a chemical mechanism that sits between these levels of complexity, realistically simulating the formation and destruction of ozone without being too slow to run. We compare this new mechanism against measurements taken of the atmosphere and the preexisting, simpler chemical mechanism and show that the new mechanism greatly enhances the amount of ozone that is produced.

show abstract

“…It is quite clear from Figure 3 that the averaging of deconvoluted profiles can yield prior-free atmospheric state representations that correspond with the initial retrievals within their uncertainty, although the tropospheric ozone concentration remains an issue for the examples shown here. For the retrievals with a sufficient DOF, e.g., more than 5 like the nadir UV and limb retrievals, deconvoluted profiles are a viable alternative in the creation of spatiotemporally averaged (Level-3) data, thus avoiding smoothing difference errors and the difficulties that arise in the need for and application of averaged averaging kernels [4,5].…”

Section: Retrieval Averagingmentioning

confidence: 99%

“…It is virtually impossible to tell whether significant structures originate from the measurement or from the a priori [2]. Furthermore, depending on the nature of the a priori information, profile retrievals at different locations are no longer statistically independent, which complicates their averaging and assimilation [4,5]. Finally, the data volume of a complete set of diagnostic parameters can be enormous, or users might not deal with this diagnostic information, risking misinterpretation of the data.…”

Section: Introductionmentioning

confidence: 99%

Removing Prior Information from Remotely Sensed Atmospheric Profiles by Wiener Deconvolution Based on the Complete Data Fusion Framework

et al. 2022

View full text Add to dashboard Cite

A method is developed that removes a priori information from remotely sensed atmospheric state profiles. This consists of a Wiener deconvolution, whereby the required cost function is obtained from the complete data fusion framework. Asserting that the deconvoluted averaging kernel matrix has to equal the unit matrix, results in an iterative process for determining a profile-specific deconvolution matrix. In contrast with previous deconvolution approaches, only the dimensions of this matrix have to be fixed beforehand, while the iteration process optimizes the vertical grid. This method is applied to ozone profile retrievals from simulated and real measurements co-located with the Izaña ground station. Individual profile deconvolutions yield strong outliers, including negative ozone concentration values, but their spatiotemporal averaging results in prior-free atmospheric state representations that correspond to the initial retrievals within their uncertainty. Averaging deconvoluted profiles thus looks like a viable alternative in the creation of harmonized Level-3 data, avoiding vertical smoothing difference errors and the difficulties that arise with averaged averaging kernels.

show abstract

The application of mean averaging kernels to mean trace gas distributions

Cited by 8 publications

References 15 publications

Seasonal, interannual and decal variability of Tropospheric Ozone in the North Atlantic: Comparison of UM-UKCA and remote sensing observations for 2005–2018

Seasonal, interannual and decal variability of Tropospheric Ozone in the North Atlantic: Comparison of UM-UKCA and remote sensing observations for 2005–2018

The Common Representative Intermediates Mechanism Version 2 in the United Kingdom Chemistry and Aerosols Model

Removing Prior Information from Remotely Sensed Atmospheric Profiles by Wiener Deconvolution Based on the Complete Data Fusion Framework

Contact Info

Product

Resources

About