ORCID Profile
0000-0001-7057-194X
Current Organisation
Universität Leipzig
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Publisher: Copernicus GmbH
Date: 25-01-2020
Abstract: Abstract. Radiative forcing provides an important basis for understanding and predicting global climate changes, but its quantification has historically been done independently for different forcing agents, involved observations to varying degrees, and studies have not always included a detailed analysis of uncertainties. The Copernicus Atmosphere Monitoring Service reanalysis is an optimal combination of modelling and observations of atmospheric composition. It provides a unique opportunity to rely on observations to quantify the monthly- and spatially-resolved global distributions of radiative forcing consistently for six of the largest forcing agents: carbon dioxide, methane, tropospheric ozone, stratospheric ozone, aerosol-radiation interactions, and aerosol-cloud interactions. These radiative forcing estimates account for adjustments in stratospheric temperatures, but do not account for rapid adjustments in the troposphere. On a global average and over the period 2003–2016, stratospherically adjusted radiative forcing of carbon dioxide has averaged +1.84 W m−2 (5–95% confidence interval: 1.46 to 2.22 W m−2) relative to 1750 and increased at a rate of 17 % per decade. The corresponding values for methane are +0.45 (0.35 to 0.55) W m−2 and 3 % per decade, but with a clear acceleration since 2007. Ozone radiative forcing averages +0.32 (0 to 0.64) W m−2 and aerosol radiative forcing averages −1.37 (−2.17 to −0.57) W m−2. Both have been relatively stable since 2003. Taking the six forcing agents together, there no indication of a slowdown or acceleration in the rate of increase in anthropogenic radiative forcing over the period. These ongoing radiative forcing estimates will monitor the impact on the Earth’s energy budget of the dramatic emission reductions towards net-zero that are needed to limit surface temperature warming to the Paris Agreement temperature targets. Indeed, such impacts should be clearly manifested in radiative forcing before being clear in the temperature record. In addition, this radiative forcing dataset can provide the input distributions needed by researchers involved in monitoring of climate change, detection and attribution, interannual to decadal prediction, and integrated assessment modelling. The data generated by this work are available at 0.24380/ads.1hj3y896 (Bellouin et al., 2020).
Publisher: Copernicus GmbH
Date: 08-07-2013
Abstract: Abstract. An error in the calculation of the emitted number of primary sulfate particles for a given mass of emitted elementary sulfur has recently been identified in HAM, i.e. the aerosol module utilised in the ECHAM-HAM aerosol climate model. Correcting for this error substantially alters the estimates of top-of-atmosphere radiative forcing due to aerosol indirect effects from global shipping emissions (year 2000) as presented in Peters et al. (2012). Here, we shortly present these new results.
Publisher: Copernicus GmbH
Date: 22-09-2023
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-13250
Abstract: Clouds are classified into types, classes, or regimes. The World Meteorological Organization distinguishes stratus and cumulus clouds and three altitude layers. Cloud types exhibit very different radiative properties and interact in numerous ways with aerosol particles in the atmosphere. However, it has proven difficult to define cloud regimes objectively and from remote sensing data, hindering the understanding we have of the processes and adjustments involved.Building on the method we previously developed, we combine synoptic observations and passive satellite remote-sensing retrievals to constitute a database of cloud types and cloud properties to eventually train a cloud classification algorithm. The cloud type labels come from the global marine meteorological observations dataset (UK Met Office, 2006) which is comprised of near-global synoptic observations. This data record reports back information about cloud type and other meteorological quantities at the surface. The cloud classification model is built on different cloud-top and cloud optical properties (Level 2 products MOD06/MYD06 from the MODIS sensor) extracted temporally close to the observation time and on a 128km x 128km grid around the synoptic observation location. To make full use of the large quantity of remote sensing data available and to investigate the variety in cloud settings, a convolutional variational auto-encoder (VAE) is applied as a dimensionality reduction tool in a first step. Furthermore, such model architecture allows to account for spatial relationships while describing non-linear patterns in the input data. The cloud classification task is subsequently performed drawing on the constructed latent representation of the VAE. Associating information from underneath and above the cloud enables to build a robust model to classify cloud types. For the training we specify a study domain in the Atlantic ocean around the equator and evaluate the method globally. Further experiments and evaluation are done on simulation data produced by the ICON model.
Publisher: American Geophysical Union (AGU)
Date: 27-12-2011
DOI: 10.1029/2011JD016531
Publisher: American Geophysical Union (AGU)
Date: 06-2018
DOI: 10.1029/2017RG000593
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-7355
Abstract: & & Cloud base height (CBH) is an important geometric parameter of a cloud and shapes its radiative properties. The CBH is also further of practical interest in the aviation community regarding pilot visibility and aircraft icing hazards. While the cloud-top height has been successfully derived from passive imaging radiometers on satellites during recent years, the derivation of the CBH remains a more difficult challenge with these same retrievals.& & & & In our study we combine surface observations and passive satellite remote-sensing retrievals to create a database of CBH labels and cloud properties to ultimately train a machine learning model predicting CBH. The labels come from the global marine meteorological observations dataset (UK Met Office, 2006) which consists of near-global synoptic observations made on sea. This data set provides information about CBH, cloud type, cloud cover and other meteorological surface quantities with CBH being the main interest here. The features based upon which the machine learning model is trained consist in different cloud-top and cloud optical properties (Level 2 products MOD06/MYD06 from the MODIS sensor) extracted on a 127km x 127km grid around the synoptic observation point. To study the large ersity in cloud scenes, an auto-encoder architecture is chosen. The regression task is then carried out in the modelled latent space which is output by the encoder part of the model. To account for the spatial relationships in our input data the model architecture is based on Convolutional Neural Networks. We define a study domain in the Atlantic ocean, around the equator. The combination of information from below and over the cloud could allow us to build a robust model to predict CBH and then extend predictions to regions where surface measurements are not available.& &
Publisher: Copernicus GmbH
Date: 13-07-2012
Abstract: Abstract. In this study, we employ the global aerosol-climate model ECHAM-HAM to globally assess aerosol indirect effects (AIEs) resulting from shipping emissions of aerosols and aerosol precursor gases. We implement shipping emissions of sulphur dioxide (SO2), black carbon (BC) and particulate organic matter (POM) for the year 2000 into the model and quantify the model's sensitivity towards uncertainties associated with the emission parameterisation as well as with the shipping emissions themselves. Sensitivity experiments are designed to investigate (i) the uncertainty in the size distribution of emitted particles, (ii) the uncertainty associated with the total amount of emissions, and (iii) the impact of reducing carbonaceous emissions from ships. We use the results from one sensitivity experiment for a detailed discussion of shipping-induced changes in the global aerosol system as well as the resulting impact on cloud properties. From all sensitivity experiments, we find AIEs from shipping emissions to range from −0.32 ± 0.01 W m−2 to −0.07 ± 0.01 W m−2 (global mean value and inter-annual variability as a standard deviation). The magnitude of the AIEs depends much more on the assumed emission size distribution and subsequent aerosol microphysical interactions than on the magnitude of the emissions themselves. It is important to note that although the strongest estimate of AIEs from shipping emissions in this study is relatively large, still much larger estimates have been reported in the literature before on the basis of modelling studies. We find that omitting just carbonaceous particle emissions from ships favours new particle formation in the boundary layer. These newly formed particles contribute just about as much to the CCN budget as the carbonaceous particles would, leaving the globally averaged AIEs nearly unaltered compared to a simulation including carbonaceous particle emissions from ships.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-8463
Abstract: & & The generation of high quality research data is expensive. The FAIR principles were established to foster the reuse of such data for the benefit of the scientific community and beyond. Publishing research data with metadata and DataCite DOIs in public repositories makes them findable and accessible (FA of FAIR). However, DOIs and basic metadata do not guarantee the data are actually reusable without discipline-specific knowledge: if data are saved in proprietary or undocumented file formats, if detailed discipline-specific metadata are missing and if quality information on the data and metadata are not provided. In this contribution, we present ongoing work in the AtMoDat project, -a consortium of atmospheric scientists and infrastructure providers, which aims on improving the reusability of atmospheric model data.& br& & & & br& Consistent standards are necessary to simplify the reuse of research data. Although standardization of file structure and metadata is well established for some subdomains of the earth system modeling community & #8211 e.g. CMIP & #8211 , several other subdomains are lacking such standardization. Hence, scientists from the Universities of Hamburg and Leipzig and infrastructure operators cooperate in the AtMoDat project in order to advance standardization for model output files in specific subdomains of the atmospheric modeling community. Starting from the demanding CMIP6 standard, the aim is to establish an easy-to-use standard that is at least compliant with the Climate and Forecast (CF) conventions. In parallel, an existing netCDF file convention checker is extended to check for the new standards. This enhanced checker is designed to support the creation of compliant files and thus lower the hurdle for data producers to comply with the new standard. The transfer of this approach to further sub-disciplines of the earth system modeling community will be supported by a best-practice guide and other documentation. A showcase of a standard for the urban atmospheric modeling community will be presented in this session. The standard is based on CF Conventions and adapts several global attributes and controlled vocabularies from the well-established CMIP6 standard.& br& & & & br& Additionally, the AtMoDat project aims on introducing a generic quality indicator into the DataCite metadata schema to foster further reuse of data. This quality indicator should require a discipline-specific implementation of a quality standard linked to the indicator. We will present the concept of the generic quality indicator in general and in the context of urban atmospheric modeling data.& & &
Publisher: Meteorological Society of Japan
Date: 2020
Publisher: Stockholm University Press
Date: 2014
Publisher: Copernicus GmbH
Date: 16-07-2020
DOI: 10.5194/ESSD-12-1649-2020
Abstract: Abstract. Radiative forcing provides an important basis for understanding and predicting global climate changes, but its quantification has historically been done independently for different forcing agents, has involved observations to varying degrees, and studies have not always included a detailed analysis of uncertainties. The Copernicus Atmosphere Monitoring Service reanalysis is an optimal combination of modelling and observations of atmospheric composition. It provides a unique opportunity to rely on observations to quantify the monthly and spatially resolved global distributions of radiative forcing consistently for six of the largest forcing agents: carbon dioxide, methane, tropospheric ozone, stratospheric ozone, aerosol–radiation interactions, and aerosol–cloud interactions. These radiative-forcing estimates account for adjustments in stratospheric temperatures but do not account for rapid adjustments in the troposphere. On a global average and over the period 2003–2017, stratospherically adjusted radiative forcing of carbon dioxide has averaged +1.89 W m−2 (5 %–95 % confidence interval: 1.50 to 2.29 W m−2) relative to 1750 and increased at a rate of 18 % per decade. The corresponding values for methane are +0.46 (0.36 to 0.56) W m−2 and 4 % per decade but with a clear acceleration since 2007. Ozone radiative-forcing averages +0.32 (0 to 0.64) W m−2, almost entirely contributed by tropospheric ozone since stratospheric ozone radiative forcing is only +0.003 W m−2. Aerosol radiative-forcing averages −1.25 (−1.98 to −0.52) W m−2, with aerosol–radiation interactions contributing −0.56 W m−2 and aerosol–cloud interactions contributing −0.69 W m−2 to the global average. Both have been relatively stable since 2003. Taking the six forcing agents together, there is no indication of a sustained slowdown or acceleration in the rate of increase in anthropogenic radiative forcing over the period. These ongoing radiative-forcing estimates will monitor the impact on the Earth's energy budget of the dramatic emission reductions towards net-zero that are needed to limit surface temperature warming to the Paris Agreement temperature targets. Indeed, such impacts should be clearly manifested in radiative forcing before being clear in the temperature record. In addition, this radiative-forcing dataset can provide the input distributions needed by researchers involved in monitoring of climate change, detection and attribution, interannual to decadal prediction, and integrated assessment modelling. The data generated by this work are available at 0.24380/ads.1hj3y896 (Bellouin et al., 2020b).
Publisher: Copernicus GmbH
Date: 16-02-2011
Abstract: Abstract. We present a method for deriving the radiative effects of absorbing aerosols in cloudy scenes from satellite retrievals only. We use data of 2005–2007 from various passive sensors aboard satellites of the "A-Train" constellation. The study area is restricted to the tropical- and subtropical Atlantic Ocean. To identify the dependence of the local planetary albedo in cloudy scenes on cloud liquid water path and aerosol optical depth (AOD), we perform a multiple linear regression. The OMI UV-Aerosolindex serves as an indicator for absorbing-aerosol presence. In our method, the aerosol influences the local planetary albedo through direct- (scattering and absorption) and indirect (Twomey) aerosol effects. We find an increase of the local planetary albedo (LPA) with increasing AOD of mostly scattering aerosol and a decrease of the LPA with increasing AOD of mostly absorbing aerosol. These results allow us to derive the direct aerosol effect of absorbing aerosols in cloudy scenes, with the effect of cloudy-scene aerosol absorption in the tropical- and subtropical Atlantic contributing (+21.2 ± 11.1)×10−3 Wm−2 to the global top of the atmosphere radiative forcing.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Johannes Quaas.