78 research outputs found

    Turbulent Diffusion and Turbulent Thermal Diffusion of Aerosols in Stratified Atmospheric Flows

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    The paper analyzes the phenomenon of turbulent thermal diffusion in the Earth atmosphere, its relation to the turbulent diffusion and its potential impact on aerosol distribution. This phenomenon was predicted theoretically more than 10 years ago and detected recently in the laboratory experiments. This effect causes a non-diffusive flux of aerosols in the direction of the heat flux and results in formation of long-living aerosol layers in the vicinity of temperature inversions. We demonstrated that the theory of turbulent thermal diffusion explains the GOMOS aerosol observations near the tropopause (i.e., the observed shape of aerosol vertical profiles with elevated concentrations located almost symmetrically with respect to temperature profile). In combination with the derived expression for the dependence of the turbulent thermal diffusion ratio on the turbulent diffusion, these measurements yield an independent method for determining the coefficient of turbulent diffusion at the tropopause. We evaluated the impact of turbulent thermal diffusion to the lower-troposphere vertical profiles of aerosol concentration by means of numerical dispersion modelling, and found a regular upward forcing of aerosols with coarse particles affected stronger than fine aerosols.Comment: 19 pages, 10 figure

    Harmonized Dataset of Ozone Profiles from Satellite Limb and Occultation Measurements

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    In this paper, we present a HARMonized dataset of OZone profiles (HARMOZ) based on limb and occultation measurements from Envisat (GOMOS, MIPAS and SCIAMACHY), Odin (OSIRIS, SMR) and SCISAT (ACE-FTS) satellite instruments. These measurements provide high-vertical-resolution ozone profiles covering the altitude range from the upper troposphere up to the mesosphere in years 2001-2012. HARMOZ has been created in the framework of the European Space Agency Climate Change Initiative project. The harmonized dataset consists of original retrieved ozone profiles from each instrument, which are screened for invalid data by the instrument teams. While the original ozone profiles are presented in different units and on different vertical grids, the harmonized dataset is given on a common pressure grid in netCDF (network common data form)-4 format. The pressure grid corresponds to vertical sampling of similar to 1 km below 20 km and 2-3 km above 20 km. The vertical range of the ozone profiles is specific for each instrument, thus all information contained in the original data is preserved. Provided altitude and temperature profiles allow the representation of ozone profiles in number density or mixing ratio on a pressure or altitude vertical grid. Geolocation, uncertainty estimates and vertical resolution are provided for each profile. For each instrument, optional parameters, which are related to the data quality, are also included. For convenience of users, tables of biases between each pair of instruments for each month, as well as bias uncertainties, are provided. These tables characterize the data consistency and can be used in various bias and drift analyses, which are needed, for instance, for combining several datasets to obtain a long-term climate dataset. This user-friendly dataset can be interesting and useful for various analyses and applications, such as data merging, data validation, assimilation and scientific research. The dataset is available at http://www.esa-ozone-cci.org/?q=node/161 or at doi: 10.5270/esa-ozone_cci-limb_occultation_profiles-2001_2012-v_1-201308

    Relative drifts and biases between six ozone limb satellite measurements from the last decade

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    As part of European Space Agency’s (ESA) climate change initiative, high vertical resolution ozone profiles from three instruments all aboard ESA’s Envisat (GOMOS, MIPAS, SCIAMACHY) and ESA’s third party missions (OSIRIS, SMR, ACE-FTS) are to be combined in order to create an essential climate variable data record for the last decade. A prerequisite before combining data is the examination of differences and drifts between the data sets. In this paper, we present a detailed analysis of ozone profile differences based on pairwise collocated measurements, including the evolution of the differences with time. Such a diagnosis is helpful to identify strengths and weaknesses of each data set that may vary in time and introduce uncertainties in long-term trend estimates. The analysis reveals that the relative drift between the sensors is not statistically significant for most pairs of instruments. The relative drift values can be used to estimate the added uncertainty in physical trends. The added drift uncertainty is estimated at about 3% decade−1^{-1} (1σ). Larger differences and variability in the differences are found in the lowermost stratosphere (below 20 km) and in the mesosphere

    Relative Drifts and Biases Between Six Ozone Limb Satellite Measurements From the Last Decade

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    As part of European Space Agency\u27s (ESA) climate change initiative, high vertical resolution ozone profiles from three instruments all aboard ESA\u27s Envisat (GOMOS, MIPAS, SCIAMACHY) and ESA\u27s third party missions (OSIRIS, SMR, ACE-FTS) are to be combined in order to create an essential climate variable data record for the last decade. A prerequisite before combining data is the examination of differences and drifts between the data sets. In this paper, we present a detailed analysis of ozone profile differences based on pairwise collocated measurements, including the evolution of the differences with time. Such a diagnosis is helpful to identify strengths and weaknesses of each data set that may vary in time and introduce uncertainties in long-term trend estimates. The analysis reveals that the relative drift between the sensors is not statistically significant for most pairs of instruments. The relative drift values can be used to estimate the added uncertainty in physical trends. The added drift uncertainty is estimated at about 3% decade-1 (1σ). Larger differences and variability in the differences are found in the lowermost stratosphere (below 20 km) and in the mesosphere

    Evidence for a continuous decline in lower stratospheric ozone offsetting ozone layer recovery

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    Ozone forms in the Earth's atmosphere from the photodissociation of molecular oxygen, primarily in the tropical stratosphere. It is then transported to the extratropics by the Brewer–Dobson circulation (BDC), forming a protective "ozone layer" around the globe. Human emissions of halogen-containing ozone-depleting substances (hODSs) led to a decline in stratospheric ozone until they were banned by the Montreal Protocol, and since 1998 ozone in the upper stratosphere is rising again, likely the recovery from halogen-induced losses. Total column measurements of ozone between the Earth's surface and the top of the atmosphere indicate that the ozone layer has stopped declining across the globe, but no clear increase has been observed at latitudes between 60° S and 60° N outside the polar regions (60–90°). Here we report evidence from multiple satellite measurements that ozone in the lower stratosphere between 60° S and 60° N has indeed continued to decline since 1998. We find that, even though upper stratospheric ozone is recovering, the continuing downward trend in the lower stratosphere prevails, resulting in a downward trend in stratospheric column ozone between 60° S and 60° N. We find that total column ozone between 60° S and 60° N appears not to have decreased only because of increases in tropospheric column ozone that compensate for the stratospheric decreases. The reasons for the continued reduction of lower stratospheric ozone are not clear; models do not reproduce these trends, and thus the causes now urgently need to be established

    Uncertainty information in climate data records from Earth observation

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    Climate data records (CDRs) derived from Earth observation (EO) should include rigorous uncertainty information, to support application of the data in policy, climate modelling and numerical weather prediction reanalysis. Uncertainty, error and quality are distinct concepts, and CDR products should follow international norms for presenting quantified uncertainty. Ideally, uncertainty should be quantified per datum in a CDR, and the uncertainty estimates should be able to discriminate more and less certain data with confidence. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence held in the uncertainty estimate provided, or indicators of conditions violating retrieval assumptions). Errors have many sources and some are correlated across a wide range of time and space scales. Error effects that contribute negligibly to the total uncertainty in a single satellite measurement can be the dominant sources of uncertainty in a CDR on large space and long time scales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. Characterisation of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation, where possible. These principles are quite general, but the form of uncertainty information appropriate to different essential climate variables (ECVs) is highly variable, as confirmed by a quick review of the different approaches to uncertainty taken across different ECVs in the European Space Agency’s Climate Change Initiative. User requirements for uncertainty information can conflict with each other, and again a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight recommendations for good practice in providing and communicating uncertainty in EO-based climate data records

    A global climatology of the mesospheric sodium layerfrom GOMOS data during the 2002-2008 period

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    This paper presents a climatology of the mesospheric sodium layer built from the processing of 7 years of GOMOS data. With respect to preliminary results already published for the year 2003, a more careful analysis was applied to the averaging of occultations inside the climatological bins (10° in latitude-1 month). Also, the slant path absorption lines of the Na doublet around 589 nm shows evidence of partial saturation that was responsible for an underestimation of the Na concentration in our previous results. The sodium climatology has been validated with respect to the Fort Collins lidar measurements and, to a lesser extent, to the OSIRIS 2003–2004 data. Despite the important natural sodium variability, we have shown that the Na vertical column has a marked semi-annual oscillation at low latitudes that merges into an annual oscillation in the polar regions, a spatial distribution pattern that was unreported so far. The sodium layer seems to be clearly influenced by the mesospheric global circulation and the altitude of the layer shows clear signs of subsidence during polar winter. The climatology has been parameterized by time-latitude robust fits to allow for easy use. Taking into account the non-linearity of the transmittance due to partial saturation, an experimental approach is proposed to derive mesospheric temperatures from limb remote sounding measurements

    Merged SAGE II, Ozone_cci and OMPS ozone profile dataset and evaluation of ozone trends in the stratosphere

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    In this paper, we present a merged dataset of ozone profiles from several satellite instruments: SAGE II on ERBS, GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, ACE-FTS on SCISAT, and OMPS on Suomi-NPP. The merged dataset is created in the framework of the European Space Agency Climate Change Initiative (Ozone_cci) with the aim of analyzing stratospheric ozone trends. For the merged dataset, we used the latest versions of the original ozone datasets. The datasets from the individual instruments have been extensively validated and intercompared; only those datasets which are in good agreement, and do not exhibit significant drifts with respect to collocated ground-based observations and with respect to each other, are used for merging. The long-term SAGE–CCI–OMPS dataset is created by computation and merging of deseasonalized anomalies from individual instruments. The merged SAGE–CCI–OMPS dataset consists of deseasonalized anomalies of ozone in 10° latitude bands from 90° S to 90° N and from 10 to 50 km in steps of 1 km covering the period from October 1984 to July 2016. This newly created dataset is used for evaluating ozone trends in the stratosphere through multiple linear regression. Negative ozone trends in the upper stratosphere are observed before 1997 and positive trends are found after 1997. The upper stratospheric trends are statistically significant at midlatitudes and indicate ozone recovery, as expected from the decrease of stratospheric halogens that started in the middle of the 1990s and stratospheric cooling

    Chapter 4: The LOTUS regression model

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    One of the primary motivations of the LOTUS effort is to attempt to reconcile the discrepancies in ozone trend results from the wealth of literature on the subject. Doing so requires investigating the various methodologies employed to derive long-term trends in ozone as well as to examine the large array of possible variables that feed into those methodologies and analyse their impacts on potential trend results. Given the limited amount of time, the LOTUS group focused on the most common methodology of multiple linear regression and performed a number of sensitivity tests with the goal of trying to establish best practices and come to a consensus on a single regression model to use for this study. This chapter discusses the details and results of the sensitivity tests before describing the components of the final single model that was chosen and the reasons for that choice
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