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- Bock, O., et al.
(författare)
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Use of GNSS Tropospheric Products for Climate Monitoring (Working Group 3)
- 2020
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Ingår i: Advanced GNSS Tropospheric Products for Monitoring Severe Weather Events and Climate. - Cham : Springer International Publishing. - 9783030139001 ; , s. 267-402
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- There has been growing interest in recent years in the use of homogeneously reprocessed ground-based GNSS, VLBI, and DORIS measurements for climate applications. Existing datasets are reviewed and the sensitivity of tropospheric estimates to the processing details is discussed. The uncertainty in the derived IWV estimates and linear trends is around 1 kg m^2 RMS and ± 0.3 kg m^2 per decade, respectively. Standardized methods for ZTD outlier detection and IWV conversion are proposed. The homogeneity of final time series is limited however by changes in the stations equipment and environment. Various homogenization algorithms have been evaluated based on a synthetic benchmark dataset. The uncertainty of trends estimated from the homogenized times series is estimated to ±0.5 kg m^2 per decade. Reprocessed GNSS IWV data are analysed along with satellites data, reanalyses and global and regional climate model simulations. A selection of global and regional reprocessed GNSS datasets and ERA-interim reanalysis are made available through the GOP-TropDB tropospheric database and online service. A new tropo SINEX format, providing new features and simplifications, was developed and it is going to be adopted by all the IAG services.
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| 4. |
- Guerova, G., et al.
(författare)
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National Status Reports
- 2020
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Ingår i: Advanced GNSS Tropospheric Products for Monitoring Severe Weather Events and Climate. - Cham : Springer International Publishing. - 9783030139001 ; , s. 403-481
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this section a summary of the national progress reports is given. GNSS4SWEC Management Committee (MC) members provided outline of the work conducted in their countries combining input from different partners involved. In the COST Action paticipated member from 32 COST countries, 1 Near Neighbour Country and 8 Intrantional Partners from Australia, Canada, Hong Kong and USA. The text reflects the state as of 1 January 2018.
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| 8. |
- Petrescu, Ana Maria Roxana, et al.
(författare)
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The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990-2019
- 2023
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Ingår i: Earth System Science Data. - : COPERNICUS GESELLSCHAFT MBH. - 1866-3508 .- 1866-3516. ; 15:3, s. 1197-1268
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Tidskriftsartikel (refereegranskat)abstract
- Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990-2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015-2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 TgCH(4) yr(-1) (EDGARv6.0, last year 2018) and 18.4 TgCH(4) yr(-1) (GAINS, last year 2015), close to the NGHGI estimates of 17 :5 +/- 2 :1 TgCH(4) yr(-1). TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 TgCH(4) yr(-1). Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 TgCH(4) yr(-1) inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH-HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 TgCH(4) yr(-1). For N2O emissions, over the 2015-2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 TgN(2)Oyr(-1), close to the NGHGI data (0 :8 +/- 55% TgN(2)Oyr(-1)). Over the same period, the mean of TD global and regional inversions was 1.4 TgN(2)Oyr(-1) (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27 C UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced dataset srelated to figures are visualized at https://doi.org/10.5281/zenodo.7553800 (Petrescu et al., 2023).
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- Sathyendranath, Shubha, et al.
(författare)
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An Ocean-Colour Time Series for Use in Climate Studies : The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)
- 2019
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 19:19
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Tidskriftsartikel (refereegranskat)abstract
- Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.
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