• Energy Research

Cet ensemble de données contient toutes les données (au format csv) liées aux chiffres du document soumis par l'ESSD : « The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK : 1990-2017 » Petrescu, A. M. R., Qiu, C., Ciais, P., Thompson, R.L., Peylin, P., McGrath, M. J., Solazzo, E., Janssens-Maenhout, G, Tubiello, F. N., Bergamaschi, P., Brunner, D., Peters, G. P., Höglund-Isaksson, L., Regnier, P., Lauerwald, R., Bastviken, D., Tsuruta, A., Winiwarter, W., Patra, P.K., Kuhnert, M., Orregioni, G. D., Crippa, M., Saunois, M., Perugini, L., Markkanen, T., Aalto, T., Groot Zwaaftink, C.D., Yao, Y., Wilson, C., Conchedda, G., Günther, D., Leip, A., Smith, P., Haussaire, J.-M., Leppänen, A., Manning, A. J., McNorton, J., Brockmann, P., et Dolman, A. J. : La synthèse européenne consolidée des émissions de CH4 et de N2O pour l'UE27 et le Royaume-Uni : 1990-2017, Earth Syst. Sci. Data Discuss., essd-2020-367, in review, 2020. Este conjunto de datos contiene todos los datos (en formato csv) vinculados a las cifras del documento presentado por la ESSD: "The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990-2017" Petrescu, A. M. R., Qiu, C., Ciais, P., Thompson, R.L., Peylin, P., McGrath, M. J., Solazzo, E., Janssens-Maenhout, G, Tubiello, F. N., Bergamaschi, P., Brunner, D., Peters, G. P., Höglund-Isaksson, L., Regnier, P., Lauerwald, R., Bastviken, D., Tsuruta, A., Winiwarter, W., Patra, P. K., Kuhnert, M., Orregioni, G. D., Crippa, M., Saunois, M., Perugini, L., Markkanen, T., Aalto, T., Groot Zwaaftink, C. D., Yao, Y., Wilson, C., Conchedda, G., Günther, D., Leip, A., Smith, P., Haussaire, J.-M., Leppänen, A., Manning, A. J., McNorton, J., Brockmann, P., y Dolman, A. J.: The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990-2017, Earth Syst. Sci. Data Discuss., essd-2020-367, en revisión, 2020. تحتوي مجموعة البيانات هذه على جميع البيانات (بتنسيق CSV) المرتبطة بالأرقام الواردة في ورقة ESSD المقدمة: "التوليف الأوروبي الموحد لانبعاثات CH4 و N2O للاتحاد الأوروبي 27 والمملكة المتحدة: 1990-2017" بتريسكو، أ. م. ر.، تشيو، ج.، Ciais, ص. طومسون، ر .ل، بيلين، ص. ماكغراث، MJ, سولازو، هـ.، Janssens - Maenhout، ز، Tubiello, F. N., Bergamaschi, ص. برونر، د.، بيترز، جي بي، Höglund - Isaksson، ل.، رينييه، ص. لويرفالد، R., باستفيكن، د.، تسوروتا، أ.، Winiwarter، دبليو، باترا، بي كيه، Kuhnert, م.، Orregioni, جي دي، كريبا، م.، ساونوا، م.، بيروجيني، ل.، Markkanen, T., آلتو، T., جروت زوافتينك، سي. دي.، ياو، Y., ويلسون، ج.، كونشيددا، G., غونتر، د.، ليب، أ.، سميث، ص. هاوسير، ج. م.، Leppänen, أ.، مانينغ، ايه جيه، ماكنورتون، J., بروكمان، ص. ودولمان، إيه جيه: التوليف الأوروبي الموحد لانبعاثات الميثان وأكسيد النيتروز للاتحاد الأوروبي 27 والمملكة المتحدة: 1990-2017، نظام الأرض. Sci. مناقشة البيانات، essd -2020-367، قيد المراجعة، 2020. This dataset contains all data (in csv format) linked to the figures from the ESSD submitted paper: "The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990-2017" Petrescu, A. M. R., Qiu, C., Ciais, P., Thompson, R.L., Peylin, P., McGrath, M. J., Solazzo, E., Janssens-Maenhout, G, Tubiello, F. N., Bergamaschi, P., Brunner, D., Peters, G. P., Höglund-Isaksson, L., Regnier, P., Lauerwald, R., Bastviken, D., Tsuruta, A., Winiwarter, W., Patra, P. K., Kuhnert, M., Orregioni, G. D., Crippa, M., Saunois, M., Perugini, L., Markkanen, T., Aalto, T., Groot Zwaaftink, C. D., Yao, Y., Wilson, C., Conchedda, G., Günther, D., Leip, A., Smith, P., Haussaire, J.-M., Leppänen, A., Manning, A. J., McNorton, J., Brockmann, P., and Dolman, A. J.: The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990-2017, Earth Syst. Sci. Data Discuss., essd-2020-367, in review, 2020.

Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model specific uncertainties when reported. In comparing NGHGI with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions and geological sources together account for the gap between NGHGI and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).

This contains driving and output data used by ConFire in the State of Wildfire’s 2023/24 report. All NetCDF files are on regular, 0.5-degree grids on a monthly timestep over the three regions used and defined in the report. Driving Data The “Driving_data” directory contains data used to run the ConFire model and produce analyses. This directory is divided into three focal regions, with NW_Amazon corresponding to the report's “Western Amazonia”. Each region contains the following files: raw_burnt_area.nc: The original 0.25-degree burnt area dataset before being regridded for use in ConFire. nrt: Near Real Time (NRT) driving data used for driver identification. isimip3a: ISIMIP3a data used for attribution. isimip3b: ISIMIP3b GCM bias-corrected data used for future projections. NRT Within the nrt directory, data is organized by periods, with the numbers corresponding to the year range. The report utilizes the period_2013_2023 directory, which contains the NetCDF files in the table below. Filename ending with the following show: 12Annual – 12 month running mean 12monthMax – 12 month running maximum Deficity – current month over 12 month running mean Quarter – 3 month running mean Not all were used in the final analysis. For full data info, see Table 3 of the report https://doi.org/10.5194/essd-2024-218: NetCDF File Variable Used/Not Used Source Notes burnt_area.nc Burnt Area As training data cropland.nc Cropland Used HYDE Klein Goldewijk et al., 2011 d2m.nc 2m Dewpoint Temperature Used ERA5-Land Muñoz-Sabater et al. 2021 DeadFuelFoilage-cvh_C.nc Dead Foliage Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelFoilage-cvl_C.nc Dead Foliage Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelFoilage.nc Dead Foliage Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelWood-cvh_C.nc Dead Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelWood-cvl_C.nc Dead Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a DeadFuelWood.nc Dead Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-12Annual.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-12monthMax.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-Deficity.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage.nc Dead Foliage Fuel Moisture Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Foilage-Quater.nc Dead Foliage Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood-12Annual.nc Dead Wood Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood-12monthMax.nc Dead Wood Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood-Deficity.nc Dead Wood Fuel Moisture Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Dead-Wood.nc Dead Wood Fuel Moisture Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-12Annual.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-12monthMax.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-Deficity.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live.nc Live Fuel Moisture Content Used Fuel Model McNorton et al. 2024a Fuel-Moisture-Live-Quater.nc Live Fuel Moisture Content Not Used Fuel Model McNorton et al. 2024a grazing_land.nc Grazing Land Not Used lightn.nc Lightning Used LIS/OTD Cecil et al., 2014 LiveFuelFoilage-cvh_C.nc Live Leaf Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelFoilage-cvl_C.nc Live Leaf Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelFoilage.nc Live Leaf Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelWood-cvh_C.nc Live Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelWood-cvl_C.nc Live Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a LiveFuelWood.nc Live Wood Fuel Load Not Used Fuel Model McNorton et al. 2024a pasture.nc Pasture Used HYDE Klein Goldewijk et al., 2011 population_density.nc Population Density Used rangeland.nc Rangeland Not Used rural_population.nc Rural Population Used HYDE Klein Goldewijk et al., 2011 snowCover.nc Snow Cover Used ERA5-Land Muñoz-Sabater et al. 2021 t2m.nc 2m Temperature Used ERA5-Land Muñoz-Sabater et al. 2021 total_irrigated.nc Irrigated Area Not Used tp-12Annual.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 tp-12monthMax.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 tp-Deficity.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 tp.nc Precipitation Used ERA5-Land Muñoz-Sabater et al. 2021 tp-Quater.nc Precipitation Not Used ERA5-Land Muñoz-Sabater et al. 2021 urban_population.nc Urban Population Used HYDE Klein Goldewijk et al., 2011 VOD-12Annual.nc Mean & Max VOD Used Satellite (SMOS) Wigneron et al 2021 VOD-12monthMax.nc Mean & Max VOD Used Satellite (SMOS) Wigneron et al 2021 VOD-Deficity.nc Vegetation Optical Depth (VOD) Not Used Satellite (SMOS) Wigneron et al 2021 VOD.nc Vegetation Optical Depth (VOD) Used Satellite (SMOS) Wigneron et al 2021 VOD-Quater.nc Vegetation Optical Depth (VOD) Not Used Satellite (SMOS) Wigneron et al 2021 ISIMIP3a The isimip3a directory follows the structure: >/>/period_yyyy_yyyy/. >: Can be either: obsclim: Reanalysis targeting observed climate. counterclim: Detrended obsclim approximating climate without climate change. >: Currently contains only GSWP3-W5E5, with more sources to follow in subsequent years. yyyy_yyyy: Corresponds to the year range. For attribution experiments in the report, the following directories are used: Factual: obsclim/GSWP3-W5E5/period_2002_2019/ Counterfactual: counterclim/GSWP3-W5E5/period_2002_2019/ Early Industrial: counterclim/GSWP3-W5E5/period_1901_1920/ Additional details on setting the temporal range for the report can be found here. ISIMIP3b The isimip3b directory structure is similar to ISIMIP3a: >/>/period_yyyy_yyyy/. > includes: historical: Historical GCM output. ssp126 ssp370 ssp585 >: Refers to the General Circulation Model used. yyyy_yyyy: Corresponds to the year range. Both ISIMIP3a and ISIMIP3b contain the same NetCDF files, as follows: netcdf file variable used/not used source Notes consec_dry_mean.nc Max. consecutive dry days used ISIMIP3a/3b Based on precipitation crop_jules-es.nc Cropland used ISIMIP3a/3b Interpolated from annual to monthly debiased_nonetree_cover_jules-es.nc Total vegetation cover not used JULES-ES-ISIMIP VCF using ibicus Non-tree vegetated cover simulated by JULES and bias-corrected debiased_tree_cover_jules-es.nc Tree Cover not used JULES-ES-ISIMIP VCF using ibicus Annual mean tree cover bias-corrected to VCF dry_days.nc No. dry days used ISIMIP3a/3b Fractional number of days with rainfall >/, ssp126/>/, ssp370/>/, ssp585/>/: Uses the ISIMIP3b datasets outlined above, where > is one of each of the five GCMs used in ISIMIP3b. Additional Analysis The analysis in the report also utilizes 95th and 90th percentile burnt area totals. These aren't as neatly organized as the NetCDF files yet, but we’re getting there. They can be found in: figs/ _13-frac_points_0.5->-control_TS/>-control_TS/pc-%%/ points->.csv

AbstractUrban Land Surface Models (ULSMs) simulate energy and water exchanges between the urban surface and atmosphere. However, earlier systematic ULSM comparison projects assessed the energy balance but ignored the water balance, which is coupled to the energy balance. Here, we analyze the water balance representation in 19 ULSMs participating in the Urban‐PLUMBER project using results for 20 sites spread across a range of climates and urban form characteristics. As observations for most water fluxes are unavailable, we examine the water balance closure, flux timing, and magnitude with a score derived from seven indicators expecting better scoring models to capture the latent heat flux more accurately. We find that the water budget is only closed in 57% of the model‐site combinations assuming closure when annual total incoming fluxes (precipitation and irrigation) fluxes are within 3% of the outgoing (all other) fluxes. Results show the timing is better captured than magnitude. No ULSM has passed all water balance indicators for any site. Models passing more indicators do not capture the latent heat flux more accurately refuting our hypothesis. While output reporting inconsistencies may have negatively affected model performance, our results indicate models could be improved by explicitly verifying water balance closure and revising runoff parameterizations. By expanding ULSM evaluation to the water balance and related to latent heat flux performance, we demonstrate the benefits of evaluating processes with direct feedback mechanisms to the processes of interest.