The dataset contains Supplementary Materials for the article ''Catastrophic PM2.5 emissions from Siberian forest fires: impacting factors analysis'' in the Environmental Pollution journal. There are files with PM2.5 emissions from forest fires in Russia 2004-2021 and SARIMAX modelling data for impacting factors analysis.

Supplementary Figures:
- Figure 1. Total wildfires PM2.5 emissions from Russian forests (yellow colour) with the average value for 2004-2021 (grey line) and emissions trend (orange dotted line);

- Figure 2. PM2.5 emissions from wildfires in different fire protection zones during 2004-2021: ground zone (green colour), aviation zone (indigo colour) and control zone (beige colour). A) total PM2.5 emissions, Mt; B) average monthly PM2.5 emissions, kg/ha; C) average annual PM2.5 emissions, kg/ha.

- Figure 3. The location of the seven federal subjects with the highest PM2.5 emissions in Russia (schematic map);

- Figure 4. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Amur Region;

- Figure 5. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in the Buryatia Republic;

- Figure 6. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Irkutsk Region;

- Figure 7. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Khabarovsk Territory;

- Figure 8. Predictive model (SARIMAX) and satellite (CAMS) data on PM2.5 emissions in Transbaikal Territory.

We share Copernicus Atmosphere Monytoring Service PM2.5 emissions maps (GeoTIFF, EPSG:4326, 0.1 degrees). Coverage: 27.9493818283081055,42.9493612670349520 : 190.0498617200859712,78.0494651794433594.

To determine emissions from the territory of Russia, we provide shapefiles with state (EPSG:4326. Coverage: -180.0000000000000000,41.1888656599999976 : 180.00000000000000000,81.8562469499999992) and Federal subjects borders (ESRI:102025. Coverage: -4073239.7565327030606568,1966601.6932600045111030 : 3971631.5190406017936766,6412842.0674155252054334).

Also, there are initial dataset for analysis (Initital data_SARIMAX archive) and SARIMAX model settings (doc.).

This dataset provides monthly Sea Surface Salinity (SSS) data derived as part of the European Space Agency (ESA) Climate Change Initiative (CCI) programme. In this product the data has been produced at a spatial resolution of 25km and a time resolution of 1 month. This has then been spatially resampled on a 25km EASE (Equal Area Scalable Earth) grid and 15 days of time sampling. A monthly product is also available.

This first version of the CCI+SSS products is a preliminary version issued for evaluation purposes by voluntary scientists and for framing future CCI+SSS products. This product has not been fully validated yet and may contain flaws. In case you discover some, the CCI salinity team (Mngt_CCI-Salinity@argans.co.uk) are very keen to get your feedback. In case you would like to use them in a presentation or publication, please be aware of the following caveats:

CAVEATS

• The SSS random error in the weekly product is overestimated by a factor ~1.4.
• The Number of outliers is wrongly set to 'NaN' in the case where it is equal to zero.
• Products have not yet been not optimised for some issues encountered at high latitudes (i.e. remaining ice, RFI pollution, biases due to land-sea contamination and dielectric constant in cold waters).
• The criteria for flagging data close to land (including islands) are conservative and likely to be too restrictive in places.
• There is a systematic global underestimation (-0.08) of SSS starting at the beginning of the data set, and gradually disappearing at the end of 2010.
• There is a seasonal varying bias (~0.1, peaking in the middle of the year) in the Pacific North of 25°N.

Acknowledgements: The authors thank the CCI+ SSS validation team, in particular S. Guimbard (ODL) and A. Martin (NOC), for their feedback on the products, R. Catany (ARGANS) for managing the project and P. Cipollini and C. Donlon (ESA) for their sound advice.

This contains gridded non-methane volatile organic compound (NMVOC) emission inventories for India derived as part of burning studies performed during the APHH-INDIA campaign. For data files with more than 1 million rows, NASA AMES metadata headers have been provided as a separate document, which has the identical name of the data it applies to but also includes _metadata.

For years 1993, 1994, 1999, 2002, 2005, 2006, 2007, 2010, 2011 and 2016 inventories have been produced in terms of total NMVOC emission from each source sector (kg/km2). There are also two upper limit scenarios of emissions from cow dung cake combustion based on data from PPAC and PPAC supplemented with additional cow dung cake consumption for states now covered by this survey. The speciation factors of NMVOCs released from particular sources are also provided so that these years can be speciated by source simply by multiplying the total emission from each source by the ratio of species released from the source. This allows future users to produce speciated emission inventories for years other than 2011 if they need.

Gridded inventories are also provided for emissions of 21 polycyclic aromatic hydrocarbons for the year 2011 from fuelwood, cow dung cake, charcoal, liquefied petroleum gas and municipal solid waste. These are provided as total PAH emissions from a source with speciation factors also provided to allow speciation should it be required by multiplying the total NMVOC emission from a source by the speciation factors from that source.

Gridded inventories are provided for crop residue burning at 1km2 and 10km2. These were calculated with total agricultural area identified in a state from either NASA MODIS (1 km2) or Ramankutty et al. (2008) (10 km2). A second inventory was produced at 10km2 as it was felt that the NASA data offered little variation within respective states. These have been split into total emissions from each of the 5 emission factors applied, RiceEFyearlyVOCKG (for rice), WheatEFyearlyVOCKG (for wheat, coarse cereal and maize), JowarEFyearlyVOCKG (for Jowar and Bajra), MeanEFyearlyVOCKG (for 9 oilseeds, groundnut, rapeseed, mustard, sunflower, cotton, jute and mesta) and SugarcaneEFyearlyVOCKG (for sugarcane).

The inventories were produced using emission factors developed as part of the APHH-INDIA project as well as from a different publication focussed on the burning of crops. The inventories have been developed in the following manner. The emission factors used in this study come from a variety of recently published sources. All emission factors applied in this study included measurement by PTR-ToF-MS, a technique well suited to species released in significant quantities from solid fuel combustion such as small oxygenated species, phenolics and furanics. These species are often missed by GC measurement alone. Preference has been given to emission factors from studies which: (1) have many measurements (n), (2) use samples collected from India or (3) use samples collected from similar countries. Fully speciated emission factors are available from the references given. For residential fuel combustion, the emission factors measured by Stewart et al. (2021a) were used and were developed from 76 combustion experiments of fuel wood, cow dung cake, LPG and MSW samples collected from around Delhi. This study was extremely detailed and measured online, gas-phase, speciated NMVOC emission factors for up to 192 chemical species using dual-channel gas chromatography with flame ionisation detection (DC-GC-FID, n = 51), two-dimensional gas chromatography (GC×GC-FID, n = 74), proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS, n = 75) and solid-phase extraction two-dimensional gas chromatography with time-of-flight mass spectrometry (SPE-GC×GC-ToF-MS, n = 28). Comparison of these emission factors to those obtained in similar studies is provided in Stewart et al. (2021a). The emission factors used as part of this study are larger than those measured by Stockwell et al. (2016), Fleming et al. (2018) and several other studies which were based on gas chromatography techniques alone. The emission factors here measure many more NMVOC species, use techniques which target a range of species which more traditional GC analyses do not detect and make online measurements which minimise loss of intermediate-volatility and semi-volatile organic species, which may be lost through the collection of whole air samples, but have been shown to represent a large proportion of total emissions from biomass burning (Stockwell et al., 2015).

Emission factors for combustion of crop residues on fields were taken from measurements by Stockwell et al. (2015) made using PTR-ToF-MS of 115 NMVOCs (Stockwell et al., 2015) for wheat straw (n = 6), sugarcane (n=2), rice straw (n=7) and millet (n=2). This study also included the mean c... For full abstract see: https://catalogue.ceda.ac.uk/uuid/fdb8960260a64c5faf652f8f47c4df81.

This Infrared Atmospheric Sounding Interferometer (IASI) methane dataset contains height-resolved and column-averaged volume mixing ratios of atmospheric methane (CH4). It also includes column-averaged water vapour (H2O), a scale factor for the HDO (water vapour isotopologue) volume mixing ratio profile, surface temperature, effective cloud fraction, effective cloud-top pressure and scale factors for two systematic residual spectra which are jointly retrieved from the spectral range 1232.25-1290.00 cm-1 by the Rutherford Appleton Laboratory (RAL) IASI optimal estimation methane retrieval scheme. The dataset additionally contains selected a priori values and uncertainties adopted in the optimal estimation scheme and retrieval output diagnostics such as the retrieval cost and the averaging kernels.

This work was funded by the National Centre for Earth Observation (NCEO) under the UK Natural Environment Research Council (NERC) with additional funding from EUMETSAT.

Data were produced by the United Kingdom Research and Innnovation (UKRI) Science and Technology Facilities Council (STFC) Remote Sensing Group (RSG) at the Rutherford Appleton Laboratory (RAL).

This is version 2.0 of the dataset.

This dataset contains Organic aerosols, NO3-, SO4=, Cl- and NH4+ submicron concentrations in µg m-3 measured with High Resolution Time of Flight Aerosol Mass Spectrometer (HR-ToF-AMS) and Organic aerosol factors (Cooking Organic Aerosol (COA), Nitrogen-rich Hydrocarbon-like Organic Aerosol (NHOA), Solid-Fuel Organic Aerosol (SFOA), Hydrocarbon-like Organic Aerosol (HOA), Semi-Volatility Biomass Burning Organic Aerosol (SVBBOA), Low-Volatility Oxygenated Organic Aerosol (LVOOA), Semi-Volatility Oxygenated Organic Aerosol (SVOOA)) identified using positive matrix factorization. The instrument was located at the Indira Gandhi Delhi Technical University for Women (IGDTUW) from May to Nov 2018. The instrument sampled initially at 7 m above ground level, then was moved to 35 m above ground on the 5th of November 2018.

The data were collected as part of the DelhiFlux project under the Air Pollution & Human Health in a Developing Indian Megacity (APHH-India) programme, and the UKCEH’s SUNRISE programme delivering National Capability to NERC.

This dataset contains hourly online measurements of VOC mixing ratios using Gas Chromatography with Flame Ionisation Detector (GC-FID) at Indira Gandhi Delhi Technical University for Women (IGDTUW), Dehli, India. Mixing ratios are reported in parts per billion by volume (ppbV). The stationary inlet was located on the roof of a single-story building. This data was collected over two measurements periods (28/05/2018 - 05/06/2018 and 05/10/2018 - 27/10/2018), for the APHH-India DelhiFlux project, by the University of York. Data analysis was completed by Beth Nelson and Jim Hopkins at the University of York.

Mixing ratios for the following species are included: ethane, ethene, propane, propane, iso-butane, n-butane, acetylene, trans-2-butene, 1-butene, iso-butene*, cis-2-butene, cyclopentane*, iso-pentane, n-pentane, 1,3-butadiene, trans-2-pentene, 1-pentene, n-octane, n-hexane, isoprene, n-heptane, benzene, toluene, ethylbenzene, combined m,p-xylene, o-xylene, methanol, acetone, ethanol, 1,2-butadiene*, propyne*.

Date and time given in Local time as Julian day where 2018 01 01 = 0

Calibrations have been performed using a certified NPL 30 component mixture, and certified NPL 6 component mixture for o-VOC calibration. NOTE: any compound not contained therein has been assumed to have the same response factor as its closest isomer*.

The data were collected as part of the DelhiFlux project part of Air Pollution & Human Health in a Developing Indian Megacity (APHH-India) programme.

Data for Figure 3.14 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 3.14 shows wet and dry region tropical mean (30°S-30°N) annual precipitation anomalies.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.

Figure subpanels

The figure has four panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c and panel_d.

List of data provided

The dataset contains timeseries (1988-2020) of annual precipitation anomalies from

• observation (GPCP)
• reanalysis (ERA5)
• multi-model mean (CMIP6)

GPCP is the Global Precipitation Climatology Project. ERA5 is the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis of the global climate. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project.

Data provided in relation to figure

• panel_a/AR6_WG1_Chap3_Figure3_14_panel_a_wetdry.csv (timeseries for wet regions)
• panel_b/AR6_WG1_Chap3_Figure3_14_panel_b_wetdry.csv (timeseries for dry regions)
• panel_c/AR6_WG1_Chap3_Figure3_14_panel_c_wetdry.csv (scaling factors for wet regions)
• panel_d/AR6_WG1_Chap3_Figure3_14_panel_d_wetdry.csv (scaling factors for dry regions) Details on data provided in relation to each figure panel and its elements in the metadata associated to the corresponding files.

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

This dataset contains scan data from the National Centre for Atmospheric Science Atmospheric Measuring Facility's mobile X-band radar between 1st November 2016 to 4th June 2018 at Chilbolton Facility for Atmospheric and Radio Research (CFARR), UK, as part of ongoing long-term observations made by the NERC National Centre for Atmospheric Science (NCAS). The radar transmits pulses of electromagnetic radiation and measures the amount of energy backscattered to the receiver from which the location and intensity of precipitation, radial winds and polarisation parameters can be calculated.

Parameters available in these data files include: dBZ - equivalent reflectivity factor; V - radial velocity; W - spectral width; ZDR - differential reflectivity; KDP - specific differential phase shift; PhiDP - differential phase shift; RhoHV - co-polar cross correlation coefficient; SQI - signal quality index or normalized_coherent_power. A complete list of all available parameters is available on the CEDA data catalogue record for this dataset.

The sur files contain a volume of scans at different elevation angles between 0 and 90 degrees, approximately every 5-6 minutes. The rhi files contain a single cross-section scan at a given azimuth and an elevation range of 0 to 180 degrees, every 5-6 minutes.

The data are available as netCDF files to all registered CEDA users under the Open Government License.

Study of intercontinental transport of air pollutants by means of coordinated flights over the East coast of North America, the Azores and the West coast of Europe. ITOP was a component of the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT), an international initiative which coordinated the efforts of various American and European groups who developed plans for field campaigns in the summer of 2004, with the aim of improving our understanding of the factors determining air quality over the two continents and over remote regions of the North Atlantic.

This dataset contains TOMCAT atmospheric CO, NOX and O3 trajectory model output. Plot images are available for NOX, CO and O3.

Data from Laser Precipitation Monitor Model 5.4110.00.000 to measure the frequency, speed and other factors for Solid and Liquid Precipitation at Manchester Air Quality Site (MAQS), 2019 onwards.

Data for Cross-Chapter Box 3.1, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Cross-Chapter Box 3.1, Figure 1 shows 15-year trends of surface global warming for 1998-2012 and 2012-2026.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.

Figure subpanels

The figure has four panels, with data provided for panels a and b in a subdirectory named panel_ab, and for panels c and d in subdirectories named panel_c and panel_d respectively.

List of data provided

This dataset contains:

• Observed and modelled global annual mean surface temperature and surface air temperature trends for 1998-2012
• Modelled global annual mean surface air temperature trends for 2012-2026
• Observed annual mean surface temperature trends for 1998-2012
• Composite of modelled annual mean surface air temperature trends for 1998-2012

Data provided in relation to figure

Panel a: - gmst_trend_1998-2012 in panel_ab/GMST_trend.csv; HadCRUT5 for histogram, ensemble mean of HadCRUT5 and other observations for open triangles at the top, and multimodel ensemble means of CMIP5 and CMIP6 for open diamonds at the top - gsat_trend_1998-2012 in panel_ab/GSAT_trend.csv; CMIP5 and CMIP6 ensembles for histograms, ERA5 for the top filled triangle, and multimodel ensemble means of CMIP5 and CMIP6 for filled diamonds at the top

Panel b: - gmst_trend_2012-2026 in panel_ab/GMST_trend.csv; multimodel ensemble means of CMIP5 and CMIP6 for open diamonds at the top - gsat_trend_2012-2026 in panel_ab/GSAT_trend.csv; CMIP5 and CMIP6 ensembles for histograms, and multimodel ensemble means of CMIP5 and CMIP6 for filled diamonds at the top

Panel c: - tas in panel_c/TrendPattern_HadCRUT5_mean.nc; shading, with the sig attribute for cross markers

Panel d: - tas in panel_d/TrendPattern_composite.nc: shading

CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. HadCRUT5 is a gridded dataset of global historical near-surface air temperature anomalies since the year 1850.

Notes on reproducing the figure from the provided data

Multimodel ensemble means and histograms are calculated after weighting each ensemble member with the inverse of the ensemble size of the same model.

The values for panels c and d are stored with the K/year unit but scaled to the K/decade, therefore they need to be multiplied by a factor of 10 in order to be consistent with the plotted values.

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

This dataset contains includes Organic aerosols, NO3, SO4, NH4 and Cl concentrations in ugm-3 measured with Compact Time of Flight Aerosol Mass Spectromete made during the Post-Monsoon periods of the APHH Delhi campaigns in 2018 at Indira Gandhi Delhi Technical University for Women (IGDTUW) site and India Meteorological Department site. Organic aerosol factors (HOA, LVOOA, BBOA, COA and SVOOA were identified using PMF factorization.

The data were collected as part of the DelhiFlux project part of Air Pollution & Human Health in a Developing Indian Megacity (APHH-India) programme.