Global Emissions Timeseries

This manuscript (permalink) was automatically generated from openclimatedata/global-emissions@a801c5d on October 8, 2019.

Authors

Unreviewed, work-in-progress …

Abstract

Introduction

Anthropogenic emissions of greenhouse gases and aerosols are the source of global and regional climate change [1]. Climate models use past emission or atmospheric concentration data to test and calibrate their parameters. (TODO Solar and Volcanic forcing) A global emission dataset is especially relevant for simple climate models which are used in assessments of climate policies or as the climate component in integrated assessment models. For historical emissions usually the historical emissions estimates from the Representative Concentrations Pathways [2] have been used. However this dataset is tied to the update cycle of the Coupled Model Intercomparison Project (TODO citation), which led to the last historic estimate ending in 2005. A publicly available, unified and regularly updated dataset of past emissions, agreed-on or widely accepted by the community, does not yet exist. This leads to fragmentation, with modeling groups using different datasets that are often difficult to reproduce.

This hinders comparability between models because deviations can originate both from input data or model differences. Transferability and reproducibility of model results cannot be guaranteed if the input data and the way they are created are not openly available.

We here provide a dataset of global emissions that is created from openly available sources. The dataset and the source code to produce it are version-controlled and published alongside. This makes it easy to update and reproduce the dataset.

Building a global emissions dataset which covers all emissions and forcings which are needed to drive simple climate models requires a “mosaic” approach of combining different data sources [3]. Projects like the Community Emissions Data System (CEDS) for Historical Emissions [4] might allow to obtain all sources from a single source with a unified methodology but they currently don’t cover all required gases.

The inputs used in current multi-gas simple climate models are listed in Table 1 .

Table 1: Gases used in Simple Climate Models and available in this dataset
name description unit
CO2ffi Carbon Dioxide emissions from Fossil Fuels and Industrial Processes GtC
CO2luc Carbon Dioxide emissions from Land-Use and Land-Use Change GtC
CH4 Methane emissions MtCH4
SO2 Sulfur dioxide emissions MtSO2
CO Carbon monoxide emissions MtCO
NMVOC Non-Methane Volatile Organic Compounds Mt
NOx Nitrogen oxide emissions (nitric oxide (NO) and nitrogen dioxide (NO2)) MtN
BC Black Carbon emissions Mt
OC Organic Carbon emissions Mt
NH3 Ammonia emissions MtNH3
HFC134a
HFC23
HFC32
HFC125
HFC143a
HFC227ea
HFC245fa
SF6
CF4
C2F6
CFC12
CFC11
CFC113
CFC114
CFC115
CH3CCl3
CCl4
CH3Cl
CH3Br
Halon1211
Halon1301
Halon2402

Section TODO presents the data sources that were considered and section TODO describes the choice of sources for each gas to build the global dataset.

The dataset is available in CSV format and the formats needed as inputs for simple climate models like MAGICC6[5], Hector [6], or FAIR[7].

Data Sources

Global Carbon Budget

The Global Carbon Budget [8] is an annually updated publication assessing anthropogenic CO₂ emissions and their redistribution in the atmosphere, as well as the ocean and terrestrial biosphere. It contains estimates of CO₂ emissions from fossil fuels and industry, mainly based on data from CDIAC [9] and data submitted to the UNFCCC. CO₂ process emissions from cement production are a subsector of Fossil Fuel and Industrial emissions. In the Global Carbon Budget 2018 cement emissions data from [10] improves on previous estimates by using detailed national statistics and data submitted to the UNFCCC as well as more detailed emissions factors. CO₂ emissions from land use, land-use change, and forestry are included as the net sum of antropogenic activities which can be sources and sinks of emissions. In the Global Carbon Budget 2018 the estimate of land-use emissions is based on the average of two bookkeeping models and dynamic global vegetation models (DGVMs) are used to evaluate the uncertainties and complement the assessment.

PRIMAP-hist

The PRIMAP-hist national historical emissions time series [11] combines several datasets to generate a nationally resolved emissions time series for the Kyoto greenhouse gases. Included are the main greenhouse gases CO₂, CH4, N2O, as well as the fluorinated gases (F-gases): HFCs, PFCs, and SF6, and NF3. The main sources for fossil fuel and industrial related emissions are data reported to the UNFCCC, CDIAC, and Edgar. The different sources are prioritized and missing data is downscaled from regional or global estimates. In that way it improves over global estimates by using nationally reported data where available. Missing data in the future is linearly interpolated using past trends.

The latest availabe version is 2.0 [12].

CEDS

The Community Emissions Data System (CEDS) [3] provides historical emissions data for anthropogenic chemically reactive gases (CO, CH4, NH3, NOx, SO2, NMVOCs), carbonaceous aerosols (black and organic carbon), as well as CO2 from a unified methodology in a software system planned to be released as open-source software in 2018. An extension to other gases like N2O or F-gases by the community has been discussed (TODO doi:10.5194/gmd-2017-43-AC1). The CEDS dataset is used as input data in the Coupled Model Intercomparison Project phase 6 (CMIP6). Emissions from agricultural waste burning are not included but can be found in the Open Biomass Burning Emissions data sources described below. More information on CEDS can be found at the project website http://www.globalchange.umd.edu/ceds/.

EDGAR

http://edgar.jrc.ec.europa.eu/overview.php?v=432&SECURE=123 https://doi.org/10.2904/JRC_DATASET_EDGAR [13]

Open Biomass Burning Emissions

[14]

Overview of Sources and Gas Availability

Source Gases Coverage Years
Global Carbon Budget CO₂ for Fossil Fuel and Industrial and Landuse Global TODO
CO2 Emissions from Cement Production CO₂ (Cement) Global 1900 - 2017
PRIMAP-hist CO2, CH4, N2O, HFCs, PFCs, SF6 Global (Energy, Industrial Processes and Agriculture) 1850 – 2016
CEDS BC, OC, SO2, NOx, NH3, CH4, CO, NMVOC, CO2 Global 1750 – 2014
EDGAR v4.3.2 (Greenhouse Gases) CO₂, CH4, N2O TODO 1970 – 2012
EDGAR v4.3.2 (Global Air Pollutant Emissions) CO, NOx, NMVOC, CH4, NH3, SO2, BC, OC TODO 1970 – 2012
Open Biomass Burning Emissions BC,OC, SO2, N2O, NOx, NH3, CH4, CO, NMVOC Open biomass burning emissions (forests, grasslands, agricultural waste burning on fields, peatlands) 1750 – 2015

Inputs

CO₂

Fossil Fuel Industrial

The main contributor to global emissions are fossil fuel and industrial emissions which are available from multiple sources. They are usually based on energy statistics and cement production data. The longest time series from 1751 to 2017 is available in the Global Carbon Budget 2018 [15], mainly based on data from CDIAC [9] and data submitted to the UNFCCC. PRIMAP-hist [11] in its version 2.0 [12] has data from 1850 to 2016 and is based on the same and other sources as the Global Carbon Budget, but does not include bunker emissions (international aviation and shipping) and, since version 2.0, land-use emissions. The EDGAR v4.3.2 dataset [16] (in review) covers the years 1970 to 2012.

Figure 1 shows the sources together from 1990.

Figure 1: Global Fossil Fuel and Industrial CO₂ Emissions from various datasets
Figure 1: Global Fossil Fuel and Industrial CO₂ Emissions from various datasets

Land-Use

Figure 2: Global Land-Use-Change CO₂ Emissions from various datasets
Figure 2: Global Land-Use-Change CO₂ Emissions from various datasets

Methane (CH₄)

PRIMAP-hist from 1850 to 2015 and RCP data [2] until 2005.

EDGAR http://edgar.jrc.ec.europa.eu/overview.php?v=432&SECURE=123 https://doi.org/10.2904/JRC_DATASET_EDGAR [13]

Figure 3: Global Methane (CH₄) Emissions
Figure 3: Global Methane (CH₄) Emissions

N₂O

PRIMAP-hist from 1850 to 2015 and RCP data [2] until 2005.

Figure 4: Global N₂O Emissions
Figure 4: Global N₂O Emissions

SOx

CEDS [3]

GBBE [14]

Figure 5: Global SO2 Emissions
Figure 5: Global SO2 Emissions

CO

CEDS [3]

GBBE [14]

Figure 6: Global CO Emissions
Figure 6: Global CO Emissions

NMVOC

CEDS [3] GBBE [14]

Figure 7: Global NMVOC Emissions
Figure 7: Global NMVOC Emissions

NOx

CEDS [3]

GBBE [14]

Figure 8: Global NOx Emissions
Figure 8: Global NOx Emissions

BC

CEDS [3]

GBBE [14]

Figure 9: Global BC Emissions
Figure 9: Global BC Emissions

OC

CEDS [3]

GBBE [14]

Figure 10: Global OC Emissions
Figure 10: Global OC Emissions

NH₃

CEDS [3]

GBBE [14]

Figure 11: Global NH3 Emissions
Figure 11: Global NH3 Emissions

Halocarbons

Volcanic Forcing

Volcanic eruptions inject ash and sulphate aerosol precursor gases (mostly SO2) into the atmosphere. When the latter reach the stratosphere they cause radiative forcing by reflecting sunlight back to space and absorbing terrestrial thermal radiation [17,18]. While the complex climate models in CMIP6 take detailed grid-based monthly resolved stratospheric aerosol data as input[19], simple climate models usually directly take a volcanic radiative forcing time series as input. This time series is added to the forcing calculated from emissions or concentrations thus enabling better matching of historical short-term temperature variations. Volcanic radiative forcing timeseries are estimated by taking monthly or annual averages of Stratospheric Aerosal Optical Depths (SAOD) and multiplying with a conversion factor to go from optical depth \(\tau\) to radiative forcing.

MAGICC6 uses monthly forcing data from the NASA GISS model with optical depth to radiative forcing scaling factor of \(−23.5 \tau\).

The RCP[2] mid-year forcing timeseries available at http://www.pik-potsdam.de/~mmalte/rcps/ uses the same data and scaling factor. It further scales by 0.7 to obtain a better fit with historical temperature data and shifts the data by \(0.2 W/m^2\) to have the mean over the historical period, control run period as well as future forcing be equal to zero. This timeseries is used as default volcanic radiative forcing input in the Hector model [6].

The FaIR model uses volcanic aerosol extinction data from input4MIP/CMIP6 and a scaling factor of \(−18\tau\) from HadGEM3. Prior to 1850 the CMIP5 timeseries is scaled and adjusted to match the new post-1850 data[7]. FaIR further includes the volcanic radiative forcing timeseries from Annex II of the Working Group I report of AR5 (Table AII.1.2)[20].

Figure 12 shows these timeseries in comparison.

Figure 12: Volcanic Radiative Forcing Time Series
Figure 12: Volcanic Radiative Forcing Time Series

Figure 13 shows a comparison of volcanic radiative forcing since 1979 until the present with updates from recent studies[21,22] as well as updated data which was used in CMIP5 [17,23] and the series used in FaIR by default.

Figure 13: Volcanic Radiative Forcing Time Series since 1979
Figure 13: Volcanic Radiative Forcing Time Series since 1979

To be able to better match forcing output from complex models in its calibration MAGICC6 uses a volcanic scaling factor with a range between 0 and 1 in its probabilistic calibration[24]. In its emulation of CMIP3 forcings they had a range range from 0.2 to 0.7 with a value around 0.7 for most models[5]. Hector, which uses the pre-scaled RCP data by default introduced a volcanic scaling factor in version 2.2.0 with a default value of 1, allowing for values greater than 1.

While the datasets currently used in SCMs have different end years a general question is how to extend volcanic forcing for future scenarios. Meinshausen et al.[25] proposes three options for constant forcing:

Using the last option and setting the 20th century mean to zero consistency with a long-term eqilibrium of the climate system with the negative radiative forcing is assumed, leading to a quiescent year forcing of around \(0.2 W/m^2\) in the RCP dataset. The timeseries shipping with MAGICC6 has no such adjustment. FaIR defines the period of 1850 to 2014 as zero, leading to slightly higher quiescent year forcing of around \(0.1 W/m^2\)[7].

TODO:

VolMIP [26]

Solar Irradiance

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