the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil and biofuel CO2 emission inventories
Ruben Urraca
Greet Janssens-Maenhout
Nicolás Álamos
Lucas Berna-Peña
Monica Crippa
Sabine Darras
Sitjn Dellaert
Hugo Denier van der Gon
Mark Dowell
Nadine Gobron
Claire Granier
Giacomo Grassi
Marc Guevara
Diego Guizzardi
Kevin Gurney
Nicolás Huneeus
Sekou Keita
Jeoren Kuenen
Ana Lopez-Noreña
Enrique Puliafito
Geoffrey Roest
Simone Rossi
Antonin Soulie
Antoon Visschedijk
Abstract. Gridded bottom-up inventories of CO2 emissions are needed in global CO2 inversion schemes as priors to initialize transport models, and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project.
CoCO2-MOSAIC 1.0 provides gridded (0.1°×0.1°) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015 to 2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (South-East Asia) and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 that is regionally accepted as a reference, and as so we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO2ff and CO2bf emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. Most regional inventories provide large emitters (energy, manufacturing) as point sources improving the agreement with the CoCO2 1.0 global power plant database. All super-emitting pixels from regional inventories contain a power station whereas several super-emitters from global inventories are likely incorrectly geo-located. CoCO2- MOSAIC 1.0 is freely available at zenodo (https://doi.org/10.5281/zenodo.7092358) (Urraca et al., 2023) and at the JRC Data Catalogue.
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Ruben Urraca et al.
Status: open (extended)
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RC1: 'Comment on essd-2023-210', Anonymous Referee #1, 01 Sep 2023
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Useful and important work that certainaly needs to be documented.
Development of datasets where best and most recent regional information can be used and integrated into global products is obviously needed. While initially there might be some trade offs between overall global consistency and level of detail included allowing for, for example, improved spatial features, there is a long term advantage that will lead to identifying and resolving issues or quickly improving quality for the global product. At the same time, such work can feedback to local/regional developers to address some of the issues identified so that further versions will be seamlessly integrated in global work to keep it up to date. Some of the aspects how this work can serve and stimulate the process of continues improvement of both regional and global products could be reflected in the paper and conclusions.
In general, this is a very 'dry' technical text which could benefit from some simplification to highlight key elements of work and challenges when compiling the inventory while leaving some of the detailed descriptions to SI. I have been struggling a little to find key information about the data flow or process in the main text while liked very much the very well written concise information about the inventories in the SI; probably the first time where I enjoyed reading, or going through, SI more than the main paper ;-).
More specific comments:
Line 36-37: I am a bit confused reading the sentence starting with 'Most regional inventories...'. Not sure what are the authors saying; isn't it that typically the local/regional inventories will have more information that global products and so will improve over those? Here it reads as they are compared against the global LPS database that is a benchmark?
Line 67-74: Could authors provide a more explicit information as to what and why information may be lost due to these requirements? The following sentences provide examples but why is it that a more detailed regional information (if available) cannot be included in globally consistent inventories?
Table 2: In the third row thee is '-' for the column of CO2. What does it mean? i'd expect reference to ff, bf...
Line 150: Could authors add a word of rationally why biofuels are included and agr and wildfires are not?
Table 3: Category 4C refers to 'waste incineration' and so i assume since this is under energy sector refers to incineration with energy recovery. Do you cover also emissions (and where) from open burning of municipal or industrial waste?
Table 3 on page 8: Is this really a continuation of Table 3? It seems to have a different structure.
Table 4: See question above on open waste burning, i.e, municipal or industrial waste. Is this covered or not in the 'waste incineration category'...I assume not but then do these inventories account for that?
Section 2.2.3. The Table 3/4 are not easy to plow through and while they contain some detailed information, I was wondering if there is a way to develop and overview table or a map that can show which (global or regional) inventory is used for a given region/sector indicating where gap-fills are done and with which inventory. Stay at a general level to give an overview rather than a lot of details. The detailed tables can be included in the SI and the won space could be possibly used to explain what challenges are still remaining, even after gap-filling so that the national/regional em inventory developers could potentially address them. Current text and information in the tables makes it, in my view, very difficult to get an overview.
Line 175: I realize that maybe the difference will be small but in principle, one probably can allocate the information to respective countries by splitting emissions by area within the pixel belonging to each specific country?
Table 6: Suggest reducing use of 'swd' ene, Res, tro+trn+.... can these be spelled out like in other columns? These three letter codes are also not explained anywhere in the paper; i know several can be easily guessed..but not all
Line 347: The sentence starting with 'Small differences..." Looking at the Fig 3, they seem larger than in totals?
Line 390: China is typically not classified as part of 'South East Asia' but either as part of 'East Asia' or 'North East Asia'
Lie 460 to 477: Some of these statements and discussion is referring to great details and I wonder if this can be simplified highlighting typical (occasionally occurring) features of misplaced, lost information and refer to some specific examples which can be moved to SI.
Conclusions: I am missing here clear statements why this inventory shall be used and is an advancement compared to other work. Further, bring in key identified gaps/problems and suggest either solutions or how further work could resolve them.
Citation: https://doi.org/10.5194/essd-2023-210-RC1
Ruben Urraca et al.
Data sets
CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil and biofuel CO2 emission inventories Ruben Urraca, Greet Janssens-Maenhout, Nicolás Álamos, Lucas Berna-Peña, Monica Crippa, Sabine Darras, Stijn Dellaert, Hugo Denier van der Gon, Mark Dowell, Nadine Gobron, Claire Granier, Giacomo Grassi, Marc Guevara, Diego Guizzardi, Kevin Gurney, Nicolás Huneeus, Sekou Keita, Jeroen Kuenen, Ana Lopez-Noreña, Enrique Puliafito, Geoffrey Roest, Simone Rossi, Antonin Soulie, Antoon Visschedijk https://doi.org/10.5281/zenodo.7092358
Ruben Urraca et al.
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