the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Indicators of Global Climate Change 2022: Annual update of large-scale indicators of the state of the climate system and the human influence
Piers Maxwell Forster
Christopher J. Smith
Tristram Walsh
William F. Lamb
Matthew D. Palmer
Karina von Schuckmann
Blair Trewin
Myles Allen
Robbie Andrew
Arlene Birt
Alex Borger
Tim Boyer
Jiddu A. Broersma
Lijing Cheng
Frank Dentener
Pierre Friedlingstein
Nathan Gillett
José M. Gutiérrez
Johannes Gütschow
Mathias Hauser
Bradley Hall
Masayoshi Ishii
Stuart Jenkins
Robin Lamboll
June-Yi Lee
Colin Morice
Christopher Kadow
John Kennedy
Rachel Killick
Vaishali Naik
Glen Peters
Anna Pirani
Julia Pongratz
Aurélien Ribes
Joeri Rogelj
Debbie Rosen
Carl-Friedrich Schleussner
Sonia Seneviratne
Sophie Szopa
Peter Thorne
Robert Rohde
Maisa Rojas Corradi
Dominik Schumacher
Russell Vose
Kirsten Zickfeld
Xuebin Zhang
Valérie Masson-Delmotte
Panmao Zhai
Abstract. Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles.
We base this update on the assessment methods used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report, updating the monitoring datasets and to produce updated estimates for key climate indicators including emissions, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth’s energy imbalance, warming attributed to human activities, the remaining carbon budget and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.7883758, Smith et al., 2023). As they are traceable and consistent with IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel.
The indicators show that human induced warming reached 1.14 [0.9 to 1.4] °C over the 2013–2022 period and 1.26 [1.0 to 1.6] °C in 2022. Human induced warming is increasing at an unprecedented rate of over 0.2 °C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 57 ± 5.6 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there are signs that emission levels are starting to stabilise, and we can hope that a continued series of these annual updates might track a real-world change of direction for the climate over this critical decade.
Piers Maxwell Forster et al.
Status: closed
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RC1: 'Comment on essd-2023-166', Greet Janssens-Maenhout, 09 May 2023
General comment
In line with IPCC’s values to provide unbiased, traceable and transparent information and conform with the FAIR principles for sharing data, this paper documents important indicators for closely monitoring the climate change that can be attributed to human activities. While the IPCC AR (FAR until AR6) were giving similar information at relative large timesteps of 5+ years, the ESSD paper series will now continue to give for these indicators such information on an annual basis. The period covered are 10 years, 2013-2022, while AR6 covered 2010-2019. Given the acceleration in climate change and urgency for stepping up climate actions, this is most appreciated. The dataset is fully available on zenodo, authored by 13 coauthors of the ESSD paper.
However, it would have been useful to just extend the period with 3 yr before and 3 yr after the AR6 period, covering 2007-2022 for two reasons: (i) the average over the 10 yr 2010-2019 can be confronted with averages over a symmetrically extended period, (ii) 2008 saw a dip in emissions due to financial crisis and 2020 due to COVID. In the aftermath of the financial crisis, we saw a stabilising of the emissions, (Cfr. https://www.pbl.nl/en/publications/trends-in-global-co2-emissions-2013-report) but this seemed only a temporarily slow down. How can we be sure about a definite stabilising in the aftermath of COVID? It would be interesting to compare the shocks of 2008 and of 2020 and their impact on the successive years 2008-2010 en 2020-2022.
The indicators cover emissions (GHG as well as SLCF), GHG concentration, radiative forcing, surface temperature change, Earth energy imbalance, warming attributed to human activities, carbon budget and global temperature extremes. Unfortunately, indicators related to the water cycle (closely interacting with the carbon cycle) are not part of the set of indicators. While surface temperature change is a prime indicator (Fig.8 being one of the most important figures), other indicators such as soil moisture and water availability might have more direct impact on humans and nature. (Similarly, the addition of the global temperature extremes is much valued.)
Specific remarks:
Lines 78, 1234: Stabilising emission trend might be risky to claim. A slowing down of the increase might be a more prudent and correct claim.
Lines 66, 149: Emissions: please specify that these are emissions of GHGs and SLCF.
Line 200: FFI = fossil fuel and industry. It is then further explained that industrial process emissions are meant. It might be useful and clarifying to say that e.g. biomass and biofuel used in industry is not included.
Line 201: Not only deforestation but also forest degradation might be added.
Line 204: F-gases are also emitted by military activities (in the past 20% shares have been estimated). It might be clarifying to mention that these are excluded?
Line 220: there are other global inventories worth mentioning here:
- Carbon monitor https://www.nature.com/articles/s43017-023-00406-z
- TNO CAMS CO2 global emissions inventory https://coco2-project.eu/data-portal
Line 232 and line 260: the uncertainty of CO2-LULUCF is not consistent: +/- 2.6 versus +/- 2.8. Please clarify
Line 286: CH4: recent progress has been made: please take up insights from E. Nisbet et al., 2023 (DOI: 10.22541/essoar.167689502.25042797/v1) and from Z. Zhang et al., 2023 (https://www.nature.com/articles/s41558-023-01629-0)
Line 282: not only an adjustment. It seems to me that also the uncertainty got significantly reduced (from 6.6 to 5.7 Gt CO2e). Please clarify if this is also due to improvements of the LULUCF emission estimates.
Line 300: Table 1: surprisingly the projection for 2022 has the same uncertainty as the calculated value for 2021. Is the extra uncertainty for the projection so small?
Line 338: another source of SLCF emissions is Crippa et al. ESSD (2018)
Line 355: Although CEDS gives estimates for 1750, it covers also 1970. The methodology to calculate the emission estimate for 2019 is much more similar to the values in 1970 than in 1750. Why not selecting also 1970 as like for CO2e instead of 1750?
Line 400: Also here an extra column with the value for 1970 could be useful.
Line 523: when changing the dataset from IEA to IATA, is there a jump? Can this be assessed with the IATA data backwards before 2020?
Line 615, Table 4: I do not understand a trend here for the aerosol cloud interactions? Any comment on these three values?
Line 810, states to assume “recent” linear trends. I can understand that the latest increment can be linearly assessed, but is the entire trend also linearly assessed (given the multivariate linear regression method in line 850)?
Line 1025: the global surface temperature is “close” to linearly proportional to the cumulative global CO2 (not CO2e?) emissions. The causal relationship between the global surface temperature and the cumulative emissions, can be assessed with the response on a significant change. Can this be done for the change of CO2 in 2020?
Line – Table 8: It is difficult to “interpret” the different levels of likehood to limit the global warming, I would have thought that we’ll can consume our 3 yr margin (150 GtCO2e), for inducing > 1,5 deg C with 67% likelihood, but in the conclusions in Table 10, it is claimed that we exhausted already our margin and have already now a chance of 1 in 3 to exceed the 1,5 degC. How do the two Tables link?
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CC3: 'Reply on RC1', Piers M. Forster, 13 May 2023
Thank you for your very timely and helpful review. I will discuss with my co-authors about extending the period earlier as I think your argument here makes a lot of sense when looking for changes over deacdes.
On the indicators set. This first year we focussed on a smaller set to build an estimate of human induced warming. It future years we would really like to exppand the set and the community to bring in additional indictors - and water cycle ones are top of our list. We'll respond properly to all your comments, but it the mean time thanks again!
Citation: https://doi.org/10.5194/essd-2023-166-CC3 -
CC7: 'Reply on RC1', Robin Lamboll, 16 May 2023
Regarding your last question, the two main and summary tables of carbon budgets include different types of uncertainty in the reported values. We will clarify and align them for the next iteration.
Citation: https://doi.org/10.5194/essd-2023-166-CC7 -
AC1: 'Reply on RC1', Piers M. Forster, 24 May 2023
Thank you for your support for the effort, and your understanding of its need and timeliness. Your review has been very helpful both for revising the framing of the paper and making corrections and clarifications resulting from your detailed comments. We have considered each comment carefully and taken most on board in our revised paper.
General comment
In line with IPCC’s values to provide unbiased, traceable and transparent information and conform with the FAIR principles for sharing data, this paper documents important indicators for closely monitoring the climate change that can be attributed to human activities. While the IPCC AR (FAR until AR6) were giving similar information at relative large timesteps of 5+ years, the ESSD paper series will now continue to give for these indicators such information on an annual basis. The period covered are 10 years, 2013-2022, while AR6 covered 2010-2019. Given the acceleration in climate change and urgency for stepping up climate actions, this is most appreciated. The dataset is fully available on zenodo, authored by 13 coauthors of the ESSD paper.
Thank you for the support for the effort.
However, it would have been useful to just extend the period with 3 yr before and 3 yr after the AR6 period, covering 2007-2022 for two reasons: (i) the average over the 10 yr 2010-2019 can be confronted with averages over a symmetrically extended period, (ii) 2008 saw a dip in emissions due to financial crisis and 2020 due to COVID. In the aftermath of the financial crisis, we saw a stabilising of the emissions, (Cfr. https://www.pbl.nl/en/publications/trends-in-global-co2-emissions-2013-report) but this seemed only a temporarily slow down. How can we be sure about a definite stabilising in the aftermath of COVID? It would be interesting to compare the shocks of 2008 and of 2020 and their impact on the successive years 2008-2010 en 2020-2022.
This is an interesting idea which we discussed as an author team. Potentially it could be really useful to compare emissions and responses to financial crises. We thought though that adding extra periods to the paper in this instance would make it overly complex and also reduce the focus on making a direct comparison to AR6. The data is available though and we think this would be useful for others to follow up in detail. It should, however, be noted that the 2007-2009 and 2020-2022 periods, particularly the latter, included significant La Niña events which would be likely to confound any assessment of the 2008/2020 shocks.
The indicators cover emissions (GHG as well as SLCF), GHG concentration, radiative forcing, surface temperature change, Earth energy imbalance, warming attributed to human activities, carbon budget and global temperature extremes. Unfortunately, indicators related to the water cycle (closely interacting with the carbon cycle) are not part of the set of indicators. While surface temperature change is a prime indicator (Fig.8 being one of the most important figures), other indicators such as soil moisture and water availability might have more direct impact on humans and nature. (Similarly, the addition of the global temperature extremes is much valued.)
Adding indicators related to the water cycle and extremes has always been the eventual aim of the author team and we very much hope to include these in future updates. The extremes of temperatures are shown to indicate where we may go in the future. Including them in this first iteration was challenging as there was more work involved to establish our approach to following the IPCC processes and more subjective assessment and wider literature review required, especially to assess regional extremes. We hope to have more next year though, so please watch this space! In response to your comment, we now clarify the existing scope and state our ambition in the introduction.
Specific remarks:
Lines 78, 1234: Stabilising emission trend might be risky to claim. A slowing down of the increase might be a more prudent and correct claim.
We agree and have modified the wording using your suggestion, thank you.
Lines 66, 149: Emissions: please specify that these are emissions of GHGs and SLCF.
Both are included and we now clarify.
Line 200: FFI = fossil fuel and industry. It is then further explained that industrial process emissions are meant. It might be useful and clarifying to say that e.g. biomass and biofuel used in industry is not included.
We now clarify this as requested.
Line 201: Not only deforestation but also forest degradation might be added.
We now clarify this as requested.
Line 204: F-gases are also emitted by military activities (in the past 20% shares have been estimated). It might be clarifying to mention that these are excluded?
This paragraph is setting the scene so we think this is too much detail to add here. We now point out later in this section that the data choice we make to use atmospheric concentrations for F-gas emissions works around known issues in inventory reporting and the exclusion of military applications.
Line 220: there are other global inventories worth mentioning here:
- Carbon monitor https://www.nature.com/articles/s43017-023-00406-z
- TNO CAMS CO2 global emissions inventory https://coco2-project.eu/data-portal
These are two very useful research efforts and initiatives but they are not yet at the level of established global inventories that are cited here, so we would prefer to keep the text as is.
Line 232 and line 260: the uncertainty of CO2-LULUCF is not consistent: +/- 2.6 versus +/- 2.8. Please clarify
Line 232 refers to the uncertainty range reported by the GCB, which is calculated as a one standard deviation range. Line 260 refers to the uncertainty range reported in this study, which follows the IPCC AR6 WG3 convention of a 90% confidence interval.
Line 286: CH4: recent progress has been made: please take up insights from E. Nisbet et al., 2023 (DOI: 10.22541/essoar.167689502.25042797/v1) and from Z. Zhang et al., 2023 (https://www.nature.com/articles/s41558-023-01629-0)
We agree, and have added a short paragraph with these references.
Line 282: not only an adjustment. It seems to me that also the uncertainty got significantly reduced (from 6.6 to 5.7 Gt CO2e). Please clarify if this is also due to improvements of the LULUCF emission estimates.
Clarified, you are correct.
Line 300: Table 1: surprisingly the projection for 2022 has the same uncertainty as the calculated value for 2021. Is the extra uncertainty for the projection so small?
These are taken from the GCB estimate directly. For fossil fuel, the annual growth in emission for 2022 is 1% with a one-sigma range: 0.1 to 1.9%). Given the GCB uncertainty on the annual estimate (5%), the overall uncertainty for the GCB 2022 estimate is still around 5%. For land use emissions, the uncertainty for the 2022 GCB projection is assumed to be the same as for previous years estimates: 0.7 GtC.
Line 338: another source of SLCF emissions is Crippa et al. ESSD (2018)
This text is making a specific point on uncertainty. The 2022 EDGAR update is cited earlier.
Line 355: Although CEDS gives estimates for 1750, it covers also 1970. The methodology to calculate the emission estimate for 2019 is much more similar to the values in 1970 than in 1750. Why not selecting also 1970 as like for CO2e instead of 1750?
1750 is used specifically for the radiative forcing estimate.
Line 400: Also here an extra column with the value for 1970 could be useful.
Other periods are all in the repository, but here we wanted to focus on data periods specifically needed for the arguments in the paper. Hence the choice.
Line 523: when changing the dataset from IEA to IATA, is there a jump? Can this be assessed with the IATA data backwards before 2020?
This has now been moved to the supplement - the sources are equivalent for where they overlap.
Line 615, Table 4: I do not understand a trend here for the aerosol cloud interactions? Any comment on these three values?
Comments have been added to the table - from reduced aerosol precursors and some saturation in the system.
Line 810, states to assume “recent” linear trends. I can understand that the latest increment can be linearly assessed, but is the entire trend also linearly assessed (given the multivariate linear regression method in line 850)?
This point is about future temperatures not being needed as recent trends are roughly linear, as explained earlier in the paragraph. The multivariate linear regression is more about the scaling factors - text has been deleted to avoid confusion as it repeated the explanation given earlier but could be confusing.
Line 1025: the global surface temperature is “close” to linearly proportional to the cumulative global CO2 (not CO2e?) emissions. The causal relationship between the global surface temperature and the cumulative emissions, can be assessed with the response on a significant change. Can this be done for the change of CO2 in 2020?
It could be, with lots of caveats around non-CO2, but this would be a research topic beyond the scope of this paper.
Line – Table 8: It is difficult to “interpret” the different levels of likehood to limit the global warming, I would have thought that we’ll can consume our 3 yr margin (150 GtCO2e), for inducing > 1,5 deg C with 67% likelihood, but in the conclusions in Table 10, it is claimed that we exhausted already our margin and have already now a chance of 1 in 3 to exceed the 1,5 degC. How do the two Tables link?
Table 10 has been updated and text added. There was a legacy issue with a revision of the paper that led to Tables 10 and 8 not being consistent. Thank you for spotting this. It’s to do with whether the zero emission commitment is included or excluded from the carbon budget uncertainty estimate. AR6 WGI SPM excluded it, so we follow their approach and now clarify this.
Citation: https://doi.org/10.5194/essd-2023-166-AC1
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RC2: 'Comment on essd-2023-166', Anonymous Referee #2, 10 May 2023
The authors are to be complimented for the amount of work and effort they put into this study. They have addressed most of the AR6 components and indicators. I am not commenting on the technical choice of methods, because their intention of updating AR6 is valid and greatly needed, but I find it a bit too long and descriptive, withmany detailed methodological sections. If this is targeted to scientist already familiar with the AR6 methods and GCB structure, I strongly recommend to move to a Supplementary file a lot of the methods (e.g., sections 4 (ERFs), 5 and perhaps some parts of 8, 9) and detailed tables, e.g., Table 3. I think the discussion and focus should be mostly given to section 7, where the summary of Results should have priority, perhaps merged with section 12. Now they are a bit hidden and one might get lost in methods until it arrives to read the end sections.
After reading the Introduction, it was not completely clear to me what else was aimed at, except for updating the indicators in AR6; if the annual update of the IPCC AR6 report is the main purpose, then it might confuse the policy makers, being a scientific publication and not an official recognized IPCC report, using slightly different data sets from those in AR6; if authors intend to also support the COP negotiations, then I see it lengthy and detailed. It also needs a more focused and concrete “bullet point” type of section including valid points for negotiations. I like the idea of having the “Reasons for change” column from Table 4 which could transform into a “summary actions for policy makers” recommendations in a final section, discussing the key figures such as: Figures 1, 2 and 7 and overview tables like Table 7.
After analyzing and updating all these indicators, what are the main messages, conclusions? Where do actions need to happen (e.g., GHG emissions, human forcing? etc.). Figure 7 is a good example.
The authors did not mention any indicator and/or analysis regarding the water availability, drought and sea level rise. I think it’s worth mentioning it in a short section.
Perhaps is not the aim here, but I was wondering why satellite-based observations are still underrepresented in such studies? UNFCCC parties start to look more into complementing their NGHGIs with these estimates, e.g., CAMS or other GOSAT, TROPOMI based inversions. They do exist and provide more and more valid estimates for the last years. They could be added to Figures 1 or 5 as an extra column, or at least mentioned in the Introduction.
Some specific comments:
Line 77: I would add “global emission levels are starting to stabilize…”. This message sounds a bit too optimistic, and this “stabilization” is only triggered by few developed nations managing their GHG emissions, while in most developing countries, emissions continue to increase.
To keep the time lines, I would move the lines 124-134 after lines 106-113 and reduce the length of this COP21 dedicated paragraph.
Line 201: degradation and natural disturbances
Line 204: Waste sector, important for CH4 emissions, is not mentioned.
Lines 215-220: Somewhere in Appendix or Supplement I would detail on the naming of these notable datasets of GCB. Please mention which EDGAR version is used…line 226 talks about EDGARv6.0 in AR6 while Figure 1 has EDGARv7.0
There is a bit of confusion reading the lines 215 – 235. First the authors mention that EDGAR is used in this study (line 218), then they describe all data sets from AR6 WGIII and on line 240 they mention that they don’t use EDGAR. Perhaps a simple table ‘AR6 data sets vs. this study’ would help summarizing the data?
It would be good to explain why the authors consider bookkeeping models as representative for estimating the CO2-LULUCF sources/sinks? How about FAOSTAT, DGVMs, CAMS?
Line 227: authors mention that in AR6 the CH4 and N2O emissions from GFED (please mention if 4.1) biomass combustion was added to EDGAR. This might create some double counting because EDGAR reports only anthropogenic emissions and emissions reported by GFED include as well agricultural waste burning and peat fires (in some countries considered managed). It is not clear to me how it’s done in this study, are GFED CH4 and N2O emissions still added to the PRIMAP-hist emissions? Did you add only the wild fires?
You mention on line 245 the specific data choices, I do agree that higher-tier methods are needed, and I would be glad to see in the future updates the inclusion of inversions.
Figure 1: please add to the caption the fact that both AR6 and current study datasets are represented.
Table 1: please correct CO2-LUCF with CO2-LULUCF
I would move Figure 4 to Appendix
Figure 5: I assume all bars should have written the periods, a bit more difficult for the shorter ones. Why not adding the left and right periods on top of the bars like: a) Decade-average warming given by observations for 2010-2019 (left) and 2013-2022 (right) . And similar to b) and c) panels.
I find Figure 7 very informative, summarizing and concluding well the findings.
Zenodo data:
Please give a shorter name to the files in the “carbon_budgets” folder, error when trying to open.
The info contained by each yml file (details on the contact author and original repository of the source code) should also be added to the README file. In this way one reads the summary of data provided by the study without having to open all the file, sone by one, unless is interested in the data.
How about individual time series from the data sets used in Section 2 (the 3 bookkeeping models, EDGAR times series, GFED, GCB? Could also be added to the greenhouse_gas_emissions_1750-2021.csv.
Please add a line in the README file: “.md and YML format files can be opened by any text editor (Notepad etc.)”.
Citation: https://doi.org/10.5194/essd-2023-166-RC2 -
CC4: 'Reply on RC2', Piers M. Forster, 13 May 2023
Thank you for your very fast and very helpful review.
Your comments on the framing are insightful and are like discussions among the author team and their evolution. We set out to update IPCC, but even only 2 years after publication, we found that a pure update without change to method was not possible for all indicators. Secondly scientists love to tinker with calibrations and things like the NOAA GHG calibration changed. Although less than a 1% effect, if we had stuck to an old calibration, our IPCC numbers would begin to depart from NOAA numbers which would be more accurate. So, in the end we were forced to do more of an assessment than we set out to do - hence the length. The method change column in Table 8 was a direct result of this and I think these method changes will be useful to track.
Despite this the authors thought we wanted to honour the IPCC approach as much as possible and be policy relevant but not policy prescriptive, so I'm not sure about your idea of attaching policy recommendations, although we want to use the paper to make them. We will discuss how to address this point. I certainly think we can be clearer with the intention with the intro.
On length, we erred on the side of caution and put everything in the paper, we will seek advice from the editor, but I think your ideas about what we could move out would really help focus the paper and make it more readable and useful to a wider audience.
Thank you so much.
Piers
Citation: https://doi.org/10.5194/essd-2023-166-CC4 -
AC2: 'Reply on RC2', Piers M. Forster, 24 May 2023
Thank you for your extremely helpful review of both the paper and the data repository. We have revised the paper, taking into account the comments, accepting most of them.
The authors are to be complimented for the amount of work and effort they put into this study. They have addressed most of the AR6 components and indicators. I am not commenting on the technical choice of methods, because their intention of updating AR6 is valid and greatly needed, but I find it a bit too long and descriptive, withmany detailed methodological sections. If this is targeted to scientist already familiar with the AR6 methods and GCB structure, I strongly recommend to move to a Supplementary file a lot of the methods (e.g., sections 4 (ERFs), 5 and perhaps some parts of 8, 9) and detailed tables, e.g., Table 3. I think the discussion and focus should be mostly given to section 7, where the summary of Results should have priority, perhaps merged with section 12. Now they are a bit hidden and one might get lost in methods until it arrives to read the end sections.
These are excellent suggestions for focussing the paper, thank you. We have essentially made them, additionally moving out the appendices and the three detailed methods from Section 7. We have also shortened the introduction, see response below.
After reading the Introduction, it was not completely clear to me what else was aimed at, except for updating the indicators in AR6; if the annual update of the IPCC AR6 report is the main purpose, then it might confuse the policy makers, being a scientific publication and not an official recognized IPCC report, using slightly different data sets from those in AR6; if authors intend to also support the COP negotiations, then I see it lengthy and detailed. It also needs a more focused and concrete “bullet point” type of section including valid points for negotiations. I like the idea of having the “Reasons for change” column from Table 4 which could transform into a “summary actions for policy makers” recommendations in a final section, discussing the key figures such as: Figures 1, 2 and 7 and overview tables like Table 7.
We have refocused the introduction to remove much of the policy framing and shortened it. In response to reviewer 4 also, we want to follow the IPCC approach and keep the paper policy relevant but not policy prescriptive. We therefore do not think that adding negotiations points would be useful. Rather we want to build material around the paper to address these aspects, keeping the paper policy neutral. We are aiming to keep it as IPCC-like as possible and form conclusions in a similar way. Hopefully, the shorter and more focussed paper is more compelling.
After analyzing and updating all these indicators, what are the main messages, conclusions? Where do actions need to happen (e.g., GHG emissions, human forcing? etc.). Figure 7 is a good example.
These are brought forward in the last two paragraphs of section 12 and the abstract. We think this is the right level of messages from the paper.
The authors did not mention any indicator and/or analysis regarding the water availability, drought and sea level rise. I think it’s worth mentioning it in a short section.
This is something we wish to do in future years and now cover this in the introduction when addressing the paper’s scope, see also the reply to reviewer 1.
Perhaps is not the aim here, but I was wondering why satellite-based observations are still underrepresented in such studies? UNFCCC parties start to look more into complementing their NGHGIs with these estimates, e.g., CAMS or other GOSAT, TROPOMI based inversions. They do exist and provide more and more valid estimates for the last years. They could be added to Figures 1 or 5 as an extra column, or at least mentioned in the Introduction.
In this paper we stick close to AR6 methods, Bastos et al 2022 https://doi.org/10.1186/s13021-022-00214-w for example, shows that more work is still necessary to align them with bottom up methods. We now allude to future possibilities in the conclusions and will consider again for future updates.
Some specific comments:
Line 77: I would add “global emission levels are starting to stabilize…”. This message sounds a bit too optimistic, and this “stabilization” is only triggered by few developed nations managing their GHG emissions, while in most developing countries, emissions continue to increase.
We have now reworded to change the tone in line with your comments.
To keep the time lines, I would move the lines 124-134 after lines 106-113 and reduce the length of this COP21 dedicated paragraph.
These paragraphs have been shortened and removed in response to reviewer 4.
Line 201: degradation and natural disturbances
Added details
Line 204: Waste sector, important for CH4 emissions, is not mentioned.
Added.
Lines 215-220: Somewhere in Appendix or Supplement I would detail on the naming of these notable datasets of GCB. Please mention which EDGAR version is used…line 226 talks about EDGARv6.0 in AR6 while Figure 1 has EDGARv7.0
GCB uses several datasets and we think it is best for the interested reader to refer to their paper. We now mention that although EDGAR version 7 is available, we use PRIMAP.
There is a bit of confusion reading the lines 215 – 235. First the authors mention that EDGAR is used in this study (line 218), then they describe all data sets from AR6 WGIII and on line 240 they mention that they don’t use EDGAR. Perhaps a simple table ‘AR6 data sets vs. this study’ would help summarizing the data?
The first paragraph describes the sort of data sets employed. The second one is exclusive to those used in AR6 and the third gives our updates approach. We have tried to clarify the text in the first paragraph that “not all these datasets were employed in this update.” We have not added a table to keep things as simple as possible - we already have a lot of tables.
It would be good to explain why the authors consider bookkeeping models as representative for estimating the CO2-LULUCF sources/sinks? How about FAOSTAT, DGVMs, CAMS?
As specified in the text we follow AR6 and GCB standard practise. Other approaches exist that quantify land-use related CO2 fluxes, but they are not directly comparable since their definitions differ from the bookkeeping estimates as used in the annual Global Carbon Budgets, where effects of environmental changes are excluded from the LULUCF fluxes (Pongratz et al., 2021). Estimates based on DGVMs include the “loss of additional sink capacity”, that is, they attribute to LULUCF the loss of the hypothetical sink that the forests, had they not been cleared for agriculture, would have created in response to environmental changes (predominantly beneficial effects on plant growth due to rising atmospheric CO2). Their estimate of the LULUCF flux is thus a substantially higher emission term to the atmosphere (Obermeier et al., 2021). Further, by using empirical carbon densities, bookkeeping models are assumed to be more complete in their representation of management activities than the process-based DGVMs. DGVMs are thus used to quantify uncertainties around the bookkeeping estimates, but not as direct estimate of the LULUCF flux. Data sets such as the national GHG inventories by UNFCCC and FAOSTAT assume different system boundaries and distinguish fluxes by area (managed vs unmanaged) rather than by (anthropogenic vs natural) drivers. They attribute parts of the fluxes due to environmental changes to the LULUCF flux, when they occur on managed land, which thus includes a substantial sink term in the LULUCF flux estimate, turning it from a substantial global source to a global sink term. UNFCCC and bookkeeping estimates can be “translated” into each other and are largely consistent then (Friedlingstein et al., 2022; Grassi et al., 2023). Differences exist between UNFCCC and FAOSTAT in particular with respect to coverage of non-biomass carbon pools and non-forest land-use types (Grassi et al., 2022). CO2 fluxes from land can also be estimated from satellite measurements of atmospheric CO2 through inversions (Deng et al. 2022). However, currently, inversions have coarse spatial scale and country-level fluxes are relatively poorly constrained (Bastos et al. 2022). Again, the distinction between anthropogenic and natural drivers is problematic (Petrescu et al., 2021) and the key reason why models are used to estimate the LULUCF fluxes.
We have added the following text as a new paragraph in the manuscript:
“There are also varying conventions used to quantify CO2-LULUCF fluxes. These include the use of bookkeeping models, dynamic global vegetation models (DVGMs), and the national inventory approach (Pongratz et al. 2021). Each differs in terms of their applied system boundaries and definitions, and are not directly comparable. However, efforts to “translate” between bookkeeping estimates and national inventories using DVGMs have demonstrated a degree of consistency between the varying approaches (Friedlingstein et al., 2022; Grassi et al., 2023).”
This choice is discussed later in the paper, from line 245 in the submitted version.
Line 227: authors mention that in AR6 the CH4 and N2O emissions from GFED (please mention if 4.1) biomass combustion was added to EDGAR. This might create some double counting because EDGAR reports only anthropogenic emissions and emissions reported by GFED include as well agricultural waste burning and peat fires (in some countries considered managed). It is not clear to me how it’s done in this study, are GFED CH4 and N2O emissions still added to the PRIMAP-hist emissions? Did you add only the wild fires?
We have investigated this important issue and found that while early drafts of the AR6 WGIII report included fire emissions from GFED, the final published draft did not. Indeed, fire emissions are an active area of development among the different teams compiling inventories - so the risk of double counting such emissions could well arise in this and subsequent editions of the indicator update. Therefore, to ensure consistency with AR6 we edit out references to GFED in the manuscript and exclude these emissions from the reported data.
You mention on line 245 the specific data choices, I do agree that higher-tier methods are needed, and I would be glad to see in the future updates the inclusion of inversions.
We will endeavor to do this.
Figure 1: please add to the caption the fact that both AR6 and current study datasets are represented.
We think the comment about the starred data used for the assessment satisfies this need and adding further text might confuse the reader.
Table 1: please correct CO2-LUCF with CO2-LULUCF
Done.
I would move Figure 4 to Appendix
We would prefer to keep it here as it clearly shows the increasing rate energy absorption by the climate system.
Figure 5: I assume all bars should have written the periods, a bit more difficult for the shorter ones. Why not adding the left and right periods on top of the bars like: a) Decade-average warming given by observations for 2010-2019 (left) and 2013-2022 (right) . And similar to b) and c) panels.
Only the first in the series have - this is now covered in the caption.
I find Figure 7 very informative, summarizing and concluding well the findings.
Thank you.
Zenodo data:
Please give a shorter name to the files in the “carbon_budgets” folder, error when trying to open.
Done.
The info contained by each yml file (details on the contact author and original repository of the source code) should also be added to the README file. In this way one reads the summary of data provided by the study without having to open all the file, sone by one, unless is interested in the data.
Done. Please note that rather than listing email contacts we have linked to the homepages of the relevant data contributor; while their email addresses are already mostly on their websites in plain text and contained in the YML files, we choose not to list them here in order to not provide another source for spam email crawlers.
How about individual time series from the data sets used in Section 2 (the 3 bookkeeping models, EDGAR times series, GFED, GCB? Could also be added to the greenhouse_gas_emissions_1750-2021.csv.
Thank you for the suggestion. Now, all of the emissions data has been added as an additional file in the greenhouse gas emissions dataset directory of the repository.
Please add a line in the README file: “.md and YML format files can be opened by any text editor (Notepad etc.)”.
Done.
Citation: https://doi.org/10.5194/essd-2023-166-AC2
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CC4: 'Reply on RC2', Piers M. Forster, 13 May 2023
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CC1: 'Comment on essd-2023-166', Rasmus Benestad, 10 May 2023
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CC2: 'Reply on CC1', Piers M. Forster, 13 May 2023
Thank you for taking the time to read and review our paper. I agree that we are focussing here on temperture realted metrics. This was a choice to build the necessary steps to estimate human induced warming, which is widlly used in policy contexts around Paris temperture targets.
This being sai, in future years I would love to see both the set of indicators expand and the community involbed also expand. We will revisit the citations we use. Again, we are not trying to completey redo the IPCC asessment and start with the basis of their far more comprehensive literature review. Most of the papers cited are methodological as we use a set of methods generated by the authors. However, we will consider how best to bring your useful suggestions in whwn we revise, thank you
Citation: https://doi.org/10.5194/essd-2023-166-CC2 -
AC5: 'Reply on CC1', Piers M. Forster, 24 May 2023
We thank you for the support for such an exercise and your helpful review. In line with comments by other reviewers, we have rewritten the introduction to make the scope of indicators assessed much clearer. We hope to extend the scope to include water cycle indicators in future years. In terms of the literature cited, we deliberately chose to build off IPCC AR6 reports. Rather than make a new comprehensive assessment, we use high level citations to chapters in this report. The other literature cited is focussed on the methodological descriptions for completeness, hence the authors earlier publications are cited where necessary. Nevertheless, we broadly agree with your point that the IPCC needs to evolve in how it makes literature assessments. In the future we want to consider how to bring in elements of systematic review, but more work is needed in this area.
Citation: https://doi.org/10.5194/essd-2023-166-AC5
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CC2: 'Reply on CC1', Piers M. Forster, 13 May 2023
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RC3: 'Comment on essd-2023-166', Matthew Jones, 10 May 2023
It's a fantastic initiative to provide running updates of all the work that underpins the IPCC assessments. This work should serve as a vital reference point for those interested in tracking (anthropogenic) climate change across a variety of indices.
The paper is extremely well written! Mercifully, the content is far more accessible than the volumous IPCC chapters that it builds on. Yet I felt that all the key concepts and background were gently spelled out, and the discussion of dataset, methods and uncertainties were covered with refreshing clarity - providing all the key information, but not so much that the text becomes convoluted. The consistent discussion of differences since AR6 and SR1.5 is also a helpful aid to the reader throughout the text.
Overall, this is a wonderful contribution to the literature that should make many aspects of future IPCC assessments far more streamlined.
I have only some minor comments/suggestions:
[75] "Human induced warming is increasing at an unprecedented rate of over 0.2 °C per decade." - is this over the past decade, or extrpolated from the 2022 value? (I imagine the former, but it's not clear when reading on from the following sentence, so it would be good to specify).
[Table 1] It might be nice to split the CH4 and N2O emissions from into their FFI and LUCF components, like for CO2? I think this is possible and straightforward when using the PRIMAP-hist dataset.
[Tables 1-3] Could you report the values for consistent periods/years across these tables? e.g. 1750, 1850, 1970-1979, ... 2010-2019, 2019, 2021, 2022.
[All results tables] As an extension of the comment above, could you provide values consistently across all results tables? I think this would measurably help the reader to digest the array of indicators provided, without getting too caught up with tracking different time periods and so on. I imagine this would help a lot with communicating the results beyond the paper - e.g. in a lecture setting.
[Figure 7] Personally, I think it would be more intuitive to flip this figure along its vertical axis, so that emissions are at the top, then concentrations, then warming at the bottom. I didn't find it natural to start at the bottom and work up.
[1214] "Indeed, our results point to the opposite: continued high levels of greenhouse gas emissions, combined with improvements in air quality, are reducing the level of aerosol cooling - leading to an unprecedented rate of human-induced warming". Perhaps rephrase for clarity?: "continued high levels of greenhouse gas emissions, combined with reduced aerosol cooling under improved air quality, have led to an unprecedented rate of human-induced warming".
Citation: https://doi.org/10.5194/essd-2023-166-RC3 -
CC5: 'Reply on RC3', Piers M. Forster, 13 May 2023
Dear Matt
Thank you for your generous review comments. They are really helpful. Another reviewer thought some of the methods could be move to supplementary material - but you seem to like them in the paper? We probably need to seek advice from the editor here!
All the best - and thank you
Piers
Citation: https://doi.org/10.5194/essd-2023-166-CC5 -
AC3: 'Reply on RC3', Piers M. Forster, 24 May 2023
Thank you so much for these very supportive comments. They make the effort involved worthwhile. We address the minor issues below and make appropriate changes where we can (some might have to wait until next time).
I have only some minor comments/suggestions:
[75] "Human induced warming is increasing at an unprecedented rate of over 0.2 °C per decade." - is this over the past decade, or extrpolated from the 2022 value? (I imagine the former, but it's not clear when reading on from the following sentence, so it would be good to specify).
“Over the 2013-2022 period” has been added to the abstract.
[Table 1] It might be nice to split the CH4 and N2O emissions from into their FFI and LUCF components, like for CO2? I think this is possible and straightforward when using the PRIMAP-hist dataset.
To avoid complexity and to follow the AR6 approach, we decided not to do this as this probably would require a fuller assessment to do properly. We will revisit it next year.
[Tables 1-3] Could you report the values for consistent periods/years across these tables? e.g. 1750, 1850, 1970-1979, ... 2010-2019, 2019, 2021, 2022.
[All results tables] As an extension of the comment above, could you provide values consistently across all results tables? I think this would measurably help the reader to digest the array of indicators provided, without getting too caught up with tracking different time periods and so on. I imagine this would help a lot with communicating the results beyond the paper - e.g. in a lecture setting.
We completely agree and set out to do just this. However, when digging into the data for the different sections, we needed to make specific date range choices to be consistent with AR6 and SR1.5. Unfortunately, these reports were not consistent across their chapters. Adding additional periods to the table to satisfy both AR6 compatibility and internal consistency would make the paper unwieldy. In this first iteration we especially wanted to show the provenance with AR6, so chose date ranges accordingly. Furthermore, in many sections of the paper such as global mean temperature, we rely on AR6 uncertainty approaches which are specific to certain averaging periods. Data for all periods are provided online, so hopefully this helps? We will revisit it again next year.
[Figure 7] Personally, I think it would be more intuitive to flip this figure along its vertical axis, so that emissions are at the top, then concentrations, then warming at the bottom. I didn't find it natural to start at the bottom and work up.
Here we choose to replicate the exact arrangement made in the AR6 synthesis report to show the provenance. This had stakeholder and designer input - so although we intend to agree, who are we to argue 🙂
[1214] "Indeed, our results point to the opposite: continued high levels of greenhouse gas emissions, combined with improvements in air quality, are reducing the level of aerosol cooling - leading to an unprecedented rate of human-induced warming". Perhaps rephrase for clarity?: "continued high levels of greenhouse gas emissions, combined with reduced aerosol cooling under improved air quality, have led to an unprecedented rate of human-induced warming".
Your wording is adopted. Thank you.
Citation: https://doi.org/10.5194/essd-2023-166-AC3
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CC5: 'Reply on RC3', Piers M. Forster, 13 May 2023
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RC4: 'Comment on essd-2023-166', Albertus J. (Han) Dolman, 15 May 2023
This is a good and much needed paper. The idea of providing annual updates on IPCC indicators is laudable. Since most of the work relates to IPCC methods or updated versions based on published work, I have little detailed comments to make.
However, I find the introduction unnecessary cumbersome and almost too apologetic. Clearly state the aims: providing annual updates of key indicators that are changing at annual time scales and provide references to changing methods. The audience when you publish in ESDD are scientist, not policymakers
I do miss a good rationale for the present selection of indicators. A conceptual diagram, assuming the authors want to stick for now to the energy cycle, how GHGs link to ERF, to warming, EIB might help. Now one wonders where the indicator of, for instance sea level is not used. Extremes are included, but only temperature, why not precipitation?
Few minor details.
L92. BAMS also does GHGs
L248 Not sure investment rate is an adequate description of the quality of a dataset. Another reason to use the UNFCC estimates, such as improved monitoring of inventories might be more useful.
L262. I would argue that given the uncertainty, the two values are the same, similarly l 275, there is mention of a substantial and downward revision, all very much within the uncertainty bounds, so please be more careful with the wording here. Similarly using the word “stable” is maybe a bit too positive if you compare only two years.
L400. I wonder if Table 4 is not better places in supplementary data.
Despite these comments I think the authors have done a wonderful job and I look forward seeing the indicators published in the dashboard (where I would like to make sure that the different components in the dashboard do reflect the same data and numbers as in the policy facing dashboard)
Citation: https://doi.org/10.5194/essd-2023-166-RC4 -
CC6: 'Reply on RC4', Piers M. Forster, 15 May 2023
Dear Han
Thank you so much for your review, it is really helpful. On reading our paper again I think you are spot on with framing of the introduction. We were trying to show the policy relevence but in the end I think the balance is wrong and we should focus it more on the science aims of the paper. This would also help shorten and focus the text. We'll respond to this and the other comments properly soon.
Best wishes
Piers
Citation: https://doi.org/10.5194/essd-2023-166-CC6 -
AC4: 'Reply on RC4', Piers M. Forster, 24 May 2023
Thank you for this very helpful review. We agree with your issues around the framing and have shortened and focused the introduction. We have specifically reviewed much of the policy angle discussion to make it clearer that we are following IPCC of being policy relevant but policy neutral. We have strengthened the rationale over indicator choice in the introduction and explicitly explain why we do not cover precipitation extremes in this first iteration. We hope to in future updates.
Few minor details.
L92. BAMS also does GHGs
Text added to clarify.
L248 Not sure investment rate is an adequate description of the quality of a dataset. Another reason to use the UNFCC estimates, such as improved monitoring of inventories might be more useful.
“Investment” changed to “best use of country-level improvements in data gathering infrastructures”.
L262. I would argue that given the uncertainty, the two values are the same, similarly l 275, there is mention of a substantial and downward revision, all very much within the uncertainty bounds, so please be more careful with the wording here. Similarly using the word “stable” is maybe a bit too positive if you compare only two years.
We agree, wording adjusted to make the text less definitive.
L400. I wonder if Table 4 is not better places in supplementary data.
Change made as requested and table moved to the Supplement - I think you meant old table 3.
Despite these comments I think the authors have done a wonderful job and I look forward seeing the indicators published in the dashboard (where I would like to make sure that the different components in the dashboard do reflect the same data and numbers as in the policy facing dashboard)
Thank you. Your help and advice for the dashboard development would be much appreciated.
Citation: https://doi.org/10.5194/essd-2023-166-AC4
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CC6: 'Reply on RC4', Piers M. Forster, 15 May 2023
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CC8: 'Comment on essd-2023-166', Leon Simons, 16 May 2023
Thank you for your invaluable work to provide an annual update of large-scale indicators of the state of the climate system and the human influence. During this time of apparent rapid changes in both the anthropogenic forcing and the Earth's Energy Imbalance, the need for and value of a timely update can hardly be overstated.
A significant anthropogenic forcing change since 2019 still seems to be absent from your assessment. On January 1st 2020 new regulation from the International Maritime Organization (IMO 2020) came into effect. This is thought to have caused a reduction in annual SOx emissions (over the oceans) of about 8.5 Tg yr-1 (Corbett et al. 2016, see attached).
The effect on global net forcing is still uncertain. Past models give a range of 0.027 W/m² (Bilsback et al 2020) to 0.36 W/m² (Lauer et al. 2007). Wall et al. (2022) estimated based on observations that the cloud-mediated RF from anthropogenic sulphate aerosols before 2020 was −1.11±0.43 W m² over the global ocean. Because of the short atmospheric lifetime of sulphates, a significant part of this sulphate cooling might be attributable to shipping (the relative contribution was not assessed by Wall et al.).
Thank you again for your invaluable work.
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CC9: 'Reply on CC8', Christopher Smith, 18 May 2023
Thank you Leon for your positive comments.
Shipping emissions estimates would be provided by the Community Emissions Data System (CEDS), but data is only available until 2019 at present. We use proxy estimates from activity data for 2020, 2021, and 2022. You are correct that the legislated shipping emissions reductions of 8.5 Tg (around 10% of global sulfur emissions) are not explicitly included in our estimates for 2020, 2021 and 2022.
We are not certain to what extent that including the expected 8.5 Tg reductions for 2020-22 would lead to double-counting. COVID-19 caused a slowdown of global trade activity during 2020 at least, and it is likely that shipping emissions reductions were therefore less than 8.5 Tg as the baseline (which we do capture) would be lower.
If we had a good idea of the relative reduction in SO2 that resulted from the legislation we could include its effects without double-counting. One paper we now cite in section 4 that provides an analysis of this (Watson-Parris et al. 2022) suggests a reduction of 80% in SO2 emissions but this could be an overestimate if there has been some under compliance with the legislation post-2020 (and overcompliance before then). It also seems unlikely that a rapid change in how fuel is processed could be achieved so quickly. In practice, the reduction may have been less than 80%. The separation into legislation effects and COVID effects are unfortunately conflated.
A reduction of 8.5 Tg in SO2 emissions is similar to the 9 Tg we already estimate for the 2019 to 2022 period (Table 2), from which we estimate an increase in aerosol-cloud interactions of +0.07 W m-2. The sulfur contribution to aerosol-radiation interactions from this level of reduction is +0.02 W m-2. Assuming linear behaviour (the aerosol-cloud interaction relationship is not linear, but for a 10% change it is a good assumption), we could add another +0.09 W m-2 if we did indeed underestimate the reduction by 8.5 Tg. This sounds small but would add around 50% to the 2019-2022 forcing change. For reasons stated above, we believe that a difference of 8.5 Tg between our estimate and any ground truth including shipping is an upper bound, but concede that the difference is probably larger than zero.
The second effect that may alter forcing is in the pattern of shipping. Over the marine stratocumulus regions reductions in emitted SO2 may have a greater effect on weakening (i.e. increasing the positivity of) the aerosol-cloud forcing than the same amount of SO2 emitted over land (Watson-Parris et al. 2022; Yuan et al. 2022). The upper bound of this effect was recently estimated to be 0.27 W m-2. We note to consider the differing spatial effects of shipping emissions in future versions of the dataset if it turns out to be backed up by more research.
By the time of the release of the 2023 update, we would expect CEDS to have emissions updated beyond 2020, for which we will have more reliable estimates of recent changes in aerosol radiative forcing. As a living dataset, the most recent years are naturally subject to greater uncertainty than a period slightly further back in time that has been subject to greater study and benefits from more independent estimates (e.g. in the case of short-lived forcer emissions, the mid-2010s), and the dataset will be continually revised upon release of updated data even when it pertains to years before the most recent.
Watson-Parris et al. 2022: https://www.pnas.org/doi/10.1073/pnas.2206885119
Yuan et al. 2022: https://www.science.org/doi/10.1126/sciadv.abn7988
Citation: https://doi.org/10.5194/essd-2023-166-CC9
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CC9: 'Reply on CC8', Christopher Smith, 18 May 2023
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RC5: 'Comment on essd-2023-166', Martin Heimann, 17 May 2023
This is a very useful update since AR6 of key climate change indicators. The authors have made a very nice job, and the manuscript reads extremely well. I have only a few comments:
General comments:
1) I second Han Dolman’s review comment in that section 1 could be significantly shortened and focused on the key objectives of this manuscript, i.e. providing an update of key climate change indicators. The overall rationale for providing a science based assessment has been well covered in the IPCC reports.
2) Section 9, climate and weather extremes. As stated in the text, this section is a placeholder for extreme weather indicators in future updates of this effort. For now, only an update of an extreme temperature indicator is discussed. Should not the title of this section be modified to reflect this restriction? Since this section and Figure 6 provide an “update” of Figure 11.2 in AR6, one might mention here as a footnote that Figure 11.2 in the final version of AR6 has a wrong time axis (data appear to go up to the year 2023, while the report was published in 2021…).
Minor comments:
p 11, Table 1: For consistency, why not have an additional column with the decade 2010-2019? I understand the reason to show the last decade, but the gap between 2009 and 2012 is somewhat puzzling.
Section 3, Table 3: I’d reverse the ordering of the columns so that time runs to the right, similar to all the other tables. An additional column with the annual increment in 2022 or perhaps averaged over 2019-2022, would also be useful.
Section 4, l 530: Wording: The time period 2009-2019 covers the entire solar cycle 24, not just the “solar minimum”.
Section 8, remaining carbon budget. Simply for putting these numbers into perspective, it would be helpful if in the text not only the emissions between the base year 2020 and 2022 were mentioned, but also the total historical emissions up to the base year or perhaps the total from the previous decade (i.e. from the numbers in table 1 in section 2).
Citation: https://doi.org/10.5194/essd-2023-166-RC5 -
AC6: 'Reply on RC5', Piers M. Forster, 24 May 2023
Thank you for the positive endorsement, your review has been very helpful to us: thank you for taking the time.
General comments:
1) I second Han Dolman’s review comment in that section 1 could be significantly shortened and focused on the key objectives of this manuscript, i.e. providing an update of key climate change indicators. The overall rationale for providing a science based assessment has been well covered in the IPCC reports.
We agree with this point and have removed the overly policy-focused paragraph to reframe the introduction. This also helps clarify the purpose of the paper.
2) Section 9, climate and weather extremes. As stated in the text, this section is a placeholder for extreme weather indicators in future updates of this effort. For now, only an update of an extreme temperature indicator is discussed. Should not the title of this section be modified to reflect this restriction? Since this section and Figure 6 provide an “update” of Figure 11.2 in AR6, one might mention here as a footnote that Figure 11.2 in the final version of AR6 has a wrong time axis (data appear to go up to the year 2023, while the report was published in 2021…).
Good idea, thank you. We have changed the title of section 9 to “Examples of climate and weather extremes : maximum temperature over land” .We got our rulers out to confirm the last point on Figure 11.2 in AR6 Chapter 11 is for 2020, so these data are ok.
Minor comments:
p 11, Table 1: For consistency, why not have an additional column with the decade 2010-2019? I understand the reason to show the last decade, but the gap between 2009 and 2012 is somewhat puzzling.
Done.
Section 3, Table 3: I’d reverse the ordering of the columns so that time runs to the right, similar to all the other tables. An additional column with the annual increment in 2022 or perhaps averaged over 2019-2022, would also be useful.
We have reversed the order but prefer to keep single years as this builds to radiative forcing. The uncertainty in trends needs to be thought about more for minor species so this is not added this year.
Section 4, l 530: Wording: The time period 2009-2019 covers the entire solar cycle 24, not just the “solar minimum”.
This has now been corrected (revised text is now in the Supplement), and we thank you for pointing this out.
Section 8, remaining carbon budget. Simply for putting these numbers into perspective, it would be helpful if in the text not only the emissions between the base year 2020 and 2022 were mentioned, but also the total historical emissions up to the base year or perhaps the total from the previous decade (i.e. from the numbers in table 1 in section 2).
This is a good idea and we have adopted your suggestion and added the historic budget until the end of 2019 following the AR6 WGI SPM Table 2.
Citation: https://doi.org/10.5194/essd-2023-166-AC6
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AC6: 'Reply on RC5', Piers M. Forster, 24 May 2023
Status: closed
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RC1: 'Comment on essd-2023-166', Greet Janssens-Maenhout, 09 May 2023
General comment
In line with IPCC’s values to provide unbiased, traceable and transparent information and conform with the FAIR principles for sharing data, this paper documents important indicators for closely monitoring the climate change that can be attributed to human activities. While the IPCC AR (FAR until AR6) were giving similar information at relative large timesteps of 5+ years, the ESSD paper series will now continue to give for these indicators such information on an annual basis. The period covered are 10 years, 2013-2022, while AR6 covered 2010-2019. Given the acceleration in climate change and urgency for stepping up climate actions, this is most appreciated. The dataset is fully available on zenodo, authored by 13 coauthors of the ESSD paper.
However, it would have been useful to just extend the period with 3 yr before and 3 yr after the AR6 period, covering 2007-2022 for two reasons: (i) the average over the 10 yr 2010-2019 can be confronted with averages over a symmetrically extended period, (ii) 2008 saw a dip in emissions due to financial crisis and 2020 due to COVID. In the aftermath of the financial crisis, we saw a stabilising of the emissions, (Cfr. https://www.pbl.nl/en/publications/trends-in-global-co2-emissions-2013-report) but this seemed only a temporarily slow down. How can we be sure about a definite stabilising in the aftermath of COVID? It would be interesting to compare the shocks of 2008 and of 2020 and their impact on the successive years 2008-2010 en 2020-2022.
The indicators cover emissions (GHG as well as SLCF), GHG concentration, radiative forcing, surface temperature change, Earth energy imbalance, warming attributed to human activities, carbon budget and global temperature extremes. Unfortunately, indicators related to the water cycle (closely interacting with the carbon cycle) are not part of the set of indicators. While surface temperature change is a prime indicator (Fig.8 being one of the most important figures), other indicators such as soil moisture and water availability might have more direct impact on humans and nature. (Similarly, the addition of the global temperature extremes is much valued.)
Specific remarks:
Lines 78, 1234: Stabilising emission trend might be risky to claim. A slowing down of the increase might be a more prudent and correct claim.
Lines 66, 149: Emissions: please specify that these are emissions of GHGs and SLCF.
Line 200: FFI = fossil fuel and industry. It is then further explained that industrial process emissions are meant. It might be useful and clarifying to say that e.g. biomass and biofuel used in industry is not included.
Line 201: Not only deforestation but also forest degradation might be added.
Line 204: F-gases are also emitted by military activities (in the past 20% shares have been estimated). It might be clarifying to mention that these are excluded?
Line 220: there are other global inventories worth mentioning here:
- Carbon monitor https://www.nature.com/articles/s43017-023-00406-z
- TNO CAMS CO2 global emissions inventory https://coco2-project.eu/data-portal
Line 232 and line 260: the uncertainty of CO2-LULUCF is not consistent: +/- 2.6 versus +/- 2.8. Please clarify
Line 286: CH4: recent progress has been made: please take up insights from E. Nisbet et al., 2023 (DOI: 10.22541/essoar.167689502.25042797/v1) and from Z. Zhang et al., 2023 (https://www.nature.com/articles/s41558-023-01629-0)
Line 282: not only an adjustment. It seems to me that also the uncertainty got significantly reduced (from 6.6 to 5.7 Gt CO2e). Please clarify if this is also due to improvements of the LULUCF emission estimates.
Line 300: Table 1: surprisingly the projection for 2022 has the same uncertainty as the calculated value for 2021. Is the extra uncertainty for the projection so small?
Line 338: another source of SLCF emissions is Crippa et al. ESSD (2018)
Line 355: Although CEDS gives estimates for 1750, it covers also 1970. The methodology to calculate the emission estimate for 2019 is much more similar to the values in 1970 than in 1750. Why not selecting also 1970 as like for CO2e instead of 1750?
Line 400: Also here an extra column with the value for 1970 could be useful.
Line 523: when changing the dataset from IEA to IATA, is there a jump? Can this be assessed with the IATA data backwards before 2020?
Line 615, Table 4: I do not understand a trend here for the aerosol cloud interactions? Any comment on these three values?
Line 810, states to assume “recent” linear trends. I can understand that the latest increment can be linearly assessed, but is the entire trend also linearly assessed (given the multivariate linear regression method in line 850)?
Line 1025: the global surface temperature is “close” to linearly proportional to the cumulative global CO2 (not CO2e?) emissions. The causal relationship between the global surface temperature and the cumulative emissions, can be assessed with the response on a significant change. Can this be done for the change of CO2 in 2020?
Line – Table 8: It is difficult to “interpret” the different levels of likehood to limit the global warming, I would have thought that we’ll can consume our 3 yr margin (150 GtCO2e), for inducing > 1,5 deg C with 67% likelihood, but in the conclusions in Table 10, it is claimed that we exhausted already our margin and have already now a chance of 1 in 3 to exceed the 1,5 degC. How do the two Tables link?
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CC3: 'Reply on RC1', Piers M. Forster, 13 May 2023
Thank you for your very timely and helpful review. I will discuss with my co-authors about extending the period earlier as I think your argument here makes a lot of sense when looking for changes over deacdes.
On the indicators set. This first year we focussed on a smaller set to build an estimate of human induced warming. It future years we would really like to exppand the set and the community to bring in additional indictors - and water cycle ones are top of our list. We'll respond properly to all your comments, but it the mean time thanks again!
Citation: https://doi.org/10.5194/essd-2023-166-CC3 -
CC7: 'Reply on RC1', Robin Lamboll, 16 May 2023
Regarding your last question, the two main and summary tables of carbon budgets include different types of uncertainty in the reported values. We will clarify and align them for the next iteration.
Citation: https://doi.org/10.5194/essd-2023-166-CC7 -
AC1: 'Reply on RC1', Piers M. Forster, 24 May 2023
Thank you for your support for the effort, and your understanding of its need and timeliness. Your review has been very helpful both for revising the framing of the paper and making corrections and clarifications resulting from your detailed comments. We have considered each comment carefully and taken most on board in our revised paper.
General comment
In line with IPCC’s values to provide unbiased, traceable and transparent information and conform with the FAIR principles for sharing data, this paper documents important indicators for closely monitoring the climate change that can be attributed to human activities. While the IPCC AR (FAR until AR6) were giving similar information at relative large timesteps of 5+ years, the ESSD paper series will now continue to give for these indicators such information on an annual basis. The period covered are 10 years, 2013-2022, while AR6 covered 2010-2019. Given the acceleration in climate change and urgency for stepping up climate actions, this is most appreciated. The dataset is fully available on zenodo, authored by 13 coauthors of the ESSD paper.
Thank you for the support for the effort.
However, it would have been useful to just extend the period with 3 yr before and 3 yr after the AR6 period, covering 2007-2022 for two reasons: (i) the average over the 10 yr 2010-2019 can be confronted with averages over a symmetrically extended period, (ii) 2008 saw a dip in emissions due to financial crisis and 2020 due to COVID. In the aftermath of the financial crisis, we saw a stabilising of the emissions, (Cfr. https://www.pbl.nl/en/publications/trends-in-global-co2-emissions-2013-report) but this seemed only a temporarily slow down. How can we be sure about a definite stabilising in the aftermath of COVID? It would be interesting to compare the shocks of 2008 and of 2020 and their impact on the successive years 2008-2010 en 2020-2022.
This is an interesting idea which we discussed as an author team. Potentially it could be really useful to compare emissions and responses to financial crises. We thought though that adding extra periods to the paper in this instance would make it overly complex and also reduce the focus on making a direct comparison to AR6. The data is available though and we think this would be useful for others to follow up in detail. It should, however, be noted that the 2007-2009 and 2020-2022 periods, particularly the latter, included significant La Niña events which would be likely to confound any assessment of the 2008/2020 shocks.
The indicators cover emissions (GHG as well as SLCF), GHG concentration, radiative forcing, surface temperature change, Earth energy imbalance, warming attributed to human activities, carbon budget and global temperature extremes. Unfortunately, indicators related to the water cycle (closely interacting with the carbon cycle) are not part of the set of indicators. While surface temperature change is a prime indicator (Fig.8 being one of the most important figures), other indicators such as soil moisture and water availability might have more direct impact on humans and nature. (Similarly, the addition of the global temperature extremes is much valued.)
Adding indicators related to the water cycle and extremes has always been the eventual aim of the author team and we very much hope to include these in future updates. The extremes of temperatures are shown to indicate where we may go in the future. Including them in this first iteration was challenging as there was more work involved to establish our approach to following the IPCC processes and more subjective assessment and wider literature review required, especially to assess regional extremes. We hope to have more next year though, so please watch this space! In response to your comment, we now clarify the existing scope and state our ambition in the introduction.
Specific remarks:
Lines 78, 1234: Stabilising emission trend might be risky to claim. A slowing down of the increase might be a more prudent and correct claim.
We agree and have modified the wording using your suggestion, thank you.
Lines 66, 149: Emissions: please specify that these are emissions of GHGs and SLCF.
Both are included and we now clarify.
Line 200: FFI = fossil fuel and industry. It is then further explained that industrial process emissions are meant. It might be useful and clarifying to say that e.g. biomass and biofuel used in industry is not included.
We now clarify this as requested.
Line 201: Not only deforestation but also forest degradation might be added.
We now clarify this as requested.
Line 204: F-gases are also emitted by military activities (in the past 20% shares have been estimated). It might be clarifying to mention that these are excluded?
This paragraph is setting the scene so we think this is too much detail to add here. We now point out later in this section that the data choice we make to use atmospheric concentrations for F-gas emissions works around known issues in inventory reporting and the exclusion of military applications.
Line 220: there are other global inventories worth mentioning here:
- Carbon monitor https://www.nature.com/articles/s43017-023-00406-z
- TNO CAMS CO2 global emissions inventory https://coco2-project.eu/data-portal
These are two very useful research efforts and initiatives but they are not yet at the level of established global inventories that are cited here, so we would prefer to keep the text as is.
Line 232 and line 260: the uncertainty of CO2-LULUCF is not consistent: +/- 2.6 versus +/- 2.8. Please clarify
Line 232 refers to the uncertainty range reported by the GCB, which is calculated as a one standard deviation range. Line 260 refers to the uncertainty range reported in this study, which follows the IPCC AR6 WG3 convention of a 90% confidence interval.
Line 286: CH4: recent progress has been made: please take up insights from E. Nisbet et al., 2023 (DOI: 10.22541/essoar.167689502.25042797/v1) and from Z. Zhang et al., 2023 (https://www.nature.com/articles/s41558-023-01629-0)
We agree, and have added a short paragraph with these references.
Line 282: not only an adjustment. It seems to me that also the uncertainty got significantly reduced (from 6.6 to 5.7 Gt CO2e). Please clarify if this is also due to improvements of the LULUCF emission estimates.
Clarified, you are correct.
Line 300: Table 1: surprisingly the projection for 2022 has the same uncertainty as the calculated value for 2021. Is the extra uncertainty for the projection so small?
These are taken from the GCB estimate directly. For fossil fuel, the annual growth in emission for 2022 is 1% with a one-sigma range: 0.1 to 1.9%). Given the GCB uncertainty on the annual estimate (5%), the overall uncertainty for the GCB 2022 estimate is still around 5%. For land use emissions, the uncertainty for the 2022 GCB projection is assumed to be the same as for previous years estimates: 0.7 GtC.
Line 338: another source of SLCF emissions is Crippa et al. ESSD (2018)
This text is making a specific point on uncertainty. The 2022 EDGAR update is cited earlier.
Line 355: Although CEDS gives estimates for 1750, it covers also 1970. The methodology to calculate the emission estimate for 2019 is much more similar to the values in 1970 than in 1750. Why not selecting also 1970 as like for CO2e instead of 1750?
1750 is used specifically for the radiative forcing estimate.
Line 400: Also here an extra column with the value for 1970 could be useful.
Other periods are all in the repository, but here we wanted to focus on data periods specifically needed for the arguments in the paper. Hence the choice.
Line 523: when changing the dataset from IEA to IATA, is there a jump? Can this be assessed with the IATA data backwards before 2020?
This has now been moved to the supplement - the sources are equivalent for where they overlap.
Line 615, Table 4: I do not understand a trend here for the aerosol cloud interactions? Any comment on these three values?
Comments have been added to the table - from reduced aerosol precursors and some saturation in the system.
Line 810, states to assume “recent” linear trends. I can understand that the latest increment can be linearly assessed, but is the entire trend also linearly assessed (given the multivariate linear regression method in line 850)?
This point is about future temperatures not being needed as recent trends are roughly linear, as explained earlier in the paragraph. The multivariate linear regression is more about the scaling factors - text has been deleted to avoid confusion as it repeated the explanation given earlier but could be confusing.
Line 1025: the global surface temperature is “close” to linearly proportional to the cumulative global CO2 (not CO2e?) emissions. The causal relationship between the global surface temperature and the cumulative emissions, can be assessed with the response on a significant change. Can this be done for the change of CO2 in 2020?
It could be, with lots of caveats around non-CO2, but this would be a research topic beyond the scope of this paper.
Line – Table 8: It is difficult to “interpret” the different levels of likehood to limit the global warming, I would have thought that we’ll can consume our 3 yr margin (150 GtCO2e), for inducing > 1,5 deg C with 67% likelihood, but in the conclusions in Table 10, it is claimed that we exhausted already our margin and have already now a chance of 1 in 3 to exceed the 1,5 degC. How do the two Tables link?
Table 10 has been updated and text added. There was a legacy issue with a revision of the paper that led to Tables 10 and 8 not being consistent. Thank you for spotting this. It’s to do with whether the zero emission commitment is included or excluded from the carbon budget uncertainty estimate. AR6 WGI SPM excluded it, so we follow their approach and now clarify this.
Citation: https://doi.org/10.5194/essd-2023-166-AC1
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RC2: 'Comment on essd-2023-166', Anonymous Referee #2, 10 May 2023
The authors are to be complimented for the amount of work and effort they put into this study. They have addressed most of the AR6 components and indicators. I am not commenting on the technical choice of methods, because their intention of updating AR6 is valid and greatly needed, but I find it a bit too long and descriptive, withmany detailed methodological sections. If this is targeted to scientist already familiar with the AR6 methods and GCB structure, I strongly recommend to move to a Supplementary file a lot of the methods (e.g., sections 4 (ERFs), 5 and perhaps some parts of 8, 9) and detailed tables, e.g., Table 3. I think the discussion and focus should be mostly given to section 7, where the summary of Results should have priority, perhaps merged with section 12. Now they are a bit hidden and one might get lost in methods until it arrives to read the end sections.
After reading the Introduction, it was not completely clear to me what else was aimed at, except for updating the indicators in AR6; if the annual update of the IPCC AR6 report is the main purpose, then it might confuse the policy makers, being a scientific publication and not an official recognized IPCC report, using slightly different data sets from those in AR6; if authors intend to also support the COP negotiations, then I see it lengthy and detailed. It also needs a more focused and concrete “bullet point” type of section including valid points for negotiations. I like the idea of having the “Reasons for change” column from Table 4 which could transform into a “summary actions for policy makers” recommendations in a final section, discussing the key figures such as: Figures 1, 2 and 7 and overview tables like Table 7.
After analyzing and updating all these indicators, what are the main messages, conclusions? Where do actions need to happen (e.g., GHG emissions, human forcing? etc.). Figure 7 is a good example.
The authors did not mention any indicator and/or analysis regarding the water availability, drought and sea level rise. I think it’s worth mentioning it in a short section.
Perhaps is not the aim here, but I was wondering why satellite-based observations are still underrepresented in such studies? UNFCCC parties start to look more into complementing their NGHGIs with these estimates, e.g., CAMS or other GOSAT, TROPOMI based inversions. They do exist and provide more and more valid estimates for the last years. They could be added to Figures 1 or 5 as an extra column, or at least mentioned in the Introduction.
Some specific comments:
Line 77: I would add “global emission levels are starting to stabilize…”. This message sounds a bit too optimistic, and this “stabilization” is only triggered by few developed nations managing their GHG emissions, while in most developing countries, emissions continue to increase.
To keep the time lines, I would move the lines 124-134 after lines 106-113 and reduce the length of this COP21 dedicated paragraph.
Line 201: degradation and natural disturbances
Line 204: Waste sector, important for CH4 emissions, is not mentioned.
Lines 215-220: Somewhere in Appendix or Supplement I would detail on the naming of these notable datasets of GCB. Please mention which EDGAR version is used…line 226 talks about EDGARv6.0 in AR6 while Figure 1 has EDGARv7.0
There is a bit of confusion reading the lines 215 – 235. First the authors mention that EDGAR is used in this study (line 218), then they describe all data sets from AR6 WGIII and on line 240 they mention that they don’t use EDGAR. Perhaps a simple table ‘AR6 data sets vs. this study’ would help summarizing the data?
It would be good to explain why the authors consider bookkeeping models as representative for estimating the CO2-LULUCF sources/sinks? How about FAOSTAT, DGVMs, CAMS?
Line 227: authors mention that in AR6 the CH4 and N2O emissions from GFED (please mention if 4.1) biomass combustion was added to EDGAR. This might create some double counting because EDGAR reports only anthropogenic emissions and emissions reported by GFED include as well agricultural waste burning and peat fires (in some countries considered managed). It is not clear to me how it’s done in this study, are GFED CH4 and N2O emissions still added to the PRIMAP-hist emissions? Did you add only the wild fires?
You mention on line 245 the specific data choices, I do agree that higher-tier methods are needed, and I would be glad to see in the future updates the inclusion of inversions.
Figure 1: please add to the caption the fact that both AR6 and current study datasets are represented.
Table 1: please correct CO2-LUCF with CO2-LULUCF
I would move Figure 4 to Appendix
Figure 5: I assume all bars should have written the periods, a bit more difficult for the shorter ones. Why not adding the left and right periods on top of the bars like: a) Decade-average warming given by observations for 2010-2019 (left) and 2013-2022 (right) . And similar to b) and c) panels.
I find Figure 7 very informative, summarizing and concluding well the findings.
Zenodo data:
Please give a shorter name to the files in the “carbon_budgets” folder, error when trying to open.
The info contained by each yml file (details on the contact author and original repository of the source code) should also be added to the README file. In this way one reads the summary of data provided by the study without having to open all the file, sone by one, unless is interested in the data.
How about individual time series from the data sets used in Section 2 (the 3 bookkeeping models, EDGAR times series, GFED, GCB? Could also be added to the greenhouse_gas_emissions_1750-2021.csv.
Please add a line in the README file: “.md and YML format files can be opened by any text editor (Notepad etc.)”.
Citation: https://doi.org/10.5194/essd-2023-166-RC2 -
CC4: 'Reply on RC2', Piers M. Forster, 13 May 2023
Thank you for your very fast and very helpful review.
Your comments on the framing are insightful and are like discussions among the author team and their evolution. We set out to update IPCC, but even only 2 years after publication, we found that a pure update without change to method was not possible for all indicators. Secondly scientists love to tinker with calibrations and things like the NOAA GHG calibration changed. Although less than a 1% effect, if we had stuck to an old calibration, our IPCC numbers would begin to depart from NOAA numbers which would be more accurate. So, in the end we were forced to do more of an assessment than we set out to do - hence the length. The method change column in Table 8 was a direct result of this and I think these method changes will be useful to track.
Despite this the authors thought we wanted to honour the IPCC approach as much as possible and be policy relevant but not policy prescriptive, so I'm not sure about your idea of attaching policy recommendations, although we want to use the paper to make them. We will discuss how to address this point. I certainly think we can be clearer with the intention with the intro.
On length, we erred on the side of caution and put everything in the paper, we will seek advice from the editor, but I think your ideas about what we could move out would really help focus the paper and make it more readable and useful to a wider audience.
Thank you so much.
Piers
Citation: https://doi.org/10.5194/essd-2023-166-CC4 -
AC2: 'Reply on RC2', Piers M. Forster, 24 May 2023
Thank you for your extremely helpful review of both the paper and the data repository. We have revised the paper, taking into account the comments, accepting most of them.
The authors are to be complimented for the amount of work and effort they put into this study. They have addressed most of the AR6 components and indicators. I am not commenting on the technical choice of methods, because their intention of updating AR6 is valid and greatly needed, but I find it a bit too long and descriptive, withmany detailed methodological sections. If this is targeted to scientist already familiar with the AR6 methods and GCB structure, I strongly recommend to move to a Supplementary file a lot of the methods (e.g., sections 4 (ERFs), 5 and perhaps some parts of 8, 9) and detailed tables, e.g., Table 3. I think the discussion and focus should be mostly given to section 7, where the summary of Results should have priority, perhaps merged with section 12. Now they are a bit hidden and one might get lost in methods until it arrives to read the end sections.
These are excellent suggestions for focussing the paper, thank you. We have essentially made them, additionally moving out the appendices and the three detailed methods from Section 7. We have also shortened the introduction, see response below.
After reading the Introduction, it was not completely clear to me what else was aimed at, except for updating the indicators in AR6; if the annual update of the IPCC AR6 report is the main purpose, then it might confuse the policy makers, being a scientific publication and not an official recognized IPCC report, using slightly different data sets from those in AR6; if authors intend to also support the COP negotiations, then I see it lengthy and detailed. It also needs a more focused and concrete “bullet point” type of section including valid points for negotiations. I like the idea of having the “Reasons for change” column from Table 4 which could transform into a “summary actions for policy makers” recommendations in a final section, discussing the key figures such as: Figures 1, 2 and 7 and overview tables like Table 7.
We have refocused the introduction to remove much of the policy framing and shortened it. In response to reviewer 4 also, we want to follow the IPCC approach and keep the paper policy relevant but not policy prescriptive. We therefore do not think that adding negotiations points would be useful. Rather we want to build material around the paper to address these aspects, keeping the paper policy neutral. We are aiming to keep it as IPCC-like as possible and form conclusions in a similar way. Hopefully, the shorter and more focussed paper is more compelling.
After analyzing and updating all these indicators, what are the main messages, conclusions? Where do actions need to happen (e.g., GHG emissions, human forcing? etc.). Figure 7 is a good example.
These are brought forward in the last two paragraphs of section 12 and the abstract. We think this is the right level of messages from the paper.
The authors did not mention any indicator and/or analysis regarding the water availability, drought and sea level rise. I think it’s worth mentioning it in a short section.
This is something we wish to do in future years and now cover this in the introduction when addressing the paper’s scope, see also the reply to reviewer 1.
Perhaps is not the aim here, but I was wondering why satellite-based observations are still underrepresented in such studies? UNFCCC parties start to look more into complementing their NGHGIs with these estimates, e.g., CAMS or other GOSAT, TROPOMI based inversions. They do exist and provide more and more valid estimates for the last years. They could be added to Figures 1 or 5 as an extra column, or at least mentioned in the Introduction.
In this paper we stick close to AR6 methods, Bastos et al 2022 https://doi.org/10.1186/s13021-022-00214-w for example, shows that more work is still necessary to align them with bottom up methods. We now allude to future possibilities in the conclusions and will consider again for future updates.
Some specific comments:
Line 77: I would add “global emission levels are starting to stabilize…”. This message sounds a bit too optimistic, and this “stabilization” is only triggered by few developed nations managing their GHG emissions, while in most developing countries, emissions continue to increase.
We have now reworded to change the tone in line with your comments.
To keep the time lines, I would move the lines 124-134 after lines 106-113 and reduce the length of this COP21 dedicated paragraph.
These paragraphs have been shortened and removed in response to reviewer 4.
Line 201: degradation and natural disturbances
Added details
Line 204: Waste sector, important for CH4 emissions, is not mentioned.
Added.
Lines 215-220: Somewhere in Appendix or Supplement I would detail on the naming of these notable datasets of GCB. Please mention which EDGAR version is used…line 226 talks about EDGARv6.0 in AR6 while Figure 1 has EDGARv7.0
GCB uses several datasets and we think it is best for the interested reader to refer to their paper. We now mention that although EDGAR version 7 is available, we use PRIMAP.
There is a bit of confusion reading the lines 215 – 235. First the authors mention that EDGAR is used in this study (line 218), then they describe all data sets from AR6 WGIII and on line 240 they mention that they don’t use EDGAR. Perhaps a simple table ‘AR6 data sets vs. this study’ would help summarizing the data?
The first paragraph describes the sort of data sets employed. The second one is exclusive to those used in AR6 and the third gives our updates approach. We have tried to clarify the text in the first paragraph that “not all these datasets were employed in this update.” We have not added a table to keep things as simple as possible - we already have a lot of tables.
It would be good to explain why the authors consider bookkeeping models as representative for estimating the CO2-LULUCF sources/sinks? How about FAOSTAT, DGVMs, CAMS?
As specified in the text we follow AR6 and GCB standard practise. Other approaches exist that quantify land-use related CO2 fluxes, but they are not directly comparable since their definitions differ from the bookkeeping estimates as used in the annual Global Carbon Budgets, where effects of environmental changes are excluded from the LULUCF fluxes (Pongratz et al., 2021). Estimates based on DGVMs include the “loss of additional sink capacity”, that is, they attribute to LULUCF the loss of the hypothetical sink that the forests, had they not been cleared for agriculture, would have created in response to environmental changes (predominantly beneficial effects on plant growth due to rising atmospheric CO2). Their estimate of the LULUCF flux is thus a substantially higher emission term to the atmosphere (Obermeier et al., 2021). Further, by using empirical carbon densities, bookkeeping models are assumed to be more complete in their representation of management activities than the process-based DGVMs. DGVMs are thus used to quantify uncertainties around the bookkeeping estimates, but not as direct estimate of the LULUCF flux. Data sets such as the national GHG inventories by UNFCCC and FAOSTAT assume different system boundaries and distinguish fluxes by area (managed vs unmanaged) rather than by (anthropogenic vs natural) drivers. They attribute parts of the fluxes due to environmental changes to the LULUCF flux, when they occur on managed land, which thus includes a substantial sink term in the LULUCF flux estimate, turning it from a substantial global source to a global sink term. UNFCCC and bookkeeping estimates can be “translated” into each other and are largely consistent then (Friedlingstein et al., 2022; Grassi et al., 2023). Differences exist between UNFCCC and FAOSTAT in particular with respect to coverage of non-biomass carbon pools and non-forest land-use types (Grassi et al., 2022). CO2 fluxes from land can also be estimated from satellite measurements of atmospheric CO2 through inversions (Deng et al. 2022). However, currently, inversions have coarse spatial scale and country-level fluxes are relatively poorly constrained (Bastos et al. 2022). Again, the distinction between anthropogenic and natural drivers is problematic (Petrescu et al., 2021) and the key reason why models are used to estimate the LULUCF fluxes.
We have added the following text as a new paragraph in the manuscript:
“There are also varying conventions used to quantify CO2-LULUCF fluxes. These include the use of bookkeeping models, dynamic global vegetation models (DVGMs), and the national inventory approach (Pongratz et al. 2021). Each differs in terms of their applied system boundaries and definitions, and are not directly comparable. However, efforts to “translate” between bookkeeping estimates and national inventories using DVGMs have demonstrated a degree of consistency between the varying approaches (Friedlingstein et al., 2022; Grassi et al., 2023).”
This choice is discussed later in the paper, from line 245 in the submitted version.
Line 227: authors mention that in AR6 the CH4 and N2O emissions from GFED (please mention if 4.1) biomass combustion was added to EDGAR. This might create some double counting because EDGAR reports only anthropogenic emissions and emissions reported by GFED include as well agricultural waste burning and peat fires (in some countries considered managed). It is not clear to me how it’s done in this study, are GFED CH4 and N2O emissions still added to the PRIMAP-hist emissions? Did you add only the wild fires?
We have investigated this important issue and found that while early drafts of the AR6 WGIII report included fire emissions from GFED, the final published draft did not. Indeed, fire emissions are an active area of development among the different teams compiling inventories - so the risk of double counting such emissions could well arise in this and subsequent editions of the indicator update. Therefore, to ensure consistency with AR6 we edit out references to GFED in the manuscript and exclude these emissions from the reported data.
You mention on line 245 the specific data choices, I do agree that higher-tier methods are needed, and I would be glad to see in the future updates the inclusion of inversions.
We will endeavor to do this.
Figure 1: please add to the caption the fact that both AR6 and current study datasets are represented.
We think the comment about the starred data used for the assessment satisfies this need and adding further text might confuse the reader.
Table 1: please correct CO2-LUCF with CO2-LULUCF
Done.
I would move Figure 4 to Appendix
We would prefer to keep it here as it clearly shows the increasing rate energy absorption by the climate system.
Figure 5: I assume all bars should have written the periods, a bit more difficult for the shorter ones. Why not adding the left and right periods on top of the bars like: a) Decade-average warming given by observations for 2010-2019 (left) and 2013-2022 (right) . And similar to b) and c) panels.
Only the first in the series have - this is now covered in the caption.
I find Figure 7 very informative, summarizing and concluding well the findings.
Thank you.
Zenodo data:
Please give a shorter name to the files in the “carbon_budgets” folder, error when trying to open.
Done.
The info contained by each yml file (details on the contact author and original repository of the source code) should also be added to the README file. In this way one reads the summary of data provided by the study without having to open all the file, sone by one, unless is interested in the data.
Done. Please note that rather than listing email contacts we have linked to the homepages of the relevant data contributor; while their email addresses are already mostly on their websites in plain text and contained in the YML files, we choose not to list them here in order to not provide another source for spam email crawlers.
How about individual time series from the data sets used in Section 2 (the 3 bookkeeping models, EDGAR times series, GFED, GCB? Could also be added to the greenhouse_gas_emissions_1750-2021.csv.
Thank you for the suggestion. Now, all of the emissions data has been added as an additional file in the greenhouse gas emissions dataset directory of the repository.
Please add a line in the README file: “.md and YML format files can be opened by any text editor (Notepad etc.)”.
Done.
Citation: https://doi.org/10.5194/essd-2023-166-AC2
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CC4: 'Reply on RC2', Piers M. Forster, 13 May 2023
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CC1: 'Comment on essd-2023-166', Rasmus Benestad, 10 May 2023
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CC2: 'Reply on CC1', Piers M. Forster, 13 May 2023
Thank you for taking the time to read and review our paper. I agree that we are focussing here on temperture realted metrics. This was a choice to build the necessary steps to estimate human induced warming, which is widlly used in policy contexts around Paris temperture targets.
This being sai, in future years I would love to see both the set of indicators expand and the community involbed also expand. We will revisit the citations we use. Again, we are not trying to completey redo the IPCC asessment and start with the basis of their far more comprehensive literature review. Most of the papers cited are methodological as we use a set of methods generated by the authors. However, we will consider how best to bring your useful suggestions in whwn we revise, thank you
Citation: https://doi.org/10.5194/essd-2023-166-CC2 -
AC5: 'Reply on CC1', Piers M. Forster, 24 May 2023
We thank you for the support for such an exercise and your helpful review. In line with comments by other reviewers, we have rewritten the introduction to make the scope of indicators assessed much clearer. We hope to extend the scope to include water cycle indicators in future years. In terms of the literature cited, we deliberately chose to build off IPCC AR6 reports. Rather than make a new comprehensive assessment, we use high level citations to chapters in this report. The other literature cited is focussed on the methodological descriptions for completeness, hence the authors earlier publications are cited where necessary. Nevertheless, we broadly agree with your point that the IPCC needs to evolve in how it makes literature assessments. In the future we want to consider how to bring in elements of systematic review, but more work is needed in this area.
Citation: https://doi.org/10.5194/essd-2023-166-AC5
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CC2: 'Reply on CC1', Piers M. Forster, 13 May 2023
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RC3: 'Comment on essd-2023-166', Matthew Jones, 10 May 2023
It's a fantastic initiative to provide running updates of all the work that underpins the IPCC assessments. This work should serve as a vital reference point for those interested in tracking (anthropogenic) climate change across a variety of indices.
The paper is extremely well written! Mercifully, the content is far more accessible than the volumous IPCC chapters that it builds on. Yet I felt that all the key concepts and background were gently spelled out, and the discussion of dataset, methods and uncertainties were covered with refreshing clarity - providing all the key information, but not so much that the text becomes convoluted. The consistent discussion of differences since AR6 and SR1.5 is also a helpful aid to the reader throughout the text.
Overall, this is a wonderful contribution to the literature that should make many aspects of future IPCC assessments far more streamlined.
I have only some minor comments/suggestions:
[75] "Human induced warming is increasing at an unprecedented rate of over 0.2 °C per decade." - is this over the past decade, or extrpolated from the 2022 value? (I imagine the former, but it's not clear when reading on from the following sentence, so it would be good to specify).
[Table 1] It might be nice to split the CH4 and N2O emissions from into their FFI and LUCF components, like for CO2? I think this is possible and straightforward when using the PRIMAP-hist dataset.
[Tables 1-3] Could you report the values for consistent periods/years across these tables? e.g. 1750, 1850, 1970-1979, ... 2010-2019, 2019, 2021, 2022.
[All results tables] As an extension of the comment above, could you provide values consistently across all results tables? I think this would measurably help the reader to digest the array of indicators provided, without getting too caught up with tracking different time periods and so on. I imagine this would help a lot with communicating the results beyond the paper - e.g. in a lecture setting.
[Figure 7] Personally, I think it would be more intuitive to flip this figure along its vertical axis, so that emissions are at the top, then concentrations, then warming at the bottom. I didn't find it natural to start at the bottom and work up.
[1214] "Indeed, our results point to the opposite: continued high levels of greenhouse gas emissions, combined with improvements in air quality, are reducing the level of aerosol cooling - leading to an unprecedented rate of human-induced warming". Perhaps rephrase for clarity?: "continued high levels of greenhouse gas emissions, combined with reduced aerosol cooling under improved air quality, have led to an unprecedented rate of human-induced warming".
Citation: https://doi.org/10.5194/essd-2023-166-RC3 -
CC5: 'Reply on RC3', Piers M. Forster, 13 May 2023
Dear Matt
Thank you for your generous review comments. They are really helpful. Another reviewer thought some of the methods could be move to supplementary material - but you seem to like them in the paper? We probably need to seek advice from the editor here!
All the best - and thank you
Piers
Citation: https://doi.org/10.5194/essd-2023-166-CC5 -
AC3: 'Reply on RC3', Piers M. Forster, 24 May 2023
Thank you so much for these very supportive comments. They make the effort involved worthwhile. We address the minor issues below and make appropriate changes where we can (some might have to wait until next time).
I have only some minor comments/suggestions:
[75] "Human induced warming is increasing at an unprecedented rate of over 0.2 °C per decade." - is this over the past decade, or extrpolated from the 2022 value? (I imagine the former, but it's not clear when reading on from the following sentence, so it would be good to specify).
“Over the 2013-2022 period” has been added to the abstract.
[Table 1] It might be nice to split the CH4 and N2O emissions from into their FFI and LUCF components, like for CO2? I think this is possible and straightforward when using the PRIMAP-hist dataset.
To avoid complexity and to follow the AR6 approach, we decided not to do this as this probably would require a fuller assessment to do properly. We will revisit it next year.
[Tables 1-3] Could you report the values for consistent periods/years across these tables? e.g. 1750, 1850, 1970-1979, ... 2010-2019, 2019, 2021, 2022.
[All results tables] As an extension of the comment above, could you provide values consistently across all results tables? I think this would measurably help the reader to digest the array of indicators provided, without getting too caught up with tracking different time periods and so on. I imagine this would help a lot with communicating the results beyond the paper - e.g. in a lecture setting.
We completely agree and set out to do just this. However, when digging into the data for the different sections, we needed to make specific date range choices to be consistent with AR6 and SR1.5. Unfortunately, these reports were not consistent across their chapters. Adding additional periods to the table to satisfy both AR6 compatibility and internal consistency would make the paper unwieldy. In this first iteration we especially wanted to show the provenance with AR6, so chose date ranges accordingly. Furthermore, in many sections of the paper such as global mean temperature, we rely on AR6 uncertainty approaches which are specific to certain averaging periods. Data for all periods are provided online, so hopefully this helps? We will revisit it again next year.
[Figure 7] Personally, I think it would be more intuitive to flip this figure along its vertical axis, so that emissions are at the top, then concentrations, then warming at the bottom. I didn't find it natural to start at the bottom and work up.
Here we choose to replicate the exact arrangement made in the AR6 synthesis report to show the provenance. This had stakeholder and designer input - so although we intend to agree, who are we to argue 🙂
[1214] "Indeed, our results point to the opposite: continued high levels of greenhouse gas emissions, combined with improvements in air quality, are reducing the level of aerosol cooling - leading to an unprecedented rate of human-induced warming". Perhaps rephrase for clarity?: "continued high levels of greenhouse gas emissions, combined with reduced aerosol cooling under improved air quality, have led to an unprecedented rate of human-induced warming".
Your wording is adopted. Thank you.
Citation: https://doi.org/10.5194/essd-2023-166-AC3
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CC5: 'Reply on RC3', Piers M. Forster, 13 May 2023
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RC4: 'Comment on essd-2023-166', Albertus J. (Han) Dolman, 15 May 2023
This is a good and much needed paper. The idea of providing annual updates on IPCC indicators is laudable. Since most of the work relates to IPCC methods or updated versions based on published work, I have little detailed comments to make.
However, I find the introduction unnecessary cumbersome and almost too apologetic. Clearly state the aims: providing annual updates of key indicators that are changing at annual time scales and provide references to changing methods. The audience when you publish in ESDD are scientist, not policymakers
I do miss a good rationale for the present selection of indicators. A conceptual diagram, assuming the authors want to stick for now to the energy cycle, how GHGs link to ERF, to warming, EIB might help. Now one wonders where the indicator of, for instance sea level is not used. Extremes are included, but only temperature, why not precipitation?
Few minor details.
L92. BAMS also does GHGs
L248 Not sure investment rate is an adequate description of the quality of a dataset. Another reason to use the UNFCC estimates, such as improved monitoring of inventories might be more useful.
L262. I would argue that given the uncertainty, the two values are the same, similarly l 275, there is mention of a substantial and downward revision, all very much within the uncertainty bounds, so please be more careful with the wording here. Similarly using the word “stable” is maybe a bit too positive if you compare only two years.
L400. I wonder if Table 4 is not better places in supplementary data.
Despite these comments I think the authors have done a wonderful job and I look forward seeing the indicators published in the dashboard (where I would like to make sure that the different components in the dashboard do reflect the same data and numbers as in the policy facing dashboard)
Citation: https://doi.org/10.5194/essd-2023-166-RC4 -
CC6: 'Reply on RC4', Piers M. Forster, 15 May 2023
Dear Han
Thank you so much for your review, it is really helpful. On reading our paper again I think you are spot on with framing of the introduction. We were trying to show the policy relevence but in the end I think the balance is wrong and we should focus it more on the science aims of the paper. This would also help shorten and focus the text. We'll respond to this and the other comments properly soon.
Best wishes
Piers
Citation: https://doi.org/10.5194/essd-2023-166-CC6 -
AC4: 'Reply on RC4', Piers M. Forster, 24 May 2023
Thank you for this very helpful review. We agree with your issues around the framing and have shortened and focused the introduction. We have specifically reviewed much of the policy angle discussion to make it clearer that we are following IPCC of being policy relevant but policy neutral. We have strengthened the rationale over indicator choice in the introduction and explicitly explain why we do not cover precipitation extremes in this first iteration. We hope to in future updates.
Few minor details.
L92. BAMS also does GHGs
Text added to clarify.
L248 Not sure investment rate is an adequate description of the quality of a dataset. Another reason to use the UNFCC estimates, such as improved monitoring of inventories might be more useful.
“Investment” changed to “best use of country-level improvements in data gathering infrastructures”.
L262. I would argue that given the uncertainty, the two values are the same, similarly l 275, there is mention of a substantial and downward revision, all very much within the uncertainty bounds, so please be more careful with the wording here. Similarly using the word “stable” is maybe a bit too positive if you compare only two years.
We agree, wording adjusted to make the text less definitive.
L400. I wonder if Table 4 is not better places in supplementary data.
Change made as requested and table moved to the Supplement - I think you meant old table 3.
Despite these comments I think the authors have done a wonderful job and I look forward seeing the indicators published in the dashboard (where I would like to make sure that the different components in the dashboard do reflect the same data and numbers as in the policy facing dashboard)
Thank you. Your help and advice for the dashboard development would be much appreciated.
Citation: https://doi.org/10.5194/essd-2023-166-AC4
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CC6: 'Reply on RC4', Piers M. Forster, 15 May 2023
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CC8: 'Comment on essd-2023-166', Leon Simons, 16 May 2023
Thank you for your invaluable work to provide an annual update of large-scale indicators of the state of the climate system and the human influence. During this time of apparent rapid changes in both the anthropogenic forcing and the Earth's Energy Imbalance, the need for and value of a timely update can hardly be overstated.
A significant anthropogenic forcing change since 2019 still seems to be absent from your assessment. On January 1st 2020 new regulation from the International Maritime Organization (IMO 2020) came into effect. This is thought to have caused a reduction in annual SOx emissions (over the oceans) of about 8.5 Tg yr-1 (Corbett et al. 2016, see attached).
The effect on global net forcing is still uncertain. Past models give a range of 0.027 W/m² (Bilsback et al 2020) to 0.36 W/m² (Lauer et al. 2007). Wall et al. (2022) estimated based on observations that the cloud-mediated RF from anthropogenic sulphate aerosols before 2020 was −1.11±0.43 W m² over the global ocean. Because of the short atmospheric lifetime of sulphates, a significant part of this sulphate cooling might be attributable to shipping (the relative contribution was not assessed by Wall et al.).
Thank you again for your invaluable work.
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CC9: 'Reply on CC8', Christopher Smith, 18 May 2023
Thank you Leon for your positive comments.
Shipping emissions estimates would be provided by the Community Emissions Data System (CEDS), but data is only available until 2019 at present. We use proxy estimates from activity data for 2020, 2021, and 2022. You are correct that the legislated shipping emissions reductions of 8.5 Tg (around 10% of global sulfur emissions) are not explicitly included in our estimates for 2020, 2021 and 2022.
We are not certain to what extent that including the expected 8.5 Tg reductions for 2020-22 would lead to double-counting. COVID-19 caused a slowdown of global trade activity during 2020 at least, and it is likely that shipping emissions reductions were therefore less than 8.5 Tg as the baseline (which we do capture) would be lower.
If we had a good idea of the relative reduction in SO2 that resulted from the legislation we could include its effects without double-counting. One paper we now cite in section 4 that provides an analysis of this (Watson-Parris et al. 2022) suggests a reduction of 80% in SO2 emissions but this could be an overestimate if there has been some under compliance with the legislation post-2020 (and overcompliance before then). It also seems unlikely that a rapid change in how fuel is processed could be achieved so quickly. In practice, the reduction may have been less than 80%. The separation into legislation effects and COVID effects are unfortunately conflated.
A reduction of 8.5 Tg in SO2 emissions is similar to the 9 Tg we already estimate for the 2019 to 2022 period (Table 2), from which we estimate an increase in aerosol-cloud interactions of +0.07 W m-2. The sulfur contribution to aerosol-radiation interactions from this level of reduction is +0.02 W m-2. Assuming linear behaviour (the aerosol-cloud interaction relationship is not linear, but for a 10% change it is a good assumption), we could add another +0.09 W m-2 if we did indeed underestimate the reduction by 8.5 Tg. This sounds small but would add around 50% to the 2019-2022 forcing change. For reasons stated above, we believe that a difference of 8.5 Tg between our estimate and any ground truth including shipping is an upper bound, but concede that the difference is probably larger than zero.
The second effect that may alter forcing is in the pattern of shipping. Over the marine stratocumulus regions reductions in emitted SO2 may have a greater effect on weakening (i.e. increasing the positivity of) the aerosol-cloud forcing than the same amount of SO2 emitted over land (Watson-Parris et al. 2022; Yuan et al. 2022). The upper bound of this effect was recently estimated to be 0.27 W m-2. We note to consider the differing spatial effects of shipping emissions in future versions of the dataset if it turns out to be backed up by more research.
By the time of the release of the 2023 update, we would expect CEDS to have emissions updated beyond 2020, for which we will have more reliable estimates of recent changes in aerosol radiative forcing. As a living dataset, the most recent years are naturally subject to greater uncertainty than a period slightly further back in time that has been subject to greater study and benefits from more independent estimates (e.g. in the case of short-lived forcer emissions, the mid-2010s), and the dataset will be continually revised upon release of updated data even when it pertains to years before the most recent.
Watson-Parris et al. 2022: https://www.pnas.org/doi/10.1073/pnas.2206885119
Yuan et al. 2022: https://www.science.org/doi/10.1126/sciadv.abn7988
Citation: https://doi.org/10.5194/essd-2023-166-CC9
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CC9: 'Reply on CC8', Christopher Smith, 18 May 2023
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RC5: 'Comment on essd-2023-166', Martin Heimann, 17 May 2023
This is a very useful update since AR6 of key climate change indicators. The authors have made a very nice job, and the manuscript reads extremely well. I have only a few comments:
General comments:
1) I second Han Dolman’s review comment in that section 1 could be significantly shortened and focused on the key objectives of this manuscript, i.e. providing an update of key climate change indicators. The overall rationale for providing a science based assessment has been well covered in the IPCC reports.
2) Section 9, climate and weather extremes. As stated in the text, this section is a placeholder for extreme weather indicators in future updates of this effort. For now, only an update of an extreme temperature indicator is discussed. Should not the title of this section be modified to reflect this restriction? Since this section and Figure 6 provide an “update” of Figure 11.2 in AR6, one might mention here as a footnote that Figure 11.2 in the final version of AR6 has a wrong time axis (data appear to go up to the year 2023, while the report was published in 2021…).
Minor comments:
p 11, Table 1: For consistency, why not have an additional column with the decade 2010-2019? I understand the reason to show the last decade, but the gap between 2009 and 2012 is somewhat puzzling.
Section 3, Table 3: I’d reverse the ordering of the columns so that time runs to the right, similar to all the other tables. An additional column with the annual increment in 2022 or perhaps averaged over 2019-2022, would also be useful.
Section 4, l 530: Wording: The time period 2009-2019 covers the entire solar cycle 24, not just the “solar minimum”.
Section 8, remaining carbon budget. Simply for putting these numbers into perspective, it would be helpful if in the text not only the emissions between the base year 2020 and 2022 were mentioned, but also the total historical emissions up to the base year or perhaps the total from the previous decade (i.e. from the numbers in table 1 in section 2).
Citation: https://doi.org/10.5194/essd-2023-166-RC5 -
AC6: 'Reply on RC5', Piers M. Forster, 24 May 2023
Thank you for the positive endorsement, your review has been very helpful to us: thank you for taking the time.
General comments:
1) I second Han Dolman’s review comment in that section 1 could be significantly shortened and focused on the key objectives of this manuscript, i.e. providing an update of key climate change indicators. The overall rationale for providing a science based assessment has been well covered in the IPCC reports.
We agree with this point and have removed the overly policy-focused paragraph to reframe the introduction. This also helps clarify the purpose of the paper.
2) Section 9, climate and weather extremes. As stated in the text, this section is a placeholder for extreme weather indicators in future updates of this effort. For now, only an update of an extreme temperature indicator is discussed. Should not the title of this section be modified to reflect this restriction? Since this section and Figure 6 provide an “update” of Figure 11.2 in AR6, one might mention here as a footnote that Figure 11.2 in the final version of AR6 has a wrong time axis (data appear to go up to the year 2023, while the report was published in 2021…).
Good idea, thank you. We have changed the title of section 9 to “Examples of climate and weather extremes : maximum temperature over land” .We got our rulers out to confirm the last point on Figure 11.2 in AR6 Chapter 11 is for 2020, so these data are ok.
Minor comments:
p 11, Table 1: For consistency, why not have an additional column with the decade 2010-2019? I understand the reason to show the last decade, but the gap between 2009 and 2012 is somewhat puzzling.
Done.
Section 3, Table 3: I’d reverse the ordering of the columns so that time runs to the right, similar to all the other tables. An additional column with the annual increment in 2022 or perhaps averaged over 2019-2022, would also be useful.
We have reversed the order but prefer to keep single years as this builds to radiative forcing. The uncertainty in trends needs to be thought about more for minor species so this is not added this year.
Section 4, l 530: Wording: The time period 2009-2019 covers the entire solar cycle 24, not just the “solar minimum”.
This has now been corrected (revised text is now in the Supplement), and we thank you for pointing this out.
Section 8, remaining carbon budget. Simply for putting these numbers into perspective, it would be helpful if in the text not only the emissions between the base year 2020 and 2022 were mentioned, but also the total historical emissions up to the base year or perhaps the total from the previous decade (i.e. from the numbers in table 1 in section 2).
This is a good idea and we have adopted your suggestion and added the historic budget until the end of 2019 following the AR6 WGI SPM Table 2.
Citation: https://doi.org/10.5194/essd-2023-166-AC6
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AC6: 'Reply on RC5', Piers M. Forster, 24 May 2023
Piers Maxwell Forster et al.
Data sets
Indicators of Global Climate Change 2022 Chris Smith, Tristram Walsh, Piers Forster, Nathan Gillett, Mathias Hauser, William Lamb, Robin Lamboll, Matthew Palmer, Aurélien Ribes, Dominik Schumacher, Sonia Seneviratne, Blair Trewin, and Karina von Schuckmann https://doi.org/10.5281/zenodo.7883758
Model code and software
Indicators of Global Climate Change Chris Smith, Tristram Walsh, Piers Forster, Nathan Gillett, Mathias Hauser, William Lamb, Robin Lamboll, Matthew Palmer, Aurélien Ribes, Dominik Schumacher, Sonia Seneviratne, Blair Trewin, and Karina von Schuckmann https://github.com/ClimateIndicator
Remaining carbon budget calculation Robin Lamboll and Joeri Rogelj https://github.com/Rlamboll/AR6CarbonBudgetCalc
Piers Maxwell Forster et al.
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