the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 May 2025
Indicators of Global Climate Change 2024: annual update of key indicators of the state of the climate system and human influence
Abstract. In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets (published here https://doi.org/10.5281/zenodo.15327155 Smith et al., 2025a) to produce updated estimates for key indicators of the state of the climate system: net emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. This year, we additionally include indicators for sea-level rise and land precipitation change. We follow methods as closely as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report.
The indicators show that human activities are increasing the Earth’s energy imbalance and driving faster sea-level rise compared to the AR6 assessment. For the 2015–2024 decade average, observed warming relative to 1850–1900 was 1.24 [1.11 to 1.35] °C, of which 1.23 [1.0 to 1.5] °C was human-induced. The 2024 observed record in global surface temperature (1.52°C best estimate) is well above the best estimate of human-caused warming (1.36°C). However, the 2024 observed warming can still be regarded as a typical year, considering the human induced warming level and the state of internal variability associated with the phase of El Niño and Atlantic variability. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.27 [0.2–0.4] °C per decade over 2015–2024. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 53.6 ± 5.2 GtCO2e per year over the last decade (2014–2023), as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track decreases or increases in the rate of the climatic changes presented here.
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RC1: 'Comment on essd-2025-250', John Dunne, 16 May 2025
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The manuscript “Indicators of Global Climate Change 2023: annual update of key indicators of the state of the climate system and human influence” by Forster et al provides an incremental annual update from Forster et al., 2023 “Indicators of Global Climate Change 2022: annual update of large-scale indicators of the state of the climate system and human influence” as an update from analysis and assessment from methods used in the IPCC Sixth Assessment Report Working Group I Chapters 3: Human Influence on the Climate System“ and “Chapter 7: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity”. The primary new information is update and extension to data on forcings and temperature responses through another year (2023) with the main points indicated in the abstract attributing 100% of historical decadal warming to human activity: “The indicators show that, for the 2014–2023 decade average, observed warming was 1.19 [1.06 to 1.30] °C, of which 1.19 [1.0 to 1.4] °C was human-induced.” Pointing out the extreme temperature of 2023 “For the single-year average, human-induced warming reached 1.31 [1.1 to 1.7] °C in 2023 relative to 1850–1900. The best estimate is below the 2023-observed warming record of 1.43 [1.32 to 1.53] °C, indicating a substantial contribution of internal variability in the 2023 record.” And revising the decadal trends slightly upward mostly due to increased confidence from the “0.2 °C per decade“ assessed in AR6: “Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.26 [0.2–0.4] °C per decade over 2014–2023. This high rate of warming is caused by a combination of net greenhouse gas emissions being at a persistent high of 53.5:4 GtCO2e yr-1 over the last decade, as well as reductions in the strength of aerosol cooling.” As such, the information provided is highly useful as an interim (annual) process level synthesis between the phases of formal IPCC Assessment (currently scoped for 2028-2030).
My main scientific criticism is in the interpretation that the authors are able to attribute all 1.19°C of the 1.19°C of observed decadal warming as human-induced as there are some questionable implications of these central values being the same, first the idea that the authors can discount any role of decadal to centennial scale climate variability of potentially playing any significant role, and second that the alignment of these central values seems to imply that the human-induced warming response of the real world was just as likely to be more severe as less severe than what has occurred. This seems more likely a flaw in the approach in ignoring potential sources of uncertainty than a statement of absolute alignment between effective radiative forcing and global response. Of course, the authors do provide an uncertainty range for each number, and the modeled response range/uncertainty of 0.4°C is indeed slightly larger than the observational uncertainty of 0.24°C, but it is hard for me to translate that into an overall central value statement that implies that the forcing and response in the Earth system (particularly before the satellite record) is known to be 100% attributable. Do the authors really think we know the system that well? Or is it more appropriate to say that the authors are confident that at least 1.0/1.19 = 84% of the observed warming is human-induced and that the uncertainty includes that 100% might be human-induced? This type of statement would seem more in line with the historical context of model-based attribution.
My main communication criticism of the current manuscript is the lack of reference to/contextualization in the introduction and motivation for the present work against the two other community level annual reports offered annually on this topic, namely the BAMS "State of the Climate" and WMO "State of the Global Climate" reports put out every year. The big difference I see is that the present analysis goes beyond the observations into process level effective radiative forcing and attributed human-induced response using the same (at times updated) methods rigorously assessed in AR6. This oversight is particularly odd given that both products are referenced later in the manuscript, so the authors are obviously aware of them and use them as sources in their analysis.
Technical suggestions:
Abstract: remove second “on the global climate system” in second sentence.
Abstract: remove “(AR6)” and (WGI)” as these acronyms are not used in the abstract.
Abstract: remove or rephrase “can be trusted by all parties involved” – the question of “trust” is separate from whether the research result is a consistent extension of previously trusted methods as a new research result.
Abstract: remove “its direction of travel”
Introduction, first paragraph: “is there any other provenance to the IGCC activity such as endorsement or sponsorship by WCRP, IPCC, specific government initiatives etc? Funding sources?”
Introduction, first paragraph: Here is where a brief statement on why this IGCC effort is necessary given that BAMS already puts out the "State of the Climate" and WMO "State of the Global Climate" every year. How is this different/complementary? The use of the same methods as in AR6? Is it more comprehensive of forcings, model information and budgets in this way compared to BAMSWMO focus only on observations? See above for suggested language.
Introduction, second paragraph: “last year” should specify that the focus of Forster et al., 2023 was an extension of the methods through 2022.
Introduction, second paragraph: “the remaining carbon budget” is only relevant when specified for particular climate warming thresholds, so should indicate the remaining carbon budget for what? 1.5C? 2C? 3C?... add "to policy-relevant temperature thresholds".
Introduction, third paragraph: The statement that the AR6 methods are not being used exactly but are including “evolving methodological improvements” seems to conflict with the objective of using the same methods as AR6 to be “trusted”… what if one person’s “methodological improvement” is another person’s wayward foray? Who will be the judge that UNFCCC parties should “trust” this new method as much or more than those that were vetted through the AR6 process?
Introduction, fourth paragraph: Suggest replacing “The update” with “This annual update”
Treatment of wildfires – The statement “The GCB methodology includes CO2 emissions from deforestation and forest degradation fires but excludes wildfires, which are assumed to be natural even if climate change affects their intensity and frequency” should also note that these fires are treated as “natural” even when they are ignited by human activity to clarify that many "wildfires" are due to human activities. Also, it is not clear how fires ignited purposely for ongoing land use on croplands (e.g. sugarcane) are treated?
Table 1: Should note that “GHG” in row one signifies the total of the other rows.
Table 2: Given that none of the numbers for 2019 and 2022 are exactly the same as in Forster et al., 2023 – It should be noted in the caption that they are all updated.
Section 4: I am not sure I agree with the statement that wildfires are a “climate feedback” in “it is not easy to determine how much of the biomass burning contribution is from natural wildfires in response to 2023’s anomalously warm year, which would be a climate feedback rather than a forcing.” In cases where the fires were ignited by human activity. Certainly, “it is not easy”, but I think it is even harder than the authors indicate.
Section 11: The statement “This annual update traced to IPCC methods can provide a reliable, timely source of trustworthy information.” Should add something like “joins the State of the Climate (BAMS) and State of Global Climate (WMO) reports”
Citation: https://doi.org/10.5194/essd-2025-250-RC1 -
CC1: 'Comment on essd-2025-250', Gareth S. Jones, 21 May 2025
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I welcome the opportunity to comment on the latest IGCC paper, before the deadline of 22nd June 2025.
The Indicators of Global Climate Change reports are ambitious. However, to be of value to "evidence-based decision-making", they need to be more robust about presentation of uncertainties, dataset choice, and differences of opinion.
This study would be much more useful for other scientists and policy-makers if it did not try to link itself to the IPCC assessment cycles so closely.
The studies have gone through much less review than IPCC reports have.
While the authors have tried to follow FAIR principles - like the IPCC - there are too many cases of data and code not being publicly accessible.
It seems arbitrary which IPCC methods are followed "as closely as possible" and which are modified or completely new.
Inconsistencies between the IGCC studies for results of the same periods make it difficult for anyone to "Track ... between IPCC report cycles". Many differences in "updates" for latest year/decade will be due to changes in method and data source, and may not be as representative of climate change as is often suggested.
AR7 could be hindered if there is an attempt to force a continuation of a later IGCC paper with it.
Using terms like "WGIII update" & "IPCC update" give impression this is an official IPCC update, when it is not.In below:
F23 = Forster et al. (2024) ESSD
F24 = Forster et al. (2023) ESSD
F25 = Forster et al. (2025) under review with ESSD
General comments
----------------* FAIR principles: Not all code for producing the figures is available. The github and zenodo links are somewhat confusing with discrepancies with what is in Section 13 and what is in the references. Dataset version numbers are not always given. There are no direct links to datasets within the manuscripts. It looks like the authors expect readers to hunt throughout the github/zenodo links to find the links. These links are not easily found ... if at all. There are mistakes in some of the repositories, which have been there since F23.
* Lack of uncertainties
There seems to be an increase in the number of values being presented without any uncertainties, over what was in F23 and F24.
For instance, no uncertainties are given for the observed temperatures for 2024 anywhere in the paper, despite it being a prominent result.
Various statements are not supported when consideration of the different sources of uncertainty are accounted for.
* Inconsistencies for results from report to report
How can one interpret differences in results for the latest year/decade between reports, when values for the same periods change from report to report? e.g.,
1) First five columns of numbers in Table 1 are different to values for the same periods in Table 1 in F23, Table 1 in F24 and with the rows of three of the periods in Table 2.1 in IPCC AR6 WGIII.
2) Values in Table 2 for "2019 (updated)" differ from those for "2019" in table 2 in F23 and table 2 in F24
3) Values in Table 6 for "repeat calculations" for 2010-2019 and 2017 differ from the "repeat calculations in F23 (table 6) and F24 (Table 6)
4) Table S2 has subtly different concentrations, for same years in F23 (table S1) and F24 (Table S2)
* New science
In various places new analyses are presented. These really should be presented in separate studies, to allow a full peer-review assessment to take place.
e.g., the analysis in Figure 8, the method to estimate volcanic forcing in 2024 (SI S5.5.2), or the analysis in S7 "2023 and 2024 temperature anomalies".
There also appears to be a lack of consistency with assumptions, choices and data used in some of these new analyses differing to what is used in the rest of the paper.
* New volcanic forcing
The volcanic forcing in Figure 5 looks quite different to figure 3 in F24. It is due to a different volcanic forcing dataset being used. The proposed CMIP7 dataset has not yet been peer reviewed, and given the very different methods to construct it, there are questions which really need to be addressed before it gets used in anger. e.g., The aerosol distribution following the Mt Agung eruption was robustly observed to be mostly in the Southern Hemisphere, but the dataset has a pretty much hemispheric symmetrical distribution.
* Aliasing
When comparing results for adjacent overlapping periods it is harder to interpret the results than the authors suggest, due to aliasing. The significance of such changes should be treated cautiously.
e.g., In Section 7.1 and table 5, interannual variability projects onto the differences between overlapping 10 year means.
The reported 0.15C increase between 2011-2020 and 2015-2024 is actually half of the difference between 2011-2014 and 2021-2024, so has more "noise" than differences between non-overlapping 10 year means.
* Kadow GMST observations
It is astonishing that an observational temperature dataset is still being used that has not been documented (there is no paper/report describing the Kadow model using the HadCRUT5 observational dataset), and is not publicly accessible.
* Human-induced warming attribution
I continue to be troubled by the lack of a presentation of the strength/weaknesses of the different methods.
They all use global mean temperatures, GWI - monthly or annual means, KCC - a mixture of multidecadal and annual means, ROF - five year means. This means statistical over/under fitting is of concern and interpreting similarities and differences between the results of the different methods challenging.
The GWI method uses a simple model but with no attempt to assess over/under fitting, which is more prone to end-effects than the authors claim, and so it is difficult to understand whether the fit is just a coincidence.
The ROF method uses an approach that effectively has to trade off between variance and bias in its estimates of the climate noise covariance matrix. In normal uses of the method a residual consistency test is done to see if some measure of over/under fitting is noticeable - although that is not discussed in the manuscript (if it was done).
The KCC method assumes that the model responses and observations are interchangable, and uses a residual of model responses subtracted from the observations to deduce noise characteristics, so in effect starts out with an assumed anthropogenic and natural attribution. It also assumes the anthropogenic component of the forced response is just the smoothed part of the historical (anthropogenic and natural) response, thus any multidecadal natural responses present will contaminate the "anthropogenic" response used.Fundamentally the net anthropogenic attribution is less informative than the authors portray. All the methods will end up with attributed anthropogenic trends that track the observed trends, as long as long term trends of natural responses is small (Section 6.1.2 in Allen et al, "Quantifying anthropogenic influence on recent near-surface temperature change", Surv Geophys (2006)
"We are fitting all model-simulated signals to the same data so it is not surprising that, after the fitting is done, the scaled model-simulated responses are brought into agreement with each other.") The wider divergence of attributed GHG and OHF supports that view (Figure S8). Including spatial information, could help in gaining confidence in the results, as discussed in Allen et al. 2006.All statistical model/observation analysis methods have different strengths and weaknesses. It does not mean they are perfect, or that they are not useful. Without giving a more fuller context it is not possible for a reader to interpret the results in an informative way.
Specific comments
-----------------L93 No uncertainties are given. If they were it would challenge the "well above" statement.
L137 Not all the data is available in the zenodo repository. Some links are there but not to all of the datasets e.g., one of the observed GMST datasets, precipitation, sea level, GHG concentrations ...
Figure 4 top panel, missing N2O label on y-axis.
Table 2 Are the values in column "2019 (WG1 for ERF estimates)" correct? They appear identical to the F24 table 2 "2019 emission" column. I tried to find the "WG1" values in AR6 but couldn't find them.
L502-513 Given the later deep dive into trying to assign causation to year-to-year changes, it seems curious that changes from year to year in natural forcings are glossed over here.
L512-513 The volcanic forcing is not negative according to figure S2, just less positive than recent years.
Section 7.2 and elsewhere. No uncertainties are given for the 2024 GMST value of 1.52C. F24 give uncertainties of about +/-0.1C (90% range).
L 634-635 Given F24 reported uncertainties on GMST of +/-0.1C, it is not "likely" that 2024 is warmer than 1.5C, but rather "as likely as not" - using IPCC convention.
L641-642 Attribution has not been discussed yet, so a reader might be unaware that the "human-induced warming" estimate has a "likely" uncertainty range of 1.1C-1.7C (Table 6). If that is accounted for then the probability is not "1 chance out of 6", but approaching 1 in 2.
L642-644 and Figure 8 I could not find the exact same "framework" in IPCC AR6 WG1 chap 3. Cross-chapter box 3.1 is close, but that does not mention "anthropogenic global warming levels". I do not follow what is being done here. This really needs to be in a study of its own, to get closer attention.
Figure 9 Giving the observed warming for 2024 on the right hand side would be nice.
Table 6 Observed first column for "2010-2019" has different uncertainties to that "Quoted from AR6 Chapter 3 Sect. 3.3.1.1.2 Table 3.1" Observed final column "2024" has no uncertainties. It appears the last two columns are "trend-based" (table S5) but equivalent columns in F23 (table 6) and F24 (table 6) are "single year".
Figure 10 is very different to figure 8 in F24. Bizarrely the natural changes are smaller than shown in F24. I don't understand how this is the case, especially given the amplification in volcanic forcing (Figure 5).
Section 11 "Global Land precipitation" How was the GHCN v4 dataset processed. It is provided as station measurements, and not as a gridded dataset. How this is processed is important.
Figure 12 This looks very different to figure 2.15c in AR6 WG1. The change in climatology period is having a striking impact on the evolution of the datasets that have unchanged versions. This should probably be noted.
SI L174 Durack 2025 is not in references, but if it is https://egusphere.copernicus.org/preprints/2025/egusphere-2024-3729/ then that paper is a review of what happened up to CMIP6 and does not describe what is planned for CMIP7.
SI S5.5.1 and S5.5.2 I don't quite understand the reasoning behind the effort to try to update the volcanic forcing for 2024, but not the solar forcing.
SI L191-208 Giving uncertainties for the reported values here would give helpful context about confidence in the values.
SI Section S7 the individual observation temperature dataset changes over the periods assessed are not given. This is important for understanding structural uncertainty in the observed trends.
SI L322 - 384 "2023 and 2024 temperature anomalies" This analysis needs to be in its own paper. Given the lack of update to solar forcing and the effort to update the volcanic forcing in section S5.5 it seems odd that here different estimates are used and relied on. In the main text (L487) it is stated that it is "problematic" to attribute year-to-year trends to aerosol forcing. How does that sit with what is being described here? Also I believe it uses a different observed temperature than elsewhere in this paper.
SI L322 - 384 "2023 and 2024 temperature anomalies" This analysis looks familiar. Might it be worth mentioning it has been published before (albeit it not in a peer reviewed journal). https://wmo.int/publication-series/state-of-global-climate-2024
SI L389 "combine measurements of near-surface temperature over land and in some cases over ice" There were no measurements of air temperature over ice, except in some very rare weather ship observations in Arctic circle.
SI L448 "some CMIP6 models may have unrealistically high decadal variability" ... and some will have low decadal variability! As we can't unambiguously observe it, we can't assess which models have realistic decadal variability or not.
SI S8.1 There are inconsistencies here about the importance of differences between GMST and GSAT with what is said in S8.2.1 and S8.2.2. It also rather simplifies the complexities of trying to account for the difference in models and observational datasets, especially over sea-ice when coupled models differ considerably in sea-ice coverage, and there are very few direct measurements of temperature.
SI L509 It might be worth mentioning that the "7%" increase in the best estimate of anthropogenic warming happened when HadCRUT5 had a ~7% increase in warming over HadCRUT4 for 2010-2019. This supports my earlier argument that net anthropogenic attribution is not as informative as we would like.
SI Table S4 how are the GSAT and GMST results identical for GWI and KCC. They both use GSAT from coupled models at some point. Section S8.2.1 and S8.2.2 try to claim that GMST and GSAT are effectively the same, despite what is said in Section S8.1 and S8.2.3. Can the authors make up their minds? ;-)
SI S8.3 I don't think it is explained how the GMST attributed results are estimated from the GSAT results.
SI S8.3 The results are presented against the assessed observed temperature changes, but the analyses were done using HadCRUT5. How much impact does this have?
Citation: https://doi.org/10.5194/essd-2025-250-CC1 -
CC2: 'Comment on essd-2025-250', Richard Allan, 23 May 2025
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The annual update by Forster et al. provides a valuable and timely assessment of ongoing climate change, particularly given the heightened attention on the sustained record global warmth, elevated Earth Energy Imbalance (EEI) and the questions over how aerosol, cloud feedbacks and internal variability are contributing, which are important to answer in regard to the magnitude and rate of near term climate change. I focused mainly on Sections 6 and 7.2 and provide some suggestions below including a number of relevant references that were not assessed.
1) L546 Although EEI dropped in 2024 from a peak of nearly 2 Wm-2 from mid 2022 to mid 2023, due to greater outgoing longwave radiation during the mature phase of the 2023/2024 El Niño, levels did not fall as low as previous mature El Niño evens such as 2016 or 2010 (Allan & Merchant, 2025 ERL doi:10.1088/1748-9326/adb448; Mauritsen et al. 2025 AGU Adv. doi:10.1029/2024AV001636).
2) L541 - the increased EEI corresponds with an accelleration of ocean warming, not merely an increase (e.g. Merchant et al. 2025 ERL doi:10.1088/1748-9326/adaa8a)
3) L551 - We also showed the EEI increase to originate from increases in absorbed sunlight associated with clouds over the ocean, with hotspots over Californian and Namibian stratocumulus zones (Allan & Merchant, 2025).
4) L635 - is this an IPCC likely exceeded 1.5oC? A recent WMO synthesis stated 1.55 ± 0.13 based on the 90% confidence range mostly relating to the 1981-2020 minus 1850-1900 IPCC offset applied. I think the 90% confidence range covers 1.645 standard deviations and the 66% level just below 1 standard deviation so this may also span below 1.5°C. "Likely" is probably fine but “More likely than not” (>50%) may be more correct. An uncertainty could be shown in Fig.7 or at least mentioned in the caption.
5) L651 - the probability of the large jump in global temperatures was increased by the fact the El Niño followed an extended La Niña 2020-2022 (Raghuraman et al. 2024 ACP doi:10.5194/acp-24-11275-2024)
6) L677 - the ocean surface warming is only reconcilable with the raised EEI if the mixed layer shallowed or was compounded by heat from the deeper ocean sequestered during the extended La Niña (Allan & Merchant, 2025 ERL). Shallowing of the mixed layer was liklely to have played a role in the Atlantic (England et al. 2025 Nature in press, published June 19th) while cooling in the 100-300m ocean layer implied a reversal of the downward heat flux below the mixed layer (Minobe et al. 2025 doi:10.1038/s41612-025-00996-z; Allan & Merchant, 2025). It is also likely that a smaller proportion of the EEI directly heated the ocean as more energy was used in heating the atmosphere and probably the land surface (Allan & Merchant 2025 ERL; Minobe et al. 2025).
7) L678 - see also England et al. (2025) Nature (in press) regarding the North Atlantic warming (due out 19 June)
8) L687 - also Allan & Merchant (2025) ERL relating to the mid 2022 to mid 2023 energy imbalance and the reduced absorption of sunlight over the cloudy ocean
9) L689 - for global temperatures to stabilise, the peak EEI and decades of decline are required (Mauritsen et al. 2025 AGU Advances, doi:10.1029/2024AV001636, Fig. 2) emphasising the importance of tracking this quantity and serious implications of the risk to the relevant observing systems
10) L1787 - repeated authors from von Schuckmenn et al. 2023 reference
11) Table3 - I was initially confused by the solar ERF estimate in Figure 5a being for a single year verses the whole solar cycle estimate used and assumed in Table 3. Perhaps a separate symbol could be used for the full solar cycle estimate (and updated to 2014-2024 or corrected for the solar cycle)? However, the single year value is useful since it does contribute a small amount to the larger EEI in 2024 (e.g. Hansen et al. 2025 SPSD doi:10.1080/00139157.2025.2434494; Merchant et al. 2025 ERL doi:10.1088/1748-9326/adaa8a).
12) Section 8.2 - a couple of studies have noted that the level of annual warmth and the sustained warmth over many months makes it inevitabole that the 1.5 degree Celsius above pre-industrial thershold will be breached (Cannon 2025 Nature Clim. 10.1038/s41558-025-02247-8; Bevacqua et al. 2025 Nature Clim. doi:10.1038/s41558-025-02246-9) without a drastic increase in mitigation (or a large explosive volcanic eruption) so this could be mentioned.
13) Section 8.3 could also consider Samset et al. (2023) Comm. Earth Env. doi:10.1038/s43247-023-01061-4 and a preprint by Rahmstorf & Foster doi:10.21203/rs.3.rs-6079807/v1
14) Section 1 final paragraph - a bulleted or numbered list may work better for the reader to point to the many sections
Citation: https://doi.org/10.5194/essd-2025-250-CC2
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Indicators of Global Climate Change 2024 (v2025.05.02) C. Smith et al. https://doi.org/10.5281/zenodo.15327155
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