Monday, September 6, 2021

Social media: Most of recent rigorous studies found weak average associations with well-/ill-being that were close to zero

Valkenburg, Patti M. 2021. “The Effects of Social Media Use on Adult Well-being and Ill-being: What We Know and What We Need to Know.” PsyArXiv. September 6. doi:10.31234/

Abstract: Research into the impact of social media use (SMU) on well-being (e.g., happiness) and ill-being (e.g., depression) has exploded in the past years. From 2019 to August 2021, 26 reviews have been published: nine meta-analyses, nine systematic reviews, and eight narrative reviews that together included hundreds of empirical studies. The aim of this umbrella review was to synthesize the results of these reviews. Even though the meta-analyses were largely based on the same evidence, they yielded inconsistent effect sizes, especially for time spent on SM, active SMU, and passive SMU. This umbrella review explains why these effects sizes disagree, summarizes the gaps in the literature, and ends with some important recommendations for future reviews and empirical research.

Dating violence victimization disparities across sexual orientation of a population-based sample of adolescents

Petit, M.-P., Blais, M., & Hébert, M. (2021). Dating violence victimization disparities across sexual orientation of a population-based sample of adolescents: An adverse childhood experiences perspective. Psychology of Sexual Orientation and Gender Diversity, Sep 2021.

Abstract: Sexual minority youth are more vulnerable to adverse childhood experiences (ACEs) and several associated negative consequences. It remains unknown whether such vulnerability explain their excess risk for teen dating violence victimization (TDVV) documented previously. It is also unclear whether risk and protective factors associated with TDVV in the general adolescent population are also shared by sexual minority youth. Using longitudinal data from a representative sample of Quebec adolescents in a relationship (n = 4,515), the current study aimed to (a) test whether the differential exposure to risk factors account for TDVV disparities, and (b) to determine whether established TDVV correlates vary by sexual attraction and lifetime sexual partners’ gender. Multilevel models revealed that participants with multigender sexual attraction or sexual partners were at increased risk for TDVV and nearly all risk factors examined, including ACEs, compared with different-gender and, to a lesser extent, same-gender peers. The association between sexual orientation and TDVV remained significant when ACEs were added, but not when all other risk and protective factors were accounted for. The patterns of risk and protection factors related to TDVV greatly differed across sexual orientation. The only common correlate of TDVV across groups was TDV perpetration. ACEs were associated with TDVV across all sexual orientation groups, but not after adjusting for other risk and protective factors. These findings suggest that negative consequences of ACEs better explain TDVV disparities than ACEs alone. Trauma-informed interventions aiming at building resilience among youth, especially among multigender groups, might support dating violence prevention. 

Taking the HITRAN database of gaseous absorption spectra as a source of analysis: Climate sensitivity to future increases in CO2 concentration is about 0.50K, including positive feedback effects of H2O, & climate sensitivities to CH4 & N2O are almost undetectable

David Coe, Walter Fabinski, Gerhard Wiegleb, The Impact of CO2, H2O and Other “Greenhouse Gases” on Equilibrium Earth Temperatures, International Journal of Atmospheric and Oceanic Sciences. Vol. 5, No. 2, 2021, pp. 29-40. doi: 10.11648/j.ijaos.20210502.12

Abstract: It has long been accepted that the “greenhouse effect”, where the atmosphere readily transmits short wavelength incoming solar radiation but selectively absorbs long wavelength outgoing radiation emitted by the earth, is responsible for warming the earth from the 255K effective earth temperature, without atmospheric warming, to the current average temperature of 288K. It is also widely accepted that the two main atmospheric greenhouse gases are H2O and CO2. What is surprising is the wide variation in the estimated warming potential of CO2, the gas held responsible for the modern concept of climate change. Estimates published by the IPCC for climate sensitivity to a doubling of CO2 concentration vary from 1.5 to 4.5°C based upon a plethora of scientific papers attempting to analyse the complexities of atmospheric thermodynamics to determine their results. The aim of this paper is to simplify the method of achieving a figure for climate sensitivity not only for CO2, but also CH4 and N2O, which are also considered to be strong greenhouse gases, by determining just how atmospheric absorption has resulted in the current 33K warming and then extrapolating that result to calculate the expected warming due to future increases of greenhouse gas concentrations. The HITRAN database of gaseous absorption spectra enables the absorption of earth radiation at its current temperature of 288K to be accurately determined for each individual atmospheric constituent and also for the combined absorption of the atmosphere as a whole. From this data it is concluded that H2O is responsible for 29.4K of the 33K warming, with CO2 contributing 3.3K and CH4 and N2O combined just 0.3K. Climate sensitivity to future increases in CO2 concentration is calculated to be 0.50K, including the positive feedback effects of H2O, while climate sensitivities to CH4 and N2O are almost undetectable at 0.06K and 0.08K respectively. This result strongly suggests that increasing levels of CO2 will not lead to significant changes in earth temperature and that increases in CH4 and N2O will have very little discernable impact.

Keywords: Carbon Dioxide, Climate Sensitivity, Greenhouse Effect, Climate Change

5. Other Considerations

5.1. The Impact of Clouds

The obvious impact of clouds is to increase the reflectivity of the earth thus reducing the level of incoming solar radiance I0 which will have a cooling influence on the earth. However, this paper is concerned with the effects of retained IR emissions from the earth. Cloud cover will not affect the absorbance of atmospheric greenhouse gases, but it will impact upon the total energy absorbed and possibly upon the energy retention factor “n”, in ways that would be difficult to quantify. However, by using the current average earth condition, which includes cloud, as a calibration point to determine an effective value for ”n” consistent with the mean earth temperature of 288K, the current average impact of cloud has, in effect, already been taken into account. This however does not, in itself, identify what this impact is. The structure of clouds is diverse and complex. It is close to impossible to derive a set of equations to describe the formation, structure and impact of clouds on the retention of absorbed energy and hence the radiative balance of the earth. This paper has so far relied upon the extensive HITRAN spectral database, basic physics and simple mathematics to determine values for climate sensitivity. Any attempts to estimate the further impact of cloud would be based upon speculation only and would not be appropriate for this paper.

5.2. Effect of Recently Increased Atmospheric CO2

It is of some interest to calculate the increase in temperature that has occurred due to the increase in atmospheric CO2 levels from the 280ppm prior at the start of the industrial revolution to the current 420ppm registered at the Mona [Mauna!] Loa Observatory. (K. W. Thoning et. al. 2019) [17]. The HITRAN calculations show that atmospheric absorptivity has increased from 0.727 to 0.730 due to the increase of 140ppm CO2, resulting in a temperature increase of 0.24Kelvin. This is, therefore, the full extent of anthropogenic global warming to date.

6. Conclusions

In order to satisfy radiative equilibrium at the “top of the atmosphere” (TOA) at an average earth temperature of 288Kelvin, only 61.5% of the earth’s radiated energy should be transmitted through to space, leaving 38.5% to be absorbed and retained by the atmosphere/earth. Use of the HITRAN data base of gaseous absorption spectra shows the current atmospheric absorption to be 73.0% of total radiative emissions of which 52.74% must be retained by the earth/atmosphere to satisfy the current TOA equilibrium. This is a simple expression of the current earth temperature equilibrium. The 38.5% retained radiation absorption comprises 35.3% attributed to H2O, 3.0% to CO2 and a mere 0.2% to CH4 and N2O combined. From this it follows that the 33Kelvin warming of the earth from 255Kelvin, widely accepted as the zero-atmosphere earth temperature, to the current average temperature of 288Kelvin, is a 29.4K increase attributed to H2O, 3.3K to CO2 and 0.3K to CH4 and N2O combined. H2O is by far the dominant greenhouse gas, and its atmospheric concentration is determined solely by atmospheric temperature. Furthermore, the strength of the H2O infra-red absorption bands is such that the radiation within those bands is quickly absorbed in the lower atmosphere resulting in further increases in H2O concentrations having little further effect upon atmospheric absorption and hence earth temperatures. An increase in average Relative Humidity of 1% will result in a temperature increase of 0.03Kelvin. By comparison CO2 is a bit player. It however does possess strong spectral absorption bands which, like H2O, absorb most of the radiated energy, within those bands, in the lower atmosphere. It also suffers the big disadvantage that most of its absorption bands are overlapped by those of H2O thus reducing greatly its effectiveness. In fact, the climate sensitivity to a doubling of CO2 from 400ppm to 800ppm is calculated to be 0.45 Kelvin. This increases to 0.50 Kelvin when feedback effects are taken into account. This figure is significantly lower than the IPCC claims of 1.5 to 4.5 Kelvin. The contribution of CH4 and N2O is miniscule. Not only have they contributed a mere 0.3Kelvin to current earth temperatures, their climate sensitivities to a doubling of their present atmospheric concentrations are 0.06 and 0.08 Kelvin respectively. As with CO2 their absorption spectra are largely overlapped by the H2O spectra again substantially reducing their impact. It is often claimed that a major contributor to global warming is the positive feedback effect of H2O. As the atmosphere warms, the atmospheric concentration of H2O also increases, resulting in a further increase in temperature suggesting that a tipping point might eventually be reached where runaway temperatures are experienced. The calculations in this paper show that this is simply not the case. There is indeed a positive feedback effect due to the presence of H2O, but this is limited to a multiplying effect of 1.183 to any temperature increase. For example, it increases the CO2 climate sensitivity from 0.45K to 0.53K. A further feedback, however, is caused by a reduction in atmospheric absorptivity as the spectral radiance of the earth’s emitted energy increases with temperature, with peak emissions moving slightly towards lower radiation wavelengths. This causes a negative feedback with a temperature multiplier of 0.9894. This results in a total feedback multiplier of 1.124, reducing the effective CO2 climate sensitivity from 0.53 to 0.50 Kelvin. Feedback effects play a minor role in the warming of the earth. There is, and never can be, a tipping point. As the concentrations of greenhouse gases increase, the temperature sensitivity to those increases becomes smaller and smaller. The earth’s atmosphere is a near perfect example of a stable system. It is also possible to attribute the impact of the increase in CO2 concentrations from the pre-industrial levels of 280ppm to the current 420ppm to an increase in earth mean temperature of just 0.24Kelvin, a figure entirely consistent with the calculated climate sensitivity of 0.50 Kelvin. The atmosphere, mainly due to the beneficial characteristics and impact of H2O absorption spectra, proves to be a highly stable moderator of global temperatures. There is no impending climate emergency and CO2 is not the control parameter of global temperatures, that accolade falls to H2O. CO2 is simply the supporter of life on this planet as a result of the miracle of photosynthesis.

Reduced Emotional Intelligence in Children Aged 9–10 caused by the COVID-19 Pandemic Lockdown

Reduced Emotional Intelligence in Children Aged 9–10 caused by the COVID-19 Pandemic Lockdown. Katya Martín-Requejo, Sandra Santiago-Ramajo. Mind, Brain, and Education, September 3 2021.

Abstract: It is necessary to know the influence of the current pandemic situation on children's emotional intelligence (EI). Therefore, this study aimed to analyze the difference in 34 Spanish children's EI (aged 9–10) caused by the lockdown. EI was measured with the BarOn Emotional Intelligence Inventory (EQ-i:YV). Results have revealed a reduction in EI, specifically on intrapersonal, interpersonal, and adaptability scales (all p < .01). Thus, the study highlights the negative influence of lockdown situation on children's EI and considering the impact this may have at a cognitive, social, or academic level, it would be convenient to promote its development at school.


This study has revealed a reduction in EI and intrapersonal, interpersonal, and adaptability scales. This pandemic situation is having a negative impact on children's emotions (Berasategi et al., 2020; Kontoangelos et al., 2020; Liu, Huang, Shi, & Lu, 2020) and this study shows that this, in turn, negatively impacts on children's EI. Because children are more vulnerable to negative effects in adverse situations, it could explain the observed reduction (Yan Jiao et al., 2020). Therefore, these authors highlight that the prolonged situation they have had to deal with during the lockdown has increased negative emotions, favoring the reduction of EI. In contrast to previous studies (Berasategi et al., 2020; Kontoangelos et al., 2020; Liang et al., 2020; Wang et al., 2020a; Wang et al., 2020b), stress management has shown a slight improvement but no significant difference. These results could indicate that children have had to assess the stressful situation and make a cognitive effort to try to minimize its emotional impact (Echavarría, 2012).

Low levels of EI decrease well-being and academic performance (Extremera-Pacheco & Fernández-Berrocal, 2004), which highlights the need to enhance EI development in all children affected by this pandemic situation. Therefore, it is necessary to develop personal resources and transversal skills that allow for an effective confrontation of this situation (Allodola et al., 2020). The school represents a key in this process, because in addition to promoting academic development, it also influences the socioemotional development of students (Wang et al., 2020a; Wang et al., 2020b).

The main limitations of the study are the small sample size and that EQ-i:YV was applied under different conditions (because of the lockdown). Therefore, the results can only be generalized to children aged 9–10 years, so for future studies, it would be convenient to study other age groups in a larger sample and carry out data under the same conditions.

The study concludes by highlighting a decrease in children's (aged 9–10) EI and its scales (intrapersonal, interpersonal, and adaptability) after the lockdown by COVID-19. The pandemic situation, in addition to cognitive and academic aspects, is also affecting student's EI, so it would be advisable to enhance its development at school. Thus, the possible consequences that low levels of EI may have on cognitive, psychological, or academic processes could be mitigated.

The Physical Science Basis of Climate Change, IPCC AR6 WGI — some quality issues

We will next show you some numbers of the last IPCC report, AR6 WGI, Climate Change 2021: The Physical Science Basis—Summary for Policymakers, August 2021, full report (downloaded Aug 09 2021).

We know that the following comments, taken in isolation, are an unfair presentation of the work that many hard-working scientists did in the last decades, scientists that who are not militant, but who want, really, sincerely, to understand how Nature works.

But we also think these error margins, the wide amplitude of some of the data's uncertainties that the sovereigns are working with, are not being scrutinized as needed. The governments will make laws taking the summaries and press releases at face value, paying no attention to quality issues that we think the population at large deserve to know.

These uncertainties are due to many causes; among them, lack of enough data, lack of time and effort to replicate previous work, lack of precision of our technologies, human beings' lack of interest while doing their jobs, lack of honesty (hopefully, in very few instances!) and others. Also, lack of luck is also one of the roots of these issues.

These comments are our small, non-exhaustive, very selective collection of what we could call strange data tolerances. We don't know whether they should, or should not, change the lawmaker's views about what it must be done in the future about the climate. We do not know if current laws (and others in the works) are right or not (in the meaning of "more right than wrong"), but would like these data to be discussed and laws eventually fixed to take the data tolerances in account.

Our comments follow.  ***WORK IN PROGRESS***

>>> >>> >>> p 39 (pages as in the PDF document, not chapter pages)

Table SPM.2 Estimates of historical CO2 emissions and remaining carbon budgets

Historical cumulative CO2 emissions from 1850 to 2019 (GtCO2)

1.07 (0.8–1.3; likely range) 2390 (± 240; likely range)

This figure, 2390 GtCO2, has a more complex range than the likely one, ± 240 GtCO2. Second note to this table (appears as 'Likelihood of limiting global warming to temperature limit*(2)'):

*(2) [...] Uncertainties related to historical warming (±550 GtCO2) and non-CO2 forcing and response (±220 GtCO2) are partially addressed by the assessed uncertainty in TCRE, but uncertainties in recent emissions since 2015 (±20 GtCO2) and the climate response after net zero CO2 emissions are reached (±420 GtCO2) are separate.

[p 106, Table TS.3 says large figures like 550 Gt are not fully additional]

>>> >>> >>> p 86

TS.2.5 The Cryosphere (lines 41-2)

[...] Under RCP2.6 and RCP8.5, respectively, glaciers are projected to lose 18% ± 13% and 36% ± 20% of their current mass over the 21st century (medium confidence). {2.3.2, 3.4.3, 9.5.1, 9.6.1}

>>> >>> >>> p 244

Box 1.2: Special Reports in the sixth IPCC assessment cycle: key findings

1) Observations of climate change

49 Anthropogenic global warming was estimated to be increasing at 0.2±0.1°C per decade (high confidence)

50 and likely matches the level of observed warming to within ±20%. [...]

>>> >>> >>> p 455

2.2.6 Aerosols

10 [...] The ERF associated with aerosol-radiation interactions for 2011 (relative to 1750) was estimated to be -0.45 ± 0.5 W m-2

11 and of aerosol-cloud interaction estimated as -0.45 [-1.2–0.0] W m-2. [...]

>>> >>> >>> p 456

2.2.7 Land use and land cover

49 AR5 assessed that land use change very likely increased the Earth’s albedo with a radiative forcing of -0.15 (± 0.10) W m–2. [...]

>>> >>> >>> p 457

28 [...]. Ward et al. (2014) examined

29 the combined effects of biophysical and biogeochemical processes, obtaining an RF of 0.9 ± 0.5 W m-2 29 since

30 1850 that was driven primarily by increases in land-use related GHG emissions from deforestation and

31 agriculture (Ward and Mahowald, 2015). According to a large suite of historical simulations, the biophysical

32 effects of changes in land cover (i.e. increased surface albedo and decreased turbulent heat fluxes) led to a

33 net global cooling of 0.10 ± 0.14 °C at the surface (SRCCL). Available model simulations suggest that

34 biophysical and biogeochemical effects jointly may have contributed to a small global warming of 0.078 ±

35 0.093 °C at the surface over about the past two centuries (SRCCL), with a potentially even larger warming

36 contribution over the Holocene as a whole (He et al., 2014).

>>> >>> >>> p 462

2.3.1 Atmosphere and Earth's surface Surface temperatures Temperatures of the deep past (65 Ma to 8 ka)

28 This compares with two other SST estimates for 125 ka of 0.5°C ± 0.3°C (± 2 SD) warmer at 125 ka relative

29 to 1870–1889 (Hoffman et al., 2017), and about 1.4°C (no uncertainty stated) warmer at 125 ka relative to

30 1850–1900 (Friedrich and Timmermann, 2020; reported relative to 10–5 ka and adjusted here by 0.4°C;

31 (Kaufman et al., 2020a)). The average of these post-AR5 global SST anomalies is 1°C. Commensurately

32 (Figure 3.2b), GMST is estimated to have been roughly 1.1°C above 1850-1900 values, although this value

33 could be too high if peak warmth was not globally synchronous (Capron et al., 2017). A further estimate of

34 peak GMST anomalies of 1.0°C–3.5°C (90% range; adjusted here to 1850–1900 by adding 0.2°C) based on

35 59 marine sediment cores (Snyder, 2016) is considerably warmer than remaining estimates and are therefore

36 given less weight in the final assessment. [...]

41 New GMST reconstructions for the LGM fall near the middle of AR5’s very likely range, which was based

42 on a combination of proxy reconstructions and model simulations. Two of these new reconstructions use

43 marine proxies to reconstruct global SST that were scaled to GMST based on different assumptions. One

44 indicates that GMST was 6.2 [4.5 to 8.1°C; 95% range] cooler than the late Holocene average (Snyder,

50 [...]. The

51 coldest multi-century period of the LGM in the Hansen et al. (2013c) reconstruction is 4.3°C colder than

52 1850–1900. This compares to land- and SST-only estimates of about -6.1°C ± 2°C and -2.2°C ± 1°C,

53 respectively (2 SD), [...]

>>> >>> >>> p 483 Global precipitation

Table 2.6

Trends in annual precipitation (mm yr-1 per decade)

             1901-2019      1960-2019      1980-2019

GPCCv2020    1.01*± 0.99  1.67 ± 3.23    5.60 ± 6.38

CRU TS 4.04  0.57 ± 2.08  0.17 ± 3.12    5.75* ± 5.09

GHCNv4       3.19*± 1.48  5.03* ± 4.87  11.06* ± 9.17

GPCPv2.3                                 5.41* ± 5.20

* Trend values significant at the 10% level.

42 In summary, globally averaged land precipitation has likely increased since the middle of the 20th century

43 (medium confidence), with low confidence in trends prior to 1950. A faster increase in global land

44 precipitation was observed since the 1980s (medium confidence), with large interannual variability and

45 regional heterogeneity. [...]

>>> >>> >>> p 497 Glacier mass

5  [...]. Between 2006 and 2015 the global glacier mass

6  change assessed by SROCC was –278 ± 113 Gt yr-1

>>> >>> >>> p 502 Ocean temperature, heat content and thermal expansion

13 [...]. SROCC reported linear warming trends for the 0–700 m and 700–2000 m layers of

14 the ocean of 4.35 ± 0.8 and 2.25 ± 0.64 ZJ yr-1 over 1970-2017; 6.28 ± 0.48 and 3.86 ± 2.09 ZJ yr-1 over

15 1993–2017;

>>> >>> >>> p 507 Sea level

28 [...]. Over the last about 1.5 kyr, the most

29 prominent century-scale GMSL trends include average maximum rates of lowering and rising of -0.7 ± 0.5

30 mm yr-1 30 (2 SD) over 1020–1120 CE, and 0.3 ± 0.5 (2 SD) over 1460–1560, respectively.

>>> >>> >>> p 509 Ocean circulation Atlantic Meridional Overturning circulation (AMOC)

24 Worthington et al., 2020). Direct indications from in-situ observations report a –2.5 ± 1.4 Sv change between

25 1993 and 2010 across the OVIDE section, superimposed on large interannual to decadal variability (Mercier

26 et al., 2015). At 41°N and 26°N, a decline of –3.1 ± 3.2 Sv per decade and –2.5 ± 2.1 Sv per decade

27 respectively has been reported over 2004–2016 (Baringer et al., 2018; Smeed et al., 2018). However, Moat et

28 al. (2020) report an increase in AMOC strength at 26°N over 2009–2018. [...]

>>> >>> >>> p 748

3.3 Human Influence on the Atmosphere and Surface

3.3.1 Temperature Surface Temperature

10 [...]. Ribes et al. (2021) imply a contribution of internal

11 variability of −0.02 ± 0.16°C to warming between 2010-2019 and 1850-1900, assuming independence

12 between errors in the observations and in the estimate of the forced response. [...]

>>> >>> >>> p 778

3.4 Human Influence on the Cryosphere

3.4.3 Glaciers and Ice Sheets Glaciers

24 since 1850 is attributable to anthropogenic influence. While Marzeion et al. (2014) found that anthropogenic

25 influence contributed only 25 ± 35% of glacier mass loss for the period 1851-2010, their naturally-forced

26 simulations exhibited a substantial negative mass balance, which Roe et al. (2020) argued is unrealistic.

27 Moreover, Marzeion et al. (2014) estimated that anthropogenic influence contributed 69 ± 24% of glacier

28 mass loss for the period 1991 to 2010, consistent with a progressively increasing fraction of mass loss

29 attributable to anthropogenic influence found by Roe et al. (2020)

>>> >>> >>> p 789 Sea Level Change Attribution

53 effect - only contributing 9 ± 18% of the change over the same period

>>> >>> >>> p 1028

10 [...] GSAT:

11 0.28±0.30°C (mean ± standard deviation); global land precipitation: 0.026±0.011 mm/day; September Arctic

12 sea-ice area: –0.32±0.53 million km2 [...]

>>> >>> >>> p 1158

Contemporary Trends of Greenhouse Gases

30 It is unequivocal that the increase of CO2, ***CH4, and N2O*** in the atmosphere over the industrial era is

31 the result of human activities (***very high confidence***). This assessment is based on multiple lines of

32 evidence including atmospheric gradients, isotopes, and inventory data. [...]


32 [...] During the last measured decade,

33 global average annual anthropogenic emissions of CO2, CH4, and N2O, reached the highest levels in human

34 history at 10.9 ± 0.9 PgC yr-1 (2010–2019), ***335–383*** Tg CH4 yr-1 (2008–2017), and ***4.2–11.4*** TgN yr-1

35 (2007–2016), respectively (high confidence). {5.2.1, 5.2.2, 5.2.3, 5.2.4; Figures 5.6, 5.13, 5.15}.

[Clarification: It is unequivocal that emissions for ***all three*** are the result of human activities, but CH4 emissions uncertainty is 335–383 Tg y-1 & N2O's is 4.2–11.4 TgN yr-1...]

>>> >>> >>> p 1161

Biogeochemical Implications of Carbon Dioxide Removal and Solar Radiation Modification

46 [...]. For simultaneously

47 cumulative CO2 emissions and removals of greater than or equal to 100 PgC, CO2 emissions are 4 ± 3%

48 more effective at raising atmospheric CO2 than CO2 removals are at lowering atmospheric CO2.

>>> >>> >>> p 1170 Holocene Changes

53 The early Holocene decrease in CO2 concentration by about 5 ppm (Schmitt et al., 2012) has been attributed

54 to post-glacial regrowth in terrestrial biomass and a gradual increase in peat reservoirs over the Holocene,

55 resulting in the sequestration of several hundred PgC (Yu et al., 2010; Nichols and Peteet, 2019).

[Clarification: This means that even if the tundra and other peat places "melt," as some say, we will only deliver a maximum of 5ppm to the atmosphere, since it is estimated that 3-41 Pg (yes, the spread is that large) will be delivered per 1K that avrg temp will increase (p 1160, p 1219 says 18 Pg/K, 3-41Pg), and Yu et al. say 500 Pg are stored in peats and other sinks (I had to download the paper because the report says only "several hundred PgC").]

>>> >>> >>> p 1173

5.2.1 CO2: Trends, Variability and Budget Anthropogenic CO2 Emissions

39 to 1750 can be estimated by subtracting the post-1750 LULUCF flux from Table 5.1 from the combined soil

40 and vegetation losses until today; they would then amount to 328 (161–501) PgC assuming error ranges are

41 independent.[...]

44 [...]. Low confidence is assigned to pre-industrial emissions estimates.

>>> >>> >>> p 1179 Land CO2 Fluxes: Historical and Contemporary Variability and Trends Trend in Land-Atmosphere CO2 Exchange

17 [...]. Estimated as the

18 residual from the mass balance budget of fossil fuel CO2 emissions minus atmospheric CO2 growth and the

19 ocean CO2 sink, the global net land CO2 sink (including both land CO2 sink and net land use change

20 emission) increased from 0.3 ± 0.6 PgC yr-1 during the 1960s to 1.8 ± 0.8 PgC yr-1 during 2010s

>>> >>> >>> p 1188-9

Table 5.2: Global CH4 budget

                                             2000-2009            2008-2017

Atmospheric growth rate (ppb yr-1)             2 ± 4                7 ± 3

>>> >>> >>> p 1191

Cross-Chapter Box 5.2: Drivers of atmospheric methane changes during 1980–2019

47 [...]. The mean growth rate decreased from 15 ± 5 ppb yr-1 in the

48 1980s to 0.48 ± 3.2 ppb yr-1 during 2000–2006 (the so-called quasi-equilibrium phase) [..]

>>> >>> >>> p 1205

5.3.3 Ocean Interior Change Ocean Memory – Acidification in the Ocean Interior

6 [...]. For example, ocean circulation contributes a pH change of –0.013 ± 0.013

>>> >>> >>> p 1220 Biological Drivers of Future Ocean Carbon Uptake

7 [...]. The projected global multi-model mean

8 change in PP in 13 models run under the SSP5−8.5 scenario project is −3 ± 9% (2080–2099 mean values

9 relative to 1870–1899 ± the inter-model standard deviation; Kwiatkowski et al., 2020). Under the low

10 emission, high-mitigation scenario SSP1−2.6, the global change in PP is −0.56 ± 4%. [...]

16 In CMIP5 models run under RCP8.5, particulate organic carbon (POC) export flux is projected to decline by

17 1–12% by 2100 (Taucher and Oschlies 2011; Laufkoetter et al. 2015). Similar values are predicted in 18

18 CMIP6 models, with declines of 2.5–21.5% (median –14%) or 0.2–2 GtC (median –0.8 GtC) between 1900

19 and 2100 under the SSP5–8.5 scenario. [...]

>>> >>> >>> p 1223 Evaluation of Historical Carbon Cycle Simulations 1 in Concentration-Driven Runs

14 The land carbon cycle components of historical ESM simulations show a larger range, with simulated

15 cumulative land carbon uptake (1850–2014) spanning the range from –47 to +21 GtC, compared to the GCP

16 estimate of –12 ± 50 GtC (Figure 5.22b). [...]

>>> >>> >>> p 1224 Coupled Climate-Carbon Cycle Projections

12 [...]. There is indeed some evidence for that with ensemble mean γL [=sensitivity of land carbon storage to temperature] moving from –58 ± 38

13 GtC K-1 to –33 ± 33 GtC K-1. [...]

>>> >>> >>> p 1230

5.4.8 Combined Biogeochemical Climate Feedback

9 cycle’s response to climate (0.24 ± 0.17 W m-2 °C-1, corresponding to γL+O of –50 ± 34 PgC °C-1), and

10 emissions from permafrost thaw (0.09 [0.02–0.20] W m-2 °C-1, corresponding to γ of –18 [3–41] PgC °C-1,

11 mean and 5–95th percentile range) (Figure 5.29a). This estimate does not include an estimate of the fire

12 related CO2 feedback (range: 0.01–0.06 W m-2 °C-1), as only limited evidence was available to inform its

13 assessment. The sum (mean and 5–95th percentile range) of feedbacks from natural emissions of CH4

14 including permafrost thaw, and N2O (0.05 [0.02–0.09] W m-2 °C-1), and feedbacks from aerosol and

15 atmospheric chemistry (–0.20 [–0.41 to 0.01] W m-2 °C-1) leads to an estimate of the non-CO2

16 biogeochemical feedback parameter of –0.15 [–0.36 to 0.06] W m-2 °C-1. [...]


29 [...] including a feedback term of –11 (–18 to –5) PgCeq °C-1 (5th–95th percentile range, –40 (–62 to –18) Gt

30 CO2eq °C-1) from natural CH4 and N2O sources. The biogeochemical feedback from permafrost thaw leads to

31 a combined linear feedback term of –21 ± 12 PgCeq °C-1 (1 standard deviation range –77 ± 44 Gt CO2eq °C-

32 1). For the integration of these feedbacks in the assessment of the remaining carbon budget (Section 5.5.2),

33 two individual non-CO2 feedbacks (tropospheric ozone, and methane lifetime) are captured in the AR6-

34 calibrated emulators (Box 7.1). Excluding those two contributions, the resulting combined linear feedback

35 term for application in Section 5.5.2 is assessed at a reduction of 7 ± 27 PgCeq °C-1 (1 standard deviation

36 range, –26 ± 97 PgCeq °C-1). For the same reasons as for the feedback terms expressed in W m-2 °C-1 (see

37 above), there is overall low confidence in the magnitude of these feedbacks.

>>> >>> >>> p 1247

5.5.2 Remaining Carbon Budget Assessment Adjustments for Other not Represented Feedbacks

32 [...[. The remainder of these independent Earth system feedbacks combine to a

33 feedback of about 7 ± 27 PgC K-1 (1-sigma range, or 26 ± 97 GtCO2 °C-1). Overall, Section 5.4.8 assessed

34 there to be low confidence in the exact magnitude of these feedbacks and they represent identified additional

35 amplifying factors that scale with additional warming and mostly increase the challenge of limiting global

36 warming to or below specific temperature levels.

>>> >>> >>> p 1257 Symmetry of Carbon Cycle Response to Positive and Negative CO2 Emissions

41 [...]. For all models, the fraction of CO2 remaining in the atmosphere after an emission is larger than the

42 fraction of CO2 remaining out of the atmosphere after a removal (by 4 ± 3%; mean ± standard deviation). [...]

>>> >>> >>> p 1450

42 Table 6.3: Global tropospheric ozone budget terms and burden based on multi-model estimates and observations for

43 present conditions. All uncertainties quoted as 1 σ. Values of tropospheric ozone burden with asterisk

44 indicate average over the latitudinal zone 60oN- 60oS.

Period                           STE (Tg yr–1)

~2000 time slice (1995–2004)      626 ± 781

~2010 time slice (2005–2014)      628 ± 804

STE: stratosphere–troposphere exchange

>>> >>> >>> p 1452

6.3.2 Ozone (O3) Tropospheric ozone

6 magnitude of the global positive trend since 1997 [is]

7 [...] 0.83± 0.85 Tg yr-1 in the satellite ensemble [...]

>>> >>> >>> p 1474

6.4.3 Climate responses to SLCFs

54 [...]. The ensemble mean global mean surface temperature decreases by

55 0.66±0.51 °C while decreasing by 0.97±0.54 °C for Northern Hemisphere and 0.34±0.2 °C for Southern [Hemisphere]

>>> >>> >>> p 1489

6.6.2 Attribution of temperature and air pollution changes to emission sectors and regions Residential and Commercial cooking, heating

1 The net climate impact of the residential sector is warming in the near term of [...]

2 [...] +0.0014±0.0012°C for biofuel use [...]

>>> >>> >>> p 1490 Shipping

31 [...]. One year of global present-day shipping emissions, not considering impact of recent low sulphur fuel

32 regulation (IMO, 2016), are estimated to cause net cooling in the near term (-0.0024±0.0025°C) and slight

33 warming (+0.00033±0.00015°C) on a 100-year horizon (Lund et al., 2020). Land transportation

49 [...]. One year
50 pulse of present day emissions has a small net global temperature effect on short time-scales
51 (+0.0011±0.0045°C), [...]

>>> >>> >>> p 1632

Chapter 7: The Earth’s energy budget, climate 2 feedbacks, and climate sensitivity Changes in the global energy inventory

table  7.1

                           1971 to 2018                           1993 to 2018                    2006 to 2018

                     Energy Gain (ZJ)          %           Energy Gain (ZJ)       % Energy Gain (ZJ) %

Ocean            396.0 [285.7 to 506.2]    91.0    263.0 [194.1 to 331.9]   90.9

0-700 m         241.6 [162.7 to 320.5]   55.6     151.5 [114.1 to 188.9]   52.4

700-2000 m   123.3 [96.0 to 150.5]    28.3       82.8 [59.9 to 105.6]   28.6

> 2000 m           31.0 [15.7 to 46.4]    7.1          28.7 [14.5 to 43.0]    9.9

[...continue table...]

138.8 [86.4 to 191.3]       90.7

75.4 [48.7 to 102.0]         49.3

49.7 [29.0 to 70.4]           32.4

13.8 [7.0 to 20.6]              9.0

Land 21.8 [18.6 to 25.0] 5.0 13.7 [12.4 to 14.9]

4.7 7.2 [6.6 to 7.8]


Cryosphere 11.5 [9.0 to 14.0]

2.7 8.8 [7.0 to 10.6]

3.0 5.4 [3.9 to 6.8]


Atmosphere 5.6 [4.6 to 6.7]

1.3 3.8 [3.2 to 4.3]

1.3 1.6 [1.2 to 2.1]


1971 to 2018 1993 to 2018 2006 to 2018

TOTAL 434.9 [324.5 to 545.5] ZJ        <<< [ZJ values magnify perception of quality issues]

289.2 [220.3 to 358.2] ZJ

153.1 [100.6 to 205.5] ZJ



0.57 [0.43 to 0.72] W m-2              <<< [Wm-2 values diminish perception of quality issues]

0.72 [0.55 to 0.89] W m-2

0.79 [0.52 to 1.06] W m-2

>>> >>> >>> p 1636-7

7.2 Earth’s energy budget and its changes through time

7.2.2 Changes in Earth’s energy budget Changes in the global energy inventory

53 Combining the likely range of integrated radiative forcing (Box 7.2, Figure 1b) with the central estimate of

54 integrated radiative response (Box 7.2, Figure 1c) gives a central estimate and likely range of 340 [47 to 662]

55 ZJ (Box 7.2, Figure 1f). Combining the likely range of integrated radiative response with the central estimate

1 of integrated radiative forcing gives a likely range of 340 [147 to 527] ZJ 1 (Box 7.2, Figure 1f). Both

2 calculations yield an implied energy gain in the climate system that is consistent with an independent

3 observation-based assessment of the increase in the global energy inventory expressed relative to the

4 estimated 1850-1900 Earth energy imbalance (Box 7.2, Figure 1a; Section 7.5.2) with a central estimate and

5 very likely range of 284 [96 to 471] ZJ (high confidence) (Box 7.2, Figure 1d; Table 7.1). Estimating the total

6 uncertainty associated with radiative forcing and radiative response remains a scientific challenge and

7 depends on the degree of correlation among the two (Box 7.2, Figure 1f). However, the central estimate of

8 observed energy change falls well with the estimated likely range assuming either correlated or uncorrelated

9 uncertainties. [...]

>>> >>> >>> p 1644 Halogenated species

30 The tropospheric adjustments to chlorofluorocarbons (CFCs), specifically CFC-11 and CFC-12, have been

31 quantified as 13% ± 10% and 12% ± 14% of the SARF respectively (Hodnebrog et al., 2020b). The assessed

32 adjustment to CFCs is therefore 12 % ± 13% with low confidence due to the lack of corroborating studies.

>>> >>> >>> p 1645-6 Stratospheric water vapour

51 Since AR5 the SARF from methane-induced stratospheric water vapour changes has been calculated in two

52 models (Winterstein et al., 2019; O’Connor et al., 2021), both corresponding to 0.09 W m-2 (1850 to 2014, [where is the uncertainty?]

53 by scaling the Winterstein et al., 2019 study). This is marginally larger than the AR5 assessed value of

54 0.07±0.05 W m-2 (Myhre et al., 2013b). However, O’Connor et al. (2021) found the ERF to be

55 approximately zero due to a negative cloud adjustment. [...]

3 [...]. The assessed ERF is therefore
4 0.05±0.05 W m-2 with a lower limit reduced to zero and the central value and upper limit reduced to allow
5 for adjustment terms. This still encompasses the two recent SARF studies. There is medium confidence in the
6 SARF from agreement with the recent studies and AR5. There is low confidence in the adjustment terms. Synthesis

26 [...]. The ERF for stratospheric water vapour is slightly reduced. The combined ERF from ozone and

27 stratospheric water vapour has increased since AR5 by 0.10 ± 0.50 W m-2 (high confidence), although the

28 uncertainty ranges still include the AR5 values. 


>>> >>> >>> p 1648

7.3.3 Aerosols Observation-based lines of evidence

22 find a best estimate of IRFari of −0.4 W m−2. The updated IRFari estimates above are all scattered around the

23 midpoint of the IRFari range of −0.35 ± 0.5 W m−2 assessed by AR5 (Boucher et al., 2013).

32 [...]. The assessed best estimate and very likely IRFari range from observation-based evidence

33 is therefore –0.4 ± 0.4 W m-2 , but with medium confidence due to the limited number of studies available. Model-based lines of evidence

54 [...]. They attributed the weaker

55 estimate relative to AR5 (–0.35 ± 0.5 W m-2; Myhre et al., 2013a) to stronger absorption by organic aerosol,

>>> >>> >>> p 1649

7 The above estimates support a less negative central estimate and a slightly narrower range compared to those

7 The above estimates support a less negative central estimate and a slightly narrower range compared to those
8 reported for IRFari from ESMs in AR5 of –0.35 [–0.6 to –0.13] W m-2. The assessed central estimate and
9 very likely IRFari range from model-based evidence alone is therefore –0.2 ± 0.2 W m-2 for 2014 relative to
10 1750, with medium confidence due to the limited number of studies available. [...] 
29 [...]; they estimated the ERFari (accounting for a small
30 contribution from longwave radiation) to be –0.27 ± 0.35 W m-2. [...]  
33 The corresponding estimate emerging from the Radiative Forcing Model Intercomparison Project (RFMIP,
34 Pincus et al., 2016) is –0.25 ± 0.40 W m-2 (Smith et al., 2020a), which is generally supported by single 
35 model studies published post-AR5 [...] 38 land surface cooling (Table 7.6). Based on the above, ERFari from model-based evidence is assessed to be –
39 0.25 ± 0.25 W m-2. Overall assessment of IRFari and ERFari

43 The observation-based assessment of IRFari of –0.4 ± 0.4 W m-2 and the corresponding model-based

44 assessment of –0.2 ± 0.2 W m-2 can be compared to the range of –0.45 W m-2 to –0.05 W m-2 that emerged

45 from a comprehensive review in which an observation-based estimate of anthropogenic AOD was combined

46 with model-derived ranges for all relevant aerosol radiative properties (Bellouin et al., 2019). Based on the

47 above, IRFari is assessed to be –0.25 ± 0.2 W m-2 (medium confidence).


49 ERFari from model-based evidence is –0.25 ± 0.25 W m-2, which suggests a small negative adjustment

50 relative to the model-based IRFari estimate, consistent with the literature discussed in Adding this

51 small adjustment to our assessed IRFari estimate of –0.25 W m-2, and accounting for additional uncertainty

52 in the adjustments, ERFari is assessed to –0.3 ± 0.3 (medium confidence). This assessment is consistent with

53 the 5% to 95 % confidence range for ERFari in Bellouin et al. (2019) of –0.71 to –0.14 W m-2, and notably

54 implies that it is very likely that ERFari is negative. [...] 

>>> >>> >>> p 1650

Table 7.6: Present-day ERF due to changes in aerosol-radiation interactions (ERFari) and changes in aerosol-cloud interactions (ERFaci), and total aerosol ERF (ERFari+aci)

                                                    ERFari (W m-2)|ERFaci (W m-2)|ERFari+aci (W m-2)

CMIP6 average and 5 to 95%     –0.25 ± 0.40    –0.86 ± 0.57    –1.11 ± 0.38

confidence range (2014–1850)

CMIP5 average and 5 to 96%     –0.27 ± 0.35    –0.96 ± 0.55    –1.23 ± 0.48

confidence range (2000–1860)

>>> >>> >>> p 1651-2

Table 7.7: Studies quantifying aspects of the global ERFaci that are mainly based on satellite retrievals and were published since AR5. All forcings/adjustments as global annual mean values in W m-2. [...] Published uncertainty ranges are converted to 5%–95 % confidence intervals, and “n/a” indicates that the study did not provide an estimate for the relevant IRF/ERF.

IRFaci               LWP adjustment     Cloud fraction adjustment     Reference
–0.6±0.6                 n/a                n/a                       Bellouin et al. (2013a)
–0.4 [–0.2 to –1.0]      n/a                n/a                       Gryspeerdt et al. (2017)
–1.0±0.4                 n/a                n/a                       McCoy et al. (2017a)
 n/a                     n/a               –0.5 [–0.1 to –0.6]        Gryspeerdt et al. (2016)
 n/a                    +0.3 to 0           n/a                       Gryspeerdt et al. (2019)
–0.8±0.7                 n/a                n/a                       Rémy et al. (2018)
–0.53                   +0.15               n/a                       Toll et al. (2019)                
–1.14 [–1.72 to –0.84]   n/a                n/a                       Hasekamp et al. (2019)
–1.2 to –0.6             n/a                n/a                       McCoy et al. (2020)
–0.69 [–0.99 to –0.44]   n/a                n/a                       Diamond et al. (2020)
–0.5 ± 0.5                        n/a         –0.5 ± 0.5                Chen et al. (2014)

>>> >>> >>> p 1652

47 Summarising the above findings related to statistical relationships and causal aerosol effects on cloud

48 properties, there is high confidence that anthropogenic aerosols lead to an increase in cloud droplet

49 concentrations. Taking the average across the studies providing IRFaci estimates discussed above and

50 considering the general agreement among estimates (Table 7.7), IRFaci is assessed to be –0.7 ± 0.5 W m-2

51 (medium confidence).

>>> >>> >>> p 1654

5 [...]. These

6 three studies together suggest a global Cf adjustment that augments ERFaci relative to IRFaci by –0.5 ± 0.4

7 W m–2 (medium confidence). For global estimates of the LWP adjustment, evidence is even scarcer.

8 Gryspeerdt et al. (2019) derived an estimate of the LWP adjustment using a method similar to Gryspeerdt et

9 al. (2016). They estimated that the LWP adjustment offsets 0 to 60% of the (negative) IRFaci (0 to +0.3 W

10 m-2). Supporting an offsetting LWP adjustment, Toll et al. (2019) estimated a moderate LWP adjustment of

11 29% (+0.15 W m-2). The adjustment due to LWP is assessed to be small, with a central estimate and very

12 likely range of 0.2 ± 0.2 W m–2 , but with low confidence due to the limited number of studies available.

14 Combining IRFaci and the associated adjustments in Cf and LWP (adding uncertainties in quadrature),

15 considering only liquid-water clouds and evidence from satellite observations alone, the central estimate and

16 very likely range for ERFaci is assessed to be –1.0 ± 0.7 W m–2 (medium confidence). The confidence level

17 and wider range for ERFaci compared to IRFaci reflect the relatively large uncertainties that remain in the

18 adjustment contribution to ERFaci.

>>> >>> >>> p 1655

10 From model-based evidence, ERFaci is assessed to –1.0 ± 0.8 W m-2 (medium confidence). Overall assessment of ERFaci

17 The assessment of ERFaci based on observational evidence alone (–1.0 ± 0.7 W m-2) is very similar to the

18 one based on model-evidence alone (–1.0 ± 0.8 W m-2), in strong contrast to what was reported in AR5. This

19 reconciliation of observation-based and model-based estimates is the result of considerable scientific

20 progress and reflects comparable revisions of both model-based and observation-based estimates. The strong

21 agreement between the two largely independent lines of evidence increases confidence in the overall

22 assessment of the central estimate and very likely range for ERFaci of –1.0 ± 0.7 W m-2 (medium

23 confidence). The assessed range is consistent with but narrower than that reported by the review of Bellouin

24 et al. (2019) of –2.65 to –0.07 W m-2. The difference is primarily due to a wider range in the adjustment

25 contribution to ERFaci in Bellouin et al. (2019), however adjustments reported relative to IRFaci ranging

26 from 40% to 150% in that study are fully consistent with the ERFaci assessment presented here. Energy budget constraints on the total aerosol ERF

46 [...]. A recent review of 19 such estimates reported a

47 mean of –0.77 W m-2 for the total aerosol ERF, and a 95% confidence interval of –1.15 W m-2 to

48 –0.31 W m-2 (Forest, 2018). Adding to that review, a more recent study using the same approach reported an

49 estimate of total aerosol ERF of –0.89 [–1.82 to –0.01] W m-2 (Skeie et al., 2018). However, in the same

50 study, an alternative way of incorporating ocean heat content in the analysis produced a total aerosol ERF

51 estimate of –1.34 [–2.20 to –0.46] W m-2, illustrating the sensitivity to the manner in which observations are

52 included. A new approach to inverse estimates took advantage of independent climate radiative response

53 estimates from eight prescribed SST and sea-ice concentration simulations over the historical period to

54 estimate the total anthropogenic ERF. From this a total aerosol ERF of –0.8 [–1.6 to +0.1] W m-2 was

55 derived (valid for near-present relative to the late 1800s).

>>> >>> >>> p 1656 Overall assessment of total aerosol ERF

38 In AR5 (Boucher et al., 2013), the overall assessment of total aerosol ERF (ERFari+aci) used the median of

39 all ESM estimates published prior to AR5 of –1.5 [–2.4 to –0.6] W m-2 as a starting point, but placed more

40 confidence in a subset of models that were deemed more complete in their representation of aerosol-cloud

41 interactions. These models, which included aerosol effects on mixed-phase, ice and/or convective clouds,

42 produced a smaller estimate of –1.38 W m-2. Likewise, studies that constrained models with satellite

43 observations (five in total), which produced a median estimate of –0.85 W m-2, were given extra weight.

44 Furthermore, a longwave ERFaci of 0.2 W m-2 was added to studies that only reported shortwave ERFaci

45 values. Finally, based on higher resolution models, doubt was raised regarding the ability of ESMs to

46 represent the cloud adjustment component of ERFaci with fidelity. The expert judgement was therefore that

47 aerosol effects on cloud lifetime were too strong in the ESMs, further reducing the overall ERF estimate. The

48 above lines of argument resulted in a total aerosol assessment of –0.9 [–1.9 to –0.1] W m-2 in AR5.

>>> >>> >>> p 1657

15 [...]. Based on this, ERFari and ERFaci for 2014 relative to 1750 are assessed

16 to –0.3 ± 0.3 W m-2 and –1.0 ± 0.7 W m-2, respectively.

26 Combining the lines of evidence and adding uncertainties in quadrature, the ERFari+aci estimated for 2014

27 relative to 1750 is assessed to be –1.3 [–2.0 to –0.6] W m-2 (medium confidence). The corresponding range

28 from Bellouin et al. (2019) is –3.15 to –0.35 W m-2, thus there is agreement for the upper bound while the

29 lower bound assessed here is less negative. A lower bound more negative than -2.0 W m-2 is not supported by

30 any of the assessed lines of evidence. There is high confidence that ERFaci contributes most (75–80%) to the

31 total aerosol effect (ERFari+aci). In contrast to AR5 (Boucher et al., 2013), it is now virtually certain that the

32 total aerosol ERF is negative. Figure 7.5 depicts the aerosol ERFs from the different lines of evidence along

33 with the overall assessments.

40 [...]. Consistent with Chapter 2, Figure 2.10, the change in aerosol ERF from about 2014 to

41 2019 is assessed to be +0.2 W m-2, but with low confidence due to limited evidence. Aerosols are therefore

42 assessed to have contributed an ERF of –1.1 [–1.7 to –0.4] W m–2 over 1750–2019 (medium confidence).

>>> >>> >>> p 1658

7.3.4 Other agents Land use

46 quantification of land use forcing in CMIP6 models (excluding one outlier) (Smith et al., 2020a) found an

47 IRF of –0.15 ± 0.12 W m–2 (1850 to 2014), and an ERF (correcting for land surface temperature change) of -

48 0.11 ± 0.09 W m–2. This shows a small positive adjustment term (mainly from a reduction in cloud cover.

49 CMIP5 models show an IRF of –0.11 [–0.16 to –0.04] W m-2 (1850 to 2000) after excluding unrealistic

50 models (Lejeune et al., 2020).

>>> >>> >>> p 1659

9 The contribution of irrigation (mainly to low cloud amount) is assessed as –0.05 [–0.1 to 0.05] W m-2 for the

10 historical period (Sherwood et al., 2018).

12 Since the CMIP5 and CMIP6 modelling studies are in agreement with Ghimire et al. (2014), that study is

13 used as the assessed albedo ERF. Adding the irrigation effect to this gives an overall assessment of the ERF

14 from land use change of –0.20 ± 0.10 W m-2 (medium confidence). [...] Contrails and aviation-induced cirrus

21 ERF from contrails and aviation-induced cirrus is taken from the assessment of Lee et al. (2020), at 0.057

22 [0.019 to 0.098] W m–2 in 2018 (see Chapter 6, Section 6.6.2 for an assessment of the total effects of

23 aviation). This is rounded up to address its low confidence and the extra year of air traffic to give an assessed

24 ERF over 1750–2019 of 0.06 [0.02 to 0.10]. This assessment is given low confidence due to the potential for

25 missing processes to affect the magnitude of contrails and aviation-induced cirrus ERF. Light absorbing particles on snow and ice

31 [...]. The SARF from LAPs on

32 snow and ice was assessed to +0.04 [+0.02 to +0.09] W m-2 (Boucher et al., 2013), a range appreciably lower

33 than the estimates given in AR4 (Forster et al., 2007).

>>> >>> >>> p 1664 Summary ERF assessment

5 The total anthropogenic ERF over the industrial era (1750–2019) is estimated as 2.72 [1.96 to 3.48] W m–2
6 (Table 7.8; Annex III) (high confidence). [...]

>>> >>> >>> p 1665 Temperature Contribution of forcing agents

46 The total human forced GSAT change from 1750–2019 is calculated to be 1.29 [1.00 to 1.65] °C (high

47 confidence). [...]

48 [...]. The calculated GSAT

49 change is comprised of a well-mixed greenhouse gas warming of 1.58 [1.17 to 2.17] °C (high confidence), a

50 warming from ozone changes of 0.23 [0.11 to 0.39] °C (high confidence), a cooling of –0.50 [–0.22 to –0.96]

51 °C from aerosol effects (medium confidence). [...]

53 [...]. There is also a –0.06 [–0.15 to +0.01] °C contribution from surface

54 reflectance changes which dominated by land-use change (medium confidence). Changes in solar and

55 volcanic activity are assessed to have together contributed a small change of –0.02 [–0.06 to +0.02] °C since [1750.]

>>> >>> >>> p 1666

11 The emulator gives a range of GSAT response for the 1750 to the 1850–1900 period of 0.09 [0.04 to 0.14 ]

12 °C from a anthropogenic ERFs. These results are used as a line of evidence for the assessment of this change

13 in Chapter 1 (Cross-Chapter Box 1.2), which gives an overall assessment of 0.1 °C [likely range -0.1 to 0.3]

14 °C.

>>> >>> >>> p 1677

7.4 Climate feedbacks

7.4.2 Assessing climate feedbacks Water vapour and temperature lapse rate feedbacks

7 [...]. The total

8 stratospheric feedback is assessed at 0.05 ± 0.1 W m–2 °C–1 (one standard deviation).

>>> >>> >>> p 1678

36 [...]. The value of the

37 global surface albedo feedback is assessed to be αA = 0.35 W m-2 °C-1, with a very likely range from 0.10 to

38 0.60 W m–2 °C–1 and a likely range from 0.25 to 0.45 W m–2 °C–1 with high confidence.

>>> >>> >>> p 1680 Assessment 1 for individual cloud regimes

High-cloud altitude feedback.

16 The high-cloud altitude feedback was estimated to be 0.5 W m–2°C–1 based on GCMs in AR5, but is revised,

17 using a recent re-evaluation that excludes aliasing effects by reduced low-cloud amounts, downward to 0.22

18 ± 0.12 W m–2 °C–1 (one standard deviation) (Zhou et al., 2014; Zelinka et al., 2020). [...]

47 [...]. Also, there is a positive feedback due to increase of optically thin cirrus clouds in the tropopause

48 layer, estimated to be 0.09 ± 0.09 W m-2 °C–1 (one standard deviation) (Zhou et al., 2014). [...] 

>>> >>> >>> p 1681

Tropical high-cloud amount feedback.

4 [...]. Taking observational estimates altogether and methodological

5 uncertainty into account, the global contribution of the high-cloud amount feedback is assessed to –0.15 ±

6    0.2 W m–2 °C–1 (one standard deviation).

35 [...]. Based on the combined estimate using LESs and the cloud controlling factor analysis, the global

36 contribution of the feedback due to marine low clouds equatorward of 30° is assessed to be 0.2 ± 0.16 W m–2

37 °C–1 (one standard deviation), for which the range reflects methodological uncertainties. 

Land cloud feedback.

46 [...]. The mean estimate of the

47 global land cloud feedback in CMIP5 models is smaller than the marine low cloud feedback, 0.08 ± 0.08 W

48 m–2 °C–1 (Zelinka et al., 2016). These values are nearly unchanged in CMIP6 (Zelinka et al., 2020). [...]

50 [...]. Therefore, the feedback

51 due to decreasing land clouds is assessed to be 0.08 ± 0.08 W m–2 °C–1 (one standard deviation) with low

52 confidence.


>>> >>> >>> p 1682

Extratropical cloud optical depth feedback.

51 [...]. Quantitatively, the global contribution of this feedback is

52 assessed to have a value of –0.03 ± 0.05 W m–2 °C–1 (one standard deviation) by combining estimates based

53 on observed interannual variability and the cloud controlling factors.

>>> >>> >>> p 1683

Arctic cloud feedback.

15 [...]. The observational estimates are sensitive to the analysis period and 

16 the choice of reanalysis data, and a recent estimate of the TOA cloud feedback over 60°–90°N using

17 atmospheric reanalysis data and CERES satellite observations suggests a regional value ranging from –0.3 to

18 0.5 W m–2 °C–1, which corresponds to a global contribution of –0.02 to 0.03 W m–2 °C–1 (Zhang et al.,

19 2018b). Based on the overall agreement between ESMs and observations, the Arctic cloud feedback is

20 assessed small positive and has the value of 0.01 ± 0.05 W m–2 °C–1 (one standard deviation). The assessed

21 range indicates that a negative feedback is almost as probable as a positive feedback, and the assessment that

22 the Arctic cloud feedback is positive is therefore given low confidence. Synthesis for the net cloud feedback

34 [...]. By assuming that uncertainty of individual cloud

35 feedbacks is independent of each other, their standard deviations are added in quadrature, leading to the

36 likely range of 0.12 to 0.72 W m–2 °C–1 and the very likely range of –0.10 to 0.94 W m–2 °C–1 (Table 7.10).



40 [...]. The observational estimate,

41 which is sensitive to the period considered, based on two atmospheric reanalyses (ERA-Interim and

42 MERRA) and TOA radiation budgets derived from the CERES satellite observations for the years 2000–

43 2010 is 0.54 ± 0.7 W m–2 °C–1 (one standard deviation) (Dessler, 2013) and overlaps with the assessed range

44 of the net cloud feedback.

>>> >>> >>> p 1685 Non-1 CO2 biogeochemical feedbacks

10 [...]. This leaves the wetland CH4, land

11 N2O, and ocean N2O feedbacks, the assessed mean values of which sum to a positive feedback parameter of

12 +0.04 [0.02 to 0.06] W m–2 °C–1 (Chapter 5, Section 5.4.7). Other non-CO2 biogeochemical feedbacks that

13 are relevant to the net feedback parameter are assessed in Chapter 6, Section 6.4.5 (Table 6.8). These

14 feedbacks are associated with sea salt, dimethyl sulphide, dust, ozone, biogenic volatile organic compounds,

15 lightning, and CH4 lifetime, and sum to a negative feedback parameter of –0.20 [–0.41 to +0.01] W m–2 °C–1.

16 The overall feedback parameter for non-CO2 biogeochemical feedbacks is obtained by summing the Chapter

17 5 and Chapter 6 assessments, which gives –0.16 [–0.37 to +0.05] W m–2 °C–1. [...]

>>> >>> >>> p 1686 Biogeophysical feedbacks

15 Given the limited number of studies, we take the full range of estimates discussed above for the

16 biogeophysical feedback parameter, and assess the very likely range to be from zero to +0.3 W m-2 °C-1, with

17 a central estimate of +0.15 W m-2 °C-1 (low confidence). [...] Synthesis of biogeophysical and non-CO2 biogeochemical feedbacks

25 The non-CO2 biogeochemical feedbacks are assessed in Section to be –0.16 [–0.37 to +0.05] W m–

26 2 °C–1 and the biogeophysical feedbacks are assessed in Section to be +0.15 [0 to +0.3] W m-2 °C-1.

27 The sum of the biogeophysical and non-CO2 biogeochemical feedbacks is assessed to have a central value of

28 -0.01 W m–2 °C–1 and a very likely range from –0.27 to +0.25 W m–2 °C–1 (see Table 7.10). [...]

>>> >>> >>> p 1688 Synthesis

6 [...]. The net climate feedback is assessed to be –1.16 W m–2 °C–1, likely from –1.54 to –0.78 W

7 m–2 °C–1, and very likely from –1.81 to –0.51 W m–2°C–1.

>>> >>> >>> p 1703-4 Dependence of feedbacks on temperature patterns

51 Recent studies based on simulations of 1% yr–1 CO2 increase (1pctCO2) or abrupt4xCO2 as analogues for

52 historical warming suggest characteristic values of α’ = +0.05 W m–2 °C–1 (–0.2 to +0.3 W m–2 °C–1 range

53 across models) based on CMIP5 and CMIP6 ESMs (Armour 2017, Lewis and Curry 2018, Dong et al. 2020).

54 Using historical simulations of one CMIP6 ESM (HadGEM3-GC3.1-LL), Andrews et al., (2019) find an

55 average feedback parameter change of α’ = +0.2 W m–2 °C–1(–0.2 to +0.6 W m–2 °C–1 range across four

56 ensemble members). Using historical simulations from another CMIP6 ESM (GFDL CM4.0), Winton et al.

1 (2020) find an average feedback parameter change of α’ = +1.5 W m–2 °C–1(+1 1.2 to +1.7 W m–2 °C–1 range

2 across three ensemble members). [...]


>>> >>> >>> p 1707

7.5.1 Estimates of ECS and TCR based on process understanding

20 In summary, the ECS based on the assessed values of Δ𝐹𝐹2×CO2 and α is assessed to have a median value of

21 3.4°C with a likely range of 2.5–5.1 °C and very likely range of 2.1–7.7 °C. To this assessed range of ECS,

22 the contribution of uncertainty in α is approximately three times as large as the contribution of uncertainty in

23 Δ𝐹 2×CO2.

>>> >>> >>> p 1708

44 In summary, the process-based estimate of TCR is assessed to have the central value of 2.0°C with the likely

45 range from 1.6 to 2.7°C and the very likely range from 1.3 to 3.1°C (high confidence). The upper bound of

46 the assessed range was slightly reduced from AR5 but can be further constrained using multiple lines of

47 evidence (Section 7.5.5).

>>> >>> >>> p 1710 Estimates of ECS and TCR based on the global energy budget

38 [...]. Several lines of evidence,

41 [...] suggest a 1850-

42 1900 Earth energy imbalance of 0.2 ± 0.2 W m–2. [...]

48 [...]. The ERF change between 1850–1900 and 2006–2019 is estimated to be ΔF = 2.20 [1.53 to 2.91]

49 W m–2 (Section 7.3.5) [...] 

52 [Estimation of TCR is] 1.9 [1.3 to 2.7]°C and an effective ECS of 2.5 [1.6–4.8] °C. [...]

>>> >>> >>> p 1711

46 The net radiative feedback change between the historical warming pattern and the projected equilibrium

47 warming pattern in response to CO2 forcing (α’) is estimated to be in the range 0.0 to 1.0 W m–2 °C–1 (Figure

48 7.15). Using the value α’ = +0.5 ± 0.5 W m–2 °C –1 to represent this range illustrates the effect of changing

49 radiative feedbacks on estimates of ECS. While the effective ECS inferred from historical warming is 2.5

50 [1.6–4.8] °C , ECS = ΔF2×CO2/(–α + α’) is 3.5 [1.7–13.8] °C. For comparison, values of α’ derived from the

51 response to historical and idealized CO2 forcing within coupled climate models (Armour, 2017; Lewis and

52 Curry, 2018; Andrews et al., 2019; Dong et al., 2020; Winton et al., 2020) can be approximated as α’ = +0.1

53 ± 0.3 W m–2 °C–1 (Section, corresponding to a value of ECS of 2.7 [1.7–5.9] °C. [...]

>>> >>> >>> p 1712 Estimates of ECS and TCR based on climate model emulators

51 2013). Emulators generally produced estimates of effective ECS between 1°C and 5°C and ranges of TCR

52 between 0.9°C and 2.6°C. Padilla et al. (2011) use a simple global-average emulator with two timescales

53 (see Supplementary Material 7.SM.2 and Section to estimate a TCR of 1.6 [1.3 to 2.6] °C. Using the

54 same model, Schwartz (2012) finds TCR in the range 0.9–1.9°C [...]

>>> >>> >>> p 1713

1 [...]. Using an eight-box

2 representation of the atmosphere–ocean–terrestrial system constrained by historical warming, Goodwin

3 (2016) found an effective ECS of 2.4 [1.4 to 4.4] °C while Goodwin (2018) found effective ECS to be in the

4 range 2–4.3°C when using a prior for ECS based on paleoclimate constraints.


7 [...], Skeie et al.

8 (2018) estimate a TCR of 1.4 [0.9 to 2.0] °C and a median effective ECS of 1.9 [1.2 to 3.1] °C. Using a

9 similar emulator comprised of land and ocean regions and an upwelling-diffusive ocean, with global surface

10 temperature and ocean heat content datasets through 2011, Johansson et al. (2015) find an effective ECS of

11 2.5 [2.0 to 3.2] °C. [...]


21 The median estimates of TCR and effective ECS inferred from emulator studies generally lie within the 5%

22 to 95% ranges of the those inferred from historical global energy budget constraints (1.3 to 2.7°C for TCR

23 and 1.6 to 4.8°C for effective ECS). [...]

>>> >>> >>> p 1714 Assessment of ECS and TCR based on the instrumental record

54 Global energy budget constraints indicate a central estimate (median) TCR value of 1.9°C and that TCR is

55 likely in the range 1.5°C to 2.3°C and very likely in the range 1.3°C to 2.7°C (high confidence). [...]

>>> >>> >>> p 1721 Emergent constraints using global or near-global surface temperature change

38 [...]. To address this limitation an

39 emergent constraint on 1970–2005 global warming was demonstrated to yield a best estimate ECS of 2.83

40 [1.72 to 4.12] °C (Jiménez-de-la-Cuesta and Mauritsen, 2019). The study was followed up using CMIP6

41 models yielding a best estimate ECS of 2.6 [1.5 to 4.0] °C based on 1975–2019 global warming (Nijsse et

42 al., 2020), [...]

53 A study that developed an emergent constraint based on the response to the Mount Pinatubo 1991 eruption

54 yielded a best estimate of 2.4 [likely range 1.7–4.1] °C (Bender et al., 2010). When accounting for ENSO

55 variations they found a somewhat higher best estimate of 2.7°C, [...]

>>> >>> >>> p 1722

11 [...]. Recently it was proposed by Cox et

12 al. (2018a) to use variations in the historical experiments of the CMIP5 climate models as an emergent

13 constraint giving a median ECS estimate of 2.8 [1.6 to 4.0] °C. [...]

20 [...]. Contrary to

21 constraints based on paleoclimates or global warming since the 1970s, when based on CMIP6 models a

22 higher, yet still well-bounded ECS estimate of 3.7 [2.6 to 4.8] °C is obtained (Schlund et al., 2020). [...]

30 [...] yield a median of 3.3 [2.4 to

31 4.5] °C (Dessler and Forster, 2018). [...]

>>> >>> >>> p 1723 Assessed ECS and TCR based on emergent constraints

32 [...]. This leads to the assessment

33 that ECS inferred from emergent constraints is very likely 1.5 to 5°C with medium confidence.

38 [...]. In the simplest form Gillett et al. (2012) regressed the

39 response of one model to individual historical forcing components to obtain a tight range of 1.3–1.8°C, but

40 later when an ensemble of models was used the range was widened to 0.9–2.3°C (Gillett et al., 2013), [...]

43 [...]. Another study used the response to the Pinatubo volcanic

44 eruption to obtain a range of 0.8–2.3°C (Bender et al., 2010). A tighter range, notably at the lower end, was

45 found in an emergent constraint focusing on the post-1970s warming exploiting the lower spread in aerosol

46 forcing change over this period (Jiménez-de-la-Cuesta and Mauritsen, 2019). Their estimate was 1.67 [1.17

47 to 2.16] °C. Two studies tested this idea: Tokarska et al. (2020) estimates TCR was 1.60 [0.90 to 2.27] °C

48 based on CMIP6 models, whereas Nijsse et al. (2020) found 1.68 [1.0 to 2.3] °C, and in both cases there was

49 a small sensitivity to choice of ensemble with CMIP6 models yielding slightly lower values and ranges.

50 Combining these studies gives a best estimate of 1.7°C and a very likely range of TCR of 1.1–2.3°C with

51 high confidence.

7.5.5 Combined assessment of ECS and TCR

43 [...]. In summary, based on multiple lines of evidence

44 the best estimate of ECS is 3°C, it is likely within the range 2.5 to 4°C and very likely within the range 2 to

45 5°C. [...]

>>> >>> >>> p 1727

1 estimate TCR is 1.8°C, it is likely 1.4 to 2.2°C and very likely 1.2 to 2.4°C. The assessed ranges are all

2 assigned high confidence due to the high level of agreement among the lines of evidence.

>>> >>> >>> p 1735 Carbon cycle responses and other indirect contributions

18 [...]. As values have only been calculated in two simple parameterised carbon cycle

19 models the uncertainty is assessed to be ±100%. Due to few studies and a factor of two difference between

20 them, there is low confidence that the magnitude of the carbon cycle response is within the higher end of this

21 uncertainty range, but high confidence that the sign is positive.

41 [...]. The

42 contribution from stratospheric water vapour is 0.4 ± 0.4 ×10–4 W m-2 ppb (CH4)-1, [...]

46 [...]. This is now increased to –1.7 ppb methane per ppb N2O (based on a methane

47 lifetime decrease of 4% ± 4% for a 55 ppb increase in N2O (Thornhill et al., 2021b) [...]

>>> >>> >>> p 1736

13 [...]. For biogenic methane the soil uptake and

14 removal of partially-oxidised products is equivalent to a sink of atmospheric CO2 of 0.7 ± 0.7 kg per kg

15 methane. [...]

>>> >>> >>> p 1825

7.SM.1.3 Historical (1750-2019) effective radiative forcing time series

7.SM.1.3.1 Best estimate historical time series

26 [...]. For solar

27 ERF, the Chapter 7 assessment of +0.01 ± 0.07 W m-2 is for the 6754 BCE to 1744 CE pre-industrial period

28 to the 2009–2019 solar cycle.

7.SM.1.3.2 Uncertainties in the historical best estimate time series

50 [...] yielding contributions to ERFari of +0.3 ± 0.2

51 W m–2 for BC, –0.4 ± 0.2 W m–2 for sulphate, –0.09 ± 0.07 W m–2 for OC and –0.11 ± 0.05 W m–2 for nitrate

52 for the 2005–2014 mean with respect to 1750. [...]. 


55 2005–2014 mean ERFaci of –1.0 ± 0.7 W m-2 with respect to 1750. [...]

>>> >>> >>> p 1827

Table 7.SM.3: ERF from ozone precursors in AerChemMIP experiments (Thornhill et al., 2021b), and radiative efficiencies derived for emissions-based SSP pathways. [...]

14  species | Contribution to ozone ERF 1850-2014, Wm–2 | Scale factor to reproduce 1850-2014 ozone ERF | Radiative efficiency for ozone ERF


Ozone-depleting       –0.11 ± 0.10                          1.27                    𝛽ODH = –0.125 ± 0.113 mW m–2 ppt-1

halocarbons (ODH)

CO                    +0.07 ± 0.06                          1.27                    𝛽CO = 0.155 ± 0.131 mW m-2 MtCO-1 yr

NMVOC                 +0.04 ± 0.04                          1.27                    𝛽NMVOC = 0.329 ± 0.328 mW m–2 MtNMVOC-1 yr

NOx                   +0.20 ± 0.11                          1.27                    𝛽NOx = 1.797 ± 0.983 mW m–2 MtNO2 yr–1


Sum                   +0.37 ± 0.18                      +0.47 ± 0.24 W m–2 (total ozone ERF)          - - -

>>> >>> >>> p 1829

27 The two-layer model can be calibrated to emulate the climate response of individual CMIP models (Geoffroy

28 et al., 2013b, 2013a) using abrupt4xCO2 experiments. Calibrations are performed for 44 CMIP6 models

29 resulting in parameter estimates (mean and standard deviation) of 𝐶 = 8.1 ± 1.0 W yr m–2 °C–1, 𝐶d = 110 ± 63

30 W yr m–2 °C–1, 𝛾 = 0.62 ± 0.13 W m–2 °C–1, 𝜀 = 1.34 ± 0.41, 𝜅 = 0.84 ± 0.38 W m–2 °C–1. [...]

>>> >>> >>> p 1830

1  (Section 7.SM.2.1). The climate feedback

2 parameter 𝛼 is sampled from a truncated Gaussian distribution (truncated at ±2 standard deviations) with

3 mean –1.33 W m-2 °C–1 and standard deviation 0.5 W m–2 °C–1. [...]

11 (1) the time series of historical GSAT to the Chapter 2 (Cross Chapter Box 2.3) assessment from 1850–

12 2020 with a root-mean-square error of 0.135°C or less, approximately recreating the headline 1850–

13 1900 to 1995–2014 assessment of 0.67–0.98°C (Cross Chapter Box 2.3, very likely range);

14 (2) the assessment of ocean heat uptake from Section from 1971–2018 within the likely range of

15 329–463 ZJ;

16 (3) CO2 concentrations to the 2014 very likely range of 397.1 ± 0.4 ppm (Table 2.1);

17 (4) the airborne fraction from a 1% per year CO2 increase simulation to the range assessed in Section

18 5.5.1 of 53 ± 6% (1 standard deviation).

25 [...] As a comparison, the ECS from this

26 constrained set has a median and 5–95% ranges of ECS and TCR of 2.95 [2.05 – 5.07]°C and 1.81 [1.36–

27 2.46]°C respectively, compared to the Chapter 7 best estimates and very likely ranges of 3.0 [2.0–5.0]°C for

28 ECS and 1.8 [1.2 – 2.4]°C for TCR.

>>> >>> >>> p 1854 

7.SM.6 Tables of greenhouse gas lifetimes, 1 radiative efficiencies and metrics

3 Table 7.SM.8: Estimated uncertainty in the GWP and GTP for CH4 showing the total uncertainty as a percentage of

4 the best estimate (expressed as 5-95% confidence interval), and the uncertainty by component of the

5 total emission metric calculation (radiative efficiency, chemistry feedbacks, atmospheric lifetime, CO2

6 (combined uncertainty in radiative efficiency and CO2 impulse response), carbon cycle response, fate

7 of oxidized fossil methane, and impulse-response function. Uncertainties in individual terms are taken

8 from Section 7.6, except for the CO2 impulse response which comes from (Joos et al., 2013). The

9 impulse-response uncertainties are calculated by taking 1.645×standard deviation of the GTPs

10 generated from 600 ensemble members of the impulse response derived from FaIRv1.6.2 and

11 MAGICC7.5.1 (Section 7.SM.4.2)

Metric            [...]      Total uncertainty (%)

GWP20   20 14 9 18 3 2 0              32

GWP100  20 14 14 26 5 7 0             40

GWP500  20 14 14 29 5 26 0            48

GTP50   20 14 37 22 17 22 31          64

GTP100  20 14 18 28 8 60 38           83

>>> >>> >>> p 1877  Future Water Cycle Changes

Chapter 8: Water cycle changes

35 [...] Global

36 annual precipitation over land is projected to increase on average by 2.4 [–0.2 to 4.7] % (very likely range) in

37 the SSP1-1.9 low-emission scenario and by 8.3 [0.9 to 12.9] % in the SSP5-8.5 high-emission scenario

>>> >>> >>> p  1887

8.2 Why should we expect water cycle changes?

8.2.1 Global water cycle constraints

Hydrological sensitivity (η)

26 [...] Values obtained from six CMIP5 models simulating the

27 Last Glacial Maximum and pre-industrial period (ηa=1.6-3.0 % per oC) are larger than for each

28 corresponding 4xCO2 experiment (ηa=1.3–2.6 % per oC) due to differences in the mix of forcings, vegetation

29 and land surface changes and a higher thermodynamic % per oC evaporation scaling in the colder state (Li et

30 al., 2013b). Updated estimates across comparable experiments from 22 CMIP5/CMIP6 models (Rehfeld et

31 al., 2020) display a consistent range (ηa=1.7±0.6 % per oC; Figure 8.4; Section Confirming ηa in

32 observations (Figure 8.4) is difficult due to measurement uncertainty, varying rapid adjustments to radiative

33 forcing and unforced variability (Dai and Bloecker, 2019; Allan et al., 2020).

38 [...] Thus, global

39 precipitation appears more sensitive to radiative forcing from sulphate aerosols (2.8±0.7 % per oC; ηa ≈η)

40 than GHGs (1.4±0.5 % per oC; ηa<η) while the response to black carbon aerosol can be negative (-3.5±5.0 %

41 per oC; ηa<<η) due to strong atmospheric solar absorption (Samset et al., 2016). [...]

44 [...] Global mean precipitation

45 increases after complete removal of present day anthropogenic aerosol emissions (see also Section 4.4.4) in

46 four different climate models (ηa = 1.6-5.5% per oC) are mainly attributed to sulphate aerosol as opposed to

47 other aerosol species (Samset et al., 2018b).[...]

54 Hydrological sensitivity is generally lower over land but with a large uncertainty range (η = -0.1 to 3.0 % per

55 oC GSAT) relative to the oceans (η = 2.3 to 3.3 % per oC) based on multi-model 4xCO2 CMIP6 simulations [...]

>>> >>> >>> p 1903 Water vapour and its transport

36 [...] CMIP5 simulations underestimate the observed decreases in relative

37 humidity over much of global land during 1979-2015 (Douville and Plazzotta, 2017; Dunn et al., 2017) even

38 when observed SSTs are prescribed (-0.05 to -0.25 %/decade compared with an observed rate of -0.4 to -0.8

39 %/decade). It is not yet clear if this discrepancy is related to internal variability or can be explained by

40 deficiencies in models (Vannière et al., 2019; Douville et al., 2020) or observations (Willett et al., 2014).

>>> >>> >>> p 1909 Runoff, streamflow and flooding

22 [...] Up to 30–50% of the recent multi-decadal

23 decline in streamflow across the Colorado River Basin can be attributed to anthropogenic warming and its

24 impacts on snow and evapotranspiration [...]

>>> >>> >>> p 1915 Groundwater

35 Increasing global freshwater withdrawals, primarily associated with the expansion of irrigated agriculture in

36 drylands, have led to global groundwater depletion that has an estimated range of ~100 and ~300 km3 yr-1

37 from hydrological models and volumetric-based calculations (Bierkens and Wada, 2019). The magnitude of

38 this change is such that its estimated contribution to global sea-level rise is in the order of 0.3 to 0.9 mm yr−1

>>> >>> >>> p 1934 Global water cycle intensity and P-E over land and oceans

47 Over global land there is a small range in global mean multi-model mean precipitation increase across

48 scenarios in the mid-term (2.6-4.0%), which widens (to 2.6-8.8%) in the long-term (Table 8.1). The long

49 term projections are consistent with the Chapter 4 assessment that global annual precipitation over land is

50 projected to increase on average by 2.4 [-0.2 to 4.7] % (very likely range) in the SSP1-1.9 low-emission

51 scenario and by 8.3 [0.9 to 12.9] % in the SSP5-8.5 high-emission scenario by 2081–2100 relative to 1995–

52 2014. [...]

>>> >>> >>> p 1949 Freshwater reservoirs

6 Glaciers

22 [...] The projected global glacier mass loss over 2015-2100 is 29 000 ± 20 000 Gt for SSP1-2.6

23 to 58 000 ± 30 000 Gt for SSP5-8.5 (Section 9.5.1). [...]

>>> >>> >>> p 1955

Table 8.2: Monsoon mean water cycle projections in the medium term (2041-2060) and long term (2081-2100) relative to present day (1995-2014), showing present day mean and 90% confidence range across CMIP6 models (historical experiment) and projected mean changes and the 90% confidence range across the same set of models and a range of shared socioeconomic scenarios. All statistics are in units of mm/day. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).

>>> >>> >>> p 1956

>>> >>> >>> p 1984 Amplification of drought by dust

27 [...]. Modern-day dust emissions

28 are dominated by natural sources (Ginoux et al., 2012), although human emissions may contribute 10–60%

29 of the global atmospheric dust load (Webb and Pierre, 2018). Paleo-dust records suggest that human factors

30 (land use change and landscape disturbance) may have doubled global dust emissions between1750 and the

31 last quarter of the 20th century (Hooper and Marx, 2018) (Section 2.2.6).

Chapter 9: Ocean, cryosphere and sea level change

>>> >>> >>> p 2155

16 Ocean Heat and Salinity

18 At the ocean surface, temperature has on average increased by 0.88 [0.68–1.01] °C from 1850-1900 to

19 2011-2020, with 0.60 [0.44–0.74] °C of this warming having occurred since 1980. The ocean surface

20 temperature is projected to increase from 1995–2014 to 2081–2100 on average by 0.86 [0.43–1.47,

21 likely range] °C in SSP1-2.6 and by 2.89 [2.01–4.07, likely range] °C in SSP5-8.5. [...]

24 [...]. At least 83% of the ocean surface will very

25 likely warm over the 21st century in all SSP scenarios. {2.3.3, 9.2.1}

29 [...]. Ocean heat content

30 has increased from 1971 to 2018 by [0.28–0.55] yottajoules and will likely increase until 2100 by 2 to 4

31 times that amount under SSP1-2.6 and 4 to 8 times that amount under SSP5-8.5. [...]

44 [...] with marine heatwaves at global scale becoming 4 [2–9, likely range] times more frequent in 2081–2100

45 compared to 1995–2014 under SSP1-2.6, and 8 [3–15, likely range] times more frequent under SSP5-8.5.

>>> >>> >>> p 2156

1 [...] the global 0–200 m stratification is now assessed to have increased about twice as much as

2 reported by the SROCC, with a 4.9 ± 1.5% increase from 1970 to 2018 (high confidence) and even higher

3 increases at the base of the surface mixed layer. Upper-ocean stratification will continue to increase

4 throughout the 21st century (virtually certain). {9.2.1}

>>> >>> >>> p 2157

Ice Sheets

9 The Greenland Ice Sheet has lost 4890 [4140–5640] Gt mass over the period 1992–2020, equivalent to

10 13.5 [11.4–15.6] mm global mean sea level rise. The mass-loss rate was on average 39 [–3 to 80] Gt yr–1

11 over the period 1992–1999, 175 [131 to 220] Gt yr–1 over the period 2000–2009 and 243 [197 to 290] Gt

12 yr–1 over the period 2010–2019. [...]

17 The Antarctic Ice Sheet has lost 2670 [1800–3540] Gt mass over the period 1992–2020, equivalent to

18 7.4 [5.0–9.8] mm global mean sea level rise. The mass-loss rate was on average 49 [–2 to 100] Gt yr–1

19 over the period 1992–1999, 70 [22 to 119] Gt yr–1 over the period 2000–2009 and 148 [94 to 202] Gt yr–1

20 over the period 2010–2019. [...]

28 [...]. The related contribution to global

29 mean sea level rise until 2100 from the Greenland Ice Sheet will likely be 0.01–0.10 m under SSP 1-2.6,

30 0.04–0.13 m under SSP2-4.5 and 0.09–0.18 m under SSP5-8.5, while the Antarctic Ice Sheet will likely

31 contribute 0.03–0.27 m under SSP1-2.6, 0.03–0.29 m under SSP2-4.5 and 0.03–0.34 m under SSP5-8.5.


45 Glaciers lost 6200 [4600–7800] Gt of mass (17.1 [12.7–21.5] mm global mean sea level equivalent) over

46 the period 1993 to 2019 and will continue losing mass under all SSP scenarios (very high confidence).

51 [...] even if global temperature is stabilized (very high confidence) [g]laciers will lose 29,000 [9,000–49,000] Gt

>>> >>> >>> p 2158

1  and 58,000 [28,000–88,000] Gt over the period 2015–2100 for RCP2.6 and RCP8.5, respectively (medium

2 confidence), which represents 18 [5–31] % and 36 [16–56] % of their early-21st-century mass, respectively.


10 [...]. Permafrost

11 warmed globally by 0.29 [0.17–0.41, likely range] °C between 2007 and 2016 (medium confidence). [...]


21 [...]. The observed

22 sensitivity of Northern Hemisphere snow cover extent to Northern Hemisphere land surface air temperature

23 for 1981–2010 is –1.9 [–2.8 to –1.0, likely range] million km2 per 1°C throughout the snow season. [...]

Sea Level

32 Global mean sea level (GMSL) rose faster in the 20th century than in any prior century over the last

33 three millennia (high confidence), with a 0.20 [0.15–0.25] m rise over the period 1901 to 2018 (high

34 confidence). GMSL rise has accelerated since the late 1960s, with an average rate of 2.3 [1.6–3.1] mm

35 yr-1 over the period 1971–2018 increasing to 3.7 [3.2–4.2] mm yr-1 over the period 2006–2018 (high

36 confidence).[...]

>>> >>> >>> p 2159

7 [...]. In total, such extreme sea levels

8 will occur about 20 to 30 times more frequently by 2050 and 160 to 530 times more frequently by 2100

9 compared to the recent past [...]

16 [...]. Considering only processes for which projections can be made with at least medium

17 confidence, relative to the period 1995–2014 GMSL will rise by 2050 between 0.18 [0.15–0.23, likely

18 range] m (SSP1-1.9) and 0.23 [0.20–0.30, likely range] m (SSP5-8.5), and by 2100 between 0.38 [0.28–

19 0.55, likely range] m (SSP1-1.9) and 0.77 [0.63–1.02, likely range] m (SSP5-8.5). [...]

21 [..]. These likely range projections do not include those ice-sheet-related

22 processes that are characterized by deep uncertainty. {9.6.3}

24 Higher amounts of GMSL rise before 2100 could be caused by earlier-than-projected disintegration of

25 marine ice shelves, the abrupt, widespread onset of Marine Ice Sheet Instability and Marine Ice Cliff

26 Instability around Antarctica, and faster-than-projected changes in the surface mass balance and

27 discharge from Greenland. These processes are characterised by deep uncertainty arising from limited

28 process understanding, limited availability of evaluation data, uncertainties in their external forcing and high

29 sensitivity to uncertain boundary conditions and parameters. In a low-likelihood, high-impact storyline,

30 under high emissions such processes could in combination contribute more than one additional meter of sea

31 level rise by 2100. {9.6.3, Box 9.4}

33 Beyond 2100, GMSL will continue to rise for centuries due to continuing deep ocean heat uptake and

34 mass loss of the Greenland and Antarctic Ice Sheets, and will remain elevated for thousands of years

35 (high confidence). Considering only processes for which projections can be made with at least medium

36 confidence and assuming no increase in ice-mass flux after 2100, relative to the period 1995–2014, by 2150,

37 GMSL will rise between 0.6 [0.4–0.9, likely range] m (SSP1-1.9) and 1.4 [1.0–1.9, likely range] m (SSP5-

38 8.5). By 2300, GMSL will rise between 0.3 m and 3.1 m under SSP1-2.6, between 1.7 m and 6.8 m under

39 SSP5-8.5 in the absence of Marine Ice Cliff Instability, and by up to 16 m under SSP5-8.5 considering

40 Marine Ice Cliff Instability (low confidence). {9.6.3}

9.2 Oceans

9.2.1 Ocean surface

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3 It is virtually certain that SST will continue to increase in the 21st century at a rate depending on future

4 emission scenario. The future global mean SST increase projected by CMIP6 models for the period 1995-

5 2014 to 2081-2100 is 0.86°C (5-95% range: 0.43-1.47°C) under SSP1-2.6, 1.51 °C [1.02-2.19°C] under

6 SSP2-4.5, 2.19°C (1.56-3.30°C) under SSP3-7.0, and 2.89°C (2.01-4.07°C) under SSP5-8.5 (Figure 9.3).

7 While under SSP1-2.6, the CMIP6 ensemble consistently projects that it is very likely at least 83% of the

8 world ocean surface will have warmed by 2100, under SSP5-8.5, at least 98% of the world ocean surface

9 will have warmed. [...]

BOX 9.2: Marine Heatwaves

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11 they project MHWs will become 4 (5-95% range: 2-9) times more frequent in 2081-2100 compared to 1995-

12 2014 under SSP1-2.6, or 8 (3-15) times more frequent under SSP5-8.5.



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