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 https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Full_Report.pdf (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
2.3.1.1 Surface temperatures
2.3.1.1.1 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
2.3.1.3.4 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
2.3.2.3 Glacier mass
5 [...]. Between 2006 and 2015 the global glacier mass
6 change assessed by SROCC was –278 ± 113 Gt yr-1
>>> >>> >>> p 502
2.3.3.1 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
2.3.3.3 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
2.3.3.4 Ocean circulation
2.3.3.4.1 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
3.3.1.1 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
3.4.3.1 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
3.5.3.2 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. [...]
But...
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
5.1.2.3 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
5.2.1.1 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
5.2.1.4 Land CO2 Fluxes: Historical and Contemporary Variability and Trends
5.2.1.4.1 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
5.3.3.1 Ocean Memory – Acidification in the Ocean Interior
6 [...]. For example, ocean circulation contributes a pH change of –0.013 ± 0.013
>>> >>> >>> p 1220
5.4.4.2 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
5.4.5.1 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
5.4.5.3 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
5.5.2.2.5 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
5.6.2.1.4 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)
6.3.2.1 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
6.6.2.2 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
6.6.2.3.2 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).
49 [...]. One year50 pulse of present day emissions has a small net global temperature effect on short time-scales51 (+0.0011±0.0045°C), [...]
Chapter 7: The Earth’s energy budget, climate 2 feedbacks, and climate sensitivity
7.2.2.2 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]
4.7
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]
3.5
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]
1.1
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
Heating
Rate
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
7.2.2.2 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
7.3.2.4 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
7.3.2.6 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 therefore4 0.05±0.05 W m-2 with a lower limit reduced to zero and the central value and upper limit reduced to allow5 for adjustment terms. This still encompasses the two recent SARF studies. There is medium confidence in the6 SARF from agreement with the recent studies and AR5. There is low confidence in the adjustment terms.
7.3.2.7 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
7.3.3.1.1 Observation-based lines of evidence
7.3.3.1.2 Model-based lines of evidence22 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.
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 to10 1750, with medium confidence due to the limited number of studies available. [...]
29 [...]; they estimated the ERFari (accounting for a small30 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 single35 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.
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).
48
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 7.3.3.1.2. 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
>>> >>> >>> 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).
7.3.3.2.3 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.
7.3.3.3 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
7.3.3.4 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
7.3.4.1 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). [...]
7.3.4.2 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.
7.3.4.3 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
7.3.5.2 Summary ERF assessment
6 (Table 7.8; Annex III) (high confidence). [...]5 The total anthropogenic ERF over the industrial era (1750–2019) is estimated as 2.72 [1.96 to 3.48] W m–2
>>> >>> >>> p 1665
7.3.5.3 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
7.4.2.2 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
7.4.2.4.2 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.
7.4.2.4.3 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
7.4.2.5.1 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
7.4.2.5.2 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). [...]
7.4.2.5.3 Synthesis of biogeophysical and non-CO2 biogeochemical feedbacks
25 The non-CO2 biogeochemical feedbacks are assessed in Section 7.4.2.5.1 to be –0.16 [–0.37 to +0.05] W m–
26 2 °C–1 and the biogeophysical feedbacks are assessed in Section 7.4.2.5.2 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
7.4.2.7 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
7.4.4.3 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
7.5.2.1 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 7.4.4.3), corresponding to a value of ECS of 2.7 [1.7–5.9] °C. [...]
>>> >>> >>> p 1712
7.5.2.2 Estimates of ECS and TCR based on climate model emulators51 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 7.5.1.2) 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 [...]
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
7.5.2.5 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
7.5.4.1 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
7.5.4.3 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
7.6.1.3 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 7.2.2.2 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 8.4.1.1). 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
8.3.1.2 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
8.3.1.5 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
8.3.1.7.4 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
8.4.1.1 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
8.4.1.7 Freshwater reservoirs
6 8.4.1.7.1 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
8.6.2.3 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.
Glaciers
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.
Permafrost
10 [...]. Permafrost
11 warmed globally by 0.29 [0.17–0.41, likely range] °C between 2007 and 2016 (medium confidence). [...]
Snow
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
>>> >>> >>> p 2166
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
>>> >>> >>> p 2171
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.
***WORK IN PROGRESS***
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