closely linked to precipitation formation (Piepgrass et al. 1982; Petersen and Rutledge 1998), upper tropospheric water vapor concentrations (Price and Asfur 2006), and the ice providing for cirrus formation (Kent et al. 1995), allowing lightning to act as a proxy measure for some of these difficult-to-quantify meteorological parameters.

Lightning is currently monitored with lightning imagers on satellites (Christian et al. 2003; Blakeslee et al. 2020) and by commercial ground-based lightning detection networks based on radio wave detection (e.g., DiGangi et al. 2021; Said et al. 2010; Virts et al. 2013). The global climatology of lightning, based on satellite optical observations, is shown in Fig. SB2.1a (Christian et al. 2003; Goodman and Christian 1993; Blakeslee et al. 2020; Rudlosky and Virts 2020). The global lightning climatology follows solar insolation with a longitudinal migration that corresponds to a diurnal temperature variation (Price 1993) and a latitudinal migration that corresponds to an annual temperature variation (Christian et al. 2003; Williams 2020). The strong continental dominance of lightning has origins in both thermodynamics (Williams and Stanfield 2002a) and in aerosol-modulated cloud physics (Rosenfeld et al. 2008; Stolz et al. 2015). Their relative contributions are currently in debate, where the strong continental dominance of cloud condensation nuclei (CCN) is recognized (Williams et al. 2002b).

Satellite measurements and global lightning detection networks have not been operational long enough to detect substantial long-term changes in lightning activity (Fig. SB2.1b; Williams et al. 2019). Therefore, scientific studies use alternative methods over longer time scales to address climate questions. The thunder day is a WMO observation that has been carried out for more than a century in many countries (Brooks 1925) and represents a 24-hour day, for which thunder was heard at official national meteorological stations. Evidence has accrued that thunder days have been increasing over the last century, specifically at high latitudes (Kitagawa et al. 1989; Williams 2009; Pinto et al. 2013; Lavigne et al. 2019). Significant increases in thunderstorm activity over Africa since the 1990s have been detected using climate proxy data from reanalysis (Harel and Price 2020).

The declaration of the WMO elevates lightning flashes to a climate quantity due to its importance in climate change. It is generally agreed that lightning activity increases as the climate warms (Williams 1992, 1994; Price and Rind 1994; Romps et al. 2014), based on both considerations of the Clausius-Clapeyron relation and on the empirical evidence that CAPE increases with temperature in global climate models (Del Genio et al. 2007). The evidence for lightning increases in the Arctic (Holzworth et al. 2021), where the annually averaged Arctic near-surface air temperature increased by 3.1°C from 1971 to 2019, i.e., three times faster than the global average (AMAP 2021), which is consistent with a thermodynamic influence on lightning Fig. SB2.2. Global aerosols may decline as alternative energy sources and the reliance on fossil fuels are considered, as exemplified during the COVID-19 lockdown, such that the reduction of aerosols may diminish lightning via cloud microphysics (Stolz et al. 2015; Williams et al. 2002b). Lightning flashes also serve as a diagnostic for key manifestations of climate variability associated with deep convection. Examples include climate induced severe local convective storms accompanied by extraordinary lightning (Zipser et al. 2006; Holzworth et al. 2019; Virts and Goodman 2020; Peterson et al. 2022), the El Niño-Southern Oscillation (Goodman et al. 2000; Chronis et al. 2008; Satori et al. 2009; Williams et al. 2021), the Madden Julian Oscillation (Anyamba et al. 2000), planetary waves (Grandt 1992; Satori et al. 2009), and warming in the Arctic (Bieniek et al. 2020; Holzworth et al. 2021; Chen et al. 2021; Finney 2021).

<figure><img src="image_85.png" /><figcaption>Fig. SB2.2. Arctic lightning densities (strokes km⁻² yr⁻¹) recorded by the World Wide Lightning Location Network (WWLLN) and averaged over Jun–Aug during the years 2010–14, 2015–20, and 2021. The lightning flash densities increased during 2015–20 compared to 2010–14. In 2021, northern Europe and much of northern Russia continued to experience higher overall lightning densities. Eastern Russia and northern North America generally experienced less lightning than the 2015–20 period.</figcaption></figure>
Lightning is a major source of NOx in the atmosphere (Gordillo-Vázquez et al. 2019; Schumann and Huntrieser 2007; Price et al. 1997), which contributes to climate change in a feedback loop (IPCC 2021). In particular, lightning NOx is a precursor to tropospheric ozone formation which is a greenhouse gas and also a designated ECV. The climate assessment of lightning needs to differentiate lightning occurrence frequencies against the effects of natural and man-made aerosol (Thornton et al. 2017; Wang et al. 2018; Williams 2020), which is also an ECV.

Lightning is becoming more important as a natural hazard, due to increasing vulnerability of critical infrastructures, such as electrical power grid and communications, and the growth in global population and associated lightning casualties, primarily in developing countries. Even if lightning activity remained constant in the future, its societal impact will increase dramatically. If lightning also increases with climate change, this impact may worsen.

### g. Atmospheric composition

#### 1. LONG-LIVED GREENHOUSE GASES—X. Lan, B. D. Hall, G. Dutton, J. Mühle, J. W. Elkins, and I. J. Vimont

Atmospheric burdens of many greenhouse gases, especially the long-lived greenhouse gases

(LLGHGs) carbon dioxide ($CO_2$), methane ($CH_4$), and nitrous oxide ($N_2O$), have been increasing significantly since the industrial revolution, mainly as a result of human activity. The 2021 growth of atmospheric $CH_4$ reached another record high since systematic measurement started in 1983, while annual growth of $CO_2$ was the fifth highest since 1958 and $N_2O$ was the third highest since 2001.

Carbon dioxide is the most important and prevalent anthropogenic GHG. In 2021, the annually averaged atmospheric $CO_2$ abundance at Mauna Loa Observatory (MLO) reached 416.5 ± 0.1 ppm (parts per million by moles in dry air; 1-σ uncertainties). Globally averaged $CO_2$, derived from remote marine boundary layer measurements, was 414.7 ± 0.1 ppm in 2021 (Fig. 2.50a; www.gml.noaa.gov/ccgg/trends). This is 49% above the atmospheric pre-industrial abundance of $CO_2$ of ~278 ppm, based on air extracted from ice in Greenland and Antarctica (Etheridge et al. 1996). Annual growth in global mean $CO_2$ has risen steadily from 0.6 ± 0.1 ppm yr⁻¹ in the early 1960s to an average of 2.4 ppm yr⁻¹ during the last decade, 2011–20 (Fig. 2.50a). The annual increase in global mean $CO_2$ in 2021 was 2.6 ± 0.1 ppm (Table 2.10), the fifth highest since the accurate measurements started in 1958.

The main driver of increasing atmospheric $CO_2$ is fossil fuel (FF) burning,

<figure><img src="image_86.png" /></figure>

Fig. 2.50. Global mean dry-air surface mole fractions (approximately weekly data in black, left axis) and annual change (red, right axis) of (a) $CO_2$ (ppm), (b) $CH_4$ (ppb), and (c) $N_2O$ (ppb) derived from the NOAA Global Greenhouse Gases Reference Network. Deseasonalized trend curves (see Dlugokencky et al. 1994b for methods) are shown in blue. $N_2O$ data prior to 2001 are too sparse to allow robust estimates of annual growth rates.
<table><caption>Table 2.10. Summary table of long-lived greenhouse gases for 2021 (CO₂ mole fractions are in ppm, N₂O and CH₄ in ppb, and all others in ppt).</caption><thead><tr><th>Industrial Designation or Common Name</th><th>Chemical Formula</th><th>AGGI</th><th>Radiative Efficiency (W m<sup>-2</sup> ppb<sup>-1</sup>)<sup>a</sup></th><th>Radiative Forcing (W m<sup>-2</sup>)</th><th>Mean surface mole fraction, 2021 (change from 2020)<sup>b</sup></th><th>Lifetime (years)</th></tr></thead><tbody><tr><td>Carbon Dioxide</td><td>CO<sub>2</sub></td><td>Y</td><td>1.37 × 10<sup>-5</sup></td><td>2.08</td><td>414.7 (2.3)</td><td>—</td></tr><tr><td>Methane</td><td>CH<sub>4</sub></td><td>Y</td><td>3.63 × 10<sup>-4</sup></td><td>0.52</td><td>1895.8 (16.7)</td><td>9.1</td></tr><tr><td>Nitrous Oxide</td><td>N<sub>2</sub>O</td><td>Y</td><td>3.00 × 10<sup>-3</sup></td><td>0.20</td><td>334.2 (1.2)<sup>c</sup></td><td>123</td></tr><tr><td colspan="7"><strong>Chlorofluorocarbons</strong></td></tr><tr><td>CFC-11</td><td>CCl<sub>3</sub>F</td><td>Y</td><td>0.26</td><td>0.059</td><td>222.2 (–1.8)<sup>c</sup></td><td>52</td></tr><tr><td>CFC-12</td><td>CCl<sub>2</sub>F<sub>2</sub></td><td>Y</td><td>0.32</td><td>0.161</td><td>493.9 (–3.2)<sup>c</sup></td><td>102</td></tr><tr><td>CFC-113</td><td>CCl<sub>2</sub>FCCIF<sub>2</sub></td><td>Y</td><td>0.30</td><td>0.021</td><td>68.5 (–0.5)<sup>c</sup></td><td>93</td></tr><tr><td colspan="7"><strong>Hydrochlorofluorocarbons</strong></td></tr><tr><td>HCFC-22</td><td>CHCl<sub>2</sub>F<sub>2</sub></td><td>Y</td><td>0.21</td><td>0.052</td><td>248.9 (1.1)</td><td>11.9</td></tr><tr><td>HCFC-141b</td><td>CH<sub>3</sub>CCl<sub>2</sub>F</td><td>Y</td><td>0.16</td><td>0.004</td><td>24.6 (0.01)</td><td>9.4</td></tr><tr><td>HCFC-142b</td><td>CH<sub>3</sub>CCIF<sub>2</sub></td><td>Y</td><td>0.19</td><td>0.004</td><td>21.5 (–0.2)</td><td>18</td></tr><tr><td colspan="7"><strong>Hydrofluorocarbons</strong></td></tr><tr><td>HFC-134a</td><td>CH<sub>2</sub>FCF<sub>3</sub></td><td>Y</td><td>0.16</td><td>0.017</td><td>118.9 (5.8)</td><td>14</td></tr><tr><td>HFC-152a</td><td>CH<sub>3</sub>CHF<sub>2</sub></td><td>Y</td><td>0.10</td><td>&lt; 0.001</td><td>7.2 (0.2)</td><td>1.6</td></tr><tr><td>HFC-143a</td><td>CH<sub>3</sub>CF<sub>3</sub></td><td>Y</td><td>0.16</td><td>0.004</td><td>26.5 (1.6)</td><td>51</td></tr><tr><td>HFC-125</td><td>CHF<sub>2</sub>CF<sub>3</sub></td><td>Y</td><td>0.23</td><td>0.007</td><td>34.7 (3.4)</td><td>30</td></tr><tr><td>HFC-32</td><td>CH<sub>2</sub>F<sub>2</sub></td><td>N</td><td>0.11</td><td>0.002</td><td>23.6 (3.1)</td><td>5.4</td></tr><tr><td>HFC-23</td><td>CHF<sub>3</sub></td><td>Y</td><td>0.18</td><td>0.006</td><td>34.9 (0.2)</td><td>228</td></tr><tr><td>HFC-365mfc</td><td>CH<sub>3</sub>CF<sub>2</sub>CH<sub>2</sub>C</td><td>N</td><td>0.22</td><td>&lt; 0.001</td><td>1.05 (0.03)</td><td>8.9</td></tr><tr><td>HFC-227ea</td><td>CF<sub>3</sub>CHFCF<sub>3</sub></td><td>N</td><td>0.26</td><td>&lt; 0.001</td><td>1.87 (0.17)</td><td>36</td></tr><tr><td colspan="7"><strong>Chlorocarbons</strong></td></tr><tr><td>Methyl Chloroform</td><td>CH<sub>3</sub>CCl<sub>3</sub></td><td>Y</td><td>0.07</td><td>&lt; 0.001</td><td>1.2 (–0.2)</td><td>5.0</td></tr><tr><td>Carbon Tetrachloride</td><td>CCl<sub>4</sub></td><td>Y</td><td>0.17</td><td>0.013</td><td>76.2 (–1.1)<sup>c</sup></td><td>32</td></tr><tr><td>Methyl Chloride</td><td>CH<sub>3</sub>Cl</td><td>N</td><td>0.01</td><td>&lt; 0.001</td><td>547.2 (–1.7)</td><td>0.9</td></tr><tr><td colspan="7"><strong>Bromocarbons</strong></td></tr><tr><td>Methyl Bromide</td><td>CH<sub>3</sub>Br</td><td>N</td><td>0.004</td><td>&lt;&lt; 0.001</td><td>6.61 (–0.06)</td><td>0.8</td></tr><tr><td>Halon 1211</td><td>CBrClF<sub>2</sub></td><td>Y</td><td>0.29</td><td>0.001</td><td>3.03 (–0.09)</td><td>16</td></tr><tr><td>Halon 1301</td><td>CBrF<sub>3</sub></td><td>Y</td><td>0.30</td><td>0.001</td><td>3.31 (0.01)</td><td>72</td></tr><tr><td>Halon 2402</td><td>CBrF<sub>2</sub>CBrF<sub>2</sub></td><td>Y</td><td>0.31</td><td>&lt; 0.001</td><td>0.396 (–0.03)</td><td>28</td></tr><tr><td colspan="7"><strong>Fully fluorinated species</strong></td></tr><tr><td>Sulfur Hexafluoride</td><td>SF<sub>6</sub></td><td>Y</td><td>0.57</td><td>0.006</td><td>10.63 (0.35)</td><td>&gt; 600</td></tr><tr><td>PFC-14</td><td>CF<sub>4</sub></td><td>N</td><td>0.09</td><td>0.005</td><td>87.4 (0.2)</td><td>~ 50 000</td></tr><tr><td>PFC-116</td><td>C<sub>2</sub>F<sub>6</sub></td><td>N</td><td>0.25</td><td>0.001</td><td>5.03 (0.02)</td><td>~ 10 000</td></tr><tr><td>PFC-218</td><td>C<sub>3</sub>F<sub>8</sub></td><td>N</td><td>0.28</td><td>&lt; 0.001</td><td>0.721 (0.003)</td><td>~ 2600</td></tr><tr><td>PFC-318</td><td>C<sub>4</sub>F<sub>8</sub></td><td>N</td><td>0.32</td><td>&lt; 0.001</td><td>1.90 (0.01)</td><td>~ 3200</td></tr></tbody></table>

<footnote><sup>a</sup> Radiative efficiencies and lifetimes were taken from Appendix A in WMO (2018), except for SF₆ lifetime from Ray et al. (2017), CH₄ lifetime from Prather et al. (2012). For CO₂, numerous removal processes complicate the derivation of a global lifetime. AGGI = Annual Greenhouse Gas Index. For radiative forcing, see https://www.esrl.noaa.gov/gmd/aggi/aggi.html</footnote>

<footnote><sup>b</sup> Mole fractions are global, annual surface means determined from NOAA Global Greenhouse Gas Reference Network marine boundary layer sites, except for PFC-14, PFC-116, PFC-218, PFC-318, and HFC-23, which were measured by AGAGE (Mühle et al. 2010; Miller et al. 2010). Changes indicated in brackets are the differences between the 2021 and 2020 means. These values differ from the growth rates reported in the main text and figures, which represent the net addition of a LLGHG to the atmosphere over the course of a year, calculated by differencing (approximately) the 1 Jan atmospheric abundances in successive years. All values are preliminary and subject to minor updates.</footnote>

<footnote><sup>c</sup> Global mean estimates derived from multiple NOAA measurement programs (“Combined Dataset”).</footnote>
with emissions (including ~5% from cement production) increasing from 3.0 ± 0.2 Pg C yr⁻¹ in the 1960s to 9.5 ± 0.5 Pg C yr⁻¹ in the past decade (2011–20; Friedlingstein et al. 2021). About half of the overall FF emitted CO₂ since 1958 has remained in the atmosphere, while the rest has been stored by the ocean and the terrestrial biosphere. While emissions of CO₂ from FF combustion drive are increasing atmospheric burden, the interannual variability in the CO₂ growth rate is mostly driven by terrestrial biosphere exchange of CO₂, which is confirmed by measurements of its ¹³C:¹²C ratio (e.g., Keeling et al. 1985; Alden et al. 2010). Terrestrial biosphere flux variability is influenced by both temperature and moisture anomalies (Cox et al. 2013; Hu et al. 2019; Humphrey et al. 2018) often associated with ENSO.

For 2020, FF CO₂ emissions declined by 5.4%, relative to 2019, due to the COVID-19 pandemic (Friedlingstein et al. 2021). However, this reduction is not obvious in observed global atmospheric CO₂ signals, because it is a relatively small signal compared with the natural variability from terrestrial biosphere exchange. The estimated 5.4% reduction in global CO₂ emissions of 10 Pg C yr⁻¹ would result in a ~0.24 ppm decrease in global CO₂ (given a conversion factor of 2.12 Pg C ppm⁻¹; Ballantyne et al. 2012), which is within the 1 std. dev. interannual variability of 0.4 ± 0.1 ppm yr⁻¹ of CO₂ annual growth in 2010–19. Yet at least one analysis has detected regional impacts of COVID-19 on total column-CO₂ (Weir et al. 2021). Preliminary data for 2021 suggest a rebound in FF CO₂ emissions relative to 2020 of +4.9 ± 0.8% globally (Friedlingstein et al. 2021).

While a pulse of CO₂ will last in the atmosphere for thousands of years (Archer and Brovkin 2008), atmospheric CH₄ has a lifetime of about nine years, meaning that its atmospheric abundance (and radiative forcing) can be reduced much more quickly (United Nations Global Methane Assessment 2021). Global mean tropospheric CH₄ abundance increased to 1895.7 ± 0.6 ppb (parts per billion by moles in dry air) in 2021, a 162% increase compared to its pre-industrial level of 722 ± 15 ppb. Since the beginning of NOAA's systematic CH₄ measurements in 1983, global CH₄ abundance rose and then flattened prior to 2006 (Fig. 2.50b), which is consistent with an approach to steady state if there was no trend in its lifetime driven by CH₄ sinks (Dlugokencky et al. 2003). Atmospheric CH₄ growth restarted in 2007 and has significantly accelerated since 2014 (Fig. 2.50b). Preliminary measurement results show continued large growth in 2021, about 18.1 ± 0.4 ppb, following a similarly large increase seen in 2020 (15.1 ± 0.4 ppb).

Given the complexity of the CH₄ budget, the scientific community has not reached consensus on reasons for the magnitudes and long-term trends of many CH₄ sources. Fossil fuel exploitation is estimated to account for ~19% of total global CH₄ emissions since 2000 based on top-down approaches that use atmospheric CH₄ measurements and inverse models (Saunois et al. 2020). However, studies including radioactive (¹⁴C:C) or stable (¹³C:¹²C) carbon isotope ratios of CH₄ suggest a much larger fraction of fossil emissions (~30% in 1984–2016; Lassey et al. 2007; Schwietzke et al. 2016; Lan et al. 2021). Measurements of ¹³C:¹²C in CH₄ also suggest that increased emissions from biogenic sources (e.g., wetlands and ruminant animals) are the dominant drivers for the post-2006 growth (Lan et al. 2021; Chang et al. 2019; Nisbet et al. 2019; Schaefer et al. 2016; Schwietzke et al. 2016).

It is an ongoing investigation to decipher the post-2019 CH₄ surge. A reduction in the global abundance of the hydroxyl radical (OH, the main sink for CH₄) may have contributed to the growth in 2020 due to the COVID-19 pandemic reduction in major OH precursors, NOₓ and CO (Laughner et al. 2021). However, reduced OH abundance due to COVID-related pollution reductions is unlikely to contribute significantly to the continued rapid increase in 2021.

Nitrous oxide (N₂O) is an ozone-depleting LLGHG mainly emitted from natural and agricultural soils, animal manure, and the oceans (Ravishankara et al. 2009; Davidson 2009). Its atmospheric lifetime is about 123 years (Ko et al. 2013). Atmospheric N₂O has been increasing steadily throughout the industrial era, except for a brief period in the 1940s (MacFarling Meure et al. 2006; Thompson et al. 2019). The mean global atmospheric N₂O abundance in 2021 was 334.3 ± 0.1 ppb, a 24% increase over its preindustrial level of 270 ppb. The annual increase of 1.3 ± 0.1 ppb in 2021
(Fig. 2.50c) was higher than the average annual increase over 2011–20 (1.0 ± 0.2 ppb) and was the third highest growth since 2001. It is an ongoing investigation to understand the drivers for the large N₂O growth in 2021 and the record growth in 2020 (1.4 ± 0.1 ppb).

The increased burdens of LLGHGs are largely responsible for increasing global temperature (IPCC 2013). The impacts of these LLGHGs on global climate are estimated based on their abilities to change the global radiative energy balance. Compared with preindustrial times (1750), increasing atmospheric CO₂ abundance has increased radiative forcing by > 2.1 W m⁻². The increase

in CH₄ has contributed to a 0.53 W m⁻² increase in direct radiative forcing while the CH₄-related production of tropospheric O₃ and stratospheric H₂O has also contributed to ~0.30 W m⁻² indirect radiative forcing (Myhre et al. 2014). While the atmospheric burdens of some greenhouse gases, such as chlorofluorocarbons, have declined in recent decades, the combined radiative forcing of CO₂, CH₄, and N₂O, in addition to other LLGHGs (all of which are halogenated compounds), has increased each year (Fig. 2.51). In 2021, the combined radiative forcing from all LLGHGs (Table 2.10) was 3.2 W m⁻², which is 3.6 times greater than in 1950, at the start of the “Great Acceleration.” NOAA's Annual Greenhouse Gas Index (AGGI; Fig. 2.51) summarizes trends in the combined direct radiative forcing by all LLGHGs (Hofmann et al. 2006). This index represents the annual cumulative radiative forcing of LLGHGs relative to the Kyoto Protocol baseline year of 1990. The AGGI value of 1.49 in 2021 indicates an increase of 49% in radiative forcing of LLGHGs compared to 1990.

<figure><img src="image_87.png" /><figcaption>Fig. 2.51. (a) Direct radiative forcing (W m⁻²) due to five major long-lived greenhouse gases (LLGHG) and 15 minor gases (left axis; see Table 2.10 for details of the 15 minor gases) and the associated values of the NOAA AGGI (right axis). The Annual Greenhouse Gas Index (AGGI) is defined to have a value of 1 in 1990. (b) Annual increase in direct radiative forcing (W m⁻²).</figcaption></figure>

## 2. OZONE-DEPLETING SUBSTANCES—I. J. Vimont, B. D. Hall, G. Dutton, S. A. Montzka, C. Siso, M. Crotwell, and M. Gentry

Halogenated trace gases affect the radiative energy balance of the atmosphere and climate through their direct absorption of infrared energy and through their ability to deplete stratospheric ozone (Karpechko and Maycock 2018). Halogenated compounds, such as chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), chlorocarbons, and bromocarbons are controlled by the 1987 Montreal Protocol (https://www.unep.org/ozonaction/who-we-are/about-montreal-protocol) and its subsequent amendments.

The effect of these controls can be seen clearly in the overall decline in the atmospheric abundance of many of the controlled gases (Engel and Rigby 2018); however, even after production ceases, declines in atmospheric abundance vary by compound because these chemicals have different lifetimes, as well as different sizes of “banks” (i.e., reservoirs of produced, but not yet emitted chemicals). For example, by 2021, CFC-11 and CFC-12 had declined from their peak abundance by only around 17% and 9% despite a reported global phase-out by 2010, whereas methyl chloroform (phased out in 2015) had decreased by 99% (Fig. 2.52; Table 2.10). These differences are explained by 50-yr and 100-yr lifetimes for the CFCs versus 5 years for methyl chloroform, and the presence of substantial (and leaky) banks for the CFCs, but not for methyl chloroform.
<figure><img src="image_88.png" /><figcaption>Fig. 2.52. Global mean abundances (mole fractions) at Earth's surface (parts per trillion = nmol mol⁻¹ in dry air) for several halogenated gases, many of which also deplete stratospheric ozone. See Table 2.10 for the 2021 global mean mole fractions of these and other gases.</figcaption></figure>

Long-term monitoring of the abundances of these species has proved to be important for ensuring the success of the Montreal Protocol. The decline of CFC-11 mole fractions in the atmosphere slowed down unexpectedly after 2012, which led to the discovery of renewed increases in global emissions through 2018 (Montzka et al. 2018; Rigby et al. 2019). Shortly after this discovery was announced in 2018, the mole fraction decline observed for CFC-11 accelerated to rates that were comparable to those measured before 2012. One possible, yet unverified, cause of the observed decline in CFC-11 global emissions is a decrease in unreported production of this chemical (Montzka et al. 2021).

HCFCs are another important set of ozone-depleting compounds controlled by the Montreal Protocol, and overall trends in recent years reflect reduced levels of production and consumption. A select number of HFCs, which are used as substitutes for ozone depleting substances (ODSs) in some applications, are scheduled for phase down by the Kigali Amendment to the Montreal Protocol, but these controls are only now coming into effect so have not substantially altered their mole fraction tendencies; some HFCs, such as HFC-134a, are steadily increasing in the atmosphere.

Equivalent effective stratospheric chlorine (EESC) is a measure of the reactive ozone-depleting halogen loading at a given time and place in the stratosphere, calculated from global mean surface mole fractions and consideration of mixing processes, time-dependent trace gas destruction in the stratosphere, and the relative ozone-destruction efficiency of bromine versus chlorine (Daniel et al. 1995; Montzka et al. 1996; Newman et al. 2007). Midlatitude EESC is calculated using a 3-year mean age-of-air, while Antarctic EESC is calculated with a 5.5-year mean age-of-air, as detailed in Newman et al. (2007). The abundance of reactive halogen in the midlatitude stratosphere is lower than in the Antarctic stratosphere, because air reaching the Antarctic has been in the stratosphere longer and has been transported to higher altitudes, which leads to more ODS destruction and release of reactive halogen (Montzka, Reimann et al. 2011). Most of the reactive
halogen in the atmosphere is due to CFCs, and they contribute strongly to EESC (Fig. 2.53) and still have high abundance in the atmosphere.

At the beginning of 2021, EESC in the midlatitudes was 1549 ppt, which represents a 20% decrease from its maximum value of 1936 ppt in 1997 (https://www.gml.noaa.gov/odgi; Fig. 2.53a). Antarctic EESC was 3659 ppt at the beginning of 2021, a 12% decrease from its maximum value of 4152 in 2001 and 2002 (https://www.gml.noaa.gov/odgi; Fig. 2.53b). To provide context for changes in the EESC, the Ozone Depleting Gas Index (ODGI) was developed. The ODGI is derived for both the midlatitude and Antarctic stratosphere by rescaling EESC values in these regions, where an ODGI of 100 represents the peak EESC value, and 0 represents the value of EESC at 1980 (Hoffmann and Montzka 2009; https://www.gml.noaa.gov/odgi). Ozone destruction had already begun by 1980, but a return of stratospheric halogen to levels last seen in 1980 would represent a major milestone for the Montreal Protocol. At the beginning of 2021, the Antarctic ODGI (ODGI-A) was 75.3 and midlatitude ODGI (ODGI-M) was 50.1. Reactive halogen

abundance in the Antarctic stratosphere has declined 24.7% (100% minus 75.3%) of the way back to the 1980 benchmark value. In the midlatitudes, the decline is nearly half of that needed to reach the 1980 benchmark value. Currently, ODGI-A and ODGI-M are projected to reach 0 around 2076 and 2049, respectively (Carpenter et al. 2018; https://www.gml.noaa.gov/odgi).

### 3. AEROSOLS—S. Rémy, N. Bellouin, Z. Kipling, M. Ades, A. Benedetti, and O. Boucher

Aerosols represent a serious public health issue in many countries and hence are subject to monitoring and forecasting worldwide as part of air quality policies. Atmospheric aerosols also play an important role in the climate system, by scattering and absorbing radiation and by affecting the life cycle, optical properties and precipitation activity of clouds (IPCC 2021).

The Copernicus Atmosphere Monitoring Service (CAMS; http://atmosphere.copernicus.eu) runs a near-real time (NRT) global analysis of aerosols and trace gases. The CAMS project also produced a reanalysis of global aerosols and trace gases that covers the years 2003 to 2021: the CAMS reanalysis (Inness et al. 2019), by combining state of the art numerical modeling and aerosol remote sensing retrievals from MODIS (Levy et al. 2013) and the Advanced Along Track Scanning Radiometer (AATSR; Popp et al. 2016). Verification of aerosol optical depth (AOD) at 500 nm, against independent AERONET observations, shows that the CAMS reanalysis has a smaller bias and error than its predecessors: the CAMS interim reanalysis (Flemming et al. 2017) and the MACC reanalysis (Inness et al. 2013). This section uses data exclusively from the CAMS reanalysis.

<figure><img src="image_89.png" /><figcaption>Fig. 2.53. Equivalent effective stratospheric chlorine (EESC, ppt) for the (a) midlatitude and (b) Antarctic stratosphere derived from surface measurements. The EESC values represent EESC on 1 Jan of each year.</figcaption></figure>
AOD at 550 nm in 2021 (Fig. 2.54a) shows maxima over the polluted regions of India and China, as well as from dust over the Sahara and the Middle East. High values from seasonal or occasional extreme fires are seen over equatorial Africa and Siberia. The high values over Hawaii and close to Mexico City are a known artifact of the CAMS reanalysis related to volcanic outgassing. Figure 2.54b shows the time series of monthly and annual globally-averaged total AOD during 2003–21. Figure 2.54b shows strong seasonality, driven mainly by dust episodes between March and July in the Sahara, Middle East, and Taklimakan/ Gobi deserts and seasonal biomass burning in Africa, South America, and Indonesia. Globally averaged AOD in 2021 was on average slightly higher than in 2020, by 0.6%, but with marked seasonal differences: the boreal summer months saw some of the highest values since 2003, but the winter months saw the lowest values since 2014/15. A series of exceptional fires in Siberia and North America contributed to the high values in July and August.

These exceptionally large biomass burning aerosol events caused the positive anomalies

relative to the 2003–20 mean over Siberia and parts of North America in Plates 2.1u,v, while the positive anomalies over India and Iran were mostly caused by anthropogenic pollution events and are consistent with a positive AOD trend in these regions (see Figs. 2.55b,c). These fires also explain most of the extreme aerosol days shown in Plates 2.1u,v. Two large volcanic eruptions occurred in 2021. The Cumbre Vieja volcano (September) in the Canary Islands explains a positive anomaly there, while the Caribbean eruption of La Soufriere in Saint Vincent (April) injected a small amount of aerosols into the stratosphere. Dust storm activity was, in general, less than usual over most of the Sahara and Taklimakan, while the negative AOD anomalies over East Asia, Europe, and the Amazon basin can be explained by ongoing long-term decreasing trends of aerosol emissions and burden in these regions.

These trends are shown in Figs. 2.55b (2003–21) and 2.55c (2012–21). Between 2003 and 2021, there are significant negative AOD trends over most of the United States, Europe, East Asia, and the Amazon basin, the latter from reduced deforestation and associated burning activity. Positive trends are noted over Siberia, driven by biomass burning events, as well as over India and Iran, driven by an increase in anthropogenic emissions (Satheesh et al. 2017). Between 2012 and 2021, the picture is slightly different: there is no decreasing trend over the United States and the Amazon basin, indicating that most of the 2003–21 trend can be explained by changes between 2003 and 2012. Similarly, the 2012–21 negative trend over Europe is smaller than the 2003–21 trend, while it is the opposite over East Asia, which is consistent with the observed decrease of most anthropogenic emissions there since around 2012 (Li et al. 2017). A stronger positive trend between 2012 and 2021 is noted over most of Iran, while over the same period the positive trend over India is smaller than the 2003–21 trend.

<figure><img src="image_90.png" /><figcaption>Fig. 2.54. (a) Global aerosol optical depth (AOD) at 550 nm in 2021. (b) Global average of total AOD at 550 nm averaged over monthly (red) and annual (blue) periods for 2003–21.</figcaption></figure>
<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /></figure>

<figure><img src="image_93.png" /><figcaption>Fig. 2.55. (a) Total AOD at 550 nm averaged over the period 2003–21. Note the regional differences, with much greater total AOD values over parts of northern Africa, the Arabian Peninsula, southern Asia, and eastern China. Linear trends of total AOD (AOD unit yr⁻¹) for (b) 2003–21 and (c) 2012–21. Only trends that are statistically significant (95% confidence level) are shown. Regions with decreasing trends include the eastern United States, most of Europe, parts of Brazil and China, as well as the Korean Peninsula and Japan.</figcaption></figure>

The AOD climatology between 2003 and 2021 (Fig. 2.55a) is close to the 2021 mean; it shows maxima over the highly populated regions of India and China, mainly caused by anthropogenic emissions, as well as over the Sahara, Middle East, and Taklimakan/Gobi from desert dust, and over central Africa, Indonesia, and the Amazon basin from fire emissions. The high values over Hawaii and close to Mexico City are a known artifact of the CAMS reanalysis related to volcanic outgassing.
Anthropogenic AOD and radiative forcing resulting from aerosol–radiation (RFari) and aero-
sol–cloud interactions (RFaci) are shown in Fig. 2.56 for 2021 and the period 2003–21. They are
estimated using the methods described in Bellouin et al. (2020). The year 2021 was character-
ized by lower anthropogenic AOD and weak RFari and RFaci relative to the past 19 years. This
decreasing trend is not yet statistically significant but is consistent with the decreasing trends
in industrial and smoke aerosols seen in many regions, as mentioned above. The AOD anoma-
lies for 2021 shown in Plate 2.1u exert a relatively weak top-of-atmosphere radiative forcing in
part because they are mostly caused by aerosols that are fairly absorbing, which decreases their
ability to scatter radiation back to space. These aerosols, however, still exert a sizeable radiative
forcing at the surface.

<figure><img src="image_94.png" /><figcaption>Fig. 2.56. CAMSRA (a) 2021 average of anthropogenic aerosol optical depth (AOD); (b) global annual average of anthropogenic AOD from 2003 to 2021. Radiative forcing (W m⁻²) in the shortwave (SW) spectrum due to (c),(d) aerosol–radiation (RFari) and (e),(f) aerosol–cloud interactions (RFaci). The left column shows the average distribution for the period 2003–21. The right column shows a time series of global averages for the same period, with the 1-σ uncertainties of these estimates shown in gray.</figcaption></figure>
4. **STRATOSPHERIC OZONE**—M. Weber, W. Steinbrecht, C. Arosio, R. van der A, S. M. Frith, J. Anderson, L. M. Ciasto, M. Coldewey-Egbers, S. Davis, D. Degenstein, V. E. Fioletov, L. Froidevaux, D. Hubert, D. Loyola, C. Roth, A. Rozanov, V. Sofieva, K. Tourpali, R. Wang, and J. D. Wild

Ninety percent of atmospheric ozone resides in the stratosphere with a maximum in the lower stratosphere. Stratospheric ozone protects Earth's biosphere from harmful ultraviolet (UV) radiation. Increases in anthropogenic ozone-depleting substances (ODS) thinned stratospheric ozone until the mid-1990s. The phase-out of ODS, mandated by the Montreal Protocol in the late 1980s (section 2g2), slowed stratospheric ozone loss, with some regions now showing a slow recovery. In addition, the rate and even the sign of long-term ozone changes depend on changes in chemical composition and stratospheric circulation caused by increasing concentrations of long-lived greenhouse gases (LLGHG) and varies by region and altitude. The clearest signs of ozone recovery related to ODS changes are evident in the upper stratosphere (WMO 2018).

The annual mean total ozone distribution in 2021 (Plate 2.1x) shows generally negative ozone global anomalies, except for two bands centered near 20° latitude on both sides of the equator, where ozone is higher by about 5 DU than the decadal mean (1998–2008). This pattern (low ozone in the inner tropics and high ozone in the outer tropics) is typical during the easterly wind shear phase of the quasi-biennial oscillation (QBO-east). During QBO-east, the meridional stratospheric circulation is generally stronger, resulting in enhanced ozone transport into the subtropical latitudes (Baldwin et al. 2001; Weber et al. 2011; Lawrence et al. 2020; Plate 2.1x). Negative total ozone anomalies in the Southern Hemisphere extratropics are possibly related to the combination of the unusually long-lasting Antarctic ozone hole of 2020, extending into 2021, and the large ozone hole in the second half of 2021. The westerly phase of the QBO in late autumn of 2020 likely resulted in weaker ozone transport and higher polar ozone deficits in the Northern Hemispheric winter of 2021.

Figure 2.57 shows the long-term evolution of annual total column ozone for different zonal bands (near-global, NH, tropics, and SH), and for polar caps in March (for the NH cap) and October (for the SH cap). These are the months when polar ozone losses are usually at their maximum after a cold stratospheric winter in the respective hemispheres, which occurs every year in the SH ("ozone hole") but is more sporadic in the NH (see sections 6h and 5j, respectively). Total ozone shows above-average total ozone levels in 2021 in the outer tropical/subtropical region (Plate 2.1x). At middle latitudes, total ozone is at the lower range of the values from the last two decades (Figs. 2.57b,e; Plate 2.1x). Total ozone was near the minimum annual mean values observed during the entire 43-year satellite observation period in the SH extratropics and above Antarctica in October (Figs. 2.57d,e; see section 6h).

ODS-related total ozone changes since 1996 are on the order of +0.5% decade⁻¹ in the extratropics of both hemispheres, but opposing long-term changes in atmospheric dynamics contributed to near-zero overall trends in the NH extratropics from 2000 to present (Coldewey-Egbers et al. 2022; Weber et al. 2022). Mean total ozone levels during the period 2017–20 are still 4% and 5% below the 1964–1980 mean in the extratropics of the NH and SH extratropics, respectively (Figs. 2.57b,d; Weber et al. 2022).

Figure 2.58 shows ozone time series at two altitudes in the lower (50 hPa/22 km altitude) and upper stratosphere (2 hPa/42 km altitude). The ozone evolution at both levels is broadly consistent with the projected range from various models of the Phase 1 Chemistry Climate Model Initiative (CCMI) using current scenarios of ODS and GHG changes (thick gray line in Fig. 2.57a and shaded area in Fig. 2.58; SPARC/IO3C/GAW 2019).

In 2021 SH extratropical lower stratospheric ozone was close to the lowest values seen in the last decade but higher than in 2020. The lower values are related to the above-average sizes of the Antarctic ozone holes in 2020 and 2021 (see section 6h).

The earliest and clearest sign of ODS-related ozone recovery was detected in the upper stratosphere, where dynamic variability plays a lesser role (e.g., Newchurch et al. 2003; Godin-Beekmann
<figure><img src="image_95.png" /></figure>

Fig. 2.57. Time series of annual mean total column ozone (DU) for (a) global (60°S–60°N), (b) NH (35°–60°N), (c) tropics (20°S–20°N), and (d) SH (35°–60°S); and (e) polar (60°–90°) total column ozone in Mar (NH) and Oct (SH), the months when polar ozone losses usually are largest. Data are from WOUDC (World Ozone and Ultraviolet Radiation Data Centre) ground-based measurements combining Brewer, Dobson, SAOZ (Système D'Analyse par Observations Zénithales), and filter spectrometer data (red: Fioletov et al. 2002, 2008); the BUV/SBUV/SBUV2/OMPS merged products from NASA (V8.7 dark blue, Frith et al. 2014, 2017), and NOAA (V8.8, light blue: J. D. Wild and L. M. Ciasto, person. comm. 2019); the GOME/SCIAMACHY/GOME-2 products GSG from University of Bremen (dark green, Weber et al. 2022), and GTO from ESA/DLR (light green, Coldewey-Egbers et al. 2015; Garane et al. 2018). MSR-2 (purple) assimilates nearly all ozone datasets after corrections based on the ground-based data (van der A et al. 2015). All datasets have been bias-corrected by subtracting averages for the reference period 1998–2008 and adding back the mean of these averages. The dotted gray lines in each panel show the average ozone level for 1964–80 calculated from the WOUDC data. The thick gray line (panel a) shows the median from chemistry-climate (CCMI)-1 ref C2 model runs (SPARC/IO3C/GAW 2019). Most of the observational data for 2021 are preliminary.
et al. 2022). Upper stratospheric ozone has shown an increase of about +2% decade⁻¹ since the late 1990s (e.g., Steinbrecht et al. 2017; Arosio et al. 2019; Szelag et al. 2020; Sofieva et al. 2021; Godin-Beekmann et al. 2022). In general, ozone observations in the lower stratosphere suggest little change or even a continuing decline over the last two decades (Fig. 2.58; Ball et al. 2018, 2020; Chipperfield et al. 2018; Wargan et al. 2018; Godin-Beekmann et al. 2022).

<figure><img src="image_96.png" /></figure>

Fig. 2.58. Annual mean anomalies of ozone (%) in (a–c) the upper stratosphere near 42-km altitude or 2-hPa pressure and (d–f) in the lower stratosphere, near 22 km or 50 hPa for the NH (35°–60°N; a,d), tropics (20°S–20°N; b,e), and SH (35°–60°S; c,f), respectively. Anomalies are GOZCARDS referenced to the 1998–2008 baseline. Colored lines are long-term records obtained by merging different limb (SWOOSH, SAGE+OSIRIS, SAGE+CCI+OMPS-L, SAGE+SCIAMACHY+OMPS-L) or nadir-viewing (SBUV, OMPS-N) satellite instruments. The nadir-viewing instruments have much coarser altitude resolution than the limb-viewing instruments. This can cause differences in some years, especially at 50 hPa. The black line is from merging ground-based ozone records at seven NDACC stations employing differential absorption lidars and microwave radiometers. See Steinbrecht et al. (2017), WMO (2018), and Arosio et al. (2018) for details on the various datasets. Gray shaded area shows the range of chemistry–climate model simulations from CCMI-1 refC2 (SPARC/IO3C/GAW, 2019). Ozone data for 2021 are not yet complete for all instruments and are still preliminary.
5. STRATOSPHERIC WATER VAPOR—S. M. Davis, K. H. Rosenlof, D. F. Hurst, H. Vömel, and R. Stauffer

The amount of water vapor (WV) entering the stratosphere is controlled to a large degree by temperature variability in the tropical tropopause layer (TTL; ~14–19 km) and particularly at the cold-point tropopause (CPT), with more WV entering the stratosphere when CPT temperatures are higher. Variations in this so-called entry value of water vapor exerts a strong influence on overall stratospheric WV concentrations, as methane oxidation in the upper stratosphere and mesosphere represents the only other consistent source for WV in the stratosphere. Thus, processes that lead to variations in TTL and CPT temperatures on various timescales can affect stratospheric WV on a global scale.

For quantifying interannual changes in stratospheric WV, the *Aura* satellite's Microwave Limb Sounder (MLS) instrument provides a nearly continuous global (82°S–82°N) record of measurements dating back to August 2004. In 2021, de-seasonalized tropical (15°S–15°N) WV anomalies from *Aura* MLS were positive (wet) for all but one month of the year in the lowermost stratosphere at 82 hPa (~17 km; Fig. 2.59b). These WV anomalies ranged from –0.02 ppm (parts per million, i.e., µmol mol⁻¹) in June to +0.6 ppm in November, corresponding to deviations from the climatological monthly mean of –1% and +16%, respectively.

<figure><img src="image_97.png" /><figcaption>Fig. 2.59. (a) Time series of vertical profiles of tropical (15°S–15°N) lower stratospheric water vapor (WV) anomalies and (b) latitudinal distributions of WV anomalies (ppm) at 82 hPa. Both are based on version 5.0 *Aura* MLS data. Anomalies are differences from the mean 2004–20 WV mixing ratios for each month. (b) Propagation of tropical lower stratospheric WV anomalies (ppm) to higher latitudes in both hemispheres as well as the influences of dehydrated air masses from the Antarctic polar vortex as they are transported toward the SH midlatitudes at the end of each year. Tick marks denote the beginning of each year.</figcaption></figure>
The tropical WV anomalies ascend in the so-called “tropical tape recorder” (i.e., the height-
time plot of tropical-average WV showing the imprint of TTL temperatures on WV entering the
stratosphere [Mote et al. 1996]) as illustrated in Fig. 2.59a, which shows a wet anomaly beginning
around the middle of 2021 at 100 hPa and ascending for the duration of the year. At 100 hPa, the
de-seasonalized tropical WV anomalies for August 2021 (+0.4 ppm, 8% above average) and Sep-
tember 2021 (+0.5 ppm, +11%) were the wettest over the MLS record for their respective months.
In the following months, the strong positive anomalies were observed at the next highest MLS
levels (82 hPa and 68 hPa) as the air ascended into the stratosphere as part of the mean meridional
overturning circulation. The 82-hPa level had its wettest October on record (+0.4 ppm, +10%) in
2021, as did November (+0.4 ppm, +9%) and December (+0.4 ppm, +10%) at the 68 hPa level. The
progression of these strong wet anomalies is illustrated further in Fig. 2.60, which shows maps of
the anomalies as they propagate upward. In addition to propagating upwards, the 2021 tropical

<figure><img src="image_98.png" /><figcaption>Fig. 2.60. Deseasonalized monthly lower stratospheric Aura Microwave Limb Sounder (MLS) version 5.0 anomalies (ppm; 2004–20 base period) at (a) 100 hPa in Sep 2021, (b) 82 hPa in Oct 2021, and (c) 68 hPa in Dec 2021.</figcaption></figure>
WV anomaly exhibits a typical “U-shaped” behavior with time in the latitude-time plane at 82 hPa, as the anomalies propagate poleward in each hemisphere (Fig. 2.59b).

The behavior of lowermost stratospheric WV, observed by Aura MLS, is broadly consistent with balloon-borne frost point hygrometer soundings at five locations, as shown in Fig. 2.61. The newest version 5.0 of the MLS data, as well as the previous 4.2 version, are included in this figure to illustrate the reduction in drift relative to the frost point (FP) data in the newest version 5.0 data. Although the drift is not completely removed, the 2021 WV anomalies in v5.0 data are ~0.1 ppm less than in v4.2 for most sites. At the tropical stations, the WV anomalies are highly correlated with the tropical CPT temperature anomalies, as expected.

In 2021, the tropical CPT temperatures were anomalously high throughout the entire year, with an annual mean anomaly of +0.77K. It is well established that interannual variations in CPTs are correlated with interannual variability in climate phenomena such as the El Niño-Southern Oscillation (ENSO) and the quasi-biennial oscillation (QBO) in equatorial stratospheric winds (Dessler et al. 2014).

La Niña conditions were present for all months of 2021, except June and July (see section 4b). In boreal winter, La Niña is known to result in weaker tropical lower stratospheric upwelling, anomalously higher CPTs, and enhanced water vapor in the tropical lower stratosphere (e.g., Garfinkel

<figure><img src="image_99.png" /></figure>

Fig. 2.61. Lower stratospheric water vapor (WV) anomalies (ppm) over five balloon-borne frost point (FP) hygrometer stations. (a–e) each shows the lower stratospheric anomalies of individual FP soundings (black) and of monthly zonal averages for versions 4.2 (orange) and 5.0 (green) of Microwave Limb Sounder (MLS) data at 82 hPa in the 5° latitude band containing the FP station (orange). The new MLS v5.0 data includes a correction for a drift contained in the earlier version 4.2 data (Hurst et al. 2016; Livesey et al. 2021), which were used in previous *State of the Climate* reports (e.g., Davis et al. 2021). High-resolution FP vertical profile data were averaged between 70 hPa and 100 hPa to emulate the MLS averaging kernel for 82 hPa. Each MLS monthly zonal mean was determined from 2000–3000 profiles. Anomalies for MLS and FP data are calculated relative to the 2004–20 period for sites except for Lindenberg (2009–21) and Hilo (2011–21). Tropical CPT anomalies based on the MERRA-2 reanalysis (d, blue curve), which are generally well correlated with the tropical lower stratospheric WV anomalies, are the driving force behind the variations in tropical WV during 2021.
et al. 2021). The positive anomalies in tropical lower stratospheric WV at the beginning of 2021 and again at the end of the year are consistent with the known behavior associated with La Niña.

Equatorial winds from the Singapore radiosonde wind data, which are a commonly used proxy for the QBO phase, were westerly at 50 hPa throughout 2021, until shifting to easterly in December. The QBO westerly phase is associated with anomalously weak tropical upwelling and anomalously high temperatures. Thus, although no formal attribution is attempted here, the combination of a La Niña phase and QBO westerlies likely contributed to the anomalously high CPTs and enhanced tropical lowermost stratospheric WV in 2021.

## 6. TROPOSPHERIC OZONE—O. R. Cooper, J. R. Ziemke, and K.-L. Chang,

Tropospheric ozone is a short-lived climate forcer that either originates naturally in the stratosphere or is produced in situ by photochemical reactions involving sunlight and precursor gases, such as nitrogen oxides (NOₓ), non-methane volatile organic compounds, methane, and carbon monoxide (Archibald et al. 2020). Tropospheric ozone has a strong seasonal cycle that peaks in either spring or summer in response to the regional availability of sunlight, ozone precursors, and long-range transport (Cooper et al. 2014). Ozone precursors can originate naturally from wildfires, biogenic hydrocarbon emissions, lightning NOₓ, and biogenic NOₓ emissions from soils, and also from anthropogenic sources such as fossil fuel and biofuel combustion or crop burning. Tropospheric ozone also acts as a pollutant near the surface, impacting human health and vegetation (Fleming et al. 2018; Mills et al. 2018).

Long-term trends of tropospheric ozone were recently assessed by the IPCC (Gulev et al. 2021; Naik et al. 2021). An ensemble of chemistry-climate models indicates that the tropospheric ozone burden (TOB) has increased by 45% since 1850. The model-estimated present-day TOB of 347 ± 28 Tg agrees well with the observed value of 338 ± 6 Tg determined from satellite products and ozonesondes. An observation-based quantification of TOB is not possible prior to 1998 due to insufficient global coverage by satellites and ozonesondes (Gaudel et al. 2018). However, model estimates of a constant global increase of tropospheric ozone since the 1950s are consistent with the observed increase since the mid-1990s (Tarasick et al. 2019).

Combined Aura Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) satellite ozone measurements (OMI/MLS) indicate increasing TOB since the record began in 2004 (Ziemke et al. 2019). In 2021, broad regions of positive tropospheric column ozone (TCO) anomalies were found in the NH midlatitudes (~1.2 DU; 4%), with smaller anomalies of ~1 DU or less elsewhere (Plate 2.1y). Hemispheric and global TOB and their 95% confidence levels for 2021 were 159 ± 6 Tg (0°–60°N), 148 ± 8 Tg (0°–60°S), and 307 ± 10 Tg (60°S–60°N). Globally (60°S–60°N), the 2004–21 TOB increase was 1.48 ± 0.40 Tg yr⁻¹, or ~9% (Fig. 2.62). Spatially, the trends are overwhelmingly positive,

<figure><img src="image_100.png" /><figcaption>**Fig. 2.62.** Monthly averages (solid lines) and 12-month running means (dashed lines) of OMI/MLS tropospheric ozone burdens (Tg) from Oct 2004 through Dec 2021. (a) 60°S–60°N monthly averages with 12-month running mean, (b) monthly averages and running means for the NH tropics and midlatitudes, and (c) monthly averages and running means for the SH tropics and midlatitudes. Slopes of linear fits to the data are presented with their 95% confidence-level uncertainties. Vertical resolution of OMI/MLS monthly TCO is ~3 km about the tropopause with ~2 DU (7%) precision regionally; trend uncertainties are about 0.5 DU decade⁻¹ (1.5% decade⁻¹).</figcaption></figure>
reaching ~+3.2 DU decade⁻¹ (~+1% yr⁻¹) east of China and Southeast Asia (Fig. 2.63), consistent with model simulations of increasing fossil fuel emissions from Southeast, East, and South Asia (Y. Zhang et al. 2016; Ziemke et al. 2019) and also consistent with ozone trends since the mid-1990s based on in situ observations in the boundary layer and free troposphere (Gaudel et al. 2020; Chang et al. 2022). Models indicate that ozone produced in these areas is transported northward and eastward in the free troposphere over the North Pacific Ocean (Zhang et al. 2020) as supported by the trend patterns in Fig. 2.63. Positive trends in the SH extra-tropics have been linked to a broadening of the Hadley circulation (Lu et al. 2018).

<figure><img src="image_101.png" /><figcaption>Fig. 2.63. Linear trends in OMI/MLS tropospheric column ozone (DU decade⁻¹) on a 5° × 5° grid from Oct 2004 through Dec 2021. Circles denote trends with *p*-values < 0.05. Trends were calculated using a multivariate linear regression model (e.g., Randel and Cobb 1994 and references therein) that included a seasonal cycle fit and the Niño 3.4 index as an ENSO proxy; trend uncertainties included autoregressive adjustment via Weatherhead et al. (1998).</figcaption></figure>

At the surface, six baseline sites are available for quantifying multi-decadal ozone trends through the end of 2021 (Fig. 2.64; Table 2.11). At northern high latitudes, ozone increased at a rate of 0.57 ± 0.33 ppbv decade⁻¹ since 1973 at Barrow Observatory, but decreased by 2.43 ± 0.97 ppbv decade⁻¹ since 2000 at Summit, Greenland. At northern midlatitudes, ozone decreased by 0.96 ± 1.22 ppbv decade⁻¹ since 1988 at Tudor Hill, Bermuda, but with large fluctuations. Mauna Loa Observatory, Hawaii, is located at the interface of the tropics and northern midlatitudes, allowing the ozone record to be split into mutually exclusive times series representing moist air (primarily a tropical origin) and dry air (primarily a midlatitude origin). Ozone in the MLO dry air (midlatitude) increased by 2.04 ± 0.41 ppbv decade⁻¹ since 1974, while ozone in the MLO moist air (tropical) increased by 1.00 ± 0.38 ppbv decade⁻¹. In the southern high latitudes ozone at Arrival Heights, Antarctica has changed little since 1996. The trend at the South Pole, the most remote location on Earth, is +0.36 ± 0.37 ppbv decade⁻¹ since 1975. While these data provide a range of trends at remote locations, they are too sparse to provide a global mean surface trend, and surface trends do not necessarily reflect trends in the free troposphere (Cooper et al. 2020).

Recent ozonesonde, lidar, and FTIR observations show a small ozone decrease in the NH mid- and lower troposphere (1–8 km) in 2020 in response to ozone precursor emissions reductions associated with the COVID-19 pandemic (Steinbrecht et al. 2021; Miyazaki et al. 2021). The decrease was strongest in the northern midlatitudes, with reductions of 5–6% above Europe and western North America (Chang et al. 2022). Future assessments will determine if this decrease was a temporary fluctuation or if it will have a long-term impact on the TOB trend.
<figure><img src="image_102.png" /></figure>

Fig. 2.64. (a) Nighttime monthly mean ozone values (ppb) at Mauna Loa (MLO), split into mutually exclusive times series, representing moist air (yellow, primarily a tropical origin) and dry air (black, primarily a midlatitude origin) based on observed relative humidity values (Gaudel et al. 2018). (b) Monthly mean surface ozone (ppb) at Barrow Observatory, Alaska (gray), Summit, Greenland (orange), Tudor Hill, Bermuda (blue), Arrival Heights, Antarctica (red), and South Pole (green). Monthly means are produced for months with at least 50% data availability, using observations from all 24 hours of the day. The locations of each site are listed in Table 2.11. (c) The same time series after conversion to monthly anomalies referenced to the monthly climatological values over 2000–20 and smoothed variability based on the LOWESS (locally weighted scatterplot smoothing) regression.

<table><caption>Table 2.11. Ozone trends at the six baseline monitoring sites shown in Fig. 2.64. Trends are estimated by the generalized least squares method, based on monthly anomalies referenced to the monthly climatological values over the period 2000–20 (Chang et al. 2021) and reported with 95% confidence intervals and *p*-values.</caption><thead><tr><th>Site name<br>Latitude, longitude, elevation<br>(m)</th><th>Years with data</th><th>trend, ppbv decade<sup>-1</sup></th><th><i>p</i>-value</th></tr></thead><tbody><tr><td>Summit, Greenland<br>72.6°N, 38.5°W, 3238 m</td><td>2000–present</td><td>-2.43 ± 0.97</td><td><i>p</i>&lt;0.01</td></tr><tr><td>Barrow, Alaska<br>71.3°N, 156.6°W, 11 m</td><td>1973–present</td><td>0.57 ± 0.33</td><td><i>p</i>=0.00</td></tr><tr><td>Tudor Hill, Bermuda<br>32.3°N, 64.9°W, 30 m</td><td>1988–98,<br>2003–present</td><td>-0.96 ± 1.22</td><td><i>p</i>=0.12</td></tr><tr><td>Mauna Loa Observatory (MLO),<br>Hawaii<br>19.5°N, 155.6°W, 3397 m</td><td>1973–present</td><td>2.04 ± 0.41 (dry air)<br>1.00 ± 0.38 (moist air)</td><td><i>p</i>&lt;0.01<br><i>p</i>&lt;0.01</td></tr><tr><td>Arrival Heights, Antarctica<br>77.8°S, 166.8°W, 50 m</td><td>1996–present</td><td>0.34 ± 0.59</td><td><i>p</i>=0.25</td></tr><tr><td>South Pole, Antarctica<br>90.0°S, 59.0°E, 2840 m</td><td>1975–present</td><td>0.36 ± 0.37</td><td><i>p</i>=0.05</td></tr></tbody></table>
7. CARBON MONOXIDE—J. Flemming and A. Inness

Carbon monoxide (CO) is emitted into the atmosphere by incomplete combustion from anthro-
pogenic sources and from wildfires. The chemical production of CO in the atmosphere from form-
aldehyde as part of the oxidation chains of methane (CH₄), isoprene, and other volatile organic
trace gases (Stein et al. 2014) is larger than these admissions. Oxidation of CO with the hydroxyl
radical (OH) is the main loss process for CO. The greater abundance of OH in summer is a main
driver for the typical CO seasonal cycle that peaks in boreal and austral winter. Carbon monoxide
is an indirect short-lived climate forcer because it is an important precursor for tropospheric ozone
(Szopa et al. 2021; section 2g6) and because it impacts OH, which controls the lifetime of CH₄.

Carbon monoxide concentrations doubled between the 1850s and the 2000s based on model
studies (Griffiths et al. 2020). Based on a limited number of ice core samples, Northern Hemisphere
(NH) CO levels were the highest (around 160 ppb) in the 1970s (Petrenko et al. 2013) but later de-
clined by 30 ppb to 130 ppb by 2008. Surface CO concentrations have been measured as part of
the global atmospheric watch (GAW) network using in situ sensors and flask observations in a
routine way by NOAA's Global Monitoring Laboratory and other agencies since the 1990s (WMO
2021a). Tropospheric CO is observed, in situ, by the In-service Aircraft for a Global Observing
System (IAGOS) aircraft observation program (Nédélec et al. 2015) and the Network for the Detec-
tion of Atmospheric Composition Change (NDACC; De Mazière et al. 2018) of ground-based remote
sensing Fourier Transform Infrared Spectroscopy (FTIR) instruments, which provides atmospheric
CO profiles. The advent of CO satellite sensors measuring CO in the early 2000s allowed more
detailed monitoring of the global CO burden (Worden at al. 2013; Yin et al. 2015; Bucholz et al.
2021) in particular by assimilating these observations in atmospheric composition reanalyses
(Flemming et al. 2017; Gaubert et al. 2017; Inness et al. 2019). The trend of the CO burdens and CO
surface concentrations since the early 2000s varies spatially, but there is qualitative agreement
between satellite-derived trends in CO burden and surface trends obtained from background in
situ observations: the NH shows a decline of CO varying between 0.3% and 1.3% yr⁻¹, while SH
values did not change significantly (Szopa et al. 2021).

Figure 2.65a shows a time series of the monthly global burden of CO from CAMS reanalysis for the period 2003–21. The total CO burden has reduced by 1.4 Tg yr⁻¹ (based on a linear trend), and piecewise trends for the periods 2003–07, 2008–09, and 2009–21 are –3.1, –14.3, and 0.0 Tg yr⁻¹ (Flemming and Inness 2018).

The spatial distribution of the annual CO anomalies, with respect to the period 2003–21, is
shown in Plate 2.1z. Stronger-than-usual wildfire activity in northeastern Russia, starting in sum-
mer 2021 led to a widespread positive CO anomaly in mid- and higher latitudes of the NH, which
was further increased by active wildfires in Canada and the western United States in August.
This led to the highest monthly mean CO burdens in the Arctic region (60°–90°N) for the period
covered by the CAMS reanalysis (Fig. 2.65b). Positive anomalies also occurred over India, caused
by intensive agricultural waste burning in January and February. La Niña conditions in the tropi-
cal Pacific resulted in a pronounced negative CO anomaly over maritime Southeast Asia in the
autumn period due to lower-than-normal biomass burning (Inness et al. 2015).

CAMS produced a retrospective analysis of CO, aerosols, and ozone for the period 2003–21 by
assimilating satellite retrievals of atmospheric composition with the ECMWF model (Inness et
al. 2019). The CAMS reanalysis-assimilated thermal infrared (TIR) total column CO retrievals (V6
from 2003 to 2016, near-real-time (NRT) V7 from January 2017 to June 2019, NRT V8 from July 2019
onward) from the MOPITT instrument (Deeter et al. 2014, 2017, 2019) globally, only excluding obser-
vations poleward of 65°N/S, using the ECWMF 4D-VAR data assimilation system. Anthropogenic
emissions were taken from the MACCity inventory (Granier et al. 2011) that accounts for projected
emission trends according to the IPCC Representative Concentration Pathway 8.5 scenario, but
COVID-19 related emissions modifications were not applied. Biomass burning emissions (section
2h3) were taken from the Global Fire Assimilation System (v1.2; Kaiser et al. 2012) that is based on
MODIS fire radiative power retrievals (Giglio et al. 2016). A monthly mean climatology of biogenic emissions was taken from the MEGAN2.1 model following Sindelarova et al. (2014).

<figure><img src="image_103.png" /><figcaption>Fig. 2.65. Time series of the mean burden of CO for the (a) globe and (b) Arctic from the CAMS reanalysis and a piecewise linear trend for the periods 2003–07, 2008, and 2009–20.</figcaption></figure>

h. Land surface properties

1. LAND SURFACE ALBEDO DYNAMICS—G. Duveiller and N. Gobron

The land surface was predominantly darker than normal during 2021 compared to the 2003–20 baseline in terms of visible broadband white-sky albedo and normal for near-infrared albedo (see Plates 2.1ac and 2.1ad, respectively). The patterns of surface albedo largely follow the dynamics of snow cover and vegetation dynamics, and 2021 was no exception. Several regions across the world had brighter surfaces due to above-average snow cover in either the beginning of the year (south/central United States, Spain, large parts of northern Europe), the end of the year (north-western America and easternmost Russia, parts of the Tibetan plateau), or both (northeastern China). Lack of snow darkened the overall surface albedo in central North America and Quebec, Canada, in southeastern Europe, and in various parts of Russia (section 2c5). Where vegetation is greener (positive FAPAR anomalies; section 2h2), its contributions darken the surface overall by reducing the visible albedo, while near-infrared albedo rises slightly as denser vegetation scatters more light in this part of the spectrum. However, an exception to this case appeared in southern Africa where a decrease in near-infrared albedo occurred despite the increase in vegetation, possibly due to anomalously wet conditions. In contrast, the strong soil moisture deficit associated with the drought in the La Plata basin (section 2d10) resulted in an increase in visible albedo due to the drier conditions.
<figure><img src="image_104.png" /></figure>

Fig. 2.66. Zonally averaged (a) white sky visible and (b) near-infrared albedo anomalies (%) for the period 2003–21 using a 2003–20 base period.

<figure><img src="image_105.png" /></figure>

Surface albedo also largely follows the trends of decreasing snow cover observed in the past years (Figs. 2.66, 2.67). Also, as higher temperatures and CO₂ fertilization increase vegetation cover, the surface darkens considerably in terms of visible albedo and brightens slightly in terms of near-infrared albedo. The year 2021 seems to confirm that this general trend is also applicable to visible albedo anomalies in the Southern Hemisphere, when a return to negative values was observed after two years of positive values.

This analysis is based on satellite records of visible and near-infrared white-sky albedo estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the *Aqua* and *Terra* satellite platforms (Schaaf et al. 2002). White-sky albedo, also known as bi-hemispherical reflectance, is defined as the fraction of radiation that is reflected in the

absence of a direct radiation component and when the diffuse radiation component is isotropic. Various studies have shown that these products well-represent ground properties, whether it is ice sheets (Stroeve et al. 2013) or vegetation (Cescatti et al. 2012). The baseline reference period is 2003–20, covering the extent of the MODIS record where data from both satellite platforms (*Terra* and *Aqua*) are available.

<figure><img src="image_106.png" /></figure>

Fig. 2.67. Global (black lines), NH (blue), and SH (red) land surface (a) visible and (b) near-infrared albedo anomalies (%) for the period 2003–21 using a 2003–20 base period. Dotted lines denote each monthly period; solid lines indicate the 6-month running averaged mean.

## 2. TERRESTRIAL VEGETATION DYNAMICS—N. Gobron

The fraction of absorbed photosynthetically active radiation (FAPAR) is used to track the overall land productivity associated with atmospheric CO₂ fixation. FAPAR anomalies in 2021 relative to the 1998–2020 mean show large surface variations, in terms of values and coverage, of vegetation productivity worldwide (Plate 2.1ae).
The greatest negative anomalies occurred over Central Asia, which had a record heatwave (see section 7g). The African continent also had strong negative anomalies in the east, over Somalia and Kenya, which were present during the entire year but strongest at the end of the year. To a lesser extent, the western coast from the Tropic of Capricorn to Nigeria also experienced numerous extreme events, with both droughts and floods that resulted in lower-than-average FAPAR. Negative anomalies were also present over far southern Africa and the southern half of Madagascar due to persistent drought inducing crop losses and other vegetation cover. The North American continent showed several negative hot-spots, including Montana, North Dakota, and Saskatchewan and along the Pacific coast, from California to Oregon, due to a series of wildfires that resulted from an exceptional heatwave and drought. Northeastern South America had low FAPAR in Guyana and Suriname. Small in extent but still significant, the Brazilian states of Rio Grande and Paraiba, Parque del Gran Chaco Kaa-Lya also had below-average FAPAR in 2021. Over Australia, Nullarbor Plain and a zone from Adelaide to Queensland, had low FAPAR. Over Alaska and northeastern Russia, with highest record temperatures during summer, there was below-average photosynthetic activity.

The most noticeable positive anomalies, most likely due to heavy rain linked to La Niña, took place in Botswana and northeastern Namibia, as well as eastern South Sudan. To a lesser extent, further positive anomalies in Africa were seen over the belt of humid savannas in the Sahel, from Liberia, Côte d'Ivoire, Ghana, Togo, and the south of Chad. These positive events were also enhanced by high precipitation totals over these regions (see section 2d4). In South America, positive anomalies occurred over regions such as northwestern Venezuela, the state of Marahao (Brazil), and Argentina over La Mesopotamia and La Pampa. Southwestern Europe—comprised of Portugal, Spain, France, and around Moldova—had positive anomalies. A large part of north-western Russia, mainly covered by tundra, also had positive anomalies. A large positive anomaly occurred over New South Wales and southern Queensland in Australia due to heavy precipitation in 2021, and another affected Malaysia.

Figure 2.68 shows the latitudinal anomalies average from 1998 to 2021 compared to the base period 1998–2020. In 2021, the positive behavior extended globally, with the exception of a few locations, highlighting the greenness of the terrestrial surfaces. The Southern Hemisphere (SH) was affected by strong negative anomalies, i.e., below –0.03, from 2002 to 2014, except in 2010–12,

for which vegetation had recovered from severe and persistent droughts (Gobron and Belward 2011) and slightly negative anomalies in 2019/20. Fig. 2.69 shows the global and hemispherical anomalies, with more seasonal variability in the SH than in the Northern Hemisphere (NH). Global seasonal anomalies have been positive since 2013, following the NH trend. SH was generally positive but with monthly negative events during its summer period, e.g., 2015/16; 2018/19; 2019/20, and 2020/21. SH data reveal two positive extreme peaks in 2000 and 2017, while the lowest values occurred in 2008/09. The NH experienced fewer extreme negative events, e.g., values below –0.03, compared to the SH. In 2021, only positive FAPAR anomalies were recorded for both SH and NH averages.

<figure><img src="image_107.png" /><figcaption>Fig. 2.68. Zonally averaged fraction of absorbed photosynthetically active radiation (FAPAR) anomalies for the period 1998–2021 (1998–2020 base period).</figcaption></figure>
Earth observations are important for monitoring the terrestrial photosynthetic activity worldwide. They are used to infer FAPAR, an essential climate variable (as defined by GCOS [2016]). The 2021 study merged 24 years of global FAPAR monthly products based on three optical sensors from 1998 to 2021 (Gobron et al. 2010; Pinty et al. 2011; Gobron and Robustelli 2013). Uncertainties of each dataset were derived through various means, such as error propagation technique and comparisons against multiple proxies using ground measurements and radiative transfer simulations, that all provide an estimate of the uncertainties and biases. This long-term FAPAR dataset presents an estimated global average uncertainty close to 5%–10% when comparing to ground-based measurements.

### 3. BIOMASS BURNING—

J. W. Kaiser and G. R. van der Werf

The year 2021 illustrated how two distinct trends that have emerged in global biomass burning over the last decade shaped current pyrogeography: a declining trend in many savanna regions related to agricultural expansion and an increasing trend in many forested regions where climate change has increased the flammability of the landscape. On one hand, 2021 was the fourth-lowest fire year in the Global Fire Assimilation System (GFAS) record (1837 Tg C; 11% below the 2003–20 average; Table 2.12; global map shown in Fig. 2.70), and fire activity in tropical Asia was the lowest since at least 2003. On the other hand, 2021 saw extreme regional fire activity in boreal North America and Siberia, as well as the western United States. After the extreme fires of 2019 and 2020 in the Arctic Circle and southeastern Australia, fire activity in these regions was again near and below average, respectively.

<figure><img src="image_108.png" /><figcaption>Fig. 2.69. Global (black/gray lines), Northern Hemisphere (blue), and Southern Hemisphere (red) fraction of absorbed photosynthetically active radiation (FAPAR) anomalies for the period 1998–2021 (1998–2020 base period). Dotted lines denote each monthly period; solid lines indicate the 6-month running averaged mean.</figcaption></figure>

<table><caption>Table 2.12. Annual continental-scale biomass burning budgets in terms of carbon emission (Tg C yr⁻¹). (Source: GFASv1.4.)</caption><thead><tr><th rowspan="2">Time Period<br>Quantity<br>Tg C yr<sup>-1</sup></th><th colspan="2">2003–20</th><th colspan="2">2021</th></tr><tr><th>Mean value<br>(Range)</th><th>Value</th><th>Value</th><th>Anomaly (%)</th></tr></thead><tbody><tr><td>Global</td><td>2062<br>(1781–2421)</td><td>1837</td><td></td><td>-225 (-11%)</td></tr><tr><td>North America</td><td>30°–75°N<br>190°–330°E</td><td>85<br>(57–114)</td><td>114</td><td>+28 (+33%)</td></tr><tr><td>Central America</td><td>13°–30°N<br>90°–330°E</td><td>52<br>(38–72)</td><td>46</td><td>-6 (-12%)</td></tr><tr><td>South America</td><td>13°S–60°N<br>190°–330°E</td><td>368<br>(242–537)</td><td>316</td><td>-52 (-14%)</td></tr><tr><td>Europe and<br>Mediterranean</td><td>30°–75°N<br>330°–60°E</td><td>42<br>(28–72)</td><td>34</td><td>-9 (-21%)</td></tr><tr><td>N. Hem. Africa</td><td>0°–30°N<br>330°–60°E</td><td>421<br>(308–494)</td><td>372</td><td>-49 (-12%)</td></tr><tr><td>S. Hem. Africa</td><td>0°–35°S<br>330°–60°E</td><td>477<br>(429–532)</td><td>476</td><td>-1 (0%)</td></tr><tr><td>Northern Asia</td><td>30°–75°N<br>60°–190°E</td><td>199<br>(116–436)</td><td>256</td><td>+57 (+29%)</td></tr><tr><td>South-East Asia</td><td>10°–30°N<br>60°–190°E</td><td>122<br>(86–162)</td><td>111</td><td>-11 (-9%)</td></tr><tr><td>Tropical Asia</td><td>10°S–10°N<br>60°–190°E</td><td>166<br>(37–475)</td><td>27</td><td>-139 (-83%)</td></tr><tr><td>Australia</td><td>10°–50°S<br>60°–190°E</td><td>129</td><td>86</td><td>-43 (-34%)</td></tr><tr><td>Arctic</td><td>67°–90°N<br>0°–360°E</td><td>8<br>(1–37)</td><td>7</td><td>-1 (-9%)</td></tr><tr><td>Western United<br>States</td><td>30°–49°N<br>230°–260°E</td><td>19 (8–42)</td><td>37</td><td>+18 (+96%)</td></tr></tbody></table>
Global fire emissions are generally dominated by savanna burning. For example, African fire emissions account for roughly half of total fire carbon emissions, and fires here and in many other savanna regions have decreased over the past decade. This trend is partly driven by agricultural expansion into savanna ecosystems and associated fragmentation of the landscape (Andela et al. 2017). The trend continued in 2021, with Africa north of the equator observing emissions 12% below the 2003–20 average and the seven years with the lowest global fire activity in the GFAS record all occurring since 2013; however, emissions were close to average south of the equator in Africa and thus did not contribute to the trend. Fire activity in tropical Asia, including Indonesia, was the lowest on record (Fig. 2.71); fire activity in this region is strongly modulated by precipitation anomalies associated with the El Niño-Southern Oscillation. Environmental protection policies may also have contributed to the low fire activity.

At higher latitudes, northern Asia and North America experienced particularly intense fire seasons in 2021, with anomalies of +29% and +33%, respectively. The continental-scale budgets are dominated by boreal fires. Nevertheless, the wildfires in the western United States were second only to those of 2020, consuming twice as much biomass as the long-term average (Fig. 2.71).

<figure><img src="image_109.png" /><figcaption>Fig 2.70. Global map of fire activity in 2021 in terms of carbon consumption (g C m⁻² yr⁻¹). (Source: GFASv1.4.)</figcaption></figure>

<figure><img src="image_110.png" /><figcaption>Fig. 2.71. Time series of annual (squares) and monthly (lines) regional fire activity, in terms of carbon consumption for (a) the western United States, (b) northern Asia, and (c) tropical Asia. (Source: GFASv1.4.)</figcaption></figure>
GFAS produces global fire emission estimates in near real-time for the Copernicus Atmosphere Monitoring Service (CAMS; Kaiser et al. 2012). It is based on the MODIS Fire Radiative Power products (Giglio et al. 2016). Here, we use consistent reprocessing with input from MODIS Collection 6 for the entire period of 2003–21. The 14% bias, with respect to Collection 5, has been corrected and the satellite and observation time-specific bias correction factors from Hüser et al. (2018) have been applied for 17 August–2 September 2020 in order to compensate for the outage of observations from MODIS onboard the Aqua satellite. The time series in Plate 2.1ae also puts the GFAS time series, which begins in 2003, in the context of GFED4s, which is mostly based on burnt area observation and dates back to 1997 (van der Werf et al. 2017).

4. PHENOLOGY OF PRIMARY PRODUCERS—D. L. Hemming, O. Anneville, Y. Aono, J. Garforth, A. Menzel, J. O’Keefe, T. Park, A. D. Richardson, T. Rutishauser, T. H. Sparks, S. J. Thackeray, A. van Vliet, and Y. Yuan

During 2021, the phenology, of satellite-derived, PhenoCam-derived, terrestrial, and aquatic records, indicate a generally earlier start and longer growing season across the globe relative to the 2000–20 baseline, with the exception of extreme April temperature impacts in Europe. The satellite-derived (MODIS) normalized difference vegetation index (NDVI; Park et al. 2016) across Northern Hemisphere land (NH, > 30°N) revealed earlier mean start of season (SOS<sub>M</sub>; -1.8 days) and later end of season (EOS<sub>M</sub>, +4.2 days) relative to the baseline (SOS<sub>M</sub> = day 135, 15 May and EOS<sub>M</sub> = day 283, 10 October; Fig. 2.72). These differences were associated with higher spring (+0.24°C) and autumn (+0.54°C) temperatures from the MERRA-2 reanalysis (Gelaro et al. 2017) and resulted in a 6-day longer growing season in 2021. Regionally, SOS<sub>M</sub> occurred earlier across western and

<figure><img src="image_111.png" /><figcaption>Fig. 2.72. (a) Time series of area-mean anomalies (days relative to 2000–20 baseline) in MODIS NDVI-based vegetation growing season onset (SOSM, green) and MERRA-2 spring (Mar–May, pink) temperature for Northern Hemisphere (> 30°N). (b) Same as (a) but for the end of growing season (EOSM, green) and autumn (Sep–Nov, pink) temperature. Note, temperature scale reversal for panel (a). (c),(d) Spatial pattern of (c) SOSM and (d) EOSM anomalies in 2021 with respect to the baseline. Highlights identify the location of sites shown in Figs. 2.73 and 2.74 and discussed in the text (Country mean phenology data: yellow; site PhenoCam and phenology observations: magenta; lake phytoplankton: blue).</figcaption></figure>
northeastern Eurasia (EA), and northeastern North America (NA) and later across central EA and NA (Fig. 2.72c). A striking earlier SOS<sub>M</sub> (–13 days) over western Russia (35°–75°E, 69°–57°N) was associated with an anomalous spring warm spell (+2.7°C). Most EA and NA regions showed a later EOS<sub>M</sub>, whereas earlier EOS<sub>M</sub> was observed in southwestern EA. The regions displaying early EOS<sub>M</sub> were mostly temperate grass and shrublands, which experienced a drier summer and autumn seasons in 2021 (JRC 2021). Two decades of MODIS record show long-term trends of earlier SOS<sub>M</sub> and later EOS<sub>M</sub> (SOS<sub>M</sub>: –1.74 ± 0.42 days decade<sup>-1</sup>, *p* < 0.001; EOS<sub>M</sub>: 1.93 ± 0.47 days decade<sup>-1</sup>, *p* < 0.001).

PhenoCam data (Seyednasrollah et al. 2019) help link the coarse resolution of satellite-derived phenology with fine resolution visual observations on organisms and ecosystems (Richardson 2019). PhenoCam-derived estimates (2008–21) of SOS (SOS<sub>PC</sub>) and EOS (EOS<sub>PC</sub>) at Harvard Forest, a deciduous forest in Massachusetts (United States), were compared with ground observations of red oak (Quercus rubra; SOS<sub>RO</sub> and EOS<sub>RO</sub>; Richardson and O’Keefe 2009; O’Keefe 2021), and MODIS (SOS<sub>M</sub> and EOS<sub>M</sub>) for the associated pixel (Figs. 2.73a,b). SOS<sub>PC</sub> and EOS<sub>PC</sub> are strongly correlated with SOS<sub>RO</sub> (r = 0.90) and EOS<sub>RO</sub> (r = 0.83), and their timings were similar. Although SOS<sub>PC</sub> and EOS<sub>M</sub> were strongly correlated (r = 0.77), SOS<sub>PC</sub> was later by 12 ± 3 days (Fig. 2.73b). The correlation between EOS<sub>PC</sub> and EOS<sub>M</sub> was weaker (r = 0.58), and EOS<sub>PC</sub> was earlier by 47 ± 10 days (Fig. 2.73a). In 2021, SOS<sub>PC</sub>, SOS<sub>RO</sub>, and SOS<sub>M</sub> were 8, 8, and 4 days earlier and EOS<sub>PC</sub>, EOS<sub>RO</sub>, and EOS<sub>M</sub> were 13, 6, and 11 days later than in 2020 (Figs. 2.73a,b). EOS<sub>PC</sub> was the latest in the PhenoCam series. All three Harvard Forest records showed a longer growing season in 2021 than 2020, with the PhenoCam showing the largest change where the earlier SOS<sub>PC</sub> and later EOS<sub>PC</sub> yielded a growing season that, at 178 days, was three weeks longer than 2020, and 11 days longer than the 2011–20 mean (167 days). First leaf (SOS<sub>PO</sub>) and leaf falling/bare tree (EOS<sub>PO</sub>) dates for pedunculate oak (Quercus robur) from Germany (D), the United Kingdom (UK), and the Netherlands (NL) are

<figure><img src="image_112.png" /><figcaption>Fig. 2.73. Day of year of spring and autumn vegetation phenology indicators for (a),(b) Harvard Forest, Massachusetts, where (a) start and (b) end of season days are derived from MODIS remote sensing (black), PhenoCam observations and red oak (Quercus rubra) single-tree ground observations (green and orange); (c),(d) Germany, United Kingdom, and the Netherlands, where country-mean first leaf (green) and bare tree or leaf fall (orange) days are derived from ground observations of pedunculate oak (Quercus robur); and (e) Kyoto, Japan, showing first full bloom days for cherry blossom, Prunus jamasakura from ground observations (pink).</figcaption></figure>
presented in Figs. 2.73c,d. The mean SOS<sub>PO</sub> and EOS<sub>PO</sub> for the 2000–20 baseline was 27 (D), 24 (UK), and 20 (NL) April, and 5, 30, and 26 November, respectively. Both events were strongly influenced by temperature; in general, it has been shown that SOS<sub>PO</sub> advances by 4–6 days per °C increase in mean February–April temperature, and EOS<sub>PO</sub> is delayed 2–4 days per °C increase in September–October temperature (Menzel et al. 2020). April 2021 temperatures were relatively cold

in these countries, resulting in SOS<sub>PO</sub> dates 10 (D), 3 (UK), and 13 (NL) days later than the baseline, while EOS<sub>PO</sub> dates were mixed; 6 days earlier in NL but 2 and 4 days later in D and UK. The net result was a shorter 2021 oak season in each country (see Kendon et al. 2022).

In Kyoto, Japan, full bloom dates (FBD) for a native cherry tree species, *Prunus jama-sakura*, were acquired from historical documents (Aono and Kazui 2008) and updated with current observations. In 2021, the FBD was 26 March (day 85), which was 9.5 days earlier than the 2000–20 baseline mean and the earliest in the entire record, which began in AD 801, breaking the previous earliest date of 27 March in the year 1409.

Monitoring data on lake water concentrations of the photosynthetic pigment chlorophyll-a were available to estimate spring phytoplankton phenology in 1 SH and 10 NH lakes (Fig. 2.74). Seasonal timing was quantified for start of season (SOS<sub>L sensu</sub> Park et al. 2016), day of maximum concentration (DOM<sub>L</sub>), and center of gravity (COG<sub>L</sub>; Edwards and Richardson 2004). Lake basins showed great interannual variation and mixed phenological behavior in 2021, relative to the 2000–20 baseline. 2021 SOS<sub>L</sub> and COG<sub>L</sub> were both earlier than the baseline interquartile range in 4 of the 11 lakes and 5 of the 11 for DOM<sub>L</sub>. Earlier growth

<figure><img src="image_113.png" /><figcaption>**Fig. 2.74.** Phenological metrics based on lake chlorophyll-a concentrations, as a proxy of phytoplankton biomass: (a) start of season, (b) day of maximum, and (c) center of gravity (a measure of the timing of the peak throughout the growing season). Boxplots show variation during the 2000–20 baseline period, and red dots show 2021 values. Dashed line identifies Northern Hemisphere (Blelham Tarn in United Kingdom [UK], Bourget in France, Esthwaite Water in UK, Geneva in France/Switzerland, Kasumigaura in Japan, Kinneret in Israel, Loch Leven in UK, Mjøsa in Norway, north and south basins of Windermere in UK) and Southern Hemisphere (Taupo in New Zealand) lakes.</figcaption></figure>
typically occurs in deeper lakes where thermal stratification is an important trigger of spring phytoplankton growth (Sommer et al. 1986).

## 5. VEGETATION OPTICAL DEPTH—W. Dorigo, R. M. Zotta, R. van der Schalie, W. Preimesberger, L. Moesinger, and R. A. M. de Jeu

Vegetation optical depth (VOD) is a parameter describing the interaction of microwave radiance with vegetation. It is closely related to vegetation above-ground biomass (Mialon et al. 2020), leaf area index (Vreugdenhil et al. 2017), gross primary production (Teubner et al. 2019; Wild et al. 2022), and canopy water content (Konings et al. 2017). Thus, it is a valuable indicator of ecosystem health, agricultural drought, and crop status (Crocetti et al. 2020; Moesinger et al. 2022).

In 2021, VOD anomaly patterns largely resembled those of 2020 (Dorigo et al. 2021). Wide-spread patterns of negative VOD anomalies occurred in large parts of Russia, Central Asia, and Mongolia. For some of these predominantly rain-fed agricultural areas, below-average cereal production was reported by the UN Food and Agricultural Organization. Clear below-average VOD values were also detected in western Africa, Angola, and Namibia, the southwestern tip of Africa, Mozambique, and southern Madagascar, which were already observed in 2020 and persisted throughout 2021 (Plate 2.1ag). In Madagascar, ongoing droughts have reportedly led to crop failure, according to UN reports. Although some parts of Australia show lower-than-usual VOD, many areas, particularly in the east, shifted from a negative state in 2020 (Dorigo et al. 2021) to a positive one in 2021, as predominantly wet conditions have prevailed following a major drought in 2017–19 (see section 7h4). The most evident region with above-average VOD was centered around Botswana and Namibia in southeastern Africa. Compared to 2020, VOD anomalies in this region became more positive and covered a larger area. Above-average rainfall amounts that are commonly associated with La Niña in this region (see section 7e) may have been responsible for this high VOD. Farther north, Uganda and South Sudan, in particular, had above-average VOD, likely related to heavy precipitation and flooding (see section 7e). Other areas of above-average VOD include parts of the Parana basin in South America, the Tigris floodplain in the Middle East, and the Indian subcontinent.

In the Southern Hemisphere, there was a clear connection between interannual variability in precipitation and vegetation activity and variations in ENSO and other climate modes (Fig. 2.75; Micalles et al. 2014; Martens et al. 2018).

La Niña conditions prevailed in 2021, and several VOD anomalies coincided with rainfall anomaly patterns typically associated with this phase, including positive VOD anomalies in northeastern Brazil, southern Africa, the northern Sahel region, eastern Australia, and India. However, for VOD, the relationship with climate modes is generally less straightforward than for moisture supply, since VOD anomalies are also affected by drivers such as temperature, radiation, CO₂ fertilization, weather extremes, lagged effects, and land management (e.g., irrigation, fertilization, logging; Gonsamo et al. 2021; Reichstein et al. 2013).

Several anomalies observed in 2021 are consistent with patterns of long-term change (Fig. 2.76). For instance,

<figure><img src="image_114.png" /><figcaption>Fig. 2.75. Yearly Ku-band VOD anomalies computed from the 1991–2020 climatology and their agreement with the Southern Oscillation Index (SOI). SOI tracks the state of the El Niño Southern Oscillation, with values > 0.7 indicating La Niña and values < -0.7 indicating El Niño episodes (Source: VODCA, http://www.bom.gov.au/climate/enso/soi/) The bottom plot shows the percentage of land pixels providing valid data for each year.</figcaption></figure>
the below-average vegetation activity in northern Mongolia and Brazil and Bolivia coincide with long-term negative trends related to land degradation and deforestation, respectively (Song et al. 2018). Above-average VOD in several regions can be linked to long-term trends in precipitation (Sahel; Dong and Sutton 2015), intensification of agricultural production (India, China), and reforestation (northeastern China; Song et al. 2018). To differentiate interannual variability from long-term change, one would need to detrend the data first (Moesinger et al. 2022). An alternative view is provided by the difference in VOD between the years 2021 and 2020 (Appendix Fig. A2.14), which reveals, for example, that although Kenya in 2021 had above-average vegetation activity, it was below that of 2020.

The VOD anomalies were computed from the VOD Climate Archive (VODCA; Moesinger et al. 2020). VODCA blends VOD observations retrieved with the Land Parameter Retrieval Model (Meesters et al. 2005; van der Schalie et al. 2017) from several space-borne radiometers, including SSM/I, TRMM, Windsat, AMSR-E, and AMSR2, into a harmonized long-term dataset. VODCA contains individual datasets for Ku-band (covering the period 1987–2021), X-band (1997–2021), and C-band (2002–21) at 0.25° spatial and daily temporal resolutions. Here, we used the VODCA Ku-band dataset, the longest available record, to compute anomalies from the long-term (1991–2020) climatology. Despite its theoretically higher sensitivity to the upper canopy, intra and interannual dynamics of Ku-band observations strongly agree with those of X-band and C-band (Moesinger et al. 2022). VOD cannot be retrieved over frozen or snow-covered areas for which they are masked in winter (Appendix Fig. A2.13).

<figure><img src="image_115.png" /><figcaption>Fig. 2.76. Time–latitude diagram of VOD anomalies (1991–2020 base period). Data are masked where no retrieval is possible or where the quality is not assured and flagged due to frozen soil, radio frequency interference, etc. (Source: VODCA.)</figcaption></figure>
# Acknowledgments

## 2.a.1 Introduction

Freya Aldred, Robert Dunn, and Kate Willett were supported by the Met Office Hadley Centre Climate Programme funded by BEIS. We thank John Kennedy, David Parker, and Katrina McNeill for their detailed comments when developing this chapter. We thank the BAMS Editor and the seven anonymous reviewers whose detailed comments improved the contents and presentation of this chapter. We also thank Atsushi Goto and Fumi Sezaki (JMA), Julien Nicolas (ECMWF), and Mike Bosilovich (NASA) for their help in providing the reanalysis data used in this chapter.

## 2.b.2 Lake Surface Temperature

Lake surface water temperatures from satellite data have been generated within the GloboLakes project funded by the UK National Environment Research Council (NE/J023345/2), with extensions funded by the EU Copernicus Climate Change Service (C3S) programme.

Part of the in situ measurements used to validate the LSWT dataset have been kindly shared by Eugene Silow (Irkutsk State University, Russia), Merja Pulkkanen (SYKE, Helsinki, Finland), Antii Raike (SYKE, Helsinki, Finland), Eric Leibensperger (Department of Physics and Astronomy, Ithaca College, Ithaca, U.S.A.), Alo Laas (Estonian University of Life Sciences, Tartu, Estonia), Michela Rogora (CNR Institute for Water Research, Italy), Martin Dokulil (Austria), Shin-ichiro Matsuzaki (National Institute for Environmental Studies, Japan), Claudia Giardino (CNR IREA, Italy), Curtis DeGasperi (King County Water and Land Resources Division, Department of Natural Resources and Parks, Seattle, Washington), Martin Schmid (Eawag, Switzerland), Don Pierson (Uppsala University, Sweden).

## 2.b.3 Night Marine Air Temperatures

R. Junod was supported by the U.S. Department of Energy (DE-SC0019296).

## 2.b.4 Land and Marine temperature extremes

Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS.

## 2.b.5 Lower Tropospheric Temperature

Work performed by Stephen Po-Chedley at Lawrence Livermore National Laboratory (LLNL) was supported by the Regional and Global Model Analysis Program of the Office of Science at the Department of Energy and performed under the auspices of the DOE under Contract DE-AC52-07NA27344.

## 2.c.1 Permafrost and ALT

The Swiss Permafrost Monitoring Network PERMOS is financially supported by MeteoSwiss in the framework of GCOS Switzerland, the Federal Office for the Environment, and the Swiss Academy of Sciences, and acknowledges the important contribution of its partner institutions and principal investigators. The French Network PermaFRANCE is financially supported by the Observatoire des Sciences de l'Univers Grenoble and the French Research Infrastructure OZCAR. The research on James Ross Island is supported by Czech Antarctic Research Programme and Czech Science Foundation project (22-28659M).
### 2.c.2 Rock Glacier Velocity

Rock glacier monitoring at Hinteres Langtalkar and Dösen rock glaciers (AT) is supported by the Hohe Tauern National Park Carinthia through its long-term permafrost monitoring program.

Laurichard (FR) survey is supported by CryobsClim "long-term Observation and Experimentation System for Environmental Research" (SOERE/All'envi-OZCAR Research Infrastructure) and the PermaFrance observatory "monitoring the mountain permafrost in the French Alps" as well as French National Research Agency in the framework of the *Investissements d'Avenir* programs: Risk@UGA (ANR-15-IDEX-02) and LabEx OSUG@2020 (ANR10 LABX56).

The Ecrins National Park helps field surveys since the early 2000s.

The Swiss Permafrost Monitoring Network PERMOS is financially supported by MeteoSwiss in the framework of GCOS Switzerland, the Federal Office for the Environment, and the Swiss Academy of Sciences. PERMOS acknowledges the important contribution of the partner institutions and principal investigators.

The time series for Central Asian rock glaciers was compiled with the ESA Permafrost_CCI project (4000123681/18/I-NB).

The time series for the Dry Andes was supported by the Centro de Estudios Avanzados en Zonas Áridas (CEAZA) and the Leading House for the Latin American Region (University of St. Gallen), grant number MOB1829.

### 2.c.4 Lake Ice Cover

We thank Alexander Mills, Huaxia Yao, Lars Rudstam, Mr. Miyasaka, Ichiro Matsuzaki, Greg Sass, Don Pierson, the Minnesota Department of Natural Resources, North Temperate Lakes Long-Term Ecological Research Network, and citizen scientist contributors from the Community Lake Ice Collaboration for providing ice phenology records for their local lakes each winter.

Funding was provided by Natural Sciences Engineering and Research Council Discovery Grant to S. Sharma.

T. Nöges was supported by the Estonian Environment Agency and Estonian Research Council project PRG709.

### 2.c.5 NH Snow Cover

This work is funded in part by NOAA's Climate Data Record (CDR) Program at the National Centers for Environmental Information.

### 2.d.1 Surface Humidity

Kate Willett was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. Adrian Simmons and David Lavers were supported by the Copernicus Climate Change Service implemented by ECMWF on behalf of the European Commission.

### 2.d.5 Land Based Precipitation Extremes

Stephen Blenkinsop is supported by the UK Natural Environment Research Council (NERC) funded FUTURE-STORMS project, grant no. NE/R01079X/1.

### 2.d.6 Cloudiness

Thank you to the following for providing data for this analysis:

- Larry Di Girolamo – University of Illinois at Urbana-Champaign, Illinois

- Guangyu Zhao – University of Illinois at Urbana-Champaign, Illinois

- Sunny Sun-Mack – Science Systems and Applications, Inc., Hampton, Virginia

- Martin Stengel – Deutscher Wetterdienst, Offenbach, Germany
**2.d.7 Runoff and River Discharge**

This work (research) was supported by the National Research Foundation of Korea (NRF) grant Funded by the Korea Government (MSIT) (2021H1D3A2A03097768) and the Japan Society for the Promotion of Science KAKENHI (21H05002) for this contribution.

**2.d.8 Groundwater and Terrestrial Water Storage**

M. Rodell and D. Wiese were supported by NASA's GRACE-FO Science Team.

They also thank the German Space Operations Center of the German Aerospace Center (DLR) for providing nearly 100% of the raw telemetry data of the twin GRACE satellites.

**2.d.9 Soil Moisture**

This work was supported by the ESA Climate Change Initiative Soil Moisture project.

**2.d.10 Drought**

Tim Osborn received funding from UK NERC (NE/S015582/1).

Ian Harris received funding from UK National Centre for Atmospheric Science (NCAS).

The research presented in the drought section was carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia.

**2.d.11 Land Evaporation**

D. G. Miralles acknowledges support from the European Research Council (ERC) under grant agreement no. 715254 (DRY-2-DRY).

M. F. McCabe is supported by the King Abdullah University of Science and Technology.

**2.e.1 Mean Sea Level Pressure and Related Modes of Variability**

David Fereday was supported by the Met Office Hadley Centre Climate Programme funded by BEIS.

**2.e.2 Surface Winds**

C. Azorin-Molina was supported by CSIC-UV-GVA and funded by RTI2018-095749-A-100, AICO/2021/023, Leonardo-FBBVA and the CSIC Interdisciplinary Thematic Platform PTI-CLIMA. R. J. H. Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS. L. Ricciardulli was supported by NASA Ocean Vector Wind Science Team grant 80HQTR19C0003.

**2.e.3 Upper Air Winds**

We thank Martin Weissmann and Anne Martin (both University of Vienna) for their support in estimating the impact of Aeolus data on global analyses in the Tropics and Michael Rennie (ECMWF) for provision of data.
## Sidebar 2.1 Lightning

The work of M. Füllekrug was sponsored by the Royal Society (UK) grant NMG/R1/180252 and the Natural Environment Research Council (UK) under grants NE/L012669/1 and NE/H024921/1.

E. Williams is supported for studies on global circuit response to climate change from the Physical and Dynamic Meteorology Program at the National Science Foundation on grant no. 6942679.

C. Price was supported in his lightning research by the Israel Science Foundation (ISF) grant 2701/17 and the Ministry of Energy grant no. 220-17-002.

S. Goodman was supported by NASA Grant 80NSSC21K0923 and NASA Contract 80GSFC20C044.

K. Virts is supported in part by the GOES-R Series Science, Demonstration, and Cal/Val Program at Marshall Space Flight Center.

D. Buechler is supported by the NASA MSFC/UAH Cooperative Agreement NNM11AA01A.

The authors wish to thank Peter Thorne at Maynooth University in Ireland and at the European Centre for Medium Range Weather Forecast (ECMWF) for suggesting and initiating this work and for recommending that lightning be made an essential climate variable.

The data used to generate Fig. SB2.1 are available from the NASA Global Hydrometeorology Resource Center DAAC, Huntsville, Alabama.

The data used to generate Fig. SB2.2 are provided by WWLLN to GHRC as part of GLM cal/val activities.

## 2.f.1 Earth Radiation Budget

This research has been supported by the NASA CERES project.

The NASA Langley Atmospheric Sciences Data Center processed the instantaneous Single Scanner Footprint (SSF) data used as input to EBAF Ed4.1 and processes the FLASHFlux TISA version 4A.

## 2.g.4 Stratospheric Ozone

Carlo Arosio, Melanie Coldewey-Egbers, Daan Hubert, Diego Loyola, Viktoria Sofieva, Alexei Rozanov, and Mark Weber are grateful to ESA's Climate Change Initiative Ozone (CCI+) project and to the EU Copernicus Climate Change Service, 312b Lot4 Ozone project for supporting the generation and extension of the GTO-ECV total ozone and SAGE-CCI-OMPS data records.

Stacey M. Frith is supported by the NASA Long Term Measurement of Ozone program WBS 479717.

Lucien Froidevaux's contribution, with the assistance of Ryan Fuller, was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA.

Daan Hubert acknowledges the partial support by the EU/ERC Horizon 2020 project GAIA-CLIM.

## 2.h.1 Albedo

The authors thank Mirko Marioni for his technical support and NASA's Land Processes Distributed Active Archive Center (LP DAAC) for providing access to the remote sensing data.

## 2.h.2 Terrestrial Vegetation Dynamics

The author thanks Mirko Marioni for his technical support and the providers of the remote sensing dataset needed to perform this research, i.e., the SeaWiFS Project (Code 970.2) and the Goddard Earth Sciences Data and Information Services Center/Distributed Active Archive Center (Code 902) at the Goddard Space Flight Center, Greenbelt, Maryland.

MERIS products were processed at the Grid On Demand facility of European Space Agency (ESA/ESRIN) using JRC software code.

The standard Two-stream Inversion Package (JRC-TIP) datasets were produced on JRC Big Data Analytics Platform (BDAP).
## 2.h.4 Phenology

Debbie Hemming acknowledges support from the Met Office Hadley Centre Climate Programme funded by BEIS and the Met Office Climate Service on Food, Farming and Natural Environment funded by Defra, and thanks all co-authors for their interesting and helpful contributions and Robert Dunn for his expertise finalizing the figures for this section.

Taejin Park acknowledges support from the NASA Earth Science Directorate (grants NNX16AO34H and 80NSSC18K0173-CMS).

Andrew Richardson acknowledges support from the National Science Foundation through the Macrosystems Biology (award 1702697) and LTER (award 1832210) programs.

John O'Keefe also acknowledges support from the National Science Foundation through the LTER (award 1832210) program.

Nature's Calendar (Woodland Trust) in the UK thanks all its volunteer recorders and support from players of People's Postcode Lottery.

De Natuurkalender (Nature's Calendar) program in the Netherlands thanks all the volunteers and school children in the GLOBE program for their many observations.

Annette Menzel and Ye Yuan acknowledge support from the Bavarian State Ministry of Science and the Arts in the context of the Bavarian Climate Research Network (BayKlif) (BAYSICS project - Bavarian Citizen Science Portal for Climate Research and Science Communication).

Orlane Anneville acknowledges support from INRAE.

Stephen Thackeray thanks Werner Eckert, Heidrun Feuchtmayr, Shin-Ichiro Matsuzaki, Linda May, Ryuichiro Shinohara, Jan-Erik Thrane, Piet Verburg, Tamar Zohary, and all field and lab workers associated with the provision of the lake chlorophyll-a data.

We acknowledge funding from Vassdragsforbundet for Mjøsa med tilløpselver (https://www.vassdragsforbundet.no/om-oss/) and Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

Data for Lakes Geneva and Bourget were contributed by The Observatory on LAkes (OLA), © SOERE OLA-IS, AnaEE-France, INRAE of Thonon-les-Bains, CIPEL, CISALB.

## 2.h.5 Vegetation Optical Depth

W. Dorigo, R.M. Zotta, and L. Moesinger, acknowledge the TU Wien Wissenschaftspreis 2015, a personal grant awarded to W. Dorigo.

We also acknowledge support from the ESA Climate Change Initiative Soil Moisture project.
Appendix 1: Chapter 2 – Acronyms

<table><tr><td>AAO</td><td>Antarctic Oscillation</td></tr><tr><td>AATSR</td><td>Advanced Along Track Scanning Radiometer</td></tr><tr><td>ALEXI</td><td>Atmosphere-Land Exchange Inverse</td></tr><tr><td>ALT</td><td>active layer thickness</td></tr><tr><td>AMSRE-E</td><td>Advanced Microwave Scanning Radiometer</td></tr><tr><td>AO</td><td>Arctic Oscillation</td></tr><tr><td>AOD</td><td>aerosol optical depth</td></tr><tr><td>ATSR</td><td>Along Track Scanning Radiometer</td></tr><tr><td>AVHRR</td><td>Advanced Very High Resolution Radiometer</td></tr><tr><td>BDC</td><td>Brewer-Dobson circulation</td></tr><tr><td>BRW</td><td>Barrow Atmospheric Baseline Observatory</td></tr><tr><td>C3S</td><td>Copernicus Climate Change Service</td></tr><tr><td>CALIOP</td><td>Cloud-Aerosol Lidar with Orthogonal Polarization</td></tr><tr><td>CAMS</td><td>Copernicus Atmosphere Monitoring Service</td></tr><tr><td>CAMSRA</td><td>Copernicus Atmosphere Monitoring Service Reanalysis</td></tr><tr><td>CCMI</td><td>Chemistry Climate Model Initiative</td></tr><tr><td>CEI</td><td>Climate Extremes Index</td></tr><tr><td>CERES</td><td>Clouds and the Earth's Radiant Energy System</td></tr><tr><td>CFC</td><td>chlorofluorocarbon</td></tr><tr><td>CH<sub>4</sub></td><td>methane</td></tr><tr><td>CI</td><td>chlorine</td></tr><tr><td>CO</td><td>carbon monoxide</td></tr><tr><td>CO<sub>2</sub></td><td>carbon dioxide</td></tr><tr><td>CPT</td><td>cold-point tropopause</td></tr><tr><td>CRU TS</td><td>Climatic Research Unit gridded Time Series</td></tr><tr><td>DDM</td><td>drainage direction map</td></tr><tr><td>DU</td><td>Dobson unit</td></tr><tr><td>EA</td><td>Eurasia</td></tr><tr><td>ECV</td><td>essential climate variable</td></tr><tr><td>EESC</td><td>equivalent effective stratospheric chlorine</td></tr><tr><td>EESC-A</td><td>equivalent effective stratospheric chlorine-Antarctic</td></tr><tr><td>EESC-M</td><td>equivalent effective stratospheric chlorine-Midlatitude</td></tr><tr><td>ENSO</td><td>El Niño-Southern Oscillation</td></tr><tr><td>EOFs</td><td>empirical orthogonal functions</td></tr><tr><td>EOS</td><td>end of season</td></tr><tr><td>ERB</td><td>Earth's radiation budget</td></tr><tr><td>ESA CCI SM</td><td>European Space Agency's Climate Change Initiative for Soil Moisture</td></tr><tr><td>ET</td><td>evapotranspiration</td></tr><tr><td>ETCCDI</td><td>WMO Expert Team in Climate Change Detection and Indices</td></tr><tr><td>FAPAR</td><td>Fraction of Absorbed Photosynthetically Active Radiation</td></tr><tr><td>FF</td><td>fossil fuel</td></tr><tr><td>GCOS</td><td>Global Climate Observing System</td></tr><tr><td>GFAS</td><td>Global Fire Assimilation System</td></tr><tr><td>GFED</td><td>Global Fire Emissions Database</td></tr><tr><td>GGGRN</td><td>NOAA's Global Greenhouse Gas Reference Network</td></tr><tr><td>GHCN</td><td>Global Historical Climatology Network</td></tr></table>
<table>
   <tr>
    <td>GHCNDEX</td>
    <td>Global Historical Climatology Network-Daily database</td>
   </tr>
   <tr>
    <td>GIN-P</td>
    <td>Global Terrestrial Network for Permafrost</td>
   </tr>
   <tr>
    <td>GLEAM</td>
    <td>Global Land Evaporation Amsterdam Model</td>
   </tr>
   <tr>
    <td>GMST</td>
    <td>global mean surface temperature</td>
   </tr>
   <tr>
    <td>GNSS</td>
    <td>Global Navigation Satellite System</td>
   </tr>
   <tr>
    <td>GPCC</td>
    <td>Global Precipitation Climatology Centre</td>
   </tr>
   <tr>
    <td>GPCP</td>
    <td>Global Precipitation Climatology Project</td>
   </tr>
   <tr>
    <td>GPS-RO</td>
    <td>Global Positioning System-Radio Occultation</td>
   </tr>
   <tr>
    <td>GRACE</td>
    <td>Gravity Recovery and Climate Experiment</td>
   </tr>
   <tr>
    <td>GRACE-FO</td>
    <td>Gravity Recovery and Climate Experiment - Follow On</td>
   </tr>
   <tr>
    <td>GTN-P</td>
    <td>Global Terrestrial Network for Permafrost</td>
   </tr>
   <tr>
    <td>HFCF</td>
    <td>hydrochlorofluorocarbon</td>
   </tr>
   <tr>
    <td>HFC</td>
    <td>hydrofluorocarbon</td>
   </tr>
   <tr>
    <td>HIRS</td>
    <td>High Resolution Infra Red Radiation Sounder</td>
   </tr>
   <tr>
    <td>HWF</td>
    <td>heat wave frequency</td>
   </tr>
   <tr>
    <td>HWM</td>
    <td>heat wave magnitude</td>
   </tr>
   <tr>
    <td>IOD</td>
    <td>Indian Ocean dipole</td>
   </tr>
   <tr>
    <td>IPA</td>
    <td>International Permafrost Association</td>
   </tr>
   <tr>
    <td>ITCZ</td>
    <td>Intertropical Convergence Zone</td>
   </tr>
   <tr>
    <td>LLGHG</td>
    <td>long-lived greenhouse gases</td>
   </tr>
   <tr>
    <td>LSA-SAF</td>
    <td>Land Surface Analysis Satellite Applications Facility</td>
   </tr>
   <tr>
    <td>LSWT</td>
    <td>lake surface water temperature</td>
   </tr>
   <tr>
    <td>LTT</td>
    <td>lower tropospheric temperature</td>
   </tr>
   <tr>
    <td>LWL</td>
    <td>lake water level</td>
   </tr>
   <tr>
    <td>MACC</td>
    <td>Monitoring Atmospheric Composition and Climate</td>
   </tr>
   <tr>
    <td>MAT</td>
    <td>marine air temperature</td>
   </tr>
   <tr>
    <td>MBL</td>
    <td>marine boundary layer</td>
   </tr>
   <tr>
    <td>MHW</td>
    <td>marine heatwave</td>
   </tr>
   <tr>
    <td>MLO</td>
    <td>Mauna Loa, Hawaii</td>
   </tr>
   <tr>
    <td>MLS</td>
    <td>Microwave Limb Sounder</td>
   </tr>
   <tr>
    <td>MODIS</td>
    <td>Moderate Resolution Imaging Spectroradiometer</td>
   </tr>
   <tr>
    <td>MOPITT</td>
    <td>Measurement of Pollution in the Troposphere</td>
   </tr>
   <tr>
    <td>MSU/AMSU</td>
    <td>Microwave Sounding Unit/Advanced Microwave Sounding Unit</td>
   </tr>
   <tr>
    <td>N<sub>2</sub>O</td>
    <td>nitrous oxide</td>
   </tr>
   <tr>
    <td>NA</td>
    <td>North America</td>
   </tr>
   <tr>
    <td>NAO</td>
    <td>North Atlantic Oscillation</td>
   </tr>
   <tr>
    <td>NDVI</td>
    <td>normalized difference vegetation index</td>
   </tr>
   <tr>
    <td>NH</td>
    <td>Northern Hemisphere</td>
   </tr>
   <tr>
    <td>NMAT</td>
    <td>night marine air temperature</td>
   </tr>
   <tr>
    <td>O<sub>3</sub></td>
    <td>ozone</td>
   </tr>
   <tr>
    <td>ODGI</td>
    <td>Ozone Depleting Gas Index</td>
   </tr>
   <tr>
    <td>ODGI-A</td>
    <td>Ozone Depleting Gas Index-Antarctic</td>
   </tr>
   <tr>
    <td>ODGI-M</td>
    <td>Ozone Depleting Gas Index-Midlatitude</td>
   </tr>
   <tr>
    <td>ODS</td>
    <td>ozone-depleting substances</td>
   </tr>
   <tr>
    <td>OH</td>
    <td>hydroxyl radical</td>
   </tr>
   <tr>
    <td>OLR</td>
    <td>outgoing longwave radiation</td>
   </tr>
   <tr>
    <td>OMI</td>
    <td>Ozone Monitoring Instrument</td>
   </tr>
   <tr>
    <td>PDO</td>
    <td>Pacific Decadal Oscillation</td>
   </tr>
   <tr>
    <td>PSC</td>
    <td>polar stratospheric cloud</td>
   </tr>
   <tr>
    <td>QBO</td>
    <td>quasi-biennial oscillation</td>
   </tr>
  </table>
<table>
   <tr>
    <td>QTP</td>
    <td>Qinghai-Tibetan Plateau</td>
   </tr>
   <tr>
    <td>RFaci</td>
    <td>radiative forcing resulting from aerosol-cloud interactions</td>
   </tr>
   <tr>
    <td>RFari</td>
    <td>radiative forcing resulting from aerosol-radiation</td>
   </tr>
   <tr>
    <td>RGK</td>
    <td>rock glacier kinematics</td>
   </tr>
   <tr>
    <td>RH</td>
    <td>relative humidity</td>
   </tr>
   <tr>
    <td>RO</td>
    <td>radio occultation</td>
   </tr>
   <tr>
    <td>RSW</td>
    <td>reflected shortwave</td>
   </tr>
   <tr>
    <td>SAM</td>
    <td>Southern Annular Mode</td>
   </tr>
   <tr>
    <td>SAR</td>
    <td>Synthetic Aperture Radar</td>
   </tr>
   <tr>
    <td>SCE</td>
    <td>snow cover extent</td>
   </tr>
   <tr>
    <td>scPDSI</td>
    <td>self-calibrating Palmer Drought Severity Index</td>
   </tr>
   <tr>
    <td>SH</td>
    <td>Southern Hemisphere</td>
   </tr>
   <tr>
    <td>SLSTR</td>
    <td>Sea and Land Surface Temperature Radiometer</td>
   </tr>
   <tr>
    <td>SOI</td>
    <td>Southern Oscillation Index</td>
   </tr>
   <tr>
    <td>SORCE</td>
    <td>Solar Radiation and Climate Experiment</td>
   </tr>
   <tr>
    <td>SOS</td>
    <td>start of season</td>
   </tr>
   <tr>
    <td>SPO</td>
    <td>South Pole Observatory</td>
   </tr>
   <tr>
    <td>SSM/I</td>
    <td>Special Sensor Microwave/Imager</td>
   </tr>
   <tr>
    <td>SSMIS</td>
    <td>Special Sensor Microwave Imager/Sounder</td>
   </tr>
   <tr>
    <td>SST</td>
    <td>sea surface temperature</td>
   </tr>
   <tr>
    <td>SSU</td>
    <td>Stratospheric Sounding Unit</td>
   </tr>
   <tr>
    <td>SW</td>
    <td>shortwave</td>
   </tr>
   <tr>
    <td>TCWV</td>
    <td>total column water vapor</td>
   </tr>
   <tr>
    <td>TIR</td>
    <td>thermal infrared</td>
   </tr>
   <tr>
    <td>TLS</td>
    <td>lower stratospheric temperature</td>
   </tr>
   <tr>
    <td>TOA</td>
    <td>top of the atmosphere</td>
   </tr>
   <tr>
    <td>TSI</td>
    <td>total solar irradiance</td>
   </tr>
   <tr>
    <td>TSIS-1</td>
    <td>Total Solar and Spectral Irradiance Sensor-1</td>
   </tr>
   <tr>
    <td>TTL</td>
    <td>tropical tropopause layer</td>
   </tr>
   <tr>
    <td>TTT</td>
    <td>tropical tropospheric temperature</td>
   </tr>
   <tr>
    <td>TWS</td>
    <td>terrestrial water storage</td>
   </tr>
   <tr>
    <td>UTH</td>
    <td>upper tropospheric (relative) humidity</td>
   </tr>
   <tr>
    <td>UV</td>
    <td>ultraviolet</td>
   </tr>
   <tr>
    <td>VOC</td>
    <td>volatile organic compound</td>
   </tr>
   <tr>
    <td>VOD</td>
    <td>vegetation optical depth</td>
   </tr>
   <tr>
    <td>VODCA</td>
    <td>vegetation optical depth Climate Archive</td>
   </tr>
   <tr>
    <td>WGMS</td>
    <td>World Glacier Monitoring Service</td>
   </tr>
   <tr>
    <td>WMO</td>
    <td>World Meteorological Organization</td>
   </tr>
   <tr>
    <td>WV</td>
    <td>water vapor</td>
   </tr>
  </table>
# Appendix 2: Supplemental Material

## 2.b.1 Surface Temperature

<figure><img src="image_116.png" /><figcaption>Fig. A2.1. JRA-55 2-m surface temperature anomalies (°C; 1991–2020 base period).</figcaption></figure>

<figure><img src="image_117.png" /><figcaption>Fig. A2.2. ERA5 2-m surface temperature anomalies (°C; 1991–2020 base period).</figcaption></figure>
<figure><img src="image_118.png" /><figcaption>Fig. A2.3. NASA surface temperature anomalies (°C; 1991–2020 base period).</figcaption></figure>

<figure><img src="image_119.png" /><figcaption>Fig. A2.4. HadCRUT5 surface temperature anomalies (°C; 1991–2020 base period).</figcaption></figure>
2.b.2 Lake Surface Temperature

<figure><img src="image_120.png" /></figure>

Fig. A2.5. Spatial distribution of the 2021 LSWT anomalies (°C) for Lake Superior, Huron, and Michigan in North America (1996–2016 base period).
2.b.4 Land Surface Temperature Extremes Indices

<figure><img src="image_121.png" /><figcaption>Fig. A2.6. Anomalies of (a) TX90p and (b) TN10p from ERA5 (1981–2010 base period).</figcaption></figure>
<figure><img src="image_122.png" /></figure>

<figure><img src="image_123.png" /><figcaption>Fig. A2.7. Rank plots highlighting locations where the highest maximum temperature (TXx) and lowest minimum temperature (TNn) in 2021 were among the highest or lowest three on record.</figcaption></figure>
<table><caption>Appendix Table 2.1. Selected extreme temperatures in 2021 relevant to the events discussed in the text. Additional records are presented in the WMO State of the Global Climate in 2021 (WMO 2022).</caption><thead><tr><td>Location</td><td>Country</td><td>Date</td><td>Value</td><td>Notes</td></tr></thead><tbody><tr><td>Clot del Tuc de la Llança</td><td>Spain</td><td>6 Jan</td><td>-34.1</td><td>National minimum record, coldest since station records began in 1956</td></tr><tr><td>Houston</td><td>Texas, USA</td><td>16 Feb</td><td>-11</td><td>Coldest since 1989</td></tr><tr><td>Dallas</td><td>Texas, USA</td><td>16 Feb</td><td>-18.9</td><td>Coldest since 1930</td></tr><tr><td>Strasbourg</td><td>France</td><td>31 Mar</td><td>26.3</td><td>March maximum record</td></tr><tr><td>Rheinau-Memprechtshofen</td><td>Germany</td><td>31 Mar</td><td>27.2</td><td>National March maximum record</td></tr><tr><td>Orléans</td><td>France</td><td>6 Apr</td><td>-5.4</td><td>April minimum record</td></tr><tr><td>Chalon-sur-Saône</td><td>France</td><td>7 Apr</td><td>-5.4</td><td>April minimum record</td></tr><tr><td>Orange</td><td>France</td><td>8 Apr</td><td>-3.2</td><td>April minimum record</td></tr><tr><td>Nova vas na Blokah</td><td>Slovenia</td><td>7 Apr</td><td>-20.6</td><td>April minimum record</td></tr><tr><td>Moscow</td><td>Russia</td><td>23 Jun</td><td>34.8</td><td>June maximum record</td></tr><tr><td>Yerevan</td><td>Armenia</td><td>24 Jun</td><td>41.1</td><td>June maximum record</td></tr><tr><td>Baku</td><td>Azerbaijan</td><td>26 Jun</td><td>40.5</td><td>June maximum record</td></tr><tr><td>Lytton</td><td>British Columbia, Canada</td><td>29 Jun</td><td>49.6</td><td>National record (by 4.6°C)</td></tr><tr><td>Utsjoki-Kevo</td><td>Lapland, Finland</td><td>5 Jul</td><td>33.6</td><td>Highest maximum since 1914</td></tr><tr><td>Death Valley</td><td>California, USA</td><td>9 Jul</td><td>54.4</td><td>Equal to hottest maximum temperature since at least 1930s.</td></tr><tr><td>Esashi</td><td>Iwate Prefecture, Japan</td><td>19 Jul</td><td>37.3</td><td>Equal to station record</td></tr><tr><td>Cizre</td><td>Turkey</td><td>20 Jul</td><td>49.1</td><td>National maximum record</td></tr><tr><td>Tbilisi</td><td>Georgia</td><td>20 Jul</td><td>40.6</td><td>National maximum record</td></tr><tr><td>Castlederg</td><td>Northern Ireland</td><td>21 Jul</td><td>31.3</td><td>Maximum record</td></tr><tr><td>Rafha</td><td>Saudi Arabia</td><td>24 Jul</td><td>50.6</td><td>—</td></tr><tr><td>Dammam</td><td>Saudi Arabia</td><td>31 Jul</td><td>50.4</td><td>Equal maximum record (set 2020)</td></tr><tr><td>Kairouan</td><td>Tunisia</td><td>2 Aug</td><td>50.3</td><td>National maximum record</td></tr><tr><td>Syracuse</td><td>Italy</td><td>11 Aug</td><td>48.8</td><td>Provisional European maximum record</td></tr><tr><td>Montoro</td><td>Cordoba, Spain</td><td>14 Aug</td><td>47.4</td><td>National maximum record</td></tr><tr><td>Makrakomi Ftiotida</td><td>Greece</td><td>2 Aug</td><td>46.3</td><td>National maximum record</td></tr></tbody></table>
2.d.1 Surface Humidity

<figure><img src="image_124.png" /></figure>

Fig. A2.8. ERA5 surface specific humidity annual average anomalies (g kg⁻¹) for 2021 (1991–2020 base period).

<figure><img src="image_125.png" /></figure>

Fig. A2.9. MERRA2 surface specific humidity annual average anomalies (g kg⁻¹) for 2021 (1991–2020 base period).
<figure><img src="image_126.png" /></figure>

Fig. A2.10. ERA5 surface relative humidity annual average anomalies (%rh) for 2021 (1991–2020 base period).

<figure><img src="image_127.png" /></figure>

Fig. A2.11. MERRA2 surface relative humidity annual average anomalies (%rh) for 2021 (1991–2020 base period).
## 2.d.5 Land based Precipitation Extremes

<table><caption>Appendix Table 2.2. Selected extreme precipitation events in 2021. The value column shows the recorded amount and accumulation period. Events are listed in the order referred to in the text. Events noted as records are for the location listed unless otherwise stated.</caption><thead><tr><td>Location</td><td>Country</td><td>Date</td><td>Value</td><td>Notes</td></tr></thead><tbody><tr><td>Paso Robles Airport, California</td><td>United States</td><td>25 Oct</td><td>39 mm (24-h)</td><td>Record 24-h total for October</td></tr><tr><td>Abbotsford</td><td>Canada</td><td>14 Nov</td><td>100.4 mm (24-h)</td><td>Record 24-h total</td></tr><tr><td>New York City</td><td>United States</td><td>1 Sep</td><td>80 mm (1-h)</td><td>Hurricane Ida set record 1-h total, breaking record of 49 mm set by Henri earlier in the month</td></tr><tr><td>Newark, New Jersey</td><td>United States</td><td>1 Sep</td><td>213.6 mm (24-h)</td><td>Record 24-h total</td></tr><tr><td>Wipperfurth-Gardenau</td><td>Germany</td><td>14–15 Jul</td><td>162.4 mm (24-h)</td><td>Record 24-h total</td></tr><tr><td>Gävle</td><td>Sweden</td><td>17–18 Aug</td><td>161.6 mm (24-h)</td><td>Record 24-h total</td></tr><tr><td>Kindee Bridge, New South Wales</td><td>Australia</td><td>20 Mar</td><td>283.4 mm (24-h)</td><td>Record 24-h total for March</td></tr><tr><td>Samuel Hill Aero, Queensland</td><td>Australia</td><td>10 Nov</td><td>340.8 mm (24-h)</td><td>Record 24-h total for November (> 3 times previous record)</td></tr><tr><td>Newbicup, Western Australia</td><td>Australia</td><td>12 Apr</td><td>74.0 mm (24-h)</td><td>Record 24-h total for April</td></tr><tr><td>Zhengzhou, Henan Province</td><td>China</td><td>20 Jul</td><td>201.9 mm (1-h)</td><td>Typhoon In-fa. 1-h total reported as national record for mainland China</td></tr><tr><td>Taiyuan, Shanxi Province</td><td>China</td><td>2–7 Oct</td><td>185.5 mm (12-h)</td><td>Regional record 12-h total for October</td></tr><tr><td>Beira</td><td>Mozambique</td><td>23 Jan</td><td>250 mm (24-h)</td><td>Close to regional monthly average rainfall</td></tr></tbody></table>
2.d.10 Soil Moisture

<figure><img src="image_128.png" /></figure>

Fig. A2.12. ESA CCI soil moisture monthly average soil moisture anomalies (m³ m⁻³) for 2021 (1991–2020 base period). Data are masked where no retrieval is possible or where the quality is not assured and flagged due to dense vegetation, frozen soil, radio frequency interference, etc.
2.h.5 Vegetation Optical Depth

<figure><img src="image_129.png" /><figcaption>Fig. A2.13. (a–l) VODCA monthly Ku-band VOD anomalies (m³ m⁻³) for 2021 (1991–2020 base period).</figcaption></figure>
<figure><img src="image_130.png" /></figure>

Fig. A2.14. Difference in average Ku-band VOD between the years 2021 and 2020. Brown/green colors indicate areas where VOD in 2021 was lower/higher than in 2020. (Source: VODCA.)
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