2022 was the third La Niña year in a row (see Sidebar 3.1 and section 4b for details), the first time such an event has formed in the twenty-first century and only the third time in the last 50 years. The last “triple La Niña” occurred after the intense 1997/98 El Niño. Figures 2.49a,b show how similar the 200-hPa velocity potential patterns are for these events, indicating persistent and widespread circulation anomalies in the tropics during the three consecutive peak La Niña phases. Negative velocity potential anomalies over the Indo-Pacific Warm Pool are consistent with enhanced convective activity in this region, and the positive anomalies to the west and east are consistent with the expected changes to the Walker Circulation. The latter describes tropospheric circulation in the zonal-vertical plane in the equatorial regions in association with zonally varying sea-surface temperatures and convective activity (Bjerknes 1969). Figures 2.49c,d show the composite anomalies of pressure vertical velocity and zonal/vertical velocities averaged over 10°S–10°N, which complements the picture of the Walker circulation. The similarity is striking in this view, particularly for the main centers of activity over Indonesia and near the date line. Even the weaker maxima and minima over eastern Africa and northeastern Brazil look qualitatively similar.

In terms of equatorial stratospheric winds, the quasi-biennial oscillation (QBO) of 2022 was quite regular in terms of wind speeds and evolution of the easterly and westerly zones, comparable to 1982, 1992, or 2015. Despite the 2021 westerly lingering around 70 hPa until mid-April,

<figure><img src="image_91.png" /><figcaption>Fig. 2.49. Composites of 200-hPa velocity potential (colors; × 10⁶ m² s⁻¹) and divergent wind anomalies (arrows; 1991–2020 base period) for three consecutive OND seasons: (a) 1998–2000 and (b) 2020–22. Composite of 10°S–10°N averaged pressure vertical velocity anomalies (colors; × 10⁻² Pa s⁻¹) and u/w anomalies (arrows; zonal wind anomaly u: m s⁻¹) for three consecutive OND seasons: (c) 1998–2000 and (d) 2020–22. (Source: ERA5.)</figcaption></figure>
thus stalling the easterly zone for this period of time, the already-formed westerly at 10 hPa descended with a normal speed of 1.13 km yr⁻¹. Its amplitude reached a maximum at the 10-hPa level in February, with a speed of 19.2 m s⁻¹, which is also within its usual range. A plot of zonal wind as a function of height and season over Singapore, which serves as a proxy for the global state of the QBO due to its zonal symmetry, is shown in Fig. 2.50a. The stratospheric state at the end of the year with one westerly shear zone present from 10 hPa to 80 hPa resembles that of 2015 (see Fig. 2.50b for a comparison of recent years), when the first major disruption of the QBO took place (Osprey et al. 2016).

<figure><img src="image_92.png" /><figcaption>Fig. 2.50. (a) Stratospheric monthly mean zonal-wind values (m s⁻¹) based on daily measurements in Singapore. Easterlies (negative values) are shown in brown, westerlies (positive) in purple. (b) Monthly profiles of Singapore zonal wind averaged for Oct, Nov, and Dec in 2015 and 2022. Westerly winds are prominent above 70 hPa for both years, though the weakening of the westerlies at 40 hPa in 2015, which lead up to the first observed quasi-biennial oscillation disruption, is already visible in Dec.</figcaption></figure>

## 4. LIGHTNING

—M. Füllekrug, E. Williams, C. Price, S. Goodman, R. Holzworth, K. Virts, D. Buechler, T. Lang, and Y. Liu

The Geostationary Lightning Mapper (GLM) on Geostationary Operational Environmental Satellites 16 and 17 reported the first lightning anomaly map covering the Western Hemisphere Americas and adjacent oceans, while the Lightning Imaging Sensor (LIS) on board the International Space Station (ISS LIS) has recorded lightning from February 2017 to present and thereby extends over 25 years of global lightning observations with previous satellites in low-Earth orbit (Blakeslee et al. 2020).

Figure 2.51a displays the average lightning density over the Americas and the Pacific Ocean calculated over 2019–22 from the GLMs. Coastlines and some topographic features, such as the Cordilleras in Central America and the Andes in South America, can act as meteorological divides for lightning densities to change on relatively small spatial scales. Over the oceans, lightning flashes indicate the tracks of thunderstorms that follow the trade winds, the westerlies at midlatitudes, and the easterlies at low latitudes. The Intertropical Convergence Zone over the Pacific Ocean is located slightly north of the equator.
The spatial distribution of anomalies in lightning density for 2022 is shown in Fig. 2.51b. Given the relatively short period of the record, it is currently expected that these anomalies exhibit an annual variability caused primarily by the long-term varying state of the climate (Williams 2020); in the case of the period 2020–22, the ongoing La Niña conditions following the El Niño event in 2018/19.

During El Niño, the three-dimensional structure of deep convection is taller and stronger (Hamid et al. 2001) than it is during La Niña. In South America, deeper storms during El Niño relate to increased convective available potential energy, a strengthening of the South American low-level jet, and a stronger upper-level jet stream (Bruick et al. 2019). Velasco and Fritsch (1987) report that large mesoscale convective systems (MCS), the most extreme lightning-producing weather systems on Earth (Zipser et al. 2006), are concentrated downwind of major mountain ranges in both North and South America. In North America, latitudinal shifts of storm tracks occur in association with El Niño and jet stream steering winds (Goodman et al. 2000). El Niño may contribute to extreme lightning climatology anomalies relative to the mean, as ~25% of the annual lightning at a single location has been shown to occur during the passage of a single MCS at midlatitudes during the strong El Niño years of 1982/83 (Goodman and MacGorman 1986).

<figure><img src="image_93.png" /><figcaption>Fig. 2.51. (a) Lightning flash rate density for the period 2019–22 from NOAA's Geostationary Lightning Mapper (GLM) on GOES 16 & 17. The black outline indicates the nominal GLM field of view for each satellite. (b) Western Hemisphere anomalies in lightning density for 2022 that are calculated relative to the 2019–21 mean. Units are fl km⁻² yr⁻¹.</figcaption></figure>
In Füllekrug et al. (2022), a distinct downturn in the reference year 2020 was apparent in the longest available uniform record of global lightning (LIS on TRMM), in a matched detector on the ISS, and in the GLM record for lightning over the Western Hemisphere (Virts et al. 2023). In contrast, the LIS record for the period 1998–2014 (Williams et al. 2019) was statistically flat. Two sustained events beginning in early 2020 could have caused the 2020 downturn: the transition from El Niño to La Niña and the aerosol reduction during the first COVID lockdown in many countries. Evidence for tropical lightning reductions in La Niña relative to El Niño is abundant (Williams 1992; Hamid et al. 2001; Yoshida et al. 2007; Satori et al. 2009; Goodman et al. 2000; Williams et al. 2021). Lightning reductions with reduced aerosol (Altaratz et al. 2017; Thornton et al. 2017; Fan et al. 2018; Wang et al. 2018; Liu et al. 2020) have a microphysical basis (Rosenfeld and Woodley 2003; Rosenfeld et al. 2008), and a global reduction in aerosol optical depth in satellite observations in 2020 (Sanap 2021) arises from reduced fossil fuel consumption and associated aerosol emissions during the COVID lockdowns (e.g., Rémy et al. 2021).

The 2019–21 reference period might therefore be slightly biased during the main COVID lockdown that occurred between March and May 2020 (Fig. 2.52). During this three-month period, the lightning densities over the Gulf of Mexico exhibit larger densities than during the 2019–21 reference period, while the central regions of North America and South America exhibit

lower lightning densities, as reported by GLM16 (Fig. 2.52a). This is consistent with lightning densities measured by ISS LIS, which offers a global view of lightning density changes (Fig. 2.52b). As a result, the two main confounding variables affecting lightning density (ENSO and aerosol loading) need to be disentangled and require some in-depth discussion in the future.

La Niña kept 2022 globally cooler than years with El Niño or ENSO-neutral conditions (section 2b1). Both the ISS LIS and the GLM records are consistent in showing that global lightning totals during the present La Niña phase have not returned to the levels of the previous El Niño in 2019. Despite the identification of consistent physical connections and a La Niña that is behaving similarly to earlier La Niñas (Williams et al. 2020), the global reductions in lightning in 2020 and thereafter are not yet fully understood.

<figure><img src="image_94.png" /><figcaption>**Fig. 2.52.** Seasonal lightning density anomalies (fl km⁻² yr⁻¹) for Mar–May 2020, potentially associated with the reduction of aerosol during the first COVID lockdown in many countries. (a) Spatial distribution of anomalies in seasonal lightning density from GLM16. (b) Global distribution of anomalies (relative to MAM 2017–19) in seasonal lightning density observed from the Lightning Imaging Sensor on board the International Space Station.</figcaption></figure>
f. Earth radiation budget

1. EARTH RADIATION BUDGET AT TOP-OF-ATMOSPHERE

—T. Wong, P. W. Stackhouse Jr., P. Sawaengphokhai, J. Garg, and N. G. Loeb

The top-of-atmosphere (TOA) Earth radia-
tion budget (ERB) is defined as the difference
between incoming total solar irradiance (TSI)
and outgoing radiation from Earth given by
the sum of reflected shortwave (RSW) and
outgoing longwave radiation (OLR). Regional
imbalances in TOA ERB drive atmospheric and
oceanic circulations. Thus, monitoring the
variability in TOA ERB is essential for under-
standing the changes in the climate system.

An analysis of Clouds and the Earth's
Radiant Energy System (CERES) TOA ERB
measurements (Table 2.7) shows that the
global annual mean OLR and TSI increased by
0.30 W m⁻² and 0.15 W m⁻², respectively, in
2022 relative to 2021 (rounded to the nearest
0.05 W m⁻²). In contrast, the global
annual-mean RSW and net radiation
decreased by 0.10 W m⁻² and 0.05 W m⁻²,
respectively, over the same period. Fig. 2.53
shows regional annual-mean maps of the dif-
ference between 2022 and 2021 in TOA OLR
and TOA RSW. The largest increases in OLR
and decreases in RSW are observed over a
large extent of the equatorial Pacific Ocean
between New Guinea and 120°W longitude.
Reductions in OLR and increases in RSW are
observed over Southeast Asia, the north-
eastern Indian Ocean, the Philippines,
Indonesia, Australia, and the tropical south-
western Pacific Ocean. These regional changes
are associated with La Niña conditions that

<figure><img src="image_95.png" /><figcaption>Fig. 2.53. Annual average top-of-atmosphere (TOA) flux dif-
ferences (W m⁻²) between 2022 and 2021 for (a) outgoing
longwave radiation (OLR) and (b) reflected shortwave
radiation (RSW). The annual-mean maps for 2022 were
derived after adjusting Dec 2022 FLASHFlux version 4A
data using the difference between CERES EBAF Ed4.2 and
CERES FLASHFlux version 4A data in 2021.</figcaption></figure>

<table><caption>Table 2.7. Global annual mean top-of-atmosphere (TOA) radiative flux changes between 2021 and 2022, the 2022 global annual mean radiative flux anomalies relative to their corresponding 2001–21 mean climatological values, and the 2-σ interannual variabilities of the 2001–21 global annual mean fluxes (all units in W m⁻²) for the outgoing longwave radiation (OLR), total solar irradiance (TSI), reflected shortwave (RSW), absorbed solar radiation (ASR, determined from TSI-RSW), and total net fluxes. All flux values have been rounded to the nearest 0.05 W m⁻² and only balance to that level of significance.</caption>
   <thead>
    <tr>
     <td>
      Global
     </td>
     <td>
      One Year Change (2022 minus 2021) (W m
      <sup>
       -2
      </sup>
      )
     </td>
     <td>
      2022 Anomaly (Relative to 2001–21) (W m
      <sup>
       -2
      </sup>
      )
     </td>
     <td>
      Climatological Mean (2001–21) (W m
      <sup>
       -2
      </sup>
      )
     </td>
     <td>
      Interannual Variability (2001–21) (W m
      <sup>
       -2
      </sup>
      )
     </td>
    </tr>
   </thead>
   <tbody>
    <tr>
     <td>
      OLR
     </td>
     <td>
      +0.30
     </td>
     <td>
      +0.30
     </td>
     <td>
      240.35
     </td>
     <td>
      ±0.60
     </td>
    </tr>
    <tr>
     <td>
      TSI
     </td>
     <td>
      +0.15
     </td>
     <td>
      +0.20
     </td>
     <td>
      340.20
     </td>
     <td>
      ±0.15
     </td>
    </tr>
    <tr>
     <td>
      RSW
     </td>
     <td>
      -0.10
     </td>
     <td>
      -0.75
     </td>
     <td>
      99.00
     </td>
     <td>
      ±1.00
     </td>
    </tr>
    <tr>
     <td>
      ASR
     </td>
     <td>
      +0.25
     </td>
     <td>
      +0.95
     </td>
     <td>
      241.15
     </td>
     <td>
      ±0.95
     </td>
    </tr>
    <tr>
     <td>
      Net
     </td>
     <td>
      -0.05
     </td>
     <td>
      +0.65
     </td>
     <td>
      0.80
     </td>
     <td>
      ±0.80
     </td>
    </tr>
   </tbody>
  </table>
persisted and intensified between 2020 and 2022, according to the Multivariate El Niño–Southern Oscillation (ENSO) index (Wolter and Timlin 1998; section 2e1). Relative to the multiyear average from 2001 to 2021, the 2022 global annual mean TOA flux anomalies are +0.30 for OLR, +0.20 for TSI, -0.75 for RSW, and +0.65 W m⁻² for total net flux (Table 2.7). These anomalies are near or within their respective 2-σ interannual variability (Table 2.7) for this period.

Throughout 2022, the global monthly mean TOA OLR anomaly remained largely positive (Fig. 2.54). The OLR anomaly dipped to a value of -0.50 W m⁻² for one month in August but recovered quickly back to positive values in the following months. These results are generally consistent with NOAA High Resolution Infrared Sounder (HIRS; Lee and NOAA CDR Program 2018) and NASA Atmospheric Infrared Sounder (AIRS; Susskind et al. 2012) OLR datasets (not shown). For the year as a whole, the 2022 global mean TOA OLR anomaly was +0.30 W m⁻². The global monthly mean TOA absorbed solar radiation (ASR; determined from TSI minus RSW) anomaly remained positive throughout 2022, peaking at +1.80 W m⁻² in June. For the year as a whole, the 2022 global mean TOA ASR anomaly was +0.95 W m⁻². The global monthly mean TOA total net anomaly, which is calculated from ASR anomaly minus OLR anomaly, also stayed mostly positive throughout 2022, reaching a maximum of +1.65 W m⁻² in June. For the year as a whole, the 2022 global mean TOA total net anomaly was +0.65 W m⁻². In terms of the global annual mean TOA ERB, the positive 2022 ASR anomaly was much larger than the negative effect of the 2022 OLR anomaly to produce the observed positive 2022 total net anomaly. Further analyses are needed to understand the significances and impacts of these observed global changes.

The TSI data were obtained from the Total Irradiance Monitor aboard the Solar Radiation and Climate Experiment (SORCE) mission (Kopp and Lean 2011), the Royal Meteorological Institute of Belgium composite dataset (Dewitte et al. 2004), and the Total Solar and Spectral Irradiance Sensor-1 (Coddington 2017) mission, all renormalized to the SORCE Version 15. The TOA RSW and TOA OLR data come from two different CERES datasets. The data for March 2000–November 2022 are based on the CERES Energy Balance and Filled (EBAF) Ed4.2 product (Loeb et al. 2009,

2012, 2018), which are constructed with measurements from the CERES instruments (Wielicki et al. 1996, 1998) aboard Terra, Aqua, and NOAA-20 spacecraft. The data for December 2022 comes from the CERES Fast Longwave and Shortwave Radiative Fluxes (FLASHFlux) version 4A product (Kratz et al. 2014), which are created using CERES measurements from Terra and Aqua spacecraft. The FLASHFlux to EBAF data normalization procedure (Stackhouse et al. 2016) results in 2-σ monthly uncertainties of ±0.35, ±0.05, ±0.15, and ±0.50 W m⁻² for the OLR, TSI, RSW, and total net radiation, respectively (rounded to nearest 0.05 W m⁻²).

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

Fig. 2.54. Time series of global monthly mean deseasonalized anomalies (W m⁻²) of top-of-atmosphere (TOA) Earth radiation budget for outgoing longwave radiation (OLR; upper), absorbed solar radiation (ASR, determined from total solar irradiance (TSI) minus RSW; middle), and total net (TSI-RSW-OLR; lower) from Mar 2000 to Dec 2022. Anomalies are relative to their calendar month climatology (2001–21). The time series show the CERES EBAF Ed4.2 1-Deg data (Mar 2000–Nov 2022) in red and the CERES FLASHFlux version 4A data (Dec 2022) in blue; see text for merging procedure. (Sources: https://ceres-tool.larc.nasa.gov/ord-tool/jsp/EBAFTOA42Selection.jsp and https://ceres-tool.larc.nasa.gov/ord-tool/jsp/FLASH_TISASelection.jsp.)
## 2. MAUNA LOA APPARENT TRANSMISSION RECORD

—J. A. Augustine, K. O. Lantz, J.-P. Vernier, and J. E. Barnes

The Mauna Loa Observatory (MLO) on Hawaii (19.536°N, 155.576°W) at 3397 m a.s.l. has made pyrheliometer measurements since 1958. Because of its high elevation, morning apparent atmospheric transmission computed from those measurements is a good proxy for stratospheric extinction.

The apparent transmission time series from 1958 through 2022 is shown in Fig. 2.55; the inset highlights new data for 2022. January and February 2022 transmissions continue at relatively stable levels of ~0.928 observed at the end of 2021. This relatively low transmission has been maintained since 2019 by a series of volcanic eruptions and wildfires (Augustine et al. 2020, 2021). A broad reduction in transmission began in March 2022 and reached a minimum of 0.919 in May. A sharp recovery in June to values observed earlier was followed by relatively stable transmissions through November (Mauna Loa erupted on 27 November, ending data collection for 2022). The springtime reduction in transmission coincided with the violent underwater eruption in January of Hunga Tonga–Hunga Haʻapai (HTHH; 20.536°S, 175.382°W; Sidebar 2.2) and the seasonal passage of dust from Asia.

Most of the 70+ volcanic eruptions in 2022 had Volcanic Explosivity Indexes (VEI) less than 2, limiting their effect on the stratosphere. HTHH's initial eruption on 13 January sent material as high as 20 km. A second more powerful eruption on 15 January (VEI 5) reached the lower mesosphere (~58 km) but within the day settled to between 25 km and 35 km (Proud et al. 2022). The westerly phase of the quasi-biennial oscillation of stratospheric winds (QBO) propagated the HTHH plume westward. The plume was concentrated at ~10°S but Cloud-Aerosol LIDAR and Infrared Pathfinder Satellite Observations (CALIPSO) imagery shows lateral transport branches in the 19 km–27 km layer reaching 30°S and 20°N in March (see supplemental material), in agreement with the Ozone Mapping and Profiler Suite (OMPS) and the Stratospheric Aerosol and Gas Experiment (SAGE) limb sounder (Taha et al. 2022). Discrete CALIPSO images at 19-day intervals in Legras et al. (2022) show the northern extent of Tonga's stratospheric plume near 20°N at least through 3 May, but by 22 May it retreated south and remained there at least through 18 July (no published data thereafter). Lidar observations at MLO first sensed the HTHH plume on 1 March at an altitude of 24.5 km. A much larger signal was observed near 24 km on 14 March, in agreement with CALIPSO, which shows the northern periphery of the plume over MLO from 14 to

16 March (see supplemental material). Distinguishing HTHH from other large volcanic stratospheric events is its relatively small sulfur dioxide injection, ~50 times less than Mt. Pinatubo (Taha et al. 2022), and stratospheric aerosol optical depth (AOD) six times less than Pinatubo at 0.015, which represents a 4–5 factor increase from background (Khaykin et al. 2022). Those discrepancies are likely due to wet deposition (Proud et al. 2022) as HTHH increased stratospheric water by 10%–13% (Millan et al. 2022; Khaykin et al. 2022). Zhu et al. (2022) suggests the added water promoted faster sulfate aerosol formation and greater extinction but shortened its long-term effect on surface radiation.

While there were several springtime dust storms in Asia in 2022, none were as severe as those in 2021. Monthly average

<figure><img src="image_97.png" /></figure>

Fig. 2.55. Apparent transmission at Mauna Loa, Hawaii, from 1958 through 2022. Red dots are monthly averages of morning apparent transmission, and the gray curve is a locally weighted scatterplot smoothing (LOWESS) fit with a six-month smoother applied. The insert is an enlargement of the newest data for 2022. The horizontal dashed gray lines represent the average transmission of the clean period before the eruption of Agung. The Dec 2022 transmission is not available because the eruption of Mauna Loa in late Nov cut power to the station.
global AOD images for 2022 from the Moderate Resolution Imaging Spectroradiometer (MODIS [Barnes et al. 1998]; https://earthobservatory.nasa.gov/global-maps/MODAL2_M_AER OD) show slightly elevated AOD over Hawaii in March, peak AOD in April, a reduction in May, and gone by June.

In summary, HTHH's plume and Asian dust were primary contributors to the reduction of apparent transmission over MLO from March through May 2022. Both sources of enhanced extinction vanished by June, returning transmission to levels observed in January and February for the remainder of the 2022 record, which ended in November.

Atmospheric transmission is calculated from the ratio of direct-normal pyrheliometer measurements at two integer solar elevations (Ellis and Pueschel 1971). Three ratios from clear-morning measurements at 2, 3, 4, and 5 atmospheric pathlengths are averaged to get representative daily value. Daily transmissions are then averaged over monthly periods. Our calculation is referred to as an "apparent" transmission because atmospheric variability at longer pathlengths adds to the uncertainty.

## Sidebar 2.2: Hunga Tonga–Hunga Haʻapai eruption

—S. DAVIS, W. RANDEL, J. AUGUSTINE, B. FRANZ, N. KRAMAROVA, T. LEBLANC, J.-P. VERNIER, X. WANG, AND M. WEBER

On 14–15 January 2022, the Hunga Tonga–Hunga Haʻapai (HTHH) submarine volcano (20.54°S, 175.4°W) erupted multiple times, injecting ash, water vapor, and sulfur dioxide ($SO_2$, an aerosol precursor) into the atmosphere. The underwater eruption on 15 January was among the strongest in the modern geophysical record, with an estimated Volcanic Explosivity Index (VEI) similar to that of Mt. Pinatubo in 1991 (VEI=6; Poli and Shapiro, 2022). The eruption produced a number of impacts, including audible sound heard ~10,000 km away, perturbations to the ionosphere, global tsunamis, as well as a Lamb wave and seismic activity similar in magnitude

to the 1883 eruption of Krakatau (Kubota et al. 2022; Matoza et al. 2022).

The plume from the HTHH eruption was observed to reach as high as ~55 km, a record in the geostationary satellite era that exceeded the ~40 km height reported for Mt. Pinatubo (Fig. SB.2.3; Carr et al. 2022). Following this transient peak in the plume height reaching the mesosphere, an umbrella of gas and ash spread out in the stratosphere between approximately 26 km and 34 km. The trace gases and aerosols injected into the stratosphere by the HTHH eruptions quickly spread as far north as 20°N in the month following the eruption, before

<figure><img src="image_98.png" /></figure>

Fig. SB2.3. (From Carr et al. 2022, their caption. Figure used under **CC BY 4.0**): Image of the plume on 15 Jan 2022 at 0430 UTC from (a) GOES-17 and (b) Himawari-8. Colored dots mark manual stereo height estimates (in km), and the white/black triangles show the volcano's location. The white arrows in panel (a) depict the shadow of a plume edge feature and a dome feature, with the shadow length and the derived height given above/below the arrow. Arrows in the lower right of each panel indicate the sun-to-pixel and satellite-to-pixel azimuths.
being transported to higher Southern Hemisphere (SH) latitudes in subsequent months and into the northern midlatitudes by the end of 2022. Here, we elaborate on some of the notable perturbations to the atmosphere, as well as some of the possible chemical and climate effects that are likely to occur in the coming years.

As the HTHH eruption occurred underwater, a vast amount of water vapor (WV) was present in the eruption plume, and this water vapor-enriched plume has had a dramatic impact on the stratosphere. By injecting water vapor and ice directly into the stratosphere, the HTHH eruption bypassed the typical tropical tropopause layer "cold trap" that normally limits the amount of water vapor entering the stratosphere (section 2g5). While previous volcanic eruptions and pyrocumulonimbus events have also injected water vapor into the stratosphere,

HTHH injected ~50 Tg H₂O to ~150 Tg H₂O, which is unprecedented in the satellite record and represents upwards of 10% of the entire stratospheric burden of WV (Khaykin et al. 2022; Millán et al. 2022; Randel et al. 2023; Vömel et al. 2022). Measurements from within the HTHH plume in the week immediately after the eruption showed water vapor mixing ratios exceeding 1000 ppmv between 25 km and 30 km (Khaykin et al. 2022; Randel et al. 2023; Vömel et al. 2022), in contrast to typical background stratospheric values of ~5 ppmv and enhancements on the order of 10 ppmv from other recent volcanoes and the early 2020 Australian wildfires (e.g., Sioris et al. 2016; Kablick et al. 2020; Schwartz et al. 2020). Enhanced water vapor amounts persisted in the stratosphere throughout 2022 (Fig. SB.2.4a). As an example, the quasi-global monthly anomaly (averaged over ~80°S–80°N, relative to the

<figure><img src="image_99.png" /><figcaption>Fig. SB2.4. Southern Hemisphere (10°S–50°S) average (a) stratospheric water vapor (H₂O) anomaly (ppm), (b) aerosol extinction (km⁻¹), (c) temperature anomaly (K), and (d) ozone (O₃) anomaly (ppm) for the period 2012–22. The start of 2022 is indicated by the vertical solid line, and the HTHH eruption is indicated by the vertical dotted line. Water vapor, ozone, and temperature anomaly data are from the Aura MLS and calculated as departures from the 2004–21 mean. Aerosol extinction at 745 nm is based on Suomi National Polar-Orbiting Partnership OMPS Limb Profiler data.</figcaption></figure>
2004–21 average) in December 2022 at 26 hPa (~25 km) from the Aura Microwave Limb Sounder (MLS) satellite was +1.1 ppmv, which corresponds to a 12-sigma and 24% deviation from the mean for that month. Because the main WV removal process is due to the slow stratospheric overturning circulation, it is expected that anomalous stratospheric water vapor will persist for a number of years.

It is estimated that HTHH emitted ~0.5 Tg SO₂ (Carn et al. 2022), which is relatively small compared to the ~20 Tg SO₂ emitted by the similarly sized Mt. Pinatubo eruption (Guo et al. 2004). Stratospheric sulfate aerosols formed anomalously quickly in the HTHH plume, likely influenced by rapid oxidation of SO₂ in the extreme water vapor plume (Carn et al. 2022; Zhu et al. 2022). The sulfate aerosol layer gradually separated from the water vapor plume and descended into the SH lower stratosphere due to gravitational settling (Fig. SB.2.4b; see also Legras et al. 2022; Wang et al. 2022), dispersing over latitudes from the tropics to the Antarctic after several months. This resulted in a five-fold increase in the stratospheric aerosol load, which is the highest in the last three decades (Khaykin et al. 2022; see also section 2g3). This stratospheric perturbation had a visible impact on SH aerosol optical depth at 550-nm anomalies in 2022 (section 2g3).

As water vapor and aerosols are radiatively active and persisted in the stratosphere throughout 2022, there are expected impacts on stratospheric temperatures (section 2b6). Enhanced stratospheric water vapor is expected to lead to local (stratospheric) cooling, while elevated stratospheric sulfate aerosol concentrations lead to local warming. Observations show cold stratospheric temperatures in 2022 that were well outside the range of previous variability (Figs. SB.2.4c, 2.11a), with corresponding anomalies in stratospheric winds and circulation that are in balance with the anomalous temperatures (Coy et al. 2022; Wang et al. 2022).

Elevated stratospheric water vapor and aerosol concentrations are expected to affect surface climate and tropospheric temperatures in a manner opposite to their stratospheric impacts (e.g., enhanced stratospheric water vapor leads to surface warming). Although unambiguous detection of a tropospheric/surface temperature signal may be nearly impossible given the potentially small magnitude of any perturbation relative to natural variability, it is estimated that the HTHH eruption increases the likelihood of exceeding an annual mean global surface temperature anomaly of 1.5°C by 7% in the five years following the eruption (Jenkins et al. 2023).

The HTHH eruption is also likely to have effects on stratospheric ozone (Fig. SB.2.4d; sections 2g4, 6h). Impacts from HTHH on stratospheric aerosols and water vapor (and other species) may be perturbing stratospheric ozone chemistry in both midlatitudes and in the polar regions, in addition to the ozone changes from forced circulation changes (Wang et al. 2022). Since the HTHH plume was primarily confined to the SH during 2022, the greatest potential effects are on SH midlatitude ozone (as already evident in Fig. SB.2.4d) and the Antarctic ozone hole. Ozone profile data and total column ozone data in 2022 reveal anomalously low ozone in the SH midlatitude lower stratosphere during 2022, in general agreement with these expectations (section 2g4). However, MLS observations inside the Antarctic vortex showed near-average water vapor (Fig. 6.15h), and other stratospheric perturbations in recent years (e.g., Australian wildfires in early 2020 and earlier volcanic eruptions such as that of La Soufrière in April 2021) as well as anomalously weak planetary wave activity in austral spring have also likely impacted the 2022 Antarctic ozone hole. Further studies are needed to quantify the role of HTHH in perturbing stratospheric ozone.
g. Atmospheric composition

# 1. LONG-LIVED GREENHOUSE GASES

—X. Lan, B. D. Hall, G. Dutton, and I. Vimont

Increases in atmospheric greenhouse gas burdens, especially the long-lived greenhouse gases (LLGHGs) carbon dioxide ($CO_2$), methane ($CH_4$), and nitrous oxide ($N_2O$), are largely responsible for increasing global temperature (Foster et al. 2021).

Carbon dioxide is the most important and prevalent anthropogenic GHG. The atmospheric pre-industrial abundance of $CO_2$ is estimated to be ~278 ppm (parts per million by moles in dry air), based on air extracted from ice in Greenland and Antarctica (Etheridge et al. 1996). Globally

averaged $CO_2$ derived from remote marine boundary layer measurements made by NOAA's Global Monitoring Laboratory was 417.1±0.1 ppm in 2022 (Fig. 2.56a), 50% higher than the pre-industrial level. 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 2013–22 with the 2022 annual growth rate at 2.2 ppm yr⁻¹ (Fig. 2.56a; the global trend is updated monthly on www.gml.noaa.gov/ccgg/trends, and uncertainties are reported as one sigma in this section).

The main driver of increasing atmospheric $CO_2$ is fossil fuel (FF) burning, with total fossil emissions (including ~5% from cement production) increasing from 3.0±0.2 Pg C yr⁻¹ in the 1960s to 9.6±0.5 Pg C yr⁻¹ in the past decade (2012–21; Friedlingstein et al. 2022). Comparing fossil emissions to the atmospheric increase, we can conclude that only about half of the fossil $CO_2$ emitted since 1958 has remained in the atmosphere, with the other half taken up by the oceans and terrestrial biosphere. While emissions of $CO_2$ from FF combustion drive its increasing atmospheric burden, the interannual variability in the $CO_2$ growth rate is mostly driven by terrestrial biosphere exchange of $CO_2$ driven by climate variability such as El Niño–Southern Oscillation (ENSO), which is confirmed by measurements of stable (¹³C:¹²C) carbon isotope ratios (e.g., Keeling and Revelle 1985; Alden et al. 2010).

Fossil $CO_2$ emissions are estimated to have declined by 5.4% in 2020 relative to 2019 due to the COVID-19 pandemic but returned to pre-COVID levels in 2021

<figure><img src="image_100.png" /></figure>

**Fig. 2.56.** Global mean dry-air remote surface mole fractions (approximately weekly data in blue and deseasonalized trend in black [see Dlugokencky et al. 1994b for methods], left axis) and annual change (red, right axis) of (a) carbon dioxide ($CO_2$, ppm), (b) methane ($CH_4$, ppb), and (c) nitrous oxide ($N_2O$, ppb) derived from the NOAA Global Greenhouse Gases Reference Network. $N_2O$ data prior to 2000 are insufficient and noisy and thus hinder the calculation of a growth rate.
(Friedlingstein et al. 2022). However, these emission changes are not reflected in observed global atmospheric CO₂ signals, because it is a relatively small signal compared to the natural variability that is driven by the large fluxes from photosynthesis and respiration of ecosystems on land.

The impacts of LLGHGs on global climate are estimated using the effective radiative forcings (ERFs) of LLGHGs, following the Intergovernmental Panel on Climate Change Sixth Assessment Report (Forster et al. 2021). Increasing atmospheric CO₂ has accounted for 66% increase in ERF by LLGHGs, by 2.26 W m⁻² in 2022 compared with preindustrial times (before 1750). The rate of increase in CO₂ ERF during 2018–22 accounted for 78% of the total increase by LLGHGs. A pulse of CO₂ will remain in the atmosphere for thousands of years (Archer and Brovkin 2008) and cause long-lasting warming (Pierrehumbert 2014).

Atmospheric CH₄ has a lifetime of ~9 years, meaning that its atmospheric abundance and ERF can be reduced much more quickly by reducing emissions compared to CO₂ (United Nations Global Methane Assessment 2021). Global mean tropospheric CH₄ abundance increased to 1911.8±0.6 ppb (parts per billion by moles in dry air) in 2022, a 165% increase compared to its pre-industrial level of 729±9 ppb (Mitchell et al. 2013). Global CH₄ increased by an average of 12±1 ppb yr⁻¹ between 1984 and 1991, followed by a smaller increase of 4±2 ppb yr⁻¹ between 1992 and 1998, and further reduced to near zero (1±3 ppb yr⁻¹) during 1999–2006. Atmospheric CH₄ growth restarted in 2007 and has significantly accelerated since 2014 (Fig. 2.56b). The highest annual CH₄ growth rates were recorded in 2020 and 2021 at 15.2±0.4 ppb and 17.7±0.4 ppb, respectively, since the beginning of NOAA's systematic CH₄ measurements in 1983. Preliminary results also reveal large growth in 2022 of 14.4±0.4 ppb. The increase in CH₄ contributed to a 0.56 W m⁻² increase in ERF in 2022 from preindustrial times while the CH₄-related production of tropospheric ozone (O₃) and stratospheric H₂O also contributes to ~0.30 W m⁻² indirect radiative forcing (Myhre et al. 2014).

Atmospheric measurements of ¹³C:¹²C of CH₄ (denoted δ¹³C-CH₄) are sensitive to different CH₄ emission and sink processes (Lan et al. 2021). The ongoing reduction in δ¹³C-CH₄ since 2008 (Michel et al. 2022) indicates increased emissions from microbial sources (Basu et al. 2022), including both anthropogenic emissions from livestock and waste/landfills and natural wetland and lakes, which have more negative δ¹³C-CH₄ signatures. A decrease in biomass burning (Worden et al. 2017) and a small increase in FF emissions (leakage, not combustion) may also play a smaller role in 2007–16 global CH₄ change (Lan et al. 2019, 2021; Oh et al. 2022; Basu et al. 2022).

Recent studies suggest a dominant role of increased tropical wetland emissions in the 2020 CH₄ surge (Feng et al. 2021; Peng et al. 2022). An increased contribution from wetland emissions also agrees with the acceleration of atmospheric δ¹³C-CH₄ reductions in 2020 and 2021 (Michel et al. 2022). Rapid increases in wetland CH₄ emissions can be an indication of ongoing climate feedback. A reduction in the hydroxyl radical (OH, the main sink for CH₄) may have contributed to the growth in 2020 due to the COVID-19 pandemic impact on major OH precursors, NOₓ, and CO (Laughner et al. 2021; Peng et al. 2022). However, COVID-19-related processes are unlikely to explain the continued rapid increase in 2021 and 2022. Additionally, Lan et al. (2021) showed that the reduction in the OH sink portion would push atmospheric δ¹³C-CH₄ upward when the atmospheric CH₄ mass balance is satisfied.

Nitrous oxide is an ozone-depleting LLGHG (Ravishankara et al. 2009) produced by microbes that rely on nitrogen substrates from natural and agricultural soils, animal manure, and the oceans (Davidson 2009). 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 2022 was 335.7±0.1 ppb, a 24% increase over its preindustrial level of 270 ppb. The annual increase of 1.3±0.1 ppb in 2020 was the largest in NOAA's measurement record since 2000, while similarly large growth rates were observed in 2021 and 2022 at 1.3±0.1 ppb and 1.3±0.1 ppb (Fig. 2.56c). Recent growth rates are larger than the average increase during 2010–19 (1.0 ± 0.2 ppb), suggesting increased emissions. The increase in atmospheric N₂O abundance contributed to a 0.22 W m⁻² increase in ERF in 2022. The rate of increase in N₂O contributed to 9% of the rate of increase in radiative forcing by all LLGHGs
in the past five years (2018–22), comparable to the 11% contribution from CH₄ increase.

The combined radiative forcing in 2022 from all LLGHGs was 3.44 W m⁻² (Fig. 2.57), which is 3.6 times larger than in 1950. The post-industrial increases in atmospheric CO₂, CH₄, and N₂O abundances have contributed to 88% of ERF increase (Fig. 2.57a), while the mean rate of ERF increase for CO₂, CH₄, and N₂O in the past five years (2018–22) accounts for 98% of the mean rate of ERF increase resulting from growth of all LLGHGs (Fig. 2.57b).

## 2. OZONE-DEPLETING SUBSTANCES AND THEIR SUBSTITUTES

—I. J. Vimont, B. D. Hall, G. Dutton, S. A. Montzka, J. Mühle, M. Crotwell, K. Petersen, S. Clingan, and D. Nance

Ozone-depleting substances (ODSs) include chlorofluorocarbons (CFCs), methyl bromide, various chlorinated hydrocarbons, halons, and the CFC replacements hydrochlorofluorocarbons (HCFCs). These compounds influence global climate both through direct absorption of infrared energy and via their ability to deplete stratospheric ozone, a strong greenhouse gas (Karpechko and Maycock 2018). Hydrofluorocarbons (HFCs) are increasingly used as ODS replacements that do not destroy ozone, but they are highly efficient infrared absorbers.

The consumption and production of these classes of halogenated compounds is controlled by the 1987 Montreal Protocol (hereafter referred to as The Protocol), and its subsequent amendments, both of which aim to limit damage to the stratospheric ozone layer and the climate. The Protocol first specified the phase-out of production and consumption for dispersive uses of ODSs starting with the CFCs, followed by halons and then HCFCs. More recently, a phase down of select HFCs with high global warming potentials (GWPs) used as ODS substitutes has been mandated through the Kigali Amendment to The Protocol. Importantly, the Kigali Amendment was signed by China in 2021 and by the United States in 2022, further strengthening the ability of The Protocol and the global community to reduce the impacts of these gases on the climate.

Even as production of these controlled substances ends, other factors affect their atmospheric abundance. Their atmospheric lifetimes vary considerably, such that long-lived chlorofluorocarbons like CFC-11 and CFC-12 have only declined 18% and 10% from their peak atmospheric abundances (in 1994 and 2003, respectively), while the shorter-lived solvent methyl chloroform has declined by 99% (Fig. 2.58). Production of CFC-11 and CFC-12 was reported to have been globally phased out in 2010, while methyl chloroform (CH₃CCl₃) was reported to have been globally phased out by 2015. Additionally, the slow long-term release or sudden leakage of chemicals from some applications (e.g., insulating foams and refrigeration units) plays an important role in maintaining emissions even after production has been phased out.

It is important to note that for compounds controlled by The Protocol, the scheduled phase-down or phase-out of production of these compounds is agreed upon, and efforts to accomplish this are self-reported by individual countries to the United Nations Ozone Secretariat. Recent studies of CFC-11 exemplify the critical need for continued monitoring of these compounds in the atmosphere to ensure the success of The Protocol. The unexpected slowdown in the reduction of the atmospheric abundance of CFC-11 after 2012 led to the discovery of renewed, unreported emissions through to 2018 in an apparent violation of The Protocol (Montzka et al.

<figure><img src="image_101.png" /><figcaption>Fig. 2.57. (a) Effective radiative forcing (W m⁻²) due to long-lived greenhouse gases (LLGHGs; see Table 2.8 for details on industrial gases). (b) Annual increase in direct radiative forcing (W m⁻²).</figcaption></figure>
2018; Rigby et al. 2019). Since then, an accelerated decline in abundance has been measured, suggesting that the problem may be mostly resolved (Montzka et al. 2021; Park et al. 2021).

Tangentially, signatories to the Kigali Amendment pledged to begin controlling select HFCs under The Protocol, including HFC-23 and HFC-134a. Reported abatement of HFC-23 independent of the Kigali Amendment suggests that emissions of this high-GWP compound should have been decreasing in recent years (e.g., Stanley et al. 2020 and references therein). However, measurements show its global abundance increasing by about 1.0 ppt (parts per trillion by moles in dry air) per year (since 2013), reaching 35.9 ppt in 2022 (Table 2.8 and Fig. 2.58), indicating that emissions are instead increasing (Stanley et al. 2020; Park et al. 2023). Likewise, the most abundant

<table><caption>Table 2.8. Summary table of long-lived greenhouse gases for 2022 (CO₂ mixing ratios are in ppm, N₂O and CH₄ in ppb, and all others in ppt).</caption><thead><tr><th>Compound Class</th><th>Industrial Designation or Common Name</th><th>Chemical Formula</th><th>ERF<sup>a</sup></th><th>Rad. Efficiency (W m<sup>-2</sup> ppb<sup>-1</sup>)<sup>b</sup></th><th>Rad. Forcing<sup>a</sup> (ERF/SARF) (W m<sup>-2</sup>)</th><th>Mean surface mole fraction, 2022 [change from prior year]<sup>c</sup></th><th>Lifetime (yrs)<sup>b</sup></th></tr></thead><tbody><tr><td>Acidic oxide</td><td>Carbon Dioxide</td><td>CO<sub>2</sub></td><td>Y</td><td>1.33 x 10<sup>-5</sup></td><td>2.3</td><td>417.1 [2.4]</td><td></td></tr><tr><td>Alkane</td><td>Methane</td><td>CH<sub>4</sub></td><td>Y</td><td>3.88 x 10<sup>-4</sup></td><td>0.56</td><td>1911.9 [13.0]</td><td>9.1</td></tr><tr><td>Nitride</td><td>Nitrous Oxide</td><td>N<sub>2</sub>O</td><td>Y</td><td>3.2 x 10<sup>-3</sup></td><td>0.22</td><td>335.7 [1.3]<sup>d</sup></td><td>123</td></tr><tr><td>Chlorofluorocarbon</td><td>CFC-11</td><td>CCl<sub>3</sub>F</td><td>N(Y)<sup>e</sup></td><td>0.26</td><td>0.057(0.064)</td><td>219.6 [-2.1]<sup>d</sup></td><td>52</td></tr><tr><td>Chlorofluorocarbon</td><td>CFC-12</td><td>CCl<sub>2</sub>F<sub>2</sub></td><td>N(Y)<sup>e</sup></td><td>0.32</td><td>0.157(0.176)</td><td>489.7 [-3.5]<sup>d</sup></td><td>102</td></tr><tr><td>Chlorofluorocarbon</td><td>CFC-113</td><td>CCl<sub>2</sub>FCCIF<sub>2</sub></td><td>N</td><td>0.30</td><td>0.020</td><td>67.8 [-0.5]<sup>d</sup></td><td>93</td></tr><tr><td>Hydrochlorofluorocarbon</td><td>HCFC-22</td><td>CHClIF<sub>2</sub></td><td>N</td><td>0.21</td><td>0.052</td><td>248.8 [-0.1]</td><td>11.9</td></tr><tr><td>Hydrochlorofluorocarbon</td><td>HCFC-141b</td><td>CH<sub>3</sub>CCl<sub>2</sub>F</td><td>N</td><td>0.16</td><td>0.004</td><td>24.6 [0.0]</td><td>9.4</td></tr><tr><td>Hydrochlorofluorocarbon</td><td>HCFC-142b</td><td>CH<sub>3</sub>CCIF<sub>2</sub></td><td>N</td><td>0.19</td><td>0.004</td><td>21.2 [-0.3]</td><td>18</td></tr><tr><td>Hydrofluorocarbon</td><td>HFC-134a</td><td>CH<sub>2</sub>FCF<sub>3</sub></td><td>N</td><td>0.17</td><td>0.021</td><td>124.5 [5.6]</td><td>14</td></tr><tr><td>Hydrofluorocarbon</td><td>HFC-152a</td><td>CH<sub>3</sub>CHF<sub>2</sub></td><td>N</td><td>0.10</td><td>&lt;0.001</td><td>7.4 [0.2]</td><td>1.6</td></tr><tr><td>Hydrofluorocarbon</td><td>HFC-143a</td><td>CH<sub>3</sub>CF<sub>3</sub></td><td>N</td><td>0.17</td><td>0.005</td><td>27.5 [1.8]</td><td>51</td></tr></tbody></table>

<footnote><sup>a</sup> Effective Radiative Forcing (ERF) calculated by multiplying the stratospheric-temperature adjusted radiative efficiency (SARF) by the global mole fraction (in ppb) and then applying a tropospheric adjustment factor for the species indicated based on recommended values from chapters 6 and 7 in the IPCC AR6 WGI Report. The Radiative Forcing column is either ERF (where indicated) or SARF. The adjustments to the SARF are CO2: 5% ± 5%, CH4: -14% ± 15%, N2O: 7% ± 13–16%.</footnote>

<footnote><sup>b</sup> Radiative efficiencies and lifetimes were taken from Appendix A in WMO (2018) and Hodnebrog et al. (2020a), 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>c</sup> Mole fractions are global, annual, midyear surface means determined from the NOAA cooperative global air sampling network (Hofmann et al. 2006), 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 2022 and 2021 means, the relevant quantities for calculating radiative forcing. These changes are somewhat different from the 2022 annual increases reported in {LLGHG SECTION), which are determined as the difference between 1 Jan 2022 and 1 Jan 2021. All values are preliminary and subject to minor updates.</footnote>

<footnote><sup>d</sup> Global mean estimates derived from multiple NOAA measurement programs ("Combined Dataset").</footnote>

<footnote><sup>e</sup> ERF calculated values for CFC-11 and CFC-12 are highly uncertain but recommended by the IPCC AR6 WGI Report. Thus, they are included in parentheses here as the lower confidence value. The adjustment to the SARF for these values is 12% ± 13% (Hodnebrog et al. (2020b).</footnote>
<table><thead><tr><th>Compound Class</th><th>Industrial Designation or Common Name</th><th>Chemical Formula</th><th>ERF<sup>a</sup></th><th>Rad. Efficiency (W m<sup>-2</sup> ppb<sup>-1</sup>)<sup>b</sup></th><th>Rad. Forcing<sup>a</sup> (ERF/SARF) (W m<sup>-2</sup>)</th><th>Mean surface mole fraction, 2022 [change from prior year]<sup>c</sup></th><th>Lifetime (yrs)<sup>b</sup></th></tr></thead><tbody><tr><td>Hydrofluorocarbon</td><td>HFC-125</td><td>CHF<sub>2</sub>CF<sub>3</sub></td><td>N</td><td>0.23</td><td>0.007</td><td>37.0 [3.7]</td><td>30</td></tr><tr><td>Hydrofluorocarbon</td><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>26.3 [3.5]</td><td>5.4</td></tr><tr><td>Hydrofluorocarbon</td><td>HFC-23</td><td>CHF<sub>3</sub></td><td>N</td><td>0.18</td><td>0.006</td><td>35.9 [0.9]</td><td>228</td></tr><tr><td>Hydrofluorocarbon</td><td>HFC-365mfc</td><td>CH<sub>3</sub>CF<sub>2</sub>CH<sub>2</sub>CF<sub>3</sub></td><td>N</td><td>0.22</td><td>&lt;0.001</td><td>1.07 [0.02]</td><td>8.9</td></tr><tr><td>Hydrofluorocarbon</td><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>2.04 [0.17]</td><td>36</td></tr><tr><td>Chlorocarbon</td><td>Methyl Chloroform</td><td>CH<sub>3</sub>CCl<sub>3</sub></td><td>N</td><td>0.07</td><td>&lt;0.001</td><td>1.1 [-0.1]</td><td>5.0</td></tr><tr><td>Chlorocarbon</td><td>Carbon Tetrachloride</td><td>CCl<sub>4</sub></td><td>N</td><td>0.17</td><td>0.013</td><td>75.5 [-1.2]<sup>d</sup></td><td>32</td></tr><tr><td>Chlorocarbon</td><td>Methyl Chloride</td><td>CH<sub>3</sub>Cl</td><td>N</td><td>0.01</td><td>&lt;0.001</td><td>547.5 [0.2]</td><td>0.9</td></tr><tr><td>Chlorocarbon</td><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>Chlorocarbon</td><td>Halon 1211</td><td>CBrClF<sub>2</sub></td><td>N</td><td>0.29</td><td>0.001</td><td>2.93 [-0.1]</td><td>16</td></tr><tr><td>Chlorocarbon</td><td>Halon 1301</td><td>CBrF<sub>3</sub></td><td>N</td><td>0.30</td><td>0.001</td><td>3.31 [0.0]</td><td>72</td></tr><tr><td>Chlorocarbon</td><td>Halon 2402</td><td>CBrF<sub>2</sub>CBrF<sub>2</sub></td><td>N</td><td>0.31</td><td>&lt;0.001</td><td>0.397 [0.001]</td><td>28</td></tr><tr><td>Fully fluorinated species</td><td>Sulfur Hexafluoride</td><td>SF<sub>6</sub></td><td>N</td><td>0.57</td><td>0.006</td><td>11.02 [0.37]</td><td>&gt;600</td></tr><tr><td>Fully fluorinated species</td><td>PFC-14</td><td>CF<sub>4</sub></td><td>N</td><td>0.09</td><td>0.005</td><td>88.5 [1.0]</td><td>~50,000</td></tr><tr><td>Fully fluorinated species</td><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>5.15 [0.12]</td><td>~10,000</td></tr><tr><td>Fully fluorinated species</td><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>0.74 [0.02]</td><td>~2600</td></tr><tr><td>Fully fluorinated species</td><td>PFC-318</td><td>c-C<sub>4</sub>F<sub>8</sub></td><td>N</td><td>0.32</td><td>&lt;0.001</td><td>1.99 [0.09]</td><td>~3200</td></tr></tbody></table>

<footnote><sup>a</sup> Effective Radiative Forcing (ERF) calculated by multiplying the stratospheric-temperature adjusted radiative efficiency (SARF) by the global mole fraction (in ppb) and then applying a tropospheric adjustment factor for the species indicated based on recommended values from chapters 6 and 7 in the IPCC AR6 WGI Report. The Radiative Forcing column is either ERF (where indicated) or SARF. The adjustments to the SARF are CO2: 5% ± 5%, CH4: -14% ± 15%, N2O: 7% ± 13–16%.</footnote>

<footnote><sup>b</sup> Radiative efficiencies and lifetimes were taken from Appendix A in WMO (2018) and Hodnebrog et al. (2020a), 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>c</sup> Mole fractions are global, annual, midyear surface means determined from the NOAA cooperative global air sampling network (Hofmann et al. 2006), 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 2022 and 2021 means, the relevant quantities for calculating radiative forcing. These changes are somewhat different from the 2022 annual increases reported in {LLGHG SECTION), which are determined as the difference between 1 Jan 2022 and 1 Jan 2021. All values are preliminary and subject to minor updates.</footnote>

<footnote><sup>d</sup> Global mean estimates derived from multiple NOAA measurement programs ("Combined Dataset").</footnote>

<footnote><sup>e</sup> ERF calculated values for CFC-11 and CFC-12 are highly uncertain but recommended by the IPCC AR6 WGI Report. Thus, they are included in parentheses here as the lower confidence value. The adjustment to the SARF for these values is 12% ± 13% (Hodnebrog et al. (2020b).</footnote>
<figure><img src="image_102.png" /><figcaption>Fig. 2.58. Global mean abundances (mole fractions) at Earth's surface (ppt = nmol mol⁻¹ in dry air) for a suite of halogenated gases, most of which deplete stratospheric ozone. See Table 2.8 for the 2022 global mean mole fractions of these and other gases. All compounds, except hydrofluorocarbon (HFC)-23, are mid-year global means taken from https://gml.noaa.gov/aftp/data/hats/. HFC-23 data derived from AGAGE mid-year global means taken from https://ageg2.eas.gatech.edu/data_archive/global_mean/global_mean_ms.txt.</figcaption></figure>

HFC, HFC-134a, a common mobile air-conditioning fluid, has increased by an average of 5.8 ppt yr⁻¹ since 2012, reaching 124.5 ppt in 2022 (Table 2.8; Fig. 2.58).

One measure of the reactive halogen loading at a given time and place in the stratosphere is the equivalent effective stratospheric chlorine (EESC; Daniel et al. 1995; Montzka et al. 1996; Newman et al. 2007). The presence of reactive halogen radicals in the stratosphere is mostly due to the breakdown of CFCs, which still have high abundances in the atmosphere and contribute strongly to EESC. While EESC provides a measure of reactive stratospheric halogen (Fig. 2.59), it is useful to scale the EESC to provide context relative to stratospheric ozone recovery, and thus the Ozone Depleting Gas Index (ODGI) was developed (Hoffmann and Montzka 2009). For a full description of both EESC and ODGI, see https://gml.noaa.gov/odgi/. Briefly, EESC and the ODGI are separated

<figure><img src="image_103.png" /><figcaption>Fig. 2.59. The equivalent effective stratospheric chlorine in the Antarctic and midlatitudes (EESC(A) and EESC(ML), respectively) values represent EESC on 1 Jan of each year since 1970. Dashed lines represent tropospheric measurement-derived scenarios, based on past measurements and, for the future, full adherence to all controls from The Protocol based on the WMO/UNEP 2018 Ozone Assessment. Solid lines depict inferred stratospheric changes based on the measured tropospheric curves. In 2022, midlatitude and Antarctic EESC were at 1537 ppt and 3635 ppt, which represent reductions of 21% and 12.5% in stratospheric reactive halogen loading from their peaks. Translating this to the Ozone Depleting Gas Index (ODGI), the midlatitude ODGI is 48.6 and the Antarctic ODGI is 74.1, meaning the stratospheric reactive halogen loading has declined 52.4% and 25.9% relative to the 1980 benchmark reactive halogen abundance.</figcaption></figure>

into two categories, midlatitude and Antarctic, because transport processes cause widely different reactive halogen abundances in these stratospheric regions. ODGI is derived from a simple scaling of EESC such that an ODGI of 100 represents the peak EESC value, and an ODGI of 0 represents the value of EESC in 1980 (Hoffmann and Montzka 2009). Using the ODGI, recovery of the stratospheric ozone layer is expected to reach 1980 levels in 2049 for the midlatitudes and 2076 in the Antarctic (Fig. 2.59).
### 3. AEROSOLS

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

Atmospheric aerosols 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 AR6, chapter 6; Szopa et al. 2021). Aerosols also represent a serious public health issue in many countries, and hence are subject to monitoring and forecasting as part of air quality policies. There is also growing evidence that aerosols influence ecosystems through changes in the quality and quantity of light and deposition flux of nutrients over land and ocean (Mahowald et al. 2017).

The Copernicus Atmosphere Monitoring Service (CAMS; http://atmosphere.copernicus.eu) runs a near-real-time global analysis of aerosols and trace gases. It also produces a reanalysis of global aerosols and trace gases covering the years 2003–22 (CAMS Reanalysis [CAMSRA]; Inness et al. 2019), by combining state-of-the-art numerical modeling and in the case of aerosols, remote sensing retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS; Levy et al. 2013) and the Advanced Along Track Scanning Radiometer (AATSR; Popp et al. 2016).

Aerosol optical depth (AOD) at 550 nm and particulate matter ≤2.5µm (PM2.5) concentrations (µg m⁻³) in 2022 (Figs. 2.60a,b) show maxima over India and China, mostly from anthropogenic sources, as well as from dust over the Sahara and the Middle East. High values from seasonal or occasional extreme fires are noted over equatorial Africa, Siberia, parts of North America, and the Amazon basin. There is strong seasonality in AOD (Fig. 2.60c), driven mainly by dust episodes

<figure><img src="image_104.png" /><figcaption>Fig. 2.60. (a) Global aerosol optical depth (AOD) at 550 nm in 2022; (b) global surface PM2.5 concentrations (µg m⁻³) in 2022; and (c) global average of total AOD at 550 nm averaged over monthly (red) and annual (blue) periods for 2003–22.</figcaption></figure>
between March and July in the Sahara, Middle East, and Taklimakan/Gobi desert and seasonal biomass burning in Africa, South America, and Indonesia. Globally averaged AOD in 2022 was the lowest on record, slightly lower than in 2021 and 2020. The summer maximum was much less pronounced than in 2021, as there were fewer fire emissions in 2022.

The 2022 AOD (Plates 2.1v,w) was much less impacted by large fire events than in 2021. Positive anomalies due to fires are found above parts of the Amazon basin, following an active fire season in July and August 2022, and parts of Alaska. South Africa also experienced exceptional fires from the end of July to early October, which led to a large positive AOD anomaly and a number of exceptional AOD days (Plate 2.1x). Dust storm activity was in general lower than usual over most of the Sahara except over its northwest fringe, while the Taklimakan and most of the Arabian Peninsula experienced a higher-than-usual amount of dust. Most of the Southern Hemisphere witnessed a small positive AOD anomaly (see Plates 2.1v,w) between January and March 2022 associated with the Hunga Tonga-Hunga Ha'apai (HTHH) eruption of 13–15 January. This eruption led to the largest stratospheric aerosol disturbance since the June 1991 Mt. Pinatubo eruption (Legras et al. 2022), although HTHH mostly impacted the Southern Hemisphere (see Sidebar 2.2). Monthly stratospheric AOD increased by 0.05 to 0.1 AOD units over the most affected areas (west of Australia, eastern equatorial Pacific Ocean), where there were also signals in terms of number of extreme AOD days. The negative anomalies of AOD over East Asia, Europe, and the Amazon basin (positive anomalies over India and Iran) are associated with ongoing decreasing (increasing) trends in these regions.

The AOD and PM2.5 2003–22 and 2012–22 trends are shown in Figs. 2.61a–d and are generally consistent. Between 2003 and 2022, there is a strong negative trend over most of United States, Europe, East Asia, and parts of the Amazon basin, the latter from reduced deforestation activity. Positive trends are noted over parts of Siberia, driven by biomass burning events, as well as over India and Iran, driven by an increase in anthropogenic emissions of aerosol constituents (e.g., Wang et al. 2021). Between 2012 and 2022, the picture is slightly different: there is no decreasing

<figure><img src="image_105.png" /><figcaption>Fig. 2.61. (a),(b) linear trends of total aerosol optical depth (AOD unit yr⁻¹) and PM2.5 (µg m⁻³ yr⁻¹) for 2003–22; and (c),(d) linear trends of total AOD (AOD unit yr⁻¹) and PM2.5 (µg m⁻³ yr⁻¹) for 2012–22. 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>
trend over the Amazon basin, showing that most of the 2003–22 reduction can be explained by the evolution from 2003 to 2012. The 2012–22 negative trend over Europe and the eastern United States is weaker than the 2003–22 trend, while over East Asia it is stronger, which is consistent with the observed decrease of anthropogenic emissions predominantly occurring since 2012 (Li et al. 2017; Wang et al. 2021). A stronger positive trend between 2012 and 2022 is noted over most of Iran, while over the same period the positive trend over India is smaller than the 2003–22 trend.

Anthropogenic AOD and radiative forcing resulting from aerosol-radiation (RFari) and aerosol-cloud interactions (RFaci; see Bellouin et al. 2020 for details) are shown in Fig. 2.62 for 2022 and the period 2003–22. 2022 is the fifth consecutive year showing a decrease in the average anthropogenic AOD and of the absolute magnitude of RFari and RFaci. This is qualitatively consistent with a reduction in anthropogenic aerosol load (Quaas et al. 2022).

<figure><img src="image_106.png" /><figcaption>Fig. 2.62. CAMSRA (a) 2022 average of anthropogenic aerosol optical depth (AOD); (b) global annual average of anthropogenic AOD from 2003 to 2022. Radiative forcing in the shortwave (SW) spectrum due to (c), (d) aerosol-radiation (RFari) and (e), (f) aerosol-cloud interactions (RFaci). The left column shows the distributions for the year 2022. The right column shows time series of global averages for the period 2003–22, 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. Loyola, A. Rozanov, V. Sofieva, K. Tourpali, R. Wang, T. Warnock, and J. D. Wild

Stratospheric ozone protects Earth's biosphere from harmful ultraviolet (UV) radiation. The phase-out of ozone-depleting substances (ODSs) mandated by the Montreal Protocol and its Amendments (section 2g2) stopped the continuous decline of stratospheric ozone observed before the mid-1990s (Fig. 2.63). Some regions indicate a slow recovery attributed to the ODS decline, most notably the upper stratosphere (Figs. 2.64a–c; WMO 2022; Arosio et al. 2019; Sofieva, et al. 2021; Coldewey-Egbers et al. 2022; Godin-Beekmann et al. 2022; Weber et al. 2022). The rate and the sign of long-term ozone changes depend on changes in chemical composition (e.g., ODSs) and stratospheric circulation, which vary by region and altitude and are partly due to increasing long-lived greenhouse gases (LLGHGs). Both stratospheric cooling due to LLGHG and ODS decline are expected to reduce stratospheric ozone loss outside the polar region (Stolarski et al. 2015).

Relative to a base period of 1998–2008, 2022 annual mean total ozone anomalies poleward of 30° latitude in each hemisphere were mostly negative, while positive anomalies were observed at lower latitudes and in the tropics (Plate 2.1y). These anomalies are related to the La Niña (https://psl.noaa.gov/enso/mei/) and the mostly westerly shear phase of the quasi-biennial oscillation (above 30 hPa) in 2022. The associated weakening of the tropical upwelling and Brewer-Dobson circulation (BDC) leads to higher ozone in the tropics and reduced ozone transport into high latitudes and, at the same time, decreases polar stratospheric temperatures in winter/spring, thereby enhancing spring polar ozone losses (Domeisen et al. 2019).

Fig. 2.63. Time series of annual mean total column ozone (DU) in (a)–(d) four zonal bands and (e) polar (60°–90°) total column ozone in Mar (Northern Hemisphere, NH) and Oct (Southern Hemisphere, SH), the months when polar ozone losses usually are largest. Data are from World Ozone and Ultraviolet Radiation Data Centre (WOUDC) ground-based measurements combining Brewer, Dobson, Système D'Analyse par Observations Zénithales (SAOZ), 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 (SBUV V8.6, OMPS V4r1; light blue; Wild and Long pers. comm., 2019); the GOME/SCIAMACHY/GOME-2/OMPS/(TROPOMI) products GSG from University of Bremen (dark green; Weber et al. 2022), and GTO from the ESA/DLR dataset (light green; Coldewey-Egbers et al. 2015; Garane et al. 2018). MSR2 (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 lines in (a) show the median ozone level from CCMI-1 ref C2 model runs (SPARC/IO3C/GAW 2019). Most of the observational data for 2022 are preliminary.

<figure><img src="image_107.png" /></figure>
The variability in lower stratospheric ozone is largest in winter/spring in both hemispheres, which drives the annual mean variations, as seen in Figs. 2.63 and 2.64.

The various annual mean time series of total ozone (Fig. 2.63) convey the same picture as observed in Plate 2.1y. At midlatitudes (35°–60°) in both hemispheres (Figs. 2.63b,d), the annual mean total ozone in 2022 was close to the long-term mean (1998–2008) in the Northern Hemisphere and at the lower end of values during the last decade in the Southern Hemisphere (SH). Particularly striking are the very low 2022 values in the SH, which are close to the all-time low of the previous sixty years. Very low stratospheric ozone is also evident at the 50-hPa level (Fig. 2.64f). Contrastingly, 50-hPa ozone and total columns from selected datasets (WOUDC,

GSG, GTO) are close to the maximum observed during the last two decades (Figs. 2.63c, 2.64e).

In addition to the effect of La Niña, the underwater volcanic eruption from Hunga Tonga–Hunga Haʻpai (HTHH) in January 2022 may have contributed to this low annual mean SH ozone in 2022. HTHH injected large quantities of aerosols and water vapor into the stratosphere that reduced stratospheric temperatures and modified chemical reaction cycles (sections 2g5, 2g3; Sidebar 2.2; Bourassa et al. 2022; Millán et al. 2022; Vömel et al. 2022). The weakening of the residual BDC in the SH caused by HTHH contributed to the drop in SH middle lower stratospheric and column ozone in 2022 (Coy et al. 2022; Wang et al. 2022). The transport of enhanced aerosol levels into the polar region, and circulation-driven lower polar temperatures may have caused additional Antarctic ozone losses (Wang et al. 2022). While the anomalously weak planetary wave activity in austral spring was the main cause of the deeper Antarctic ozone holes during the last three years (section 6i), recent studies suggest that Australian wildfires in December 2019, volcanic events of La Soufrière in April 2021, and HTHH in early 2022 contributed to the low ozone levels in the lower stratosphere at southern midlatitudes

<figure><img src="image_108.png" /></figure>

**Fig. 2.64.** 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 pressure for three zonal bands: (a),(d) 35°N–60°N, (b),(e) 20°S–20°N (tropics), and (c),(f) 35°S–60°S. Anomalies are with respect to the 1998–2008 base period. Colored lines are long-term records obtained by merging different limb (GOZCARDS, SWOOSH, 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 Network for the Detection of Atmospheric Composition Changes (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 2022 are not yet complete for all instruments and are still preliminary.
(Figs. 2.63d, 2.64f) and deeper Antarctic ozone holes (Figs. 2.64f; Rieger et al. 2021; Ansmann et al. 2022; Yook et al. 2022; Strahan et al. 2022; Wang et al. 2022, Solomon et al. 2023).

Ozone profile data (Fig. 2.64f) confirm the low total ozone at southern midlatitudes. Apart from this, Fig. 2.64 shows ozone values in 2022 that are generally consistent with expectations from model simulations of the Phase 1 Chemistry Climate Model Initiative (CCMI) based on current scenarios of ODS and greenhouse gas changes (thick gray line in Fig. 2.63a; shaded area in Fig. 2.64; SPARC/IO3C/GAW 2019): 1) slow but noticeable recovery of ozone in the upper stratosphere over the last 20 years (WMO 2022; Godin-Beekmann et al. 2022), with observations in recent years closer to the lower end of the model simulations; and 2) little or no recovery of ozone in the lower stratosphere, with recent midlatitude observations at the lower end of the simulations (Ball et al. 2020; Thompson et al. 2021; Godin-Beekmann et al. 2022; WMO 2022).

## 5. STRATOSPHERIC WATER VAPOR

—S. M. Davis, K. H. Rosenlof, D. F. Hurst, H. Vömel, and R. Stauffer

Normally, water vapor (WV) entering the stratosphere is regulated by temperatures in the tropical tropopause layer (TTL; ~14 km–19 km), with higher WV concentrations occurring when TTL temperatures are higher. However, the 14–15 January 2022 eruptions of the Hunga Tonga–Hunga Haʻapai (HTHH) submarine volcano (20.54°S, 175.4°W) injected an amount of water vapor (~50 Tg–150 Tg) into the stratosphere that is unprecedented in the satellite record and represents upwards of 10% of the entire stratospheric burden of WV (Carr et al. 2022; Khaykin et al. 2022; Legras et al. 2022; Millán et al. 2022; Proud et al. 2022; Vömel et al. 2022; see also Sidebar 2.2). By being injected at between approximately 26 km and 34 km, WV associated with the HTHH eruption bypassed the TTL “cold trap” and resulted in a dramatic perturbation to WV and other stratospheric species (e.g., ozone, section 2g4) that will likely persist for years.

This direct injection of WV into the stratosphere by HTHH is evident in the so-called “tropical tape recorder” (Mote et al. 1996) plot (Fig. 2.65a). The WV anomaly appears suddenly in early 2022 between roughly 40 hPa and 10 hPa and then ascends through the stratosphere as part of the meridional overturning circulation. Within the tropical latitude band (15°S–15°N), this unprecedented zonal-mean monthly-mean anomaly (relative to the 2004–21 mean) peaked at 6.4 ppm (parts per million, i.e., µmol mol⁻¹) above the climatological normal of 4.1 ppm

**Fig. 2.65.** (a) Latitude–time contour of tropical (15°S–15°N) lower-stratospheric water vapor (WV) anomalies, with the +2 ppm and +4 ppm values shown as yellow and red contour lines, respectively. (b),(c) Latitude–time contour of WV anomalies at (b) 26 hPa and (c) 82 hPa, respectively. All panels are based on version 5.01 Aura MLS data, which has collected near-global (82°S–82°N) measurements since Aug 2004. Anomalies are differences from the mean 2004–21 WV mixing ratios (ppm) for each month. (a) shows the unprecedented injection of water vapor directly into the stratosphere by the Hunga Tonga–Hunga Haʻapai eruption. (b) shows the southward propagation of the plume at 26 hPa, while (c) shows a more general propagation of tropical lower-stratospheric WV anomalies 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 Southern Hemisphere midlatitudes at the end of each year. Tick marks denote the beginning of each year.

<figure><img src="image_109.png" /></figure>
at 26 hPa (~25 km) in March after the enhancement spread northward to fill this band, corresponding to a deviation from the climatological monthly zonal mean of 160% (31 standard deviations). A latitude-time cross-section of WV anomalies at 26 hPa (Fig. 2.65b) shows that the HTHH plume quickly spread as far north as 20°N immediately following the eruption, before being transported into the Southern Hemisphere in subsequent months. Maps of WV anomalies at 82 hPa (~17 km) and 26 hPa reveal the impact on lower- and mid-stratospheric WV, respectively, from the quiescent period in December 2021 through to the aftermath of the eruption in February (Fig. 2.66).

Even though they pale in comparison to the mid-stratospheric impacts, tropical lower stratospheric WV anomalies were positive (wet) for all months in 2022 (Figs. 2.65a,c). These anomalies were greatest in February (the first full month post-eruption of HTHH), with values of +1.1 ppm, corresponding to deviations from the climatological monthly mean of 40%. Over the tropical latitude band, anomalies were either the most positive on record or second-most positive between February and July. In addition to propagating upwards, the 82-hPa WV anomalies also propagated poleward in each hemisphere (Fig. 2.65c).

Lower stratospheric WV observed by Aura Microwave Limb Sounder (MLS) is consistent with balloon-borne frost point hygrometer soundings (Fig. 2.67). As is well known, at the tropical stations the WV anomalies are highly correlated with the tropical cold-point tropopause (CPT) temperature anomalies (Figs. 2.67c,d).

<figure><img src="image_110.png" /><figcaption>Fig. 2.66. Deseasonalized monthly lower stratospheric Aura MLS vapor (WV) anomalies (ppm; 2004–21 base period) at (a),(c),(e) 82 hPa and (b),(d),(f) 26 hPa for (a),(b) Dec 2021, (c),(d) Jan 2022, and (e),(f) Feb 2022. Contours of WV anomalies of +2, +4, and +8 ppm are shown in yellow, red, and cyan, respectively.</figcaption></figure>
In 2022, the tropical CPT temperatures were anomalously high throughout the entire year except for July (deseasonalized monthly anomaly of -0.04 K), with an annual mean anomaly of +0.73 K. Interannual variations in CPT temperatures are correlated with interannual variability in climate modes, such as ENSO and the quasi-biennial oscillation (QBO) in equatorial stratospheric winds (e.g., Randel et al. 2004), so these are briefly discussed below.

The criteria for La Niña was met throughout 2022 (see section 4b for details). La Niña con-

ditions in boreal winter are typified by weaker tropical lower stratospheric upwelling, anomalously warmer CPTs, and enhanced WV in the tropical lower stratosphere (e.g., Garfinkel et al. 2021). The observed positive anomalies in tropical lower-stratospheric WV at the beginning and end of 2022 are thus consistent with the expected behavior associ- ated with a La Niña.

The QBO phase was easterly through to September until switching to westerly for the remainder of 2022. The QBO westerly phase is associ- ated with anomalously weak tropical upwelling and warm temperatures. Thus, the La Niña and QBO easterlies had offsetting effects on CPTs (and hence WV) at the begin- ning of 2022, whereas the QBO westerlies and La Niña at the end of 2022 likely con- tributed to the anomalously warm CPTs and enhanced lower-stratospheric WV at that time.

<figure><img src="image_111.png" /></figure>

Fig. 2.67. Lower-stratospheric water vapor (WV) anomalies (ppm) over five balloon-borne frost point (FP) hygrometer stations. Each panel shows the lower stratospheric anomalies of individual FP soundings (black) and of monthly zonal averages from MLS data at 82 hPa in the 5° latitude band containing the FP station (red). 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 2004–21 period for all sites except for Lindenberg (2009–21) and Hilo (2011–21). Tropical cold-point tropopause temperature (CPT) anomalies based on the MERRA-2 reanalysis (c,d, blue curve) are generally well correlated with the tropical lower stratospheric WV.

## 6. TROPOSPHERIC OZONE

— O. R. Cooper, J. R. Ziemke, and K.-L. Chang

Tropospheric ozone is the third most important greenhouse gas, after carbon dioxide and methane. It contributes to almost all of the effective radiative forcing due to ozone (tropospheric and stratospheric), estimated by the IPCC Sixth Assessment Report to be 0.47 (0.24 to 0.70) W m⁻² (Forster et al. 2021). A short-lived climate forcer, its lifetime is on the order of three to
four weeks (Archibald et al. 2020) and, there-
fore, its global distribution is highly variable
(Gaudel et al. 2018). In situ observations
are too sparse and infrequent to provide an
accurate quantification of the global distri-
bution and trends in tropospheric ozone,
although in some areas they are abundant
enough to provide reliable regional-scale
trends (Tarasick et al. 2019; Cooper et al.
2020; Gaudel et al. 2020; Gulev et al. 2021;
Chang et al. 2022).

While the current generation of atmo-
spheric chemistry models is showing
reasonable skill in quantifying the global
tropospheric ozone burden (TOB) and repro-
ducing long-term trends (Skeie et al. 2020;
Szopa et al. 2021; Christiansen et al. 2022;
Fiore et al. 2022; Wang et al. 2022), their
estimates vary, and further development
is required before they can provide reliable
near-real time estimates of the global ozone
distribution. Instruments on polar-orbiting
satellites are our best means for monitoring
tropospheric ozone on the global scale.
While currently available satellite products
have limited vertical resolution, they can
report tropospheric column ozone values,
which can be summed to provide near-global
estimates of the tropospheric ozone burden.

Since 2012, *State of the Climate* reports have relied on the combined Aura Ozone Monitoring Instrument and Microwave Limb Sounder satellite ozone measurements (OMI/MLS) to quantify the near-global tropospheric ozone burden and trends (Ziemke et al. 2019). Vertical resolution of OMI/MLS monthly tropospheric column ozone (TCO) is ~3 km near the tropopause with ~2 Dobson units (DU; 7%) precision regionally; trend uncertainties are about 0.5 DU decade⁻¹ (1.5% decade⁻¹). In 2022, the strongest positive TCO anomalies (relative to 2005–21) occurred from East Asia to the northeastern North Pacific (~>1.2 DU; 3%), while the negative anomalies were weak and were

limited to Australia and New Zealand (Plate
2.1z). Hemispheric and global TOB were
160±6 Tg (0°–60°N), 149±6 Tg (0°–60°S), and
309±8 Tg (60°S–60°N) for 2022 (95% confi-
dence ranges). Globally (60°S–60°N), the
2004–22 TOB increase was approximately
1.50±0.37 Tg yr⁻¹, equal to a total increase of
~8% since 2004 (Fig. 2.68). Spatially, the
trends are overwhelmingly positive
(Fig. 2.69), with the strongest trends occur-
ing in the tropics, consistent with the
conclusions of the IPCC AR6, which assessed

<figure><img src="image_112.png" /><figcaption>Fig. 2.69. Linear trends in OMI/MLS tropospheric column ozone (DU decade⁻¹) on a 5° x 5° grid from Oct 2004 through Dec 2022. 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 El Niño-Southern Oscillation proxy; trend uncertainties included autoregressive adjustment via Weatherhead et al. (1998).</figcaption></figure>

<figure><img src="image_113.png" /><figcaption>Fig. 2.68. Monthly averages (solid lines) and 12-month running means (dashed lines) of OMI/MLS tropospheric ozone burdens (Tg) from Oct 2004 through Dec 2022 for (a) 60°S–60°N (black), (b) the Northern Hemisphere tropics (red) and midlatitudes (dark red), and (c) the Southern Hemisphere tropics (light blue) and midlatitudes (dark green). Slopes of linear fits to the data are presented with their 95% confidence-level uncertainties.</figcaption></figure>
observed tropospheric ozone trends from the late twentieth century to 2016–18 (Gulev et al. 2021).

At the surface, six baseline sites are available for quantifying multi-decadal ozone trends through the end of 2022 (Fig. 2.70; Table 2.9). Trends are estimated by the generalized least squares method, based on monthly anomalies referenced to the monthly climatological values over 2000–20 (Chang et al. 2021). At northern high latitudes, ozone has increased at the rate of 0.50±0.32 ppbv decade⁻¹ (p<0.01) since 1973 at Barrow Observatory, Alaska, while ozone at Summit, Greenland, has decreased by 2.25±0.92 ppbv decade⁻¹ (p<0.01) since 2000. At northern midlatitudes, ozone has decreased by 0.97±1.15 ppbv decade⁻¹ (p=0.09) since 1988 at Tudor Hill,

Bermuda, but with large fluctuations. Nighttime observations at Mauna Loa Observatory (MLO), Hawaii, are representative of the lower free troposphere of the central North Pacific Ocean and show a positive trend of 0.92±0.40 ppbv decade⁻¹ (p<0.01) since 1973. In the southern high latitudes, ozone at Arrival Heights, Antarctica, changed little since 1996, with a weak increase of 0.27±0.56 ppbv decade⁻¹ (p=0.33). Ozone at South Pole, the most remote location on Earth, increased by 0.35±0.35 ppbv decade⁻¹ (p=0.05) 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, which have been overwhelmingly positive since the 1990s (Gulev et al. 2021; Fiore et al. 2022), in agreement with OMI/MLS tropospheric column product.

<figure><img src="image_114.png" /><figcaption>Fig. 2.70. (a) Monthly mean surface ozone (ppb) at Barrow Observatory, Alaska (gray), Summit, Greenland (orange), Tudor Hill, Bermuda (blue), Mauna Loa, Hawaii (purple), 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 2.9. (b) The same time series after conversion to monthly anomalies referenced to the monthly climatological values over 2000–20 and smoothed variability based on the locally weighted scatterplot smoothing (LOWESS) regression.</figcaption></figure>

<table><caption>Table 2.9. Ozone trends at the six baseline monitoring sites shown in Fig. 2.70. Trends are estimated by the generalized least squares method, based on monthly anomalies referenced to the monthly 2000–20 base period (Chang et al. 2021), and reported with 95% confidence intervals and p-values.</caption><thead><tr><th>Site name — latitude, longitude, elevation (m)</th><th>Yrs with data</th><th>Trend, ppbv decade<sup>-1</sup></th><th>p-value</th></tr></thead><tbody><tr><td>Summit, Greenland — 72.6°N, 38.5°W, 3238 m</td><td>2000–present</td><td>-2.25±0.92</td><td>p&lt;0.01</td></tr><tr><td>Barrow, Alaska — 71.3°N, 156.6°W, 11 m</td><td>1973–present</td><td>0.50±0.32</td><td>p&lt;0.01</td></tr><tr><td>Tudor Hill, Bermuda — 32.3°N, 64.9°W, 30 m</td><td>1988–1998, 2003–present</td><td>-0.97±1.15</td><td>p=0.09</td></tr><tr><td>Mauna Loa Observatory (MLO), Hawaii — 19.5°N, 155.6°W, 3397 m</td><td>1973–present</td><td>0.92±0.40</td><td>p&lt;0.01</td></tr><tr><td>Arrival Heights, Antarctica — 77.8°S, 166.8°W, 50 m</td><td>1996–present</td><td>0.27±0.56</td><td>p=0.33</td></tr><tr><td>South Pole, Antarctica — 90.0°S, 59.0°E, 2840 m</td><td>1975–present</td><td>0.35±0.35</td><td>p=0.05</td></tr></tbody></table>
# 7. CARBON MONOXIDE

—J. Flemming and A. Inness

Carbon monoxide (CO) is an indirect climate forcing agent because of its chemical feedbacks with the hydroxyl radical (OH), which controls the atmospheric lifetime of methane ($CH_4$), and because of its role as a precursor for tropospheric ozone (Szopa et al. 2021, section 6.3.3.2). Both methane and tropospheric ozone are also short-lived climate pollutants. CO is emitted into the atmosphere by combustion processes originating from anthropogenic sources, such as road transport and energy generation, as well as from natural sources, such as wildfires and biogenic emissions. Of similar or larger size than these emissions is the chemical production of CO in the atmosphere from formaldehyde as part of the oxidation chains of $CH_4$, isoprene, and other volatile organic trace gases (Zheng et al. 2019). Oxidation of CO by reaction with OH is the main loss process for CO, resulting in an atmospheric lifetime of one to two months. The greater abundance of OH in the summer of each hemisphere is a main reason for the typical CO seasonal cycle that peaks in winter.

Monthly and annual global mean total columns of CO together with combined annual anthropogenic and wildfire emissions for the period 2003–22 are shown in Fig. 2.71. The year 2022 has

the lowest overall global CO burden and the lowest total CO emissions in the period. The low emissions in 2022 were a consequence of less intense wildfire activity and a general decrease in anthropogenic CO emissions. Figure 2.71 suggests a good qualitative agreement between the variability and trends of global CO burden (lines) and global CO emissions (points) for the study period. However, the CO emissions and the CO burden are not perfectly correlated, which is an indication of the additional influence of the varied chemical production and destruction of CO on its global burden. Furthermore, while the CO wildfire emissions and CO burden were inferred from satellite observations, the anthropogenic

<figure><img src="image_115.png" /><figcaption>Fig. 2.71. Time series of the area-averaged global total column of carbon monoxide (CO) from the CAMS reanalysis (× 10¹⁸ molecules cm⁻², left axis, black: monthly mean values; cyan: annual mean values) and annual total global CO emissions (Tg yr⁻¹, right axis, red circles) for the period 2003–22.</figcaption></figure>

emissions are only projections based on inventories and the biogenic emissions are from a modeled multi-year climatology, both with considerable uncertainties for the details for 2022.

The spatial distribution of the annual anomalies of 2022 with respect to the period 2003–22 is shown in Plate 2.1aa. The mid and high latitudes show large-scale negative anomalies throughout the year. These were most pronounced in the summer seasons when the wildfire activity was lower than in some previous years (e.g., 2021, 2019) that saw strong wildfires. The positive CO anomalies in northern India can be attributed to anthropogenic sources, such as agricultural fires. The continuation of La Niña conditions in the tropical Pacific resulted in a pronounced negative anomaly over Maritime Southeast Asia during September–November. Stronger-than-usual wildfire activity in tropical and southern Africa and tropical South America led to positive CO anomalies, particularly in the second half of 2022.

The Copernicus Atmosphere Monitoring Service (CAMS; https://atmosphere.copernicus.eu/) has produced a retrospective analysis of CO, aerosols, and ozone since 2003 by assimilating satellite retrievals of atmospheric composition with the European Centre for Medium-Range Weather Forecasts (ECMWF) model (Inness et al. 2019). This CAMS reanalysis assimilated global Thermal Infra-Red 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 to present) of the Measurement of Pollution in the Troposphere instrument (Deeter et al. 2014, 2017, 2019), excluding observations poleward of 65° N/S, using the ECWMF 4D-VAR data assimilation system. The anthropogenic emissions were taken from the Monitoring Atmospheric Composition and Climate megacity (MACCity) inventory
(Granier et al. 2011) that accounts for projected emission trends according to the Representative Concentration Pathway (RCP) 8.5 scenario, but COVID-19-related emissions modifications were not applied. Biomass burning emissions were taken from the Global Fire Assimilation System (v1.2; Kaiser et al. 2012; section 2h3) that is based on MODIS fire radiative power retrievals (Giglio et al. 2016). Monthly mean biogenic CO emissions simulated by the Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1) model following Sindelarova et al. (2014) were used for the period 2003–17, after a monthly-mean climatology derived from the 2003–17 simulations was applied.

h. Land surface properties

1. TERRESTRIAL SURFACE ALBEDO DYNAMICS

—G. Duveiller and N. Gobron

The changes in brightness of the land's surface, referred to as its albedo, emerge from the combined effect of multiple land processes, notably a darkening in the visible part of the spectrum caused by increased vegetation growth, a brightening due to dry conditions, and a strong effect depending on the presence of snow. In 2022, the land surface was overall darker compared to the reference period of 2003–20. This darkening can be attributed to a combination of greener-than-usual regions along with considerable snow cover deficits in the Northern Hemisphere (section 2c5), which were not offset by the brightening that could have been expected following the multiple heatwaves that occurred in 2022 (section 2b4).

La Niña was responsible for increased precipitation in several areas during 2022, leading to greening (and thus darkening), generating distinct features in the global anomaly maps (see Plate 2.1ab for anomalies in visible albedo and compare to anomalies in precipitation), notably in southern Africa, eastern Australia, and northeastern Brazil. Snow deficits in the Arctic, in Eastern Europe, and in a small area of the midwestern United States further characterize the darkening patterns in 2022, while larger and longer extents of snow cover in North America and Tibet brightened these latter areas. Drier conditions following heatwaves did not offset the darkening effects, with localized exceptions in eastern Africa, Paraguay and Argentina, Mexico and the central United States, and Turkey.

The patterns of surface albedo anomalies consolidate the darkening trend over the years, specifically for visible albedo (Figs. 2.72, 2.73). Surface albedo is generally known to be decreasing considerably in the Arctic due to reductions of terrestrial snow cover, snow cover fraction over sea ice, and sea-ice extent, driven mostly by increasing surface air temperature and declining snowfall (Zhang et al. 2019; see Chapter 5). Negative trends of albedo have also been reported over the vegetated surfaces in various regions, even though land-cover change and other effects can

<figure><img src="image_116.png" /><figcaption>Fig. 2.72. Zonally averaged (a) white sky visible (%) and (b) near infrared (%) albedo anomalies for the period 2003–22 using a 2003–20 base period.</figcaption></figure>
brighten the land as well (Chrysoulakis et al. 2019). The non-snow-related albedo reductions are in line with increasing trends in the fraction of absorbed photosynthetically active radiation (FAPAR; section 2h2), that could possibly be associated with increasing trends in greening across some areas, while browning occurs in others (Cortés et al. 2021).

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). Also known as bi-hemispherical reflectance, it is defined as the fraction of radiation that is reflected by the surface in the absence of a direct radiation component and when the diffuse radiation component is isotropic. This situation would correspond to a hypothetical overcast or foggy day, in which the sky would be white (and hence the name: white-sky albedo). Various studies have shown that these products represent ground properties well, whether it be ice sheets (Stroeve et al. 2013) or vegetation (Cescatti et al. 2012). The baseline reference period used here 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_117.png" /><figcaption>**Fig. 2.73.** Global (black lines), Northern Hemisphere (blue), and Southern Hemisphere (red) land surface (a) visible and (b) near infrared albedo anomalies (%; 2003–20 base period) for the period 2003–22. Dotted lines denote each monthly period; solid lines indicate the six-month running averaged mean.</figcaption></figure>

## 2. TERRESTRIAL VEGETATION DYNAMICS

—N. Gobron

The fraction of absorbed photosynthetically active radiation (FAPAR) provides evidence on the amount of vegetation and its health status, and is, therefore, important in assessing the primary productivity of the vegetative cover, the associated fixing of atmospheric carbon dioxide and the energy balance at the surface. FAPAR anomalies in 2022 compared to the 1998–2020 average show wide variations in terms of value and geographic coverage of vegetation productivity worldwide (Plate 2.1ad).

The largest negative anomalies (decreased plant photosynthesis) occurred in North America, across Alaska and the Yukon territory, Kansas, and Texas through to northeast Mexico and in East Africa (Somalia, Ethiopia, Kenya, Uganda, and Tanzania). Negative anomalies also occurred in South America (Argentina, Paraguay, and Bolivia). To a lesser extent, negative anomalies appeared on the Pacific zones of Colombia and Ecuador, southern zone of Chile, and Atlantic coast of Angola and Namibia. Madagascar, Morocco, and northern Algeria were also affected. Several negative hotspots concerned southwest and northern Australia, western Russia, and central Europe.

The most noticeable positive anomalies (increased vegetated photosynthesis) appeared over northern Canada, northeastern Brazil, Botswana, southern Africa, southern and eastern Asia, and eastern Australia. Smaller anomalies were noticeable over the Sahel region and over northern Russia.

The negative anomalies over the boreal forest in Alaska and the Yukon were due to spring wildfires associated with earlier snowmelt. Summer rainfall was lower and later than normal, with higher temperatures than usual (see section 7b). There were precipitation deficits from Kansas in the United States to the northeast of Mexico throughout the year, which affected shrubland growth. Heatwaves occurred in January and July over northern Argentina and Paraguay,
respectively, and temperatures were the highest on record at both the start and the end of 2022 with a strong deficit of precipitation over these regions, as well as Bolivia (see section 7d). East African vegetation health declined in the spring as the temperature was higher than normal with a rainy season ending with cumulatively low rainfall. Strong seasonal negative anomalies occurred over Europe in spring and summer and over the northern United States in autumn;

however, these seasonal events were not detectable in the annual anomaly as positive anomalies occurred in other seasons.

The Arctic ecosystem, characterized by low tundra vegetation over northern Canada (Nunavut) and northern Russia (Kara Sea) shows a greening trend due to higher temperatures during the summer. Terrestrial photosynthesis was enhanced over eastern China and India, with vegetation growth increasing since 2015 due to land use changes, along with an intensification of the production of multiple crops (Gobron 2019; Chen et al. 2019). Northeastern Brazil, with tropical forests along the coast and savanna, had a positive FAPAR annual anomaly of above +0.4, as both precipitation and temperature were above normal. Similar conditions occurred over Botswana, South Africa, and eastern Australia, which correspond to the typical impact of La Niña.

Figure 2.74 shows the average latitudinal anomalies from 1998 to 2022 compared to the reference period 1998–2020. In 2022, the anomaly was positive at nearly all latitudes, apart from a few places such as above the equator and south of 30°S, highlighting the greening of land surfaces. The Southern Hemisphere (SH) was affected by negative anomalies (i.e., less than -0.04), from 2002 to 2014, except in 2010–12, and in 2019/20.

Figure 2.75 shows the global and hemispheric anomalies, with more seasonal variability in the SH than in the Northern Hemisphere (NH). FAPAR anomalies over the SH were positive before 2002, started to be

negative until 2010/11 and during the summer periods in 2015/16, 2018/19, and 2019/20, but were positive for all of 2022. The NH experienced fewer negative events compared to the SH. Since 2021/22, both the SH and NH recorded only positive FAPAR anomalies.

Space-based earth observations are crucial for monitoring terrestrial photosynthetic activity worldwide. Optical sensors are used to infer FAPAR, an essential climate variable (as defined by GCOS [2016, 2022]). The 2022 analysis merges 25 years of global FAPAR monthly products based on three optical sensors from 1998 to 2022 (Gobron et al. 2010; Pinty et al. 2011; Gobron and Robustelli 2013). Uncertainties of each dataset were derived through error propagation techniques and comparisons against multiple proxies using ground-based measurements and radiative transfer simulations that all provide an estimate of the uncertainties and biases. This long-term FAPAR dataset has an estimated uncertainty close to 5%–10%.

<figure><img src="image_118.png" /><figcaption>Fig. 2.74. Zonally averaged fraction of absorbed photosynthetically active radiation (FAPAR) anomalies for 1998–2022 (1998–2020 base period).</figcaption></figure>

<figure><img src="image_119.png" /><figcaption>Fig. 2.75. Global (black lines), Northern Hemisphere (blue), and Southern Hemisphere (red) fraction of absorbed photosynthetically active radiation (FAPAR) anomalies for 1998–2022 (1998–2020 base period). Dotted lines denote each monthly period; solid lines indicate the six-month running averaged mean.</figcaption></figure>
### 3. BIOMASS BURNING

—J. W. Kaiser and M. Parrington

The year 2022 continued to illustrate the two distinct trends that have emerged in global biomass burning over the last decade that are shaping current pyrogeography: a declining trend in many savanna regions related to agricultural expansion and an increasing trend in many forested regions where climate change with severe drought periods increases the flammability of the landscape (Plate 2.1ae). Here we characterize the amount of biomass burning, also referred to here as “fire activity” and more widely as wildfires, with the amount of carbon that is consumed by fire; 80%–95% of this is emitted as carbon dioxide, depending on fire type (smoldering vs. flaming), 1%–2% is emitted as particulate matter and subject to deposition within a few days, and the remainder (CO, CH$_4$, and others) is further oxidized to carbon dioxide in the atmosphere. In a stable ecosystem, virtually all of this carbon dioxide is assimilated again on time scales of years by re-growth of vegetation. In the current situation however, 20% is estimated to contribute to the long-term buildup of atmospheric carbon dioxide (Zheng et al. 2023).

On one hand, 2022 had the lowest fire activity in the Global Fire Assimilation System (GFAS) record (1603 Tg C, 22% below the 2003–20 average; Table 2.10; Plate 1.1), with fire activity in tropical Asia at its lowest since at least 2003. On the other hand, there was significant regional fire activity in boreal North America, southwestern and Central Europe, and central South

<table><caption>Table 2.10. Annual continental-scale biomass burning budgets in terms of carbon emission (Tg C yr⁻¹) from GFASv1.4. The Arctic and western United States are listed as subregions of frequent interest; their values are contained in those for North America plus Northern Asia.</caption><thead><tr><th>Name of Region</th><th>Location</th><th>2003–20 Mean value<sup>a</sup> (min–max)</th><th>2022 Value<sup>a</sup></th><th>2022 Anomaly<sup>a</sup> (%)</th></tr></thead><tbody><tr><td>Global</td><td>–</td><td>2062 (1781–2421)</td><td>1603</td><td>-460 (-22%)</td></tr><tr><td>North America</td><td>30°N–75°N, 190°E–330°E</td><td>85 (57–114)</td><td>77</td><td>-8 (-10%)</td></tr><tr><td>Central America</td><td>13°N–30°N, 190°E–330°E</td><td>52 (38–72)</td><td>45</td><td>-7 (-14%)</td></tr><tr><td>South America</td><td>60°S–13°N, 190°E–330°E</td><td>368 (242–537)</td><td>376</td><td>+9 (+2%)</td></tr><tr><td>Europe and Mediterranean</td><td>30°N–75°N, 330°E–60°E</td><td>42 (28–72)</td><td>37</td><td>-5 (-12%)</td></tr><tr><td>N. Hem. Africa</td><td>0°–30°N, 330°E–60°E</td><td>421 (308–494)</td><td>333</td><td>-88 (-21%)</td></tr><tr><td>S. Hem. Africa</td><td>0°–35°S, 330°E–60°E</td><td>477 (429–532)</td><td>450</td><td>-27 (-6%)</td></tr><tr><td>Northern Asia</td><td>30°N–75°N, 60°E–190°E</td><td>199 (116–436)</td><td>110</td><td>-89 (-45%)</td></tr><tr><td>Southeast Asia</td><td>10°N–30°N, 60°E–190°E</td><td>122 (86–162)</td><td>69</td><td>-53 (-44%)</td></tr><tr><td>Tropical Asia</td><td>10°N°–10°S, 60°E–190°E</td><td>166 (27–475)</td><td>22</td><td>-144 (-87%)</td></tr><tr><td>Australia</td><td>10°S–50°S, 60°E–190°E</td><td>129 (60–232)</td><td>83</td><td>-46 (-36%)</td></tr><tr><td>Arctic (sub-region)</td><td>67°N–90°N, 0°–360°</td><td>8 (1–37)</td><td>7</td><td>-1 (-17%)</td></tr><tr><td>Western United States (sub-region)</td><td>30°N–49°N, 230°E–260°E</td><td>19 (8–42)</td><td>16</td><td>-2 (-12%)</td></tr></tbody></table>

<footnote><sup>a</sup>Quantity given in Tg C yr⁻¹</footnote>
America (Plate 2.1ae; Fig. 2.76). After the extreme fires of 2019 and 2020, fire activity in the Arctic Circle and southeast Australia was again near and below average, respectively.

Global fire emissions are dominated by savanna burning except for rare episodes of extreme tropical peat burning during El Niño conditions. African fire emissions account for roughly half of the total fire carbon emissions in the Global Fire Emissions Database (GFED) and GFAS time series, and fires over savanna regions have decreased over the past decade (Andela et al. 2017). This trend continued in 2022 over Northern Hemisphere Africa with emissions 21% below the 2003–20 average, marking the third successive year with lower fire activity than any years in the record before 2019 (Fig. 2.77a). Southern Hemisphere Africa also contributed to the trend, albeit to a lesser degree. Fire activity in tropical Asia—including Indonesia—was the lowest on record (Fig. 2.77c); here, wet La Niña-related conditions continued as the dominant physical driver. While the long-term global trend is partly driven by agricultural expansion into savanna ecosystems and its associated fragmentation of the landscape, unusual patterns of high or low rainfall and more or less environmental protection also influence interannual variability on top of the declining trend.

South America was the only continental region to experience above-average activity (+2%; Fig. 2.77b) in 2022, due to the increased numbers of fires across northern Argentina, Paraguay, Bolivia, and some parts of the Amazon. In this region, 2020 and 2022 saw the highest fire activity of the last 12 years, which is consistent with the significant droughts in much of this region

(section 2d10) as well as increased deforestation in the Amazon since 2019 for Brazil (http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes). At higher northern latitudes, North America and northern Asia experienced negative anomalies (–10% and –45%, respectively) overall, although there was regionally increased fire activity during the summer months for Alaska, the Yukon and Northwest Territories, and Khabarovsk Krai.

GFAS is operated to produce global fire emission estimates in near real-time by the Copernicus Atmosphere Monitoring Service (CAMS; Kaiser et al. 2012). It is based on the MODIS

<figure><img src="image_120.png" /><figcaption>Fig. 2.76. Global map of fire activity in 2022 in terms of carbon consumption (g C m⁻² yr⁻¹). (Source: GFASv1.4.)</figcaption></figure>

<figure><img src="image_121.png" /><figcaption>Fig. 2.77. Regional time series of monthly (lines in Tg C month⁻¹) and annual (squares in Tg C yr⁻¹) biomass burning activity in (a) Northern Hemisphere Africa, (b) South America, and (c) tropical Asia.</figcaption></figure>
Fire Radiative Power products (Giglio et al. 2016). Here, we use consistent reprocessing with input from MODIS Collection 6 for the entire period of 2003–22. 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 in order to compensate for several outages of observations from the MODIS instruments during 2022. The *Aqua* and *Terra* satellites carrying MODIS have been in drifting orbits during 2022 with an overall shift of the local equator crossing times of <15 minutes (https://aqua.nasa.gov/sites/default/files/AquaStatus.pdf; https://terra.nasa.gov/wp-content/uploads/2022/09/Orbit-Changes-Terra.pdf); we consider this to be negligible for GFAS. The time series in Plate 1.1 also puts GFAS 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, T. Crimmins, N. Estrella, A. Menzel, I. Mrekaj, J. O’Keefe, T. Park, A. D. Richardson, J. Rozkošný, T. Rutishauser, T. H. Sparks, S. J. Thackeray, A. J. H. van Vliet, and F. West.

During 2022, land surface phenology derived from the MODIS Normalized Difference Vegetation Index (NDVI, a dimensionless index of the difference between visible and near-infrared reflectance of vegetation cover, where higher values indicate denser, green vegetation) over the Northern Hemisphere (>30°N; Park et al. 2016), was compared to NDVI over the 2000–20 baseline period (Fig. 2.78). The hemispheric mean Start and End of Season ($SOS_M$ and $EOS_M$) for the

<figure><img src="image_122.png" /></figure>

Fig. 2.78. (a) Time series of area-mean anomalies (days relative to 2000–20 baseline) in MODIS Normalized Difference Vegetation Index (NDVI)-based vegetation-growing-season onset (start of season, $SOS_M$, green) and satellite-derived (MERRA-2) spring (Mar–May, pink) temperature for the Northern Hemisphere. (b) Same as (a) but for growing season end (end of season, $EOS_M$, green) and autumn (Sep–Nov, pink) temperature. Note the temperature scale reversal for (a). (c), (d) Spatial pattern of (c) $SOS_M$ and (d) $EOS_M$ anomalies in 2022 with respect to the baseline. Highlights identify the location of sites shown in Figs. 2.79 and 2.80 and discussed in the text (country mean phenology data, yellow; site PhenoCam and phenology observations, magenta; lake phytoplankton, blue).
baseline period is 16 May and 11 October, respectively, and in 2022, SOS<sub>M</sub> was 1.1 days earlier and EOS<sub>M</sub> was similar to the baseline (Figs. 2.78a,b; Table 2.11). Regionally, earlier SOS<sub>M</sub> occurred across central and northeastern Eurasia (EA), Alaska, and northern Canada. The warmer spring (+0.23°C) led to 2.5 days earlier SOS<sub>M</sub> in EA. Most of North America (NA) experienced later SOS<sub>M</sub> (+1.7 days) in 2022 (Fig. 2.78c) due to the colder (–0.18°C) and wetter (+0.05 mm day<sup>-1</sup>) spring, particularly over U.S. croplands. Northern NA and western EA showed later EOS<sub>M</sub> (+0.8 days) whereas earlier EOS<sub>M</sub> (–0.4 days) was observed in southern NA and eastern EA (Fig. 2.78d). Time series of the two decades of the MODIS record show continuous advancement and delay trends in SOS<sub>M</sub> and EOS<sub>M</sub> (SOS<sub>M</sub>: –1.6±0.4 days decade<sup>-1</sup>, p<0.001; EOS<sub>M</sub>: +1.2±0.5 days decade<sup>-1</sup>, p=0.07).

PhenoCam data (Seyednasrollah et al. 2019) helped link the coarse resolution of satellite-derived phenology with fine-resolution visual observations on organisms and ecosystems (Richardson 2019). PhenoCam estimates (2008–22) 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

<table><caption>Table 2.11. Start of season (SOS), end of season (EOS), and full bloom dates (FBD; for native cherry tree observations only) for MODIS mean across the Northern Hemisphere (NH MODIS, >30°N), land phenology records in USA (Harvard: PhenoCam, red oak, and MODIS mean across Harvard Forest; USA National Phenology Network, USA-NPN, mean covering northeastern United States), Europe oak records (Germany, Netherlands, Slovakia, UK, and MODIS mean across UK) and Japan (native cherry tree observations and MODIS mean across Japan). The baseline period is 2000–20 for all records except PhenoCam and NPN, which have baseline periods of 2008–22 and 2011–22, respectively, spanning the available observations. Negative/positive values represent earlier/later dates.</caption><thead><tr><th>Location/Record</th><th>SOS/FBD 2022 (date)</th><th>SOS/FBD Baseline (date)</th><th>SOS/FBD Difference 2022-Baseline (days)</th><th>EOS 2022 (date)</th><th>EOS Baseline (date)</th><th>EOS Difference 2022-Baseline (days)</th></tr></thead><tbody><tr><td>NH MODIS</td><td>15 May</td><td>16 May</td><td>-1</td><td>11 Oct</td><td>11 Oct</td><td>0</td></tr><tr><td>Harvard MODIS</td><td>26 Apr</td><td>24 Apr</td><td>-2</td><td>1 Dec</td><td>5 Dec</td><td>+3</td></tr><tr><td>Harvard PhenoCam</td><td>9 May</td><td>7 May</td><td>-2</td><td>16 Oct</td><td>22 Oct</td><td>+5</td></tr><tr><td>Harvard red oak</td><td>13 May</td><td>6 May</td><td>-7</td><td>18 Oct</td><td>19 Oct</td><td>0</td></tr><tr><td>USA-NPN</td><td>7 May</td><td>6 May</td><td>-1</td><td>27 Sep</td><td>3 Oct</td><td>+6</td></tr><tr><td>UK MODIS</td><td>6 Apr</td><td>30 Mar</td><td>-7</td><td>23 Dec</td><td>12 Dec</td><td>-10</td></tr><tr><td>Germany</td><td>29 Apr</td><td>28 Apr</td><td>+1</td><td>12 Nov</td><td>6 Nov</td><td>+6</td></tr><tr><td>Netherlands</td><td>17 Apr</td><td>20 Apr</td><td>-3</td><td>19 Dec</td><td>27 Nov</td><td>+22</td></tr><tr><td>Slovakia</td><td>1 May</td><td>26 Apr</td><td>+6</td><td>14 Oct</td><td>18 Oct</td><td>-4</td></tr><tr><td>UK</td><td>19 Apr</td><td>24 Apr</td><td>-5</td><td>10 Dec</td><td>1 Dec</td><td>+9</td></tr><tr><td>Japan MODIS</td><td>9 Apr</td><td>21 Apr</td><td>-12</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Japan</td><td>1 Apr</td><td>6 Apr</td><td>-5</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
2021) and MODIS (SOS<sub>M</sub> and EOS<sub>M</sub>) for the associated pixel (Figs. 2.79a,b). These were also compared with red oak observations contributed to *Nature's Notebook*, the United States National Phenology Network's (NPN) monitoring across the northeastern United States (Rosemartin et al. 2014; Crimmins et al. 2022). In 2022, SOS<sub>PC</sub>, SOS<sub>RO</sub>, and SOS<sub>M</sub> were zero, three, and two days later, respectively, compared to 2021. EOS<sub>PC</sub>, EOS<sub>RO</sub>, and EOS<sub>M</sub> were 17, 7, and 13 days earlier compared to 2021 (Figs. 2.79a,b). Interannual variability of start and end of season for Harvard Forest are broadly consistent with those from the NPN, which underscore the value of volunteer-contributed data for tracking phenology at local to continental scales. The earlier EOS<sub>PC</sub> in 2022 yielded a growing season of 160 days, more than two weeks shorter compared to 2021 and a full week shorter compared to the 2011–20 average (167±7 days; Table 2.11).

‘First leaf’/’start of leaf unfolding’ (SOS₀) and ‘leaf falling’/’bare tree’ (EOS₀) dates for oak (*Quercus robur* and/or *Q. petraea*) from Germany (D), the United Kingdom (UK), Netherlands (NL), and Slovakia (SK) are presented (Figs. 2.79c,d). In 2022, SOS₀ dates varied across Europe from 5 and 3 days earlier than the baseline (in UK and NL) to 1 and 6 days later (in D and SK), while EOS₀ dates were earlier by 4 days (SK) and later by 6, 9, and 22 days (D, UK, and NL; Table 2.11).

Start- and end-of-season events across Europe are strongly influenced by temperature (Menzel et al. 2020); SOS₀ advances by four to six days per 1°C increase in the mean February–April temperature, and EOS₀ is delayed by two to four days per 1°C increase in the mean September–October temperature. In 2022, the later EOS₀ dates in D, UK, and NL were, in part, associated with unusually high autumn temperatures, encouraging leaf activity and resulting in a longer 2022 ‘oak season’ at these locations (for UK, see Kendon et al. 2022). In SK, high spring, summer, and autumn air temperatures and soil moisture deficits in 2022 encouraged later leaf out and early leaf fall and led to the shortest oak season since 2000.

<figure><img src="image_123.png" /><figcaption>**Fig. 2.79.** Day of year of spring (green shades) and autumn (orange and brown) vegetation phenology indicators for (a),(b) Harvard Forest, Massachusetts, derived from PhenoCam, red oak ground observations, MODIS remote sensing (dashed), and USA-National Phenology Network (NPN) regional-scale means for red oak (calculated across the northeastern states of Pennsylvania, New Jersey, New York, Connecticut, Rhode Island, Massachusetts, Vermont, New Hampshire, and Maine, ±1 std. error shaded), (c),(d), Germany, UK, Netherlands, and Slovakia mean oak and MODIS Europe mean, and (e) Kyoto, Japan, full bloom day observations for cherry trees and MODIS Japan mean start of season.</figcaption></figure>
In Kyoto, Japan, full bloom dates (FBD) for a native cherry tree species, *Prunus jamasakura*, were acquired from historical documents dating back to 812 AD (Aono and Kazui 2008) and updated with current observations at Arashiyama, which are compiled from daily observations made by railway passengers at train stations and recorded in newspapers and on web sites (Fig. 2.79e). In 2022, FBD was five days earlier than the 2000–20 mean (Table. 2.11).

Monitoring data on lake water concentrations of the photosynthetic pigment chlorophyll-a were available to estimate spring phytoplankton phenology in 1 Southern Hemisphere and 10 Northern Hemisphere lakes (Fig. 2.80). Seasonal timing was quantified for start of season (SOS_L; Park et al. 2016), day of maximum concentration (DOM_L), and center of gravity (COG_L, an estimate of the mid-point of the plankton bloom; Edwards and Richardson 2004). Lake basins showed great interannual variation and mixed phenological behavior in 2022 relative to the 2000–20 baseline. The Norway lake, Mjøsa, and the Southern Hemisphere lake in New Zealand, Taupo, showed different seasonal changes to other lakes related to snow melt and southern season, respectively. SOS_L occurred earlier than the baseline median for most (8 of 11) lakes, and DOM_L occurred later in most lakes (8 of 11). For COG_L, no consistent pattern was observed.

## 5. VEGETATION OPTICAL DEPTH

—R. M. Zotta, R. van der Schalie, W. Preimesberger, L. Moesinger, R. A. M. de Jeu, and W. Dorigo

Microwave radiation emitted or reflected by Earth's surface is strongly affected by available surface water, including that which is stored in living biomass. The portion of the radiance attenuated by the canopy is expressed by vegetation optical depth (VOD), a parameter used in radiative transfer models to describe radiance interaction with vegetation. VOD is closely related to canopy water content (Konings et al. 2017), leaf area index (Vreugdenhil et al. 2017), and gross primary production (Teubner et al. 2019; Wild et al. 2022) and is a good indicator of vegetation response to climate variability. Positive

<figure><img src="image_124.png" /><figcaption>**Fig. 2.80.** 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. Boxplots show variation during the 2000–20 baseline period, and red dots show 2022 values. Dashed line identifies Northern Hemisphere (Blelham Tarn in 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>
VOD anomalies correspond to above-average vegetation development, while negative VOD anomalies indicate stressed or underdeveloped vegetation.

Globally, the year 2022 saw similar annual VOD anomaly patterns to 2021 and 2020 (Dorigo et al. 2022, 2021). In the Southern Hemisphere, where there is a clear connection between vegetation activity and variations in the El Niño–Southern Oscillation (Miralles et al. 2014; Martens et al. 2017), anomalies became positive (Figs. 2.81, 2.82). The relationship with climate modes is not straightforward, however, as VOD is also affected by drivers such as moisture availability, temperature, radiation, carbon dioxide fertilization, and land management (Gonsamo et al. 2021; Reichstein et al. 2013).

Widespread positive anomalies are seen in southern Africa and Australia (Plate 2.1af). In these regions, the patterns became more positive compared to 2021 (Supp. Fig. A2.9), coinciding with the persistence of La Niña throughout 2022, which brought above-average rainfall (see sections 7e5, 7h4). Other regions where positive patterns have prevailed include the Sahel, India, and northeastern China. Negative VOD anomalies occurred in the Great Plains in North America, the Parana River Basin in South America, and eastern Africa (Plate 2.1af). Most of these negative patterns coincided with precipitation deficits associated with La Niña and/or the negative Indian Ocean dipole mode (section 2e1; Mo et al. 2009; Santos et al. 2021; Anderson et al. 2022; Barlow et al. 2002). The negative VOD anomalies in the Great Plains (Plate 2.1af) coincide with reports of poor vegetation health issued by the NOAA National Centers for Environmental Information (NOAA 2023) for the American West, central and southern Plains, through the Mississippi Valley, and into the northeast. The negative patterns in the Parana River basin occurred in a region with intense wildfire activity (section 2h3), facilitated by dry conditions that persisted for a fourth consecutive year (Neumann et al. 2021). Below-average precipitation trends continued in East Africa (Anderson et al. 2022) and intensified towards the end of the year, leading to strong negative VOD anomalies (Plate 2.1af). Other remarkable negative VOD anomalies were observed in Europe from July to September (Supp. Fig. A2.10). Notably, in Spain, France, central and northern Italy, central Germany, Hungary, Romania, Slovenia,

<figure><img src="image_125.png" /><figcaption>**Fig. 2.81.** Yearly CXKu vegetation optical depth (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. The SOI has been divided by 10, so that values >0.7 indicate La Niña and values <-0.7 indicate El Niño episodes. (Source: VODCA; http://www.bom.gov.au/climate/enso/soi/.)</figcaption></figure>

<figure><img src="image_126.png" /><figcaption>**Fig. 2.82.** Time–latitude diagram of vegetation optical depth (VOD) anomalies (1991–2020 base period). Data are masked where no retrievals is possible, where no vegetation is present (bare soil), or where the quality is not assured and flagged due to frozen soil, radio frequency interference, etc. (Source: VODCA.)</figcaption></figure>
and Croatia, the main summer crops were severely affected due to exceptionally hot and dry weather conditions (Baruth et al. 2022). Negative VOD prevailed in Algeria and Morocco (Plate 2.1af) as well, with the Food and Agriculture Organization (FAO) reporting below-average cereal production due to widespread drought (FAO 2022). In China, vegetation conditions were affected by a precipitation deficit and heatwave in August (Toreti et al. 2022), which appear as negative VOD anomalies in central China, especially during July, August, and September (Supp. Fig. A2.10).

Several long-term patterns resulting from land-use change also prevailed in 2022 (Plate 2.1af; Dorigo et al. 2021). Due to deforestation and land degradation, below-average VOD occurred in Mongolia, Bolivia, Paraguay, and Brazil (Song et al. 2018). The intensification of agricultural production and reforestation led to above-average VOD in India and northeastern China, respectively (Song et al. 2018).

The VOD anomalies were computed from the VOD Climate Archive (VODCA; Moesinger et al. 2020). VODCA fuses VOD observations derived from several space-borne radiometers (Special Sensor Microwave/Imager [SSM/I], Tropical Rainfall Measuring Mission [TRMM], WindSat, Advanced Microwave Scanning Radiometer-Earth Observing System [AMSR-E] and Advanced Microwave Scanning Radiometer 2 [AMSR2]) through the Land Parameter Retrieval Model (Meesters et al. 2005; van der Schalie et al. 2017) into a harmonized, long-term (1987–2022) dataset. VODCA version 2 contains individual products for Ku-band (covering 1987–2022), X-band (1997–2022), C-band (2002–22), and L-band (2010–22), as well as a multi-frequency product called VODCA CXKu (1987–2022) which blends the highly correlated Ku-, X-, and C-band observations. All VODCA products are at 0.25° spatial and daily temporal resolutions. Here, we used VODCA CXKu to compute anomalies from the long-term (1991–2020) climatology. VODCA CXKu is indicative of upper canopy dynamics.
# Acknowledgments

## 2.a.1 Overview

The editors thank the BAMS special editor, Mike Alexander, the five anonymous reviewers, as well as David Parker, John Kennedy, Katrina MacNeill, and Josh Blannin for their insight, thoughts, and suggestions when developing this chapter. We also thank Fumi Sezaki (JMA), Julien Nicolas (ECMWF), and Mike Bosilovich (NASA) for their help in providing the reanalysis data used in this chapter. R. J. H. Dunn and K. M. Willett were supported by the Met Office Hadley Centre Climate Programme funded by BEIS. R. J. H. Dunn also thanks Bill Little for his assistance with the plots.

## 2.b.2 Lake surface water temperature

Lake surface water temperatures from satellite data have been generated within the Climate Change Initiative Lakes project funded by the European Space Agency (4000125030/18/I-NB) with adaptation and extensions funded by the EU Copernicus Climate Change Service (C3S) programme. Part of the in situ data used for the validation of the satellite data and for this report have kindly been made publicly available by the Fisheries and Oceans Canada (FOC), the National Data Buoy Center from NOAA, the Hungarian Meteorological Office, the Upper Great Lakes Observing System (UGLOS), the North Temperate Lakes Long-Term Ecological Research (NTL-LTER), and the Swedish Infrastructure for Ecosystem Science (SITES), and the King County Water and Land Resources Division, Washington. The authors gratefully acknowledge the late Alon Rimmer for always supplying data for Lake Kinneret. The in situ temperature measurements for Lake Baikal were supported by Foundation for support of applied ecological studies "Lake Baikal" (https://baikalfoundation.ru/project/tochka-1/). Temperature data for the UK lakes were supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCaPE program delivering National Capability.

## 2.b.3 Night marine air temperature

R. C. Cornes, T. Cropper and E. C. Kent were supported by the NERC NC CLASS programme (NE/R015953/1) and the NERC GloSAT project (NE/S015647/2). R. Junod was supported by funding from the U.S. Department of Energy (DE-SC0023332).

## 2.b.4 Surface temperature extremes

R. J. H. Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS.

## 2.b.5 Tropospheric temperature

Work performed by Stephen Po-Chedley at Lawrence Livermore National Laboratory (LLNL) was supported by LDRD 23-FS-009 and performed under the auspices of the DOE under Contract DE-AC52-07NA27344.

## 2.c.1 Permafrost temperatures and active layer thickness

The Circumpolar Active Layer Monitoring (CALM) program is funded by the US NSF Project OPP-1836377. 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. Research on James Ross Island was supported by the Czech Antarctic Research Programme and the Czech Science Foundation project (GM22-28659M). The Chinese Permafrost Monitoring Network is financially supported by the Chinese National Science Foundation (41931180) and the Cryosphere Research Station on Qinghai-Xizang Plateau, CAS.
### 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. The 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.3 Alpine glaciers

The World Glacier Monitoring Service data collection system is the basis for nearly all data used in this section.

### 2.c.4 Lake ice

We thank Alexander Mills, Huaxia Yao, Merja Pulkkanen, Shin-Ichiro Matsuzaki, Jan Henning L'Abée-Lund, Don Pierson, Lars Rudstam, Beth Kohlman, BJ Bauer, Douglas Pierzina, Bonny Pederson, Brendan Wiltse, Carol Wendorf, Cheryl and Dorothy Zingler, Craig Hillman, Calvin Maurer, Dale Robertson, David Kahan, Doug Fitzgerald, Earl Cook, Gay Alberts Ruby, Greg Sass, John Maier, James and Sharon Fenner, Susan Reineking, Jerry Evans, Jerry Sondreal, Tom Stangl, Jim and Judy Daugherty, Joel Rasmussen, John and Catherine Bart, Joy Krubsack, Kay and Rich Rezanka, Kay Wepfer, Ken Blumenfeld, Pete Boulay, Holly Waterfield, Larry Peterson, Lolita Olson, Lowell Dague, Marge Kellor, Mark Biller, Michael Kolecheck, Michael Bradley, Michael Allen, Michael Traufler, Mike, Jeff, and Thomm Backus, Mickey and Dennis Chick, Ann LaLiberte, Nancy Steenport, Patricia Bebak, Patrick Collins, Peter Bearup, Joe Jenkins, Bob Katzenberger, Ronald Jones, Sandra Anderson, Scott Schoepp, Travis Campbell, Susan Verhaalen, Thomas Sommerfeldt, Water Dahlke III, Rachel Dahlke, William Hanson, and Clare and Dan Shirley for their dedication and efforts in collecting and sharing *in situ* ice phenological records over the past 32+ years with us.

### 2.c.5 Northern Hemisphere snow cover extent

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. 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 Precipitation extremes

The National Center for Atmospheric Research is sponsored by the National Science Foundation under Cooperative Agreement No. 1852977.

### 2.d.6 Cloudiness

CERES EBAF data were obtained from the NASA Langley Research Center CERES ordering tool at https://ceres.larc.nasa.gov/data/.
### 2.d.7 Lake water level

B. M. Kraemer recognizes support from the grant "SeeWandel: Life in Lake Constance - the Past, Present and Future" within the framework of the Interreg V programme "Alpenrhein-Bodensee-Hochrhein (Germany/ Austria/Switzerland/Liechtenstein)" whose funds are provided by the European Regional Development Fund as well as the Swiss Confederation and cantons. M.F. Meyer was supported by a USGS Mendenhall Fellowship from the Water Mission Area. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

### 2.d.8 Groundwater and terrestrial water storage

Work on the Groundwater and Terrestrial Water Storage section was funded by NASA's GRACE-FO Science Team.

### 2.d.9 Soil moisture

This study uses satellite soil moisture observations from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS): Soil moisture gridded data from 1978 to present. (Accessed on 19-01-2023), 10.24381/cds.d7782f18

### 2.d.10 Drought

Jonathan Barichivich was supported by the European Research Council (ERC) under the Horizon Europe research and innovation programme (ERC-starting grant CATES, grant agreement No. 101043214). 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. 101088405 (HEAT). M.F. McCabe and H.E. Beck are supported by the King Abdullah University of Science and Technology.

### 2.e.2 Surface winds

C. Azorin-Molina was supported by CSIC-UV-GVA and funded by AICO/2021/023, LINCGLOBAL-CSIC ref. INCGLO0023, INTRAMURAL-CSIC ref. 202230I068, and 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. Z. Zeng was supported by the National Natural Science Foundation of China grant 42071022.

### 2.e.4 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. ISS LIS acknowledges support from the NASA Science Mission Directorate.
**2.f.1 Earth radiation budget at top of atmosphere**

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 Ed4.2 and processes the FLASHFlux TISA version 4A.

**2.g.3 Aerosols**

Samuel Rémy, Melanie Ades, and Mihai Alexe are grateful to the EU Copernicus Atmosphere Monitoring Service (CAMS), which supports the CAMS reanalysis used in the aerosols section.

**2.g.4 Stratospheric ozone**

Carlo Arosio, Melanie Coldewey-Egbers, Diego Loyola, Viktoria Sofieva, Alexei Rozanov, and Mark Weber are grateful to the EU Copernicus Climate Change Service, 312b Lot4 Ozone and to ESA's Climate Change Initiative Ozone (CCI+) projects for supporting the generation and extension of the GTO-ECV total ozone and SAGE-CCI-OMPS data records. Mark Weber, Carlo Arosio, Kleareti Tourpali, and Alexei Rozanov are grateful for the support by the ESA project "Ozone Recovery from Merged Observational Data and Model Analysis (OREGANO)". 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. Jeannette Wild was supported by NOAA grant NA19NES4320002 (Cooperative Institute for Satellite Earth System Studies - CISESS) at the University of Maryland/ESSIC. Melanie Coldewey-Egbers and Diego Loyola acknowledge the partial support by the DLR projects MABAK and INPULS.

**2.g.6 Tropospheric ozone**

O. R. Cooper and K.-L. Chang were supported by NOAA cooperative agreement NA22OAR4320151.

**2.g.7 Carbon monoxide**

The Copernicus Atmosphere Monitoring Service is funded by the European Union.

**2.h.2 Terrestrial vegetation dynamics**

N. Gobron 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, MD 20771. MERIS products were processed at the Grid On Demand facility of the European Space Agency (ESA/ESRIN) using JRC software code.

**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.3 Biomass burning**

J. W Kaiser and M. Parrington were supported by the Copernicus Atmosphere Monitoring Service implemented by ECMWF on behalf of the European Commission. Data processing facilities were provided by the Max Planck Institute for Chemistry, Mainz, Germany.

**2.h.4 Phenology of primary producers**

Debbie Hemming acknowledges support from the Met Office Hadley Centre Climate Programme funded by BEIS, and thanks all co-authors for their interesting and helpful contributions. 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. Theresa Crimmins and the USA-NPN acknowledge support from the National Science Foundation through the Division of Biological Infrastructure (award 2031660), the US Fish and Wildlife Service (agreements F16AC01075 and F19AC00168), and the US Geological Survey (G14AC00405 and G18AC00135). The USA National Phenology Network thanks the many participants contributing phenology observations to *Nature's Notebook*. 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 Nicole Estrella acknowledge support from the Bavarian State Ministry of Science and the Arts in the context of the Bavarian Climate Research Network (BayKlif) (Bavarian Citizen Science Portal for Climate Research and Science Communication [BAYSICS project]). The Slovak Hydrometeorological Institute thanks all its volunteer observers for participating in the phenological observation program. 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 of alpine LAkes (OLA), © SOERE OLA-IS, AnaEE-France, INRAE of Thonon-les-Bains, CIPEL, CISALB.

**2.h.5 Vegetation optical depth**

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

We also acknowledge support from the ESA Climate Change Initiative and the Copernicus Climate Change Service (C3S).

**Sidebar 2.1 Humid heat extremes**

Kate Willett was supported by the Met Office Hadley Centre Climate Programme funded by BEIS.
# Appendix 1: Chapter 2 – Acronyms

<table><tr><td>4D-VAR</td><td>four-dimensional variational data assimilation</td></tr><tr><td>AAO</td><td>Antarctic Oscillation</td></tr><tr><td>AATSR</td><td>Advanced Along Track Scanning Radiometer</td></tr><tr><td>AGAGE</td><td>Advanced Global Atmospheric Gases Experiment</td></tr><tr><td>AIRS</td><td>Atmospheric Infrared Sounder</td></tr><tr><td>ALT</td><td>active layer thickness</td></tr><tr><td>AMSR2</td><td>Advanced Microwave Scanning Radiometer 2</td></tr><tr><td>AMSR-E</td><td>Advanced Microwave Scanning Radiometer-Earth Observing System</td></tr><tr><td>AMSU</td><td>Advanced Microwave Sounding Unit</td></tr><tr><td>AO</td><td>Arctic Oscillation</td></tr><tr><td>AOD</td><td>aerosol optical depth</td></tr><tr><td>ASCAT</td><td>Advanced Scatterometer</td></tr><tr><td>ASR</td><td>absorbed solar radiation</td></tr><tr><td>BDC</td><td>Brewer-Dobson circulation</td></tr><tr><td>BOM</td><td>Bureau of Meteorology</td></tr><tr><td>BUV</td><td>backscattering ultraviolet</td></tr><tr><td>C3S</td><td>Copernicus Climate Change Service</td></tr><tr><td>CALIPSO</td><td>Cloud-Aerosol LIDAR and Infrared Pathfinder Satellite Observations</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>CCI</td><td>Climate Change Initiative</td></tr><tr><td>CCMI</td><td>Chemistry Climate Model Initiative</td></tr><tr><td>CDR</td><td>climate data record</td></tr><tr><td>CEI4</td><td>Climate Extremes Index component 4</td></tr><tr><td>CERES EBAF</td><td>Clouds and the Earth's Radiant Energy System Energy Balance and Filled</td></tr><tr><td>CFC-11</td><td>trichlorofluoromethane</td></tr><tr><td>CFC</td><td>chlorofluorocarbon</td></tr><tr><td>CH<sub>3</sub>CCl<sub>3</sub></td><td>methyl chloroform</td></tr><tr><td>CH<sub>4</sub></td><td>methane</td></tr><tr><td>CHIRPS</td><td>Climate Hazards Group Infrared Precipitation with Station data</td></tr><tr><td>CLASSnmat</td><td>Climate Linked Atlantic Sector Science night marine air temperature</td></tr><tr><td>CMIP</td><td>Coupled Model Intercomparison Project</td></tr><tr><td>CMIP6</td><td>Phase 6 of the Coupled Model Intercomparison Project</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>COG<sub>L</sub></td><td>center of gravity</td></tr><tr><td>COSMIC</td><td>Constellation Observing System for Meteorology, Ionosphere, and Climate</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>DOM<sub>L</sub></td><td>day of maximum concentration</td></tr><tr><td>DU</td><td>Dobson units</td></tr><tr><td>EBAF</td><td>Energy Balance and Filled</td></tr><tr><td>ECMWF</td><td>European Centre for Medium-Range Weather Forecasts</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>ENSO</td><td>El Niño–Southern Oscillation</td></tr><tr><td>EOS</td><td>end of season</td></tr><tr><td>ERA5</td><td>European Centre for Medium-Range Weather Forecasts Reanalysis version 5</td></tr><tr><td>ERB</td><td>Earth radiation budget</td></tr></table>
<table>
   <tr>
    <td>ERFs</td>
    <td>effective radiative forcings</td>
   </tr>
   <tr>
    <td>ESA</td>
    <td>European Space Agency</td>
   </tr>
   <tr>
    <td>ET</td>
    <td>Penman-Monteith Potential Evapotranspiration</td>
   </tr>
   <tr>
    <td>ETCCDI</td>
    <td>Expert Team in Climate Change Detection and Indices</td>
   </tr>
   <tr>
    <td>EUR</td>
    <td>Eurasia</td>
   </tr>
   <tr>
    <td>EUR</td>
    <td>Europe</td>
   </tr>
   <tr>
    <td>FAO</td>
    <td>Food and Agriculture Organization</td>
   </tr>
   <tr>
    <td>FAPAR</td>
    <td>fraction of absorbed photosynthetically active radiation</td>
   </tr>
   <tr>
    <td>FBD</td>
    <td>full bloom dates</td>
   </tr>
   <tr>
    <td>FF</td>
    <td>fossil fuel</td>
   </tr>
   <tr>
    <td>FLASHFlux</td>
    <td>Fast Longwave and Shortwave Radiative Fluxes</td>
   </tr>
   <tr>
    <td>FP</td>
    <td>frost point</td>
   </tr>
   <tr>
    <td>GAW</td>
    <td>Global Atmosphere Watch</td>
   </tr>
   <tr>
    <td>GCM</td>
    <td>general circulation model</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>GHCND</td>
    <td>Global Historical Climatology Network Daily</td>
   </tr>
   <tr>
    <td>GHCNDEX</td>
    <td>Global Historical Climatology Network Index</td>
   </tr>
   <tr>
    <td>GISTEMP</td>
    <td>GISS Surface Temperature Analysis</td>
   </tr>
   <tr>
    <td>GLEAM</td>
    <td>Global Land Evaporation Assessment Model</td>
   </tr>
   <tr>
    <td>GLM</td>
    <td>Geostationary Lightning Mapper</td>
   </tr>
   <tr>
    <td>GNSS</td>
    <td>global navigation satellite system</td>
   </tr>
   <tr>
    <td>GOES</td>
    <td>Geostationary Operational Environmental Satellite</td>
   </tr>
   <tr>
    <td>GOME</td>
    <td>Global Ozone Monitoring Experiment</td>
   </tr>
   <tr>
    <td>GOZCARDS</td>
    <td>Global Ozone Chemistry and Related Trace Gas Data Records for the Stratosphere</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>GPS 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>G-REALM</td>
    <td>Global Reservoir and Lake Monitoring</td>
   </tr>
   <tr>
    <td>GSL</td>
    <td>Global Snow Lab</td>
   </tr>
   <tr>
    <td>GTN-P</td>
    <td>Global Terrestrial Network for Permafrost</td>
   </tr>
   <tr>
    <td>GWP</td>
    <td>global warming potential</td>
   </tr>
   <tr>
    <td>H₂O</td>
    <td>water vapor</td>
   </tr>
   <tr>
    <td>HadCRUT5</td>
    <td>Hadley Centre/Climatic Research Unit Temperature version 5</td>
   </tr>
   <tr>
    <td>HadISD3</td>
    <td>Hadley Centre Integrated Surface Database version 3</td>
   </tr>
   <tr>
    <td>HadISDH</td>
    <td>Hadley Centre Integrated Surface Database Humidity</td>
   </tr>
   <tr>
    <td>HadSST</td>
    <td>The Met Office Hadley Centre's sea surface temperature dataset</td>
   </tr>
   <tr>
    <td>HCFC</td>
    <td>hydrochlorofluorocarbon</td>
   </tr>
   <tr>
    <td>HFC</td>
    <td>hydrofluorocarbon</td>
   </tr>
   <tr>
    <td>HIRS</td>
    <td>High-Resolution Infrared Sounder</td>
   </tr>
   <tr>
    <td>HTHH</td>
    <td>Hunga Tonga-Hunga Ha'apai</td>
   </tr>
   <tr>
    <td>IOD</td>
    <td>Indian Ocean dipole</td>
   </tr>
   <tr>
    <td>IPCC AR6</td>
    <td>Intergovernmental Panel on Climate Change Sixth Assessment Report</td>
   </tr>
   <tr>
    <td>ISS</td>
    <td>International Space Station</td>
   </tr>
   <tr>
    <td>ITCZ</td>
    <td>Intertropical Convergence Zone</td>
   </tr>
   <tr>
    <td>JRA-55</td>
    <td>Japanese 55-year Reanalysis</td>
   </tr>
   <tr>
    <td>LIS</td>
    <td>Lightning Imaging Sensor</td>
   </tr>
   <tr>
    <td>LLGHG</td>
    <td>long-lived greenhouse gases</td>
   </tr>
   <tr>
    <td>LOWESS</td>
    <td>locally weighted scatterplot smoothing</td>
   </tr>
  </table>
<table>
  <tr>
    <td>LSWT</td>
    <td>lake-surface water temperature</td>
  </tr>
  <tr>
    <td>LTT</td>
    <td>lower-tropospheric temperature</td>
  </tr>
  <tr>
    <td>LWCRE</td>
    <td>longwave cloud radiative effect</td>
  </tr>
  <tr>
    <td>MACCity</td>
    <td>Monitoring Atmospheric Composition and Climate megacity</td>
  </tr>
  <tr>
    <td>MAT</td>
    <td>marine air temperature</td>
  </tr>
  <tr>
    <td>MCS</td>
    <td>marine cold-spell</td>
  </tr>
  <tr>
    <td>MCS</td>
    <td>mesoscale convective systems</td>
  </tr>
  <tr>
    <td>MEGAN2.1</td>
    <td>Model of Emissions of Gases and Aerosols from Nature version 2.1</td>
  </tr>
  <tr>
    <td>MERRA-2</td>
    <td>Modern-Era Retrospective Analysis for Research and Applications version 2</td>
  </tr>
  <tr>
    <td>MHW</td>
    <td>marine heatwave</td>
  </tr>
  <tr>
    <td>MLO</td>
    <td>Mauna Loa Observatory</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>MSLP</td>
    <td>mean sea-level pressure</td>
  </tr>
  <tr>
    <td>MSU</td>
    <td>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>NAE</td>
    <td>North Atlantic/European</td>
  </tr>
  <tr>
    <td>NAO</td>
    <td>North Atlantic Oscillation</td>
  </tr>
  <tr>
    <td>NDACC</td>
    <td>Network for the Detection of Atmospheric Composition Changes</td>
  </tr>
  <tr>
    <td>NDVI</td>
    <td>Normalized Difference Vegetation Index</td>
  </tr>
  <tr>
    <td>NH</td>
    <td>Northern Hemisphere</td>
  </tr>
  <tr>
    <td>NIWA</td>
    <td>National Institute of Water and Atmospheric Research</td>
  </tr>
  <tr>
    <td>NMAT</td>
    <td>night marine air temperature</td>
  </tr>
  <tr>
    <td>NOAAGlobalTemp</td>
    <td>NOAA Merged Land Ocean Global Surface Temperature Analysis</td>
  </tr>
  <tr>
    <td>NPN</td>
    <td>National Phenology Network</td>
  </tr>
  <tr>
    <td>NRT</td>
    <td>near real time</td>
  </tr>
  <tr>
    <td>O<sub>3</sub></td>
    <td>tropospheric ozone</td>
  </tr>
  <tr>
    <td>ODGI</td>
    <td>Ozone Depleting Gas Index</td>
  </tr>
  <tr>
    <td>ODS</td>
    <td>Ozone-depleting substance</td>
  </tr>
  <tr>
    <td>OH</td>
    <td>hydroxyl radical</td>
  </tr>
  <tr>
    <td>OISST</td>
    <td>Optimum Interpolation Sea Surface Temperature</td>
  </tr>
  <tr>
    <td>OLR</td>
    <td>outgoing longwave radiation</td>
  </tr>
  <tr>
    <td>OMI</td>
    <td>Ozone Monitoring Instrument</td>
  </tr>
  <tr>
    <td>OMPS</td>
    <td>Ozone Mapping and Profiler Suite</td>
  </tr>
  <tr>
    <td>OMPS-L</td>
    <td>Ozone Mapping and Profiler Suite-Limb</td>
  </tr>
  <tr>
    <td>OMPS-N</td>
    <td>Ozone Mapping and Profiler Suite-Nadir</td>
  </tr>
  <tr>
    <td>PATMOS-X</td>
    <td>Pathfinder Atmospheres – Extended</td>
  </tr>
  <tr>
    <td>PERMOS</td>
    <td>Swiss Permafrost Monitoring Network</td>
  </tr>
  <tr>
    <td>PNA</td>
    <td>Pacific/North American</td>
  </tr>
  <tr>
    <td>q</td>
    <td>specific humidity</td>
  </tr>
  <tr>
    <td>QBO</td>
    <td>quasi-biennial oscillation</td>
  </tr>
  <tr>
    <td>QuikSCAT</td>
    <td>Quick Scatterometer</td>
  </tr>
  <tr>
    <td>RCP</td>
    <td>Representative Concentration Pathway</td>
  </tr>
  <tr>
    <td>RFaci</td>
    <td>aerosol-cloud interactions</td>
  </tr>
  <tr>
    <td>RFari</td>
    <td>aerosol-radiation</td>
  </tr>
  <tr>
    <td>RGIK</td>
    <td>rock glacier inventories and kinematics</td>
  </tr>
  <tr>
    <td>RGV</td>
    <td>rock glacier velocities</td>
  </tr>
  <tr>
    <td>RH</td>
    <td>relative humidity</td>
  </tr>
  <tr>
    <td>RSS</td>
    <td>Remote Sensing Systems</td>
  </tr>
  <tr>
    <td>RSW</td>
    <td>reflected shortwave</td>
  </tr>
</table>
<table>
   <tr>
    <td>SAGE</td>
    <td>Stratospheric Aerosol and Gas Experiment</td>
   </tr>
   <tr>
    <td>SAM</td>
    <td>Southern Annular Mode</td>
   </tr>
   <tr>
    <td>SAOZ</td>
    <td>Système D'Analyse par Observations Zénithales</td>
   </tr>
   <tr>
    <td>SAWS</td>
    <td>South African Weather Service</td>
   </tr>
   <tr>
    <td>SBUV</td>
    <td>Solar Backscatter Ultraviolet Radiometer</td>
   </tr>
   <tr>
    <td>SCE</td>
    <td>snow-cover extent</td>
   </tr>
   <tr>
    <td>SCIAMACHY</td>
    <td>Scanning Imaging Absorption Spectrometer for Atmospheric Cartography</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>SO<sub>2</sub></td>
    <td>sulfur dioxide</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>SPARC</td>
    <td>Spatial Analysis and Resource Characterization</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>SW</td>
    <td>shortwave</td>
   </tr>
   <tr>
    <td>SWCRE</td>
    <td>shortwave cloud radiative effect</td>
   </tr>
   <tr>
    <td>SWOT</td>
    <td>Surface Water and Ocean Topography</td>
   </tr>
   <tr>
    <td>TCO</td>
    <td>tropospheric column ozone</td>
   </tr>
   <tr>
    <td>TCWV</td>
    <td>total column water vapor</td>
   </tr>
   <tr>
    <td>TMI</td>
    <td>TRMM Microwave Imager</td>
   </tr>
   <tr>
    <td>TOA</td>
    <td>top-of-atmosphere</td>
   </tr>
   <tr>
    <td>TOB</td>
    <td>tropospheric ozone burden</td>
   </tr>
   <tr>
    <td>TRMM</td>
    <td>Tropical Rainfall Measuring Mission</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>T<sub>w</sub></td>
    <td>wet bulb temperature</td>
   </tr>
   <tr>
    <td>TWS</td>
    <td>terrestrial water storage</td>
   </tr>
   <tr>
    <td>UAHNMAT</td>
    <td>University of Alabama in Huntsville night-time marine air temperature</td>
   </tr>
   <tr>
    <td>UT</td>
    <td>upper tropospheric</td>
   </tr>
   <tr>
    <td>UTH</td>
    <td>upper-tropospheric humidity</td>
   </tr>
   <tr>
    <td>UV</td>
    <td>ultraviolet</td>
   </tr>
   <tr>
    <td>VEI</td>
    <td>Volcanic Explosivity Index</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>w.e.</td>
    <td>water equivalent</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>WOUDC</td>
    <td>World Ozone and Ultraviolet Radiation Data Centre</td>
   </tr>
   <tr>
    <td>WV</td>
    <td>water vapor</td>
   </tr>
  </table>
Appendix 2: Chapter 2 – Supplemental Material

<figure><img src="image_127.png" /><figcaption>Fig. A2.1. NASA GISS 2-m surface temperature anomalies (°C; 1991–2020 base period).</figcaption></figure>

<figure><img src="image_128.png" /><figcaption>Fig. A2.2. ERA5 2-m surface temperature anomalies (°C; 1991–2020 base period).</figcaption></figure>
<figure><img src="image_129.png" /><figcaption>Fig. A2.3. JRA-55 2-m surface temperature anomalies (°C; 1991–2020 base period).</figcaption></figure>

<figure><img src="image_130.png" /><figcaption>Fig. A2.4. HadCRUT5 2-m surface temperature anomalies (°C; 1991–2020 base period).</figcaption></figure>
<figure><img src="image_131.png" /></figure>

<figure><img src="image_132.png" /></figure>

<figure><img src="image_133.png" /></figure>

Fig. A2.5. (a) Extreme temperature anomalies in 2022 based on GHCNDEX for (a) hottest day of the year (°C: TXx), (b) annual number of warm days (TX90p), and (c) annual number of cool nights (TN10p). Base period is 1961–90.
<figure><img src="image_134.png" /></figure>

<figure><img src="image_135.png" /></figure>

<figure><img src="image_136.png" /></figure>

Fig. A2.6. Maps indicating grid cells where the temperature indices for 2022 ranked in the three highest or three lowest values based on GHCNDEX since 1951 for (a) hottest day of the year (TXx), (b) annual number of warm days (TX90p), and (c) annual number of cool nights (TN10p).
<figure><img src="image_137.png" /><figcaption>Fig. A2.7. Monthly average global lower-tropospheric temperature (LTT) anomalies (°C; 1991–2020 base period) for (a) radiosonde, (b) satellite, and (c) reanalysis datasets. Time series are smoothed using a 12-month running average. Annual averages are displayed for the RATPAC dataset.</figcaption></figure>
<figure><img src="image_138.png" /><figcaption>Fig. A2.8. Monthly average soil moisture anomalies for 2022 (m³ m⁻³; 1991–2020 base period). Data are masked where no retrieval is possible or where the quality is not assured and flagged, for example due to dense vegetation, frozen soil, or radio frequency interference. (Source: C3S Soil Moisture.)</figcaption></figure>
2.f.2 Mauna Loa Apparent Transmission

Simulation of Tonga aerosol dispersion in the layer 19-27 km for 8-18 Mar 2022, derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data.

https://figshare.com/articles/media/CALIOP_Latm-GMAO-19_27km_v2-10_20MAR-22Mp4/22704607/1

2.h.5 Vegetation Optical Depth

<figure><img src="image_139.png" /></figure>

Fig. A2.9. Difference in average CXKu vegetation optical depth (VOD) between the years 2022 and 2021. Brown/green colors indicate areas where VOD in 2022 was lower/higher than in 2021. (Source: VODCA.)
<figure><img src="image_140.png" /><figcaption>Fig. A2.10. VODCA monthly CXKu vegetation optical depth (VOD) anomalies for 2022 (1991–2020 base period). VOD cannot be retrieved over frozen or snow-covered areas, which is why they are masked in winter.</figcaption></figure>
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