section 6d), which was linked to strengthened westerly winds around the continent. This pattern was also associated with a stronger-than-normal subtropical jet stream extending from eastern Australia to Chile and Argentina (e.g., Montecinos et al. 2011). The negative phase of the PSA showed a statistically significant increasing trend on an annual basis from 1979 to 2014 and is consistent with positive trends in the SAM, making it an important SH diagnostic (Irving and Simmonds 2016).

<figure><img src="image_93.png" /><figcaption>Fig. 2.47. Northern Hemisphere circulation in 2023. (a)–(d) seasonal mean sea level pressure (MSLP) anomalies with respect to the 1991–2020 base period, shown as percentiles based on the 1959–2023 period. (e) Jul/Aug summer North Atlantic Oscillation (NAO) index for the period 1959–2023 (Source: ERA5 [Hersbach et al. 2020].)</figcaption></figure>
<figure><img src="image_94.png" /><figcaption>Fig. 2.48. Southern Hemisphere circulation in 2023. Seasonal mean sea-level pressure (MSLP) anomalies (hPa; 1991–2020 base period) for (a) DJF 2022/23, (b) MAM 2023, (c) JJA 2023, and (d) SON 2023. (Source: ERA5 reanalysis.) (e) Daily Antarctic Oscillation (AOO) index time series (Source: NOAA Climate Prediction Center.)</figcaption></figure>

## 2. LAND AND OCEAN SURFACE WINDS

—C. Azorin-Molina, R. J. H. Dunn, L. Ricciardulli, C. A. Mears, J. P. Nicolas, T. R. McVicar, Z. Zeng, and M. G. Bosilovich

Annual mean wind speed at ~10 m above the ground was anomalously low over Northern Hemisphere lands in 2023 (–0.035 m s⁻¹) with respect to the 1991–2020 climatology (Table 2.8). This negative anomaly was primarily driven by the decline in winds observed in North America (–0.168 m s⁻¹) and, secondarily, in Europe (–0.011 m s⁻¹). This contrasts with the interhemispheric asymmetry of positive anomalies in South America (+0.145 m s⁻¹) and Central (+0.076 m s⁻¹) and East (+0.032 m s⁻¹) Asia (Plate 2.1v). After decades of “stilling” (McVicar et al. 2012), a weak “reversal” of winds occurred around the 2010s (Zeng et al. 2019) with almost neutral anomalies
dominating the last decade (Fig. 2.49a). Moreover, the observed changes are dominated by a declining frequency of winds at >3 m s⁻¹ (Fig. 2.49c) and particularly at >10 m s⁻¹ (Fig. 2.49d), which in 2023 reached the third lowest frequency value during the 1973–2023 record.

The assessment of terrestrial surface wind speed anomalies, trends, and multidecadal variability is based on: 1) the HadISD3 observational dataset (1973–2023; Dunn et al. 2012, 2016, 2019) with stations selected for completeness; 2) the ERA5 reanalysis (1979–2023; Hersbach et al. 2020; Bell et al. 2021); and 3) the MERRA-2 reanalysis (1980–2023; Gelaro et al. 2017). Reanalyses underestimated anomalies and failed in reproducing the decadal variability of both the observed “stilling” and “reversal”; however, their agreement with station observations improved since the mid-1990s (Fig. 2.49b; e.g. Torralba et al. 2017; Wohland et al. 2019).

The average land wind speed has declined across the Northern Hemisphere over the last 45 years (–0.053 m s⁻¹ decade⁻¹ for the period 1979–2023), with a noticeable inter-hemispheric asymmetry of surface winds changes (Zha et al. 2021). Table 2.8 reports this opposite sign in trends between the northern continents, where the strongest negative trend is recorded in North America (–0.072 m s⁻¹ decade⁻¹) and the strongest positive trend in South America (+0.052 m s⁻¹ decade⁻¹). Due to the cessation of the “stilling” over the last decade, the global negative trend is of lesser magnitude compared to previous reports (Azorin-Molina et al. 2023a). The asymmetric trends between hemispheres are partly captured by the ERA5 reanalysis (Fig. 2.50a; Deng et al. 2021).

<figure><img src="image_95.png" /><figcaption>Fig. 2.49. Land surface Northern Hemisphere (20°N–70°N) and regional surface wind speed anomaly time series (ms⁻¹; 1991–2020 reference period): (a) HadISD3 observational dataset (1973–2023), (b) ERA5 (1970–2023 masked to only those grid boxes which contain one of the HadISD stations used in this section), and MERRA-2 (1980–2023 complete land surface) reanalyses. (c), (d) The HadISD3 occurrence frequencies (% yr⁻¹) for wind speeds (c) >3 m s⁻¹ and (d) >10 m s⁻¹.</figcaption></figure>

<table><caption>Table 2.8. Northern Hemisphere (20°N–70°N) and regional statistics for land surface wind speed (m s⁻¹) using the observational HadISD3 dataset for 1979–2023.</caption><thead><tr><th>Region</th><th>Mean Wind Speed<br>1991–2020<br>(m s<sup>-1</sup>)</th><th>Wind Speed Anomaly<br>2023<br>(m s<sup>-1</sup>)</th><th>Wind Speed Trend<br>1979–2023 (m s<sup>-1</sup> decade<sup>-1</sup>),<br>and 5th to 95th percentile confidence range</th><th>Number of stations</th></tr></thead><tbody><tr><td>Northern Hemisphere</td><td>3.302</td><td>-0.035</td><td>-0.053 (-0.067 → -0.040)</td><td>2874</td></tr><tr><td>North America</td><td>3.642</td><td>-0.168</td><td>-0.072 (-0.086 → -0.053)</td><td>841</td></tr><tr><td>Europe</td><td>3.644</td><td>-0.011</td><td>-0.049 (-0.069 → -0.033)</td><td>931</td></tr><tr><td>Central Asia</td><td>2.738</td><td>+0.076</td><td>-0.069 (-0.105 → -0.041)</td><td>304</td></tr><tr><td>East Asia</td><td>2.711</td><td>+0.032</td><td>-0.027 (-0.042 → -0.013)</td><td>540</td></tr><tr><td>South America</td><td>3.452</td><td>+0.145</td><td>+0.052 (+0.036 → +0.071)</td><td>101</td></tr></tbody></table>
Changes in ocean surface winds for the period 1988–2023 were assessed using two products: 1) ERA5 and 2) satellite-based products as the merged radiometer winds (including Special Sensor Microwave/Imager [SSM/I], the Special Sensor Microwave Imager/Sounder [SSMIS], the Advanced Microwave Scanning Radiometer for the Earth Observation Satellite [AMSRE] and Advanced Microwave Scanning Radiometer 2 [AMSR2], Tropical Rainfall Measuring Mission's [TRMM] Microwave Imager [TMI], and WindSat), and the scatterometers Quick Scatterometer (QuikSCAT) and Advanced Scatterometer (ASCAT; Wentz 1997; Wentz et al. 2007, 2015; Ricciardulli and Wentz 2015; Ricciardulli and Manaster 2021), all processed at Remote Sensing Systems (RSS). Ocean wind speed anomalies were slightly negative in 2023: satellite radiometers (RSS, -0.055 m s⁻¹), satellite scatterometers (ASCAT, -0.038 m s⁻¹), and reanalyses (ERA5, -0.050 m s⁻¹, MERRA-2, -0.132 m s⁻¹; Fig. 2.51). Spatially (Plate 2.1v), there was a localized weak positive anomaly in the southern equatorial eastern Pacific, and a large negative anomaly in the tropical Atlantic, almost unprecedented over the past 30 years (2010 being the closest). The strong positive anomaly pattern seen in 2022 in the western and central equatorial Pacific Ocean (Azorin-Molina et al. 2023a) reversed to a weak negative pattern in 2023 (due to El Niño, albeit much weaker than in 1997 and 2015). Much smaller positive anomalies, or even negative ones, were observed in the Southern Ocean. Over the Indian Ocean, negative anomalies dominated except in a strip west of Sumatra. Due to the weak ocean surface winds in 2023, long-term trends (1988–2023) are of lesser magnitude with respect to previous reports (RSS Radiometers: <+0.1 m s⁻¹ decade⁻¹; ERA5: +0.03 m s⁻¹ decade⁻¹ over 60°S–60°N) but have similar spatial patterns (Fig. 2.50a). Positive trends dominate over the south Pacific trade winds, the Southern Ocean, the Bering Sea, and near coastlines, while negative trends persist across midlatitude ocean areas and the whole Indian Ocean.

Widespread warm sea-surface temperatures (coincident with El Niño, sections 2b1, 2b3, 3b) might have weakened pressure gradients and driven negative wind speed anomalies in 2023. Changes in both land and

<figure><img src="image_96.png" /><figcaption>(b)</figcaption></figure>

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

Fig. 2.50. Wind speed trends (m s⁻¹ decade⁻¹) from the (a) ERA5 reanalysis output over land/ice and Remote Sensing Systems (RSS) satellite radiometers (Special Sensor Microwave/Imager [SSM/I], Special Sensor Microwave Imager / Sounder [SSMIS], Tropical Rainfall Measuring Mission's Microwave Imager [TMI], Advanced Microwave Scanning Radiometer 2 [AMSR2], Advanced Microwave Scanning Radiometer for the Earth Observing System [ASMR-E], and WindSat) over ocean for the period 1988–2023 (shaded areas) and (b) observational HadISD3 dataset over land (circles) for the period 1979–2023.

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

Fig. 2.51. Annual global mean wind speed anomalies (m s⁻¹; 1991–2020 baseline) over the ocean from satellite radiometers and scatterometers, ERA5, and MERRA-2 reanalyses.
ocean winds have been mainly attributed to decadal ocean–atmosphere oscillations characterized as the decadal variations of climate indices such as the tropical North Atlantic, North Atlantic Oscillation, and Pacific Decadal Oscillation (Zeng et al. 2019); however, changes in the pressure gradient (Zhang et al. 2021) forced by the anthropogenic warming partly explain the interhemispheric asymmetry with negative (positive) trends in the Northern (Southern) Hemisphere (Zha et al. 2021). Secondary drivers are linked to anemometer biases (Azorin-Molina et al. 2023b; Liu et al. 2024), encoding data issues (Dunn et al. 2022), and land cover changes (Minola et al. 2022).

### 3. UPPER AIR WINDS

—L. Haimberger, M. Mayer, P. Rohini, C. T. Sabeerali, V. Schenzinger, and O. P. Sreejith

Anomalies such as the El Niño–Southern Oscillation have profound impacts on upper-level circulation. Climate projections predict changes of upper-level wind patterns, such as the Hadley cell or jet stream intensity. Therefore, it is important to monitor the observed upper air winds. The 2023 global mean wind speed anomaly at 850 hPa (Fig. 2.52a) became negative (–0.1 m s⁻¹) in the second half of the year in a manner typical of El Niño conditions. Hence, linear trends decreased very slightly (0.02 m s⁻¹ decade⁻¹ to 0.04 m s⁻¹ decade⁻¹ for the period 1991–2023).

The Southern Annular Mode (SAM) Index ( Marshal 2003) stayed high in 2023 (0.77) whereas the closely related Antarctic Oscillation index returned toward normal albeit still-positive values

(0.24). This is consistent with the zonal 850-hPa wind speed anomalies between 70°S and 50°S (Fig. 2.52b), which were more than +1 m s⁻¹ in the first and last months of the year (consistent with mostly positive values in this belt in Plate 2.1w), but were near zero or even slightly negative during austral winter. The positive wind speed trend in this latitude belt remained highly significant for the period 1991–2023, between 0.2 m s⁻¹ decade⁻¹ and 0.26 m s⁻¹ decade⁻¹, consistent with section 2e1.

The pattern of strong easterlies (negative anomalies in a region with negative wind climatology) wind at 850 hPa over September–December in 2023 (Plate 2.1w; fourth strongest in 50 years over the area 60°E–90°E, 10°S–10°N, stronger only in 2019, 2010, 1997) over the equatorial Indian Ocean is related to both strongly positive Indian Ocean dipole (IOD) (see section 4f) and El Niño (see section 4b) indices during that time. Due to El Niño, the easterlies were weaker (positive anomalies) in the tropical

central and east Pacific. There was a relatively prominent westerly wind anomaly (positive anomaly) over western and central Europe during the whole year, but it was strongest in the summer and autumn months, which likely contributed to above-average precipitation over west-central Europe at the same time.

We investigate the impact of major tropical climate anomalies on the upper-air divergent circulation by assessing the anomalous 200-hPa velocity potential in October–December (OND) 2023 (Fig. 2.53a). A strong positive anomaly was present over the Indo-Pacific warm pool, indicative of upper-air convergence and sinking motion associated with the strongly positive IOD and Oceanic Niño Index (ONI). Combined with ascending motion over the tropical west Indian Ocean and connected by strong anomalous easterly (negative) flows over the central Indian Ocean (Plate 2.1w), this anomaly resulted in strong zonal-vertical circulation.

<figure><img src="image_99.png" /><figcaption>Fig. 2.52. Annual anomalies of (a) global mean, (b) 70°S–50°S belt mean wind speed (m s⁻¹; 1991–2020 base period) at 850 hPa from four reanalyses (ERA5 [Hersbach et al. 2020], ERA-Interim [Dee et al. 2011], MERRA-2 [Gelaro et al. 2017], and JRA-55 [Kobayashi et al. 2015]). The numbers in parentheses are linear trends in m s⁻¹ decade⁻¹ for the period 1991–2023. The ERA-Interim time series ends in 2019.</figcaption></figure>
<figure><img src="image_100.png" /><figcaption>Fig. 2.53. (a) 200-hPa (colors) velocity potential (× 10⁶ m² s⁻¹) and (arrows) divergent wind anomalies (m s⁻¹; 1991–2020 base period) for OND 2023; stippling indicates regions with anomalies exceeding 1.65 std. dev. of the seasonal anomalies; (b) evolution of anomalous velocity potential (× 10⁶ m² s⁻¹) in the equatorial central Pacific (5°S–5°N, 170°E–130°W) for the four strongest El Niño years since 1991. The Nov Oceanic Niño Index (ONI) for the four years is provided in the legend. (Source: ERA5.) (c) Anomalies of pressure vertical velocity (shaded; units: × 10⁻² Pa s⁻¹) and u/w anomalies (arrows) averaged over the region 10°S–10°N (zonal wind anomaly [u] unit: m s⁻¹, pressure vertical velocity anomaly [w] unit: × 10⁻² Pa s⁻¹).</figcaption></figure>

The IOD is also associated with the negative velocity potential anomaly over the western Indian Ocean and central Africa, and the ONI is associated with a negative anomaly over the central equatorial Pacific (both related to enhanced convection in their respective regions). Although the 2023 El Niño event was one of the strongest of the past decades, the velocity potential anomaly in the central Pacific was rather weak. The region was chosen based on the location of the typically strongest precipitation response to El Niño (see, e.g., Fig. 3b in Mayer et al. 2013). The relatively weak upper-air wind response to the 2023 El Niño conditions (also noted in section 2e2) is consistent with the surprisingly weak coupling of Pacific equatorial surface winds to central-to-eastern Pacific warm sea-surface temperature (SST) anomalies during the development phase of the event. Thus, the weakening of the Pacific Walker cell was modest during 2023 despite the strong warm Pacific SST anomalies.

There was a strong negative velocity potential anomaly over the far eastern tropical Pacific and Central America, which is consistent with the highly active eastern North Pacific hurricane season (see section 4g3). Together with high oceanic heat content, it likely contributed to the explosive development of Hurricane Otis that made landfall near Acapulco on 25 October (see section 4g3 and Sidebar 4.1 for details).

Figure 2.53c depicts anomalies in pressure vertical velocity and zonal/vertical velocities averaged over the region spanning from 10°S to 10°N in OND 2023. Consistent with Fig. 2.53a, positive anomalies in pressure vertical velocity were observed over the tropical central/east Pacific, indicating ascending motion associated with El Niño. Particularly noteworthy is the stronger ascending motion observed during OND compared to the June–August season, suggesting the intensification of El Niño as the season progressed.

In 2023, the quasi-biennial oscillation (QBO) of stratospheric zonal-mean zonal winds completed its regular westerly phase after 12.7 months. It reached its maximum value of 16.1 m s⁻¹ at the 40-hPa level in April. The newly formed easterly descended from the 10-hPa pressure level to
60-hPa at a rate of 1.1±0.5 km month⁻¹, which is quite fast. Descent tends to stall between 40 hPa and 50 hPa in many years, but this did not happen in 2023. A new westerly had already formed at the 10-hPa level in late November, starting a new QBO cycle for 2024.

## 4. THUNDER HOURS

—M. Füllekrug, E. Williams, C. Price, S. Goodman, R. Holzworth, J. Lapierre, E. DiGangi, R. Said, M. McCarthy, K. Virts, A. M. Grimm, and Y. Liu

The lifetime of an ordinary thunderstorm is about one hour, and thunder can be heard over a ~15 km radius. Based on this, the definition of a thunder hour is that at least two lightning flashes occurred within one hour and 15 km from a given location. The mapping of thunder hours enables the characterization of thunderstorm frequencies around the world (DiGangi et al. 2021) that are indicative of high-impact weather including high wind speeds, intense rainfall, large hail, and lightning hazards. (Füllekrug et al. 2022 and references therein). Thunder hours can be derived from optical, radio, and sonic remote sensing and result in maps that offer a statistically robust measure of the frequency of deep convection—on time scales ranging from hours to decades—that is suitable for climate studies.

This contribution describes the first global climatic thunder hour anomaly map calculated from composite radio remote sensing using three different ground-based global lightning detection networks for comparison with optical remote sensing using the Geostationary Lightning

Mapper (GLM) on board the NOAA GOES-16 (Rudlosky and Virts 2021). Vaisala's Global Lightning Detection Network (GLD360; Said et al. 2013), Earth Network's Total Lightning Network (ENTLN; Zhu et al, 2022), and the University of Washington's World-Wide Lightning Location Network (WWLLN; Holzworth et al. 2021) radio-locate lightning flashes around the world. The lightning occurrence times and locations are subsequently used to calculate the total number of thunder hours separately for each network and for each year from 2018 to 2023 with a geographic resolution of 0.05° × 0.05°, which corresponds to a spatial resolution of ~5.56 km × 5.56 km at the equator. Subsequently, the total number of thunder hours in 2023 is averaged across all three networks, revealing up to ~500 thunder hours in the Americas, Central Africa, and the Maritime Continent in Southeast Asia (Fig. 2.54a). The global thunder hour anomaly within the field of view of GLM in 2023 (Fig. 2.55) exhibits remarkable agreement with the anomaly calculated from the ground-based global lightning detection networks (Fig. 2.54b).

Thunder hour anomalies in 2023 were calculated against the preceding five-year average of annual thunder hours (2018–22). The anomaly of up to ~200 additional thunder hours over the eastern tropical Pacific (Fig. 2.54b) is attributed to increased convection associated with above-average SSTs (see sections 2b2, 3b) and El Niño (see sections 2d5, 4b).

<figure><img src="image_101.png" /></figure>

<figure><img src="image_102.png" /><figcaption>Fig. 2.54. (a) Total number of thunder hours for 2023 averaged from three ground-based global lightning detection networks (Vaisala Global Lightning Detection Network [GLD360], Advanced Environmental Monitoring Earth Networks Total Lightning Network [AEM ENTLN], and the University of Washington's World Wide Lightning Location Network [UW WWLLN]) and (b) thunder hour anomalies for 2023 (base period is 2018–22).</figcaption></figure>
Above-average numbers of thunder hours and precipitation in southeastern South America has been attributed to a teleconnection between weather patterns in northwestern and southeastern South America in austral spring, albeit before the peak of El Niño's SST anomaly (Grimm 2003; Grimm and Natori 2006; Adler et al. 2017). The similarity of this feature with an increased number of days and hours with lightning during the 1997/98 El Niño along the northern Gulf of Mexico basin (Goodman et al. 2000) suggests a common physical mechanism. However, negative thunder hour anomalies prevailed along the northern Gulf of Mexico in 2023, possibly because the impact of El Niño on the location of the subtropical jet stream over North America is largest in Northern Hemisphere winter (Manney et al. 2021, Fig. 11 top row).

A third area of above-average numbers of thunder hours in 2023 is evident in the northern part of the Maritime Continent, where previous studies have shown maximum lightning responsiveness to “Super El Niño” events (Hansen et al. 2006;

Williams et al. 2021), which are declared when the SST anomaly exceeds 2°C. The negative anomaly in the southern portion of the Maritime Continent is potentially attributed to a cold anomaly in SSTs (sections 2b3, 3b). A more detailed characterization of thunder hour anomalies over land and ocean is the subject of ongoing research.

Finally, our analysis shows a positive anomaly in thunder hours in the western United States and Canada during 2023. This was coincident with extreme wildfires in Canada over the summer (sections 2h3; Sidebar 7.1). Thunder hours are indicative of high-impact weather as part of weather and climate disasters documented for the United States (Bartow-Gillies et al. 2023).

<figure><img src="image_103.png" /><figcaption>Fig. 2.55. Thunder hour anomaly for 2023 calculated from NOAA's Geostationary Lightning Mapper on GOES-16. This compares well to the 2023 anomalies calculated from ground-based lightning detection networks (Fig 2.54b).</figcaption></figure>
# 1. EARTH RADIATION BUDGET AT TOP-OF-ATMOSPHERE

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

The Earth radiation budget (ERB) at top-of-atmosphere (TOA) involves the exchange of incoming total solar irradiance (TSI) and outgoing radiation from Earth given by the sum of reflected shortwave (RSW) and outgoing longwave radiation (OLR). This balance is crucial in understanding Earth's climate system and global temperature variations. Over the last 20 years, the observed climate system has been experiencing an increasing net positive imbalance, representing a surplus of energy to the Earth-atmosphere system (Loeb et al. 2022; von Schuckmann et al. 2023). This observed net positive imbalance continued in 2023, albeit with significant changes in all ERB components corresponding to the transition from La Niña to El Niño.

An analysis of CERES (Clouds and the Earth's Radiant Energy System) TOA ERB measurements (Table 2.9) shows that the global annual mean OLR, TSI, and net radiation increased by 0.60 W m⁻², 0.10 W m⁻², and 0.30 W m⁻², respectively, in 2023 relative to 2022 (rounded to the nearest 0.05 W m⁻²). In contrast, the global annual mean RSW decreased by 0.80 W m⁻² over the same period. Relative to the 2001–22 climatology, the 2023 global annual mean anomalies for all TOA radiative flux components (Table 2.9) are greater than their corresponding 2-sigma interannual variability; this is the first time this has occurred in the CERES record. These large TOA radiative flux anomalies are indicative of the extremely large climate anomalies that occurred in 2023. The TOA radiative impact of the La Niña to El Niño transition is shown in Fig. 2.56 as regional annual mean difference maps in OLR and RSW between 2023 and 2022.

<figure><img src="image_104.png" /><figcaption>Fig. 2.56. Annual average top-of-atmosphere flux differences (W m⁻²) between 2023 and 2022 for (a) outgoing longwave radiation (OLR) and (b) reflected shortwave radiation (RSW). The annual mean maps for 2023 were derived after adjusting Dec 2023 FLASHFlux version 4B data using the difference between CERES EBAF Ed4.2 and CERES FLASHFlux version 4B data in 2022.</figcaption></figure>

<table><caption>Table 2.9. Global annual mean top-of-atmosphere (TOA) radiative flux changes between 2022 and 2023, the 2023 global annual mean radiative flux anomalies relative to their corresponding 2001–22 mean climatological values, the mean 2001–22 climatological values, and the 2-sigma interannual variabilities of the 2001–22 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><th>Global</th><th>One Year Change<br>(2023 minus 2022)<br>(W m<sup>-2</sup>)</th><th>2023 Anomaly<br>(Relative to 2001–22)<br>(W m<sup>-2</sup>)</th><th>Climatological Mean<br>(2001–22)<br>(W m<sup>-2</sup>)</th><th>Interannual Variability<br>(2001–22)<br>(W m<sup>-2</sup>)</th></tr></thead><tbody><tr><td>OLR</td><td>+0.60</td><td>+0.85</td><td>240.35</td><td>±0.65</td></tr><tr><td>TSI</td><td>+0.10</td><td>+0.25</td><td>340.20</td><td>±0.15</td></tr><tr><td>RSW</td><td>-0.80</td><td>-1.50</td><td>99.00</td><td>±1.05</td></tr><tr><td>ASR</td><td>+0.90</td><td>+1.75</td><td>241.20</td><td>±1.05</td></tr><tr><td>Net</td><td>+0.30</td><td>+0.90</td><td>0.85</td><td>±0.85</td></tr></tbody></table>
The largest reductions in OLR and increases in RSW, indicative of the increases in deep convection, are observed spanning from the tropical western Pacific (north of Australia) eastward in the equatorial region and southeastward into the South Pacific Ocean. The largest increases in OLR and decreases in RSW are observed over the Indian Ocean and the Maritime Continent, extending northeastward into the subtropical northern Pacific and southeastward over Australia in the subtropical southern Pacific Ocean.

The global monthly mean TOA OLR anomaly showed large variability in 2023 (Fig. 2.57), dropping to a minimum of -0.70 W m⁻² for May, then peaking in October at +1.90 W m⁻². This is the largest range in monthly OLR anomaly for the CERES climatology for a given year. This variability is consistent with NOAA HIRS (Lee and NOAA CDR Program 2018) and NASA AIRS (Susskind et al. 2012) OLR datasets (not shown). The 2023 global annual mean TOA OLR anomaly was +0.85 W m⁻². The global monthly mean TOA absorbed solar radiation (ASR, determined from TSI minus RSW) anomaly increased throughout 2023, peaking at +2.35 W m⁻² in August before slightly decreasing over the last few months. For the year as a whole, the 2023 global annual mean TOA ASR anomaly was +1.75 W m⁻². The global monthly mean TOA total net anomaly, which is calculated from ASR anomaly minus OLR anomaly, also stayed positive throughout 2023, peaking at +1.80 W m⁻² in April, but decreasing strongly to about +0.15 W m⁻² by October. The global annual mean TOA total net anomaly for 2023 was +0.90 W m⁻², representing a continuation of positive net imbalance in 2023 (known as the Earth energy imbalance) that has been observed through the 2020s (Loeb et al. 2021, 2022; von Schuckmann et al. 2023). That positive net imbalance continued to grow in early 2023 but appears to have been interrupted by the onset of the strong El Niño. Further analyses are needed to understand the significances and impacts of these observed global changes.

The TSI data are from a “Community-Consensus TSI Composite” using the methodology defined by Dudok de Wit et al. (2017). The TOA RSW and TOA OLR data come from two different

CERES datasets. The data for March 2000–November 2023 are based on the CERES EBAF edition 4.2 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 2023 comes from the CERES FLASHFlux version 4B product (Kratz et al. 2014), which are created using CERES measurements from *Terra* and NOAA-20 spacecraft. The FLASHFlux to EBAF data normalization procedure (Stackhouse et al. 2016) results in 2-sigma monthly uncertainties of ±0.40 W m⁻², ±0.00 W m⁻², ±0.30 W m⁻², and ±0.45 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_105.png" /></figure>

Fig. 2.57. Time series of global monthly mean deseasonalized anomalies (W m⁻²) of top-of-atmosphere Earth radiation budget for outgoing longwave radiation (OLR; top), absorbed solar radiation (ASR, determined from total solar irradiance [TSI] minus reflected shortwave [RSW]; middle), and total net (TSI-RSW-OLR; lower) from Mar 2000 to Dec 2023. Anomalies are relative to their calendar month climatology (2001–22). Time series show the CERES EBAF Ed4.2 1-Deg data (Mar 2000–Nov 2023) in red and the CERES FLASHFlux version 4B data (Dec 2023) marked by the blue dot; 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 L. Soldo

The time series of monthly mean apparent atmospheric transmission from pyrheliometer measurements at NOAA's Mauna Loa Observatory (MLO) in Hawaii (19.536°N, 155.576°W, 3397 m a.s.l.) is one of the longest geophysical records, dating back to 1958. However, its extension to 2023 is abbreviated due to damage sustained from the eruption of Mauna Loa on 27 November 2022. Ten meters of lava buried approximately a mile of the road leading to the station and demolished the power lines. Power was restored in July 2023.

The apparent atmospheric transmission time series through 2023 is shown in Fig. 2.58. Lack of operations in the first half of the year precluded sampling of the perennial springtime passage of Asian dust that usually causes a reduction in transmission (Augustine et al. 2023; Bodhaine et al. 1981). Until November 2023, transmission levels are maintained at the relatively low levels (0.926±0.0026) that have been observed since 2018. A composite of Stratospheric Aerosol and Gas Experiment (SAGE) limb sounder imagery (Leckey et al. 2021; https://sage.nasa.gov/sageiii-iss/browse_images/expedited/) suggests that this long period of relatively low transmission may have been sustained by a series of volcanic eruptions, including Ambae-1 and -2 in 2018, Raikoke and Ulawun in 2019, Taal in 2020, Soufriere in 2021, and Hunga Tonga-Hunga Ha'apai (HTHH) in 2022. Although SAGE imagery covers only +10° to -10° latitude, Yu et al. (2023) attribute elevated stratospheric aerosols at 15.5 km and 18.5 km from balloon-borne Portable Optical Particle Spectrometer soundings at Boulder, Colorado (40°N), and the Tibetan Plateau (25°N-36°N) to those eruptions, indicating that the volcanic aerosols did spread northward over Mauna Loa.

High levels of stratospheric water vapor from HTHH may have also contributed to the low transmission after January 2022. However, successive upticks in November and December of 2023 (to 0.93) may hint at the onset of a recovery. That tendency continued into January 2024, when the MLO transmission remained >0.93 (not shown). A possible cause is the switching of the quasi-biennial oscillation to an easterly phase around October 2023, and the significant drying of the lower stratosphere thereafter, which has been confirmed by integrated water vapor measurements at Mauna Loa. Less absorption in the near-infrared from that drying likely contributed to the increase in transmission from November 2023 through January 2024.

According to the Smithsonian/U.S. Geological Survey Weekly Volcanic Activity Report, 72 eruptions occurred in 2023. Of those, only one, specifically Lascar in Chile, attained a Volcanic Explosivity Index (VEI)

of 3, with two others registering VEIs of 2 and 1. Other 2023 eruptions were, or have so far been too weak to be classified as explosive. Analyses of the CALIPSO data show that the plume from the January 2022 explosive eruption of HTHH remains in the lower stratosphere but is confined mainly to the Southern Hemisphere. However, CALIPSO and SAGE continue to show a weak but diminishing presence of that plume at the latitude of MLO at least through June and October 2023, respectively.

The primary aerosol event of 2023 was unprecedented wildfires across Canada from May through September that impacted air quality throughout the Northern Hemisphere (Wang et al. 2023; see Sidebar 7.1 for details). Pyrocumulus thunderstorms generated by some of those fires as well as solar heating of black carbon within initial plumes likely lofted smoke into the lower

<figure><img src="image_106.png" /><figcaption>Fig. 2.58. Apparent transmission at Mauna Loa, Hawaii, from 1958 through 2023. Red dots are monthly averages of morning apparent transmission, and the gray curve is a lowess fit with a six-month smoother applied. Inset shows new data for 2023. The gray horizontal dashed line represents the average transmission of the clean period (Ammann et al. 2003; Solomon et al. 2011) before the eruption of Agung. Transmission is not available from Jan through Jun 2023 (gray areas in main figure and inset) because lava from the eruption of Mauna Loa in late Nov 2022 cut power to the station.</figcaption></figure>
stratosphere; however, a Copernicus Atmospheric Monitoring Service (CAMS) global analysis of daily mean organic matter aerosol optical depth at 550 nm (Garrigues et al. 2022) from May through September shows that smoke was confined mostly north of 40°N. A modeling study by Wang et al. (2023) shows very low concentrations of particulate matter (PM2.5) aerosol (<1 µg m⁻³) over Hawaii from late June through September from wildfires in Canada and East Asia, but no presence of it there in the other months of the year. This evidence indicates that wildfire smoke may not have had a significant impact on MLO transmission in 2023.

To calculate apparent atmospheric transmission, three ratios of successive clear-morning pyrheliometer measurements made near the summit at solar air masses of 2, 3, 4, and 5 are averaged to get a representative daily transmission (Ellis and Pueschel 1971). The mean of daily transmissions for a particular month is considered to be a representative monthly transmission. It is referred to as “apparent” because atmospheric variability at longer pathlengths increases the uncertainty of the measurements.
In 2023, the atmospheric burdens of the long-lived greenhouse gases (LLGHGs) carbon dioxide ($CO_2$), methane ($CH_4$), and nitrous oxide ($N_2O$), that are largely responsible for increasing global temperature (Forster et al. 2023; IPCC 2013), continued to rise to record-high levels. $CO_2$ is the most important and abundant anthropogenic GHG, and in 2023, globally averaged $CO_2$—as derived from remote marine boundary layer measurements made by NOAA's Global Monitoring Laboratory—reached 419.3±0.1 ppm (parts per million by moles in dry air; Fig. 2.59a; Table 2.10; uncertainties are reported as one sigma in this section). This is a 50% increase from the pre-industrial level of ~278 ppm (Etheridge et al. 1996). Annual growth in global mean $CO_2$ has risen from 0.6±0.1 ppm yr⁻¹ in the early 1960s to an average of 2.5 ppm yr⁻¹ during 2014–23 (Fig. 2.59a; Lan et al. 2024a). $CO_2$ growth in 2023 was 2.8 ppm, the fourth highest in the record since the 1960s.

The main driver of increasing atmospheric $CO_2$ is fossil fuel (FF) burning, with emissions increasing from 3.0±0.2 Pg C yr⁻¹ in the 1960s to 9.6±0.5 Pg C yr⁻¹ (including cement production) in the past decade (2013–22; Friedlingstein et al. 2023). Together with the measured atmospheric increase, we can conclude that about 45% of the FF-emitted $CO_2$ since 1958 has remained in the atmosphere, with the remaining portion entering the oceans and terrestrial biosphere (Friedlingstein et al. 2023). While increasing emissions of $CO_2$ from FF combustion are roughly monotonic, the $CO_2$ growth rate varies from year to year (standard deviation = 0.4 ppm in 2014–23) with variability mostly driven by terrestrial biosphere exchange of $CO_2$, as confirmed by measurements of the ¹³C:¹²C ratio in atmospheric $CO_2$ (e.g., Keeling et al. 1985; Alden et al. 2010). The El Niño–Southern Oscillation (ENSO) is the main driver of this interannual variability (Betts et al. 2016; Liu et al. 2017), which impacts photosynthetic $CO_2$ uptake, respiratory release, and fires.

Atmospheric $CH_4$ is the second most important LLGHG, and in 2023 its atmospheric abundance reached 1922.6±0.6 ppb (parts per billion by moles in dry air; Lan

et al. 2024b), about 2.6 times its pre-industrial level of 729±9 ppb (Mitchell et al. 2013). Global $CH_4$ increased by an average rate of 11.7±1.4 ppb yr⁻¹ between 1984 and 1991, followed by a smaller increase of 4.4±1.8 ppb yr⁻¹ between 1992 and 1998, and further reduced to near zero (0.5±3.0 ppb yr⁻¹) during 1999–2006. Atmospheric $CH_4$ growth restarted in 2007 and has accelerated since 2014 and further accelerated in 2020–22 with an average rate of increase of 15.4±2.0 ppb yr⁻¹ (Fig. 2.59b). Its growth remained high in 2023 at about 11.1±0.4 ppb, which was the fifth-highest annual growth rate since the renewed growth started in 2007.

Atmospheric $CH_4$ is emitted by anthropogenic sources such as fossil fuel exploitation, livestock, waste and landfill, and rice cultivation, as well as natural sources such as wetlands and shallow lakes. The ongoing reduction in atmospheric δ¹³C-$CH_4$ since 2008 (Michel et al. 2022) indicates increased emissions from microbial sources (Basu et al. 2022), including emissions from livestock as well as natural wetland and lakes, which have more negative δ¹³C-$CH_4$ signatures. Small

<figure><img src="image_107.png" /><figcaption>Fig. 2.59. Global mean dry air remote surface mole fractions (approximately weekly data in blue and the deseasonalized trend in black [see Dlugokencky et al. 1994 for methods]; left axis) and annual change (red, right axis) of (a) $CO_2$ (ppm), (b) $CH_4$ (ppb), and (c) $N_2O$ (ppb) derived from the NOAA Global Greenhouse Gases Reference Network. $N_2O$ data prior to 2000 are insufficient to accurately calculate its growth rate.</figcaption></figure>
increases in FF emissions may also play a role in the post-2006 global CH₄ increase (Oh et al. 2023; Lan et al. 2019, 2021; Basu et al. 2022). The contribution of hydroxyl radical, the main sink for CH₄, is still uncertain, but it is less likely to be a major contributor (Zhao et al. 2019; Lan et al. 2021). Recent studies suggest a dominant role of increased tropical wetland emissions in the post-2020 CH₄ surge (Feng et al. 2021; Peng et al. 2022), and sustained increases in wetland CH₄ emissions may be an indication of an emerging carbon climate feedback (Nisbet et al. 2023; Zhang et al. 2023). An increased contribution from wetland emissions is also consistent with the acceleration in the decline of atmospheric δ¹³C-CH₄ in 2020–22 (Michel et al. 2022).

Nitrous oxide (N₂O) is a potent greenhouse gas with an atmospheric lifetime of 120 years (Tian et al. 2023). It is produced by microbes that rely on nitrogen substrates from natural and agricultural soils, animal manure, and the oceans (Davidson 2009), and increased

agricultural emissions related to fertilizer usage are the major source of its long-term increase (Tian et al. 2023). The mean global atmospheric abundance of N₂O in 2023 was 336.7±0.1 ppb, a 25% increase over its pre-industrial level of 270 ppb (Rubino et al. 2019). Recent growth reached an average rate of 1.3±0.1 ppb yr⁻¹ from 2020 to 2022 (Fig. 2.59c), larger than the average rate between 2010 and 2019 (1.0±0.2 ppb yr⁻¹), strongly suggesting increased emissions. The N₂O growth rate in 2023 was 1.0±0.1 ppb.

The impacts of LLGHGs on global climate can be estimated using the effective radiative forcing (ERF) of LLGHGs, the change of radiative energy caused by added LLGHGs to the atmosphere, following the Intergovernmental Panel on Climate Change's Sixth Assessment Report (Forster et al. 2021). Increasing atmospheric CO₂ has accounted for 64% of the increase in ERF by LLGHGs, reaching 2.28 W m⁻² in 2023 (Fig. 2.60) compared with preindustrial times (1750). The increase in CH₄ contributed a 0.56 W m⁻² increase in ERF between 1750 and 2023 while the CH₄-related production of tropospheric ozone and stratospheric water vapor also contributes to ~0.30 W m⁻² indirect radiative forcing (Myhre et al. 2014). The increase in atmospheric N₂O abundance contributed to a 0.22 W m⁻² increase in ERF between 1750 and 2023.

<figure><img src="image_108.png" /><figcaption>Fig. 2.60. (a) Effective radiative forcing (W m⁻²) due to long-lived greenhouse gases (LLGHGs; see Table 2.10 for details on industrial gases). (b) Annual increase in direct radiative forcing (W m⁻²).</figcaption></figure>

<table><caption>Table 2.10. Summary table of long-lived greenhouse gases for 2023 (CO₂ mole fractions and changes from prior year, in brackets, 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, 2023 [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 × 10<sup>-5</sup></td><td>2.28</td><td>419.3 [2.2]</td><td></td></tr><tr><td>Alkane</td><td>Methane</td><td>CH<sub>4</sub></td><td>Y</td><td>3.88 × 10<sup>-4</sup></td><td>0.56</td><td>1922.6 [10.6]</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 × 10<sup>-3</sup></td><td>0.22</td><td>336.7 [1.0]</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>217.1 [-2.4]<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>0.156(0.174)</td><td>485.4 [-4.3]<sup>d</sup></td><td>102</td></tr><tr><td>Chlorofluorocarbon</td><td>CFC-113</td><td>CCl<sub>2</sub>FCClF<sub>2</sub></td><td>N</td><td>0.30</td><td>0.020</td><td>67.1 [-0.6]<sup>d,f</sup></td><td>93</td></tr></tbody></table>
<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, 2023 [change from prior year]<sup>c</sup></th><th>Lifetime (yrs)<sup>b</sup></th></tr></thead><tbody><tr><td>Hydrochlorofluorocarbon</td><td>HCFC-22</td><td>CHClF<sub>2</sub></td><td>N</td><td>0.21</td><td>0.052</td><td>247.5 [-1.3]</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>24.5 [-0.1]</td><td>9.4</td></tr><tr><td>Hydrochlorofluorocarbon</td><td>HCFC-142b</td><td>CH<sub>3</sub>CClF<sub>2</sub></td><td>N</td><td>0.19</td><td>0.004</td><td>21.0 [-0.2]</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>129.5 [5.0]</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 [0.0]</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>28.4 [1.7]</td><td>51</td></tr><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>0.009</td><td>38.8 [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>28.3 [3.7]</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>0.007</td><td>36.8 [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 [0.00]</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>2.20 [0.16]</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>0.98 [-0.12]</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>73.8 [-0.9]<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>549.9 [2.7]</td><td>0.9</td></tr><tr><td>Bromocarbon</td><td>Methyl Bromide</td><td>CH<sub>3</sub>Br</td><td>N</td><td>0.004</td><td>&lt;0.001</td><td>6.47 [-0.05]</td><td>0.8</td></tr><tr><td>Bromocarbon</td><td>Halon 1211</td><td>CBrClF<sub>2</sub></td><td>N</td><td>0.29</td><td>0.001</td><td>2.84 [-0.09]</td><td>16</td></tr><tr><td>Bromocarbon</td><td>Halon 1301</td><td>CBrF<sub>3</sub></td><td>N</td><td>0.30</td><td>0.001</td><td>3.32 [0.01]</td><td>72</td></tr><tr><td>Bromocarbon</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>0.396 [-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>11.40 [0.38]</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>89.4 [0.09]</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>5.24 [0.09]</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>0.76 [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>2.10 [0.11]</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 Intergovernmental Panel on Climate Change Sixth Assessment Report Working Group I (IPCC AR6 WGI) Report. The Radiative Forcing column is either ERF (where indicated) or SARF. The adjustments to the SARF are CO<sub>2</sub>: 5% ± 5%, CH<sub>4</sub>: -14% ± 15%, N<sub>2</sub>O: 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 SF<sub>6</sub> lifetime from Ray et al. (2017), CH<sub>4</sub> lifetime from Prather et al. (2012). For CO<sub>2</sub>, 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 the Advanced Global Atmospheric Gases Experiment (AGAGE; Mühle et al. 2010; Miller et al. 2010). Changes indicated in brackets are the differences between the 2023 and 2022 means, the relevant quantities for calculating radiative forcing. These changes are somewhat different from the 2023 annual increases reported in Section 2.g.1, which are determined as the difference between 1 Jan 2023 and 1 Jan 2024. 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>

<footnote><sup>f</sup>Measurements of CFC-113 are known to be a combination of CFC-113 and CFC-113a, with CFC-113a contributing approximately 0.4 ppt to CFC-113.</footnote>
## 2. OZONE-DEPLETING SUBSTANCES

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

Since 1987, the Montreal Protocol and its Amendments (The Protocol; https://ozone.unep.org/treaties/montreal-protocol) have regulated the production and consumption of ozone-depleting substances (ODSs) and their replacement compounds. The broad categories of these compounds are the chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons, and perfluorocarbons (CFCs, HCFCs, HFCs, and PFCs, respectively), as well as halons and methyl bromide. While the primary goal of controlling ODSs through The Protocol was to limit damage to the ozone layer by limiting production for dispersive uses, these controls (and the subsequent amendments controlling the replacement compounds) have also reduced their radiative impact. Through the 2016 Kigali Amendment, The Protocol also addresses some HFCs that do not destroy stratospheric ozone, but are strong greenhouse gases. As of 2023, 155 nations have ratified the Kigali Amendment, which will aid the global effort to reduce the impacts of these gases on the climate.

Phase-out of the production for dispersive use is not the end of emissions of a chemical, nor are emissions the only factor controlling the atmospheric abundance of a trace gas species. Existing reservoirs of gases, such as those in insulating foams, are known as banks and continue to emit controlled chemicals for years after the final phase-out has occurred. The atmospheric lifetime, or rate of destruction, of a chemical in the atmosphere dictates how quickly a compound is removed, and these lifetimes vary over a large range between different species. As an example, CFC-11 and CFC-12 were reported to be globally phased out in 2010, but have long atmospheric lifetimes and large banks that continue to emit both compounds. These two gases have declined by only 18% and 10%, respectively, from their peak atmospheric abundances in 1994 and 2003

(Fig. 2.61). Conversely, methyl chloroform ($CH_3Cl_3$) has relatively few banks and a short lifetime and, despite having been phased out in 2015, has declined by 99% from its peak abundance in the atmosphere.

While the transition from CFCs to HCFCs resulted in an increase in the atmospheric abundance of several HCFCs during the 1990s and 2000s, the mole fractions of two of the three most abundant HCFCs (HCFC-22 and HCFC-141b) have not increased from 2021 to 2023, suggesting that their mole fractions may have peaked (Fig. 2.61; Table 2.10). The third most abundant HCFC, HCFC-142b, has been declining since about 2020 (Fig. 2.61; Table 2.10). Mole fractions of several HFCs, used as replacements for HCFCs, have increased substantially since their introduction in the mid-1990s, in particular HFC-134a, HFC-32, and HFC-23 (Fig. 2.61; Table 2.10).

Additionally, chemicals controlled under The Protocol are still allowed to be used as feedstocks for newer-generation products. Feedstock use may play a role in renewed release of ozone-depleting substances, such as CFC-112, CFC-113a, CFC-114a, and CFC-115 (e.g., Western et al. 2023). While these new releases do not yet pose a risk to the recovery

<figure><img src="image_109.png" /><figcaption>Fig. 2.61. Global mean abundances (mole fractions) at Earth's surface (ppt = nmol mol⁻¹ in dry air) for several halogenated gases, many of which also deplete stratospheric ozone. See Table 2.10 for the 2023 global mean mole fractions of these and other gases.</figcaption></figure>
of the ozone layer, continued emissions could begin to impact ozone layer recovery in the future (Western et al. 2023).

While global measurements of ODSs mainly represent the composition of the planetary boundary layer close to Earth's surface, destruction of the ozone layer is dependent on the amount of reactive halogen in the stratosphere. In order to track progress towards the ozone layer's recovery, equivalent effective stratospheric chlorine (EESC) is used as a measure of the reactive halogen loading in the stratosphere based on global tropospheric measurements, atmospheric transport (i.e., estimates of the mean age of the air in different parts of the stratosphere), and chemical reactivity (Daniel et al. 1995; Montzka et al. 1996; Newman et al. 2007). The destruction of the CFCs is the primary source of stratospheric reactive halogen and strongly contributes to the overall EESC. However, it is useful to scale the EESC relative to a benchmark by using the Ozone Depleting Gas Index (ODGI) to provide a more intuitive measure of the progress towards ozone layer recovery. The ODGI assesses the EESC relative to 1980, where an ODGI of 0 represents the EESC level in 1980, and an ODGI of 100 represents peak EESC (Hoffmann and Montzka 2009). The EESC, and therefore also the ODGI, are reported for the midlatitudes and

the Antarctic, which spans the range of ozone layer recovery due to differences in transport processes in the stratosphere. The midlatitude EESC is expected to return to 1980 levels around 2045, while the Antarctic EESC is expected to recover by the 2070s (Fig. 2.62; https://gml.noaa.gov/odgi/).

### 3. TROPOSPHERIC AEROSOLS

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

Aerosols represent a serious public health issue in many countries and are subject to monitoring and forecasting as part of air quality policies. They also impact weather and climate 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).

The Copernicus Atmosphere Monitoring Service (CAMS, https://atmosphere.copernicus.eu) produces a reanalysis of global aerosols and trace gases that covers the years 2003–23 (i.e., CAMSRA; Inness et al. 2019) by combining state-of-the-art numerical modeling and aerosol 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). This analysis uses data exclusively from the CAMS reanalysis, focusing on aerosol optical depth at 550 nm and surface particulate matter (PM2.5) concentrations.

Aerosol optical depth (AOD) at 550 nm and PM2.5 in 2023 show maxima over the polluted regions of India and China, as well as from dust over the Sahara and the Middle East (Figs. 2.63a,b). High values arose from seasonal vegetation fires in equatorial Africa and occasional extreme

<figure><img src="image_110.png" /></figure>

Fig. 2.62. The values of equivalent effective stratospheric chlorine in the Antarctic and midlatitudes (EESC[A] and EESC[ML], respectively) 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 World Meteorological Organization (WMO)/United Nations Environment Programme 2018 Ozone Assessment. Solid arrows indicate currently predicted dates for the return of EESC to 1980s levels. Solid lines depict inferred stratospheric changes based on the measured tropospheric curves. In 2023, midlatitude and Antarctic EESC were 1526 ppt and 3610 ppt, respectively, which represents a respective reduction of 21% and 13% in stratospheric reactive halogen loading from its peak. Translating this to the Ozone Depleting Gas Index (ODGI), the midlatitude ODGI is 47.1 and the Antarctic ODGI is 72.8, meaning the stratospheric reactive halogen loading has declined by 52.9% and 27.2%, respectively, relative to the 1980 benchmark reactive halogen abundance.
fires, most notably across large parts of high-latitude North America, eastern Siberia, and parts of the Amazon basin (section 2h3; see Sidebar 7.1). Figure 2.63c shows the time series of monthly and yearly globally averaged total AOD during 2003–23. There is strong seasonality in AOD, driven mainly by dust episodes between March and July in the Sahara, Middle East, and the Taklamakan/Gobi deserts as well as seasonal biomass burning in Africa, South America, and Indonesia. Globally averaged AOD in 2023 was the lowest on record, on par with 2022. The summer maximum was slightly higher than in 2022 and significantly lower than in 2021, as the large fires in Canada in 2023 (see Sidebar 7.1) were compensated by lower-than-usual fire emissions elsewhere (section 2h3).

The AOD anomalies at 550 nm and PM2.5 anomalies (Plates 2.1x,y) are dominated by the exceptional fire events during summer 2023 over western and eastern Canada (section 2h3; see Sidebar 7.1) and the associated transported plumes over the North Atlantic. Positive anomalies due to fires are also seen over eastern Siberia, while the number of fires and associated emissions from equatorial Africa continued the downward trend of the last two decades. Dust storm activity was lower than usual over Northern Hemisphere (NH) deserts. The negative anomalies

<figure><img src="image_111.png" /><figcaption>Fig. 2.63. (a) Global aerosol optical depth (AOD) at 550 nm in 2023; (b) global surface fine particulate matter (PM2.5) concentrations (µg m⁻³) in 2023; (c) global average of total AOD at 550 nm averaged over monthly (red) and annual (blue) periods for 2003–23; and (d) monthly AOD anomalies at 550 nm for Jul 2023 compared to the Jul 2003–22 average, highlighting the extreme nature of the Canadian fires.</figcaption></figure>
of AOD and PM2.5 over East Asia, Europe, and the Amazon basin (positive anomalies over India and Iran) can be explained by ongoing decreasing (increasing) trends in these regions. The exceptional nature of the summer 2023 Canada fires is highlighted by Plate 2.1z, which shows the number of extreme AOD days in 2023, and by Fig. 2.63d, which focuses on the AOD 550 nm anomaly in July 2023 over the North Hemisphere.

The AOD at 550 nm and PM2.5 trends for 2003–23 and 2012–23 are shown in Figs. 2.64a–d. The trends in AOD and PM2.5 are generally co-located. Between 2003 and 2023, there is a significant negative trend for both AOD and PM2.5 over most of the 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, which are driven by increased wildfire, as well as over India and Iran, which are driven by an increase in anthropogenic emissions (Satheesh et al. 2017). The trends between 2012 and 2023 show some contrast to those between 2003 and 2023: a stronger decrease over China reflecting a decrease in anthropogenic emissions (Quaas et al. 2022), smaller decreasing trends over Amazonia, Europe, and the United States, and new increases over Bolivia and Paraguay caused by a series of years with large fire events.

Anthropogenic AOD and radiative forcing resulting from aerosol-radiation (RFari) and aerosol-cloud interactions (RFaci) are shown in Fig. 2.65 for 2023 and the period 2003-23, as computed following Bellouin et al. (2020). There was a small increase in anthropogenic AOD in 2023 compared to 2022 (0.061 versus 0.059) and, consequently, aerosol radiative forcing has become slightly more negative by an estimated 0.05 W m⁻². These results are in contrast to the decreasing trend in anthropogenic AOD that started in 2018, but cannot yet signify a longer-term reversal.

<figure><img src="image_112.png" /><figcaption>Fig. 2.64. (a),(b) Linear trends of total aerosol optical depth (AOD; AOD unit yr⁻¹) and fine particulate matter (PM2.5; µg m⁻³ yr⁻¹) for 2003–23; and (c),(d) linear trends of total AOD (AOD unit yr⁻¹) and PM2.5 (µg m⁻³ yr⁻¹) for 2012–23. Only trends that are statistically significant (95% confidence level) are shown.</figcaption></figure>
<figure><img src="image_113.png" /></figure>

<figure><img src="image_114.png" /></figure>

<figure><img src="image_115.png" /></figure>

<figure><img src="image_116.png" /></figure>

<figure><img src="image_117.png" /></figure>

<figure><img src="image_118.png" /></figure>

Fig. 2.65. CAMSRA (a) 2023 average of anthropogenic aerosol optical depth (AOD); (b) global annual average of anthropogenic AOD from 2003 to 2023. 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 2023. The right column shows time series of global averages for the period 2003–23, with the 1-σ uncertainties of these estimates shown in gray.

4. TROPOSPHERIC OZONE

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

Tropospheric ozone is a short-lived climate forcer with a global distribution that varies regionally, vertically, and on seasonal and interannual time scales (Forster et al. 2021; Szopa et al. 2021), posing a challenge for trend detection (Chang et al. 2021; Fiore et al. 2022). Atmospheric chemistry models indicate an approximately 40% increase of the tropospheric ozone burden (TOB) since the nineteenth century, and limited observations since the early and mid-twentieth century are consistent with the model estimates (Tarasick et al. 2019). Widespread in situ and satellite observations also record an increase of TOB since the mid-1990s (Gulev et al. 2021); however, new satellite-based observations suggest that the increase of TOB came to an end in 2020 in response to diminished ozone precursor emissions during the economic downturn that was triggered by the coronavirus (COVID-19) pandemic (Miyazaki et al. 2020; Ziemke et al. 2021). With three additional years (2021–23) of no growth in TOB as described below, the year 2020 may be a change point in the TOB record.

The unusual period (2020–23) of no growth in TOB (Fig. 2.66) coincides with observations of negative ozone anomalies (–5%) in the free troposphere above western North America and
Europe during 2020, which were recorded by ozonesondes, infrared spectrometers, and commercial aircraft (Steinbrecht et al. 2021; Clark et al. 2021; Chang et al. 2022, 2023). These anomalies were strongest in summer, when photochemical production is most active, and they are similar in magnitude to negative ozone anomalies detected at high-elevation rural sites in western North America and Europe (Putero et al. 2023). Model simulations of the COVID-19 period indicate that reduced emissions of ozone precursor gases across the Northern Hemisphere led to the ozone decreases (Miyazaki et al. 2020; Steinbrecht et al. 2021), reaching levels similar to those measured in the mid-1990s when ozone precursor emissions were less than 2019 levels (Chang et al. 2022). The models also indicate that the 2020 ozone anomalies were not caused by the unusual ozone depletion event that occurred above the Arctic during the spring of 2020 (Steinbrecht et al. 2021; Wang et al. 2023).

The combined Aura Ozone Monitoring Instrument and Microwave Limb Sounder satellite ozone measurements (OMI/MLS) provide a continuous record of the TOB from 60°S to 60°N for the period 2004–23 (Ziemke et al. 2019). The vertical resolution of OMI/MLS monthly tropospheric column ozone is ~3 km near the tropopause with a regional precision of ~2 Dobson units (DU; 7%); trend uncertainties are about 0.5 DU decade⁻¹ (1.5% decade⁻¹). Positive tropospheric column ozone anomalies were widespread across the Northern Hemisphere in 2023 (relative to 2005–22), with peak values above South Asia, East Asia, and the North Pacific Ocean, while relatively weak negative anomalies occurred above southern Africa, Australia, and New Zealand (Plate 2.1aa). Global TOB (60°S–60°N) increased at the rate of 1.06±0.48 Tg yr⁻¹ for the first 15 years of the record (2004–19), equal to a total increase of ~5% (Fig. 2.66). There was a slight drop in TOB in 2020, likely due to reduced ozone precursor emissions during the COVID-19 pandemic, as described above (Fig. 2.66). The tropospheric ozone burden remained at similar levels during 2021–23, mainly driven by decreases in northern midlatitudes (Figs. 2.66a–c). Regionally, the strongest positive trends (2004–23) have occurred above

South and East Asia and across much of the North Pacific Ocean, along with Amazonia (Fig. 2.67). Weak but widespread ozone decreases are present above North America, Europe, Central Asia, Siberia, northern and southern Africa, Australia, and New Zealand, with the strongest decreases above North Africa and the western Mediterranean.

<figure><img src="image_119.png" /><figcaption>Fig. 2.66. Monthly averages (solid lines) and 12-month running means (dashed lines) of Ozone Monitoring Instrument (OMI)/Microwave Limb Sounder (MLS) tropospheric ozone burdens (Tg) from Oct 2004 through Dec 2023 for (a) 60°S–60°N (black), (b) the Northern Hemisphere tropics (red) and midlatitudes (dark red), and (c) the Southern Hemisphere tropics (blue) and midlatitudes (green). Slopes of linear fits to the data are presented with their 95% confidence-level uncertainties.</figcaption></figure>

<figure><img src="image_120.png" /><figcaption>Fig. 2.67. Linear trends in Ozone Monitoring Instrument (OMI)/Microwave Limb Sounder (MLS) tropospheric column ozone (DU decade⁻¹) on a 5° × 5° grid from Oct 2004 through Dec 2023. 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>
Tropospheric ozone burden trends cannot be assessed from surface records because surface trends are often decoupled from the trends in the free troposphere above (Gulev et al. 2021), and the limited availability of long-term surface records precludes the construction of a data record that is globally representative; however, long-term surface records at remote locations are critical for evaluating the performance of global chemistry-climate models. Ozone trends from six such sites are reported here, based on records more than 20 years in length (Fig. 2.68; Table 2.11). Two records—those of the Mauna Loa Observatory in Hawaii, and the Barrow Atmospheric Observatory in Alaska—now span 50 years and report positive surface ozone trends of 0.93±0.39 ppbv decade⁻¹ and 0.50±0.34 ppbv decade⁻¹, respectively. The 48-year record at South Pole also reports a positive albeit weaker trend of 0.32±0.34 ppbv decade⁻¹. The observations show no trend at Arrival Heights, Antarctica, since 1996. There is some evidence of a decrease at Tudor Hill, Bermuda, since 1988 (−0.81±1.10 ppbv decade⁻¹), as well as clear evidence of a decrease at Summit, Greenland, since 2000 (−2.00±0.93 ppbv decade⁻¹).

<figure><img src="image_121.png" /><figcaption>Fig. 2.68. (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 2.11. (b) As in panel (a), except the time series have been converted to monthly anomalies, referenced to the monthly climatological values over 2000–20, and smoothed using a locally weighted scatterplot smoothing regression.</figcaption></figure>

<table><caption>Table 2.11. Surface ozone trends at the six baseline monitoring sites shown in Fig. 2.68 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>-2.00±0.93</td><td>p&lt;0.01</td></tr><tr><td>Barrow Atmospheric Observatory, Alaska — 71.3°N, 156.6°W, 11 m</td><td>1973–present</td><td>0.50±0.34</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>-0.81±1.10</td><td>p=0.14</td></tr><tr><td>Mauna Loa Observatory (MLO), Hawaii — 19.5°N, 155.6°W, 3397 m</td><td>1973–present</td><td>0.93±0.39</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>0.23±0.53</td><td>p=0.39</td></tr><tr><td>South Pole, Antarctica — 90.0°S, 59.0°E, 2840 m</td><td>1975–present</td><td>0.32±0.34</td><td>p=0.06</td></tr></tbody></table>

## 5. STRATOSPHERIC AEROSOLS

—S. Khaykin, G. Taha, T. Leblanc, T. Sakai, I. Morino, B. Liley, and S. Godin-Beekmann

Stratospheric aerosols play a large role in the chemical and radiative balance of the atmosphere (Kremser et al. 2016). Explosive volcanic eruptions may directly inject sulfur dioxide (SO₂) and ash into the stratosphere, leading to significant perturbations of stratospheric aerosol burden at hemispheric and global scales lasting from several months to several years. Another important source of particulate matter in the stratosphere is the increasingly intense wildfires (Peterson et al. 2021; Fromm et al. 2022).
Figure 2.69 shows 24 years of stratospheric aerosol optical depth (sAOD) observations by the ground-based Network for the Detection of Atmospheric Composition Change lidars at Observatoire de Haute Provence (OHP), France (43.9°N), and Lauder observatory, New Zealand (45.0°S), together with zonally averaged satellite data. These stations, antipodally located on the globe, respectively represent the northern and southern extratropics. The OHP time series (Fig. 2.69a) from 2000 to 2023 is largely modulated by several moderate volcanic eruptions as well as by the extreme British Columbia pyrocumulonimbus wildfire outbreak in August 2017 (Peterson et al. 2018), which led to a prolonged perturbation of stratospheric aerosol composition and burden. The largest impact on the NH sAOD in terms of magnitude and longevity of the perturbation was generated by the Raikoke volcanic eruption in 2019. The decay of the Raikoke sAOD perturbation appears to be longer than those of other midlatitude eruptions of similar magnitude. This is possibly due to the diabatic lofting of ash-rich Raikoke plumes that were shown to self-organize into persistent stratospheric anticyclones (Khaykin et al. 2022a). Such behavior has previously been reported for the wildfire smoke aerosols (Khaykin et al. 2020) that contain highly absorptive black carbon; however, it was unexpected for the volcanic aerosols, composed primarily of non-absorbing sulfates.

Significant sAOD perturbations in the Southern Hemisphere (SH; Fig. 2.69b) were nearly absent for more than two decades until the 2015 Calbuco volcanic eruption in Chile. The record-breaking 2019/20 "Australian New Year Super Outbreak" (ANYSO) wildfires boosted the SH sAOD to four times the background level according to Stratospheric Aerosol and Gas Experiment (SAGE) III and surpassed the Raikoke-induced NH perturbation (Khaykin et al. 2020), which was deemed

<figure><img src="image_122.png" /><figcaption>Fig. 2.69. Time series of monthly mean stratospheric aerosol optical depth (sAOD) at 532 nm of the stratospheric layer between 380 K and 1000 K potential temperature from ground-based lidars at (a) French Observatoire de Haute Provence (OHP, 43.9°N, 5.7°E, LiO₃S, and LTA lidars, red and green curves) and (b) New Zealand's Lauder station (45.0°S, 169.7°E, Lauder aerosol lidar, red curve) and the corresponding monthly/zonal-mean values from satellite observations within 40°N–50°N and 40°S–50°S latitude bands from the International Space Station's Stratospheric Aerosol and Gas Experiment (SAGE) III instrument (blue curves) and GloSSAC (Global Satellite-based Stratospheric Aerosol Climatology) merged satellite record (black curves). The embedded panels display the log-scaled time series from the beginning of the GloSSAC record. The literal notations indicate the most significant volcanic eruptions: El Chichon (EC), Pinatubo (Pi), Kasatochi (Ka), Sarychev (Sa), Nabro (Na), Raikoke (Ra), Calbuco (Ca), and Hunga Tonga–Hunga Haʻapai (HTHH); and wildfire events: Pacific Northwest Event (PNE; British Columbia, Canada), and Australian New Year Super Outbreak (ANYSO).</figcaption></figure>
the 30-year high at that time (Leblanc et al. 2020). The ANYSO outbreak led to a prolonged perturbation in the entire SH with the decay exceeding one year.

More recently, the 30-year global sAOD record has been surpassed again, following the eruption of the Hunga Tonga–Hunga Haʻapai (HTHH) volcano on 15 January 2022, which was marked by extreme explosiveness with aerosols reaching an altitude above 50 km (Carn et al. 2022; Khaykin et al. 2022b). Aerosol layers were detected by the Ozone Mapping and Profiler Suite–Limb Profiler (OMPS-LP) instrument above 40 km, though the bulk of HTHH aerosols was bounded within the 20-km to 30-km layer (Taha et al. 2023).

The meridional evolution of the sAOD (Fig. 2.70a) shows that the HTHH-induced perturbation was mostly restricted to the tropical belt during the first four months after the eruption, although some transport into southern high latitudes occurred as early as February 2022 (Khaykin et al. 2022b). The transport of the bulk of volcanic material into the southern extratropics occurred in June 2022; however, its further penetration towards the pole was hindered by a strong transport barrier at the edge of the Antarctic stratospheric vortex that had been fully established by that time (Manney et al. 2023). By early 2023, the HTHH aerosols had spread across the entire SH and, unlike in 2022, were then entrained by the 2023 Antarctic vortex.

While the bulk of the HTHH sAOD perturbation has been restricted to the tropical belt and the SH, limited transport to the northern extratropics can be seen by tracking the anomalies in the aerosol extinction vertical profiles. Figure 2.70b displays the meridional transport of the HTHH sulfate aerosols expressed as the potential temperature of the peak extinction ratio (ER; aerosol-to-molecular extinction ratio) from OMPS-LP observations. The data suggest that the first intrusions into the northern extratropics occurred in April 2022, after which ground-based lidars in the NH extratropics started reporting weak yet distinct aerosol layers in the mid-stratosphere (Khaykin et al. 2022b). Further NH midlatitude intrusion episodes occurred during November 2022–January 2023. The vertical evolution of the peak ER (Fig. 2.70b) reveals that sedimentation of HTHH sulfate aerosols was slower in the tropics, where it is partly compensated by upwelling, and faster towards the poles.

Additional stratospheric aerosols detected in the NH mid- and high latitudes from April through November 2023 (Figs. 2.70a,b) can be traced to the eruption of Shiveluch volcano in the Kamchatka peninsula on 14 April 2023, as well as to a series of wildfire outbreaks in Canada and Siberia that led to a significant season-wide pollution of the lowermost stratosphere with smoke aerosols.

<figure><img src="image_123.png" /><figcaption>Fig. 2.70. Time–latitude evolution of the stratospheric aerosol from OMPS-LP observations at 997 nm in 2022/23. (a) Zonal-mean stratospheric aerosol optical depth (sAOD). (b) Mean potential temperature of the stratospheric peak of extinction ratio for the samples with ERmax>6, which corresponds to departures beyond ~7 sigma of the background variability in the given bin. This method allows for the detection of optically thin yet distinct aerosol layers, which are hard to spot using zonally averaged sAOD. The dashed arrows in (b) indicate the episodes of Hunga Tonga–Hunga Haʻapai (HTHH) aerosol intrusions into the Northern Hemisphere extratropics. The large and small triangles in (a) and (b) indicate the eruptions of HTHH and Shiveluch, respectively, whereas the black stars in (a) and red stars in (b) indicate wildfire events with measurable stratospheric impact in Canada and Russia during summer 2023.</figcaption></figure>
# 6. STRATOSPHERIC OZONE

—M. Weber, W. Steinbrecht, C. Arosio, R. van der A, S. M. Frith, J. Anderson, L. M. Ciasto, M. Coldewey-Egbers, S. Davis, D. Degenstein, V. E. Fioletov, L. Froidevaux, D. Hubert, D. Loyola, A. Rozanov, V. Sofieva, K. Tourpali, R. Wang, T. Warnock, and J. D. Wild

Stratospheric ozone protects the ecosystem from harmful ultraviolet radiation. The total ozone column is an indicator of the level of protection from this radiation. About 90% of the total column amount resides in the stratosphere, and the number of ozone molecules is maximum at about 20-km to 25-km altitude (lower stratosphere), an altitude range that is called the ozone layer. Long-term changes in stratospheric ozone are governed by declining stratospheric halogens (chemistry) from man-made ozone-depleting substances (ODSs) and by the current and future greenhouse gas concentrations in the atmosphere (chemistry and circulation; WMO 2022).

In 2023, total column ozone was, on average, slightly lower compared to the 1998–2008 reference period in the NH, while in the SH from 35°S to 60°S, it was higher by up to 10 DU–15 DU

(Plate 2.1ab). Antarctic total column ozone was close to the long-term mean except for in a small region south of Australia (Plate 2.1ab). The year 2023 ends a series of three years with below-average ozone values for the SH extratropics. The SH total column ozone was unusually low in 2022 (Figs. 2.71d,e), mainly due to circulation changes but also due to enhanced chemical destruction following the HTHH volcanic eruption in January 2022 (e.g., Santee et al. 2022; Evan et al. 2023; Fleming et al. 2024). In the tropics (Fig. 2.71c), total ozone was higher by a few DU compared to previous years but was within the year-to-year variability (two sigma) of the last two decades. Globally, total ozone levels

Fig. 2.71. 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 the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) ground-based measurements combining Brewer, Dobson, 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; Jeannette Wild, NOAA, 2024, personal communication); the GOME/SCIAMACHY/ GOME-2/OMPS/TROPOMI products (GSG) from University of Bremen (dark green, Weber et al. 2022), and GTO from the EU’s German Aerospace Center (DLR; light green; Coldewey-Egbers et al. 2022; Garane et al. 2018). MSR-2 (purple) assimilates nearly all ozone datasets after corrections based on the ground-based data (van der A et al. 2015). The dotted gray lines in each panel show the average total column ozone level for 1964–80 calculated from the WOUDC data. Most of the observational data for 2023 are preliminary. The thick white lines in (a)–(d) show the median from chemistry-climate CCMI-2022 ref D2 model runs (Plummer et al. 2021). The model data have been smoothed using a three-point triangle function. The gray-shaded areas provide the 80% percentile range for the model data. All datasets have been bias-corrected by subtracting individual data averages and adding the multi-instrument mean in the reference period (1998–2008).

<figure><img src="image_124.png" /></figure>
in 2023 were close to the long-term average of the last 20 years and broadly agree with projections from chemistry-climate models (CCMs) using current scenarios of ODSs and greenhouse gases, as shown in Figs. 2.71a–d.

Figures 2.72d,e show that ozone at 50 hPa (~22-km altitude) in the tropics and northern midlatitudes behaved similarly to the total column (Figs. 2.71b,c). In 2023, the NH annual mean was nearly unchanged from previous years (Fig. 2.72d). Ozone at 50 hPa was slightly higher in the tropics in 2023 but still within the year-to-year variability of the last decade (Fig. 2.72e), and larger than in 2022 by about 5% in the SH (Fig. 2.72f), bringing it closer to the long-term average. In the upper stratosphere (2 hPa or 42-km altitude; Figs. 2.72a–c), ozone observations show a clear increase since the mid-1990s, averaging 0.2±0.15% yr⁻¹. The 2023 annual means follow the long-term trend, again in general agreement with the broad range predicted by CCMs.

In the SH midlatitude, elevated total column ozone (Plate 2.1ab; Fig. 2.71d) and ozone in lower stratosphere (Fig. 2.72f) in 2023 compared to 2022 is probably related to the strong El Niño that started to emerge in the middle of 2023. El Niños are linked to a strengthening of the Brewer-Dobson (BD) circulation and a weakening of the polar vortex, which both increase extratropical ozone by enhancing ozone transport from the tropical stratosphere to higher latitudes and by reducing the potential for the formation of widespread polar stratospheric clouds and subsequent large chemical ozone depletion in polar spring (e.g., Domeisen et al. 2022; Butchart 2014). The quasi-biennial oscillation (QBO) was in its westerly phase from September 2022 until the end of boreal summer 2023. This is associated with a weaker BD circulation and typically results in lower extratropical and higher tropical ozone columns. In the first half of 2023, this resulted in lower stratospheric ozone in the NH (e.g., Baldwin et al. 2001). The QBO turned easterly during the second half of 2023, coinciding with the strengthening of El Niño. The combined effect on SH ozone resulted in positive anomalies at southern midlatitudes (Plate 2.1ab; Figs. 2.71d,e, 2.72f).

<figure><img src="image_125.png" /><figcaption>Fig. 2.72. Annual mean anomalies of ozone (%) in (a)–(c) the upper stratosphere near 42-km altitude or 2-hPa pressure, and (d)–(f) the lower stratosphere, near 22 km or 50 hPa for three zonal bands: 35°N–60°N, 20°S–20°N (tropics), and 35°S–60°S, respectively. Anomalies are with respect to the 1998–2008 baseline. Colored lines are long-term records obtained by merging different limb (GOZCARDS, SWOOSH, SAGE+CCI+OMPS_L, SAGE+OSIRIS+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 determined from merging ground-based ozone records at seven Network for the Detection of Atmospheric Composition Change (NDACC) stations employing differential absorption lidars and microwave radiometers. See Steinbrecht et al. (2017), Arosio et al. (2019), and Godin-Beekmann (2022) for details on the various datasets. Gray-shaded areas show the range of chemistry-climate model simulations from CCMI-1 refC2 (SPARC/IO3C/GAW 2019). Ozone data for 2023 are not yet complete for all instruments and are still preliminary.</figcaption></figure>
# 7. STRATOSPHERIC WATER VAPOR

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

In the aftermath of the January 2022 eruption of the Hunga Tonga–Hunga Haʻapai (HTHH) volcano (20.5°S, 175.4°W), which injected ~50 Tg–150 Tg water vapor (WV) into the stratosphere (3.5%–10% of the entire stratospheric burden; Millán et al. 2022; Vömel et al. 2022), WV concentrations remained at or near record-high levels through much of the stratosphere in 2023. By being injected into the tropical stratosphere between approximately 26 km (22 hPa) and 34 km (6 hPa), air from the eruption bypassed the tropical tropopause layer (TTL) “cold trap” that normally controls the amount of WV entering the stratosphere (Fig. 2.73a). This dramatic perturbation to WV and other stratospheric species (e.g., ozone; section 2g6) is expected to persist for years.

At the beginning of 2023, the HTHH stratospheric WV perturbation had already been transported upward within the rising branch of the Brewer-Dobson circulation in the tropics (Fig. 2.73a) and poleward into each hemisphere (Fig. 2.73b). The majority of the WV perturbation was still in the SH in January 2023. This hemispheric asymmetry was caused by the location and timing of the eruption, which was followed by strong poleward transport up to the SH polar vortex edge in the 2022 austral winter.

In 2023, the HTHH WV perturbation continued to spread poleward and downward in the NH (Figs. 2.74c,f,i). By the end of the year, elevated WV was evident in the lower stratosphere at high northern latitudes down to ~68 hPa/~19 km (Fig. 2.74i). Whereas the tropical (15°S–15°N) mean WV was at a record level in the mid-stratosphere (near ~30 hPa/24 km) for much of 2022 (relative to the 2004–21 mean), monthly WV anomalies in 2023 were at record levels in the upper stratosphere (Fig. 2.73a). For example, at and above 10 hPa/31 km, anomalies were 1.2 ppm to 1.8 ppm (parts per million, i.e., µmol mol⁻¹), corresponding to a deviation from the climatological mean of ~25%–30% (~10 std. dev.).

Even though the mid- and upper-stratospheric WV anomalies were most dramatic in 2023, lower-stratospheric WV anomalies (near 82 hPa/17 km) were also positive (wet) for all months (e.g., Figs. 2.73a,c), following a general trend towards more positive anomalies in the last five years, which is also evident in frost point measurements from the set of balloon-launching stations with long-term records (Fig. 2.75).

<figure><img src="image_126.png" /><figcaption>Fig. 2.73. (a) Vertical–time contour of tropical (15°S–15°N) lower-stratospheric water vapor (WV) anomalies, with the +2-, +3-, and +4-ppm values shown as yellow, red, and cyan 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 5.0 Aura Microwave Limb Sounder (MLS) data, which has collected near-global (82°S–82°N) measurements since Aug 2004. Anomalies are differences from the mean 2004–2021 water vapor mixing ratios (ppm) for each month. (a) shows the unprecedented injection of water vapor directly into the stratosphere by the Hunga Tonga–Hunga Haʻapai (HTHH) eruption, followed by its slow ascent through the tropical stratosphere. (b) shows the southward propagation of the plume at 26 hPa in 2022, followed by the downward transport of the HTHH-related anomalies in 2023, while (c) shows a more typical propagation of interannual-varying tropical lower-stratospheric WV anomalies to higher latitudes in both hemispheres following the second-warmest coldpoint and record wet tropical lower-stratospheric WV in 2023. (c) also shows 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.</figcaption></figure>
<figure><img src="image_127.png" /><figcaption>Fig. 2.74. Deseasonalized monthly lower stratospheric Aura Microwave Limb Sounder (MLS) anomalies (ppm; 2004–21 base period) at (a),(d),(g), 82 hPa and (b),(e),(h) 26 hPa. (c),(f),(i) show latitude–height cross-sections of the water vapor (WV) anomalies. Data are shown for Jan 2023 (top row), Jul 2023 (middle row), and Dec 2023 (bottom row). WV anomalies of +2 ppm and +3 ppm are shown with yellow and red contour lines, respectively. Hatching in the right column shows where the zonal-mean WV was at record values for the given month.</figcaption></figure>

The 2023 WV anomalies in the lowermost tropical stratosphere are expected to be primarily caused by anomalies in tropical tropopause temperatures, although a contribution from WV-impacted air from higher latitudes via mixing is also plausible. Considering the whole time series, lower-stratospheric WV anomalies from the Aura Microwave Limb Sounder (MLS) and frost point hygrometers are highly correlated with tropical (15°S–15°N) cold-point tropopause (CPT) temperature anomalies (Figs. 2.75b,c). In 2023, tropical CPT temperatures were the second highest on record (annual mean anomaly was +0.73 K; second only to 2022, which was +0.76 K), and MLS tropical stratospheric WV entry values at 82 hPa were at their highest recorded levels in the 20-year MLS record (2023 annual mean anomaly was +0.39 ppm, compared to +0.27 ppm in 2022).
La Niña conditions were present at the beginning of 2023 (see section 4b), which during boreal winter are typified by weaker tropical lower-stratospheric upwelling and anomalously warmer CPTs (e.g., Garfinkel et al. 2021). The positive tropical lower-stratospheric WV anomalies at the beginning of 2023 are thus consistent with the expected La Niña response. Following a brief transition to neutral conditions, El Niño conditions emerged in May and strengthened through the rest of the year (see section 4b). The net effect of ENSO on water vapor is complex, but there is some evidence of moistening associated with strong El Niño events (Garfinkel et al. 2018).

The quasi-biennial oscillation (QBO) phase at 70 hPa was westerly throughout 2023 (section 2e3). The QBO westerly phase is associated with anomalously weak tropical upwelling and warm temperatures, which can impact the CPT. Thus, the QBO westerlies and La Niña likely enhanced lower-stratospheric WV at the beginning of 2023, while the effects of the strong El Niño may have contributed to the record-breaking stratospheric entry values of water vapor later in the year.

## 8. CARBON MONOXIDE

—J. Flemming and A. Inness

The global burden of carbon monoxide (CO) increased in 2023 compared to previous years due to exceptionally high emissions from wildfires in Canada (Plate 2.1ac;

Fig. 2.76; section 2h3; see Sidebar 7.1). Western Canada (i.e., Northwest Territories, Alberta) experienced increased fire frequency from May to September, and eastern Canada (i.e., Quebec) was the center of wildfires in May and June (see Sidebar 7.1). The resulting emissions led to increased total-column CO values of up to 25% in the affected areas and the outflow region over the Atlantic.

Carbon monoxide is emitted into the atmosphere by combustion processes originating from anthropogenic sources, such as road transport and energy generation, as well as from wildfires (Szopa et al. 2021). 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 methane, isoprene, and other volatile organic trace gases. Oxidation of CO by reaction with the hydroxyl radical is the main loss process for CO, resulting in an atmospheric lifetime of one to two months. The presence of CO is one of the factors that controls the abundance of tropospheric ozone, which is a short-lived pollutant and climate forcer (section 2g4).

According to the CAMS (https://atmosphere.copernicus.eu/) reanalysis of atmospheric composition (Inness et al. 2019), the global CO burden has decreased over the last 20 years (Fig. 2.76a). The decrease is likely caused by both decreased anthropogenic emissions in most parts of the world, as well as a strong decrease in fire frequency compared to the early 2000s, mainly in South America. In recent years, positive global and regional CO anomalies have occurred because of intense wildfires related to exceptional regional meteorological conditions such as the peat fires in Indonesia in 2015 that were associated with El Niño conditions, or boreal wildfires such

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

Fig. 2.75. Lower-stratospheric water vapor (WV) anomalies over four 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 Microwave Limb Sounder (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 Hilo (2011–21). Tropical cold-point tropopause anomalies based on the MERRA-2 reanalysis ([b],[c], blue curve) are generally well correlated with the tropical lower-stratospheric WV anomalies.
as those in Siberia during a heatwave in 2021. The positive CO anomalies in 2023 in North America (Fig. 2.76b) and western Australia were also caused by increased wildfire activity supported by exceptionally warm and dry conditions, some possibly related to the onset of El Niño in May 2023. The main areas of seasonal savanna wildfires, such as tropical Africa and tropical South America, showed negative anomalies in 2023 (Plate 2.1ac).

CAMS 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 reanalysis assimilated global thermal infrared total-column CO retrievals (V6 from 2003 to 2016; NRT V7 from January 2017 to June 2019; NRT V8 from July 2019 to present) of the Measurement of Pollution in the Troposphere (MOPITT) instrument (Deeter et al. 2014, 2017, 2019), excluding observations poleward of 65°N and 65°S, using the ECWMF four-dimensional variational assimilation (4D-VAR) data assimilation system. Anthropogenic emissions were taken from the MACCity inventory (Granier et al. 2011) that accounts for projected emission trends according to the Representative Concentration Pathways (RCP) 8.5 scenario, but COVID-19-related emissions modifications were not applied. Biomass burning emissions were taken from the Global Fire Assimilation System (v1.4; Kaiser et al. 2012; section 2h3) that is based on MODIS fire radiative power retrievals

(Giglio et al. 2016). Monthly mean biogenic 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 which a monthly-mean climatology derived from the 2003–17 simulations was applied.

<figure><img src="image_129.png" /><figcaption>Fig. 2.76. Time series of the area-averaged monthly mean (red) and annual mean (blue, yearly mean [YM]) total column carbon monoxide (CO; × 10¹⁸ molecules cm⁻²) over (a) the whole globe and (b) North America (30°N–70°N, 55°W–165°W, land points only for b) from the CAMS reanalysis for the period 2003–23.</figcaption></figure>
The terrestrial surface albedo is the ratio between the solar radiation reflected by Earth's surface and the incident light. It is a key forcing parameter controlling the partitioning of radiative energy between the atmosphere and the surface. Increases in vegetation lead to a "darkening" effect, as more radiation is absorbed by plants in the visible spectrum. At the same time, the near-infrared albedo increases slightly, as healthy vegetation tends to reflect this part of the spectrum. Desertification and the related exposure of bare soil or increases in snowpack lead to a brighter surface and higher surface albedo.

In 2023, the normalized anomaly (2003–20 base period) of white-sky albedo in the visible broadband (Plate 2.1ad) indicated a notable surface darkening (values below –15%) of more than 17% of the land surface, compared to 6% recorded in 2022 (Duveiller and Gobron 2023). The darkening effect over Quebec and Nunavut and over large parts of Siberia was affected by the early melting of surface snow, starting in the second quarter of the year (section 2c5), together with the continuous decline in surface snow cover since the beginning of this century (Young 2023). The decreases in visible surface albedo recorded in central and eastern Europe, eastern China, western India, Japan, northern Australia, and sub-Saharan Africa were associated with a slight increase of the near-infrared albedo (Plate 2.1ae) as vegetation density increased over these areas (section 2h2).

In some regions, such as in eastern Australia, western Africa, and the Arabian Peninsula, 2023 displayed opposite signs of both albedo anomalies with respect to 2022. These fluctuations can be attributed to vegetation dynamics that are sensitive to water availability and temperature variations. Positive anomalies recorded over the central United States and Alaska resulted from above-average snow cover, which even extended during late spring after snowstorms in some of these regions.

The patterns of the zonally averaged albedo anomalies in the visible (Fig. 2.77a) and near-infrared (Fig. 2.77b) parts of the spectrum follow the darkening trend of previous years. Both figures show the large interannual variations related to seasonal snow in winter and spring at mid- and high-northern latitudes, but also the general trend in increased vegetation greening during summer periods. Persistent negative anomalies are noticeable for 2022 and 2023 between 15°N and 30°N in the visible albedo (with weak anomalies in the near-infrared domain), mainly due to the increase in vegetation density over China and India. Persistent negative anomalies in 2022/23 in both visible and near-infrared domains are detected between 10°S and 30°S, indicating a deviation from average conditions mainly over southern Africa and Australia.

The amplitude of the globally smoothed average anomaly (solid black line) is within

<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /></figure>

Fig. 2.77. Zonally averaged (a) white sky visible (%) and (b) near-infrared (%) broadband land surface albedo anomalies for the period 2003–23 (2003–20 base period).
±4% in the visible domain during 2003–23 (and within ±1% for the near-infrared; Fig. 2.78). The year 2023 is characterized by a trend to more negative anomalies in both the visible and near-infrared domains, driven by the dominant contribution from the Northern Hemisphere regions.

This analysis was based on satellite products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board the *Aqua* and *Terra* satellite platforms to generate a long-term record from 2002 to 2022 (Schaaf et al. 2002). The 2023 data are from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi NPP. Although VIIRS has been assessed as a strong candidate for the continuation of the MODIS archive (Liu at al. 2017), a small difference between VIIRS and MODIS surface albedo was noted; VIIRS 2023 data were bias-corrected accordingly. The anomalies were calculated at a 10-day frequency, based on the 2003–20 reference period.

<figure><img src="image_132.png" /><figcaption>**Fig. 2.78.** Global (black lines), Northern Hemisphere (blue), and Southern Hemisphere (red) land surface (a) visible and (b) near-infrared broadband albedo normalized anomalies (%; 2003–20 base period) for the period 2003–23. Dotted lines denote each 10-day period; solid lines indicate the 12-month running averaged mean.</figcaption></figure>

## 2. TERRESTRIAL VEGETATION DYNAMICS

—N. Gobron and F. Cappucci

The fraction of absorbed photosynthetically active radiation (FAPAR) reveals the amount of vegetation as well as its health and is important for assessing primary productivity and the associated fixing of atmospheric carbon dioxide by plants. FAPAR anomalies in 2023 compared to the 1998–20 average show a greater extent of positive values than negative values in the level of vegetation productivity across the world (Plate 2.1af).

Positive anomalies (increased plant photosynthesis) over the northern-central region of the United States and southern-central Canada (i.e., Great Plains) indicate that vegetation health recovered from last year due to above-average precipitation associated with higher temperatures. In contrast, fire events over the Quebec region (section 2h3) resulted in negative annual anomalies (decreased plant photosynthesis) as more than five million hectares of boreal forest burnt (see Sidebar 7.1 for details). The positive annual anomalies over Europe highlighted that Earth's surfaces continued the greening trend due to higher temperatures as well as plentiful rainfall.

Positive anomalies over southern Brazil and Paraguay were due to the transition to El Niño, which started in spring with extreme rainfall (section 2d4). In contrast, severe heatwaves, especially in Chile and southwestern Argentina (see section 7d3), impacted vegetation health, leading to negative FAPAR anomalies. The Central African Republic showed positive anomalies that were due to above-normal precipitation during the rainy seasons, whereas Namibia suffered from drought during the first half of the year (see section 7e). A major part of northern and southeastern Australia had positive FAPAR anomalies, as both precipitation and temperature were above normal (see section 7h4).

Figure 2.79 shows that all latitudes—though more markedly over the Southern Hemisphere (SH)—were affected by negative anomalies (i.e., less than –0.04) from 2002 to 2014, and that positive patterns were dominant in both hemispheres afterward. In 2023, the monthly anomalies were positive at nearly all latitudes, apart from a few places such as south of 20°S. Regions around 50°S had strong negative patterns at both the start and end of the year.

Figure 2.80 shows the global and hemispheric anomalies, with more seasonal variability in the less-landed SH than in the Northern Hemisphere (NH). FAPAR anomalies over the SH were positive before 2002, then negative until 2010. Thereafter, there were positive peaks in 2011,
2014, 2016/17, and 2022, with few negative months after 2013. The NH was positive in 1998, negative from 1999 to 2013, and positive thereafter. This trend towards positive values is linked to the trend for surface temperatures over land (see Fig. 2.1b). FAPAR annual anomalies were 0.013 (0.006) for NH (SH) in 2023 (with record values set in the NH [>0.02] at the end of the year).

Optical space sensors are used to infer FAPAR, an essential climate variable of the Global Climate Observation System (GCOS 2022). The 2023 analysis merges 26 years of global FAPAR products based on four optical sensors: Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Envisat/MERIS, Terra-Aqua/MODIS and Sentinel-3/Ocean and Land Colour Instrument (OLCI) from 1998 to 2023 (Gobron et al. 2010, 2022; 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 average uncertainty of ~5%–10%.

### 3. BIOMASS BURNING

—J. W. Kaiser, M. Parrington, and D. Armenteras

Two distinct trends that have emerged in global biomass burning over the last decade continued in 2023. Many savanna regions, which dominate global fire emissions, saw a decline related to agricultural expansion, while many forested regions—where climate change with severe drought periods increases the flammability of the landscape (e.g., Xing and Wang 2023 for the Arctic)—experienced longer and more intense wildfire episodes (Plate 2.1ag). The amount of biomass burning, referred to here as “fire activity” and more commonly as wildfires, is characterized here as the amount of carbon that is consumed by fire and emitted into the atmosphere. Of this, 80%–95% is emitted as carbon dioxide ($CO_2$), and the rest is oxidized to $CO_2$ in the atmosphere or released as particulate matter. In a stable ecosystem,

virtually all of this $CO_2$ is assimilated again by the regrowth of vegetation. Currently however, 20% is estimated to contribute to the long-term build-up of atmospheric $CO_2$ (Zheng et al. 2023).

Global annual total estimated fire emissions were close to the 2003–20 average in 2023 in the GFASv1.4 dataset (Table 2.12). However, 2022 had the lowest global emissions in GFAS, and the 2023 emissions represent an increase of 26% from 2022. The years 2019 and 2023 have similar

<figure><img src="image_133.png" /><figcaption>Fig. 2.79. Zonally averaged fraction of absorbed photosynthetically active radiation (FAPAR) anomalies for 1998–2023 (1998–2020 base period).</figcaption></figure>

<figure><img src="image_134.png" /><figcaption>Fig. 2.80. Global (black lines), Northern Hemisphere (blue), and Southern Hemisphere (red) fraction of absorbed photosynthetically active radiation (FAPAR) anomalies for 1998–2023 (1998–2020 base period). Dotted lines denote each monthly period; solid lines indicate the six-month running averaged mean.</figcaption></figure>

<figure><img src="image_135.png" /><figcaption>Fig. 2.81. Global map of fire activity ($g C m^{-2} yr^{-1}$) in 2023 in terms of carbon consumption. (Source: CAMS-GFASv1.4.)</figcaption></figure>
emission budgets, and both are higher than any other year after 2015. The increase in 2023 was driven by anomalously large-scale wildfires that burned persistently in forests across Canada from May to September (Fig. 2.81; Plate 2.1ag; see Sidebar 7.1). These wildfires consumed five times more biomass than the average for the 2003–20 reference period and three times more than the previously recorded maximum (2014; cf. Fig. 2.82a). In contrast, the United States experienced its lowest annual total fire emissions on record, and wildfire emissions from boreal Eurasia were 28% below the 2003–20 average.

African fire carbon emissions accounted for roughly half of the total global emissions during the 2000s but their contribution has since shrunk to ~40%. The decreasing trend in savanna regions continued in 2023 over Northern-Hemisphere Africa with emissions 20% below the 2003–20 average, the third successive year with lower fire activity than any years in the record prior to 2019 (Fig. 2.82b). Southern-Hemisphere Africa and South America also contributed to the

<figure><img src="image_136.png" /><figcaption>Fig. 2.82. Regional time series of monthly (lines in Tg C month⁻¹) and annual (symbols in Tg C yr⁻¹) biomass burning activity for (a) Canada, (b) Northern-Hemisphere Africa, and (c) tropical Asia.</figcaption></figure>

<table><caption>Table 2.12. Annual continental-scale biomass burning budgets in terms of carbon emission (Tg C yr⁻¹). (Source: CAMS-GF-ASv1.4.)</caption><thead><tr><th>Name of Region</th><th>Location</th><th>Biomass Burning 2003–20 Mean value (Range)</th><th>Biomass Burning 2023 Value</th><th>Biomass Burning 2023 Anomaly (%)</th></tr></thead><tbody><tr><td>Global</td><td>–</td><td>2052 (1776–2388)</td><td>1996</td><td>-53 (-3%)</td></tr><tr><td>North America</td><td>30°N–75°N, 190°E–330°E</td><td>88 (60–116)</td><td>265</td><td>+177 (-201%)</td></tr><tr><td>Central America</td><td>13°N–30°N, 190°E–330°E</td><td>49 (35–67)</td><td>53</td><td>+4 (+8%)</td></tr><tr><td>South America</td><td>13°N–60°S, 190°E–330°E</td><td>368 (243–540)</td><td>338</td><td>-30 (-8%)</td></tr><tr><td>Europe and Mediterranean</td><td>30°N–75°N, 330°E–60°E</td><td>41 (27–70)</td><td>29</td><td>-12 (-30%)</td></tr><tr><td>N. Hem. Africa</td><td>0°–30°N, 330°E–60°E</td><td>412 (333–479)</td><td>331</td><td>-81 (-20%)</td></tr><tr><td>S. Hem. Africa</td><td>0°–35°S, 330°E–60°E</td><td>486 (433–548)</td><td>459</td><td>-27 (-6%)</td></tr><tr><td>Northern Asia</td><td>30°N–75°N, 60°E–190°E</td><td>204 (118–446)</td><td>147</td><td>-57 (-28%)</td></tr><tr><td>Southeast Asia</td><td>10°N–30°N, 60°E–190°E</td><td>120 (85–157)</td><td>116</td><td>-4 (-3%)</td></tr><tr><td>Tropical Asia</td><td>10°N–10°S, 60°E–190°E</td><td>161 (33–464)</td><td>80</td><td>-80 (-50%)</td></tr><tr><td>Australia</td><td>10°S–50°S, 60°E–190°E</td><td>123 (54–226)</td><td>177</td><td>+54 (+44%)</td></tr><tr><td>Canada</td><td>47°N–75°N, 219°E–310°E</td><td>46 (10–80)</td><td>240</td><td>+194 (+421%)</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>
trend. Increased wildfire emissions related to warmer and drier conditions occurred in tropical regions of Australia between September and November (Plate 2.1ag). Fire activity in tropical Asia—including Indonesia—increased relative to the previous three years (Fig. 2.82c) but was significantly lower than the increased emissions of 2006, 2015, and 2019 despite the El Niño and positive Indian Ocean dipole-related conditions. In this region, extreme fires are driven by the combination of agricultural use of fires on plantations, in particular for palm oil and pulp production, and drought conditions during El Niño years, which lead to a high persistence of fires on peatlands that have become exposed by deforestation of tropical rainforest. The relatively low fire activity across the region in 2023 indicates that stricter policies by the Indonesian government that restrict the use of agricultural fires are largely effective.

While South America overall experienced moderately below-average fire activity (–8%; Plate 2.1ag), seasonal fires increased in several regions. Chile experienced its second-highest wildfire activity for any January–February period in 20 years, with almost 4 Tg C emissions (Fig. 2.81). In the same period, the Corrientes region in Argentina reached its second-highest emissions on record. The latter part of the year saw a spike in fires across Bolivia and some Brazilian states (including parts of the Amazon), which was largely driven by drought conditions in the Pantanal wetlands (section 2d11). Bolivia experienced its highest fire activity since 2010, with the peak shifting to October and November (peak monthly emissions of 30 Tg C in November) instead of the usual August and September. Fire emissions from the Brazilian Amazon continued the recent trend with below-average fire since 2003 emissions in Mato Grosso but increased fires in Amazonas (e.g., the highest for the month of June since 2007 [de Oliveira et al. 2023] and the highest for November [https://atmosphere.copernicus.eu/2023-year-intense-global-wildfire-activity]) despite decreased deforestation in the Brazilian Amazon relative to 2022 (http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes); this suggests that fire types other than those related to deforestation have become relatively more important in South America. The increasing wildfires indicate a possible effect of the 2023 El Niño, which favors hot and dry conditions and increases general vegetation flammability, but also a lag effect of a period of weakened enforcement of environmental laws that favored old pastures burning earlier in the dry season (de Oliveira et al. 2023).

The GFAS is operated by the Copernicus Atmosphere Monitoring Service (CAMS) and produces global fire emission estimates (Kaiser et al. 2012) in near-real-time based on the MODIS Fire Radiative Power products (Giglio et al. 2016). A combination of real-time and consistently reproduced products by CAMS are used here, with input from MODIS Collection 6 for the entire period of 2003–23. The biases with respect to Collection 5 and between satellites have been corrected. The time series in Plate 1.1 also places GFAS in the context of GFED4s, which is primarily based on burnt area observation and dates to 1997 (van der Werf et al. 2017).

# 4. PHENOLOGY OF PRIMARY PRODUCERS

—D. L. Hemming, O. Anneville, Y. Aono, T. Crimmins, N. Estrella, S. -I. Matsuzaki, A. Menzel, I. Mrekaj, J. O'Keefe, A. D. Richardson, J. Rozkošný, T. Rutishauser, R. Shinohara, S. J. Thackeray, A. J. H. van Vliet, and J. Garforth.

Vegetation phenology, “the rhythm of the seasons”, is strongly affected by climate variations and can influence the local and global climate via modifications in the land–atmosphere exchanges of energy, moisture, and carbon (Hassan et al. 2024). A range of satellite- and surface-based observations monitor phenological variability across space and time.

PhenoCam (http://phenocam.nau.edu/) is a network of over 800 automated digital cameras monitoring phenological changes in a wide range of ecosystems around the world (Richardson 2019; Seyednasrollah et al. 2019). The highest-density and longest-running PhenoCam sites (of which there are over 50 with more than 10 years of observations) are in the United States. Indicators of start of season (SOS<sub>PC</sub>) and end of season (EOS<sub>PC</sub>) were estimated from PhenoCam data and ground observation (GO) of red oak (*Quercus rubra*; SOS<sub>GO</sub>, EOS<sub>GO</sub>) in Harvard Forest, a deciduous forest in Massachusetts in the United States (Richardson and O'Keefe 2009; O'Keefe
2023), and from red oak observations across the northeastern United States contributed to *Nature's Notebook* (SOS<sub>NN</sub>, EOS<sub>NN</sub>), the USA-National Phenology Network's (USA-NPN) phenology monitoring platform (Rosemartin et al. 2014; Crimmins et al. 2022). Interannual variations in the start and end of season dates at Harvard Forest are broadly consistent with the broader-scale USA-NPN data (Figs. 2.83a,b; Table 2.13). In 2023, SOS<sub>PC</sub>, SOS<sub>GO</sub>, and SOS<sub>NN</sub> were 6, 11, and 13 days earlier, respectively, than in 2022, while EOS<sub>PC</sub>, EOS<sub>GO</sub>, and EOS<sub>NN</sub> were 11, 8, and 0 days later than in 2022. SOS<sub>PC</sub> (EOS<sub>PC</sub>) was 5 days earlier (5 days later) than the 2011–20 baseline mean, resulting in a growing season length of 177 days, 17 days longer than in 2022, and 10 days longer than the baseline (167±7 days).

The USA-NPN's extended Spring Index (SI-x), a model that reflects the onset of spring-season biological activity (Schwartz et al. 2013; Crimmins et al. 2017), estimated widespread earlier “first leaf” in 2023 across the eastern United States and later first leaf across the western United States compared with 2022 or the 2011–20 mean (Figs. 2.83a,b). These estimates were consistent with the SOS<sub>PC</sub> and SOS<sub>GO</sub> observations at Harvard Forest and SOS<sub>PC</sub> observations from six other sites across the country (see Fig. 2.83 for details). They were more than 14 days earlier or later than recent years across many parts of the United States, largely due to warmer- (and cooler-) than-average late winter/spring 2023 temperatures across the eastern (and western) United States.

Start- and end-of-season indicators for native oak trees (*Quercus robur* and/or *Quercus petraea*) at European sites in Germany (D), Netherlands (NL), Slovakia (SK), and the United Kingdom (UK) are represented by observations of first leaf (SOS) and leaf fall or bare tree (EOS; Table 2.13; Figs. 2.84c,d). These events have been shown to be strongly influenced by spring and winter temperatures across Europe (Menzel et al. 2020). In 2023, SOS across all four European countries was later than usual. Compared to the 2000–20 mean, SOS dates in D, NL, SK, and UK were later by 3, 1, 5, and 2 days, respectively, and EOS dates were later by 8, 11, 6, and 4 days (Table 2.13). The later SOS and EOS dates across Europe were in part associated with relatively cool spring temperatures, delaying leaf out, followed by warm autumn temperatures, which

<figure><img src="image_137.png" /></figure>

Fig. 2.83. 2023 ‘first leaf’ date anomalies across the United States relative to (a) 2022 and (b) the 2011–20 baseline, estimated using the USA National Phenological Network’s (USA-NPN) extended Spring Index (SI-x) model (Source: https://www.usanpn.org/data). Negative (green) values show earlier first leaf and positive (brown) values are later estimates for 2023. First leaf SI-x anomalies are generally consistent with start of season PhenoCam (SOS<sub>PC</sub>) anomalies at the following six sites highlighted as points in (a): 1) a deciduous forest in Indiana (Morgan Monroe State Forest, SOS<sub>PC</sub> = 15 days earlier than in 2022, and 6 days earlier than 2011–20); 2) a deciduous forest in Pennsylvania (Susquehanna Shale Hills Critical Zone Observatory, SOS<sub>PC</sub> = 18 days earlier than in 2022); 3) a deciduous forest in Louisiana (Russell Sage State Wildlife Management Area, SOS<sub>PC</sub> = 18 days earlier than in 2022); 4) a sagebrush site in Oregon (Eastern Oregon Agricultural Research Center, SOS<sub>PC</sub> = 29 days later than in 2022); 5) a grassland site in Montana (Butte, SOS<sub>PC</sub> = 6 days earlier than in 2022, but 1 day later than during 2011–20); and 6) a wooded shrubland site in Arizona (Grand Canyon National Park, SOS<sub>PC</sub> = 12 days later than in 2022).
encouraged later leaf activity (for UK, see Kendon et al. 2023). The 2023 EOS in D was the latest since 2000, and this was associated with the warmest September temperatures on record (since 1881; Deutscher Wetterdienst [DWD] 2023; section 7f3). In SK, below-average temperatures during the end of March and April increased the prevalence of nocturnal frosts and delayed the first leaf onset, while extremely warm September and above-average October temperatures combined with ample precipitation resulted in the latest EOS dates across SK in 2023 since 2000. While the phenological timing of leaf out shifted later in the season in 2023, the length of the growing season for oak at the European locations was close to the baseline mean.

In Kyoto, Japan, full bloom dates (FBD) for a native cherry tree (Prunus jamasakura) have been recorded since 812 AD (Aono and Kazui 2008). For the Arashiyama district of Kyoto, these are updated with daily observations at train stations that are recorded in newspapers and on websites by railway passengers. In 2023, the FBD was the earliest on record for Arashiyama at 12 days earlier than the baseline (2000–20 mean; Table 2.13; Fig. 2.84e).

Monitoring data on lake water concentrations of the photosynthetic pigment chlorophyll-a were available to estimate spring phytoplankton phenology (nine Northern Hemisphere and one Southern Hemisphere lakes are reported; Fig. 2.85). The seasonal timing was estimated for start of season (SOS<sub>L</sub>; Park et al. 2016), day of maximum concentration (DOM<sub>L</sub>), and center of gravity (COG<sub>L</sub>), which is an estimate of the mid-point of the plankton bloom (Edwards and Richardson 2004). The lake basins showed great interannual variation and mixed phenological behavior in 2023 relative to 2000–20. The SOS<sub>L</sub> and COG<sub>L</sub> occurred earlier than the baseline median for most of the lakes—7 and 8 of 10, respectively—whereas no consistent pattern was observed for DOM<sub>L</sub>.

<table><caption>Table 2.13. Day of year (doy, equivalent to Julian day) and date of start of season (SOS), end of season (EOS), and full bloom date (FBD; cherry tree observations only) for land phenology records in USA (Harvard: PhenoCam, red oak, and USA National Phenology Network [USA-NPN] mean covering northeastern USA), Europe oak records (Germany, Netherlands, Slovakia, and United Kingdom), and Japan (native cherry tree observations in Japan). The baseline period is 2000–20 for all records except PhenoCam and USA-NPN which have baseline periods of 2008–22 and 2011–22, respectively, spanning the available observations. Growing season length for 2023 and the baseline mean are calculated as EOS minus SOS or FBD as appropriate for the record. Negative/positive values represent earlier/later dates for 2023 relative to the baseline mean.</caption><thead><tr><th>Location/ Record</th><th>SOS/FBD 2023 (doy, date)</th><th>SOS/FBD Baseline (doy, date)</th><th>SOS/FBD Difference 2023 – Baseline (days)</th><th>EOS 2023 (doy, date)</th><th>EOS Baseline (doy, date)</th><th>EOS Difference 2023 – Baseline (days)</th><th>Growing season EOS–SOS 2023 (days)</th><th>Growing season EOS–SOS Baseline mean (days)</th></tr></thead><tbody><tr><td>Harvard PhenoCam</td><td>123 3 May</td><td>128 8 May</td><td>-5</td><td>300 27 Oct</td><td>295 22 Oct</td><td>+5</td><td>177</td><td>167</td></tr><tr><td>Harvard red oak</td><td>122 2 May</td><td>128 8 May</td><td>-6</td><td>299 26 Oct</td><td>293 20 Oct</td><td>+6</td><td>177</td><td>165</td></tr><tr><td>Northeastern USA-NPN</td><td>113 23 Apr</td><td>125 5 May</td><td>-12</td><td>265 22 Sep</td><td>278 5 Oct</td><td>-13</td><td>152</td><td>153</td></tr><tr><td>Germany</td><td>121 1 May</td><td>118 28 Apr</td><td>+3</td><td>318 14 Nov</td><td>310 6 Nov</td><td>+8</td><td>197</td><td>192</td></tr><tr><td>Netherlands</td><td>111 21 Apr</td><td>110 20 Apr</td><td>+1</td><td>342 8 Dec</td><td>331 27 Nov</td><td>+11</td><td>231</td><td>221</td></tr><tr><td>Slovakia</td><td>121 1 May</td><td>116 26 Apr</td><td>+5</td><td>297 24 Oct</td><td>291 18 Oct</td><td>+6</td><td>176</td><td>175</td></tr><tr><td>UK</td><td>116 26 Apr</td><td>114 24 Apr</td><td>+2</td><td>339 5 Dec</td><td>335 1 Dec</td><td>+4</td><td>223</td><td>221</td></tr><tr><td>Japan</td><td>84 25 Mar</td><td>96 6 Apr</td><td>-12</td><td>--</td><td>--</td><td>--</td><td>--</td><td>--</td></tr></tbody></table>
<figure><img src="image_138.png" /></figure>

Fig. 2.84. Day of year of spring (greens) and autumn (oranges) vegetation phenology indicators for: (a), (b) Harvard Forest, Massachusetts, USA, derived from PhenoCam (PC), ground observations (GO) of red oak (Quercus rubra), and the USA-National Phenology Network (USA-NPN) regional-scale means of red oak observations (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, United Kingdom, Netherlands, and Slovakia mean of native oak observations (*Quercus robur* and/or *Quercus patrea*), and (e) Kyoto (Arashiyama district), Japan, full bloom date observations of native cherry trees (*Prunus jamasakura*).

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

Fig. 2.85. 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 2023 values. Nine lakes are in the Northern Hemisphere (Blelham Tarn [United Kingdom], Bourget [France], Esthwaite Water [United Kingdom], Geneva [France/Switzerland], Kasumigaura [Japan], Kinneret [Israel], Mjøsa [Norway], north and south basins of Windermere [United Kingdom]), and one lake is in the Southern Hemisphere (Taupo [New Zealand]).
# 5. VEGETATION OPTICAL DEPTH

—R. M. Zotta, R. van der Schalie, T. Frederikse, W. Preimesberger, R. de Jeu, and W. Dorigo

Vegetation optical depth (VOD) derived from space-borne passive microwave radiometers is a non-dimensional parameter used in radiative transfer models to describe the interaction between radiance emitted from Earth's surface and vegetation and is an indicator of the water stored in plant structures. It has found utility in a wide range of studies, including drought- and vegetation-condition monitoring (Moesinger et al. 2022; Vreugdenhil et al. 2022). Positive VOD anomalies indicate above-average vegetation abundance, while negative VOD anomalies indicate underdeveloped or stressed vegetation.

Several trends resulting from land-use changes manifest prominently in VOD anomalies (Plate 2.1ah; Dorigo et al. 2021; Zotta et al. 2023), which are calculated as deviations from the 1991–2020 climatology. Notably, negative annual VOD anomalies in regions like Mongolia, Bolivia, Paraguay, and Brazil reflect the impacts of deforestation and land degradation, while positive anomalies in areas such as India and northeastern China signify agricultural intensification and reforestation efforts (Song et al. 2018). In order to exclude such long-term trends and isolate the year-to-year anomalies, we also look at the differences in VOD between 2023 and 2022 (Appendix Fig. A2.7).

In 2023, annual VOD anomaly patterns differed from those in recent years (e.g., Zotta et al. 2023; 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 (ENSO; Miralles et al. 2014; Martens et al. 2017), overall VOD was lower than in 2022 (Figs. 2.86, 2.87).

Compared to 2022, substantial increases in VOD can be observed across regions in North America, northern Australia, and the Horn of Africa (Appendix Fig. A2.7). In northern Australia, the patterns are likely due to above-average rainfall (sections 2d4, 7h4) driving vegetation growth. In the Horn of Africa, predominantly positive annual (Plate 2.1ah) and high monthly (Appendix Fig. A2.8) VOD anomalies in November and December coincided with heavy rains associated with El Niño (section 2d4) and with the switch of the Indian Ocean dipole from negative to positive in September 2023 (see section 4f). The remarkable increases in VOD across Ethiopia, Somalia, and Kenya (Appendix Fig. A2.7) were likely due to favorable growing conditions caused by large precipitation amounts and were also captured in other satellite-borne vegetation indicators (FEWS NET 2023). In North America, the increase in VOD can likely be attributed to vegetation recovery after persistent dry conditions, which ameliorated in many regions such as the northeastern United States, the western portion of the Great Plains, California, and in parts of the Great Lakes and the Southeast (NOAA/NCEI 2023). Strong positive VOD anomalies

<figure><img src="image_140.png" /><figcaption>Fig. 2.86. Yearly vegetation optical depth (VOD) anomalies computed from the 1991–2020 climatology and their agreement with the Southern Oscillation Index (SOI). The SOI tracks the state of the El Nino–Southern Oscillation. (Source: VOD Climate Archive [VODCA]; http://www.bom.gov.au/climate/enso/soi/.)</figcaption></figure>

<figure><img src="image_141.png" /><figcaption>Fig. 2.87. Time–latitude diagram of vegetation optical depth (VOD) anomalies (1991–2020 base period). Data are masked where no retrieval is possible, or where the quality is not assured and flagged due to frozen soil, radio frequency interference, etc. (Source: VOD Climate Archive [VODCA].)</figcaption></figure>
in November and December (Appendix Fig. A2.8) support the findings of crop reports that announced favorable conditions for winter wheat across the United States and Canada, especially in Ontario and Manitoba (GeoGlam 2023).

Similar to soil moisture (section 2d10), VOD decreased substantially across southern Africa in 2023, where ENSO is one of the strongest drivers impacting agricultural production (OCHA 2023). Here, optical satellite observations of vegetation health indicate stressed vegetation (section 2h2; NOAA/NCEI 2023a). In the Maghreb (west and central north Africa) and northern Africa, where satellite observations from the Gravity Recovery and Climate Experiment (GRACE) have indicated persistently low soil moisture and groundwater (section 2d9; NOAA/NCEI 2023b), patterns of decreased VOD and negative monthly VOD anomalies across most of the year have prevailed. VOD also decreased in Central America and Mexico. An extremely dry period led to crop damage and losses observed in FAPAR anomalies (section 2h2; Toreti et al. 2023). In the Amazon basin, the decrease in VOD and the negative monthly anomalies across most of the year were likely caused by severe heatwaves and below-average rainfall linked with ENSO. In Spain, the decrease in VOD coincided with sparse rainfall and heatwaves that dominated in spring and summer (sections 2b1, 2d4).

The VOD data are from the VOD Climate Archive v2 (VODCA v2; Moesinger et al. 2020; Zotta et al. 2024a,b). VODCA merges VOD observations 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 long-term, harmonized dataset. Here, we used VODCA CXKu, a multi-frequency product that blends C-, X-, and Ku band observations, has a spatial resolution of 0.25°, and provides daily observations.
# Acknowledgments

## a Introduction

Robert Dunn and Kate Willett were supported by the Met Office Hadley Centre Climate Programme funded by the U.K. Department for Science, Innovation and Technology (DSIT). The editors thank the five anonymous reviewers and also Lizzie Fuller, John Kennedy, David Parker, and Ruben Urraca Valle for their insight, thoughts, and suggestions when developing this chapter. We also thank Fumi Sezaki (Japanese Meteorological Association [JMA]), Julien Nicolas (European Centre for Medium-Range Weather Forecasts [ECMWF]), and Mike Bosilovich (NASA) for their help in providing the reanalysis data used in this chapter.

## b2 Lake surface 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 funded by the E.U. Copernicus Climate Change Service (C3S) programme and extensions by the U.K. Earth Observation Climate Information Service (EOCIS) project (NE/X019071/1). 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 Fisheries and Oceans Canada (FOC), the National Data Buoy Center (NDBC) from NOAA, the Hungarian Meteorological Office, the Upper Great Lakes Observing System (UGLOS), and the North Temperate Lakes Long-Term Ecological Research (NTL-LTER). The authors gratefully acknowledge the late Alon Rimmer for always supplying data for Lake Kinneret. Data from Lake Zurich were provided by the City of Zurich Water Supply and by the Amt für Abfall, Wasser, Energie und Luft of the Canton of Zurich.

## b3 Night marine air temperature

R. C. Cornes was 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).

## b4 Surface temperature extremes

Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by the Department for Science, Innovation and Technology (DSIT).

## b5 Tropospheric temperature

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

## c1 Permafrost temperatures and active-layer thickness

Research on James Ross Island was supported by the Czech Antarctic Research Programme and Czech Science Foundation project (GM22-28659M). The Circumpolar Active Layer Monitoring (CALM) program is funded by the National Science Foundation (NSF) Project 1836377. The Svalbard permafrost data is part the Svalbard Integrated Earth Observing System (SIOS). 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 contribution of its partner institutions. The French Network PermaFRANCE is financially supported by the Observatoire des Sciences de l'Univers Grenoble and the French Research Infrastructure OZCAR. The Chinese Permafrost Monitoring Network is financially supported by the Chinese National Science Foundation (41931180) and Cryosphere Research Station on Qinghai-Xizang Plateau, CAS.

## c2 Rock glacier velocity

Rock glacier monitoring at Hinteres Langtalkar and Dösen rock glaciers (AT) is supported by the Hohe Tauern National Park Carinthia through its long-term permafrost monitoring program. Laurichard (FR)
survey is supported by 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 the 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 has helped field surveys since the early 2000s. The Swiss Permafrost Monitoring
Network (PERMOS) is financially supported by MeteoSwiss in the framework of Global Climate Observing
System (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 investiga-
tors. 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.

c4 Lake ice cover

We thank Al and Sue Stangel, Alexander Mills, Ann LaLiberte, Beth Kohlman, BJ Bauer, Bob Katzenberger, Bonny Pederson, Brendan Wiltse, Brian D. Neill, Brian Vlach, John Barten, Rick Brascke, Calvin Maurer, Carl C. Nelson, Carol Wendorf, Cheryl and Dorothy Zingler, Clare and Dan Shirley, Craig Hillman, Dale Robertson, Dan Brumm, Daniel L. Anderson, Dave and Lynda J. Urshan, David Kahan, Don McClanathan, Donald and Mureil Fornasiere, Doug Fitzgerald, Douglas Pierzina, Dr. Neal D. Mundahl, Duane Williams and Edie Evarts, Duncan A. Brown, Earl Cook, Fred Buckley, Gary Teigen, Gay Alberts Ruby, Gene Cooper, George Grevich, Greg Sass, Holly Waterfield, Huaxia Yao, James and Sharon Fenner, James W. Danielson, Jan Henning L'Abée-Lund, Jeff Goelzer, Jeffrey G. Lowe, Jerry Evans, Jerry Sondreal, Jim and Judy Daugherty, Joe Jenkins, Joel Rasmussen, John and Catherine Bart, John Maier, Jonathan Ross, Dan Drumm, Dr. Lesly Knoll, Joy Krubsack, Kay and Rich Rezanka, Kay Olson, Kay Wepfer, Ken Blumenfeld, Larry and Marlene Lotto, Larry Peterson, Lars Rudstam, Lolita Olson, Lowell Dague, Marge Kellor, Mark Biller, Mark Holland, Mark J. and Rosie Peters, Mary Jane Dillingham, Mary Lou Fry, Randell Fry, Merja Pulkkanen, Michael Allen, Michael Bradley, Michael Kolecheck, Michael Traufler, Mickey and Dennis Chick, Mike, Jeff, and Thomm Backus, Molly Hibbard, Morris and Doris Whiting, Mr. and Mrs. Jay R. Mackie, Mr. and Mrs. William Bergersen, Mrs. Gale Wheeler, Mrs. Kathy Elhard, Mrs. Margery Armstrong, Myron Hagelstrom, Nancy Putnam, Nancy Steenport, Park Manager (L.C.), Patricia Bebak, Patrick Collins, Paulette Janssen, Pete Boulay, Peter Bearup, Rachel Dahlke, Raymond (Joe) Jenkins, Richard L. Tamke, Rock Anderson, Ron Pabich, Ronald Jones, Ross Swain, Sally Ketchen, Sandra Anderson, Scott Schoepp, Sharon Natzel, Shin-Ichiro Matsuzaki, Susan Reineking, Susan Verhaalen, Tana McNutt, Theodore (Ted) Peters, Thomas Sommerfeldt, Tom Stangl, Travis Campbell, Virgil Luehrs, Walt and Nancy Quillinan, Walter R. Brown, Water Dahlke III, William and Brenda Jones, and William Hanson, for their dedication and efforts to collecting and sharing in situ ice phenological records with us.

c5 Northern Hemisphere continental snow cover extent

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

d1 Surface humidity

Kate Willett was supported by the Met Office Hadley Centre Climate Programme funded by the Department for Science, Innovation and Technology (DSIT). Adrian Simmons and David Lavers were supported by the Copernicus Climate Change Service (C3S) implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission.

d2 Humid-heat extremes over land

Kate Willett was supported by the U.K.-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China under the International Science Partnerships Fund (ISPF).
Cassandra Rogers is supported with funding from the Australian Government under the National Environmental Science Program.

Thank you to Tim Cowan from the University of Southern Queensland and Blair Trewin from the Bureau of Meteorology for providing initial reviews of the humid heat extremes over land section.

## d7 Cloudiness

Clouds and Earth's Radiant Energy Systems Energy Balanced and Filled (CERES EBAF) data were obtained from the NASA Langley Research Center CERES ordering tool at https://ceres.larc.nasa.gov/data/.

## d8 Lake water storage

B. M. Kraemer acknowledges support from the University of Freiburg Chair of Environmental Hydrosystems. M. F. Meyer and M. E. Harlan were supported by the United States Geological Survey (USGS) Mendenhall Fellowships 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. R. S. La Fuente was funded by the Irish HEA Landscape programme and Dundalk Institute of Technology (DkIT) Research Office. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

## d9 Groundwater and terrestrial water storage

Work on the Groundwater and Terrestrial Water Storage section was funded by NASA's Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) Science Team.

## d10 Soil moisture

This study uses satellite soil moisture observations from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2018): Soil moisture gridded data from 1978 to present. C3S CDS. (Accessed on 16 01 2024), 10.24381/cds.d7782f18.

## d11 Monitoring drought using the Self Calibrating Palmer Drought Severity Index

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 the U.K. Natural Environment Research Council (NERC) (NE/S015582/1). Ian Harris received funding from U.K. 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.

## d12 Land evaporation

D. G. Miralles acknowledges support from the European Research Council (ERC) under grant agreement no. 101088405 (HEAT). H. E. Beck is supported by the King Abdullah University of Science and Technology.

## e2 Surface winds

C. Azorin-Molina was supported by CSIC-UV-GVA and funded by AICO/2021/023, LINCGLOBAL-CSIC ref. INCGLO0023, and PTI-CLIMA.

Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by the Department for Business, Energy and Industrial Strategy (BEIS).

L. Ricciardulli was supported by NASA Ocean Vector Wind Science Team grant 80NSSC23K0984. Z. Zeng was supported by the National Natural Science Foundation of China grant 42071022.

## e4 Lightning

The work of M. Füllekrug was sponsored by the Royal Society (U.K.) grant NMG/R1/180252 and the Natural Environment Research Council (U.K.) 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 (NSF) 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. 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.

## f1 Earth radiation budget at top-of-atmosphere

This research has been supported by the NASA Clouds and Earth's Radiant Energy Systems (CERES) project. The resources of the NASA Langley Atmospheric Sciences Data Center are utilized to process the instantaneous Single Scanner Footprint (SSF) data used as input to Energy Balance and Filled (EBAF) Ed4.2 and processes the FLASHFlux TISA version 4B. We further acknowledge the efforts of Walt Miller and Pam Mlynczak of the CERES team who monitor CERES data production and produce the FLASHFlux TISA 4B monthly averaged data products, respectively.

## g3 Tropospheric aerosols

The data used for this section were provided by the European Union Copernicus Atmosphere Monitoring Service (CAMS).

## g4 Tropospheric ozone

The NOAA Proving Ground and Risk Reduction (PGRR) Program supported the contributions by O. R. Cooper and K.-L. Chang. K.-L. Chang was also supported by NOAA cooperative agreement NA22OAR4320151. Funding for J. Ziemke for this research was provided in part by NASA NNH14ZDA001N-DSCOVR, NASA Suomi NPP and Joint Polar Satellite System (JPSS) Satellites Standard Products for Earth System Data Records, and Code 614 programmatic support for long-term ozone trends.

## g5 Stratospheric aerosols

Lidar observations at Observatory de Haute-Provence (OHP) are funded by the French National Centre for Scientific Research (CNRS) National Institute of Sciences of the Universe (INSU) and CNES. S. Khaykin's work is supported by the ANR PyroStrat 21-CE01-335 0007-01 project. We thank Christine David, Alain Hauchecorne, Julien Jumelet, Philippe Keckhut (LATMOS), and the lidar station operators for their contributions to long-term monitoring of stratospheric aerosol at OHP. Lidar observation at Lauder are funded in part by the Greenhouse gases Observing SATellite (GOSAT) project. We thank Richard Querel (National Institute of Water and Atmospheric Research [NIWA]), Osamu Uchino, Tomohiro Nagai (Meteorological Research Institute [MRI]) and Yushitaka Jin (MRI) for their contributions to long-term monitoring of stratospheric aerosol at Lauder.

## g6 Stratospheric ozone

Carlo Arosio, Melanie Coldewey-Egbers, Diego Loyola, Viktoria Sofieva, Alexei Rozanov, and Mark Weber are grateful to the E.U. Copernicus Climate Change Service (C3S), C3S2_312a_Lot2 Ozone, and to European Space Agency's (ESA) Climate Change Initiative Ozone (CCI+) projects for supporting the generation and extension of the GTO-ECV total ozone and SAGE-CCI-OMPS data records. Carlo Arosio, Viktoria Sofieva, Kleareti Tourpali, Alexei Rozanov, and Mark Weber are grateful for the support of 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 (JPL), 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's Earth System Science Interdisciplinary Center (ESSIC). Melanie Coldewey-Egbers and Diego Loyola acknowledge the partial support by the German Aerospace Center (DLR) projects MABAK and INPULS.
## h2 Terrestrial vegetation dynamics

The authors thank the providers of the remote sensing dataset needed to perform this analysis and the Joint Research Centre's (JRC) Big Data Analytics Platform (BDAP).

## h4 Phenology of primary producers

Debbie Hemming acknowledges support from the Met Office Hadley Centre Climate Programme funded by the Department for Science, Innovation and Technology (DSIT), and thanks all co-authors for their interesting and helpful contributions. Andrew Richardson acknowledges support from the National Science Foundation (NSF) Long Term Ecological Research (LTER) program (award 1832210). John O'Keefe also acknowledges support from the NSF through the LTER (award 1832210) program. Nature's Calendar (Woodland Trust) in the U.K. thanks all its volunteer recorders and support from players of People's Postcode Lottery. Theresa Crimmins and the USA National Phenology Network (USA-NPN) acknowledge support from the NSF through the Division of Biological Infrastructure (award 2031660), the US Fish and Wildlife Service (agreements F16AC01075 and F19AC00168) and the U.S. Geological Survey (G14AC00405 and G18AC00135). The USA-NPN 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]). The Slovak Hydrometeorological Institute thanks all its volunteer observers for participating in the phenological observation program. Orlane Anneville acknowledges support from the National Institute of Agricultural Research (INRAE). Stephen Thackeray thanks Werner Eckert, Heidrun Feuchtmayr, Shin-Ichiro Matsuzaki, 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 U.K. Status, Change and Projections of the Environment (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.

## h5 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 European Space Agency (ESA) Climate Change Initiative and the Copernicus Climate Change Service (C3S).

## Sidebar 2.1: Impacts of extreme temperatures in 2023

Robert Dunn and Kate Willett were supported by the Met Office Hadley Centre Climate Programme.
# Appendix 1: 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>AEM ENTLN</td><td>Advanced Environmental Monitoring Earth Networks Total Lightning Network</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>ANYSO</td><td>Australian New Year Super Outbreak</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>BD</td><td>Brewer-Dobson</td></tr><tr><td>C3S</td><td>Copernicus Climate Change Service</td></tr><tr><td>CALM</td><td>Circumpolar Active Layer Monitoring</td></tr><tr><td>CAMS</td><td>Copernicus Atmosphere Monitoring Service</td></tr><tr><td>CCI</td><td>Climate Change Initiative</td></tr><tr><td>CCM</td><td>chemistry climate model</td></tr><tr><td>CERES</td><td>Clouds and the Earth's Radiant Energy System</td></tr><tr><td>CFC</td><td>chlorofluorocarbon</td></tr><tr><td>CH<sub>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>CO</td><td>carbon monoxide</td></tr><tr><td>CO<sub>2</sub></td><td>carbon dioxide</td></tr><tr><td>COG</td><td>center of gravity</td></tr><tr><td>COVID-19</td><td>Coronavirus disease 2019</td></tr><tr><td>cp</td><td>specific heat capacity of air at constant pressure</td></tr><tr><td>CPT</td><td>cold-point tropopause</td></tr><tr><td>CRE</td><td>cloud radiative effect</td></tr><tr><td>DOM</td><td>day of maximum concentration</td></tr><tr><td>DWD</td><td>Deutscher Wetterdienst</td></tr><tr><td>DZAA</td><td>depth of zero annual amplitude</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>EESC</td><td>equivalent effective stratospheric chlorine</td></tr><tr><td>EESC(A)</td><td>equivalent effective stratospheric chlorine in the Antarctic</td></tr><tr><td>EESC(ML)</td><td>equivalent effective stratospheric chlorine in the midlatitudes</td></tr><tr><td>ENSO</td><td>El Niño-Southern Oscillation</td></tr><tr><td>EOCIS</td><td>Earth Observation Climate Information Service</td></tr><tr><td>EOF</td><td>empirical orthogonal function</td></tr><tr><td>EOS</td><td>end of season</td></tr><tr><td>ER</td><td>extinction ratio</td></tr><tr><td>ERB</td><td>Earth radiation budget</td></tr><tr><td>ERF</td><td>effective radiative forcing</td></tr><tr><td>ESA</td><td>European Space Agency</td></tr><tr><td>ET</td><td>evapotranspiration</td></tr><tr><td>EUR</td><td>Europe</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>FEWS NET</td><td>Famine Early Warning Systems Network</td></tr></table>
<table>
   <tr>
    <td>FF</td>
    <td>fossil fuel</td>
   </tr>
   <tr>
    <td>FP</td>
    <td>frost point</td>
   </tr>
   <tr>
    <td>GAM</td>
    <td>General Additive Model</td>
   </tr>
   <tr>
    <td>GCOS</td>
    <td>Global Climate Observing System</td>
   </tr>
   <tr>
    <td>GISS</td>
    <td>Goddard Institute for Space Studies</td>
   </tr>
   <tr>
    <td>GLAMOS</td>
    <td>Glacier Monitoring Switzerland</td>
   </tr>
   <tr>
    <td>GLD360</td>
    <td>Global Lightning Detection Network</td>
   </tr>
   <tr>
    <td>GLEAM</td>
    <td>Global Land Evaporation Amsterdam Model</td>
   </tr>
   <tr>
    <td>GLIMS</td>
    <td>Global Land Ice Measurements from Space</td>
   </tr>
   <tr>
    <td>GLM</td>
    <td>Geostationary Lightning Mapper</td>
   </tr>
   <tr>
    <td>GloSSAC</td>
    <td>Global Satellite-based Stratospheric Aerosol Climatology</td>
   </tr>
   <tr>
    <td>GO</td>
    <td>ground observations</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>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>GSL</td>
    <td>Global Snow Lab</td>
   </tr>
   <tr>
    <td>HCFC</td>
    <td>hydrochlorofluorocarbon</td>
   </tr>
   <tr>
    <td>HFC</td>
    <td>hydrofluorocarbon</td>
   </tr>
   <tr>
    <td>HTHH</td>
    <td>Hunga Tonga-Hunga Ha'apai</td>
   </tr>
   <tr>
    <td>INRAE</td>
    <td>Université Grenoble Alpes</td>
   </tr>
   <tr>
    <td>IOD</td>
    <td>Indian Ocean dipole</td>
   </tr>
   <tr>
    <td>IPCC</td>
    <td>Intergovernmental Panel on Climate Change</td>
   </tr>
   <tr>
    <td>ITCZ</td>
    <td>Intertropical Convergence Zone</td>
   </tr>
   <tr>
    <td>LLGHG</td>
    <td>long-lived greenhouse gas</td>
   </tr>
   <tr>
    <td>LSWT</td>
    <td>lake surface water temperature</td>
   </tr>
   <tr>
    <td>LTT</td>
    <td>lower-tropospheric temperature</td>
   </tr>
   <tr>
    <td>Lv</td>
    <td>latent heat of vaporization</td>
   </tr>
   <tr>
    <td>LWCRE</td>
    <td>longwave cloud radiative effect</td>
   </tr>
   <tr>
    <td>LWS</td>
    <td>lake water storage</td>
   </tr>
   <tr>
    <td>MC</td>
    <td>Maritime Continent</td>
   </tr>
   <tr>
    <td>MCM</td>
    <td>million cubic meters</td>
   </tr>
   <tr>
    <td>MCS</td>
    <td>mesoscale convective system</td>
   </tr>
   <tr>
    <td>MCS</td>
    <td>marine cold spell</td>
   </tr>
   <tr>
    <td>MEGAN2.1</td>
    <td>Model of Emissions of Gases and Aerosols from Nature version 2.1</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>N2O</td>
    <td>nitrous oxide</td>
   </tr>
   <tr>
    <td>NA</td>
    <td>North America</td>
   </tr>
   <tr>
    <td>NAO</td>
    <td>North Atlantic Oscillation</td>
   </tr>
   <tr>
    <td>NDACC</td>
    <td>Network for the Detection of Atmospheric Composition Changes</td>
   </tr>
   <tr>
    <td>NH</td>
    <td>Northern Hemisphere</td>
   </tr>
   <tr>
    <td>NMAT</td>
    <td>night marine air temperature</td>
   </tr>
   <tr>
    <td>NN</td>
    <td>Nature's Notebook</td>
   </tr>
   <tr>
    <td>O3</td>
    <td>tropospheric ozone</td>
   </tr>
   <tr>
    <td>OCHA</td>
    <td>Office for the Coordination of Humanitarian Affairs</td>
   </tr>
   <tr>
    <td>ODGI</td>
    <td>Ozone Depleting Gas Index</td>
   </tr>
   <tr>
    <td>ODS</td>
    <td>ozone-depleting substance</td>
   </tr>
  </table>
<table>
   <tr>
    <td>OHP</td>
    <td>Observatoire de Haute Provence</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-LP</td>
    <td>Ozone Mapping and Profiler Suite-Limb Profiler</td>
   </tr>
   <tr>
    <td>ONI</td>
    <td>Oceanic Niño Index</td>
   </tr>
   <tr>
    <td>PC</td>
    <td>PhenoCam</td>
   </tr>
   <tr>
    <td>PERMOS</td>
    <td>Swiss Permafrost Monitoring Network</td>
   </tr>
   <tr>
    <td>PM2.5</td>
    <td>fine particulate matter</td>
   </tr>
   <tr>
    <td>PNA</td>
    <td>Pacific/North American</td>
   </tr>
   <tr>
    <td>PSA</td>
    <td>Pacific-South American</td>
   </tr>
   <tr>
    <td>q</td>
    <td>specific humidity</td>
   </tr>
   <tr>
    <td>QBO</td>
    <td>quasi-biennial oscillation</td>
   </tr>
   <tr>
    <td>QTP</td>
    <td>Qinghai-Tibet Plateau</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 velocity</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>
   <tr>
    <td>Rx1day</td>
    <td>one-day maximum accumulation</td>
   </tr>
   <tr>
    <td>SAGE</td>
    <td>Stratospheric Aerosol and Gas Experiment</td>
   </tr>
   <tr>
    <td>SAM</td>
    <td>Southern Annular Mode</td>
   </tr>
   <tr>
    <td>sAOD</td>
    <td>stratospheric aerosol optical depth</td>
   </tr>
   <tr>
    <td>SCE</td>
    <td>snow cover extent</td>
   </tr>
   <tr>
    <td>scPDSI</td>
    <td>self-calibrating Palmer Drought Severity Index</td>
   </tr>
   <tr>
    <td>SH</td>
    <td>Southern Hemisphere</td>
   </tr>
   <tr>
    <td>SNAO</td>
    <td>summer North Atlantic Oscillation</td>
   </tr>
   <tr>
    <td>SO₂</td>
    <td>sulfur dioxide</td>
   </tr>
   <tr>
    <td>SOI</td>
    <td>Southern Oscillation Index</td>
   </tr>
   <tr>
    <td>SOS</td>
    <td>start of season</td>
   </tr>
   <tr>
    <td>SSM/I</td>
    <td>Special Sensor Microwave/Imager</td>
   </tr>
   <tr>
    <td>SSMIS</td>
    <td>Special Sensor Microwave Imager/Sounder</td>
   </tr>
   <tr>
    <td>SST</td>
    <td>sea surface temperature</td>
   </tr>
   <tr>
    <td>SSU</td>
    <td>Stratospheric Sounding Unit</td>
   </tr>
   <tr>
    <td>SW</td>
    <td>shortwave</td>
   </tr>
   <tr>
    <td>SWCRE</td>
    <td>shortwave cloud radiative effect</td>
   </tr>
   <tr>
    <td>T</td>
    <td>dry-bulb air temperature</td>
   </tr>
   <tr>
    <td>Ta</td>
    <td>air temperature</td>
   </tr>
   <tr>
    <td>TCWV</td>
    <td>total column water vapor</td>
   </tr>
   <tr>
    <td>TEC</td>
    <td>total energy content</td>
   </tr>
   <tr>
    <td>Teq</td>
    <td>equivalent temperature</td>
   </tr>
   <tr>
    <td>TLS</td>
    <td>lower stratosphere temperature</td>
   </tr>
   <tr>
    <td>TLT</td>
    <td>lower tropospheric temperature</td>
   </tr>
   <tr>
    <td>TMI</td>
    <td>Tropical Rainfall Measuring Mission's 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>Tq</td>
    <td>latent temperature</td>
   </tr>
   <tr>
    <td>TRMM</td>
    <td>Tropical Rainfall Measuring Mission</td>
   </tr>
  </table>
<table><tr><td>TSI</td><td>total solar irradiance</td></tr><tr><td>TTL</td><td>tropical tropopause layer</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>T<sub>w</sub>X</td><td>daily maximum wet-bulb temperatures</td></tr><tr><td>T<sub>x</sub></td><td>zonal mean profiles</td></tr><tr><td>USA-NPN</td><td>USA National Phenology Network</td></tr><tr><td>UT</td><td>upper tropospheric</td></tr><tr><td>UTH</td><td>upper-tropospheric humidity</td></tr><tr><td>UW WWLLN</td><td>University of Washington's World Wide Lightning Location Network</td></tr><tr><td>VEI</td><td>Volcanic Explosivity Index</td></tr><tr><td>VIIRS</td><td>Visible Infrared Imaging Radiometer Suite</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: Datasets and sources

## Section 2b Temperature

<table><thead><tr><td>Sub-section</td><td>General Variable or Phenomenon</td><td>Specific dataset or variable</td><td>Source</td></tr></thead><tbody><tr><td>2b1</td><td>Temperature, [Near] Surface</td><td>Berkeley Earth</td><td>http://berkeleyearth.org/data/</td></tr><tr><td>2b1</td><td>Temperature, [Near] Surface</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2b1</td><td>Temperature, [Near] Surface</td><td>HadCRUT5 Global Temperature</td><td>https://www.metoffice.gov.uk/hadobs/hadcrut5/</td></tr><tr><td>2b1</td><td>Temperature, [Near] Surface</td><td>CRUTEM5</td><td>https://www.metoffice.gov.uk/hadobs/crutem5/</td></tr><tr><td>2b1, 2b3</td><td>Temperature, [Near] Surface</td><td>HadSST4</td><td>https://www.metoffice.gov.uk/hadobs/hadsst4/</td></tr><tr><td>2b1, 2b4</td><td>Temperature, [Near] Surface</td><td>JRA-55 Atmospheric Reanalysis</td><td>https://search.diasjp.net/en/dataset/JRA55</td></tr><tr><td>2b1</td><td>Temperature, [Near] Surface</td><td>JRA-3Q</td><td>https://search.diasjp.net/en/dataset/JRA3Q</td></tr><tr><td>2b1, 2b2</td><td>Temperature, [Near] Surface</td><td>NASA/GISS Global Temperature V4</td><td>https://data.giss.nasa.gov/gistemp/</td></tr><tr><td>2b1</td><td>Temperature, [Near] Surface</td><td>NOAA/NCEI NOAAGlobalTemp</td><td>https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>Full Lake Surface Temperature Water dataset</td><td>https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-lake-water-temperature</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>National Buoy Data Center Great Lakes Buoys</td><td>https://www.ndbc.noaa.gov/mobile/region.php?reg=great_lakes</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>Balaton Lakes</td><td>https://odp.met.hu/climate/observations_hungary/hourly/historical/</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>Canadian Lakes</td><td>https://www.meds-sdmm.dfo-mpo.gc.ca/isdm-gdsi/waves-vagues/data-donnees/index-eng.asp</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>Biel and Thun Lakes (Switzerland); Biwa and Mikata Lakes (Japan)</td><td>https://www.die3seen.ch/, https://portal.gemstat.org/applications/public.html?publicuser=PublicUser</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>Trout Lake</td><td>https://portal.edirepository.org/nis/mapbrowse?scope=knb-iter-ntl&amp;identifier=116&amp;revision=27</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>ESA CCI LAKES LSWT v2.0.2</td><td>https://catalogue.ceda.ac.uk/uuid/a07deacaffb8453e93d57ee214676304</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>Sentinel 3 Sea and Land Surface Temperature Radiometer (SLSTR)</td><td>https://sentinel.esa.int/web/sentinel/user-guides/sentinel-3-slstr/overview</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific dataset or variable</th><th>Source</th></tr></thead><tbody><tr><td>2b3</td><td>Nighttime marine Air Temperature</td><td>CLASSnmat</td><td>https://catalogue.ceda.ac.uk/ uuid/5bbf48b128bd488dbb10a56111feb36a</td></tr><tr><td>2b3</td><td>Nighttime marine Air Temperature</td><td>UAHNMATv1</td><td>https://www.nsstc.uah.edu/climate/, https://doi.org/10.1002/joc.6354</td></tr><tr><td>2b4</td><td>Sea Surface Temperature</td><td>NOAA Optimum Interpolation Sea Surface Temperature (OISST) v2.1</td><td>https://www.ncei.noaa.gov/products/optimum-interpolation-sst</td></tr><tr><td>2b4</td><td>Temperature, [Near] Surface</td><td>GHCNDEX</td><td>https://www.climdex.org/</td></tr><tr><td>2b4</td><td>Temperature, [Near] Surface</td><td>MERRA-2</td><td>http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/</td></tr><tr><td>2b4, 2b5</td><td>Temperature, Upper Atmosphere</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2b4, 2b5</td><td>Temperature, Upper Atmosphere</td><td>JRA-55 Atmospheric Reanalysis</td><td>http://jra.kishou.go.jp/JRA-55/index_en.html</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>MERRA-2</td><td>http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>NOAA/NESDIS/STAR MSU v5</td><td>https://www.star.nesdis.noaa.gov/data/mscat/MSU_AMSU_v5.0/ Monthly_Atmospheric_Layer_Mean_Temperature/</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>RAOBCORE, RICH</td><td>https://imgw.univie.ac.at/forschung/klimadiagnose/raobcore/</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>RATPAC A2</td><td>https://www.ncei.noaa.gov/products/weather-balloon/radiosonde- atmospheric-temperature-products</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>RSS v4.0</td><td>https://www.remss.com/measurements/upper-air-temperature/</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>UAH MSU v6.0</td><td>https://www.nsstc.uah.edu/data/msu/v6.0/</td></tr><tr><td>2b5</td><td>Sea Surface Temperature</td><td>Niño 3.4 Index</td><td>https://psl.noaa.gov/gcos_wgsp/Timeseries/Nino34/</td></tr><tr><td>2b6</td><td>Temperature, Upper Atmosphere</td><td>Aura MLS</td><td>https://disc.gsfc.nasa.gov/datasets/ML2T_005/summary</td></tr></tbody></table>
Section 2c Cryosphere

<table><thead><tr><td>Sub-section</td><td>General Variable or Phenomenon</td><td>Specific dataset or variable</td><td>Source</td></tr></thead><tbody><tr><td>2c1</td><td>Permafrost</td><td>Global Terrestrial Network for Permafrost (GTN-P)</td><td>http://gtnpdatabase.org/</td></tr><tr><td>2c1</td><td>Permafrost</td><td>GTN-P global mean annual ground temperature data for permafrost</td><td>https://doi.org/10.1594/PANGAEA.884711</td></tr><tr><td>2c1</td><td>Permafrost</td><td>Permafrost Temperature at Chinese (QTP) sites</td><td>https://nsidc.org/data/GGD700/versions/1</td></tr><tr><td>2c1</td><td>Permafrost</td><td>Permafrost Temperature at French sites</td><td>https://permafrance.osug.fr</td></tr><tr><td>2c1</td><td>Permafrost</td><td>Permafrost Temperature at Norwegian sites</td><td>https://cryo.met.no/</td></tr><tr><td>2c1, 2c2</td><td>Permafrost</td><td>Permafrost Temperature at Swiss sites (PERMOS)</td><td>https://www.permos.ch, https://www.permos.ch/doi/permos-dataset-2022-1</td></tr><tr><td>2c1</td><td>Active Layer Depth</td><td>Circumpolar Active Layer Monitoring (CALM)</td><td>https://www.gwu.edu/~calm/</td></tr><tr><td>2c2</td><td>Rock Glacier Velocity</td><td>Regional Rock Glacier Velocity</td><td>Available from authors upon request. Austria: V. Kaufmann and A. Kellerer-Pirklbauer, Central Asia: A. Kääb, Dry Andes: S. Vivero, France: X. Bodin, D. Cusicanqui and E. Thibert, Switzerland: R. Delaloye, J. Noetzli and C. Pellet</td></tr><tr><td>2c3</td><td>Glacier Mass, Area or Volume</td><td>World Glacier Monitoring Service</td><td>http://dx.doi.org/10.5904/wgms-fog-2022-09</td></tr><tr><td>2c3</td><td>Glacier Area</td><td>Copernicus Sentinel-2 MSI image</td><td>https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/overview</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>Lake ice clearance and formation data for Green Lakes Valley, 1968 - ongoing. ver 5. Environmental Data Initiative</td><td>https://portal.edirepository.org/nis/mapbrowse?scope=knb-iter-nwt&identifier=106&revision=6</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>Global Lake and River Ice Phenology Database, Version 1</td><td>https://doi.org/10.7265/N5W66HP8</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>Mountain Lake Biology, Chemistry, Physics, and Climate Data since 1959 at Castle Lake ver 1. Environmental Data Initiative</td><td>https://doi.org/10.6073/pasta/a8e3b81cfe5864731b29ad42506c65d7</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>Great Lakes Annual Maximum Ice Cover (%)</td><td>https://www.glerl.noaa.gov/data/ice/</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific dataset or variable</th><th>Source</th></tr></thead><tbody><tr><td>2c4</td><td>Lake Ice</td><td>Great Lakes Ice</td><td>www.glerl.noaa.gov/data/ice</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>Geographic variation and temporal trends in ice phenology in Norwegian lakes during a century, Dryad</td><td>https://datadryad.org/stash/dataset/doi:10.5061/dryad.bk3j9kd9x</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>lake surface water temperature and ice cover in subalpine Lake Lunz, Austria</td><td>https://doi.org/10.1080/20442041.2017.1294332</td></tr><tr><td>2c4</td><td>Temperature, [Near] Surface</td><td>NASA/GISS Global Temperature</td><td>https://data.giss.nasa.gov/gistemp/</td></tr><tr><td>2c5</td><td>Snow Properties</td><td>Northern Hemisphere (NH) Snow Cover Extent (SCE), Version 1</td><td>doi:10.7289/V5N014G9, https://www.snowcover.org</td></tr></tbody></table>

<table><caption>Section 2d Hydrological cycle</caption><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific dataset or variable</th><th>Source</th></tr></thead><tbody><tr><td>2d1</td><td>Humidity, [Near] Surface</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2d1, 2d2</td><td>Humidity, [Near] Surface</td><td>HadISDH</td><td>www.metoffice.gov.uk/hadobs/hadisdh, https://catalogue.ceda.ac.uk/uuid/251474c7b09449d8b9e7aeaf1461858f</td></tr><tr><td>2d1</td><td>Humidity, [Near] Surface</td><td>JRA-55 Atmospheric Reanalysis</td><td>http://jra.kishou.go.jp/JRA-55/index_en.html</td></tr><tr><td>2d1</td><td>Humidity, [Near] Surface</td><td>MERRA-2</td><td>https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>COSMIC</td><td>http://cosmic-io.cosmic.ucar.edu/cdaac/index.html</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>GNSS Ground-Based Total Column Water Vapor</td><td>https://doi.org/10.25326/68</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>JRA-55 Atmospheric Reanalysis</td><td>http://jra.kishou.go.jp/JRA-55/index_en.html</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>MERRA-2</td><td>https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>SSM/I -AMSR-E Ocean Total Column Water Vapor</td><td>http://www.remss.com</td></tr><tr><td>2d4</td><td>Humidity, Upper Atmosphere</td><td>Upper Troposphere Humidity (UTH)</td><td>Available on request to Brian Soden (bsoden@miami.edu)</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific dataset or variable</th><th>Source</th></tr></thead><tbody><tr><td>2d4</td><td>Humidity, Upper Atmosphere</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2d4</td><td>Humidity, Upper Atmosphere</td><td>High Resolution Infrared Sounder (HIRS)</td><td>https://www.ncei.noaa.gov/products/climate-data-records/hirs-ch12-brightness-temperature</td></tr><tr><td>2d4</td><td>Temperature, Upper Atmosphere</td><td>NOAA/NESDIS/STAR MSU v5</td><td>https://www.star.nesdis.noaa.gov/pub/smcd/emb/mscat/data/MSU_AMSU_v5.0/Monthly_Atmospheric_Layer_Mean_Temperature/</td></tr><tr><td>2d5, 2d6</td><td>Precipitation</td><td>GPCC</td><td>www.dwd.de/EN/ourservices/gpcc/gpcc.html</td></tr><tr><td>2d5</td><td>Precipitation</td><td>Global Precipitation Climatology Project (GPCP) v2.3</td><td>https://www.dwd.de/EN/ourservices/gpcc/gpcc.html, https://www.ncei.noaa.gov/products/global-precipitation-climatology-project</td></tr><tr><td>2d6</td><td>Precipitation</td><td>HadEX3</td><td>https://www.metoffice.gov.uk/hadobs/hadex3/</td></tr><tr><td>2d6</td><td>Precipitation</td><td>Climate Extremes Index Component 4</td><td>https://www.ncdc.noaa.gov/extremes/cei/</td></tr><tr><td>2d6</td><td>Precipitation</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5</td></tr><tr><td>2d6</td><td>Precipitation</td><td>GHCNDEX</td><td>https://www.climdex.org</td></tr><tr><td>2d6</td><td>Precipitation</td><td>MERRA-2</td><td>https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/</td></tr><tr><td>2d7</td><td>Cloud properties</td><td>PATMOS-x v6.0</td><td>https://www.ncei.noaa.gov/products/climate-data-records/avhrr-hirs-cloud-properties-patmos</td></tr><tr><td>2d7</td><td>Cloud Properties</td><td>Aqua MODIS C6.1 MYD08_M3</td><td>https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MYD08_M3</td></tr><tr><td>2d7</td><td>Cloud Properties</td><td>Clouds and the Earth's Radiant Energy System Energy Balance and Filled (CERES EBAF) v4.2</td><td>https://ceres.larc.nasa.gov/data/</td></tr><tr><td>2d7</td><td>Modes of Variability</td><td>Multivariate ENSO Index (MEI) v2</td><td>https://psl.noaa.gov/enso/mei/</td></tr><tr><td>2d8</td><td>Lake Water Storage</td><td>'GloLakes' lake and reservoir storage</td><td>https://doi.org/10.5194/essd-16-201-2024</td></tr><tr><td>2d9</td><td>Groundwater and terrestrial water storage</td><td>GRACE / GRACE-FO</td><td>https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI GRID RL06.1_V3</td></tr><tr><td>2d10</td><td>Soil Moisture</td><td>Copernicus Climate Change Service (C3S) v202012 product based on the ESA Climate Change Initiative for Soil Moisture (ESA CCI SM) v05.2 merging algorithm</td><td>https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-soil-moisture?tab=form</td></tr></tbody></table>
<table><thead><tr><td>Sub-section</td><td>General Variable or Phenomenon</td><td>Specific dataset or variable</td><td>Source</td></tr></thead><tbody><tr><td>2d11</td><td>Drought</td><td>Climatic Research Unit gridded Time Series (CRU TS) 4.07</td><td>https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/</td></tr><tr><td>2d12</td><td>Land Evaporation</td><td>Climatic Research Unit gridded Time Series (CRU TS) 4.07</td><td>https://www.gleam.eu/</td></tr><tr><td>2d1</td><td>Modes of Variability</td><td>Southern Oscillation Index</td><td>https://crudata.uea.ac.uk/cru/data/soi/</td></tr></tbody></table>

## Section 2e Atmospheric circulation

<table><thead><tr><td>Sub-section</td><td>General Variable or Phenomenon</td><td>Specific dataset or variable</td><td>Source</td></tr></thead><tbody><tr><td>2e1</td><td>Modes of Variability</td><td>Antarctic Oscillation (AAO)/Southern Annular Mode (SAM)</td><td>https://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.aao.index.b790101.current.ascii</td></tr><tr><td>2e1</td><td>Pressure, Sea Level or Near-Surface</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2e2</td><td>Modes of Variability</td><td>Antarctic Oscillation (AAO)/Southern Annular Mode (SAM)</td><td>https://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.aao.index.b790101.current.ascii</td></tr><tr><td>2e2</td><td>Wind, [Near] Surface</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2e2</td><td>Wind, [Near] Surface</td><td>HadISD v3.3.0.2022f</td><td>https://hadleyserver.metoffice.gov.uk/hadisd/v330_2022f/index.html</td></tr><tr><td>2e2</td><td>Wind, [Near] Surface</td><td>Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2)</td><td>http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/</td></tr><tr><td>2e2</td><td>Wind [Near Surface]</td><td>Remote Sensing System (RSS) Merged 1-deg monthly radiometer winds</td><td>https://www.remss.com/measurements/wind/</td></tr><tr><td>2e2</td><td>Wind [Near Surface]</td><td>Remote Sensing System (RSS) Advanced Scatterometer (ASCAT)</td><td>https://www.remss.com/missions/ascat/</td></tr><tr><td>2e2</td><td>Wind [Near Surface]</td><td>Remote Sensing System (RSS) QuickScat4</td><td>https://www.remss.com/missions/qscat/</td></tr><tr><td>2e3</td><td>Wind [Upper Atmosphere]</td><td>Quasi biennial Oscillation (QBO)</td><td>https://www.atmohub.kit.edu/data/singapore2023.dat</td></tr><tr><td>2e3</td><td>Modes of Variability</td><td>Antarctic Oscillation (AAO), Southern Annular Mode (SAM)</td><td>https://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/aao/aao.shtml, http://www.nerc-bas.ac.uk/icd/gjma/sam.html</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific dataset or variable</th><th>Source</th></tr></thead><tbody><tr><td>2e3</td><td>Wind [Upper Atmosphere]</td><td>ERA5 hourly data on pressure levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)</td><td>https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview</td></tr><tr><td>2e3</td><td>Wind [Upper Atmosphere]</td><td>ERA-Interim</td><td>www.ecmwf.int/en/research/climate-reanalysis/era-interim</td></tr><tr><td>2e3</td><td>Wind [Upper Atmosphere]</td><td>MERRA-2</td><td>http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/</td></tr><tr><td>2e3</td><td>Wind [Upper Atmosphere]</td><td>JRA-55 Atmospheric Reanalysis</td><td>http://jra.kishou.go.jp/JRA-55/index_en.html</td></tr><tr><td>2e4</td><td>Lightning</td><td>GOES-R Geostationary Lightning Mapper (GLM) Gridded Data Products V1</td><td>http://doi.org/10.7289/V5KH0KK6</td></tr></tbody></table>

## Section 2f Earth's radiation budget

<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific dataset or variable</th><th>Source</th></tr></thead><tbody><tr><td>2f1</td><td>TOA Earth Radiation Budget</td><td>CERES Energy Balanced and Filled version 4.2</td><td>https://ceres-tool.larc.nasa.gov/ord-tool/jsp/EBAFTOA42Selection.jsp</td></tr><tr><td>2f1</td><td>TOA Earth Radiation Budget</td><td>CERES FLASHflux version 4A</td><td>https://ceres-tool.larc.nasa.gov/ord-tool/jsp/FLASH_TISASelection.jsp</td></tr><tr><td>2f1</td><td>TOA Earth Radiation Budget</td><td>Community-Consensus TSI Composit</td><td>https://spot.colorado.edu/~koppg/TSI/TSI_Composite-SIST.txt</td></tr><tr><td>2f2</td><td>Solar Transmission, Apparent</td><td>Mauna Loa Observatory</td><td>https://www.esrl.noaa.gov/gmd/webdata/grad/mloapt/mauna_loa_transmission.dat</td></tr><tr><td>2f2</td><td>Cloud Aerosol</td><td>Cloud-Aerosol LIDAR and Infrared Pathfinder Satellite Observations (CALIPSO)</td><td>http://www-calipso.larc.nasa.gov</td></tr><tr><td>2f2</td><td>Ozone, Stratospheric</td><td>Stratospheric Aerosol and Gas Experiment (SAGE) limb sounder</td><td>https://sage.nasa.gov/sageiii-iss/browse_images/expedited/</td></tr></tbody></table>

## Section 2g Atmospheric composition

<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific dataset or variable</th><th>Source</th></tr></thead><tbody><tr><td>2g1</td><td>Trace Gases</td><td>Atmospheric Gas trends</td><td>https://gml.noaa.gov/ccgg/trends/</td></tr><tr><td>2g1</td><td>Trace Gases</td><td>Global Greenhouse Gas Reference Network</td><td>https://gml.noaa.gov/ccgg/about.html</td></tr></tbody></table>
<table><thead><tr><td>Sub-section</td><td>General Variable or Phenomenon</td><td>Specific dataset or variable</td><td>Source</td></tr></thead><tbody><tr><td>2g1</td><td>Trace Gases</td><td>Atmospheric Greenhouse Gas Index (AGGI)</td><td>www.esrl.noaa.gov/gmd/aggi</td></tr><tr><td>2g2</td><td>Trace Gases</td><td>Halocarbons and other Atmospheric Trace Species</td><td>https://gml.noaa.gov/aftp/data/hats/</td></tr><tr><td>2g2</td><td>Trace Gases</td><td>Advanced Global Atmospheric Gases Experiment</td><td>https://agege2.eas.gatech.edu/data_archive/global_mean/global_mean_ms.txt</td></tr><tr><td>2g2</td><td>Trace Gases</td><td>Ozone-Depleting Gas Index (ODGI)</td><td>www.esrl.noaa.gov/gmd/odgi</td></tr><tr><td>2g3</td><td>Aerosols</td><td>Advanced Along Track Scanning Radiometer (AATSR)</td><td>https://earth.esa.int/eogateway/instruments/aatsr</td></tr><tr><td>2g3</td><td>Aerosols</td><td>Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA)</td><td>https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-radiative-forcing-auxilliary-variables?tab=overview</td></tr><tr><td>2g4</td><td>Ozone, Tropospheric</td><td>NOAA Global Monitoring Laboratory</td><td>https://gml.noaa.gov/aftp/data/ozwv/SurfaceOzone/</td></tr><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>OHP LTA lidar</td><td>https://www-air.larc.nasa.gov/missions/ndacc/data.html?station=haute.provence/ames/lidar/</td></tr><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>OHP LiO3S lidar</td><td>https://www-air.larc.nasa.gov/missions/ndacc/data.html?station=haute.provence/ames/lidar/</td></tr><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>Lauder aerosol lidar</td><td>https://www-air.larc.nasa.gov/missions/ndacc/data.html?station=lauder/ames/lidar/</td></tr><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>SAGE III v5.3</td><td>https://asdc.larc.nasa.gov/project/SAGE%20III-ISS/g3bssp_53</td></tr><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>GloSSAC v2.</td><td>https://asdc.larc.nasa.gov/project/GloSSAC</td></tr><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>OMPS-LP v2.1</td><td>https://disc.gsf.c.na.gov/datasets/OMPS_NPP_LP_L2_AER_DAILY_2/summary</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>GOME/SCIAMACHY/GOME2 (GSG) Merged Total Ozone</td><td>http://www.iup.uni-bremen.de/UVSAT/datasets/merged-wfdoas-total-ozone</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>GOME/SCIAMACHY/GOME2 (GTO) Merged Total Ozone</td><td>https://atmos.eoc.dlr.de/gto-ecv</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>GOZCARDS ozone profiles</td><td>https://www.earthdata.nasa.gov/esds/competitive-programs/measures/gozcards</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>Multi Sensor Reanalysis (MSR-2) of total ozone</td><td>http://www.temis.nl/protocols/O3global.html</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>NASA BUV/SBUV/OMPS v8.7 (MOD) Merged Ozone</td><td>https://acd-ext.gsfc.nasa.gov/Data_services/merged/</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific dataset or variable</th><th>Source</th></tr></thead><tbody><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>NOAA SBUV V8.6 OMPS V4r1 cohesive data set (COH)</td><td>ftp://ftp.cpc.ncep.noaa.gov/SBUV_CDR/</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>Network for the Detection of Atmospheric Composition Change (NDACC) lidar, microwave and FTIR</td><td>https://www-air.larc.nasa.gov/missions/ndacc</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>CCMI-2022 model runs</td><td>https://blogs.reading.ac.uk/ccmi/ccmi-2022/</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>SAGE-CCI-OMPS</td><td>https://climate.esa.int/en/projects/ozone/data</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>SAGE/OSIRIS</td><td>Bourassa et al. (2018) doi:10.5194/amt-11-489-2018</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>SAGE-SCIA-OMPS</td><td>Arosio et al., (2018) doi:10.5194/amt-2018-275</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>SWOOSH</td><td>www.esrl.noaa.gov/csd/groups/csd8/swoosh/</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>WOUDC Ground-based Ozone</td><td>ftp.tor.ec.gc.ca; cd /pub/woudc/Projects Campaigns/ZonalMeans</td></tr><tr><td>2g7</td><td>Stratospheric Water Vapor</td><td>the Aura Microwave Limb Sounder version 5.0 data, as merged into SWOOSH</td><td>www.esrl.noaa.gov/csd/groups/csd8/swoosh/</td></tr><tr><td>2g7</td><td>Tropopause Temperature</td><td>MERRA-2</td><td>http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/</td></tr><tr><td>2g7</td><td>Stratospheric Water Vapor</td><td>NOAA Frostpoint Hygrometer (FPH)</td><td>https://gml.noaa.gov/aftp/data/ozwv/WaterVapor/</td></tr><tr><td>2g7</td><td>Stratospheric Water Vapor</td><td>Cryogenic Frostpoint Hygrometer (CFH)</td><td>https://ndacc.org</td></tr><tr><td>2g8</td><td>Trace Gases</td><td>Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) for Carbon Monoxide</td><td>https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-radiative-forcing-auxilliary-variables?tab=overview</td></tr></tbody></table>

## Section 2h Land surface properties

<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific dataset or variable</th><th>Source</th></tr></thead><tbody><tr><td>2h1</td><td>Albedo</td><td>MODIS/Terra+Aqua BRDF/ Albedo Albedo Daily L3 Global 0.05Deg CMG V061</td><td>https://lpdaac.usgs.gov/products/mcd43c3v061/</td></tr><tr><td>2h1</td><td>Albedo</td><td>VIIRS VNP43C3 Collection 1.0</td><td>https://lpdaac.usgs.gov/products/vnp43c3v001 https://doi.org/10.5067/VIIRS/VNP43C3.001</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific dataset or variable</th><th>Source</th></tr></thead><tbody><tr><td>2h2</td><td>fraction of absorbed photosynthetically active radiation (FAPAR)</td><td>JRC TIP MODIS</td><td>https://fapar.jrc.ec.europa.eu</td></tr><tr><td>2h2</td><td>FAPAR</td><td>MERIS</td><td>https://fapar.jrc.ec.europa.eu</td></tr><tr><td>2h2</td><td>FAPAR</td><td>SeaWiFS FAPAR</td><td>http://fapar.jrc.ec.europa.eu/</td></tr><tr><td>2h2</td><td>FAPAR</td><td>OLCI</td><td>https://dataspace.copernicus.eu/</td></tr><tr><td>2h3</td><td>Biomass, Greenness or Burning</td><td>GFAS v1.4</td><td>https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-fire-emissions-gfas</td></tr><tr><td>2h3</td><td>Biomass, Greenness or Burning</td><td>Global Fire Emissions Database</td><td>https://www.globalfiredata.org/data.html</td></tr><tr><td>2h3</td><td>Deforestation</td><td>PRODES Amazonia</td><td>http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes</td></tr><tr><td>2h4</td><td>Phenology</td><td>MODIS Normalized Difference Vegetative Index</td><td>https://modis.gsfc.nasa.gov/data/dataprod/mod13.php</td></tr><tr><td>2h4</td><td>Temperature [Near] Surface</td><td>MERRIS-2 monthly temperature</td><td>https://goldsmr4.gesdisc.eosdis.nasa.gov/data/MERRA2_MONTHLY/M2TMNXLND.5.12.4/</td></tr><tr><td>2h4</td><td>Phenology</td><td>USA-National Phenology Network (NPN) phenology data</td><td>https://www.usanpn.org/data/observational</td></tr><tr><td>2h4</td><td>Phenology</td><td>USA-National Phenology Network (NPN) Spring Index raster data products</td><td>https://data.usanpn.org/geoserver-request-builder/</td></tr><tr><td>2h4</td><td>Phenology</td><td>German oak phenology data</td><td>https://opendata.dwd.de/</td></tr><tr><td>2h4</td><td>Phenology</td><td>Harvard Forest</td><td>https://harvardforest1.fas.harvard.edu/exist/apps/datasets/showData.html?id=hf003</td></tr><tr><td>2h4</td><td>Phenology</td><td>Natures Calendar</td><td>https://naturescalendar.woodlandtrust.org.uk/</td></tr><tr><td>2h4</td><td>Phenology</td><td>PhenoCam</td><td>http://phenocam.sr.unh.edu</td></tr><tr><td>2h4</td><td>Phenology</td><td>UK Cumbrian lakes data</td><td>https://catalogue.ceh.ac.uk/documents/bf30d6aa-345a-4771-8417-ffbcf8c08c28/</td></tr><tr><td>2h5</td><td>Vegetation Optical Depth</td><td>Global Long-term Microwave Vegetation Optical Depth Climate Archive (VODCA) v2</td><td>https://researchdata.tuwien.ac.at/records/t74ty-tcx62</td></tr><tr><td>2h5</td><td>Modes of Variability</td><td>Southern Oscillation Index</td><td>http://www.bom.gov.au/climate/enso/soi/</td></tr></tbody></table>
## Sidebar 2.2 Near-surface equivalent temperature as a key climate change metric

<table><thead><tr><td>Sub-section</td><td>General Variable or Phenomenon</td><td>Specific dataset or variable</td><td>Source</td></tr></thead><tbody><tr><td>SB2.2</td><td>Temperature [Near] Surface</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>SB2.2</td><td>Dewpoint</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>SB2.2</td><td>Pressure [Near] Surface</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>SB2.2</td><td>Humidity [Near] Surface</td><td>HadISDH</td><td>www.metoffice.gov.uk/hadobs/hadisdh, https://catalogue.ceda.ac.uk/uuid/251474c7b09449d8b9e7aeaf1461858f</td></tr></tbody></table>
Appendix 3: Supplemental materials

<figure><img src="image_142.png" /><figcaption>Fig. A2.1. Global surface temperature anomalies. (°C; Source: NASA-GISTEMPv4.)</figcaption></figure>

<figure><img src="image_143.png" /><figcaption>Fig. A2.2. Global surface temperature anomalies. (°C; Source: HadCRUT5.)</figcaption></figure>
<figure><img src="image_144.png" /><figcaption>Fig. A2.3. Global surface temperature anomalies. (°C; Source: ERA5).</figcaption></figure>

<figure><img src="image_145.png" /><figcaption>Fig. A2.4. Global surface temperature anomalies. (°C; Source: JRA-55.)</figcaption></figure>

<figure><img src="image_146.png" /><figcaption>Fig. A2.5. Global surface temperature anomalies. (°C; Source: JRA-3Q.)</figcaption></figure>
<figure><img src="image_147.png" /><figcaption>Fig. A2.6. Monthly average soil moisture anomalies for 2023 (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, permanent ice cover, or radio frequency interference. (Source: C3S Soil Moisture.)</figcaption></figure>
<figure><img src="image_148.png" /><figcaption>Fig. A2.7. The difference in average CXKu vegetation optical depth (VOD) between the years 2022 and 2023. Brown (green) colors indicate areas where VOD in 2023 were lower (higher) than in 2022. (Source: VODCA.)</figcaption></figure>

<figure><img src="image_149.png" /><figcaption>Fig. A2.8. VODCA monthly CXKu vegetation optical depth (VOD) anomalies in 2023 (1991–2020 base period). VOD cannot be retrieved over frozen or snow-covered areas, which is why they are masked out in winter.</figcaption></figure>
<table><caption>Table A2.1. Notable precipitation events, with prior known record where long-term observations are available. (Source: GHCNDEX unless otherwise noted.)</caption><thead><tr><td>Date</td><td>Index</td><td>Amount (mm)</td><td>Prior Record Year (Amount in mm)</td><td>Location</td><td>Reference</td></tr></thead><tbody><tr><td>01/02/23</td><td>Rx5day</td><td>653.2</td><td>1987 (336.5)</td><td>Beverley Springs, Australia</td><td></td></tr><tr><td>01/19/23</td><td>Rx1day</td><td>104</td><td></td><td>Antosyhihy, Madagascar</td><td>Davies (2023a)</td></tr><tr><td>02/14/23</td><td>Rx1day</td><td>183.8</td><td></td><td>Whangarei, New Zealand</td><td>Murray (2023)</td></tr><tr><td>02/14/23</td><td>Rx5day</td><td>408.7</td><td></td><td>Glenbervie Forest, Northland, New Zealand</td><td>Murray (2023)</td></tr><tr><td>02/14/23</td><td>Rx5day</td><td>447</td><td></td><td>The Pinnacles, New Zealand</td><td>Murray (2023)</td></tr><tr><td>02/19/23</td><td>Rx1day</td><td>682</td><td></td><td>Bertioga, Brazil</td><td>Davies (2023b)</td></tr><tr><td>02/24/23</td><td>Rx1day</td><td>672</td><td></td><td>Marromeu, Mozambique</td><td>United Nations Office for the Coordination of Humanitarian Affairs (2023)</td></tr><tr><td>08/03/23</td><td>Rx5day</td><td>488</td><td>1998 (264.4)</td><td>Almora Station, Australia</td><td></td></tr><tr><td>09/03/23</td><td>Rx5day</td><td>421</td><td>2009 (294)</td><td>Undilla Station, Australia</td><td>Bureau of Meteorology (2024)</td></tr><tr><td>03/16/23</td><td>Rx1day</td><td>152.4</td><td>1962 (71.1)</td><td>Hat Creek, California, USA</td><td></td></tr><tr><td>04/12/23</td><td>Rx1day</td><td>571.5</td><td>2003 (259.6)</td><td>Fort Lauderdale, Florida, USA</td><td></td></tr><tr><td>05/02/23</td><td>Rx1day</td><td>182.6</td><td></td><td>Mushubati, Rwanda</td><td>World Meteorological Organization (WMO; 2023)</td></tr><tr><td>05/24/23</td><td>Rx1day</td><td>304.8</td><td>2018 (230.1)</td><td>Agat, Guam</td><td></td></tr><tr><td>06/23/23</td><td>Rx1day</td><td>156.6</td><td></td><td>Retiro, Chile</td><td>Davies (2023c)</td></tr><tr><td>07/07/23</td><td>Rx5day</td><td>160.3</td><td>2001 (95.4)</td><td>Delsbo, Sweden</td><td></td></tr><tr><td>07/20/23</td><td>Rx1day</td><td>198.6</td><td></td><td>Maharashtra, India</td><td>Davies (2023d)</td></tr><tr><td>07/28/23</td><td>Rx1day</td><td>649.8</td><td></td><td>Mulugu, India</td><td>The Watchers (2023)</td></tr><tr><td>08/19/23</td><td>Rx5day</td><td>371.9</td><td></td><td>Termas de Chillán, Chile</td><td>Davies (2023e)</td></tr><tr><td>08/22/23</td><td>Rx1day</td><td>73.7</td><td>2016 (38.1)</td><td>Rosette, Utah, USA</td><td></td></tr><tr><td>08/24/23</td><td>Rx1day</td><td>123</td><td>2016 (73.6)</td><td>Harrow, Canada</td><td></td></tr><tr><td>08/29/23</td><td>Rx5day</td><td>744.8</td><td></td><td>Wangjiayuan Reservoir, China</td><td>Du Yan (2023)</td></tr></tbody></table>
<table><thead><tr><td>Date</td><td>Index</td><td>Amount (mm)</td><td>Prior Record Year (Amount in mm)</td><td>Location</td><td>Reference</td></tr></thead><tbody><tr><td>09/04/23</td><td>Rx5day</td><td>1096.2</td><td></td><td>Zagora Pelion, Greece</td><td>WMO (2023)</td></tr><tr><td>09/05/23</td><td>Rx1day</td><td>759.6</td><td></td><td>Zagora Pelion, Greece</td><td>WMO (2023)</td></tr><tr><td>09/07/23</td><td>Rx1day</td><td>425</td><td>1926 (534.1)</td><td>Hong Kong Observatory</td><td>Hong Kong Observatory (2024)</td></tr><tr><td>09/07/23</td><td>Rx1h</td><td>158.1</td><td>2008 (145.5)</td><td>Hong Kong Observatory</td><td>Hong Kong Observatory (2024)</td></tr><tr><td>09/08/23</td><td>Rx1day</td><td>391.5</td><td>2013 (272)</td><td>Mobara City, Japan</td><td>Davies (2023f)</td></tr><tr><td>09/11/23</td><td>Rx1day</td><td>414.1</td><td></td><td>Al Badya, Libya</td><td>WMO (2023)</td></tr><tr><td>09/25/23</td><td>Rx1day</td><td>216</td><td></td><td>Dwarsberg, South Africa</td><td>Maswanganye (2023)</td></tr><tr><td>10/24/23</td><td>Rx1day</td><td>406</td><td></td><td>Al Ghadya Airport, Yemen</td><td>Davies (2023g)</td></tr><tr><td>10/29/23</td><td>Rx5day</td><td>1125.8</td><td>1995 (704.2)</td><td>Danang, Vietnam</td><td></td></tr><tr><td>12/13/23</td><td>Rx5day</td><td>1933.8</td><td>1996 (1265.6)</td><td>Whyanbeel Valley, Australia</td><td></td></tr><tr><td>12/13/23</td><td>Rx5day</td><td>1592.8</td><td>2018 (557.2)</td><td>White Cliff Point, Australia</td><td></td></tr><tr><td>12/13/23</td><td>Rx5day</td><td>1295</td><td>2004 (745)</td><td>Copperlode Dam, Australia</td><td></td></tr><tr><td>12/21/23</td><td>Rx5day</td><td>211.4</td><td>2022 (127)</td><td>Green Cape, Australia</td><td></td></tr></tbody></table>