into the Arctic Ocean was slightly below normal, particularly in summer. The Mackenzie and Nelson Rivers (North America) and the Lena River (Asia) experienced low discharge, whereas the Ob River (Asia) exhibited above-normal discharge (see section 5h for more details). The Atlantic basin faced severe drought conditions, receiving the lowest discharge in the series. Below-normal discharge persisted throughout the year, intensifying during the beginning of 2024 when El Niño was present. Major rivers, including the Amazon, Paraná, and Mississippi (America) as well as the Congo and Nile (Africa) had below-normal discharge. Exceptions to this pattern were the flooding that affected Rio Grande Do Sul (Brazil) in late April (see Sidebar 7.1; Dijk et al. 2025) and the Congo basin in January (WMO 2025b), the latter of which was the worst in six decades. Unlike the Atlantic basin, the Indian Ocean experienced its highest discharge from rivers in the time series, with above-average discharge spanning both wet seasons, while values remained normal during dry seasons. Severe flooding impacted Bangladesh in August (Dijk et al. 2025). The Pacific Ocean also received above-normal discharge, with the largest positive anomalies occurring between April and July.

The river discharge and runoff data used in this analysis are derived from the historical simulation of the Global Flood Awareness System version 4 (GloFASv4) of the Copernicus Emergency Management Service (Joint Research Centre - European Commission 2025). GloFASv4 employs the LISFLOOD-OS hydrological model (Burek et al. 2013), incorporating surface fields that represent land characteristics (Choulga et al. 2024) and parameters calibrated against discharge records from nearly 2000 stations, or regionalized where necessary. The historical simulation spans from 1979 to the present, utilizing a 0.05-degree grid and a daily temporal resolution. ERA5 (Hersbach et al. 2020) serves as the meteorological forcing input.

# 10. GROUNDWATER AND TERRESTRIAL WATER STORAGE

—M. Rodell and D. N. Wiese

In addition to the continued diminishment of polar ice sheets and glaciers, five regions around the world experienced major (>15 cm) changes in terrestrial water storage (TWS) from 2023 to 2024 (Plate 2.1t). By far the largest of these changes resulted from drought that encompassed most of Brazil and its neighbors to the north. For the equatorial portion of this region, it was a continuation of drought from 2023 (see Plate 2.1u), dropping TWS to record lows. A second major TWS change was a wet event that straddled Uruguay, northwestern Argentina, and southern Brazil, a reversal from the prior year (see section 7d). Changes in the rest of South America were small, though eastern Brazil remained wetter than normal. In contrast, TWS changes in North America were relatively unsubstantial, with some recovery from drought in the central plains of the United States and eastern Mexico, and continued TWS declines in north-central Canada and in the southwestern United States and northern Mexico, with record-low TWS and subsequent wildfires in some parts of these regions.

Remarkably large changes occurred in two regions of Africa. Zambia was at the center of a widespread TWS decline, extending a drought that has been its worst in at least two decades, while Tanzania, already experiencing pluvial conditions in prior years, accumulated even more TWS. Except for the Zambian drought region and another region in western equatorial Africa, a majority of sub-Saharan Africa gained water as the most intense (in terms of extent, duration, and TWS anomalies) wet event in the 23-year TWS record continued (Rodell and Li 2023), associated with unusually heavy rainfall. This led to all-time high TWS and flooding in several African drainage basins including those of the Niger, Congo, Nile, and Senegal Rivers, as well as Lake Chad and eastern lake basins.

The fifth region to experience a major change in TWS was the northern half of Australia's Northern Territory, which was pounded by rain from tropical cyclones in February and March (see section 4g7 for details), continuing northern Australia's wetting trend from the previous year. Much of Australia gained water, except for small declines along the northwestern and southeastern coasts. TWS remained elevated in much of New South Wales and Victoria.

Despite high temperatures, rainfall alleviated drought conditions in western Europe and even caused flooding in southern Spain; however, drought worsened in the Balkan Peninsula. A large area of western Russia and Kazakhstan experienced wetter-than-normal conditions
in 2024 as did parts of Southeast Asia, excluding southern India and Cambodia. TWS changes were mixed in the rest of Asia, with TWS remaining depleted in the Arabian Peninsula, the Middle East, and eastward into south central Asia, due to a combination of drought and overexploitation of water resources (Rodell et al. 2018).

Figures 2.42 and 2.43 display deseasonalized time series of monthly zonal-mean and global-mean TWS anomalies. Gaps indicate intervals when satellite observations were unavailable. We excluded regions from the averages where TWS declines are dominated by ice sheet and glacier ablation: Antarctica, Greenland, the Gulf Coast of Alaska, polar islands, High Mountain Asia, alpine western Canada, and the southern Andes. A 20°-wide zone of drying in the northern midlatitudes remained stable in 2024. Except for the northern portion, a 23°-wide equatorial zone of wetting diminished as the massive drought in South America counteracted the pluvial in Africa. South of that zone, TWS declines in South America and southern Africa overpowered the increase in northern Australia. A gradual decline in the boreal latitudes reflects ongoing drought in Canada and lingering depressed levels of TWS in much of northern Eurasia. Consistent with the end of El Niño, global-mean non-ice TWS (Fig. 2.43) recovered somewhat in 2024 from a near-record low a few months prior, but it remained in a lower range that was established after an abrupt decline during 2014–16 (Rodell et al. 2024).

TWS anomalies are derived from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite observations of Earth's time-varying gravity field (Tapley et al. 2004; Landerer et al. 2020). Uncertainty in these estimates is about 1 cm–2 cm equivalent height of water over a 500,000 km² region at midlatitudes (Wiese et al. 2016). Satellite observations are used

because in situ measurements of groundwater, soil moisture, surface waters, snow, and ice (the components of TWS) do not provide the spatial density and vertical completeness required to monitor TWS at continental scales. On multi-year timescales, groundwater is typically the primary contributor of variations in TWS, except in the humid tropics and cold regions where surface water and ice/snow, respectively, are dominant (Getirana et al. 2017).

<figure><img src="image_88.png" /><figcaption>Fig. 2.42. Zonal means of monthly terrestrial water storage anomalies—excluding those in Antarctica, Greenland, the Gulf Coast of Alaska, polar islands, High Mountain Asia, alpine western Canada, and the southern Andes—in cm equivalent height of water, based on gravity observations from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO). The anomalies are relative to a 2002–20 base period.</figcaption></figure>

<figure><img src="image_89.png" /><figcaption>Fig. 2.43. Global average terrestrial water storage anomalies from Gravity Recovery and Climate Experiment (GRACE; gray) and GRACE Follow-On (GRACE-FO; black)—excluding those in Antarctica, Greenland, the gulf coast of Alaska, polar islands, High Mountain Asia, alpine western Canada, and the southern Andes—in cm equivalent height of water, relative to a 2002–20 base period.</figcaption></figure>
# 11. SOIL MOISTURE

—J. Lems, W. Preimesberger, A. Gruber, D. D. Kovács, S. Hahn, M. Formanek, B. L. Harris, N. Rodriguez-Fernandez, T. Frederikse, R. Kidd, R. A. M. de Jeu, and W. A. Dorigo

Soil moisture is a crucial factor in land-atmosphere interactions, influencing surface air temperature, precipitation generation, and extreme weather events, including heatwaves and wildfires (Seneviratne et al. 2010).

In 2024, global soil moisture conditions were wetter than the 1991–2020 average (Fig. 2.44), with notable regional contrasts (Plate 2.1v). America, southern Africa, northern Europe, and Asia experienced drier-than-average conditions, while eastern South America, East Africa

(including the Sahel), India, East Asia, and northern Australia saw above-average soil moisture levels. The Northern Hemisphere experienced wetter-than-normal conditions, while the Southern Hemisphere remained drier than average (Fig. 2.44). This contrast closely resembled that of 2023 (Hirschi 2024); however, the Northern Hemisphere saw a notable increase in soil moisture compared to the previous year. Consistent with the trend of the past five years, the most pronounced wet anomalies in the Northern Hemisphere were concentrated between the equator and 30°N (Fig. 2.45).

A strong El Niño in the first quarter of the year contributed to drier and warmer conditions in North America, Southeast Asia, Australia, and southern Africa (Song 2018; Hoell et al. 2017; Figs. 2.32, 2.46). In April, the El Niño transitioned to a neutral phase, which aligned with a return to normal soil moisture conditions in the Southern Hemisphere towards the end of 2024 (Fig. 2.44).

Wetter-than-normal conditions were present throughout most of the year in northern Australia, with wet anomalies that were particularly widespread in March (Fig. 2.46). Similarly, strong wet anomalies were also observed in India, starting in May and lasting through to the end of the year. In August and September, anomalously high soil moisture in Afghanistan and Pakistan was associated with heavy rain and flash floods. Also, parts of East Asia experienced noticeable wetter-than-normal conditions in 2024, similar to the last couple of years.

The Sahel experienced prolonged wet anomalies from August to November, with soil moisture amounts twice as high as normal in some areas. Meanwhile, northern and southern Africa faced extreme drought conditions, particularly southern Africa, which remained dry even after El Niño subsided.

Widespread drier-than-normal soil moisture conditions persisted across North and South America throughout 2024, with the

<figure><img src="image_90.png" /><figcaption>Fig. 2.44. (top) Time series of global (black), Northern Hemisphere (purple), and Southern Hemisphere (orange) monthly surface soil moisture anomalies (m³ m⁻³) for the period 1991–2024 (1991–2020 base period), and (bottom) the valid observations as a percentage (%) of total global land surface. 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: Copernicus Climate Change Service [C3S] Soil Moisture.)</figcaption></figure>

<figure><img src="image_91.png" /><figcaption>Fig. 2.45. Time-latitude diagram of monthly surface soil moisture anomalies (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: Copernicus Climate Change Service [C3S] Soil Moisture.)</figcaption></figure>
continent experiencing the most significant negative soil moisture anomaly globally. Following a record-breaking warm and dry October (NCEI 2024), the drought footprint reached a nationwide record in the United States (Fig. 2.46), with 47% of the nation under moderate to extreme drought (section 2d12). September was exceptionally dry in inland South America and in Eastern Europe, primarily affecting Ukraine. Generally, Europe exhibited a distinct east-west contrast, with Western Europe experiencing a wetter-than-average summer, while Eastern and Southern Europe remained dry for most of the year.

Soil moisture was observed by microwave satellite remote sensing of the upper few centimeters of the soil layer, as provided by the COMBINED product of the Copernicus Climate Change Service (C3S) version 202312 (Dorigo et al. 2025). C3S combines multi-sensor data in the 1978-2024 period through statistical merging (Gruber et al. 2017, 2019). Wet and dry anomalies here refer to the deviation from the 1991-2020 average. Note that changes in spatiotemporal coverage (both between seasons and periods, e.g., resulting from the inclusion of additional sensors) can introduce uncertainties in the domain-averaged soil moisture time series (e.g., Bessenbacher et al. 2023).

<figure><img src="image_92.png" /><figcaption>Fig. 2.46. Monthly average soil moisture anomalies for 2024 (m³ m⁻³; 1991–2020 average). 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: Copernicus Climate Change Service [C3S] Soil Moisture.)</figcaption></figure>
12. MONITORING DROUGHT USING THE SELF-CALIBRATING PALMER DROUGHT SEVERITY INDEX

—J. Barichivich, T. J. Osborn, I. Harris, A. Gollop, G. van der Schrier, and P. D. Jones

The self-calibrating Palmer Drought Severity Index (scPDSI) for 1950–2024 indicates a decline in global drought severity and extent in 2024, following the record-high peak in late 2023 (Barichivich et al. 2024; Fig. 2.47). Extreme drought (scPDSI ≤-4) affected around 5% of the global land throughout 2024, down from over 7% in July–August 2023. Severe and extreme drought combined (scPDSI ≤-3) stabilized near 12% of the global land area after reaching a record 17% in July 2023. Similarly, moderate or worse drought (scPDSI ≤-2) affected about 23% of global land in 2024 compared to 28% in mid-2023. Most of this decline occurred in regions where drought conditions of 2023 transitioned to normal or wetter conditions, particularly in the La Plata Basin, much of non-Mediterranean Europe, and the midlatitudes of Central Asia (Fig. 2.48). Meanwhile, severe drought persisted in southwestern and northern North America, parts of tropical South America, the Mediterranean and northwest Africa, southern Africa, parts of the Middle East, southern Australia, and Mongolia (Plate 2.1w).

In Canada, 2024 ranked as the driest year on the nationally averaged yearly scPDSI for the 1950–2024 period. In the United States, severe-to-extreme drought conditions persisted through much of Arizona and New Mexico. Mexico and most countries across Central and South America experienced a mix of moderate drought and normal conditions (Plate 2.1w). On a country-averaged basis, 2024 was the third-driest year in both Peru and Brazil, with vast areas of the Amazon basin enduring extreme drought. By mid-October, the Rio Negro at Manaus, a major tributary of the Amazon River, recorded its lowest water levels since records began in 1902 (updated from Barichivich et al. 2018). In contrast, Chile experienced a moisture recovery in 2024 following a prolonged megadrought (Garread et al. 2025). In terms of average drought severity, 2024 ranked as the 18th-driest year since 1950, following the driest years on record in 2021 and 2022.

Northwest Africa experienced record or near-record drought levels (Plate 2.1w), with 2024 being the driest year in the Canary Islands and Morocco, second driest in Algeria, and third driest in Tunisia. In southeastern Europe, it was the driest year in Greece and third driest in Bulgaria and Romania. In Italy, 2024 was ranked sixth driest for country-average drought severity, with the top six drought years all occurring in the last eight years.

Although uncertain due to sparser observations, moisture patterns in tropical Africa did not change much in 2024 with respect to 2023 (Fig. 2.48). Southern Africa experienced a continuation of drought conditions seen since 2018, and its severity continued mostly as moderate (Plate 2.1w). In Australia, the moisture pattern in 2024 also remained similar to that in 2023, with the coastal parts of the

<figure><img src="image_93.png" /><figcaption>Fig. 2.47. Percentage of global land area (excluding ice sheets and deserts) with self-calibrating Palmer Drought Severity Index (scPDSI) indicating moderate (<-2), severe (<-3), and extreme (<-4) drought for each month of 1950–2024. Inset: each month of 2024 (shading) compared with 2023 (dashed lines).</figcaption></figure>

<figure><img src="image_94.png" /><figcaption>Change in categories from 2023 to 2024 (self-calibrating PDSI)</figcaption></figure>

<caption>Fig. 2.48. Change in drought categories from 2023 to 2024 (mean self-calibrating Palmer Drought Severity Index [scPDSI] for 2024 minus mean scPDSI for 2023). Increases in drought severity are indicated by negative values (brown), and decreases by positive values (green). No calculation is made where a drought index is meaningless (gray areas: ice sheets or deserts with approximately zero mean precipitation).</caption>
country continuing under moderate drought (Plate 2.1w). Wet conditions seen through most of India and Southeast Asia since 2022 continued during 2024. Most of the previous severe-to-extreme drought conditions through China and Kazakhstan shifted to normal or wet conditions, but Mongolia saw continued drought.

Hydrological drought results from a period of abnormally low precipitation, sometimes exacerbated by a concurrent increase in evapotranspiration (ET). Its occurrence can be apparent in reduced river discharge (section 2d9), groundwater storage, (section 2d10), and/or soil moisture (section 2d11), depending on season and duration of the event. Here, the scPDSI (Wells et al. 2004; van der Schrier et al. 2013) is presented, using global precipitation and Penman-Monteith Potential ET from the Climatic Research Unit terrestrial series (CRU TS 4.09) dataset (Harris et al. 2020). A simple water balance at the core of the scPDSI estimates actual evapotranspiration, soil moisture content, and runoff based on the input precipitation and potential loss of moisture to the atmosphere. Estimated soil moisture categories are calibrated over the complete 1901–2024 period to ensure that “extreme” droughts and pluvials (wet periods) relate to events that do not occur more frequently than in approximately 2% of the months. This calibration affects direct comparison with other hydrological cycle variables in Plate 2.1w that use a different baseline period. All country rankings mentioned above are based on the ranking of spatial averages of annual scPDSI for each country for the 1950–2024 period; pre-1950 data are not used for rankings because observational coverage is poor in some countries.

## 13. LAND EVAPORATION

—D. G. Miralles, O. M. Baez-Villanueva, O. Bonte, E. Tronquo, S. Haghdoost, and H. E. Beck

A clear understanding of the spatial and temporal dynamics of terrestrial evaporation is fundamental for evaluating the impacts of natural and anthropogenic forcing on hydrological systems, since evaporation serves as a direct nexus between the energy and water balances. The global-mean land evaporation in 2024 aligned well with the multidecadal trend of approximately +0.4 mm year⁻¹ (Fig. 2.49), a trend that has been attributed to an increase in atmospheric evapotransitive demand with global warming (Brutsaert 2017) and global greening (Yang et al. 2023). Despite 2024 being overall an El Niño year, which usually implies negative global evaporation anomalies due to the proliferation of drought conditions in the Southern Hemisphere (Martens et al. 2018; Miralles et al. 2014), it was also the warmest year on record (section 2b1). The latter explains the high mean evaporation in the Northern Hemisphere and the subsequent positive global anomaly, which is not an isolated event but contributes to the long-term increase witnessed since the early 1980s (Fig. 2.49).

The latitudinal anomaly profiles in Fig. 2.50 reveal the overall positive anomalies in Northern Hemisphere regions, which are consistent with warm temperatures in Europe, Asia, and North

<figure><img src="image_95.png" /></figure>

Fig. 2.49. Land evaporation anomaly (mm yr⁻¹; 1991–2020 base period) for the Northern Hemisphere, Southern Hemisphere, and the entire globe (purple, orange, and black solid lines, respectively). Linear trends in evaporation (dashed lines) and the Niño-3.4 index (right axis, shaded area) are also shown. (Source: Global Land Evaporation Amsterdam Model version 4 [GLEAM4] and NOAA Physical Sciences Laboratory [PSL].)

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

Fig. 2.50. Zonal mean terrestrial evaporation anomalies (mm month⁻¹; relative to 1991–2020). (Source: Global Land Evaporation Amsterdam Model version 4 [GLEAM4].)
America (section 2b1). The largest negative anomalies occurred in the latitudinal band from 0° to 30°S, and they reflect the extraordinary drought conditions experienced in South America and southern Africa in 2024 (sections 2d11, 2d12). Drought conditions tend to be associated with high atmospheric water demand, which initially enhances evaporation and accelerates soil desiccation. However, as soil moisture decreases, evaporation rates decline, leading to negative evaporation anomalies in later stages of the drought event (Miralles et al. 2019). This pattern is reflected in the positive evaporation anomalies at those latitudes during the first half of 2024, which then transitioned into negative anomalies in the second half (Fig. 2.50).

In terms of global patterns, there was a mix of positive and negative evaporation anomalies across different regions in 2024, reflecting a complex interplay of meteorological variables and land surface processes (Plate 2.1x). As expected during El Niño years, lower-than-usual evaporation occurred as a result of soil moisture limitations in southern Africa and most of South America; as mentioned above, those soil moisture limitations were caused by persistent drought events (Marengo et al. 2024). On the other hand, the lower-than-usual evaporation in western North America is less typical of El Niño conditions, and was also triggered by precipitation scarcity (section 2d5). Most of the world was, however, dominated by positive evaporation anomalies, as expected given the high temperatures in 2024 (Plate 2.1x). Major tropical forested regions experienced higher evaporation than normal, as the high atmospheric demand for water increased transpiration despite the negative anomalies in precipitation leading to lower-than-usual interception loss (i.e., water evaporating directly from wet canopies during and after precipitation events).

The evaporation estimates used in this analysis were obtained from the Global Land Evaporation Amsterdam Model version 4 (GLEAM4; Miralles et al. 2025), which integrates satellite observations and reanalysis data. GLEAM4 separately estimates the main components of terrestrial evaporation, including soil evaporation, transpiration, interception loss, and sublimation, and its estimates are routinely validated against in situ measurements from eddy-covariance flux towers and other ground-based observations. Despite recent advancements, uncertainties remain high, particularly in regions with sparse observational data. Ongoing efforts aim to refine different evaporation components by leveraging new satellite missions and improved reanalysis products. Future developments are expected to leverage emerging technologies from thermal missions—such as Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS; Fisher et al. 2020) and Thermal Infrared Imaging Satellite for High-resolution Natural Resource Assessment (TRISHNA; Lagouarde et al. 2018), along with hyper-resolution optical remote sensing from CubeSats (McCabe et al. 2017)—to improve global evaporation monitoring and understanding of its response to climate change.
## 1. MEAN SEA LEVEL PRESSURE AND RELATED MODES OF VARIABILITY

—D. Fereday, D. Campos, and G. Macara

Mean sea level pressure (MSLP) variability is characterized by large-scale modes that drive weather and climate anomalies and extremes. These modes include the Arctic Oscillation, the North Atlantic Oscillation (NAO), and the Pacific–North American (PNA) pattern in the Northern Hemisphere (NH) as well as the Southern Annular Mode (SAM; also known as the Antarctic Oscillation) in the Southern Hemisphere (SH; Kaplan 2011). Because of its direct tropical impact and important extratropical teleconnections to both hemispheres (Capotondi et al. 2015; Yeh et al. 2018), the El Niño–Southern Oscillation (ENSO) is one of the most significant global climate drivers (see section 4b for details).

In the NH, the NAO index fluctuated through the winter into early spring (December–March), remaining overall fairly neutral (Figs. 2.51a,e). An important driver of winter circulation variability in the NH is the stratospheric polar vortex. The vortex was unusually variable in winter 2023/24, with two major sudden stratospheric warmings (SSWs) occurring in January and March (Fig. 2.51e). Such events tend to favor a negative NAO (Baldwin and Dunkerton 2001), leaving northern Europe colder and drier, and southern Europe milder and wetter. However, this does not always occur (Kodera et al. 2016), and neither SSW in 2024 produced a strong negative NAO response (Lee at al. 2025). Nevertheless, the NH temperature anomalies for January–March 2024 resembled those seen in other El Niño winters that experienced an SSW (Ciasto and Butler 2024), with northeast North America having been warmer and southwest North America and northern Eurasia having been cooler. In NH spring, low-pressure anomalies over western Europe

(Fig. 2.51b) were consistent with increased precipitation (see section 7f2). In NH summer, MSLP anomalies in North America remained weak (Fig. 2.51c). The summer NAO, an important driver of summer European climate (Folland et al. 2009), was also near-neutral. In December 2024, the PNA was positive, as was the winter NAO, consistent with warmer conditions in the eastern United States and drier conditions in the western United States (see section 7b2).

The Southern Oscillation Index (SOI), calculated by the MSLP difference between Tahiti and Darwin, highlights the atmospheric component of ENSO (Allan et al. 1996; Kaplan 2011). During 2024, the SOI transitioned slowly from negative to positive values through the year, associated with the decay of El Niño and neutral conditions in the second half of the year that gave way to La Niña-like conditions in the tropical Pacific Ocean by the end of 2024 (see section 4b for details). During the austral winter, with neutral SOI conditions, a pattern similar to the Pacific–South American (PSA) pattern (O’Kane and Franzke 2025) developed in the South Pacific, allowing the presence of an area of above-normal MSLP centered over the Bellingshausen Sea (Fig. 2.52c). This atmospheric blocking pattern was associated with wetter-than-normal conditions in south-central Chile during the winter (e.g., Rutllant and Fuenzalida 1991; Campos and Rondanelli 2023; see also Fig. 2.46; see section 7d3) despite the

<figure><img src="image_97.png" /><figcaption>Fig. 2.51. Northern Hemisphere mean sea level pressure (MSLP) for 2024. Panels (a)–(d): seasonal mean MSLP for Dec 2023 to Nov 2024. Fields are anomalies with respect to a 1991–2020 climatology, expressed as percentiles. (Source: ERA5.) Panel (e): daily North Atlantic Oscillation (NAO) index for Dec 2023 to Mar 2024 defined as Azores minus Iceland MSLP. Black lines show the dates of the two major sudden stratospheric warmings. (Source: National Centers for Environmental Prediction reanalysis version 1 [NCEPv1].)</figcaption></figure>
midterm megadrought (Garreaud et al. 2020). MSLP was also above-normal over and to the east of New Zealand during austral winter, associated with drier-than-normal conditions over the country during June and July and the country's third-warmest winter on record.

The annual MSLP anomalies in the SH (Plate 2.1y) resemble the positive phase of the SAM, which is the leading mode of extratropical variability in the SH (Fogt and Marshall 2020), explaining up to 34% of the variance of the extratropical atmospheric circulation. Two prominent centers of positive anomalies were observed in the extratropics: one in the southern Pacific Ocean and one in the southern Indian Ocean. At a seasonal scale, positive SAM conditions prevailed during the first months of the year where El Niño conditions were present (74% of the days between January and April; Figs. 2.52a,e). In the austral autumn, MSLP was higher than normal south of Australia, with below-average rainfall across southern parts of the country. In contrast, significant flooding affected large parts of central and northern parts of Australia, associated mostly with a monsoon trough (see section 4f) and Severe Tropical Cyclone Megan in March (see section 4g7). More frequent southwesterly winds than normal contributed to New Zealand observing its coolest autumn since 2012. During the austral winter (June–August), a period of nearly 45 consecutive days with the SAM in the negative phase was registered around August (Fig. 2.52e), associated with the positioning of the blocking high in the Bellingshausen Sea and an increase of the subtropical jet stream. From September to December, the SAM was variable, spending approximately half of the time in each phase (Fig. 2.52e).

## 2. LAND AND OCEAN SURFACE WINDS

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

Northern Hemisphere land surface wind speeds at ~10 m above ground in 2024 were generally lower with respect to the 1991–2020 climatology (Plate 2.1z), with an anomaly (–0.034 m s⁻¹) similar to that reported for 2023 (Azorin-Molina et al. 2024; Table 2.9). The most notable spatial feature in the wind speed anomalies is an interhemispheric asymmetry, characterized by negative values in all northern regions (except for Central Asia) and positive values in South America. The “reversal” observed around the 2010s (Zeng et al. 2019), following decades of “stilling” (McVicar et al. 2012), is weakening as shown in Fig. 2.53a. In recent years, weak variations have been observed in the frequency of moderate (>3 m s⁻¹, Fig. 2.53c) and strong (>10 m s⁻¹, Fig. 2.53d) winds, although a declining trend persists for both categories over the period 1973–2024.

Land surface winds are assessed by comparing observations with reanalyses: 1) the Met Office Hadley Centre Integrated Surface Dataset 3 (HadISD3) observational dataset (1973–2024; Dunn et al. 2012, 2016; Dunn 2019); 2) the Global Historical Climatology Network hourly (GHCNh) observational dataset (1974–2023; Menne et al. 2025); 3) ERA5 (1979–2024; Hersbach et al. 2020; Bell et al. 2021); and 4) MERRA-2 (1980–2024; Gelaro et al. 2017). Atmospheric reanalyses can generally reproduce the climatology of station-based wind observations; however, they tend to underestimate the magnitude of observed anomalies and fail to accurately capture the multidecadal variability, even though their performance has consistently improved since the

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

Fig. 2.52. Southern Hemisphere mean sea level pressure (MSLP) for 2023/24. Panels (a)–(d): seasonal-mean MSLP for Dec 2023 to Nov 2024. Fields are anomalies with respect to a 1991–2020 climatology. (Source: ERA5.) Panel (e): daily Antarctic Oscillation (AAO) index for Jan–Dec 2024. (Source: NOAA National Center for Environmental Prediction).
mid-1990s (Fig. 2.53b; e.g., Torralba et al. 2017; Wohland et al. 2019).

Over the period 1979–2024, terrestrial wind speed declined (–0.052 m s⁻¹ decade⁻¹) across all regions in the NH (Table 2.9), with the strongest decline observed in North America (–0.069 m s⁻¹ decade⁻¹) and the weakest in East Asia (–0.027 m s⁻¹ decade⁻¹). The interhemispheric asymmetry in the long-term changes of wind speeds persists (Zha et al. 2021): despite limited SH land observations, positive trends are reported in South America (+0.054 m s⁻¹ decade⁻¹). This dipole in trend direction between hemispheres is partly captured by ERA5 (Fig. 2.54a).

In 2024, globally averaged ocean wind speed anomalies (relative to the 1991–2020 base period) were opposite in sign compared to land surfaces, with slightly positive values for radiometers (Remote Sensing Systems [RSS], +0.051 m s⁻¹) and close to zero for scatterometers (Advanced Scatterometer [ASCAT], –0.003 m s⁻¹) and reanalysis (ERA5, +0.006 m s⁻¹); see Fig. 2.55. Satellite wind datasets from RSS include the merged radiometers (Special Sensor Microwave/Imager [SSM/I] series, Special Sensor Microwave Imager/Sounder [SSMIS] series, Advanced Microwave Scanning Radiometer 2 [AMSR2] and AMSR for the Earth Observing System [AMSR-E], Tropical Rainfall Measuring Mission Microwave Imager [TMI], WindSat, and Global Precipitation Measurement Microwave Imager [GMI]), and the Quick Scatterometer (QuickSCAT) and ASCAT scatterometers (Wentz 1997, 2015; Wentz et al. 2007, 2024;

<figure><img src="image_99.png" /><figcaption>Fig. 2.53. Land surface Northern Hemisphere (20°N–70°N) and regional surface wind speed anomaly time series (m s⁻¹; 1991–2020 reference period). Panel (a) shows the Met Office Hadley Centre Integrated Surface Dataset 3 (HadISD3) observational dataset (1973–2024) in solid lines and Global Historical Climatology Network hourly (GHCNh; 1974–2023) in dashed lines, and (b) ERA5 (1970–2024) and MERRA-2 (1980–2024). The lower panels show HadISD3 occurrence frequencies (% yr⁻¹) for wind speeds (c) >3 m s⁻¹ and (d) >10 m s⁻¹, with the same legend as in (a).</figcaption></figure>

<table><caption>Table 2.9. Northern Hemisphere (20°N–70°N) and regional statistics for land surface wind speed (m s⁻¹) using the observational HadISD3 dataset for 1979–2024.</caption><thead><tr><th>Region</th><th>Mean Wind Speed 1991–2020 (m s<sup>-1</sup>)</th><th>Wind Speed Anomaly 2024 (m s<sup>-1</sup>)</th><th>Wind Speed Trend 1979–2024 (m s<sup>-1</sup> decade<sup>-1</sup>)</th><th>Confidence Interval 5% (m s<sup>-1</sup> decade<sup>-1</sup>)</th><th>Confidence Interval 95% (m s<sup>-1</sup> decade<sup>-1</sup>)</th><th>Number of Stations</th></tr></thead><tbody><tr><td>Northern Hemisphere</td><td>3.297</td><td>-0.034</td><td>-0.052</td><td>-0.066</td><td>-0.040</td><td>2811</td></tr><tr><td>North America</td><td>3.645</td><td>-0.075</td><td>-0.069</td><td>-0.084</td><td>-0.052</td><td>845</td></tr><tr><td>Europe</td><td>3.640</td><td>-0.048</td><td>-0.049</td><td>-0.067</td><td>-0.035</td><td>866</td></tr><tr><td>Central Asia</td><td>2.741</td><td>+0.052</td><td>-0.065</td><td>-0.098</td><td>-0.039</td><td>303</td></tr><tr><td>East Asia</td><td>2.720</td><td>-0.036</td><td>-0.027</td><td>-0.041</td><td>-0.014</td><td>538</td></tr><tr><td>South America</td><td>3.451</td><td>+0.116</td><td>+0.054</td><td>+0.039</td><td>+0.072</td><td>101</td></tr></tbody></table>
Ricciardulli and Wentz 2015; Ricciardulli and Manaster 2021). The ocean wind anomaly map for 2024 (Plate 2.1z) shows notable regional features, including: 1) a large negative anomaly in the tropical Atlantic, similar to 2023 but slightly reduced in both extent and magnitude; 2) a positive anomaly in the tropical Pacific, indicating the transition from El Niño to La Niña-like conditions; 3) slightly negative anomalies over the Indian Ocean; and 4) a dominance of moderate positive anomalies over the Southern Ocean, Greenland Sea, and North Pacific Ocean. The comparison between the RSS ASCAT anomaly time series and maps and ERA5 shows they are consistent both temporally and spatially. In the long term (1988–2024), ocean winds exhibit slightly positive trends (Fig. 2.55) for both RSS (+0.030 m s⁻¹ decade⁻¹) and ERA5 (+0.030 m s⁻¹ decade⁻¹) averaged over 60°S–60°N, and display similar spatial patterns (Fig. 2.54a) as those shown for the anomalies. Spatially, the trend map reveals a positive trend in the central Pacific and Southern Ocean, consistently observed in many of the past years.

The causes behind the opposite land (negative) and ocean (positive) anomalies are not yet attributable (see discussion in McVicar et al. 2012, for example). Long-term trends and multidecadal variability (stilling versus reversal) of land and ocean surface winds have primarily been attributed to decadal ocean–atmosphere oscillations (Zeng et al. 2019). Anthropogenic global warming is also driving the opposite trends between the Northern (negative) and Southern (positive) Hemisphere (Zha et al. 2021) due to changes in the pressure gradient (Zhang et al. 2021). Changes in land cover (Minola et al. 2022), data encoding issues (Dunn et al. 2022a), and biases in anemometer readings (Azorin-Molina et al. 2023; Liu et al. 2024) are likely secondary drivers of the observed trends and variability.

### 3. UPPER-AIR WINDS

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

Global atmospheric circulation patterns from the surface to stratospheric levels can be strongly impacted by oceanic temperatures and ENSO state. During October–December (OND; Plate 2.1aa) in 2024, a weakly negative Indian Ocean dipole (IOD) and La Niña conditions were indicated by the positive–negative 850-hPa zonal wind dipole over the equatorial

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

<figure><img src="image_101.png" /><figcaption>Fig. 2.54. Wind speed trends (m s⁻¹ decade⁻¹) from the (a) ERA5 output over land/ice and Remote Sensing System (RSS) satellite radiometers (Special Sensor Microwave/Imager [SSM/I], Special Sensor Microwave Imager/Sounder [SSMIS], Tropical Rainfall Measuring Mission Microwave Imager [TMI], AMSR2 [Advanced Microwave Scanning Radiometer 2], ASMR-E [Advanced Microwave Scanning Radiometer for the Earth Observing System], WindSat, and Global Precipitation Measurement Microwave Imager [GMI]) over ocean for 1988–2024 (shaded areas), and (b) the observational Met Office Hadley Centre Integrated Surface Dataset 3 [HadISD3] dataset over land (circles) for 1979–2024.</figcaption></figure>

<figure><img src="image_102.png" /><figcaption>Fig. 2.55. Annual global anomalies of global mean wind speed (m s⁻¹; 1991–2020 base period) over the ocean from merged satellite radiometers, the Quick Scatterometer (QuickSCAT) and Advanced Scatterometer (ASCAT), and ERA5 and MERRA-2 reanalyses.</figcaption></figure>
Indian and western Pacific Ocean basins, feeding the convective maximum over the Indonesian region with moisture. However, eastward wind anomalies covered the Niño-3.4 region of the eastern Pacific, along with westward wind anomalies near the South American coast, converging on positive sea surface temperature (SST) anomalies in the far eastern Pacific.

The year 2024 as a whole was relatively normal; the global wind anomaly was slightly positive (Fig. 2.53a). Trends for the period 1991–2024 were negative, with MERRA-2 having been the only outlier. In the Southern Ocean, the Antarctic Oscillation (AOO) anomaly turned from positive to moderately negative in the second half of 2024, leaving the significantly positive trends of the past three decades practically unchanged also for 1991–2024 (from 0.21 [MERRA, JRA55] to 0.23 [JRA-3Q] and 0.32 [ERA5] m s⁻¹ decade⁻¹, not shown).

The imprint of ENSO in eastward wind anomalies of the Niño-3.4 region (5°S–5°N, 170°W–120°W), where El Niño events lead to significant reduction of the predominant easterlies (e.g., Trenberth 1997), is considered. Since the 1982/83 El Niño, the eastward anomalies appear to have weakened, even during strong El Niño events, in terms of temperature. This has led to a significant negative trend of ~0.4±0.2 m s⁻¹ decade⁻¹ (across reanalyses) over the period 1991–2024 (Fig. 2.56). The 2023/24 El Niño, which coincided with a negative Pacific Decadal Oscillation (PDO) index, had a relatively weak westerly wind anomaly despite simultaneous global record temperatures. Generally, the westerly wind anomaly maxima appear stronger if the PDO and ENSO are in phase, in accord with Yoon and Yeh (2010).

Looking further back, it is noteworthy that the apparent “quiet” period from the strong 1940–42 El Niño until 1982, in terms of Niño-3.4 westerly wind anomalies, was associated with a mostly negative PDO. This is considered a robust result in terms of data quality, at least at the 850-hPa level, since there are enough surface wind data assimilated, and there are also long radiosonde station records nearby (e.g., Marshall Islands back to 1948).

Figure 2.57b depicts anomalies in pressure vertical velocity and zonal/vertical velocities averaged over the region spanning from 10°S to 10°N for the OND season. Although the conventional Oceanic Niño Index (ONI) indicated neutral ENSO conditions until November 2024 (see section 4b), other indices, such as the relative ONI (L’Heureux et al. 2024) and the Multivariate

ENSO Index (https://psl.noaa.gov/enso/mei/), signaled La Niña conditions beginning in mid-2024. Indeed, the 2024 OND season displayed a pronounced descending motion over the central/eastern Pacific, similar to a classical La Niña pattern (see Fig. 2.49 in Mayer et al. 2023). At the same time, a strong ascending motion was observed over the Indo-Pacific Warm Pool, while descending motion prevailed over the tropical western Indian Ocean.

Anomalies of upper-tropospheric circulation in boreal autumn 2024, as reflected by 200-hPa velocity potential and divergent wind anomalies (Fig. 2.57a), were also consistent with La Niña conditions. Anomalously strong divergence prevailed in the Indo-Pacific Warm Pool, indicating enhanced convection, while anomalously strong convergence was seen in the central to eastern tropical Pacific, indicating anomalously strong sinking. Anomalous upper-air divergence extending well into the eastern Indian Ocean was consistent with a weakly positive IOD. The magnitude of 200-hPa divergent wind anomalies over the equatorial Pacific was weaker than during the protracted La Niña of 2020–22, consistent with the only

<figure><img src="image_103.png" /></figure>

Fig. 2.56. Eastward wind speed anomalies (m s⁻¹; 1991–2020 climatology) averaged (a) globally and (b) over the Nino-3.4 region (5°S–5°N, 170°W–120°W), as depicted from two fourth-generation (MERRA-2 [Gelaro et al. 2017] and JRA-55 [Kobayashi et al. 2015]) and two fifth-generation (the Japanese Reanalysis for Three Quarters of a Century [JRA-3Q; Kosaka et al. 2024] and ERA5 [Hersbach et al. 2020] reanalyses). The Pacific Decadal Oscillation (PDO) index from the NOAA Physical Sciences Laboratory (PSL) Ensemble SST product is shown in panel (b) with the zero shifted for better visibility.
weak negative SST anomalies in OND 2024. The enhanced upper-tropospheric divergence in the Caribbean and central North Atlantic was consistent with the active 2024 hurricane season from mid-September to mid-November (see section 4g2 for details). The weak anomalous convergence over the east central tropical Pacific region was in line with the below-average Pacific hurricane activity (see section 4g3).

The quasi-biennial oscillation in 2024 was near-normal. The stratospheric zonal-mean zonal wind was dominated by a westerly shear zone, which descended from the 12-hPa level down to 80 hPa at about $1.37\pm0.87$ km month$^{-1}$, which is faster than average, but not unusual. The maximum easterly amplitude occurred in February at 20hPa (–31.6 m s$^{-1}$) and the maximum westerly amplitude at 40 hPa in October (13.8 m s$^{-1}$). This amplitude is on the weaker end for a westerly shear zone, but still not an outlier in the historical record. The new easterly formed in October and started descending from the 10-hPa level.

## 4. NOVEL LIGHTNING FLASH DENSITIES FROM SPACE

—M. Füllekrug, E. Williams, C. Price, S. Goodman, R. Holzworth, S.-E. Enno, and B. Viticchie

The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) recently launched the Lightning Imager (LI) onboard the Meteosat Third Generation (MTG) geostationary satellite in support of climate monitoring and lightning warning alerts as a proxy

measure for high-impact weather. MTG-LI started to deliver publicly available lightning flash occurrence times and locations on 4 July 2024, 1500 UTC (EUMETSAT 2024). The optical emissions from lightning flashes are recorded with four cameras with slightly overlapping field of views which cover Europe, Africa, the Middle East, the eastern part of South America, and a large part of the Atlantic Ocean (EUMETSAT 2023). South America is also covered by the Geostationary Lightning Mapper (GLM) on the Geostationary Operational Environmental Satellite 16 (GOES-16) (Rudlosky and Virts 2021). The field of view of MGT-LI extends from 80°S to 80°N latitude to cover part of the Antarctic and Arctic, where lightning occurrence may increase as a result of fast rising near-surface temperatures (Holzworth et al. 2021). The lightning flash densities were accumulated in a map with a spatial resolution of $0.1^\circ \times 0.1^\circ$ from July 2024 to January 2025 (Fig. 2.58). These lightning flash densities are largest ~177 fl km$^{-2}$ yr$^{-1}$ along the western side of the Virunga Mountains as part of the Albertine

<figure><img src="image_104.png" /><figcaption>Fig. 2.57. (a) 200-hPa (colors) velocity potential ($\times 10^6$ m$^2$ s$^{-1}$) and (arrows) divergent wind anomalies (m s$^{-1}$) with respect to the 1991–2020 climatology for Oct–Dec 2024; stippling indicates regions with anomalies exceeding 1.65 standard deviations of the seasonal anomalies. Based on ERA5 data. (b) Anomalies of pressure vertical velocity (shaded; units: $\times 10^{-2}$ Pa s$^{-1}$) and u/w anomalies (arrows) averaged over the region 10°S–10°N (zonal [divergent + rotational] wind anomaly [u] unit: m s$^{-1}$, pressure vertical velocity anomaly [w] unit: $\times 10^{-2}$ Pa s$^{-1}$).</figcaption></figure>

<figure><img src="image_105.png" /><figcaption>Fig. 2.58. Lightning flash densities (fl km$^2$ yr$^{-1}$) calculated from the European Organisation for the Exploitation of Meteorological Satellites' (EUMETSAT) Lightning Imager (LI) on the Meteosat Third Generation (MTG) geostationary satellite from Jul 2024 to Jan 2025. The largest lightning flash densities, ~177 fl km$^2$ yr$^{-1}$, are found west of the Virunga Mountains and Lake Kivu along the border between East and Central Africa. Many lightning flashes are detected over the Atlantic and Indian Oceans as part of thunderstorm systems driven by the trade winds.</figcaption></figure>
Rift and Lake Kivu in Africa, where Rwanda, the Democratic Republic of Congo, and Uganda meet. Although lightning is mainly a continental phenomenon (Füllekrug et al. 2022), relatively large flash densities were observed over the Atlantic and Indian Oceans. These lightning flash densities follow the outflow of thunderstorms from the continents, for example westerlies at midlatitudes and easterlies at low latitudes. It is thought that many of the optical pulses recorded by MTG-LI are caused by in-cloud (IC) lightning flashes, which are more common than cloud-to-ground (CG) lightning flashes (Rakov and Uman 2003).

The lightning flash densities over the oceans and the continents have different seasonal variability. Figure 2.59 shows the differences between lightning flash densities during July and December 2024. Over the continents, the lightning flash densities migrate seasonally following the solar insolation, which increases surface temperature and thereby facilitates the development of deep convection. Over the oceans, the lightning flash densities are larger in December compared to July. Four key areas can be distinguished: 1) the Mediterranean Sea, the relative warmth of which interacts with cold European air to enhance instability and deep convection during the winter months, and where

numerous particularly intense lightning discharges commonly known as superbolts occur (Holzworth et al. 2019); 2) the Cape Verde Islands, possibly linked to the Atlantic winter storm tracks; 3) the Mid-Atlantic off the coast of western Africa linked to the Intertropical Convergence Zone (ITCZ); and 4) the southern Atlantic off the coast of southern Brazil, due to cooler continental air in winter (July) moving out over the warm Brazil and South Atlantic currents. It is thought that the increase of lightning flash densities over the oceans are caused by the larger heat storing capacity of the oceans when compared to the continents during the winter months, and that warmer ocean currents might assist the initiation of deep convection (Virts et al. 2015; Füllekrug et al. 2002). In this context, it is interesting to note that Lake Victoria (Africa) exhibits a different diurnal cycle when compared to the land around it (Virts and Goodman 2020). It is expected that such regional and seasonal climatological differences will become more evident when MTG-LI lightning flashes accumulate over time, allowing for more detailed analyses with particular emphasis on annual anomalies. Thunderstorms over Lake Victoria are an important area of study because of the risks to local fishers (Thiery et al. 2016; Roberts et al. 2022). Another interesting area of research with EUMETSAT's novel lightning flash densities is the propagation of atmospheric instabilities associated with thunderstorms and lightning across the Atlantic, which have the potential to develop into hurricanes in the Caribbean and the southeastern United States (Price et al. 2009).

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

<figure><img src="image_107.png" /><figcaption>Fig. 2.59. Lightning flash densities (fl km$^2$ yr$^{-1}$) for (a) Jul 2024 and (b) Dec 2024.</figcaption></figure>
# 1. EARTH RADIATION BUDGET AT TOP-OF-ATMOSPHERE

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

The Earth radiation budget (ERB) at top-of-atmosphere (TOA) is defined by 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 vital in understanding Earth's climate system and global temperature variations. Over the past two decades, the climate system has amassed a large net positive imbalance, representing a sizeable surplus of energy to the Earth-atmosphere system (Loeb et al. 2021, 2022; von Schuckmann et al. 2023). This net positive imbalance continued in 2024 with a global annual mean of +0.85 W m⁻² despite the transition from El Niño to La Niña-like conditions.

An analysis of the Clouds and the Earth's Radiant Energy System (CERES) TOA ERB measurements (Table 2.10) shows that the global annual-mean OLR increased by 0.35 W m⁻² relative to 2023, and RSW by 0.65 W m⁻², while the corresponding TSI component remained unchanged in 2024 relative to 2023 (rounded to the nearest 0.05 W m⁻²). As a consequence of the increase in both OLR and RSW, the global annual-mean net radiation decreased by 1.00 W m⁻². Figure 2.60 shows regional annual-mean difference maps in OLR and RSW between 2024 and 2023. The largest reductions in OLR and increases in RSW, indicative of the increases in deep convection, are observed over the tropical Indian Ocean, Australia, the region just east and southeast of

<figure><img src="image_108.png" /><figcaption>Fig. 2.60. Annual average top-of-atmosphere flux differences (W m⁻²) between 2024 and 2023 for (a) outgoing longwave radiation (OLR) and (b) reflected shortwave radiation (RSW). The annual mean maps for 2024 were derived after adjusting Dec 2024 Fast Longwave and Shortwave Radiative Fluxes (FLASHFlux) version 4C data using the difference between Clouds and the Earth's Radiant Energy System (CERES) EBAF Ed4.2.1 and CERES FLASHFlux version 4C data in 2023.</figcaption></figure>

<table><caption>Table 2.10. Global annual mean top-of-atmosphere (TOA) radiative flux changes between 2023 and 2024, the 2024 global annual mean radiative flux anomalies relative to their corresponding 2001–23 mean climatological values, the mean 2001–23 climatological values, and the 2-sigma interannual variabilities of the 2001–23 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 (Net). 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>(2024 minus 2023)<br>(W m<sup>-2</sup>)</th><th>2024 Anomaly<br>(Relative to 2001–23)<br>(W m<sup>-2</sup>)</th><th>Climatological Mean<br>(2001–23)<br>(W m<sup>-2</sup>)</th><th>Interannual Variability<br>(2001–23)<br>(W m<sup>-2</sup>)</th></tr></thead><tbody><tr><td>OLR</td><td>+0.35</td><td>+1.15</td><td>240.40</td><td>±0.70</td></tr><tr><td>TSI</td><td>+0.00</td><td>+0.25</td><td>340.20</td><td>±0.20</td></tr><tr><td>RSW</td><td>+0.65</td><td>-0.85</td><td>98.90</td><td>±1.20</td></tr><tr><td>ASR</td><td>-0.65</td><td>+1.10</td><td>241.30</td><td>±1.25</td></tr><tr><td>Net</td><td>-1.00</td><td>-0.05</td><td>0.90</td><td>±0.95</td></tr></tbody></table>
the Australian continent, and the South Pacific Convergence Zone. The largest increases in OLR and decreases in RSW are observed to cover the entire extent of the equatorial Pacific Ocean. There is also an area of increased RSW off the west coast of South America that does not have a corresponding OLR decrease; this may be due to a thickening of stratocumulus clouds there. These large regional changes represent the response of the climate system as it transitioned from El Niño into La Niña-like conditions. Additional studies are required to understand fully the effects of this El Niño-Southern Oscillation (ENSO) transition as well as other climate drivers on the observed regional changes in the Earth radiation budget during the past year. While the 2024 global annual-mean anomalies, relative to their 2001–23 climatology, for TSI, RSW, and net anomalies (Table 2.10) were near or within their corresponding 2-sigma interannual variability, the 2024 OLR anomaly continued to be outside the range of natural variability observed during the past two decades. This large 2024 OLR anomaly illustrates that the Earth climate system continues to labor excessively to remove the large surplus of energy currently stored within its system.

The global monthly-mean TOA OLR anomaly varied between +0.80 W m⁻² and +1.55 W m⁻² in 2024 (Fig. 2.61). This variability is consistent with NOAA's High-resolution Infrared Radiation Sounder (HIRS; Lee and NOAA CDR Program 2018) and NASA's Atmospheric Infrared Sounder (AIRS; Susskind et al. 2012) OLR datasets (not shown). The 2024 global annual-mean TOA OLR anomaly was +1.15 W m⁻². The global monthly-mean TOA absorbed solar radiation (ASR, determined from TSI minus RSW) anomaly also remained positive between +0.55 W m⁻² and +1.65 W m⁻² in 2024. The 2024 global annual-mean TOA ASR anomaly was +1.10 W m⁻². The global monthly-mean TOA total net anomaly, which is calculated from ASR anomaly minus OLR anomaly, varied between -1.00 and +0.35 W m⁻² in 2024. The global annual-mean TOA total net anomaly for 2024 was -0.05 W m⁻². Further analyses are needed to understand the significance 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 are from two different CERES datasets. The data for March 2000–November 2024 are based on the CERES Energy Balanced and Filled (EBAF) Ed4.2.1 product (Loeb et al. 2009, 2012, 2018), which were constructed with measurements from the CERES instruments (Wielicki et al. 1996, 1998) aboard Terra, Aqua, and NOAA-20 spacecraft. The data for December 2024 are from the CERES Fast Longwave and Shortwave Radiative Fluxes (FLASHFlux) version 4C product (Kratz et al. 2014), which were 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, ±0.45 W m⁻², and ±0.50 W m⁻² for the OLR, TSI, RSW, and total net radiation, respectively (rounded to the nearest 0.05 W m⁻²).

<figure><img src="image_109.png" /><figcaption>**Fig. 2.61.** 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; bottom) from Mar 2000 to Dec 2024. Anomalies are relative to their calendar month climatology (2001–23). The time series show the Clouds and the Earth's Radiant Energy (CERES) EBAF Ed4.2.11-Deg data (Mar 2000–Nov 2024) in solid red line and the CERES Fast Longwave and Shortwave Radiative Fluxes (FLASHFlux) version 4C data (Dec 2024) in solid blue dot; see text for merging procedure (Sources: https://ceres-tool.larc.nasa.gov/ord-tool/jsp/EBAFTOA421Selection.jsp; https://ceres-tool.larc.nasa.gov/ord-tool/jsp/FLASH_TISASelection.jsp).</figcaption></figure>
—J. A. Augustine, L. Soldo, K. O. Lantz, A. Baron, K. Smith, E. Asher, and J.-P. Vernier

Since 2018, the Northern Hemisphere (NH) lower stratosphere (LS) has been continuously infused with aerosols from a series of volcanic eruptions (e.g., Raikoke, Ulawun, La Soufriere, Hunga) and wildfire events (e.g., Pacific Northwest; Boone et al. 2020). These perturbations are evident from both a composite, multi-platform analysis from the Global Space-based Stratospheric Aerosol Climatology (GloSSAC; Thomason et al. 2018; Kovilakam et al. 2020) and from aerosol extinction time series imagery from the Stratospheric Aerosol and Gas Experiment (SAGE-III) limb sounder on board the International Space Station (ISS). Although the Hunga eruption occurred in the Southern Hemisphere (20.54°S, its stratospheric plume extended to the latitude of the Mauna Loa Observatory (MLO, 19.536°N, 155.576°W, elevation 3397 m a.s.l.) on the Big Island of Hawaii (Augustine et al. 2024). These events have kept the apparent atmospheric transmission at MLO near or below 0.93 since 2018.

Broadband pyrheliometer measurements of the direct solar beam at MLO have been used to derive atmospheric transmission since 1958 (Ellis and Pueschel 1971). The high altitude and pristine environment there makes transmission a fitting proxy of the extent and variability of stratospheric aerosols. Bodhaine et al. (1981) demonstrated that the principal tropospheric influence on the transmission is the perennial passage of dust from springtime storms in Asia.

The complete time series (Fig. 2.62) begins with a very clean period, until the Agung eruption in 1963. That period serves as a baseline-level clean stratosphere, as there were no impactful volcanic events from the mid-1930s to 1963 (Ammann et al. 2003; Sato et al. 1993). Since Agung, a series of eruptions have kept the transmission generally lower than pre-1963 baseline levels.

The extended transmission reduction from 2018 through 2024 is evident in Fig. 2.62. New data for 2024, shown in the inset of Fig. 2.62, have an annual mean of 0.926±0.00387. The year began with a relatively high transmission of ~0.93 in January and February as the LS was recovering from the Hunga eruption. Reduction of the transmission in March and April was probably caused by dust because, after a lull in 2023, several strong spring storms in the desert regions of northwest China and Mongolia brought dust to Japan and Korea and then to Mauna Loa. The latter is supported by the aerosol optical depth (AOD) product from NASA's Moderate Resolution Imaging

Spectroradiometer (https://earthobservatory.nasa.gov/global-maps/MODAL2_M_AEROD). It shows relatively high AOD over Hawaii from March through May, followed by dramatic clearing in June as easterly tropospheric winds became more persistent. The sharp minimum in May was likely due to both Asian dust and stratospheric plumes from explosive eruptions of Ruang in Indonesia on 16 and 29 April 2024, which sent eruptive plumes 21,000 m and 19,000 m above mean sea level, well into the LS. Other NH explosive eruptions in 2024, e.g., Kanlaon in the Philippines, did not penetrate the tropopause. An aerosol layer in the LS centered at ~5°S is apparent in a latitudinal cross section of the aerosol extinction coefficient from SAGE for August 2024 (https://sage.nasa.gov/2024/09/sage-iii-iss-science-highlight/). Residual Hunga aerosol was supplemented in the tropics by Ruang and now extends beyond 30°N, overlying MLO.

<figure><img src="image_110.png" /><figcaption>**Fig. 2.62.** Apparent transmission at Mauna Loa, Hawaii, from 1958 through 2024. Red dots are monthly averages of morning transmission and the black curve is a locally weighted scatterplot smoothing (LOWESS) fit with a six-month smoother applied. Inset shows new data for 2024. Horizontal dashed lines represent the average transmission of the clean period before the eruption of Agung (Ammann et al. 2003; Sato et al. 1993). The shaded area represents the period from Dec 2022 through Jun 2023 when the station was down due to the eruption of Mauna Loa in late Nov 2022.</figcaption></figure>
<figure><img src="image_111.png" /></figure>

Fig. 2.63. Particle number concentration profiles at standard temperature and pressure (STP; number STP cm⁻³) from the Portable Optical Particle Spectrometer (POPS) aboard the NOAA Global Monitoring Laboratory balloon platform launched from Hilo, Hawaii, ~60 km east of Mauna Loa. The shaded area highlights lower-stratosphere (LS) aerosol signals in Jun, Jul, Aug, and Oct 2024 that arose from the Ruang eruptions in Apr 2024.

Trajectories from the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT; Stein et al. 2015) reveal that the stratospheric plume from Ruang reached the longitude of MLO around 14 May. NOAA balloon sondes (Todt et al. 2023; Asher et al. 2024) from Hilo, Hawaii, ~60 km east of MLO, on 6 June, 10 July, 7 August, and 9 October carrying the Portable Optical Particle Spectrometer (POPS; Gao et al. 2016) confirm the presence of the Ruang signal between 17,000 m and 22,000 m a.s.l. (Fig. 2.63). That signal was also sampled by a NASA balloon at Baura, Brazil, on 1 June 2024 (https://science.larc.nasa.gov/balneo/). The highest aerosol number concentrations were observed in June (200 Number cm⁻³) and July (300 Number cm⁻³), with back trajectories indicating that LS signals at Hilo were from Ruang. Slowly increasing transmission after

July that is apparent in the inset of Fig. 2.62, along with the sequence of sondes in Fig. 2.63, reflect a diminishing Ruang signal to the end of the year.

Major wildfire activity in 2024 occurred in South and Central America and Canada from July through September (section 2h3). However, trajectory and the Copernicus Atmosphere Monitoring Service's (CAMS) analyses (https://atmosphere.copernicus.eu/south-america-sees-historic-emissions-during-2024-wildfire-season) suggest that smoke from those fires had little to no presence over Hawaii.
In 2024, the atmospheric burdens of the three most important long-lived greenhouse gases (LLGHGs), carbon dioxide ($CO_2$), methane ($CH_4$), and nitrous oxide ($N_2O$), showed no sign of slowing in their increases. The globally averaged $CO_2$ level at Earth's surface in 2024—as derived from remote marine boundary layer measurements made by NOAA's Global Monitoring Laboratory—reached 422.8±0.1 ppm (parts per million by moles in dry air; Fig. 2.64a; Table 2.11; uncertainties are reported as one sigma in this section), a 52% increase from the pre-industrial level of ~278 ppm (Etheridge et al. 1996). Globally averaged $CH_4$ and $N_2O$ levels reached 1930.0±0.6 ppb (parts per billion by moles in dry air) and 337.7±0.1 ppb in 2024, which are 165% and 25% increases from pre-industrial levels, respectively. Yet again, these three LLGHGs are setting record highs.

Carbon dioxide is the most important and abundant anthropogenic greenhouse gas (GHG). 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 2.4 ppm yr⁻¹ during 2011–20 (Lan et al. 2025a). The increase in $CO_2$ by 3.4 ppm from 2023 to 2024 tied with that of 2015/16 as the highest on record since systematic measurements started in the 1960s. The main driver of increasing atmospheric $CO_2$ is fossil fuel (FF) burning; overall emissions, including cement production, increased from 3.0±0.2 Pg C yr⁻¹ in the 1960s to 9.7±0.5 Pg C yr⁻¹ in the past decade (2014–23; Friedlingstein et al. 2025). Emissions in 2024 are estimated at 10.2±0.5 Pg C yr⁻¹ (Friedlingstein et al. 2025). Together with the measured atmospheric increase, it is estimated 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. 2025). 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 2015–24) with variability mostly driven by terrestrial biosphere exchange of $CO_2$. The El Niño–Southern Oscillation (ENSO) changes regional temperature and precipitation patterns and influences photosynthetic $CO_2$ uptake, respiratory release, and fires. It is the main driver of $CO_2$ interannual variability (Betts et al. 2016; Liu et al. 2017). The record-high $CO_2$ increase in 2024 was likely driven by the record-high global temperature and large fire carbon emissions during the year.

Atmospheric $CH_4$ is the second most important LLGHG, and in 2024 its globally averaged abundance at Earth's surface reached 1930.0±0.6 ppb (Lan et al. 2025b),

<figure><img src="image_112.png" /><figcaption>**Fig. 2.64.** Global mean dry-air remote surface mole fractions (approximately weekly data in blue and deseasonalized trend in black; left axis) and instantaneous growth rates (red, right axis) calculated as time derivatives of deseasonalized trend curves of (a) carbon dioxide ($CO_2$), (b) methane ($CH_4$), and (c) nitrous oxide ($N_2O$) derived from marine boundary layer measurements from the NOAA Global Greenhouse Gases Reference Network. See Dlugokencky et al. (1994) for methods. $N_2O$ data prior to 2000 are insufficient and noisy, and therefore hinder the calculation of a growth rate.</figcaption></figure>
about 2.65 times its pre-industrial level of 729±9 ppb (Mitchell et al. 2013). Global CH₄ increased by an average rate of 11.5±1.4 ppb yr⁻¹ between 1984 and 1991, followed by a smaller increase of 5.4±1.8 ppb yr⁻¹ between 1992 and 1998, and a further reduced rate near zero (0.7±3.0 ppb yr⁻¹) between 1999 and 2006. Atmospheric CH₄ growth restarted in 2007 and has accelerated since 2014, and further accelerated in 2020–24 with an average rate of increase of 12.6±2.0 ppb yr⁻¹ (Fig. 2.64b). Atmospheric CH₄ increased by 8.4±0.4 ppb from 2023 to 2024.

Atmospheric CH₄ is emitted by anthropogenic sources such as fossil fuel exploitation, livestock, waste and landfills, and rice cultivation areas, as well as natural sources such as wetlands and shallow lakes. The ongoing reduction in atmospheric δ¹³C-CH₄ since 2008 (Michel et al. 2024) indicates increased emissions from microbial sources (Basu et al. 2022), including emissions from livestock as well as natural wetlands and lakes, which have more negative δ¹³C-CH₄ signatures. Small 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 the hydroxyl radical, the main sink for CH₄, is still uncertain, but is less likely to be a major contributor (Morgenstern et al. 2025; 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 (Lin et al. 2024; L. Feng et al. 2022; Peng et al. 2022). Sustained increases in wetland CH₄ emissions may be an indication of an emerging carbon climate feedback (Nisbet et al. 2023; Zhang et al. 2023).

Nitrous oxide (N₂O) is a potent greenhouse gas with an atmospheric lifetime of 120 years (Tian et al. 2024). It is produced by microbes that rely on nitrogen from natural and agricultural soils, animal manure, and the oceans (Davidson 2009). Increased agricultural emissions related to fertilizer usage are the major source of its long-term increase (Tian et al. 2023). The average global atmospheric abundance of N₂O in 2024 was 337.7±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. 2.64c), larger than the average rate between 2010 and 2019 (1.0±0.2 ppb yr⁻¹), strongly suggesting increased emissions (Tian et al. 2023). Atmospheric N₂O increased by 1.0±0.1 ppb from 2023 to 2024.

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 approach used in the Intergovernmental Panel on Climate Change's Sixth Assessment Report (Forster et al. 2021). Increased atmospheric abundances of LLGHGs are largely responsible for increasing global temperature (Forster et al. 2023; IPCC 2013). Since the industrial era (1750), increasing atmospheric CO₂ has accounted for 64% of the increase in ERF by LLGHGs, reaching 2.33 W m⁻² in 2024 (Fig. 2.65). The increase in CH₄ contributed a 0.57 W m⁻² increase in ERF between 1750 and 2024 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 0.23 W m⁻² increase in ERF between 1750 and 2024.

<figure><img src="image_113.png" /><figcaption>Fig. 2.65. (a) Effective radiative forcing (W m⁻²) due to long-lived greenhouse gases (LLGHGs; see Table 2.11 for details on industrial gases). (b) Annual increase in effective radiative forcing (W m⁻² yr⁻¹) smoothed by a 10-year running average.</figcaption></figure>
<table><caption>Table 2.11. Summary table of long-lived greenhouse gases for 2024 (CO₂ mixing ratios are in ppm, N₂O and CH₄ in ppb, and all others in ppt).</caption><thead><tr><th>Compound Class</th><th>Industrial Designation or Common Name</th><th>Chemical Formula</th><th>ERF<sup>a</sup></th><th>Effective Rad. Efficiency (W m<sup>-2</sup> ppb<sup>-1</sup>)<sup>b</sup></th><th>Effective Rad. Forcing<sup>a</sup> (ERF or SARF) (W m<sup>-2</sup>)</th><th>Mean Surface Mole Fraction, 2024 [Change from Prior Year]<sup>c</sup></th><th>Lifetime (yrs)<sup>b</sup></th></tr></thead><tbody><tr><td>Acidic Oxide</td><td>Carbon Dioxide</td><td>CO<sub>2</sub></td><td>Y</td><td>1.33 x 10<sup>-5</sup></td><td>2.33</td><td>422.8 [3.4]</td><td></td></tr><tr><td>Alkane</td><td>Methane</td><td>CH<sub>4</sub></td><td>Y</td><td>3.89 x 10<sup>-4</sup></td><td>0.57</td><td>1930.0 [8.4]</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>3.20 x 10<sup>-3</sup></td><td>0.23</td><td>337.7 [1.0]</td><td>109</td></tr><tr><td>Chlorofluorocarbons</td><td>CFC-11</td><td>CCl<sub>3</sub>F</td><td>N(Y)<sup>e</sup></td><td>0.30</td><td>0.057 (0.063)</td><td>214.5 [-2.7]<sup>d</sup></td><td>52</td></tr><tr><td>Chlorofluorocarbons</td><td>CFC-12</td><td>CCl<sub>2</sub>F<sub>2</sub></td><td>N(Y)<sup>e</sup></td><td>0.36</td><td>0.156 (0.172)</td><td>481.5 [-3.9]<sup>d</sup></td><td>102</td></tr><tr><td>Chlorofluorocarbons</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>66.6 [-0.6]<sup>d,f</sup></td><td>93</td></tr><tr><td>Hydrochlorofluorocarbons</td><td>HCFC-22</td><td>CHClF<sub>2</sub></td><td>N</td><td>0.21</td><td>0.052</td><td>245.3 [-2.2]</td><td>11.6</td></tr><tr><td>Hydrochlorofluorocarbons</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>24.4 [-0.1]</td><td>8.8</td></tr><tr><td>Hydrochlorofluorocarbons</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>20.7 [-0.3]</td><td>17.1</td></tr><tr><td>Hydrofluorocarbons</td><td>HFC-134a</td><td>CH<sub>2</sub>FCF<sub>3</sub></td><td>N</td><td>0.17</td><td>0.022</td><td>134.7 [5.2]</td><td>13.5</td></tr><tr><td>Hydrofluorocarbons</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>7.73 [0.38]</td><td>1.5</td></tr><tr><td>Hydrofluorocarbons</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>32.4 [1.7]</td><td>52</td></tr><tr><td>Hydrofluorocarbons</td><td>HFC-125</td><td>CHF<sub>2</sub>CF<sub>3</sub></td><td>N</td><td>0.23</td><td>0.01</td><td>47.5 [4.0]</td><td>31</td></tr><tr><td>Hydrofluorocarbons</td><td>HFC-32</td><td>CH<sub>2</sub>F<sub>2</sub></td><td>N</td><td>0.11</td><td>0.003</td><td>38.2 [4.4]</td><td>5.3</td></tr><tr><td>Hydrofluorocarbons</td><td>HFC-23</td><td>CHF<sub>3</sub></td><td>N</td><td>0.19</td><td>0.007</td><td>37.8 [1.0]</td><td>228</td></tr><tr><td>Hydrofluorocarbons</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>0.24</td><td>&lt;0.001</td><td>1.03 [0.04]</td><td>8.9</td></tr><tr><td>Hydrofluorocarbons</td><td>HFC-227ea</td><td>CF<sub>3</sub>CHF<sub>CF</sub><sub>3</sub></td><td>N</td><td>0.27</td><td>&lt;0.001</td><td>2.39 [0.19]</td><td>36</td></tr><tr><td>Chlorocarbons</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>0.87 [-0.11]</td><td>5</td></tr><tr><td>Chlorocarbons</td><td>Carbon Tetrachloride</td><td>CCl<sub>4</sub></td><td>N</td><td>0.17</td><td>0.012</td><td>73.0 [-0.8]<sup>d</sup></td><td>30</td></tr><tr><td>Chlorocarbons</td><td>Methyl Chloride</td><td>CH<sub>3</sub>Cl</td><td>N</td><td>0.005</td><td>&lt;0.001</td><td>550 [5]</td><td>0.9</td></tr><tr><td>Bromocarbons</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>6.52 [0.08]</td><td>0.8</td></tr><tr><td>Bromocarbons</td><td>Halon 1211</td><td>CBrClF<sub>2</sub></td><td>N</td><td>0.31</td><td>0.001</td><td>2.75 [-0.09]</td><td>16</td></tr><tr><td>Bromocarbons</td><td>Halon 1301</td><td>CBrF<sub>3</sub></td><td>N</td><td>0.31</td><td>0.001</td><td>3.3 [0]</td><td>72</td></tr><tr><td>Bromocarbons</td><td>Halon 2402</td><td>CBrF<sub>2</sub>CBrF<sub>2</sub></td><td>N</td><td>0.33</td><td>&lt;0.001</td><td>0.390 [-0.005]</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>0.007</td><td>11.8 [0.4]</td><td>850-1280</td></tr><tr><td>Fully Fluorinated Species</td><td>PFC-14</td><td>CF<sub>4</sub></td><td>N</td><td>0.1</td><td>0.005</td><td>90.4 [1.0]</td><td>~50,000</td></tr><tr><td>Fully Fluorinated Species</td><td>PFC-116</td><td>C<sub>2</sub>F<sub>6</sub></td><td>N</td><td>0.26</td><td>0.001</td><td>5.33 [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>0.78 [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>0.33</td><td>&lt;0.001</td><td>2.18 [0.08]</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 CO₂: 5%±5%, CH₄: -14%±15%, N₂O: 7%±13%–16%</footnote>

<footnote><sup>b</sup>Effective radiative efficiencies and lifetimes were taken from Appendix A in WMO (2022) and Hodnebrog et al. (2020a), except CH₄ which is from Prather et al. (2012). For CO₂, numerous removal processes complicate the derivation of a global lifetime. AGGI = Annual Greenhouse Gas Index. For radiative forcing, see https://www.esrl.noaa.gov/gmd/aggi/aggi.html</footnote>

<footnote><sup>c</sup>Mole fractions are global, annual, midyear surface means determined from the NOAA Cooperative Global Air Sampling Network (Hofmann et al. 2006), except for PFC-14, PFC-116, PFC-218, PFC-318, and HFC-23, which were measured by the Advanced Global Atmospheric Gases Experiment (AGAGE; Mühle et al. 2010; Miller et al. 2010). Changes indicated in brackets are the differences between the 2024 and 2023 means. 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 0.3 ppt to the 2024 reported abundance of CFC-113.</footnote>
## 2. OZONE-DEPLETING GASES

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

Since 1987, the Montreal Protocol (Montreal Protocol 1989) 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 chemicals. Controlled chemicals include chlorofluorocarbons, hydrochlorofluorocarbons, and hydrofluorocarbons, (CFCs, HCFCs, and HFCs, respectively), as well as halons and methyl bromide. While The Protocol was initially enacted to limit damage to the stratospheric ozone layer by limiting ODS production for dispersive uses, these controls have also limited the impact of these gases on Earth's radiation budget. Through the 2016 Kigali Amendment, The Protocol also limits production and consumption of some HFCs that do not destroy stratospheric ozone, but like the CFCs and HCFCs they replace, are strong greenhouse gases.

Emissions of a chemical do not necessarily cease once production has been phased out for dispersive uses, nor are emissions the only factor controlling the atmospheric abundance of a trace gas species. Reservoirs that exist, e.g., in equipment and insulating foams (known as “banks”) can continue to emit controlled chemicals for years after the final phase-out of production has occurred. The observed atmospheric trends of ODSs and their replacements (Fig. 2.66) result from the combination of emissions and the rate at which compounds degrade via loss processes such as photolysis. As an example, CFC-11 and CFC-12 production for dispersive use was scheduled to be globally phased out in 2010, but they have long atmospheric lifetimes and remain present in large banks that continue to emit both compounds. CFC-12, the most abundant CFC, declined by 4 ppt in 2024 to 481.4 ppt (Table 2.11). Conversely, methyl chloroform ($CH_3CCl_3$) has relatively small banks and a short lifetime and, having been phased out in 2015, has declined in the atmosphere to 0.87 ppt, 99% lower than its peak abundance.

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 the three most

abundant HCFCs (HCFC-22, HCFC-141b, and HCFC-142b) have started to decline (Fig. 2.66; Table 2.11). For example, HCFC-22 declined by 2.2 ppt in 2024 to 245.3 ppt. The combined radiative forcing from these three HCFCs peaked in 2021 and is now declining (Western et al. 2024). In contrast, 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. There is substantial interest in HFC-23 because it is a potent greenhouse gas and is emitted primarily as a by-product of HCFC-22 production (UNEP 2024). The production of chlorinated and fluorinated compounds such as HFCs and some plastics can also result in emissions of ODSs. Recent increases in abundances and emissions of CFC-13, CFC-112a, CFC-113a, CFC-114a, and CFC-115 (not shown), chemicals controlled by the Montreal Protocol, have been identified and could be related to uses and processes not controlled by the Montreal Protocol (Western et al. 2023). Of these, CFC-115 is the most abundant at ~8.8 ppt.

While global measurements of ODSs mainly represent the composition of the planetary boundary layer close to Earth's surface,

<figure><img src="image_114.png" /><figcaption>Fig. 2.66. Global mean abundances (mole fractions) at Earth's surface (ppt = pmol mol⁻¹ in dry air) for several halogenated gases, many of which deplete stratospheric ozone. See Table 2.11 for the 2024 global mean mole fractions of these and other gases.</figcaption></figure>
destruction of the ozone layer is dependent on
the amount of reactive halogen in the strato-
sphere. 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 globally distributed
surface measurements, atmospheric trans-
port, and chemical reactivity (Daniel et al.
1995; Newman et al. 2007). While EESC
provides a measure of reactive stratospheric
halogen, it is also useful to scale the EESC to
provide context relative to stratospheric ozone
recovery. The Ozone Depleting Gas Index
(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, which occurred in 1996/97 at midlati-
tudes and in 2001/02 over Antarctica. The
EESC and, therefore, also the ODGI, are
reported for the midlatitudes and the Antarctic
in order to capture the range of ozone layer
recovery timescales due to differences in trans-
port and chemical degradation processes in
the stratosphere. At the beginning of 2024,

reactive halogen in the Antarctic (represented by air with a mean transit time from the surface of 5.5 years) had decreased 28% from the peak relative to the 1980 benchmark. Likewise, reactive halogen in the midlatitude region (represented by air with a mean transit time from the surface of three years) declined by 55% (Fig. 2.67; https://gml.noaa.gov/odgi/).

3. TROPOSPHERIC AEROSOLS

—S. Rémy, N. Bellouin, M. Parrington, M. Ades, M. Alexe, A. Benedetti, O. Boucher, E. di Tomaso, and

Z. Kipling

Atmospheric aerosols play an important role in the climate system by scattering and absorbing radiation, and by affecting the life cycle, optical properties, and precipitation activity of clouds (IPCC AR6, chapter 6; Szopa et al. 2021). Aerosols in the boundary layer also represent a serious public health issue in many countries and are thus subject to monitoring and forecasting as part of air quality policies.

The Copernicus Atmosphere Monitoring Service (CAMS; https://atmosphere.copernicus.eu) runs near-real-time (NRT) global analyses and forecasts of aerosols and trace gases. CAMS also produces a reanalysis of global aerosols and trace gases that covers the years between 2003 and 2024 (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) onboard the Terra and Aqua satellites (Levy et al. 2013) and the Advanced Along Track Scanning Radiometer (AATSR) onboard the Envisat satellite (Popp et al. 2016). This section uses data exclusively from the CAMS reanalysis, focusing on aerosol optical depth at 550 nm in the middle of the visible light spectrum (AOD550), as well as surface particulate matter (PM2.5) concentrations. AOD550 is a vertically integrated quantity while PM2.5 is a surface parameter.

AOD550 and PM2.5 absolute values in 2024 (Plates 2.1ab, ac, respectively) show maxima from pollution over the industrial regions of India and China, as well as from dust over the Sahara and the Middle East. High values in 2024 also arose from seasonal vegetation fires in equatorial Africa and the southern Amazon Basin, and occasionally from extreme fires, most notably across large parts of Canada and eastern Siberia. A strong seasonality appears in AOD550, driven mainly by dust episodes between March and July in the Sahara, Middle East, and Taklimakan/Gobi

<figure><img src="image_115.png" /><figcaption>Fig. 2.67. Equivalent effective stratospheric chlorine (EESC) calculated for air representative of the Antarctic (green) and midlatitude (blue) stratosphere (EESC[A] and EESC[ML], respectively). 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 blue and green arrows indicate currently predicted dates for the return of EESC to 1980s levels in midlatitudes (year 2048) and over the Antarctic (year 2076), respectively. Solid lines depict inferred stratospheric changes based on the measured tropospheric curves.</figcaption></figure>
regions, as well as seasonal biomass burning emissions in tropical regions of Africa, South America, and Indonesia (Fig. 2.68c). Globally averaged AOD550 in 2024 was the highest since 2019, driven by higher-than-usual fire activity over large parts of North and South America as well as equatorial Africa.

The AOD550 anomalies (Plate 2.1ab) are dominated by the large fire events over western Brazil, Canada, equatorial Africa, and eastern Siberia. The PM2.5 anomalies mostly match those in AOD, except for the PM2.5 anomalies caused by large fires, which caused elevated aerosol plumes with a clear AOD signature, but without a PM2.5 one. Over parts of South America, a series of recent years with positive anomalies led to a positive trend of AOD since 2012. Dust storm activity was higher than usual over the western Sahara (with the signal mostly in PM2.5) and the southern Arabian Peninsula, and lower than usual over the eastern Sahara. The negative anomalies of AOD and PM2.5 over East Asia, the eastern United States, and Europe can be explained by ongoing decreasing long-term trends in these regions. Conversely, the positive anomalies over India and Iran are associated with increasing long-term trends over these regions. The exceptional nature of the fires in the summer of 2024 in South America, Canada, and equatorial Africa is highlighted by Plate 2.1ad, which shows the number of extreme AOD days in 2024 as compared to the climatological distribution of daily AOD over the 2003–24 period. Interestingly, for the South American fires, the highest number of extreme AOD days is found over the Pacific and Atlantic Oceans from transported plumes of smoke originating from Bolivia and southern Brazil. Fires also caused a significant number over parts of Siberia, while the high values over India are mostly from anthropogenic sources.

The AOD550 nm and PM2.5 2003–24 trends (Fig. 2.69a,b) are generally collocated, although discrepancies can occur, particularly in

regions mainly affected by fires and dust transport. Between 2003 and 2024, there is a significant negative trend in both quantities over most of the United States, Europe, East Asia, and most of the eastern Sahara. The first three can be attributed to a decrease in anthropogenic emissions while the last is caused mainly by a decrease of desert dust emissions. Positive trends in AOD are noted over parts of Siberia, driven by biomass burning events, as well as over India and Iran, driven by an increase in anthropogenic emissions (Satheesh et al. 2017). Interestingly, the positive trend in AOD over Iran and the Indian subcontinent is not matched by a corresponding positive trend in PM2.5. This means that in the CAMS reanalysis, the increasing trend in aerosol burden over these areas is simulated aloft and not at surface, which could be an artefact, or because of elevated aerosol plumes such as those from desert dust or fire events.

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

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

<figure><img src="image_118.png" /><figcaption>Fig. 2.68. (a) Global aerosol optical depth (AOD) at 550 nm in 2024. (b) Global surface particulate matter (PM2.5) concentrations (µg m⁻³) in 2024. (c) Global average of total AOD at 550 nm averaged over monthly (red) and annual (blue) periods for the period 2003–24.</figcaption></figure>
<figure><img src="image_119.png" /></figure>

Fig. 2.69. (a),(b) linear trends of total aerosol optical depth (AOD yr⁻¹) and particulate matter (PM2.5), respectively, (µg m⁻³ yr⁻¹) for the period 2003–24. Only trends that are statistically significant (95% confidence level) are shown. Regions with decreasing trends include the eastern United States, most of Europe, and parts of Brazil and China, as well as the Korean peninsula and Japan.

Tarasick et al. 2019; Gulev et al. 2021; Chang et al. 2024). On the global scale, the tropospheric ozone burden (TOB) increased from the mid-1990s to 2019, based on a range of in situ and satellite observations. Last year's *State of the Climate* report (Cooper et al. 2024a) reported an apparent leveling-off of ozone for the period 2020–23, mainly at northern midlatitudes, likely initiated by the economic downturn associated with the Coronavirus disease 2019 (COVID-19) pandemic. However, the updated tropospheric ozone product from the Aura Ozone Monitoring Instrument (OMI) and the Microwave Limb Sounder (MLS) satellite instruments (based on the new OMI Collection 4 total ozone) does not support a sustained leveling-off of ozone on the global scale (Fig. 2.70a), and the long-term trend for the period 2004–24 is positive. Notably, 2024 experienced the highest TOB since the OMI/MLS record began in 2004.

Anthropogenic AOD and radiative forcing resulting from aerosol–radiation interactions (RFari) and aerosol–cloud interactions (RFaci) are shown in Appendix Fig. A2.12 for 2024 and the period 2003–24. They are estimated using the methods described in Bellouin et al. (2020). There was a small increase in anthropogenic AOD again this year, but aerosol radiative forcing has remained fairly flat recently.

4. TROPOSPHERIC OZONE

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

Tropospheric ozone contributes to almost all of ozone's effective radiative forcing (tropospheric and stratospheric), estimated to be 0.51 (0.25–0.76) W m⁻² for the period 1750–2023 (Forster et al. 2024). A short-lived climate forcer, its lifetime is on the order of three to four weeks (Archibald et al. 2020) and, therefore, its global distribution is highly variable and difficult to quantify (Gaudel et al. 2018;

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

Fig. 2.70. 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 2024 for (a) near global (60°S–60°N; black), (b) the Northern Hemisphere tropics (0°–30°N; red) and midlatitudes (30°N–60°N; dark red), and (c) the Southern Hemisphere tropics (0°–30°S; blue) and midlatitudes (30°S–60°S; green). Slopes of linear fits to the data are presented with their 95%-confidence-level uncertainties.
Averaged over the entire year of 2024, and relative to 2005–23, positive ozone anomalies were widespread across the Northern Hemisphere (NH), with the largest anomalies found above central Asia, the tropical North Atlantic, the eastern North Pacific, and western North America (Plate 2.1ae). Anomalies in the Southern Hemisphere (SH) were generally positive across the tropics and across much of the midlatitudes, with weaker positive anomalies across southern Australia and the central South Pacific Ocean.

Over the full 20-year record, global (60°S–60°N) TOB increased at the average rate of 1.20±0.34 Tg yr⁻¹, equal to a total increase of ~8% (Fig. 2.70). Regarding the impact of COVID-19, the updated OMI/MLS product shows a brief leveling-off of ozone from 2019 to 2020 and into 2021, similar to other satellite products (Ziemke et al. 2022); however, by the end of 2021, ozone began to increase again through 2024. Regionally, the strongest trends

(2004–24) have occurred above central and southern Asia and also across western North America and central South America (Fig. 2.71). Strong ozone decreases were not observed above any region of the globe.

Ozone trends at the surface are often decoupled from the trends in the free troposphere (Gulev et al. 2021; Chang et al. 2023), and the availability of long-term surface ozone records is too limited spatially to produce a globally representative surface ozone product (Sofen et al. 2016). However, global chemistry climate models can estimate surface ozone trends, and their output can be evaluated against reliable observations. Ozone trends from six surface sites are reported here, all located in remote environments and suitable for evaluating coarse resolution global models. These records are now 25 to 50 years in length (Fig. 2.72; Table 2.12). In the Arctic, Barrow Atmospheric Observatory (at sea level) reported a weak positive trend of 0.45±0.30 ppbv decade⁻¹, while the high-elevation site of Summit, Greenland, showed decreasing ozone since 2000 (−1.89±0.85 ppbv decade⁻¹). In the remote North Atlantic Ocean, Tudor Hill, Bermuda (sea level),

<figure><img src="image_121.png" /><figcaption>Fig. 2.71. 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 2024. 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>

<table><caption>Table 2.12. Ozone trends at the six baseline monitoring sites shown in Fig. 2.72. 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. The trends at the Mauna Loa Observatory (MLO) were adjusted to account for interannual meteorological variability following the methods of Chang et al. (2024), spanning the period 1974–2024.</caption><thead><tr><td>Site Name — Latitude, Longitude, Elevation</td><td>Years With Data</td><td>Trend, ppbv decade<sup>-1</sup></td><td>p-value</td></tr></thead><tbody><tr><td>Summit, Greenland — 72.6°N, 38.5°W, 3238 m</td><td>2000–present</td><td>-1.89±0.85</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>0.45±0.30</td><td>p&lt;0.01</td></tr><tr><td>Tudor Hill, Bermuda — 32.3°N, 64.9°W, 30 m</td><td>1988–98, 2003–present</td><td>-0.65±1.04</td><td>p=0.21</td></tr><tr><td>Mauna Loa Observatory (MLO), Hawaii — 19.5°N, 155.6°W, 3397 m</td><td>1973–present</td><td>1.21±0.30</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>-0.04±0.52</td><td>p=0.88</td></tr><tr><td>South Pole, Antarctica — 90.0°S, 59.0°E, 2840 m</td><td>1975–present</td><td>0.35±0.33</td><td>p=0.03</td></tr></tbody></table>
reported high interannual variability and a weak negative trend (−0.65±1.04 ppbv decade⁻¹), while Mauna Loa (3400 m a.s.l.) in the tropical North Pacific Ocean showed a clear ozone increase since 1973 (1.21±0.30 ppbv decade⁻¹). The Antarctic site of Arrival Heights showed no trend since 1996, while ozone has increased slightly at South Pole (0.35±0.33 ppbv decade⁻¹).

The combined OMI/MLS satellite product (Ziemke et al. 2019) has been reported by the *State of the Climate* reports since 2012, covering most of the globe (60°S–60°N). The product now provides a continuous record of TOB spanning a full 20 years (2004–24). This edition of the *State of the Climate* report uses the latest version of the product, based on the new OMI Collection 4 L1b retrievals (Kleipool et al. 2022), which correct for instrument drift through the end of 2024. The vertical resolution of OMI/MLS monthly tropospheric column ozone is ~3 km near the tropopause with a regional precision (standard deviation) of ~2 Dobson units (DU; ~7%) in all latitude bands; trend uncertainties are about 0.5 DU decade⁻¹ (1.5% decade⁻¹).

# 5. STRATOSPHERIC AEROSOLS

—S. Khaykin, G. Taha, T. Sakai, I. Morino, and B. Liley

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 ($SO_2$) and ash into the stratosphere, leading to significant perturbations of stratospheric aerosol burden at hemispheric and global scales that can last from several months to several years. Another important emerging source of particulate matter in the stratosphere is injection of smoke from wildfires via associated pyrocumulonimbus (pyroCb)—the fire-generated storms that can overshoot the tropopause (Peterson et al. 2021).

Despite the continued decay of the stratospheric aerosol perturbation produced by the eruption of the Hunga submarine volcano in January 2022, the stratospheric aerosol optical depth (sAOD) remained elevated above the background level in 2024 (Fig. 2.73a). Due to its extreme explosiveness, the Hunga eruption produced aerosol layers throughout the stratosphere (Taha et al. 2022) and resulted in the largest perturbation of the global sAOD in the last three decades (Khaykin et al. 2022). Augmenting the Hunga perturbation were additional lower stratospheric injections (up to 20 km–21 km) from two consecutive eruptions of the Ruang volcano in North Sulawesi, Indonesia, on 18 and 29 April 2024 (Dodangodage et al. 2025). Extrapolation of the stable decay of the Hunga southern hemispheric sAOD during early 2024 leads to an inference of the total lifetime of the Hunga-induced stratospheric aerosol load of 2.5 years, spanning from mid-January 2022 to mid-July 2024.

The eruption of Ruang ended the era of Hunga-induced sAOD perturbation. The Ruang aerosols spread throughout the tropics in less than two months and were transported farther into the southern extratropics during the austral winter (Fig. 2.73a). Figures 2.73b–e display quarterly zonal-mean extinction ratio (ER, aerosol-to-molecular extinction ratio) from the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) observations, summarizing the stratospheric aerosol latitude–altitude distribution during 2024. During the first three months of 2024, the Hunga aerosols were still present in the tropical mid-stratosphere (21 km–29 km) and the southern extratropical lower stratosphere (Fig. 2.73b). During the three months following the

<figure><img src="image_122.png" /><figcaption>Fig. 2.72. (a) Monthly mean surface ozone (ppbv) at Barrow Observatory, Alaska (gray), Summit, Greenland (orange), Tudor Hill, Bermuda (blue), Mauna Loa, Hawaii (purple), Arrival Heights, Antarctica (red), and the 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 2.12. (b) The same time series after conversion to monthly anomalies referenced to the monthly climatological values over 2000–20 and smoothed using locally weighted scatterplot smoothing (LOWESS) regression (Chang et al. 2021).</figcaption></figure>
Ruang eruption, the Ruang aerosols were observed at 18 km-23 km together with a possible contribution from remnants of Hunga above that layer (Fig. 2.73c). During July-September 2024, the Ruang layer intensified, and the isentropic transport of its aerosols to the southern extra-tropical lowermost stratosphere can be seen (Fig. 2.73d). Further transport of Ruang aerosols towards the southern high latitudes and their upwelling in the tropics to altitudes of 19 km-24 km in October-December, as well as a modest transport into the northern extratropics during this period, can be inferred from Fig. 2.73e.

<figure><img src="image_123.png" /><figcaption>(b) NOAA-21 OMPS-LP extinction ratio 997 nm, Jan-Mar 2024</figcaption></figure>

<figure><img src="image_124.png" /><figcaption>(c) NOAA-21 OMPS-LP extinction ratio 997 nm, Apr-Jun 2024</figcaption></figure>

<figure><img src="image_125.png" /><figcaption>(d) NOAA-21 OMPS-LP extinction ratio 997 nm, Jul-Sep 2024</figcaption></figure>

<figure><img src="image_126.png" /><figcaption>(e) NOAA-21 OMPS-LP extinction ratio 997 nm, Oct-Dec 2024</figcaption></figure>

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

Fig. 2.73. Global evolution of the stratospheric aerosol from the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-21 Ozone Mapping and Profiler Suite-Limb Profile (OMPS-LP) observations at 997 nm during 2022-24. (a) zonal-mean stratospheric aerosol optical depth (sAOD). The triangles indicate the eruptions of Hunga (Hu), Shiveluch (Sh), and Ruang (Ru) volcanoes, whereas the stars indicate the wildfire events with measurable stratospheric impact in Canada and Russia during summer 2023. (b)–(e) NOAA-21 OMPS-LP three-month zonal mean latitude-altitude section of the aerosol-to-molecular extinction ratio (ER) during 2024. The thick black line is the tropopause altitude, and the thin black lines are the potential temperature levels in Kelvin (K).
Figure 2.74 shows three decades of sAOD observations by ground-based Network for the Detection for Stratospheric Change (NDACC) lidars at Observatoire de Haute Provence (OHP; 43.9°N) and Lauder observatory (45°S) together with the corresponding zonally averaged satellite data from the International Space Station's (ISS) Stratospheric Aerosol and Gas Experiment III (SAGE III) instrument and the Global Space-based Stratospheric Aerosol Climatology (GloSSAC). These stations, located nearly antipodally on the globe, respectively represent the northern and southern extratropics. The OHP time series (Fig. 2.74a) during the twenty-first century is largely modulated by several moderate volcanic eruptions as well as by the extreme Pacific Northwest Event (PNE) wildfire outbreak in August 2017 (Khaykin et al. 2018; Peterson et al. 2018). The largest impact on the NH sAOD was caused by the Raikoke eruption in June 2019. Since that time, the OHP sAOD remained elevated, owing to contributions of boreal wildfires and the transport of Hunga aerosols (Khaykin et al. 2024). In late 2024, a subtle increase in sAOD was most likely related to the transport of Ruang aerosol into northern midlatitudes.

In the southern midlatitude stratosphere (Fig. 2.74b), the most significant sAOD perturbations were caused by the 2019/20 Australian New Year (ANY) wildfire super outbreak (Khaykin et al. 2020; Peterson et al. 2021) and the eruption of the Hunga submarine volcano in January 2022. The massive transport of Ruang sulfates into the southern extratropics is reflected in the SH extratropical sAOD enhancement peaking in October 2024 (Fig. 2.74b).

Unlike 2023, which had multiple pyrocumulonimbus clouds in the NH that reached the lowest stratosphere (Zhang et al. 2024), there were no significant stratospheric intrusions of the smoke during 2024, and most of the aerosol seen in the NH stratosphere originated from the Hunga and Ruang eruptions.

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

Fig. 2.74. Time series of monthly mean aerosol optical depth at 532 nm of the stratospheric overworld (380 K; 33 km) from ground-based lidars at (a) French Haute-Provence Observatory (OHP, 43.9°N, 5.7°E, Stratospheric Ozone Lidar at OHP [LiO3S] and the Lidar Temperature Aerosol [LTA] instruments) and (b) New Zealand's Lauder station (45°S, 179°E, Lauder aerosol lidar) and the corresponding monthly/zonal-mean values from satellite observations within 40°N–50°N and 50°S–40°S latitude bands from the ISS's Stratospheric Aerosol and Gas Experiment III (SAGE III) instrument and Global Space-based Stratospheric Aerosol Climatology (GloSSAC) merged satellite record. 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 Chichón (EC), Pinatubo (Pi), Kasatochi (Ka), Sarychev (Sa), Nabro (Na), Raikoke (Ra), Calbuco (Ca), Hunga (Hu), Ruang (Ru); and wildfire events: Pacific Northwest Event (PNE, British Columbia, Canada) and Australian New Year (ANY) super outbreak.
# 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, J. de Laat, D. Loyola, A. Rozanov, V. Sofieva, K. Tourpali, R. Wang, T. Warnock, and J. D. Wild

About 90% of total column ozone resides in the stratosphere; only 10% resides in the troposphere. In 2024, total column ozone was well above the average of 1998–2008 over most of the globe except for two narrow zonal bands in the tropics and a patch over Antarctica (Plate 2.1af). In the NH, anomalies reached values of +60 DU or more in some regions, for example the Canadian Arctic. The time series in Fig. 2.75b show that the 2024 annual zonal mean at northern midlatitudes (35°N–60°N) was close to the high values observed during the 1960s. In March 2024, Arctic (60°N–90°N) total column ozone reached 475 DU, the highest value seen since 1979 (Fig. 2.75e). The variation in annual-mean total column ozone in the extratropics is largely driven by variations in the stratospheric circulation in winter/early spring. During boreal winter/spring 2024, the Brewer–Dobson (BD) circulation, which transports ozone from the tropical source region to middle and high latitudes, was particularly strong (Newman et al. 2024). Combined with the lower stratospheric quasi-biennial oscillation (QBO) in its easterly phase and the strong El Niño conditions in the first half of 2024, total column ozone was reduced in the tropics and strongly enhanced in the extratropics (Figs. 2.75a,b,d; Plate 2.1af; Baldwin et al. 2001; Oman et al. 2013; Butchart 2014; Domeisen et al. 2019).

In the SH midlatitudes and in October in the Antarctic (Fig. 2.75d,e), the last two years were closer to the high end of the range of interannual variability, ending the series of years of low total column ozone from 2020 to 2022, caused by Australian wildfires (Solomon et al. 2023) and a series of volcanic eruptions, including Hunga (Santee et al. 2023; Fleming et al. 2024).

Generally, observed total column ozone values in recent years have tended to be at the low end of projections from chemistry climate models (CCMs; see Figs. 2.75a–d), based on current scenarios for ODSs and GHGs. Overall, the data show the slow path of ozone recovery due to the ODS phase-out by the Montreal Protocol and its Amendments (WMO 2022).

<figure><img src="image_129.png" /></figure>

Fig. 2.75. 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. Values are plotted at the tick mark start of each year. The dotted gray lines in each panel show the average ozone level for 1964–1980 calculated from the World Ozone and Ultraviolet Radiation Data Centre's (WOUDC) data. Most of the observational data for 2024 are preliminary. The thick white lines in (a)–(d) show the median from Chemistry-Climate Model Initiative (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 80th percentile range. All datasets have been bias-corrected by subtracting individual data averages and adding the multi-instrument mean from the reference period 1998–2008.
Figure 2.76 shows the evolution of ozone profiles at two stratospheric levels and for three latitude bands. The 2-hPa level (or 42-km altitude) represents the upper stratosphere (Figs. 2.76a–c), and the 50-hPa level (or 22-km altitude) the lower stratosphere (Figs. 2.76d–f).

Ozone in the upper stratosphere is controlled to a large degree by photochemistry. The year 2024 continued the slow upper-stratospheric ozone increase due to declining ODSs and cooling of the upper stratosphere, as predicted by models (e.g., WMO 2022), although observed values in recent years have tended to be at the lower end of expectations from CCM simulations (gray-shaded region in Figs. 2.76a–c).

Ozone in the lower stratosphere (Figs. 2.76d–f) is controlled to a large degree by transport variations and is the main contributor to the already discussed total column ozone variations. Consistent with the strong El Niño and the easterly shear phase of the QBO in the lower stratosphere from January to April, ozone values were very low in the tropical band in 2024 (Fig. 2.76e; see also the El Niño years 1998 and 2016). In the Northern Hemisphere extratropical band in 2024 (Fig. 2.76d), ozone at 50 hPa was near the high end of recent values for almost all individual datasets. However, the enhancement was not as large as that seen for total column ozone in Fig. 2.75b, because a large fraction of the total column enhancement in 2024 came from levels lower than 50 hPa. In the SH (Fig. 2.76f) in 2024, ozone at 50 hPa from the zonal-mean satellite datasets again approached the range predicted by CCMs, ending the low excursions from 2020 to 2022 due to the Australian wildfires and recent volcanic eruptions, events which were not considered in the CCM projections.

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

Fig. 2.76. Annual mean anomalies of ozone in the (a)–(c) upper stratosphere near 42-km altitude or 2-hPa pressure, and in the (d)–(f) lower stratosphere near 22 km or 50 hPa for three zonal bands: (a),(d) 35°N–60°N, (b),(e) 20°S–20°N (tropics), and (c),(f) 35°S–60°S, respectively. Anomalies are referenced to the 1998–2008 baseline. Annual means are plotted at the tick marks of the start of each year. Colored lines are long-term records obtained by merging different limb (Global Ozone Chemistry and Related Trace Gas Data Records for the Stratosphere [GOZCARDS], Stratospheric Water and Ozone Satellite Homogenized [SWOOSH], Stratospheric Aerosol and Gas Experiment [SAGE]+Climate Change Initiative [CCI]+Ozone Mapping and Profiler Suite Limb Profiler [OMPS-LP], SAGE+Scanning Imaging Absorption Spectrometer for Atmospheric Chartography [SCIAMACHY]+OMPS-LP, SAGE+Optical Spectrograph and InfraRed Imaging System [OSIRIS]+OMPS-LP) and nadir-viewing (Solar Backscatter Ultraviolet Radiometer [SBUV], OMPS Nadir Profile [OMPS-NP]) satellite instruments. The nadir-viewing instruments have a much coarser altitude resolution than the limb-viewing instruments. This can cause differences in some years, especially at 50 hPa. Red dots are results from ground-based Umkehr data (Petropavlovskikh et al. 2025). The black line is 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), WMO (2022), and Arosio et al. (2019) for details on the various datasets. Gray shaded area shows the range of chemistry-climate model simulations from the Chemistry-Climate Model Initiative (CCMI)-1 refC2 (SPARC/IO3C/GAW 2019). Ozone data for 2024 are not yet complete for all instruments and are still preliminary.
# 7. STRATOSPHERIC WATER VAPOR

—S. M. Davis, K. H. Rosenlof, E. A. Asher, H. Vömel, and R. M. Stauffer

In 2024, stratospheric water vapor (WV) continued to be strongly influenced by the January 2022 eruption of the Hunga volcano (20.5°S, 175.4°W), which injected 150 Tg of WV into the stratosphere (~10% of the entire stratospheric burden; Millán et al. 2022; Vömel et al. 2022). In addition to the ongoing influence from the Hunga eruption, deseasonalized tropical lower-stratospheric WV anomalies (using a climatological base period of 2004–21) started the year positive following record-high values in 2023 (Davis et al. 2024), but then became negative for much of the year before returning to positive in November and December 2024 (Figs. 2.77a,c, 2.78, 2.79). Overall, 2024 continued the positive WV anomalies observed in the last five years in the global stratosphere (Konopka et al. 2022; Zolghadrshojaee et al. 2024) with some notable variability that was likely due to natural fluctuations.

Zonal-mean WV provided by satellite measurements (Fig. 2.77) shows that the lingering positive anomalies from Hunga were evident in the tropical upper stratosphere (above 10 hPa) in 2024 (Fig. 2.77a). In the tropics (Figs. 2.77a, 2.78), the positive anomaly decreased as the year progressed, with a return to near-normal values around July, likely driven by upward vertical transport of drier air that entered the stratosphere more recently and was not impacted by the Hunga eruption. At the start of 2024, the lingering effects of the Hunga WV perturbation were evident in the middle stratosphere (e.g., 26 hPa, Fig. 2.77b; see also Fig. 2.78) at extratropical latitudes in both hemispheres. Positive WV anomalies, including some record values (see hatched regions, Fig. 2.78), were also present in the lower stratosphere throughout 2024. Attribution of these extratropical lower-stratosphere anomalies is subject to ongoing research, but they are plausibly explained as some combination of poleward transport of the positive tropical lowermost stratosphere anomalies from the end of 2023 (Figs. 2.77a,c) as well as downward transport of Hunga-impacted air from above.

Even though the mid- and upper-stratospheric WV anomalies were still dramatically perturbed by Hunga in 2024, tropical lower-stratospheric WV anomalies (i.e., at pressures greater than ~30 hPa) followed a more typical progression influenced by other factors. Anomalies in this region are expected to be primarily caused by anomalies in tropical tropopause temperatures, which control freeze-drying of air ascending into the stratosphere (Mote et al. 1996). Indeed, lower-stratospheric satellite WV anomalies are highly correlated with tropical (15°S–15°N) cold-point tropopause (CPT) temperature anomalies (Figs. 2.79b,c). While 2022 and 2023 had the first- and second-warmest MERRA-2 tropical CPT temperatures on record (the annual-mean anomaly was +0.76 K in 2022 and +0.73 K in 2023), 2024 was cooler than average (–0.23 K), ranking 39th out of 45 years (since 1980, based on the MERRA-2 reanalysis). Accordingly, while 2023 and 2022 ranked as the first- and

<figure><img src="image_131.png" /><figcaption>Fig. 2.77. (a) Pressure–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. The black line shows the MERRA-2 tropical cold point pressure. (b), (c) Latitude–time contour of WV anomalies at (b) 26 hPa (middle stratosphere) and (c) 82 hPa (lower stratosphere), respectively. All panels are based on merged satellite data from the Stratospheric Water and Ozone Satellite Homogenized version 2.7 (SWOOSH v2.7) dataset (a combination of Aura Microwave Limb Sounder (MLS), Atmospheric Chemistry Experiment—Fourier Transform Spectrometer (ACE-FTS), and Stratospheric Aerosol and Gas Experiment III (SAGE III)/ISS WV data, Davis et al. 2016). Anomalies are differences from the mean 2004–21 water vapor mixing ratios (ppm) for each month. Tick marks denote the beginning of each year.</figcaption></figure>
second-most positive annual-mean tropical (15°S-15°N) WV anomalies at 82 hPa (+0.39 ppm in 2023 and +0.27 ppm in 2022; parts per million, i.e., µmol mol⁻¹) based on the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) combined WV record (Davis et al. 2016), 2024 was near the median (10th out of the 20 years since 2005 with sufficient satellite sampling of the tropics) with an anomaly of +0.07 ppm. As is typical, the tropical lower-stratosphere WV anomalies propagate both upward and to higher latitudes, becoming evident at other frost-point measurement stations with a lag of several months at subtropical and midlatitude stations (Hurst et al. 2011).

Two important drivers of interannual variations in CPT temperatures and stratospheric WV concentrations entering the stratosphere are the ENSO and the QBO in equatorial stratospheric winds. Strong El Niño conditions were present at the beginning of 2024 through January–March, followed by a transition to ENSO-neutral conditions by April–June, and then La Niña-like conditions from September through the end of the year (see section 4f). During boreal winter, both La Niña and strong El Niño conditions are associated with a wetter tropical lowermost stratosphere (e.g., Garfinkel et al. 2021), which seems consistent with conditions at the beginning and end of 2024. However, also during the first half of 2024, there was easterly QBO shear between the 50-hPa and 70-hPa levels (section 2e3), which is consistent with stronger upwelling and cold CPT temperatures (e.g., Fig. 2.79c). This behavior is consistent with the anomalously dry

<figure><img src="image_132.png" /><figcaption>Fig. 2.78. Latitude–pressure cross-sections of zonally averaged water vapor (WV) anomalies (ppm; 2004–21 base period). Hatching shows where the zonal-mean WV was at record values for the given month. Black lines show the monthly mean tropopause pressure from the MERRA-2 reanalysis.</figcaption></figure>
<figure><img src="image_133.png" /></figure>

**Fig. 2.79.** Lower-stratospheric water vapor (WV) anomalies over four balloon-borne frost point (FP) hygrometer stations. Each panel shows the lower-stratospheric anomalies (relative to each station) of individual FP soundings (black diamonds) and of monthly zonal averages from Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) data at 82 hPa in the 5° latitude band containing the FP station (red lines). High-resolution FP vertical profile data were averaged between 70 hPa and 100 hPa for comparison with the SWOOSH 82 hPa level. Anomalies for SWOOSH and FP data are calculated relative to the 2004–21 period for all sites except Hilo (2011–21). Tropical cold-point tropopause temperature anomalies based on the MERRA-2 reanalysis ([b],[c], blue lines) are generally well correlated with the tropical lower-stratospheric WV anomalies.

According to the CAMS (https://atmosphere.copernicus.eu/) reanalysis of atmospheric composition, produced by the European Center for Medium-Range Weather Forecasts (ECMWF) and detailed in Inness et al. (2019), the global CO burden decreased between 2003 and 2014 because of decreased anthropogenic emissions in most parts of the world, along with a strong decrease in fire activities in South America. In recent years, positive global and regional CO anomalies occurred because of regionally intensified wildfires related to exceptional meteorological conditions, such as the intensive peat fires in Indonesia in 2015—which caused the highest global CO burden since 2003 (Fig. 2.80)—and the exceptional wildfires in South America in 2024. The latter led to the highest CO burden over the continent in the CAMS reanalysis record (Fig. 2.81). The increased fire intensity in South America was caused by anomalous dry and hot conditions, which started in mid-2023 (de Laat et al. 2025). While fires in the Amazon

conditions in the tropical lower stratosphere during the middle of the year (Figs. 2.77a,c, 2.78c–f). After June, the 70-hPa–50-hPa wind shear then reversed to westerly for the remainder of the year, which is consistent with the anomalously wet conditions in the tropical lowermost stratosphere at the end of 2024.

## 8. CARBON MONOXIDE

—J. Flemming and A. Inness

The global burden of carbon monoxide (CO) in 2024 was the second highest since 2003, mainly because of exceptionally high emissions from wildfires in South America (Fig. 2.80). The emitted CO was transported in the adjacent outflow regions over the Pacific and Atlantic Oceans, leading to positive CO anomalies throughout the SH midlatitudes and tropics (Plate 2.1ag).

CO is emitted into the atmosphere by anthropogenic combustion processes, such as road transport and energy generation, as well as from wildfires. Similar in size to, or even larger 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 (OH) is the main loss process for CO, resulting in an atmospheric lifetime of 1–2 months. The presence of CO contributes to the production of tropospheric ozone, a relatively short-lived species among radiatively important molecules that affect the climate.

<figure><img src="image_134.png" /></figure>

**Fig. 2.80.** Global monthly-mean (red) and yearly-mean (YM; blue) of the total-column carbon monoxide from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis for the period 2003–24.
region of Brazil and Bolivia in August–September 2024 were the main cause of the increased CO burden, fires in other regions contributed as well: Venezuela experienced strong fires in January–March, and the Pantanal wetlands in Brazil, the world's largest tropical wetland area, suffered from intensive peat fires in May.

As in previous years, boreal wildfires over Canada and Eastern Siberia in summer led to regional positive CO anomalies against the background of decreasing CO burden in the NH. Less-intensive fire activity over many parts of central and southern Africa contributed to a localized negative CO anomaly in 2024. India is a region where anthropogenic sources, including the burning of agricultural waste in winter, continued to increase the regional CO burden.

CAMS has produced a retrospective analysis of CO, aerosols, and ozone since 2003 by assimilating satellite retrievals of atmospheric composition with the ECMWF model (Inness et al. 2019). This CAMS 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) satellite instrument (Deeter et al. 2014, 2017, 2019), excluding observations poleward of 65°N/S, using the ECWMF four-dimensional variational assimilation (4D-VAR) data assimilation system. The anthropogenic emissions were taken from the Monitoring Atmospheric Composition and Climate and CityZen (MACCity) inventory (Granier et al. 2011) that accounts for projected emission trends according to the emission scenario Representation 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 (GFASv1.2; 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 (MEGAN) 2.1 following Sindelarova et al. (2014) were used for the period 2003–17, and after 2017 a monthly climatology derived from the 2003–17 data was applied.

<figure><img src="image_135.png" /><figcaption>Fig. 2.81. Monthly-mean (red) and yearly-mean (YM; blue) of the total column carbon monoxide from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis over South America (70°S–10°N, 83°W–30°W) for the period 2003–24.</figcaption></figure>
## Sidebar 2.2: Operational satellite instruments monitor a range of indirect short-lived climate forcers

—O. R. COOPER, E. PENNINGTON, J. WORDEN, K. BOWMAN, S. KONDRAGUNTA, Z. WEI, AND K. YANG

Earth's effective radiative forcing (ERF) due to total anthropogenic activities since 1750 is 2.79 (1.78 to 3.61) W m⁻², which is a balance between the warming effect of greenhouse gases and the cooling effect of aerosols and land use change (surface albedo changes and effects of irrigation; Forster et al. 2024). Long-lived greenhouse gases (LLGHGs: carbon dioxide [CO₂], nitrous oxide [N₂O], and halogenated greenhouse gases) accumulate in the atmosphere on decadal to centennial time scales, while short-lived climate forcers (SLCFs) have lifetimes ranging from a few hours to about two decades. SLCFs that exert a climate effect through their radiative forcing are known as direct SLCFs (e.g., ozone, methane, primary and secondary aerosols), while indirect SLCFs are precursors of other direct climate forcers (IPCC 2021). For example, carbon monoxide (CO) and nitrogen oxides (NOₓ = NO+NO₂) are ozone precursors, while ammonia (NH₃) is a precursor of secondary aerosols (e.g., ammonium nitrate).

In terms of SLCFs, previous editions of the *State of the Climate* report have focused on methane, tropospheric and stratospheric ozone (direct SLCFs; sections 2g1, 2g4, 2g6), and carbon monoxide (indirect SLCF; section 2g8). The *State of the Climate* also provides updates on aerosols (sections 2g3, 2g5), but no distinction is made between primary aerosols (direct SLCFs) and secondary aerosols (indirect SLCFs). Advances in satellite instrumentation and retrieval algorithms now allow for operational monitoring of several indirect SLCFs; here, three operational satellite products that can improve understanding of the regional and global scale production of tropospheric ozone and secondary aerosols are described.

Tropospheric column nitrogen dioxide (NO₂): NO₂ is an indirect SLCF that reacts with CO, methane, and volatile organic compounds (VOCs) in the presence of sunlight to produce ozone, which is both a SLCF and an air pollutant detrimental to human health and vegetation (Monks et al. 2015). Over the past two decades, several satellite instruments have monitored changes in tropospheric column NO₂, allowing scientists to identify regions where ozone production is increasing or decreasing (Duncan et al. 2016; He et al. 2024). The Ozone Mapping and Profiler Suite (OMPS) instrument is currently monitoring NO₂ on three NOAA operational polar-orbiting satellites (Suomi National Polar-orbiting Partnership [SNPP] since 2012, NOAA-20 since 2018, and NOAA-21 since 2023; Huang et al. 2022). Figure SB2.4a presents annual average tropospheric column NO₂ for the most recent five years (2020–24), showing well-known hotspots associated with fossil fuel combustion across North America, Europe, the Middle East, South Asia, East Asia, and southern Africa. A region of frequent

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

<figure><img src="image_137.png" /><figcaption>Fig. SB2.4. (a) Ozone Mapping and Profiler Suite (OMPS) tropospheric-column nitrogen dioxide (NO₂), 2020–24; (b) Anomalies of OMPS tropospheric column NO₂ for the period 2020–24, with reference to 2012–19. Units are µmol m⁻². Gray areas indicate regions with low data availability, and portions of South America and the South Atlantic are omitted due to instrument interference caused by the South Atlantic Anomaly (Finlay et al. 2020).</figcaption></figure>

biomass burning is also visible above south-central Africa. Figure SB2.4b presents the 2020–24 period as anomalies with respect to 2012–19. Previously documented NO₂ decreases have persisted across Mexico City (Mexico), South Africa, the northeastern United States, western Europe, and especially across East Asia (Duncan et al. 2016; Elshorbany et al. 2024). Notable increases are found above western Texas, eastern Europe, Iraq, Iran, and eastern India (Gyawali 2023; Elshorbany et al. 2024).

**Ammonia (NH₃):** Ammonia is produced from agricultural practices (Behera et al. 2013), fossil fuel combustion, and wildfires (Lindaas et al. 2021). When this indirect SLCF combines with nitric acid (HNO₃; an oxidation product of NO₂) it forms
ammonium nitrate ($NH_4NO_3$), a secondary aerosol and SLCF, which reflects sunlight (Nowak et al. 2012). The Cross-Track Infrared Sounder (CrIS) flies on the same NOAA operational satellites as the OMPS instrument, and provides midday retrievals of column ammonia, with a peak sensitivity of around 700 hPa (Bowman 2021a). Figure SB2.5a presents average CrIS ammonia for the year 2024, revealing three major hotspots. The enhancements above northeastern China and the Indo-Gangetic Plain are associated with anthropogenic activity and are similar to previous years (2020–23, not shown). In contrast, the enhancement above central South America is produced by wildfires and is elevated compared to previous years. Peak ammonia concentrations occurred in September (Fig. SB2.5b) during a period of record-breaking wildfires across Bolivia (CAMS 2024; sections 2g8, 2h3). The ammonia plume was prevented from spreading westward by the Andes Mountains and instead advected eastward across the South Atlantic Ocean.

**Peroxyacetyl nitrate (PAN):** Acyl peroxynitrates (PANs) are a class of thermally unstable reservoir species for nitrogen oxides, commonly produced by wildfires and fossil fuel combustion (Juncosa Calahorrano et al. 2021). CrIS detects PANs with highest sensitivity in the mid-troposphere (450 hPa; Bowman 2021b), with peroxyacetyl nitrate (PAN) being the

most abundant species (Payne et al. 2022). PAN is stable at low temperatures and can therefore transport $NO_x$ over long distances, especially in the free troposphere. When PAN descends to warmer layers of the atmosphere, its thermal decomposition releases $NO_x$, which is then available for ozone production (Fischer et al. 2014). CrIS detected enhanced PANs above North America during summer 2023, produced by the record-breaking Canadian wildfire season (Cooper et al. 2024b). Canada experienced its second strongest wildfire season in 2024 (CAMS 2024; section 2h3), and the CrIS PANs product shows strong enhancements above Canada and the central United States during July 2024 (Fig. SB2.6a), similar to the enhancements above the downwind region of the North Atlantic Ocean and to the enhancements above East Asia that are attributed to anthropogenic activity. By September, the PANs hotspots had shifted to the biomass burning regions of South America and southern Africa, with plumes of PANs extending from both regions into the South Atlantic Ocean (Fig. SB2.6b).

These examples demonstrate the unique capability of satellites to monitor indirect SLCFs on a global scale. The benefits of this knowledge go beyond the simple understanding of trace gas distributions, because the chemistry and transport connections between these trace gases amplify their impact on climate and air quality (Szopa et al. 2021). For example,

<figure><img src="image_138.png" /><figcaption>Fig. SB2.5. (a) Cross-Track Infrared Sounder (CrIS) ammonia ($NH_3$) averaged over the entire year of 2024; (b) CrIS $NH_3$ averaged over Sep 2024. Units are $m mol m^{-2}$. Both images show $NH_3$ for the total atmospheric column.</figcaption></figure>

<figure><img src="image_139.png" /><figcaption>Fig. SB2.6. (a) Cross-Track Infrared Sounder (CrIS) acyl peroxynitrates (PANs) averaged over Jul 2024; (b) CrIS PANs averaged over Sep 2024. Units are ppb. Both images show PANs between 825 hPa and 215 hPa.</figcaption></figure>
NO₂, NH₃, and PAN are all produced by biomass burning, and wildfires (many of which are ignited by humans) are becoming more intense due to climate change (Byrne et al. 2024). The connections of these indirect SLCFs with respect to ERF are as follows: NO₂ is a precursor of PAN, which then transports NOₓ downwind where it can produce ozone far from its origin (potential ERF increase); the ozone produced from NO₂ subsequently impacts methane (direct SLCF) by reducing its lifetime (potential ERF decrease); NH₃ and NO₂ generated by fires can produce secondary aerosols (potential ERF decrease).

With existing and expanded data assimilation methods, satellite retrievals of indirect SLCFs can be assimilated into global atmospheric chemistry models, such as the Unified Forecast System (UFS; Jacobs 2021), Goddard Earth Observing System Composition Forecast Modeling System (GEOS-CF; Keller et al. 2021), or the Copernicus Atmosphere Monitoring Service (CAMS) European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis of atmospheric composition (Innes et al. 2019), to quantify their impact on air quality and Earth's radiation budget.
# 1. TERRESTRIAL SURFACE ALBEDO DYNAMICS

— F. Cappucci, R. Urraca, and N. Gobron

The terrestrial surface albedo is a key variable controlling the amount of radiative energy absorbed by Earth's surface. Defined as the nondimensional ratio of reflected solar radiation to incident irradiance, the terrestrial surface albedo is influenced by a range of factors. An increase in vegetation density causes a “darkening” effect (negative anomalies) in the visible spectrum (0.3 µm–0.7 µm), as vegetation absorbs more radiation, while near-infrared (0.7 µm–5.0 µm) albedo increases slightly, due to healthy vegetation's higher reflectivity. In contrast, desertification or snow-covered surfaces result in a greater shortwave (0.3 µm–5.0 µm) albedo.

The 2024 anomaly (2003–20 reference period) of white sky albedo in visible spectrum (Plate 2.1ah, Fig. 2.82a) continued the darkening trend of Earth's surface as already seen in previous years (Cappucci et al. 2024; Duveiller and Gobron 2023).

The decrease in shortwave (Plate 2.1aj) white sky surface albedo over Canada was driven by the exceptional heatwaves that affected the region during the first half of 2024, which contributed to early snowmelt and large-scale wildfires. The decline in the shortwave albedo over central Europe, Scandinavia, and Greenland's coastline is also linked to the loss of snow and ice in all these areas (section 2c5).

The albedo brightening (positive anomaly) in the visible spectrum (Plate 2.1ah) over South America, (mainly Brazil, Bolivia, and Paraguay) and over southern Africa (including Zambia, Zimbabwe, Namibia, and Botswana) can be attributed to vegetation decline resulting from prolonged drought during the second and third quarters of the year, often associated with high temperatures (sections 2b1, 2d12). Surface darkening in the visible spectrum over northeastern Brazil and the Horn of Africa was characteristic of El Niño and the Indian Ocean dipole, respectively,

where abundant precipitation occurring during the first half of the year contributed to healthier vegetation. A decrease in visible surface albedo, accompanied by an increase in near-infrared spectrum (Plate 2.1ai), was recorded in central and eastern Europe, eastern China, Japan, northern Australia, and sub-Saharan northeast Africa, characteristic of an increase in vegetation activity driven by the above-average precipitation that occurred over these areas during the first half of the year (section 2d6).

The patterns of the zonally averaged albedo anomalies in the shortwave (Fig. 2.83c) during 2024 exhibit a large decline in albedo at high latitudes (above 60°N), most evident in spring driven by early snowmelt, a tendency already consolidated since early in the century (Young 2023). The increased vegetation density over China and India has resulted in lowering the visible albedo (persistent negative anomalies) and increasing the near-infrared albedo (positive anomalies), which is clear between 10°N and 30°N for 2023 and 2024 (Figs. 2.83a,b, respectively). Over the southern tropical zone, surface visible brightening is evident during the second and third quarters of 2024, and is associated with a decline in vegetation health, particularly over South America and southern Africa. Between 30°S and 40°S, a persistent

<figure><img src="image_140.png" /></figure>

Fig. 2.82. Global (black lines), Northern Hemisphere (purple), and Southern Hemisphere (orange) land surface (a) visible, (b) near-infrared, and (c) shortwave broadband albedo anomalies (%; 2003–20 reference period) for the period 2003–24. Dotted lines denote monthly values; solid lines indicate the 12-month running averaged mean.
surface darkening trend in both visible and
near-infrared domains, which had already
started in 2022, is also evident in 2024.

The globally smoothed average anomaly (solid black line) enables the detection of long-term patterns of darkening or brightening in the terrestrial surface albedo over time. Although its amplitude shows minimal variability during the 2003–24 period, with fluctuations within ±3% in the visible domain and ±2% in the near-infrared and shortwave domains (Fig. 2.82), notable trends emerge.

A positive plateau in global shortwave albedo from 2018 to 2020 was followed by consecutive drops from 2021, with a persistent decline emerging in mid-2023 and consolidating through 2024 (Fig. 2.82c). Furthermore, 2024 saw the third-darkest (negative) shortwave anomalies since 2003 both globally (-0.58%) and in the Northern Hemisphere (NH; -0.96%). The acceleration of albedo decline in 2023/24 coincides with the record-positive anomalies in absorbed solar radiation (ASR), especially in 2023 (Fig. 2.61; section 2f1), as well as global surface temperature during these years. Surface albedo anomalies have a weak contribution to ASR on a global scale due to cloud masking, which attenuates surface albedo effect on ASR by a factor of about three. However, the decline in albedo caused by snow and ice loss above 55°N and ice loss above 55°S dominates polar ASR anomalies, contributing around +0.03°C of the +0.22°C ASR-driven warming in 2023 (Goessling et al. 2025).

This analysis is 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 and 2024 data are derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi NPP satellite. Although the similarity in spectral band configuration makes VIIRS the optimal continuation of the MODIS archive (Liu et al. 2017), a small difference between VIIRS and MODIS surface albedo was noted, with VIIRS data being bias-corrected accordingly.

## 2. TERRESTRIAL VEGETATION DYNAMICS

—N. Gobron and F. Cappucci

The fraction of absorbed photosynthetically active radiation (FAPAR) measures the amount of radiation absorbed by plant canopies. FAPAR is a key indicator of vegetation density, health, and productivity, and plays a crucial role in evaluating how effectively plants absorb carbon dioxide from the atmosphere. According to the FAPAR record in 2024, extreme deviations from

<figure><img src="image_141.png" /></figure>

<figure><img src="image_142.png" /></figure>

<figure><img src="image_143.png" /></figure>

Fig. 2.83. Zonally averaged white-sky (a) visible, (b) near-infrared, and (c) shortwave broadband land surface albedo anomalies (%) for the period 2003–24 using a 2003–20 reference period.
the 1998–2020 average (above 0.04 and below –0.04) accounted for 13% of land areas, with 11% showing an increase in productivity and 2% showing a decrease.

As illustrated in Plate 2.1ak, Earth's surfaces continued their greening trend in 2024 due to both higher temperatures (section 2b1) as well as plentiful rainfall (section 2d5). This is especially noticeable over continental Europe, excluding Bulgaria and Ukraine. The North American Arctic experienced an exceptional level of tundra greenness, with northern Alaska and central and eastern Canada all showing significantly above-average levels of greenness (Frost et al. 2024). In contrast, northeastern Siberia was again suffering from highly active wildfires. Northeastern Brazil vegetation recovered from 2023, particularly during the first half of the year. Positive anomaly patterns were seen in eastern Africa—including Kenya, Somalia, and Ethiopia—where, remarkably, overall green conditions were present due to extreme rainfall at the end of 2023; however, vegetated productivity declined in the second half of the year, especially in Somalia due to below-average rainfall between October and December. Northern and eastern Australia reported greener annual conditions, building on the higher-than-normal levels seen during the first six months of the year.

In Canada, forests in Quebec still bear the scars of fires in 2023, as evidenced by negative annual anomalies indicating that the area has not yet fully recovered. Additionally, there were persistent and large-scale fires that affected British Columbia and Alberta in July 2024 (section 2h3), and the number of wildfires increased in the Northwest Territories through August following heatwaves across the region.

The prolonged spring heatwaves that affected Central America contributed to vegetation decline in Chihuahua (Mexico) and Yucatan (Guatemala), and set records in high temperature and drought. Conversely, the state of Nuevo León (Mexico) experienced a significant increase in photosynthetic activity due to recovery of vegetation surfaces from last year.

Several regions in South America, including the Amazon and other areas of Brazil and Bolivia, experienced an extended period of drought, characterized by low rainfall and high temperatures. This led to severe summer wildfires affecting the forests. While the Sahel received excess rainfall (section 2d5), several countries in southern Africa, including Namibia, Botswana, Nigeria, Zambia, and Mozambique, experienced severe rainfall deficits and record-high temperatures. India and Pakistan, along with China, still exhibited a strong annual greenness due to agricultural intensification (Park et al. 2023) despite occasional heatwaves and floods.

The monthly anomalies of longitude-averaged FAPAR from 1998 to 2024 (compared to the 1998–2020 base period, Fig. 2.84) reveal that all latitudes between 2002 and 2013, particularly in the Southern Hemisphere (SH), experienced significant vegetation decline (values below –0.04). This was followed by an increasing greenness of surfaces in both hemispheres thereafter. Furthermore, during 2024, positive values were observed at almost all latitudes, with exceptionally high values (exceeding +0.04) noted around 50°N at the start of the year and shifting to around 10°N in the latter part of the year.

Figure 2.85 shows the global and hemispheric results, with the SH showing greater seasonal variability than the NH. FAPAR monthly anomalies over the SH were positive before 2002, in 2011, and after 2014, except for 2019. There were strong positive peaks (above +0.01) during the summers of 2000, 2017, and 2023, corresponding to fewer extreme events such as fire or drought, compared to the negative peaks (below –0.01) in 2008/09. A severe drought that affected a significant portion of South America and southern Africa led to an extreme negative deviation of –0.015 in October 2024. The NH was positive in 1998 but negative from 1999 to 2013 and positive thereafter, reaching a peak

<figure><img src="image_144.png" /><figcaption>Fig. 2.84. Zonally averaged fraction of absorbed photosynthetically active radiation (FAPAR) anomalies for the period 1998–2024 (1998–2020 base period).</figcaption></figure>
(above 0.014) in spring 2024. This greening trend is directly linked to the increase of land surface temperatures but also to the carbon dioxide fertilization effect (Zhu et al. 2016).

FAPAR, an essential climate variable (GCOS 2022), was estimated using optical space sensors. The 2024 analysis combines 27 years of global products from four optical sensors: the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS; 1998–2002); Envisat/ Medium Resolution Imaging Spectrometer (MERIS; 2003–10), *Terra-Aqua/MODIS* (2011–16), and EU Copernicus Sentinel-3/Ocean and Land Colour Instrument (OLCI; 2017–24; Gobron et al. 2010, 2022; Pinty et al. 2011).

Uncertainties of the four datasets were derived through error propagation techniques and comparisons against multiple proxies using ground-based measurements (Bai et al.

2022) and radiative transfer simulations, all of which provide an estimate of the uncertainties and biases. This long-term FAPAR dataset has an estimated average uncertainty of approximately 5% to 10%.

### 3. BIOMASS BURNING

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

Two distinct trends in global biomass burning were identified over the last decade, both of which persisted into 2024. Many savanna regions, which are responsible for most global fire emissions, have experienced a decline related to agricultural expansion. In contrast, many forested, wetland, and boreal regions have witnessed an increase in both the duration and intensity of wildfire episodes (see Plate 2.1al). This is attributable to climate change, which has led to increased drought periods and, consequently, heightened flammability of the landscape (e.g., Xing and Wang 2023 for the Arctic). The magnitude of biomass burning, denoted as “fire activity” or “wildfires”, is here described as the quantity of carbon that is consumed by fire and released into the atmosphere. Of this, 80% to 95% is emitted as carbon dioxide (CO₂), with the remainder being oxidized to CO₂ in the atmosphere or released as particulate matter. In a stable ecosystem, the bulk of this CO₂ is typically assimilated through vegetation re-growth. However, it is estimated that 20% currently contributes to the long-term buildup of atmospheric CO₂ (Zheng et al. 2023).

Global annual total estimated fire emissions were below the 2003–20 average in 2024 by approximately 7%, but were still the second highest since 2016 (after 2023), according to the Copernicus Atmosphere Monitoring Service's (CAMS) Global Fire Assimilation System version 1.4 (GFASv1.4; Fig. 2.86; Table 2.13). The global total was affected by anomalously large-scale wildfires that burned persistently in forests across South and North America at different points throughout the year (Figs. 2.87, 2.88). In the latter, Canada again experienced an extreme year with fire activity above any year between 2003 and 2022, with the annual total having been second only to the record fires of 2023 (Fig. 2.88). The western United States experienced its seventh-highest annual total fire emissions since 2003 with a +22% anomaly. The year 2024 also saw an increase in

<figure><img src="image_145.png" /><figcaption>Fig. 2.85. Global (black line), Northern Hemisphere (purple), and Southern Hemisphere (orange) fraction of absorbed photosynthetically active radiation (FAPAR) anomalies for 1998–2024 (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_146.png" /><figcaption>Fig. 2.86. Global monthly carbon consumption (Tg C month⁻¹) in biomass burning.</figcaption></figure>
<table><caption>Table 2.13. Annual continental-scale biomass burning budgets in terms of carbon emission (Tg C yr⁻¹) from the Copernicus Atmosphere Monitoring Service Global Fire Assimilation System version 1.4 (CAMS-GFASv1.4).</caption><thead><tr><th>Name of Region</th><th>Location</th><th>Biomass Burning 2003–20 Mean value (Range)</th><th>Biomass Burning 2024 Value</th><th>Biomass Burning 2024 Anomaly (%)</th></tr></thead><tbody><tr><td>Global</td><td>–</td><td>1954 (1669–2293)</td><td>1812</td><td>-142 (-7%)</td></tr><tr><td>North America</td><td>30°N–75°N, 190°E–330°E</td><td>86 (58–114)</td><td>148</td><td>+62 (+71%)</td></tr><tr><td>Central America</td><td>13°N–30°N, 190°E–330°E</td><td>43 (29–62)</td><td>46</td><td>+3 (+7%)</td></tr><tr><td>South America</td><td>13°N–60°S, 190°E–330°E</td><td>352 (225–526)</td><td>486</td><td>+134 (+38%)</td></tr><tr><td>Europe and Mediterranean</td><td>30°N–75°N, 330°E–60°E</td><td>30 (16–58)</td><td>22</td><td>-8 (-28%)</td></tr><tr><td>N. Hem. Africa</td><td>0°–30°N, 330°E–60°E</td><td>394 (314–457)</td><td>305</td><td>-89 (-23%)</td></tr><tr><td>S. Hem. Africa</td><td>0°–35°S, 330°E–60°E</td><td>482 (429–544)</td><td>446</td><td>-36 (-7%)</td></tr><tr><td>Northern Asia</td><td>30°N–75°N, 60°E–190°E</td><td>185 (97–425)</td><td>156</td><td>-29 (-16%)</td></tr><tr><td>South-East Asia</td><td>10°N–30°N, 60°E–190°E</td><td>116 (80–153)</td><td>86</td><td>-31 (-26%)</td></tr><tr><td>Tropical Asia</td><td>10°N–10°S, 60°E–190°E</td><td>149 (24–448)</td><td>24</td><td>-125 (-84%)</td></tr><tr><td>Australia</td><td>10°S–50°S, 60°E–190°E</td><td>117 (49–221)</td><td>94</td><td>-22 (-19%)</td></tr><tr><td>Canada</td><td>47°N–75°N, 219°E–310°E</td><td>46 (10–80)</td><td>111</td><td>+65 (+141%)</td></tr><tr><td>Arctic</td><td>67°N–90°N, 0°–360°</td><td>7 (1–35)</td><td>16</td><td>+8 (+117%)</td></tr></tbody></table>

wildfire emissions from boreal Eurasia, following persistent wildfires in the Sakha Republic and Amur Oblast in the east of Russia. The first contributed to the third strongest wildfires in the Arctic after 2019/20 (Fig. 2.88). Overall, however, North Asia experienced a negative anomaly of -16%.

South America experienced its highest fire activity since 2007, but approximately equal with 2010, at 38% above the 2003–20 mean (Fig. 2.88). The spring seasonal fires in the northern tropics of South America led to Venezuela, Guyana, Suriname, and Brazil's Roraima state all experiencing their highest annual total emissions. In central regions, Bolivia, the Pantanal

wetlands, and some parts of the Brazilian Amazon experienced historic fires with the highest emissions of the past two decades, largely driven by drought conditions (section 2d11). Bolivia experienced its highest fire activity since at least 2003, with each month during January to November exceeding its 2003–20 monthly mean. Fires in Pantanal burned 1.2 million hectares between January and August, 14% of which were on either indigenous lands or protected natural areas including the Pantanal wetlands (Alencar et al. 2024). In Brazil, Amazon fire emissions peaked higher than any year since 2010, with emissions in the states of Mato Grosso and Pará well above average, as El Niño-driven drought (see section 7d; Marengo et al. 2024)

<figure><img src="image_147.png" /><figcaption>Fig. 2.87. Global map of fire activity in 2024 in terms of carbon consumption (g C m⁻² yr⁻¹). (Source: Copernicus Atmosphere Monitoring Service Global Fire Assimilation System version 1.4 [CAMS-GFASv1.4.])</figcaption></figure>
significantly increased fire risk despite reduced deforestation. Earlier seasonal drying also heightened flammability, leading to widespread fire outbreaks (Feron et al. 2024).

African fire carbon emissions accounted for roughly half of the total global emissions during the 2000s, but their contribution shrunk to ~40% during recent years (Fig. 2.87; Plate 2.1al). The decrease in savanna regions persisted in 2024 over Northern Hemisphere Africa with emissions 23% below the 2003–20 average, the fourth successive year with lower fire activity than any years in the record prior to 2019. Southern Hemisphere Africa also contributed to the trend despite increased fire activity in several regions, including the central African tropical forest. Fire activity in southeast and tropical Asia generally continued reducing trends. In the latter—including Indonesia—El Niño-induced drought combined with land-clearing fires on palm, pulp, and rice plantations have led to extreme fires in the past. However, the fire activity of 2024 was

lower than during any year from 2003 to 2016 (Fig. 2.88) despite the El Niño conditions at the beginning of the year. Increased wildfire emissions occurred in tropical regions of Australia between September and November, related to warmer and drier conditions (Plate 2.1al).

GFAS is operated by 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 near-real-time and consistently reprocessed products are used here, with input from MODIS Collection 6 for the entire period of 2003–24. The biases with respect to Collection 5 and between satellites have been corrected, and a more extensive spurious signal mask has been applied. Archived MODIS input is used for 1 January 2003 to 18 December 2016 and August 2024, and near-real-time (NRT) input is used otherwise. The replacement for August 2024 is necessary due to corrupt NRT input, and uses an updated land cover map. The time series in Fig. 2.86 also places GFAS in the context of the Global Fire Emissions Database version 4.1 (GFED4s), which is primarily based on burnt area observation and dates back to 1997 (van der Werf et al. 2017).

<figure><img src="image_148.png" /><figcaption>Fig. 2.88. Regional time series of monthly (lines in Tg C month⁻¹) and annual (symbols in Tg C yr⁻¹) biomass burning activity in (a) Canada, (b) South America, (c) the Arctic, and (d) tropical Asia.</figcaption></figure>

## 4. PHENOLOGY

—D. L. Hemming, O. Anneville, Y. Aono, T. Crimmins, N. Estrella, R. Holliday, 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 influenced by climate variations and can modify climate through land-atmosphere exchanges of energy, moisture, and carbon (Hassan et al. 2024).

PhenoCam (https://phenocam.nau.edu/) is a network of over 900 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 (there are over 75 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 (Table 2.14) and ground observation 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 and VanScoy 2024), 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 (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 larger-scale data from USA NPN's *Nature's Notebook* program (Figs. 2.89a,b; Table 2.15). In 2024, SOS<sub>PC</sub>, SOS<sub>GO</sub>, and SOS<sub>NN</sub> were three, six, and four days later, respectively, than in 2023, while EOS<sub>PC</sub>, EOS<sub>GO</sub>, and EOS<sub>NN</sub> were seven, four, and four days earlier. SOS<sub>PC</sub> (EOS<sub>PC</sub>) was two days earlier (one day earlier) than the 2011–20 baseline mean, resulting in a growing season length of 168 days: 10 days shorter than in 2023, and one day longer than the baseline.

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 later “first leaf” in 2024 across the eastern and southern United States and earlier first leaf across the western and central United States compared with 2023. In contrast, compared with the baseline (2011–20 mean), first leaf in 2024 was early across most of the eastern United States, and late across the west (Figs. 2.89a,b). Consistent with the SOS<sub>PC</sub> and SOS<sub>GO</sub> observations at Harvard Forest, first leaf was later (by three days) in 2024 than in 2023 and earlier (by one day) in 2024 compared to the baseline. Likewise, first leaf and SOS<sub>PC</sub> observations from a selection of nine other sites across the USA showed consistent patterns (see Fig. 2.89 for details).

Start- and end-of-season indicators for native oak trees (*Quercus robur* and/or *Quercus petraea*) at European sites in Germany (D), the Netherlands (NL), Slovakia (SK), and the

<figure><img src="image_149.png" /><figcaption>Fig. 2.89. 2024 “first leaf” date anomalies across the United States relative to (a) 2023 and (c) the 2011–20 baseline, estimated using the USA National Phenological Network (USA NPN) extended Spring Index (SI-x) model (Source: USA NPN data—https://www.usanpn.org/data). Negative (green) values show earlier first-leaf estimates in 2024 and positive (brown) are later. First-leaf SI-x anomalies are generally consistent with start of season PhenoCam (SOS<sub>PC</sub>) anomalies at the 10 selected sites highlighted on map (a), see Table 2.14 for details. The scatter plot (b, inset in [a]) shows PhenoCam date anomalies on the x axis, and first-leaf SI-x date anomalies on the y axis (Deming regression, y = 1.02±0.04 x – 0.29±0.49, r<sup>2</sup> = 0.69). Map (c) shows the four sites from (a) for which the data record is long enough to calculate the 2011–20 baseline.</figcaption></figure>

<table><caption>Table 2.14. PhenoCam sites across USA used in this assessment.</caption><thead><tr><td>PhenoCam Site Name (#)</td><td>Location</td><td>Ecosystem Type</td></tr></thead><tbody><tr><td>ARS Great Basin (1)</td><td>Reynolds Creek, Idaho</td><td>Shrub Community</td></tr><tr><td>NEON Site – D10 Central Plains (2)</td><td>Arikaree River, Colorado</td><td>Grass Community</td></tr><tr><td>Nine Mile Prairie, University of Nebraska (3)</td><td>Lancaster County, Nebraska</td><td>Grass Community</td></tr><tr><td>University of Illinois Energy Farm (4)</td><td>Urbana, Illinois</td><td>Restored Prairie Community</td></tr><tr><td>Morgan Monroe State Forest (5)</td><td>Indiana</td><td>Deciduous Forest</td></tr><tr><td>Bartlett Experimental Forest (6)</td><td>New Hampshire</td><td>Deciduous Forest</td></tr><tr><td>Susquehanna Shale Hills Critical Zone Observatory (7)</td><td>Pennsylvania</td><td>Deciduous Forest</td></tr><tr><td>Russell Sage State Wildlife Management Area (8)</td><td>Louisiana</td><td>Deciduous Forest</td></tr><tr><td>NEON Site – D08 Ozarks Complex (9)</td><td>Lenoir Landing, Alabama</td><td>Deciduous Forest</td></tr><tr><td>Harvard Forest (H)</td><td>Petersham, Massachusetts</td><td>Deciduous Forest</td></tr></tbody></table>
United Kingdom (UK) are represented by observations of first leaf (SOS) and leaf fall or “bare tree” (EOS; Table 2.15; Figs. 2.90c,d). They have been shown to be strongly influenced by spring and autumn/winter temperatures across Europe (Menzel et al. 2020). Compared to the baseline (2000–20 mean), SOS dates in D, NL, SK, and UK were all earlier by 11, 13, 12, and 8 days, respectively, while EOS dates were mostly later by 2, 5, 3, and 0 days (Table 2.15). Spring temperatures in D, NL, UK, and SK were much warmer (record high in SK; see section 7f) than average, resulting in very early 2024 SOS in D and SK, and the earliest SOS on record in NL and UK. Above-average temperatures in autumn/winter combined with ample precipitation across most of Europe resulted in generally later-than-average EOS dates. In some locations of SK, where extreme summer drought occurred (see section 7f), very early yellowing and leaf fall was observed at the end of August. With the warmer autumn/winter and consequently later EOS dates, the growing season length in 2024 was significantly longer than the baseline.

In Kyoto, Japan, the full bloom date (FBD) for native cherry tree (Prunus jamasakura) in 2024 was two days earlier than the baseline (2000–20 mean; Table 2.15; Fig. 2.90e), but 10 days later than the record set in 2023. Annual FBDs have been recorded since 812 AD (Aono and Kazui 2008), and for the Arashiyama district of Kyoto, they are recorded in newspapers and on web sites from daily observations at train stations by railway passengers.

Monitoring data on lake water concentrations of the photosynthetic pigment chlorophyll-a were available to estimate spring phytoplankton phenology in 10 Northern Hemisphere lakes (Fig. 2.91). 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 2024 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—8 and 7 of 10, respectively—whereas no consistent pattern was observed for DOM<sub>L</sub> (5 earlier and 5 later than the baseline median).

<table><caption>Table 2.15. Day of year (doy) 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 2011–20. Growing season length for 2024 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 2024 relative to the baseline.</caption><thead><tr><th rowspan="2">Location / Record</th><th>SOS/FBD 2024</th><th>SOS/FBD Baseline</th><th>SOS/FBD Difference 2024 - Baseline</th><th>EOS 2024</th><th>EOS Baseline</th><th>EOS Difference 2024 - Baseline</th><th>Growing Season EOS - SOS 2024</th><th>Growing Season EOS - SOS Baseline Mean</th></tr><tr><th>(doy, date)</th><th>(doy, date)</th><th>(days)</th><th>(doy, date)</th><th>(doy, date)</th><th>(days)</th><th>(days)</th><th>(days)</th></tr></thead><tbody><tr><td>Harvard PhenoCam</td><td>125 4 May</td><td>127 6 May</td><td>-2</td><td>293 19 Oct</td><td>294 20 Oct</td><td>-1</td><td>168</td><td>167</td></tr><tr><td>Harvard Red oak</td><td>128 7 May</td><td>128 7 May</td><td>0</td><td>295 21 Oct</td><td>293 19 Oct</td><td>+2</td><td>167</td><td>164</td></tr><tr><td>USA-NPN</td><td>120 29 Apr</td><td>126 5 May</td><td>-6</td><td>268 24 Sep</td><td>276 2 Oct</td><td>-8</td><td>148</td><td>150</td></tr><tr><td>Germany</td><td>107 17 Apr</td><td>118 28 Apr</td><td>-11</td><td>312 8 Nov</td><td>310 6 Nov</td><td>+2</td><td>205</td><td>192</td></tr><tr><td>Netherlands</td><td>97 6 Apr</td><td>110 20 Apr</td><td>-13</td><td>336 1 Dec</td><td>331 27 Nov</td><td>+5</td><td>239</td><td>221</td></tr><tr><td>Slovakia</td><td>104 14 Apr</td><td>116 26 Apr</td><td>-12</td><td>294 20 Oct</td><td>291 17 Oct</td><td>+3</td><td>190</td><td>175</td></tr><tr><td>UK</td><td>106 15 Apr</td><td>114 23 Apr</td><td>-8</td><td>334 29 Nov</td><td>334 29 Nov</td><td>0</td><td>228</td><td>221</td></tr><tr><td>Japan</td><td>95 4 Apr</td><td>97 6 Apr</td><td>-2</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
<figure><img src="image_150.png" /></figure>

Fig. 2.90. Day of year of spring (green shades) and autumn (orange and yellow) vegetation phenology indicators for (a),(b) Harvard Forest, Massachusetts, 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, with ±1 std. error shaded); (c),(d) Germany, United Kingdom, Netherlands, and Slovakia mean of native oak observations (*Quercus robur* or *Quercus petraea*), and (e) full bloom date observations of native cherry trees (*Prunus jamasakura*) in Kyoto (Arashiyama district), Japan.

<figure><img src="image_151.png" /></figure>

<figure><img src="image_152.png" /></figure>

<figure><img src="image_153.png" /></figure>

Fig. 2.91. 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 2024 values. The 10 lakes are in the Northern Hemisphere (Blelham Tarn in the United Kingdom, Bourget in France, Esthwaite Water in the United Kingdom, Geneva in France/Switzerland, Kasumigaura in Japan, Kinneret in Israel, Loch Leven in the United Kingdom, Mjøsa in Norway, and north and south basins of Windermere in the United Kingdom).
# 5. VEGETATION OPTICAL DEPTH

—R. M. Zotta, W. Preimesberger, R. De Jeu, T. Frederikse, and W. Dorigo

Vegetation optical depth (VOD) is a radiative transfer model parameter derived from space-borne passive microwave sensors that indicates the attenuation of Earth's emitted or reflected radiation by vegetation. It serves as an indicator of vegetation water content (VWC) and vegetation density. VOD has proven valuable in many applications, including vegetation condition monitoring (Moesinger et al. 2022; Vreugdenhil et al. 2022). Positive VOD anomalies indicate above-average vegetation development, while negative anomalies indicate stressed or underdeveloped vegetation with lower VWC compared to normal conditions, either due to stressed vegetation, overall lower biomass, or both.

In 2024, the overall VOD anomaly in the NH was similar to that observed in 2023 (Zotta et al. 2023; Fig. 2.92). In contrast, in the SH, where vegetation development is influenced by variations in the El Niño–Southern Oscillation (ENSO; Miralles et al. 2014; Martens et al. 2017), overall VOD was lower than in 2023 (Figs. 2.92, 2.93). Here, negative anomalies prevailed, especially in the first quarter of the year (Appendix Fig. A2.13), during the strong El Niño event.

Widespread positive anomalies (Plate 2.1am), which intensified relative to 2023 (Appendix Fig. A2.14), were observed in large regions across Australia and parts of eastern Africa. In Australia, these patterns were likely driven by above-average rainfall (section 7h4) boosting vegetation growth. Substantial positive anomaly patterns were observed during the first half of 2024 across large parts of Kenya, Somalia, Ethiopia, and South Sudan (Appendix Fig. A2.13), likely a consequence of two consecutive seasons of wetter-than-normal conditions facilitating vegetation recovery after an extended drought period (WFP 2024). Additionally, notable positive VOD anomaly patterns emerged in the Sahel starting in September, likely driven by substantial rainfall (section 7e5) that boosted vegetation activity. At the monthly time scale (Appendix Fig. A2.13), striking positive patterns were observed in North America across portions of the Plains and Midwest, where exceptionally warm temperatures led to favorable winter crop conditions (USDA and USDC 2024) in the year's first quarter. Strong positive VOD anomaly patterns also emerged in Spain in October, likely due to record rainfall amounts (section 7f4).

Large-scale negative anomaly patterns in VOD were identified across multiple regions, including extended areas in Africa, Mexico, Central and South America, and Eastern Europe (Plate 2.1am). During the first quarter of 2024, lower-than-usual VOD continued to be evident in southern

<figure><img src="image_154.png" /></figure>

Fig. 2.92. Yearly vegetation optical depth (VOD) anomaly averages computed from the 1991–2020 climatology and yearly Niño-3.4 index. The Niño-3.4 index tracks the state of the El Nino–Southern Oscillation (ENSO). A negative Niño-3.4 index corresponds to a negative ENSO phase. (Source: VOD Climate Archive version 2 [VODCA v2]; NOAA Physical Sciences Laboratory [https://psl.noaa.gov/data/timeseries/month/DS/Nino34/].)

<figure><img src="image_155.png" /></figure>

Fig. 2.93. Time–latitude diagram of monthly vegetation optical depth (VOD) anomalies (1991–2020 base period). Data are masked where no retrieval is possible or where the quality of the retrieval is not assured and flagged due to frozen soil, radio frequency interference, etc. (Source: VOD Climate Archive version 2 [VODCA v2].)
Africa (Appendix Fig. A2.13), where ENSO is a significant factor in agricultural productivity (UNOCHA 2024). Specifically, negative VOD anomalies in February and March were recorded in Namibia, Botswana, Zimbabwe, Zambia, and Angola. These regions experienced their driest conditions in decades (Van Dijk et al. 2025), which deteriorated the vegetation (UNOCHA 2024). In Zambia, severe drought and high temperatures devastated agricultural production (Van Dijk et al. 2025). In the Americas, Mexico experienced a prolonged dry spell which, combined with intense heatwaves (section 7b3), severely impacted crop growth (Climate.gov 2024). The reduction in VOD observed in the Pantanal region in Brazil is likely a result of extreme drought (NASA 2024). Persistent drought conditions significantly affected vegetation condition across West, Central, and North Africa during the first half of the year, contributing to the observed negative VOD anomalies (Appendix Fig. A2.13; Toreti et al. 2024a). Similarly, declines in VOD in Romania, Ukraine, and southern Russia are likely attributable to extended drought conditions that adversely affected local vegetation (Toreti et al. 2024b).

Long-term patterns associated with land-use changes persisted in 2024 (Plate 2.1am; Zotta et al. 2023). Regions including northern Mongolia, Bolivia, Paraguay, and Brazil experienced below-average VOD due to deforestation and land degradation (Song et al. 2018). Conversely, intensified agricultural practices in India and reforestation efforts in northeastern China contributed to above-average VOD (Song et al. 2018).

The VOD data originate from the VOD Climate Archive version 2 (VODCA v2, Zotta et al. 2024a,b). VODCA combines VOD observations derived with the Land Parameter Retrieval Model (Meesters et al. 2005; van der Schalie et al. 2017) from various space-borne radiometers—including the Special Sensor Microwave/Imager (SSM/I), Tropical Rainfall Measuring Mission (TRMM), WindSat, Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2)—into a long-term, harmonized dataset. Here, VODCA CXKu, which integrates C-, X-, and Ku-band observations and provides daily data at 0.25° spatial resolution, was used.
# Acknowledgments

## a Introduction

R.J.H.D., J.B., and K.M.W. were supported by the Met Office Hadley Centre Climate Programme funded by the U.K. Department for Science, Innovation and Technology (DSIT). G.A.M. is supported by NOAA. The editors thank Emily Carlisle, John Kennedy, David Parker, Jessica Poulton, Nick Rayner, Ruben Urraca, and the five anonymous reviewers for their insight, thoughts, and suggestions while reviewing this chapter. We also thank Fumi Sezaki and Ayaka Bunno (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. Many of the figures in this chapter were produced with the help of the Matplotlib version 3.7.2 (Hunter et al. 2007), Iris version 3.7.0 (Hattersley et al. 2023), and Cartopy version 0.21.1 (Elson et al. 2022) packages.

## b2 Lake Surface Temperatures

Lake surface water temperatures from satellite data have been generated within the Climate Change Initiative Lakes project funded by the European Space Agency (ESA) (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 the Fisheries and Oceans Canada (FOC), the National Data Buoy Center 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.

## b3 Night Marine Air Temperatures

R.C. and E. K. were supported by the Natural Environmental Research Council (NERC) through National Capability funding (AtlantiS: NE/Y005589/1). R. J. was supported by funding from the U.S. Department of Energy (DOE) (DE-SC0023332).

## b4 Surface Temperature Extremes

R.J.H.D. was supported by the Met Office Hadley Centre Climate Programme funded by 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 (RGMA) of the Office of Science at the DOE and performed under the auspices of the DOE under Contract DE-AC52-07NA27344. Work performed by Cheng-Zhi Zou was supported by NOAA/Office of Low Earth Orbit (LEO) Proving Ground and Risk Reduction (PGRR) Program.

## b7 Equivalent Temperature

Paul Stoy was supported by the U.S. National Science Foundation (NSF) (Hydrological Sciences award 2422397). Tom Matthews and Tom Wood were supported by a U.K. Research and Innovation Future Leaders Fellowship (grant MR/X03450X/1).

## c1 Permafrost Temperatures and Active-Layer Thickness

Research on James Ross Island was supported by the Czech Antarctic Research Programme and the Czech Science Foundation project (GM22-28659M). The Circumpolar Active Layer Monitoring (CALM) program is funded by the NSF Project 1836377. The Svalbard permafrost data is part of the Svalbard Integrated Earth Observing System (SIOS). 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, and acknowledges the contribution of its partner
institutions. The French Network PermaFRANCE is financially supported by the Grenoble Observatory for Sciences of the Universe and the French Research Infrastructure Critical Zone Observatories: Research and Application (OZCAR). The Chinese Permafrost Monitoring Network is financially supported by the Chinese National Science Foundation (41931180) and Cryosphere Research Station on Qinghai-Xizang Plateau, Chinese Academy of Sciences (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 French National Research Agency in the framework of the Investments for the Future programs: Risk@UGA (ANR-15-IDEX-02) and LabEx OSUG@2020 (ANR10 LABX56). The Ecrins National Park has been supporting field surveys since the early 2000s. PERMOS is financially supported by MeteoSwiss in the framework of GCOS Switzerland, the Federal Office for the Environment, and the Swiss Academy of Sciences. PERMOS acknowledges the important contribution of the partner institutions and principal investigators. The time series for Central Asian rock glaciers was compiled within the ESA Permafrost Climate Change Initiative (Permafrost_CCI) project (4000123681/18/I-NB). The time series for the Dry Andes was supported by the Center for Advanced Studies in Arid Zones (CEAZA) and the Leading House for the Latin American Region (University of St. Gallen), grant number MOB1829.

## c3 Alpine Glaciers

The World Glacier Monitoring Service mass balance dataset is the primary data used in the section.

## c4 Lake Ice Cover

We thank Al and Sue Stangel, Alexander Mills, Ann LaLiberte, Beth Kohlman, B.J. 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 Pierson, 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, Martin Kainz, Joy Krubsack, Kay and Rich Rezanka, Kay Olson, Kay Wepfer, 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, 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, Sogee Spinner, 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 collect and share in situ ice phenological records with us. A part of this research was enabled by an intern, Jessica Ollinik, from the Canadian Institute of Ecology and Evolution's Living Data Project, which is funded by the Government of Canada through a Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Training Experience (CREATE) grant.
### 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.

### d1 Surface Humidity

Kate Willett was supported by the Met Office Hadley Centre Climate Programme funded by DSIT.

### d2 Humid-Heat extremes

Kate Willett was supported by the U.K.-China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China under the International Science Partnerships Fund (ISPF). Kate Willett was also supported by the Met Office Hadley Centre Climate Programme funded by DSIT.

Cassandra Rogers was supported by the Australian Climate Service.

Thank you to Mitchell Black and Ulrike Bende-Michl from the Bureau of Meteorology for providing initial reviews of this section.

### d6 Precipitation Extremes

The National Center for Atmospheric Research is sponsored by the National Science Foundation under Cooperative Agreement No. 1852977. Stephen Blenkinsop was supported by the UK NERC funded grant no. NE/Y503241/1.

### d7 Cloudiness

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

### d9 River Discharge

The Global Flood Awareness System (GloFAS) is part of the Copernicus Emergency Management Service, which is funded by the European Union Space Program.

### d10 Groundwater and Terrestrial Water Storage

This work was supported by NASA's Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) Science Team.

### d11 Soil Moisture

This study uses satellite soil moisture observations from the C3S's Climate Data Store (CDS; 2025): Soil moisture gridded data from 1978 to present. Accessed on 11 February 2025, 10.24381/cds.d7782f18

### d12 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. NERC (NE/S015582/1). Ian Harris and Amee Gollop 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.

### d13 Land Evaporation

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

### e1 Mean Sea Level Pressure and Related Modes of Variability

David Fereday was supported by the Met Office Hadley Centre Climate Programme funded by DSIT.
## e2 Surface Winds

C.A.M. was supported by the Spanish National Research Council (CSIC), University of Valencia (UV), Generalitat Valenciana (GVA) and funded by PROMETEO Grant CIPROM/2023/38, International Global Change Laboratory (LINCGLOBAL)-CSIC ref. LINCG24042, and the Interdisciplinary Thematic Platform for Climate and Climate Services (PTI-CLIMA). R.J.H.D. was supported by the Met Office Hadley Centre Climate Programme funded by DSIT. L.R. was supported by NASA Ocean Vector Wind Science Team grant 80NSSC23K0984. Z.Z. was supported by the National Natural Science Foundation of China grant 42071022.

## e4 Lightning

The work of M.F. was sponsored by the Royal Society (U.K.) grant NMG/R1/180252 and NERC (U.K.) under grants NE/L012669/1 and NE/H024921/1.

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

C.P. 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.G. 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 ECMWF for suggesting and initiating this work and for recommending that lightning be made an essential climate variable. The Meteosat Third Generation Lightning Imager (MTG-LI) data used to generate the Figures are available from https://user.eumetsat.int/resources/user-guides/mtg-data-access-guide.

## f1 Earth Radiation Budget at Top-of-Atmosphere

This research has been supported by the NASA 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 EBAF Ed4.2.1 and processes the Fast Longwave and Shortwave Radiative Fluxes (FLASHFlux) Time Interpolated and Spatially Averaged (TISA) version 4C. 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 version 4C monthly averaged data products, respectively.

## f2 Mauna Loa Clear-Sky Atmospheric Solar Transmission

Key balloon data from Hilo used in this 2024 update of the Mauna Loa apparent atmospheric transmission would not have been possible without the generous cooperation afforded by Global Monitoring Laboratory (GML) staff, Matthew Martinsen, Darryl Kuniyuki, and David Nardini, at the Mauna Loa Observatory.

## g1 Long-Lived Greenhouse Gases

This research was supported in part by NOAA cooperative agreement NA22OAR4320151, for the Cooperative Institute for Earth System Research and Data Science (CIESRDS).

## g2 Ozone-Depleting Gases

This research was supported in part by NOAA cooperative agreement NA22OAR4320151, for the Cooperative Institute for Earth System Research and Data Science (CIESRDS).

## g3 Tropospheric Aerosols

This research has been supported by the Copernicus Atmospheric Monitoring Service (CAMS) program managed by ECMWF (Framework Agreement ECMWF/Copernicus/2021/CAMS2_35_HYGEOS) on behalf of the European Commission. A large number of observational datasets are used in the CAMS reanalysis; the authors would like to thank all the actors that created and made public the remote sensing products assimilated in the CAMS Reanalysis: NASA, NOAA, the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and the ESA.
## 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. P. Effertz was supported by NOAA cooperative agreement NA22OAR4320151. Funding for J. Ziemke for this research was provided in part by NASA NNH14ZDA001N-DSCOVR, NASA Suomi National Polar-orbiting Partnership (SNPP) 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 the Haute-Provence Observatory (OHP) are funded by the French National Centre for Scientific Research/National Institute of Sciences of the Universe (CNRS/INSU) and the National Centre for Space Studies (CNES). S. Khaykin's work is supported by National Agency for Research (ANR) PyroStrat 21-CE01-335 0007-01 project. We thank Christine David, Alain Hauchecorne, Julien Jumelet, Philippe Keckhut (Laboratory for Atmospheres, Observations, and Space [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. G. Taha's work is supported by the NASA Earth Science Division grant 80NSSC23K1037.

## g6 Stratospheric Ozone

Carlo Arosio, Melanie Coldewey-Egbers, Diego Loyola, Viktoria Sofieva, Alexei Rozanov, and Mark Weber are grateful to the E.U. C3S, C3S2_312a_Lot2 Ozone, and to the ESA's Climate Change Initiative Ozone (CCI+) projects for supporting the generation and extension of the Global Ozone Monitoring Experiment-type Total Ozone Essential Climate Variable (GTO-ECV) total ozone and Stratospheric Aerosol and Gas Experiment (SAGE) CCI Ozone Mapping and Profiler Suite (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). The OSIRIS Team is grateful for the support of the Canadian Space Agency since 1993. The NASA Long Term Measurement of Ozone program WBS 479717 supports Stacey M. Frith. Lucien Froidevaux's contribution, with the assistance of Ryan Fuller, was performed at the California Institute of Technology's Jet Propulsion Laboratory (JPL), 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 "Methods for analyzing and assessing changes in the atmosphere and the climate system" (MABAK) and INPULS.

## g7 Stratospheric Water Vapor

We would like to acknowledge assistance with water vapor sonde launches from NOAA GML staff in Boulder, Colorado, and Hilo, Hawaii, and international collaborators in San Jose, Costa Rica, and Lauder, New Zealand. This research was supported by the NASA Upper Atmosphere Composition Observations (UACO) program.

## g8 Carbon Monoxide

CAMS is funded by the E.U.

## 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) (https://doi.org/10.5281/zenodo.10214201).
## h4 Phenology

Deborah Hemming and Rebecca Holliday acknowledge support from the Met Office Hadley Centre Climate Programme funded by DSIT. Andrew Richardson acknowledges support from the NSF LTER (award 1832210); John O'Keefe and Greta VanScoy also acknowledge 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 (NPN) acknowledge support from the NSF through the Division of Biological Infrastructure (award 2404760), the U.S. 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 Global Learning and Observations to Benefit the Environment (GLOBE) program for their many observations. 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, Alba Alemany, Shin Ichiro Matsuzaki, Ryuichiro Shinohara, Jan-Erik Thrane, Linda May, 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/). Windermere, Blelham Tarn, Esthwaite Water, and Loch Leven monitoring were supported by NERC award numbers NE/R016429/1 and NE/Y006208/1 as part of the U.K. Status, Change and Projections of the Environment (UK-SCAPE) and U.K. Challenges Programmes delivering National Capability. Data for Lakes Geneva and Bourget were contributed by the Observatory on LAkes (OLA), © Observation and Experimentation System for Environmental Research (SOERE) OLA-IS, AnaEE-France, INRAE of Thonon-les-Bains, International Commission for the Protection of the Waters of Lake Geneva (CIPEL), Intercommunity Committee for the Sanitation of Lake Bourget (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 ESA CCI and the C3S.

## Sidebar 2.1 Super Extreme Land Surface Temperature Hotspots

This study has been funded through the ESA within the framework of the Land Surface Temperature project under the CCI (LST_cci), contract number 4000123553/18/I-NB, and was supported by NERC [NERC grant reference number NE/X019071/1, "U.K. EO Climate Information Service"] and the U.K.–China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China under the International Science Partnerships Fund (ISPF). The authors are grateful to the ESA for creating the CCI program, which has strengthened the consistency of the many research communities related to developing, processing, qualifying, and using satellite CDRs.
# Appendix 1: Acronyms

<table><tr><td>4D-VAR</td><td>four-dimensional variational 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>ACE-FTS</td><td>Atmospheric Chemistry Experiment—Fourier Transform Spectrometer</td></tr><tr><td>AIRS</td><td>Atmospheric Infrared Sounder</td></tr><tr><td>ALT</td><td>active-layer thickness</td></tr><tr><td>AMSR2</td><td>Advanced Microwave Scanning Radiometer 2</td></tr><tr><td>AMSR-E</td><td>Advanced Microwave Scanning Radiometer for the Earth Observing System</td></tr><tr><td>ANR</td><td>National Agency for Research</td></tr><tr><td>ANT_EAST</td><td>East Antarctic</td></tr><tr><td>ANT_PEN</td><td>Antarctic Peninsula</td></tr><tr><td>ANT_SVL</td><td>Antarctic: southern Victoria Land</td></tr><tr><td>ANY</td><td>Australian New Year</td></tr><tr><td>AOD</td><td>aerosol optical depth</td></tr><tr><td>AOD550</td><td>aerosol optical depth at 550 nm</td></tr><tr><td>ASCAT</td><td>Advanced Scatterometer</td></tr><tr><td>ASR</td><td>absorbed solar radiation</td></tr><tr><td>AT</td><td>Austria</td></tr><tr><td>BAR_WS</td><td>Barents Sea region—West Siberia</td></tr><tr><td>BCS</td><td>Beaufort Chukchi Sea—Arctic Alaska and Mackenzie Delta region</td></tr><tr><td>BD</td><td>Brewer-Dobson</td></tr><tr><td>BDAP</td><td>Big Data Analytics Platform</td></tr><tr><td>C3S</td><td>Copernicus Climate Change Service</td></tr><tr><td>Ca</td><td>Calbuco</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>CAMSRA</td><td>Copernicus Atmosphere Monitoring Service reanalysis</td></tr><tr><td>CAN</td><td>Canada</td></tr><tr><td>CAS</td><td>Chinese Academy of Sciences</td></tr><tr><td>CCI+</td><td>Climate Change Initiative Ozone</td></tr><tr><td>CCM</td><td>chemistry climate model</td></tr><tr><td>CCMI</td><td>Chemistry-Climate Model Initiative</td></tr><tr><td>CDR</td><td>Climate Data Record</td></tr><tr><td>CEAZA</td><td>Center for Advanced Studies in Arid Zones</td></tr><tr><td>CENT_SIB</td><td>Central Siberia</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>CG</td><td>cloud-to-ground</td></tr><tr><td>CH</td><td>Switzerland</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>CI</td><td>confidence interval</td></tr><tr><td>CIESRDS</td><td>Cooperative Institute for Earth System Research and Data Science</td></tr><tr><td>CIPEL</td><td>International Commission for the Protection of the Waters of Lake Geneva</td></tr><tr><td>CISALB</td><td>Intercommunity Committee for the Sanitation of Lake Bourget</td></tr><tr><td>CISESS</td><td>Cooperative Institute for Satellite Earth System Studies</td></tr><tr><td>CLASSnmat</td><td>Climate Linked Atlantic Sector Science Night Marine Air Temperature</td></tr><tr><td>CNES</td><td>National Centre for Space Studies</td></tr><tr><td>CNRS</td><td>French National Centre for Scientific Research</td></tr></table>