<table><caption>Table 2.8. Global and regional statistics for land surface wind speed (m s⁻¹) using the observational HadISD3 dataset for 1979–2020.</caption><thead><tr><th>Region</th><th>Mean 1981–2010 (m s<sup>-1</sup>)</th><th>Anomaly 2020 (m s<sup>-1</sup>)</th><th>Trend 1979–2020 (m s<sup>-1</sup> decade<sup>-1</sup>) and 5th–95th percentile confidence range</th><th>Number of stations</th></tr></thead><tbody><tr><td>Globe (excluding Australia)</td><td>3.324</td><td>+0.052</td><td>-0.056 (-0.065 → -0.040)</td><td>2554</td></tr><tr><td>North America</td><td>3.709</td><td>-0.084</td><td>-0.080 (-0.092 → -0.069)</td><td>578</td></tr><tr><td>Europe</td><td>3.677</td><td>+0.082</td><td>-0.042 (-0.053 → -0.032)</td><td>765</td></tr><tr><td>Central Asia</td><td>2.890</td><td>+0.178</td><td>-0.089 (-0.116 → -0.060)</td><td>258</td></tr><tr><td>East Asia</td><td>2.726</td><td>+0.051</td><td>-0.028 (-0.037 → -0.015)</td><td>459</td></tr></tbody></table>

<figure><img src="image_82.png" /><figcaption>Fig. 2.42. Wind speed trends (m s⁻¹ decade⁻¹) for the observational HadISD3 dataset (circles) over land, and ERA5 reanalysis output over land/ice and RSS satellite radiometers (SSM/I, SSMIS, TMI, GMI, AMSR2, AMSR-E, and WindSat) over ocean for 1988–2020 (shaded areas).</figcaption></figure>

Ocean surface winds for 1988–2020 were assessed using: (i) reanalyses (MERRA-2, ERA5, and 20CRv3) and (ii) satellite-based products including the Special Sensor Microwave/Imager (SSM/I), the Special Sensor Microwave Imager/Sounder (SSMIS), the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), and microwave imagers TMI, GMI, WindSat, QuikSCAT, and ASCAT (Wentz 1997; Wentz et al. 2007, 2015; Ricciardulli and Wentz 2015; Ricciardulli 2016). For 2020, satellite radiometers showed a near-zero global mean wind speed anomaly over ocean (−0.013 m s⁻¹; Fig. 2.43), consistent with the satellite scatterometers (ASCAT) and with MERRA-2; whereas ERA5 shows a positive anomaly and biases compared to the other products. In general, the magnitudes of any positive wind speed anomalies in 2020 were weak (Plate 2.1v), except in the Arctic Ocean, the North Atlantic Ocean, the southern fringe of both the South Pacific and South Atlantic Oceans, and the Bering Sea. In contrast, negative wind speed anomalies dominated tropical and subtropical ocean surfaces in 2020, particularly in the Atlantic and Indian Oceans. The ocean wind speed trend from satellite radiometers is nearly zero (+0.002 m s⁻¹ decade⁻¹) for 1988–2020 (Fig. 2.42), with a dominance of negative regional trends (moderate ones in the Indian Ocean and western Pacific Ocean), except for the positive trends found in the Southern Ocean, and the Pacific and Atlantic trade winds south of the equator (Young and Ribal 2019)
<figure><img src="image_83.png" /><figcaption>Fig. 2.43. Annual global mean wind speed anomalies (m s⁻¹; 1981–2010 base period) over the ocean from satellite radiometers and scatterometers, and reanalysis outputs. The values for the first year of both ASCAT and QuikSCAT are based on 6 months of deseasonalized data (monthly anomaly compared to monthly climatology).</figcaption></figure>

Decadal-scale variations and trends of land and ocean surface winds are likely driven by internal decadal ocean–atmosphere oscillations (Zeng et al. 2019) and anthropogenic greenhouse gas forcing (Deng et al. 2021), respectively, with vegetation growth (Vautard et al. 2010), urbanization (Chen et al. 2020), and instrumentation issues (Azorin-Molina et al. 2018b) having a lesser impact. After decades of a slowdown of winds over land, the recent reversal, which continued in 2020, is increasing potential wind energy across the world (Zeng et al. 2019). The most challenging research questions now are to predict how long the positive anomalies will last and to estimate future wind projections given a changing climate with a direct impact on the wind energy sector.

### 3) *Upper air winds*—L. Haimberger, M. Mayer, and V. Schenzinger

The 2020 global mean wind speed anomaly at 850 hPa has slightly recovered from a minimum in late 2019 to values close to the long term (1981–2010) average (Fig. 2.44). However, the clear positive trend over the past 40 years remains in all four included reanalyses. The wind time series start now in 1950, thanks to the backward extension of ERA5 (Bell et al. 2021). In the NH extratropics, there is no evidence of wind stilling at 850 hPa, in contrast to surface winds (section 2e2).

Plate 2.1w shows the meridional (positive northward) wind anomaly averaged over September–December 2020 at 850 hPa. That period showed a pronounced wave train with wavenumber 4 around 55°N, with particularly strong southerly winds of up to 4 m s⁻¹ in the 4-month average over northeastern Europe and far eastern Newfoundland. This pattern, together with ongoing climate change, led to the highest autumn temperatures on record in this region (Copernicus 2021). The dynamics that led to this pattern need to be fully investigated but are likely an example of enhanced resonance of Rossby waves in a warming climate (Petoukhov et al. 2013; Mann et al. 2019; Wills et al. 2019).

Turning to higher altitudes, we first assess the impact of tropical climate anomalies on upper-tropospheric circulation through inspection of 200-hPa

<figure><img src="image_84.png" /><figcaption>Fig. 2.44. Annual anomalies of global mean wind speed (m s⁻¹; 1981–2010 base period) at 850 hPa from four reanalyses (ERA5, ERA-Interim, MERRA-2, JRA-55). The numbers in parentheses are linear trends in m s⁻¹ decade⁻¹ for the period 1980–2020. The y-axis range matches that for the land surface winds (Fig. 2.41).</figcaption></figure>
velocity potential. After a strong positive IOD event in 2019, IOD conditions returned to neutral in 2020 (section 4f). In the Pacific, cold equatorial sea surface temperature (SST) anomalies appeared from early boreal summer 2020 and developed into a moderate La Niña event had formed by the end of the year (if defined by ONI index as >- 1.0 value, see section 4b).

Figure 2.45 depicts the imprint of these major tropical climate anomalies on upper air circulation. In August–September (Fig. 2.45a), negative velocity potential anomalies were present over the tropical Indian Ocean, with local minima over the northwestern and southeastern parts of the basin, indicating widespread, positive upper-air divergence anomalies. This differs from 2019 when there was a clear east–west dipole in the Indian Ocean velocity potential anomalies. An explanation for this difference is that SSTs were above normal both in the western and eastern equatorial Indian Ocean in 2020 (suggestive of a positive state of the Indian Ocean Basin Mode; Yang et al. 2007) but with more pronounced anomalies in the east (see also section 4f). The generally positive SST anomalies were favorable for enhanced upper-air divergence. At the same time, positive velocity potential anomalies were present over the western Pacific, consistent with reduced atmospheric convection associated with the developing La Niña event.

Velocity potential anomalies shifted eastward in October–November (Fig. 2.45b). Negative anomalies were centered over the Indo-Pacific Warm Pool. This is consistent with enhanced atmospheric convection over the eastern Indian Ocean associated with positive SST anomalies in this region. Moreover, in October–November changes to the Pacific Walker Circulation related to the negative SST anomalies of the now more mature La Niña event were centered farther eastward in the Pacific. This is reflected in positive velocity potential anomalies centered east of the date line arising from suppressed convection and likely contributed to the negative anomaly over the Indo-Pacific Warm Pool.

La Niña years provide more favorable conditions for Atlantic hurricanes, and indeed the late 2020 hurricane season was exceptionally intense (see section 4g). The imprint of the strong

<figure><img src="image_85.png" /><figcaption>Fig. 2.45. Anomalous 200-hPa velocity potential (× 10⁶ m² s⁻¹) and divergent winds (m s⁻¹) averaged over (a) Aug–Sep and (b) Oct–Nov 2020 (1981–2010 base period) based on ERA5 data.</figcaption></figure>
hurricane activity can also be seen in Fig. 2.45b, with a prominent negative anomaly over the Caribbean. Such a pattern favored hurricane activity, but likely was also enhanced by the strong upper-air divergence in the hurricanes once they had formed.

After the anomaly around the 2015/16 year transition (Osprey et al. 2016), the quasi-biennial oscillation (QBO) saw its second disruption in 2019/20. In both cases, a thin layer of westerlies appeared to be split off the descending westerly phase around 40 hPa and propagated upward through the stratosphere (Fig. 2.46). While these winds were overall weaker in the recent anomaly, the disruption, as measured by the explained variance of the first two empirical orthogonal functions (EOFs), was much stronger (Anstey et al. 2020). Typically, the first two EOFs explain around 90% of the vertical wind variance. In the 2015/16 disruption, this value dropped to around 60% and in 2019/20 down to 20%.

While the anomaly in 2015/16 was associated with unusually high wave-momentum fluxes from the NH (Osprey et al. 2016), the 2019/20 anomaly was probably caused by meridional momentum fluxes from the SH (Anstey et al. 2020). The QBO pattern stabilized again around May 2020. The combination of the QBO phase shifts following the disruptions results in the phase again aligning with the expectation from the historical record (Anstey et al. 2020). However, with long-term changes in the tropical circulation like increased upwelling, it remains to be seen whether the QBO returns to its regular cycle for a longer period of time.

<figure><img src="image_86.png" /><figcaption>Fig. 2.46. Monthly mean stratospheric zonal winds (m s⁻¹) at Singapore with 2016 and 2020 highlighted by arrows (FU Berlin, 2021). Purple shades show westerly winds; orange colors show easterly winds.</figcaption></figure>

f. Earth radiation budget

1) *Earth radiation budget at top-of-atmosphere*—P. W. Stackhouse Jr., T. Wong, P. Sawaengphokhai, A. C. Wilber, S. K. Gupta, D. P. Kratz, and N. G. Loeb

The energetic state of the Earth–atmosphere system is defined by the balance of the incoming total solar irradiance (TSI), the reflected shortwave (RSW), and the outgoing longwave radiation (OLR) from Earth. This balance defines Earth’s radiation budget (ERB) at the top of the atmosphere (TOA) and its regional distribution drives atmosphere and ocean circulations.

An analysis of all Clouds and the Earth's Radiant Energy System (CERES) ERB measurements (Table 2.9) shows that 2020 global annual mean OLR increased by ~0.20 W m⁻² and RSW increased by ~0.40 W m⁻² relative to their corresponding values in 2019 (rounded to nearest 0.05 W m⁻²).
<table><caption>Table 2.9. Global annual mean top of atmosphere (TOA) radiative flux changes (W m⁻²) between 2019 and 2020, the 2020 global annual mean radiative flux anomalies relative to their corresponding 2001–19 mean climatological values (also shown), and the 2-sigma interannual variabilities of the 2001–19 global annual mean fluxes for the outgoing longwave radiation (OLR), total solar irradiance (TSI), reflected shortwave (RSW), absorbed shortwave (SW; TSI minus RSW), absorbed SW (TSI – RSW), and total net fluxes (TSI minus RSW minus OLR)). 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></th><th>One-Year Change<br>(2020 minus 2019)<br>(W m<sup>-2</sup>)</th><th>2020 Anomaly<br>(Relative to Climatology)<br>(W m<sup>-2</sup>)</th><th>Climatological Mean<br>(2001–19)<br>(W m<sup>-2</sup>)</th><th>Interannual Variability<br>(2001–19)<br>(W m<sup>-2</sup>)</th></tr></thead><tbody><tr><td>OLR</td><td>0.20</td><td>+0.65</td><td>240.20</td><td>±0.65</td></tr><tr><td>TSI</td><td>0.05</td><td>-0.05</td><td>340.00</td><td>±0.15</td></tr><tr><td>RSW</td><td>0.40</td><td>-0.70</td><td>99.00</td><td>±1.00</td></tr><tr><td>TSI – RSW</td><td>-0.40</td><td>+0.65</td><td>241.00</td><td>±0.95</td></tr><tr><td>Net</td><td><strong>-0.60</strong></td><td><strong>0.00</strong></td><td><strong>0.80</strong></td><td><strong>±0.80</strong></td></tr></tbody></table>

Over the same timeframe, the global annual mean TSI increased by 0.05 W m⁻², showing a steady increase, possibly toward the next solar maximum. The sum of these components amounts to a decrease of ~0.60 W m⁻² in the global annual mean total net radiation relative to 2019. Figure 2.47 shows the annual mean regional difference maps in the OLR and RSW between 2019 and 2020. The largest reductions in OLR and increases in RSW are observed over the tropical Indian Ocean extending over Indonesia and southeastward over and east of Australia (a recovery from a 2019 Indian Ocean dipole event; see sections 2a1, 2d7, 4f), as well as a feature in the tropical Atlantic. The largest increases in OLR and reductions in RSW were observed in the tropical western and south-central Pacific regions. These regional differences appear associated with the change from near-neutral ENSO conditions at the end of 2019 to La Niña conditions by August 2020 that persisted through the end of the year (see section 2e1, 4b). Also noted are broad areas of moderate OLR increase and RSW decrease over both North and South America corresponding to reduced cloudiness and increased surface warmth in these regions (see sections 2d4, 2d7, 2d11 and 2b1). Relative to the 2001–19 climatological average, the 2020 global annual mean flux anomalies are +0.65, -0.05, -0.70, and +0.00 W m⁻²

<figure><img src="image_87.png" /><figcaption>Fig. 2.47. Annual average top of atmosphere (TOA) flux differences between 2020 and 2019 (W m⁻²) for the (top panel) OLR and (bottom panel) TOA reflected shortwave (RSW). The annual mean maps for 2020 were derived after adjusting December 2020 FLASHFlux v4A using the difference between EBAF and FF v4A in 2019. The pattern of differences shows several significant features including changes over the Indian and tropical western and south-central Pacific Oceans. The tropical Indian/Pacific Ocean pattern is dominated by an atmospheric shift from neutral ENSO conditions during the latter half of 2019 and early 2020 to La Niña conditions that persisted from Aug through Dec 2020 and also includes a transition from an IOD event in 2019 (see section 4f).</figcaption></figure>
<figure><img src="image_88.png" /><figcaption>Fig. 2.48. Time series of global monthly mean deseasonalized anomalies (W m⁻²) of top-of-atmosphere (TOA) Earth radiation budget for outgoing longwave radiation (OLR; upper); absorbed shortwave (total solar irradiance [TSI] minus reflected shortwave [RSW]; middle); and total net (TSI minus RSW minus OLR; lower) from Mar 2000 to Dec 2020. Anomalies are relative to their calendar month climatology (2001–19). Time series shows the CERES EBAF Ed4.1 1-Deg data (Mar 2000–Nov2020) in red and the CERES FLASHFlux version 4A data (Dec 2020) in blue; see text for merging procedure. (Sources: https://ceres-tool.larc.nasa.gov/ord-tool/jsp/EBAF41Selection.jsp and https://ceres-tool.larc.nasa.gov/ord-tool/jsp/FLASH_TISASelection.jsp.)</figcaption></figure>

for OLR, TSI, RSW, and total net flux, respectively (Table 2.9), all at or within their corresponding 2-sigma interannual variability (Table 2.9).

The global monthly mean anomaly time series of TOA fluxes (Fig. 2.48) reveal that the global monthly mean OLR anomaly remained positive throughout the first half of 2020 at a level of about +1.00 W m⁻². During the second half of 2020, the OLR anomalies remained positive, but decreased to less than +0.50 W m⁻² except for November, which spiked to +1.30 W m⁻². This large November OLR anomaly is consistent with the values obtained from the NOAA HIRS OLR (Lee and NOAA CDR Program 2011) and NASA AIRS OLR (Susskind et al. 2012) datasets (not shown). The global monthly mean absorbed shortwave (SW; TSI minus RSW) anomaly began 2020 at +0.20 W m⁻², peaked in May at around +1.35 W m⁻², and then decreased sharply after August, ending the year with a value of -0.10 W m⁻². For the year as a whole, the 2020 global annual mean absorbed SW anomaly was +0.65 W m⁻². The global monthly mean total net

anomaly, which is calculated from the absorbed SW anomaly minus the OLR anomaly, began 2020 with a value of -0.05 W m⁻², reached a maximum value of +0.75 W m⁻² in August, then decreased rapidly to about -0.90 W m⁻² in November, ending the year at -0.45 W m⁻². The positive OLR anomalies approximately balanced the positive absorbed SW anomalies in 2020, resulting in a global annual mean total net anomaly of 0.0 W m⁻². The total net anomaly decreased by ~1.65 W m⁻² between August and November 2020. Although this corresponds to the onset of the 2020 La Niña, more analysis is required for definitive attribution. Long-term trend analyses that include the last month of the merged dataset are discouraged because of the natural fluctuation in ERB components, uncertainty from the data-merging process, and potential for drift in the FLASHFlux product.

The TSI data used in this study are provided by the Total Irradiance Monitor aboard the Solar Radiation and Climate Experiment (SORCE) mission (Kopp and Lean 2011) and the Royal Meteorological Institute of Belgium composite dataset (Dewitte et al. 2004), both renormalized to the SORCE Version 15. Starting in February 2020, data from the Total Solar and Spectral Irradiance Sensor-1 (TSIS-1, Coddington, 2017) mission on board the International Space Station is normalized to SORCE Version 15. The RSW and OLR data were obtained from the CERES instruments (Wielicki et al. 1996, 1998) aboard Terra and Aqua spacecraft. The time series (Fig. 2.48) were constructed from the CERES EBAF (Energy Balanced And Filled) Ed4.1 product (Loeb et al. 2009, 2012, 2018) for March 2000–November 2020 and from the CERES Fast Longwave and Shortwave Radiative Fluxes (FLASHFlux) version 4A product (Kratz et al. 2014) for December 2020. The normalization of the FLASHFlux data (Stackhouse et al. 2016) results in 2-sigma monthly uncertainties of ±0.47, ±0.07, ±0.24, and ±0.58 W m⁻² for the OLR, TSI, RSW, and total net radiation, respectively.
Initiated in 1958 as part of the International Geophysical Year, one of the longest records of atmospheric transmission, i.e., the percent of top of atmosphere (TOA) solar radiation that reaches the surface, has been recorded on the island of Hawaii at 3397 m above mean sea level, just below the summit of the Mauna Loa volcano. Because of the clean nature of the atmosphere over Mauna Loa, its elevation and vertical separation from the marine boundary layer, atmospheric transmission there is considered a proxy of stratospheric aerosol loading. One exception is the effect from the annual transport of Asian dust over Hawaii at high-tropospheric levels in springtime (Bodhaine et al. 1981).

The updated time series of “apparent” transmission (see definition below) through 2020 is presented in Fig. 2.49. Plotted are monthly averages and a 6-month-smoothed fit that reveals intra-annual variability caused mainly by springtime Asian dust. The most pronounced features are deviations caused by three major volcanic eruptions: Agung, Indonesia, in 1963; El Chichon, Mexico, in 1982; and Pinatubo, Philippines, in 1991. Resultant deep reductions in transmission are followed by slow recoveries that last up to 8 years and reflect the long residence time of aerosols in the stratosphere. For reference, the horizontal dashed line in Fig. 2.49 represents the average transmission prior to Agung (0.934) when the stratosphere was exceptionally clean. That level of stratospheric purity has been achieved only briefly over the 62-year time series, most notably in the late 1970s and from the late 1990s into the early 2000s. A steady decrease from 2002 to 2010 is associated with a series of tropical and high-latitude volcanic eruptions (Andersson et al. 2015; Vernier et al. 2011), each of which affected the stratosphere for a year or less (Augustine et al. 2020). Apparent transmission over Mauna Loa increases after 2010 and remains relatively stable through 2018. A slow decline is apparent through 2019 from the eruptions of Mt. Raikoke on the Kuril Islands north of Japan in June 2019 and Mt. Ulawun in Papua New Guinea in June and August 2019.

The only major volcanic event in 2020 was the explosive eruption of Mt. Taal in the Philippines on 12 January, but there is no indication from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) space-borne lidar that it significantly affected the stratosphere. The most notable aerosol events of 2020 were wildfires in Australia from December 2019 to early 2020 (Kablick et al. 2020) and the record-setting Saharan dust event in June (Francis et al. 2020). However, neither affected Mauna Loa. Stratospheric aerosols from Australian wildfire pyrocumulus had limited cross-equatorial transport, and Saharan dust reached just ~3–7 km in altitude and

only affected the troposphere in the low-latitude North Atlantic Ocean, the Caribbean, and eastern North America (Francis et al. 2020). Aerosols from wildfire pyrocumulus in the western United States that began in August 2020, especially in central California, were observed in the lower stratosphere in September by CALIOP and in September and October by SAGE III aboard the International Space Station (see https://appliedsciences.nasa.gov/our-impact/news/californias-creek-fire-blasts-smoke-stratosphere). Satellite visualization shows a relatively small amount of that smoke reaching Hawaii in late August and September 2020 and is probably partially responsible for the transmission decrease in September

<figure><img src="image_89.png" /><figcaption>Fig. 2.49. Apparent transmission at Mauna Loa, Hawaii, from 1958 through 2020. Red dots are monthly average morning transmissions, the gray curve is a fit with a 6-month smoother applied, and the dashed horizontal line is the average transmission for the clean period before the eruption of Agung. Insert is an enlargement of the newest data for 2020.</figcaption></figure>
apparent in the inset of Fig. 2.49. Residual effects from the 2019 eruptions of Raikoke and, to a lesser extent Ulawun, were likely responsible for maintaining a relatively low transmission of 0.9265 ± 0.0029 in 2020. CALIOP shows those stratospheric volcanic plumes much reduced from 2019 levels but still present at the latitude of Mauna Loa through October 2020, which is the extent of CALIOP data analyzed. Transmission reached the annual minimum of 0.919 in April, presumably from the addition of springtime Asian dust.

Atmospheric transmission is defined as the ratio of the solar beam intensity at the surface to that at the top of the atmosphere over a vertical path. Given the impracticality of that calculation, Ellis and Pueschel (1971) showed mathematically equivalency of vertical transmission to the ratio of surface solar beam measurements at two distinct integer path lengths. However, because broadband transmission is influenced by path length, that calculation is referred to as “apparent” transmission. Here, a representative daily apparent transmission is the mean of three successive ratios of pyrheliometer measurements at 2, 3, 4, and 5 atmospheric path lengths. Only morning data are considered because upslope winds typically contaminate afternoon measurements with marine-layer aerosols. Individual points in Fig. 2.49 represent the average of all acceptable morning transmissions within a particular month. Neither the radiometer calibration factor nor the solar intensity at TOA are needed, resulting in a precise time series back to 1958.

g. Atmospheric composition

1) *Long-lived greenhouse gases*—X. Lan, P. Tans, B. D. Hall, G. Dutton, J. Mühle, J. W. Elkins, and I. Vimont

Increased atmospheric burdens of long-lived greenhouse gases (LLGHGs) are the dominant driver of warming climate (IPCC AR5 2013). Carbon dioxide ($CO_2$), methane ($CH_4$), and nitrous oxide ($N_2O$) are naturally present in the atmosphere but have been greatly increased by human activity in the industrial era.

Systematic measurements of atmospheric $CO_2$ began at Mauna Loa, Hawaii (MLO), in 1958, when $CO_2$ was approximately 315 ppm (parts per million by moles in dry air). In 2020, annually averaged $CO_2$ at MLO reached 414.2 ± 0.1 ppm (all uncertainties are reported as 1 sigma [σ] in this section), while globally averaged $CO_2$ derived from remote marine boundary layer (MBL) measurements from NOAA's Global Greenhouse Gas Reference Network (GGGRN) was 412.5 ± 0.1 ppm (Fig. 2.50a; gml.noaa.gov/ccgg/trends). Both levels were the highest since the systematic measurements of $CO_2$ started. The globally averaged level represents an increase of 48% over pre-industrial values of 278 ppm (Etheridge et al. 1996). Annual growth in global mean $CO_2$ has accelerated from 0.8 ± 0.3 ppm yr⁻¹ (± 1 σ for interannual variability) in the 1960s to an average of 2.4 ± 0.4 ppm yr⁻¹ during 2010–19 (Fig. 2.50a). The annual increase in global mean $CO_2$ in 2020 was 2.5 ± 0.1 ppm. In 2020, the radiative forcing due to anthropogenic $CO_2$ increased to 2.11 W m⁻² relative to pre-industrial times (1750 CE; Table 2.10; gml.noaa.gov/gmd/aggi/; Hofmann et al. 2006).

The main driver of increasing atmospheric $CO_2$ is fossil fuel (FF) burning, with emissions (including a minor amount from cement production) between 2010 and 2019 averaging 9.4 ± 0.5 Pg C yr⁻¹ (Friedlingstein et al. 2020). If all of this $CO_2$ remained in the atmosphere, the average 2010–19 increase would have been 4.4 ppm yr⁻¹ instead of 2.4 ppm yr⁻¹. Thus, only about 55% of FF-emitted $CO_2$ in 2010–19 has remained in the atmosphere, while the rest has been stored by the ocean and the terrestrial biosphere. While emissions of $CO_2$ from FF combustion drive its increasing atmospheric burden, the large interannual variability in the $CO_2$ growth rate is mostly driven by terrestrial biospheric exchange of $CO_2$, which is confirmed by stable carbon isotope (¹³C) measurements (e.g., Keeling et al. 1985; Alden et al. 2010). Terrestrial biosphere flux variability is influenced by both temperature and moisture anomalies (Cox et al. 2013; Hu et al. 2019; Humphrey et al. 2018). For example, the terrestrial impacts of the strong El Niño that peaked in late-2015 contributed to a strong global $CO_2$ increase of 3.0 ppm yr⁻¹ (Betts et al. 2016). Because El Niño–Southern Oscillation (ENSO) changed from neutral to La Niña during 2020, it is not surprising that the observed $CO_2$ increase was near the 2010–19 mean.
<figure><img src="image_90.png" /><figcaption>Fig. 2.50. Global monthly mean dry-air surface mole fractions (black, left axis) and annual increases (red, right axis) of (a) carbon dioxide (CO₂), (b) methane (CH₄), and nitrous oxide (N₂O) derived from NOAA Global Greenhouse Gases Reference Network marine boundary layer measurement sites. Deseasonalized trend curves (see Dlugokencky et al. 1994 for methods) are shown in blue; annual increases are defined as 1 Jan minus 1 Jan of consecutive years from the trend line. N₂O data prior to 1995 are insufficient and noisy, thus hindering the calculation of a growth rate.</figcaption></figure>

Anthropogenic CO₂ emissions are estimated to have decreased by about 6%–7% due to reduced human activities during the COVID-19 pandemic (Friedlingstein et al. 2020; Le Quere et al. 2020; BP Statistical Review of the World Energy 2021). However, this reduction is not obvious in observed global atmospheric CO₂ signals, because it is a relatively small signal compared with natural variability that is driven by the large fluxes from photosynthesis and respiration of ecosystems on land. The estimated ~6%–7% reduction in global CO₂ emissions of ~10 Pg C yr⁻¹ would result in a ~0.3 ppm decrease in global CO₂ (given a conversion factor of 2.12 Pg C ppm⁻¹; Ballantyne et al. 2012), which is within the 1-σ interannual variability of CO₂ annual growth in 2010–19 (0.4 ± 0.1 ppm yr⁻¹). The impact of emission changes during COVID-19 may be more discernible in urban atmospheric CO₂ measurements because most of the emission reductions come from urban areas (A. J. Turner et al. 2020).

Methane is the second-most important anthropogenic greenhouse gas after CO₂. Its abundance in the atmosphere increased to 1879.2 ± 1.0 ppb (parts per billion by moles in dry air) in 2020, a 160% increase compared to its pre-industrial level of 722 ± 15 ppb. Since the beginning of NOAA's systematic CH₄ measurements in 1983, the global CH₄ annual increase has varied between -4.9 and 14.8 ppb yr⁻¹ (red line in Fig. 2.50b) as a result of the

changing balance between emissions and chemical destruction. The CH₄ annual increase averaged 11.4 ± 1.4 ppb yr⁻¹ from 1983 to 1992, followed by a strong decrease to 4.4 ± 1.8 ppb yr⁻¹ between 1992 and 1998, and further reduced to near zero (0.5 ± 3.0 ppb yr⁻¹) during 1999–2006. The rise and then flattening of the global methane abundance is consistent with an approach to steady state if there was no trend in its lifetime (Dlugokencky et al. 2003). Atmospheric CH₄ growth restarted again in 2007 and the average growth rate after 2014 was higher than 2007–14 (red line in Fig. 2.50b). The annual increase in 2020 was 14.8 ± 0.5 ppb, which is by far the largest annual increase since systematic atmospheric CH₄ measurements began. There is no obvious explanation at this time for this large anomaly. CH₄ now contributes 0.52 W m⁻² in direct radiative forcing (Table 2.10) relative to pre-industrial times, while the CH₄-related production of tropospheric ozone (O₃) and stratospheric water vapor (H₂O) contributes ~0.3 W m⁻² in indirect radiative forcing (Myhre et al. 2013).
<table><caption>Table 2.10. Summary table of long-lived greenhouse gases for 2020 (Carbon dioxide [CO₂] abundances [mole fractions] are in ppm, nitrous oxide [N₂O] and methane [CH₄] in ppb, and all others in ppt).</caption><thead><tr><th>Industrial Designation or Common Name</th><th>Chemical Formula</th><th>Included in the AGGI<sup>a</sup>?</th><th>Radiative Efficiency (W m<sup>-2</sup> ppb<sup>-1</sup>)<sup>b</sup></th><th>Rad. Forcing (W m<sup>-2</sup>)</th><th>Mean surface mole fraction, 2020 (change from 2019)<sup>c</sup></th><th>Lifetime (years)</th></tr></thead><tbody><tr><td>Carbon Dioxide</td><td>CO<sub>2</sub></td><td>Y</td><td>1.37 × 10<sup>-5</sup></td><td>2.11</td><td>412.5 (2.5)</td><td>—</td></tr><tr><td>Methane</td><td>CH<sub>4</sub></td><td>Y</td><td>3.63 × 10<sup>-4</sup></td><td>0.52</td><td>1879.2 (12.7)</td><td>9.1</td></tr><tr><td>Nitrous Oxide</td><td>N<sub>2</sub>O</td><td>Y</td><td>3.00 × 10<sup>-3</sup></td><td>0.21</td><td>333.0 (1.2)<sup>d</sup></td><td>123</td></tr><tr><td colspan="7"><strong>Chlorofluorocarbons</strong></td></tr><tr><td>CFC-11</td><td>CCl<sub>3</sub>F</td><td>Y</td><td>0.26</td><td>0.058</td><td>224.5 (–1.8)<sup>c</sup></td><td>52</td></tr><tr><td>CFC-12</td><td>CCl<sub>2</sub>F<sub>2</sub></td><td>Y</td><td>0.32</td><td>0.159</td><td>497.3 (–3.9)<sup>c</sup></td><td>102</td></tr><tr><td>CFC-113</td><td>CCl<sub>2</sub>FCCl</td><td>Y</td><td>0.30</td><td>0.021</td><td>69.0 (–0.7)<sup>c</sup></td><td>93</td></tr><tr><td colspan="7"><strong>Hydrochlorofluorocarbons</strong></td></tr><tr><td>HCFC-22</td><td>CHClF<sub>2</sub></td><td>Y</td><td>0.21</td><td>0.052</td><td>246.4 (1.4)</td><td>11.9</td></tr><tr><td>HCFC-141b</td><td>CH<sub>3</sub>CCl<sub>2</sub></td><td>Y</td><td>0.16</td><td>0.004</td><td>24.2 (0.03)</td><td>9.4</td></tr><tr><td>HCFC-142b</td><td>CH<sub>3</sub>CClF</td><td>Y</td><td>0.19</td><td>0.004</td><td>21.6 (–0.2)</td><td>18</td></tr><tr><td colspan="7"><strong>Hydrofluorocarbons</strong></td></tr><tr><td>HFC-134a</td><td>CH<sub>2</sub>FCF<sub>3</sub></td><td>Y</td><td>0.16</td><td>0.018</td><td>111.5 (5.1)</td><td>14</td></tr><tr><td>HFC-152a</td><td>CH<sub>3</sub>CHF<sub>2</sub></td><td>Y</td><td>0.10</td><td>&lt;0.001</td><td>6.2 (–0.03)</td><td>1.6</td></tr><tr><td>HFC-143a</td><td>CH<sub>3</sub>CF<sub>3</sub></td><td>Y</td><td>0.16</td><td>0.004</td><td>24.9 (1.5)</td><td>51</td></tr><tr><td>HFC-125</td><td>CHF<sub>2</sub>CF<sub>3</sub></td><td>Y</td><td>0.23</td><td>0.007</td><td>33.0 (3.0)</td><td>30</td></tr><tr><td>HFC-32</td><td>CH<sub>2</sub>F<sub>2</sub></td><td>N</td><td>0.11</td><td>0.002</td><td>21.1 (2.9)</td><td>5.4</td></tr><tr><td>HFC-23</td><td>CHF<sub>3</sub></td><td>Y</td><td>0.18</td><td>0.006</td><td>33.7 (1.3)</td><td>228</td></tr><tr><td>HFC-365mfc</td><td>CH<sub>3</sub>CF<sub>2</sub>C</td><td>N</td><td>0.22</td><td>&lt;0.001</td><td>1.0 (0.02)</td><td>8.9</td></tr><tr><td>HFC-227ea</td><td>CF<sub>3</sub>CHFC</td><td>N</td><td>—</td><td>&lt;0.001</td><td>1.70 (0.15)</td><td>36</td></tr><tr><td colspan="7"><strong>Chlorocarbons</strong></td></tr><tr><td>Methyl Chloroform</td><td>CH<sub>3</sub>CCl<sub>3</sub></td><td>Y</td><td>0.07</td><td>&lt;0.001</td><td>1.4 (–0.2)</td><td>5.0</td></tr><tr><td>Carbon Tetrachloride</td><td>CCl<sub>4</sub></td><td>Y</td><td>0.17</td><td>0.013</td><td>86.8 (–0.9)<sup>c</sup></td><td>32</td></tr><tr><td>Methyl Chloride</td><td>CH<sub>3</sub>Cl</td><td>N</td><td>0.01</td><td>&lt;0.001</td><td>546.6 (8.7)</td><td>0.9</td></tr><tr><td colspan="7"><strong>Bromocarbons</strong></td></tr><tr><td>Methyl Bromide</td><td>CH<sub>3</sub>Br</td><td>N</td><td>0.004</td><td>&lt;0.001</td><td>6.70 (0.15)</td><td>0.8</td></tr><tr><td>Halon 1211</td><td>CBrClF<sub>2</sub></td><td>Y</td><td>0.29</td><td>0.001</td><td>3.11 (–0.10)</td><td>16</td></tr><tr><td>Halon 1301</td><td>CBrF<sub>3</sub></td><td>Y</td><td>0.30</td><td>0.001</td><td>3.31 (0.0)</td><td>72</td></tr><tr><td>Halon 2402</td><td>CBrF<sub>2</sub>CB</td><td>Y</td><td>0.31</td><td>&lt;0.001</td><td>0.40 (0.0)</td><td>28</td></tr><tr><td colspan="7"><strong>Fully fluorinated species</strong></td></tr><tr><td>Sulfur Hexafluoride</td><td>SF<sub>6</sub></td><td>Y</td><td>0.57</td><td>0.006</td><td>10.3 (0.3)</td><td>&gt;600</td></tr><tr><td>PFC-14</td><td>CF<sub>4</sub></td><td>N</td><td>0.09</td><td>0.005</td><td>86.4 (0.9)</td><td>~50000</td></tr><tr><td>PFC-116</td><td>C<sub>2</sub>F<sub>6</sub></td><td>N</td><td>0.25</td><td>0.001</td><td>4.94 (0.09)</td><td>~10000</td></tr><tr><td>PFC-218</td><td>C<sub>3</sub>F<sub>8</sub></td><td>N</td><td>0.28</td><td>&lt;0.001</td><td>0.70 (0.02)</td><td>~2600</td></tr><tr><td>PFC-318</td><td>c-C<sub>4</sub>F<sub>8</sub></td><td>N</td><td>0.32</td><td>&lt;0.001</td><td>1.82 (0.06)</td><td>~3200</td></tr></tbody></table>

<footnote><sup>a</sup> Annual Greenhouse Gas Index (AGGI). See https://gml.noaa.gov/aggi/ for more information</footnote>

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

<footnote><sup>c</sup> Mole fractions are global, annual surface means for the 2020 determined from the NOAA Global Greenhouse Gas Reference Network, except for PFC-14, PFC-116, PFC-218, PFC-318, and HFC-23, which were measured by AGAGE (Mühle et al. 2010; Miller et al. 2010). Changes indicated in brackets are the differences between the 2020 and 2019 means, the relevant quantities for calculating radiative forcing. These changes are somewhat different from the 2020 annual increases reported in 2.g.1, which are determined as the difference between Jan. 1, 2021 and Jan. 1, 2020. 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>
Given the complexity of the CH₄ budget and the uncertainty in CH₄ source partitioning, the magnitudes and long-term trends of many CH₄ sources are still uncertain. FF exploitation is estimated to account for ~19% of total global CH₄ emissions since 2000 based on top-down approaches that use atmospheric CH₄ measurements and inverse models (Saunois et al. 2020). However, studies including the radiocarbon (¹⁴C) or stable carbon (¹³C) isotopes of CH₄ suggest a much larger fraction of FF emissions (~30%; Lassey et al. 2007; Schwietzke et al. 2016). Measurements of δ¹³C-CH₄ also suggest that increased emissions from biogenic sources, from natural and/or anthropogenic origins, are the dominant drivers for the post-2006 growth (Chang et al. 2019; Nisbet et al. 2019; Schaefer et al. 2016; Schwietzke et al. 2016). Global atmospheric δ¹³C-CH₄ has become more depleted since 2008, which is consistent with an increased contribution from biogenic sources with more negative δ¹³C-CH₄ signatures. Increased wetland emissions may play a role in the post-2006 renewed increase (Yin et al. 2020), but further investigation is required to better quantify wetland emissions given the large uncertainties associated with wetland emission areas and processes controlling wetland CH₄ emissions (Bloom et al. 2017). A decrease in biomass burning and a small increase in FF emissions (Worden et al, 2017) may also play a smaller role in post-2006 global CH₄ change (Lan et al. 2021).

Methane is removed from the atmosphere mainly by reaction with hydroxyl radical (OH); OH has a very short lifetime (~1 s), which makes it difficult to constrain by direct observations. While recent studies suggest that a decreasing OH sink may not be the dominant driver for the post-2006 renewed increase in global atmospheric CH₄ (Fujita et al. 2020), uncertainties remain in the temporal variations of the OH sink and other CH₄ sinks such as oxidation by tropospheric chlorine (Cl; Hossaini et al. 2016; Gromov et al. 2018) and soils. The global soil CH₄ sink is estimated to be ~30 Tg yr⁻¹; however, up to a 77% decrease was reported for 1988–2015 based on long-term measurements and data reviews (Ni and Groffman 2018). A decrease in the soil CH₄ sink is consistent with an observed global decrease in δ¹³C-CH₄ (Lan et al. 2021).

Nitrous oxide (N₂O) is an important LLGHG that also depletes stratospheric ozone (Ravishankara et al. 2009). Atmospheric N₂O has been increasing steadily throughout the industrial era except for a brief period in the 1940s (MacFarling Meure et al. 2006; Thompson et al. 2019). The mean global atmospheric N₂O abundance in 2020 was 333.0 ± 0.1 ppb, while the annual increase in 2020 was 1.4 ± 0.1 ppb (Fig. 2.50c), and a 23% increase over pre-industrial levels of 270 ppb. The 1.4 ppb increase in the annual mean is similar to average rate of increase of 1.0 ± 0.2 ppb yr⁻¹ over the past decade (2010–19), but slightly larger than the average rate in previous decade (2000–09) of 0.7 ± 0.2 ppb yr⁻¹. The observed increase in atmospheric N₂O over preindustrial levels is mostly caused by nitrogen-containing fertilizers and manure used for agriculture (Davidson 2009). A recent study found that anthropogenic N₂O emissions have increased by 30% since 1980, with significant contributions from developing countries such as Brazil, China, and India (Tian et al. 2020). Radiative forcing from N₂O in 2020 is now 0.21 W m⁻² relative to pre-industrial times (Table 2.10).

The combined radiative forcing in 2020 from major and minor LLGHGs was 3.2 W m⁻² (Fig. 2.51). Annual increases in radiative forcing correspond roughly with variability in CO₂, since CO₂ is responsible for about 65% of radiative forcing by LLGHGs and its increase during 2015–20 accounts for 82% of total increase in radiative forcing.
<figure><img src="image_91.png" /><figcaption>Fig. 2.51. (a) Direct radiative forcing (W m⁻²) due to five major long-lived greenhouse gases (LLGHG) and 15 minor gases (left axis) and the associated values of the NOAA AGGI (right axis). The Annual Greenhouse Gas Index (AGGI) is defined to have a value of one in 1990. (b) Annual increase in direct radiative forcing (W m⁻²).</figcaption></figure>

2) **Ozone-depleting substances**—I. J. Vimont, B. D. Hall, S. A. Montzka, G. Dutton, C. Siso, M. Crotwell, and M. Gentry

Our climate is affected by the presence of halogenated trace gases in the atmosphere. This group of compounds includes, but is not limited to, chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), chlorinated hydrocarbons, and halons. These compounds both directly (via radiative forcing) and indirectly (via ozone depletion in the stratosphere) influence the radiative balance of the atmosphere (Karpechko and Maycock 2018).

The Montreal Protocol (1987) and its subsequent amendments regulate the production and consumption of these ozone-depleting substances (ODS) and other compounds. These controls started in the late 1980s by phasing out production of CFCs and were followed by the reduction and phaseout of halons in the 1990s and early 2000s. The year 2020 marks the near-complete phaseout of HCFCs in developed countries. These controls have resulted in declines in atmospheric abundance for many of these gases (Engel and Rigby, 2018). CFC-11 and CFC-12 declined 16% ± 1% and 8% ± 0.3%, respectively, from their maximum values by 2020 (Fig. 2.52). Additionally, while the reduction of CFC-11 in the atmosphere slowed after 2012 owing to an unexpected increase in emissions that were likely linked to unreported production of CFC-11 (Montzka et al. 2018; Rigby et al.

<figure><img src="image_92.png" /><figcaption>Fig. 2.52. Global mean abundances (mole fractions) at Earth's surface (ppt = nmol mol⁻¹ in dry air) for several halogenated gases, many of which also deplete stratospheric ozone. See Table 2.10 for the 2020 global mean mole fractions of these and other gases.</figcaption></figure>
2019), continued monitoring has shown an accelerated decline from 2018 to 2019, signaling a decline in these emissions (Montzka et al. 2021; Park et al. 2021).

As HCFCs replaced CFCs, their abundances increased in the atmosphere. More recently, however, as the phaseout of HCFC production nears completion, growth rates of atmospheric HCFC-22, HCFC-141b, and HCFC-142b have slowed (Fig. 2.52). Notably, atmospheric abundances of both HCFC-141b and HCFC-142b have remained nearly constant since 2018 (Table 2.10; Fig. 2.52). With the near-complete phaseout of HCFCs in developed nations scheduled for 2020, and significant reductions in production and consumption occurring in developing countries, the atmospheric abundances of these compounds may soon begin to decline. The Kigali Amendment to the Montreal Protocol has mandated the phase-down of HFCs, which are substitutes for ODS. While these compounds do not contribute to ozone destruction, they contribute to radiative forcing, and atmospheric abundances for most have been increasing in the atmosphere (Table 2.10, Fig. 2.52). HFC-134a is the largest contributor to radiative forcing among the HFCs, and its global abundance increased by 5.3 ppt (4.7%) from 2019 to 2020, similar to the mean yearly increase over the last decade (~5.5 ppt yr⁻¹; Fig. 2.52).

In order to quantify the overall efficacy of ozone-destroying halogen in the stratosphere, equivalent effective stratospheric chlorine (EESC) is calculated from the weighted global average surface abundance of ozone-depleting gases (Daniel et al. 1995). The weights represent the ozone-destruction efficiency of the halogens contained in each ODS, the destruction rates of each ODS in the stratosphere, and transport and mixing processes within the stratosphere (Montzka

et al. 1996; Newman et al. 2007). EESC is calculated for the Antarctic (EESC-A) and the midlatitude (EESC-M) stratosphere. The abundance of reactive halogen in the Antarctic stratosphere is higher than in midlatitudes because air reaching the Antarctic stratosphere has been in the stratosphere longer and has been transported to higher altitudes, factors that lead to both ODS destruction and release of reactive halogen (Montzka and Reimann et al. 2011). CFCs, despite their decreasing global abundance, contribute strongly to EESC (Fig. 2.53), because they account for most of the reactive halogen present in the atmosphere today.

At the beginning of 2020, EESC-A was 3685 ppt, and EESC-M was 1562 ppt, decreases of 25 ppt and 12 ppt, respectively, relative to 2019. To put these values into context of stratospheric reactive halogen reduction, the Ozone Depleting Gas Index (Hoffmann and Montzka, 2009, gml.noaa.gov/odgi/) is defined for both the Antarctic and midlatitude stratosphere (ODGI-A and ODGI-M). This index is defined as 100 for the peak EESC and 0 at the 1980 level of EESC, for both the Antarctic and midlatitude stratosphere. Even though ozone destruction was occurring

<figure><img src="image_93.png" /><figcaption>Fig. 2.53. Equivalent effective stratospheric chlorine (EESC, ppt) for the midlatitude and Antarctic stratosphere derived from surface measurements. The EESC values represent EESC on 1 Jan of each year.</figcaption></figure>
in 1980, returning the stratosphere to 1980 levels of reactive halogen would represent a major ac-
complishment for the Montreal Protocol and the global community. At the beginning of 2020, the
ODGI-A was 77, and the ODGI-M was 52, representing progress of 23% and 48% toward the 1980
benchmarks, respectively. Carpenter et al. (2018) estimate that ODGI-A will reach zero around
2070, and ODGI-M will reach zero around 2045, assuming all other factors remain constant.

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

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 (Boucher et al. 2013). Aerosols also represent a serious public health issue in many countries, and hence are subject to monitoring and forecasting as part of air quality policies. There is also growing evidence that aerosols influence ecosystems through changes in the quality and quantity of light (over land) and deposition flux of nutrients (over land and ocean) such as iron (e.g., Hamilton et al. 2019).

The Copernicus Atmosphere Monitoring Service (CAMS; http://atmosphere.copernicus.eu) runs a near-real time global analysis of aerosols and trace gases. The CAMS project also produced a reanalysis of global aerosols and trace gases that covers the years 2003–20, named the CAMS reanalysis (CAMSRA; Inness et al. 2019) by combining state-of-the-art numerical modeling and aerosol remote-sensing retrievals from MODIS (Levy et al. 2013) and the Advanced Along Track Scanning Radiometer (AATSR) (Popp et al. 2016). Verification of aerosol optical depth (AOD) at 550 nm against independent AERONET observations shows that the CAMS reanalysis has a smaller bias and error than its predecessors, the CAMS interim reanalysis (Flemming et al. 2017)

and the Monitoring Atmospheric Composition
and Climate (MACC) reanalysis (Inness et al.
2013). This section uses data exclusively from
the CAMS reanalysis. Here, we assess aero-
sols in terms of the AOD at 550 nm because
this wavelength corresponds to the middle
of the visible part of the spectrum, and be-
cause many remote-sensing products provide
retrievals at this wavelength.

The time series of monthly and yearly glob-
ally-averaged total AOD during 2003–20 are
depicted in Fig. 2.54b, showing strong season-
ality, driven mainly by dust episodes between
March and July in the Sahara, Middle East,
and the Taklimakan/Gobi Desert and seasonal
biomass burning in Africa, South America,
and Indonesia. There is no significant trend
over the period, but extreme events such as
the July–October 2015 fires over Indonesia
associated with El Niño can have an impact
on the global mean. Globally averaged AOD
in 2020 was on average lower than in 2019,
with a less pronounced summer maximum,
mostly because of less intense biomass burn-
ing in the Northern Hemisphere summer and
autumn over Indonesia and parts of equato-
rial Africa as well as Canada. Figure 2.54a
shows the geographical distribution of AOD in

<figure><img src="image_94.png" /><figcaption>Fig. 2.54. (a) Global aerosol optical depth (AOD) at 550 nm in 2020 from CAMSRA. (b) Global average of total AOD at 550 nm for monthly (red) and annual (blue) periods for 2003–20.</figcaption></figure>
2020, with maxima from anthropogenic aerosols over India and China, as well as less pronounced maxima over the Arabian Peninsula and parts of the Sahara from dust, and over equatorial Africa from biomass burning aerosols.

Average AOD between 2003 and 2020 (Fig. 2.55a) is marked by high values over the highly populated regions of India and China, mainly caused by anthropogenic emissions. High AOD values over the Sahara and Middle East are from dust, while the maxima over central Africa, Indonesia, the Amazon Basin, and parts of Siberia are caused by biomass burning. The high values over Hawaii and close to Mexico City are a known artifact of the CAMS reanalysis related to volcanic outgassing.

<figure><img src="image_95.png" /><figcaption>Fig. 2.55. (a) Total aerosol optical depth (AOD) at 550 nm averaged over the period 2003–20 from CAMSRA. Note the regional differences, with much greater total AOD values over parts of northern Africa, the Arabian Peninsula, southern Asia, and eastern China. (b) Linear trends of total AOD (AOD unit yr⁻¹) for 2003–20 and 2012–20. (c) Only trends that are statistically significant (95% confidence level) are shown. Regions with decreasing trends include the eastern United States, most of Europe, parts of Brazil and China, as well as the Korean peninsula and Japan.</figcaption></figure>
As compared to the 2003–19 average from the CAMS reanalysis, total AOD in 2020 shows nega-
tive anomalies over most of Europe, Africa, and East Asia, as well as parts of the Amazon basin,
United States, and Canada (Plate 2.1x). The negative anomalies over the eastern United States,
Europe, and China/Japan are part of a longer trend over these regions (Fig. 2.55b). As shown by
Fig. 2.55c, the trend is more negative over China for 2012–20 than for 2003–20, which is consistent
with the observed decrease of anthropogenic aerosol emissions there since around 2012 (Li et al.
2017). The 2012–20 trends are positive or not significant over much of the Amazon basin, while
the 2003–20 trends are mostly negative over the same area, showing that most of the decrease in
AOD occurred before 2012. Reduced anthropogenic emissions because of COVID-19 lockdowns
may have contributed to local AOD anomalies, although the impact was probably more important
for surface particulate matter (PM<sub>2.5</sub>).

Positive anomalies of total AOD in 2020 (Plate 2.1x) were found over parts of Brazil and Bolivia, Siberia, and the western United States. These positive anomalies are associated with large fire events. The positive anomaly over southeast Australia and large parts of the southern Pacific Ocean were caused by the extreme fires over New South Wales in late 2019 and early 2020. The positive anomalies over parts of western Africa and the Atlantic Ocean were caused by an extreme dust event in June 2020, while the positive anomaly over Iran was caused by meteorological conditions that favored severe pollution events (Broomandi et al. 2020). The positive anomaly over the Indian subcontinent corresponds to a long-term trend of increasing anthropogenic aerosol emissions (Satheesh et al. 2017), as shown in Figs. 2.55b,c. Plate 2.1y shows the ratio of AOD at 550 nm in 2020 to the 2003–19 average, which gives a measure of the relative importance of the anomalies as compared to climatological values. The highest relative anomalies in 2020 are almost all associated with fire events (Siberia, southeastern Australia, western United States, southwestern Brazil), except over Iran. The exceptional severity of the Australian fires of early 2020 and of the associated plume over the southern Pacific is highlighted in Plate 2.1z, which shows the number of days in 2020 with daily AOD at 550 nm above the 99.9th percentile of the 2003–19 daily values. The same plot also shows the exceptional nature of the fires that affected the western United States in August and September 2020, as well as the dust plume that crossed the Atlantic from western Africa to the Caribbean Sea in June 2020.

Anthropogenic AOD and radiative forcing resulting from aerosol-radiation (RFari) and
aerosol-cloud interactions (RFaci) are shown in Fig. 2.56 for the period 2003-20. They are esti-
mated using the methods described in Bellouin et al. (2020). 2020 was characterized by a small an-
thropogenic AOD and a weak RFari and RFaci in the context of the past 18 years. This may be partly
due to regional decreases in aerosol primary and precursor emissions caused by the response
to the COVID-19 pandemic. Such decreases have, for example, reduced aerosol radiative effects
off the coast of China, at least in cloud-free conditions (Ming et al. 2020), which would weaken
RFari. The data suggest a weakening trend in aerosol radiative forcing starting around 2015, but
the trend would need to be sustained over a longer period to become statistically significant.
<figure><img src="image_96.png" /></figure>

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

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

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

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

<figure><img src="image_101.png" /><figcaption>Fig. 2.56. CAMSRA (a) 2020 average of anthropogenic aerosol optical depth (AOD); (b) global annual average of anthropogenic AOD from 2003 to 2020. Radiative forcing in the shortwave (SW) spectrum due to (c),(d) aerosol-radiation (RFari) and (e),(f) aerosol-cloud interactions (RFaci). The left column shows the average distribution for the period 2003–2020. The right column shows time series of global averages for the same period, with the 1-σ uncertainties of these estimates shown in gray.</figcaption></figure>

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

Stratospheric ozone protects Earth's biosphere from harmful ultraviolet (UV) solar radiation. The total ozone column determines how much UV reaches the surface. Most of the ozone resides in the lower stratosphere ("ozone layer"), where it is recovering slowly from anthropogenic Ocean Depleting Substances (ODS). Clearer signs of ozone recovery, due to the phase-out of ODSs mandated by the Montreal Protocol in the late 1980s (section 2g2), are seen in the upper stratosphere (WMO 2018).

The year 2020 was remarkable because the annual mean anomaly of total column ozone was negative for most of the globe (Plate 2.1aa). This negative anomaly was due to the combination of
very low polar ozone during Arctic winter/spring (Manney et al. 2020; Inness et al. 2020; Dameris et al. 2021) and a large and unusually long-lasting Antarctic ozone hole (see sections 5j and 6h, respectively). Low winter/spring polar ozone is a consequence of stable and cold stratospheric winter vortices with very low temperatures that permit wide-spread formation of polar stratospheric clouds (PSC), chlorine activation, and large polar ozone depletion (Solomon et al. 1999, 2015).

Figure 2.57 shows time series of Arctic and Antarctic daily minimum total column ozone. Generally, Arctic minimum total ozone increases from early winter (November) to spring (April). However, in cold Arctic winters, with stratospheric temperatures sufficiently low for persistent PSC formation (~195 K), minimum total ozone decreases over the winter, due to both chemical loss and reduced poleward ozone transport related to a weak Brewer-Dobson circulation (BDC; Lawrence et al. 2020). In March 2020 record low column values slightly below 220 Dobson unit (DU) were reached (Inness et al. 2020, Dameris et al. 2021), less than in previous cold winters (e.g., 2010/11). Even these record minimum values are, however, higher than values observed in the Southern Hemisphere (SH) ozone hole. Chemical ozone losses of up to 2.8 ppm near 18 km altitude and 88 DU (vortex average) by the end of March 2020 were similar to losses observed in March 2011, but due to the larger polar vortex area, the ozone mass loss was higher in 2020 and reached a new record after the previous record in 2011 (Manney et al. 2020; Weber et al. 2021). Without the Montreal Protocol phaseout of ODS, this chemical ozone loss would have been even higher (Feng et al. 2021). Above Antarctica, minimum total column ozone remained extremely low in 2020 and only rose rapidly at the end of November about 2 months later than in 2016, which had a winter with an average size ozone hole.

The low total ozone levels during winter/spring in both hemispheres contributed significantly to the annual mean low ozone anomaly (Plate 2.1aa). Zonally averaged annual mean total column ozone was as much as 20 and 60 DU below the long-term mean of 1998–2008 at northern middle and Arctic latitudes, respectively. The band of positive anomalies in the outer tropics along with

<figure><img src="image_102.png" /><figcaption>Fig. 2.57. Annual cycle of daily minimum total column ozone values (Dobson Units [DU]) in the polar regions between 50° and 90° in both hemispheres derived from the European GOME-type Total Ozone Essential Climate Variable (GTO) satellite record from Jul 1995 to Jun 2019 and TROPOMI data thereafter. The black line shows the GTO mean annual cycle in the north polar region. The thin gray lines indicate the maximum and minimum values of the observed daily minima from Jul 1995 to Jun 2019. The light gray shading denotes the 10th percentile and 90th percentile, the dark gray shading the 30th percentile and 70th percentile, respectively. The cyan dashed line shows the upper limit of 220 DU that defines the edge of the Antarctic ozone hole. Total ozone minimum time series are shown for winter/spring 2010/11 (blue) and 2019/20 (magenta) in the Northern Hemisphere (Jul–Jun) and in Antarctic winter/spring 2016 (red) and 2020 (orange) in the Southern Hemisphere (Jan–Dec). Updated from Dameris et al. (2021).</figcaption></figure>
the negative anomalies at high latitudes are a typical pattern during quasi-biennial oscillation (QBO) westerly phases, as explained in previous reports. The Arctic Oscillation (AO) index was at

a record high during Northern Hemisphere (NH) winter/spring and contributed to the very low ozone observed in the NH extratropics resulting from a very weak meridional circulation (Lawrence et al. 2020).

Figure 2.58 shows the long-term evolution of total column ozone for different zonal bands. Except for the polar region (NH: March mean; SH: October mean) annual mean total column ozone is shown. Following the decline until the middle 1990s due to ODS increases, total column ozone has remained at a steady level with substantial year-to-year variability during the last 2 decades, but still well below the 1964–80 mean (indicated by the dashed line). Near-global mean total column ozone (Fig. 2.58a) is on average still about 2% below the 1964–80 mean. The median ozone from the Chemistry Climate Model Initiative (CCMI) model simulations (SPARC/IO3C/GAW 2019), accounting for ODS and greenhouse gas changes, is in good agreement with observations. This shows that ozone observations are consistent with the expected slow ozone recovery due to the phasing out of certain ODSs (section 2g2). In 2020, the annual means in all latitude bands, as well as the Arctic March and Antarctic October mean (Fig. 2.58), were all below the decadal average of 1998–2008, but were within the variability observed in recent years (except March 2020).

Figure 2.59 shows the ozone evolution at different altitudes in the stratosphere. Ozone in the upper stratosphere showed a large decline in the 1980s caused by ODS increases, which was stopped in the late 1990s, thanks to the ODS phase-out mandated by the Montreal Protocol. Since about 2000, upper stratospheric ozone has been in a phase of slow recovery. In

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

Fig. 2.58. Time series of annual mean total column ozone (Dobson Units [DU]) in (a)–(d) four zonal bands, and (e) polar (60°–90°) total column ozone in Mar (Northern Hemisphere) and Oct (Southern Hemisphere), the months when polar ozone losses usually are largest. Red: WOUDC ground-based measurements combining Brewer, Dobson, SAOZ, and filter spectrometer data (Fioletov et al. 2002, 2008). Dark blue and light blue: BUV/SBUV/SBUV2 V8.6/OMPS merged products from NASA (MOD V8.6, Frith et al. 2014, 2017) and NOAA (Wild and Long, personal communication, 2019), respectively. Dark green and light green: GOME/SCIAMACHY/GOME-2 products GSG from University of Bremen (Weber et al. 2018) and GTO (additionally includes OMI and TROPOMI) from ESA/DLR (Coldewey-Egbers et al. 2015; Garane et al. 2018). Purple: MSR-2, which assimilates nearly all available ozone datasets after corrections based on the ground-based data (van der A et al. 2015). All datasets have been bias-corrected by subtracting averages for the reference period 1998–2008 and adding back the mean of these averages. The dashed gray lines in each panel show the average ozone level for 1964–1980 calculated from the WOUDC data. The thick orange line shows the median from CCMI model runs (SPARC/IO3C/GAW, 2019). Most of the observational data for 2020 are preliminary.
<figure><img src="image_104.png" /></figure>

Fig. 2.59. Annual mean anomalies of ozone in the upper stratosphere (top three panels) near 42-km altitude or 2-hPa pressure, and in the lower stratosphere (bottom three panels, near 22 km or 50 hPa for three zonal bands: 35°–60°N, 20°S–20°N (tropics), 35°–60°S respectively. Anomalies are referenced to the 1998–2008 baseline. Colored lines are long-term records obtained by merging different limb (GOZCARDS, SWOOSH, SAGE+OSIRIS, SAGE+CCI+OMPS-L, SAGE+SCIAMACHY+OMPS-L, SAGE+OSIRIS+OMPS-L) or nadir-viewing (SBUV, OMPS-N) satellite instruments. The nadir-viewing instruments have much coarser altitude resolution than the limb-instruments. This can cause differences in some years, especially at 50 hPa. The black line is from merging ground-based ozone records at seven NDACC stations employing differential absorption lidars and microwave radiometers. See Steinbrecht et al. (2017), WMO (2018), and Arosio et al. (2018) for details on the various datasets. Gray-shaded area shows the range of simulations from CCMI (SPARC/IO3C/GAW 2019). At the time of publication, ozone data for 2020 were not yet complete for all instruments and were still preliminary.

all recent years, including 2020, ozone values in the upper stratosphere from most datasets were above the 1998–2008 average, consistent with expectations from the CCMI simulations (gray shaded range in Fig. 2.59; SPARC/IO3C/GAW 2019). In the lower stratosphere, however, long-term ozone variations are dominated by meteorological and transport variations (e.g., Chipperfield et al. 2018), and Fig. 2.59 shows no clear sign of ozone increases in this region over the last 20 or so years. This is consistent with total column ozone in Fig. 2.58. In 2020, lower

stratospheric values were at the low end of recent years, and also at the low end of the model predictions. The tropical (20°S–20°N) long-term ozone decline is linked to the acceleration of the meridional Brewer-Dobson circulation (Ball et al. 2018; Chipperfield et al. 2018; WMO 2018). The low annual mean 2020 values of lower stratospheric ozone in the northern and southern extratropical 35°–60° latitude bands, however, similar to the generally low total column ozone (Plate 2.1aa; Fig. 2.58), are the result of the weak meridional Brewer-Dobson circulation in winter in both hemispheres.

### 5) Stratospheric water vapor—S. M. Davis, K. H. Rosenlof, D. F. Hurst, and H. Vömel

Variations in stratospheric water vapor (WV) occur over a wide range of timescales and can impact stratospheric ozone (Dvortsov and Solomon 2001) and surface climate (Solomon et al. 2010). Such variations are forced by prominent modes of seasonal and interannual dynamical variability that influence temperatures in the tropical tropopause layer (TTL; ~14–19 km). In general, the amount of WV entering the stratosphere is controlled by the lowest temperature encountered by an ascending air mass (i.e., through the Clausius-Clapeyron relationship), with more WV entering the stratosphere when TTL temperatures are warmer. As a result, processes that cause temporal variability in TTL temperatures also lead to global-scale variability in stratospheric WV.
<figure><img src="image_105.png" /><figcaption>Fig. 2.60. (a) Time series of vertical profiles of tropical (15°S–15°N) lower stratospheric water vapor (WV) anomalies and (b) latitudinal distributions of WV anomalies at 82 hPa. Both are based on version 4.2 Aura MLS data from the SWOOSH v2.6 5° zonal mean product (Davis et al. 2016). Anomalies are differences from the mean 2004–20 water vapor mixing ratios (ppm) for each month. (c) shows the propagation of tropical lower stratospheric WV anomalies to higher latitudes in both hemispheres as well as the influences of dehydrated air masses from the Antarctic polar vortex as they are transported toward the SH midlatitudes at the end of each year. Tick marks denote the beginning of each year.</figcaption></figure>

In 2020, WV anomalies in the tropical lower stratosphere were positive (wet). Figure 2.60 shows the vertical-time cross section of tropical-averaged WV anomalies from the *Aura* satellite Microwave Limb Sounder (MLS; Fig. 2.60a), as well as the latitudinal distribution of WV anomalies as a function of time in the base of the stratosphere at 82 hPa (~17 km; Fig. 2.60b). The vertical-time plot shows a substantial region (in time and space) of positive water vapor anomalies ascending into the stratosphere starting in mid-2019 and continuing throughout 2020.

The 2020 *Aura* MLS (version 4.2) annual tropical average (15°S–15°N) WV anomaly at 82 hPa was 0.4 ppm (parts per million, i.e., µmol mol⁻¹), or 11% above the annual average since 2005. Monthly WV anomalies ranged from +0.1 ppm (+2%) in October to +0.8 ppm (+22%) in December, which was the fifth-wettest anomaly in the *Aura* MLS record dating back to August 2004. The tropical WV anomaly time series in 2020 is U-shaped, with strong positive anomalies at the beginning and end of the year and weak positive anomalies in the middle (Fig. 2.60b). The qualitative behavior of lowermost stratospheric WV observed by *Aura* MLS is consistent with balloon-borne frost-point hygrometer soundings at five locations, as shown in Fig. 2.61.

In 2020, tropical cold-point tropopause (CPT) temperature anomalies were positive (warm) from January through April, negative from May through August, and positive from September through the end of the year (blue line, Figs. 2.61c,d). The annual mean tropical cold-point anomaly was +0.48 K.

In general, interannual variations in CPTs are correlated with interannual variability in modes of climate variability such as the ENSO and QBO in equatorial stratospheric winds. These phenomena partly impact CPTs through their modulation of upwelling of air in the tropical lower stratosphere and the associated temperature response. Although we do not attempt formal attribution of the CPT and lower stratospheric WV variability to QBO and ENSO, below we discuss the changes in the phases of QBO and ENSO during 2020 as they pertain to WV variability.

The QBO westerly shear phase is associated with a negative upwelling anomaly and cold temperatures, whereas the reverse is true for easterly shear. Equatorial winds from the Singapore radiosonde wind data, which are a commonly used proxy for the QBO phase, were westerly at 70 hPa at the beginning of 2020, but transitioned to easterly from May through September, before returning back to westerly for the final 3 months of 2020 (sections 2b5, 2e3; see Fig. 2.46). The wind shear between 70 hPa and 100 hPa was positive (westerly over easterly) for all of 2020, with the exception of July and August. It is possible that these anomalies impacted tropical CPTs, as the most negative CPT anomaly of the year (–0.4 K) occurred in August (Fig. 2.61d).
<figure><img src="image_106.png" /></figure>

Fig. 2.61. Lower stratospheric water vapor (SWV) anomalies over five balloon-borne frost point (FP) hygrometer stations. Each panel shows the lower stratospheric anomalies of individual FP soundings (black) and of monthly zonal averages of Microwave Limb Sounder (MLS) retrievals at 82 hPa in the 5° latitude band containing the FP station (red). High-resolution FP vertical profile data were averaged between 70 hPa and 100 hPa to emulate the MLS averaging kernel for 82 hPa. Each MLS monthly zonal mean was determined from 2000–3000 profiles. Anomalies for MLS and FP data are calculated relative to the 2004–20 period for sites except for Lindenberg (2009–20) and Hilo (2011–20). Tropical CPT anomalies based on the MERRA-2 reanalysis (d, blue curve), which are generally well correlated with the tropical lower stratospheric WV anomalies, are the driving force behind the variations in tropical WV during 2020.

ENSO was in a neutral phase in the first half of 2020 before transitioning to La Niña in August and remaining in that phase through the end of the year (see sections 2e1, 4b). In boreal winter, La Niña is known to result in weaker tropical lower stratospheric upwelling, anomalously higher cold-point temperatures, and enhanced water vapor in the tropical lower stratosphere (Calvo et al. 2010; Garfinkel et al. 2018; Simpson et al. 2011). The large positive tropical anomalies in the lower stratospheric WV observed at the end of 2020 are consistent with the known behavior associated with a La Niña. This narrow band of positive anomalies in December 2020 is shown in contrast
<figure><img src="image_107.png" /><figcaption>Fig. 2.62. Deseasonalized monthly lower stratospheric Microwave Limb Sounder (MLS) anomalies (ppm; 2004–20 base period) centered on 82 hPa in (a) Dec 2019 and (b) Dec 2020 from the *Aura* MLS.</figcaption></figure>

to the same month of the previous year in Fig. 2.62.

In addition to the tropical stratospheric WV features in 2020, there are several notable higher latitude features. First, strong negative WV anomalies at high southern latitudes at 82 hPa in the last several months of 2020 (Figs. 2.60b, 2.62b) are likely the signal of anomalously strong dehydration associated with the very strong and persistent Antarctic vortex (see sections 2b5, 6h). The remarkably long-lived and stable Antarctic vortex likely explains why the dry anomaly is most noticeable at the very end of the year rather than earlier in austral spring.

Additionally, the positive tropical lower stratospheric WV anomaly discussed previously appears to be somewhat shifted toward the SH in the early part of the year (Fig. 2.60b). This anomaly may be related to the rapid injection of wet tropospheric air into the stratosphere by Australian bushfires of record-breaking intensity at the beginning of 2020 (Kablick et al. 2020; Khaykin et al. 2020). Of course, this anomaly occurs in concert with widespread positive WV anomalies in the tropics, so it is not possible

to quantitatively determine the contribution of the fires to stratospheric WV with the analysis presented here. Further modeling and analysis should be able to shed light on the contribution of the Australian bushfires to stratospheric WV levels in 2020 in the context of other sources of variability such as QBO and ENSO.

**6) Tropospheric ozone**—J. R. Ziemke and O. R. Cooper

Tropospheric ozone is the third-most effective climate-forcing greenhouse gas following CO₂ and CH₄ (IPCC 2013). Average global radiative forcing due to tropospheric ozone is +0.4 ± 0.2 W m⁻² and thus contributes to net warming of the atmosphere. In addition, tropospheric ozone is a surface pollutant damaging to vegetation and human health (Fleming et al. 2018; Mills et al. 2018), and it is the primary producer of OH radical (OH), which is the main oxidant of tropospheric pollutants. The sources for tropospheric ozone include transport from the stratosphere, non-combustive, non-biogenic volatile organic compound (VOC) sources such as solvents or fuel evaporation, photochemical production from precursors that include non-methane biogenic hydrocarbons, CH₄, lightning NOₓ, and also emissions generated from the combustion of fossil fuels and biomass burning (Young et al. 2013, 2018; Monks et al. 2015; McDonald et al. 2018; Archibald et al. 2020). The main drivers of planetary-scale variability of tropospheric ozone include dynamical forcing from the ENSO and Walker circulation in the tropics, and “weather system” baroclinic waves in midlatitudes (Chandra et al. 1998, 2009; Sun et al. 2014; Ziemke et al. 2015). The main drivers of small-scale patterns in tropospheric ozone are local emissions of ozone precursors, both anthropogenic and natural, and ozone surface deposition driven mainly by vegetation
(Archibald et al. 2020). The large temporal variability of tropospheric ozone from diurnal to inter-annual timescales makes it difficult to determine decadal trends from regional to global scales (Neu et al. 2014; Cooper et al. 2014; Lin et al. 2014; Barnes et al. 2016; Strode et al. 2019; Tarasick et al. 2019).

Since 2012, all *State of the Climate* reports have provided updates on global tropospheric ozone based on independent measurements from ground- and satellite-based instruments (Ziemke and

Cooper 2019, and references therein). Due to limited annual updates of ground-based observations, these reports have relied primarily on combined *Aura* Ozone Monitoring Instrument (OMI) and MLS satellite ozone measurements (Ziemke et al. 2019). Vertical resolution of OMI/MLS monthly tropospheric column ozone is 2–3 km about the tropopause with ~2 DU (7%) precision in regional measurements; uncertainties in calculated trends are about 0.5 DU decade⁻¹ (1.5% decade⁻¹). OMI/MLS data show broad regions of positive 2020 tropospheric ozone column anomalies (relative to the 2005–19 average) of ~1.2 DU (4%) in the NH midlatitudes, with smaller anomalies of ~1 DU or less elsewhere (Plate 2.1ab). Hemispheric and global average tropospheric ozone burdens and their 95% confidence levels for 2020 were 160 ± 7 Tg (0°–60°N), 145 ± 8 Tg (0°–60°S), and 304 ± 8 Tg (60°S–60°N; Fig. 2.63). Trends and their 95% confidence levels (in units Tg yr⁻¹) in hemispheric and 60°S–60°N burdens from October 2004 through December 2020 are shown in Fig. 2.63; these trends correspond to increases of about 0.50 ± 0.15% yr⁻¹ for all three curves. Spatially, the trends are overwhelmingly positive, the strongest of which are ~ +3.2 DU decade⁻¹ (~ +1% yr⁻¹) above India and East/Southeast Asia, extending eastward over the North Pacific Ocean (Fig. 2.64). These trends are consistent with model estimates based on strengthening emissions of ozone precursors from Southeast, East, and South Asia, primarily due to fossil fuel combustion (Zhang et al. 2016b; Lin et al. 2014; Ziemke et al. 2019) and with NH ozone trends (1994–2016) as observed by instrumented commercial aircraft (Gaudel et al. 2020).

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

Fig. 2.63. Monthly averages of Ozone Monitoring Instrument / Microwave Limb Sounder (OMI/MLS) tropospheric ozone burdens (Tg) from Oct 2004 through Dec 2020. The top curve (black) shows 60°S–60°N monthly averages (solid) with 12-month running mean (dashed). The bottom two curves show monthly averages (solid) and running means (dashed) for the Northern Hemisphere (red) and Southern Hemisphere (blue). Slopes of linear fits to the data are presented with their 95% confidence-level uncertainties.

<figure><img src="image_109.png" /></figure>

Fig. 2.64. Linear trends in Ozone Monitoring Instrument / Microwave Limb Sounder (OMI/MLS) tropospheric column ozone (DU decade⁻¹) on a 5° × 5° grid from Oct 2004 through Dec 2020. Asterisks denote trends with *p*-values less than 0.05. Trends were calculated using a multivariate linear regression model (e.g., Randel and Cobb 1994, and references therein) that included a seasonal cycle fit and the Niño-3.4 index as an ENSO proxy; trend uncertainties included autoregressive adjustment via Weatherhead et al. (1998).
Models indicate that ozone produced in these areas is transported northward and eastward in the free troposphere over the North Pacific Ocean (Zhang et al. 2020), as supported by the trend patterns in Fig. 2.64. Positive trends in the SH extra-tropics have been linked to a broadening of the Hadley circulation (Lu et al. 2018a).

Three long-term baseline monitoring sites with quality assured data are available for updating surface ozone trends through 2020: 1) Mauna Loa Observatory (MLO), Hawaii (19.5°N, 155.6°W, 3397 m a.s.l.); 2) South Pole Observatory (SPO), Antarctica (90°S, 59°E, 2840 m a.s.l.); and 3) Barrow Atmospheric Baseline Observatory (BRW), near Utqiaġvik, Alaska (71.3°N, 156.6°W, 11 m a.s.l.). Continuous ozone measurements began at MLO in September 1973, at SPO in January 1975, and at BRW in March 1973, with reliable observations available at SPO for the years 1961–63 and at MLO for the years 1957–59 (Tarasick et al. 2019). Observations at remote baseline sites are important for understanding long-term ozone trends in the boundary layer, but they do not necessarily match the trends in the free troposphere, which have been overwhelmingly positive since the mid-1990s, as measured by ozonesondes, lidars, and commercial aircraft (Cooper et al. 2020).

Ozone levels at BRW in the Arctic increased by 3 ppbv (11%) since 1973. The limited data at MLO and SPO from the 1950s and 1960s indicate that ozone levels at these remote high-elevation sites were similar in the mid-twentieth century despite being located in different hemispheres. Ozone levels at SPO have changed little since the 1960s, with only a slight increase of ~2 ppbv (6%) from 1975 to 2020 (Fig. 2.65). In contrast, ozone levels at MLO increased at the rate of 0.14 ± 0.05 ppbv yr⁻¹, resulting in a 17% increase since 1973. MLO experiences high inter-annual ozone level variability due to its location in the transition region between tropical and extratropical air masses. The ozone level trend in the extratropical air masses can be isolated by focusing on the dry air masses, which tend to originate at higher altitudes and latitudes to the west and northwest of MLO (Gaudel et al. 2018). The trend in the dry air masses is 50% greater compared to the trend using all air masses (10.1 ppbv total increase since 1974, or 24%), which implies that the site is influenced by ozone level increases in upwind regions to the west and northwest, most likely Asia where surface and free tropospheric ozone levels have generally increased over the past 2 decades due to increased anthropogenic emissions of ozone precursor gases (Zhang et al. 2016b; Cohen et al. 2018; Lu et al. 2018b; Gaudel et al. 2018, 2020).

<figure><img src="image_110.png" /><figcaption>Fig. 2.65. Monthly median ozone (ppbv) at Barrow Observatory (Mar 1973–Dec 2020, green) and South Pole (Jan 1975–Dec 2020, black) using data from all hours of the day. Additional data from South Pole are shown for the early 1960s. Also shown are nighttime monthly median ozone values at Mauna Loa (MLO) calculated with all available data for months with at least 50% data availability, Oct 1973–Oct 2020 (blue), with early observations from the late 1950s. In addition, the monthly median values associated with dry air masses (orange) at MLO are included (dewpoint < the climatological monthly 40th percentile, and a sample size of at least 24 individual hourly nighttime observations). Trends (solid straight lines) are based on least-squares linear regression fit through the monthly values (1970s–2020), and reported with 95% confidence intervals and *p*-values. The MLO and South Pole trend lines are extrapolated back in time to the late 1950s (dashed lines).</figcaption></figure>
Carbon monoxide (CO) plays a significant role in determining the abundance of climate-forcing gases like CH₄ and tropospheric ozone through OH chemistry (Hartmann et al. 2013). CO is therefore regarded as an indirect climate-forcing agent. Sources of CO include incomplete fossil fuel and biomass combustion and in situ production via the oxidation of CH₄, isoprene, and other organic trace gases. Combustion and atmospheric chemical sources typically produce similar amounts of CO on the global scale but vary in space and time because of the varying distribution of anthropogenic and biomass burning CO emissions as well as biogenic isoprene emissions.

CAMS (https://atmosphere.copernicus.eu) produced a retrospective analysis of CO, aerosols, and ozone for the period 2003–20 by assimilating satellite retrievals of atmospheric composition with the ECMWF model (Inness et al. 2019). This CAMS reanalysis assimilated thermal infrared (TIR) column CO retrievals of the Measurement of Pollution in the Troposphere (MOPITT) instrument (Deeter et al. 2014) globally, only excluding observations polewards of 65°N/S using the ECMWF 4D-VAR data assimilation system. The anthropogenic CO emissions used in the ECMWF model were taken from the MACC/CityZEN EU projects (MACCity) inventory (Granier et al. 2011), which estimates emission trends according to the IPCC RCP 8.5 scenario. No COVID-19 pandemic-related emissions modifications for 2020 were applied in the assimilation. Anthropogenic biomass burning emissions were taken from the Global Fire Assimilation System (GFAS) v1.2 (Kaiser et al. 2012; see section 2h3).

Figure 2.66 shows the time series of the monthly mean global burden of CO from the CAMS reanalysis for the period 2003–20. The total burden in 2020 was similar to the burden in the previous years, with the exception of the year 2015 when the global CO burden was dominated by emissions from fires in Indonesia (Huijnen et al. 2016). Approximated with a linear trend over the whole period, the total global CO burden has declined by –1.5 Tg CO yr⁻¹, and as piecewise trends by –3.1, –14.0, and +0.1 Tg CO yr⁻¹ for 2003–07, 2008, and 2009–20, respectively, following Flemming and Inness (2019). Figure 2.67 shows clean marine boundary layer (MBL) mean surface CO for five zonal bands based on measurements of weekly air samples collected at MBL sites in the NOAA Global Greenhouse Gas Reference Network (Novelli et al. 2003; Pétron et al. 2020). The global negative trend seen in both surface and satellite records is dominated by the decrease of the CO burden in the mid- and high-latitudes of the NH, likely as a result of reductions in vehicle emissions (Wang et al. 2012). The tropics and the SH exhibit no trends or a small positive trend.

The spatial patterns of the 2020 annual CO total column anomalies (Plate 2.1ac) agree with the multi-year trends and show about 0%–5% higher values throughout the SH and most of the tropics and negative values for most of the NH mid- and high-latitudes. The most noticeable negative

<figure><img src="image_111.png" /><figcaption>Fig. 2.66. Time series of monthly global carbon monoxide (CO) burdens (Tg CO) from the total column CO output from the CAMSRA (TCCO CAMSRA) and a piecewise linear trend for the periods 2003–07, 2008, and 2009–20. The red line indicates the year 2020.</figcaption></figure>
<figure><img src="image_112.png" /></figure>

Fig. 2.67. Time series of carbon monoxide (CO) over the clean marine boundary layer for the polar NH (53.1°–90°N, black), temperate NH (17.5°–53.1°N, green), tropics (17.5°S–17.5°N, red), temperate SH (17.5°–53.1°S, dark blue), and SH (53.1°–90°S, magenta) for the period 1991–2020.

anomaly in 2020 appeared over maritime Southeast Asia (Indonesia) and was caused by low fire activity in the region, because La Niña conditions were starting to evolve in (austral) spring 2020. Positive anomalies occurred over the western United States and over northeast Siberia (Yakutia), caused by intense biomass burning in boreal summer and early autumn 2020 (see also sections 2g3, 2h3). The widespread positive anomaly over the southern South Pacific Ocean was the result of long-range transport of CO plumes originating from intense fires in southwestern Australia between December 2019 and January 2020. The observed increase in tropical and SH MBL CO in 2020 was also most likely caused by the Australian fires.

The reduction in anthropogenic CO emission during the COVID-19 pandemic has

been estimated to be up to 30% in North America and Europe and 10%–20% over China during the height of the lockdown measures (Foster et al. 2020; Doumbia et al. 2021). The CAMSRA CO total columns over Europe and North America were the lowest since 2003 for the period February–April (Fig. 2.68). However, the attribution of these anomalies to COVID-19-related emission reduction is complicated by the multi-year negative CO trends in the regions and the unquantified influence of other factors such as meteorological conditions.

<figure><img src="image_113.png" /></figure>

Fig. 2.68. Seasonal cycle of monthly global carbon monoxide (CO; ppb) total column (10¹⁸ molec. cm⁻²) over (a) Europe and (b) North America for all years in the 2003–19 period (blue) and for 2020 (red) from the CO total column output from the CAMSRA (TCCOsfc CAMSRA).
1) *Land surface albedo dynamics*—G. Duveiller and N. Gobron

The presence and absence of snow cover dominates the large-scale spatial patterns of the land surface albedo for 2020 (Plates 2.1ad, ae). The most prominent feature was a strong reduction in albedo in Europe from western Germany to Moscow, and from southern Scandinavia to the Balkans, corresponding to a large deficit in snow cover over this area, particularly during the January–March 2020 period (see sections 2c2, 7f), when temperatures were particularly high (see section 2b1). This same effect occurred to a lesser extent in the eastern half of the United States. Reductions in the snow cover extent during spring over large parts of Siberia also contributed to reducing the overall albedo of the Northern Hemisphere (NH). Conversely, an anomalously high duration of snow cover resulted in a rise in albedo in several parts of the world. For northeastern China, the Tibetan plateau, and central parts of North America, excess snow cover duration occurred in boreal winter (January–March), for Canada and northern Scandinavia in boreal spring (April–June), and for Patagonia in austral winter (July–September). All left a clear mark in the annual maps of albedo, both in the visible and near infrared parts of the spectrum (Plates 2.1ad, ae).

Beyond the strong effect of snow, the land surface albedo dynamics were affected by vegetation growth, which darkens the surface, and by dry climatic conditions, which typically lighten the surface due to either the hastening of leaf senescence or the drying up of bare soil. In 2020,

warm conditions across most of the globe and during considerable portions of the year contributed to the development of greener surfaces (see section. 2h2), which translated to lower visible albedo. This was particularly evident over India, northern East Africa, and southeastern Australia, all of which had higher-than-average soil moisture during large parts of the year (see section 2d10). On the other hand, drought conditions increased visible albedo in southern Africa (mostly in Mozambique) and in central South America (around the Gran Chaco but also extending eastward and northward, see section 2d11). Rain deficits attributable to the development of La Niña in late 2020 further exacerbated the dry conditions in these regions of South America (see section 7d). However, drier conditions in the later part of the year over various parts of the NH did not considerably alter the albedo patterns that were mostly dominated by spring snow cover.

Overall, 2020 contributed to the general darkening trend of the land surface with respect to visible albedo (Figs. 2.69, 2.70). There was, however, a clear separation between NH and Southern Hemisphere (SH), with the SH seeing its second consecutive brighter year than the 2003–10 baseline, and following a steady

<figure><img src="image_114.png" /><figcaption>Fig. 2.69. Zonally averaged (a) white sky visible and (b) near infrared albedo anomalies (%) for the period 2003–20 using a 2003–10 baseline period.</figcaption></figure>
<figure><img src="image_115.png" /><figcaption>Fig. 2.70. Global (black/gray lines), NH (blue), and SH (red) land surface (a) visible and (b) near infrared albedo anomalies for the period 2003–20 using a 2003–10 baseline period. Dotted lines denote each monthly period; solid lines indicate the 6-month running averaged mean.</figcaption></figure>

brightening trend since its lowest point on the satellite record in 2017. With respect to the albedo in the near infrared part of the spectrum, the overall trend was toward a slight brightening, which was more pronounced in the SH than in the NH.

This analysis of the land surface albedo relied exclusively on satellite information. Surface albedo was retrieved from multispectral surface reflectance measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Aqua and Terra satellite platforms (Schaaf et al. 2002). Satellite retrievals are probably the most accurate way to assess surface albedo at the global level as they rely on a limited set of assumptions. They have been shown to provide sufficiently accurate estimates when compared to ground measurements both on ice sheets (Stroeve et al. 2013) and over different types

of vegetation (Cescatti et al. 2012). The MODIS albedo products provide separate estimations for different parts of the shortwave electromagnetic spectrum, allowing this analysis to focus separately on the visible and the near infrared parts of the spectrum. Furthermore, the analysis was based on estimation of white-sky albedo (bi-hemispherical reflectance), which is defined as albedo in the absence of a direct radiation component and when the diffuse radiation component is isotropic.

## 2) Terrestrial vegetation dynamics—N. Gobron

The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) plays a critical role in assessing the primary productivity of canopies, the associated fixation of atmospheric CO₂, and the energy balance of the surface. FAPAR anomalies from the 1998–2010 average show significant regional variations in the vegetation productivity conditions worldwide in 2020 (Plate 2.1af). The SH appeared similar to typical La Niña conditions, with largest negative anomalies (brown) occurring over Argentina, Paraguay, and Chile and largest positive (dark blue) in eastern Brazil, Malaysia, and Indonesia (see section 2e1). However, there were negative anomalies in regions such as Mozambique, southern Madagascar, and most of Australia, except the southeast. To a lesser extent, negative regional anomalies were observed near the northwest coast of the South American continent.

In the NH, the largest negative anomalies were observed over herbaceous vegetation in Norway, northern Sweden, and Canada. Local negative occurrences were notable in the north-central United States (Colorado, Iowa, North Dakota, and Wisconsin), but also in California, as well as in the Russian Far East, including Kamchatka. The biggest positive anomalies occurred in eastern China and India and north Pakistan, South Sudan, and Kenya, followed by southern and central Europe. Similar to 2016, some arctic regions in Russia and Canada also showed strong positive anomalies.

La Niña had an impact on vegetation health by contributing heavy rains over Indonesia, India, and western Brazil. In contrast, Argentina, Paraguay, and Chile recorded a strong negative anomaly that increased from June to December due to the dry conditions caused by La Niña coupled with above-normal temperatures (see section 2d4). At the start of the dry season in April,
northern Mozambique and western Madagascar suffered from rainfall deficits that induced the negative annual anomalies. Strong negative anomalies were also observed over some regions of Australia that suffered from the devastating 2019/20 summer bushfires, although vegetation partially recovered locally with adequate rains.

The European summer heat wave (see sections 2b3, 7f) significantly impacted the vegetation productivity, as can be seen in the annual anomaly of herbaceous vegetation in Sweden and Norway. The rest of Europe was also strongly affected during spring and summer, but this was not apparent per se in the annual analysis. During spring, Canadian vegetation suffered from very cold temperatures that affected the annual value. The western and central United States experienced both high temperatures and dry conditions in much of the year, and the impacts on central states were perceived in the annual study. Terrestrial photosynthesis was enhanced over eastern China, with vegetation growth noticeable since 2015 due to important changes in

the main land use, with a net increase in leaf area mainly from croplands (Gobron 2019; Chen et al. 2019). The strong positive anomalies over some northern latitudes were largely due to the warm spring that was ideal for vegetation growth. Furthermore, heavy rains enhanced the positive anomalies in China and East Africa.

Figure 2.71 shows the latitudinal anomalies average from 1998 to 2020 compared to the base period 1998–2010. The strong seasonal deviations mainly include positive anomalies north of 20°N after 2014. In 2020, this positive behavior extended south of the equator. Negative anomalies from 2002 to 2014 affected the SH, except in 2010–12. Around 30°S, 2019/20 anomalies again became again negative.

Figure 2.72 draws the global and bihemispherical anomalies, revealing more seasonal oscillations in the SH than in the NH. Analysis of SH data reveals two positive extreme peaks in 2000 and 2017, while extreme minima events occurred in 2008–09. Afterwards, SH anomalies increased with interannual variations and were positive since 2014. The NH experienced fewer extreme negative events compared to the SH, and its photosynthetic activity increased from 2010 to 2017 and, after a brief decline in late 2017/early 2018, increased again to a high value in 2020. The global average has been positive since 2010 with a positive trend.

Earth observations measurements are fundamental for monitoring the activity of vegetation worldwide. These observations are used to infer FAPAR,

<figure><img src="image_116.png" /><figcaption>Fig. 2.71. Zonally averaged Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomalies for 1998–2020 (1998–2010 base period).</figcaption></figure>

<figure><img src="image_117.png" /><figcaption>Fig. 2.72. Global (black/gray lines), Northern Hemisphere (blue), and Southern Hemisphere (red) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomalies for 1998–2020 (1998–2010 base period). Dotted lines denote each monthly period; solid lines indicate the 6-month running averaged mean.</figcaption></figure>
an essential climate variable (as defined by GCOS [2016]). The 2020 analysis merged 23 years of global FAPAR monthly products based on three optical sensors from 1998 to 2020 (Gobron et al. 2010; Pinty et al. 2011; Gobron and Robustelli 2013; the base period is 1998–2010). Comparisons between each dataset and with multiple proxies using ground measurements provide an estimate of the uncertainties and biases. This long-term global FAPAR dataset presents an estimated average uncertainty close to 5%–10%.

### 3) Biomass burning—J. W. Kaiser and G. R. van der Werf

The year 2020 illustrates how two distinct trends that have emerged in global biomass burning over the last decade shape current pyrogeography: a declining trend in many savanna regions related to agricultural expansion and an increasing trend in many forested regions where climate change increases the flammability of the landscape. It was one of the lowest fire years in the Global Fire Assimilation System (GFAS) record (13% below the 2003–10 average), but there was also extreme regional fire activity (Table 2.11). This activity included the continuation of fires in southeastern Australia early in the year that had started in late 2019, fires above the Arctic circle in Russia, and in the western United States.

Global fire emissions are generally dominated by savanna burning (Fig. 2.73). For example, African fire emissions comprise roughly half of total global fire carbon emissions; but fires here and in many other savanna regions have decreased over the past decade (Andela et al. 2017). This trend continued in 2020 with Africa north (south) of the equator seeing emissions

11% (12%) below the 2003–10 average. Given the dominance of these regions, this reduction was reflected in the global total of 1741 Tg C emissions from biomass burning in 2020, which was the fourth lowest of the past 18 years. The lowest fire year in this period was 2018 with 1661 Tg C, followed by 2017 and 2013 with 1683 Tg C and 1690 Tg C, respectively. While the long-term trend is partly driven by agricultural expansion into savanna ecosystems and associated fragmentation of the landscape, anomalous rainfall years also influence interannual variability on top of the declining trend.

Tropical forests in the Amazon saw the highest fire activity since 2012, surpassing the year 2019, which attracted more media attention at that time. The emerging upward trend is also supported by independent VIIRS observations (https://globalfiredata.org/pages/2020/09/22/amazon-fire-activity-in-2020-surpasses-2019; Schroeder et al. 2014). In contrast, fire activity in tropical

<table><caption>Table 2.11. Annual continental-scale biomass burning budgets in terms of carbon emission (Tg C yr⁻¹) from Global Fire Assimilation System (GFASv1.4).</caption><thead><tr><th>Time period</th><th>2003–10</th><th colspan="2">2020</th></tr><tr><th>Quantity in Tg C yr⁻¹</th><th>Latitude/longitude</th><th>Mean value (range)</th><th>Value</th><th>Anomaly (percent)</th></tr></thead><tbody><tr><td>Global</td><td></td><td>2010<br>(1828–2272)</td><td>1741</td><td>-269 (-13%)</td></tr><tr><td>North America</td><td>30°–75°N<br>190°–330°E</td><td>79<br>(63–109)</td><td>65</td><td>-14 (-18%)</td></tr><tr><td>Central America</td><td>13°–30°N<br>190°–330°E</td><td>42<br>(31–58)</td><td>42</td><td>0 (0%)</td></tr><tr><td>South America</td><td>13°–60°S<br>190°–330°E</td><td>427<br>(255–524)</td><td>418</td><td>-9 (-2%)</td></tr><tr><td>Europe and Mediterranean</td><td>30°–75°N<br>330°–60°E</td><td>37<br>(29–62)</td><td>30</td><td>-7 (-17%)</td></tr><tr><td>N. Hem. Africa</td><td>0°–30°N<br>330°–60°E</td><td>419<br>(353–453)</td><td>374</td><td>-45 (-11%)</td></tr><tr><td>S. Hem. Africa</td><td>0°–35°S<br>330°–60°E</td><td>484<br>(444–528)</td><td>426</td><td>-58 (-12%)</td></tr><tr><td>Northern Asia</td><td>30°–75°N<br>60°–190°E</td><td>176<br>(99–418)</td><td>193</td><td>+17 (+10%)</td></tr><tr><td>South-East Asia</td><td>10°–30°N<br>60°–190°E</td><td>128<br>(107–150)</td><td>104</td><td>-24 (-18%)</td></tr><tr><td>Tropical Asia</td><td>10°S–10°N<br>60°–190°E</td><td>118<br>(38–228)</td><td>23</td><td>-95 (-80%)</td></tr><tr><td>Australia</td><td>10°–50°S<br>60°–190°E</td><td>99<br>(47–137)</td><td>64</td><td>-34 (-35%)</td></tr><tr><td>Arctic</td><td>67°–90°N<br>0°–60°E</td><td>4<br>(0–11)</td><td>37</td><td>+32 (+724%)</td></tr><tr><td>Western United States</td><td>30°–49°N<br>230°–260°E</td><td>15<br>(7–25)</td><td>42</td><td>+27 (+183%)</td></tr></tbody></table>
<figure><img src="image_118.png" /><figcaption>Fig. 2.73. Global map of fire activity in 2020 in terms of carbon consumption (g C m⁻² yr⁻¹). (Source: Global Fire Assimilation System GFASv1.4.)</figcaption></figure>

<figure><img src="image_119.png" /><figcaption>Fig. 2.74. Time series of annual (squares) and monthly (lines) regional fire activity in terms of carbon consumption: (a) Arctic, (b) New South Wales and Victoria, (c) western United States, and (d) South America. (Source: Global Fire Assimilation System GFASv1.4.)</figcaption></figure>

Asia—including Indonesia—was one of the lowest on record, related to relatively wet conditions as La Niña started to evolve during the fire season.

At higher latitudes, 2020 was record breaking in both southeastern Australia and the western United States, where extreme heat and drought contributed to unprecedented fire conditions (Van Oldenborgh et al. 2020). While Australia as a whole did not exhibit anomalies (Table 2.11) due to the dominance of savanna fires in the northern part of the country and because the fire season in southeastern Australia started in 2019, the combined 2019–20 southeastern Australian fire season was unprecedented. For example, emissions were the highest since at least 2003 (Fig. 2.74), burnt area in New South Wales was the largest since at least 1968 with more than 5 million ha, and ~43% of the total Australian coverage of Eucalyptus forests and woodlands burned (Bowman et al. 2021). In the western United States, total fire emissions almost tripled compared to the 2003–10 mean and thus continued the recent upward trend (Fig. 2.74).

The Arctic experienced its highest fire year in 2020, surpassing the record set in 2019 (Kaiser et al. 2020; Sidebar 5.1) by 34%. Each of the last 5 years have thus seen more Arctic fires than the preceding year (Fig. 2.74). Most of the fires occurred in Arctic Asia, with Arctic America, and also all of Canada plus Alaska, experiencing their lowest fire year on record in 2020. While the fires burned within the Arctic circle and partly affected thawed permafrost, the largest fire complex was still in thinly forested regions and not in tundra, which leaves the long-term possibility of a partial uptake of the emitted carbon through re-growth.

The time series in Plate 1.1ac puts GFAS in the context of the Global Fire

Emissions Database, version 4 with small fires (GFED4s), which is mostly based on burnt area observation and dates back to 1997 (van der Werf et al. 2017). It shows that the global fire emissions during the 1997–98 El Niño remain unsurpassed.
GFAS produces global fire emission estimates in near real-time for the Copernicus Atmosphere Monitoring Service (Kaiser et al. 2012). Here, we used a consistent reprocessing of 2003–20 based on Collection 6 of the MODIS Fire Radiative Power product (Giglio et al. 2016). The 14% bias with respect to Collection 5 has been corrected and the satellite- and observation time-specific bias correction factors from Hüser et al. (2018) were applied for 17 August–2 September 2020 in order to compensate for the outage of observations from MODIS onboard the *Aqua* satellite.

4) *Phenology of primary producers*—D. L. Hemming, J. Garforth, J. O'Keefe, T. Park, A. D. Richardson, T. Rutishauser, T. H. Sparks, and S. J. Thackeray

During 2020, the satellite-derived (MODIS) normalized difference vegetation index (NDVI; Park et al. 2016) across the NH landmass (>30°N) revealed an earlier mean start of season (SOS<sub>M</sub>), later end of season (EOS<sub>M</sub>), and 1.8 days longer growing season, compared to the 2000–10 baseline (Fig. 2.75). These coincided with the second-warmest spring and warmest autumn on record across the NH (NOAA 2020). Regional differences resulted in 2.1 days earlier and 0.9 days later SOS<sub>M</sub> in Eurasia (EA) and North America (NA), respectively. No clear signal in EOS<sub>M</sub> was observed across NA, whereas, later EOS<sub>M</sub> was dominant across EA (Fig. 2.75d). Overall, about 55% and 65% of the NH showed earlier SOS<sub>M</sub> and later EOS<sub>M</sub> in 2020. These spatial variations also correlate with spring and autumn surface temperature (section 2b1; NASA MERRA-2, Gelaro et al. 2017), which were 0.5°C (+1.2°C for EA, -0.95°C for NA) and 0.7°C (+1.0°C for EA, -0.05°C for NA) warmer in 2020 compared to the baseline. NH trends of earlier SOS<sub>M</sub> and later (less significant) EOS<sub>M</sub> over the last 21 years were noted in MODIS NDVI (SOS<sub>M</sub> = -2.3 ± 0.7 days decade<sup>-1</sup>, *p* = 0.01; EOSM = 1.3 ± 0.9 days decade<sup>-1</sup>, *p* = 0.18) while significant differences in magnitude were observed between

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

Fig. 2.75. (a) Time series of area-mean anomalies (days; base period 2000–10) in MODIS NDVI-based start of season (SOS) (green) and MERRA-2 spring (Mar–May, pink) temperature (°C) for Northern Hemisphere (>30°N). (b) Same as (a) but for end of season (EOS) (green) and autumn (Sep–Nov, pink) temperature. Note temperature scale reversal for (a). 2020 spatial pattern of (c) SOS and (d) EOS anomaly with respect to the baseline. Highlighted points and box identify the location of phenology sites shown in Fig. 2.76. (United States Harvard Forest, Massachusetts PhenoCam and Red oak [pink point]; United Kingdom mean Pedunculate oak [yellow box]; Lake Kasumigaura, Japan, Lake Kinneret, Israel and Müggelsee, Germany [green points]).
NA (SOSM = -0.05 ± 0.5 days decade⁻¹, *p* = 0.24; EOSM = 2.0 ± 0.6 days decade⁻¹, *p* = 0.01) and EA (SOSM = -1.7 ± 0.6 days decade⁻¹, *p* = 0.02; EOSM = 0.1 ± 0.8 days decade⁻¹, *p* = 0.89), indicating asymmetric extension of the growing season at the continental scale.

PhenoCam data across NA (Seyednasrollah et al. 2019) provide a link between the coarse and fine resolutions of satellite monitoring and site-level observations on individual organisms (Richardson et al. 2019). We compared PhenoCam-derived estimates (2008–20, n = 13) of start of season (SOSPC) and end of season (EOSPC) at Harvard Forest, a deciduous forest in Massachusetts, United States, with ground observations of Red oak (*Quercus rubra*) phenology (SOSRO = 50% budburst and EOSRO = 50% autumn color; Richardson and O’Keefe 2009, 2019), and MODIS SOSM and EOSM for the associated pixel (Figs. 2.76a,b). SOSPC and EOSPC were strongly correlated with SOSRO and EOSRO (r = 0.90 and 0.81, respectively) and their timings were similar. Although SOSPC and SOSM were strongly correlated (r = 0.79), SOSPC was later by 12 ± 3 days (Fig. 2.76a). The correlation between EOSPC and EOSM was weaker (r = 0.48), and EOSPC was earlier on average by 48 ± 12 days (Fig. 2.76b). These differences may be explained in part by a mix of land cover types covered by the MODIS 5-km pixel. In 2020, SOSPC (day 137, 16 May, ± 2 days) was 6 days later than in 2019, and consistent with the change for SOSRO, which was 9 days later in 2020 than 2019. EOSPC in 2020 (day 293, 19 October, ± 1 day) was unchanged, while EOSRO was 4 days later than 2019. In comparison, SOSM was 3 days earlier than 2019 and EOSM remained unchanged (Figs. 2.76a,b). Later SOSPC in 2020 was related to relatively cold spring temperatures and resulted in a growing season 5 days shorter than in 2019. 2020 had the shortest growing season observed at Harvard Forest in the last 13 years.

Dates of Pedunculate oak (*Quercus robur*) “first leaf” (SOSPO) and “bare tree” (EOSPO) recorded by citizen scientists across the United Kingdom have been collated by the Woodland Trust since 1999 (Collinson and Sparks 2008). The mean SOSPO for the 2000–09 baseline was 26 April (day 116), and EOSPO was 30 November (day 334), giving a 218-day growing season length (Figs. 2.76c,d). Both events were strongly influenced by temperature; SOSPO advances by approximately 6 days for every 1°C increase in mean February–April temperature, and EOSPO is delayed by approximately 3 days for every 1°C increase in October temperature. The year 2020, like 2019, had a very warm spring, and this resulted in the earliest United Kingdom SOSPO in the 20-year series (10 days earlier than the in-situ baseline). October temperature was similar to recent years, and the EOSPO date

<figure><img src="image_121.png" /><figcaption>Fig. 2.76. Start of season (SOS) and end of season (EOS) phenology indicators derived from (a),(b) Harvard Forest, Massachusetts, United States, PhenoCam (SOSPC and EOSPC), Red oak ground observations (SOSRO and EOSRO), and MODIS remote sensing (SOSM and EOSM), and (c),(d) United Kingdom mean Pedunculate oak (SOSPO and EOSPO) and MODIS.</figcaption></figure>
(note these were predicted from the temperature relationship due to COVID-19 monitoring restric-
tions) was approximately 2 days later than the baseline. The net result was a United Kingdom
“oak season” 12 days longer than the baseline.

In 2020, monitoring data on lake water concentrations of the photosynthetic pigment chloro-
phyll-a were available to estimate the spring phytoplankton peak in three NH lake basins (Lake
Kasumigaura in Japan, Lake Kinneret in Israel, and Müggelsee in Germany). Some in situ lake
monitoring schemes were inactive in 2020 due to COVID-19 restrictions. The mean start of spring
bloom during the 2000–10 baseline in these lakes ranged from 22 March (day 81, Lake Kasum-
igaura) to 21 April (day 111, Lake Kinneret). Spring peak was earlier in 2020 than the baseline in
two lake basins (by 15 to 38 days), but later for Müggelsee (by 4 days). This variation between
sites may relate to differences in climate or other factors that interact with climate to influence
seasonal ecosystem behavior.

Sidebar 2.3. **Long-term monitoring of vegetation state through passive microwave satellites**—W. DORIGO, L. MOESINGER, R. VAN DER SCHALIE, R. M. ZOTTA, T. SCANLON, AND R. A. M. DE JEU

Microwave radiation emitted or reflected by the land sur-
face is strongly affected by available water, including that stored
in living biomass. The all-weather, sunlight-independent observ-
ing capacity of microwave satellites makes them complementary
to satellites in the optical domain traditionally used to observe
vegetation characteristics (Becker and Choudhury 1988; see
section 2h2.). Particularly for areas with frequent cloud cover,
such as the humid tropics, microwave satellites provide novel
insights into vegetation dynamics, although with lower spatial
detail (Rodriguez-Fernandez et al. 2018).

The portion of the radiance attenuated by the canopy is
expressed by its vegetation optical depth (VOD), a parameter
used in radiative transfer models to describe radiance interac-
tion with vegetation. Long seen as a by-product of soil moisture
retrievals (see section 2d10), VOD is increasingly proven to be
a valuable indicator of land surface conditions itself. While
VOD is not a biogeophysical variable per se, various studies
have shown its close relationship to vegetation above-ground
biomass (Mialon et al. 2020, Rodriguez-Fernandez et al. 2018),
leaf area index (Vreugdenhil et al. 2017), gross primary produc-
tion (Teubner et al. 2019), or canopy water content (Konings
et al. 2017). Since VOD is wavelength-dependent and, with

increasing frequency, increasingly sensitive to the upper veg-
etation layer (Li et al. 2021), VOD estimates at low frequencies
(L-band) are more closely related to forest biomass (Chaparro
et al. 2019), while higher frequency observations (C-, X-, and
Ku-band) show closer agreement with seasonal leaf dynamics
and photosynthetic activity (Teubner et al. 2018; Fig. SB2.5)
and, hence, are valuable phenological indicators (see section
2h4; Pfeil et al. 2020).

VOD products from various frequencies have been used
to monitor global terrestrial carbon dynamics (Liu et al. 2015),
assess the severity of agricultural droughts (Van Dijk et al. 2013;
Crocetti et al. 2020), assess crop yield (Chaparro et al. 2018),
model fire occurrence (Forkel et al. 2017, 2019) and terrestrial
evaporation (Martens et al. 2017), and monitor land degrada-
tion (Liu et al. 2013) and deforestation (van Marle et al. 2015).

VOD observations from several available meteorological and
Earth observation satellites, including SSM/I, TRMM, AMSR-E, and
AMSR2, have been retrieved with the Land Parameter Retrieval
Model (Meesters et al. 2005; Van der Schalie et al. 2018) and
amalgamated into the long-term VOD Climate Archive (VODCA),
which allows for studying variability and change at climatic
time scales (Moesinger et al. 2020). VODCA contains individual
<figure><img src="image_122.png" /><figcaption>Fig. SB2.5. (a),(b) Global maps of average vertical optical depth (VOD; unitless) for the period 2010–20 for L- and X-band, respectively. Note that VOD is wavelength-dependent and hence has a different range for different bands. (c) ESA CCI above-ground biomass (Mg ha⁻¹) for 2017, (d) Average LAI (m² m⁻²) from MODIS for 2010–20.</figcaption></figure>

datasets for Ku-band (covering the period 1987–2020), X-band (1997–2020), and C-band (2002–20) at 0.25° spatial and daily temporal resolutions. Because of the superior length of the data record, we used the VODCA Ku-band dataset to compute anomalies from the long-term (1991–2010) mean seasonal cycle. This frequency has a noted higher sensitivity to the upper canopy than the other frequencies and is thus a suitable indicator of foliage biomass and water content dynamics over space and time.

The year 2020 saw lower-than-usual VOD values (Appendix Fig. A2.12 in large parts of North and South America, central and southern Africa, most of Australia, and in a wide belt from eastern Europe, through Russia and Mongolia, to northern China and Korea. Some of these regions had to cope with strong agricultural droughts and crop yield losses, e.g., Argentina and Ukraine (see sections 7d3, 7f6). In early 2021, in southern Madagascar over one million people were at the brink of famine because of yield losses, according to the UN Global Disaster Alert and Coordination System. Above-normal vegetation activity was observed in the central United States, northeastern Brazil, the Sahel, eastern and central southern Africa, India, and large parts of Eurasia. Many of these regions were much

wetter than usual in 2020 (section 2d10). For example, eastern Africa was repeatedly struck by torrential rainfall, flooding, and landslides throughout the first half of the year (see sections 2d5, 7e4), while India received 109% of its typical rainfall during its monsoon season (see section 7g4).

Vegetation dynamics are not only driven by water availability, as they are the result of complex interactions of multiple drivers (e.g., precipitation, temperature, radiation, carbon dioxide fertilization), weather extremes, lagged effects due to deep rooting systems, and land management (Nemani et al. 2003; Reichstein et al. 2013). For example, in regions or seasons where plant growth is traditionally limited by low temperatures or radiation, plant growth may be anti-correlated with precipitation, as precipitation events are characterized by more cloud cover and, hence, lower temperatures. A good example is the dry, warm, and sunny April in Europe in 2019 (see section 7f; Appendix Fig. A.SB2.1d), which clearly boosted vegetation development in the same month. In May, while soil moisture conditions had returned to normal, VOD showed a lagged drought response to the soil water depletion in early spring for several consecutive months (Appendix Figs. A.SB2.1e,f).
Worldwide, but particularly in the global south, interannual VOD conditions can be linked to variations in the El Niño–Southern Oscillation (ENSO; Fig. SB2.6), which is characterized by predominantly dry conditions during El Niño and mostly wet conditions during La Niña episodes (see sections 2d4, 2d10). This connection between VOD and ENSO confirms previous studies based on optical data (e.g., Poulter et al. 2014) that, at the global scale, interannual vegetation activity is largely controlled by moisture supply. Although 2020 saw a transition from weak El Niño-like conditions at the start of the year to a moderate La Niña toward the end (see sections 2e1, 4b), this is not clearly reflected by the yearly and monthly VOD anomaly patterns (Appendix Fig. A.SB2.1), which show a mixture of typical El Niño-like patterns (e.g., wet and, hence, green conditions

in eastern Africa and dry conditions in southern Africa and Australia) and patterns typically observed during La Niña episodes (e.g., wetter conditions in northeastern Brazil and drier conditions in Argentina; see section 2e1).

Global long-term VOD trends are slightly positive (Fig. SB2.6; Moesinger et al. 2020) and in line with greening trends derived from observations in the optical domain (e.g., Forzieri et al. 2017; see section 2h2), thus affirming the usability of VOD for detecting and attributing long-term changes in vegetation activity (Liu et al. 2013). Through its multiple facets, long-term VOD observations perfectly complement the available suite of Earth observation tools to solve the complex puzzle of the effects of climate change on our biosphere.

<figure><img src="image_123.png" /><figcaption>Fig. SB2.6. Yearly Ku-band virtual optical depth (VOD) anomalies juxtaposed with the Southern Oscillation Index Lines indicate the global and hemispheric VOD while the shading is the southern oscillation index (SOI) (red: La Niña, negative; blue: El Niño, positive). (Source: VODCA, http://www.bom.gov.au/climate/current/soi2.shtml.) The bottom plot shows the percentage of land pixels that provides valid data for each year.</figcaption></figure>
# Acknowledgments

## 2a1 (Overview)

Freya Aldred, Robert Dunn, and Kate Willett were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra.

We thank Rob Chadwick and David Parker, the five anonymous reviewers, and the BAMS Editor for their detailed comments when reviewing the chapter. We also thank Atsushi Goto (JMA), Julien Nicolas (ECMWF) and Mike Bosilovich (NASA) for the provision of the reanalysis data used in this chapter.

## 2b2 (Lake surface water temperature)

Lake surface water temperatures from satellite data have been generated within the GloboLakes project funded by the UK National Environment Research Council (NE/J023345/2) with extensions funded by the EU Copernicus Climate Change Service (C3S) programme. The in situ measurements used to validate the LSWT dataset have been kindly shared by Eugene Silow (Irkutsk State University, Russia); Merja Pulkkanen (SYKE, Helsinki, Finland); Antii Raike (SYKE, Helsinki, Finland); Eric Leibensperger (Department of Physics and Astronomy, Ithaca College, Ithaca, New York); Alo Laas (Estonian University of Life Sciences, Tartu, Estonia); Michela Rogora (CNR Institute for Water Research, Italy); Martin Dokulil (Austria); Shin-ichiro Matsuzaki (National Institute for Environmental Studies, Japan); Claudia Giardino (CNR IREA, Italy); Curtis DeGasperi (King County Water and Land Resources Division, Department of Natural Resources and Parks, Seattle, Washington); Martin Schmid (Eawag, Switzerland); and Don Pierson (Uppsala University, Sweden).

Lake Water Level timeseries are generated by LEGOS/CLS as part of the Theia Land programme supported by CNES with extensions funded by the EU Copernicus Climate Change Service (C3S) programme and by ESA through the Climate Change Initiative (CCI+) project. Valery Vluglinskii (SHI, Russia) provided water level in situ time series on 16 lakes in Russia allowing to validate the LWL products.

## 2b3 (Land and marine temperature extremes)

Robert J. H. Dunn was supported by the Hadley Centre Climate Programme funded by BEIS and Defra.

## 2b4 (Tropospheric temperature)

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

## 2b5 (Stratospheric temperature and winds)

The National Center for Atmospheric Research is sponsored by the U.S. National Science Foundation.

### Sidebar 2.1 (Night marine air temperature)

Richard Cornes, David Berry, and Elizabeth Kent were funded by the NERC CLASS programme (NE/R015953/1), the NERC GloSAT project (NE/S015647/2), and the Copernicus Climate Change Service (C3S_311a_Lot2). The UAHNMATv1 work was supported by the U.S. Department of Energy (DE-SC0005330 and DE-SC0019296. Nick Rayner was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra.

## 2c1 (Permafrost thermal state)

PERMOS is financially supported by MeteoSwiss in the framework of GCOS Switzerland, the Federal Office for the Environment, and the Swiss Academy of Sciences and acknowledges the important contribution of the partner institutions and principal investigators. The French Network PermaFRANCE is financially supported by OSUG (Observatoire des Sciences de l'Univers Grenoble) and the French Research Infrastructure OZCAR. Czech Antarctic Research Programme is supported by Czech Ministry of Education, Youth and Sport project VAN2020/1.
**2c2 (Northern Hemisphere continental snow cover extent)**

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

**2c4 (Lake ice)**

We thank Kevin Blagrave, Nikolay Granin, Lars Rudstam, and Merja Pulkanen for providing updates to in situ lake ice phenology records.

**Sidebar 2.2 (Rock glacier kinematics)**

Rock glacier monitoring at Hinteres Langtalkar and Dösen rock glaciers (AT) was supported by different national and international projects during the last decades. The authority of the Hohe Tauern National Park is one of the most important supporters of this monitoring. Laurichard (FR) survey is supported by CryobsClim "long-term Observation and Experimentation System for Environmental Research" (SOERE/All'envi-OZCAR Research Infrastructure) and the PermaFrance observatory "monitoring the mountain permafrost in the French Alps." The Ecrins National Park helps field surveys since the early 2000's. The Swiss Permafrost Monitoring Network PERMOS is financially supported by MeteoSwiss in the framework of GCOS Switzerland, the Federal Office for the Environment and the Swiss Academy of Sciences. PERMOS acknowledges the important contribution of the partner institutions and principal investigators.

**2d1 (Surface humidity)**

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

**2d5 (Land-based precipitation extremes)**

Mari Tye was supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977.

**2d6 (Lake water levels)**

B. M. Kraemer received support from the 2017–2018 Belmont Forum and BiodivERsA joint call for research proposals, under the BiodivScen ERA-Net COFUND program, and with the funding organizations German Science Foundation (AD 91/22-1). This work benefited from B. M. Kraemer’s participation in the Global Lake Ecological Observatory Network (GLEON).

**2d8 (River discharge and runoff)**

Hyungjun Kim was supported by the Japan Society for the Promotion of Science KAKENHI (16H06291 and 18KK0117) for this contribution.

**2d10 (Soil moisture)**

The ESA CCI SM datasets and the authors were supported by ESA's Climate Change Initiative for Soil Moisture (Contract No. 4000104814/11/I-NB and 4000112226/14/I-NB) and in addition the authors were supported by activities under provision of services to Copernicus Climate Change Service (Contract No. ECMWF/COPERNICUS/2018/C3S_312b_Lot4_EODC/SC2).

**2d11 (Monitoring global drought using the self-calibrating Palmer Drought Severity Index)**

J. Barichivich acknowledges the FONDECYT Grant 1181956. Tim Osborn received funding from UK NERC (NE/S015582/1). Ian Harris received funding from UK National Centre for Atmospheric Science (NCAS). The research presented in the drought section was carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia.
**2d12 (Land evaporation)**

Diego G. Miralles acknowledges support from the European Research Council (ERC) under grant agreement no. 715254 (DRY-2-DRY). A. Koppa and D. Rains acknowledge support from the Belgian Science Policy Office (BELSPO) in the frame of the STEREO III program projects ALBERI (SR/00/373) and ET-Sense (SR/02/377). M. F. McCabe is supported by the King Abdullah University of Science and Technology.

**2e2 (Land and ocean surface winds)**

Robert J. H. Dunn was supported by the Hadley Centre Climate Programme funded by BEIS and Defra. L. Ricciardulli and Carl A. Mears are supported by the NASA Ocean Vector Wind Science Team (OVWST) program.

**2f1 (Earth radiation budget at top-of-atmosphere)**

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

**2g4 (Stratospheric ozone)**

Carlo Arosio, Melanie Coldewey-Egbers, Daan Hubert, Diego Loyola, Victoria Sofieva, Alexei Rozanov, and Mark Weber are grateful to ESA's Climate Change Initiative Ozone project and to the EU Copernicus Climate Change Service 312b Lot4 Ozone project for supporting the generation and extension of the GTO-ECV total ozone and SAGE-CCI-OMPS data records. Stacey M. Frith is supported by the NASA Long Term Measurement of Ozone program WBS 479717. Lucien Froidevaux's contribution, with the assistance of Ryan Fuller, was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. Daan Hubert acknowledges the partial support by the EU/ERC Horizon 2020 project GAIA-CLIM.

**2h1 (Land surface albedo dynamics)**

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

**2h2 (Terrestrial vegetation dynamics)**

The author thanks Monica Robustelli for her technical support and the providers of the remote sensing dataset needed to perform this research, i.e., the SeaWiFS Project (Code 970.2) and the Goddard Earth Sciences Data and Information Services Center/Distributed Active Archive Center (Code 902) at the Goddard Space Flight Center, Greenbelt, Maryland 20771. MERIS products were processed at the Grid On Demand facility of European Space Agency (ESA/ESRIN) using JRC software code.

**2h4 (Phenology of primary producers)**

Debbie Hemming acknowledges support from the Met Office Hadley Centre Climate Programme funded by BEIS and Defra, and thanks Robert Dunn for his expertise finalizing the figures. Taejin Park acknowledges support from the NASA Earth Science Directorate (grants NNX16AO34H and 80NSSC18K0173-CMS). Andrew Richardson acknowledges support from the National Science Foundation through the Macrosystems Biology (award 1702697) and LTER (award 1832210) programs. Nature's Calendar thanks all its volunteer recorders, without whom it could not function. Stephen Thackeray thanks Rita Adrian, Tamar Zohary, Kazuhiro Komatsu and Shin-Ichiro Matsuzaki for collecting and providing lake chlorophyll-a data.

**Sidebar 2.3 (Long-term monitoring of vegetation state through passive microwave satellites)**

Wouter A. Dorigo, L. Moesinger, and R. M. Zotta acknowledge the TU Wien Wissenschaftspreis 2015, a personal grant awarded to Wouter Dorigo.
Chapter 2 – Acronyms

<table>
   <tr>
    <td>
     AAO
    </td>
    <td>
     Antarctic Oscillation
    </td>
   </tr>
   <tr>
    <td>
     AATSR
    </td>
    <td>
     Advanced Along Track Scanning Radiometer
    </td>
   </tr>
   <tr>
    <td>
     ALEXI
    </td>
    <td>
     Atmosphere-Land Exchange Inverse
    </td>
   </tr>
   <tr>
    <td>
     ALT
    </td>
    <td>
     active layer thickness
    </td>
   </tr>
   <tr>
    <td>
     AMSRE-E
    </td>
    <td>
     Advanced Microwave Scanning Radiometer
    </td>
   </tr>
   <tr>
    <td>
     AO
    </td>
    <td>
     Arctic Oscillation
    </td>
   </tr>
   <tr>
    <td>
     AOD
    </td>
    <td>
     aerosol optical depth
    </td>
   </tr>
   <tr>
    <td>
     ATSR
    </td>
    <td>
     Along Track Scanning Radiometer
    </td>
   </tr>
   <tr>
    <td>
     AVHRR
    </td>
    <td>
     Advanced Very High Resolution Radiometer
    </td>
   </tr>
   <tr>
    <td>
     BDC
    </td>
    <td>
     Brewer-Dobson circulation
    </td>
   </tr>
   <tr>
    <td>
     BRW
    </td>
    <td>
     Barrow Atmospheric Baseline Observatory
    </td>
   </tr>
   <tr>
    <td>
     C3S
    </td>
    <td>
     Copernicus Climate Change Service
    </td>
   </tr>
   <tr>
    <td>
     CALIOP
    </td>
    <td>
     Cloud-Aerosol Lidar with Orthogonal Polarization
    </td>
   </tr>
   <tr>
    <td>
     CAMS
    </td>
    <td>
     Copernicus Atmosphere Monitoring Service
    </td>
   </tr>
   <tr>
    <td>
     CAMSRA
    </td>
    <td>
     Copernicus Atmosphere Monitoring Service Reanalysis
    </td>
   </tr>
   <tr>
    <td>
     CCMI
    </td>
    <td>
     Chemistry Climate Model Initiative
    </td>
   </tr>
   <tr>
    <td>
     CEI
    </td>
    <td>
     Climate Extremes Index
    </td>
   </tr>
   <tr>
    <td>
     CERES
    </td>
    <td>
     Clouds and the Earth's Radiant Energy System
    </td>
   </tr>
   <tr>
    <td>
     CFC
    </td>
    <td>
     chlorofluorocarbon
    </td>
   </tr>
   <tr>
    <td>
     CH4
    </td>
    <td>
     methane
    </td>
   </tr>
   <tr>
    <td>
     Cl
    </td>
    <td>
     chlorine
    </td>
   </tr>
   <tr>
    <td>
     CO
    </td>
    <td>
     carbon monoxide
    </td>
   </tr>
   <tr>
    <td>
     CO2
    </td>
    <td>
     carbon dioxide
    </td>
   </tr>
   <tr>
    <td>
     CPT
    </td>
    <td>
     cold-point tropopause
    </td>
   </tr>
   <tr>
    <td>
     CRU TS
    </td>
    <td>
     Climatic Research Unit gridded Time Series
    </td>
   </tr>
   <tr>
    <td>
     DDM
    </td>
    <td>
     drainage direction map
    </td>
   </tr>
   <tr>
    <td>
     DU
    </td>
    <td>
     Dobson unit
    </td>
   </tr>
   <tr>
    <td>
     EA
    </td>
    <td>
     Eurasia
    </td>
   </tr>
   <tr>
    <td>
     ECV
    </td>
    <td>
     essential climate variable
    </td>
   </tr>
   <tr>
    <td>
     EESC
    </td>
    <td>
     equivalent effective stratospheric chlorine
    </td>
   </tr>
   <tr>
    <td>
     EESC-A
    </td>
    <td>
     equivalent effective stratospheric chlorine-Antarctic
    </td>
   </tr>
   <tr>
    <td>
     EESC-M
    </td>
    <td>
     equivalent effective stratospheric chlorine-Midlatitude
    </td>
   </tr>
   <tr>
    <td>
     ENSO
    </td>
    <td>
     El Niño-Southern Oscillation
    </td>
   </tr>
   <tr>
    <td>
     EOFs
    </td>
    <td>
     empirical orthogonal functions
    </td>
   </tr>
   <tr>
    <td>
     EOS
    </td>
    <td>
     end of season
    </td>
   </tr>
   <tr>
    <td>
     ERB
    </td>
    <td>
     Earth's radiation budget
    </td>
   </tr>
   <tr>
    <td>
     ESA CCI SM
    </td>
    <td>
     European Space Agency's Climate Change Initiative for Soil Moisture
    </td>
   </tr>
   <tr>
    <td>
     ET
    </td>
    <td>
     evapotranspiration
    </td>
   </tr>
   <tr>
    <td>
     ETCCDI
    </td>
    <td>
     WMO Expert Team in Climate Change Detection and Indices
    </td>
   </tr>
   <tr>
    <td>
     FAPAR
    </td>
    <td>
     Fraction of Absorbed Photosynthetically Active Radiation
    </td>
   </tr>
   <tr>
    <td>
     FF
    </td>
    <td>
     fossil fuel
    </td>
   </tr>
   <tr>
    <td>
     GCOS
    </td>
    <td>
     Global Climate Observing System
    </td>
   </tr>
   <tr>
    <td>
     GFAS
    </td>
    <td>
     Global Fire Assimilation System
    </td>
   </tr>
   <tr>
    <td>
     GFED
    </td>
    <td>
     Global Fire Emissions Database
    </td>
   </tr>
   <tr>
    <td>
     GGGRN
    </td>
    <td>
     NOAA's Global Greenhouse Gas ReferenceNetwork
    </td>
   </tr>
  </table>
<table>
   <tr>
    <td>GHCN</td>
    <td>Global Historical Climatology Nework</td>
   </tr>
   <tr>
    <td>GHCNDEX</td>
    <td>Global Historical Climatology Network-Daily database</td>
   </tr>
   <tr>
    <td>GIN-P</td>
    <td>Global Terrestrial Network for Permafrost</td>
   </tr>
   <tr>
    <td>GLEAM</td>
    <td>Global Land Evaporation Amsterdam Model</td>
   </tr>
   <tr>
    <td>GMST</td>
    <td>global mean surface temperature</td>
   </tr>
   <tr>
    <td>GNSS</td>
    <td>Global Navigation Satellite System</td>
   </tr>
   <tr>
    <td>GPCC</td>
    <td>Global Precipitation Climatology Centre</td>
   </tr>
   <tr>
    <td>GPCP</td>
    <td>Global Precipitation Climatology Project</td>
   </tr>
   <tr>
    <td>GPS-RO</td>
    <td>Global Positioning System-Radio Occultation</td>
   </tr>
   <tr>
    <td>GRACE</td>
    <td>Gravity Recovery and Climate Experiment</td>
   </tr>
   <tr>
    <td>GRACE-FO</td>
    <td>Gravity Recovery and Climate Experiment - Follow On</td>
   </tr>
   <tr>
    <td>GTN-P</td>
    <td>Global Terrestrial Network for Permafrost</td>
   </tr>
   <tr>
    <td>HFCF</td>
    <td>hydrochlorofluorocarbon</td>
   </tr>
   <tr>
    <td>HFC</td>
    <td>hydrofluorocarbon</td>
   </tr>
   <tr>
    <td>HIRS</td>
    <td>High Resolution Infra Red Radiation Sounder</td>
   </tr>
   <tr>
    <td>HWF</td>
    <td>heat wave frequency</td>
   </tr>
   <tr>
    <td>HWM</td>
    <td>heat wave magnitude</td>
   </tr>
   <tr>
    <td>IOD</td>
    <td>Indian Ocean dipole</td>
   </tr>
   <tr>
    <td>IPA</td>
    <td>International Permafrost Association</td>
   </tr>
   <tr>
    <td>ITCZ</td>
    <td>Intertropical Convergence Zone</td>
   </tr>
   <tr>
    <td>LLGHG</td>
    <td>long-lived greenhouse gases</td>
   </tr>
   <tr>
    <td>LSA-SAF</td>
    <td>Land Surface Analysis Satellite Applications Facility</td>
   </tr>
   <tr>
    <td>LSWT</td>
    <td>lake surface water temperature</td>
   </tr>
   <tr>
    <td>LTT</td>
    <td>lower tropospheric temperature</td>
   </tr>
   <tr>
    <td>LWL</td>
    <td>lake water level</td>
   </tr>
   <tr>
    <td>MACC</td>
    <td>Monitoring Atmospheric Composition and Climate</td>
   </tr>
   <tr>
    <td>MAT</td>
    <td>marine air temperature</td>
   </tr>
   <tr>
    <td>MBL</td>
    <td>marine boundary layer</td>
   </tr>
   <tr>
    <td>MHW</td>
    <td>marine heatwave</td>
   </tr>
   <tr>
    <td>MLO</td>
    <td>Mauna Loa, Hawaii</td>
   </tr>
   <tr>
    <td>MLS</td>
    <td>Microwave Limb Sounder</td>
   </tr>
   <tr>
    <td>MODIS</td>
    <td>Moderate Resolution Imaging Spectroradiometer</td>
   </tr>
   <tr>
    <td>MOPITT</td>
    <td>Measurement of Pollution in the Troposphere</td>
   </tr>
   <tr>
    <td>MSU/AMSU</td>
    <td>Microwave Sounding Unit/Advanced Microwave Sounding Unit</td>
   </tr>
   <tr>
    <td>N<sub>2</sub>O</td>
    <td>nitrous oxide</td>
   </tr>
   <tr>
    <td>NA</td>
    <td>North America</td>
   </tr>
   <tr>
    <td>NAO</td>
    <td>North Atlantic Oscillation</td>
   </tr>
   <tr>
    <td>NDVI</td>
    <td>normalized difference vegetation index</td>
   </tr>
   <tr>
    <td>NH</td>
    <td>Northern Hemisphere</td>
   </tr>
   <tr>
    <td>NMAT</td>
    <td>night marine air temperature</td>
   </tr>
   <tr>
    <td>O<sub>3</sub></td>
    <td>ozone</td>
   </tr>
   <tr>
    <td>ODGI</td>
    <td>Ozone Depleting Gas Index</td>
   </tr>
   <tr>
    <td>ODGI-A</td>
    <td>Ozone Depleting Gas Index-Antarctic</td>
   </tr>
   <tr>
    <td>ODGI-M</td>
    <td>Ozone Depleting Gas Index-Midlatitude</td>
   </tr>
   <tr>
    <td>ODS</td>
    <td>ozone-depleting substances</td>
   </tr>
   <tr>
    <td>OH</td>
    <td>hydroxyl radical</td>
   </tr>
   <tr>
    <td>OLR</td>
    <td>outgoing longwave radiation</td>
   </tr>
   <tr>
    <td>OMI</td>
    <td>Ozone Monitoring Instrument</td>
   </tr>
  </table>
<table>
   <tr>
    <td>PDO</td>
    <td>Pacific Decadal Oscillation</td>
   </tr>
   <tr>
    <td>PSC</td>
    <td>polar stratospheric cloud</td>
   </tr>
   <tr>
    <td>QBO</td>
    <td>quasi-biennial oscillation</td>
   </tr>
   <tr>
    <td>QTP</td>
    <td>Qinghai-Tibetan Plateau</td>
   </tr>
   <tr>
    <td>RFaci</td>
    <td>radiative forcing resulting from aerosol-cloud interactions</td>
   </tr>
   <tr>
    <td>RFari</td>
    <td>radiative forcing resulting from aerosol-radiation</td>
   </tr>
   <tr>
    <td>RGK</td>
    <td>rock glacier kinematics</td>
   </tr>
   <tr>
    <td>RH</td>
    <td>relative humidity</td>
   </tr>
   <tr>
    <td>RO</td>
    <td>radio occultation</td>
   </tr>
   <tr>
    <td>RSW</td>
    <td>reflected shortwave</td>
   </tr>
   <tr>
    <td>SAM</td>
    <td>Southern Annular Mode</td>
   </tr>
   <tr>
    <td>SAR</td>
    <td>Synthetic Aperture Radar</td>
   </tr>
   <tr>
    <td>SCE</td>
    <td>snow cover extent</td>
   </tr>
   <tr>
    <td>scPDSI</td>
    <td>self-calibrating Palmer Drought Severity Index</td>
   </tr>
   <tr>
    <td>SH</td>
    <td>Southern Hemisphere</td>
   </tr>
   <tr>
    <td>SLSTR</td>
    <td>Sea and Land Surface Temperature Radiometer</td>
   </tr>
   <tr>
    <td>SOI</td>
    <td>Southern Oscillation Index</td>
   </tr>
   <tr>
    <td>SORCE</td>
    <td>Solar Radiation and Climate Experiment</td>
   </tr>
   <tr>
    <td>SOS</td>
    <td>start of season</td>
   </tr>
   <tr>
    <td>SPO</td>
    <td>South Pole Observatory</td>
   </tr>
   <tr>
    <td>SSM/I</td>
    <td>Special Sensor Microwave/Imager</td>
   </tr>
   <tr>
    <td>SSMIS</td>
    <td>Special Sensor Microwave Imager/Sounder</td>
   </tr>
   <tr>
    <td>SSMIS</td>
    <td>Special Sensor Microwave Imager/Sounder</td>
   </tr>
   <tr>
    <td>SST</td>
    <td>sea surface temperature</td>
   </tr>
   <tr>
    <td>SSU</td>
    <td>Stratospheric Sounding Unit</td>
   </tr>
   <tr>
    <td>SW</td>
    <td>shortwave</td>
   </tr>
   <tr>
    <td>TCWV</td>
    <td>total column water vapor</td>
   </tr>
   <tr>
    <td>TIR</td>
    <td>thermal infrared</td>
   </tr>
   <tr>
    <td>TLS</td>
    <td>lower stratospheric temperature</td>
   </tr>
   <tr>
    <td>TOA</td>
    <td>top of the atmosphere</td>
   </tr>
   <tr>
    <td>TSI</td>
    <td>total solar irradiance</td>
   </tr>
   <tr>
    <td>TSIS-1</td>
    <td>Total Solar and Spectral Irradiance Sensor-1</td>
   </tr>
   <tr>
    <td>TTL</td>
    <td>tropical tropopause layer</td>
   </tr>
   <tr>
    <td>TTT</td>
    <td>tropical tropospheric temperature</td>
   </tr>
   <tr>
    <td>TWS</td>
    <td>terrestrial water storage</td>
   </tr>
   <tr>
    <td>UTH</td>
    <td>upper tropospheric (relative) humidity</td>
   </tr>
   <tr>
    <td>UV</td>
    <td>ultraviolet</td>
   </tr>
   <tr>
    <td>VOC</td>
    <td>volatile organic compound</td>
   </tr>
   <tr>
    <td>VOD</td>
    <td>vegetation optical depth</td>
   </tr>
   <tr>
    <td>VODCA</td>
    <td>vegetation optical depth Climate Archive</td>
   </tr>
   <tr>
    <td>WGMS</td>
    <td>World Glacier Monitoring Service</td>
   </tr>
   <tr>
    <td>WMO</td>
    <td>World Meteorological Organization</td>
   </tr>
   <tr>
    <td>WV</td>
    <td>water vapor</td>
   </tr>
  </table>
APPENDIX 2: Supplemental Material

<figure><img src="image_124.png" /><figcaption>2b1 Surface air temperature</figcaption></figure>

Fig. A2.1. Global 2-m surface temperature anomalies (°C; 1981–2010 base period).
(Source: JRA-55.)

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

Fig. A2.2. Global 2-m surface temperature anomalies (°C; 1981–2010 base period).
(Source: ERA5.)
<figure><img src="image_126.png" /><figcaption>Fig. A2.3. Global surface temperature anomalies (°C; 1981–2010 base period).
(Source: NASA GISS.)</figcaption></figure>

<figure><img src="image_127.png" /><figcaption>Fig. A2.4. Global surface temperature anomalies (°C; 1981–2010 base period).
(Source: HadCRUT5.)</figcaption></figure>
<figure><img src="image_128.png" /><figcaption>Fig. A2.5. Specific humidity annual average anomaly (g kg⁻¹; base period 1981–2010)
(Source: ERA5.)</figcaption></figure>

<figure><img src="image_129.png" /><figcaption>Fig. A2.6. Specific humidity annual average anomaly (g kg⁻¹; base period 1981–2010)
(Source: MERRA-2.)</figcaption></figure>
Section 2d2 Total column water vapor

<figure><img src="image_130.png" /><figcaption>Fig. A2.7. Relative humidity annual average anomaly (%rh; base period 1981–2010)
(Source: ERA5.)</figcaption></figure>

Section 2d3 Upper tropospheric humidity

<figure><img src="image_131.png" /><figcaption>Fig. A2.8. Annual average TCWV anomalies (mm; 1981–2010 base period). The data are from satel-
lite radiometers over the oceans (RSS) and from satellite RO over land. Data from GNSS stations are
plotted as filled circles.</figcaption></figure>
<figure><img src="image_132.png" /><figcaption>Fig. A2.9. Annual average UTH anomalies (%rh; 2001–10 base period)
(Source: HIRS UTH dataset.)</figcaption></figure>
MERRA-2 R10mm - Number of heavy precipitation days

<figure><img src="image_133.png" /><figcaption>Fig. A2.10a.</figcaption></figure>

ERA5 R10mm - Number of heavy precipitation days

<figure><img src="image_134.png" /><figcaption>Fig. A2.10b.</figcaption></figure>
<figure><img src="image_135.png" /><figcaption>Fig. A2.10c.</figcaption></figure>

<figure><img src="image_136.png" /><figcaption>Fig. A2.10d. Anomalies of 2020 indices relative to a 1981–2010 base period for R10mm (days) derived from (a) MERRA-2 (Gelaro et al. 2017) and (b) ERA5 (Hersbach et al. 2020); Rx5day (mm) derived (c[a–d]) seasonally relative to a 1961–1990 base period from GHCNDEX (Donat et al. 2013) and (d) annually relative to a 1982–2016 base period from GPCC (Schamm et al. 2013).</figcaption></figure>
Section 2d5 Land-based precipitation extremes

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

Fig. A2.11. (a–l) Monthly average soil moisture anomalies for 2020 (m³ m⁻³; 1991–2010 base period). Data are masked where no retrieval is possible or where the quality is not assured and flagged due to dense vegetation, frozen soil, radio frequency interference, etc. (Source: ESA CCI Soil Moisture.)
Section 2d10 Soil moisture

<figure><img src="image_138.png" /></figure>

Fig. A2.12. (a–l) Monthly global Ku-band VOD anomalies (unitless; 1991–2010 reference period). High values indicate favorable vegetation conditions, and low values indicate lower vegetation activity than normal. Data are masked where no retrieval is possible due to sparse vegetation or frozen soils/snow cover. (Source: VODCA.)
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