The Tropics

Howard J. Diamond; Carl J. Schreck; Adam Allgood; Emily J. Becker; Eric S. Blake; Francis G. Bringas; Suzana J. Camargo; Lin Chen; Caio A.S. Coelho; Nicolas Fauchereau; Chris Fogarty; Stanley B. Goldenberg; Gustavo Goni; Daniel S. Harnos; Qiong He; Zeng-Zhen Hu; Philip J. Klotzbach; John A. Knaff; Arun Kumar; Michelle L’Heureux; Chris W. Landsea; I-I. Lin; Andrew M. Lorrey; Jing-Jia Luo; Andrew D. Magee; Richard J. Pasch; Alexandre B. Pezza; Matthew Rosencrans; Jozef Rozkošný; Blair C. Trewin; Ryan E. Truchelut; Bin Wang; Hui Wang; Kimberly M. Wood

doi:10.1175/BAMS-D-23-0078.1

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Fig. 4.1.

Time series of the Oceanic Niño Index (ONI, °C) from mid-2020 through 2022. Overlapping three-month seasons are labeled on the x-axis, with initials indicating the first letter of each month in the season. Blue bars indicate negative values that are less than −0.5°C. ONI values are derived from the ERSSTv5 dataset and are based on departures from the 1991–2020 period monthly means (Huang et al. 2017).

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Fig 4.2.

Mean sea-surface temperature (SST; left) and SST anomaly (right) for (a),(b) DJF 2021/22, (c),(d) MAM 2022, (e),(f) JJA 2022, and (g),(h) SON 2022. Units are in °C. The bold contour for SST is located at 30°C. Anomalies are departures from the 1991–2020 seasonal adjusted OIv2.1 climatology (Huang et al. 2020).

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Fig 4.3.

Equatorial depth–longitude section of Pacific Ocean temperature anomalies (°C) averaged between 5°S and 5°N during (a) DJF 2021/22, (b) MAM 2022, (c) JJA 2022, and (d) SON 2022. The 20°C isotherm (thick solid line) approximates the center of the oceanic thermocline. The gray dashed line shows the climatology of the 20°C isotherm based on 1991–2020. Anomalies are departures from the 1991–2020 period monthly means. Data are from GODAS; Behringer 2007.

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Fig. 4.4.

Outgoing longwave radiation (OLR) anomalies (W m⁻²) during (a) DJF 2021/22, (b) MAM 2022, (c) JJA 2022, and (d) SON 2022. Anomalies are departures from the 1991–2020 period monthly means. Data are from Liebmann and Smith (1996).

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Fig. 4.5.

Anomalous 850-hPa wind vectors and zonal wind speed (shading) during (a) DJF 2021/22, (b) MAM 2022, (c) JJA 2022, and (d) SON 2022. The reference wind vector is located at the bottom right. Anomalies are departures from the 1991–2020 period monthly means. Data are from the NCEP/NCAR reanalysis (Kalnay et al. 1996).

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Fig. 4.6.

Anomalous 200-hPa wind vectors and zonal wind speed (shading) during (a) DJF 2021/22, (b) MAM 2022, (c) JJA 2022, and (d) SON 2022. The reference wind vector is located at the bottom right. Anomalies are departures from the 1991–2020 period monthly means. Data are from the NCEP/NCAR reanalysis (Kalnay et al. 1996).

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Fig. 4.7.

(a) Time–longitude section with (a) outgoing longwave radiation (OLR) anomalies (W m⁻²; Schreck et al. 2018) and (b) 200-hPa velocity potential anomalies (× 10⁶ m² s⁻¹) from the CFSR (Saha et al. 2014). Both variables are averaged over 10°S–10°N. Time increases downward on this graph, beginning with Jan 2022 at the top and ending with Jan 2023 at the bottom. Negative anomalies indicate enhanced convection, and positive anomalies indicate suppressed convection. Contours identify anomalies filtered for the Madden-Julian Oscillation (MJO; black) and atmospheric Kelvin waves (red). Contours are drawn at ±12 W m⁻² and ±4 × 10⁶ m² s⁻¹ with the enhanced (suppressed) convective phase of these phenomena indicated by solid (dashed, MJO only) contours. Anomalies are departures from the 1991–2020 base period daily means.

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Fig. 4.8.

(a) Time–longitude section for 2022 of anomalous 850-hPa zonal wind (m s⁻¹) averaged between 10°S and 10°N. Contours identify anomalies filtered for the Madden-Julian Oscillation (MJO; black) and atmospheric Kelvin waves (red). (b) Time–longitude section for 2022 of the anomalous equatorial Pacific Ocean heat content, calculated as the mean temperature anomaly (°C) between 0-m and 300-m depth. Yellow/red (blue) shading indicates above- (below-) average heat content. Relative warming (dashed lines) and cooling (dotted lines) due to downwelling and upwelling equatorial oceanic Kelvin waves are indicated. Anomalies are departures from the 1991–2020 base period pentad means. Data in (b) are derived from GODAS (Behringer et al. 1998).

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Fig. 4.9.

Wheeler and Hendon (2004) Real-time Multivariate Madden-Julian Oscillation (RMM) index for (a) Jan–Mar, (b) Apr–Jun, (c) Jul–Sep, and (d) Oct–Dec 2022. Each point represents the Madden-Julian Oscillation (MJO) amplitude and location on a given day, and the connecting lines illustrate its propagation. Amplitude is indicated by distance from the origin, with points inside the circle representing weak or no MJO. The eight phases around the origin identify the region experiencing enhanced convection, and counter-clockwise movement is consistent with eastward propagation.

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Fig. 4.10.

Rainfall rate (mm day⁻¹) for (a) Jan–Mar, (b) Apr–Jun, (c) Jul–Sep, and (d) Oct–Dec 2022. The separate panels for each quarter show the rainfall cross-section between 30°S and 20°N (solid line) and the 1991–2020 climatology (dotted line), separately for four 30° sectors from 150°E–180° to 120°W–90°W. (Source: MSWEP v2.8.0.)

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Fig. 4.11.

Rainfall anomalies (mm day⁻¹) for Apr–Jun 2022. The anomalies are calculated with respect to the 1991–2020 climatology. (Source: MSWEP v2.8.0.)

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Fig. 4.12.

Rainfall rate (mm day⁻¹) for the Jan–Mar 2022 quarter, for each year from 1979 to 2022, averaged over the longitude sector 150°W–180°E. The cross-sections are color-coded according to NOAA’s Oceanic Niño Index (with a threshold of ±0.5°C), except 2022 which is shown in black. Dotted lines are individual years, and solid lines are the average overall years in each ENSO phase. The inset legend indicates how many years went into each composite. (Source: MSWEP v2.8.0.)

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Fig. 4.13.

Jan–May (a) South American and high-latitude mean sea-level pressure (MSLP) anomalies (hPa; Kalnay et al. 1996), and (b) precipitation anomalies (mm day⁻¹) over the Atlantic sector. MSLP anomalies are calculated with respect to the 1991–2020 climatology and are derived from NCEP/NCAR reanalysis (Kalnay et al. 1996). Precipitation anomalies are calculated with respect to the 2001–2021 climatology and are derived from IMERG (Huffman et al. 2014).

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Fig. 4.14.

(a) Atlantic Intertropical Convergence Zone (ITCZ) position inferred from outgoing longwave radiation (Liebmann and Smith 1996) during Mar 2022. The colored thin lines indicate the approximate position for the six pentads of the month. The black thick line indicates the Atlantic ITCZ climatological position for Mar. The sea-surface temperature (SST) anomalies (°C) for Mar 2022 calculated with respect to the 1982–2020 climatology are shaded. The two boxes indicate the areas used for the calculation of the Atlantic index in panel (b), which shows the monthly OISST (Reynolds et al. 2002) anomaly time series averaged over the South Atlantic sector (SA region: 5°S–5°N, 10°W–50°W) minus the SST anomaly time series averaged over the North Atlantic sector (NA region: 5°N–25°N, 20°W–50°W) for the period 2017–22, forming the Atlantic index. The positive phase of the index indicates favorable conditions for enhanced Atlantic ITCZ activity south of the equator.

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Fig. 4.15.

Seasonal mean precipitation anomalies (mm day⁻¹) and 850-hPa wind anomalies (m s⁻¹) for (a) the Southern Hemisphere (SH) summer monsoon season: Nov 2021–Apr 2022 and (b) the Northern Hemisphere (NH) summer monsoon season: May–Oct 2022. The anomalies are departures from the 1991–2020 climatology. Red lines outline the global monsoon precipitation domain. Two criteria define the monsoon domains: 1) the annual precipitation range (summer-minus-winter mean) exceeds 300 mm, and 2) the summer precipitation is >55% of the total annual precipitation amount, where summer here means May–Sep for the NH and Nov–Mar for the SH (Wang and Ding 2008). The blue rectangular boxes denote the regions where the regional monsoon precipitation indices are measured. The dotted area represents the dry region where the local summer precipitation rate is below 1 mm day⁻¹. (Source: GPCP; Huffman et al. 2009.)

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Fig. 4.16.

(a)–(h). Temporal variations of summer monsoon precipitation (black lines) and low-level circulation (red lines) indices for eight regional monsoons. The precipitation indices represent the anomalous precipitation rate averaged over the blue rectangular box regions shown in Fig. 4.15. The corresponding circulation indices are defined in Table 4.1. All indices were normalized by their corresponding standard deviation (ordinate) derived for the period of 1979/80–2021/22. Numbers shown in the bottom right of each panel denote the correlation coefficient between the seasonal mean precipitation and circulation indices (sample size: 43). Dashed lines indicate ±0.5 std. dev. The summer monsoon seasons are May–Oct for the Northern Hemisphere and Nov–Apr for the Southern Hemisphere. (Data source: GPCP for precipitation; ERA5 [Hersbach et al. 2020] for circulation).

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Fig. 4.17.

(a) Northern Hemisphere (NH) summer (May–Oct) land monsoon precipitation anomaly (blue) normalized by its standard deviation (std. dev.). The climatological mean NH summer land monsoon precipitation (mean) and std. dev. are shown in the lower right panel (mm day⁻¹). Numbers shown in each panel’s top right denote the correlation coefficient (R) between the seasonal mean precipitation anomaly and the simultaneous Niño-3.4 index (red). Dashed lines indicate ±0.5. (b) As in (a) except for the Southern Hemisphere (SH) summer (Nov–Apr). Note that the land monsoon precipitation excludes the monsoon rainfall over the oceanic monsoon domain. (Source: GPCP for precipitation; HadISST and ERSSTv5 for SST.)

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Fig. 4.18.

(a) Monthly anomalies of sea-surface temperature (SST; °C; solid lines) and precipitation (mm day⁻¹; dashed lines) of the Indian Ocean dipole (IOD) in the eastern pole (IODE; 10°S−0°, 90°E−110°E; blue lines) and the western pole (IODW; 10°S−10°N, 50°E−70°E; red lines). (b) As in (a), but for the IOD index (measured by the SST difference between IODW and IODE, green line) and surface zonal wind anomaly (m s⁻¹) in the central equatorial IO (Ucio; 5°S−5°N, 70°E−90°E; black line). (c) As in (a), but for the SST anomalies in the Niño-3.4 region (5°S−5°N, 170°W−120°W; black line) and the tropical IO (IOB; 20°S−10°N, 40°E−120°E; red line). Anomalies are relative to the 1982–2022 base period. (Sources: NOAA OISST [Reynolds et al. 2002]; monthly CMAP precipitation analysis [http://ftpprd.ncep.noaa.gov/pub/precip/cmap/]; and JRA-55 atmospheric reanalysis [Ebita et al. 2011].)

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Fig. 4.19.

(a) Sep–Nov IOD index (DMI) based on sea-surface temperature (SST) data from OISSTv2. (b) Annual DMI time series for all negative IOD events.

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Fig. 4.20.

Sea-surface temperature (SST) anomalies (°C, colored scale) and 200-hPa velocity potential (× 10⁶ m² s⁻¹, contoured with an interval of 1; solid/dashed/bold curves denote positive/negative/zero values) during (a) Dec 2021−Feb 2022, (b) Mar−May 2022, (c) Jun−Aug 2022, and (d) Sep−Nov 2022. Anomalies were calculated relative to the climatology over the period 1982–2022. (Sources: NOAA OISST [Reynolds et al. 2002] and JRA-55 atmospheric reanalysis [Ebita et al. 2011].)

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Fig. 4.21.

(a) Global summary of tropical cyclone (TC) tracks; (b) global TC counts; and (c) global accumulated cyclone energy (ACE) values (× 10⁴ kt²). Horizontal lines on (b) and (c) are the 1991–2020 normals.

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Fig. 4.22.

(a) Seasonal Atlantic hurricane activity during 1950–2022 for named storms (blue), hurricanes (orange), and major hurricanes (gray). (b) The annual accumulated cyclone energy (ACE) index for 1950–2022 expressed as percent of the 1951–2020 median value. ACE is calculated by summing the squares of the six-hourly maximum sustained surface wind speed (kt) for all periods while the storm is at least tropical-storm strength. Black and orange lines correspond to NOAA’s classifications for above-normal and below-normal seasons, respectively (https://www.cpc.ncep.noaa.gov/products/outlooks/Background.html). The thick red horizontal line at 165% denotes the threshold for an extremely active season. Note that there is a low-bias in activity during the 1950s through early 1970s due to the lack of satellite imagery and technique (Dvorak) to interpret tropical-cyclone intensity for systems over the open ocean. (c) 2022 Atlantic basin storm tracks. (Source: HURDAT2 [Landsea and Franklin 2013].)

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Fig. 4.23.

(a) Aug–Oct 2022 sea-surface temperature (SST) anomalies (°C). (b),(c) Time series of Aug–Oct area-averaged SST anomalies (black) and 5-pt running mean of the time series (red): (b) In the Main Development Region (MDR, green box in (a) spanning 20°W–87.5°W and 9.5°N–21.5°N), and (c) difference between the MDR and the global tropics (20°S–20°N). Anomalies are departures from the 1991–2020 period means. The green horizontal line in (b) and (c) highlights the zero value, a critical reference value. (Source: ERSST-v5 [Huang et al. 2017].)

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Fig. 4.24.

(a) Jul–Sep 2022 anomalous outgoing longwave radiation (OLR; W m⁻²), with negative (positive) values indicating enhanced (suppressed) convection. (b) Time series of Jul–Sep total OLR (black) and 5-yr running mean of the time series (red) averaged over the African Sahel region (red box in (a) spanning 20°W–0° and 12.5°N–17.5°N). (c) Jul–Sep 2022 anomalous 200-hPa velocity potential (× 10⁶ m² s⁻¹) and divergent wind vectors (m s⁻¹). In (a), (c), the green box denotes the Atlantic Main Development Region. Anomalies are departures from the 1991–2020 means. The green horizontal line in (b) highlights the 240 value, a critical reference value for relating OLR to convection. (Sources: Liebmann and Smith [1996] for OLR; NCEP/NCAR Reanalysis [Kalnay et al. 1996].)

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Fig. 4.25.

Aug–Oct 2022: (a) 200-hPa streamfunction (contours, interval is 5 × 10⁶ m² s⁻¹) and anomalies (shaded); (b) anomalous 700-hPa cyclonic relative vorticity (shaded, × 10⁻⁶ s⁻¹), wind speed (contours), and vector winds (m s⁻¹); (c) anomalous 1000-hPa heights (shaded, m) and vector winds. Vector scales differ for each panel and are below right of the color bar. Green box denotes the Main Development Region. Anomalies are departures from the 1991–2020 means. (Source: NCEP/NCAR reanalysis [Kalnay et al. 1996].)

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Fig. 4.26.

Aug–Oct magnitude of the 200-hPa–850-hPa vertical wind shear (m s⁻¹): (a) 2022 anomalous magnitude and vector. (b), (c) Time series of Aug–Oct vertical shear magnitude (black) and 5-yr running mean of the time series (red) averaged over (b) the Main Development Region (green box in (a), spanning 87.5W°–20°W and 9.5°N–21.5°N) and (c) the Gulf of Mexico (21.5°N–30°N and 97.5°W–80°W). The green horizontal lines in (b) and (c) highlight the value 10, a critical reference value where shear is thought to inhibit tropical cyclone development on monthly and seasonal time scales. Anomalies are departures from the 1991–2020 means. (Source: NCEP/NCAR reanalysis [Kalnay et al. 1996].)

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Fig. 4.27.

(a) Aug 2022 magnitude of the 200-hPa–850-hPa vertical wind shear (m s⁻¹) showing anomalous magnitude and vector; (b) time series of vertical shear magnitude (black) and 5-yr running mean of the time series (red) averaged over the Main Development Region (MDR; green box in (a)), with the green horizontal line at 10, a critical reference value where shear is thought to inhibit tropical cyclone development on a monthly and seasonal time scales: (c) Aug 2022 MDR specific humidity deviations from normal (percent). Brown (green) shading represents below- (above-) normal values, with the thin black line representing no deviation; (d) Aug 2022 200-hPa streamfunction (contours, interval is 5 × 10⁶ m² s⁻¹) and anomalies (shaded). (Source: NCEP/NCAR reanalysis [Kalnay et al. 1996].)

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Fig 4.28.

Sep 2022 conditions: (a) Main Development Region specific humidity deviations from normal (%). Brown (green) shading represents below- (above-) normal values, with the thin black line representing no deviation; (b) time series of vertical shear magnitude (black) and five-year running mean of the time series (red) averaged over the MDR (green box in Fig. 4.23a). The green horizontal line is at 10, a critical reference value where shear is thought to inhibit tropical cyclone development on a monthly and seasonal time scales.

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Fig. 4.29.

Annual eastern North Pacific (a) storm counts by category during 1970–2022, with the 1991–2020 average by category denoted by each dashed line, and (b) accumulated cyclone energy (ACE) during 1970–2022, with 2022 highlighted in orange and the 1991–2020 average denoted by the dashed line. (c) Daily eastern North Pacific ACE for the 1991–2020 average (solid black) and during 2022 (solid green); accumulated daily ACE for the 1991–2020 average (dashed blue) and during 2022 (dashed orange).

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Fig. 4.30.

15 May–30 Nov 2022 anomaly maps of (a) sea-surface temperature (SST; °C; Banzon and Reynolds 2013), (b) outgoing longwave radiation (OLR; W m⁻²; Schreck et al. 2018), (c) 200-hPa–850-hPa vertical wind shear (m s⁻¹) vector (arrows) and scalar (shading) anomalies, and (d) 850-hPa wind (m s⁻¹, arrows) and zonal wind (shading) anomalies. Anomalies are relative to the annual cycle from 1991 to 2020. Letters denote where each tropical cyclone attained tropical-storm intensity. Wind data are obtained from CFSR (Saha et al. 2014).

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Fig. 4.31.

Longitude–time Hovmöller diagram of 5°N–15°N average outgoing longwave radiation (OLR; W m⁻²; Schreck et al. 2018). Unfiltered anomalies from a daily climatology are shaded. Negative anomalies (green) indicate enhanced convection. Anomalies filtered for Kelvin waves are contoured in blue at −10 W m⁻² and Madden-Julian Oscillation (MJO)-filtered anomalies in black at ±10 W m⁻² (dashed for positive, solid for negative). Letters are centered on the longitude and time when each tropical cyclone attained tropical-storm intensity.

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Fig. 4.32.

(a) Number of tropical storms (TS), typhoons (TY), and major typhoons (MTY ≥96 kt) per year in the western North Pacific (WNP) for the period 1945–2022 based on JTWC. (b) Number of tropical cyclones (TCs; all storms which reach TS intensity or higher) from 1951 to 1976; number of TS, severe tropical storms (STS) and TY from 1977 to 2022 based on JMA. (c) Cumulative number of tropical cyclones with TS intensity or higher (named storms) per month in the WNP in 2022 (black line), and climatology (1991–2020) as box plots (interquartile range: box, median: red line, mean: blue asterisk, values in the top or bottom quartile: blue crosses, high [low] records in the 1945–2021 period: red diamonds [circles]). (e) As in (c) but for the number of typhoons. (d),(f) Number of typhoons and super typhoons (≥130 kt) per month in 2022 (black line) and the climatological mean (blue line). The red diamonds and circles denote the maximum and minimum monthly historical records, and the blue error bars show the climatological interquartile range for each month (in the case of no error bars, the upper and/or lower percentiles coincide with the median). (Sources: 1945–2021 JTWC best-track dataset; 2022 JTWC preliminary operational track data for panels (a) and (c)–(f); 1951–2022 Regional Specialized Meteorological Center-Tokyo, Japan Meteorological Agency [JMA] best-track dataset for (b).)

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Fig. 4.33.

(a) Accumulated cyclone energy (ACE) per year in the western North Pacific for 1945–2022. The solid blue line indicates the median for the 1991–2020 climatology. (b) ACE per month in 2022 (black line) and the median during 1991–2020 (blue line), where the blue error bars indicate the climatological interquartile range. In cases of no error bars, the upper and/or lower percentiles coincide with the median. The red diamonds and circles denote the maximum and minimum values, respectively, per month during the 1945–2021 period. (Source: 1945–2021 JTWC best-track dataset, 2022 JTWC preliminary operational track data.)

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Fig. 4.34.

Jul–Oct (JASO) 2022: (a) sea-surface temperature (SST) anomalies (°C) and the tracks of all 2022 storms with colors denoting their intensity; (b) potential intensity anomalies (m s⁻¹); (c) relative humidity 600-hPa anomalies (%); (d) genesis potential index anomalies and first position of JASO 2022 storms marked with an asterisk; and (e) zonal winds at 850 hPa (m s⁻¹). (Data sources: SST: ERSSTv5 [Huang et al. 2017]; other environmental fields: ECMWF ERA5 reanalysis [Hersbach et al. 2020], tracks and first position: JTWC preliminary operational track data.)

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Fig. 4.35.

Annual tropical cyclone statistics for the North Indian Ocean basin for 1990–2022: (a) storm tracks for 2022; (b) number of named storms, cyclones, and major cyclones; and (c) accumulated cyclone energy (ACE; × 10⁴ kt²). Horizontal lines, representing the 1991–2020 climatology, are included in both (b) and (c).

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Fig. 4.36.

Oct–Dec 2022 North Indian Ocean (NIO) anomaly maps of (a) SST (°C; Banzon and Reynolds 2013), (b) outgoing longwave radiation (OLR; W m⁻²; Schreck et al. 2018); (c) 200-hPa–850-hPa vertical wind shear (m s⁻¹) vector (arrows) and scalar anomalies (shading), and (d) 850-hPa winds (m s⁻¹, arrows) and zonal wind anomalies (shading). Anomalies are relative to the annual cycle from 1991 to 2020. Letter symbols denote where each NIO tropical cyclone attained its initial tropical-storm intensity, and the red circle represents an unnamed tropical storm. (Source: wind data from CFSR [Saha et al. 2014].)

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Fig. 4.37.

Annual tropical cyclone statistics for the South Indian Ocean basin for 1980–2022: (a) storm tracks for the basin, (b) number of named storms, cyclones, and major cyclones, and (c) accumulated cyclone energy (ACE; × 10⁴ kt²). Horizontal lines represent the 1991–2020 climatology.

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Fig. 4.38.

Jan–Apr 2022 South Indian Ocean (SIO) anomaly maps of (a) sea-surface temperature (SST; °C; Banzon and Reynolds 2013), (b) outgoing longwave radiation (OLR; W m⁻²; Schreck et al. 2018); (c) 200-hPa–850-hPa vertical wind shear (m s⁻¹) vector (arrows) and scalar anomalies (shading), and (d) 850-hPa winds (m s⁻¹, arrows) and zonal wind anomalies (shading). Anomalies are relative to the annual cycle from 1991 to 2020. Letter symbols denote where each SIO tropical cyclone attained its initial tropical-storm intensity (Source: wind data from CFSR [Saha et al. 2014].)

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Fig. 4.39.

Annual tropical cyclone statistics for the Australian basin for the period 1980–2022: (a) storm tracks for the basin, (b) number of named storms, cyclones, and major cyclones, and (c) accumulated cyclone energy (ACE; × 10⁴ kt²). Horizontal lines represent the 1991–2020 climatology.

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Fig. 4.40.

Annual tropical cyclone statistics for the Southwest Pacific basin for the period 1990–2022: (a) storm tracks, (b) number of named storms, cyclones, and major cyclones, and (c) accumulated cyclone energy (ACE; × 10⁴ kt²). Horizontal lines represent the 1991–2020 climatology.

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Fig. 4.41.

Global anomalies of tropical cyclone heat potential (TCHP; kJ cm⁻²) during 2022. The boxes indicate the seven regions where TCs occur: from left to right, southwest Indian, North Indian, west North Pacific, southeast Indian, South Pacific, east Pacific, and North Atlantic (shown as Gulf of Mexico and tropical Atlantic separately). The green lines indicate the trajectories of all tropical cyclones reaching at least Category 1 intensity (one-minute average wind ≥64 kt, 34 m s⁻¹) and above during Nov 2021–Apr 2022 in the Southern Hemisphere and Jun–Nov 2022 in the Northern Hemisphere. The numbers above each box correspond to the number of Category 1 and above cyclones that traveled within that box. The Gulf of Mexico conditions are shown in the inset in the lower right corner.

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Fig. 4.42.

Tropical cyclone heat potential (TCHP) anomaly difference between the 2022 and 2021 tropical cyclone seasons (kJ cm⁻²; Jun–Nov in the Northern Hemisphere and Nov–Apr in the Southern Hemisphere). The Gulf of Mexico conditions are shown in the inset in the lower right corner.

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Fig. SB4.1.

(a) Track of Hurricane Fiona at 6-hr intervals with select milestones labeled along the track. Triangles denote pre- and post-tropical phases. Two enhanced infrared GOES-16 satellite insets 24 hours apart show the rapid transformation of Fiona from tropical to post-tropical. (b) Sea-level pressure analysis (every 4 hPa, select contour labels marked, e.g., 52 = 952 hPa) from NOAA’s Ocean Prediction Center five hours after landfall. Minimum central pressure was ~940 hPa. Direction of travel shown by red arrow. (c) Highlighted regions in Atlantic Canada where the most significant storm impacts were observed.

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Fig. SB4.2.

Hurricane Ian (left) peak inundation levels relative to North American VerticalDatum of 1988 from the United States Geological Survey Flood Event Viewer (Bowyer and MacAfee 2005) for southwest Florida and (right) storm total rainfall estimates (inches). Right panel courtesy of NWS Melbourne, Florida.

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Fig. SB4.3.

(a) Track of all eastern North Pacific tropical cyclones from 1991 to 2020 with Hurricane Kay’s track highlighted in red. (b) Daily PRISM precipitation averaged within 25 km of Tucson (blue), Phoenix (orange), Flagstaff (green), Yuma (red), Albuquerque (violet), and El Paso (brown). Gray shading indicates times during which a tropical cyclone existed for at least 24 hours within the region north of 20°N and between 120°W and the North American coastline labeled by the first letter of each storm: Celia, Howard, Javier, Kay, and Madeline.

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Fig. A4.1.

Land-only percent of normal precipitation during (a) JJA 2022 and (b) SON 2022 (relative to a 1961–1990 base period). The figure is provided by NOAA NCEI and the data are from GHCN-M version 4beta (Menne et al. 2018).

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Article Type:: Research Article

The Tropics

Howard J. Diamond

Howard J. Diamond NOAA/OAR Air Resources Laboratory, College Park, Maryland

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Carl J. Schreck

Carl J. Schreck North Carolina State University, North Carolina Institute for Climate Studies, Cooperative Institute Satellite Earth System Studies, Asheville, North Carolina

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Adam Allgood

Adam Allgood NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center, College Park, Maryland

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Emily J. Becker

Emily J. Becker University of Miami Rosenstiel School of Marine and Atmospheric Science, Miami, Florida

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Eric S. Blake

Eric S. Blake NOAA/NWS National Hurricane Center, Miami, Florida

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Francis G. Bringas

Francis G. Bringas NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida

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Suzana J. Camargo

Suzana J. Camargo Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

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Lin Chen

Lin Chen Institute for Climate and Application Research (ICAR)/KLME/ILCEC/CIC-FEMD, Nanjing University of Information Science and Technology, Nanjing, China

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Caio A.S. Coelho

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Nicolas Fauchereau

Nicolas Fauchereau National Institute of Water and Atmospheric Research, Ltd., Auckland, New Zealand

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Chris Fogarty

Chris Fogarty Canadian Hurricane Centre, Dartmouth, Canada

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Stanley B. Goldenberg

Stanley B. Goldenberg NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida

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Gustavo Goni

Gustavo Goni NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida

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Daniel S. Harnos

Daniel S. Harnos NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center, College Park, Maryland

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Qiong He

Qiong He Earth System Modeling Center, Nanjing University of Information Science and Technology, Nanjing, China

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Zeng-Zhen Hu

Zeng-Zhen Hu NOAA/NWS Climate Prediction Center, College Park, Maryland

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Philip J. Klotzbach

Philip J. Klotzbach Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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John A. Knaff NOAA/NESDIS Center for Satellite Applications and Research, Fort Collins, Colorado

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Online Publication:: 06 Sep 2023

Print Publication:: 01 Sep 2023

DOI:: https://doi.org/10.1175/BAMS-D-23-0078.1

Page(s):: S207–S270

Published Online:: 06 Sep 2023

Displayed acceptance dates for articles published prior to 2023 are approximate to within a week. If needed, exact acceptance dates can be obtained by emailing amsjol@ametsoc.org.

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Corresponding author: Howard J. Diamond / howard.diamond@noaa.gov

4. Table of Contents

List of authors and affiliations	S210
a. Overview	S212
b. ENSO and the tropical Pacific	S213
c. Tropical intraseasonal activity	S218
d. Intertropical convergence zones	S221
1. Pacific	S221
2. Atlantic	S222
e. Global monsoon summary	S224
f. Indian Ocean dipole	S228
g. Tropical cyclones	S230
1. Overview	S230
2. Atlantic Basin	S232
3. Eastern North Pacific and central North Pacific basins	S239
4. Western North Pacific basin	S243
5. North Indian Ocean	S248
6. South Indian Ocean	S250
7. Australian basin	S253
8. Southwest Pacific basin	S255
h. Tropical cyclone heat potential	S256
Sidebar 4.1: Hurricanes Fiona and Ian: A pair of impactful North Atlantic major hurricanes	S259
Sidebar 4.2: Tropical cyclone contributions during the 2022 North American monsoon	S262
Appendix 1: Chapter 4 – Acronyms	S263
Appendix 2: Chapter 4 – Supplemental Materials	S265
References	S266

Please refer to Chapter 8 (Relevant Datasets and Sources) for a list of all climate variables and datasets used in this chapter for analyses, along with their websites for more information and access to the data.

a. Overview

—H. J. Diamond and C. J. Schreck

In 2022, the El Niño–Southern Oscillation (ENSO) continued a multi-year La Niña event spanning the period from 2020 to 2022. La Niña conditions started in July–September 2020 and have lasted nearly continuously for over two years, with a brief period of ENSO-neutral conditions between May–July and June–August 2021. In 2022, La Niña fluctuated between weak and moderate strength, with an Oceanic Niño Index (ONI) value of −1.1°C in March–May (peak ONI values between −1.0° and −1.4°C are defined to be moderate strength) and weakening to −0.8°C in June–August. Following the Northern Hemisphere summer, La Niña strengthened again with a peak intensity of −1.0°C in August–October and September–November 2022.

For the global tropics, the NOAA Merged Land Ocean Global Surface Temperature analysis (NOAA GlobalTemp; Vose et al. 2021) indicates that the combined average land and ocean surface temperature (measured 20°S–20°N) was 0.01°C above the 1991–2020 average, tying with 2004 and 2006 as the 17th-warmest year for the tropics in the 173-year data record. The five warmest years in the tropics since 1850 have all occurred since 2015. Data from the Global Precipitation Climatology Project indicate a mean annual total precipitation value of 1413 mm across the 20°S–20°N latitude band over land. This is 9 mm above the 1991–2020 average and ranks 20th wettest for the 1979–2022 period of record.

Globally, 85 named tropical cyclones (TCs; ≥34 kt; or ≥17 m s⁻¹) were observed during the 2022 Northern Hemisphere season (January–December 2022) and the 2021/22 Southern Hemisphere season (July–June 2021/22; see Table 4.2), as documented in the International Best Track Archive for Climate Stewardship version 4 (Knapp et al. 2010). Overall, this number was near the 1991–2020 global average of 87 TCs but below the 96 TCs reported during the 2021 season (Diamond and Schreck 2022) and the all-time record 104 named storms in 1992.

Of the 85 named storms, 40 reached tropical-cyclone strength and 16 reached major tropical-cyclone strength. Both of these counts were below their 1991–2020 averages. The accumulated cyclone energy (ACE; an integrated metric of the strength, frequency, and duration of tropical storms and hurricanes) was the lowest on record since reliable data began in 1981. No basin was more active than normal in terms of ACE. The North Atlantic, eastern North Pacific, and South Indian Ocean basins had near-normal activity. The other basins were all less active than normal, including the western North Pacific, which had its third consecutive season with below-normal activity. Three storms reached Category 5 on the Saffir-Simpson Hurricane Wind Scale during 2022. Two were from the western North Pacific: Super Typhoons Hinnamnor and Noru. The third was Hurricane Ian in the North Atlantic, which was upgraded to Category 5 during post-season analysis (Bucci et al. 2023). This was the fewest Category 5 storms globally since 2017.

The 14 named storms in the North Atlantic during 2022 were the fewest observed since 2015 when 11 named storms developed and well below the 21 named storms in 2021. Eight hurricanes developed in 2022, one more than occurred in 2021 and near the 1991–2020 average of seven. Two major hurricanes occurred, which was slightly below the 1991–2020 average of three and half as many as occurred in 2021. The 2022 North Atlantic hurricane season was classified by NOAA’s National Hurricane Center as a near-normal season based on ACE, ending the streak of six consecutive above-normal seasons (2016–21). Even during near-normal seasons, a single hurricane can bring devastation to an area. Hurricane Ian caused over 100 deaths and more than $100 billion (U.S. dollars) in damage, making it the third-costliest hurricane in U.S. history. Hurricane Fiona caused extreme flooding in Puerto Rico before making landfall in Canada as the country’s strongest storm on record in terms of pressure. Both storms are featured in Sidebar 4.1 as well as in section 4g2.

While we do not normally report on volcanic eruptions, given the climatic impact that a large volcanic eruption can have, we would be remiss in not mentioning the eruption of the Hunga Tonga-Hunga Ha'apai (HTHH) in the southwest island nation of Tonga on 15 January 2022. HTHH ranked a 5.7 on the Volcanic Explosivity Index, alongside other history makers like Vesuvius in 79 CE and Mount St. Helens in 1980 (Besl 2023). The injection of water into the atmosphere was unprecedented in both magnitude (far exceeding any previous values in the 17-year Aura Microwave Limb Sounder record) and altitude (penetrating into the mesosphere). Millán et al (2022) estimates that the mass of water injected into the stratosphere to be 146±5 Tg, or~10% of the stratospheric burden.

It may take several years for the water plume to dissipate, and it is thought that this eruption could impact climate, not through surface cooling due to sulfate aerosols, but rather through possible surface warming due to the radiative forcing from the excess stratospheric water vapor. Similar to the climate effects (albeit cooling) of Mount Pinatubo in the Philippines in 1991, but unlike other eruptions its size, HTHH had a relatively low sulfur dioxide content. While it has been theorized that it may have added only 0.004°C of global cooling in 2022 (Zuo et al. 2022), it may take a few more years to fully determine if this eruption had any possible long-term climate effects.

b. ENSO and the tropical Pacific

—Z.-Z. Hu, M. L’Heureux, A. Kumar, and E. Becker

The El Niño–Southern Oscillation (ENSO) is an ocean and atmosphere-coupled climate phenomenon that occurs across the tropical Pacific Ocean. Its warm and cold phases are called El Niño and La Niña, respectively. For historical purposes, NOAA’s Climate Prediction Center classifies and assesses the strength and duration of El Niño and La Niña events using the Oceanic Niño Index (ONI; shown for mid-2020 through 2022 in Fig. 4.1). The ONI is the three-month (seasonal) running average of sea-surface temperature (SST) anomalies in the Niño-3.4 region (5°S–5°N, 170°W–120°W), currently calculated as the departure from the 1991–2020 base period mean¹. El Niño is classified when the ONI is at or greater than +0.5°C for at least five consecutive, overlapping seasons, while La Niña is classified when the ONI is at or less than −0.5°C for at least five consecutive, overlapping seasons.

Fig. 4.1.

Citation: Bulletin of the American Meteorological Society 104, 9; 10.1175/BAMS-D-23-0078.1

The time series of the ONI (Fig. 4.1) shows a multi-year La Niña event spanning 2020–22 (Fang et al. 2023). La Niña conditions started in July–September 2020 and have lasted nearly continuously for over two years, with a brief period of ENSO-neutral conditions between May–July and June–August (JJA) 2021 (Fig. 4.1). In 2022, La Niña fluctuated between moderate and weak strength with an ONI value of −1.1°C in March–May (MAM; peak ONI value between −1.0° and −1.4°C is defined to be moderate strength) and weakening to −0.8°C in June–August. Following the Northern Hemisphere summer, La Niña str

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The Tropics

a. Overview

b. ENSO and the tropical Pacific