The Tropics

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

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

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

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

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

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

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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 Centro de Previsão do Tempo e Estudos Climáticos/National Institute for Space Research, Center for Weather Forecasts and Climate Studies, Cachoeira Paulista, Brazil

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

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Chris Fogarty Canadian Hurricane Centre, Dartmouth, Canada

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

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

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

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

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

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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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Arun Kumar NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center, College Park, Maryland

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Michelle L’Heureux NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center, College Park, Maryland

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Chris W. Landsea NOAA/NWS National Hurricane Center, Miami, Florida

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I-I. Lin National Taiwan University, Taipei, Taiwan

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Andrew M. Lorrey National Institute of Water and Atmospheric Research, Ltd., Auckland, New Zealand

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Jing-Jia Luo Institute for Climate and Application Research, Nanjing University of Information Science and Technology, Nanjing, China

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Andrew D. Magee Centre for Water, Climate and Land, School of Environmental and Life Sciences, University of Newcastle, Callaghan, Australia

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Richard J. Pasch NOAA/NWS National Hurricane Center, Miami, Florida

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Alexandre B. Pezza Greater Wellington Regional Council, Wellington, New Zealand

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Matthew Rosencrans NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center, College Park, Maryland

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Jozef Rozkošný Slovak Hydrometeorological Institute, Bratislava, Slovakia

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Blair C. Trewin Australian Bureau of Meteorology, Melbourne, Australia

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Ryan E. Truchelut WeatherTiger, Tallahassee, Florida

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Bin Wang School of Ocean and Earth Science and Technology, Department of Meteorology, University of Hawaii; International Pacific Research Center, Honolulu, Hawaii

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Hui Wang NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center, College Park, Maryland

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Kimberly M. Wood Department of Geosciences, Mississippi State University, Mississippi State, Mississippi

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Open access

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

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

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

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

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.
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).

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 strengthened again with a peak intensity of −1.0°C in August–October and September–November (SON). Sidebar 3.1 in Chapter 3 describes the triple La Niña event.

(i) Oceanic conditions

Figure 4.2 displays the three-monthly mean SST (left column) and SST anomalies (right column) during December–February (DJF) 2021/22 through September–November (SON) 2022. Consistent with La Niña, below-average SSTs persisted across most of the equatorial Pacific Ocean during the year. During DJF (Fig. 4.2b), the strongest SST anomalies on the equator exceeded −2.0°C in a small portion of the eastern equatorial Pacific (between 120°E and 80°W), implying a strengthening of the cold tongue (Fig. 4.2a). During MAM, the negative SST anomalies strengthened in the central equatorial Pacific and expanded westward (Fig. 4.2d). Below-average SSTs were weakest across the equatorial Pacific in JJA, but remained in excess of −1.0°C in small regions of the central and far eastern Pacific (Fig. 4.2f). The western Pacific warm pool remained contracted to the west during most of the year, with the 30°C isotherm nearly vanishing during JJA (Fig. 4.2e). During SON, below-average SSTs re-strengthened in the central and eastern equatorial Pacific (Fig. 4.2h). A horseshoe-like pattern of above-average SSTs stretched from the western tropical Pacific to the extratropical North and South Pacific Oceans during all seasons.

Fig 4.2.
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).

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

Consistent with the evolution of SST anomalies and La Niña, the subsurface temperature anomalies were a dipole-like pattern centered along the thermocline in the western and eastern Pacific Ocean (Kumar and Hu 2014). The positive temperature anomalies were centered in the western and central equatorial Pacific, while negative temperature anomalies were strongest in the eastern Pacific throughout the year. These anomalies reflect a steeper-than-average thermocline slope (solid line) with shallow anomalies in the eastern Pacific and deep anomalies in the western Pacific (Fig. 4.3). Negative subsurface temperature anomalies also persisted within the mixed layer near the date line. The slope of the thermocline was steepest in SON, which was also when the anomalous subsurface temperature gradient was strongest (Fig. 4.3d). These subsurface features were relatively weaker in MAM and JJA (Figs. 4.3b,c).

Fig 4.3.
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.

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

(ii) Atmospheric circulation

In 2022, the large-scale tropical atmospheric circulation anomalies were also consistent with La Niña and persisted through the year. Figure 4.4 shows outgoing longwave radiation (OLR) anomalies, which is a proxy for tropical convection and rainfall. Typically, during La Niña, convection is suppressed (positive OLR, brown shading) over the western and central tropical Pacific and enhanced (negative OLR, green shading) over the Maritime Continent. Relative to the other seasons in the year, the dipole-like pattern in precipitation anomalies was shifted eastward during DJF 2021/22, with suppressed convection located just to the east of the date line and enhanced convection over the western tropical Pacific (Fig. 4.4a). The anomalies then shifted westward after DJF with suppressed convection expanding into the western tropical Pacific and enhanced convection shifting over western Indonesia (Fig. 4.4b). During MAM 2022, convection over the date line was further suppressed, which occurred at the same time the ONI value reached its peak. Corresponding to the seasonal cycle, the region of enhanced precipitation over the Maritime Continent extended farther northwards toward the Philippines during DJF 2021/22 and MAM 2022. Following boreal spring, enhanced rainfall anomalies became mainly confined to the equator and south of the equator during JJA and SON, with anomalies also increasing in intensity (Figs. 4.4c,d).

Fig. 4.4.
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).

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

Similar to convection, the lower- and upper-level wind anomalies were reflective of La Niña throughout the year. Stretching across most of the equatorial Pacific Ocean (Fig. 4.5), the tropical low-level 850-hPa easterly trade winds were enhanced. The low-level easterly wind anomalies were strongest over the eastern Pacific during DJF 2021/22 (Fig. 4.5a). During the other seasons (MAM through SON), the low-level easterly wind anomalies strengthened and expanded over the western tropical Pacific Ocean (Figs. 4.5b–d). The upper-level 200-hPa westerly wind anomalies prevailed throughout the year over most of the equatorial Pacific Ocean (Fig. 4.6). Like the low-level winds, upper-level westerly wind anomalies also expanded farther to the west after DJF (Figs. 4.6b–d). During all seasons, an anomalous cyclonic circulation couplet straddled the equator in both hemispheres (Fig. 4.6). At times, two pairs of cyclonic anomalies were evident, such as in MAM 2022, with centers around 160°E and 120°W, respectively. Overall, the lower- and upper-level wind anomalies (Figs. 4.5, 4.6) and rainfall anomalies across the tropical Pacific (Fig. 4.4) were indicative of an enhanced equatorial Walker circulation over the tropical Pacific. Collectively, these oceanic and atmospheric anomalies reflected the well-known, basin-wide atmospheric and oceanic coupling of the La Niña phenomenon (Bjerknes 1969).

Fig. 4.5.
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).

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

Fig. 4.6.
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 botto