Abstract
A substantial number of studies have been published since the Ninth International Workshop on Tropical Cyclones (IWTC-9) in 2018, improving our understanding of the effect of climate change on tropical cyclones (TCs) and associated hazards and risks. These studies have reinforced the robustness of increases in TC intensity and associated TC hazards and risks due to anthropogenic climate change. New modeling and observational studies suggested the potential influence of anthropogenic climate forcings, including greenhouse gases and aerosols, on global and regional TC activity at the decadal and century time scales. However, there are still substantial uncertainties owing to model uncertainty in simulating historical TC decadal variability in the Atlantic, and the limitations of observed TC records. The projected future change in the global number of TCs has become more uncertain since IWTC-9 due to projected increases in TC frequency by a few climate models. A new paradigm, TC seeds, has been proposed, and there is currently a debate on whether seeds can help explain the physical mechanism behind the projected changes in global TC frequency. New studies also highlighted the importance of large-scale environmental fields on TC activity, such as snow cover and air-sea interactions. Future projections on TC translation speed and medicanes are new additional focus topics in our report. Recommendations and future research are proposed relevant to the remaining scientific questions and assisting policymakers.
Original language | English |
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Pages (from-to) | 216-239 |
Number of pages | 24 |
Journal | Tropical Cyclone Research and Review |
Volume | 12 |
Issue number | 3 |
DOIs | |
Publication status | Published - Sept 2023 |
Bibliographical note
Funding Information:To circumvent data heterogeneity issues for trend analysis, Kossin et al. (2020) used the HURSAT homogenized records of TC intensities from satellite imagery data for the period 1979–2017 to show a significant increase in the global exceedance probability and the proportion (Cat. 3–5 relative to Cat. 1–5) of major hurricanes, as well as an increase in global TC intensity (Fig. 1a–c). A subsequent study by Jewson and Lewis (2020) demonstrated that the increase in the proportion of major TCs (Cat. 3–5) compared to all TCs with Cat. 1–5 is mainly driven by a decrease in the number of Cat. 1–5 TCs and, to a small extent, by a slight increase in the number of Cat. 3–5. Based on a potential intensity (PI) metric derived from the ERA5 dataset for the period 1979–2018, Emanuel (2020) also supported the notion of an increase in major TC wind speeds. Using multiple-century reanalyses and models, Chand et al. (2022) found a declining trend in global TC frequency over the 20th century.TCs in the NA basin are influenced by multiple internal and external forcings such as multidecadal climate variability and localized aerosol effects, often leading to conflicting conclusions on causes and robustness of historical trends. For example, increasing trends are noticed in most basin wide TC metrics (such as number of named storms, hurricanes and major hurricanes) over the past three decades (Klotzbach et al. 2022) and over the 20th century in raw (unadjusted) data, but no significant trend is found when an extended period of reconstructed (adjusted for ship-track density) observational data since the mid-twentieth century is considered for hurricanes or major hurricanes (Vecchi et al. 2021, Fig. 2). The notion of “no significant century-scale trend” was also supported by Chan et al. (2021), who used bias-corrected SST to simulate TC trends for the basin with a global atmospheric model. Unadjusted U.S. landfalling hurricane and major hurricane frequency since the late 1800s also indicate no significant long-term trend (e.g., Vecchi et al. 2021). An increase in the frequency of TCs in the northeastern Atlantic sector was also observed (Lima et al. 2021).TC storm surge height is, amongst other factors, strongly influenced by TC intensity, size, and track, and can be further amplified by a shallow coastline and local bathymetry (Ramos-Valle et al. 2020; Bloemendaal et al. 2019). Recently published global databases on present-climate storm surge reconstructions (Tadesse and Wahl 2021) and storm surge return periods (Dullaart et al. 2020) can support trend and risk analyses of TC storm surge heights, as in the case of Japan in the last 40 years (Islam et al. 2022). Tide gauge observations revealed that sea level rise has been the primary contributor to changes in total water levels over the past decades, and will likely continue to be an important contributor in the future (Fox-Kemper et al. 2021). Similar results on the global scale were also found by Muis et al. (2020), who showed that towards the end of the century and under the RCP4.5 scenario, 10-year water levels would increase by approximately 0.5m in the global tropics. This change is predominantly driven by future sea level rise. Furthermore, an increase in TC intensity is expected to directly contribute to further increased storm surge heights (Knutson et al. 2020). Mori et al. (2019) found that future changes in TC activity will increase storm surge heights by 10–30 % around 15°–35°N, with increases of 0.3–0.45m near Japan, while Chen et al. (2020) obtained an increase of ∼8.5 % in storm surge heights over the Pearl River Delta, towards the end of the century (RCP8.5). For the US, end-of-the-century estimates of the 100-year flood level at the RCP8.5 forcing scenario were found to occur approximately every 1–30 years (Marsooli et al. 2019; Mayo and Lin 2022).We thank Drs. Tsung-Lin Hsieh and Jie Chen (GFDL) for their comments on this manuscript. SJC acknowledges support from NSF (AGS 20–43142 and AGS 22–17618), NOAA (NA21OAR4310344), DOE (DE SC0023333) and the Vetlesen Foundation. SSC acknowledges funding support from the Climate Systems Hub of the Australian Government's National Environmental Science Program (NESP). I-JM acknowledges the financial supports from the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2022-00144325) and the Ministry of Education (Basic Science Research Program, 2021R1A2C1005287). CMP acknowledges support from the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Earth and Environmental Systems Modeling (EESM) Program, under Early Career Research Program Award Number DE-SC0021109. MJR acknowledges support from the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. YY acknowledges support from the. Ministry of Education, Culture, Sports, Science and Technology (MEXT) as “Program for. Promoting Researches on the Supercomputer Fugaku” (JPMXP1020200305).
Funding Information:
We thank Drs. Tsung-Lin Hsieh and Jie Chen (GFDL) for their comments on this manuscript. SJC acknowledges support from NSF (AGS 20–43142 and AGS 22–17618), NOAA (NA21OAR4310344), DOE (DE SC0023333) and the Vetlesen Foundation . SSC acknowledges funding support from the Earth Systems and Climate Change Hub of the Australian Government's National Environmental Science Program (NESP). I-JM acknowledges the financial supports from the National Research Foundation of Korea ( NRF ) grant funded by the Korea government ( MSIT ) (No. RS-2022-00144325) and the Ministry of Education (Basic Science Research Program, 2021R1A2C1005287). CMP acknowledges support from the U.S. Department of Energy , Office of Science , Office of Biological and Environmental Research (BER) , Earth and Environmental Systems Modeling (EESM) Program, under Early Career Research Program Award Number DE-SC0021109. MJR acknowledges support from the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. YY acknowledges support from the.
Publisher Copyright:
© 2023 The Shanghai Typhoon Institute of China Meteorological Administration
Funding
To circumvent data heterogeneity issues for trend analysis, Kossin et al. (2020) used the HURSAT homogenized records of TC intensities from satellite imagery data for the period 1979–2017 to show a significant increase in the global exceedance probability and the proportion (Cat. 3–5 relative to Cat. 1–5) of major hurricanes, as well as an increase in global TC intensity (Fig. 1a–c). A subsequent study by Jewson and Lewis (2020) demonstrated that the increase in the proportion of major TCs (Cat. 3–5) compared to all TCs with Cat. 1–5 is mainly driven by a decrease in the number of Cat. 1–5 TCs and, to a small extent, by a slight increase in the number of Cat. 3–5. Based on a potential intensity (PI) metric derived from the ERA5 dataset for the period 1979–2018, Emanuel (2020) also supported the notion of an increase in major TC wind speeds. Using multiple-century reanalyses and models, Chand et al. (2022) found a declining trend in global TC frequency over the 20th century.TCs in the NA basin are influenced by multiple internal and external forcings such as multidecadal climate variability and localized aerosol effects, often leading to conflicting conclusions on causes and robustness of historical trends. For example, increasing trends are noticed in most basin wide TC metrics (such as number of named storms, hurricanes and major hurricanes) over the past three decades (Klotzbach et al. 2022) and over the 20th century in raw (unadjusted) data, but no significant trend is found when an extended period of reconstructed (adjusted for ship-track density) observational data since the mid-twentieth century is considered for hurricanes or major hurricanes (Vecchi et al. 2021, Fig. 2). The notion of “no significant century-scale trend” was also supported by Chan et al. (2021), who used bias-corrected SST to simulate TC trends for the basin with a global atmospheric model. Unadjusted U.S. landfalling hurricane and major hurricane frequency since the late 1800s also indicate no significant long-term trend (e.g., Vecchi et al. 2021). An increase in the frequency of TCs in the northeastern Atlantic sector was also observed (Lima et al. 2021).TC storm surge height is, amongst other factors, strongly influenced by TC intensity, size, and track, and can be further amplified by a shallow coastline and local bathymetry (Ramos-Valle et al. 2020; Bloemendaal et al. 2019). Recently published global databases on present-climate storm surge reconstructions (Tadesse and Wahl 2021) and storm surge return periods (Dullaart et al. 2020) can support trend and risk analyses of TC storm surge heights, as in the case of Japan in the last 40 years (Islam et al. 2022). Tide gauge observations revealed that sea level rise has been the primary contributor to changes in total water levels over the past decades, and will likely continue to be an important contributor in the future (Fox-Kemper et al. 2021). Similar results on the global scale were also found by Muis et al. (2020), who showed that towards the end of the century and under the RCP4.5 scenario, 10-year water levels would increase by approximately 0.5m in the global tropics. This change is predominantly driven by future sea level rise. Furthermore, an increase in TC intensity is expected to directly contribute to further increased storm surge heights (Knutson et al. 2020). Mori et al. (2019) found that future changes in TC activity will increase storm surge heights by 10–30 % around 15°–35°N, with increases of 0.3–0.45m near Japan, while Chen et al. (2020) obtained an increase of ∼8.5 % in storm surge heights over the Pearl River Delta, towards the end of the century (RCP8.5). For the US, end-of-the-century estimates of the 100-year flood level at the RCP8.5 forcing scenario were found to occur approximately every 1–30 years (Marsooli et al. 2019; Mayo and Lin 2022).We thank Drs. Tsung-Lin Hsieh and Jie Chen (GFDL) for their comments on this manuscript. SJC acknowledges support from NSF (AGS 20–43142 and AGS 22–17618), NOAA (NA21OAR4310344), DOE (DE SC0023333) and the Vetlesen Foundation. SSC acknowledges funding support from the Climate Systems Hub of the Australian Government's National Environmental Science Program (NESP). I-JM acknowledges the financial supports from the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2022-00144325) and the Ministry of Education (Basic Science Research Program, 2021R1A2C1005287). CMP acknowledges support from the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (BER), Earth and Environmental Systems Modeling (EESM) Program, under Early Career Research Program Award Number DE-SC0021109. MJR acknowledges support from the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. YY acknowledges support from the. Ministry of Education, Culture, Sports, Science and Technology (MEXT) as “Program for. Promoting Researches on the Supercomputer Fugaku” (JPMXP1020200305). We thank Drs. Tsung-Lin Hsieh and Jie Chen (GFDL) for their comments on this manuscript. SJC acknowledges support from NSF (AGS 20–43142 and AGS 22–17618), NOAA (NA21OAR4310344), DOE (DE SC0023333) and the Vetlesen Foundation . SSC acknowledges funding support from the Earth Systems and Climate Change Hub of the Australian Government's National Environmental Science Program (NESP). I-JM acknowledges the financial supports from the National Research Foundation of Korea ( NRF ) grant funded by the Korea government ( MSIT ) (No. RS-2022-00144325) and the Ministry of Education (Basic Science Research Program, 2021R1A2C1005287). CMP acknowledges support from the U.S. Department of Energy , Office of Science , Office of Biological and Environmental Research (BER) , Earth and Environmental Systems Modeling (EESM) Program, under Early Career Research Program Award Number DE-SC0021109. MJR acknowledges support from the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. YY acknowledges support from the.
Funders | Funder number |
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EESM | DE-SC0021109 |
Earth and Environmental Systems Modeling | |
UK-China Research and Innovation Partnership Fund | |
Vetlesen foundation | |
National Science Foundation | AGS 22–17618, AGS 20–43142 |
National Science Foundation | |
U.S. Department of Energy | DE SC0023333 |
U.S. Department of Energy | |
National Oceanic and Atmospheric Administration | NA21OAR4310344 |
National Oceanic and Atmospheric Administration | |
Office of Science | |
Biological and Environmental Research | |
Newton Fund | |
National Retail Federation | |
Ministry of Education, Culture, Sports, Science and Technology | JPMXP1020200305 |
Ministry of Education, Culture, Sports, Science and Technology | |
Ministry of Education | 2021R1A2C1005287 |
Ministry of Education | |
Ministry of Science, ICT and Future Planning | RS-2022-00144325 |
Ministry of Science, ICT and Future Planning | |
National Research Foundation of Korea |