Wei Mei’s research while affiliated with University of North Carolina at Chapel Hill and other places

The small sample size of tropical cyclone (TC) genesis in the observations prevents us from fully characterizing its spatiotemporal variations. Here we take advantage of a large ensemble of 60-km-resolution atmospheric simulations to address this issue over the northwest Pacific (NWP) during 1951–2010. The variations in annual TC genesis density are explored separately on interannual and decadal time scales. The interannual variability is dominated by two leading modes. One is characterized by a dipole pattern, and its temporal evolution is closely linked to the developing ENSO. The other mode features high loadings in the central part of the basin, with out-of-phase changes near the equator and date line, and tends to occur during ENSO decay years. On decadal time scales, TC genesis density variability is primarily controlled by one mode, which exhibits an east–west dipole pattern with strong signals confined to south of 20°N and is tied to the interdecadal Pacific oscillation–like sea surface temperature anomalies. Further, we investigate the seasonal evolution of the ENSO effect on TC genesis density. The results highlight the distinct impacts of the two types of ENSO (i.e., eastern Pacific vs central Pacific) on TC genesis density in the NWP during a specific season and show the strong seasonal dependency of the TC genesis response to ENSO. Although the results from the observations are not as prominent as those from the simulations because of the small sample size, the high consistency between them demonstrates the fidelity of the model in reproducing TC statistics and variability in the observations.

Plain Language Summary While many studies have been devoted to understanding the processes and mechanisms underlying the sea surface temperature (SST) cooling induced by tropical cyclones (TCs), few studies have attempted to predict the spatial and temporal evolution of the sea surface temperature (SST) cooling triggered by TCs. In this study, we proposed to achieve this goal by building a model using an efficient and robust machine learning‐based method. The constructed model uses 12 predictors associated with TC characteristics (e.g., intensity, and translation speed) and pre‐storm ocean states (e.g., mixed layer depth). The model performs well in producing the TC‐induced spatial structure and temporal evolution of the cold wake and can capture most of the variance in the observed SST response. We quantified the relative importance of the 12 predictors, and found that TC intensity, translation speed and size, and pre‐storm mixed layer depth and SST dominate in deciding the magnitude of the SST response. The results and proposed method have important implications for predicting the response of ocean primary production to the TC wind pump effects.

Theory1 and numerical modelling2 suggest that tropical cyclones (TCs) will strengthen with rising ocean temperatures. Even though models have reached broad agreement on projected TC intensification3–5, observed trends in TC intensity remain inconclusive and under active debate6–10 in all ocean basins except the North Atlantic, where aircraft reconnaissance data greatly reduce uncertainties11. The conventional satellite-based estimates are not accurate enough to ascertain the trend in TC intensity6,11, suffering from contamination by heavy rain, clouds, breaking waves and spray12. Here we show that weak TCs (that is, tropical storms to category-1 TCs based on the Saffir–Simpson scale) have intensified in all ocean basins during the period 1991–2020, based on huge amounts of highly accurate ocean current data derived from surface drifters. These drifters have submerged ‘holy sock’ drogues at 15 m depth to reduce biases induced by processes at the air–sea interface and thereby accurately measure near-surface currents, even under the most destructive TCs. The ocean current speeds show a robust upward trend of ~4.0 cm s−1 per decade globally, corresponding to a positive trend of 1.8 m s−1 per decade in the TC intensity. Our analysis further indicates that globally TCs have strengthened across the entirety of the intensity distribution. These results serve as a historical baseline that is crucial for assessing model physics, simulations and projections given the failure of state-of-the-art climate models in fully replicating these trends13. Both drifter current observations and satellite-based tropical cyclone (TC)-induced sea surface cooling demonstrate that weak TCs have intensified in recent decades.

The variability and predictability of tropical cyclone genesis frequency (TCGF) during 1973–2010 at both basinwide and sub-basin scales in the northwest Pacific are investigated using a 100-member ensemble of 60-km-resolution atmospheric simulations that are forced with observed sea surface temperatures (SSTs). The sub-basin regions include the South China Sea (SCS) and the four quadrants of the open ocean. The ensemble-mean results well reproduce the observed interannual-to-decadal variability of TCGF in the southeast (SE), northeast (NE), and northwest (NW) quadrants, but show limited skill in the SCS and the southwest (SW) quadrant. The skill in the SE and NE quadrants is responsible for the model’s ability to replicate the observed variability in basinwide TCGF. Above-normal TCGF is tied to enhanced relative SST (i.e., local SST minus tropical-mean SST) either locally or to the southeast of the corresponding regions in both the observations and ensemble mean for the SE, NE, and NW quadrants, but only in the ensemble mean for the SCS and the SW quadrant. These results demonstrate the strong SST control of TCGF in the SE, NE, and NW quadrants; both empirical and theoretical analyses suggest that ensembles of ∼10, 20, 35, and 15 members can capture the SST-forced TCGF variability in these three sub-basin regions and the entire basin, respectively. In the SW quadrant and the SCS, TCGF contains excessive noise, particularly in the observations, and thus shows low predictability. The variability and predictability of the large-scale atmospheric environment and synoptic-scale disturbances and their contributions to those of TCGF are also discussed.

Using reanalysis and high-resolution ensemble simulations, we characterize cold season (December−March) North Atlantic (NA) atmospheric river (AR) tracks by grouping them into four distinct clusters; then for each cluster, we link the year-to-year variations in track count to large-scale climate variability and examine the climatological effects of the cluster on extreme precipitation and winds. The four clusters share similar prevailing AR track orientation, but differ in AR genesis locations and dominate over different regions. Cluster 1, with the longest average track of the four clusters, originates near the U.S. East Coast during La Niña and positive North Atlantic Oscillation (NAO) years and produces extreme precipitation and winds primarily over the eastern coast of North America. Cluster 2, which is weak in intensity and short-lived, forms north of 30°N of the open ocean during positive NAO years and contributes to more than 25% of the precipitation and wind extremes along the coasts of Northwestern Europe. Cluster 3, with the strongest intensity and longest duration among the four clusters, is generated surrounding the Gulf of Mexico during El Niño and negative NAO years and produces respectively more than 50% and 40% of the extreme precipitation and wind events over the eastern U.S. Cluster 4, the smallest and weakest among the four clusters, is favored under negative NAO conditions and generates roughly 25% of the extreme precipitation and winds along the coast of the Iberian Peninsula. The similarities and discrepancies between reanalysis and model simulations and among different member simulations are also discussed.

The sea surface temperature (SST)-forced and internal variability in cold-season (December–March) atmospheric river (AR) occurrence frequency during 1951–2010 over the North Atlantic (NA) basin are examined using a 30-member ensemble of high-resolution atmospheric simulations. The first leading mode of the forced variability features a north–south wobbling pattern modulated by an out-of-phase combination of El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). Co-existing El Niño and negative NAO act to shift ARs equatorward, whereas concurrent La Niña and positive NAO tend to displace ARs poleward. The second leading mode is characterized by a meridional concentration and dispersion of AR occurrence at a basin scale and can be linked to the Scandinavian pattern and the SST difference between the central and easternmost tropical Pacific. The third leading mode is dominated by an oscillation of AR occurrence north and south of 40°N in the eastern NA basin, and modulated by an in-phase combination of ENSO and the NAO. Its time series exhibits a significant upward trend, which can be linked to the SST warming in the Indo-western Pacific since the 1970s. The internal variability in cold-season NA AR occurrence frequency is then quantified by means of the signal-to-noise ratio. The calculations show that the internal variability is relatively weak over the Great Antilles and central-to-eastern US but extremely strong over Northwestern Europe, which can be attributed to the strong SST control associated with ENSO and the chaotic variations of the NAO, respectively.

Portions of East Asia often experienced extremely heavy rainfall events over the last decade. Intense atmospheric rivers (ARs), eddy transports of moisture over the middle latitudes, contributed significantly to these events. Although previous studies pointed out that landfalling ARs will become more frequent under global warming, the extent to which ARs produce extreme rainfall over East Asia in a warmer climate remains unclear. Here we evaluate changes in the frequency and intensity of AR‐related extreme heavy rainfall under global warming using a set of high‐resolution global and regional atmospheric simulations. We find that both the AR‐related water vapor transport and rainfall intensify over the southern and western slopes of mountains over East Asia in a warmer climate. ARs are responsible for a large fraction of the increase in the occurrence of extreme rainfall in boreal spring and summer. ARs will bring unprecedented extreme rainfall over East Asia under global warming.

This study quantifies the contributions of tropical sea surface temperature (SST) variations during the boreal warm season to the interannual-to-decadal variability in tropical cyclone genesis frequency (TCGF) over the Northern Hemisphere ocean basins. The first seven leading modes of tropical SST variability are found to affect basin-wide TCGF in one or more basins, and are related to canonical El Niño–Southern Oscillation (ENSO), global warming (GW), the Pacific Meridional Mode (PMM), Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO) and Atlantic Meridional Mode (AMM). These modes account for approximately 58%, 50% and 56% of the variance in basin-wide TCGF during 1969–2018 over the North Atlantic (NA), Northeast Pacific (NEP) and Northwest Pacific (NWP), respectively. The SST effect is weak on TCGF variability in the North Indian Ocean. The dominant SST modes differ among the basins: ENSO, the AMO, AMM and GW for the NA; ENSO and the AMO for the NEP; and the PMM, interannual AMO and GW for the NWP. A specific mode may have opposite effects on TCGF in different basins, particularly between the NA and NEP. Sliding-window multiple linear regression analyses show that the SST effects on basin-wide TCGF are stable in time in the NA and NWP, but strengthen after the mid-1970s in the NEP. The SST effects on local TC genesis and occurrence frequency are also explored, and the underlying physical mechanisms are examined by diagnosing a genesis potential index and its components.

Atmospheric rivers (ARs) are an important concern in regional water management; however, little is known about the AR impacts on hydrology in East Asia (EA). This study analyzes the characteristics of storms, precipitation (P), streamflow (Q), and runoff coefficient (R) in the Namgang-dam basin in Korea related to ARs for 2000–2013, as well as the sensitivity of the analysis results to AR detection methods, using observed P, Q, and three different AR inventories. The basin experiences 37.3 storms annually, of which 54% are AR storms that provide over 60% of the annual P and Q. The AR (non-AR) storms are dominant in storm frequency and the storm-total P and Q for January–July (August–December) with peaks in July (August). The monthly AR frequency varies closely with the seasonal variations in the EA monsoon and the North Pacific storm track which modulate the number of extratropical cyclones. The AR storms produce most of the extreme events; they also generate larger storm-mean P and Q than the non-AR storms for all months. The seasonal variations in R are related to the total (AR- and non-AR storms combined) P through the seasonal soil water variations, making the AR effects on R unclear. Considering 95% confidence intervals, the AR storms are distinguished well from the non-AR storms in storm frequency and the storm-total and storm-mean P and Q. The sensitivity to AR inventories is not critical in quantifying the AR-storm characteristics and their impacts on hydrologic variables except for R.