Meiyu Rainfall Research Articles

Improvement of decadal predictions of monthly extreme Mei-yu rainfall via a causality guided approach

Abstract While the improved performance of climate prediction systems has allowed better predictions of the East Asian Summer Monsoon rainfall to be made, the ability to predict extreme Mei-yu rainfall (MYR) remains a challenge. Given that large scale climate modes (LSCMs) tend to be better predicted by climate prediction systems than local extremes, one useful approach is to employ causality-guided statistical models (CGSMs), which link known LSCMs to improve MYR prediction. However, previous work suggests that CGSMs trained with data from 1979–2018 might struggle to model MYR in the pre-1978 period. One hypothesis is that this is due to potential changes in causal processes, which modulate MYR in different phases of the multidecadal variability, such as the Pacific decadal oscillation (PDO). In this study, we explore this hypothesis by constructing CGSMs for different PDO phases, which reflect the different phases of specific causal process, and examine the difference in quality as well as with respect to difference drivers and thus causal links between CGSMs of different PDO phases as well as the non-PDO phase specific CGSMs. Our results show that the set of predictors of CGSMs is PDO phase specific. Furthermore, the performance of PDO phase specific CGSMs are better than the non-PDO phase specific CGSMs. To demonstrate the added value of CGSMs, the PDO phase specific versions are applied to the latest UK Met Office decadal prediction system, DePreSys4, and it is shown that the root-mean squared errors of MYR prediction based on PDO phase specific CGSMs is consistently smaller than the MYR predicted based on the direct DePreSys4 extreme rainfall simulations. We conclude that the use of a causality approach improves the prediction of extreme precipitation based solely on known LSCMs because of the change in the main drivers of extreme rainfall during different PDO-phases.

Storyline attribution of human influence on a record-breaking spatially compounding flood-heat event.

Attribution of compound events informs preparedness for emerging hazards with disproportionate impacts. However, the task remains challenging because space-time interactions among extremes and uncertain dynamic changes are not satisfactorily addressed in the well-established attribution framework. For attributing the 2020 record-breaking spatially compounding flood-heat event in China, we conduct a storyline attribution analysis by designing simulation experiments via a weather forecast model, quantifying component-based attributable changes, and comparing with historical flow analogs. We quantify that given the large-scale circulation, anthropogenic influence to date has exacerbated the extreme Mei-yu rainfall in the mid-lower reaches of the Yangtze River during June-July 2020 by ~6.5% and warmed the co-occurring seasonal extreme heat in South China by ~1°C. Our projections show a further intensification of the compound event by the end of this century, with moderate emissions making the rainfall totals ~14% larger and the season ~2.1°C warmer in South China than the 2020 status.

Moisture sources and isotopic composition of the 2020 extraordinary and persistent Meiyu rainfall in the Yangtze River valley modulated by large-scale circulations

Meiyu rainfall is an important climate phenomenon in East Asia, but its interannual variability has amplified in recent decades. To gain a better understanding of the underlying atmospheric processes of torrential Meiyu rainfall, we investigated the moisture sources and isotopic composition of the 2020 extraordinary and persistent Meiyu rainfall in the Yangtze River valley from June 19 to July 10 using an isotopic regional spectral model. During the Meiyu period, significant amounts of Indian Ocean moisture were transported to Yangtze River valley by Indian monsoonal southwesterlies, especially in the middle levels between 850 hPa and 600 hPa. Whereas moderate amounts of East Asian continent moisture were distributed principally within the boundary layer below 850 hPa. Besides, tiny amounts of South China Sea moisture were transported by the western Pacific subtropical high to the middle and lower reaches of Yangtze River valley within the boundary layer below 900 hPa. With respect to isotopic characteristics, the lowest δ2H and the highest d-excess values of the Indian Ocean moisture were attributable to more rainout and below-cloud evaporation during long-distance transport. In contrast, the isotopic signals of the East Asian continent moisture and the South China Sea moisture were relatively preserved. The 2020 Meiyu rainfall exhibited significant intraseasonal variability and was classified into heavy and light Meiyu periods based on rainfall amount. Heavy Meiyu rainfall with northward migration were characterized by enhanced Indian Ocean moisture with lower δ2H and higher d-excess, resulting from the westward expansion of the western Pacific subtropical high and the deepened East Asian mid-latitude trough.

Hourly and Sub-Hourly Rainfall under Synoptic Patterns during the Anomalous Meiyu Season 2020

The 2020 Meiyu season has received extensive attention due to its record-breaking rainfall in the Yangtze–River Huai Basin (YHRB) region of China. Although its rainfall features have been well studied on various time scales, the sub-hourly/hourly rainfall features are unknown. In this study, a wavelet analysis was applied to 1 min rainfall data from 480 national rain gauges across the YHRB, and hourly synoptic patterns during the Meiyu season were grouped using an obliquely rotated principal component analysis in T-mode (PCT). The results suggest that variances on the sub-hourly and hourly scales contributed 63.4% of the 2020 Meiyu rainfall. The hourly synoptic variations in the Meiyu season can be categorized into three major patterns: weak synoptic forcing (P1), a convergence line (P2), and a vortex (P3). The rainfalls under P1 were spatially dispersed over the YHRB and on the shortest time scale, with a 70.4% variance from sub-hourly to hourly rainfalls. P2 had a peak wavelet variance around 30 min–1 h, with rainfalls concentrated to the south of the convergent line. The rainfalls under P3 were locally distributed with a longer duration of around 1–4 h. Compared with the climate mean, hourly rainfall frequencies are indispensable to understanding the 2020 accumulated Meiyu rainfall anomaly. This research highlights the dominant role of synoptic patterns on the temporal and spatial features of the Meiyu rainfall.

Impact of anomalous Eurasian blocking activities on the East Asian Meiyu rainfall

An extreme East Asian Meiyu rainfall in both amount and duration occurred along Yangtze River valley during June–July of 2020, however, possible mid-high latitude signatures causing this super Meiyu have not been well identified. This study explores the cause of the Meiyu rainfall from the aspect of anomalous Eurasian blocking activities with a two-dimensional blocking index, using the Japanese 55-yr Reanalysis for 1979–2020. The major findings are as follows. Variabilities of the Eurasian blocking activities are primarily characterized by a tripole pattern with three centers over the Baltic Sea, the Ural Mountains, and the Sea of Okhotsk, respectively. The tripole pattern is associated with two zonally-oriented Rossby wavetrains which may originate from the rainfall anomaly in central Europe. Corresponding to a positive phase of the tripole pattern, the northern wavetrain through energy dispersion tends to induce an anomalous anticyclone (i.e., enhanced blocking) over the Sea of Okhotsk, while the southern wavetrain tends to induce an anomalous cyclone over the Sea of Japan which is conducive to a southward intrusion of more high-latitude cold airs. Consequently, a meridional dipole anomalous circulation pattern over the northeastern Asia is formed, favoring more East Asian Meiyu rainfall. In 2020, the Eurasian blocking activities exhibit a significantly positive phase of the tripole pattern and considerably contribute to the super Meiyu. The results identified in this study highlight the importance of the Eurasian blocking activities in the East Asian Meiyu and provide a new perspective for the prediction of the Meiyu rainfall with mid-high latitude signatures.

The Combined Effects of the Tropical and Extratropical Quasi-biweekly Oscillations on the Record-setting Mei-yu Rainfall in the Summer of 2020

The Combined Effects of the Tropical and Extratropical Quasi-biweekly Oscillations on the Record-setting Mei-yu Rainfall in the Summer of 2020

Contribution of the Tibetan Plateau Snow Cover to the Record-breaking Rainfall Over the Yangtze River Valley in June 2020

ABSTRACT In June 2020, the Yangtze River Valley (YRV) experienced the most severe and long-persisting rainfall in past decades, causing hundreds of people’s death and huge property loss. Based on the high-resolution MODIS/Terra snow cover data for the 2000–2021 period and the linear baroclinic model (LBM), this study investigates the dynamic influence and the potentially predictable source from the Tibetan Plateau (TP) snow cover (TPSC) on this record-breaking YRV rainfall. The TPSC anomalies in the critical area (76°−83°E, 28°−35°N) can persist for more than six months and induce a Rossby wave train propagating northeastward. For the excessive TPSC, barotropic negative geopotential height anomalies prevail over northern East Asia and positive geopotential height anomalies over the northwestern Pacific. Such a circulation configuration favours a deepened cold vortex over Northeast China and the westward-developing western Pacific subtropical high, which leads to an intensified Meiyu-Baiu-Changma front and rich Meiyu rainfall. The LBM sensitive experiments can reproduce the above physical processes associated with the TPSC anomalies, realistically. Moreover, the quantitative contributions of the indices representing the TPSC and the three oceans (Pacific, Indian Ocean, and Atlantic) to the record-breaking Meiyu rainfall are examined, and the TPSC index explains most of the total variance of the rainfall anomaly among the indices, reaching 42.75%. This highlights the importance of the TP thermal forcing to the extreme climate conditions in June 2020.

Effects of aerosols on the forecasting of Mei-yu frontal storms over the Yangtze–Huai River valley

The evolutions of Mei-yu frontal storms during the Mei-yu season in 2018 were simulated in the Weather Research and Forecasting with Chemistry (WRF/Chem) model to investigate the effects of aerosols on the prediction of clouds and precipitation. Two groups of daily launched forecast experiments were conducted with a duration of 17 days, by turning the chemistry–aerosol module on (WRF/Chem) and off (WRF), and additional cloud droplet number concentration (Nd) experiments were perturbed in WRF. Comparison between the two groups of experiments suggested that activating the chemistry–aerosol module improved the forecast skill for precipitation exceeding 5 mm/h during the Mei-yu season, while had no positive forecast skill on precipitation exceeding 0.1 mm/h in terms of both traditional and neighborhood spatial verifications. In contrast to the uniform Nd distribution adopted in WRF, the inhomogeneous Nd distribution in WRF/Chem agreed better with in-situ aircraft observations collected during the field campaign. As Nd increases in WRF experiments, the warm clouds decreased and deep convective clouds significantly increased with more convective updraft mass flux and net heat budget. Scavenging in Mei-yu frontal storms generally reduced the median Nd in the WRF/Chem experiment. The fewer and larger cloud particles in WRF/Chem promoted the conversion of cloud to rain and the accretion of cloud by rain, thereby leading to invigoration of warm clouds and precipitation efficiency, which explains the improved forecast skill for precipitation exceeding 5 mm/h. Enhanced warm-rain processes caused fewer cloud particles for further ascent and ultimately resulted in suppression of deep convective clouds. The preliminary findings in this study highlights the potential significance of incorporating chemistry–aerosol schemes in the operational forecasting of Mei-yu rainfall.

Evaluation of the CAMS-CSM Model in predicting a Northeast Cold Vortex Event at the early Onset of 2020 Mei-yu season

Intimately related to the Asian summer monsoon, the Mei-yu rainfall is also strongly influenced by atmospheric circulation in the middle to high latitudes, especially the Northeast Cold Vortex (NECV), and thus making prediction of the Mei-yu rainfall a challenging issue. The prediction skills of the CAMS-CSM (full name as the Chinese Academy of Meteorological Sciences Climate System Model) for a NECV event at the early onset of 2020 Mei-yu season and the associated Mei-yu rainfall are evaluated. Hindcast experiments with two different horizontal resolutions are employed to assess the climate model in predicting the synoptic system. The ERA5 reanalysis data and the CN05.1 precipitation data are adopted for comparison. Results indicate that both the middle-resolution (T106, ∼1°) and high-resolution (T255, ∼0.47°) version of the CAMS-CSM model are able to capture this NECV event, while showing some biases on its intensity, duration and location. Overpredictions in the strength and duration of the NECV are found in the T106 version, which could be attributed to a stronger dry invasion from higher levels during the development stage, an intensified Okhotsk blocking high, and a weakened upper-level jet at the decaying phase. As for the T255 version, a stronger NECV is predicted further north than the reanalysis, and sources of the biases are identified, including northward displacements of the high-level dry invasion and the westerly jet. Further investigation suggests that the T106 version performs better than the T255 version in predicting the Mei-yu rainfall because of a more accurate prediction on the location of the NECV. Positive biases in the cold advection and Mei-yu front are identified in the T106 version, contributing to a stronger Mei-yu rainfall than the observation. The Mei-yu rainfall is poorly predicted in T255 version due to prediction errors in the location of the NECV-induced cold advection. Therefore, improving the prediction skill of the NECV, not only its intensity but also location, is of vital significance to achieve a better prediction of the Mei-yu rainfall.

Possible Impact of Early Spring Arctic Sea Ice on Meiyu Cessation over the Yangtze–Huaihe River Basin

The timing of the cessation of Meiyu is closely connected to the amount of Meiyu rainfall and the commencement of the rainy season in North China. Accurately forecasting the Meiyu withdrawal date (MWD) over the Yangtze–Huaihe River basin (YHRB) has significant implications for the prevention and mitigation of flooding in eastern China. This study observed an intimate out-of-phase relationship between MWD variations and early spring (March and April) Arctic Sea ice area (SIA) anomalies to the north of the Chukchi and Beaufort Seas, as well as SIA anomalies to the north of the Queen Elizabeth Islands (75° N–82° N, 170° E–130° W and 82° N–86° N, 130° W–80° W, respectively) on the interannual timescale. As such, these can be considered key Arctic Sea ice domains connected to Meiyu cessation in the YHRB. The Arctic SIA anomalies in the key domains persist from early spring to early summer (May and June), thus exerting a lag modulation effect on year-to-year changes in Meiyu cessation, which can be demonstrated through observational analysis and results from the Community Earth System Model Large Ensemble Numerical Simulation (CESM-LENS) project. Specifically, the preceding negative SIA anomalies in the key domains are linked to a planetary-scale Rossby wave-like pattern emanating over areas to the northwest of the Chukchi Sea. This tele-connected wave-like pattern is conducive to the generation and maintenance of a quasi-barotropic “north-low–south-high” meridional see-saw pattern over the East Asian–Western North Pacific sector in July, which is a pivotal circulation pattern responsible for delayed Meiyu termination. Furthermore, the situation is the opposite in response to increased sea ice in these key domains within the Arctic. This study proposes a significant cryospheric forcing indicator for Meiyu cessation over the YHRB, which may provide helpful information for operational forecasting of the withdrawal timing of the Meiyu over the YHRB.

Effects of Direct Assimilation of FY-4A AGRI Water Vapor Channels on the Meiyu Heavy-Rainfall Quantitative Precipitation Forecasts

In this study, the regional Weather Research and Forecasting model (WRF)-based quantitative precipitation forecasts (QPFs) are conducted for an extreme Meiyu rainfall event over East Asia in 2020. The data of water vapor channels 9 and 10 from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite are assimilated through the Gridpoint Statistical Interpolation (GSI) system. It shows that a reasonable amount of assimilated AGRI data can produce reasonable water vapor increments, compared to the too sparse or dense assimilated AGRI observations. In addition, the critical success indexes (CSIs) of the precipitation forecasts within 72 h are obviously improved. The enhanced variational bias correction (VarBC) scheme is applied to remove the air-mass and scan-angle biases, and the mean observation-minus-background (O − B) values before and after the VarBC of channel 9 are −1.185 and 0.02 K, respectively, and those of channel 10 are −0.559 and −0.01 K, respectively. Assimilating the upper-level channel 9 data of AGRI (EXP_WV9) lead to a neutral-to-positive effect on QPFs, compared to the control run (CTL), which is based on the assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) data. In particular, the CSIs from 42 to 72 h are significantly improved. However, the assimilation of the AGRI channel 10 (EXP_WV10) shows a neutral-to-negative effect on QPFs in this study, probably due to the complicated surface situations. This study confirms the feasibility of assimilating the water vapor channel data of FY4A AGRI in the GSI system and highlights the importance of assimilating AGRI channel 9 data to improve the QPFs of the Meiyu rainfall event.

Idealized simulations of Mei-yu rainfall in Taiwan under uniform southwesterly flow using a cloud-resolving model

Abstract. In this study, idealized cloud-resolving simulations are performed for horizontally uniform and steady southwesterly flow at fixed direction–speed combinations to investigate rainfall characteristics and the role of the complex topography in Taiwan during the Mei-yu season without the influence of a front or other disturbances. Eight directions (180 to 285∘, every 15∘) and eight speeds (5 to 22.5 m s−1, every 2.5 m s−1) are considered, and near-surface relative humidity is also altered (from 55 %–100 %) in a subset of these tests to further examine the effects of moisture content, yielding a total 109 experiments each having a integration length of 50 h. Three rainfall regimes that correspond to different ranges of the wet Froude number (Frw) are identified from the idealized simulations (with a grid size of 2 km). The low-Frw regime (Frw ≤ ∼ 0.3) is where the island circulation from thermodynamic effects is the main driver of rainfall in local afternoon. The lower the wind speed and Frw are, the more widespread the rainfall is, as well as its amount. On the other hand, the high-Frw regime (Frw ≥ ∼ 0.4) occurs when the flow of at least 12.5 m s−1 impinges on Taiwan terrain at a large angle (not parallel). This favors the flow-over scenario, and topographic rainfall production becomes dominant through mechanical uplift of unstable air. In this scenario, the faster and wetter the flow is, the heavier the rainfall on the windward slopes is, and maximum amounts typically occur at wind directions from 240–255∘. Between the two regimes above, a third, mixed regime also exists. The idealized results are discussed for their applicability to the real atmosphere.

A Causality-guided Statistical Approach for Modeling Extreme Mei-yu Rainfall Based on Known Large-scale Modes—A Pilot Study

Extreme Mei-yu rainfall (MYR) can cause catastrophic impacts to the economic development and societal welfare in China. While significant improvements have been made in climate models, they often struggle to simulate local-to-regional extreme rainfall (e.g., MYR). Yet, large-scale climate modes (LSCMs) are relatively well represented in climate models. Since there exists a close relationship between MYR and various LSCMs, it might be possible to develop causality-guided statistical models for MYR prediction based on LSCMs. These statistical models could then be applied to climate model simulations to improve the representation of MYR in climate models.In this pilot study, it is demonstrated that skillful causality-guided statistical models for MYR can be constructed based on known LSCMs. The relevancy of the selected predictors for statistical models are found to be consistent with the literature. The importance of temporal resolution in constructing statistical models for MYR is also shown and is in good agreement with the literature. The results demonstrate the reliability of the causality-guided approach in studying complex circulation systems such as the East Asian summer monsoon (EASM). Some limitations and possible improvements of the current approach are discussed. The application of the causality-guided approach opens up a new possibility to uncover the complex interactions in the EASM in future studies.

Precipitation Microphysics during the Extreme Meiyu Period in 2020

Previous studies have reported the large-scale meteorological conditions and dynamic causes of the extreme period of meiyu rainfall in 2020. However, the microphysical properties of meiyu precipitation during this period remain unclear. We used the Global Precipitation Measurement 2ADPR orbital precipitation dataset, the IMERG gridded precipitation dataset and the ERA5 reanalysis dataset to study the characteristics of meiyu precipitation over the Yangtze Plain during the extreme meiyu period in 2020 and historical meiyu periods from 2014 to 2019. The results showed that the average daily rainfall during the 2020 meiyu period was 1.5 times higher than the historical average as a result of the super-strong water vapor flux in the low- to mid-level layers of the atmosphere. The amplitude of nocturnal low-level water vapor transport during the 2020 meiyu period was twice the historical average and, therefore, the diurnal peak of meiyu rainfall at 0630 LST in 2020 was significantly earlier than the historical average. The moisture transport was the dominant moisture supply for precipitation during the 2020 meiyu period, whereas the moisture convection contributed less than in the meiyu periods of 2014–2019. This led to the precipitation in the 2020 meiyu period having a higher concentration of smaller droplets than the historical average. There were lower proportions of size-sorting evaporation and break-up processes in the liquid-phase precipitation processes in the 2020 meiyu than the historical average, but a higher proportion of coalescence processes. These results provide a factual basis for the simulation and forecast of precipitation during extreme meiyu periods.

MJO Phase Swings Modulate the Recurring Latitudinal Shifts of the 2020 Extreme Summer‐Monsoon Rainfall Around Yangtse

AbstractConsidering the unprecedented extremity of the Meiyu episodes in early summer of 2020, here the authors perform a comprehensive study of the intraseasonal evolution characteristics of the observed rainfall round Yangtse as well as the potential modulating effect of Madden‐Julian Oscillation (MJO). Results suggest that the Meiyu rainfall manifests as recurring latitudinal shifts around Yangtse during June–July of 2020, corresponding well to the continued swings of MJO circulation and convection between Phase 1 and Phase 2. The MJO affects the meridional migration of the 2020 Meiyu rainfall mainly through regulating the westward extended ridge line and southwesterly of the anomalous anticyclone over the western North Pacific (WNP). Specifically, the northeastward propagation of boreal summer MJO from Phase 1 to 2 shifts the ridge line of the anomalous WNP anticyclone northeastward. Consequently, the associated southwesterly anomaly over the southeastern China penetrates to the north of Yangtse and enhances the lower‐tropospheric moisture flux convergence, supporting heavy precipitation there. However, when the MJO swings back to Phase 1, the anomalous southwesterly and moisture flux convergence retreat southward, so does the Meiyu rain band. The present results advanced our understanding of the interactions between the latitudinal shifts of extreme Meiyu rainfall episodes and MJO Phase swings.

Reduced Probability of 2020 June–July Persistent Heavy Mei-yu Rainfall Event in the Middle to Lower Reaches of the Yangtze River Basin under Anthropogenic Forcing

"Reduced Probability of 2020 June–July Persistent Heavy Mei-yu Rainfall Event in the Middle to Lower Reaches of the Yangtze River Basin under Anthropogenic Forcing" published on Mar 2022 by American Meteorological Society.

Subseasonal Variation of 2020 Meiyu Rainfall With Extremely Long Duration Over the Yangtze–Huaihe River Basin in China

An observed fact is proposed that the 2020 record-breaking Meiyu over the Yangtze–Huaihe River Basin (YHRB) is characteristic of significant subseasonal variation. The anomalously enhanced rainfall experiences a southward retreat from the northern YHRB in June to the southern YHRB in July. The meridional shift of abnormal rainbelt is closely related to the different anomalous Meiyu front circulations. The first stage features a warm Meiyu front with an extremely strong northward transport of warm-and-wet air, whereas a cold front forms with a convergence between the strong northeasterlies and southwesterlies in July. Another significant subseasonal variation of the 2020 Meiyu is that the Meiyu rainfall evolves in a periodic quasi-biweekly oscillation (QBWO). On the scale of the QBWO, the cold air activities over East Asia contribute significantly to Meiyu; each intense southward invasion of cold air corresponds well to the enhancement of Meiyu rainfall. However, the location of the western Pacific subtropical high, the subtropical East Asia summer monsoon (SEASM), and the intensity of the East Asia subtropical westerly jet exhibit opposite QBWO characteristics in June and July. For an increasing (decreasing) of Meiyu rainfall, the western Pacific subtropical high marches northward (retreats southward), the subtropical East Asia summer monsoon weakens (strengthens), and the East Asia subtropical westerly jet accelerates (decelerates) in June. The opposite holds true in July. Further analyses reveal that the filtered cyclone over Northeast China benefiting the southward intrusion of cold air is associated with the eastward propagation of a wave train over the mid-high latitudes in June, whereas it arises from both the westward propagation of a wave train over the mid-high latitudes and the northward propagation of cyclonic circulation from the tropics in July.

Symbiotic Relationship between Mei-Yu Rainfall and the Morphology of Mei-Yu Front

Abstract Observational evidence from a heavy precipitation event of the 2020 extreme mei-yu season is presented here to reveal a symbiotic relationship between mei-yu rainfall and the morphology of the mei-yu front. The two influence each other through dynamical and thermodynamic feedbacks and evolve in a coherent way to generate cyclic behaviors. Specifically, an intense and band-shaped mei-yu front leads to symmetrical instability in the lower atmospheric layer and convective instability in the middle atmospheric layer, forming a rainband along the front. The mei-yu front and its associated instability subsequently weaken as a result of rainfall, and the front is bent by the process of tilting frontolysis. Deep convective instability in the middle and lower layers develops in the warm, humid prefrontal area, and triggers isolated heavy rainfall replacing the original rainband south of the bent front. This warm sector precipitation then strengthens the front through tilting and diabatic heating frontogenesis. A stronger front recovers its initial band shape, and the associated rainfall also resumes the form of a rainband along the front. Analyses of potential energy associated with instability, water vapor convergence, and cross-frontal circulation are carried out to illustrate key processes of this mei-yu front–rainfall cycle. The implications of this symbiotic relationship for simulating and predicting extreme rainfall associated with mei-yu fronts are presented.

The role of El Niño in the extreme Mei-yu rainfall in 2020

Mei-yu is a wide-spread rain band occurring in early summer over the East Asia, especially over the middle and lower Yangtze River valley (YRV). Devastating floods in the YRV were often associated with the heavy Mei-yu in the decaying phase of extremely strong El Niño events. However, the unusually heavy Mei-yu and flood in 2020 were preceded only by a weak El Niño in the central equatorial Pacific, and thus caught many forecasters by surprise. Here we try to find the cause of this peculiarity through data analysis and a set of model experiments. Our results indicate that the positive Indian Ocean basin mode (IOBM) and the sudden decay of the CP El Niño in mid-2020 significantly contributed to ascending anomaly over the Maritime Continent, which enhanced the subtropical high over the South China Sea and the convergence over the YRV. It is the zonal temperature gradient across the tropical Indo-Pacific Ocean before and during the mei-yu season, rather than the magnitude of the preceding El Niño or Indian Ocean warming, that determines the strength and duration of the Mei-yu. The results highlight the combined effects of the weak CP El Niño and positive IOBM on extreme Mei-yu event.

Subseasonal forecast barrier of the North Atlantic oscillation in S2S models during the extreme mei-yu rainfall event in 2020

Subseasonal forecast barrier of the North Atlantic oscillation in S2S models during the extreme mei-yu rainfall event in 2020