 So our next speaker is Dr. Yuhei Takaya. Yuhei is a researcher in the Atmosphere, Ocean, and Earth System Modeling Research Department for the MRI, JMA at in Japan. Yuhei also gave a talk during the colloquium couple of weeks back. Thanks, Yuhei. Can you hear me? Yeah. Do you see my screen? Yeah. OK. OK. Thank you, Anish, for the introduction. And I would like to thank the organizers, the Anish, and Judith for a lot of work. And today I am going to talk about the enhanced Mayu Bayu rainfall in early summer 2020. I particularly focus on the influence of the previous IOD event, Indian Ocean Dipole event. And every season, we experience a lot of extremes, the sub-season time scale and season time scale. And the extreme events are not necessarily to be explained by the some dominant variability, including the ocean variability. But if we can identify the strong influence or strong driver for the sub-seasonal time scale variability, then we expect some predictability for that event. So in this talk, I focus on the ocean variability in the Indian Ocean. And we may be all of you familiar with the answer. The answer is the most dominant source of the predictability for the sub-seasonal time scale. But for the Asian monsoon, the Indian Ocean is also important. First, I'd like to acknowledge my collaborators, the Ichiro Ishikawa at Shiyaki Kobayashi and Hirokazu Endo and Tomaki Ose at the MRI. And this work was published in GRL last year. OK, maybe you know that in early summer 2020, we experienced mercury-enhanced Mayu bio-rainfall. And that event caused a lot of flooding in China as well as in Japan. And here, I'm showing the rainfall anomaly over the Asian region. Here, I'm showing the rainfall and moisture flux anomalies. And on the right, we see the Mayu bio-frontal zone rainfall, the rainfall amounts in the red box. And in 2020, it's the highest rainfall season. And the rainfall amount is about the 1.5 exceeds 1.5 times the crime torch. There are several reasons for this event. And here, I'm showing the figure from the JMA, Japan Meteorological Agency. This figure explains the possible factors for this extreme event. The first one is the southwestward extension of western north Pacific subtropical high. That brings the amount, a lot of moisture, to the Mayu bio-frontal zone. And there was an upper level top over the Hiroshi. And we observed the southwest shift of jet. And if we looked at the ocean condition, we observed the warm Indian Ocean. Actually, we know that the warm Indian Ocean causes the enhanced Mayu bio-frontal. Here, I'm showing the figure from the Kosaka at all, 2013. This figure explains if we have the warm Indian Ocean, then we often observe the anomalous anti-cycling cyclations over the tropical western north Pacific. And we also experienced the enhanced rainfall. Here, they show the Yantt-Riefer flow. And the Yantt-Riefer flows increased in the warm Indian Ocean condition. This variability is called the Indian Western Pacific Capacitor Mode. This condition often occurs after the El Niño. So here, they show the lag correlations of the Indian Ocean temperature, sea-sub-temperature, with respect to the previous Niño 3.4 SST. And we see the positive correlations between the previous winter SST, Niño 3.4, SST, and the tropical Indian Ocean temperature, sea-sub-temperature. So first, I checked the influence of the Indian Ocean conditions. So we conducted the sensitivity experiments. The Y experiment is the X control, the control experiment. This is the original season of predictions started from the end of April 2020. This figure shows the SST anomalies during the June-July period in the X control experiment. And the second experiment, IO-CLM, is the experiment with the prescribed impostery climatological SST condition is imposed in the Indian Ocean area. So here, I'm showing the SST anomalies in June-July period. And we see the anomalies over the Indian Ocean because we nuts the SST to the model plan torch. And here, we see the observed SST conditions. And we see a good resemblance between the control experiment and observations. So here, we see the SST precipitation anomalies in X control and X IO-CLM. So basically, if we remove the SST anomalies in the Indian Ocean regions, we don't see the suppressed convection over the Tokyo-Western-North And also, we don't see the enhanced precipitation over the May-U-Bio rainfall. And here, this figure is a zoom up of the precipitation anomalies, a difference of the precipitation between the X control and X IO-CLM experiments. And basically, the research shows that the warm Indian Ocean condition is one of the causes for the extreme May-U-Bio rainfall in 2020. And here, I'm showing the atmospheric circulation map. And again, here, we see the sea level pressure anomalies in observation and control experiment and sensitivity experiment. And the X control reproduces the anomalous anti-cyclic circulation and high pressure anomaly over the tropical-western-North Pacific as observed, but we don't see the anomalous sea level pressure in the sensitivity experiment. But the one question remains, as I said, the warm Indian Ocean in summer often occur after the El Nino events. But in 2019, we didn't observe the clear, typical El Nino event. Instead, actually, in autumn 2019, we observed the positive phase of the Indian Ocean diphmol and the high SST in the Western Central Equatorial Pacific as shown here. And this SST condition induced the history wind anomalies over the equatorial Indian Ocean. This wind, surface wind anomalies, enhanced, induced the record-strong, down-wading loss V-rape, as shown here. Here, I'm showing the subsurface temperature anomalies during the November to January. And we see very strong oceanic down-wading loss V-rape in the South Indian Ocean. And here, I'm showing the Hofmehrer diagram. The x-axis is the lat launch shield, and the y-axis is the time. Times goes to the downward. And we see westward propagating loss V-rape. Here. And this event, this event is actually very strong compared with the previous events. Here, I'm showing the Hofmehrer diagram of the subsurface temperature anomalies along the six-degree toss in the Indian Ocean. And here, we see the 2020 event. And the figure, bottom figure shows the maximum and minimum subsurface temperature anomalies in the Indian Ocean at the six-degrees south. And actually, the maximum temperature is on par with the previous strong event in 1997, 1998, a linear event. Then, as I said, the ocean loss V-rape propagated the westward, and the loss V-rape warmed the SST in the South Western Tokyo Indian Ocean. As I showed here, this is the SST anomalies during the February to April 2020. And then, the warmed SST in the spring weakened the monsoon flow in early summer 2020. And the weakened monsoon flow warmed the SST in the North Indian Ocean and the South China Sea. Here, we see the SST anomalies during May to July 2020, and we see high SST anomaly in the North Indian Ocean and the South China Sea. And in early summer 2020, this SST condition enhanced the IPOC mode in the Western Pacific Ocean Kebashite mode, and that caused the South West for the extension of subtropical high over the tropical Western North Pacific and the intense fire media value rainfall. So we can trace back the extreme media value event in 2020 to the previously Indian Ocean Typhoon event. But this is not the end of story. Actually, we know that the other influence of the isotropic circulations on the media value rainfall, here I'm showing the intermember correlation between media value rainfall and the atmospheric freeze, and set 500 and 200 hectopascal zone of wind and seedable pressure. As we expect, if we have the stronger Western North Pacific subtropical high or the southwest extension, then we have more rainfall over the media value rainfall from the zone. If we have stronger 12 over the EOC or over Japan, then we intend to have more rainfall. So there is an influence of the isotropic atmospheric freeze. So these results support the explanation shown earlier in this presentation. To summarize, the enhanced media value rainfall in early summer 2020 was associated with the Indian Ocean iPod mode with the future of North Indian Ocean and South China Sea warming. The Indian Ocean warming and the iPod mode was excited in an unusual way with the strong Indian Ocean iPod in 2019 instead of preceding the new. This case may exemplify another forcing process that can trigger the iPod mode, causing the enormous summer climate in Japan in Asia. So for the sub-season predictions or the extreme events in sub-season time scale, it's very important to understand the underlying processes to cause the extreme events. And if we can identify some predictive sources or the precursor or external forcing, then we may predict the extreme event in the sub-season and season time scale. Thank you for your kind attention. Great. Thanks very much, Yuhei, a very interesting case and also connection to historical cases. Any questions for Yuhei? I had one, Yuhei, in terms of the links to also the polar, like, arctic circulation. There's been some work showing the, I think it's more recent from a group in FSU, T.N. Krishnamurti and others that there is a link between the monsoon strength or monsoon oscillations and the arctic sea ice as well. Does interactions of the tropics and the polar circulation also influence this meubio rainfall? And, yeah, how does the polar region's variability influence this? Yes, I have a very good study. And it's possible to influence the polaration influence on the meubio rainfall. Because if we have some forcing in the polar region, and if we can trace the Rossby wave train that can cause the meubio rainfall, maybe we can attribute the meubio rainfall to the polar condition. OK, on the long-term chart that you showed, I didn't see specific links to the extreme low arctic sea ice in fall, like 2012, I think was one of the case. And then 2019, where the arctic sea ice was the lowest in the observed record. It was not obvious to me that those years had extreme anomalies in the meubio. Yes, if we look at the observation, we may see some wave train. So we can attribute the polar influence. But in the extra tropics, we have chaotic variability. And even we specify the sea ice conditions, it's very difficult to reproduce the observed conditions with only the polar region. So there is a difficulty for the attribution studies, in particular, the signal to noise ratio is low. So even in the tropical influence, the actually the signal to noise ratio is low. So the predicted anomaly is very weak compared with the observation. So I don't know how we can overcome this problem. But yeah, this is the limit for the attribution study, I think. Right. OK, yeah. Thanks again, Yuhain. Thank you for your really nice talk at 1 a.m. local time for you. Thank you for staying up late for us. Thank you.