 네, 고맙습니다, 프랫. 오늘의 주장은 앤소 텔레코넥션의 비교적 변화입니다. 특히, 아라비아, 피니슬라의 비교적 변화입니다. 저는 3개의 교수는, 프로세먼즈오, 이루파인, 킹압드라치스, 아라비아, 면술이 있었고, 프레드가 있었습니다. 그리고, 사이언스에 대해 말하자마자, 새로운 센터에서 한국의 아라비아에서 소개해드렸습니다. 이 센터는 여기 7년 전, 우리는 6년 전의 ICTP에서 시작했습니다. 그리고, 글로벌 클라멘 모델링과 시즌의 prediction, 그리고 기술 클라멘 모델링, 그리고 여러 가지 기술 클라멘이 있습니다. 그리고 최근 KAU가 우리의 컴퓨터를 보내줄 것입니다. 그래서 우리가 많은 시뮬레이션을 만드는 것입니다. 이 센터는 아랍 지역의 유닛 센터입니다. 그리고 기술 클라멘 자력이 이 시즌의 기념에 대해 공개되었습니다. 랩세션과 문조의 기업을 준비할 수 있습니다. 사실 이 새로운 크라미아센터는 사우디에서 사회에 대해 제작한 이 사회에서 연구하는 사회의 사연입니다. 사회의 사회의 사회에 관한 사회의 사회의 사회의 사회입니다. 이 사회의 사회의 사회의 사회는 제 여성과 제 prend the science in Arab society. The collaborators of course, of course, the collaborators of course, ICTP, Fred and other people, Columbia University, Columbia University, Columbia University, Michael Tibet, and myself from Seoul National University, and myself from Seoul University, and the Columbia research unit of East Anglia University, the University of East Anglia. 마즈오는 또 다른 연구자들과 함께 일하고 있습니다. 그리고 마즈오는 여기 있습니다. 그래서 이 센터에 대해 이야기하실 수 있습니다. 이 센터는 크라멘 모델을 개발하여, 또한 시즌의 prediction, 그리고 헤브리의 prediction forecast에 의해, 왜 prediction of rainfall? 사실 이곳은 디지털 지역입니다. 그러나, 우리 사우디가 annual cycle, annual variability도 있습니다. 그리고 오픈, 프라덴을 볼 수 있습니다. 2009년, 100mm의 랜폴이 벌어졌습니다. 이 이벤트는 100명을 더 많이 죽인 것입니다. 그래서 사회적 경험에 의해 랜폴의 경험에 의해 그래서 우리의 시간에 아주 기쁘게 보셨던 것 같아요. 그리고 만소와 저희는 학교에 대한 중요성을 보여주기 시작했죠. 하지만... 저는... 그게 좀 더 어렵더라고요. 하지만 이 학교에 관심을 받았죠. 그리고 그녀는 학교 센터에 적용되었죠. 그래서 그 센터에 적용되었죠. 어제, 이 년 17일에 우리의 일회장에 또 다른 기회가 있었습니다. 만소오가 저에게 말했습니다. 이 큰 기회는 또 다른 기회가 있습니다. 랜폼, 사우디, 그리고 웨스티즌의 불가능한 variable of the inter-annual variation of the total precipitationVAV Is fluctuating fisherman significantly from a minus 45mm. And maximum is over 150mm per season. So that large inter-research fluctuations and in order to make a long conserve forecast. We have to understand the fluctuation of this so fourth thing to do is to 글로벌 pattern associated with this. And we try to find out the relationship of this with Anso. And the correlation was nothing, almost zero, for over 100 years. But if you do sliding correlations with 21 year window, sometimes big correlations happen. Here, this is an example. We use data from 1950 because global circulation data is available after that. And as you can see, by the way, this is the correlation between African, Arabian Peninsula rainfall and Renew 3.4. And here, 60s and 70s, the regional rainfall is negatively correlated with Anso, and positively correlated in recent 30 year period. I mean, I was very much interested in, after seeing this, I mean, the correlation even changed the sign for last 30 years or so. So, we start to investigate why this Anso influence even changed the sign. La Nina produces more precipitation. And in recent years, La Nina produces more precipitation in recent 30 years. So, we divide 30 years by 1980 from 1950 to 1980 and 1980 to 2010. And this is the correlation pattern of SST with AP rainfall. And as you can see, all tropical cooling produces more precipitation over here. But in recent 30 years, I mean, eastern Pacific warming actually produce more precipitation. And as you can see, I mean, a linear pattern actually influence, I mean, Arabian Peninsula precipitation in recent 30 years. And when you correlate this AP rainfall on the 200mB geopotamian height, this is a pattern. So, I mean, large-scale circulation pattern actually influence, I mean, a digital area of Saudi precipitation over here. In this two figures, there is a common feature here, over here. I mean, upper-level negative geopotamian height actually produces more precipitation over here. See? But the connectivity to other region in geopotamian height is different. All negative in early 30-year period and positive, mostly positive in recent 30-year period. And this pattern is similar to Anso. And this pattern is similar, not very exact same, but close to La Nina. So, we try to investigate what is the teleconnection pattern of, I mean, teleconnection pattern of Anso for this two period. And this is, no, before that, yes, this is Anso, the correlation pattern of geopotamian height correlated with linear 3.4 for, I mean, early 30-year period and late 30-year period. As you can see, Anso, actually, Linear produces all warming over the tropics in early 30-year period. And it is expanded over here, in the Indian region. But in recent years, Anso actually produces warming over here, but it is shrink. And the negative geopotamian height becomes evident. So, this pattern is similar to this. And this pattern is completely negative. That's why AP rainfall is correlated with La Nina condition. Now, what is the SST pattern associated with La Nina? This is early 30-year period. And this is recent 30-year period. As you can see, very strong warming associated with La Nina in early 30-year period and cooling, but it is not very distinctive in the western Pacific. However, in the recent period, La Nina company, very strong negative SST over western Pacific and relatively weak SST anomaly in the Indian Ocean. So that over here, the SST pattern is quite different. So, we try to reproduce this with the model, with the ICTP speedy model, with the prescribed SST anomaly with this and with this. And this is the results. I mean, this is a 200mB geopotamian height response with prescribed SST anomaly over the globe. And this is the response in early 30-year period. And this is late 30-year period. As you can see, there is a distinctive difference over this location in South Asia including Arabian Peninsula over here. So, we reproduced these differences, geopotamian height differences by prescribing SST anomaly. So, the SST anomaly is responsible. And if we divide Pacific and Indian Ocean, this is the result. So, I mean, upper panel is the reason. Okay, sorry. Pacific response and Indian Ocean response. So, Pacific mainly make the difference. Particularly, western Pacific SST pattern produces a negative over here. And Indian Ocean plays some role. But western Pacific plays a major role for the difference. And what about linking this to the global circulation mode? So, we made a year of analysis of geopotamian height anomaly in early 30-year period. And this is the first UF. As you know and well known, the PNA pattern is reproduced. And if you correct this time series with SST, this is the SST pattern. So, we know that ENSO produces PNA. And this pattern is actually well known after Horan, Warris, and Warris, and Gatchelot papers in early 1980s. So, I mean, the data is actually the same. The period is actually the same. So, ENSO and PNA. And the second mode is actually the trend mode. I'm not going to talk about the second mode. And in early 30-year period, the first mode is this one, pretty similar to ENSO. But more or less generally oriented, I call journal mode. But very close to PNA. And the associate SST pattern is like this. So, warming all over the tropics seems to produce this kind of pattern. And more interestingly, this is second mode. Actually, this second mode is ENSO mode, which produces the pattern like this. But pattern looks similar to PNA. But when you compare these two, this is first mode. And this is second mode in recent 30-year period. As you can see, there is similarity in western hemisphere. But differences in the eastern hemisphere, particularly over here, you see? The positive and the negative. So that, I mean, the distinctive mode actually shows the circulation differences over South Asia and even in the eastern hemisphere. This sign is different here. And the SST pattern, as you can see here in early 30-year period, very strong Indian Ocean linkage and relatively weak western Pacific SST anomaly. But as you can see, very strong western Pacific SST anomaly and relatively weak Indian Ocean SST anomaly. And so, this pattern, I mean, difference of the PNA pattern and eastern Pacific circulation differences of the principal mode seems to be very important for the not only, I mean, South Asian climate, but also Asian monsoon as well. So we are going to further attention on this pattern and further make on the research. And finally, then what is the possible reason? One possible reason might be from Atlantic Ocean. This is one example. This is the linear Indian Ocean relationship has been changed. This is the sliding correlation and this is AMO times AMO index. As you can see, there is some negative correlations, not perfect. But it indicates that AMO might influence to a linear and Indian Ocean relationship. And this is my final slide. This is again sliding correlation between NAO and Arabian Peninsula rainfall. As you can see, there is also very distinctive sign change relationship for the linear influence to the Arabian Peninsula precipitation. Positive and becomes negative. And similarly, I already showed this one. Negative to positive with and so. So if you correlate with NAO and linear, the correlation is not that good. Almost not significant. But if you correlate with AP rainfall, the correlation becomes some importance. Important correlations becomes appeared. So Atlantic Ocean may influence Indian Ocean and also influence regional rainfall in South Asia as well as Sahara precipitation as well. And thank you very much. And this is summary and thank you very much. And this is our recent publication on this topic. Thank you.