Interdisciplinary Dialogue: Real-World Applications in Numerical Ocean Modeling
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Qiao, Fangli
The ocean, a source and support of life on the earth, is one of the most complicated systems for scientists to simulate accurately. Numerical ocean models, running on the world’s most cutting-edge supercomputers, are the key tool for understanding and forecasting oceanic phenomena. Improving the accuracy of numerical models requires finer resolutions and incorporating more reasonable physical processes, and recent increases in computing power have enabled more detailed calculations on even finer grids. In this interdisciplinary dialogue we will address such questions as – what is state-of-the-art in numerical ocean modeling? And what are the current trends in model improvement? We will consider several applications of the surface wave-tide-circulation coupled numerical ocean model, such as nuclear radiation spread prediction following damage to the Fukushima Nuclear Power in 2011, and the rescue of lives at sea after boats capsized near Phuket in 2018. We will also discuss the history and challenges of numerical ocean model development and how ocean models and supercomputing relate to our everyday lives.
 Good morning, everyone. My name is Fang Liqiao from the first state of the oceanography, the Ministry of Natural Resources of the Republic of China, located in Qingdao. My topic will be new ocean, typhoon and cleft models with surface wave. That means we will focus on the coupling process. So this is outline. The first is a background and a challenge we faced. So why? First why we focus on the ocean? As we know, ocean covers more than 70% of the global surface and more than 97% water resources in the global and more than 90% from the greenhouse gases. And for the oxygen, it's between 50% to 70% and in fact the ocean controls the climate. So that's why we focus on the ocean. And we know recently we are considering the UN decades of ocean science for sustainable development for 10 years. And for these decades, we're focused on six, mainly six parts, the clean ocean, predicted ocean, health ocean, sea ocean, productive ocean, transparent ocean. So today I will share some ideas with you on the predicted ocean. So for the predicted ocean, the time scale is from days to centuries. The first is days forecast. For days forecast, which can be used for ships, safety, for the typhoon and the hurricane and storm surge, and also for tourism. We enjoy the ocean along the coastline. And this is for seasonal prediction. And when the seasonal prediction is for the disaster prevention and the reduction. And also very important is for the flood and drought, which is key important for agriculture. Mostly we focus on the flood and drought in the land. But in fact, it's controlled by the ocean. And the next is for climate change. As we know, the ocean controls the climate. What affect, I think, play a key role in the climate system. And also, this climate change will also affect the fishing. So this is the background. And then we analyze the challenge we face for ocean generation models. This challenge lasts for half a century. The simulated from the model, the sea surface temperature is overhead in summer and the sub-surface is over cooling. Wow, the mixed layer depth is too shallow. Here we show. So this is from observed mixed layer depth. As we know, mixed layer depth is so important for fishery, for climate change. And this is for model, so much difference. And for the typhoon, this challenge lasts for several decades already. So this is the trick. This for the last 20 years. This is forecast error. So the typhoon trick is reducing. That means we have better and better ability to forecast the typhoon or hurricane, the trick. But for the intensity, no ending progress during the past several decades. So this is our challenge. And for the climate models, it's a tropical bias. The tropical bias, for example, is here, it's too cold, it's too warm. You may say that maybe this is not a good model. In fact, this is a leading one. This model is from ANCA. We run the model for 300 years and last 50 years take average. So this is the simulation error. So even in this area, the error can be much larger than four degrees. So this is our climate system. So how to solve these problems or challenge? So as we know, the ocean model is based on governing equation. So we need to go back to the governing equation. And this term is a mixing or called turbulence. So this is with high uncertainty. And we guess this should be related with this term. And the other is for the AC flux. AC flux is also kind of a turbulence process. And we probably don't know the role of we're breaking AC heat flux and the role of wave in AC momentum flux. For example, previously we think if the wind in this direction and the wind stress always the same direction, we always use this term in the model. But from an observation, it's totally different. Sometimes in low wind conditions, the wind stress can be opposite to the wind direction. So how to consider this process is a challenge. So next, what is turbulence? The turbulence is the most important I saw the problem with classical physics. So if we go back to Heisenberg, Heisenberg is Nobel Prize winner. He said, why meet God? I'm going to ask him two questions. So why relativity and why turbulence? So I really believe he will have an answer for the first. So this is how difficult of this problem we faced. For the mixed layer depth and the sea surface temperature over heating always tell us the mixing the turbulence in the opposite not enough. So where does this energy come from? As we know, this is the surface wave. When the wind forces the ocean, more than 90% of energy is into the wind, into the wave. But the wave effect is not coincided in the ocean model because it's a previously we think that the wave is so small scale. The wave length is only 100 meters. But for the circulation, we have tens of thousands of kilometers. So so much difference. So we always separate this is where this is where this ocean circulation. For the research community for the model is totally different. But we noticed we proposed the idea this kind of breaking this kind of non-breaking will can generate turbulence. So how to get this one is a long story. So today I just show the result. So we express this is a way we do the mixing and we try to understand the physics. This means that this kind of mixing is related with the amplitude and the stock drift. So this is a new proposed idea. And because previously we never show, we never show this kind of non-breaking will can generate turbulence. So when we propose this idea, a lot of debate. So could you show us why? Why, you know, the way we say you're rotational, you should not generate turbulence. Could you show me? Could you show me? How can generate turbulence? So this is the laboratory experiment we published on JPU in 2010. So we as a bottom, we have a refer editor and we monitor the temperature evolution. So this is at the beginning. At the beginning, the bottom, the temperature is cold and the surface is warm. So it's quite similar to the ocean. And we keep it to nothing, it will take about one day or well mixed. This is a molecular mixing. And then we produce the non-breaking will. With this will, the mixing can be finished within half hour. So it's clearly demonstrated the will can generate very strong turbulence. Then we needed to in-circuit observation to support this idea. So this is one publication from Sutherland on ocean science. So the red one is from our game and the black one is from observation. So you can see these two lines fit quite well. That means the will in the mixing in the upper ocean play a dominant role. Unfortunately, we did not consider this process in the ocean model. So this is the first part. The second part is a surface-will tied circulation coupled model. So we call this a state of art models. First, we check how important the surface will. At the beginning, we introduced, so this is mixed-layer depth from observation. This mixed-layer depth from model, so much different. The color bar is the same. By concept, we will get this one. So this one is very similar to the observation. So this is from one model. And here, I think a lot of experts know NEMO. So we did the, we based on NEMO to some model experiment. These four different kind of resolutions. It's all got two resolutions, two degree by two degree, one degree by one degree, and a quarter by quarter. So this is the NEMO model. The sub-surface here is a code. So it's independent of model resolution. So what's the contribution of the surface-will? So this is the contribution of the surface-will. By concept, the way, totally. So this systematic error nearly disappeared. So this is, we show how important the surface-will is, the development of ocean models. The next is why we should concept the title, precisely in the coupled model. So first, I will show you here. So this is China's, this is Qingdao's here, and we show this area. So this, in this area, it's very special. Because the summertime, the summertime, this from satellite, this is the wind, and the color is the surface temperature. And from the theory of acumen transport, if the wind is this direction, the acumen transport to this part. So in this part, it should be downwelling. So downwelling, so the surface temperature should be higher, but from observation, it's quite low. It's indicative of upwelling. As we know, upwelling is very important for fishery. And we do some numerical experiment. If we have a coupled model, we can clearly simulate this upwelling process. But if we close the tide, without the tide, so the upwelling disappear, we only have the downwelling. So this means in the coastal area, the tidal mixing is very important for the generation of upwelling. So based on these understandings, we developed the global surface wave tied to the secretion coupled model. The resolution is 0.1 degree by 0.1 degrees. So this is for the global ocean. And this system already into operational. The third part is surface wave in typhoon model. Okay. Oh, the typhoon model, the typhoon model, this is error. So for the weak typhoon, always the forecast is too strong. For the strong typhoon, it's always too weak. So we consider the wheel process, wheel spring and wheel mixing by these two processes. So this is heat flux, it's without a wheel and with wheel. So the latent heat flux and sensible heat flux totally different. By coincide, the surface wheel in the coupled model. So the black line is from data and this is from a coupled model. The coupled model is very close to the data. And then you may say, oh, this is a handcast, you may tune the model, in fact it's not. So this is from ECMWF. As we know, Europe center play a leading role for the typhoon forecast. And they tried for this, for the same typhoon, they tried for many kind of numerical experiments. And the difference is quite large. So the force is surface wheel in climbing model. Climbing model at the beginning I introduced it. So this is bias. And this is contribution of surface wheel. By coincide, surface wheel. So this can be, this is cold, this is warm. This is cold, this is warm. So this is what we expected. And because we changed the sea surface temperature and the precipitation, which also were changed, much improved. And the GFDR use our scheme to test their type of model. So this is from observation. It's different kind of schemes. And this is our scheme. They're concluding as our scheme is much closer to the observation. So this is the fourth part. The fifth is high efficient parallel scheme and the operational ocean forecast system. As we know, as the computer resources get more and more powerful, the resolution for the ocean and climbing model is higher and higher. But it's how to use the computer resources we need as high efficient parallel schemes. So this is, we developed different kind of schemes. And finally, we developed for the global ocean, for surface-to-were model is one kilometer by one kilometer. And the computer resources use the whole type of light. It's more than 10 million CPU cores. And then we developed the operational forecast system. And this forecast system is shared by the global community and published by the iOS Westpack. So everybody can download and this is from cell phone. And the whole about the verification. So we compared our operational forecast system with different kind of from UK, from France, from USA, from Canada. And our is the error. The forecasting error is the smallest. So this is summary. So we demonstrated that small scale surface will play a key role in improving global ocean simulation models, typhoon models, and climbing models. High efficient parallel schemes are crucially important for coupled high resolution models. And we have a lot of applications, including extreme events such as Fukushima nuclear leakage, certain rescue for the offshore plankton in last year, oil spill. And also for Arctic bloom, also called the green tide. So that's all. Thank you so much. [?]. [?]. Thank you very much for a very nice presentation. Actually, for me, as well, coming from dynamics, but from another domain, it's very nice to see the similarities and challenges in computation. You very well described. You described this across the domains of ocean, typhoon, and climate modeling. Motivated by this presentation, I would like to ask you what it is that you consider the state of the art in numerical ocean modeling. So for the state of our art, we have considered as a coupling different kind of processes. We coupled the surface with tide and circulation, coupled together. So previously, as I introduced, so this is for different research groups. And for the operational forecast, it's totally different. Over well with well, tide is tide, and the circulation is circulation. And we proposed some ideas to couple this through process. This couple is mainly based on the two processes. One is the computer resources. As we know, if we coupled so many processes, the computation cost will be much higher. And we need more computer resources. No, the computer is more and more powerful. And the other is clearly understand the physical process. As we know, when we talk about coupling, maybe some type of not real coupling. For example, for the well. The well breaking can generate turbulence. The well breaking, if we consider this well breaking process, it also can cover the well and the circulation. But this process doesn't work. It's because the well breaking can only affect very shallow area. It can only affect in the order of well amplitude. Well amplitude is like 1 meter, 2 meters. But what we proposed is the non-breaking process. This process can affect much deeper. It's in the order of a well length. And when a well length is 100 meters, and 100 meters is very important for the uphorsing. So this is the difference. Yes. So this is a problem across scales, I guess. I would say based on your background, I have realized you have really more than 30 years of experience in the particular domain. And I wonder if you now think back in your work from the early stages until now, how would you describe the history of progress, let's say, in this domain, but also the developments in your works? How do you see this placed across these 30 years? OK. So this has two parts. One is for the challenge we faced. The second is how to solve this challenge. For the challenge we faced, for the ocean models, as I introduced. The simulated, the sea surface temperature, is over heating. Over heating means, as we know, in summertime, the ocean gets heat from the atmosphere. So the surface is quite warm, if not have strong mixing. And this heat can be accumulated into the surface. So the surface, the surface temperature, is over heating. And the subsurface, because the leak of heat transport in the subsurface is too cold. If we have more mixing, and this problem can be resolved. So this is the challenge we faced. Although it shows a different part as the sea surface temperature, subsurface temperature, and mixed layer, in fact, the key point is when it's mixing. So for the history of the ocean circulation models, as we know, the first ocean circulation model is developed in GFDR, United States. In 1967, it's more than half a century ago. So from then on, we have two parts to support the ocean model development. One is high resolution, more and more computer resources. The high resolution, at the beginning, the resolution is 5 degrees, but now, for the global ocean, normally it's 0.1 by 0.1. It's much better at the beginning. So this is one direction. The other is for the physical process. So as we know, in the ocean model, the mixing term is the most important. While the mixing term, the first one, at the beginning, we use the mixing term as a constant. This is very simple. Take it as a constant. And then, in 1982, developed the Malaya model, turbulent global model. So this model is introduced as a scheme. So everybody uses this scheme. And then in 1994, very large, also from Inka, United States, they proposed the KPP scheme. And all this, you can notice, so this progress is very slowly. Not so many publications, because this is really very hard problem. So recently, we consigned as a non-breaking wheel process. And this process, in fact, we do some numerical experiment. If we can choose different kind of turbulent clover schemes, and all of them cannot simulate the operation correctly. But if we close, we don't use these turbulent clover schemes. We only use surface wheel, who was the response of the ocean's village model. So we get a very nice result. That means the surface wheel is played a dominant role. So unfortunately, previously in the model, we did not consider this process. But if we consider this process, I think it's our model ability will be much, much improved. And maybe on this, so it is obvious. And this actually relates to my domain, which is fairly different from yours, but where as well, the solution of the governing equations for dynamics will play a role for us in structural dynamics, for example. Or in monitoring, we have to make sure we're able to fuse these models with data. So the speed in which we can solve them makes a difference. And I noticed in your recent paper in 2019, you mentioned as well that numerical and parametrization improvements will continue to define the state of the art in numerical ocean modeling. In concert with integration and co-analysis of observations. In this respect, what is your view regarding the developments in the near future along this line? So what would be, let's say, the future of numerical ocean computation? So for the development of ocean models, I think we have three parts we could contribute. The first is physical process. For the model, the physics is the first important thing. And as we shown by introducing the coupling process, the simulation result is totally different. So this is one process. The other, the second is for the high resolution. As we know, if we have a very close resolution, some process like the eddy, a methodical eddy, could not be recognized by the model. So we need high resolution models. And of course, this high resolution model need to be supported by the very strong, very powerful computer systems. So this is the second. And the third is some new technology, such as data simulation and artificial intelligence. So data simulation, we can merge the observation with the model and get very good initial conditions. And for the artificial intelligence, previously, we used our experience. Maybe we tried to find the difference from the model and the observation. But now, with the new technology, we can identify the difference between model and the observation. Yeah, very quickly. This is a very good point. Actually, let me build on this. You mentioned that modeling can come with errors. This is true across fields. You even show in your presentation, you have discussed biases, the erroneous temperature model in the North Atlantic, often leading to propagations of errors in the resulting models. My question to you is, what do you deem as trends in model improvement? You already mentioned artificial intelligence, the potential to use observations and data. How can we decrease, or how can we increase our confidence in what the model predictions are? OK. So the confidence is we should reduce the uncertainty. Yes, exactly. Well, reduce the uncertainty, first, we need some tools, such as Monte Carlo, such as the Bayesian analysis to quantify the uncertainty. More important, it's how to reduce the uncertainty. So yesterday, we have a very nice presentation for the client model. For the client model, if we run the model, the model is fixed, if we run on different platforms, different computers, the result is different. And even on the same computer, if very small perturbations are the initial conditions, such as maybe 1,000 degrees, like a temperature, very, very small disturbance, and the climate system gets the result is totally different. And even everything is fixed, we don't have any disturbance at the initial conditions. If we choose different CPU processors, sometimes if we have more computer resources, we would like to have more CPU cores. And the difference with more and less CPU resources, the difference is quite obvious. So one of the solutions is we make an ensemble. We ensemble means we run the same model for many times and take an average and identify some of the autoscopes and could not be coincided and take the average as the model result. So this is, of course, it is a very high cost. This is a good point. We should not be presenting maybe these results if they come with uncertainties as deterministic, but rather in their full probabilistic, let's say, form. Since you discussed verification, which is basically how do we verify a model against maybe other available models, I wanted to ask if you could elaborate also on the topic of validation, which would be how can we actually compare the predictions of our models against observations and data. And where does this data usually come from in your case, let's say? To give you an example, in my case, data comes directly from sensors that we would attach on systems and where we monitor the dynamics. In your case, we're talking about a completely different scale, obviously. So where do the observations come from? OK. So the data for the ocean, when it's from satellite, the satellite can have the global observation of the, mainly, sea surface, the sea surface temperature, the wind level, these processes. And the other is from the more resistant. We have some boos in the ocean. This is high cost. And some data is from the ship course. And from ship course, we can also get some data to validate our model. So the validate model is very important. Because first I make a joke. I'm a modeler. So I decided that some modelers always try to be honest. But our forecast is always, if not always, often it's not correct. Or uncertain. Yeah, yeah, yeah. So model is model. It's reality. Reality is totally different. So we need to compare the data from different sensors with the model results and try to identify the bias. With this bias, then we try to improve our physical understanding how to improve the model. So this is the purpose of validation in the ocean model. In fact, for ocean model, it's not only the validation. But another process for data simulation is also very important. Data simulation is combined to observe the data with the model as a control. So with some technique and the inital condition, it's much closer to the real. And then we base on this and then we do forecasts for the future. Yes, in fact, we do something similar. We call it model updating. And this is the way in which we can fuse. Yeah, yeah, quite similar. And I agree this gives us models that are closer to reality, which is important for really modeling systems that are true to life. Now, maybe we can touch upon one of the topics that have to do with the applications of these models. And what I have seen in your work is that you have been using these also to tackle extreme events. So you have work that has dealt with nuclear radiation spread following the Fukushima nuclear power catastrophe in 2011 and also the rescue of lives at sea after capsizing of boats in Phuket and so forth. Maybe you can elaborate a bit on these applications that have to do with extreme events and recovery, essentially. OK, so very good. So could I use a protector? Because the time is not enough, so here I have several sliders. So this, as we know, in March of 2011, the Fukushima had a nuclear leakage. And after that, the government responses very quickly. For example, in China, it happened in 11th. And on 13th, I'm on the way from Beijing back to Qingdao. And on the airport, I noticed, oh, this is forecast for three days. The three-day forecast affected this area. So the next question is, as we know, in these areas, the wind is from west to east. It's for three days. So how about one month? How about one year? So after I go back, I push our group to do this forecast, these three days, five days, 10 days. And this is for one month. After one month, the whole North Hamlet Ferry is affected by this accident. Of course, the concentration is very, very low. And we compare this data. After 16 months, this data is from Japan. From surface to 800 meters along 165 degrees, this is from south to north. And this is observation. And this is our model result. So if you compare, this is quite similar. That means our model, this is based on a couple model. So this model has very good performance for the forecast of this area. And this is for the ocean. In the end of the year, the process is very, very quickly. Within one month, it affected the whole North Hamlet Ferry. But this is in the ocean. In the ocean, it's one year. It's two years. And this part is 2015. And this yellow one is for 2020. It's very close to this one. And for here, even we can have 20, 37. So many, many years later. So this is propagated to this part and then come back. So very, very slowly, this process. So this is animation for the surface concentration. And for the certain rescue, as we know, last year, this is a Pukit Island. Pukit Island. These two ships capsized in this area. And we have cooperation with Thailand. And after the accident, the Thailand government sent a message to us to ask, so how to, could you identify the certain rescue area? Because this is the location. So we should start here, start here, or start here. So we may forecast, one day forecast, two day forecast, three forecast. And then finally, the result is confirmed by the real observation. All the people, all the missing people, is in this area. So this is one example. So I find two very important points here. The fact that the application of these methods really assists practically governments in recovery. And that one has to operate with different means to do short-term forecasting, something very fast, and then long-term on how this evolves in a greater scale. On the other hand, there exist also events that are not necessarily, are not extreme events. But they are harmful, for example, for the balance of our ecosystems. You have relevant work on the algae bloom, for example, which is there. I found, is there a rapid increase or accumulation in the population of algae in water systems? How can ocean models be applied in such a situation? OK. It's for the management, like. So we can still use the, could you transfer to the, so this one, or this one, we can escape. So this is Qingdao. Qingdao is our city as a located in this area. And in 2008, we will have Olympic rectum in Qingdao area. And this is just before the Olympic game. So this is called algae bloom. It's very serious. So what we need to know, where this algae bloom come from. So this is for 2008. 2008, as Qingdao is here. So all this area will accumulate. This is under the effect of the surface of the wind and the sea surface current. So it's accumulated to the Qingdao. And two years later, this is 2010. 2010, even when in this area is very close to Qingdao, it's only 30 kilometers. So the local government is very nervous. They want to know, oh, where this year the algae bloom will also seriously affect the Qingdao or not. So the government asked me to provide some prediction. And for that year, the prediction is, yes, it's coming in this area and then turn direction. So there are no much effect on the Qingdao city. So this is our conclusion. So they said, oh, are you serious? This is because we need many people, many boats to deal with this emergency. So are you sure? Because it is very short term prediction, only several days. So I told them, not so accurate, but the pattern is correct. We are sure that the majority of the algae blooms were not affected in the Qingdao. So at last, we confirmed this result. It's very interesting. Actually, I promised that I would open the questions for the audience. And we have less than five minutes left. So maybe if there are questions or one question from the audience that we could take. Yes? Maybe we can use a microphone if possible. Or maybe. Yeah, great, if you have one. Yeah, you sort of mentioned data simulation and passing. But obviously, in order to do a good forecast, you need to have a very good initial condition. And it's absolutely crucial. And I was wondering what technique you use for the ocean data simulation and how you make sure that basically the observations arrive timely in the ocean. Because the observations on the ocean are not like in the atmosphere, basically. They are very sparse. They arrive maybe not very timely. Can you say something about that? Yeah. For the ocean forecast, the initial condition is very important. So we need to do some data simulation. What we use is a common filter, ensemble common filter. So as we know, this ensemble means we need some members. As a present, we select 10. If we select 20 members, that means the cost will be 20 times higher. So as a present, we use 10 members, use ensemble filter as data simulation scheme. I think we're now out of time. So I give you the floor again. Yeah, since we have another session to follow right after this, if you have more questions, you can try to communicate with Fali or Alaini afterwards. So let's thank our interviewee and interviewer again. Thank you so much.
 Good morning, everyone.  My name is Fang Liqiao from the first state of the oceanography, the Ministry of Natural  Resources of the Republic of China, located in Qingdao.  My topic will be new ocean, typhoon and cleft models with surface wave.  That means we will focus on the coupling process.  So this is outline.  The first is a background and a challenge we faced.  So why?  First why we focus on the ocean?  As we know, ocean covers more than 70% of the global surface and more than 97% water  resources in the global and more than 90% from the greenhouse gases.  And for the oxygen, it's between 50% to 70% and in fact the ocean controls the climate.  So that's why we focus on the ocean.  And we know recently we are considering the UN decades of ocean science for sustainable  development for 10 years.  And for these decades, we're focused on six, mainly six parts, the clean ocean, predicted  ocean, health ocean, sea ocean, productive ocean, transparent ocean.  So today I will share some ideas with you on the predicted ocean.  So for the predicted ocean, the time scale is from days to centuries.  The first is days forecast.  For days forecast, which can be used for ships, safety, for the typhoon and the hurricane  and storm surge, and also for tourism.  We enjoy the ocean along the coastline.  And this is for seasonal prediction.  And when the seasonal prediction is for the disaster prevention and the reduction.  And also very important is for the flood and drought, which is key important for agriculture.  Mostly we focus on the flood and drought in the land.  But in fact, it's controlled by the ocean.  And the next is for climate change.  As we know, the ocean controls the climate.  What affect, I think, play a key role in the climate system.  And also, this climate change will also affect the fishing.  So this is the background.  And then we analyze the challenge we face for ocean generation models.  This challenge lasts for half a century.  The simulated from the model, the sea surface temperature is overhead in summer and the  sub-surface is over cooling.  Wow, the mixed layer depth is too shallow.  Here we show.  So this is from observed mixed layer depth.  As we know, mixed layer depth is so important for fishery, for climate change.  And this is for model, so much difference.  And for the typhoon, this challenge lasts for several decades already.  So this is the trick.  This for the last 20 years.  This is forecast error.  So the typhoon trick is reducing.  That means we have better and better ability to forecast the typhoon or hurricane, the  trick.  But for the intensity, no ending progress during the past several decades.  So this is our challenge.  And for the climate models, it's a tropical bias.  The tropical bias, for example, is here, it's too cold, it's too warm.  You may say that maybe this is not a good model.  In fact, this is a leading one.  This model is from ANCA.  We run the model for 300 years and last 50 years take average.  So this is the simulation error.  So even in this area, the error can be much larger than four degrees.  So this is our climate system.  So how to solve these problems or challenge?  So as we know, the ocean model is based on governing equation.  So we need to go back to the governing equation.  And this term is a mixing or called turbulence.  So this is with high uncertainty.  And we guess this should be related with this term.  And the other is for the AC flux.  AC flux is also kind of a turbulence process.  And we probably don't know the role of we're breaking AC heat flux and the role of wave  in AC momentum flux.  For example, previously we think if the wind in this direction and the wind stress always  the same direction, we always use this term in the model.  But from an observation, it's totally different.  Sometimes in low wind conditions, the wind stress can be opposite to the wind direction.  So how to consider this process is a challenge.  So next, what is turbulence?  The turbulence is the most important I saw the problem with classical physics.  So if we go back to Heisenberg, Heisenberg is Nobel Prize winner.  He said, why meet God?  I'm going to ask him two questions.  So why relativity and why turbulence?  So I really believe he will have an answer for the first.  So this is how difficult of this problem we faced.  For the mixed layer depth and the sea surface temperature over heating always tell us the  mixing the turbulence in the opposite not enough.  So where does this energy come from?  As we know, this is the surface wave.  When the wind forces the ocean, more than 90% of energy is into the wind, into the wave.  But the wave effect is not coincided in the ocean model because it's a previously we think  that the wave is so small scale.  The wave length is only 100 meters.  But for the circulation, we have tens of thousands of kilometers.  So so much difference.  So we always separate this is where this is where this ocean circulation.  For the research community for the model is totally different.  But we noticed we proposed the idea this kind of breaking this kind of non-breaking will  can generate turbulence.  So how to get this one is a long story.  So today I just show the result.  So we express this is a way we do the mixing and we try to understand the physics.  This means that this kind of mixing is related with the amplitude and the stock drift.  So this is a new proposed idea.  And because previously we never show, we never show this kind of non-breaking will can generate  turbulence.  So when we propose this idea, a lot of debate.  So could you show us why?  Why, you know, the way we say you're rotational, you should not generate turbulence.  Could you show me?  Could you show me?  How can generate turbulence?  So this is the laboratory experiment we published on JPU in 2010.  So we as a bottom, we have a refer editor and we monitor the temperature evolution.  So this is at the beginning.  At the beginning, the bottom, the temperature is cold and the surface is warm.  So it's quite similar to the ocean.  And we keep it to nothing, it will take about one day or well mixed.  This is a molecular mixing.  And then we produce the non-breaking will.  With this will, the mixing can be finished within half hour.  So it's clearly demonstrated the will can generate very strong turbulence.  Then we needed to in-circuit observation to support this idea.  So this is one publication from Sutherland on ocean science.  So the red one is from our game and the black one is from observation.  So you can see these two lines fit quite well.  That means the will in the mixing in the upper ocean play a dominant role.  Unfortunately, we did not consider this process in the ocean model.  So this is the first part.  The second part is a surface-will tied circulation coupled model.  So we call this a state of art models.  First, we check how important the surface will.  At the beginning, we introduced, so this is mixed-layer depth from observation.  This mixed-layer depth from model, so much different.  The color bar is the same.  By concept, we will get this one.  So this one is very similar to the observation.  So this is from one model.  And here, I think a lot of experts know NEMO.  So we did the, we based on NEMO to some model experiment.  These four different kind of resolutions.  It's all got two resolutions, two degree by two degree, one degree by one degree,  and a quarter by quarter.  So this is the NEMO model.  The sub-surface here is a code.  So it's independent of model resolution.  So what's the contribution of the surface-will?  So this is the contribution of the surface-will.  By concept, the way, totally.  So this systematic error nearly disappeared.  So this is, we show how important the surface-will is,  the development of ocean models.  The next is why we should concept the title,  precisely in the coupled model.  So first, I will show you here.  So this is China's, this is Qingdao's here,  and we show this area.  So this, in this area, it's very special.  Because the summertime, the summertime,  this from satellite, this is the wind,  and the color is the surface temperature.  And from the theory of acumen transport,  if the wind is this direction,  the acumen transport to this part.  So in this part, it should be downwelling.  So downwelling, so the surface temperature  should be higher, but from observation,  it's quite low.  It's indicative of upwelling.  As we know, upwelling is very important for fishery.  And we do some numerical experiment.  If we have a coupled model,  we can clearly simulate this upwelling process.  But if we close the tide, without the tide,  so the upwelling disappear, we only have the downwelling.  So this means in the coastal area,  the tidal mixing is very important  for the generation of upwelling.  So based on these understandings,  we developed the global surface wave  tied to the secretion coupled model.  The resolution is 0.1 degree by 0.1 degrees.  So this is for the global ocean.  And this system already into operational.  The third part is surface wave in typhoon model.  Okay.  Oh, the typhoon model,  the typhoon model, this is error.  So for the weak typhoon, always the forecast is too strong.  For the strong typhoon, it's always too weak.  So we consider the wheel process,  wheel spring and wheel mixing by these two processes.  So this is heat flux, it's without a wheel and with wheel.  So the latent heat flux and sensible heat flux  totally different.  By coincide, the surface wheel in the coupled model.  So the black line is from data  and this is from a coupled model.  The coupled model is very close to the data.  And then you may say, oh, this is a handcast,  you may tune the model, in fact it's not.  So this is from ECMWF.  As we know, Europe center play a leading role  for the typhoon forecast.  And they tried for this, for the same typhoon,  they tried for many kind of numerical experiments.  And the difference is quite large.  So the force is surface wheel in climbing model.  Climbing model at the beginning I introduced it.  So this is bias.  And this is contribution of surface wheel.  By coincide, surface wheel.  So this can be, this is cold, this is warm.  This is cold, this is warm.  So this is what we expected.  And because we changed the sea surface temperature  and the precipitation, which also were changed,  much improved.  And the GFDR use our scheme to test their type of model.  So this is from observation.  It's different kind of schemes.  And this is our scheme.  They're concluding as our scheme is much closer  to the observation.  So this is the fourth part.  The fifth is high efficient parallel scheme  and the operational ocean forecast system.  As we know, as the computer resources get more and more  powerful, the resolution for the ocean and climbing model  is higher and higher.  But it's how to use the computer resources we need  as high efficient parallel schemes.  So this is, we developed different kind of schemes.  And finally, we developed for the global ocean,  for surface-to-were model is one kilometer by one kilometer.  And the computer resources use the whole type of light.  It's more than 10 million CPU cores.  And then we developed the operational forecast system.  And this forecast system is shared by the global community  and published by the iOS Westpack.  So everybody can download and this is from cell phone.  And the whole about the verification.  So we compared our operational forecast system  with different kind of from UK, from France, from USA,  from Canada.  And our is the error.  The forecasting error is the smallest.  So this is summary.  So we demonstrated that small scale surface  will play a key role in improving global ocean simulation  models, typhoon models, and climbing models.  High efficient parallel schemes are  crucially important for coupled high resolution models.  And we have a lot of applications,  including extreme events such as Fukushima nuclear leakage,  certain rescue for the offshore plankton in last year,  oil spill.  And also for Arctic bloom, also called the green tide.  So that's all.  Thank you so much.  [?].  [?].  Thank you very much for a very nice presentation.  Actually, for me, as well, coming from dynamics,  but from another domain, it's very nice  to see the similarities and challenges in computation.  You very well described.  You described this across the domains of ocean, typhoon,  and climate modeling.  Motivated by this presentation, I would like to ask you  what it is that you consider the state of the art  in numerical ocean modeling.  So for the state of our art, we have  considered as a coupling different kind of processes.  We coupled the surface with tide and circulation,  coupled together.  So previously, as I introduced, so this  is for different research groups.  And for the operational forecast,  it's totally different.  Over well with well, tide is tide,  and the circulation is circulation.  And we proposed some ideas to couple this through process.  This couple is mainly based on the two processes.  One is the computer resources.  As we know, if we coupled so many processes,  the computation cost will be much higher.  And we need more computer resources.  No, the computer is more and more powerful.  And the other is clearly understand the physical process.  As we know, when we talk about coupling,  maybe some type of not real coupling.  For example, for the well.  The well breaking can generate turbulence.  The well breaking, if we consider this well breaking  process, it also can cover the well and the circulation.  But this process doesn't work.  It's because the well breaking can only  affect very shallow area.  It can only affect in the order of well amplitude.  Well amplitude is like 1 meter, 2 meters.  But what we proposed is the non-breaking process.  This process can affect much deeper.  It's in the order of a well length.  And when a well length is 100 meters,  and 100 meters is very important for the uphorsing.  So this is the difference.  Yes.  So this is a problem across scales, I guess.  I would say based on your background,  I have realized you have really more than 30 years of experience  in the particular domain.  And I wonder if you now think back  in your work from the early stages until now,  how would you describe the history of progress,  let's say, in this domain, but also the developments  in your works?  How do you see this placed across these 30 years?  OK.  So this has two parts.  One is for the challenge we faced.  The second is how to solve this challenge.  For the challenge we faced, for the ocean models,  as I introduced.  The simulated, the sea surface temperature,  is over heating.  Over heating means, as we know, in summertime,  the ocean gets heat from the atmosphere.  So the surface is quite warm, if not have strong mixing.  And this heat can be accumulated into the surface.  So the surface, the surface temperature, is over heating.  And the subsurface, because the leak of heat transport  in the subsurface is too cold.  If we have more mixing, and this problem can be resolved.  So this is the challenge we faced.  Although it shows a different part  as the sea surface temperature, subsurface temperature,  and mixed layer, in fact, the key point is when it's mixing.  So for the history of the ocean circulation models,  as we know, the first ocean circulation model is developed  in GFDR, United States.  In 1967, it's more than half a century ago.  So from then on, we have two parts  to support the ocean model development.  One is high resolution, more and more computer resources.  The high resolution, at the beginning,  the resolution is 5 degrees, but now, for the global ocean,  normally it's 0.1 by 0.1.  It's much better at the beginning.  So this is one direction.  The other is for the physical process.  So as we know, in the ocean model,  the mixing term is the most important.  While the mixing term, the first one, at the beginning,  we use the mixing term as a constant.  This is very simple.  Take it as a constant.  And then, in 1982, developed the Malaya model,  turbulent global model.  So this model is introduced as a scheme.  So everybody uses this scheme.  And then in 1994, very large, also from Inka, United States,  they proposed the KPP scheme.  And all this, you can notice, so this progress is very slowly.  Not so many publications, because this is really  very hard problem.  So recently, we consigned as a non-breaking wheel process.  And this process, in fact, we do some numerical experiment.  If we can choose different kind of turbulent clover schemes,  and all of them cannot simulate the operation correctly.  But if we close, we don't use these turbulent clover schemes.  We only use surface wheel, who was the response  of the ocean's village model.  So we get a very nice result.  That means the surface wheel is played a dominant role.  So unfortunately, previously in the model,  we did not consider this process.  But if we consider this process, I think  it's our model ability will be much, much improved.  And maybe on this, so it is obvious.  And this actually relates to my domain,  which is fairly different from yours,  but where as well, the solution of the governing equations  for dynamics will play a role for us in structural dynamics,  for example.  Or in monitoring, we have to make sure  we're able to fuse these models with data.  So the speed in which we can solve them makes a difference.  And I noticed in your recent paper in 2019,  you mentioned as well that numerical and parametrization  improvements will continue to define the state of the art  in numerical ocean modeling.  In concert with integration and co-analysis of observations.  In this respect, what is your view  regarding the developments in the near future along this line?  So what would be, let's say, the future of numerical ocean  computation?  So for the development of ocean models,  I think we have three parts we could contribute.  The first is physical process.  For the model, the physics is the first important thing.  And as we shown by introducing the coupling process,  the simulation result is totally different.  So this is one process.  The other, the second is for the high resolution.  As we know, if we have a very close resolution,  some process like the eddy, a methodical eddy,  could not be recognized by the model.  So we need high resolution models.  And of course, this high resolution model  need to be supported by the very strong, very powerful  computer systems.  So this is the second.  And the third is some new technology,  such as data simulation and artificial intelligence.  So data simulation, we can merge the observation  with the model and get very good initial conditions.  And for the artificial intelligence,  previously, we used our experience.  Maybe we tried to find the difference from the model  and the observation.  But now, with the new technology,  we can identify the difference between model and the observation.  Yeah, very quickly.  This is a very good point.  Actually, let me build on this.  You mentioned that modeling can come with errors.  This is true across fields.  You even show in your presentation,  you have discussed biases, the erroneous temperature model  in the North Atlantic, often leading  to propagations of errors in the resulting models.  My question to you is, what do you  deem as trends in model improvement?  You already mentioned artificial intelligence,  the potential to use observations and data.  How can we decrease, or how can we  increase our confidence in what the model predictions are?  OK.  So the confidence is we should reduce the uncertainty.  Yes, exactly.  Well, reduce the uncertainty, first,  we need some tools, such as Monte Carlo, such as the Bayesian  analysis to quantify the uncertainty.  More important, it's how to reduce the uncertainty.  So yesterday, we have a very nice presentation  for the client model.  For the client model, if we run the model,  the model is fixed, if we run on different platforms,  different computers, the result is different.  And even on the same computer, if very small perturbations  are the initial conditions, such as maybe 1,000 degrees,  like a temperature, very, very small disturbance,  and the climate system gets the result  is totally different.  And even everything is fixed, we don't  have any disturbance at the initial conditions.  If we choose different CPU processors,  sometimes if we have more computer resources,  we would like to have more CPU cores.  And the difference with more and less CPU resources,  the difference is quite obvious.  So one of the solutions is we make an ensemble.  We ensemble means we run the same model for many times  and take an average and identify some of the autoscopes  and could not be coincided and take the average as the model  result.  So this is, of course, it is a very high cost.  This is a good point.  We should not be presenting maybe these results  if they come with uncertainties as deterministic,  but rather in their full probabilistic, let's say, form.  Since you discussed verification, which is basically  how do we verify a model against maybe other available models,  I wanted to ask if you could elaborate also  on the topic of validation, which would be how can we  actually compare the predictions of our models  against observations and data.  And where does this data usually come from in your case,  let's say?  To give you an example, in my case,  data comes directly from sensors that we would attach  on systems and where we monitor the dynamics.  In your case, we're talking about a completely different scale,  obviously.  So where do the observations come from?  OK.  So the data for the ocean, when it's from satellite,  the satellite can have the global observation of the,  mainly, sea surface, the sea surface temperature, the wind  level, these processes.  And the other is from the more resistant.  We have some boos in the ocean.  This is high cost.  And some data is from the ship course.  And from ship course, we can also get some data  to validate our model.  So the validate model is very important.  Because first I make a joke.  I'm a modeler.  So I decided that some modelers always  try to be honest.  But our forecast is always, if not always,  often it's not correct.  Or uncertain.  Yeah, yeah, yeah.  So model is model.  It's reality.  Reality is totally different.  So we need to compare the data from different sensors  with the model results and try to identify the bias.  With this bias, then we try to improve our physical understanding  how to improve the model.  So this is the purpose of validation in the ocean model.  In fact, for ocean model, it's not only the validation.  But another process for data simulation  is also very important.  Data simulation is combined to observe the data with the model  as a control.  So with some technique and the inital condition,  it's much closer to the real.  And then we base on this and then we do forecasts for the future.  Yes, in fact, we do something similar.  We call it model updating.  And this is the way in which we can fuse.  Yeah, yeah, quite similar.  And I agree this gives us models that are closer to reality,  which is important for really modeling systems that  are true to life.  Now, maybe we can touch upon one of the topics  that have to do with the applications of these models.  And what I have seen in your work is  that you have been using these also to tackle extreme events.  So you have work that has dealt with nuclear radiation  spread following the Fukushima nuclear power catastrophe  in 2011 and also the rescue of lives  at sea after capsizing of boats in Phuket and so forth.  Maybe you can elaborate a bit on these applications that  have to do with extreme events and recovery, essentially.  OK, so very good.  So could I use a protector?  Because the time is not enough, so here I  have several sliders.  So this, as we know, in March of 2011,  the Fukushima had a nuclear leakage.  And after that, the government responses very quickly.  For example, in China, it happened in 11th.  And on 13th, I'm on the way from Beijing back to Qingdao.  And on the airport, I noticed, oh,  this is forecast for three days.  The three-day forecast affected this area.  So the next question is, as we know,  in these areas, the wind is from west to east.  It's for three days.  So how about one month?  How about one year?  So after I go back, I push our group  to do this forecast, these three days, five days, 10 days.  And this is for one month.  After one month, the whole North Hamlet Ferry  is affected by this accident.  Of course, the concentration is very, very low.  And we compare this data.  After 16 months, this data is from Japan.  From surface to 800 meters along 165 degrees,  this is from south to north.  And this is observation.  And this is our model result.  So if you compare, this is quite similar.  That means our model, this is based on a couple model.  So this model has very good performance  for the forecast of this area.  And this is for the ocean.  In the end of the year, the process is very, very quickly.  Within one month, it affected the whole North Hamlet Ferry.  But this is in the ocean.  In the ocean, it's one year.  It's two years.  And this part is 2015.  And this yellow one is for 2020.  It's very close to this one.  And for here, even we can have 20, 37.  So many, many years later.  So this is propagated to this part and then come back.  So very, very slowly, this process.  So this is animation for the surface concentration.  And for the certain rescue, as we know, last year,  this is a Pukit Island.  Pukit Island.  These two ships capsized in this area.  And we have cooperation with Thailand.  And after the accident, the Thailand government  sent a message to us to ask, so how to,  could you identify the certain rescue area?  Because this is the location.  So we should start here, start here, or start here.  So we may forecast, one day forecast, two day forecast,  three forecast.  And then finally, the result is confirmed  by the real observation.  All the people, all the missing people, is in this area.  So this is one example.  So I find two very important points here.  The fact that the application of these methods  really assists practically governments in recovery.  And that one has to operate with different means  to do short-term forecasting, something very fast,  and then long-term on how this evolves in a greater scale.  On the other hand, there exist also  events that are not necessarily, are not extreme events.  But they are harmful, for example,  for the balance of our ecosystems.  You have relevant work on the algae bloom, for example,  which is there.  I found, is there a rapid increase or accumulation  in the population of algae in water systems?  How can ocean models be applied in such a situation?  OK.  It's for the management, like.  So we can still use the, could you transfer to the,  so this one, or this one, we can escape.  So this is Qingdao.  Qingdao is our city as a located in this area.  And in 2008, we will have Olympic rectum in Qingdao area.  And this is just before the Olympic game.  So this is called algae bloom.  It's very serious.  So what we need to know, where this algae bloom come from.  So this is for 2008.  2008, as Qingdao is here.  So all this area will accumulate.  This is under the effect of the surface of the wind  and the sea surface current.  So it's accumulated to the Qingdao.  And two years later, this is 2010.  2010, even when in this area is very close to Qingdao,  it's only 30 kilometers.  So the local government is very nervous.  They want to know, oh, where this year the algae bloom will  also seriously affect the Qingdao or not.  So the government asked me to provide some prediction.  And for that year, the prediction is, yes,  it's coming in this area and then turn direction.  So there are no much effect on the Qingdao city.  So this is our conclusion.  So they said, oh, are you serious?  This is because we need many people, many boats  to deal with this emergency.  So are you sure?  Because it is very short term prediction, only several days.  So I told them, not so accurate, but the pattern is correct.  We are sure that the majority of the algae blooms were not  affected in the Qingdao.  So at last, we confirmed this result.  It's very interesting.  Actually, I promised that I would open the questions  for the audience.  And we have less than five minutes left.  So maybe if there are questions or one question  from the audience that we could take.  Yes?  Maybe we can use a microphone if possible.  Or maybe.  Yeah, great, if you have one.  Yeah, you sort of mentioned data simulation and passing.  But obviously, in order to do a good forecast,  you need to have a very good initial condition.  And it's absolutely crucial.  And I was wondering what technique  you use for the ocean data simulation  and how you make sure that basically the observations arrive  timely in the ocean.  Because the observations on the ocean are not  like in the atmosphere, basically.  They are very sparse.  They arrive maybe not very timely.  Can you say something about that?  Yeah.  For the ocean forecast, the initial condition  is very important.  So we need to do some data simulation.  What we use is a common filter, ensemble common filter.  So as we know, this ensemble means  we need some members.  As a present, we select 10.  If we select 20 members, that means the cost  will be 20 times higher.  So as a present, we use 10 members,  use ensemble filter as data simulation scheme.  I think we're now out of time.  So I give you the floor again.  Yeah, since we have another session to follow right  after this, if you have more questions,  you can try to communicate with Fali or Alaini afterwards.  So let's thank our interviewee and interviewer again.  Thank you so much.
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