 So, yeah, thank you, Johanan, and good morning, everybody. I think the simplest way to demonstrate the KDL variability is just to look at the evolution of globally average of surface air temperature globally during the last decades. So, you see on the left just the trend pattern during, or for 1980 to 2014, and you see, of course, that the yellow color and the orange color is dominating. So, there is, of course, global warming during that time, but you see also a lot of regional variation, okay? And these regional variations are reminiscent of some of the climate modes, the KDL climate modes that we know in the Pacific. You see some indications of the Pacific, the KDL oscillation or variability or in the KDL Pacific oscillation, which are, of course, somehow linked. In the Atlantic, you see some North-South asymmetry, okay, which may be due to the Atlantic multi-decay oscillation or variability. And in the Southern Ocean, you see also some signals which don't really fit the canonical global warming pattern, and this may be due to Southern Ocean centennial variability. You also see readily decadal variability if you look at the time series. So, Northern Hemisphere averaged surface air temperature, which is the red curve, and Southern Hemisphere averaged surface air temperature. Sometimes they go hand in hand, sometimes they tend to diverge. This may also tell us something about the mechanisms of these decadal fluctuations. But to make the long story short, I think the KDL variability is so obvious that we have not really to discuss whether it is the KDL variability and clearly the KDL variability must be a major topic of climate research. Now, I have a few points I would like to discuss, first of all, internal variability, so you can conceptually divide climate variability into internal and external variability. Then I would like to discuss briefly the role of model bias. Then another interesting topic, which also Johanan alluded to, is the decadal modulation of inter-annual variability, and so is the canonical example. And finally, I will say a few words about decadal predictability. So, internal variability, we are dealing here with, the climate system is composed of several subsystems, and these several subsystems climate subcomponents have different intrinsic timescales, and you can basically, if you are concerned with decadal variability, then you see from this diagram that the ocean plays a key role in that, at least as far as internal variability is concerned. And, you know, since there was so much variability, internal variability in the atmosphere, since the atmospheric variability is so energetic, I think it makes sense to study internal variability in a stochastic concept, which means that the atmosphere basically drives the ocean, and then there may be some feedbacks, of course, from the ocean back to the atmosphere. And you can basically define three types of stochastic models. So one on the very left is the local model, where you have no spatial coherence, where the ocean mix there just integrates the heat flux variability. Then in the middle we see that you can refine that model by introducing spatial coherence in the atmosphere. And finally, and this is what I shall focus on, you can include dynamical feedbacks, ocean dynamical feedbacks, and you can basically split them into feedbacks arising from the wind-driven circulation and feedbacks arising from the thermal-highline circulation. So let me just discuss very briefly the effects of the North Atlantic Oscillation on the ocean circulation, both the wind-driven and the thermal-highline circulation. So I don't really have time to also discuss the Pacific, but I'm pretty sure this will be done during other talks. So the leading pattern of atmospheric variability in winter in the Atlantic sector is the North Atlantic Oscillation. Okay, and the North Atlantic Oscillation has a spectrum which is almost white, which means that there is, of course, decadent variability, and according to the stochastic concept, the ocean would highlight the decadent variability. And as I said, you can impact both the wind-driven circulation and the thermal-highline circulation. Here is from our own model, so I would like to apologize. I basically show results from our own model from the climate model, the KCM, but this is not because I believe this is the best model in the world. This is simply because these results are readily available to me, but I think they basically carry over to most of the model. So the North Atlantic Oscillation variability has an impact on the wind-driven circulation, as we see here, expressed by the barotropic stream function, which describes the gyro circulation, and what we see here is lag regressions of the NAO index with the barotropic stream function. And you see there is a signal emerging at lag zero, and then it kind of persists for several years. And this, then, of course, has typical signatures in the sea surface temperature, which could then impact the atmosphere, but I'm not going into this. All I want to say here is that the wind-driven circulation or the response of the gyro circulation is kind of short-lived, which means that it can explain multi-year, maybe decadal variability, but not longer-term variability. So then, if you are interested in multi-decadal variability, then you have to invoke the thermo-highline circulation or the meridional overturning circulation, and this automatically leads us into the Atlantic multi-decadal oscillation or variability. It can be defined by sea surface temperature and the pattern is kind of monopolar with one sign in the whole North Atlantic, but with some focus in the northern part, south of Greenland. Again, if we turn to the KCM, you can basically see that the NAO drives the overturning circulation, so you see a cross-correlation in the lower right between the NAO index and an AMOC index, and you see clearly that the NAO leads the AMOC by several years. If you look at the pattern, which is above, you see clearly it is the NAO and the variability in the overturning circulation, then what you see on the upper right drives surface air temperature changes and these green contours are the explained variants, so you explain quite a lot of variants in the surface air temperature by the variability of the AMOC, and that variability that you get is kind of similar to what you know from observations about the AMO or AMV pattern. Now, this all happens basically, so the NAO affects the heat fluxes over the sub-polar North Atlantic, that drives variability in the mixed layer depths, okay, and the heat flux, which is on the upper right, is basically damping the surface air temperature variability and again, if you look at the cross-correlation, you clearly see that the variability in the mixed layer depths, which is forced by the NAO, basically drives the overturning circulation, and you see also from the cross-correlation that the timescale is multi-decadent if you just look at the maxima and the minima of the cross-correlation function. So second point, very quickly, external variability, so we all know there is variability in the solar input arriving at the Earth, and I would just like to discuss very briefly one paper that we published, which deals with the response of the overturning circulation to a periodic solar forcing. So what we did here was that we used our model and forced it by periodically varying solar constant, and the period here, we did several periods, the period here is 60 years. Okay, we see some wave flats for three quantities, global SAT, northern hemisphere SAT, and the AMOG, and what you expect, of course, is that the global temperature varies exactly with a period of 60 years. The same is true for the northern hemisphere temperature, but the AMOG does not vary with the 60-year period. So we have a rather strong forcing here, plus minus two watts per meter squared. You can see, therefore, I put the dashed line in there, that the AMOG varies with a quasi-centennial period. Okay, so there is obviously not a simple relationship between forcing and response, at least on regional time scales, or in terms of regional spatial scales, or in terms of the ocean circulation. So I can't really go into details here. Here you see, in green, the forcing, and in red, you see the response of the AMOG given by the leading mode, and you clearly see the AMOG is not entrained by the forcing, and so this is the conclusion from that study. So the AMOG is not entrained by the forcing frequency and response with one of its quasi-centennial modes, which is not the leading mode of the control run, okay? And the bottom line is the spatial surface forcing pattern matters, okay? So what is excited here is the third mode, okay? The leading mode is the multi-decadal one, okay? Then we have a multi-centennial one in the control run, and this is the third energetic mode, which is somewhere hidden in the red background noise, but that one is excited by the solar forcing, but the periodically solar forcing. So understanding the response to external forcing in terms of the ocean circulation, I think is important. Now role of model bias, many models, many CMAP models and also our model, Saffa from large salinity and sea surface temperature biases in the North Atlantic, and you see on the upper left the salinity bias in the climate model, in the lower left, you see the salinity bias if the freshwater flux correct over the North Atlantic the salinity, okay? And by definition, salinity bias then basically gone in that region, okay? You see it doesn't have much impact elsewhere. Now on the right, you see how sea surface temperature is affected. If you don't correct, freshwater flux correct the model, you see a huge sea surface temperature bias. Now in the bottom right, you see the freshwater flux corrected. So it doesn't touch upon or we don't correct temperature or heat flux or so, okay? You see the bias is strongly reduced, at least half. All right? So correcting salinity, sea surface salinity only over the North Atlantic has quite some impact. And this impacts the Atlantic marine general overturning circulation. So in red, you see the variability of the AMOC and the flux corrected run. In black, you see the AMOC in the non-flux corrected run. So in the simulation, which has strong salinity biases in the North Atlantic. And this also impacts the AMO or the AMV pattern. On the right, you see the pattern in the flux corrected run. In the left, you see the pattern in the non-flux corrected run. And you clearly see that the link to the AMOC, these are the regressions to the AMOC index, that the relationship to the AMOC is much, much stronger in the freshwater flux corrected run. And this immediately would have also or would impact the predictability of the decadal variability. And so this brings us to the decadal variability or to the decadal predictability. Johanan has shown this already. So this is just a kind of teaser. Potential predictability, which was pioneered by Bohr. So you see most of the decadal predictability in the extra topics. All right. I want to discuss one topic here, which deals with the response, the atmospheric response to extra topical SSTs, okay? So that's a longstanding problem. And there are so many conflicting results. And if we want to understand the decadal predictability, I think we need also to understand how does the atmosphere respond to extra topical SST anomalies. So what we did here is, first of all, we used the high-revolution atmosphere model T213, which is about 50 kilometers globally. Before, yeah, we forced the model by this PDO-like SST anomaly. So the canonical way how these experiments are done is you have an SST climatology and you superimpose a SST anomaly. This is not what we did. We used daily varying background SSTs, observed SSTs actually from 81 to 1990, 981 to 1990 and superimposed this SST anomaly, which is constant during that time, okay? So we have a daily varying background SST. And what we find is, in the top, you see sea level pressure response, 500 hectopascal geopotential height response and zonal velocity response at the same level. We see a strong response, okay? A strong equivalent barotropic response. Eddy mediated, I can't go into this, and highly significant and highly robust. We did tons of sensitivity experiments and the major result was that you need the daily varying background SST. If you don't have the daily varying background SST, you don't get a significant response to this extra tropical SST anomaly, okay? Then we repeated this with the background SST, daily varying SST of 1991 to 2000, and the response is gone, okay? So there is clearly a state dependence of the atmospheric response and this has, we need to understand this in terms of the decadal changes in the background SST. And what I would like to allude to is, if you look at the daily variability of the level of daily variability, it goes down, okay? Both in the Western and in the Eastern. And that's the main reason why we can't reproduce the response that we had during the decade 1981, 1990, during the decade 91, 2000. So we need the daily variability in the background, okay? To obtain a large scale, a significant large scale atmospheric response to extra tropical SST anomalies. Last point, decadal modulation of interannual variability, and so is a nice example. So I've highlighted here the time period since 1980. We see some increase in variability. We had three big El Ninos, okay, 82, 83, 97, 98, and the current one, 2015. And the big question, of course, is this already an expression of that global warming is impact answer, is answer getting stronger, or the just decadal variability, okay? I think nobody can really answer this question, but this is, of course, one of the present questions. So if ENSO would intensify in response to global warming, this would have major economic and societal implication. So this is some scientific questions I put up. I think you probably would pose similar questions, so we still are lacking a good understanding of the mechanisms of internal variability. This, of course, has to do with the shortness of the instrumental record, and hopefully, Palio Records will help us in getting more insight into that. Then what is the climate response to decadal variability in external forcing? Okay, it could be solar, it could be volcanic forcing. Then the relative roads of internally and externally driven decadal variability, model bias. I think model bias, you know, the models, frankly, you know, have so large biases, even the best models that I think we have to put a big question marks behind the results that we obtain from these models, then decadal modulation of internal variability, and so the canonical example, and here the interactions between the tropics and the exotropics come into play, and quantification of decadal predictability potential is pending, and the estimates that we have are highly model dependent, okay, so we haven't really reached the consensus on that. So thanks for your attention.