Video: A NASA Perspective on El Niño

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Steven Pawson is the Chief of the Global Modeling and Assimilation Ofifice at NASA Goddard Space Flight Center

Steven Pawson is the Chief of the Global Modeling and Assimilation Ofifice at NASA Goddard Space Flight Center

In this video, Steven Pawson discussed how NASA uses computer models to build up a complete three-dimensional picture of El Niño in the ocean and atmosphere. Pawson is an atmospheric scientist and the chief of the Global Modeling and Assimilation Office at NASA’s Goddard Space Flight Center in Greenbelt, Maryland.


Well, thank you for the nice introduction. I thought I was going to tell you everything about El Nino today, and when I first started planning this presentation, I was thinking I was going to show satellite observations of clouds in Ethiopia, how the monsoon goes down in India, fires in South America. Flooding everywhere. And when it comes down to it, there’s just so much to say about El Nino.

I actually ended up dropping a lot of those aspects from the presentation. I wanted to try to give just a zoom-in on the Pacific Ocean where El Nino is really happening. And then a look at some of the things that are happening around the world because of that. But really, I want to try to give you a sense of a little bit of how El Nino works, what it really is and also how NASA has so many satellite observations that can contribute to our understanding of the phenomenon. And also how we can use those with numerical models to do predictions of how things are going to evolve in the future.
And in doing this I want to try to give you that NASA and NOA play in some ways similar but actually very different roles. And that NOA is an operational agency, and they have a fleet of operational satellites which they can use and do long-term products. NASA comes a bit more from the space science point of view. We’re doing research types of instruments, so maybe some of them are a little short-lived. But they set up standards and demonstrate that observations can be made of different processes. And that they can be used into the future.

And that I think gives NASA an incentive to continue the time series of observations or it gives us a means of convincing NOAA to try to take on that kind of observing. And what I want to try to do today is just give you a flavor of how we work in that regard, of how NASA makes sort of revolutionary observations at certain times. And how we can use those to help put together the big picture of the processes at work. I’ve tried to put a little history in as well, to try to show us how those people from like a century ago were able to just take a few points of information spread over time and put those altogether, and actually understand pretty much what we still understand today.
It’s just that now we have a lot more detail from the number of observations we have. This is a slide. I started collecting things from newspapers. I might be seeing a little problem here. And there’s a lot of — over the world, like different newspapers are publishing stories about El Nino’s back and droughts in Australia. There’s flooding in Southern California. A lot of El Nino related damage in the Philippines and Indonesia and things like that. There’s been a lot of headline news about El Nino. And also, has it been affecting the droughts in California? Has it been affecting these big storms that came up the east coast of the US? And I’m thinking about that. They all make the news and get into the newspapers.

So my overview, which has an O at the front of it usually, is basically to say what is El Nino. I’d like to give an overview of what it looks like. Yeah, I set the formats of these slides on a stretched screen and it’s kind of truncated on the 4×3 I think. El Nino in history. So look at the historical observations, and then moving into NASA’s observations before we go to the work that we actually do in the Global Modeling and Assimilation office, which is using those observations alongside computer models, high-end computer models, to make detailed analyses of the three-dimensional structure of El Nino.

And forecasts of how it’s going to behave over the next few months. And then looking at the view from space of different parts of the climate system that are affected by El Nino and a little bit of a summary at the end. So start off with a basic description of El Nino. What is El Nino? And basically looking at the oceans and the signals that El Nino has.
So I’m going to start with a map of global sea surface temperature. So this is what’s called a Robinson projection, so it goes in a bit at the poles and it gives you roughly the sense that the tropics are very large. And if we look at this map —

So this is sea surface temperature and basically the Reynolds sea surface time series is a very famous product. It’s developed at NOAA. And it’s used to produce essentially global maps at least every month of the temperatures of the surface of the ocean. And to see what you expect. At the poles where there’s less sunlight, it gets cooled. It’s cold so those temperatures are blue. They are just in the low few Celsius.

Then by the time you get to the tropics, you can see the temperatures are well into the 20’s Celsius. Much warmer water. And in particular what we’d like to look at today in this region of the Pacific Ocean. And we can see that over by Indonesia, the maritime continent north of Australia in the western Pacific, the temperatures really are up to above 28 Celsius. And that’s kind of a magic number.

Because 28 Celsius is about the temperature that we need to have for deep, powerful convection to happen. So basically over that region we’re going to get strong clouds forming, very deep clouds and storms. And in the normal case, there’s a lot of precipitation in this region. And we can see what’s called the intertropical convergence zone, which is essentially a warm tongue extending over to the east Pacific. That hits Central America.

And if we look right on the equator, we can see cooler waters here off the coast of Peru. And that is associated with the currents we’ll talk about in a few moments. But this is the basic structure of temperature over the Pacific Ocean. Warm on the west, cooler on the east. At least in the southern hemisphere and under the equator itself. There’s that gradient. And that’s essentially the normal state of the circulation over there.

And this is basically pointing out what I just said. And this black rectangle is essentially showing us that the main region of interest for El Nino — so when you think about it, it’s the tropical Pacific Ocean. But this projection does a fairly good job of preserving areas. This is actually about between 30 north and 30 south is half of the globe in terms of surface area.

And this is almost half of that in terms of the longitudinal extent. So El Nino is a huge thing. It’s affecting — variations in El Nino are really affecting the global temperatures because it’s such a large area of the globe that we’re looking at. What we tend to show when we’re looking at El Nino and other phenomena is the anomaly. And that’s the departure from the long-term average in this sense.

So we look at an average from something like 1980-2000 and we do that and we subtract that from the actual year that we’re looking at. And that’s essentially telling us how different is this year from a normal year, if a normal year exists? And this is December of 2013 again, the month that we just saw.

And we can see that in this tropical region here, there are really very small differences from the normal situation here. So this is what we call a neutral year. There was no El Nino and there is no La Nina. Yeah? We’re not going to talk too much about La Nina today. La Nina is essentially just an intensification of the normal state. So where it’s cold over here and the average is just a little bit colder than normal average, and probably a little bit warmer over here.

The more interesting one is the El Nino, and this is the plot from the previous December, 2015, the one that just passed. And here we can see a very different situation. Now we’ve got a slightly cool anomaly over the western Pacific, but here over the eastern Pacific we can see a very, very large red colored contour that’s showing us temperatures more than two degrees above the typical situation.

And this is the development of the El Nino case, where the warmth has basically moved across the Pacific to the other side. And now south of the equator in Peru there’s these warm ocean temperatures there. And that’s showing up as that anomaly. There are other features you can see too in the other oceans. But they are less of a concern today. Except one thing here. I’m just going to talk back to the 2013 case.

Already we’re seeing this very warm anomaly in the Pacific Ocean off the west coast of the US. And that’s been referred to quite a bit in the press as the blob and things like that. It’s basically a warm anomaly off the coast of California and the entire west coast here. And this is probably related to the drought that we’ve been experiencing in California and on the west coast over the past few years. This has been quite persistent. And it’s led to the anomalously low rainfalls over the west coast, the site of the drought. Or if it hasn’t led to it, it’s associated with it. It’s all part of the same sort of large-scale sort of patterns of variations in the atmosphere. And you can see when we go back to 2015, that’s still apparent there as well.

And that’s probably going to be important just towards the end of the presentation when we talk a little about California. So that’s essentially what I wanted to show at the beginning. Now just one more thing. If we look at this map here, we can see that this is directly compiled from observations. So observations are coming in from ships. They’re coming in from satellite observations of the ocean temperatures. And from satellites especially, they’re very spotty because there are a lot of clouds in the way and things like that. So you can see that spottiness come through on these maps. Especially outside the tropics, it’s got a bit of a spotty structure. Whereas on this plot, this is essentially the same field, just using the same observations more or less with a few extra ones added. This is what happens when we actually assimilate those same observations.

And essentially assimilation is the art or mathematics really of putting together observations into a very complex computer model. And we’re essentially, if you interpolate you’d have information about this point here and this point here and you could draw a straight line between them and come up with some values from in between.
What assimilation is essentially doing is a very sophisticated interpolation. And the model that we’re using to build that structure in between the observation points is based on the dynamical equations of motion. So we’re actually using that to use water masses around when we do it for the ocean. And that gives us a good estimate of the state of the ocean based on the observations in other places.

So it’s essentially a very expensive three-dimensional interpolation that we’re doing to come up with optimize analyses of the entire globe. And we’ll see later we get the three-dimensional structure of the oceans as well from just a few bits of information about the vertical profiles in the ocean.

And the point here being if we toggle back and forth for a moment, here it’s very noisy from the pure observations. Here we get a much smoother and much more sort of realistic, dynamical looking field that’s taking into account the motions of the ocean currents and the surface winds blowing over it that help define these fields.

And so that’s essentially the work that the Global Modelling and Assimilation office is doing in this. And here again, as part of what we do, this is showing us sections across the equator from the Indonesian side of the Pacific to the South American side of the Pacific. And it’s showing depth of the ocean  to about 300 meters below the surface.

And this is the temperature anomaly as a function of depth of the ocean for December of 2013 where we see just a very weak one or two degree — region that’s one or two degrees warmer than normal. And for December of 2015, the strong El Nino year that you saw at the top. And here you can see that this temperature anomaly that we were looking at on the surface just a moment ago is really, really prevalent below the surface. And it reaches about 7 degrees Celsius on this South American side of the Pacific.

So under the ocean surface. Now as I said, this analysis is made using the surface ocean observations. It’s made using an analysis of the atmospheric state so we know the winds in some detail. And it’s also made using just very, very few profiles of the ocean temperature and salinity structure from some buoys that are moored over in the Pacific at certain locations.

But really it’s just a handful of information that’s actually measured about that. And we use the model to fill in the gaps and come up with a three-dimensional picture. The other thing that we sometimes use, and I actually believe it’s actually not in this run, but we sometimes use measurements of the sea surface height that we made from space.
And we’ll be looking at those in a while. And essentially that’s useful because obviously from space you can’t see below the surface of the ocean in most cases. Especially optically thick. So all you can do is measure the top temperatures. One thing you can measure at the surface, if you can measure the height of the ocean you’re essentially saying, “If that water in there is warm, it’s going to have a larger volume, so it’s going to push the height up.”

So these surface height observations are telling us about the temperature of the water over a huge, deep layer beneath the surface. And so that’s the type of thing that can go into getting this analysis as well.

And so moving away from that and going back to kind of the schematic — and it’s always tempting to try to draw my schematics of what’s happening. And actually these days if you look around the web you really find some fantastic ones. And this one I think sums up what’s happening and what we’ve just talked about. It’s essentially showing the ocean surface, showing the normal warm temperatures over to the western part of the Pacific.

And then the deep convection above that, and this circulation in the atmosphere that goes up in that region and it completely goes across and down. And then the trade winds they’re called, running across the ocean’s surface up there. And that’s called the trade winds because of the ancient shipping routes that sailors used to make use of those to get across the Pacific. Essentially they’d be blown across there.

And it’s showing underneath the ocean surface, it’s showing the thermocline. The thermocline is essentially the boundary of the water that’s mixed up by turbulence caused by wind stirring it. And the deep ocean waters, which are not affected by the surface winds so much. And so what we’re seeing is that the thermocline is not far below the surface on the Indonesian side. But then on the Peruvian side here there’s basically upwelling. We’ll talk about that again in a moment.

And that’s pushing the cold, nutrient-rich water from the deep ocean upwards on the coast. And so the layer that’s actually affected by the atmosphere is very thin at that edge of the ocean. So that’s the normal state. And then if we look at the El Nino state we can see the warmer temperatures moving across the equator. The thermocline tips so we don’t get so much of the upwelling over the eastern side of the Pacific.

And the deep convection in the atmosphere moves over to the central Pacific and also a little bit more over to the South American part of the atmosphere. And as I said earlier, the La Nina case, which is the opposite of El Nino, is basically an intensification of this state. And typically the thermocline may even meet the surface just off the coast. So it’s just an intensification of how cold it is over that side of the Pacific and a little bit warmer over the other side.

And I’ve tried to give a citation to most of the figures I do use from the web, although this is — I think that’s the original site. These images float around all over the place, so it’s hard to know who did that one first. So just looking at this, the Pacific then essentially, in normal years in the central Pacific region we’ve got sort of moderately warm temperatures. In El Nino years it gets much warmer.

And this is then used as a metric of how strong the El Nino events are. These four regions, Ninos one, two, three and four — the red shaded regions — early locations used to define the strength of the El Nino events. And the reason they’re called one, two, three and four I think is because these are places where the ships used to sail. And so they actually made measurements using — you know, they threw a bucket over the side, pulled the water up and put a thermometer in it, basically. And they get measurements of the ocean surface temperature. And that’s still done today by research vessels.

And so these were four regions where ships sailed. And it’s obviously due to trades between the ports of South America here and the Asian ports on these lengths. What we typically use these days to define the El Nino is this region we call Nino 3.4, which has got a little bit of 4 and a little bit of three. And I think people converge on that just because it really shows a decent signal that we can relate to what we regard as Enzo. And then if we look at the time series of the Nino 3.4 region, this is essentially showing us how things change over the years. This one begins in 1950, and it goes up to actually the present, to about February of 2016. This is again compiled by NOAA. There’s this one of the labs in College Park.

And essentially you can see these very cold years. Basically if it gets half a degree cooler than normal, it then turns into La Nina, at least if it persists for three months. And if it gets half a degree warmer than normal and persists for three months, then it’s regarded as El Nino. And of great interest are these three big El Nino events in 1982-83, ’97-98 and then this present year. You can see that at least over the last 50 years these three events stand out.

And this one was quite widely studied and it’s a very interesting one in many ways, because it actually coincides with a major volcanic eruption as well, El Chichon. So it’s actually hard to use that to decide what the climate anomalies are, especially with El Nino, because there are also big climate anomalies associated with all the volcanic sulfates and all that buzzing to the high atmosphere. So that one is less useful, at least less useful in terms of defining climate anomalies. But as we get through to 1997-98, we’re getting to the point where we have a fairly clean atmosphere so we can look at the climate anomalies. And the other great thing is this is around the time when NASA did start having some research satellites available. And in particular the TRIM mission that measured global precipitation had just been launched when this started. And we also had some measurements of the color of the ocean, which tells us about how many phyto pints are in it and things like that.

So this starts to get interesting for NASA. And then of course the present one is as strong as by this measure that one. In some measures it’s a little stronger. In other measures it’s not quite so strong. So this again is giving us an opportunity to look at El Nino with a very modern fleet of satellite instruments alongside everything else that we have. And I think just basically the forecasts of this extremely strong El Nino that started coming out towards the middle of last year were really alarming because people remembered what happened with the 1997-98 one. You know, that was a big, global scale climate response. It starts with that warm water by Peru. Really leads to a lot of anomalous rainfall in Peru.

A lot of rainfall in a mountainous terrain like that leads to a catastrophic landslide. So a lot of environmental damage by the flooding and landslides. So there was a lot of concern about that happening this year as well. And I was going to show a bit more about that, but I didn’t. And so I’ll say now that actually, even though there was some strong rainfall in Peru, it didn’t turn out to be quite as bad. Because if we look at these anomalies, El Nino one and El Nino two are basically telling us about the temperatures just by Peru.

They were not quite so strong this year as they were in 1997-98. So essentially there’s been a tendency for the El Ninos to be peaking in more of the central pacific rather than right on the eastern Pacific in recent years. Even though this one is really big and bigger in some ways than ’97-98, its horizontal extent wasn’t quite as large. So it didn’t have such a big signature down here. That means that any weather systems that are moving into Peru just don’t get quiet as much energy from the oceans beneath them as they go in a little less water because it’s not quite so warm.

And so the rain has probably not been quite as bad as it was in ’97-98. That’s not to say there hasn’t been any severe storms in Peru, but it probably wasn’t as bad as they were dreading in the beginning of this. So with that, I’ve tried to give sort of an overview of the basic characteristics of El Nino and what it looks like. I want to just briefly go through a little bit about the historical role of El Nino. And it seemed good to start with an old map of Peru.

So that’s the Peruvian coast, and you can see that north is pointing that way, so orient yourself. And this is actually — this map was actually drawn up by Mercator and one of his colleagues. And there’s actually a Mercator map projection that’s you know, very widely used to this day. It’s essentially a regular latitude/longitude grid. And you know, you really — I just always found it impressive these people would be out on ships and making measurements. And they’d get some fairly accurate maps of the coast. They even put ships out in the sea and things like that.

You know, sometimes you find monsters. I don’t know about these ones. So this is a nice one. And you can see on this too they’ve really made effort to show the very tall mountains, the Andes, very close to the coast in Peru. Which rise very dramatically up there. So that’s an old map.

And this is a map of basically the ocean circulation of South America. Again, a pretty old map. This is the coast of South America. And essentially the Humboldt current runs up the coast in the ocean. And as we get close to the equator, that starts to be pushed by the winds going off the continent. Also by the earth’s rotation, kind of encourages that to flip away.

So as we get to Peru and Ecuador, that current is moving away from the coast. And that’s essentially dragging this water up from below. So that deep water from the deep cold ocean is going to get drawn up from the deep ocean. And that comes up just around here. And as the settlers in the 17th century and way back started moving into Peru, they basically started discovering that there was a lot of fishing, especially anchovies off the coast.

And essentially what’s happening there is this cooler water that’s upwelling from the ocean floor is bring up iron and other nutrients. And that encourages the growth of phytoplankton in that water. It’s got plenty of light to grow as well, so it’s really the ideal conditions. And these anchovy fisheries are essentially still one of the biggest fish sources for the entire world.

But what actually happened is that the old fisherman noticed that every few years the fish would go away. And this is basically the ocean warms. This is the El Nino coming in. The ocean warms. That upwelling of the deep cold waters to the surface is cut off. The phytoplankton, you know, they’re upwelling somewhere else. So the fish go away and feed elsewhere. So essentially there’s a dramatic decline in the fisheries in those years when there’s an El Nino.

And the name El Nino actually comes — it was given to it by the Spanish settlers in Peru and Ecuador. Essentially this was happening around the end of the year, around Christmastime and they used to call this the boy child, or El Nino. And that’s where the name comes from. And then La Nina, I think the name came some years later because it wasn’t so dramatic. You know, you still got the fish in the La Nina. So for those people it wasn’t so critical.

And so that’s loosely how things are working around the coast of South America. And just flipping onto a very modern view, this is a NASA view. This is actually from the CWB Satellite which was observing around 1997 and 1998 after it was first launched. So CWB measures the color of the ocean essentially at different wavelengths. And the color of the ocean, if it’s very green, means there’s a lot of phytoplankton in there. If it’s very blue, it means it’s very clean so there’s no life.

So you can infer from this the amount of phytoplankton in the ocean. And you can then infer where the fish may be and things like that. But these plots are essentially showing that in December of 1997, an El Nino year, we have to look at the scale and be careful here. This is a logarithmic scale here. So this dark blue has 10 times less chlorophyll than this pale blue. And by the time you get to white, that’s 100 times less.

So between there and there, there’s about 100 times less chlorophyll here than there is there, basically. And this is the El Nino year. You’re seeing not particularly much, but compare that to December 1998, a year later. So ’97 was the first year of measurement from CWB. ’98 you’re seeing a very white region. So you’re in the few milligrams per cubic meter of chlorophyll in those regions.

So we’re essentially able to show using space-based measurements the thing that the Peruvian fishermen knew all those years ago, that the phytoplankton had gone away in the El Nino event. And there they are in the La Nina in this case. So you can actually see this from space. And we’ve now got a really long time series. About 20 years of measurements of ocean color. And there’s a new mission that just got funded to take that way into the future.

So you know, we’re able to do sort of climatological studies now of ocean color and infer phytoplankton changes over time. We have models that run and do sort of diatom and multi-bodied phytoplankton that kind of change over time. And that’s one of the great things that we actually do with our satellite observations. So going back to history, and we come to Sir Gilbert Walker who is also English. He was a mathematician and physicist and essentially he was posted to India as the director of the meteorological observatories there. And he just heard about, you know, there had been — I’m sorry, I’m not saying this well. In the late 19th century there had been famines in India because the monsoons had collapsed. And Walker knew about other things. He knew about pressure measurements, surface pressure measurements over the Pacific Ocean. And he basically had an army of staff in India working on time series analysis essentially.

And what we now do very routinely on the computer he had people doing for him. And he was able to establish sort of relationships between different variables that were being measured in the atmosphere in that time. And it turns out that the circulation with the ascent over Indonesia and then the transport across the atmosphere and descent near South America, that’s now called the Walker circulation, named after him. And he’s basically the person that put this all together. And what he actually defined was what’s called the southern oscillation, which was a seesaw in surface pressure, the surface pressure difference between Tahiti in the min-Pacific and Darwin in Australia.

So he was able to deduce that those things moved out of phase. And this was the change of mass of the atmosphere. He didn’t realize at that point that was El Nino. It was
Burke I guess in the 1950’s or ’60’s who put that all together and realized that essentially the southern oscillation in the atmospheric surface pressures is essentially the same signal as the El Nino event in the tropical Pacific ocean. You know, but coming up with these things with just a few point measurements of meteorological conditions of the colonial sort of stations that they had set up over time.

So this is a much more modern map. It was in the 2007 IPCC report. And it’s showing essentially this Darwin sudden oscillation index, the Tahiti Darwin surface pressure change. And in this case actually we have to be a little bit careful. A positive value is actually La Nina. A negative value is an El Nino. They’re anti-correlated between them, the Pacific surface temperature. But essentially what it’s saying is that when we have a — so a negative anomaly in this pressure difference is corresponding with — this will be a positive anomaly in the surface temperature. So the El Nino has warmer temperatures over the eastern Pacific than has that particular signal in the surface pressure difference.

So you can see a signal here in the surface. That temperature in the surface pressure. This is essentially the Walker circulation. When it’s going up there, the pressure is lower. Down here the pressure is higher and that seesaws. And the precipitation, essentially the clouds are moving over the mid-Pacific in the El Nino event. So in the El Nino the precipitation is increased in the mid-Pacific over Indonesia. So that’s the coherent dynamical framework. Walker put that together. You know, we’ve got a lot more observations and a lot more time to have done that in the meantime, but that still holds in those things. So that’s then showing essentially what people were able to do in historical times with very few observations. And now let’s move a little into the time when we have NASA observations. And I want to emphasize the view from space showing the advantages that a satellite can have over these in situ observations.

We start with just repeating the map of where we’re looking in the El Nino 3.4 region. And this time, I’m going to show the historical ship tracks as well. This is obviously not NASA observations. This is ship observations that were made I think towards the end of last century. And this is averaged over a long time, so there are a lot of tracks in that. And unfortunately the Pacific on this is over here. But you can see there’s the coast of South America. You can see fairly dense networks of ships off Peru. And then crossing the trade winds, crossing the Pacific and coming into Asia.

You can see, you know, there’s some very interesting things about this because the ships, if they wanted to go around here, they had to go all the way around the Cape, so going past Africa. So if we look then at a more modern ship map, we can see the Panama and Suez Canals have opened up and that has totally changed where the ships go. You know, the distances are much shorter going through. So really very few ships now are going through these regions. But many more are passing through the channels, through the Red Sea, through the Mediterranean and then through the Panama Canal.

But still, the point being for both the historical time and the present time, yes, you do get quite a lot of information from ships, but it’s very, very selective where you actually have that information. It’s analogous really to the weather observations that we have, you know, for the weather forecasts. Essentially we have a lot of balloon observations over land, so North America, Europe, Asia is all dense in observations. But really very, very few of those over the oceans because there is no one there to observe things, and similarly in unpopulated land regions, don’t have many observations there either. So essentially, knowledge of surface temperature from ships is essentially only coming from where the ships sail.

So what NASA does is basically provide a lot of different types of observations. I’m actually going to skip this slide, and I’m going to show this one, maybe.

And this is basically showing NASA has about 20 satellites in orbit right now. Most of them polar orbiting, which means that they essentially just go round and round and round in a circle and the earth spins underneath them. And because the earth turns once a day, we basically get a map of things high-globe from each satellite over each day when it’s in a polar orbit. And this is the fleets. You can see some of them chasing each other around. That’s actually called the A-train. [Laughter] That’s the name. Basically, they’re all trying to measure essentially the same air masses, one after the other, so we get information about different properties of it. What one interesting one, it’s always the same with this movie. As soon as you want to start speaking about something, it disappears around the back.

So there’s the Space Station. There, it’s gone again. But a very different type of orbit is the International Space Station. Here it comes around there. So that’s in a much different orbit, it’s much more inclined, so it’s essentially going around the earth more frequently. What that means is you actually, whereas these polar orbiters, you only see one time of morning and one time of night, the Space Station, you basically go through the whole day, and you can see things at all times of day. And that can be really useful information for things that vary strongly through the day, if you’re able to see them from that orbit. No, and you know, a whole bunch of things that [inaudible] you just sort of go by. That’s actually a radar, and it’s measuring the roughness of the ocean’s surface so you’re able to get information about waves and things like that from that.

Oh, seems to have stopped. You know, OCO2 down there is measuring carbon dioxide in the atmosphere. SNAP, which seems to have gotten distorted as well as stopped, is measuring soil moisture from space. That’s a fairly new mission, you know. And actually, we had one called Aquarius up there. That failed, unfortunately, last August. We’re hoping we’re going to be able to use some measurements from SNAP to replace Aquarius and get information about the surface salinity of the oceans, the saltiness of the oceans. And that’s also a really important part of the like, the circulation budget, essentially you’ve got cold, fresh water coming as ice melts in high latitudes. And also, you know, colder water comes up. I’m sorry, and then where it rains onto the oceans, that’s diluting salt, so it’s fresher there as well. So with things like that, we’re able to put together a big picture of, you know, how precipitation’s happening and how it’s affecting the ocean. How if ice is melting in the polar regions, that’s affecting the salinity of the oceans. And that’s sort of what drags the large-scale ocean’s circulations that we have.

I wanted to talk a little bit about the sea surface height. I alluded to that earlier. As I said, you basically can’t measure temperature below the ocean’s surface from space. So, but what you can do is kind of infer that if it’s warm, it’s going to be higher. So essentially, this series of satellites, they’re actually international partnerships with Europe, and they’re led by the jet propulsion laboratory in California. We’ve just launched Jason-3, which is the third one in the series of Jasons. Jason-2 was launched in 2008, Jason-1 in 2001. And before that, we had TOPEX/Poseidon which was launched in 1992 and lasted through 2006. So we’ve got a long time series now of about 20 years of information about the height of the oceans. And this is a, you know, as I said, sort of an integrated quantity of the vertical temperature profile. And this is an example of, again, an anomaly map for February 22nd of this year. So really quite recent. And it’s showing us essentially the same thing that we’ve been looking at with the temperatures. Where it’s warm, we’ve got a high anomaly in the oceans’ surface, you know. So it’s warm, it’s thicker, it’s raised up. And here over the Eastern Pacific, it’s lower than normal. So that’s the signature of the El Nino in the ocean height. Now how we measure that is actually very interesting. Essentially you, you — you would, the altitude of the spacecraft is measured very accurately in reference to a certain geoid, it’s called, a reference level. And then by beaming a radar down, it knows how much signal is sent. It times the time for it to come back, the signal to come back. And then it can calculate the path between the spacecraft and the height of the ocean. So with those two pieces of information about the height of the satellite above this reference level, and the distance from the satellite to the ocean’s surface, which you can measure very precisely, it can actually measure the height of the ocean’s surface to within — I think it’s about three centimeters. From an orbit about — I think it’s about 300 — no, 850 kilometers above the earth, 500 miles up. So, you know, that’s the type of information we can get from space that we cannot get from down here.

You can’t actually measure the height from down here. You’d be on a ship, and you don’t know how high you are to start with. Yeah, so. And another type of observation that we can make. This is actually rainfall, and again, it’s comparing the rainfall measurements on the left for 2014, on the right for 2015. And it’s two different months. I think it’s September and October. September at the top, October at the bottom. And if you look at this carefully, you can see the differences with the El Nino states. And when you look at it from here, you can’t see any difference at all. And so maybe we’ll just skip over that one. There is definitely a signal of — it’s actually difficult to get the reference there. I should stand near the microphone, sorry. It’s essentially, I think that’s the equator, and this is closer to the equator than –. I’m sorry. This is side in 2015, are closer to the equator than these ones. And you can see actually, yeah, it is more red over the mid-Pacific than it is here. So, you know, there’s — if you know what you’re looking for, you can actually see the signal of El Nino in those observations. And those really are just two of the different types of observations that we have from NASA that help us put together sort of a quantitative picture of what’s really happening in the El Nino event. Now the ocean height, that goes along with the sea surface temperature that we actually get from NOA largely. And this again, the Global Precipitation Mission, the TRMM was a predecessor of the Global Precipitation mission. If you remember, that’s the one that crashed into the Indian Ocean about a year ago. It ran out of fuel, and then it just basically — they descend back through the atmosphere then. But now there’s the Global Precipitation Mission which should give us information about rainfall and clouds for the next few years as well.

So I wanted to give a brief look at our computer-based analysis and prediction, and I see I’m actually running out of time a little bit, so I have to be careful. But essentially, I already mentioned we’re taking a lot of these observations that are made from earth and also combining that with observations from space, and using advanced analysis techniques to come up with analyses of three-dimensional structure of the oceans, as well as the atmosphere. And once we have a good estimate of the ocean’s state on any day, we can actually start to predict what it’s going to look like in the future. So every month we ‘rerunning a suite of forecasts that’s called an ensemble. I think right now we run 17 numbers each month. Some of them start on different days, and some of them we tweak the actual state because, you know, there is some uncertainty in it. So we try to build up some suite of ensembles and then we can make a forecast. And these are then the forecasts that we ran last August. We’d already in about May, we’d been starting to see a signal of a very, very strong positive temperature anomaly in this Nino 3.4 region. And this is the ensemble. Often, when you run an ensemble, this is the zero line that you’ll get if you’re going up there, and if you’re going down there. And, you know, the average of them all is roughly what you put out as the best guess of where we’re going to go. In this case, we’d already seen the observations of sea surface temperature ramping up, so we want to start with, and we’re predicting then, a very warm anomaly. That’s about, you know, three and a half degrees on the average, but some of them were almost five degrees, and most of them were about two and a half degrees. So we were predicting fairly early on a very strong El Nino for this year, and we were a bit worried about it because some of the other centers weren’t predicting something that strong. But actually it turned out as the year went by, the Australians put out a press release saying it’s going to be the strongest El Nino ever. And it sent out these two or three groups that were forecasting a very strong event, and were actually right. And this is what’s called the National Multi-Model Ensemble.

This is a project organized by NOA, and they gather this information from different centers that make the forecasts. And what I just showed you from out groups is all of the different ensemble members. You can then make an average for each group, and this is the averages for each of a number of groups. We’re on this with the yellow one. We are the strongest number; one of the Canadian models is a little weaker. And the average is here. Most of the models were clustered just a little bit colder than the average of all of them, and that’s just because you have two fairly high ones above it. So you know, there was a good consensus this year that there was going to be a very strong El Nino, and you know, in the mid-Pacific, it really looked like it was going to be the strongest one. This is a, this is a history map showing our forecasts that began in August of each year, so essentially you’re seeing this El Ninos and La Nina’s going along. This was, the blue line here is the observations, those Reynolds Sea Surface Temperature observations, and here — so here you are in January and February. It’s very warm. And here is our forecast. So we’d greatly overestimated that forecast in that previous event in ’97 and ’98. But generally, you know, we do a reasonably good job, and we were a bit concerned about this one being so strong because on the basis of this one, we were thinking, “Well, okay, we’re saying it’s strong, but it’s going to be down here.” But actually, and unfortunately, I couldn’t find the plot that shows it. But we actually did a pretty good job. This was one of the strongest El Ninos ever in the mid-Pacific. So these forecasts of sea surface temperatures in that region were pretty accurate. And so what’s going to happen now? We’ve gone through this time. We’ve predicted a big El Nino, now here are the forecasts that begin in January of 2016. Here you can see we’re in the pink now so we’re up there somewhere, the two and a half, three degrees. And as time goes by, we’re predicting that to fall away and just get loosely negative. So essentially, the El Nino’s dying out. Some of the groups are now predicting a, you know, a reasonably strong La Nina event is going to happen later this year. On the basis of this we couldn’t really say that our ensemble is doing that. But you know, that’s still a possibility. There’s often a precedent that when you have a strong El Nino event one year, the following year you get a reasonably strong La Nina event afterwards. So this is like an over-recovery of the thing. So that’s one of the uses of the analyses, making these forecasts.

What I’d like to show now is a movie, and this is going to show us the sea surface temperatures. And throughout 2015, and this is the view from the top, essentially, so this is from our analyses, and it’s showing essentially how the warmth has built up and propagated across the ocean. This is made from daily or subjects-daily data, so you can see a lot of movement. And that’s starting again. And now what’s going to happen, it’s going to tilt on its side in a moment, and you can start seeing this as a function of depth in the ocean. So there you go, so now you’re not just seeing the surface, but you’re seeing the situation under the ocean’s surface. And that’s starting again now in January 2015, and you can see how the anomalies are building up in the lower layers, then really moving over to the ocean’s surface as the event matures. So it’s November. It’s now a very mature El Nino event in January and February. And stops there. It’s already dying off a little bit. Yeah, you see, you can put it to January. So you know, we’ve peaked here, and that’s dying out now. But this is, you know, that’s basically a summary diagram of what happened this year based on our best estimates using as many observations as we could use. Now, I was — I would just talk about the drought in California at this point.

The view from space. So essentially, this is a NASA view of the drought in California. It’s essentially looking at the vegetation derived by MODIS, which is an instrument on the EOS Aqua satellite. And essentially, anything brown means that there’s way less vegetation there than there should be. And so compare this with maps of high temperatures. This essentially means, you know, it’s been hot, dry. The plants haven’t survived. So we’re on strong anomaly there down, you know, through much of the West Coast, in the mountains close to the coast as well. So when we heard about this big El Nino coming, there’s been precedents in the past to say okay, a large El Nino means a lot of rainfall in California. And so has there been rainfall in California? Well, here’s another view. This is NASA’s GPN satellite. This is essentially going to build up showing you accumulated rainfall over each month for November, December. No, sorry. December, January, and February. And yes, you can see we got quite a lot of precipitation on the northern part of the West Coast, but really not a huge lot over the Sierra Nevada in Southern California. We got plenty on the Gulf Coast, you know, some storms. And you’re going to see one event that we got, the snowfall event, and you know, which was a typical East Coast storm. So on the basis of this, at least until mid-February, there have not been a huge lot of rain in California. And this is a map again from NOA sort of summarizing. It’s the average precipitation between December, over December, January, and February. And typically in a strong El Nino year, it’s a little cooler up here, warmer down here, and blue here means wetter. So a lot more rain on the West Coast, and typically across the Gulf Coast, too. It’s kind of well-known the Gulf of Mexico gets pretty wet in the El Nino events. And also people thought this year, 2015-2016, not so much rain over in the Gulf. Not enough, particularly wet. Also not a whole lot of rain in California. There’s been a lot of rain in the Pacific Northwest, but not so much in California. So really we haven’t had this massive deluge throughout the entire winter. I think though since this map was made, there has — there was actually some fairly severe rainfall just a couple of weeks ago, so that didn’t make this map. But in general, this idea that a strong El Nino means a lot of rain throughout the winter in California is not holding up this year.

Now, there’s a couple possible reasons for that. One of them might be that warm blob off the coast that’s keeping things warm out there and directing the storm tracks slightly differently than normal. But more generally, I think our knowledge of El Ninos and their impacts on North American rainfall is actually based on very few events. It may just be that every single one of them, you know, just like the events themselves, each one is very different. Maybe the response in the higher latitudes is also affected by other factors. Just the positions of the waves that affect where the storm tracks go, and that. So I think it’s not such a clear-cut thing that strong El Nino automatically means rain in California. And we’ve known for a long time that moderately sized El Nino, there’s really no correlation between California, Northern California and rainfall and the El Nino pattern. So, you know, we’re probably in the situation where we have so few large events that we don’t know statistically what really happens. So the summary and outlook: I’m trying to keep this short. We do have a broad understanding of El Nino and its importance in climate variability. I’ve tried to give you a few examples today of, you know, about the structure of El Nino itself, and the long-term effects. To do that thoroughly, you could have a lecture every week for three months, and you know, I think that would be too much. So you know, I can encourage people to just to go away and take a look. And the traditional viewpoint of the measurements from ships and from the ground is supplemented by NASA satellite observations. And essentially with that, we’re able to get global observations of many different quantities that we can put today using our complex data simulation systems. And in that way, we can build more complete pictures of the entire earth system and how different processes start interacting. And that’s one of the more, you know, for me, that’s the really interesting part about this right now.

We’ve got essentially an unprecedented number of satellites flying, and we’re able to see so many different parts of the earth’s system now than we could a few years ago. But, you know, this is a very exciting time to be working in this science. You know, barely touched on the broad spectrum of NASA observations of the earth’s system, but I hope I’ve given you some flavor of what we have. And even despite all this, I think there are a number of points where, you know, our understanding isn’t all there. And so what we’re really trying to do is bring together these satellite observations with the models so that we can improve the representation of processes and improve the fidelity of these predictions, not only of the nine months seasonal forecasts, but all the IPCC models that do climates assessments. Some of those have, you know, reasonably good El Nino representation. Some of them, it’s too weak, or some it’s too strong. Some of them, it hits the wrong, periodicities and things. And that got better between the 2007 and 2013 climate reports, but they’re still not there. So there’s really a great hope that we can use our observations to improve these models and come up with a better prediction of climate and the role of El Nino in climate. And also even the role of El Nino in a changing climate isn’t well understood. You know, we know surface temperatures are warming. We’ve had this general temperature increase, even basic things now, like if we’re looking at a 40-year time series, we can’t assume that the average El Nino is not affected by climate change. We need to stop thinking that the average should be something that increases slowly with time, and then we might come up with slightly different conclusions of how these things happen. You know, so I think those are some of the things that I’m hoping we can use NASA satellite observations to address in the future. I just want to thank everyone here as well as my colleagues, our funding and computing center in particular. We keep them really busy with our things. And I’ll end up with some contact information if anyone needs it. So I’d like to thank you very much for your attention, and thanks for coming.

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