and i don't remember hearing much about the government then, and people just sort of took charge of what was going on with their own problems and handled it. and i don't remember hearing about any national guard or anything like that out there helping, and we lived close to the guadalupe river. and it was -- i mean, it was at flood stage, but i don't remember anybody saying, oh, it's a disaster, natural disaster. this was in the 194e's. the government didn't really have something on the ground people should be preparing for things of this nature. they shouldn't be waiting for >> texas governor greg abbott will be leaving reporters.

while we wait for that to start, a discussion from florida state university. >> hello, thanks a lot. actually, i only wear one hat, also. i really think of myself as a scientist. really, that is all i do. but if you do science for a long enough time, they put you in positions like being the chair of a department. so it is not something that you seek out, but it is something that happens because you do a lot of science. so i like to think of myself as a person who just focuses on one thing. actually, two things, hurricanes and tornadoes. i focus on two things, but they

are connected in that they are these violent windstorms that cause lots of damage and casualties. so we have to think about what might happen in the future. so really what i do, and this is this idea of just wearing one hat, i spend all of my time thinking about what hurricanes and what tornadoes might be like in the future. so you are going to see a lot of science today in the sense that you are going to see a lot of graphs. ok, we use a lot of graphs the cause that is the way we make comparisons. science is about comparing this with that. this is not a science class. i am not going to apologize, because i think that is the window of understanding science. but i want you to know that these are my graphs. these are not graphs i got off the internet. is is not someone else's argument about what has happened, and there are a lot of arguments and a lot of opinions. what you will hear today from me is what i do and what i think about on a daily basis. before i begin, i would like to thank felicia for inviting me to

do this. i got a chance to tour the facility with doreen this afternoon to wonderful facility. this is really an outstanding facility that i think you should be very proud of. and folks associated with fsu should know more about this fsu coastal and marine lab. a large part of the reason for that is because of what has been done over the last decade. i chair the department of geography, not a very big department. we have about 10, 12 faculty, depending on how you count. but it is very dynamic, and it is increasingly associated with what folks do down here at the lab. hurricane center native -- i will start out with hurricanes. if you kind of drift off on this part, and you might, when i get to tornadoes, you know i am

about halfway done. we are going to talk about hurricanes to start out. you are obviously familiar with hurricanes in a generic sense. we can track them and look at them from space, and they are these powerful things. so we know it hurricanes are certainly like today. in fact, last year we had a hurricane come very close to this part of the world, so they are in our consciousness constantly. when, where, how often -- these are things we know about, especially if you live know the coast. but what about the future? are we in for a greater risk of these storms? unfortunately, there is no simple way to get answers to these questions. they are very important. there is just no simple way. why is that? why can't we just do some kind of calculation on the back of the envelope and work it out? well, first of all, the theory

is very limited it we do not have a theory of climate. if someone said this is a theory of how climate works, it is nonsense and we have parts of theories of how things work, but we do not have a general theory of how climate works. and we certainly do not know everything about what drives a hurricane to besides basic stuff, so theory is limited. models, which are good at forecasting where a hurricane might go, given that there is one out there, they do not really represent the atmosphere in the ocean in an adequate way, at least on the scale of climate. finally, we do not have enough data. we do not have a way to look back in the prehistoric times. it is very difficult to look back. we just do not have enough data.

and the data that we do have kind of very and quality -- kind of vary in quality. of course, social media is not going to help us out or do you will get a lot of opinions and a lot of bickering. what is the solution? how does jim elsner spend his days figuring out this problem? well, i try to put things together. i put the theory together with the models in the data. i will talk about two theories. these are the two theories that allow us to get some answers about what hurricanes might be like in the future. you probably are not physicists or statisticians, so i will not go that deep, but these are the deep structures and which we can hang our hats on to try to understand what might it be like in the future. we will start with thermodynamics. this is my thermodynamics slide from popular mechanics, which basically describes how a hurricane operates.

all you have to keep in mind is a couple prepositions, ok? the first one is in. the second one is up. the third one is out. in, up, and out. that is the circulation of a hurricane, which you are probably not aware of it you think of circulation like this, these things that are spinning. that is the wind that will destroy your home, produce the surge, slide your house. but that is not the circulation that drives the hurricane. it is the in, up, and out. if you leave from here with those three prepositions, i have done my job. the in part is where the air comes in at low levels, near the ground, near the ocean's surface.

it gets its heat and moisture. it rises in the center of the hurricane and also the other thunderstorms surrounding the hurricane. it exhausts at higher levels. so it takes in high temperatures and exhausts the heat at much lower temperatures. it is kind of, not even kind of, it is almost exactly opposite of your refrigerator. it is how things day cold in your refrigerator, you are exhausting at a high temperatures. here you are exhausting at low temperatures. this is what you call a heat engine. it is an extremely efficient heat engine. these describe a hurricane that has a heat engine, a way of converting the heat and moisture of that motion into the winds ocean into the winds that you feel circulating. in, up, and out. so that is a theory, and it is based on thermodynamics that was worked out in the 18th century, and it is called the carnot heat engine.

with that heat engine theory, with that theory, we can work out how strong a hurricane can get. i will use this mpi to abbreviate how strong can those winds actually rotate, right? 60 miles per hour, 80 miles per hour, 100, 100 50 -- that is what i will call maximum potential intensity, the speed. that is just related to how warm the ocean is. sst is an abbreviation we use for how warm that ocean is. the warmer the ocean, the hider -- hired the intensity of the storm. -- the higher the intensity of the storm. they are proportional. more motion, more energy. this is a basic theory. this was worked out by a good friend of mine at m.i.t. he worked out this theory of how hurricanes and tens of i and how intensify andnes

how they get strong and their maximum potential, when energy is derived directly from how warm the water is to it so if you go summing in the ocean tomorrow and you feel that heat, that is that heat, because of this carnot engine theory, that drives the hurricanes. you think, that is kind of symbol, you can figure out how warm it will be and we can figure out intensity storms will be. if you are thinking ahead, you are thinking, why is this so complicated? warmer oceans, more energy. first, you have this thing done wn here, the denominator. that is the upper level. so the colder it is, so that his downstairs, colder it is, also the stronger the storm can be. so warm aloft, strong storm. colder aloft, you can get a very strong hurricane. you can get arctic hurricanes. if this is cold enough, even if this is not that warm.

you say, ok, he has everything there, why does he need -- he has his theory here, his friend can work this out. but here is the problem right here. blf, boundary layer fluxes -- the thing here is that the hurricanes start to makes up the ocean andup the produce all kinds of sea spray, so there's a lot of complexity going on. it is not clear how much this is going to go up given this. we know the oceans are warming up. they are warming up, primarily due to greenhouse gases. they are warming up, we should get stronger storms. how much longer? who knows, because of this be it we cannot work it out because of these boundary layer fluxes. ok, so that is one theory. bring in the second theory. the first one again to review. in, up, and out, the theory of

how storms intensify through the heat engine. your car has an engine, and it works on very similar idea, not nearly as efficient as a hurricane. the other theory is from statistics. this might even be farther from your experience. let me see if i can humor you a little bit. it was worked out in the mid-20th century, about 1955. for example, we record the highest wind speed in 10 consecutive hurricanes. so there is a hurricane out there now, let's say, and after two days it is gone, but we know how strong it got. let's say it got to 34.5 meters per second. why am i using meters per second? because i am a scientist. i apologize for that, but that is what we use.

if you need to convert, you just double that. that is miles per hour, so about 60 miles per hour. about how fast you go in your car, 60 miles per hour. this was the first hurricane, how strong it got. the next one, let's say, hit puerto rico and then died. and it had a maximum intensity of 44.2. you can imagine doing this for each hurricane. simply tell me how strong it got. i am going to put all those strengths down here. some get stronger than others. so this is the set of 10 wind speeds for the last 10 hurricanes. we can order these. this is how they occurred in time. this is more interesting from a statistical point of view. this was the weakest. this was the strongest. we just order them up.

this tells us that 20% of the hurricanes of these last 10 have winds that exceeded 61 meters per second. these two in red exceeded that amount of wind speed. 10% exceeded this amount. so we have percentages and threshold wind speeds. those two things make statisticians drool because they can connect the dots through this extreme value theory. so we can work up the spirit ago -- so we can work up these theoretical highest wind speeds. this gives a limiting intensity from the data. so from the theory thermodynamics, you can work of the maximum potential intensity, and from statistics, you can

work out the limited intensity. ok, so we have this limiting intensity as indicated by this red line. we take our hurricanes, ok, over the last set of years, and we can look at how strong the got. this is how fast they were rotating in meters per second. and we can plot them and fit a a curve through there. the black dots represent that curve. notice what happens to those black dots. they start to flatten out. that level here for this set of storms is right at 75 meters per second. that is what i mean by limiting intensity. that is a brilliant statistical theory. it tells us about the maximum you can get given the set of values you have. think about that, folks. think about it. i would say 30 people are in this room right now, and if i

measured the height of every one of you and then ranked them from shortest to tallest, i would have a single tall person in this room, but i would not say that that is the tallest person that could ever be in this room, right? but i want to know that. i want to know what would be the tallest. so i would have another lecture next week and would get another 25 people, a different group of people, and i would get another tall person. now i have two tell people. -- two tall people. that is what i do here. i'm getting all the set of tall people and then fitting a curve through that, and i can then extrapolate to get the possible tallest person. wonderful theory. it is embedded in the mathematics of statistics. so i got to about things going on here. this limiting intensity, a

statistical quantity that we can use to compare with emmanuel's theory of maximum potential intensity. hopefully you are still with me. i have this maximum digital intensity from theory. i have this statistical limited intensity from statistics. how should we make that comparison? it turns out that the limiting intensity is the absolute limit, that is that the tallest person is not that important, it is how limiting intensity changes with the ocean temperature. that is the key component here. how do we get at this? it turns out hurricanes, of course, occur over oceans where the temperatures are not uniform. so maybe if you pay attention to hurricanes, you will recognize this season. this is a plot of all the hurricanes in one season. i did not label which season it was. as a professor, i want to test my students. what season is this, if you remember? it was a pretty active season. >> [inaudible] >> close, very close to 2005. 2004 is an excellent guess.

they look very similar. 2005, we had lots of hurricanes and got hit a couple times here in florida. but you notice underneath the tracks, each of these white lines, actually, they are a series of hourly dots of where that hurricane was, at least the center of it. underneath those tracks are the ocean temperatures. they do not all of her over the occur over thell same temperature, right? so what we can do is use space -- as felicia said, i was trained as a scientist, but i really became a scientist when i became a geographer. although atmospheric science is an important discipline for understanding how the atmosphere

works, geography lets you put pieces together. it allows you to leverage space. it allows you to think about things spatially. when you start leveraging space, you can get a lot more bang for your buck. let me show you what i mean. we can grid up the domain. this is our atlantic basin. we recognize florida here. this is where our hurricanes form. we can look at two hurricanes, one here and one here. and the hurricane hours are gray until it gets into one of those hexagons, and then it turns black. i should say it is not a hurricane until it gets to black, but that is about hurricane intensity. so it is a weak storm that becomes a hurricane. i can count how many hurricanes occurred in each of the boxes, each of the hexagons. this is another key component of my talk, besides the three prepositions, which hopefully you do not forget. i want you to keep in mind the

difference between the frequency of storms and the intensity of storms. this is a key idea of trying to understand what might happen in the future. i am going to count them right here. these boxes had one, these had two, the others had no hurricanes. but i can do that for all the hurricanes that have occurred over many years. this is a 50-year plot. you can see were hurricanes are most common. in this box here, just off the north carolina coast, and then this box here. i bet, especially this one here, comes as somewhat of a surprise to you. this is where more hurricanes occur than here, more than here, more than here. more hurricanes out here and here than there are in here. so it is not about frequency, not just about frequency, because it does not give you the answer that you might think. this gets a little complicated,

but just humor me for another five minutes here. so i take a time period, 1981 through 2010. this is the number of hurricanes set have occurred in these boxes over that period of time. the darker blue indicates more hurricanes, the lighter blue, fewer hurricanes. simple. i will point to two, c and d. two hexagons, c and d. i can tell you how strong each hurricane was. i am getting back to the theory. strong each how hurricane was. each hurricane gets to have one value when he goes through that hexagon. i will take his highest value. this plot probably looks more familiar to you. this is where the storms are

strong. this is where they are frequent. this is where they are strong. c represents fewer, fewer, but stronger. fewer, stronger. more. less. less intense. more hurricanes, not as strong. ok? that is the key to this puzzle. the future might be about fewer but stronger. ok? and i know that does not sound like he is going to tell you whether my house is going to get -- i do not work at that scale. what i work at is the scale of trying to understand how the climate conspires to create hurricanes in this aggregate. so this is how i work it out. i have these two boxes, and i can say, for example, for box c, where there is fewer of them, i get a stronger limiting intensity. and for d, were there are fewer -- where there are more but less

intense, i get a lower limiting intensity. this is only 50 meters per second. second.70 meters per i am halfway done. sorry about that, only halfway done with the hurricane. i have the limiting intensities now. i have this nice special geographic -- a meteorologist would never think of this, but geographers would think about this all the time. it is blank space to understand how things are happening over time. i have my limiting intensity, so i can put a limiting intensity for each of my boxes, and i also have my ocean temperatures. ah-ha. no i have what i need, the limiting intensity and ocean

temperature, and together i get this beautiful plot. here are my ocean temperatures. here is how strong storms can get. and that slope represents the sensitivity, the sensitivity of of hurricane intensity to ocean warmth. that is exactly close to eight meters per second -- about 16 miles per hour for every degree celsius warming or two degrees fahrenheit of warming. that is how much stronger hurricanes we can expect to get, just based on combining the theory with the data. that is a beautiful result, because it has never been worked out before how to get that sensitivity. it is a fundamental component of how the atmosphere works and produces. why is this important? you say, well, ok, eight meters per second, plus our minus one. why is that important? turns out that hurricanes are

getting stronger by one meters per decade. it does not sound like a lot, but we can see the strongest storms, so for every year i can group the storms by their intensity. this is the medium intensity, and that is changing much but -- and that is not changing much but these upper quartiles are going up and going up at about one meter per second per decade. this is where it gets interesting, because the losses increase by -- here is your economic damage in billions of dollars. these indicate how much more damage we can expect for every meter per second increase in the wind speed. so this is important, folks, because it maps onto the next aggregation of the next 20, 30, 50, 100 years more of hurricanes that we will see this kind of change.

forget about the fact that we're -- that we will maybe build more stuff, have more stuff in the way. this is completely based on the theory of how hurricanes operate, so extremely important. ok, just a real quick summary. we can understand my hurricanes might be like in the future by combining the theory with the data. theory, in, up, and out, heat engines. wonderfulta, this extreme value theory. looks like the strongest hurricanes are stronger by about eight meters per second per degree of ocean warming. you can take that to the bank. this amounts to about one meters per second per decade, which translate to about a 5% increase in losses per decade, independent of how much we are exposing.

if we are exposing more, of course, you will get more. the keeping exposure constant, that is the increase. of course, you will say, well, there are lots of other factors. you take one factor. you are right, that affects sensitivity, share, upper level, etc. if we put those in our model, and we have done this, it is not very strong. it is not really affect it here it but there could be something i am missing. as a scientist, you always reserve the right to be wrong and to be found wrong. that is part of our job. but because it is part of our job, we think about this all the time. ok, let me talk about tornadoes. then we will stop and you can ask me questions. maybe tornadoes a somewhat mysterious to you compared to hurricanes. but as a scientist, to me, it is the same idea, this idea that the atmosphere produces these very episodic and extreme

events, whether it is the hurricane or the tornado. tornadoes are much smaller from -- much smaller phenomenon, but winds blow faster and do much more damage, on average, in the hurricanes. generally, this focuses on looking at the annual counts. if you look at the number of ef one plus tornadoes, we rate these tornadoes on the frujita scale. about an 80 mile per hour hurricane or stronger, you do not really see any trend in the number of storms. this does not imply that the tornado climate is stationary.

but it often stifles the discussion. we find that the number of days with tornadoes provide additional information about tornadoes and their occurrence. we do not have any theory to hang our hats on with regard to tornadoes might be like in the future. we do have something to go on with the data. fortunately, we have become much better at observing when it is then in the past. if you look at the long record of tornadoes, there is generally an increase in the numbers, but that is because we probably are able to communicate what we see and there are just more people paying attention. we have a population bias. this graph clearly shows this population bias. this is distanced to nearest city center. this is the number of tornado reports scaled by an area in

square kilometers. so this is within 10 square kilometers, a function of distance from a city, every city. this is every city. and you see a higher rate, between 1.2 and 1.4 tornadoes, per 10 square kilometers and near the city to less than one, about .8 from the city. if you want to go out and say cities cause tornadoes, you can get front-page headlines, right? but clearly, you probably want to step and say, is that the causal mechanism? maybe i have this backwards. it is likely that this is due to the fact that there's people say that the cities are will people can report it, and efficient mechanism of getting observations into record books. so that is what we are seeing

here. but what is really interesting, i did the same thing and 10-year periods, starting with 1961 to 1970, going to decades in one year at a time. what do you see in this plot as you go down? a couple things. very interesting. ok? to me, i will call this a snake plot. looks like these snakes, snakes that have been alerted to you. what is happening to those snake plots over time? maybe it is hard to see this bottom one. it is starting to flatten out. we do not see much of an urban effect relative to a rule affect today than we did back here. it is higher. those are the two main points of the plot. beautiful. clearly, it is not the cities that are causing the hurricanes, because this would not go flat

if that was the case. i just bring this up, folks, not because i like to look at snake plots, but i do spend a lot of time making nice plots, ok, but that is how i think, the way i try to put the pieces together. i say, well, if i did it this way, i should see that. if i do not, i am surprised. here is your number of tornadoes over time since about 1954. this is only the stronger tornadoes. it bounces around from year-to-year, sometimes as many as 900. 2011 was a big year. but there is no real trend. we're seeing more tornadoes as the climate is warming up, clearly the climate has warmed, but we're not seeing more tornadoes, not seeing fewer tornadoes.

but we are seeing fewer tornado days. so the same number of tornadoes but they are occurring on fewer days. this is work i did with my wife. she is sitting in the audience. she was instrumental in trying to get me to make these plots. so we're seeing fewer days with tornadoes, and that is the key, folks. suppose i count the number of days, number of days with at least 10 tornadoes. i told you i would have lots of graphs. number of days with at least n-tornadoes, so n is four here. about 35 days in 1954 where there were at least 10 tornadoes reported. tornadoes, i

should say. that bounces around. as you get to the larger end, to 16 and then to 32, you start to see this upward trend. what are we seeing? although the number of tornadoes is flat, the days in which there are big outbreaks is increasing. ok? it is like the atmosphere saves itself for a big day. to put it in anthropogenic terms. but that is what we are seeing it, and it is alarming. so we can do this in a slightly different way, talk about the probability of a day with at least 10 tornadoes. four here, is going up 8, 16, going up even more, and 32, going way up. just getting bigger outbreaks. what is happening? that is where my head is when i wake up in the morning. sorry. why is this happening, folks?

well, that is the -- i can do this slightly differently, thinking about it in terms of percent changed, different decades, but no matter which way you do it, it is not the number of events, but it is the big events. it is kind of like the hurricane problems. i said the strongest storms are getting stronger, no doubt about that. quantified it. we're not seeing more hurricanes, but the stronger ones are getting stronger. we are not seeing more tornadoes, but they are coming in bigger bunches. so there are fewer days without -- there are fewer days with tornadoes, but when they are coming, they are coming in bunches. there is a large-scale hypothesis that was put out. as soon as i tweeted this result, i got a lot of folks tweeting back.

they were saying -- i think about this as efficiency. i actually like that way of thinking about it. i like that way of thinking about hurricanes, too. the atmosphere is getting more efficient. when you think about the storms getting stronger, it is about efficiency. the time and which hurricanes intensifies not going to change. that is because they have to come off africa, move across the ocean, and the only have so much time to get strong. that is not going to change. the oceans are not getting wider, at least on the scale of humans. so they are going to have the same amount of time but getting stronger, which means they are more efficient. i like this idea of efficiency. same thing with tornadoes. we're not seeing more, but the atmosphere tends to be more efficient a jumping them from efficient at dropping them

from the clouds. so we call this the large-scale hypothesis. it involves large-scale dynamics, like wind shear. so my first thought based on this is the areas. we have a big day, and the area over which the 20 dozen dropping out of the sky -- they do not actually dropped out of the sky, tornadoes actually start from the ground-up. pro tip, they start from the ground-up. i think waterspouts are probably different. but the tornadoes we talking about here start from the ground-up. looks like they come from the cloud because the condensation comes from the cloud. that is what we see, the condensation first. but the spin starts of the ground. it is spinning up here and kind of gets together and speeds up. we can have another lecture on that, but here, i am thinking maybe the atmosphere is just getting better over a larger area.

let's look at a particular day, just one day in 2008, may 8. there were a number of tornadoes, but they occurred in two different regions. so i think about this is a cluster of tornadoes. this is a cluster of tornadoes. i just draw a box. i am a geographer, so i make these maps and draw out where the tornadoes occurred. what i think is happening, my hypothesis was that these things are getting bigger. more of them, so these areas are getting larger. the areas of these clusters are getting largers. this is an example of two clusters. this red dot is a tornado that represents the center of this set of tornadoes, the closest to the center. that is the middle tornado. it is like the median or the

mean, the medoid. i am going to get rid of all tornadoes -- oops. ok. >> that is a new one. >> is it? anyway, not so important. what is important is that i was wrong about my hypothesis. this is the mean number of clusters. we're not seeing more. that is pretty flat. although it is going up, it is not significant. maybe there's a few more -- the, but it's not total area of clusters is flat.