Hi everybody. Today I’m visiting the University of Oxford and I’m here to talk to Professor Tim Palmer about climate models. Tim, on the weekend I read this article in the New York Times. It was titled “How scientists got climate change so wrong” and it was mostly about weather extremes and said that climate change has been much more abrupt than climate scientists expected.
And I was wondering if that’s really the whole story. Because I kind of remember that people were talking about tipping points and unstable equilibria already in the 80s. So, I was a little bit surprised about this and I thought maybe I should I should ask you what do you think about this.
Well, the first thing to say is it’s kind of interesting that now for decades having been attacked by the climate skeptic stroke denier community that the models are somehow useless, now the attack is coming from the other side saying the models are somehow too conservative and not telling us enough about the extremes.
Now I guess there’s a couple of points to make here. The first thing is from a scientific point of view most of the focus of the model predictions has been on global mean temperature and the reason for that is sort of you know because that’s you know that’s that’s the basic thing that increased carbon dioxide is doing to the to the atmosphere, it’s increasing the surface temperature and by measuring the the global average of this what you’re actually doing is is is measuring, or predicting a quantity where the signal-to-noise is maximal, the signal being the effect of the carbon dioxide forcing that we’re putting into the atmosphere and the noise being the internal variability of climate, the fluctuations that really have nothing to do with increasing levels of co2 but just arise from the the natural chaotic variability of the atmosphere.
So when we go on to global scales this the chaotic variability is actually at a minimum and the impact of the co2 forcing is at a maximum so from a from a science point of view that actually is a very kind of robust indicator of how carbon dioxide is changing the climate, it’s warming the global temperature.
And actually I would say that the models from the 20th century through to the present day have been remarkably accurate in predicting the rise in global temperature. So, from that point of view I don’t I don’t think the models have been you know under estimating the effect of carbon dioxide on global warming.
However, when we come to talk about more regional extremes so things like particular heat waves – could be over Europe or United States – or you know flooding events or intense hurricanes or tropical cyclones or indeed as you say kind of tipping point types of phenomena, then you’re dealing with a situation where the internal variability of the atmosphere is much greater and the signal therefore relatively speaking is smaller, so it becomes the more difficult statistical exercise.
But on the other hand this is exactly what people want to know. I mean nobody physically is affected by global mean temperature but they are affected by extremes of weather. And I think basically what this is the article is is correctly pointing to is the need you know now we’ve established beyond doubt I would say that humankind is warming the planet we need to think in much more detail and much more your much more accuracy what this implies for regional extremes of temporal extremes of weather and climate so there are two issues here one is you know one is is developing this sort of statistical techniques where we can be confident in saying that such and such a weather event or climatic event had a had an anthropogenic if had an anthropogenic component in other words part of it was due to the fact that we are increasing co2 levels but it also puts for very much an onus on climate models to be able to simulate these extremes well and actually that’s an area where I think we can still improve things considerably so I think that the article I think it got a it it sort of exaggerated some aspects of the issue particularly in relation to global mean temperature but it correctly drew attention to the fact that we do need to focus much more on our ability to simulate and predict and assess how extremes of weather and climate are being affected by climate change forcing so speaking about the quality of the predictions you told me something about this figure 9.
8 in the IPCC report and this took me forever to understand but I will I will try to summarize what’s in the figure and had you told me if that’s if that’s about correct so what you see in the figure is the temperature anomaly for a period of years and so the temperature anomaly is the it’s the global temperature basically up to a reference value and this reference value on this figure is is in the yellow region which is from the years 61 to 1990 and so all the thin squiggly lines are the predictions from the different models and the red line is the average from the models and the three black lines are the data from different organizations and so what I what I didn’t understand for mr.
Craig it’s not the data from different organizations is how different organizations analyze the data to the the common data sets to produce estimates of global temperature yeah so so what what I didn’t understand forever was what’s this little bar on the right side where it says mean temperature so that’s the actual temperature the absolute temperature that these models predict in this region from 61 to 1990 so basically tells you that the spread in the in the absolute temperatures is much larger than the uncertainty you know the little squiggles in these models in in the whole region where they have data right so I think what this figure tells us is that the models all agree that there is a certain trend you know looks pretty good you know in terms of forecasting but it also tells me that you know the models have some difficulty getting they are full of temperatures right yeah it just highlights the fact I think I think perhaps it might be worth backing off a bit here and saying that these models are attempt to represent the climate of the earth from pretty much first principles you know from the basic laws of physics so from Newton’s laws of motion as expressed in you know the what are called the navier-stokes equations of fluid mechanics to equations which basically represent the laws of thermodynamics in a in a sort of in terms of differential equations coupled together with laws which express more quantum mechanical laws which express how photons from the Sun are absorbed by different molecules and so on in the atmosphere and re-radiated back to space within the infrared so these are all very basic sort of equations they’re not you know it’s not that we’re just kind of guessing empirically how we think the the world works you know by just sort of drawing equations out of a hat and putting them into a computer these are the basic laws of physics now if you look at it from that point of view trying to get exactly the right you know good to simulate exactly the right surface temperature of the earth which you have to remember you know over the oceans the surface temperature is is a very sort of complicated balance between you know regions of weather whether the ocean is ocean water is sinking and other areas where it’s rising to the surface and regions where the Sun warms the surface other regions where you know you’re under cloud and there’s very little Sun so getting the surface temperature not only requires for example getting the dynamics of the ocean right it requires getting all that cloud cover right in the right place so it’s a really really really complicated and difficult thing to get right that’s the first thing to say and the little bar on the right hand side is just pointing out that actually you know it’s a kind of manifestation of that problem because the range of estimates of global mean temperature from the models actually range over a few degrees which is which is much larger than this trend in temperature that we’ve seen over the last you know 6070 years or so now I don’t think this particularly undermines there where it doesn’t undermine the projections of global temperature it doesn’t it doesn’t I don’t think it casts any doubt that the trends in in temperature that we’ve seen over the the last 70 years are indeed directly associated with human emission of carbon dioxide but what it indicates is that you know we still have some way to go before we can say we have simulated the climate system to the extent that you really can’t tell now if I you know if I show you output from a climate model you can’t tell whether you’re looking at a model or the real world we still have some way to go to do that and that’s particularly becomes particularly important and more at the regional level for example you know we talked a little bit earlier about tipping points and these are these are kind of what you might call very nonlinear transitions sudden transitions in the in the climate system getting these right actually does depend on getting the actual absolute values of temperature right so for example if you take the melting of you know I mean there’s a concern that the melting of Greenland ice and actually the sort of disintegration of the Greenland ice core caused by you know the lubrication of the surface the bedrock from from melting water I mean that requires models to get that absolute that the temperature right because water fresh water at these freezes at 0 degrees so if you have a two degree bias or something I mean that you’re going to get that process wrong another example is is possible tipping points for the bias here where you know either due to heat or or combination of heat and and an availability of moisture of rainfall you know a forest can suddenly become no longer self-sustaining and will collapse but again that to be able to model that requires getting these temperatures and rainfall amounts not right in a kind of anomalous sense but getting right absolutely and all that I think that what that bar on the right hand side I don’t think it should make us doubt at all that the temperature is warming due to co2 but what it what it’s indicative of is the fact that we you know particularly now as climate really starts to become an important societal issue we’ve got to step up a gear in getting our models bias-free yeah so this bar on the right side this was one of the things that our dinner around and about this figure the other thing is that I find it peculiar that you have the prediction from these models but the predictions don’t have any uncertainty attached to them which is what what I would expect would be the output of such a model so my understanding is that the the figure that for the projection of the increase in temperature until the year 2100 or something in the IPCC report it has an uncertainty and that’s basically the spread in the projections from the different models not actually the uncertainty from the models okay I mean so the first thing to say is that the whole philosophy underlying the IPCC report is that it’s it’s an assessment of the you know of the state of the art of climate science as as determined by the peer-reviewed publications that exist at the time the report is written now there are many climate Institute’s around the world you know typically certainly virtually all of the bigger countries of the world have their own weather or climate Institute’s and and they they have their own climate models they might be they might be literally their own model produced by scientists in-house or they might have taken the code from from another institution and maybe done some modifications and you know produced produced results for that model now many Institute’s do not have sufficient computing resources to actually run the model itself in a kind of ensemble mode where they might produce you know 50 projections where you try to buy a number of different possible ways of representing uncertainty in the particularly the so called sub grid parameterization so that’s the most unser and part of climate models you have to represent processes cloud processes perhaps the most important uh which are occurring on scales where where the model can’t resolve the grids of spacing between the grid points in the model is larger than that you know typical size of the cloud I mean many climate models have grid spacings of many tens that you know you may be up to 100 kilometers or as individual clouds they’re just you know a few kilometres and big ones induce comatose in the horizontal horizontal sorry in the horizontal yes it would be less in the vertical so many climate Institute’s don’t have the computational resources to to try to explore the uncertainty in the you know in the very in the in the sub grid parameterizations so they would they would typically just have one one run or one or two let’s say other other Institute’s may have may have multiple ensemble integrations where they do I mean the Met Office the UK Met Office is a good example where they produce very large ensembles of climate change integrations where they try to exactly do what you say try to perturb the uncertain parts of the of the models maybe using some kind of stochastic process and then run these but for these IPCC assessments you know to avoid being dominated by you know if one Institute had a hundred runs and the others only had one you know you’d be dominated by the center which had a hundred so I suppose you know a way of a way of dealing that with that is just to make the assumption that the ensemble of all of these different models is itself a reasonable representation of model uncertainty now you can argue whether that’s true or not and I would argue certainly on the again coming down to the regional scale that’s probably not a good assumption but I think for these global mean temperatures it’s not a bad so what do you think are the main reasons that the predictions from the different models diverge well there’ll be a little bit of divergence from chaos if you like that you know if you just started them from infinitesimally different initial start initial starting conditions the butterfly effect will actually produce a certain amount of spread but that’s probably not the that isn’t really the major contribution to uncertainty it comes from the uncertainty in in how to represent processes which you know are important for climate but where you don’t have the computational resources to resolve them so you have to parameterize to use the bit of the jug and parameterize these sub grid processes with very simple well it’s relatively simple anyway formula which you know which and then so you have a closure formula so there is a basic assumption somewhere where you would say okay if I know the temperature in a grid box and I know the humidity in the grid box and you know maybe some other variables the wind and so on I can predict in a bulk sense what the cloud the amount of cloud in that grid box you know whether it’ll be completely cloud free or completely cloud covered or you know 50/50 or something like that half covered and half creek so so there’d be a formula which would be based on these these resolved scale variables now you know in reality there isn’t such a formula you know it’s not like nature you can’t go up and you know read of a textbook on physics and discover what that formula is because there is no formula like that so different groups may come up with different formulae different closure schemes for you know for various reasons they may have some datasets which other groups don’t have which may be supports their formula or whatever it is I mean my own view is that the only way to deal with this objectively is to express all of these sub closure schemes in a stochastic way using kind of some ideas of random variables and just acknowledge that that’s actually from from a basic physics point of view that is the best way to represent uncertain processes but but in any case the origin of the uncertainty is this sub grid the sub grid parameterizations and you know of them of all of them the most important our cloud processes but there’s also other things to do with you know how you represent topography the mountains of the earth you know if you have a very sharp flow that’s blocking so if you have a very sharp barrier that’s blocking some flow the the the width of the barrier might actually be too small to represent with your grid if your grid you’re with your finite grid so you have to try to represent that blocking effect in a in a more approximate way I mean that’s just one example but and the oceans you know the oceans have what are called a mesoscale Eddie’s which are really important for determining how strong currents like the Gulf Stream or the Kuroshio Current in the Pacific are but again you know modern-day climate models the ocean the ocean part of these climate models is the resolution is I mean we’re getting starting to get getting close to being able to resolve these types of ocean eddies but we’re not really there yet so they have to be parameterized and again that’s the source of uncertainty so I guess this brings up the obvious question what can be done about it well you see the the the issue you know the the interesting thing from my point of view is that climate has to have gone in the last few years from something that it’s always been potentially of societal concern but I think a lot of a lot of scientists felt that although you know the the societal concerns were important in a way that the the the tools that they had were if you like primarily being used for scientific research to really understand you know the way in which for example co2 in house co2 emissions would impact on different parts of the climate system it was it was a yeah I mean you know it’s a scientific endeavor but what’s literally happened you know in the last year or two is that it suddenly become this incredibly pressing societal issue you know we’re seeing all around the world these quite devastating weather events which are you know affecting people’s lives and society has got to know what can they expect in the future and how can they better prepare for the future what sorts of you know buildings do we need to withstand these extremes where should we be living to withstand these extremes you know can the can the human body actually literally exist in parts of the world where temperatures and humidity –zz become once they reach a certain level so it’s kind of gone from a you know a society important that fundamentally scientific question – one that really is societally crucial and I think therefore as a result of that we’ve got to think much more in a much more pressing way about making these models fully realistic and accurate and really trying to eliminate where possible these these parameters which just are just to approximate and we know in a way the the the bottom line is the resolution of the model in other words the spacing between the grid points that’s what you know that’s having having these grid spacings of many tens or hundreds of kilometres means that many of these important processes key types of cloud processes ocean meso-scale eddies, the flow over orography, topography, whatever you want to call it, have to be parametrized.
We know if we can get the grid down to, say, about one kilometer globally then we can eliminate certain – not all parameterizations – but probably the most important ones. And I feel, given this new sort of urgency to try to be able to answer questions which governments around the world and individuals around the world are asking about and that and the New York Times article drew attention to about extremes how you know how I mean we’re you know this week for example in the UK there was considerable flooding in in an area near Doncaster hundreds of people had to leave their houses I mean that’s the kind of key question a government wants to know how much more frequent will that type of situation occur in the future the you know poor people that suffered in Mozambique under these tropical, enormously powerful tropical cyclones.
Again we want to know how much more frequent are these unbelievably intense tropical cyclones going to be. So, we’ve got to develop models where these biases and so on you know are eliminated. And we can do that in principle, but like all scientific big projects it requires a certain amount of of investment.
And it’s primarily investment in supercomputing it’s to do with supercomputing. So you know I would certainly agree with that that this is information that we need so I find it a little bit ironic that I keep hearing that the science is basically done so I I have another quote which I found in The Guardian in an article that appeared last week.
It says “For ordinary citizens it is important to recognize that scientists have done their job. It is up to us to force our leaders to act upon what we know.” Ok, I mean that particular quote was I think referring to the issue of trying to cut our emissions of carbon dioxide I would agree I think more or less with that quote if all that we were talking about was do we have enough evidence to make a decision about cutting our carbon emissions because in a way you know it’s like every decision you make in life you don’t necessarily have to know exactly what’s going to happen to make that decision you have to you have to know the threat that you’re facing and whether the decision is justified given that threat now the one thing that you know climate models have been quite unequivocal about is actually that as we if we if we continue to emit co2 as we have as we are doing now and as we have done then by 2100 although we can say that you know the most likely amount of global warming might be you know might be systems maybe three or four degrees we know from these ensembles of integrations that there is a tail which goes out to more than that could go out to six or seven or or even more than that degrees now again since then I don’t know that sounds a lot but for anyone who knows their climatology that really is captain strophic so you know I mean I as a scientist don’t want to be I don’t want to say that you know that means that we must cut our emissions immediately because that’s a political statement but I think the politicians in principle have that have enough information to make that decision so that the fact there is this threat this risk not only of you know very undesirable levels of climate change but actually you know catastrophic that was the climate change the risk is quite clear and the only way to reduce that risk is to reduce our emissions so from that point of view I agree with the statement but to say that the science is is kind of all done and dusted and the scientists are not needed anymore misses the you know the other aspect of the problem which is that you know even if we cut our emissions to zero tomorrow we’ve already put into the climate system a certain amount of climate change that will continue.
Now we’re not going to cut our emissions tomorrow, we’re going to carry on for sure for many decades to come, and so we are faced with a change in climate, and we are faced with decisions on how to make society more resilient to that change in climate.
And I think nowhere is that more important than in the developing world who after all have had absolutely nothing to do with this problem, I mean they have not caused it in the most minute way, but in a way they’re suffering they’re likely to suffer the most either from extreme levels of drought to these really occasional but exceptionally damaging storms or to levels of you know as I was saying to you earlier, to periods where temperatures and humidity could get so high collectively that the human body can no longer lose heat either by sweating or any other means so you know then that becomes an existential threat so that’s sort of what we’ve got to be do better I think in quantifying and and that that puts very much the onus of climate change at the regional level not just the global mean temperature you know my own view is that this is a little bit like you know the the the famous Marshall Plan for bailing out Europe after the Second World War where the u.
s. pumped you know large amounts of money into stimulating the European climate or the European economy not particularly for an altruistic reason but because they feared the spread of communism and they wanted to stop that now you could very much view that the the whole investment in climate adaptation in the developing world could similarly be viewed at a very you know self-interested level in the sense that we’re already seeing you know migration you know in Europe from Africa and the Middle East in the United States from Central America and South America and there are certainly aspects of climate change in the reasons why people are migrating now this is potentially nothing compared to what it could be like you know in in later in this century and so I think a kind of modern-day Marshall Plan by the developed world to try to make life just more bearable in the developing world would would you know like like the Marshall Plan to stop communist and this would be to try to keep people in place and say actually you know living where you are is not so bad but if it becomes unbearable then then the trickle of migration that we’re seeing now will become a torrent and so that’s that’s where again I think the climate science is not done and dusted because we don’t yet have a good and I would say reliable picture of how these extremes of climate at the regional level are changing and whether for example these tipping points as they’re called colloquially but these kind of sudden rapid changes in climate which cannot be reversed, I mean that’s the key point about tipping point you can’t reverse it, once it’s flipped into this new state it’s irreversible.
This by the way has this this this is actually where this actually has a kind of a feedback into this question of emissions reduction because there’s certainly a body of political thought which says well cutting our carbon emissions today is really really difficult for various political reasons but we don’t have to worry because in 50 years time we’ll have developed the technology which will enable us to suck the co2 out you know will will suck it out of the atmosphere and dump it underground and so we know we don’t have to be too kind of aggressive in our emissions cuts today because that will be a technology that will be there in the future which will help us.
Now the point about that is if in that period before before the technology has been developed if it ever can be developed which is a major question mark I would say if we have undergone these tipping points for example in Greenland ice or some of the biosphere or indeed at oceans some ocean dynamics might potentially have that capability then you’ve gone to a stake that you can’t reverse.
So sucking the co2 out of the air once the tipping points happened won’t do any good at all you’re not going to recover back to where you were so again that’s an area actually where I’m slightly contradicting myself because I was saying that we perhaps need all we need we have as much knowledge as we need to put into place emissions cuts but I think the question about whether we can delay emissions in the hope that sort of the sucking it out of the air at some future stage will occur that’s going to be totally ineffective if we actually have crossed some of these tipping point things and that’s much that requires knowledge of the climate system at that much more regional level and also at a much more detailed level because invoice is never to involve quite nonlinear processes which are which are hard to you know simulate accurately and and that’s where you need good models I think that’s a good place to stop thank you for your time thanks everybody for watching see you again next week hi everybody today I’m visiting the University of Oxford and I’m here to talk to Professor Tim Palmer about climate models Tim on the weekend I read this article in the New York Times there was titled how scientists got climate change so wrong and it was mostly about weather extremes and said that climate change has been much more abrupt than climate scientists expected and I was wondering if that’s really the whole story because I kind of remember that people were talking about tipping points and unstable equilibria already in the 80s so I was a little bit surprised about this and I thought maybe I should I should ask you what do you think about this well the first thing to say is it’s kind of interesting that now for decades having been attacked by the climate skeptic stroke denier community that the models are somehow useless now the attack is coming from the other side saying the models are somehow too too conservative and not telling us enough about the extremes now I guess there’s a couple of points to make here the first thing is from a scientific point of view most of the focus of the model predictions has been of global mean temperature and the reason for that is sort of you know because that’s you know that’s that’s the basic thing that increased carbon dioxide is doing to the to the atmosphere it’s increasing the surface temperature and by measuring the the global average of this what you’re actually doing is is is measuring a coil predicting a quantity where the signal-to-noise is maximum maximal the signal being the effect of the carbon dioxide forcing that we’re putting into the atmosphere and the noise being the internal variability of climate the fluctuations that really have nothing to do with increasing levels of co2 but just arise from the the natural chaotic variability of the atmosphere so when we go on to global scales this the chaotic variability is actually at a minimum and the impact of the co2 forcing is at a maximum so from a from a science point of view that actually is a very kind of robust indicator of how carbon dioxide is changing the climate it’s warming the global temperature and actually I would say that the models from the 20th century through to the present day have been remarkably accurate in predicting the rise in global temperature so if from that point of view I don’t I don’t think the models have been you know under estimating the effect of carbon dioxide on global warming however when we come to talk about more regional extremes so things like particular heat waves could be over Europe or United States or you know flooding events or intense hurricanes or tropical cyclones or indeed as you say kind of tipping point types of phenomena then you’re dealing with a situation where the internal variability of the atmosphere is much greater and the signal therefore relatively speaking is smaller so it becomes the more difficult statistical exercise but on the other hand this is exactly what people want to know I mean nobody physically is affected by global mean temperature but they are affected by extremes of weather and I think basically what this is the article is is correctly pointing to is the need you know now we’ve established beyond doubt I would say that humankind is warming the planet we need to think in much more detail and much more your much more accuracy what this implies for regional extremes of temporal extremes of weather and climate so there are two issues here one is you know one is is developing this sort of statistical techniques where we can be confident in saying that such and such a weather event or climatic event had a had an anthropogenic if had an anthropogenic component in other words part of it was due to the fact that we are increasing co2 levels but it also puts for very much an onus on climate models to be able to simulate these extremes well and actually that’s an area where I think we can still improve things considerably so I think that the article I think it got a it it sort of exaggerated some aspects of the issue particularly in relation to global mean temperature but it correctly drew attention to the fact that we do need to focus much more on our ability to simulate and predict and assess how extremes of weather and climate are being affected by climate change forcing so speaking about the quality of the predictions you told me something about this figure 9.
8 in the IPCC report and this took me forever to understand but I will I will try to summarize what’s in the figure and had you told me if that’s if that’s about correct so what you see in the figure is the temperature anomaly for a period of years and so the temperature anomaly is the it’s the global temperature basically up to a reference value and this reference value on this figure is is in the yellow region which is from the years 61 to 1990 and so all the thin squiggly lines are the predictions from the different models and the red line is the average from the models and the three black lines are the data from different organizations and so what I what I didn’t understand for mr.
Craig it’s not the data from different organizations is how different organizations analyze the data to the the common data sets to produce estimates of global temperature yeah so so what what I didn’t understand forever was what’s this little bar on the right side where it says mean temperature so that’s the actual temperature the absolute temperature that these models predict in this region from 61 to 1990 so basically tells you that the spread in the in the absolute temperatures is much larger than the uncertainty you know the little squiggles in these models in in the whole region where they have data right so I think what this figure tells us is that the models all agree that there is a certain trend you know looks pretty good you know in terms of forecasting but it also tells me that you know the models have some difficulty getting they are full of temperatures right yeah it just highlights the fact I think I think perhaps it might be worth backing off a bit here and saying that these models are attempt to represent the climate of the earth from pretty much first principles you know from the basic laws of physics so from Newton’s laws of motion as expressed in you know the what are called the navier-stokes equations of fluid mechanics to equations which basically represent the laws of thermodynamics in a in a sort of in terms of differential equations coupled together with laws which express more quantum mechanical laws which express how photons from the Sun are absorbed by different molecules and so on in the atmosphere and re-radiated back to space within the infrared so these are all very basic sort of equations they’re not you know it’s not that we’re just kind of guessing empirically how we think the the world works you know by just sort of drawing equations out of a hat and putting them into a computer these are the basic laws of physics now if you look at it from that point of view trying to get exactly the right you know good to simulate exactly the right surface temperature of the earth which you have to remember you know over the oceans the surface temperature is is a very sort of complicated balance between you know regions of weather whether the ocean is ocean water is sinking and other areas where it’s rising to the surface and regions where the Sun warms the surface other regions where you know you’re under cloud and there’s very little Sun so getting the surface temperature not only requires for example getting the dynamics of the ocean right it requires getting all that cloud cover right in the right place so it’s a really really really complicated and difficult thing to get right that’s the first thing to say and the little bar on the right hand side is just pointing out that actually you know it’s a kind of manifestation of that problem because the range of estimates of global mean temperature from the models actually range over a few degrees which is which is much larger than this trend in temperature that we’ve seen over the last you know 6070 years or so now I don’t think this particularly undermines there where it doesn’t undermine the projections of global temperature it doesn’t it doesn’t I don’t think it casts any doubt that the trends in in temperature that we’ve seen over the the last 70 years are indeed directly associated with human emission of carbon dioxide but what it indicates is that you know we still have some way to go before we can say we have simulated the climate system to the extent that you really can’t tell now if I you know if I show you output from a climate model you can’t tell whether you’re looking at a model or the real world we still have some way to go to do that and that’s particularly becomes particularly important and more at the regional level for example you know we talked a little bit earlier about tipping points and these are these are kind of what you might call very nonlinear transitions sudden transitions in the in the climate system getting these right actually does depend on getting the actual absolute values of temperature right so for example if you take the melting of you know I mean there’s a concern that the melting of Greenland ice and actually the sort of disintegration of the Greenland ice core caused by you know the lubrication of the surface the bedrock from from melting water I mean that requires models to get that absolute that the temperature right because water fresh water at these freezes at 0 degrees so if you have a two degree bias or something I mean that you’re going to get that process wrong another example is is possible tipping points for the bias here where you know either due to heat or or combination of heat and and an availability of moisture of rainfall you know a forest can suddenly become no longer self-sustaining and will collapse but again that to be able to model that requires getting these temperatures and rainfall amounts not right in a kind of anomalous sense but getting right absolutely and all that I think that what that bar on the right hand side I don’t think it should make us doubt at all that the temperature is warming due to co2 but what it what it’s indicative of is the fact that we you know particularly now as climate really starts to become an important societal issue we’ve got to step up a gear in getting our models bias-free yeah so this bar on the right side this was one of the things that our dinner around and about this figure the other thing is that I find it peculiar that you have the prediction from these models but the predictions don’t have any uncertainty attached to them which is what what I would expect would be the output of such a model so my understanding is that the the figure that for the projection of the increase in temperature until the year 2100 or something in the IPCC report it has an uncertainty and that’s basically the spread in the projections from the different models not actually the uncertainty from the models okay I mean so the first thing to say is that the whole philosophy underlying the IPCC report is that it’s it’s an assessment of the you know of the state of the art of climate science as as determined by the peer-reviewed publications that exist at the time the report is written now there are many climate Institute’s around the world you know typically certainly virtually all of the bigger countries of the world have their own weather or climate Institute’s and and they they have their own climate models they might be they might be literally their own model produced by scientists in-house or they might have taken the code from from another institution and maybe done some modifications and you know produced produced results for that model now many Institute’s do not have sufficient computing resources to actually run the model itself in a kind of ensemble mode where they might produce you know 50 projections where you try to buy a number of different possible ways of representing uncertainty in the particularly the so called sub grid parameterization so that’s the most unser and part of climate models you have to represent processes cloud processes perhaps the most important uh which are occurring on scales where where the model can’t resolve the grids of spacing between the grid points in the model is larger than that you know typical size of the cloud I mean many climate models have grid spacings of many tens that you know you may be up to 100 kilometers or as individual clouds they’re just you know a few kilometres and big ones induce comatose in the horizontal horizontal sorry in the horizontal yes it would be less in the vertical so many climate Institute’s don’t have the computational resources to to try to explore the uncertainty in the you know in the very in the in the sub grid parameterizations so they would they would typically just have one one run or one or two let’s say other other Institute’s may have may have multiple ensemble integrations where they do I mean the Met Office the UK Met Office is a good example where they produce very large ensembles of climate change integrations where they try to exactly do what you say try to perturb the uncertain parts of the of the models maybe using some kind of stochastic process and then run these but for these IPCC assessments you know to avoid being dominated by you know if one Institute had a hundred runs and the others only had one you know you’d be dominated by the center which had a hundred so I suppose you know a way of a way of dealing that with that is just to make the assumption that the ensemble of all of these different models is itself a reasonable representation of model uncertainty now you can argue whether that’s true or not and I would argue certainly on the again coming down to the regional scale that’s probably not a good assumption but I think for these global mean temperatures it’s not a bad so what do you think are the main reasons that the predictions from the different models diverge well there’ll be a little bit of divergence from chaos if you like that you know if you just started them from infinitesimally different initial start initial starting conditions the butterfly effect will actually produce a certain amount of spread but that’s probably not the that isn’t really the major contribution to uncertainty it comes from the uncertainty in in how to represent processes which you know are important for climate but where you don’t have the computational resources to resolve them so you have to parameterize to use the bit of the jug and parameterize these sub grid processes with very simple well it’s relatively simple anyway formula which you know which and then so you have a closure formula so there is a basic assumption somewhere where you would say okay if I know the temperature in a grid box and I know the humidity in the grid box and you know maybe some other variables the wind and so on I can predict in a bulk sense what the cloud the amount of cloud in that grid box you know whether it’ll be completely cloud free or completely cloud covered or you know 50/50 or something like that half covered and half creek so so there’d be a formula which would be based on these these resolved scale variables now you know in reality there isn’t such a formula you know it’s not like nature you can’t go up and you know read of a textbook on physics and discover what that formula is because there is no formula like that so different groups may come up with different formulae different closure schemes for you know for various reasons they may have some datasets which other groups don’t have which may be supports their formula or whatever it is I mean my own view is that the only way to deal with this objectively is to express all of these sub closure schemes in a stochastic way using kind of some ideas of random variables and just acknowledge that that’s actually from from a basic physics point of view that is the best way to represent uncertain processes but but in any case the origin of the uncertainty is this sub grid the sub grid parameterizations and you know of them of all of them the most important our cloud processes but there’s also other things to do with you know how you represent topography the mountains of the earth you know if you have a very sharp flow that’s blocking so if you have a very sharp barrier that’s blocking some flow the the the width of the barrier might actually be too small to represent with your grid if your grid you’re with your finite grid so you have to try to represent that blocking effect in a in a more approximate way I mean that’s just one example but and the oceans you know the oceans have what are called a mesoscale Eddie’s which are really important for determining how strong currents like the Gulf Stream or the Kuroshio Current in the Pacific are but again you know modern-day climate models the ocean the ocean part of these climate models is the resolution is I mean we’re getting starting to get getting close to being able to resolve these types of ocean eddies but we’re not really there yet so they have to be parameterized and again that’s the source of uncertainty so I guess this brings up the obvious question what can be done about it well you see the the the issue you know the the interesting thing from my point of view is that climate has to have gone in the last few years from something that it’s always been potentially of societal concern but I think a lot of a lot of scientists felt that although you know the the societal concerns were important in a way that the the the tools that they had were if you like primarily being used for scientific research to really understand you know the way in which for example co2 in house co2 emissions would impact on different parts of the climate system it was it was a yeah I mean you know it’s a scientific endeavor but what’s literally happened you know in the last year or two is that it suddenly become this incredibly pressing societal issue you know we’re seeing all around the world these quite devastating weather events which are you know affecting people’s lives and society has got to know what can they expect in the future and how can they better prepare for the future what sorts of you know buildings do we need to withstand these extremes where should we be living to withstand these extremes you know can the can the human body actually literally exist in parts of the world where temperatures and humidity –zz become once they reach a certain level so it’s kind of gone from a you know a society important that fundamentally scientific question – one that really is societally crucial and I think therefore as a result of that we’ve got to think much more in a much more pressing way about making these models fully realistic and accurate and really trying to eliminate where possible these these parameters which just are just to approximate and we know in a way the the the bottom line is the resolution of the model in other words the spacing between the grid points that’s what you know that’s having having these grid spacings of many tens or hundreds of kilometres means that many of these important processes key types of cloud processes ocean ocean music scale Eddie’s the flow over our ography topography wouldn’t they call it have to be parametrized we know if we can get the grid down to say about one kilometer globally then we can eliminate certain not all parameterizations but probably the most important ones and I feel given this new sort of urgency to try to be able to answer questions which governments around the world and individuals around the world are asking about and that and the New York Times article drew attention to about extremes how you know how I mean we’re you know this week for example in the UK there was considerable flooding in in an area near Doncaster hundreds of people had to leave their houses I mean that’s the kind of key question a government wants to know how much more frequent will that type of situation occur in the future the you know poor people that suffered in Mozambique under these tropical enormous ly powerful tropical cyclones again want to know how much more frequent are these unbelievably intense tropical cyclones going to be so we’ve got to develop models where these biases and so on you know are eliminated and we can do that in principle but like all scientific big projects it requires a certain amount of of investment and it’s primarily investment in supercomputing is to do with supercomputing so you know I would certainly agree with that that this is information that we need so I find it a little bit ironic that I keep hearing that the science is basically done so I I have another quote which I found in The Guardian in an article that appeared last week it says for ordinary citizens it is important to recognize that scientists have done that job it is up to us to force our leaders to act upon what we know ok I mean that particular quote was I think referring to the issue of trying to cut our emissions of carbon dioxide I would agree I think more or less with that quote if all that we were talking about was do we have enough evidence to make a decision about cutting our carbon emissions because in a way you know it’s like every decision you make in life you don’t necessarily have to know exactly what’s going to happen to make that decision you have to you have to know the threat that you’re facing and whether the decision is justified given that threat now the one thing that you know climate models have been quite unequivocal about is actually that as we if we if we continue to emit co2 as we have as we are doing now and as we have done then by 2100 although we can say that you know the most likely amount of global warming might be you know might be systems maybe three or four degrees we know from these ensembles of integrations that there is a tail which goes out to more than that could go out to six or seven or or even more than that degrees now again since then I don’t know that sounds a lot but for anyone who knows their climatology that really is captain strophic so you know I mean I as a scientist don’t want to be I don’t want to say that you know that means that we must cut our emissions immediately because that’s a political statement but I think the politicians in principle have that have enough information to make that decision so that the fact there is this threat this risk not only of you know very undesirable levels of climate change but actually you know catastrophic that was the climate change the risk is quite clear and the only way to reduce that risk is to reduce our emissions so from that point of view I agree with the statement but to say that the science is is kind of all done and dusted and the scientists are not needed anymore misses the you know the other aspect of the problem which is that you know even if we cut our emissions to zero tomorrow we’ve already put into the climate system a certain amount of climate change that will continue now we’re not going to cut our emissions tomorrow we’re going to carry on for sure for many decades to come and so we are faced with a change in climate and we are faced with decisions on how to make society more resilient to that change in climate and I think nowhere is that more important than in the developing world who after all have had absolutely nothing to do with this problem I mean they have not caused it in the most minut way but in a way they’re suffering they’re likely to suffer the most either from extreme levels of drought to these really occasional but exceptionally damaging storms or to levels of you know as I st.
you earlier to periods where temperatures and humidity could get so high collectively that the human body can no longer lose heat either by sweating or any other means so you know then that becomes an existential threat so that’s sort of what we’ve got to be do better I think in quantifying and and that that puts very much the onus of climate change at the regional level not just the global mean temperature you know my own view is that this is a little bit like you know the the the famous Marshall Plan for bailing out Europe after the Second World War where the u.
s. pumped you know large amounts of money into stimulating the European climate or the European economy not particularly for an altruistic reason but because they feared the spread of communism and they wanted to stop that now you could very much view that the the whole investment in climate adaptation in the developing world could similarly be viewed at a very you know self-interested level in the sense that we’re already seeing you know migration you know in Europe from Africa and the Middle East in the United States from Central America and South America and there are certainly aspects of climate change in the reasons why people are migrating now this is potentially nothing compared to what it could be like you know in in later in this century and so I think a kind of modern-day Marshall Plan by the developed world to try to make life just more bearable in the developing world would would you know like like the Marshall Plan to stop communist and this would be to try to keep people in place and say actually you know living where you are is not so bad but if it becomes unbearable then then the trickle of migration that we’re seeing now will become a torrent and so that’s that’s where again I think the climate science is not done and dusted because we don’t yet have a good and I would say reliable picture of how these extremes of climate at the regional level are changing and whether for example these tipping points as they’re called colloquially but these kind of sudden rapid changes in climate which cannot be reversed I mean that’s the key point about tipping point you can’t reverse it once it’s flipped into this new state isn’t it’s irreversible this by the way has this this this is actually where this actually has a kind of a feedback into this question of emissions reduction because there’s certainly a body of political thought which says well cutting our carbon emissions today is really really difficult for various political reasons but we don’t have to worry because in 50 years time we’ll have developed the technology which will enable us to suck the co2 out you know will will suck it out of the atmosphere and dump it underground and so we know we don’t have to be too kind of aggressive in our emissions cuts today because that will be a technology that will be there in the future which will help us now the point about that is if in that period before before the technology has been developed if it ever can be developed which is a major question mark I would say if we have undergone these tipping points for example in Greenland ice or some of the biosphere or indeed at oceans some ocean dynamics might potentially have that capability then you’ve gone to a stake that you can traverse so sucking the co2 out of the air once the tipping points happened won’t do any good at all you’re not going to recover back to where you were so again that’s an area actually where I’m slightly contradicting myself because I was saying that we perhaps need all we need we have as much knowledge as we need to put into place emissions cuts but I think the question about whether we can delay emissions in the hope that sort of the sucking it out of the air at some future stage will occur that’s going to be totally ineffective if we actually have crossed some of these tipping point things and that’s much that requires knowledge of the climate system at that much more regional level and also at a much more detailed level because invoice is never to involve quite nonlinear processes which are which are hard to you know simulate accurately and and that’s where you need good models I think that’s a good place to stop thank you for your time thanks everybody for watching see you again next week.
Source: https://youtube.com/watch?v=-fkCo_trbT8
