While global climate models do a good job of simulating the Earth’s climate, they are not perfect.
| Carbon Brief’s series on climate modelling |
Advances in knowledge and computing power mean models are constantly revised and improved. As models become ever more sophisticated, scientists can generate a more accurate representation of the climate around us.
But this is a never-ending quest for greater precision.
In the third article in our week-long climate modelling series, Carbon Brief asked a range of climate scientists what they think the main priorities are for improving climate models over the coming decade.
These are their responses, first as sample quotes, then, below, in full:
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Prof Pete Smith Chair in plant and soil science University of Aberdeen |
We’ve got a great representation of these things within ecosystem models that we tend to use uncoupled and we just run those on the land surface. We’ve got a good detailed representation of some of those processes in those models – but those aren’t all yet into the ESMs. So getting that level of detail in I think is important, as well as improving the regional downscaling and improving the resolution of those ESMs.
That used to be limited by computing power, but that’s no longer a limitation. So we can get that extra level of detail into the models and check that that’s an appropriate level of detail, of course – because a more complex model is not necessarily a better model.
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Dr Kate Marvel Theoretical physicist NASA Goddard Institute for Space Studies |
Number two is better cloud simulation. Clouds are hard for models to get right and we know that different climate models don’t agree on how hot it’s going to get, in large part because they don’t agree on what clouds will do in the future. If we can get climate models to more credibly simulate current cloud patterns and observed cloud changes, this might reduce the uncertainty in future projections
Three is better observations. Satellites have been a real game-changer for climate research, but they’re not perfect. We need to keep evaluating our models against observational data and this is difficult in the presence of observational uncertainty. Long-term global datasets are often cobbled together from many different satellite and ground-based observations, and different measurements of the same variable often disagree. Dedicated long-term measurement devices like the instruments on NASA’s Afternoon Constellation (“A-train”) of satellites will help us understand reality better and this will allow us to benchmark and re-evaluate our models.
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Prof John Mitchell Principal research fellow Met Office Hadley Centre |
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Prof Daniela Jacob Director Climate Service Center Germany |
If you look at the scientific questions behind this, then I think the most important areas to look at are clouds, how to simulate clouds, the development of clouds, the life cycle of clouds, the land surface. The representation of the land cover and the land management is something which needs to be looked at.
Of course, there are many, many other questions. It really depends on what you want to use the model for. All climate models, global or regional, are made for a specific purpose. I think that’s important to have in mind. Not all models can do the same and they are not all good in the same way.
For us, the priority is to simulate the water cycle correctly. I was very interested in getting the precipitation amounts, locations and frequency, intensity, times, weird rains correct to get the runoff simulated.
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Prof Kevin Trenberth Distinguished senior scientist National Center for Atmospheric Research |
- Precipitation. Every model does this poorly and it is socially acceptable. It has to change. By precipitation I mean all characteristics: frequency, intensity, duration, amount, type (snow vs rain etc) at hourly resolution.
- Aerosols. The indirect effects of aerosols on clouds are poorly done. Some processes are included, but all models are incomplete and the result is nothing like observations. This affects climate sensitivity.
- Clouds. This is more generic and relates to sub-grid scale processes.
- Land-surface heterogeneity: this is a resolution issue and deals also with complexity.
- Air-sea interaction and the oceans. This also relates to mixing in the ocean, the mixed layer depth and ocean heat storage and exchanges.
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Prof Piers Forster Director Priestley International Centre for Climate University of Leeds |
It is intimately tied with observations but there’s also been a huge advance in the last 10 years in the way we can observe the way clouds work. We have unprecedented satellite instruments up there currently, that can really observe clouds in a far more sophisticated way than we ever have been able to before.
They’re fantastic, and by exploiting these wonderful observations we’ve got, I think we can really test the way these climate models work.
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Dr Lesley Ott Research meteorologist NASA Goddard Space Flight Center |
The other thing that is particularly important, which is my research area, is understanding carbon–climate interactions. Right now, one thing that not a lot of people know is that 50% of human emissions get absorbed by plants on the land and the oceans and that’s been a really valuable resource in limiting climate change to the effects we’re seeing today. If we didn’t have that valuable resource we’d be seeing things progress much more quickly, in terms of CO2 concentrations and global warming. The problem is we don’t understand those processes well enough to know if they’re going to continue. We’re seeing a lot of energy both with atmospheric observations and new observations of the land’s surface and I hope we’re going to continue to see progress.
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Dr Syukuro Manabe Senior meteorologist Princeton University |
What we have to do now is more of the things that I was doing in the old days when I used a simpler parameterisation of the sub-grid scale process, but keeping basic physics such as the hydrodynamical equation, radiative transfer, etc. That model is run much faster than the so-called Earth system model which they now use for the IPCC [Intergovernmental Panel on Climate Change]. And then using a much faster computer you can run a large number of numerical experiments where you can change one factor at a time, as if the model were a virtual laboratory. You can then see how the model is responding to that change.
(This is an extract taken from the Carbon Brief Interview with Manabe conducted in July 2015.)
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Prof Stephen Belcher Chief scientist Met Office |
For example, the kind of heatwaves we’ve seen in Europe – we had one in 2003 and 2006 – just how severe will they become and how frequent might they become? Some of the wet winters we’ve been having in Europe as well – are they going to become the new normal, or are will they just remain unusual events? So, having climate models that can really give us the precision around these extreme weather and climate events is definitely one priority.
The other priority is that in order to achieve the goals of the Paris Agreement, we’ll need to have a very close eye on the amount of carbon we emit into the atmosphere and the amount of CO2 that remains in the atmosphere. There are other factors in the climate system that drive the concentration of CO2 and hence global warming. For example, we know that as the planet warms, permafrost might melt and emit greenhouse gases of their own – warming the planet still further. But our quantitative estimate of that permafrost and the warming that might give are not very quantitatively accurate at the moment.
Secondly, about half of the CO2 we release into the atmosphere is absorbed either by plants on land or into the ocean and tightening up those numbers is really important. As we approach the targets given in Paris, the amount of precision we need on these allowable carbon budgets – to meet the temperature changes – is going to get sharper and sharper, and so we’re going to need better climate models to address those carbon budget issues.
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Prof Drew Shindell Nicholas professor of Earth sciences Duke University |
In particular, better computer power [is needed] because we do have some observations and some process understanding, but they happen at very fine spatial and temporal scales, and that’s the hardest thing to model because it takes an enormous amount of computer power.
We can get better observations from things like satellite data, but a lot of that is very challenging because the uppermost level of clouds blocks everything below and then you can’t see what’s really going on. You can fly airplanes and get detailed information, but for one short period of time and one short area. Those are really challenging things to improve from an observational perspective – and require immense computer power.
I would say that as far as advancing our ability to really look at the issue of climate change, I think one of the things we really need to do is to make our models interact more between the physical sciences and the social economics, and to really understand the link a little more closely between climate change and the drivers and impacts of climate change.
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Prof Michael Taylor Deputy dean and senior lecturer University of the West Indies |
Certainly advances in representing topography at a finer scale – putting the mountains in the right place, achieving the right height for the small scale – would represent significant improvements for the small islands. And improvements in coastal processes, the dynamics of coastal climate would represent improvements for the small island community.
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Prof Stefan Rahmstorf Head of Earth systems analysis Potsdam Institute for Climate Impact Research |
There is another effect which is the changes in the atmospheric circulation, including the jet stream. That’s one area of research that we are working on currently which has a really big impact on extreme weather events and it’s this kind of phenomena that we need to understand much better.
I’ve had a longstanding interest in palaeoclimate. The last few million years have been generally colder with ice ages, but if you go way back in time for many millions of years, there are much warmer climates on Earth and we are very interested in modelling these. But it is quite difficult because of the long time scales that you have to do deal with so you can’t use the models that are used to simulate a hundred years or two hundred years. You have to design models that are highly computationally efficient to study palaeoclimate.
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Dr James Hansen Climate scientist Columbia University |
But then the effective heat capacity, the surface temperature, depends on the rate of mixing of the ocean water and I have presented evidence from a number of different ways that models tend to be too diffusive because of numerical reasons and coarse resolution and wave parameter rise, motions in the ocean. It can tend to exaggerate the mixing and, therefore, make the heat capacity more effective.
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Dr Doug McNeall Researcher in climate change impacts Met Office Hadley Centre |
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Dr Ronald Stouffer Senior research climatologist and group head of the Climate and Ecosystems Group at the Geophysical Fluid Dynamics Laboratory (GFDL) Princeton University |
- Evaluating and understanding climate response to changes in radiative forcing (greenhouse gases and aerosols).
- Improving the cloud simulation (distribution 3D and radiative properties). This is of first importance for better estimates of the climate sensitivity.
- Improving the ocean simulation particularly in the Southern Ocean. Models do a fairly poor job currently and this is a very important region for the uptake of heat and carbon from human activities.
- Higher model resolution. This helps provide improved local information on climate change. It also reduces the influence of physical parameterisations in models (a known problem).
- Improve the carbon simulation and modelling in general. Modelling land carbon changes is particularly a challenge do to the importance of small local scales.
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Prof Adam Scaife Physicist and head of monthly-to-decadal prediction Met Office Hadley Centre |
It is the top priority of my research group to try to solve this problem to improve our climate predictions and, depending on the answer, it could affect predictions on all timescales from medium range forecasts, through monthly, seasonal, decadal and even climate change projections.
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Dr Jatin Kala Lecturer in atmospheric science and ARC DECRA fellow Murdoch University, Perth |
- Improving our abilities in simulating climate extremes.
- Improving the skill of climate models in simulating key modes of natural climate variability.
- Moving towards unstructured climate model domains (current models use a square/rectangular domain, but using mesh approaches is the next step).
- More realistic representation of vegetation processes.
- Improving convection parameterisations
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Dr Katharine Hayhoe Climate Science Center director Texas Tech University |
We’re also learning that natural variability is really important when we’re looking over time scales of anywhere from the next year or two to even a couple of decades in the future. Natural variability is primarily controlled by exchange of heat between the ocean and the atmosphere, but it is an extremely complex process and if we want to develop better near-term predictive skills – which is looking not at what’s going to happen in the next three months but what’s going to happen between the next year and 10 years or 20 years or so – if we want to expand our understanding there, we have to understand natural variability better than we do today.
(This is an extract taken from the Carbon Brief Interview with Hayhoe conducted in November 2017.)
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Dr Chris Jones Lead researcher in vegetation and carbon cycle modelling Met Office Hadley Centre |
When we start to get into the details that really affect people, that’s where the models are not yet perfect, and that’s partly because we can’t represent them in enough fine scale detail. There is always a big push as soon as we get a new computer to try and increase the resolution that we represent, and we’ve seen them get better and better in that respect over the years.
The other aspect and something that I work on is increasingly trying to look at the interactions between climate and ecosystems, and if what that allows us to do is to inform climate negotiations around things like carbon budgets, so how much CO2 can we emit to stay within a certain target.
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Prof Christian Jakob Centre deputy director School of Earth, Atmosphere and Environment Monash University |
Other priorities would be to improve the physical realism of the models, in particular the representation of precipitation and clouds, and to significantly increase the model development “workforce” in the relevant areas.
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Prof Richard Betts Head of climate impacts University of Exeter & Met Office Hadley Centre |
The other thing we need to do is to find ways to represent the other aspects of the climate system that aren’t always captured in the climate models [such as] tipping points, non-linearities. They don’t always, or hardly ever, emerge from the models. You can artificially force the models to do this. We know these things have happened in the real climate in the past. We need to find ways to reproduce these in a completely realistic way so that we can do a full risk assessment of future climate change including these surprises that may occur.
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Dr Bill Hare Director Climate Analytics |
One of the underdeveloped areas, including in IPCC assessment reports, is evaluating what are the avoidable impacts [of climate change]. It’s very hard to find a coherent survey of avoidable impacts in an IPCC assessment reports. I think we need to be getting at that so we can better inform policymakers about what the benefits are of taking some of the big transformational steps that, while economically beneficial, are definitely going to cause political problems as incumbent power producers and others try and defend their turf.
(This is an extract taken from the Carbon Brief Interview with Hare conducted in November 2017.)
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Prof Detlef van Vuuren Senior researcher PBL Netherlands Environmental Assessment Agency |
For me, broadening the representation of different factors would have a
higher priority than deepening the existing process representation. I
think quite a number of key Earth processes are still not very well
represented, including things like the role of land use, but also pollution
and nutrients. I would see that as a high priority. Activities are
going on in this area, no doubt. But I personally think that the balance
might shift still in this direction.
Second, ensuring somehow that we keep older versions of the models “active”. The idea sounds attractive to me that in addition of having ever better models, but still being slow despite progress in computing power, we would also the ability to have fast model runs. This could be used for more uncertainty runs, having larger ensembles, exploring a wider range of types of scenarios.
Finally, I would expect that there will be a further representation of the human system in Earth system models (ESMs) and that integrated assessment models (IAMs) will try to be more geographically explicit – in order to better represent local processes, such as water management and presence of renewable energy. These together might mean that there is the agenda of merging ESMs and IAMs more. I think this is interesting, but, at the same time, it is also very challenging as both communities already are rather interdisciplinary (so one would risk having models based on different philosophies and being too complex to understand the results).
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Second, ensuring somehow that we keep older versions of the models “active”. The idea sounds attractive to me that in addition of having ever better models, but still being slow despite progress in computing power, we would also the ability to have fast model runs. This could be used for more uncertainty runs, having larger ensembles, exploring a wider range of types of scenarios.
Finally, I would expect that there will be a further representation of the human system in Earth system models (ESMs) and that integrated assessment models (IAMs) will try to be more geographically explicit – in order to better represent local processes, such as water management and presence of renewable energy. These together might mean that there is the agenda of merging ESMs and IAMs more. I think this is interesting, but, at the same time, it is also very challenging as both communities already are rather interdisciplinary (so one would risk having models based on different philosophies and being too complex to understand the results).
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