Long-term climate variability is the range of temperatures and weather patterns experienced by the Earth over a scale of thousands of years. New research suggests it could fall as the world warms.
A study using data taken from fossils and ice cores finds that long-term temperature variability decreased four-fold from the Last Glacial Maximum (LGM) around 21,000 years ago to the start of the Holocene around 11,500 years ago. Within this period, natural processes caused the planet to warm by around 3-8C.
If future global emissions are not curbed, human-driven global warming could cause further large declines in long-term temperature variability, the lead author tells Carbon Brief, which may have far-reaching effects on the world’s seasons and weather.
However, it is still unclear how a decline in long-term variability could affect the frequency of extreme weather events, she adds. This is because the chances of an extreme event happening could be influenced by both short- and long-term climate variability, as well as global temperature rise.
Digging up the past
The new study, published in Nature, is the first to make a global assessment of how long-term temperature variability changed from the LGM to the Holocene.
During the LGM, the world’s last major ice age, snow covered much of Asia, Europe and North America. Yet, within a few thousand years, global temperatures rose by around 3-8C, causing the ice to thaw and the world to enter its current geological period, the Holocene.
The cause of this temperature rise is still disputed by scientists, but research suggests the natural release of large stores of CO2 from the world’s oceans may have played a role.
To work out how long-term climate variability changed over the period, the researchers analysed data taken from ancient ice cores, marine sediments and animal and plant fossils stretching back thousands of years.
Scientists are able to analyse some of these samples – which are known as proxy records – by looking at the ratios between different chemical isotopes.
Combining data derived from different parts of the world and time periods allows scientists to create a picture of past temperature change, explains Dr Kira Rehfeld, a research fellow at the British Antarctic Survey and the Alfred-Wegener Institute for Polar and Marine Research (AWI) in Potsdam, Germany. She tells Carbon Brief:
“We set out and started collecting more and more records that we could use to get a more general picture of changing climate variability for temperature. It’s taken us three and a half years to find enough records and to develop the methodology to be able to analyse them.”
The researchers then compared data taken from the LGM and the Holocene to help them work out how global temperatures could have changed over large time scales. Rehfeld says:
“We don’t look at the variability in terms of just temperature rise, we look at the ratio of the variability. So we divide the variability of the LGM by the variability of the Holocene. That way we can compare records that have very different origins.”
The research finds that, from the LGM to the Holocene, long-term temperature variability fell by a factor of four.
However, some parts of the world experienced larger changes in temperature than others, the study notes.
This is shown on the chart below, where dark blues show areas that experienced a large amount of temperature change from the LGM to the Holocene, whereas light blue shows areas that experienced less change.
On the chart, symbols are used to show the location of ice cores (circle), marine sediments (diamond), lacustrine – or lake – sediment (triangle) and tree fossil data (square). Colours are used to show samples from the Holocene and LGM (red), the Holocene (orange) and the LGM (purple).
Global temperature change from the Last Glacial Maximum to the Holocene. Dark blue indicates high temperature change while light blue shows low temperature change. Symbols show the location of ice cores (circle), marine sediments (diamond), lacustrine sediment (triangle) and tree fossil data (square). Colours show samples from the Holocene and LGM (red), the Holocene (orange) and the LGM (purple). Source: Rehfeld et al. (2018)
The findings show that the world’s poles experienced a larger change in temperature than the equator over the time period. These changes led to an overall decline in long-term temperature variability, the research finds.
The difference in warming between the poles and the equator could be down to a process known as “polar amplification”, Rehfeld says.
Polar amplification is the phenomenon that any change in the impact of sunlight on the Earth tends to have a larger effect on the poles than the equator.
This is thought to be because as warming causes sea ice near the poles to melt, energy from the sun that would have been reflected away by the ice is instead absorbed by the ocean. Because of this, surface temperatures near the poles start to rise at an accelerated rate.
The findings reinforce the prediction that future climate change driven by humans will cause a larger increase in temperature at the poles than at the equator, Rehfeld says:
“The temperature difference between the poles and the equator has decreased as the Earth warms due to polar amplification. This relates to a change in overall long timescale temperature variability.
“If you take that and extrapolate that into the future, warming could be larger at the poles. The temperature difference is then further reduced, which would translate into a reduction of overall temperature variability.”
Carbon Brief previously reported on how the effect of climate change on polar amplification could cause the amount of wind available for power generation to fall in the northern hemisphere.
Although long-term variability is expected to fall, this does not mean that short-term variability will also be reduced, Rehfeld says:
“The question we’re asking is what would a warmer world than today look like? If we can translate our changes in the temperature gradient, then that would mean, theoretically, that long timescale variability in the future will be reduced. But that doesn’t mean that short timescale variability will be reduced.”
Short-term climate variability is a term typically used to describe the natural range of temperatures and weather patterns experienced by the Earth within shorter periods.
For example, after an extreme weather event, scientists often carry out single attribution studies to determine how the likelihood of such an event could have been influenced by climate change and short-term climate variability.
It is still not clear how a reduction in long-term variability will affect the frequency and severity of extreme weather events, Rehfeld says:
“There seems to be a correlation. This change in long timescale climate variability could have influences on extreme events and seasonal variability.
“Based on what we know about how extreme events work, if we have a broader distribution of temperatures then we should have more extreme events. However, what we perceive as extreme events, like floods or heatwaves, is not reflected in our datasets.”
In other words, scientific theory suggests that declines in long-term climate variability could lead to fewer extreme events. However, the timescale used in the study was too broad to reflect short-term events, such as floods and heatwaves.
The findings are “interesting”, but could hold “limited relevance” to understanding future climate change, which is occuring at a much faster rate than the warming observed from the LGM to the Holocene, says Prof Amanda Maycock, a research fellow from the University of Leeds who was not involved in the new study. She tells Carbon Brief:
“Current surface temperature changes and associated changes in climate variability and extremes are occurring much more rapidly than the multi-centennial timescales considered in the study.”
The datasets collated in the study could be used to help climate models simulate more long-term changes in climate variability, says Dr Lauren Gregorie, an academic research fellow at the University of Leeds, who was also not involved in the study. She tells Carbon Brief:
“What I find particularly interesting is that while models do simulate a reduction in variability, they tend to underestimate that change compared to the records [used in the study]. There is a great opportunity to use our knowledge of past climate change to test and improve climate models. Unfortunately, there’s currently very little funding to do this kind of work.”
However, less than a century ago, climate models were little more than an idea; basic equations roughly sketched out on paper. After the second world war, though, the pace of development quickened dramatically, particularly in the US.
By the late 1960s, policymakers were being presented with the models’ findings, which strongly reinforced the theory that the continued rise in human-caused greenhouse gas emissions would alter the global climate in profound ways.
In the interactive timeline above, Carbon Brief charts more than 50 key moments in the history of climate modelling.
Such moments include…
Guy Callendar’s seminal paper published in 1938.
The first computerised, regional weather forecast in 1950.
Norman Phillips’ first general circulation model in 1956.
The establishment of a modelling group at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado, in 1964.
Syukuro Manabe and Richard Wetherald’s seminal climate modelling study in 1967.
The Met Office’s first general circulation model in 1972.
The Charney Report in 1979.
James Hansen’s three scenarios published in 1988.
The first Intergovernmental Panel on Climate Change (IPCC) report published in 1990.
The Coupled Model Intercomparison Project (CMIP) launched in 1995.
The IPCC’s fifth assessment report published in 2013.
Scroll through the various slides within the interactive timeline, above, by clicking on the arrows. Or you can use the calendar above each slide to jump to a particular moment within the history.
Despite the huge strides taken since the earliest climate models, there are some climatic processes that they do not simulate as accurately as scientists would like.
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:
Prof Pete Smith: “We can get that extra level of detail into the models and check that that’s an appropriate level of detail because a more complex model is not necessarily a better model.”
Dr Kate Marvel: “Higher resolution is the first priority. Right now, climate models have to approximate many physical processes that turn out to be very important.”
Prof John Mitchell: “The top priorities should be reducing uncertainties in climate sensitivity and reducing uncertainties in radiative forcing – particularly that associated with aerosols.”
Prof Daniela Jacob: “It’s important that models will be able to simulate local characteristics, so that they are able to simulate the climate in a city, in mountainous regions, along the coast.”
Prof Kevin Trenberth: “Precipitation. Every model does this poorly and it is socially acceptable. It has to change.”
Prof Piers Forster: “The biggest uncertainty in our climate models has been their inability to simulate clouds correctly. They do a really bad job and they have done ever since they first began.”
Dr Lesley Ott: “Understanding carbon-climate interactions. We don’t understand those processes well enough to know if they’re going to continue.”
Dr Syukuro Manabe: “As the models get ever more complicated – or, as some people say, sophisticated – no one person can appreciate what’s going on inside them.”
Prof Stephen Belcher: “Having climate models that can give us the precision around extreme weather and climate events is definitely one priority.”
Prof Drew Shindell: “One of the key uncertainties is clouds, understanding the physics behind clouds and how clouds interact with aerosol particles.”
Prof Michael Taylor: “I think there is still some difficulty in understanding the land and sea border and any advances within models would be an advantage for island states.”
Prof Stefan Rahmstorf: “I think a key challenge is non-linear effects, or tipping points. For example, the Gulf stream system. We still don’t know how close we are to a threshold there.”
Dr James Hansen: “The fundamental issue about climate change is the delayed response of a system and that’s due to the ocean’s heat capacity.”
Dr Doug McNeall: “We need to be adding more processes, modelling new things, and also we need to be modelling finer detail so we can better explain the climate.”
Dr Ronald Stouffer: “Improving the ocean simulations, particularly in the Southern Ocean. This is a very important region for the uptake of heat and carbon from human activities.”
Prof Adam Scaife: “There is a signal-to-noise problem evident in climate models which means that, in some mid-latitude regions, predicted climate signals are too weak.”
Dr Jatin Kala: “More realistic representation of vegetation processes.”
Dr Katharine Hayhoe: “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.”
Dr Chris Jones: “I think the major gaps would include the ability to trust climate models at finer and finer scales, which, ultimately, is what people want to know.”
Prof Christian Jakob: “In my view, the highest priority is to have more people involved in the model development process so that more new ideas can be generated and implemented.”
Prof Richard Betts: “We need to represent the other aspects of the climate system that aren’t always captured in the climate models [such as] tipping points, nonlinearities.”
Dr Bill Hare: “One of the underdeveloped areas, including in IPCC assessment reports, is evaluating what are the avoidable impacts [of climate change].”
Prof Detlef van Vuuren: “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 think the ESMs [Earth system models] have a pretty good representation of many of the processes, but because they’re trying to cover the whole Earth, then you have a relatively simple description of most things in the models. There are still ESMs, for example, that have a very limited representation of nutrients – so, for example, nitrogen and phosphorus limitations on plant growth in the future.
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.
Higher resolution is the first priority. Right now, climate models have to approximate many physical processes that turn out to be very important; air flowing over mountain ranges, for example, or small eddies mixing water in the ocean. This is because it’s too hard to get the large and small scales right: there’s simply no computer powerful enough to keep track of very small and large scales simultaneously. Different models make different approximations and this contributes to uncertainty in their projections. But as computing power increases, we’ll be able to explicitly capture a lot of the small-scale effects that are very important to regional climate. You can think of this as sharpening the blurry picture of climate change.
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.
The top priorities should be reducing uncertainties in climate sensitivity, getting a better understanding of the effect of climate change on atmospheric circulation (critical for understanding of regional climate change, changes in extremes) and reducing uncertainties in radiative forcing – particularly those associated with aerosols.
I think from a societal point of view, it’s important that models will be able to simulate local characteristics, so that they are able to simulate the local climate in parts of a city, in mountainous regions, in valleys, along the coast. There are still limitations in the climate models. Although they’ve made a lot of progress over the last decades, we still do not really know how climate is changing on a local scale.
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.
The top priorities over the next decade for improving climate models are:
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.
By far the biggest uncertainty in our climate models has been their inability to simulate clouds correctly. They do a really bad job and they have done ever since they first began. And this has all sorts of knock-on effects. It gives a big uncertainty in projections going further forward in time because we don’t understand the way they work, and it also gives big uncertainty to things like extreme precipitation – so that we don’t understand rainfall extremes that well. So we have all these big uncertainties from our incorrect simulation of clouds.
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.
One area that’s really critical is cloud–aerosol interactions. It’s something that we really don’t know too much about, we’re seeing some tantalising evidence that there could be important effects but on a global scale it’s very hard to understand. For us, in our office, it took a lot of work to get our model to run with the kind of cloud microphysics and aerosol microphysics that would actually allow us to study that. We’re now at that point where we are starting to do that kind of work and I think you’re going to see in the next five or ten years a lot more research on that.
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.
As the models get ever more complicated – or, as some people say, sophisticated – no one person can appreciate what’s going on inside them.
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.
I think the Paris Agreement really changed the agenda for climate science. And at the Met Office, we’re really focused on two aspects of improving climate models. The first is understanding extreme events and the risks associated with extreme weather and climate events – in the current climate, but also in a future climate.
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.
Carbon budget: A carbon budget is the maximum amount of carbon that can be released into the atmosphere while keeping a reasonable chance of staying below a given temperature rise. The Intergovernmental Panel on Climate Change (IPCC) first adopted the concept of carbon budgets in its 2013 report. Budgets are typically expressed in gigatonnes of carbon (GtC) or carbon dioxide (GtCO2). To convert the former to the latter, multiply by 3.67.
Carbon budget: A carbon budget is the maximum amount of carbon that can be released into the atmosphere while keeping a reasonable chance of staying below a given temperature rise. The Intergovernmental Panel on… Read More
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.
One of the key uncertainties is clouds, understanding the physics behind clouds and how clouds interact with aerosol particles. That has, unfortunately, also been a key uncertainty for a long time and is likely to remain one.
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.
I think for us there is still some difficulty in understanding the land and sea border and certainly any advances in differentiating that land-sea contrast within the model would be an advantage for island states – especially small island states.
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.
I think a key challenge is non-linear effects, or tipping points. For example, the Gulf stream system. We still don’t know how close we are to a threshold there. We know there is one because we know these non-linear phenomena are very sensitively dependent on the exact state of the system and so models still widely disagree on how stable or unstable the Gulf stream system will be under global warming in the future.
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.
The fundamental issue about climate change, the difficulty, is the delayed response of a system and that’s due to the ocean’s heat capacity.
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.
As we’ve gone through time, climate models have got more complex, so that’s not only been due to increasing resolution, but also adding more processes. I think we need to continue both of those trends. We need to be adding more processes, modelling new things and also we need to be modelling finer detail so we can better explain the climate. We need to better explain the impacts of climate on the systems we care about, such as the human systems, ecological, carbon cycle systems. If you make the model better, if you make it look more like reality, it means that your knowledge of how the system will change gets better.
Dr Ronald Stouffer Senior research climatologist and group head of the Climate and Ecosystems Group at the Geophysical Fluid Dynamics Laboratory (GFDL) Princeton University
The top priorities over the next decade for improving climate models are:
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.
There is a signal-to-noise problem evident in climate models which means that, in some mid-latitude regions, predicted climate signals are too weak. This possibility was realised in the past and has actually been around in climate models for many years.
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.
Climate modelling is an enormous undertaking. I think few people realise just how complex these models are. As soon as there’s a new supercomputer available anywhere in the world, there’s a climate model waiting to be run on it because we know that many of our physical processes right now are not being directly represented. They have to be “parameterised” because they occur at spatial or time scales that are smaller than the grids in the time steps that we use. So the smaller the spatial grids and the smaller the time step we use in the model, the better we’re able to actually explicitly resolve the physical processes in the climate.
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.
I think the major gaps would include the ability to trust climate models at finer and finer scales, which, ultimately, is what people want to know. At a global scale we understand the physics very well about how greenhouse gases trap energy in the atmosphere, and so the models do a pretty good job of the global scale energy balance and how the world as a planet warms up. We can recreate the 20th century global climate patterns pretty well and we know why that is.
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.
In my view, the highest priority is to have more people involved in the model development process so that more new ideas can be generated and implemented. This has proven difficult.
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.
It’s worth saying at first that they are remarkably good already at simulating the general patterns of climate, the general circulation of the atmosphere and the past trend of global temperatures. But we still see systematic biases in some of the models so we have to often correct for these biases when looking at other models for impact studies. It would be good to be able to eliminate that because that introduces another level of uncertainty and inconsistency. Say we could have detailed, realistic, regional climates that don’t require this adjustment that would be a major victory.
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.
I think one of the important issues is to be doing modelling of the climate system is consequences of fully 1.5C pathways and maybe even more than that. This would allow us to begin to understand how we could prevent some of the major tipping point problems that we can already foresee coming, even for 1.5C warming, and to try and understand what it would take to protect and sustain important natural ecosystems such as coral reefs, or to prevent ice sheet disintegration.
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.
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.
Integrated Assessment Models: IAMs are computer models that analyse a broad range of data – e.g. physical, economic and social – to produce information that can be used to help decision-making. For climate research, specifically, IAMs are typically used to project future greenhouse gas emissions and climate impacts, and the benefits and costs of policy options that could be implemented to tackle them.
Integrated Assessment Models: IAMs are computer models that analyse a broad range of data – e.g. physical, economic and social – to produce information that can be used to help decision-making. For climate research, specifically,… Read More
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).
Much of the public discussion around climate change has focused on how much the Earth will warm over the coming century. But climate change is not limited just to temperature; how precipitation – both rain and snow – changes will also have an impact on the global population.
While the models used by climate scientists generally agree on how different parts of the Earth will warm, there is much less agreement about where and how precipitation will change.
In the fifth and final article in our week-long climate modelling series, Carbon Brief explores where the models agree and disagree about future changes in precipitation.
Move evaporation and more water vapour
There are some basic physical processes that inform scientists’ expectations of how precipitation will respond in a warming world. With higher temperatures comes greater evaporation and surface drying, potentially contributing to the intensity and duration of drought.
However, as the air warms its water-holding capacity increases, particularly over the oceans. According to the Clausius-Clapeyron equation, the air can generally hold around 7% more moisture for every 1C of temperature rise. As such, a world that is around 4C warmer than the pre-industrial era would have around 28% more water vapour in the atmosphere.
RCP8.5: The RCPs (Representative Concentration Pathways) are scenarios of future concentrations of greenhouse gases and other forcings. RCP8.5 is a scenario of “comparatively high greenhouse gas emissions“ brought about by rapid population growth, high energy demand, fossil fuel dominance and an absence of climate change policies. This “business as usual” scenario is the highest of the four RCPs and sees atmospheric CO2 rise to around 935ppm by 2100, equivalent to 1,370ppm once other forcings are included (in CO2e). The likely range of global temperatures by 2100 for RCP8.5 is 4.0-6.1C above pre-industrial levels.
RCP8.5: The RCPs (Representative Concentration Pathways) are scenarios of future concentrations of greenhouse gases and other forcings. RCP8.5 is a scenario of “comparatively high greenhouse gas emissions“ brought about by rapid population growth,… Read More
But this increased moisture will not fall evenly across the planet. Some areas will see increased precipitation, while other areas are expected to see less due to shifting weather patterns and other factors.
The figure below shows projected percentage change in precipitation between the current climate (represented by the 1981-2000 average) and the end of the century (2081-2100) in the average of all of the climate models featured in in the latest Intergovernmental Panel on Climate Change (IPCC) report (CMIP5), using the high-end warming scenario (RCP8.5).
Purple colors show areas where precipitation will increase, while orange areas indicate less future rain and snow.
CMIP5 RCP8.5 multimodel average percent change in total precipitation (rain and snow) between 1981-2000 and 2081-2100. Uses one run for each model, 38 models total. Data from KNMI Climate Explorer; map by Carbon Brief.
On average, warming is expected to result in dry areas becoming drier and wet areas becoming wetter, especially in mid- and high-latitude areas. (This is not always true over land, however, where the effects of warming are a bit more complex.)
The average of the models shows large increases in precipitation near the equator, particularly in the Pacific Ocean. They also show more precipitation in the Arctic and Antarctic, where cold temperatures currently limit how much water vapour the air can hold.
The Mediterranean region is expected to have around 20% less precipitation by 2100 in an RCP8.5 world, with similar reductions also found in southern Africa. Western Australia, Chile, and Central America/Mexico may all become around 10% drier.
RCP2.6: The RCPs (Representative Concentration Pathways) are scenarios of future concentrations of greenhouse gases and other forcings. RCP2.6 (also sometimes referred to as “RCP3-PD”) is a “peak and decline” scenario where stringent mitigation and carbon dioxide removal technologies mean atmospheric CO2 concentration peaks and then falls during this century. By 2100, CO2 levels increase to around 420ppm – around 20ppm above current levels – equivalent to 475ppm once other forcings are included (in CO2e). By 2100, global temperatures are likely to rise by 1.3-1.9C above pre-industrial levels.
RCP2.6: The RCPs (Representative Concentration Pathways) are scenarios of future concentrations of greenhouse gases and other forcings. RCP2.6 (also sometimes referred to as “RCP3-PD”) is a “peak and decline” scenario where stringent mitigation… Read More
These changes tend to increase proportionately with warming; if the Earth warmed only 2C in an aggressive mitigation scenario such as RCP2.6 rather than 4C, the percent change in precipitation would be roughly half as large.
However, the simple picture painted by the average of all the models shown above hides profound differences. There are actually relatively few areas that all the models agree will become wetter or drier. Climate models are not perfect and projections of future average precipitation changes may become more consistent as models continue to improve.
There are 39 different climate models within CMIP5 that provide estimates of precipitation changes in the future. Unlike for temperature, where models show a general degree of agreement about future regional changes, different models may have the same region becoming much wetter or much drier in a warming world.
The figure below shows expected percent change in precipitation between the current climate and the end of the century in Australia, with purple areas indicating increased precipitation and orange indicating reductions. While the average of all the models – shown on the left – has a modest 5 to 10% reduction in precipitation over most of the country, some individual models show much greater changes.
For example, the Australian CSIRO model – middle panel – projects precipitation decreases of around 50% on average by the end of the century. In stark contrast, the Chinese FGOALS model projects a 30% average increase in precipitation by 2100, with almost no areas experiencing less rainfall.
CMIP5 RCP8.5 multimodel mean percent change in total precipitation (rain and snow) between 1981-2000 and 2081-2100 for Australia, as well as individual CSIRO-Mk3 and FGOALS runs. Data from KNMI Climate Explorer; maps by Carbon Brief.
Similar results can be found for many other regions of the world. The figure below shows the driest projection and wettest projections for each different part of the world across all the CMIP5 models, represented by the 10th and 90th percentile of all the models (e.g. the 10% of models that show the most reduction in precipitation and the 10% that show the most increase in precipitation for any region of the world).
In at least one model much of the world outside high-latitude areas and the tropical oceans shows sizable drying. Similarly, you can find at least one model where nearly any given location in the world gets wetter.
RCP8.5 10th percentile of mean precipitation change (left map) and 90th percentile (right map) for total precipitation (rain and snow) for each 1×1 latitude/longitude gridcell between 1981-2000 and 2081-2100. Uses one run for each model, 38 models total. Data from KNMI Climate Explorer; maps by Carbon Brief.
This means that average annual precipitation projections by climate models should be approached cautiously. This creates a challenge decision-makers who need to plan for changes that will occur in their country, as relying on the output of any one model may mask dramatic disagreements.
However, disagreements between the models in future precipitation changes in some regions does not mean that the models are useless for this purpose.
Where do models agree?
While models disagree on how average precipitation will change in many parts of the world, there are some areas where nearly all the models tell the same story about future changes.
The figure below shows the same annual average change in precipitation between today and the end of the century, but adds dots to indicate areas where at least nine out of 10 models agree on the direction of change.
As first figure, but with areas where 90% of the models agree on the sign of the change highlighted with dots. Data from KNMI Climate Explorer; map by Carbon Brief.
Here there is widespread agreement among the models that both the tropical Pacific and high-latitude areas will have more precipitation in the future. India, Bangladesh and Myanmar will all become wetter, as will much of northern China.
The models largely agree that the Mediterranean region and southern Africa will have less precipitation in the future. They also agree on reduced precipitation in southwest Australia around Perth, in southern Chile, the west coast of Mexico and over much of the tropical and subtropical Atlantic ocean.
Interestingly, despite all the focus on drought in the state of California, there is no consensus among climate models that the region will experience less precipitation on average in a warmer world.
Different changes in different seasons
A limitation of looking at annual precipitation changes is that they can mask some seasonal effects.
The figure below shows projected changes in future precipitation broken down by season, along with dots in regions where at least nine out of 10 of models agree on the direction of the change for each season.
A few things stand out when looking at projected seasonal changes. In winter there are greater reductions in precipitation projected over northern Africa, but no agreement on increases in precipitation over India or much of South Asia.
In spring, models agree that southern California will experience less rainfall. In summer, reductions in precipitation in southern Africa are particularly strong, while in autumn increases in rainfall over India, Bangladesh and the Sahara region all stand out.
Increases in extreme precipitation
Models also generally agree that precipitation, when it does occur, will become more intense nearly everywhere. Unlike average annual precipitation, almost the entire world is expected to see an increase in extreme precipitation as it warms.
Models suggest most of the world will have a 16-24% increase in heavy precipitation intensity by 2100. In other, words, heavy rain is likely to get heavier.
You can see this in the figure below. Here percent changes in heavy precipitation events by the end of the century are shown per degree warming that we experience, and dots represent areas of the map where 90% of the models agree. Red areas show decreases in heavy precipitation, while blue areas indicate increases.
The largest increases in heavy precipitation events on land are expected to occur over central Africa and South Asia. On the other hand, North Africa, Australia, Southern Africa, and Central America may not see a noticeable increase in heavy precipitation.
Percent change in heavy precipitation per degree warming, defined as the heaviest daily precipitation event of the year for each location. Figure adapted from Fischer et al 2014.
Temperature and precipitation influence drought
While changes in rainfall and snow in a warming world are highly uncertain for many parts of the world, changes in future precipitation are only part of the story.
Just as important is temperature, which influences whether precipitation takes the form of snow or rain and controls how much “snowpack” accumulates.
The snowpack is the snow that accumulates in mountains during winter and provides fresh water to the valleys below as it melts in spring and summer. It is an important contributor to many rivers, and impacts river flow and water availability for agriculture, particularly in regions, such as California, where precipitation is concentrated in winter.
Temperatures also impact the rate of evaporation, with higher temperatures leading to faster soil moisture loss and an increased need for irrigation in agriculture.
This means that, even for regions that are likely to get wetter, this will be largely offset by temperature-driven drying.
As Dr Benjamin Cook at the NASA Goddard Institute for Space Studies tells Carbon Brief, while changes in future precipitation is uncertain, the drying associated with warmer temperatures is much more widespread:
“The drought-climate change story is actually pretty complicated. While the impact of climate change on precipitation is fairly uncertain, we do expect with warming that many areas will experience more soil moisture droughts and declining runoff and streamflow resulting in an overall increase in drought risk and severity.
“The general consensus is that precipitation will decline in subtropical regions, places like the [US] Southwest and the Mediterranean. But the warming effect and impact of warming on evapotranspiration and associated drying happens over a much, much larger region.”
Changes in average precipitation is much more difficult for climate models to predict than temperature. There are many parts of the world where models disagree whether there will be more or less rain and snow in the future. However, there are some regions, particularly the Mediterranean and southern Africa, where nearly all models suggest rainfall will decrease. Similarly, increases in rainfall are expected in high latitude areas, as well as much of South Asia.
There is much more agreement by the models that a warming climate will increase the severity of extreme rainfall and snowfall almost everywhere. A warmer world will, they project, also increase soil evaporation and reduce snowpack, exacerbating droughts even in the absence of reduced precipitation.