How do climate models work

That said, models are increasingly collaborative efforts, which is often reflected in their names. As its name suggests, the model is a product of a collaboration between thousands of scientists and is freely available to download and run.

He tells Carbon Brief:. And, by comparing what the different models and the different sets of research say, you can judge which things to have confidence in, where they agree, and where we have less confidence, where there is disagreement. That guides the model development process. If there was just one model, or one modelling centre, there would be much less of an idea of its strengths and weaknesses, says Jones.

And while the different models are related — there is a lot of collaborative research and discussion that goes on between the groups — they do not usually go to the extent of using the same lines of code. He explains:. Mouse over the individual centres in the map to find out more about them. The majority of modelling centres are in North America and Europe. However, it is worth noting that the CMIP5 list is not an exhaustive inventory of modelling centres — particularly as it focuses on institutions with global climate models.

This means the list does not include centres that concentrate on regional climate modelling or weather forecasting, says Jones:. Many models are available under licence to the scientific community at no cost. These generally require signing of a licence that defines the terms of use and distribution of the code.

The institute points out that the main purpose of the licence agreement is to let it know who is using the models and to establish a way of getting in touch with the users. It says:. This is the spirit behind the following licence agreement…It is also important to provide feedback to the model developers, to report about errors and to suggest improvements of the code. With so many institutions developing and running climate models, there is a risk that each group approaches its modelling in a different way, reducing how comparable their results will be.

CMIP is a framework for climate model experiments, allowing scientists to analyse, validate and improve GCMs in a systematic way. So, CMIP was designed to be a way to bring into line all the climate model experiments that different modelling centres were doing. Since its inception in , CMIP has been through several generations and each iteration becomes more sophisticated in the experiments that are being designed.

A new generation comes round every years. Setting the models up in the same way and using the same inputs means that scientists know that the differences in the climate change projections coming out of the models is down to differences in the models themselves.

This is the first step in trying to understand what is causing those differences. The output that each modelling centre produces is then loaded on a central web portal , managed by the Program for Climate Model Diagnosis and Intercomparison PCMDI , which scientists across many disciplines and from all over the world can then freely and openly access.

In addition, the CMIP Panel oversees the design of the experiments and datasets, as well as resolving any problems. So far, 21 MIPs have been endorsed, Eyring says:. Reproduced with permission from Simpkins This might include, for example, comparing the model projections against actual global surface temperatures over the past century. This can be a useful way to validate models. There are hindcasts for the historical temperature record present , over the past 2, years using various climate proxies, and even over the past 20, years.

Specific events that have a large impact on the climate, such as volcanic eruptions, can also be used to test model performance. The climate responds relatively quickly to volcanic eruptions, so modellers can see if models accurately capture what happens after big eruptions, after waiting only a few years. Studies show models accurately project changes in temperature and in atmospheric water vapour after major volcanic eruptions. For example, researchers check to see if the average temperature of the Earth in winter and summer is similar in the models and reality.

They also compare sea ice extent between models and observations, and may choose to use models that do a better job of representing the current amount of sea ice when trying to project future changes. For many parts of the climate system, the average of all models can be more accurate than most individual models.

Researchers have found that forecasts can show better skill, higher reliability and consistency when several independent models are combined. One way to check if models are reliable is to compare projected future changes against how things turn out in the real world. This can be hard to do with long-term projections, however, because it would take a long time to assess how well current models perform.

Recently, Carbon Brief found that models produced by scientists since the s have generally done a good job of projecting future warming. The video below shows an example of model hindcasts and forecasts compared to actual surface temperatures. Not too badly… … Follow the link in our bio to read more.

As mentioned above, scientists do not have a limitless supply of computing power at their disposal, and so it is necessary for models to divide up the Earth into grid cells to make the calculations more manageable. This means that at every step of the model through time, it calculates the average climate of each grid cell.

For example, the height of the land surface will be averaged across a whole grid cell in a model, meaning it potentially overlooks the detail of any physical features such as mountains and valleys. Similarly, clouds can form and dissipate at scales that are much smaller than a grid cell. Parameterisations are one of the main sources of uncertainty in climate models.

A list of 20 climate processes and properties that typically need to be parameterised within global climate models. In many cases, it is not possible to narrow down parameterised variables into a single value, so the model needs to include an estimation.

Scientists run tests with the model to find the value — or range of values — that allows the model to give the best representation of the climate. While it is a necessary part of climate modelling, it is not a process that is specific to it.

Dr James Screen , assistant professor in climate science at the University of Exeter , describes how scientists might tune their model for the albedo reflectivity of sea ice. There is some uncertainty range associated with observations of albedo. So whilst developing their models, modelling centres may experiment with slightly different — but plausible — parameter values in an attempt to model some basic features of the sea ice as closely as possible to our best estimates from observations.

For example, they might want to make sure the seasonal cycle looks right or there is roughly the right amount of ice on average. This is tuning. Therefore, they need to test their parameter values in order to give sensible model output for key variables. It is derived from the Latin word albus, meaning white. Snow and ice tend to have a higher albedo than, for example, soil, forests and open water.

As most global models will contain parameterisation schemes, virtually all modelling centres undertake model tuning of some kind. A survey in pdf found that, in most cases, modellers tune their models to ensure that the long-term average state of the climate is accurate — including factors such as absolute temperatures , sea ice concentrations, surface albedo and sea ice extent.

This process involved adjusting parameterisations particularly of clouds — microphysics, convection and cloud fraction — but also snow, sea ice albedo and vegetation.

Rather, if a reasonable choice of parameters leads to model results that differ dramatically from observed climatology , modellers may decide to use a different one. Similarly, if updates to a model leads to a wide divergence from observations, modellers may look for bugs or other factors that explain the difference.

There is a lot of discussion on this point in the community, but everyone is clear this needs to be made more transparent. These biases occur because models are a simplification of the climate system and the large-scale grid cells that global models use can miss the detail of the local climate. The protection is supposed to last for the next decades, so you have to account for future changes in rainfall over your river catchment.

Climate models, even if they resolve the relevant weather systems, may be biased compared to the real world. For the water engineer, who runs the climate model output as an input for a flood risk model of the valley, such biases may be crucial, says Maraun:.

But the model simulates positive temperatures, rainfall and a flash flood. In other words, taking the large-scale climate model output as is and running it through a flood model could give a misleading impression of flood risk in that specific valley.

Essentially, scientists compare long-term statistics in the model output with observed climate data. Using statistical techniques, they then correct any biases in the model output to make sure it is consistent with current knowledge of the climate system. Bias correction is often based on average climate information, Maraun notes, though more sophisticated approaches adjust extremes too. The bias correction step in the modelling process is particularly useful when scientists are considering aspects of the climate where thresholds are important, says Hawkins.

An example comes from a study , co-authored by Hawkins, on how shipping routes could open through Arctic sea ice because of climate change. If the climate model simulates too much or too little ice for the present day in a particular location then the projections of ship route viability will also be incorrect. In other words, by using bias correction to get the simulated sea ice in the model for the present day right, Hawkins and his colleagues can then have more confidence in their projections for the future.

Russian icebreaker at the North Pole. Credit: Christopher Michel via Flickr. Typically, bias correction is applied only to model output, but in the past it has also been used within runs of models, explains Maraun:. Recent advances in modelling mean flux corrections are largely no longer necessary.

However, some researchers have put forward suggestions that flux corrections could still be used to help eliminate remaining biases in models, says Maraun:. So by nudging the model to keep its simulations of the North Atlantic Ocean on track based on observed data , the idea is that this may produce, for example, more accurate simulations of rainfall for Europe.

In other words, if a model is not producing enough rainfall in Europe, it might be for reasons other than the North Atlantic, explains Maraun. For example, it might be because the modelled storm tracks are sending rainstorms to the wrong region. This reinforces that point that scientists need to be careful not to apply bias correction without understanding the underlying reason for the bias, concludes Maraun:.

Weather and climate are sometimes used interchangeably, but scientists, meteorologists and researchers study and model them differently. To make these predictions , meteorologists use weather data and forecast models to determine current and future atmospheric conditions. Because weather takes place hour by hour, forecast models use current atmospheric and oceanic conditions to predict future weather.

The forecast takes into account humidity, temperature, air pressure, wind speed and direction, as well as cloud cover. Geographic location, proximity to water, urban structures, latitude and elevation can also influence the weather you experience. Weather models work at resolutions high enough to generate different predictions for neighboring towns, in some cases, but only over short timescales of about two weeks maximum.

Essentially, climate models are an extension of weather forecasting. But whereas weather models make predictions over specific areas and short timespans, climate models are broader and analyze long timespans. They predict how average conditions will change in a region over the coming decades. Illustration of the three-dimensional grid of a climate model. Image: Ruddiman. Climate models include more atmospheric, oceanic and land processes than weather models do—such as ocean circulation and melting glaciers.

These models are typically generated from mathematical equations that use thousands of data points to simulate the transfer of energy and water that takes place in climate systems. Scientists use climate models to understand complex earth systems. These models allow them to test hypotheses and draw conclusions on past and future climate systems.

This can help them determine whether abnormal weather events or storms are a result of changes in climate or just part of the routine climate variation.

For example, when predicting tropical cyclones during hurricane season, scientists can use climate models to predict the number of tropical storms that may form off the coast and in what regions they are likely to make landfall. When creating climate models, scientists use one of three common types of simple climate models: energy balance models, intermediate complexity models, and general circulation models.

These models use numbers to simplify the complexities that exist when taking into account all the factors that affect climate, like atmospheric mixing and ocean current. This model takes into account surface temperatures from solar energy, albedo or reflectivity, and the natural cooling from the earth emitting heat back out into space.

To predict climate, scientists use an equation that represents the amount of energy coming in versus going out, to understand the changes in heat storage—for example, as more heat-absorbing CO2 fills up the atmosphere.

Scientists then take this equation and plug it into box models that represent a square of land within a three-dimensional grid, to express climate in a region or even across a continent. These geographical features allow intermediate complexity models to simulate large-scale climate scenarios such as glacial fluctuations, ocean current shifts, and atmospheric composition changes over long timescales.

An ice core. General circulation models are the most complex and precise models for understanding climate systems and predicting climate change. These models include information regarding the atmospheric chemistry, land type, carbon cycle, ocean circulation and glacial makeup of the isolated area.

This type of model also uses a three-dimensional grid, with each box representing around square kilometers of land, air, or sea, which is better resolution than the typical to kilometers per box. This model is more sophisticated than the energy balance and intermediate complexity models, but it does require a larger amount of computing time—each simulation could take several weeks to run.

For many decades, scientists have been collecting data on climate using cores from ice, trees, and coral, as well as carbon dating. From this research they have discovered details about past human activity, temperature changes in our oceans, periods of extreme drought, and much more.

As more data points are collected, they increase the accuracy of existing climate models. This enhances climate forecasting, because past climate data helps to establish a baseline for typical climate systems. From there, researchers establish climate variables that they want to keep the same, like cloud cover, and variables they want to test, like increased carbon dioxide, to evaluate hypotheses about future changes.

These could estimate anything from sea level rise to increased temperatures and risk of drought and forest fires. If the model accurately predicts past events that we know happened, then it should be pretty good at predicting the future, too. And the more we learn about past and present conditions, the more accurate these models become.

Each RCP indicates the amount of climate forcing, expressed in Watts per square meter, that would result from greenhouse gases in the atmosphere in The rate and trajectory of the forcing is the pathway. Like their predecessors, these values are used in setting up climate models.

Learn more about RCPs ». Around the world, different teams of scientists have built and run models to project future climate conditions under various scenarios for the next century. The model results project that global temperature will continue to increase, but show that human decisions and behavior we choose today will determine how dramatically climate will change in the future.

Unlike weather forecasts, which describe a detailed picture of the expected daily sequence of conditions starting from the present, climate models are probabilistic, indicating areas with higher chances to be warmer or cooler and wetter or drier than usual.

Climate models are based on global patterns in the ocean and atmosphere, and records of the types of weather that occurred under similar patterns in the past. View maps showing short-term climate forecasts ». What's the difference between climate and weather? How do weather observations become climate data? How do we observe today's climate? One of the big challenges for scientists is creating climate models that display the earth in high-resolution.

Higher resolution models will allow scientists to zoom in on certain regions, such as the South of England, and see the effects of climate change on local scales. Scientists divide the earth into a three dimensional grid to run climate simulations. Previously, these grid squares were around km by km. Now, scientists are able to calculate the climate using a grid that is 25km by 25km.

This means we can see individual weather systems, like storms. Scientists are also trying to create climate models that take more aspects of the climate into account. The latest generation of climate models are known as Earth System Models. This is a specific type of model that incorporates chemical and biological processes into the picture.