Here you can learn about the models and methods used in the production of climate indicators. This is a basis for understanding the possible choices in the tools available at the Climate Information Portal.

Climate Models and Ensembles

What is a climate model?

Climate models are numerical descriptions of the atmosphere in computational codes.  We use them for calculating the future and historic climate. Scientists often use climate models to study how the climate may change when the composition of the atmosphere changes with, for example, changed levels of greenhouse gases and aerosols. The models can be seen as scientific laboratories for experimenting and exploring hypothesis.

The climate models are three-dimensional mathematical descriptions of the climate system: the atmosphere, land surface, oceans, lakes and ice. In a climate model, the atmosphere is divided into boxes, so-called grids, along the earth’s surface that extend up into the air, as visualized in the figure.

Schematic of the representation of the land surface, ocean, and atmosphere in a climate model, with specific processes calculated within model grid boxes and information transfer between boxes.

The motions of the atmosphere and the preservation of energy, water and mass follow well-known physical laws that can be described by mathematical formulas. For each grid various processes are calculated resulting in variables, such as heat, wind, clouds and rain. A global climate model (GCM) describes many processes both in the atmosphere, ocean, land and cryosphere, such as formation of glaciers and ice-caps.

It requires a lot of computer power to run a climate model, and even though the computing capacity is constantly increasing, calculations in the global climate models are still made with a rather sparse grid of several hundred kilometers. This means that the level of detail for a local or regional scale is low in the global model. However, if you want to study a smaller part of the Earth in more detail, you can use so called regional climate models (RCM). In a regional model, the grid is positioned over a smaller area, which means that you can get a denser grid (and more detail, e.g. 50 kilometers) from lower computational power. Exchange with the area outside the region is governed by the results of a global climate model. In this way the models are linked. The use of results from a global model for higher resolution by applying a regional model is called dynamic regional downscaling.

What is a model ensemble?

Across the globe there are many different climate models being developed. They all describe the main well-known physical laws that govern the atmospheric and oceanic motions, but there are processes that are less well known that can only be estimated with different assumptions. This is done in different ways and at varying complexity by the different models. This means that all climate models will give somewhat different results, and still it is not possible to state which one is more correct. The magnitude of these differences can be large or small depending on model, region, season, variable etc. Some models may perform well for a specific region/season/variable and worse in other aspects. So even though the climate models are advanced, they are not a perfect description of the climate system. The differences between the climate models carry important and useful information that describes the uncertainty in our knowledge about current and future climate.

When exploring climate change, one should therefore use several different models, i.e. model ensembles. The spread in the results of the ensemble can be significant, partly because models describe climatological processes in different ways. The advantage of model ensembles is that if the same response is seen in several models, the result is considered to be more robust. If the responses are different in different models the result is considered to be less robust.

Scientist collaborate under different projects and programs. Two important and global programs are: Coupled Model Inter-comparison Project (CMIP) and Coordinated Regional Climate Downscaling experiment (CORDEX).  Both are endorsed by the World Meteorological Organisation (WMO).


What is CMIP?

Coupled Model Inter-comparison Project (CMIP) is the standard experimental protocol for studying the output of coupled atmosphere-ocean global climate models. CMIP provides a community-based infrastructure that supports climate model diagnosis, validation, inter-comparison, documentation and data access. This framework enables a diverse community of scientists to analyze the global models in a systematic fashion, a process which helps in making models better. The international climate modelling community has participated in this project since it began in 1995.

CMIP has been going through several phases, from CMIP1 to CMIP3, then synchronized with the IPCC assessment reports’ numbering with CMIP5 and CMIP6. Each time with some changes to the modelling protocols and standards, e.g. the emission scenarios (RCPs in CMIP5 and SSPs in CMIP6). CMIP7 is a work in progress by the modelling groups around the world. CMIP are endorsed by the World Climate Research Programme (WCRP).

CMIP in short:

  • Global Climate Models (GCM).
  • Mostly coarse spatial resolution (several hundred kilometres)
  • To understand climate drivers, differences in model results.
  • Work in progress = CMIP7
  • More than 100 modelling groups around the world.

What is CORDEX?

The Coordinated Regional Climate Downscaling EXperiment (CORDEX) is responsible for advancing and coordinating the science and application of regional climate downscaling models through global partnerships. The reason according to CORDEX being that Regional Climate Models (RCM) and Empirical Statistical Downscaling (ESD), applied over a limited area and driven by GCMs can provide information on smaller scales, supporting more detailed impact and adaptation assessment and planning, which is vital in many vulnerable regions of the world.

Both CMIP and CORDEX are international collaborations between scientific institutions. They are also diagnostic model inter-comparison projects (model development) for different regions across the world. The programs also help in evaluating how realistic the models are in simulating the recent past as well as provide projections of future climate change.

CMIP6, CMIP5 and CORDEX are endorsed by the World Climate Research Program (WCRP). Information from CMIP5 and CORDEX experiments are summarized in the Intergovernmental Panel on Climate Change (IPCC) reports, starting from the IPCC Fifth Assessment Report (IPCC AR5), and CMIP6 are introduced in the assessment report as of the IPCC Sixth Assessment Report (IPCC AR6). CORDEX experiments are frequently used in regional assessment reports. The CORDEX regions sometimes differ regarding their spatial resolution.

CORDEX in short:

  • Regional Climate Models (RCMs) for dynamical downscaling of GCM results.
  • Often 50 km spatial resolution, some areas with 12 km or less.
  • To understand regional climate processes e.g. from local topography.
  • Suitable for impact studies.

Emission Scenarios

What is an emission scenario?

Emission scenarios describe possible anthropogenic emissions of greenhouse gases, based on assumptions about the future development of the world’s economy, population growth, globalization, transition to environmentally friendly technology and more. All this affects the level of greenhouse gas emissions, which in turn affects the greenhouse effect. One way of measuring how the greenhouse effect will change in the future is to estimate radiative forcing, which is measured in power per square meter (W/m2). More greenhouse gases in the atmosphere lead to a higher additional radiative forcing. Such scenarios are called Representative Concentration Pathways (RCP), and are in CMIP6 accompanied by further information about e.g. land-use changes in the Shared Socio-economic Pathways (SSP).

What is RCP?

Future greenhouse gas emissions and concentrations are difficult to predict and depend on future developments such as future population growth, economic growth, energy use, uptake of renewable energy, technological change, deforestation and land use. The climate-modelling community has developed four Representative Concentration Pathways (RCPs). The four RCPs span a large range of future global warming scenarios. RCPs are space and time and dependent trajectories of future greenhouse gas concentrations and different pollutants caused by different human activities. RCPs quantify future greenhouse gas concentrations and the radiative forcing (additional energy taken up by the Earth system), due to increases in climate change pollution. Read more about the RCPs in the article “What do different RCPs mean?”.

What is SSP?

Apart from RCP scenarios, Shared Socio-economic Pathways (SSPs) are also used in the climate models. These pathways are a separate complementary effort to look at factors such as population, education, economic growth, urbanization and technological development. Theoretically, each SSP can be used alongside the RCPs as long as it goes with the narrative (which will be explained below). For example, depending on the levels of emissions and what kind of mitigation actions that are taken. While the RCPs are used as input to evaluate the level of global warming in the future considering the levels of emissions, the SSPs look at the possibilities of emission reduction. SSPs can be combined with climate impact scenarios (as RCP scenarios) as well as sets of policy assumptions to study for example the interactions between climate change, related climate impacts, vulnerability to these and possible actions. The radiative forcing is described with W/m2 for the SSPs (as with the RCPs) and with the scenario number. For example, SSP1-2.6 (1 being the SSP scenario and 2.6 being the radiative forcing). The SSP-scenarios (Shared Socio-economic Pathways) are five scenarios that describes different socio-economic developments with impact on climate. Factors such as emissions of carbon dioxide, other greenhouse gases and the change of land use are crucial to describe the evolution of the future anthropogenic climate forcing and the climate scenarios need to take these factors into account. The SSPs differ in terms of, among other things, population development, equality, energy use and global carbon dioxide emissions. In all SSPs, the global economy is growing. Read more about the characteristics of the SSPs in the article What do different SSPs mean?.
SSP narratives describe alternative socio-economic developments and result in different emission scenarios used by the climate models.

None of the SSPs are more likely than the other but the world can develop in several different ways depending on decisions in a number of different areas, where different paths are possible. All SSPs pose different major challenges for emission reductions and adaptation. However, no actual climate policy is included in these scenarios. Although climate policies can be explored in studies based on the conditions provided by the scenarios, for example to achieve a certain emission reduction.

Climate Scenarios and Indicators

What is a climate scenario?

A climate scenario is a description of a possible future evolution of the climate, as calculated by a climate model based on an emission scenario. Climate scenarios are several possible climate developments that require a long chain of assumptions and calculations. The description of the scenario can be in the form of, for example, a map, a diagram or a table. The values can be absolute numbers, differences, or related to a value like for example time. The reference period 1981-2010 is used in the Climate Information Portal.

What is a climate indicator?

A climate indicator describes some aspect of the climate for a given time period. It can for example be the average temperature, the global radiation, or the number of days with snow cover. It can also be based on hydrological impact models, and for example describe the annual river discharge or soil moisture. Typically, the climate indicators are presented as deviations from a reference period to describe how climate is changing a certain variable.

Bias adjustment

What is bias adjustment?

The complex climate system, with many interdependent processes that are not always fully known or described in the climate model, inevitably leads to the climate model producing systematic deviations from observed values. These systematic deviations are called model bias. The bias can be of different magnitude for different variables and for different regions of the world. Most often, these deviations do not pose a problem for calculating climate indicators where the focus is on differences between a historical and a future scenario. However, some indicators are based on absolute limits, such as the number of days with frost, tropical nights, or precipitation above a certain level. A bias in the climate model can affect these indicators so that their interpretation is misleading. To avoid such issues, the variables are bias adjusted using a so-called bias adjustment (or bias correction) method, which defines a mapping of the range of model values to an observed range of values. The method used here is called quantile mapping, and two different versions of this technique are used depending on the production time of the indicators. The CMIP5 (those using RCP emission scenarios) models are bias adjusted using the Distribution Based Scaling (Yang et al., 2010), and the CMIP6 uses the newly developed method MIdAS (Berg et al., 2022).

Bias adjustment so that climate model output become more similar to observations or reference data. The bias adjustment identifies a statistical connection between values in the model to values of similar frequency in the observed climate. A transfer function is then mapping the model values (horizontal arrow) to remove systematic deviations (bias).

Water Impacts

What are water impacts?​

The Climate Information Portal shows climate change effects on hydrological variables along with the meteorological indicators. Climate change is affecting the hydrological cycle and the resulting consequences on land is calculated with the hydrological impact model World Wide HYPE (WWH). The HYPE model calculates the discharge in rivers, surface runoff and other hydrological parameters based on mathematical representations of storage and flow processes in and on the ground, and in lakes and streams.

Hype model
Schematic representation of the HYPE model processes.

The WWH model divides the world in river catchments based on topography to simulate how water flows through the landscape. Water balance equations calculate water storage and exchange with e.g. vegetation, soil, groundwater, glaciers, as well as routing of water through rivers, lakes and reservoirs with human regulations. To some extent irrigation and water abstractions are also included. Bias adjusted climate model data of daily mean precipitation, and daily mean, minimum and maximum temperature is used as input to simulate the hydrological impacts from climate change with WWH, assuming that other factors affecting hydrology remain the same as today.