Questions related to climate projections

Questions related to climate projections concern initialization and what are the data needed in the models. Transient climate simulations and time-slice experiments are also discussed as well as differences between numerical weather prediction and climate projections.

What is the difference between climate projections and a weather forecast?

A weather forecast starts from a known initial state that is as close to the actual state of the system as given by available observations. The weather forecast can provide relevant information for a few days to at most a few weeks. A climate projection starts from an arbitrary model state and can be run for decades to centuries.
After a few days to some week(s) the predictability of the weather vanishes and continuing a simulation with a weather prediction model will not give an accurate weather forecasts at these time horizons. At longer time scales of weeks to months, processes working on slower time scales can have an impact on the weather and even if single events cannot be forecasted it may be possible to say that it will be warmer or colder than normal, or wetter or drier than normal. Such forecasts are relying on slowly varying processes in the climate system. For instance, if a large-scale warm anomaly in sea surface temperatures is found in an area this will impact the region for a long time which can be simulated by a forecast model. A climate projection, on the other hand, is not useful for predicting the actual state of the system (weather) for the first weeks of a simulation as the initial state is not close to the observed state of the climate system. However, the climate model can be integrated for long time periods and will generate possible weather situations that resemble those in the real climate system. Assessed over long-enough times this would represent the climate and its variability even if the exact evolution over time can't be compared to the actual evolution of the climate system in a meaningful way.
Any users of forecast data (seasonal to decadal) are not served by the current EURO-CORDEX data.
Users of climate projections must be aware that direct comparisons of time series of weather events between model and the real atmosphere are not meaningful because climate model can provide information on weather information only in statistical sense over a long period (typically 30-years) of time.

How do you start a climate change projection and why is this important?

A simulation with a climate model requires an initial state for each individual grid cells in the model (atmosphere, ocean, land surface). Given this state the exact evolution over time will differ between different simulations. This implies that two simulations starting from slightly different initial conditions in the 19th century will differ in their details also in scenarios for the 21st century.
Most climate model projections in global climate models start in pre-industrial time, which is often taken to be sometimes in the second half of the 19th century. For this time period (or any other historical time period) there are not enough observations to start a model from a purely observational state. Instead, an arbitrary state is taken from a long-term global climate model integration with the same climate model representing pre-industrial conditions.
Important in a climate model integration is that the model starts from a spun-up state to avoid spurious drift in the system that would result from an integration starting from a state far away from equilibrium for the model. For global climate models, notably the slow time scales of the ocean imply that spin-up times of several centuries are needed. This is achieved in the pre-industrial control runs from which initial states for climate projections are taken. For regional models not involving the global ocean circulation, such as the EURO-CORDEX models, climate processes are acting at shorter time scales. Depending on area, slower processes involving soil moisture may, still, call for longer spin-up times of several years.

What determines the evolution of a climate projection?

The exact evolution of the climate in a climate projection is governed by the initial state of the system, of the imposed forcing conditions and of how the climate model responds to the forcing.
As the initial state in a global climate model differs from the real state of the climate system there will necessarily be a mismatch between various modes of variability in the real system and in the climate integration. This means, for instance, that El Nino episodes or phases with stronger or weaker westerlies over the North Atlantic (strong or weak North Atlantic Oscillation conditions) may be out of phase. Such discrepancies can influence climate at scales covering years to decades and is one important source of uncertainty in climate change projections (e.g. Hawkins and Sutton, 2009).
Forcing conditions are imposed using, first, historical forcing conditions as reconstructed until a certain point in time, when scenarios for future climate forcing comes into place. For the RCP-scenarios used in CORDEX, the historical forcing is applied until 2005. From 2006 and onwards, future scenarios are used for providing forcing conditions. For the newer SSP-RCP-scenarios that are used in CMIP6, the transition takes place at the shift from 2015 to 2016.
Summarizing for a CORDEX regional climate model projection, the evolution is determined by:

  1. The initial conditions in the underlying global climate model, typically starting in 1850.
  2. The initial conditions in the regional model starting around 1960.
  3. The forcing conditions from the global climate model imposed on the boundaries.
  4. The local forcing conditions, including changes in greenhouse gases, aerosols and land-use, imposed in the RCM.
  5. How the RCM responds to these changes in forcing.

Note that this list is not in order of importance but rather in a sequential way starting from the GCM simulation ending up at the regional and local scale as simulated by the RCM.


Hawkins E and Sutton R (2009) The potential to narrow uncertainty in regional climate predictions Bulletin of the American Meteorological Society, 90, pp. 1095-1107, 10.1175/2009BAMS2607.1