The climate indicators provided are the end result of a long chain of model simulations and statistical calculations. Here, we provide a general overview of how to estimate local changes in climate indicators with the necessary steps for an impact assessment.
A starting point is the emissions scenarios. What would you like to look at? Business-as-usual or a moderate path? Scenarios for the evolution of greenhouse gas emissions are provided by Representative Concentration Pathways (RCPs) and are often presented for a high emission path representing “business-as-usual” (RCP8.5), and a more moderate path with a stabilization of the resulting radiative forcing (RCP4.5).
Global Climate Models (GCMs) are then applied to calculate climate projections forced by the different future scenarios. The various GCMs differ in their sensitivity to the greenhouse gas forcing, and also in how they simulate different processes in the Earth’s climate; each having their own strengths and weaknesses. Ensemble statistics, e.g. the average of all models, are therefore generally considered more reliable than using a single model projection.
GCMs have a typical horizontal resolution of several hundred kilometers, and since most hydrological processes occur at much smaller scales, a further downscaling of the GCM information is necessary. This is performed by Regional Climate Models (RCMs), which nest into the GCM and provide finer scale information of 50 km or less.
It is common practice to apply bias correction or adjustment, a statistical method that removes various errors from the climate model so that they become more similar to observations (local gridded or station data) or reanalysis, and thus more useful and suitable for hydrological applications or other impact models.
For example, if you are interested in local hydrological changes, at the end of this chain of models and methods, a range of hydrological models can be applied to provide an ensemble of estimations of the hydrological response to the climate projections.
Since climate, in contrast to weather, is more evident from long term statistics, e.g. the average over thirty years, indicators are produced to show how the climate changes between different time periods and for a given emission scenario, which is shown for a range of possible combinations of emission pathways, GCMs, RCMs, and hydrological models. Each model step in the production chain includes uncertainties and therefore an ensemble of projections is used to account for the spread of possible climate impacts in the future. The exact future still remains unknown but the indicators show tendencies and future risks generated by climate change.