To handle uncertainties, climate modelers produce ensembles of model results. This is to explore sensitivities and make assessments about future climate change by using different scenarios for the future. For instance, by producing projections of climate change in a range of different climate models, or with a single model starting from different initial conditions. The result is an ensemble of climate projections, which are all considered as possible futures.

Bias adjustments are normally performed before impact analysis to make the climate model results correspond to observations. This step will align the climate models for the historical period where the bias adjustment is calibrated, but may also introduce further uncertainties for the future climate. Moreover, bias adjustment methods may lead to different implications for the final analysis, e.g. inconsistency between corrected variables if this is done separately. The final part of the model chain, the hydrological impact models, may respond differently to climate change due to different interpretation of drivers for flow generation in the model set-up or assumptions in the model structure.

Impact studies use long-term average values. For instance, water management is always local, and the local scale is already exposed to large variation in weather patterns. This means that climate impact may not be evident on a year-to-year basis, but some events may become more frequent, or prolonged, if analyzed over a longer time period. Therefore, climate impact assessments often use 30-year averages to explore changes. In practice this may be too short a period for local conditions as they are so variable. If the trend is small and the variability large (often in precipitation and river flow) it may be very difficult to detect changes beyond natural variability.