This question is related to and extension of my previous question which was about the methodology used to predict future climatic extremes:
Although the answer to that question is quite detailed and informative, it is still not clear to me whether the models used in global climate (GC) simulations can predict rare (~1000-year return period events).
- I understand that if we have multi-thousand simulations, we can calculate the 0.1% quantile, but I do not know whether the applied models can predict rare events reliably. Under reliably, I mean that in some alignment with the observed extremes or with the extremes statistically inferred from the observations.
- All the papers I saw so far, which dealing with extremes due to climate change, are considering 20-year return period events as maximum and comparing the simulations to observed quantiles. This is one source of my suspicion regarding the reliability of GCMs.
- Are GCMs calibrated to observed mean or multiple quantiles? Daily, monthly, annual, etc. averages are used? Averages of larger areas or grid points (corresponding to the applied resolutions) are adopted in the calibration, validation processes? What are the typically used target parameters used in the calibrations?
- Here is an example to make it more clear: one wants to determine the probability distribution function of extreme wind speed in a given location from daily measurements/simulation results. The question: If we take one simulation for the 1950-2000 period and assume for a moment that model uncertainty is negligible compared to climate variability, can we treat the simulation data as a sample with more or less equivalent confidence than that of the actual observations, corresponding to the same period, to infer large return period extremes?
One way to check this is would be to compare the large return period extremes inferred from observed data and from multiple simulations, I have not seen any papers with above 20-year return level comparison yet. I see that we cannot expect very accurate models regarding e.g. 1000-year extremes, because we cannot reliably calibrate and validate GCMs:
- The available measurements are from a 100-200 year observation period (at best);
- We do not have too much data from different climatic conditions (there might be some phenomena which are not (or not adequately) taken into account in current models, we are somehow biased by the observed, prior 100 year conditions.
Any help, idea, reference, link are welcomed.