Assessing consistency across climate datasets for the potential detectability of extreme events in seasonal forecasting using agroclimatic indicators

Seasonal forecasts are medium-range climate predictions that, used for calculating agroclimatic indicators, might potentially help land managers for best decision making. To assess their reliability seasonal forecasts are commonly contrasted against observed datasets, e.g. gridded data coming from reanalysis, classifying yearly pixel conditions in into/out of the norm events (i.e. using the 33th and 66th percentiles along a time series to define the occurrence of out of the norm events). Potential differences in the shape of the probability distribution across observed climate datasets might influence the results in the validation procedure of seasonal forecasting since the definition of out of the norm events depends on the properties of the statistical distribution. Here, we assess for different agroclimatic indicators related with water availability, vegetation thermal needs and fire risk, the spatial patterns of skewness using a range of climate datasets, i.e. ERA5, EOBS and WFDEI along a 30 year period. Skewness represents the degree of asymmetry of the probability distribution evidencing locations in which out of the norm events highly differ from mean conditions which might suggest a potentially higher detectability. Common spatial patterns of great skewness (either positive or negative) across observed dataset might suggest areas with high and consistent detectability whereas contrasting patterns might suggest higher uncertainty for the validation procedure.