EGU2020-20631, updated on 10 Apr 2024
https://doi.org/10.5194/egusphere-egu2020-20631
EGU General Assembly 2020
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Jose Maria Costa Saura1,2, Valentina Bacciu1,2, Valentina Mereu1,2, Antonio Trabucco1,2, and Donatella Spano1,2
Jose Maria Costa Saura et al.
  • 1Euromediterranean Center on Climate Change (CMCC)), IAFES division, Sassari, Italy (jmcostasaura@unisss.it)
  • 2Agraria, University of Sassari, Sassari, Italy

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, E-OBS 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.

How to cite: Costa Saura, J. M., Bacciu, V., Mereu, V., Trabucco, A., and Spano, D.: Assessing consistency across climate datasets for the potential detectability of extreme events in seasonal forecasting using agroclimatic indicators, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20631, https://doi.org/10.5194/egusphere-egu2020-20631, 2020.

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