Abstract
Abstract. This paper presents a system to perform large-ensemble climate stochastic forecasts. The system is based on random analogue sampling of sea-level pressure data from the NCEP reanalysis. It is tested to forecast a North Atlantic Oscillation (NAO) index and the daily average temperature in five European stations. We simulated 100-member ensembles of averages over lead times from 5 days to 80 days in a hindcast mode, i.e., from a meteorological to a seasonal forecast. We tested the hindcast simulations with the usual forecast skill scores (CRPS or correlation) against persistence and climatology. We find significantly positive skill scores for all timescales. Although this model cannot outperform numerical weather prediction, it presents an interesting benchmark that could complement climatology or persistence forecast.
Highlights
Stochastic weather generators (SWGs) have been devised to simulate many and long sequences of climate variables that yield realistic statistical properties (Semenov and Barrow, 1997)
This paper presents tests of such a system to forecast temperatures in Europe and an index of the North Atlantic Oscillation (NAO)
The two datasets (NAO and temperature) are treated separately because the simulations are done with two different analogue computations (Sect. 3.1)
Summary
Stochastic weather generators (SWGs) have been devised to simulate many and long sequences of climate variables that yield realistic statistical properties (Semenov and Barrow, 1997). Their main practical use has been to investigate the probability distribution of local variables such as precipitation, temperature, or wind speed, and their impacts on agriculture (Carter, 1996), energy (Parey et al, 2014), or ecosystems (Maraun et al, 2010). This allows us to simulate spatially coherent multivariate fields (Yiou, 2014; Sparks et al, 2018) and can be used for downscaling (Wilks, 1999)
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