We are convinced that the climatology (or even the persistence) does not necessarily represent a good proxy for the value the users may see in these predictions. We think this is really important from a climate service perspective, because the existence of simple alternative models with similar skill [of dynamical seasonal forecasts] could represent a stimulus for further research whilst at the same time providing a natural benchmark for evaluating more complex kind of predictions.

What is users' next best alternative to the use of dynamical seasonal predictions?


The recent development of climate predictions has shown their potential in providing users with information that could be used to inform decision on a seasonal time-horizon. This in turns offers a way of developing resilience to climate change through adaptation to seasonal variability. An effective way to assess to value of this information is to compare the quality of the forecast (assessed by its verification attributes) to the quality of the forecast from a system based on simpler assumption (and thus cheaper to run). For this purpose, climatology and persistence are commonly used. We are presenting here a general methodology based on a Markov Chain process trained at a seasonal time-scale which can be used as a simple benchmark in assessing the skill and thus the value of the predictions. We demonstrate that in spite of its absolute simplicity our methodology easily outperforms not only climatology but also most of the seasonal predictions system at least in some location such as continental Europe. We suggest that Markov Chain could represent a useful next best alternative for a number of users and thus should be used as a more realistic benchmark.