Multi-model skill assessment of seasonal temperature and precipitation forecasts over Europe

Abstract

There is now a wide range of forecasts and observations of seasonal climatic conditions that can be used across a range of application sectors, including hydrological risk forecasting, planning and management. As we rely more on seasonal climate forecasts, it becomes essential to also assess its quality to ensure its intended use. In this study, we provide the most comprehensive assessment of seasonal temperature and precipitation ensemble forecasts of the EUROSIP multi-model forecasting system over Europe. The forecasts from the four individual climate models within the EUROSIP are assessed using both deterministic and probabilistic approaches. One equally and two unequally Weighted Multi-Models (WMMs) are also constructed from the individual models, for both climate variables, and their respective forecasts are also assessed. Consistent with existing literature, we find limited seasonal climate prediction skill over Europe. A simple equally WMM system performs better than both unequally WMM combination systems. However, the equally WMM system does not always outperform the single best model within the EUROSIP multi-model. Based on the results, it is recommended to assess seasonal temperature and precipitation forecast of individual climate models as well as their multi-model mean for a comprehensive overview of the forecast skill.

Post-processing of seasonal predictions – Case studies using the EUROSIP hindcast data base

“…The second main result is the predictability of summer temperature in the Mediterranean region obtained without de-trending the observed and forecast data. Significant skill (anomaly correlation) is also shown by Mishra et al (2018) in their skill assessment of EUROSIP for the forecasts of summer temperature with one-month lead time for areas including the CS4 and CS6 domains of this study for three models. The skill for CS6 is also supported by their results with a probabilistic skill score and for the multi-model.…”

“…In their assessment of the skill of EUROSIP, Mishra et al (2018) select a common available period of 1992-2012. As we have seen that sample length and ensemble size are critical, we take these two parameters as integral parts of a seasonal prediction system and we propose to take advantage of the full hindcast information and evaluate each model on the longest series available and pool the ensembles together, each bringing all its members.…”

Toward a Regional-Scale Seasonal Climate Prediction System over Central Italy Based on Dynamical Downscaling

“…For the European winter season, factors such as stratospheric processes, soil moisture, sea-ice extension, and North Atlantic Oscillation (NAO) phase have been identified as sources of predictability [7][8][9]. Specifically, for the winter season evidence indicates a stratospheric variability that significantly impacts seasonal temperature anomalies [10].…”

“…However, a poorer predictability characterizes the mid-latitudes compared to tropical latitudes. In fact, the latter are affected by a larger chaotic component which limits the skill/quality of the current seasonal forecast systems [9].…”

“…In recent years, many studies considered regional-scale dynamically downscaled SFSs, mainly focused on tropical Asian regions and Africa (see e.g., [18][19][20][21]). Based on the author's knowledge, few studies involving regional-scale SFC over the European domain exist [9,22,23], and even less focused on the Mediterranean [24] basin and the Italian peninsula [25]. However, in recent years, an interesting multi-partner initiative aimed at investigating inter-annual variability mechanisms and at providing tailored climate services mainly on a seasonal time scale in the Mediterranean basin has been established (MEDSCOPE, https://www.medscope-project.eu/).…”