Coupling forecast calibration and data‐driven downscaling for generating reliable, high‐resolution, multivariate seasonal climate forecast ensembles at multiple sites. (19th November 2019)

• Record Type: Journal Article
• Authors: Schepen, Andrew; Everingham, Yvette; Wang, Quan J.
• Is Part Of: International journal of climatology. Volume 40:Number 4(2020)
• Page Start: 2479
• Page End: 2496
• Publication Date: 2019-11-19
• Subjects: bias‐correction -- forecast verification -- multivariate -- post‐processing -- seasonal forecasting; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.6346
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract: Calibration and downscaling of ensemble GCM forecasts is becoming increasingly important for hydrological and agricultural modelling in support of the management and protection of valuable natural resources. Moreover, skilful and reliable daily forecast sequences are required to drive decision support models that operate on a daily time step. While downscaling of daily GCM outputs has been developed extensively in climate impacts studies, much less attention has been paid to the downscaling of ensemble GCM forecasts, which has the confronting aspect of low and diminishing skill with increasing lead time. Evidence is building that simple bias‐correction methods that do not model correlation between forecasts and observations, nor attempt to correct spatial, temporal and inter‐variable correlations, produce downscaled forecasts that perform poorly in applications models. Thus downscaling GCM forecasts requires inclusion of a full calibration component to reduce bias, improve reliability, capture skill where it is available and remove negative skill. In this study, we propose a new methodology for coupling a full GCM forecast calibration with empirical methods to (a) instil correct temporal, spatial and inter‐variable correlations in ensembles and (b) perform a multivariate downscaling of ensembles to daily sequences at multiple sites. Through a case study application, the proposed methodology is shown to produce skilful monthly–seasonal forecasts of rainfall, temperature and solar radiation at regional spatial scales. It also produces realistic and coherent multivariate daily sequences at multiple sub‐grid locations. The new methodology can be applied to more effectively integrate climate forecasts into hydrological and crop models and to support proactive decision‐making in agriculture and natural resources management. Abstract : A novel methodology (see figure) is developed to couple a rigorous GCM forecast calibration with data‐driven downscaling to generate high‐resolution and multivariate climate forecasts in the form of ensembles. Skilful and reliable seasonal forecasts are downscaled using historical data patterns to obtain much more realistic temporal, spatial and inter‐variable relationships compared to raw forecasts. The new forecasts are promising to enable better use of GCM forecasts in agriculture and water resources management where existing methods result in unreliable linkages and poor forecasts.