Coupling forecast calibration and data‐driven downscaling for generating reliable, high‐resolution, multivariate seasonal climate forecast ensembles at multiple sites

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.

Seasonal forecasts offer economic benefit for hydrological decision making in semi-arid regions

“…monthly; per lead time) and spatial (basin-averages) resolutions are preserved and (3) no absolute values or event thresholds (e.g., mm) are used. Nevertheless, more sophisticated post-processing measures, such as multivariate bias correction 40 or even dynamical downscaling 41 may show an effect on the forecast value.…”

Post-processing sub-seasonal to seasonal climate forecasts under climate change

“…Downscaling is often required to generate local-scale climate forecasts requested by the stakeholders from raw GCM outputs that are in coarse resolution (Stockdale et al, 2010). Spatial and temporal downscaling is mostly achieved by the statistical techniques (Jacob et al, 2020;Nury et al, 2019;Schepen et al, 2019), such as analogue downscaling (Hwang and Graham, 2013;Shao and Li, 2013), nonhomogeneous hidden Markov model (Pineda and Willems, 2016), and weather generator (Han et al, 2017). Dynamical methods, such as regional climate models, are also readily available for the downscaling purpose, but they are computationally expensive and do not have obvious advantages over statistical methods.…”

“…Developing reliable tools in post-processing GCM forecasts as well as linking the post-processed forecasts with impact models (e.g. hydrological and crop models) are vital for delivering timely forecasts to practical applications (Schepen et al, 2020a). Integrating the trend-aware forecastcalibration model into the end-to-end forecast workflow may produce more user-oriented forecasts for water resource management, agriculture, and other climate-sensitive sectors in a changing climate.…”