The Climate-System Historical Forecast Project: Providing Open Access to Seasonal Forecast Ensembles from Centers around the Globe

Abstract

UNCERTAINTY IN SEASONAL FORE CAST ING. Any prediction of the future evolution of the Earth system requires an associated assessment of its uncertainty. This is true whether the forecast is for the days ahead or is a longer-term prediction for the following months and seasons. For seasonal forecasts, the uncertainty associated with inexact initial conditions, which can grow rapidly in time, is usually addressed by running multiple forecasts with perturbations applied to the initial state of the ocean and atmosphere (Arribas et al. 2011; Stockdale et al. 2011). The idea is that the perturbed initial conditions are of a suitable magnitude to represent the uncertainty in the observational measurements and the analysis tools that are used to process them. As the forecast evolves, the differences between the forecasts, known as the ensemble "spread," should therefore reflect the typical forecast error, or "uncertainty"; in other words, the eventual real-world evolution should be contained within the cluster of this forecast ensemble. In tandem, uncertainty in forecasts is also contributed to by our inexact representations of the Earth system physics. This contribution to uncertainty is sampled by employing different Earth system models (Yun et al. 2005; Weisheimer et al. 2009; Smith et al. 2013), the so-called multimodel approach, which is often supplemented by the use of perturbations to physical processes, known as stochastic physics schemes, to

The Subseasonal Experiment (SubX): A Multimodel Subseasonal Prediction Experiment

“…Until recently, it has been difficult to assess the skill of subseasonal predictions. Re-forecast databases consisted of monthly or seasonal predictions that were not initialized frequently enough to capture the full range of subseasonal variability (e.g., NMME, DEMETER, CHFP, ENSEM-BLES, APCC/CliPAS) (Kirtman et al (2014); Palmer et al (2004); Tompkins et al (2017); Weisheimer and Reyes (2009); Wang et al (2008)) or weather predictions that did not extend to long enough lead-times for subseasonal predictions (e.g. TIGGE, GEFS 2nd generation reforecasts) (Swinbank et al (2016); Hamill et al (2013)).…”

Evaluation and statistical downscaling of East Asian summer monsoon forecasting in BCC and MOHC seasonal prediction systems

“…In recent decades, climate prediction systems have been much improved in many aspects, such as the resolution, initial condition, ensemble size and model physics, as well as ensemble size. These improvements are mainly attributed to the improved computing resources, and they have facilitated coupled general circulation models (CGCMs) to be a standard tool for seasonal climate prediction (Yang et al, ; Weisheimer et al, ; Zhu and Shukla, ; Ma and Wang, ; Tompkins et al, ; Zhu et al, ). However, the prediction skill of the EASM climate depends on the models and monsoon index selections.…”

Seasonal prediction skill of East Asian summer monsoon in CMIP5 models

“…To better predict the short-to long-term climate, the World Climate Research Programme (WCRP) launched two new projects: the Climate-system Historical Forecast Project (CHFP; Kirtman and Pirani, 2009;Tompkins et al, 2017) and the Subseasonal-to-Seasonal (S2S) Prediction Project (Vitart et al, 2017). The two projects coordinate most climate modelling research groups and provide a large range of forecast datasets.…”