DELPHI
Produces physically consistent future-climate equivalents of a location's observed or generated weather, using CMIP6 (or other) climate-ensemble information.
DELPHI takes an existing hourly weather record (for example, a SAGE-generated ensemble) and reprojects it onto a chosen future climate. The transformation is driven by per-variable, per-month, per-quantile change signals derived from a climate model ensemble, with secondary closure adjustments (diurnal range, wet-day frequency, radiation envelope) that keep the futurised series physically self-consistent across all variables.
The key invariant is the separation between local absolute climate and the model-derived change signal: absolute local climate is provided by the input record, while only the change signal is taken from the ensemble. Because change factors are differences (or ratios) between each GCM's own future and its own baseline, stationary GCM biases cancel by construction. DELPHI currently supports three approaches to the futurisation process: scenario means (SSP1-2.6 … SSP5-8.5), storyline corners on the ΔT–ΔP plane, and IPCC AR6 Global Warming Levels. The case study below showcases all three. Each approach answers a different question about the local response.
Built on Quantile Delta Mapping (Cannon, Sobie & Murdock, 2015) combined with IPCC AR6 Atlas Global Warming Level windows and a distance-weighted storyline-cluster framing (Ruane & McDermid 2017; Shepherd et al. 2018). Change signals are derived from the climate ensemble and applied to the local generator; the models therefore contribute only the change factor, not absolute local climate.
- 01Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015). Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate, 28(17), 6938–6959. https://doi.org/10.1175/JCLI-D-14-00754.1
- 02IPCC (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the IPCC (Atlas & Cross-Chapter Box ATLAS.1). Cambridge University Press. https://doi.org/10.1017/9781009157896
- 03Ruane, A. C., & McDermid, S. P. (2017). Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment. Earth Perspectives, 4, 1. https://doi.org/10.1186/s40322-017-0036-4
- 04Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S., Dima-West, I. M., … Zenghelis, D. A. (2018). Storylines: an alternative approach to representing uncertainty in physical aspects of climate change. Climatic Change, 151, 555–571. https://doi.org/10.1007/s10584-018-2317-9
A SAGE ensemble, futurised through three methods
Scenario means, ΔT–ΔP storyline corners, and IPCC AR6 Global Warming Levels. The same Hasselt generator gives three complementary readings of the local response.
Sample year · present vs futurised · Hasselt · 8,760 hours
Same SAGE member, same timestamps: the storm timing, dry spells and diurnal cycle are identical by construction. Only the climate is different, so any gap between the two lines is pure climate signal. Switch between scenarios, storyline corners and IPCC AR6 global warming levels to feel how DELPHI reshapes the year.