Choose Cal-Adapt Data Sources to Use for Historical and Future Projections

[dlebauer]

Preferred characteristics:

• Consistent (methods, resolution) historical + future inputs
• Sub-daily
• Small grid size
• Ensembles if available
• Annually updateable (at least for inventories)

[dlebauer]

Summary

• Temporal resolution
  • LOCA, LOCA2 only at daily resolution
  • Sub-daily only available from dynamical downscaling (WRF CMIP6 or ERA5 reanalysis)
• Historical --> future consistency:
  • WRF CMIP6: historical (1980–2014) and future (2015–2100) at the same resolution, using same methods but note that CMIP6 is not a reanalysis
  • LOCA/LOCA2 also span historical+future but are daily only; could be downscaled.
• Ensembles:
  • LOCA/LOCA2: broad multi-model ensembles (15–30+ GCMs).
  • WRF CMIP6: smaller ensemble (currently ~4–6 GCMs, expected to expand).
  • Reanalyses (ERA5, WRF–ERA): well constrained (and only historical).

Considerations

WRF CMIP6 provides hourly, 3km gridded data for both historical and future runs. Provides ensembles across GCMs and 3 SSPs

WRF+CMIP6 provides consistency between historical (calibration, validation, inventory) future projections, ensuring that the statistical properties of training data are consistent with forecast data (GCM model physics, grid/downscaling, error etc).

Open Question How to handle annual updates?

• Stick with static dataset and update based on crops? (MVP)
• Downscale / bias correct with reanalysis data?

Overview of Cal-Adapt climate datasets

Dataset	Spatial resolution	Time step	Ensemble / models	Historical coverage	Future coverage	Variables	Notes
LOCA (CMIP5)	California; ~6 km (1/16 degree) grid	Daily	32 GCMs + ensemble stats	1950–2005	2006–2100 (RCP4.5/8.5)	tasmax, tasmin, pr	daily only. 1)
LOCA2–Hybrid (CMIP6, CA)	California; 3 km grid	Daily	~15 GCMs + ensemble mean	1950–2015	2015–2100 SSPs (Table 1)	Daily tasmin, tasmax, pr, winds, humidity, solar	2
WRF CMIP6	California; ~3km (also 9&45 km)	Hourly	Multi-model - CMIP6 GCMs	1980–2014 (downscaled GCM “historical”)	2015–2100 (SSPs)	20+ met vars (e.g., T, P, RH, winds, radiation)	Sub-daily, physics-consistent; both hist+future in same WRF framework.4
WRF–ERA (historical)	California; 45 km	Hourly	Reanalysis-driven WRF (no ensemble)	1980–2014	—	Many WRF met vars	Historical reanalysis baseline consistent with WRF physics.5)
ERA5 (downscaled)	California 3 km, 9 km	Hourly	Single reanalysis (no multi-GCM)	1979–present	—	Broad met suite	6
LOCA2–NA (CMIP6, North America)	NA domain 6 km grid	Daily	CMIP6 multi-model	1950–2015	2015–2100	Similar core vars	NA coverage; pairs with CA-focused LOCA2–Hybrid. 7

References

EMISSIONS TRAJECTORY	DEFINITION	DESCRIPTION	OUTCOME
SSP2-4.5	4.5 watts per meter squared of radiative forcing in the year 2100	Emissions continue at present levels to mid-century and then decline	2.7°C warmer at end of century
SSP3-7.0	7.0 watts per meter squared of radiative forcing by the year 2100	Emissions roughly double from current levels by 2100	3.6°C warmer at end of century
SSP5-8.5	8.5 watts per meter squared of radiative forcing by the year 2100	Emissions roughly double from current levels by 2050	4.4°C warmer at end of century

Table 1: Three of the common greenhouse gas trajectory scenarios hosted on the Analytics Engine. 8