AE Data
Analytics Engine Data Catalog
The following tables show a list of all raw projections data available on the Analytics Engine. This includes future climate projections and historical climate downscaled from Global Climate Models (GCMs) and a historical reconstruction (reanalysis data based on modeling and empirical weather observations).
Note: The different datasets are very nuanced, and they are not interchangeable. Please review our upcoming Guidance documentation to understand the available data in full.
Data Selection
The data available on the Analytics Engine were identified and selected through a rigorous skill evaluation process based on the parent GCM’s ability to capture several critical aspects of California climate characteristics. These models were then downscaled via different complex techniques for California. More details on the GCM selection process are available here.
Global Climate Models
Various CMIP6 GCMs are available, each of which may be used to explore possible future climate projections with built-in assumptions, methodologies, and limitations. We recommend, where possible, using as many GCMs in your workflows in order to capture the full range of possibilities.
Downscaling Methods
Downscaled data is available via two methods
- Downscaled by UCLA using a dynamical method with the Weather Research and Forecasting model (WRF)
- Downscaled by Scripps Institution of Oceanography using a hybrid-statistical downscaling method, Localized Constructed Analogs version 2 (LOCA2-Hybrid)
Spatial Resolution
- 45-km (WRF only)
- 9-km - Western Electricity Coordinating Council (WECC) region (WRF only)
- 3-km - California (CA) resolution (WRF and LOCA2-Hybrid)
Of particular note is the nuance that the 3km data is not “just” a narrowing of projections data but a qualitatively more accurate representation of the data than the 45km.
Shared Scenario Pathways
Various Shared Scenario Pathways (SSPs) representing global emissions trajectories from intermediate to higher trajectories are available on the Analytics Engine for the LOCA2-Hybrid data. SSP2-4.5, SSP3-7.0, and SSP5-8.5 each represent a combination of narratives (2, 3, and 5) and Representative Concentration Pathways (RCP) (4.5, 7.0, and 8.5). For example, SSP2-4.5 utilizes the SSP 2 narrative and RCP 4.5. All the WRF runs use SSP3-7.0.
Details of Available Data
Data Foundation
This table demonstrates the underlying data created by our colleagues at Scripps Institution of Oceanography and UCLA, highlighting the method of downscaling, model availability, and bias correction:
| Dynamically Downscaled / WRF | Hybrid-Statistically Downscaled / LOCA2-Hybrid |
|---|---|
| Institution: UCLA | Institution: Scripps |
|
Models: 8 Unique Models CESM2, CNRM-ESM2-1, EC-Earth3, EC-Earth3-Veg, FGOALS-g3, MIROC6, MPI-ESM1-2-HR, TaiESM1 |
Models: 15 Unique Models ACCESS-CM2, CESM2-LENS, CNRM-ESM2-1, EC-Earth3, EC-Earth3-Veg, FGOALS-g3, GFDL-ESM4, HadGEM-GC31-LL, INM-CM5-0, IPSL-CM6A-LR, KACE-1-0-G, MIROC, MPI-ESM1-2-HR, MRI-ESM2-0, TaiESM1 |
|
Bias Correction*: A priori: EC-Earth3, EC-Earth3-Veg, MIROC6, TaiESM1, MPI-ESM1-2-HR Other models are not bias-corrected |
Bias correction*: All 15 models |
| Native resolution: Hourly | Native resolution: Daily |
|
Other pre-aggregated daily and monthly datasets are available via the
data catalog * Details of the bias correction method are available here. |
|
Downscaled Data
The following table showcases data available within Analytics Engine products, including, but not limited to, the Data Catalog and accessible via JupyterHub notebooks.
Hybrid-Statistical Downscaled Data: LOCA2-Hybrid
This downscaling method created data for 15 GCMs with a total of 199 ensemble runs.
The following table shows the LOCA2-Hybrid data constituents available by variable:
-
Per SSP there are two counts
- GCM is the count of GCMs that were downscaled that have that variable in at least one ensemble run
- Ensembles is the count of ensemble runs that contain that variable
- All LOCA2-Hybrid data is downscaled at a native daily resolution. A pre-aggregated version of the LOCA2-Hybrid data at monthly resolution is also available on the Analytics Engine.
| Total precipitation (pr) | Max air temperature at 2m (tasmax) | Min air temperature at 2m (tasmin) | Specific humidity at 2m (huss) | Max relative humidity (hursmax) | Min relative humidity (hursmin) | Wind speed at 10m (wspeed) | West-east component of wind (uas) | North-south component of wind (vas) | Shortwave flux at the surface (rsds) | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Historical | GCMs | 15 | 15 | 15 | 13 | 13 | 13 | 11 | 11 | 11 | 14 |
| Ensembles | 70 | 70 | 70 | 51 | 51 | 51 | 46 | 46 | 46 | 51 | |
| SSP2-4.5 | GCMs | 14 | 14 | 14 | 12 | 12 | 12 | 11 | 11 | 11 | 14 |
| Ensembles | 33 | 33 | 33 | 25 | 25 | 25 | 24 | 24 | 24 | 30 | |
| SSP3-7.0 | GCMs | 14 | 14 | 14 | 12 | 12 | 12 | 10 | 10 | 10 | 13 |
| Ensembles | 62 | 62 | 62 | 45 | 45 | 45 | 38 | 38 | 38 | 45 | |
| SSP5-8.5 | GCMs | 13 | 13 | 13 | 11 | 11 | 11 | 11 | 11 | 11 | 13 |
| Ensembles | 34 | 34 | 34 | 25 | 25 | 25 | 26 | 26 | 26 | 29 | |
Dynamical Downscaled Data: WRF
This downscaling method created data for 8 GCMs with 1 ensemble run in each emissions scenario.
The following table shows the WRF data simulations available:
-
There are two kinds of WRF simulations
- Projections Simulations is the count of GCMs that were downscaled per emissions scenario and spatial resolution
- Reanalysis Simulations is a downscaled ensemble run of the ERA5 reanalysis dataset providing an observationally constrained historical reconstruction
- All WRF data is downscaled at a native hourly resolution.
* The Analytics Engine also provides a pre-aggregated version of the WRF
data at daily (noted by asterisk) and monthly resolutions for four of the
WRF simulations.
** Additional simulations from 1 model (CESM2) for SSP2-4.5 and SSP5-8.5 at 9
km spatial resolution are also available on the Analytics Engine. These simulations
are a part of the larger suite of dynamically downscaled GCMs by UCLA, of which
the CEC-supported simulations are a subset. More information is available here.
| Simulations | ||||
|---|---|---|---|---|
| Projections Simulations | ||||
| Emissions Scenario | Temporal Resolution | |||
| Daily* | Hourly | |||
| Spatial Resolutions | 3 km (CA) | Historical | 8 | 8 |
| SSP2-4.5 | 0 | 0 | ||
| SSP3-7.0 | 8 | 8 | ||
| SSP5-8.5 | 0 | 0 | ||
| 9 km (WECC) | Historical | 8 | 8 | |
| SSP2-4.5** | 1 | 1 | ||
| SSP3-7.0 | 8 | 8 | ||
| SSP5-8.5 | 1 | 1 | ||
| 45 km | Historical | 8 | 8 | |
| SSP2-4.5 | 1 | 1 | ||
| SSP3-7.0 | 8 | 8 | ||
| SSP5-8.5 | 1 | 1 | ||
| Reanalysis Simulations | ||||
| Spatial Resolutions | 3 km (CA) | Historical Reconstruction WRF_ERA5 | 1 | 1 |
| 9 km (WECC) | 1 | 1 | ||
| 45 km | 1 | 1 | ||
| Variables: Air temperature, wind speed, total precipitation, relative humidity, solar radiation, + 19 others | ||||
Current Analytics Engine Data Catalog
Datasets available to download and analyze through Cal-Adapt: Analytics Engine
The following table provides information on all available datasets to download and analyze through the Cal-Adapt: Analytics Engine in a searchable format. Each downscaled model is provided and is listed by its "source" GCM, experiment or emissions scenario, variant (the individual ensemble set-up), temporal frequency, and spatial resolution for each variable. Click the magnifying glass icon to begin custom searching of data.
| LOCA2 | UCSD - Scripps Institute of Oceanography | ACCESS-CM2 | historical | r1i1p1f1 | Daily | hursmax | 3-km (d03) | s3://cadcat/loca2/ucsd/access-cm2/historical/r1i1p1f1/day/hursmax/d03/ |
| LOCA2 | UCSD - Scripps Institute of Oceanography | ACCESS-CM2 | historical | r1i1p1f1 | Daily | hursmin | 3-km (d03) | s3://cadcat/loca2/ucsd/access-cm2/historical/r1i1p1f1/day/hursmin/d03/ |
| LOCA2 | UCSD - Scripps Institute of Oceanography | ACCESS-CM2 | historical | r1i1p1f1 | Daily | huss | 3-km (d03) | s3://cadcat/loca2/ucsd/access-cm2/historical/r1i1p1f1/day/huss/d03/ |
| LOCA2 | UCSD - Scripps Institute of Oceanography | ACCESS-CM2 | historical | r1i1p1f1 | Daily | pr | 3-km (d03) | s3://cadcat/loca2/ucsd/access-cm2/historical/r1i1p1f1/day/pr/d03/ |
| LOCA2 | UCSD - Scripps Institute of Oceanography | ACCESS-CM2 | historical | r1i1p1f1 | Daily | rsds | 3-km (d03) | s3://cadcat/loca2/ucsd/access-cm2/historical/r1i1p1f1/day/rsds/d03/ |
| LOCA2 | UCSD - Scripps Institute of Oceanography | ACCESS-CM2 | historical | r1i1p1f1 | Daily | tasmax | 3-km (d03) | s3://cadcat/loca2/ucsd/access-cm2/historical/r1i1p1f1/day/tasmax/d03/ |
| LOCA2 | UCSD - Scripps Institute of Oceanography | ACCESS-CM2 | historical | r1i1p1f1 | Daily | tasmin | 3-km (d03) | s3://cadcat/loca2/ucsd/access-cm2/historical/r1i1p1f1/day/tasmin/d03/ |
| LOCA2 | UCSD - Scripps Institute of Oceanography | ACCESS-CM2 | historical | r1i1p1f1 | Daily | uas | 3-km (d03) | s3://cadcat/loca2/ucsd/access-cm2/historical/r1i1p1f1/day/uas/d03/ |
| LOCA2 | UCSD - Scripps Institute of Oceanography | ACCESS-CM2 | historical | r1i1p1f1 | Daily | vas | 3-km (d03) | s3://cadcat/loca2/ucsd/access-cm2/historical/r1i1p1f1/day/vas/d03/ |
| LOCA2 | UCSD - Scripps Institute of Oceanography | ACCESS-CM2 | historical | r1i1p1f1 | Daily | wspeed | 3-km (d03) | s3://cadcat/loca2/ucsd/access-cm2/historical/r1i1p1f1/day/wspeed/d03/ |
Data Catalog Updates
October 29, 2024
WRF GCM EC-Earth3-Veg 3km switched to bias-adjusted version with addtional variables at all scales:
- cape
- cin
- lcl
- lfc
- pblh
September 5, 2024
Add hourly 45km for latest WRF GCMs
June 19, 2024
Additional Climate Projection Data:
- Sea Level Rise (SLR) - https://cadcat.s3.amazonaws.com/index.html#hmet/
Hourly sea level rise projections data generated by Scripps Institution of Oceanography for 13 tidal stations along the California coast and San Francisco Bay covering the 1950-2100 time period.
- Hydrologic Data: NOAH MP and VIC models - https://wrf-cmip6-noversioning.s3.amazonaws.com/index.html#ben_temp/d03_3km/CEC/2_NOAH_MP_SIMULATIONS/
Daily gridded streamflow and VIC hydrology data generated by UCLA for California covering 4 projections scenarios.
May 31, 2024
LOCA2 monthly precipitation data were regenerated as there was a mistake in how units were dealt with and a different calculation needed to be done.
April 5, 2024
Deployed updated information about data on the Cal-Adapt Analytics Engine
November 15, 2023
Latest WRF GCM added:
- EC-Earth3
- MIROC6
- MPI-ESM1-2-HR
- TaiESM1
August 7, 2023
Added annual maxima for tasmax (yrmax)
June 1, 2023
Fix incorrectly named LOCA2-Hybrid variables:
- windspeed -> wspeed
- rel_humind_max -> hursmax
- rel_humid_min -> hursmin
May 22, 2023
Added LOCA2-Hybrid monthly aggregates
May 15, 2023
Added rh, rsds, and wspeed:
- hursmax
- hursmin
- rsds
- wspeed
April 7, 2023
Added rh min/max, sw_dwn for regridded WRF:
- rh_max
- rh_min
- sw_dwn
April 3, 2023
Added regridded WRF to the LOCA2-Hybrid grid
March 23, 2023
Added LOCA2-Hybrid uas, vas:
- uas
- vas
Drop HadGEM3 huss due to data error.
February 17, 2023
Added LOCA2-Hybrid huss:
- huss
February 15, 2023
Added LOCA2-Hybrid tasmin:
- tasmin
February 14, 2023
Added LOCA2-Hybrid tasmax, precip:
- tasmax
- pr
November 15, 2022
Added hourly total precip:
- prec
Added multi-model ensembles for all hourly variables:
- ensmean
March 18, 2022
Added 9km WRF data
March 14, 2022
Added WRF CESM2 historical 45km data
February 1, 2022
Added ERA5 9km data
December 7, 2021
Added WRF CESM2 ssp3-7.0 45km data
November 22, 2021
Added WRF CESM2 historical 45km data
November 17, 2021
Added WRF ERA hourly 45km data