Memo on the Development
and Availability of
Dynamically Downscaled
Projections Using WRF
Prepared by Stefan Rahimi, UCLA
April, 2022●●●

This research is funded by the California Energy Commission (CEC) through its Electric
Program Investment Charge (EPIC) Program, which invests in scientific andtechnological research to accelerate the transformation of the electricity sector to meetthe state’s energy and climate goals.
The research project, EPC-20-006, will integrate the latest downscaling approaches
applied to the recently produced global climate models (GCMs) with an engagement
process to develop a robust, usable, set of climate projections applicable for California.
This memo and data here within are being shared to support transparent and timely
consideration of interim deliverables that are relevant for energy stakeholders and all
those interested in California’s next generation of climate projections. The memo does
include data that was part of non-CEC leveraged projects.

This memorandum is submitted to the CEC by UC San Diego’s Scripps Institution of
Oceanography. The report meets deliverable requirements under Task 4 of the California
Energy Commission’s Project EPC-20-006: Development of Climate Projections for California
and Identification of Priority Projections.

Synopsis
This document describes a new suite of dynamically downscaled climate-scale datadelivered to CEC under award EPC-20-006, “Development of Climate Projections for
California and Identification of Priority Projections”, Task 4, as part of a broader
downscaling effort. Specifically, select global climate model (GCM) projections from the
6th coupled model intercomparison project (CMIP6) are physically transformed to highspatial resolutions from 1980 through 2100 by a regional climate model. The resultsfrom the dynamically downscaled projections will contain spatial patterns of climate thatevolve throughout the 21st century, including changes in this dynamic system, that will
be used as training for LOCA, a statistical downscaling approach (Task 5). A historicalreanalysis is also dynamically downscaled from 1950-2021. The memorandum
describes dynamical downscaling and then provides information about the synthesis
and structure of the dynamically downscaled projections and reanalysis. A similar
method that is described here will be employed for targeted case studies (Task 8) thatwill be available later in the project. While the focus is mainly on the dynamicallydownscaled reanalysis and 4 GCMs, we also touch on the other GCMs that have been
downscaled via leveraged projects and may be of interest to the energy sector andbeyond.

1.

Introduction

GCMs are the primary tool by which future climate projections are created; however,they do not have sufficiently small grid spacing to allow for facility-level and watershedspecific forecasts. Thus, some type of technique must be used to synthesize plausible
future projections that capture local geographic complexities that may drive local
weather, hydrology, and climate (e.g., mountains, coastlines, lakes, etc.). The term
‘downscaling’ generally describes this synthesis process, and its etymology is tied to theconcept that in nature, energy in the atmosphere tends to be transferred from larger
processes or systems to smaller processes or systems. An example of this is alandfalling atmospheric river (AR); its kinetic energy (i.e., winds) originates from largescale horizontal temperature differences and the earth’s rotation. The smaller-scaleprecipitation segments associated with the AR derive their energy from the parent storm
system and serve as a conduit by which energy is passed on to even smaller scales of
motion, driving processes such as turbulence, as the overall system attempts todistribute energy equally, everywhere. Downscaling of GCMs is the term used todescribe the umbrella of techniques by which smaller-scale weather, hydrologic, andclimate features can be estimated by using physics and statistics.
For dynamical downscaling, a mere seven laws of nature are used by a regional climate
model (RCM) to arrive at the desired high-resolution end-product. Specifically, the RCM‘ingests’ the GCM-simulated temperature, horizontal winds, moisture content, sea
surface temperatures, soil properties, and atmospheric pressure fields at sub-daily timeintervals, integrating a high-resolution solution across a limited area of the planet. The
ingested GCM information serves to constrain the RCM solution, which itself contains
2high-resolution topography and other geo-features needed to simulate a more spatiallyheterogeneous future across a given area.
Broad applications of dynamical downscaling across large GCM ensembles arehampered by computational resource limitations, and GCM data availability. From acomputational standpoint, each of our GCM simulations require the equivalent of nearly
26,000 personal computers run continuously and simultaneously for almost 9-days
without pause; this amounts to a very expensive computational endeavor. Regarding
GCM data availability, only a subset of CMIP6 GCMs have saved the 3-dimensional
atmospheric drivers having sufficient temporal resolution, sufficiently-high verticalresolution, and proper model initialization needed by a regional atmospheric model for
dynamical downscaling. Furthermore, only a portion of the CMIP6 GCMs grade out assufficiently skillful in reproducing observed atmospheric processes and regional weather
and climate variability.
Amongst a growing set of dynamical downscaling-capable RCMs, the Weather
Research and Forecasting (WRF) model is one of the most widely maintained and used
RCMs for research and operational applications, and it has a superior customizability
compared to other RCMs in terms of selecting its physical parameters and solvers.
Thus, we were able to select a physical configuration of the model tailor-made forsimulating western US weather and climate (briefly described in 2.1.1 and in Rahimi etal., 2022). Our compliance with best practices in WRF effectively limits the number of
GCM simulation candidates from thousands (accounting ~35 GCMs from internationalmodeling centers, different emissions pathways, and multiple ensemble members ofeach) to a mere ~150. We add that, of these 150 GCM simulations, only 13 are
relatively independent of one another in terms of their physical solvers,
parameterizations, and dynamic cores (variations of the 7 fundamental physical laws).
Lastly, the plethora of available RCMs use different physical solvers and dynamic cores
to downscale the GCMs which can introduce uncertainty. For instance, a RCM may
have a default convective scheme better suited for tropical climates that may not be as
apt for modeling in the midlatitudes. Thus, its solution may differ from that of another
RCM which uses a more generalized convective scheme; choosing the best RCM andoptions within is a nontrivial process. The ability of RCMs to represent regional weather
differently adds to uncertainty in the finalized downscaled result, and theaforementioned GCM data requirements prevent dynamically downscaling from being
applied to a greater number of GCMs simulations. Nonetheless, dynamical downscaling
provides physically-derived continuous weather and climate evolution information whichis extremely valuable in assessing future weather extremes and climate.

3

2.

Development of Model Set up for Dynamical Downscaling

All testing and production of the dynamical downscaled data were conducted on the
NCAR-Wyoming Cheyenne supercomputing cluster. Although Dr. Rahimi secured thecompute resources to complete the experiments, we also acknowledge the generous
support of Dr. Zachary Lebo at the University of Wyoming via the UCAR Computational
Information Systems Lab (see supporting letter in proposal documents).

2.1 Conducting the simulations
2.1.1 Testing and reanalysis-driven experiments
The RCM used in dynamical downscaling is the WRF model version 4.1.3. With ourprime goal being to dynamically downscale CMIP6 GCMs, the general decision wasmade to dynamically downscale GCM forcing datasets to a 45-km grid first and use thatsimulations output to drive an inner 9-km simulations (see grids in Fig. 1a) via one-way
nesting, meaning the larger grid only pushed the data to the smaller high-resolution grid.
Finally, two 3-km experiments (one over California and one over Wyoming) were driven
using the 9-km simulation outputs. The California nest supports EPC-20-006 and the
Wyoming nest supports another project that provided the computing resources. WRF
simulations are carried out on 39 vertical levels, with a varying vertical grid spacing on
the order of 100 meters in the boundary layer and stretching to hundreds to a few
thousands of meters higher up. This ‘stretching’ of the vertical grid allows for features in
the lower troposphere, which are characterized by fine vertical variations than those
aloft, to be better simulated while meteorological features in the middle and upper
troposphere with smaller vertical variations are still sufficiently resolved.
WRF is an extremely sophisticated but complex RCM (Skamarock et al., 2019),
containing tens of thousands of combinations of base physical solver packages
wrapped up in 3-4 million lines of code. To identify a preferred set of physical solver
options for downscaling with WRF across the western US and California, we conducted
21 year-long tests in which either the North American Regional Reanalysis (Mesinger etal., 2006) or the European Center for Medium-range Weather Forecasting's 5th
Reanalysis (ERA5) (Hersbach et al., 2020) were downscaled for water year 2010
(described in Rahimi et al., 2022) onto the grids shown in Fig. 1a, and tests prioritized
the 9-km and 3-km grids. We began by identifying a base set of solvers commonly usedin other published studies across the region. As solvers were updated throughout
testing, general reductions in precipitation, snow, wind, and to a lesser degree
streamflow biases were noted at weather (SNOTEL and METAR) and hydrologic(GAGES-II) stations in Fig. 1b, especially with increasing horizontal resolution (smaller
grid spacings). ERA5-driven tests generally outperformed NARR-driven tests. The
superior performance in ERA5-driven tests, coupled with its longer historical scope(1950 onwards) compared to NARR, motivated us to choose ERA5 as our forcing
reanalysis.

4Figure 1. (a) WRF domain coverage for the 45-km, 9-km, and 3-km experiments. The filled color contours represent the terrain
height [m] in the highest-resolution domain that covers the given pixel. presents the two subregions that we consider in our targetedevaluations (blue outline), specifically the Sierra Nevada (SN) and the Northern Rocky Mountains (NRMs), as well as in situ
SNOTEL (squares), METAR (stars), and GAGES-II (red crosshatches) data locations used in WRF performance evaluations.

Our test results informed a subsequent downscaling effort in which ERA5 was
dynamically downscaled from 1 August 1950 through 1 September 2021, on the samegrids shown in 1a. To simulate a 70-year period in a reasonable real-world time frame,
we chose to simulate each fiscal year, defined from 1 August through 1 September ofthe following year (13 months), simultaneously and in parallel, independent of eachother, from 1950-2021, discarding the first month of these discrete 13-month
simulations as spin-up. The spin-up period is nominally designed to allow WRF’s
simulated soil properties to reach an equilibrium state, although it should be noted thatspin-up periods of a year or more are desired in hydrologic modeling. Nonetheless, wethen reconstructed the full time series by stitching together the remaining data inchronological order. We note that, without this parallelization approach, the time tocomplete the ERA5 simulation would be increased from roughly 9 days to 1 year. Eachfiscal year was initialized to the ERA5 state, and spectral nudging of the large-scalemeteorological patterns was implemented to prevent WRF from drifting too far from theforcing reanalysis state; features with spatial wavelengths of ~1,500 km or larger arepreserved in downscaling, while WRF integrated its own internal higher-resolution
meteorological features.
The downscaled ERA5 product provides a useful baseline historical dataset bywhich to compare downscaled GCMs across the western US and California over climate
time scales. As can be seen in Rahimi et al., 2022, however, this dataset ischaracterized by biases that must be acknowledged by end-users including (i) a highelevation wet bias on the order of 5%, (ii) a summertime wet bias across the desert
southwest of less than 1 mm day-1 (but can be ~100%), and finally a low (high) elevation
warm (cold) temperature bias that can be as large as a few Kelvin in magnitude on
seasonal time scales. Biases in a subset of widely-used surface variables will mostly beremoved from the dataset by the LOCA team’s bias correction procedure (part of Task
5), which is not discussed here.

52.1.2 GCM simulations
In addition to the ERA5-driven WRF simulation, we dynamically downscale four CMIP6
GCMs from 1980-2100 using the same yearly discretization approach and the same setof physics solvers configured in WRF. We only downscale four GCMs to 3 km due tothe high computational costs involved in the dynamical downscaling process of even asingle GCM. Limited compute resources, in addition to the more than 30 GCMs
reporting to CMIP6, each potentially containing multiple future emissions scenarios andrealizations, facilitates a need for a GCM screening process described below.
Here, we dynamically downscale GCMs subject to the following requirements:
1. GCM-simulated atmospheric data are available at sub-daily time intervals as weseek to resolve the synoptic-scale evolution of weather patterns from the parent
simulation. Specifically, 4 sub-daily fields are required, and we require a total of 8
(preferably daily) state/kinematic variables from the land surface and atmosphere
for WRF to yield physical results. If any of these variables are absent, we cannot
effectively downscale the GCM.
2. The GCM must exhibit acceptable performance in simulating the observed
northern hemispheric circulation patterns, Pacific Ocean oceanic conditions, andother atmospheric properties characteristic of eastern Pacific Basin/western
North American climate relative to other CMIP6 GCMs over the historical period.
The evaluation process is described below.
3. After determining a GCM shortlist in (2), we further refine potential GCMs basedon their linear independence from each other, as some GCMs share the samecode bases.
4. Finally, we select our GCMs based on the spread in their respective futureclimate change signals. For instance, two of our selected GCMs described below
have wetter futures across California while the other two have a neutral or drier
future across California
As discussed above, models are selected for downscaling by a multi-stepevaluation process that prioritizes both the skill of models over the Western US and thebalanced representation of future climate scenarios. The skill of models is evaluated bycomparison to ERA5 reanalysis data in the historical period through two sets of metrics.
The first set evaluates model performance on temperature and precipitation over
several timescales within the downscaling domain. The second set evaluates largerscale patterns of circulation and variability across the northern hemisphere that are
particularly important for creating realistic boundary conditions for the regional model.
Models that perform well across both sets of metrics are prioritized for downscaling.
Within the available realizations of the top performing models, the final set for
downscaling is selected to achieve a balance of model diversity, a representative rangeof overall change to temperature and precipitation, and a set of storylines that capture
significant climate events such as droughts and heatwaves that are helpful for regional
adaptation planning. More information on the GCM selection process can be found in
the Evaluation of CMIP6 GCMs Relevant for California Report by W. Kranz et al. (Task
3) submitted to the CEC.

6

Based the GCM selection process described above, the dynamically downscaled GCMs
for CEC are:
1. CESM2 r11i1p1f1
a. historical from 1980-2014
b. SSP3-7.0 from 2015-2100
c. 45 km, 9km, and 3 km domain
2. CNRM-ESM2-1 r1i1p1f2
a. historical from 1980-2014
b. SSP3-7.0 from 2015-2100
c. 45 km, 9km, and 3 km domain
3. EC-Earth3-Veg r1i1p1f1
a. historical from 1980-2014
b. SSP3-7.0 from 2015-2100
c. 45 km, 9km, and 3 km domain
4. FGOALS-g3 r1i1p1f1
a. historical from 1980-2014
b. SSP3-7.0 from 2015-2100
c. 45 km, 9km, and 3 km domain
Please note: No pre-downscaling bias correction is applied to any of these 4
WRF-downscaled GCM simulations.
Figure 2 depicts future changes in cumulative annual water year precipitation from our 4
GCMs (top row of images) and their dynamically downscaled counterparts (bottom row
of images).

7

Figure 2. Future (2070-2100 mean) minus present-day (1980-2010 mean) differences in cumulative annual water year
precipitation in [mm] for the four WRF-downscaled GCMs. The top row shows the climate change signal from the native GCMs
while the bottom row shows the same but on the dynamically downscaled 9-km grid. Note that the WRF solution generally
preserves the large-scale characteristics of the parent GCM.

CMIP6 historical GCM output is used to drive WRF from 1 August 1980 through 31
December 2014, while CMIP6 ScenarioMIP GCM output is used to drive WRF from 1
January 2015 through 1 September 2100. Anthropogenic emissions from the third
Shared Socioeconomic Pathway (SSP3) with an end-century top-of-the-atmosphereradiative forcing of 7 W m-2 is (SSP3-7.0) are used beyond 2014. We chose SSP3-7.0
as our future emissions scenario because its future change has a large signal-to-noise
ratio and because it is increasingly unlikely that the maximum emissions scenario(SSP5-8.5) will be realized due to increasing international mitigation efforts. We notethat the LOCA Hybrid downscaling (Task 5) will include a much larger suite of models
and three different SSPs.
As a part of UCLA’s participation in NCAR’s Advanced Scientific Discovery program on
their new supercomputer, Derecho, 15-20 additional GCMs are to be dynamicallydownscaled by the end of 2022. That downscaling will use the same WRF methods as
discussed above and will also be made available to the IOUs, as well as the energy andclimate community. The additional GCM simulations that will be dynamicallydownscaled will be primarily based on the requirements of the Advanced Scientific
Discovery Program, which awarded the supercomputing time enabling the dynamicallydownscaling of additional simulations. However, the additional dynamically downscaled
GCM simulations also benefit EPC-20-006 and thus, we will use the GCMs selection
process described above and the input from stakeholders, particularly feedback from8the Working Group meeting on Feb 4th focused on model selection, as possible todetermine which models will be additionally downscaled.
For the CEC, the variables listed below are provided on the 45-, 9-, and 3-km California
WRF grid. Please note that for other tasks within EPC-20-006 the WRF results will be
used in different ways by the LOCA and hydrologic modeling teams. We emphasize thatdynamically downscaled data as part of this Task 4 and presented below are not bias
corrected in any way following downscaling. As WRF testing and early model runs
leveraged other projects, the initial set of variables were chosen based on feedback
from hydrologic, fire, and land-surface modelers and researchers. We evaluated thevariables against the Synthesis of Variables, Spatial and Temporal Scales required fromall use-cases, which was shared in the EPC-20-006 2021 Quarter 4 progress report. An
important variable identified from the use cases was wind gusts, however, maximum
hourly wind gusts could not be saved as it would have increased our computationalcosts by 20% and thus could not be accommodated.
Below are the 21 hourly variables we provide.
Name
1. 2-m temperature
2. 2-m specific humidity
3. Surface pressure
4. 10-m u-component of the wind (grid relative)
5. 10-m v-component of the wind (grid relative)
6. Snow water equivalent
7. Skin temperature
8. Non-convective precipitation (cumulative)
9. Convective precipitation (cumulative)
10. Cumulative snowfall equivalent
11. Diffuse downwelled solar radiation
12. Surface upwelled solar radiation (all sky)
13. Surface upwelled solar radiation (clear sky)
14. Surface downwelled solar radiation (all sky)
15. Surface downwelled solar radiation (clear sky)
16. Surface upwelled longwave radiation (all sky)
17. Surface upwelled longwave radiation (clear sky)
18. Surface downwelled longwave radiation (all sky)
19. Surface downwelled longwave radiation (clear sky)
20. Surface runoff
21. Sub-surface runoff

Units
[K]
[kg kg-1][Pa][m s-1][m s-1]
[mm]
[K]
[mm]
[mm]
[mm]
[W m-2][W m-2][W m-2][W m-2][W m-2][W m-2][W m-2][W m-2][W m-2][mm s-1][mm s-1]

9

As with the hourly outputs, we provide 37 variables post-processed daily time scales.
Noting that convective precipitation is only nonzero in the 45- and 9-km simulations, we
provide:Name
1. 2-m average temperature
2. 2-m minimum temperature
3. 2-m maximum temperature
4. Maximum hourly precipitation
5. 2-m specific humidity
6. Maximum 10-m wind speed
7. Snow water equivalent
8. Precipitation rate
9. Snow precipitation rate
10. Relative humidity
11. Integrated vapor transport (zonal and meridional
components; earth relative)
12. Ice water path
13. Liquid water path
14. Soil moisture
15. Soil temperature
16. Skin temperature
17. Surface pressure
18. Surface runoff
19. Sub-surface runoff
20. Evaporation
21. Evapotranspiration
22. Downwelled SW at surface (> 0 into sfc)
23. Downwelled LW at surface (> 0 into sfc)
24. Net SW flux at the surface (> 0 into sfc)
25. Net LW flux at surface (> 0 into atm)
26. Sensible heat flux at surface (> 0 into atn)
27. Latent heat flux at surface (> 0 into atm)
28. Ground heat flux at surface (> into atm)
29. 3-D q
30. 3-D w
31. 10-m u, v (earth relative)
32. 3-D u (earth relative)
33. 3-D v (earth relative)
34. 3-D geopotential height

Units
[K]
[K]
[K]
[mm h-1][kg kg-1][m s-1][mm][mm d-1][mm d-1][0-100][kg s-1 m-1]

Label
‘t2’
‘t2min‘t2max’‘prec_max’‘q2’‘wspd10max’‘snow’‘prec’‘prec_snow’
‘rh’
‘ivt’

[kg m-2]‘iwp’[kg m-2]‘lwp’[m3 m-3]‘soil_m’[K]‘soil_t’[K]‘tskin’[Pa]‘psl’[mm d-1]‘sfc_runoff’[mm d-1] ‘subsfc_runoff’[mm d-1]‘evap_sfc’[mm d-1]‘etrans_sfc’[W m-2]‘sw_dwn’[W m-2]‘lw_dwn’[W m-2]‘sfc_sfc’[W m-2]‘lw_sfc’[W m-2]‘sh_sfc’[W m-2]‘lh_sfc’[W m-2]‘gh_sfc’[kg kg-1]‘q_3d[m s-1]‘w_3d[m s-1]‘uv10’[m s-1]‘u_3d’[m s-1]‘v_3d’[m2 s-2]‘phi_3d’
1035. 3-D temperature
36. Convective precipitation
37. Mean 10-m wind speed[K]‘t_3d’[mm d-1]‘prec_c’[m s-1] ‘wspd10mean’

The full 6-hourly WRF datastream for all grids (45-, 9-, and 3-km) in its native output
form can be found in the Amazon S3 bucket described in the data access section
below. The best way to examine the file contents is by loading a 6-hourly file intomemory and examining the data keys. More than 200 variables are provided in each 6hourly file.
In addition to the four dynamically downscaled GCMs for the CEC mentioned above, in
our data catalog (https://dept.atmos.ucla.edu/alexhall/downscaling-cmip6), there are
additional GCM downscaled simulations. These simulations are a combination of ourlarger research effort to better understand and test methods that are used to
dynamically downscaling GCMs. Some of the runs were conducted prior to the start ofthis project and others were conducted to address other research projects’ objectives.
Specifically, we were examining the effects of pre-dynamically downscaling (a priori)
bias correction. As a result, several of these experiments are simply variations of four
aforementioned GCMs in which a pre-downscaling bias correction of the mean-state
was applied following Bruyère et al., 2014.This is discussed more below in Section 4.
Simulations in this category were generally downscaled to 9-km with the exception of
EC-Earth3-Veg. The variables for these simulations are the same as is listed above. Asof 29 March 2022, the following GCMs have also been dynamically downscaled:
1. CESM2 r11i1p1f1 for SSP2-4.5 down to 9-km
a. historical from 1980-2014
b. SSP2-4.5 from 2015-2100
2. CESM2 r11i1p1f1 for SSP5-8.5 down to 9-km
a. historical from 1980-2014
b. SSP5-8.5 from 2015-2100
3. CESM2 r11i1p1f1 for SSP3-7.0 with pre-downscaling bias correction down to 9km
a. historical from 1980-2014
b. SSP3-7.0 from 2015-2100
4. MPI-ESM1-2-LR for SSP3-7.0 down to 9-km
a. historical from 1980-2014
b. SSP3-7.0 from 2015-2100
5. MPI-ESM1-2-LR for SSP3-7.0 with pre-downscaling bias correction down to 9km
a. historical from 1980-2014
b. SSP3-7.0 from 2015-2100
6. CNRM-ESM2-1 for SSP3-7.0 with pre-downscaling bias correction down to 9-km
a. historical from 1980-2014
b. SSP3-7.0 from 2015-2100

117. EC-Earth3-Veg for SSP3-7.0 with pre-downscaling bias correction down to 3-km
for California and Wyoming grids
a. historical from 1980-2014
b. SSP3-7.0 from 2015-2100
8. FGOALS-g3 for SSP3-7.0 with pre-downscaling bias correction down to 9-km
a. historical from 1980-2014
b. SSP3-7.0 from 2015-2100
We note that the CESM2 non-SSP3-7.0 experiments share a common downscaled
historical period (1980-2014). The additional simulations may be of interest to IOUs andclimate researchers which is why they are mentioned here.

3.

Data uses within EPC-20-006 and beyond

For EPC-20-006, WRF results will be used in different ways by the LOCA andhydrologic modeling teams. We emphasize that dynamically downscaled data as partof Task 4 and presented above are not bias corrected in any way following
downscaling. However, select downscaled WRF variables will be bias corrected before
ingestion into LOCA (Task 5). The WRF data will also be used by the hydrologic
modeling team to produce streamflow projections (Task 6). As described in the section
4, below, the WRF data are subdivided into 3 tiers which can be used for various
purposes. The hourly data can be used to create hourly climatologies and serve as
input for land surface models, for example. The daily post-processed data arelightweight and can be easily used in geospatial physical analysis. Finally, the 6-hourly
data can be used for more intensive analyses using the complete set of outputs from
WRF, and the data can be used to create boundary conditions for even higherresolution WRF experiments (beyond 3-km resolution).

4.

Impacts of pre-downscaling bias correction

Although bias correction of the GCM simulations prior to WRF downscaling (i.e., apriori) was not used in EPC-20-006, the effects of a priori bias correction on the
dynamical downscaled data is an open research question. Below we provide somepreliminary evaluation of a priori bias correction of GCMs versus not. Figure 3 shows
the vertical profile of GCM wintertime mean historical (1980-2014) biases intemperature and zonal wind relative to ERA5 across the eastern Pacific and western
North America (20°-55°N and 140°-90°W mean). A tropospheric cold bias, a low-levelinstability bias, and a strong vertical shear bias are prevalent in the CMIP6 ensemble
mean (30 GCMs, thick black curves). These three types of biases favor enhanced
precipitation. When these mean-state biases are removed, we see significant drying of
WRF’s dynamically downscaled solution on the 9-km grid at SNOTEL locations across
California’s Sierra Nevada, both in terms of precipitation and snow water equivalent(Figure 4). Ultimately, the decision to not a priori bias correct the GCMs for EPC-20-006
was made because there was not sufficient time and computational resources to12evaluate what uncertainty the bias correction may add to the finalized high-resolution
product.

Figure 3. Vertical profiles of historical (1980-2014) GCM mean-state biases in (left) temperature and (right) zonal wind averaged
over 20°-55°N and 140°-90°W. The gray shading shows the spread from 30 CMIP6 GCMs, while the thick black curve denotes thebias in the ensemble mean. Colored curves are for 5 of our CMIP6 Historical simulations.

Figure 4. Historical (1980-2010) site mean (left) cumulative precipitation and (right) snow water equivalent in mm from various
bias corrected downscaled products on the 9-km grid (solid lines). Dashed curves show the difference between the bias corrected
and non-bias corrected time series; negative values indicate a wetter solution in the non-bias corrected experiment. Green
hatches show the differences between the ERA5-driven WRF experiment and SNOTEL observations; negative values indicate a
wet bias in WRF.

13

5.

AccessAll data discussed above is located in an open data bucket on Amazon S3. See bucket
details at https://registry.opendata.aws/wrf-cmip6/. Amazon provides open-source
software that allows for free and fast data transfers from S3 to your local devices via the
Amazon Web Service Command Line Interface (AWS CLI). Specifically, all WRF data
can be accessed on SE at:
1. ERA5 downscaled reanalysi – s3://wrf-cmip6noversioning/downscaled_products/reanalysis/era5/
2. CESM2 downscaled GCM – s3://wrf-cmip6noversioning/downscaled_products/gcm/cesm2_r11i1p1f1_historical/ ands3://wrf-cmip6noversioning/downscaled_products/gcm/cesm2_r11i1p1f1_ssp370/
3. CNRM-ESM2-1 downscaled GCM – s3://wrf-cmip6noversioning/downscaled_products/gcm/cnrm-esm2-1_r1i1p1f2_historical/ ands3://wrf-cmip6- noversioning/downscaled_products/gcm/cnrm-esm21_r1i1p1f2_ssp370/
4. EC-Earth3-Veg downscaled GCM – s3://wrf-cmip6noversioning/downscaled_products/gcm/ec-earth3-veg_r1i1p1f1_historical/ ands3://wrf-cmip6- noversioning/downscaled_products/gcm/ec-earth3veg_r1i1p1f1_ssp370/
5. FGOALS-g3 downscaled GCM – s3://wrf-cmip6noversioning/downscaled_products/gcm/fgoals-g3_r1i1p1f1_historical/ ands3://wrf-cmip6- noversioning/downscaled_products/gcm/fgoalsg3_r1i1p1f1_ssp370/

6.

References

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