Memo on the Evaluation of
Downscaled GCMs Using
WRF
Prepared by Stefan Rahimi, UCLA
October, 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
In this document, we evaluate the performance of a developing suite of dynamicallydownscaled global climate models (GCMs) with emphasis on four projections as part of
EPC-20-006, Task 4. Specifically, GCMs from the 6th Coupled Model Intercomparison
Project (CMIP6) are downscaled here using the Weather Research and Forecasting(WRF) model. More information on the development of these 4 dynamically downscaled
projections can be found in the Memo on the Development and Availability of
Dynamically Downscaled Projections Using WRF - another Task 4 deliverable of the
California Energy Commission’s Project EPC-20-006. The GCMs that were dynamicallydownscaled were preselected based on their abilities to simulate the atmosphericcirculation (both in the Northern Hemisphere and across California), extremes, andCalifornia’s temperature and precipitation; as well as availability of data at 6-hourintervals on native GCM levels to drive WRF. However, large differences between
historical downscaled WRF simulations and observations exist and are discussed in thisdocument. In addition, we highlight the range in results of climate projections from theensemble of dynamically downscaled models as well as focus in on the effects of a predownscaling bias correction of the mean-state fields used to drive WRF.
1. Introduction
Dynamical downscaling is a popular technique used in the synthesis of physically based
high-resolution weather and climate projections. Conducted over a limited area of theplanet, historical reanalyses are often dynamically downscaled using the Weather
Research and Forecasting (WRF; Skamarock et al., 2019) model to reproduce focusedlandscape-scale atmospheric and surface conditions (Liu et al., 2011, 2017; Rahimi etal., 2022; Rasmussen et al., 2014). The high-resolution output allows us to study
weather- and climate-relevant processes that may unfold across sparsely observed
regions, gain physical insights into their land-atmosphere drivers, and their high-spatial
resolution/frequency outputs can be used to drive land-surface, hydrologic, and firemodels. Regional climate models (RCMs) such as WRF are typically designed todownscale reanalysis, as scientists seek to improve their physical solvers and numerics
to increase the precision and confidence of forecasts by benchmarking simulation
results against historically observed events. Indeed, dynamical downscaling, when
implemented properly, may even yield solutions within the bounds of observational
uncertainties (Lundquist et al., 2019).
Direct dynamical downscaling of GCMs is far less common than dynamicallydownscaling reanalyses in regional climate modeling for a variety of reasons but stillyields a result based solely on computational physics (Bruyère et al., 2014; Coppola etal., 2020, 2021; Huang et al., 2020; Komurcu et al., 2018; Wang et al., 2016; Wang &Kotamarthi, 2015; Zobel et al., 2017, 2018). Direct dynamical downscaling has
important strengths relative to other downscaling methods. For example, dynamical
downscaling of GCMs doesn’t assume stationarity (Lanzante et al., 2018) in its creationof the high resolution predictands as is the case in other forms of downscaling (e.g.,
statistical). The dynamically downscaled solution can in fact resolve plausible extreme
weather events that may only emerge in an unobserved future (or historical) climate2

state. Additionally, RCMs use the primitive governing equations to integrate a diversityof physical quantities that cannot be obtained through statistical methods or via machinelearning techniques owing to lacking observations and subsequent training data. For
example, statistically downscaled precipitation and temperature, even when obtainedusing multivariate relationships, may contain no information about water vapor content,
surface pressure, cloud depth, etc. Finally, and perhaps most fundamentally, the use ofphysics to arrive at the downscaled result means that physically interpretable feedbacks
between the landscape and the overlying atmosphere can be interactively simulated.
Despite these strengths, however, dynamical downscaling must be applied with caution
owing to large biases within the driving GCMs which may be propagated to andamplified by an RCM’s physical solvers during downscaling, and these biases should be
dealt with via the use of bias correction.
Additionally, barriers to using RCMs to dynamically downscale GCMs exist because: 1.
RCMs rely heavily on the actual data of the GCMs (which may not include allneeded/desired variables), 2. RCM configurations may not be designed to ingest GCM
data as boundary conditions, and 3. the high computational cost to dynamicallydownscale GCMs. Because of these barriers, large ensembles of dynamicallydownscaled landscape-resolving (10 km grid spacing) climate projections generally
remain out of reach.
As part of EPC-20-006, we provide a set of four dynamically downscaled GCM
projections and a dynamically downscaled reanalysis (Task 4) for use in hybrid
downscaling (Task 5) and hydrologic modeling (Task 6) across the western UnitedStates (WUS). Specifically, we provide simulations on a convective-permitting grid thatcovers all of California as well as another suite of 9-km simulations with coverage
across 11 WUS states, including the whole of the Western Electricity Coordinating
Council (WECC) region. In the following sections, we present an overview of theseprojections and our methodology, a characterization of their historical performance andfuture change signal, and a broad comparison to the greater ensemble of which they
are a part of. We will also present a high-level summary on how these WRF(dynamically downscaled) projections are to be used within a statistical downscaling
framework to develop a hybrid downscaling methodology, as well as serve as input data
for the hydrologic simulations also carried out as part of this project.
2. Methodology
The development of high-resolution and physically based climate ensembles presents
researchers with some major practical difficulties (the barriers discussed above). We
address these difficulties below, and we provide justification for our RCM setup.
2.1 Parallelization approach
WRF simulations are conducted for the period 1 August 1980 through 1 September
2100. We downscale each year of integration separately and in parallel; each modelyear is initialized to the driving GCM/reanalysis state. In this way, an N-year simulationis completed in the same amount of wall clock time as a 1-year experiment, where N is
the total number of years. For each year of integration, we choose the beginning of theretained WRF output to coincide with minimum snowpack across the western United3

States (September 1). This approach minimizes the dependence of the simulations on
soil moisture and snowpack derived from the previous water year, which is poorly
represented by the coarse-resolution driving datasets (GCM or reanalysis). Given that
we require 1 month of spin-up, WRF is initialized on August 1 to surface and threedimensional data from the GCM/reanalysis and integrated through September 1 of thefollowing year (13 months, including the spin-up month) on 39 atmospheric levels. Thisapproach is like Zobel et al. (2017, 2018), who initialized WRF experiments at yearlyintervals but only allowed for 1 day of model spin-up.
For the purposes of accurately simulating streamflow, which is a main priority of
Task 6, fully time-continuous land-surface simulations need to be conducted. This isbecause soil moisture/temperature conditions may take years to properly equilibrate to
the local weather, and these soil properties have a large impact on subsequent
streamflow. WRF’s parallelization procedure, which is advantageous for executing
simulations in weeks instead of years, is performed to the detriment of time continuity insimulating the surface and subsurface runoff with high precision. To address the issueof continuity in simulating streamflow for Task 6, atmospheric fields from WRF, which
are of high accuracy, are used to drive offline hydrology models that are continuous(such as WRF-Hydro and VIC used in Task 6).
2.2 Model options, grid, and physics
Our final model setup, applied to the reanalysis-driven and all GCM-driven simulations,
was informed through the results of 21 year-long sensitivity experiments overviewed in
Rahimi et al. (2022). Superficially, these test experiments examined the result sensitivity
to user choices in spectral nudging strength, microphysics and cumulus
parameterization choices, land surface model solver choices, and reanalysis choice. We
conducted these experiments because large dry biases in snow water equivalent andprecipitation were found when we used parameterizations based on recently publishedWRF studies across the WUS, which is the focus region for this work. For a morequantitative overview of our testing procedure, see section 2 of Rahimi et al. (2022). We
assert that extensive testing over the complete annual cycle is generally a best practicewhen trying to identify a set of model parameterizations/solvers across a focus region
prior to downscaling over a climatology period.
In our finalized WRF setup, we dynamically downscale all simulations to a 45-km gridon which the horizontal winds, temperature, and geopotential height are relaxed to theirrespective reanalysis/GCM-simulated fields via spectral nudging for wavelengths
greater than 1,500 km in length above the planetary boundary layer; smaller waveforms
were allowed to evolve freely on the WRF grid. One-way nesting is then used to
dynamically downscale the 45-km result to a 9-km grid on which spectral nudging is not
implemented. Specifically, output from the 45-km simulations provides the lateralboundary conditions for the 9-km experiments. Finally, we one-way nest from 9- to 3-km
without spectral nudging. All grids are shown in Figure 1. The lateral boundary
conditions are updated at 6-hourly intervals, and adaptive time stepping is used. On the45- and 9-km grids, convective precipitation is parameterized following Tiedtke (1989)
and Zhang et al. (2011); the cumulus scheme is disabled in the 3-km experiments which4are assumed to be convective permitting. P3 microphysics is used (Morrison &Milbrandt, 2015), shortwave and longwave radiation schemes of Iacono et al. (2008) are
implemented, and the Noah land surface model with multiparameterizations (Noah-MP)
is used (Niu et al., 2011).
2.3 Reanalysis-driven experiment
As informed by model testing, we dynamically downscaled the European Centre for
Medium-range Weather Forecasting's fifth Reanalysis (ERA5; Hersbach et al., 2020)
from 1 September 1980 to 31 August 2020 as described in Rahimi et al. (2022). For
purposes of this project we also dynamically downscaled ERA5 from 1 September 1950
through 31 August 1980 and from 1 September 2020 through 31 August 2021 toprovide a 70-year downscaled reanalysis from 1950 through 2021. While a generalcharacterization of biases in the 40-year downscaled reanalysis is presented in Rahimi
et al. (2022) below we advertise the full 70-year downscaled product, named ERA5, as
we assess biases in dynamically downscaled GCMs.

Figure 1. The pane on the left shows the WRF grids with shaded topography, while theWestern Electricity Coordinating Council (WECC) region is shaded in blue. The rightpane shows our analysis subregions (dashed lines) for WRF comparisons to SNOw
TELemetry sites (black boxes).
2.4 GCM-driven experiments
Next, we dynamically downscale four CMIP6 GCMs from 1980 to 2100. These GCMs
were selected because, at the time of selection, of the GCMs with the requisite data todrive WRF, they exhibited the most skill compared to other CMIP6 GCMs. The GCM
selection process is described in the, Memorandum on Evaluating Global Climate
Models for Studying Regional Climate Change in California an interim deliverable for5this project’s task 3. As mentioned previously, computational limitations preclude ourteam from dynamically downscaling more GCMs to the 3-km California grid under thisproject; however, many more GCMs are currently being downscaled to 9-km across thewestern United States with funding from other sources.
To account for the impacts of greenhouse gases on the model energetics, updating
annual and Northern Hemispheric mean CMIP6 historical carbon dioxide and methane
concentrations are used in WRF’s longwave radiation scheme from 1980-2014, whileanthropogenic emissions from the third Shared Socioeconomic Pathway (SSP3) with an
end-century top-of-the-atmosphere radiative forcing of 7 W m-2 is (SSP3-7.0) are usedfrom 2014 to 2100. Table 1 shows a list of the GCMs provided to this project (EPC-20006).
Table 1. Listing of dynamically downscaled CMIP6 GCMs provided under EPC-20-006.
The GCM resolutions are approximate since many CMIP6 GCMs have stretchedhorizontal grids near the poles (e.g., FGOALS-g3).
GCM

1 CESM2
2 CNRM3
4

ESM2-1ECEarth3Veg
FGOALSg3

Modeling
centerNCAR
CNRMCERFACS
EC-Earth
Consortiu
m
CAS

SSP

Variant

GCM resolution(latitude/longitude)
0.94°x1.25°
1.4°x1.4°

Abbreviation

r11i1p1f1
r11i1p1f2

WRF
grids
45,9,3
45,9,3

3-7.0
3-7.0
3-7.0

r11i1p1f1

45,9,3

0.7°x0.7°

ECE

3-7.0

r11i1p1f1

45,9,32.1°x2.0°

FGOALSCESM2
CNRM

Modeling centers report many more results on isobaric surfaces versus native modellevels in CMIP6 given the much smaller data volume of the former. In WRF, asufficiently high vertical resolution is recommended to force the model. Additionally, 6hourly forcing updates are generally recommended, but most modeling centers do notreport the requisite 6-hourly data to drive WRF, although many do report daily data. To
increase the number of potential downscalable GCMs, our team conducted tests inwhich (i) data on a mere 6 pressure levels was used to drive WRF and (ii) a daily forcing
interval was used. In both instances, large differences between the control (forced with
6-hourly data on 25 isobaric surfaces) and the two test cases were noted, forcing us toproceed adherent to WRF’s best practices.
2.5 Sea surface temperatures
Sea surface temperatures (SSTs) are prescribed to update daily in WRF to thereanalysis/GCM values, except for the Flexible Global Ocean-Atmosphere-Land System(FGOALS) which only contained monthly SST output in downscaling. For FGOALS,
linear interpolation was used to upsample monthly SST means, valid at the midpoint ofeach month, to daily values.

6

SST warmness in the Gulf of California (GoC) is known to have a strong impact on the
North American Monsoon, which provides roughly a third of Arizona and New Mexico’s
annual precipitation (Mitchell et al., 2002). As downscaling progressed, we began toconsider the consequences of the fact that the GoC is not resolved in CMIP6 GCMs
because of their coarse resolutions. As a result, there is no SST information from GCMs
across the GoC that can be used to directly prescribe SSTs in the WRF-resolved GoC.
For CESM2 and CNRM, GoC SSTs were extrapolated from the adjacent Pacific SSTs.
However, the SST climatologies of the open Pacific (directly west of the California Sur)and the GoC can be vastly different; specifically, the open Pacific SSTs are cooler andexhibit a weaker seasonal variability. Furthermore, we found a strong anticorrelation
between the climatological GoC entrance region temperature and the along-axis GoC
SST gradient, indicating that we may be able to apply a linear relationship to extrapolate
the GoC SSTs. In the GoC, we thus apply the following linear extrapolation,𝜕𝜕𝜕𝜕
𝑇𝑇𝐺𝐺𝐺𝐺𝐺𝐺 = ]𝐸𝐸𝐸𝐸𝐸𝐸5 (𝑛𝑛) + 𝑇𝑇𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒,𝐺𝐺𝐺𝐺𝐺𝐺,
𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕

where ]𝐸𝐸𝐸𝐸𝐸𝐸5 is the monthly varying climatological GoC temperature gradient and is𝜕𝜕𝜕𝜕always positive as SSTs are always warmer further south and east, 𝑛𝑛 is the along-GoC
axis coordinate, and 𝑇𝑇𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒,𝐺𝐺𝐺𝐺𝐺𝐺 is the GoC entrance temperature, which is resolved in
GCMs. A schematic showcasing the origins of this relationship is given in Figure 2.

Hence, for EC-Earth3-Veg (ECE) and FGOALS (and 16 of 18 GCMs in the remaining
greater ensemble), we applied the above linear extrapolation relating the GoC entrance
region meant SST, a feature resolved in all GCMs, to the GoC SSTs by using a monthly
varying SST gradient, derived from ERA5 over the 1980-2014 period.
3. Historical performance
We intentionally narrow the scope of our historical analysis to temperature andprecipitation for a variety of reasons. Firstly, certain variables such as simulated snow
water equivalent, streamflow, and evapotranspiration will be provided to end-users by
driving calibrated offline hydrologic simulations (Task 6) using bias corrected WRFsimulated precipitation and temperature as inputs, thus yielding a result morecomparable to the observed climatology. Secondly, these variables have highconfidence historical reference datasets available due to sufficient recording ofobservations. Other variables, such as near-surface wind, do not have this. Third,
precipitation and temperature are of fundamental importance to this project’s statistical
downscaling and hydrologic modeling teams. Finally, we chose to dynamicallydownscale CESM2, CNRM, FGOALS, and ECE based on their skill in simulating
precipitation and temperature, as well as their future diversity in their changes. Although
we assess the skill of the GCMs, the dynamically downscaled GCM results may belargely different than an observed data product owing to (i) internal climate variability
differences and (ii) large GCM biases that are propagated into WRF during
downscaling, resulting in an amplified bias, even in the GCMs that performed well in the
Task 3 evaluation.

7

We examine California statewide mean biases in the wet season (October throughMarch climatology) and in the warm season (June through September climatology)
between 1980 and 2014. Figures 3 and 4 show the historical climatology of wet andwarm season 2-m temperature (T2MEAN) amongst the ERA5-driven and GCM-driven
experiments, along with a climatology from the Parameter-elevation Regressions on
Independent Slopes Model (PRISM; PRISM Climate Group, 2021). PRISM is acommonly used observationally based gridded product that is often used to assess a
RCM’s skill in predicting temperature and precipitation.

Figure 2. Subpanel (a) shows the coastal geometry from the 30-km-resolution ERA5
product along with 5 transects parallel the coastline within the Gulf of California (GoC)
and the Open Pacific. Monthly climatological (1980-2014) temperatures along thesetransects are shown in panels (b) and (c) respectively; we note large differences inmonthly temperature magnitudes and seasonal variability between the two regions,
respectively. Subpanel (d) shows the climatological temperature gradient magnitudesfor (dashed) the Open Pacific and (solid) the GoC averaged over the 5 transects,
respectively. Subpanel (e) shows the climatological GoC Entrance region temperatureby month. For panels (d) and (e), the month of January (December) is denoted by 0 (11)on the abscissa.
As expected, the heterogeneity of the WRF result reflects the geographic diversity ofCalifornia, although the results are characterized by notable biases (Figure 5). During
the wet season, an ERA5 warm bias of 1°C is simulated in the Central Valley.
Meanwhile, ERA5 can be locally 1-4°C cooler than PRISM across the high Sierra andportions of northern California, although these locations are sparsely observed whichraises concerns regarding PRISM’s fidelity. This bias profile (warm at low-elevations
and cold at higher elevations) is consistent with other studies such as Rahimi et al.
(2022) and Liu et al., (2017), the cause of which is actively being researched by WRF
developers. For the GCMs, the biases are qualitatively like those in ERA5, with theCentral Valley showcasing an even larger warm bias (3°C). CESM2 and ECE (CNRM
8and FGOALS) have a smaller (larger) Sierra cold bias than ERA5 during the wet
season.

Figure 3. Historical climatology (1980-2014) of wet season (October through Marchmean) of 2 m temperature (T2MEAN) from 5 dynamically downscaled products at 3 km
and PRISM.

Figure 4. Historical climatology (1980-2014) of warm season (June through Septembermean) of 2 m temperature (T2MEAN) from 5 dynamically downscaled products at 3 km
and PRISM.
During the warm season, the bias pattern is more complex than in the wet season.
ERA5 is the least-biased product, again showcasing a cold bias of 1-2°C in the high9Sierra (Fig. 5). ERA5 also exhibits a cold bias of similar magnitude across the northern
California coast. On the other hand, CESM and ECE are generally characterized bywarm biases almost everywhere in the state, with biases approaching 4°C in portions of
the Central Valley and eastern Sierra; biases in these two simulations are minimized
across the Sierra and are even smaller than ERA5. CNRM and FGOALS can locally bemore than 4°C colder than PRISM along the spine of the Sierra, while CNRM is 3°C
warmer than PRISM in the Central Valley. Finally, aside from the Central Valley,
FGOALS is 2-5°C cooler than PRISM across portions of northern and southeastern
California.
WRF-simulated wet season precipitation (PREC) is expectedly correlated with
California’s complex topography (Figure 6), and its biases are more uniform in sign thanthose of T2MEAN for the downscaled GCMs in that they are almost universally wet(Figure 7). Additionally, the bias magnitudes grow with increasing elevation. ERA5 is by
far the most comparable with PRISM with wet biases of 3-4 mm/d at the highest
elevations of the high Sierra where confidence in PRISM’s fidelity is reduced. CESM2
and ECE are the least biased GCMs but still see biases that exceed 9 mm/d locally
across the high Sierra and across northern California. Like the T2MEAN results, CNRM
and FGOALS are the most biased of the four downscaled GCMs for wet season
precipitation, with nearly the entirety of the Sierra seeing >10 mm/d more than PRISM
over the historical period.

Figure 5. Climatologies of wet (left) and warm (right) season T2MEAN biases amongst
the various downscaled products relative to PRISM.
Next, we examine monthly California-mean temperature and precipitation biases byquantile (Figure 8). For T2MEAN, ERA5 most skillfully reproduces the reference
quantiles from PRISM; ERA5 in fact exhibits superior performance compared to the
GCM simulations for PREC and 2 m maximum temperature (T2MAX). ERA5 biases areconstant across quantiles – near zero for T2MEAN and T2MAX and around +2K for 2 m
minimum temperature (T2MIN). For PREC ERA5 exhibits a bias of less than 0.2 mm/dthat increases to 0.54 mm/d and 0.72 mm/d for the 90th and 95th percentiles,
respectively.

10

Figure 6. Historical climatology (1980-2014) of wet season (October through Marchmean) of precipitation (PREC) from 5 dynamically downscaled products at 3 km and
PRISM.

Figure 7. Historical climatology of wet season (October through March) biases inprecipitation (PREC).

11

Figure 8. Observed (abscissa) versus WRF-simulated (ordinate) monthly-meanvariables by quantile (1st to 99th incremented by 1) for the various downscaled products
averaged over California. Quantiles were computed by resampling the daily time series
to monthly means and averaging across the entire state of California. The stars denotethe 25th, 50th, 75th, 90th, and 99th percentiles, while circles are used for all otherquantiles. An addition anal EC-Earth experiment is presented here, ‘b ECE’. This
experiment is identical to ECE except that a mean-state bis correction is applied to the
GCM inputs prior to downscaling.
When viewed by quantile, the dynamically downscaled GCMs continue to present muchlarger biases relative to ERA5. It appears all downscaled GCMs have issues withsimulating the amplitude of the daily temperature cycle as evidenced by their uniform
cold bias in T2MAX and warm bias in T2MIN. FGOALS and CNRM are more biased
than CESM2 and ECE for T2MIN, T2MAX, and T2MEAN, while ECE, ERA5, and bias
corrected ECE (b ECE; discussed shortly) are minimally biased. Overall, cold biases in
T2MEAN are driven by cold biases in T2MAX and is most probably related to wet
median precipitation bias of 0.57 mm/d, 1.89 mm/d, 0.73 mm/d, and 1.34 mm/d, in
CESM2, CNRM, ECE, and FGOALS, respectively. Magnitudes in PREC biases growwith increasing quantile; the 90th quantile biases are 1.69 mm/d, 3.12 mm/d, 2.03 mm/d,
and 3.88 mm/d for CESM2, CNRM, ECE, and FGOALS, respectively. For the 99th
percentile, biases of 2.70 mm/d, 5.40 mm/d, 5.00 mm/d, and 6.81 mm/d are simulated
for CESM2, CNRM, ECE, and FGOALS, respectively.
We note the minimal biases in b ECE, especially for precipitation, which can even besmaller than those in ERA5; this experiment was conducted following a correction ofmean-state biases relative to ERA5 prior to downscaling. The removal of these biases,
12

which can be quite large (see below), can increase the realism of dynamicallydownscaled results but may also introduce uncertainty in the dynamically downscaled
signal (hence the choice not to use this form of bias correction for EPC-20-006).
While the California-mean biases in temperature and precipitation are remarkably large,
they are at least somewhat physically interpretable and consistent with some of themean-state biases from the parent GCMs that are propagated into WRF during
dynamical downscaling. Despite a comprehensive processed-based GCM evaluation
prior to downscaling, we found there to be large biases in the climatological mean of
certain variables used to drive WRF. Here, we define a mean-state variable bydecomposing its full signal into a linear summation of a climatological mean and its
deviation from the mean. For example, for temperature 𝑇𝑇, the decomposition yields
𝑇𝑇(𝑥𝑥, 𝑦𝑦, 𝑧𝑧, 𝑡𝑡) = 𝑇𝑇(𝑥𝑥, 𝑦𝑦, 𝑧𝑧) + 𝑇𝑇′(𝑥𝑥, 𝑦𝑦, 𝑧𝑧, 𝑡𝑡). 𝑇𝑇 is computed over the climatology period (19802014), and its bias is found by direct comparisons with ERA5. We show climatologicalwintertime biases for temperature and zonal wind, averaged over the eastern Pacificand western North America (20°-55°N and 140°-90°W area-weighted) in Figure 9.

Figure 9. Vertical profiles of historical (1980-2014) climatological wintertime biases in(left) temperature and (right) zonal wind across the eastern Pacific and western North
America. Colored curves indicate the biases of the dynamically downscaled simulations.
The thick black curve represents the CMIP6 ensemble mean (30 GCMs, 1 realizationeach), while the gray shading depicts the CMIP6 model spread.
CMIP6 GCMs are generally characterized by the following mean-state biases acrossthis sector of the planet, shown in Fig. 9:
1. A lower tropospheric instability bias between the surface and 850 hPa: Thisbias manifests in lapse rate magnitudes that are ~3°C/km larger in the CMIP6
ensemble mean than in ERA5. The resultant increase in available potential
13

energy via parcel theory, if realized by parcels that achieve their level of freeconvection, may lead to increased precipitation relative to ERA5.
2. A general tropospheric cold bias of around 2°C: This has consequences forsurface temperatures across high terrain (such as the Sierra) and precipitation
phase. For the former, many land-surface models (including Noah-MP; usedhere) use a threshold surface temperature that determines the rain/snow
partitioning to classify impacting hydrometeors as rain or snow. As this cooler air
impinges on topographic barriers, the surface will thus generally remain cooler
than it would otherwise in ERA5. Thus, falling precipitation is more likely to beclassified as snow rather than rain. As snow accumulates, an albedo feedback
ensues which acts to keep surface temperatures cooler than they would
otherwise become in a snow-free environment. For the latter, parcels rising in acooler atmosphere will be more likely to achieve saturation and initiate nucleation
processes (both liquid and ice). This cold bias may therefore increase theprecipitation efficiency across the region in downscaled GCMs compared to
ERA5.
3. A westerly zonal wind bias throughout the troposphere that increases withincreasing height: Any lower-tropospheric upslope flow will thus be enhanced,
leading to enhanced updraft velocities and increased nucleation within clouds(and thus increased precipitation capabilities). In the CMIP6 mean, the bias is on
the order of 0.5 m/s in the lower troposphere and increases to around 2 m/s near
the tropopause.
4. A zonal wind shear bias throughout the troposphere: Storms that evolve in a
sufficiently sheared environment may have a greater longevity as their fallinghydrometeors are deposited away from the main updraft. Increased storm
lifetimes may act to increase precipitation in dynamically downscaled GCM
simulations.
Preliminary results from this evaluation suggest that thermodynamic mean-state GCM
biases (relating to temperature, lapse rates, and moisture; bullets 1 and 2 above) aremost correlated with dynamically downscaled precipitation biases. Meanwhile, meanstate GCM biases in wind are anticorrelated, except across the Pacific Northwest,
where the removal of wind biases are found to improve dynamically downscaled
precipitation biases.
As it pertains to the above mean-state biases, CESM2 and ECE are the least biased intropospheric temperature compared to CNRM and FGOALS, and the lower-troposphericinstability bias in ECE is virtually absent. CESM2 and ECE also have the smallest zonal
wind biases in the lower half of the troposphere compared to CNRM and FGOALS.
These mean-state biases may begin to explain their relatively smaller precipitationbiases compared to CNRM and FGOALS (Fig. 7). Furthermore, the spatiality of the
precipitation biases seems to indicate that the mean-state biases are exacerbated byforcing from local topography. Additionally, biases in high-frequency weather-scale
winds, temperature, and moisture may be contributing to biases in precipitation. Indeed,even though extensive testing of WRF was conducted to optimize its simulation of
weather and climate across the western US (Rahimi et al., 2022), and even though
14

GCMs were chosen based on how well they simulated the regional synoptics (Task 3
Memorandum on Evaluating Global Climate Models for Studying Regional Climate
Change in California), large internal GCM biases, characteristic of the greater CMIP6
ensemble, remain, leading to large differences between the downscaled GCMs and
PRISM. We postulate that this general behavior, namely hyperactive precipitation, canbe expected when downscaling a given CMIP6 GCM across this region using thisconfiguration of WRF.
4. Diversity of future precipitation and temperature changes for WRF projections
for this project
We begin by examining the future changes in precipitation and temperature. We
intentionally selected GCMs to downscale based on how wet and warm their futures
became and ensured that their respective future projections were relatively balanced
and unique from each other. Following precipitation and temperature, we examine
future changes in other variables from our four dynamically downscaled GCMs.
4.1 Historically evaluated variables
The dynamically downscaled GCMs showcase a diversity of future changes in terms oftemperature and precipitation, and these projected changes can locally be vastlydifferent between the parent GCM and its respective downscaled solution. All anomalies
in this section are computed as the 2070-2100 climatology minus the 1980-2010
climatology 1. The large-scale GCM signal is generally preserved in downscaling for all
GCMs for both the wet and warm seasons (Figures 10 and 11). Additionally, a widely
forecast inland-directed gradient to the warming presents in all simulations (both GCM
and downscaled), as well as generally maximized warming anomalies during the warm
season (Fig. 10). CRNM and ECE seem to warm the most in the future, while FGOALS
sees much smaller levels of warming (around 2°C) in the California state mean.
Prominent California landscape features such as the Salton Sea, the Sierra Nevada,and several southern California mountain ranges distinguish the dynamicallydownscaled products from their parent GCMs as WRF can resolve these geographic
features and their subsequent impact on local weather and climate. As a result, locallymaximized wet season warming, possibly associated with the snow-albedo feedback, is
projected in proximity to California high points in CESM2, CNRM, and ECE, while amore homogeneous warming pattern is simulated in FGOALS. During the warm season,
CNRM and ECE see warming of nearly 6°C in the future along the spine of the Sierra;
this region only warms by ~4°C in the future in their respective GCM parents. Even the
0.7°-grid spacing ECE GCM showcases large differences with its downscaled
counterpart locally, particularly during the wet season.
As with T2MEAN, the large-scale change in PREC is generally preserved indownscaling, and the four GCMs depict a diversity of plausible futures (Figure 11).
1

The entirety of the CMIP6 historical time period (1980-2014) was considered for bias evaluation purposes. Since,
for the climate change calculations, we use the last 30 years of the 21st century in computing the end-century
climatology, it made sense to use a 30-year historical reference period in the calculations of the future change signal
as opposed to a 35-year historical climatology.

15

During the wet season, CESM2 and FGOALS universally project a wetter future across
California, with the maximum increase in precipitation collocated with California’scomplex terrain features. Meanwhile, although the ECE and CNRM GCMs simulate
future drying across most, if not all, of California, their downscaled counterparts provide
a signal of increased heterogeneity with pronounced wetting along the spine of the
Sierra in CNRM and other regions of complex terrain. We also see wetting in thenorthern Central Valley in ECE, a feature that is absent in the parent GCM. For thewarm season, differences between the dynamically downscaled simulations and theparent GCMs are more pronounced, and CESM2, CNRM, ECE, and FGOALS all
predict a wetter future in the Sierra Nevada. CNRM amplifies the drying of its parent
GCM across most of California, with locally maximized drying of 1 mm/d across thenorthern upslope of the Sierra. It appears that the RCM amplifies the change signal of
the parent GCM, and the larger GCM/RCM differences during the warm season may be
associated with seasonally weaker large-scale forcing for ascent, which would lessen
the impacts of spectral nudging (conducted on the 45-km grid) on the downstream
higher resolution WRF products (i.e., 9- and 3-km). Finally, the cooler temperature
response across California in CESM2 and especially FGOALS may be the result ofoffsetting cooling associated with projected increases in precipitation.
We also find shifts in the distributions of daily T2MEAN and PREC (Figure 12). Across
California, an expected warming profile to the distribution is noted, with the weakest(strongest) warming signal simulated in FGOALS (ECE). For PREC meanwhile,simulations project quite different futures for lower-intensity precipitation events (< 10mm/d), with CESM2 and FGOALS wetting and CNRM and ECE drying. For extreme
precipitation rates however (> 100 mm/d), all simulations predict higher-intensity
extremes by the end of the 21st century. This predicted wettening of the tail has been
forecast in other studies using CESM2 across California (Swain et al., 2018).
4.2 Other variables
Here we examine high resolution future changes in certain variables which cannot bederived physically using any of the other modeling methods in EPC-20-006.
During the wet season, beginning with 10 meter wind speeds and 2 meter vapor
pressure deficit (VPD; defined as the difference between the saturation vapor pressure
and the vapor pressure), we find a diversity of futures in our downscaled simulations(Figures 13 and 14). VPD increases everywhere, with the maximum increases at lower
elevations and away from areas of complex terrain, indicating that surface evaporation
will preferentially increase at these locations in the future. Future increases in VPD
generally increase with south and eastward extend across the state, and FGOALS
seems to be an outlier in terms of wet season VPD anomalies, which are smaller inmagnitude relative to CESM2, CNRM, and ECE. Wind speeds on the other hand
generally decrease in the future in all simulations, and the decreases tend to be larger
over regions of complex terrain and at higher elevations. Diversity in the future change
signal presents for winds offshore; CNRM, ECE, and FGOALS all simulate weakening
future winds, while CESM2 simulates slight strengthening.

16

Figure 10. Future anomalies in temperature across California for the dynamicallydownscaled GCMs during the wet and warm seasons. The top row is the native GCM
change and the bottom row is the change in 3 km WRF data. Anomalies are computed
as the 2070-2100 means minus the 1980-2010 mean.
During the warm season, VPD increases are larger relative to the wet season, withagain a diversity of future evaporative potential with the same spatiality of the change asin the wet season. FGOALS again generally simulates the smallest increase in VPD
values, while CNRM and especially ECE see locally maximized future increases in VPD
across the Central Valley (ECE and CNRM warm the most comparatively). For winds,
striking consistency in the downscaled result is noted across several regions ofCalifornia:
1. Winds are weaker in the future across the spine of the Sierra
2. Winds are weaker in the future off the California coast17

3. Winds are stronger in the future across portions of the Sierra Madre
4. Winds are stronger in the future across the San Francisco Bay area and innerchannel. This is probably due to a strong projected eastward-directed gradient to
the warming pattern, with stronger inland warming, yielding an anomalous pressure
gradient force. Increased wind speeds manifest as the mass response is funneledthrough the channel via Bernoulli effects.
5. Winds are weaker in the future across the San Gabriel
Furthermore, winds strengthen in the future in the San Bernadino mountains in CESM2
and CNRM but weaken in ECE and FGOALS.
5. Data uses in EPC-20-006
The WRF data will be used for purposes of the EPC-20-006 project; specifically by thestatistical downscaling (Task 5) and hydrology (Task 6) teams.
WRF data will be bias corrected before use by the statistical downscaling team as
training data in LOCA, their statistical downscaling product. This marriage of WRF and
LOCA births a novel hybrid downscaling framework and is a cornerstone feature of
EPC-20-006. Additionally, WRF hourly outputs, including precipitation and temperature,are being used to drive continuous, calibrated hydrology experiments offline.
An important advance that is provided by the EPC-20-006 project is that it develops twodata sets by applying two different downscaling approaches. End users are given theoption of deciding which data is best suited for their particular applications. The
dynamically downscaled projections that are described in this memo and in the first
Task 4 memo contain physically consistent downscaled data with high temporal
resolution: 6 hourly for all output and up to hourly for some variables. The dynamical
downscaled data is used in the EPC-20-006 project in training the statistical
downscaling (resulting in the LOCA hybrid data), which is more computationallyeconomical than the dynamical downscaling and thus able to produce projections frommany global climate models under different possible greenhouse gas andsocioeconomic scenarios. Training from dynamical model simulations over the 21stCentury averts the traditional "stationarity assumption" that climate patterns in the past
will also be the same in the future. However, as described in this memo, the dynamical
downscaling simulations have large biases for some variables. These biases will be ofconcern to some users, so the LOCA hybrid data, which is daily and includes fewer
variables, will be biased corrected. Depending on the needs of the analysis, thedynamical or hybrid downscaled data may be the best suited for certain end-uses.

18

Figure 11. Same as in Fig. 10 but for PREC.
6. Data accessTo recap, 6-hourly hourly, and daily WRF data from the discussed simulations (see theMemo on the Development and Availability of Dynamically Downscaled Projections
Using WRF) on a 45-km grid that covers western North America, a 9-km grid thatcovers the entirety of the western United States, and a 3-km convective-permitting gridwith California-wide coverage.
Data discussed delivered to CEC in this task, Task 4, as well as data from the larger
ensemble delivered for other research grants, are 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 toyour local devices via the Amazon Web Service Command Line Interface (AWS CLI).
Specifically, Task 4 WRF data can be accessed on S3 at:
19

1. ERA5 downscaled reanalysis– 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/
All other dynamically downscaled GCM data can be found in s3://wrf-cmip6noversioning/downscaled_products/gcm/.

Figure 12. Future changes in the distribution of (left) T2MEAN and (right) PREC acrossthe entire state of California. Each distribution is calculated by considering the fullannual cycle, binning daily quantities for all California gridcells in the historical (19802010) and future (2070-2100) periods. The distribution differences are then computed.

20

Figure 13. Future climatological changes in (left) 10 m wind speed and (right) vapor
pressure deficit (VPD) during the wet season (October to March). As in previous figures,
anomalies are computed as the 2070-2100 means minus the 1980-2010 mean.
Different from previous figures in which all non-California gridcells were masked, we
included usable gridcells outside of the state here for winds due to recent interest in
predicting offshore winds.

Figure 14. Same as in Fig. 13, but for the warm season (June through September).
21

Find previous memos referend here:
Krantz, W., Pierce, D., Goldenson, N., and Cayan, D. (2021). Projected climate, heatwaves and extreme weather for the 21st Century,
https://www.energy.ca.gov/sites/default/files/202209/20220907_CDAWG_MemoEvaluating_GCMs_EPC-20-006_Nov2021-ADA.pdf
Rahimi, S. (2022). Memo on the development and availability of dynamical downscaled
projections using WRF, https://www.energy.ca.gov/sites/default/files/202209/20220907_CDAWG_MemoDynamicalDownscaling_EPC-20-006_May2022-ADA.pdf
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Appendix I. Additional WRF projections across the western United States
In this section, we advertise the greater ensemble of GCMs, of which the four CECfunded WRF projections are a small part. We proceed by stating that, although thesesimulations are not funded by CEC, we support their use following their publication.
They are also located in the open data bucket on Amazon S3. See bucket details at
https://registry.opendata.aws/wrf-cmip6/.
Initially, 9 different GCM (including the 4 CEC WRF projections) are downscaled 9-km
across the western United States. For CESM2, two additional simulations wereconducted for the SSP2-4.5 and SSP5-8.5 emissions pathways. Future changes incumulative water year precipitation for our entire ensemble are shown in Figure 15.
Additionally, each of the SSP3-7.0 GCMs are dynamically downscaled as a pair ofsimulations, one using standard dynamical downscaling, and a second which sees amean-state bias correction applied to the GCM inputs before WRF downscaling (a predownscaling bias correction) following Bruyère et al. (2014). The reference dataset used
for bias correction is the ERA5 reanalysis, and the bias correction is only applied to the
3-dimensional horizontal winds, temperature, and specific humidity, as well as surfacepressure, mean sea-level pressure, and sea-surface temperatures. The mean-state bias
correction is applied monthly. In summary, a total of 20 simulations are dynamicallydownscaled to 9-km. In addition to the four 3-km simulations provided to CEC, and fifth
3-km experiment was completed across California from 1980-2100 for ECE with a predownscaling bias correction (b ECE). A summary of all total experiments is given in
Figure 16.

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Figure 15. Future (2070-2100 mean) minus historical (1980-2100) differences incumulative water year precipitation for parent GCMs (left) and their downscaled
counterparts (right) within our full ensemble. Yellow highlighted GCMs are thosepromised to CEC, while asterisks indicate that GCMs have a companion simulation inwhich a mean-state bias correction was applied prior to downscaling. For non-SSP3-7.0
simulations, we include the associated SSPs in the labeling. This chart is valid as of 21
October 2022.
While the climate change signal of cumulative water year precipitation is quite variable
across our downscaled GCM ensemble, we note three items:
1. The large-scale change signal in precipitation from the parent GCM continues tobe preserved following dynamical downscaling, regardless of whether predownscaling bias correction is used (Figure 15).
2. The change signal is generally quite similar between the non-bias corrected andthe bias corrected downscaled experiments, although we do note some large
local differences in the change signal (e.g., CNRM across the Sierra; Figure 17)
3. There is a strong landscape heterogeneity in the expression of the downscaled
climate change signal that cannot be resolved in the native GCM.

25

Figure 16. Graphic showing where the CEC WRF projections (denoted by the lightningbolts) fall within the larger 20-member ensemble. The numbers in each square denotethe highest resolution grid downscaled to. For example, a 3 indicates that the GCM has
been downscaled to 45-, 9-, and 3-km. Here, BC is an acronym for pre-downscaling
bias correction.

Figure 17. Future (2070-2100 mean) minus historical (1980-2100) differences incumulative water year precipitation for experiments in which pre-downscaling bias
correction was and was not applied on the 9-km western US grid.
Finally, we briefly examine the effects of pre-downscaling bias correction on thedownscaled GCMs at 9-km grid spacings. Figure 18 shows the historical daily
26

climatologies of precipitation and its future change at SNOTEL sites across various
western US subregions shown in Fig. 2.

Figure 18 Cumulative precipitation climatologies for the bias corrected experiments(solid lines) at SNOTEL sites across the southwest (SW) south-central Rockies (SCR),
north-central Rockies (NCR), and the Pacific Northwest (PNW) regions (see Fig. 2). The
black dashed curve shows the ERA5 climatology, while the hatching shows thedifferences between ERA5 and SNOTEL (positive values = wetter than observed). The
dashed lines in the show the BC’d minus the non-BC’d differences in cumulative
precipitation for the downscaled GCMs.
High-level takeaways from Figs. 18 and 19 include:
1. For the historical period (Fig. 18), bias corrected (BC’d) experiments are
generally drier than their non-BC’d counterparts in terms of their cumulative
precipitation as indicated by the dashed curves for each GCM denoting the
cumulative difference between the BC’d and non-BC’d experiments (negativemeans that the BC’d experiments are drier). We also note the small differences
between ERA5 and SNOTEL observations (hatch scatter).
2. Generally, biases in cumulative precipitation are reduced as the result of the predownscaling bias correction, and GCM results are more like ERA5 results. In
some cases, however, drying of the climatologies increases the bias (e.g., acrossthe NCR in MPI).
3. Even after the removal of mean-state biases and subsequent drying of the WRF
solution, large biases can remain (e.g., FGOALS in the PNW and the SCR),
possibly attributable to higher-frequency biases which are not removed from the
GCM inputs. This issue could also be due to an assumed decoupling between
the mean-state and deviations from the mean-state in our adopted bias
correction approach.
27

4. Bias correction generally preserves the sign of the change signal (Fig. 19).
Exceptions to this include MPI and CNRM across the SCR; however, we notethat the change signal in cumulative precipitation across these regions isrelatively small.
5. For the single simulation for which multiple emissions scenarios were
downscaled (CESM2), the change signal modification because of bias correction
is generally much smaller than the modification associated with changing
emissions scenarios. We emphasize that this conclusion can only be applied to
CESM2 and may not necessarily hold for other GCMs.

Figure 19. (left) Future changes in cumulative annual precipitation for the non-BC’dexperiments and (right) percent changes relative to the historical period for the non-bias
corrected experiments. Bias-corrected experimental percent changes are also given inparentheses. The historical (future) period encompasses the 1980-2010 (2070-2100)
period.

28