Dynamical Downscaling(convective permitting scales):Is it worth it?
Roy Rasmussen, Ethan Gutmann and Andreas Prein, NCAR

Presentation at the International Workshop on Climate Downscaling
Studies
October 2017

Schematic of WRF Physics Parameterization Interactions

Key issues for all models andscales:• Are precipitation intensity, frequency, duration,sequence and phase right?
These aspects all have implications forsoil moisture, runoff and surface hydrology
and thus for surface feedbacks.

Can we create a convective parameterization tocapture the observed precipitation dynamics orshould we move to convective permitting
resolution (> 4km)?
Can we capture the benefits of convective
permitting through statistical or hybrid
downscaling?

Are global climate models good enough for regional andconvective permitting climate models to downscalewater related quantities? What about the CIMP5 model
runs?

Covey et al. 2003

Key issues for all models and scales:• Are precipitation intensity, frequency,duration, sequence and phase right?
These aspects all have implications forsoil moisture, runoff and surface hydrology
and thus for surface feedbacks.
• The timing and duration of precipitation
events can be examined systematically byexploiting the diurnal cycle of precipitation in
the warm season over North America andextending results to other continents.

Mesoscale Convective Systems (MCS) downstream ofmountains

Afternoon

Next morningc = 15 m s
Cumulonimbus

-1

MCS: cumulonimbus
familyMesoscale
downdraftElevated heating determines start
position & start time of travelingconvection~1000 km

JJA 1996 – 2002
% Time Radar Precipitation Echo

Carbone and colleagues

Phase of diurnal cycle

Knievel et al. (2004)

Diurnal Cycle of Convective Precipitation for JJA
Observed Frequency 1976-97
Time of maximum

CCSM Frequency 1983-88
Time of maximum

Modeled frequency occurs about 2 hours earlier than observed
Dai and Trenberth 2004

% of time precipitating

Intensity (mm/day)
TRMM Satellite
Dai, J. Climate, Precipitation
Characteristics in 18 Coupled
Climate Models, 2006

GCM Model

Recent paper by Stephens et al. (2010) entitled:“Dreary state of precipitation in globalmodels”
confirms these results using CloudSat data.

Annual-mean frequency (%of time, left column) andintensity (mm/day, rightcolumn) of daily precipitation(>1 mm/day) events from
TRMM satellite observations(top panels, 3B42 data set,1998-2003 mean) and four
different coupled models(1991-2000 mean).

Note theunderestimatesof intensity andoverestimates offrequency of precip.
in the models.

Orogenic MCS downstream of mountain ranges

Laing & Fritsch (1997)

Organized convection parameterization challenge:‘Ordinary’ deep convection(conventional paradigm)

Organized deep convection(new paradigm)

cSingle grid volume

Spans many grid volumes• Entraining (‘mixing’) plume model Organized quasi-laminar-flow models• Shear not taken into account• Shear affects organization, transport• Propagation not considered• Propagation integral part of organization• Closed system (mass compensationwithin grid volume)• Open system (environment and far-field
an integral part of system)• Weak interaction with environment• Strong interaction with environment

Organized and ordinary
convection may occur in thesame grid volume

Why Convection-Permitting Climate Modeling?

4-day rain accumulation from Hurricane Harvey[The Washington Post]

Simulation Domain and Setup

WRF 4 km | 1359 x 1015 grid cells
13 years (2001-13)
ERA-Interim

Liu et al. 2016, Clim. Dyn.

Physics
 Microphysics
Thompson aerosol-aware[Thompson and Eidhammer 2014]

 Radiation RRTMG [Iacono et al. 2008]
 Land-surface model NOAH-MP
 Boundary layer YSU [Hong et al. 2006]

Spectral Nudging
U, V, T, and ZG above the PBL

Simulating on the Storm-scale
Convective outbreak in May 2010
Observation

WRF 4 km• Objective based analysis allows to evaluate model on thestorm scale

MCSs in current
climate simulation[accepted in Climate Dynamics]

ModeledObserved (stage-IV)

MCS in Texas during March 2007

Method for ObjectBased Diagnostic
Evaluation (MODE)
Time Domain(MTD)-------Under thesupervision andsupport of Randy
BullockMCS in Texas during March 2007
Observed (stage-IV)

ModeledMCS
Characteristics
Speed
Lifetime

Size
Maximum Intensity

4 km WRF model is able to simulate theprecipitation form MCSs realistically

Total Precipitation…

MCS attributes – JJA Central U.S.

Observation
Model• Realistic representationof MCS attributes• But underestimation ofMCS frequency

probability
probability[][]MCSMaximum
Precipitation
MCS
MCS
Movement
Precipitation
Speed
VolumeMCS
MCS
Lifetime
Size

MCSs per YearMCS attributes – JJA Central U.S.

Observation
Model

Similar good results forvariety of feature tracking
setupsMCS Size

probability []Precipitation Threshold:
Smoothing Radius:

5 mm/h32 km

Original setup

2.5 mm/h
64 km
Leads to larger MCSs due
to lower precipitationthresholdConvective Diurnal Cycle
Afternoon Peak
Noon PeakNighttime PeakObservations

WRF 36 km

WRF 4 km

Improved simulation of diurnal cycle of of the amount, intensity,and frequency of precipitation at 4 km compared to 36 km withconvective parameterization[Scaff et al. submitted]

Summertime rainfall diurnal cycle in Western U.S.
WRF 36 km

WRF 4 km

AmountFrequency

Non-MCS
precipitation wellsimulated at 4 km
(convectivepermitting) but
poorly handled inregional models(36 km)!

Intensity[Mooney et al. 2016]

Future MCSs
[submitted to Nature Climate Change]

WRF Future Climate Simulation

Pseudo Global Warming(PGW) [Schär et al. 1996,
Rasmussen et al. 2011]• Monthly averaged
climate change
perturbations from 19
CMIP5 GCMs• Delta 2071 to 2100 –1976 to 2005  RCP8.5• Thermodynamic
response of climatechange• No changes in weather
patterns / moisture
convergence• No issues with internalvariability

MCS Tracks & Intensities
Future+30 % maximum

MCS total precipitation – Mid Atlantic+60 % volume

Conclusion

Can statistical or hybrid downscaling
capture this behavior?

• Statistical:- Correlates GCM large scale fields with observations- GCM typically uses 1-D cloud parameterization that does
not capture movement of convective clouds from cell tocell.
- If fundamental diurnal cycle of convection not captured in
the GCM, large scale fields incorrect and not necessarily
correlated with the physical processes producing theprecipitation over the central U.S. (~70% due topropagating MCS systems).
- While winter precipitation over the continental U.S.
captured by statistical downscaling, summer convection
poorly handled.
- Not possible to deal with changes in MCS frequency,
intensity and size.

Changes in MCS Dynamics andThermodynamics – Mid Atlantic
Rotated MCS environments

More Favorable+ Increased CAPE+ Higher cloud top+ Increased vertical
moisture transport+ Deeper warm
cloud layerLess Favorable
₋ Increasing stability
₋ Less Rel. Humidity

Can statistical or hybrid downscaling
capture this behavior?
• Can Hybrid downscaling provide some of the advantages ofdynamical downscaling without the large cost?
- ICAR system (Gutmann et al. 2016) uses linear solution of flow field driven by
the GCM large scale variables- Allows for high resolution simulations similar to dynamical downscaling
spatial resolution used for Colorado Headwaters (4 km, Rasmussen et al.
2011) but 100 times faster due to the simplified dynamics.
- Shows promise in capturing orographic precipitation that is largely driven bylarge scale flow interaction with local topography- Limitations: Blocked flow not well captured. Thus, weak flow and/or steep topography
are situations that may not be well simulated.

- Current weakness: Convection.
- Research to date has shown poor performance in capturing convection storms,
especially MCS type systems.

1. GCM typically uses 1-D cloud parameterization that does not capture
movement of convective clouds from cell to cell and thus does not capture thelikely adjustments to the large scale flow that occurs with large MCSs. Diurnal
cycle of precipitation incorrect over the continental U.S., keyed to local noon
and not delayed as observed.
2. If fundamental diurnal cycle of convection not captured in the GCM, large
scale fields incorrect and not necessarily correlated with the physical process
producing the precipitation over the central U.S. (~70% due to propagating MCS
systems).

What about regional modeling?
• Regional modeling at 25-50 km horizontal resolution nests
driven by GCM or reanalysis boundary conditions has
shown skill in capturing mesoscale features such as
land/sea breezes and hurricanes at significantly lessexpensive that convective permitting models. Can thismodel be used to provide improved large scale fields toperform statistical or hybrid downscaling? Can it be usedby itself to answer key climate change questions?
- Depends:- Can be used to examine changes in frequency of large scale
mesoscale systems such as hurricanes, however, the tradeoff is thatthe frequency of hurricanes formed in both the current and futureclimate sensitive to the convective parameterization used. Thus,another uncertainty is introduced.
- As shown in this talk, a 36 km regional model is unable to capture
the dynamics and physics of MCS type convective systems, so
convective permitting simulations required to examine future changes
in intensity, size, storm speed, and frequency.

Sensitivity of hurricane frequency at 36 km
to model physics

Summary• Is dynamical downscaling worth it?

• Depends on the region, the driving model and the problem asking.
• Region:• Regions dominated by convection systems should consider dynamical downscaling
as simpler techniques such as ICAR not able to capture convection well yet. Phdstudent??
• Examples of regions that could benefit from dynamical downscaling include regions
subject to hurricanes and summer convection downstream of significant orography.

• Driving model• GCM or Regional model• Need to bias correct large scale model before using to drive dynamical downscaling
model.

• Problem:• Temperature or precipitation? Mean or extreme weather? Hard to simulate manyyears with dynamical downscaling.
• Local? High resolution simulations provide more accurate local effects and better
estimate of physical processes including future climate changes.
• Need to estimate uncertainty? ICAR or statistical downscaling may be the method ofchoice. Able to downscale many GCM ensemble members. If use dynamical
downscaling, consider using stochastic physics to get at likely physics uncertainty.

Challenges to address regarding downscaling:

1. Appropriate representation of the land surface, including soil
structure and moisture
2. Approprate representation of convection
3. Appropriate representation of extremes (hourly precipitation, etc.)
4. Limitations of the data that global models are downscaled to.
5. Appropriate representation of sub-grid scale cloud and radiation
feedbacks (even at cloud permitting scales).
6. Appropriate representation of precipitation phase (terrainsmoothing)
7. Representation of vertical motion
8. Representation of microphysical processes
9. Appropriate representation of snow melting due to slope andaspect considerations

Thank You!
[rasmus@ucar.edu]

The high resolution WRF simulation data for the current and future climate overCONUS produced by our NCAR group is now available for download via NCAR's
Research Data Archive website. https://rda.ucar.edu/datasets/ds612.0/. To download,
sign in first (top of the page on the url). Each variable from hourly 2D and 3-hrly 3D
data are archived separately.
How to site this dataset:
Rasmussen, R., and C. Liu. 2017. High Resolution WRF Simulations of the Current and Future
Climate of North America. Research Data Archive at the National Center for AtmosphericResearch, Computational and Information Systems
Laboratory. https://doi.org/10.5065/D6V40SXP. Accessed† dd mmm yyyy.