Climate Downscaling for the
National Climate Assessment:Practices and Challenges
Liqiang Sun
Cooperative Institute for Climate and Satellites (CICS)
North Carolina State University (NCSU), andNational Centers for Environmental Information (NCEI)

Acknowledgements§ Kenneth Kunkel, Laura Stevens, Jim Biard, and SarahChampion (NCSU)§ David Easterling (NCEI)§ David Pierce and Daniel Cayan (UCSD)§ NA-CORDEX group§ Terence Thompson (Logistics Management Institute, VA)

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Outline§ National Climate Assessment (NCA)§ Climate Downscaling for NCA§ Challenges and Paths Forward

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National Climate Assessment (NCA)
The NCA is an important resource for understanding
and communicating climate change science andimpacts in the United States. It informs the nation
about already observed changes, the current status of
the climate, and anticipated trends for the future. TheNCA report process integrates scientific informationfrom multiple sources and sectors to highlight key
findings and significant gaps in our knowledge.
http://www.globalchange.gov/
http://scenarios.globalchange.gov/scenarios/climate
http://nca2014.globalchange.gov/report4NCA Regions5

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Climate Downscaling for NCA
Why Downscaling?
§ Actionable regional/local climate information§ Climate assessment: top-down and bottom-up
approaches§ Climate extremes

Derived Variables
Annual number of days > 86F
Annual number of days > 90F
Annual number of days > 95F
Annual number of days > 100F
Annual number of days > 105F
Annual number of days > 110F
Annual number of days > 115F
Annual number of days Tmin < 28F
Annual number of days Tmin > 75F
Annual number of days Tmin > 80F
Annual number of days Tmin > 85F
Annual number of days Tmin > 90F
Annual number of days > 1 inch
Annual number of days > 2 inches
Annual number of days > 3 inches
Annual number of days > 4 inches
Annual maximum number of consecutive dry days
Annual maximum number of consecutive wet days
Annual maximum 1-day precipitation
Annual maximum 5-day precipitation

Annual number of icing (Tmax < 32F)days
Annual number of frost (Tmin < 32F)days
Annual highest 5-day maximum temperature
Annual lowest 5-day minimum temperature
Annual lowest 5-day minimum temperature
Annual highest 1-day maximum temperature
Annual lowest minimum temperature
Annual lowest minimum temperature
Cooling degree days
Heating degree days
Growing degree days, base 50 (F)Date of the first fall freeze
Date of the last spring freeze
Length of the frost-free season
Length of the growing season (28F threshold)
Length of the growing season (41F threshold)Annual number of days with precipitation greater than the 99th percentile
Annual total precipitation greater than the 99th percentile
Annual number of days with maximum temperature lower than the 1st percentile
Annual number of days with maximum temperature greater than the 99th percentile
Annual number of days with minimum temperature lower than the 1st percentile
Annual number of days with minimum temperature greater than the 99th percentile

Climate Extremes§ projected changes for future 30-year periods, respect tothe reference period of 1976-2005, using multi-modelensemble.

Climate Data
§ National Weather Service’s Cooperative Observer
Network (COOP)§ NA-CORDEX§ LOCA

Long-term Precipitation Stations adequacy of data to detect and understand the past changes

NA-CORDEX Simulations
ERA-Int

CRCM5
CRCM5(OURANOS(UQAM))
0.44°
0.44°†
0.22°
0.11°

RCA4RegCM4WRF

CanRCM4 HIRHAM50.44°

50km
25km

50km
25km

0.44°
0.22°

50km
25km

50km
25km

0.44°
0.44°
0.44°
0.44°†
0.22°†

0.22°†

0.44°
50km*
25km*

0.22°†

8.5
0.44°
0.22°
0.44°
0.22°

0.44°

CanESM2

MPI-ESMMRRCPECS (°C)

4.5

HadGEM2ES

MPI-ESMLR

0.44°

4.5
3.7
8.5
4.5

50km†
25km†

8.5

0.44°
0.44°
0.44°
0.44°

EC-EARTH‡
GFDLESM2M

0.22°†

4.6

0.44°
0.44°
50km
25km

50km
25km

4.5
8.5
2.6
4.5
8.5
4.5
8.5

Access

PoC

PoC

ESGF

PoC

PoC

CCCma

ESGF

Institution

UQAM

OURANOSSMHI

Iowa State*NCAR

U Arizona

CCCma

DMI

Modeler

K. Winger

S. Biner

G. Nikulin

R. Arritt*M. Bukovsky

C. Castro,
H-I Chang

J. Scinocca

O. Christensen

3.6
3.4~3.32.4

https://na-cordex.org/simulation-matrix
12Scripps Localized Constructed Analogs (LOCA) dataset§ 32 CMIP5 models§ Historical: 1950-2005. RCP 4.5
and RCP 8.5: 2006-2100 (2099some models)§ Climatological period: 1950-99§ Interpolated model calendars tostandard calendar w/leap days
§ North America 24.5 N to 52.8 N
at 1/16th degree resolution§ Daily Tmin, Tmax, Precip
Slide from D. Pierce

ACCESS1-0ACCESS1-3
CCSM4
CESM1-BGC
CESM1-CAM5
CMCC-CM
CMCC-CMS
CNRM-CM5CSIRO-Mk3-6-0
CanESM2
EC-EARTH
FGOALS-g2
GFDL-CM3
GFDL-ESM2G
GFDL-ESM2M
GISS-E2-H
GISS-E2-R

HadGEM2-AO
HadGEM2-CC
HadGEM2-ES
IPSL-CM5A-LR
IPSL-CM5A-MR
MIROC-ESM
MIROC-ESMCHEMMIROC5MPI-ESM-LR
MPI-ESM-MR
MRI-CGCM3
NorESM1-M
bcc-csm1-1
bcc-csm1-1-m
inmcm4

Weighting Method
Weighting§ Independence Weights - Inter-model
distances computed as simple root
mean square differences are used tocalculate independence weights§ Skill Weights - The RMSE distances
between each model and theobservations are used to calculate skillweights§ An overall weight is then computed as
the product of the skill weight and theindependence weight.

Variables
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.

Surface Temperature (seasonal)
Mean Precipitation (seasonal)
TOA Shortwave Flux (seasonal)
TOA Longwave Flux (seasonal)
Vertical Temperature Profile (seasonal)
Vertical Humidity Profile (seasonal)
Surface Pressure (seasonal)
Coldest Night
Coldest Day
Warmest Night
Warmest day
Seasonal max. 5-day total precip.

Sanderson et al. 201614

Change (%) in annual total precipitation greaterthan the 99th percentile
RCP8.5: 2070-2099 minus 1976-2005
LOCA

NA-CORDEX

Difference15

Annual highest 5-day
maximum temperature

Annual lowest 5-day
minimum temperature

Annual number of icing/frost days

Annual number of days when
max temperature > 95oF

Length of the growing season(41oF threshold)Annual Maximum 1-day
precipitation (%)

Annual number ofDays > 2 inches

Annual Maximum Number of Consecutive Dry/Wet Days

Challenges and Paths Forward“Regional” focus of NCA imposes significant scientific andtechnical challenges.
1. Right-scaling climate models – Downscaling tools should bethoroughly evaluated.
2. Matching model outputs to sectorial needs - Identifying
appropriate climate data and climate information forvulnerability and impact assessments and impacts-related
research.
3. Attribution of climate extremes – linking the extremes toweather systems
4. Uncertainty – Identifying the ensemble size for regionalmodels– Ensemble size varies with the variables of interested21

Correlation between SST and precipitation

Li, Sun and Dai 2017

Plant hardiness Zones

Causes of major extreme precipitation events

Kunkel et al. 201224

Fronts• Deep learning neural network• Inputs: surface values ofo air pressureo air temperatureo specific humidity
o west-east and south-north wind velocity components• Training dataset: 14-year set of fronts manuallydrawn by the National Weather Service and digitized
into polylines• Grid cell values of the probability of the presence offive types of frontal boundaries (cold, warm,stationary, occluded, and trough)Front Identification Comparison
Example26

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Cold Front Climatology (2003-2016)

National Weather Service Manually
Analyzed Fronts (2004-2016)

Deep Learning Neural Network
automated front detection

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CMIP5 ETC analysis
Perform extensive analyses of CMIP5 model simulations,
identifying the occurrence of ETCs causing heavy
precipitation for historical and future simulations.

Extratropical Cyclone Future
Change: 2070-2099
Increases ineastern U.S. inwinterDecreases in fall
everywhere30

How to choose the number ofensembles?

SAMPLING ERRORS
N=8

N=16

N=24

N=39Converges like

23 N

11+ S 2S = Signal-to-noise ratio
N = ensemble size
“True” rms divide by2.
M. Tippett

Summary§ Climate Downscaling has been extensively used in theNational Climate Assessment.
1)

2)
3)

Temperature extremes are most directly affected by the climatechange: heat waves are projected to increase in frequency,intensity, and duration, and cold snaps are projected to diminish
in their frequency, but not necessarily in their intensity.
Precipitation extremes are projected to become more frequentand more intense
annual maximum number of consecutive wet days will
significantly increase in the eastern U.S. and annual maximum
consecutive dry days will significantly increase in the western
U.S.

Summary (cont’d)§ The possibility exists to enhance climate information andincrease our confidence in future climate change using
dynamical downscaling
1) Focus on climate variables that are both relevant and adding
values by regional models. RCMs can resolve the topographic
features and many physical processes such as convectively
driven storms and monsoon-topography interaction at very highresolution. We do anticipate that RCMs will provide a realistic
indication of how the characteristics of weather events willchange in a warming world.
2) Focus on the physical mechanisms of regional/local climatechanges. The full set of dynamical fields available from RCMs
will allow diagnostic investigation of these changes tocomplement the GCM projections.

Climate Explorer 2.0§ The Climate Explorer offers customizable graphs andmaps of observed and projected temperature,
precipitation, and related climate variables for every
county in the contiguous United States.

https://toolkit.climate.gov/climate-explorer2/35Buncombe County36

THANKS
ありがとう37