Using Downscaled Data in the Real World: Sharing Experiences

Julie WinklerMichigan State University

Introduction• Describe the selection and application of climate

projections for four climate change assessments that vary in terms of:– assessment objectives – climate variables of interest– relevant spatial and temporal scales– nature of the “downstream” impact models that employ the

downscaled climate projections – geographic focus

• The assessments share in common an “end-to-end” approach (also referred to as “top-down” approach)

Assessment Projects

• Potential impacts of climate change on perennial crop production and tourism/recreation in Michigan

• Vulnerability of understory bamboo habitat and panda distribution in China’s Quinling Mountains to climate change

• Climate change impacts on corn and soybean production in the Upper Great Lakes Region of the United States

• Development of an integrated framework for climate change impact assessments for international market systems with long-term investments

SOUR CHERRY PRODUCTION IN MICHIGAN

Background/Considerations

• Production occurs in the lake-modified regions along Lake Michigan

• Damaging spring temperatures are the major limiting factor on perennial crop yield in Michigan, with precipitation during pollination a secondary factor

• Downstream models were phenology and yield models – These models were developed at the “point” (i.e., local) level

using climate observations from COOP stations with relatively long-term records

– Considerable stakeholder involvement

Climate Projections• “Custom” projections (i.e., developed specifically for the project)

– Local scale (i.e., individual sites)– Daily time step– Maximum temperature, minimum temperature, wet/dry days,

precipitation amount • Indices (e.g., frequency of freezing temperatures after reaching heat

accumulation thresholds)– Statistical downscaling of GCM simulations – Perfect Prog approach

• Multiple regression using surface and upper-level “circulation” variables as predictors

– Ensemble• four GCMs, two greenhouse gas emissions scenarios (SRES A2 and B2), several

“variants” of downscaling transfer functions – Web-based user tools for stakeholders

• 15 Locations

• 4 climate parameters– Tmax– Tmin– Wet/dry days– Precipitation amount

• 4 GCMs – CCC CGCM2– HadCM3– MPI ECHAM4– NCAR CSM1.2

• 2 Emission scenarios– A2, B2

• Multiple empirical downscaling methodologies

A2, B2multiple downscalingmethodologies

HadleyCanadian

ECHAMNCAR

Advantages/Limitations• Advantages

– Projections had the temporal and spatial resolution necessary for impact models

– Local site factors implicitly included– Spatial autocorrelation retained– Research team intimately familiar with nature of the projections and their

limitations– Large ensemble to characterize uncertainty

• Limitations– Considerable time, effort, and expense to develop the projections– Projections available for only a small number of locations – Projections for climate variables developed separately– Local site factors (e.g., Lake Michigan) included implicitly rather than explicitly– Explained variance for precipitation transfer functions was small– Working with a large scenario ensemble caused some angst among team

members– Stationarity?

References• Web site: www.pileus.msu.edu• Winkler, J.A., J.A. Andresen, J. Bisanz, G.S. Guentchev, J.

Nugent, K. Piromsopa, N. Rothwell, C. Zavalloni, J. Clark, H.K. Min, A. Pollyea, and H. Prawiranta, 2012: Michigan’s Tart Cherry Industry: Vulnerability to Climate Variability and Change In S.C. Pryor [Ed] Climate Change in the Midwest: Impacts, Risks, Vulnerability and Adaptation, Indiana University Press, 104-116. ISBN: 978-0-253-00682-0

• Winkler, J.A., J.P. Palutikof, J.A. Andresen, and C.M. Goodess, 1997: The simulation of daily time series from GCM output. Part 2: A sensitivity analysis of empirical transfer functions for downscaling GCM simulations. Journal of Climate, 10, 2514-2532.

UNDERSTORY BAMBOO HABITAT AND PANDA DISTRIBUTION IN CHINA’S QUINLING MOUNTAINS

Background/Considerations

• Elevation a key consideration• Bioclimatic models developed by the research team

– Dependent variable: • bamboo presence data from field plots covering the

elevational range of the distributions of three dominant bamboo species within the Qinling Mountains

– Independent variables (from WorldClim database): • gridded values of 19 bioclimatic variables• Long-term (1950-2000) averages• 30×30 arc second resolution (ca. 1 km2)• thin plate spline interpolation

Climate Projections• “Off the shelf” projections:

– WORLDCLIM• Three time slices 2010 – 2039, 2040 – 2069 and 2070 – 2099) [IPCC TAR]• Four GCMs (CCSR/NIES, CGCM2, CSIRO-Mk2 and HadCM3) [IPCC TAR]• SRES A2 and B2 greenhouse gas emissions scenarios • ca. 1 km2 resolution

– International Center for Tropical Agriculture (CIAT)• One future time slice (2040 – 2069) • 15 GCMs (IPCC AR5)• SRES A2 greenhouse gas emissions scenario• ca. 1 km2 resolution

• Projections in the form of “deltas” from a reference period

Advantages/Limitations• Advantages

– Readily available, ease of use– Fine resolution– Includes widely-used bioclimate variables– Explicit consideration of topographic variations

• Disadvantages– “black box”– sensitivity of projections to different interpolation algorithms is

unknown– difficult for users to evaluate– needed to “piece together” projections from two sources to cover

time period of interest

Reference• Tuanmu, M-N, A. Viña, J.A. Winkler, Y. Li, W. Xu, Z. Ouyang, and J. Liu, 2013: Climate

change impacts on understory bamboo species and giant pandas in China’s Qinling Mountains. Nature Climate Change, 3: 249–253 doi:10.1038/nclimate1727.

Projected future distributions of climatically suitable areas (CSAs) in 2070 – 2099 for the three bamboo species studied under the climate projections from four IPCC TAR GCMs

GCM-related uncertainty of projected changes in giant panda habitat area for the time slice of 2040 – 2069 under the SRES A2 greenhouse gas emissions

scenario.

CORN AND SOYBEAN PRODUCTION IN THE UPPER GREAT LAKES REGION OF THE UNITED STATES

Background/Considerations

• Goal was to evaluate potential latitudinal shifts/expansion of corn and soybean production in the Upper Great Lakes region

• Impacts models employed were:– CERES-Maize– CROPGRO-Soybean

• Interested in county-level yield• Required climate variables:

– Daily maximum temperature, minimum temperature, precipitation, solar radiation

Climate Projections • Developed from NARCCAP simulations

– Used 8 RCM/GCM simulations (CRCM/ccsm, CRCM/cgcm3, HRM3/hadcm3, HRM3/gfdl, RCM3/cgcm3, RCM3/gfdl, WRFG/ccsm, and WRFG/cgcm3)

– SRES A2 greenhouse gas emissions scenario– 50 km2 resolution– Time slices:

• future period (2041-2070)• historical periods (1971-2000)

• Used “delta” method (calculated by month) to adjust for biases and downscale to local level

• Adjusted daily time series of precipitation and maximum and minimum temperature for 34 USHCN stations across the study region– The stations were selected for their representativeness of the regional

climate variations and the quality (i.e., percent missing values) of their time series

– Employed a climate regionalization (PCA/cluster analysis) – Counties were assigned to stations

CRCM

/ccsm

CRCM

/cgcm

3HR

M3/gf

dlHR

M3/ha

dcm3

RCM3

/cgcm

3RC

M3/gf

dlWR

FG/cc

smWR

FG/cg

cm3

Temp. Change<=1.01.0 - 1.51.5 - 2.0

2.0 - 2.52.5 - 3.03.0 - 3.5

3.5 - 4.0>4.0

% Precip Change<=-10-10 - -5-5 - 00 - 5

5 - 1010 - 1515 - 2020 - 25

25 - 30>30

Projected changes in maximum temperature (left), minimum temperature (middle), and precipitation (right) between the future (2041-2070) and historical (1971-2000) period for the eight NARCCAP models.

MCLN

GMDW

ALGN

NWBR

BWLR

MDLNZMRT

PRDC

THBR

STBG

NWUM

SPNR

STHV

PSTN

PRDM

OWSO

OCNT

MNSG

MWESMILA

MSFD

IRWD

HART

GRFR

FDLCETWS

PRMN

CDWR

CLQT

CBGN

BRHD

BRWT

ARGL

ANBR

ADRN

MADA

Climate Region123

45

67

Climate StationsRepresentative Stations

Selection of representative climate stations for the regional climate change impact assessment on corn and soybean production in the Upper Midwest based on county-climate memberships (above) and their assignment to counties (below). Four characters are the abbreviation to distinguish the representative climate stations with colors indicated counties assigned with the same representative climate station.

Advantages/Limitations

• Advantages– Realistic location/outline of Great Lakes – Delta method is simple to apply

• Limitations– Small number of USHCN stations and non-uniform

distribution – Lost some of the spatial detail available from NARCCAP

simulations– Did not consider changes in variability or frequency of

wet/dry days– Stationarity assumption

CRCM-ccsm HRM3-gfdl

RCM3-cgcm3

WRFG-ccsm

CRCM-cgcm3

HRM3-hadcm3 RCM3-gfdl

WRFG-cgcm3 Average

CRCM-ccsm HRM3-gfdl

RCM3-cgcm3

WRFG-ccsm

CRCM-cgcm3

HRM3-hadcm3 RCM3-gfdl

WRFG-cgcm3 Average

ET

Yield

Yield Changes (%)25 - 5050 - 100100 - 150>150

<=-50-50 - -25-25 - 00 - 25

ET Changes (%)<=-15-15 - -10-10 - -5-5 - 0

0 - 55 - 1010 - 15>15

Change in the median of cumulative seasonal evapotranspiration (ET, above) and crop yields (Yield, below) between the historical (1971-2000) and the future (2041-2070) period for corn production in the Upper Midwest at the reference level of CO2 (370 ppm) concentration

References

• Perdinan, 2013. Crop production and future climate change in a high latitude region: a case study for the Upper Great Lakes region of the United States. PhD Dissertation. Michigan State University. Completed May, 2013.

DEVELOPMENT OF AN INTEGRATED FRAMEWORK FOR CLIMATE CHANGE IMPACT ASSESSMENTS FOR INTERNATIONAL MARKET SYSTEMS WITH LONG-TERM INVESTMENTS (CLIMARK)

Background/Considerations• Impetus came from stakeholders of the tart cherry industry• Traditional local/regional climate impact and adaptation

assessments do not consider important spatial and temporal interactions for international market systems

• Other important production regions include central and eastern Europe

• Assume that supply and demand are linked through international trade

• Local, daily climate projections needed for several locations within each of the major production regions

Expanded Assessment FrameworkClimate Projections

Climate projections for production regions for Time Slice #1

Climate projections for production regions for Time Slice #2

Climate projections for production regions for Time Slice #3

Base Situation(industry structure, economic factors, and regional

constraints)

Between Time Slice Projected Changes

Adaptation (e.g., cultivars, growing regions)

Regional constraints (e.g., infrastructure, institutions)

Economic factors (e.g., consumer preferences, income)

Between Time Slice Projected Changes

Adaptation (e.g., cultivars, growing regions)

Regional constraints (e.g., infrastructure, institutions)

Economic factors (e.g., consumer preferences, income)

Major System Components for Time Slice #3:•Regional climate scenarios•Phenology and yield models•Trade model

Major System Components for Time Slice #1:•Regional climate scenarios•Phenology and yield models•Trade model

Major System Components for Time Slice #2:•Regional climate scenarios•Phenology and yield models•Trade model

Climate Projections

• Hybrid downscaling– Start with dynamically-downscaled projections:

• NARCCAP (mid-century time slice)• ENSEMBLES (21st century)

– Apply bias correction and empirical downscaling• Hybrid projections supplemented with statistically-

downscaled projections using simple “delta” approach applied to CMIP5 model output for 21st century

Need for Bias Correction

Observed and simulated values of minimum temperature for winter (December, January, February).

Types of Bias Correction and Empirical Downscaling Techniques

N

iiia xfyL

1

)();(,min

Accuracy-driven:

Goal is to minimize overall prediction error

Distribution-driven:

Goal is to minimize error of fitted distribution

);( such that

)();(,min1

iwiobs

N

iiid

xfFzF

xfzL

Examples: MLR and its variants (ridge and lasso regression), analog methods, nonlinear models (neural networks, HMM)

Examples: quantile mapping (QM), histogram equalization (Piani et al)

MLRCDF: Multi-Objective Regression

N

iiidiia xfzLxfyL

1

)();(,);(,min

• Current techniques are designed to optimize either accuracy or fit to observed distribution, but not both

• MLRCDF: a multi-objective regression method that combines both objective functions

– controls the trade-off between accuracy and fitting the distribution

*adjusted time stamp for time of observation

QQ plot for daily precipitation over the test period for unadjusted and adjusted output from WRFG driven by the NCEP reanalysis The blue line corresponds to the QQ line while the dotted black line is the diagonal. Top row: Eau Claire; Middle row: Maple City; Bottom row: Hart

Advantages/Limitations

• Advantages– Capture some of the benefits of both dynamic and

statistical downscaling• Limitations

– Only one future time slice for NARCCAP– NARCCAP and ENSEMBLES do not use the same

emissions scenario– Time consuming – More ensemble members?

References• Winkler, J.A., S. Thornsbury, M. Artavio, F.-M. Chmielewski, D. Kirschke, S.

Lee, M. Liszewska, S. Loveridge, P.-N. Tan, S. Zhong, J.A. Andresen, J.R. Black, R. Kurlus, D. Nizalov, N. Olynk, Z. Ustrnul, C. Zavalloni, J.M. Bisanz, G. Bujdosó, L. Fusina, Y. Henniges, P. Hilsendegen, K. Lar, L. Malarzewski, T. Moeller, R. Murmylo, T. Niedzwiedz, O. Nizalova, H. Prawiranata, N. Rothwell, J. van Ravensway, H. von Witzke, and M. Woods, 2010: Multi-regional climate change assessments for international market systems with long-term investments: A conceptual framework. Climatic Change, 103, 445-470. DOI 10.1007/s10584-009-9781-1.

• Abraham, Z., P.-N.Tan, Perdinan, J. A. Winkler, S. Zhong, and M. Liszewskak, 2013: Distribution Regularized Regression Framework for Climate Modeling. Proceedings of SIAM International Conference on Data Mining (SDM-2013), Austin, Texas. Available at: http://knowledgecenter.siam.org/333SDM/333SDM/1

Closing Remarks

• Choice of climate projections is influenced by:– Assessment goals – Demands of impact models

• Hybrid downscaling is likely to become more common