Using Downscaled Data in the Real World: Sharing Experiences

1. Using Downscaled Data in the Real World: Sharing Experiences Julie Winkler Michigan State University

2. 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)

3. 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

4. Sour Cherry Production in Michigan

5. 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

6. 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

7. NCAR • 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 ECHAM Canadian Hadley A2, B2 multiple downscaling methodologies

9. 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?

10. 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.

11. Understory bamboo habitat and panda distribution in China’s Quinling Mountains

12. 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

13. 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

14. 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

15. 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. 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. 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

16. Corn and soybean production in the Upper Great Lakes Region of the United States

17. 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

18. 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

19. 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.

20. 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.

21. 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

22. 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

23. 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.

24. Development of an integrated framework for climate change impact assessments for international market systems with long-term investments (CLIMARK)

25. 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

26. Climate Projections Base Situation (industry structure, economic factors, and regional constraints) Expanded Assessment Framework • Major System Components for Time Slice #1: • Regional climate scenarios • Phenology and yield models • Trade model Climate projections for production regions for Time Slice #1 Between Time Slice Projected Changes Economic factors (e.g., consumer preferences, income) Climate projections for production regions for Time Slice #2 Adaptation (e.g., cultivars, growing regions) Regional constraints (e.g., infrastructure, institutions) • Major System Components for Time Slice #2: • Regional climate scenarios • Phenology and yield models • Trade model Climate projections for production regions for Time Slice #3 Between Time Slice Projected Changes • Major System Components for Time Slice #3: • Regional climate scenarios • Phenology and yield models • Trade model Economic factors (e.g., consumer preferences, income) Adaptation (e.g., cultivars, growing regions) Regional constraints (e.g., infrastructure, institutions)

27. 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

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

29. Types of Bias Correction and Empirical Downscaling Techniques Accuracy-driven: Goal is to minimize overall prediction error Examples: MLR and its variants (ridge and lasso regression), analog methods, nonlinear models (neural networks, HMM) Distribution-driven: Goal is to minimize error of fitted distribution Examples: quantile mapping (QM), histogram equalization (Piani et al)

30. 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 MLRCDF: Multi-Objective Regression *adjusted time stamp for time of observation

31. 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

32. 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?

33. 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

34. Closing Remarks • Choice of climate projections is influenced by: • Assessment goals • Demands of impact models • Hybrid downscaling is likely to become more common