Motivation

Temperature:1. Heating Degree Days: Cold weather measure used to

commonly used to evaluate energy needs and costs for heating, HDD= 18.3 - (Daily High Temp + Daily Low Temp)/2 (in degrees Celsius)

2. Cooling Degree Days: Hot weather measure used to evaluate energy needs and costs for cooling, CDD=(Daily High Temp + Daily Low Temp)/2 - 18.3 (in degrees Celsius)

3. Heat Wave Intensity [1,2]: Hottest consecutive 3-day daily minimum temperature average in a given year, relates to human mortality

Precipitation:1. Average Daily Volume: Indicates daily global mean

precipitation calculated at different resolution.2. Extremes: T-year Return Level [3]: Level of

precipitation expected to be exceeded once in every T years. Computed using Extreme Value estimators.

Scale Dependence of Uncertainty in HDDIndices Average Daily Precipitation

Geog. Information Sci. & Tech. Group Computational Sci. & Eng. DivisionOak Ridge National LaboratoryOak Ridge, TN 37831-6017Tel.: +1 (865) 241-1305Fax: +1 (865) 241- 6261

This research was primarily funded by the Laboratory Directed Research and Development (LDRD) Program of the Oak Ridge National Laboratory (ORNL), managed by UT-Battelle, LLC, for the U.S. Department of Energy under Contract DE-AC05-00OR22725.

Regional Climate Model (RCM)-based dynamical downscaling of Global Climate Model (GCM) may lead to improved prediction of climate change, extreme weather and hydrologic events at local scales.As derived from GCMs, uncertainty of extreme events at local scales is large; better estimates of uncertainty are important for local stakeholders & policymakers decisions.

Auroop R. Ganguly, Ph.D.Email: gangulyar@ornl.gov http://www.ornl.gov/knowledgediscovery/ClimateExtremes

¶ Point of Contact

Uncertainty and Extremes Analysis to Evaluate Dynamical Downscaling of Climate Models Debasish Das1, 3, Evan Kodra1, 4, Karsten Steinhaeuser1, 5, Shih-Chieh Kao1, Auroop R Ganguly1, Marcia L Branstetter1,

David J Erickson1, Raymond Flanery1, Maria Martinez Gonzalez2, Cynthia Hays6, Anthony W King2, Christopher Lenhardt2, Robert Oglesby6, Robert M. Patton1, Clinton M Rowe6, Alexandre Sorokine1, Chad Steed1

1 CSE Division, Oak Ridge National Laboratory (ORNL), TN; 2 ES Division, ORNL, TN; 3 CIS Dept., Temple University, PA; 4 Statistics, Operations, and Management Science, University of Tennessee, Knoxville, TN; 5 CS Dept., University of Notre Dame, IN; 6 School of Natural Resources, University of Nebraska, Lincoln, NE;

GC13A-0707

Data Bias Reduction in CDDAnalysis of WRF Output: Conceptual

Limitations and Future Work

Fig 8

Fig 7: The average of the spatial mean precipitation for each day of the year calculated from 2000-2005 for d01 (top), d02 (middle) and d03 WRF downscaling of

CCSM (red) and NCEP (blue)

References

Extremes: 30-Year Return Level

(1) Meehl GA, Tebaldi C (2004) More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305: 994–997.(2) Ganguly AR, Steinhaeuser K, Erickson III DJ, Branstetter M, Parish ES, Singh N, Drake JB, Buja L (2009) Higher trends but larger uncertainty and geographic variability in 21st century temperature and heat waves. PNAS 106 (37): 15555 – 15559.(3) Coles S (2001) An Introduction to Statistical Modeling of Extreme Values, Springer.

Hypothesis Tested

Increases in spatial resolution in dynamical downscaling to 4 km improves accuracy and reduces uncertainty in local to regional simulation of climate and weather extremes in regions of complex topographyAccordingly, we investigated

1. Bias-variance trade-off for temperature and precipitation depending on spatial resolution.

2. Scale dependence of uncertainty in prediction.

●Community Climate Simulation Model (CCSM) 3.0 – A GCM at ~128-km – is downscaled with Weather Research and Forecasting Model (WRF) – An RCM – at three resolutions.

●D01: 64-km WRF●D02: 16-km WRF●D03: 4-km WRF

●Lower resolution runs interpolated in space to 4 km for comparative evaluation●National Centers for Environmental Prediction (NCEP)-4 km reanalysis runs used as surrogate for observed.●6 years data (2000-2005) used for evaluation.

Fig 1: Coupled CCSM3-WRF

simulations at 64, 16, 4 km

resolutions

Is there a bias-variance trade-off with increased resolution? The average Heating Degree Days (HDD), averaged over space, shows a consistent decrease in bias as the model resolution for downscaling increases (Fig 2).The average HDD, averaged over space, shows a slight decrease in standard error upon downscaling (Fig 3).The average HDD, averaged over space, shows a consistent decrease in spatial variance of standard error as the model resolution for downscaling increases (Fig 4).

Bias in cooling degree days decays steadily with finer model resolutions (Fig 5).Would the variance exhibit a similar pattern?Is a bias-variance tradeoff expected given finer-scale physics competing with added model complexity?

Fig. 2 Fig. 3 Fig. 4

Variance Reduction in Heat Wave IntensityThe average Heat Wave Intensity, when averaged over space, shows a consistent decrease in spatial variance of error as the model resolution for downscaling increases (Fig. 6). This could suggest a constant bias correction may be adequate with such output. This may not be the case with output from one of the lower resolution model outputs.

-5 0 5 10 15 20

Variance of Standard Error (degree C)

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Spatially Variance of Standard Error in Heat Wave Intensity

CCSM 3: No Downscalin

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CCSM 3: 64-km WRF

CCSM 3: 16-km WRF

CCSM 3: 4-km WRF

Fig. 6

Average daily precipitation appears better captured with higher resolution dynamical downscaling.

30-Year Return levels calculated with Peak-Over-Threshold appear to suggest that the effect of the global model is less visible but the effect of downscaling dominates. Unfortunately, short WRF runs apparently yield increasingly noisy spatial fields at higher resolutions (below, right column), for return levels.

One limitation of this work is the difficulty in finding a suitable reference dataset for skill evaluation. Although NCEP reanalysis are used as observation, they are forced through the same regional model as CCSM, creating potential serious confounding in results. It is difficult to decipher whether apparent dramatic skill increases are results of real skill improvement from resolving complex terrain or rather just the result of the regional model dominating both CCSM and NCEP. In addition, while results involving temperature may approach credibility, certainly 6 years is not adequate to make any insights regarding precipitation-related indices. especially return levels, which require many more years of data. These issues need to be addressed in future.

In future work, to solve these types of issue, creative approaches may be taken and/or better, higher resolution observational-datasets need to be sought. Additionally, longer integrations are needed to evaluate indices such as return values.

Fig 5: Top to bottom, left to right: CCSM, d01, d02, and d03. Blue values indicate a cold bias (less CDD) from the output, while red indicate a warm bias (more CDD). As resolution increases, bias appears to decrease.

Bias-Variance TradeoffsWith Spatial Scales

Scale Dependence in Bias and Variance

Large shifts in regional climate patterns

Change in extreme event statistics over space-time

Temperature ExtremesHeating and Cooling Degree Days

Heat Waves: Intensity-Duration-Frequency

Precipitation ExtremesIntensity-Duration-Frequency

Degree of Surprise: “Extremes Volatility”

Temperature Mean ProcessesTemporal and Geographical Trends

Spatial-Temporal Dependence Processes

Precipitation Mean ProcessesTemporal and Geographical Trends

Spatial-Temporal Dependence Processes

Bias and Uncertainty QuantificationWith Downscaling Resolutions

Stakeholder-relevant Metrics: Energy and Water Sectors

Geographical and TemporalAnalysis of Bias / Variance

Extreme Value Theory and User-Centric Extremes

Novel Climate Science Insights

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