Evaluating Projected Changes in Mean Processes, Extreme Events, and their Spatio-Temporal Dependence Structures

Auroop R. Ganguly*1, Karsten Steinhaeuser1, Evan Kodra1, Shih-Chieh Kao1, Subimal Ghosh2

1Oak Ridge National Laboratory 2Indian Institute of Technology, Bombay

Computational Sciences and EngineeringOak Ridge National Laboratory

1, Bethel Valley RoadOak Ridge, TN 37831-6017

Tel.: +1 (865) 241-1305Fax: +1 (865) 241- 6261

Auroop R. GangulyGeographic Information Science & Technology (GIST) GroupEmail: gangulyar@ornl.gov

CREDITS: This research was funded by the Oak Ridge National Laboratory (ORNL), managed by UT Battelle, LLC, for the U.S. Department of Energy under Contract DE-AC05-00OR22725. This manuscript was authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains, and the publisher by accepting the article for publication, acknowledges that the United States Government retains, a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

*Contact Author Information

Uncertainty in Projections of Regional Climate Patterns Best Individual Model Versus Multi-Model Averages

Temperature errors from 2000-2007 (top)Gaussian and stationary errors (left)Interannual change (bottom)

GHG scenario difference (top)Ensemble differences (right)

For stationary Gaussian error distributions, translation to risk is based on standard deviations from mean values

Ganguly, Steinhaeuser, et al. (2009): PNAS

Precipitation trajectories, per IPCC SRES GHG emissions , appear similar to temperature (below) which influences water holding capacity

Precipitation is well-projected at the extratropics (left), although uncertainties remain, but not so well in the tropics. Temperature projections agree well with observations; precipitation projections do not (above)

Significant uncertainties remain at regional scales, especially in projections of hydrologic variables. Precipitation projections for 2030 from two climate models, NASA-GISS and MIROC (in Japan), show differences in the Western and Southeastern US.

While climate land models (CLM within CCSM3) appear to show increased runoff, the Himalayan warming could melt glaciers and cause major Indian rivers to turn seasonal. Uncertainties in land glacier melt imply uncertainties in projections of potential impacts.

Warming in Himalayas (left), higher runoff in Indian rivers, and current population count (right)

There is evidence that, in some cases, a best individual model may be a better predictor than any combination. Here, the case we present is 30-year moving average monsoon anomaly over India. Seven models known to be skilled in modeling precipitation in India were studied. One model in particular showed remarkable skill in modeling monsoon anomalies over time, while others did not. In almost all cases, adding additional models decreased skill.

Precipitation Extremes

More intense rainfall extremes are expected in the 21st century on the average: This can be inferred from both observations and climate models with extreme value theory

References Kao & Ganguly; Ganguly, Kao, Steinhaeuser et al. (in preparation)

Higher trends but larger uncertainty of temperature extremes in the 21st century

Introduction

Increased temperature and heat waves from CCSM A1FI projection comparing 2090-2099 with current decade.

Trend corrections and confidence bounds developed by comparing model outputs with reanalysis data provide enhanced guidance to policy makers

Potential impacts on human mortality, food security, water availability, infrastructures, and energy consumption: Ganguly et al. (PNAS, 2009)

Global-average temperature (top left) and heat-wave intensities (top)

A Drought Index (Standardized Precipitation Index or SPI) on left and the Precipitation Extremes Volatility Index (PEVI) on rightCitations on QDR Website:http://www.ornl.gov/knowledgediscovery/QDR/

Challenges in Translation of Capabilities to CMIP5

CMIP5 model datasets will be higher resolution in both space (from typically more than 1 X 1 to as small as 0.25 X 0.25) and time (from daily or 6-hourly to 3-hourly). This translates to the need for increased computational power and, in some cases, improvements in algorithms to implement much of the methodology showcased here. However, in most cases, only an incremental effort in adapting these capabilities is expected.

Temperature Extremes and Uncertainty

Various diagnostic and analytical capabilities have been developed and tested using CMIP3 model datasets. These capabilities will be used or further developed and used with CMIP5 datasets as they become available, potentially providing the community with new insights. In addition, the repetition of such analyses will provide an opportunity to compare CMIP5 model projections and skill with those of CMIP3. In doing so, scientists will be able to evaluate the degree and arenas of improvement from CMIP3 to CMIP5. Of particular interest is the potential improvement in regional modeling, hydrologic variables, and extremes. Ultimately, such improvements may prove to have value for impacts and mitigation assessments.

Intensity-Duration-Frequency Case: Cold ExtremesRight: The counts in each grid cell indicate the number of years out of 2091-2100 where cold extreme intensity (an analogue of heat wave intensity) measure surpasses the average cold extreme calculated from 1991-2000. These counts are performed at each grid cell. There is evidence that cold extremes will persist in the future at present levels. Bias evaluation suggests these models do relatively well in hindcast mode. Similar insights are found for cold extreme duration (bottom left) but not frequency (bottom right.).

Time Series Forecasts: Adding Next Best Model

Time

-0.3

-0.2

-0.1

0.0

0.1

0.2

1. BCCR2. +INM3. +GISS4. +CSIRO5. +MIROC MED6. +PCM7. +MIROC HI

1900 1910 1920 1930 1940 1950 1960 1970

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Model Performance - MAA

Number of ModelsBCCR Rank=1/127, MMA Rank=60/127

MA

PE

1 2 3 4 5 6 7

Left, above: Beginning with the most skillful model and continually adding the next best model to the combination yields increasingly worse forecasts.Right, above: Box plots of all 127 possible combinations of 7 models. The best individual model outperforms every other model, including the multi-model average. With increasing number of models, skill appears to asymptote to a suboptimal value.Below: Skill metric presentation of left panel, above. Skill decreases with each addition.

Above: For INGV ECHAM4, if we simply take a mean decadal change in cold intensity, we do not gain the same insights.

Spatio-Temporal Dependence in Extremes

A quantification of the spatio-temporal dependence among precipitation extremes is important for investigating the properties of intense storms as well as flood or flash-flood related hazards. Such an analysis should be useful not only in this case but in evaluating dependence structures in various extremes towards understanding, prediction of and preparation for extreme events.

Reference: Kodra, Steinhaeuser, Ganguly, in review

Observed spatial temporal correlation (left) and observed tail dependence (right) with one specific grid in the Brazilian highlands. Refernece: Kuhn et al. (AWR 2007) & Khan et al. (WRR 2007)

Reference: Ganguly, Ghosh, Kodra, in progress

0.8

0.9

1.0

1.1

1.2

1.3

1.4

Change in Skill when Adding Next Best Model

Model

MA

PE

BCCR +INM +GISS +CSIRO +MIROC MED +PCM +MIROC HI

GC41B-0912