Grantee Research Project Results
2006 Progress Report: Statistical Approaches to Detection and Downscaling of Climate Variability and Change
EPA Grant Number: R829402C006Subproject: this is subproject number 006 , established and managed by the Center Director under grant R829402
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: UT Center for Infrastructure Modeling and Management
Center Director: Hodges, Ben R.
Title: Statistical Approaches to Detection and Downscaling of Climate Variability and Change
Investigators: Wuebbles, Donald J. , Cai, Airong , Hertel, Anne , Tiao, George , Hayhoe, Katharine , Vrac, Mathieu , Stein, Michael
Institution: University of Illinois Urbana-Champaign , University of Chicago , University of Illinois at Chicago
Current Institution: University of Illinois Urbana-Champaign , Texas Tech University , University of Chicago
EPA Project Officer: Packard, Benjamin H
Project Period: March 12, 2002 through March 11, 2007
Project Period Covered by this Report: March 12, 2005 through March 11, 2006
RFA: Environmental Statistics Center (2001) RFA Text | Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Environmental Statistics , Human Health , Aquatic Ecosystems
Objective:
This project applies statistical techniques to analyze global climate model simulations of present-day and future atmospheric circulation patterns and their relationship to surface climate, in order to determine likely impacts from future climate change.
Here, we summarize the key research questions addressed by this project, briefly describe our findings in each area, present on-going research avenues, and list the presentations and submitted journal articles that have resulted from the various components of this project to date.
Progress Summary:
Research Question 1: To What Extent Are Global Models Able to Reproduce the Dominant Circulation Patterns of the Atmosphere and Their Relationship to Surface Climate Across the United States and Around the World?
Atmospheric circulation patterns—including cyclical phenomena such as El Niño, as well as every-day high and low pressure systems that pass across the continent each week—are key to determining surface climate conditions. To this end, our first research question examines the extent to which the latest global climate model simulations prepared for the Intergovernmental Panel on Climate Change Fourth Assessment Report, are able to simulate these large-scale atmospheric patterns. Their performance in this area is highly indicative of their overall skill in capturing the behavior of the real-world climate system.
We first examined six large-scale oscillatory patterns known to be linked (or teleconnected) to surface climate: El Niño/Southern Oscillation, the North Atlantic and Pacific Decadal Oscillations, the Arctic and Antarctic Oscillations, and the Pacific North American Pattern. Simulations by eight of the latest global climate models (Parallel Climate Model [PCM], Community Climate System Model version 3 [CCSM3], HadCM3, Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration Climate Model, version 2.1 [GFDL CM2.1], European Community – Hamburg Model, version 5 [ECHAM5], Commonwealth Scientific and Industrial Research Organisation (Australia) climate system model, version 2 [CSIRO Mk-2], Model for Interdisciplinary Research on Climate [ MIROC-med], and the Coupled Global Climate Model, version 3 [CGCM3]) show that, in general, the models are able to simulate the spatial and temporal characteristics of these patterns. Detailed statistical analysis reveals, however, that models have a tendency to over estimate the spatial strength of the pattern and produce time series that oscillate too quickly relative to the real world. Moreover, some models produce patterns that are clearly closer to “observed” patterns—based on reanalysis fields—than other models, allowing us to identify those best able to reproduce specific features of atmospheric circulation.
We then examined global climate model ability to simulate primary day-to-day atmospheric circulation patterns over North America. Using both a hierarchical and an Expectation Maximization (EM) algorithm to cluster reanalysis geopotential height fields, we identified five primary patterns that showed a strong seasonal dependence. Two patterns tended to appear most frequently during winter months, two were typical of “transitional” spring and autumn months, and one tended to dominate summer months. Using the PCM and CCSM models, we found that these models were able to reproduce the same five seasonal patterns, although with a bias towards an early emergence of the summer pattern in spring months and longer duration into the fall as compared with reanalysis-based pattern frequencies.
Over the past 12 months, this work has generated four conference presentations or journal submissions:
Hayhoe K, Wuebbles D, Hertel A. Observed and modeled climate variability over the United States associated with major teleconnection patterns. Presented at the U.S. Climate Change Science Program Workshop, Climate Science in Support of Decision Making, Arlington, VA, November 14-16, 2005.
Hayhoe K, Wake C, Anderson B, Bradbury J, DeGaetano A, Hertel A, Liang X, Zhu J, Maurer E, Wuebbles D. Translating global change into regional trends: climate drivers of past and future trends in the U.S. Northeast. Bulletin of the American Meteorological Society (in review, 2006).
Hertel A, Hayhoe K, Wuebbles D. Climate variability over the U.S. associated with major teleconnection patterns. Presented at the 86th Annual Meeting of the American Meteorological Society, Atlanta, GA, January 12-16, 2006.
Vrac M, Hayhoe K, Stein M. Identification and inter model comparison of seasonal circulation patterns over North America. International Journal of Climatology 2006 15 Dec [Epub ahead of print] 10.1002/joc.1422.
Research Question 2: When Can Anthropogenic Climate Change Be Expected to Produce a Statistically Significant Impact on Regional Patterns of Seasonal Temperature and Precipitation?
Human-driven climate change is already manifesting itself in many ways around the globe, including rising temperatures and sea level, melting ice sheets, and advances in the timing of spring phenology. However, many of these changes are not yet large enough to affect the consciousness of the general public or regional planners, who often make decisions that will affect a city or a region’s ability to cope with climate and weather events over future years and decades.
Based on historical simulations from the same eight global climate models listed above that take into account observed natural variability and human emissions over the past century, we divide the globe into 256 grid cells and fit a linear statistical model to the simulated seasonal temperature trends in each grid cell. Using a historical database of monthly observed surface temperatures from 1960 through 2000, we then calculate inter annual variability in seasonal temperatures. From the observationally based variance and model-based trend estimates, we then estimate the year in which a seasonal temperature trend will become significant at the 95th percentile level or higher for each grid cell.
We find that the opposing factors of variance and trends often tend to cancel each other out, with relatively long detection times being found for both equatorial regions with small variance and small trends as well as for high latitude areas with relatively high trends but also high variance. Instead, we find that continental locations sufficiently poleward to have a significant trend—but close enough to the coasts so as to enjoy the moderating effects of the ocean on interannual variability—are the most sensitive “indicator” regions where temperature trends either have already or are likely to become significant within the next decade.
Over the past 12 months, this work has generated two conference presentations or journal submissions:
Cai A, Hayhoe K, Tiao G. Statistical trend detection and application to surface temperature trends. Presented at the 86th Annual Meeting of the American Meteorological Society, Atlanta, GA, January 12-16, 2006.
Cai A, Hayhoe K, Tiao G, Wuebbles D. How soon is now? Statistical detection of climate-driven trends in surface temperature (manuscript in preparation, 2006).
Research Question 3: What Impact Is Anthropogenic Climate Change Expected to Have on Atmospheric Circulation? How Will This Affect Climate Averages and Extremes at the Earth’s Surface?
Changes in the heat content of the atmosphere due to increasing concentrations of greenhouse gases are projected to drive substantial changes in atmospheric circulation and surface climate over the coming century. Here, we use future simulations driven by a range of emission scenarios, in order to capture the uncertainty in future projections due to the human choices and activities that determine our emissions, to examine projected changes in key circulation features and surface climate.
Using the seasonal circulation patterns identified in research question 2, we first examine projected shifts in monthly frequencies using simulations of climate over the coming century. In general, we see a trend towards earlier emergence of transition patterns in winter, and of summer patterns in spring and fall, relative to present-day simulations and reanalysis-based pattern frequencies. This is consistent with a picture of a warming world and suggests that surface temperature trends over North America may be at least partially driven by earlier and more prolonged summer-like circulation patterns.
We next use the global model simulations to calculate projected changes in 10 indices of climate extremes, five related to temperature and five to precipitation. These extremes measure projected changes in heat wave days, warm nights, annual temperature ranges, dry days, extreme rainfall events, and precipitation intensity. We first find that the historical trends simulated by the models over the past century are remarkably consistent with observed trends over that same time period. Turning to the future, model simulations present a consistent picture of a warmer and wetter world. Dramatic increases in heat-related extreme events are seen around the world, as well as increases in both heavy rainfall and drought events across much of the Northern Hemisphere.
Finally, the same model simulations are used to examine changes in the strength and frequency of the six primary teleconnection patterns examined earlier in research question 1. In general, we find that the frequency of El Niño events increases, and many patterns appear to strengthen under a higher emissions scenario in the future. Under a lower emissions scenario, little change is seen. These results seem to suggest a possible threshold of change. If we remain below a certain level of change, atmospheric teleconnection patterns may not be affected; beyond that, however, atmospheric circulation patterns begin to alter as do their impacts on surface climate and weather patterns around the world.
Over the past 12 months, this work has generated three conference presentations or journal submissions:
Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA. Going to the extremes: an intercomparison of model- simulated historical and future changes in extreme events. Climatic Change 2006;79(3-4):185-211.
Vrac M, Hayhoe K, Stein M. Climate-driven shifts in seasonal circulation patterns over North America (manuscript in preparation, 2006).
Hertel A, Hayhoe K, Cai A, Wuebbles D. The influence of future climate change on six global teleconnection patterns (manuscript in preparation, 2006).
Research Question 4: To What Degree Are Statistical Methods Able to Downscale Global Climate Model Simulations to Reproduce Climate Means and Extremes at the Local to Regional Scale?
Present-day computing resources currently limit the spatial resolution of global climate model simulations to one degree or higher. However, most of the impacts of climate change are expected to be significantly modified by local- and regional-scale features. For that reason, we rely on downscaling methods to develop relationships between large-scale and local-scale climate fields in order to determine the likely future impact of global climate change at the regional level.
To this end, we have developed a categorization and transition modeling method to provide accurate and rapid simulations of local-scale precipitation features, based on statistically defined weather states, at low computational cost. This statistical method is based on a stochastic weather typing approach. Two different kinds of weather states are defined: “circulation” patterns, developed by a mixture model applied to large-scale National Centers for Environmental Prediction (NCEP) reanalysis data, and “precipitation” patterns, developed by a hierarchical ascending clustering method applied directly to the observed rainfall amounts in Illinois. By modeling the transition probabilities from one pattern to another by a nonhomogeneous Markov model, we see that the precipitation states allow us to model conditional distributions and local simulated intensities of precipitation more accurately than with the traditional approach based on upper-air patterns alone.
Over the past 12 months, this work has generated three conference presentations or journal submissions:
Vrac M, Stein M, Hayhoe K. Statistical downscaling of precipitation through a nonhomogeneous stochastic weather typing approach. Climate Dynamics (in press, 2006).
Hayhoe K, Vrac M, Stein M. Statistical downscaling of precipitation through mixture-model clustering and nonhomogeneous transition probabilities. Presented at the 86th Annual Meeting of the American Meteorological Society, Atlanta, GA, January 12-16, 2006.
Vrac M, Stein M, Hayhoe K. A general method for validating statistical downscaling of AOGCM output: application to nonhomogenous stochastic weather typing (manuscript in preparation, 2006).
Journal Articles on this Report : 1 Displayed | Download in RIS Format
Other subproject views: | All 12 publications | 7 publications in selected types | All 5 journal articles |
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Other center views: | All 120 publications | 74 publications in selected types | All 52 journal articles |
Type | Citation | ||
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Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA. Going to the extremes: an intercomparison of model-simulated historical and future changes in extreme events. Climatic Change 2006;79(3-4):185-211. |
R829402 (Final) R829402C006 (2006) R829402C006 (Final) |
Exit Exit |
Supplemental Keywords:
RFA, Economic, Social, & Behavioral Science Research Program, Scientific Discipline, Air, Health Risk Assessment, climate change, Air Pollution Effects, Environmental Statistics, Ecological Risk Assessment, biostatistics, health risk analysis, particulate matter, risk assessment, environmental monitoring, environmental risks, air pollution, climate models, data analysis, environmental indicators, infant mortality, ambient airborne particulate matter, statistical methodsRelevant Websites:
http://galton.uchicago.edu/~cises/ Exit
Progress and Final Reports:
Original AbstractMain Center Abstract and Reports:
R829402 UT Center for Infrastructure Modeling and Management Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R829402C001 Detection of a Recovery in Stratospheric and Total Ozone
R829402C002 Integrating Numerical Models and Monitoring Data
R829402C003 Air Quality and Reported Asthma Incidence in Illinois
R829402C004 Quasi-Experimental Evidence on How Airborne Particulates Affect Human Health
R829402C005 Model Choice Stochasticity, and Ecological Complexity
R829402C006 Statistical Approaches to Detection and Downscaling of Climate Variability and Change
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.
Project Research Results
5 journal articles for this subproject
Main Center: R829402
120 publications for this center
52 journal articles for this center