DETECTION AND DESIGNATION
Characterization, detection, and designation of observed extreme events.
Extreme weather has large effects on human and natural systems. Through the use of observations, climate model and statistical techniques, CASCADE researchers examine how changes in the natural environment have impacted recent weather extremes.
Increased understanding of the influence of environmental drivers on current extreme weather increases confidence in projections of changes in future extreme weather statistics. The centerpiece modeling effort of the CASCADE Detection and Designation team is the C20C+ (Climate of the 20th Century) experiment. CASCADE is a primary contributor to the World Climate Research Program (WCRP) coordinated international project. This multi-model effort aims to aid event attribution by building a database of ensemble climate model simulations describing the “world that was” in a realistic-as-possible configuration and the “worlds that might have been” in a counterfactual configuration where environmental drivers have not changed.
Associating changes in the behavior of extreme events to specific environmental drivers requires a systematic characterization of extreme events in the recent past. Recent advances in simulation capabilities and statistical methodologies allow us to focus on the impacts of a wide range of environmental drivers at regional-to-local scales, to focus on factors impacting the spatial and temporal co-occurrence of extremes, and to simulate how we might expect extremes to change in the future.
UNDERSTANDING STRANGE WEATHER
Understanding how changes in the ocean and atmosphere make weather events more extreme.
Climate extremes – such as hurricanes, major floods, and heat waves – not only stress society, they push the bounds of what modern climate models can simulate.
While extreme weather events often have impacts at relatively small (city-wide) scales, they are often driven by planetary scale forces. Observing and simulating these events requires datasets and models with high fidelity at a wide range of scales. CASCADE is making novel use of self-similarity* in the atmosphere to define new standards for how model performance should change as model scale changes from ‘city’ to ‘planetary’. The CASCADE team is using the Department of Energy’s new Accelerated Climate Model for Energy (ACME) to simulate past weather and is using these new standards to evaluate the model. Through a tight collaboration with ACME developers, these insights are being translated in to improved model fidelity at a wide range of scales.
Designating and projecting changes in extremes requires a well-developed understanding of the processes that drive changes in extremes. In particular, for the overall goal of the CASCADE SFA, it is necessary to understand how have changes in the physical behavior of the coupled system altered the frequencies of occurrence and the characteristics of extreme climate events? To address this issue, it is also necessary to advance our understanding of the processes governing the properties of extremes that are being investigated within the CASCADE SFA. The SFA team focuses specifically on the processes that drive multivariate extremes, the processes that drive changes in the spatio-temporal characteristics of extremes, and the fidelity with which these processes are represented in climate models.
*self-similarity means that the statistics of the atmosphere change predictably depending on the scale at which the statistics are evaluated
UNCERTAINTY OF EXTREMES
Statistical methods to quantify changes in extreme weather in light of uncertainty.
Extremes are by definition rare. Statistical methods are critical for characterizing extreme weather in time and space, estimating changes in extreme weather, and quantifying our uncertainty about the frequency and trends in extremes.
The Statistics team is developing, implementing and advising on statistical methods for characterizing extremes in observations and model output. We are particularly focused on detection and designation of changes in extreme events, quantifying the evidence that the probability of extreme events are changing over time and that changes are caused by environmental drivers. As part of this work we are addressing the question of uncertainty characterization. A key focus is to identify the leading sources of uncertainty in our understanding of weather extremes (e.g., initial condition uncertainty/sampling uncertainty, forcing uncertainty, model parameter uncertainty).
Studies that aim to detect and attribute changes in extreme events have numerous sources of uncertainty, including parametric uncertainty, structural uncertainty, and even methodological uncertainty. In the current paradigm, these sources of uncertainty are dealt with in a piecemeal fashion that can result in overconfident statements of causation. This could cause, and in fact has produced, conflicts among causation statements on the same event. The sensitivity of event causation conclusions to the various sources of uncertainty remains sparsely investigated but is demonstrably important. The SFA team focuses on performing a multifaceted set of modeling experiments and analyses designed specifically to characterize, and if possible quantify, the importance of structural uncertainty, parametric uncertainty, and methodological uncertainty on our understanding of various classes of events.
TECA Analysis
CASCADE research teams require sophisticated pattern detection and statistical analysis capabilities
- Detection of extremes is a pre-requisite for most proposed methodologies
- UQ and D&A teams rely on GEV and spatial extreme analysis for characterization
- Statistical methodologies are a cross-cutting theme throughout the SFA
CASCADE science teams require automated “Big Data” analytics
- ILIAD 10,000 member ensembles will result in ~1PB datasets
- CAM5 0.25° runs on Mira will produce 2-3 PB datasets
- We will process O(100GB) observation and O(TB) CMIP-5 datasets
Precipitation over 20 years
Observed twenty year return values of seasonal maximum daily precipitation. Units: Inches/day. Understanding the seasonal cycle of extreme precipitation is a critical part of CASCADE objectives. In the US, the meteorology of extreme storms varies greatly across seasons. The stats and software teams have developed R and Python based tools enabling robust descriptions of extreme temperature and precipitation.
Map-Reduce paradigm
TECA uses the Map-Reduce paradigm for scaling climate analytics tools on platforms such as Hopper and Mira. See Scalable Algorithms under Resources for more information.
C20C+ highlights
The figure shows an Australian heat wave as an example use of the C20C+ model output by the CASCADE Detection and Attribution team. We first apply a number of tests to ensure that the model and observational data are “fit for purpose” for attribution purposes (middle panel). The right panel shows the distribution of each 300 member ensemble for the world that was (red) and the world that might have been (blue) showing a clear shift towards higher temperature. Thus the odds of the actual heat wave have increased.
Recent C20C+ highlights include:
- Completion of actual and counterfactual 50 member ensembles from 1960-2013 using the Community Atmospheric Model, CAM5.1.
- Completion of an additional 250 ensemble members for each CAM5.1 experiment from 2011-2013.
- Distribution of CAM5.1 model output on the Federated Earth System Grid.
- Acquisition of C20C+ model data from the Japanese modeling group, MIROC.
3rd US NCA figure 2
CASCADE team members Chris Paciorek and Michael Wehner prepared this figure describing changes in extreme temperature and precipitation for review articles in the Bulletin of the American Meteorological Society as source material for the 3rd US National Climate Assessment.
The figure set shows change over 1950-2007 in estimated 20-year return value (oC) for (a) upper tail of daily maximum temperature, max(Tmax) (b) lower tail of daily maximum temperature, min(Tmax) (c) upper tail of daily minimum temperature, max(Tmin) and (d) lower tail of daily minimum temperature, min(Tmin). Results are based on fitting extreme value statistical models with a linear trend in the location parameter to exceedances of a location-specific threshold (greater than the 99th percentile for upper tail and less than the 1th percentile for lower tail). Circles indicate z-score for the estimated change (estimate divided by its standard error), with absolute z-scores exceeding 1, 2, and 3 indicated by open circles of increasing size.
Source: Peterson, et al. (2013) Monitoring and Understanding Changes in Heatwaves, Coldwaves, Floods and Droughts in the United States: State of Knowledge, Bulletin of the American Meteorological Society June 2013, 821-834, DOI: 10.1175/BAMS-D-12-00066.1, Supplement DOI: 10.1175/BAMS-D-12-00066.2
3rd US NCA figure 1
CASCADE team members Chris Paciorek and Michael Wehner prepared this figure describing changes in extreme temperature and precipitation for review articles in the Bulletin of the American Meteorological Society as source material for the 3rd US National Climate Assessment. The figure shows changes in observed twenty year return value of the daily accumulated precipitation from 1948 to 2010. Units: inches. Only locations for which data from at least 2/3 of the days in the 1948-2010 period were recorded are included in this analysis. The change in return period threshold at each station is shown by a circle whose relative size portrays its statistical significance.
Source: Kunkel et al. (2013) Monitoring and Understanding Trends in Extreme Storms: State of Knowledge, Bulletin of the American Meteorological Society, 94, 499–514, 10.1175/BAMS-D-11-00262.1
COMPUTATION AND PREDICTION
High performance computing to detect and predict changes in weather extremes.
The CASCADE computation and predictions team is developing scientific tools, workflow patterns, and scalable algorithms that can process massive model output on modern HPC systems.
The computation and predictions team is tightly integrating the detection system with the attribution framework so that statistics from the detection analyses automatically yield the probability distribution functions required to produce quantitative attribution and projection statements for extreme events. In a related effort, we are integrating event detection and analysis with the ILIAD ((InitiaLized-ensemble Identify, Analyze, Develop) framework to ensure that probabilities of event detection do not depend on model configuration, thereby mitigating the resolution dependence of hurricane detection.
The CASCADE research portfolio requires extensive computational and statistical infrastructure. Much of the SFA research requires implementation of novel statistical methods. Likewise, the formal application of UQ methods for extremes requires the implementation of a surrogate model and new developments in emulator methodology. Further, all of the SFA’s analyses require sophisticated, robust, and parallelizable data analysis tools to operate on the enormous datasets that we use (O (100{1000) TB). Therefore, the SFA focuses on three main research and development foci to support the broader goals of the project: methodological development for systematic event causation at ne spatial scales, development of a statistical framework for the holistic uncertainty characterization work, and development of a multilevel model emulator for extremes.
TECA Analysis
CASCADE research teams require sophisticated pattern detection and statistical analysis capabilities
- Detection of extremes is a pre-requisite for most proposed methodologies
- UQ and D&A teams rely on GEV and spatial extreme analysis for characterization
- Statistical methodologies are a cross-cutting theme throughout the SFA
CASCADE science teams require automated “Big Data” analytics
- ILIAD 10,000 member ensembles will result in ~1PB datasets
- CAM5 0.25° runs on Mira will produce 2-3 PB datasets
- We will process O(100GB) observation and O(TB) CMIP-5 datasets
Precipitation over 20 years
Observed twenty year return values of seasonal maximum daily precipitation. Units: Inches/day. Understanding the seasonal cycle of extreme precipitation is a critical part of CASCADE objectives. In the US, the meteorology of extreme storms varies greatly across seasons. The stats and software teams have developed R and Python based tools enabling robust descriptions of extreme temperature and precipitation.
Map-Reduce paradigm
TECA uses the Map-Reduce paradigm for scaling climate analytics tools on platforms such as Hopper and Mira. See Scalable Algorithms under Resources for more information.
C20C+ highlights
The figure shows an Australian heat wave as an example use of the C20C+ model output by the CASCADE Detection and Attribution team. We first apply a number of tests to ensure that the model and observational data are “fit for purpose” for attribution purposes (middle panel). The right panel shows the distribution of each 300 member ensemble for the world that was (red) and the world that might have been (blue) showing a clear shift towards higher temperature. Thus the odds of the actual heat wave have increased.
Recent C20C+ highlights include:
- Completion of actual and counterfactual 50 member ensembles from 1960-2013 using the Community Atmospheric Model, CAM5.1.
- Completion of an additional 250 ensemble members for each CAM5.1 experiment from 2011-2013.
- Distribution of CAM5.1 model output on the Federated Earth System Grid.
- Acquisition of C20C+ model data from the Japanese modeling group, MIROC.
3rd US NCA figure 2
CASCADE team members Chris Paciorek and Michael Wehner prepared this figure describing changes in extreme temperature and precipitation for review articles in the Bulletin of the American Meteorological Society as source material for the 3rd US National Climate Assessment.
The figure set shows change over 1950-2007 in estimated 20-year return value (oC) for (a) upper tail of daily maximum temperature, max(Tmax) (b) lower tail of daily maximum temperature, min(Tmax) (c) upper tail of daily minimum temperature, max(Tmin) and (d) lower tail of daily minimum temperature, min(Tmin). Results are based on fitting extreme value statistical models with a linear trend in the location parameter to exceedances of a location-specific threshold (greater than the 99th percentile for upper tail and less than the 1th percentile for lower tail). Circles indicate z-score for the estimated change (estimate divided by its standard error), with absolute z-scores exceeding 1, 2, and 3 indicated by open circles of increasing size.
Source: Peterson, et al. (2013) Monitoring and Understanding Changes in Heatwaves, Coldwaves, Floods and Droughts in the United States: State of Knowledge, Bulletin of the American Meteorological Society June 2013, 821-834, DOI: 10.1175/BAMS-D-12-00066.1, Supplement DOI: 10.1175/BAMS-D-12-00066.2
3rd US NCA figure 1
CASCADE team members Chris Paciorek and Michael Wehner prepared this figure describing changes in extreme temperature and precipitation for review articles in the Bulletin of the American Meteorological Society as source material for the 3rd US National Climate Assessment. The figure shows changes in observed twenty year return value of the daily accumulated precipitation from 1948 to 2010. Units: inches. Only locations for which data from at least 2/3 of the days in the 1948-2010 period were recorded are included in this analysis. The change in return period threshold at each station is shown by a circle whose relative size portrays its statistical significance.
Source: Kunkel et al. (2013) Monitoring and Understanding Trends in Extreme Storms: State of Knowledge, Bulletin of the American Meteorological Society, 94, 499–514, 10.1175/BAMS-D-11-00262.1
TECA Analysis
/in Computation And Predictions /by cascadesfaCASCADE research teams require sophisticated pattern detection and statistical analysis capabilities
CASCADE science teams require automated “Big Data” analytics
Precipitation over 20 years
/in Uncertainty of Extremes /by cascadesfaObserved twenty year return values of seasonal maximum daily precipitation. Units: Inches/day. Understanding the seasonal cycle of extreme precipitation is a critical part of CASCADE objectives. In the US, the meteorology of extreme storms varies greatly across seasons. The stats and software teams have developed R and Python based tools enabling robust descriptions of extreme temperature and precipitation.
Map-Reduce paradigm
/in Computation And Predictions /by cascadesfaTECA uses the Map-Reduce paradigm for scaling climate analytics tools on platforms such as Hopper and Mira. See Scalable Algorithms under Resources for more information.
C20C+ highlights
/in Detection and Designation /by cascadesfaRecent C20C+ highlights include:
3rd US NCA figure 2
/in Uncertainty of Extremes /by cascadesfaThe figure set shows change over 1950-2007 in estimated 20-year return value (oC) for (a) upper tail of daily maximum temperature, max(Tmax) (b) lower tail of daily maximum temperature, min(Tmax) (c) upper tail of daily minimum temperature, max(Tmin) and (d) lower tail of daily minimum temperature, min(Tmin). Results are based on fitting extreme value statistical models with a linear trend in the location parameter to exceedances of a location-specific threshold (greater than the 99th percentile for upper tail and less than the 1th percentile for lower tail). Circles indicate z-score for the estimated change (estimate divided by its standard error), with absolute z-scores exceeding 1, 2, and 3 indicated by open circles of increasing size.
Source: Peterson, et al. (2013) Monitoring and Understanding Changes in Heatwaves, Coldwaves, Floods and Droughts in the United States: State of Knowledge, Bulletin of the American Meteorological Society June 2013, 821-834, DOI: 10.1175/BAMS-D-12-00066.1, Supplement DOI: 10.1175/BAMS-D-12-00066.2
3rd US NCA figure 1
/in Uncertainty of Extremes /by cascadesfaCASCADE team members Chris Paciorek and Michael Wehner prepared this figure describing changes in extreme temperature and precipitation for review articles in the Bulletin of the American Meteorological Society as source material for the 3rd US National Climate Assessment. The figure shows changes in observed twenty year return value of the daily accumulated precipitation from 1948 to 2010. Units: inches. Only locations for which data from at least 2/3 of the days in the 1948-2010 period were recorded are included in this analysis. The change in return period threshold at each station is shown by a circle whose relative size portrays its statistical significance.
Source: Kunkel et al. (2013) Monitoring and Understanding Trends in Extreme Storms: State of Knowledge, Bulletin of the American Meteorological Society, 94, 499–514, 10.1175/BAMS-D-11-00262.1