RESEARCH AREAS – CASCADE

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[av_heading heading=’DETECTION AND DESIGNATION’ tag=’h3′ style=’blockquote modern-quote’ size=” subheading_active=’subheading_below’ subheading_size=’15’ margin=” margin_sync=’true’ padding=’10’ color=” custom_font=” av-medium-font-size-title=” av-small-font-size-title=” av-mini-font-size-title=” av-medium-font-size=” av-small-font-size=” av-mini-font-size=” av_uid=’av-jk2s9gh9′ admin_preview_bg=”]
Characterization, detection, and designation of observed extreme events.
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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 20^th 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.

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[av_heading tag=’h3′ padding=’10’ heading=’UNDERSTANDING STRANGE WEATHER’ color=” style=’blockquote modern-quote’ custom_font=” size=” subheading_active=’subheading_below’ subheading_size=’15’ custom_class=” admin_preview_bg=” av-desktop-hide=” av-medium-hide=” av-small-hide=” av-mini-hide=” av-medium-font-size-title=” av-small-font-size-title=” av-mini-font-size-title=” av-medium-font-size=” av-small-font-size=” av-mini-font-size=” margin=” av_uid=’av-og1et’]
Understanding how changes in the ocean and atmosphere make weather events more extreme.
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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
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[av_heading tag=’h3′ padding=’10’ heading=’UNCERTAINTY OF EXTREMES’ color=” style=’blockquote modern-quote’ custom_font=” size=” subheading_active=’subheading_below’ subheading_size=’15’ custom_class=” admin_preview_bg=” av-desktop-hide=” av-medium-hide=” av-small-hide=” av-mini-hide=” av-medium-font-size-title=” av-small-font-size-title=” av-mini-font-size-title=” av-medium-font-size=” av-small-font-size=” av-mini-font-size=” margin=” av_uid=’av-dp899′]
Statistical methods to quantify changes in extreme weather in light of uncertainty.
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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.

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[av_heading tag=’h3′ padding=’10’ heading=’COMPUTATION AND PREDICTION’ color=” style=’blockquote modern-quote’ custom_font=” size=” subheading_active=’subheading_below’ subheading_size=’15’ custom_class=” admin_preview_bg=” av-desktop-hide=” av-medium-hide=” av-small-hide=” av-mini-hide=” av-medium-font-size-title=” av-small-font-size-title=” av-mini-font-size-title=” av-medium-font-size=” av-small-font-size=” av-mini-font-size=” margin=” av_uid=’av-b6sut’]
High performance computing to detect and predict changes in weather extremes.
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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.

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