| NSF Org: |
AGS Division of Atmospheric and Geospace Sciences |
| Recipient: |
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| Initial Amendment Date: | June 6, 2014 |
| Latest Amendment Date: | May 25, 2016 |
| Award Number: | 1408897 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Ming Cai
AGS Division of Atmospheric and Geospace Sciences GEO Directorate for Geosciences |
| Start Date: | July 1, 2014 |
| End Date: | June 30, 2018 (Estimated) |
| Total Intended Award Amount: | $433,031.00 |
| Total Awarded Amount to Date: | $433,031.00 |
| Funds Obligated to Date: |
FY 2015 = $159,564.00 FY 2016 = $178,538.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
3203 N DOWNER AVE # 273 MILWAUKEE WI US 53211-3153 (414)229-4853 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
3200 North Cramer Street Milwaukee WI US 53211-3029 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Climate & Large-Scale Dynamics |
| Primary Program Source: |
01001516DB NSF RESEARCH & RELATED ACTIVIT 01001617DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.050 |
ABSTRACT
Improved understanding of Northern Hemisphere decadal climate variability is an issue of tremendous societal importance. The goal of this project is to document simulated decadal variability and to analyze the dynamics of decadal climate shifts in global climate model simulations. Using a novel hybrid dynamical/statistical modeling framework to study the dynamics of the decadal climate shifts and relate them to climate processes operating on shorter, intraseasonal-to-interannual time scales, this work will shed insights into how various components of the climate system interact.
The researchers hypothesize that decadal climate shifts - defined as abrupt changes in the climates long-term state and variability that tend to happen with decadal frequency - arise as a result of collective behavior of climate subsystems due to interplay between occurrences of synchronized states and variable coupling strength within the climate network. This mechanism, which is consistent with the theory of synchronized chaos, appears to be a very robust mechanism operating in the climate system. It has been found in instrumental records, in forced and unforced climate simulations, as well as in proxy records spanning several centuries. The tendency for the climate subsystems to synchronize/couple their intra-seasonal 'beats' appears to be enhanced in certain phases of the slowly varying Meridional Overturning Circulation in the Atlantic (AMOC), which is dominated by the decadal time scales. This work will thus provide quantitative estimates of climate predictability in the Northern Hemisphere associated with the coupled climate subsystems simulated by comprehensive climate models.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The major goal of this project was to enhance our understanding of the observed multidecadal climate variability via analyses of global climate simulations using the ensemble of state-of-the-art climate models within the Climate Model Intercomparison Project, phase 5 (CMIP-5). Multidecadal climate variability can be externally forced or intrinsic to climate system. We hypothesized that the intrinsic variability can be isolated and described, statistically, in terms of collective behavior of interacting climate sub-systems leading to climate shifts that can persist for decades. We have previously developed a suite of diagnostic techniques designed to address and analyze such a collective behavior and applied these techniques to the analysis of observations. In this project, we extended these analyses to CMIP5 simulations. The major outcomes from this project are as follows:
(1) We developed a suite of semi-empirical attribution approaches for isolating forced and internal variability in observations and climate model simulations. Using these methodologies, we conducted a careful analysis of multidecadal climate variability in CMIP5 models and observations. By comparing different methods for isolating forced and internal components of climate variability in historical simulations of climate models, we demonstrated superior performance of the novel approach developed in the present project. This project's main outcome is in providing a compelling evidence for striking departures of CMIP5 models' multidecadal variability from the observed multidecadal variability, and in verifying low-dimensional spatiotemporal structure of these departures dubbed "stadium wave". We believe that diagnosing dynamical causes of the stadium wave's absence from CMIP5 simulations may potentially be one of the most important near-term tasks of global climate modeling efforts.
(2) Using the tools from the Information Theory, we obtained a compact description of multi-scale interactions in the observed ENSO and documented systematic failure of state-of-the-art, as well as conceptual climate models to capture these interactions. Our results point to a crucial role of biennial climate variability in ENSO. In a nutshell, low-frequency ENSO dynamics affects - directly and indirectly, through the interactions with the annual cycle) the phase and amplitude of the biennial variability, which leads to intermittent synchronizations of multiple biennial modes a.k.a. extreme ENSO events. This part of work was completed in collaboration with Institute of Computer Science (Czech Republic).
(3) Within this project, we also developed a novel strategy for constructing truly multi-scale empirical ?coupled? climate models and applied it to multi-scale modeling of global sea-level pressure and surface air temperature over North America. Using these models, we produced and published a data set for a 100- member ensemble empirical-model simulation of surface temperature. This library provides a valuable resource for scientific analyses of historical temperature variability (including extremes) and a variety of probabilistic applications. Such multi-scale empirical models were also used as an integral part of the objective space-time filters designed for identification of multidecadal model-data differences in item (1) above.
(4) The previous item involved collaboration between the PI and two other members of the UWM Department of Mathematical Sciences, one of which is an expert in Actuarial Science that directly stemmed from this project; this collaboration was co-funded by the American Society of Actuaries (SOA). Another promising collaboration that was initiated during this project is between UWM and a private forecasting firm — Air Worldwide Corporation —based in Boston, MA.
(5) This project contributed to the activities associated with the US AMOC Science Team; the results from the project were included in 2014 and 2016 US AMOC reports.
(6) The results of this project were published in nine peer-reviewed papers and reported on in seventeen national and international conference presentations/seminars (five of them invited).
(7) This project trained three MS students and provided supplementary training for a visiting PhD student.
Intellectual Merit:
- Global climate modeling is of utmost societal important in light of the prospects of global warming and nonlinear climate change. Our results on model assessment with regards to network dynamics and collective behavior provide an important and potentially influential perspective on the field's state of the art.
- The identification of ubiquitous and very pronounced multidecadal climate variability of atmospheric teleconnections in observations and lack thereof in current-generation climate models is a major finding which will significantly affect our assessment of the causes of the 20th century climate change and may guide the routes for further model development. The same applies to the identification of causal interactions instrumental in ENSO dynamics.
- The high-dimensional empirical climate emulators developed, in part, within this project, have numerous applications in climate downscaling, prediction and modeling
- The new identification tools from information theory utilized in this project provide novel means of diagnosing the causality in climate dynamics
Broader Impacts:
- The high-dimensional empirical models developed in this project can be utilized in essentially any discipline, and are in this sense "globally" significant; in particular, they have a great commercialization potential in risk-assessment applications
Last Modified: 07/03/2018
Modified by: Sergey V Kravtsov
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