| NSF Org: |
OCE Division Of Ocean Sciences |
| Recipient: |
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| Initial Amendment Date: | March 29, 2018 |
| Latest Amendment Date: | December 9, 2021 |
| Award Number: | 1756883 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Baris Uz
bmuz@nsf.gov (703)292-4557 OCE Division Of Ocean Sciences GEO Directorate for Geosciences |
| Start Date: | September 1, 2018 |
| End Date: | August 31, 2023 (Estimated) |
| Total Intended Award Amount: | $625,040.00 |
| Total Awarded Amount to Date: | $625,040.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
110 INNER CAMPUS DR AUSTIN TX US 78712-1139 (512)471-6424 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
10100 Burnet Rd., ROC/Bldg. 196 Austin TX US 78758-4445 |
| 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): |
PHYSICAL OCEANOGRAPHY, Climate & Large-Scale Dynamics |
| Primary Program Source: |
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| 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
La Niña, a recurrent cooling pattern over the tropical Pacific Ocean, has been linked to reduced wintertime precipitation across the southern tier of the United States. Historical observations show that La Niña events can last two years or longer, a feature that could make their associated drought impacts more persistent. Forecast systems are generally able to predict the onset of La Niña, as they virtually always follow El Niño. However, little is known about the predictability of multi-year La Niña events because current operational ENSO forecasts are limited to 8 months. This project addresses a series of questions that are critical to improve our ability to predict these events. The project will determine the processes, initial ocean states, and models that can produce skillful multi-year predictions of tropical Pacific. Predicting whether La Niña will return for a second year is critical for predicting the duration of associated droughts throughout the world. Results from this project can potentially improve our ability to predict both the strength and duration of US droughts caused by La Niña.
Observations show that La Niña events tend to last for an additional year, causing persistent drought and flooding impacts in regions throughout the world. Forecasts of these 2-year La Niña events are not routinely generated by operational prediction systems because they focus on shorter lead times, typically up to eight months. This project will demonstrate the feasibility of skillful predictions of 2-year La Niña. Multi-year forecasts will be performed using the Community Earth System Model Version 1 (CESM1), a model that simulates realistic and highly predictable 2-year La Niña. Model dependence of 2-year predictability will be explored using decadal climate predictions performed by four climate models that participated in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). These retrospective forecasts (hindcasts) performed with CESM1 and CMIP5 will be used to explore the physical processes responsible for the predictability of historical 2-year La Niña. Advanced diagnostics will be used to attribute the processes causing forecast spread. Discrepancies in the hindcasts of historical 2-year La Niña events relative to their observed trajectory will be analyzed to identify their predictable and unpredictable drivers. An existing suite of seasonal predictions performed with CESM1 will be extended to produce 2-year hindcasts initialized in March, June, and September. Together with the existing suite of decadal predictions initialized in November, the proposed extension will be used to explore the impact of different lead times, initial state, and seasonality.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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.
There is immense societal benefit from skillful multi-year climate forecasts since decision-making occurs on these timescales. Climate fluctuations on these timescales are largely generated by the El Niño/Southern Oscillation (ENSO) phenomenon, but its predictive limited to lead times of 9 or fewer months. Predictive skill at lead times beyond 9 months is not firmly established.
In this project, we demonstrated that the long-lead predictability of ENSO could arise from particular sequences of ENSO events. For instance, persistent La Niña states lasting 2 or more years appear highly predictable, particularly after a strong El Niño event (DiNezio et al. 2017a; DiNezio et al. 2017b; Wu et al., 2019; Wu et al. 2021b). Conversely, El Niño states lasting multiple years might be predictable based on the onset season (Wu et al., 2019; Wu et al. 2021a; Wu et al. 2021b). These studies, some funded by this project, developed dynamical theories of ENSO and connected them to potential predictable multi-year sequences. However, these studies used hindcasts performed with a single climate model, the Community Earth System Model (CESM), and contained a limited number of events for retrospective validation. Evidence for multi-year predictability from other numerical models is sparse and not systematically explored. Therefore, in this project, we also assess predictive skill across a multi-model ensemble using a hybrid dynamical-statistical method.
In this study, we confirm that the 2-year predictability of La Niña following a strong El Niño is a robust feature of many models used in operational forecasts. Strong El Niño events provide forecasts of opportunity in which we have high confidence in multi-year predictions of ENSO. The opposite is also shown; forecasts initialized during other ENSO states (weak El Niño, Neutral, and La Niña) do not have predictive skills past 12 months. These results hold regardless of the climate model used to make the predictions as shown using 1,000s of years of retrospective climate forecasts made with 11 different state-of-the-art climate models (Figure 1).
Funding from this project was also used to explore changes in the dynamics of 2-year La Niña in a warming climate. Preliminary results show that ENSO could become more oscillatory as the climate warms making it more predictable, particularly making the onset of El Niño more consistent following a La Niña event.
Last Modified: 12/06/2023
Modified by: Yuko Okumura
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