| NSF Org: |
AGS Division of Atmospheric and Geospace Sciences |
| Recipient: |
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| Initial Amendment Date: | October 29, 2013 |
| Latest Amendment Date: | April 13, 2020 |
| Award Number: | 1338427 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Eric DeWeaver
edeweave@nsf.gov (703)292-8527 AGS Division of Atmospheric and Geospace Sciences GEO Directorate for Geosciences |
| Start Date: | May 1, 2014 |
| End Date: | May 31, 2021 (Estimated) |
| Total Intended Award Amount: | $5,500,000.00 |
| Total Awarded Amount to Date: | $5,699,932.00 |
| Funds Obligated to Date: |
FY 2015 = $2,199,932.00 FY 2016 = $1,500,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
4400 UNIVERSITY DR FAIRFAX VA US 22030-4422 (703)993-2295 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4400 University Drive Fairfax VA US 22030-4422 |
| 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, EarthCube |
| 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
This award provides continued funding for the Center for Ocean-Land-Atmosphere Studies (COLA). COLA is a climate science research center established to explore, establish and quantify the variability and predictability of Earth's climate variations on seasonal to decadal time scales, and to harvest this predictability for societally beneficial predictions. The Center is jointly funded by NSF, NOAA and NASA.
Work supported through this award includes activities devoted to 1) basic research on predictability on intraseasonal, seasonal, interannual, and decadal timescales; 2) evaluation of the predictability, skill, and fidelity of US national climate models; and 3) contributions to the development of next generation seamless prediction systems. Research performed under item 1 includes testing of land data assimilation schemes in multiple models, performing hindcasts of El Nino/Southern Oscillation (ENSO) events investigate inter-event diversity of ENSO, performing dynamical prediction experiments for the Indian monsoon, and determining the dependence of drought probability on surface boundary conditions including land cover change. Work under item 2 focuses on the use of optimal spatial structures derived from information theoretic analysis, which represent the most predictable modes, or modes for which predictability differs the most between two models. This activity is intended to support climate prediction efforts at US national centers and contribute to COLA's research-to-operations effort. Work under item 3 involves collaborators at the NOAA National Centers for Environmental Prediction (NCEP) and includes the development of optimal methods of initializing high-resolution coupled models including version 2 of the Coupled Forecast System (CFSv2), a model used operationally at NCEP.
The work has broader impacts due to its focus on research leading to improved climate prediction, given the substantial societal consequences of climate variability and change. In addition, COLA benefits the US climate research enterprise through community integration, education, seminars, workshops, and software and information services. COLA also serves an important function in transferring the results of basic climate science research on predictability and prediction into operational use.
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.
This project has investigated the predictability of Earth?s current climate on time scales of a few days to a few decades. Predictability in this context is narrowly defined to mean that a predicted probability distribution can be confidently said to differ from the climatological probability distribution. Given the unpredictability of instantaneous states of the atmosphere at a lead time of about two weeks, predictability at time scales longer than that applies only to spatial or temporal averages and represents a confident estimate of the signal that stands out above the noise associated with day-to-day weather. In this project, hundreds of simulations and retrospective forecasts of climate variability, made using sophisticated, complex models of the global, coupled Earth system, were used to evaluate and quantify the predictability of phenomena, including the Madden-Julian Oscillation (MJO), El Nino and the Southern Oscillation (ENSO), monsoons in south Asia, east Asia and the Americas, and droughts, floods and other extreme events. The major outcomes of this project include the following:
- In an unprecedented series of retrospective seasonal forecasts made for the past 60 years, it was determined that the predictability of ENSO has not significantly changed, within the bounds of uncertainty and changes in the global observing system, despite substantial changes in the background state due to global warming.
- Interactions of the land surface and the atmosphere are major contributors to predictability at time scales of 3-5 weeks, which is intermediate between the time scales of a few days, at which predictability is dominated by the atmospheric initial conditions, and a few months, when ENSO and other ocean-atmosphere interactions are the drivers of predictability.
- There are substantial interactions in both directions between sub-seasonal fluctuations in tropical convection (MJO and other intraseasonal variations) and ENSO, such that the sub-seasonal fluctuations can trigger or otherwise affect the onset and demise of ENSO events, and ENSO can modulate the occurrence, magnitude and progression of sub-seasonal events.
- The Indian (or south Asian) monsoon is affected by sea surface temperature fluctuations in the Pacific Ocean (ENSO) and the Indian Ocean, snow cover in Eurasia, and local soil moisture anomalies. While these influences have been known for a long time, the results of this project have refined and quantified these relationships to a much greater degree that heretofore available.
- The predictability of sub-seasonal and seasonal phenomena in realistic Earth system models is sensitive to the spatial resolution employed, such that increasing spatial resolution in either the atmospheric component, the oceanic component, or both, tends to increase the predictability. This is despite the increase in variability at small scales that is found in higher resolution models.
- A new method for including the effects of temperature and moisture in preconditioning the atmospheric boundary layer to trigger convection was developed and applied to good effect.
- Several new statistical methods were developed to better quantify predictability, to attribute causes of trends, and to reduce model bias a posteriori.
Last Modified: 10/03/2021
Modified by: James Kinter
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