Table of contents (30 pp limit, approved by nsf on 19 October 2012) – 1 pg introduction
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- 2. RESULTS FROM PRIOR RESEARCH
- Ensemble ENSO Hindcasts Initialized from Multiple Ocean Analyses A Statistical–Dynamical Estimate of Winter ENSO Teleconnections in a Future Climate
- Land-Climate Interactions GLACE-2 Prediction Skill from Land Initialization Rebound of Predictability over US Great Plains
- Demonstration of Decadal Predictability and Prediction Skill COLA’s Contribution to CMIP5 Decadal Predictions High Spatial Resolution
- 3.1.2 Decadal Predictability and Prediction: Natural and Forced Variability
- 3.2 Evaluate Fidelity of National Climate Models, with Actionable Feedback to National Model Development Centers 3.2.1 Multivariate Skill Assessment
- 3.2.4 Multi-Model Interactive Ensemble 3.3 Toward the Next Generation Seamless System for Operational Climate Forecasting
- 3.3.2 Practical Methods of Initializing High-Resolution Coupled Models 3.3.3 Define RD Pathway and Address Operational R2O and O2R Issues
- NOTE: Items highlighted in yellow are missing and need to be filled in.
- COLA has made important scientific contributions
- COLA has a unique capability to run coupled models from multiple major institutions
- COLA has developed new approaches for climate research
- COLA has made fundamental contributions to climate applications of information theory
- COLA provides and supports a unique, widely-used suite of tools
| Unified Days-to-Decades Predictability of Weather and Climate
Principal Investigator: J. Kinter Co-Investigators: B. Cash, T. DelSole, P. Dirmeyer, B. Huang, B. Klinger, V. Krishnamurthy, J. Lu, E. Schneider, J. Shukla, C. Stan, D. Straus TABLE OF CONTENTS (30 pp limit, approved by NSF on 19 October 2012) – 1 pg 1. INTRODUCTION – 4 pp 2. RESULTS FROM PRIOR RESEARCH – 10 pp
3. PROPOSED WORK – 15 pp 3.1 Predictability from Days to Decades in a Changing Climate 3.1.1 ISI Predictability and Prediction 3.1.1.1 Land-Atmosphere Interaction 3.1.1.1.1 Land-driven Predictability 3.1.1.1.2 Improved Land-driven Prediction 3.1.1.1.3 Coupling Between Land Surface and Boundary Layer 3.1.1.2 Ocean-Atmosphere Interaction 3.1.1.2.1 What is the origin of inter-event diversity of ENSO events? 3.1.1.2.2 Ensemble prediction: uncertainty of ocean initial state 3.1.1.2.3 Dynamical pathways for low-frequency mid-latitude variability 3.1.1.2.4 Oceanic Fronts, Western Boundary Currents and Storm Tracks 3.1.1.2.5 Tropical-Extratropical and Trop-Stratosphere Coupling 3.1.1.2.6 Persistent Atmospheric Eddies and Blocking 3.1.1.3 Internally- and Externally-Generated Changes in ISI Predictability TIM WILL PREPARE A LONG PARAGRAPH (PROOF OF CONCEPT IN PRELIMINARY WORK) 3.1.1.3.1 Attribution of Climate Events 3.1.2 Decadal Predictability and Prediction: Natural and Forced Variability 3.1.2.1 Decadal predictability and prediction 3.1.2.2 Mechanisms for decadal variability and its predictability 3.1.2.2.1 Linear Inverse Modeling 3.1.2.2.2 AMOC Variability
3.3.1.1 Mean climate and annual cycle 3.3.1.2 Understanding the sources of climate model bias
3.3.1.3 Intraseasonal Variability and Predictability 3.3.1.4 Seasonal Variability and Predictability: Tropical Heating and ENSO 3.3.1.5 Interannual variability- ENSO, IOD and Atlantic Ocean 3.3.1.5.1 Evaluation of ENSO Prediction 3.3.1.5.2 Exploring Indian Ocean predictability and its implication for the Asian Monsoon 3.3.1.5.3 Potential Predictability of Tropical Atlantic Variability 3.3.1.6 COLA Monsoon Forecast 3.3.1.7 New methodologies to compare models 3.3.1.8 Predictability of Regional Climate: Scientific Basis for Adaptation Strategies 3.3.2 Practical Methods of Initializing High-Resolution Coupled Models 3.3.3 Define R&D Pathway and Address Operational R2O and O2R Issues 3.4 Broader Impacts and Service to the Nation – 2 pp
NOTE: Items highlighted in yellow are missing and need to be filled in. Items highlighted in green (references, figures and sections and pointers to them) need to be cross-checked. 1. INTRODUCTION - 5 pp Since 1993 (1984?), the Center for Ocean-Land-Atmosphere Studies (COLA1) has been a world-leading scientific research center devoted to an improved understanding of climate variability, predictability and change. It maintains a position of preeminence and national leadership that is crucial to the nation’s climate research enterprise and contributes toward providing better climate forecast services. Its scientists lead the nation in efforts to provide a better scientific description of the predictability and variability of climate, which is essential for the design of better prediction and attribution systems. The COLA mode of organization has fostered collaboration among a group of excellent climate scientists that has led to several achievements. First, and most importantly, COLA has made several important scientific contributions that have led to significant progress in our understanding of the physical climate system. Second, COLA is uniquely capable of diagnosing and experimenting with multiple U.S. national climate models. Third, COLA scientists have developed several innovative techniques for obtaining a deeper understanding of the roles of different climate system components and, importantly, how the coupling among them influences predictability. Fourth, COLA has developed methods to frame predictability and prediction issues in probabilistic terms. Fifth, COLA has contributed a widely-used suite of tools for geophysical data analysis and display that has become an essential element of the rapid progress our field has made in the past two (three?) decades. Each of these points is described below. With this proposal, COLA will continue and expand the line of research that has established a scientific basis for predictability in a changing climate and fostered the transition of climate predictability research results to operational use by the world’s weather and climate services. The proposed work will build on prior results to establish a unified probabilistic framework for predictability of variations over days to decades in a changing climate. It will include rigorous multi-scale evaluation of physical processes and mechanisms of climate variability at days-to-decades time scales in national models. The work will be carried out in a multi-model context using state-of-the-art national models, and it will enable COLA to contribute directly to the development of the next generation (2018) seamless prediction system for U.S. operational climate forecasting.
The main contribution of COLA research over the past two decades has been to establish a scientific basis for quantitative dynamical seasonal prediction (DSP) that is grounded in both classical predictability theory and makes use of state-of-the-art numerical models of the physical climate system. The many aspects of these scientific contributions are detailed in nearly 600 peer-reviewed publications that COLA has produced over the past 20 years (URL). Highlights of the most recent work are provided in Section 2, Results from Prior Support.
COLA has developed the capability to diagnose and experiment with each of the national models supported by the National Science Foundation (NSF) and the Department of Energy (DoE; the Community Climate System Model or CCSM, now called the Community Earth System Model or CESM); the National Oceanic and Atmospheric Administration (NOAA; the Climate Forecast System or CFS, from the NOAA National Centers for Environmental Prediction or NCEP, and the Coupled Model from the NOAA Geophysical Fluid Dynamics Laboratory or GFDL); and the National Aeronautics and Space Administration (NASA; the GEOS model from the NASA Global Modeling and Assimilation Office or GMAO); as well as the global atmosphere and coupled models from the European Centre for Medium-range Weather Forecasts (ECMWF; System4 or the Ensemble Prediction System). By adopting a national models approach, COLA has set itself apart from the model development centers, thereby enabling it to formulate and execute experiments that test each model’s unique virtues and potential for improvement. The collaboration with the ECMWF, enabled by COLA’s special relationship with ECMWF, has led to several important breakthroughs in the seamless weather and climate prediction area. Running large experiments with multiple large models has required COLA to develop the capability to use the Nation’s shared high-performance computing resources and managing and manipulating the diverse output from different models. In the past several years, COLA has competed for, been awarded and made productive use of tens of millions of core-hours on the largest and fastest supercomputers available to civilian researchers (e.g. Kraken and Athena at the National Institute for Computational Studies or NICS; Ranger at the Texas Advanced Computing Center or TACC; Jaguar and Hopper at the DoE’s Oak Ridge National Laboratory and National Energy Research Supercomputing Center or NERSC; BlueWaters at the National Center for Supercomputer Applications or NCSA; and a series of machines culminating with the current Yellowstone cluster at the National Center for Atmospheric Research or NCAR). COLA has generated or obtained several petabytes of output from these models, building a core competency in data acquisition and management and assisting collaborators from other institutions.
COLA has developed or contributed to several new methods that have become standard or are soon to be adopted by climate researchers, including:
COLA research has developed the capability to frame predictability and prediction issues in cutting edge information-theoretic terms, developing a series of both deterministic and probabilistic metrics for quantifying predictability and prediction skill, which has immensely clarified the issues confronting the research program and enabled new research studies. This is further described in Section 2.1.
Directory: pub -> kinter -> Projects pub -> Acm word Template for sig site pub -> The german unification, 1815-1870 pub -> Baltic Olympiads in Informatics: Challenges for Training Together pub -> Preparation of Papers for ieee transactions on medical imaging pub -> Forthcoming meetings and conferences pub -> Asilomar Conference on Circuits, Systems & Computers, Pacific Grove, ca, November 1978, pp. 55 58 pub -> Forthcoming meetings and conferences pub -> Harmonised compatibility and sharing conditions for video pmse in the 7 9 ghz frequency band, taking into account radar use Projects -> Revolutionizing Climate Modeling – Project Athena: a multi-Institutional, International Collaboration Download 250.48 Kb. Share with your friends:
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