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Sort by: Title Principal Investigator (s) Task Force Year Initially Funded
Year Initially Funded: 2015
Task Force:
Final Report:

Sub-Seasonal to Seasonal Predictability of Weather Statistics Using NMME

View abstract
"High-impact weather events exact a huge toll on society every year. There is much current interest in the potential of sub-seasonal forecasting to fill the gap between weather forecasts on the one hand and seasonal forecasts on the other. Sub-seasonal forecasting implies shorter averaging periods (such as pentads, weeks) than the 3-month periods typically considered by seasonal forecasts, but high-impact weather events are often daily scale and fall below even these averaging periods. An important unifying idea that we will pursue here is the concept of predicting specific high-impact weather events, but in the absence of temporal and spatial specificity. The concept involves two components: weather-within-climate, which defines climate (and its prediction) in terms of the odds of weather events happening sometime within a target period, but without specifying precisely when; and weather-within-regions, which expresses the odds of weather events happening somewhere within a region, but, again, without specifying precisely where. Our past work on weather-within-climate has demonstrated that the even seasonal forecasts can be expressed in such terms: the predictability of daily rainfall frequency is usually higher than seasonal rainfall totals in the tropics. Similarly, just as long-range forecasts of high-impact weather events necessarily lack temporal specificity, our earlier work indicates that there may be gains in predictive skill for high-impact weather events by relaxing the spatial specificity of the forecasts. By analog with the temporal case, it may be more useful to provide estimates of the probability of an extreme event pooled over a region rather than trying to predict the occurrence of an event at a precise location.

We propose to use the National Multi-Model Ensemble (NMME) database of seasonal and forecasts to investigate the predictability of sub-seasonal high-impact weather events, and to develop an MME-based downscaling system for their prediction at continental scale. Two downscaling methodologies will be used, namely IRI’s Climate Predictability Tool (CPT), based on canonical correlation analysis, together with a hidden Markov model (HMM) toolkit based on generalized linear models and non-homogeneous HMMs. The former will be applied to a hierarchical set of daily high-impact weather statistics constructed from station- or high-resolution gridded observational data, using NMME models as predictor datasets. These CPT experiments will be complemented by a NMME+HMM approach in which we will generate stochastic daily sequences of rainfall at local scale, and then aggregate these both temporally and spatially to assess the predictability of spatio-temporal aggregates. By comparing the HMM sequences from the NMME with similar sequences based on reanalyses we will also be able to diagnose the ability of the NMME to predict realistic sequences of weather, and thereby identify possible systematic errors that may limit the models' ability to predict high-impact weather events. This exploratory pilot study will deliver new NMME prediction products through evaluations of the predictability of both heavy rainfall and drought events in the NMME models."

Principal Investigator (s): Mason, Simon (Columbia University/IRI)

Co-PI (s): Andrew Robertson (IRI)
Year Initially Funded: 2015
Task Force:
Final Report:

Application of the NMME for the Intraseasonal Prediction of Tropical Cyclones over the Atlantic and North Pacific Basins

View abstract
"This proposal is directed to the NOAA OAR CPO FY2015 and targets the Competition of MAPP – North American Multi-Model Ensemble (NMME) system evaluation and application, Area B: Exploration of new application of NMME system predictions. The objective of this project is to develop a suite of dynamical–statistical forecast models for intraseasonal forecasts of Atlantic and North Pacific basin tropical cyclone (TC) activities using the data from the NMME-Phase 2 system.

Introduction to the problem: Tropical cyclones have significant social and economic impacts. Their activities exhibit large intraseasonal variability, which is modulated by the Madden–Julian oscillation (MJO). A below-normal hurricane season may have certain periods with active TC activity, and vice versa. Therefore, issuing skillful intraseasonal forecasts of Atlantic and PacificTC activity in a timely manner would be important and beneficial for the TC-affected areas.

Rationale: With the development of the NMME-Phase 2 system, data at higher temporal resolution (daily and 6 hourly) are becoming available. An evaluation of CFSv2, one of the NMME models, indicates that the MJO is better represented with a higher prediction skill in CFSv2 than in CFSv1. Based on the performance of the NMME in the experimental seasonal climate prediction, it is reasonable to expect that the intraseasonal forecast skill of the MJO will be further improved with the NMME-Phase 2 system. The multi-year retrospective forecasts in the NMME-Phase 2 system offer a unique opportunity to develop and test the dynamical–statistical models for the forecasts of 30-day mean Atlantic and North Pacific TC activities.

Brief summary of the work to be completed:
(1) To establish the empirical relationships between the observed 30-day mean tropical cyclone activity and the NMME-Phase 2 system predicted ocean/atmosphere conditions for the same 30-day moving window throughout the entire hurricane season for the tropical North Atlantic, eastern and western tropical North Pacific regions, respectively, based on the 1982–2010 data, and identify potential predictors for the intraseasonal TC forecast;
(2) To apply a hybrid dynamical–statistical model for the intraseasonal tropical cyclone forecast with the multiple linear regression method and cross-validate the forecasting system over the 1982–2010 period using the NMME-Phase 2 system hindcast suites; and
(3) To test the model for real-time intraseasonal forecasts for the 2016 hurricane season and implement the model into operations at NCEP/CPC. Relevance to the Competition: This project will explore new applications of the NMME-Phase 2 system, test and evaluate new prediction products for intraseasonal tropical cyclone activity. Therefore, it is highly relevant to the Competition of MAPP, Area B. The project will also support the NCEP/CPC Global Tropics Hazards and Benefits Outlooks. The proposed work will help accomplish the NOAA’s long-term climate goal by improving one of the core capabilities, namely, predictions and projections, and addressing the societal challenge of the changes in high-impact extremes of weather and climate, as described in the NOAA’s Next-Generation Strategic Plan."

Principal Investigator (s): Schemm, Jae-Kyung (NOAA/CPC)

Co-PI (s): Hui Wang (NOAA/CPC, Innovim)
Year Initially Funded: 2015
Task Force:
Final Report:

Evaluating Sudden Stratospheric Warmings and NAM Predictability in the NMME Phase-2 System

View abstract
"The Northern Annular Mode (NAM) dominates variability of the Northern Hemisphere (NH) wintertime extratropical circulation in the troposphere and stratosphere. Changes in the tropospheric NAM directly alter NH mid-latitude temperature and precipitation patterns and increase chances for extreme winter weather in major population centers. Numerous studies have concluded that dynamical stratosphere-troposphere coupling constitutes an important forced component of NAM variability, with changes in the strength of the stratospheric polar vortex typically preceding large changes in the tropospheric NAM. These tropospheric NAM changes can then persist for 30 to 45 days following the initial stratospheric perturbation. Additionally, sudden stratospheric warmings (SSWs; i.e., weakenings of the stratospheric polar vortex) may also be predictable through documented anomalous tropospheric circulation patterns that precede them, possibly further extending the lead-times for tropospheric NAM predictions. However, evaluation of these stratosphere-troposphere coupling features in operational and coupled climate models yields mixed results. Generally, the models correctly simulate conditions that precede SSWs but incorrectly simulate the downward propagation of the anomalies into the troposphere. Therefore, tropospheric NAM predictability remains limited to one to two weeks.

Our proposed research will study the predictability of the wintertime tropospheric NAM in the hindcast simulations of the North American Multi-Model Ensemble Phase-2 (NMME-2) model suite. We will specifically evaluate SSWs and their relationship with the subsequent evolution of the tropospheric NAM. The research plan consists of three tasks. First, we will evaluate the general characteristics of the stratospheric and tropospheric NAM in the NMME-2 models. Metrics of model performance will include mean biases in the spatial pattern of the NAM, persistence of each phase of the NAM, and absolute errors in associated teleconnection patterns. Second, using NMME-identified SSW events, we will composite pre- and post-SSW circulation patterns in both the troposphere and stratosphere and compare the simulated patterns to those derived from reanalysis. We will also examine wave diagnostics to understand where models differ dynamically from reanalysis. The final task will examine lag-composites from the NMME-2 models based on reanalysis-identified (i.e., verified) SSW event dates and also involve scoring the forecast skill of the NMME-2 hindcasts to those verified events. In this way, we will quantify how well the models capture actual events and score absolute errors in meteorological fields and wave diagnostics to understand why models may have missed other events.

This proposal is submitted for consideration under the NOAA Modeling, Analysis, Predictions, and Projections Competition for North American Multi-Model Ensemble System Evaluation and Application. The proposed research aims to quantify stratosphere-troposphere coupling processes and NAM predictability within the NMME-2 system in order to identify model biases and subsequently improve sub-seasonal winter forecasts. Our work will document explicitly “the representation of climate phenomena underpinning known intraseasonal to interannual predictability sources and examine the linkages between those sources and prediction skill or lack thereof in the system.” explicitly sought by the call. Furthermore, because of the observed link between major SSWs and significant changes in tropospheric weather (including cold air outbreaks and winter storms), our study satisfies the interest of the competition to “evaluat[e] the prediction of large-scale, extended lead time conditions conducive to extremes.” Improving sub-seasonal forecasts from the models will heighten societal preparedness for potential extreme winter weather and therefore directly address the objectives set forth in NOAA’s long-term goal of “Weather-Ready Nation” outlined in NOAA’s Next-Generation Strategic Plan."

Principal Investigator (s): Furtado, Jason (Atmospheric and Environmental Research)

Co-PI (s): Judah Cohen (AER), Dan Collins (NOAA/CPC), Emily Becker (NOAA/CPC)
Year Initially Funded: 2015
Task Force:
Final Report:

Prediction of Atmospheric Rivers in NMME

View abstract
"Heavy winter precipitation in the western United States (US) is significantly affected by long narrow bands of water vapor transport from the tropical/subtropical North Pacific into mid-latitudes, termed “Atmospheric Rivers” (ARs). Because of strong water vapor transport, ARs can produce hazardous hydro-meteorological extremes (heavy precipitation, mountain snowpack), while ARs are also the main agent of water resources in the western US. Accurate prediction of ARs with lead times of month to season is urgent, since it can help people to mitigate potential damages from the natural hazards. However, assessment of potential predictability and actual prediction skill of ARs has not been attempted with climate models.

Although a large proportion of ARs activity is related directly to synoptic weather conditions, recent studies have explored the linkage between ARs activity and modes of large-scale climate variability, such as El Niño Southern Oscillation (ENSO). Knowing that the state-of-the-art climate models can properly predict ENSO for several months, we can expect that ARs activity can be predicted to a certain extent in monthly to seasonal timescales. The objective of the proposed research is to understand the predictability of Atmospheric Rivers in climate models. To achieve the objective, the main steps of the proposed work are: (1) to assess the ARs activity (water vapor transport, AR frequency and duration) in NMME phase-II hindcasts, (2) to evaluate actual prediction skill of ARs activity for months to seasons, (3) to investigate the source of ARs predictability by understanding the processes related to ENSO, and (4) to evaluate models’ capability in representing the ENSO-ARs processes.

This proposal directly addresses MAPP’s focus area “North American Multi-Model Ensemble system evaluation and application” as it evaluates the performance of NMME system predictions, in particular the ARs prediction by understanding the processes related to ENSO. The outcomes of this project support NOAA's long-term goals and directly contributes to the NOAA's NGSP by addressing its objective for “improved scientific understanding of the changing climate system and its impacts""."

Principal Investigator (s): Kim, Hyemi (Stony Brook University)

Co-PI (s):
Year Initially Funded: 2015
Task Force:
Final Report:

Forecasting risk of seasonal temperature extremes with the North American Multi-Model Ensemble

View abstract
"Problem statement: The North American Multi-Model Ensemble (NMME) includes global ensemble forecasts at lead times of weeks to months since 2011 from several leading climate models. Approximately 30 years of hindcasts from each model are available for calibrating the model forecast skill. It is hypothesized that suitably bias-corrected and calibrated probabilistic forecasts based on NMME can help predict the risk of temperature extremes at monthly to seasonal lead times. Temperature extremes impact many sectors, including agriculture, water resources, energy, transportation, and health.

Work plan: We will apply and extend methods that we and others have developed for quantifying and improving forecast skill, including trend extrapolation, quantification of forecast information gain and over- or under-confidence, and different approaches to probability distribution estimation and model weighting, to provide NOAA with usable probability forecasts and with diagnostic outputs for model improvement. The research will be oriented toward developing and testing general algorithms that can be applied to operational forecasts of any desired climate properties with limited manual tuning. The PI will solicit feedback from the NOAA Climate Prediction Center (CPC) operational prediction branch, which has expressed interest in the problem, to develop pilot products of interest to CPC operations and stakeholders.

Relevance to competition: This proposal targets exploration of new applications within the NMME System Evaluation and Application competition. The proposed work will seek to explore a novel application of NMME output for providing warnings of heightened risk of temperature extremes, at time scales and lead times of 1-3 months. The results from the proposed work will extend and apply previous findings across NMME models and forecast products, resulting in pilot probabilistic seasonal temperature forecasts that could find a wide variety of applications.

Relevance to NOAA goals: NOAA’s Next-Generation Strategic Plan goals of im-
proved scientific understanding of the climate system and of providing climate services to complement currently available weather analysis and forecast services are challenged by the current gap in prediction tools for the intraseasonal to interannual timescale. This project will help NOAA move toward filling this gap by developing scientific and computational tools to utilize the predictive abilities in current and future generations of numerical models to produce well-calibrated probabilistic forecasts of quantities of practical interest."

Principal Investigator (s): Krakauer, Nir (City University of New York, City College)

Co-PI (s):
Year Initially Funded: 2015
Task Force:
Final Report:

Towards week-2 to week-4 excessive heat outlooks: Evaluation of the forecast skill of the North American Multi-Model Ensemble system

View abstract
"In this research work we will quantify the value that the NMME Phase-2 system adds to the prediction skill of the subseasonal excessive heat outlook system with focus on human health (SEHOS-H) currently in development at NOAA’s Climate Prediction Center (CPC) and the Earth System Science Interdisciplinary Center (ESSIC) of the University of Maryland. The development of the SEHOS-H was motivated by the fact that adaptation to more frequent, more intense and longer lasting heat waves, as projected by the IPCC, necessitates excessive heat information at increasing lead time utilizing all sources of available predictability. Known sources of predictability at subseasonal lead times include the Madden-Julian Oscillation (MJO) and patterns of anomalous mid-latitude atmospheric planetary waves with a wavenumber of 5 that precede heat waves over the contiguous U.S. (CONUS) by 15–20 days. These sources of predictability have to be considered in conjunction with interfering, even slower modes of variability such as ENSO.

In ongoing work, the SEHOS-H uses ensemble forecasts of 2-meter apparent and dry-bulb temperature calculated by the NCEP Global Ensemble Prediction system (GEFS) for forecasts during Week-2 and the Climate Forecast System (CFS) for forecasts during Week 3-4. The SEHOS-H is based on effects of heat on human health and defines the intensity of heat events as the integral of the standard NOAA heat index over the number of consecutive days that the heat index exceeds a certain percentile threshold. Additional seasonal weighting of each day is performed in order to take into account acclimatization to heat. This weighting was optimized by using mortality data. Verification of this system is done by comparing the forecasts to observations considering: (1) the number of heat events successfully forecast by the system, (2) the number of false alerts, (3) the number of missed alerts and finally, (4) the forecast skill for the intensity of heat events.

Multi-model ensemble forecasting has been shown to be more skillful than each individual model forecast. This consideration was the motivation for the current proposal which seeks to compare the forecast skill of the baseline experimental SEHOS-H to the forecast skill of the SEHOS-H forced by every possible combination from the NMME Phase-2 models. We will first assess the forecast skill of apparent and dry-bulb temperature for all NMME models and their combinations and compare them to the baseline GEFS/CFS. We will then use these fields to forecast the duration and intensity of heat events thus considering the non-linear relations between atmospheric conditions and human health. Verification will be based on the Climate Test Bed (CTB) protocol and use the suggested categorical and probabilistic verification techniques.

This research will document the importance of the NMME system as part of a subseasonal excessive heat outlook system and the realism in the representation by the NMME models of subseasonal variability relevant to extreme heat patterns over the CONUS. As such this proposal is highly relevant to the targeted competition as well as highly visible due to recent initiatives announced at both the NOAA and White House level in recent months."

Principal Investigator (s): Vintzileos, Augustin (University of Maryland, ESSIC)

Co-PI (s): Jon Gottschalck (NOAA/CPC)
Year Initially Funded: 2015
Task Force:
Climate Model Development Task Force
Final Report:

Modeling and Data Infrastructure in Support of NOAA’s Global Models

View abstract
"Software infrastructure for modeling and data services is needed to enable distributed and collaborative development of high performance, coupled models and the management and dissemination of model data and metadata. Development challenges include the difficulty of implementing common infrastructure standards across organizations, the growing complexity of models and modeling techniques, the increasing volume of model outputs, and the need to adapt to a continually changing computational environment. At NOAA, a specific challenge is the development of a coupled operational modeling system that can be configured for prediction over a range of temporal scales, spatial scales, and ensemble configurations.

We propose to engage in the following activities:
1. ESMF-based infrastructure support for modeling and data services. The Earth System Modeling Framework (ESMF) software is used to build and couple weather, climate, and related models, and to remap grids in data analysis and visualization applications. ESMF has been deployed in operational and research models at NOAA and other centers. We propose to maintain the software in partnership with other agencies (port, test, release, etc.), and to develop new ESMF capabilities for emerging scientific and technical requirements.

2. Development partnership with the CESM. The Community Earth System Model (CESM) is a critical development partner for ESMF, as CESM uses grid remapping and other elements of the ESMF software, and provides requirements and active feedback that enable the framework to track current climate research directions. Further, CESM’s open source code, excellent documentation, and robust governance structure have helped to create an open, collaborative community of users and contributors. We plan to leverage CESM best practices for NOAA global models.

3. Development of the coupled NEMS system for a more capable, user-friendly CFS and a single framework for EMC/CPC. The NOAA Environmental Modeling System (NEMS) is an ESMF-based coupled model capable of spanning weather to climate time scales. We will continue integration of atmosphere, ocean, sea ice, wave, and land components within NEMS with the goal of creating a candidate model for the operational Climate Forecast System (CFS) version 3.

4. Climate data product development and operation. We will continue development of a suite of interconnected capabilities for documenting, searching, accessing, and performing operations on climate data. A main task is to evolve the Earth System CoG collaboration environment, which is becoming the primary user interface to the Earth System Grid Federation (ESGF) distributed data archive, to support CMIP6 and other MIPs. Tasks will include integrating CoG with the visualization capabilities in GrADS.

The work outlined here directly addresses the need for common modeling and data infrastructure, identified in multiple national reports and in the NOAA Next Generation Strategic Plan. Modeling activities will directly benefit NOAA’s seamless suite of forecast products, particularly the gap at lead times of 2-4 weeks; will provide a platform for more systematically addressing the issues of model biases; and will enable smoother, more efficient, and more rigorous transition to operations of research developments at partner institutions and in the broader research community. Improvements to data products will promote accessibility, understanding, and collaborative analysis of climate model outputs."

Principal Investigator (s): DeLuca, Cecelia (NESII, University of Colorado/CIRES)

Co-PI (s): Jim Kinter (COLA), V. Balaji (NOAA/GFDL), Mark Iredell (NOAA/EMC), Mariana Vertenstein (NCAR), Dean Williams (LLNL), Robert Ferraro (JPL)
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Advancing Drought Monitoring and Prediction Using a Multi-Index Multivariate Framework

View abstract
Studies of the suitability of drought indices for different applications indicate that a thorough depiction of drought events requires joint analysis of the covariation of multiple indicators such as rainfall, runoff and soil moisture conditions. However, current approaches do not objectively combine separate indicators into an overall assessment of drought for managing water resources and assessing impacts of climate variability. To address this need, we propose to:
- Expand, test and implement a multivariate drought analysis framework for combining multiple drought indicators probabilistically to improve the understanding of drought onset, development and termination. Indicators include precipitation, soil moisture and runoff, which are used in describing multiple drought aspects (meteorological, agricultural, hydrological).
- Assess the proposed multi-index approach as applied to the detection of drought characteristics such as onset, development and termination, and contrast performance with univariate approaches or subjective combinations.
- Diagnose physical underpinnings of variations in multivariate index performance for different droughts, with emphasis on the MAPP Drought Task Force (DTF) case studies. The index responds to covariation in water stress for multiple indices, and thus distinguishes false wet/dry signals by individual drought indicators.
- Use the proposed multivariate multi-index approach to assess 1-9 months drought predictions using seasonal forecast datasets (primarily from CFSv2 and NMME).
- Support the National Integrated Drought Information System (NIDIS) and the United States Drought Monitor (USDM), focusing both nationwide and with emphasis on prediction of severe droughts in the southwestern U.S. and associated decision support.

The proposed project coordinates with the National Drought Mitigation Center and the California Dept. of Water Resources, and with technical linkages to the NOAA/NCEP and the DTF. The proposed work follows the capability assessment protocol introduced by the DTF, and addresses two primary objectives of the MAPP-Drought program: (a) improving methodologies for global to regional-scale analysis and predictions and (b) developing integrated assessment and prediction capabilities relevant to decision makers, and particularly the opportunity to integrate diverse data sources. The project goals also support NOAA’s strategic objectives in the climate area, namely, “to identify causes and effects of [changes in climate variability and their impacts], produce accurate predictions, identify risks and vulnerabilities, and inform decisions”.

Principal Investigator (s): AghaKouchak, Amir (University of California, Irvine)

Co-PI (s): Mark Svoboda (National Drought Mitigation Center, University of Nebraska-Lincoln), Andy Wood (NCAR)
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Subseasonal Predictability of US Heat Waves/Droughts Associated with Planetary Wave Events

View abstract
In this proposed project, we focus on summertime droughts and associated heat waves that last for 5 days to about 2 weeks over the continental US. Our overall goal is to investigate the prospects for predicting the likelihood of these events as a result of their connections with intraseasonal fluctuations in the midlatitude planetary waves and the potential for predicting those fluctuations.

Prospects for predicting droughts and heat waves based on their connections to intraseasonal planetary wave variations may not seem promising. After all, typical instantaneous states are unpredictable after about 10 days due to the chaotic nature of the atmosphere. This fact, however, does not preclude the possibility that time-averaged, forced or unusually predictable patterns could be predictable on longer time scales. Until now, the search for sources of intraseasonal predictability has primarily focused on atmospheric phenomena driven by Madden-Julian Oscillation (MJO) or Asian summer monsoons, or by boundary conditions including sea surface temperature and soil moisture. But our recent study based on a 12,000-year integration of an atmospheric general circulation model found that a midlatitude planetary wave pattern that features a zonal wavenumber-5 structure can enhance probability forecasts of US heat waves/droughts up to 15-20 days ahead. Since that analysis found internal midlatitude dynamics is primarily responsible for this pattern, those results open up a new possibility for predicting the likelihood of US droughts and heat waves.

Here we seek support to build on this promising result. The primary purposes include: (a) confirming the robustness of the result by repeating analysis for other models and reanalysis fields and determining whether more than one atmospheric pattern may give predictability of droughts and heat waves, (b) elucidating certain processes (boundary conditions or remote heating) might enhance the predictability of the heat waves/droughts we found, (c) assessing whether the predictability we found is properly represented in National Multi-model Ensemble (NMME) phase II models, and (d) transferring our knowledge to a framework suitable for operational predictions.

By performing our analysis, relating it to NMME forecasts and adapting it to operational settings, this proposal targets priorities of the MAPP program solicitation: (a) research to advance understanding, monitoring, and prediction of drought and (b) research to advance NOAA’s operational systems for climate prediction. Also, all activities are tightly related to the NOAA Next-Generation Strategic Plan (NGSP) objectives of “improving scientific understanding of the changing climate system and its impacts” and “climate mitigation and adaptation”.

Principal Investigator (s): Branstator, Grant (NCAR)

Co-PI (s): Joe Tribbia (NCAR), Haiyan Teng (NCAR)
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Collaborative Research: Towards predicting persistent drought conditions associated with consecutive La Nina years

View abstract
La Ni˜na – the cold phase of the El Ni˜no/Southern Oscillation (ENSO) phenomenon – is associated with drought over the US. In contrast, El Ni˜no – the warm phase of ENSO – brings increased precipitation to the southern portion of the country. Importantly, these two phases of the ENSO cycle exhibits asymmetric duration: while El Ni˜no events typically terminate after one year, La Ni˜na events, in contrast, commonly extend over two or more consecutive years.

We propose a suite of numerical simulations and observational analyses to advance the understanding of the predictability of persistent drought conditions over the US during La Ni˜na years. Our project addresses two overlooked issues of relevance to the prediction of La Ni˜na droughts over North America. First, the predictability of the duration of La Ni˜na events is unknown. Second, observations show that droughts initiated by La Ni˜na intensify and expand during the second year of the event. Thus additional mechanisms such as seasurface temperature anomalies from outside the tropical Pacific, or local land-atmosphere interactions must play an important role, exacerbating the drought’s magnitude and spatial extent. We will address these gaps by 1) diagnosing the physical processes responsible for the second year intensification of La Ni˜na droughts from a suite of observational datasets and simulations, 2) evaluating the ability of operational forecast systems to simulate 2–yr La Ni˜na events and their predictability, and 3) quantifying the potential predictability of 2–yr La Ni˜na events in a “perfect model” framework.

Skillful prediction of multi–year La Ni˜na could have a large impact in the ability to predict persistent drought conditions over a large portion of the United States. While much emphasis has been placed upon the onset of drought, the fact that historical La Ni˜na droughts have lasted more than 1 year is a reminder of how important the prediction of La Ni˜na duration/termination is. The proposed research will explore the dynamics of these processes in observations, forecast systems, and climate models with the ultimate goal of quantifying how predictable the duration of La Ni˜na droughts is. In particular we will quantify whether the return of La Ni˜na for a consecutive year can be predicted 18 and 6 months in advance.

Addressing the two gaps outlined above will directly contribute to the objectives of MAPP’s competition: “Research to Advance Understanding, Monitoring, and Prediction of Drought” because it will lead to more skillful and reliable drought forecasts at regional scales and adequate lead times needed by drought stakeholders. The core capabilities and research efforts resulting from our project will allow progress to be made toward the provision of sustained, reliable, and timely climate services related to water resources. Having the capacity to provide accurate predictions on drought termination several seasons in advance could greatly reduce the severity of social and economic damage caused by drought, a leading natural hazard for North America. Determining whether the duration of La Ni˜na droughts can be predicted directly contributes to NOAAs long-term climate goal because it improves the scientific understanding of the changing climate system and its impacts as stated by the first objective outlined by NOAA’s Next Generation Strategic Plan.

Principal Investigator (s): DiNezio, Pedro (University of Hawaii)

Co-PI (s): Yuko Okumura (University of Texas), Clara Deser (NCAR)
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The Modeling, Analysis, Predictions, and Projections (MAPP) Program's mission is to enhance the Nation's capability to understand and predict natural variability and changes in Earth's climate system. The MAPP Program supports development of advanced climate modeling technologies to improve simulation of climate variability, prediction of future climate variations from weeks to decades, and projection of long-term future climate conditions. To achieve its mission, the MAPP Program supports research focused on the coupling, integration, and application of Earth system models and analyses across NOAA, among partner agencies, and with the external research community.

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Download our program brochure (pdf).