Funding Opportunities & Funded Projects

FY23 Research Opportunities

For both competitions, Science for the 21st Century Western U.S. Hydroclimate and Products for Areas of Climate Risk, and Projections for Societally-Relevant Problems

LOIs are due September 1, 2022 by 5pm and Full Proposals are due November 21, 2022 by 5pm.



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Multi-model seasonal sea level forecasts for the U.S. Coast

Coastal high water events are increasing in frequency and severity as global ocean levels rise. With higher relative sea levels, minor coastal flooding is occurring more often during periods of higher astronomical tides. If combined with above-normal seasonal sea levels, often associated with climate-driven variability in the ocean, coastal flooding becomes more severe. Such total high water events expose coastlines to potentially damaging storm-related flooding, yet no seasonal prediction of coastal high water exists on a national scale.

Regional sea levels are affected by the winds, as well as ocean circulation changes. In the Pacific, sea level variations (±30 cm) associated with the El Niño-Southern Oscillation (ENSO), as well as more local processes such as eddies and upwelling or downwelling, impact Hawaii, U.S.-affiliated Pacific Islands (USAPI), and the West Coast. In the Atlantic, sea level anomalies also emerge during El Niño from seasonal changes in storm tracks and winds, impacting the East Coast. On more frequent timescales, fluctuations in the Gulf Stream can produce sea level anomalies (±15 cm) along the U.S. mid- and south-Atlantic Coasts. With recent advancements in forecasting seasonal climate variability using state-of-the-art coupled ocean-atmosphere models, which have the ability to assimilate and predict sea level, come the opportunity to predict the potential for future high water events many months in advance for the U.S. Coast.

Our goal is to construct multi-model forecasts based on seasonal prediction systems and evaluate their skill across the NOAA tide gauge network in the continental U.S., as well as the NOAA and University of Hawaii Sea Level Center (UHSLC) network in Hawaii, the USAPI, the Gulf of Mexico, and Caribbean. Preliminary work suggests that forecast skill can also be improved in some regions by incorporating statistical relationships between observed and predicted sea level variability or by connecting it with the modes of atmospheric variability. From the multi-model forecasts we will provide regional seasonal sea level anomaly outlooks nationwide. A geospatial web portal will be developed to deliver the outlooks, such as high-water alert calendars, which can be combined or incorporated into new or existing NOAA coastal-flood products.

Our team will focus on three objectives by performing the following tasks to deliver a prototype seasonal prediction system:

i. We will explore the processes responsible for sea level variability on monthly to interannual timescales in the Pacific, Atlantic, Gulf of Mexico, and Caribbean coastal regions.

ii. We will process sea level forecasts from operational as well as experimental modeling frameworks to develop a prototype ensemble seasonal prediction system for coastal sea level anomalies.

iii. We will use the multi-model prediction system to provide monthly outlooks for seasonal sea level anomalies across the Nation.

We aim to deliver a framework for seasonal sea level forecasting which strengthens the production of climate data and information that informs the management of climate-related risks. Our forecast framework seeks to reduce the residual between predicted tides and observed water levels by predicting relative sea level changes.

Climate Risk Area: Coastal Inundation

Principal Investigator (s): Mark Merrifield (University of Hawai’i at Mānoa (UH)), Arun Kumar (NOAA/CPC), Gary Mitchum (University of South Florida)

Co-PI (s):Matthew Widlansky (UH Sea Level Center), Philip Thompson (UH Sea Level Center), H. Annamalai (International Pacific Research Center), William Sweet (NOAA/NOS/CO-OPS), Eric Leuliette (NOAA/STAR), John Marra (NOAA/NCEI)

Task Force: Marine Prediction

Year Initially Funded:2017

Competition: Research to explore seasonal prediction of coastal high water levels and changing living marine resources

Final Report:

Probabilistic Seasonal Prediction of the Distribution of Fish and Marine Mammals in the Northeast US

Distributional shifts related to climate variability and oceanographic conditions have been observed across a broad range of marine taxa, and these shifts have both negative and positive socioeconomic consequences. The Northeast United States Large Marine Ecosystem (NEUS LME), a highly productive marine ecosystem that supports important commercial and recreational fisheries, has experienced some of the highest rates of warming in the last few decades. Climate-driven shifts in fish distribution have been observed in this region for most fish species and for whole fish communities. In addition to distributional changes, climate-driven environmental change can influence the phenology of marine processes such as the timing of breeding or spawning, seasonal movements and migrations. Marine organisms show species-specific responses to changing thermal regimes, and thus distributional overlap between species can be strongly impacted by climate-driven change. Therefore, quantifying the biophysical links driving species distributions and understanding how seasonality and climate-driven change together impact living marine resources is imperative to predicting the impacts of future change.

Bycatch, the incidental capture of non-target species in fisheries, is an important source of mortality for several marine mammal and fish species in the NEUS LME, and is strongly impacted by both species overlap and overlap between fishermen and non-target species. In addition to impacts on non-target species, bycatch is a concern for commercial fishermen as it can increase costs and decrease yield. Bycatch could be reduced by incorporating dynamic environmental variables to more precisely estimate spatio-temporal limits on the distribution of marine mammals and commercially important fish. Accurately predicting the distribution of living marine resources on seasonal timescales would be particularly beneficial since it would allow fishing and management approaches to be adjusted based on environmental conditions. Recently developed state-of-the-art seasonal climate models (e.g NMME, S2S) now provide the unprecedented opportunity to make significant strides in the seasonal predictions of living marine resources. However, despite the advantage of the hybrid model in seasonal prediction, this method has been applied only to limited properties of climate phenomena such as hurricane activity. Here we propose to use output from these climate models to generate probabilistic predictions of forage fish and marine mammal distribution in order to inform dynamic management of protected species in the NEUS LME. Specifically, this work will develop bycatch reduction tools to inform decision making for fishermen and managers by highlighting regions that fishermen could avoid in order to decrease the likelihood of bycatch.

The proposed research directly addresses priorities of the MAPP program. A major goal of the MAPP program is to increase the resilience and intelligence of coastal communities through improved products and services relevant to NOAA. The proposed work will contribute to this goal by elucidating the impacts of climate-driven environmental variability on fish and protected marine mammal species, and by generating probabilistic predictions regarding the biological impacts of forecasted changes in the NEUS LME. These products can then be used to inform the management of commercially and ecologically important species, and to provide information required for dynamic management. The proposed studies are thus consistent with the mission of NOAA  MAPP “to enhance the Nation’s capability to predict variability and changes in the Earth’s climate system” and directly contribute to NOAA’s long-term goals, especially for “(1) improved scientific understanding of the changing climate system and its impacts” and “(3) mitigation and adaptation choices supported by sustained, reliable, and timely climate services”.

Climate Risk Area: Marine Ecosystems

Principal Investigator (s): Lesley Thorne (Stony Brook University)

Co-PI (s):Janet Nye (Stony Brook University), Hyemi Kim (Stony Brook University)

Task Force: Marine Prediction

Year Initially Funded:2017

Competition: Research to explore seasonal prediction of coastal high water levels and changing living marine resources

Final Report:

Seasonal Forecasting Applications for Ecosystem Based Fisheries Management in the Eastern Bering Sea

As part of the NOAA Integrated Ecosystem Assessment (IEA) Program, the NMFS Alaska Fisheries Science Center, the OAR Pacific Marine Environmental Laboratory, and the Joint Institute for the Study of the Atmosphere and Ocean at the University of Washington have collaborated to produce seasonal forecasts of the eastern Bering Sea as part of the North Pacific Fishery Management Council’s Ecosystem Status Report. The forecasts incorporate dynamically-downscaling climate information into a regional ocean model coupled to a nutrient- phytoplankton-zooplankton model for the Bering Sea (called Bering10K hereafter). Bering10K model has been tested for the past four years with promising results for a 9-month lead-time forecast of the Bering Sea Cold Pool, a major habitat feature of bottom temperature that determines fish and crab recruitment and distribution. As fish and crab from the eastern Bering Sea account for over 40% of the annual catches in the United States, we envision seasonal forecasts of the Cold Pool, sea-ice cover, ocean temperature, and other outputs to be of interest to numerous stakeholders, from resource management agencies and coastal communities to research institutions and industry service providers. To date, our forecasts have provided direct guidance, in the management council setting, for determining the outlook for key stocks such as Bering Sea walleye pollock and snow crab.

This project would further develop our seasonal forecasting ability, focusing on both technical improvement and applicability. Our goals are to: 1) conduct systematic re-forecasting experiments, driving our regional model with an ensemble of global re-forecasts to assess regional skill and sources of predictability; 2) expand from our present use of NOAA’s Climate Forecast System (CFS) to include other members of the North American Multi-Model Ensemble (NMME); 3) fine tune our regional models to improve accuracy in 9-month forecasts of key environmental features such as sea-ice cover, cold pool, and water column temperature; 4) develop seasonal outlooks of environmental indices as well as fish and crab distribution and recruitment which are management relevant and consistent with ecosystem based management; and 5) conduct a workshop in collaboration with stakeholders from management, fishing industry and Alaskan Native communities to identify additional type, format, extent and frequency of information that would be most useful to those stakeholders for forecast delivery, and design and deliver final products accordingly.

This proposal targets the MAPP competition, specifically on the priority to predict seasonal impacts on the distribution and abundance of fish stocks or other living marine resources. This project will directly increase the production, delivery, and use of climate-related information in fisheries management by producing and applying seasonal environmental predictions directly to the management of living marine resources as per the MAPP topical area. In particular, we have established a direct link between the Bering10K model and fisheries management: seasonal forecasts relevant to managed stocks are provided each year to the North Pacific Fishery Management Council in the direct context of groundfish and crab quota setting, informing final decisions on modifying recommended quotas. Thus, it responds directly to the NOAA long-term climate goals of applying modeling and prediction to maintaining the sustainability of marine ecosystems.

Climate Risk Area: Marine Ecosystems

Principal Investigator (s): Kerim Aydin (NOAA/AFSC), Albert Hermann (University of Washington), Michael Alexander (NOAA/ESRL), Phyllis Stabeno (NOAA/PMEL)

Co-PI (s):Wei Cheng (JISAO, UW/PMEL, OAR, NOAA Affiliate), Kelly Kearney (JISAO, UW/AFSC, NMFS, NOAA Affiliate), Ivonne Ortiz (JISAP, UW/AFSC, NMFS, NOAA Affiliate)

Task Force: Marine Prediction

Year Initially Funded:2017

Competition: Research to explore seasonal prediction of coastal high water levels and changing living marine resources

Final Report:

Understanding and Quantifying the Predictability of Marine Ecosystem Drivers in the California Current System

The California Current upwelling system (CCS) supports one of the most productive marine ecosystems in the world and is a primary source of ecosystem services for the U.S. including fishing, shipping, and recreation. Despite the empirical evidence of ENSO influence upon the California Current marine ecosystems, the detailed influence of different ENSO events is unclear, and the degree of predictability of the various ecosystem drivers for specific tropical Pacific conditions has never been quantified. The goal of this proposal is to: 1) Use high-resolution ocean reanalysis of the CCS to link the physical drivers of the CCS ecosystem (temperature, upwelling velocity, alongshore & cross-shore transport) to local climate forcing functions (e.g. alongshore winds, wind stress curl, heat fluxes, precipitation and river runoff) at seasonal timescale; 2) Use long reanalysis products (e.g. SODAsi.3, 20CRv2c, CERA-20C) in combination with multiple linear regression and Singular Value Decomposition to objectively link the climate forcing functions variations in the CCS region with conditions (e.g. sea surface temperature, thermocline depth, sea surface height, tropical wind stresses) in the tropical Pacific that can optimally force them at seasonal timescales; and 3) Use Linear Inverse Modeling (LIM) and the North American Multi Model Ensemble (NMME) to determine the predictability and uncertainty of the forcing functions along the CCS region, compare the LIM and NMME forecast skills, and explore possible sources of error in the NMME models.

The proposed research will directly address the first objective of the call, "Explore how selected modes of climate or ocean variability relate to seasonal variations in fields such as sea level height and ocean temperature that are of primary relevance to predictability for the topical areas of the call, and evaluate the seasonal prediction skill of these modes”, in that it explores how the leading mode of tropical Pacific climate variability (e.g. ENSO) will affect a set of variables, including sea level height and ocean temperature, that are fundamental to ecosystem dynamics in the California Current System. The proposed study also sets the foundation for the development of a probabilistic prediction system, as described in objective 3 of the call, for use in predicting components of the ecosystem that are relevant to the marine resources managed by NOAA NMFS. This study will also utilize and evaluate the latest versions of the century-long SODAsi and 20CR reanalysis products, and also provide an evaluation of a NOAA-sponsored operational forecast system, the North American Multi-Model Ensemble (NMME) in the area of ecosystem predictions.

Climate Risk Area: Marine Ecosystems

Principal Investigator (s): Arthur Miller (Scripps Institution of Oceanography)

Co-PI (s):Antonietta Capotondi (NOAA/ESRL/PSD), Emanuele Di Lorenzo (Georgia Institute of Technology)

Task Force: Marine Prediction

Year Initially Funded:2017

Competition: Research to explore seasonal prediction of coastal high water levels and changing living marine resources

Final Report: Final Report.pdf

Using a synoptic climatological framework to assess predictability of anomalous coastal sea levels in NOAA high priority areas

Introduction to the problem: Changes in sea levels have been studied on many spatiotemporal levels, from the local to the global, and short-term to long-term, as well as secular trends. One of the key drivers in seasonal fluctuations in coastal sea levels are ambient atmospheric conditions. Thus, the ability to predict anomalous sea levels should be viewed within the context of the ability to predict the modes of atmospheric variability that affect these seasonal anomalies. The improvement of mid-range to seasonal forecasts of atmospheric conditions has long been a priority of the weather/climate modeling world, and the North American Multi-Model Ensemble (NMME) experiment has been designed to help overcome a number of uncertainties in climate predictions. The ability of models to forecast anomalous sea levels can thus be examined in light of their ability to predict atmospheric circulation. Rationale and objectives: We focus on two main objectives. First, we will assess the relationship between short-term to seasonal-term atmospheric circulation patterns and anomalous coastal sea-level values for all oceanic tidal gauges in the conterminous United States from 1982-2016. Our hypothesis is that the occurrence of extreme atmospheric circulation patterns, as well as the anomalous frequency of these patterns, can be associated with anomalous sea levels locally and regionally on multiple timescales. Second, we will assess the ability of the NMME to successfully simulate both the arrays of atmospheric circulation patterns that are identified, in terms of their overall frequency, persistence, and seasonality, as well as anomalous sea levels using the relationships that were developed. Summary of the work to be completed: We will obtain tidal gauge data for the conterminous US, and classify circulation patterns (CP) using multiple variables, via self-organizing maps. The relationship between CPs and anomalous sea levels will be analyzed by examining the short-term and seasonal-term relationships between anomalous sea-level values and individual CPs, and then modeling the time series with non-linear autoregressive models with exogenous input (NARX models). The output of the NARX model will not evaluate the relationship between atmospheric circulation and anomalous sea level, but also the role of individual drivers. Once this is complete, forecast data from the NMME will be used to evaluate the ability of the model to reproduce observed synoptic circulation patterns, as well as modeled sea-level anomalies.

Climate Risk Area: Coastal Inundation

Principal Investigator (s): Scott Sheridan (Kent State University)

Co-PI (s):Cameron Lee (Kent State University) Key People: Doug Pirhalla (NOAA/CSC), Varis Ransibrahmanakul (NOAA/NOS)

Task Force: Marine Prediction

Year Initially Funded:2017

Competition: Research to explore seasonal prediction of coastal high water levels and changing living marine resources

Final Report:

Development of a Monitoring and Prediction System for Flash Droughts over the United States

The goal of this proposal is to develop an objective drought seasonal outlook (ODSO) over the Conterminous United States (CONUS) for agricultural and hydrological users. The ODSO will utilize both statistical and dynamical forecasts, and will include estimates of their uncertainties. The idea is that forecasters can use the ODSO as their ‘first guess’ for the NOAA Drought Outlook. The proposed project will advance CPC/NOAA operational capabilities for prediction of drought by incorporating the results of past NOAA/MAPP-funded (and other) drought research. To achieve our goal, we plan (1) to better understand drought evolution from onset to demise based on observations of precipitation (P) and temperature (Tair), and model- reconstructed soil moisture (SM), snow water equivalent (SWE, where applicable), evapotranspiration (ET) and runoff (RO) using a suite of land surface models (LSMs) that comprise an updated NLDAS system from 1916-near present; (2) to examine forcings associated with drought and to test statistical tools such as ensemble canonical correlation analysis (ECCA) for forecasting of drought indices; (3) to evaluate the ability of an ensemble of global forecast and land surface models (GCM_LSM) to predict drought development by diagnosing sources of model errors, with global forecasts taken from the North American Multi Model Ensemble (NMME) archive, (4) to utilize the ECCA to optimally combine statistical forecasts and error-corrected dynamical forecasts into an ODSO. The proposed work will directly contribute to the MAPP objectives of advancing drought understanding and prediction. The proposed research will also contribute to the NIDIS objectives of (1) improving drought prediction skill and (2) improving drought information systems. The PI and co-PI will continue to contribute to NOAA’s Drought Task Force (DTF) research to better understand the physical mechanisms and advance the ability to predict various aspects of drought including onset, duration and recovery. The work we propose will be readily transferrable to operations, and in fact we propose to implement a prototype system at CPC that will draw from the operational North American Land Data Assimilation System (NLDAS) and will produce information for CPC Drought Outlook forecasters in near real-time. The TRL level of the proposed project is TR5 (System/subsystem/component validation in relevant environment).

Principal Investigator (s): Lettenmaier, Dennis (UCLA)

Co-PI (s):Kingtse Mo (NOAA/CPC)

Task Force: Drought

Year Initially Funded:2017

Competition: Advancing drought understanding, monitoring and prediction

Final Report: Final Report.pdf

Biosphere-atmosphere regulations of droughts assessed using microwave and solar-induced fluorescence observations and improved plant water stress representation

The proposed work will encompass three intertwined approaches: 1) a better representation of plant water stress and soil moisture, by implementing a plant hydraulic model and new stomatal conductance model in the NOAH land model of the CFS operational forecast model; 2) linking the model surface fluxes and vegetation water status to remote sensing observations of surface fluxes based on solar-induced fluorescence (SIF – a proxy for photosynthesis), and plant water stress strategy based on microwave vegetation optical depth (VOD – directly related to vegetation water status), and 3) defining a quantitative assessment of the model improvement through changes in land-atmosphere interaction using an improved causal statistical model that explicitly defines the time scales of the feedback from weekly to interannual, and its impact on droughts.

In part 1, we will implement a plant hydraulic model that directly resolves the transport of water throughout the plant xylem and an improved stomatal conductance model based on optimal carbon gain. This will define a physically-based response of plants to water stress through either stomatal closure (demand break down) under reduced leaf water potential or through xylem cavitation (supply break down).

In part 2, we will constrain this new model and avoid over-parameterization by making use of a recent vegetation water stress index derived from vegetation optical depth (VOD) measurements from the ASMR satellite, as it can directly sense the canopy water status at a spatial scale (~25 to 50km) relevant to weather and land-surface modeling prediction. We will also use novel estimates of surface fluxes based on solar-induced fluorescence (SIF) measurements to further constrain the dynamics of plant transpiration and the new model parameterization.

We will test the implemented models with typical metrics (2m temperature and humidity, precipitation and geopotential heights) but also with a new causal statistical technique based on multivariate Granger causality. We recently showed that such a technique could be used to assess vegetation-atmosphere feedback and to isolate the time scales of the feedback from monthly to interannual in coupled land-(ocean-)atmosphere models. We will extend this analysis to non-stationary/non-periodic time series using temporal wavelets (as opposed to Fourier transform currently used) so that we can further highlight the time dependence of the feedback in the coupled model. This will further help refine the model during droughts.

This work is directly relevant to the MAPP drought competition 1 as well as to NOAA’s long-term climate goals as it “advances the understanding, monitoring, and prediction of drought [with] improved understanding of predictability relevant to drought, [and] model development.” It also uses “new monitoring [...] products”. It will also improve subseasonal to seasonal prediction, which is critical for water resource and ecosystem health forecasts.

Principal Investigator (s): Gentine, Pierre (Columbia University)

Co-PI (s):Rongqian Yang (NOAA EMC), Michael Ek (NOAA/EMC), Alexandra Konings (Stanford University)

Task Force: Drought

Year Initially Funded:2017

Competition: Advancing drought understanding, monitoring and prediction

Final Report: Gentine_NA17OAR4310127_NOAA_2019_2020_report.pdf

Clarifying the influence of the multiscale coupling between land surface, shallow and deep convection, and large-scale circulation on the predictability of summer drought over the US Great Plains

Current dynamic seasonal predictions show lower prediction skills over the US Great Plains than over the eastern and western United States (US). This is particularly so over that region during the warm season (May through August). The dynamical seasonal prediction systems have virtually no prediction skill, for the warm season rainfall over this region. This lack is in large part because the predictability provided by ENSO and tropical Atlantic sea surface temperature anomalies (SSTA) becomes weaker in the warm season. Internal atmospheric variability has significant impact on warm season rainfall, but without strong feedbacks, it can only sustain an anomalous drought circulation for a few weeks. Many studies have suggested land surface feedbacks as the main source of the dry memory. However, such feedbacks cannot sustain dry memory for much more than a month.

Extreme droughts are resulted from dry memory lasting from 3 to 8 months. An empirical model based on such drought memory shows higher skills for seasonal prediction of warm season rainfall anomalies than those of the dynamic models over the US Great Plains. Thus, there is more predictability than that shown by the current dynamic prediction system. However, the processes behind such higher empirical predictability are unclear, limiting our ability to tap into it as an additional source of predictability for the warm season drought. This proposal addresses this knowledge gap by clarifying the following questions:

  • How does the interplay between anomalous land surface conditions, shallow and deep convection, diabatic heating, and large-scale anticyclonic circulation influence the onset, duration and demise of the drought on a seasonal scale over the Great Plains?
  • What role do sub-seasonal atmospheric variability play in the onset and demise of the drought memory on a seasonal scale?
  • What are the causes of drought memory in dynamic models such as those of CFSv2, CCSM4, CESM1 being weak and short-lived?

We will use CFSR, MERRA, NARR reanalyses, and NLDAS land surface product to diagnose the large-scale atmospheric and land surface anomalous conditions. We will also use long-term ground based and satellite datasets, such as the NCDC rainfall data, the North American Soil Moisture Database (NASMD) data, the Vegetation Drought Response Index (VegDRI), the Sun Induced Fluorescence (SIF), CloudSat data, to diagnose the anomalous conditions of cloud, rainfall, surface temperature, soil moisture and vegetation. The Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric measurements and Oklahoma Mesonet will be used for detailed process studies. We will evaluate the seasonal predictions from the CFSv2, CCSM4 and CESM1 of the North-American Multi-Model Ensemble (NMME) prediction system.

Climate Risk Area: Water Resources

Principal Investigator (s): Fu, Rong (UCLA)

Co-PI (s):

Task Force: Drought

Year Initially Funded:2017

Competition: Advancing drought understanding, monitoring and prediction

Final Report:

Collaborative Research: Toward operational predictions of persistent drought driven by multi-year La Niña

The interannual cooling of the equatorial Pacific associated with La Niña events has been known to cause drought conditions over the southern tier of the US by displacing the subtropical jet and storm track northward. Whereas El Niño usually terminates after one year, La Niña often persists for 2 years or longer, exacerbating its impacts. It is therefore critical to predict the occurrence of such events with sufficient lead time. Our ongoing work funded by the NOAA CPC MAPP Program laid the groundwork for the prediction of La Niña-driven drought. Through “perfect model” experiments using the Community Earth System Model version 1 (CESM1), we showed that the duration of La Niña is predictable with lead times of 18-24 months. Furthermore, our observational analyses indicate that multi-year La Niña events have robust, sustained impacts on cold season precipitation deficits over the southern half of the US. To further advance our research toward operational predictions of persistent drought driven by multi-year La Niña, we propose the following three sets of research activities:

Task 1 (La Niña duration): We will assess the predictability of La Niña duration in the real world using two suites of long-range CESM1 forecasts initialized with observed oceanic conditions every July and November during 1954-2014. July and November are chosen because they correspond to the peak of El Niño and subsequent discharge of heat from the equatorial Pacific, respectively, after which La Niña typically develops. We will analyze the predictability of La Niña duration and the dynamical processes underlying that predictability for all events occurring during 1954-2014.

Task 2 (La Niña teleconnections): We will assess the predictability of atmospheric teleconnections forced by La Niña by quantifying the influence of various factors including internal atmospheric variability using the long-range CESM1 forecasts and two other suites of atmospheric model simulations in which tropical Pacific SSTs are constrained to observed values during 1880-2015. We will also analyze the mechanisms responsible for the evolution of the teleconnections over the course of multi-year La Niña events using a simple atmospheric model, as well as the atmospheric component of CESM1.

Task 3 (La Niña impacts on drought): We will investigate how the cold season precipitation deficits induced by La Niña teleconnections affect drought over the southern US during the course of multi-year La Niña events through analyses of observational data. We will use soil moisture and accumulated precipitation anomalies as indicators of drought. In particular, we will examine how precipitation deficits during the second cold season of multi-year La Niña events increase the severity and duration of drought compared to single-year La Niña events. We will also explore the predictability of La Niña-driven drought with the same suites of atmospheric model simulations used in Task 2.

Persistent drought driven by multi-year La Niña exerts tremendous socioeconomic impacts on the southern tier of the US. The proposed research activities will fill important gaps in our current understanding of La Niña-driven drought by exploring the predictability of three underlying processes: the duration of La Niña (Task 1); atmospheric teleconnections driven by La Niña (Task 2); and impacts of La Niña teleconnections on drought (Task 3). The project will thus represent a major contribution toward the Drought Task Force activities and a stated objective of the MAPP Competition: “advancing drought understanding, monitoring and prediction”, as well as NOAA’s long-term climate goal: “an informed society anticipating and responding to climate and its impacts”.

Climate Risk Area: Water Resources

Principal Investigator (s): Okumura, Yuko (University of Texas)

Co-PI (s):Pedro DiNezio (University of Texas at Austin), Clara Deser (NCAR)

Task Force: Drought

Year Initially Funded:2017

Competition: Advancing drought understanding, monitoring and prediction

Final Report: NA17OAR4310145_Final Report (1).pdf

Developing an automated weekly probabilistic and categorical drought outlook based on U.S. Drought Monitor and ensemble prediction

Drought monitoring and prediction are critical components of the NOAA-led National Integrated Drought Information System (NIDIS). The U.S. Drought Monitor (USDM) has played an important role in gathering, synthesizing and disseminating drought information to a range of users and stakeholders. The USDM is reasonably realistic with multiple sources of information; it is simple and easy to understand with five intensity categories; and it is up-to-date with a fixed weekly release. These features have made the USDM very successful and popular among drought users and stakeholders. It is the USDM drought map that policymakers and media use in discussion of drought and in allocating drought relief. However, on the prediction side, there is currently no suitable drought outlook that matches up with the USDM although monthly and seasonal drought outlooks are issued each month by CPC.

Our team has identified five important gaps in NOAA’s drought prediction capability, and this proposed project will develop an automated, weekly probabilistic and categorical drought outlook to fill these gaps. The proposed research builds off existing operational products, such as the USDM, LIS-based NLDAS, CFSv2 and NMME seasonal forecast, and research outcomes such as the weekly ensemble drought prediction system developed at MSU and the statistical modeling framework for producing probabilistic forecast of USDM drought categories from monthly drought indicators. All the model and data products are readily available; the key methods for utilizing these model and data products to produce a probabilistic and categorical drought outlook have been developed and tested in recent research. This project is a natural step towards integration of the them to produce the desired drought outlook that is multiple model- based, objective, probabilistic, categorical, and can be run on a weekly schedule to match exactly the USDM schedule. To achieve this, the project comprises of six well designed tasks from multimodel offline simulation and seasonal forecast with the LIS-based NLDAS framework to the development and evaluation of the ordinal regression model for predicting the USDM drought categories. The combination of dynamical modeling and statistical modeling is the major strength of this project. These tasks are well connected to ensure the success of the project. This proposed drought outlook system can potentially be seamlessly integrated with the current USDM to provide simple, easy-to-understand and up-to-date drought forecast information to users of USDM.

This proposal responds directly to Competition 1 (Advancing drought understanding, monitoring and prediction) of the MAPP program for FY2017. More specifically, the objectives of this project are in line with several priority areas highlighted in the MAPP information sheet. The project will help to advancing drought prediction system and outlooks operated, used, and produced by NOAA that contribute to the Drought Early Warning System effort. It will also contribute to the development of new national-scale monitoring and forecast products that can help integrate the results of research advances into improved information for managers and communities. From a practical perspective, the automated system with improved skill will provide additional assistance to CPC forecasters to improve their drought outlook.

Climate Risk Area: Water Resources

Principal Investigator (s): Luo, Lifeng (Michigan State University)

Co-PI (s):Youlong Xia (NOAA/EMC), ZENGCHAO HAO (Beijing Normal University, China)

Task Force: Drought

Year Initially Funded:2017

Competition: Advancing drought understanding, monitoring and prediction

Final Report:

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