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Sort by: Title Principal Investigator (s) Task Force Year Initially Funded
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Understanding predictability of Long Lived North American Drought Regimes

View abstract
A defining feature of U.S. drought variability is its regime-like behavior. The 1930s, 1950s, and the 2000s stand out as national-scale dry epochs, while the 1980’s and 1990’s were comparatively wet periods. The dry epochs are not merely the result of a single, especially severe drought event, but are typically plagued by a series of recurring droughts. These sometimes strike the same portion of the nation year-over-year (e.g., southern Plains in the 1950s), while at other times, drought tends to progress and appears to spread across the contiguous U.S. over an extended period (e.g. in the 2000s). Concerns that a new regime of sustained and severe drought may now be taking hold over the U.S. grain belt, harkening perhaps to the 1930s, has been triggered by the recent drought events in the Great Plains that began in 2011 over southern portions and expanded to central portions in 2012.

This proposed project seeks to understand why the contiguous U.S. experiences distinct decadal-scale modulations in drought. While individual seasonal drought events have been extensively studied, and for which considerable progress is being made to improve their predictability, less is known about the regime-like behavior. This project seeks to understand if drought regimes are a consequence of slowly evolving forcings of climate, and whether those forcings and their accompanying impacts possess predictability. In particular, are drought regimes due principally to low frequency oceanic forcings (e.g. Atlantic and Pacific decadal-like ocean variations), or are they mainly a statistical residual (or rectified response) of higher frequency ocean fluctuations linked to El Niño – Southern Oscillation (ENSO)? Can prolonged dry periods arise from atmosphere-land coupled variability alone without ocean forcing, and if so what are the implications for predictability? In this sense, the project seeks to understand whether the various US drought epochs of the last century have had common causes. Further, given the recent proliferation of severe U.S. drought during a period in which national temperatures were their warmest on record, the question arises if the role of climate change is now exerting an appreciable effect on sustained drought risks.

The project proposes mechanistic studies using model simulations and initialized decadal predictions to understand the causes for, and predictability of, US drought regimes. The focus is on long-term factors, such as decadal ocean variability and anthropogenically-driven trends, though cognizant of possible rectification resulting from seasonal SST variations. In parallel with climate simulations that will explore the physical processes causing decadal dry and wet epochs, a set of decadal predictions will assess existing capabilities to anticipate such epochs. Climate simulations spanning the last century will be used to understand how climate change forcing has acted to influence the risks of sustained droughts.

The goals of the proposal are closely aligned with the CPO/MAPP FY14 announcement of opportunity research foci of “Research to Advance Understanding, Monitoring, and Prediction of Drought - Understanding Predictability of Past Droughts over North America.” More specifically to “...consider mechanistic studies involving model simulations and predictions to examine processes such as the role of land surface conditions, oceanic conditions, and atmospheric feedbacks; long-term factors, such as decadal variability or anthropogenically3 driven trends versus immediate meteorological causes.” Proposal objectives also align closely with the NOAA’s Next Generation Strategic Plan (NGSP) objectives to (a) “Improved scientific understanding of the changing climate system and its impacts”, and (b) “Assessments of current and future states of the climate system that identify potential impacts and inform science, service, and stewardship decisions.”

Principal Investigator (s): Kumar, Arun (NOAA/CPC)

Co-PI (s): Martin Hoerling (NOAA/ESRL), Siegfried Schubert (NASA/GSFC)
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Objective monitoring and prediction system for drought classification over the continental United States

View abstract
This proposal responds to the 2014 solicitation for CPO’s Modeling, Analysis, Prediction and Projection (MAPP) program. The proposal specifically responds to area 1) of the solicitation, Research to Advance Understanding, Monitoring, and Prediction of Drought, and within that priority, Focus area 2), advancing the development of a national drought monitoring and prediction system. This is a joint proposal from NOAA’s Climate Prediction Center (CPC) and the University of Washington; with parallel submissions from both entities.

The U.S Drought Monitor (USDM) and the seasonal and monthly Drought Outlooks (USDO) are used by water resources managers, government and state agencies in their planning efforts which are intended to reduce the severity of the impacts of drought to U.S. society. The USDM classifies drought into categories D0-D4 (moderate to severe). The USDO predicts the development of drought in terms of changes in the same categories.
One major gap in the drought information system that underlies the USDM and USDO is that the suite of NOAA climate prediction models does not explicitly use the D0-D4 categories. Instead, NOAA’s drought monitoring and prediction capabilities are based on the North American Land Data Assimilation Systems (NLDAS), which use model-predicted soil moisture and runoff (typically expressed as percentiles relative to historical runs of four component land surface models) in lieu of observations (which are not available over domains as large as the continental U.S.). While monitoring systems based on sources like NLDAS are able to detect droughts, they are challenged by classification of drought into the D0 to D4 categories in part due to uncertainties among multiple drought indicators, models and assimilation systems. While the USDM authors use both subjective and objective information in the USDM (the former to incorporate “on the ground” observations into their drought identifications), they have difficulty in the use of NLDAS-based objective information because it is formulated in a fundamentally different manner than the USDM classifications. For the same reason, there is at present no well formulated method of incorporating objective forecasts of drought categories into the USDO.

We propose to explore an objective scheme for drawing boundaries between the D0-D4 classes used by the USDM. Our approach will be based on multiple drought indices that will be derived from NLDAS outputs, from which we will form an ensemble mean index. We will then remap the mean index to a uniform distribution by using the climatology of the ensemble (percentiles) averages. To assess uncertainties in the classifications, we plan to use a concurrence measure among indices. The classification scheme we propose to develop will provide information about drought severity, as well as the representativeness of the ensemble mean index. Forecasts of the indices will be derived using the National Multi-Model Ensemble (NMME) system to force the four land surface models (LSMs) that operate within NLDAS (NMME_LSM). The initial conditions for each LSM will be taken from NLDAS, which drives the LSMs with observed forcings. We expect that the objective drought classification nowcasts and forecasts that we propose to develop based on the NMME_LSM will fill a major gap in the drought information system widely used within the U.S., and will provide drought forecasters with a mechanism to issue reproducible drought forecasts.

Principal Investigator (s): Mo, Kingtse (NOAA/CPC)

Co-PI (s): Dennis Lettenmaier (University of California, Los Angeles)
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Towards an Enhanced Probabilistic National Drought Forecasting

View abstract
Improved long-range hydrologic drought forecasts can help mitigate drought impacts-they provide guidance to agencies responsible for water allocation, water conservation, and the mitigation of other adverse impacts such as wild land fires. Both observations and models have indicated an increasing trend in drought events that could be linked to recent global climate change.

With respect to drought monitoring and prediction, the U.S. Drought Monitor (DM) and the NOAA Climate Prediction Center’s (CPC) US Drought Outlook (DO), are the primary operational tools available to water managers dealing with drought contingencies. However, both the DM and DO depend on a range of tools from simple statistical indices to water balance models driven by statistical forecasts. MAPP supports developing an integrated drought prediction capability that incorporates research advances into operational seasonal to intra-seasonal climate and hydrologic prediction by means of multiple data sources, hydrologic modeling and data assimilation. The program puts special emphasis on integration of dynamical prediction systems with statistical methods where improved initial condition and probabilistic prediction are considered the key elements in advancing the drought prediction system. The probabilistic framework is expected to improve the characterization of uncertainties, the accuracy, reliability and confidence in drought prediction as the main undertakings under this program.

Uncertainties are an unfortunate yet inevitable part of any forecasting system. Within the context of seasonal hydrologic predictions, these uncertainties can be attributed to three causes: our imperfect characterization of initial conditions, an incomplete knowledge of future climate and errors within computational models. In order to effectively manage these uncertainties, each of these factors must be quantified, providing a framework to reduce uncertainty and accurately convey persistent predictive uncertainty. Remote sensing-based real-time snow extent fields can, in some regions, help characterize one of the key components used to define drought, which is critically important for identifying conditions that could flicker seasonal droughts.

Here, we propose a three-year collaborative research project that will quantify and reduce the major uncertainties involved in drought forecasting by implementing state of the art of data assimilation methods and multivariate statistical drought forecasting by means of copula functions. This is a combined dynamical-statistical approach that is designed to characterize the uncertainties. Multivariate framework allows developing joint distribution function of drought predictors including initial condition estimated in the form of snow and soil moisture storages using data assimilation, and also streamflow observation and simulation. The framework is compared with a purely dynamical framework where the ensemble streamflow prediction (ESP) is generated by deriving the hydrologic model with the bias-corrected climate forecasts. We will focus in particular on those observed variables that have the greatest potential for improving hydrological forecasts in different regions of the U.S. During this process we examine the effectiveness of newly developed postprocessing methods applicable for climate forecast in generating the ESP and accordingly drought forecast. Assessment of prediction skills using deterministic and probabilistic approaches that evaluate the reliability and confidence is conducted.

This proposal responds to the second area solicited on the MAPP Information Sheet "B. Advancing the Development of a National Drought Monitoring and Prediction System, in particular the consideration of the opportunity to integrate state-of-the-art dynamical prediction systems, statistical methodologies, and improved initial conditions through data assimilation to develop more accurate and skillful regional-scale probabilistic drought predictions on intra-seasonal to seasonal time scales”. Assessing the prediction skills will be according to the metrics identified by the MAPP drought task force, working group 1 (http://mappdroughttaskforce.wikispaces.com/) where the PI has been actively engaged in identifying and selecting those metrics. The proposed research will expand on past NOAA-MAPP, -CSTAR and -CPPA funded research by the PI and others in the development and application of hydrologic forecasting with a special attention to drought. The proposed project aligns well with the NOAA’s long-term climate goal as described in NOAA’s Next Generation Strategic Plan in particular on: 1) improved scientific understanding of the changing climate system and its impacts, and 2) mitigation and adaptation choices supported by sustained, reliable, and timely climate services.

Principal Investigator (s): Moradkhani, Hamid (Portland State University)

Co-PI (s): N/A
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

High-Resolution Integrated Drought Monitoring

View abstract
Drought information is being increasingly relied upon at county and sub-county spatial scales, yet most in situ observation-based tools are only available at much coarser resolution. The NOAA/NWS River Forecast Centers operationally produce high-resolution multi-sensor precipitation estimates (MPE) at ~4 km spatial resolution, and these data have become an invaluable resource for local, state, and national drought monitoring. Recent efforts by members of this project team have led to operational production and public web delivery of daily updated Standardized Precipitation Indices (SPI) with national coverage using MPE as the primary input. However, SPI is a relatively crude index for drought, and even the most sophisticated index, while useful, provides much less information than a well-calibrated land surface model.

We are now in the process of developing long-term bias correction methods to convert MPE into a reliable measure of long-term precipitation deficits and surpluses. We propose to use this biascorrected MPE to calculate drought indices such as the Standardized Precipitation Evapotranspiration Index (SPEI) and the various Palmer Drought Indices (PDI), both of which can be more informative than SPI because they incorporate effects of evaporation as well as precipitation. Gridded daily temperature data will be obtained from daily PRISM temperature grids, MODIS Daily Land Surface Temperature, or NWS national gridded MOS of daily maximum and minimum temperatures. As with our present SPI analyses, these gridded indices would be publicly available over the web through a convenient interface and would be downloadable as shapefiles.

In addition, we shall develop and test the implementation of bias-corrected MPE as input into the North American Land Data Assimilation System (NLDAS). Using the Noah land surface model, we shall perform comparison runs with and without bias-correction and evaluate impacts on soil moisture profiles and streamflow. The results will be evaluated using streamflow gauge measurements in natural basins. The initial comparison will be performed with MPE aggregated to the 1/8 degree grid that is conventional for NLDAS, but as NLDAS migrates to higher spatial resolution, the benefits of full-resolution bias-corrected MPE will be tested and are expected to be even larger. The methods being developed are not resolution-specific and can continue to be applied even as MPE methods and resolution change.

This project squarely addresses the Modeling, Analysis, Predictions, and Projections (MAPP) competition priority of advancing the development of a national drought monitoring and prediction system. The project explicitly involves drought monitoring, and since precipitation deficits precede drought impacts, having high-resolution meteorological drought information can provide the basis for drought impact prediction. The project specifically addresses the NOAA’s Strategic Plan by providing improved “assessment of current and future states of the climate systems that identify potential impacts” and by providing “timely climate services” (NGSP 2013). The output from this project will directly benefit many existing NOAA initiatives, including drought.gov, NIDIS, NLDAS, and the U.S. Drought Monitor.

Principal Investigator (s): Nielsen-Gammon, John (Texas A&M University)

Co-PI (s): Ryan Boyles (NC State University), Michael Ek (NOAA/EMC), Rebecca Cumbie (NC State University), Youlong Xia (NOAA/EMC)
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Development of probabilistic drought intensification forecasts using the GOES-BASED Evaporative Stress Index

View abstract
We propose to develop a drought early warning system based on satellite-derived maps of evapotranspiration (ET) and forecast output from the National Multi Model Ensemble (NMME) that will provide probabilistic drought intensification forecasts over weekly to monthly time scales. Recent examples of rapid drought development have clearly demonstrated the need for a reliable drought early warning system capable of providing vulnerable stakeholders additional time to prepare for worsening drought conditions.

The proposed study will use the Evaporative Stress Index (ESI) dataset generated with the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance model using GOES thermal infrared imagery. The ESI represents standardized anomalies in the ratio of actual-topotential ET, and has been shown to agree well with standard precipitation-based drought indices and with the U.S. Drought Monitor (USDM). Because ALEXI computes ET using remotely sensed land surface temperatures, which respond quickly to changes in soil moisture content, the ESI is often able to detect increasing water stress sooner than other drought metrics, thereby making it a useful drought early warning tool. Temporal changes in the ESI (referred to as ΔESI) have been shown to provide valuable information about the rate of drought intensification, thus, a Rapid Change Index (RCI) product encapsulating the cumulative magnitude of ΔESI anomalies has also been developed. Preliminary work has revealed a strong relationship between the magnitude of the RCI and subsequent changes in the USDM drought depiction.

In this work, probabilistic drought intensification forecasts will be generated each week across the contiguous U.S. based on the RCI and NMME forecast output. New insight into the causes of rapid drought development will be gained through detailed analyses of soil moisture, rainfall, and atmospheric anomalies both preceding and accompanying notable flash drought events in recent years. Refinements will be made to the RCI-based probabilities through development of synergistic methods that combine drought early warning signals from multiple data sources, such as the ESI, Standardized Precipitation Index, and the North American Land Data Assimilation System, and through evaluation of alternative forms of RCI computation. After evaluating the efficacy of the RCI-derived drought intensification probabilities, new methods will be devised to incorporate ensemble forecasts of temperature and rainfall from the NMME as a means of further enhancing their forecast skill. The drought forecast products will be relevant to multiple end users, including authors of the Climate Prediction Center Seasonal and Monthly Drought Outlook products.

The proposed project will benefit the MAPP initiative through the development of an innovative probabilistic drought early warning and forecasting tool that will support decision-making and risk characterization by vulnerable stakeholders. The development of robust drought intensification forecasts with high spatial resolution addresses the NOAA Next-Generation Strategic Plan by providing early warning of worsening drought conditions that will support regional drought preparation and mitigation activities. Timely delivery of the probabilistic forecasts through the NIDIS user portal will help inform the public about the possibility of rapidly worsening drought conditions.

Principal Investigator (s): Otkin, Jason (University of Wisconsin-Madison)

Co-PI (s): Martha Anderson (USDA-ARS), Mark Svoboda (University of Nebraska-Lincoln), Chris Hain (University of Maryland), Xiwu Zhan (NOAA/NESDIS/STAR)
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Advancing probabilistic drought monitoring with the North American Land Data Assimilation System (NLDAS) through multisensor ensemble data assimilation

View abstract
Background and Objectives
The North American Land Data Assimilation System (NLDAS) has a long successful history of producing surface meteorology and precipitation datasets used as forcing for land-surface models (LSMs) to produce soil moisture, snow cover, and runoff/streamflow products. These products have been used by both MAPP and non-MAPP researchers for applications including drought and streamflow monitoring, as well as initial conditions for drought forecast systems and for short-term weather forecasts. To maintain the state-of-the-art nature of this land data assimilation system, several NLDAS upgrades are proposed, including the addition of three community LSMs that include a prognostic water table to help characterize hydrological drought. The assimilation of recent terrestrial water storage and surface soil moisture products, as well as incorporating the effects of irrigation, should also better represent evolving drought conditions. Probabilistic historic drought analysis and probabilistic real-time drought monitoring capabilities enabled by the Land Information System (LIS) architecture used for NLDAS can provide a “probability of drought” rather than a single deterministic drought index.

Brief Summary of Work to be Completed
The proposed work will include the following elements: 1) Adding improvements and capabilities to the NLDAS data production and drought monitoring system. This effort will include adding the latest versions of the Noah-MP and the CLM LSMs, in addition to the NASA Catchment LSM added under previous MAPP funding, to take advantage of their groundwater sub-modules to obtain a more complete picture of drought conditions; 2) Assimilating GRACE terrestrial water storage and SMAP surface soil moisture products into the NLDAS system for better diagnosis of drought and improvement of initial land conditions. This task will also include the effects of irrigation using maps of irrigated area derived from MODIS; and 3) Performing probabilistic historical drought analysis as well as real-time monitoring to both advance our understanding of drought and provide a measure of drought uncertainty.

Relevance to Program Announcement Research Area
The proposed work is in response to MAPP Competition: Research to Advance Understanding, Monitoring, and Prediction of Drought and will be primarily relevant to focus area B: Advancing the Development of a National Drought Monitoring and Prediction System. The improvements to the NLDAS drought monitoring and data products will be made by assimilating multiple data sources into community models to objectively evaluate the impact of model and data upgrades on modeled soil moisture, snow, and groundwater products with the goal to improve drought analysis and monitoring. The improved land-surface states will also provide initial conditions to a complimentary drought prediction system also being proposed under this call. The probabilistic drought analysis supported by our ensemble data assimilation system is also relevant to the goals of the MAPP program, and will represent a significant advance in drought monitoring and assessment.

Principal Investigator (s): Peters-Lidard, Christa (NASA/GSFC)

Co-PI (s): David Mocko (NASA/GSFC), Sujay Kumar (NASA/GSFC), Shugong Wang (NASA/GSFC), Michael Ek (NOAA/EMC), Youlong Xia (NOAA/EMC), Jiarui Dong (NOAA/EMC)
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Precursor conditions to onset and breakdown of agricultural drought over the United States corn belt region

View abstract
The proposed work targets the NOAA competition “Modeling, Analysis, Predictions, and Projections (MAPP), Competition 5: Research to Advance Understanding, Monitoring, and Prediction of Drought. The proposed work would fulfill both points 2) advancing the development of a national drought monitoring and Prediction system and objective 1) understanding the predictability of past droughts. The proposed work will generate a ~60 year record to identify both the onset and termination dates or pentads of intraseasonal and longer period agricultural drought events in the United States Midwest Corn Belt Region (CBR) based on an index of anomalous crop maturation reconstructed from temperature and rainfall data as well as on the NOAA crop moisture index (CMI). Time lag composites and time extended empirical orthogonal function analysis applied to these events will show the average pathway as well as other leading pathways to transition toward or away from drought conditions. These pathways will be analyzed to reveal evolving precursor patterns of drought onset or termination. Then, the predictability of these different types of drought transition events will be assessed in the climate forecast system (CFS) V2 and in the global ensemble forecasting system (GEFS) reforecast datasets, with skill benchmarked against each other, against a statistical approach based on identification of precursor patterns, and against climatology. The statistical algorithms will ultimately predict anticipated fractions of full corn crop given model forecasts and the observation or prediction of precursor patterns of drought onset or termination.

The proposed work would broaden NOAA’s drought monitoring and prediction systems by developing critical crop-specific algorithms for evaluation and prediction of intraseasonal drought development and termination events that conveniently associate directly with agricultural and economic outcomes of such events. The proposed research would advance understanding, monitoring, and prediction of drought and would provide information about the precursor patterns to drought transition along with an online real time prediction system for the probabilities of such transitions.

Results will fulfill NOAA’s Next Generation Strategic Plan by 1) improving our scientific understanding of drought and its impacts, 2) developing algorithms to track and predict drought onset or termination events in ways that might help agricultural decision makers and commodity markets, 3) making drought prediction services more directly applicable to public needs, including in agriculture, and 4) increasing public awareness of the weather and climate patterns that tend to lead to development or decline of drought events and the state of the art in predicting development or termination of drought.

Principal Investigator (s): Roundy, Paul (University of Albany, SUNY)

Co-PI (s): N/A
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

The Dynamical Mechanisms and Potential Predictability of Indian and Pacific Ocean Influences on Seasonal North American Drought

View abstract
Seasonal drought prediction for North America is currently limited to the impacts of tropical Pacific sea surface temperature (SST) anomalies in the winter half year. Predictability is small in winters without strong El Ni˜nos or La Ni˜nas and in the summer half year. However, droughts are year round events and summer droughts can be extremely costly via impacts on agriculture. Does this situation represent the limits of predictability or could predictability be improved based on atmospheric responses to other tropical SST anomalies? Further, could prediction on the basis of tropical Pacific SST anomalies be refined by taking account of the differing patterns of anomalies and the potential different patterns of atmospheric and precipitation response? These questions will be addressed in this proposal. The work proposed combines two thrusts. The first is an extensive set of numerical experiments with comprehensive atmosphere models and diagnostic models. 100-member super-ensembles of climate model simulations will be generated in which various realistic Pacific and Indian Ocean SST anomalies are switched on, creating dis-equilibrium, and the day-by-day transient adjustment of the atmospheric circulation and precipitation back to statistical equilibrium is analyzed. This modeling approach allows establishment of cause and effect in the response to SST anomalies in a way that is impossible by analyzing observations, or models forced with realistically evolving SSTs, where the atmosphere is always in statistical equilibrium with the SSTs. At least two different climate models will be used. The transient adjustment in the climate model simulations will be fully diagnosed using nonlinear storm track models, a linear stationary wave model and a linear quasi-geostrophic model of transient eddy propagation. In this way the transient eddy-mean flow interactions that connect tropical SST anomalies and the precipitation over North America will be fully understood. Imposed SST anomaly patterns will include eastern Pacific centered El Ni˜nos and La Ni˜nas, El Ni˜no-Modoki patterns centered in the central Pacific, and Indian Ocean anomalies. Ground truth will be analyses of observed relations between the SST anomalies and reanalyzed circulation and instrumental precipitation fields. The work will allow a more comprehensive understanding than available to date of how tropical Pacific and Indian Ocean SST anomalies can impact North American precipitation and drought, what matters in the SST anomalies, what the strength of the relations are, how this depends on season, and, most importantly, what the physical mechanisms are that couple the mean and transient atmospheric circulation and the moisture budget. The second thrust will apply the lessons learned to analysis of two specific great North American droughts: the post 1998 drought that still goes on but with a focus on the 1998 to 2004 period, and the early to mid 1890s drought which was a turning point in the economic and agricultural development of the American West.

Relevance to NOAA’s goals: The proposal will help fulfill aims of the MAPP program on “Research to Advance Understanding, Monitoring and Prediction of Drought” with stated goal of “understanding predictability of past droughts over North America” via an intensive modeling and observational study of general predictability offered by tropical SST anomalies and detailed analysis of two historical North American droughts and will help advance NOAA’s goal to provide useful support to the social challenge of dealing with “climate impacts on water resources”.

Relevance to society as a whole: Improved drought prediction could potentially be used to guide adaptation strategies and minimize the tremendous costs to individuals, organizations and the entire nation that currently occur when drought strikes and water becomes limited for agricultural, municipal and other uses. Such advances require a clear assessment of what the limits of predictability are and what cannot be predicted in advance and must remain as inevitable surprises.

Principal Investigator (s): Seager, Richard (Columbia University/LDEO)

Co-PI (s): Mingfang Ting (Columbia University/LDEO), Isla Simpson (Columbia University/LDEO), Naomi Henderson (Columbia University/LDEO), Dong Eun Lee (Columbia University/LDEO)
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Advancing predictive understanding of North American Drought: The role of the North Atlantic SST

View abstract
The United States experienced record-breaking droughts in recent years and the physical mechanisms and the predictability of such severe and persistent droughts are not entirely understood. While the tropical Pacific plays a key role in U.S. precipitation fluctuations on a year-to-year basis through the impact of ENSO-related sea surface temperature (SST) anomalies, particularly during the winter season, the persistence and severity of U.S. droughts cannot be explained entirely by the tropical Pacific SST fluctuations. Many previous studies have shown the important role played by the North Atlantic SST in causing U.S. rainfall and temperature anomalies as well as the combined effect of the Pacific and the Atlantic SST on the U.S. hydroclimate variability. However, there is no systematic examination of the impacts of North Atlantic conditions on U.S. drought predictability.

The frequency and severity of droughts across North America has been modulated by the phase of the Atlantic Multidecadal Variability (AMV) over the historical period. The decadal oscillations in U.S. West hydroclimate (associated with ENSO) reach extreme severity during the warm and neutral phases of AMV, such as in the 1930s and the 1950s when the U.S. Great Plains and the Southwest experienced the extremely dry conditions of the Dust Bowl and the persistent Texas drought, respectively. While when AMV was in its cold phase in the early 1900s and from 1965 to 1995 droughts were less frequent or severe. The recent increases in drought severity in the Central U.S. concurs with the cold phase of the Pacific Decadal Oscillation but also with the current warm phase of AMV.

The proposed work includes two major components: (1) Mechanisms of North Atlantic SST impact on U.S. drought: We will conduct atmospheric GCM experiments using the NCAR CAM5 as well as IRI’s operational seasonal forecast models (currently running ECHAM4.5), with prescribed tropical and subpolar North Atlantic SST to understand the dynamical linkages between SST and U.S. precipitation and temperature. (2) Predictability of U.S. precipitation given AMV phases: Given the longer time scale of the AMV and the advancements in the field of decadal prediction carried out as part of the CMIP5, we will examine in detail how predictability may be improved by knowing the state of the AMV while the rest of the oceans are varying seasonally and interannually.

This proposal is submitted to NOAA MAPP program and address MAPP’s research priority “To advance understanding, monitoring, and prediction of drought” and the focus area 1) understanding predictability of past droughts over North America. It will also help with the focus area 2) advancing the development of a national drought monitoring and prediction system, through improving IRI’s seasonal forecasts. The proposed work will build on recent NOAA supported research of AMOC and the AMV done by the Lamont PIs and the expertise of the IRI collaborators in the areas of seasonal and interannual climate predictions.

Principal Investigator (s): Ting, Mingfang (Columbia University/LDEO)

Co-PI (s): Yochanan Kushnir (Columbia University/LDEO), Dong Eun Lee (Columbia University/LDEO), Anthony Barnston (Columbia University/IRI), and Richard Seager (Columbia University/LDEO)
Year Initially Funded: 2014
Task Force:
Drought Task Force
Final Report:

Subseasonal development of past North American droughts: The role of stationary Rossby waves in a changing climate

View abstract
Drought is one of the most economically expensive recurring natural disasters to affect North America. Accurate drought predictions from weeks to a season in advance require an advanced understanding of predictability of drought development on subseasonal time scales and an improvement in the representation of drought processes in model prediction systems. Here we propose to investigate mechanisms and predictability of North American drought development on subseasonal time scales by studying the role of stationary Rossby waves. Recent research by the PI and Co-Is has shown that these waves play a key role in atmospheric circulation and surface meteorological variability on subseasonal time scales and that they have been crucial in
the development of recent warm season droughts and heat waves over North America (e.g. the 1988, 1998 and 2012 summer droughts). This proposal, which builds on our prior work, will shed new light on the role of stationary Rossby waves in the development of North American droughts, the physical processes that initiate and sustain the stationary Rossby waves, and the predictability of these waves.

Our proposed work has three thrusts. First, through a comprehensive diagnosis of observations and reanalyses, we will investigate the physical mechanisms by which leading quasi-stationary subseasonal atmospheric circulation variability affected the development of past North American droughts. We will also diagnose initiation and maintenance of these circulation anomalies by determining the key regional forcing anomalies. Second, we will investigate the predictability of the stationary Rossby waves, using a set of idealized Atmospheric General Circulation Model (AGCM) experiments and a series of short-term GCM hindcasts for selected past North American droughts. The potential sources of predictability for the stationary Rossby waves will be explored by investigating the roles of subseasonal SST processes in tropics and subtropics, extratropical air-sea interaction and land feedbacks over North America in initiating and sustaining the stationary Rossby waves. Additionally, the influence of model climate drift on model hindcasts of stationary Rossby waves and our understanding of their predictability will be addressed. Third, we will investigate the modulating effects of El Niño – Southern Oscillation (ENSO) and climate variations on decadal and longer time scales (including the Pacific Decadal Variability [PDV], Atlantic Multi-decadal Variability [AMV], and the globally warming trend) on the characteristics and frequency of occurrence of the stationary Rossby waves that affect North America. This will proceed by examining the changes they exert on the optimal stationary wave forcing distribution for the leading subseasonal atmospheric circulation patterns using reanalyses, and the statistics of stationary Rossby waves in a set of idealized AGCM runs forced with Sea Surface Temperature (SST) anomaly patterns associated with these climate variations.

The proposed work directly targets the priority area “Research to Advance Understanding, Monitoring, and Prediction of Drought” solicited by the FY 2014 NOAA MAPP Program. The expected outcome of the proposed work is an improved understanding of the predictability of the development of North American droughts on subseasonal time scales. It is expected to contribute to NOAA’s long-term goal of climate adaptation and mitigation through “Improved scientific understanding of the changing climate system and its impacts”.

Principal Investigator (s): Wang, Hailan (NASA/GSFC)

Co-PI (s): Siegfried Schubert (NASA/GSFC), Randal Koster (NASA/GSFC)
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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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