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Sort by: Title Principal Investigator (s) Task Force Year Initially Funded
Year Initially Funded: 2010
Task Force:
Drought Task Force
Final Report:

Multi-Model Ensemble Combination and Conditional Stochastic Weather Generation Tool for Improved Streamflow Forecast

View abstract

Skillful basin wide streamflow forecasts at short (1-2 weeks) and long (seasonal and longer) time scales are important for efficient water resources management. This is particularly so in the Western US, which is semi-arid and its limited water resources are stressed due to unprecedented socio economic growth. The skillful ensemble hydrologic forecasts require (i) skillful hydrometeorological outlooks, (ii) suite of models – physical and statistical that captures the physical and climate features of the basin and provide ensemble forecasts conditioned on the outlooks and, (iii) an optimal combination tool. The outlooks have to be based on the short term weather forecasting information from NOAA/NWS and the seasonal climate forecast from NOAA. Current forecasts are provided by River Forecasting Centers based on a single physical model with limited ensemble generating capability and recent research suggests that a multimodel ensemble forecasting approach provides enhanced skills in the forecast than any single model. To this end this research proposes to develop two key tools - (i) a conditional stochastic weather generator to provide daily weather ensembles based on the NWS short term and NOAA seasonal outlooks and in-situ data including land surface observations to drive the RFC’s physical model to provide ensemble streamflow forecast and, (ii) an optimal multi-model ensemble combination to provide a combined ensemble forecast from physical and statistical models. We will demonstrate the framework by applying it to the Upper Colorado River Basin. The forecasts in this basin are critical for efficient operation and management of major reservoirs and consequently, the impacts on water resources, agriculture, hydropower and aquatic environment in the South Western and Inter mountain region of Western US.

Approach Proposed

The work we propose will involve the following streams:

(1) Development of tools to “translate” short term and seasonal forecasts from NWS and NCEP, respectively, to basin scale ensemble hydrometeorological forecasts.

(2) Drive the physical model with the hydrometeorological ensembles to obtain ensemble streamflow forecasts.

(3) Develop a multi-site statistical ensemble streamflow forecast model

(4) Develop an optimal combination tool to combine these and other available forecasts to provide a multi-model ensemble forecast.

(5) Work with the water managers (USBR) in the basin to implement these forecasts for operations and management.


Principal Investigator (s): Balaji, Rajagopalan (University of Colorado)

Co-PI (s):
Year Initially Funded: 2010
Task Force:
Final Report:

Dynamical Climate Predictability of the NCEP CFS in the Stratosphere and the Statistical Downscaling for Climate Prediction in the Troposphere

View abstract

The primary objective of our proposed study is to explore a new source for the prediction skill of climate variability from intra-seasonal to interannual time scales, namely the stratospheretroposphere coupling. We envision that in addition to well researched ocean-atmosphere interaction (ENSO) and land-surface interaction, this may be the new avenue of progress through modes of variability that involve the stratosphere, or more generally the hemispheric mass circulation from the tropical troposphere into the high latitude stratosphere, and back through the troposphere.

The three main proposed research activities are

Task A: To systematically evaluate the prediction skill for stratospheric anomalies in the 25-year (1982-2006) retrospective seasonal climate predictions made by NCEP’s Climate Forecast System (CFS).

Task B: To explore the way of ‘specifying’ surface weather or its statistics (over a month) from (predicted values of) a few predictable modes in the stratosphere. In other words, we propose to identify some statistical diagnostic tools on the relationships between stratospheric circulation anomalies and the statistical distribution (or changes in the statistics) of tropospheric/surface synoptic scale weather events.

Task C: To explore how to utilize the information derived from the model-based stratosphere prediction and statistical-based stratosphere-troposphere coupling to improve the climate prediction skill of surface weather beyond the lead time of 2 weeks. We will develop a new set of products for winter season climate predictions at various lead times from a month to a season or longer.

The overall goal of the proposed research is to utilize the extra NWP skill in the stratosphere beyond inherent predictability time scale to be identified in “Task A” for climate predictions of tropospheric circulation anomalies by “downscaling” the stratospheric climate prediction (Task C) via the statistical diagnostic relations between the stratospheric and tropospheric anomalies to be identified in Task B. In essence, we propose a new hybrid climate prediction strategy: predicting the stratospheric circulation anomalies by a dynamically based general circulation model beyond the inherent predictability time scale and using the simultaneous diagnostic relation between the stratospheric and tropospheric circulation anomalies for climate predictions of the tropospheric climate variability. The proposed hybrid prediction strategy for troposphere/surface climate variability at seasonal and interannual time scales is akin to the strategy for weather forecasts in 1960/70s, which was to predict 500 hPa circulation using dynamical models and to forecasts surface conditions using (simultaneous) statistical downscaling from the 500 hPa to the surface (e.g., MOS).

Principal Investigator (s): Cai, Ming (Florida State University)

Co-PI (s): van den Dool, Huug (NOAA/CPC)
Year Initially Funded: 2010
Task Force:
Final Report:

Resolving the role of groundwater in multi-scale land-atmosphere dynamics using simulation, sensor networks and satellites: Juniata River Basin

View abstract

 

Prediction of flood and drought has proven particularly challenging in small upland river basins (1 to 10,000 km2 and 1st to 4th order channel networks) which represent the major water generating areas to downstream, higher order rivers. Current prediction schemes for the most part, rely upon statistical methods, not physics-based prognostic models at these scales. Further, while advances in weather prediction have come from improved representation of soil moisture and vegetation fluxes, existing land surface schemes using in NWP models are limited to vertical moisture transport in the soil column and largely ignore deeper soil moisture processes and ground water. This proposal investigates the value of integrating a physics-based, highly dataconstrained model of ground water hydrology for a river basin in central Pennsylvania into flood/drought prediction, and into NWP models.

We will address two primary hypotheses: 1) A bedrock-to surface layer hydrologic modeling system, driven by satellite observations of the land surface and meteorological reananalyses, will improve simulation of flood and drought conditions in the Juniata River basin at 1-10,000 km2 spatial scales; and 2) Reanalysis of the ground water hydrology of the Juniata river basin using PIHM will significantly improve the accuracy of predictions of the basin-wide, daily surface energy balance at time scales where groundwater hydrology is predictable (days to months), relative to a prediction that does not include explicit modeling of ground water hydrology. The project will bring together resources including the Penn State Integrated Hydrologic Model (PIHM), the National Science Foundation sponsored Critical Zone Observatory (CZO) at Shale Hills, PA, and the Penn State ensemble Kalman filter (EnKF) data assimilation system. PIHM will be implemented across the Juniata River basin, initialized with high quality, static land surface characteristics, driven by a combination of North American Regional Reanalysis (NARR) meteorological and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation inputs, and optimized using basin-wide stream flow and ground water table data for the period from 2000 through 2010. The optimized model and basin-wide hydrologic reanalyses will then be used to evaluate the skill of the model-data assimilation system in predicting flood/drought conditions in the basin as well as sensible and latent heat fluxes in the basin relative to current operational models. In the short-term, we anticipate that this reanalysis of basin hydrology could be used to improve flood and drought forecasting in this and other river basins. Longer-term goals of the research are to describe the influence of seasonal, inter-annual and decadal climate variability and change on extreme events (floods and droughts), and to progress towards a fully-coupled, multiscale hydrologic and atmospheric modeling system that could yield important benefits in long-term weather forecasting. 

In terms of the FY2010 Climate Prediction Program for the Americas request for proposals, this proposal squarely addresses the request to “improve hydrologic predictions at regional scales at intraseasonal to interannual time scales,” and to, “improve understanding and process modeling of land surface physics including soil moisture, vegetation, snowpack, groundwater and processes in complex terrain,” two of the three elements called for in the request. This project has relevance to the first element of the call in that it has the potential to contribute to “climate predictability at intraseasonal to interannual time scales focusing on land memory effects.” The research also addresses CPPA priority 3. “Climate-based hydrologic and water management applications at regional scales”.

We will address two primary hypotheses: 1) A bedrock-to surface layer hydrologic modeling system, driven by satellite observations of the land surface and meteorological reananalyses, will improve simulation of flood and drought conditions in the Juniata River basin at 1-10,000 km2 spatial scales; and 2) Reanalysis of the ground water hydrology of the Juniata river basin using PIHM will significantly improve the accuracy of predictions of the basin-wide, daily surface energy balance at time scales where groundwater hydrology is predictable (days to months), relative to a prediction that does not include explicit modeling of ground water hydrology. The project will bring together resources including the Penn State Integrated Hydrologic Model (PIHM), the National Science Foundation sponsored Critical Zone Observatory (CZO) at Shale Hills, PA, and the Penn State ensemble Kalman filter (EnKF) data assimilation system. PIHM will be implemented across the Juniata River basin, initialized with high quality, static land surface characteristics, driven by a combination of North American Regional Reanalysis (NARR) meteorological and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation inputs, and optimized using basin-wide stream flow and ground water table data for the period from 2000 through 2010. The optimized model and basin-wide hydrologic reanalyses will then be used to evaluate the skill of the model-data assimilation system in predicting flood/drought conditions in the basin as well as sensible and latent heat fluxes in the basin relative to current operational models. In the short-term, we anticipate that this reanalysis of basin hydrology could be used to improve flood and drought forecasting in this and other river basins. Longer-term goals of the research are to describe the influence of seasonal, inter-annual and decadal climate variability and change on extreme events (floods and droughts), and to progress towards a fully-coupled, multiscale hydrologic and atmospheric modeling system that could yield important benefits in long-term weather forecasting.


Principal Investigator (s): Duffy, Christopher (Pennsylvania State University)

Co-PI (s): Davis, Kenneth (Pennsylvania State University); Zhang, Fuqing (Pennsylvania State University)
Year Initially Funded: 2010
Task Force:
Final Report:

Collaborative Research: Analysis of IPCC-AR5 and CFS model simulated stratosphere-troposphere coupling and its link to Eurasian snow cover variability

View abstract

The goals of the proposed research are to (a) analyze the output from IPCC AR5 class of models and the NCEP Climate Forecast System (CFS) hindcasts to assess the impact of stratosphere-troposphere (ST) coupling and Eurasian snow variability on the winter climate of North America, (b) further quantify atmospheric predictability associated with interannual snow variability using an operational prediction model, and (c) work with the operational community to improve climate forecasts from the intraseasonal to the interannual time scales by incorporating the predictive potential of snow variability into operational practices. The proposed work specifically addresses the goal of CPPA “to evaluate the ability of the IPCC-AR5 class models to simulate and predict ISI climate” and “improve understanding of climate predictability at ISI time scales focusing on stratosphere-troposphere coupling, land memory effects and weather-climate links.”

Skillful climate predictions throughout the extratropics remain a challenge for both statistical and numerical models. For the winter season, ST coupling is now understood to play an important role in winter surface anomalies, especially for those that persist for longer than synoptic time-scales and therefore are important for determining seasonal means. Furthermore, a statistically significant link has been demonstrated between Eurasian snow cover extent and major ST coupling events. Snow cover anomalies often lead ST coupling events by two to three months, making snow cover a potential predictor of winter climate anomalies. Previous analysis has shown that the two regions where snow cover has the highest potential for skillful prediction are East Asia and the eastern United States; the latter will be the focus of this proposal.

Our proposed research will focus on diagnosing model output and performing additional experiments designed to study ST coupling and its link to snow cover variability. Our initial analysis will study archived atmospheric state variables from control (i.e., preindustrial) and time-evolving GHG (i.e., climate of 20th Century and climate projection) GCM experiments from the IPCC AR4 and AR5 class of models. We will then compare the analysis from the IPCC AR4/AR5 models with a similar analysis using existing hindcast output from the CFS model. We will diagnose correlations between simulated snow cover variability and atmospheric temperatures, geopotential heights, winds and energy flux (Eliassen-Palm flux or the three dimensional wave activity flux) and compare with observed co-variability between snow cover and the atmosphere. This analysis will (a) assess the simulation of ST coupling in the IPCC AR5 class of models and in the CFS model compared against the observations, (b) quantify the influence of snow variability on ST coupling and climate variability over North America in the IPCC AR5 models and from the CFS hindcasts by comparing with the observations, and (c) provide an assessment of how much improvement in the simulation of the pathway by which snow anomalies influence climate variability over North America has been accomplished by comparing the analysis from the IPCC AR5 models and the CFS with a similar analysis performed from the IPCC AR4 class of models. Demonstrating improved simulations of ST coupling in the CFS model will enhance credibility of the CFS forecasts.

We will also carry out additional experiments in an effort to assess predictability of atmospheric anomalies associated with the interannual snow variability using an operational prediction model. Experiments will be designed to force the CFS with observed snow cover variability. Model output of atmospheric response to observed snow cover forcing will be analyzed and compared with observations and with archived CFS data where snow cover has not been prescribed. In a second set of GCM experiments we will identify and isolate large ST coupling events. Then we will re-run the model with initialized atmospheric conditions that preceded the stratosphere-troposphere coupling events but with varying amounts of snow cover. This set of experiments will help us determine whether the modeled ST coupling events were altered or modified by changes in snow cover extent.

A reasonable concern is that the CFS model cannot adequately simulate observed snowatmosphere coupling and therefore snow experiments with the CFS will only yield negative results. However, for future model development it is critical to understand and document model errors and deficiencies in order to spur future model improvements, and we are only proposing enough model analysis and experiments to determine the CFS capabilities in regard to this important coupling. Whether realistic snow cover improves seasonal prediction or not, the results will be shared with the operational modeling community. Results from the analysis of archived model data and original GCM experiments will be submitted for publication.

Principal Investigator (s): Cohen, Judah (AER)

Co-PI (s): Kumar, Arun (NOAA/CPC); Amy Butler (NOAA/CPC)
Year Initially Funded: 2010
Task Force:
CMIP5 Task Force
Final Report:

An integrated view of the American Monsoon Systems: observations, models and probabilistic forecasts

View abstract

Climate Prediction Program for the Americas (CPPA) Science Plan indentified the need to “explore a unified approach to understand the North American (NAMS) and South American (SAMS) monsoons systems, which constitute the two extremes of the annual cycle over the Americas and possible linkages between the two systems.” This proposal will contribute to the CPPA implementation strategy by focusing on the interactions between the two systems and identification of common sources and limits of summer season predictability in the AMS. The main theme of this proposal is to develop a unified view of the AMS. Specifically, it addresses the FY2010 CPPA research priority of Predictability and prediction of intraseasonal to interannual (ISI) climate variations and related impacts over the Americas. The proposal will also evaluate the ability of global models from the World Climate Research Program (WCRP) Coupled Model Intercomparison Project (CMIP) to simulate the variability of the AMS in the present climate.

The project is comprised of four interconnected main goals. First, the project will investigate the extent to which the annual evolution of NAMS and SAMS and their temporal variability on ISI time scales can be represented with metrics that effectively describe changes in precipitation and atmospheric circulation in the Americas. Second, this will identify regional physical processes and teleconnections that control the interactions between NAMS and SAMS. Third, this project will evaluate the skill of WCRP CMIP coupled models in representing the observed variations in the AMS. Lastly, this project will implement diagnostic monitoring tools, identify sources of potential predictability and develop probabilistic forecasts of the AMS on subseasonal to seasonal scales.

Specific objectives are:

I. Develop and validate indices for a unified approach to monitor and forecast the variability of the monsoon systems in the Americas.

II. Investigate the associations between the two monsoon systems, the importance of regional processes and remote atmosphere-ocean variations on ISI time scales in explaining these linkages.

III. Examine the degree to which simulations from the WCRP Coupled Model Intercomparison Project (CMIP-3 and CMIP-5) realistically represent the AMS and associations between the monsoons in the Americas.

IV. Use NCEP Climate Forecast System (CFS) model outputs (reforecasts and operational) to develop probabilistic forecasts of the American Monsoon Systems on subseasonal to seasonal lead times. Identify potential predictability sources of the AMS on ISI time scales.

Principal Investigator (s): Carvalho, Leila (ICESS)

Co-PI (s): Jones, Charles (UC Santa Barbara)
Year Initially Funded: 2010
Task Force:
Final Report:
Webster_Final_Report.pdf

Prediction of Intraseasonal Variability in the Americas

View abstract

Successful strategic and tactical decisions that support water resource management, agriculture, and hydroelectric power generation in the Americas, especially in monsoon areas, are only possible if precipitation forecasts on subseasonal to seasonal time scales are skillful. Subseasonal variability of precipitation is evident across the globe, including in North, South and Central America. During the boreal summer, large intraseasonal variability extends eastward across the Pacific Ocean over the equatorial and subtropical regions of North America. During the boreal winter, similar patterns of variance exist but with maximum variance in the southern hemisphere with maxima over Brazil and the South Atlantic Convergence Zone. Previous studies have suggested the existence of a link between North and South America intraseasonal variability with that in the Indo-West pacific basin. Despite its importance modulating climate and weather across the globe, numerical prediction models do not forecast skillfully and robustly intraseasonal variability. The skill of empirical predictions scheme is higher, but also have limitations predicting extreme events. In addition, empirical schemes are often deterministic and do not allow for a direct assessment of error growth associated with sensitivity to initial conditions. This proposal provides a hybrid methodology to improve the forecasting skill of extended rainfall predictions in the Americas by combining numerical weather predictions and empirical schemes into a single system capable of providing probabilistic forecasts using the different ensemble members available in numerical weather prediction models. To design an optimal operational hybrid scheme we will first assess the prediction and simulation skill of intraseasonal variability in the Americas from numerical weather prediction and climate models. We will also assess the interannual variability of intraseasonal variability in the Americas, and its potential impact on intraseasonal prediction. The proposed hybrid scheme uses the forecast circulation structure from each ensemble member and separates it into different temporal scales by projecting the numerical forecasts onto the most important multivariate spatio-temporal modes of variability and then uses the banded numerical forecasts as predictors in the hybrid empirical system.


Principal Investigator (s): Hoyos, Carlos (Georgia Tech)

Co-PI (s): Webster, Peter (Georgia Tech); Agudelo, (Georgia Tech); Kim, (Georgia Tech)
Year Initially Funded: 2010
Task Force:
Drought Task Force
Final Report:

Atmosphere-Land Coupling and the Predictability of North American Drought

View abstract

The proposed research is based on the hypothesis that the predictability of persistent large-scale drought is due the competition among three processes:

(i) The nature of local coupled atmosphere-land feedbacks (i.e., strength, growth rate, saturation)

(ii) The predictability limiting affects of atmospheric noise or stochastic forcing

(iii) The remote forcing from low frequency global SST variability (e.g., AMO, PDO, NPO…).

We propose to test this hypothesis through a series of modeling experiments that isolate the relative importance of coupled atmosphere-land feedbacks vs. atmospheric stochastic forcing vs. remote SST forcing. These experiments include using the novel interactive ensemble coupling strategy (Kirtman and Shukla 2002) previously used to isolate coupled ocean-atmosphere feedbacks vs. atmospheric stochastic forcing, extended to the problem of atmosphere-land interactions. Part of our modeling strategy builds on the success of the US Clivar drought WG (http://www.usclivar.org/Organization/drought-wg.html) and the international Global Land-Atmosphere Coupling Experiment (GLACE) by explicitly leveraging their experimental protocol. We have chosen to focus on the question of North American drought because of its societal importance to US interests; however, the approach is equally applicable to terrestrial hydro-climate predictability on multiple space and time scales throughout the globe.


Principal Investigator (s): Kirtman, Benjamin (University of Miami/CIMAS)

Co-PI (s): Burgman, Robert (University of Miami/CIMAS), Mapes, Brian (University of Miami/CIMAS); Zhang, Chidong (University of Miami/CIMAS)
Year Initially Funded: 2010
Task Force:
CMIP5 Task Force
Final Report:

Changes in Intraseasonal to Interannual Variability of the Pan American Monsoons Under a Warmer Climate and Their Impacts on Extreme Events Assessed by the CMIP5 Models and Observations

View abstract

We propose to characterize the changes of intraseasonal, seasonal and interannual variability (ISI) and their impact on extreme events over the Pan America monsoon region as simulated and projected by the Coupled Model Inter-comparison Phase Five (CMIP5) and National Oceanic and Atmospheric Administration (NOAA) Climate Forecast System (CFS) models. Our analysis will first focus on observations of changes in rainfall and temperature characteristics and extreme weather events in the recent past and their underlying mechanisms. The observational results will be used to assess the skills of the CMIP5 class models in reproducing the observed ISI variability and their changes, in focusing on the mechanisms that control ISI variability in the Pan American monsoon regions and their links to sea surface temperature (SST) changes over the adjacent oceans, the local land surface process and the extratropical synoptic weather systems, as well as bias estimations. The results of this model evaluation will be used to filter-out “unrealistic” models from the climate projections and also for model bias corrections.

The proposal aims to address the following questions:

a) Will the spatial patterns, ISI variabilities and statistical distributions of temperature and rainfall shift significantly in a warming climate in the Pan American monsoon region? How would such changes impact the intensity and frequency of the droughts, floods and heat waves?

b) What external forcings are responsible for these changes and how would local land surface feedbacks contribute to these changes? What processes are key in determining the influence of these forcings? How are changes in the North American and the South American monsoon connected?

c) How realistically can global climate models simulate the key process that control changes of ISI in the Pan American monsoon region? How can such model evaluation be used to reduce random error and biases in climate projections? We will extensively use observations and reanalysis products including in situ observations from surface and upper air meteorological networks, remote sensing datasets, the North American Regional Reanalysis (NARR), the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis (CDAS), the North-American Land Data Assimilation System (NLDAS) and the CFS reanalysis (CFSR) when it becomes available. We will also analyze daily and monthly outputs from ensemble simulations of the CMIP5 class models for the pre-industrial scenario, the 20th century simulations forced by estimated increase of the anthropogenic forcing, and the 21st century under..


Principal Investigator (s): Fu, Rong (University of Colorado)

Co-PI (s): Mo, Kingtse (NOAA/CPC); Han, Weiqin (NOAA/CPC)
Year Initially Funded: 2010
Task Force:
Final Report:
Karnauskas_Final_Report.pdf

The American Midsummer Drought: Causal Mechanisms and Seasonal–to–Interannual Predictability

View abstract

The Intra–Americas Sea (IAS) region– including the northeastern tropical Pacific Ocean, Caribbean Sea, Gulf of Mexico, western tropical Atlantic Ocean, and all adjacent landforms–represents a fascinating natural climate laboratory due to a confluence of diverse oceanic, orographic, atmospheric, and remote influences. The IAS region is also home to a large portion of humanity whose livelihood depends critically upon the spatiotemporal variability of precipitation. Throughout most of the IAS region, the rainy season spans roughly May through October with a break in precipitation in July–August known as the midsummer drought (MSD). This feature of the rainfall climatology is highly unique to the IAS region, and is particularly evident over Central America and the adjacent northeastern tropical Pacific Ocean. Indeed, the MSD is such a pervasive phenomenon that crop insurance programs incorporate what little information is known of the MSD in pricing and triggering policies in Central America. Since the recognition of the MSD as a regular climatological feature in the early 1960s, much effort has been directed toward characterizing and understanding the MSD. Both local processes (e.g., SST–convection–radiation feedback) and aspects of the general circulation (e.g., the North Atlantic subtropical high) have been shown to influence the MSD. To date, however, a unifying explanation for the very existence of the MSD has yet to emerge. As a result, our understanding of the interannual variability and– most importantly– predictability of the MSD is only in a nascent stage. Seasonal–to–interannual climate predictions for the IAS region would benefit greatly from an understanding of the causal mechanisms for the existence and variability of the MSD. We propose to first focus on analysis of observations: satellite and in situ measurements, as well as state–of–the–art global and regional reanalyses to diagnose the dominant mechanisms of the MSD in the IAS region. Secondly, we will use state–of–the–art general circulation models to test specific hypotheses regarding the dominant mechanisms of the MSD and its variability. This approach will allow us to thoroughly examine and identify the features of the global atmospheric circulation and, especially, the role of the ocean, that are crucial for predicting seasonal hydroclimate variability in the IAS region.


Principal Investigator (s): Karnauskaus, Kristopher (WHOI)

Co-PI (s): Giannini; Seager, Richard (Columbia University; Busalacchi, Tony (University of Maryland/ESSIC)
Year Initially Funded: 2010
Task Force:
Final Report:

Climatic Predictability of Extreme Floods in the United States

View abstract

Of all climate-related disasters, floods account for the largest average annual losses. Only a limited climatic perspective on floods in the United States exists. This includes the identification of the seasonality and typical mechanisms (e.g., frontal or connective precipitation) important for floods by subregion. Climate change analyses have led to either no clear assessment of changes in flood potential, or to projections of dramatically increased frequency of extreme floods. The anticipated intensification of the atmospheric hydrological cycle and the increased atmospheric moisture holding capacity under warming, render increasing flood risk plausible. However, it is unclear whether the climatic processes associated with extreme floods are well modeled in global and regional climate models, and whether such models provide predictability for assessing the frequency and intensity of rainfall responsible for extreme floods in the United States with spatial specificity relevant for hydrological analysis of floods.

Our work shows that extreme floods (annual exceedance probability less than ~ 0.1) in most river basins in the United States are associated with a distinct atmospheric moisture transport pattern, where the moisture source is typically in the oceans rather than associated with local convection. Over much of the Western United States, we have been able to demonstrate statistical predictability of the annual maximum flood conditional on pre-season Pacific SSTs. For a region in Brazil we are able to demonstrate that the annual maximum flood at each of the stations can be modeled using concurrent large scale, seasonal climate predictors, and a spatial scaling model for the flood process indexed to the drainage area of the site. Consequently, our hypothesis is that river basins aggregate the spatio-temporal climate signal in terms of synoptic and seasonal atmospheric moisture transport in a way that allows empirical connections to be drawn between slowly varying climate fields and the severity, incidence and location of extreme floods over N. America. If these connections can be quantitatively assessed, modeled and understood, then a basis for assessing changes in flood risk using GCMs or empirical methods could be developed for seasonal prediction and for climate change projections.

The research proposed here seeks to develop an exploratory statistical-dynamical approach for “downscaling” flood risk from climate models through an analysis of the causal structure of the entire ocean-atmosphere-land chain of the flood process. This entails (a) use of historical, reanalysis and GCM data for the diagnostic analyses of the causal structure from the spatiotemporal hydroclimatic data associated with the extreme floods in each of the regions of the United States; (b) Bayesian model development for assessing the conditional probability distributions across the causal chain, leading to a conditional flood risk estimate given either GCM state variables or observed/re-analysis data fields, and (c) assessments of projections of flood risk at selected locations for the upcoming season or for a climate change scenario.


Principal Investigator (s): Lall, Upmanu (IRI/Columbia University)

Co-PI (s): Kushnir, Yochanan (IRI/Columbia University); Robertson, Andrew (IRI/Columbia University); Nakamura, Jennifer (IRI/Columbia University)
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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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