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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Weather-type based cross-timescale diagnostics of CMIP6-era models

The objective of this project is to perform a process-based multi-timescale diagnostic of CMIP5 and CMIP6-era Earth System Models using a weather-typing dynamical approach. The proposed
work focuses on how accurately extreme rainfall events, both wet and dry, are represented over the US in CMIP5/6 models. Although the project will emphasize the present and next generation
of NOAA/GFDL models, to guarantee robustness other available models will also be diagnosed. This project will develop process-informed cross-timescale tools to diagnose CMIP5/6 historical and climate-change projections over North America based on the methodology of large-scale recurrent, persistent weather types (WTs), also known as large-scale meteorological patterns (LSMPs). These regimes provide a dynamically informative intermediary between the large- scale drivers of climate variability and change from sub-seasonal to decadal timescales, and mid- latitude high-impact weather events, through the mechanism of synoptic control. The proposed work will provide an urgently-needed process-level understanding on rainfall extremes in CMIP5/6 simulations, and develop standard metrics that model developers and users can apply to these models easily. These will allow model developers to quickly assess the impacts of changes in parameters, and will enable users to better assess confidence levels on projections of return intervals of extreme rainfall events.


The proposed work will build on recent previous work by the team demonstrating the effectiveness of the approach to both (1) cross-timescale diagnostics of rainfall over North and South America, and (2) diagnose GCM model performance in a suite of GFDL forecast models.

Expected deliverables of the project include (a) general open-source software package to perform weather-type based cross-timescale diagnostics of climate models, including new process-based metrics that can be added to the MAPP diagnostics Task Force framework, and documentation for the software package; and (b) an online “diagnostic atlas” containing the process-based metrics (e.g., WT spatial patterns and frequencies of occurrences at different timescales, extreme rainfall composite analysis for different thresholds, anomaly correlations to climate drivers) available via the IRI Data Library.

Principal Investigator (s): Angel Munoz (Columbia University)

Co-PI (s):Vecchi, Gabriel (Princeton Unviersity), Ming Zhao (NOAA/GFDL)

Task Force: Model Diagnostics Task Force

Year Initially Funded:2018

Competition: Addressing Key Issues in CMIP6-era Earth System Models

Final Report:

Variability of Rossby Wave Breaking and its Impacts on the Large-scale Circulation and Extreme Weathers: Implications for S2S Prediction and Predictability

"Rossby wave breaking (RWB) is characterized by large-scale, irreversible overturning of potential vorticity (PV) on isentropic surfaces. The eddy-mean interaction involved in RWB is an important process for the maintenance and variability of the midlatitude jet, and mixing associated with RWB plays an important role in moisture and momentum transport between the tropics and extratropics. In particular, extratropical PV intrusion modulates the moisture distribution in the subtropical dry zone, which affects the infrared energy loss and is an important factor in the global energy budget.

RWB is also closely related to extreme weathers, such as blocking and atmospheric rivers. Our recent study revealed a strong relationship between RWB and Atlantic tropical cyclones (TC). Anomalously frequent RWB enhances the equatorward intrusion of cold, dry extratropical air and leads to a significant reduction in precipitable water over the tropical/subtropical Atlantic and an increase in vertical wind shear, both hindering TC formation and intensification. The correlation of Atlantic hurricane counts with a basinwide RWB frequency index is comparable to the correlation with the Atlantic Main Development Region SST, and stronger than the correlation with the ENSO.

In this project, we propose to i) examine the variability and impacts of RWB on the subseasonal to seasonal (S2S) time scales using reanalysis and observational data; ii) assess the representation of RWB in global prediction systems and investigate model deficiencies using the WWRP/THORPEX/WCRP S2S prediction project database; and iii) investigate the implications of the findings for the S2S prediction and predictability, especially for the prediction and predictability of Atlantic tropical cyclones.

Studies on S2S prediction have primarily focused on tropical forcing and processes (such as the MJO and tropical SST) in the past. This project complements those efforts through its unique perspective in emphasizing the extratropical impacts on tropical circulation and extreme weathers and stressing the link between midlatitude transient eddies and the S2S prediction and predictability. The ultimate goal of the project is to improve the prediction skill of global models on the S2S time scales, and it is well aligned with the focus of the NOAA MAPP Competition 2 to “improve the understanding of predictability” and “advance the prediction of phenomena occurring on S2S time scales”. It is also highly relevant to the NOAA’s long-term climate goal to improve the scientific understanding of the changing climate system and its impacts. This project team consists of scientists from research institutes and operational centers. Their combined expertise in atmospheric dynamics, global modeling, and operational forecasting together forms the particular strength of this project, and will also facilitate transition from research to operations and help to bridge the gap between numerical weather forecasting and short-term climate prediction."

Principal Investigator (s): Zhuo Wang (University of Illinois at Urbana-Champaign)

Co-PI (s):Melinda Peng (NRL), Stan Benjamin (NOAA/ESRL)

Task Force: S2S Prediction Task Force

Year Initially Funded:2016

Competition: S2S

Final Report: Wang_ NA16OAR4310080_final.pdf

Variability and Predictability of the Atlantic Warm Pool and Its Impacts on Extreme Events in North America

Our current/previous NOAA/CPO-funded research has pointed out the importance of the Atlantic Warm Pool (AWP) for summer climate and extreme events in the Western Hemisphere. AWP variability occurs on seasonal, interannual, multidecadal, and secular (global warming) timescales, with large AWPs being almost three times larger than small ones. The effect of the AWP is to weaken the North Atlantic subtropical high (NASH) and strengthen the summer continental low over the North American monsoon region. A large AWP also weakens the southerly Great Plains low-level jet, which results in reduced northward moisture transport from the AWP to the central U. S. and thus decreases the summer rainfall over the central United States. A large AWP increases the number of Atlantic hurricanes by reducing vertical wind shear and increasing the moist static instability of the troposphere, and influences the hurricane steering flow changes that become unfavorable for hurricanes to make landfall in the United States. Our research also suggests that the AWP serves as a link between the Atlantic Multidecadal Oscillation (AMO) and climate and hurricane activity. Despite its importance, almost of all stateof-the-art coupled models exhibit serious biases in the AWP region, which limit the seasonal prediction of AWP-related climate and extreme events.

We propose to continue our investigation of the AWP using fully coupled climate models. Two specific areas of proposed work are (1) diagnosing the CMIP5 outputs to assess model biases near the AWP region and to understand their skill in simulating the mechanisms and climate impacts of AWP variability, and (2) performing coupled model experiments using CESM1 (also called CCSM4) and analyzing the Climate Forecast System version 2 (CFSv2) reforecasts to assess and improve predictability of the AWP and its impacts on climate and extreme events such as hurricanes, flood and drought in North America. The diagnostic analyses will mainly focus on variability of the AWP, and its impacts on the NASH, the Caribbean low level jet and its moisture transport, and the Great Plains low-level jet and its moisture transport. Other areas of the focus in the diagnostic analyses include the relationships of rainfall in the U.S. with the AWP, the external influences (such as ENSO, the AMO, and the NAO) versus local ocean-atmosphere processes on AWP variability, and the relationships among environmental factors contributing to hurricane activity. We will perform CESM1 model simulations with and without realistic initialization of the AWP to explore the impact of AWP initialization on seasonal forecasts. We will also examine the influences of model resolution and deep convective parameterizations in CESM1 on AWP SST and rainfall biases. One of the tasks is to analyze the CFSv2 reforecasts to explore its skill for seasonal predication of the AWP and AWP-related climate and extreme events. The CESM1 experiments and the analysis of CFSv2 reforecasts are designed to identify the sources that contribute to the model biases, thus provide a basis for improving model simulations and predictions. In collaboration with scientists at NOAA/CPC, we will attempt to transition research results to operations at NOAA/CPC. The proposed work directly contributes to all of four topics listed in the NOAA/CPO MAPP FY12 Priority Area 3 of "modeling of IAS climate processes associated with extremes over North America". It is hoped that over a longer time frame, this work will result in the regional implementation of data- and model-based outlooks for flood/drought in the United States, hurricanes and climate variability, when successfully combined with land-based models.

Principal Investigator (s): Wang, Chunzai (NOAA/AMOL)

Co-PI (s):Lee, S.-K. (NOAA/AMOL); Enfield, D. (NOAA/AMOL)

Task Force: Climate Prediction Task Force

Year Initially Funded:2012

Competition: Modeling of Intra-Americas Sea climate processes associated with extremes over North America

Final Report: LEE_Final_Report_FY2012.pdf

Validation of regional ocean model hindcasts for zooplankton biomass via comparisons with thirty years of field sampling data in a rapidly changing ecosystem

The eastern Bering Sea (EBS) is one of the most productive ecosystems on the planet and
produces the largest fish catch by volume in the United States. The EBS is currently
undergoing rapid ecological change, having experienced record-low seasonal sea-ice in the
past two years. To effectively manage fisheries in the face of rapid change, predicting the
response of the ecosystem to this forcing is imperative and requires the application of
modeling. The Regional Ocean Model System (ROMS) is widely used in the EBS, North
Pacific, and nationwide to conduct forecasts and hindcasts of ocean physics. In the EBS, the
10km resolution ROMS-NPZ model (Bering10K) includes multiple functional groups for
primary and secondary producers; however, there has been relatively little work validating
these components of the model. We propose to compare spatial and temporal patterns in
Bering10K predictions of secondary producers with field-sampling records of spatial and
temporal variation. In particular, field observations of zooplankton community have not
been compared to model output. A rich time series of data on zooplankton abundance,
collected with OAR, NMFS, and NOS support are available for this comparison. The lack of
comparison is critical in that zooplankton link changes in primary production to fish
through interactions, beginning with the first year of life. Survival of juvenile fish through
their first winter relates strongly to eventual recruitment and zooplankton availability
plays a key role; zooplankton remain key diet components of many commercially and
ecologically important species after recruitment to the fishery. This lack of model to
observational comparisons also makes it difficult to assess whether Bering10K hindcasts
and forecasts of secondary producers capture observed spatial and temporal variability,
and therefore provide credible estimates in locations and times without field data. This
uncertainty then makes it impractical to use the Bering10K model to inform the short-term
(1-5 year) forecasts/hindcasts of fish distribution that are currently used in the Alaska
pollock stock assessment and to develop adaptive sampling designs for fisheries (cod,
pollock) and protected species (e.g., subarctic cetaceans, including the critically
endangered N. Pacific right whale). This proposal aims to evaluate the Bering 10K output
by comparison to field observations of the zooplankton community. This comparison will
be evaluated using several statistical approaches and will identify areas for model
improvement. Furthermore, we will use the comparisons to test short time-scale scenarios
designed to simulate recent warming trends. The overall outcome of this research will be to
advance the capability of Bering 10K to predict zooplankton biomass, providing vital
ecosystem information that can be used to estimate fisheries production in a rapidly changing
Bering Sea.

Climate Risk Area: Marine Ecosystems

Principal Investigator (s): David Kimmel

Co-PI (s):James T. Thorson, Darren J. Pilcher, Kelly Kearney, Patrick Ressler

Task Force: Marine Ecosystem Task Force

Year Initially Funded:2020

Competition: Modeling Climate Impacts on the Predictability of Fisheries and Other Living Marine Resources

Final Report:

USING UNFORCED VARIABILITY OF LOW CLOUD ‘HOT-SPOTS’ TO DEVELOP BETTER CONSTRAINTS ON EARTH’S CLIMATE SENSITIVITY

Developing better constraints on Earth’s Equilibrium Climate Sensitivity (ECS) is one of the
central goals of climate science, but despite decades of work, there is still large uncertainty in
Earth’s ECS. The response of low clouds to warming has been identified as the primary source of
this uncertainty and, while recent evidence suggests that low cloud cover is reduced in warmer
climates, uncertainty around the sign and, especially, the magnitude of the low cloud response is
responsible for much of the spread in climate models’ ECS.

To further refine our understanding of the link between low clouds and ECS, and to develop
potential constraints on the behavior of low clouds with warming, we propose studying the
unforced variability of low-level clouds and their governing meteorological conditions over the
global ocean in order to identify specific geographic regions (“hot-spots”) in which the
variability of low cloudiness is especially indicative of models’ response to warming. The
proposed project will combine data from simulations with comprehensive climate models,
including from CMIP6 and from perturbed physics ensembles with models developed at NOAA’s
Geophysical Fluid Dynamics Laboratory (GFDL), with observations to (1) evaluate model skill
in representing the meteorological conditions governing low cloudiness, (2) identify ‘hot-spot’
regions in which unforced low cloud variability is strongly related to the forced low cloud
response, and (3) combine metrics of model skill in simulating the governing meteorology and of
low cloud variability in the hot-spots with observations to develop emergent constraints on
Earth’s ECS. A number of steps will be taken to ensure that the emergent constraints are robust –
both physically and statistically – and not simply the result of data-mining. The proposed
analysis will consider variability on time-scales from monthly to the 2-5 year time-scales of the
El Nino Southern Oscillation, and will consider low cloud regions over the entire global ocean.
Comprehensively characterizing low cloud variability, and its relationship with the governing
meteorological conditions, in CMIP5/6 and GFDL models is an important additional benefit of
the proposed project.

This project fits squarely within the aims of the MAPP competition, and addresses Priority Areas
A, B and C, as we will quantify uncertainty associated with low clouds in CMIP6 models (and in
climate models developed at GFDL), develop new metrics for assessing climate models, and use
observations to assess the models and develop emergent constraints. Additionally, the project
will provide a more refined view of the causes of intermodel differences in ECS and a better
understanding of the relationships between model parameters and low cloud variability in the
GFDL models. More broadly, the project will help NOAA prepare for the potential impacts from
increased atmospheric CO2 concentrations by reducing uncertainty in Earth’s ECS and by
improving the models used by NOAA to forecast future climate states.

 

Principal Investigator (s): Nicholas Lutsko

Co-PI (s):Joel Norris, Ming Zhao, David Paynter

Task Force: Climate Sensitivity Task Force

Year Initially Funded:2020

Competition: Constraining Models’ Climate Sensitivity

Final Report:

Using Historical Surface Data to Verify the Twentieth Century Reanalysis for Oceanographic Applications

The Twentieth Century (20CRv.2) Reanalysis Project of Compo et al. (2010) more than doubles the time span covered by atmospheric reanalyses, and in addition uses an assimilation methodology that provides information about the accuracy of the reanalysis.

Thus the 20CRv.2 potentially offers wonderful advantages for use in multi-decadal ocean circulation and climate studies. This proposal will help fulfill that promise by supporting exploration of the surface winds from the 20CRv.2 in two stages. The first stage will involve comparison of the 20CRv.2 and other multi-decadal reanalyses to wind observations such as those contained in ICOADS (which were not used in the 20CRv.2 assimilation) to estimate accuracy various space and timescales and to detect the presence of time-dependent biases. The second stage will involve examination of output from a simulation of an ocean general circulation model driven by reanalysis winds in comparison with the historical hydrographic record.

Principal Investigator (s): Carton, James (University of Maryland)

Co-PI (s):Grodsky, Semyon (University of Maryland)

Task Force:

Year Initially Funded:2011

Competition: Evaluate Recently Developed Reanalysis Projects

Final Report: Carton_Final_Report_FY2011.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:

Upgrading the CPC operational ocean monitoring to an eddy-permitting global ocean analysis using the Hybrid Global Ocean Data Assimilation System as a replacement for GODAS

A number of troubling weaknesses have been found in the ocean monitoring tools used by the CPC. Precipitated by the international Ocean Reanalysis Intercomparison Project (ORA-IP) and the Observing System Experiments for evaluating the TAO array, Co-PI Xue identified large systematic errors in salinity and velocity fields in the operational Global Ocean Data Assimilation System (GODAS) and Climate Forecast System Reanalysis (CFSR), as well as a serious issue in fitting to observations too strongly in both GODAS and CFSR. The ocean-alone GODAS is forced by the NCEP Reanalysis 2, has an outdated ocean model (MOM3), a low resolution (1ox1o), atmospheric fluxes from an old atmospheric reanalysis, and lack of a sea-ice model. The coupled CFSR suffered a serious issue of climatology shift around 1999 due to assimilation of ATOV satellite observations. Both GODAS and CFSR had large departures from the ensemble mean of multiple international ocean reanalysis products during a large data gap in the TAO array in late 2012, most likely due to the outdated data assimilation scheme (3DVar).

As part of a previous CPO MAPP project, a Hybrid 3DVar/EnKF Global Ocean Data Assimilation System (Hybrid-GODAS) was implemented, and was evaluated using real data for a 21-year reanalysis. PI Penny showed that the Hybrid-GODAS produced significantly improved thermohaline structure compared to the 3DVar-based GODAS. Comparing GODAS and CFSR with the new Hybrid-GODAS, Co-PI Xue has found significant improvements in the temperature, salinity, sea surface height and velocity analysis against observations.

We propose to transition the Hybrid-GODAS to TRL 8, ready for evaluation and implementation into operations at NCEP. We use the recently released GFDL Modular Ocean Model version 6 (MOM6) at 1⁄4ox1⁄4o horizontal resolution and Sea-Ice Simulator (SIS2), which is the ocean component of GFDL’s CM4 earth system model for the next series of Coupled Model Intercomparison Project (CMIP6) experiments. We will implement near real-time monitoring of the ocean/sea-ice state by assimilating oceanic variables such as in situ temperature and salinity profiles, surface drifter data, and along-track satellite measurements of sea surface height, temperature, and salinity. A companion project (PI: Carton) will prepare sea-ice data feeds and perform specialized assimilation of observed sea-ice data into the ice model using the Local Ensemble Transform Kalman Filter (LETKF). Pending positive results from Carton’s team, the sea-ice data can be included into the operational Hybrid-GODAS with minimal effort. The proposed upgrades will significantly improve our capability in monitoring and understanding not only temperature but also salinity and velocity variability that play a critical role in the evolution of El Niño / Southern Oscillation (ENSO) and therefore seasonal climate forecasts.

Principal Investigator (s): Steve Penny (University of Maryland)

Co-PI (s):Jim Carton (UMD), Yan Xue (NOAA/CPC), David Behringer (NOAA/EMC), Laury Miller (NOAA/EMC)

Task Force:

Year Initially Funded:2016

Competition: Climate Test Bed

Final Report: NA16OAR4310140_Carton_final-report.pdf

Understanding the variability and projecting future changes of biogeochemistry in the California Current Upwelling System

 In the California Current System (CCS), variability in both surface and
subsurface biogeochemical variables affects the dynamics of economically important living marine
resources. Future variability and trends in the biogeochemistry of the CCS upwelling ecosystem may
be driven by several potentially confounding factors that span a range of spatiotemporal scales.
Physical factors include both local processes, such as changes in along-shore wind magnitude and
stratification, and remote processes, such as changes in the relative contributions of different source
waters and in their biogeochemical properties. However, most future climate projections are based
on Earth System Models (ESMs) with resolutions too coarse to properly resolve coastal winds and
upwelling dynamics. Therefore, more accurate representations of upwelling dynamics and
biogeochemical variability from high-resolution models are needed to provide actionable
information to those managing living marine resources. The main goal of this project is to
understand and quantify the variability of the physical mechanisms that drive changes in
the biogeochemistry of the California Current Upwelling System in response to
anthropogenic climate change. We will analyze biogeochemical variables (e.g., oxygen, nutrients,
pH, Chl-a) from historical observations and a set of high-resolution biogeochemical reanalyses, and
produce future projections from an eddy-permitting regional ocean circulation model (ROMS)
coupled with a biogeochemical model and forced with bias-corrected CMIP6 output to accomplish
three objectives: (1) Quantify and examine how future climate change will alter the relative
contribution of source water masses to the CCS and the biogeochemical properties within those
source waters; (2) Quantify and examine local changes in coastal upwelling and biogeochemistry in
response to future climate change; and (3) Quantify the relative contributions of local vs. remote
forcing to future changes in the biogeochemistry of the CCS.

Relevance to the Competition and NOAA’s Long-Term Climate Goal and Broader impacts:
The proposed research will directly address Priority Area A of the competition, “Identify key
climate/oceanic processes that affect ocean biogeochemistry of relevance to fisheries and other living marine resources in NMFS areas of interest across climate timescales”, in that it explores and quantifies future changes in physical drivers (local vs. remote) affecting the biogeochemistry in the California Current Upwelling System, which impacts the ecosystem across all trophic levels through changing oxygen, nutrient and pH levels. It also addresses the Priority Area C “Improve the modeling of climate-ocean predictability pathways and its representation in prediction/projection systems”, by producing a set of high-resolution coupled physical-biogeochemical projections forced by CMIP6 output. This project will also address a number of priorities identified in the NMFS National Climate Science Strategy, including “understanding mechanisms of change”, “projecting future conditions”, and “tracking change to provide early warnings”.

Climate Risk Area: Marine Ecosystems

Principal Investigator (s): Mercedes Pozo Buil

Co-PI (s):Michael Jacox, Desiree Tommasi, Jerome Fiechter, Steven Bograd, Ryan Rykaczewski

Task Force: Marine Ecosystem Task Force

Year Initially Funded:2020

Competition: Modeling Climate Impacts on the Predictability of Fisheries and Other Living Marine Resources

Final Report:

Understanding the sources of US drought predictability using seasonal reforecasts of sixty years (1958-2017) initialized with multiple land analyses

Based on a set of recently completed ensemble seasonal reforecasts covering 1958-2014 (to be extended to 2017) using the NCEP Climate Forecast System, version 2 (CFSv2) and initialized from observation-based ocean, land and atmospheric states, we propose to evaluate the predictive skill of US droughts over 60 years and to identify the sources of prediction skill in the ocean, land and global climate trends. As far as we know, this will be the first time that a record of this length of seasonal reforecasts would be used for the study of drought, comparable to the Atmospheric Model Intercomparison Project (AMIP) simulations that are widely used in drought mechanism studies. Taking advantage of this long reforecast dataset, together with AMIP simulations by the CFSv2 atmospheric component with large ensemble sizes, we plan to conduct the following studies of US drought predictions: (1) We will examine whether there are differences in US drought predictioskill in the 1960s-1970s, 1980s-1990s and 2000s-present, associated with different phases of the Pacific decadal oscillation. For this purpose, we will use an advanced statistical method to extract the predictable patterns of the US precipitation from ensemble predictions and examine the connection of these patterns to the ocean, land and atmospheric forcing factors during each of these three periods. We will also examine the mechanisms and model prediction skills of selected major drought events during each of these three periods. Sensitivity experiments will be conducted to identify the potential contributions of specific SST anomalies on the particular events. (2) We will examine the contributions of the observation-based land initial states to the seasonal predictions of the US precipitation and drought events. For this purpose, we will conduct land initialization experiments in which climatological soil moisture will be used to initialize a selected group of drought events. The comparison of these experiments with the reforecasts will isolate the effects of the initial land signals. Furthermore, we will conduct a set of reforecasts using an alternative set of model-based land initial states and specified persisted anomalies to assess the effects of the uncertainty of the land initial states. (3) Using the 60-year reforecasts and the AMIP runs, we will examine the global change trends in evapotranspiration and its potential influence on US droughts by comparing the variability of the temperature, evapotranspiration and precipitation between earlier and later periods (e.g., 1960s-1970s vs. 2000s-present) associated with observed CO2 forcing and initialized or specified boundary conditions. This proposal addresses two of the principal objectives in the MAPP drought project, i.e., developing a better understanding of sources of predictability toward improving predictions of drought onset, evolution, and termination on subseasonal to interannual timescales, and understanding the role of the temperature and evapotranspiration in affecting droughts. The Center for Ocean-Land-Atmosphere Studies and the US Climate Prediction Center will work closely to conduct this collaborative research.

Climate Risk Area: Water Resources

Principal Investigator (s): Huang, Bohua (GMU/COLA)

Co-PI (s):Chul-Su Shin (GMU/COLA), Paul Dirmeyer (GMU/COLA), Arun Kumar (NOAA/CPC)

Task Force: Drought

Year Initially Funded:2017

Competition: Advancing drought understanding, monitoring and prediction

Final Report:

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