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

Metrics for general circulation model biases in extratropical cyclone clouds and precipitation: evaluating their skill and identifying processes to be improved

View abstract
"The general circulation models that participate in the CMIP5 exercise exhibit cloud and precipitation biases in the midlatitudes, a region where extratropical cyclones play an active role. This in turn causes long-term biases for the prediction of average and extreme precipitation events, as well as the global energy balance. It is thus paramount to better understand which processes in the models contribute to these biases and provide a set of metrics to help in their evaluation.

As such, the objective of this project is to create a set of process-oriented metrics to evaluate model ability to produce realistic extratropical cyclones. These metrics encompass the characteristics of the storms on the planetary, synoptic and frontal scales. The metrics are designed to inform on model skill in accurately representing the large-scale and the cyclone-
scale dynamics, as well as the cyclone moisture content, temperature, cloud and radiation fields and precipitation. Furthermore, the proposed analysis methods will help identify the limitations of current parameterization schemes.

This project is based on a set of tools that the team has developed over the years that use model gridded outputs to: (a) locate and track extratropical cyclones, (b) locate the warm and cold fronts, (c) measure cyclone-local characteristics through the use of compositing techniques and (d) use conditional subsetting to identify the sensitivity of cyclone cloud and precipitation to changes in dynamics and thermodynamics. It also combines a comprehensive set of satellite observations and reanalysis output using both state-of-the-art and innovative approaches to assess models beyond the simple map-to-map comparison. The metrics will provide (1) a measure of a model’s ability to reproduce the atmospheric conditions within the storms, and (2) the model’s ability to predict the right response in cloud and precipitation to changes in atmospheric conditions. Furthermore, specific metrics are designed to inform on the reliability of components of the model that directly participate in the formation of clouds and precipitation, namely their convection, cloud and boundary layer schemes.

The outcome of the project will be a package of numerical codes that generate the process-based metrics proposed herein and compare them with reference metrics. The reference metrics are a series of storm-specific observations and reanalysis products that we will generate. Additionally, in our work we will test the process-based metrics using output from the GCMs participating in CMIP5. Both the numerical code for the metrics analysis and the data for the reference metrics will be made freely accessible via NOAA-CREST website.

The project directly addresses the objectives of the NOAA MAPP-Process-Oriented Evaluation of Climate and Earth System Models and Derived Projections (ID 2488569) call, namely to “develop and integrate process-oriented metrics into U.S. modeling centers’ diagnostic packages to support the evaluation and development of next-generation climate and Earth system models”. This project fits within NOAA’s long term goal of “Climate Adaption and Mitigation” by addressing their objective of “Improved scientific understanding of the changing climate system and its impacts” and “Assessments of current and future states of the climate system”."

Principal Investigator (s): Booth, James (City University of New York, City College)

Co-PI (s): Catherine Naud (Columbia University), Zhengzhao Luo (City University of New York, City College), Leo Donner, Chris Golaz (NOAA/GFDL), Anthony Del Genio (NASA/GISS), Justin Small (NCAR)
Year Initially Funded: 2015
Task Force:
Model Diagnostics Task Force
Final Report:

Development of process-oriented metrics for ENSO-induced teleconnection over North America and U.S. Affiliated Pacific Islands in Climate models

View abstract
"In climate models, to demonstrate the hypothesis that the sea surface temperature (SST) anomalies associated with ENSO serves as the source predictability of seasonal to interannual climate anomalies over North America and U.S. affiliated Pacific Islands (USAPI) require translating these SST anomalies into precipitation and latent heating anomalies by models’ physical parameterizations. While CMIP5 (Coupled Model Intercomparison Project Phase 5) assessment studies suggest certain improvement in representing ENSO-related SST anomalies, representation of precipitation anomalies along the equatorial central Pacific and therefore tropical to extratropical teleconnection have not improved. Climate models’ fidelity in representing ENSO and associated teleconnection require detailed assessment of models’ ability in representing “moist convective processes”. Specifically, during the life-cycle of ENSO, a detailed evaluation of various entropy elements in forcing convection and factors that determine vertical structure of diabatic heating (such as cloud intensity and height) needs to be performed. Such a process-oriented diagnostics will lead to identification of source of model errors and provide pathways for model improvement.

We propose processes-oriented metrics to be applied on the 45+ CMIP5 models as well as on the hindcasts performed with the North American Multi-Model Ensemble Phase 2 (NMME-2). To assess observational uncertainty, similar metrics will be obtained from all available reanalysis products (CFSR, NCEP, ERA-Interim, JRA-25 and MERRA). We identify 4 major objectives that deem highly relevant to the present focus. Diagnostics are developed to understand: (i) processes that shape tropical precipitation climatology; (ii) processes in determining precipitation anomalies during different phases of ENSO and their different roles between equatorial central vs eastern Pacific precipitation anomalies; (iii) processes that determine the vertical structure of diabatic heating anomalies and associated teleconnection, and (iv) processes that account for regional and remote precipitation anomalies. Deliverables include objectively oriented and physically based metrics for climate models’ performance in representing precipitation anomalies along the equatorial Pacific, North America and the USAPI.

Our goals lie in identifying sources of model errors in physical parameterization, and provide clear pathways for model improvement. Our proposed research targets the MAPP competition that focuses on “Process-oriented evaluation of climate and Earth system models and derived projections”. Specifically, the competition identified research focus - “Area A: Metrics for climate and Earth system model development”. Process-based diagnostics will lead to selection of a subset models could contribute to other crosscutting (RISA and National climate change assessment) programs of NOAA."

Principal Investigator (s): Annamalai, Hariharasubramanian (University of Hawaii)

Co-PI (s): Arun Kumar (NOAA/CPC)
Year Initially Funded: 2015
Task Force:
Model Diagnostics Task Force
Final Report:

Process orientated metrics of land surface-atmospheric interactions for diagnosing coupled model simulations of land surface hydro-meteorological extremes

View abstract
"Introduction to the Problem: Hydro-meteorological extremes such as droughts and heat waves have enormous impacts on water resources, agriculture, health, energy production and infrastructure. Understanding how these events have varied in the past and how they are expected to change in the future are key to mitigation and adaptation. Land-atmosphere (L-A) interactions and feedbacks are increasingly acknowledged as important processes that contribute to climate variability and can amplify droughts and heat waves through changes in partitioning of surface fluxes and interactions with the atmospheric boundary layer. Climate models are central to understanding future changes, but they continue to show problems in depicting climate extremes and the processes that lead to their development and persistence, despite incremental improvements in model resolution and more comprehensive treatment of physical processes. Their future projections are therefore inherently uncertain, especially as L-A interactions are expected to intensify in the future and play a more important role in modulating these extremes.

Rationale: Given the potential for high impacts of droughts and heat waves, and our general lack of understanding of future changes in these extreme events, there is a pressing need to evaluate coupled models for their representation of surface fluxes and L-A interactions at the process level. We propose to develop and test a suite of process-based metrics to diagnose the coupling and feedbacks between the land and the atmosphere, and apply these to climate models to help identify deficiencies in parameterizations. This has potential to improve our understanding of the contribution of the land to climate variability and its role in amplifying extreme events, as well as to lead to model developments that provide better understanding of past changes and reduction of uncertainties in future projected changes. This work leverages from the PI’s experience and ongoing activities in understanding large-scale variability of the land surface and its feedbacks with climate, particularly changes in extreme events.

Summary of work to be completed: We will evaluate the observational uncertainties in surface climate, hydrology and L-A interactions from it-situ, remote sensing and observationally constrained models, globally, with a focus on the U.S. We will develop and test a suite of process-based diagnostic metrics on the observational data, with a focus on droughts and heat waves. These metrics will be applied to the CMIP5 ensemble for the historical simulation and a set of future scenarios. The metrics will be used to identify deficiencies in coupled model parameterizations, attribute historic and future changes to L-A interactions, and link the robustness of future projections to historic performance.

Relevance to the Competition and NOAA’s long-term climate goal: This work is central to the mission of the Climate Program Office’s MAPP program to “enhance the Nation's capability to predict variability and change in Earth's climate system” by focusing on improvement of climate models in the realm of L-A interactions that is not well understood but is increasingly acknowledged as being important. As such it directly adheres to NOAA’s long-term climate goal of adaptation and mitigation, by specifically addressing the goal to “improve scientific understanding of the changing climate system and its impacts” by evaluating coupled climate models at the process level so that past changes can be diagnosed and the uncertainties in future changes evaluated."

Principal Investigator (s): Sheffield, Justin (Princeton University)

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

Process-oriented Diagnosis and Metrics Development for the Madden-Julian Oscillation Based on Climate Simulations

View abstract
"The Madden-Julian Oscillation (MJO) exerts significant influences on global climate and weather including in North America, and serves as a critical basis of the “Seamless Prediction” concept by bridging the forecasting gap between medium- to long-range weather forecasts and short-term climate prediction. The MJO, however, remains poorly represented in state-of-the-art general circulation models (GCMs) as well as NWP models, which leaves us greatly disadvantaged in undertaking climate change studies, particularly in projecting future changes in extreme events that are significantly modulated by the MJO.

In this proposed study, built upon both the PI’s (Jiang, co-organizer of the MJOTF/GASS MJO Project) and Co-PI’s (Maloney, co-chair of the WGNE MJO Task Force and NOAA MAPP CMIP5 Task Force) extensive experiences in studies on process-oriented metrics for the MJO, as well as the co-I’s (Zhao) and collaborator’s (Lin) expertises in GFDL model development, we propose to further explore key physical processes for realistic MJO simulations in GCMs by diagnosing observations and multi-model simulations including the 27 model datasets collected from the MJO Task Force/GASS MJO project and GFDL GCM simulations. In particular, we plan to comprehensively evaluate several processes previously considered to be important for the MJO, including feedbacks between environmental moisture and convection, convection and its induced circulation, and cloud-induced radiative heating and convection. Findings from this project will be developed into effective process-oriented metrics to assess reasons for MJO fidelity and particularly provide insight into key GCM deficiencies in MJO simulations, which further will provide valuable guidance for model development. These metrics will be built into standard software packages as part of collective efforts coordinated by the Type-1 project, and be installed at GFDL and other modeling centers. This proposed work will significantly contribute to GFDL’s model development efforts related to CMIP6 and IPCC AR6, particularly regarding representation of the MJO.

This proposal is strongly relevant to one of the NOAA NGSP’s long-term goal, “toward an improved scientific understanding of the changing climate system”, by advancing core capabilities in “understanding and modeling” and “predictions and projections”, as well as societal challenges in “climate impacts on water resources” and “changes in extremes of weather and climate”. In particular, this proposal directly addresses MAPP Program’s FY15 goal for “Process oriented evaluation of climate and Earth system models and derived projections”, particularly in the Area A: “Metrics for climate and Earth system model development”, as a type-2 subproject with the specific focus on the MJO."

Principal Investigator (s): Jiang, Xianan (University of California, Los Angeles)

Co-PI (s): Eric Maloney (Colorado State University), Ming Zhao (GFDL/NOAA)
Year Initially Funded: 2015
Task Force:
Model Diagnostics Task Force
Final Report:

Development of a Framework for Process-Oriented Diagnosis of Global Models

View abstract
"A critical need exists to improve the diagnosis of global climate and forecasting models. For example, models continue to be plagued by common biases in tropical convection and its variability, including well-known trade-offs between the quality of the mean state and of intraseasonal convective variability such as the Madden Julian Oscillation (MJO). A key need is incorporation of process-oriented diagnostics into standard diagnostics packages that can be applied to development versions of the models, allowing the application of diagnostics to be repeatable across multiple model versions. A significant barrier is the lack of a mechanism for getting community-developed diagnostics into the modeling center development process. The proposed work involves close collaboration between diagnostic developers and modeling centers to develop a common and extensible mechanism for rapid dissemination of process-oriented diagnostics across modeling centers. As demonstration of the process, we will implement critical diagnostics for tropical convection and its variability.

Proposed goals will be met as follows:
1) A leadership team with diagnostic developers and software developers at GFDL and NCAR will be formed to develop best practices for implementation of diagnostics into the centers' standard evaluation packages.
2) GFDL and NCAR will coordinate development of a software framework that allows sharing of diagnostics, with an eye to extensibility to other centers (and other diagnostics) during the project lifetime. This coordinated effort will include development of software tools/standards that individual PIs may use to craft their diagnostics into a form easily useable by multiple modeling centers.
3) Initial emphasis will be placed on process-oriented diagnostics related to tropical convection and its variability. The modeling centers have a critical need for diagnostics in this area, making this topic an attractive one for a pilot diagnostics effort. PIs Maloney and Neelin have led development of existing process-oriented diagnostics that will serve as initial test diagnostics for this effort. Continued development of tropical convection diagnostics will also form a key component of the proposed work.
4) The PIs will also coordinate with international efforts that are developing general diagnostics frameworks such as those at PCMDI and the European EMBRACE project, to ensure that the efforts developed here are complementary rather than duplicative of these efforts.
5) This proposal will also provide a mechanism for PIs of the Type 2 proposals funded under this MAPP call to incorporate their new diagnostics into the diagnostics stream of modeling centers. The PIs have developed collaborations with several potential Type 2 PIs at the proposal stage to aid this integration.

Relevance to NOAA: This proposal directly addresses the FFO “MAPP - Process-oriented evaluation of climate and Earth system models and derived projections” by developing a Type 1 core team to lead development of a mechanism for inclusion of process-oriented diagnostics into standard packages of modeling centers, and also conducting scientific development of new process-oriented diagnostics. We will aid NOAA’s NGSP by improving models to provide more accurate “assessments of current and future states of the climate system that identify potential impacts and inform science, service, and stewardship decisions.”"

Principal Investigator (s): Maloney, Eric (Colorado State University/CIRA)

Co-PI (s): Yi Ming (NOAA/GFDL), Andrew Gettelman (NCAR), David Neelin (UCLA)
Year Initially Funded: 2015
Task Force:
Model Diagnostics Task Force
Final Report:

Diurnal Metrics for Evaluating GFDL and Other Climate Models

View abstract
"The diurnal cycle is a fundamental feature of Earth's climate. Because of its short time scales and close coupling to surface and atmospheric processes, the simulation of the diurnal cycle provides an ideal test bed for evaluating many aspects of model physics. Despite recent improvements in model resolution and parameterizations, the diurnal amplitude and phase in surface temperature, cloudiness, convection, precipitation and other fields still differ considerably from observations in many climate models. These diurnal biases reflect deficiencies in various physical processes simulated by the models. While there exist many observational datasets with sub-daily resolution, most of them cannot be readily used to evaluate models, and current model evaluation packages often contain very limited data for evaluating the diurnal cycle. Based on our previous work on studying the diurnal cycle and its simulation in models, here we propose to a) develop a new set of diurnal metrics and link them to specific underlying processes for evaluating model physics, and b) apply the diurnal metrics to diagnose and identify deficiencies in the GFDL and other CMIP5 models.

Specifically, we propose to 1) compile a new dataset with high temporal-resolution (hourly to 6-hourly) from surface and satellite observations, field experiments, research sites, and atmospheric reanalyses for studying the diurnal cycle and evaluating models; 2) apply the new dataset to quantify the diurnal cycle and study its underlying processes in various fields over the globe, including surface daily maximum (Tmax) and minimum (Tmin) temperatures, precipitation frequency, intensity and amount, cloud cover, humidity and others; 3) design a new set of effective diurnal metrics and link them to specific physical processes based on analyses of observational data; and 4) apply these diurnal metrics and associated linkages to physical processes to diagnose deficiencies in GFDL and other CMIP5 models by analyzing sub-daily output from these models.

The new diurnal data set and diurnal metrics developed in this project will greatly enhance current model evaluation packages. Our second task will improve our understanding of the diurnal cycle and its underlying physical processes. This understanding is necessary for developing constructive diurnal metrics for evaluating physical processes in models, while tasks 3 and 4 will directly help improve models, especially the GFDL model.

A unique feature of this proposal is that it utilizes the expertise of the PI and others on this proposal in studying the diurnal cycle to identify specific physical processes underlying each of the major diurnal variations (e.g., in Tmax and Tmin or the low-level jet over the central U.S.), so that a modeler can use this information to examine specific areas in his/her model when a diurnal bias is found. Another strength is that it includes two leading modelers from GFDL who have a strong desire to improve the simulation of the diurnal cycle in GFDL's new models. This collaboration will lead to real model improvements.

Relevance: This proposal is for MAPP Competition - Process-oriented evaluation of climate and Earth system models and derived projections (Area A, Type 2), which emphasizes projects to ""develop and apply process-oriented metrics to evaluate simulated climate phenomena with strong theoretical and observational bases"". The diurnal cycle is a well-studied, fundamental feature of Earth's climate. The focus of our diurnal metrics on the sub-daily processes and our emphasis on linking diurnal biases to underlying physical processes make our metrics truly process-oriented. We will also apply the new diurnal metrics to diagnose the simulation of the diurnal cycle in the GDFL and other models. Thus, this proposal is directly responsive to the MAPP competition. Improving climate models and our understanding of the diurnal cycle is also an important step to achieve NOAA's long-term climate goal to ""improved scientific understanding of the changing climate system and its impacts""."

Principal Investigator (s): Dai, Aiguo (University of Albany, SUNY)

Co-PI (s): Junhong Wang (SUNY), Chris Golaz (NOAA/GFDL), Ming Zhao (NOAA/GFDL)
Year Initially Funded: 2015
Task Force:
Model Diagnostics Task Force
Final Report:

Evaluation and Diagnosis of the Atlantic Meridional Overturning Circulation 3D Structure in Climate Models

View abstract
"The Atlantic Meridional Overturning Circulation (AMOC), with its large heat and freshwater transports and interaction with the atmosphere and sea-ice, plays a fundamental role in establishing the mean state and the temporal variability of the Earth’s climate system. Existing studies have shown a wide spread of AMOC state in the current state-of-art climate models, CIMIP5, and hence a better understanding of the driving mechanisms is urgently needed. The current analysis, however, typically quantifies the AMOC as one maximum volume transport stream function at certain latitudes. This overly simplified AMOC index, while important, is insufficient for formulating a comprehensive picture of the AMOC structure across the entire Atlantic and characterizing its fundamental role in the climate system, such as transport of heat/freshwater and water mass transformation.

Recognizing this problem, here we propose a collaborative effort to conduct more comprehensive analyses on the structure of the AMOC in climate models. The analyze are built on observational results and high-resolution ocean simulations, and include a) AMOC transport on temperature-salinity plane and density spaces across trans-Atlantic sections at different latitudes, b) water mass transportation due to surface buoyancy forcing as well as diapycnal/isoptcnal mixing in the ocean interior, and c) diapycnal velocity. The overall goals are 1) to derive a better and more comprehensive diagnosis for evaluating the AMOC representation, including time mean structure and temporal variability, in current climate models, and 2) to identify and understand the key physical process or mechanisms that lead to the wide spread of the AMOC state among the CMIP5 models as well as the AMOC variability in individual models.

This project is a direct contribution to the “Process-oriented evaluation of climate and earth system models and derived projections” in the area A: metric for climate and earth model development. The proposed analysis will help a) evaluate the AMOC structure in current earth system models, compared to observations and high-resolution models; and b) isolate the model biases on AMOC structure due to contributions from air-sea interaction and/or oceanic advection and mixing process. Botha re critical for the ongoing efforts to improve our scientific understanding of the changing climate system and its impact, and to enhance our capability to predict climate variability and changes in climate and earth system models."

Principal Investigator (s): Xu, Xiaobiao (Florida State University)

Co-PI (s): Eric Chassignet (FSU), Molly Baringer (NOAA/AOML), Shenfu Dong (NOAA/AOML)
Year Initially Funded: 2015
Task Force:
Final Report:

Identifying and Assessing Gaps in Subseasonal to Seasonal Prediction Skill

View abstract
"Estimates of predictability together with calculations of current prediction skill are often used to define the gaps in our prediction capabilities on subseasonal to seasonal timescales and to inform the scientific issues that must be addressed to build the next forecast system. However, different methods for estimating predictability can produce substantially different estimates of the upper limit of skill, leading to different conclusions regarding the gaps in our prediction capabilities. This project proposes to (1) systematically quantify estimates of the upper limits of predictability, (2) assess similarities and differences between predictability estimates and understand the reasons for differences between them, and (3) compare predictability estimates with current skill to identify gaps in our prediction capabilities.

To accomplish these objectives, we will apply several methods for estimating predictability to the North American Multimodel Ensemble (NMME) data as well as several other publically available datasets and compare them with each other and current prediction skill globally for monthly, seasonal and week 2-4 forecasts and for measures of specific phenomena (e.g. El Niño and the Southern Oscillation, Madden-Julian Oscillation, North Atlantic Oscillation). Additionally, we will evaluate extreme temperature, precipitation, and associated circulation anomalies. The outcome will provide information on the regions, variables, and phenomena where predictability estimates agree about whether current skill has or has not reached the limits of predictability and provide information on when and where we do not know the predictability limits. In the situations where the predictability limit is not clear because of disagreement in the estimates, we will diagnose the signal and noise from the methods to understand the source of the discrepancy. Where there is clear potential for more skill that has yet to be realized, the variability of the signal and noise from forecast to forecast, and its relationship to prediction skill will be evaluated to better understand current gaps in prediction capabilities.

Relevance: This project focuses on North American Multi-Model Ensemble System evaluation and application by assessing predictability and identifying the gaps in current prediction capabilities using the NMME re-forecasts. The analysis will evaluate predictability and current prediction skill globally and for specific phenomena, including extreme events such as heat waves, cold spells, and extreme precipitation anomalies. The focus on weeks 2-4 is a current priority for NOAA, and the results of this work will have a bearing on plans for model-based forecasts to operational climate prediction."

Principal Investigator (s): Pegion, Kathy (George Mason University)

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

NMME Precipitation and Temperature Forecasts for the Continental United States and Europe: Diagnostic Evaluation and Development of Multi Model Applications

View abstract
"The main goals of this proposal are to: 1) examine the potential value and applicability of five global circulation models (GCMs) part of the North American Multi-Model Ensemble (NMME) project in forecasting monthly and seasonal precipitation and temperature over the continental United States and Europe; 2) Develop a multi-model averaging procedure to increase the forecast skill of these models.

We will focus on the continental United States and Europe and evaluate the forecast quality of five GCMs part of the NMME project in forecasting seasonal precipitation and temperature. The GCMs we consider are two GFDL models (CM2.1, and FLORb01), NASA-GMAO-GEOS-5, COLA-RSMAS-CCSM3, and CCCma-CanCM4. The outputs from these models are available from the early 1980s to the present. They have a resolution of 1-degree and monthly, with a forecast lead time up to one year. We will also focus on extreme precipitation and temperature. More specifically, we will examine the skill of the models in forecasting extended periods with high temperature and/or low precipitation (leading to drought conditions), and periods characterized by extreme precipitation (leading to flooding). The forecasts from these models are compared against gridded monthly temperature and precipitation data created by the PRISM Climate Group for the United States and the E-OBS data for Europe.

Aspects of forecast quality are quantified using a diagnostic skill score decomposition that allows the evaluation of the potential skill and conditional and unconditional biases associated with these forecasts. This skill score will allow for a better understanding of the utility of these models for flood and drought predictions. It also represents a diagnostic tool that could provide model developers feedback about strengths and weaknesses of their models.

Quantification of the forecast quality represents the necessary step towards improving on the models’ skill. We will apply a simple new Bayesian model averaging procedure that leverages the strength of each model at different lead times for different months. Using the relationship between hindcast model forecasts and observations from the verification, the procedure assigns weights to each historical observation given the multi-model forecasts; the weights represent the likelihood of each historical outcome given the multi-model forecasts. The weighted samples of observations not only define an optimal bias-corrected multi-model ensemble forecast, they can also be used to selectively weight historical forcings as an atmospheric ensemble pre-processor method for hydrologic forecasting. This Bayesian multi-model weighting procedure will be performed at the pixel scale for all the months/seasons and lead times.

This proposal is relevant to NOAA’s Next Generation Strategic Plan (NGSP) because it provides information valuable to “assessments of current and future states of the climate system that identify potential impacts and inform science, service, and stewardship decisions,” to improve “freshwater resource management,” and to reduce “loss of life, property, and disruption from high-impact events,” which are some of the NGSP’s objectives. In evaluating the temperature and precipitation forecasts from five GCMs part of the NMME project, our goals lie squarely with those of the Modeling, Analysis, Predictions, and Projections (MAPP) Program and solicitation. In particular the proposed work will result in the evaluation of “the prediction of large-scale, extended lead time conditions conducive to extremes such as heat waves, extreme precipitation.” Moreover, the development of weighted ensemble predictions aligns with the “application of the NMME for the development of new prediction products.”"

Principal Investigator (s): Villarini, Gabriele (University of Iowa)

Co-PI (s): Allen Bradley (University of Iowa)
Year Initially Funded: 2015
Task Force:
Final Report:

Assessing Phase 2 NMME (NMME-2) forecasts for improved seasonal predictions of drought and water management

View abstract
"The over-arching goal of the proposed project is to assess and document the NMME Phase-2 seasonal forecasts for hydrological seasonal forecasts of drought and water management and compare these to the skill from the Phase-1. The availability under NMME-2 of a broader suite of variables, and daily forecasts rather than monthly, offers the potential for significant improvements that need to be documented through forecast experiments and systematic analysis.

Over the last 15 years NOAA’s Climate Program Office (CPO) has fostered the development of objective hydrologic and drought monitoring through support for the North American Land Data Assimilation System (NLDAS), experimental drought and hydrological forecasting using CFSv1 and more recently CFSv2, and the experimental North American Multi-Model Ensemble project (NMME). PI Wood has participated in these developments through supported projects in NLDAS, the NCEP’s Climate Test Bed and NMME. PI Wood’s current drought and hydrological seasonal forecast system is based on NMME Phase 1 (NMME-1), which utilizes monthly mean forecasts as inputs to his VIC land surface model and produces drought indices (SPI, a soil moisture based drought index, river discharge and seasonal temperature index).

As NMME transitions into phase 2 (NMME-2), there are two major differences that will impact its use for hydrological seasonal forecasting and related applications in drought and water management: the higher temporal resolution of the available forecasts – primarily daily – and equally important the availability of many more forecast variables, including surface meteorology and energy variables that can be used to force hydrological models, and surface water budget variables such as soil moisture, runoff and evapotranspiration.

Proposed Research. The higher temporal resolution of the forecasts and the new forecast variables in the NMME-2 archive offers a number of opportunities for advancing seasonal drought and hydrological forecasting. These include the following:
1. Given that NMME-2 provides high temporal resolution forecast products for many additional variables, how will these new products contribute to improved drought and hydrologic seasonal forecasts?
2. To what extent will seasonal forecast skill from NMME-2 predictions change when compared to predictions based on NMME-1? For the first time ever, the variables needed as inputs to hydrological models will be offered at high temporal resolutions from seasonal forecast models. This allows the impact on drought and water availability forecasts to be assessed and quantified.
3. How can drought and hydrologic forecasts for month 1 be improved using NMME-2? The PI’s group has tested the usefulness of the first 14 day forecast from GEFSv2 in a seasonal hydrologic forecast system based on monthly CFSv2 forecasts. During the proposed project, similar experiments with NMME-2 will be carried out within a multi-model framework, either with or without GEFSv2 for weeks 1-2. This will advance the usefulness of a seamless forecast system.

Relevance to the MAPP Program. Assessing whether NMME-2 will lead to improved seasonal forecast applications and skill is central to determining its usefulness in NCEP operations – especially for the needs of NIDIS and the Climate Prediction Center. Additionally, the needs of the private sector for seasonal forecasts are growing, and assessing the usefulness of NMME-2 for water management and agricultural crop management as well as extremes (drought and wet periods; heat waves and cold seasons) is critical. The proposed project will offer research results that can address these needs."

Principal Investigator (s): Wood, Eric (Princeton University)

Co-PI (s):
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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.

Learn more...

Download our program brochure (pdf).