Funded Projects

Program (s): Climate Variability & Predictability

Competition: COM and CVP: Innovative Ocean Dataset/Product Analysis and Development for support of the NOAA Observing and Climate Modeling Communities

Award Number: GC21-411a, GC21-411b | View Publications on Google Scholar

Ocean observations are used for initializing and validating sub seasonal to seasonal forecasts using coupled models; creating synthesis products, for monitoring evolution of ocean conditions; developing climate data records to monitor the influence of slow trends; and for improving understanding of the physics affecting the climate system. With continued investments in ocean observations, and upcoming enhancements in the tropical Pacific observing system (TPOS), increasing the utilization of ocean observations has been identified as one of the key challenges. There is also a consensus in the community that there is a longstanding disconnect between the investments in observations and their utilization in model-based analysis and prediction systems. A real-time comparison between observations and model-based analysis products will represent a direct use of ocean observations for which a need has been long perceived but remains to be realized. Towards bridging the gap between the observation and modeling communities, the goal of this project is to develop a capability for the real-time comparison of in situ ocean observations with operational analysis products. The scope of the project will focus on the observations from moorings in the tropical Pacific. This is because El Niño-Southern Oscillation (ENSO) is one of the most important modes of coupled variability with largest societal impacts, and further, because the TPOS is currently going through evolutionary changes. To accomplish the goals of the project outlined above, the following tasks will be completed: (a) Identify ocean and atmosphere mooring data to be used in the real-time model assessment and set up procedures to update the observational database in real-time. (b) For atmospheric and ocean analyses, set up corresponding procedures to update the model database in real-time. (c) Develop procedures to compare time-series of model analysis with observations and develop a web interface to disseminate the information to the community. (d) Utilization of assembled observational and modeled data bases to address science questions of relevance in understanding coupled climate variability in the tropics. The goals of the proposal are highly relevant to the focus of the present call to “...develop an observations-based product for climate monitoring or modeling application” that “enables improved climate modeling or monitoring (e.g., enables future climate model evaluation, validation, process-oriented diagnostics)”. The project will also address other foci of the call for “Evaluating current methods and approaches for ocean observing and modeling, and the ability of observed and modeled data/products to reproduce physical or biogeochemical processes, climate phenomena, or interactions between Earth System components on different timescales.”

Program (s): Climate Variability & Predictability

Competition: Pre-Field Modeling Studies in Support of TPOS Process Studies, a Component of TPOS 2020

Award Number: GC18-907 | View Publications on Google Scholar

As part of the Tropical Pacific Observing System 2020 (TPOS 2020) first report, several process studies were identified that would guide development of the observing system and lead to improved understanding and predictability of the Pacific climate system. Here we focus on prefield phase needs of the process study “Air-sea interaction at the eastern edge of the western Pacific warm pool (WPWP)”. This study builds upon previous studies in the WPWP (e.g., TOGA COARE) by focusing on the interactions at the front at the eastern edge of the WPWP. Towards the implementation of the process study to understand air-sea interaction at the eastern edge of the WPWP, this proposal is primarily a model-based study to provide some necessary insights for the design of the field phase of the experiment. Our objectives are to (a) explore the multi-timescale near-surface salinity variations at the Warm Pool eastern edge (WPEE), and (b) To identify possible sampling requirements (and strategies) that may be essential to capture this variability. To achieve the objectives, the following tasks are proposed: 1) A coupled simulation using the modified Coupled Forecast System version 2 (CFSv2) with 1-m vertical resolution in the upper ocean will be conducted. Diagnostics related to the sea surface salinity (SSS) front at the WPEE and multi-timescale SSS variability will be made to enhance our understanding of air-sea interaction in the presence of barrier layer over this region; 2) Mimicking a realistic combination of sampling variables, sampling locations/frequencies and sampling technologies that may be viable for observing the variability associated WPEE, a set of “synthetic observations” will be constructed based on the above coupled model simulation; and 3) Observing system simulation experiments (OSSEs) will be performed by assimilating the “synthetic observations” into an ocean data assimilation to obtain an ocean analysis. Comparisons with the original coupled simulation will be made to ascertain if the proposed observational strategies will be adequate to capture essential features of WPEE. We anticipate that the proposed research will enhance our understanding of processes associated with multi-timescale near-surface salinity at the warm pool eastern edge and identify possible sampling requirements (and strategies) essential to capture them. The outcomes from the project will not only improve our understanding of air-sea interaction at the eastern edge of the warm pool, but will also help guide the pre-cruise planning and field campaign development for TPOS 2020, and further, in the design of the sustained observing system.

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

We propose conducting a series of ocean reanalyses of the 20th Century (1890-2005) using SODA to study tropical Pacific decadal variablility, its influence on El Niño, and the atmospheric teleconnections that lead to decadal climate change across North America. The study will use the SODA ocean data assimilation framework in conjunction with the recently released atmospheric reanalysis of the 20th Century to generate a state estimate of the global oceans. In addition to the baseline run, we will conduct a series of “data thinning” experiments whereby we degrade the observations to replicate data coverage for various periods of time throughout the 20th Century to calculate error in the ocean state estimate. In addition to the ocean reanalyses, we will use results from the reanalyses to drive an atmospheric general circulation model to sudy the impact of improved SST information on the modeled climate of North America. For the atmospheric modeling component we plan to utilize the Community Atmosphere Model 3 (CAM3) developed and distributed by the National Center for Atmospheric Research (NCAR). We will force the T85 resolution (1.4 degree) version of this model with the SST anomaly from SODA as well as SST reconstructions (such as HadISST) and compare the results to the results from the NCAR multi-century control runs. These control runs have been extensively analyzed and provide an accepted atmosphere background state with which we can compare our suite of runs. We will verify the model results, both from SODA and from the atmosphere model with 100-year long records from sources such as 18O from corals from the tropical Pacific, sea level records from coastal tide guages, and from observations such as global precipitation rates that are currently available. 

The proposed research contributes to the goals of the Climate Variability Program by expanding our understanding of decadal climate variability, by providing initial conditions for decadal prediction models, and by exploring the causes of North American climate change. The resulting reanalysis will be available on our web site (soda.tamu.edu) to other researchers interested in topics such as AMOC and rapid climate transitions during the 20th Century.

Program (s): Climate Variability & Predictability

Competition: OAR/CPO/CVP - NWS/OSTI/Modeling Division - Joint Competition to Advance Process Understanding and Representation of Precipitation in Models

Award Number: NA22OAR4310603 NA22OAR4310604 | View Publications on Google Scholar

Integration of the NOAA Unified Forecast System (UFS) Seasonal Forecast System (SFS) into the seasonal research and forecasting communities, including University of Miami and the Climate Prediction Center (CPC) relies on assessment of skill and biases of precipitation over North America in both hindcasts and realtime forecasts. The National Center for Atmospheric Research (NCAR)’s enhanced Model Evaluation Tools (METplus) verification framework is intended to be used to verify the UFS, and is currently being onboarded for operational use at CPC due to its large library of verification metrics and community support approach. A currently funded collaborative effort between NCAR and CPC shows that METplus requires more development to seamlessly integrate with seasonal climate data, such as UFS-SFS and the North American Multi- Model Ensemble (NMME) (part a). Moreover, CPC seasonal forecasters rely on the state of primary climate drivers to forecast seasonal precipitation, and information on these drivers is imperative to the seasonal climate research and modeling communities. Thus, the assessment of the impact of El Niño Southern Oscillation (ENSO), decadal trends, etc. on North American precipitation variability is key to diagnosing the utility of any dynamical models used in seasonal forecasting and research. Though ENSO plays a key role in precipitation variability, other climate drivers should also be considered. For example, key internal forcing mechanisms such as the Pacific Decadal Oscillation (PDO) and its impact on North American precipitation in seasonal forecast systems must be assessed, as well as the representation of in-situ drivers such as soil moisture and snow cover (part b). Collaboratively, we will create a verification framework utilizing METplus to allow streamlined assessment of probabilistic seasonal precipitation forecast skill, including hindcast and conditional skill related to the above key drivers within the UFS-SFS. An additional goal will be that it can be easily expanded to any climate model ensemble. The development, documentation, and demonstration of these process-based model capabilities will provide valuable feedback to the UFS model development team and community, with the potential to improve the key modes of variability that impact seasonal precipitation forecasts.

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

The PIs propose to examine tropical Pacific biases. They propose a different approach for understanding the systematic errors and why promising sensitivities fail to translate from one model to the next. They suggest that the errors in the mean state are, at least in part, due to errors in the simulated ENSO; and that the errors in the simulated ENSO are due to errors in the statistics of the tropical atmospheric weather. That is, if there are large errrors in the simulation of weather statistics, then the climatic simulation is seriously degraded. The PIs hypothesize that the changes - or lack thereof - in the weather statistics can explain the large differences in model sensitivity. They propose a series of novel weather noise forced CGCM simulations designed to understand the differences in coupled model biases and sensitivities. These experiments leverage their experitise with the NOAA Climate Forecast System, the NCAR Community Climate System Model and the interactive ensemble coupling strategy that has been developed by the PI. 

Program (s): Climate Variability and Predictability

Competition:

Award Number: NA13OAR4310167 | View Publications on Google Scholar

An improved understanding of Madden-Julian Oscillation (MJO) dynamics and predictability is of utmost importance for extending weather forecasts and developing seamless climate predictions that are increasingly in demand for mitigation and adaptation to climate change. The lack of understanding and inadequate treatment of multi-scale convective organization and air-sea interaction are among the major stumbling blocks for the numerical prediction of intraseasonal variations.
Our preliminary examination of the development of the wet phase of the MJO event (heretofore referred to as “MJO initiation”), which occurred in late November 2011 over the Indian Ocean (IO), suggests that DYNAMO observations have provided an unprecedented dataset from a “natural laboratory” experiment.

This proposal articulates an endeavor to investigate aspects of MJO initiation, including the onset and development stages of an MJO wet phase, over the IO. The objective is to understand the roles of the multi-scale convective organization and interaction as well as air-sea interaction during MJO initiation. The overarching questions that serve as foci for the proposed research are:

1) How is deep convection organized during MJO initiation in the DYNAMO period (for each individual active MJO event)? What are controlling factors for the wet phase onset? How and to what extent do convective and eddy upscale transports of heat, momentum, and moisture impact MJO initiation?

2) How and to what extent does oceanic mixed layer/SST feedback influence MJO wet phase onset? What is the role of the precipitation-salinity-SST feedback in MJO development?

We hypothesize that MJO initiation is not a static thermodynamic process and must be conditioned by dynamical processes and involve feedbacks from meso-synoptic scale convectively coupled systems. The multi-scale and air-sea interaction play a significant role in MJO initiation. Our specific efforts focus on two major thrust areas dealing with the oceanic MJO initiation specifically arising from (a) multi-scale convective organization and interaction during MJO initiation and (b) upper ocean response and feedback in MJO initiation. To meet the challenge of understanding these nonlinear processes, we shall adopt a strategy of synergetic analysis by combining diagnostic and hierarchical numerical modeling approaches.

This proposal is submitted to one of the priority areas of NOAA/CPO/ESS Program: “Understanding and improving prediction of tropical convection using results from the DYNAMO Field Campaign.” This project will extensively use data collected during DYNAMO and focus on two physical processes deemed to be critical to MJO initiation: “the dynamic evolution of the cloud population and air-sea interaction.”

Completion of this project will make a significant contribution to NOAA’s goal in delivering cutting-edge extended range and seamless predictions to the nation and global community, which would serve as a foundation for climate adaptation and mitigation. Specifically, this project is expected to contribute to NOAA’s Next Generation Strategic Plan (NGSP)’s five-year climate objectives to improve scientific understanding of changing climate and its impact.

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

Understanding the predictability of the tropical Atlantic variability (TAV) is crucial for short-term climate prediction in the Atlantic sector. Two major TAV mechanisms are regional air-sea interaction and remote El Niño/Southern Oscillation (ENSO) influence, both potentially predictable on the seasonal-to-interannual time scales. Mid-latitude seasonal atmospheric anomalies over the North and South Atlantic may also be useful precursors for the tropical anomalies in subsequent seasons. In a dynamical forecast model, these mechanisms and their interactions should be represented realistically and initialized accurately. This study examines the TAV predictability using a coupled ocean-atmosphere general circulation model (CGCM) with realistic ocean/atmosphere initial states. In particular, we would like to understand what kind of initial surface and subsurface anomalies within the Atlantic Ocean can damp or amplify the remote ENSO influences, and vice versa. We will also examine under what conditions the midlatitude anomalies can stimulate major tropical air-sea feedback on seasonal time scales. 

Our previous studies have shown that this CGCM can simulate the major TAV features realistically. For this study, we propose further improvements in its simulation and initialization. We will conduct an empirical CGCM error correction that prescribes observational climatological monthly mean low-cloud amount in the model while allowing the anomalous model sea surface temperature (SST)-low cloud feedback. The mean cloud correction targets specifically the inadequate simulation of low cloud fraction over the southeastern tropical Atlantic, which is a major component of the current CGCM systematic bias and unrealistic annual cycle in this region. The preserved anomalous SST-low cloud feedback is important for the interannual variability. To minimize the CGCM initial shock, a new model initialization technique will be tested. We will conduct a series of coupled “initialization runs”, in which the coupled system is nudged continuously toward the observed climate anomalies derived from the oceanatmosphere analyses. Initial conditions from these runs should be more in balance between the ocean and atmosphere and likely represent the low-frequency signals better than the instantaneous initial ocean-atmosphere states separately generated by uncoupled atmospheric and oceanic data assimilation systems. To take into account the potentially significant uncertainty of current oceanic analysis in the tropical Atlantic Ocean, we will use several different ocean analysis products to generate an ensemble of oceanic states with sufficient spread. Using the improved CGCM, we will conduct a set of hindcast experiments for 1981-2005 to establishing its predictive skill in the tropical Atlantic. Further sensitivity case studies will be conducted using our regional coupling strategy with the observed or climatological SST anomalies prescribed in the tropical Pacific to study the relative roles of the ENSO forcing and regional air-sea interaction. To link this study more directly to operational prediction, we plan to conduct some experiments with the NCEP Climate Forecast System (CFS), which has been successfully installed at COLA.

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

All climate models predict a weakening of the tropical atmospheric circulation in response to anthropogenic increases in greenhouse gases. Analysis of climate model simulations from the CMIP4 archive indicate that the atmospheric circulation may weaken by as much as 25% by the end of the century (Vecchi and Soden, 2006). However, observed variability of the Walker circulation over the past few decades appears dominated by unforced internal variability (Burgman et al. 2008). This decadal variability in the atmospheric circulation also appears to be amplified by the response of low-level clouds (Clement et al. 2009), and hence these clouds may be an important component of decadal variability. Circulation changes can have significant impacts on cloud feedbacks in response to anthropogenic warming, particularly marine stratocumulus clouds, which have been identified as one of the main sources of uncertainty in global warming projections. While our prior work has identified patterns of decadal variability in the tropical circulation, the causes of these changes and their implications for climate are still not known. For example, what are the relative contributions of internally-generated variability and external forcing? What role does the ocean play in generating decadal atmospheric variability? What role do low clouds play on decadal and longer-timescales? We propose to address these questions with a modeling and diagnostic study that is focused on three separate tasks:

• Task I: Determine what aspects of low-frequency changes in the atmospheric circulation over recent decades can be attributed to unforced internal variability, or to external (anthropogenic and natural) climate forcings using both observations and CMIP5 climate model simulations under different forcing scenarios.

• Task II: Investigate the mechanisms of variability in the tropical atmospheric circulation using idealized climate model simulations

• Task III: Evaluate the impacts of atmospheric circulation changes on cloud feedback from marine stratocumulus clouds.

The proposed work will address the FY 2010 priority of 'understanding the causes of climate variability over the observational record,' and we will 'attempt to quantify the roles of radiative forcing and natural climate variability for explaining the observed climate record.'

Program (s): Climate Variability and Predictability

Competition:

Award Number: | View Publications on Google Scholar

Several recent observational studies suggest that precipitation may be increasing at a much faster rate than currently predicted by GCMs. These discrepancies appear at time-scales ranging from interannual, to decadal, to centennial and have important implications for future projections of climate change, the reliability of the observing system and the monitoring of the global water cycle. If true, such a bias in model projections would have substantial repercussions - not only for the modeling of the atmospheric energy and water budgets, but also for the model projections of the response of the atmospheric and oceanic circulation to increased CO2. However, the veracity of the satellite-observed changes in precipitation remains in question due, in large part, to uncertainties in the retrieval of precipitation from passive microwave sensors. 

The PIs propose to better understand the cause of these discrepancies by performing a detailed comparison of SSMI observations and GFDL GCM simulations using a “model-to-satellite” approach in which model output is used to directly simulate the radiances which would be observed by the satellite under those conditions. The advantages of this strategy are that it avoids many of the assumptions that are required when performing retrievals and it provides a model-simulated quantity that is directly comparable to what is actually observed by the satellite. Any assumptions involved in the performing forward radiance simulation are made explicit and can be varied in a controlled framework to examine their sensitivity. 

They propose to apply this strategy for comparing model-simulated microwave radiances from the GFDL GCM to the satellite-observed radiances from SSMI. From this comparison they hope to better understand the cause of bias between observed and model-simulated precipitation response to a warming climate. 

Program (s): Climate Variability & Predictability

Competition: Pre-Field Modeling Studies in Support of TPOS Process Studies, a Component of TPOS 2020

Award Number: NA18OAR4310403 | View Publications on Google Scholar

The First Report of TPOS 2020 (Tropical Pacific Observing System 2020, tpos2020.org) recommended several process studies, including Pacific Upwelling and Mixing Physics (PUMP), and Air-sea Interaction at the eastern edge of the Warm Pool (WPEE). We are proposing a modeling and assimilation study in support of these process studies, at the large-scale end of an expected hierarchy of models. The state estimates will form a reanalysis for siting smaller-scale studies and the adjoint model can be used to probe sensitivities that can inform sampling. Data withholding experiments will also be performed to assess the values of different components of the observing systems, as well as their usefulness for testing and improving models. The overall goal of these studies is to improve the ocean models and initializations for better predictability of the coupled Pacific climate system in support of better environmental prediction, to help refine the TPOS 2020 implementation, and to assess an assimilation system as a key component of the observing system. We will produce a series of overlapping Four-dimensional variational (4D-Var) state estimates for the tropical Pacific covering 2010-2019 at a resolution of 1/3 degree or better. Each estimate is a free forward model run that has had initial conditions, forcing, and other controls adjusted so that it is consistent with observations and can provide diagnoses of mass, heat, and salt fluxes. They will be used to support embedded process studies and to assess regions of good and bad model skill to provide guidance for model improvement and designing observing systems. The model domain will include the entire tropical Pacific, and so will contain both the PUMP upwelling region and the warm pool region. We will work collaboratively with the NOAA labs and other investigators to provide a dynamically-consistent, property-conserving, large-scale context and boundary conditions for the hierarchy of process models as mentioned in the Call. The state estimates will enforce the model dynamics over assimilation windows of up to 4 months or longer, to be determined experimentally as part of the research. Each fit tests the model as a hypothesis for the dynamical explanation of the observations, and the controls estimated as part of the assimilation process will be examined to identify model errors. Cross-validation will come from comparisons to withheld observations and from forecasts beyond the time range of each estimate. In this way, the assimilation serves as a process experiment by directly testing the compatibility of the proposed dynamics, as quantified in the model, with the observations. The state estimates can be used as part of a diverse set of methods and models to provide the outer context and large-scale budgets for the inner models.