The role of the Indian Ocean sector and sea surface salinity for prediction of the coupled Indo-Pacific system

The purpose of this dissertation is to evaluate the potential downstream influence of the Indian Ocean (IO) on El Niño/Southern Oscillation (ENSO) forecasts through the oceanic pathway of the Indonesian Throughflow (ITF), atmospheric teleconnections between the IO and Pacific, and assimilation of IO observations. Also the impact of sea surface salinity (SSS) in the Indo-Pacific region is assessed to try to address known problems with operational coupled model precipitation forecasts. The ITF normally drains warm fresh water from the Pacific reducing the mixed layer depths (MLD). A shallower MLD amplifies large-scale oceanic Kelvin/Rossby waves thus giving ~10% larger response and more realistic ENSO sea surface temperature (SST) variability compared to observed when the ITF is open. In order to isolate the impact of the IO sector atmospheric teleconnections to ENSO, experiments are contrasted that selectively couple/decouple the interannual forcing in the IO. The interannual variability of IO SST forcing is responsible for 3 month lagged widespread downwelling in the Pacific, assisted by off-equatorial curl, leading to warmer NINO3 SST anomaly and improved ENSO validation (significant from 3-9 months). Isolating the impact of observations in the IO sector using regional assimilation identifies large-scale warming in the IO that acts to intensify the easterlies of the Walker circulation and increases pervasive upwelling across the Pacific, cooling the eastern Pacific, and improving ENSO validation (r ~ 0.05, RMS~0.08C). Lastly, the positive impact of more accurate fresh water forcing is demonstrated to address inadequate precipitation forecasts in operational coupled models. Aquarius SSS assimilation improves the mixed layer density and enhances mixing, setting off upwelling that eventually cools the eastern Pacific after 6 months, counteracting the pervasive warming of most coupled models and significantly improving ENSO validation from 5-11 months. In summary, the ITF oceanic pathway, the atmospheric teleconnection, the impact of observations in the IO, and improved Indo-Pacific SSS are all responsible for ENSO forecast improvements, and so each aspect of this study contributes to a better overall understanding of ENSO. Therefore, the upstream influence of the IO should be thought of as integral to the functioning of ENSO phenomenon

Database of Observations: Ocean/Marine Perspectives

NASA GMAO is one of the contributing agencies in the Joint Center for Satellite Data Assimilation (JCSDA). One of the projects of the JCSDA is the Joint Effort for Data Assimilation Integration (JEDI). The JEDI framework needs a database of observations of the earth system. This talk is about planning for the ocean observations to be used in the JEDI based assimilation system at GMAO, NASA. We present preliminary requirements of such an observational database and scope out issues that need multi-agency attention in future

Sea surface topography fields of the tropical Pacific from data assimilation

Time series of maps of monthly tropical Pacific dynamic topography anomalies from 1979 through 1985 were constructed by means of assimilation of tide gauge and expendable bathythermograph (XBT) data into a linear model driven by observed winds. Estimates of error statistics were calculated and compared to actual differences between hindcasts and observations. Four experiments were performed as follows: one with no assimilation, one with assimiation of sea level anomaly data from eight selected island tide gauge stations, one with assimilation of dynamic height anomalies derived from XBT data, and one with both XBT and tide gauge data assimilated. Data from seven additional tide gauge stations were withheld from the assimilation process and used for verification in all four experiments. Statistical objective maps based on data alone were also constructed for comparison purposes. The dynamic response of the model without assimilation was, in general, weaker than the observed response. Assimilation resulted in enhanced signal amplitude in all three assimilation experiments. RMS amplitudes of statistical objective maps were only strong near observing points. In large data-void regions these maps show amplitudes even weaker than the wind-driven model without assimilation. With few exceptions the error estimates generated by the Kalman filter appeared quite reasonable. Since the error processes cannot be assumed to be white or stationary, we could find no straightforward way to test the formal statistical hypothesis that the time series of differences between the filter output and the actual observations were drawn from a population with statistics given by the Kalman filter estimates. The autocovariance of the innovation sequence, i.e., the sequence of differences between forecasts before assimilation and observations, has long been used as an indicator of how close a filter is to optimality. We found that the best filter we could devise was still short of the goal of producing a white innovation sequence. In this and earlier studies, little sensitivity has been found to the parameters under our direct control. Extensive changes in the assumed error statistics make only marginal differences. The same is true for long time and space scale behavior of different models with richer physics and finer resolution. Better data assimilation results will probably require relaxation of the assumptions of stationarity and serial independence of the errors. Formulation of such detailed noise models will require longer time series, with the attendant problems of matching very different data sets

An optimized design for a moored instrument array in the tropical Atlantic Ocean

This paper presents a series of observing system simulation experiments (OSSEs) which are intended as a design study for a proposed array of instrumented moorings in the tropical Atlantic Ocean. Fields of TOPEX/Poseidon sea surface height anomalies are subsampled with the goal being reconstruction of the original fields through the use of reduced-space Kalman filter data assimilation at a restricted number of locations. Our approach differs from typical identical and fraternal twin experiments in that real observed data (i.e., TOPEX/Poseidon data) are subsampled and used in place of synthetic data in all phases of the OSSEs. In this way the question of how closely a particular model-generated data set resembles nature is avoided. Several data assimilation runs are performed in order to optimize the location of a limited number of moorings for the proposed Pilot Research Moored Array in the Tropical Atlantic (PIRATA). Results of experiments in which data are assimilated at 2°N, 2°S and the equator and the longitude is systematically varied by 5° show that the greatest impact of the assimilated data occurs when the observations are taken between 15°W and 30°W. Next, a more systematic technique is presented which allows us to determine optimal points in an objective fashion by applying a least squares regression approach to reconstruct the errors on a dense array of points from the data misfits at any three selected points. The forecast error structure from the Kalman filter is used in a novel way to assess the optimality of mooring locations. From a large sample of triads of points, the optimal mooring locations are found to be along the equator at 35°W, 20°W, and 10°W. Additional experiments are performed to demonstrate the efficacy of the initial and final PIRATA configurations and the added value that can be expected from PIRATA observations beyond existing expendable bathythermograph observations

Sea Ice Outlook for September 2017: June Report - NASA Global Modeling and Assimilation Office

The GMAO seasonal forecast is produced from coupled model integrations that are initialized every five days, with seven additional ensemble members generated by coupled model breeding and initialized on the date closest to the beginning of the month. The main components of the AOGCM are the GEOS-5 atmospheric model, the MOM4 ocean model, and CICE sea ice model. Forecast fields were re-gridded to the passive microwave grid for averaging

Sea Ice Outlook for September 2017 July Report - NASA Global Modeling and Assimilation Office

The GMAO seasonal forecast is produced from coupled model integrations that are initialized every five days, with seven additional ensemble members generated by coupled model breeding and initialized on the date closest to the beginning of the month. The main components of the AOGCM are the GEOS-5 atmospheric model, the MOM4 ocean model, and CICE sea ice model. Forecast fields were re-gridded to the passive microwave grid for averaging

NASA GMAO GEOS S2S Prediction System: Metrics, Post-Processing and Products

In this presentation we present an overview of the GMAO Sub-Seasonal and Seasonal Prediction System, current users and products, and methods for validation and evaluation of the system. Methods for evaluation include baseline evaluations metrics, the ability to simulate key modes of variability, and evaluation of new development areas

Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

Background Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. Methods We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. Results We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P Conclusion Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.Peer reviewe

Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer.

In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10-8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10-14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10-10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10-8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10-8). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene

Impact of Aquarius sea surface salinity observations on coupled forecasts for the tropical Indo-Pacific Ocean

23 pages, 12 figures, 1 tableThis study demonstrates the impact of gridded in situ and Aquarius sea surface salinity (SSS) on coupled forecasts for August 2011 until February 2014. Assimilation of all available subsurface temperature (ASSIM-Tz) is chosen as the baseline and an optimal interpolation of all in situ salinity (ASSIM-Tz-SSSIS) and Aquarius SSS (ASSIM-T z-SSSAQ) are added in separate assimilation experiments. These three are then used to initialize coupled experiments. Including SSS generally improves NINO3 sea surface temperature anomaly validation. For ASSIM-Tz-SSSIS, correlation is improved after 7 months, but the root mean square error is degraded with respect to ASSIM-Tz after 5 months. On the other hand, assimilating Aquarius gives significant improvement versus ASSIM-Tz for all forecast lead times after 5 months. Analysis of the initialization differences with the baseline indicates that SSS assimilation results in an upwelling Rossby wave near the dateline. In the coupled model, this upwelling signal reflects at the western boundary eventually cooling the NINO3 region. For this period, coupled models tend to erroneously predict NINO3 warming, so SSS assimilation corrects this defect. Aquarius is more efficient at cooling the NINO3 region since it is relatively more salty in the eastern Pacific than in situ SSS which leads to increased mixing and upwelling which in turn sets up enhanced west-to-east SST gradient and intensified Bjerknes coupling. A final experiment that uses subsampled Aquarius at in situ locations infers that high-density spatial sampling of Aquarius is the reason for the superior performance of Aquarius versus in situ SSS. Key Points Assimilation of sea surface salinity (SSS) improves coupled forecasts Aquarius outperforms in situ SSS assimilation SSS assimilation imparts a relative improved upwelling signal © 2014. American Geophysical Union. All Rights ReservedThis research is supported by NASA Physical Oceanography grant NNX12AN08G and the Ocean Salinity Science Team (NNX09AU74G). Ballabrera-Poy was supported by the MIDAS-7 AYA2012-39356-C05-03 Spanish grantPeer Reviewe