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Geophysical validation of TOPEX/Poseidon altimetry products
The objectives of this study are the following: (1) to assess the quality of the sea level observed by altimetry on a global scale, especially in tropical regions where the atmospheric effects are the most critical, and to prepare the altimeter data for their assimilation into tropical Oceanic General Circulation Models; and (2) to validate and calibrate the altimeter-derived sea state parameters, to assimilate these parameters into numerical models, and to estimate the altimeter's sea state and electromagnetic biases.
An Assimilation of TOPEX/POSEIDON Data
This report summarizes progress in a recent study of assimilating altimetric data into a global ocean general circulation model.
Progress Toward Implementation of Coupled Ocean/Atmosphere in Unified Forward Operator (UFO)
The (Sea-Ice Ocean and Coupled Assimilation) SOCA project focuses on meshing atmospheric and marine data assimilation efforts to create innovative data assimilation systems. One of the main goals ofSOCA is to make use of surface-sensitive radiances to constrain sea-ice and upper ocean fields (e.g., salinity, temperature, sea-ice fraction, sea-ice temperature, etc.). the focus of this research is to build first elements toward an ocean/sea-ice/atmosphere coupled data assimilation capability, with a focus on supporting and developing the assimilation of observations sensitive to multiple sub-domains. We Setup the existing Joint Effort for Data assimilation Integration (JEDI) coupled UFO (CRTM related) for surface sensitive microwave radiances (SST and SSS) focusing on Global Precipitation Measurement (GPM) Imager (GMI) and Soil Moisture Active Passive (SMAP).
Interannual Variability of Tropical Ocean Evaporation: A Comparison of Microwave Satellite and Assimilation Results
Remote sensing methodologies for turbulent heat fluxes over oceans depend on driving bulk formulations of fluxes with measured surface winds and estimated near surface thermodynamics from microwave sensors of the Special Sensor Microwave Imager (SSM/I) heritage. We will review recent work with a number of SSM/I-based algorithms and investigate the ability of current data sets to document global, tropical ocean-averaged evaporation changes in association with El Nino and La Nina SST changes. We show that in addition to interannual signals, latent heat flux increases over the period since late 1987 range from approx. .1 to .6 mm/ day are present; these represent trends 2 to 3 times larger than the NCEP Reanalysis. Since atmospheric storage cannot account for the difference, and since compensating evapotranspiration changes over land are highly unlikely to be this large, these evaporation estimates cannot be reconciled with ocean precipitation records such as those produced by the Global Precipitation Climatology Project, GPCP. The reasons for the disagreement include less than adequate intercalibration between SSM/I sensors providing winds and water vapor for driving the algorithms, biases due to the assumption that column integrated water vapor mirrors near surface water vapor variations, and other factors as well. The reanalyses have their own problems with spin-up during assimilation, lack of constraining input data at the ocean surface, and amplitude of synoptic transients.
Reconstruction of the 1997/1998 El Nino from TOPEX/POSEIDON and TOGA/TAO Data Using a Massively Parallel Pacific-Ocean Model and Ensemble Kalman Filter
Two massively parallel data assimilation systems in which the model forecast-error covariances are estimated from the distribution of an ensemble of model integrations are applied to the assimilation of 97-98 TOPEX/POSEIDON altimetry and TOGA/TAO temperature data into a Pacific basin version the NASA Seasonal to Interannual Prediction Project (NSIPP)ls quasi-isopycnal ocean general circulation model. in the first system, ensemble of model runs forced by an ensemble of atmospheric model simulations is used to calculate asymptotic error statistics. The data assimilation then occurs in the reduced phase space spanned by the corresponding leading empirical orthogonal functions. The second system is an ensemble Kalman filter in which new error statistics are computed during each assimilation cycle from the time-dependent ensemble distribution. The data assimilation experiments are conducted on NSIPP's 512-processor CRAY T3E. The two data assimilation systems are validated by withholding part of the data and quantifying the extent to which the withheld information can be inferred from the assimilation of the remaining data. The pros and cons of each system are discussed.
OGCM Simulations of Equatorial Pacific Current and Temperature to ERS-1, FSU and NMC Surface Winds and to Assimilation of Subsurface Temperature Data
The relative accuracies of three surface wind data products for the tropical Pacific Ocean during April 1992 to March 1994 were examined by analyzing temperature and current fields along the equator, which were simulated with an ocean general circulation model. Simulations were made with and without assimilation of surface and subsurface temperature data. Simulated currents were compared with observations at three sites (170oW, 140oW, 110oW) at the equator. Model-generated currents and temperatures indicated that the ERS-1 westward wind speeds were low compared to the FSU and NMC winds. With data assimilation, the agreement between simulated and observed currents was highest at 170oW and lowest at 110oW.
Constraining Oceanic Sources and Sinks of CO2 by Inverse Modeling: A First Step Toward Assimilation of In Situ and Remote Data
The primary goal of this collaborative project, lead by Nicholas Gruber at UCLA, is to better quantify the air-sea fluxes of pre-industrial and anthropogenic carbon dioxide CO2 for the global ocean with a newly developed inverse method. The method involves using ocean general circulation models (OGCMs) to characterize the subsurface distribution of tracers injected for different regions of the ocean surface. By linearly combining a series of these basis (or Greens) functions, one should be able to recreate any arbitrary ocean tracer field. In our case, we are using the global database of ocean dissolved inorganic carbon (DIC) from the WOCE/JGOFS global CO2 survey from the 1990s. The observed DIC field has been partitioned into pre-industrial and anthropogenic components using the empirical C* technique. We then solve for an optimal combination of the regional basis functions that best match the two sets observations in a least-squares sense with singular value decomposition. This in turn provides solutions for the regional air-sea CO2 fluxes (pre-industrial and anthropogenic) that are independent of more traditional estimates derived from surface water disequilibria in the partial pressure of CO2 and wind speed gas exchange parameterizations. We are apply the method to a suite of nine different OGCMs to test the robustness of the derived surface fluxes fields to differences in ocean circulation. Together with a collegue Keith Lindsay at NCAR, S. Doney's role in this project was to create the appropriate steady-state and temporally evolving basis functions from the NCAR CSM-1 ocean circulation model. Because of a slow start across the project in defining a common set of experimental protocols, we requested and were given a one year, no cost extension. Both the steady-state and temporally evolving basis functions were delivered to the UCLA group for further analysis. Additional information is included in the original extended abstract.
Combining Satellite and in Situ Data with Models to Support Climate Data Records in Ocean Biology
The satellite ocean color data record spans multiple decades and, like most long-term satellite observations of the Earth, comes from many sensors. Unfortunately, global and regional chlorophyll estimates from the overlapping missions show substantial biases, limiting their use in combination to construct consistent data records. SeaWiFS and MODIS-Aqua differed by 13% globally in overlapping time segments, 2003-2007. For perspective, the maximum change in annual means over the entire Sea WiFS mission era was about 3%, and this included an El NinoLa Nina transition. These discrepancies lead to different estimates of trends depending upon whether one uses SeaWiFS alone for the 1998-2007 (no significant change), or whether MODIS is substituted for the 2003-2007 period (18% decline, P less than 0.05). Understanding the effects of climate change on the global oceans is difficult if different satellite data sets cannot be brought into conformity. The differences arise from two causes: 1) different sensors see chlorophyll differently, and 2) different sensors see different chlorophyll. In the first case, differences in sensor band locations, bandwidths, sensitivity, and time of observation lead to different estimates of chlorophyll even from the same location and day. In the second, differences in orbit and sensitivities to aerosols lead to sampling differences. A new approach to ocean color using in situ data from the public archives forces different satellite data to agree to within interannual variability. The global difference between Sea WiFS and MODIS is 0.6% for 2003-2007 using this approach. It also produces a trend using the combination of SeaWiFS and MODIS that agrees with SeaWiFS alone for 1998-2007. This is a major step to reducing errors produced by the first cause, sensor-related discrepancies. For differences that arise from sampling, data assimilation is applied. The underlying geographically complete fields derived from a free-running model is unaffected by solar zenith angle requirements and obscuration from clouds and aerosols. Combined with in situ dataenhanced satellite data, the model is forced into consistency using data assimilation. This approach eliminates sampling discrepancies from satellites. Combining the reduced differences of satellite data sets using in situ data, and the removal of sampling biases using data assimilation, we generate consistent data records of ocean color. These data records can support investigations of long-term effects of climate change on ocean biology over multiple satellites, and can improve the consistency of future satellite data sets.
Operational seasonal and interannual predictions of ocean conditions
Dr. Leetmaa described current work at the U.S. National Meteorological Center (NMC) on coupled systems leading to a seasonal prediction system. He described the way in which ocean thermal data is quality controlled and used in a four dimensional data assimilation system. This consists of a statistical interpolation scheme, a primitive equation ocean general circulation model, and the atmospheric fluxes that are required to force this. This whole process generated dynamically consist thermohaline and velocity fields for the ocean. Currently routine weekly analyses are performed for the Atlantic and Pacific oceans. These analyses are used for ocean climate diagnostics and as initial conditions for coupled forecast models. Specific examples of output products were shown both in the Pacific and the Atlantic Ocean.
Tidal Prediction
Tides are the most predictable of oceanographic phenomena, due both to the simplicity and predictability of the astronomical forcing and to the near linearity of the ocean's dynamical response. In the classical and simplest scenario, tidal prediction is based on harmonic analysis of past measurements at a fixed location. Limits to predictability arise because isolated astronomical spectral lines are broadened into "cusps" of incoherent energy, for example through interactions with non-tidal flows. Tidal prediction at locations without past measurements has historically been a major challenge, but, owing to near-global observations of modern satellite altimeters, the empirical harmonic approach now yields reasonably accurate predictions throughout most of the open ocean. Advances in numerical modeling and data assimilation allow these predictions to be refined (especially in shallow seas where observations remain insufficient to directly constrain tidal wave structure) and extended to include tidal currents. We review recent progress in the development of global and regional-scale tidal prediction capabilities, summarize accuracy of available charts, and briefly consider outstanding issues. Satellite altimetry has also helped unravel the global tidal energy budget and has clarified the role of internal tides as a sink of tidal energy. We summarize these results, and then turn to the challenging problem of predicting internal tides. To the extent that low modes remain coherent with the surface tide, elevations can be directly mapped. We review current efforts in this direction, which are already producing charts with predictive capability. However, internal tides (especially higher modes) interact much more strongly with lower-frequency ocean flows, so a significant fraction of this tidal signal is intermittent and incoherent. We close with a brief review of ongoing efforts to model global tides in combination with wind-forced ocean motions.
SWOT Applications for WRF-Hydro Modeling in Alaska
The Surface Water Ocean Topography (SWOT) mission, launching next year, will provide high-spatial resolution measurements of terrestrial surface water, including global rivers with widths greater than 50-100 m. SWOT measurements are naturally suited for stream hydrology, and many previous studies have worked to quantify the impact of SWOT observations on the modeling of channel flow. This work highlights the application of SWOT for WRF-Hydro modeling in Alaska for data assimilation and model calibration to support ongoing National Oceanic and Atmospheric Administration (NOAA) National Water Model development. Results demonstrate the effectiveness of using SWOT discharge estimates to calibrate WRF-Hydro in ungauged basins, and quantifies the impact of SWOT data assimilation on WRF-Hydro performance.
Synthesis and Assimilation Systems - Essential Adjuncts to the Global Ocean Observing System
Ocean assimilation systems synthesize diverse in situ and satellite data streams into four-dimensional state estimates by combining the various observations with the model. Assimilation is particularly important for the ocean where subsurface observations, even today, are sparse and intermittent compared with the scales needed to represent ocean variability and where satellites only sense the surface. Developments in assimilation and in the observing system have advanced our understanding and prediction of ocean variations at mesoscale and climate scales. Use of these systems for assessing the observing system helps identify the strengths of each observation type. Results indicate that the ocean remains under-sampled and that further improvements in the observing system are needed. Prospects for future advances lie in improved models and better estimates of error statistics for both models and observations. Future developments will be increasingly towards consistent analyses across components of the Earth system. However, even today ocean synthesis and assimilation systems are providing products that are useful for many applications and should be considered an integral part of the global ocean observing and information system.
Synergistic and Collaborative Development Strategies for FV3 Powered Next Generation Unified Global Modeling System
The GFDL (Geophysical Fluid Dynamics Laboratory, NOAA - National Oceanic and Atmospheric Administration) Finite-Volume Cubed-Sphere Dynamical Core (FV3) is a scalable and flexible dynamical core capable of both hydrostatic and non-hydrostatic atmospheric simulations. FV3 has been chosen as the dynamical core for the Next Generation Global Prediction System project (NGGPS), designed to upgrade the current NCEP (National Centers for Environmental Prediction, NOAA) operational Global Forecast System (GFS) to run as a unified, fully-coupled system in NOAA's Environmental Modeling System infrastructure. FV3 dynamic core has a long history of serving as the main engine for global atmospheric models at various Government and Academic Research Laboratories including NOAA: GFDL Climate Modeling Suite (AM4 (Atmosphere Modeling 4), CM4 (Climate Model 4) ESM4 (Earth System Model 4), Hiram (HIgh Resolution Atmospheric Model)); NASA: GMAO (Global Modeling and Assimilation Office) Goddard Earth Observing System Model (GEOS); and NCAR (National Center for Atmospheric Research) Community Earth System Model (CESM). The three primary stakeholders in FV3 (GFDL, GMAO, EMC (Environmental Modeling Center - NOAA)) have embarked on synergistic and collaborative strategies with focus on the advancement of non-hydrostratic dynamic core, physics, chemistry, and data assimilation efforts, leveraging collective strengths of each of the partnering agencies. This talk presents the ongoing plans for model advancements at GFDL, GMAO and EMC, with special emphasis on contributions towards developing community based unified global modeling system for operational and research applications at respective organizations. Future plans include extending the collaborations to the development of earth system components including GMAO's aerosol chemistry models (GOCART/MAM (Goddard Chemistry Aerosol Radiation and Transport / Modal Aerosol Model)), Land Information System (LIS), and advanced data assimilation techniques; and GFDL's Modular Ocean Model (MOM) and Sea Ice Simulator (SIS) for transition to operations at NCEP.
Fortnightly Earth Rotation, Ocean Tides, and Mantle Anelasticity
Sustained accurate measurements of earth rotation are one of the prime goals of Global Geodetic Observing System (GGOS). We here concentrate on the fortnightly (Mf) tidal component of earth-rotation data to obtain new results concerning anelasticity of the mantle at this period. The study comprises three parts: (1) a new determination of the Mf component of polar motion and length-of-day from a multi-decade time series of space-geodetic data; (2) the use of the polar-motion determination as one constraint in the development of a hydrodynamic ocean model of the Mf tide; and (3) the use of these results to place new constraints on mantle anelasticity. Our model of the Mf ocean tide assimilates more than fourteen years of altimeter data from the Topex/Poseidon and Jason-1 satellites. The polar motion data, plus tide-gauge data and independent altimeter data, give useful additional information, with only the polar motion putting constraints on tidal current velocities. The resulting ocean-tide model, plus the dominant elastic body tide, leaves a small residual in observed length-of-day caused by mantle anelasticity. The inferred effective tidal 0 of the anelastic body tide is 90 and is in line with a omega-alpha frequency dependence with alpha in the range 0.2--0.3.
NASA's Carbon Monitoring System Flux-Pilot Project: A Multi-Component Analysis System for Carbon-Cycle Research and Monitoring
The importance of greenhouse gas increases for climate motivates NASA s observing strategy for CO2 from space, including the forthcoming Orbiting Carbon Observatory (OCO-2) mission. Carbon cycle monitoring, including attribution of atmospheric concentrations to regional emissions and uptake, requires a robust modeling and analysis infrastructure to optimally extract information from the observations. NASA's Carbon-Monitoring System Flux-Pilot Project (FPP) is a prototype for such analysis, combining a set of unique tools to facilitate analysis of atmospheric CO2 along with fluxes between the atmosphere and the terrestrial biosphere or ocean. NASA's analysis system is unique, in that it combines information and expertise from the land, oceanic, and atmospheric branches of the carbon cycle and includes some estimates of uncertainty. Numerous existing space-based missions provide information of relevance to the carbon cycle. This study describes the components of the FPP framework, assessing the realism of computed fluxes, thus providing the basis for research and monitoring applications. Fluxes are computed using data-constrained terrestrial biosphere models and physical ocean models, driven by atmospheric observations and assimilating ocean-color information. Use of two estimates provides a measure of uncertainty in the fluxes. Along with inventories of other emissions, these data-derived fluxes are used in transport models to assess their consistency with atmospheric CO2 observations. Closure is achieved by using a four-dimensional data assimilation (inverse) approach that adjusts the terrestrial biosphere fluxes to make them consistent with the atmospheric CO2 observations. Results will be shown, illustrating the year-to-year variations in land biospheric and oceanic fluxes computed in the FPP. The signals of these surface-flux variations on atmospheric CO2 will be isolated using forward modeling tools, which also incorporate estimates of transport error. The results will be discussed in the context of interannual variability of observed atmospheric CO2 distributions.
An evaluation of the National Meteorological Center weekly hindcast of upper-ocean temperature along the eastern Pacific equator in January 1992
The upper-ocean temperature distribution along the Pacific equator from 139 to 103 deg W was observed in January 1992 with temperature profiles recorded from a ship and inferred from an ocean general circulation model calculation involving data assimilation (i.e., hindcast). An El Nino episode was in progress. The l00-m-thick mixed layer depth, the mixed-layer temperature, and the depth-averaged temperature below the thermocline were similar in both data products. Considerable differences occurred in the representation of the 15-25 C thermocline, such as the depth-averaged temperatures above and below the 20 C isotherm, the east-west slope of the 20 C isotherm, and a 1000-km-wide depression. The longitudinal-averaged root-mean-square difference between the hindcast and observed depths of the center of the thermocline was 17 m. Most of the disparities could be attributed to a high wavenumber transient event that the model-based assimilation system was not intended to resolve.
On the physical consistency of data assimilation and a computationally effective approximate smoother
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