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At least 91 records · Page 5

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

Developments in ocean data assimilation (DA) and observing system technologies are intertwined. New observation types lead to new DA methods, and new DA methods such as Coupled Data Assimilation can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners are encouraged to make better use of observations that are already available, for example in strongly coupled data assimilation where ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate,as well as initializing operational long-range prediction models. There are remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean observing system throughout its history, the presence of biases and drifts in models, and simplifying assumptions made in the DA methods. From a governance point of view, more support is needed to interface the observing community and the ocean DA community. For prediction applications, the ocean DA community must work with the ocean observing community to establish protocols for rapid communication of ocean observing data on NWP timescales. There is potential for new observations to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of numerical weather prediction covering hours to weeks, out to multiple decades. It is highly encouraged that communication be fostered between thesecommunities to allow operational prediction centers the ability to provide guidance to the design of a sustained and adaptive observing network.

Ocean reanalysis↗

Reduced viscosity steadily weakens oceanic currents

The viscosity of both air and water is temperature dependent. A rising temperature leads to an increased viscosity for air but a decreased viscosity for water. As climate becomes warmer, this increased air viscosity can partly inhibit the reduction of wind stress over the ocean, and the reduced water viscosity causes less downward momentum and heat transport. As these opposing effects of warming on air and water viscosity are not included in the state-of-the-art climate models, the understanding of their potential impacts on the response of the climate system to the anthropogenic warming is lacking. Here, via analyzing the Simple Ocean Data Assimilation oceanic reanalysis dataset, we show that the ocean heat content increases at a rate of ~1.3 × 10 22 J/yr over 35 years, which leads to a continuous reduction of oceanic viscosity. As a result, the ocean vertical shear enhances with a shoaling of the mixed layer depth and a reduced vertical linkage in the ocean. Our calculations show a reduction of the oceanic kinetic energy at a rate of ~2.4 × 10 16 J/yr. Potentially, this could generate far-reaching impacts on the energy storage of the climate system and, hence, could pace the global warming. Thus, it is important to include the temperature-dependent viscosity in our climate models. Freshwater discharged from polar ice sheets and mountain glaciers also contributes to the reduction in oceanic viscosity but, at present, to a lesser extent than that in oceanic warming. Reduced oceanic viscosity, therefore, is an important, but hitherto overlooked, response to a warming climate and contributes to many recent weather extremes including heavier rainfall rates in hurricanes, slackening of the polar vortex, and oceanic heat waves.

54 ENVIRONMENTAL SCIENCES↗

A technique for the reduction and analysis of ocean spectral data

Some initial investigations of a method of reducing and analyzing raw ocean spectral data are presented. The method avoids most problems associated with atmospheric effects, and requires the application of little, if any, calibration information to the data.

White, P. G.↗

Sampling strategies and four-dimensional assimilation of altimetric data for ocean monitoring and prediction

Numerical experiments using simulated altimeter data were conducted in order to examine the assimilation of altimeter-derived sea surface heights into numerical ocean circulation models. A reduced-gravity, primitive equation circulation model of the Gulf of Mexico was utilized; the Gulf of Mexico was chosen because of its amenability to modeling and the ability of low vertical-mode models to reproduce the observed dynamical features of the Gulf circulation. The simulated data were obtained by flying an imaginary altimeter over the model ocean and sampling the model sea surface just as real altimeter would observe the true ocean. The data were used to initialize the numerical model and the subsequent forecast was compared to the true numerical solution. Results indicate that for a stationary, circular eddy, approximately three to four tracks (either ascending or descending) across the eddy are sufficient to ensure adequate spatial resolution.

Kindle, J. C.↗

The importance of altimeter and scatterometer data for ocean prediction

The prediction of ocean circulation using satellite altimeter data is discussed. Three classes of oceanic response to atmospheric forcing are outlined and examined. Storms, surface waves, eddies, and ocean currents were evaluated in terms of forecasting time requirements. Scatterometer and radiometer applications to ocean prediction are briefly reviewed.

Hurlburt, H. E.↗

Ocean Color Data at the Goddard DAAC

The apparent color of the ocean is determined by the interactions of incident light with substances or particles present in the water. The most significant constituents are free-floating photosynthetic organisms (phytoplankton) and inorganic particulates. Phytoplankton contain chlorophyll, which absorbs light at blue and red wavelengths and transmits in the green. Particulate matter can reflect and absorb light, which reduces the clarity (light transmission) of the water. Substances dissolved in water can also affect its color. Observations of ocean color from space, utilizing sensors specially designed to detect the small amount of light radiating from the sea surface, provide a global picture of the patterns of biological productivity in the world's oceans. For that reason, ocean color remote sensing data is a vital resource for biological oceanography. Unlike the limited area of the ocean that can be investigated from a research ship, data from a satellite sensor covers a large region and provides a comprehensive view of the marine environment.

Source record↗

A Compilation of Global Bio-Optical in Situ Data for Ocean Colour Satellite Applications – Version Three

A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately.

ocean color↗

Pyroxene stability and the composition of the lunar magma ocean

Data on the liquidus stability fields of pyroxene phases in lunar-like systems are used to predict the crystallization sequence and residual liquid composition of lunar magma oceans. Fractional and equilibrium crystallization of major and trace elements are modeled quantitatively through use of analytical expressions for liquidus phase boundaries. According to the model, a magma ocean that differentiated chiefly at low pressure and contained 6-8 wt% A1203 and a subchondritic Ca/Al ratio probably formed the lunar crust. The chondrite-normalized pattern of heavy REE depletion in the magma ocean could have developed by self-contamination of extensively fractionated liquids, or could have been imparted during accretion.

Longhi, J.↗

The analysis of ocean optical data

A new oceanographic instrument to measure underwater optical, biological and physical properties simultaneously has been built and used extensively at sea. The Bio-Optical Profiling System (BOPS) was designed for the rapid acquisition of data to accommodate shipboard 'synoptic' sampling strategies, often in conjunction with concurrent aircraft and satellite sensors. The data rates and associated quantity of data from the BOPS are some orders of magnitude larger than those traditionally encountered in optical oceanography. The rapid acquisition of optical data in a wide range of environmental conditions requires new methodologies in ocean optical data analysis.

Smith, R. C.↗

Skill Assessment in Ocean Biological Data Assimilation

There is growing recognition that rigorous skill assessment is required to understand the ability of ocean biological models to represent ocean processes and distributions. Statistical analysis of model results with observations represents the most quantitative form of skill assessment, and this principle serves as well for data assimilation models. However, skill assessment for data assimilation requires special consideration. This is because there are three sets of information in the free-run model, data, and the assimilation model, which uses Data assimilation information from both the flee-run model and the data. Intercom parison of results among the three sets of information is important and useful for assessment, but is not conclusive since the three information sets are intertwined. An independent data set is necessary for an objective determination. Other useful measures of ocean biological data assimilation assessment include responses of unassimilated variables to the data assimilation, performance outside the prescribed region/time of interest, forecasting, and trend analysis. Examples of each approach from the literature are provided. A comprehensive list of ocean biological data assimilation and their applications of skill assessment, in both ecosystem/biogeochemical and fisheries efforts, is summarized.

Gregg, Watson W.↗

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.

Gregg, Watson↗

Wave propagation in the marginal ice zone - Model predictions and comparisons with buoy and synthetic aperture radar data

Ocean wave dispersion relation and viscous attenuation by a sea ice cover are studied for waves propagating into the marginal ice zone (MIZ). The Labrador ice margin experiment (LIMEX), conducted on the MIZ off the east coast of Newfoundland, Canada in March 1987, provided aircraft SAR imagery, ice property and wave buoy data. Wave energy attenuation rates are estimated from SAR data and the ice motion package data that were deployed at the ice edge and into the ice pack, and compared with a model. It is shown that the model data comparisons are quite good for the ice conditions observed during LIMEX 1987.

Liu, Antony K.↗

Effect on atmospheric CO2 from seasonal variations in the high latitude ocean

Data from the North Pacific gyre, Bering Sea, and North Atlantic show large seasonal fluctuations in the pCO2 of surface waters. The seasonal variation in these latitudes apparently has a generic pattern: higher surface water pCO2 in winter and lower in summer. Satellite data will eventually help decipher the relative effects of temperature and biological production in the seasonal carbon cycle, but as yet little work has been done on what possible role the seasonality of pCO2 in the high latitudes might have on the average value of atmospheric pCO2. A model is developed that shows the average value for atmospheric pCO2 depends upon the ratio of the rates at which the ocean/atmosphere system moves toward equilibrium values during the summer and winter conditions of the high latitude ocean.

Volk, Tyler↗

Some analysis on the diurnal variation of rainfall over the Atlantic Ocean

Data collected from the GARP Atlantic Tropical Experiment (GATE) was examined. The data were collected from 10,000 grid points arranged as a 100 x 100 array; each grid covered a 4 square km area. The amount of rainfall was measured every 15 minutes during the experiment periods using c-band radars. Two types of analyses were performed on the data: analysis of diurnal variation was done on each of grid points based on the rainfall averages at noon and at midnight, and time series analysis on selected grid points based on the hourly averages of rainfall. Since there are no known distribution model which best describes the rainfall amount, nonparametric methods were used to examine the diurnal variation. Kolmogorov-Smirnov test was used to test if the rainfalls at noon and at midnight have the same statistical distribution. Wilcoxon signed-rank test was used to test if the noon rainfall is heavier than, equal to, or lighter than the midnight rainfall. These tests were done on each of the 10,000 grid points at which the data are available.

Gill, T.↗

Data access for scientific problem solving

An essential ingredient in scientific work is data. In disciplines such as Oceanography, data sources are many and volumes are formidable. The full value of large stores of data cannot be realized unless careful thought is given to data access. JPL has developed the Pilot Ocean Data System to investigate techniques for archiving and accessing ocean data obtained from space. These include efficient storage and rapid retrieval of satellite data, an easy-to-use user interface, and a variety of output products which, taken together, permit researchers to extract and use data rapidly and conveniently.

Brown, James W.↗

Proceedings of the Ocean Climate Data Workshop

The First Consultative Meeting on Responsible National Oceanographic Data Centres (RNODC's) and Climate DataServices met in February 1988 and made a number of recommendations related to improving services to meet the needs of climate programmes. Included in these discussions was a recommendation for a Workshop on Ocean Climate Data Management. This workshop will be talking about ways to establish a Global Ocean Observing System (GOOS).

Churgin, James↗

Synthesis of Ocean Observations Using Data Assimilation for Operational, Real-Time and Reanalysis Systems: A More Complete Picture of the State of the Ocean

Ocean data assimilation is increasingly recognized as crucial for the accuracy of realtime ocean prediction systems and historical re-analyses. The current status of ocean data assimilation in support of the operational demands of analysis, forecasting and reanalysis is reviewed, focusing on methods currently adopted in operational and realtime prediction systems. Significant challenges associated with the most commonly employed approaches are identified and discussed. Overarching issues faced by ocean data assimilation are also addressed, and important future directions in response to scientific advances, evolving and forthcoming ocean observing systems and the needs of stakeholders and downstream applications are discussed.

modeling↗