1. Monthly sampling trips were taken to 9 National Reference Stations to measure chlorophyll-a, salinity, total alkalinity, and temperature over 4 years.
2. The document proposes using measurements of sea surface salinity, sea surface temperature, and chlorophyll-a from satellites combined with an algorithm to model total alkalinity and pH levels around the Australian coastline to monitor ocean acidification.
3. Models were developed using measurements from the 9 stations. The non-conservative model, which accounts for processes affecting total alkalinity beyond salinity, provided a better fit with errors generally between 4-10 μmolkg-1.
Greetings all,
This month’s newsletter is devoted to ocean indices aiming at a better understanding of the state of the ocean climate. Ocean
climate indices can be linked to major patterns of climate variability and usually have a significant social impact. The estimation of
the ocean climate indices along with their uncertainty is thus crucial: It gives an indication of our ability to measure the ocean. It is
as well a useful tool for decision making. Ocean climate indices also provide an at-a-glance overview of the state of the ocean
climate, and a way to talk to a wider audience about the ocean observing system. Several groups of experts are now working on
various ocean indicators using ocean forecast models, satellite data and reanalysis models in observing system simulation
experiments, among which the OOPC, NOAA and MERSEA/Boss4Gmes communities for example:
http://ioc3.unesco.org/oopc/state_of_the_ocean/index.php
http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/
http://www.aoml.noaa.gov/phod/cyclone/data/method.html
http://www.mersea.eu.org/Indicators-with-B4G.html
Scientific articles about Ocean indices in the present Newsletter are displayed as follows: The first article by Von Schuckmann et
al. is dealing with the estimation of global ocean indicators from a gridded hydrographic field. Then, Crosnier et al. are describing
the need to conduct intercomparison of model analyses and forecast in order for experts to provide a reliable scientific expertise
on ocean climate indicators. The next article by Coppini et al. is telling us about ocean indices computed from the Mediterranean
Forecasting System for the European Environment Agency and Boss4Gmes. Then Buarque et al. are revisiting the Tropical
Cyclone Heat Potential Index in order to better represent the ocean heat content that interacts with Hurricane. The last article by Greiner et al. is dealing with the assessment of robust ocean indicators and gives an example with oceanic predictors for the
Sahel precipitations.
The next July 2009 newsletter will review the current work on data assimilation and its techniques and progress for operational
oceanography.
We wish you a pleasant reading.
Trace gas batch inverse problems are often formulated in a Bayesian framework that require minimization of an objective function that takes as an input atmospheric measurements of trace gas concentrations, prior estimates of fluxes, and a transport operator that describes the influence of the sources of fluxes on measurements. As part of minimization, batch inverse problems require computation of covariance matrices that describes the error in measurements and prior fluxes. Most of the computational/data bottlenecks in these inverse problems occur in estimating the transport operator that require processing of terabytes of output generated from a Weather model. Typically, this output is stored on tape storage system that needs to copied or moved into an intermediary storage system for computing the transport operator and finally the covariance matrices that are used in inverse problems. This operation of bringing data to the algorithm is an inefficient and time-delaying way to solve these problems and therefore necessitates development of methods that can work on partitioned observations and transport operator and compute covariance matrices and inverse estimates of fluxes at locations of data storage.
Greetings all,
This month’s newsletter is devoted to ocean indices aiming at a better understanding of the state of the ocean climate. Ocean
climate indices can be linked to major patterns of climate variability and usually have a significant social impact. The estimation of
the ocean climate indices along with their uncertainty is thus crucial: It gives an indication of our ability to measure the ocean. It is
as well a useful tool for decision making. Ocean climate indices also provide an at-a-glance overview of the state of the ocean
climate, and a way to talk to a wider audience about the ocean observing system. Several groups of experts are now working on
various ocean indicators using ocean forecast models, satellite data and reanalysis models in observing system simulation
experiments, among which the OOPC, NOAA and MERSEA/Boss4Gmes communities for example:
http://ioc3.unesco.org/oopc/state_of_the_ocean/index.php
http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/
http://www.aoml.noaa.gov/phod/cyclone/data/method.html
http://www.mersea.eu.org/Indicators-with-B4G.html
Scientific articles about Ocean indices in the present Newsletter are displayed as follows: The first article by Von Schuckmann et
al. is dealing with the estimation of global ocean indicators from a gridded hydrographic field. Then, Crosnier et al. are describing
the need to conduct intercomparison of model analyses and forecast in order for experts to provide a reliable scientific expertise
on ocean climate indicators. The next article by Coppini et al. is telling us about ocean indices computed from the Mediterranean
Forecasting System for the European Environment Agency and Boss4Gmes. Then Buarque et al. are revisiting the Tropical
Cyclone Heat Potential Index in order to better represent the ocean heat content that interacts with Hurricane. The last article by Greiner et al. is dealing with the assessment of robust ocean indicators and gives an example with oceanic predictors for the
Sahel precipitations.
The next July 2009 newsletter will review the current work on data assimilation and its techniques and progress for operational
oceanography.
We wish you a pleasant reading.
Trace gas batch inverse problems are often formulated in a Bayesian framework that require minimization of an objective function that takes as an input atmospheric measurements of trace gas concentrations, prior estimates of fluxes, and a transport operator that describes the influence of the sources of fluxes on measurements. As part of minimization, batch inverse problems require computation of covariance matrices that describes the error in measurements and prior fluxes. Most of the computational/data bottlenecks in these inverse problems occur in estimating the transport operator that require processing of terabytes of output generated from a Weather model. Typically, this output is stored on tape storage system that needs to copied or moved into an intermediary storage system for computing the transport operator and finally the covariance matrices that are used in inverse problems. This operation of bringing data to the algorithm is an inefficient and time-delaying way to solve these problems and therefore necessitates development of methods that can work on partitioned observations and transport operator and compute covariance matrices and inverse estimates of fluxes at locations of data storage.
C5.04: GO-SHIP: A component of the sustained ocean observing system - Bernade...Blue Planet Symposium
The Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP) brings together scientists with interests in physical oceanography, the carbon cycle, marine biogeochemistry and ecosystems, and other users and collectors of ocean interior data, and coordinates a network of globally sustained hydrographic sections as part of the global ocean/climate observing system including physical oceanography, the carbon cycle, marine biogeochemistry and ecosystems.
GO-SHIP provides approximately decadal resolution of the changes in inventories of heat, freshwater, carbon, oxygen, nutrients and transient tracers, covering the ocean basins from coast to coast and full depth (top to bottom), with global measurements of the highest required accuracy to detect these changes. The GO-SHIP principal scientific objectives are: (1) understanding and documenting the large-scale ocean water property distributions, their changes, and drivers of those changes, and (2) addressing questions of how a future ocean that will increase in dissolved inorganic carbon, become more acidic and more stratified, and experience changes in circulation and ventilation processes due to global warming and altered water cycle.
Modelling Fault Reactivation, Induced Seismicity, and Leakage During Underground CO2 Injection, Jonny Rutquvist - Geophysical Modelling for CO2 Storage, Leeds, 3 November 2015
Pore scale dynamics and the interpretation of flow processes - Martin Blunt, Imperial College London, at UKCCSRC specialist meeting Flow and Transport for CO2 Storage, 29-30 October 2015
Passive seismic monitoring for CO2 storage sites - Anna Stork, University of Bristol at UKCCSRC specialist meeting Geophysical modelling for CO2 storage, monitoring and appraisal, 3 November 2015
Joint Indonesia-UK Conference on Computational Chemistry 2015Dasapta Erwin Irawan
The following there slides were made for Joint Indonesia-UK Conference on Computational Chemistry 2015, consists of three abstracts:
1. Generalised mixed model of water quality in Cikapundung Riverbank using R
Author: Dasapta Erwin Irawan1*, Cut Novianti Rachmi2, Prana Ugi3, Dwi Suhandoko1, Ahmad Darul1, Nurjana Joko Trilaksono1
2. PCA computation to detect water interactions in Cikapundung Riverbank using R
Author: Dasapta Erwin Irawan1*, Cut Novianti Rachmi2, Prana Ugi3, Dwi Suhandoko1, Ahmad Darul1, Nurjana Joko Trilaksono1
3. Landfill Plume Identification : a Review
Author: Ramadhan, F.R1., Nafisah, L.A1., Yosandian, Hazmanu1., and Irawan, D.E 2.
Freshwater Lake Mapping and its Volumetric Estimation in the Glaciated Valley of Chhombu in Sikkim Himalayas Using High-Resolution Optical (Sentinel-2 MSI) Imagery.
Arindam Chowdhury North Eastern Hill University, India)
Examination of Total Precipitable Water using MODIS measurements and Comparis...inventionjournals
In this research, precipitable water vapor, as the most effective character in the production of biomass is estimated using remote sensing techniques. Total Precipitable Water (TPW) was estimated using measurements in the Near Infrared bands of the MODIS. To examine the level of confidence in TPW deriving, a simultaneous in situ measurement by Radiosonde and ground-based Global Positioning System (GPS) was carried out. The TPW as results in Radiosonde and GPS was accomplished using the relevant physical equations and base on wet delay troposphere, respectively. Results showed a high correlation among the values of TPW derived from MODIS banding ratio, Radiosonde and GPS data at the Mehrabad station. Also, Using the ratio of the apparent reflectance in the water vapor absorption band to reflectance in non-absorbing band, the atmospheric water vapor transparency was mapped, that the maps showed a high correlation between apparent reflectance and TPW MODIS as their statistical results showed an inverse negative relationship(R²= -0.97).
Monitoring water pollution in the River Ganga with innovations in airborne remote sensing and drone technology.
Rajiv Sinha (Indian Institute of Technology Kanpur)
C5.04: GO-SHIP: A component of the sustained ocean observing system - Bernade...Blue Planet Symposium
The Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP) brings together scientists with interests in physical oceanography, the carbon cycle, marine biogeochemistry and ecosystems, and other users and collectors of ocean interior data, and coordinates a network of globally sustained hydrographic sections as part of the global ocean/climate observing system including physical oceanography, the carbon cycle, marine biogeochemistry and ecosystems.
GO-SHIP provides approximately decadal resolution of the changes in inventories of heat, freshwater, carbon, oxygen, nutrients and transient tracers, covering the ocean basins from coast to coast and full depth (top to bottom), with global measurements of the highest required accuracy to detect these changes. The GO-SHIP principal scientific objectives are: (1) understanding and documenting the large-scale ocean water property distributions, their changes, and drivers of those changes, and (2) addressing questions of how a future ocean that will increase in dissolved inorganic carbon, become more acidic and more stratified, and experience changes in circulation and ventilation processes due to global warming and altered water cycle.
Modelling Fault Reactivation, Induced Seismicity, and Leakage During Underground CO2 Injection, Jonny Rutquvist - Geophysical Modelling for CO2 Storage, Leeds, 3 November 2015
Pore scale dynamics and the interpretation of flow processes - Martin Blunt, Imperial College London, at UKCCSRC specialist meeting Flow and Transport for CO2 Storage, 29-30 October 2015
Passive seismic monitoring for CO2 storage sites - Anna Stork, University of Bristol at UKCCSRC specialist meeting Geophysical modelling for CO2 storage, monitoring and appraisal, 3 November 2015
Joint Indonesia-UK Conference on Computational Chemistry 2015Dasapta Erwin Irawan
The following there slides were made for Joint Indonesia-UK Conference on Computational Chemistry 2015, consists of three abstracts:
1. Generalised mixed model of water quality in Cikapundung Riverbank using R
Author: Dasapta Erwin Irawan1*, Cut Novianti Rachmi2, Prana Ugi3, Dwi Suhandoko1, Ahmad Darul1, Nurjana Joko Trilaksono1
2. PCA computation to detect water interactions in Cikapundung Riverbank using R
Author: Dasapta Erwin Irawan1*, Cut Novianti Rachmi2, Prana Ugi3, Dwi Suhandoko1, Ahmad Darul1, Nurjana Joko Trilaksono1
3. Landfill Plume Identification : a Review
Author: Ramadhan, F.R1., Nafisah, L.A1., Yosandian, Hazmanu1., and Irawan, D.E 2.
Freshwater Lake Mapping and its Volumetric Estimation in the Glaciated Valley of Chhombu in Sikkim Himalayas Using High-Resolution Optical (Sentinel-2 MSI) Imagery.
Arindam Chowdhury North Eastern Hill University, India)
Examination of Total Precipitable Water using MODIS measurements and Comparis...inventionjournals
In this research, precipitable water vapor, as the most effective character in the production of biomass is estimated using remote sensing techniques. Total Precipitable Water (TPW) was estimated using measurements in the Near Infrared bands of the MODIS. To examine the level of confidence in TPW deriving, a simultaneous in situ measurement by Radiosonde and ground-based Global Positioning System (GPS) was carried out. The TPW as results in Radiosonde and GPS was accomplished using the relevant physical equations and base on wet delay troposphere, respectively. Results showed a high correlation among the values of TPW derived from MODIS banding ratio, Radiosonde and GPS data at the Mehrabad station. Also, Using the ratio of the apparent reflectance in the water vapor absorption band to reflectance in non-absorbing band, the atmospheric water vapor transparency was mapped, that the maps showed a high correlation between apparent reflectance and TPW MODIS as their statistical results showed an inverse negative relationship(R²= -0.97).
Monitoring water pollution in the River Ganga with innovations in airborne remote sensing and drone technology.
Rajiv Sinha (Indian Institute of Technology Kanpur)
Increasing interest by governments worldwide on reducing CO2 released into the atmosphere form a nexus of of opportunity with enhanced oil recovery which could benefit mature oil fields in nearly every country. Overall approximately two-thirds of original oil in place (OOIP) in mature conventional oil fields remains after primary or primary/secondary recovery efforts have taken place. CO2 enhanced oil recovery (CO2 EOR) has an excellent record of revitalizing these mature plays and can dramatically increase ultimate recovery. Since the first CO2 EOR project was initiated in 1972, more than 154 additional projects have been put into operation around the world and about two-thirds are located in the Permian basin and Gulf coast regions of the United States. While these regions have favorable geologic and reservoir conditions for CO2 EOR, they are also located near large natural sources of CO2.
In recent years an increasing number of projects have been developed in areas without natural supplies, and have instead utilized captured CO2 from a variety of anthropogenic sources including gas processing plants, ethanol plants, cement plants, and fertilizer plants. Today approximately 36% of active CO2 EOR projects utilize gas that would otherwise be vented to the atmosphere. Interest world-wide has increased, including projects in Canada, Brazil, Norway, Turkey, Trinidad, and more recently, and perhaps most significantly, in Saudi Arabia and Qatar. About 80% of all energy used in the world comes from fossil fuels, and many industrial and manufacturing processes generate CO2 that can be captured and used for EOR. In this 30 minute presentation a brief history of CO2 EOR is provided, implications for utilizing captured carbon are discussed, and a demonstration project is introduced with an overview of characterization, modeling, simulation, and monitoring actvities taking place during injection of more than a million metric tons (~19 Bcf) of anthropogenic CO2 into a mature waterflood.
Longer versions of the presentation can be requested and can cover details of geologic and seimic characterization, simulation studies, time-lapse monitoring, tracer studies, or other CO2 monitoring technologies.
Classification of storm water and sea water samples by zero-, first- and seco...IJERA Editor
This paper deals with the quality of storm water and its recipient sea water. For this purpose, UV spectroscopy
and pattern recognition methods were used. The treatment of the zero-order spectral data showed that almost all
storm water samples were classified into two groups. The treatment of the first-order derivative spectral data
showed that each of these groups can be divided into two subgroups, with few samples common, while the
second-order derivatization has highlighted the final group of the common samples. Finally, sea water samples
were classified into two groups after processing of the spectral data. The majority of the samples was classified
to the first group and the rest of them to the second group.
This presentation focuses, how carbon dioxide plays dirty role in Ocean Acidification and Global Warming. I have analyzed data and presented it with some real samples collected from Visakhapatnam, India. Thank you!
An Experimental Study on the Migration of Pb in the Groundwater Table Fluctua...NOMADPOWER
As a result of fluctuations in the shallow groundwater table, hydrodynamic conditions change alongside environmental conditions and hydrogeochemical processes to affect pollutant migration. The study aimed to investigate the migration, adsorption, and desorption characteristics of Pb on fine, medium, and coarse sand in the water table fluctuation zone by using several laboratory methods, including the kinetic aspects of Pb2+ adsorption/desorption and water table fluctuation experiments.
Assessment of impact of climatic change on groundwater quality around igbokod...
Poster_2
1. 1. Collect observations
Monthly sampling trips to nine
National Reference stations (NRS)
took measurements of:
• Chlorophyll-a
• Salinity
• Total Alkalinity
• Temperature
Data for up to four years and
methods of collection are
available through the IMOS portal[8].
Development of Remote Sensing for the monitoring of Ocean Acidification
around the Australian Coastline: A proposal
Baldry, Kimberlee1,2,3
Supervisors: Hardman-Mountford3, Nick; Greenwood, Jim3
1University of Western Australia
2Recipient of SGS Brian Doran Scholarship
3CSIRO Oceans and Atmosphere, Floreat
Abstract
The Australian Coastline is over 36,000 km long and comprises of diverse and unique marine life. It is important to understand climatic effects such as ocean acidification
which can impact this marine life. Remote sensing is a new area of data collection which uses satellite data to model parameters of interest. Recently, sea surface salinity
(SSS) measurements from space have become available through measurements of microwave emissions from the Earths surface. These are collected using the Soil Moisture
and Ocean Salinity (SMOS) instrument[1]. Together with sea surface temperature (SST) and chlorophyll-a (Chl-a) measurements, an algorithm can be used for modelling the
total alkalinity (TA) of Australian coastal waters[2]. Once this has been achieved, the constructed model can then be used to estimate pH levels around the Australian
Coastline. The challenge in this method lies within the development of such an algorithm, which is robust enough to predict pH with acceptable error.
Total Alkalinity (TA) is a measure of the buffering capacity of a water mass[3]. It is related to pH, pCO2 and dissolved inorganic carbon (DIC) through ocean carbonate
chemistry (Fig. 1). Calculations for pH can be performed using the program CO2SYS[4].
CO2 (g)
CO2 (aq) +H2O (l) ↔ H2CO3 (aq)↔HCO3
- + H+
TA
Figure 1: A simplistic representation of the ocean carbonate system. CO2 is dissolved into
the ocean which introduces H+ ions into the system. If TA is reduced, the ocean has less
buffering capacity to counteract the change in H+ ions, increasing pH
TA is related to salinity (SSS) linearly through the convective mixings of a water
mass;
𝑇𝐴 = 𝑎𝑆𝑆𝑆 + 𝑏
This relationship is referred to as the conservative relationship as it is does not
change within one water mass[5].
In reality other processes effect TA (calcification, primary productivity and the
mixing of different water masses)[6,7]. Adjusting the previous relationship to account
for this yields a non-conservative relationship;
𝑇𝐴 = 𝑎𝑆𝑆𝑆 + 𝑏𝑆𝑆𝑇 + 𝑐𝐶𝐻𝐿 + 𝑑
Figure 2: National Reference Station Locations
2. Models[9]
Neural Networks (NN)
• Offers a good fit with low residual
standard error (RSE)
• Poor extrapolation
NRS CONSERVATIVE NON-CONSERVATIVE
n RSE n RSE
COASTAL 1064 10.86 644 8.17
DARWIN 58 9.48 27 6.35
ESPERANCE 48 6.02 36 5.51
KANGAROO ISLAND 98 5.37 15 3.37
MARIA ISLAND 187 4.01 120 3.46
NINGALOO 29 5.82 18 2.36
NORTH STRADBROKE
ISLAND
168 4.50 133 4.17
PORT HACKING BAY 167 2.76 96 2.55
ROTTNEST ISLAND 119 4.79 67 3.53
YONGALA 191 8.63 132 8.10
3. Conservative or Non-Conservative?
The non-conservative relationship gives the better fit. In general, a good model
can predict TA with RSE of 4-10μmolkg-1 [10]..
5. Modelling pH Non-Conservatively
• The conservative model calculates pH levels with RSE below 0.01 for the regional
models.
• The coastal model offers a model with a bounded error (0.04 approx.) which shows
variable fit across locations.
6. What’s Next?
The problems with the proposed model include:
• Not suitable for short term monitoring (<10years); pH has been shown to increase
by 0.0017 units/year[2]
• Not all NRS are modelled by the coastal model so only a discrete model exists
• Models are only proven to hold for the dataset
The next steps for improvement involve:
1. Cross-validataing TA calculations with observations outside the data set
2. Using cruise data from the Southern Surveyor to investigate the continuity of the
model around the coastline
3. Applying the continuous model to satellite data and cross-validataing with
corresponding in-situ data
Figure 4: Representation of the difference between pH calculated from observed TA and
calculated TA for regional (red) and coastal (blue) models. The box range represents the RSE and
the extrema represents the maximum and minimum values.
Figure 3: How data is fitted by MLR (left) and NN (right)
Table1: The RSE and number of observations (n) for each NRS, including a combined NRS
model (coastal), for the different relationships
Multi-Linear Regression (MLR)
• Good extrapolation
7. References
1. Font, J., et al., SMOS: The challenging sea surface salinity measurement from space. Proceedings of the IEEE,
2010. 98(5): p. 649-665.
2. Sun, Q., D. Tang, and S. Wang, Remote-sensing observations relevant to ocean acidification. International
Journal of Remote Sensing, 2012. 33(23): p. 7542-7558.
3. Dickson, A.G., AN EXACT DEFINITION OF TOTAL ALKALINITY AND A PROCEDURE FOR THE ESTIMATION OF
ALKALINITY AND TOTAL INORGANIC CARBON FROM TITRATION DATA. Deep-Sea Research Part a-
Oceanographic Research Papers, 1981. 28(6): p. 609-623.
4. Lewis, E., D. Wallace, and L.J. Allison, Program developed for CO2 system calculations. 1998: Carbon Dioxide
Information Analysis Center, managed by Lockheed Martin Energy Research Corporation for the US
Department of Energy Tennessee.
5. Lee, K., et al., Global relationships of total alkalinity with salinity and temperature in surface waters of the
world's oceans. Geophysical Research Letters, 2006. 33(19): p. 5.
6. Cai, W.J., et al., Alkalinity distribution in the western North Atlantic Ocean margins. Journal of Geophysical
Research-Oceans, 2010. 115: p. 15.
7. Jiang, Z.P., et al., Variability of alkalinity and the alkalinity-salinity relationship in the tropical and subtropical
surface ocean. Global Biogeochemical Cycles, 2014. 28(7): p. 729-742.
8. Proctor, R., K. Roberts, and B. Ward, A data delivery system for IMOS, the Australian Integrated Marine
Observing System. Advances in Geosciences, 2010. 28(28): p. 11-16
9. Moussa, H., et al., A comparison of Multiple Non-linear regression and neural network techniques for sea
surface salinity estimation in the tropical Atlantic ocean based on satellite data. ESAIM: Proceedings and
Surveys, 2015. 49: p. 65-77.
10. Riebesell, U., et al., Guide to best practices for ocean acidification research and data reporting. 2010:
Publications Office of the European Union Luxembourg.