Line-of-Sight Observables Algorithms for the <Emphasis Type="Italic">Helioseismic and Magnetic Imager</Emphasis> (HMI) Instrument Tested with <Emphasis Type="Italic">Interferometric Bidimensional Spectrometer</Emphasis> (IBIS) Observations

The Helioseismic and Magnetic Imager (HMI) instrument onboard the Solar Dynamics Observatory produces line-of-sight (LOS) observables (Doppler velocity, magnetic-field strength, Fe i line width, line depth, and continuum intensity) as well as vector magnetic-field maps at the solar surface. The accuracy of LOS observables is dependent on the algorithm used to translate a sequence of HMI filtergrams into the corresponding observables. Using one hour of high-cadence imaging spectropolarimetric observations of a sunspot in the Fe i line at 6173 Å through the Interferometric Bidimensional Spectrometer installed at the Dunn Solar Telescope, and the Milne–Eddington inversion of the corresponding Stokes vectors, we test the accuracy of the observables algorithm currently implemented in the HMI data-analysis pipeline: the MDI-like algorithm. In an attempt to improve the accuracy of HMI observables, we also compare this algorithm to others that may be implemented in the future: a least-squares fit with a Gaussian profile, a least-squares fit with a Voigt profile, and the use of second Fourier coefficients in the MDI-like algorithm.     

Learning from CDM SD tool experience for Article 6.4 of the Paris Agreement

The Paris Agreement (PA) emphasizes the intrinsic relationship between climate change and sustainable development (SD) and welcomes the 2030 agenda for the global Sustainable Development Goals (SDGs). Yet, there is a lack of assessment approaches to ensure that climate and development goals are achieved in an integrated fashion and trade-offs avoided. Article 6.4 of the PA introduces a new Sustainable Mitigation Mechanism (SMM) with the dual aim to contribute to the mitigation of greenhouse gas emissions and foster SD. The Kyoto Protocol’s Clean Development Mechanism (CDM) has a similar objective and in 2014, the CDM SD tool was launched by the Executive Board of the CDM to highlight the SD benefits of CDM activities. This article analyses the usefulness of the CDM SD tool for stakeholders and compares the SD tool’s SD reporting requirements against other flexible mechanisms and multilateral standards to provide recommendations for improvement. A key conclusion is that the Paris Agreement’s SMM has a stronger political mandate than the CDM to measure that SD impacts are ‘real, measurable and long-term’. Recommendations for an improved CDM SD tool are a relevant starting point to develop rules, modalities, and procedures for SD assessment in Article 6.4 as well as for other cooperative mitigation approaches.

POLICY RELEVANCE

Research findings are relevant for developing the rulebook of modalities and procedures for Article 6.4 of the Paris Agreement, which introduces a new mechanism for mitigation of greenhouse gas emissions and sustainable development. Lessons learnt from the CDM SD tool and recommendations for enhanced SD assessment are discussed in context of Article 6 cooperative approaches, and make a timely contribution to inform negotiations on the rulebook agreed by the Conference of the Parties serving as the Meeting of the Parties to the Paris Agreement.     

Induction of heat shock protein (hsp)60 in Isochrysis galbana exposed to sublethal preparations of dispersant and Prudhoe Bay crude oil

Adaptation to sublethal exposure to crude oil by phytoplankton is poorly understood. Use of chemical dispersants for oil spill remediation increases petroleum hydrocarbon concentrations in water, while exposing marine organisms to potentially toxic concentrations of dispersant. Heat shock proteins (hsps) have been found to serve as an adaptive and protective mechanism against environmental stresses. The objective of this project was to examine the induction of hsps in Isochrysis galbana, a golden-brown algae, following exposure to the water-accommodated fraction (WAF) of Prudhoe Bay crude oil (PBCO) and PBCO chemically dispersed with Corexit 9527® (dispersed oil: DO). Initial experiments using ³⁵S-labeled amino acids and 2-dimensional electrophoresis with subsequent western blotting identified and confirmed hsp60, a member of the chaperonin family of stress proteins, as being efficiently induced by heat shock in this species. One-dimensional SDS PAGE and western blotting, with hsp60 antibodies and chemiluminesence detection, were used to quantitate hsp60 following exposure to a range of environmental temperatures and concentrations of WAF and DO preparations. I. galbana cultured in 22 parts per thousand (‰) salinity showed a statistically significant increase (p<0.05) in hsp60 after exposure to 25, 30, and 35°C for 1 h compared to controls (20°C), while 34‰ cultures showed no significant increase in hsp60 with increasing temperature. Cultures (22 and 34‰) exposed to varying doses of WAF and DO exhibited statistically significant (p<0.05) increases in levels of hsp60 in all cases. A dose-related response was observed in all exposures (r²≥0.90) with the exception of 34‰ WAF. In independent exposures, a dose-related hsp60 response to naphthalene, a relatively abundant water-soluble aromatic hydrocarbon, was observed in both 22 and 34‰ cultures. Results of this study are consistent with previous studies in other species documenting increases in hsp60 levels with exposure to xenobiotics. Further studies are investigating the protective function of hsp60 against the toxic effects of exposure to WAF and DO preparations.     

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets

In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.     

Seagrass ecophysiology meets ecological genomics: are we ready?

In March 2011, the Ecophysiology and Genetics Working Groups of the European Science Foundation COST Action ES 0906, entitled Seagrass Productivity: From Genes to Ecosystem Management, organized an exploratory workshop entitled “Linking Ecophysiology and Ecogenomics in Seagrass Systems”. The goal of the workshop was to discuss how to integrate comparative gene expression studies with physiological processes such as photosynthetic performance, carbon and nitrogen utilization and environmental adaptation. The main questions discussed for integrative research related to mechanisms of carbon utilization, light requirements, temperature effects and natural variation in pH and ocean acidification. It was concluded that the seagrass research community is still in the nascent stages of linking ecophysiology with genomic responses, as carbon and nitrogen metabolism of seagrasses have not been sufficiently well studied. Likewise, genomic approaches have only been able to assign meaningful interpretations to a handful of differentially expressed genes. Nevertheless, the way forward has been established.     

Significance of river–aquifer interactions for reach-scale thermal patterns and trout growth potential in the Motueka River, New Zealand

To assess whether reaches of the Motueka River (New Zealand) that gain water from groundwater were likely to represent significant cold-water refugia for brown trout during periods of high water temperatures, water temperature was monitored for more than 18 months in two gaining reaches of the Motueka River and three reaches that were predicted to be losing water to groundwater. These data were used to predict brown trout (Salmo trutta) growth in gaining and losing reaches. Groundwater inputs had a small effect on water temperature at the reach-scale and modelling suggests that the differences observed were unlikely to result in appreciable differences in trout growth. Several coldwater patches were identified within the study reach that were up to 3.5°C cooler than the mainstem, but these were generally shallow and were unlikely to provide refuge for adult trout. The exception was Hinetai Spring, which had a mean water temperature of close to 16°C during the period January–March, when temperatures in the mainstem regularly exceeded 19°C. Trout were observed within the cold-water plume at the mouth of Hinetai Stream, which would allow them to thermoregulate when mainstem temperatures are unfavourable while still being able to capitalise on food resources available in the mainstem.     

Geochemistry of groundwater in front of a warm‐based glacier in Southeast Greenland

Groundwater in front of warm‐based glaciers is likely to become a more integrated part of the future proglacial hydrological system at high latitudes due to global warming. Here, we present the first monitoring results of shallow groundwater chemistry and geochemical fingerprinting of glacier meltwater in front of a warm‐based glacier in Southeast Greenland (Mittivakkat Gletscher, 65° 41′ N, 37° 48′ W). The groundwater temperature, electrical conductivity and pressure head were monitored from August 2009 to August 2011, and water samples were collected in 2009 and analyzed for major ions and water isotopes (δD, δ¹⁸O). The 2 yrs of monitoring revealed that major outbursts of glacier water during the ablation season flushed the proglacial aquifer and determined the groundwater quality for the next 2–8 weeks until stable chemical conditions were reached again. Water isotope composition shows that isotopic fractionation occurs in both groundwater and glacier meltwater, but fractionation due to evaporation from near‐surface soil moisture prior to infiltration has the most significant effect. This study shows that groundwater in Low Arctic Greenland is likely to possess a combined geochemical and isotopic composition, which is distinguishable from other water sources in the proglacial environment. However, the shallow groundwater composition at a given time is highly dependent on major outbursts of glacier water in the previous months.     

Numerical Modeling of Atoll Island Hydrogeology

We implemented Ayers and Vachers' (1986) inclusive conceptual model for atoll island aquifers in a comprehensive numerical modeling study to evaluate the response of the fresh water lens to selected controlling climatic and geologic variables. Climatic factors include both constant and time-varying recharge rates, with particular attention paid to the effects of El Niño and the associated drought it brings to the western Pacific. Geologic factors include island width; hydraulic conductivity of the uppermost Holocene-age aquifer, which contains the fresh water lens; the depth to the contact with the underlying, and much more conductive, Pleistocene karst aquifer, which transmits tidal signals to the base of the lens; and the presence or absence of a semiconfining reef flat plate on the ocean side. Sensitivity analyses of steady-steady simulations show that lens thickness is most strongly sensitive to the depth to the Holocene-Pleistocene contact and to the hydraulic conductivity of the Holocene aquifer, respectively. Comparisons between modeling results and published observations of atoll island lens thicknesses suggest a hydraulic conductivity of approximately 50 m/d for leeward islands and approximately 400 m/d for windward islands. Results of transient simulations show that lens thickness fluctuations during average seasonal conditions and El Niño events are quite sensitive to island width, recharge rate, and hydraulic conductivity of the Holocene aquifer. In general, the depletion of the lens during drought conditions is most drastic for small, windward islands. Simulation results suggest that recovery from a 6-month drought requires about 1.5 years.