Bol’shakovskii gabbro massif as a fragment of the Southern Urals zone of Early Carboniferous rift

In this paper new data on the absolute age and geochemistry of rocks of the Bol’shakovskii massif, situated in the central part of the Aramil-Sukhteli zone of the Southern Urals, are given. The obtained values are evidence for its Visean age. By the geological-petrographic and petro- and geochemical features, the rocks of the Bol’shakovskii complex differ sharply from ophiolite-type gabbroids, although they reveal a substantial similarity with the gabbro-granite formation of the Magnitogorsk megazone. The Bol’shakovskii massif is situated in the northern branch of the South Urals zone of Early Carboniferous riftogenesis; and its formation is most probably associated with magmatism events during the rift regime in the died_out island arc.     

Probabilistic life cycle analysis model for evaluating electric power infrastructure risk mitigation investments

One effect of climate change may be increased hurricane frequency or intensity due to changes in atmospheric and geoclimatic factors. It has been hypothesized that wetland restoration and infrastructure hardening measures may improve infrastructure resilience to increased hurricane frequency and intensity. This paper describes a parametric decision model used to assess the tradeoffs between wetland restoration and infrastructure hardening for electric power networks. We employ a hybrid economic input–output life-cycle analysis (EIO-LCA) model to capture: construction costs and life-cycle emissions for transitioning from the current electric power network configuration to a hardened network configuration; construction costs and life-cycle emissions associated with wetland restoration; and the intrinsic value of wetland restoration. Uncertainty is accounted for probabilistically through a Monte Carlo hurricane simulation model and parametric sensitivity analysis for the number of hurricanes expected to impact the project area during the project cycle and the rate of wetland storm surge attenuation. Our analysis robustly indicates that wetland restoration and undergrounding of electric power network infrastructure is not preferred to the “do-nothing” option of keeping all power lines overhead without wetland protection. However, we suggest a few items for future investigation. For example, our results suggest that, for the small case study developed, synergistic benefits of simultaneously hardening infrastructure and restoring wetlands may be limited, although research using a larger test bed while integrating additional costs may find an enhanced value of wetland restoration for disaster loss mitigation.     

Nowcasting with INCA During SNOW-V10

A Central-European nowcasting system which has been developed for use in mountainous terrain is tested in the Whistler/Vancouver area as part of the SNOW-V10 experiment. The integrated nowcasting through comprehensive analysis system provides hourly updated gridded forecasts of temperature, humidity, and wind, as well as precipitation forecasts which are updated every 15 min. It is based on numerical weather prediction (NWP) output and real-time surface weather station and radar data. Verification of temperature, relative humidity, and wind against surface stations shows that forecast errors are significantly reduced in the nowcasting range compared to those of the driving NWP model. The main contribution to the improvement comes from the implicit bias correction due to use of the latest observations. Relative humidity shows the longest lasting effect, with >50 % reduction of mean absolute error up to +4 h. For temperature and wind speed this percentage is reached after +2 and +3 h, respectively. Two cases of precipitation nowcasting are discussed and verified qualitatively.     

The importance of reduced meat and dairy consumption for meeting stringent climate change targets

For agriculture, there are three major options for mitigating greenhouse gas (GHG) emissions: 1) productivity improvements, particularly in the livestock sector; 2) dedicated technical mitigation measures; and 3) human dietary changes. The aim of the paper is to estimate long-term agricultural GHG emissions, under different mitigation scenarios, and to relate them to the emissions space compatible with the 2 °C temperature target. Our estimates include emissions up to 2070 from agricultural soils, manure management, enteric fermentation and paddy rice fields, and are based on IPCC Tier 2 methodology. We find that baseline agricultural CO₂-equivalent emissions (using Global Warming Potentials with a 100 year time horizon) will be approximately 13 Gton CO₂eq/year in 2070, compared to 7.1 Gton CO₂eq/year 2000. However, if faster growth in livestock productivity is combined with dedicated technical mitigation measures, emissions may be kept to 7.7 Gton CO₂eq/year in 2070. If structural changes in human diets are included, emissions may be reduced further, to 3–5 Gton CO₂eq/year in 2070. The total annual emissions for meeting the 2 °C target with a chance above 50 % is in the order of 13 Gton CO₂eq/year or less in 2070, for all sectors combined. We conclude that reduced ruminant meat and dairy consumption will be indispensable for reaching the 2 °C target with a high probability, unless unprecedented advances in technology take place.     

Automated Image Registration for Hydrologic Change Detection in the Lake-Rich Arctic

Multitemporal remote sensing provides a unique tool to track lake dynamics at the pan-Arctic scale but requires precise registration of thousands of satellite images. This is a challenging task owing to a dearth of stable features to be used as tie points [(TPs), i.e., control points] in the dynamic landscapes. This letter develops an automated method to precisely register images in the lake-rich Arctic. The core premise of the method is that the centers of lakes are generally stable even if their shorelines are not. The proposed procedures first extract lakes in multitemporal satellite images, derive lake centroids and match them between images, and then use the centroids of stable lakes as TPs for image registration. The results show that this approach can achieve subpixel registration accuracy, outcompeting the conventional manual methods in both efficiency and accuracy. The proposed method is fully automated and represents a feasible way to register images for lake change detection at the pan-Arctic scale.      

On the use of IPCC-class models to assess the impact of climate on Living Marine Resources

The study of climate impacts on Living Marine Resources (LMRs) has increased rapidly in recent years with the availability of climate model simulations contributed to the assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Collaboration between climate and LMR scientists and shared understanding of critical challenges for such applications are essential for developing robust projections of climate impacts on LMRs. This paper assesses present approaches for generating projections of climate impacts on LMRs using IPCC-class climate models, recommends practices that should be followed for these applications, and identifies priority developments that could improve current projections. Understanding of the climate system and its representation within climate models has progressed to a point where many climate model outputs can now be used effectively to make LMR projections. However, uncertainty in climate model projections (particularly biases and inter-model spread at regional to local scales), coarse climate model resolution, and the uncertainty and potential complexity of the mechanisms underlying the response of LMRs to climate limit the robustness and precision of LMR projections. A variety of techniques including the analysis of multi-model ensembles, bias corrections, and statistical and dynamical downscaling can ameliorate some limitations, though the assumptions underlying these approaches and the sensitivity of results to their application must be assessed for each application. Developments in LMR science that could improve current projections of climate impacts on LMRs include improved understanding of the multi-scale mechanisms that link climate and LMRs and better representations of these mechanisms within more holistic LMR models. These developments require a strong baseline of field and laboratory observations including long time series and measurements over the broad range of spatial and temporal scales over which LMRs and climate interact. Priority developments for IPCC-class climate models include improved model accuracy (particularly at regional and local scales), inter-annual to decadal-scale predictions, and the continued development of earth system models capable of simulating the evolution of both the physical climate system and biosphere. Efforts to address these issues should occur in parallel and be informed by the continued application of existing climate and LMR models.     

Advances in location modeling: GIS linkages and contributions

Geographic information systems (GIS) have matured and proven to be an enabling technology, one that is important to many disciplines. Location analysis is also a field that has matured and continues to evolve. In fact, the combination of GIS and location science is at the forefront of advances in spatial analysis capabilities, offering substantial potential for continued and sustained theoretical and empirical evolution. This paper provides an overview of location analysis and discusses GIS. The paper highlights how GIS has contributed to location science in terms of data input, visualization, problem solution and theoretical advances. The significance of GIS in this context is that it is far more than a mere spatial data input mechanism, which is a commonly held misconception within geography, operations research and other allied disciplines. In contrast to other reviews, the focus in this paper is to highlight the theoretical foundations of location analysis and modeling and how GIS is contributing to important advancements in this field. An overall contribution of the paper is providing a perspective on spatial analysis and how associated specialty areas are evolving and thriving, particularly as a component of GIScience.     

Jinshanjiangite and bafertisite from the Gremyakha-Vyrmes Alkaline Complex,Kola Peninsula

Jinshanjiangite (acicular crystals up to 2 mm in length) and bafertisite (lamellar crystals up to 3 × 4 mm in size) have been found in alkali granite pegmatite of the Gremyakha-Vyrmes Complex, Kola Peninsula. Albite, microcline, quartz, arfvedsonite, zircon, and apatite are associated minerals. The dimensions of a monoclinic unit cell of jinshanjiangite and bafertisite are: a = 10.72(2), b=13.80(2), c = 20.94(6) Å, β = 97.0(5)° and a = 10.654(6), b = 13.724(6), c = 10.863(8) Å, β = 94.47(8)°, respectively. The typical compositions (electron microprobe data) of jinshanjiangite and bafertisite are: (Na_0.57Ca_0.44)_Σ1.01(Ba_0.57K_0.44)_Σ1.01 (Fe_3.53Mn_0.30Mg_0.04Zn_0.01)_Σ3.88(Ti_1.97Nb_0.06Zr_0.01)_Σ2.04(Si_3.97Al_0.03O₁₄)O_2.00(OH_2.25F_0.73O_0.02)_Σ3.00 and (Ba_1.98Na_0.04K_0.03)_Σ2.05(Fe_3.43Mn_0.37Mg_0.03)_Σ3.83(Ti_2.02Nb_0.03)_Σ2.05 (Si_3.92Al_0.08O₁₄)(O_1.84OH_0.16)_Σ2.00(OH_2.39F_1.61)_Σ3.00, respectively. The minerals studied are the Fe-richest members of the bafertisite structural family.