Holocene ice dynamics and bottom-water formation associated with Cape Darnley polynya activity recorded in Burton Basin,East Antarctica

A multi-proxy study including sedimentological, mineralogical, biogeochemical and micropaleontological methods was conducted on sediment core PS69/849-2 retrieved from Burton Basin, MacRobertson Shelf, East Antarctica. The goal of this study was to depict the deglacial and Holocene environmental history of the MacRobertson Land–Prydz Bay region. A special focus was put on the timing of ice-sheet retreat and the variability of bottom-water formation due to sea ice formation through the Holocene. Results from site PS69/849-2 provide the first paleo-environmental record of Holocene variations in bottom-water production probably associated to the Cape Darnley polynya, which is the second largest polynya in the Antarctic. Methods included end-member modeling of laser-derived high-resolution grain size data to reconstruct the depositional regimes and bottom-water activity. The provenance of current-derived and ice-transported material was reconstructed using clay-mineral and heavy-mineral analysis. Conclusions on biogenic production were drawn by determination of biogenic opal and total organic carbon. It was found that the ice shelf front started to retreat from the site around 12.8 ka BP. This coincides with results from other records in Prydz Bay and suggests warming during the early Holocene optimum next to global sea level rise as the main trigger. Ice-rafted debris was then supplied to the site until 5.5 cal. ka BP, when Holocene global sea level rise stabilized and glacial isostatic rebound on MacRobertson Land commenced. Throughout the Holocene, three episodes of enhanced bottom-water activity probably due to elevated brine rejection in Cape Darnley polynya occured between 11.5 and 9 cal. ka BP, 5.6 and 4.5 cal. ka BP and since 1.5 cal. ka BP. These periods are related to shifts from warmer to cooler conditions at the end of Holocene warm periods, in particular the early Holocene optimum, the mid-Holocene warm period and at the beginning of the neoglacial. In contrast, between 7.7 and 6.7 cal. ka BP, brine rejection shut down, maybe owed to warm conditions and pronounced open-water intervals.     

A Two-Day Case Study: Comparison of Turbulence Data from an Unmanned Aircraft System with a Model Chain for Complex Terrain

The airborne measurement platform MASC-3 (Multi-Purpose Airborne Sensor Carrier) is used for measurements over a forested escarpment in the Swabian Alps to evaluate the wind field. Data from flight legs between 20 and 200 m above the ground on two consecutive days with uphill (westerly) flow in September 2018 are analyzed. In the lowest 140 m above the ground a speed-up is found with increased turbulence and changes in wind direction directly over the escarpment, whereas in the lowest 20 to 50 m above the ground a deceleration of the flow is measured. Additionally, simulation results from a numerical model chain based on the Weather Research and Forecasting (WRF) model and an OpenFOAM (Open Source Field Operation and Manipulation) model, developed for complex terrain, are compared to the data captured by MASC-3. The models and measurements compare well for the mean wind speed and inclination angle.

     

Determining the amount of overstepping required to nucleate garnet during Barrovian regional metamorphism,Connecticut Valley Synclinorium

The novel method of inclusion barometry coupled with the calculation of the required affinity for garnet nucleation is applied to three samples from the previously well‐characterized Connecticut Valley Synclinorium in central Vermont. Raman shifts for quartz inclusions record a range of maximum peak shifts of the quartz 464 cm^?1 peak from 2.4 to 3.0 cm^?1. Temperature of garnet nucleation was constrained by calculating mineral assemblage diagrams in the MnNCKFMASHT system and plotting the intersection of quartz inclusion in garnet barometry (QuiG, quartz‐in‐garnet) with Zr‐in‐rutile thermometry. Utilizing the intersection of Zr‐in‐rutile thermometry with QuiG barometry, garnet nucleation is inferred to have occurred within a P–T range of ~8.6–9.5 kbar and ~560–575°C. These P–T conditions for garnet nucleation are significantly higher than calculated equilibrium garnet‐in isograds for the three samples. Affinities for garnet nucleation were calculated as the difference between the free energy of a fictive garnet composition based on the matrix assemblage and the free energy of the nucleated garnet. The calculated nucleation affinity varied from 300 to 600 kJ/mol O for St–Ky grade samples. These results suggest that the assumption that metamorphism proceeds as a sequence of near‐equilibrium conditions cannot, in general, be made for regional metamorphic terranes. This body of work agrees with numerous recent studies showing that garnet‐producing reactions must be overstepped in order to for garnet to nucleate.     

Calibration of imperfect models to biased observations

The problem of assimilating biased and inaccurate observations into inadequate models of the physical systems from which the observations were taken is common in the petroleum and groundwater fields. When large amounts of data are assimilated without accounting for model error and observation bias, predictions tend to be both overconfident and incorrect. In this paper, we propose a workflow for calibration of imperfect models to biased observations that involves model construction, model calibration, model criticism and model improvement. Model criticism is based on computation of model diagnostics which provide an indication of the validity of assumptions. During the model improvement step, we advocate identification of additional physically motivated parameters based on examination of data mismatch after calibration and addition of bias correction terms. If model diagnostics indicates the presence of residual model error after parameters have been added, then we advocate estimation of a “total” observation error covariance matrix, whose purpose is to reduce weighting of observations that cannot be matched because of deficiency of the model. Although the target applications of this methodology are in the subsurface, we illustrate the approach with two simplified examples involving prediction of the future velocity of fall of a sphere from models calibrated to a short-time series of biased measurements with independent additive random noise. The models into which the data are assimilated contain model errors due to neglect of physical processes and neglect of uncertainty in parameters. In every case, the estimated total error covariance is larger than the true observation covariance implying that the observations need not be matched to the accuracy of the measuring instrument. Predictions are much improved when all model improvement steps were taken.     

Landslide prediction from machine learning

Predicting where and when landslides are likely to occur in a specific region of interest remains a key challenge in natural hazards research and mitigation. While the basic mechanics of slope‐failure initiation and runout can be cast into physical and numerical models, a scarcity of sufficiently detailed and real‐time measurements of soil, rock‐mass and groundwater conditions prohibits accurate landslide forecasting. Researchers are therefore increasingly exploring multivariate data analysis techniques from the fields of data mining or machine learning in order to approximate future occurrences of landslides from past distribution patterns. This work has elucidated patterns of spatial susceptibility, but temporal forecasts have remained largely empirical. Most machine learning techniques achieve overall success rates of 75–95 percent. Whilst this may seem very promising, issues remain with data input quality, potential overfitting and commensurate inadequate choice of prediction models, inadvertent inclusion of redundant or noise variables, and technical limits to predicting only certain types and sizes of landslides. Simpler models provide only slightly inferior predictions to more complex models, and should guide the way for a more widespread application of data mining in regional landslide prediction. This approach should especially be communicated to planners and decision makers. Future research may want to develop: (1) further best‐practice guidelines for model selection; (2) predictions of occurrence and runout of large slope failures at the regional scale; and (3) temporal forecasts of landslides.     

Trading more food: Implications for land use,greenhouse gas emissions,and the food system

The volume of agricultural trade increased by more than ten times throughout the past six decades and is likely to continue with high rates in the future. Thereby, it largely affects environment and climate. We analyse future trade scenarios covering the period of 2005–2045 by evaluating economic and environmental effects using the global land-use model MAgPIE (“Model of Agricultural Production and its Impact on the Environment”). This is the first trade study using spatially explicit mapping of land use patterns and greenhouse gas emissions. We focus on three scenarios: the reference scenario fixes current trade patterns, the policy scenario follows a historically derived liberalisation pathway, and the liberalisation scenario assumes a path, which ends with full trade liberalisation in 2045.Further trade liberalisation leads to lower global costs of food. Regions with comparative advantages like Latin America for cereals and oil crops and China for livestock products will export more. In contrast, regions like the Middle East, North Africa, and South Asia face the highest increases of imports. Deforestation, mainly in Latin America, leads to significant amounts of additional carbon emissions due to trade liberalisation. Non-CO₂ emissions will mostly shift to China due to comparative advantages in livestock production and rising livestock demand in the region. Overall, further trade liberalisation leads to higher economic benefits at the expense of environment and climate, if no other regulations are put in place.     

Lateglacial environmental and climatic changes at the Maloja Pass,Central Swiss Alps,as recorded by chironomids and pollen

Chironomids and pollen were studied in a radiocarbon-dated sediment sequence obtained from a former lake near the Maloja Pass in the Central Swiss Alps (1865 m a.s.l.) to reconstruct the Lateglacial environment. Pollen assemblages imply a vegetation development around the Maloja Pass from shrub tundra at the beginning of the Allerød to coniferous forest during the early Holocene with a lowering of the timberline during the Younger Dryas. Chironomid assemblages are characterized by several abrupt shifts in dominant taxa through the Lateglacial. The occurrence of taxa able to survive hypoxia in the second part of the Allerød and during the Preboreal, and their disappearance at the onset of the Younger Dryas cold phase suggest summer thermal stratification and unfavourable hypolimnetic oxygen conditions in the palaeo-lake during the warmer periods of the Lateglacial interstadial and early Holocene. Mean July air temperatures were reconstructed using a chironomid-temperature transfer function from the Alpine region. The pattern of reconstructed temperature changes agrees well with the Greenland δ¹⁸O record and other Lateglacial temperature inferences from Central Europe. The inferred July temperatures of ca 10.0 °C during most of the Allerød were slightly lower than modern values (10.8 °C) and increased up to ca 11.7 °C (i.e., above present-day values) at the end of the Allerød. The first part of the Younger Dryas was colder (ca 8.8 °C) than the second part (ca 9.8 °C). During most of the Preboreal, the temperatures persisted within the limits of 13.5–14.5 °C (i.e., ca 3 °C above present-day values). The amplitudes of temperature changes at the Allerød–Younger Dryas–Preboreal transitions were ca 3.5–4.0 °C. The temperature reconstruction also shows three short-lived cooling events of ca 1.5–2.0 °C, which may be attributed to the centennial-scale Greenland Interstadial events GI-1d and GI-1b, and the Preboreal Oscillation.     

Atmospheric wet deposition of PAHs to the sea-surface microlayer

Sea-surface microlayer (SML) and subsurface seawater samples (SSW) collected from Singapore's coastal environment were analyzed for 14 polycyclic aromatic hydrocarbons (PAHs) in the dissolved (DP) and suspended particulate phase (SPM). Samples were collected prior to and after rainfall events to ascertain the contribution of wet atmospheric deposition of PAH enrichment to the SML. The concentration ranges of summation operatorPAHs in the SML before rain and after wet deposition were 2.6-46.2 ngL(-1) and 4.3-278.0 ngL(-1), respectively, for the DP and 3.8-31.4 ngL(-1) and 12.8-1280 ngL(-1), respectively, for the SPM. Load factors (i.e. concentration after wet deposition relative to before wet deposition) of the atmospheric wet deposition for DP and SPM ranged from 1.4 to 42.9 and 1.2 to 337, respectively. This study provides the first data on PAH concentration, enrichment (i.e. concentration of PAHs in SML relative to subsurface water) and load factors in the SML before and after wet deposition to the ocean surface.