Glacial–interglacial dynamics of Antarctic firn columns: comparison between simulations and ice core air-δ15N measurements

Correct estimation of the firn lock-in depth is essential for correctly linking gas and ice chronologies in ice core studies. Here, two approaches to constrain the firn depth evolution in Antarctica are presented over the last deglaciation: outputs of a firn densification model, and measurements of δ15N of N2 in air trapped in ice core, assuming that δ15N is only affected by gravitational fractionation in the firn column. Since the firn densification process is largely governed by surface temperature and accumulation rate, we have investigated four ice cores drilled in coastal (Berkner Island, BI, and James Ross Island, JRI) and semi-coastal (TALDICE and EPICA Dronning Maud Land, EDML) Antarctic regions. Combined with available ice core air-δ15N measurements from the EPICA Dome C (EDC) site, the studied regions encompass a large range of surface accumulation rates and temperature conditions. Our δ15N profiles reveal a heterogeneous response of the firn structure to glacial–interglacial climatic changes. While firn densification simulations correctly predict TALDICE δ15N variations, they systematically fail to capture the large millennial-scale δ15N variations measured at BI and the δ15N glacial levels measured at JRI and EDML – a mismatch previously reported for central East Antarctic ice cores. New constraints of the EDML gas–ice depth offset during the Laschamp event (~41 ka) and the last deglaciation do not favour the hypothesis of a large convective zone within the firn as the explanation of the glacial firn model–δ15N data mismatch for this site. While we could not conduct an in-depth study of the influence of impurities in snow for firnification from the existing datasets, our detailed comparison between the δ15N profiles and firn model simulations under different temperature and accumulation rate scenarios suggests that the role of accumulation rate may have been underestimated in the current description of firnification models.

Transient simulations of the carbon and nitrogen dynamics in northern peatlands: from the Last Glacial Maximum to the 21st century

The development of northern high-latitude peatlands played an important role in the carbon (C) balance of the land biosphere since the Last Glacial Maximum (LGM). At present, carbon storage in northern peatlands is substantial and estimated to be 500 ± 100 Pg C (1 Pg C = 1015 g C). Here, we develop and apply a peatland module embedded in a dynamic global vegetation and land surface process model (LPX-Bern 1.0). The peatland module features a dynamic nitrogen cycle, a dynamic C transfer between peatland acrotelm (upper oxic layer) and catotelm (deep anoxic layer), hydrology- and temperature-dependent respiration rates, and peatland specific plant functional types. Nitrogen limitation down-regulates average modern net primary productivity over peatlands by about half. Decadal acrotelm-to-catotelm C fluxes vary between −20 and +50 g C m−2 yr−1 over the Holocene. Key model parameters are calibrated with reconstructed peat accumulation rates from peat-core data. The model reproduces the major features of the peat core data and of the observation-based modern circumpolar soil carbon distribution. Results from a set of simulations for possible evolutions of northern peat development and areal extent show that soil C stocks in modern peatlands increased by 365–550 Pg C since the LGM, of which 175–272 Pg C accumulated between 11 and 5 kyr BP. Furthermore, our simulations suggest a persistent C sequestration rate of 35–50 Pg C per 1000 yr in present-day peatlands under current climate conditions, and that this C sink could either sustain or turn towards a source by 2100 AD depending on climate trajectories as projected for different representative greenhouse gas concentration pathways.

Discontinuity in the responses of ecosystem processes and multifunctionality to altered soil community composition

Ecosystem management policies increasingly emphasize provision of multiple, as opposed to single, ecosystem services. Management for such "multifunctionality" has stimulated research into the role that biodiversity plays in providing desired rates of multiple ecosystem processes. Positive effects of biodiversity on indices of multifunctionality are consistently found, primarily because species that are redundant for one ecosystem process under a given set of environmental conditions play a distinct role under different conditions or in the provision of another ecosystem process. Here we show that the positive effects of diversity (specifically community composition) on multifunctionality indices can also arise from a statistical fallacy analogous to Simpson's paradox (where aggregating data obscures causal relationships). We manipulated soil faunal community composition in combination with nitrogen fertilization of model grassland ecosystems and repeatedly measured five ecosystem processes related to plant productivity, carbon storage, and nutrient turnover. We calculated three common multifunctionality indices based on these processes and found that the functional complexity of the soil communities had a consistent positive effect on the indices. However, only two of the five ecosystem processes also responded positively to increasing complexity, whereas the other three responded neutrally or negatively. Furthermore, none of the individual processes responded to both the complexity and the nitrogen manipulations in a manner consistent with the indices. Our data show that multifunctionality indices can obscure relationships that exist between communities and key ecosystem processes, leading us to question their use in advancing theoretical understanding-and in management decisions-about how biodiversity is related to the provision of multiple ecosystem services.

Simulating Carbon Stocks and Fluxes of an African Tropical Montane Forest with an Individual-Based Forest Model

Tropical forests are carbon-dense and highly productive ecosystems. Consequently, they play an important role in the global carbon cycle. In the present study we used an individual-based forest model (FORMIND) to analyze the carbon balances of a tropical forest. The main processes of this model are tree growth, mortality, regeneration, and competition. Model parameters were calibrated using forest inventory data from a tropical forest at Mt. Kilimanjaro. The simulation results showed that the model successfully reproduces important characteristics of tropical forests (aboveground biomass, stem size distribution and leaf area index). The estimated aboveground biomass (385 t/ha) is comparable to biomass values in the Amazon and other tropical forests in Africa. The simulated forest reveals a gross primary production of 24 tcha-1yr-1. Modeling above- and belowground carbon stocks, we analyzed the carbon balance of the investigated tropical forest. The simulated carbon balance of this old-growth forest is zero on average. This study provides an example of how forest models can be used in combination with forest inventory data to investigate forest structure and local carbon balances.

Temperature Prediction Model for Bone Drilling Based on Density Distribution and In Vivo Experiments for Minimally Invasive Robotic Cochlear Implantation

Surgical robots have been proposed ex vivo to drill precise holes in the temporal bone for minimally invasive cochlear implantation. The main risk of the procedure is damage of the facial nerve due to mechanical interaction or due to temperature elevation during the drilling process. To evaluate the thermal risk of the drilling process, a simplified model is proposed which aims to enable an assessment of risk posed to the facial nerve for a given set of constant process parameters for different mastoid bone densities. The model uses the bone density distribution along the drilling trajectory in the mastoid bone to calculate a time dependent heat production function at the tip of the drill bit. Using a time dependent moving point source Green's function, the heat equation can be solved at a certain point in space so that the resulting temperatures can be calculated over time. The model was calibrated and initially verified with in vivo temperature data. The data was collected in minimally invasive robotic drilling of 12 holes in four different sheep. The sheep were anesthetized and the temperature elevations were measured with a thermocouple which was inserted in a previously drilled hole next to the planned drilling trajectory. Bone density distributions were extracted from pre-operative CT data by averaging Hounsfield values over the drill bit diameter. Post-operative [Formula: see text]CT data was used to verify the drilling accuracy of the trajectories. The comparison of measured and calculated temperatures shows a very good match for both heating and cooling phases. The average prediction error of the maximum temperature was less than 0.7 °C and the average root mean square error was approximately 0.5 °C. To analyze potential thermal damage, the model was used to calculate temperature profiles and cumulative equivalent minutes at 43 °C at a minimal distance to the facial nerve. For the selected drilling parameters, temperature elevation profiles and cumulative equivalent minutes suggest that thermal elevation of this minimally invasive cochlear implantation surgery may pose a risk to the facial nerve, especially in sclerotic or high density mastoid bones. Optimized drilling parameters need to be evaluated and the model could be used for future risk evaluation.

Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

Simulating surface wind over complex terrain is a challenge in regional climate modelling. Therefore, this study aims at identifying a set-up of the Weather Research and Forecasting Model (WRF) model that minimises system- atic errors of surface winds in hindcast simulations. Major factors of the model configuration are tested to find a suitable set-up: the horizontal resolution, the planetary boundary layer (PBL) parameterisation scheme and the way the WRF is nested to the driving data set. Hence, a number of sensitivity simulations at a spatial resolution of 2 km are carried out and compared to observations. Given the importance of wind storms, the analysis is based on case studies of 24 historical wind storms that caused great economic damage in Switzerland. Each of these events is downscaled using eight different model set-ups, but sharing the same driving data set. The results show that the lack of representation of the unresolved topography leads to a general overestimation of wind speed in WRF. However, this bias can be substantially reduced by using a PBL scheme that explicitly considers the effects of non-resolved topography, which also improves the spatial structure of wind speed over Switzerland. The wind direction, although generally well reproduced, is not very sensitive to the PBL scheme. Further sensitivity tests include four types of nesting methods: nesting only at the boundaries of the outermost domain, analysis nudging, spectral nudging, and the so-called re-forecast method, where the simulation is frequently restarted. These simulations show that restricting the freedom of the model to develop large-scale disturbances slightly increases the temporal agreement with the observations, at the same time that it further reduces the overestimation of wind speed, especially for maximum wind peaks. The model performance is also evaluated in the outermost domains, where the resolution is coarser. The results demonstrate the important role of horizontal resolution, where the step from 6 to 2 km significantly improves model performance. In summary, the combination of a grid size of 2 km, the non-local PBL scheme modified to explicitly account for non-resolved orography, as well as analysis or spectral nudging, is a superior combination when dynamical downscaling is aimed at reproducing real wind fields.

Enhanced Recovery after Urological Surgery: A Contemporary Systematic Review of Outcomes, Key Elements, and Research Needs

CONTEXT Enhanced Recovery after Surgery (ERAS) programs are multimodal care pathways that aim to decrease intra-operative blood loss, decrease postoperative complications, and reduce recovery times. OBJECTIVE To overview the use and key elements of ERAS pathways, and define needs for future clinical trials. EVIDENCE ACQUISITION A comprehensive systematic MEDLINE search was performed for English language reports published before May 2015 using the terms "postoperative period," "postoperative care," "enhanced recovery after surgery," "enhanced recovery," "accelerated recovery," "fast track recovery," "recovery program," "recovery pathway", "ERAS," and "urology" or "cystectomy" or "urologic surgery." EVIDENCE SYNTHESIS We identified 18 eligible articles. Patient counseling, physical conditioning, avoiding excessive alcohol and smoking, and good nutrition appeared to protect against postoperative complications. Fasting from solid food for only 6h and perioperative liquid-carbohydrate loading up to 2h prior to surgery appeared to be safe and reduced recovery times. Restricted, balanced, and goal-directed fluid replacement is effective when individualized, depending on patient morbidity and surgical procedure. Decreased intraoperative blood loss may be achieved by several measures. Deep vein thrombosis prophylaxis, antibiotic prophylaxis, and thermoregulation were found to help reduce postsurgical complications, as was a multimodal approach to postoperative nausea, vomiting, and analgesia. Chewing gum, prokinetic agents, oral laxatives, and an early resumption to normal diet appear to aid faster return to normal bowel function. Further studies should compare anesthetic protocols, refine analgesia, and evaluate the importance of robot-assisted surgery and the need/timing for drains and catheters. CONCLUSIONS ERAS regimens are multidisciplinary, multimodal pathways that optimize postoperative recovery. PATIENT SUMMARY This review provides an overview of the use and key elements of Enhanced Recovery after Surgery programs, which are multimodal, multidisciplinary care pathways that aim to optimize postoperative recovery. Additional conclusions include identifying effective procedures within Enhanced Recovery after Surgery programs and defining needs for future clinical trials.