Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

The Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP) was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH4) emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO2) forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013). Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO2 concentration). The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH4 fluxes from wetland systems. Even though modelling of wetland extent and CH4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models.

Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP)

Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH4). Increased wetland CH4 emissions could act as a positive feedback to future warming. The Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large-scale wetland characteristics and corresponding CH4 emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO2) forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO2 concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prognostically, while other models relied on remotely sensed inundation datasets, or an approach intermediate between the two. Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH4 emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH4 emissions, but large variation between the models remains. For annual global CH4 emissions, the models vary by ±40% of the all-model mean (190 Tg CH4 yr−1). Second, all models show a strong positive response to increased atmospheric CO2 concentrations (857 ppm) in both CH4 emissions and wetland area. In response to increasing global temperatures (+3.4 °C globally spatially uniform), on average, the models decreased wetland area and CH4 fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH4 fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatial scale comparable to model grid cells (commonly 0.5°). This limitation severely restricts our ability to model global wetland CH4 emissions with confidence. Our simulated wetland extents are also difficult to evaluate due to extensive disagreements between wetland mapping and remotely sensed inundation datasets. Fourth, the large range in predicted CH4 emission rates leads to the conclusion that there is both substantial parameter and structural uncertainty in large-scale CH4 emission models, even after uncertainties in wetland areas are accounted for.

Greenland accumulation and its connection to the large-scale atmospheric circulation in ERA-Interim and paleoclimate simulations

Changes in Greenland accumulation and the stability in the relationship between accumulation variability and large-scale circulation are assessed by performing time-slice simulations for the present day, the preindustrial era, the early Holocene, and the Last Glacial Maximum (LGM) with a comprehensive climate model. The stability issue is an important prerequisite for reconstructions of Northern Hemisphere atmospheric circulation variability based on accumulation or precipitation proxy records from Greenland ice cores. The analysis reveals that the relationship between accumulation variability and large-scale circulation undergoes a significant seasonal cycle. As the contributions of the individual seasons to the annual signal change, annual mean accumulation variability is not necessarily related to the same atmospheric circulation patterns during the different climate states. Interestingly, within a season, local Greenland accumulation variability is indeed linked to a consistent circulation pattern, which is observed for all studied climate periods, even for the LGM. Hence, it would be possible to deduce a reliable reconstruction of seasonal atmospheric variability (e.g., for North Atlantic winters) if an accumulation or precipitation proxy were available that resolves single seasons. We further show that the simulated impacts of orbital forcing and changes in the ice sheet topography on Greenland accumulation exhibit strong spatial differences, emphasizing that accumulation records from different ice core sites regarding both interannual and long-term (centennial to millennial) variability cannot be expected to look alike since they include a distinct local signature. The only uniform signal to external forcing is the strong decrease in Greenland accumulation during glacial (LGM) conditions and an increase associated with the recent rise in greenhouse gas concentrations.

Methane emissions from floodplains in the Amazon Basin: challenges in developing a process-based model for global applications

Tropical wetlands are estimated to represent about 50% of the natural wetland methane (CH4) emissions and explain a large fraction of the observed CH4 variability on timescales ranging from glacial–interglacial cycles to the currently observed year-to-year variability. Despite their importance, however, tropical wetlands are poorly represented in global models aiming to predict global CH4 emissions. This publication documents a first step in the development of a process-based model of CH4 emissions from tropical floodplains for global applications. For this purpose, the LPX-Bern Dynamic Global Vegetation Model (LPX hereafter) was slightly modified to represent floodplain hydrology, vegetation and associated CH4 emissions. The extent of tropical floodplains was prescribed using output from the spatially explicit hydrology model PCR-GLOBWB. We introduced new plant functional types (PFTs) that explicitly represent floodplain vegetation. The PFT parameterizations were evaluated against available remote-sensing data sets (GLC2000 land cover and MODIS Net Primary Productivity). Simulated CH4 flux densities were evaluated against field observations and regional flux inventories. Simulated CH4 emissions at Amazon Basin scale were compared to model simulations performed in the WETCHIMP intercomparison project. We found that LPX reproduces the average magnitude of observed net CH4 flux densities for the Amazon Basin. However, the model does not reproduce the variability between sites or between years within a site. Unfortunately, site information is too limited to attest or disprove some model features. At the Amazon Basin scale, our results underline the large uncertainty in the magnitude of wetland CH4 emissions. Sensitivity analyses gave insights into the main drivers of floodplain CH4 emission and their associated uncertainties. In particular, uncertainties in floodplain extent (i.e., difference between GLC2000 and PCR-GLOBWB output) modulate the simulated emissions by a factor of about 2. Our best estimates, using PCR-GLOBWB in combination with GLC2000, lead to simulated Amazon-integrated emissions of 44.4 ± 4.8 Tg yr−1. Additionally, the LPX emissions are highly sensitive to vegetation distribution. Two simulations with the same mean PFT cover, but different spatial distributions of grasslands within the basin, modulated emissions by about 20%. Correcting the LPX-simulated NPP using MODIS reduces the Amazon emissions by 11.3%. Finally, due to an intrinsic limitation of LPX to account for seasonality in floodplain extent, the model failed to reproduce the full dynamics in CH4 emissions but we proposed solutions to this issue. The interannual variability (IAV) of the emissions increases by 90% if the IAV in floodplain extent is accounted for, but still remains lower than in most of the WETCHIMP models. While our model includes more mechanisms specific to tropical floodplains, we were unable to reduce the uncertainty in the magnitude of wetland CH4 emissions of the Amazon Basin. Our results helped identify and prioritize directions towards more accurate estimates of tropical CH4 emissions, and they stress the need for more research to constrain floodplain CH4 emissions and their temporal variability, even before including other fundamental mechanisms such as floating macrophytes or lateral water fluxes.

Sensitive Questions in Online Surveys: An Experimental Comparison of the RRT and the Crosswise Model

Self-administered online surveys provide a higher level of privacy protection to respondents than surveys administered by an interviewer. Yet, studies show that asking sensitive questions is problematic also in self-administered mode. Because respondents might not be willing to reveal the truth and provide answers that are subject to social desirability bias, the validity of prevalence estimates of sensitive behaviors gained via online surveys can be challenged. A wellknown method to combat these problems is the Randomized Response Technique (RRT). However, convincing evidence that the RRT provides more valid estimates than direct questioning in online mode is still lacking. Moreover, an alternative approach called the Crosswise Model (CM) has recently been suggested to overcome some of the deficiencies of the RRT. We therefore conducted an experimental study in which different implementations of the RRT and the CM have been tested and compared to direct questioning. Our study is a large-scale online survey on sensitive behaviors by students such as cheating in exams and paper plagiarism. The results of the study reveal poor per-formance of the RRT, while the CM yielded significantly higher estimates of sensitive behaviors than direct questioning. We conclude that the CM is a promising approach for asking sensitive questions in self-administered surveys.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

A hybrid method for large-scale short-term scheduling of make-and-pack production processes

Due to the ongoing trend towards increased product variety, fast-moving consumer goods such as food and beverages, pharmaceuticals, and chemicals are typically manufactured through so-called make-and-pack processes. These processes consist of a make stage, a pack stage, and intermediate storage facilities that decouple these two stages. In operations scheduling, complex technological constraints must be considered, e.g., non-identical parallel processing units, sequence-dependent changeovers, batch splitting, no-wait restrictions, material transfer times, minimum storage times, and finite storage capacity. The short-term scheduling problem is to compute a production schedule such that a given demand for products is fulfilled, all technological constraints are met, and the production makespan is minimised. A production schedule typically comprises 500–1500 operations. Due to the problem size and complexity of the technological constraints, the performance of known mixed-integer linear programming (MILP) formulations and heuristic approaches is often insufficient. We present a hybrid method consisting of three phases. First, the set of operations is divided into several subsets. Second, these subsets are iteratively scheduled using a generic and flexible MILP formulation. Third, a novel critical path-based improvement procedure is applied to the resulting schedule. We develop several strategies for the integration of the MILP model into this heuristic framework. Using these strategies, high-quality feasible solutions to large-scale instances can be obtained within reasonable CPU times using standard optimisation software. We have applied the proposed hybrid method to a set of industrial problem instances and found that the method outperforms state-of-the-art methods.

Simulating Cortical Development as a Self Constructing Process: A Novel Multi-Scale Approach Combining Molecular and Physical Aspects

Current models of embryological development focus on intracellular processes such as gene expression and protein networks, rather than on the complex relationship between subcellular processes and the collective cellular organization these processes support. We have explored this collective behavior in the context of neocortical development, by modeling the expansion of a small number of progenitor cells into a laminated cortex with layer and cell type specific projections. The developmental process is steered by a formal language analogous to genomic instructions, and takes place in a physically realistic three-dimensional environment. A common genome inserted into individual cells control their individual behaviors, and thereby gives rise to collective developmental sequences in a biologically plausible manner. The simulation begins with a single progenitor cell containing the artificial genome. This progenitor then gives rise through a lineage of offspring to distinct populations of neuronal precursors that migrate to form the cortical laminae. The precursors differentiate by extending dendrites and axons, which reproduce the experimentally determined branching patterns of a number of different neuronal cell types observed in the cat visual cortex. This result is the first comprehensive demonstration of the principles of self-construction whereby the cortical architecture develops. In addition, our model makes several testable predictions concerning cell migration and branching mechanisms.

Retrospective analysis of a nonforecasted rain-on-snow flood in the Alps – a matter of model limitations or unpredictable nature?

A rain-on-snow flood occurred in the Bernese Alps, Switzerland, on 10 October 2011, and caused significant damage. As the flood peak was unpredicted by the flood forecast system, questions were raised concerning the causes and the predictability of the event. Here, we aimed to reconstruct the anatomy of this rain-on-snow flood in the Lötschen Valley (160 km2) by analyzing meteorological data from the synoptic to the local scale and by reproducing the flood peak with the hydrological model WaSiM-ETH (Water Flow and Balance Simulation Model). This in order to gain process understanding and to evaluate the predictability. The atmospheric drivers of this rain-on-snow flood were (i) sustained snowfall followed by (ii) the passage of an atmospheric river bringing warm and moist air towards the Alps. As a result, intensive rainfall (average of 100 mm day-1) was accompanied by a temperature increase that shifted the 0° line from 1500 to 3200 m a.s.l. (meters above sea level) in 24 h with a maximum increase of 9 K in 9 h. The south-facing slope of the valley received significantly more precipitation than the north-facing slope, leading to flooding only in tributaries along the south-facing slope. We hypothesized that the reason for this very local rainfall distribution was a cavity circulation combined with a seeder-feeder-cloud system enhancing local rainfall and snowmelt along the south-facing slope. By applying and considerably recalibrating the standard hydrological model setup, we proved that both latent and sensible heat fluxes were needed to reconstruct the snow cover dynamic, and that locally high-precipitation sums (160 mm in 12 h) were required to produce the estimated flood peak. However, to reproduce the rapid runoff responses during the event, we conceptually represent likely lateral flow dynamics within the snow cover causing the model to react "oversensitively" to meltwater. Driving the optimized model with COSMO (Consortium for Small-scale Modeling)-2 forecast data, we still failed to simulate the flood because COSMO-2 forecast data underestimated both the local precipitation peak and the temperature increase. Thus we conclude that this rain-on-snow flood was, in general, predictable, but requires a special hydrological model setup and extensive and locally precise meteorological input data. Although, this data quality may not be achieved with forecast data, an additional model with a specific rain-on-snow configuration can provide useful information when rain-on-snow events are likely to occur.

Daily ozone cycle in the stratosphere: global, regional and seasonal behaviour modelled with the Whole Atmosphere Community Climate Model

The Whole Atmosphere Community Climate Model (WACCM) is utilised to study the daily ozone cycle and underlying photochemical and dynamical processes. The analysis is focused on the daily ozone cycle in the middle stratosphere at 5 hPa where satellite-based trend estimates of stratospheric ozone are most biased by diurnal sampling effects and drifting satellite orbits. The simulated ozone cycle shows a minimum after sunrise and a maximum in the late afternoon. Further, a seasonal variation of the daily ozone cycle in the stratosphere was found. Depending on season and latitude, the peak-to-valley difference of the daily ozone cycle varies mostly between 3 and 5% (0.4 ppmv) with respect to the midnight ozone volume mixing ratio. The maximal variation of 15% (0.8 ppmv) is found at the polar circle in summer. The global pattern of the strength of the daily ozone cycle is mainly governed by the solar zenith angle and the sunshine duration. In addition, we find synoptic-scale variations in the strength of the daily ozone cycle. These variations are often anti-correlated to regional temperature anomalies and are due to the temperature dependence of the rate coefficients k2 and k3 of the Chapman cycle reactions. Further, the NOx catalytic cycle counteracts the accumulation of ozone during daytime and leads to an anti-correlation between anomalies in NOx and the strength of the daily ozone cycle. Similarly, ozone recombines with atomic oxygen which leads to an anti-correlation between anomalies in ozone abundance and the strength of the daily ozone cycle. At higher latitudes, an increase of the westerly (easterly) wind cause a decrease (increase) in the sunshine duration of an air parcel leading to a weaker (stronger) daily ozone cycle.

New Constraints on Dark Matter Effective Theories from Standard Model Loops

We consider an effective field theory for a gauge singlet Dirac dark matter particle interacting with the standard model fields via effective operators suppressed by the scale Λ≳1  TeV. We perform a systematic analysis of the leading loop contributions to spin-independent Dirac dark matter–nucleon scattering using renormalization group evolution between Λ and the low-energy scale probed by direct detection experiments. We find that electroweak interactions induce operator mixings such that operators that are naively velocity suppressed and spin dependent can actually contribute to spin-independent scattering. This allows us to put novel constraints on Wilson coefficients that were so far poorly bounded by direct detection. Constraints from current searches are already significantly stronger than LHC bounds, and will improve in the near future. Interestingly, the loop contribution we find is isospin violating even if the underlying theory is isospin conserving.

Reconstructing the subsurface ocean decadal variability using surface nudging in a perfect model framework

Initialising the ocean internal variability for decadal predictability studies is a new area of research and a variety of ad hoc methods are currently proposed. In this study, we explore how nudging with sea surface temperature (SST) and salinity (SSS) can reconstruct the threedimensional variability of the ocean in a perfect model framework. This approach builds on the hypothesis that oceanic processes themselves will transport the surface information into the ocean interior as seen in ocean-only simulations. Five nudged simulations are designed to reconstruct a 150 years ‘‘target’’ simulation, defined as a portion of a long control simulation. The nudged simulations differ by the variables restored to, SST or SST + SSS, and by the area where the nudging is applied. The strength of the heat flux feedback is diagnosed from observations and the restoring coefficients for SSS use the same time-scale. We observed that this choice prevents spurious convection at high latitudes and near sea-ice border when nudging both SST and SSS. In the tropics, nudging the SST is enough to reconstruct the tropical atmosphere circulation and the associated dynamical and thermodynamical impacts on the underlying ocean. In the tropical Pacific Ocean, the profiles for temperature show a significant correlation from the surface down to 2,000 m, due to dynamical adjustment of the isopycnals. At mid-tohigh latitudes, SSS nudging is required to reconstruct both the temperature and the salinity below the seasonal thermocline. This is particularly true in the North Atlantic where adding SSS nudging enables to reconstruct the deep convection regions of the target. By initiating a previously documented 20-year cycle of the model, the SST + SSS nudging is also able to reproduce most of the AMOC variations, a key source of decadal predictability. Reconstruction at depth does not significantly improve with amount of time spent nudging and the efficiency of the surface nudging rather depends on the period/events considered. The joint SST + SSS nudging applied verywhere is the most efficient approach. It ensures that the right water masses are formed at the right surface density, the subsequent circulation, subduction and deep convection further transporting them at depth. The results of this study underline the potential key role of SSS for decadal predictability and further make the case for sustained largescale observations of this field.

Sensitive Questions in Online Surveys: An Experimental Evaluation of the Randomized Response Technique and the Crosswise Model

Self-administered online surveys provide a higher level of privacy protection to respondents than surveys administered by an interviewer. Yet, studies indicate that asking sensitive questions is problematic also in self-administered surveys. Because respondents might not be willing to reveal the truth and provide answers that are subject to social desirability bias, the validity of prevalence estimates of sensitive behaviors from online surveys can be challenged. A well-known method to overcome these problems is the Randomized Response Technique (RRT). However, convincing evidence that the RRT provides more valid estimates than direct questioning in online surveys is still lacking. A new variant of the RRT called the Crosswise Model has recently been proposed to overcome some of the deficiencies of existing RRT designs. We therefore conducted an experimental study in which different implementations of the RRT, including two implementations of the crosswise model, were tested and compared to direct questioning. Our study is a large-scale online survey (N = 6,037) on sensitive behaviors by students such as cheating in exams and plagiarism. Results indicate that the crosswise-model RRT---unlike the other variants of RRT we evaluated---yields higher prevalence estimates of sensitive behaviors than direct questioning. Whether higher estimates are a sufficient condition for more valid results, however, remains questionable.

General distress, hopelessness—suicidal ideation and worrying in adolescence: Concurrent and predictive validity of a symptom-level bifactor model for clinical diagnoses

BACKGROUND: Clinical disorders often share common symptoms and aetiological factors. Bifactor models acknowledge the role of an underlying general distress component and more specific sub-domains of psychopathology which specify the unique components of disorders over and above a general factor. METHODS: A bifactor model jointly calibrated data on subjective distress from The Mood and Feelings Questionnaire and the Revised Children's Manifest Anxiety Scale. The bifactor model encompassed a general distress factor, and specific factors for (a) hopelessness-suicidal ideation, (b) generalised worrying and (c) restlessness-fatigue at age 14 which were related to lifetime clinical diagnoses established by interviews at ages 14 (concurrent validity) and current diagnoses at 17 years (predictive validity) in a British population sample of 1159 adolescents. RESULTS: Diagnostic interviews confirmed the validity of a symptom-level bifactor model. The underlying general distress factor was a powerful but non-specific predictor of affective, anxiety and behaviour disorders. The specific factors for hopelessness-suicidal ideation and generalised worrying contributed to predictive specificity. Hopelessness-suicidal ideation predicted concurrent and future affective disorder; generalised worrying predicted concurrent and future anxiety, specifically concurrent generalised anxiety disorders. Generalised worrying was negatively associated with behaviour disorders. LIMITATIONS: The analyses of gender differences and the prediction of specific disorders was limited due to a low frequency of disorders other than depression. CONCLUSIONS: The bifactor model was able to differentiate concurrent and predict future clinical diagnoses. This can inform the development of targeted as well as non-specific interventions for prevention and treatment of different disorders.

Neutrino physics with multi-ton scale liquid xenon detectors

We study the sensitivity of large-scale xenon detectors to low-energy solar neutrinos, to coherent neutrino-nucleus scattering and to neutrinoless double beta decay. As a concrete example, we consider the xenon part of the proposed DARWIN (Dark Matter WIMP Search with Noble Liquids) experiment. We perform detailed Monte Carlo simulations of the expected backgrounds, considering realistic energy resolutions and thresholds in the detector. In a low-energy window of 2–30 keV, where the sensitivity to solar pp and 7Be-neutrinos is highest, an integrated pp-neutrino rate of 5900 events can be reached in a fiducial mass of 14 tons of natural xenon, after 5 years of data. The pp-neutrino flux could thus be measured with a statistical uncertainty around 1%, reaching the precision of solar model predictions. These low-energy solar neutrinos will be the limiting background to the dark matter search channel for WIMP-nucleon cross sections below ~2X 10-48 cm2 and WIMP masses around 50 GeV c 2, for an assumed 99.5% rejection of electronic recoils due to elastic neutrino-electron scatters. Nuclear recoils from coherent scattering of solar neutrinos will limit the sensitivity to WIMP masses below ~6 GeV c-2 to cross sections above ~4X10-45cm2. DARWIN could reach a competitive half-life sensitivity of 5.6X1026 y to the neutrinoless double beta decay of 136Xe after 5 years of data, using 6 tons of natural xenon in the central detector region.