Fingerprinting of the Atlantic meridional overturning circulation in climate models to aid in the design of proxy investigations

It is desirable to design proxy investigations that target regions where properties reconstructed from calibrated parameters potentially carry high-fidelity information concerning changes in large-scale climate systems. Numerical climate models can play an important role in this task, producing simulations that can be analyzed to produce spatial “fingerprints” of the expected response of various properties under a variety of different scenarios. We will introduce a new method of fingerprinting the Atlantic meridional overturning circulation (AMOC) that not only provides information concerning the sensitivity of the response at a given location to changes in the large-scale system, but also quantifies the linearity, monotonicity and symmetry of the response. In this way, locations that show high sensitivities to changes in the AMOC, but that exhibit, for example, strongly nonlinear behavior can be avoided during proxy investigations. To demonstrate the proposed approach we will use the example of the response of seawater temperatures to changes in the strength of the AMOC. We present results from an earth-system climate model which has been perturbed with an idealized freshwater forcing scenario in order to reduce the strength of the AMOC in a systematic manner. The seawater temperature anomalies that result from the freshwater forcing are quantified in terms of their sensitivity to the AMOC strength in addition to the linearity and monotonicity of their response. A first-order reversal curve (FORC) approach is employed to investigate and quantify the irreversibility of the temperature response to a slowing and recovering AMOC. Thus, FORCs allow the identification of areas that are unsuitable for proxy reconstructions because their temperature versus AMOC relationship lacks symmetry.     

Multi-Source Emission Determination Using an Inverse-Dispersion Technique

Inverse-dispersion calculations can be used to infer atmospheric emission rates through a combination of downwind gas concentrations and dispersion model predictions. With multiple concentration sensors downwind of a compound source (whose component positions are known) it is possible to calculate the component emissions. With this in mind, a field experiment was conducted to examine the feasibility of such multi-source inferences, using four synthetic area sources and eight concentration sensors arranged in different configurations. Multi-source problems tend to be mathematically ill-conditioned, as expressed by the condition number κ. In our most successful configuration (average κ = 4.2) the total emissions from all sources were deduced to within 10% on average, while component emissions were deduced to within 50%. In our least successful configuration (average κ = 91) the total emissions were calculated to within only 50%, and component calculations were highly inaccurate. Our study indicates that the most accurate multi-source inferences will occur if each sensor is influenced by only a single source. A “progressive” layout is the next best: one sensor is positioned to “see” only one source, the next sensor is placed to see the first source and another, a third sensor is placed to see the previous two plus a third, and so on. When it is not possible to isolate any sources κ is large and the accuracy of a multi-source inference is doubtful.     

The Delphi technique and judgmental forecasting

The Delphi technique for judgmental forecasting by expert groups is described and the controversy surrounding its use is summarized. The technique clearly does not eliminate all unwanted psychological effects on group judgment. Furthermore, the design of most Delphi studies makes it impossible to separate the signal from the noise in expert judgment. A methodological standard for evaluating judgmental forecasts is proposed.     

High-resolution climate simulation of the last glacial maximum

The climate of the last glacial maximum (LGM) is simulated with a high-resolution atmospheric general circulation model, the NCAR CCM3 at spectral truncation of T170, corresponding to a grid cell size of roughly 75 km. The purpose of the study is to assess whether there are significant benefits from the higher resolution simulation compared to the lower resolution simulation associated with the role of topography. The LGM simulations were forced with modified CLIMAP sea ice distribution and sea surface temperatures (SST) reduced by 1°C, ice sheet topography, reduced CO₂, and 21,000 BP orbital parameters. The high-resolution model captures modern climate reasonably well, in particular the distribution of heavy precipitation in the tropical Pacific. For the ice age case, surface temperature simulated by the high-resolution model agrees better with those of proxy estimates than does the low-resolution model. Despite the fact that tropical SSTs were only 2.1°C less than the control run, there are many lowland tropical land areas 4–6°C colder than present. Comparison of T170 model results with the best constrained proxy temperature estimates (noble gas concentrations in groundwater) now yield no significant differences between model and observations. There are also significant upland temperature changes in the best resolved tropical mountain belt (the Andes). We provisionally attribute this result in part as resulting from decreased lateral mixing between ocean and land in a model with more model grid cells. A longstanding model-data discrepancy therefore appears to be resolved without invoking any unusual model physics. The response of the Asian summer monsoon can also be more clearly linked to local geography in the high-resolution model than in the low-resolution model; this distinction should enable more confident validation of climate proxy data with the high-resolution model. Elsewhere, an inferred salinity increase in the subtropical North Atlantic may have significant implications for ocean circulation changes during the LGM. A large part of the Amazon and Congo Basins are simulated to be substantially drier in the ice age—consistent with many (but not all) paleo data. These results suggest that there are considerable benefits derived from high-resolution model regarding regional climate responses, and that observationalists can now compare their results with models that resolve geography at a resolution comparable to that which the proxy data represent.     

Species richness,diversity and density of understory vegetation along disturbance gradients in the Himalayan conifer forest

We investigated whether species richness, diversity and density of understory herbaceous plants differed along logging(gap) and grazing(primarily by cattle) disturbance gradients, and sought to identify drivers of richness, diversity and density of understory vegetation of logged sites. A factorial experiment was conducted in the mixed conifer forest of Gidakom in Western Bhutan. Levels of the logging treatment included small(0.15 – 0.24 ha), medium(0.25 – 0.35 ha) and large(0.36 – 1.31 ha) gaps. The grazing treatment included grazed(primarily by cattle) and ungrazed(where herbivores were excluded by a fence) plots nested within each gap. Data were collected from 12 gaps(4 replicates at each level of logging) using the point intercept method. Shannon Weaver Diversity and Margalef's indices were used to estimate species diversity and describe species richness, respectively. Soil samples were analyzed for pH and nutrients. The interaction effect of logging and grazing was significant(p≤0.001) only on species diversity. Relative to ungrazed areas, species diversity was significantly higher(0.01≤p≤0.05) in medium grazed gaps. Under grazed conditions, soil P was negatively correlated with gap size and species diversity. While species diversity was positivelycorrelated(0.01≤p≤0.05) with soil N in grazed plots species richness was positively correlated(0.001≤p≤0.01) with soil N in ungrazed plots. Relative density of Yushania microphylla and Carex nubigena were higher under ungrazed conditions. Our study suggests that the combined effect of cattle grazing and logging results in higher species diversity of understory vegetation in medium and grazed gaps in mixed conifer forests of Bhutan,whereas increase or decrease in relative density of major species is determined primarily by the independent effects of grazing and logging. From management perspective, forest managers must refrain from creating large gaps to avoid loss of nutrients(mainly P and N), which may eventually affect tree regeneration. Managers intending to maintain understory vegetation diversity must consider the combined effects of grazing and logging, ensuring low to moderate grazing pressure.     

Integrated and Participatory Research Approaches towards Sustainable Livelihoods and Ecosystems in Mountainous Regions

Mountainous regions cover about 27 per cent of the world’s land surface and are home to some 22 per cent of the global population (UNEP 2002). A much greater number of people depend on mountain environments for a wide range of services, including clean water, energy, timber, biodiversity, recreation, and protection from environmental hazards, such as landslides and floods. At the same time, mountain areas are extremely vulnerable ecosystems and under continuous threat of environmental degrad…     

Detection and extraction of signals from the epoch of reionization using higher-order one-point statistics

Detecting redshifted 21-cm emission from neutral hydrogen in the early Universe promises to give direct constraints on the epoch of reionization (EoR). It will, though, be very challenging to extract the cosmological signal (CS) from foregrounds and noise which are orders of magnitude larger. Fortunately, the signal has some characteristics which differentiate it from the foregrounds and noise, and we suggest that using the correct statistics may tease out signatures of reionization. We generate mock data cubes simulating the output of the Low Frequency Array (LOFAR) EoR experiment. These cubes combine realistic models for Galactic and extragalactic foregrounds and the noise with three different simulations of the CS. We fit out the foregrounds, which are smooth in the frequency direction, to produce residual images in each frequency band. We denoise these images and study the skewness of the one-point distribution in the images as a function of frequency. We find that, under sufficiently optimistic assumptions, we can recover the main features of the redshift evolution of the skewness in the 21-cm signal. We argue that some of these features – such as a dip at the onset of reionization, followed by a rise towards its later stages – may be generic, and give us a promising route to a statistical detection of reionization.     

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while one-third of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.     

Resistivity of reservoir sandstones and organic rich shales on the Barents Shelf:Implications for interpreting CSEM data

Marine controlled source electromagnetic(CSEM)data have been utilized in the past decade during petroleum exploration of the Barents Shelf,particularly for de-risking the highly porous sandstone reservoirs of the Upper Triassic to Middle Jurassic Realgrunnen Subgroup.In this contribution we compare the resistivity response from CSEM data to resistivity from wireline logs in both water-and hydrocarbon-bearing wells.We show that there is a very good match between these types of data,particularly when reservoirs are shallow.CSEM data,however,only provide information on the subsurface resistivity.Careful,geology-driven interpretation of CSEM data is required to maximize the impact on exploration success.This is particularly important when quantifying the relative re-sistivity contribution of high-saturation hydrocarbon-bearing sandstone and that of the overlying cap rock.In the presented case the cap rock comprises predominantly organic rich Upper Jurassic-Early Cretaceous shales of the Hekkingen Formation(i.e.a regional source rock).The resistivity response of the reservoir and its cap rock become merged in CSEM data due to the transverse resistance equivalence principle.As a result of this,it is imperative to understand both the relative contributions from reservoir and cap rock,and the geological sig-nificance of any lateral resistivity variation in each of the units.In this contribution,we quantify the resistivity of organic rich mudstone,i.e.source rock,and reservoir sandstones,using 131 exploration boreholes from the Barents Shelf.The highest resistivity(＞10,000 Ωm)is evident in the hydrocarbon-bearing Realgrunnen Subgroup which is reported from 48 boreholes,43 of which are used for this study.Pay zone resistivity is primarily controlled by reservoir quality(i.e.porosity and shale fraction)and fluid phase(i.e.gas,oil and water saturation).In the investigated wells,the shale dominated Hekkingen Formation exhibits enhanced resistivity compared to the background(i.e.the underlying and overlying stratigraphy),though rarely exceeds 20Ωm.Marine mudstones typically show good correlation between measured organic richness and resistivity/sonic velocity log signatures.We conclude that the resistivity contribution to the CSEM response from hydrocarbon-bearing sandstones out-weighs that of the organic rich cap rocks.