Near-shore swell estimation from a global wind-wave model: Spectral process,linear, and artificial neural network models

Estimation of swell conditions in coastal regions is important for a variety of public, government, and research applications. Driving a model of the near-shore wave transformation from an offshore global swell model such as NOAA WaveWatch3 is an economical means to arrive at swell size estimates at particular locations of interest. Recently, some work (e.g. Browne et al. [Browne, M., Strauss, D., Castelle, B., Blumenstein, M., Tomlinson, R., 2006. Local swell estimation and prediction from a global wind-wave model. IEEE Geoscience and Remote Sensing Letters 3 (4), 462–466.]) has examined an artificial neural network (ANN) based, empirical approach to wave estimation. Here, we provide a comprehensive evaluation of two data driven approaches to estimating waves near-shore (linear and ANN), and also contrast these with a more traditional spectral wave simulation model (SWAN). Performance was assessed on data gathered from a total of 17 near-shore locations, with heterogenous geography and bathymetry, around the continent of Australia over a 7 month period. It was found that the ANNs out-performed SWAN and the non-linear architecture consistently out-performed the linear method. Variability in performance and differential performance with regard to geographical location could largely be explained in terms of the underlying complexity of the local wave transformation.     

The effects on water mass formation of surface mixed layer timedependence and entrainment fluxes

An air-sea buoyancy flux out of the ocean between the surface outcroppings of different isopycnals must be balanced by a convergence of advective and diffusive fluxes of buoyancy across those isopycnals (Walin, 1982; Tziperman, 1986; Garrett et al., 1995). For steady conditions, the diapycnal diffusive flux due to vertical mixing in the surface mixed layer is very small, so that the advective buoyancy flux dominates (Speer, 1993; Garrett et al., 1995). The associated advective buoyancy flux can then be used to estimate the volume flux of water out of the base of the surface mixed layer. The resulting thermodynamic algorithm provides a valuable estimate of water mass formation in the ocean.In contrast, for the time-dependent real ocean with horizontal and vertical gradients of the horizontal buoyancy gradient, diurnal and seasonal mixed layer deepening and entrainment in the presence of a buoyancy jump at the base of the mixed layer contributes to the annual volume flux out of the base of the deepest (wintertime) mixed layer. The mismatch between the predictions of the ideal algorithm and measured rates of water mass formation (Speer, 1997) may thus be partly due to mixed layer processes rather than diapycnal mixing in the thermocline.     

Meiofauna of the deep Eastern Mediterranean Sea: distribution and abundance in relation to bacterial biomass, organic matter composition and other environmental factors

Quantitative information on the abundance and biomass of metazoan meiofauna was obtained from samples collected at 15 deep-sea stations in the Eastern Mediterranean Sea (533–2400m). Meiofaunal abundance was compared to bacterial biomass and other environmental factors such as the total sedimentary organic matter content, the concentrations of the main biochemical classes of organic compounds (i.e. proteins, carbohydrates and lipids) and to ATP. To estimate the sedimentation potential of primary organic matter, sediment bound chloroplastic pigment equivalents (CPE) were assayed. Meiofaunal density was very low ranging from 4 ind.10cm⁻² (Station A4, 1658m depth) to 290 ind.10cm⁻² (Station A12, 636m depth). Nematodes were the numerically dominant taxon (68% of total meiofauna) and were usually confined to the top 6cm of the sediments. Total meiofaunal biomass ranged from 2.78μgC 10cm⁻² (Station A4) to 598.34μgC 10cm⁻² (Station 15A). There was a significant decrease in the density of metazoan meiofauna with water depth. Bacterial biomass largely dominated the total biomass (as the sum of bacterial and meiofaunal biomass) with an average of 73.2% and accounted for 35.8% of the living biomass (as ATP carbon) whereas meiofaunal biomass accounted only for 6.56%. Bacterial biomass was significantly related to the DNA concentrations of the sediment. A significant correlation between ATP concentration and CPE content was also found. No correlations were found between meiofauna, ATP and CPE, or between meiofauna and bacterial parameters. The significant relationship between meiofaunal density and the ratio of labile organic matter/total organic matter indicates that deep-sea meiofauna inhabiting an extremely oligotrophic environment (such as the Eastern Mediterranean) may be more nutritionally dependent upon the quality than on the quantity of sedimentary organic matter.     

Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models

Traditional approaches to develop 3D geological models employ a mix of quantitative and qualitative scientific techniques,which do not fully provide quantification of uncertainty in the constructed models and fail to optimally weight geological field observations against constraints from geophysical data.Here,using the Bayesian Obsidian software package,we develop a methodology to fuse lithostratigraphic field observations with aeromagnetic and gravity data to build a 3D model in a small(13.5 km×13.5 km)region of the Gascoyne Province,Western Australia.Our approach is validated by comparing 3D model results to independently-constrained geological maps and cross-sections produced by the Geological Survey of Western Australia.By fusing geological field data with aeromagnetic and gravity surveys,we show that 89%of the modelled region has>95%certainty for a particular geological unit for the given model and data.The boundaries between geological units are characterized by narrow regions with<95%certainty,which are typically 400-1000 m wide at the Earth's surface and 500-2000 m wide at depth.Beyond~4 km depth,the model requires geophysical survey data with longer wavelengths(e.g.,active seismic)to constrain the deeper subsurface.Although Obsidian was originally built for sedimentary basin problems,there is reasonable applicability to deformed terranes such as the Gascoyne Province.Ultimately,modification of the Bayesian engine to incorporate structural data will aid in developing more robust 3D models.Nevertheless,our results show that surface geological observations fused with geophysical survey data can yield reasonable 3D geological models with narrow uncertainty regions at the surface and shallow subsurface,which will be especially valuable for mineral exploration and the development of 3D geological models under cover.     

Dissecting indices of aridity for assessing the impacts of global climate change

There is great interest in understanding how climate change will impact aridity through the interaction of precipitation changes with rising temperatures. The Aridity Index (AI), Climatic Moisture Deficit (CMD), and Climatic Moisture Surplus (CMS) are metrics commonly used to quantify and map patterns in aridity and water cycling. Here we show that these metrics have different patterns of change under future climate—based on an ensemble of nine general circulation climate models—and the different metrics are appropriate for different purposes. Based on these differences between the metrics, we propose that aridity can be dissected into three different types—hydrological (CMS), agricultural (CMD), and meteorological. In doing this, we propose a novel modified version of the Aridity Index, called AI+, that can be useful for assessing changes in meteorological aridity. The AI?+?is based on the same ratio between precipitation and evapotranspiration as the traditional AI, but unlike the traditional AI, the AI?+?only accounts for changes to precipitation during months when precipitation is less than reference/potential evapotranspiration (i.e. there is a deficit). Moreover, we show that the traditional AI provides a better estimate of change in moisture surplus driven by changes to precipitation during the wet season, rather than changes in deficit that occur during the drier seasons. These results show that it is important to select the most appropriate metric for assessing climate driven changes in aridity.     

Identifying thresholds and barriers to adaptation through measuring climate sensitivity and capacity to change in an Australian primary industry

Primary producers, including graziers, crop farmers and commercial fishers are especially vulnerable to climate change because they depend on highly climate-sensitive natural resources. Adaptation to climate change will make a major difference to the severity of the impacts experienced. However, individuals (resource users) can erect sometimes seemingly peculiar barriers to potential adaptation options that need to be addressed if adaptation is to be effective. Our aim was to understand the nature of barriers to change for cattle graziers in the northern Australian rangelands. We conceptualised barriers as adverse reactions where resource users are unlikely to contemplate adaptations that threaten core values or perceptions about themselves. We assumed that resource users that were more sensitive to climate change impacts—or more dependent on the resource—were more proximate to thresholds of coping and thus more likely to erect barriers, especially people with little adaptive capacity. Given that climate sensitivity and adaptive capacity are important components of vulnerability, our approach was to conduct a vulnerability assessment to identify potential but important barriers to change. Data from 240 graziers suggest that graziers in northern Australia might be especially vulnerable to climate change because their identity, place attachment, low employability, weak networks and dependents can make them sensitive to change, and their sensitivity can be compounded by a low adaptive capacity. We argue that greater attention needs to be placed on the social context of climate change impacts and on the processes shaping vulnerability and adaptation, especially at the scale of the individual.     

The regional forcing of Northern hemisphere drought during recent warm tropical west Pacific Ocean La Niña events

Northern Hemisphere circulations differ considerably between individual El Niño-Southern Oscillation events due to internal atmospheric variability and variation in the zonal location of sea surface temperature forcing over the tropical Pacific Ocean. This study examines the similarities between recent Northern Hemisphere droughts associated with La Niña events and anomalously warm tropical west Pacific sea surface temperatures during 1988–1989, 1998–2000, 2007–2008 and 2010–2011 in terms of the hemispheric-scale circulations and the regional forcing of precipitation over North America and Asia during the cold season of November through April. The continental precipitation reductions associated with recent central Pacific La Niña events were most severe over North America, eastern Africa, the Middle East and southwest Asia. High pressure dominated the entire Northern Hemisphere mid-latitudes and weakened and displaced storm tracks northward over North America into central Canada. Regionally over North America and Asia, the position of anomalous circulations within the zonal band of mid-latitude high pressure varied between each La Niña event. Over the northwestern and southeastern United States and southern Asia, the interactions of anomalous circulations resulted in consistent regional temperature advection, which was subsequently balanced by similar precipitation-modifying vertical motions. Over the central and northeastern United States, the spatial variation of anomalous circulations resulted in modest inter-seasonal temperature advection variations, which were balanced by varying vertical motion and precipitation patterns. Over the Middle East and eastern Africa, the divergence of moisture and the advection of dry air due to anomalous circulations enhanced each of the droughts.     

Diagnosis of the marine low cloud simulation in the NCAR community earth system model (CESM) and the NCEP global forecast system (GFS)-modular ocean model v4 (MOM4) coupled model

We present a diagnostic analysis of the marine low cloud climatology simulated by two state-of-the-art coupled atmosphere–ocean models: the National Center for Atmospheric Research community earth system model version 1 (CESM1) and the National Center for Environmental Predictions global forecasting system-modular ocean model version 4 (GFS-MOM4) coupled model. In the CESM1, the coastal stratocumulus (Sc)-topped planetary boundary layers (PBLs) in the subtropical Eastern Pacific are well-simulated but the climatological transition from Sc to shallow cumulus (Cu) is too abrupt and occurs too close to the coast. By contrast, in the GFS-MOM4 the coastal Sc amount and PBL depth are severely underestimated while the transition from Sc to shallow Cu is “delayed” and offshore Sc cover is too extensive in the subtropical Eastern Pacific. We discuss the possible connections between these differences in the simulations and differences in the parameterizations of shallow convection and boundary layer turbulence in the two models.     

Evaluating Weather Research and Forecasting Model Sensitivity to Land and Soil Conditions Representative of Karst Landscapes

Due to their particular physiographic, geomorphic, soil cover, and complex surface-subsurface hydrologic conditions, karst regions produce distinct land–atmosphere interactions. It has been found that floods and droughts over karst regions can be more pronounced than those in non-karst regions following a given rainfall event. Five convective weather events are simulated using the Weather Research and Forecasting model to explore the potential impacts of land-surface conditions on weather simulations over karst regions. Since no existing weather or climate model has the ability to represent karst landscapes, simulation experiments in this exploratory study consist of a control (default land-cover/soil types) and three land-surface conditions, including barren ground, forest, and sandy soils over the karst areas, which mimic certain karst characteristics. Results from sensitivity experiments are compared with the control simulation, as well as with the National Centers for Environmental Prediction multi-sensor precipitation analysis Stage-IV data, and near-surface atmospheric observations. Mesoscale features of surface energy partition, surface water and energy exchange, the resulting surface-air temperature and humidity, and low-level instability and convective energy are analyzed to investigate the potential land-surface impact on weather over karst regions. We conclude that: (1) barren ground used over karst regions has a pronounced effect on the overall simulation of precipitation. Barren ground provides the overall lowest root-mean-square errors and bias scores in precipitation over the peak-rain periods. Contingency table-based equitable threat and frequency bias scores suggest that the barren and forest experiments are more successful in simulating light to moderate rainfall. Variables dependent on local surface conditions show stronger contrasts between karst and non-karst regions than variables dominated by large-scale synoptic systems; (2) significant sensitivity responses are found over the karst regions, including pronounced warming and cooling effects on the near-surface atmosphere from barren and forested land cover, respectively; (3) the barren ground in the karst regions provides conditions favourable for convective development under certain conditions. Therefore, it is suggested that karst and non-karst landscapes should be distinguished, and their physical processes should be considered for future model development.     

La Niña diversity and Northwest Indian Ocean Rim teleconnections

The differences in tropical Pacific sea surface temperature (SST) expressions of El Niño-Southern Oscillation (ENSO) events of the same phase have been linked with different global atmospheric circulation patterns. This study examines the dynamical forcing of precipitation during October–December (OND) and March–May (MAM) over East Africa and during December–March (DJFM) over Central-Southwest Asia for 1950–2010 associated with four tropical Pacific SST patterns characteristic of La Niña events, the cold phase of ENSO. The self-organizing map method along with a statistical distinguishability test was used to isolate La Niña events, and seasonal precipitation forcing was investigated in terms of the tropical overturning circulation and thermodynamic and moisture budgets. Recent La Niña events with strong opposing SST anomalies between the central and western Pacific Ocean (phases 3 and 4), force the strongest global circulation modifications and drought over the Northwest Indian Ocean Rim. Over East Africa during MAM and OND, subsidence is forced by an enhanced tropical overturning circulation and precipitation reductions are exacerbated by increases in moisture flux divergence. Over Central-Southwest Asia during DJFM, the thermodynamic forcing of subsidence is primarily responsible for precipitation reductions, with moisture flux divergence acting as a secondary mechanism to reduce precipitation. Eastern Pacific La Niña events in the absence of west Pacific SST anomalies (phases 1 and 2), are associated with weaker global teleconnections, particularly over the Indian Ocean Rim. The weak regional teleconnections result in statistically insignificant precipitation modifications over East Africa and Central-Southwest Asia.