Changes in water quality following tidal inundation of coastal lowland acid sulfate soil landscapes

This study examines the remediation of surface water quality in a severely degraded coastal acid sulfate soil landscape. The remediation strategy consisted of partial restoration of marine tidal exchange within estuarine creeks and incremental tidal inundation of acidified soils, plus strategic liming of drainage waters. Time-series water quality and climatic data collected over 5 years were analysed to assess changes in water quality due to this remediation strategy. A time-weighted rainfall function (TWR) was generated from daily rainfall data to integrate the effects of antecedent rainfall on shallow groundwater levels in a way that was relevant to acid export dynamics. Significant increases in mean pH were evident over time at multiple monitoring sites. Regression analysis at multiple sites revealed a temporal progression of change in significant relationships between mean daily electrical conductivity (EC) vs. mean daily pH, and TWR vs. mean daily pH. These data demonstrate a substantial decrease over time in the magnitude of creek acidification per given quantity of antecedent rainfall. Data also show considerable increase in soil pH (2–3 units) in formerly acidified areas subject to tidal inundation. This coincides with a decrease in soil pe, indicating stronger reducing conditions. These observations suggest a fundamental shift has occurred in sediment geochemistry in favour of proton-consuming reductive processes. Combined, these data highlight the potential effectiveness of marine tidal inundation as a landscape-scale acid sulfate soil remediation strategy.     

A tool to navigate overlaps in fragmented ocean governance

Implementation of marine ecosystem-based management requires improved understanding of existing governance, including gaps and overlaps resulting from fragmented management. Focusing on overlaps, this paper presents a technique using text analysis to assist in the identification of agencies and laws involved in overlaps from a system perspective. The overlaps analysis uses term frequency counts on ocean laws and regulations in conjunction with relevant agency authority. Such information delivered in a transparent user-friendly presentation can help policymakers and other constituents of ecosystem-based management to find existing overlaps as a step to facilitate improved coordination.     

Nutrient transport from an artificial upwelling of deep sea water

The transport of nutrient-rich, deep sea water from an artificial upwelling pipe has been simulated. A numerical model has been built within a commercial Computational Fluid Dynamics (CFD) package. The model considers the flow of the deep sea water after it is ejected from the pipe outlet in a negatively buoyant plume (densimetric Froude number = −2.6), within a stably stratified ocean environment subject to strong ocean current cross flow. Two cross-flow profiles were tested with momentum flux ratios equal to 0.92 and 3.7. The standard k-ε turbulence model has been employed and a range of turbulent Schmidt and Prandtl numbers tested. In all cases the results show that the rapid diffusion of heat and salinity at the pipe outlet causes the plume to attain neutral buoyancy very rapidly, preventing strong fountain-like behavior. At the higher momentum flux ratio fountain-like behavior is more pronounced close to the pipe outlet. The strong cross-current makes horizontal advection the dominant transport process downstream. The nutrient plume trajectory remains largely within one relatively thin stratified layer, making any ocean cross-flow profile less important. Very little unsteady behavior was observed. The results show that the nutrient is reduced to less than 2% of its inlet concentration 10 meters downstream of the inlet and this result is largely independent of turbulent Prandtl or Schmidt number. Initial results would suggest that if such an artificial upwelling were to be viable for an ocean farming project, a large number of upwelling pipes would be necessary. Further work will have to determine the minimum nutrient concentration required to sustain a viable phytoplankton population and the required spacing between upwelling pipes.     

Jason Microwave Radiometer Performance and On-Orbit Calibration

Results are presented from the on-orbit calibration of the Jason Microwave Radiometer (JMR). The JMR brightness temperatures (TBs) are calibrated at the hottest and coldest ends of the instrument's dynamic range, using Amazon rain forest and vicarious cold on-Earth theoretical brightness temperature references. The retrieved path delay values are validated using collocated TOPEX Microwave Radiometer and Radiosonde Observation path delay (PD) values. Offsets of 1-4 K in the JMR TBs and 8-12 mm in the JMR PDs, relative to TMR measurements, were initially observed. There were also initial TB offsets of 2 K between the satellite's yaw state. The calibration was adjusted by tuning coefficients in the antenna temperature calibration algorithm and the antenna pattern correction algorithm. The calibrated path delay values are demonstrated to have no significant bias or scale errors with consistent performance in all nonprecipitating weather conditions. The uncertainty of the individual path delay measurements is estimated to be 0.74 cm ± 0.15, which exceeds the mission goal of 1.2 cm RMS.     

Testing and calibrating parametric wave transformation models on natural beaches

To provide coastal engineers and scientists with a detailed inter-comparison of widely used parametric wave transformation models, several models are tested and calibrated with extensive observations from six field experiments on barred and unbarred beaches. Using previously calibrated (“default”) values of a free parameter γ, all models predict the observations reasonably well (median root-mean-square wave height errors are between 10% and 20%) at all field sites. Model errors can be reduced by roughly 50% by tuning γ for each data record. No tuned or default model provides the best predictions for all data records or at all experiments. Tuned γ differ for the different models and experiments, but in all cases γ increases as the hyperbolic tangent of the deep-water wave height, H_o. Data from two experiments are used to estimate empirical, universal curves for γ based on H_o. Using the new parameterization, all models have similar accuracy, and usually show increased skill relative to using default γ.     

Climate change projections for the surface ocean around New Zealand

The future status of the surface ocean around New Zealand was projected using two Earth System Models and four emission scenarios. By 2100 mean changes are largest under Representative Concentration Pathway 8.5 (RCP8.5), with a +2.5°C increase in sea surface temperature, and decreases in surface mixed layer depth (15%), macronutrients (7.5–20%), primary production (4.5%) and particle flux (12%). Largest macronutrient declines occur in the eastern Chatham Rise and subantarctic waters to the south, whereas dissolved iron increases in subtropical waters. Surface pH projections, validated against subantarctic time-series data, indicate a 0.335 decline to ～7.77 by 2100. However, projected pH is sensitive to future CO₂ emissions, remaining within the current range under RCP2.6, but decreasing below it by 2040 with all other scenarios. Sub-regions vulnerable to climate change include the Chatham Rise, polar waters south of 50°S, and subtropical waters north of New Zealand, whereas the central Tasman Sea is least affected.     

Using seasonal hindcasts to understand the origin of the equatorial cold tongue bias in CGCMs and its impact on ENSO

The cold equatorial SST bias in the tropical Pacific that is persistent in many coupled OAGCMs severely impacts the fidelity of the simulated climate and variability in this key region, such as the ENSO phenomenon. The classical bias analysis in these models usually concentrates on multi-decadal to centennial time series needed to obtain statistically robust features. Yet, this strategy cannot fully explain how the models errors were generated in the first place. Here, we use seasonal re-forecasts (hindcasts) to track back the origin of this cold bias. As such hindcasts are initialized close to observations, the transient drift leading to the cold bias can be analyzed to distinguish pre-existing errors from errors responding to initial ones. A time sequence of processes involved in the advent of the final mean state errors can then be proposed. We apply this strategy to the ENSEMBLES-FP6 project multi-model hindcasts of the last decades. Four of the five AOGCMs develop a persistent equatorial cold tongue bias within a few months. The associated systematic errors are first assessed separately for the warm and cold ENSO phases. We find that the models are able to reproduce either El Niño or La Niña close to observations, but not both. ENSO composites then show that the spurious equatorial cooling is maximum for El Niño years for the February and August start dates. For these events and at this time of the year, zonal wind errors in the equatorial Pacific are present from the beginning of the simulation and are hypothesized to be at the origin of the equatorial cold bias, generating too strong upwelling conditions. The systematic underestimation of the mixed layer depth in several models can also amplify the growth of the SST bias. The seminal role of these zonal wind errors is further demonstrated by carrying out ocean-only experiments forced by the AOCGCMs daily 10-meter wind. In a case study, we show that for several models, this forcing is sufficient to reproduce the main SST error patterns seen after 1 month in the AOCGCM hindcasts.     

Diagnosis and testing of low-level cloud parameterizations for the NCEP/GFS model using satellite and ground-based measurements

The objective of this study is to investigate the quality of clouds simulated by the National Centers for Environmental Prediction global forecast system (GFS) model and to examine the causes for some systematic errors seen in the simulations through use of satellite and ground-based measurements. In general, clouds simulated by the GFS model had similar spatial patterns and seasonal trends as those retrieved from passive and active satellite sensors, but large systematic biases exist for certain cloud regimes especially underestimation of low-level marine stratocumulus clouds in the eastern Pacific and Atlantic oceans. This led to the overestimation (underestimation) of outgoing longwave (shortwave) fluxes at the top-of-atmosphere. While temperature profiles from the GFS model were comparable to those obtained from different observational sources, the GFS model overestimated the relative humidity field in the upper and lower troposphere. The cloud condensed water mixing ratio, which is a key input variable in the current GFS cloud scheme, was largely underestimated due presumably to excessive removal of cloud condensate water through strong turbulent diffusion and/or an improper boundary layer scheme. To circumvent the problem associated with modeled cloud mixing ratios, we tested an alternative cloud parameterization scheme that requires inputs of atmospheric dynamic and thermodynamic variables. Much closer agreements were reached in cloud amounts, especially for marine stratocumulus clouds. We also evaluate the impact of cloud overlap on cloud fraction by applying a linear combination of maximum and random overlap assumptions with a de-correlation length determined from satellite products. Significantly better improvements were found for high-level clouds than for low-level clouds, due to differences in the dominant cloud geometry between these two distinct cloud types.     

Regeneration in Gap Models: Priority Issues for Studying Forest Responses to Climate Change

Recruitment algorithms in forest gap models are examined withparticular regard to their suitability for simulating forestecosystem responses to a changing climate. The traditional formulation of recruitment is found limiting in three areas. First, the aggregation of different regeneration stages (seedproduction, dispersal, storage, germination and seedling establishment) is likely to result in less accurate predictionsof responses as compared to treating each stage separately. Second, the related assumptions that seeds of all species are uniformly available and that environmental conditions arehomogeneous, are likely to cause overestimates of future speciesdiversity and forest migration rates. Third, interactions between herbivores (ungulates and insect pests) and forest vegetation are a big unknown with potentially serious impactsin many regions. Possible strategies for developing better gapmodel representations for the climate-sensitive aspects of eachof these key areas are discussed. A working example of a relatively new model that addresses some of these limitations is also presented for each case. We conclude that better modelsof regeneration processes are desirable for predicting effectsof climate change, but that it is presently impossible to determine what improvements can be expected without carrying outrigorous tests for each new formulation.