Effect of Conditioning Randomly Heterogeneous Transmissivity on Temporal Hydraulic Head Measurements in Transient Two-Dimensional Aquifer Flow

We investigated the effect of conditioning transient, two-dimensional groundwater flow simulations, where the transmissivity was a spatial random field, on time dependent head data. The random fields, representing perturbations in log transmissivity, were generated using a known covariance function and then conditioned to match head data by iteratively cokriging and solving the flow model numerically. A new approximation to the cross-covariance of log transmissivity perturbations with time dependent head data and head data at different times, that greatly increased the computational efficiency, was introduced. The most noticeable effect of head data on the estimation of head and log transmissivity perturbations occurred from conditioning only on spatially distributed head measurements during steady flow. The additional improvement in the estimation of the log transmissivity and head perturbations obtained by conditioning on time dependent head data was fairly small. On the other hand, conditioning on temporal head data had a significant effect on particle tracks and reduced the lateral spreading around the center of the paths.     

Effect of Conditioning Randomly Heterogeneous Transmissivity on Temporal Hydraulic Head Measurements in Transient Two-Dimensional Aquifer Flow

We investigated the effect of conditioning transient, two-dimensional groundwater flow simulations, where the transmissivity was a spatial random field, on time dependent head data. The random fields, representing perturbations in log transmissivity, were generated using a known covariance function and then conditioned to match head data by iteratively cokriging and solving the flow model numerically. A new approximation to the cross-covariance of log transmissivity perturbations with time dependent head data and head data at different times, that greatly increased the computational efficiency, was introduced. The most noticeable effect of head data on the estimation of head and log transmissivity perturbations occurred from conditioning only on spatially distributed head measurements during steady flow. The additional improvement in the estimation of the log transmissivity and head perturbations obtained by conditioning on time dependent head data was fairly small. On the other hand, conditioning on temporal head data had a significant effect on particle tracks and reduced the lateral spreading around the center of the paths.     

Sensitivity of modern climate to the presence,strength and salinity of Mediterranean-Atlantic exchange in a global general circulation model

Mediterranean Outflow Water (MOW) is thought to be a key contributor to the strength and stability of Atlantic Meridional Overturning Circulation (AMOC), but the future of Mediterranean-Atlantic water exchange is uncertain. It is chiefly dependent on the difference between Mediterranean and Atlantic temperature and salinity characteristics, and as a semi-enclosed basin, the Mediterranean is particularly vulnerable to future changes in climate and water usage. Certainly, there is strong geologic evidence that the Mediterranean underwent dramatic salinity and sea-level fluctuations in the past. Here, we use a fully coupled atmosphere–ocean General Circulation Model to examine the impact of changes in Mediterranean-Atlantic exchange on global ocean circulation and climate. Our results suggest that MOW strengthens and possibly stabilises the AMOC not through any contribution towards NADW formation, but by delivering relatively warm, saline water to southbound Atlantic currents below 800 m. However, we find almost no climate signal associated with changes in Mediterranean-Atlantic flow strength. Mediterranean salinity, on the other hand, controls MOW buoyancy in the Atlantic and therefore affects its interaction with the shallow-intermediate circulation patterns that govern surface climate. Changing Mediterranean salinity by a factor of two reorganises shallow North Atlantic circulation, resulting in regional climate anomalies in the North Atlantic, Labrador and Greenland-Iceland-Norwegian Seas of ±4 °C or more. Although such major variations in salinity are believed to have occurred in the past, they are unlikely to occur in the near future. However, our work does suggest that changes in the Mediterranean’s hydrological balance can impact global-scale climate.     

Transverse Dispersion of a Kinetically Sorbing Solute

A recursion formulation for the transverse spreading of a solute is developed, and under conditions of steady flow in a stratified aquifer, the transport of a linearly sorbing solute undergoing nonequilibrium sorption is studied. The effect of spatial variability in the velocity field and the sorption kinetics are modeled to see the combined effect of the two processes on the spreading of the solute injected at a point in the aquifer. The main result of this work is a transport model based on a discrete formulation that includes local dispersion and leads to nonasymptotic behavior in the spreading of the plume in a direction normal to the mean flow velocity.     

NIST RM 8301 Boron Isotopes in Marine Carbonate (Simulated Coral and Foraminifera Solutions): Inter‐laboratory δ11B and Trace Element Ratio Value Assignment

The boron isotopic ratio of ¹¹B/¹⁰B (δ¹¹B_SRM951) and trace element composition of marine carbonates are key proxies for understanding carbon cycling (pH) and palaeoceanographic change. However, method validation and comparability of results between laboratories requires carbonate reference materials. Here, we report results of an inter‐laboratory comparison study to both assign δ¹¹B_SRM951 and trace element compositions to new synthetic marine carbonate reference materials (RMs), NIST RM 8301 (Coral) and NIST RM 8301 (Foram) and to assess the variance of data among laboratories. Non‐certified reference values and expanded 95% uncertainties for δ¹¹B_SRM951 in NIST RM 8301 (Coral) (+24.17‰ ± 0.18‰) and NIST RM 8301 (Foram) (+14.51‰ ± 0.17‰) solutions were assigned by consensus approach using inter‐laboratory data. Differences reported among laboratories were considerably smaller than some previous inter‐laboratory comparisons, yet discrepancies could still lead to large differences in calculated seawater pH. Similarly, variability in reported trace element information among laboratories (e.g., Mg/Ca ± 5% RSD) was often greater than within a single laboratory (e.g., Mg/Ca < 2%). Such differences potentially alter proxy‐reconstructed seawater temperature by more than 2 °C. These now well‐characterised solutions are useful reference materials to help the palaeoceanographic community build a comprehensive view of past ocean changes.     

Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM

A new parametric bias correction method for precipitation with an extension for extreme values is compared to an empirical and an existing parametric method. The bias corrections are applied to the regional climate model COSMO-CLM (consortium for small-scale modelling – climate limited area modelling) with a resolution of 4.5 km for the time periods 1991–2000 and 2091–2100. In addition to a comparison in a cross-validation framework, a focus is laid on the investigation of extreme value correction and the effect of the bias correction on the climate change signal. According to the statistical methods used in this study, it was found that the empirical method outperforms both parametric alternatives. However, due to the limited length of the available time series, some outliers occurred, and all methods had problems correcting extreme values. The climate change signal is moderately influenced by all three methods, and the power of climate change detection is reduced. The largest effect was found for the number of dry days and the mean daily intensity, which are considerably altered after correction.     

Joint conditional simulations and the spectral approach for flow modeling

The use of data to condition single random fields has a well-established history. However, the joint use of data from several cross-correlated random fields is not as well developed. For example, the use of both transmissivity and head data in a steady state 2-d stochastic flow problem is essentially an inverse problem that is very important for both flow and transport predictions. This problem is addressed here by using a combination of numerical simulation and analytical methods and its application illustrated. The type of information conveyed by the different data categories is explored. The results presented are especially interesting in that head and transmissivity each give different information: Head values would appear to constrain the geometry of the paths while transmissivity data yields information about travel times. The linearized model is expanded to an iterative procedure and the true conditional distribution at several locations is compared with the iterative solution.The problem mentioned above is one with a special transfer function specified by the flow equation. In the second part of the paper a Fast Fourier Transform method for generation and conditioning of two or more random fields is introduced. This procedure is simple to implement, fast and very flexible.     

Joint conditional simulations and the spectral approach for flow modeling

The use of data to condition single random fields has a well-established history. However, the joint use of data from several cross-correlated random fields is not as well developed. For example, the use of both transmissivity and head data in a steady state 2-d stochastic flow problem is essentially an inverse problem that is very important for both flow and transport predictions. This problem is addressed here by using a combination of numerical simulation and analytical methods and its application illustrated. The type of information conveyed by the different data categories is explored. The results presented are especially interesting in that head and transmissivity each give different information: Head values would appear to constrain the geometry of the paths while transmissivity data yields information about travel times. The linearized model is expanded to an iterative procedure and the true conditional distribution at several locations is compared with the iterative solution.The problem mentioned above is one with a special transfer function specified by the flow equation. In the second part of the paper a Fast Fourier Transform method for generation and conditioning of two or more random fields is introduced. This procedure is simple to implement, fast and very flexible.