Global mean surface temperature (GMST) fluctuates over decadal to multidecadal time scales. Patterns of internal variability are partly responsible, but the relationships can be conflated by anthropogenically forced signals. Here we adopt a physically based method of separating internal variability from forced responses to examine how trends in large‐scale patterns, specifically the Interdecadal Pacific Oscillation (IPO) and Atlantic Multidecadal Variability (AMV), influence GMST. After removing the forced responses, observed variability of GMST is close to the central estimates of Coupled Model Intercomparison Project phase 5 simulations, but models tend to underestimate IPO variability at time scales >10 years, and AMV at time scales >20 years. Correlations between GMST trends and these patterns are also underrepresented, most strongly at 10‐ and 35‐year time scales, for IPO and AMV, respectively. Strikingly, models that simulate stronger variability of IPO and AMV also exhibit stronger relationships between these patterns and GMST, predominately at the 10‐ and 35‐year time scales, respectively.

This paper presents a new analytic approach for estimating the local power coefficient of a turbine experiencing anisotropic local blockage effects. Data-driven methods are employed first to approximately obtain a known analytic expression for isotropic local blockage effects, and then deployed to find candidate expressions for anisotropic local blockage effects. The dataset for the analysis of anisotropic local blockage is collected from 3D Reynolds-averaged Navier-Stokes (RANS) simulations of an infinitely wide array of actuator disks for nearly 2,000 different blockage configurations. This study builds upon previous work in array optimisation of both tidal and wind turbines, where the local power coefficient may increase substantially for optimal array configuration. A brief discussion on the relationship between the local blockage and wind farm blockage is also provided. Other theoretical approaches and possible refinements to the presented analytic model are also discussed.

Predicting the performance of large turbine arrays requires the understanding of many physical factors, such as array geometry, turbine operation, inflow conditions and turbulent wake mixing. Due to the large parameter space that an array may be optimised over, low-order models with low computational cost are often employed. This paper extends one of these models, the inviscid–viscous coupled model, for multi-row turbine modelling. Firstly, an extension to the inviscid actuator disc theory is presented by removing the limit on the number of discrete streamtubes computed. The extended model allows for the quantification of the impact of shear in the bypass and core flows separately. In particular, it is shown that averaging a sheared bypass flow profile can result in a substantial over-prediction of the power of a turbine in a laterally bounded flow as the effective blockage of the flow increases. The model is also used to confirm that an approximation using a limited number of streamtubes in some previous applications of the inviscid–viscous approach has a negligible impact on the results. Secondly, we explore the performance of a multi-row array with either uniform or varying turbine resistance across different rows. Results suggest that by varying resistance across rows, the array may outperform the uniform resistance case. The performance gain is dependent, however, on the arrangement and inter-turbine spacing both in the spanwise and streamwise directions.