Impacts of accelerating deployment of offshore windfarms on near-surface climate

The European Union has set the ambitious goal of becoming climate neutral by 2050, which has stimulated renewable energy production and accelerated the deployment of offshore wind energy in the North Sea. Here, a high-resolution regional climate model was used to investigate the impact on the sea surface climate of large-scale offshore wind farms that are proposed for the North Sea. The results show a significant reduction in the air-sea heat fluxes and a local, annual mean net cooling of the lower atmosphere in the wind farm areas down to more than 2.0 Wm^-2, due to a decrease in 10 m wind speed and turbulent kinetic energy and an increase in low-level clouds. Mean surface winds decreased by approximately 1 ms^-1 downstream of wind farms. Furthermore, an increase of approximately 5% in mean precipitation was found over the wind farm areas. At a seasonal timescale, these differences are higher during winter and autumn than in other seasons. Although the offshore wind farms reduce the heat transport from the ocean to the atmosphere in the region of large wind farms, the atmospheric layers below the hub height show an increase in temperature, which is on the order of up to 10% of the climate change signal at the end of the century, but it is much smaller than the interannual climate variability. In contrast, wind speed changes are larger than projected mean wind speed changes due to climate change. Our results suggest that the impacts of large clustered offshore wind farms should be considered in climate change impact studies. Moreover, the identified offshore windfarm impacts on the sea surface climate and the introduced spatial pattern in atmospheric conditions, in particular the modeled wind speed changes, suggest potential impacts on local ocean dynamics and the structure of the marine ecosystem. This should be considered in future scenarios for the North Sea marine environment and taken into account as a structuring influence in the offshore environment.

A Satellite-Observed Substantial Decrease in Multiyear Ice Area Export through the Fram Strait over the Last Decade

Revealing the changes in the Fram Strait (FS) multiyear ice (MYI) export is crucial due to their climate relevance in the context of the loss rate of MYI being faster than that of the total ice in the Arctic. Here, we estimated winter (October–April) MYI area export through the FS over the last 2 decades by using updated MYI concentration data retrieved from active and passive microwave satellite observations. We divided the period into two regimes relative to the ice index: D1 (2002/03–2010/11) and D2 (2012/13–2019/20). The observed variations of winter MYI exports D2 were compared with those of the previous decade D1. The results show that the MYI area exports display strong interannual variability. A significant decrease in MYI export for the periods between D1 and D2 is noted. On average, the wintertime MYI area exports declined sharply by 22% from 3.82 × 105 km2 in D1 to 3.00 × 105 km2 in D2. In addition, the percentage of MYI in the total sea ice outflow through the FS (PCM) also decreased distinctly from 72% in D1 to 59% in D2. Statistics show that weekly sea ice drift across the strait can explain 76% of the MYI area export variability. Furthermore, the dominant atmospheric drivers contributing to the decline in MYI area export during D2 were examined. In the last decade (D2), the strengthened low pressure in the North Atlantic sector, combined with an eastward shift in the axis of dipole anomaly (DA), resulted in reduced MYI advection from the Beaufort Sea and Siberian Coast toward the FS. Moreover, weakened cyclonic activity south of the FS also contributed to the reduction in MYI export during D2.

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets

Landscape attributes that vary with microtopography, such as active layer thickness (ALT), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km² area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r² = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT, consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.