Abstract. We propose that considering mesoscale wind direction changes in the computation of wind farm cluster wakes could reduce the uncertainty of engineering wake modelling tools. The relevance of mesoscale wind direction changes is investigated using a wind climatology of the German Bight area covering 30 years, derived from the New European Wind Atlas (NEWA). Furthermore, we present a new solution for engineering modelling tools that accounts for the effect of such changes on the propagation of cluster wakes. Mesoscale wind direction changes are found to exceed 7° per 100 km in 50 % of all cases and are particularly large in the lower partial load range, which is associated with strong wake formation. Here, the quartiles reach up to 20° per 100 km. Especially on a horizontal scale of several tens to a hundred kilometers, wind direction changes are relevant. Both the temporal and spatial scale at which large wind direction changes occur depend on the presence of pressure systems. Furthermore, atmospheric conditions which promote far-reaching wakes were found to align with a strong turning in 14.6 % of the cases. In order to capture these mesoscale wind direction changes in engineering model tools, a wake propagation model was implemented into the Fraunhofer IWES wind farm and wake modelling software flappy. The propagation model derives streamlines from the horizontal velocity field and forces the single turbine wakes along these streamlines. This model has been qualitatively evaluated by simulating the flow around wind farm clusters in the German Bight with data from the mesoscale atlas of NEWA and comparing the results to Synthetic Aperture Radar (SAR) measurements for selected situations. The comparison reveals that the flow patterns are in good agreement if the underlying mesoscale data capture the velocity field well. For such cases, the new model provides an improvement compared to the baseline approach of engineering models, which assumes a straight-line propagation of wakes. The streamline and the baseline model have been further compared in terms of their quantitative effect on the energy yield. Simulating two neighbouring wind farm clusters over a time period of 10 years, it is found that there are no significant differences across the models when computing the total energy yield of both clusters. However, extracting the wake effect of one cluster on the other, the two models show a difference of about 1 %. Even greater differences are commonly observed when comparing single situations. Therefore, we claim that the model has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.

Interannual sea surface temperature (SST) variations in the tropical Atlantic Ocean lead to anomalous atmospheric circulation and precipitation patterns with important ecological and socioeconomic consequences for the semiarid regions of sub-Saharan Africa and northeast Brazil. This interannual SST variability is characterized by three modes: an Atlantic meridional mode featuring an anomalous cross-equatorial SST gradient that peaks in boreal spring; an Atlantic zonal mode (Atlantic Niño mode) with SST anomalies in the eastern equatorial Atlantic cold tongue region that peaks in boreal summer; and a second zonal mode of variability with eastern equatorial SST anomalies peaking in boreal winter. Here we investigate the extent to which there is any seasonality in the relationship between equatorial warm water recharge and the development of eastern equatorial Atlantic SST anomalies. Seasonally stratified cross-correlation analysis between eastern equatorial Atlantic SST anomalies and equatorial heat content anomalies (evaluated using warm water volume and sea surface height) indicate that while equatorial heat content changes do occasionally play a role in the development of boreal summer Atlantic zonal mode events, they contribute more consistently to Atlantic Niño II, boreal winter events. Event and composite analysis of ocean adjustment with a shallow water model suggest that the warm water volume anomalies originate mainly from the off-equatorial northwestern Atlantic, in agreement with previous studies linking them to anomalous wind stress curl associated with the Atlantic meridional mode.

Abstract. We propose that considering mesoscale wind direction changes in the computation of wind farm cluster wakes could reduce the uncertainty of engineering wake modeling tools. The relevance of mesoscale wind direction changes is investigated using a wind climatology of the German Bight area covering 30 years, derived from the New European Wind Atlas (NEWA). Furthermore, we present a new solution for engineering modeling tools that accounts for the effect of such changes on the propagation of cluster wakes. The mesoscale wind direction changes relevant to the operation of wind farm clusters in the German Bight are found to exceed 11∘ in 50 % of all cases. Particularly in the lower partial load range, which is associated with strong wake formation, the wind direction changes are the most pronounced, with quartiles reaching up to 20∘. Especially on a horizontal scale of several tens of kilometers to 100 km, wind direction changes are relevant. Both the temporal and spatial scales at which large wind direction changes occur depend on the presence of synoptic pressure systems. Furthermore, atmospheric conditions which promote far-reaching wakes were found to align with a strong turning in 14.6 % of the cases. In order to capture these mesoscale wind direction changes in engineering model tools, a wake propagation model was implemented in the Fraunhofer IWES wind farm and wake modeling software flappy (Farm Layout Program in Python). The propagation model derives streamlines from the horizontal velocity field and forces the single turbine wakes along these streamlines. This model has been qualitatively evaluated by simulating the flow around wind farm clusters in the German Bight with data from the mesoscale atlas of the NEWA and comparing the results to synthetic aperture radar (SAR) measurements for selected situations. The comparison reveals that the flow patterns are in good agreement if the underlying mesoscale data capture the velocity field well. For such cases, the new model provides an improvement compared to the baseline approach of engineering models, which assumes a straight-line propagation of wakes. The streamline and the baseline models have been further compared in terms of their quantitative effect on the energy yield. Simulating two neighboring wind farm clusters over a time period of 10 years, it is found that there are no significant differences across the models when computing the total energy yield of both clusters. However, extracting the wake effect of one cluster on the other, the two models show a difference of about 1 %. Even greater differences are commonly observed when comparing single situations. Therefore, we claim that the model has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.