The response of atmospheric CO 2 and climate to the reconstructed variability in solar irradiance and radiative forcing by volcanoes over the last millennium is examined by applying a coupled physical-biogeochemical climate model that includes the Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM) and a simplified analogue of a coupled atmosphere-ocean general circulation model. The modeled variations of atmospheric CO 2 and Northern Hemisphere (NH) mean surface temperature are compatible with reconstructions from different Antarctic ice cores and temperature proxy data. Simulations where the magnitude of solar irradiance changes is increased yield a mismatch between model results and CO 2 data, providing evidence for modest changes in solar irradiance and global mean temperatures over the past millennium and arguing against a significant amplification of the response of global or hemispheric annual mean temperature to solar forcing. Linear regression (r = 0.97) between modeled changes in atmospheric CO 2 and NH mean surface temperature yields a CO 2 increase of about 12 ppm for a temperature increase of 1°C and associated precipitation and cloud cover changes. Then, the CO 2 data range of 12 ppm implies that multi-decadal NH temperature changes between 1100 and 1700 AD had to be within 1°C. Modeled preindustrial variations in atmospheric d 13 C are small compared to the uncer-tainties in ice core d 13 C data. Simulations with natural forcings only suggest that atmospheric CO 2 would have remained around the preindustrial concentration of 280 ppm without anthropogenic emissions. Sensitivity experiments show that atmospheric CO 2 closely follows decadal-mean temperature changes when changes in ocean circulation and ocean-sediment interactions are not important. The response in terrestrial carbon storage to factorial changes in temperature, the seasonality of temperature, precipitation, and atmospheric CO 2 has been determined.

ScaleX is a collaborative measurement campaign, collocated with a long-term environmental observatory of the German Terrestrial Environmental Observatories (TERENO) network in the mountainous terrain of the Bavarian Prealps, Germany. The aims of both TERENO and ScaleX include the measurement and modeling of land surface–atmosphere interactions of energy, water, and greenhouse gases. ScaleX is motivated by the recognition that long-term intensive observational research over years or decades must be based on well-proven, mostly automated measurement systems, concentrated in a small number of locations. In contrast, short-term intensive campaigns offer the opportunity to assess spatial distributions and gradients by concentrated instrument deployments, and by mobile sensors (ground and/or airborne) to obtain transects and three-dimensional patterns of atmospheric, surface, or soil variables and processes. Moreover, intensive campaigns are ideal proving grounds for innovative instruments, methods, and techniques to measure quantities that cannot (yet) be automated or deployed over long time periods. ScaleX is distinctive in its design, which combines the benefits of a long-term environmental-monitoring approach (TERENO) with the versatility and innovative power of a series of intensive campaigns, to bridge across a wide span of spatial and temporal scales. This contribution presents the concept and first data products of ScaleX-2015, which occurred in June–July 2015. The second installment of ScaleX took place in summer 2016 and periodic further ScaleX campaigns are planned throughout the lifetime of TERENO. This paper calls for collaboration in future ScaleX campaigns or to use our data in modelling studies. It is also an invitation to emulate the ScaleX concept at other long-term observatories.

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Abstract. Wake measurements of a scanning Doppler lidar mounted on the nacelle of a full-scale wind turbine during a wake-steering experiment were used for the characterization of the wake flow, the evaluation of the wake-steering set-up, and the validation of analytical wake models. Inflow-scanning Doppler lidars, a meteorological mast, and the supervisory control and data acquisition (SCADA) system of the wind turbine complemented the set-up. Results from the wake-scanning Doppler lidar showed an increase in the wake deflection with the yaw angle and that the wake deflection was not in all cases beneficial for the power output of a downstream turbine due to a bias of the inflow wind direction perceived by the yawed wind turbine and the wake-steering design implemented. Both observations could be reproduced with an analytical model that was initialized with the inflow measurements. Error propagation from the inflow measurements that were used as model input and the power coefficient of a waked wind turbine contributed significantly to the model uncertainty. Lastly, the span-wise cross section of the wake was strongly affected by wind veer, masking the effects of the yawed wind turbine on the wake cross sections.

Accurate prediction of wind turbine wakes is important for more efficient design and operation of wind parks. Volumetric wake measurements of nacelle-mounted Doppler lidars are used to characterize the wake of a full-scale wind turbine and to validate an analytical wake model that incorporates the effect of wind veer. Both, measurements and model prediction, show an elliptical and tilted spanwise cross-section of the wake in the presence of wind veer. The error between model and measurements is reduced compared to a model without the effect of wind veer. The characterization of the downwind velocity deficit development and wake growth is robust. The wake tilt angle can only be determined for elliptical wakes.