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The transition from land to sea affects the wind field in coastal regions. From the perspective of near-coastal offshore wind farms, the coastal transition complicates the task of energy resource assessment by, for example, introducing non-homogeneity into the free wind field. To help elucidate the matter, we quantify the average horizontal wind speed gradients at progressively increasing distances from the German coast using two years of hourly ERA5 reanalysis data, and further describe the dependence of wind speed gradients on the measurement height, atmospheric stability, and season. A vertical wind lidar located on Norderney Island near the German mainland acts as our observational reference for the ERA5 data, where a good agreement (R2=0.93$R^2 =\nobreak 0.93$) is found despite the relatively coarse ERA5 data resolution. Interestingly, the comparison of lidar data with the higher-resolution Weather Research and Forecasting (WRF) mesoscale model yields good but relatively weaker agreement (R2=0.85$R^2 =\nobreak 0.85$). The ERA5 data reveal that, for flow over the North Sea originating from the German mainland from the south, the wind speed at 10 m (110 m) above sea level increases by 30 % (20 %) some 80 km from the coast on average, and by 5 % at larger heights. An increased stratification increases the horizontal wind speed gradient at 10 m above sea level but decreases it at 110 m. Case studies using satellite and flight measurements are first analyzed to help reveal some of the underlying mechanisms governing horizontal wind speed gradients, including cases of decreasing wind speed with increasing distance from the coast, in which stable flow of warm air over the colder sea leads to an overall deceleration of the flow. The accuracy of offshore resource assessment appears to profit from utilising the horizontal wind speed gradient information contained in ERA5 reanalysis data.

European wintertime precipitation is known to be skilfully estimated in reanalysis data and model simulations since it is highly correlated with large scale, low frequency modes of variability, namely the North Atlantic Oscillation (NAO). Since the NAO is mainly a wintertime mode of variability, the skill of estimating precipitation becomes more limited in other seasons, most importantly in summer, when precipitation is mainly a result of mesoscale convection. In this study, we use the Weather Research and Forecast (WRF) model, to show the added value of using a high resolution, convection-permitting model to estimate precipitation extremes. The results show that WRF succeeds to correct the failure of ERA-Interim reanalysis to capture the positive trends over the last decades of European extreme precipitation in summer and transition seasons, that are indicated by observational data (E-OBS) and previous literature. Partial improvements are evident using ERA5 reanalysis, specifically in Spring and in Autumn. In winter, changes in European extreme precipitation over the last decades are dominated by variations in the NAO index, and are well reproduced both in reanalysis data and in the high resolution WRF downscaling.

The recently observed warming of west Spitsbergen fjords has led to anomalous sea-ice conditions and has implications for the marine ecosystem. We investigated long-term trends of maximum temperature of Atlantic Water (AW) in two west Spitsbergen fjords. The data set is composed of more than 400 oceanographic stations for Isfjorden and Grønfjorden (78.1°N), spanning from 1876 to 2009. Trends throughout the last century (1912–2009) indicate an increase of 1.9°C and 2.1°C in the maximum temperature during autumn for Isfjorden and Grønfjorden, respectively. A recent warming event in the beginning of the 21st century is found to be more than 1°C higher than the second warmest period in the time series. Mean sea-level pressure (MSLP) data from ERA-40 and ERA-Interim data sets produced by the European Centre for Medium-Range Weather Forecasts and mean temperature in the core of the West Spitsbergen Current (WSC) at the Sørkapp Section along 76.3°N were used to explain the variability of the maximum temperature. A correlation analysis confirmed previous findings, showing that variability in the oceanography of the fjords can be explained mainly by two external factors: AW temperature variability in the WSC and regional patterns of the wind stress field. To take both processes into consideration, a multiple regression model accounting for temperature in the WSC core and MSLP over the area was developed. The predicted time series shows a reasonable agreement with observed maxima temperature in Isfjorden for the period 1977–2009 (N=24), with a statistically significant multiple correlation coefficient of 0.60 (R2=0.36) at P

Watershed-scale streamflow routing models play a key role in real-time flood forecasting, flood risk evaluation, and water resource management, but the accuracy of modeled streamflow depends on both the spatial resolution and the accuracy of the runoff data used as the model forcing. The production of new, higher-resolution runoff datasets therefore motivates an evaluation of the relative improvement in streamflow model performance. This study evaluates streamflow routing output for the Mississippi River Basin, with the simulations driven by three different runoff products from the European Centre for Medium-Range Weather Forecasts. The first runoff product is the ERA-Interim surface and subsurface runoff dataset (ERAI), which was projected to a grid with a resolution of ∼ 39 km at the equator. Second, this study considers the ERA5 reanalysis dataset (ERA5-25km) placed on a quarter degree grid (∼ 25 km resolution at the equator). The simulations which generated ERA5-25km included land–atmosphere coupling and land data assimilation. The third runoff product is the ERA5-Land dataset (ERA5L-9km), which was produced in an offline simulation at 9-km resolution without land–atmosphere coupling or land data assimilation, forced by the coarser ERA5 meteorological input. For each runoff input, streamflow was simulated by applying the Routing Application for Parallel computation of Discharge (RAPID) model to the vectorized NHDPlus stream network, which comprised 1.2 million reaches within the study area. Comparison of modeled and measured daily and monthly mean discharge from 60 USGS gages reveals several broad trends. Streamflow simulated using the ERAI runoff tends to generate an earlier peak monthly discharge, while peak discharge from the ERA5L-9km runoff systematically exceeds peak discharge from the ERA5-25km runoff. Basin-aggregated model performance varies by metric, with discharge simulated using the ERA5-25km runoff having the smallest median root-mean-square error even as the discharge modeled using ERA5L-9km runoff has the smallest median Kling-Gupta efficiency. Model performance also varies spatially, with all three simulations exhibiting the least accurate results in the western region of the Mississippi River Basin, and by drainage area, with more accurate results generally obtained for larger drainages. These results suggest that streamflow model accuracy will benefit from the use of either ERA5-25km or ERA5L-9km runoff in place of ERAI. However, the selection of ERA5-25km versus ERA5L-9km depends on regional considerationsas well as which components of model performance are most critical to the user. • ERA5 runoff outperforms ERA-Interim for simulating Mississippi (MRB) streamflow. • ERA5 and ERA5-Land runoff generate comparable streamflow output when aggregated. • Defining a "best" input depends on geographic position and performance statistic. • Despite unmodeled reservoirs, streamflow performance improves for large drainages. • In the eastern MRB, ERA5-Land runoff generated the most accurate flood recurrence. [ABSTRACT FROM AUTHOR]