Present-day precipitation–temperature scaling relations indicate that hourly precipitation extremes may have a response to warming exceeding the Clausius–Clapeyron (CC) relation; for the Netherlands the dependency on surface dewpoint temperature follows 2 times the CC relation (2CC). The authors’ hypothesis—as supported by a simple physical argument presented here—is that this 2CC behavior arises from the physics of convective clouds. To further investigate this, the large-scale atmospheric conditions accompanying summertime afternoon precipitation events are analyzed using surface observations combined with a regional reanalysis. Events are precipitation measurements clustered in time and space. The hourly peak intensities of these events again reveal a 2CC scaling with the surface dewpoint temperature. The temperature excess of moist updrafts initialized at the surface and the maximum cloud depth are clear functions of surface dewpoint, confirming the key role of surface humidity on convective activity. Almost no differences in relative humidity and the dry temperature lapse rate were found across the dewpoint temperature range, supporting the theory that 2CC scaling is mainly due to the response of convection to increases in near-surface humidity, while other atmospheric conditions remain similar. Additionally, hourly precipitation extremes are on average accompanied by substantial large-scale upward motions and therefore large-scale moisture convergence, which appears to accelerate with surface dewpoint. Consequently, most hourly extremes occur in precipitation events with considerable spatial extent. Importantly, this event size appears to increase rapidly at the highest dewpoint temperature range, suggesting potentially strong impacts of climatic warming.

Previously observed twice-Clausius-Clapeyron (2CC) scaling for extreme precipitation at hourly time scales has led to discussions about its origin. The robustness of this scaling is assessed by analyzing a subhourly dataset of 10-min resolution over the Netherlands. The results confirm the validity of the previously found 2CC scaling for extreme convective precipitation.Using a simple entraining plume model, an idealized deep convective environmental temperature profile is perturbed to analyze extreme precipitation scaling from a frequently used relation based on the column condensation rate. The plume model simulates a steady precipitation increase that is greater than ClausiusClapeyron scaling (super-CC scaling). Precipitation intensity increase is shown to be controlled by a flux of moisture through the cloud base and in-cloud lateral moisture convergence. Decomposition of this scaling relation into a dominant thermodynamic and additional dynamic component allows for better understanding of the scaling and demonstrates the importance of vertical velocity in both dynamic and thermodynamic scaling. Furthermore, systematically increasing the environmental stability by adjusting the temperature perturbations from constant to moist adiabatic increase reveals a dependence of the scaling on the change in environmental stability. As the perturbations become increasingly close to moist adiabatic, the scaling found by the entraining plume model decreases to CC scaling. Thus, atmospheric stability changes, which are expected to be dependent on the latitude, may well play a key role in the behavior of precipitation extremes in the future climate.

No abstract

Number of tables in main text: 1 Number of figures in main text: 3 Number of references in main text: 41

Large-eddy simulations with strong lateral forcing representative of precipitation over the Netherlands are performed to investigate the influence of stability, relative humidity (RH), and moisture convergence on precipitation. Furthermore, a simple climate perturbation is applied to analyze the precipitation response to increasing temperatures. Precipitation is decomposed to distinguish between processes affecting the precipitating area and the precipitation intensity. It is shown that amplification of the moisture convergence and destabilization of the atmosphere both lead to an increase in precipitation, but on account of different effects: atmospheric stability mainly influences the precipitation intensity, whereas the moisture convergence mainly controls the precipitation area fraction. Extreme precipitation intensities show qualitatively similar sensitivities to atmospheric stability and moisture convergence. Precipitation increases with RH due to an increase in area fraction, despite a decrease in intensity. The precipitation response to the climate perturbation shows a stronger response for the precipitation intensity than the overall precipitation, with no clear dependency on changes in atmospheric stability, moisture convergence, and relative humidity.

Research on relations between atmospheric conditions and extreme precipitation is important to understand and model present-day climate extremes and assess how precipitation extremes might evolve in a future climate. Here we present a statistical analysis of the relation between large-scale conditions and hourly precipitation at midlatitudes, by using observations of the Netherlands combined with a regional reanalysis. The aim is to gain a better understanding of the typical large-scale atmospheric conditions and large-scale forcing associated with extreme hourly precipitation and determine the typical differences between cases of extreme precipitation and weaker events. To avoid double counting, we perform an event-based analysis and consider the hourly peak intensity, rather than all hourly data. Atmospheric large-scale profiles consistently show a clear separation between precipitation deciles, characterized by increasing instability and moisture content of the atmosphere for more extreme precipitation. Furthermore, stronger events are characterized by larger atmospheric forcing preceding the event, which primarily relates to vertical motions. Based on these results, four atmospheric parameters, describing atmospheric moisture, stability and large-scale convergence, are analyzed as potential indicators of strong precipitation events. Despite positive relations between these parameters and the peak intensity, their correlations are found to be weak. Key Points:• Climatological analysis of relations between atmospheric conditions and precipitation extremes • Heavy precipitation events occur under warmer, moister conditions, under stronger LS convergence • Four atmospheric parameters were evaluated as precipitation indicators, but correlations were weak Correspondence to:At midlatitudes, strong convection tends to occur ahead of synoptic disturbances, where upward vertical motions lead to moistening and destabilization of the atmosphere [Doswell and Bosart, 2001]. Studies have tried to connect extreme precipitation to cyclones [Pfahl and Wernli, 2012] and fronts [Catto and Pfahl, 2013]. A large amount of 6-hourly precipitation extremes were associated with fronts. Since many precipitation extremes occur under a combination of convective and large-scale precipitation, in this study we will not make an explicit distinction between the different precipitation types.

The net greenhouse gas benefits of wind turbines compared to their fossil energy counterparts depend on location-specific wind climatology and the turbines’ technological characteristics. Assessing the environmental impact of individual wind parks requires a universal but location-dependent method. Here, the greenhouse gas payback time for 4161 wind turbine locations in northwestern Europe was determined as a function of (i) turbine size and (ii) spatial and temporal variability in wind speed. A high-resolution wind atlas (hourly wind speed data between 1979 and 2013 on a 2.5 by 2.5 km grid) was combined with a regression model predicting the wind turbines’ life cycle greenhouse gas emissions from turbine size. The greenhouse gas payback time of wind turbines in northwestern Europe varied between 1.8 and 22.5 months, averaging 5.3 months. The spatiotemporal variability in wind climatology has a particularly large influence on the payback time, while the variability in turbine size is of lesser importance. Applying lower-resolution wind speed data (daily on a 30 by 30 km grid) approximated the high-resolution results. These findings imply that forecasting location-specific greenhouse gas payback times of wind turbines globally is well within reach with the availability of a high-resolution wind climatology in combination with technological information.

No abstract