Over the past 60 years, both average daily precipitation intensity and extreme precipitation have increased in many regions 1-3 . Part of these changes, or even individual events 4,5 , have been attributed to anthropogenic warming 6,7 . Over the Black Sea and Mediterranean region, the potential for extreme summertime convective precipitation has grown 8 alongside substantial sea surface temperature increase. A particularly devastating convective event experienced in that region was the July 2012 precipitation extreme near the Black Sea town of Krymsk 9 . Here we study the e ect of sea surface temperature (SST) increase on convective extremes within the region, taking the Krymsk event as a showcase example. We carry out ensemble sensitivity simulations with a convection-permitting atmospheric model and show the crucial role of SST increase in the extremeness of the event. The enhancement of lower tropospheric instability due to the current warmer Black Sea allows deep convection to be triggered, increasing simulated precipitation by more than 300% relative to simulations with SSTs characteristic of the early 1980s. A highly nonlinear precipitation response to incremental SST increase suggests that the Black Sea has exceeded a regional threshold for the intensification of convective extremes. The physical mechanism we identify indicates that Black Sea and Mediterranean coastal regions may face abrupt amplifications of convective precipitation under continued SST increase, and illustrates the limitations of thermodynamical bounds for estimating the temperature scaling of convective extremes.Extreme precipitation responds sensitively to both dynamical and thermodynamical forcings 10-12 . Changes in precipitation extremes can occur owing to changes in evaporation, the increased saturation vapour pressure in a warmer climate 13 , and changes in storm dynamics 12 . Global climate models project increased heavy precipitation, mainly over the tropics and high latitudes 14,15 . As temperature increases, large-scale precipitation extremes tend to scale at about 7% K −1 , along the thermodynamical bounds given by the Clausius-Clapeyron (CC) relation 3 . Convective extremes, however, are strongly influenced by mesoscale dynamics and may scale well above the CC rate 10,11 . Local factors, such as orography and moisture availability, can also impact the convective response to temperature increase.The Krymsk precipitation extreme saw a daily precipitation total that exceeded all previous annual daily maxima since 1936 by a factor of two (Fig. 1a), and a flash flood that killed over 170 people 9 . On the basis of statistical evidence from the pre-2012 record,
Climate model resolution can affect both the climate change signal and present-day representation of extreme precipitation. The need to parametrize convective processes raises questions about how well the response to warming of convective precipitation extremes is captured in such models. In particular, coastal precipitation extremes can be sensitive to sea surface temperature (SST) increase. Taking a recent coastal precipitation extreme as a showcase example, we explore the added value of convection-permitting models by comparing the response of the extreme precipitation to a wide range of SST forcings in an ensemble of regional climate model simulations using parametrized and explicit convection. Compared at the same spatial scale, we find that the increased local intensities of vertical motion and precipitation in the convection-permitting simulations play a crucial role in shaping a strongly nonlinear extreme precipitation response to SST increase, which is not evident when convection is parametrized. In the convection-permitting simulations, SST increase causes precipitation intensity to increase only until a threshold is reached, beyond which further SST increase does not enhance the precipitation. This flattened response results from an improved representation of convective downdrafts and near-surface cooling, which damp the further intensification of precipitation by stabilizing the lower troposphere locally and also create cold pools that cause subsequent convection to be triggered at sea, rather than by the coastal orography. These features are not well represented in the parametrized convection simulations, resulting in precipitation intensity having a much more linear response to increasing SSTs.
In conclusion, the results of blinded ring testing and comparison to new ISO WG7 acceptance criteria indicate that a new in vitro SPF test method meets (and exceeds) these minimum criteria and is an interesting candidate for possible deployment as an industry test methodology.
Short‐duration, high‐impact precipitation events in the extratropics are invariably convective in nature, typically occur during the summer, and are projected to intensify under climate change. The occurrence of convective precipitation is strongly regulated by the diurnal convective cycle, peaking in the late afternoon. Here we perform very high resolution (convection‐permitting) regional climate model simulations to study the scaling of extreme precipitation under climate change across the diurnal cycle. We show that the future intensification of extreme precipitation has a strong diurnal signal and that intraday scaling far in excess of overall scaling, and indeed thermodynamic expectations, is possible. We additionally show that, under a strong climate change scenario, the probability maximum for the occurrence of heavy to extreme precipitation may shift from late afternoon to the overnight/morning period. We further identify the thermodynamic and dynamic mechanisms which modify future extreme environments, explaining both the future scaling's diurnal signal and departure from thermodynamic expectations.
Abstract. High-resolution climate data O(1 km) at the catchment scale can be of great value to both hydrological modellers and end users, in particular for the study of extreme precipitation. While dynamical downscaling with convection-permitting models is a valuable approach for producing quality high-resolution O(1 km) data, its added value can often not be realized due to the prohibitive computational expense. Here we present a novel and flexible classification algorithm for discriminating between days with an elevated potential for extreme precipitation over a catchment and days without, so that dynamical downscaling to convectionpermitting resolution can be selectively performed on highrisk days only, drastically reducing total computational expense compared to continuous simulations; the classification method can be applied to climate model data or reanalyses. Using observed precipitation and the corresponding synoptic-scale circulation patterns from reanalysis, characteristic extremal circulation patterns are identified for the catchment via a clustering algorithm. These extremal patterns serve as references against which days can be classified as potentially extreme, subject to additional tests of relevant meteorological predictors in the vicinity of the catchment. Applying the classification algorithm to reanalysis, the set of potential extreme days (PEDs) contains well below 10 % of all days, though it includes essentially all extreme days; applying the algorithm to reanalysis-driven regional climate simulations over Europe (12 km resolution) shows similar performance, and the subsequently dynamically downscaled simulations (2 km resolution) well reproduce the observed precipitation statistics of the PEDs from the training period. Additional tests on continuous 12 km resolution historical and future (RCP8.5) climate simulations, downscaled in 2 km resolution time slices, show the algorithm again reducing the number of days to simulate by over 90 % and performing consistently across climate regimes. The downscaling framework we propose represents a computationally inexpensive means of producing high-resolution climate data, focused on extreme precipitation, at the catchment scale, while still retaining the advantages of convection-permitting dynamical downscaling.
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