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Sub-daily extreme precipitation can generate fast hydro-geomorphic hazards such as flash floods and debris flows, which cause fatalities and damages especially in mountainous regions. Reliable projections of extreme future precipitation is fundamental for risk management and adaptation strategies. Convection-permitting climate models (CPMs) esplicitely resolve large convective systems and represent local processes, especially sub-daily extreme precipitation, more realistically than coarser resolution models, thus leading to higher confidence in their projections. Given their high computation cost, however, the available CPM simulations cover relatively short time periods (10–20 years), too short for deriving precipitation frequency analyses with conventional extreme value methods based on annual maxima or threshold exceedances.In this work, we evaluate the potential of a non-asymptotic approach based on “ordinary” events, the so-called Simplified Metastatistical Extreme Value (SMEV), to provide information on the future change of short-duration precipitation extremes. We focus on a complex-orography region in the Eastern Italian Alps, where significant changes in sub-daily annual maxima have been already observed. The study is based on COSMO-crCLIM model simulations at 2.2 km resolution under the RCP8.5 scenario and uses three 10-year time periods: historical 1996-2005 (the control period), near-future 2041-2050 and far future 2090-2099. We estimate extreme precipitation for durations ranging from 1 h to 24 h and assess the projected changes with respect to the control period. Specifically, we analyze annual maxima, return levels up to 50 years, and the parameters of the statistical model. A bootstrap procedure is used to evaluate the uncertainty of the estimates, and a permutation test is applied to assess the statistical significance of the projected changes. We compare our results with a modified Generalized Extreme Value (GEV) approach, recently applied for the study of extremes in CPM future time periods.We found that annual maxima and higher return levels exhibit a general increase in the future especially for the far future and the shorter event durations. On average, the magnitude of the far future change decreases with the precipitation temporal scale. The changes show an interesting spatial organization that can be associated with the orography of the region: significant future increases are mostly located at high elevations, while lowlands and coastal zones show no clear pattern.This work shows that SMEV reduces the uncertainty in the estimates of higher return levels compared to GEV and can thus provide improved estimates of their future changes from short CPM runs. These findings advance our knowledge about the projected changes in extreme precipitation and their spatial distribution at the different time scales. They can thus help improving risk management and adaptation strategies.

The Mediterranean basin is well recognized as one of the main climate change hotspots; besides, this region is one the most active cyclogenetic area of the Northern Hemisphere with a large number of intense cyclones occurring every year mainly during winter and fall. The climatology of Mediterranean cyclones has been deeply investigated in the past years, leading to a high agreement on the tracks density, seasonal cycle and favorite locations of cyclogenesis. Nevertheless, open questions still remain on the future evolution of Mediterranean cyclogenesis and associated impacts. Mediterranean cyclones typically present weaker intensities, smaller sizes and shorter lifetimes than tropical cyclones or other mid-latitude cyclones that develop over open oceans. However, they are often responsible for extreme precipitation and wind events leading to severe socio-economic and environmental impacts especially over densely populated regions and coastal areas. Thus, studying the feedbacks of air-sea interactions on Mediterranean cyclones will bring to a better understanding of both the contribution of cyclones to the variability in and extremes of the regional climate and the impacts on the marine ecosystems as well as the associated risks in maritime transportation and coastal structures.This study aims to investigate the added values of the ocean-atmosphere coupling in regional climate models in reproducing Mediterranean cyclones. To this end, two simulations are performed using the ENEA-REG regional earth system model at 12 km over the Med-CORDEX domain. The first experiment uses the mesoscale WRF model with prescribed Sea Surface Temperature (SST), while in the second WRF is coupled to the MITgcm ocean model. Different tracking methods, based on sea level pressure, are used to account for the uncertainties linked with mathematical and physical definitions of cyclone itself. The simulations are validated against ERA5 reanalysis dataset in terms of their ability to reproduce the Mediterranean cyclone climatology as well as to represent sub-daily fields linked to the cyclones (e.g. SST, heat fluxes, wind, precipitation). Here we show how the coupling of the atmosphere with an interactive ocean model affects the number of cyclones along with their intensity and duration.

Viticulture is strictly related with weather and climate. Italy is a world leader in the wine business but also a known hot-spot for climate change. In the last decades, Italian winegrowers already experiences the effect of climate change, especially in terms of warmer growing season, more frequent and longer drought periods, increased frequency of weather extremes as well as shifts in phenological phases, that increase the exposure of the plant at frost risk. This study investigates the impact of climate variability and change on grape yield at local scale in three wine consortiums. Using climate variables from the E-OBS observational dataset, we computed a range of bioclimatic indices, selected by the International Organisation of Vine and Wine (OIV), and correlated them to grape yield data from three wine consortiums in in northern and central Italy. The collaboration with consortiums allows to include in the analysis other factors, besides climate, that influences wine productivity like vineyard management, policies and market.We evaluate how the interannual variability and the changes in the bioclimatic indices impact on grape productivity in the study areas using a single regression approach. We also combined the bioclimatic indices into a multi-regression analysis to investigate if a more complex methodologyincreases the portion of total yield variability explained, in comparison with the single regression approach. Keyword: climate change, agroclimatic service, wine, local scale

Climate change is causing increased risks linked to extreme weather events. In order to develop effective adaptation strategies and policies, there is an urgent need for methodologies able to assess how the socio-economic risks associated with extreme climate-related events will change in the coming decades especially at local scale. The development of these methodologies require the expertise from many different scientific disciplines, including: modelling of global and local climatic phenomena, assessment of the intensity and probability of extreme events, representation of their impacts on the society and quantification of the associated risk.In this work we propose a methodological chain linking the risk of extreme events in a changing climate with both direct and indirect impacts on the socio-economic sector from regional to local scale. The proposed chain integrates the knowledge of three scientific fields: climatologists, engineers and macro-economist. Here, we present agreements and differences between communities (e.g., aim, terminology, methodology, etc.), and evaluate advantages and constrains of the combined used of high-resolution regional climate models, engineering risk assessment models and economic input-output models compared to the state of the art in this field.To illustrate the advantages of the proposed methodology and its practical feasibility, we present preliminary results from an applied pilot study in the Italian context.

Extreme precipitation events (EPE), especially those leading to floods and landslides, are devastating to society. Predicting these events in advance can help disaster managers to carry out plans of action to respond effectively to any oncoming adverse events. Sub-seasonal forecasts, which aim to predict the weather with 2 weeks to 2 months in advance, can help to provide valuable and actionable information to disaster managers. Given the potential usefulness to end users, it is vital to assess the skill of sub-seasonal forecasts in predicting EPEs. However, given that precipitation is known to be a difficult variable to predict, the lead time at which forecasts are skilful may be limited. This study, therefore, aims to assess at which lead time sub-seasonal forecasts of atmospheric drivers of EPEs are skilful.The study investigates the skill of the European Centre for Medium-Range Weather Forecast (ECMWF) sub-seasonal reforecast in predicting EPE over Italy from 2001 to 2020. A total of 100 EPEs are used as case studies. The variables evaluated are total precipitation, mean sea level pressure, geopotential height at 500 hPa and specific humidity at 850 hPa. Variables are averaged over the 5 days surrounding the date of the EPE. ERA5 is used as the reference dataset. Both deterministic and probabilistic metrics are used to assess the skill of the reforecast.Results show that the skill for precipitation is limited to the first two weeks. Nevertheless, the ECMWF sub-seasonal product is skilful in predicting the atmospheric fields associated with the selected EPEs, such as MSLP and geopotential height, showing both reliability and discrimination beyond two weeks.

Understanding the impact of orography on the probability distribution of extreme precipitation at short (i.e., sub-daily) temporal scales, as well as on extreme-rainfall causative processes, is critical for managing risk from rainfall-triggered natural hazards in mountainous regions. High-resolution convection-permitting models (CPMs) are crucial for this type of analysis since they better represent convective processes key to short-duration extremes. Here, we assess the ability of multi-model CPM ensemble CORDEX-FPS to represent the upper tail of sub-daily precipitation in a complex-orography region in the Eastern Italian Alps. In this area, different orographic impacts on sub-daily precipitation upper tail were reported at different event durations, and significant temporal trends in precipitation intensity were reported during the last few decades, making it a challenging and interesting test case for CPM simulations. An ensemble of six CPMs with a horizontal grid spacing of ∼2.2 km, driven by ERA-Interim reanalysis, are analysed and evaluated against ∼180 rain gauges. Since CPM simulations are too short (10 years) for analysing extremes using conventional methods, we use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value, SMEV), which was proven to provide reliable results even using short time records. We explore how the model spread vary with elevation and the ability of the multi-model mean to reproduce the distribution parameters and the extreme quantiles up to 100-year return period at different elevations.

Atmospheric blocking is known to be one of the most important drivers of large-scale atmospheric variability at mid-high latitudes. Blocking events consist of a disruption and/or deceleration of the mean westerly circumpolar flow, and are generally associated with large-scale high-pressure patterns, which may be connected with the occurrence of climate extremes, such as heat waves and cold spells. Atmospheric dynamics in the Arctic region may be very important in shaping the spatial and temporal patterns of blocking at mid-high latitudes, and consequently the occurrence of associated climate extremes. In particular, Arctic Amplification (AA), namely the recent amplified warming in the Arctic region compared to lower latitudes, has recently been argued to have an impact on blocking patterns and behaviour at mid-high latitudes.The objective of this study is to investigate the most relevant mechanisms playing a role in the relationship between blocking and Arctic Amplification, by analysing the variability and frequency of the associated spatial patterns at various timescales using variables from the ERA5 reanalysis dataset for the time interval 1959-2022. Blocking events are detected based on geopotential height gradients between mid- and high-latitude regions, while Arctic Amplification is quantified as the difference of 1000hPa temperature between high and mid latitudes.The climatological number of events per year and their average lifetime, along with the long-term trends and their relationship with the AA are analysed. Furthermore, possible mechanisms linking blocking variability and the AA are explored through the analysis of the jetstream dynamics and teleconnection patterns in the Northern Hemisphere.

Climate change and its repercussions on society are gathering interest among scientists from different fields. In particular, the observed temperature increase of the last decades is reason for concern given the resulting change in frequency and intensity of extreme events. In the next decades, global warming is expected to lead to a significant increase of short-lived high-intensity precipitation, which is likely to increase the risk for flash floods especially in urban areas, landslides, and debris flow. Moreover, the joint occurrence of intense precipitation and strong wind is particularly hazardous for the forested mountainous catchments. In fact, the extensive uprooting may strongly enhance the triggering of landslides and debris flows, thus leading to the formation of large woody debris, as seen during the Vaia extreme storm that occurred in 2018 in northeastern Italy. In this context, the aim of our study is twofold: (1) evaluate the fields of hourly precipitation and wind from convection-permitting models (CPM) against observations from stations and reanalysis datasets at CP resolution; (2) applying common bias correction techniques (i.e., empirical quantile mapping), as well as deep learning algorithms, like convolutional neural networks, that are able to analyse non-linear spatial relationships along with the possibility to incorporate multivariable bias correction procedures. Preliminary results show a general good agreement between CP and observational datasets at CP resolution, which would eventually benefit the bias correction methods. A better modelling and understanding of the changes brought by climate change, could eventually lead to an improved design and more appropriate application of risk mitigation strategies.

Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state of the art for these projections, as they resolve convective processes that are key to short-duration extremes. Recent observational studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This "reverse orographic effect"could be related to processes which are subgrid even for CPMs. To quantify the reliability of future projections of extreme short-duration precipitation in mountainous regions, it is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands however, CPM simulations are still too short for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value: SMEV) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We analyze an ERA-Interim-driven Consortium for Small-Scale Modeling (COSMO-crCLIM, convection-resolving Climate Modelling) simulation (2000-2009; 2.2 km resolution), and we use hourly precipitation from 174 rain gauges in an orographically complex area in northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes, as compared to observational data. We focus on extremes as high as the 20-year return levels. While overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation, implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias-correction approaches should account for orography. SMEV's capability of estimating reliable rare extremes from short periods promises further applications on short-time-period CPM projections and model ensembles., Hydrology and Earth System Sciences, 27 (5), ISSN:1027-5606, ISSN:1607-7938

Estimating future short-duration extreme precipitation in mountainous regions is fundamental for risk management. High-resolution convection-permitting models (CPMs) represent the state of the art for these projections, as they resolve convective processes that are key to short-duration extremes. Recent observational studies reported a decrease in the intensity of extreme hourly precipitation with elevation. This “reverse orographic effect” could be related to processes which are subgrid even for CPMs. To quantify the reliability of future projections of extreme short-duration precipitation in mountainous regions, it is thus crucial to understand to what extent CPMs can reproduce this effect. Due to the computational demands however, CPM simulations are still too short for analyzing extremes using conventional methods. We use a non-asymptotic statistical approach (Simplified Metastatistical Extreme Value: SMEV) for the analysis of extremes from short time periods, such as the ones of CPM simulations. We analyze an ERA-Interim-driven Consortium for Small-Scale Modeling (COSMO-crCLIM, convection-resolving Climate Modelling) simulation (2000–2009; 2.2 km resolution), and we use hourly precipitation from 174 rain gauges in an orographically complex area in northeastern Italy as a benchmark. We investigate the ability of the model to simulate the orographic effect on short-duration precipitation extremes, as compared to observational data. We focus on extremes as high as the 20-year return levels. While overall good agreement is reported at daily and hourly duration, the CPM tends to increasingly overestimate hourly extremes with increasing elevation, implying that the reverse orographic effect is not fully captured. These findings suggest that CPM bias-correction approaches should account for orography. SMEV's capability of estimating reliable rare extremes from short periods promises further applications on short-time-period CPM projections and model ensembles.

The RESILIENCE project aims at developing an integrated methodology for assessing the impact of climatic variations and changes on the intense precipitation and wind regimes, and on the consequent triggering of flash floods, debris-flows and wind-related forest damages. A significant increase of short and intense precipitation is expected in the next future due to global warming, with consequent impacts on flash floods and hydro-geomorphic hazards such as shallow landslides and debris flows. Despite their societal importance, only few studies have explored potential climate change effects on these hydrological and hydro-geological processes. In fact, no accepted estimates of such changes to be used in engineering practice or environmental management planning exist so far, nationally or regionally.The RESILIENCE project tries to address this specific knowledge gap. Two recent scientific advances are at the basis of the development of RESILIENCE. The first advance is the advent of high-resolution climate models, also called Convection-Permitting Climate Models (CPM), which improve the representation of both precipitation and wind field at the sub-daily scales compared to the standard coarser resolution Regional Climate Models. However, due to their computational costs, simulations are currently available for only short (typically ten years) time slices and few emission scenarios. These time series are too short to provide reliable statistics of extremes if analyzed using the classical extreme value methods. A second recent advance in the field of extreme value theory, the Metastatistical Extreme Value Distribution (MEVD), allows to overcome this limitation: it provides reliable extreme event probability estimates even from short time series, as in the case of CPM outputs, since it is based on all “ordinary events'' in the series instead of just yearly maxima or a few “peak-over-threshold” values per year as in the traditional methods.Given this background, and focusing on the Veneto region in Italy as a study area, the specific objectives of RESILIENCE are 1) to quantify near (2041-2050) and far (2090-2099) future changes in precipitation and wind extremes probability at sub-daily temporal scales with respect to the baseline (1996-2005) using the MEVD approach and high-resolution COSMO-CLM simulations, 2) to quantify the associated future impacts on flash floods, debris flows and forest damages, 3) to provide data and hazard models to support flood and forest risk management plans in the Italian North-East accounting for future climate changes.RESILIENCE brings together an interdisciplinary group of scientists, from hydrologists, to climate modelers, to statisticians, to forest science experts, and is based on the interaction with three key Project Stakeholders. The project results will be communicated and disseminated to a wide audience of residents in the Veneto region and beyond, through collaborations with Museums, Academies and Local Authorities.

We investigate the spatial and temporal patterns of annual and seasonal biases in extreme precipitation simulated by a convection permitting climate model forced with reanalysis data (Era Interim driven COSMO-CLM model) across a 10-years period (2000-2009). Specifically, we aim at developing an adjustment procedure able to preserve extremes at different temporal and spatial scales, and which can be applied to future scenarios. The biases are here defined as the ratio between simulated and observed rainfall at 130 rain gauges. The quantile-based analysis reveals a general overestimation for gauges located in foothill or mountain areas (elevation > 500 m asl), and a general underestimation over lowland sites. This behavior is recurrent for various investigated quantities, such as annual or seasonal biases, and their counterparts estimated upon Extreme precipitation Values (EV) or wet periods. We also observe a temporal heterogeneity of the biases estimated in different years or seasons. Dry years (e.g. 2003) are characterized by remarkably high biases, while those estimated upon springs and autumns data within the investigation period are generally overestimated and underestimated, respectively. We explore two preliminary adjustment approaches: Quantile Mapping (QM) and Linear Scaling (LS) adjustments. QM corrects simulated rainfall series but can affect the rainfall temporal autocorrelation. Conversely, LS preserves the autocorrelation but fails in the correction of seasonal and annual biases. EV-related biases are not properly corrected, and further statistical methods need to be formulated to correct EV simulated rainfall while both respecting their ACF and taking into account orographic effects.

Increasing extreme precipitation intensity at short duration is reported in recent literature and related to the global warming. For improving risk management and adaptation to changing climate, it is important to estimate the changes in hourly extremes, because they cause numerous hydro-geological hazards. High resolution climate models (Convection-permitting models, CPMs) resolve the scales at which convective processes occur, and can provide higher confidence in the future estimates of hourly precipitation than coarser resolution models. However, since actual CPM runs are available for short time slices (10–20 years), estimation of extremes by using classical Extreme Value approaches is difficult. Novel methods based on the concept of ordinary event have shown the capacity of deriving reliable frequency analyses from short data records, and they can be successfully applied to CPMs. Recent literature reported distinct orographic effects on precipitation extremes. In particular, decreasing intensity with elevation for hourly extremes is found (“reverse orographic effect”) as contrasted with the orographic enhancement of precipitation for long durations. The reverse orographic effect was tentatively associated to orography-induced turbulence. These processes could be sub-grid even for CPMs, so it is crucial to understand whether and how CPMs can represent the orographic effect before using the simulations to project future extremes in mountainous areas. We focus our study on an orographically complex area in the Eastern Italian Alps. Precipitation data comes from: i) ~170 5-min resolution rain gauges (our benchmark), ii) CPM simulations from COSMO model, run at 2.2 km spatial resolution and 1h time-resolution. The model is driven with ERA Interim re-analyses for the period 2000-2009. A storm-based statistical method is applied to both observed and simulated time series, and we use a Weibull distribution for modelling the upper tail of ordinary events. We derive the distribution parameters and extreme return levels up to 20-year return period for durations between 1 and 24 h. We look at their dependence on elevation, and we quantify the bias between observations and CPM, the dependence of the biases with elevation. Spatial patterns in the CPM biases on the annual maxima and the modelled return levels emerge for short durations, while a general better agreement between model and observation is found at the daily duration. For the 1h return levels, the bias depends on elevation, with increasing overestimation with elevation, which implies a weak representation of the reverse orographic effect. This work shows that we can have reliable estimates of high return levels from short CPM runs by using proper statistical methods. The results can improve our understanding of the changes in the meteorological processes underlying the changes in the precipitation extremes, and could help us develop adjustment approaches accounting for the role of orography at multiple durations.

Fast hydro-geomorphic hazards such as flash floods and debris flows cause numerous fatalities and large damage, and are triggered by sub-daily extreme precipitation. Projecting future changes in these extremes is thus of great importance for risk management and adaptation strategies. High-resolution climate models, called convection-permitting models (CPMs), represent land-surface characteristics and small-scale processes in the atmosphere, such as convection, more realistically than coarser resolution models. Subdaily extremes could be better represented, and thus CPMs provide higher confidence in the estimate of future changes in extreme precipitation. However, the existing CPM runs are available for relatively short time periods (10–20 years at most) that are too short for deriving precipitation frequency analyses with conventional extreme value methods. Here, we evaluate the potential of a novel statistical approach based on many “ordinary” events rather than just yearly maxima or a few values over a high threshold. This method has the potential to provide reliably estimate of rare return levels from short data record, thus offering the chance to be effectively applied to the analysis of CPM data for reliable frequency analysis on future precipitation. We focus on an Eastern Alpine transect, characterized by a complex orography, where significant changes in sub-daily annual maxima have been already observed. We estimate future changes under the RCP8.5 scenario using COSMO-crCLIM model simulations at 2.2 km resolution. We focus on three 10-year time slices (historical 1996-2005, near-future 2041-2050, and far future 2090-2099). A bias assessment is also performed by comparing the estimated extremes from the historical time-slice to the ones from long records of observed precipitation. We estimate extreme precipitation for duration ranging from 1 h to 24 h and assess the changes between the time periods. Specifically, we analyze: annual maxima, return levels, and parameters of the statistical model. Although the storms' frequency will generally decrease in the region, the mean annual maxima exhibit a general increase in the near and far future, especially at shorter durations. The change in the extreme return levels shows a similar trend, with larger increase in the far future at the shorter duration. Interestingly, the changes show a spatial organization that can be associated to the orographic features of the area: the stronger increasing changes are located in the high elevation zone, while in the lowlands weak decrease and weak increase emerge in the near and far future, respectively. This analysis demonstrates the possibility to have reliable estimates of future extreme precipitation from short CPM runs by using a novel method based on ordinary-events. The relevant findings from this analysis are useful for improving our knowledge about the projected future changes in extreme precipitation and thus for improving the strategies for risk management and adaptation.

Subdaily extreme precipitation may trigger fast hydro-geomorphic responses, such as flash floods and debris flows, which cause numerous fatalities and large damage. High-resolution climate models, called convection-permitting models (CPMs), represent convective processes more realistically than coarser resolution models. These processes are crucial for the correct representation of subdaily extremes, and thus CPMs provide higher confidence in the estimate of future extreme precipitation. However, because of their high computational cost, the existing CPM runs are available for relatively short time periods (10–20 years at most) that are too short for deriving precipitation frequency analyses with conventional extreme value methods. Recent approaches are based on many “ordinary” events rather than on just yearly maxima or a few values over a high threshold. They demonstrate the capability to provide reliably estimate of return levels associated with long return periods from short data record, thus they offer the chance to be effectively applied to the analysis of CPM data.In the present study, we estimate the changes in subdaily precipitation extremes from COSMO-crCLIM model simulations at 2.2 km resolution, for three 10-year time slices (historical 1996-2005, near-future 2041-2050, and far future 2090-2099 – under the RCP8.5 scenario). We focus on the Eastern Alpine transect, characterized by a complex orography, where significant changes in subdaily annual maxima have been already observed. We apply an ordinary-event statistical method for the estimation of the extreme precipitation with duration ranging from 1 h to 24 h. We analyze the changes between the time periods in both the annual maxima, the quantiles, and the distribution parameters. We find that, although the storms' frequency will generally decrease in the region, the mean annual maxima will increase continuously in the near and far future, especially at shorter durations. Investigation of extreme return levels shows a similar trend, with larger changes in the far future at the shorter duration. Interestingly, the emerging spatial patterns in the changes can be associated to the orographic features of the study area: the stronger increasing changes are located in the high elevation zone, while the flat zone shows weak decrease and weak increase in the near and far future, respectively.These analysis and results are useful for improving our knowledge about the projected future changes in extreme precipitation and thus for improving the strategies for risk management and adaptation.

A significant increase in the intensity of extreme precipitation of short duration is expected due to the global warming, and is already reported in recent literature. Estimating the changes in hourly extremes in climate models is of fundamental importance for improving risk management and adaptation to changing climate, because they cause numerous hydrological and geomorphological hazard. Convection-permitting models (CPMs) can resolve the scales at which convective processes occur, and thus provide higher confidence in the future estimates of hourly precipitation than coarser resolution models. However, the existing CPM simulations cover relatively short time periods (10–20 years), and this prevents the use of conventional extreme value methods for the estimation of extremes. Novel methods based on the concept of ordinary event have shown the potential of deriving accurate frequency analyses from short data records, and they can be successfully applied to CPMs.Recent studies reported distinct orographic impacts on precipitation extremes. In particular, a “reverse orographic effect” was reported for hourly durations, meaning that the intensity of hourly extremes tends to decrease with elevation, as opposed to the orographic enhancement of precipitation for long durations. This reverse orographic effect was tentatively associated to orography-induced turbulence. As these processes could be sub-grid even for CPMs, it is crucial to understand whether and how CPMs are able to represent these effects before using these simulations to project future extremes in mountainous areas.Here, we focus on a complex-orography area in North-eastern Italy and we use hourly precipitation records from ~150 5-min resolution rain gauges (our benchmark) and CPM simulations from COSMO model, run at 2.2 km resolution. The model is driven with ERA Interim reanalyses for the period 2000-2009. By applying a storm-based statistical method to both observed and simulated time series, we model the ordinary events tails using a Weibull distribution. We compute the distribution parameters and the extreme quantiles up to 20-year return period for 9 durations between 1 and 24 hours, and we evaluate: their dependence on elevation, the bias between the observation and the CPM, the dependence of the biases with elevation.We find evident spatial patterns in the CPM biases on the annual maxima and the modelled quantiles, especially for short durations. The bias significantly depends on elevation, with increasing overestimation of the 1-hour quantiles with elevation. This seems to confirm our hypothesis that CPMs cannot well represent the “reversed orographic effect”. These findings can improve our understanding of the changes in the meteorological processes underlying the changes in the precipitation extremes, and could help us develop adjustment approaches that can account for the role of orography at multiple durations.

For the first time, a model at a resolution on par with operational weather forecast models has been used for national climate scenarios. An ensemble of 12 climate change projections at convection-permitting (2.2 km) scale has been run for the United Kingdom, as part of the UK Climate Projections (UKCP) project. Contrary to previous studies, these show greater future increases in winter mean precipitation in the convection-permitting model compared with the coarser (12 km) driving model. A large part (60%) of the future increase in winter precipitation occurrence over land comes from an increase in convective showers in the 2.2 km model, which are most likely triggered over the sea and advected inland with potentially further development. In the 12 km model, increases in precipitation occurrence over the sea, largely due to an increase in convective showers, do not extend over the land. This is partly due to known limitations of the convection parameterization scheme, used in conventional coarse-resolution climate models, which acts locally without direct memory and so has no ability to advect diagnosed convection over the land or trigger new showers along convective outflow boundaries. This study shows that the importance of accurately representing convection extends beyond short-duration precipitation extremes and the summer season to projecting future changes in mean precipitation in winter.

For the first time, we analyze 2.2 km UK Met Office Unified Model convection-permitting model (CPM) projections for a pan-European domain. These new simulations represent a major increase in domain size, allowing us to examine the benefits of CPMs across a range of European climates. We find a change to the seasonality of extreme precipitation with warming. In particular, there is a relatively muted response for summer, which contrasts with much larger increases in autumn and winter. This flattens the hourly extreme precipitation seasonal cycle across Northern Europe which has a summer peak in the present climate. Over the Western Mediterranean, where autumn is the main extreme precipitation season, there is a regional increase in hourly extreme precipitation frequency, but local changes for lower precipitation thresholds are often insignificant. For mean precipitation, decreases are projected across Europe in summer, smaller decreases in autumn, and increases in winter; comparable changes are seen in the driving general circulation model (GCM) simulations. The winter mean increase is accompanied by a large decrease of winter mean snowfall. Comparing the driving GCM projections with the CPM ones, the CPMs show a robust enhanced intensification of precipitation extremes at the convection-permitting scale compared to coarser resolution climate model projections across various European regions for summer and autumn. Electronic supplementary material The online version of this article (10.1007/s00382-020-05192-8) contains supplementary material, which is available to authorized users.

In the last years, many countries in Europe have been experiencing an increased frequency of extreme precipitation leading to natural disasters like floods and landslides. In Italy, the majority of the country’s natural disasters have been related to extreme precipitation. Floods and landslides have led to the country experiencing great loss in its social and economic structure. Early warning systems are important to stakeholders such as Disaster Risk Managers to make informed decisions in relation to a forecasted disaster.Extreme precipitation is often associated with specific circulation patterns. Precursor information about atmospheric circulation patterns can therefore act as an indicator of an oncoming extreme precipitation event. The objective of this work is to identify the weather circulation patterns associated with extreme precipitation events over Italy.E-OBS precipitation datasets were used to identify the most intense extreme precipitation events for each season for the period 1990-2020 across Italy. Mean sea level pressure and 500 hPa geopotential height from the ERA5 dataset were used to identify circulation anomalies associated with the extreme events. The analysis is performed by clustering extreme precipitation events into three homogeneous climatic zones in Italy defined following the Köppen-Geiger classification.Results show that extreme precipitation events are always associated with an intense low pressure system located within the Euro-Mediterranean region. Depending on the location of precipitation extremes across different climatic zones, low pressure location changes, also modifying the atmospheric circulation and the associated moisture transport. Namely, for precipitation extremes occurring in the Italian peninsula, the low pressure is located in central-western Europe, while for extremes in Sardinia and Sicily, low pressure is in the Mediterranean.

Compared to standard regional climate models (RCMs), convection-permitting models (CPMs) provide an improved representation of sub-daily precipitation statistics and extremes thanks mainly to the possibility to switch off the deep convection parameterisation, a known source of model error and uncertainties. The more realistic representation of local processes in CPMs leads to a greater confidence in their projections of future changes in short-duration precipitation extremes.The quantification of uncertainties on future changes at this resolution has been barely touched. Using the first-ever ensemble of CPMs run within the UK Climate Projections project, Fosser et al. (2020) found that the climate change signal for extreme summer precipitation may converge in CPMs in contrast to RCMs, thanks to a more realistic representation of the local storm dynamics.Here we use the first multi-model CPMs ensemble over the greater Alpine region, run under the auspices of the World Climate Research Programme’s (WCRP) Coordinated Regional Downscaling Experiment Flagship Pilot Study on Convective phenomena at high resolution over Europe and the Mediterranean (Coppola et al. 2020). Several statistics are used to determine the uncertainties in the climate change signal trying to disentangle model uncertainties from natural variability. We found that the contribution of model to the total uncertainties is substantially reduced in CPMs compared to the driving models in summer. This is likely linked to the removal of the uncertainties associated with the convective parameterisation and to a more realistic representation of convective and local dynamical processes in the CPMs. Fosser G, Kendon EJ, Stephenson D, Tucker S (2020) Convection‐Permitting Models Offer Promise of More Certain Extreme Rainfall Projections. Geophys Res Lett 47:0–2. doi: 10.1029/2020GL088151Coppola, E., Sobolowski, S., Pichelli, E. et al.A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean. Clim Dyn55, 3–34 (2020). https://doi.org/10.1007/s00382-018-4521-8

In the context of the accelerating global warming, surface air temperature in the Arctic region is increasing faster than the Northern Hemisphere or global average. This phenomenon is expression of the so-called Arctic Amplification (AA), and is related to the interaction of several processes, including the observed reduction in sea ice and the associated ice-albedo feedback. The AA potentially affects atmospheric circulation patterns in the Northern Hemisphere, namely by modifying the westerly jet stream dynamics and the occurrence of weather regimes. In particular, atmospheric blocking at mid-to-high latitudes may be subject to significant variations in formation, frequency, spatial patterns and intensity due to of the changes in the AA-induced changes in atmospheric circulation.The objective of this study is to investigate the dynamics of atmospheric blocking in the Northern Hemisphere, by analysing the variability and frequency of the associated spatial patterns at decadal time scales. To this aim, the ERA5 reanalysis is used, and blocking events are detected based on geopotential height gradients between mid and high latitude regions.Results highlight that blocking is associated with the occurrence of extreme events, in particular with increased likelihood of heatwaves and cold spells in the blocking high. Moreover, impacts are observed in the region adjacent to the blocking high, due to the persistent deflection of synoptic disturbances. The relationship between AA and blocking events is also analysed, to identify possible mechanisms controlling the variability of atmospheric blocking in the last decades.

Italy is a world leader for viticulture and wine business. According to the 2021 National Wine Market Forum, promoted by National Wine Union (Uiv), the wine business in Italy is expected to have an annual turnover of 11 billion euros in 2022, keeping Italy at second place in the world trade market ranking. Our study aims to understand the impact of climate change on wine production in Italy to provide useful information to winegrowers and stakeholders involved in the wine business to make their activities more sustainable and more resilient to climate change. The climate variables that most influence grape growth are: temperature, precipitation, and evapotranspiration. Starting from these variables, we calculate a range of bioclimatic indices, selected following the International Organisation of Vine and Wine Guidelines (OIV), to be correlated with grape yield data. Using observations from the E-OBS gridded dataset, we investigate how the bioclimatic indices changed in the last 39 years (1980-2019), and the impact of these changes on grape productivity aggregated at the regional (NUTS2) scale. The Italian Statistic Institute (ISTAT) provides yearly grape yield data for each region, which allows us to account for specific regional grape characteristics and wine production policies. Our results show low and not statistically significant correlations between individual bioclimatic indices and yields in most of the NUTS2 aggregations. Climate is not the only factor that influences wine productivity. In fact, vineyard management, policies, and markets can play a major role and those data are not included in the ISTAT dataset. The study highlights the need for higher quality data, including their metadata, and the active involvement of local businesses in this type of impact study.

Subdaily extreme precipitation may trigger fast hydro-geomorphic responses, such as flash floods and debris flows, which cause numerous fatalities and large damage. Compared to coarser resolution models, high-resolution models, called convection-permitting (CPMs), more realistically represent convective processes that are key for the correct representation of subdaily extremes, and thus provide higher confidence in the future extreme estimates. However, due to the high computational demands, the existing CPM simulations are only available for relatively short time periods (10–20 years at most), too short for deriving precipitation frequency analyses with conventional approaches. Recent extreme value analysis methods, based on all “ordinary” observations rather than on just yearly maxima or a few values over a high threshold, offer an opportunity for exploiting these short data records to reliably estimate return levels associated with long return periods. Here, we examine subdaily precipitation extremes from three 10-year time slices (historical 1996-2005, near-future 2041-2050, and far future 2090-2099 – under the RCP8.5 scenario) of COSMO-crCLIM model simulations at 2.2 km resolution. We focus on the Eastern Alpine transect characterised by a complex orography, where significant changes in subdaily annual maxima have been already observed. We find that, although the storms' frequency will decrease in the region, the mean annual maxima will increase continuously in the near and far future, especially at shorter durations. Investigation of extreme return levels shows a similar trend, with larger changes in the far future. A shift in the seasonality is also reported, with extremes moving from late summer-autumn (historical), to autumn (near future), and autumn-winter (far future).

Convection‐permitting climate models (CMPs) give a much more realistic representation of sub-daily precipitation statistics compared to coarser resolution climate models, thanks to the explicit representation of convection. Their higher spatial and temporal resolution allows to used them directly to study future changes in the frequency, intensity, and spatiotemporal patterns of heavy rainfall over complex terrain. However, the high computational requirements of CPM runs restricts the existing simulations to relatively short time periods (10–20 years), too short for deriving precipitation frequency analyses with conventional approaches. Alternative methods, based on the so called Metastatistical Extreme Value Distribution, were recently proposed (e.g. Marani and Ignaccolo, 2015) for deriving frequency analyses from shorter data records, promising improved applications based on CPMs. These approaches rely on the concept of ordinary events, which are all the independent events that share the statistical properties of extremes: once the upper tail of the distribution of ordinary events is known, it is possible to derive an extreme value distribution by explicitly considering their yearly occurrence frequency.Here, we investigate the CPM ability to represent the upper tail of sub-daily precipitation in a complex-orography region in the Eastern Italian Alps. In this area, different orographic impacts on sub-daily precipitation upper tail were reported at different durations (Formetta et al., 2021), and significant temporal trends in their intensity were reported during the last few decades (Libertino et al., 2019), making it a challenging and interesting test case for CPM simulations. As CPM we used the COSMO model run at 2.2 km resolution over Europe, driven with ERA Interim for the period 2000-2009. We use 180 rain gauges to benchmark the CPM simulation. CPM time series are extracted for the grid points corresponding to the rain gauges, and hourly time series are created from both stations and CPMs. In each time series, independent storms are separated by 24-hour dry hiatuses, and ordinary events for 9 durations between 1 and 24 hours are defined as the corresponding peak intensity of each storm. Ordinary events upper tails are modeled using a Weibull distribution (two-parameter stretched exponential), which was previously reported to well reproduce the statistics of extremes in the area. The ability of CPMs to reproduce the model parameters and extreme quantiles up to 100-year return period, and their dependence on elevation are evaluated, together with the dependence of the biases with elevation. A general overestimation is found for annual maxima (10-40%), and the estimated quantiles (10-60%), especially for short durations. The bias significantly depends on elevation, with increasing overestimation of the 1-hour quantiles with elevation. It seems that CPMs cannot represented well the “reversed orographic effect” reported by previous studies.

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius–Clapeyron (CC) rate (6–7% K −1 ), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.

Compared to standard regional climate models (RCMs), convection-permitting models (CPMs) provide an improved representation of sub-daily precipitation statistics and extremes thanks mainly to the possibility to switch off the deep convection parameterisation, a known source of model error and uncertainties. The more realistic representation of local processes in CPMs leads to a greater confidence in their projections of future changes in short-duration precipitation extremes. Recent literature on CPMs seems to agree on a future increase of extreme precipitation, above Clausius‐Clapeyron scaling in some cases, which is likely to have severe socio-economic impacts.The quantification of uncertainties on future changes at this resolution has been barely touched. Using the first‐ever ensemble of CPMs run within the UK Climate Projections project, Fosser et al. (2020) found that the climate change signal for extreme summer precipitation may converge in CPMs in contrast to RCMs, thanks to a more realistic representation of the local storm dynamics.Here we use the first multi-model CPMs ensemble over the greater Alpine region, run under the auspices of the World Climate Research Programme’s (WCRP) Coordinated Regional Downscaling Experiment Flagship Pilot Study on Convective phenomena at high resolution over Europe and the Mediterranean (Coppola et al. 2020). In our analysis we compared the uncertainties in the CPMs ensemble to the driving models following a similar method to Fosser et al. (2020). In this presentation we will show if multi-model CPMs can really provide more certain extreme rainfall projections then their parent coarser resolution models. Fosser G, Kendon EJ, Stephenson D, Tucker S (2020) Convection‐Permitting Models Offer Promise of More Certain Extreme Rainfall Projections. Geophys Res Lett 47:0–2. doi: 10.1029/2020GL088151Coppola, E., Sobolowski, S., Pichelli, E. et al. A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean. Clim Dyn 55, 3–34 (2020). https://doi.org/10.1007/s00382-018-4521-8

In Italy the wine industry is an economic asset representing the 8% of the annual turnover of the Food & Beverage sector, according to Unicredit Industry Book 2019. Viticulture is strongly influenced by weather and climate, and winegrowers in Europe have already experienced the impact of climate change in terms of more frequent drought periods, warmer and longer growing seasons and an increased frequency of weather extremes. These changes impact on both yield production and wine quality.Our study aims to understand the impact of climate change on wine production, to estimate the risks associated with climate factors and to suggest appropriate adaptation measurement. The weather variables that most influence grape growth are: temperature, precipitation and evapotranspiration. Starting for these variables we calculate a range of bioclimatic indices, selected following the International Organisation of Vine and Wine Guidelines (OIV), and correlate these with wine productivity data. According to the values of different indices it is possible to determine the more suitable areas for wine production, where we expect higher productivity, although the climate is not the only factor influencing yield.Using the convection-permitting models (CPMs – 2.2 horizontal resolution) we investigate how the bioclimatic indices changed in the last 20 years, and the impact of this change on grapes productivity. We look at possible climate trends and at the variation in the frequency distribution of extreme weather events. The CPMs are likely the best available option for this kind of impact studies since they allow a better representation of surface and orography field, explicitly resolve deep convection and show an improved representation of extremes events. In our study, we compare CPMs with regional climate models (RCMs – 12 km horizontal resolution) to evaluate the possible added value of high resolution models for impact studies. To compare models' output to observation the same analysis it carried out using E-OBS dataset.Through our impact study, we aim to provide a tool that winegrower and stakeholders involved in the wine business can use to make their activities more sustainable and more resilient to climate change.

We investigate the effect of using convection-permitting models (CPMs) spanning a pan-European domain on the representation of precipitation distribution at a climatic scale. In particular we compare two 2.2 km models with two 12 km models run by ETH Zurich (ETH-12 km and ETH-2.2 km) and the Met-Office (UKMO-12 km and UKMO-2.2 km). The two CPMs yield qualitatively similar differences to the precipitation climatology compared to the 12 km models, despite using different dynamical cores and different parameterization packages. A quantitative analysis confirms that the CPMs give the largest differences compared to 12 km models in the hourly precipitation distribution in regions and seasons where convection is a key process: in summer across the whole of Europe and in autumn over the Mediterranean Sea and coasts. Mean precipitation is increased over high orography, with an increased amplitude of the diurnal cycle. We highlight that both CPMs show an increased number of moderate to intense short-lasting events and a decreased number of longer-lasting low-intensity events everywhere, correcting (and often over-correcting) biases in the 12 km models. The overall hourly distribution and the intensity of the most intense events is improved in Switzerland and to a lesser extent in the UK but deteriorates in Germany. The timing of the peak in the diurnal cycle of precipitation is improved. At the daily time-scale, differences in the precipitation distribution are less clear but the greater Alpine region stands out with the largest differences. Also, Mediterranean autumnal intense events are better represented at the daily time-scale in both 2.2 km models, due to improved representation of mesoscale processes.

UK Climate Projections 2018 (UKCP18) included land and marine projections and were published in 2018 to replace UKCP09. The land projections had three components, and all were designed to provide more information on future weather compared to UKCP09. The first component updated the UKCP09 probabilistic projections by including newer CMIP5 data and focussing on seasonal means from individual years rather than 30-year averages. The probabilistic projections represent the wider uncertainty. The second two components (global and regional projections) both had the aim of providing plausible examples of future climate, but at different resolutions.The global projections were a combination of 13 CMIP5 models and a 15-member perturbed parameter ensemble (PPE) of coupled simulations for 1900-2100 using CMIP5 RCP8.5 from 2005 onwards. The PPE was provided at 60km atmosphere, quarter degree ocean and the large-scale conditions from twelve of the members were used to drive the regional model at both 12km and 2.2km resolution. These plausible examples are more useful for providing information about weather in a future climate to support a storyline approach for decision making.The talk will present examples of new ways to use UKCP18 compared to UKCP09. We will show how the global projections can be used to understand that the recent record winter daily maximum temperature in the UK in 2019 had a large contribution from internal variability and what this means for breaking the record in a warming climate. We also use an example from China to demonstrate one way to exploit information at different time scales, looking at how a circulation index, which is predictable and related to tropical cyclone landfall, changes in a future climate.Finally, we show that while the enhanced resolution of the global and regional projections has improved our capability to provide climate information linked to the better representation of circulation, they lack diversity in some of the key drivers of future climate. Therefore, a key way forward will be to find an appropriate balance between the need for better diversity (e.g. multiple ensembles such as CMIP or PPEs) and the need for an appropriate resolution to retain this new capability.

Convection-permitting models (CPMs) provide a better representation of sub-daily precipitation statistics and convective processes, both on climate and NWP time scales, mainly thanks to the possibility to switch off the parameterisation of convection. The improved realism of these models gives us greater confidence in their ability to project future changes in short-duration precipitation extremes.The first 12-member ensemble of convection-permitting climate simulations over the UK was completed within the latest updates to the UK Climate Projections (UKCP). The 20-year long CPM simulations for present-day and end of century periods are nested in an ensemble of regional climate model (RCM) simulations over Europe driven by a global climate model ensemble. In the driving ensembles, uncertain parameters in the model physics are varied within plausible bounds to sample uncertainty. Although no perturbations are applied directly to the CPMs, this project allow us to provide a first-ever estimate of uncertainty at convection-permitting scale and thus provide UK risk assessment studies with more reliable climate change projections at local and hourly scales.Here we will present results looking at the uncertainty in future changes in hourly precipitation extremes across the CPM ensemble, and how this differs from the driving RCM ensemble.

For the first time internationally a model at a resolution on par with operational weather forecast models has been used for national climate scenarios. As part of the UK Climate Projections (UKCP) project, an ensemble of 12 projections at 2.2km resolution have been carried out over the UK. These were launched in September 2019, with the aim of providing an improved simulation of extreme precipitation and also other high-impact events at local scales for the coming decades. At such high (2.2km) resolution, convection can be represented explicitly (‘permitted’) without the need for a parameterisation scheme, leading to a much more realistic representation of hourly precipitation characteristics, including extremes. In this talk initial results from the UKCP local (2.2km) projections will be presented. This includes new understanding of changes in winter mean precipitation, as well as projected changes in hourly precipitation extremes and the frequency of hot spells. I will also discuss remaining outstanding issues and the future outlook for convective-scale climate modelling.

Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

High Impact Weather (HIW), particularly Heavy Precipitation Events (HPE), are common phenomena affecting the Western Mediterranean (WMED) especially in the fall period. Understanding and evaluating the capability to adequately represent such events in model simulations is one of the main goals of the Hydrological cycle in the Mediterranean Experiment (HyMeX) and the main motivation of this investigation. In order to gain a better knowledge of the model representation of HPE and related processes we perform a seamless multi-model intercomparison at the event scale. Limited area model runs (grid spacing from 2 to 20 km) at weather and climate timescales are considered, four with parameterized and five with explicit convection. The performance of the nine models is compared by analysing precipitation, as well as convection-relevant parameters. An Intensive Observation Period (IOP12) from the HyMeX-SOP1 (Special Observation Period) is used to illustrate the results. During IOP12, HPE affected the north-western Mediterranean region, from Spain to Italy, as a consequence of Mesoscale Convective Systems (MCSs) which initiated and intensified in the area of investigation. Results show that: (i) the timing of the maximum precipitation seems to be linked with the representation of large-scale conditions rather than differences among models, (ii) Convection Permitting Models (CPMs) exhibit differences among each other, but better represent the short-intense convective events. All four convection-parameterised models produce a large number of weak and long-lasting events. Regional Climate Models (RCMs) capture the occurrence of the event but produce notably lower precipitation amount and hourly intensities than CPMs and Numerical Weather Prediction (NWP) models with parameterized convection. (iii) These differences do not seem to come from mean moisture or Convective Available Potential Energy (CAPE) which are in the same range for all models, but rather from differences in the variability and vertical distribution of moisture and the triggering of deep convection.

International audience; In this study we investigate the scaling of precipitation extremes with temperature in the Mediterranean region by assessing against observations the present day and future regional climate simulations performed in the frame of the HyMeX and MED-CORDEX programs. Over the 1979–2008 period, despite differences in quantitative precipitation simulation across the various models, the change in precipitation extremes with respect to temperature is robust and consistent. The spatial variability of the temperature–precipitation extremes relationship displays a hook shape across the Mediterranean, with negative slope at high temperatures and a slope following Clausius–Clapeyron (CC)-scaling at low temperatures. The temperature at which the slope of the temperature–precipitation extreme relation sharply changes (or temperature break), ranges from about 20 °C in the western Mediterranean to

A major source of uncertainty in regional climate model (RCM) simulations arises from the parameterisation of sub-grid scale convection. With increasing model resolution, approaching the so-called convection permitting scale, it is possible to switch off most of the convection parameterisations. A set of simulations using COSMO-CLM model has been carried out at different resolutions in order to investigate possible improvements and limitations resulting from increased horizontal resolution. For our analysis, 30 years were simulated in a triple nesting setup with 50, 7 and 2.8 km resolutions, with ERA40 reanalysis data at the lateral boundaries of the coarsest nest. The investigation area covers the state of Baden-Wurttemberg in southwestern Germany, which is a region known for abundant orographically induced convective precipitation. A very dense network of high temporal resolution rain gauges is used for evaluation of the model simulations. The purpose of this study is to examine the differences between the 7 and 2.8 km resolutions in the representation of precipitation at sub-daily timescales, and the atmospheric conditions leading to convection. Our results show that the highest resolution of RCM simulations significantly improves the representation of both hourly intensity distribution and diurnal cycle of precipitation. In addition, at convection permitting scale the atmospheric fields related to convective precipitation show a better agreement with each other. The results imply that higher spatial resolution partially improves the representation of the precipitation field, which must be the way forward for regional climate modelling.

Using the CRAY XE-6 at the HLRS high performance computing facilities provides the possibility to study various aspects of the regional climate employing the regional climate model COSMO-CLM. The research activities of the group “Regional Climate and Water Cycle” at the KIT focus on the regional atmospheric water cycle and, especially, on extremes and different goals are pursued in the individual research projects. Different regions and orographies are studied using different resolutions from 50 to 3 km. Furthermore, different time spans are investigated and computational capacities from 2 to 500 node-hours per year (Wall Clock Time) are required. The analyses comprise decadal climate simulations of Germany, Europe and Africa to assess regional decadal climate predictability. Further, climate projections are carried out for Baden-Wurttemberg (Germany) and novel ensemble generating techniques are implemented to better describe the involved uncertainties. High resolution (3 km) experiments are performed for Baden-Wurttemberg to study extremes and the effects of climate change on soil erosion. Moreover, the possibilities of adaption to climate change for Baden-Wurttemberg are analysed, with focus on extremes and combination of extremes (such as dry soil and extreme precipitation).

The scope of regional climate simulations carried out at the Institute for Meteorology and Climate Research (IMK) of Karlsruhe Institute of Technology (KIT) using the regional climate model (RCM) COSMO-CLM (CCLM) has been extended during the last years. From the focus first on Southwest Germany the area of interest has then been extended to the whole of Germany for the assessment of changes in flood risk for medium and small mountainous river catchments (CEDIM project: www.cedim.de), and within the frame of CORDEX (Coordinated Regional climate Downscaling Experiment, http://wcrp.ipsl.jussieu.fr/SF_RCD_CORDEX.html) also to the whole African continent. CORDEX aims to provide a framework to evaluate and benchmark RCMs and to design a set of experiments to produce climate projections for use in impact and adaption studies and as input to the IPCC 5th Assessment Report (AR5).

[1] A detailed analysis of the tidal regime in Venice, Italy, during the last century shows that the frequency and magnitude of high tides are correlated to interdecadal climatic oscillations. The monthly high tide maxima and the average elevation of all high tides are negatively correlated to the North Atlantic Oscillation (NAO), to the Arctic Oscillation (AO), to the East Atlantic – West Russian oscillation (EA-WR), and to the Polar Eurasia teleconnection (POL). The correlation is high during winter months for all four indices, whereas in the fall, when most of the city floods occur, the AO and the EA-WR exert a stronger influence on the tidal regime. During negative phases of the climate indices both the average elevation of high tides and the frequency of flooding increase consistently, with negative effects on the city and its monuments.