Given the regional surface network of the Umbria region, a mountainous area located in central Italy, the observed hourly temperature time series from 2010 to 2017 were analysed by applying basic and extended quality control procedures following World Meteorological Organization (WMO) standards. The validation procedure consisted of automatic quality control, producing validated data with metadata subsequently recorded in the NetCDF format. After these controls, data were checked manually and an extended procedure was applied to reconstruct the temperature time series for missing data. The spatiotemporal method used to reconstruct the data was a linear interpolation for 1 hr gaps and the empirical orthogonal function (EOF) algorithm for gaps ≥ 2 hr. The introduction of a complete and homogeneous data set of hourly reanalysis ERA5 (from the European Center for Medium-Range Weather Forecasts-ECMWF) allowed for the reconstruction of the longest gaps with statistical and physical consistency. The final product of the study is a continuous station time series of hourly temperatures that will be available to the public by the end of 2020; a daily version of the original time series is already available on the regional website.

The current evolution of numerical weather prediction models, climate applications, warning and decision support systems needs more information at increasingly finer scales. In this context, mesoscale meteorological networks (mesonets) can provide essential observations for the international community. However, they often suffer from the absence of a national and international coordination, scarce maintenance, inadequate data quality and redundancy. An integrated network design and the implementation of a unified quality management system could reveal the full socio‐economical benefits of mesonet information. This study provides a general procedure to realize an efficient and high‐quality mesonet starting from existing fragmented networks. The process starts by defining a network quality management system (NQMS), which is responsible for the station maintenance and the data quality control (QC) procedures. Stations are first classified based on their primary purpose, their landscape and the instruments siting and exposure in the station enclosure. Then, their quality performances are evaluated by a complex QC system made by numerous QC tests, whose specifications are tailored to the main surface observations. Finally, an integrated network design procedure is provided to identify observational lack and planning site interventions. The design is based on the purpose of the network and all the information gathered by the NQMS. Spatial, meteorological, climate and financial considerations are then used to decide whether to add, remove or modify observations. This procedure is tested in the Umbria region, Central Italy, where its implementation would lead to a considerable advancement in terms of regional weather and climate services.

Radiative-convective equilibrium (RCE) of an ensemble of clouds has been used as an example of a statistical equilibrium state of the atmosphere able to mimic the tropical part of the climate system (K. Emanuel et al., 2014). Indeed, given the crucial importance of the balance between radiative cooling and latent heating due to condensation and surface-sensitive heat flux in the Tropics in the Earth's global balance, the RCE state of the atmosphere has become a proxy to study the link between global circulation and convection (Held et al., 1993;Pauluis & Held, 2002a, 2002bRandall et al., 1994;Yano & Plant, 2012). The implicit assumption in this is that the tropical circulation may be larger than the small-scale convective processes of an ensemble of clouds and that the statistical equilibrium implied by the RCE state is based on processes in near-equilibrium on different space and time scales with the large-scale flow. This point was brought to general attention when the increased computing capability available made it possible to run three-dimensional high-resolution simulations (Bretherton et al., 2005;Tompkins & Craig, 1998) and to study the sensitivity of RCE states using models with enhanced dimensions of the grid reaching the dimensions of mesoscale processes, with explicit moist variables and different physics

Even if the sensitivity of vegetation phenology to climate change has been accepted on global and continental scales, the correlation between global warming and phenotypic variability shows a modulated answer depending on altitude, latitude, and the local seasonal thermal trend. To connect global patterns of change with local effects, we investigated the impact of the observed signal of warming found in Central Italy on two different willow species, Salix acutifolia and Salix smithiana, growing in three phenological gardens of the International Phenological Gardens’ network (IPG) located in different orographic positions. The time series of temperatures and phenological data for the period 2005–2018 were analysed first to find trends over time in the three gardens and then to correlate the recent local warming and the change in the two species phenology. The results confirmed the correlation between phenological trends and local trend of temperatures. In particular: budburst showed a trend of advancement of 1.4 days/year on average in all three gardens; flowering showed a divergent pattern between the gardens of either advancement of 1.0 days/year on average or delay of 1.1 days/year on average; while senescence showed a delay reaching even 3.3 days/year, although significant in only two gardens for both species. These trends were found to be correlated mainly with the temperatures of the months preceding the occurrence of the phase, with a shift in terms of days of the year (DOY) of the two species. Our conclusion is that the observed warming in Central Italy played a key role in controlling the phenophases occurrences of the two willow species, and that the orographic forcing leads to the different shift in DOY of phenophases (from 5 to 20 days) due to the local thermal forcing of the three phenological gardens.

Recent analyses of satellite and surface observations reported a negative annual rainfall trend in central Italy. The complex orography of the Apennines and the strong influence of climate change in the Mediterranean basin complicates the explanation of such trends and their spatial variability. This work aims at describing the link between circulation weather types, orography and precipitation patterns observed in central Italy, as a first step towards the understanding of such climate trends. Using ERA5 reanalysis data from 1951 to 2019, four weather types are identified as most responsible for the spatial variability of rainfall in central Italy. They are associated with cyclonic circulations characterized by high water vapour transport coming from west, southwest, south-east and north-east. The analysis of wind speed and precipitation climatology for the period 1951-2019, as derived from both surface observa-

Climate change has a strong impact on inland water bodies such as lakes. This means that the increase in lake temperature recorded in recent decades-in Europe as well-can change the evaporation regime of the lakes. This, together with the variation of the water cycle, in particular precipitation, implies that the water mass balance of lakes may vary due to climate change. Water mass balance modeling is therefore of paramount importance to monitor lakes in the context of global warming. Although many studies have focused on such a modeling, there is no shared approach that can be used for any lake across the globe, irrespective of the size. This becomes even more problematic for shallow and small lakes, for which few studies exist. For this reason, in this paper the use of reanalysis data, in particular ERA5-Land provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), is proposed for the mass balance modeling. In fact, ERA5-Land has a global coverage and it is the only data source comprising a specific model for lakes, the Fresh-water Lake model (FLake). The chosen case study is the Trasimeno lake, a small and shallow lake located in Central Italy. The use of the reanalysis was preceded by data validation by considering both ground-based and satellite observations. The results show that there is a good agreement between the observed monthly variation of the lake level, ΔH, and the corresponding values of the water storage, δ, computed by means of the ERA5-Land data (Pearson coefficient larger than 70%). Discrepancies between observations and the ERA5-Land data happen in periods characterized in Europe by an extreme climate anomaly. This promising result encourages the use of ERA5-Land for other lakes.

Spontaneous aggregation of deep convection is a common feature of idealized numerical simulations of the tropical atmosphere in Radiative-Convective Equilibrium (RCE). However, at coarse grid resolution where deep convection is not fully resolved, the occurrence of this phenomenon is extremely sensitive to subgrid-scale processes. This study focuses on the role played by mixing and entrainment, either provided by the turbulence model or the implicit numerical dissipation. We have analyzed the results of two different models, WRF and SAM, and we have compared different configurations by varying the turbulence models, the numerical schemes and the horizontal spatial resolution. At coarse grid resolution (3 km), removing turbulent mixing prevents the occurrence of Convective Self-Aggregation (CSA) in low numerical diffusion models, while delaying it in high numerical diffusion models. When the horizontal grid resolution is refined to 1 km (thus reducing the implicit numerical dissipation), CSA is achieved only by increasing the explicit turbulent mixing. In this case, CSA was found to occur even with a small amount of shallow clouds. Therefore, this study suggests that the sensitivity of CSA to horizontal grid resolution is not primarily due to the corresponding decrease in shallow clouds. Instead, it is found that turbulent mixing and dissipation at small scales regulate the amplitude of humidity perturbations introduced by convection in the free troposphere: the greater the dissipation at small scales, the greater the size and the strength of humidity perturbations in the free troposphere that can destabilize the RCE state.

The Radiative-Convective Equilibrium (RCE) of two models exhibiting convective aggregation has been compared. The goal of the work, following the suggestion from the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP), is to identify key parameters controlling self-aggregation in RCE for both models and discuss the processes controlled by these parameters in order to find the simulations similarities and to test their differences. The two models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), have different physical and numerical formulations. This allowed us to compare the sensitivity to processes related to self-aggregation. When self-aggregation occurs, the two models present similar statistics for what concerns precipitation, warming, and drying of the atmosphere and anvil cloud area reduction (leading to an "Iris effect'), within the spread of the RCEMIP values. On the other hand, they differ both in the degree of organization and the organization feedback: SAM is strongly organized (is on the highest quartile of the RCEMIP for the Iorg Index) and the convective organization is achieved by cloud-radiative feedback; ARPS is weakly organized (on the multi-model average of the RCEMIP for the Iorg Index) and the moisture-convection feedback is leading to the convective organization. The prevalence of one mechanism over the other has been found in the interaction between the microphysics and the sub-cloud layer properties. This comparison suggests that, in order to have a robust measure of climate sensitivity, climate models should include both types of convective organization mechanisms as shown by the two models.