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Reanalysis and satellite data indicate a decreasing precipitation area in recent decades, affecting local water resources and precipitation intensities. We have used CMIP6 simulations to test the hypothesis of a shrinking precipitation area in a warming climate. Our analyses reveal that SSP5-8.5 projections show a robust decrease in the precipitation area between 50 °S and 50 °N, and globally in 75% of the simulations. The new findings support the observed relationship, although to a lesser extent than earlier found in reanalysis and satellite data. We find a poleward shift of precipitation, increasing the daily precipitation area in the Arctic from 18% to 28%. At lower latitudes the precipitation area is reduced due to a decreasing occurrence of precipitation. These changes are related to the expansion of low relative humidity zones in the lower-to-mid troposphere, specifically at the poleward edges of the subtropics. [ABSTRACT FROM AUTHOR]

Reanalyses are utilized for calculating climatological trends due to their focus on temporal consistency. ERA5 reanalysis family has proven to be a valuable and widely used product for trend extraction. This study specifically examines long‐term trends in total annual precipitation across two climatic hotspots: the Alps and Italy. It is acknowledged by reanalysis producers that variations in the observational systems used for data assimilation impact water cycle components like precipitation. This understanding highlights the need of assessing to what extent temporal variations in ERA5 precipitation amounts are solely a result of climate variations and the influence of changes in the observational system impacting simulation accuracy. Our research examines the differences between ERA5 and similar reanalyses against homogenized, trend‐focused observational datasets. We find that discerning the climatological signal within ERA5 adjustments for observational system variations is challenging. The trend in ERA5 from 1940 to 1970 shows distinct patterns over the Alps and, to a lesser extent, Italy, diverging from later ERA5 trends and those in other reanalyses. Notably, ERA5 shows an increasing, although nonlinear, trend in the deviation between ERA5 and the observational datasets. Improving future reanalysis interpretability could involve adopting a model‐only integration for the same period, akin to the ERA‐20C and ERA‐20CM approach. [ABSTRACT FROM AUTHOR]

Surface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short‐range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state‐of‐the‐art in global reanalyses and supplies initial and boundary conditions for higher‐resolution regional reanalyses designed to capture finer‐scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy. This study analyses the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991–2020 period. The analyses encompass ERA5, ERA5‐Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High‐Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR‐REA_IT). The validation we conduct pertains to both the spatial distribution of 30‐year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography. Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, and Sardinia. Conversely, VHR‐REA_IT shows a warm bias in the Po Valley up to 3°C in summer. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy and correlation with observational fields. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to <0.5°C. The results of this study empower reanalysis users across multiple sectors to gain a more profound insight into the capabilities and constraints of different reanalysis products. The knowledge and the characterization of the reanalyses t2m bias against observations can indeed be crucial when incorporating these products into their research and practical applications. [ABSTRACT FROM AUTHOR]

We conducted an analysis of hydrological cycle variations across 13 regions of varying sizes distributed across different continents. The analysis is based on five reanalysis datasets of daily precipitation, all produced by the European Centre on Medium‐Range Weather Forecasts (ECMWF): ERA5 high‐resolution, ERA5 ensemble, CERA‐20C, ERA20‐C and ERA20‐CM. We examined several climate indicators, including the daily mean precipitation, the 75th and 99th percentiles, the precipitation area fraction and the area fractions with precipitations exceeding 10 and 20 mm. We evaluated the ability of the reanalyses to capture precipitation at specific spatial scales using scale‐separation diagnostics based on 2D wavelet decomposition. The climatological energy spectra of precipitation derived from the analysis describe the scales that each reanalysis can accurately reproduce, serving as a unique signature for each dataset. We compared the spatial scales that were comparable across the different reanalyses and examined the temporal trends of energy on those scales. The results indicate that the hydrological cycle is undergoing changes in all regions, with some variations observed across different regions. Common features include an increase in intense precipitation events and a decrease in the corresponding spatial extent. The ensemble of ERA5 reanalyses exhibited the smallest effective resolution, as determined by the scale‐separation method, and displayed more pronounced trends compared to other reanalyses. Notably, an acceleration of changes is evident in the last 20 years. However, Central Asia may be an exception, showing relatively less noticeable changes in the hydrological cycle. [ABSTRACT FROM AUTHOR]

Both the total amount of precipitation falling on Earth's surface and the fraction of the surface area on which it falls represent two key global climate indicators for Earth's global hydrological cycle. We show that the fraction of Earth's surface area receiving daily precipitation is closely connected to the global statistics of local wet-day frequency and mean precipitation intensity, based on the ERA5 reanalysis. Our analysis of the global statistical distribution of local temporal mean precipitation intensity μ revealed a close link between (1) its global spatial average ⟨ μ ⟩ and (2) the total daily precipitation falling on Earth's surface divided by the global surface area fraction on which it falls. This correlation highlights an important connection, since the wet-day frequency and the mean precipitation intensity represent two key parameters that may be used to approximately infer the probability of heavy rainfall on local scales. We also found a close match between the global mean surface temperature and both the total mass of 24-h precipitation falling on Earth's surface as well as surface area receiving 24-h precipitation in the ERA5 data, highlighting the dependency between the greenhouse effect and the global hydrological cycle. Moreover, the total planetary precipitation and the daily precipitation area represent links between the global warming and extreme precipitation amounts that traditionally have not been included in sets of essential climate indicators. A simple back-of-the-envelope calculation suggests that half of Δ ⟨ μ ⟩ / Δ T = 0.47 mm / day can be explained by increased 24-h precipitation and half by a reduced fractional area of 24-h precipitation. [ABSTRACT FROM AUTHOR]

Verif is an open-source tool for verifying weather predictions against a ground truth. It is suitable for a range of applications and designed for iterative product development involving fine-tuning of algorithms, comparing methods, and addressing scientific issues with the product. The tool generates verification plots based on user-supplied input files containing predictions and observations for multiple point-locations, forecast lead times, and forecast initialization times. It supports over 90 verification metrics and diagrams and can evaluate deterministic and probabilistic predictions. An extensive set of command-line flags control how the input data are aggregated, filtered, stratified, and visualized. The broad range of metrics and data manipulation options allows the user to gain insight from both summary scores and detailed time series of individual weather events. Verif is suitable for many applications, including assessing numerical weather prediction models, climate models, reanalyses, machine learning models, and even the fidelity of emerging observational sources. The tool has matured through long-term development at the Norwegian Meteorological Institute and the University of British Columbia. Verif comes with an extensive wiki page and example input files covering a wide range of prediction applications, allowing students and researchers interested in verification to get hands-on experience with real-life datasets. This article describes the functionality of Verif version 1.3 and shows how the tool can be used for effective product development. [ABSTRACT FROM AUTHOR]

The ERA5 global reanalysis has been compared against a high‐resolution regional reanalysis (COSMO‐REA6) by means of scale‐separation diagnostics based on 2d Haar discrete wavelet transforms. The presented method builds upon existing methods and enables the assessment of bias, error and skill for individual spatial scales, separately. A new skill score (evaluated against random chance) and the Symmetric Bounded Efficiency are introduced. These are compared to the Nash‐Sutcliffe and the Kling‐Gupta Efficiencies, evaluated on different scales, and the benefits of symmetric statistics are illustrated. As expected, the wavelet statistics show that the coarser resolution ERA5 products underestimate small‐to‐medium scale precipitation compared to COSMO‐REA6. The newly introduced skill score shows that the ERA5 control member (EA‐HRES), despite its higher variability, exhibits better skill in representing small‐to‐medium scales with respect to the smoother ensemble members. The Symmetric Bounded Efficiency is suitable for the inter‐comparison of reanalyses, since it is invariant with respect to the order of comparison. [ABSTRACT FROM AUTHOR]

We present a comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national meteorological services, the latter used as reference values. The empirical distributions of the crowdsourced observations in the surroundings of reference stations are used to assess accuracy and precision of crowdsourced data. We found that reference values are typically within the distribution of the crowdsourced data. However, as the amount of precipitation increases, the spread of the crowdsourced distribution increases and the reference values are more and more frequently found towards the right tail of the distribution. These results indicate that accuracy and precision of crowdsourced data change as precipitation increases. We have studied the sensitivity of our results to the size of the neighbourhood chosen around the reference stations and we show that by aggregating the values over those neighbourhoods, crowdsourced data can be trusted in determining precipitation occurrence. We have assessed the variability of precipitation within small neighbourhoods (of radius 1, 3 and 5 km) and we provide estimates on the basis of the precipitation amounts. Our study quantifies the variability of hourly precipitation over small regions, of the size of the so-called "unresolved spatial scales" in limited area models, based on three years of data collected at several places in Scandinavia. [ABSTRACT FROM AUTHOR]

Crowdsourced meteorological observations are becoming more prevalent and in some countries their spatial resolution already far exceeds that of traditional networks. However, due to the larger uncertainty associated with these observations, quality control (QC) is an essential step. Spatial QC methods are especially well suited for such dense networks since they utilize information from nearby stations. The performance of such methods usually depends on the choice of their parameters. There is, however, currently no specific procedure on how to choose the optimal settings of such spatial QC methods. In this study we present a framework for tuning a spatial QC method for a dense network of meteorological observations. The method uses artificial errors in order to perturb the observations to simulate the effect of having errors. A cost function, based on the hit and false alarm rate, for optimizing the spatial QC method is introduced. The parameters of the spatial QC method are then tuned such that the cost function is optimized. The application of the framework to the tuning of a spatial QC method for a dense network of crowdsourced observations in Denmark is presented. Our findings show that the optimal settings vary with the error magnitude, time of day, and station density. Furthermore, we show that when the station network is sparse, a better performance of the spatial QC method can be obtained by including crowdsourced observations from another denser network. [ABSTRACT FROM AUTHOR]

Strong historical and predicted future warming over high-latitudes prompt significant effects on agricultural and forest ecosystems. Thus, there is an urgent need for spatially-detailed information of current thermal growing season (GS) conditions and their past changes. Here, we deployed a large network of weather stations, high-resolution geospatial environmental data and semi-parametric regression to model the spatial variation in multiple GS variables (i.e. beginning, end, length, degree day sum [GDDS, base temperature + 5 °C]) and their intra-annual variability and temporal trends in respect to geographical location, topography, water and forest cover, and urban land use variables over northern Europe. Our analyses revealed substantial spatial variability in average GS conditions (1990–2019) and consistent temporal trends (1950–2019). We showed that there have been significant changes in thermal GS towards earlier beginnings (on average 15 days over the study period), increased length (23 days) and GDDS (287 °C days). By using a spatial interpolation of weather station data to a regular grid we predicted current GS conditions at high resolution (100 m × 100 m) and with high accuracy (correlation ≥ 0.92 between observed and predicted mean GS values), whereas spatial variation in temporal trends and interannual variability were more demanding to predict. The spatial variation in GS variables was mostly driven by latitudinal and elevational gradients, albeit they were constrained by local scale variables. The proximity of sea and lakes, and high forest cover suppressed temporal trends and inter-annual variability potentially indicating local climate buffering. The produced high-resolution datasets showcased the diversity in thermal GS conditions and impacts of climate change over northern Europe. They are valuable in various forest management and ecosystem applications, and in adaptation to climate change. [ABSTRACT FROM AUTHOR]

Gridded analyses of observed precipitation are an important data resource for environmental modelling, climate model evaluation and climate monitoring. In Europe, datasets that resolve the rich mesoscale variations widely exist for the national territories, but similar datasets covering the entire continent are more recent. Here, we evaluate daily precipitation in two newly available pan‐European datasets: E‐OBS (v19.0e), a statistical analysis from rain‐gauge data, and ERA5, the new global reanalysis from ECMWF. Special interest is on how the refinements of grid spacing, the methodological upgrades and the quantification of uncertainty (ensemble), bear on capabilities at the mesoscale. The evaluation is conducted in three subregions, the Alps, the Carpathians and Fennoscandia, and involves as reference high‐quality regional datasets derived from dense rain‐gauge data. The study suggests that E‐OBS and ERA5 agree qualitatively well with the reference datasets. Major mesoscale patterns in the climatology (mean, wet‐day frequency, 95% quantile) are reproduced. The improvement over earlier versions of the datasets is evident. ERA5 was found to overestimate mean precipitation in all regions, related to too many wet days. The accuracy of E‐OBS was found to depend on station density, with spatial and temporal variations clearly less accurate in data sparse regions. In comparison, E‐OBS turned out to be superior to ERA5 in regions with dense data, but the two datasets are on a par in regions with sparse data, and partly ERA5 has advantages. For both datasets we find that the spatial resolution is coarser than the grid spacing, with overly smooth fields and an underestimation of high quantiles. Also, both datasets were found to be clearly overconfident in their uncertainty characterization (too small ensemble spread). Overall, the two datasets advance the characterization of precipitation on a pan‐European scale, but users are advised to take residual limitations into account in applications. [ABSTRACT FROM AUTHOR]

Gridded analyses of observed precipitation are an important data resource for environmental modelling, climate model evaluation and climate monitoring. In Europe, datasets that resolve the rich mesoscale variations widely exist for the national territories, but similar datasets covering the entire continent are more recent. Here, we evaluate daily precipitation in two newly available pan-European datasets: E-OBS (v19.0e), a statistical analysis from rain-gauge data, and ERA5, the new global reanalysis from ECMWF. Special interest is on how the refinements of grid spacing, the methodological upgrades and the quantification of uncertainty (ensemble), bear on capabilities at the mesoscale. The evaluation is conducted in three subregions, the Alps, the Carpathians and Fennoscandia, and involves as reference high-quality regional datasets derived from dense rain-gauge data. The study suggests that E-OBS and ERA5 agree qualitatively well with the reference datasets. Major mesoscale patterns in the climatology (mean, wet-day frequency, 95% quantile) are reproduced. The improvement over earlier versions of the datasets is evident. ERA5 was found to overestimate mean precipitation in all regions, related to too many wet days. The accuracy of E-OBS was found to depend on station density, with spatial and temporal variations clearly less accurate in data sparse regions. In comparison, E-OBS turned out to be superior to ERA5 in regions with dense data, but the two datasets are on a par in regions with sparse data, and partly ERA5 has advantages. For both datasets we find that the spatial resolution is coarser than the grid spacing, with overly smooth fields and an underestimation of high quantiles. Also, both datasets were found to be clearly overconfident in their uncertainty characterization (too small ensemble spread). Overall, the two datasets advance the characterization of precipitation on a pan-European scale, but users are advised to take residual limitations into account in applications. [ABSTRACT FROM AUTHOR]

There are few commonly used indicators that describe the state of Earth's global hydrological cycle and here we propose three indicators to capture how an increased greenhouse effect influences the global hydrological cycle and the associated rainfall patterns. They are: i) the 24-hr global total rainfall, ii) the global surface area with daily precipitation, and iii) the global mean precipitation intensity. With a recent progress in both global satellite observations and reanalyses, we can now estimate the global rainfall surface area to provide new insights into how rainfall intensity changes over time. Based on the ERA5 reanalysis, we find that the global area of daily precipitation decreased from 43 to 41% of the global area between 1950 and 2020, whereas the total daily global rainfall increased from 1440 Gt to 1510 Gt per day. However, the estimated 24-hr global precipitation surface area varies when estimated from different reanalyses and the estimates are still uncertain. To further investigate historical variations in the precipitation surface area, we carried out a wavelet analysis of 24-hr precipitation from the ERA5 reanalysis that indicated how the rainfall patterns have changed over time. Our results suggest that individual precipitation systems over the globe have shrunk in terms of their spatial extent while becoming more intense throughout the period 1950--2020. Hence, the wavelet results are in line with an acceleration of the rate of the global hydrological cycle, combined with a diminishing global area of rainfall. [ABSTRACT FROM AUTHOR]

Hourly precipitation over a region is often simultaneously simulated by numerical models and observed by multiple data sources. An accurate precipitation representation based on all available information is a valuable result for numerous applications and a critical aspect of climate monitoring. The inverse problem theory offers an ideal framework for the combination of observations with a numerical model background. In particular, we have considered a modified ensemble optimal interpolation scheme. The deviations between background and observations are used to adjust for deficiencies in the ensemble. A data transformation based on Gaussian anamorphosis has been used to optimally exploit the potential of the spatial analysis, given that precipitation is approximated with a gamma distribution and the spatial analysis requires normally distributed variables. For each point, the spatial analysis returns the shape and rate parameters of its gamma distribution. The ensemble-based statistical interpolation scheme with Gaussian anamorphosis for precipitation (EnSI-GAP) is implemented in a way that the covariance matrices are locally stationary, and the background error covariance matrix undergoes a localization process. Concepts and methods that are usually found in data assimilation are here applied to spatial analysis, where they have been adapted in an original way to represent precipitation at finer spatial scales than those resolved by the background, at least where the observational network is dense enough. The EnSI-GAP setup requires the specification of a restricted number of parameters, and specifically, the explicit values of the error variances are not needed, since they are inferred from the available data. The examples of applications presented over Norway provide a better understanding of EnSI-GAP. The data sources considered are those typically used at national meteorological services, such as local area models, weather radars, and in situ observations. For this last data source, measurements from both traditional and opportunistic sensors have been considered. [ABSTRACT FROM AUTHOR]

Hourly precipitation over a region is often simultaneously simulated by numerical models and observed by multiple data sources. An accurate precipitation representation based on all available information is a valuable result for numerous applications and a critical aspect of climate. Inverse problem theory offers an ideal framework for the combination of observations with a numerical model background. In particular, we have considered a modified ensemble optimal interpolation scheme, that takes into account deficiencies of the background. An additional source of uncertainty for the ensemble background has been included. A data transformation based on Gaussian anamorphosis has been used to optimally exploit the potential of the spatial analysis, given that precipitation is approximated with a gamma distribution and the spatial analysis requires normally distributed variables. For each point, the spatial analysis returns the shape and rate parameters of its gamma distribution. The Ensemble-based Statistical Interpolation scheme with Gaussian AnamorPhosis (EnSI-GAP) is implemented in a way that the covariance matrices are locally stationary and the background error covariance matrix undergoes a localization process. Concepts and methods that are usually found in data assimilation are here applied to spatial analysis, where they have been adapted in an original way to represent precipitation at finer spatial scales than those resolved by the background, at least where the observational network is dense enough. The EnSI-GAP setup requires the specification of a restricted number of parameters and specifically the explicit values of the error variances are not needed, since they are inferred from the available data. The examples of applications presented provide a better understanding of the characteristics of EnSI-GAP. The data sources considered are those typically used at national meteorological services, such as local area models, weather radars and in-situ observations. For this last data source, measurements from both traditional and opportunistic sensors have been considered. [ABSTRACT FROM AUTHOR]

Citizen weather stations are rapidly increasing in prevalence and are becoming an emerging source of weather information. These low-cost consumer-grade devices provide observations in real time and form parts of dense networks that capture high-resolution meteorological information. Despite these benefits, their adoption into operational weather prediction systems has been slow. However, MET Norway recently introduced observations from Netatmo's network of weather stations in the postprocessing of near-surface temperature forecasts for Scandinavia, Finland, and the Baltic countries. The observations are used to continually correct errors in the weather model output caused by unresolved features such as cold pools, inversions, urban heat islands, and an intricate coastline. Corrected forecasts are issued every hour. Integrating citizen observations into operational systems comes with a number of challenges. First, operational systems must be robust and therefore rely on strict quality control procedures to filter out unreliable measurements. Second, postprocessing methods must be selected and tuned to make use of the high-resolution data that at times can contain conflicting information. Central to resolving these challenges is the need to use the massive redundancy of citizen observations, with up to dozens of observations per square kilometer, and treating the data source as a network rather than a collection of individual stations. We present our experiences with introducing citizen observations into the operational production chain of automated public weather forecasts. Their inclusion shows a clear improvement to the accuracy of short-term temperature forecasts, especially in areas where existing professional stations are sparse. [ABSTRACT FROM AUTHOR]

In science, poor quality input data will invariably lead to faulty conclusions, as in the spirit of the saying "garbage in, garbage out". Atmospheric sciences make no exception and correct data is crucial to obtain a useful representation of the real world in meteorological, climatological and hydrological applications. Titan is a computer program for the automatic quality control of meteorological data that has been designed to serve real-time operational applications that process massive amounts of observations measured by networks of automatic weather stations. The need to quality control third-party data, such as citizen observations, within a station network that is constantly changing was an important motivation that led to the development of Titan. The quality control strategy adopted is a sequence of tests, where several of them utilize the expected spatial consistency between nearby observations. The spatial continuity can also be evaluated against independent data sources, such as numerical model output and remote sensing measurements. Examples of applications of Titan for the quality control of near-surface hourly temperature and precipitation over Scandinavia are presented. In the case of temperature, this specific application has been integrated into the operational production chain of automatic weather forecasts at the Norwegian Meteorological Institute (MET Norway). Titan is an open source project and it is made freely available for public download. One of the objectives of the Titan project is to establish a community working on common tools for automatic quality control, and the Titan program represents a first step in that direction for MET Norway. Further developments are necessary to achieve a solution that satisfies more users, for this reason we are currently working on transforming Titan into a more flexible library of functions. [ABSTRACT FROM AUTHOR]

seNorge_2018 is a collection of observational gridded datasets over Norway for daily total precipitation: daily mean, maximum, and minimum temperatures. The time period covers 1957 to 2017, and the data are presented over a high-resolution terrain-following grid with 1 km spacing in both meridional and zonal directions. The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a 20-year-long history and seNorge_2018 is its newest member, the first providing daily minimum and maximum temperatures. seNorge datasets are used for a wide range of applications in climatology, hydrology, and meteorology. The observational dataset is based on MET Norway's climate data, which have been integrated by the "European Climate Assessment and Dataset" database. Two distinct statistical interpolation methods have been developed, one for temperature and the other for precipitation. They are both based on a spatial scale-separation approach where, at first, the analysis (i.e., predictions) at larger spatial scales is estimated. Subsequently they are used to infer the small-scale details down to a spatial scale comparable to the local observation density. Mean, maximum, and minimum temperatures are interpolated separately; then physical consistency among them is enforced. For precipitation, in addition to observational data, the spatial interpolation makes use of information provided by a climate model. The analysis evaluation is based on cross-validation statistics and comparison with a previous seNorge version. The analysis quality is presented as a function of the local station density. We show that the occurrence of large errors in the analyses decays at an exponential rate with the increase in the station density. Temperature analyses over most of the domain are generally not affected by significant biases. However, during wintertime in data-sparse regions the analyzed minimum temperatures do have a bias between 2 ∘ C and 3 ∘ C. Minimum temperatures are more challenging to represent and large errors are more frequent than for maximum and mean temperatures. The precipitation analysis quality depends crucially on station density: the frequency of occurrence of large errors for intense precipitation is less than 5% in data-dense regions, while it is approximately 30 % in data-sparse regions. The open-access datasets are available for public download at daily total precipitation (10.5281/zenodo.2082320,); and daily mean (10.5281/zenodo.2023997,), maximum (10.5281/zenodo.2559372,), and minimum (10.5281/zenodo.2559354,) temperatures. [ABSTRACT FROM AUTHOR]

The influence of uncertainties in gridded observational reference data on regional climate model (RCM) evaluation is quantified on a pan‐European scale. Three different reference data sets are considered: the coarse‐resolved E‐OBS data set, a compilation of regional high‐resolution gridded products (HR) and the European‐scale MESAN reanalysis. Five high‐resolution ERA‐Interim‐driven RCM experiments of the EURO‐CORDEX initiative are evaluated against each of these references over eight European sub‐regions and considering a range of performance metrics for mean daily temperature and daily precipitation. The spatial scale of the evaluation is 0.22°, that is, the grid spacing of the coarsest data set in the exercise (E‐OBS). While the three reference grids agree on the overall mean climatology, differences can be pronounced over individual regions. These differences partly translate into RCM evaluation uncertainty. For most cases observational uncertainty is smaller than RCM uncertainty. Nevertheless, for individual sub‐regions and performance metrics observational uncertainty can dominate. This is especially true for precipitation and for metrics targeting the wet‐day frequency, the pattern correlation and the distributional similarity. In some cases the spatially averaged mean bias can also be considerably affected. An illustrative ranking exercise highlights the overall effect of observational uncertainty on RCM ranking. Over individual sub‐domains, the choice of a specific reference can modify RCM ranks by up to four levels (out of five RCMs). For most cases, however, RCM ranks are stable irrespective of the reference. These results provide a twofold picture: model uncertainty dominates for most regions and for most performance metrics considered, and observational uncertainty plays a minor role. For individual cases, however, observational uncertainty can be pronounced and needs to be definitely taken into account. Results can, to some extent, also depend on the treatment of precipitation undercatch in the observational reference. [ABSTRACT FROM AUTHOR]

Climate models show that global warming will disproportionately influence high‐latitude regions and indicate drastic changes in, among others, seasonal snow cover. However, current continental and global simulations covering these regions are often run at coarse grid resolutions, potentially introducing large errors in computed fluxes and states. To quantify some of these errors, we have assessed the sensitivity of an energy‐balance snow model to changes in grid resolution using a multiparametrization framework for the spatial domain of mainland Norway. The framework has allowed us to systematically test how different parametrizations, describing a set of processes, influence the discrepancy, here termed the scale error, between the coarser (5 to 50‐km) and finest (1‐km) resolution. The simulations were set up such that liquid and solid precipitation was identical between the different resolutions, and differences between the simulations arise mainly during the ablation period. The analysis presented in this study focuses on evaluating the scale error for several variables relevant for hydrological and land surface modelling, such as snow water equivalent and turbulent heat exchanges. The analysis reveals that the choice of method for routing liquid water through the snowpack influences the scale error most for snow water equivalent, followed by the type of parametrizations used for computing turbulent heat fluxes and albedo. For turbulent heat exchanges, the scale error is mainly influenced by model assumptions related to atmospheric stability. Finally, regions with strong meteorological and topographic variability show larger scale errors than more homogenous regions. Key Points: Scale sensitivity for an energy balance snow model depends on the choice of model parameterizationsThe method for computing turbulent heat fluxes influences the scale error largelyScale errors are larger in regions with high meteorological and topographic variability [ABSTRACT FROM AUTHOR]

The 1981–2010 monthly precipitation climatologies for Norway at 1 km resolution are presented. They are computed by an interpolation procedure (HCLIM+RK) combining the output from a numerical model with the in situ observations. Specifically, the regional climate model data set HCLIM‐AROME, based on the dynamical downscaling of the global ERA‐Interim reanalysis onto 2.5 km resolution, is considered together with 2009 rain‐gauges located within the model domain. The precipitation climatologies are defined by superimposing the grid of 1981–2010 monthly normals from the numerical model and the kriging interpolation of station residuals. The combined approach aims at improving the quality of gridded climatologies and at providing reliable precipitation gradients also over those remote Norwegian regions not covered by observations, especially over the northernmost mountainous areas. The integration of rain‐gauge data greatly reduces the original HCLIM‐AROME biases. The HCLIM+RK errors obtained from the leave‐one‐out station validation turn out to be lower than those provided by two considered interpolation schemes based on observations only: a multi‐linear local regression kriging (MLRK) and a local weighted linear regression (LWLR). As average over all months, the mean absolute (percentage) error is 10.0 mm (11%) for HCLIM+RK, and 11.4 (12%) and 11.6 mm (12%) for MLRK and LWLR, respectively. In addition, by comparing the results at both station and grid cell level, the accuracy of MLRK and LWLR is more sensitive to the spatial variability of station distribution over the domain and their interpolated fields are more affected by discontinuities and outliers, especially over those areas not covered by the rain‐gauge network. The obtained HCLIM+RK climatologies clearly depict the main west‐to‐east gradient occurring from the orographic precipitation regime of the coast to the more continental climate of the inland and it allows to point out the features of the climatic subzones of Norway. The paper presents the 1981–2010 monthly precipitation climatologies over Norway at 1‐km grid spacing. The climatologies are computed by an interpolation scheme (HCLIM+RK) combining the in situ observations with the regional climate model data set HCLIM‐AROME, based on the dynamical downscaling of the global ERA‐Interim reanalysis. The comparison with methods using observations only proved that HCLIM+RK improves the accuracy of Norwegian climatologies and provides reliable precipitation patterns also over the remote areas not covered by rain‐gauges. [ABSTRACT FROM AUTHOR]

seNorge_2018 is a collection of observational gridded datasets over Norway for: daily total precipitation; daily mean, maximum and minimum temperatures. The time period covers 1957 to 2017, and the data are presented over a high-resolution terrain-following grid with 1 km spacing in both meridional and zonal directions. The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a twenty-year long history and seNorge_2018 is its newest member, the first providing daily minimum and maximum temperatures. seNorge datasets are used for a wide range of applications in climatology, hydrology and meteorology. The observational dataset is based on MET Norway's climate data, which has been integrated by the European Climate Assessment and Dataset database. Two distinct statistical interpolation methods have been developed, one for temperature and the other for precipitation. They are both based on a spatial scale-separation approach where, at first, the analysis (i.e., predictions) at larger spatial scales are estimated. Subsequently they are used to infer the small-scale details down to a spatial scale comparable to the local observation density. Mean, maximum and minimum temperatures are interpolated separately, then physical consistency among them is enforced. For precipitation, in addition to observational data, the spatial interpolation makes use of information provided by a climate model. The analysis evaluation is based on cross-validation statistics and comparison with a previous seNorge version. The analysis quality is presented as a function of the local station density. We show that the occurrence of large errors in the analyses decays at an exponential rate with the increase in the station density. Temperature analyses over most of the domain are generally not affected by significant biases. However, during wintertime in data-sparse regions the analyzed minimum temperatures do have a bias between 2 °C and 3 °C. Minimum temperatures are more challenging to represent and large errors are more frequent than for maximum and mean temperatures. The precipitation analysis quality depends crucially on station density: the frequency of occurrence of large errors for intense precipitation is less than 5 % in data-dense regions, while it is approximately 30 % in data-sparse regions. . he open-access datasets are available20for public download at: daily total precipitation (DOI: https://doi.org/10.5281/zenodo.2082320, Lussana, 2018b); daily mean (DOI: https://doi.org/10.5281/zenodo.2023997, Lussana, 2018c) , maximum (DOI: https://doi.org/10.5281/zenodo.2559372, Lussana, 2018e) and minimum (DOI: https://doi.org/10.5281/zenodo.2559354, Lussana, 2018d) temperatures. [ABSTRACT FROM AUTHOR]

ABSTRACT The stationarity of rainfall annual maxima statistics is investigated in a northern Italian area characterized by important spatial variations in orography and precipitation climatology. Climatic changes in Italian recorded temperature series are widely acknowledged. In 2016, though, there is still discussion on the stationarity of the statistics of precipitation and its extremes in Italy. By means of standard hypothesis testing techniques, it is shown, for the first time in Italy, that changes in rainfall annual maxima climatology are indeed present in the period 1950-2005, relevant with regard to global climate change. In particular, in the rainy mountainous northwestern part of Lombardy, adjacent to the Swiss and Piedmont border, the annual maxima distribution for 6 to 24-h event durations significantly moves towards higher values when going from the first half (1950-1977) to the second half (1978-2005) of the period. It is remarkable, moreover, that for 1-h duration the distribution significantly moves towards lower values, suggesting a possible change in the precipitation regime. After compensating for data density changes by 'bootstrap' resampling, a median increase for all durations has also been confirmed for the (less rainy) Alpine area in northeastern Lombardy. Such changes in climatology have practical importance, because stationarity is a customary assumption when rainfall annual maxima frequencies are estimated for civil engineering. [ABSTRACT FROM AUTHOR]

The conventional climate datasets based on observations only are a widely used source of information for climate and hydrology. On the Norwegian mainland, the seNorge datasets of daily mean temperature and total precipitation amount constitute a valuable meteorological input for snow- and hydrological simulations which are routinely conducted over such a complex and heterogeneous terrain. A new seNorge version (seNorge2) has been released recently and to support operational applications for civil protection purposes, it must be updated daily and presented on a high-resolution grid (1 km of grid spacing). The archive goes back to 1957. The seNorge2 statistical interpolation schemes can provide high-resolution fields for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The statistical schemes build upon classical spatial interpolation methods, such as Optimal Interpolation and successive-correction schemes, and introduce original approaches. For both temperature and precipitation, the spatial interpolation exploits the concept of (spatial) scale-separation and the first-guess field is derived from the observed data. Furthermore, the geographical coordinates and the elevation are used as complementary information. The evaluation of the seNorge2 products is presented both from a general point of view, through systematic cross-validations, and specifically as the meteorological input in the operational model chains used for snow- and hydrological simulations. The seNorge snow model is used for simulation of snow fields and the DDD (Distance Distribution Dynamics) rainfall-runoff model is the hydrological model used. The evaluation points out important information for the future seNorge2 developments: the daily mean temperature fields constitute an accurate and precise dataset, on average the predicted temperature is an unbiased estimate of the actual temperature and its precision (at grid points) varies between 0.8 °C and 2.4 °C; the daily precipitation fields provide a reasonable estimate of the actual precipitation, the cross-validation shows that on average the precision of the estimates (at grid points) is about &pm;20 %, though a systematic underestimation of precipitation occurs in data-sparse areas and for intense precipitation. Both the seNorge snow and the DDD models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The dataset described in this article is available for public download at http://doi.org/10.5281/zenodo.845733. [ABSTRACT FROM AUTHOR]

A research-based understanding of permafrost distribution at a sufficient spatial resolution is important to meet the demands of science, education and society. We present a new permafrost map for Norway, Sweden and Finland that provides a more detailed and updated description of permafrost distribution in this area than previously available. We implemented the CryoGRID1 model at 1 km2 resolution, forced by a new operationally gridded data-set of daily air temperature and snow cover for Finland, Norway and Sweden. Hundred model realisations were run for each grid cell, based on statistical snow distributions, allowing for the representation of sub-grid variability of ground temperature. The new map indicates a total permafrost area (excluding palsas) of 23 400 km2 in equilibrium with the average 1981-2010 climate, corresponding to 2.2 per cent of the total land area. About 56 per cent of the area is in Norway, 35 per cent in Sweden and 9 per cent in Finland. The model results are thoroughly evaluated, both quantitatively and qualitatively, as a collaboration project including permafrost experts in the three countries. Observed ground temperatures from 25 boreholes are within ± 2 °C of the average modelled grid cell ground temperature, and all are within the range of the modelled ground temperature for the corresponding grid cell. Qualitative model evaluation by field investigators within the three countries shows that the map reproduces the observed lower altitudinal limits of mountain permafrost and the distribution of lowland permafrost. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT In the region of the European Alps, national and regional meteorological services operate rain-gauge networks, which together, constitute one of the densest in situ observation systems in a large-scale high-mountain region. Data from these networks are consistently analyzed, in this study, to develop a pan-Alpine grid dataset and to describe the region's mesoscale precipitation climate, including the occurrence of heavy precipitation and long dry periods. The analyses are based on a collation of high-resolution rain-gauge data from seven Alpine countries, with 5500 measurements per day on average, spanning the period 1971-2008. The dataset is an update of an earlier version with improved data density and more thorough quality control. The grid dataset has a grid spacing of 5 km, daily time resolution, and was constructed with a distance-angular weighting scheme that integrates climatological precipitation-topography relationships. Scales effectively resolved in the dataset are coarser than the grid spacing and vary in time and space, depending on station density. We quantify the uncertainty of the dataset by cross-validation and in relation to topographic complexity, data density and season. Results indicate that grid point estimates are systematically underestimated (overestimated) at large (small) precipitation intensities, when they are interpreted as point estimates. Our climatological analyses highlight interesting variations in indicators of daily precipitation that deviate from the pattern and course of mean precipitation and illustrate the complex role of topography. The daily Alpine precipitation grid dataset was developed as part of the EU funded EURO4M project and is freely available for scientific use. [ABSTRACT FROM AUTHOR]