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.
This study assesses the ability of global and high‐resolution regional reanalyses to reproduce surface air temperature over Italy during 1991–2020, comparing their field against an observational dataset specifically gridded for each reanalysis. Both seasonal and annual climatological values and the daily anomaly records were validated (in figure, ERA5 climatological bias).
The spatial and temporal consistency of seasonal air temperature and precipitation in eight widely used gridded observation-based climate datasets (CANGRD, CRU-TS3.1, CRUTEM4.1, GISTEMP, GPCC, GPCP, ...HadCRUT3, and UDEL) and eight reanalyses (20CR, CFSR, ERA-40, ERA-Interim, JRA25, MERRA, NARR, and NCEP2) was evaluated over the Canadian Arctic for the 1950-2010 period. The evaluation used the CANGRD dataset, which is based on homogenized temperature and adjusted precipitation from climate stations, as a reference. Dataset agreement and bias were observed to exhibit important spatial, seasonal, and temporal variability over the Canadian Arctic with the largest spread occurring between datasets over mountain and coastal regions and over the Canadian Arctic Archipelago. Reanalysis datasets were typically warmer and wetter than surface observation-based datasets, with CFSR and 20CR exhibiting biases in total annual precipitation on the order of 300 mm. Warm bias in 20CR exceeded 12°C in winter over the western Arctic. Analysis of the temporal consistency of datasets over the 1950-2010 period showed evidence of discontinuities in several datasets as well as a noticeable increase in dataset spread in the period after approximately 2000. Declining station networks, increased automation, and the inclusion of new satellite data streams in reanalyses are potential contributing factors to this phenomenon. Evaluation of trends over the 1950-2010 period showed a relatively consistent picture of warming and increased precipitation over the Canadian Arctic from all datasets, with CANGRD giving moistening trends two times larger than the multi-dataset average related to the adjustment of the station precipitation data. The study results indicate that considerable care is needed when using gridded climate datasets in local or regional scale applications in the Canadian Arctic.
Determining avalanche activity corresponding to given snow and meteorological conditions is an old problem of high practical relevance. To address it, numerous statistical forecasting models have ...been developed, but intercomparisons of their efficiency on very large datasets are seldom. In this work, an approach combining random forests with class-balancing is presented and systematically compared with competing methods currently described in the avalanche literature. On more than 50 years of daily avalanche observations, in the 23 massifs of the French Alps, the competing classifiers are evaluated on their ability to distinguish three classes of avalanche activity: non-avalanche days, days with moderate activity, and days with high activity. Moreover, the variables of higher importance in the random forest classifiers are shown to be coherent with current avalanche literature and a clustering based on these variable importance separates massifs which are known to have different avalanche activities. Our approach opens perspectives to support operational avalanche forecasting.
•New approach combining random forests with class-balancing for avalanche prediction.•Systematic comparison with most existing approaches.•Probative results for the 23 massifs of the French Alps using more than 50 years of daily data.•Physical coherence of the main selected variables.•Perspectives to support operational avalanche forecasting.
The available wind power resource worldwide at altitudes between 500 and 12,000 m above ground is assessed for the first time. Twenty-eight years of wind data from the reanalyses by the National ...Centers for Environmental Prediction and the Department of Energy are analyzed and interpolated to study geographical distributions and persistency of winds at all altitudes. Furthermore, intermittency issues and global climate effects of large-scale extraction of energy from high-altitude winds are investigated.
Southern Greenland is home to a number of weather systems characterized by high speed low‐level winds that are the result of topographic flow distortion. These systems include tip jets, barrier winds ...and katabatic flows. Global atmospheric reanalyses have proven to be important tools in furthering our understanding of these systems and their role in the climate system. However, there is evidence that their mesoscale structure may be poorly resolved in these global products. Here output from the regional Arctic System Reanalysis (ASRv1–30 km and ASRv2–15 km grid resolutions) are compared to the global ERA‐Interim Reanalysis (ERA‐I–80 km grid resolution), focusing on their ability to represent winds in the vicinity of southern Greenland. Comparisons are made to observations from surface and upper‐air stations, as well as from research aircraft flights during the Greenland Flow Distortion Experiment (GFDex). The ERA‐I reanalysis has a tendency to underestimate high wind speeds and overestimate low wind speeds, which is reduced in ASRv1 and nearly eliminated in ASRv2. In addition, there is generally a systematic reduction in the root‐mean‐square error between the observed and the reanalysis wind speeds from ERA‐I to ASRv1 to ASRv2, the exception being low‐level marine winds where the correspondence is similar in all reanalyses. Case‐studies reveal that mesoscale spatial features of the wind field are better captured in ASRv2 as compared to the ERA‐I or ASRv1. These results confirm that a horizontal grid size on the order of 15 km is needed to characterize the impact that Greenland's topography has on the regional wind field and climate. However even at this resolution, there are still features of the wind field that are under‐resolved.
The ERA5 reanalysis, recently made available by the European Centre for Medium-Range Weather Forecasts (ECMWF), is a new reanalysis product at a high resolution replacing ERA-Interim and is ...considered to provide the best climate reanalysis over Greenland to date. However, so far little is known about the performance of ERA5 over the Greenland Ice Sheet (GrIS). In this study, we compare the near-surface climate from the new ERA5 reanalysis to ERA-Interim, the Arctic System Reanalysis (ASR) as well as to a state-of-the-art polar regional climate model (MAR). The results show (1) that ERA5 does not outperform ERA-Interim significantly when compared with near-surface climate observations over GrIS, but ASR better models the near-surface temperature than both ERA reanalyses. (2) Polar regional climate models (e.g., MAR) are still a useful tool to downscale the GrIS climate compared to ERA5, as in particular the near-surface temperature in summer has a key role for representing snow and ice processes such as the surface melt. However, assimilating satellite data and using a more recent radiative scheme enable both ERA and ASR reanalyses to represent more satisfactorily than MAR the downward solar and infrared fluxes. (3) MAR near-surface climate is not affected when forced at its lateral boundaries by either ERA5 or ERA-Interim. Therefore, forcing polar regional climate models with ERA5 starting from 1950 will enable long and homogeneous surface mass balance reconstructions.
In this study, the temperature biases and the ability of the ERA-5 product to reproduce the LiDAR variability in the 30–80 km altitude range were evaluated for the period 2005–2020, both for the ...winter and the summer months. During winter, temperatures from the ERA-5 dataset were in good agreement with LiDAR observations up to 45 km, while in the mesosphere, almost 70% of the ERA-5 profiles were cooler than those from LiDAR, except around 65 km. During summer, negative biases of −3 K were observed up to the stratopause, while significant positive biases of more than +10 K were found in the mesosphere. For the winter months, the variability observed by LiDAR, even during sudden stratospheric warming (SSWs) events, was reproduced accurately by the model in the upper stratosphere, but not in the mesosphere. Surprisingly, the LiDAR variability mainly due to propagating gravity waves in the summertime was also not reproduced by ERA-5 in the whole middle atmosphere. The model uncertainty associated with this variability, evaluated afterward with a new method, grew as expected with altitude and was more significant in winter than summer. A principal component analysis of the fluctuations of the temperature differences between the LiDAR and ERA-5 was performed to investigate the vertical coupling between 30 km and 70 km. The three first vertical modes illustrated 76% and 78% of the fluctuations of the temperature difference profiles in summer and winter, respectively, confirming the connection between the studied layers. The leading modes of the summer (49%) and winter (42%) possessed an anti-correlation between the upper stratosphere and the mesosphere, where fluctuations increased (at least ±5 K at 65 km) for both seasons due to the coarse vertical resolution in the model. The other modes showed an agreement between the LiDAR and ERA-5 fluctuations in the upper stratosphere and had a wave-like structure mainly located in the mesosphere, confirming that the model either overlooked or simulated imprecisely the gravity waves, leading to mesospheric inversions. Finally, SSWs impacted the ERA-5 temperature (deviation of ±3K) some days before and after its trigger around the stratopause.
Temporal evolution of relationships between surface temperature and modes of low‐frequency circulation variability is compared between five reanalyses (20CRv3, 20CRv2c, ERA‐20C, JRA‐55, and NCEP‐1) ...in winter from 1958 to 2010 over the northern Extratropics. The relationships are evaluated using 15‐year running correlations between temperature anomalies (from the CRU TS v. 4.03 data set) and the intensity of circulation modes (detected in 500 hPa heights by rotated principal component analysis). The analysis, utilizing mean absolute differences between time series of running correlations, points to the large agreement between ERA‐20C, JRA‐55, and NCEP‐1. Circulation modes in those reanalyses are highly similar, which in turn lead to the agreement in temporal development of correlations. In contrast, relationships of some circulation modes with temperature in 20CRv3 and 20CRv2c differ due to differences in the position, strength, and shape of the action centres. This concerns circulation modes located over Eurasia and the Atlantic, mainly North Atlantic Oscillation and Eurasian mode type 1 (EU1). Composite maps, calculated for all running periods, indicate dissimilar temporal evolution of action centres in both 20CR reanalyses. Increased differences in correlations occur mainly during periods when the position and strength of action centres diverge the most. Relationships of circulation modes located over North America and the Pacific with temperature share large resemblance between all reanalyses, including those from the 20CR family. Differences appear to be smaller in 20CRv3 compared to the preceding version, 20CRv2(c), suggesting that the development of the 20CR reanalysis has succeeded in correcting and diminishing biases.
Temporal evolution (defined by running correlations) of relationships between temperature and circulation modes is compared between five reanalyses from 1958 to 2010. The large spatial similarity of modes between reanalyses leads to similar running correlations; this concerns the JRA‐55, NCEP‐1, and ERA‐20C reanalyses. In the 20CRv2c and 20CRv3 reanalyses, relationships with circulation modes located over Eurasia and the Atlantic, mainly North Atlantic Oscillation and Eurasian mode type 1, evolve differently due to differences in the position, strength, and shape of the action centres.
Ocean reanalyses aims to provide the best estimate of the time-varying ocean multi-scale structure. However, due to different ocean models, data assimilation methods and observation data, the ocean ...structure described by various reanalyses products has different degrees of error. Quantifying the uncertainties of relevant ocean variables with different reanalyses products is an important application of reanalyses products. In this study, we use deep learning to capture the development trend of sea surface height uncertainties in the South China Sea region and predict the future uncertainties. In order to better characterize the development trend of sea surface height uncertainties in the region, we used three high-resolution ocean reanalyses products as the data basis for the experiment. The final experimental results indicate that deep learning can accurately capture the uncertainties of sea surface height described by three types of reanalyses data without requiring any physical prior knowledge.
A set of four eddy-permitting global ocean reanalyses produced in the framework of the MyOcean project have been compared over the altimetry period 1993–2011. The main differences among the ...reanalyses used here come from the data assimilation scheme implemented to control the ocean state by inserting reprocessed observations of sea surface temperature (SST), in situ temperature and salinity profiles, sea level anomaly and sea-ice concentration. A first objective of this work includes assessing the interannual variability and trends for a series of parameters, usually considered in the community as essential ocean variables: SST, sea surface salinity, temperature and salinity averaged over meaningful layers of the water column, sea level, transports across pre-defined sections, and sea ice parameters. The eddy-permitting nature of the global reanalyses allows also to estimate eddy kinetic energy. The results show that in general there is a good consistency between the different reanalyses. An intercomparison against experiments without data assimilation was done during the MyOcean project and we conclude that data assimilation is crucial for correctly simulating some quantities such as regional trends of sea level as well as the eddy kinetic energy. A second objective is to show that the ensemble mean of reanalyses can be evaluated as one single system regarding its reliability in reproducing the climate signals, where both variability and uncertainties are assessed through the ensemble spread and signal-to-noise ratio. The main advantage of having access to several reanalyses differing in the way data assimilation is performed is that it becomes possible to assess part of the total uncertainty. Given the fact that we use very similar ocean models and atmospheric forcing, we can conclude that the spread of the ensemble of reanalyses is mainly representative of our ability to gauge uncertainty in the assimilation methods. This uncertainty changes a lot from one ocean parameter to another, especially in global indices. However, despite several caveats in the design of the multi-system ensemble, the main conclusion from this study is that an eddy-permitting multi-system ensemble approach has become mature and our results provide a first step towards a systematic comparison of eddy-permitting global ocean reanalyses aimed at providing robust conclusions on the recent evolution of the oceanic state.
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