European Centre Research Articles

Estimating global precipitation fields by interpolating rain gauge observations using the local ensemble transform Kalman filter and reanalysis precipitation

Abstract. It is crucial to improve global precipitation estimates for a better understanding of water-related disasters and water resources. This study proposes a new methodology to interpolate global precipitation fields from ground rain gauge observations using the algorithm of the local ensemble transform Kalman filter (LETKF), a computationally efficient ensemble data assimilation method, in which the first guess and its error covariance are developed based on the reanalysis data of precipitation from the European Centre for Medium-Range Weather Forecasts (ERA5). For the estimation for each date, the climatological ensembles are constructed using the ERA5 data 10 years before and after that date, and thereafter they are utilized to obtain the first guess and its error covariance. Additionally, the global rain gauge observations provided by the National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) are used for observation inputs in the LETKF algorithm. Our estimates have better agreements with independent rain gauge observations compared to the existing precipitation estimates of the NOAA CPC in general. Because we utilized the same rain gauge observations for the inputs of our estimation as those used in the NOAA CPC product, this indicates that the proposed estimation method is superior to that of the NOAA CPC (i.e., optimal interpolation). Our proposed method had the advantage of constructing a physically consistent first guess and its error variance using reanalysis data for interpolating precipitation fields. Furthermore, validations against independent rain gauge observations showed that our estimates are largely improved in mountainous or rain-gauge-sparse regions compared to the CPC estimates, indicating strong benefits of the proposed method for such regions.

Prioritisation of infectious diseases from a public health perspective: a multi-criteria decision analysis study, France, 2024.

BackgroundWithin the International Health Regulations framework, the French High Council for Public Health was mandated in 2022 by health authorities to establish a list of priority infectious diseases for public health, surveillance and research in mainland and overseas France.AimOur objective was to establish this list.MethodsA multi-criteria decision analysis was used, as recommended by the European Centre for Disease Prevention and Control. A list of 95 entities (infectious diseases or groups of these, including the World Health Organization (WHO)-labelled 'Disease X') was established by 17 infectious disease experts. Ten criteria were defined to score entities: incidence rate, case fatality rate, potential for emergence and spread, impact on the individual, on society, on socially vulnerable groups, on the healthcare system, and need for new preventive tools, new curative therapies, and surveillance. Each criterion was assigned a relative weight by 77 multidisciplinary experts. For each entity, 98 physicians from various specialties rated each criterion against the entity, using a four-class Likert-type scale; the ratings were converted into numeric values with a nonlinear scale and respectively weighted to calculate the entity score.ResultsFifteen entities were ranked as high-priorities, including Disease X and 14 known pathologies (e.g. haemorrhagic fevers, various respiratory viral infections, arboviral infections, multidrug-resistant bacterial infections, invasive meningococcal and pneumococcal diseases, prion diseases, rabies, and tuberculosis).ConclusionThe priority entities agreed with those of the WHO in 2023; almost all were currently covered by the French surveillance and alert system. Repeating this analysis periodically would keep the list updated.

Evaluating downscaled products with expected hydroclimatic co-variances

Abstract. There has been widespread adoption of downscaled products amongst practitioners and stakeholders to ascertain risk from climate hazards at the local scale (e.g., ∼ 5 km resolution). Such products must nevertheless be consistent with physical laws to be credible and of value to users. Here we evaluate statistically and dynamically downscaled products by examining local co-evolution of downscaled temperature and precipitation during convective and frontal precipitation events (two mechanisms testable with just temperature and precipitation). We find that two widely used statistical downscaling techniques (Localized Constructed Analogs version 2, LOCA2, and Seasonal Trends and Analysis of Residuals Empirical Statistical Downscaling Model, STAR-ESDM) generally preserve expected co-variances during convective precipitation events over the historical and future projected intervals as compared to European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and two observation-based data products (Livneh and nClimGrid-Daily). However, both techniques dampen future intensification of frontal precipitation that is otherwise robustly captured in global climate models (i.e., prior to downscaling) and with process-based dynamical downscaling across five different regional climate models. In the case of LOCA2, this leads to appreciable underestimation of future frontal precipitation event intensity. This study is one of the first to quantify a likely ramification of the stationarity assumption underlying statistical downscaling methods and identify a phenomenon where projections of future change diverge depending on data production method employed. Finally, our work proposes expected co-variances during convective and frontal precipitation as useful evaluation diagnostics that can be universally applied to a wide range of statistically downscaled products.

Recent Trends in Extreme Temperature Events Across the Contiguous United States

ABSTRACTExtreme heat events (EHEs) are becoming prevalent across the globe and are a major factor in terms of temperature‐related mortality in the United States (US). In this study, we compare trends in extreme temperature events (ETEs) across the Contiguous US, from 3 reanalysis products, namely: European Centre for Medium‐Range Weather Forecasts Reanalysis Version 5 (ERA5), Modern‐Era Retrospective Analysis for Research and Applications Version 2 (MERRA2) and North American Regional Reanalysis (NARR). We focused on the trends (1980–2022) in absolute extreme heat and cold events (ECE) as well as seasonally relative extreme heat and cold events (REHE and RECE). ETEs are defined based on a duration‐intensity metric calculated from excess apparent temperature factors, based on the exceedance of apparent temperature beyond local percentile thresholds while incorporating an acclimatisation factor. Our results show that the reanalysis data sets generally produced consistent climatology of ETEs, though with some inconsistencies in their number and spatial distribution. ETE trends in the study region are spatially heterogeneous and were more consistent between MERRA2 and ERA5. Nonetheless, all data sets agree that the frequency of EHEs is significantly increasing in the western parts of the US, whereas REHEs are significantly increasing in the southern parts. The highest increase in the frequency of EHEs occurs in southern California and Nevada, while REHE trends are maximal in Florida. RECEs are significantly decreasing more in spatial scale and magnitude than ECEs, especially towards the coastal regions. The highest decrease in RECEs is in Florida peninsula, southern California and Nevada. The data sets show inconsistency in ECE trends. Trends in excess temperature factors further indicated that extreme cold conditions are decreasing faster compared to the increasing trends of extreme heat conditions. Our findings highlight the need for improving the monitoring of ETEs across the US and for policies that mitigate the impact of ETEs on biological systems.

Consideration of the cloud motion for aircraft-based stereographically derived cloud geometry and cloud top heights

Abstract. Cloud geometry and in particular cloud top heights can be derived from 2-D camera measurements by applying a stereographic method to data from an overflight over a scene of clouds (see, e.g., Kölling et al., 2019). Although airplane overpasses are relatively fast, cloud motion with the wind is important and can result in errors in the cloud localization. Here, the impact of the wind is investigated using the method from Kölling et al. (2019) for measurements of the airborne hyperspectral imaging system spectrometer of the Munich Aerosol Cloud Scanner (specMACS). Further, a method for cloud motion correction using model winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) is presented. It is shown that the update is important as the original algorithm without the cloud motion correction can over- or underestimate the cloud top heights by several hundred meters, depending on the wind speed and the relative wind direction. This is validated using data from the Elucidating the role of clouds–circulation coupling in climate (EUREC4A) campaign and realistic 3-D radiative transfer simulations. From the comparison of the derived cloud top heights with the expected ones from the model input, an average accuracy of the cloud top heights of less than (20±140) m (mean deviation and 1 standard deviation) is estimated for the updated method.

Probabilistic weather forecasting with machine learning.

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP)1, which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations2,3. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range WeatherForecasts4. GenCast is an ML weather prediction method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude-longitude resolution, for more than 80 surface and atmospheric variables, in 8 min. It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production. This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently.

A revised ocean mixed layer model for better simulating the diurnal variation in ocean skin temperature

Abstract. Sea surface temperature (SST) is a crucial parameter in climate, weather, and ocean sciences due to its decisive role in ocean–atmosphere interactions. Identifying errors in the prognostic scheme used by the current European Centre for Medium-range Weather Forecasts (ECMWF) model for predicting the diurnal variation in ocean skin temperature led to a revisit and revision of the ocean mixed layer (OML) model. Validation of the revised model was conducted by comparing simulated temperatures at the sub-skin level and 1 m depth with observations from shipborne infrared measurements and buoys. These comparisons revealed a strong correlation, with an absolute mean deviation of less than 0.1 K and a standard deviation under 0.5 K, which are found to be comparable to errors in satellite observations of SST. Given that these results are derived from the same model simulations, the error statistics for the simulated skin temperature and its diurnal variation should have the same degree of accuracy. Furthermore, the simulation results closely align with anticipated solar radiation distributions, whereas ERA5 ocean skin temperature shows a significant lack of alignment with solar radiation. Consequently, the revised OML model shows promising potential for improving the simulation of diurnal SST variations in weather and climate models.

The impact of quasi-biennial oscillation (QBO) disruptions on diurnal tides over the low- and mid-latitude mesosphere and lower thermosphere (MLT) region observed by a meteor radar chain

Abstract. A quasi-biennial oscillation (QBO) disruption is a very rare phenomenon in which QBO westward wind is temporarily interrupted by the occurrence of a band of westward wind in the tropical stratosphere. This phenomenon is important as it could greatly affect the global atmospheric circulation, especially in the mesosphere. Past observational and modelling studies have shown the QBO varying mesospheric diurnal tide, but the mechanism is still not fully understood. In this study, we report on the strong response of mesospheric diurnal tides to the two QBO disruptions that occurred in 2015–2016 and 2019–2020 and their possible mechanisms. The diurnal tidal winds are observed by a meteor radar chain, consisting of meteor radars located at Kunming (25.6° N, 103.8° E), Wuhan (30.5° N, 114.2° E), Mengcheng (33.4° N, 116.5° E), Beijing (40.3° N, 116.2° E), and Mohe (53.5° N, 122.3° E) in China. These observations provide clear evidence that mesospheric diurnal tides are unusually weakened (by ∼ −6 m s−1) during these QBO disruptions, over Kunming, Wuhan, Mengcheng, and Beijing. Using the Specific Dynamics version of the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (SD-WACCM-X) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset, the analysis indicates that the QBO wind affects mid-latitude mesospheric diurnal tides by modulating both the solar radiative absorption by subtropical stratospheric ozone (∼ 5 to 0.5 hPa) and the tidal–gravity wave interaction in the mesosphere (∼ 60 to 100 km). Thus, these unexpected QBO disruptions provide an opportunity to better understand the coupling between climate change and middle-atmospheric dynamics.

Perspective of explosive cyclones with three shapes of upper-level potential vorticity over the northern Atlantic Ocean

The characteristics of explosive cyclones (ECs) with three shapes of upper-level potential vorticity (PV), namely “streamer”, “hook”, and “treble clef” shapes were jointly examined by composite and diagnostic approaches, by using the fifth-generation global atmospheric reanalysis data (ERA5) issued by European Centre for Medium-Range Weather Forecasts (ECMWF). Totally, 48 ECs over the northern Atlantic Ocean with typical evolution from “streamer” to “hook”, and then to “treble clef” shape of PV were selected for composite analyses. It turns out that the upper-level PV exhibits “streamer”, “hook” and “treble clef” features at early, rapid deepening and mature stages of EC, respectively. The intrinsic essences of three shapes of upper-level PV associated with different stages of EC were linked. In the upper troposphere, positive PV tendency is mainly derived from positive horizontal advection of PV (HADV), which is essentially generated by high-PV air trapped in upper-level jet and cyclonic circulation. Positive HADV is the main factor driving the evolution of upper-level PV from “streamer” to “hook”, and then to “treble clef”. In the middle troposphere, positive PV tendency is primarily associated with positive vertical advection of PV (VADV). This is due to the upper-level high-PV anomalies from stratospheric intrusion and the lower-level high-PV anomalies generated by diabatic heating both extend vertically to the middle troposphere. In the lower troposphere, diabatic heating (DIAB) overwhelmingly produce positive PV tendency. These three PV anomalies are usually vertically overlain and connected to form PV “tower” that runs through the whole troposphere. The formation of PV “tower” may strengthen cyclonic circulation, which is in turn conducive to the explosive deepening of cyclone.

EVALUATION OF DAILY SATELLITE AND REANALYSIS OF RAINFALL DATA OVER SOUTH SUMATRA REGION

The limitations of good rainfall data due to constraints on direct measurements can be overcome by using satellite data or reanalysis data. The use of this data must, of course, go through a validation process first. This research aims to evaluate daily data from the Tropical Rainfall Measurement Mission (TRMM) version 3B42RT (TRMM_3B42RT) and European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) data against data collected from 17 rain gauges in the Sumatra region South. Evaluation is carried out based on the Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Bias Error (MBE) values between the data. In addition, the estimation capabilities of TRMM_3B42RT and ERA5 were evaluated based on the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) values. The results show a very high correlation between TRMM_3B42RT and ERA5 with rain gauge data, especially in terms of monthly data. These values (monthly data) for TRMM_3B42RT and ERA5 data are 0.3-0.9 and 0.2-0.9, respectively. The RMSE values of TRMM_3B42RT and ERA5 data in monthly analysis are 75-250mm/month and 100-180mm/month, respectively. The forecasting performance of TRMM_3B42RT and ERA5 shows good results, especially for moderate rainfall in daily data and heavy rainfall in monthly data. The results of this analysis show tha the monthly data TRMM_3B42RT is more in line with the station data and can be used in further research.

History of higher education in Ukraine between Sovietization, linguistic discrimination and internationalization (1890-1972)

This article aims to reconstruct the main aspects of the development of the Ukrainian system of higher education from its birth to the 1970s. It provides an overview of the historiographical debate, covering not only higher education but also the role played by intellectuals and the history of nationalities. After an introduction describing the history of higher education in the Ukrainian territories during the modern period, the article is divided into four parts analyzing, respectively, the development of higher education from its start in the modern University of Bologna era to the last decades of the nineteenth century, before Ukraine’s existence as a nation-state; the reform of higher education after the October Revolution until World War II, with reference to the Soviet system; the impact of World War II on these reforms; and the role of UNESCO’s European Centre for Higher Education (from 1972). This makes it possible to grasp the salient stages of a development that thanks to the succession of governments was very complex and was also marked by internal tensions and nationalist overtones.

Trends of temperature and total precipitable water, as well as the trend of surface pressure induced by CO2.

Two total precipitable water (TPW) monthly time series derived from hourly analyses and from 1:30 am and 1:30 pm (local time) analyses, respectively, from European Centre for Medium-Range Weather Forecasts Reanalysis model version 5 (ERA5) data are practically identical. The TPW is increasing as surface and lower tropospheric temperatures are warming. Surface pressure is rising as well because of increased mass from atmospheric carbon dioxide and TPW above the surface. The close similarity of trends using hourly and twice-daily analyses suggest that using well-calibrated observations from a single sun-synchronous polar orbiting satellite, such as Suomi-NPP Advanced Technology Microwave Sounder measurements, can be sufficient to characterize global trends in climate-sensitive surface temperatures and TPW.

Childhood Mandatory Vaccinations: Current Situation in European Countries and Changes Occurred from 2014 to 2024.

Background/Objectives: Vaccination is one of the most effective public health interventions, preventing millions of deaths globally each year. However, vaccine hesitancy, driven by misinformation and reduced disease risk perception, has led to declining vaccination rates and the resurgence of vaccine-preventable diseases (VPDs) in Europe. In response to this, countries have implemented various strategies, including mandatory and recommended vaccination programs. The objective of this study is to map the current European landscape of pediatric vaccination policies, and the variations that have occurred in the last decade. Methods: This rapid review was conducted on PubMed, Google, and the European Centre for Disease Prevention and Control website, to collect all vaccination schedules in EU/EEA countries in 2024 and all documents focusing on the introduction of mandatory vaccines during the last decade. Results: As of 2024, 13 countries had at least one mandatory pediatric vaccination, with France, Hungary, and Latvia requiring all but one vaccine. In contrast, 17 countries had no mandatory vaccinations, relying only on recommendations. Between 2014 and 2024, six countries (Croatia, France, Germany, Hungary, Italy, and Poland) introduced or extended mandatory vaccinations. Conclusions: European vaccination policies show significant variation. Effective programs depend on robust healthcare systems, public trust, and adaptable strategies to address vaccine hesitancy and the resurgence of VPDs.

A Reanalysis Precipitation Integration Method Utilizing the Generalized Three-Cornered Hat Approach and High-Resolution, Gauge-Based Datasets

The development of high-precision, long-term, hourly-scale precipitation data is essential for understanding extreme precipitation events. Reanalysis systems are particularly promising for this type of research due to their long-term observations and wide spatial coverage. This study aims to construct a more robust precipitation dataset by integrating three widely-used reanalysis precipitation estimates: Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA2), Climate Forecast System Reanalysis (CFSR), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). A novel integration method based on the generalized three-cornered hat (TCH) approach is employed to quantify uncertainties in these products. To enhance accuracy, the high-density daily precipitation data from the Asian Precipitation-Highly-Resolved Observation Data Integration Towards Evaluation (APHRODITE) dataset is used for correction. Results show that the TCH method effectively identifies seasonal and spatial uncertainties across the products. The TCH-weighted product (TW), calculated using signal-to-noise ratio weighting, outperforms the original reanalysis datasets across various watersheds and seasons. After correction with APHRODITE data, the enhanced integrated product (ATW) significantly improves accuracy, making it more suitable for extreme precipitation event analysis. Quantile mapping was applied to assess the ability of TW and ATW to represent extreme precipitation. Both products showed improved accuracy in regional average precipitation, with ATW demonstrating superior improvement. This integration method provides a robust approach for refining reanalysis precipitation datasets, contributing to more reliable hydrological and climate studies.

Assessing the impact of SARS-CoV-2 on influenza-like illness surveillance trends in the community during the 2023/2024 winter in England

ObjectivesInfluenza-like-illness (ILI) is a commonly used symptom categorization in seasonal disease surveillance focusing on influenza in community and clinical settings. However, SARS-CoV-2 often causes presentation with a similar symptom profile. We explore how SARS-CoV-2-positive individuals can influence surveillance trends for the World Health Organization, the United States Centre for Disease Control, and the European Centre for Disease Control (ECDC) ILI criteria. MethodsHarnessing the Winter COVID-19 Infection Study in England, a cohort study, the prevalence of different ILI definitions is modeled using multilevel regression and poststratification using age and spatial stratifications with temporal smoothing. Trends over time across stratifications were compared for SARS-CoV-2 positive and negative individuals to understand differences in ILI trends. Symptom presentation across positive and negative SARS-CoV-2 cases were compared. ResultsSARS-CoV-2 symptom profiles are shown to overlap with the ILI case definitions, particularly for “cough” and “fever”, causing SARS-CoV-2 positive individuals to be frequently detected as ILI cases. The trend of SARS-CoV-2 positives is a substantial component of the ILI-modeled trend, driving an earlier perceived peak in prevalence. The ECDC symptom definition was most influenced by SARS-CoV-2 positive individuals. ConclusionsUsing a large community cohort we show how SARS-CoV-2 can impact ILI surveillance trends. SARS-CoV-2 makes up a substantial part of the community ILI burden and public health messaging should reflect this when discussing ILI. We show ILI is no longer a strong proxy for influenza activity alone.

Characterizing Long-term Astroclimate Parameters at the Muztagh-Ata Site in the Pamir Plateau with ERA5 and MERRA-2 Data

Abstract The Muztagh-Ata site, an excellent high-altitude ground-based astronomical observing site was discovered and monitored in the eastern part of the Pamir Plateau in the southwestern Xinjiang Uygur Autonomous Region of China. This site has been systematically monitored using various observational parameters since the spring of 2017. Yet, the site lacks long-term monitoring and statistical characterization of key variables such as: precipitable water vapour (PWV) and air temperature. These factors directly influence whether a site is suitable for hosting large ground-based telescopes across optical, infrared, submillimeter, and millimeter wavelengths in later stages. In this study, we utilized atmospheric reanalysis datasets from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and ERA5, the fifth edition of the European Centre for Medium-Range Weather Forecasts (ECMWF). These datasets were combined with local observations from the weather station at the Muztagh-Ata site. Following the validation of ground-based and satellite data, we conducted a comparative analysis of PWV and temperature trends at the Muztagh-Ata site over a period of 24 years. The weighted annual mean nighttime temperature and PWV increase at rates of 0.18 ∼ 0.38 ○C decade−1 and 0.02 ∼ 0.15 mm decade−1, respectively. The nighttime PWV slightly decrease during the winter with rates of −0.01 ∼ −0.03 mm decade−1. This findings reveal that the PWV and temperature variation patterns at the Muztagh-Ata site are consistently stable, particularly in the results derived from the ERA5 dataset. The comprehensive conditions at this site are highly suitable and advantageous for hosting large optical, infrared, submillimeter, and millimeter wavelengths telescope installations.

Process Evaluation of Subseasonal North Atlantic Oscillation Prediction in the ECMWF Ensemble Forecast System

AbstractThis study evaluates the prediction skill of the North Atlantic Oscillation (NAO) pattern and its associated energy budget as simulated by the European Center for Medium‐Range Weather Forecasts ensemble forecast system. By classifying NAO events into high‐ and low‐skill cases, we analyzed the stationarity of NAO patterns and the role of baroclinic energy conversion in NAO prediction. In both positive and negative NAO phases, high‐skill cases exhibited more stationary NAO patterns than low‐skill cases. The analysis of processes indicates that high‐skill NAO cases are due to stronger baroclinic maintenance of NAO, with its initial position at the climatological thermal trough, whereas low‐skill NAO cases result from forecast biases in wave propagation from the North Pacific. Specifically, biases in baroclinic energy conversion in the meridional direction from week 2 lead to weak advection of the eddy available potential energy (EAPE), resulting in lower prediction skill.

Methods and Evaluation of AI-Based Meteorological Models for Zenith Tropospheric Delay Prediction

Zenith Tropospheric Delay (ZTD) is a significant error source affecting the accuracy of certain space geodetic measurements. This study evaluates the performance of Artificial Intelligence (AI) based meteorological models, such as Fengwu and Pangu, in estimating real-time ZTD. The results from these AI models were compared with those obtained from the Global Navigation Satellite System (GNSS), the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5), and the third generation of the Global Pressure–Temperature data model (GPT3) to assess their accuracy across different time intervals, seasons, and geographic locations. The findings reveal that AI-driven models, particularly Fengwu, offer higher long-term forecasting accuracy. An analysis of data from 81 stations throughout 2023 indicates that Fengwu’s 7-day ZTD forecast achieved an RMSE of 2.85 cm when compared to GNSS-derived ZTD. However, in oceanic regions and areas with complex climatic dynamics, the Fengwu model exhibited a larger error compared to in other land regions. Additionally, seasonal variations and station altitude were found to influence the accuracy of ZTD predictions, emphasizing the need for detailed modeling in complex climatic zones.

The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators

Abstract. Supercooled liquid clouds are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We take advantage of the Macquarie Island Cloud and Radiation Experiment (MICRE) to perform an analysis of observed and simulated cloud processes over the Southern Ocean in a region and season dominated by supercooled liquid clouds. Using a single-column version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), we compare two different cloud microphysical schemes to ground-based observations of cloud, precipitation, and radiation over a 2.5-month period (1 January–17 March 2017). Both schemes are able to reproduce aspects of the cloud and radiation observations during MICRE to within the uncertainty of the data when the thermodynamic profile is prescribed with relaxation. There are differences in water mass and representation of reflectivity between the schemes. A sensitivity study of the cloud microphysics schemes, one a bulk one-moment scheme and the other a two-moment scheme with prediction of mass and number, indicates that several key processes create differences between the schemes. Surface radiative fluxes and total water path are highly sensitive to the formation and fall speed of precipitation. The prediction of hydrometeor number with the two-moment scheme yields a better comparison with observed reflectivity and radiative fluxes, despite predicting higher liquid water contents than observed. With the two-moment scheme, we are also able to test the sensitivity of the results to the input of liquid cloud condensation nuclei (CCN) and ice nuclei (IN). The cloud properties and resulting radiative effects are found to be sensitive to the CCN and IN concentrations. More CCN and IN increase liquid and ice water paths, respectively. Thus, both the dynamic environment and aerosols, integrated through the cloud microphysics, are important for properly representing Southern Ocean cloud radiative effects.

Major Artifacts in ERA5 2‐m Air Temperature Trends Over Antarctica Prior to and During the Modern Satellite Era

AbstractGlobal reanalyzes are widely used for investigations of Antarctic climate variability and change. The European Centre for Medium‐Range Weather Forecasts 5th generation reanalysis (ERA5) is well regarded and spans 1940 to today. We investigate whether ERA5 reliably represents the 2‐m air temperature trends across the 1940–2022 (83 years) period at seasonal and annual time scales. We compare ERA5 temperatures with an observation‐based temperature reconstruction for Antarctica (RECON) that has monthly resolution for 1958–2022, the period of reliable observational availability. Results for individual stations are also examined. ERA5 anomalously warms Antarctica in relation RECON especially for the period prior to 1979 when satellite observations over the Southern Ocean were sparse. Trend hotspots that are shown to be artifacts are found at three locations and are present until today. The results demonstrate that ERA5 temperature trends can be questionable even today, but variability is well captured after 1979.