European Centre forMedium-Range Weather Research Articles

Uncertainty growth and forecast reliability during extratropical cyclogenesis

Abstract. In global numerical weather prediction, the strongest contribution to ensemble variance growth over the first few days is at synoptic scales. Hence it is particularly important to ensure that this synoptic-scale variance is reliable. Here we focus on wintertime synoptic-scale growth in the North Atlantic storm track. In the 12 h background forecasts of the Ensemble of Data Assimilations (EDA) from the European Centre for Medium-Range Weather Forecasts (ECMWF), we find that initial variance growth at synoptic scales tends to be organized in particular flow situations, such as during the deepening of cyclones (cyclogenesis). Both baroclinic and diabatic aspects may be involved in the overall growth rate. However, evaluation of reliability through use of an extended error–spread equation indicates that the ECMWF ensemble forecast, which is initialized from the EDA but with additional singular vector perturbations, appears to have too much variance at a lead time of 2 d and that this over-spread is associated with cyclogenesis situations. Comparison of variance growth rates and reliability with other forecast systems within The International Grand Global Ensemble (TIGGE) archive indicates some sensitivity to the model or its initialization. For the ECMWF ensemble forecast, sensitivity experiments suggest that a large part of the total day-2 spread in cyclogenesis cases is associated with the growth of EDA uncertainty, but up to 25 % can be associated with the additional singular vector perturbations to the initial conditions and up to 25 % with the representation of model uncertainty. The sensitivities of spread to resolution, the explicit representation of convection, and the assimilation of local observations are also considered. The study raises the question of whether the EDA now successfully represents initial uncertainty (and the enhanced growth rates associated with cyclogenesis) to the extent that singular vector perturbations could be reduced in magnitude to improve storm track reliability. This would leave a more seamless forecast system, allowing short-range diagnostics to better help improve the model and model-uncertainty representation, which could be beneficial throughout the forecast range.

Sensitivity of simulating Typhoon Haiyan (2013) using WRF: the role of cumulus convection, surface flux parameterizations, spectral nudging, and initial and boundary conditions

Abstract. Typhoon (TY) Haiyan was one of the most intense and highly destructive tropical cyclones (TCs) to affect the Philippines. As such, it is regarded as a baseline for extreme TC hazards. Improving the simulation of such TCs will not only improve the forecasting of intense TCs but will also be essential in understanding the potential sensitivity of future intense TCs with climate change. In this study, we investigate the effects of model configuration in simulating TY Haiyan using the Weather Research Forecasting (WRF) Model. Sensitivity experiments were conducted by systematically altering the choice of cumulus schemes, surface flux options, and spectral nudging. In addition to using the European Centre for Medium-Range Weather Forecasts Reanalysis fifth-generation (ERA5) single high-resolution realization as initial and boundary conditions, we also used 4 of the 10 lower-resolution ERA5 data assimilation system (EDA) ensemble members as initial and boundary conditions. Results indicate a high level of sensitivity to cumulus schemes, with a trade-off between using Kain–Fritsch and Tiedtke schemes that have not been mentioned in past studies of TCs in the Philippines. The Tiedtke scheme simulates the track better (with a lower mean direct positional error, DPE, of 33 km), while the Kain–Fritsch scheme produces stronger intensities (by 15 hPa minimum sea level pressure). Spectral nudging also resulted in a reduction in the mean DPE by 20 km, and varying the surface flux options resulted in the improvement of the simulated maximum sustained winds by up to 10 m s−1. Simulations using the EDA members initial and boundary conditions revealed low sensitivity to the initial and boundary conditions, having less spread than the simulations using different parameterization schemes. We highlight the advantage of using an ensemble of cumulus parameterizations to take into account the uncertainty in the track and intensity of simulating intense tropical cyclones.

Ozone, DNA-active UV radiation, and cloud changes for the near-global mean and at high latitudes due to enhanced greenhouse gas concentrations

Abstract. This study analyses the variability and trends of ultraviolet-B (UV-B, wavelength 280–320 nm) radiation that can cause DNA damage. The variability and trends caused by climate change due to enhanced greenhouse gas (GHG) concentrations. The analysis is based on DNA-active irradiance, total ozone, total cloud cover, and surface albedo calculations with the European Centre for Medium-Range Weather Forecasts – Hamburg (ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) chemistry–climate model (CCM) free-running simulations following the RCP 6.0 climate scenario for the period 1960–2100. The model output is evaluated with DNA-active irradiance ground-based measurements, satellite SBUV (v8.7) total-ozone measurements, and satellite MODerate-resolution Imaging Spectroradiometer (MODIS) Terra cloud cover data. The results show that the model reproduces the observed variability and change in total ozone, DNA-active irradiance, and cloud cover for the period 2000–2018 quite well according to the statistical comparisons. Between 50∘ N–50∘ S, the DNA-damaging UV radiation is expected to decrease until 2050 and to increase thereafter, as was shown previously by Eleftheratos et al. (2020). This change is associated with decreases in the model total cloud cover and negative trends in total ozone after about 2050 due to increasing GHGs. The new study confirms the previous work by adding more stations over low latitudes and mid-latitudes (13 instead of 5 stations). In addition, we include estimates from high-latitude stations with long-term measurements of UV irradiance (three stations in the northern high latitudes and four stations in the southern high latitudes greater than 55∘). In contrast to the predictions for 50∘ N–50∘ S, it is shown that DNA-active irradiance will continue to decrease after the year 2050 over high latitudes because of upward ozone trends. At latitudes poleward of 55∘ N, we estimate that DNA-active irradiance will decrease by 8.2 %±3.8 % from 2050 to 2100. Similarly, at latitudes poleward of 55∘ S, DNA-active irradiance will decrease by 4.8 % ± 2.9 % after 2050. The results for the high latitudes refer to the summer period and not to the seasons when ozone depletion occurs, i.e. in late winter and spring. The contributions of ozone, cloud, and albedo trends to the DNA-active irradiance trends are estimated and discussed.

OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3

Abstract. In this paper, we report on the first implementation of atmospheric chemistry and aerosol as part of the European Centre for Medium-Range Weather Forecasts (ECMWF) OpenIFS model. OpenIFS is a portable version of ECMWF's global numerical weather prediction model. Modules and input data for model cycle CY43R3, which have been developed as part of the Copernicus Atmosphere Monitoring Service (CAMS), have been ported to OpenIFS with the modified CB05 tropospheric chemistry scheme, the bulk bin tropospheric aerosol module, and the option to use Belgian Assimilation System for Chemical ObsErvations (BASCOE)-based stratospheric ozone chemistry. We give an overview of the model, and describe the datasets used for emissions and dry deposition, which are similar to those used in the model configuration applied to create the CAMS reanalysis. We evaluate two reference model configurations with and without the stratospheric chemistry extension against standard observational datasets for tropospheric ozone, surface carbon monoxide (CO), tropospheric nitrogen dioxide (NO2), and aerosol optical depth. The results give basic confidence in the model implementation and configuration. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license.

Time evolution of temperature profiles retrieved from 13 years of infrared atmospheric sounding interferometer (IASI) data using an artificial neural network

Abstract. The three infrared atmospheric sounding interferometers (IASIs), launched in 2006, 2012, and 2018, are key instruments to weather forecasting, and most meteorological centres assimilate IASI nadir radiance data into atmospheric models to feed their forecasts. The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) recently released a reprocessed homogeneous radiance record for the whole IASI observation period, from which 13 years (2008–2020) of temperature profiles can be obtained. In this work, atmospheric temperatures at different altitudes are retrieved from IASI radiances measured in the carbon dioxide absorption bands (654–800 and 2250–2400 cm−1) by selecting the channels that are the most sensitive to the temperature at different altitudes. We rely on an artificial neural network (ANN) to retrieve atmospheric temperatures from a selected set of IASI radiances. We trained the ANN with IASI radiances as input and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) as output. The retrieved temperatures were validated with ERA5, with in situ radiosonde temperatures from the Analysed RadioSoundings Archive (ARSA) network and with EUMETSAT temperatures retrieved from IASI radiances using a different method. Between 750 and 7 hPa, where IASI is most sensitive to temperature, a good agreement is observed between the three datasets: the differences between IASI on one hand and ERA5, ARSA, or EUMETSAT on the other hand are usually less than 0.5 K at these altitudes. At 2 hPa, as the IASI sensitivity decreases, we found differences up to 2 K between IASI and the three validation datasets. We then computed atmospheric temperature linear trends from atmospheric temperatures between 750 and 2 hPa. We found that in the past 13 years, there is a general warming trend of the troposphere that is more important at the poles and at mid-latitudes (0.5 K/decade at mid-latitudes, 1 K/decade at the North Pole). The stratosphere is globally cooling on average, except at the South Pole as a result of the ozone layer recovery and a sudden stratospheric warming in 2019. The cooling is most pronounced in the equatorial upper stratosphere (−1 K/decade). This work shows that ANN can be a powerful and simple tool to retrieve IASI temperatures at different altitudes in the upper troposphere and in the stratosphere, allowing us to construct a homogeneous and consistent temperature data record adapted to trend analysis.

Atmospheric rivers and associated precipitation patterns during the ACLOUD and PASCAL campaigns near Svalbard (May–June 2017): case studies using observations, reanalyses, and a regional climate model

Abstract. Recently, a significant increase in the atmospheric moisture content has been documented over the Arctic, where both local contributions and poleward moisture transport from lower latitudes can play a role. This study focuses on the anomalous moisture transport events confined to long and narrow corridors, known as atmospheric rivers (ARs), which are expected to have a strong influence on Arctic moisture amounts, precipitation, and the energy budget. During two concerted intensive measurement campaigns – Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) and the Physical feedbacks of Arctic planetary boundary layer, Sea ice, Cloud and AerosoL (PASCAL) – that took place at and near Svalbard, three high-water-vapour-transport events were identified as ARs, based on two tracking algorithms: the 30 May event, the 6 June event, and the 9 June 2017 event. We explore the temporal and spatial evolution of the events identified as ARs and the associated precipitation patterns in detail using measurements from the French (Polar Institute Paul Emile Victor) and German (Alfred Wegener Institute for Polar and Marine Research) Arctic Research Base (AWIPEV) in Ny-Ålesund, satellite-borne measurements, several reanalysis products (the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA) Interim (ERA-Interim); the ERA5 reanalysis; the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2); the Climate Forecast System version 2 (CFSv2); and the Japanese 55-Year Reanalysis (JRA-55)), and the HIRHAM regional climate model version 5 (HIRHAM5). Results show that the tracking algorithms detected the events differently, which is partly due to differences in the spatial and temporal resolution as well as differences in the criteria used in the tracking algorithms. The first event extended from western Siberia to Svalbard, caused mixed-phase precipitation, and was associated with a retreat of the sea-ice edge. The second event, 1 week later, had a similar trajectory, and most precipitation occurred as rain, although mixed-phase precipitation or only snowfall occurred in some areas, mainly over the coast of north-eastern Greenland and the north-east of Iceland, and no differences were noted in the sea-ice edge. The third event showed a different pathway extending from the north-eastern Atlantic towards Greenland before turning south-eastward and reaching Svalbard. This last AR caused high precipitation amounts on the east coast of Greenland in the form of rain and snow and showed no precipitation in the Svalbard region. The vertical profiles of specific humidity show layers of enhanced moisture that were concurrent with dry layers during the first two events and that were not captured by all of the reanalysis datasets, whereas the HIRHAM5 model misrepresented humidity at all vertical levels. There was an increase in wind speed with height during the first and last events, whereas there were no major changes in the wind speed during the second event. The accuracy of the representation of wind speed by the reanalyses and the model depended on the event. The objective of this paper was to build knowledge from detailed AR case studies, with the purpose of performing long-term analysis. Thus, we adapted a regional AR detection algorithm to the Arctic and analysed how well it identified ARs, we used different datasets (observational, reanalyses, and model) and identified the most suitable dataset, and we analysed the evolution of the ARs and their impacts in terms of precipitation. This study shows the importance of the Atlantic and Siberian pathways of ARs during spring and beginning of summer in the Arctic; the significance of the AR-associated strong heat increase, moisture increase, and precipitation phase transition; and the requirement for high-spatio-temporal-resolution datasets when studying these intense short-duration events.

Characteristics of surface currents in Manado Bay, North Sulawesi, Indonesia

Manado Bay is a complex waterway located in Manado City, North Sulawesi, Indonesia. It is an entry point for the Indonesia Trough-Flow, and its circulation is affected by the seasonal winds. Manado City has no debris net on its river estuaries. Therefore, marine debris can easily be carried away by the ocean currents and accumulate in the tourism areas located along the coast of Manado Bay. Consequently, it is important to study the sea surface current circulation in Manado Bay to deal with marine debris accumulation. In the present study, we utilized the DELFT3D software to simulate the hydrodynamic circulation in Manado Bay from 2016-2017. We conducted a 2-dimension (2D) horizontal hydrodynamic simulation using tidal and wind forcing from European Centre for Medium-Range Weather (ECMWF). The simulation results indicate that the change in bathymetry and wind affect the sea surface currents. During the summer monsoon (June-August), the sea surface current flows from the northeast to the southwest with an average speed of 1.1 cm s-1. On the contrary, during the transitional monsoon 1 (September-November), the sea surface current flows from the southeast to the northwest with an average speed of 1.3 cm s-1. Meanwhile, in the winter monsoon (December-February), the sea surface current originated from the southwest flows to the east with an average velocity of 1.9 cm s-1. Then, it moves from west to east during transitional monsoon 2 (March-May) with an average speed of 1.5 cm s-1. The current speed increases whenthe water enters the strait between the Bunaken Islands due to refraction, diffraction, and shallowing effect. As current flows toward the shallower area, the current speed increases, compensating the water column reduction.

Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents

Abstract. In August 2018, the first Doppler wind lidar, developed by the European Space Agency (ESA), was launched on board the Aeolus satellite into space. Providing atmospheric wind profiles on a global basis, the Earth Explorer mission is expected to demonstrate improvements in the quality of numerical weather prediction (NWP). For the use of Aeolus observations in NWP data assimilation, a detailed characterization of the quality and the minimization of systematic errors is crucial. This study performs a statistical validation of Aeolus observations, using collocated radiosonde measurements and NWP forecast equivalents from two different global models, the ICOsahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD) and the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System (IFS) model, as reference data. For the time period from the satellite's launch to the end of December 2019, comparisons for the Northern Hemisphere (23.5–65∘ N) show strong variations of the Aeolus wind bias and differences between the ascending and descending orbit phase. The mean absolute bias for the selected validation area is found to be in the range of 1.8–2.3 m s−1 (Rayleigh) and 1.3–1.9 m s−1 (Mie), showing good agreement between the three independent reference data sets. Due to the greater representativeness errors associated with the comparisons using radiosonde observations, the random differences are larger for the validation with radiosondes compared to the model equivalent statistics. To achieve an estimate for the Aeolus instrumental error, the representativeness errors for the comparisons are determined, as well as the estimation of the model and radiosonde observational error. The resulting Aeolus error estimates are in the range of 4.1–4.4 m s−1 (Rayleigh) and 1.9–3.0 m s−1 (Mie). Investigations of the Rayleigh wind bias on a global scale show that in addition to the satellite flight direction and seasonal differences, the systematic differences vary with latitude. A latitude-based bias correction approach is able to reduce the bias, but a residual bias of 0.4–0.6 m s−1 with a temporal trend remains. Taking additional longitudinal differences into account, the bias can be reduced further by almost 50 %. Longitudinal variations are suggested to be linked to land–sea distribution and tropical convection that influences the thermal emission of the earth. Since 20 April 2020 a telescope temperature-based bias correction scheme has been applied operationally in the L2B processor, developed by the Aeolus Data Innovation and Science Cluster (DISC).

Record low ozone values over the Arctic in boreal spring 2020

Abstract. Ozone data derived from the Tropospheric Monitoring Instrument (TROPOMI) sensor on board the Sentinel-5 Precursor satellite show exceptionally low total ozone columns in the polar region of the Northern Hemisphere (Arctic) in spring 2020. Minimum total ozone column values around or below 220 Dobson units (DU) were seen over the Arctic for 5 weeks in March and early April 2020. Usually the persistence of such low total ozone column values in spring is only observed in the polar Southern Hemisphere (Antarctic) and not over the Arctic. These record low total ozone columns were caused by a particularly strong polar vortex in the stratosphere with a persistent cold stratosphere at higher latitudes, a prerequisite for ozone depletion through heterogeneous chemistry. Based on the ERA5, which is the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis, the Northern Hemisphere winter 2019/2020 (from December to March) showed minimum polar cap temperatures consistently below 195 K around 20 km altitude, which enabled enhanced formation of polar stratospheric clouds. The special situation in spring 2020 is compared and discussed in context with two other Northern Hemisphere spring seasons, namely those in 1997 and 2011, which also displayed relatively low total ozone column values. However, during these years, total ozone columns below 220 DU over several consecutive days were not observed in spring. The similarities and differences of the atmospheric conditions of these three events and possible explanations for the observed features are presented and discussed. It becomes apparent that the monthly mean of the minimum total ozone column value for March 2020 (221 DU) was clearly below the respective values found in March 1997 (267 DU) and 2011 (252 DU), which highlights the special evolution of the polar stratospheric ozone layer in the Northern Hemisphere in spring 2020. A comparison with a typical ozone hole over the Antarctic (e.g., in 2016) indicates that although the Arctic spring 2020 situation is remarkable, with total ozone column values around or below 220 DU observed over a considerable area (up to 0.9 million km2), the Antarctic ozone hole shows total ozone columns typically below 150 DU over a much larger area (of the order of 20 million km2). Furthermore, total ozone columns below 220 DU are typically observed over the Antarctic for about 4 months.

Intercomparison of freshwater fluxes over ocean and investigations into water budget closure

Abstract. The development of algorithms for the retrieval of water cycle components from satellite data – such as total column water vapor content (TCWV), precipitation (P), latent heat flux, and evaporation (E) – has seen much progress in the past 3 decades. In the present study, we compare six recent satellite-based retrieval algorithms and ERA5 (the European Centre for Medium-Range Weather Forecasts' fifth reanalysis) freshwater flux (E−P) data regarding global and regional, seasonal and interannual variation to assess the degree of correspondence among them. The compared data sets are recent, freely available, and documented climate data records (CDRs), developed with a focus on stability and homogeneity of the time series, as opposed to instantaneous accuracy. One main finding of our study is the agreement of global ocean means of all E−P data sets within the uncertainty ranges of satellite-based data. Regionally, however, significant differences are found among the satellite data and with ERA5. Regression analyses of regional monthly means of E, P, and E−P against the statistical median of the satellite data ensemble (SEM) show that, despite substantial differences in global E patterns, deviations among E−P data are dominated by differences in P throughout the globe. E−P differences among data sets are spatially inhomogeneous. We observe that for ERA5 long-term global E−P is very close to 0 mm d−1 and that there is good agreement between land and ocean mean E−P, vertically integrated moisture flux divergence (VIMD), and global TCWV tendency. The fact that E and P are balanced globally provides an opportunity to investigate the consistency between E and P data sets. Over ocean, P (nearly) balances with E if the net transport of water vapor from ocean to land (approximated by over-ocean VIMD, i.e., ∇⋅(vq)ocean) is taken into account. On a monthly timescale, linear regression of Eocean-∇⋅(vq)ocean with Pocean yields R2=0.86 for ERA5, but smaller R2 values are found for satellite data sets. Global yearly climatological totals of water cycle components (E, P, E−P, and net transport from ocean to land and vice versa) calculated from the data sets used in this study are in agreement with previous studies, with ERA5 E and P occupying the upper part of the range. Over ocean, both the spread among satellite-based E and the difference between two satellite-based P data sets are greater than E−P, and these remain the largest sources of uncertainty within the observed global water budget. We conclude that, for a better understanding of the global water budget, the quality of E and P data sets needs to be improved, and the uncertainties more rigorously quantified.

Performance of the RegCM4.6 for High-Resolution Climate and Extreme Simulations over Tibetan Plateau

This paper presents an evaluation of the Regional Climate Model version 4.6.1 (RegCM4) at a high-resolution simulation at 10 km applied over the Tibetan Plateau. This simulation covers the period from 1980 to 2010 and is nested in a RegCM4 simulation at 30-km resolution, which is driven by the main European Centre for Medium-Range Weather and Forecasting reanalysis (ERA-Interim reanalysis) dataset. A new daily observational dataset is employed as reference data to evaluate the temperature and precipitation simulations for the inner model domain and the five largest river basins that originated in the Tibetan Plateau (TP) (i.e., the source region of Yangtze River, Yellow River, Mekong River, Salween River, and Brahmaputra River). In comparison with the low-resolution model run (R30), the cold biases for the area-averaged temperature were reduced from −2.5 to −0.1° C and the wet biases in summer mean precipitation were reduced from 58% to 25% in the high-resolution model run (R10). The substantial warming trends and slight wetting trends were basically reproduced by both RegCM4 simulations. Annual mean precipitation trends from both simulations show a better agreement with the observations than the ERA-Interim, which underestimates the annual mean precipitation trends in most regions, whereas both the RegCM4 and ERA-Interim consistently underestimate the annual mean temperature trends when compared with the observations. In addition, the overall improvement in the modeling trends for annual mean temperature and precipitation in R10 is limited when compared with R30. The extreme precipitation was also captured reasonably in both RegCM4 simulations, and the better performance is detected in the R10 simulation. The findings above show that RegCM4 with a high-resolution of 10 km is capable of reproducing the major regional climate features over the TP, but a great deal of uncertainties still exist, especially in the subregion of the Brahmaputra River basin. Thus, the 10-km resolution simulation in RegCM4 may still not be fine enough to resolve the topoclimates over the complex Himalayan terrain in the Brahmaputra River basin.

Role of vegetation in representing land surface temperature in the CHTESSEL (CY45R1) and SURFEX-ISBA (v8.1) land surface models: a case study over Iberia

Abstract. Earth observations were used to evaluate the representation of land surface temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – the European Centre for Medium-Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modeling platform (SURFEX-ISBA) for the 2004–2015 period. The results showed that the daily maximum LST simulated by CHTESSEL over Iberia was affected by a large cold bias during summer months when compared against the Satellite Application Facility on Land Surface Analysis (LSA-SAF), reaching magnitudes larger than 10 ∘C over wide portions of central and southwestern Iberia. This error was shown to be tightly linked to a misrepresentation of the vegetation cover. In contrast, SURFEX simulations did not display such a cold bias. We show that this was due to the better representation of vegetation cover in SURFEX, which uses an updated land cover dataset (ECOCLIMAP-II) and an interactive vegetation evolution, representing seasonality. The representation of vegetation over Iberia in CHTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) leaf area index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement in vegetation also included a clumping approach that introduces seasonality to the vegetation cover. The results showed significant added value, removing the daily maximum LST summer cold bias completely, without reducing the accuracy of the simulated LST, regardless of season or time of the day. The striking performance differences between SURFEX and CHTESSEL were fundamental to guiding the developments in CHTESSEL highlighting the importance of using different models. This work has important implications: first, it takes advantage of LST, a key variable in surface–atmosphere energy and water exchanges, which is closely related to satellite top-of-atmosphere observations, to improve the model's representation of land surface processes. Second, CHTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis; hence systematic errors in land surface variables and fluxes are then propagated into those products. Indeed, we showed that the summer daily maximum LST cold bias over Iberia in CHTESSEL is present in the widely used ECMWF fifth-generation reanalysis (ERA5). Finally, our results provided hints about the interaction between vegetation land–atmosphere exchanges, highlighting the relevance of the vegetation cover and respective seasonality in representing land surface temperature in both CHTESSEL and SURFEX. As a whole, this work demonstrated the added value of using multiple earth observation products for constraining and improving weather and climate simulations.

Tritium as a hydrological tracer in Mediterranean precipitation events

Abstract. Climate models are in need of improved constraints for water vapor transport in the atmosphere, and tritium can serve as a powerful tracer in the hydrological cycle. Although the general principles of tritium distribution and transfer processes within and between the various hydrological compartments are known, variation on short timescales and aspects of altitude dependence are still under debate. To address questions regarding tritium sources, sinks, and transfer processes, the sampling of individual precipitation events in Corte on the island of Corsica in the Mediterranean Sea was performed between April 2017 and April 2018. Tritium concentrations of 46 event samples were compared to their moisture origin and corresponding air mass history. Air mass back-trajectories were generated from the novel high-resolution ERA5 dataset from the ECMWF (European Centre for Medium-Range Weather Forecasts). Geographical source regions with similar tritium concentrations were predefined using generally known tritium distribution patterns, such as the “continental effect”, and from data records derived at long-term measurement stations of tritium in precipitation across the working area. Our model-derived source region tritium concentrations agreed well with annual mean station values. Moisture that originated from continental Europe and the Atlantic Ocean was most distinct regarding tritium concentrations with values up to 8.8 TU (tritium units) and near 0 TU, respectively. The seasonality of tritium values ranged from 1.6 TU in January to 10.1 TU in May, and they exhibited well-known elevated concentrations in spring and early summer due to increased stratosphere–troposphere exchange. However, this pattern was interrupted by extreme events. The average altitude of trajectories was correlated with the tritium concentrations in precipitation, especially in spring and early summer and if outlier values of extreme tritium concentrations were excluded. However, in combination with the trajectory information, these outlier values proved to be valuable for improving the comprehension of tritium movement in the atmosphere. Our work shows how event-based tritium research can advance the understanding of its distribution in the atmosphere.

Wave Energy in Korean Seas from 12-Year Wave Hindcasting

In this study, a numerical simulation is performed to produce wave hindcasting data from 2007 to 2018 for the assessment of wave energy resources in the sea waters of Korea. The hindcasting data are obtained with a relatively fine spatial resolution of 1/20° covering 120–150 °E longitude and 22.4–47.6 °N latitude using the Simulating WAves Nearshore wave model (SWAN). Three different wind fields, those of the European Centre for Medium-Range Weather (ECMWF), National Centers for Environmental Prediction (NCEP), and Japan Meteorological Agency (JMA), are used for the numerical wave simulation. It is observed that the wind field dataset of JMA exhibits the best agreement with available field observation data. For this reason, the wave energy resources are evaluated based on the data hindcasted using the JMA wind field. It is found that the overall magnitudes of wave energy are larger in winter than in summer. The wave energy in August, however, is comparable to the mean wave energy during winter because of the influence of frequent high wave events caused by typhoons. The highest monthly average wave power around Yellow Sea, South Sea, East Sea, and Jeju Island are 13.3, 18.2, 13.7, and 40 kW/m, respectively.

Inter-comparison of ERA-5, ERA-interim and GPCP rainfall over the last 40 years: Process-based analysis of systematic and random differences

This study presents a comprehensive inter-comparison between two widely used rainfall datasets – the Global Precipitation Climatology Project (GPCP) and the ERA-Interim reanalysis – and the recently released ERA-5, which will replace ERA-Interim as the main European Centre for Medium-Range Weather and Forecasting (ECMWF) reanalysis by 2020. Systematic and random error components were computed for the reanalysis datasets over the 1979–2018 period using GPCP as reference. The analysis was taken at monthly timescale at 2.5° spatial resolution, limited by GPCP. Despite its resolution limitations and complex algorithms, GPCP has been extensively used for model evaluation due to its global and long-term (over 40 years) coverage. Over most of the tropics, and over localized mid-latitude regions, ERA-5 showed lower bias and unbiased root-mean squared error (ubRMSE), as well as higher correlations, compared to ERA-Interim. Throughout the rest of the globe, these error metrics displayed similar values between both reanalysis, except over localized regions of the eastern tropical Pacific, the Andes and the Himalayas, where ERA-Interim outperformed ERA-5. A process-based analysis revealed that ERA-Interim tended to overestimate deep convection and moisture flux convergence over the tropical oceans and land, leading to excessive rainfall. Similarly, ERA-Interim showed significant rainfall underestimation over the mid-latitude oceans due to underestimation of deep convection and moisture flux convergence. Both cases were significantly improved in ERA-5, likely due to its improved parameterizations and higher resolution. Indeed, the differences in monthly rainfall between the two reanalysis were mainly due to improved dynamical (circulation) rather than thermodynamical (Clausius-Clapeyron) processes or surface evaporation changes. Nonetheless, the results also revealed improved representation of the moisture sink/source patterns over the tropical oceans in ERA-5. Finally, there were significant differences in the long-term rainfall trend patterns amongst the three datasets (with differences reaching ~30%/decade), that can extend even to the sign of the trend, with the most notorious differences occurring over tropical Africa. Furthermore, ERA-5 didn’t show improved representation of these trends. In fact, the trend of global-mean rainfall in ERA-Interim was closer to GPCP than ERA-5.

Impact of high-resolution sea surface temperature representation on the forecast of small Mediterranean catchments' hydrological responses to heavy precipitation

Abstract. Operational meteo-hydrological forecasting chains are affected by many sources of uncertainty. In coastal areas characterized by complex topography, with several medium-to-small size catchments, quantitative precipitation forecast becomes even more challenging due to the interaction of intense air–sea exchanges with coastal orography. For such areas, which are quite common in the Mediterranean Basin, improved representation of sea surface temperature (SST) space–time patterns can be particularly important. The paper focuses on the relative impact of different resolutions of SST representation on regional operational forecasting chains (up to river discharge estimates) over coastal Mediterranean catchments, with respect to two other fundamental options while setting up the system, i.e. the choice of the forcing general circulation model (GCM) and the possible use of a three-dimensional variational assimilation (3D-Var) scheme. Two different kinds of severe hydro-meteorological events that affected the Calabria region (southern Italy) in 2015 are analysed using the WRF-Hydro atmosphere–hydrology modelling system in its uncoupled version. Both of the events are modelled using the 0.25∘ resolution global forecasting system (GFS) and the 16 km resolution integrated forecasting system (IFS) initial and lateral atmospheric boundary conditions, which are from the European Centre for Medium-Range Weather Forecasts (ECMWF), applying the WRF mesoscale model for the dynamical downscaling. For the IFS-driven forecasts, the effects of the 3D-Var scheme are also analysed. Finally, native initial and lower boundary SST data are replaced with data from the Medspiration project by Institut Français de Recherche pour L'Exploitation de la Mer (IFREMER)/Centre European Remote Sensing d'Archivage et de Traitement (CERSAT), which have a 24 h time resolution and a 2.2 km spatial resolution. Precipitation estimates are compared with both ground-based and radar data, as well as discharge estimates with stream gauging stations' data. Overall, the experiments highlight that the added value of high-resolution SST representation can be hidden by other more relevant sources of uncertainty, especially the choice of the general circulation model providing the boundary conditions. Nevertheless, in most cases, high-resolution SST fields show a non-negligible impact on the simulation of the atmospheric boundary layer processes, modifying flow dynamics and/or the amount of precipitated water; thus, this emphasizes the fact that uncertainty in SST representation should be duly taken into account in operational forecasting in coastal areas.

Spatio-Seasonal Variations in Long-Term Trends of Offshore Wind Speeds Over the Black Sea; an Inter-Comparison of Two Reanalysis Data

Spatio-seasonal variability of long-term trends in mean and 95th percentile wind speeds for the term between 1979 and 2016, over the Black Sea is presented. Our aim is to contribute the existing literature by presenting the inhomogeneous spatial distribution of the long-term trends in both moderate and severe wind speeds on a monthly basis. The analysis is conducted by using two different data; European Centre for Medium-Range Weather Forecasts-ERA-Interim and U.S. National Centers for Environmental Prediction-Climate Forecast System Reanalysis (CFSR) to perform a comparative analysis. The non-parametric Mann–Kendall and Sen’s Slope methods are used to determine the trends and their significance over the Black Sea. CFSR winds presented higher interannual variability than the ERA-Interim. ERA-Interim indicates that annual mean and 95th percentile wind speeds have decreasing trends down to − 0.17%/year and − 0.20%/year in the Sea of Azov, while they have an increasing trend up to 0.35%/year and 0.38%/year in the eastern part, respectively. Results indicate that wind speeds are increasing over 28% ~ 36% of the Black Sea surface area while the wind speeds are decreasing over 2% ~ 4% of the surface area. Pacific North American Oscillation presented an influence almost all over the Black Sea with statistically significant correlation coefficients over 0.5. North Atlantic Oscillation dominates over the southwestern, western and northern Black Sea with inverse correlation coefficients over 0.6. ERA-Interim and CFSR data illustrated a similar distribution pattern over the Black Sea in means of the relation of variations in wind speeds to the teleconnection indices.

Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations

Abstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole, is the world's highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impact on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric reanalyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global reanalyses with satellite and in situ data. Yet, snow in reanalyses provides critical surface information for forecast systems from the medium to sub-seasonal timescales. Here, snow depth and snow cover from four recent global reanalysis products, namely the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 and ERA-Interim reanalyses, the Japanese 55-year Reanalysis (JRA-55) and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2), are inter-compared over the TP region. The reanalyses are evaluated against a set of 33 in situ station observations, as well as against the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in situ observations provides confidence in the station data despite the relative paucity of in situ measurement sites and the harsh operating conditions. While several reanalyses show a systematic overestimation of the snow depth or snow cover, the reanalyses that assimilate local in situ observations or IMS snow cover are better capable of representing the shallow, transient snowpack over the TP region. The latter point is clearly demonstrated by examining the family of reanalyses from the ECMWF, of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. We further tested the sensitivity of the ERA5-Land model in offline experiments, assessing the impact of blown snow sublimation, snow cover to snow depth conversion and, more importantly, excessive snowfall. These results suggest that excessive snowfall might be the primary factor for the large overestimation of snow depth and cover in ERA5 reanalysis. Pending a solution for this common model precipitation bias over the Himalayas and the TP, future snow reanalyses that optimally combine the use of satellite snow cover and in situ snow depth observations in the assimilation and analysis cycles have the potential to improve medium-range to sub-seasonal forecasts for water resources applications.

Cloud responses to climate variability over the extratropical oceans as observed by MISR and MODIS

Abstract. Linear temporal trends in cloud fraction over the extratropical oceans, observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR) during the period from 2000 to 2013, are examined in the context of coincident European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data using a maximum covariance analysis. Changes in specific cloud types defined with respect to cloud-top height and cloud optical depth are related to trends in reanalysis variables. A pattern of reduced high-altitude optically thick cloud and increased low-altitude cloud of moderate optical depth is found to be associated with increased temperatures, geopotential heights, and anti-cyclonic flow over the extratropical oceans. These and other trends in cloud occurrence are shown to be correlated with changes in the El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the North Pacific index (NPI), and the Southern Annular Mode (SAM).

Assessment of precipitation error propagation in multi-model global water resource reanalysis

Abstract. This study focuses on the Iberian Peninsula and investigates the propagation of precipitation uncertainty, and its interaction with hydrologic modeling, in global water resource reanalysis. Analysis is based on ensemble hydrologic simulations for a period spanning 11 years (2000–2010). To simulate the hydrological variables of surface runoff, subsurface runoff, and evapotranspiration, we used four land surface models (LSMs) – JULES (Joint UK Land Environment Simulator), ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), SURFEX (Surface Externalisée), and HTESSEL (Hydrology – Tiled European Centre for Medium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land) – and one global hydrological model, WaterGAP3 (Water – a Global Assessment and Prognosis). Simulations were carried out for five precipitation products – CMORPH (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), 3B42V(7), ECMWF reanalysis, and a machine-learning-based blended product. As a reference, we used a ground-based observation-driven precipitation dataset, named SAFRAN, available at 5 km, 1 h resolution. We present relative performances of hydrologic variables for the different multi-model and multi-forcing scenarios. Overall, results reveal the complexity of the interaction between precipitation characteristics and different modeling schemes and show that uncertainties in the model simulations are attributed to both uncertainty in precipitation forcing and the model structure. Surface runoff is strongly sensitive to precipitation uncertainty, and the degree of sensitivity depends significantly on the runoff generation scheme of each model examined. Evapotranspiration fluxes are comparatively less sensitive for this study region. Finally, our results suggest that there is no single model–forcing combination that can outperform all others consistently for all variables examined and thus reinforce the fact that there are significant benefits to exploring different model structures as part of the overall modeling approaches used for water resource applications.