Copernicus Climate Change Service Research Articles

Intercomparisons, error assessments, and technical information on historical upper-air measurements

Abstract. Upper-air data form the backbone of weather analysis and reanalysis products, particularly in the pre-satellite era. However, they are particularly prone to errors and uncertainties, especially data from the early days of aerology. Information that allows us to better characterize the errors of radiosonde data is important. This paper reports on an attempt to collect data from historical upper-air intercomparisons and from historical error assessments reaching back to the 1930s. The digitized numerical data will be made available through Copernicus Climate Change Services; here we publish the full information that includes images, literature, and other metadata that may be relevant and can be used to inform homogenization approaches or reanalysis production. The data collection described in this paper is available on PANGAEA: https://doi.org/10.1594/PANGAEA.925860 (Imfeld et al., 2021).

Seasonal prediction of the boreal winter stratosphere

The predictability of the Northern Hemisphere stratosphere and its underlying dynamics are investigated in five state-of-the-art seasonal prediction systems from the Copernicus Climate Change Service (C3S) multi-model database. Special attention is devoted to the connection between the stratospheric polar vortex (SPV) and lower-stratosphere wave activity (LSWA). We find that in winter (December to February) dynamical forecasts initialised on the first of November are considerably more skilful than empirical forecasts based on October anomalies. Moreover, the coupling of the SPV with mid-latitude LSWA (i.e., meridional eddy heat flux) is generally well reproduced by the forecast systems, allowing for the identification of a robust link between the predictability of wave activity above the tropopause and the SPV skill. Our results highlight the importance of November-to-February LSWA, in particular in the Eurasian sector, for forecasts of the winter stratosphere. Finally, the role of potential sources of seasonal stratospheric predictability is considered: we find that the C3S multi-model overestimates the stratospheric response to El Niño–Southern Oscillation (ENSO) and underestimates the influence of the Quasi–Biennial Oscillation (QBO).

Evaluating the performance of eight drought indices for capturing soil moisture dynamics in various vegetation regions over China

Drought is pervasive global hazard and seriously impacts ecology. Particularly, vegetation drought, which is chiefly driven by soil moisture (SM) deficiency, has a direct bearing on grain production and human livelihoods. Various drought indices associated with vegetation and SM conditions have been proposed to monitor and detect vegetation drought. In this study, we evaluated the performance of eight drought indices, including Drought Severity Index (DSI), Evaporation Stress Index (ESI), Normalized Vegetation Supply Water Index (NVSWI), Temperature-Vegetation Dryness Index (TVDI), Temperature Vegetation Precipitation Dryness Index (TVPDI), Vegetation Health Index (VHI), Self-calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Evapotranspiration Index (SPEI), for capturing SM dynamic (derived from Copernicus Climate Change Service) across the six main vegetation coverage types of China. Our results showed DSI and ESI had the best overall performance. When exploring the reasons for the uncertainty of these indices (except SC-PDSI and SPEI) in the evaluation, we found that, in the non-arable regions, the time lag effect of drought indices on SM, the average state and rangeability of corresponding variables and the climatic conditions (precipitation and temperature) all impacted the performance of DSI, ESI, NVSWI, TVPDI and VHI. In the arable region, cropland types (paddy field and non-paddy field) and the uncertainty of SM data mainly caused the uncertainties of the above five indices. With regard to the TVDI, abnormalities of dry and wet edges fitting may be the primary factor affecting its performance. These results demonstrated that these drought indices with reliable and robust performance of capturing SM dynamics can be suggested to characterize the trend of SM. Certainly, this study can provide a reference for the improvement of existing drought indices and the establishment of new drought indices.

The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

A perfect prognosis downscaling methodology for seasonal prediction of local-scale wind speeds

This work provides a new methodology based on a statistical downscaling with a perfect prognosis approach to produce seasonal predictions of near-surface wind speeds at the local scale. Hybrid predictions combine a dynamical prediction of the four main Euro-Atlantic Teleconnections (EATC) and a multilinear statistical regression, which is fitted with observations and includes the EATC as predictors. Once generated, the skill of the hybrid predictions is assessed at 17 tall tower locations in Europe targeting the winter season. For comparative purposes, hybrid predictions have also been produced and assessed at a pan-European scale, using the ERA5 100 m wind speed as the observational reference. Overall, results indicate that hybrid predictions outperform the dynamical predictions of near-surface wind speeds, obtained from five prediction systems available through the Climate Data Store of the Copernicus Climate Change Service. The performance of a multi-system ensemble prediction has also been assessed. In all cases, the enhancement is particularly noted in northern Europe. By being more capable of anticipating local wind speed conditions in higher quality, hybrid predictions will boost the application of seasonal predictions outside the field of pure climate research.

Pan-European meteorological and snow indicators of climate change impact on ski tourism

Ski tourism plays a major socio-economic role in the snowy and mountainous areas of Europe such as the Alps, the Pyrenees, Nordic Europe, Eastern Europe, Anatolia, etc. Past and future climate change has an impact on the operating conditions of ski resorts, due to their reliance on natural snowfall and favorable conditions for snowmaking. However, there is currently a lack of assessment of past and future operating conditions of ski resorts at the pan-European scale in the context of climate change. The presented work aims at filling this gap, as part of the ”European Tourism” Sectoral Information System (SIS) of the Copernicus Climate Change Services (C3S). The Mountain Tourism Meteorological and Snow Indicators (MTMSI) were co-designed with representatives of the ski tourism industry, including consulting companies. They were derived from statistically adjusted EURO-CORDEX climate projections (multiple GCM/RCM pairs for RCP2.6, RCP4.5 and RCP8.5) using the UERRA 5.5 km resolution surface reanalysis as a reference, used as input to the snow cover model Crocus, with and without accounting for snow management (grooming, snowmaking). Results are generated for 100 m elevation bands for NUTS-3 geographical areas spanning all areas relevant to ski tourism in Europe. This article introduces the underpinning elements for the generation of this product, and illustrates results at the pan-European scale as well as for smaller scale case studies. A dedicated visualization app allows for easy navigation into the multiple dimensions of this dataset, thereby fulfilling the needs of a broad range of users.

Seasonal predictability of Mediterranean weather regimes in the Copernicus C3S systems

Seasonal predictions in the Mediterranean region have relevant socio-economic implications, especially in the context of a changing climate. To date, sources of predictability have not been sufficiently investigated at the seasonal scale in this region. To fill this gap, we explore sources of predictability using a weather regimes (WRs) framework. The role of WRs in influencing regional weather patterns in the climate state has generated interest in assessing the ability of climate models to reproduce them. We identify four Mediterranean WRs for the winter (DJF) season and explore their sources of predictability looking at teleconnections with sea surface temperature (SST). In particular, we assess how SST anomalies affect the WRs frequencies during winter focussing on the two WRs that are associated with the teleconnections in which the signal is more intense: the Meridional and the Anticyclonic regimes. These sources of predictability are sought in five state-of-the-art seasonal forecasting systems included in the Copernicus Climate Change Services (C3S) suite finding a weaker signal but an overall good agreement with reanalysis data. Finally, we assess the ability of the C3S models in reproducing the reanalysis data WRs frequencies finding that their moderate skill increases during ENSO intense years, indicating that this teleconnection is well reproduced by the models and yields improved predictability in the Mediterranean region.

Recent global land cover dynamics and implications for soil erosion and carbon losses from deforestation

Changes in land cover are increasingly affecting land surface properties and provision of ecosystem services. Understanding recent historical land cover changes and their interlinkages with key environmental processes is instrumental to better support strategies for land-use management. The recently released products from the European Space Agency and Copernicus Climate Change Service contain high-resolution (300 m) time series of global land cover maps from 1992 to 2018. This study investigates the land transitions in these products and explores the effects on two key environmental aspects, namely, carbon losses from deforestation and soil erosion rates. We used a powerful server for big data analysis to retain the original spatial resolution of the datasets. We found 722 Mha (5.5 % of the total ice-free land surface) of gross land cover changes, which mainly involved transitions to and from forest/agriculture. Cropland gains are 205 Mha and losses 126 Mha (net expansion of 79 Mha). Deforestation occurring in 242 Mha was mainly caused by agricultural expansion, whereas 196 Mha were afforested. Settlements show the largest relative expansion (44 Mha, +210 %), of which 67 % (29 Mha) occurred at expenses of agricultural land. Deforestation caused 12.3 (7.6/14.2) Gt Carbon losses from below- and aboveground biomass from 2010 to 2018, corresponding to 1.5 (1.0/1.8) Gt Carbon per year. Global agriculture activities have increased total soil erosion of 3.2 Gt and soil erosion rates of 0.22 Mg ha −1 yr −1 in the period 2001–2012, especially in tropical regions. The identified land transitions and changes in key environmental processes reflect a human-dominated Earth system and the indirect effects of climate change on land cover, especially in boreal ecosystems.

Datasets and approaches for the estimation of rainfall erosivity over Italy: A comprehensive comparison study and a new method

The paper considers a methodology to assess rainfall erosivity in Italy for the recent decades (1981–2010), building on datasets and materials freely available, such as those included within the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S). Twenty-one referenced empirical models to assess rainfall erosivity (R-factor) based on coarse rainfall data are tested and compared; then, a custom model is calibrated, with the support of seasonal rainfall pattern clustering by means of the Self-Organizing Map. Moreover, a large database of sub-hourly rainfall observations, covering the period 2002–2011, is collected and used for validation. Model performances are analysed at the point-scale of the rain gauges and at the spatial scale of different relevant gridded rainfall products: the fifth generation of ECMWF ReAnalysis (ERA5, ERA5-Land), the gridded European observational dataset (E-OBS), all included in the CDS, and SCIA-ISPRA (the Italian standard rainfall gridded dataset).New Hydrological Insights from the RegionThe proposed methodology provides four alternatives of spatially distributed rainfall erosivity datasets covering Italy with diverse levels of reliability. Analysis of results shows that the best performance is achieved by the combined use of the custom model and the SCIA-ISPRA dataset, followed by ERA5-Land, with the main source of error lying in the use of an empirical model instead of the rigorous model for R-factor estimation, and secondly in the use of gridded rainfall data instead of point-scale rainfall observations.

Calculation of Agro-Climatic Factors from Global Climatic Data

This manuscript aims to create large-scale calculations of agro-climatic factors from global climatic data with high granularity-climatic ERA5-Land dataset from the Copernicus Climate Change Service in particular. First, we analyze existing approaches used for agro-climatic factor calculation and formulate a frame for our calculations. Then we describe the design of our methods for calculation and visualization of certain agro-climatic factors. We then run two case studies. Firstly, the case study of Kojčice validates the uncertainty of input data by in-situ sensors. Then, the case study of the Pilsen region presents certain agro-climatic factors calculated for a representative point of the area and visualizes their time-variability in graphs. Maps represent a spatial distribution of the chosen factors for the Pilsen region. The calculated agro-climatic factors are frost dates, frost-free periods, growing degree units, heat stress units, number of growing days, number of optimal growing days, dates of fall nitrogen application, precipitation, evapotranspiration, and runoff sums together as water balance and solar radiation. The algorithms are usable anywhere in the world, especially in temperate and subtropical zones.

The Impact of Assimilating Satellite Radiance Observations in the Copernicus European Regional Reanalysis (CERRA)

The assimilation of microwave and infrared (IR) radiance satellite observations within numerical weather prediction (NWP) models have been an important component in the effort of improving the accuracy of analysis and forecast. Such capabilities were implemented during the development of the high-resolution Copernicus European Regional Reanalysis (CERRA), funded by the Copernicus Climate Change Services (C3S). The CERRA system couples the deterministic system with the ensemble data assimilation to provide periodic updates of the background error covariance matrix. Several key factors for the assimilation of radiances were investigated, including appropriate use of variational bias correction (VARBC), surface-sensitive AMSU-A observations and observation error correlation. Twenty-one-day impact studies during the summer and winter seasons were conducted. Generally, the assimilation of radiances has a small impact on the analysis, while greater impacts are observed on short-range (12 and 24-h) forecasts with an error reduction of 1–2% for the mid and high troposphere. Although, the current configuration provided less accurate forecasts from 09 and 18 UTC analysis times. With the increased thinning distances and the rejection of IASI observation over land, the errors in the analyses and 3 h forecasts on geopotential height were reduced up to 2%.

Surface Albedo Retrieval from 40-Years of Earth Observations through the EUMETSAT/LSA SAF and EU/C3S Programmes: The Versatile Algorithm of PYALUS

Land surface albedo quantifies the fraction of the sunlight reflected by the surface of the Earth. This article presents the algorithm concepts for the remote sensing of this variable based on the heritage of several developments which were performed at Méteo France over the last decade and described in several papers by Carrer et al. The scientific algorithm comprises four steps: an atmospheric correction, a sensor harmonisation (optional), a BRDF (Bidirectional Reflectance Distribution Function) inversion, and the albedo calculation. At the time being, the method has been applied to 11 sensors in the framework of two European initiatives (Satellite Application Facility on Land Surface Analysis—LSA SAF, and Copernicus Climate Change Service—C3S): NOAA-7-9-11-14-16-17/AVHRR2-3, SPOT/VGT1-2, Metop/AVHRR-3, PROBA-V, and MSG/SEVIRI. This work leads to a consistent archive of almost 40 years of satellite-derived albedo data (available in 2020). From a single sensor, up to three different albedo products with different characteristics have been developed to address the requirements of both, near real-time (NRT) (weather prediction with a demand of timeliness of 1 h) and climate communities. The evaluation of the algorithm applied to different platforms was recently made by Lellouch et al. and Sánchez Zapero et al. in 2020 which can be considered as companion papers. After a summary of the method for the retrieval of these surface albedos, this article describes the specificities of each retrieval, lists the differences, and discusses the limitations. The plan of continuity with the next European satellite missions and perspectives of improvements are introduced. For example, Metop/AVHRR-3 albedo will soon become the medium resolution sensor product with the longest NRT data record, since MODIS is approaching the end of its life-cycle. Additionally, Metop-SG/METimage will ensure its continuity thanks to consistent production of data sets guaranteed till 2050 by the member states of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). In the end, the common strategy which we proposed through the different programmes may offer an unprecedented opportunity to study the temporal trends affecting surface properties and to analyse human-induced climate change. Finally, the access to the source code (called PYALUS) is provided through an open access platform in order to share with the community the expertise on the satellite retrieval of this variable.

Skill Assessment of Copernicus Climate Change Service Seasonal Ensemble Precipitation Forecasts over Iran

Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems. Recently, the Copernicus Climate Change Service (C3S) database has been releasing monthly forecasts for lead times of up to three months for public use. This study evaluated the ensemble forecasts of three C3S models over the period 1993–2017 in Iran’s eight classified precipitation clusters for one- to three-month lead times. Probabilistic and non-probabilistic criteria were used for evaluation. Furthermore, the skill of selected models was analyzed in dry and wet periods in different precipitation clusters. The results indicated that the models performed best in western precipitation clusters, while in the northern humid cluster the models had negative skill scores. All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons. Moreover, with increasing lead time, the forecast skill of the models worsened. In terms of forecasting in dry and wet years, the forecasts of the models were generally close to observations, albeit they underestimated several severe dry periods and overestimated a few wet periods. Moreover, the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models. In general, the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran. For the clusters considered in Iran and for the long-range system versions considered, the Meteo France model had lower skill than the other models.

Study of Climate Change Detection in North-East Africa Using Machine Learning and Satellite Data

The study of climate change has become an important topic, because of its negative impact on human life. The North-East African part lacks the studies for climate change detection, despite it being one of the most affected parts worldwide. The relationship between the emission of greenhouse gases (GHGs) and climate change is an important factor to understand. To investigate this linkage, we used machine-learning models based on essential climate variables to investigate the relationship between the GHGs and the rhythm of climate variable change. The research investigates how machine learning techniques can be applied to climatic data to build an ML model that able to predict the state of climate variables for the short and long term. By selecting a candidate model, will help in climate adaptation and mitigation, also determine at what level GHGs should be kept and their corresponding concentrations in order to avoid climate events and crises. The used models are long short-term memory, autoencoders, and convolutional neural network. Alternatively, the data set has been selected from UK National Centre for Earth Observation (NCEO) and Copernicus Climate Change Services (C3Ss). We compared the performance of these techniques and the best candidate was the Head CNN; based on performance metrics such as Root-Mean-Squared-Error (RMSE): 5.378, 2.395, and 15.923, and R Coefficient: 0.607, 0.806, and 0.539 for the variables temperature, CO2, and CH4 respectively. We were able to link the greenhouse gas emission to essential climate variables with high accuracy based on the reading of this geographic area.

A decade of GOSAT Proxy satellite CH<sub>4</sub> observations

Abstract. This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4∕XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).

The human exposome and health in the Anthropocene.

‘Man is a part of nature, and his war against nature is inevitably a war against himself’ ― Rachel Carson. The term Anthropocene describes the current period, when the biophysical systems of Earth are being significantly altered by human activities. Although the great acceleration of anthropogenic influences began in the 20th century, the Industrial Revolution has often been proposed as the start of this period. Burning fossil fuels on a large scale, which was the motor of the Industrial Revolution, has disrupted the global climate, with far-reaching consequences for life on Earth. In the Intergovernmental Panel on Climate Change (IPCC) Special Report, it is stated with high confidence that human activities have significantly contributed to an increase in the global mean temperature of approximately 1°C above pre-industrial levels, and with medium confidence that trends in intensity and frequency of some climate and weather extremes have been detected, during which approximately 0.5°C of global warming occurred.1 The average global temperature will continue to rise if current greenhouse gas emissions continue. The Copernicus Climate Change Service (C3S) recently reported that the 5 warmest years on record have all occurred in the past 5 years, and that 2010–19 was the warmest decade recorded. C3S also reported that 2019 was globally the second warmest year and that atmospheric carbon dioxide (CO2) concentrations continued to rise.2 During 2019 there was extraordinary ice-melt in Greenland and wildfires consumed millions of acres in Siberia, Alaska and Australia.3,4 Brazil’s national institute for space research (INPE) also reported fires burning at a record rate in the Amazon rainforest. The INPE reported an 80% increase in deforestation compared with that in the same period in 2018. This not only threatens local ecosystems but may affect the entire planet, because intact forest absorbs and holds large amounts of CO2.5 The recent Living Planet Report by the World Wildlife Fund and Zoological Society of London provides further evidence that unsustainable human activity is threatening the planet’s natural systems.6 The report shows an overall decline of 60% in species’ population sizes between 1970 and 2014 because of factors such as overexploitation, habitat destruction (e.g. agricultural land expansion and logging), environmental pollution and climate change. The current species extinction rates are between 100 and 1000 times higher than previous background rate.6 The International Union for Conservation of Nature Red List 2019 reported that more than 27 000 species are threatened with extinction (27% of all animal, fungi and plant species assessed).7 A recent study of plants that have become extinct since Carl Linnaeus’ Species Plantarum has concluded that the number of known seed plant extinctions is more than four times that of the Red List.8 Biodiversity is a vital part of Earth’s past, present and future, and a key indicator of planetary health. The ongoing dramatic decline in biodiversity is therefore a problem, and underlying causes must be better understood for effective species conservation and human well-being. The concept of planetary health focuses on the intricate link between human health and the health of natural systems within the biosphere.9–11 Loss of biological diversity alters ecosystems functions and thereby reduces the ability to provide society with the goods and services, including food, necessary to flourish.9–11 The emergence of viruses such as the novel coronavirus (SARS-CoV-2), and global environmental change, wildlife, animal markets and food systems (which contribute largely to carbon emissions) are also interrelated.12,13 To increase awareness of current environmental problems and how complex interactions among climate change, infectious agents, environmental pollutants and other stressors, i.e. the exposome, may affect human health, this multidisciplinary review summarizes important scientific literature and discusses actions for a successful transformation to a safe, sustainable society.

Progress towards a holistic land and marine surface meteorological database and a call for additional contributions

AbstractThis paper outlines progress of the Copernicus Climate Change Service's (C3S) Global Land and Marine Observations Database service in securing data sources and introduces the data upload component. We present details of land and marine data holdings inventoried, highlighting priority needs in terms of periods, regions and Essential Climate Variables (ECVs) where additional data could bring most benefit. These holdings are being iteratively merged and integrated to best meet user needs and are served to the user via the Copernicus Climate Data Store (CDS). The secure Data Upload Server enables any data provider to share additional data and metadata with the service. We outline the process for registering as a data provider and how data sets are prioritized for integration. We encourage all data owners to share their data with the C3S service via our Data Upload Server. All unique and relevant data acquired or submitted will be also archived at the NOAA National Centers for Environmental Information World Data Center for Meteorology, Asheville, North Carolina, USA and used in their database curation efforts which are being jointly developed.

The NASA‐JAXA Global Precipitation Measurement mission – part II: New frontiers in precipitation science

The <scp>NASA‐JAXA</scp> Global Precipitation Measurement mission – part <scp>II</scp>: New frontiers in precipitation science

Comparison of open access global climate services for hydrological data

ABSTRACT There is a high demand for openly accessible hydroclimatic data for climate change adaptation. Different data sources are available, however, discrepancy between the data can confuse users and should be evaluated and explained. This study, investigates how climate impact indicators (CIIs) developed for global users in the Copernicus Climate Change Service (C3S) are comparable to other openly available global data for water and climate. We found that, for temperature, datasets are comparable and climate impacts are thus considered robust. Important discrepancies arise in the precipitation indicators. Of the CIIs analysed in this study, the hydrological CIIs differ most so they should be used with care. These differences are probably caused by model uncertainty (hydrological model, HM; global climate model, GCM), ensemble size and model selection. A HM ensemble, as well as a GCM ensemble combined with improved model performance and selection criteria, should be used to ensure high-quality global water and climate services.

WFDE5: bias-adjusted ERA5 reanalysis data for impact studies

Abstract. The WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5∘ spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components when analysed in an uncalibrated hydrological model (WaterGAP) than with the use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020b), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, C3S, 2020a) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data – allowing users to regenerate part of the dataset or apply the same approach to other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole of the year 2016, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60 (Cucchi et al., 2020).