Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023 Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramon y Cajal 2017 Grant Reference RYC‐2017‐22964 Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD) Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702 Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank IR of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887136402‐00INCT The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1) Data from Southeast Asia (excl Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003 Daily data for Mexico were provided by the Servicio Meteorologico Nacional (SMN) of Comision Nacional del Agua (CONAGUA) We acknowledge the data providers in the ECA&D project (https://wwwecadeu), the SACA&D project (https://saca-bmkgknminl), and the LACA&D project (https://ciifenknminl) We thank the three anonymous reviewers for their detailed comments which improved the manuscript

Development of an updated global land In situ‐based data set of temperature and precipitation extremes : HadEX3

The rough topography, harsh climate, and sparse monitoring stations have limited hydro-climatological studies in arid regions of Pakistan. Gauge-based gridded precipitation datasets provide an opportunity to assess the climate where stations are sparsely located. Though, the reliability of these datasets heavily depends on their ability to replicate the observed temporal variability and distribution patterns. Conventional correlation or error analyses are often not enough to justify the variability and distribution of precipitation. In the present study, mean bias error, mean absolute error, modified index of agreement, and Anderson–Darling test have been used to evaluate the performance of four widely used gauge-based gridded precipitation data products, namely, Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU); Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE), Center for Climatic Research—University of Delaware (UDel) at stations located in semi-arid, arid, and hyper-arid regions in the Balochistan province of Pakistan. The result revealed that the performance of different products varies with climate. However, GPCC precipitation data was found to perform much better in all climatic regions in terms of most of the statistical assessments conducted. As the temporal variability and distribution of precipitation are very important in many hydrological and climatic applications, it can be expected that the methods used in this study can be useful for the better assessment of gauge-based data for various applications.

https://www.mdpi.com/2073-4441/11/2/210/pdf

Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan

Traditional approaches to age-depth modeling typically assume no uncertainty for the depth value of dated intervals. However, such an assumption may not be fully valid in the case of poor coring re ...

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018PA003457

A Rapid, Deterministic Age-Depth Modeling Routine for Geological Sequences With Inherent Depth Uncertainty

Machine learning for site-adaptation and solar radiation forecasting

Climatic Research Unit temperature version 5 (CRUTEM5) is an extensive revision of our land surface air temperature data set. We have expanded the underlying compilation of monthly temperature records from 5,583 to 10,639 stations, of which those with sufficient data to be used in the gridded data set has grown from 4,842 to 7,983. Many station records have also been extended or replaced by series that have been homogenized by national meteorological and hydrological services. We have improved the identification of potential outliers in these data to better capture outliers during the reference period; to avoid classifying some real regional temperature extremes as outliers; and to reduce trends in outlier counts arising from climatic warming. Due to these updates, the gridded data set shows some regional increases in station density and regional changes in temperature anomalies. Nonetheless, the global‐mean timeseries of land air temperature is only slightly modified compared with previous versions and previous conclusions are not altered. The standard gridding algorithm and comprehensive error model are the same as for the previous version, but we have explored an alternative gridding algorithm that removes the under‐representation of high latitude stations. The alternative gridding increases estimated global‐mean land warming by about 0.1°C over the course of the whole record. The warming from 1861–1900 to the mean of the last 5 years is 1.6°C using the standard gridding (with a 95% confidence interval for errors on individual annual means of −0.11 to +0.10°C in recent years), while the alternative gridding gives a change of 1.7°C.

Land Surface Air Temperature Variations Across the Globe Updated to 2019: The CRUTEM5 Data Set

In situ climatological observations are essential for studies related to climate trends and extreme events. However, in many regions of the globe, observational records are affected by a large number of data quality issues. Assessing and controlling the quality of such datasets is an important, often overlooked aspect of climate research. Besides analysing the measurement data, metadata are important for a comprehensive data quality assessment. However, metadata are often missing, but may partly be reconstructed by suitable actions such as station inspections. This study identifies and attributes the most important common data quality issues in Bolivian and Peruvian temperature and precipitation datasets. The same or similar errors are found in many other predominantly manned station networks worldwide. A large fraction of these issues can be traced back to measurement errors by the observers. Therefore, the most effective way to prevent errors is to strengthen the training of observers and to establish a near real-time quality control (QC) procedure. Many common data quality issues are hardly detected by usual QC approaches. Data visualization, however, is an effective tool to identify and attribute those issues, and therefore enables data users to potentially correct errors and to decide which purposes are not affected by specific problems. The resulting increase in usable station records is particularly important in areas where station networks are sparse. In such networks, adequate selection and treatment of time series based on a comprehensive QC procedure may contribute to improving data homogeneity more than statistical data homogenization methods.

Identifying, attributing, and overcoming common data quality issues of manned station observations

Systematic data quality issues may occur at various stages of the data generation process They may affect large fractions of observational datasets and remain largely undetected with standard data quality control This study investigates the effects of such undetected data quality issues on the results of climatological analyses For this purpose, we quality controlled daily observations of manned weather stations from the Central Andean area with a standard and an enhanced approach The climate variables analysed are minimum and maximum temperature and precipitation About 40 % of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues These quality problems undetected with the standard quality control approach strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional temperature trends Our findings indicate that undetected data quality issues are included in important and frequently used observational datasets and hence may affect a high number of climatological studies It is of utmost importance to apply comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and large uncertainties

/pdf/effects-of-undetected-data-quality-issues-on-climatological-do2e1pz7aw.pdf

Effects of undetected data quality issues on climatological analyses

Coordinado por la Universidad de Berna, Suiza. DECADE comenzo en 2014 y finalizara a principios de 2018.

Juan Calle

Papers

Identifying, attributing, and overcoming common data quality issues of manned station observations

Effects of undetected data quality issues on climatological analyses

Atlas - Clima y eventos extremos del Altiplano Central perú-boliviano