Articles | Volume 14, issue 5
https://doi.org/10.5194/essd-14-2195-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-14-2195-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
06 May 2022
Data description paper |
| 06 May 2022
| 06 May 2022
HCPD-CA: high-resolution climate projection dataset in central Asia
Key Laboratory of Regional Climate-Environment for Temperate East Asia
(RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing 100029, China
Jinming Feng
CORRESPONDING AUTHOR
Key Laboratory of Regional Climate-Environment for Temperate East Asia
(RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing 100029, China
Zhongwei Yan
Key Laboratory of Regional Climate-Environment for Temperate East Asia
(RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing 100029, China
Key Laboratory of Regional Climate-Environment for Temperate East Asia
(RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing 100029, China
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We modified the code of the Weather Research and Forecasting Model (WRF) v3.8.1 to include the forcing components more than the Greenhouse Gases and evaluate the impact of forcing configurations on the climate simulation results in China. It showed that different external forcing configurations in WRF could result in considerable impact on the annual temperature and precipitation trend, which was stronger than parameterization schemes but was weaker than spectral nudging.
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Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Jinming Feng, Meng Luo, Jun Wang, Yuan Qiu, Qizhong Wu, and Ke Wang
EGUsphere, https://doi.org/10.5194/egusphere-2023-867, https://doi.org/10.5194/egusphere-2023-867, 2023
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Atmos. Chem. Phys., 21, 4825–4848, https://doi.org/10.5194/acp-21-4825-2021, https://doi.org/10.5194/acp-21-4825-2021, 2021
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We assessed the influence of the greening trend on BVOC emission in China. The comparison among different scenarios showed that vegetation changes resulting from land cover management are the main driver of BVOC emission change in China. Climate variability contributed significantly to interannual variations but not much to the long-term trend during the study period.
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A 7-year record of vertical profiles of radar measurements and precipitation estimates at Dumont d'Urville, Adélie Land, East Antarctica
Long-term monthly 0.05° terrestrial evapotranspiration dataset (1982–2018) for the Tibetan Plateau
High-resolution (1 km) all-sky net radiation over Europe enabled by the merging of land surface temperature retrievals from geostationary and polar-orbiting satellites
Atmospheric and surface observations during the Saint John River Experiment on Cold Season Storms (SAJESS)
Year-long buoy-based observations of the air–sea transition zone off the US west coast
The historical Greenland Climate Network (GC-Net) curated and augmented level-1 dataset
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: a comprehensive long-term dataset of remote sensing observations
Global high-resolution drought indices for 1981–2022
CHESS-SCAPE: high-resolution future projections of multiple climate scenarios for the United Kingdom derived from downscaled United Kingdom Climate Projections 2018 regional climate model output
Quality-controlled meteorological datasets from SIGMA automatic weather stations in northwest Greenland, 2012–2020
A dataset of energy, water vapor, and carbon exchange observations in oasis–desert areas from 2012 to 2021 in a typical endorheic basin
Derivation and compilation of lower-atmospheric properties relating to temperature, wind, stability, moisture, and surface radiation budget over the central Arctic sea ice during MOSAiC
CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023
Dataset of Stable Isotopes of Precipitation in the Eurasian Continent
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A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations
A Global Gridded Dataset for Cloud Vertical Structure from Combined CloudSat and CALIPSO Observations
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The EUPPBench postprocessing benchmark dataset v1.0
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Database of the Italian disdrometer network
East Asia Reanalysis System (EARS)
Data rescue of historical wind observations in Sweden since the 1920s
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EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset
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The AntAWS dataset: a compilation of Antarctic automatic weather station observations
HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020
A long-term 1 km monthly near-surface air temperature dataset over the Tibetan glaciers by fusion of station and satellite observations
A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)
GSDM-WBT: global station-based daily maximum wet-bulb temperature data for 1981–2020
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The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019)
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This paper presents 7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica. The main characteristics of the dataset are outlined in a short statistical study. Interannual and seasonal variability are also investigated. Then, we extensively describe the processing method to retrieve snowfall profiles from the radar data. Lastly, a brief comparison is made with two climate models as an application example of the dataset.
Ling Yuan, Xuelong Chen, Yaoming Ma, Cunbo Han, Binbin Wang, and Weiqiang Ma
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Land surface temperature and surface net radiation are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions, and the few high-resolution datasets available have large gaps due to cloud cover. Here, we present a continuous daily product for both variables across Europe for 2018–2019 obtained by combining observations from geostationary as well as polar-orbiting satellites.
Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
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The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick, Canada, to investigate the types of precipitation that can lead to ice jams and flooding along the river. We deployed meteorological instruments, took precipitation measurements and photographs of snowflakes, and launched weather balloons. These data will help us to better understand the atmospheric conditions that can affect local communities and townships downstream during the spring melt season.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
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Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
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The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Giovanni Chellini, Rosa Gierens, Kerstin Ebell, Theresa Kiszler, Pavel Krobot, Alexander Myagkov, Vera Schemann, and Stefan Kneifel
Earth Syst. Sci. Data, 15, 5427–5448, https://doi.org/10.5194/essd-15-5427-2023, https://doi.org/10.5194/essd-15-5427-2023, 2023
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We present a comprehensive quality-controlled dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region, and substantially warm the surface. However, our understanding of microphysical processes in these clouds is incomplete. This dataset includes a comprehensive set of variables which allow for extensive investigation of such processes in LLMPCs at the site.
Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
Earth Syst. Sci. Data, 15, 5449–5466, https://doi.org/10.5194/essd-15-5449-2023, https://doi.org/10.5194/essd-15-5449-2023, 2023
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Drought is undeniably one of the most intricate and significant natural hazards with far-reaching consequences for the environment, economy, water resources, agriculture, and societies across the globe. In response to this challenge, we have devised high-resolution drought indices. These indices serve as invaluable indicators for assessing shifts in drought patterns and their associated impacts on a global, regional, and local level facilitating the development of tailored adaptation strategies.
Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data, 15, 5371–5401, https://doi.org/10.5194/essd-15-5371-2023, https://doi.org/10.5194/essd-15-5371-2023, 2023
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CHESS-SCAPE is a suite of high-resolution climate projections for the UK to 2080, derived from United Kingdom Climate Projections 2018 (UKCP18), designed to support climate impact modelling. It contains four realisations of four scenarios of future greenhouse gas levels (RCP2.6, 4.5, 6.0 and 8.5), with and without bias correction to historical data. The variables are available at 1 km resolution and a daily time step, with monthly, seasonal and annual means and 20-year mean-monthly time slices.
Motoshi Nishimura, Teruo Aoki, Masashi Niwano, Sumito Matoba, Tomonori Tanikawa, Tetsuhide Yamasaki, Satoru Yamaguchi, and Koji Fujita
Earth Syst. Sci. Data, 15, 5207–5226, https://doi.org/10.5194/essd-15-5207-2023, https://doi.org/10.5194/essd-15-5207-2023, 2023
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We presented the method of data quality checks and the dataset for two ground weather observations in northwest Greenland. We found that the warm and clear weather conditions in the 2015, 2019, and 2020 summers caused the snowmelt and the decline in surface reflectance of solar radiation at a low-elevated site (SIGMA-B; 944 m), but those were not seen at the high-elevated site (SIGMA-A; 1490 m). We hope that our data management method and findings will help climate scientists.
Shaomin Liu, Ziwei Xu, Tao Che, Xin Li, Tongren Xu, Zhiguo Ren, Yang Zhang, Junlei Tan, Lisheng Song, Ji Zhou, Zhongli Zhu, Xiaofan Yang, Rui Liu, and Yanfei Ma
Earth Syst. Sci. Data, 15, 4959–4981, https://doi.org/10.5194/essd-15-4959-2023, https://doi.org/10.5194/essd-15-4959-2023, 2023
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We present a suite of observational datasets from artificial and natural oases–desert systems that consist of long-term turbulent flux and auxiliary data, including hydrometeorological, vegetation, and soil parameters, from 2012 to 2021. We confirm that the 10-year, long-term dataset presented in this study is of high quality with few missing data, and we believe that the data will support ecological security and sustainable development in oasis–desert areas.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data, 15, 4983–4995, https://doi.org/10.5194/essd-15-4983-2023, https://doi.org/10.5194/essd-15-4983-2023, 2023
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Observations from the MOSAiC expedition relating to lower-atmospheric temperature, wind, stability, moisture, and surface radiation budget from radiosondes, a meteorological tower, radiation station, and ceilometer were compiled to create a dataset which describes the thermodynamic and kinematic state of the central Arctic lower atmosphere between October 2019 and September 2020. This paper describes the methods used to develop this lower-atmospheric properties dataset.
Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
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This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
Longhu Chen, Qinqin Wang, Guofeng Zhu, Xinrui Lin, Dongdong Qiu, Yinying Jiao, Siyu Lu, Rui Li, Gaojia Meng, and Yuhao Wang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-384, https://doi.org/10.5194/essd-2023-384, 2023
Revised manuscript accepted for ESSD
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We have compiled data regarding stable precipitation isotopes from 1930 sampling points throughout the Eurasian continent since 1961, accumulating a total of 51,753 data records. This collected data has undergone preprocessing and statistical analysis. Moreover, we analyzed the spatiotemporal distribution of stable precipitation isotopes across the Eurasian continent and their interrelationships with meteorological elements.
Jinghua Xiong, Abhishek, Li Xu, Hrishikesh A. Chandanpurkar, James S. Famiglietti, Chong Zhang, Gionata Ghiggi, Shenglian Guo, Yun Pan, and Bramha Dutt Vishwakarma
Earth Syst. Sci. Data, 15, 4571–4597, https://doi.org/10.5194/essd-15-4571-2023, https://doi.org/10.5194/essd-15-4571-2023, 2023
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To overcome the shortcomings associated with limited spatiotemporal coverage, input data quality, and model simplifications in prevailing evaporation (ET) estimates, we developed an ensemble of 4669 unique terrestrial ET subsets using an independent mass balance approach. Long-term mean annual ET is within 500–600 mm yr−1 with a unimodal seasonal cycle and several piecewise trends during 2002–2021. The uncertainty-constrained results underpin the notion of increasing ET in a warming climate.
Boyang Jiao, Yucheng Su, Qingxiang Li, Veronica Manara, and Martin Wild
Earth Syst. Sci. Data, 15, 4519–4535, https://doi.org/10.5194/essd-15-4519-2023, https://doi.org/10.5194/essd-15-4519-2023, 2023
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This paper develops an observational integrated and homogenized global-terrestrial (except for Antarctica) SSRIH station. This is interpolated into a 5° × 5° SSRIH grid and reconstructed into a long-term (1955–2018) global land (except for Antarctica) 5° × 2.5° SSR anomaly dataset (SSRIH20CR) by an improved partial convolutional neural network deep-learning method. SSRIH20CR yields trends of −1.276 W m−2 per decade over the dimming period and 0.697 W m−2 per decade over the brightening period.
Lukas Frank, Marius Opsanger Jonassen, Teresa Remes, Florina Roana Schalamon, and Agnes Stenlund
Earth Syst. Sci. Data, 15, 4219–4234, https://doi.org/10.5194/essd-15-4219-2023, https://doi.org/10.5194/essd-15-4219-2023, 2023
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The Isfjorden Weather Information Network (IWIN) provides continuous meteorological near-surface observations from Isfjorden in Svalbard. The network combines permanent automatic weather stations on lighthouses along the coast line with mobile stations on board small tourist cruise ships regularly trafficking the fjord during spring to autumn. All data are available online in near-real time. Besides their scientific value, IWIN data crucially enhance the safety of field activities in the region.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Veronique Bouchet, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Detlef Stammer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-376, https://doi.org/10.5194/essd-2023-376, 2023
Revised manuscript accepted for ESSD
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines are proposed as international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Jiye Leng, Jing M. Chen, Wenyu Li, Xiangzhong Luo, Mingzhu Xu, Jane Liu, Rong Wang, Cheryl Rogers, Bolun Li, and Yulin Yan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-328, https://doi.org/10.5194/essd-2023-328, 2023
Revised manuscript accepted for ESSD
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We produced a long-term global two-leaf gross primary productivity (GPP) and evapotranspiration (ET) dataset at the hourly time step by integrating a diagnostic process-based model with dynamic parameterizations. The new dataset provides us with a unique opportunity to study carbon and water fluxes at sub-daily time scales and advance our understanding of ecosystem functions in response to transient environmental changes.
Jingya Han, Chiyuan Miao, Jiaojiao Gou, Haiyan Zheng, Qi Zhang, and Xiaoying Guo
Earth Syst. Sci. Data, 15, 3147–3161, https://doi.org/10.5194/essd-15-3147-2023, https://doi.org/10.5194/essd-15-3147-2023, 2023
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Constructing a high-quality, long-term daily precipitation dataset is essential to current hydrometeorology research. This study aims to construct a long-term daily precipitation dataset with different spatial resolutions based on 2839 gauge observations. The constructed precipitation dataset shows reliable quality compared with the other available precipitation products and is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling.
William Bertrand, Jennifer E. Kay, John Haynes, and Gijs de Boer
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-265, https://doi.org/10.5194/essd-2023-265, 2023
Revised manuscript accepted for ESSD
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The vertical structure of clouds has a profound effect on global energy flows, air circulation, and the hydrologic cycle. Two satellite instruments, CloudSat radar and CALIPSO lidar, have taken complementary measurements of cloud vertical structure for over a decade. Here we present the 3S-GEOPROF-COMB product, a globally-gridded satellite data product combining CloudSat and CALIPSO observations of cloud vertical structure.
Christian Borger, Steffen Beirle, and Thomas Wagner
Earth Syst. Sci. Data, 15, 3023–3049, https://doi.org/10.5194/essd-15-3023-2023, https://doi.org/10.5194/essd-15-3023-2023, 2023
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This study presents a long-term data set of monthly mean total column water vapour (TCWV) based on measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. We describe how the TCWV values are retrieved from UV–Vis satellite spectra and demonstrate that the OMI TCWV data set is in good agreement with various different reference data sets. Moreover, we also show that it fulfills typical stability requirements for climate data records.
Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem
Earth Syst. Sci. Data, 15, 2635–2653, https://doi.org/10.5194/essd-15-2635-2023, https://doi.org/10.5194/essd-15-2635-2023, 2023
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A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
Santiago Beguería, Dhais Peña-Angulo, Víctor Trullenque-Blanco, and Carlos González-Hidalgo
Earth Syst. Sci. Data, 15, 2547–2575, https://doi.org/10.5194/essd-15-2547-2023, https://doi.org/10.5194/essd-15-2547-2023, 2023
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A gridded dataset on monthly precipitation over mainland Spain between spans 1916–2020. The dataset combines ground observations from the Spanish National Climate Data Bank and new data rescued from meteorological yearbooks published prior to 1951, which almost doubled the number of weather stations available during the first decades of the 20th century. Geostatistical techniques were used to interpolate a regular 10 x 10 km grid.
Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Olaf Conrad, Niklaus E. Zimmermann, and Katja Frieler
Earth Syst. Sci. Data, 15, 2445–2464, https://doi.org/10.5194/essd-15-2445-2023, https://doi.org/10.5194/essd-15-2445-2023, 2023
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We present the first 1 km, daily, global climate dataset for climate impact studies. We show that the high-resolution data have a decreased bias and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.
Elisa Adirosi, Federico Porcù, Mario Montopoli, Luca Baldini, Alessandro Bracci, Vincenzo Capozzi, Clizia Annella, Giorgio Budillon, Edoardo Bucchignani, Alessandra Lucia Zollo, Orietta Cazzuli, Giulio Camisani, Renzo Bechini, Roberto Cremonini, Andrea Antonini, Alberto Ortolani, Samantha Melani, Paolo Valisa, and Simone Scapin
Earth Syst. Sci. Data, 15, 2417–2429, https://doi.org/10.5194/essd-15-2417-2023, https://doi.org/10.5194/essd-15-2417-2023, 2023
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The paper describes the database of 1 min drop size distribution (DSD) of atmospheric precipitation collected by the Italian disdrometer network over the last 10 years. These data are useful for several applications that range from climatological, meteorological and hydrological uses to telecommunications, agriculture and conservation of cultural heritage exposed to precipitation. Descriptions of the processing and of the database organization, along with some examples, are provided.
Jinfang Yin, Xudong Liang, Yanxin Xie, Feng Li, Kaixi Hu, Lijuan Cao, Feng Chen, Haibo Zou, Feng Zhu, Xin Sun, Jianjun Xu, Geli Wang, Ying Zhao, and Juanjuan Liu
Earth Syst. Sci. Data, 15, 2329–2346, https://doi.org/10.5194/essd-15-2329-2023, https://doi.org/10.5194/essd-15-2329-2023, 2023
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A collection of regional reanalysis datasets has been produced. However, little attention has been paid to East Asia, and there are no long-term, physically consistent regional reanalysis data available. The East Asia Reanalysis System was developed using the WRF model and GSI data assimilation system. A 39-year (1980–2018) reanalysis dataset is available for the East Asia region, at a high temporal (of 3 h) and spatial resolution (of 12 km), for mesoscale weather and regional climate studies.
John Erik Engström, Lennart Wern, Sverker Hellström, Erik Kjellström, Chunlüe Zhou, Deliang Chen, and Cesar Azorin-Molina
Earth Syst. Sci. Data, 15, 2259–2277, https://doi.org/10.5194/essd-15-2259-2023, https://doi.org/10.5194/essd-15-2259-2023, 2023
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Newly digitized wind speed observations provide data from the time period from around 1920 to the present, enveloping one full century of wind measurements. The results of this work enable the investigation of the historical variability and trends in surface wind speed in Sweden for
the last century.
Ulrike Herzschuh, Thomas Böhmer, Chenzhi Li, Manuel Chevalier, Raphaël Hébert, Anne Dallmeyer, Xianyong Cao, Nancy H. Bigelow, Larisa Nazarova, Elena Y. Novenko, Jungjae Park, Odile Peyron, Natalia A. Rudaya, Frank Schlütz, Lyudmila S. Shumilovskikh, Pavel E. Tarasov, Yongbo Wang, Ruilin Wen, Qinghai Xu, and Zhuo Zheng
Earth Syst. Sci. Data, 15, 2235–2258, https://doi.org/10.5194/essd-15-2235-2023, https://doi.org/10.5194/essd-15-2235-2023, 2023
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Climate reconstruction from proxy data can help evaluate climate models. We present pollen-based reconstructions of mean July temperature, mean annual temperature, and annual precipitation from 2594 pollen records from the Northern Hemisphere, using three reconstruction methods (WA-PLS, WA-PLS_tailored, and MAT). Since no global or hemispheric synthesis of quantitative precipitation changes are available for the Holocene so far, this dataset will be of great value to the geoscientific community.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, https://doi.org/10.5194/essd-15-1441-2023, 2023
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EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather radar data to provide local precipitation information every hour and combined with rain gauge data to obtain good precipitation estimates. EURADCLIM provides a much better reference for validation of weather model output and satellite precipitation datasets. It also allows for climate monitoring and better evaluation of extreme precipitation events and their impact (landslides, flooding).
Alfonso Ferrone and Alexis Berne
Earth Syst. Sci. Data, 15, 1115–1132, https://doi.org/10.5194/essd-15-1115-2023, https://doi.org/10.5194/essd-15-1115-2023, 2023
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This article presents the datasets collected between November 2019 and February 2020 in the vicinity of the Belgian research base Princess Elisabeth Antarctica. Five meteorological radars, a multi-angle snowflake camera, three weather stations, and two radiometers have been deployed at five sites, up to a maximum distance of 30 km from the base. Their varied locations allow the study of spatial variability in snowfall and its interaction with the complex terrain in the region.
José Dias Neto, Louise Nuijens, Christine Unal, and Steven Knoop
Earth Syst. Sci. Data, 15, 769–789, https://doi.org/10.5194/essd-15-769-2023, https://doi.org/10.5194/essd-15-769-2023, 2023
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This paper describes a dataset from a novel experimental setup to retrieve wind speed and direction profiles, combining cloud radars and wind lidar. This setup allows retrieving profiles from near the surface to the top of clouds. The field campaign occurred in Cabauw, the Netherlands, between September 13th and October 3rd 2021. This paper also provides examples of applications of this dataset (e.g. studying atmospheric turbulence, validating numerical atmospheric models).
Peng Yuan, Geoffrey Blewitt, Corné Kreemer, William C. Hammond, Donald Argus, Xungang Yin, Roeland Van Malderen, Michael Mayer, Weiping Jiang, Joseph Awange, and Hansjörg Kutterer
Earth Syst. Sci. Data, 15, 723–743, https://doi.org/10.5194/essd-15-723-2023, https://doi.org/10.5194/essd-15-723-2023, 2023
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We developed a 5 min global integrated water vapour (IWV) product from 12 552 ground-based GPS stations in 2020. It contains more than 1 billion IWV estimates. The dataset is an enhanced version of the existing operational GPS IWV dataset from the Nevada Geodetic Laboratory. The enhancement is reached by using accurate meteorological information from ERA5 for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. The dataset is recommended for high-accuracy applications.
Yaozhi Jiang, Kun Yang, Youcun Qi, Xu Zhou, Jie He, Hui Lu, Xin Li, Yingying Chen, Xiaodong Li, Bingrong Zhou, Ali Mamtimin, Changkun Shao, Xiaogang Ma, Jiaxin Tian, and Jianhong Zhou
Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, https://doi.org/10.5194/essd-15-621-2023, 2023
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Our work produces a long-term (1979–2020) high-resolution (1/30°, daily) precipitation dataset for the Third Pole (TP) region by merging an advanced atmospheric simulation with high-density rain gauge (more than 9000) observations. Validation shows that the produced dataset performs better than the currently widely used precipitation datasets in the TP. This dataset can be used for hydrological, meteorological and ecological studies in the TP.
Yetang Wang, Xueying Zhang, Wentao Ning, Matthew A. Lazzara, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Paolo Grigioni, Petra Heil, Elizabeth R. Thomas, David Mikolajczyk, Lee J. Welhouse, Linda M. Keller, Zhaosheng Zhai, Yuqi Sun, and Shugui Hou
Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, https://doi.org/10.5194/essd-15-411-2023, 2023
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Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
Hui Zhang, Ming Luo, Yongquan Zhao, Lijie Lin, Erjia Ge, Yuanjian Yang, Guicai Ning, Jing Cong, Zhaoliang Zeng, Ke Gui, Jing Li, Ting On Chan, Xiang Li, Sijia Wu, Peng Wang, and Xiaoyu Wang
Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, https://doi.org/10.5194/essd-15-359-2023, 2023
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We generate the first monthly high-resolution (1 km) human thermal index collection (HiTIC-Monthly) in China over 2003–2020, in which 12 human-perceived temperature indices are generated by LightGBM. The HiTIC-Monthly dataset has a high accuracy (R2 = 0.996, RMSE = 0.693 °C, MAE = 0.512 °C) and describes explicit spatial variations for fine-scale studies. It is freely available at https://zenodo.org/record/6895533 and https://data.tpdc.ac.cn/disallow/036e67b7-7a3a-4229-956f-40b8cd11871d.
Jun Qin, Weihao Pan, Min He, Ning Lu, Ling Yao, Hou Jiang, and Chenghu Zhou
Earth Syst. Sci. Data, 15, 331–344, https://doi.org/10.5194/essd-15-331-2023, https://doi.org/10.5194/essd-15-331-2023, 2023
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To enrich a glacial surface air temperature (SAT) product of a long time series, an ensemble learning model is constructed to estimate monthly SATs from satellite land surface temperatures at a spatial resolution of 1 km, and long-term glacial SATs from 1961 to 2020 are reconstructed using a Bayesian linear regression. This product reveals the overall warming trend and the spatial heterogeneity of warming on TP glaciers and helps to monitor glacier warming, analyze glacier evolution, etc.
Tao Zhang, Yuyu Zhou, Kaiguang Zhao, Zhengyuan Zhu, Gang Chen, Jia Hu, and Li Wang
Earth Syst. Sci. Data, 14, 5637–5649, https://doi.org/10.5194/essd-14-5637-2022, https://doi.org/10.5194/essd-14-5637-2022, 2022
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We generated a global 1 km daily maximum and minimum near-surface air temperature (Tmax and Tmin) dataset (2003–2020) using a novel statistical model. The average root mean square errors ranged from 1.20 to 2.44 °C for Tmax and 1.69 to 2.39 °C for Tmin. The gridded global air temperature dataset is of great use in a variety of studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting.
Jianquan Dong, Stefan Brönnimann, Tao Hu, Yanxu Liu, and Jian Peng
Earth Syst. Sci. Data, 14, 5651–5664, https://doi.org/10.5194/essd-14-5651-2022, https://doi.org/10.5194/essd-14-5651-2022, 2022
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We produced a new dataset of global station-based daily maximum wet-bulb temperature (GSDM-WBT) through the calculation of wet-bulb temperature, data quality control, infilling missing values, and homogenization. The GSDM-WBT covers the complete daily series of 1834 stations from 1981 to 2020. The GSDM-WBT dataset handles stations with many missing values and possible inhomogeneities, which could better support the studies on global and regional humid heat events.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
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In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Craig D. Smith, Eva Mekis, Megan Hartwell, and Amber Ross
Earth Syst. Sci. Data, 14, 5253–5265, https://doi.org/10.5194/essd-14-5253-2022, https://doi.org/10.5194/essd-14-5253-2022, 2022
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It is well understood that precipitation gauges underestimate the measurement of solid precipitation (snow) as a result of systematic bias caused by wind. Relationships between the wind speed and gauge catch efficiency of solid precipitation have been previously established and are applied to the hourly precipitation measurements made between 2001 and 2019 in the automated Environment and Climate Change Canada observation network. The adjusted data are available for download and use.
Minghu Ding, Xiaowei Zou, Qizhen Sun, Diyi Yang, Wenqian Zhang, Lingen Bian, Changgui Lu, Ian Allison, Petra Heil, and Cunde Xiao
Earth Syst. Sci. Data, 14, 5019–5035, https://doi.org/10.5194/essd-14-5019-2022, https://doi.org/10.5194/essd-14-5019-2022, 2022
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The PANDA automatic weather station (AWS) network consists of 11 stations deployed along a transect from the coast (Zhongshan Station) to the summit of the East Antarctic Ice Sheet (Dome A). It covers the different climatic and topographic units of East Antarctica. All stations record hourly air temperature, relative humidity, air pressure, wind speed and direction at two or three heights. The PANDA AWS dataset commences from 1989 and is planned to be publicly available into the future.
Zen Mariani, Laura Huang, Robert Crawford, Jean-Pierre Blanchet, Shannon Hicks-Jalali, Eva Mekis, Ludovick Pelletier, Peter Rodriguez, and Kevin Strawbridge
Earth Syst. Sci. Data, 14, 4995–5017, https://doi.org/10.5194/essd-14-4995-2022, https://doi.org/10.5194/essd-14-4995-2022, 2022
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Environment and Climate Change Canada (ECCC) commissioned two supersites in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) to provide new and enhanced automated and continuous altitude-resolved meteorological observations as part of the Canadian Arctic Weather Science (CAWS) project. These observations are being used to test new technologies, provide recommendations to the optimal Arctic observing system, and evaluate and improve the performance of numerical weather forecast systems.
Eva Beele, Maarten Reyniers, Raf Aerts, and Ben Somers
Earth Syst. Sci. Data, 14, 4681–4717, https://doi.org/10.5194/essd-14-4681-2022, https://doi.org/10.5194/essd-14-4681-2022, 2022
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This paper presents crowdsourced data from the Leuven.cool network, a citizen science network of around 100 low-cost weather stations distributed across Leuven, Belgium. The temperature data have undergone a quality control (QC) and correction procedure. The procedure consists of three levels that remove implausible measurements while also correcting for between-station and station-specific temperature biases.
Auguste Gires, Jerry Jose, Ioulia Tchiguirinskaia, and Daniel Schertzer
Earth Syst. Sci. Data, 14, 3807–3819, https://doi.org/10.5194/essd-14-3807-2022, https://doi.org/10.5194/essd-14-3807-2022, 2022
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The Hydrology Meteorology and Complexity laboratory of École des Ponts ParisTech (https://hmco.enpc.fr) has made a data set of high-resolution atmospheric measurements (rainfall, wind, temperature, pressure, and humidity) available. It comes from a campaign carried out on a meteorological mast located on a wind farm in the framework of the Rainfall Wind Turbine or Turbulence project (RW-Turb; supported by the French National Research Agency – ANR-19-CE05-0022).
Bastian Kirsch, Cathy Hohenegger, Daniel Klocke, Rainer Senke, Michael Offermann, and Felix Ament
Earth Syst. Sci. Data, 14, 3531–3548, https://doi.org/10.5194/essd-14-3531-2022, https://doi.org/10.5194/essd-14-3531-2022, 2022
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Conventional observation networks are too coarse to resolve the horizontal structure of kilometer-scale atmospheric processes. We present the FESST@HH field experiment that took place in Hamburg (Germany) during summer 2020 and featured a dense network of 103 custom-built, low-cost weather stations. The data set is capable of providing new insights into the structure of convective cold pools and the nocturnal urban heat island and variations of local temperature fluctuations.
Fan Mei, Mikhail S. Pekour, Darielle Dexheimer, Gijs de Boer, RaeAnn Cook, Jason Tomlinson, Beat Schmid, Lexie A. Goldberger, Rob Newsom, and Jerome D. Fast
Earth Syst. Sci. Data, 14, 3423–3438, https://doi.org/10.5194/essd-14-3423-2022, https://doi.org/10.5194/essd-14-3423-2022, 2022
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This work focuses on an expanding number of data sets observed using ARM TBS (133 flights) and UAS (seven flights) platforms by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research, such as the aerosol–cloud interaction in the boundary layer.
Vera Thiemig, Goncalo N. Gomes, Jon O. Skøien, Markus Ziese, Armin Rauthe-Schöch, Elke Rustemeier, Kira Rehfeldt, Jakub P. Walawender, Christine Kolbe, Damien Pichon, Christoph Schweim, and Peter Salamon
Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, https://doi.org/10.5194/essd-14-3249-2022, 2022
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EMO-5 is a free and open European high-resolution (5 km), sub-daily, multi-variable (precipitation, temperatures, wind speed, solar radiation, vapour pressure), multi-decadal meteorological dataset based on quality-controlled observations coming from almost 30 000 stations across Europe, and is produced in near real-time. EMO-5 (v1) covers the time period from 1990 to 2019. In this paper, we have provided insight into the source data, the applied methods, and the quality assessment of EMO-5.
Qian He, Ming Wang, Kai Liu, Kaiwen Li, and Ziyu Jiang
Earth Syst. Sci. Data, 14, 3273–3292, https://doi.org/10.5194/essd-14-3273-2022, https://doi.org/10.5194/essd-14-3273-2022, 2022
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We used three machine learning models and determined that Gaussian process regression (GPR) is best suited to the interpolation of air temperature data for China. The GPR-derived results were compared with that of traditional interpolation techniques and existing data sets and it was found that the accuracy of the GPR-derived data was better. Finally, we generated a gridded monthly air temperature data set with 1 km resolution and high accuracy for China (1951–2020) using the GPR model.
Yinghong Jing, Xinghua Li, and Huanfeng Shen
Earth Syst. Sci. Data, 14, 3137–3156, https://doi.org/10.5194/essd-14-3137-2022, https://doi.org/10.5194/essd-14-3137-2022, 2022
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Snow variation is a vital factor in global climate change. Satellite-based approaches are effective for large-scale environmental monitoring. Nevertheless, the high cloud fraction seriously impedes the remote-sensed investigation. Therefore, a recent 20-year cloud-free snow cover collection in China is generated for the first time. This collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues.
Cited articles
Bao, J., Feng, J., and Wang, Y.: Dynamical downscaling simulation and future
projection of precipitation over China, J. Geophys. Res.-Atmos., 120, 8227–8243, 2015.
Bruyère, C. L., Done, J. M., Holland, G. J., and Fredrick, S.: Bias
corrections of global models for regional climate simulations of high-impact
weather, Clim. Dynam., 43, 1847–1856, https://doi.org/10.1007/s00382-013-2011-6, 2014.
Burunciuc, L.: Natural disasters cost Central Asia $10 billion a year –
Are we doing enough to prevent them?, World Bank Blogs, https://blogs.worldbank.org/europeandcentralasia/natural-disasters-cost-central-asia-10-billion-year-are-we-doing-enough (last access: 5 May 2022), 2020.
Chen, X., Bai, J., Li, X., Luo, G., Li, J., and Li, B. L.: Changes in land
use/land cover and ecosystem services in Central Asia during 1990–2009,
Curr. Opin. Environ. Sustain., 5, 116–127, https://doi.org/10.1016/j.cosust.2012.12.005, 2013.
Dee, D. P., Uppala, S. M., Simmons, A., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M., Balsamo, G., and Bauer, D. P.: The
ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, 2011.
Déqué, M., Rowell, D. P., Lüthi, D., Giorgi, F., Christensen, J.
H., Rockel, B., Jacob, D., Kjellström, E., de Castro, M., and van den
Hurk, B.: An intercomparison of regional climate simulations for Europe:
assessing uncertainties in model projections, Climatic Change, 81, 53–70, https://doi.org/10.1007/s10584-006-9228-x, 2007.
Di Luca, A., de Elía, R., and Laprise, R.: Potential for added value in
precipitation simulated by high-resolution nested Regional Climate Models
and observations, Clim. Dynam., 38, 1229–1247, https://doi.org/10.1007/s00382-011-1068-3, 2012.
Di Luca, A., de Elía, R., and Laprise, R.: Potential for small scale
added value of RCM's downscaled climate change signal, Clim. Dynam., 40,
601–618, https://doi.org/10.1007/s00382-012-1415-z, 2013.
Done, J. M., Holland, G. J., Bruyère, C. L., Leung, L. R., and
Suzuki-Parker, A.: Modeling high-impact weather and climate: lessons from a
tropical cyclone perspective, Climatic Change, 129, 381–395, https://doi.org/10.1007/s10584-013-0954-6, 2015.
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS Opinions “Should we apply bias correction to global and regional climate model data?”, Hydrol. Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012.
Fischer, A. M., Keller, D. E., Liniger, M. A., Rajczak, J., Schär, C.,
and Appenzeller, C.: Projected changes in precipitation intensity and
frequency in Switzerland: a multi-model perspective, Int. J.
Climatol., 35, 3204–3219, https://doi.org/10.1002/joc.4162, 2015.
Frenken, K.: Irrigation in Central Asia in figures, Food and Agriculture
Organization of the United Nations, https://doi.org/10.13140/RG.2.1.2770.8247, 2013.
Gabriel, K. A. and Kimon, K.: Analysis of scenarios integrating the INDCs,
EUR – Scientific and Technical Research Reports, JRC nr: JRC97845, 2015.
Gao, X., Pal, J. S., and Giorgi, F.: Projected changes in mean and extreme
precipitation over the Mediterranean region from a high resolution double
nested RCM simulation, Geophys. Res. Lett., 33, L03706, https://doi.org/10.1029/2005GL024954, 2006.
Gessner, U., Naeimi, V., Klein, I., Kuenzer, C., Klein, D., and Dech, S.:
The relationship between precipitation anomalies and satellite-derived
vegetation activity in Central Asia, Global Planet. Change, 110,
74–87, https://doi.org/10.1016/j.gloplacha.2012.09.007, 2013.
Giorgi, F.: Thirty Years of Regional Climate Modeling: Where Are We and
Where Are We Going next?, J. Geophys. Res.-Atmos., 124,
5696–5723, https://doi.org/10.1029/2018jd030094, 2019.
Giorgi, F., Shields Brodeur, C., and Bates, G. T.: Regional Climate Change
Scenarios over the United States Produced with a Nested Regional Climate
Model, J. Climate, 7, 375–399, https://doi.org/10.1175/1520-0442(1994)007<375:RCCSOT>2.0.CO;2, 1994.
Giorgi, F., Torma, C., Coppola, E., Ban, N., Schär, C., and Somot, S.:
Enhanced summer convective rainfall at Alpine high elevations in response to
climate warming, Nat. Geosci., 9, 584–589, https://doi.org/10.1038/ngeo2761, 2016.
Guo, D., Zhang, Y., Gao, X., Pepin, N., and Sun, J.: Evaluation and ensemble
projection of extreme high and low temperature events in China from four
dynamical downscaling simulations, Int. J. Climatol., 41,
E1252–E1269, 2021.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU
TS monthly high-resolution gridded multivariate climate dataset, Sci.
Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc.,
146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hong, C., Zhang, Q., Zhang, Y., Tang, Y., Tong, D., and He, K.: Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects, Geosci. Model Dev., 10, 2447–2470, https://doi.org/10.5194/gmd-10-2447-2017, 2017.
Im, E. S., Coppola, E., Giorgi, F., and Bi, X.: Local effects of climate
change over the Alpine region: A study with a high resolution regional
climate model with a surrogate climate change scenario, Geophys. Res.
Lett., 37, L05704, https://doi.org/10.1029/2009GL041801, 2010.
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer,
L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G.,
Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A.,
Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N.,
Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C.,
Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D.,
Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C.,
Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new
high-resolution climate change projections for European impact research,
Reg. Environ. Chang., 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014.
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda,
M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva, A.,
Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin, E.
L., Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner, M.
A., García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K.,
Gómez-Navarro, J. J., Alemán, J. J. G., Gutiérrez, C.,
Gutiérrez, J. M., Güttler, I., Haensler, A., Halenka, T., Jerez, S.,
Jiménez-Guerrero, P., Jones, R. G., Keuler, K., Kjellström, E.,
Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P.,
Montávez, J. P., Navarra, A., Nikulin, G., de Noblet-Ducoudré, N.,
Panitz, H.-J., Pfeifer, S., Piazza, M., Pichelli, E., Pietikäinen,
J.-P., Prein, A. F., Preuschmann, S., Rechid, D., Rockel, B., Romera, R.,
Sánchez, E., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L.,
Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, K.,
and Wulfmeyer, V.: Regional climate downscaling over Europe: perspectives
from the EURO-CORDEX community, Reg. Environ. Chang., 20, 52, https://doi.org/10.1007/s10113-020-01606-9, 2020.
Ji, Z. and Kang, S.: Double-nested dynamical downscaling experiments over
the Tibetan Plateau and their projection of climate change under two RCP
scenarios, J. Atmos. Sci., 70, 1278–1290, 2013.
Jiang, R., Sun, L., Sun, C., and Liang, X.-Z.: CWRF downscaling and
understanding of China precipitation projections, Clim. Dynam., 57, 1079–1096, https://doi.org/10.1007/s00382-021-05759-z, 2021.
Jung, C.-Y., Shin, H.-J., Jang, C. J., and Kim, H.-J.: Projected change in
East Asian summer monsoon by dynamic downscaling: Moisture budget analysis,
Asia-Pac. J. Atmos. Sci., 51, 77–89, 2015.
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014.
Kotlarski, S., Lüthi, D., and Schär, C.: The elevation dependency of
21st century European climate change: an RCM ensemble perspective,
Int. J. Climatol., 35, 3902–3920, https://doi.org/10.1002/joc.4254, 2015.
Li, J., Chen, H., Zhang, C., and Pan, T.: Variations in ecosystem service value in response to land use/land cover changes in Central Asia from 1995–2035, PeerJ, 7, e7665, https://doi.org/10.7717/peerj.7665, 2019.
Li, X., Che, T., Li, X., Wang, L., Duan, A., Shangguan, D., Pan, X., Fang,
M., and Bao, Q.: CASEarth Poles: Big Data for the Three Poles, B.
Am. Meteorol. Soc., 101, E1475–E1491, https://doi.org/10.1175/bams-d-19-0280.1, 2020.
Liang, X.-Z., Kunkel, K. E., Meehl, G. A., Jones, R. G., and Wang, J. X. L.:
Regional climate models downscaling analysis of general circulation models
present climate biases propagation into future change projections,
Geophys. Res. Lett., 35, L08709, https://doi.org/10.1029/2007GL032849, 2008.
Ma, X., Zhu, J., Yan, W., and Zhao, C.: Projections of desertification
trends in Central Asia under global warming scenarios, Sci. Total
Environ., 781, 146777, https://doi.org/10.1016/j.scitotenv.2021.146777, 2021.
Mannig, B., Müller, M., Starke, E., Merkenschlager, C., Mao, W., Zhi,
X., Podzun, R., Jacob, D., and Paeth, H.: Dynamical downscaling of climate
change in Central Asia, Global Planet. Change, 110, 26–39, https://doi.org/10.1016/j.gloplacha.2013.05.008, 2013.
Micklin, P.: The Aral Sea disaster, in: Annual Review of Earth and Planetary
Sciences, Annu. Rev. Earth Pl. Sc., 47–72, 2007.
Monaghan, A. J., Steinhoff, D. F., Bruyere, C. L., and Yates, D.: NCAR CESM Global Bias-Corrected CMIP5 Output to Support WRF/MPAS Research, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6DJ5CN4, 2014.
Narama, C., Kääb, A., Duishonakunov, M., and Abdrakhmatov, K.:
Spatial variability of recent glacier area changes in the Tien Shan
Mountains, Central Asia, using Corona (∼ 1970), Landsat
(∼ 2000), and ALOS (∼ 2007) satellite data,
Global Planet. Change, 71, 42–54, 2010.
Ozturk, T., Turp, M. T., Türkeş, M., and Kurnaz, M. L.: Projected
changes in temperature and precipitation climatology of Central Asia CORDEX
Region 8 by using RegCM4.3.5, Atmos. Res., 183, 296–307, https://doi.org/10.1016/j.atmosres.2016.09.008, 2017.
Palazzi, E., Mortarini, L., Terzago, S., and von Hardenberg, J.:
Elevation-dependent warming in global climate model simulations at high
spatial resolution, Clim. Dynam., 52, 2685–2702, https://doi.org/10.1007/s00382-018-4287-z, 2019.
Pan, X., Guo, X., Li, X., Niu, X., Yang, X., Feng, M., Che, T., Jin, R.,
Ran, Y., Guo, J., Hu, X., and Wu, A.: National Tibetan Plateau Data Center:
Promoting Earth System Science on the Third Pole, B. Am.
Meteorol. Soc., 102, E2062–E2078, https://doi.org/10.1175/bams-d-21-0004.1, 2021.
Pepin, N., Bradley, R. S., Diaz, H. F., Baraer, M., Caceres, E. B.,
Forsythe, N., Fowler, H., Greenwood, G., Hashmi, M. Z., Liu, X. D., Miller,
J. R., Ning, L., Ohmura, A., Palazzi, E., Rangwala, I., Schöner, W.,
Severskiy, I., Shahgedanova, M., Wang, M. B., Williamson, S. N., Yang, D.
Q., and Mountain Research Initiative, E. D. W. W. G.: Elevation-dependent
warming in mountain regions of the world, Nat. Clim. Change, 5, 424–430, https://doi.org/10.1038/nclimate2563, 2015.
Pierce, D. W., Das, T., Cayan, D. R., Maurer, E. P., Miller, N. L., Bao, Y.,
Kanamitsu, M., Yoshimura, K., Snyder, M. A., and Sloan, L. C.: Probabilistic
estimates of future changes in California temperature and precipitation
using statistical and dynamical downscaling, Clim. Dynam., 40, 839–856,
2013.
Qiu, Y.: High-resolution climate projection dataset in Central Asia
(1986–2005 and 2031–2050), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Meteoro.tpdc.271759, 2021.
Qiu, Y., Hu, Q., and Zhang, C.: WRF simulation and downscaling of local
climate in Central Asia, Int. J. Climatol., 37, 513–528, https://doi.org/10.1002/joc.5018, 2017.
Qiu, Y., Feng, J., Yan, Z., Wang, J., and Li, Z.: High-resolution dynamical
downscaling for regional climate projection in Central Asia based on
bias-corrected multiple GCMs, Clim. Dynam., 58, 777–791, https://doi.org/10.1007/s00382-021-05934-2,
2021.
Racherla, P., Shindell, D., and Faluvegi, G.: The added value to global
model projections of climate change by dynamical downscaling: A case study
over the continental US using the GISS-ModelE2 and WRF models, J.
Geophys. Res.-Atmos., 117, D20118, https://doi.org/10.1029/2012JD018091, 2012.
Rangwala, I., Sinsky, E., and Miller, J. R.: Amplified warming projections
for high altitude regions of the northern hemisphere mid-latitudes from
CMIP5 models, Environ. Res. Lett., 8, 024040, https://doi.org/10.1088/1748-9326/8/2/024040, 2013.
Schulzweida, U.: CDO User Guide (Version 2.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.5614769, 2021.
Seddon, A. W., Macias-Fauria, M., Long, P. R., Benz, D., and Willis, K. J.:
Sensitivity of global terrestrial ecosystems to climate variability, Nature,
531, 229–232, 2016.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M.,
Wang, W., and Powers, J. G.: A description of the Advanced Research WRF
version 3. NCAR Technical note-475+ STR, 2008.
Sorg, A., Bolch, T., Stoffel, M., Solomina, O., and Beniston, M.: Climate
change impacts on glaciers and runoff in Tien Shan (Central Asia), Nat.
Clim. Change, 2, 725–731, https://doi.org/10.1038/nclimate1592, 2012.
Tang, J., Niu, X., Wang, S., Gao, H., Wang, X., and Wu, J.: Statistical
downscaling and dynamical downscaling of regional climate in China: Present
climate evaluations and future climate projections, J. Geophys.
Res.-Atmos., 121, 2110–2129, https://doi.org/10.1002/2015JD023977, 2016.
The NCAR Command Language: (Version 6.6.2) [Software],
Boulder, Colorado, UCAR/NCAR/CISL/TDD, https://doi.org/10.5065/D6WD3XH5, 2019.
Thurman, M.: Natural disaster risks in Central Asia: a synthesis, UNDP/BCPR,
Regional Disaster Risk Reduction Asvisor, Europe and CIS, https://www.eurasia.undp.org/content/rbec/en/home/library/crisis_prevention_and_recovery/Natural_disaster_risks_in_Central_Asia_A_synthesis.html (last access: 5 May 2022), 2011.
Torma, C., Giorgi, F., and Coppola, E.: Added value of regional climate
modeling over areas characterized by complex terrain – Precipitation over
the Alps, J. Geophys. Res.-Atmos., 120, 3957–3972, https://doi.org/10.1002/2014JD022781, 2015.
Vautard, R., Gobiet, A., Jacob, D., Belda, M., Colette, A., Déqué,
M., Fernández, J., García-Díez, M., Goergen, K., Güttler,
I., Halenka, T., Karacostas, T., Katragkou, E., Keuler, K., Kotlarski, S.,
Mayer, S., van Meijgaard, E., Nikulin, G., Patarčić, M., Scinocca,
J., Sobolowski, S., Suklitsch, M., Teichmann, C., Warrach-Sagi, K.,
Wulfmeyer, V., and Yiou, P.: The simulation of European heat waves from an
ensemble of regional climate models within the EURO-CORDEX project, Clim.
Dynam., 41, 2555–2575, https://doi.org/10.1007/s00382-013-1714-z, 2013.
Wang, J. and Kotamarthi, V. R.: High-resolution dynamically downscaled
projections of precipitation in the mid and late 21st century over North
America, Earth's Future, 3, 268-288, 2015.
Wang, W., Barker, D., Bray, J., Bruyere, C., Duda, M., Dudhia, J., Gill, D., and Michalakes, J.: User’s guide for advanced research WRF (ARW) modeling system version 3, Mesoscale & Microscale Meteorology Division, National Center for Atmospheric Research, 2007.
Wang, X., Huang, G., Liu, J., Li, Z., and Zhao, S.: Ensemble projections of
regional climatic changes over Ontario, Canada, J. Climate, 28,
7327–7346, 2015.
Wang, Y., Feng, J., Luo, M., Wang, J., and Qiu, Y.: Uncertainties in
simulating central Asia: Sensitivity to physical parameterizations using
Weather Research and Forecasting model, Int. J.
Climatol., 40, 5813–5828, https://doi.org/10.1002/joc.6567,
2020.
Xu, Z. and Yang, Z.-L.: An Improved Dynamical Downscaling Method with GCM
Bias Corrections and Its Validation with 30 Years of Climate Simulations,
J. Climate, 25, 6271–6286, https://doi.org/10.1175/JCLI-D-12-00005.1, 2012.
Zhang, C., Lu, D., Chen, X., Zhang, Y., Maisupova, B., and Tao, Y.: The
spatiotemporal patterns of vegetation coverage and biomass of the temperate
deserts in Central Asia and their relationships with climate controls,
Remote Sens. Environ., 175, 271–281, https://doi.org/10.1016/j.rse.2016.01.002, 2016.
Zhu, X., Wei, Z., Dong, W., Ji, Z., Wen, X., Zheng, Z., Yan, D., and Chen,
D.: Dynamical downscaling simulation and projection for mean and extreme
temperature and precipitation over central Asia, Clim. Dynam., 54, 3279–3306, https://doi.org/10.1007/s00382-020-05170-0, 2020.
Zittis, G., Hadjinicolaou, P., Klangidou, M., Proestos, Y., and Lelieveld,
J.: A multi-model, multi-scenario, and multi-domain analysis of regional
climate projections for the Mediterranean, Reg. Environ. Chang.,
19, 2621–2635, https://doi.org/10.1007/s10113-019-01565-w, 2019.
Zou, L. and Zhou, T.: Future summer precipitation changes over CORDEX-East
Asia domain downscaled by a regional ocean-atmosphere coupled model: A
comparison to the stand-alone RCM, J. Geophys. Res.-Atmos., 121, 2691–2704, https://doi.org/10.1002/2015JD024519, 2016.
Zou, L. and Zhou, T.: Dynamical downscaling of East Asian winter monsoon
changes with a regional ocean–atmosphere coupled model, Q. J. Roy. Meteor. Soc., 143, 2245–2259,
https://doi.org/10.1002/qj.3082, 2017.
Short summary
A high-resolution climate projection dataset in central Asia, named the HCPD-CA dataset, is derived from the dynamically downscaled results based on three bias-corrected global climate models and contains 4 geostatic variables and 10 meteorological elements that are widely used to drive ecological and hydrological models. This dataset can serve as a scientific basis for assessing the potential impacts of projected climate changes over central Asia on many sectors.
A high-resolution climate projection dataset in central Asia, named the HCPD-CA dataset, is...
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