Articles | Volume 12, issue 2
https://doi.org/10.5194/essd-12-1191-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-1191-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
28 May 2020
Data description paper |
| 28 May 2020
| 28 May 2020
High-resolution (1 km) Polar WRF output for 79° N Glacier and the northeast of Greenland from 2014 to 2018
Climate System Research Group, Institute of Geography,
Friedrich-Alexander-Universität Erlangen-Nürnberg, 90158 Erlangen–Nuremberg, Germany
Thomas Mölg
Climate System Research Group, Institute of Geography,
Friedrich-Alexander-Universität Erlangen-Nürnberg, 90158 Erlangen–Nuremberg, Germany
Emily Collier
Climate System Research Group, Institute of Geography,
Friedrich-Alexander-Universität Erlangen-Nürnberg, 90158 Erlangen–Nuremberg, Germany
Related authors
Jenny V. Turton, Philipp Hochreuther, Nathalie Reimann, and Manuel T. Blau
The Cryosphere, 15, 3877–3896, https://doi.org/10.5194/tc-15-3877-2021, https://doi.org/10.5194/tc-15-3877-2021, 2021
Short summary
Short summary
We assess the climatic controls of melt lake development, melt duration, melt extent, and the spatial distribution of lakes of 79°N Glacier. There is a large interannual variability in the areal extent of the lakes and the maximum elevation of lake development, which is largely controlled by the summertime air temperatures and the snowpack thickness. Late-summer lake development can be prompted by spikes in surface mass balance. There is some evidence of inland expansion of lakes over time.
Jenny V. Turton, Amélie Kirchgaessner, Andrew N. Ross, John C. King, and Peter Kuipers Munneke
The Cryosphere, 14, 4165–4180, https://doi.org/10.5194/tc-14-4165-2020, https://doi.org/10.5194/tc-14-4165-2020, 2020
Short summary
Short summary
Föhn winds are warm and dry downslope winds in the lee of a mountain range, such as the Antarctic Peninsula. Föhn winds heat the ice of the Larsen C Ice Shelf at the base of the mountains and promote more melting than during non-föhn periods in spring, summer and autumn in both model output and observations. Especially in spring, when they are most frequent, föhn winds can extend the melt season by over a month and cause a similar magnitude of melting to that observed in summer.
Thomas Mölg, Jan Christoph Schubert, Annette Debel, Steffen Höhnle, Kathy Steppe, Sibille Wehrmann, and Achim Bräuning
Geosci. Commun. Discuss., https://doi.org/10.5194/gc-2023-5, https://doi.org/10.5194/gc-2023-5, 2023
Preprint under review for GC
Short summary
Short summary
We examine the understanding of weather and climate impacts on forest health in high school students. Climate physics, tree ring science and educational research collaborate to provide an online platform that captures the students’ observations, showing they verbalize the measured weather and the basic tree responses well. However, students hardly detect the causal connections. This result will help refine future classroom concepts and public climate change communication on changing forests.
Annelies Voordendag, Brigitta Goger, Rainer Prinz, Tobias Sauter, Thomas Mölg, Manuel Saigger, and Georg Kaser
EGUsphere, https://doi.org/10.5194/egusphere-2023-1395, https://doi.org/10.5194/egusphere-2023-1395, 2023
Short summary
Short summary
Wind-driven snow redistribution affects glacier mass balance. A case study of Hintereisferner glacier in Austria used high-resolution observations and simulations to model snow redistribution. Simulations matched observations, showing the potential of the model for studying snow redistribution on other mountain glaciers.
Jonathan P. Conway, Jakob Abermann, Liss M. Andreassen, Mohd Farooq Azam, Nicolas J. Cullen, Noel Fitzpatrick, Rianne H. Giesen, Kirsty Langley, Shelley MacDonell, Thomas Mölg, Valentina Radić, Carleen H. Reijmer, and Jean-Emmanuel Sicart
The Cryosphere, 16, 3331–3356, https://doi.org/10.5194/tc-16-3331-2022, https://doi.org/10.5194/tc-16-3331-2022, 2022
Short summary
Short summary
We used data from automatic weather stations on 16 glaciers to show how clouds influence glacier melt in different climates around the world. We found surface melt was always more frequent when it was cloudy but was not universally faster or slower than under clear-sky conditions. Also, air temperature was related to clouds in opposite ways in different climates – warmer with clouds in cold climates and vice versa. These results will help us improve how we model past and future glacier melt.
Jenny V. Turton, Philipp Hochreuther, Nathalie Reimann, and Manuel T. Blau
The Cryosphere, 15, 3877–3896, https://doi.org/10.5194/tc-15-3877-2021, https://doi.org/10.5194/tc-15-3877-2021, 2021
Short summary
Short summary
We assess the climatic controls of melt lake development, melt duration, melt extent, and the spatial distribution of lakes of 79°N Glacier. There is a large interannual variability in the areal extent of the lakes and the maximum elevation of lake development, which is largely controlled by the summertime air temperatures and the snowpack thickness. Late-summer lake development can be prompted by spikes in surface mass balance. There is some evidence of inland expansion of lakes over time.
Emily Collier and Thomas Mölg
Earth Syst. Sci. Data, 12, 3097–3112, https://doi.org/10.5194/essd-12-3097-2020, https://doi.org/10.5194/essd-12-3097-2020, 2020
Short summary
Short summary
As part of a recent project that aims to investigate the impact of climate change on forest ecosystems in Bavaria, we developed a high-resolution atmospheric dataset, BAYWRF, for this region that covers the period of September 1987 to August 2018. The data reproduce observed variability in recent meteorological conditions well and provide a useful tool for linking large-scale climate change to local impacts on economic, societal, ecological, and agricultural processes.
Jenny V. Turton, Amélie Kirchgaessner, Andrew N. Ross, John C. King, and Peter Kuipers Munneke
The Cryosphere, 14, 4165–4180, https://doi.org/10.5194/tc-14-4165-2020, https://doi.org/10.5194/tc-14-4165-2020, 2020
Short summary
Short summary
Föhn winds are warm and dry downslope winds in the lee of a mountain range, such as the Antarctic Peninsula. Föhn winds heat the ice of the Larsen C Ice Shelf at the base of the mountains and promote more melting than during non-föhn periods in spring, summer and autumn in both model output and observations. Especially in spring, when they are most frequent, föhn winds can extend the melt season by over a month and cause a similar magnitude of melting to that observed in summer.
Catrin Stadelmann, Johannes Jakob Fürst, Thomas Mölg, and Matthias Braun
The Cryosphere, 14, 3399–3406, https://doi.org/10.5194/tc-14-3399-2020, https://doi.org/10.5194/tc-14-3399-2020, 2020
Short summary
Short summary
The glaciers on Kilimanjaro are unique indicators for climatic changes in the tropical midtroposphere of Africa. A history of severe glacier area loss raises concerns about an imminent future disappearance. Yet the remaining ice volume is not well known. Here, we reconstruct ice thickness maps for the two largest remaining ice bodies to assess the current glacier state. We believe that our approach could provide a means for a glacier-specific calibration of reconstructions on different scales.
Thomas Mölg, Douglas R. Hardy, Emily Collier, Elena Kropač, Christina Schmid, Nicolas J. Cullen, Georg Kaser, Rainer Prinz, and Michael Winkler
Earth Syst. Dynam., 11, 653–672, https://doi.org/10.5194/esd-11-653-2020, https://doi.org/10.5194/esd-11-653-2020, 2020
Short summary
Short summary
The glaciers on Kilimanjaro summit are like sample spots of the climate in the tropical mid-troposphere. Measurements of air temperature, air humidity, and precipitation with automated weather stations show that the differences in these meteorological elements between two altitudes (~ 5600 and ~ 5900 m) vary significantly over the day and the seasons, in concert with airflow dynamics around the mountain. Knowledge of these variations will improve atmosphere and cryosphere models.
Stephan Peter Galos, Christoph Klug, Fabien Maussion, Federico Covi, Lindsey Nicholson, Lorenzo Rieg, Wolfgang Gurgiser, Thomas Mölg, and Georg Kaser
The Cryosphere, 11, 1417–1439, https://doi.org/10.5194/tc-11-1417-2017, https://doi.org/10.5194/tc-11-1417-2017, 2017
Kai-Uwe Eiselt, Frank Kaspar, Thomas Mölg, Stefan Krähenmann, Rafael Posada, and Jens O. Riede
Adv. Sci. Res., 14, 163–173, https://doi.org/10.5194/asr-14-163-2017, https://doi.org/10.5194/asr-14-163-2017, 2017
Short summary
Short summary
As one element of the SASSCAL initiative (a cooperation of Angola, Botswana, Namibia, Zambia, South Africa and Germany) networks of automatic weather stations have been installed or improved in Southern Africa. Here we compare interpolation methods for monthly minimum and maximum temperatures which were calculated from hourly measurements. The best interpolation results have been achieved combining multiple linear regression with three dimensional inverse distance weighted interpolation.
Kjetil S. Aas, Thorben Dunse, Emily Collier, Thomas V. Schuler, Terje K. Berntsen, Jack Kohler, and Bartłomiej Luks
The Cryosphere, 10, 1089–1104, https://doi.org/10.5194/tc-10-1089-2016, https://doi.org/10.5194/tc-10-1089-2016, 2016
Short summary
Short summary
A high-resolution, coupled atmosphere--climatic mass balance (CMB) model is applied to Svalbard for the period 2003 to 2013. The mean CMB during this period is negative but displays large spatial and temporal variations. Comparison with observations on different scales shows a good overall model performance except for one particular glacier, where wind strongly affects the spatial patterns of CMB. The model also shows considerable sensitivity to model resolution, especially on local scales.
R. Prinz, L. I. Nicholson, T. Mölg, W. Gurgiser, and G. Kaser
The Cryosphere, 10, 133–148, https://doi.org/10.5194/tc-10-133-2016, https://doi.org/10.5194/tc-10-133-2016, 2016
Short summary
Short summary
Lewis Glacier has lost > 80 % of its extent since the late 19th century. A sensitivity study using a process-based model assigns this shrinking to decreased atmospheric moisture without increasing air temperatures required. The glacier retreat implies a distinctly different coupling between the glacier's surface-air layer and its surrounding boundary layer, underlining the difficulty of deriving palaeoclimates for larger glacier extents on the basis of modern measurements of small glaciers.
E. Collier, F. Maussion, L. I. Nicholson, T. Mölg, W. W. Immerzeel, and A. B. G. Bush
The Cryosphere, 9, 1617–1632, https://doi.org/10.5194/tc-9-1617-2015, https://doi.org/10.5194/tc-9-1617-2015, 2015
Short summary
Short summary
We investigate the impact of surface debris on glacier energy and mass fluxes and on atmosphere-glacier feedbacks in the Karakoram range, by including debris in an interactively coupled atmosphere-glacier model. The model is run from 1 May to 1 October 2004, with a simple specification of debris thickness. We find an appreciable reduction in ablation that exceeds 5m w.e. on glacier tongues, as well as significant alterations to near-surface air temperatures and boundary layer dynamics.
M. Hofer, B. Marzeion, and T. Mölg
Geosci. Model Dev., 8, 579–593, https://doi.org/10.5194/gmd-8-579-2015, https://doi.org/10.5194/gmd-8-579-2015, 2015
E. Collier, L. I. Nicholson, B. W. Brock, F. Maussion, R. Essery, and A. B. G. Bush
The Cryosphere, 8, 1429–1444, https://doi.org/10.5194/tc-8-1429-2014, https://doi.org/10.5194/tc-8-1429-2014, 2014
S. MacDonell, C. Kinnard, T. Mölg, L. Nicholson, and J. Abermann
The Cryosphere, 7, 1513–1526, https://doi.org/10.5194/tc-7-1513-2013, https://doi.org/10.5194/tc-7-1513-2013, 2013
E. Collier, T. Mölg, F. Maussion, D. Scherer, C. Mayer, and A. B. G. Bush
The Cryosphere, 7, 779–795, https://doi.org/10.5194/tc-7-779-2013, https://doi.org/10.5194/tc-7-779-2013, 2013
T. Mölg, F. Maussion, W. Yang, and D. Scherer
The Cryosphere, 6, 1445–1461, https://doi.org/10.5194/tc-6-1445-2012, https://doi.org/10.5194/tc-6-1445-2012, 2012
Related subject area
Meteorology
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
ET-WB: water-balance-based estimations of terrestrial evaporation over global land and major global basins
An integrated and homogenized global surface solar radiation dataset and its reconstruction based on a convolutional neural network approach
IWIN: the Isfjorden Weather Information Network
A 7-year record of vertical profiles of radar measurements and precipitation estimates at Dumont d’Urville, Adélie Land, East Antarctica
A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations
A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range
The EUPPBench postprocessing benchmark dataset v1.0
MOPREDAScentury: a long-term monthly precipitation grid for the Spanish mainland
Year-long Buoy-Based Observations of the Air–Sea Transition Zone off the U.S. West Coast
CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies
Database of the Italian disdrometer network
East Asia Reanalysis System (EARS)
Data rescue of historical wind observations in Sweden since the 1920s
LegacyClimate 1.0: a dataset of pollen-based climate reconstructions from 2594 Northern Hemisphere sites covering the last 30 kyr and beyond
EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset
Atmospheric and surface observations during the Saint John River Experiment on Cold Season Storms (SAJESS)
Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica
Combined wind lidar and cloud radar for high-resolution wind profiling
An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020
TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations
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
Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region
The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019)
The PANDA automatic weather station network between the coast and Dome A, East Antarctica
Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites
Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium
High-resolution all-sky land surface temperature and net radiation over Europe
Combined high-resolution rainfall and wind data collected for 3 months on a wind farm 110 km southeast of Paris (France)
Sub-mesoscale observations of convective cold pools with a dense station network in Hamburg, Germany
Observational data from uncrewed systems over Southern Great Plains
EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe
GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning
STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China
A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis
Historical and future weather data for dynamic building simulations in Belgium using the regional climate model MAR: typical and extreme meteorological year and heatwaves
Hourly historical and near-future weather and climate variables for energy system modelling
HCPD-CA: high-resolution climate projection dataset in central Asia
Development of East Asia Regional Reanalysis based on advanced hybrid gain data assimilation method and evaluation with E3DVAR, ERA-5, and ERA-Interim reanalysis
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Valentin Wiener, Marie-Laure Roussel, Christophe Genthon, Étienne Vignon, Jacopo Grazioli, and Alexis Berne
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-301, https://doi.org/10.5194/essd-2023-301, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica are presented in the present paper. 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 2 climate models as an application example of the dataset.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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 Discuss., https://doi.org/10.5194/essd-2023-115, https://doi.org/10.5194/essd-2023-115, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Our understanding, ability to observe and model air-sea processes has been identified as a principal limitation to our ability to predict future climate. 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 along the coast of California. In this article, we present details of the post-processing, algorithms and analysis.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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).
Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-59, https://doi.org/10.5194/essd-2023-59, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Dominik Rains, Isabel Trigo, Emanuel Dutra, Sofia Ermida, Darren Ghent, Petra Hulsman, Jose Gómez-Dans, and Diego Gonzales Miralles
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-302, https://doi.org/10.5194/essd-2022-302, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
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 by combining observations from geostationary as well as polar-orbiting satellites.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Falu Hong, Wenfeng Zhan, Frank-M. Göttsche, Zihan Liu, Pan Dong, Huyan Fu, Fan Huang, and Xiaodong Zhang
Earth Syst. Sci. Data, 14, 3091–3113, https://doi.org/10.5194/essd-14-3091-2022, https://doi.org/10.5194/essd-14-3091-2022, 2022
Short summary
Short summary
Daily mean land surface temperature (LST) acquired from satellite thermal sensors is crucial for various applications such as global and regional climate change analysis. This study proposed a framework to generate global spatiotemporally seamless daily mean LST products (2003–2019). Validations show that the products outperform the traditional method with satisfying accuracy. Our further analysis reveals that the LST-based global land surface warming rate is 0.029 K yr−1 from 2003 to 2019.
Sébastien Doutreloup, Xavier Fettweis, Ramin Rahif, Essam Elnagar, Mohsen S. Pourkiaei, Deepak Amaripadath, and Shady Attia
Earth Syst. Sci. Data, 14, 3039–3051, https://doi.org/10.5194/essd-14-3039-2022, https://doi.org/10.5194/essd-14-3039-2022, 2022
Short summary
Short summary
This data set provides historical (1980–2014) and future (2015–2100) weather data for 12 cities in Belgium. This data set is intended for architects or building or energy designers. In particular, it makes available to all users hourly open-access weather data according to certain standards to recreate a Typical and an Extreme Meteorological Year. In addition, it provides hourly data on heatwaves from 1980 to 2100. Weather data were produced from the outputs of the MAR model simulations.
Hannah C. Bloomfield, David J. Brayshaw, Matthew Deakin, and David Greenwood
Earth Syst. Sci. Data, 14, 2749–2766, https://doi.org/10.5194/essd-14-2749-2022, https://doi.org/10.5194/essd-14-2749-2022, 2022
Short summary
Short summary
There is a global increase in renewable generation to meet carbon targets and reduce the impacts of climate change. Renewable generation and electricity demand depend on the weather. This means there is a need for high-quality weather data for energy system modelling. We present a new European-level, 70-year dataset which has been specifically designed to support the energy sector. We provide hourly, sub-national climate outputs and include the impacts of near-term climate change.
Yuan Qiu, Jinming Feng, Zhongwei Yan, and Jun Wang
Earth Syst. Sci. Data, 14, 2195–2208, https://doi.org/10.5194/essd-14-2195-2022, https://doi.org/10.5194/essd-14-2195-2022, 2022
Short summary
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.
Eun-Gyeong Yang, Hyun Mee Kim, and Dae-Hui Kim
Earth Syst. Sci. Data, 14, 2109–2127, https://doi.org/10.5194/essd-14-2109-2022, https://doi.org/10.5194/essd-14-2109-2022, 2022
Short summary
Short summary
The East Asia Regional Reanalysis (EARR) system is developed based on the advanced hybrid gain data assimilation method (AdvHG) using the Weather Research and Forecasting (WRF) model and conventional observations. Based on EARR, high-resolution regional reanalysis and reforecast fields are produced with 12 km horizontal resolution over East Asia for the period 2010–2019. Compared to ERA5, EARR represents precipitation better for January and July 2017 over East Asia.
Cited articles
Banzon, V., Smith, T. M., Chin, T. M., Liu, C., and Hankins, W.: A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies, Earth Syst. Sci. Data, 8, 165–176, https://doi.org/10.5194/essd-8-165-2016, 2016.
Bennartz, R., Shupe, M. D., Turner, D. D., Walden, V. P., Steffen, K., Cox,
C. J., Kulie, M. S., Miller, B. B., and Pettersen, C.: July 2012 Greenland melt
extent enhanced by low-level liquid clouds, Nature, 496, 83–86, https://doi.org/10.1038/nature12002, 2013.
Bowden, J. H., Nolte, C. G., and Otte, T. L: Simulating the impact of the
large-scale circulation on the 2-m temperature and precipitation
climatology, Clim. Dynam., 40, 1903–1920, https://doi.org/10.1007/s00382-012-1440-y, 2012.
Bromwich, D. H., Hines, K. M., and Bai, L.: Development and testing of Polar
Weather Research and Forecasting model: 2. Arctic Ocean, J. Geophys.
Res., 114, D08122, https://doi.org/10.1029/2008JD010300, 2009.
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model
with the Penn State-NCAR MM5 modeling system. Part 1: Model implementation
and sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/15200493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Cho, H., Jun, S.-Y., Ho, C.-H., and McFarquhar, G.: Simulations of winter Arctic
clouds and associated radiation fluces using different cloud microphysics
schemes in the Polar WRF: Comparisons with CloudSat, CALIPSO and CERES,
J. Geophys. Res.-Atmos., 125, e2019JD031413, https://doi.org/10.1029/2019JD031413, 2020.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, I., Biblot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Greer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Mong-Sanz, B. M., Morcette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
DuVivier, A. K. and Cassano, J. J.: Evaluation of WRF Model Resolution on
Simulated Mesoscale Winds and Surface Fluxes near Greenland, Mon. Weather
Rev., 141, 941–963, https://doi.org/10.1175/MWR-D-12-00091.1, 2013.
ENVEO: Greenland Calving Front Dataset, 1990–2017, v3.0, Greenland Ice Sheet
CCI, available at: http://cryoportal.enveo.at (last access: 21 May 2020), 2019.
European Space Agency: Climate Change Initiative landuse product, available
at: https://www.esalandcover-cci.org/, last access: 5 September 2019.
Fausto, R. S. and van As, D.: Programme for monitoring of the Greenland ice
sheet (PROMICE): Automatic weather station data, Version: v03, Dataset, Geological Survey of Denmark and Greenland, https://doi.org/10.22008/promice/data/aws, 2019.
Fettweis, X., Box, J. E., Agosta, C., Amory, C., Kittel, C., Lang, C., van As, D., Machguth, H., and Gallée, H.: Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR model, The Cryosphere, 11, 1015–1033, https://doi.org/10.5194/tc-11-1015-2017, 2017.
Hines, K. M. and Bromwich, D. H.: Development and Testing of Polar Weather
Research and Forecasting (WRF) Model. Part I: Greenland Ice Sheet
Meteorology, Mon. Weather Rev., 136, 1971–1989, https://doi.org/10.1175/2007MWR2112.1, 2008.
Hines, K. M. and Bromwich, D. H: Simulation of Late Summer Arctic Clouds
during ASCOS with Polar WRF, Mon. Weather Rev., 145, 521–541, https://doi.org/10.1175/MWR-D-16-0079.1, 2017.
Hines, K. M., Bromwich, D. H., Bai, L.-S., Barlage, M., and Slater, A. G.:
Development and Testing of Polar WRF. Part III: Arctic Land, J. Climate,
24, 26–48, https://doi.org/10.1175/2010JCLI3460.1, 2011.
Hines, K. M., Bromwich, D. H., Bai, L., Bitz, C. M., Powers, J. G., and Manning,
K. W.: Sea Ice Enhancements to Polar WRF, Mon. Weather Rev., 143,
2363–2385, https://doi.org/10.1175/MWR-D-14-00344.1, 2015.
Hong, S.-Y., Noh, Y., Dudhia, J., Hong, S.-Y., Noh, Y., and Dudhia, J.: A New
Vertical Diffusion Package with an Explicit Treatment of Entrainment
Processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Howat, I. and Eddy, A: Multi-decadal retreat of Greenland's
marine-terminating glaciers, J. Glaciology, 57, 389-396,
https://doi.org/10.3189/002214311796905631, 2011.
Janjić, Z. I.: The Step-Mountain Eta Coordinate Model: Further
Developments of the Convection, Viscous Sublayer, and Turbulence Closure
Schemes, Mon. Weather Rev., 122, 927–945, https://doi.org/10.1175/15200493(1994)122<0927:TSMECM>2.0.CO;2, 1994.
Joughin, I., Smith, B. E., Howat, I. M., Scambos, T., and Moon, T.: Greenland
flow variability from ice-sheet-wide velocity mapping, J. Glaciol., 56, 415–430,
https://doi.org/10.3189/002214310792447734, 2010.
Kain, J. S: The Kain–Fritsch Convective Parameterization: An Update, J.
App. Met., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Khan, S. A., Kjær, K. H., Bevis, M., Bamber, J. L., Wahr, J., Kjeldsen,
K. K., Bjørk, A. A., Korsgaard, N. J., Stearns, L. A., van den Broeke, M. R., Liu, L., Larsen, N. K., and Muresan, I. S.: Sustained mass loss of the
northeast Greenland ice sheet triggered by regional warming, Nat. Clim.
Change, 4, 292–299, https://doi.org/10.1038/nclimate2161,
2014.
Kuipers Munneke, P., Smeets, C. J. P. P., Reijmer, C. H., Oerlemans, J., van
de Wal., R. S. W., and van den Broeke, M. R.: The K-transect on the western
Greenland Ice Sheet: Surface energy balance (2003–2016), Arct. Antarct. Alp. Res., 50, e1420952, https://doi.org/10.1080/15230430.2017.1420952, 2018.
Lachlan-Cope, T., Listowski, C., and O'Shea, S.: The microphysics of clouds over the Antarctic Peninsula – Part 1: Observations, Atmos. Chem. Phys., 16, 15605–15617, https://doi.org/10.5194/acp-16-15605-2016, 2016.
Larsen, N. K., Levy, L. B., Carlson, A. E., Buizert, C., Olsen, J., Strunk,
A., Bjørk, A. A., and Skov, D. S.: Instability of the Northeast Greenland
Ice Stream over the last 45,000 years, Nat. Commun., 9, 1872,
https://doi.org/10.1038/s41467-018-04312-7, 2018.
Leeson, A. A., Eastoe, E., and Fettweis, X.: Extreme temperature events on Greenland in observations and the MAR regional climate model, The Cryosphere, 12, 1091–1102, https://doi.org/10.5194/tc-12-1091-2018, 2018.
Listowski, C. and Lachlan-Cope, T.: The microphysics of clouds over the Antarctic Peninsula – Part 2: modelling aspects within Polar WRF, Atmos. Chem. Phys., 17, 10195–10221, https://doi.org/10.5194/acp-17-10195-2017, 2017.
Lo, J. C.-F., Yang, Z.-L., and Pielke Sr., R. A.: Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model, J. Geophys. Res.-Atmos., 113, D09112, https://doi.org/10.1029/2007JD009216, 2008.
Mayer, C., Schaffer, J., Hattermann, T., Floricioiu, D., Krieger, L., Dodd,
P. A., Kanzow, T., Licciulli, C., and Schannwell, C.: Large ice loss
variability at Nioghalvfjerdsfjorden Glacier, Northeast Greenland, Nat.
Commun., 9, 2768, https://doi.org/10.1038/s41467-018-05180-x,
2018.
Mernild, S. H., Liston, G. E., van As, D., Hasholt, B., and Yde, J. C.:
High-resolution ice sheet surface mass-balance and spatiotemporal runoff
simulations: Kangerlussuaq, west Greenland, Arct. Antarct. Alp. Res., 50, S100008, https://doi.org/10.1080/15230430.2017.1415856, 2018.
Mottram, R., Boberg, F., Langen, P., Yang, S., Rodehacke, C., Christensen,
J., and Madsen, M.: Surface mass balance of the Greenland ice sheet in the
regional climate model HIRHAM5: Present state and future prospects, Low
Temp. Sci., 75, 105–115, 2017a.
Mottram, R., Nielsen, K. P., Gleeson, E., and Yang, X.: Modelling Glaciers in the HARMONIE-AROME NWP model, Adv. Sci. Res., 14, 323–334, https://doi.org/10.5194/asr-14-323-2017, 2017b.
Mouginot, J., Rignot, E., Scheuchl, B., Fenty, I., Khazendar, A., Morlighem,
M., Buzzi, A., and Paden, J.: Fast retreat of Zachariæ Isstrøm,
northeast Greenland, Science, 350, 1357–1361, https://doi.org/10.1126/SCIENCE.AAC7111, 2015.
Münchow, A., Schaffer, J., and Kanzow, T: Ocean circulation connecting
Fram Strait to Glaciers off North-East Greenland: Mean flows, topographic
Rossby waves, and their forcing, J. Phys. Oceanogr., 50, 509–530,
https://doi.org/10.1175/JPO-D-19-0085.1, 2020.
NCAR: NOAA Optimum Interpolation 1/4 Degree Daily Sea Surface Temperature Analysis, Research Data Archive at the National Center for Atmospheric Research,National Centers for Environmental Information, NESDIS, NOAA, and U.S. Department of Commerce, Computational and Information Systems Laboratory, https://doi.org/10.5065/EM0T-1D34, 2007.
NCAR: Weather Research and Forecasting Model, National Centre for
Atmospheric Research, available at: https://www.mmm.ucar.edu/weather-research-and forecasting-model,last access: 1 October 2019.
Niwano, M., Aoki, T., Hashimoto, A., Matoba, S., Yamaguchi, S., Tanikawa, T., Fujita, K., Tsushima, A., Iizuka, Y., Shimada, R., and Hori, M.: NHM–SMAP: spatially and temporally high-resolution nonhydrostatic atmospheric model coupled with detailed snow process model for Greenland Ice Sheet, The Cryosphere, 12, 635–655, https://doi.org/10.5194/tc-12-635-2018, 2018.
Noël, B., van de Berg, W. J., Machguth, H., Lhermitte, S., Howat, I., Fettweis, X., and van den Broeke, M. R.: A daily, 1 km resolution data set of downscaled Greenland ice sheet surface mass balance (1958–2015), The Cryosphere, 10, 2361–2377, https://doi.org/10.5194/tc-10-2361-2016, 2016.
Ohio State University: Polar Weather Research and Forecasting Model, available at: http://polarmet.osu.edu/PWRF/, last
access: 29 July 2019.
Otte, T. L., Nolte, C. G., Otte, M. J., and Bowden, J. H.: Does Nudging Squelch
the Extremes in Regional climate modeling?, J. Climate, 25, 7046–7066,
https://doi.org/10.1175/JCLID-12-00048.1, 2012.
Pal., S., Change, H.-I., Castro, C. L., and Dominguez, F.: Credibility of
convection-permitting modeling to improve seasonal precipitation forecasting
in the southwestern United States, Front. Earth Sci., 7, 11, https://doi.org/10.3389/feart.2019.00011, 2019.
Pedersen, S. H., Tamstorf, M. P., Abermann, J., Westergaard-Nielsen, A., Lund, M., Skov, K., Sigsgaard, C., Mylius, M. R., Hansen, B. U., Liston, G. E., and Schmidt, N. M.: Spatiotemporal characteristics of seasonal snow cover in Northeast Greenland from in situ observations, Arct. Antarct. Alp. Res., 48, 653–671, https://doi.org/10.1657/AAAR0016-028,
2016.
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J.,
Gill, D. O., Coen, J. L., and Duda, M. G.: The Weather Research and Forecasting
Model: Overview, System Efforts, and Future Directions, B. Am. Meteorol.
Soc., 98, 1717–1737, https://doi.org/10.1175/BAMS-D-15-00308.1, 2017.
Rignot, E., Fenty, I., Xu, Y., Cai, C., and Kemp, C: Undercutting of
marine-terminating glaciers in West Greenland, Geophys. Res. Lett.,
42, 5909–5917, https://doi.org/10.1002/2015GL064236, 2015.
Schaffer, J., von Appen, W.-J., Dodd, P. A., Hofstede, C., Mayer, C., de
Steur, L., and Kanzow, T.: Warm water pathways toward Nioghalvfjerdsfjorden
Glacier, Northeast Greenland, J. Geophys. Res.-Oceans, 122, 4004–4020,
https://doi.org/10.1002/2016JC012462, 2017.
Shepherd, A., Ivins, E., Rignot, E., et al.: Mass balance of the Greenland Ice Sheet from 1992 to
2018, Nature, 579, 233–239, https://doi.org/10.1038/s41586-019-1855-2, 2020.
Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric
model for weather research and forecasting applications. J. Computat.
Phys., 227, 3465–3485, https://doi.org/10.1016/j.jcp.2007.01.037, 2008.
Tedesco, M., Fettweis, X., Mote, T., Wahr, J., Alexander, P., Box, J. E., and Wouters, B.: Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional climate model and reanalysis data, The Cryosphere, 7, 615–630, https://doi.org/10.5194/tc-7-615-2013, 2013.
Turton, J. V., Mölg, T., and Van As, D.: Atmospheric Processes and
Climatological Characteristics of the 79N Glacier (Northeast Greenland),
Mon. Weather Rev., 147, 1375–1394, https://doi.org/10.1175/MWR-D-18-0366.1, 2019a.
Turton, J. V., Mölg, T., and Collier, E.: NEGIS_WRF model
output, Open Science Framework Repository,
https://doi.org/10.17605/OSF.IO/53E6Z, 2019b.
van As, D. and Fausto, R.: Programme for Monitoring of the Greenland Ice
Sheet (PROMICE): first temperature and ablation records, Geolog. Survey
Denmark Greenland Bulletin, 23, 73–76, 2011.
van As, D., Bøggild, C. E., Nielsen, S., Ahlstrøm, A. P., Fausto, R. S., Podlech, S., and Andersen, M. L.: Climatology and ablation at the South Greenland ice sheet margin from automatic weather station observations, The Cryosphere Discuss., 3, 117–158, https://doi.org/10.5194/tcd-3-117-2009, 2009.
van den Broeke, M., Box, J., Fettweis, X., Hanna, E., Noël, B., Tedesco,
M., van As, D., van de Berg, W. J., and van Kampenhout, L.: Greenland Ice Sheet
Surface Mass Loss: Recent Developments in Observation and Modeling, Curr.
Clim. Change Rep., 3, 345–356, https://doi.org/10.1007/s40641-017-0084-8, 2017.
Wang, C., Graham, R. M., Wang, K., Gerland, S., and Granskog, M. A.: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, 2019.
Wilson, N. J. and Straneo, F.: Water exchange between the continental shelf and the cavity beneath Nioghalvfjerdsbrae (79 North Glacier), Geophys. Res. Lett., 42, 7648–7654, https://doi.org/10.1002/2015GL064944, 2015.
Wilson, A. B., Bromwich, D. H., and Hines, K. M.: Evaluation of Polar WRF
forecasts on the Arctic System Reanalysis domain: Surface and upper air
analysis, J. Geophys. Res., 116, D11112, https://doi.org/10.1029/2010JD015013, 2011.
Short summary
The Northeast Greenland Ice Stream drains approximately 12 % of the entire Greenland ice sheet and could contribute over 1 m of sea level rise if it were to completely disappear. However, this region is a relatively new research area. Here we provide an atmospheric modelling dataset from 2014 to 2018, which includes many meteorological and radiation variables. The model data have been compared to weather stations and show good agreement. This dataset has many future applications.
The Northeast Greenland Ice Stream drains approximately 12 % of the entire Greenland ice sheet...
Altmetrics
Final-revised paper
Preprint