Articles | Volume 10, issue 3
https://doi.org/10.5194/essd-10-1427-2018
© Author(s) 2018. 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-10-1427-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief communication
| 
09 Aug 2018
Brief communication |
| 09 Aug 2018
| 09 Aug 2018
PRIMAP-crf: UNFCCC CRF data in IPCC 2006 categories
M. Louise Jeffery
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research (PIK), Member of the
Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Johannes Gütschow
Potsdam Institute for Climate Impact Research (PIK), Member of the
Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Robert Gieseke
Potsdam Institute for Climate Impact Research (PIK), Member of the
Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Ronja Gebel
Potsdam Institute for Climate Impact Research (PIK), Member of the
Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Related authors
Johannes Gütschow, M. Louise Jeffery, Robert Gieseke, Ronja Gebel, David Stevens, Mario Krapp, and Marcia Rocha
Earth Syst. Sci. Data, 8, 571–603, https://doi.org/10.5194/essd-8-571-2016, https://doi.org/10.5194/essd-8-571-2016, 2016
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This paper provides the methodology for the creation of historical country-resolved time series of greenhouse gas emissions from different datasets which are individually incomplete in terms of years, gases, and/or countries. The combination of datasets is carried out using the PRIMAP model (www.primap.org). The resulting time series can be viewed interactively on www.pik-potsdam.de/primap-live. It will be used for climate policy analysis, e.g. the historical responsibility for climate change.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
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This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Piers M. Forster, Christopher J. Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Sonia I. Seneviratne, Blair Trewin, Xuebin Zhang, Myles Allen, Robbie Andrew, Arlene Birt, Alex Borger, Tim Boyer, Jiddu A. Broersma, Lijing Cheng, Frank Dentener, Pierre Friedlingstein, José M. Gutiérrez, Johannes Gütschow, Bradley Hall, Masayoshi Ishii, Stuart Jenkins, Xin Lan, June-Yi Lee, Colin Morice, Christopher Kadow, John Kennedy, Rachel Killick, Jan C. Minx, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sophie Szopa, Peter Thorne, Robert Rohde, Maisa Rojas Corradi, Dominik Schumacher, Russell Vose, Kirsten Zickfeld, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 15, 2295–2327, https://doi.org/10.5194/essd-15-2295-2023, https://doi.org/10.5194/essd-15-2295-2023, 2023
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This is a critical decade for climate action, but there is no annual tracking of the level of human-induced warming. We build on the Intergovernmental Panel on Climate Change assessment reports that are authoritative but published infrequently to create a set of key global climate indicators that can be tracked through time. Our hope is that this becomes an important annual publication that policymakers, media, scientists and the public can refer to.
Francesco N. Tubiello, Kevin Karl, Alessandro Flammini, Johannes Gütschow, Griffiths Obli-Laryea, Giulia Conchedda, Xueyao Pan, Sally Yue Qi, Hörn Halldórudóttir Heiðarsdóttir, Nathan Wanner, Roberta Quadrelli, Leonardo Rocha Souza, Philippe Benoit, Matthew Hayek, David Sandalow, Erik Mencos Contreras, Cynthia Rosenzweig, Jose Rosero Moncayo, Piero Conforti, and Maximo Torero
Earth Syst. Sci. Data, 14, 1795–1809, https://doi.org/10.5194/essd-14-1795-2022, https://doi.org/10.5194/essd-14-1795-2022, 2022
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The paper presents results from the new FAOSTAT database on food system emissions, covering all countries over the time series 1990–2019. Results indicate and further clarify – updated to 2019 – the relevance of emissions from crop and livestock production processes within the farm gate; from conversion of natural ecosystems to agriculture, such as deforestation and peat degradation; and from use of fossil fuels for energy and other industrial processes along food supply chains.
Annika Günther, Johannes Gütschow, and Mairi Louise Jeffery
Geosci. Model Dev., 14, 5695–5730, https://doi.org/10.5194/gmd-14-5695-2021, https://doi.org/10.5194/gmd-14-5695-2021, 2021
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The mitigation components of the nationally determined contributions (NDCs) under the Paris Agreement are essential in our fight against climate change. Regular updates with increased ambition are requested to limit global warming to 1.5–2 °C. The new and easy-to-update open-source tool NDCmitiQ can be used to quantify the NDCs' mitigation targets and construct resulting emissions pathways. In use cases, we show target uncertainties from missing clarity, data, and methodological challenges.
Johannes Gütschow, M. Louise Jeffery, Annika Günther, and Malte Meinshausen
Earth Syst. Sci. Data, 13, 1005–1040, https://doi.org/10.5194/essd-13-1005-2021, https://doi.org/10.5194/essd-13-1005-2021, 2021
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Climate policy analysis needs scenarios of future greenhouse gas emission to assess countries' emission targets and current trends. The models generating these scenarios work on a regional resolution. Scenarios are often made available only on a very coarse regional resolution. In this paper we use per country projections of gross domestic product (GDP) from the Shared Socio-Economic Pathways (SSPs) to derive country-level data from published regional emission scenarios.
Johannes Gütschow, M. Louise Jeffery, Robert Gieseke, Ronja Gebel, David Stevens, Mario Krapp, and Marcia Rocha
Earth Syst. Sci. Data, 8, 571–603, https://doi.org/10.5194/essd-8-571-2016, https://doi.org/10.5194/essd-8-571-2016, 2016
Short summary
Short summary
This paper provides the methodology for the creation of historical country-resolved time series of greenhouse gas emissions from different datasets which are individually incomplete in terms of years, gases, and/or countries. The combination of datasets is carried out using the PRIMAP model (www.primap.org). The resulting time series can be viewed interactively on www.pik-potsdam.de/primap-live. It will be used for climate policy analysis, e.g. the historical responsibility for climate change.
Related subject area
Atmospheric chemistry and physics
MAP-IO: an atmospheric and marine observatory program on board Marion Dufresne over the Southern Ocean
Retrieving ground-level PM2.5 concentrations in China (2013–2021) with a numerical-model-informed testbed to mitigate sample-imbalance-induced biases
Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd)
Visibility-derived aerosol optical depth over global land from 1959 to 2021
Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology
A Level 3 monthly gridded ice cloud dataset derived from 12 years of CALIOP measurements
IPB-MSA&SO4: a daily 0.25° resolution dataset of in situ-produced biogenic methanesulfonic acid and sulfate over the North Atlantic during 1998–2022 based on machine learning
Indicators of Global Climate Change 2023: annual update of key indicators of the state of the climate system and human influence
Version 1 NOAA-20/OMPS Nadir Mapper Total Column SO2 Product: Continuation of NASA Long-term Global Data Record
A 10 km daily-level ultraviolet radiation predicting dataset based on machine learning models in China from 2005 to 2020
Multiwavelength, aerosol lidars at Maïdo supersite, Reunion Island, France: instruments description, data processing chain and quality assessment
Multi-year high time resolution measurements of fine PM at 13 sites of the French Operational Network (CARA program): Data processing and chemical composition
The Total Carbon Column Observing Network's GGG2020 data version
Global anthropogenic emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service simulations of air quality forecasts and reanalyses
Deep Convective Microphysics Experiment (DCMEX) coordinated aircraft and ground observations: microphysics, aerosol, and dynamics during cumulonimbus development
High-resolution physicochemical dataset of atmospheric aerosols over the Tibetan Plateau and its surroundings
Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research
PM2.5 concentrations based on near-surface visibility at 4011 sites in the Northern Hemisphere from 1959 to 2022
GHOST: A globally harmonised dataset of surface atmospheric composition measurements
The Tibetan Plateau space-based tropospheric aerosol climatology: 2007–2020
PalVol v1: a proxy-based semi-stochastic ensemble reconstruction of volcanic stratospheric sulfur injection for the last glacial cycle (140 000–50 BP)
The GERB Obs4MIPs Radiative Flux Dataset: A new tool for climate model evaluation
Four decades of global surface albedo estimates in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) climate data record
Large synthesis of in situ field measurements of the size distribution of mineral dust aerosols across their lifecycle
Ground- and ship-based microwave radiometer measurements during EUREC4A
Shortwave and longwave components of the surface radiation budget measured at the Thule High Arctic Atmospheric Observatory, Northern Greenland
Cloud condensation nuclei concentrations derived from the CAMS reanalysis
A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS
Changes of air pollutant emissions in China during two clean air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI)
12 years of continuous atmospheric O2, CO2 and APO data from Weybourne Atmospheric Observatory in the United Kingdom
CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations
Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets
High-resolution aerosol data from the top 3.8 kyr of the East Greenland Ice coring Project (EGRIP) ice core
A database of aircraft measurements of carbon monoxide (CO) with high temporal and spatial resolution during 2011–2021
A first global height-resolved cloud condensation nuclei data set derived from spaceborne lidar measurements
A monthly 1° resolution dataset of daytime cloud fraction over the Arctic during 2000–2020 based on multiple satellite products
Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method
Spatially coordinated airborne data and complementary products for aerosol, gas, cloud, and meteorological studies: the NASA ACTIVATE dataset
Network for the Detection of Atmospheric Composition Change (NDACC) Fourier transform infrared (FTIR) trace gas measurements at the University of Toronto Atmospheric Observatory from 2002 to 2020
Deconstruction of tropospheric chemical reactivity using aircraft measurements: the Atmospheric Tomography Mission (ATom) data
Spatial variability of Saharan dust deposition revealed through a citizen science campaign
Radiative sensitivity quantified by a new set of radiation flux kernels based on the ECMWF Reanalysis v5 (ERA5)
Updated observations of clouds by MODIS for global model assessment
An investigation of the global uptake of CO2 by lime from 1930 to 2020
An extensive database of airborne trace gas and meteorological observations from the Alpha Jet Atmospheric eXperiment (AJAX)
Two years of volatile organic compound online in situ measurements at the Site Instrumental de Recherche par Télédétection Atmosphérique (Paris region, France) using proton-transfer-reaction mass spectrometry
Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products of atmospheric trace gas columns
Crowdsourced Doppler measurements of time standard stations demonstrating ionospheric variability
Isotopic measurements in water vapor, precipitation, and seawater during EUREC4A
A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina
Pierre Tulet, Joel Van Baelen, Pierre Bosser, Jérome Brioude, Aurélie Colomb, Philippe Goloub, Andrea Pazmino, Thierry Portafaix, Michel Ramonet, Karine Sellegri, Melilotus Thyssen, Léa Gest, Nicolas Marquestaut, Dominique Mékiès, Jean-Marc Metzger, Gilles Athier, Luc Blarel, Marc Delmotte, Guillaume Desprairies, Mérédith Dournaux, Gaël Dubois, Valentin Duflot, Kevin Lamy, Lionel Gardes, Jean-François Guillemot, Valérie Gros, Joanna Kolasinski, Morgan Lopez, Olivier Magand, Erwan Noury, Manuel Nunes-Pinharanda, Guillaume Payen, Joris Pianezze, David Picard, Olivier Picard, Sandrine Prunier, François Rigaud-Louise, Michael Sicard, and Benjamin Torres
Earth Syst. Sci. Data, 16, 3821–3849, https://doi.org/10.5194/essd-16-3821-2024, https://doi.org/10.5194/essd-16-3821-2024, 2024
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The MAP-IO program aims to compensate for the lack of atmospheric and oceanographic observations in the Southern Ocean by equipping the ship Marion Dufresne with a set of 17 scientific instruments. This program collected 700 d of measurements under different latitudes, seasons, sea states, and weather conditions. These new data will support the calibration and validation of numerical models and the understanding of the atmospheric composition of this region of Earth.
Siwei Li, Yu Ding, Jia Xing, and Joshua S. Fu
Earth Syst. Sci. Data, 16, 3781–3793, https://doi.org/10.5194/essd-16-3781-2024, https://doi.org/10.5194/essd-16-3781-2024, 2024
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Surface PM2.5 data have gained widespread application in health assessments and related fields, while the inherent uncertainties in PM2.5 data persist due to the lack of ground-truth data across the space. This study provides a novel testbed, enabling comprehensive evaluation across the entire spatial domain. The optimized deep-learning model with spatiotemporal features successfully retrieved surface PM2.5 concentrations in China (2013–2021), with reduced biases induced by sample imbalance.
Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang
Earth Syst. Sci. Data, 16, 3565–3577, https://doi.org/10.5194/essd-16-3565-2024, https://doi.org/10.5194/essd-16-3565-2024, 2024
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Long-term, open-source, gap-free daily ground-level PM2.5 and PM10 datasets for India (LongPMInd) were reconstructed using a robust machine learning model to support health assessment and air quality management.
Hongfei Hao, Kaicun Wang, Chuanfeng Zhao, Guocan Wu, and Jing Li
Earth Syst. Sci. Data, 16, 3233–3260, https://doi.org/10.5194/essd-16-3233-2024, https://doi.org/10.5194/essd-16-3233-2024, 2024
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In this study, we employed a machine learning technique to derive daily aerosol optical depth from hourly visibility observations collected at more than 5000 airports worldwide from 1959 to 2021 combined with reanalysis meteorological parameters.
Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, and Veronika Eyring
Earth Syst. Sci. Data, 16, 3001–3016, https://doi.org/10.5194/essd-16-3001-2024, https://doi.org/10.5194/essd-16-3001-2024, 2024
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CCClim displays observations of clouds in terms of cloud classes that have been in use for a long time. CCClim is a machine-learning-powered product based on multiple existing observational products from different satellites. We show that the cloud classes in CCClim are physically meaningful and can be used to study cloud characteristics in more detail. The goal of this is to make real-world clouds more easily understandable to eventually improve the simulation of clouds in climate models.
David Winker, Xia Cai, Mark Vaughan, Anne Garnier, Brian Magill, Melody Avery, and Brian Getzewich
Earth Syst. Sci. Data, 16, 2831–2855, https://doi.org/10.5194/essd-16-2831-2024, https://doi.org/10.5194/essd-16-2831-2024, 2024
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Clouds play important roles in both weather and climate. In this paper we describe version 1.0 of a unique global ice cloud data product derived from over 12 years of global spaceborne lidar measurements. This monthly gridded product provides a unique vertically resolved characterization of the occurrence and properties, optical and physical, of thin ice clouds and the tops of deep convective clouds. It should provide significant value for cloud research and model evaluation.
Karam Mansour, Stefano Decesari, Darius Ceburnis, Jurgita Ovadnevaite, Lynn M. Russell, Marco Paglione, Laurent Poulain, Shan Huang, Colin O'Dowd, and Matteo Rinaldi
Earth Syst. Sci. Data, 16, 2717–2740, https://doi.org/10.5194/essd-16-2717-2024, https://doi.org/10.5194/essd-16-2717-2024, 2024
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We propose and evaluate machine learning predictive algorithms to model freshly formed biogenic methanesulfonic acid and sulfate concentrations. The long-term constructed dataset covers the North Atlantic at an unprecedented resolution. The improved parameterization of biogenic sulfur aerosols at regional scales is essential for determining their radiative forcing, which could help further understand marine-aerosol–cloud interactions and reduce uncertainties in climate models
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
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This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Can Li, Nickolay A. Krotkov, Joanna Joiner, Vitali Fioletov, Chris McLinden, Debora Griffin, Peter J. T. Leonard, Simon Carn, Colin Seftor, and Alexander Vasilkov
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-168, https://doi.org/10.5194/essd-2024-168, 2024
Revised manuscript accepted for ESSD
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Sulfur dioxide (SO2), a poisonous gas from human activities and volcanoes, causes air pollution, acid rain, and changes to climate and ozone layer. Satellites have been used to monitor SO2 globally, including remote areas. Here we describe a new satellite SO2 dataset from the OMPS instrument that flies on the NOAA-20 satellite. Results show that the new dataset agrees well with the existing ones from other satellites and can help to continue the global monitoring of SO2 from space.
Yichen Jiang, Su Shi, Xinyue Li, Chang Xu, Haidong Kan, Bo Hu, and Xia Meng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-111, https://doi.org/10.5194/essd-2024-111, 2024
Revised manuscript accepted for ESSD
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Limited UV measurements hindered further investigation of its health effects. This study used a machine learning algorithm to predict UV radiation at daily level and 10 km resolution with high accuracy in mainland China in 2005–2020. Then, uneven spatial distribution and population exposure risks as well as increased temporal trend of UV radiation were found in China. The long-term and high-quality UV dataset could further facilitate health-related research in the future.
Dominique Gantois, Guillaume Payen, Michaël Sicard, Valentin Duflot, Nicolas Marquestaut, Thierry Portafaix, Sophie Godin-Beekmann, Patrick Hernandez, and Eric Golubic
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-93, https://doi.org/10.5194/essd-2024-93, 2024
Revised manuscript accepted for ESSD
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In this work, we describe three instruments measuring interactions between aerosols (particles of various origins) and light over Reunion Island since 2012. Aerosols influence directly or indirectly the temperature in the atmosphere and can interact with clouds. Details can be found about how we derived aerosol properties from our measurements, and how we assessed the quality of our data before sharing it with the scientific community. A good correlation was found between the three instruments.
Hasna Chebaicheb, Joel F. de Brito, Tanguy Amodeo, Florian Couvidat, Jean-Eudes Petit, Emmanuel Tison, Gregory Abbou, Alexia Baudic, Mélodie Chatain, Benjamin Chazeau, Nicolas Marchand, Raphaele Falhun, Florie Francony, Cyril Ratier, Didier Grenier, Romain Vidaud, Shouwen Zhang, Gregory Gille, Laurent Meunier, Caroline Marchand, Véronique Riffault, and Olivier Favez
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-80, https://doi.org/10.5194/essd-2024-80, 2024
Revised manuscript accepted for ESSD
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Long-term (2015–2021) quasi-continuous measurements have been obtained at 13 French urban sites using online mass spectrometry, to acquire comprehensive chemical composition of submicron particulate matter. The results show their spatial and temporal differences and confirm the predominance of organics in France (40–60 %). These measurements can be used for many future studies such as trend and epidemiological analyses, or comparisons with chemical transport models.
Joshua L. Laughner, Geoffrey C. Toon, Joseph Mendonca, Christof Petri, Sébastien Roche, Debra Wunch, Jean-Francois Blavier, David W. T. Griffith, Pauli Heikkinen, Ralph F. Keeling, Matthäus Kiel, Rigel Kivi, Coleen M. Roehl, Britton B. Stephens, Bianca C. Baier, Huilin Chen, Yonghoon Choi, Nicholas M. Deutscher, Joshua P. DiGangi, Jochen Gross, Benedikt Herkommer, Pascal Jeseck, Thomas Laemmel, Xin Lan, Erin McGee, Kathryn McKain, John Miller, Isamu Morino, Justus Notholt, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Haris Riris, Constantina Rousogenous, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Steven C. Wofsy, Minqiang Zhou, and Paul O. Wennberg
Earth Syst. Sci. Data, 16, 2197–2260, https://doi.org/10.5194/essd-16-2197-2024, https://doi.org/10.5194/essd-16-2197-2024, 2024
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This paper describes a new version, called GGG2020, of a data set containing column-integrated observations of greenhouse and related gases (including CO2, CH4, CO, and N2O) made by ground stations located around the world. Compared to the previous version (GGG2014), improvements have been made toward site-to-site consistency. This data set plays a key role in validating space-based greenhouse gas observations and in understanding the carbon cycle.
Antonin Soulie, Claire Granier, Sabine Darras, Nicolas Zilbermann, Thierno Doumbia, Marc Guevara, Jukka-Pekka Jalkanen, Sekou Keita, Cathy Liousse, Monica Crippa, Diego Guizzardi, Rachel Hoesly, and Steven J. Smith
Earth Syst. Sci. Data, 16, 2261–2279, https://doi.org/10.5194/essd-16-2261-2024, https://doi.org/10.5194/essd-16-2261-2024, 2024
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Anthropogenic emissions are the result of transportation, power generation, industrial, residential and commercial activities as well as waste treatment and agriculture practices. This work describes the new CAMS-GLOB-ANT gridded inventory of 2000–2023 anthropogenic emissions of air pollutants and greenhouse gases. The methodology to generate the emissions is explained and the datasets are analysed and compared with publicly available global and regional inventories for selected world regions.
Declan L. Finney, Alan M. Blyth, Martin Gallagher, Huihui Wu, Graeme J. Nott, Michael I. Biggerstaff, Richard G. Sonnenfeld, Martin Daily, Dan Walker, David Dufton, Keith Bower, Steven Böing, Thomas Choularton, Jonathan Crosier, James Groves, Paul R. Field, Hugh Coe, Benjamin J. Murray, Gary Lloyd, Nicholas A. Marsden, Michael Flynn, Kezhen Hu, Navaneeth M. Thamban, Paul I. Williams, Paul J. Connolly, James B. McQuaid, Joseph Robinson, Zhiqiang Cui, Ralph R. Burton, Gordon Carrie, Robert Moore, Steven J. Abel, Dave Tiddeman, and Graydon Aulich
Earth Syst. Sci. Data, 16, 2141–2163, https://doi.org/10.5194/essd-16-2141-2024, https://doi.org/10.5194/essd-16-2141-2024, 2024
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The DCMEX (Deep Convective Microphysics Experiment) project undertook an aircraft- and ground-based measurement campaign of New Mexico deep convective clouds during July–August 2022. The campaign coordinated a broad range of instrumentation measuring aerosol, cloud physics, radar signals, thermodynamics, dynamics, electric fields, and weather. The project's objectives included the utilisation of these data with satellite observations to study the anvil cloud radiative effect.
Jianzhong Xu, Xinghua Zhang, Wenhui Zhao, Lixiang Zhai, Miao Zhong, Jinsen Shi, Junying Sun, Yanmei Liu, Conghui Xie, Yulong Tan, Kemei Li, Xinlei Ge, Qi Zhang, and Shichang Kang
Earth Syst. Sci. Data, 16, 1875–1900, https://doi.org/10.5194/essd-16-1875-2024, https://doi.org/10.5194/essd-16-1875-2024, 2024
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A comprehensive aerosol observation project was carried out in the Tibetan Plateau (TP) and its surroundings in recent years to investigate the properties and sources of atmospheric aerosols as well as their regional differences by performing multiple intensive field observations. The release of this dataset can provide basic and systematic data for related research in the atmospheric, cryospheric, and environmental sciences in this unique region.
Xiaoyong Zhuge, Xiaolei Zou, Lu Yu, Xin Li, Mingjian Zeng, Yilun Chen, Bing Zhang, Bin Yao, Fei Tang, Fengjiao Chen, and Wanlin Kan
Earth Syst. Sci. Data, 16, 1747–1769, https://doi.org/10.5194/essd-16-1747-2024, https://doi.org/10.5194/essd-16-1747-2024, 2024
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The Himawari-8/9 level-2 operational cloud product has a low spatial resolution and is available only during the daytime. To supplement this official dataset, a new dataset named the NJIAS Himawari-8/9 Cloud Feature Dataset (HCFD) was constructed. The NJIAS HCFD provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the years 2016–2022 by 30 retrieved cloud variables. The NJIAS HCFD has been demonstrated to outperform the official dataset.
Hongfei Hao, Kaicun Wang, Guocan Wu, Jianbao Liu, and Jing Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-96, https://doi.org/10.5194/essd-2024-96, 2024
Revised manuscript accepted for ESSD
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In this study, daily PM2.5 concentrations are estimated from 1959 to 2022 using a machine learning method at 4011 terrestrial sites in the Northern Hemisphere based on hourly atmospheric visibility data, which are extracted from the Meteorological Terminal Aviation Routine Weather Report (METAR).
Dene Bowdalo, Sara Basart, Marc Guevara, Oriol Jorba, Carlos Pérez García-Pando, Monica Jaimes Palomera, Olivia Rivera Hernandez, Melissa Puchalski, David Gay, Jörg Klausen, Sergio Moreno, Stoyka Netcheva, and Oksana Tarasova
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-397, https://doi.org/10.5194/essd-2023-397, 2024
Revised manuscript accepted for ESSD
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GHOST: Globally Harmonised Observations in Space and Time, represents one of the biggest collection of harmonised measurements of atmospheric composition at the surface. In total, 7,275,148,646 measurements from 1970–2023, of 227 different components, from 38 reporting networks, are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties.
Honglin Pan, Jianping Huang, Jiming Li, Zhongwei Huang, Minzhong Wang, Ali Mamtimin, Wen Huo, Fan Yang, Tian Zhou, and Kanike Raghavendra Kumar
Earth Syst. Sci. Data, 16, 1185–1207, https://doi.org/10.5194/essd-16-1185-2024, https://doi.org/10.5194/essd-16-1185-2024, 2024
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We applied several correction procedures and rigorously checked for data quality constraints during the long observation period spanning almost 14 years (2007–2020). Nevertheless, some uncertainties remain, mainly due to technical constraints and limited documentation of the measurements. Even though not completely accurate, this strategy is expected to at least reduce the inaccuracy of the computed characteristic value of aerosol optical parameters.
Julie Christin Schindlbeck-Belo, Matthew Toohey, Marion Jegen, Steffen Kutterolf, and Kira Rehfeld
Earth Syst. Sci. Data, 16, 1063–1081, https://doi.org/10.5194/essd-16-1063-2024, https://doi.org/10.5194/essd-16-1063-2024, 2024
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Volcanic forcing of climate resulting from major explosive eruptions is a dominant natural driver of past climate variability. To support model studies of the potential impacts of explosive volcanism on climate variability across timescales, we present an ensemble reconstruction of volcanic stratospheric sulfur injection over the last 140 000 years that is based primarily on tephra records.
Jacqueline Elizabeth Russell, Richard John Bantges, Helen Elizabeth Brindley, and Alejandro Bodas-Salcedo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-4, https://doi.org/10.5194/essd-2024-4, 2024
Revised manuscript accepted for ESSD
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We present a dataset of top of atmosphere diurnally resolved reflected solar and emitted thermal energy for Earth-system model evaluation. The multi-year, monthly hourly dataset, derived from observations made by the Geostationary Earth Radiation Budget instrument, covers the range 60° N–60° S, 60° E–60° W at one degree resolution. Comparison with two versions of the Hadley Centre Global Environmental model highlight how the data can be used to assess updates to key model parameterisations.
Aku Riihelä, Emmihenna Jääskeläinen, and Viivi Kallio-Myers
Earth Syst. Sci. Data, 16, 1007–1028, https://doi.org/10.5194/essd-16-1007-2024, https://doi.org/10.5194/essd-16-1007-2024, 2024
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We describe a new climate data record describing the surface albedo, or reflectivitity, of Earth's surface (called CLARA-A3 SAL). The climate data record spans over 4 decades of satellite observations, beginning in 1979. We conduct a quality assessment of the generated data, comparing them against other satellite data and albedo observations made on the ground. We find that the new data record in general matches surface observations well and is stable through time.
Paola Formenti and Claudia Di Biagio
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-481, https://doi.org/10.5194/essd-2023-481, 2024
Revised manuscript accepted for ESSD
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Particles from deserts and semi-vegetated areas (mineral dust) are important for the Earth climate, and the human health, notably depending on their size. In this paper we collect and made de synthesis of a body of those observations since 1972 in order to provide researchers modelling the Earth climate as well as researchers developing satellite observations from space a simple way of confronting their results and understanding their validity.
Sabrina Schnitt, Andreas Foth, Heike Kalesse-Los, Mario Mech, Claudia Acquistapace, Friedhelm Jansen, Ulrich Löhnert, Bernhard Pospichal, Johannes Röttenbacher, Susanne Crewell, and Bjorn Stevens
Earth Syst. Sci. Data, 16, 681–700, https://doi.org/10.5194/essd-16-681-2024, https://doi.org/10.5194/essd-16-681-2024, 2024
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This publication describes the microwave radiometric measurements performed during the EUREC4A campaign at Barbados Cloud Observatory (BCO) and aboard RV Meteor and RV Maria S Merian. We present retrieved integrated water vapor (IWV), liquid water path (LWP), and temperature and humidity profiles as a unified, quality-controlled, multi-site data set on a 3 s temporal resolution for a core period between 19 January 2020 and 14 February 2020.
Daniela Meloni, Filippo Calì Quaglia, Virginia Ciardini, Annalisa Di Bernardino, Tatiana Di Iorio, Antonio Iaccarino, Giovanni Muscari, Giandomenico Pace, Claudio Scarchilli, and Alcide di Sarra
Earth Syst. Sci. Data, 16, 543–566, https://doi.org/10.5194/essd-16-543-2024, https://doi.org/10.5194/essd-16-543-2024, 2024
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Solar and infrared radiation are key factors in determining Arctic climate. Only a few sites in the Arctic perform long-term measurements of the surface radiation budget (SRB). At the Thule High Arctic Atmospheric Observatory (THAAO, 76.5° N, 68.8° W) in Northern Greenland, solar and infrared irradiance measurements were started in 2009. These data are of paramount importance in studying the impact of the atmospheric (mainly clouds and aerosols) and surface (albedo) parameters on the SRB.
Karoline Block, Mahnoosh Haghighatnasab, Daniel G. Partridge, Philip Stier, and Johannes Quaas
Earth Syst. Sci. Data, 16, 443–470, https://doi.org/10.5194/essd-16-443-2024, https://doi.org/10.5194/essd-16-443-2024, 2024
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Aerosols being able to act as condensation nuclei for cloud droplets (CCNs) are a key element in cloud formation but very difficult to determine. In this study we present a new global vertically resolved CCN dataset for various humidity conditions and aerosols. It is obtained using an atmospheric model (CAMS reanalysis) that is fed by satellite observations of light extinction (AOD). We investigate and evaluate the abundance of CCNs in the atmosphere and their temporal and spatial occurrence.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
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A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Lei Kong, Xiao Tang, Zifa Wang, Jiang Zhu, Jianjun Li, Huangjian Wu, Qizhong Wu, Huansheng Chen, Lili Zhu, Wei Wang, Bing Liu, Qian Wang, Duohong Chen, Yuepeng Pan, Jie Li, Lin Wu, and Gregory R. Carmichael
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-477, https://doi.org/10.5194/essd-2023-477, 2023
Revised manuscript accepted for ESSD
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A new long-term inversed emission inventory for Chinese air quality (CAQIEI) is developed in this study which contains constrained monthly emissions of NOx, SO2, CO, PM2.5, PM10 and NMVOC in China from 2013 to 2020 with a horizontal resolution of 15km. Emissions of different air pollutants and their changes during 2013–2020 were investigated, and compared with previous emission inventories, which sheds new light on the complex variations of the air pollutant emissions in China.
Karina E. Adcock, Penelope A. Pickers, Andrew C. Manning, Grant L. Forster, Leigh S. Fleming, Thomas Barningham, Philip A. Wilson, Elena A. Kozlova, Marica Hewitt, Alex J. Etchells, and Andy J. Macdonald
Earth Syst. Sci. Data, 15, 5183–5206, https://doi.org/10.5194/essd-15-5183-2023, https://doi.org/10.5194/essd-15-5183-2023, 2023
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We present a 12-year time series of continuous atmospheric measurements of O2 and CO2 at the Weybourne Atmospheric Observatory in the United Kingdom. These measurements are combined into the term atmospheric potential oxygen (APO), a tracer that is not influenced by land biosphere processes. The datasets show a long-term increasing trend in CO2 and decreasing trends in O2 and APO between 2010 and 2021.
Nikos Benas, Irina Solodovnik, Martin Stengel, Imke Hüser, Karl-Göran Karlsson, Nina Håkansson, Erik Johansson, Salomon Eliasson, Marc Schröder, Rainer Hollmann, and Jan Fokke Meirink
Earth Syst. Sci. Data, 15, 5153–5170, https://doi.org/10.5194/essd-15-5153-2023, https://doi.org/10.5194/essd-15-5153-2023, 2023
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This paper describes CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI, which was created based on observations from geostationary Meteosat satellites. CLAAS-3 cloud properties are evaluated using a variety of reference datasets, with very good overall results. The demonstrated quality of CLAAS-3 ensures its usefulness in a wide range of applications, including studies of local- to continental-scale cloud processes and evaluation of climate models.
Sandip S. Dhomse and Martyn P. Chipperfield
Earth Syst. Sci. Data, 15, 5105–5120, https://doi.org/10.5194/essd-15-5105-2023, https://doi.org/10.5194/essd-15-5105-2023, 2023
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There are no long-term stratospheric profile data sets for two very important greenhouse gases: methane (CH4) and nitrous oxide (N2O). Along with radiative feedback, these species play an important role in controlling ozone loss in the stratosphere. Here, we use machine learning to fuse satellite measurements with a chemical model to construct long-term gap-free profile data sets for CH4 and N2O. We aim to construct similar data sets for other important trace gases (e.g. O3, Cly, NOy species).
Tobias Erhardt, Camilla Marie Jensen, Florian Adolphi, Helle Astrid Kjær, Remi Dallmayr, Birthe Twarloh, Melanie Behrens, Motohiro Hirabayashi, Kaori Fukuda, Jun Ogata, François Burgay, Federico Scoto, Ilaria Crotti, Azzurra Spagnesi, Niccoló Maffezzoli, Delia Segato, Chiara Paleari, Florian Mekhaldi, Raimund Muscheler, Sophie Darfeuil, and Hubertus Fischer
Earth Syst. Sci. Data, 15, 5079–5091, https://doi.org/10.5194/essd-15-5079-2023, https://doi.org/10.5194/essd-15-5079-2023, 2023
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The presented paper provides a 3.8 kyr long dataset of aerosol concentrations from the East Greenland Ice coring Project (EGRIP) ice core. The data consists of 1 mm depth-resolution profiles of calcium, sodium, ammonium, nitrate, and electrolytic conductivity as well as decadal averages of these profiles. Alongside the data a detailed description of the measurement setup as well as a discussion of the uncertainties are given.
Chaoyang Xue, Gisèle Krysztofiak, Vanessa Brocchi, Stéphane Chevrier, Michel Chartier, Patrick Jacquet, Claude Robert, and Valéry Catoire
Earth Syst. Sci. Data, 15, 4553–4569, https://doi.org/10.5194/essd-15-4553-2023, https://doi.org/10.5194/essd-15-4553-2023, 2023
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To understand tropospheric air pollution at regional and global scales, an infrared laser spectrometer called SPIRIT was used on aircraft to rapidly and accurately measure carbon monoxide (CO), an important indicator of air pollution, during the last decade. Measurements were taken for more than 200 flight hours over three continents. Levels of CO are mapped with 3D trajectories for each flight. Additionally, this can be used to validate model performance and satellite measurements.
Goutam Choudhury and Matthias Tesche
Earth Syst. Sci. Data, 15, 3747–3760, https://doi.org/10.5194/essd-15-3747-2023, https://doi.org/10.5194/essd-15-3747-2023, 2023
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Aerosols in the atmosphere that can form liquid cloud droplets are called cloud condensation nuclei (CCN). Accurate measurements of CCN, especially CCN of anthropogenic origin, are necessary to quantify the effect of anthropogenic aerosols on the present-day as well as future climate. In this paper, we describe a novel global 3D CCN data set calculated from satellite measurements. We also discuss the potential applications of the data in the context of aerosol–cloud interactions.
Xinyan Liu, Tao He, Shunlin Liang, Ruibo Li, Xiongxin Xiao, Rui Ma, and Yichuan Ma
Earth Syst. Sci. Data, 15, 3641–3671, https://doi.org/10.5194/essd-15-3641-2023, https://doi.org/10.5194/essd-15-3641-2023, 2023
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We proposed a data fusion strategy that combines the complementary features of multiple-satellite cloud fraction (CF) datasets and generated a continuous monthly 1° daytime cloud fraction product covering the entire Arctic during the sunlit months in 2000–2020. This study has positive significance for reducing the uncertainties for the assessment of surface radiation fluxes and improving the accuracy of research related to climate change and energy budgets, both regionally and globally.
Yuan Wang, Qiangqiang Yuan, Tongwen Li, Yuanjian Yang, Siqin Zhou, and Liangpei Zhang
Earth Syst. Sci. Data, 15, 3597–3622, https://doi.org/10.5194/essd-15-3597-2023, https://doi.org/10.5194/essd-15-3597-2023, 2023
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We propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless global XCO2 and XCH4 products. Results show that the proposed method achieves a satisfactory accuracy that distinctly exceeds that of CAMS-EGG4 and is superior or close to those of GOSAT and OCO-2. In particular, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission for COVID-19.
Armin Sorooshian, Mikhail D. Alexandrov, Adam D. Bell, Ryan Bennett, Grace Betito, Sharon P. Burton, Megan E. Buzanowicz, Brian Cairns, Eduard V. Chemyakin, Gao Chen, Yonghoon Choi, Brian L. Collister, Anthony L. Cook, Andrea F. Corral, Ewan C. Crosbie, Bastiaan van Diedenhoven, Joshua P. DiGangi, Glenn S. Diskin, Sanja Dmitrovic, Eva-Lou Edwards, Marta A. Fenn, Richard A. Ferrare, David van Gilst, Johnathan W. Hair, David B. Harper, Miguel Ricardo A. Hilario, Chris A. Hostetler, Nathan Jester, Michael Jones, Simon Kirschler, Mary M. Kleb, John M. Kusterer, Sean Leavor, Joseph W. Lee, Hongyu Liu, Kayla McCauley, Richard H. Moore, Joseph Nied, Anthony Notari, John B. Nowak, David Painemal, Kasey E. Phillips, Claire E. Robinson, Amy Jo Scarino, Joseph S. Schlosser, Shane T. Seaman, Chellappan Seethala, Taylor J. Shingler, Michael A. Shook, Kenneth A. Sinclair, William L. Smith Jr., Douglas A. Spangenberg, Snorre A. Stamnes, Kenneth L. Thornhill, Christiane Voigt, Holger Vömel, Andrzej P. Wasilewski, Hailong Wang, Edward L. Winstead, Kira Zeider, Xubin Zeng, Bo Zhang, Luke D. Ziemba, and Paquita Zuidema
Earth Syst. Sci. Data, 15, 3419–3472, https://doi.org/10.5194/essd-15-3419-2023, https://doi.org/10.5194/essd-15-3419-2023, 2023
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The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol–cloud–meteorology interactions. HU-25 Falcon and King Air aircraft conducted systematic and spatially coordinated flights over the northwest Atlantic Ocean. This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes.
Shoma Yamanouchi, Stephanie Conway, Kimberly Strong, Orfeo Colebatch, Erik Lutsch, Sébastien Roche, Jeffrey Taylor, Cynthia H. Whaley, and Aldona Wiacek
Earth Syst. Sci. Data, 15, 3387–3418, https://doi.org/10.5194/essd-15-3387-2023, https://doi.org/10.5194/essd-15-3387-2023, 2023
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Nineteen years of atmospheric composition measurements made at the University of Toronto Atmospheric Observatory (TAO; 43.66° N, 79.40° W; 174 m.a.s.l.) are presented. These are retrieved from Fourier transform infrared (FTIR) solar absorption spectra recorded with a spectrometer from May 2002 to December 2020. The retrievals have been optimized for fourteen species: O3, HCl, HF, HNO3, CH4, C2H6, CO, HCN, N2O, C2H2, H2CO, CH3OH, HCOOH, and NH3.
Michael J. Prather, Hao Guo, and Xin Zhu
Earth Syst. Sci. Data, 15, 3299–3349, https://doi.org/10.5194/essd-15-3299-2023, https://doi.org/10.5194/essd-15-3299-2023, 2023
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The Atmospheric Tomography Mission (ATom) measured the chemical composition in air parcels from 0–12 km altitude on 2 km horizontal by 80 m vertical scales for four seasons, resolving most scales of chemical heterogeneity. ATom is one of the first missions designed to calculate the chemical evolution of each parcel, providing semi-global diurnal budgets for ozone and methane. Observations covered the remote troposphere: Pacific and Atlantic Ocean basins, Southern Ocean, Arctic basin, Antarctica.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
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Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Han Huang and Yi Huang
Earth Syst. Sci. Data, 15, 3001–3021, https://doi.org/10.5194/essd-15-3001-2023, https://doi.org/10.5194/essd-15-3001-2023, 2023
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We present a newly generated set of ERA5-based radiative kernels and compare them with other published kernels for the top of the atmosphere and surface radiation budgets. For both, the discrepancies in sensitivity values are generally of small magnitude, except for temperature kernels for the surface, likely due to improper treatment in the perturbation experiments used for kernel computation. The kernel bias is not a major cause of the inter-GCM (general circulation model) feedback spread.
Robert Pincus, Paul A. Hubanks, Steven Platnick, Kerry Meyer, Robert E. Holz, Denis Botambekov, and Casey J. Wall
Earth Syst. Sci. Data, 15, 2483–2497, https://doi.org/10.5194/essd-15-2483-2023, https://doi.org/10.5194/essd-15-2483-2023, 2023
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This paper describes a new global dataset of cloud properties observed by a specific satellite program created to facilitate comparison with a matching observational proxy used in climate models. Statistics are accumulated over daily and monthly timescales on an equal-angle grid. Statistics include cloud detection, cloud-top pressure, and cloud optical properties. Joint histograms of several variable pairs are also available.
Longfei Bing, Mingjing Ma, Lili Liu, Jiaoyue Wang, Le Niu, and Fengming Xi
Earth Syst. Sci. Data, 15, 2431–2444, https://doi.org/10.5194/essd-15-2431-2023, https://doi.org/10.5194/essd-15-2431-2023, 2023
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We provided CO2 uptake inventory for global lime materials from 1930–2020, The majority of CO2 uptake was from the lime in China.
Our dataset and the accounting mathematical model may serve as a set of tools to improve the CO2 emission inventories and provide data support for policymakers to formulate scientific and reasonable policies under
carbon neutraltarget.
Emma L. Yates, Laura T. Iraci, Susan S. Kulawik, Ju-Mee Ryoo, Josette E. Marrero, Caroline L. Parworth, Jason M. St. Clair, Thomas F. Hanisco, Thao Paul V. Bui, Cecilia S. Chang, and Jonathan M. Dean-Day
Earth Syst. Sci. Data, 15, 2375–2389, https://doi.org/10.5194/essd-15-2375-2023, https://doi.org/10.5194/essd-15-2375-2023, 2023
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The Alpha Jet Atmospheric eXperiment (AJAX) flew scientific flights between 2011 and 2018 providing measurements of carbon dioxide, methane, ozone, formaldehyde, water vapor and meteorological parameters over California and Nevada, USA. AJAX was a multi-year, multi-objective, multi-instrument program with a variety of sampling strategies resulting in an extensive dataset of interest to a wide variety of users. AJAX measurements have been published at https://asdc.larc.nasa.gov/project/AJAX.
Leïla Simon, Valérie Gros, Jean-Eudes Petit, François Truong, Roland Sarda-Estève, Carmen Kalalian, Alexia Baudic, Caroline Marchand, and Olivier Favez
Earth Syst. Sci. Data, 15, 1947–1968, https://doi.org/10.5194/essd-15-1947-2023, https://doi.org/10.5194/essd-15-1947-2023, 2023
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Long-term measurements of volatile organic compounds (VOCs) have been set up to better characterize the atmospheric chemistry at the SIRTA national facility (Paris area, France). Results obtained from the first 2 years (2020–2021) confirm the importance of local sources for short-lived compounds and the role played by meteorology and air mass origins in the long-term analysis of VOCs. They also point to a substantial influence of anthropogenic on the monoterpene loadings.
Ka Lok Chan, Pieter Valks, Klaus-Peter Heue, Ronny Lutz, Pascal Hedelt, Diego Loyola, Gaia Pinardi, Michel Van Roozendael, François Hendrick, Thomas Wagner, Vinod Kumar, Alkis Bais, Ankie Piters, Hitoshi Irie, Hisahiro Takashima, Yugo Kanaya, Yongjoo Choi, Kihong Park, Jihyo Chong, Alexander Cede, Udo Frieß, Andreas Richter, Jianzhong Ma, Nuria Benavent, Robert Holla, Oleg Postylyakov, Claudia Rivera Cárdenas, and Mark Wenig
Earth Syst. Sci. Data, 15, 1831–1870, https://doi.org/10.5194/essd-15-1831-2023, https://doi.org/10.5194/essd-15-1831-2023, 2023
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This paper presents the theoretical basis as well as verification and validation of the Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products.
Kristina Collins, John Gibbons, Nathaniel Frissell, Aidan Montare, David Kazdan, Darren Kalmbach, David Swartz, Robert Benedict, Veronica Romanek, Rachel Boedicker, William Liles, William Engelke, David G. McGaw, James Farmer, Gary Mikitin, Joseph Hobart, George Kavanagh, and Shibaji Chakraborty
Earth Syst. Sci. Data, 15, 1403–1418, https://doi.org/10.5194/essd-15-1403-2023, https://doi.org/10.5194/essd-15-1403-2023, 2023
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This paper summarizes radio data collected by citizen scientists, which can be used to analyze the charged part of Earth's upper atmosphere. The data are collected from several independent stations. We show ways to look at the data from one station or multiple stations over different periods of time and how it can be combined with data from other sources as well. The code provided to make these visualizations will still work if some data are missing or when more data are added in the future.
Adriana Bailey, Franziska Aemisegger, Leonie Villiger, Sebastian A. Los, Gilles Reverdin, Estefanía Quiñones Meléndez, Claudia Acquistapace, Dariusz B. Baranowski, Tobias Böck, Sandrine Bony, Tobias Bordsdorff, Derek Coffman, Simon P. de Szoeke, Christopher J. Diekmann, Marina Dütsch, Benjamin Ertl, Joseph Galewsky, Dean Henze, Przemyslaw Makuch, David Noone, Patricia K. Quinn, Michael Rösch, Andreas Schneider, Matthias Schneider, Sabrina Speich, Bjorn Stevens, and Elizabeth J. Thompson
Earth Syst. Sci. Data, 15, 465–495, https://doi.org/10.5194/essd-15-465-2023, https://doi.org/10.5194/essd-15-465-2023, 2023
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One of the novel ways EUREC4A set out to investigate trade wind clouds and their coupling to the large-scale circulation was through an extensive network of isotopic measurements in water vapor, precipitation, and seawater. Samples were taken from the island of Barbados, from aboard two aircraft, and from aboard four ships. This paper describes the full collection of EUREC4A isotopic in situ data and guides readers to complementary remotely sensed water vapor isotope ratios.
Melisa Diaz Resquin, Pablo Lichtig, Diego Alessandrello, Marcelo De Oto, Darío Gómez, Cristina Rössler, Paula Castesana, and Laura Dawidowski
Earth Syst. Sci. Data, 15, 189–209, https://doi.org/10.5194/essd-15-189-2023, https://doi.org/10.5194/essd-15-189-2023, 2023
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We explored the performance of the random forest algorithm to predict CO, NOx, PM10, SO2, and O3 air quality concentrations and comparatively assessed the monitored and modeled concentrations during the COVID-19 lockdown phases. We provide the first long-term O3 and SO2 observational dataset for an urban–residential area of Buenos Aires in more than a decade and study the responses of O3 to the reduction in the emissions of its precursors because of its relevance regarding emission control.
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Short summary
Developed countries are required to report detailed greenhouse gas emissions data to the UN on an annual basis. The reporting tables are complex, do not fit well with existing hierarchical reporting guidelines, and are not machine-readable. We present a processed version of the reported data in a consistent hierarchy, and in a format that is machine-readable and easy-to-use. The emissions data are also aggregated into
basketsof gases using global warming equivalency metrics from IPCC reports.
Developed countries are required to report detailed greenhouse gas emissions data to the UN on...
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