Articles | Volume 16, issue 2
https://doi.org/10.5194/essd-16-1007-2024
© Author(s) 2024. 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-16-1007-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Four decades of global surface albedo estimates in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) climate data record
Meteorological Research, Finnish Meteorological Institute, Helsinki, 00560, Finland
Emmihenna Jääskeläinen
Meteorological Research, Finnish Meteorological Institute, Helsinki, 00560, Finland
Viivi Kallio-Myers
Meteorological Research, Finnish Meteorological Institute, Helsinki, 00560, Finland
Related authors
Emmihenna Jääskeläinen, Kerttu Kouki, and Aku Riihelä
Hydrol. Earth Syst. Sci., 28, 3855–3870, https://doi.org/10.5194/hess-28-3855-2024, https://doi.org/10.5194/hess-28-3855-2024, 2024
Short summary
Short summary
Snow cover is an important variable when studying the effect of climate change in the Arctic. Therefore, the correct detection of snowfall is important. In this study, we present methods to detect snowfall accurately using satellite observations. The snowfall event detection results of our limited area are encouraging. We find that further development could enable application over the whole Arctic, providing necessary information on precipitation occurrence over remote areas.
Adriano Lemos and Aku Riihelä
EGUsphere, https://doi.org/10.5194/egusphere-2024-869, https://doi.org/10.5194/egusphere-2024-869, 2024
Short summary
Short summary
Here we used satellite imagery to measure snow depth in northern Finland and compared to on-site weather stations from 2019–2022. We correlated snow depths and vegetation coverage, and found thicker snow over non-vegetated areas and frozen water bodies due to the satellite's sensitivity. Our estimates showed underestimated results of snow depth and need further investigation, but they highlight the potential in monitoring seasonal snow changes, particularly where direct measurements are lacking.
Kerttu Kouki, Kari Luojus, and Aku Riihelä
The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, https://doi.org/10.5194/tc-17-5007-2023, 2023
Short summary
Short summary
We evaluated snow cover properties in state-of-the-art reanalyses (ERA5 and ERA5-Land) with satellite-based datasets. Both ERA5 and ERA5-Land overestimate snow mass, whereas albedo estimates are more consistent between the datasets. Snow cover extent (SCE) is accurately described in ERA5-Land, while ERA5 shows larger SCE than the satellite-based datasets. The trends in snow mass, SCE, and albedo are mostly negative in 1982–2018, and the negative trends become more apparent when spring advances.
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.
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
Short summary
Short summary
We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
Terhikki Manninen, Emmihenna Jääskeläinen, Niilo Siljamo, Aku Riihelä, and Karl-Göran Karlsson
Atmos. Meas. Tech., 15, 879–893, https://doi.org/10.5194/amt-15-879-2022, https://doi.org/10.5194/amt-15-879-2022, 2022
Short summary
Short summary
A new method for cloud-correcting observations of surface albedo is presented for AVHRR data. Instead of a binary cloud mask, it applies cloud probability values smaller than 20% of the A3 edition of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record provided by the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT. According to simulations, the 90% quantile was 1.1% for the absolute albedo error and 2.2% for the relative error.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
Short summary
Short summary
The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
Aku Riihelä, Michalea D. King, and Kati Anttila
The Cryosphere, 13, 2597–2614, https://doi.org/10.5194/tc-13-2597-2019, https://doi.org/10.5194/tc-13-2597-2019, 2019
Short summary
Short summary
We used a 1982–2015 time series of satellite observations to examine changes in surface reflectivity (albedo) of the Greenland Ice Sheet. We found notable decreases in albedo over most of the ice sheet margins in July and August, particularly over the west coast and between 2000 and 2015. The results indicate that significant melt now occurs in areas 50 to 100 m higher up the ice sheet relative to the early 1980s. The albedo decrease is consistent and covarying with modelled ice sheet mass loss.
P. Räisänen, A. Luomaranta, H. Järvinen, M. Takala, K. Jylhä, O. N. Bulygina, K. Luojus, A. Riihelä, A. Laaksonen, J. Koskinen, and J. Pulliainen
Geosci. Model Dev., 7, 3037–3057, https://doi.org/10.5194/gmd-7-3037-2014, https://doi.org/10.5194/gmd-7-3037-2014, 2014
Short summary
Short summary
Snowmelt influences greatly the climatic conditions in spring. This study evaluates the timing of springtime end of snowmelt in the ECHAM5 model. A key finding is that, in much of northern Eurasia, snow disappears too early in ECHAM5, in spite of a slight cold bias in spring. This points to the need for a more comprehensive treatment of the surface energy budget. In particular, the surface temperature for the snow-covered and snow-free parts of a climate model grid cell should be separated.
K.-G. Karlsson, A. Riihelä, R. Müller, J. F. Meirink, J. Sedlar, M. Stengel, M. Lockhoff, J. Trentmann, F. Kaspar, R. Hollmann, and E. Wolters
Atmos. Chem. Phys., 13, 5351–5367, https://doi.org/10.5194/acp-13-5351-2013, https://doi.org/10.5194/acp-13-5351-2013, 2013
A. Riihelä, T. Manninen, V. Laine, K. Andersson, and F. Kaspar
Atmos. Chem. Phys., 13, 3743–3762, https://doi.org/10.5194/acp-13-3743-2013, https://doi.org/10.5194/acp-13-3743-2013, 2013
Emmihenna Jääskeläinen, Kerttu Kouki, and Aku Riihelä
Hydrol. Earth Syst. Sci., 28, 3855–3870, https://doi.org/10.5194/hess-28-3855-2024, https://doi.org/10.5194/hess-28-3855-2024, 2024
Short summary
Short summary
Snow cover is an important variable when studying the effect of climate change in the Arctic. Therefore, the correct detection of snowfall is important. In this study, we present methods to detect snowfall accurately using satellite observations. The snowfall event detection results of our limited area are encouraging. We find that further development could enable application over the whole Arctic, providing necessary information on precipitation occurrence over remote areas.
Adriano Lemos and Aku Riihelä
EGUsphere, https://doi.org/10.5194/egusphere-2024-869, https://doi.org/10.5194/egusphere-2024-869, 2024
Short summary
Short summary
Here we used satellite imagery to measure snow depth in northern Finland and compared to on-site weather stations from 2019–2022. We correlated snow depths and vegetation coverage, and found thicker snow over non-vegetated areas and frozen water bodies due to the satellite's sensitivity. Our estimates showed underestimated results of snow depth and need further investigation, but they highlight the potential in monitoring seasonal snow changes, particularly where direct measurements are lacking.
Yurii Batrak, Bin Cheng, and Viivi Kallio-Myers
The Cryosphere, 18, 1157–1183, https://doi.org/10.5194/tc-18-1157-2024, https://doi.org/10.5194/tc-18-1157-2024, 2024
Short summary
Short summary
Atmospheric reanalyses provide consistent series of atmospheric and surface parameters in a convenient gridded form. In this paper, we study the quality of sea ice in a recently released regional reanalysis and assess its added value compared to a global reanalysis. We show that the regional reanalysis, having a more complex sea ice model, gives an improved representation of sea ice, although there are limitations indicating potential benefits in using more advanced approaches in the future.
Kerttu Kouki, Kari Luojus, and Aku Riihelä
The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, https://doi.org/10.5194/tc-17-5007-2023, 2023
Short summary
Short summary
We evaluated snow cover properties in state-of-the-art reanalyses (ERA5 and ERA5-Land) with satellite-based datasets. Both ERA5 and ERA5-Land overestimate snow mass, whereas albedo estimates are more consistent between the datasets. Snow cover extent (SCE) is accurately described in ERA5-Land, while ERA5 shows larger SCE than the satellite-based datasets. The trends in snow mass, SCE, and albedo are mostly negative in 1982–2018, and the negative trends become more apparent when spring advances.
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.
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
Short summary
Short summary
We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
Terhikki Manninen, Emmihenna Jääskeläinen, Niilo Siljamo, Aku Riihelä, and Karl-Göran Karlsson
Atmos. Meas. Tech., 15, 879–893, https://doi.org/10.5194/amt-15-879-2022, https://doi.org/10.5194/amt-15-879-2022, 2022
Short summary
Short summary
A new method for cloud-correcting observations of surface albedo is presented for AVHRR data. Instead of a binary cloud mask, it applies cloud probability values smaller than 20% of the A3 edition of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record provided by the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT. According to simulations, the 90% quantile was 1.1% for the absolute albedo error and 2.2% for the relative error.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
Short summary
Short summary
The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
Aku Riihelä, Michalea D. King, and Kati Anttila
The Cryosphere, 13, 2597–2614, https://doi.org/10.5194/tc-13-2597-2019, https://doi.org/10.5194/tc-13-2597-2019, 2019
Short summary
Short summary
We used a 1982–2015 time series of satellite observations to examine changes in surface reflectivity (albedo) of the Greenland Ice Sheet. We found notable decreases in albedo over most of the ice sheet margins in July and August, particularly over the west coast and between 2000 and 2015. The results indicate that significant melt now occurs in areas 50 to 100 m higher up the ice sheet relative to the early 1980s. The albedo decrease is consistent and covarying with modelled ice sheet mass loss.
Karl-Göran Karlsson, Kati Anttila, Jörg Trentmann, Martin Stengel, Jan Fokke Meirink, Abhay Devasthale, Timo Hanschmann, Steffen Kothe, Emmihenna Jääskeläinen, Joseph Sedlar, Nikos Benas, Gerd-Jan van Zadelhoff, Cornelia Schlundt, Diana Stein, Stefan Finkensieper, Nina Håkansson, and Rainer Hollmann
Atmos. Chem. Phys., 17, 5809–5828, https://doi.org/10.5194/acp-17-5809-2017, https://doi.org/10.5194/acp-17-5809-2017, 2017
Short summary
Short summary
The paper presents the second version of a global climate data record based on satellite measurements from polar orbiting weather satellites. It describes the global evolution of cloudiness, surface albedo and surface radiation during the time period 1982–2015. The main improvements of algorithms are described together with some validation results. In addition, some early analysis is presented of some particularly interesting climate features (Arctic albedo and cloudiness + global cloudiness).
Emmihenna Jääskeläinen, Terhikki Manninen, Johanna Tamminen, and Marko Laine
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-180, https://doi.org/10.5194/amt-2016-180, 2016
Revised manuscript not accepted
P. Räisänen, A. Luomaranta, H. Järvinen, M. Takala, K. Jylhä, O. N. Bulygina, K. Luojus, A. Riihelä, A. Laaksonen, J. Koskinen, and J. Pulliainen
Geosci. Model Dev., 7, 3037–3057, https://doi.org/10.5194/gmd-7-3037-2014, https://doi.org/10.5194/gmd-7-3037-2014, 2014
Short summary
Short summary
Snowmelt influences greatly the climatic conditions in spring. This study evaluates the timing of springtime end of snowmelt in the ECHAM5 model. A key finding is that, in much of northern Eurasia, snow disappears too early in ECHAM5, in spite of a slight cold bias in spring. This points to the need for a more comprehensive treatment of the surface energy budget. In particular, the surface temperature for the snow-covered and snow-free parts of a climate model grid cell should be separated.
K.-G. Karlsson, A. Riihelä, R. Müller, J. F. Meirink, J. Sedlar, M. Stengel, M. Lockhoff, J. Trentmann, F. Kaspar, R. Hollmann, and E. Wolters
Atmos. Chem. Phys., 13, 5351–5367, https://doi.org/10.5194/acp-13-5351-2013, https://doi.org/10.5194/acp-13-5351-2013, 2013
A. Riihelä, T. Manninen, V. Laine, K. Andersson, and F. Kaspar
Atmos. Chem. Phys., 13, 3743–3762, https://doi.org/10.5194/acp-13-3743-2013, https://doi.org/10.5194/acp-13-3743-2013, 2013
Related subject area
Domain: ESSD – Global | Subject: Atmospheric chemistry and physics
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
An investigation of the global uptake of CO2 by lime from 1930 to 2020
Isotopic measurements in water vapor, precipitation, and seawater during EUREC4A
Global Carbon Budget 2022
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Cited articles
Anttila, K., Manninen, T., Jääskeläinen, E., Riihelä, A., and Lahtinen, P.: The role of climate and land use in the changes in surface albedo prior to snow melt and the timing of melt season of seasonal snow in northern land areas of 40 N–80 N during 1982–2015, Remote Sens., 10, 1619, https://doi.org/10.3390/rs10101619, 2018.
Aoki, S.: Breakup of land-fast sea ice in Lützow-Holm Bay, East Antarctica, and its teleconnection to tropical Pacific sea surface temperatures, Geophys. Res. Lett., 44, 3219–3227, 2017.
Beringer, J., Chapin III, F. S., Thompson, C. C., and McGuire, A. D.: Surface energy exchanges along a tundra-forest transition and feedbacks to climate, Agr. Forest Meteorol., 131, 143–161, 2005.
Briegleb, B. P., Minnis, P., Ramanathan, V., and Harrison, E.: Comparison of regional clear-sky albedos inferred from satellite observations and model computations, J. Appl. Meteorol. Clim., 25, 214–226, 1986.
Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R., and Tait, A. M.: Dynamic World, Near real-time global 10 m land use land cover mapping, Sci. Data, 9, 251, https://doi.org/10.1038/s41597-022-01307-4, 2022.
Budyko, M. I.: The effect of solar radiation variations on the climate of the Earth, Tellus, 21, 611–619, 1969.
Cao, Y., Liang, S., Chen, X., and He, T.: Assessment of sea ice albedo radiative forcing and feedback over the Northern Hemisphere from 1982 to 2009 using satellite and reanalysis data, J. Climate, 28, 1248–1259, 2015.
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, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. -J., Park, B. -K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. -N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011.
Dech, S., Holzwarth, S., Asam, S.,Andresen, T., Bachmann, M., Boettcher, M., Dietz, A., Eisfelder, C., Frey, C., Gesell, G., Gessner, U., Hirner, A., Hofmann, M., Kirches, G., Klein, D., Klein, I., Kraus, T., Krause, D., Plank, S., Popp, T., Reinermann, S., Reiners, P., Roessler, S., Ruppert, T., Scherbachenko, A., Vignesh, R., Wolfmueller, M., Zwenzner, H., and Kuenzer, C.: Potential and Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience, Remote Sens., 13, 3618, https://doi.org/10.3390/rs13183618, 2021.
Driemel, A., Augustine, J., Behrens, K., Colle, S., Cox, C., Cuevas-Agulló, E., Denn, F. M., Duprat, T., Fukuda, M., Grobe, H., Haeffelin, M., Hodges, G., Hyett, N., Ijima, O., Kallis, A., Knap, W., Kustov, V., Long, C. N., Longenecker, D., Lupi, A., Maturilli, M., Mimouni, M., Ntsangwane, L., Ogihara, H., Olano, X., Olefs, M., Omori, M., Passamani, L., Pereira, E. B., Schmithüsen, H., Schumacher, S., Sieger, R., Tamlyn, J., Vogt, R., Vuilleumier, L., Xia, X., Ohmura, A., and König-Langlo, G.: Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017), Earth Syst. Sci. Data, 10, 1491–1501, https://doi.org/10.5194/essd-10-1491-2018, 2018.
Fausto, R. S., van As, D., Mankoff, K. D., Vandecrux, B., Citterio, M., Ahlstrøm, A. P., Andersen, S. B., Colgan, W., Karlsson, N. B., Kjeldsen, K. K., Korsgaard, N. J., Larsen, S. H., Nielsen, S., Pedersen, A. Ø., Shields, C. L., Solgaard, A. M., and Box, J. E.: Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station data, Earth Syst. Sci. Data, 13, 3819–3845, https://doi.org/10.5194/essd-13-3819-2021, 2021.
Guo, H., Wang, X., Wang, T., Ma, Y., Ryder, J., Zhang, T., Liu, D., Ding, J., Li, Y., and Piao, S.: Spring snow-albedo feedback analysis over the Third Pole: results from satellite observation and CMIP5 model simulations, J. Geophys. Res.-Atmos., 123, 750–763, 2018.
He, T., Liang, S., and Song, D. X.: Analysis of global land surface albedo climatology and spatial-temporal variation during 1981–2010 from multiple satellite products, J. Geophys. Res.-Atmos., 119, 10–281, 2014.
Heidinger, A. K., Straka III, W. C., Molling, C. C., Sullivan, J. T., and Wu, X.: Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record, Int. J. Remote Sens., 31, 6493–6517, 2010.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020.
Hofmann, M. and Seckmeyer, G.: A New Model for Estimating the Diffuse Fraction of Solar Irradiance for Photovoltaic System Simulations, Energies, 10, 248, https://doi.org/10.3390/en10020248, 2017.
Jääskeläinen, E., Manninen, T., Tamminen, J., and Laine, M.: The Aerosol Index and Land Cover Class Based Atmospheric Correction Aerosol Optical Depth Time Series 1982–2014 for the SMAC Algorithm, Remote Sens., 9, 1095, https://doi.org/10.3390/rs9111095, 2017.
Jääskeläinen, E., Manninen, T., Hakkarainen, J., and Tamminen, J.: Filling gaps of black-sky surface albedo of the Arctic sea ice using gradient boosting and brightness temperature data, Int. J. Appl. Earth Obs., 107, 102701, https://doi.org/10.1016/j.jag.2022.102701, 2022.
Jiao, Z., Ding, A., Kokhanovsky, A., Schaaf, C., Bréon, F.-M., Dong, Y., Wang, Z., Liu, Y., Zhang, X., Yin, S., Cui, L., Mei, L., and Chang, Y.: Development of a snow kernel to better model the anisotropic reflectance of pure snow in a kernel-driven BRDF model framework, Remote Sens. Environ., 221, 198–209, 2019.
Jin, Z., Qiao, Y., Wang, Y., Fang, Y., and Yi, W.: A new parameterization of spectral and broadband ocean surface albedo, Opt. Express, 19, 26429–26443, 2011.
Karlsson, J. and Svensson, G.: Consequences of poor representation of Arctic sea-ice albedo and cloud-radiation interactions in the CMIP5 model ensemble, Geophys. Res. Lett., 40, 4374–4379, 2013.
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Fokke Meirink, J., Devasthale, A., Hanschmann, T., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Schlundt, C., Stein, D., Finkensieper, S., Håkansson, N., and Hollmann, R.: CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Atmos. Chem. Phys., 17, 5809–5828, https://doi.org/10.5194/acp-17-5809-2017, 2017.
Karlsson, K. G., Johansson, E., Håkansson, N., Sedlar, J., and Eliasson, S.: Probabilistic Cloud Masking for the Generation of CM SAF Cloud Climate Data Records from AVHRR and SEVIRI Sensors, Remote Sens., 12, 713, https://doi.org/10.3390/rs12040713, 2020.
Karlsson, K.-G., Stengel, M., Meirink, J. F., Riihelä, A., Trentmann, J., Akkermans, T., Stein, D., Devasthale, A., Eliasson, S., Johansson, E., Håkansson, N., Solodovnik, I., Benas, N., Clerbaux, N., Selbach, N., Schröder, M., and Hollmann, R.: 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, Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, 2023a.
Karlsson, K.-G., Riihelä, A., Trentmann, J., Stengel, M., Solodovnik, I., Meirink, J. F., Devasthale, A., Jääskeläinen, E., Kallio-Myers, V., Eliasson, S., Benas, N., Johansson, E., Stein, D., Finkensieper, S., Håkansson, N., Akkermans, T., Clerbaux, N., Selbach, N., Schröder, M., and Hollmann, R.: CLARA-A3: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data – Edition 3, Satellite Application Facility on Climate Monitoring [data set], https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003, 2023b.
Kashiwase, H., Ohshima, K. I., Nihashi, S., and Eicken, H.: Evidence for ice-ocean albedo feedback in the Arctic Ocean shifting to a seasonal ice zone, Sci. Rep., 7, 8170, https://doi.org/0.1038/s41598-017-08467-z, 2017.
Key, J. R., Wang, X., Stoeve, J. C., and Fowler, C.: Estimating the cloudy-sky albedo of sea ice and snow from space, J. Geophys. Res.-Atmos., 106, 12489–12497, 2001.
Kleipool, Q., Rozemeijer, N., van Hoek, M., Leloux, J., Loots, E., Ludewig, A., van der Plas, E., Adrichem, D., Harel, R., Spronk, S., ter Linden, M., Jaross, G., Haffner, D., Veefkind, P., and Levelt, P. F.: Ozone Monitoring Instrument (OMI) collection 4: establishing a 17-year-long series of detrended level-1b data, Atmos. Meas. Tech., 15, 3527–3553, https://doi.org/10.5194/amt-15-3527-2022, 2022.
Kokhanovsky, A. A.: The Broadband Albedo of Snow, Front. Environ. Sci., 9,. https://doi.org/10.3389/fenvs.2021.757575, 2021.
Kokhanovsky, A., Lamare, M., Danne, O., Brockmann, C., Dumont, M., Picard, G., Arnaud, L., Favier, V., Jourdain, B., Le Meur, E., Di Mauro, B., Aoki, T., Niwano, M., Rozanov, V., Korkin, S., Kipfstuhl, S., Freitag, J., Hoerhold, M., Zuhr, A., Vladimirova, D., Faber, A.-K., Steen-Larsen, H. C., Wahl, S., Andersen, J. K., Vandecrux, B., van As, D., Mankoff, K. D., Kern, M., Zege, E., and Box, J. E.: Retrieval of snow properties from the Sentinel-3 Ocean and Land Colour Instrument, Remote Sens., 11, 2280, https://doi.org/10.3390/rs11192280, 2019.
Konzelmann, T. and Ohmura, A.: Radiative fluxes and their impact on the energy balance of the Greenland ice sheet, J. Glaciol., 41, 490–502, 1995.
Kouki, K., Anttila, K., Manninen, T., Luojus, K., Wang, L., and Riihelä, A.: Intercomparison of snow melt onset date estimates from optical and microwave satellite instruments over the northern hemisphere for the period 1982–2015, J. Geophys. Res.-Atmos., 124, 11205–11219, 2019.
Liang, S.: Narrowband to broadband conversions of land surface albedo I: Algorithms, Remote Sens. Environ., 76, 213–238, 2001.
Light, B., Dickinson, S., Perovich, D. K., and Holland, M. M.: Evolution of summer Arctic sea ice albedo in CCSM4 simulations: Episodic summer snowfall and frozen summers, J. Geophys. Res.-Oceans, 120, 284–303, 2015.
Liu, Y., Wang, Z., Sun, Q., Erb, A. M., Li, Z., Schaaf, C. B., Zhang, X., Román, M. O., Scott, R. L., Zhang, Q., Novick, K. A., Syndonia Bret-Harte, M., Petroy, S., and SanClements, M.: Evaluation of the VIIRS BRDF, Albedo and NBAR products suite and an assessment of continuity with the long term MODIS record, Remote Sens. Environ., 201, 256–274, 2017.
Lucht, W., Schaaf, C. B., and Strahler, A. H.: An algorithm for the retrieval of albedo from space using semiempirical BRDF models, IEEE T. Geosci. Remote, 38, 977–998, https://doi.org/10.1109/36.841980, 2000.
Malinka, A., Zege, E., Heygster, G., and Istomina, L.: Reflective properties of white sea ice and snow, The Cryosphere, 10, 2541–2557, https://doi.org/10.5194/tc-10-2541-2016, 2016.
Manninen, T., Andersson, K., and Riihelä, A.: Topography correction of the CM-SAF surface albedo product SAL, in: EUMETSAT Meteorological Satellite Conference Proceedings, Oslo, Norway, 5–9 September 2011, Poster #37, EUM P. 59, ISBN 978-92-9110-093-4, ISSN 1011-3932, Meteorological Conferences, EUMETSAT, 2011.
Manninen, T., Jääskeläinen, E., and Riihelä, A.: Black and white-sky albedo values of snow: In situ relationships for AVHRR-based estimation using CLARA-A2 SAL, Can. J. Remote Sens., 45, 350–367, 2019.
Manninen, T., Jääskeläinen, E., Siljamo, N., Riihelä, A., and Karlsson, K.-G.: Cloud-probability-based estimation of black-sky surface albedo from AVHRR data, Atmos. Meas. Tech., 15, 879–893, https://doi.org/10.5194/amt-15-879-2022, 2022.
Nakamura, K., Aoki, S., Yamanokuchi, T., and Tamura, T.: Interactive movements of outlet glacier tongue and landfast sea ice in Lützow-Holm Bay, East Antarctica, detected by ALOS-2/PALSAR-2 imagery, Science of Remote Sensing, 6, 100064, https://doi.org/10.1016/j.srs.2022.100064, 2022.
Perovich, D. K., Grenfell, T. C., Light, B., and Hobbs, P. V.: Seasonal evolution of the albedo of multiyear Arctic sea ice, J. Geophys. Res.-Oceans, 107, SHE-20, https://doi.org/10.1029/2000JC000438, 2002.
Pinty, B., Lattanzio, A., Martonchik, J. V., Verstraete, M. M., Gobron, N., Taberner, M., Widlowski, J.-L., Dickinson, R. E., and Govaerts, Y.: Coupling Diffuse Sky Radiation and Surface Albedo, J. Atmos. Sci., 62, 2580–2591, 2005.
Pohl, C., Istomina, L., Tietsche, S., Jäkel, E., Stapf, J., Spreen, G., and Heygster, G.: Broadband albedo of Arctic sea ice from MERIS optical data, The Cryosphere, 14, 165–182, https://doi.org/10.5194/tc-14-165-2020, 2020.
Polvani, L. M., Banerjee, A., and Schmidt, A.: Northern Hemisphere continental winter warming following the 1991 Mt. Pinatubo eruption: reconciling models and observations, Atmos. Chem. Phys., 19, 6351–6366, https://doi.org/10.5194/acp-19-6351-2019, 2019.
Rahman, H. and Dedieu, G.: SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum, Remote Sens., 15, 123–143, 1994.
Riihelä, A., Manninen, T., Laine, V., Andersson, K., and Kaspar, F.: CLARA-SAL: a global 28 yr timeseries of Earth's black-sky surface albedo, Atmos. Chem. Phys., 13, 3743–3762, https://doi.org/10.5194/acp-13-3743-2013, 2013.
Román, M. O., Schaaf, C. B., Lewis, P., Gao, F., Anderson, G. P., Privette, J. L., Strahler, A. H., Woodcock, C. E., and Barnsley, M.: Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes, Remote Sens. Environ., 114, 738–760, 2010.
Roujean, J. L., Leroy, M., and Deschamps, P. Y.: A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data, J. Geophys. Res.-Atmos., 97, 20455–20468, 1992.
Ryan, J. C., Hubbard, A., Box, J. E., Brough, S., Cameron, K., Cook, J. M., Cooper, M., Doyle, S. H., Edwards, A., Holt, T., Irvine-Fynn, T., Jones, C., Pitcher, L. H., Rennermalm, A. K., Smith, L. C., Stibal, M., and Snooke, N.: Derivation of high spatial resolution albedo from UAV digital imagery: application over the Greenland Ice Sheet, Front. Earth Sci., 5, 40, https://doi.org/10.3389/feart.2017.00040, 2017.
Sánchez-Zapero, J., Martínez-Sánchez, E., Camacho, F., Wang, Z., Carrer, D., Schaaf, C., ... & Cosh, M.: Surface ALbedo VALidation (SALVAL) Platform: Towards CEOS LPV Validation Stage 4–Application to Three Global Albedo Climate Data Records, Remote Sens., 15, 1081, https://doi.org/10.3390/rs15041081, 2023.
Schaaf, C. and Wang, Z.: MODIS/Terra+Aqua BRDF/Albedo Black Sky Albedo Shortwave Daily L3 Global 30ArcSec CMG V061, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43D51.061, 2021.
Schaepman-Strub, G., Schaepman, M. E., Painter, T. H., Dangel, S., and Martonchik, J. V.: Reflectance quantities in optical remote sensing – definitions and case studies, Remote Sens. Environ., 103, 27–42, 2006.
Sellers, W. D.: A global climatic model based on the energy balance of the earth-atmosphere system, J. Appl. Meteorol. Clim., 8, 392–400, 1969.
Smith, M. M., Light, B., Macfarlane, A. R., Perovich, D. K., Holland, M. M., and Shupe, M. D.: Sensitivity of the Arctic sea ice cover to the summer surface scattering layer, Geophys. Res. Lett., 49, e2022GL098349, https://doi.org/10.1029/2022GL098349, 2022.
Thackeray, C. W. and Hall, A.: An emergent constraint on future Arctic sea-ice albedo feedback, Nat. Clim. Change, 9, 972–978, 2019.
Tian, L., Zhang, Y., and Zhu, J.: Decreased surface albedo driven by denser vegetation on the Tibetan Plateau, Environ. Res. Lett., 9, 104001, https://doi.org/10.1088/1748-9326/9/10/104001, 2014.
Tokinaga, H. and National Center for Atmospheric Research Staff (Eds.): The Climate Data Guide: WASWind: Wave and Anemometer-based Sea Surface Wind, https://climatedataguide.ucar.edu/climate-data/waswind-wave-and-anemometer-based-sea-surface-wind (last access: 15 February 2024), 2022.
Tomasi, C., Lupi, A., Mazzola, M., Stone, R. S., Dutton, E. G., Herber, A., Radionov, V. F., Holben, B. N., Sorokin, M. G., Sakerin, S. M., Terpugova, S. A., Sobolewski, P. S., Lanconelli, C., Petkov, B. H., Busetto, M., and Vitale, V.: An update on polar aerosol optical properties using POLAR-AOD and other measurements performed during the International Polar Year, Atmos. Environ., 52, 29–47, 2012.
Trlica, A., Hutyra, L. R., Schaaf, C. L., Erb, A., and Wang, J. A.: Albedo, land cover, and daytime surface temperature variation across an urbanized landscape, Earth's Future, 5, 1084–1101, 2017.
Urraca, R., Lanconelli, C., Cappucci, F., and Gobron, N.: Assessing the Fitness of Satellite Albedo Products for Monitoring Snow Albedo Trends, IEEE T. Geosci. Remote Sens., 61, 4404817, https://doi.org/10.1109/TGRS.2023.3281188, 2023.
Vazquez, J.: Nimbus-7 SMMR Ocean Products: 1979–1984, PO.DAAC., CA, USA, [data set], https://doi.org/10.5067/SMMRN-2WAF0, 1997.
Venter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., and Singh, G.: Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover, Remote Sens., 14, 4101, https://doi.org/10.3390/rs14164101, 2022.
Vihma, T., Jaagus, J., Jakobson, E., and Palo, T.: Meteorological conditions in the Arctic Ocean in spring and summer 2007 as recorded on the drifting ice station Tara, Geophys. Res. Lett., 35, https://doi.org/10.1029/2008GL034681, 2008.
Wang, Z., Schaaf, C. B., Strahler, A. H., Chopping, M. J., Román, M. O., Shuai, Y., Woodcock, C. E., Hollinger, D. Y., and Fitzjarrald, D. R.: Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods, Remote Sens. Environ., 140, 60–77, 2014.
Wang, Z., Schaaf, C., Lattanzio, A., Carrer, D., Grant, I., Román, M., Camacho, F., Yu, Y., Sánchez-Zapero, J., and Nickeson, J.: Global Surface Albedo Product Validation Best Practices Protocol. Version 1.0, in:, Best Practice for Satellite Derived Land Product Validation, edited by: Wang, Z., Nickeson, J., and Román, M., p. 45, Land Product Validation Subgroup (WGCV/CEOS), https://doi.org/10.5067/DOC/CEOSWGCV/LPV/ALBEDO.001, 2019.
Wentz, F. J.: A well-calibrated ocean algorithm for SSM/I, J. Geophys. Res., 102, 8703–8718, 1997.
Wu, A., Li, Z., and Cihlar, J.: Effects of land cover type and greenness on advanced very high resolution radiometer bidirectional reflectances: Analysis and removal, J. Geophys. Res.-Atmos., 100, 9179–9192, 1995.
Yang, F., Mitchell, K., Hou, Y. T., Dai, Y., Zeng, X., Wang, Z., and Liang, X. Z.: Dependence of land surface albedo on solar zenith angle: Observations and model parameterization, J. Appl. Meteorol. Clim., 47, 2963–2982, 2008.
Xiong, X., Stamnes, K., and Lubin, D.: Surface albedo over the Arctic Ocean derived from AVHRR and its validation with SHEBA data, J. Appl. Meteorol. Clim., 41, 413–425, 2002.
Zhang, R., Wang, H., Fu, Q., Rasch, P. J., and Wang, X.: Unraveling driving forces explaining significant reduction in satellite-inferred Arctic surface albedo since the 1980s, P. Natl. Acad. Sci. USA, 116, 23947–23953, 2019.
Short summary
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.
We describe a new climate data record describing the surface albedo, or reflectivitity, of...
Altmetrics
Final-revised paper
Preprint