Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5573-2022
© Author(s) 2022. 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-14-5573-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 Dec 2022
Data description paper |
| 16 Dec 2022
Global climate-related predictors at kilometer resolution for the past and future
Philipp Brun
CORRESPONDING AUTHOR
Land Change Science, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Niklaus E. Zimmermann
Land Change Science, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Chantal Hari
Land Change Science, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Climate and Environmental Physics, Physics Institute, University of
Bern, 3012 Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, Switzerland
Loïc Pellissier
Land Change Science, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Ecosystems and Landscape Evolution, Institute of Terrestrial
Ecosystems, Department of Environmental System Science, ETH Zürich, 8092
Zürich, Switzerland
Dirk Nikolaus Karger
Land Change Science, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
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Johanna Teresa Malle, Giulia Mazzotti, Dirk Nikolaus Karger, and Tobias Jonas
EGUsphere, https://doi.org/10.5194/egusphere-2023-1832, https://doi.org/10.5194/egusphere-2023-1832, 2023
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Land surface processes are crucial for the exchange of carbon, nitrogen and energy in the earth system. Using detailed meteorological and land-use data, we found that higher resolution improved not only the model representation of snow cover, but also plant productivity and water returned to the atmosphere. Only by combining high-resolution models with high-quality input data can we accurately represent complex spatially heterogeneous processes and improve our understanding of the earth system.
Florian Zellweger, Eric Sulmoni, Johanna T. Malle, Andri Baltensweiler, Tobias Jonas, Niklaus E. Zimmermann, Christian Ginzler, Dirk N. Karger, Pieter De Frenne, David Frey, and Clare Webster
EGUsphere, https://doi.org/10.5194/egusphere-2023-1549, https://doi.org/10.5194/egusphere-2023-1549, 2023
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The microclimatic conditions experienced by organisms living close to the ground are not well represented in currently used climate datasets derived from weather stations. Therefore, we measured and mapped ground microclimate temperatures at 10 meter spatial resolution across Switzerland, using a novel radiation model. Our results reveal a high variability in microclimates across different habitats and will help to better understand climate and land use impacts on biodiversity and ecosystems.
Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Olaf Conrad, Niklaus E. Zimmermann, and Katja Frieler
Earth Syst. Sci. Data, 15, 2445–2464, https://doi.org/10.5194/essd-15-2445-2023, https://doi.org/10.5194/essd-15-2445-2023, 2023
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We present the first 1 km, daily, global climate dataset for climate impact studies. We show that the high-resolution data have a decreased bias and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.
Tobias Siegfried, Aziz Ul Haq Mujahid, Beatrice Sabine Marti, Peter Molnar, Dirk Nikolaus Karger, and Andrey Yakovlev
EGUsphere, https://doi.org/10.5194/egusphere-2023-520, https://doi.org/10.5194/egusphere-2023-520, 2023
Preprint archived
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Our study investigates climate change impacts on water resources in Central Asia's high-mountain regions. Using new data and a stochastic soil moisture model, we found increased precipitation and higher temperatures in the future, leading to higher water discharge despite decreasing glacier melt contributions. These findings are crucial for understanding and preparing for climate change effects on Central Asia's water resources, with further research needed on extreme weather event impacts.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Matthew Forrest, and Michel Bechtold
EGUsphere, https://doi.org/10.5194/egusphere-2023-281, https://doi.org/10.5194/egusphere-2023-281, 2023
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Our paper provides an overview of all observational climate-related and socio-economic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes are already induced by climate change.
Dirk Nikolaus Karger, Michael P. Nobis, Signe Normand, Catherine H. Graham, and Niklaus E. Zimmermann
Clim. Past, 19, 439–456, https://doi.org/10.5194/cp-19-439-2023, https://doi.org/10.5194/cp-19-439-2023, 2023
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Here we present global monthly climate time series for air temperature and precipitation at 1 km resolution for the last 21 000 years. The topography at all time steps is created by combining high-resolution information on glacial cover from current and Last Glacial Maximum glacier databases with the interpolation of an ice sheet model and a coupling to mean annual temperatures from a global circulation model.
Ionuț Iosifescu Enescu, David Hanimann, Dominik Haas-Artho, Marius Rüetschi, Dirk Nikolaus Karger, Gian-Kasper Plattner, Martin Hägeli, Rebecca Kurup Buchholz, Lucia de Espona, Niklaus E. Zimmermann, and Loïc Pellissier
Abstr. Int. Cartogr. Assoc., 3, 119, https://doi.org/10.5194/ica-abs-3-119-2021, https://doi.org/10.5194/ica-abs-3-119-2021, 2021
Ionuț Iosifescu Enescu, David Hanimann, Dominik Haas-Artho, Marius Rüetschi, Dirk Nikolaus Karger, Gian-Kasper Plattner, Martin Hägeli, Rebecca Kurup Buchholz, Lucia de Espona, Niklaus E. Zimmermann, and Loïc Pellissier
Abstr. Int. Cartogr. Assoc., 3, 120, https://doi.org/10.5194/ica-abs-3-120-2021, https://doi.org/10.5194/ica-abs-3-120-2021, 2021
Damiano Righetti, Meike Vogt, Niklaus E. Zimmermann, Michael D. Guiry, and Nicolas Gruber
Earth Syst. Sci. Data, 12, 907–933, https://doi.org/10.5194/essd-12-907-2020, https://doi.org/10.5194/essd-12-907-2020, 2020
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Phytoplankton sustain marine life, as they are the principal primary producers in the global ocean. Despite their ecological importance, their distribution and diversity patterns are poorly known, mostly due to data limitations. We present a global dataset that synthesizes over 1.3 million occurrences of phytoplankton from public archives. It is easily extendable. This dataset can be used to characterize phytoplankton distribution and diversity in current and future oceans.
Zhen Zhang, Niklaus E. Zimmermann, Jed O. Kaplan, and Benjamin Poulter
Biogeosciences, 13, 1387–1408, https://doi.org/10.5194/bg-13-1387-2016, https://doi.org/10.5194/bg-13-1387-2016, 2016
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This study investigates improvements and uncertainties associated with estimating global inundated area and wetland CH4 emissions using TOPMODEL. Different topographic information and catchment aggregation schemes are evaluated against seasonal and permanently inundated wetland observations. Reducing uncertainty in prognostic wetland dynamics modeling must take into account forcing data as well as topographic scaling schemes.
D. R. Schmatz, J. Luterbacher, N. E. Zimmermann, and P. B. Pearman
Clim. Past Discuss., https://doi.org/10.5194/cpd-11-2585-2015, https://doi.org/10.5194/cpd-11-2585-2015, 2015
Revised manuscript not accepted
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Global climate model output for the Last Glacial Maximum (LGM) is downscaled to a very high resolution using the change factor method. We develop two new methods to extend current baseline climate to the LGM coastline so that the final data cover all terrestrial area at LGM. Results are gridded data for temperature, precipitation and 19 bioclimatic variables which are often used in studies on climate change impact on biological diversity, glacial refugia or migration during Holocene warming.
Related subject area
Domain: ESSD – Land | Subject: Biogeosciences and biodiversity
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2022
CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada
Quantifying exchangeable base cations in permafrost: a reserve of nutrients about to thaw
Routine monitoring of western Lake Erie to track water quality changes associated with cyanobacterial harmful algal blooms
The Portuguese Large Wildfire Spread database (PT-FireSprd)
Thirty-meter map of young forest age in China
GRiMeDB: the Global River Methane Database of concentrations and fluxes
A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests
Fire weather index data under historical and shared socioeconomic pathway projections in the 6th phase of the Coupled Model Intercomparison Project from 1850 to 2100
A remote-sensing-based dataset to characterize the ecosystem functioning and functional diversity in the Biosphere Reserve of the Sierra Nevada (southeastern Spain)
A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT
A global database on holdover time of lightning-ignited wildfires
National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the global stocktake
Spatiotemporally consistent global dataset of the GIMMS Leaf Area Index (GIMMS LAI4g) from 1982 to 2020
Mammals in the Chornobyl Exclusion Zone's Red Forest: a motion-activated camera trap study
Maps with 1 km resolution reveal increases in above- and belowground forest biomass carbon pools in China over the past 20 years
AnisoVeg: anisotropy and nadir-normalized MODIS multi-angle implementation atmospheric correction (MAIAC) datasets for satellite vegetation studies in South America
TiP-Leaf: a dataset of leaf traits across vegetation types on the Tibetan Plateau
Forest structure and individual tree inventories of northeastern Siberia along climatic gradients
A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020
Global land surface 250 m 8 d fraction of absorbed photosynthetically active radiation (FAPAR) product from 2000 to 2021
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Harmonized gap-filled datasets from 20 urban flux tower sites
Holocene spatiotemporal millet agricultural patterns in northern China: a dataset of archaeobotanical macroremains
The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil
Airborne SnowSAR data at X and Ku bands over boreal forest, alpine and tundra snow cover
The Landscape Fire Scars Database: mapping historical burned area and fire severity in Chile
Aridec: an open database of litter mass loss from aridlands worldwide with recommendations on suitable model applications
LegacyPollen 1.0: a taxonomically harmonized global late Quaternary pollen dataset of 2831 records with standardized chronologies
Muyi Li, Sen Cao, Zaichun Zhu, Zhe Wang, Ranga B. Myneni, and Shilong Piao
Earth Syst. Sci. Data, 15, 4181–4203, https://doi.org/10.5194/essd-15-4181-2023, https://doi.org/10.5194/essd-15-4181-2023, 2023
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Long-term global Normalized Difference Vegetation Index (NDVI) products support the understanding of changes in vegetation under environmental changes. This study generates a consistent global NDVI product (PKU GIMMS NDVI) from 1982–2022 that eliminates the issue of orbital drift and sensor degradation in Advanced Very High Resolution Radiometer (AVHRR) data. More accurate than its predecessor (GIMMS NDVI3g), it shows high temporal consistency with MODIS NDVI in describing vegetation trends.
Parisa Sarzaeim, Francisco Muñoz-Arriola, Diego Jarquin, Hasnat Aslam, and Natalia De Leon Gatti
Earth Syst. Sci. Data, 15, 3963–3990, https://doi.org/10.5194/essd-15-3963-2023, https://doi.org/10.5194/essd-15-3963-2023, 2023
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A genomic, phenomic, and climate database for maize phenotype predictability in the US and Canada is introduced. The database encompasses climate from multiple sources and OMICS from the Genomes to Fields initiative (G2F) data from 2014 to 2021, including codes for input data quality and consistency controls. Earth system modelers and breeders can use CLIM4OMICS since it interconnects the climate and biological system sciences. CLIM4OMICS is designed to foster phenotype predictability.
Elisabeth Mauclet, Maëlle Villani, Arthur Monhonval, Catherine Hirst, Edward A. G. Schuur, and Sophie Opfergelt
Earth Syst. Sci. Data, 15, 3891–3904, https://doi.org/10.5194/essd-15-3891-2023, https://doi.org/10.5194/essd-15-3891-2023, 2023
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Permafrost ecosystems are limited in nutrients for vegetation development and constrain the biological activity to the active layer. Upon Arctic warming, permafrost degradation exposes organic and mineral soil material that may directly influence the capacity of the soil to retain key nutrients for vegetation growth and development. Here, we demonstrate that the average total exchangeable nutrient density (Ca, K, Mg, and Na) is more than 2 times higher in the permafrost than in the active layer.
Anna G. Boegehold, Ashley M. Burtner, Andrew C. Camilleri, Glenn Carter, Paul DenUyl, David Fanslow, Deanna Fyffe Semenyuk, Casey M. Godwin, Duane Gossiaux, Thomas H. Johengen, Holly Kelchner, Christine Kitchens, Lacey A. Mason, Kelly McCabe, Danna Palladino, Dack Stuart, Henry Vanderploeg, and Reagan Errera
Earth Syst. Sci. Data, 15, 3853–3868, https://doi.org/10.5194/essd-15-3853-2023, https://doi.org/10.5194/essd-15-3853-2023, 2023
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Western Lake Erie suffers from cyanobacterial harmful algal blooms (HABs) despite decades of international management efforts. In response, the US National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs. Here we describe the data collected from this monitoring program from 2012 to 2021.
Akli Benali, Nuno Guiomar, Hugo Gonçalves, Bernardo Mota, Fábio Silva, Paulo M. Fernandes, Carlos Mota, Alexandre Penha, João Santos, José M. C. Pereira, and Ana C. L. Sá
Earth Syst. Sci. Data, 15, 3791–3818, https://doi.org/10.5194/essd-15-3791-2023, https://doi.org/10.5194/essd-15-3791-2023, 2023
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We reconstructed the spread of 80 large wildfires that burned recently in Portugal and calculated metrics that describe how wildfires behave, such as rate of spread, growth rate, and energy released. We describe the fire behaviour distribution using six percentile intervals that can be easily communicated to both research and management communities. The database will help improve our current knowledge on wildfire behaviour and support better decision making.
Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter M. Atkinson
Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, https://doi.org/10.5194/essd-15-3365-2023, 2023
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Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
Emily H. Stanley, Luke C. Loken, Nora J. Casson, Samantha K. Oliver, Ryan A. Sponseller, Marcus B. Wallin, Liwei Zhang, and Gerard Rocher-Ros
Earth Syst. Sci. Data, 15, 2879–2926, https://doi.org/10.5194/essd-15-2879-2023, https://doi.org/10.5194/essd-15-2879-2023, 2023
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The Global River Methane Database (GRiMeDB) presents CH4 concentrations and fluxes for flowing waters and concurrent measures of CO2, N2O, and several physicochemical variables, plus information about sample locations and methods used to measure gas fluxes. GRiMeDB is intended to increase opportunities to understand variation in fluvial CH4, test hypotheses related to greenhouse gas dynamics, and reduce uncertainty in future estimates of gas emissions from world streams and rivers.
Xueqin Yang, Xiuzhi Chen, Jiashun Ren, Wenping Yuan, Liyang Liu, Juxiu Liu, Dexiang Chen, Yihua Xiao, Qinghai Song, Yanjun Du, Shengbiao Wu, Lei Fan, Xiaoai Dai, Yunpeng Wang, and Yongxian Su
Earth Syst. Sci. Data, 15, 2601–2622, https://doi.org/10.5194/essd-15-2601-2023, https://doi.org/10.5194/essd-15-2601-2023, 2023
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We developed the first time-mapped, continental-scale gridded dataset of monthly leaf area index (LAI) in three leaf age cohorts (i.e., young, mature, and old) from 2001–2018 data (referred to as Lad-LAI). The seasonality of three LAI cohorts from the new Lad-LAI product agrees well at eight sites with very fine-scale collections of monthly LAI. The proposed satellite-based approaches can provide references for mapping finer spatiotemporal-resolution LAI products with different leaf age cohorts.
Yann Quilcaille, Fulden Batibeniz, Andreia F. S. Ribeiro, Ryan S. Padrón, and Sonia I. Seneviratne
Earth Syst. Sci. Data, 15, 2153–2177, https://doi.org/10.5194/essd-15-2153-2023, https://doi.org/10.5194/essd-15-2153-2023, 2023
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We present a new database of four annual fire weather indicators over 1850–2100 and over all land areas. In a 3°C warmer world with respect to preindustrial times, the mean fire weather would increase on average by at least 66% in both intensity and duration and even triple for 1-in-10-year events. The dataset is a freely available resource for fire danger studies and beyond, highlighting that the best course of action would require limiting global warming as much as possible.
Beatriz P. Cazorla, Javier Cabello, Andrés Reyes, Emilio Guirado, Julio Peñas, Antonio J. Pérez-Luque, and Domingo Alcaraz-Segura
Earth Syst. Sci. Data, 15, 1871–1887, https://doi.org/10.5194/essd-15-1871-2023, https://doi.org/10.5194/essd-15-1871-2023, 2023
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This dataset provides scientists, environmental managers, and the public in general with valuable information on the first characterization of ecosystem functional diversity based on primary production developed in the Sierra Nevada (Spain), a biodiversity hotspot in the Mediterranean basin and an exceptional natural laboratory for ecological research within the Long-Term Social-Ecological Research (LTSER) network.
Shengli Tao, Zurui Ao, Jean-Pierre Wigneron, Sassan Saatchi, Philippe Ciais, Jérôme Chave, Thuy Le Toan, Pierre-Louis Frison, Xiaomei Hu, Chi Chen, Lei Fan, Mengjia Wang, Jiangling Zhu, Xia Zhao, Xiaojun Li, Xiangzhuo Liu, Yanjun Su, Tianyu Hu, Qinghua Guo, Zhiheng Wang, Zhiyao Tang, Yi Y. Liu, and Jingyun Fang
Earth Syst. Sci. Data, 15, 1577–1596, https://doi.org/10.5194/essd-15-1577-2023, https://doi.org/10.5194/essd-15-1577-2023, 2023
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We provide the first long-term (since 1992), high-resolution (8.9 km) satellite radar backscatter data set (LHScat) with a C-band (5.3 GHz) signal dynamic for global lands. LHScat was created by fusing signals from ERS (1992–2001; C-band), QSCAT (1999–2009; Ku-band), and ASCAT (since 2007; C-band). LHScat has been validated against independent ERS-2 signals. It could be used in a variety of studies, such as vegetation monitoring and hydrological modelling.
Jose V. Moris, Pedro Álvarez-Álvarez, Marco Conedera, Annalie Dorph, Thomas D. Hessilt, Hugh G. P. Hunt, Renata Libonati, Lucas S. Menezes, Mortimer M. Müller, Francisco J. Pérez-Invernón, Gianni B. Pezzatti, Nicolau Pineda, Rebecca C. Scholten, Sander Veraverbeke, B. Mike Wotton, and Davide Ascoli
Earth Syst. Sci. Data, 15, 1151–1163, https://doi.org/10.5194/essd-15-1151-2023, https://doi.org/10.5194/essd-15-1151-2023, 2023
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This work describes a database on holdover times of lightning-ignited wildfires (LIWs). Holdover time is defined as the time between lightning-induced fire ignition and fire detection. The database contains 42 datasets built with data on more than 152 375 LIWs from 13 countries in five continents from 1921 to 2020. This database is the first freely-available, harmonized and ready-to-use global source of holdover time data, which may be used to investigate LIWs and model the holdover phenomenon.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
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Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Sen Cao, Muyi Li, Zaichun Zhu, Junjun Zha, Weiqing Zhao, Zeyu Duanmu, Jiana Chen, Yaoyao Zheng, and Yue Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-68, https://doi.org/10.5194/essd-2023-68, 2023
Revised manuscript accepted for ESSD
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The long-term global LAI products are critical supports to characterize vegetation dynamics under environmental changes. This study presents an updated GIMMS LAI product (GIMMS LAI4g; 1982−2020) based on PKU GIMMS NDVI and massive Landsat LAI samples. With higher accuracy than other LAI products, GIMMS LAI4g removes the effects of orbital drift and sensor degradation in AVHRR data. It also presents a better temporal consistency before and after 2000 and a more reasonable global vegetation trend.
Nicholas A. Beresford, Sergii Gashchak, Michael D. Wood, and Catherine L. Barnett
Earth Syst. Sci. Data, 15, 911–920, https://doi.org/10.5194/essd-15-911-2023, https://doi.org/10.5194/essd-15-911-2023, 2023
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Camera traps were established in a highly contaminated area of the Chornobyl Exclusion Zone (CEZ) to capture images of mammals. Over 1 year, 14 mammal species were recorded. The number of species observed did not vary with estimated radiation exposure. The data will be of value from the perspectives of effects of radiation on wildlife and also rewilding in this large, abandoned area. They may also have value in future studies investigating impacts of recent Russian military action in the CEZ.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
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This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Ricardo Dalagnol, Lênio Soares Galvão, Fabien Hubert Wagner, Yhasmin Mendes de Moura, Nathan Gonçalves, Yujie Wang, Alexei Lyapustin, Yan Yang, Sassan Saatchi, and Luiz Eduardo Oliveira Cruz Aragão
Earth Syst. Sci. Data, 15, 345–358, https://doi.org/10.5194/essd-15-345-2023, https://doi.org/10.5194/essd-15-345-2023, 2023
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The AnisoVeg dataset brings 22 years of monthly satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for South America at 1 km resolution aimed at vegetation applications. It has nadir-normalized data, which is the most traditional approach to correct satellite data but also unique anisotropy data with strong biophysical meaning, explaining 55 % of Amazon forest height. We expect this dataset to help large-scale estimates of vegetation biomass and carbon.
Yili Jin, Haoyan Wang, Jie Xia, Jian Ni, Kai Li, Ying Hou, Jing Hu, Linfeng Wei, Kai Wu, Haojun Xia, and Borui Zhou
Earth Syst. Sci. Data, 15, 25–39, https://doi.org/10.5194/essd-15-25-2023, https://doi.org/10.5194/essd-15-25-2023, 2023
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The TiP-Leaf dataset was compiled from direct field measurements and included 11 leaf traits from 468 species of 1692 individuals, covering a great proportion of species and vegetation types on the highest plateau in the world. This work is the first plant trait dataset that represents all of the alpine vegetation on the TP, which is not only an update of the Chinese plant trait database, but also a great contribution to the global trait database.
Timon Miesner, Ulrike Herzschuh, Luidmila A. Pestryakova, Mareike Wieczorek, Evgenii S. Zakharov, Alexei I. Kolmogorov, Paraskovya V. Davydova, and Stefan Kruse
Earth Syst. Sci. Data, 14, 5695–5716, https://doi.org/10.5194/essd-14-5695-2022, https://doi.org/10.5194/essd-14-5695-2022, 2022
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We present data which were collected on expeditions to the northeast of the Russian Federation. One table describes the 226 locations we visited during those expeditions, and the other describes 40 289 trees which we recorded at these locations. We found out that important information on the forest cannot be predicted precisely from satellites. Thus, for anyone interested in distant forests, it is important to go to there and take measurements or use data (as presented here).
Shaoyang He, Yongqiang Zhang, Ning Ma, Jing Tian, Dongdong Kong, and Changming Liu
Earth Syst. Sci. Data, 14, 5463–5488, https://doi.org/10.5194/essd-14-5463-2022, https://doi.org/10.5194/essd-14-5463-2022, 2022
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This study developed a daily, 500 m evapotranspiration and gross primary production product (PML-V2(China)) using a locally calibrated water–carbon coupled model, PML-V2, which was well calibrated against observations at 26 flux sites across nine land cover types. PML-V2 (China) performs satisfactorily in the plot- and basin-scale evaluations compared with other mainstream products. It improved intra-annual ET and GPP dynamics, particularly in the cropland ecosystem.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
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The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Hannah Adams, Jane Ye, Bhaleka D. Persaud, Stephanie Slowinski, Homa Kheyrollah Pour, and Philippe Van Cappellen
Earth Syst. Sci. Data, 14, 5139–5156, https://doi.org/10.5194/essd-14-5139-2022, https://doi.org/10.5194/essd-14-5139-2022, 2022
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Climate warming and land-use changes are altering the environmental factors that control the algal
productivityin lakes. To predict how environmental factors like nutrient concentrations, ice cover, and water temperature will continue to influence lake productivity in this changing climate, we created a dataset of chlorophyll-a concentrations (a compound found in algae), associated water quality parameters, and solar radiation that can be used to for a wide range of research questions.
Mathew Lipson, Sue Grimmond, Martin Best, Winston T. L. Chow, Andreas Christen, Nektarios Chrysoulakis, Andrew Coutts, Ben Crawford, Stevan Earl, Jonathan Evans, Krzysztof Fortuniak, Bert G. Heusinkveld, Je-Woo Hong, Jinkyu Hong, Leena Järvi, Sungsoo Jo, Yeon-Hee Kim, Simone Kotthaus, Keunmin Lee, Valéry Masson, Joseph P. McFadden, Oliver Michels, Wlodzimierz Pawlak, Matthias Roth, Hirofumi Sugawara, Nigel Tapper, Erik Velasco, and Helen Claire Ward
Earth Syst. Sci. Data, 14, 5157–5178, https://doi.org/10.5194/essd-14-5157-2022, https://doi.org/10.5194/essd-14-5157-2022, 2022
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We describe a new openly accessible collection of atmospheric observations from 20 cities around the world, capturing 50 site years. The observations capture local meteorology (temperature, humidity, wind, etc.) and the energy fluxes between the land and atmosphere (e.g. radiation and sensible and latent heat fluxes). These observations can be used to improve our understanding of urban climate processes and to test the accuracy of urban climate models.
Keyang He, Houyuan Lu, Jianping Zhang, and Can Wang
Earth Syst. Sci. Data, 14, 4777–4791, https://doi.org/10.5194/essd-14-4777-2022, https://doi.org/10.5194/essd-14-4777-2022, 2022
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Here we presented the first quantitative spatiotemporal cropping patterns spanning the Neolithic and Bronze ages in northern China. Temporally, millet agriculture underwent a dramatic transition from low-yield broomcorn to high-yield foxtail millet around 6000 cal. a BP under the influence of climate and population. Spatially, millet agriculture spread westward and northward from the mid-lower Yellow River (MLY) to the agro-pastoral ecotone (APE) around 6000 cal. a BP and diversified afterwards.
Kailiang Yu, Johan van den Hoogen, Zhiqiang Wang, Colin Averill, Devin Routh, Gabriel Reuben Smith, Rebecca E. Drenovsky, Kate M. Scow, Fei Mo, Mark P. Waldrop, Yuanhe Yang, Weize Tang, Franciska T. De Vries, Richard D. Bardgett, Peter Manning, Felipe Bastida, Sara G. Baer, Elizabeth M. Bach, Carlos García, Qingkui Wang, Linna Ma, Baodong Chen, Xianjing He, Sven Teurlincx, Amber Heijboer, James A. Bradley, and Thomas W. Crowther
Earth Syst. Sci. Data, 14, 4339–4350, https://doi.org/10.5194/essd-14-4339-2022, https://doi.org/10.5194/essd-14-4339-2022, 2022
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We used a global-scale dataset for the surface topsoil (>3000 distinct observations of abundance of soil fungi versus bacteria) to generate the first quantitative map of soil fungal proportion across terrestrial ecosystems. We reveal striking latitudinal trends. Fungi dominated in regions with low mean annual temperature (MAT) and net primary productivity (NPP) and bacteria dominated in regions with high MAT and NPP.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Alejandro Miranda, Rayén Mentler, Ítalo Moletto-Lobos, Gabriela Alfaro, Leonardo Aliaga, Dana Balbontín, Maximiliano Barraza, Susanne Baumbach, Patricio Calderón, Fernando Cárdenas, Iván Castillo, Gonzalo Contreras, Felipe de la Barra, Mauricio Galleguillos, Mauro E. González, Carlos Hormazábal, Antonio Lara, Ian Mancilla, Francisca Muñoz, Cristian Oyarce, Francisca Pantoja, Rocío Ramírez, and Vicente Urrutia
Earth Syst. Sci. Data, 14, 3599–3613, https://doi.org/10.5194/essd-14-3599-2022, https://doi.org/10.5194/essd-14-3599-2022, 2022
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Achieving a local understanding of fire regimes requires high-resolution, systematic and dynamic data. High-quality information can help to transform evidence into decision-making. Taking advantage of big-data and remote sensing technics we developed a flexible workflow to reconstruct burned area and fire severity data for more than 8000 individual fires in Chile. The framework developed for the database can be applied anywhere in the world with minimal adaptation.
Agustín Sarquis, Ignacio Andrés Siebenhart, Amy Theresa Austin, and Carlos A. Sierra
Earth Syst. Sci. Data, 14, 3471–3488, https://doi.org/10.5194/essd-14-3471-2022, https://doi.org/10.5194/essd-14-3471-2022, 2022
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Plant litter breakdown in aridlands is driven by processes different from those in more humid ecosystems. A better understanding of these processes will allow us to make better predictions of future carbon cycling. We have compiled aridec, a database of plant litter decomposition studies in aridlands and tested some modeling applications for potential users. Aridec is open for use and collaboration, and we hope it will help answer newer and more important questions as the database develops.
Ulrike Herzschuh, Chenzhi Li, Thomas Böhmer, Alexander K. Postl, Birgit Heim, Andrei A. Andreev, Xianyong Cao, Mareike Wieczorek, and Jian Ni
Earth Syst. Sci. Data, 14, 3213–3227, https://doi.org/10.5194/essd-14-3213-2022, https://doi.org/10.5194/essd-14-3213-2022, 2022
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Pollen preserved in environmental archives such as lake sediments and bogs are extensively used for reconstructions of past vegetation and climate. Here we present LegacyPollen 1.0, a dataset of 2831 fossil pollen records from all over the globe that were collected from publicly available databases. We harmonized the names of the pollen taxa so that all datasets can be jointly investigated. LegacyPollen 1.0 is available as an open-access dataset.
Cited articles
Aguilos, M., Sun, G., Noormets, A., Domec, J.-C., McNulty, S., Gavazzi, M.,
Prajapati, P., Minick, K. J., Mitra, B., and King, J.: Ecosystem
Productivity and Evapotranspiration Are Tightly Coupled in Loblolly Pine
(Pinus taeda L.) Plantations along the Coastal Plain of the Southeastern
U.S., Carbon and Water Cycles in Coastal Forests under Climate Change and Variability, Forests, 12, 1123, https://doi.org/10.3390/f12081123, 2021.
Alduchov, O. A. and Eskridge, R. E.: Improved Magnus Form Approximation of
Saturation Vapor Pressure, J. Appl. Meteorol., 35, 601–609,
https://doi.org/10.1175/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2, 1996.
Allen, C. D., Macalady, A. K., Chenchouni, H., Bachelet, D., McDowell, N.,
Vennetier, M., Kitzberger, T., Rigling, A., Breshears, D. D., Hogg, E. H., Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim,
J.-H., Allard, G., Running, S. W., Semerci, A., and Cobb, N.: A global
overview of drought and heat-induced tree mortality reveals emerging climate
change risks for forests, For. Ecol. Manage., 259, 660–684,
https://doi.org/10.1016/j.foreco.2009.09.001, 2010.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop
evapotranspiration – guidelines for computing crop water requirements, FAO
Irrig. Drain. Pap., 56, ISBN 9251042195, 1998.
Anandhi, A.: Growing degree days – Ecosystem indicator for changing diurnal
temperatures and their impact on corn growth stages in Kansas, Ecol. Indic.,
61, 149–158, https://doi.org/10.1016/j.ecolind.2015.08.023, 2016.
Andrade, A. M. D., Michel, R. F. M., Bremer, U. F., Schaefer, C. E. G. R.,
and Simões, J. C.: Relationship between solar radiation and surface
distribution of vegetation in Fildes Peninsula and Ardley Island, Maritime
Antarctica, Int. J. Remote Sens., 39, 2238–2254,
https://doi.org/10.1080/01431161.2017.1420937, 2018.
Araújo, M. B. and Rahbek, C.: How Does Climate Change Affect
Biodiversity?, Science, 313, 1396–1397,
https://doi.org/10.1126/science.1131758, 2006.
Arguez, A. and Vose, R. S.: The Definition of the Standard WMO Climate
Normal: The Key to Deriving Alternative Climate Normals, Bull. Am. Meteorol.
Soc., 92, 699–704, https://doi.org/10.1175/2010BAMS2955.1, 2011.
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., and Courchamp, F.:
Impacts of climate change on the future of biodiversity, Ecol. Lett., 15,
365–377, https://doi.org/10.1111/j.1461-0248.2011.01736.x, 2012.
Bobrowski, M., Weidinger, J., and Schickhoff, U.: Is New Always Better?
Frontiers in Global Climate Datasets for Modeling Treeline Species in the
Himalayas, Atmosphere (Basel), 12, 543,
https://doi.org/10.3390/atmos12050543, 2021.
Böhner, J. and Antonic, O.: Land-Surface Parameters Specific to
Topo-Climatology, in: Geomorphometry: Concepts, Software, Applications,
195–226, https://doi.org/10.1016/S0166-2481(08)00008-1, 2009.
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and
Zemp, M.: The Concept of Essential Climate Variables in Support of Climate
Research, Applications, and Policy, Bull. Am. Meteorol. Soc., 95,
1431–1443, https://doi.org/10.1175/BAMS-D-13-00047.1, 2014.
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y.,
Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P.,
Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A.,
Cugnet, D., D'Andrea, F., Davini, P., Lavergne, C., Denvil, S., Deshayes,
J., Devilliers, M., Ducharne, A., Dufresne, J., Dupont, E., Éthé,
C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M., Gardoll, S.,
Gastineau, G., Ghattas, J., Grandpeix, J., Guenet, B., Guez, Lionel, E.,
Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A.,
Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N.,
Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S.,
Madec, G., Madeleine, J., Maignan, F., Marchand, M., Marti, O., Mellul, L.,
Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton,
Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima,
A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M.,
Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and
Evaluation of the IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Syst.,
12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020.
Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L., and Karger, D. N.:
CHELSA-BIOCLIM+ A novel set of global climate-related predictors at
kilometre-resolution, EnviDat [data set], https://doi.org/10.16904/envidat.332, 2022.
Callaghan, T. V., Johansson, M., Brown, R. D., Groisman, P. Y., Labba, N.,
Radionov, V., Bradley, R. S., Blangy, S., Bulygina, O. N., Christensen, T.
R., Colman, J. E., Essery, R. L. H., Forbes, B. C., Forchhammer, M. C.,
Golubev, V. N., Honrath, R. E., Juday, G. P., Meshcherskaya, A. V., Phoenix,
G. K., Pomeroy, J., Rautio, A., Robinson, D. A., Schmidt, N. M., Serreze, M.
C., Shevchenko, V. P., Shiklomanov, A. I., Shmakin, A. B., Sköld, P.,
Sturm, M., Woo, M., and Wood, E. F.: Multiple Effects of Changes in Arctic
Snow Cover, Ambio, 40, 32–45, https://doi.org/10.1007/s13280-011-0213-x,
2011.
Cayton, H. L., Haddad, N. M., Gross, K., Diamond, S. E., and Ries, L.: Do
growing degree days predict phenology across butterfly species?, Ecology,
96, 1473–1479, https://doi.org/10.1890/15-0131.1, 2015.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015.
Daly, C., Taylor, G. H., and Gibson, W. P.: The PRISM approach to mapping
precipitation and temperature, in: Proc 10th AMS Conf Appl. Climatol.,
20–23, https://prism.oregonstate.edu/documents/pubs/1997appclim_PRISMapproach_daly.pdf (last access: 12 December 2022), 1997.
Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain elevation
data 2010 (GMTED2010), Earth Resources Observation And Science (EROS) Center, https://doi.org/10.5066/F7J38R2N, 2011.
Datta, A., Schweiger, O., and Kühn, I.: Origin of climatic data can
determine the transferability of species distribution models, 59, 61–76,
https://doi.org/10.3897/neobiota.59.36299, 2020.
Dawson, T. E.: Fog in the California redwood forest: ecosystem inputs and
use by plants, Oecologia, 117, 476–485,
https://doi.org/10.1007/s004420050683, 1998.
Dunn, R. J. H.: HadISD version 3: monthly updates, Hadley Centre Technical Note, https://digital.nmla.metoffice.gov.uk/digitalFile_13890750-fb6f-42c7-92df-1c4504621fae/ (last access: last access: 12 December 2022), 2019.
Easterling, D. R., Meehl, G. A., Parmesan, C., Changnon, S. A., Karl, T. R.,
and Mearns, L. O.: Climate Extremes: Observations, Modeling, and Impacts,
Science, 289, 2068–2074,
https://doi.org/10.1126/science.289.5487.2068, 2000.
Easterling, D. R., Kunkel, K. E., Wehner, M. F., and Sun, L.: Detection and
attribution of climate extremes in the observed record, Weather Clim.
Extrem., 11, 17–27, https://doi.org/10.1016/j.wace.2016.01.001, 2016.
Elsen, P. R., Monahan, W. B., Dougherty, E. R., and Merenlender, A. M.:
Keeping pace with climate change in global terrestrial protected areas, Sci.
Adv., 6, eaay0814, https://doi.org/10.1126/sciadv.aay0814, 2020.
Evans, B. M., Walker, D. A., Benson, C. S., Nordstrand, E. A., and Petersen,
G. W.: Spatial interrelationships between terrain, snow distribution and
vegetation patterns at an arctic foothills site in Alaska, Ecography, 12, 270–278, https://doi.org/10.1111/j.1600-0587.1989.tb00846.x, 1989.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R.
J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project
Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9,
1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
FAO: FAOCLIM 2: world-wide agroclimatic data, Environment and Natural Resources, Working paper No. 5 (CD-ROM), FAO [data set], https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1028000/ (last access: 13 December 2022), 2001.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1 km spatial resolution
climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315,
https://doi.org/10.1002/joc.5086, 2017.
Fourcade, Y., Besnard, A. G., and Secondi, J.: Paintings predict the
distribution of species, or the challenge of selecting environmental
predictors and evaluation statistics, Glob. Ecol. Biogeogr., 27, 245–256,
https://doi.org/10.1111/geb.12684, 2018.
Gholz, H. L.: Environmental Limits on Aboveground Net Primary Production,
Leaf Area, and Biomass in Vegetation Zones of the Pacific Northwest,
Ecology, 63, 469–481, https://doi.org/10.2307/1938964, 1982.
Grier, C. G. and Running, S. W.: Leaf Area of Mature Northwestern Coniferous
Forests: Relation to Site Water Balance, Ecology, 58, 893–899,
https://doi.org/10.2307/1936225, 1977.
Grossiord, C., Buckley, T. N., Cernusak, L. A., Novick, K. A., Poulter, B.,
Siegwolf, R. T. W., Sperry, J. S., and McDowell, N. G.: Plant responses to
rising vapor pressure deficit, New Phytol., 226, 1550–1566,
https://doi.org/10.1111/nph.16485, 2020.
Gutjahr, O., Putrasahan, D., Lohmann, K., Jungclaus, J. H., von Storch, J.-S., Brüggemann, N., Haak, H., and Stössel, A.: Max Planck Institute Earth System Model (MPI-ESM1.2) for the High-Resolution Model Intercomparison Project (HighResMIP), Geosci. Model Dev., 12, 3241–3281, https://doi.org/10.5194/gmd-12-3241-2019, 2019.
Hannah, L.: Protected Areas and Climate Change, Ann. N. Y. Acad. Sci., 1134,
201–212, https://doi.org/10.1196/annals.1439.009, 2008.
Hargreaves, G. H. and Samani, Z. A.: Reference Crop Evapotranspiration from
Temperature, Appl. Eng. Agric., 1, 96–99, https://doi.org/10.13031/2013.26773, 1985.
Hartman, M. D., Parton, W. J., Derner, J. D., Schulte, D. K., Smith, W. K.,
Peck, D. E., Day, K. A., Del Grosso, S. J., Lutz, S., Fuchs, B. A., Chen,
M., and Gao, W.: Seasonal grassland productivity forecast for the U.S. Great
Plains using Grass-Cast, 11, e03280, https://doi.org/10.1002/ecs2.3280, 2020.
Hauser, G., Rais, O., Morán Cadenas, F., Gonseth, Y., Bouzelboudjen, M.,
and Gern, L.: Influence of climatic factors on Ixodes ricinus nymph
abundance and phenology over a long-term monthly observation in Switzerland
(2000–2014), Parasit. Vectors, 11, 289,
https://doi.org/10.1186/s13071-018-2876-7, 2018.
Hay, L. E., Wilby, R. L., and Leavesley, G. H.: A Comparison Of Delta Change
And Downscaled Gcm Scenarios For Three Mountainous Basins In The United
States 1, JAWRA J. Am. Water Resour. Assoc., 36, 387–397,
https://doi.org/10.1111/j.1752-1688.2000.tb04276.x, 2000.
Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting,
J., Shevliakova, E., Winton, M., Zhao, M., Bushuk, M., Wittenberg, A. T.,
Wyman, B., Xiang, B., Zhang, R., Anderson, W., Balaji, V., Donner, L.,
Dunne, K., Durachta, J., Gauthier, P. P. G., Ginoux, P., Golaz, J. -C.,
Griffies, S. M., Hallberg, R., Harris, L., Harrison, M., Hurlin, W., John,
J., Lin, P., Lin, S. -J., Malyshev, S., Menzel, R., Milly, P. C. D., Ming,
Y., Naik, V., Paynter, D., Paulot, F., Ramaswamy, V., Reichl, B., Robinson,
T., Rosati, A., Seman, C., Silvers, L. G., Underwood, S., and Zadeh, N.:
Structure and Performance of GFDL's CM4.0 Climate Model, J. Adv. Model.
Earth Syst., 11, 3691–3727, https://doi.org/10.1029/2019MS001829, 2019.
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G.
B. M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G. B., Walsh,
M. G., and Gonzalez, M. R.: SoilGrids1km – Global Soil Information Based
on Automated Mapping, PLoS One, 9, e105992,
https://doi.org/10.1371/journal.pone.0105992, 2014.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLoS One, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017.
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., 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., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global
reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Hijmans, R. J.: raster: Geographic Data Analysis and Modeling, r-project [code],
https://cran.r-project.org/package=raster (last access: 22 August 2019), 2019.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A.:
Very high resolution interpolated climate surfaces for global land areas,
Int. J. Climatol., 25, 1965–1978, https://doi.org/10.1002/joc.1276, 2005.
Hogg, E. H.: Temporal scaling of moisture and the forest-grassland boundary
in western Canada, Agric. For. Meteorol., 84, 115–122,
https://doi.org/10.1016/S0168-1923(96)02380-5, 1997.
Hogg, E. H., Michaelian, M., Hook, T. I., and Undershultz, M. E.: Recent
climatic drying leads to age-independent growth reductions of white spruce
stands in western Canada, Glob. Chang. Biol., 23, 5297–5308,
https://doi.org/10.1111/gcb.13795, 2017.
Howden, S. M., Soussana, J.-F., Tubiello, F. N., Chhetri, N., Dunlop, M.,
and Meinke, H.: Adapting agriculture to climate change, Proc. Natl. Acad.
Sci., 104, 19691–19696, https://doi.org/10.1073/pnas.0701890104, 2007.
Hufkens, K., Friedl, M. A., Keenan, T. F., Sonnentag, O., Bailey, A.,
O'Keefe, J., and Richardson, A. D.: Ecological impacts of a widespread frost
event following early spring leaf-out, Glob. Chang. Biol., 18, 2365–2377,
https://doi.org/10.1111/j.1365-2486.2012.02712.x, 2012.
Iio, A., Hikosaka, K., Anten, N. P. R., Nakagawa, Y., and Ito, A.: Global
dependence of field-observed leaf area index in woody species on climate: a
systematic review, Glob. Ecol. Biogeogr., 23, 274–285,
https://doi.org/10.1111/geb.12133, 2014.
IPBES: The IPBES regional assessment report on biodiversity and ecosystem
services for Europe and Central Asia., edited by: Rounsevell, M., Fischer,
M., Torre-Marin Rando, A., and Mader, A., Secretariat of the
Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem
Services, Bonn, Germany, 892 pp., https://doi.org/10.5281/zenodo.3237428, 2018.
IPCC: Renewable Energy Sources and Climate Change Mitigation: Special Report
of the Intergovernmental Panel on Climate Change, edited by: Edenhofer, O.,
Pichs-Madruga, R., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S.,
Zwickel, T., Eickemeier, P., Hansen, G., Schlömer, S., and von Stechow,
C., Cambridge University Press, Cambridge, ISBN 9781107607101, 2011.
IPCC: Climate Change 2022: Impacts, Adaptation, and Vulnerability.
Contribution of Working Group II to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O.,
Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K.,
Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V.,
Okem, A., and Rama, B., Cambridge University Press, United Kingdom, https://doi.org/10.1017/9781009325844., 2022.
Irmak, S.: Evapotranspiration, in: Encyclopedia of Ecology, edited by: Jørgensen, S. E. and Fath, B. D., Academic Press,
1432–1438, https://doi.org/10.1016/B978-008045405-4.00270-6, 2008.
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H.,
Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., and Kessler, M.:
Climatologies at high resolution for the earth's land surface areas, Sci.
Data, 4, 170122, https://doi.org/10.1038/sdata.2017.122, 2017.
Karger, D. N., Schmatz, D. R., Dettling, G., and Zimmermann, N. E.:
High-resolution monthly precipitation and temperature time series from 2006
to 2100, Sci. Data, 7, 248, https://doi.org/10.1038/s41597-020-00587-y,
2020.
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H.,
Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., and Kessler, M.:
Climatologies at high resolution for the earth's land surface areas, EnviDat [data set],
https://doi.org/10.16904/envidat.228.v2.1, 2021a.
Karger, D. N., Wilson, A. M., Mahony, C., Zimmermann, N. E., and Jetz, W.:
Global daily 1 km land surface precipitation based on cloud cover-informed
downscaling, Sci. Data, 8, 307, https://doi.org/10.1038/s41597-021-01084-6,
2021b.
Karger, D. N., Kessler, M., Lehnert, M., and Jetz, W.: Limited protection
and ongoing loss of tropical cloud forest biodiversity and ecosystems
worldwide, Nat. Ecol. Evol., 5, 854–862,
https://doi.org/10.1038/s41559-021-01450-y, 2021c.
Karger, D. N., Lange, S., Hari, C., Reyer, C. P. O., Conrad, O., Zimmermann, N. E., and Frieler, K.: CHELSA-W5E5: Daily 1 km meteorological forcing data for climate impact studies, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-367, in review, 2022.
Kawamiya, M., Hajima, T., Tachiiri, K., Watanabe, S., and Yokohata, T.: Two
decades of Earth system modeling with an emphasis on Model for
Interdisciplinary Research on Climate (MIROC), Prog. Earth Planet. Sci., 7,
64, https://doi.org/10.1186/s40645-020-00369-5, 2020.
Knauer, J., El-Madany, T. S., Zaehle, S., and Migliavacca, M.: Bigleaf – An
R package for the calculation of physical and physiological ecosystem
properties from eddy covariance data, PLoS One, 13, e0201114,
https://doi.org/10.1371/journal.pone.0201114, 2018.
Körner, C., Paulsen, J., and Spehn, E. M.: A definition of mountains and
their bioclimatic belts for global comparisons of biodiversity data, Alp.
Bot., 121, 73, https://doi.org/10.1007/s00035-011-0094-4, 2011.
Lange, S.: ISIMIP3b bias adjustment fact sheet, Inter-Sectoral Impact Model, Intercomparison Project, 40 pp., https://www.isimip.org/documents/413/ISIMIP3b_bias_adjustment_fact_sheet_Gnsz7CO.pdf (last access: 12 December 2022), 2021.
Larcher, W.: Ökophysiologie der Pflanzen: Leben und
Stressbewältigung der Pflanzen in ihrer Umwelt, 5th edn., Verlag Eugen
Ulmer, Stuttgart, 394 pp., ISBN 3825280748, 1994.
Leng, G. and Hall, J.: Crop yield sensitivity of global major agricultural
countries to droughts and the projected changes in the future, Sci. Total
Environ., 654, 811–821, https://doi.org/10.1016/j.scitotenv.2018.10.434,
2019.
Lenihan, J. M.: Ecological response surfaces for North American boreal tree
species and their use in forest classification, J. Veg. Sci., 4, 667–680,
https://doi.org/10.2307/3236132, 1993.
Levins, R.: The Strategy Of Model Building In Population Biology, Am. Sci.,
54, 421–431, 1966.
Lieth, H.: Modeling the Primary Productivity of the World, in: Primary Productivity of the Biosphere, Springer, vol. 14, 237–263, https://doi.org/10.1007/978-3-642-80913-2_12, 1975.
Liu, W., Ye, T., Jägermeyr, J., Müller, C., Chen, S., Liu, X., and
Shi, P.: Future climate change significantly alters interannual wheat yield
variability over half of harvested areas, Environ. Res. Lett., 16, 094045,
https://doi.org/10.1088/1748-9326/ac1fbb, 2021.
Masia, S., Trabucco, A., Spano, D., Snyder, R. L., Sušnik, J., and
Marras, S.: A modelling platform for climate change impact on local and
regional crop water requirements, Agric. Water Manag., 255, 107005,
https://doi.org/10.1016/j.agwat.2021.107005, 2021.
Maussion, F., Scherer, D., Finkelnburg, R., Richters, J., Yang, W., and Yao, T.: WRF simulation of a precipitation event over the Tibetan Plateau, China – an assessment using remote sensing and ground observations, Hydrol. Earth Syst. Sci., 15, 1795–1817, https://doi.org/10.5194/hess-15-1795-2011, 2011.
Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., and
Finkelnburg, R.: Precipitation Seasonality and Variability over the Tibetan
Plateau as Resolved by the High Asia Reanalysis, J. Clim., 27, 1910–1927,
https://doi.org/10.1175/JCLI-D-13-00282.1, 2014.
Monteith, J. L.: Evaporation and environment, Symp. Soc. Exp. Biol., 19,
205–234, 1965.
Muñoz Sabater, J.: ERA5-Land monthly averaged data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.68d2bb30, 2019.
Muñoz‐Sabater, J.: ERA5-Land monthly averaged data from 1950 to 1980, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.68d2bb30, 2021.
Neilson, R. P.: A Model for Predicting Continental-Scale Vegetation
Distribution and Water Balance, Ecol. Appl., 5, 362–385,
https://doi.org/10.2307/1942028, 1995.
Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C.,
Tucker, C. J., Myneni, R. B., and Running, S. W.: Climate-Driven Increases
in Global Terrestrial Net Primary Production from 1982 to 1999, Science, 300, 1560–1563, https://doi.org/10.1126/science.1082750, 2003.
Nobel, P. S.: Wind as an Ecological Factor, in: Physiological Plant Ecology
I, Springer Berlin Heidelberg, Berlin, Heidelberg, vol. 12/A, 475–500,
https://doi.org/10.1007/978-3-642-68090-8_16, 1981.
O'Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter,
T. R., Mathur, R., and van Vuuren, D. P.: A new scenario framework for
climate change research: the concept of shared socioeconomic pathways, Clim.
Change, 122, 387–400, https://doi.org/10.1007/s10584-013-0905-2, 2014.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K.,
Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok,
K., Levy, M., and Solecki, W.: The roads ahead: Narratives for shared
socioeconomic pathways describing world futures in the 21st century, Glob.
Environ. Chang., 42, 169–180,
https://doi.org/10.1016/j.gloenvcha.2015.01.004, 2017.
Ooms, J.: magick: Advanced Graphics and Image-Processing in R, magick [code],
https://CRAN.R-project.org/package=magick (last access: 12 December 2022), 2020.
Ouisse, T., Bonte, D., Lebouvier, M., Hendrickx, F., and Renault, D.: The
importance of relative humidity and trophic resources in governing
ecological niche of the invasive carabid beetle Merizodus soledadinus in the
Kerguelen archipelago, J. Insect Physiol., 93–94, 42–49,
https://doi.org/10.1016/j.jinsphys.2016.08.006, 2016.
Paulsen, J. and Körner, C.: A climate-based model to predict potential
treeline position around the globe, Alp. Bot., 124, 1–12,
https://doi.org/10.1007/s00035-014-0124-0, 2014.
Pebesma, E. J. and Bivand, R. S.: Classes and methods for spatial data in
{R}, R News, 5, 9–13, 2005.
Pollock, L. J., Thuiller, W., and Jetz, W.: Large conservation gains
possible for global biodiversity facets, Nature, 546, 141–144,
https://doi.org/10.1038/nature22368, 2017.
Prentice, I. C., Cramer, W., Harrison, S. P., Leemans, R., Monserud, R. A.,
and Solomon, A. M.: Special Paper: A Global Biome Model Based on Plant
Physiology and Dominance, Soil Properties and Climate, J. Biogeogr., 19,
117, https://doi.org/10.2307/2845499, 1992.
Pryor, S. C. and Hahmann, A. N.: Downscaling Wind, in: Oxford Research
Encyclopedia of Climate Science, Oxford University Press,
https://doi.org/10.1093/acrefore/9780190228620.013.730, 2019.
R Development Core Team: R: A Language and Environment for Statistical
Computing, http://www.r-project.org (last access: 12 December 2022), 2008.
Santini, M., Noce, S., Antonelli, M., and Caporaso, L.: Complex drought
patterns robustly explain global yield loss for major crops, Sci. Rep., 12,
5792, https://doi.org/10.1038/s41598-022-09611-0, 2022.
Schimel, D. S.: Terrestrial ecosystems and the carbon cycle, Glob. Chang.
Biol., 1, 77–91, https://doi.org/10.1111/j.1365-2486.1995.tb00008.x, 1995.
Schultz, J.: The Ecozones of the World, The Ecological Divisions of the Geosphere, Springer Berlin Heidelberg, Berlin,
Heidelberg, https://doi.org/10.1007/3-540-28527-X, 2005.
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire,
A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S.,
Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson,
C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald,
A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M.,
Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B.,
Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble,
J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson,
E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G.,
and Zerroukat, M.: UKESM1: Description and Evaluation of the U.K. Earth
System Model, J. Adv. Model. Earth Syst., 11, 4513–4558,
https://doi.org/10.1029/2019MS001739, 2019.
Seneviratne, S. I., Easterling, D., Goodess, C. M., Kanae, S., Kossin, J.,
Luo, Y., Marengo, J., McInnes, K., Rahimi, M., Reichstein, M., Sorteberg,
A., Vera, C., and Zhang, X.: Changes in climate extremes and their impacts
on the natural physical environment, in: Managing the Risks of Extreme
Events and Disasters to Advance Climate Change Adaptation, edited by: Field,
C. B., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L.,
Mastrandrea, M. D., Mach, K. J., Plattner, G.-K., Allen, S. K., Tignor, M.,
and Midgley, P. M., A Special Report of Working Groups I and II of the
Intergovernmental Panel on Climate Change (IPCC), Cambridge, UK and New
York, NY, USA, 109–230, https://doi.org/10.7916/d8-6nbt-s431, 2012.
Singer, M. B., Asfaw, D. T., Rosolem, R., Cuthbert, M. O., Miralles, D. G.,
MacLeod, D., Quichimbo, E. A., and Michaelides, K.: Hourly potential
evapotranspiration at 0.1∘ resolution for the global land surface
from 1981-present, Sci. Data, 8, 224,
https://doi.org/10.1038/s41597-021-01003-9, 2021.
Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric
model for weather research and forecasting applications, J. Comput. Phys.,
227, 3465–3485, https://doi.org/10.1016/j.jcp.2007.01.037, 2008.
Sloat, L. L., Davis, S. J., Gerber, J. S., Moore, F. C., Ray, D. K., West,
P. C., and Mueller, N. D.: Climate adaptation by crop migration, Nat.
Commun., 11, 1243, https://doi.org/10.1038/s41467-020-15076-4, 2020.
Sonntag, D.: Important new values of the physical constants of 1986, vapor
pressure formulations based on the ITS-90 and psychrometric formulae,
Z. Meteorol., 70, 340–344, 1990.
Sparks, A. H., Hengl, T., and Nelson, A.: GSODR: Global Summary Daily
Weather Data in R, J. Open Source Softw., 2, 177,
https://doi.org/10.21105/joss.00177, 2017.
Suwal, M. K., Huettmann, F., Regmi, G. R., and Vetaas, O. R.: Parapatric
subspecies of Macaca assamensis show a marginal overlap in their predicted
potential distribution: Some elaborations for modern conservation
management, Ecol. Evol., 8, 9712–9727, https://doi.org/10.1002/ece3.4405,
2018.
Thuiller, W., Lavorel, S., Araújo, M. B., Sykes, M. T., and Prentice, I.
C.: Climate change threats to plant diversity in Europe, Proc. Natl. Acad.
Sci., 102, 8245–8250, https://doi.org/10.1073/pnas.0409902102, 2005.
Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N., and Zimmermann, N.
E.: Uncertainty in ensembles of global biodiversity scenarios, Nat. Commun.,
10, 1446, https://doi.org/10.1038/s41467-019-09519-w, 2019.
Global Surface Summary of Day (GSOD): Index of /data/global-summary-of-the-day, NOAA [data set],
https://www.ncei.noaa.gov/data/global-summary-of-the-day/, last access: 15 October 2022.
Weibull, W.: A Statistical Distribution Function of Wide Applicability, J.
Appl. Mech., 18, 293–297, https://doi.org/10.1115/1.4010337, 1951.
Willis, K. J. and Bhagwat, S. A.: Biodiversity and Climate Change, Science, 326, 806–807, https://doi.org/10.1126/science.1178838, 2009.
WMO: Guide to Instruments and Methods of Observation, 8th edn., World
Meteorological Organization, Geneva, 548 pp., ISBN 9789263100085, 2018.
Woodward, F. I.: Climate and plant distribution, Cambridge University Press,
Cambridge, 192 pp., ISBN 9780521282147, 1987.
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S.,
Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H.,
Shindo, E., Mizuta, R., Obata, A., Adachi, Y., and Ishii, M.: The
Meteorological Research Institute Earth System Model Version 2.0,
MRI-ESM2.0: Description and Basic Evaluation of the Physical Component, J.
Meteorol. Soc. Japan. Ser. II, 97, 931–965,
https://doi.org/10.2151/jmsj.2019-051, 2019.
Zeng, Z., Ziegler, A. D., Searchinger, T., Yang, L., Chen, A., Ju, K., Piao,
S., Li, L. Z. X., Ciais, P., Chen, D., Liu, J., Azorin-Molina, C., Chappell,
A., Medvigy, D., and Wood, E. F.: A reversal in global terrestrial stilling
and its implications for wind energy production, Nat. Clim. Chang., 9,
979–985, https://doi.org/10.1038/s41558-019-0622-6, 2019.
Zhang, K., Bosch-Serra, A. D., Boixadera, J., and Thompson, A. J.:
Investigation of Water Dynamics and the Effect of Evapotranspiration on
Grain Yield of Rainfed Wheat and Barley under a Mediterranean Environment: A
Modelling Approach, PLoS One, 10, e0131360,
https://doi.org/10.1371/journal.pone.0131360, 2015.
Zhang, T.: Influence of the seasonal snow cover on the ground thermal
regime: An overview, Rev. Geophys., 43, 2004RG000157, https://doi.org/10.1029/2004RG000157, 2005.
Ziese, M., Rauthe-Schöch, A., Becker, A., Finger, P., Meyer-Christoffer,
A., and Schneider, U.: GPCC Full Data Daily Version.2018 at 1.0∘:
Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and
Historic Data, https://doi.org/10.5676/DWD_GPCC/FD_D_V2018_100, 2018.
Zomer, R. J., Xu, J., and Trabucco, A.: Version 3 of the Global Aridity
Index and Potential Evapotranspiration Database, Sci. Data, 9, 409,
https://doi.org/10.1038/s41597-022-01493-1, 2022.
Zscheischler, J., Westra, S., van den Hurk, B. J. J. M., Seneviratne, S. I.,
Ward, P. J., Pitman, A., AghaKouchak, A., Bresch, D. N., Leonard, M., Wahl,
T., and Zhang, X.: Future climate risk from compound events, Nat. Clim.
Chang., 8, 469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018.
Short summary
Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.
Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant,...
