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opencc-by-4.0Dec 2021View details →

TMax index: spatial heterogeneity of climate change as an experiential basis for skepticism

To evaluate how the spatial heterogeneity of climate change affects the public’s willingness to accept scientific results that the climate is changing, we propose an index that accurately measures local changes in climate based on the number of days per year for which the year of the record high temperature is more recent than the year of the record low temperature. TMax index is calculated using the Global Historical Climatology Network (GHCN) dataset. Please refer to the PNAS paper for details on how the index is calculated.

opencc-by-4.0Dec 2021View details →

Model output data and figures' code for Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security

Model output data and figures' code for "Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security" in Nature Food (DOI: 10.1038/s43016-022-00464-4)

opencc-zeroDec 2021View details →

Too hot for the devil? Did climate change cause the mid-Holocene extinction of the Tasmanian devil (Sarcophilus harrisii) from mainland Australia?

The possible role of climate change in late Quaternary animal extinctions is hotly debated, yet few studies have investigated its direct effects on animal physiology to assess whether past climate changes might have had significant impacts on now-extinct species. Here we test whether climate change could have imposed physiological stress on the Tasmanian devil (Sarcophilus harrisii) during the mid-Holocene, when the species went extinct on mainland Australia. Physiological values for the devil were quantified using mechanistic niche models of energy and water requirements for thermoregulation, and soil-moisture-based indices of plant stress from drought to indirectly represent food and water availability. The spatial pervasiveness, extremity, and frequency of physiological stresses were compared between a period of known climatic and presumed demographic stability (8000-6010 BP) and the extinction period (5000-3010 BP). We found no evidence of widespread negative effects of climate on physiological parameters for the devil on the mainland during its extinction window. This leaves cultural and demographic changes in the human population or competition from the dingo (Canis dingo) as the main contending hypotheses to explain mainland loss of the devil in the mid-Holocene.

opencc-zeroJan 2022View details →

Variable vulnerability to climate change in New Zealand lizards

Aim: The primary drivers of species and population extirpations have been habitat loss, overexploitation, and invasive species, but human-mediated climate change is expected to be a major driver in future. To minimise biodiversity loss, conservation managers should identify species vulnerable to climate change and prioritise their protection. Here, we estimate climatic suitability for two speciose taxonomic groups, then use phylogenetic analyses to assess vulnerability to climate change. Location: Aotearoa New Zealand (NZ) Taxa: NZ lizards: diplodactylid geckos and eugongylinae skinks Methods: We built correlative species distribution models (SDMs) for NZ geckos and skinks to estimate climatic suitability under current climate and 2070 future-climate scenarios. We then used Bayesian phylogenetic mixed models (BPMMs) to assess vulnerability for both groups with predictor variables for life history traits (body size and activity phase) and current distribution (elevation and latitude). We explored two scenarios: an unlimited dispersal scenario, where projections track climate, and a no-dispersal scenario, where projections are restricted to areas currently identified as suitable. Results: SDMs projected vulnerability to climate change for most modelled lizards. For species' ranges projected to decline in climatically suitable areas, average decreases were between 42–45% for geckos and 33–91% for skinks, although area did increase or remain stable for a minority of species. For the no-dispersal scenario, the average decrease for geckos was 37–52% and for skinks was 33–52%. Our BPMMs showed phylogenetic signal in climate change vulnerability for both groups, with elevation increasing vulnerability for geckos, and body size reducing vulnerability for skinks. Main conclusions: NZ lizards showed variable vulnerability to climate change, with most species' ranges predicted to decrease. For species whose suitable climatic space is projected to disappear from within their current range, managed relocation could be considered to establish populations in regions that will be suitable under future climates.

opencc-zeroJan 2022View details →

Thermal tolerance in Drosophila: repercussions for distribution, community coexistence and responses to climate change

Here we combined controlled experiments and field surveys to determine if estimates of heat tolerance predict distributional ranges and phenology of different Drosophila species in southern South America. We contrasted thermal death time curves, which consider both magnitude and duration of the challenge to estimate heat tolerance, against the thermal range where populations are viable based on field surveys in an 8-yr longitudinal study. We observed a strong correspondence of the physiological limits, the thermal niche for population growth, and the geographic ranges across studied species, which suggests that the thermal biology of different species provides a common currency to understand how species will respond to warming temperatures both at a local level and throughout their distribution range. Our approach represents a novel analytical toolbox to anticipate how natural communities of ectothermic organisms will respond to global warming.

opencc-by-4.0Feb 2022View details →

Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions (v3.0)

Agricultural land resources – a global suitability evaluation (v3.0) Local climate, soil and topography determine the conditions under which agricultural crops are suitable for growth or not. The methodology uses a fuzzy logic approach that is described in Zabel et al. (2014). The approach is based on Liebig's law of the minimum. Accordingly, plant suitability is determined not by total available resources, but by the scarcest resource. The limiting factor depends on the local environmental conditions and the crop-specific requirements, that are taken from literature.  Determining Agricultural Suitability Agricultural suitability is calculated for each of 5 climate models (GFDL, HadGEM2, IPSL, MIROC and NorESM1) from the AR5 ISIMIP fast track protocol. Daily climate model data for temperature, precipitation and solar radiation are statistically downscaled to 30 arc seconds spatial resolution. A monthly bias-correction is applied using WorldClim data. The provided suitability data refers to the model median over the 5 climate simulations. Soil data is taken from the Harmonized World Soil Database (HWSD) v1.21. Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Soil depth is taken into account according to Pelletier et al. (2015). Topography data is applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the suitability of crops and is considered in this approach. Agricultural Suitability The agricultural suitability data is provided at a spatial resolution of 30 arc seconds (approximately 1 km2 at the equator). The dataset contains four time periods (1980-2009, 2010-2039, 2040-2069, 2070-2099) and two climate change scenarios (RCP2.6 and RCP 8.5). Agricultural suitability is provided for rainfed conditions and for irrigated conditions seperately. Additionally, we provide a dataset in which the current irrigation areas according to Maier et al. (2018) are applied. The suitability is provided for 23 food, feed, fibre, and 1st and 2nd generation bio-energy crops. An 'overall suitability' is provided for all crops that considers the most suitable crop on each pixel. Additionally, we provide a dataset excluding 2nd generation bioenergy crops (18-23) from the overall aggregation of crops. <table> <caption>Food, feed, fiber and first-generation bioenergy crops</caption> <tbody> <tr> <td>Barley</td> <td>Potato</td> <td>Sugarbeet</td> </tr> <tr> <td>Cassava</td> <td>Rapeseed</td> <td>Sugarcane</td> </tr> <tr> <td>Groundnut</td> <td>Rice</td> <td>Sunflower</td> </tr> <tr> <td>Maize</td> <td>Rye</td> <td>Summer wheat</td> </tr> <tr> <td>Millet</td> <td>Sorghum</td> <td>Winter wheat</td> </tr> <tr> <td>Oilpalm</td> <td>Soybean</td> <td> </td> </tr> </tbody> </table> <table> <caption> Second-generation bioenergy crops </caption> <tbody> <tr> <td>Jatropha</td> <td>Reed canary grass</td> </tr> <tr> <td>Miscanthus</td> <td>Eucalyptus</td> </tr> <tr> <td>Switchgrass</td> <td>Willow</td> </tr> </tbody> </table> Growing Season Adaptation The agricultural suitability considers the adaptation of the growing season. For each pixel and crop, the growing season is optimized throughout the year, taking the annual course of precipitation, temperature, and solar radiation as well as their interplay, into account. Most Suitable Crop The most suitable crop for each pixel is provided in the data. Please note that a value of 126 means that no crop suitable and 127 means that multiple crops have the same suitability. Further information Detailled information are available in the following publications: Zabel F, Putzenlechner B, Mauser W (2014) Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLOS ONE 9(9): e107522. doi: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">10.1371/journal.pone.0107522</a> Cronin, J., Zabel, F., Dessens, O., Anandarajah, G. (2020): Land suitability for energy crops under scenarios of climate change and land-use. GCB Bioenergy, 12(8). doi: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcbb.12697">10.1111/gcbb.12697</a> Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data 9, 527. doi: <a href="https://doi.org/10.1038/s41597-022-01632-8">10.1038/s41597-022-01632-8</a> Meier, J., Zabel, F., Mauser, W. (2018): A global approach to estimate irrigated areas – a comparison between different data and statistics. Hydrol. Earth Syst. Sci., 22, 1119–1133, 2018. doi: <a href="https://hess.copernicus.org/articles/22/1119/2018/">10.5194/hess-22-1119-201</a> Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D. (2016), A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, J. Adv. Model. Earth Syst., 8, 41– 65, doi: <a href="https://doi.org/10.1002/2015MS000526">10.1002/2015MS000526</a>. Improvements in v3.0 Compared to the previous version (<a href="https://zenodo.org/record/3748350">v2.0</a>), this version (v3.0) uses updated input data for soil (HWSD v1.21) and high resolution irrigated areas (Maier et al. 2018), and additionally considers soil depth (Pelletier et al. 2016). Moreover, the suitability is calculated for an ensemble of 5 climate models, and is available for more crops, including a number of second generation bioenergy crops. Contact Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department of Geography, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)

opencc-by-4.0Jan 2021View details →

Data and code: Climate policy accelerates structural changes in energy employment

The file contains code to create the figures used in main text and supplementary information of the paper Climate policy accelerates structural changes in energy employment. To run the RMD file and see the resulting figures, press Knit on R studio (requires the package knitr), or else see the attached HTML file, already created through such a process.

opencc-by-4.0Feb 2022View details →

Data from: Central Mongolian lake sediments reveal new insights on climate change and equestrian empires in the Eastern Steppes

The data set includes the results of ICP-OES, CNS, biomarker, and stable isotope analyses published in the research paper: Struck, J., Bliedtner, M., Strobel, P., Taylor, W., Biskop, S., Plessen, B., Klaes, B., Bittner, L., Jamsranjav, B., Salazar, G., Szidat, S., Brenning, A., Bazarradnaa, E., Glaser, B., Zech, M., Zech, R.: Central Mongolian lake sediments reveal new insights on climate change and equestrian empires in the Eastern Steppes. Scientific Reports, 12, 2829, (2022). DOI: https://doi.org/10.1038/s41598-022-06659-w For further information, in particular, the analyses and methods applied, we refer the reader/user to the original research paper and the supporting information published in Scientific Reports.

opencc-by-4.0Sep 2021View details →

Summer Rainfall Scenarios and Climate Change Factor Projections over Wanzhou County, China

This dataset consists of rainfall scenarios and ensemble projections of extreme daily rainfall and mean summer season rainfall over Wanzhou County, China. Precipitation Reference Period (1979-2018) The reference scenario rainfall covers the period of 1979-2018, and is derived from the China Meteorological Forcing Dataset (https://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/). The extreme daily rainfall (in mm/day) is derived from Gumbel distributions fitted to monthly maximum daily rainfall covering the months of June to August. A spatial distribution of return periods from 2, 5, 10 20, 50 and 100 years for this scenario were derived and included in this dataset. The mean seasonal rainfall scenario covers the average daily rainfall (in mm/day) for the months of May to July to represent antecedent rainfall conditions of that could trigger shallow landslides during the summer season. <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.1 degrees x 0.1 degrees</li> <li>Time period: 1979-2018</li> <li>Data Format: .csv files (.xyz file extensions)</li> <li>Variable: Rainfall (pr)</li> <li>Units: mm/day </li> </ul> Ensemble Projections and Climate Change Factors The ensemble climate change projections cover two periods: Mid-21st Century (2021-2060) and Late-21st Century (2061-2100). The influence of climate change is assessed through climate change factors that represent a multiplicative factor of change between present and future climate model outputs. The ensemble projections are the mean climate change factor derived from four bias-corrected Regional Climate Model outputs. The ensemble consisted of the results REMO2015 and RegCM4 models that dynamically downscaled HadGEM2-ES, MPI-ESM-ML, and MPI-ESM-MR model outputs (https://esgf-data.dkrz.de/search/cordex-dkrz/). The bias correction was performed using the quantile delta method. An empirical transfer function for daily rainfall was used to derive the mean seasonal rainfall scenario, while a parametric (Gumbel distribution) transfer function was used to derive on the monthly maxima for the extreme daily rainfall scenarios. <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.22 degrees x 0.22 degrees</li> <li>Time periods: Mid-21st Century (2021-2060) & Late-21st Century (2061-2100)</li> <li>Data Format: .csv files</li> <li>Variable: Climate Change Factor (ccf)</li> <li>Unit: Dimensionless</li> <li>Included ensemble projection statistics: <ul> <li>Standard deviation (sd)</li> <li>Coefficient of Variation (cv)</li> </ul> </li> </ul>

opencc-by-4.0Feb 2022View details →

Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events

Data, code and supplementary Figures for paper "Probabilistic simulation of big climate data for robust quantification of changes in compound hazard events".

Dataset on Alternaria disease on rocket under simulated climate change conditions

This dataset is related to disease severity caused by the Alternaria spp. isolates tested in different temperature and CO2 combinations on cultivated rocket and published in https://doi.org/10.3920/WMJ2016.2108 (Figure 1) and in https://doi.org/10.1007/s42161-018-0125-8,

Data for the Manuscript 'Phenotypic Variation from Waterlogging in Multiple Perennial Ryegrass Varieties under Climate Change Conditions'

Experimental data supporting the findings of the manuscript 'Phenotypic Variation from Waterlogging in Multiple Perennial Ryegrass Varieties under Climate Change Conditions'. This dataset will be made publicly available when the manuscript has been accepted for journal publication unless exceptional conditions become apparent.

Assessment of future wind speed and wind power changes over South Greenland using the MAR regional climate model : MAR ouptuts and KATABATA weather stations timeseries

Daliy MARv3.12 outputs and KATABATA weather stations timeseries used in : Lambin, C., Fettweis, X., Kittel, C., Fonder, M., & Ernst, D. (2022).Assessment of future wind speed and wind power changes over South Greenland using the Modèle Atmosphérique Régional regional climate model. International Journal of Climatology, 43(1),558–574. https://doi.org/10.1002/joc.7795574

opencc-by-4.0Dec 2021View details →

Future water level, discharge, and flood maps under climate change and infrastructure impacts along the Cambodian Mekong.

Baseline and future (2036-2065) river water levels and discharges at 4 gauging stations along the Cambodian Mekong (Kratie, Kampong Cham, Chrouy Changva, and Neak Loeung) under different scenarios of climate change (RCP 4.5 and 8.5) and infrastructural developments. Average depth and duration flood maps are also included for each scenario.   A full description of the methods and results can be found in the article:  Alexander J. Horton, Nguyen V. K. Triet, Long P. Hoang, Sokchhay Heng, Panha Hok, Sarit Chung, Jorma Koponen, and Matti Kummu. (2022). The Cambodian Mekong floodplain under future development plans and climate change. Nat. Hazards Earth Syst. Sci.

opencc-by-4.0Oct 2021View details →

Supporting data for ``Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land'"

Here we have the processed data used in the preprint ``'Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land'' The README.md file includes explanations about the data in the repository.

opencc-zeroAug 2022View details →

Data for: Growing faster, longer or both? Modelling plastic response of Juniperus communis growth phenology to climate change

Aim: Plant growth and phenology plastically respond to changing climatic conditions both in space and time. Species-specific levels of growth plasticity determine biogeographical patterns and the adaptive capacity of species to climate change. However, a direct assessment of spatial and temporal variability in radial-growth dynamics is complicated, as long records of cambial phenology do not exist. Location: 16 sites across European distribution margins of Juniperus communis L. (the Mediterranean, the Arctic, the Alps and the Urals). Time period: 1940-2016 Major taxa studied: Juniperus communis Methods: We applied the Vaganov-Shashkin process-based model of wood formation to estimate trends in growing season duration and growth kinetics since 1940. We assumed that J. communis would exhibit spatially and temporally variable growth patterns reflecting local climatic conditions. Results: Our simulations indicate regional differences in growth dynamics and plastic responses to climate warming. Mean growing season duration is the longest at Mediterranean sites and, recently, there is a significant trend towards its extension of up to 0.44 days per year. However, this stimulating effect of longer growing season is counteracted by declining summer growth rates caused by amplified drought stress. Consequently, overall trends in simulated ring-widths are marginal in the Mediterranean. By contrast, durations of growing seasons in the Arctic show lower and mostly non-significant trends. However, spring and summer growth rates follow increasing temperatures, leading to a growth increase of up to 0.32 % per year. Main conclusions: This study highlights the plasticity in growth phenology of widely distributed shrubs to climate warming–an earlier onset of cambial activity that offsets the negative effects of summer droughts in the Mediterranean and, conversely, an intensification of growth rates during the short growing seasons in the Arctic. Such plastic growth responsiveness allows woody plants to adapt to the local pace of climate change.

Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)

This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX). The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute  for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France);  Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015. Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant. The objectives of CLIMAX  include  better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate. <ol> <li> EXPERIMENT DESIGN </li> </ol> We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005). We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022). We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named historical CO2 (hist CO2); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named constant CO2 (const CO2). We ran both CO2 experiments under Land Use Change (LUC) and Potential Natural Vegetation (PNV) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. LUC historical CO2; 2. LUC constant CO2; 3. PNV historical CO2; 4. PNV constant CO2  (Rezende et al., 2022). 2. DATA DESCRIPTION The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs. 2.1 SOFTWARE The data were manipulated, worked, standardized, converted using the software: Climate Data Operators (cdo) version 1.7.0, Grid Analysis and Display System (Grads) (<a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>) version 2.0.2, and RStudio Desktop version 1.3.1093 (R Core Team, 2020), through command lines and several scripts developed for this purpose. The figures were generated  with Grads, and RStudio, and some images were enhanced  with Gimp version 2.8.22. All the software used is freeware.    2.2 PRIMARY DATA FROM SIMULATIONS Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning  over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010. Variables: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation  (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment). The naming of the files is according to the following rules: DGVM_forcing_vegetation cover_CO2 concentration_attribute DGVMs;     InLand (INLAND)     LPJ-G (LPJ-GUESS)     LPJmL (LPJmL4)     ORCHI (ORCHIDEE) forcings:      gld - GLDAS     gsw – GSWP3     wat – WATCH+WFDEI   vegetation cover:     LU – Land Use Change     PNV – Potential Natural Vegetation   CO2 concentration:     CO2 – historical CO2     noCO2 – constant CO2 = 278 ppm   variables:     E – evaporation     Et – transpiration     gpp – Gross Primary Productivity     npp - Net Primary Productivity (not used in the experiment)   Example:     inLand_gld_LU_noCO2_E.nc    2.3 SUPPLEMENTARY DATA These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude). Files are named  according to the following rules: variable_season_forcing_DGVM_vegetation cover CO2 concentration_region variable:     inter – loss by interception   season:     D – dry season     R – rainy season   forcings:                 gl - GLDAS                gs – GSWP3                wa – WATCH+WFDEI   DGVMs:     in - INLAND      lg - LPJ-GUESS             lm - LPJmL4             or – ORCHIDEE   vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   region     SA – southern Amazon   Example:     inter_D_gl_in_LC_SA.nc 2.4 PROCESSED DATA Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation  (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).   Files are named according to the following rules:   season_DGVM_forcing_vegetation cover CO2 concentration_region season     D – dry season     R – rainy season   DGVMs:     in - INLAND      lg - LPJ-GUESS            lm - LPJmL4            or - ORCHIDEE   forcings:             Gl - GLDAS            Gs – GSWP3            Wa – WATCH+WFDEI      vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   Example: D_in_Gs_LN.nc 2.5 DERIVED DATA   The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1).    <table> <tbody> <tr> <td>                                   WUE = GPP / Tr </td> <td> (Eq. 1) </td> </tr> </tbody> </table>   These data refer to the study region: southern Amazon and apply to only one scenario:  Land Use Change and historic CO2. Files are named  naming of according to the following rules:   Variable:     WUE – Water Use Efficiency   season     D – dry season     R – rainy season   DGVMs:     in - INLAND      lg - LPJ-GUESS            lm - LPJmL4            or - ORCHIDEE   forcings:                 Gl - GLDAS                Gs – GSWP3                Wa – WATCH+WFDEI      vegetation cover      L – Land Use Change     P – Potential Natural Vegetation   CO2 concentration     C – historical CO2     N – constant CO2 = 278 ppm   region     SA – southern Amazon   Example: wue_D_lm_Gl_LC_SA.nc   How to cite this work: Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on  Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022. References <a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015. Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a> Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.               Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg. Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6. Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on  Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. 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Data for manuscript: Ecological lags govern the pace and outcome of plant community responses to 21st century climate change

These data were used in the analyses reported in Block et al. "Ecological lags govern the pace and outcome of plant community responses to 21st century climate change".

Climate transition at the Eocene - Oligocene influenced by bathymetric changes to the Atlantic - Arctic oceanic gateways

This data includes input and output fields of the Norwegian Earth System model simulations targeting the Eocene-Oligocene boundary, and the data accompanies a scientific article with the same name (DOI 10.1073/pnas.2115346119). This data is aimed to provide users enough data to reproduce the figures appearing in the article and to analyze the steady state behavior beyond what is discussed in the article. We also provide the ocean bathymetry and atmospheric topography that allows others to create a model setup similar to what is used in the article. For most files there are two output periods 1450-1500 is a common period for the experiments: Late Eocene and cases 1-5. This period the average over the last 50 years of the experiments that were branched of the common spinup at year 1000. The latter period 2650-2700 includes continuation of the Late Eocene and Case 2 simulations and their lower CO2 counterparts (branched of from their parent simulations at year 2000). For more information of the simulations and the setup, we refer to the parent article. In addition to the output files, we also share the grid files of the ocean model (grid*.nc) and the topography used by the atmospheric model (ATM_TOPO.nc). Note that we did not change the land mask between the experiments which is why there is only one ATM_TOPO, but one grid file per each experiment.