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CYGNSS Level 3 Climate Data Record Version 1.0

This dataset contains the Version 1.0 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 2 month latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the SDR v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v2.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. The SDR v2.1 L1 algorithm assumes a constant GPS transmit power and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce SDR v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v2.1 L1 data so there is also some loss of samples that were present in SDR v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.

CYGNSS Level 2 Climate Data Record Version 1.2

This dataset contains the Version 1.2 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. The Version 1.2 CDR represents is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.1 (https://doi.org/10.5067/CYGNS-L2X31 ) using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L2C11 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.1 L1 data so there is also some loss of samples that were present in SDR v3.1.

CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record Version 1.1

This dataset contains the first release, Version 1.1, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 1-2 month latency from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR v1.1 ocean surface wind speed estimates with the auxiliary parameters provided by the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS. Additional information on the CYGNSS L2 CDR v1.1 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C11.

CYGNSS Level 3 Climate Data Record Version 1.1

This dataset contains the Version 1.1 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 1 to 2 month latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.1 CDR is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 (https://doi.org/10.5067/CYGNS-L3X30 ) using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L3C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.0 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. CDR v1.1 does not include a Young Seas with Limited Fetch (YSLF) wind speed product and investigators requiring wind speed measurements in and near the inner core of tropical cyclones should use the SDR v3.0 YSLF wind speed product. A YSLF wind speed product is omitted because the trackwise correction algorithm, which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds, is inherently biased toward fully developed sea state conditions. The constraint improves wind speed retrieval performance in fully developed seas but produces underestimates in YSLF conditions. It should also be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.0 L1 data so there is also some loss of samples that were present in SDR v3.0.

CYGNSS Level 2 Climate Data Record Version 1.0

This dataset contains the Version 1.0 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 2 months (or better) from the last recorded measurement time. The Version 1.0 CDR represents the first climate-quality release and is a collection of reanalysis products derived from the SDR v2.1 Level 1 data. Calibration accuracy and long term stability are improved relative to the SDR v2.1 using a new trackwise correction algorithm which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds. Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v2.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v2.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for variations in the transmit power level of the GPS signals measured by the CYGNSS bistatic radar receivers. The SDR v2.1 L1 algorithm assumes a constant GPS transmit power and variations in it can be misinterpreted as variations in the L1 data and in subsequent L2 science data products derived from them. The GPS constellation consists of several different satellite models (a.k.a. block types) and the level of transmit power variation differs between them. The more recent Block IIF models (which account for ~37% of the GPS constellation) have significantly larger variations than the older models and, for this reason, they have been screened out and not used to produce SDR v2.1 L2 or L3 science data products. Trackwise correction eliminates the need for this screening so CDR L2 and L3 data products now include Block IIF samples. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v2.1 L1 data so there is also some loss of samples that were present in SDR v2.1. Overall, there is a significant increase in sampling and improvement in spatial coverage with the CDR products.

CYGNSS Level 3 Climate Data Record Version 1.2

This dataset contains the Version 1.2 CYGNSS Level 3 Climate Data Record which provides the average wind speed and mean square slope (MSS) on a 0.2x0.2 degree latitude by longitude equirectangular grid obtained from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The Level 2 Delay Doppler Map (DDM) data are used in the direct processing of the average wind speed and MSS data that are binned on the Level 3 grid. A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. A single netCDF-4 data file is produced for each day of operation with an approximate 5 days latency. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). The Version 1.2 CDR is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.1 (https://doi.org/10.5067/CYGNS-L3X31 ) using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L3C11 ), which was derived from SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.1 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.1. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.1 L1 data so there is also some loss of samples that were present in SDR v3.1.

CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record Version 1.0

This dataset contains the first release, Version 1.0, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 1-2 month latency from the Delay Doppler Mapping Instrument (DDMI) aboard the CYGNSS satellite constellation. The Cyclone Global Navigation Satellite System (CYGNSS) is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR v1.0 ocean surface wind speed estimates with the auxiliary parameters provided by the NASA Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS. Additional information on the CYGNSS L2 CDR v1.0 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C10.

CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record Version 1.2

This dataset contains the third release, Version 1.2, of the CYGNSS Level 2 Ocean Surface Heat Flux Climate Data Record (CDR), which provides the time-tagged and geolocated ocean surface heat flux parameters with 25x25 kilometer footprint resolution with 6-7 day latency from the Delay Doppler Mapping Instrument (DDMI) aboard the Cyclone Global Navigation Satellite System (CYGNSS) constellation. CYGNSS is a NASA Earth System Science Pathfinder Mission designed to collect the first frequent space-based measurements of surface wind speeds in the inner core of tropical cyclones. The Coupled Ocean-Atmosphere Response Experiment (COARE) version 3.5 algorithm combines CYGNSS L2 CDR v1.2 ocean surface wind speed estimates with the auxiliary parameters provided by the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5) to produce latent and sensible heat fluxes and their respective transfer coefficients. More information on how the data is produced and validated can be found in the dataset user guide (see Documentation tab). More information on the CYGNSS mission, spacecraft, instrumentation and related datasets is available here: https://podaac.jpl.nasa.gov/CYGNSS . Additional information on the CYGNSS L2 CDR v1.2 wind speed dataset is available here: https://doi.org/10.5067/CYGNS-L2C12 .

CYGNSS Level 2 Climate Data Record Version 1.1

This dataset contains the Version 1.1 CYGNSS Level 2 Climate Data Record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 1 to 2 months from the last recorded measurement time. The Version 1.1 CDR represents is a collection of reanalysis products derived from the SDR v3.0 Level 1 data (https://doi.org/10.5067/CYGNS-L1X30 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 (https://doi.org/10.5067/CYGNS-L2X30 ) using the same trackwise correction algorithm as was used by CDR v1.0 (https://doi.org/10.5067/CYGNS-L2C10 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. CDR Level 2 and 3 products (ocean surface wind speed, mean square slope, and latent and sensible heat flux) are generated from the CDR L1 data using the v3.0 SDR data processing algorithms. These products also exhibit improved calibration accuracy and stability over SDR v3.0. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the leading edge slope of the Doppler-integrated delay waveform (LES). The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. CDR v1.1 does not include a Young Seas with Limited Fetch (YSLF) wind speed product and investigators requiring wind speed measurements in and near the inner core of tropical cyclones should use the SDR v3.0 YSLF wind speed product. A YSLF wind speed product is omitted because the trackwise correction algorithm, which constrains the average value of the L1 data using MERRA-2 reanalysis wind speeds, is inherently biased toward fully developed sea state conditions. The constraint improves wind speed retrieval performance in fully developed seas but produces underestimates in YSLF conditions. It should also be noted that the trackwise correction algorithm cannot be successfully applied to all SDR v3.0 L1 data so there is also some loss of samples that were present in SDR v3.0.

CYGNSS Level 1 Climate Data Record Version 1.2

This Level 1 (L1) dataset contains the Version 1.2 Climate Data Record (CDR) of the geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 1 week. The Version 1.2 CDR is a collection of reanalysis products derived from the SDR v3.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X31 ). Calibration accuracy and long term stability are improved relative to SDR v3.0 using the same trackwise correction algorithm as was used by CDR v1.1 (https://doi.org/10.5067/CYGNS-L1C11 ), which was derived from SDR v2.1 Level 1 data (https://doi.org/10.5067/CYGNS-L1X21 ). Details of the algorithm are provided in the Trackwise Corrected CDR Algorithm Theoretical Basis Document. Trackwise correction is applied to the two primary CYGNSS L1 science data products, the normalized bistatic radar cross section (NBRCS) and the LES. The correction compensates for small errors in the Level 1 calibration, due e.g. to uncertainties in the GPS transmitting antenna gain patterns and the CYGNSS receiving antenna gain patterns. It should be noted that the trackwise correction algorithm cannot be successfully applied to all v3.1 SDR L1 data, so there is also some loss of samples that were present in v3.1.

opencc-by-4.0Jan 2020View details →

Water isotope data for "Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction"

iCESM1.2 simulated seawater oxygen isotopes for the Early Eocene Citation: Zhu, J., Poulsen, C. J., Otto-Bliesner, B. L., Liu, Z., Brady, E. C., & Noone, D. C. (2020). Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction. Earth and Planetary Science Letters, 537, 116164. <a href="https://doi.org/10.1016/j.epsl.2020.116164">https://doi.org/10.1016/j.epsl.2020.116164</a> <ul> <li>Data set includes climatology (12 months) sea-surface temperature (TEMP) and sea-surface oxygen isotope ratio (R18O) from four Eocene simulations with 1×, 3×, 6×, and 9× preindustrial level of CO2 (284.7 ppmv), and a preindustrial simulation.</li> <li>Climatology was calculated from averaging data over the last 100 years of each simulation.</li> <li>Seawater d18O = (R18O - 1.0) * 1000.0</li> <li>TEMP and R18O are on the POP ocean grid (~1°; see here: <a href="http://www.cesm.ucar.edu/models/cesm1.2/pop2/">http://www.cesm.ucar.edu/models/cesm1.2/pop2/</a>).</li> </ul>

opencc-by-4.0Feb 2020View details →

Model simulation data used in "Coupling aerosols to (cirrus) clouds in the global aerosol-climate model EMAC-MADE3" (Righi et al., Geosci. Model Dev., 2020)

This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (Geosci. Model Dev., 2020). An overview of the numerical experiments performed for this study is given in the file "experiments.dat".

Data for the parameterization of radiative transfer processes in urban climate models

Radiative Transfer Model (RTM) is a key component in microscale building resolving urban climate models (UCM), which are used to simulate the flow within urban area. We use different parameterizations of RTMs in the model system <a href="https://gmd.copernicus.org/articles/13/1335/2020/gmd-13-1335-2020.html">PALM</a> version 6.0 to show how much detail modellers should include in their simulation. We introduce the output PALM model results for two examples: (1) A simplified urban geometry consisting of an urban crossing (UC) and (2) a realistic urban domain located at the town square Ernst-Reuter-Platz in Charlottenburg in Berlin (ER). The netCDF files contain the radiative flux received by each surface in the domains, including the shortwave (direct and diffuse) radiation as well as the longwave radiation. Also, the data set includes the 3D flow variables (u, v, w) and the potential temperature. The model drivers (input data) for both examples are included as well. The data set consists of the following model input/output data: 1) Simplified urban domain (UC): <ul> <li>Input driver for the model PALM for UC (UC_model_driver.tar.gz)</li> <li>Radiation fluxes for UC when using RTM_01: radiation for horizontal surfaces (UC_RTM_01.nc)</li> <li>Radiation fluxes for UC when using RTM_02: sky view effect (building shadows) (UC_RTM_02.nc)</li> <li>Radiation fluxes for UC when using RTM_03: vegetation interaction with SW radiation (UC_RTM_03.nc)</li> <li>Radiation fluxes for UC when using RTM_04: receiving radiation from surface emission (UC_RTM_04.nc)</li> <li>Radiation fluxes for UC when using RTM_05: vegetation interaction with LW radiation (UC_RTM_05.nc)</li> <li>Radiation fluxes for UC when using RTM_06: single reflection (UC_RTM_06.nc)</li> <li>Radiation fluxes for UC when using RTM_07: vegetation interaction with reflected radiation (UC_RTM_07.nc)</li> <li>Radiation fluxes for UC when using RTM_08: multiple reflections (UC_RTM_08.nc)</li> <li>3D data for the UC reference case which includes u,v,w,theta</li> </ul> 2) Realistic urban domain (ER): <ul> <li>Input driver for the model PALM for ER (ER_model_driver)</li> <li>Radiation fluxes for ER when using RTM_01: radiation for horizontal surfaces (ER_RTM_01.nc)</li> <li>Radiation fluxes for ER when using RTM_02: sky view effect (building shadows) (ER_RTM_02.nc)</li> <li>Radiation fluxes for ER when using RTM_03: vegetation interaction with SW radiation (ER_RTM_03.nc)</li> <li>Radiation fluxes for ER when using RTM_04: receiving radiation from surface emission (ER_RTM_04.nc)</li> <li>Radiation fluxes for ER when using RTM_05: vegetation interaction with LW radiation (ER_RTM_05.nc)</li> <li>Radiation fluxes for ER when using RTM_06: single reflection (ER_RTM_06.nc)</li> <li>Radiation fluxes for ER when using RTM_07: vegetation interaction with reflected radiation (ER_RTM_07.nc)</li> <li>Radiation fluxes for ER when using RTM_08: multiple reflections (ER_RTM_08.nc)</li> <li>3D data for the ER reference case which includes u,v,w,theta</li> </ul> For more information and analysis, please check out the relevant publication in the international journal Geoscientific Model Development: Salim et. al, Importance of radiative transfer processes in urban climate models:A study based on the PALM model system 6.0, submitted to GMD.

"Shifts in Phytoplankton Composition and Stepwise Climate Change during the Middle Miocene" - Age-depth models and calcareous nannofossil census data

This is a data supplement to the paper "Shifts in Phytoplankton Composition and Stepwise Climate Change during the Middle Miocene" (Paleoceanography and Paleoclimatology). Data Set S1. Age-depth models. This data set includes the file SI_Tables S2-S5_Henderiks_etal.xlsx containing raw age-depth tie point compilations for each site and sample age estimates, as well as the final, site-specific input files and output (age assignments) from the Undatable Matlab software Version 1.1 (Lougheed and Obrochta, 2019; https://doi.org/10.1029/2018PA003457). The age-depth models presented in this study can be reproduced by running the age-depth model input files in the Undatable graphical user interface (GUI), whereby the necessary settings for the specific number of Monte Carlo iterations, xfactor and bootstrapping are contained in the header of the input files. Note that input and output files are grouped in two zipped folders: a cm- and meter-depth scale version (the latter decreases computing time and produced the age-depth plots shown in Figures S1 and S2 of the paper). Data Set S2. Calcareous nannofossil census data. The file SI_ds02_Henderiks_etal.xlsx consists of two separate data sheets: 1. Middle Miocene nannofossil abundance estimates (N/g) and genus-level census counts (%, ±95% CI) at 5 different Atlantic deep-sea sites (Sites 982, 608, 925, 926 and 1264). 2. Middle Miocene census counts (%, ±95% CI) of Coccolithus and Reticulofenestra morphospecies and size categories for Sites 982, 608, 925 and 926.

Data repository: Cytotype distributions of the common Dandelion (Taraxacum section Ruderalia) and the Cuckoo flower (Cardamine pratensis) in Europe and how these are affected by climate change

This map contains all datasets and R-scripts used to write the paper with the same name. This map also contains the original research proposal on which the datasets are based. The datasets contain location data on the cytotypes of the common Dandelion and the Cuckoo flower in Europe. The data has been collected using a meta-analysis. The R-scripts present are for data preparation, SDM's for current and future scenarios and bioclimatic variable preparation.

High-resolution gridded climate data for Europe based on bias-corrected EURO-CORDEX: the ECLIPS-2.0 dataset

We developed a new climate dataset for Europe referred to as ECLIPS (European CLimate Index ProjectionS), which contains gridded data for 80 annual, seasonal, and monthly climate variables for two past (1961-1990, 1991-2010) and five future periods (2011-2020, 2021-2140, 2041-2060, 2061-2080, 2081-2100). The future data are based on five Regional Climate Models (RCMs)driven by two greenhouse gas concentration scenarios, RCP 4.5 and 8.5. The  ECLIPS dataset has two versions; ECLIPS 1.1 contains data with spatial resolution of 0.11° × 0.11°, which is the resolution of underlying RCMs. ECLIPS1.1 is available at <a href="https://doi.org/10.5281/zenodo.1181780">https://doi.org/10.5281/zenodo.1181780</a>. The ECLIPS 2.0 presented here contains a subset of climate indices of ECLIPS 1.1, downscaled to the resolution of  30 arcsec by means of the delta correction approach. Both ECLIPS versions were evaluated by testing their relationship with independent station data from the European Climate Assessment (ECA) dataset. Correlations of the empirical testing data to ECLIPS 1.1 ranged from 0.63 to 0.78,and to ECLIPS 2.0 from 0.78 to 0.93. suggesting substantial improvement due to downscaling. A large number of climate projections, time periods and indices as well as the availability of these data at two different spatial resolutions can support diverse studies across a range of disciplines and thus extend our understanding of climate-sensitive dynamics of many social-ecological systems The zipfile ECLIPS2.0 contains 5 folders with subfolders File naming system for the subfolders / folder are  as follows ECLIPS2.0_196191: past climate 1961-1990: < climate index><period> ECLIPS2.0_199110: past climate 1991-2010 < climate index><period> ECLIPS2.0_45 : future climate RCP4.5 <subfolder-Model name> < climate index><period> ECLIPS2.0_85 : future climate RCP8.5 <subfolder-Model name> < climate index><period> Incase zpfile reader 7zip is not available, please install from here: <a href="https://www.7-zip.org/">https://www.7-zip.org/</a>

opencc-zeroJul 2020View details →

dryad28/100

Data from: Microgeography, not just latitude, drives climate overlap on mountains from tropical to polar ecosystems

An extension of the climate variability hypothesis is that relatively stable climate, such as that of the tropics, induces distinct thermal bands across elevation that render dispersal over tropical mountains difficult compared to temperate mountains. Yet, ecosystems are not thermally static in space-time, especially at small scales, which might render some mountains greater thermal isolators than others. Here, we provide an extensive investigation of temperature drivers from fine to coarse scales, and demonstrate that the degree of overlap in temperatures at high and low elevations on mountains is driven by more than just absolute mountain height and latitude. We compiled a database of 29 mountains spanning 6 continents to characterize "thermal overlap" by vertically stratified microhabitats, biomes, and owing to seasonal changes in foliage, demonstrating via mixed-effects modeling that micro- and mesogeography more strongly influence thermal overlap than macrogeography. Impressively, an increase of one meter of vertical microhabitat height generates an increase in overlap equivalent to a 5.26° change in latitude. In addition, forested mountains have reduced overlap – 149% lower – relative to non-forested mountains. We provide evidence in support of a climate hypothesis that emphasizes microgeography as a determinant of dispersal, demographics, and behavior, thereby refining classical theory of macroclimate variability as a prominent driver of biogeography.

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Data from: Climate change and landscape-use patterns influence recent past distribution of giant pandas

Climate change is one of the most pervasive threats to biodiversity globally, yet the influence of climate relative to other drivers of species depletion and range contraction remain difficult to disentangle. Here, we examine climatic and non-climatic correlates of giant panda (Ailuropoda melanoleuca) distribution using a large-scale 30-year dataset to evaluate whether a changing climate has already influenced panda distribution. We document several climatic patterns, including increasing temperatures, and alterations to seasonal temperature and precipitation. We found that while climatic factors were the most influential predictors of panda distribution, their importance diminished over time, while landscape variables have become relatively more influential. We conclude that the panda's distribution has been influenced by changing climate, but conservation intervention to manage habitat is working to increasingly offset these negative consequences.

opencc-zeroJun 2020View details →

opencc-zeroAug 2020View details →

Supplementary material 2 from: Datta A, Schweiger O, Kühn I (2020) Origin of climatic data can determine the transferability of species distribution models. NeoBiota 59: 61-76. https://doi.org/10.3897/neobiota.59.36299

Multimodel inference table