| Gaia: | Data and coding scripts for Seddon et al. (2016) Nature (DOI 10.1038/nature16986). We derived monthly time-series of four key terrestrial ecosystem variables at 0.05 degree (~5km) resolution from observations by the MODIS sensor on Terra (AM) for the period February 2010-December 2013 inclusive, and developed a method to identify vegetation sensitivity to climate variability over this period (see Methods in main paper).
This ORA item contains all data and files required to run the analysis described in the paper. Data required to run the script are provided in six zip files evi.zip, temp.zip, aetpet.zip, cld.zip, stdev.zip, numpxl.zip, each containing 167 text files, one per month of available data, in addition to a supporting files folder. Details are as follows.
supporting_files.zip : This directory includes computer code and additional supporting files. Please see the 'read me.txt' file within this directory for more information.
evi.zip: ENHANCED VEGETATION INDEX (EVI). We used the MOD13C2 product (Huete et al 2002) which comprises monthly, global EVI at 0.05 degree resolution. In some cases where no clear-sky observations are available, the MOD13C2 version 5 product replaces no-data values with climatological monthly means, so we removed these values where appropriate.
EVI format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = dimensionless scale factor = 10000 (divide the value by 10000 to get EVI) filenames = yyyymmevi.txt
numpxl.zip - COUNTS OF THE NUMBER OF PIXELS USED IN EVI CALCULATION. The MOD13C2 product is the result of a spatially and temporally averaged mosaic of higher resolution (1km pixels). Data in this directory represent the number of 1km observations used to calculate the MODIS EVI product. See the online documentation for more details (Solano et al. 2010).
numpxl format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = counts filenames = yyyy_mm_numpxl_pt05deg.txt
stdev.zip - STANDARD DEVIATION OF EVI VALUES. Standard deviation of the monthly EVI observations. See discussion in numpxl.zip item (above) and the online documentation for more details (Solano et al. 2010).
stdev format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = dimensionless scale factor = 10000 (divide the value by 10000 to get EVI) filenames = yyyy_mm_stdev_pt05deg.txt
temp.zip: AIR TEMPERATURE. We used the MOD07_L2 Atmospheric Profile product (Seeman et al 2006) as a measure of air temperature. Five-minute swaths of Retrieved Temperature Profile were projected to geographic co-ordinates. Pixels from the highest available pressure level, corresponding to the temperature closest to the Earth's surface, were selected from each swath. Swaths were then mean-mosaicked into global daily grids, and the daily global grids were mean-composited to monthly grids of air temperature.
Air temperature format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = degrees C scale factor = 1 (divide the value by 1 to get Air temperature) filenames = yyyymmtemp.txt
aetpet.zip: WATER AVAILABILITY. We used the MOD16 Global Evapotranspiration product (Mu et al 2011) to calculate the monthly 0.05 degree ratio of Actual to Potential Evapotranspiration (AET/PET).
AET/PET format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = dimensionless scale factor = 10000 (divide the value by 10000 to get AET/PET) filenames = yyyymmaetpet.txt
cld.zip - CLOUDINESS. We used the MOD35_L2 Cloud Mask product (Ackerman et al 2010). This product provides daily records on the presence of cloudy vs cloudless skies, and we used this to make an index of the proportion of of cloudy to clear days in a given pixel. After conversion to geographic co-ordinates, five-minute swaths at 1-km resolution were reclassed as clear sky or cloudy, and these daily swaths were mean-mosaicked to global coverages, mean composited from daily to monthly, and mean-aggregated from 1km to 0.05 degree.
cld format = ascii text file projection = geographic projection spatial resolution = 0.05 degrees min x = -180 max x = 180 min y = -60 max x = 90 rows = 3000 cols = 7200 bit depth = 16 bit signed integer nodata (sea) = -9999 missing data (on land) = -999 units = percentage of days in the month which were cloudy scale factor = 100 (divide the value by 100 to get percentage cloudy days) filenames = yyyymmcld.txt
References
Ackerman, S. et al. (2010) Discriminating clear-sky from cloud with MODIS: Algorithm Theoretical Basis Document (MOD35), Version 6.1. (URL: ttp://modis- atmos.gsfc.nasa.gov/_docs/MOD35_A TBD_Collection6.pdf)
Huete, A. et al. (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195–213.
Mu, Q., Zhao, M., Running, S.R. (2011) Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment 115, 1781-1800
Seeman, S. W., Borbas, E. E., Li, J., Menzel, W. P. & Gumley, L. E. (2006) MODIS Atmospheric Profile Retrieval Algorithm Theoretical Basis Document, Version 6 (URL: http://modis-atmos.gsfc.nasa.gov/_docs/MOD07_atbd_v7_April2011.pdf)
Solano, R. et al. (2010) MODIS Vegetation Index User’s Guide (MOD13 Series)
Version 2.00, May 2010 (Collection 5) (URL: http://vip.arizona.edu/documents/MODIS/MODIS_VI_UsersGuide_01_2012.pdf) Seddon et al. (2016) Nature (DOI 10.1038/nature16986) ABSTRACT: Identification of properties that contribute to the persistence and resilience of ecosystems despite climate change constitutes a research priority of global significance. Here, we present a novel, empirical approach to assess the relative sensitivity of ecosystems to climate variability, one property of resilience that builds on theoretical modelling work recognising that systems closer to critical thresholds respond more sensitively to external perturbations. We develop a new metric, the Vegetation Sensitivity Index (VSI) which identifies areas sensitive to climate variability over the past 14 years. The metric uses time-series data of MODIS derived Enhanced Vegetation Index (EVI) and three climatic variables that drive vegetation productivity (air temperature, water availability and cloudiness). Underlying the analysis is an autoregressive modelling approach used to identify regions with memory effects and reduced response rates to external forcing. We find ecologically sensitive regions with amplified responses to climate variability in the arctic tundra, parts of the boreal forest belt, the tropical rainforest, alpine regions worldwide, steppe and prairie regions of central Asia and North and South America, the Caatinga deciduous forest in eastern South America, and eastern areas of Australia. Our study provides a quantitative methodology for assessing the relative response rate of ecosystems – be they natural or with a strong anthropogenic signature – to environmental variability, which is the first step to address why some regions appear to be more sensitive than others and what impact this has upon the resilience of ecosystem service provision and human wellbeing.
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