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REFERENCE RMD FILE:

1 Summary

This data prep markdown calls a series of scripts to download and analyze sea ice data for the habitat subgoal and coastal protection goal. See data layers documentation for more information.

2 Updates from previous assessment

No changes to methods from previous assessment. We now download the data from a HTTPS website, rather than the defunct FTP website used previously. Instructions for downloading have been updated below.

3 Data Source

Reference:
Cavalieri, D.J., Parkinson, C.L., Gloersen, P. and Zwally, H. (1996). Sea ice concentrations from Nimbus-7 SMMR and DMSP SMM/I-SSMIS passive microwave data. 1979-2014. Boulder, Colorado, USA: NASA National Snow and Ice Data Center Distributed Active Archive Center (http://dx.doi.org/10.5067/8GQ8LZQVL0VL).

Downloaded: August 8, 2019

Description: Monthly sea ice extent data.

Data can be downloaded here: https://nsidc.org/data/nsidc-0051

3.1 Downloading the raw data

To download the raw data, you must go to this website: https://nsidc.org/data/nsidc-0051, and search "nt_??????_" to access the monthly files.

This is because all monthly files follow the format “nt_YYYYMM_SSS_vVV_R.bin”. Click “Order Files” and wait until your order is processed. Once processed (should only take a couple of minutes, you will receive an email with instructions), download the zip file and place it into the /NSIDC_Seaice/v2019/raw folder on Mazu and extract it.

The zip file contains numbered folders, each with two files in them, .xml and .bin files.

We need to create “north” and “south” folders and place the .bin files within them. To do this, search for .xml in the finder, and place all of these files in a “xml” folder. Now move all of the north .bin files into the newly created “north” folder by the same process, just searching for “n.bin” in the finder. Repeat the same process for the south files, searching for “s.bin” instead.

The final folder structure looks like this, where you have created new “north”, “south”, and “xml” folders:

For complete documentation and more information about data access, please see:

http://nsidc.org/data/nsidc-0051.html

If you wish to be notified of updates or corrections to these data, please register with NSIDC User Services by sending e-mail to: nsidc@nsidc.org

Identify yourself as a user of “Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data (NSIDC-0051).” Include your name, e-mail address, postal address, and telephone number.

If you have questions, please contact NSIDC User Services.

CONTACT INFORMATION: User Services National Snow and Ice Data Center CIRES, 449 UCB University of Colorado Boulder, CO USA 80309-0449 Phone: +1 303-492-6199 Fax: +1 303-492-2468 E-mail:

Time range: 1970-2018

4 Methods

4.1 Setup

Load all relevant libraries, establish/define parameters and commonly used pathnames. Manually change scenario and data years in file pathnames code chunk to reflect the most recent data (d) and current assessment year (v) in setup code chunk.

## This file makes it easier to process data for the OHI global assessment
##  by creating the following objects:
## 
##  * dir_M = identifies correct file path to Mazu (internal server) based on your operating system
##  * mollCRS = the crs code for the mollweide coordinate reference system we use in the global assessment
##  * regions_shape() = function to load global shapefile for land/eez/high seas/antarctica regions
##  * ohi_rasters() = function to load two rasters: global eez regions and ocean region
##  * region_data() = function to load 2 dataframes describing global regions 
##  * rgn_syns() = function to load dataframe of region synonyms (used to convert country names to OHI regions)
##  * low_pop() = function to load dataframe of regions with low and no human population
##  * UNgeorgn = function to load dataframe of UN geopolitical designations used to gapfill missing data

4.2 Location of Maps

These maps of the OHI regions were made by the PreparingSpatialFiles.R script. If there are changes to the OHI regions, the PreparingSpatialFiles.R script will need to be run. Additionally, it is critical to walk through the ObtainingData.R script if any of the spatial files have been modified (this saves the files as spatial points).

The original polygon files are used from: git-annex/globalprep/_raw_data/NSIDC_SeaIce/v2015. However, we use the fasterize package to rasterize the polygons and save them to: git-annex/globalprep/_raw_data/NSIDC_SeaIce/v2017.

4.4 Function 1

Collects the data for each month/year from mazu and add to raster stack that is saved in tmp folder as: n_rasters_points.rdata or s_rasters_points.rdata. And, if it doesn’t already exist, it converts the region shapefile into a raster points file. See ObtainingData.R script for more details.

4.5 Function 2

Using the data from the .rdata files created from Function 1 with the ObtainingData.R script, this function calculates status and trend for shoreline ice and ice edge habitat. Data is saved in intermediate (int) folder:

  • Habitat: n_IceEdgeHabitat.csv, s_IceEdgeHabitat.csv
  • Coastal Protection: n_IceShoreProtection.csv, s_IceShoreProtection.csv

4.6 Final calculations and organization

Read in ice edge habitat and ice shore protection csv-format datasets, remove anamolous eez regions with minimal ice cover, remove disputed regions. Bind these datasets and convert to units of km^2. Save seaice health, extent, trend, and extent data.

n_edge <- read.csv("int/n_IceEdgeHabitat_ref1979to2000.csv")
s_edge <- read.csv("int/s_IceEdgeHabitat_ref1979to2000.csv")
edge <- rbind(n_edge, s_edge)
edge  <- edge %>%
  dplyr::filter(Reference_avg1979to2000monthlypixels != 0) %>%
  dplyr::filter(!(rgn_id %in% c(59, 141, 219, 4, 172, 94))) %>%  # anomolous eez regions with very little ice cover
  dplyr::filter(!(rgn_id %in% c("248300","258510","258520","258600","258700"))) %>% # cut due to minimal ice (<200km2/yr - avg of months)
  dplyr::filter(rgn_nam != "DISPUTED") %>%
  dplyr::mutate(habitat="seaice_edge")

n_shore <- read.csv("int/n_IceShoreProtection_ref1979to2000.csv")
s_shore <- read.csv("int/s_IceShoreProtection_ref1979to2000.csv")
shore <- rbind(n_shore, s_shore)
shore <- shore %>%
  dplyr::filter(Reference_avg1979to2000monthlypixels != 0) %>%
  dplyr::filter(!(rgn_id %in% c(59, 89, 177, 178))) %>%  # anomolous eez regions with very little ice cover
  dplyr::filter(rgn_nam != "DISPUTED") %>%
  dplyr::mutate(habitat = "seaice_shoreline")

data <- rbind(edge, shore)
data  <- data %>%
  dplyr::mutate(km2 = Reference_avg1979to2000monthlypixels/12 * (pixel/1000)^2)

write.csv(data, "int/sea_ice.csv", row.names = FALSE)

## health data
health <- data %>%
  dplyr::filter(rgn_typ == "eez") %>%
  dplyr::select(rgn_id, habitat, dplyr::starts_with("pctdevR")) %>%
  tidyr::gather("year", "health", -(1:2)) %>%
  dplyr::mutate(year = substring(year, 9, 12)) %>%
  dplyr::mutate(year = as.numeric(year)) %>%
  dplyr::mutate(health = ifelse(health > 1, 1, health))

write.csv(health, "output/hab_ice_health_eez.csv", row.names = FALSE)

## trend data
trend <- data %>%
  dplyr::filter(rgn_typ == "eez") %>%
  dplyr::select(rgn_id, habitat, dplyr::starts_with("Trend")) %>%
  tidyr::gather("year", "trend", -(1:2)) %>%
  dplyr::mutate(year = substring(year, 13, 16)) %>%
  dplyr::mutate(year = as.numeric(year)) %>%
  dplyr::mutate(trend = trend * 5) %>%
  dplyr::mutate(trend = ifelse(trend > 1, 1, trend)) %>%
  dplyr::mutate(trend = ifelse(trend < (-1), -1, trend))

write.csv(trend, "output/hab_ice_trend_eez.csv", row.names = FALSE)

## seaice extent data
extent <- data %>%
   dplyr::filter(rgn_typ == "eez") %>%
   dplyr::mutate(year = 2016) %>% # extent not updated each year (historic extent of the sea ice habitat); updated last 2016 bc of source methods
  dplyr::select(rgn_id, habitat, year, km2)

write.csv(extent, "output/hab_ice_extent_eez.csv", row.names = FALSE) # save seaice extent data

4.7 Data Checks

  1. Compare to last year’s data. There should be a strong correlation between these data.
  2. Pair with region names for sanity check.