Record Details

Monthly Aggregated NEX-GDDP Ensemble Climate Projections: Historical (1985–2005) and RCP 4.5 and RCP 8.5 (2006–2080)

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

View Archive Info
 
 
Field Value
 
Title Monthly Aggregated NEX-GDDP Ensemble Climate Projections: Historical (1985–2005) and RCP 4.5 and RCP 8.5 (2006–2080)
 
Identifier https://doi.org/10.7910/DVN/ZNEJMS
 
Creator Peter, Brad
Messina, Joseph
Moragoda, Nishani
 
Publisher Harvard Dataverse
 
Description Monthly Aggregated NEX-GDDP Ensemble Climate Projections: Historical (1985–2005) and RCP 4.5 and RCP 8.5 (2006–2080)



This dataset is a monthly-scale aggregation of the NEX-GDDP: NASA Earth Exchange Global Daily Downscaled Climate Projections processed using Google Earth Engine (Gorelick 2017). The native delivery on Google Earth Engine is at the daily timescale for each individual CMIP5 GCM model. This dataset was created to facilitate use of NEX-GDDP and reduce processing times for projects that seek an ensemble model with a coarser temporal resolution. The aggregated data have been made available in Google Earth Engine via 'users/cartoscience/GCM_NASA-NEX-GDDP/NEX-GDDP-PRODUCT-ID_Ensemble-Monthly_YEAR' (see code below on how to access), and all 171 GeoTIFFS have been uploaded to this dataverse entry.



Relevant links:

https://www.nasa.gov/nex

https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp


https://esgf.nccs.nasa.gov/esgdoc/NEX-GDDP_Tech_Note_v0.pdf


https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-GDDP
https://journals.ametsoc.org/view/journals/bams/93/4/bams-d-11-00094.1.xml
https://rd.springer.com/article/10.1007/s10584-011-0156-z#page-1



The dataset can be accessed within Google Earth Engine using the following code:


var histYears = ee.List.sequence(1985,2005).getInfo()
var rcpYears = ee.List.sequence(2006,2080).getInfo()

var path1 = 'users/cartoscience/GCM_NASA-NEX-GDDP/NEX-GDDP-'
var path2 = '_Ensemble-Monthly_'
var product

product = 'Hist'
var hist = ee.ImageCollection(
histYears.map(function(y) {
return ee.Image(path1+product+path2+y)
})
)

product = 'RCP45'
var rcp45 = ee.ImageCollection(
rcpYears.map(function(y) {
return ee.Image(path1+product+path2+y)
})
)

product = 'RCP85'
var rcp85 = ee.ImageCollection(
rcpYears.map(function(y) {
return ee.Image(path1+product+path2+y)
})
)

print(
'Hist (1985–2005)', hist,
'RCP45 (2006–2080)', rcp45,
'RCP85 (2006–2080)', rcp85
)

var first = hist.first()
var tMax = first.select('tasmin_1')
var tMin = first.select('tasmax_1')
var tMean = first.select('tmean_1')
var pSum = first.select('pr_1')

Map.addLayer(tMax, {min: -10, max: 40}, 'Average min temperature Jan 1985 (Hist)', false)
Map.addLayer(tMin, {min: 10, max: 40}, 'Average max temperature Jan 1985 (Hist)', false)
Map.addLayer(tMean, {min: 10, max: 40}, 'Average temperature Jan 1985 (Hist)', false)
Map.addLayer(pSum, {min: 10, max: 500}, 'Accumulated rainfall Jan 1985 (Hist)', true)

https://code.earthengine.google.com/5bfd9741274679dded7a95d1b57ca51d



Ensemble average based on the following models:
ACCESS1-0,BNU-ESM,CCSM4,CESM1-BGC,CNRM-CM5,
CSIRO-Mk3-6-0,CanESM2,GFDL-CM3,GFDL-ESM2G,
GFDL-ESM2M,IPSL-CM5A-LR,IPSL-CM5A-MR,MIROC-ESM-CHEM,
MIROC-ESM,MIROC5,MPI-ESM-LR,MPI-ESM-MR,MRI-CGCM3,
NorESM1-M,bcc-csm1-1,inmcm4



Each annual GeoTIFF contains 48 bands (4 variables across 12 months)—


Temperature: Monthly mean (tasmin, tasmax, tmean)


Precipitation: Monthly sum (pr)



Bands 1–48 correspond with:
tasmin_1,
tasmax_1,
tmean_1,
pr_1,
tasmin_2,
tasmax_2,
tmean_2,
pr_2,
tasmin_3,
tasmax_3,
tmean_3,
pr_3,
tasmin_4,
tasmax_4,
tmean_4,
pr_4,
tasmin_5,
tasmax_5,
tmean_5,
pr_5,
tasmin_6,
tasmax_6,
tmean_6,
pr_6,
tasmin_7,
tasmax_7,
tmean_7,
pr_7,
tasmin_8,
tasmax_8,
tmean_8,
pr_8,
tasmin_9,
tasmax_9,
tmean_9,
pr_9,
tasmin_10,
tasmax_10,
tmean_10,
pr_10,
tasmin_11,
tasmax_11,
tmean_11,
pr_11,
tasmin_12,
tasmax_12,
tmean_12,
pr_12



*Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, pp.18–27.



Project information:

SEAGUL: Southeast Asia Globalization, Urbanization, Land and Environment Changes


http://seagul.info/

https://lcluc.umd.edu/projects/divergent-local-responses-globalization-urbanization-land-transition-and-environmental


This project was made possible by the the NASA Land-Cover/Land-Use Change Program (Grant #: 80NSSC20K0740)
 
Subject Earth and Environmental Sciences
 
Contributor Peter, Brad