Open data
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Deployed on AWS
The NEX-DCP30-CMIP6 dataset contains daily, monthly, and annual downscaled CMIP6 projections over CONUS at 30-arcsecond hoirzontal resolution for maximum temperature, minimum temperature, precipitation, and vapor pressure.
Overview
The NEX-DCP30-CMIP6 dataset contains daily, monthly, and annual downscaled CMIP6 projections over CONUS at 30-arcsecond hoirzontal resolution for maximum temperature, minimum temperature, precipitation, and vapor pressure.
Features and programs
Open Data Sponsorship Program
This dataset is part of the Open Data Sponsorship Program, an AWS program that covers the cost of storage for publicly available high-value cloud-optimized datasets.
Pricing
This is a publicly available data set. No subscription is required.
Legal
Content disclaimer
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Delivery details
AWS Data Exchange (ADX)
AWS Data Exchange is a service that helps AWS easily share and manage data entitlements from other organizations at scale.
Open data resources
Available with or without an AWS account.
- How to use
- To access these resources, reference the Amazon Resource Name (ARN) using the AWS Command Line Interface (CLI). Learn more
- Description
- Downscaled Climate Projections over CONUS
- Resource type
- S3 bucket
- Amazon Resource Name (ARN)
- arn:aws:s3:::nasa-nex-dcp30-cmip6
- AWS region
- us-east-1
- AWS CLI access (No AWS account required)
- aws s3 ls --no-sign-request s3://nasa-nex-dcp30-cmip6/
Resources
Vendor resources
Support
Contact
Bridget Thrasher (bridget@climateanalyticsgroup.org )
Managed By
National Aeronautics and Space Administration (NASA) Ames Research Center
How to cite
Downscaled CMIP6 projections over CONUS at 30-arcsecond horizontal resolution was accessed on DATE from https://registry.opendata.aws/nasa-nex-dcp30-cmip6 .
License
There are no restrictions on the use of this data.
Similar products
The NEX-HIDCP-CMIP6 dataset contains high-resolution daily downscaled CMIP6 projections over Hawai'i for maximum temperature, minimum temperature, and precipitation. This dataset compliments the NEX-DCP30-CMIP6 collection of downscaled projections over CONUS.
The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate
scenarios derived from the General Circulation Model (GCM) runs
conducted under the Coupled Model Intercomparison Project Phase 6
(CMIP6) and across two of the four "Tier 1" greenhouse gas emissions
scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6
GCM runs were developed in support of the Sixth Assessment Report
of the Intergovernmental Panel on Climate Change (IPCC AR6). This
dataset includes downscaled projections from ScenarioMIP model
runs for which daily scenarios were produced and distributed
through the Earth System Grid Federation. The purpose of this dataset
is to provide a set of global, high resolution, bias-corrected
climate change projections that can be used to evaluate climate
change impacts on processes that are sensitive to finer-scale climate
gradients and the effects of local topography on climate conditions.
![Hybrid statistical-dynamic downscaling based on multi-model ensembl[...]](https://d1ewbp317vsrbd.cloudfront.net/9ef172d6-2c6d-4e2e-9dae-44ffa478d91d.png)
GCMs under CMIP6 have been widely used to investigate climate change impacts and put forward associated adaptation and mitigation strategies. However, the relatively coarse spatial resolutions (usually 100~300km) preclude their direct applications at regional scales, which are exactly where the analysis (e.g., hydrological model simulation) is performed. To bridge this gap, a typical approach is to ‘refine’ the information from GCMs through regional climate downscaling experiments, which can be conducted statistically, dynamically, or a combination thereof. Statistical downscaling establishes relationships between large-scale climate indicators and small-scale climate variables in the reference (historical) period. Subsequently, these relationships are kept unchanged in the future and used to predict the future variables. On the other hand, dynamical downscaling operates based on the physical processes and the associated interactions in the climate systems and thus can produce a f[...]
![Hybrid statistical-dynamic downscaling based on multi-model ensembl[...]](https://d1ewbp317vsrbd.cloudfront.net/7b19ec7d-5c86-4a02-ba66-84c3cc1e7674.png)
GCMs under CMIP6 have been widely used to investigate climate change impacts and put forward associated adaptation and mitigation strategies. However, the relatively coarse spatial resolutions (usually 100~300km) preclude their direct applications at regional scales, which are exactly where the analysis (e.g., hydrological model simulation) is performed. To bridge this gap, a typical approach is to ‘refine’ the information from GCMs through regional climate downscaling experiments, which can be conducted statistically, dynamically, or a combination thereof. Statistical downscaling establishes relationships between large-scale climate indicators and small-scale climate variables in the reference (historical) period. Subsequently, these relationships are kept unchanged in the future and used to predict the future variables. On the other hand, dynamical downscaling operates based on the physical processes and the associated interactions in the climate systems and thus can produce a f[...]