Identification

Title

GARD-LENS: A downscaled large ensemble dataset for understanding the future climate and its uncertainties

Alternative title(s)

d619000

Abstract

<p>The Generalized Analog Regression Downscaling method Large Ensemble (GARD-LENS) dataset is comprised of daily precipitation, mean temperature, and temperature range over the Contiguous U.S., Alaska, and Hawaii at 12 km, 4 km, and 1 km resolutions, respectively. GARD-LENS downscales three CMIP6 global climate model large ensembles, CESM2, CanESM5, and EC-Earth3, totaling 200 ensemble members. GARD-LENS is the first downscaled SMILE (single model initial-condition large ensemble), providing information about the role of internal climate variability at high resolutions. GARD LENS uses GMET as a training dataset for the period 1980-2014, although Hawaii GMET data is only available for 1990-2014. The total dataset consists of 200 ensemble member files per region per variable (e.g., 200 files for t_mean for CONUS), for a total of 1800 files and a total dataset size of roughly 12 TB.</p> <p>The 150-year record of this large ensemble dataset provides ample data for assessing trends and extremes and allows users to robustly assess internal variability, forced climate signals, and time of emergence at high resolutions. As the need for high resolution, robust climate datasets continues to grow, GARD-LENS will be a valuable tool for scientists and practitioners who wish to account for internal variability in their future climate analyses and adaptation plans.</p>

Resource type

dataset

Resource locator

https://gdex.ucar.edu/datasets/d619000/

protocol: https

name: Dataset Description

description: Related Link

function: information

https://gdex.ucar.edu/datasets/d619000/dataaccess/

protocol: https

name: Data Access

description: Related Link

function: download

Unique resource identifier

code

codeSpace

Dataset language

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

climatologyMeteorologyAtmosphere

Keywords

Keyword set

keyword value

dataset

originating controlled vocabulary

title

reference date

date type

revision

effective date

2021-03-30

Keyword set

keyword value

CESM > NCAR Community Earth System Model

originating controlled vocabulary

title

reference date

date type

revision

effective date

2025-10-03

Keyword set

keyword value

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > PRECIPITATION AMOUNT

originating controlled vocabulary

title

reference date

date type

revision

effective date

2025-10-03

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

1950

End position

2100

Dataset reference date

date type

publication

effective date

2024-07-12

Frequency of update

notPlanned

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Creative Commons Attribution 4.0 International License

Limitations on public access

None

Responsible organisations

Responsible party

organisation name

email address

datahelp@ucar.edu

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact

organisation name

NSF NCAR Geoscience Data Exchange

email address

datahelp@ucar.edu

web address

https://gdex.ucar.edu

name: NSF NCAR Geoscience Data Exchange

description: The Geoscience Data Exchange (GDEX), managed by the Computational and Information Systems Laboratory (CISL) at NSF NCAR, contains a large collection of meteorological, atmospheric composition, and oceanographic observations, and operational and reanalysis model outputs, integrated with NSF NCAR High Performance Compute services to support atmospheric and geosciences research.

function: download

responsible party role

pointOfContact

Metadata date

2025-10-09T01:19:44Z

Metadata language