Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6
Abstract Efforts to diagnose the risks of a changing climate often rely on downscaled and bias-corrected climate information, making it important to understand the uncertainties and potential biases of this approach. Here, we perform a variance decomposition to partition uncertainty in global climat...
Main Authors: | David C. Lafferty, Ryan L. Sriver |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2023-09-01
|
Series: | npj Climate and Atmospheric Science |
Online Access: | https://doi.org/10.1038/s41612-023-00486-0 |
Similar Items
-
Author Correction: Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6
by: David C. Lafferty, et al.
Published: (2024-01-01) -
High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method
by: Fasil M. Rettie, et al.
Published: (2023-07-01) -
Precipitation Projection in Cambodia Using Statistically Downscaled CMIP6 Models
by: Seyhakreaksmey Duong, et al.
Published: (2023-12-01) -
Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany
by: Frank Kreienkamp, et al.
Published: (2020-11-01) -
South American Monsoon Lifecycle Projected by Statistical Downscaling with CMIP6-GCMs
by: Michelle Simões Reboita, et al.
Published: (2023-08-01)