Modeling general circulation model bias via a combination of localized regression and quantile mapping methods
<p>General circulation model (GCM) outputs are a primary source of information for climate change impact assessments. However, raw GCM data rarely are used directly for regional-scale impact assessments as they frequently contain systematic error or bias. In this article, we propose a novel ex...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-02-01
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| Series: | Advances in Statistical Climatology, Meteorology and Oceanography |
| Online Access: | https://ascmo.copernicus.org/articles/9/1/2023/ascmo-9-1-2023.pdf |
| _version_ | 1797934968677072896 |
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| author | B. J. Washington L. Seymour T. L. Mote |
| author_facet | B. J. Washington L. Seymour T. L. Mote |
| author_sort | B. J. Washington |
| collection | DOAJ |
| description | <p>General circulation model (GCM) outputs are a primary source of information for
climate change impact assessments. However, raw GCM data rarely are used directly for
regional-scale impact assessments as they frequently contain systematic error or bias. In this
article, we propose a novel extension to standard quantile mapping that allows for a continuous
seasonal change in bias magnitude using localized regression. Our primary goal is to examine the
efficacy of this tool in the context of larger statistical downscaling efforts on the tropical
island of Puerto Rico, where localized downscaling can be particularly challenging. Along the
way, we utilize a multivariate infilling algorithm to estimate missing data within an incomplete
climate data network spanning Puerto Rico. Next, we apply a combination of multivariate
downscaling methods to generate in situ climate projections at 23 locations across Puerto Rico
from three general circulation models in two carbon emission scenarios: RCP4.5 and RCP8.5.
Finally, our bias-correction methods are applied to these downscaled GCM climate projections.
These bias-correction methods allow GCM bias to vary as a function of a user-defined season
(here, Julian day). Bias is estimated using a continuous curve rather than a moving window or
monthly breaks. Results from the selected ensemble agree that Puerto Rico will continue to warm
through the coming century. Under the RCP4.5 forcing scenario, our methods indicate that the dry
season will have increased rainfall, while the early and late rainfall seasons will likely have a
decline in total rainfall. Our methods applied to the RCP8.5 forcing scenario favor a wetter
climate for Puerto Rico, driven by an increase in the frequency of high-magnitude rainfall events
during Puerto Rico's early rainfall season (April to July) as well as its late rainfall season
(August to November).</p> |
| first_indexed | 2024-04-10T18:07:12Z |
| format | Article |
| id | doaj.art-7dcdbea14eb54fbbb48cadbd7f42136e |
| institution | Directory Open Access Journal |
| issn | 2364-3579 2364-3587 |
| language | English |
| last_indexed | 2024-04-10T18:07:12Z |
| publishDate | 2023-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Advances in Statistical Climatology, Meteorology and Oceanography |
| spelling | doaj.art-7dcdbea14eb54fbbb48cadbd7f42136e2023-02-02T12:36:06ZengCopernicus PublicationsAdvances in Statistical Climatology, Meteorology and Oceanography2364-35792364-35872023-02-01912810.5194/ascmo-9-1-2023Modeling general circulation model bias via a combination of localized regression and quantile mapping methodsB. J. Washington0L. Seymour1T. L. Mote2Department of Statistics, University of Georgia, Athens, GA, USA Department of Statistics, University of Georgia, Athens, GA, USA Department of Geography, University of Georgia, Athens, GA, USA<p>General circulation model (GCM) outputs are a primary source of information for climate change impact assessments. However, raw GCM data rarely are used directly for regional-scale impact assessments as they frequently contain systematic error or bias. In this article, we propose a novel extension to standard quantile mapping that allows for a continuous seasonal change in bias magnitude using localized regression. Our primary goal is to examine the efficacy of this tool in the context of larger statistical downscaling efforts on the tropical island of Puerto Rico, where localized downscaling can be particularly challenging. Along the way, we utilize a multivariate infilling algorithm to estimate missing data within an incomplete climate data network spanning Puerto Rico. Next, we apply a combination of multivariate downscaling methods to generate in situ climate projections at 23 locations across Puerto Rico from three general circulation models in two carbon emission scenarios: RCP4.5 and RCP8.5. Finally, our bias-correction methods are applied to these downscaled GCM climate projections. These bias-correction methods allow GCM bias to vary as a function of a user-defined season (here, Julian day). Bias is estimated using a continuous curve rather than a moving window or monthly breaks. Results from the selected ensemble agree that Puerto Rico will continue to warm through the coming century. Under the RCP4.5 forcing scenario, our methods indicate that the dry season will have increased rainfall, while the early and late rainfall seasons will likely have a decline in total rainfall. Our methods applied to the RCP8.5 forcing scenario favor a wetter climate for Puerto Rico, driven by an increase in the frequency of high-magnitude rainfall events during Puerto Rico's early rainfall season (April to July) as well as its late rainfall season (August to November).</p>https://ascmo.copernicus.org/articles/9/1/2023/ascmo-9-1-2023.pdf |
| spellingShingle | B. J. Washington L. Seymour T. L. Mote Modeling general circulation model bias via a combination of localized regression and quantile mapping methods Advances in Statistical Climatology, Meteorology and Oceanography |
| title | Modeling general circulation model bias via a combination of localized regression and quantile mapping methods |
| title_full | Modeling general circulation model bias via a combination of localized regression and quantile mapping methods |
| title_fullStr | Modeling general circulation model bias via a combination of localized regression and quantile mapping methods |
| title_full_unstemmed | Modeling general circulation model bias via a combination of localized regression and quantile mapping methods |
| title_short | Modeling general circulation model bias via a combination of localized regression and quantile mapping methods |
| title_sort | modeling general circulation model bias via a combination of localized regression and quantile mapping methods |
| url | https://ascmo.copernicus.org/articles/9/1/2023/ascmo-9-1-2023.pdf |
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