Probabilistic projections of climate change for the mid-Atlantic region of the United States: Validation of precipitation downscaling during the historical era

Liang Ning, Michael E. Mann, Robert Crane, Thorsten Wagener

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

This study uses a statistical downscaling method based on self-organizing maps (SOMs) to produce highresolution, downscaled precipitation estimates over the state of Pennsylvania in the mid-Atlantic region of the United States. The SOMs approach derives a transfer function between large-scale mean atmospheric states and local meteorological variables (daily point precipitation values) of interest. First, the SOM was trained using seven coarsely resolved atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis dataset to model observed daily precipitation data from 17 stations across Pennsylvania for the period 1979-2005. Employing the same coarsely resolved variables from nine general circulation model (GCM) simulations taken from the historical analysis of the Coupled Model Intercomparison Project, phase 3 (CMIP3), the trained SOM was subsequently applied to simulate daily precipitation at the same 17 sites for the period 1961-2000. The SOM analysis indicates that the nine model simulations exhibit similar synoptic-scale features to the (NCEP) observations over the 1979-2007 training interval. An analysis of the sea level pressure signatures and the precipitation distribution corresponding to the trainedSOMshows that it is effective in differentiating characteristic synoptic circulation patterns and associated precipitation. The downscaling approach provides a faithful reproduction of the observed probability distributions and temporal characteristics of precipitation on both daily and monthly time scales. The downscaled precipitation field shows significant improvement over the raw GCM precipitation fields with regard to observed average monthly precipitation amounts, average monthly number of rainy days, and standard deviations of monthly precipitation amounts, although certain caveats are noted.

Original languageEnglish (US)
Pages (from-to)509-526
Number of pages18
JournalJournal of Climate
Volume25
Issue number2
DOIs
StatePublished - Jan 1 2012

Fingerprint

downscaling
climate change
general circulation model
sea level pressure
prediction
transfer function
simulation
timescale
analysis
distribution

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

@article{2a701eaae10d4c7c8c4c915bf8dd1d45,
title = "Probabilistic projections of climate change for the mid-Atlantic region of the United States: Validation of precipitation downscaling during the historical era",
abstract = "This study uses a statistical downscaling method based on self-organizing maps (SOMs) to produce highresolution, downscaled precipitation estimates over the state of Pennsylvania in the mid-Atlantic region of the United States. The SOMs approach derives a transfer function between large-scale mean atmospheric states and local meteorological variables (daily point precipitation values) of interest. First, the SOM was trained using seven coarsely resolved atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis dataset to model observed daily precipitation data from 17 stations across Pennsylvania for the period 1979-2005. Employing the same coarsely resolved variables from nine general circulation model (GCM) simulations taken from the historical analysis of the Coupled Model Intercomparison Project, phase 3 (CMIP3), the trained SOM was subsequently applied to simulate daily precipitation at the same 17 sites for the period 1961-2000. The SOM analysis indicates that the nine model simulations exhibit similar synoptic-scale features to the (NCEP) observations over the 1979-2007 training interval. An analysis of the sea level pressure signatures and the precipitation distribution corresponding to the trainedSOMshows that it is effective in differentiating characteristic synoptic circulation patterns and associated precipitation. The downscaling approach provides a faithful reproduction of the observed probability distributions and temporal characteristics of precipitation on both daily and monthly time scales. The downscaled precipitation field shows significant improvement over the raw GCM precipitation fields with regard to observed average monthly precipitation amounts, average monthly number of rainy days, and standard deviations of monthly precipitation amounts, although certain caveats are noted.",
author = "Liang Ning and Mann, {Michael E.} and Robert Crane and Thorsten Wagener",
year = "2012",
month = "1",
day = "1",
doi = "10.1175/2011JCLI4091.1",
language = "English (US)",
volume = "25",
pages = "509--526",
journal = "Journal of Climate",
issn = "0894-8755",
publisher = "American Meteorological Society",
number = "2",

}

TY - JOUR

T1 - Probabilistic projections of climate change for the mid-Atlantic region of the United States

T2 - Validation of precipitation downscaling during the historical era

AU - Ning, Liang

AU - Mann, Michael E.

AU - Crane, Robert

AU - Wagener, Thorsten

PY - 2012/1/1

Y1 - 2012/1/1

N2 - This study uses a statistical downscaling method based on self-organizing maps (SOMs) to produce highresolution, downscaled precipitation estimates over the state of Pennsylvania in the mid-Atlantic region of the United States. The SOMs approach derives a transfer function between large-scale mean atmospheric states and local meteorological variables (daily point precipitation values) of interest. First, the SOM was trained using seven coarsely resolved atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis dataset to model observed daily precipitation data from 17 stations across Pennsylvania for the period 1979-2005. Employing the same coarsely resolved variables from nine general circulation model (GCM) simulations taken from the historical analysis of the Coupled Model Intercomparison Project, phase 3 (CMIP3), the trained SOM was subsequently applied to simulate daily precipitation at the same 17 sites for the period 1961-2000. The SOM analysis indicates that the nine model simulations exhibit similar synoptic-scale features to the (NCEP) observations over the 1979-2007 training interval. An analysis of the sea level pressure signatures and the precipitation distribution corresponding to the trainedSOMshows that it is effective in differentiating characteristic synoptic circulation patterns and associated precipitation. The downscaling approach provides a faithful reproduction of the observed probability distributions and temporal characteristics of precipitation on both daily and monthly time scales. The downscaled precipitation field shows significant improvement over the raw GCM precipitation fields with regard to observed average monthly precipitation amounts, average monthly number of rainy days, and standard deviations of monthly precipitation amounts, although certain caveats are noted.

AB - This study uses a statistical downscaling method based on self-organizing maps (SOMs) to produce highresolution, downscaled precipitation estimates over the state of Pennsylvania in the mid-Atlantic region of the United States. The SOMs approach derives a transfer function between large-scale mean atmospheric states and local meteorological variables (daily point precipitation values) of interest. First, the SOM was trained using seven coarsely resolved atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis dataset to model observed daily precipitation data from 17 stations across Pennsylvania for the period 1979-2005. Employing the same coarsely resolved variables from nine general circulation model (GCM) simulations taken from the historical analysis of the Coupled Model Intercomparison Project, phase 3 (CMIP3), the trained SOM was subsequently applied to simulate daily precipitation at the same 17 sites for the period 1961-2000. The SOM analysis indicates that the nine model simulations exhibit similar synoptic-scale features to the (NCEP) observations over the 1979-2007 training interval. An analysis of the sea level pressure signatures and the precipitation distribution corresponding to the trainedSOMshows that it is effective in differentiating characteristic synoptic circulation patterns and associated precipitation. The downscaling approach provides a faithful reproduction of the observed probability distributions and temporal characteristics of precipitation on both daily and monthly time scales. The downscaled precipitation field shows significant improvement over the raw GCM precipitation fields with regard to observed average monthly precipitation amounts, average monthly number of rainy days, and standard deviations of monthly precipitation amounts, although certain caveats are noted.

UR - http://www.scopus.com/inward/record.url?scp=84856952281&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84856952281&partnerID=8YFLogxK

U2 - 10.1175/2011JCLI4091.1

DO - 10.1175/2011JCLI4091.1

M3 - Article

AN - SCOPUS:84856952281

VL - 25

SP - 509

EP - 526

JO - Journal of Climate

JF - Journal of Climate

SN - 0894-8755

IS - 2

ER -