Downscaling climate variability associated with quasi-periodic climate signals: A new statistical approach using MSSA

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Abstract

A statistical method is introduced to downscale hydroclimatic variables while incorporating the variability associated with quasi-periodic global climate signals. The method extracts statistical information of distributed variables from historic time series available at high resolution and uses Multichannel Singular Spectrum Analysis (MSSA) to reconstruct, on a cell-by-cell basis, specific frequency signatures associated with both the variable at a coarse scale and the global climate signals. Historical information is divided in two sets: a reconstruction set to identify the dominant modes of variability of the series for each cell and a validation set to compare the downscaling relative to the observed patterns. After validation, the coarse projections from Global Climate Models (GCMs) are disaggregated to higher spatial resolutions by using an iterative gap-filling MSSA algorithm to downscale the projected values of the variable, using the distributed series statistics and the MSSA analysis. The method is data adaptive and useful for downscaling short-term forecasts as well as long-term climate projections. The method is applied to the downscaling of temperature and precipitation from observed records and GCM projections over a region located in the US Southwest, taking into account the seasonal variability associated with ENSO.

Original languageEnglish (US)
Pages (from-to)65-75
Number of pages11
JournalJournal of Hydrology
Volume398
Issue number1-2
DOIs
StatePublished - Feb 15 2011

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climate signal
downscaling
global climate
climate
climate modeling
El Nino-Southern Oscillation
spatial resolution
time series
analysis
method
temperature

Keywords

  • MSSA
  • Precipitation
  • Statistical downscaling

ASJC Scopus subject areas

  • Water Science and Technology

Cite this

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title = "Downscaling climate variability associated with quasi-periodic climate signals: A new statistical approach using MSSA",
abstract = "A statistical method is introduced to downscale hydroclimatic variables while incorporating the variability associated with quasi-periodic global climate signals. The method extracts statistical information of distributed variables from historic time series available at high resolution and uses Multichannel Singular Spectrum Analysis (MSSA) to reconstruct, on a cell-by-cell basis, specific frequency signatures associated with both the variable at a coarse scale and the global climate signals. Historical information is divided in two sets: a reconstruction set to identify the dominant modes of variability of the series for each cell and a validation set to compare the downscaling relative to the observed patterns. After validation, the coarse projections from Global Climate Models (GCMs) are disaggregated to higher spatial resolutions by using an iterative gap-filling MSSA algorithm to downscale the projected values of the variable, using the distributed series statistics and the MSSA analysis. The method is data adaptive and useful for downscaling short-term forecasts as well as long-term climate projections. The method is applied to the downscaling of temperature and precipitation from observed records and GCM projections over a region located in the US Southwest, taking into account the seasonal variability associated with ENSO.",
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author = "Julio Ca{\~n}{\'o}n and Francina Dominguez and Valdes, {Juan B}",
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AU - Cañón, Julio

AU - Dominguez, Francina

AU - Valdes, Juan B

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N2 - A statistical method is introduced to downscale hydroclimatic variables while incorporating the variability associated with quasi-periodic global climate signals. The method extracts statistical information of distributed variables from historic time series available at high resolution and uses Multichannel Singular Spectrum Analysis (MSSA) to reconstruct, on a cell-by-cell basis, specific frequency signatures associated with both the variable at a coarse scale and the global climate signals. Historical information is divided in two sets: a reconstruction set to identify the dominant modes of variability of the series for each cell and a validation set to compare the downscaling relative to the observed patterns. After validation, the coarse projections from Global Climate Models (GCMs) are disaggregated to higher spatial resolutions by using an iterative gap-filling MSSA algorithm to downscale the projected values of the variable, using the distributed series statistics and the MSSA analysis. The method is data adaptive and useful for downscaling short-term forecasts as well as long-term climate projections. The method is applied to the downscaling of temperature and precipitation from observed records and GCM projections over a region located in the US Southwest, taking into account the seasonal variability associated with ENSO.

AB - A statistical method is introduced to downscale hydroclimatic variables while incorporating the variability associated with quasi-periodic global climate signals. The method extracts statistical information of distributed variables from historic time series available at high resolution and uses Multichannel Singular Spectrum Analysis (MSSA) to reconstruct, on a cell-by-cell basis, specific frequency signatures associated with both the variable at a coarse scale and the global climate signals. Historical information is divided in two sets: a reconstruction set to identify the dominant modes of variability of the series for each cell and a validation set to compare the downscaling relative to the observed patterns. After validation, the coarse projections from Global Climate Models (GCMs) are disaggregated to higher spatial resolutions by using an iterative gap-filling MSSA algorithm to downscale the projected values of the variable, using the distributed series statistics and the MSSA analysis. The method is data adaptive and useful for downscaling short-term forecasts as well as long-term climate projections. The method is applied to the downscaling of temperature and precipitation from observed records and GCM projections over a region located in the US Southwest, taking into account the seasonal variability associated with ENSO.

KW - MSSA

KW - Precipitation

KW - Statistical downscaling

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