Quantifying Differences between 2-m Temperature Observations and Reanalysis Pressure-Level Temperatures in Northwestern North America

In northwestern North America, which is a large area with complex physiography, Climatic Research Unit (CRU) Time Series, version 2.1, (TS 2.1) gridded monthly mean 2-m temperatures are systematically lower than interpolated monthly averaged North American Regional Reanalysis (NARR) pressure-level t...

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Published inJournal of applied meteorology and climatology Vol. 50; no. 4; pp. 916 - 929
Main Authors Reuten, Christian, Moore, R. Dan, Clarke, Garry K. C.
Format Journal Article
LanguageEnglish
Published Boston, MA American Meteorological Society 01.04.2011
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Abstract In northwestern North America, which is a large area with complex physiography, Climatic Research Unit (CRU) Time Series, version 2.1, (TS 2.1) gridded monthly mean 2-m temperatures are systematically lower than interpolated monthly averaged North American Regional Reanalysis (NARR) pressure-level temperatures—in particular, in the winter. Quantification of these differences based on CRU gridded observations can be used to estimate pressure-level temperatures from CRU 2-m temperatures (1901–2002) that predate the NARR period (since 1979). Such twentieth-century pressure-level temperature fields can be used in glacier mass-balance modeling and as an alternative to calibrating general circulation model control runs, avoiding the need for accurate boundary layer parameterization. In this paper, an approach is presented that is transferable to moisture, wind, and other 3D fields with potential applications in wind power generation, ecology, and air quality. At each CRU grid point, the difference between CRU and NARR is regressed against seven predictors in CRU (mean temperature, daily temperature range, precipitation, vapor pressure, cloud cover, and number of wet and frost days) for the period of overlap between CRU and NARR (1979–2002). Bayesian model averaging (BMA) is used to avoid overfitting the CRU–NARR differences and underestimating uncertainties. In cross validations, BMA provides reliable posterior predictions of the CRU–NARR differences and outperforms predictions from three alternative models: the constant model (24-yr mean), the regression model of highest Bayesian model probability, and the full model retaining all seven predictors in CRU.
AbstractList In northwestern North America, which is a large area with complex physiography, Climatic Research Unit (CRU) Time Series, version 2.1, (TS 2.1) gridded monthly mean 2-m temperatures are systematically lower than interpolated monthly averaged North American Regional Reanalysis (NARR) pressure-level temperatures—in particular, in the winter. Quantification of these differences based on CRU gridded observations can be used to estimate pressure-level temperatures from CRU 2-m temperatures (1901–2002) that predate the NARR period (since 1979). Such twentieth-century pressure-level temperature fields can be used in glacier mass-balance modeling and as an alternative to calibrating general circulation model control runs, avoiding the need for accurate boundary layer parameterization. In this paper, an approach is presented that is transferable to moisture, wind, and other 3D fields with potential applications in wind power generation, ecology, and air quality. At each CRU grid point, the difference between CRU and NARR is regressed against seven predictors in CRU (mean temperature, daily temperature range, precipitation, vapor pressure, cloud cover, and number of wet and frost days) for the period of overlap between CRU and NARR (1979–2002). Bayesian model averaging (BMA) is used to avoid overfitting the CRU–NARR differences and underestimating uncertainties. In cross validations, BMA provides reliable posterior predictions of the CRU–NARR differences and outperforms predictions from three alternative models: the constant model (24-yr mean), the regression model of highest Bayesian model probability, and the full model retaining all seven predictors in CRU.
Abstract In northwestern North America, which is a large area with complex physiography, Climatic Research Unit (CRU) Time Series, version 2.1, (TS 2.1) gridded monthly mean 2-m temperatures are systematically lower than interpolated monthly averaged North American Regional Reanalysis (NARR) pressure-level temperatures––in particular, in the winter. Quantification of these differences based on CRU gridded observations can be used to estimate pressure-level temperatures from CRU 2-m temperatures (1901–2002) that predate the NARR period (since 1979). Such twentieth-century pressure-level temperature fields can be used in glacier mass-balance modeling and as an alternative to calibrating general circulation model control runs, avoiding the need for accurate boundary layer parameterization. In this paper, an approach is presented that is transferable to moisture, wind, and other 3D fields with potential applications in wind power generation, ecology, and air quality. At each CRU grid point, the difference between CRU and NARR is regressed against seven predictors in CRU (mean temperature, daily temperature range, precipitation, vapor pressure, cloud cover, and number of wet and frost days) for the period of overlap between CRU and NARR (1979–2002). Bayesian model averaging (BMA) is used to avoid overfitting the CRU–NARR differences and underestimating uncertainties. In cross validations, BMA provides reliable posterior predictions of the CRU–NARR differences and outperforms predictions from three alternative models: the constant model (24-yr mean), the regression model of highest Bayesian model probability, and the full model retaining all seven predictors in CRU.
In northwestern North America, which is a large area with complex physiography, Climatic Research Unit (CRU) Time Series, version 2.1, (TS 2.1) gridded monthly mean 2-m temperatures are systematically lower than interpolated monthly averaged North American Regional Reanalysis (NARR) pressure-level temperatures-in particular, in the winter. Quantification of these differences based on CRU gridded observations can be used to estimate pressure-level temperatures from CRU 2-m temperatures (1901-2002) that predate the NARR period (since 1979). Such twentieth-century pressure-level temperature fields can be used in glacier mass-balance modeling and as an alternative to calibrating general circulation model control runs, avoiding the need for accurate boundary layer parameterization. In this paper, an approach is presented that is transferable to moisture, wind, and other 3D fields with potential applications in wind power generation, ecology, and air quality. At each CRU grid point, the difference between CRU and NARR is regressed against seven predictors in CRU (mean temperature, daily temperature range, precipitation, vapor pressure, cloud cover, and number of wet and frost days) for the period of overlap between CRU and NARR (1979-2002). Bayesian model averaging (BMA) is used to avoid overfitting the CRU-NARR differences and underestimating uncertainties. In cross validations, BMA provides reliable posterior predictions of the CRU-NARR differences and outperforms predictions from three alternative models: the constant model (24-yr mean), the regression model of highest Bayesian model probability, and the full model retaining all seven predictors in CRU. [PUBLICATION ABSTRACT]
Author Moore, R. Dan
Clarke, Garry K. C.
Reuten, Christian
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  fullname: Clarke, Garry K. C.
  organization: Department of Earth and Ocean Sciences, The University of British Columbia, Vancouver, Canada
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10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2
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10.1016/j.agrformet.2005.12.011
10.2151/jmsj.85.369
10.1175/BAMS-83-11-1631
10.1002/joc.1181
10.1007/s00704-006-0280-2
10.1256/qj.04.176
10.1175/MWR-2859.1
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Keywords Temperature distribution
mass balance
General circulation models
Vapor pressure
atmospheric precipitation
Predictor
Regional scope
Time series
Pressure distribution
Air quality
digital simulation
monthly average
Three dimensional field
North America
Modeling
Century 20th
parametrization
Winter
daily average
winds
Glacier balance
Reanalysis
Temperature range
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Snippet In northwestern North America, which is a large area with complex physiography, Climatic Research Unit (CRU) Time Series, version 2.1, (TS 2.1) gridded monthly...
Abstract In northwestern North America, which is a large area with complex physiography, Climatic Research Unit (CRU) Time Series, version 2.1, (TS 2.1)...
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SubjectTerms Air quality
Atmospheric sciences
Bayesian analysis
Bayesian networks
Bayesian theory
Boundary layer parameters
Boundary layers
Climate models
Climatic zones
Climatology
Cloud cover
Daily temperatures
Datasets
Earth, ocean, space
Electric power generation
Exact sciences and technology
External geophysics
General circulation models
Glaciers
Homogenization
Humidity
Linear regression
Mass balance
Mass balance of glaciers
Mathematical models
Mean temperatures
Meteorology
Modeling
Modelling
Moisture effects
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Title Quantifying Differences between 2-m Temperature Observations and Reanalysis Pressure-Level Temperatures in Northwestern North America
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Volume 50
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