Estimating Sampling Errors in Large-Scale Temperature Averages

Estimating Sampling Errors in Large-Scale Temperature Averages A method is developed for estimating the uncertainty (standard error) of observed regional, hemispheric, and global-mean surface temperature series due to incomplete spatial sampling. Standard errors estimated at the grid-box level SE 2 == S 2 (1 −− r̄ )/(1 ++ ( n −− 1) r̄ ) depend upon three parameters: the number of site records ( n ) within each box, the average interrecord correlation ( r̄ ) between these sites, and the temporal variability ( S 2 ) of each grid-box temperature time series. For boxes without data ( n == 0), estimates are made using values of S 2 interpolated from neighboring grid boxes. Due to spatial correlation, large-scale standard errors in a regional-mean time series are not simply the average of the grid-box standard errors, but depend upon the effective number of independent sites ( N eff ) over the region. A number of assumptions must be made in estimating the various parameters, and these are tested with observational data and complementary results from multicentury control integrations of three coupled general circulation models (GCMs). The globally complete GCMs enable some assumptions to be tested in a situation where there are no missing data; comparison of parameters computed from the observed and model datasets are also useful for assessing the performance of GCMs. As most of the parameters are timescale dependent, the resulting errors are likewise timescale dependent and must be calculated for each timescale of interest. The length of the observed record enables uncertainties to be estimated on the interannual and interdecadal timescales, with the longer GCM runs providing inferences about longer timescales. For mean annual observed data on the interannual timescale, the 95%% confidence interval for estimates of the global-mean surface temperature since 1951 is ±±0.12°°C. Prior to 1900, the confidence interval widens to ±±0.18°°C. Equivalent values on the decadal timescale are smaller: ±±0.10°°C (1951––95) and ±±0.16°°C (1851––1900). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Climate American Meteorological Society

Estimating Sampling Errors in Large-Scale Temperature Averages

Journal of Climate, Volume 10 (10) – May 23, 1996

Loading next page...
 
/lp/american-meteorological-society/estimating-sampling-errors-in-large-scale-temperature-averages-5jOqoq6nA1
Publisher
American Meteorological Society
Copyright
Copyright © 1996 American Meteorological Society
ISSN
1520-0442
DOI
10.1175/1520-0442(1997)010<2548:ESEILS>2.0.CO;2
Publisher site
See Article on Publisher Site

Abstract

A method is developed for estimating the uncertainty (standard error) of observed regional, hemispheric, and global-mean surface temperature series due to incomplete spatial sampling. Standard errors estimated at the grid-box level SE 2 == S 2 (1 −− r̄ )/(1 ++ ( n −− 1) r̄ ) depend upon three parameters: the number of site records ( n ) within each box, the average interrecord correlation ( r̄ ) between these sites, and the temporal variability ( S 2 ) of each grid-box temperature time series. For boxes without data ( n == 0), estimates are made using values of S 2 interpolated from neighboring grid boxes. Due to spatial correlation, large-scale standard errors in a regional-mean time series are not simply the average of the grid-box standard errors, but depend upon the effective number of independent sites ( N eff ) over the region. A number of assumptions must be made in estimating the various parameters, and these are tested with observational data and complementary results from multicentury control integrations of three coupled general circulation models (GCMs). The globally complete GCMs enable some assumptions to be tested in a situation where there are no missing data; comparison of parameters computed from the observed and model datasets are also useful for assessing the performance of GCMs. As most of the parameters are timescale dependent, the resulting errors are likewise timescale dependent and must be calculated for each timescale of interest. The length of the observed record enables uncertainties to be estimated on the interannual and interdecadal timescales, with the longer GCM runs providing inferences about longer timescales. For mean annual observed data on the interannual timescale, the 95%% confidence interval for estimates of the global-mean surface temperature since 1951 is ±±0.12°°C. Prior to 1900, the confidence interval widens to ±±0.18°°C. Equivalent values on the decadal timescale are smaller: ±±0.10°°C (1951––95) and ±±0.16°°C (1851––1900).

Journal

Journal of ClimateAmerican Meteorological Society

Published: May 23, 1996

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off