How do we tell which estimates of past climate change are correct?

Sherwood, Steven C.; Titchner, Holly A.; Thorne, Peter; McCarthy, Mark

doi:10.1002/joc.1825

Cited by 7 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Monthly correction methods warrant more study. The correlations between the error metrics based on the time series themselves and break detection scores are modest (Sherwood et al, 2009), as well as for the detection scores amongst each other. The use of detection scores as sole performance criterion should thus be discouraged.…”

Section: Homogenizationmentioning

confidence: 93%

Benchmarking homogenization algorithms for monthly data

et al. 2012

View full text Add to dashboard Cite

Abstract. The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added.Published by Copernicus Publications on behalf of the European Geosciences Union. V. K. C. Venema et al.: Benchmarking monthly homogenization algorithmsParticipants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, stateof-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

show abstract

Section: Homogenizationmentioning

confidence: 93%

Benchmarking homogenization algorithms for monthly data

et al. 2012

View full text Add to dashboard Cite

show abstract

“…Evans et al 1998;Mann and Rutherford 2002;Bunn et al 2004;Smerdon et al 2010;. As Sherwood et al (2009) suggest, ''methods for reconstructing climate should be evaluated by testing them on simulated datasets constructed as realistically as possible, including suspected proxy/instrumental artifacts or biases and sampling patterns.'' Here, we generate ''psuedoclimate'' time series (C) meant to mimic a possible, realistic transient climate signal.…”

Section: Methodsmentioning

confidence: 99%

Tree-Ring-Reconstructed Summer Temperatures from Northwestern North America during the Last Nine Centuries*

et al. 2013

View full text Add to dashboard Cite

Northwestern North America has one of the highest rates of recent temperature increase in the world, but the putative ''divergence problem'' in dendroclimatology potentially limits the ability of tree-ring proxy data at high latitudes to provide long-term context for current anthropogenic change. Here, summer temperatures are reconstructed from a Picea glauca maximum latewood density (MXD) chronology that shows a stable relationship to regional temperatures and spans most of the last millennium at the Firth River in northeastern Alaska. The warmest epoch in the last nine centuries is estimated to have occurred during the late twentieth century, with average temperatures over the last 30 yr of the reconstruction developed for this study in the Common Era (CE)] approximately 1.38 6 0.48C warmer than the long-term preindustrial mean (1100-1850 CE), a change associated with rapid increases in greenhouse gases. Prior to the late twentieth century, multidecadal temperature fluctuations covary broadly with changes in natural radiative forcing. The findings presented here emphasize that tree-ring proxies can provide reliable indicators of temperature variability even in a rapidly warming climate.

show abstract

“…Overall, adjusted series are visually somewhat more spatially homogeneous than the input data trends, lending some support to the findings detailed in section 3 regarding the efficacy of the PHA when applied either directly or indirectly to DTR records. However, use of a spatial smoothness criteria alone to ascertain efficacy of adjustment approaches may be misleading [ Sherwood et al , ].…”

Section: Analysis Of Gridded Fields and Regional Averagesmentioning

confidence: 99%

Reassessing changes in diurnal temperature range: A new data set and characterization of data biases

et al. 2016

View full text Add to dashboard Cite

It has been a decade since changes in diurnal temperature range (DTR) globally have been assessed in a stand-alone data analysis. The present study takes advantage of substantively improved basic data holdings arising from the International Surface Temperature Initiative's databank effort and applies the National Centers for Environmental Information's automated pairwise homogeneity assessment algorithm to reassess DTR records. It is found that breakpoints are more prevalent in DTR than other temperature elements and that the resulting adjustments have a broader distribution. This strongly implies that there is an overarching tendency, across the global meteorological networks, for nonclimatic artifacts to impart either random or anticorrelated rather than correlated biases in maximum and minimum temperature series. Future homogenization efforts would likely benefit from simultaneous consideration of DTR and maximum and minimum temperatures, in addition to average temperatures. Estimates of change in DTR are relatively insensitive to whether adjustments are calculated directly or inferred from adjustments returned for the maximum and minimum temperature series. The homogenized series exhibit a reduction in DTR since the midtwentieth century globally (À0.044 K/decade). Adjustments serve to approximately halve the long-term global reduction in DTR in the basic "raw" data. Most of the estimated DTR reduction occurred over 1960-1980. In several regions DTR has apparently increased over 1979-2012, while globally it has exhibited very little change (À0.016 K/decade). Estimated changes in DTR are an order of magnitude smaller than in maximum and minimum temperatures, which have both been increasing rapidly on multidecadal timescales (0.186 K/decade and 0.236 K/decade, respectively, since the midtwentieth century).

show abstract

How do we tell which estimates of past climate change are correct?

Cited by 7 publications

References 12 publications

Benchmarking homogenization algorithms for monthly data

Benchmarking homogenization algorithms for monthly data

Tree-Ring-Reconstructed Summer Temperatures from Northwestern North America during the Last Nine Centuries*

Reassessing changes in diurnal temperature range: A new data set and characterization of data biases

Contact Info

Product

Resources

About