Confidence intervals in optimal fingerprinting

DelSole, Timothy; Trenary, Laurie; Yan, Xiaolei; Tippett, Michael K.

doi:10.1007/s00382-018-4356-3

Cited by 40 publications

(46 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The best estimate of scaling factor of AA signal is greater than zero for TX10p at 5% significance level over GLB but the residual consistency test did not pass. These do not necessarily indicate that AA signal is weak, rather, they may be a reflection of the difficulties in separating highly co-linear signals in detection and attribution analyses as discussed in previous studies (Jones et al 2013, Schurer et al 2018, DelSole et al 2019. The weak NAT signal cannot be distinguished from internal climate variability in any regions.…”

Section: Three-signal Detection Resultsmentioning

confidence: 72%

Human influence on frequency of temperature extremes

Sun

Zhang

et al. 2020

Environ. Res. Lett.

View full text Add to dashboard Cite

We investigate the influence of external forcings on the frequency of temperature extremes over land at the global and continental scales by comparing HadEX3 observations and simulations from the Coupled Model Intercomparison Programme Phase 6 (CMIP6) project. We consider four metrics including warm days and nights (TX90p and TN90p) and cold days and nights (TX10p and TN10p). The observational dataset during 1951-2018 shows continued increases in the warm days and nights and decreases in the cold days and nights in most land areas in the years after 2010. The area of the so-called 'warming hole' in North America is much reduced in 1951-2018 compared with that in 1951-2010. The comparison between observation and simulations based on an optimal fingerprinting method shows that the anthropogenic forcing, dominated by greenhouse gases, plays the most important role in the changes of the frequency indices. Changes in CMIP6 multi-model mean response to all forcing need to be scaled down to best match the observations, indicating that the multi-model ensemble mean may have overestimated the observed changes. Analyses that involve signals from anthropogenic and natural external forcings confirm that the anthropogenic signal can be detected over global land as a whole and for most continents in all temperature indices. Analyses that include signals from greenhouse gas (GHG), anthropogenic aerosol (AA) and natural external (NAT) forcings show that the GHG signal is detected in all indices over the globe and most continents while the AA signal can be detected mainly in the warm extremes but not the cold extremes over the globe and most continents. The effect of NAT is negligible in most land areas. GHG's warming effect is offset partially by AA's cooling effect. The combined effects from both explain most of the observed changes over the globe and continents.

show abstract

Section: Three-signal Detection Resultsmentioning

confidence: 72%

Human influence on frequency of temperature extremes

Sun

Zhang

et al. 2020

Environ. Res. Lett.

View full text Add to dashboard Cite

show abstract

“…2 are robust. A lower perfect-model detection rate and wider CIs with CESM are consistent with smaller ratios of the ensemble mean trend to the spread of trends (SI Appendix, Table S1). There is a caveat that the widely used method for estimating the scaling factor CIs tends to underestimate their width (32). As such, the detection results presented may actually be less robust, although we are unable to test and fix this issue.…”

Section: Attribution Of the Spatiotemporal Evolutionmentioning

confidence: 82%

Human influence has intensified extreme precipitation in North America

Kirchmeier‐Young

Zhang

2020

Proc. Natl. Acad. Sci. U.S.A.

172

View full text Add to dashboard Cite

Precipitation extremes have implications for many facets of both the human and natural systems, predominantly through flooding events. Observations have demonstrated increasing trends in extreme precipitation in North America, and models and theory consistently suggest continued increases with future warming. Here, we address the question of whether observed changes in annual maximum 1- and 5-d precipitation can be attributed to human influence on the climate. Although attribution has been demonstrated for global and hemispheric scales, there are few results for continental and subcontinental scales. We utilize three large ensembles, including simulations from both a fully coupled Earth system model and a regional climate model. We use two different attribution approaches and find many qualitatively consistent results across different methods, different models, and different regional scales. We conclude that external forcing, dominated by human influence, has contributed to the increase in frequency and intensity of regional precipitation extremes in North America. If human emissions continue to increase, North America will see further increases in these extremes.

show abstract

“…The findings of DelSole et al. (2019) were valid for the case when the statistical characteristics of internal variability are known perfectly. With real climate data, however, the internal variability is unknown and must be estimated.…”

Section: Introductionmentioning

confidence: 68%

“…Since these problems arise for small SNR, it is plausible that the minimum SNR of any linear combination of variables is an important diagnostic for TLS. This minimum is the TCN and typically grows with ensemble size (DelSole et al., 2019). For large TCN, the forced signals are large and distinct, and the corresponding scaling factors have confidence intervals that approximate those from OLS.…”

Section: Resultsmentioning

confidence: 99%

Comparing Methods of Uncertainty Estimation in Optimal Fingerprinting

Trenary

DelSole

Tippett

2020

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

The performances of different uncertainty estimates in multipattern optimal fingerprinting are compared. The comparisons are performed in a perfect model context using the Canadian Earth System Model Large Ensemble to provide a clean comparison in the absence of model error or inconsistencies in internal variability. Performance is quantified in terms of the accuracy (correct coverage) of the confidence intervals for the fingerprint scaling factors. Sensitivity to temporal and spatial smoothing is tested. The most accurate method for interval estimation differs between variables, with a maximum likelihood method being most accurate for temperature and a bootstrap method being most accurate for precipitation. These differences can only partially be explained in terms of a relevant measure of signal‐to‐noise ratio.

show abstract

Confidence intervals in optimal fingerprinting

Cited by 40 publications

References 27 publications

Human influence on frequency of temperature extremes

Human influence on frequency of temperature extremes

Human influence has intensified extreme precipitation in North America

Comparing Methods of Uncertainty Estimation in Optimal Fingerprinting

Contact Info

Product

Resources

About