On Determining the Statistical Significance of Climate Experiments with General Circulation Models

Chervin, Robert M.; Schenider, Stephen H.

doi:10.1175/1520-0469(1976)033<0405:odtsso>2.0.co;2

Cited by 169 publications

(61 citation statements)

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“…Near-surface air temperature differences from the simulations with and without urban surfaces were then compared with the standard deviation among the random-perturbation simulations within each model surface grid cell with a non-directional t-test in a manner similar to in Chervin and Schneider [1976]. The resulting confidence levels (CL) in each model grid cell of the statistical significance of the spatial temperature changes are shown in Fig.…”

Section: Urbanheatislandsimulationresultsmentioning

confidence: 99%

Effects of Urban Surfaces and White Roofs on Global and Regional Climate

2012

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Abstract. Land use, vegetation, albedo, and soil-type data are combined in a global model that accounts for roofs and roads at near their actual resolution to quantify the effects of urban surface and white roofs on climate. In 2005, ~0.128% of the Earth's surface contained urban landcover, half of which was vegetated. Urban landcover was modeled over 20 years to increase gross global warming (warming before cooling due to aerosols and albedo change are accounted for) by 0.06-0.11 K and population-weighted warming by 0.16-0.31 K, based on two simulations under different conditions. As such, the urban heat island (UHI) effect may contribute to 2-4% of gross global warming, although the uncertainty range is likely larger than the model range presented, and more verification is needed. This may be the first estimate of the UHI effect derived from a global model while considering both UHI local heating and large-scale feedbacks.Previous data estimates of the global UHI, which considered the effect of urban areas but did not treat feedbacks or isolate temperature change due to urban surfaces from other causes of urban temperature change, imply a smaller UHI effect but of similar order. White roofs change surface albedo and affect energy demand. A worldwide conversion to white roofs, accounting for their albedo effect only, was calculated to cool population-weighted temperatures by ~0.02 K but to warm the Earth overall by ~0.07 K. White-roof local cooling may also affect energy use, thus emissions, a factor not accounted for here. As such, conclusions here regarding white roofs apply only to the assumptions made. 3 1.IntroductionUrban areas are generally warmer than vegetated areas around them since urban surfaces reduce evapotranspiration and have sufficiently different heat capacities, thermal conductivities, albedos, and emissivities to enhance urban warming [Howard, 1833;Oke, 1982]. Several studies have estimated, from data analysis, that the globally-averaged urban heat island (UHI) effect may contribute ≤0.1 K to global temperature changes since the preindustrial era [Jones, 1990;Easterling, 1997;Hansen et al., 1999;Peterson, 2003;Parker, 2006]. The IPCC Fourth Assessment Report concluded that the UHI may have increased temperatures ~0.065 over land and ~0.022 K globally from 1900-2008 [IPCC, 2007. The IPCC global estimate was scaled from the land estimate assuming no UHI heating or feedbacks over the ocean. Data analysis studies of the UHI do not account for feedbacks of changes in local temperatures, moisture, and their gradients to large-scale weather systems, either due to traceable effects or to deterministic chaotic variation. Furthermore, such studies cannot distinguish temperature changes in urban areas due to the UHI from those due to greenhouse gases, carbon dioxide domes over cities [Jacobson, 2010a], cooling or warming aerosol particles, transmission or use of electricity, stationary or mobile combustion, or human respiration, which also occur in urban areas. As such, numerical modeling is needed to ...

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Section: Urbanheatislandsimulationresultsmentioning

confidence: 99%

Effects of Urban Surfaces and White Roofs on Global and Regional Climate

2012

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“…A slightly different approach,butwith a verysimilar flavor, was applied by Chervin and Schneider [52] inassessing the statistical significance of predictions based on globalclimatemodels.…”

Section: Sunspotsmentioning

confidence: 99%

Testing for nonlinearity in time series: the method of surrogate data

Theiler

Eubank

Longtin

et al. 1992

Physica D: Nonlinear Phenomena

3,406

2,533

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Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or o_herwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any ager, cy thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.By acceplance of mta article. Thepubhsher rec_gn_-"eS that the U.S Government retains a nonexcluslve, royalty-free hcenlm1opubhll_ or reproduce tr_e0ubhSr_edform Of Ih,S conlr_buhOn,or tO allow omer$ to do so. for U S Government purposes T_e Los Alamos National Laboratory requests thal the 10ubllsheridefltlfy this art,cle as work performed under the ouspfceI of the U S Depo_menl at Energy ASTEB Lo8LosAa os a, ona a0o a,or ABSTRACTWe describe a statistical approach for identifying nonlinearity in time series; in particular, we want to avoid claims of chaos when simpler models (such as linearly correlated noise) can explain the data. The method requires a careful statement of the null hypothesis which characterizes a candidate linear process, the generation of an ensemble of "surrogate" data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. We present algorithms for generating surrogate data under various null hypotheses, and we show the results of numerical experiments on artificial data using correlation dimension, Lyapunov exponent, and forecasting error as discriminating statistics.Finally, we consider a number of experimental time series m including sunspots, electroencephalogram (EEG) signals, and fluid convection --and evaluate the statistical significance of the evidence for nonlinear structure in each case.

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“…Hotelling's T 2 is a multivariate generalization of the ordinary t-statistic that is appropriately used to examine differences in climate-model simulations (Chervin and Schneider 1976). Like the t-statistic, Hotelling's T 2 scales the difference between the means by a measure of the variability of the groups of observations being compared such that small values of the statistic could result from small differences in the means, or large variability among (in this case) models.…”

Section: Comparison Of Pmip2 and Cmip5 Simulationsmentioning

confidence: 99%

Climate model benchmarking with glacial and mid-Holocene climates

et al. 2013

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Past climates provide a test of models' ability to predict climate change. We present a comprehensive evaluation of state-of-the-art models against Last Glacial Maximum and mid-Holocene climates, using reconstructions of land and ocean climates and simulations from the Palaeoclimate Modelling and Coupled Modelling Intercomparison Projects. Newer models do not perform better than earlier versions despite higher resolution and complexity. Differences in climate sensitivity only weakly account for differences in model performance. In the glacial, models consistently underestimate land cooling (especially in winter) and overestimate ocean surface cooling (especially in the tropics). In the mid-Holocene, models generally underestimate the precipitation increase in the northern monsoon regions, and overestimate summer warming in central Eurasia. Models generally capture large-scale gradients of climate change but have more limited ability to reproduce spatial patterns. Despite these common biases, some models perform better than others.

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On Determining the Statistical Significance of Climate Experiments with General Circulation Models

Cited by 169 publications

References 0 publications

Effects of Urban Surfaces and White Roofs on Global and Regional Climate

Effects of Urban Surfaces and White Roofs on Global and Regional Climate

Testing for nonlinearity in time series: the method of surrogate data

Climate model benchmarking with glacial and mid-Holocene climates

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