Atmospheric changes through 2012 as shown by iteratively homogenized radiosonde temperature and wind data (IUKv2)

Sherwood, Steven C.; Nishant, Nidhi

doi:10.1088/1748-9326/10/5/054007

Cited by 45 publications

(43 citation statements)

References 20 publications

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“…These biases include step-like mechanisms that do not cancel over time, whereas the amplitude of internal variability diminishes with time scale. Therefore, any residual data issues will become more apparent the longer the time scale [Santer et al, 2005;Thorne et al, 2007;Sherwood and Nishant, 2015]. Although homogenization techniques remove many of these biases, some biases may remain [Santer et al, 2005;Thorne et al, 2007Thorne et al, , 2011aSherwood and Nishant, 2015].…”

Section: Datamentioning

confidence: 99%

“…Some studies argue that models significantly overestimate the tropospheric warming response and are therefore seriously flawed [Douglass et al, 2007;McKitrick et al, 2010]. Other studies, however, suggest that remaining discrepancies are not statistically significant once observational uncertainties and natural internal variability are taken into account [Sherwood et al, 2005;Thorne et al, 2007;Santer et al, 2008;Thorne et al, 2011aThorne et al, , 2011bMitchell et al, 2013;Sherwood and Nishant, 2015;Po-Chedley et al, 2015]. Yet other studies cannot unambiguously conclude whether discrepancies are caused by incorrect model behavior or observational uncertainties [Fu et al, 2011;Po-Chedley and Fu, 2012;Seidel et al, 2012;Santer et al, 2016].…”

Section: Introductionmentioning

confidence: 99%

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Internal variability in simulated and observed tropical tropospheric temperature trends

Suárez-Gutiérrez

Thorne

et al. 2017

Geophysical Research Letters

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We explore the extent to which internal variability can reconcile discrepancies between observed and simulated warming in the upper tropical troposphere. We compare all extant radiosonde‐based estimates for the period 1958–2014 to simulations from the Coupled Model Intercomparison Project phase 5 multimodel ensemble and the 100 realization Max Planck Institute large ensemble. We consider annual mean temperatures and all available 30‐and 15‐year trends. Most observed trends fall within the ensemble spread for most of the record, and trends calculated over 15‐year periods show better agreement than 30‐year trends, with generally larger discrepancies for the older observational products. The simulated amplification of surface warming aloft in the troposphere is consistent with observations, and the linear correlation between surface and simultaneous tropospheric warming trends decreases with trend length. We conclude that trend differences between observations and simulations of tropical tropospheric temperatures are dominated by observational uncertainty and chaotic internal variability rather than by systematic errors in model performance.

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Internal variability in simulated and observed tropical tropospheric temperature trends

Suárez-Gutiérrez

Thorne

et al. 2017

Geophysical Research Letters

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“…As shown in Fig. 1.5, tropospheric warming coupled with stratospheric cooling is seen in the climate record, at least over the part of the Anthropocene for which modern measurements of atmospheric temperature profiles exist (Sherwood and Nishant 2015). About two-thirds of the cooling of the upper stratosphere for the time period 1979-2005 has been attributed to rising GHGs, with the remainder attributed to humaninduced depletion of stratospheric O 3 (Mitchell 2016).…”

Section: Rising Temperaturementioning

confidence: 93%

“…The patterns of tropospheric warming, stratospheric cooling, and drop in the tropospheric lapse rate (i.e., more warming aloft than at the surface) illustrated in Fig. 1.5 are seen throughout the global atmosphere, in addition to the tropics (Sherwood and Nishant 2015). Figure 1.6 shows CO 2 from Mauna Loa Observatory (Keeling et al 1976) and global annual average CO 2 (Ballantyne et al 2012) provided by NOAA ESRL at:…”

Section: Methodsmentioning

confidence: 99%

“…All uncertainties represent 5-95 % confidence intervals and are given as a range, rather than a plus and minus value, since some are asymmetric about the mean. Figure 1.5 shows a profile of the change in temperature over the time period 1959-2012, based on radiosonde observations collected in the latitude range 30°S to 30°N (Sherwood and Nishant 2015). Data reflect the Iterative Universal Kriging (Solomon et al 2010) (IUKv2) processing.…”

Section: Methodsmentioning

confidence: 99%

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Earth’s Climate System

et al. 2017

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This chapter provides an overview of the factors that influence Earth's climate. The relation between reconstructions of global mean surface temperature and estimates of atmospheric carbon dioxide (CO 2 ) over the past 500 million years is first described. Vast variations in climate on geologic time scales, driven by natural fluctuations of CO 2 , are readily apparent. We then shift attention to the time period 1765 to present, known as the Anthropocene, during which human activity has strongly influenced atmospheric CO 2 , other greenhouse gases (GHGs), and Earth's climate. Two mathematical concepts essential for quantitative understanding of climate change, radiative forcing and global warming potential, are described. Next, fingerprints of the impact of human activity on rising temperature and the abundance of various GHGs over the course of the Anthropocene are presented. We conclude by showing Earth is in the midst of a remarkable transformation. In the past, radiative forcing of climate represented a balance between warming due to rising GHGs and cooling due to the presence of suspended particles (aerosols) in the troposphere. There presently exists considerable uncertainty in the actual magnitude of radiative forcing of climate due to tropospheric aerosols, which has important consequences for our understanding of the climate system. In the future, climate will be driven mainly by GHG warming because aerosol precursors are being effectively removed from pollution sources, due to air quality legislation enacted in response to public health concerns.

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The distribution of precipitation and the spread in tropical upper tropospheric temperature trends in CMIP5/AMIP simulations

Fueglistaler

Radley

Held

2015

Geophysical Research Letters

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Reconciling observations and simulations of tropical upper tropospheric temperature trends remains an important problem in climate science. Examining atmospheric models running over observed sea surface temperatures (SSTs), Flannaghan et al. (2014) show that this reconciliation is affected by the SST data set used and that a precipitation‐weighted SST (PSST) is valuable in explaining this result. Here we show that even for Coupled Model Intercomparison Project Phase 5 Atmospheric Model Intercomparison Project (CMIP5 AMIP) simulations forced with identical SSTs, tropical upper tropospheric temperature trends across models (and between ensemble members) show a substantial spread (standard deviation ∼10% of the average trend). About 60% of this spread between ensemble means, as well as deviations from the ensemble means, can be explained by PSST calculated from the time‐evolving precipitation in each model run. Both PSST and atmospheric temperature trends show statistical evidence for systematic differences between models. We conclude that the response of precipitation patterns to changes in SST patterns is a significant source of uncertainty for tropical temperature trends.

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Atmospheric changes through 2012 as shown by iteratively homogenized radiosonde temperature and wind data (IUKv2)

Cited by 45 publications

References 20 publications

Internal variability in simulated and observed tropical tropospheric temperature trends

Internal variability in simulated and observed tropical tropospheric temperature trends

Earth’s Climate System

The distribution of precipitation and the spread in tropical upper tropospheric temperature trends in CMIP5/AMIP simulations

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