Assessing uncertainty in estimates of atmospheric temperature changes from MSU and AMSU using a Monte-Carlo estimation technique

Mears, C. A.; Wentz, F. J.; Thorne, Peter; Bernie, Dan

doi:10.1029/2010jd014954

Cited by 76 publications

(155 citation statements)

References 32 publications

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“…First and foremost, such estimates are an emerging field and to date several distinct approaches have arisen (e.g., Kennedy et al 2011b;Morice et al 2012;Thorne et al 2011a;Mears et al 2011;Williams et al 2012). These reflect both the importance of such estimates, which allow users to assess the sensitivity of their analyses to observational uncertainties in a more informed manner, and also the real challenges in making such estimates.…”

Section: The Ersstv4 Parametric Uncertainty Estimationmentioning

confidence: 99%

“…This is unsurprising given the sequential nature of the processing as outlined in section 2. Indeed, based upon a tacitly stated assumption of such nonlinearity existing in dataset construction techniques more generally, many emergent parametric uncertainty estimates for both in situ (e.g., Kennedy et al 2011b;Morice et al 2012;Thorne et al 2011a) and satellite (Mears et al 2011) data products have used Monte Carlo estimation techniques to quantify their parametric uncertainties. To our knowledge, this is the first time that the need or otherwise for such a step has been formally quantified and proven, at least in an observational climate record reconstruction context for a given algorithmic approach.…”

Section: B Testing For Nonlinearitymentioning

confidence: 99%

“…That other datasets fall outside the parametric uncertainty estimates of a single dataset is not unique to SST. For example, Mears et al (2011) found that for their Microwave Sounding Unit dataset parametric uncertainty estimates, other independently derived datasets fell outside their estimates over 50% of the time.…”

Section: Comparison To Other Sst Estimates and Inferencesmentioning

confidence: 99%

See 2 more Smart Citations

Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4): Part II. Parametric and Structural Uncertainty Estimations

Liu

Huang

Thorne³

et al. 2015

Journal of Climate

206

210

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Described herein is the parametric and structural uncertainty quantification for the monthly Extended Reconstructed Sea Surface Temperature (ERSST) version 4 (v4). A Monte Carlo ensemble approach was adopted to characterize parametric uncertainty, because initial experiments indicate the existence of significant nonlinear interactions. Globally, the resulting ensemble exhibits a wider uncertainty range before 1900, as well as an uncertainty maximum around World War II. Changes at smaller spatial scales in many regions, or for important features such as Niño-3.4 variability, are found to be dominated by particular parameter choices.Substantial differences in parametric uncertainty estimates are found between ERSST.v4 and the independently derived Hadley Centre SST version 3 (HadSST3) product. The largest uncertainties are over the mid and high latitudes in ERSST.v4 but in the tropics in HadSST3. Overall, in comparison with HadSST3, ERSST.v4 has larger parametric uncertainties at smaller spatial and shorter time scales and smaller parametric uncertainties at longer time scales, which likely reflects the different sources of uncertainty quantified in the respective parametric analyses. ERSST.v4 exhibits a stronger globally averaged warming trend than HadSST3 during the period of 1910-2012, but with a smaller parametric uncertainty. These global-mean trend estimates and their uncertainties marginally overlap.Several additional SST datasets are used to infer the structural uncertainty inherent in SST estimates. For the global mean, the structural uncertainty, estimated as the spread between available SST products, is more often than not larger than the parametric uncertainty in ERSST.v4. Neither parametric nor structural uncertainties call into question that on the global-mean level and centennial time scale, SSTs have warmed notably.

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Section: The Ersstv4 Parametric Uncertainty Estimationmentioning

confidence: 99%

Section: B Testing For Nonlinearitymentioning

confidence: 99%

Section: Comparison To Other Sst Estimates and Inferencesmentioning

confidence: 99%

See 1 more Smart Citation

Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4): Part II. Parametric and Structural Uncertainty Estimations

Liu

Huang

Thorne³

et al. 2015

Journal of Climate

206

210

View full text Add to dashboard Cite

show abstract

“…Where the propagation of these is highly non-linear, they can be estimated via ensemble techniques analogous to the NWP approach, as done by Liu et al (2015). Rather than present an ensemble of retrievals, Mears et al (2011) produced an ensemble of estimated errors (as perturbations about the measured value presume it is the mean of the true distribution).…”

Section: Atmosmentioning

confidence: 99%

Known and unknown unknowns: uncertainty estimation in satellite remote sensing

Povey

Grainger

2015

Atmos. Meas. Tech.

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Abstract. This paper discusses a best-practice representation of uncertainty in satellite remote sensing data. An estimate of uncertainty is necessary to make appropriate use of the information conveyed by a measurement. Traditional error propagation quantifies the uncertainty in a measurement due to well-understood perturbations in a measurement and in auxiliary data -known, quantified "unknowns". The underconstrained nature of most satellite remote sensing observations requires the use of various approximations and assumptions that produce non-linear systematic errors that are not readily assessed -known, unquantifiable "unknowns". Additional errors result from the inability to resolve all scales of variation in the measured quantity -unknown "unknowns". The latter two categories of error are dominant in underconstrained remote sensing retrievals, and the difficulty of their quantification limits the utility of existing uncertainty estimates, degrading confidence in such data. This paper proposes the use of ensemble techniques to present multiple self-consistent realisations of a data set as a means of depicting unquantified uncertainties. These are generated using various systems (different algorithms or forward models) believed to be appropriate to the conditions observed. Benefiting from the experience of the climate modelling community, an ensemble provides a user with a more complete representation of the uncertainty as understood by the data producer and greater freedom to consider different realisations of the data.

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“…Another way to explore the uncertainty would be to produce plausible ensemble estimates of HadISDH, as was done for HadCRUT4 or Remote Sensing Systems' Microwave Sounding Units product (Mears et al, 2011). This is the first time that a global humidity estimate has been given any measure of uncertainty.…”

Section: K M Willett Et Al: An Updateable Land Surface Specific Humentioning

confidence: 99%

HadISDH: an updateable land surface specific humidity product for climate monitoring

et al. 2013

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HadISDH is a near-global land surface specific humidity monitoring product providing monthly means from 1973 onwards over large-scale grids. Presented herein to 2012, annual updates are anticipated. HadISDH is an update to the land component of HadCRUH, utilising the global high-resolution land surface station product HadISD as a basis. HadISD, in turn, uses an updated version of NOAA's Integrated Surface Database. Intensive automated quality control has been undertaken at the individual observation level, as part of HadISD processing. The data have been subsequently run through the pairwise homogenisation algorithm developed for NCDC's US Historical Climatology Network monthly temperature product. For the first time, uncertainty estimates are provided at the grid-box spatial scale and monthly timescale.

HadISDH is in good agreement with existing land surface humidity products in periods of overlap, and with both land air and sea surface temperature estimates. Widespread moistening is shown over the 1973–2012 period. The largest moistening signals are over the tropics with drying over the subtropics, supporting other evidence of an intensified hydrological cycle over recent years. Moistening is detectable with high (95%) confidence over large-scale averages for the globe, Northern Hemisphere and tropics, with trends of 0.089 (0.080 to 0.098) g kg⁻¹ per decade, 0.086 (0.075 to 0.097) g kg⁻¹ per decade and 0.133 (0.119 to 0.148) g kg⁻¹ per decade, respectively. These changes are outside the uncertainty range for the large-scale average which is dominated by the spatial coverage component; station and grid-box sampling uncertainty is essentially negligible on large scales. A very small moistening (0.013 (−0.005 to 0.031) g kg⁻¹ per decade) is found in the Southern Hemisphere, but it is not significantly different from zero and uncertainty is large. When globally averaged, 1998 is the moistest year since monitoring began in 1973, closely followed by 2010, two strong El Niño years. The period in between is relatively flat, concurring with previous findings of decreasing relative humidity over land

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Assessing uncertainty in estimates of atmospheric temperature changes from MSU and AMSU using a Monte-Carlo estimation technique

Cited by 76 publications

References 32 publications

Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4): Part II. Parametric and Structural Uncertainty Estimations

Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4): Part II. Parametric and Structural Uncertainty Estimations

Known and unknown unknowns: uncertainty estimation in satellite remote sensing

HadISDH: an updateable land surface specific humidity product for climate monitoring

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