Uncertainty of the stratospheric/tropospheric temperature trends in 1979–2008: multiple satellite MSU, radiosonde, and reanalysis datasets

Xu, Jianjun

doi:10.5194/acp-11-10727-2011

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…To combine two such data sets, one of them needs to be converted to the vertical coordinate of the other, requiring knowledge of the vertical temperature profile, and also to a common concentration unit, requiring local temperatures. For long-term ozone trends, where changes are on the order of a few percent per decade, the uncertainties in long-term stratospheric temperature records can confound such conversions between vertical coordinate systems and concentration units (Davis and Rosenlof, 2012;Thompson et al, 2012;Wang et al, 2011;McLinden and Fioletov, 2011;Xu and Powell, 2011). Any artificial trend in stratospheric temperature structure, which affects the altitudes of pressure levels, can alias into ozone trends (WMO, 2015;McLinden and Fioletov, 2011;Rosenfield et al, 2005).…”

Section: Issues Related To Merging Data From Several Recordsmentioning

confidence: 99%

Intercomparison of vertically resolved merged satellite ozone data sets: interannual variability and long-term trends

et al. 2015

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Section: Issues Related To Merging Data From Several Recordsmentioning

confidence: 99%

Intercomparison of vertically resolved merged satellite ozone data sets: interannual variability and long-term trends

et al. 2015

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“…These time-varying errors can introduce spurious trends without being eliminated by data assimilation systems. Many spurious variations in climate signals were also identified in the early-generation reanalyses (Bengtsson et al, 2004;Andersson et al, 2005;Chen et al, 2008;Wang, 2016a, 2017a;Schoeberl et al, 2012;Xu and Powell, 2011;Hines et al, 2000;Cornes and Jones, 2013). Therefore, reanalyses produced using the existing reanalysis strategy may not accurately capture climate trends (Trenberth et al, 2008), even though they may contain relatively accurate estimates of synoptic or interannual variations in the Earth's atmosphere.…”

Section: Introductionmentioning

confidence: 99%

On the suitability of current atmospheric reanalyses for regional warming studies over China

Zhou

Wang

2018

Atmos. Chem. Phys.

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Abstract. Reanalyses are widely used because they add value to routine observations by generating physically or dynamically consistent and spatiotemporally complete atmospheric fields. Existing studies include extensive discussions of the temporal suitability of reanalyses in studies of global change. This study adds to this existing work by investigating the suitability of reanalyses in studies of regional climate change, in which land-atmosphere interactions play a comparatively important role. In this study, surface air temperatures (T a ) from 12 current reanalysis products are investigated; in particular, the spatial patterns of trends in T a are examined using homogenized measurements of T a made at ∼ 2200 meteorological stations in China from 1979 to 2010. The results show that ∼ 80 % of the mean differences in T a between the reanalyses and the in situ observations can be attributed to the differences in elevation between the stations and the model grids. Thus, the T a climatologies display good skill, and these findings rebut previous reports of biases in T a . However, the biases in theT a trends in the reanalyses diverge spatially (standard deviation = 0.15-0.30 • C decade −1 using 1 • × 1 • grid cells). The simulated biases in the trends in T a correlate well with those of precipitation frequency, surface incident solar radiation (R s ) and atmospheric downward longwave radiation (L d ) among the reanalyses (r = −0.83, 0.80 and 0.77; p < 0.1) when the spatial patterns of these variables are considered. The biases in the trends in T a over southern China (on the order of −0.07 • C decade −1 ) are caused by biases in the trends in R s , L d and precipitation frequency on the order of 0.10, −0.08 and −0.06 • C decade −1 , respectively. The biases in the trends in T a over northern China (on the order of −0.12 • C decade −1 ) result jointly from those in L d and precipitation frequency. Therefore, improving the simulation of precipitation frequency and R s helps to maximize the signal component corresponding to regional climate. In addition, the analysis of T a observations helps represent regional warming in ERA-Interim and JRA-55. Incorporating vegetation dynamics in reanalyses and the use of accurate aerosol information, as in the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), would lead to improvements in the modelling of regional warming. The use of the ensemble technique adopted in the twentieth-century atmospheric model ensemble ERA-20CM significantly narrows the uncertainties associated with regional warming in reanalyses (standard deviation = 0.15 • C decade −1 ).

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“…R s values based on the revised Ångström-Prescott equation (Wang et al, 2015;Yang et al, 2006;Wang, 2014) are used in this study. The derived R s values consider the effects of Rayleigh scattering, water vapour absorption, and ozone absorption (Wang et al, 2015;Yang et al, 2006) and can accurately reflect the effects of aerosols and clouds on R s over China Tang et al, 2011). Several intensive studies have reported that the derived R s values can accurately depict the interannual, decadal and long-term variations in R s (Wang et al, , 2015Wang, 2014).…”

Section: Observational Datasetsmentioning

confidence: 99%

Reply to referee #1

Wang¹

2018

Preprint

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Reanalyses are widely used because they add value to routine observations by generating physically or dynamically consistent and spatiotemporally complete atmospheric fields. Existing studies include extensive discussions of the temporal suitability of reanalyses in studies of global change. This study adds to this existing work by investigating the suitability of reanalyses in studies of regional climate change, in which land-atmosphere interactions play a comparatively important role. In this study, surface air temperatures (T a ) from 12 current reanalysis products are investigated; in particular, the spatial patterns of trends in T a are examined using homogenized measurements of T a made at ∼ 2200 meteorological stations in China from 1979 to 2010. The results show that ∼ 80 % of the mean differences in T a between the reanalyses and the in situ observations can be attributed to the differences in elevation between the stations and the model grids. Thus, the T a climatologies display good skill, and these findings rebut previous reports of biases in T a . However, the biases in theT a trends in the reanalyses diverge spatially (standard deviation = 0.15-0.30 • C decade −1 using 1 • × 1 • grid cells). The simulated biases in the trends in T a correlate well with those of precipitation frequency, surface incident solar radiation (R s ) and atmospheric downward longwave radiation (L d ) among the reanalyses (r = −0.83, 0.80 and 0.77; p < 0.1) when the spatial patterns of these variables are considered. The biases in the trends in T a over southern China (on the order of −0.07 • C decade −1 ) are caused by biases in the trends in R s , L d and precipitation frequency on the order of 0.10, −0.08 and −0.06 • C decade −1 , respectively. The biases in the trends in T a over northern China (on the order of −0.12 • C decade −1 ) result jointly from those in L d and precipitation frequency. Therefore, improving the simulation of precipitation frequency and R s helps to maximize the signal component corresponding to regional climate. In addition, the analysis of T a observations helps represent regional warming in ERA-Interim and JRA-55. Incorporating vegetation dynamics in reanalyses and the use of accurate aerosol information, as in the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), would lead to improvements in the modelling of regional warming. The use of the ensemble technique adopted in the twentieth-century atmospheric model ensemble ERA-20CM significantly narrows the uncertainties associated with regional warming in reanalyses (standard deviation = 0.15 • C decade −1 ).

show abstract

Uncertainty of the stratospheric/tropospheric temperature trends in 1979–2008: multiple satellite MSU, radiosonde, and reanalysis datasets

Cited by 14 publications

References 10 publications

Intercomparison of vertically resolved merged satellite ozone data sets: interannual variability and long-term trends

Intercomparison of vertically resolved merged satellite ozone data sets: interannual variability and long-term trends

On the suitability of current atmospheric reanalyses for regional warming studies over China

Reply to referee #1

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