Evaluation of Surface Fluxes in ERA-Interim Using Flux Tower Data

Zhou, Chunlüe; Wang, Kaicun

doi:10.1175/jcli-d-15-0523.1

Cited by 31 publications

(33 citation statements)

References 50 publications

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“…1 and Table 2). Similar biases in T a within various regions worldwide have been widely reported by previous studies (Mao et al, 2010;Pitman and Perkins, 2009;Reuten et al, 2011;Wang and Zeng, 2012;Zhou et al, 2017;Zhou and Wang, 2016b). However, we found that the spatial patterns in the differences in T a are well correlated with the elevation differences between models and stations, as reflected by correlation coefficients (r) of 0.85 to 0.94 (Figs.…”

Section: Resultssupporting

confidence: 89%

“…The R s heats the surface, and the portion of this radiation that becomes the sensible heat flux heats the air near the surface (Zhou and Wang, 2016b, c;Wang and Dickinson, 2013). Part of the energy absorbed by the surface is released back to space as outgoing longwave radiation; some of this radiation is reflected by clouds and is influenced by atmospheric water vapour, further warming the near-surface air (Wang and Dickinson, 2013).…”

Section: Key Factors Regulating Regional Temperature Changementioning

confidence: 99%

“…Extensive assessment studies have reported that most reanalyses display a certain level of performance in terms of their absolute values (Betts et al, 1996(Betts et al, , 1998Zhou and Wang, 2016b), interannual variability (Lin et al, 2014;Lindsay et al, 2014;Wang, 2016a, 2017a;Wang and Zeng, 2012), distributions (Gervais et al, 2014;Heng et al, 2014;Mao et al, 2010) and relationships among variables (Niznik and Lintner, 2013;Cash III et al, 2015;Zhou et al, 2017;Zhou and Wang, 2016b;Betts, 2004) over regions worldwide. However, these aspects of reanalyses still contain certain errors that restrict the general use of reanalyses, especially in climate applications.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, observation error incorporates systematic and random errors in instruments and their replacements, errors in data reprocessing and representation error, which arises due to the spatiotemporal incompleteness of observations (Dee and Uppala, 2009;Desroziers et al, 2005). Model error refers mainly to the inadequate representation of physical processes in NWP models (Peña and Toth, 2014;Bengtsson et al, 2007), such as the lack of time-varying surface conditions such as vegetation growth (Zhou and Wang, 2016b;Trigo et al, 2015), and incomplete cloud-precipitation-radiation parameterizations (Fujiwara et al, 2017;Dolinar et al, 2016). Assimilation error describes errors that arise in the mapping of the model space to the observation space and errors in the topologies of cost functions (Dee, 2005;Dee and Da Silva, 1998;Lahoz and Schneider, 2014;Parker, 2016).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultssupporting

confidence: 89%

Section: Key Factors Regulating Regional Temperature Changementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhou

Wang

2018

Atmos. Chem. Phys.

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“…ERA-Interim is a global atmospheric reanalysis gridded dataset available from 1979 (real time dataset from 1989 onwards) [40] at various horizontal resolutions ranging from 0.125° to 3° [41]. Meteorological variables and fluxes derived from 0.125° resolution ERA-Interim dataset roughly matches the ground observations in a study performed by Zhou and Wang [42]. ERA-Interim uses the roughness length and drag coefficient to represent the …”

Section: Methodsmentioning

confidence: 67%

Land-Air Interactions over Urban-Rural Transects Using Satellite Observations: Analysis over Delhi, India from 1991–2016

2017

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Abstract:Over the past four decades Delhi, India, has witnessed rapid urbanization and change in land use land cover (LULC) pattern, with most of the cultivable areas and wasteland being converted into built-up areas. Presently around 40% land is under built-up area, a drastic rise of 30% from 1977. The effect of changing LULC, at a local scale, on various variables-land surface temperature (LST), normalized difference vegetation index (NDVI), emissivity, albedo, evaporation, Bowen ratio, and planetary boundary layer (PBL) height, from 1991-2016, is investigated. To assess the spatio-temporal dynamics of land-air interactions, we select two different 100 km transects covering the NE-SW and NW-SE expanse of Delhi and its adjoining areas. High NDVI and emissivity is found for regions with green cover and drastic reduction is noted in built-up area clusters. In both of the transects, land surface variations manifest itself in patterns of LST variation. Parametric and non-parametric correlations are able to statistically establish the land-air interactions in the city. NDVI, an indirect indicator for LULC classes, significantly helps in understanding the modifications in LST and ultimately air temperature. Significant, strong positive relationships exist between skin temperature and evaporation, skin temperature and PBL height, and PBL height and evaporation, providing insights into the meteorological changes that are associated with urbanization.

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Land surface temperature over global deserts: Means, variability, and trends

Zhou

Wang

2016

JGR Atmospheres

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Land surface air temperature (LSAT) has been a widely used metric to study climate change. Weather observations of LSAT are the fundamental data for climate change studies and provide key evidence of global warming. However, there are very few meteorological observations over deserts due to their uninhabitable environment. This study fills this gap and provides independent evidence using satellite‐derived land surface temperatures (LSTs), benefiting from their global coverage. The frequency of clear sky from MODerate Resolution Imaging Spectroradiometer (MODIS) LST data over global deserts was found to be greater than 94% for the 2002–2015 period. Our results show that MODIS LST has a bias of 1.36°C compared to ground‐based observations collected at 31 U.S. Climate Reference Network (USCRN) stations, with a standard deviation of 1.83°C. After bias correction, MODIS LST was used to evaluate existing reanalyses, including ERA‐Interim, Japanese 55‐year Reanalysis (JRA‐55), Modern‐Era Retrospective Analysis for Research and Applications (MERRA), MERRA‐land, National Centers for Environmental Prediction (NCEP)‐R1, and NCEP‐R2. The reanalyses accurately reproduce the seasonal cycle and interannual variability of the LSTs, but their multiyear means and trends of LSTs exhibit large uncertainties. The multiyear averaged LST over global deserts is 23.5°C from MODIS and varies from 20.8°C to 24.5°C in different reanalyses. The MODIS LST over global deserts increased by 0.25°C/decade from 2002 to 2015, whereas the reanalyses estimated a trend varying from −0.14 to 0.10°C/decade. The underestimation of the LST trend by the reanalyses occurs for approximately 70% of the global deserts, likely due to the imperfect performance of the reanalyses in reproducing natural climate variability.

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Evaluation of Surface Fluxes in ERA-Interim Using Flux Tower Data

Cited by 31 publications

References 50 publications

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

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

Land-Air Interactions over Urban-Rural Transects Using Satellite Observations: Analysis over Delhi, India from 1991–2016

Land surface temperature over global deserts: Means, variability, and trends

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