Intercomparison of Various Surface Latent Heat Flux Fields

Kubota, Masahisa; Kano, Atsuko; Muramatsu, Hidenori; Tomita, Hiroyuki

doi:10.1175/1520-0442(2003)016<0670:iovslh>2.0.co;2

Cited by 55 publications

(59 citation statements)

References 14 publications

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“…The data such as latent heat flux and land surface temperature are all at daily timescale in NCEP-R1 and NCEP-R2. In general, latent heat flux data in reanalysis are estimated using a bulk formula, and latent heat flux LH is a product of the wind speed, the difference between the surface saturation humidity and the surface air humidity, the density of air, and the exchange coefficient for latent heat flux [Kubota et al, 2003]. …”

Section: Analysis Proceduresmentioning

confidence: 99%

Evaporation variability under climate warming in five reanalyses and its association with pan evaporation over China

Feng

2015

JGR Atmospheres

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With the motivation to identify actual evapotranspiration (AE) variability under climate warming over China, an assessment is made from five sets of reanalysis data sets [National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR), NCEP-Department of Energy (NCEP-DOE), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Interim Reanalysis, and Japanese 55-year Reanalysis (JRA-55)]. Based on comparison with AE estimates calculated using the Budyko equation, all five reanalysis data sets reasonably reproduce the spatial patterns of AE over China, with a clearly southeast-northwest gradient. Overall, JRA-55 (NCEP-DOE) gives the lowest (highest) reanalysis evaporation (RE) values. From 1979 to 2013, dominant modes of RE among five reanalyses are extracted using multivariate empirical orthogonal function analysis. Accordingly, the interdecadal variation of RE is likely driven by the change of temperature, and the interannual variation is constrained by the water supply conditions. Under climate warming, RE increase in the Northwest China, Yangtze-Huaihe river basin, and South China, while they decrease in Qinghai-Tibet Plateau, and northern and Northeast China. Moreover, the relationship between RE and pan evaporation (PE) are comprehensively evaluated in space-time. Negative correlations are generally confirmed in nonhumid environments, while positive correlations exist in the humid regions. Our analysis supports the interpretation that the relationship between PE and AE was complementary with water control and proportional with energy control. In view of data availability, important differences in spatial variability and the amount of RE can be found in Northwest China, the Qinghai-Tibet Plateau, and the Yangtze River Basin. Generally speaking, NCEP-NCAR and MERRA have substantial problems on describing the long-term change of RE; however, there are some inaccuracies in the JRA-55 estimates when focusing on the year-to-year variation.

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Section: Analysis Proceduresmentioning

confidence: 99%

Evaporation variability under climate warming in five reanalyses and its association with pan evaporation over China

Feng

2015

JGR Atmospheres

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“…The monthly flux climatology by Da Silva et al, (1994) has been excluded; the product is available only from 1945 to 1993, limiting the temporal overlap with the analysed satellite and hybrid products (Table I). In addition, previous comparisons (Chou et al, 2003;Kubota et al, 2003) have shown Da Silva et al (1994) to have severe limitations related to sampling patterns (particularly in the southern ocean (SO) where the product has very little variability), and some versions of the product have biases related to assumptions regarding ocean budgets (Da Silva et al, 1994). Also excluded is the 2009 update of the NOC-Southampton in situ analysis (Berry and Kent, 2009 Bentamy (personal communication, 2007), the IFREMER surface turbulent fluxes are created using physical properties of active and passive satellite instrument measurements, empirical and Table I.…”

Section: In Situmentioning

confidence: 99%

“…Both operational and research users continue to seek the 'best' estimate of air-sea fluxes for their applications. At present, many users are working with older, outdated flux products that have been shown to have severe limitations (Bony et al, 1997;Putman et al, 2000;Smith et al, 2001;Chou et al, 2003;Kubota et al, 2003). In fact, several of the flux products considered in this manuscript have been updated since this work was completed; however, the community will continue to use the nine analysed products so that an assessment of their characteristics and limitations is valuable.…”

Section: Introductionmentioning

confidence: 99%

“…Several recent comparisons have focused on the LHF (Chou et al, 2003;Kubota et al, 2003;Bourras, 2006). Kubota et al (2003) and Chou et al (2003) both take the approach of differencing the product developed by their group with one or more products from other researchers.…”

Section: Introductionmentioning

confidence: 99%

“…Kubota et al (2003) and Chou et al (2003) both take the approach of differencing the product developed by their group with one or more products from other researchers. This approach tends to favor the product developed 'inhouse' as the benchmark, making conclusions relative to that product.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of nine monthly air–sea flux products

Smith

Hughes

Bourassa

2010

Intl Journal of Climatology

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A comparison is conducted between nine monthly turbulent air-sea flux products. The analysis includes in situ-based (Florida State University fluxes, FSU3 and National Oceanography Centre, NOC), satellite-based (Hamburg Ocean-Atmosphere Parameters from Satellite data, HOAPS2 and the French Research Institute for Exploitation of the Sea, IFREMER), hybrid (Goddard Satellite-based Surface Turbulent Fluxes, GSSTF2 and objectively analysed fluxes, OAFLUX), and reanalysis (National Centers for Environmental Prediction, NCEPR2, Japanese 25-year reanalysis, JRA, and European Centre for Medium Range Weather Forecasts reanalysis, ERA-40) products. Objectives include documenting the varying analysis methodologies and quantifying the differences and similarities between the nine products. Recommendations are made for developers of future flux products and to guide users to select products most suitable for their application.The comparison examines turbulent fluxes of heat and momentum along with the forcing variables (air temperature, wind speed, humidity, and ocean skin temperature) that are necessary to estimate turbulent fluxes. The wide range of turbulent flux parameterisations, sampling patterns, and averaging techniques within the products are described, including some of the difficulties product differences pose when trying to compare or apply the individual products. Global comparisons of monthly means tend to reveal similar spatial patterns in latent heat flux (LHF) and sensible heat flux (SHF) for the nine products; however, the magnitudes and patterns of variability (expressed as maps of standard deviations) are widely different. Basin scale and regional analysis further reveals large differences in the products (in some cases the interquartile ranges (IQRs) do not overlap for different products), but also reveals potential sources of the differences. For example, some of the variations in LHF can be explained by large differences in the distribution of specific humidity between the products. As a final analysis, we examine how each product represents the variations in turbulent fluxes in the equatorial Pacific (EP) Ocean. This analysis provides an example of how the choice of a flux product, and understanding the strengths and weaknesses of that product, can alter research findings.

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Using an ensemble data set of turbulent air‐sea fluxes to evaluate the IPSL climate model in tropical regions

2015

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Citation:Gȃinuşȃ-Bogdan, A., P. Braconnot, and J. Servonnat (2015), Using an ensemble data set of turbulent air-sea fluxes to evaluate the IPSL climate model in tropical regions, J. Geophys. Res. Atmos., 120, 4483-4505, doi:10.1002 Abstract Ocean-atmosphere interactions represent a key component of the hydrological cycle in tropical regions and their variability has profound influences on low-latitude climate. In order to evaluate how climate models represent these fluxes while taking into account the observational uncertainties, we assemble a comprehensive database of 14 climatological surface flux products, including in situ-based, satellite, hybrid, and reanalysis data sets. We find that the large observational uncertainties are reflected in the climatological magnitudes, as well as in the spatial patterns and seasonal variations and that, for the most part, they do not carry specific signatures of product type. This data ensemble allows us to draw several conclusions on the current representation of the intertropical turbulent air-sea fluxes in the atmospheric component of the Intitut Pierre Simon Laplace-Coupled Model 5A, when forced by observed sea surface temperatures. Despite significantly underestimated near-surface wind speeds over the entire tropical oceans domain, the atmospheric model produces generally well represented zonal and meridional wind stress values, and only weak biases in the spatial patterns and seasonality. The simulated latent heat flux develops a bias pattern matching that of the wind speed, but with no systematic underestimation. Compared to the same reference, the sensible heat flux is overestimated over the entire region of interest, in response to a significant overestimation of the sea-air temperature contrast. The observational ensemble and analyses presented in this paper offer a good framework for large-scale model surface flux evaluation.

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Intercomparison of Various Surface Latent Heat Flux Fields

Cited by 55 publications

References 14 publications

Evaporation variability under climate warming in five reanalyses and its association with pan evaporation over China

Evaporation variability under climate warming in five reanalyses and its association with pan evaporation over China

A comparison of nine monthly air–sea flux products

Using an ensemble data set of turbulent air‐sea fluxes to evaluate the IPSL climate model in tropical regions

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