Assimilation and Modeling of the Atmospheric Hydrological Cycle in the ECMWF Forecasting System

Andersson, Erik; Bauer, Péter; Beljaars, Anton; Chevallier, F.; Hólm, Elías; Janisková, Marta; Kållberg, Per; Kelly, Graeme; Lopez, Philippe; McNally, A. P.; Moreau, E.; Simmons, A. J.; Thépaut, Jean‐Noël; Tompkins, Adrian M.

doi:10.1175/bams-86-3-387

Cited by 147 publications

(126 citation statements)

References 63 publications

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“…The Hadley cell is a purely divergent circulation, which is not well constrained by observations and is therefore sensitive to biases in the assimilating models [Sardeshmukh and Hoskins, 1987]. A well-documented problem with ERA40's tropical humidity leads to spurious latent heating, driving a strong Hadley cell which survives in the analysis [Andersson et al, 2005]. On the other hand, both stationary and transient eddy stresses in ERA40 agree well those in NCEP ( Figure 2c).…”

Section: Multi-model Ensemble-meanmentioning

confidence: 88%

Hadley cell bias in climate models linked to extratropical eddy stress

Caballero¹

2008

Geophysical Research Letters

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Recent theoretical and observational work shows that the Reynolds stresses due to large‐scale extratropical eddies play a key role in determining Hadley cell strength. This implies that errors in the representation of extratropical eddies in climate models could force a bias in the tropical circulation. Here, this hypothesis is assessed using output from 17 coupled climate models, focusing on Northern Hemisphere winter. Inter‐model variability in Hadley cell strength, tropical temperature and tropical humidity is found to be significantly correlated with inter‐model differences in stationary eddy stress. Thus, a significant fraction of the tropical climate bias found in current climate models may be forced from the extratropics.

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Section: Multi-model Ensemble-meanmentioning

confidence: 88%

Hadley cell bias in climate models linked to extratropical eddy stress

Caballero¹

2008

Geophysical Research Letters

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“…In the ERA-40, ship winds are assimilated at measured height when known; otherwise, they are assumed to be at 25 m. As noted in Section 2.1, a long-term trend existing in anemometer heights might add a small bias to the fluxes and forcing parameters in ERA-40 over the oceans; however, the low weight of ship observations (as compared to radiosonde and satellite data) in ERA-40 might render this bias undetectable. Finally, the ERA-40 analyses are found to be more moist over the tropical oceans due to the assimilation of satellite data (Andersson et al, 2004), which produces excessive tropical oceanic precipitation and likely impacts the LHF.…”

Section: Reanalysismentioning

confidence: 99%

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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“…Overestimation of rainfall by ERA-40 may be linked to an overestimation by the model of moisture over the Indian Ocean which is then advected over the land. Excessive humidity over tropical oceans in the ERA-40 model, and its effect on precipitation have been discussed by Andersson et al (2005) and Uppala et al (2005).…”

Section: Climatological Spatial Variabilitymentioning

confidence: 99%

Evaluation of reanalysis rainfall estimates over Ethiopia

Diro

Grimes

Black

et al. 2008

Intl Journal of Climatology

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There is a pressing need for good rainfall data for the African continent both for humanitarian and climatological purposes. Given the sparseness of ground-based observations, one source of rainfall information is Numerical Weather Prediction (NWP) model outputs. The aim of this article is to investigate the quality of two NWP products using Ethiopia as a test case. The two products evaluated are the ERA-40 and NCEP reanalysis rainfall products. Spatial, seasonal and interannual variability of rainfall have been evaluated for Kiremt (JJAS) and Belg (FMAM) seasons at a spatial scale that reflects the local variability of the rainfall climate using a method which makes optimum use of sparse gauge validation data.We found that the spatial pattern of the rainfall climatology is captured well by both models especially for the main rainy season Kiremt. However, both models tend to overestimate the mean rainfall in the northwest, west and central regions but underestimate in the south and east. The overestimation is greater for NCEP in Belg season and greater for ERA-40 in Kiremt Season. ERA-40 captures the annual cycle over most of the country better than NCEP, but strongly exaggerates the Kiremt peak in the northwest and west. The overestimation in Kiremt appears to have been reduced since the assimilation of satellite data increased around 1990. For both models the interannual variability is less well captured than the spatial and seasonal variability.

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Assimilation and Modeling of the Atmospheric Hydrological Cycle in the ECMWF Forecasting System

Cited by 147 publications

References 63 publications

Hadley cell bias in climate models linked to extratropical eddy stress

Hadley cell bias in climate models linked to extratropical eddy stress

A comparison of nine monthly air–sea flux products

Evaluation of reanalysis rainfall estimates over Ethiopia

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