The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3

Andersson, Axel; Fennig, Karsten; Klepp, Christian; Bakan, Stephan; Graßl, Hartmut; Schulz, Jörg B.

doi:10.5194/essd-2-215-2010

Cited by 173 publications

(191 citation statements)

References 51 publications

(66 reference statements)

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“…Second, the interplay of the SPCZ (South Pacific Convergence Zone), the subtropical gyre and the westerly storm tracks centered at 34 • S (Saez et al, 2009). According to Vincent (1993), the SPCZ is an extended and persistent cloud band which axis stretches from New Guinea east-southeastward to about 30 • S and 120 • W. To investigate the effect of the SPCZ on the climate of Rapa Nui, we analysed the precipitation climatology between the years 1987-2005 from HOAPS-3 (Andersson et al, 2007). The dataset from HOAPS-3 provides a spatial resolution of 0.5 • on a global grid.…”

Section: Analysis Of Observational Datamentioning

confidence: 99%

Simulated climate variability in the region of Rapa Nui during the last millennium

Junk¹,

Claußen

2011

Clim. Past

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Abstract. Rapa Nui, an isolated island in the Southeast Pacific, was settled by the Polynesians most likely around 1200 AD and was discovered by the Europeans in 1722 AD. While the Polynesians presumably found a profuse palm woodland on Rapa Nui, the Europeans faced a landscape dominated by grassland. Scientists have examined potential anthropogenic, biological and climatic induced vegetation changes on Rapa Nui. Here, we analyse observational climate data for the last decades and climate model results for the period 800-1750 AD to explore the potential for a climatic-induced vegetation change. A direct influence of the ENSO phenomenon on the climatic parameters of Rapa Nui could not be found in the model simulations. Furthermore, strong climatic trends from a warm Medieval Period to a Little Ice Age or rapid climatic fluctuations due to large volcanic eruptions were not verifiable for the Rapa Nui region, although they are detectable in the simulations for many regions world wide. Hence, we tentatively conclude that largescale climate changes in the oceanic region around Rapa Nui might be too small to explain strong vegetation changes on the island over the last millennium.

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Section: Analysis Of Observational Datamentioning

confidence: 99%

Simulated climate variability in the region of Rapa Nui during the last millennium

Junk¹,

Claußen

2011

Clim. Past

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“…The HOAPS precipitation was retrieved from a neural network algorithm that takes the SSM/I brightness temperature and the precipitation from the ECMWF model as training data [Andersson et al, 2010]. Both the GPCP [Adler et al, 2003;Huffman et al, 2009] and the CMAP [Xie and Arkin, 1997] use multiple satellite and rain gauge data sets with some differences in their input data and merging techniques [Yin et al, 2004].…”

Section: Data Setsmentioning

confidence: 99%

“…In HOAPS, evaporation data were obtained from all of the available Special Sensor Microwave Imager (SSM/I) observation data based on the Coupled Ocean-atmosphere Response Experiment (COARE) bulk flux algorithm version 2.6a [Bradley et al, 2000;Fairall et al, 1996] for latent heat flux parameterization. The nearsurface wind speed and specific air humidity were retrieved from the SSM/I brightness temperature, and the sea surface temperature data from the Advanced Very High Resolution Radiometer (AVHRR) measurements were used [Andersson et al, 2010]. The GSSTF latent heat flux was retrieved based on the bulk flux model [Chou, 1993], using the SSM/I surface wind, surface air humidity, and near-surface air and sea surface temperatures from the National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis as the input data [Chou et al, 2003;Shie, 2010].…”

Section: Data Setsmentioning

confidence: 99%

Atmospheric water balance over oceanic regions as estimated from satellite, merged, and reanalysis data

2013

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[1] The column integrated atmospheric water balance over the ocean was examined using satellite-based and merged data sets for the period from 2000 to 2005. The data sets for the components of the atmospheric water balance include evaporation from the HOAPS, GSSTF, and OAFlux and precipitation from the HOAPS, CMAP, and GPCP. The water vapor tendency was derived from water vapor data of HOAPS. The product for water vapor flux convergence estimated using satellite observation data was used. The atmospheric balance components from the MERRA reanalysis data were also examined. Residuals of the atmospheric water balance equation were estimated using nine possible combinations of the data sets over the ocean between 60 N and 60 S. The results showed that there was considerable disagreement in the residual intensities and distributions from the different combinations of the data sets. In particular, the residuals in the estimations of the satellite-based atmospheric budget appear to be large over the oceanic areas with heavy precipitation such as the intertropical convergence zone, South Pacific convergence zone, and monsoon regions. The lack of closure of the atmospheric water cycle may be attributed to the uncertainties in the data sets and approximations in the atmospheric water balance equation. Meanwhile, the anomalies of the residuals from the nine combinations of the data sets are in good agreement with their variability patterns. These results suggest that significant consideration is needed when applying the data sets of water budget components to quantitative water budget studies, while climate variability analysis based on the residuals may produce similar results.

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“…The HOAPS-III data set (Andersson et al, 2007) is a satellite based climatology of precipitation, turbulent heat fluxes and freshwater budget (evaporation minus precipitation) that includes also related atmospheric state variables over the global oceans. The main advantage of this dataset is that daily, pentad and monthly means of various air-sea interaction parameters, such as sea surface temperature, (Platnick et al, 2003;Fetzer et al, 2006;Olsen et al, 2007).…”

Section: Datasetsmentioning

confidence: 99%

“…In the present study, the temporal evolution of double ITCZs in the Indian Ocean is characterized using the newly released Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) pentad (5-day) Version III dataset of EUMETSAT's Satellite Application Facility on Climate Monitoring (Andersson et al, 2007, Schulz et al, 2009 (Zhang, 2001). This is because the life period of double ITCZs varies from 1 to 12 days with an average life span of 5-6 days (Saha, 1971), which cannot be fully resolved in analysis of monthly mean precipitation.…”

Section: Introductionmentioning

confidence: 99%

A multi-sensor climatological view of double ITCZs over the Indian Ocean

Kumar

Devasthale

Levy

et al. 2011

International Journal of Remote Sensing

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We characterize climatological features of Double Inter Tropical Convergence Zones (DITCZs) over the western Indian Ocean during November-December by a synergistic analysis of the Satellite Application (1997, 2002 and 2006), in sharp contrast to the DITCZs in the eastern Pacific Ocean, where they are absent during ENSOs. Further, these convergence zones on either side of the equator are of short duration, approximately 3-6 pentads during November and December. All satellite sensors data sets consistently capture major features of DITCZ. As an accurate simulation of DITCZs in coupled global climate models remains a challenge, results from the present study would provide platform for evaluating these models.

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The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3

Cited by 173 publications

References 51 publications

Simulated climate variability in the region of Rapa Nui during the last millennium

Simulated climate variability in the region of Rapa Nui during the last millennium

Atmospheric water balance over oceanic regions as estimated from satellite, merged, and reanalysis data

A multi-sensor climatological view of double ITCZs over the Indian Ocean

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