Bridging the gap between climate models and impact studies: the <scp>FORESEE</scp> Database

Dobor, Laura; Barcza, Zoltán; Hlásny, Tomáš; Havasi, Ágnes; Horváth, Ferenc; Ittzés, Péter; Bartholy, Judit

doi:10.1002/gdj3.22

Cited by 46 publications

(33 citation statements)

References 60 publications

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“…To investigate the effects of weather variability on the vegetation activity and greenness, we used the FORESEE database [24]. This freely available meteorological database contains observed and projected daily maximum/minimum temperature and precipitation fields for Central Europe on a regular grid with a spatial resolution of 1/6° × 1/6°, covering the years between 1951 and 2100.…”

Section: Meteorological Databasementioning

confidence: 99%

Identification of Years with Extreme Vegetation State in Central Europe Based on Remote Sensing and Meteorological Data

Kern

Marjanović

Dobor

et al. 2017

SEEFOR

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Section: Meteorological Databasementioning

confidence: 99%

Identification of Years with Extreme Vegetation State in Central Europe Based on Remote Sensing and Meteorological Data

Kern

Marjanović

Dobor

et al. 2017

SEEFOR

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“…To investigate the effects of weather variability on the vegetation activity and greenness, we used the FORESEE (Open Database for Climate Change Related Impact Studies in Central Europe) database [65]. This freely available meteorological database contains observed and projected daily maximum/minimum temperature and precipitation fields for Central Europe on a regular grid with a spatial resolution of 1/6 • × 1/6 • , covering the 1951-2100 time period.…”

Section: The Applied Meteorological Databasementioning

confidence: 99%

Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013

2016

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Abstract:Remote sensing provides invaluable insight into the dynamics of vegetation with global coverage and reasonable temporal resolution. Normalized Difference Vegetation Index (NDVI) is widely used to study vegetation greenness, production, phenology and the responses of ecosystems to climate fluctuations. The extended global NDVI3g dataset created by Global Inventory Modeling and Mapping Studies (GIMMS) has an exceptional 32 years temporal coverage. Due to the methodology that was used to create NDVI3g inherent noise and uncertainty is present in the dataset. To evaluate the accuracy and uncertainty of application of NDVI3g at regional scale we used Collection-6 data from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on board satellite Terra as a reference. After noise filtering, statistical harmonization of the NDVI3g dataset was performed for Central Europe based on MOD13 NDVI. Mean seasonal NDVI profiles, start, end and length of the growing season, magnitude and timing of peak NDVI were calculated from NDVI3g (original, noise filtered and harmonized) and MODIS NDVI and compared with each other. NDVI anomalies were also compared and evaluated using simple climate sensitivity metrics. The results showed that (1) the original NDVI3g has limited applicability in Central Europe, which was also implied by the significant disagreement between the NDVI3g and MODIS NDVI datasets; (2) the harmonization of NDVI3g with MODIS NDVI is promising since the newly created dataset showed improved quality for diverse vegetation metrics. For NDVI anomaly detection NDVI3g showed limited applicability, even after harmonization. Climate-NDVI relationships are not represented well by NDVI3g. The presented results can help researchers to assess the expected quality of the NDVI3g-based studies in Central Europe.

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“…This is inevitable because each data set utilizes different raw data, data quality control, and analysis methodology [15,[47][48][49]. For example, the SNRs for the annual mean precipitation and its inter-annual variability exceeded 5 in most parts of the study domain.…”

Section: Discussionmentioning

confidence: 99%

Uncertainties in the Estimation of Global Observational Network Datasets of Precipitation over West Africa

Oj¹,

Oe²,

La³

et al. 2017

J Climatol Weather Forecasting

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This study assessed the uncertainty in estimating long-term mean precipitation, its inter-annual variability, and linear trend of three network observation datasets over West Africa. A reference data, defined as a multi-dataset ensemble of precipitation observations of the Climate Research Unit (CRU) of the University of East Anglia, the Global Precipitation Climatology Centre (GPCC) and the University of Delaware (UDEL), all at horizontal resolutions of 0.5° by 0.5° were obtained and used in this study. Uncertainties in these climatological parameters of precipitation at both annual and seasonal time scales were examined in terms of inter-dataset variability using signalto-noise ratio (SNR), correlation, root-mean-square errors and the normalised standard deviation. Results showed that the mean, inter-annual variability and trends climatology varied for different datasets. The three datasets had good agreement (SNR>5) in terms of the annual mean precipitation and its inter-annual variability in most parts of West Africa. However, the agreement between the datasets was poor in the very dry Sahel parts of northern Niger, Mali, and Mauritania (SNR ≤ 1) due to very little precipitation and possibility of relatively low station density in these regions of complex terrain. In terms of correlation (0.89 ≤ r ≤ 0.98), and normalised standard deviation, NSD (0.8 ≤ NSD ≤ 1.7), the uncertainties in the spatial variations in linear trend were larger than mean precipitation and their inter-annual variability for both annual and seasonal scales. The long-term annual precipitation trend in the region is highly uncertain except in a few small areas. observational errors are much more problematic, because their effects become relatively more pronounced as greater numbers of observations are aggregated. In this case, the author believed that averaging observations together from many different instruments/sources would tend to reduce the contribution of systematic observational errors to the uncertainty of the average.A number of researchers and institutions have developed observation-based gridded analysis datasets of global or regional coverage with fine spatial resolutions [8][9][10][11][12][13][14]. These network of observation datasets provide precipitation and/or surface air temperatures over extended periods of multiple decades at spatial resolutions of 0.5° or finer. This is, of course, a substantial improvement over previous generation data sets that are typically at much coarser (e.g. 2.5°) horizontal resolutions [15]. These recent fine-scale datasets allow us to better examine the regional precipitation and temperature climatology and to perform more reliable evaluations of today's highresolution climate simulations, especially over the regions of complex terrain, that are important for climate-change impact assessments and climate model evaluations [16].

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Bridging the gap between climate models and impact studies: the FORESEE Database

Cited by 46 publications

References 60 publications

Identification of Years with Extreme Vegetation State in Central Europe Based on Remote Sensing and Meteorological Data

Identification of Years with Extreme Vegetation State in Central Europe Based on Remote Sensing and Meteorological Data

Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013

Uncertainties in the Estimation of Global Observational Network Datasets of Precipitation over West Africa

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