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  • Other Sources  (3)
  • bias correction  (2)
  • ENSO  (1)
  • John Wiley & Sons, Ltd.  (3)
  • Institute for Advanced Sustainability Studies (IASS)
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  • English  (3)
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  • 1
    Publication Date: 2023-01-14
    Description: Climate model simulations typically exhibit a bias, which can be corrected using statistical approaches. In this study, a geostatistical approach for bias correction of daily precipitation at ungauged locations is presented. The method utilizes a double quantile mapping with dry day correction for future periods. The transfer function of the bias correction for the ungauged locations is established using distribution functions estimated by ordinary kriging with anisotropic variograms. The methodology was applied to the daily precipitation simulations of the entire CORDEX‐Africa ensemble for a study region located in the West African Sudanian Savanna. This ensemble consists of 23 regional climate models (RCM) that were run for three different future scenarios (RCP 2.6, RCP 4.5, and RCP 8.5). The evaluation of the approach for a historical 50‐year period (1950–2005) showed that the method can reduce the inherent strong precipitation bias of RCM simulations, thereby reproducing the main climatological features of the observed data. Moreover, the bias correction technique preserves the climate change signal of the uncorrected RCM simulations. However, the ensemble spread is increased due to an overestimation of the rainfall probability of uncorrected RCM simulations. The application of the bias correction method to the future period (2006–2100) revealed that annual precipitation increases for most models in the near (2020–2049) and far future (2070–2099) with a mean increase of up to 165mm⋅a−1 (18%). An analysis of the monthly and daily time series showed a slightly delayed onset and intensification of the rainy season.
    Description: Adapting water management strategies to future precipitation projected by climate models is associated with high uncertainty in sparsely gauged catchments. Kriging was utilized to estimate distribution parameters for ungauged locations in a West African region to perform a bias correction of the CORDEX‐Africa ensemble. The application of the bias correction method revealed higher annual precipitation amounts and an intensifaction of the rainy season but only little change to the onset of the rainy season.
    Description: German Federal Ministry of Education and Research, Bonn (BMBF), West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL)
    Keywords: ddc:551.6 ; bias correction ; climate change ; CORDEX‐Africa ; geostatistical approaches ; precipitation ; quantile mapping ; West Africa
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2024-01-24
    Description: 〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Reliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.〈/p〉
    Description: Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347
    Description: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview
    Description: https://cdc.dwd.de/portal/
    Keywords: ddc:551.5 ; bias correction ; circulation patterns ; ERA5 ; extreme events ; heavy precipitation ; simulated annealing ; statistical downscaling
    Language: English
    Type: doc-type:article
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  • 3
    Publication Date: 2024-02-06
    Description: El Niño–Southern Oscillation (ENSO) is one of the most important modes of climate variability on interannual timescales. We aim to find out whether a change in ENSO frequency can be predicted for the nearer future. We analyse the unforced pre‐industrial control run and the forced 1%/year CO〈sub〉2〈/sub〉 increase run for an ensemble of 43 general circulation models that participated in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We assume that the uncertainty of ENSO frequency trend estimates from an ensemble is caused by apparent trends as well as model differences. The part of the uncertainty caused by apparent trends is estimated from the pre‐industrial control simulations. As a measure for ENSO frequency, we use the number of El Niño‐ and La Niña‐like months in a moving 30‐year time window. Its linear decadal trend is calculated for every member. The multimember mean of the trend for both experiments is less than 0.7 events per decade. Given that the standard error is of the same order of magnitude, we consider this a negligible trend. The uncertainties are large in both experiments and we can attribute most of the intermember variability to apparent trends due to natural variability rather than different model reactions to CO〈sub〉2〈/sub〉 forcing. This means that the impact of intermodel differences might have been overstated in previous studies. Apparent trends make it very difficult to make reliable predictions of changes in ENSO frequency based on 120‐year time series.
    Description: The 1pctCO2 and piControl ensembles from CMIP6 are analysed for 43 models with a focus on changes in ENSO frequency. We find that most of the intermember variability can be attributed to natural variability instead of model differences. Therefore, the uncertainty can only marginally be reduced and it is very difficult to reliably predict changes in ENSO frequency on a timescale of 150 years.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: https://doi.org/10.5281/zenodo.6841964
    Keywords: ddc:551.6 ; climate change ; CMIP6 ; ENSO ; ENSO frequency
    Language: English
    Type: doc-type:article
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