scholarly journals Improving pan-european hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations

2011 ◽  
Vol 8 (2) ◽  
pp. 3883-3936 ◽  
Author(s):  
R. Rojas ◽  
L. Feyen ◽  
A. Dosio ◽  
D. Bavera
Keyword(s):  
Impact Assessment ◽  
Extreme Events ◽  
Bias Correction ◽  
Large Scale ◽  
Transfer Functions ◽  
Time Window ◽  
Control Period ◽  
Maximum Temperature ◽  
Data Set ◽  
Climate Simulations

Abstract. In this work we asses the benefits of removing bias in climate forcing data used for hydrological climate change impact assessment at pan-European scale, with emphasis on floods. Climate simulations from the HIRHAM5-ECHAM5 model driven by the SRES-A1B emission scenario are corrected for bias using a histogram equalization method. As predictand for the bias correction we employ gridded interpolated observations of precipitation, average, minimum, and maximum temperature from the E-OBS data set. Bias removal transfer functions are derived for the control period 1961–1990. These are subsequently used to correct the climate simulations for the control period, and, under the assumption of a stationary error model, for the future time window 2071–2100. Validation against E-OBS climatology in the control period shows that the correction method performs successfully in removing bias in average and extreme statistics relevant for flood simulation over the majority of the European domain in all seasons. This translates into considerably improved simulations with the hydrological model of observed average and extreme river discharges at a majority of 554 validation river stations across Europe. Probabilities of extreme events derived employing extreme value techniques are also more closely reproduced. Results indicate that projections of future flood hazard in Europe based on uncorrected climate simulations, both in terms of their magnitude and recurrence interval, are likely subject to large errors. Notwithstanding the inherent limitations of the large-scale approach used herein, this study strongly advocates the removal of bias in climate simulations prior to their use in hydrological impact assessment.

scholarly journals Improving pan-European hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations

2011 ◽  
Vol 15 (8) ◽  
pp. 2599-2620 ◽  
Author(s):  
R. Rojas ◽  
L. Feyen ◽  
A. Dosio ◽  
D. Bavera
Keyword(s):  
Impact Assessment ◽  
Extreme Events ◽  
Bias Correction ◽  
Large Scale ◽  
Transfer Functions ◽  
Time Window ◽  
Control Period ◽  
Maximum Temperature ◽  
Data Set ◽  
Climate Simulations

Abstract. In this work we asses the benefits of removing bias in climate forcing data used for hydrological climate change impact assessment at pan-European scale, with emphasis on floods. Climate simulations from the HIRHAM5-ECHAM5 model driven by the SRES-A1B emission scenario are corrected for bias using a histogram equalization method. As target for the bias correction we employ gridded interpolated observations of precipitation, average, minimum, and maximum temperature from the E-OBS data set. Bias removal transfer functions are derived for the control period 1961–1990. These are subsequently used to correct the climate simulations for the control period, and, under the assumption of a stationary error model, for the future time window 2071–2100. Validation against E-OBS climatology in the control period shows that the correction method performs successfully in removing bias in average and extreme statistics relevant for flood simulation over the majority of the European domain in all seasons. This translates into considerably improved simulations with the hydrological model of observed average and extreme river discharges at a majority of 554 validation river stations across Europe. Probabilities of extreme events derived employing extreme value techniques are also more closely reproduced. Results indicate that projections of future flood hazard in Europe based on uncorrected climate simulations, both in terms of their magnitude and recurrence interval, are likely subject to large errors. Notwithstanding the inherent limitations of the large-scale approach used herein, this study strongly advocates the removal of bias in climate simulations prior to their use in hydrological impact assessment.

A novel method to identify subseasonal clustering episodes of extreme precipitation events and their contribution to large accumulations

2021 ◽  
Author(s):  
Jérôme Kopp ◽  
Pauline Rivoire ◽  
S. Mubashshir Ali ◽  
Yannick Barton ◽  
Olivia Martius
Keyword(s):  
Time Scales ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Time Window ◽  
Heat Waves ◽  
Reanalysis Data ◽  
Data Set ◽  
Temporal Clustering ◽  
Extreme Precipitation Events ◽  
Precipitation Events

<p>Temporal clustering of extreme precipitation events on subseasonal time scales is a type of compound event, which can cause large precipitation accumulations and lead to floods. We present a novel count-based procedure to identify subseasonal clustering of extreme precipitation events. Furthermore, we introduce two metrics to characterise the frequency of subseasonal clustering episodes and their relevance for large precipitation accumulations. The advantage of this approach is that it does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this methodology to the ERA5 reanalysis data set, we identify regions where subseasonal clustering of annual high precipitation percentiles occurs frequently and contributes substantially to large precipitation accumulations. Those regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southeast of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the subseasonal time window (here 2 – 4 weeks). The procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (e.g. for drought years). <span>For a complementary study on the subseasonal clustering of European extreme precipitation events and its relationship to large-scale atmospheric drivers, please refer to Barton et al.</span></p>

scholarly journals Identification of Droughts and Heat Waves in Germany with Regional Climate Networks

2020 ◽  
Author(s):  
Gerd Schädler ◽  
Marcus Breil
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Heat Waves ◽  
Regional Climate ◽  
Clustering Coefficient ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Published Data ◽  
Data Set ◽  
Climate Networks

Abstract. Regional Climate Networks (RCNs) are used to identify heat waves and droughts in Germany and two subregions for the summer half years resp. summer seasons of the period 1951 to 2019. RCNs provide information for whole areas (in contrast to the point-wise information from standard indices), the underlying nodes can be distributed arbitrarily, they are easy to 5 construct and provide details otherwise difficult to avail of like extent, intensity and collective behaviour of extreme events. The RCNs were constructed on the regular 0.25 degree grid of the E-Obs data set. The season-wise correlation of time series of daily maximum temperature Tmax and precipitation were used to construct the adjacency matrix of the networks. Metrics to identify extremes were the edge density, the 90th percentile of the correlations and the average clustering coefficient, which turned out to be highly correlated; they increased considerably during extreme events. The standard indices for comparison 10 were the effective drought and heat index (EDI and EHI) respectively, based on the same time series, and complemented by other published data. Our results show that the RCNs are able to identify severe extremes in all cases and moderate extremes in most cases. An interesting finding is that during average years, the distribution of the node degrees is close to the Poisson distribution, characteristic of random networks, while for extreme years the distribution is more uniform and heavy tailed.

scholarly journals Improved Analyses of Changes and Uncertainties in Sea Surface Temperature Measured In Situ since the Mid-Nineteenth Century: The HadSST2 Dataset

2006 ◽  
Vol 19 (3) ◽  
pp. 446-469 ◽  
Author(s):  
N. A. Rayner ◽  
P. Brohan ◽  
D. E. Parker ◽  
C. K. Folland ◽  
J. J. Kennedy ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Bias Correction ◽  
Measurement Errors ◽  
Large Scale ◽  
Sea Surface ◽  
Data Set ◽  
Total Uncertainty ◽  
Hadley Centre ◽  
Measurement Practices

Abstract A new flexible gridded dataset of sea surface temperature (SST) since 1850 is presented and its uncertainties are quantified. This analysis [the Second Hadley Centre Sea Surface Temperature dataset (HadSST2)] is based on data contained within the recently created International Comprehensive Ocean–Atmosphere Data Set (ICOADS) database and so is superior in geographical coverage to previous datasets and has smaller uncertainties. Issues arising when analyzing a database of observations measured from very different platforms and drawn from many different countries with different measurement practices are introduced. Improved bias corrections are applied to the data to account for changes in measurement conditions through time. A detailed analysis of uncertainties in these corrections is included by exploring assumptions made in their construction and producing multiple versions using a Monte Carlo method. An assessment of total uncertainty in each gridded average is obtained by combining these bias-correction-related uncertainties with those arising from measurement errors and undersampling of intragrid box variability. These are calculated by partitioning the variance in grid box averages between real and spurious variability. From month to month in individual grid boxes, sampling uncertainties tend to be most important (except in certain regions), but on large-scale averages bias-correction uncertainties are more dominant owing to their correlation between grid boxes. Changes in large-scale SST through time are assessed by two methods. The linear warming between 1850 and 2004 was 0.52° ± 0.19°C (95% confidence interval) for the globe, 0.59° ± 0.20°C for the Northern Hemisphere, and 0.46° ± 0.29°C for the Southern Hemisphere. Decadally filtered differences for these regions over this period were 0.67° ± 0.04°C, 0.71° ± 0.06°C, and 0.64° ± 0.07°C.

scholarly journals HydroGFD3.0: a 25 km global near real-time updated precipitation and temperature data set

2020 ◽  
Author(s):  
Peter Berg ◽  
Fredrik Almén ◽  
Denica Bozhinova
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Horizontal Resolution ◽  
Maximum Temperature ◽  
Historical Period ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Time Data ◽  
Data Set

Abstract. HydroGFD (Hydrological Global Forcing Data) is a data set of bias adjusted reanalysis data for daily precipitation, and minimum, mean, and maximum temperature. It is mainly intended for large scale hydrological modeling, but is also suitable for other impact modeling. The data set has an almost global land area coverage, excluding the Antarctic continent, at a horizontal resolution of 0.25°, i.e. about 25 km. It is available for the complete ERA5 reanalysis time period; currently 1979 until five days ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology, and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number or wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly, such that no additional input data about the number of wet days is necessary. The daily mean temperature is adjusted toward the monthly mean of the observations, and applied to 1 h timesteps of the ERA5 reanalysis. Daily mean, minimum and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access to the data set at https://doi.org/10.5281/zenodo.3871707.

scholarly journals MORPHODYNAMIC PROCESSES IN ESTUARIES - COMPARISON OF MARINE AND LIMNIC TIDAL FLATS

2011 ◽  
Vol 1 (32) ◽  
pp. 77
Author(s):  
Thorsten Albers ◽  
Dagmar Much ◽  
Nino Ohle ◽  
Nicole Von Lieberman ◽  
Eva Falke
Keyword(s):  
Extreme Events ◽  
Tidal Flat ◽  
Large Scale ◽  
Spatial Scales ◽  
Field Measurements ◽  
Weather Conditions ◽  
Water Levels ◽  
Seasonal Effects ◽  
Data Set ◽  
Investigation Area

Tidal flat areas in estuaries are affected by strong morphodynamics. Changes of sedimentation and erosion occur on different temporal and spatial scales. These changes challenge the responsible authorities due to the high importance of sufficient navigation channel depths and the ecological importance of those unique zones. In cooperation with the Hamburg Port Authority the Hamburg University of Technology runs broad field measurements on tidal flat areas in the Elbe estuary. The results provide a fundamental data set to improve the knowledge about morphodynamic processes. For more than 3 years water levels, waves, current parameters and suspended sediment concentrations are being recorded continuously and in a high resolution at different positions on a marine investigation area. Therefore, ADCP’s, optical backscatter sensors (OBS) and pressure transducers (PT) are used. To observe the consequences of the morphodynamic processes, the bathymetry of the investigation areas is determined with a multi-beam echo sounder (MBES) in frequent intervals as well as after extreme events like storm surges. Derived from the field data certain patterns of erosion, sediment transport and sedimentation could be observed depending on tidal currents, waves and large scale weather conditions. Seasonal effects are analyzed as well as the influence of extreme events. The observed processes are compared with data from a limnic investigation area, where a second, shorter field study was carried out.

Comparing the Lasso Predictor-Selection and Regression Method with Classical Approaches of Precipitation Bias Adjustment in Decadal Climate Predictions

2020 ◽  
Vol 148 (10) ◽  
pp. 4339-4351
Author(s):  
Jingmin Li ◽  
Felix Pollinger ◽  
Heiko Paeth
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Transfer Functions ◽  
Classical Model ◽  
Phase Relationship ◽  
Regression Method ◽  
Added Value ◽  
Model Bias ◽  
Shrinkage Factor ◽  
Bias Adjustment

AbstractIn this study, we investigate the technical application of the regularized regression method Lasso for identifying systematic biases in decadal precipitation predictions from a high-resolution regional climate model (CCLM) for Europe. The Lasso approach is quite novel in climatological research. We apply Lasso to observed precipitation and a large number of predictors related to precipitation derived from a training simulation, and transfer the trained Lasso regression model to a virtual forecast simulation for testing. Derived predictors from the model include local predictors at a given grid box and EOF predictors that describe large-scale patterns of variability for the same simulated variables. A major added value of the Lasso function is the variation of the so-called shrinkage factor and its ability in eliminating irrelevant predictors and avoiding overfitting. Among 18 different settings, an optimal shrinkage factor is identified that indicates a robust relationship between predictand and predictors. It turned out that large-scale patterns as represented by the EOF predictors outperform local predictors. The bias adjustment using the Lasso approach mainly improves the seasonal cycle of the precipitation prediction and, hence, improves the phase relationship and reduces the root-mean-square error between model prediction and observations. Another goal of the study pertains to the comparison of the Lasso performance with classical model output statistics and with a bivariate bias correction approach. In fact, Lasso is characterized by a similar and regionally higher skill than classical approaches of model bias correction. In addition, it is computationally less expensive. Therefore, we see a large potential for the application of the Lasso algorithm in a wider range of climatological applications when it comes to regression-based statistical transfer functions in statistical downscaling and model bias adjustment.

scholarly journals A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements

2014 ◽  
Vol 14 (8) ◽  
pp. 3991-4012 ◽  
Author(s):  
S. Houweling ◽  
M. Krol ◽  
P. Bergamaschi ◽  
C. Frankenberg ◽  
E. J. Dlugokencky ◽  
...  
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Systematic Errors ◽  
Inverse Modelling ◽  
Data Set ◽  
Mixing Ratios ◽  
Before And After ◽  
Modelling Studies ◽  
The Difference ◽  
The Tropics

Abstract. This study investigates the use of total column CH4 (XCH4) retrievals from the SCIAMACHY satellite instrument for quantifying large-scale emissions of methane. A unique data set from SCIAMACHY is available spanning almost a decade of measurements, covering a period when the global CH4 growth rate showed a marked transition from stable to increasing mixing ratios. The TM5 4DVAR inverse modelling system has been used to infer CH4 emissions from a combination of satellite and surface measurements for the period 2003–2010. In contrast to earlier inverse modelling studies, the SCIAMACHY retrievals have been corrected for systematic errors using the TCCON network of ground-based Fourier transform spectrometers. The aim is to further investigate the role of bias correction of satellite data in inversions. Methods for bias correction are discussed, and the sensitivity of the optimized emissions to alternative bias correction functions is quantified. It is found that the use of SCIAMACHY retrievals in TM5 4DVAR increases the estimated inter-annual variability of large-scale fluxes by 22% compared with the use of only surface observations. The difference in global methane emissions between 2-year periods before and after July 2006 is estimated at 27–35 Tg yr−1. The use of SCIAMACHY retrievals causes a shift in the emissions from the extra-tropics to the tropics of 50 ± 25 Tg yr−1. The large uncertainty in this value arises from the uncertainty in the bias correction functions. Using measurements from the HIPPO and BARCA aircraft campaigns, we show that systematic errors in the SCIAMACHY measurements are a main factor limiting the performance of the inversions. To further constrain tropical emissions of methane using current and future satellite missions, extended validation capabilities in the tropics are of critical importance.

scholarly journals HydroGFD3.0 (Hydrological Global Forcing Data): a 25 km global precipitation and temperature data set updated in near-real time

2021 ◽  
Vol 13 (4) ◽  
pp. 1531-1545
Author(s):  
Peter Berg ◽  
Fredrik Almén ◽  
Denica Bozhinova
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Horizontal Resolution ◽  
Precipitation Anomaly ◽  
Maximum Temperature ◽  
Historical Period ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Time Data ◽  
Data Set

Abstract. HydroGFD3 (Hydrological Global Forcing Data) is a data set of bias-adjusted reanalysis data for daily precipitation and minimum, mean, and maximum temperature. It is mainly intended for large-scale hydrological modelling but is also suitable for other impact modelling. The data set has an almost global land area coverage, excluding the Antarctic continent and small islands, at a horizontal resolution of 0.25∘, i.e. about 25 km. It is available for the complete ERA5 reanalysis time period, currently 1979 until 5 d ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number of wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly such that no additional input data about the number of wet days are necessary. The daily mean temperature is adjusted toward the monthly mean of the observations and applied to 1 h time steps of the ERA5 reanalysis. Daily mean, minimum, and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access the data set at https://doi.org/10.5281/zenodo.3871707 (Berg et al., 2020).

scholarly journals Differential Credibility Assessment for Statistical Downscaling

2020 ◽  
Vol 59 (8) ◽  
pp. 1333-1349
Author(s):  
S. C. Pryor ◽  
J. T. Schoof
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Linear Models ◽  
Transfer Functions ◽  
Global Climate ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Climate Projections ◽  
Climate Extreme ◽  
Credibility Assessment

AbstractClimate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing. A framework to quantify and depict the credibility of statistically downscaled model output is presented and demonstrated. The approach employs transfer functions in the form of robust and resilient generalized linear models applied to downscale daily minimum and maximum temperature anomalies at 10 locations using predictors drawn from ERA-Interim reanalysis and two global climate models (GCM; GFDL-ESM2M and MPI-ESM-LR). The downscaled time series are used to derive several impact-relevant Climate Extreme (CLIMDEX) temperature indices that are assigned credibility based on 1) the reproduction of relevant large-scale predictors by the GCMs (i.e., fraction of regression beta weights derived from predictors that are well reproduced) and 2) the degree of variance in the observations reproduced in the downscaled series following application of a new variance inflation technique. Credibility of the downscaled predictands varies across locations and between the two GCM and is generally higher for minimum temperature than for maximum temperature. The differential credibility assessment framework demonstrated here is easy to use and flexible. It can be applied as is to inform decision-makers about projection confidence and/or can be extended to include other components of the transfer functions, and/or used to weight members of a statistically downscaled ensemble.

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