Statistical bias correction of global climate projections – consequences for large scale modeling of flood flows

Abstract. General circulation models (GCMs) project an increasing frequency and intensity of heavy rainfall events due to global climate change. This rather holds true for regions that are even expected to experience an overall decrease in average annual precipitation. Consequently, this may be attended by an increasing frequency and magnitude of flood events. However, time series of GCMs show a bias in simulating 20th century precipitation and temperature fields and, therefore, cannot directly be used to force hydrological models in order to assess the impact of the projected climate change on certain components of the hydrological cycle. For a posteriori correction, the so-called delta change approach is widely-used which adds the 30-year monthly differences for temperature or ratios for precipitation of the GCM data to each month of a historic climate data set. As the variability of the climate variables in the scenario period is not transferred, this approach is especially questionable if discharge extremes are to be analyzed. In order to preserve the variability given by the GCM, methods of statistical bias correction are applied. This study aims to investigate the impact of two methods of bias correction, the delta change approach and a statistical bias correction, on the large scale modeling of flood discharges, using the example of 25 macroscale catchments in Europe. The discharge simulation is carried out with the global integrated model WaterGAP3 (Water – Global Assessment and Prognosis). Results show that the two bias correction methods lead to distinctively different trends in future flood flows.

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A statistical bias correction technique for global climate model predicted near-surface temperature in India using the generalized regression neural network

Journal of Water and Climate Change ◽

10.2166/wcc.2022.214 ◽

2022 ◽

Author(s):

Diljit Dutta ◽

Rajib Kumar Bhattacharjya

Keyword(s):

Neural Network ◽

Surface Temperature ◽

Bias Correction ◽

Global Climate ◽

Temperature Data ◽

Generalized Regression Neural Network ◽

Emission Scenarios ◽

Statistical Bias ◽

Near Surface ◽

Statistical Bias Correction

Abstract Global climate models (GCMs) developed by the numerical simulation of physical processes in the atmosphere, ocean, and land are useful tools for climate prediction studies. However, these models involve parameterizations and assumptions for the simulation of complex phenomena, which lead to random and structural errors called biases. So, the GCM outputs need to be bias-corrected with respect to observed data before applying these model outputs for future climate prediction. This study develops a statistical bias correction approach using a four-layer feedforward radial basis neural network – a generalized regression neural network (GRNN) to reduce the biases of the near-surface temperature data in the Indian mainland. The input to the network is the CNRM-CM5 model output gridded data of near-surface temperature for the period 1951–2005, and the target to the model used for bias correcting the input data is the gridded near-surface temperature developed by the Indian Meteorological Department for the same period. Results show that the trained GRNN model can improve the inherent biases of the GCM modelled output with significant accuracy, and a good correlation is seen between the test statistics of observed and bias-corrected data for both the training and testing period. The trained GRNN model developed is then used for bias correction of CNRM-CM5 modelled projected near-surface temperature for 2006–2100 corresponding to the RCP4.5 and RCP8.5 emission scenarios. It is observed that the model can adapt well to the nature of unseen future temperature data and correct the biases of future data, assuming quasi-stationarity of future temperature data for both emission scenarios. The model captures the seasonal variation in near-surface temperature over the Indian mainland, having diverse topography appreciably, and this is evident from the bias-corrected output.

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Statistical Bias Correction for Predictions of Indian Ocean Dipole Index with Quantile Mapping Approach

10.31219/osf.io/7dq2j ◽

2021 ◽

Author(s):

Sri Nurdiati ◽

Ardhasena Sopaheluwakan ◽

Mohamad Khoirun Najib

Keyword(s):

Indian Ocean ◽

Prediction Model ◽

Bias Correction ◽

Indian Ocean Dipole ◽

Sea Surface ◽

Statistical Bias ◽

Quantile Mapping ◽

Ecmwf Model ◽

The Impact ◽

Statistical Bias Correction

The Indian Ocean Dipole (IOD) is a phenomenon of ocean-atmosphere interaction that affects climate conditions in Indonesia. The IOD index shows the difference between the western and eastern Indian Ocean sea surface temperature. The impact of the IOD can increase the risk of forest fires, floods and crop failure. Thus, an IOD index prediction model is needed to anticipate the impact of the IOD. One of prediction models of sea surface temperature is the ECMWF prediction model. However, this prediction model has systematic errors that can be corrected using a quantile mapping approach. This method corrects the systematic error of the ECMWF model by connecting the distribution between the ECMWF model and OISST in a transfer function, such as different of quantile and polynomial function. Based on the results, the linear function has the highest chance to improve the accuracy of the model. Moreover, the result shows that statistical bias correction is a good method to improve the accuracy of the ECMWF model especially in Januari-April and September-December.

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PowerSystems.jl — A power system data management package for large scale modeling

SoftwareX ◽

10.1016/j.softx.2021.100747 ◽

2021 ◽

Vol 15 ◽

pp. 100747

Author(s):

José Daniel Lara ◽

Clayton Barrows ◽

Daniel Thom ◽

Dheepak Krishnamurthy ◽

Duncan Callaway

Keyword(s):

Power System ◽

Data Management ◽

Large Scale ◽

Scale Modeling ◽

System Data ◽

Large Scale Modeling

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Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.0c01164 ◽

2021 ◽

Vol 61 (2) ◽

pp. 653-663

Author(s):

Sankalp Jain ◽

Vishal B. Siramshetty ◽

Vinicius M. Alves ◽

Eugene N. Muratov ◽

Nicole Kleinstreuer ◽

...

Keyword(s):

Deep Learning ◽

Acute Toxicity ◽

Large Scale ◽

Learning Methods ◽

End Points ◽

Scale Modeling ◽

Large Scale Modeling

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Stochastic representation of the Reynolds transport theorem: Revisiting large-scale modeling

Computers & Fluids ◽

10.1016/j.compfluid.2017.08.017 ◽

2017 ◽

Vol 156 ◽

pp. 456-469 ◽

Cited By ~ 10

Author(s):

S. Kadri Harouna ◽

E. Mémin

Keyword(s):

Large Scale ◽

Stochastic Representation ◽

Scale Modeling ◽

Large Scale Modeling ◽

Transport Theorem

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Large scale modeling of the transport, chemical transformation and mass budget of the sulfur emitted during the April 2007 eruption of Piton de la Fournaise

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-4533-2011 ◽

2011 ◽

Vol 11 (9) ◽

pp. 4533-4546 ◽

Cited By ~ 31

Author(s):

P. Tulet ◽

N. Villeneuve

Keyword(s):

Dry Deposition ◽

Large Scale ◽

Temporal Evolution ◽

Active Period ◽

General Shape ◽

So2 Emission ◽

Piton De La Fournaise ◽

Scale Modeling ◽

The Indian Ocean ◽

Large Scale Modeling

Abstract. In April 2007, the Piton de la Fournaise volcano (Réunion island) entered into its biggest eruption recorded in the last century. Due to the absence of a sensors network in the vicinity of the volcano, an estimation of degassing during the paroxysmal phase of the event has not been performed. Nevertheless, the SO2 plume and aerosols have been observed by the OMI and CALIOP space sensors, respectively. The mesoscale chemical model MesoNH-C simulates the observed bulk mass of SO2 and the general shape of the SO2 plume spreading over the Indian Ocean. Moreover, an analysis of the SO2 plume budget estimates a total SO2 release of 230 kt, among of which 60 kt have been transformed into H2SO4. 27 kt of SO2 and 21 kt of H2SO4 have been deposited at the surface by dry deposition. With this top down approach, the temporal evolution of the SO2 emission has been estimated during the most active period of the eruption. The peak of degassing was estimated at 1800 kg s−1 in the morning of 6~April. The temporal evolution of SO2 emission presented here can also be used for local studies.

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Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

Geoscientific Model Development ◽

10.5194/gmd-10-1383-2017 ◽

2017 ◽

Vol 10 (3) ◽

pp. 1383-1402 ◽

Cited By ~ 35

Author(s):

Paolo Davini ◽

Jost von Hardenberg ◽

Susanna Corti ◽

Hannah M. Christensen ◽

Stephan Juricke ◽

...

Keyword(s):

High Resolution ◽

Large Scale ◽

Climate Model ◽

Global Climate ◽

Rainfall Variability ◽

Global Climate Model ◽

Small Scale ◽

Low Resolution ◽

The Impact ◽

Stochastic Physics

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

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