scholarly journals A Hybrid Downscaling Approach for Future Temperature and Precipitation Change

2020 ◽  
Vol 59 (11) ◽  
pp. 1793-1807 ◽  
Author(s):  
Helene Birkelund Erlandsen ◽  
Kajsa M. Parding ◽  
Rasmus Benestad ◽  
Abdelkader Mezghani ◽  
Marie Pontoppidan
Keyword(s):  
Regional Climate Model ◽  
Statistical Models ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Small Scale ◽  
Regional Climate Model Output

AbstractWe used empirical–statistical downscaling in a pseudoreality context, in which both large-scale predictors and small-scale predictands were based on climate model results. The large-scale conditions were taken from a global climate model, and the small-scale conditions were taken from dynamical downscaling of the same global model with a convection-permitting regional climate model covering southern Norway. This hybrid downscaling approach, a “perfect model”–type experiment, provided 120 years of data under the CMIP5 high-emission scenario. Ample calibration samples made rigorous testing possible, enabling us to evaluate the effect of empirical–statistical model configurations and predictor choices and to assess the stationarity of the statistical models by investigating their sensitivity to different calibration intervals. The skill of the statistical models was evaluated in terms of their ability to reproduce the interannual correlation and long-term trends in seasonal 2-m temperature T2m, wet-day frequency fw, and wet-day mean precipitation μ. We found that different 30-yr calibration intervals often resulted in differing statistical models, depending on the specific choice of years. The hybrid downscaling approach allowed us to emulate seasonal mean regional climate model output with a high spatial resolution (0.05° latitude and 0.1° longitude grid) for up to 100 GCM runs while circumventing the issue of short calibration time, and it provides a robust set of empirically downscaled GCM runs.

scholarly journals Brief Communication: An update of the article "Modeling flood damages under climate change conditions – a case study for Germany"

2015 ◽  
Vol 3 (12) ◽  
pp. 7231-7245
Author(s):  
F. F. Hattermann ◽  
S. Huang ◽  
O. Burghoff ◽  
P. Hoffmann ◽  
Z. W. Kundzewicz
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Large Scale ◽  
Considerable Increase ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Flood Damages

Abstract. In our first study on possible flood damages under climate change in Germany, we reported that a considerable increase in flood related losses can be expected in future, warmer, climate. However, the general significance of the study was limited by the fact that outcome of only one Global Climate Model (GCM) was used as large scale climate driver, while many studies report that GCM models are often the largest source of uncertainty in impact modeling. Here we show that a much broader set of global and regional climate model combinations as climate driver shows trends which are in line with the original results and even give a stronger increase of damages.

scholarly journals Validation of a transient dynamical downscaling simulation approach with multi-model ensemble-based GCM bias corrections

2021 ◽  
Author(s):  
Zhongfeng Xu ◽  
Ying Han ◽  
Chi-Yung Tam ◽  
Zong-Liang Yang ◽  
Congbin Fu
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Eastern China ◽  
Summer Precipitation ◽  
Precipitation Pattern ◽  
Improved Performance

Abstract Dynamical downscaling is the most widely used physics-based approach to obtaining fine-scale weather and climate information. However, traditional dynamical downscaling approaches are often degraded by biases in the large-scale forcing. To improve the confidence in future projection of regional climate, we used a novel bias-corrected global climate model (GCM) dataset to drive a regional climate model (RCM) over the period for 1980–2014. The dynamical downscaling simulations driven by the original GCM dataset (MPI-ESM1-2-HR model) (hereafter WRF_GCM), the bias-corrected GCM (hereafter WRF_GCMbc) are validated against that driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 dataset (hereafter WRF_ERA5), respectively. The results suggest that, compared with the WRF_GCM, the WRF_GCMbc shows a 50–90% reduction in RMSEs of the climatological mean of downscaled variables (e.g. temperature, precipitation, wind, relative humidity). Similarly, the WRF_GCMbc also shows improved performance in simulating the interannual variability of downscaled variables. The RMSEs of interannual variances of downscaled variables are reduced by 30–60%. An EOF analysis suggests that the WRF_GCMbc can successfully reproduce the dominant tri-pole mode in the interannual summer precipitation variations observed over eastern China as opposed to the mono-pole precipitation pattern simulated by the WRF_GCM. Such improvements are primarily caused by the correct simulation of the location of the western North Pacific subtropical high by the WRF_GCMbc due to the GCM bias correction.

scholarly journals Dynamical Downscaling Luaran Global Climate Model (GCM) Menggunakan Model REGCM3 untuk Proyeksi Curah Hujan di Kabupaten Indramayu

Agromet ◽  
2018 ◽  
Vol 28 (1) ◽  
pp. 9
Author(s):  
Syamsu Dwi Jadmiko ◽  
Akhmad Faqih
Keyword(s):  
Regional Climate Model ◽  
Spatial Resolution ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
High Spatial Resolution ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Equations Of Motion ◽  
Daily Rainfall

Future rainfall projection can be predicted by using Global Climate Model (GCM). In spite of low resolution, we are not able specifically to describe a local or regional information. Therefore, we applied downscaling technique of GCM output using Regional Climate Model (RCM). In this case, Regional Climate Model version 3 (RegCM3) is used to accomplish this purpose. RegCM3 is regional climate model which atmospheric properties are calculated by solving equations of motion and thermodynamics. Thus, RegCM3 is also called as dynamic downscaling model. RegCM3 has reliable capability to evaluate local or regional climate in high spatial resolution up to 10 × 10 km. In this study, dynamically downscaling techniques was applied to produce high spatial resolution (20 × 20 km) from GCM EH5OM output which commonly has rough spatial resolution (1.875<sup>o</sup> × 1.875<sup>o</sup>). Simulation show that future rainfall in Indramayu is relatively decreased compared to the baseline condition. Decreased rainfall generally occurs during the dry season (July-June-August/JJA) in a range 10-20%. Study of extreme daily rainfall indicates that there is no significant increase or decrease value.

scholarly journals Filling the matrix: an ANOVA-based method to emulate regional climate model simulations for equally-weighted properties of ensembles of opportunity

2021 ◽  
Author(s):  
Ole B. Christensen ◽  
Erik Kjellström
Keyword(s):  
Regional Climate Model ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Climate Data ◽  
Large Ensembles ◽  
Climate Model Simulations

AbstractCollections of large ensembles of regional climate model (RCM) downscaled climate data for particular regions and scenarios can be organized in a usually incomplete matrix consisting of GCM (global climate model) x RCM combinations. When simple ensemble averages are calculated, each GCM will effectively be weighted by the number of times it has been downscaled. In order to facilitate more equal and less arbitrary weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM. This method is based on a traditional Analysis of Variance (ANOVA) approach. The method is applied and studied for fields of seasonal average temperature, precipitation and surface wind and for the 10-year return value of daily precipitation and of 10-m wind speed for a completely filled matrix consisting of 5 GCMs and 4 RCMs. We quantify the skill of the two averaging methods for different numbers of missing simulations and show that ensembles where lacking members have been emulated by the ANOVA technique are better at representing the full ensemble than corresponding simple ensemble averages, particularly in cases where only a few model combinations are absent. The technique breaks down when the number of missing simulations reaches the sum of the numbers of GCMs and RCMs. Also, the method is only useful when inter-simulation variability is limited. This is the case for the average fields that have been studied, but not for the extremes. We have developed analytical expressions for the degree of improvement obtained with the present method, which quantify this conclusion.

scholarly journals Filling the Matrix: An ANOVA-Based Method to Emulate Regional Climate Model Simulations for Equally-Weighted Properties of Ensembles of Opportunity

2021 ◽  
Author(s):  
Ole Bøssing Christensen ◽  
Erik Kjellström
Keyword(s):  
Regional Climate Model ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Climate Data ◽  
Large Ensembles ◽  
Climate Model Simulations

Abstract Collections of large ensembles of regional climate model (RCM) downscaled climate data for particular regions and scenarios can be organized in a usually incomplete matrix consisting of GCM (global climate model) x RCM combinations. When simple ensemble averages are calculated, each GCM will effectively be weighted by the number of times it has been downscaled. In order to facilitate more equal and less random weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM. This method is based on a traditional Analysis of Variance (ANOVA) approach. The method is applied and studied for fields of seasonal average temperature, precipitation and surface wind and for the 10-year return value of daily precipitation and of 10-m wind speed for a completely filled matrix consisting of 5 GCMs and 4 RCMs. We quantify the skill of the two averaging methods for different numbers of missing simulations and show that ensembles where lacking members have been emulated by the ANOVA technique are better at representing the full ensemble than corresponding simple ensemble averages, particularly in cases where only a few model combinations are absent. The technique breaks down when the number of missing simulations reaches the sum of the numbers of GCMs and RCMs.

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

2017 ◽  
Vol 10 (3) ◽  
pp. 1383-1402 ◽  
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).

scholarly journals Brief Communication: An update of the article "Modelling flood damages under climate change conditions – a case study for Germany"

2016 ◽  
Vol 16 (7) ◽  
pp. 1617-1622 ◽  
Author(s):  
Fred Fokko Hattermann ◽  
Shaochun Huang ◽  
Olaf Burghoff ◽  
Peter Hoffmann ◽  
Zbigniew W. Kundzewicz
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Considerable Increase ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Impact Modelling ◽  
Flood Damages

Abstract. In our first study on possible flood damages under climate change in Germany, we reported that a considerable increase in flood-related losses can be expected in a future warmer climate. However, the general significance of the study was limited by the fact that outcome of only one global climate model (GCM) was used as a large-scale climate driver, while many studies report that GCMs are often the largest source of uncertainty in impact modelling. Here we show that a much broader set of global and regional climate model combinations as climate drivers show trends which are in line with the original results and even give a stronger increase of damages.

Coordinated Global and Regional Climate Modeling*

2015 ◽  
Vol 29 (1) ◽  
pp. 17-35 ◽  
Author(s):  
J. F. Scinocca ◽  
V. V. Kharin ◽  
Y. Jiao ◽  
M. W. Qian ◽  
M. Lazare ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Climate Modeling ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Value Added ◽  
Regional Climate Modeling ◽  
Climate Projections ◽  
Climate Modelling

Abstract A new approach of coordinated global and regional climate modeling is presented. It is applied to the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) and its parent global climate model CanESM2. CanRCM4 was developed specifically to downscale climate predictions and climate projections made by its parent global model. The close association of a regional climate model (RCM) with a parent global climate model (GCM) offers novel avenues of model development and application that are not typically available to independent regional climate modeling centers. For example, when CanRCM4 is driven by its parent model, driving information for all of its prognostic variables is available (including aerosols and chemical species), significantly improving the quality of their simulation. Additionally, CanRCM4 can be driven by its parent model for all downscaling applications by employing a spectral nudging procedure in CanESM2 designed to constrain its evolution to follow any large-scale driving data. Coordination offers benefit to the development of physical parameterizations and provides an objective means to evaluate the scalability of such parameterizations across a range of spatial resolutions. Finally, coordinating regional and global modeling efforts helps to highlight the importance of assessing RCMs’ value added relative to their driving global models. As a first step in this direction, a framework for identifying appreciable differences in RCM versus GCM climate change results is proposed and applied to CanRCM4 and CanESM2.

scholarly journals Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

2011 ◽  
Vol 92 (9) ◽  
pp. 1181-1192 ◽  
Author(s):  
Frauke Feser ◽  
Burkhardt Rockel ◽  
Hans von Storch ◽  
Jörg Winterfeldt ◽  
Matthias Zahn
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Small Scale ◽  
Model Data ◽  
Polar Lows

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

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