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 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 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.

Global Instrumental Meteorological Database Before 1890 – a useful overview

2021 ◽  
Author(s):  
Elin Lundstad ◽  
Yuri Brugnera ◽  
Stefan Brönnimann
Keyword(s):  
Meteorological Parameters ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Meteorological Station ◽  
Climate Data ◽  
Data Set ◽  
Instrumental Data ◽  
The World ◽  
Climate Model Simulations

<p>This work describes the compilation of global instrumental climate data with a focus on the 18th and early 19th centuries. This database provides early instrumental data recovered for thousands of locations around the world. Instrumental meteorological measurements from periods prior to the start of national weather services are designated “early instrumental data”. Much of the data is taken from repositories we know (GHCN, ISTI, CRUTEM, Berkeley Earth, HISTALP). In addition, many of these stations have not been digitized before. Therefore,  we provide a new global collection of monthly averages of multivariable meteorological parameters before 1890 based on land-based meteorological station data. The product will be form as the most comprehensive global monthly climate data set, encompassing temperature, pressure, and precipitation as ever done. These data will be quality controlled and analyzed with respect to climate variability and they be assimilated into global climate model simulations to provide monthly global reconstructions. The collection has resulted in a completely new database that is uniform, where no interpolations are included. Therefore, we are left with climate reconstruction that becomes very authentic. This compilation will describe the procedure and various challenges we have encountered by creating a unified database that can later be used for e.g. models. It will also describe the strategy for quality control that has been adopted is a sequence of tests.</p>

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 Increased variability in Greenland Ice Sheet runoff from satellite observations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Thomas Slater ◽  
Andrew Shepherd ◽  
Malcolm McMillan ◽  
Amber Leeson ◽  
Lin Gilbert ◽  
...  
Keyword(s):  
Ocean Circulation ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Model Simulations ◽  
Climate Model Simulations

AbstractRunoff from the Greenland Ice Sheet has increased over recent decades affecting global sea level, regional ocean circulation, and coastal marine ecosystems, and it now accounts for most of the contemporary mass imbalance. Estimates of runoff are typically derived from regional climate models because satellite records have been limited to assessments of melting extent. Here, we use CryoSat-2 satellite altimetry to produce direct measurements of Greenland’s runoff variability, based on seasonal changes in the ice sheet’s surface elevation. Between 2011 and 2020, Greenland’s ablation zone thinned on average by 1.4 ± 0.4 m each summer and thickened by 0.9 ± 0.4 m each winter. By adjusting for the steady-state divergence of ice, we estimate that runoff was 357 ± 58 Gt/yr on average – in close agreement with regional climate model simulations (root mean square difference of 47 to 60 Gt/yr). As well as being 21 % higher between 2011 and 2020 than over the preceding three decades, runoff is now also 60 % more variable from year-to-year as a consequence of large-scale fluctuations in atmospheric circulation. Because this variability is not captured in global climate model simulations, our satellite record of runoff should help to refine them and improve confidence in their projections.

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.

Building synergies in regional climate services for Southeast Asia: The ASEAN Regional Climate Data, Analysis and Projections (ARCDAP) workshop series.

2020 ◽  
Author(s):  
Gerald Lim ◽  
Aurel Moise ◽  
Raizan Rahmat ◽  
Bertrand Timbal
Keyword(s):  
Climate Change ◽  
Data Analysis ◽  
Southeast Asia ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Early Warning Systems ◽  
Climate Data ◽  
Climate Services

<p>Southeast Asia (SEA) is a rapidly developing and densely populated region that is home to over 600 million people. This, together with the region’s high sensitivity, exposure and low adaptive capacities, makes it particularly vulnerable to climate change and extremes such as floods, droughts and tropical cyclones. While the last decade saw some countries in SEA develop their own climate change projections, studies were largely uncoordinated and most countries still lack the capability to independently produce robust future climate information. Following a proposal from the World Meteorological Organisation (WMO) Regional Association (RA) V working group on climate services, the ASEAN Regional Climate Data, Analysis and Projections (ARCDAP) workshop series was conceived in 2017 to bridge these gaps in regional synergies. The ARCDAP series has been organised annually since 2018 by the ASEAN Specialised Meteorological Centre (hosted by Meteorological Service Singapore) with support from WMO through the Canada-funded Climate Risk and Early Warning Systems (Canada-CREWS) initiative.</p><p>This presentation will cover the activities and outcomes from the first two workshops, as well as the third which will be held in February 2020. The ARCDAP series has so far brought together representatives from ASEAN National Meteorological and Hydrological Services (NMHSs), climate scientists and end-users from policy-making and a variety of vulnerability and impact assessment (VIA) sectors, to discuss and identify best practices regarding the delivery of climate change information, data usage and management, advancing the science etc. Notable outputs include two comprehensive workshop reports and a significant regional contribution to the HadEX3 global land in-situ-based dataset of temperature and precipitation extremes, motivated by work done with the ClimPACT2 software.</p><p>The upcoming third workshop will endeavour to encourage the uptake of the latest ensemble of climate simulations from the Coupled Model Intercomparison Project (CMIP6) using CMIP-endorsed tools such as ESMValTool. This will address the need for ASEAN climate change practitioners to upgrade their knowledge of the latest global climate model database. It is anticipated that with continued support from WMO, the series will continue with the Fourth workshop targeting the assessment of downscaling experiments in 2021.</p>

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