scholarly journals A Norwegian Approach to Downscaling

2021 ◽  
Author(s):  
Rasmus E. Benestad
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Model Performance ◽  
Large Datasets ◽  
Access To Information ◽  
Evaluation Scheme ◽  
Data Volume ◽  
Model Ensembles ◽  
Fast Access

Abstract. A description of a comprehensive geoscientific downscaling model strategy is presented outlining an approach that has evolved over the last 20 years, together with an explanation for its development, its technical aspects, and evaluation scheme. This effort has resulted in an open-source and free R-based tool, 'esd', for the benefit of sharing and improving the reproducibility of the downscaling results. Furthermore, a set of new metrics was developed as an integral part of the downscaling approach which assesses model performance with an emphasis on regional information for society (RifS). These metrics involve novel ways of comparing model results with observational data and have been developed for downscaling large multi-model global climate model ensembles. A literature search suggests that this comprehensive downscaling strategy and evaluation scheme are not widely used within the downscaling community. In addition, a new convention for storing large datasets of ensemble results that provides fast access to information and drastically saves data volume is explained.

scholarly journals Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles

2019 ◽  
Vol 32 (9) ◽  
pp. 2591-2603 ◽  
Author(s):  
Emily Hogan ◽  
Robert E. Nicholas ◽  
Klaus Keller ◽  
Stephanie Eilts ◽  
Ryan L. Sriver
Keyword(s):  
United States ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Extreme Temperature ◽  
The United States ◽  
Temperature Extremes ◽  
Warm Temperature ◽  
Climate Model Ensembles ◽  
Model Ensembles

Abstract Extreme temperature events can have considerable negative impacts on sectors such as health, agriculture, and transportation. Observational evidence indicates the severity and frequency of warm extremes are increasing over much of the United States, but there are sizeable challenges both in estimating extreme temperature changes and in quantifying the relevant associated uncertainties. This study provides a simple statistical framework using a block maxima approach to analyze the representation of warm temperature extremes in several recent global climate model ensembles. Uncertainties due to structural model differences, grid resolution, and internal variability are characterized and discussed. Results show that models and ensembles differ greatly in the representation of extreme temperature over the United States, and variability in tail events is dependent on time and anthropogenic warming, which can influence estimates of return periods and distribution parameter estimates using generalized extreme value (GEV) distributions. These effects can considerably influence the uncertainty of model hindcasts and projections of extremes. Several idealized regional applications are highlighted for evaluating ensemble skill and trends, based on quantile analysis and root-mean-square errors in the overall sample and the upper tail. The results are relevant to regional climate assessments that use global model outputs and that are sensitive to extreme warm temperature. Accompanying this manuscript is a simple toolkit using the R statistical programming language for characterizing extreme events in gridded datasets.

scholarly journals Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques

2017 ◽  
Vol 56 (12) ◽  
pp. 3245-3262 ◽  
Author(s):  
A. Wootten ◽  
A. Terando ◽  
B. J. Reich ◽  
R. P. Boyles ◽  
F. Semazzi
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Model Experiments ◽  
Complete Set ◽  
Regional Adaptation ◽  
Model Ensembles

AbstractIn recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios is typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast for precipitation and ~30% for extreme heat days (>35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a subsample of all models is available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. This article concludes with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

Global climate model evaluation and selection using the interactive tool GCMeval

2020 ◽  
Author(s):  
Kajsa Parding ◽  
Oskar A. Landgren ◽  
Andreas Dobler ◽  
Carol F. McSweeney ◽  
Rasmus E. Benestad ◽  
...  
Keyword(s):  
Model Selection ◽  
Web Application ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Model Performance ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Services ◽  
Skill Scores

<p>We present the interactive web application GCMeval, available at https://gcmeval.met.no. The tool is a useful resource for climate services by illustrating how model selection affects representation of future climate change. GCMeval was developed in a co-design process engaging users. Based on a thorough analysis of user demands, needs and capabilities, two different user groups were defined: Data users with lots of experience with data processing and Product users with a strong focus on information products. The available data, information, and user interface in GCMeval are tailored to the requirements of the data users.</p><p>In the tool, the user can select all or a subset of models from the CMIP5 and CMIP6 ensembles and assign weights to different regions, seasons, climate variables, and skill scores. The tool provides visualizations of the spread of future changes in temperature and precipitation which allows the user to study how the sub-ensemble fits in relation to the full multi-model ensemble and to compare climate model results for different regions of the world. A ranking of individual model performance for recent past climate is also provided. The tool can be used to aid in model selection for climate or impact studies, or to illustrate how an already existing selection represents the range of possible future climate outcomes.</p>

scholarly journals Similarities within a multi-model ensemble: functional data analysis framework

2019 ◽  
Vol 12 (2) ◽  
pp. 735-747 ◽  
Author(s):  
Eva Holtanová ◽  
Thomas Mendlik ◽  
Jan Koláček ◽  
Ivanka Horová ◽  
Jiří Mikšovský
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Future Climate Change ◽  
Model Ensembles

Abstract. Despite the abundance of available global climate model (GCM) and regional climate model (RCM) outputs, their use for evaluation of past and future climate change is often complicated by substantial differences between individual simulations and the resulting uncertainties. In this study, we present a methodological framework for the analysis of multi-model ensembles based on a functional data analysis approach. A set of two metrics that generalize the concept of similarity based on the behavior of entire simulated climatic time series, encompassing both past and future periods, is introduced. To our knowledge, our method is the first to quantitatively assess similarities between model simulations based on the temporal evolution of simulated values. To evaluate mutual distances of the time series, we used two semimetrics based on Euclidean distances between the simulated trajectories and based on differences in their first derivatives. Further, we introduce an innovative way of visualizing climate model similarities based on a network spatialization algorithm. Using the layout graphs, the data are ordered on a two-dimensional plane which enables an unambiguous interpretation of the results. The method is demonstrated using two illustrative cases of air temperature over the British Isles (BI) and precipitation in central Europe, simulated by an ensemble of EURO-CORDEX RCMs and their driving GCMs over the 1971–2098 period. In addition to the sample results, interpretational aspects of the applied methodology and its possible extensions are also discussed.

scholarly journals Evaluation of extreme sub-daily precipitation in high-resolution global climate model simulations

2021 ◽  
Vol 379 (2195) ◽  
pp. 20190545 ◽  
Author(s):  
Michael Wehner ◽  
Jiwoo Lee ◽  
Mark Risser ◽  
Paul Ullrich ◽  
Peter Gleckler ◽  
...  
Keyword(s):  
High Resolution ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Daily Precipitation ◽  
Flash Flood ◽  
Global Climate ◽  
Global Climate Model ◽  
Model Performance ◽  
Horizontal Resolution

We examine the resolution dependence of errors in extreme sub-daily precipitation in available high-resolution climate models. We find that simulated extreme precipitation increases as horizontal resolution increases but that appropriately constructed model skill metrics do not significantly change. We find little evidence that simulated extreme winter or summer storm processes significantly improve with the resolution because the model performance changes identified are consistent with expectations from scale dependence arguments alone. We also discuss the implications of these scale-dependent limitations on the interpretation of simulated extreme precipitation. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.

Evaluation of CMIP6 Models Toward Dynamical Downscaling Over Eight CORDEX Domains

2021 ◽  
Author(s):  
Meng-Zhuo Zhang ◽  
Zhongfeng Xu ◽  
Ying Han ◽  
Weidong Guo
Keyword(s):  
Model Selection ◽  
Large Scale ◽  
Evaluation Method ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Model Performance ◽  
Uncertainty Range ◽  
Multiple Variables

Abstract Both reliability and independence of global climate model (GCM) simulation are essential for model selection to generate a reasonable uncertainty range of dynamical downscaling simulations. In this study, we evaluate the performance and interdependency of 37 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in terms of seven key large-scale driving fields over eight CORDEX domains. A multivariable integrated evaluation method is used to evaluate and rank the models’ ability to simulate multiple variables in terms of their climatological mean and interannual variability. The results suggest that the model performance varies considerably with seasons, domains, and variables evaluated, and no model outperforms in all aspects. However, the multi-model ensemble mean performs much better than any individual model. Among 37 CMIP6 models, the MPI-ESM1-2-HR, FIO-ESM-2-0, and MPI-ESM1-2-LR rank top three due to their overall good performance across all domains. To measure the model interdependency in terms of multiple fields, we define the similarity of multivariate error fields between pairwise models. Our results indicate that the dependence exists between most of the CMIP6 models, and the models sharing the same idea or/and concept generally show less independence. Furthermore, we hierarchically cluster the top 15 models based on the similarity of multivariate error fields to facilitate the model selection. Our evaluation can provide useful guidance on the selection of CMIP6 models based on their performance and relative independence, which helps to generate a more reliable ensemble of dynamical downscaling simulations with reasonable inter-model spread.

High bit rate ACTS experiments for performing global science - Keck Telescope and global climate model

1996 ◽  
Author(s):  
Larry Bergman ◽  
J. Gary ◽  
Burt Edelson ◽  
Neil Helm ◽  
Judith Cohen ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Bit Rate ◽  
Global Science ◽  
High Bit Rate

scholarly journals A comparison of ship and satellite measurements of cloud properties with global climate model simulations in the southeast Pacific stratus deck

2010 ◽  
Vol 10 (14) ◽  
pp. 6527-6536 ◽  
Author(s):  
M. A. Brunke ◽  
S. P. de Szoeke ◽  
P. Zuidema ◽  
X. Zeng
Keyword(s):  
Diurnal Cycle ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Cloud Fraction ◽  
Cloud Base ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Southeast Pacific ◽  
Cloud Thickness

Abstract. Here, liquid water path (LWP), cloud fraction, cloud top height, and cloud base height retrieved by a suite of A-train satellite instruments (the CPR aboard CloudSat, CALIOP aboard CALIPSO, and MODIS aboard Aqua) are compared to ship observations from research cruises made in 2001 and 2003–2007 into the stratus/stratocumulus deck over the southeast Pacific Ocean. It is found that CloudSat radar-only LWP is generally too high over this region and the CloudSat/CALIPSO cloud bases are too low. This results in a relationship (LWP~h9) between CloudSat LWP and CALIPSO cloud thickness (h) that is very different from the adiabatic relationship (LWP~h2) from in situ observations. Such biases can be reduced if LWPs suspected to be contaminated by precipitation are eliminated, as determined by the maximum radar reflectivity Zmax>−15 dBZ in the apparent lower half of the cloud, and if cloud bases are determined based upon the adiabatically-determined cloud thickness (h~LWP1/2). Furthermore, comparing results from a global model (CAM3.1) to ship observations reveals that, while the simulated LWP is quite reasonable, the model cloud is too thick and too low, allowing the model to have LWPs that are almost independent of h. This model can also obtain a reasonable diurnal cycle in LWP and cloud fraction at a location roughly in the centre of this region (20° S, 85° W) but has an opposite diurnal cycle to those observed aboard ship at a location closer to the coast (20° S, 75° W). The diurnal cycle at the latter location is slightly improved in the newest version of the model (CAM4). However, the simulated clouds remain too thick and too low, as cloud bases are usually at or near the surface.

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