scholarly journals Exploring Perturbed Physics Ensembles in a Regional Climate Model

2012 ◽  
Vol 25 (13) ◽  
pp. 4582-4599 ◽  
Author(s):  
Omar Bellprat ◽  
Sven Kotlarski ◽  
Daniel Lüthi ◽  
Christoph Schär
Keyword(s):  
Regional Climate Model ◽  
Annual Cycle ◽  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Total Cloud Cover ◽  
Observational Uncertainty ◽  
Perturbed Physics ◽  
Perturbed Physics Ensembles

Abstract Perturbed physics ensembles (PPEs) have been widely used to assess climate model uncertainties and have provided new estimates of climate sensitivity and parametric uncertainty in state-of-the-art climate models. So far, mainly global climate models were used to generate PPEs, and little work has been conducted with regional climate models. This paper discusses the parameter uncertainty in two PPEs of a regional climate model driven by reanalysis data for the present climate over Europe. The uncertainty is evaluated for the variables of 2-m temperature, precipitation, and total cloud cover, with a focus on the annual cycle, interannual variability, and selected extremes. The authors show that the simulated spread of the PPEs encompasses the observations at a regional scale in terms of the annual cycle and the interannual variability, provided observational uncertainty is taken into account. To rank the PPEs a new skill metric is proposed, which takes into account observational uncertainty and natural variability. The metric is a generalization of the climate prediction index (CPI) and is compared to metrics used in other studies. The consideration of observational uncertainty is particularly important for total cloud cover and reveals that current observations do not allow for a systematic evaluation of high precipitation intensities over the entire European domain. The skill framework is additionally used to identify important model parameters, which are of interest for an objective model calibration.

scholarly journals Evaluation of the Rossby Centre Regional Climate Model Rainfall Simulations over West Africa Using Large-Scale Spatial and Temporal Statistical Metrics

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 802 ◽  
Author(s):  
Gnim Tchalim Gnitou ◽  
Tinghuai Ma ◽  
Guirong Tan ◽  
Brian Ayugi ◽  
Isaac Kwesi Nooni ◽  
...  
Keyword(s):  
West Africa ◽  
Boundary Conditions ◽  
Data Quality ◽  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Global Climate Models ◽  
Observational Uncertainty ◽  
Ensemble Mean

Climate models are usually evaluated to understand how well the modeled data reproduce specific application-related features. In Africa, where multisource data quality is an issue, there is a need to assess climate data from a general perspective to motivate such specific types of assessment, but mostly to serve as a basis for data quality enhancement activities. In this study, we assessed the Rossby Centre Regional Climate Model (RCA4) over West Africa without targeting any application-specific feature, while jointly evaluating its boundary conditions and accounting for observational uncertainties. Results from this study revealed that the RCA4 signal highly modifies the boundary conditions (global climate models (GCMs) and reanalysis data), resulting in a significant reduction of their biases in the dynamically downscaled outputs. The results, with respect to the observational ensemble members, are in line with the differences between the observation datasets. Among the RCA4 simulations, the ensemble mean outperformed all individual simulations regardless of the statistical metric and the reference data used. This indicates that the RCA4 adds value to GCMs over West Africa, with no influence of observational uncertainty, and its ensemble mean reduces model-related uncertainties.

scholarly journals Evaluation and Bias Correction of Regional Climate Model Results Using Model Evaluation Measures

2012 ◽  
Vol 51 (9) ◽  
pp. 1670-1684 ◽  
Author(s):  
Robert Schoetter ◽  
Peter Hoffmann ◽  
Diana Rechid ◽  
K. Heinke Schlünzen
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Model Evaluation ◽  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Total Cloud Cover ◽  
Evaluation Measures

AbstractFor the assessment of regional climate change the reliability of the regional climate models needs to be known. The main goal of this paper is to evaluate the quality of climate model data that are used for impact research. Temperature, precipitation, total cloud cover, relative humidity, and wind speed simulated by the regional climate models Climate Local Model (CLM) and Regional Model (REMO) are evaluated for the metropolitan region of Hamburg in northern Germany for the period 1961–2000. The same evaluation is performed for the global climate model ECHAM5 that is used to force the regional climate models. The evaluation is based on comparison of the simulated and observed climatological annual cycles and probability density functions of daily averages. Several model evaluation measures are calculated to assure an objective model evaluation. As a very selective model evaluation measure, the hit rate of the percentiles is introduced for the evaluation of daily averages. The influence of interannual climate variability is considered by determining confidence intervals for the model evaluation measures by bootstrap resampling. Evaluation shows that, with some exceptions, temperature and wind speed are well simulated by the climate models; whereas considerable biases are found for relative humidity, total cloud cover, and precipitation, although not for all models in all seasons. It is shown that model evaluation measures can be used to decide for which meteorological parameters a bias correction is reasonable.

scholarly journals Regional climate change projections for the Barents region

2016 ◽  
Author(s):  
Andreas Dobler ◽  
Jan Erik Haugen ◽  
Rasmus Emil Benestad
Keyword(s):  
Sea Ice ◽  
Regional Climate Model ◽  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Barents Sea ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Barents Region ◽  
The Barents Sea

Abstract. Regional climate models can provide estimates for quantities that are difficult to study in empirical studies, such as cloud cover, wind, sea-ice or dependencies between variables. In this study, the regional climate model COSMO-CLM was used to simulate local climate conditions over the Barents region and provide projections for the three emission scenarios RCP2.6, RCP4.5 and RCP8.5. The results indicate that the most pronounced local warming can be expected in winter in the high Arctic near the present sea-ice border. The changes reach up to 20K, resulting in future temperatures close to melting. Similar spatial patterns are seen for changes in precipitation and wind in all scenarios, but with different amplitudes. Precipitation sensitivities, however, show the highest values along the west coast of Norway and in the Arctic during summer. For clouds, the projections show a decrease in winter mean cloud cover over sea and an increase over land, dominated by changes in low layer clouds. Over the Barents sea, convective cloud fraction is projected to increase, together with an increases in convective and total precipitation. In contrast to the COSMO-CLM and two other regional climate models taken into account, the ensemble mean of the driving global models shows an increasing trend in total cloud cover over the Barents sea. An analysis of the opposing trends reveals that there is an added value in the regional climate model projections for the Barents region.

Regional Climate Model Inter-Comparison for Antarctica within a Data Science Framework

2021 ◽  
Author(s):  
Jeremy Carter ◽  
Amber Leeson ◽  
Andrew Orr ◽  
Christoph Kittel ◽  
Melchior van Wessem
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate Change ◽  
Near Surface ◽  
Virtual Lab ◽  
Model Variability ◽  
The Antarctic

<p>Understanding the surface climatology of the Antarctic ice sheet is essential if we are to adequately predict its response to future climate change. This includes both primary impacts such as increased ice melting and secondary impacts such as ice shelf collapse events. Given its size, and inhospitable environment, weather stations on Antarctica are sparse. Thus, we rely on regional climate models to 1) develop our understanding of how the climate of Antarctica varies in both time and space and 2) provide data to use as context for remote sensing studies and forcing for dynamical process models. Given that there are a number of different regional climate models available that explicitly simulate Antarctic climate, understanding inter- and intra model variability is important.</p><p>Here, inter- and intra-model variability in Antarctic-wide regional climate model output is assessed for: snowfall; rainfall; snowmelt and near-surface air temperature within a cloud-based virtual lab framework. State-of-the-art regional climate model runs from the Antarctic-CORDEX project using the RACMO, MAR and MetUM models are used, together with the ERA5 and ERA-Interim reanalyses products. Multiple simulations using the same model and domain boundary but run at either different spatial resolutions or with different driving data are used. Traditional analysis techniques are exploited and the question of potential added value from more modern and involved methods such as the use of Gaussian Processes is investigated. The advantages of using a virtual lab in a cloud based environment for increasing transparency and reproducibility, are demonstrated, with a view to ultimately make the code and methods used widely available for other research groups.</p>

scholarly journals Global Radiative Flux and Cloudiness Variability for the Period 1959–2010 in Belgium: A Comparison between Reanalyses and the Regional Climate Model MAR

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 262 ◽  
Author(s):  
Coraline Wyard ◽  
Sébastien Doutreloup ◽  
Alexandre Belleflamme ◽  
Martin Wild ◽  
Xavier Fettweis
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Radiative Flux ◽  
Added Value ◽  
Historical Trends ◽  
The Mean

The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (Eg↓) that are found in reanalysis products and global models, as they allow for a finer spatial resolution and a finer parametrisation of surface and atmospheric processes. In this study, we assess the ability of the MAR («Modèle Atmosphérique Régional») RCM to reproduce observed changes in Eg↓, and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizontal resolution of 5 km for the period 1959–2010 by forcing MAR with different reanalysis products: ERA40/ERA-interim, NCEP/NCAR-v1, ERA-20C, and 20CRV2C. Measurements of Eg↓ from the Global Energy Balance Archive (GEBA) and from the Royal Meteorological Institute of Belgium (RMIB), as well as cloud cover observations from Belgocontrol and RMIB, were used for the evaluation of the MAR model and the forcing reanalyses. Results show that MAR enables largely reducing the mean biases that are present in the reanalyses. The trend analysis shows that only MAR forced by ERA40/ERA-interim shows historical trends, which is probably because the ERA40/ERA-interim has a better horizontal resolution and assimilates more observations than the other reanalyses that are used in this study. The results suggest that the solar brightening observed since the 1980s in Belgium has mainly been due to decreasing cloud cover.

Comparison of different infiltration schemes in the regional climate model REMO

2021 ◽  
Author(s):  
Daniel Abel ◽  
Katrin Ziegler ◽  
Felix Pollinger ◽  
Heiko Paeth
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Root Zone ◽  
Regional Climate ◽  
Climate Projections ◽  
Soil Hydrology ◽  
Spatiotemporal Resolution ◽  
Development Fund ◽  
Near Surface

<p>The European Regional Development Fund-Project BigData@Geo aims to create highly resolved climate projections for the model region of Lower Franconia in Bavaria, Germany. These projections are analyzed and made available to local stakeholders of agriculture, forestry, and viniculture as well as general public. Since regional climate models’ spatiotemporal resolution often is too coarse to deal with such local issues, the regional climate model REMO is improved within the frame of the project in cooperation with the Climate Service Center Germany (GERICS).</p><p>Accurate and highly resolved climate projections require realistic modeling of soil hydrology. Thus, REMO’s original bucket scheme is replaced by a 5-layer soil scheme. It allows for the representation of water below the root zone. Evaporation is possible solely from the top layer instead of the entire bucket and water can flow vertically between the layers. Consequently, the properties and processes change significantly compared to the bucket scheme. Both, the bucket and the 5-layer scheme, use the improved Arno scheme to separate throughfall into infiltration and surface runoff.</p><p>In this study, we examine if this scheme is suitable for use with the improved soil hydrology or if other schemes lead to better results. For this, we (1) modify the improved Arno scheme and further introduce the infiltration equations of (2) Philip as well as (3) Green and Ampt. First results of the comparison of these four different schemes and their influence on soil moisture and near-surface atmospheric variables are presented.</p>

scholarly journals Improving Climate Sensitivity of Deep Lakes within a Regional Climate Model and Its Impact on Simulated Climate

2014 ◽  
Vol 27 (8) ◽  
pp. 2886-2911 ◽  
Author(s):  
Val Bennington ◽  
Michael Notaro ◽  
Kathleen D. Holman
Keyword(s):  
Regional Climate Model ◽  
Interannual Variability ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Global Climate Models ◽  
Deep Lakes ◽  
Community Land Model ◽  
Lake Model ◽  
Lake Temperature

Abstract Regional climate models aim to improve local climate simulations by resolving topography, vegetation, and land use at a finer resolution than global climate models. Lakes, particularly large and deep lakes, are local features that significantly alter regional climate. The Hostetler lake model, a version of which is currently used in the Community Land Model, performs poorly in deep lakes when coupled to the regional climate of the International Centre for Theoretical Physics (ICTP) Regional Climate Model, version 4 (RegCM4). Within the default RegCM4 model, the lake fails to properly stratify, stifling the model’s ability to capture interannual variability in lake temperature and ice cover. Here, the authors improve modeled lake stratification and eddy diffusivity while correcting errors in the ice model. The resulting simulated lake shows improved stratification and interannual variability in lake ice and temperature. The lack of circulation and explicit mixing continues to stifle the model’s ability to simulate lake mixing events and variability in timing of stratification and destratification. The changes to modeled lake conditions alter seasonal means in sea level pressure, temperature, and low-level winds in the entire model domain, highlighting the importance of lake model selection and improvement for coupled simulations. Interestingly, changes to winter and spring snow cover and albedo impact spring warming. Unsurprisingly, regional climate variability is not significantly altered by an increase in lake temperature variability.

scholarly journals Climate Model Evaluation in the Presence of Observational Uncertainty: Precipitation Indices over the Contiguous United States

2019 ◽  
Vol 20 (7) ◽  
pp. 1339-1357 ◽  
Author(s):  
Peter B. Gibson ◽  
Duane E. Waliser ◽  
Huikyo Lee ◽  
Baijun Tian ◽  
Elias Massoud
Keyword(s):  
United States ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Climate Model Evaluation ◽  
Observational Uncertainty ◽  
Precipitation Indices

Abstract Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large divergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.

scholarly journals Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

2018 ◽  
Author(s):  
Huikyo Lee ◽  
Alexander Goodman ◽  
Lewis McGibbney ◽  
Duane Waliser ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Evaluation System ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Climate Data ◽  
Climate Model Evaluation

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

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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