Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region

2021 ◽  
Author(s):  
Michael Notaro ◽  
Yafang Zhong ◽  
Pengfei Xue ◽  
Christa Peters-Lidard ◽  
Carlos Cruz ◽  
...  
Keyword(s):  
Great Lakes ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Cold Season ◽  
Cold Bias ◽  
Great Lakes Region ◽  
Next Generation ◽  
Lake Model

AbstractAs Earth’s largest collection of fresh water, the Laurentian Great Lakes have enormous ecological and socio-economic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake-atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014-2015 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.

scholarly journals Lake and climate models linkage: a 3-D hydrodynamic contribution

2005 ◽  
Vol 4 ◽  
pp. 57-62 ◽  
Author(s):  
L. F. Leon ◽  
D. Lam ◽  
W. Schertzer ◽  
D. Swayne
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Heat Fluxes ◽  
Large Lakes ◽  
Atmospheric Sciences ◽  
Lake Model ◽  
Realistic Representation ◽  
Heat Exchanges

Abstract. Under a Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) project, targeted to study the feasibility to link regional climate models with lake models, one of the tasks was to consider such a coupling in large lakes. The objective is to provide detailed information on temperature and circulation distributions of the lake to take into account the spatial variability for temperature and the heat exchange through the water's surface. The major contribution of this work is focused on realistic representation of the heat fluxes and temperature distributions to and from lakes especially during the thermally stratified ice-free periods. This paper presents the detailed 3-D ELCOM model applied in Lake Erie in order to produce, at the surface layer of the lake, the spatial distribution of temperature and heat exchanges that eventually can be coupled with a regional climate model (CRCM). Preliminary results will be presented on how this lake model may improve the regional climate models, which currently do not consider such large lake circulation effects.

scholarly journals High-Resolution Simulations of Wintertime Precipitation in the Colorado Headwaters Region: Sensitivity to Physics Parameterizations

2011 ◽  
Vol 139 (11) ◽  
pp. 3533-3553 ◽  
Author(s):  
Changhai Liu ◽  
Kyoko Ikeda ◽  
Gregory Thompson ◽  
Roy Rasmussen ◽  
Jimy Dudhia
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Cold Season ◽  
Cloud Microphysics ◽  
Physics Parameterizations ◽  
Heating Profiles

Abstract An investigation was conducted on the effects of various physics parameterizations on wintertime precipitation predictions using a high-resolution regional climate model. The objective was to evaluate the sensitivity of cold-season mountainous snowfall to cloud microphysics schemes, planetary boundary layer (PBL) schemes, land surface schemes, and radiative transfer schemes at a 4-km grid spacing applicable to the next generation of regional climate models. The results indicated that orographically enhanced precipitation was highly sensitive to cloud microphysics parameterizations. Of the tested 7 parameterizations, 2 schemes clearly outperformed the others that overpredicted the snowfall amount by as much as ~30%–60% on the basis of snow telemetry observations. Significant differences among these schemes were apparent in domain averages, spatial distributions of hydrometeors, latent heating profiles, and cloud fields. In comparison, model results showed relatively weak dependency on the land surface, PBL, and radiation schemes, roughly in the order of decreasing level of sensitivity.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

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.

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Data ◽  
Urban Rural

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>

A statistical framework for evaluating EURO-CORDEX simulations and derived drought characteristics

2021 ◽  
Author(s):  
David J. Peres ◽  
Alfonso Senatore ◽  
Paola Nanni ◽  
Antonino Cancelliere ◽  
Giuseppe Mendicino ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Circulation Model ◽  
Regional Climate Models ◽  
Monitoring Networks ◽  
Global Circulation Model ◽  
Drought Characteristics ◽  
Precipitation And Temperature

<p>Regional climate models (RCMs) are commonly used for assessing, at proper spatial resolutions, future impacts of climate change on hydrological events. In this study, we propose a statistical methodological framework to assess the quality of the EURO-CORDEX RCMs concerning their ability to simulate historic observed climate (temperature and precipitation). We specifically focus on the models’ performance in reproducing drought characteristics (duration, accumulated deficit, intensity, and return period) determined by the theory of runs at seasonal and annual timescales, by comparison with high-density and high-quality ground-based observational datasets. In particular, the proposed methodology is applied to the Sicily and Calabria regions (Southern Italy), where long historical precipitation and temperature series were recorded by the ground-based monitoring networks operated by the former Regional Hydrographic Offices. The density of the measurements is considerably greater than observational gridded datasets available at the European level, such as E-OBS or CRU-TS. Results show that among the models based on the combination of the HadGEM2 global circulation model (GCM) with the CLM-Community RCMs are the most skillful in reproducing precipitation and temperature variability as well as drought characteristics. Nevertheless, the ranking of the models may slightly change depending on the specific variable analysed, as well as the temporal and spatial scale of interest. From this point of view, the proposed methodology highlights the skills and weaknesses of the different configurations, aiding on the selection of the most suitable climate model for assessing climate change impacts on drought processes and the underlying variables.</p>

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