scholarly journals Exploring the Parameters Space of the Regional Climate Model COSMO-CLM 5.0 for the CORDEX Central Asia Domain

2020 ◽  
Author(s):  
Emmanuele Russo ◽  
Silje Lund Sørland ◽  
Ingo Kirchner ◽  
Martijn Schaap ◽  
Christoph C. Raible ◽  
...  
Keyword(s):  
Central Asia ◽  
Parameter Uncertainty ◽  
Climate Models ◽  
Climate Model ◽  
Optimal Parameter ◽  
Regional Climate ◽  
Model Parameters ◽  
Study Results ◽  
Calibration Methods ◽  
Parameter Values

Abstract. The parameter uncertainty of a climate model represents the spectrum of the results obtained by perturbing its empirical and unconfined parameters used to represent sub-grid scale processes. In order to assess a model reliability and to better understand its limitations and sensitivity to different physical processes, the spread of model parameters needs to be carefully investigated. This is particularly true for Regional Climate Models (RCMs), whose performances are domain-dependent. In this study, the parameter space of the RCM COSMO-CLM is investigated for the CORDEX Central Asia domain, using a Perturbed Physics Ensemble (PPE) obtained by performing 1-year long simulations with different parameter values. The main goal is to characterize the parameter uncertainty of the model, and to determine the most sensitive parameters for the region. Moreover, the presented experiments are used to study the effect of several parameters on the simulation of selected variables for sub-regions characterized by different climate conditions, assessing by which degree it is possible to improve model performances by properly selecting parameter inputs in each case. Finally, the paper explores the model parameter sensitivity over different domains, tackling the question of transferability of an RCM model setup to different regions of study. Results show that only a sub-set of model parameters present relevant changes in model performances for different parameter values. Importantly, for almost all parameter inputs, the model shows an opposite behavior among different clusters and regions. This indicates that conducting a calibration of the model against observations to determine optimal parameter values for the Central Asia domain is particularly challenging: in this case, the use of objective calibration methods is highly necessary. Finally, the sensitivity of the model to parameters perturbation for Central Asia is different than the one observed for Europe, suggesting that an RCM should be re-tuned, and its parameter uncertainty properly investigated, when setting up model-experiments to different domains of study.

scholarly journals Exploring the parameter space of the COSMO-CLM v5.0 regional climate model for the Central Asia CORDEX domain

2020 ◽  
Vol 13 (11) ◽  
pp. 5779-5797
Author(s):  
Emmanuele Russo ◽  
Silje Lund Sørland ◽  
Ingo Kirchner ◽  
Martijn Schaap ◽  
Christoph C. Raible ◽  
...  
Keyword(s):  
Central Asia ◽  
Parameter Space ◽  
Parameter Uncertainty ◽  
Climate Model ◽  
Regional Climate ◽  
Model Performance ◽  
Small Scale ◽  
Model Parameters ◽  
Study Results ◽  
Parameter Values

Abstract. The parameter uncertainty of a climate model represents the spectrum of the results obtained by perturbing its empirical and unconfined parameters used to represent subgrid-scale processes. In order to assess a model's reliability and to better understand its limitations and sensitivity to different physical processes, the spread of model parameters needs to be carefully investigated. This is particularly true for regional climate models (RCMs), whose performance is domain dependent. In this study, the parameter space of the Consortium for Small-scale Modeling CLimate Mode (COSMO-CLM) RCM is investigated for the Central Asia Coordinated Regional Climate Downscaling Experiment (CORDEX) domain, using a perturbed physics ensemble (PPE) obtained by performing 1-year simulations with different parameter values. The main goal is to characterize the parameter uncertainty of the model and to determine the most sensitive parameters for the region. Moreover, the presented experiments are used to study the effect of several parameters on the simulation of selected variables for subregions characterized by different climate conditions, assessing by which degree it is possible to improve model performance by properly selecting parameter inputs in each case. Finally, the paper explores the model parameter sensitivity over different domains, tackling the question of transferability of an RCM model setup to different regions of study. Results show that only a subset of model parameters present relevant changes in model performance for different parameter values. Importantly, for almost all parameter inputs, the model shows an opposite behaviour among different clusters and regions. This indicates that conducting a calibration of the model against observations to determine optimal parameter values for the Central Asia domain is particularly challenging: in this case, the use of objective calibration methods is highly necessary. Finally, the sensitivity of the model to parameter perturbation for Central Asia is different than the one observed for Europe, suggesting that an RCM should be retuned, and its parameter uncertainty properly investigated, when setting up model experiments for different domains of study.

scholarly journals Sensitivity studies with the Regional Climate Model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

2019 ◽  
Author(s):  
Emmanuele Russo ◽  
Ingo Kirchner ◽  
Stephan Pfahl ◽  
Martijn Schaap ◽  
Ulrich Cubasch
Keyword(s):  
Regional Climate Model ◽  
Central Asia ◽  
Climate Models ◽  
Climate Model ◽  
Reference Model ◽  
Developing Economies ◽  
Regional Climate ◽  
Model Performance ◽  
Mitigation Strategies ◽  
Climate Projections

Abstract. Due to its extension, geography and the presence of several under-developed or developing economies, the Central Asia domain of the Coordinated Regional climate Downscaling Experiment (CORDEX) is one of the most vulnerable regions on Earth to the effects of climate changes. Reliable information on potential future changes with high spatial resolution acquire significant importance for the development of effective adaptation and mitigation strategies for the region. In this context, Regional Climate Models (RCMs) play a fundamental role. In this paper, the results of a set of sensitivity experiments with the regional climate model COSMO-CLM version 5.0, for the Central Asia CORDEX domain, are presented. Starting from a reference model setup, general model performance is evaluated for present-days, testing the effects of a set of singular physical parameterizations and their mutual interaction on the simulation of monthly and seasonal values of three variables that are important for impact studies: 2-meter temperature, precipitation and diurnal temperature range. The final goal of this study is two-fold: having a general overview of model performance and its uncertainties for the considered region and determining at the same time an optimal model configuration. Results show that the model presents remarkable deficiencies over different areas of the domain. The combined change of the albedo taking into consideration the ratio of forest fractions and the soil conductivity taking into account the ratio of liquid water and ice in the soil, allows to achieve the best improvements in model performance in terms of climatological means. Importantly, the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features, and that could positively affect the repartition of incoming radiation. The results for the mean climate appear to be independent of the observational dataset used for evaluation and of the boundary data employed to force the simulations. On the other hand, due to the large uncertainties in the variability estimates from observations, the use of different boundaries and the model internal variability, it has not been possible to rank the different simulations according to their representation of the monthly variability. This work is the first ever sensitivity study of an RCM for the CORDEX Central Asia domain and its results are of fundamental importance for further model development and for future climate projections over the area.

scholarly journals Dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs

2021 ◽  
Author(s):  
Yuan Qiu ◽  
Jinming Feng ◽  
Zhongwei Yan ◽  
Jun Wang
Keyword(s):  
Climate Change ◽  
Central Asia ◽  
Climate Changes ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
High Elevation ◽  
Global Climate Models ◽  
Climate Projections

Abstract Central Asia (CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need to achieve robust projection of regional climate change. In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9km-resolution regional climate simulations in CA for the present (1986–2005) and future (2031–2050) periods. Dynamical downscaling based on multiple bias-corrected GCM outputs obtains numerous added values not only in reproducing the historical climate but also in projecting the climate changes in CA, in comparison to the original GCMs. The regional climate model (RCM) simulations indicate significant warming over CA in the near-term future, with the regional mean increase of annual daily mean temperature (Tmean) in a range of 1.63–2.01℃, relative to the present period. This increase is expected to be higher north of ~ 45°N in each season except summer and the high-elevation areas have a weaker warming signal than the plains through the year. The season with the largest warming rate is not consistent among the RCM simulations, highlighting the necessity of using multiple GCMs as the boundary conditions to give a range of the projected climate changes. A slight increase in annual precipitation is consistently projected in most plain areas, although the changes over few areas are statistically significant. The climate projections presented here serve as a robust scientific basis for assessment of future risk from climate change in CA.

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.

scholarly journals Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 174
Author(s):  
Günther Heinemann ◽  
Sascha Willmes ◽  
Lukas Schefczyk ◽  
Alexander Makshtas ◽  
Vasilii Kustov ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Open Water ◽  
The Arctic ◽  
Late Winter ◽  
Good Representation ◽  
Hourly Temperature ◽  
Ice Model

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.

scholarly journals Transferability Intercomparison: An Opportunity for New Insight on the Global Water Cycle and Energy Budget

2007 ◽  
Vol 88 (3) ◽  
pp. 375-384 ◽  
Author(s):  
E. S. Takle ◽  
J. Roads ◽  
B. Rockel ◽  
W. J. Gutowski ◽  
R. W. Arritt ◽  
...  
Keyword(s):  
Energy Budget ◽  
Climate Models ◽  
Climate Model ◽  
Water Cycle ◽  
Regional Climate ◽  
Climate Conditions ◽  
Continental Scale ◽  
Global Water Cycle ◽  
Wide Range ◽  
Global Water

A new approach, called transferability intercomparisons, is described for advancing both understanding and modeling of the global water cycle and energy budget. Under this approach, individual regional climate models perform simulations with all modeling parameters and parameterizations held constant over a specific period on several prescribed domains representing different climatic regions. The transferability framework goes beyond previous regional climate model intercomparisons to provide a global method for testing and improving model parameterizations by constraining the simulations within analyzed boundaries for several domains. Transferability intercomparisons expose the limits of our current regional modeling capacity by examining model accuracy on a wide range of climate conditions and realizations. Intercomparison of these individual model experiments provides a means for evaluating strengths and weaknesses of models outside their “home domains” (domain of development and testing). Reference sites that are conducting coordinated measurements under the continental-scale experiments under the Global Energy and Water Cycle Experiment (GEWEX) Hydrometeorology Panel provide data for evaluation of model abilities to simulate specific features of the water and energy cycles. A systematic intercomparison across models and domains more clearly exposes collective biases in the modeling process. By isolating particular regions and processes, regional model transferability intercomparisons can more effectively explore the spatial and temporal heterogeneity of predictability. A general improvement of model ability to simulate diverse climates will provide more confidence that models used for future climate scenarios might be able to simulate conditions on a particular domain that are beyond the range of previously observed climates.

scholarly journals A Mechanistically Credible, Poleward Shift in Warm-Season Precipitation Projected for the U.S. Southern Great Plains?

2017 ◽  
Vol 30 (20) ◽  
pp. 8275-8298 ◽  
Author(s):  
Melissa S. Bukovsky ◽  
Rachel R. McCrary ◽  
Anji Seth ◽  
Linda O. Mearns
Keyword(s):  
Great Plains ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Dry Season ◽  
Wet Season ◽  
Southern Great Plains ◽  
Poleward Shift

Abstract Global and regional climate model ensembles project that the annual cycle of rainfall over the southern Great Plains (SGP) will amplify by midcentury. Models indicate that warm-season precipitation will increase during the early spring wet season but shift north earlier in the season, intensifying late summer drying. Regional climate models (RCMs) project larger precipitation changes than their global climate model (GCM) counterparts. This is particularly true during the dry season. The credibility of the RCM projections is established by exploring the larger-scale dynamical and local land–atmosphere feedback processes that drive future changes in the simulations, that is, the responsible mechanisms or processes. In this case, it is found that out of 12 RCM simulations produced for the North American Regional Climate Change Assessment Program (NARCCAP), the majority are mechanistically credible and consistent in the mean changes they are producing in the SGP. Both larger-scale dynamical processes and local land–atmosphere feedbacks drive an earlier end to the spring wet period and deepening of the summer dry season in the SGP. The midlatitude upper-level jet shifts northward, the monsoon anticyclone expands, and the Great Plains low-level jet increases in strength, all supporting a poleward shift in precipitation in the future. This dynamically forced shift causes land–atmosphere coupling to strengthen earlier in the summer, which in turn leads to earlier evaporation of soil moisture in the summer, resulting in extreme drying later in the summer.

A new ensemble-based statistical methodology to verify changes in weather and climate models

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Numerical Models ◽  
Hypothesis Test ◽  
Model Development ◽  
Atmospheric Model ◽  
Statistical Hypothesis ◽  
Model Parameters ◽  
Rounding Errors ◽  
Weather And Climate

<p>Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.</p><p>Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.</p><p>We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.</p>

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>

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