scholarly journals Automatic tuning of the Community Atmospheric Model CAM5.3 by using short-term hindcasts with an improved downhill simplex optimization method

2018 ◽  
Author(s):  
Tao Zhang ◽  
Minghua Zhang ◽  
Yanluan Lin ◽  
Wei Xue ◽  
Wuyin Lin ◽  
...  
Keyword(s):  
General Circulation ◽  
Model Simulation ◽  
Computational Cost ◽  
Model Performance ◽  
Optimization Procedure ◽  
General Circulation Models ◽  
Atmospheric Model ◽  
Automatic Tuning ◽  
Short Term ◽  
Cloud Forcing

Abstract. Traditional trial-and-error tuning of uncertain parameters in global atmospheric General Circulation Models (GCM) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10 % reduction of the overall bias in CAM5, which is already a well calibrated model, based on a pre-defined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to about a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.

scholarly journals Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method

2018 ◽  
Vol 11 (12) ◽  
pp. 5189-5201 ◽  
Author(s):  
Tao Zhang ◽  
Minghua Zhang ◽  
Wuyin Lin ◽  
Yanluan Lin ◽  
Wei Xue ◽  
...  
Keyword(s):  
General Circulation ◽  
Model Simulation ◽  
Computational Cost ◽  
Model Performance ◽  
Optimization Procedure ◽  
General Circulation Models ◽  
Atmospheric Model ◽  
Automatic Tuning ◽  
Short Term ◽  
Cloud Forcing

Abstract. Traditional trial-and-error tuning of uncertain parameters in global atmospheric general circulation models (GCMs) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10 % reduction of the overall bias in CAM5, which is already a well-calibrated model, based on a predefined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.

An Object-Based Approach for Quantification of GCM Biases of the Simulation of Orographic Precipitation. Part I: Idealized Simulations

2014 ◽  
Vol 27 (24) ◽  
pp. 9139-9154 ◽  
Author(s):  
M. Soner Yorgun ◽  
Richard B. Rood
Keyword(s):  
General Circulation ◽  
Evaluation Method ◽  
Model Simulation ◽  
General Circulation Models ◽  
Atmospheric Model ◽  
Surface Fluxes ◽  
Dynamical Core ◽  
Community Atmosphere Model ◽  
Object Based ◽  
Intercomparison Project

Abstract An object-based evaluation method to quantify biases of general circulation models (GCMs) is introduced using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Idealized experiments with different topography are designed to reproduce the spatial characteristics of precipitation biases that were present in Atmospheric Model Intercomparison Project simulations using the CAM finite volume (FV) and CAM Eulerian spectral dynamical cores. Precipitation features are identified as “objects” to understand the causes of the differences between CAM FV and CAM Eulerian spectral dynamical cores. Three different mechanisms of precipitation were simulated in idealized experiments: stable upslope ascent, local surface fluxes, and resolved downstream waves. The results indicated stronger sensitivity of the CAM Eulerian spectral dynamical core to resolution. The application of spectral filtering to topography is shown to have a large effect on the CAM Eulerian spectral model simulation. The removal of filtering improved the results when the scales of the topography were resolvable. However, it reduced the simulation capability of the CAM Eulerian spectral dynamical core because of Gibbs oscillations, leading to unusable results. A clear perspective about models biases is provided from the quantitative evaluation of objects extracted from these simulations and will be further discussed in part II of this study.

scholarly journals An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)

2019 ◽  
Author(s):  
Li Wu ◽  
Tao Zhang ◽  
Yi Qin ◽  
Wei Xue
Keyword(s):  
Parameter Optimization ◽  
General Circulation ◽  
Performance Indicator ◽  
Optimization Problems ◽  
Model Performance ◽  
Optimization Methods ◽  
General Circulation Models ◽  
Atmospheric Model ◽  
Radiation Balance ◽  
Performance Metric

Abstract. Uncertain parameters in physical parameterizations of General Circulation Models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs but most of the optimization methods are unconstrained optimization methods under a given performance indicator, so that the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at top of model, which is known for its importance to the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using global means of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. And the radiation constraint is defined as the deviation of the net longwave flux at top of model (FLNT) and net solar flux at top of model (FSNT) less than 1 W m−2. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) as well as the radiation imbalance is as low as 0.1 W m−2. The proposed method provides the insight for physics-guided optimization under the premise of a profound understanding of models and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.

scholarly journals An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)

2020 ◽  
Vol 13 (1) ◽  
pp. 41-53
Author(s):  
Li Wu ◽  
Tao Zhang ◽  
Yi Qin ◽  
Wei Xue
Keyword(s):  
Parameter Optimization ◽  
General Circulation ◽  
Performance Indicator ◽  
Optimization Problems ◽  
Model Performance ◽  
Optimization Methods ◽  
General Circulation Models ◽  
Atmospheric Model ◽  
Absolute Difference ◽  
Radiation Balance

Abstract. Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimization methods under a given performance indicator. Therefore, the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at the top of the model. The radiation balance is known for its importance in the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in the Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using normalized mean square error of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m−2. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m−2. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.

scholarly journals An automatic and effective parameter optimization method for model tuning

2015 ◽  
Vol 8 (5) ◽  
pp. 3791-3822
Author(s):  
T. Zhang ◽  
L. Li ◽  
Y. Lin ◽  
W. Xue ◽  
F. Xie ◽  
...  
Keyword(s):  
General Circulation ◽  
Model Development ◽  
Computational Cost ◽  
Model Performance ◽  
Objective Evaluation ◽  
Optimization Methods ◽  
General Circulation Models ◽  
Optimization Method ◽  
Development Stage ◽  
Optimum Combination

Abstract. Physical parameterizations in General Circulation Models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.

scholarly journals An automatic and effective parameter optimization method for model tuning

2015 ◽  
Vol 8 (11) ◽  
pp. 3579-3591 ◽  
Author(s):  
T. Zhang ◽  
L. Li ◽  
Y. Lin ◽  
W. Xue ◽  
F. Xie ◽  
...  
Keyword(s):  
Simplex Method ◽  
General Circulation ◽  
Model Development ◽  
Model Performance ◽  
Parameter Tuning ◽  
General Circulation Models ◽  
Development Stage ◽  
Optimum Combination ◽  
Downhill Simplex Method ◽  
Downhill Simplex

Abstract. Physical parameterizations in general circulation models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time-consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determining the model's sensitivity to the parameters and the other choosing the optimum initial value for those sensitive parameters, are introduced before the downhill simplex method. This new method reduces the number of parameters to be tuned and accelerates the convergence of the downhill simplex method. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

scholarly journals Diagnostic study of errors in the simulation of tropical continental precipitation in general circulation models

2003 ◽  
Vol 21 (5) ◽  
pp. 1197-1207 ◽  
Author(s):  
J. Srinivasan
Keyword(s):  
Water Vapour ◽  
General Circulation ◽  
Nonlinear Function ◽  
General Circulation Models ◽  
Atmospheric Model ◽  
Large Error ◽  
Tropical Meteorology ◽  
Diagnostic Model ◽  
Diagnostic Study ◽  
Integrated Water Vapour

Abstract. A simple diagnostic model has been used to identify the parameters that induce large errors in the simulation of tropical precipitation in atmospheric General Circulation models (GCM). The GCM that have been considered are those developed by the National Center for Environmental Prediction (NCEP), the National Center for Atmospheric Research (NCAR) and the Japanese Meteorological Agency (JMA). These models participated in the phase II of the Atmospheric Model Inter-comparison Project (AMIP II) and simulated the climate for the period 1979 to 1995. The root mean-square error in the simulation of precipitation in tropical continents was larger in NCEP and NCAR simulations than in the JMA simulation. The large error in the simulation of precipitation in NCEP was due to errors in the vertical profile of water vapour. The large error in precipitation in NCAR in North Africa was due to an error in net radiation (at the top of the atmosphere). The simple diagnostic model predicts that the moisture converge is a nonlinear function of integrated water vapour. The large error in the interannual variance of rainfall in NCEP over India has been shown to be due to this nonlinearity.Key words. Meteorology and atmospheric dynamics (precipitation; tropical meteorology; convective processes)

Interannual oscillations in an ocean general circulation model coupled to a simple atmosphere model

1989 ◽  
Vol 329 (1604) ◽  
pp. 189-205 ◽  
Keyword(s):  
El Niño ◽  
General Circulation ◽  
El Nino ◽  
Southern Oscillation ◽  
Circulation Model ◽  
Coupled System ◽  
General Circulation Models ◽  
Atmospheric Model ◽  
Ocean General Circulation Model ◽  
Coupled Oscillations

A model is being developed for tropical air-sea interaction studies that is intermediate in complexity between the large coupled general circulation models (GCMS) that are coming into use, and the simple two-level models with which pioneering El Nino Southern Oscillation studies were done. The model consists of a stripped-down tropical Pacific Ocean GCM, coupled to an atmospheric model that is sufficiently simple that steady-state solutions may be found for low-level flow and surface stress, given oceanic boundary conditions. This permits examination of the nature of interannual coupled oscillations in the absence of atmospheric noise. In preliminary tests of the model the coupled system is found to undergo a Hopf bifurcation as certain parameters are varied, giving rise to sustained three to four year oscillations. For stronger coupling, a secondary bifurcation yields six month coupled oscillations during the warm phase of the El Nino-period oscillation. Such variability could potentially affect the predictability of the coupled system.

Application of Cluster Analysis to Climate Model Performance Metrics

2011 ◽  
Vol 50 (8) ◽  
pp. 1666-1675 ◽  
Author(s):  
Satoru Yokoi ◽  
Yukari N. Takayabu ◽  
Kazuaki Nishii ◽  
Hisashi Nakamura ◽  
Hirokazu Endo ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
General Circulation ◽  
Performance Metrics ◽  
Climate Model ◽  
Model Performance ◽  
General Circulation Models ◽  
Clustering Methods ◽  
Dynamic Constraints ◽  
Overall Performance

AbstractThe overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individual models that measure the reproducibility of diverse aspects of the climate. Because of physical and dynamic constraints governing the climate, a model’s performance in simulating a certain aspect of the climate is sometimes related closely to that in simulating another aspect, which results in significant intermodel correlation between performance metrics. Numerous metrics and intermodel correlations may cause a problem in understanding the evaluation and synthesizing the metrics. One possible way to alleviate this problem is to group the correlated metrics beforehand. This study attempts to use simple cluster analysis to group 43 performance metrics. Two clustering methods, the K-means and the Ward methods, yield considerably similar clustering results, and several aspects of the results are found to be physically and dynamically reasonable. Furthermore, the intermodel correlation between the cluster averages is considerably lower than that between the metrics. These results suggest that the cluster analysis is helpful in obtaining the appropriate grouping. Applications of the clustering results are also discussed.

scholarly journals On the potential use of glacier and permafrost observations for verification of climate models

1997 ◽  
Vol 25 ◽  
pp. 400-406 ◽  
Author(s):  
Martin Beniston ◽  
Wilfried Haeberli ◽  
Martin Hoelzle ◽  
Alan Taylor
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Model Performance ◽  
Regional Climate Models ◽  
General Circulation Models ◽  
Model Verification ◽  
Dominant Feature

While the capability of global and regional climate models in reproducing current climate has significantly improved over the past few years, the confidence in model results for remote regions, or those where complex orography is a dominant feature, is still relatively low. This is, in part, linked to the lack of observational data for model verification and intercomparison purposes.Glacier and permafrost observations are directly related to past and present energy flux patterns at the Earth-atmosphere interface and could be used as a proxy for air temperature and precipitation, particularly of value in remote mountain regions and boreal and Arctic zones where instrumental climate records are sparse or non-existent. It is particularly important to verify climate-model performance in these regions, as this is where most general circulation models (GCMs) predict the greatest changes in air temperatures in a warmer global climate.Existing datasets from glacier and permafrost monitoring sites in remote and high altitudes are described in this paper; the data could be used in model-verification studies, as a means to improving model performance in these regions.

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