On the Design of Dynamical Cores for Atmospheric General ...
Investigation of Model Bias of Dynamical Cores and...
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Investigation of Model Bias of Dynamical Cores and Physical Parameterizations by Object Based Methods M. Soner Yorgun, Richard B. Rood Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI Motivation Global climate models (GCM) exhibit various types of behavior depending on their underlying algorithms such as parameterizations of physical phenomena on different scales or their dynamical cores. This study aims on development of new types of statistical approaches for investigating the bias of model simulations by quantifying the relationship between them and their related underlying physics. Specifically, we want to quantify the relation of the model outputs to their numerical algorithms (i.e. finite volume vs. spectral dynamical core), and measure the impact of increasing grid resolution. The comparison between the model output and observations is made by defining the structures as “objects” rather than feature-by-feature basis. The focus of the study is on the relationship between precipitation and orography. Fig1: The domain of the study : North American West Coast Models & Domain •Previous model runs with CAM3 are used to motivate new test runs with CAM4 •Finite Volume (1 degree, 0.5 degree) •Eulerian –Spectral- (T85, T170) •Domain: North American West Coast 1. Characterization of orographic precipitation by GCM’s •Identification of special features 2. Linking the representation of orographic precipitation to GCM structure •Idealized test case runs with CAM4 (Figure 3) 3. Isolation and verification of the orographic precipitation features •Cluster analysis, geostatistics and other methods. Object Based Quantification Approach 1.Characterization of orographic precipitation by GCM’s •Significant difference between mountain range precipitation simulated by FV 0.5 degree and spectral T170. •The merger of precipitation over domain of interest is apparent for spectral T170 (white circles). Fig2: 1979-2000 long term mean precipitation plots of Global Precipitation Climatology Centre (GPCC-observations), FV 0.5 degree model and Eulerian T170 model run with CAM3 (Bala et. al., 2008). 2. Linking the representation of orographic precipitation to GCM structure Figure 3: 30 day mean total precipitation, surface geopotential (topography) for double mountain case of experimental test cases, simulated by spectral T85 (a), spectral T170 (b), FV 1 degree (c) and FV 0.5 degrees. 3. Isolation and verification of the orographic precipitation features 3a. Identification of rain areas (object boundaries) by cluster analysis 3b. Comparison of objects by geostatistics (Varography) •K means cluster analysis was used to define object boundaries. The number of clusters (K) is pre-defined and resulting clusters are identified •K=2 [ (1) Non-rain area , (2) Rain area ] •K=3 [ (1) Non-rain area, (2) Light rain area, (3) Heavy rain area ] •Cluster analysis provides flexibility in object identification depending on the choice of clusters • Cluster analysis revealed the merger of precipitation features observed for spectral T170 in figure 2. This was not visually observable in figure 3. •Setting K=3 classifies the rain areas into 2 subparts (light rain and heavy rain), which can be compared and analyzed separately. •Once the object boundaries are defined, the real precipitation values are put back over the boundaries and remaining areas are set to zero. The resulting fields are then compared using variography. Figure 4: K-means clustering results of the double mountain case for K=2 and K=3 of (a,e) spectral T85, (b, f) spectral T170, (c,g) FV 1 degree and (d,h) FV 0.5 degree. • Variography is a traditional geostatistical method which is used to measure the spatial continuity of a regionalized variable. The semi-variogram shows how the dissimilarity between Z(x) and Z(x +h), evolves with separation distance h via: where Z(x) is the spatial random variable and N is the total number of pairs. The semi-variogram values are then plotted against different separation distances. Semi-variogram can be computed over a particular direction in the field, or in all possible directions (Omni- Directional). • Idealized orographic precipitation tests with CESM1.0 CAM4 • FV (1 deg. And 0.5 deg.) vs. spectral (T85 and T170) dynamical cores. • Simple physics parameterization, 30 day simulation. • Mountain Rossby-wave testcase (Jablonowski and Williamson, 2008) applied to aqua-planet . • Two Gaussian shaped mountains (1500 m) (1 st peak @ 30N 90E and 2 nd peak @ 30N 97E ( •Two precipitation peaks occur in front of each mountain for both models. •Wider precipitation peaks with smaller max. values for spectral model. •Increase in resolution has a bigger impact on spectral model •Treatment of topography (spectral filtering) is effective on the simulation of precipitation of spectral models. •Semi-variograms carry information about the size, location, magnitude of the objects in their peaks and bends. •Directional semi-varograms can be used to pinpoint detailed information about the objects •The difference of spectral T85 is evident on Figure 5. Spectral T170 and FV 0.5 show a strong resemblance for Object 1. Figure 5: 3D surface plot of FV 0.5 degree, and Omni, 90 degree and 0 degree directional semi-variograms of all simulations for object1 of K=2. Figure 6: K=2 and K=3 Omni directional semi-varigrams of FV 0.5 degree and spectral T170, for object1 and object 2. •When the rain area is reduced for smaller scale object 2 (K=3), the difference of the semi- variogram becomes larger for spectral T170 then FV 0.5 degree. This means that a bigger amount of rain is removed from the spectral T170 object as “light rain”. Contact Info M. Soner Yorgun [email protected] Conclusions and Future Work •We were able to reproduce the orographic precipitation features that were observed in AMIP runs and observations (Figure 2), with our idealized test cases (Figure 3) •The merger of the precipitation bands for spectral T170 in AMIP runs weren’t visually obvious in idealized test case, however it was revealed by clustering of the results (Figure 4) •Our object based approach included the identification of object boundaries without hardwiring a constant threshold limit to define features, but by employing a more flexible method (cluster analysis). Depending on the choice of clusters, object boundaries are defined by characteristics of the precipitation field, which prevents misclassification. •Variography has skill in quantifying the differences between FV and spectral models, which were observed in idealized test cases. However the inherent averaging of this technique can lead to mixing of signatures of objects that are important. •A verification technique that will conserve the flexibility of object identification thresholding, but provide better verification by removing averaging will be developed.
Transcript of Investigation of Model Bias of Dynamical Cores and...
Investigation of Model Bias of Dynamical Cores and Physical Parameterizations by Object Based Methods M. Soner Yorgun, Richard B. Rood
Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI
Motivation Global climate models (GCM) exhibit various types of behavior depending on
their underlying algorithms such as parameterizations of physical phenomena on different scales or their dynamical cores. This study aims on development of new types of statistical approaches for investigating the bias of model simulations by quantifying the relationship between them and their related underlying physics.
Specifically, we want to quantify the relation of the model outputs to their numerical algorithms (i.e. finite volume vs. spectral dynamical core), and measure
the impact of increasing grid resolution. The comparison between the model output and observations is made by defining the structures as “objects” rather
than feature-by-feature basis. The focus of the study is on the relationship between precipitation and orography.
Fig1: The domain of the study : North American West
Coast
Models & Domain • Previous model runs with CAM3 are used to motivate new test runs with CAM4
• Finite Volume (1 degree, 0.5 degree)
• Eulerian –Spectral- (T85, T170)
• Domain: North American West Coast
1. Characterization of orographic precipitation by GCM’s • Identification of special features
2. Linking the representation of orographic precipitation to GCM structure
• Idealized test case runs with CAM4 (Figure 3)
3. Isolation and verification of the orographic precipitation features
• Cluster analysis, geostatistics and other methods.
Object Based Quantification Approach
1. Characterization of orographic precipitation by GCM’s
• Significant difference between mountain range precipitation simulated by FV 0.5 degree and spectral T170.
• The merger of precipitation over domain of interest is apparent for spectral T170 (white circles). Fig2: 1979-2000 long term mean precipitation plots of Global Precipitation Climatology Centre
(GPCC-observations), FV 0.5 degree model and Eulerian T170 model run with CAM3 (Bala et. al., 2008).
2. Linking the representation of orographic precipitation to GCM structure
Figure 3: 30 day mean total precipitation, surface geopotential (topography) for double mountain case of experimental test cases, simulated by spectral T85 (a),
spectral T170 (b), FV 1 degree (c) and FV 0.5 degrees.
3. Isolation and verification of the orographic precipitation features 3a. Identification of rain areas (object boundaries) by cluster analysis 3b. Comparison of objects by geostatistics (Varography)
• K means cluster analysis was used to define object boundaries. The number of clusters (K) is pre-defined and resulting clusters are identified
• K=2 [ (1) Non-rain area , (2) Rain area ]
• K=3 [ (1) Non-rain area, (2) Light rain area, (3) Heavy rain area ]
• Cluster analysis provides flexibility in object identification depending on the choice of clusters
• Cluster analysis revealed the merger of precipitation features observed for spectral T170 in figure 2. This was not visually observable in figure 3.
• Setting K=3 classifies the rain areas into 2 subparts (light rain and heavy rain), which can be compared and analyzed separately.
• Once the object boundaries are defined, the real precipitation values are put back over the boundaries and remaining areas are set to zero. The resulting fields are then compared using variography.
Figure 4: K-means clustering results of the double mountain case for K=2 and K=3 of (a,e) spectral T85, (b, f) spectral T170, (c,g) FV
1 degree and (d,h) FV 0.5 degree.
• Variography is a traditional geostatistical method which is used to measure the spatial continuity of a regionalized variable. The semi-variogram shows how the dissimilarity between Z(x) and Z(x+h), evolves with separation distance h via:
where Z(x) is the spatial random variable and N is the total number of pairs. The semi-variogram values are then plotted against different separation distances. Semi-variogram can be computed over a particular direction in the field, or in all possible directions (Omni-Directional).
• Idealized orographic precipitation tests with CESM1.0 CAM4
• FV (1 deg. And 0.5 deg.) vs. spectral (T85 and T170) dynamical cores.
• Simple physics parameterization, 30 day simulation.
• Mountain Rossby-wave testcase (Jablonowski and Williamson, 2008) applied to aqua-planet.
• Two Gaussian shaped mountains (1500 m) (1st peak @ 30N 90E and 2nd peak @ 30N 97E (
• Two precipitation peaks occur in front of each mountain for both models. • Wider precipitation peaks with smaller max. values for spectral model. • Increase in resolution has a bigger impact on spectral model
• Treatment of topography (spectral filtering) is effective on the simulation of precipitation of spectral models.
• Semi-variograms carry information about the size, location, magnitude of the objects in their peaks and bends.
• Directional semi-varograms can be used to pinpoint detailed information about the objects
• The difference of spectral T85 is evident on Figure 5. Spectral T170 and FV 0.5 show a strong resemblance for Object 1.
Figure 5: 3D surface plot of FV 0.5 degree, and Omni, 90 degree and 0 degree directional semi-variograms of all
simulations for object1 of K=2.
Figure 6: K=2 and K=3 Omni directional semi-varigrams of FV 0.5 degree and spectral T170, for object1 and object 2.
• When the rain area is reduced for smaller scale object 2 (K=3), the difference of the semi-variogram becomes larger for spectral T170 then FV 0.5 degree. This means that a bigger amount of rain is removed from the spectral T170 object as “light rain”.
Contact Info
M. Soner Yorgun [email protected]
Conclusions and Future Work
• We were able to reproduce the orographic precipitation features that were observed in AMIP runs and observations (Figure 2), with our idealized test cases (Figure 3) • The merger of the precipitation bands for spectral T170 in AMIP runs weren’t visually obvious in idealized test case, however it was revealed by clustering of the results (Figure 4) • Our object based approach included the identification of object boundaries without hardwiring a constant threshold limit to define features, but by employing a more flexible method (cluster analysis). Depending on the choice of clusters, object boundaries are defined by characteristics of the precipitation field, which prevents misclassification. • Variography has skill in quantifying the differences between FV and spectral models, which were observed in idealized test cases. However the inherent averaging of this technique can lead to mixing of signatures of objects that are important.
• A verification technique that will conserve the flexibility of object identification thresholding, but provide better verification by removing averaging will be developed.
