Quantitative characterization of spurious numerical oscillations in 48 CMIP5 models

Geil, Kerrie; Zeng, Xubin

doi:10.1002/2015gl063931

Cited by 8 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The global 1° grid uses about 10 or less grid points to represent the central Andes and other mountains surrounding the Amazon, which is not likely adequate to realistically simulate orographic gravity waves (Reinecke & Durran, ). Geil and Zeng () found unrealistic wave patterns in several variables near the Andes in many of the global climate models that participated in CMIP5.…”

Section: Summary and Discussionmentioning

confidence: 99%

Role of Troposphere‐Convection‐Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model Version 1 (CESM1)

et al. 2018

View full text Add to dashboard Cite

The Amazon forest plays an important role in the tropical circulation and global water and biogeochemical cycles. However, the common Amazon precipitation biases in global climate models undermine our ability to understand future changes in the Amazon and their global consequences. The Community Earth System Model exhibits a persistent wet season dry bias over the southwestern Amazon. The bias is also characterized by inaccurate diurnal timing and intensity of rainfall events. Analysis of a diverse set of simulations suggests that the bias originates from the land model and/or deep convection parameterization. A known land model bias in the partitioning of surface energy fluxes is found not to contribute significantly to the rainfall bias. Key model processes are identified to be the low‐level divergent circulation controlling horizontal moisture flow and the sensitivity of the modeled deep convection to the lower tropospheric moisture. The lower tropospheric moisture divergence dictates the spatiotemporal distributions of convective precipitation through parameterized entrainment mixing. While the entrainment effect improves the rainfall diurnal cycle, it suppresses convective precipitation under low‐level moisture divergence. Persistent moisture divergence is found over the southwestern Amazon and limits parameterized convective precipitation, particularly in the late afternoon, although uncertainty exists in the low‐level flow in the simulation and reanalyses. One way to sustain rainfall in the presence of low‐level divergence is a convective trigger based on both the moisture and temperature changes in the free troposphere, which is shown to help in reducing the dry bias.

show abstract

Section: Summary and Discussionmentioning

confidence: 99%

Role of Troposphere‐Convection‐Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model Version 1 (CESM1)

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Numerical noise is common in numerical models (Geil & Zeng, 2015 ), and is not easily addressed without introducing artificial diffusion. In this section we briefly discuss spurious small scale features in the simulated variables originating from the different host model dynamical cores.…”

Section: Resultsmentioning

confidence: 99%

The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness

Salzmann

Ferrachat

Tully

et al. 2022

J Adv Model Earth Syst

View full text Add to dashboard Cite

The Hamburg Aerosol Module version 2.3 (HAM2.3) from the ECHAM6.3‐HAM2.3 global atmosphere‐aerosol model is coupled to the recently developed icosahedral nonhydrostatic ICON‐A (icon‐aes‐1.3.00) global atmosphere model to yield the new ICON‐A‐HAM2.3 atmosphere‐aerosol model. The ICON‐A and ECHAM6.3 host models use different dynamical cores, parameterizations of vertical mixing due to sub‐grid scale turbulence, and parameter settings for radiation balance tuning. Here, we study the role of the different host models for simulated aerosol optical thickness (AOT) and evaluate impacts of using HAM2.3 and the ECHAM6‐HAM2.3 two‐moment cloud microphysics scheme on several meteorological variables. Sensitivity runs show that a positive AOT bias over the subtropical oceans is remedied in ICON‐A‐HAM2.3 because of a different default setting of a parameter in the moist convection parameterization of the host models. The global mean AOT is biased low compared to MODIS satellite instrument retrievals in ICON‐A‐HAM2.3 and ECHAM6.3‐HAM2.3, but the bias is larger in ICON‐A‐HAM2.3 because negative AOT biases over the Amazon, the African rain forest, and the northern Indian Ocean are no longer compensated by high biases over the sub‐tropical oceans. ICON‐A‐HAM2.3 shows a moderate improvement with respect to AOT observations at AERONET sites. A multivariable bias score combining biases of several meteorological variables into a single number is larger in ICON‐A‐HAM2.3 compared to standard ICON‐A and standard ECHAM6.3. In the tropics, this multivariable bias is of similar magnitude in ICON‐A‐HAM2.3 and in ECHAM6.3‐HAM2.3. In the extra‐tropics, a smaller multivariable bias is found for ICON‐A‐HAM2.3 than for ECHAM6.3‐HAM2.3.

show abstract

“…The condensation of moisture is a discontinuous process in time, which introduces sharp gradients that are especially problematic for spectral models. These processes are candidates to produce the Gibbs phenomenon; thereby reducing the realism of spectral model simulations [ Geil and Zeng , ; Navarra et al ., ].…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations

Yorgun

Rood

2016

J Adv Model Earth Syst

View full text Add to dashboard Cite

An object‐based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smooth topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small‐scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales.

show abstract

Quantitative characterization of spurious numerical oscillations in 48 CMIP5 models

Cited by 8 publications

References 17 publications

Role of Troposphere‐Convection‐Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model Version 1 (CESM1)

Role of Troposphere‐Convection‐Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model Version 1 (CESM1)

The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness

A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations

Contact Info

Product

Resources

About