Huang et al., The linkage between CMIP5 climate models' abilities to simulate precipitation and vector winds, Climate Dynamics, 2020
Precipitation is one of the major challenges in climate modeling. Among various factors, the large-scale atmospheric circulation plays an important role in modulating regional precipitation through dynamic processes that has been widely discussed in previous studies. However, few efforts have been made to investigate the relationship of model abilities to simulate precipitation and vector winds. Such an investigation may help to understand the source of uncertainty of precipitation simulation. Here, we examined the relationship between model performances in simulating precipitation with that in simulating vector winds by using the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Our results suggest that the model biases/uncertainties in simulating climatological mean precipitation often accompanied by the biases/uncertainties in vector wind fields. Model ability to simulate precipitation is closely related to the ability to simulate vector winds, especially over monsoon regions and the regions with warm and moist advection or high terrains, such as the South Asian and East Asian summer monsoon region, the Alaskan region, the Rocky Mountain, etc. Over these regions, the models with higher horizontal resolution tends to generate improved simulations in both the vector winds and precipitation relative to the models with coarse horizontal resolution. Besides, the model's ability to simulate vector winds, compared to simulate the zonal wind, meridional wind, and skin temperature, is more closely related to the ability to simulate precipitation. This indicates that it is more meaningful to evaluate the vector winds than the zonal or meridional wind from the perspective of improving regional precipitation simulation.
Huang et al., Evaluating vector winds in the Asian-Australian monsoon region simulated by 37 CMIP5 models, Climate Dynamics, 2019
Vector wind plays a crucial role in shaping regional climate through transferring energy and moisture. In this study, we evaluate 37 Coupled Model Intercomparison Project Phase 5 (CMIP5) models and multi-model ensembles (MME) in terms of the climatological mean state, annual cycle, and interannual variability of vector winds in the Asian-Australian monsoon (A-AM) region. Unlike most previous studies those assessed meridional and zonal wind separately, we treat vector wind as a whole by employing a recently developed vector field evaluation method. The results are summarized as follows: (1) MME exhibits the best performance in reproducing the climatological mean of vector winds, followed by CESM1-CAM5 and three MPI-ESM models. However, models still show significant biases characterized by overestimated lower level vector winds and its spatial variation. The biases are mainly rooted in the anomaly components of vector winds and are observed in the regions with complex topography. (2) CMIP5 models can well simulate the annual cycle of upper-tropospheric vector winds, especially in the extratropical regions, but show large biases and dispersion over complex terrains in the lower troposphere. (3) MME still outperforms individual model for the simulation of interannual variance of vector winds, although most CMIP5 models overestimate the strength of vector wind variability in the lower troposphere. (4) Model skills in simulating climatological means, annual cycle, and interannual variability are positively correlated with each other to a certain degree over the A-AM region, suggesting an improvement in climatological mean may lead to a better simulation in the annual cycle or interannual variability of vector winds.
Li et al., An integrated evaluation of land surface energy fluxes over China in seven reanalysis/modeling products. Journal of Geophysical Research-Atmospheres, 2017
An integrated evaluation of monthly mean land surface energy fluxes over China in seven reanalysis and land model products during the period 1979-2015 is conducted. Observations from seven field sites are used to evaluate these flux products, including four reanalysis data sets and three produced by off-line land surface models. In general, the expected seasonal variations and spatial patterns in major climatic regimes are well reproduced by all reanalysis and modeling products. However, large differences among the four reanalysis products are found, while the three off-line land surface modeling products correlate well with each other. Looking at the Bowen ratio, it is found that the off-line land surface models convert a larger fraction of surface available energy into sensible heat flux compared to the reanalysis products in all climatic regimes. There are three centers of high interannual variability in sensible heat located in West China, Northeast China, and the eastern Inner Mongolia, respectively. In addition, the sensible heat flux agrees better with observations at grassland sites than at forest sites, while the latent heat flux and net radiation are significantly overestimated at forest sites in all the flux products. Besides, mean square errors of the fluxes are decomposed into biases, correlations, and differences in standard deviation. Finally, based on a ranking system adopted to quantitatively evaluate the performance of each data set, it is found that the surface energy fluxes in ERA-Interim and JRA-25 agree well with observations and the ensemble mean of all these products remains reasonably realistic as well.
Ling et al., Evaluating CEOP model performance in semi-arid region of China, Environmental Research Letters, 2012
This study systematically evaluates simulations of near-surface temperature and precipitation using the station observations collected in the semi-arid region of China during the Coordinated Enhanced Observing Period (CEOP) from October 2002 to December 2004 (EOP3 and EOP4). The outputs being evaluated are from eight general circulation models (GCMs) archived by the Coordinated Energy and Water Cycle Observations Project (CEOP), as well as a multi-model ensemble based on these eight models. We find that the multi-model ensemble has a better performance than most of the individual models. Our results show that all individual models and the Model Analysis Comparison (MAC) ensemble mean perform much better when simulating regionally averaged temperature than precipitation. For most models, a systematically low bias is identified in the regionally averaged simulated temperatures, while a high bias exists in the simulated precipitation except in summer. For the simulated temperatures, the lowest and largest rRMSE are found in JMA and BMRC, respectively. Furthermore, temperature is always overestimated when it is between -18 and -10 oC, while the temperature is underestimated when it is greater than 6 oC; the best performance lies between -10 and 2 oC for all the models except BMRC. For the simulated precipitation, excessive rainfall is reproduced at all intervals except in ECPC-SFM, and the largest deviation is identified at the interval of 2-5 mm with a bias of 18.3%. With respect to sub-regions, the simulated temperatures are better in eastern China, but the simulated precipitation is better in the transition zone from the semi-arid region to the arid region. However, the simulation bias increases west of 100 oE, which may be associated with the complex and steep topography there. We want to stress that the MAC ensemble mean is superior to any individual models.