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Zhou, ZQ, Xie SP, Zhang GJ, Zhou WY.  2018.  Evaluating AMIP Skill in Simulating Interannual Variability over the Indo-Western Pacific. Journal of Climate. 31:2253-2265.   10.1175/jcli-d-17-0123.1   AbstractWebsite

Local correlation between sea surface temperature (SST) and rainfall is weak or even negative in summer over the Indo-western Pacific warm pool, a fact often taken as indicative of weak ocean feedback on the atmosphere. An Atmospheric Model Intercomparison Project (AMIP) simulation forced by monthly varying SSTs derived from a parallel coupled general circulation model (CGCM) run is used to evaluate AMIP skills in simulating interannual variability of rainfall. Local correlation of rainfall variability between AMIP and CGCMsimulations is used as a direct metric of AMIP skill. This "perfect model'' approach sidesteps the issue of model biases that complicates the traditional skill metric based on the correlation between AMIP and observations. Despite weak local SST-rainfall correlation, the AMIP-CGCM rainfall correlation exceeds a 95% significance level over most of the Indo-western Pacific warm pool, indicating the importance of remote (e.g., El Nino in the equatorial Pacific) rather than local SST forcing. Indeed, the AMIP successfully reproduces large-scale modes of rainfall variability over the Indo-western Pacific warm pool. Compared to the northwest Pacific east of the Philippines, the AMIP-CGCMrainfall correlation is low from the Bay of Bengal through the South China Sea, limited by internal variability of the atmosphere that is damped in CGCM by negative feedback from the ocean. Implications for evaluating AMIP skill in simulating observations are discussed.

Li, LJ, Wang B, Zhang GJ.  2015.  The role of moist processes in shortwave radiative feedback during ENSO in the CMIP5 models. Journal of Climate. 28:9892-9908.   10.1175/jcli-d-15-0276.1   AbstractWebsite

The weak negative shortwave (SW) radiative feedback (sw) during El Nino-Southern Oscillation (ENSO) over the equatorial Pacific is a common problem in the models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). In this study, the causes for the (sw) biases are analyzed using three-dimensional cloud fraction and liquid water path (LWP) provided by the 17 CMIP5 models and the relative roles of convective and stratiform rainfall feedbacks in (sw) are explored. Results show that the underestimate of SW feedback is primarily associated with too negative cloud fraction and LWP feedbacks in the boundary layers, together with insufficient middle and/or high cloud and dynamics feedbacks, in both the CMIP and Atmospheric Model Intercomparsion Project (AMIP) runs, the latter being somewhat better. The underestimations of SW feedbacks are due to both weak negative SW responses to El Nino, especially in the CMIP runs, and strong positive SW responses to La Nina, consistent with their biases in cloud fraction, LWP, and dynamics responses to El Nino and La Nina. The convective rainfall feedback, which is largely reduced owing to the excessive cold tongue in the CMIP runs compared with their AMIP counterparts, contributes more to the difference of SW feedback (mainly under El Nino conditions) between the CMIP and AMIP runs, while the stratiform rainfall plays a more important role in SW feedback during La Nina.

Subramanian, AC, Zhang GJ.  2014.  Diagnosing MJO hindcast biases in NCAR CAM3 using nudging during the DYNAMO field campaign. Journal of Geophysical Research-Atmospheres. 119:7231-7253.   10.1002/2013jd021370   AbstractWebsite

This study evaluates the Madden-Julian Oscillation (MJO) hindcast skill and investigates the hindcast biases in the dynamic and thermodynamic fields of the National Center for Atmospheric Research Community Atmosphere Model version 3. The analysis is based on the October 2011 MJO event observed during the Dynamics of the Madden-Julian Oscillation field campaign. The model captures the MJO initiation but, compared to the observations, the hindcast has a faster MJO phase speed, a dry relative humidity bias, a stronger zonal wind shear, and a weaker MJO peak amplitude. The MJO hindcast is then nudged toward the European Centre for Medium-Range Weather Forecast Reanalysis fields of temperature, specific humidity, horizontal winds, and surface pressure. The nudging tendencies highlight the model physics parameterization biases, such as not enough convective diabatic heating during the MJO initiation, not enough upper tropospheric stratiform condensation, and lower tropospheric reevaporation during the mature and decay phases and a strong zonal wind shear during the MJO evolution. To determine the role of temperature, specific humidity, and horizontal winds in the model physics parameterization errors, six additional nudging experiments are carried out, with either one or two of the fields allowed to evolve freely while the others are nudged. Results show that convection and precipitation increase when temperature or specific humidity are unconstrained and decrease when horizontal winds evolve freely or temperature alone is constrained to reanalysis. Budget analysis of moist static energy shows that the nudging tendency compensates for different process biases during different MJO phases. The diagnosis of such nudging tendencies provides a unique objective way to identify model physics biases, which usefully guides the model physics parameterization development.