The objective of LLNL Cloud-Associated Parameterizations Testbed (CAPT) Project is to examine climate model errors and test new parameterizations in weather forecast mode to better identify parameterization-related deficiencies. In this meeting, we will present our recent accomplishment and on-going work on diagnosing climate model biases using CAPT approach. The first focus is on the correspondence between mean forecast errors and climate errors by analyzing the results from Transpose AMIP II and CMIP5/AMIP climate models. The key accomplishment for this work is that most systematic errors of precipitation, clouds, and radiation processes as well as large-scale state variables in the long-term climate runs are present by day 5. Furthermore, these systematic errors across different models likely are initiated by model parameterizations. Stemmed from our analysis of T-AMIP models, the second focus of the presentation is our ongoing project, the Clouds Above the United States and Errors at the Surface (CAUSES), to evaluate the central U.S. summertime surface warm biases seen in many climate models. This is a joint GASS/RGCM/ASR intercomparison project led by PCMDI/LLNL and U.K. Met Office to identify the role of cloud, radiation, and precipitation processes in contributing to the temperature biases. We use CAPT approach and look at the growth of the error as a function of hindcast lead time. Preliminary results of CAM5 compared to data from DOE ARM SGP site and other available observations indicate that the lack of soil moisture due to insufficient precipitation in the model is likely the primary source contributing to surface energy and temperature biases. Other processes that lead to the warm biases are also explored. This study is funded by the Regional and Global Climate Modeling and Atmospheric System Research programs of the U.S. Department of Energy as part of the CAPT. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS- 653161.