Despite the importance of precipitation and moisture transport over the Tibetan Plateau for glacier mass balance, river runoff and local ecology, changes in these quantities remain highly uncertain and poorly understood. Here we use observational data and model simulations to explore the close relationship between summer rainfall variability over the southwestern Tibetan Plateau (SWTP) and that over central-eastern India (CEI), which exists despite the separation of these two regions by the Himalayas. We show that this relationship is maintained primarily by ‘up-and-over’ moisture transport, in which hydrometeors and moisture are lifted by convective storms over CEI and the Himalayan foothills and then swept over the SWTP by the mid-tropospheric circulation, rather than by upslope flow over the Himalayas. Sensitivity simulations confirm the importance of up-and-over transport at event scales, and an objective storm classification indicates that this pathway accounts for approximately half of total summer rainfall over the SWTP.

A team effort to develop a Community Integrated Earth System Model (CIESM) was initiated in China in 2012. The model was based on NCAR Community Earth System Model (Version 1.2.1) with several novel developments and modifications aimed to overcome some persistent systematic biases, such as the double Intertropical Convergence Zone problem and underestimated marine boundary layer clouds. Aerosols' direct and indirect effects are prescribed using the MACv2‐SP approach and data sets. The spin‐up of a 500‐year preindustrial simulation and three historical simulations are described and evaluated. Prominent improvements include alleviated double Intertropical Convergence Zone problem, increased marine boundary layer clouds, and better El Niño Southern Oscillation amplitude and periods. One deficiency of the model is the significantly underestimated Arctic and Antarctic sea ice in warm seasons. The historical warming is about 0.55 °C greater than observations toward 2014. CIESM has an equilibrium climate sensitivity of 5.67 K, mainly resulted from increased positive shortwave cloud feedback. Our efforts on porting and redesigning CIESM for the heterogeneous Sunway TaihuLight supercomputer are also introduced, including some ongoing developments toward a future version of the model.

A new circulation model of the western North Pacific Ocean based on the parallelized version of the Princeton Ocean Model and incorporating the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme has been developed. The new model assimilates satellite data and is tested for the period January 1 to April 3, 2012 initialized from a 24-year simulation to estimate the ocean state focusing in the South China Sea (SCS). Model results are compared against estimates based on the optimum interpolation (OI) assimilation scheme and are validated against independent Argo float and transport data to assess model skills. LETKF provides improved estimates of the western North Pacific Ocean state including transports through various straits in the SCS. In the Luzon Strait, the model confirms, for the first time, the three-layer transport structure previously deduced in the literature from sparse observations: westward in the upper and lower layers and eastward in the middle layer. This structure is shown to be robust, and the related dynamics are analyzed using the results of a long-term (18 years) unassimilated North Pacific Ocean model. Potential vorticity and mass conservations suggest a basin-wide cyclonic circulation in the upper layer of the SCS (z > −570 m), an anticyclonic circulation in the middle layer (−570 m ≥ z > −2,000 m), and, in the abyssal basin (<−2,000 m), the circulation is cyclonic in the north and anticyclonic in the south. The cyclone–anticyclone abyssal circulation is confirmed and explained using a deep-layer reduced-gravity model as being caused by overflow over the deep sill of the Luzon Strait, coupled with intense, localized upwelling west of the strait.

Graphics processing units (GPUs) are an attractive solution in many scientific applications due to their high performance. However, most existing GPU conversions of climate models use GPUs for only a few computationally intensive regions. In the present study, we redesign the mpiPOM (a parallel version of the Princeton Ocean Model) with GPUs. Specifically, we first convert the model from its original Fortran form to a new Compute Unified Device Architecture C (CUDA-C) code, then we optimize the code on each of the GPUs, the communications between the GPUs, and the I / O between the GPUs and the central processing units (CPUs). We show that the performance of the new model on a workstation containing four GPUs is comparable to that on a powerful cluster with 408 standard CPU cores, and it reduces the energy consumption by a factor of 6.8.