Responses of tropical cyclones (TCs) to CO 2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ 25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3-4-5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model's transient fullycoupled 2 × CO 2 TC activity response is largely recovered by "time-slice" experiments using time-invariant SST perturbations added to each model's own SST climatology. The TC response to SST forcing depends on each model's background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO 2 -induced warming patterns and CO 2 doubling. Isolated CO 2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC "seeds", which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO 2 concentrations, and (2) less efficient development of these"seeds" into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.

Heat waves (HWs) are projected to become more frequent and last longer over most land areas in the late 21st century, which raises serious public health concerns. Urban residents face higher health risks due to synergies between HWs and urban heat islands (UHIs) (i.e., UHIs are higher under HW conditions). However, the responses of urban and rural surface energy budgets to HWs are still largely unknown. This study analyzes observations from two flux towers in Beijing, China and reveals significant differences between the responses of urban and rural (cropland) ecosystems to HWs. It is found that UHIs increase significantly during HWs, especially during the nighttime, implying synergies between HWs and UHIs. Results indicate that the urban site receives more incoming shortwave radiation and longwave radiation due to HWs as compared to the rural site, resulting in a larger radiative energy input into the urban surface energy budget. Changes in turbulent heat fluxes also diverge strongly for the urban site and the rural site: latent heat fluxes increase more significantly at the rural site due to abundant available water, while sensible heat fluxes and possibly heat storage increase more at the urban site. These comparisons suggest that the contrasting responses of urban and rural surface energy budgets to HWs are responsible for the synergies between HWs and UHIs. As a result, urban mitigation and adaption strategies such as the use of green roofs and white roofs are needed in order to mitigate the impact of these synergies.

Abstract. High-resolution satellite precipitation products are very attractive for studying the hydrologic processes in mountainous areas where rain gauges are generally sparse. Four high-resolution satellite precipitation products are evaluated using gauge measurements over different climate zones of the Tibetan Plateau (TP) within a 6 yr period from 2004 to 2009. The four satellite-based precipitation data sets are: Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 version 6 (TMPA) and its Real Time version (TMPART), Climate Prediction Center Morphing Technique (CMOPRH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN). TMPA and CMORPH, with higher correlation coefficients and lower root mean square errors (RMSEs), show overall better performance than PERSIANN and TMPART. TMPA has the lowest biases among the four precipitation data sets, which is likely due to the correction process against the monthly gauge observations from global precipitation climatology project (GPCP). TMPA also shows large improvement over TMPART, indicating the importance of gauge-based correction on accuracy of rainfall. The four products show better agreement with gauge measurements over humid regions than that over arid regions where correlation coefficients are less than 0.5. Moreover, the four precipitation products generally tend to overestimate light rainfall (0-10 mm) and underestimate moderate and heavy rainfall (> 10 mm). Moreover, this study extracts 24 topographic variables from a DEM (digital elevation model) and uses a linear regression model to explore the bias-topography relationship. Results show that biases of TMPA and CMORPH present weak dependence on topography. However, biases of TMPART and PERSIANN present dependence on topography and variability of elevation and surface roughness plays important roles in explaining their biases.