Analyzing urban influence on extreme winter precipitation through observations and numerical simulation of two South China case studies.

This study investigates the impact of urbanization on extreme winter rainfall in the South China Greater Bay Area (GBA) through the analysis of hourly station observations and simulations using the Weather Research and Forecasting Model with the Single Layer Urban Canopy Model (WRF-SLUCM). Data from 2008 to 2017 reveal that urban areas in the GBA experience lower 99th percentile hourly winter rainfall intensity compared to surrounding rural regions. However, urban locations exhibit higher annual maximum hourly rainfall (Rmax) and very extreme rainfall events (99.99th percentile) in winter, suggesting a positive influence of urbanization on extreme winter precipitation. A case study further underscores the role of the Urban Heat Island (UHI) effect in enhancing extreme rainfall intensity and probability in the GBA urban areas. Additionally, two extreme cases were dynamically downscaled using WRF-SLUCM, involving four parallel experiments: replacing urban land use with cropland (Nourban), using historical urban land use data from 1999 (99LS), projecting near-future urban land use for 2030 (30LS), and considering 2030 urban land use without anthropogenic heat (AH) (30LS-AH0). Synoptic analysis demonstrates that cold air intrusion suppresses the GBA UHI in Case 2013 but not in Case 2015. Reduced evaporation and humidity induced by urban surfaces significantly decrease urban precipitation in Case 2013. In contrast, the persistent UHI in Case 2015 enhances local convection and land-ocean circulation, leading to increased moisture flux convergence and amplified urban precipitation intensity and probability in 30LS compared to Nourban. This amplification is primarily attributed to AH, while the change in 99LS remains insignificant. These findings suggest that urban influences on extreme precipitation in the GBA persist during winter, particularly when the UHI effect is maintained.

Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979-2100).

Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979-2014 and future scenarios (SSP245 and SSP585) for 2015-2100 with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that the bias-corrected data are of better quality than the individual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth's future climate, atmospheric environment, hydrology, agriculture, wind power, etc.

Impacts of climate change on tropical cyclones and induced storm surges in the Pearl River Delta region using pseudo-global-warming method.

We have investigated changes of western North Pacific land-falling tropical cyclone (TC) characteristics due to warmer climate conditions, using the pseudo-global-warming (PGW) technique. Historical simulations of three intense TCs making landfall in Pearl River Delta (PRD) were first conducted using the Weather Research and Forecasting (WRF) model. The same cases were then re-simulated by superimposing near- (2015-2039) and far- (2075-2099) future temperature and humidity changes onto the background climate; these changes were derived from the Coupled Model Intercomparison Project phase 5 (CMIP5) multi-model projections according to the Representative Concentration Pathway (RCP) 8.5 scenario. Peak intensities of TCs (maximum surface wind in their lifetimes) are expected to increase by ~ (3) 10% in the (near) far future. Further experiments indicate that surface warming alone acts to intensify TCs by enhancing sea surface heat flux, while warmer atmosphere acts in the opposite way by increasing the stability. In the far future, associated storm surges are also estimated to increase by about 8.5%, computed by the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model. Combined with sea level rise and estimated land vertical displacement, TC-induced storm tide affecting PRD will increase by ~1 m in the future 2075-2099 period.

Sensitivity of precipitation statistics to urban growth in a subtropical coastal megacity cluster.

This short paper presents an investigation on how human activities may or may not affect precipitation based on numerical simulations of precipitation in a benchmark case with modified lower boundary conditions, representing different stages of urban development in the model. The results indicate that certain degrees of urbanization affect the likelihood of heavy precipitation significantly, while less urbanized or smaller cities are much less prone to these effects. Such a result can be explained based on our previous work where the sensitivity of precipitation statistics to surface anthropogenic heat sources lies in the generation of buoyancy and turbulence in the planetary boundary layer and dissipation through triggering of convection. Thus only mega cities of sufficient size, and hence human-activity-related anthropogenic heat emission, can expect to experience such effects. In other words, as cities grow, their effects upon precipitation appear to grow as well.

How do the strength and type of ENSO affect SST predictability in coupled models.

The effects of amplitude and type of the El Niño-Southern Oscillation (ENSO) on sea surface temperature (SST) predictability on a global scale were investigated, by examining historical climate forecasts for the period 1982-2006 from air-sea coupled seasonal prediction systems. Unlike in previous studies, SST predictability was evaluated in different phases of ENSO and for episodes with various strengths. Our results reveal that the seasonal mean Niño 3.4 index is well predicted in a multi-model ensemble (MME), even for four-month lead predictions. However, coupled models have particularly low skill in predicting the global SST pattern during weak ENSO events. During weak El Niño events, which are also El Niño Modoki in this period, a number of models fail to reproduce the associated tri-pole SST pattern over the tropical Pacific. During weak La Niña periods, SST signals in the MME tend to be less persistent than observations. Therefore, a good ENSO forecast does not guarantee a good SST prediction from a global perspective. The strength and type of ENSO need to be considered when inferring global SST and other climate impacts from model-predicted ENSO information.