Evaluation of the Performance of CMIP6 HighResMIP on West African Precipitation

This research focuses on evaluating the High-Resolution Model Intercomparison Project (HighResMIP) simulations within the framework of the Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6). We used seven of its consortiums to study how CMIP6 reproduced the West African precipitation features during the 1950–2014 historical simulation periods. The rainfall event was studied for two sub-regions of West Africa, the Sahel and the Guinea Coast. Precipitation datasets from the Climate Research Unit (CRU) TS v4.03, University of Delaware (UDEL) v5.01, and Global Precipitation Climatology Centre (GPCC) were used as observational references with the aim of accounting for uncertainty. The observed annual peak during August, which is greater than 200, 25, and 100 mm/month in the Guinea Coast, the Sahel, and West Africa as a whole, respectively, appears to be slightly underestimated by some of the models and the ensemble mean, although all the models captured the general rainfall pattern. Global climate models (GCMs) and the ensemble mean reproduced the spatial daily pattern of precipitation in the monsoon season (from June to September) over West Africa, with a high correlation coefficient exceeding 0.8 for the mean field and a relatively lower correlation coefficient for extreme events. Individual models, such as IPSL and ECMWF, tend to show high performance, but the ensemble mean appears to outperform all other models in reproducing West African precipitation features. The result from this study shows that merely improving the horizontal resolution may not remove biases from CMIP6.

Evaluating the added values of regional climate modeling over China at different resolutions

Previous studies have suggested that dynamical downscaling to global climate models can produce improved climate simulations at regional and local scales. However, the expensive computational requirements of dynamical downscaling inevitably add a limit to the spatial resolution of the resulting regional climate simulations. In order to find a balance between computational requirements and simulation improvements, it is extremely important to investigate how the spatial resolution of regional climate simulation affects the added values of dynamical downscaling; yet, it is still not well understood. Therefore, in this study, we conduct long-term climate simulations for the entire country of China with the PRECIS regional climate model at two different spatial resolutions (i.e., 25 and 50 km). The purpose is to evaluate whether a fine-resolution model simulation, given its considerable requirements for computational resources, would add more valuable information for understanding regional climatology than a coarse-resolution model simulation. Our results show that the PRECIS can reasonably reproduce the spatial distribution of seasonal and monthly mean temperature and precipitation over the most of regions in China. However, in the process of downscaling, RCM with higher resolution cannot always produce more accurate output. In regard to precipitation simulations, compared with the host GCM, it is difficult to determine exactly a homogeneous improvement of performance in downscaling, both in terms of spatial patterns as well as magnitude of errors. For interannual variability, variations in temperature are closer to observation than precipitation and the high-resolution R25 has better skills over the northwest than R50. Moreover, except for the west, it is shown that PRECIS is able to better reproduce the probability distribution function of precipitation and some impact-relevant indices such as the number of consecutive wet days and simple precipitation intensity index in spatial distribution.

Modified Approach to Reduce GCM Bias in Downscaled Precipitation: A Study in Ganga River Basin

Reanalysis data is widely used to develop predictor-predictand models, which are further used to downscale coarse gridded general circulation models (GCM) data at a local scale. However, large variability in the downscaled product using different GCMs is still a big challenge. The first objective of this study was to assess the performance of reanalysis data to downscale precipitation using different GCMs. High bias in downscaled precipitation was observed using different GCMs, so a different downscaling approach is proposed in which historical data of GCM was used to develop a predictor-predictand model. The earlier approach is termed “Re-Obs” and the proposed approach as “GCM-Obs”. Both models were assessed using mathematical derivation and generated synthetic series. The intermodal bias in different GCMs downscaled precipitation using Re-Obs and GCM-Obs model was also checked. Coupled Model Inter-comparison Project-5 (CMIP5) data of ten different GCMs was used to downscale precipitation in different urbanized, rural, and forest regions in the Ganga river basin. Different measures were used to represent the relative performances of one downscaling approach over other approach in terms of closeness of downscaled precipitation with observed precipitation and reduction of bias using different GCMs. The effect of GCM spatial resolution in downscaling was also checked. The model performance, convergence, and skill score were computed to assess the ability of GCM-Obs and Re-Obs models. The proposed GCM-Obs model was found better than Re-Obs model to statistically downscale GCM. It was observed that GCM-Obs model was able to reduce GCM-Observed and GCM-GCM bias in the downscaled precipitation in the Ganga river basin.

Avoided heat-related mortality through climate adaptation strategies in three US cities

Heat-related mortality in US cities is expected to more than double by the mid-to-late 21st century. Rising heat exposure in cities is projected to result from: 1) climate forcings from changing global atmospheric composition; and 2) local land surface characteristics responsible for the urban heat island effect. The extent to which heat management strategies designed to lessen the urban heat island effect could offset future heat-related mortality remains unexplored in the literature. Using coupled global and regional climate models with a human health effects model, we estimate changes in the number of heat-related deaths in 2050 resulting from modifications to vegetative cover and surface albedo across three climatically and demographically diverse US metropolitan areas: Atlanta, Georgia, Philadelphia, Pennsylvania, and Phoenix, Arizona. Employing separate health impact functions for average warm season and heat wave conditions in 2050, we find combinations of vegetation and albedo enhancement to offset projected increases in heat-related mortality by 40 to 99% across the three metropolitan regions. These results demonstrate the potential for extensive land surface changes in cities to provide adaptive benefits to urban populations at risk for rising heat exposure with climate change.