Search

We describe the bias adjustment and downscaling to a resolution of 1 × 1 km of daily temperature, precipitation and solar radiation of a multimodel ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs with the local observational data set BhutanClim for Bhutan and an analysis of the results for 1996–2100. The GCMs were selected based on their ability to reproduce local temporal and spatial precipitation patterns, resulting in a total of 14 models for each shared socioeconomic pathway (SSP). The results are shown for five global warming levels (GWLs) 1.5, 2, 3, 4 and 5°C, providing insight into the projected impacts of climate change at different warming levels. The daily temperature and precipitation data are publicly available, which is of great benefit for climate impact studies, such as ecological, hydrological or infrastructure‐related studies. In this paper, we discuss showcase climate indicators. Peak‐over‐threshold climate indicators such as hot days above 30°C are relevant for human health. In the capital city of Thimphu, these are currently rare, at 4 days per year, but might increase to more than 80 for GWL5.0. Precipitation increases in the summer months, but slightly decreases in the drier winter months. Accordingly, the 3‐month SPEI drought index is projected to increase in summer, but strongly decreases in the winter months, mostly pronounced in February. The increasing severity of late‐winter and spring droughts might have a negative impact on vegetation, wildfires and water supply. [ABSTRACT FROM AUTHOR]

Moisture sources and transport processes play a critical part in hydrological cycle and determine regional precipitation. This paper utilizes the Water Accounting Model‐2layers (WAM‐2layers) and the ERA5 reanalysis data to track the sources of precipitation over the Pearl River Basin (PRB). The contribution of external moisture and the role of local recycling are investigated. The results show that during the period from 1980 to 2020, oceanic sources including the western North Pacific and Indian Oceans serve as the primary moisture sources of precipitation over the PRB. The contributions to total seasonal precipitation are respectively 62.57% in MAM, 54.79% in JJA, 43.70% in SON and 60.88% in DJF. By contrast, the contribution of local recycling is generally below 5.50%. In the dry years of 1994, 1997 and 2001, the contribution of terrestrial sources is about 19.22%; in the wet years of 1989, 2009 and 2011, the contribution is about 16.31%. The summer precipitation anomalies are mainly attributable to moisture anomalies from the Equatorial Indian Ocean in the wet years and from Southeast Asia in the dry years. Furthermore, vertically integrated moisture flux anomalies over the boundaries of the PRB are generally the result of anomalous wind rather than anomalous moisture. In the wet years, low‐pressure systems induce strong cyclonic moisture transports, increasing the PRB precipitation. In the dry years, high‐pressure anomalies over the PRB block the moisture transports from the Indian Ocean and western North Pacific. [ABSTRACT FROM AUTHOR]

The South China Sea summer monsoon (SCSSM) onset and withdrawal have critical impacts on the climate of East Asia. However, using different reanalysis data may lead to the uncertainty of the SCSSM. This study uses reanalysis datasets including JRA‐55, ERA5, NCEP‐NCAR NCEP‐1 and NCEP‐DOE NCEP‐2 to calculate onset and withdrawal dates and index of SCSSM from 1991 to 2020, analyses the uncertainty and figures out the possible reasons. Results reveal that the four reanalysis datasets have obvious uncertainty in calculating the withdrawal date of SCSSM, with the deviation being up to 3.4 pentads. Further analysis shows that large differences in thermal conditions and lead to the uncertainties. From the perspective of precipitation, Greater uncertainty exists in the relationship between monsoon retreat and September–October precipitation, NCEP1 and NCEP2 datasets show weak correlation in southern China and Indonesia. Moreover, the uncertainty can be also found in the correlations of SCSSM with global sea surface temperature (SST) anomalies. JRA‐55 and ERA5 show a strong correlation of with SST in several areas, and exhibit a distinct Pacific decadal oscillation (PDO) mode with monsoon onset, while NCEP‐1 and NCEP‐2 have a weaker correlation with SST anomalies than JRA‐55 or ERA5. Comparing multiple factors comprehensively, this paper concludes that JRA‐55 may be more representative in the monsoon study, followed by ERA data. In short, depending on the specific research questions, researchers can choose appropriate data sources to study the SCSSM based on the different aspects and the uncertainty also deserve our special attention when selecting the reanalysis datasets. [ABSTRACT FROM AUTHOR]

Climate models' capability of reproducing the present climate at both global and regional scales still needs improvements. The assessment of model performance critically depends on the data sets used as comparators/references. Reanalysis and gridded observational data sets have been frequently used for this purpose. However, none of these can be considered an accurate reference data set because of their associated uncertainties and full representativity. This paper, for the first time, uses in‐situ measurements from National Oceanic and Atmospheric Administration U.S. Climate Reference Network (USCRN) spanning the period 2006–2020 to assess daily temperature and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA‐Interim) involved in NA‐CORDEX. The assessment is also extended to the most recent and widely used Earth system reanalyses (ERA5, ERA‐Interim, MERRA2 and NARR) and a few in situ‐based gridded data sets (Daymet, PRISM, Livneh and CPC). Results show that biases for the different data sets are seasonally and subregionally dependent. On average, reanalysis and in situ‐based gridded data sets are warmer (with biases exceeding 0.3°C) than USCRN year‐round, while RCMs are colder (warmer) in winter (summer) with biases ranging from −0.5 (0.9)°C for RCMs at 0.44° to −0.2 (1.4)°C for CRCM5‐UQAM‐11. In situ‐based gridded data sets provide the best performance in most of the Contiguous United States (CONUS) regions compared to reanalyses and RCMs, but still have biases in regions such as the Western mountains and the Pacific Northwest. Furthermore, in most US subregions, reanalysis data sets do not outperform reanalysis‐driven RCMs. Likewise, for both reanalysis data sets and RCMs, temperature and precipitation biases vary considerably depending on the local orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis. [ABSTRACT FROM AUTHOR]