Search

Changing climate has altered the trends and variability of precipitation and temperature globally and thereby increasing the risk of natural and social disasters, especially in coastal climatic transitional zones such as the Huaihe river basin (HRB). This paper applies the Empirical Quantile Mapping (EQM) method for bias correction and systematically evaluates the performance of the 30 Global climate models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating precipitation and temperature over the Huaihe river basin (HRB) for 1979–2014. A suitable multi-model ensemble subset (BMME) is selected based on the Euclidean Distance (ED) synthetic metric. Results show that in comparison to the baseline period 1995–2014, precipitation (temperature) over HRB is projected to increase at the rate of around 15 mm/decade (0.1 °C/decade), 16 mm/decade (0.3 °C/decade), 20 mm/decade (0.5 °C/decade), and 15 mm/decade (0.6 °C/decade) for the period 2015–2100, under Shared Socioeconomic Pathways SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. In the long-term (2081–2100), the annual precipitation is projected to increase by 32%, 27%, 35%, and 26%, under the four scenarios, respectively. The temperature is projected to remain stable or slightly decrease under SSP1-2.6 (2 °C) and SSP2-4.5 (3 °C), while will keep rising and increasing by 6 °C under SSP5-8.5 and by 4 °C under SSP3-7.0 by 2100. The isotherm and isohyet are projected to shift northwest from southeastern coastal China to inland in the future, which is likely associated with the northwestward shift of the western Pacific anticyclone in summer. [ABSTRACT FROM AUTHOR]

• The spatiotemporal continuity of droughts was explored using a three-dimensional clustering algorithm. • Droughts can migrate hundreds of kilometers from the source sites in the agro-pastoral ecotone of northern China. • The more severe and longer duration of meteorological drought, the more likely it is to trigger agricultural drought. Understanding the spatiotemporal evolution of droughts is critical for food security and water allocation. In this study, we propose an approach to construct the linkage of the propagation from meteorological drought to agricultural drought to explore the simultaneous spatiotemporal evolution of droughts based on a 3-dimensional clustering algorithm. We first evaluate the improvement of the downscaled high-resolution outputs from the Weather Research and Forecasting (WRF) model to reanalysis data in detecting wet-dry variations in the agro-pastoral ecotone of northern China (APENC). The WRF simulation results well characterize the evolution of droughts with a high correlation coefficient of 0.82 (p <0.05), which implies that the WRF downscaling model provides reliable hydrometeorological results for assessing and analyzing long-term wet-dry variations. Subsequently, we identify and track the spatiotemporal evolution of each individual drought event in the APENC for the first time. Based on the standardized precipitation index calculated from the WRF outputs, a total of 185 drought clusters were identified during 2000–2017, with 28 drought events lasting at least 3 months duration occurring in the APENC. The characteristics of the different drought events vary considerably. Droughts in the APENC have a larger percentage of propagation to the northeast and northwest and migrated hundreds of kilometers from the source sites. The results of the propagation of meteorological droughts to agricultural droughts indicate that 53.75% of the meteorological drought events progressed further to agricultural droughts. Agricultural droughts exhibit spatial variations consistent with meteorological droughts. Agricultural droughts have 1.69 months onset lag time, 2.12 months termination lag time, and overall longer duration than meteorological droughts in the APENC. Our findings provide valuable information for the mechanism and prediction of drought propagation. [ABSTRACT FROM AUTHOR]

The regional drought predictability is strongly influenced by teleconnections factors. Among them, El Niño-Southern Oscillation (ENSO) is important to drought occurrence. However, it is still not fully understood how the probability of regional droughts corresponding to different ENSO regimes varies with time lags. In this study, the event-based coincidence analysis (ECA) is employed to statistically derive the relationships regarding time information and magnitude between conventional ENSO (c-ENSO)/ENSO Modoki (ENSO_M) episodes and drought events. The standardized precipitation evapotranspiration index (SPEI) based on station-records is used to determine meteorological droughts. Our study focuses on the largest freshwater lake of China, Poyang Lake basin (PLB), as severe droughts have occurred there frequently. Results indicate that the summer-autumn continuous drought dominates the severe droughts, with a frequent onset in August/September, and a cessation in October/November. The likelihood of drought occurrence is highest during cool phases of c-ENSO (CEN), followed by warm ENSO_M (WEM), warm c-ENSO (WEN), and cool ENSO_M (CEM). The influence of WEN/WEM on droughts is statistically significant mostly for a time lag of 10 months, whereas CEN/CEM shows no lagged influence. Furthermore, the variability of the 850 hPa wind anomalies during different ENSO episodes at various time lags is corresponding to the lagged influence of ENSO on droughts. The development and persistence of droughts is resulting from strengthened northeasterly wind anomalies over the PLB. Our findings demonstrate that the ENSO anomalies are early warning signals of droughts and hence can be beneficial for increasing the drought predictivity • A novel statistical framework to quantify the probability of droughts responding to ENSO with varied time lags is developed. • The influence of WEN/WEM on the droughts in the PLB is statistically significant mostly for a time lag of 10 months. • The strengthened northeasterly wind anomalies over the PLB likely results in the development of ENSO-induced droughts. [ABSTRACT FROM AUTHOR]

Intense precipitation events frequently occur in the Hanjiang River Basin (HRB) in China, causing major economic losses and affecting human security. Accurate precipitation simulations are sensitive to the choice of physical schemes in numerical weather prediction models. Therefore, in this study the performance of the different combinations of physical schemes in the Weather Research and Forecasting (WRF) model was investigated for the HRB. Five intense precipitation events in summer during the period 2008–2011 were selected. For simulating each intense precipitation event, an 18-member multi-physics ensemble was designed by using three cumulus (CU) schemes and four microphysics (MP) schemes. The temporal dynamics and spatial patterns of the simulated precipitation were evaluated systematically by a comparison to the China precipitation analysis reference data. A quantitative analysis using the Euclidean distance metric to synthesize the evaluation results was performed. The performed uncertainty analysis suggests that the skill of precipitation simulation is more sensitive to the choice of the CU scheme and less sensitive to the choice of the MP scheme. Our analysis shows that a suitable WRF physical configuration consists of Kain-Fritsch and WRF Single-Moment 6-class, which reaches a performance similar to that of the ensemble mean method. To our knowledge, this multi-physics ensemble study is the first effort to comprehensively evaluate the applicability of the WRF model to intense precipitation simulations over the HRB. Therefore, this study potentially contributes to the improvement of climate simulations for central China. • Cumulus schemes have primary impact on intense precipitation simulation while microphysics schemes have secondary impact. • A suitable WRF physical configuration is the combinations of the KF and WSM6 schemes. • This study potentially contributes to the improvement of climate simulations for central China. [ABSTRACT FROM AUTHOR]

Lateral terrestrial water flow contributes to the Earth system dynamics by redistributing the moisture fluxes at the land surface. Fully coupled atmospheric-hydrological modeling, which considers the lateral hydrological processes, is increasingly used to investigate the land-atmosphere interactions. Planetary boundary layer (PBL) parameterizations which depict vertical energy and mass exchanges from the surface to lower atmosphere are pivotal to atmosphere modeling applications. This study aims at quantitatively evaluating to what extend the PBL scheme influences land-atmosphere interactions and how it impacts the regional climate modeling by coupled atmospheric-hydrological modeling approach. In this case study, the hydrologically enhanced version of the Weather Research and Forecasting modeling WRF-Hydro is used as fully coupled atmospheric-hydrological modeling experiment, and the standard WRF modeling is used as the control experiment. The regional simulation is applied to the Heihe river basin, an endorheic basin located in northwest China. The atmospheric modeling is configured at convection permitting scale 3 km, and the lateral terrestrial water processes are resolved at 300 m fine hydrological sub-grid. An ensemble of WRF/WRF-Hydro simulations is produced by varying the PBL scheme (YSU, MYJ, ACM2) for three continuous years from 2008-2010. The sensitivity of land-atmosphere interactions is evaluated with a regional precipitation recycling analysis and a three-dimensional atmospheric moisture tracing method (E-tagging). The interannual variability of the regional water budgets and land-atmosphere interactions are quantified with the model ensemble. We find a low precipitation recycling and a clearly positive soil moisture-precipitation feedback triggered by lateral terrestrial water flow in the study area. Lateral terrestrial water flow increases the local precipitation recycling irrespective of the modeling variability obtained with the different PBL parameterizations. The robust impact of lateral terrestrial water flow on local precipitation recycling indicates that the fully coupled modeling should be applied for climate projections. [ABSTRACT FROM AUTHOR]