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The synoptic cold surge is a weather event that frequently occurs in the Northern Hemisphere, often causing severe damage accompanied with intense winds and precipitation. It is reported that the Arctic Oscillation and the North Atlantic Oscillation can affect the cold surge. However, on interacting with tropical circulation, the cold surge potentially links to the Antarctic sea‐ice loss with influence extending to Tropics. Therefore, this research aims to investigate the potential link between Antarctic sea‐ice loss and Northern Hemisphere cold surges and corresponding wind–precipitation compound events. To study this link, twin numerical experiments using a physically based atmosphere model with a prescribed 30% Antarctic sea‐ice loss scenario were used. The results show that the cold surge occurs more frequently in most regions of the world, except for central North America and central Eurasia. In Europe, the cold surges occur closer to the lower latitudes in the Antarctic sea‐ice loss scenario. Additionally, the Antarctic sea‐ice loss can result in intensified wind and precipitation extremes within compound events. The intensified precipitation extremes are caused by increased moisture transportation by tropical easterlies and higher local temperature after the cold surge occurrence, providing more moisture available for precipitation in East Asia and East North America, respectively. Overall, the results of the numerical experiments provide evidence that Antarctic sea‐ice loss influences cold surges in Northern Hemisphere through atmospheric teleconnections. It is important to continue investigating the link between Antarctic sea‐ice loss and weather events like cold surges, as this research can help improve our understanding of the impacts of polar climate change on lower latitudes. [ABSTRACT FROM AUTHOR]

Climate change impacts on hydrological processes can affect reservoir operational performance. Hence, the reservoir operation model, based on historical climate conditions, may not guarantee sustainable water resources management in the future. To enable stakeholders to design reliable adaptation strategies, this study aims to propose a cascading framework to quantify the impacts of climate change on the operational performance and sustainability of a multipurpose reservoir. The Danjiangkou Reservoir (DJKR), which serves as the water source for the middle route of the South-to-North Water Diversion Project in China, was selected as a case study. To achieve the aforementioned aims, bias-corrected simulations from 13 global climate models (GCMs) were first input into five hydrological models [i.e., one data-driven [deep belief network (DBN)], three conceptual [SIMHYD, HBV, and Xin'anjiang (XAJ)], and one physically-based [variable infiltration capacity (VIC)]. The simulated reservoir inflows were then fed into a 10-day reservoir simulation model where DJKR operation followed the designed operating rules to evaluate reservoir operational performance. Finally, a data envelopment analysis (DEA) model was proposed to assess reservoir sustainability under both historical (1976–2005) and future (2021–2050) climate conditions. The results show that the combination of the GCM ensembles and the SIMHYD, HBV, XAJ, and VIC models exhibit similar growth patterns in the reservoir inflow and operational benefits for the future period. However, the DBN model produces consistent decreases in most cases, which may be attributed to its inability to generate accurate estimates of extreme events. The results indicate that hydrological models may be extensively utilized in decision making with greater confidence, and the data-driven model should be interpreted with caution when used in hydrological climate change impact studies. The efficiency metrics suggest that decision makers should focus more on increasing operational benefits, which can subsequently enhance reservoir sustainability. Overall, the framework proposed in this study provides a foundation for evaluating the reservoir sustainability and adaptability to climate change from water managers' perspective. [ABSTRACT FROM AUTHOR]

Bias correction methods remove systematic differences in the distributional properties of climate model outputs with respect to observations, often as a means of pre‐processing model outputs for use in hydrological impact studies. Traditionally, bias correction is applied at each weather station individually, neglecting the dependence that exists between different sites, which could negatively affect simulations from a distributed hydrological model. In this study, three multi‐variate bias correction (MBC) methods—initially proposed to correct the inter‐variable correlation or multi‐variate dependence of climate model outputs—are used to correct biases in distributional properties and spatial dependence at multiple weather stations. To reveal the benefits of correcting spatial dependence, two distribution‐based single‐site bias correction methods are used for comparison. The effects of multi‐site correction on hydro‐meteorological extremes are assessed by driving a distributed hydrological model and then evaluating the model performance in terms of several meteorological and hydrological extreme indices. The results show that the multi‐site bias correction methods perform well in reducing biases in spatial correlation measures of raw global climate model outputs. In addition, the multi‐site methods consistently reproduce watershed‐averaged meteorological variables better than single‐site methods, especially for extreme values. In terms of representing hydrological extremes, the multi‐site methods generally perform better than the single‐site methods, although the benefits vary according to the hydrological index. However, when applying the multi‐site methods, the original temporal sequence of precipitation occurrence may be altered to some extent. Overall, all multi‐site bias correction methods are able to reproduce the spatial correlation of observed meteorological variables over multiple stations, which leads to better hydrological simulations, especially for extremes. This study emphasizes the necessity of considering spatial dependence when applying bias correction to ccc outputs and hydrological impact studies. [ABSTRACT FROM AUTHOR]

Weather generators are usually used to produce an ensemble of climate time series for vulnerability assessments and impact studies in hydrological and agricultural communities. Multisite and multivariate weather generators (MMWGs) have many advantages over single-site counterparts in terms of coupling with distributed models for the assessment of spatial variability in various impact sectors. However, the existing MMWGs usually suffer from limitations in preserving multisite and multivariate dependencies at multiple time scales, as well as preserving the low-frequency variability of climate variables. This study proposes a new MMWG which preserves low-frequency climate variability by coupling annual, monthly and daily weather generators into a single model. Specifically, the daily precipitation and temperature time series generated by a widely used multisite daily weather generator is adjusted using monthly and annual climate time series generated by a first-order linear autoregressive model. This combination preserves the multisite and multivariate attributes, as well as low-frequency variability at monthly and annual scales. The performance of the proposed MMWG was evaluated by comparing the baseline model against that of its variants with monthly or annual adjustments for climate generation. The performance was also assessed using a hydrological model over two watersheds with different hydroclimatic characteristics. The results show that the proposed weather generator performs well with respect to reproducing the marginal distributional attributes, multisite and multivariate dependencies, and climate variability at the daily, monthly and annual scales. Weather generators with either monthly or annual adjustments (and not both) only improve the simulations in multisite and multivariate dependencies and low-frequency variability at the corresponding time scales. In terms of hydrological modeling, the proposed model consistently performs better than the baseline model and its variants with only monthly or annual adjustments in representing the mean and variance of monthly and annual streamflows. It also performs better in representing the frequency distribution of mean and extreme streamflow events. Overall, the proposed MMWG can effectively produce multisite and multivariate climate time series with low-frequency variability and has a strong potential for use in climate change impact studies. [ABSTRACT FROM AUTHOR]