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Salt precipitation is a crucial process occurring during CO2 injection into saline aquifers. It significantly alters the porous space, leading to reduced permeability and impaired injectivity. While the dynamics of precipitation have been studied within porous media, our understanding of precipitation patterns and permeability evolution within rough fractures remains inadequate. Here, we conduct flow‐visualization experiments on salt precipitation, wherein dry air invades brine‐filled rough fractures under various flow rate conditions. Our observations reveal that the precipitation pattern shifts from ex situ precipitation to homogeneous form as the flow rate (capillary number Ca) increases. Through real‐time imaging of the salt precipitation process, we determine that ex situ precipitation is due to capillary‐driven backflow. This backflow phenomenon occurs when previously precipitated salt, acting as a hydrophilic porous medium, attracts the brine flow backward. As a result, precipitation occurs at a location different from the original site. We further show that the impact of capillary‐driven backflow is significant at low flow rates and is gradually suppressed as the flow rate increases. We provide a theoretical estimation for the critical Ca for the occurrence of capillary‐driven backflow. As Ca is smaller than this critical value, backflow‐precipitation positive feedback causes fracture voids to become completely clogged, thereby leading to a more substantial permeability reduction. In contrast, a homogeneous precipitation pattern tends to only partially clog the fracture voids, causing a relatively smaller permeability reduction. This study enhances our understanding of the role of capillary‐driven backflow in controlling salt precipitation and permeability reduction in fractures. Plain Language Summary Injecting CO2 into underground water layers (saline aquifers) is one way to tackle climate change by storing it away from the air. However, this process can lead to salt formation within the rock fractures, especially near the injection well, which can block the flow pathways and make it more challenging to inject additional CO2. Our research focuses on how salt forms within the rock fractures when we introduce dry air into areas filled with salty water, at different flow rates. We discover that at slower flow rate, the salt forms in patches due to a process where the salt already formed pulls more water toward it, leading to blockages. At higher flow rates, this doesn’t happen, and the salt is distributed more uniformly, causing less blockage. We identify a specific flow rate at which the transition between these two types of salt formation occurs. Understanding this can help us better manage CO2 injection strategies and make it more effective by minimizing the risk of blockages. This work is important for enhancing how we store CO2 underground, an important strategy in reducing its levels in the atmosphere and fighting global warming. Key Points We show that precipitation pattern shifts from ex situ to homogeneous form and ex situ precipitation is due to capillary‐driven backflow We verify that capillary‐driven backflow occurs when previously precipitated salt, as a hydrophilic porous medium, draws brine flow back We quantify that capillary‐driven backflow causes voids to be completely clogged, leading to a more substantial permeability reductions

It is debated whether there was strong climate seasonality during the Eocene, which provides a close geological analogy for near‐future scenarios of greenhouse gas emissions. Lithological data suggest the existence of a broad arid zone centered around 30°N paleo‐latitude, while a humid climate was supported by palaeobotanic assemblages in East Asia. Here, we report the occurrence of massive primary lacustrine dolomite and magnesite in the central East Asia during the middle Eocene. We provide a novel perspective from magnesium isotopes to link the formation of Mg‐carbonates to seasonal dry‐wet cycles. Rapid magnesium input during the rainy season and intense evaporation in the dry season likely caused the formation of magnesium carbonates in an enclosed lake. These findings provide insights into hydroclimatic seasonality during the Eocene, contributing to our understanding of the hydrological cycle response to a greenhouse climate. Plain Language Summary The Eocene epoch serves as a valuable analog for future climates. While geochemical reconstructions and model simulations have illuminated lower thermal latitudinal gradients and seasonal variations, our understanding of Eocene precipitation patterns lags, encompassing wet‐dry conditions and seasonal dynamics. To enhance our understanding of Eocene precipitation patterns, we investigated a 158‐m‐thick primary dolomite and magnesite deposition in the middle Eocene lacustrine succession of the Lushi Basin, central China. From a novel perspective, we provide evidence from magnesium isotopes to link the formation of Mg‐carbonates to climate seasonality. Clumped isotopes (∆47) and Mg isotopes provide evidence supporting the formation under specific hydroclimatic conditions. A surge in magnesium input during the rainy season, succeeded by intense evaporation in the dry season, likely led to the development of extensive Mg carbonate layers in an enclosed lake. The prevalence of seasonal variations in precipitation in the central East Asia during the middle Eocene is further substantiated by a compilation of the occurrence of Eocene lacustrine Mg‐carbonates in this region. Our findings suggest that while Eocene temperature seasonal variability was weak, significant precipitation seasonality could have coexisted. Key Points Magnesium isotopes of Eocene lacustrine dolomites and magnesites provide insight into the presence of seasonal precipitation variation Mg‐carbonate formation was linked to hydroclimatic seasonality characterized by alternation between heavy rainfall and strong evaporation Weak temperature seasonal variation and significant precipitation seasonality could have coexisted in central East Asia during the Eocene

The U.S. Midwest is an area that has been plagued by heavy and persistent precipitation leading to frequent flood events. The improved understanding of the types of weather conditions and settings associated with heavy precipitation can provide basic information to improve our preparation for and response to these events. Here we identify five weather types from daily 500-hPa geopotential height using the k-means cluster analysis. Consistent with their distinct large-scale atmospheric patterns, these weather types exert different effects on precipitation in the Midwest. Weather type 1 (WT1) features a zonally-aligned wave train propagating from the North Pacific to North America. Overall, WT2 is characterized by a wave train pattern with high (low) pressure in the western (eastern) United States. WT3 features a unique pattern with a high pressure system over the continental United States except for the northwestern United States, similar to the La-Niña forced responses. WT4 is characterized by a wave train moving from the Pacific Northwest to the North Atlantic with a strong ridge over the western United States, while WT5 features a positive geopotential height anomaly originating from the Arctic, probably influenced by the Arctic Amplification. Because of the strong moisture transport, strengthened low-level jet stream and wavy upper-level polar jet stream located in the western United States, among the five weather types WT1 exerts the strongest impacts on precipitation, accounting for up to 40% of the total precipitation over the Midwest, followed by WT5. Moreover, we detect a significant upward trend in the number of WT1 and WT5 events for 1948–2017 and their persistency, suggesting a rising risk of heavy and long-lasting precipitation across the Midwest. Overall, the weather types during summer and winter are consistent with those obtained from the analysis of the entire year, although the weather types during winter have a larger magnitude in the geopotential height anomaly. WT1 accounts for the largest contribution to total precipitation in the Midwest during summer and winter.

Westerlies Asia, which includes arid Central Asia (ACA) and arid West Asia (AWA), is characterized by water vapor transport primarily controlled by the westerlies. Recent studies have identified a dipole pattern in hydroclimate variability between ACA and AWA during both the Holocene and modern period. However, it remains unclear whether such a dipole pattern persisted over the past millennium. Our findings demonstrate that the PMIP4 multi-model simulations reveal a dipole precipitation pattern between arid Central Asia and arid West Asia over the past millennium. During the Little Ice Age (LIA), annual precipitation increased in ACA but decreased in AWA, while the opposite pattern occurred during the Medieval Climate Anomaly (MCA). This dipole precipitation pattern is attributed to seasonal differences: increased spring precipitation in ACA together with decreased summer precipitation in AWA shaped the annual precipitation anomaly during the Little Ice Age, with a reversed regime during the Medieval Climate Anomaly. Mechanistically, a negative North Atlantic Oscillation (NAO) phase during LIA springs shifted the westerly moisture transport southward, enhancing moisture supply to ACA and increasing the precipitation there. In contrast, during LIA summers, a positive NAO phase displaced the westerly northward, reducing moisture advection to AWA, while a strengthened Azores High promoted moisture outflow and descending motion, suppressing precipitation. These findings offer a paleo-hydroclimatic basis for anticipating alternating dry-wet regimes between subregions, which can inform adaptive water allocation strategies, drought and flood preparedness, and long-term infrastructure planning across Westerlies Asia in a warming world.

This study explores the impact of winter sea surface temperature (SST) anomalies in the Southern Indian Ocean on summer precipitation patterns in China, utilizing data from reanalysis sources and Coupled Model Intercomparison Project Phase 6 (CMTP6) models. The results reveal that the Southern Indian Ocean Dipole (SIOD), characterized by contrasting SST anomalies in the northeast and southwest regions, acts as a predictor for Chinese summer precipitation patterns, namely floods in the south and drought in the north. In a positive SIOD event, the southwestern Indian Ocean exhibits warmer SSTs, while the northeastern region remains cooler. A negative SIOD event shows the opposite pattern. During the positive phase of the SIOD, the winter SST distribution strengthens the 850-hPa cross-equatorial airflow, generating a robust low-level westerly jet that enhances water vapor transport to the Bay of Bengal (BoB). These air-sea interactions maintain lower SSTs in the northeastern region, which significantly increase the land-sea temperature contrast in the Northern Hemisphere during spring and summer. This strengthened thermal gradient intensifies the southwest monsoon, establishing a strong convergence zone near the South China Sea and amplifying monsoon-driven precipitation in South China. Additionally, CMTP6 models, such as NorESM2-LM and NorCPMl, which accurately simulate the SIOD pattern, effectively capture the seasonal response of cross-equatorial airflow driven by SST anomalies of Southern Indian Ocean. The result highlights the essential role of cross-equatorial airflow generated by the SIOD in forecasting cross-seasonal precipitation patterns.

The prediction of summer precipitation patterns (PPs) over eastern China is an important and topical issue in China. Predictors that are selected based on historical information may not be suitable for the future due to non-stationary relationships between summer precipitations and corresponding predictors, and might induce the instability of prediction models, especially in cases with few predictors. This study aims to investigate how to learn as much information as possible from various and numerous predictors reflecting different climate conditions. An objective prediction method based on the multinomial logistic regression (MLR) model is proposed to facilitate the study. The predictors are objectively selected from a machine learning perspective. The effectiveness of the objective prediction model is assessed by considering the influence of collinearity and number of predictors. The prediction accuracy is found to be comparable to traditionally estimated predictability, ranging between 0.6 and 0.7. The objective prediction model is capable of learning the intrinsic structure of the predictors, and is significantly superior to the prediction model with randomly-selected predictors and the single best predictor. A robust prediction can be generally obtained by learning information from plenty of predictors, although the most effective model may be constructed with fewer predictors through proper methods of predictor selection. In addition, the effectiveness of objective prediction is found to generally improve as observation increases, highlighting its potential for improvement during application as time passes.

This paper examines the projected changes in rainfall in Southeast Asia (SEA) in the twenty-first century based on the multi-model simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment–Southeast Asia (SEACLID/CORDEX–SEA). A total of 11 General Circulation Models (GCMs) have been downscaled using 7 Regional Climate Models (RCMs) to a resolution of 25 km × 25 km over the SEA domain (89.5° E–146.5° E, 14.8° S–27.0° N) for two different representative concentration pathways (RCP) scenarios, RCP4.5 and RCP8.5. The 1976–2005 period is considered as the historical period for evaluating the changes in seasonal precipitation of December–January–February (DJF) and June–July–August (JJA) over future periods of the early (2011–2040), mid (2041–2070) and late twenty-first century (2071–2099). The ensemble mean shows a good reproduction of the SEA climatological mean spatial precipitation pattern with systematic wet biases, which originated largely from simulations using the RegCM4 model. Increases in mean rainfall (10–20%) are projected throughout the twenty-first century over Indochina and eastern Philippines during DJF while a drying tendency prevails over the Maritime Continent. For JJA, projections of both RCPs indicate reductions in mean rainfall (10–30%) over the Maritime Continent, particularly over the Indonesian region by mid and late twenty-first century. However, examination of individual member responses shows prominent inter-model variations, reflecting uncertainty in the projections.

To characterize and explore the short-term climatic patterns over the last decade (Jan. 2009 to Dec. 2018), the present research has been carried out, involving time series analysis of precipitation pattern in three cities of Tamil Nadu, namely, Thanjavur, Nagapattinam, and Chennai, referring to deltaic, coastal and highly urbanized cities of Tamil Nadu, respectively. The study involves time series empirical analysis, decomposition, exponential smoothing, and various stochastic modeling. Herein, the location-specific suitable models are obtained and specific predictions are being carried out, as well.

Changes in precipitation pattern have been associated with global warming and is of more importance particularly for monsoon dependent regions such as India, which receives maximum rainfall from south-west monsoon. Indian land mass is surrounded by ocean from three sides named Arabian Sea (AS), Bay of Bengal (BOB) and rest of the Indian Ocean (IO) which makes its climate more sensitive. To understand the effect of global warming, long term (1960–2017) annually averaged in-situ sea surface temperature (SST) is studied which shows an increasing trend (~ 0.11 °C/decade; P < 0.05) with higher variations (r2AS = 0.46; r2BOB = 0.43) over AS and BOB whereas comparatively lower in magnitude (~ 0.14 °C/decade; P < 0.05) with less variation (r2IO = 0.74) over IO. Rise in SST could vary evaporation rate, moisture content, cloud temperature and initial conditions required for cloud formation. To understand this heterogeneity in conjunction with seasonal variation, present study correlates cloud microphysical properties such as cloud effective radius (CER) with SST and aerosol optical depth (AOD) at high-resolution (1° × 1°) using linear interpolation method during 2001–2016. Features of north-east monsoon captures with high (~ 0.006–0.012 kg/kg) specific humidity at 850 hPa, positive correlation (~ 0.1–0.8) of SST-CER and negative correlation (~ − 0.1 to ~ − 0.8) of AOD–CER over BOB which may imply formation of bigger droplets due to presence of more moisture and less AOD. Though these patches show prominent results, it also shows scattered interpolation signifying role of other parameters on CER. Findings would be promising with more parameters, which can be used as an input data in climate models to understand regional climate variability.

Precipitation data with high resolution and high accuracy are significantly important in numerous hydrological applications. To enhance the spatial resolution and accuracy of satellite-based precipitation products, an easy-to-use downscaling-calibration method based on a spatial random forest (SRF-DC) is proposed in this study, where the spatial autocorrelation of precipitation measurements between neighboring locations is considered. SRF-DC consists of two main stages. First, the satellite-based precipitation is downscaled by the SRF with the incorporation of high-resolution variables including latitude, longitude, normalized difference vegetation index (NDVI), digital elevation model (DEM), terrain slope, aspect, relief and land surface temperatures. Then, the downscaled precipitation is calibrated by the SRF with rain gauge observations and the aforementioned high-resolution variables. The monthly Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) over Sichuan Province, China, from 2015 to 2019 was processed using SRF-DC, and its results were compared with those of classical methods including geographically weighted regression (GWR), artificial neural network (ANN), random forest (RF), kriging interpolation only on gauge measurements, bilinear interpolation-based downscaling and then SRF-based calibration (Bi-SRF), and SRF-based downscaling and then geographical difference analysis (GDA)-based calibration (SRF-GDA). Comparative analyses with respect to root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC) demonstrate that (1) SRF-DC outperforms the classical methods as well as the original IMERG; (2) the monthly based SRF estimation is slightly more accurate than the annually based SRF fraction disaggregation method; (3) SRF-based downscaling and calibration perform better than bilinear downscaling (Bi-SRF) and GDA-based calibration (SRF-GDA); (4) kriging is more accurate than GWR and ANN, whereas its precipitation map loses detailed spatial precipitation patterns; and (5) based on the variable-importance rank of the RF, the precipitation interpolated by kriging on the rain gauge measurements is the most important variable, indicating the significance of incorporating spatial autocorrelation for precipitation estimation.