Regional Precipitation Patterns Research Articles

Impact of El Niño, Indian Ocean dipole, and Madden–Julian oscillation on land surface temperature in Kuching City Sarawak, during the periods of 1997/1998 and 2015/2016: a pilot study

ABSTRACT The severe El Niño events of 1997/1998 and 2015/2016 caused significant disruptions in Southeast Asia, particularly in Borneo, resulting in hazardous haze and acute water shortages. This study examines the influence of El Niño, the Indian Ocean Dipole (IOD), and the Madden–Julian oscillation (MJO) on regional climate, using time-series data from February 1993 to December 2020. Data from El Niño, IOD, and MJO indices were integrated with Landsat 5 and 8 land surface temperature records, allowing for a detailed analysis of their combined effects on regional temperature and precipitation patterns. Time-series trend decomposition and the generalized linear mixed model approach identified the Oceanic Niño Index (ONI) as a significant driver of temperature increases and dry spell occurrences during the peak El Niño years. On the other hand, ONI correlated strongly with mean monthly temperatures, underscoring its dominant influence. In addition, the IOD was found to significantly affect regional temperatures with a regression coefficient of 0.38867 (p = 0.0455), indicating its significant but less pronounced impact compared with ONI. These findings clarify the dynamics between key climate indices and their impact on regional climate extremes, offering critical insights for improving climate resilience and adaptation in tropical regions.

Triple coupling random forest approach for bias correction of ensemble precipitation data derived from Earth system models for Divandareh‐Bijar Basin (Western Iran)

AbstractClimate change is expected to change the frequency, duration, intensity, and pattern of precipitation, underscoring the need for accurate predictive tools. Earth system models (ESMs) serve as invaluable instruments in this endeavour, simulating climate variable variations across temporal and spatial dimensions. This study aims to develop a methodology for generating precise daily precipitation maps by rectifying biases inherent in ESM outputs. The proposed methodology includes downscaling ESM outputs to simulate historical daily grid‐based precipitation, thereby enhancing the fidelity of daily precipitation representation. For this purpose, 14 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were employed. Random forest (RF) machine learning method was used to correct biases in these ESM outputs. This study's novelty lies in integrating results of a grid‐based RF classification model, employed to distinguish between rainy and non‐rainy days, with those obtained by two RF regression models, to estimate precipitation amounts for grid cells receiving extreme and non‐extreme precipitation, to generate an ensemble of ESM outputs. The resulting method, termed the triple coupling method (EN‐RF), was validated using precipitation data from the Divandareh‐Bijar Basin in western Iran to simulate historical climate conditions. Furthermore, the accuracy of the developed triple coupling approach was compared with that of a commonly used single machine learning‐based downscaling model (EN‐Single‐RF). Comparative analysis against a commonly used single machine learning‐based downscaling model (EN‐Single‐RF) revealed the superior performance of the EN‐RF approach in replicating the intensity and distribution of daily precipitation. Furthermore, within the triple coupling framework, support vector machine (SVM) was utilized to simulate daily historical precipitation (EN‐SVM), while the quantile mapping (QM) method served as a benchmark. Comparison of the results showed superiority of the EN‐RF to other methods (EN‐Single‐RF, EN‐SVM, and QM) in terms of various accuracy metrics (Kling‐Gupta Efficiency = 0.95, mean square error = 0.22). The findings indicated the capability of the proposed triple coupling framework using the RF algorithm to simulate the spatio‐temporal distribution of precipitation using the ESM precipitation outputs. The developed framework can be used to produce reliable projections to gain deeper insights into the potential impacts of climate change on regional precipitation patterns.

Hydrological impacts of vegetation cover change in China through terrestrial moisture recycling

Terrestrial moisture recycling (TMR), characterized by a continuous process comprising green water flow (i.e., terrestrial evaporation), atmospheric transport, and terrestrial precipitation, functions as a nexus connecting hydrosphere, atmosphere, biosphere, and anthroposphere. During this process, land cover changes that impact green water flow can modify regional and remote precipitation patterns, potentially yielding far-reaching effects on water resources and human livelihoods. However, the comprehensive patterns of moisture recycling and transfer across eco-geographical regions in China, and their connection with various land cover types and vegetation transitions, remain insufficiently evaluated. This study employed an atmospheric moisture tracking model to quantify China's TMR pattern and evaluate the hydrological impacts of vegetation cover changes in China's ecosystems through TMR. The results demonstrate a significant moisture recycling ratio (52.4 %) and a considerable recycled volume (1.9 trillion m3/a) over China, characterized by pronounced moisture transfer from south to north and southwest to northeast. Among various land cover types, grasslands, croplands, and forests play pivotal supportive roles in China's TMR, contributing 738.8, 470.0, and 450.0 billion m3/a of precipitation in China, respectively. Moreover, the potential transition of vegetation between forest and cropland exerts the most significant and extensive impact on China's hydrological cycle. The conversion from forest to cropland leads to a total decrease of 44.7 billion m3/a in precipitation, whereas reforestation from cropland corresponds to a precipitation increase of 74.9 billion m3/a. This study provides a quantitative approach to comprehending the TMR pattern and its relationship with ecosystems, substantiating the significance of a comprehensive water management framework that considers the contribution of atmospheric moisture recycling and the impact of vegetation cover change.

Study on spatiotemporal dynamic characteristics of precipitation and causes of waterlogging based on a data-driven framework

The discernible alterations in regional precipitation patterns, influenced by the intersecting factors of urbanization and climate change, exert a substantial impact on urban flood disasters. Based on multi-source precipitation data, a data-driven model fusion framework was constructed to analyze the spatial and temporal dynamic distribution characteristics of precipitation in Beijing. Wavelet analysis method was used to reveal the periodic variation characteristics and multi-scale effects of precipitation, and the machine learning method was used to characterize the spatiotemporal dynamic change pattern of precipitation. Finally, geographical detector was used to explore the causes of waterlogging in Beijing. The research outcomes reveal a disparate distribution of precipitation across the year, with 78 % of the total precipitation occurring during the flood season. The principal periodic cycles observed in annual cumulative precipitation (ACP) were identified at 21, 13, and 9-year intervals. Spatially, while a decreasing trend in precipitation was observed in most areas of Beijing, 63.4 % of the region exhibited an escalating concentration trend, thereby heightening the risk of urban waterlogging. Machine learning model clustering elucidated three predominant spatial dynamic distribution patterns of precipitation in Beijing. The utilization of web crawler technology to acquire water accumulation data addressed challenges in obtaining urban waterlogging data, and validation through Landsat8 images enhanced data reliability and authenticity. Factor detection shows that road network density, topography, and precipitation were the main factors affecting urban waterlogging. These findings hold significant implications for informing flood control strategies and emergency management protocols in urban areas across China.

Characteristics of annual and seasonal precipitation in North Macedonia: change analysis and correlation with the North Atlantic Oscillation (1951–2010)

Studying the spatiotemporal precipitation characteristics in North Macedonia (1951–2010) is important as no spatially concurrent precipitation changes across the Balkan Peninsula have been identified. North Macedonia lies at the intersection between Mediterranean and continental climate zones and an improved understanding might help to better understand the regional precipitation patterns. The analysis shows a spatially consistent, high inter-annual variability, which makes trend detection difficult. Statistically significant decreasing trends were only found in seasonal precipitation at three stations. Changes in all other precipitation series were non-significant. Trends in winter, spring, and at annual scale are generally decreasing, whereas in summer are increasing. To better understand possible mechanisms behind the observed variability and change, correlations with the North Atlantic Ocsillation (NAO) were assessed. Significant and regionally concurrent correlations were detected. A strong correlation of the previous winter NAO-index with spring precipitation was found, which is valuable information for anticipatory water resources management in the region.

Detection and classification of rainfall in South America using satellite images and machine learning techniques

The study of precipitation is one of the most intriguing areas in atmospheric sciences, with significant implications for our daily lives and climate change projections. This paper explores the estimation of rainfall trends in South American regions using convolutional neural networks (CNNs). The study focuses on the application of Cloud-Net, a CNN-based model with a format similar to an autoencoder, to obtain qualitative estimates of precipitation patterns. The employed loss functions, Categorical Cross Entropy and Categorical Focal Loss, address the challenges of classifying minority categories in unbalanced data. Regional analysis was conducted, identifying days with high rainfall intensity and the predominant intensities in 25 regions. The CNN model’s performance was compared with the XGBoost algorithm, showing excellent results for extreme rainfall categories and challenging intermediate categories. Furthermore, a comparison was made with Quantitative Precipitation Estimation (QPE) data and ground measurements from rain gauges. While the CNN model provided a valuable qualitative estimate of precipitation trends, achieving precise quantitative estimation would require an extensive data set of in-situ measurements. Overall, this research demonstrates the potential of CNNs for estimating rainfall trends and understanding precipitation patterns in South American regions. The findings offer valuable insights for further applications in meteorology and environmental studies.

Precipitation variability related to atmospheric circulation patterns over the Tibetan Plateau

AbstractPrecipitation is affected by intricate atmospheric dynamic and thermodynamic processes. Horizontal winds are frequently used to represent the dynamic component as winds play a critical role in transporting moisture. Previous studies on precipitation over the Tibetan Plateau (TP) focused on the influence of summer monsoons and westerlies in isolation. However, the collective seasonal dynamics and their combined effects on the precipitation distribution remain less explored. This study aims to determine the seasonal evolutions of the wind patterns and the associated regional precipitation patterns over the TP using a neural network approach and focuses on their interannual variability and long‐term trends. A self‐organizing map (SOM) was used to classify the wind patterns based on 500 hPa winds and related precipitation from the ERA5 reanalysis. The classified wind patterns show seasonal shifts between the Asian summer monsoon circulations and the westerlies along with the westerly jets migrating between the north and south of the TP from summer to winter. The locations of abundant precipitation during the winter and transition seasons are mainly associated with variations in the intensity and locations of the strong westerlies. There is a significant positive trend in the occurrences of the summer‐type wind pattern, which has likely led to a wetter TP, and an earlier‐ended winter and advanced spring wind patterns. The interannual variability of westerlies is highly related to the variability of precipitation in the western TP during its wet season (January–April). In the eastern TP, the interannual variability of the precipitation is linked to the wind patterns associated with the westerly jets to the south of the TP, while precipitation variability in the central TP is controlled by thermodynamic components. This study reveals the spatial precipitation distributions according to the different wind patterns and identifies the contributions from atmospheric components to the regional precipitation over the TP.

Water-use strategies and functional traits explain divergent linkages in physiological responses to simulated precipitation change

As a part of global climate change, precipitation patterns in arid regions will change significantly, and the different responses of desert plants to these changes will lead to alterations in community composition, thereby impacting ecosystem stability. Thus, understanding the mechanism underlying the associations among physiological response variables considering changing precipitation is crucial. Here, water-use strategies, functional traits, and physiological processes (e.g., photosynthesis (An), transpiration (Tr), leaf water potential (Ψl), stomatal conductance (gs), and soil respiration (Rs)) were measured in a precipitation experiment with two coexisting desert riparian species to determine how water-use strategies and functional traits operate together in generating physiological response mechanisms. The results showed that the two species exhibited divergent response pathways of physiological processes following rainfall events, although both were identified as isohydric plants with stringent stomatal regulation. For the shallow-rooted species N. sphaerocarpa, gs was sensitive to changes in both surface soil moisture (Swc) and Ψl, and Swc was the primary factor influencing Rs. These results were supported by the preference for shallow water and predominance of functional traits associated with drought avoidance. For the deep-rooted species R. soongorica, variations in gs were decoupled from Swc and directly influenced by enhanced Ψl, An was the main factor affecting Rs, while Ψl negatively affected Rs. These correlations could be attributed to the preference for deep water and functional traits associated with drought tolerance. These findings suggest that R. soongorica had a stronger tolerance to environmental water deficits and may expand extensively under drier climatic conditions in the future.

Spatial and Temporal Variation Characteristics of Stable Isotopes in Precipitation and Their Relationships with Meteorological Factors in the Shiyang River Basin in China

Stable isotopes of precipitation play an important role in understanding hydrological and climatic processes of arid inland river basins. In order to better understand the difference of regional water cycle and precipitation patterns, precipitation samples were collected in Shiyang River Basin from June 2018 to May 2020, and spatial and temporal variation characteristics of stable isotopes in precipitation and their relationships with meteorological factors were discussed. The results showed that stable isotopes in precipitation showed seasonal variation characteristics, that δ2H and δ18O values were higher in summer and autumn and lower in winter and spring, and d-excess values were higher in spring and autumn and lower in winter and summer. Slopes and intercepts of the local meteoric water lines gradually increased with elevation increasing. δ18O significantly showed a positive correlation with temperature but a negative correlation with precipitation in summer. Contrary to below 15 hPa, δ18O significantly showed a negative correlation with water vapor pressure above 15 hPa. Due to the influence of meteorological factors, there are significant differences in stable isotopes of precipitation in arid inland river basins, which were more influenced by local moisture recycling in upstream area but by below-cloud secondary evaporation in mid-downstream area.

Application of Wavelet Transform for Bias Correction and Predictor Screening of Climate Data

Climate model (CM) statistical downscaling requires quality and quantity modifications of the CM’s outputs to increase further modeling accuracy. In this respect, multi-resolution wavelet transform (WT) was employed to determine the hidden resolutions of climate signals and eliminate bias in a CM. The results revealed that the newly developed discrete wavelet transform (DWT)-based bias correction method can outperform the quantile mapping (QM) method. In this study, wavelet coherence analysis was utilized to assess the high common powers and the multi-scale correlation between the predictors and predictand as a function of time and frequency. Thereafter, to rate the most contributing predictors based on potential periodicity, the average variance was calculated, which is named the Scaled Average (SA) measure. Consequently, WT along with Artificial Neural Network (ANN) were applied for bias correction and identifying the dominant predictors for statistical downscaling. The CAN-ESM5 data of Canadian climate models and INM-CM5 data of Russian climate models over two climatic areas of Iran with semi-arid (Tabriz) and humid (Rasht) weather were applied. The projection of future precipitation revealed that Tabriz will experience a 3.4–6.1% decrease in precipitation, while Rasht’s precipitation will decrease by 1.5–2.5%. These findings underscore the importance of refining CM data and employing advanced techniques to assess the potential impacts of climate change on regional precipitation patterns.

Divergent patterns and drivers of leaf functional traits of Robinia pseudoacacia and Pinus tabulaeformis plantations along a precipitation gradient in the Loess plateau, China

Changes in precipitation patterns in arid and semi-arid regions can reshape plant functional traits and significantly affect ecosystem functions. However, the synchronous responses of leaf economical, anatomical, photosynthetic, and biochemical traits to precipitation changes and their driving factors have rarely been investigated, which hinders our understanding of plants’ ecological adaptation strategies to drought tolerance in arid areas. Therefore, the leaf traits of two typical plantations (Robinia pseudoacacia, RP and Pinus tabulaeformis, PT) along the precipitation gradient in the Loess Plateau, including economical, anatomical, photosynthetic, and biochemical traits, were investigated in this study. The results show that the leaf photosynthetic traits of RP and PT increase along the precipitation gradient, whereas leaf biochemical traits decrease. The anatomical traits of PT decrease with increasing precipitation, whereas no significant variation was observed for RP. Random Forest analysis show that LNC, LDMC, Chl, and PRO are leaf traits that significantly vary with the precipitation gradient in both plantations. Correlation analysis reveals that the traits coordination of RP is better than that of PT. The LMG model was used to determine driving factors. The results suggest that MAP explains the variation of PT leaf traits better (30.38%–36.78%), whereas SCH and SPH contribute more to the variation of RP leaf traits (20.88%–41.76%). In addition, the piecewise Structural Equation Model shows that the climate and soil physical and chemical properties directly affect the selected leaf functional traits of RP, whereas only the soil chemical properties directly affect the selected leaf functional traits of PT. The results of this study contribute to the understanding of the ecological adaptation of plants to environmental gradients and highlight that correlations among leaf traits should be considered when predicting plant adaptation strategies under future global change scenarios.

Analysis of precipitation-related climatic conditions in European plain regions

Significant increases of extreme precipitation and drought events have been detected in Europe in the past few decades. In this study, 16 selected climate indices (11 recommended by the Expert Team of Climate Change and Indices) were used for the analysis of the temporal changes and spatial distribution of precipitation patterns in European plain regions during 1950–2022.Using an objective, two-step method, 14 plain regions were selected across Europe representing different climatic conditions. Precipitation time series were used from the E-OBS v.27 dataset on a 0.1° regular grid. The dataset is based on station measurements, which are available from 1950 onwards with daily temporal resolution.Our results suggest a general intensification of precipitation events over the continent, as 11 regions show a significant increase in the indices related to the intensity of extreme precipitation, and the frequency of these events increased in 10 regions. Extreme indices related to dry periods changed significantly in only a few cases. An increase may only be observed in the southern part of the continent, while a significant decrease can be seen in three regions in northern Europe.

Population dynamics of the threatened Oregon spotted frog before and after drought mitigation

AbstractAmphibians are among the most sensitive taxa to climate change, and species inhabiting arid and semiarid landscapes at the extremes of their range are especially vulnerable to drought. The Jack Creek, Oregon, USA, population of Oregon spotted frogs (Rana pretiosa) faces unique challenges because it occupies the highest elevation site in the species' extant range and one that has been transformed by loss of American beavers (Castor canadensis), which historically maintained open water. We evaluated the effects of drought mitigation (addition of excavated ponds) on relationships between local and regional water availability, inactive legacy beaver dams, and Oregon spotted frog population dynamics in the Jack Creek system. We conducted egg mass surveys and capture‐mark‐recapture sampling at a treatment reach with excavated ponds and 3 reference reaches over 13 years; surveys spanned a period before and after pond excavation at the treatment and 1 primary comparison reference reach. We analyzed data using a combination of robust design capture‐mark‐recapture estimators and generalized linear mixed models to characterize population dynamics. Adult Oregon spotted frog survival was approximately 19.5% higher at the treatment reach than the primary reference reach during the study period. Annual survival was most strongly associated with late summer vegetation greenness, a proxy for water availability, and males had higher survival than females. Among the 4 study reaches, the treatment reach consistently had higher late summer vegetation greenness, and the hydrology functioned more independently of regional precipitation patterns relative to the reference reaches; however, these dynamics were not linked to pond excavation. Breeding was concentrated in 2 legacy beaver ponds that were deepened by excavation during the study compared to an unexcavated beaver pond, 2 excavated ponds without legacy beaver dams, and 9 reference ponds. These results point to the benefit of enhancing existing beaver structures and indicate that management actions aimed at maintaining surface water for breeding in spring and saturated soils and ponded water for adults in late summer would benefit this unique population of Oregon spotted frogs in the face of drought.

Hydroclimatic trend analysis and projection in Africa tropical urban regions: Cases of Lusaka, Zambia and Kigali, Rwanda

The global climate has significantly changed primarily due to human-induced activities. The incidences of droughts and extreme floods followed by destruction have been appearing across the globe along with extensive effects on economic and public health sectors, including the human lives losses whereby, Africa is among the greatly affected regions. Several studies aver that weather-related extremes are likely to increase due to the rising impacts. This article aims to emerge the effects of a warning climate on temperature and precipitation patterns in African tropical regions for pre-emptive adaptation practices. It was conducted using nonparametric Mann-Kendall's test and Sen's slope estimator coupled with the downscaled global climate models (NEX-GDDP-CMIP6 GCMs). The study shows that the annual precipitation in Lusaka keeps decreasing though the rainfall season showed an increasing possibility. The projection under ssp245 showed a slight rainfall decrement but increased by the mid-century under ssp585. Besides, the rainfall in Kigali, presents an excess increment resulting in increased extreme events. The projection showed up to 40% annual rainfall increment by the end-century under ssp585. However, both the regions present a slight trend increment in maximum and minimum temperature. Henceforth, the results of this study may prove useful in climate change mitigation and adaptation practices on rainfed agriculture, hydroelectricity, water supply systems, and resilient hydraulic infrastructure provision in the region.

Regional polar warming linked to poleward moisture transport variability

Polar warming, ice melt and strong precipitation events are strongly affected by episodic poleward advection of warm and moist air (Woods and Caballero 2016 J. Clim. 29 4473–85; Wille et al 2019 Nat. Geosci. 12 911–6), which, in turn, is linked to variability in poleward moisture transport (PMT) (Nash et al 2018 J. Geophys. Res. Atmos. 123 6804–21). However, processes governing regional impacts of PMT as well as long-term trends remain largely unknown. Here we use an ensemble of state-of-the-art global climate models in standardized scenario simulations (1850–2100) to show that both the Arctic and the Antarctic exhibit distinct geographical patterns of PMT-related warming. Specifically, years with high PMT experience considerable warming over subarctic Eurasia and West-Antarctica (Raphael et al 2016 Bull. Am. Meteorol. Soc. 97 111–21), whereas precipitation is distributed more evenly over the polar regions. The warming patterns indicate preferred routes of atmospheric rivers (Woods and Caballero 2016 J. Clim. 29 4473–85), which may regionally enhance atmospheric moisture content, cloud cover, and downward longwave radiative heating in years with comparatively high PMT (Scott et al 2019 J. Clim. 32 665–84). Trend-analyses reveal that the link between PMT-variability and regional precipitation patterns will weaken in both polar regions. Even though uncertainties associated with intermodel differences are considerable, the advection of warm and moist air associated with PMT-variability is likely to increasingly cause mild conditions in both polar regions, which in the Arctic will reinforce sea-ice melt. Similarly, the results suggest that warm years in West-Antarctica disproportionally contribute to ice sheet melt (Trusel et al 2015 Nat. Geosci. 8 927–32), enhancing the risk of ice-sheet instabilities causing accelerated and sudden sea-level rise.

Regional pollen-based Holocene temperature and precipitation patterns depart from the Northern Hemisphere mean trends

Abstract. A mismatch between model- and proxy-based Holocene climate change, known as the “Holocene conundrum”, may partially originate from the poor spatial coverage of climate reconstructions in, for example, Asia, limiting the number of grid cells for model–data comparisons. Here we investigate hemispheric, latitudinal, and regional mean time series and time-slice anomaly maps of pollen-based reconstructions of mean annual temperature, mean July temperature, and annual precipitation from 1908 records in the Northern Hemisphere extratropics. Temperature trends show strong latitudinal patterns and differ between (sub-)continents. While the circum-Atlantic regions in Europe and eastern North America show a pronounced Middle Holocene temperature maximum, western North America shows only weak changes, and Asia mostly shows a continuous Holocene temperature increase. Likewise, precipitation trends show certain regional peculiarities such as the pronounced Middle Holocene precipitation maximum between 40 and 50∘ N in Asia and Holocene increasing trends in Europe and western North America, which can all be linked with Holocene changes in the regional circulation pattern responding to temperature change. Given a background of strong regional heterogeneity, we conclude that the calculation of global or hemispheric means, which initiated the Holocene conundrum debate, should focus more on understanding the spatiotemporal patterns and their regional drivers.

Contribution of external forcing to summer precipitation trends over the Qinghai–Tibet Plateau and Southwest China

In the past 60 years, the global climate has undergone both rapid warming and a brief warming hiatus, while regional precipitation patterns in China have also experienced diverse and complex changes. Specifically, the factors behind the opposing trends in summer precipitation between two adjacent regions—Southwest China and the Qinghai–Tibet Plateau—are particularly intricate. After evaluating the historical runs from CMIP6 models, the authors assessed the contributions of various external forcing factors that simulated the summer precipitation trends observed over the Qinghai–Tibet Plateau and Southwest China from 1961 to 2014. The findings show that, compared to other forcing factors, greenhouse gases had a significant impact on the increase in summer precipitation over the Qinghai–Tibet Plateau, while aerosols played an important role in the decrease in summer precipitation in Southwest China.摘要在过去的60年中, 全球气候经历了快速变暖和短暂的变暖停滞, 而中国的区域降水也经历了多样而复杂的变化. 本文分析了1961年至2014年外强迫因子对青藏高原和中国西南地区夏季降水趋势的影响. 观测数据显示, 青藏高原的夏季降水呈增加趋势, 而中国西南地区的夏季降水呈减少趋势, 这两个相邻地区的夏季降水变化趋势相反. 利用CMIP6数据, 本文研究了不同外强迫因子对两个区域夏季降水趋势的影响. 结果表明, 温室气体对青藏高原夏季降水的增加具有显著影响, 而气溶胶在中国西南地区夏季降水减少中起主要作用.

Multi‐Year Controls on Groundwater Storage in Seasonally Snow‐Covered Headwater Catchments

AbstractSeasonally snow‐covered catchments in the western United States supply water to growing populations as both annual snowmelt‐driven streamflow and multi‐year groundwater recharge. Although interannual variability in streamflow is driven largely by precipitation, runoff efficiency (the ratio of streamflow to precipitation) in individual catchments varies by 50% or more. Recent work suggests that interannual variability in groundwater storage, inferred from winter baseflow, is a primary control on runoff efficiency, highlighting a need to quantify both the time scales on which groundwater storage varies and the hydro‐climatic drivers of storage. Using over a century of daily stream discharge data from 10 seasonally snow‐covered catchments in northern Utah, we find that temporal variability in winter baseflow, an index of groundwater storage, measured from mean daily January discharge, exhibits a 2–5‐ and 12–15‐year periodicity, driven by regional precipitation patterns and snowmelt dynamics. Specifically, multiple linear regression (MLR) modeling using antecedent hydro‐climatic variables demonstrates that winter baseflow (groundwater storage) was positively related to 3–4 years of antecedent annual precipitation, negatively related to the previous year's mean annual temperature, and positively related to 1–4 antecedent years of snowmelt rate and duration. Because antecedent baseflow (groundwater storage) is strongly related to runoff efficiency, these results suggest that more frequent and longer droughts in a future climate will reduce surface water supplies faster than otherwise expected. More broadly, these results highlight the importance of including the influence of antecedent climate on groundwater storage when modeling and managing water supplies from seasonally snow‐covered catchments.

Pitfalls of Climate Network Construction—A Statistical Perspective

Abstract Network-based analyses of dynamical systems have become increasingly popular in climate science. Here, we address network construction from a statistical perspective and highlight the often-ignored fact that the calculated correlation values are only empirical estimates. To measure spurious behavior as deviation from a ground truth network, we simulate time-dependent isotropic random fields on the sphere and apply common network-construction techniques. We find several ways in which the uncertainty stemming from the estimation procedure has a major impact on network characteristics. When the data have a locally coherent correlation structure, spurious link bundle teleconnections and spurious high-degree clusters have to be expected. Anisotropic estimation variance can also induce severe biases into empirical networks. We validate our findings with ERA5 data. Moreover, we explain why commonly applied resampling procedures are inappropriate for significance evaluation and propose a statistically more meaningful ensemble construction framework. By communicating which difficulties arise in estimation from scarce data and by presenting which design decisions increase robustness, we hope to contribute to more reliable climate network construction in the future. Significance Statement Network-based approaches have gained renewed attention regarding the prediction of climate phenomena such as El Niño events, extreme regional precipitation patterns, anomalous polar vortex dynamics, and regarding understanding the Earth system. Even though climate networks are constructed from a limited amount of noisy data, they typically are not studied from a statistical perspective. However, such an approach is crucial: due to sampling uncertainty, climate networks unavoidably contain false and missing edges. We analyze how sampling artifacts impact the conclusions drawn from the networks and present both pitfalls and statistically robust procedures of network construction and evaluation. We aim to contribute to understanding the limitations and fully leveraging the potentials of network methods in climate and Earth system science.

Atmospheric patterns and winter precipitation over north‐western South Asia

AbstractMid‐latitude storms act as crucial atmospheric bridges between global teleconnection patterns and regional precipitation distribution during boreal winter over South Asia. In this work, we study the spatio‐temporal variability of seasonal precipitation over South Asia over the last four decades, which reveals distinct regional precipitation patterns across seasons. For the winter region of South Asia (WRSA), the second Empirical orthogonal function (EOF2) of 500 hPa geopotential heights shows a significant positive correlation with precipitation. Furthermore, using regression analysis, we estimate that the first six EOFs explain most of the interannual variability in winter precipitation during the positive phase of AMO. The results show that six standard climate indices influence winter precipitation over WRSA. We observed a decadal variability in the relationship between ENSO and winter precipitation over WRSA that is modulated by Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). The positive phase of AMO strengthens the positive relationship between ENSO and winter precipitation over WRSA. The model results also show that two other oscialltions, DMI and AAO, significantly explain the variability of winter precipitation over WRSA. The atmospheric patterns extracted in this study significantly improve winter precipitation forecasting skill of linear regression models compared to those based on standard climate indices, especially during positive AMO winters. The results from this study can be used to improve seasonal predictability of winter precipitation over WRSA.