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Snow cover provides distinct seasonal controls on the exchange of energy between Earth's surface and atmosphere, and on hydrologic cycling, and holds considerable importance to communities and ecosystems worldwide. In this work, we tackle a comprehensive review of existing snow classification approaches and the development of new globally applicable snow cover–based rules for delineating snow seasonality classes. Snow classification rules are defined using machine learning approaches, which are then applied to the 22-yr record of snow cover (2000–22) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on a 0.01° global grid. For the MODIS period of record, we find the global land surface can be effectively partitioned into five snow seasonality classes: no snow, ephemeral, transitional, seasonal, and perennial snow regimes which on average cover extents of approximately 76 (52% of global land areas), 19 (13%), 16 (11%), 18 (13%), and 16 million km2 (11%), respectively. Using the multidecadal dataset, we explore changes within snow regimes and find significant increases in the areal extent of no snow (approximately +70 000 km2 yr−1) as well as apparent losses in perennial (−3600 km2 yr−1) and seasonal snow regime coverage (−38 000 km2 yr−1). The resulting classification maps have strong agreement with in situ snow depth observations and present similar patterns to existing snow and climate classifications with notable discrepancies in cold arid regions. The framework's ability to accurately capture variations in snow persistence, snow accumulation, and melt cycling is shown, providing a reference to the current state of global snow seasonality. Significance Statement: Following a review of existing snow classification approaches, this study focuses on improving our understanding of snow-cover variability on Earth by using satellite-based observations of snow-covered area to derive a new snow classification. Satellite observations provide the best means of measuring snow cover at a global scale, helping to identify regions where its influence on energy, water, and climate is changing. The results are compared to existing climate and snow summaries, ground observations of snow depth, and include trends in snow cover over recent decades (2000–22). The resulting datasets are also made available to the broader scientific community. [ABSTRACT FROM AUTHOR]

The use of global climate model (GCM) precipitation simulations typically requires corrections for precipitation biases at subgrid spatial scales, typically at daily or monthly time scales. However, over many regions GCMs underestimate the magnitudes of multiyear precipitation extremes in the observed climate, resulting in a likely underestimation of the magnitudes of multiyear precipitation extremes in future scenarios. The objective of this study is to propose a method to extract from GCMs more realistic scenarios of multiyear precipitation extremes over time horizons of decades to one century. This proposed correction method is analogous to widely used bias correction methods, except that it is applied to variability at longer time scales than previous implementations (i.e., multiyear rather than daily or monthly). A case study of precipitation over a basin from the New York City water supply system demonstrates the potential magnitude of the underestimation of multiyear precipitation using uncorrected GCM scenarios, and the potential impact of the correction on multiyear hydrological extremes. Overall, it is a practical, conceptually simple approach meant for water supply system impact studies, but can be used for any impact studies that require more realistic multiyear extreme precipitation extreme scenarios. Significance Statement: The purpose of this study is to present a practical method to address a particular difficulty that in some regions arises in climate change impact studies: global climate models tend to underestimate the multiyear variability of precipitation over some regions, resulting in an underestimation of the magnitudes and/or intensities of prolonged droughts as well as prolonged wet periods. The method is analogous to widely used bias correction methods, except it is applied to variability at longer time scales than previous implementations (i.e., multiyear rather than daily or monthly). It is designed to provide more realistic estimates of extreme hydrological scenarios during the twenty-first century. Our particular interest is for managers of water supply systems, but the method may be of interest to others for whom multiyear precipitation extremes are critical. [ABSTRACT FROM AUTHOR]

Meteorological droughts in the continental United States (CONUS) are known to oscillate at the multidecadal time scale in response to the sea surface temperatures (SST) variability over the Pacific Ocean and the North Atlantic Ocean. While previous studies have focused on understanding the influence of SST oscillations on drought frequency over the CONUS, this information has not been integrated with global warming for future drought risk assessment at the decadal scale. In this study, we use the support vector machines (SVMs) to handle correlation between input variables for quantifying the influence of internal variability [Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO)] and global warming on the decadal changes in the severity of seasonal droughts over the CONUS during 1901–2015. The regional drivers of drought severity identified using SVMs are used for the assessment of decadal drought risk in the near future. We find internal variability as the dominant driver of decadal changes in drought severity in the southern and central Great Plains and global warming as the dominant driver for the southeastern and southwestern United States. In the southern Plains, the existing pattern of increasing drought severity is likely to persist in the near future if AMO and PDO remain in their positive and negative phases, respectively, while global warming is likely to contribute to increasing drought severity in the Southeast and Southwest. This study suggests an emerging role of global warming in drought risk over the southern states, where near-term climate change adaptation is necessary. [ABSTRACT FROM AUTHOR]

The Indus River basin is highly vulnerable to water scarcity due to increasing population, unsustainable management practices, and climate change. Yet the regional hydroclimate and precipitation dynamics remain poorly understood. Using running trend and spectral analysis with multiple gauge-based, remote sensing, and reanalysis precipitation datasets, this study analyzes precipitation temporal variability, its subregional variations, and the main seasonal drivers, particularly the South Asian monsoon. The results uncover remarkable alternation of long-term positive and negative interdecadal precipitation trends in the basin over the past half century. These trends have led to substantial changes in water input over the region at the time scales comparable to climate assessment periods (30 years), and therefore this high intrinsic variability must be accounted for in climate change adaptation studies. This study also reconstructs onset and withdrawal dates of the South Asian monsoon that exhibit interdecadal variability, but their dominant modes differ from that of annual precipitation. The findings hypothesize that higher-frequency variability in El Niño–Southern Oscillation is likely to have a pronounced impact on monsoon onset and duration in the studied region. [ABSTRACT FROM AUTHOR]

Recent analyses of extreme hydrological events across the United States, including those summarized in the recent U.S. Third National Climate Assessment (May 2014), show that extremely large (extreme) precipitation and streamflow events are increasing over much of the country, with particularly steep trends over the northeastern United States. The authors demonstrate that the increase in extreme hydrological events over the northeastern United States is primarily a warm season phenomenon and is caused more by an increase in frequency than magnitude. The frequency of extreme warm season events peaked during the 2000s; a secondary peak occurred during the 1970s; and the calmest decade was the 1960s. Cold season trends during the last 30-50 yr are weaker. Since extreme precipitation events in this region tend to be larger during the warm season than during the cold season, trend analyses based on annual precipitation values are influenced more by warm season than by cold season trends. In contrast, the magnitude of extreme streamflow events at stations used for climatological analyses tends to be larger during the cold season: therefore, extreme event analyses based on annual streamflow values are overwhelmingly influenced by cold season, and therefore weaker, trends. These results help to explain an apparent discrepancy in the literature, whereby increasing trends in extreme precipitation events appear to be significant and ubiquitous across the region, while trends in streamflow appear less dramatic and less spatially coherent. [ABSTRACT FROM AUTHOR]

The Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona covers ~150 km2 and receives the majority of its annual precipitation from highly variable and intermittent summer storms during the North American monsoon. In this study, the patterns of precipitation in the U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) 88-rain-gauge network are analyzed for July through September from 1956 to 2011. Because small-scale convective systems generate most of this summer rainfall, the total ( T), intensity ( I), and frequency ( F) exhibit high spatial and temporal variability. Although subsidiary periods may have apparent trends, no significant trends in T, I, and F were found for the study period as a whole. Observed trends in the spatial coverage of storms change sign in the late 1970s, and the multidecadal variation in I and spatial coverage of storms have statistically significant correlation with the Pacific decadal oscillation and the Atlantic multidecadal oscillation indices. Precipitation has a pronounced diurnal cycle with the highest T and F occurring between 1500 and 2200 LT, and its average fractional coverage over 2- and 12-h periods is less than 40% and 60% of the gauges, respectively. Although more gauges are needed to estimate area-averaged daily precipitation, 5-11 gauges can provide a reasonable estimate of the area-averaged monthly total precipitation during the period from July through September. [ABSTRACT FROM AUTHOR]