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AbstractDroughts that span the states of Washington, Oregon, and California are rare but devastating due to their large spatial coverage and potential loss of redundancies in water, agricultural, and fire-fighting resources. Such pan-coastal droughts [which we define using boreal summer volumetric soil moisture along the U.S. Pacific coast (32°–50°N, 115°–127°W)] require a more precise understanding of the roles played by the Pacific Ocean and internal atmospheric variability. We employ 16-member ensembles of the Community Atmosphere Model version 5 and Community Climate Model version 3 forced with observed sea surface temperatures (SSTs) from 1856 to 2012 to separate and quantify the influences of the tropical Pacific and internal atmospheric variability on pan-coastal droughts; all other boundary conditions are kept at climatological levels to explicitly isolate for the impacts of SST changes. Internal atmospheric variability is the dominant driver of pan-coastal droughts, accounting for 84% of their severity, and can reliably generate pan-coastal droughts even when ocean conditions do not favor drought. Cold phases of the Pacific Ocean play a secondary role and contribute, on average, only 16% to pan-coastal drought severity. Spatiotemporal analyses of precipitation and soil moisture along the U.S. Pacific coast corroborate these findings and identify an antiphased wet–dry dipole pattern induced by the Pacific to play a more secondary role. Our model framework expands on previous observational analyses that point to the spatially uniform forcing of internal atmospheric variability as the more dominant mode of hydroclimate variability along the U.S. Pacific coast. The secondary nature of oceanic forcing suggests limited predictability of pan-continental droughts.

Severe and persistent 21st-century drought in southwestern North America (SWNA) motivates comparisons to medieval megadroughts and questions about the role of anthropogenic climate change. We use hydrological modeling and new 1200-year tree-ring reconstructions of summer soil moisture to demonstrate that the 2000-2018 SWNA drought was the second driest 19-year period since 800 CE, exceeded only by a late-1500s megadrought. The megadrought-like trajectory of 2000-2018 soil moisture was driven by natural variability superimposed on drying due to anthropogenic warming. Anthropogenic trends in temperature, relative humidity, and precipitation estimated from 31 climate models account for 47% (model interquartiles of 35 to 105%) of the 2000-2018 drought severity, pushing an otherwise moderate drought onto a trajectory comparable to the worst SWNA megadroughts since 800 CE.

There has been widespread adoption of downscaled products amongst practitioners and stakeholders to ascertain risk from climate hazards at the local scale (e.g., ∼ 5 km resolution). Such products must nevertheless be consistent with physical laws to be credible and of value to users. Here we evaluate statistically and dynamically downscaled products by examining local co-evolution of downscaled temperature and precipitation during convective and frontal precipitation events (two mechanisms testable with just temperature and precipitation). We find that two widely used statistical downscaling techniques (Localized Constructed Analogs version 2, LOCA2, and Seasonal Trends and Analysis of Residuals Empirical Statistical Downscaling Model, STAR-ESDM) generally preserve expected co-variances during convective precipitation events over the historical and future projected intervals as compared to European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and two observation-based data products (Livneh and nClimGrid-Daily). However, both techniques dampen future intensification of frontal precipitation that is otherwise robustly captured in global climate models (i.e., prior to downscaling) and with process-based dynamical downscaling across five different regional climate models. In the case of LOCA2, this leads to appreciable underestimation of future frontal precipitation event intensity. This study is one of the first to quantify a likely ramification of the stationarity assumption underlying statistical downscaling methods and identify a phenomenon where projections of future change diverge depending on data production method employed. Finally, our work proposes expected co-variances during convective and frontal precipitation as useful evaluation diagnostics that can be universally applied to a wide range of statistically downscaled products. [ABSTRACT FROM AUTHOR]

The tree-ring-based North American Drought Atlas (NADA), Monsoon Asia Drought Atlas (MADA), and Old World Drought Atlas (OWDA) collectively yield a near-hemispheric gridded reconstruction of hydroclimate variability over the last millennium. To test the robustness of the large-scale representation of hydroclimate variability across the drought atlases, the joint expression of seasonal climate variability and teleconnections in the NADA, MADA, and OWDA are compared against two global, observation-based PDSI products. Predominantly positive (negative) correlations are determined between seasonal precipitation (surface air temperature) and collocated tree-ring-based PDSI, with average Pearson's correlation coefficients increasing in magnitude from boreal winter to summer. For precipitation, these correlations tend to be stronger in the boreal winter and summer when calculated for the observed PDSI record, while remaining similar for temperature. Notwithstanding these differences, the drought atlases robustly express teleconnection patterns associated with the El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO). These expressions exist in the drought atlas estimates of boreal summer PDSI despite the fact that these modes of climate variability are dominant in boreal winter, with the exception of the Atlantic Multidecadal Oscillation. ENSO and NAO teleconnection patterns in the drought atlases are particularly consistent with their well-known dominant expressions in boreal winter and over the OWDA domain, respectively. Collectively, our findings confirm that the joint Northern Hemisphere drought atlases robustly reflect large-scale patterns of hydroclimate variability on seasonal to multidecadal timescales over the 20th century and are likely to provide similarly robust estimates of hydroclimate variability prior to the existence of widespread instrumental data.

We illustrate efficient methods to estimate future projected average return intervals (ARIs) of flood depths in coastal regions from storm-tide data and sea-level rise (SLR) projections. A flood-water path-finding algorithm is applied to digital elevation models (DEMs) in coastal regions to determine possible flood depths at interior points, given storm-tide levels at ARIs and local SLR with uncertainty on the coast. We show that the distribution of projected ARIs of a historical baseline flood-depth is truncated log-normal in the Gumbel extreme-value approximation, and we provide analytic expressions for the means. With this approximation, projected change in flood damage over a range of ARIs can be estimated by analysis at any single ARI. Compared to flood-depth distributions, ARI distributions are less directly related to flood damage, but they have the advantage of relative insensitivity to uncertainties in DEMs and other granular details of flood-risk modeling. We illustrate with applications to Miami, Florida, and coastal North Carolina using two different DEMs. [ABSTRACT FROM AUTHOR]

The tree-ring-based North American Drought Atlas (NADA), Monsoon Asia Drought Atlas (MADA), and Old World Drought Atlas (OWDA) collectively yield a near-hemispheric gridded reconstruction of hydroclimate variability over the last millennium. To test the robustness of the large-scale representation of hydroclimate variability across the drought atlases, the joint expression of seasonal climate variability and teleconnections in the NADA, MADA, and OWDA are compared against two global, observation-based PDSI products. Predominantly positive (negative) correlations are determined between seasonal precipitation (surface air temperature) and collocated tree-ring-based PDSI, with average Pearson's correlation coefficients increasing in magnitude from boreal winter to summer. For precipitation, these correlations tend to be stronger in the boreal winter and summer when calculated for the observed PDSI record, while remaining similar for temperature. Notwithstanding these differences, the drought atlases robustly express teleconnection patterns associated with El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), the Pacific decadal oscillation (PDO), and the Atlantic multidecadal oscillation (AMO). These expressions exist in the drought atlas estimates of boreal summer PDSI despite the fact that these modes of climate variability are dominant in boreal winter, with the exception of the AMO. ENSO and NAO teleconnection patterns in the drought atlases are particularly consistent with their well-known dominant expressions in boreal winter and over the OWDA domain, respectively. Collectively, the findings herein confirm that the joint Northern Hemisphere drought atlases robustly reflect large-scale patterns of hydroclimate variability on seasonal to multidecadal time scales over the twentieth century and are likely to provide similarly robust estimates of hydroclimate variability prior to the existence of widespread instrumental data. [ABSTRACT FROM AUTHOR]

The article presents a study concerning the dual features functional support vector machine for the early detection of faulty batteries in manufacturing processes. It outlines the use of the existing original performance degradation profiles, but the approach is not effective in detecting faulty batteries which prompted the proposal of dual features functional support vector machine measure. It highlights the application of the modified floating search algorithm for the repeated feature selection, as well as the sampling plan for time-varying classification errors to determine the optimal number of test cycles. It illustrates the improved performance of the proposed approach compared to the existing method wherein the process can be applied to a wide range of manufacturing processes.