Efforts to understand the influence of historical global warming on individual extreme climate events have increased over the past decade. However, despite substantial progress, events that are ...unprecedented in the local observational record remain a persistent challenge. Leveraging observations and a large climate model ensemble, we quantify uncertainty in the influence of global warming on the severity and probability of the historically hottest month, hottest day, driest year, and wettest 5-d period for different areas of the globe. We find that historical warming has increased the severity and probability of the hottest month and hottest day of the year at >80% of the available observational area. Our framework also suggests that the historical climate forcing has increased the probability of the driest year and wettest 5-d period at 57% and 41% of the observed area, respectively, although we note important caveats. For the most protracted hot and dry events, the strongest and most widespread contributions of anthropogenic climate forcing occur in the tropics, including increases in probability of at least a factor of 4 for the hottest month and at least a factor of 2 for the driest year. We also demonstrate the ability of our framework to systematically evaluate the role of dynamic and thermodynamic factors such as atmospheric circulation patterns and atmospheric water vapor, and find extremely high statistical confidence that anthropogenic forcing increased the probability of record-low Arctic sea ice extent.
Surface weather conditions are closely governed by the large-scale circulation of the Earth's atmosphere. Recent increases in the occurrence of some extreme weather phenomena have led to multiple ...mechanistic hypotheses linking changes in atmospheric circulation to increasing probability of extreme events. However, observed evidence of long-term change in atmospheric circulation remains inconclusive. Here we identify statistically significant trends in the occurrence of atmospheric circulation patterns, which partially explain observed trends in surface temperature extremes over seven mid-latitude regions of the Northern Hemisphere. Using self-organizing map cluster analysis, we detect robust circulation pattern trends in a subset of these regions during both the satellite observation era (1979-2013) and the recent period of rapid Arctic sea-ice decline (1990-2013). Particularly substantial influences include the contribution of increasing trends in anticyclonic circulations to summer and autumn hot extremes over portions of Eurasia and North America, and the contribution of increasing trends in northerly flow to winter cold extremes over central Asia. Our results indicate that although a substantial portion of the observed change in extreme temperature occurrence has resulted from regional- and global-scale thermodynamic changes, the risk of extreme temperatures over some regions has also been altered by recent changes in the frequency, persistence and maximum duration of regional circulation patterns.
Characterizing changes in precipitation patterns over time is critical for hydrologically dependent fields like water resource management and agriculture. Here, we explore observed trends in ...interannual precipitation variability using a suite of metrics that describe changes in precipitation over time. We analyze daily in situ Global Historical Climatology Network precipitation data from 1970 to present over 17 internally consistent sub‐national United States domains using a regional Mann‐Kendall trend test. We find robustly increasing trends in annual mean precipitation and wet day frequency for most of the central and eastern U.S., but decreasing trends in the western U.S. Importantly, we identify widespread significant trends in interannual precipitation variability, with increasing variability in the southeast, decreasing variability in the far west, and mixed signals in the Rocky Mountains and north‐central U.S. Our results provide important context for water resource managers and a new observational standard for climate model performance assessments.
Plain Language Summary
While many studies have examined how annual precipitation totals and precipitation frequency have changed, few examine the variability, or consistency, of year‐over‐year precipitation. We test for these trends in daily observations across 17 regions within the United States. We find changes in yearly precipitation variability for most regions, though results in the central U.S. are mixed. We also identify rising average annual precipitation and precipitation frequency for the central and eastern U.S. and falling average annual precipitation and frequency for the western U.S. Our results are important for agriculture and water resource management and can be compared against historical climate model simulations to determine how well they reproduce observations.
Key Points
We find widespread robust changes in two measures of interannual precipitation variability across the United States
We detect increases (decreases) in annual mean precipitation and wet day frequency across the eastern (western) United States
We explore the interaction of changes in precipitation frequency and wet day precipitation intensity on interannual variability
To describe coronavirus disease 2019 (COVID-19) mortality in Chicago during the spring of 2020 and identify at the census-tract level neighborhood characteristics that were associated with higher ...COVID-19 mortality rates.
Using Poisson regression and regularized linear regression (elastic net), we evaluated the association between neighborhood characteristics and COVID-19 mortality rates in Chicago through July 22 (2514 deaths across 795 populated census tracts).
Black residents (31% of the population) accounted for 42% of COVID-19 deaths. Deaths among Hispanic/Latino residents occurred at a younger age (63 years, compared with 71 for white residents). Regarding residential setting, 52% of deaths among white residents occurred inside nursing homes, compared with 35% of deaths among black residents and 17% among Hispanic/Latino residents. Higher COVID-19 mortality was seen in neighborhoods with heightened barriers to social distancing and low health insurance coverage. Neighborhoods with a higher percentage of white and Asian residents had lower COVID-19 mortality. The associations differed by race, suggesting that neighborhood context may be most tightly linked to COVID-19 mortality among white residents.
We describe communities that may benefit from supportive services and identify traits of communities that may benefit from targeted campaigns for prevention and testing to prevent future deaths from COVID-19.
•Residential context influences COVID-19 mortality rates.•Residential context’s influence has largely been evaluated across large areas.•This work evaluates the influence of 33 neighborhood descriptors in Chicago.•The analysis is done over census tracts, and focuses on deaths outside nursing homes.•We find racial disparities in overall mortality and age of death from COVID-19.•Barriers to social distancing and health care were associated with higher COVID-19 mortality.•This describes communities that may benefit from targeted campaigns for prevention and testing
Planets residing in circumstellar habitable zones offer us the best opportunities to test hypotheses of life's potential pervasiveness and complexity. Constraining the precise boundaries of ...habitability and its observational discriminants is critical to maximizing our chances at remote life detection with future instruments. Conventionally, calculations of the inner edge of the habitable zone (IHZ) have been performed using both 1D radiative-convective and 3D general circulation models. However, these models lack interactive 3D chemistry and do not resolve the mesosphere and lower thermosphere region of the upper atmosphere. Here, we employ a 3D high-top chemistry-climate model (CCM) to simulate the atmospheres of synchronously rotating planets orbiting at the inner edge of habitable zones of K- and M-dwarf stars (between Teff = 2600 and 4000 K). While our IHZ climate predictions are in good agreement with general circulation model studies, we find noteworthy departures in simulated ozone and HOx photochemistry. For instance, climates around inactive stars do not typically enter the classical moist greenhouse regime even with high ( 10−3 mol mol−1) stratospheric water vapor mixing ratios, which suggests that planets around inactive M-stars may only experience minor water-loss over geologically significant timescales. In addition, we find much thinner ozone layers on potentially habitable moist greenhouse atmospheres, as ozone experiences rapid destruction via reaction with hydrogen oxide radicals. Using our CCM results as inputs, our simulated transmission spectra show that both water vapor and ozone features could be detectable by instruments NIRSpec and MIRI LRS on board the James Webb Space Telescope.
The intensity and spatial extent of tropical cyclone precipitation (TCP) often shapes the risk posed by landfalling storms. Here we provide a comprehensive climatology of landfalling TCP ...characteristics as a function of tropical cyclone strength, using daily precipitation station data and Atlantic U.S. landfalling tropical cyclone tracks from 1900 to 2017. We analyze the intensity and spatial extent of ≥1 mm/day TCP (Z1) and ≥50 mm/day TCP (Z50) over land. We show that the highest median intensity and largest median spatial extent of Z1 and Z50 occur for major hurricanes that have weakened to tropical storms, indicating greater flood risk despite weaker wind speeds. We also find some signs of TCP change in recent decades. In particular, for major hurricanes that have weakened to tropical storms, Z50 intensity has significantly increased, indicating possible increases in flood risk to coastal communities in more recent years.
Plain Language Summary
Heavy and widespread rainfall during landfalling tropical cyclones can cause severe damage and large financial losses. Here we investigate the differences in rainfall along tracks of tropical cyclones of different intensities. To do this, we examine the tracks of Atlantic tropical cyclones that made landfall in the southeastern and eastern United States during the 20th century. Across all major hurricanes, the largest areas and heaviest intensities of rainfall over land occur after they have weakened to tropical storms. These major hurricanes that have weakened to tropical storms also have heavier rainfall over land during the most recent six decades compared to the first six decades of our study period. Our findings indicate that after landfall occurs, the greatest risks of heavy and widespread rainfall are associated with major hurricanes that have weakened to tropical storms and that these risks may have grown in the past century.
Key Points
Precipitation extent and intensity vary strongly among categories of tropical cyclones
The largest extents and heaviest intensities of overall rainfall over land occur for major hurricanes that have weakened to tropical storms
Heavy precipitation has significantly increased between 1900–1957 and 1958–2017 for major hurricanes that have weakened to tropical storms
During the winters of 2013–2014 and 2014–2015, anomalously warm temperatures in western North America and anomalously cool temperatures in eastern North America resulted in substantial human and ...environmental impacts. Motivated by the impacts of these concurrent temperature extremes and the intrinsic atmospheric linkage between weather conditions in the western and eastern United States, we investigate the occurrence of concurrent “warm‐West/cool‐East” surface temperature anomalies, which we call the “North American winter temperature dipole.” We find that, historically, warm‐West/cool‐East dipole conditions have been associated with anomalous mid‐tropospheric ridging over western North America and downstream troughing over eastern North America. We also find that the occurrence and severity of warm‐West/cool‐East events have increased significantly between 1980 and 2015, driven largely by an increase in the frequency with which high‐amplitude “ridge‐trough” wave patterns result in simultaneous severe temperature conditions in both the West and East. Using a large single‐model ensemble of climate simulations, we show that the observed positive trend in the warm‐West/cool‐East events is attributable to historical anthropogenic emissions including greenhouse gases, but that the co‐occurrence of extreme western warmth and eastern cold will likely decrease in the future as winter temperatures warm dramatically across the continent, thereby reducing the occurrence of severely cold conditions in the East. Although our analysis is focused on one particular region, our analysis framework is generally transferable to the physical conditions shaping different types of extreme events around the globe.
Key Points
U.S. warm‐West/cool‐East dipole events associated with large circulation anomalies across the Northern Hemisphere mid‐latitudes
Historical increase in dipole events linked to increasing frequency of events when associated circulation patterns occur
Positive trend in dipole events attributable to historical anthropogenic warming, though future warming is likely to reverse trend
The characterization of changes over the full distribution of precipitation intensities remains an overlooked and underexplored subject, despite their critical importance to hazard assessments and ...water resource management. Here, we aggregate daily in situ Global Historical Climatology Network precipitation observations within 17 internally consistent domains in the United States for two time periods (1951–1980 and 1991–2020). We find statistically significant changes in wet day precipitation distributions in all domains—changes primarily driven by a shift from lower to higher wet day intensities. Patterns of robust change are geographically consistent, with increases in the mean (4.5%–5.7%) and standard deviation (4.4%–8.7%) of wet day intensity in the eastern U.S., but mixed signals in the western U.S. Beyond their critical importance to the aforementioned impact assessments, these observational results can also inform climate model performance evaluations.
Plain Language Summary
Lots of research has been done to see how precipitation event totals are affected by climate change. Instead of yearly totals or extreme precipitation, we look at how daily precipitation is changing at all intensities, which has effects on natural hazards and related risks. We group daily rain gauge measurements within 17 climate regions in the United States for two 30‐year time periods: 1951–1980 and 1991–2020. We find changes in daily precipitation intensity in all regions, changes that are mostly caused by a shift from lower to higher intensity events. We also identify a broad area within the central and eastern U.S. with consistent increases in average precipitation and its variability. Changes are mixed in the western U.S. In addition to the impacts mentioned above, our results can also be used to see how well climate models perform.
Key Points
We find consistent shifts from lower to higher daily precipitation intensities, particularly in the central and eastern United States
All contiguous United States domains show significant changes in their distributions of precipitation intensity from 1951–1980 to 1991–2020
Mean and standard deviation of wet day precipitation intensities increase for nearly all domains in the central and eastern United States
Abstract
Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require ...validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF‐Hydro) over a central California domain. Our results show WRF‐Hydro soil moisture exhibits increased correlation coefficients (
r
), reduced biases, and increased Kling‐Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well‐established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS‐adjusted WRF‐Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF‐Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO‐informed total soil thickness. The magnitude and timing of peak flow events are better captured,
r
increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre‐calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.
Plain Language Summary
In this study, we develop a method that uses field‐ and machine learning‐derived soil property estimates to improve the performance of a hydrological model to simulate observed soil water content and river flows. Specifically, we replace three of the model's soil parameters with median values of the corresponding parameters from a probabilistic soil property data set. After replacement, simulated soil water content more closely resembles observations from seven in situ observing stations. Compared to four other well‐established, satellite‐derived or model‐simulated products, our soil property‐adjusted model performs favorably. For river flows, we find the highest model performance in the case where we modify the total soil thickness according to the soil survey data set. With modified soil thickness, the timing and magnitude of high flows are much better captured and the similarity between our simulations and the observations substantially increases at almost all observing stations. We demonstrate that using soil parameters informed by state‐of‐the‐science soil characteristic data sets with higher spatial resolution and better representations of spatial heterogeneity can significantly improve model performance even before model calibration. Our methods are thus computationally efficient and may prove useful in a number of hydrological modeling contexts.
Key Points
Model simulated soil moisture and streamflow fidelity substantially improve by using SSURGO and POLARIS soil parameters
Simulated soil moisture outperforms four well‐established soil moisture products when evaluated against in situ observations
Using data‐informed soil thickness, the model's streamflow fidelity substantially increases, especially for peak flow events
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