Concept: Contiguous United States
The differential warming of land and ocean leads to many continental regions in the Northern Hemisphere warming at rates higher than the global mean temperature. Adaptation and conservation efforts will, therefore, benefit from understanding regional consequences of limiting the global mean temperature increase to well below 2°C above pre-industrial levels, a limit agreed upon at the United Nations Climate Summit in Paris in December 2015. Here, we analyze climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to determine the timing and magnitude of regional temperature and precipitation changes across the contiguous United States (US) for global warming of 1.5 and 2°C and highlight consensus and uncertainties in model projections and their implications for making decisions. The regional warming rates differ considerably across the contiguous US, but all regions are projected to reach 2°C about 10-20 years before the global mean temperature. Although there is uncertainty in the timing of exactly when the 1.5 and 2°C thresholds will be crossed regionally, over 80% of the models project at least 2°C warming by 2050 for all regions for the high emissions scenario. This threshold-based approach also highlights regional variations in the rate of warming across the US. The fastest warming region in the contiguous US is the Northeast, which is projected to warm by 3°C when global warming reaches 2°C. The signal-to-noise ratio calculations indicate that the regional warming estimates remain outside the envelope of uncertainty throughout the twenty-first century, making them potentially useful to planners. The regional precipitation projections for global warming of 1.5°C and 2°C are uncertain, but the eastern US is projected to experience wetter winters and the Great Plains and the Northwest US are projected to experience drier summers in the future. The impact of different scenarios on regional precipitation projections is negligible throughout the twenty-first century compared to uncertainties associated with internal variability and model diversity.
IMPORTANCE A strong association between infant bed sharing and sudden infant death syndrome or unintentional sleep-related death in infants has been established. Occurrences of unintentional sleep-related deaths among infants appear to be increasing. OBJECTIVES To determine the trends and factors associated with infant bed sharing from 1993 through 2010, including the association of physician advice on bed sharing. DESIGN National Infant Sleep Position study conducted with annual telephone surveys. SETTING The 48 contiguous states. PARTICIPANTS Nighttime caregivers of infants born within 7 months of each survey administration. Approximately 1000 interviews were completed annually. MAIN OUTCOMES AND MEASURES Infant bed sharing as a usual practice. RESULTS Of 18 986 participants, 11.2% reported an infant sharing a bed as a usual practice. Bed sharing increased from 1993 (6.5%) to 2010 (13.5%). Although bed sharing increased significantly among white respondents from 1993 to 2000 (P < .001), the increase from 2001 to 2010 was not significant (P = .48). Black and Hispanic respondents reported an increase in bed sharing throughout the study period, with no difference between the earlier and later periods (P = .63 and P = .77, respectively). After accounting for the study year, factors associated with increase in infant bed sharing as a usual practice included maternal educational level of less than high school compared with college or greater (adjusted odds ratio, 1.42 [95% CI, 1.12-1.79]); black (3.47 [2.97-4.05]), Hispanic (1.33 [1.10-1.61]), and other (2.46 [2.03-2.97]) maternal race or ethnicity compared with white race; household income of less than $20 000 (1.69 [1.44-1.99]) and $20 000 to $50 000 (1.29 [1.14-1.45]) compared with greater than $50 000; living in the West (1.61 [1.38-1.88]) or the South (1.47 [1.30-1.66]) compared with the Midwest; infants younger than 8 weeks (1.45 [1.21-1.73]) or ages 8 to 15 weeks (1.31 [1.17-1.45]) compared with 16 weeks or older; and being born prematurely compared with full-term (1.41 [1.22-1.62]). Almost 46% of the participants reported talking to a physician about bed sharing. Compared with those who did not receive advice from a physician, those who reported their physicians had a negative attitude were less likely to have the infant share a bed (adjusted odds ratio, 0.66 [95% CI, 0.53-0.82]), whereas a neutral attitude was associated with increased bed sharing (1.38 [1.05-1.80]). CONCLUSIONS AND RELEVANCE Our finding of a continual increase in bed sharing throughout the study period among black and Hispanic infants suggests that the current American Academy of Pediatrics recommendation about bed sharing is not universally followed. The factors associated with infant bed sharing may be useful in evaluating the impact of a broad intervention to change behavior.
The covariability of temperature (T), precipitation (P) and radiation ® is an important aspect in understanding the climate influence on crop yields. Here, we analyze county-level corn and soybean yields and observed climate for the period 1983-2012 to understand how growing-season (June, July and August) mean T, P and R influence crop yields jointly and in isolation across the CONterminous United States (CONUS). Results show that nationally averaged corn and soybean yields exhibit large interannual variability of 21% and 22%, of which 35% and 32% can be significantly explained by T and P, respectively. By including R, an additional of 5% in variability can be explained for both crops. Using partial regression analyses, we find that studies that ignore the covariability among T, P, and R can substantially overestimate the sensitivity of crop yields to a single climate factor at the county scale. Further analyses indicate large spatial variation in the relative contributions of different climate variables to the variability of historical corn and soybean yields. The structure of the dominant climate factors did not change substantially over 1983-2012, confirming the robustness of the findings, which have important implications for crop yield prediction and crop model validations.
- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 3 years ago
The contiguous United States (CONUS), especially the West, faces challenges of increasing water stress and uncertain impacts of climate change. The historical information of surface water body distribution, variation, and multidecadal trends documented in remote-sensing images can aid in water-resource planning and management, yet is not well explored. Here, we detected open-surface water bodies in all Landsat 5, 7, and 8 images (∼370,000 images, >200 TB) of the CONUS and generated 30-meter annual water body frequency maps for 1984-2016. We analyzed the interannual variations and trends of year-long water body area, examined the impacts of climatic and anthropogenic drivers on water body area dynamics, and explored the relationships between water body area and land water storage (LWS). Generally, the western half of the United States is prone to water stress, with small water body area and large interannual variability. During 1984-2016, water-poor regions of the Southwest and Northwest had decreasing trends in water body area, while water-rich regions of the Southeast and far north Great Plains had increasing trends. These divergent trends, mainly driven by climate, enlarged water-resource gaps and are likely to continue according to climate projections. Water body area change is a good indicator of LWS dynamics in 58% of the CONUS. Following the 2012 prolonged drought, LWS in California and the southern Great Plains had a larger decrease than surface water body area, likely caused by massive groundwater withdrawals. Our findings provide valuable information for surface water-resource planning and management across the CONUS.
Land use regression (LUR) is widely used for estimating within-urban variability in air pollution. While LUR has recently been extended to national and continental scales, these models are typically for long-term averages. Here we present NO2 surfaces for the continental United States with excellent spatial resolution (~100-m) and monthly-average concentrations for one decade. We investigate multiple potential data sources (e.g., satellite column and surface estimates, high- and standard-resolution satellite data, and a mechanistic model [WRF-Chem]), approaches to model building (e.g., one model for the whole country versus having separate models for urban and rural areas; monthly LURs versus temporal scaling of a spatial LUR), and spatial interpolation methods for temporal scaling factors (e.g., kriging versus inverse distance weighted). Our core approach uses NO2 measurements from U.S. EPA monitors (2000 - 2010) to build a spatial LUR and to calculate spatially-varying temporal scaling factors. The model captures 82% of the spatial and 76% of the temporal variability (population-weighted average) of monthly-mean NO2 concentrations from U.S. EPA monitors with low average bias (21%) and error (2.4 ppb). Model performance in absolute terms is similar near versus far from monitors, and in urban, suburban, and rural locations (mean absolute error: 2-3 ppb); since low-density locations generally experience lower concentrations, model performance in relative terms is better near monitors than far from monitors (mean bias: 3% versus 40%) and is better for urban and suburban locations (1%-6%) than for rural locations (78%, reflecting the relatively clean conditions in many rural areas). We apply our approach to all U.S. Census blocks in the contiguous United States to provide 132 months of publicly available, high-resolution NO2 concentration estimates.
Ecosystem carbon stocks and sequestration potential of federal lands across the conterminous United States
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 5 years ago
Federal lands across the conterminous United States (CONUS) account for 23.5% of the CONUS terrestrial area but have received no systematic studies on their ecosystem carbon © dynamics and contribution to the national C budgets. The methodology for US Congress-mandated national biological C sequestration potential assessment was used to evaluate ecosystem C dynamics in CONUS federal lands at present and in the future under three Intergovernmental Panel on Climate Change Special Report on Emission Scenarios (IPCC SRES) A1B, A2, and B1. The total ecosystem C stock was estimated as 11,613 Tg C in 2005 and projected to be 13,965 Tg C in 2050, an average increase of 19.4% from the baseline. The projected annual C sequestration rate (in kilograms of carbon per hectare per year) from 2006 to 2050 would be sinks of 620 and 228 for forests and grasslands, respectively, and C sources of 13 for shrublands. The federal lands' contribution to the national ecosystem C budget could decrease from 23.3% in 2005 to 20.8% in 2050. The C sequestration potential in the future depends not only on the footprint of individual ecosystems but also on each federal agency’s land use and management. The results presented here update our current knowledge about the baseline ecosystem C stock and sequestration potential of federal lands, which would be useful for federal agencies to decide management practices to achieve the national greenhouse gas (GHG) mitigation goal.
[This corrects the article DOI: https://doi.org/10.1289/EHP507.].
This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast’s construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011-2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year’s regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011-2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases.
Understanding historical changes in flood damage and the underlying mechanisms is critical for predicting future changes for better adaptations. In this study, a detailed assessment of flood damage for 1950-1999 is conducted at the state level in the conterminous United States (CONUS). Geospatial datasets on possible influencing factors are then developed by synthesizing natural hazards, population, wealth, cropland and urban area to explore the relations with flood damage. A considerable increase in flood damage in CONUS is recorded for the study period which is well correlated with hazards. Comparably, runoff indexed hazards simulated by the Variable Infiltration Capacity (VIC) model can explain a larger portion of flood damage variations than precipitation in 84% of the states. Cropland is identified as an important factor contributing to increased flood damage in central US while urbanland exhibits positive and negative relations with total flood damage and damage per unit wealth in 20 and 16 states, respectively. Overall, flood damage in 34 out of 48 investigated states can be predicted at the 90% confidence level. In extreme cases, ~76% of flood damage variations can be explained in some states, highlighting the potential of future flood damage prediction based on climate change and socioeconomic scenarios.
Quantitative models that predict cyanotoxin concentrations in lakes and reservoirs from nutrient concentrations would facilitate management of these resources for recreation and as sources of drinking water. Development of these models from field data has been hampered by the high proportion of samples in which cyanotoxin concentrations are below detection limits and by the high variability of cyanotoxin concentrations within individual lakes. Here, we describe a national-scale hierarchical Bayesian model that addresses these issues and that predicts microcystin concentrations from summer mean total nitrogen and total phosphorus concentrations. This model accounts for 69% of the variance in mean microcystin concentrations in lakes and reservoirs of the conterminous United States. Mean microcystin concentrations were more strongly associated with differences in total nitrogen than total phosphorus. A general approach for assessing this and similar types of models for their utility for guiding management decisions is also described.