This study quantifies worldwide health effects of the Fukushima Daiichi nuclear accident on 11 March 2011. Effects are quantified with a 3-D global atmospheric model driven by emission estimates and evaluated against daily worldwide Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) measurements and observed deposition rates. Inhalation exposure, ground-level external exposure, and atmospheric external exposure pathways of radioactive iodine-131, cesium-137, and cesium-134 released from Fukushima are accounted for using a linear no-threshold (LNT) model of human exposure. Exposure due to ingestion of contaminated food and water is estimated by extrapolation. We estimate an additional 130 (15-1100) cancer-related mortalities and 180 (24-1800) cancer-related morbidities incorporating uncertainties associated with the exposure-dose and dose-response models used in the study. We also discuss the LNT model's uncertainty at low doses. Sensitivities to emission rates, gas to particulate I-131 partitioning, and the mandatory evacuation radius around the plant are also explored, and may increase upper bound mortalities and morbidities in the ranges above to 1300 and 2500, respectively. Radiation exposure to workers at the plant is projected to result in 2 to 12 morbidities. An additional $600 mortalities have been reported due to non-radiological causes such as mandatory evacuations. Lastly, a hypothetical accident at the Diablo Canyon Power Plant in California, USA with identical emissions to Fukushima was studied to analyze the influence of location and seasonality on the impact of a nuclear accident. This hypothetical accident may cause $25% more mortalities than Fukushima despite California having one fourth the local population density due to differing meteorological conditions.

Abstract. Land use, vegetation, albedo, and soil-type data are combined in a global model that accounts for roofs and roads at near their actual resolution to quantify the effects of urban surface and white roofs on climate. In 2005, ~0.128% of the Earth's surface contained urban landcover, half of which was vegetated. Urban landcover was modeled over 20 years to increase gross global warming (warming before cooling due to aerosols and albedo change are accounted for) by 0.06-0.11 K and population-weighted warming by 0.16-0.31 K, based on two simulations under different conditions. As such, the urban heat island (UHI) effect may contribute to 2-4% of gross global warming, although the uncertainty range is likely larger than the model range presented, and more verification is needed. This may be the first estimate of the UHI effect derived from a global model while considering both UHI local heating and large-scale feedbacks.Previous data estimates of the global UHI, which considered the effect of urban areas but did not treat feedbacks or isolate temperature change due to urban surfaces from other causes of urban temperature change, imply a smaller UHI effect but of similar order. White roofs change surface albedo and affect energy demand. A worldwide conversion to white roofs, accounting for their albedo effect only, was calculated to cool population-weighted temperatures by ~0.02 K but to warm the Earth overall by ~0.07 K. White-roof local cooling may also affect energy use, thus emissions, a factor not accounted for here. As such, conclusions here regarding white roofs apply only to the assumptions made. 3 1.IntroductionUrban areas are generally warmer than vegetated areas around them since urban surfaces reduce evapotranspiration and have sufficiently different heat capacities, thermal conductivities, albedos, and emissivities to enhance urban warming [Howard, 1833;Oke, 1982]. Several studies have estimated, from data analysis, that the globally-averaged urban heat island (UHI) effect may contribute ≤0.1 K to global temperature changes since the preindustrial era [Jones, 1990;Easterling, 1997;Hansen et al., 1999;Peterson, 2003;Parker, 2006]. The IPCC Fourth Assessment Report concluded that the UHI may have increased temperatures ~0.065 over land and ~0.022 K globally from 1900-2008 [IPCC, 2007. The IPCC global estimate was scaled from the land estimate assuming no UHI heating or feedbacks over the ocean. Data analysis studies of the UHI do not account for feedbacks of changes in local temperatures, moisture, and their gradients to large-scale weather systems, either due to traceable effects or to deterministic chaotic variation. Furthermore, such studies cannot distinguish temperature changes in urban areas due to the UHI from those due to greenhouse gases, carbon dioxide domes over cities [Jacobson, 2010a], cooling or warming aerosol particles, transmission or use of electricity, stationary or mobile combustion, or human respiration, which also occur in urban areas. As such, numerical modeling is needed to ...

Determining the major sources of particles that act as cloud condensation nuclei (CCN) represents a critical step in the development of a more fundamental understanding of aerosol impacts on cloud formation and climate. Reported herein are direct measurements of the CCN activity of newly formed ambient particles, measured at a remote rural site in the Sierra Nevada Mountains of Northern California. Nucleation events in the winter of 2009 occurred during two pristine periods following precipitation, with higher gas-phase SO(2) concentrations during the second period, when faster particle growth occurred (7-8 nm/h). Amines, as opposed to ammonia, and sulfate were detected in the particle phase throughout new particle formation (NPF) events, increasing in number as the particles grew to larger sizes. Interestingly, long-range transport of SO(2) from Asia appeared to potentially play a role in NPF during faster particle growth. Understanding the propensity of newly formed particles to act as CCN is critical for predicting the effects of NPF on orographic cloud formation during winter storms along the Sierra Nevada Mountain range. The potential impact of newly formed particles in remote regions needs to be compared with that of transported urban aerosols when evaluating the impact of aerosols on clouds and climate.

[1] Biomass burning (BB) aerosol particles affect clouds through competing microphysical and radiative (semi-direct and cloud absorption) effects, each of which dominates at different degrees of aerosol loading. Here, we analyze the influence of competing aerosol effects on mixed-phase clouds, precipitation, and radiative fields over the Amazon with a climate-air pollution-weather forecast model that treats aerosol-cloud-radiative interactions physically. Extensive comparisons with remotely sensed observations and in situ measurements are performed. Both observations and model results suggest an increase in cloud optical depth (COD) with increasing aerosol optical depth (AOD) at low AODs, and a decrease in COD with increasing AOD at higher AODs in accord with previous observational and modeling studies. The increase is attributed to a combination of microphysical and dynamical effects, whereas the decrease is attributed to a dominance of radiative effects that thin and darken clouds. An analogous relationship is shown for other modeled cloud variables as well. The similarity between the remotely sensed observations and model results suggests that these correlations are physically based and are not dominated by satellite retrieval artifacts. Cloud brightening due to BB is found to dominate in the early morning, whereas cloud inhibition is found to dominate in the afternoon and at night. BB decreased the net top of the atmosphere solar+IR irradiance modestly, but with large diurnal variation. We conclude that models that exclude treatment of aerosol radiative effects are likely to over-predict the microphysical effects of aerosols and underestimate the warming due to aerosols containing black and brown carbon.

Aerosol, cloud, water vapor, and temperature profile data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are utilized to examine the impact of aerosols on clouds during the Amazonian biomass burning season in Rondônia, Brazil. It is found that increasing background column water vapor (CWV) throughout this transition season between the Amazon dry and wet seasons likely exerts a strong effect on cloud properties. As a result, proper analysis of aerosol-cloud relationships requires that data be stratified by CWV to account better for the influence of background meteorological variation. Many previous studies of aerosol-cloud interactions over Amazonia have ignored the systematic changes to meteorological factors during the transition season, leading to possible misinterpretation of their results. Cloud fraction (CF) is shown to increase or remain constant with aerosol optical depth (AOD), depending on the value of CWV, whereas the relationship between cloud optical depth (COD) and AOD is quite different. COD increases with AOD until AOD ~ 0.3, which is assumed to be due to the first indirect (microphysical) effect. At higher values of AOD, COD is found to decrease with increasing AOD, which may be due to: (1) the inhibition of cloud development by absorbing aerosols (radiative effect/semi-direct effect) and/or (2) a possible retrieval artifact in which the measured reflectance in the visible is less than expected from a cloud top either from the darkening of clouds through the addition of carbonaceous biomass burning aerosols within or above clouds or subpixel dark surface contamination in the measured cloud reflectance. If (1) is a contributing mechanism, as we suspect, then an empirically-derived increasing function between cloud drop number and aerosol concentration, assumed in a majority of global climate models, is inaccurate since these models do not include treatment of aerosol absorption in and around clouds. The relationship between aerosols and both CWV and clouds over varying land surface types is also analyzed. The study finds that the difference in CWV between forested and deforested land is not correlated with aerosol loading, supporting the assumption that temporal variation of CWV is primarily a function of the larger-scale meteorology. However, a difference in the response of CF to increasing AOD is observed between forested and deforested land. This suggests that dissimilarities between other meteorological factors, such as atmospheric stability, may have an impact on aerosol-cloud correlations between different land cover types

This study characterized the annual mean US East Coast (USEC) offshore wind energy (OWE) resource on the basis of 5 years of high‐resolution mesoscale model (Weather Research and Forecasting–Advanced Research Weather Research and Forecasting) results at 90 m height. Model output was evaluated against 23 buoys and nine offshore towers. Peak‐time electrical demand was analyzed to determine if OWE resources were coincident with the increased grid load. The most suitable locations for large‐scale development of OWE were prescribed, on the basis of the wind resource, bathymetry, hurricane risk and peak‐time generation potential. The offshore region from Virginia to Maine was found to have the most exceptional overall resource with annual turbine capacity factors (CF) between 40% and 50%, shallow water and low hurricane risk. The best summer resource during peak time, in water of ≤ 50 m depth, is found between Long Island, New York and Cape Cod, Massachusetts, due in part to regional upwelling, which often strengthens the sea breeze. In the South US region, the waters off North Carolina have adequate wind resource and shallow bathymetry but high hurricane risk. Overall, the resource from Florida to Maine out to 200 m depth, with the use of turbine CF cutoffs of 45% and 40%, is 965–1372 TWh (110–157 GW average). About one‐third of US or all of Florida to Maine electric demand can technically be provided with the use of USEC OWE. With the exception of summer, all peak‐time demand for Virginia to Maine can be satisfied with OWE in the waters off those states. Copyright © 2012 John Wiley & Sons, Ltd.

The physical processes that determine the time scale of zonal-mean-flow variability are examined with an idealized numerical model that has a zonally symmetric lower boundary. In the part of the parameter space where the time-mean zonal flow is characterized by a single (double) jet, the dominant form of zonalmean-flow variability is the zonal index (poleward propagation), and the time-mean potential vorticity gradient is found to be strong and sharp (weak and broad). The e-folding time scale of the zonal index is found to be close to 55 days, much longer than the observed 10-day time scale. The e-folding time scale of the poleward propagation is about 40 days. The long e-folding time scales for the zonal index are found to be consistent with an unrealistically strong and persistent eddy-zonal-mean-flow feedback. A calculation of the refractive index indicates that the background flow supports eddies that are trapped within midlatitudes, undergoing relatively little meridional propagation.Additional model runs are performed with an idealized mountain to investigate whether zonal asymmetry can disrupt the eddy feedback. For single-jet states, the time scale is reduced to about 30 days if the mountain height is 4 km or less. The reduction in the time scale occurs because the stationary eddies excited by the mountain alter the background flow in a manner that leads to the replacement of zonal-index events by shorter-time-scale poleward propagation. With a 5-km mountain, the time scale reverts and increases to 105 days. This threshold behavior is again attributed to a sharpening of the background zonal jet, which arises from an extremely strong stationary wave momentum flux convergence. In contrast, for double-jet states, the time scale changes only slightly and the poleward propagation is maintained in all mountain runs.

Capsule SummaryCurrent research applying artificial intelligence to the Earth and environmental sciences is progressing quickly, with emerging developments in terms of efficiency, accuracy, and discovery.