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Register a new userBiological Impact of Ozone Depletion at the End-Permian: A modeling study
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The end-Permian mass extinction is the most severe known from the fossil record. The most likely cause is massive volcanic activity associated with the formation of the Permo-Triassic Siberian flood basalts. A proposed mechanism for extinction due to this volcanic activity is depletion of stratospheric ozone, leading to increased penetration of biologically damaging Solar ultraviolet-B (UVB) radiation to Earths surface. Previous work has modeled the atmospheric chemistry effects of volcanic emission at the end-Permian. Here we use those results as input for detailed radiative transfer simulations to investigate changes in surface-level Solar irradiance in the ultraviolet-B, ultraviolet-A and photosynthetically available (visible light) wave bands. We then evaluate the potential biological effects using biological weighting functions. In addition to changes in ozone column density we also include gaseous sulfur dioxide (SO2) and sulfate aerosols. Ours is the first such study to include these factors and we find they have a significant impact on transmission of Solar radiation through the atmosphere. Inclusion of SO2 and aerosols greatly reduces the transmission of radiation across the ultraviolet and visible wavelengths, with subsequent reduction in biological impacts by UVB. We conclude that claims of a UVB mechanism for this extinction are likely overstated.
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Based on cosmological rates, it is probable that at least once in the last Gy the Earth has been irradiated by a gamma-ray burst in our Galaxy from within 2 kpc. Using a two-dimensional atmospheric model we have performed the first computation of the effects upon the Earths atmosphere of one such impulsive event. A ten second burst delivering 100 kJ/m^2 to the Earth penetrates to the stratosphere and results in globally averaged ozone depletion of 35%, with depletion reaching 55% at some latitudes. Significant global depletion persists for over 5 years after the burst. This depletion would have dramatic implications for life since a 50% decrease in ozone column density results in approximately three times the normal UVB flux. Widespread extinctions are likely, based on extrapolation from UVB sensitivity of modern organisms. Additional effects include a shot of nitrate fertilizer and NO2 opacity in the visible providing a cooling perturbation to the climate over a similar timescale. These results lend support to the hypothesis that a GRB may have initiated the late Ordovician mass extinction (Melott et al. 2004).
Social-distancing to combat the COVID-19 pandemic has led to widespread reductions in air pollutant emissions. Quantifying these changes requires a business as usual counterfactual that accounts for the synoptic and seasonal variability of air pollutants. We use a machine learning algorithm driven by information from the NASA GEOS-CF model to assess changes in nitrogen dioxide (NO$_{2}$) and ozone (O$_{3}$) at 5,756 observation sites in 46 countries from January through June 2020. Reductions in NO$_{2}$ correlate with timing and intensity of COVID-19 restrictions, ranging from 60% in severely affected cities (e.g., Wuhan, Milan) to little change (e.g., Rio de Janeiro, Taipei). On average, NO$_{2}$ concentrations were 18% lower than business as usual from February 2020 onward. China experienced the earliest and steepest decline, but concentrations since April have mostly recovered and remained within 5% to the business as usual estimate. NO$_{2}$ reductions in Europe and the US have been more gradual with a halting recovery starting in late March. We estimate that the global NO$_{x}$ (NO+NO$_{2}$) emission reduction during the first 6 months of 2020 amounted to 2.9 TgN, equivalent to 5.1% of the annual anthropogenic total. The response of surface O$_{3}$ is complicated by competing influences of non-linear atmospheric chemistry. While surface O$_{3}$ increased by up to 50% in some locations, we find the overall net impact on daily average O$_{3}$ between February - June 2020 to be small. However, our analysis indicates a flattening of the O$_{3}$ diurnal cycle with an increase in night time ozone due to reduced titration and a decrease in daytime ozone, reflecting a reduction in photochemical production. The O$_{3}$ response is dependent on season, time scale, and environment, with declines in surface O$_{3}$ forecasted if NO$_{x}$ emission reductions continue.
Tropospheric ozone (O3) is a greenhouse gas which can absorb heat and make the weather even hotter during extreme heatwaves. Besides, it is an influential ground-level air pollutant which can severely damage the environment. Thus evaluating the importance of various factors related to the O3 formation process is essential. However, O3 simulated by the available climate models exhibits large variance in different places, indicating the insufficiency of models in explaining the O3 formation process correctly. In this paper, we aim to identify and understand the impact of various factors on O3 formation and predict the O3 concentrations under different pollution-reduced and climate change scenarios. We employ six supervised methods to estimate the observed O3 using fourteen meteorological and chemical variables. We find that the deep neural network (DNN) and long short-term memory (LSTM) based models can predict O3 concentrations accurately. We also demonstrate the importance of several variables in this prediction task. The results suggest that while Nitrogen Oxides negatively contributes to predicting O3, solar radiation makes a significantly positive contribution. Furthermore, we apply our two best models on O3 prediction under different global warming and pollution reduction scenarios to improve the policy-making decisions in the O3 reduction.
Climate models are critical tools for developing strategies to manage the risks posed by sea-level rise to coastal communities. While these models are necessary for understanding climate risks, there is a level of uncertainty inherent in each parameter in the models. This model parametric uncertainty leads to uncertainty in future climate risks. Consequently, there is a need to understand how those parameter uncertainties impact our assessment of future climate risks and the efficacy of strategies to manage them. Here, we use random forests to examine the parametric drivers of future climate risk and how the relative importances of those drivers change over time. We find that the equilibrium climate sensitivity and a factor that scales the effect of aerosols on radiative forcing are consistently the most important climate model parametric uncertainties throughout the 2020 to 2150 interval for both low and high radiative forcing scenarios. The near-term hazards of high-end sea-level rise are driven primarily by thermal expansion, while the longer-term hazards are associated with mass loss from the Antarctic and Greenland ice sheets. Our results highlight the practical importance of considering time-evolving parametric uncertainties when developing strategies to manage future climate risks.
In modern surgery, a multitude of minimally intrusive operational techniques are used which are based on the punctual heating of target zones of human tissue via laser or radio-frequency currents. Traditionally, these processes are modeled by the bioheat equation introduced by Pennes, who considers Fouriers theory of heat conduction. We present an alternative and more realistic model established by the hyperbolic equation of heat transfer. To demonstrate some features and advantages of our proposed method, we apply the obtained results to different types of tissue heating with high energy fluxes, in particular radiofrequency heating and pulsed laser treatment of the cornea to correct refractive errors. Hopefully, the results of our approach help to refine surgical interventions in this novel field of medical treatment.
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