scholarly journals In and out of glacial extremes by way of dust−climate feedbacks

2018 ◽  
Vol 115 (9) ◽  
pp. 2026-2031 ◽  
Author(s):  
Gary Shaffer ◽  
Fabrice Lambert

Mineral dust aerosols cool Earth directly by scattering incoming solar radiation and indirectly by affecting clouds and biogeochemical cycles. Recent Earth history has featured quasi-100,000-y, glacial−interglacial climate cycles with lower/higher temperatures and greenhouse gas concentrations during glacials/interglacials. Global average, glacial maxima dust levels were more than 3 times higher than during interglacials, thereby contributing to glacial cooling. However, the timing, strength, and overall role of dust−climate feedbacks over these cycles remain unclear. Here we use dust deposition data and temperature reconstructions from ice sheet, ocean sediment, and land archives to construct dust−climate relationships. Although absolute dust deposition rates vary greatly among these archives, they all exhibit striking, nonlinear increases toward coldest glacial conditions. From these relationships and reconstructed temperature time series, we diagnose glacial−interglacial time series of dust radiative forcing and iron fertilization of ocean biota, and use these time series to force Earth system model simulations. The results of these simulations show that dust−climate feedbacks, perhaps set off by orbital forcing, push the system in and out of extreme cold conditions such as glacial maxima. Without these dust effects, glacial temperature and atmospheric CO2 concentrations would have been much more stable at higher, intermediate glacial levels. The structure of residual anomalies over the glacial−interglacial climate cycles after subtraction of dust effects provides constraints for the strength and timing of other processes governing these cycles.

Author(s):  
Sanne B. Geeraerts ◽  
Joyce Endendijk ◽  
Kirby Deater-Deckard ◽  
Jorg Huijding ◽  
Marike H. F. Deutz ◽  
...  

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 513
Author(s):  
Olga Fullana ◽  
Mariano González ◽  
David Toscano

In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling motivated us to analyze to what extent the assumptions involved in this seminal model are not good enough approximations to solve the firm valuation problem, causing poor model performance. The model is based on the well-known dividend discount model and the residual income valuation model, and it adds a linear information model, which is a time series model by nature. Therefore, we adopt the time series approach. In the presence of non-stationary variables, we focus our research on US-listed firms for which more than forty years of data with the required cointegration properties to use error correction models are available. The results show that the clean surplus relation assumption has no impact on model performance, while the unbiased accounting property assumption has an important effect on it. The results also emphasize the uselessness of forcing valuation models to match the value displacement property of dividends.


Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1679
Author(s):  
Jacopo Giacomelli ◽  
Luca Passalacqua

The CreditRisk+ model is one of the industry standards for the valuation of default risk in credit loans portfolios. The calibration of CreditRisk+ requires, inter alia, the specification of the parameters describing the structure of dependence among default events. This work addresses the calibration of these parameters. In particular, we study the dependence of the calibration procedure on the sampling period of the default rate time series, that might be different from the time horizon onto which the model is used for forecasting, as it is often the case in real life applications. The case of autocorrelated time series and the role of the statistical error as a function of the time series period are also discussed. The findings of the proposed calibration technique are illustrated with the support of an application to real data.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tanja Charles ◽  
Matthias Eckardt ◽  
Basel Karo ◽  
Walter Haas ◽  
Stefan Kröger

Abstract Background Seasonality in tuberculosis (TB) has been found in different parts of the world, showing a peak in spring/summer and a trough in autumn/winter. The evidence is less clear which factors drive seasonality. It was our aim to identify and evaluate seasonality in the notifications of TB in Germany, additionally investigating the possible variance of seasonality by disease site, sex and age group. Methods We conducted an integer-valued time series analysis using national surveillance data. We analysed the reported monthly numbers of started treatments between 2004 and 2014 for all notified TB cases and stratified by disease site, sex and age group. Results We detected seasonality in the extra-pulmonary TB cases (N = 11,219), with peaks in late spring/summer and troughs in fall/winter. For all TB notifications together (N = 51,090) and for pulmonary TB only (N = 39,714) we did not find a distinct seasonality. Additional stratified analyses did not reveal any clear differences between age groups, the sexes, or between active and passive case finding. Conclusion We found seasonality in extra-pulmonary TB only, indicating that seasonality of disease onset might be specific to the disease site. This could point towards differences in disease progression between the different clinical disease manifestations. Sex appears not to be an important driver of seasonality, whereas the role of age remains unclear as this could not be sufficiently investigated.


2011 ◽  
Vol 11 (12) ◽  
pp. 6049-6062 ◽  
Author(s):  
X. Yue ◽  
H. Liao ◽  
H. J. Wang ◽  
S. L. Li ◽  
J. P. Tang

Abstract. Mineral dust aerosol can be transported over the nearby oceans and influence the energy balance at the sea surface. The role of dust-induced sea surface temperature (SST) responses in simulations of the climatic effect of dust is examined by using a general circulation model with online simulation of mineral dust and a coupled mixed-layer ocean model. Both the longwave and shortwave radiative effects of mineral dust aerosol are considered in climate simulations. The SST responses are found to be very influential on simulated dust-induced climate change, especially when climate simulations consider the two-way dust-climate coupling to account for the feedbacks. With prescribed SSTs and dust concentrations, we obtain an increase of 0.02 K in the global and annual mean surface air temperature (SAT) in response to dust radiative effects. In contrast, when SSTs are allowed to respond to radiative forcing of dust in the presence of the dust cycle-climate interactions, we obtain a global and annual mean cooling of 0.09 K in SAT by dust. The extra cooling simulated with the SST responses can be attributed to the following two factors: (1) The negative net (shortwave plus longwave) radiative forcing of dust at the surface reduces SST, which decreases latent heat fluxes and upward transport of water vapor, resulting in less warming in the atmosphere; (2) The positive feedback between SST responses and dust cycle. The dust-induced reductions in SST lead to reductions in precipitation (or wet deposition of dust) and hence increase the global burden of small dust particles. These small particles have strong scattering effects, which enhance the dust cooling at the surface and further reduce SSTs.


PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0119811 ◽  
Author(s):  
Sofia Bajocco ◽  
Eleni Dragoz ◽  
Ioannis Gitas ◽  
Daniela Smiraglia ◽  
Luca Salvati ◽  
...  

2021 ◽  
Vol 257 ◽  
pp. 83-100
Author(s):  
Andrew Harvey

This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020, Harvard Data Science Review, Special Issue 1—COVID-19, https://hdsr.mitpress.mit.edu/pub/ozgjx0yn) is extended to model the relationship between two or more series and the role of common trends is discussed. Data on daily deaths from COVID-19 in Italy and the UK provides an example of leading indicators when there is a balanced growth. When growth is not balanced, the model can be extended by including a non-stationary component in one of the series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy in the spring of 2020.


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