Projected temperature changes indicate significant increase in interannual variability of U.S. maize yields

Climate change has the potential to be a source of increased variability if crops are more frequently exposed to damaging weather conditions. Yield variability could respond to a shift in the frequency of extreme events to which crops are susceptible, or if weather becomes more variable. Here we focus on the United States, which produces about 40% of the world’s maize, much of it in areas that are expected to see increased interannual variability in temperature. We combine a statistical crop model based on historical climate and yield data for 1950–2005 with temperature and precipitation projections from 15 different global circulation models. Holding current growing area constant, aggregate yields are projected to decrease by an average of 18% by 2030–2050 relative to 1980–2000 while the coefficient of variation of yield increases by an average of 47%. Projections from 13 out of 15 climate models result in an aggregate increase in national yield coefficient of variation, indicating that maize yields are likely to become more volatile in this key growing region without effective adaptation responses. Rising CO₂ could partially dampen this increase in variability through improved water use efficiency in dry years, but we expect any interactions between CO₂ and temperature or precipitation to have little effect on mean yield changes.     

Seasonal and Diurnal Ozone Variations: Observations and Modeling

Ozone mixing ratios observed by the Bordeaux microwave radiometer between 1995 and 2002 in an altitude range 25–75 km show diurnal variations in the mesosphere and seasonal variations in terms of annual and semi-annual oscillations (SAO) in the stratosphere and in the mesosphere. The observations with 10–15 km altitude resolution are presented and compared to photochemical and transport model results.Diurnal ozone variations are analyzed by averaging the years 1995–1997 for four representative months and six altitude levels. The photochemical models show a good agreement with the observations for altitudes higher than 50 km. Seasonal ozone variations mainly appear as an annual cycle in the middle and upper stratosphere and a semi-annual cycle in the mesosphere with amplitude and phase depending on altitude. Higher resolution (2 km) HALOE (halogen occultation experiment) ozone observations show a phase reversal of the SAO between 44 and 64 km. In HALOE data, a tendancy for an opposite water vapour cycle can be identified in the altitude range 40–60 km.Generally, the relative variations at all altitudes are well explained by the transport model (up to 54 km) and the photochemical models. Only a newly developed photochemical model (1-D) with improved time-dependent treatment of water vapour profiles and solar flux manages to reproduce fairly well the absolute values.     

植硅体研究及其应用的讨论

植硅体是填充于高等植物细胞组织中的非晶质含水二氧化硅颗粒，记录了它所起源的植物细胞的特征，可以在土层或沉积物中很好地保存下来。通过植硅体的形态鉴定和确定组合带，可以恢复古植被、讨论植物群落演替，推断第四纪古气候环境及其变迁，指导地层划分对比。结合数学方法可以进一步计算出各种古环境气候的参数值，从而用以研究气候波动和发现气候事件，进行全球对比研究。在考古学、土壤学、农学和医学等领域，植硅体也得到了广泛的应用。     

A global analysis of cities’ geosocial temporal signatures for points of interest hours of operation

ABSTRACT

The temporal nature of humans interaction with Points of Interest (POIs) in cities can differ depending on place type and regional location. Times when many people are likely to visit restaurants (place type) in Italy, may differ from times when many people are likely to visit restaurants in Lebanon (i.e. regional differences). Geosocial data are a powerful resource to model these temporal differences in cities, as traditional methods used to study cross-cultural differences do not scale to a global level. As cities continue to grow in population and economic development, research identifying the social and geophysical (e.g., climate) factors that influence city function remains important and incomplete. In this work, we take a quantitative approach, applying dynamic time warping and hierarchical clustering on temporal signatures to model geosocial temporal patterns for Retail and Restaurant Facebook POIs hours of operation for more than 100 cities in 90 countries around the world. Results show cities’ temporal patterns cluster to reflect the cultural region they represent. Furthermore, temporal patterns are influenced by a mix of social and geophysical factors. Trends in the data suggest social factors influence unique drops in temporal signatures, and geophysical factors influence when daily temporal patterns start and finish.     

Study on mine earthquake activity rules for Laohutai coal mine in Fushun of Liaoning

According to earthquake data of Fushun earthquake administration,the seismic analysis and statistical methods are utilized in order to analyze earthquake frequency,b-value timing and energy creep trends in Laohutai coal mine. By using least squares linear regression method,the relational expression between frequency and magnitude of mine earthquake in Laohutai coal mine is given. And the possible largest magnitude mine earthquake inferred has also been calculated. And this paper also provides a theoretical ...     

Computation of analytical partial derivatives of phase and group velocities for Rayleigh waves with respect to structural parameters

Summary The analytical expressions used to compute the partial derivatives of phase and group velocity of Rayleigh waves with respect to the P- and S-wave velocity and the density are derived and the related computer code is developed. The results of the analytical computations were satisfactorily tested against numerically determined values. Several examples of partial derivatives for a given structural model are presented.     

Potential response of pacific northwestern forests to climatic change,effects of stand age and initial composition

We used an individual-based forest simulator (a gap model) to assess the potential effects of anthropogenic climatic change on conifer forests of the Pacific Northwestern United States. Steady-state simulations suggested that forest zones could be shifted on the order of 500–1000 m in elevation, which could lead to the local extirpation of some high-altitude species. For low-elevation sites, species which currently are more abundant hundreds of kilometers to the south would be favored under greenhouse scenarios. Simulations of transient responses suggested that forest stands could show complex responses depending on initial species composition, stand age and canopy development, and the magnitude and duration of climatic warming. Assumptions about species response to temperature, which are crucial to the model's behaviors, were evaluated using data on species temperature limits inferred from regional distributions. The high level of within-species variability in these data, and other confounding factors influencing species distributions, argue against over-interpreting simulations. We suggest how we might resolve critical uncertainties with further research.     

A comparison of coincident GRACE and ICESat data over Antarctica

In this study, we present a comparison of coincident GRACE and ICESat data over Antarctica. The analysis focused on the secular changes over a 4-year period spanning from 2003 to 2007, using the recently reprocessed and publicly available data sets for both missions. The results show that the two independent data sets possess strong spatial correlations, but that there are several factors that can significantly impact the total derived ice mass variability from both missions. For GRACE, the primary source of uncertainty comes from the modelling of glacial isostatic adjustment, along with the estimates of C _2,0 and the degree one terms. For ICESat, it is shown that assumptions about firn density, rate biases, and the sampling interval of the various laser campaigns can have large effects on the results. Despite these uncertainties, the similarities that do exist indicate a strong potential for the future refinement of both GIA and mass balance estimates of Antarctica.     

The Effects of Quality Control on Decreasing Error Propagation in the LandScan USA Population Distribution Model: A Case Study of Philadelphia County

LandScan USA is a 90 m population distribution model that is used for a variety of applications, including emergency management. Models should have a measure of accuracy; however, the accuracy of population distribution models is difficult to determine due to the inclusion of multiple input datasets and the lack of quantifiable, observable (validated) data to confirm model output. Validated data enables quantification of: (1) overall model accuracy and (2) changes in model output at different levels of quality control. This article examines the effect of quality control for two national school datasets incorporated as input in LandScan USA for Philadelphia County, Pennsylvania; which had a local, validated school dataset available. The effect of each stage of quality control efforts utilized throughout the LandScan USA process were assessed to determine what level of quality control was required to have a statistically significant change of the model's population distribution. The typical level of quality control for LandScan USA resulted in 36% of schools being moved to the correct location and 20% of missing student enrollments were found, compared to 87% and 98% respectively for the validated dataset. The costs of increasing quality control resulted in a six-fold increase in labor time; however, the additional quality control did not produce statistically significant improvements in the LandScan USA model. Thus, typical quality control efforts for schools in LandScan USA produced a population distribution similar to the validated level of quality control, and can be applied with confidence for policy, planning, and emergency situations.