Solar variability, weather, and climate

Advances in the understanding of possible effects of solar variations on weather and climate are most likely to emerge by addressing the subject in terms of fundamental physical principles of atmospheric sciences and solar-terrestrial physis. The limits of variability of solar inputs to the atmosphere and the depth in the atmosphere to which these variations have significant effects are determined

Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets

Agricultural water use accounts for around 70% of the total water that is withdrawn from surface water and groundwater. We use a new, gridded, global-scale water balance model to estimate interannual variability in global irrigation water demand arising from climate data sets and uncertainties arising from agricultural and climate data sets. We used contemporary maps of irrigation and crop distribution, and so do not account for variability or trends in irrigation area or cropping. We used two different global maps of irrigation and two different reconstructions of daily weather 1963–2002. Simulated global irrigation water demand varied by ∼30%, depending on irrigation map or weather data. The combined effect of irrigation map and weather data generated a global irrigation water use range of 2200 to 3800 km3 a−1. Weather driven variability in global irrigation was generally less than ±300 km3 a−1, globally (\u3c∼10%), but could be as large as ±70% at the national scale

Weather Variability and the Tourism Industry: A Panel Data Analysis

Increasing weather variability around the world has led to many researchers examining the impacts of weather variability on vulnerable industries. For example, the tourism industry can make up a large portion of an economy’s growth, with some of the most dependent countries relying on tourism for over 40% of GDP (World Travel & Tourism Council 2014). In an attempt to better understand the relationship between weather variability and the tourism industry at the country level, this study employs a series of fixed effects panel regression models to analyze the impact of rainfall and temperature on tourism levels and growth rates among 194 countries. Variations of the model allow for the exploration of the differential impacts sustained by island and non-island countries to help determine whether island countries are more vulnerable to weather variations due to the large contribution of tourism to their economies (Uyarra et al. 2005). Results suggest that using a yearly average measure of the temperature and rainfall data does not yield useful results, while using seasonal temperature and seasonal rainfall averages appears to explain the different impacts across island and non-island countries with more consistency

VARIABLE RATE NITROGEN APPLICATION ON CORN FIELDS: THE ROLE OF SPATIAL VARIABILITY AND WEATHER

Meta-response functions for corn yields and nitrogen losses were estimated from EPIC-generated data for three soil types and three weather scenarios. These metamodels were used to evaluate variable rate (VRT) versus uniform rate (URT) nitrogen application technologies for alternative weather scenarios and policy option. Except under very dry conditions, returns per acre for VRT were higher than for URT and the economic advantage of VRT increased as realized rainfall decreased from expected average rainfall. Nitrogen losses to the environment from VRT were lower for all situation examined, except on fields with little spatial variability.Corn, environment, meta-response functions, nitrogen restriction, precision farming, site-specific management, spatial variability, weather variability, Crop Production/Industries,

Application of dynamical systems theory to global weather phenomena revealed by satellite imagery

Theoretical studies of low frequency and seasonal weather variability; dynamical properties of observational and general circulation model (GCM)-generated records; effects of the hydrologic cycle and latent heat release on extratropical weather; and Earth-system science studies are summarized

Radiation variability and correlation studies

The determination of variability of the emitted and reflected components of outgoing radiation from the earth-atmosphere system is discussed. The effects of variability on climate and weather are considered, and meteorological and climate variables to be correlated with radiation budget measurements are determined

Nonlinear dynamics of global atmospheric and Earth system processes

During the past eight years, we have been engaged in a NASA-supported program of research aimed at establishing the connection between satellite signatures of the earth's environmental state and the nonlinear dynamics of the global weather and climate system. Thirty-five publications and four theses have resulted from this work, which included contributions in five main areas of study: (1) cloud and latent heat processes in finite-amplitude baroclinic waves; (2) application of satellite radiation data in global weather analysis; (3) studies of planetary waves and low-frequency weather variability; (4) GCM studies of the atmospheric response to variable boundary conditions measurable from satellites; and (5) dynamics of long-term earth system changes. Significant accomplishments from the three main lines of investigation pursued during the past year are presented and include the following: (1) planetary atmospheric waves and low frequency variability; (2) GCM studies of the atmospheric response to changed boundary conditions; and (3) dynamics of long-term changes in the global earth system

Using life-history traits to explain bird population responses to changing weather variability

Bird population dynamics are expected to change in response to increased weather variability, an expression of climate change. The extent to which species are sensitive to effects of weather on survival and reproduction depends on their life-history traits. We investigated how breeding bird species can be grouped, based on their life-history traits and according to weather-correlated population dynamics. We developed and applied the linear trait–environment method (LTE), which is a modified version of the fourth-corner method. Despite our focus on single traits, 2 strategies—combinations of several traits—stand out. As expected, breeding populations of waterfowl species are negatively impacted by severe winters directly preceding territory monitoring, probably because of increased adult mortality. Waterfowl species combine several traits: they often breed at ground or water level, feed on plant material, are precocial and are generally short-distance or partial migrants. Furthermore, we found a decline in population growth rates of insectivorous long-distance migrants due to mild winters and warm springs in the year before territory monitoring, which may be caused by reduced reproduction due to trophic mismatches. We identify species that are expected to show the most significant responses to changing weather variability, assuming that our conclusions are based on causal relationships and that the way species, weather variables and habitat interact will not alter. Species expected to respond positively can again be roughly categorized as waterfowl species, while insectivorous long-distance migrants are mostly expected to respond negatively. As species traits play an important role in constructing functional groups that are relevant to the provisioning of ecosystem services, our study enables the incorporation of ecosystem vulnerability to climate change into such functional approache

Wealth, weather risk, and the composition and profitability of agricultural investments

Despite the growing evidence that farmers in low-income environments are risk-averse, there has been little empirical evidence on the importance of risk in shaping the actual allocation of production resources among farmers differentiated by wealth. The authors use panel data on investments in rural India to examine how the composition of productive and nonproductive asset holdings varies across farmers with different levels of total wealth and across farmers facing different degrees of weather risk. Income variability is a prominent feature of the experience of rural agents in low-income countries. The authors report evidence, based on measures of rainfall variability, that the agricultural investment portfolio behavior of farmers in such settings reflects risk aversion, due evidently to limitations on consumption-soothing mechanisms such as crop insurance or credit markets. The authors'results suggest that uninsured weather risk is a significant cause of lower efficiency and lower average incomes: a one-standard-deviation decrease in weather risk (measured by the standard deviation of the timing of the rainy season) would raise average profits by up to 35 percent among farmers in the lowest wealth quartile. Moreover, rainfall variability induces a more unequal distribution of average incomes for a given distribution of wealth. Wealthier farmers are willing to absorb significant risk without giving up profits to reduce production risk. Smaller farmers have to invest their limited wealth in ways that reduce their exposure to risk at the cost of lower profit rates. The authors found that at high levels of rainfall variability, differences in rates of profit per unit of agricultural assets were similar across classes of wealth. But over the sample range of rainfall variability, these rates of profit were always higher for the poorer farmers than for the wealthier ones, suggesting that the disadvantages of small farmers in risk diffusion are more than offset by their labor cost advantage.International Terrorism&Counterterrorism,Economic Theory&Research,Environmental Economics&Policies,Health Economics&Finance,Financial Intermediation