Search

The article focuses on an observation of changes in perspective of several television weathercasters approaches towards human-caused climate change. Topics discussed include analysis of different surveys regarding global warming issues among weathercasters such as Kris Wilson; assessment of census by the U.S. National Weather Association over an increase in response rate for global warming as an issue; and interest of weathercasters over reporting issues such as an extreme precipitation.

Among papers stating a position on anthropogenic global warming (AGW), 97 % endorse AGW. What is happening with the 2 % of papers that reject AGW? We examine a selection of papers rejecting AGW. An analytical tool has been developed to replicate and test the results and methods used in these studies; our replication reveals a number of methodological flaws, and a pattern of common mistakes emerges that is not visible when looking at single isolated cases. Thus, real-life scientific disputes in some cases can be resolved, and we can learn from mistakes. A common denominator seems to be missing contextual information or ignoring information that does not fit the conclusions, be it other relevant work or related geophysical data. In many cases, shortcomings are due to insufficient model evaluation, leading to results that are not universally valid but rather are an artifact of a particular experimental setup. Other typical weaknesses include false dichotomies, inappropriate statistical methods, or basing conclusions on misconceived or incomplete physics. We also argue that science is never settled and that both mainstream and contrarian papers must be subject to sustained scrutiny. The merit of replication is highlighted and we discuss how the quality of the scientific literature may benefit from replication. [ABSTRACT FROM AUTHOR]

Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method's skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a 'perfect model' experimental design that quantifies aspects of ESD method performance for both historical and late 21st century time periods. The experimental design tests a key stationarity assumption inherent to ESD methods - namely, that ESD performance when applied to future projections is similar to that during the observational training period. Case study results employing a single ESD method (an Asynchronous Regional Regression Model variant) and climate variable (daily maximum temperature) demonstrate that violations of the stationarity assumption can vary geographically, seasonally, and with the amount of projected climate change. For the ESD method tested, the greatest challenges in downscaling daily maximum temperature projections are revealed to occur along coasts, in summer, and under conditions of greater projected warming. We conclude with a discussion of the potential use and expansion of the perfect model experimental design, both to inform the development of improved ESD methods and to provide guidance on the use of ESD products in climate impacts analyses and decision-support applications. [ABSTRACT FROM AUTHOR]

Conceptual change theory holds that pre-existing misconceptions persist under regular instruction and interfere with student acquisition of correct concepts. This implies that effective instruction should address both concepts and common misconceptions. Here, we investigate the efficacy of incorporating corrective information regarding well-documented misconceptions on the greenhouse effect and how the climate is affected by human activities into instruction on the greenhouse effect. Specifically, we compare student acquisition of fundamental concepts related to the greenhouse effect and climate change via traditional reading-based instruction (TRI) to misconceptions reading-based instruction (MRI). Lab sections from a first year Atmospheric Sciences course at a large public university were assigned to either a TRI or MRI treatment group, yielding a total of 197 subjects who successfully completed a pretest, the treatment, an immediate post-test, and a 2-week delayed post-test. Repeated-measures analysis of variance (ANOVA) with contrasts reveals that while both treatments produced significant gains in the post-test and delayed post-test overall, only the MRI treatment produced longterm gains on misconception-related questions. These results support the conceptual change model's claims that misconceptions may persist through concept-based instruction, but may be uprooted by instruction that addresses them directly. Based on these findings, it appears that a relatively brief reading passage focused on the basic science underlying a complex phenomenon, such as the greenhouse effect and human impact on climate, can be effective in improving student understanding. In addition, specifically addressing and instructing against misconceptions may improve learner acquisition of key concepts on which misconceptions exist. [ABSTRACT FROM AUTHOR]

Climate change is expected to alter the geographic distribution of wildfire, a complex abiotic process that responds to a variety of spatial and environmental gradients. How future climate change may alter global wildfire activity, however, is still largely unknown. As a first step to quantifying potential change in global wildfire, we present a multivariate quantification of environmental drivers for the observed, current distribution of vegetation fires using statistical models of the relationship between fire activity and resources to burn, climate conditions, human influence, and lightning flash rates at a coarse spatiotemporal resolution (100 km, over one decade). We then demonstrate how these statistical models can be used to project future changes in global fire patterns, highlighting regional hotspots of change in fire probabilities under future climate conditions as simulated by a global climate model. Based on current conditions, our results illustrate how the availability of resources to burn and climate conditions conducive to combustion jointly determine why some parts of the world are fire-prone and others are fire-free. In contrast to any expectation that global warming should necessarily result in more fire, we find that regional increases in fire probabilities may be counter-balanced by decreases at other locations, due to the interplay of temperature and precipitation variables. Despite this net balance, our models predict substantial invasion and retreat of fire across large portions of the globe. These changes could have important effects on terrestrial ecosystems since alteration in fire activity may occur quite rapidly, generating ever more complex environmental challenges for species dispersing and adjusting to new climate conditions. Our findings highlight the potential for widespread impacts of climate change on wildfire, suggesting severely altered fire regimes and the need for more explicit inclusion of fire in research on global vegetation-climate change dynamics and conservation planning. [ABSTRACT FROM AUTHOR]

Climate projections at relevant temporal and spatial scales are essential to assess potential future climate change impacts on climatologically diverse regions such as the northeast United States. Here, we show how both statistical and dynamical downscaling methods applied to relatively coarse-scale atmosphere-ocean general circulation model output are able to improve simulation of spatial and temporal variability in temperature and precipitation across the region. We then develop high-resolution projections of future climate change across the northeast USA, using IPCC SRES emission scenarios combined with these downscaling methods. The projections show increases in temperature that are larger at higher latitudes and inland, as well as the potential for changing precipitation patterns, particularly along the coast. While the absolute magnitude of change expected over the coming century depends on the sensitivity of the climate system to human forcing, significantly higher increases in temperature and in winter precipitation are expected under a higher as compared to lower scenario of future emissions from human activities. [ABSTRACT FROM AUTHOR]

Global climate change has already affected the abundances, range limits, and interactions of many species. The hemlock woolly adelgid (Adelges tsugae), an invasive insect introduced to eastern North America from Japan, has decimated stands of eastern hemlock (Tsuga canadensis) and Carolina hemlock (T. caroliniana) from Georgia to Connecticut. However, its spread across central and northern New England has been slowed substantially by its inability to tolerate cold winter temperatures. Using data from previous lab and field studies collected over the past 17 years, including adelgid spread and overwintering mortality, we first characterize the temperature conditions that may limit adelgid spread. We then show how, in the future, rising winter temperatures due to climate change are likely to remove the conditions currently limiting adelgid spread, and facilitate the northward expansion as more suitable habitat becomes available. [ABSTRACT FROM AUTHOR]

• Some types of weather affect Americans' belief that they have experienced global warming. • Americans associate hot, dry days and extreme drought with global warming. • People often fail to interpret extreme rainfall or flooding as due to global warming. Public perceptions of climate change in the United States are deeply rooted in cultural values and political identities. Yet, as the public experiences extreme weather and other climate change-related impacts, their perceptions of the issue may shift. Here, we explore whether, when, and where local climate trends have already influenced perceived experiences of global warming in the United States. Using a large national survey dataset (n = 13,607), we compare Americans' experiences of climate with corresponding trends in seven different high-resolution climate indicators for the period 2008 to 2015. We find that increases in hot dry day exposure significantly increases individuals' perceptions that they have personally experienced global warming. We do not find robust evidence that other precipitation and temperature anomalies have had a similar effect. We also use multilevel modeling to explore county-level patterns of perceived experiences with climate change. Whereas the individual-level analysis describes a likely causal relationship between a changing climate and individuals' perceived experience, the multilevel model depicts county-level changes in perceived experience resulting from particular climate trends. Overall, we find that exposure to hot dry days, has a modest influence on perceived experience, independent of the political and socio-demographic factors that dominate U.S. climate opinions today. [ABSTRACT FROM AUTHOR]