Climatic Change最新文献

This study reconstructed the river freeze-up date anomalies utilizing historical river ice records from the Korean Envoys Yanxing Book (韩使燕行录) for the 60 years spanning the sixteenth to nineteenth centuries in southern Northeast China. The main changing features of river freeze-up phenology are summarized as follows: (1) The river freeze-up date was 5 days earlier than the modern mean during the sixteenth to nineteenth centuries in southern Northeast China; (2) during the study period, four colder and earlier freeze-up periods are identified of 1533–1572, 1619–1690, 1712–1729, and 1822–1888, as well as two warmer and delayed freeze-up periods of 1610–1617 and 1755–1803; (3) this study gave more detailed reconstructions on the river freeze-up phenology and temperature change than the previous studies, specifically the warmer in 1610–1617 indicated by the delayed freeze-up dates, the colder period in the 1720s indicated by the earlier freeze-up dates, and the warm period during the mid-to-late eighteenth century lasting until the 1790s at least.

If local governments reduce greenhouse gas emissions, they will not see effects unless a very large number of other actors do the same. However, reducing greenhouse gas emissions can have multiple local “co-benefits” (improved air quality, energy savings, even energy security), creating incentives for local governments to reduce emissions—if just for the local side-effects of doing so. Available empirical research yet shows a large gap between co-benefits as a rationale and an explanatory factor for climate mitigation by local governments: co-benefits are seemingly very large, but do not seem to drive local mitigation efforts. Relying on policy documents, available research, and other written sources, the present paper consists of a multiple case study addressing the link between co-benefits and climate mitigation in Moscow, Paris, and Montreal. Air quality plays a very different role in each case, ranging from a key driver of mitigation to a liability for local climate action. This heterogeneity of mechanisms in place emerges as a likely explanation for the lack of a clear empirical link between co-benefits and local mitigation in the literature. We finally discuss implications for urban climate action policy and research.

Future streamflow and nutrient projections are necessary for the development of sustainable water resources management plans and practices. Watersheds located in the Northern Lake Okeechobee (NLO) areas serve as a source of water and nutrients to Lake Okeechobee and its downstream areas, including the Everglades, in South Florida. This study investigated how projected changes in climate would affect water and nutrient loading to the lake to help develop watershed management plans for improved sustainability of South Florida. Future ensemble climate projections were prepared using the outputs of 29 Coupled Model Intercomparison Project (CMIP) Phase 5 General Circulation Models (GCMs), which were then incorporated into the Watershed Assessment Model (WAM) developed to simulate the streamflow and nutrient loading from the six study drainage watersheds to the lake. The bias-corrected GCMs projected a consistent increase in air temperature in the 2040s and 2070s under both Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. The projected changes in precipitation substantially varied depending on the GCM selections; uncertainty in the multi-model ensemble precipitation projection was propagated to the hydrological projections. The streamflow and nutrient loading projections were closely related to the projected precipitation depths. The modeling experiment results showed that the total phosphorus loads per unit area would be consistently associated with the percentages of pastureland in both historical and future periods. Overall, nutrient loads were projected to increase. Such findings indicate the need for nutrient control strategies and innovative solutions to make progress toward Lake Okeechobee water quality goals in the face of climate change.

Rangeland social-ecological systems (SESs), which make up vast tracts of Earth’s terrestrial surface, are facing unprecedented change—from climate change and vegetation transitions to large-scale shifts in human land use and changing social and economic conditions. Understanding how people who manage and depend on rangeland resources are adapting to change has been the focus of a rapidly growing body of research, which has the potential to provide important insights for climate change adaptation policy and practice. Here, we use quantitative, qualitative, and bibliometric analyses to systematically review the scope, methods, and findings of 56 studies that examine the social dimensions of adaptation in rangeland SESs. Our review focuses on studies within the climate adaptation, adaptive capacity, and adaptive decision-making sub-fields, finding that this body of research is highly diverse in its disciplinary roots and theoretical origins, and therefore uses a wide range of frameworks and indicators to evaluate adaptation processes. Bibliometric analyses revealed that the field is fragmented into distinct scholarly communities that use either adaptive capacity or adaptive decision-making frameworks, with a lack of cross-field citation. Given the strengths (and weaknesses) inherent in each sub-field, this review suggests that greater cross-pollination across the scholarship could lead to new insights, particularly for capturing cross-scale interactions related to adaptation on rangelands. Results also showed that a majority of studies that examine adaptation in either “ranching” or “rangeland” systems are geographically concentrated in few, high-income countries (i.e., USA, Australia, China), demonstrating a need to extend future research efforts to understudied regions of the globe with rangeland-based livelihoods. Finally, our review highlights the need for more translational rangeland science, where policy- and practice-relevant frameworks evaluating adaptation in rangeland SESs might be developed by co-producing research working with rangeland communities.

Newspaper coverage of climate change has the potential to shape how the public and elites define this policy problem, the solutions under consideration, and the level of climate change concern. Existing US research focuses exclusively on national coverage, which limits our knowledge of how subnational media outlets report on climate change. In contrast, this study constructs an original dataset of over 12,000 climate-change-related articles that appeared in newspapers in forty-nine US states in 2012 and 2017. We combine manual coding and automated text analysis to assess whether the content of climate change coverage varies systematically across states. Consistent with existing research on journalistic norms, our analysis suggests that coverage reflects geographic differences in the ecological effects of climate change and the specific mix of industries present in a state.

Although dependency ratio (DR) significantly affects anthropogenic emissions and thus emission abatement, previous researchers usually neglected DR’s impact on climate policy implementation. In this paper, we attempt to narrow the research gap by studying how the DR is related to the emission trading scheme (ETS) in China. To achieve this research target, a Computable General Equilibrium (CGE) model is employed to study the relation between DR and ETS. The CGE model results show that both DR and ETS negatively affect carbon emissions because they decrease labor employment and thus economic output. The DR is negatively related to emission abatement and the economic cost of the ETS, whereas the ETS relieves the negative DR impacts on the economy and emissions. These findings imply that characterized by rising DR, population aging helps achieve emission mitigation; as climate policy relieves the negative impacts of the rising DR on the economy and emissions, its implementation may be smoother in an aging society.

Rigorous statistical methods are used to examine changes in the timing of heavy storms for various storm durations, to assess the degree to which climate change has influenced heavy precipitation events. The findings indicate that the timing of heavy storms (from 5-min to 24-h duration events in Ontario, Canada) is indicating that they are now predominantly occurring earlier within the calendar year. An example of the situation is that within the period from 1960 to 2017, the mean occurrence times of heavy storms have advanced by 44 days, with maximum and minimum time advances of 86 and 21 days, respectively. Trend analysis, including the Mann-Kendall test, linear regression, and Sen’s slope method, all show that the times of heavy storms have advanced to earlier times within the calendar year. These earlier times of heavy storms will influence the natural and anthropogenic activities such as crop planning, drought management, and groundwater replenishment. This research provides new evidence and dimensions regarding the understanding of climate change, particularly related to the timing of heavy storms and various adaptation strategies (e.g., flood prevention and soil conservation).

The effects of global warming on the regional climate of southern South America (SSA) during the recent decades have been exhaustively documented with consistency throughout the literature. However, the projected changes on temperature- and precipitation-related climate hazards depict an important uncertainty, mostly in the intensity of the changes. This work assessed a set of CMIP5 and CMIP6 global climate models (GCMs) in reproducing the observed atmospheric circulation patterns (CPs) over SSA and their expected changes for the twenty-first century. Furthermore, the attribution of the seasonal precipitation changes to changes in the CPs was explored for the late future (2070–2100). GCMs were generally able to represent the variety of CPs and their seasonal frequencies, although presenting more deficiencies in capturing their respective precipitation patterns over SSA. Larger model agreement was found in the increasing and/or decreasing frequency of specific CPs for the near future (2040–2070) than for the late future, when model spread became more noticeable. Particularly, CPs associated with larger positive rainfall anomalies over southeastern South America—a hotspot for precipitation extremes—are expected to become more frequent in the near future, whereas their changes in the longer term are more uncertain. When performing an attributional study, precipitation changes showed important differences between GCMs and were often associated with changes in the intrapattern variability rather than in the CP changing frequency. In this way, the modification of the precipitation regime of SSA may not be explained only by changes in the large-scale circulation but probably also by other regional-to-local features.

Weighting climate models has recently become a more accepted approach. However, it remains a topic of ongoing discussion, especially for analyses needed at regional scales, such as hydrological assessments. Various studies have evaluated the weighting approaches for climate simulations. Yet, few case studies have assessed the impacts of weighting climate models on streamflow projections. Additionally, the methodological and location limitations of previous studies make it difficult to extrapolate their conclusions over regions with contrasting hydroclimatic regimes, highlighting the need for further studies. Thus, this study evaluates the effects of different climate model’s weighting approaches on hydrological projections over hydrologically diverse basins. An ensemble of 24 global climate model (GCM) simulations coupled with a lumped hydrological model is used over 20 North American basins to generate 24 GCM-driven streamflow projections. Six unequal-weighting approaches, comprising temperature-, precipitation-, and streamflow-based criteria, were evaluated using an out-of-sample approach during the 1976–2005 reference period. Moreover, the unequal-weighting approaches were compared against the equal-weighting approach over the 1976–2005, 2041–2070, and 2070–2099 periods. The out-of-sample assessment showed that unequally weighted ensembles can improve the mean hydrograph representation under historical conditions compared to the common equal-weighting approach. In addition, results revealed that unequally weighting climate models not only impacted the magnitude and climate change signal, but also reduced the model response uncertainty spread of hydrological projections, particularly over rain-dominated basins. These results underline the need to further evaluate the adequacy of equally weighting climate models, especially for variables with generally larger uncertainty at regional scale.