Extreme weather events and climate change concern

This paper examines whether experience of extreme weather events—such as excessive heat, droughts, flooding, and hurricanes—increases an individual’s level concern about climate change. We bring together micro-level geospatial data on extreme weather events from NOAA’s Storm Events Database with public opinion data from multiple years of the Cooperative Congressional Election Study to study this question. We find evidence of a modest, but discernible positive relationship between experiencing extreme weather activity and expressions of concern about climate change. However, the effect only materializes for recent extreme weather activity; activity that occurred over longer periods of time does not affect public opinion. These results are generally robust to various measurement strategies and model specifications. Our findings contribute to the public opinion literature on the importance of local environmental conditions on attitude formation.     

Staying cool in a changing climate: Reaching vulnerable populations during heat events

The frequency and intensity of hot weather events are expected to increase globally, threatening human health, especially among the elderly, poor, and chronically ill. Current literature indicates that emergency preparedness plans, heat health warning systems, and related interventions may not be reaching or supporting behavior change among those most vulnerable in heat events. Using a qualitative multiple case study design, we comprehensively examined practices of these populations to stay cool during hot weather (“cooling behaviors”) in four U.S. cities with documented racial/ethnic and socio-economic disparities and diverse heat preparedness strategies: Phoenix, Arizona; Detroit, Michigan; New York City, New York; and Philadelphia, Pennsylvania. Based on semi-structured in-depth interviews we conducted with 173 community members and organizational leaders during 2009–2010, we assessed why vulnerable populations do or do not participate in health-promoting behaviors at home or in their community during heat events, inquiring about perceptions of heat-related threats and vulnerability and the role of social support. While vulnerable populations often recognize heat's potential health threats, many overlook or disassociate from risk factors or rely on experiences living in or visiting warmer climates as a protective factor. Many adopt basic cooling behaviors, but unknowingly harmful behaviors such as improper use of fans and heating and cooling systems are also adopted. Decision-making related to commonly promoted behaviors such as air conditioner use and cooling center attendance is complex, and these resources are often inaccessible financially, physically, or culturally. Interviewees expressed how interpersonal, intergenerational relationships are generally but not always protective, where peer relationships are a valuable mechanism for facilitating cooling behaviors among the elderly during heat events. To prevent disparities in heat morbidity and mortality in an increasingly changing climate, we note the implications of local context, and we broadly inform heat preparedness plans, interventions, and messages by sharing the perspectives and words of community members representing vulnerable populations and leaders who work most closely with them.     

Extreme climate events in China: IPCC-AR4 model evaluation and projection

Observations from 550 surface stations in China during 1961–2000 are used to evaluate the skill of seven global coupled climate models in simulating extreme temperature and precipitation indices. It is found that the models have certain abilities to simulate both the spatial distributions of extreme climate indices and their trends in the observed period. The models’ abilities are higher overall for extreme temperature indices than for extreme precipitation indices. The well-simulated temperature indices are frost days (Fd), heat wave duration index (HWDI) and annual extreme temperature range (ETR). The well-simulated precipitation indices are the fraction of annual precipitation total due to events exceeding the 95th percentile (R95T) and simple daily intensity index (SDII). In a general manner, the multi-model ensemble has the best skill. For the projections of the extreme temperature indices, trends over the twenty-first century and changes at the end of the twenty-first century go into the same direction. Both frost days and annual extreme temperature range show decreasing trends, while growing season length, heat wave duration and warm nights show increasing trends. The increases are especially manifested in the Tibetan Plateau and in Southwest China. For extreme precipitation indices, the end of the twenty-first century is expected to have more frequent and more intense extreme precipitation. This is particularly visible in the middle and lower reaches of the Yangtze River, in the Southeast coastal region, in the west part of Northwest China, and in the Tibetan Plateau. In the meanwhile, accompanying the decrease in the maximum number of consecutive dry days in Northeast and Northwest, drought situation will reduce in these regions.     

Alpine snow cover in a changing climate: a regional climate model perspective

An analysis is presented of an ensemble of regional climate model (RCM) experiments from the ENSEMBLES project in terms of mean winter snow water equivalent (SWE), the seasonal evolution of snow cover, and the duration of the continuous snow cover season in the European Alps. Two sets of simulations are considered, one driven by GCMs assuming the SRES A1B greenhouse gas scenario for the period 1951–2099, and the other by the ERA-40 reanalysis for the recent past. The simulated SWE for Switzerland for the winters 1971–2000 is validated against an observational data set derived from daily snow depth measurements. Model validation shows that the RCMs are capable of simulating the general spatial and seasonal variability of Alpine snow cover, but generally underestimate snow at elevations below 1,000 m and overestimate snow above 1,500 m. Model biases in snow cover can partly be related to biases in the atmospheric forcing. The analysis of climate projections for the twenty first century reveals high inter-model agreement on the following points: The strongest relative reduction in winter mean SWE is found below 1,500 m, amounting to 40–80 % by mid century relative to 1971–2000 and depending upon the model considered. At these elevations, mean winter temperatures are close to the melting point. At higher elevations the decrease of mean winter SWE is less pronounced but still a robust feature. For instance, at elevations of 2,000–2,500 m, SWE reductions amount to 10–60 % by mid century and to 30–80 % by the end of the century. The duration of the continuous snow cover season shows an asymmetric reduction with strongest shortening in springtime when ablation is the dominant factor for changes in SWE. We also find a substantial ensemble-mean reduction of snow reliability relevant to winter tourism at elevations below about 1,800 m by mid century, and at elevations below about 2,000 m by the end of the century.     

IPCC AR6报告解读：短寿命气候强迫因子的气候及环境效应

影响气候变化的大气成分,依据其在大气中存留的时间,分为长寿命的温室气体和短寿命的气候强迫因子（SLCFs)。考虑到SLCFs在气候变化和大气环境中的重要作用,IPCC第六次评估报告（AR6）首次有了专门针对SLCFs的章节（第六章)。本文解读IPCC报告关于SLCFs的主要结论,特别强调AR5以来的最新结论,包括：SLCFs的定义、SLCFs排放和大气含量的变化特征及其对辐射强迫和全球气候的影响、不同共享社会经济路径（SSP）情景下SLCFs对未来气候变化和空气质量可能的影响,以及COVID-19疫情期间减排对气候变化的影响。文末也讨论了结论的不确定性以及结论对我国的启示。     

Lake surface temperatures in a changing climate: a global sensitivity analysis

We estimate the effects of climatic changes, as predicted by six climate models, on lake surface temperatures on a global scale, using the lake surface equilibrium temperature as a proxy. We evaluate interactions between different forcing variables, the sensitivity of lake surface temperatures to these variables, as well as differences between climate zones. Lake surface equilibrium temperatures are predicted to increase by 70 to 85 % of the increase in air temperatures. On average, air temperature is the main driver for changes in lake surface temperatures, and its effect is reduced by ~10 % by changes in other meteorological variables. However, the contribution of these other variables to the variance is ~40 % of that of air temperature, and their effects can be important at specific locations. The warming increases the importance of longwave radiation and evaporation for the lake surface heat balance compared to shortwave radiation and convective heat fluxes. We discuss the consequences of our findings for the design and evaluation of different types of studies on climate change effects on lakes.     

A megacity in a changing climate: the case of Kolkata

Projections by the Intergovernmental Panel on Climate Change suggest that there will be an increase in the frequency and intensity of climate extremes in the 21st century. Kolkata, a megacity in India, has been singled out as one of the urban centers vulnerable to climate risks. Modest flooding during monsoons at high tide in the Hooghly River is a recurring hazard in Kolkata. More intense rainfall, riverine flooding, sea level rise, and coastal storm surges in a changing climate can lead to widespread and severe flooding and bring the city to a standstill for several days. Using rainfall data, high and low emissions scenarios, and sea level rise of 27 cm by 2050, this paper assesses the vulnerability of Kolkata to increasingly intense precipitation events for return periods of 30, 50, and 100 years. It makes location-specific inundation depth and duration projections using hydrological, hydraulic, and urban storm models with geographic overlays. High resolution spatial analysis provides a roadmap for designing adaptation schemes to minimize the impacts of climate change. The modeling results show that de-silting of the main sewers would reduce vulnerable population estimates by at least 5 %.     

Non-stationary extreme value analysis in a changing climate

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.     

California coastal management with a changing climate

With over 2,000 miles (3,218 km) of ocean and estuarine coastline, California faces significant coastal management challenges as a result of climate change-induced sea level rise. Under high emission scenarios, recent models predict 1.4 m or more of sea level rise by 2100, accompanied by increasing storm surges. This article investigates the most important issues facing coastal managers, explores the policy tools available for adapting to the impacts of climate change, assesses institutional constraints to adaptation, and identifies priorities for future research and policy action. We find that adaptation tools exist for dealing with anticipated increases in coastal erosion and flooding, but they involve significant costs and tradeoffs. In particular, coastal armoring, such as seawalls, can protect developed coastal lands, but destroys beaches and habitat. Although California already has policies and institutions that aim to balance the competing objectives for coastal development, management agencies are at the early stages of understanding how to facilitate adaptation. Research priorities to inform coastal adaptation planning include: (i) inventorying coastal resources to provide a firmer basis for balancing decisions on property and habitat protection, (ii) identifying opportunities for coastal habitat migration, (iii) assessing the vulnerabilities of existing and planned coastal infrastructure, and (iv) experimenting with alternatives to armoring as a way of managing the changing coastline.     

Dry season precipitation over the Mesoamerican Biological Corridor is more sensitive to deforestation than to greenhouse gas driven climate change

To prevent the loss of biodiversity in northern Central America, which is one of 34 global biodiversity hotspots, the Mesoamerican Biological Corridor, a network of protected parks and reserves has been proposed. While on-going deforestation to croplands and pastures outside the protected regions is likely to effect the dry season precipitation over the regenerated and extant forests in the proposed protected regions, global climate change driven precipitation changes may also be a significant factor, at least at some locations. This study compares the effects of land cover change to the effects of elevated greenhouse gas concentrations on precipitation in the proposed areas of the Mesoamerican Biological Corridor network. Using 5 consecutive dry season simulations of the effects of land cover change that included dry, wet and normal years, and using statistically downscaled global climate model (GCM) precipitation from the fourth assessment report (AR4), a larger expanse of the proposed protected regions was found more sensitive to precipitation decreases due to land cover changes. Two specific protected regions however stand out: the Maya Highlands and some areas of the Maya lowlands that were more sensitive to global climate change driven precipitation decreases. In these protected regions it is likely that irrespective of local policies the climate change signal would dominate.     

Impact of non-outbreak insect damage on vegetation in northern Europe will be greater than expected during a changing climate

Background insect herbivory, in addition to insect outbreaks, can have an important long term influence on the performance of tree species. Since a projected warmer climate may favour insect herbivores, we use a dynamic ecosystem model to investigate the impacts of background herbivory on vegetation growth and productivity, as well as distribution and associated changes in terrestrial ecosystems of northern Europe. We used the GUESS ecosystem modelling framework and a simple linear model for including the leaf area loss of Betula pubescens in relation to mean July temperature. We tested the sensitivity of the responses of the simulated ecosystems to different, but realistic, degrees of insect damage. Predicted temperature increases are likely to enhance the potential insect impacts on vegetation. The impacts are strongest in the eastern areas, where potential insect damage to B. pubescens can increase by 4–5%. The increase in insect damage to B. pubescens results in a reduction of total birch leaf area (LAI), total birch biomass and birch productivity (Net Primary Production). This effect is stronger than the insect damage to leaf area alone would suggest, due to its second order effect on the competition between tree species. The model's demonstration that background herbivory may cause changes in vegetation structure suggests that insect damage, generally neglected by vegetation models, can change predictions of future forest composition. Carbon fluxes and albedo are only slightly influenced by background insect herbivory, indicating that background insect damage is of minor importance for estimating the feedback of terrestrial ecosystems to climate change.     

Water resources for agriculture in a changing climate: international case studies

This integrated study examines the implications of changes in crop water demand and water availability for the reliability of irrigation, taking into account changes in competing municipal and industrial demands, and explores the effectiveness of adaptation options in maintaining reliability. It reports on methods of linking climate change scenarios with hydrologic, agricultural, and planning models to study water availability for agriculture under changing climate conditions, to estimate changes in ecosystem services, and to evaluate adaptation strategies for the water resources and agriculture sectors. The models are applied to major agricultural regions in Argentina, Brazil, China, Hungary, Romania, and the US, using projections of climate change, agricultural production, population, technology, and GDP growth.For most of the relatively water-rich areas studied, there appears to be sufficient water for agriculture given the climate change scenarios tested. Northeastern China suffers from the greatest lack of water availability for agriculture and ecosystem services both in the present and in the climate change projections. Projected runoff in the Danube Basin does not change substantially, although climate change causes shifts in environmental stresses within the region. Northern Argentina's occasional problems in water supply for agriculture under the current climate may be exacerbated and may require investments to relieve future tributary stress. In Southeastern Brazil, future water supply for agriculture appears to be plentiful. Water supply in most of the US Cornbelt is projected to increase in most climate change scenarios, but there is concern for tractability in the spring and water-logging in the summer.Adaptation tests imply that only the Brazil case study area can readily accommodate an expansion of irrigated land under climate change, while the other three areas would suffer decreases in system reliability if irrigation areas were to be expanded. Cultivars are available for agricultural adaptation to the projected changes, but their demand for water may be higher than currently adapted varieties. Thus, even in these relatively water-rich areas, changes in water demand due to climate change effects on agriculture and increased demand from urban growth will require timely improvements in crop cultivars, irrigation and drainage technology, and water management.     

Evolution of the Parisian urban climate under a global changing climate

The evolution of the Parisian urban climate under a changing climate is analyzed from long-term offline numerical integrations including a specific urban parameterization. This system is forced by meteorological conditions based on present-climate reanalyses (1970–2007), and climate projections (2071–2099) provided by global climate model simulations following two emission scenarios (A1B and A2). This study aims at quantifying the impact of climate change on air temperature within the city and in the surroundings. A systematic increase of 2-meter air temperature is found. In average according to the two scenarios, it reaches +?2.0/2.4°C in winter and +?3.5/5.0°C in summer for the minimum and maximum daily temperatures, respectively. During summer, the warming trend is more pronounced in the surrounding countryside than in Paris and suburbs due to the soil dryness. As a result, a substantial decrease of the strong urban heat islands is noted at nighttime, and numerous events with negative urban heat islands appear at daytime. Finally, a 30% decrease of the heating degree days is quantified in winter between present and future climates. Inversely, the summertime cooling degree days significantly increase in future climate whereas they are negligible in present climate. However, in terms of accumulated degree days, the increase of the demand in cooling remains smaller than the decrease of the demand in heating.