Future air pollution in the Shared Socio-economic Pathways

Emissions of air pollutants such as sulfur and nitrogen oxides and particulates have significant health impacts as well as effects on natural and anthropogenic ecosystems. These same emissions also can change atmospheric chemistry and the planetary energy balance, thereby impacting global and regional climate. Long-term scenarios for air pollutant emissions are needed as inputs to global climate and chemistry models, and for analysis linking air pollutant impacts across sectors. In this paper we present methodology and results for air pollutant emissions in Shared Socioeconomic Pathways (SSP) scenarios. We first present a set of three air pollution narratives that describe high, central, and low pollution control ambitions over the 21st century. These narratives are then translated into quantitative guidance for use in integrated assessment models. The resulting pollutant emission trajectories under the SSP scenarios cover a wider range than the scenarios used in previous international climate model comparisons. In the SSP3 and SSP4 scenarios, where economic, institutional and technological limitations slow air quality improvements, global pollutant emissions over the 21^st century can be comparable to current levels. Pollutant emissions in the SSP1 scenarios fall to low levels due to the assumption of technological advances and successful global action to control emissions.     

Learning about parameter and structural uncertainty in carbon cycle models

Uncertainty in the response of the global carbon cycle to anthropogenic emissions plays a key role in assessments of potential future climate change and response strategies. We investigate how fast this uncertainty might change as additional data on the global carbon budget becomes available over the twenty-first century. Using a simple global carbon cycle model and focusing on both parameter and structural uncertainty in the terrestrial sink, we find that additional global data leads to substantial learning (i.e., changes in uncertainty) under some conditions but not others. If the model structure is assumed known and only parameter uncertainty is considered, learning is rather limited if observational errors in the data or the magnitude of unexplained natural variability are not reduced. Learning about parameter values can be substantial, however, when errors in data or unexplained variability are reduced. We also find that, on the one hand, uncertainty in the model structure has a much bigger impact on uncertainty in projections of future atmospheric composition than does parameter uncertainty. But on the other, it is also possible to learn more about the model structure than the parameter values, even from global budget data that does not improve over time in terms of its associated errors. As an example, we illustrate how one standard model structure, if incorrect, could become inconsistent with global budget data within 40 years. The rate of learning in this analysis is affected by the choice of a relatively simple carbon cycle model, the use of observations only of global emissions and atmospheric concentration, and the assumption of perfect autocorrelation in observational errors and variability. Future work could usefully improve the approach in each of these areas.     

Impacts of Climate Change on the Global Forest Sector

The path and magnitude of future anthropogenic emissions of carbon dioxide will likely influence changes in climate that may impact the global forest sector. These responses in the global forest sector may have implications for international efforts to stabilize the atmospheric concentration of carbon dioxide. This study takes a step toward including the role of global forest sector in integrated assessments of the global carbon cycle by linking global models of climate dynamics, ecosystem processes and forest economics to assess the potential responses of the global forest sector to different levels of greenhouse gas emissions. We utilize three climate scenarios and two economic scenarios to represent a range of greenhouse gas emissions and economic behavior. At the end of the analysis period (2040), the potential responses in regional forest growing stock simulated by the global ecosystem model range from decreases and increases for the low emissions climate scenario to increases in all regions for the high emissions climate scenario. The changes in vegetation are used to adjust timber supply in the softwood and hardwood sectors of the economic model. In general, the global changes in welfare are positive, but small across all scenarios. At the regional level, the changes in welfare can be large and either negative or positive. Markets and trade in forest products play important roles in whether a region realizes any gains associated with climate change. In general, regions with the lowest wood fiber production cost are able to expand harvests. Trade in forest products leads to lower prices elsewhere. The low-cost regions expand market shares and force higher-cost regions to decrease their harvests. Trade produces different economic gains and losses across the globe even though, globally, economic welfare increases. The results of this study indicate that assumptions within alternative climate scenarios and about trade in forest products are important factors that strongly influence the effects of climate change on the global forest sector.     

Natural and anthropogenic climate change: incorporating historical land cover change,vegetation dynamics and the global carbon cycle

This study explores natural and anthropogenic influences on the climate system, with an emphasis on the biogeophysical and biogeochemical effects of historical land cover change. The biogeophysical effect of land cover change is first subjected to a detailed sensitivity analysis in the context of the UVic Earth System Climate Model, a global climate model of intermediate complexity. Results show a global cooling in the range of –0.06 to –0.22 °C, though this effect is not found to be detectable in observed temperature trends. We then include the effects of natural forcings (volcanic aerosols, solar insolation variability and orbital changes) and other anthropogenic forcings (greenhouse gases and sulfate aerosols). Transient model runs from the year 1700 to 2000 are presented for each forcing individually as well as for combinations of forcings. We find that the UVic Model reproduces well the global temperature data when all forcings are included. These transient experiments are repeated using a dynamic vegetation model coupled interactively to the UVic Model. We find that dynamic vegetation acts as a positive feedback in the climate system for both the all-forcings and land cover change only model runs. Finally, the biogeochemical effect of land cover change is explored using a dynamically coupled inorganic ocean and terrestrial carbon cycle model. The carbon emissions from land cover change are found to enhance global temperatures by an amount that exceeds the biogeophysical cooling. The net effect of historical land cover change over this period is to increase global temperature by 0.15 °C.     

Boreal forest and tundra ecosystems as components of the climate system

The effects of terrestrial ecosystems on the climate system have received most attention in the tropics, where extensive deforestation and burning has altered atmospheric chemistry and land surface climatology. In this paper we examine the biophysical and biogeochemical effects of boreal forest and tundra ecosystems on atmospheric processes. Boreal forests and tundra have an important role in the global budgets of atmospheric CO₂ and CH₄. However, these biogeochemical interactions are climatically important only at long temporal scales, when terrestrial vegetation undergoes large geographic redistribution in response to climate change. In contrast, by masking the high albedo of snow and through the partitioning of net radiation into sensible and latent heat, boreal forests have a significant impact on the seasonal and annual climatology of much of the Northern Hemisphere. Experiments with the LSX land surface model and the GENESIS climate model show that the boreal forest decreases land surface albedo in the winter, warms surface air temperatures at all times of the year, and increases latent heat flux and atmospheric moisture at all times of the year compared to simulations in which the boreal forest is replaced with bare ground or tundra. These effects are greatest in arctic and sub-arctic regions, but extend to the tropics. This paper shows that land-atmosphere interactions are especially important in arctic and sub-arctic regions, resulting in a coupled system in which the geographic distribution of vegetation affects climate and vice versa. This coupling is most important over long time periods, when changes in the abundance and distribution of boreal forest and tundra ecosystems in response to climatic change influence climate through their carbon storage, albedo, and hydrologic feedbacks.     

Historical and future quantification of terrestrial carbon sequestration from a Greenhouse-Gas-Value perspective

Terrestrial ecosystems provide a range of important services to humans, including global and regional climate regulation. These services arise from natural ecosystem functioning as governed by drivers such as climate, atmospheric carbon dioxide mixing ratio, and land-use change. From the perspective of carbon sequestration, numerous studies have assessed trends and projections of the past and future terrestrial carbon cycle, but links to the ecosystem service concept have been hindered by the lack of appropriate quantitative service metrics. The recently introduced concept of the Greenhouse Gas Value (GHGV) accounts for the land-atmosphere exchanges of multiple greenhouse gases by taking into consideration the associated ecosystem pool sizes, annual exchange fluxes and probable effects of natural disturbance in a time-sensitive manner.We use here GHGV as an indicator for the carbon sequestration aspects of the climate regulation ecosystem service, and quantify it at global scale using the LPJ-GUESS dynamic global vegetation model. The response of ecosystem dynamics and ecosystem state variables to trends in climate, atmospheric carbon dioxide levels and land use simulated by LPJ-GUESS are used to calculate the contribution of carbon dioxide to GHGV. We evaluate global variations in GHGV over historical periods and for future scenarios (1850–2100) on a biome basis following a high and a low emission scenario.GHGV is found to vary substantially depending on the biogeochemical processes represented in LPJ-GUESS (e.g. carbon–nitrogen coupling, representation of land use). The consideration of disturbance events that occur as part of an ecosystem's natural dynamics is crucial for realistic GHGV assessments; their omission results in unrealistically high GHGV. By considering the biome-specific response to current climate and land use, and their projections for the future, we highlight the importance of all forest biomes for maintaining and increasing biogeochemical carbon sequestration. Under future climate and carbon dioxide levels following a high emission scenario GHGV values are projected to increase, especially so in tropical forests, but land-use change (e.g. deforestation) opposes this trend. The GHGV of ecosystems, especially when assessed over large areas, is an appropriate metric to assess the contribution of different greenhouse gases to climate and forms a basis for the monetary valuation of the climate regulation service ecosystems provide.     

The benefits of quantifying climate model uncertainty in climate change impacts assessment: an example with heat-related mortality change estimates

The majority of climate change impacts assessments account for climate change uncertainty by adopting the scenario-based approach. This typically involves assessing the impacts for a small number of emissions scenarios but neglecting the role of climate model physics uncertainty. Perturbed physics ensemble (PPE) climate simulations offer a unique opportunity to explore this uncertainty. Furthermore, PPEs mean it is now possible to make risk-based impacts estimates because they allow for a range of estimates to be presented to decision-makers, which spans the range of climate model physics uncertainty inherent from a given climate model and emissions scenario, due to uncertainty associated with the understanding of physical processes in the climate model. This is generally not possible with the scenario-based approach. Here, we present the first application of a PPE to estimate the impact of climate change on heat-related mortality. By using the estimated impacts of climate change on heat-related mortality in six cities, we demonstrate the benefits of quantifying climate model physics uncertainty in climate change impacts assessment over the more common scenario-based approach. We also show that the impacts are more sensitive to climate model physics uncertainty than they are to emissions scenario uncertainty, and least sensitive to whether the climate change projections are from a global climate model or a regional climate model. The results demonstrate the importance of presenting model uncertainties in climate change impacts assessments if the impacts are to be placed within a climate risk management framework.     

陆地生态系统与全球变化相互作用的研究进展

全球变化及其对生态系统特别是陆地生态系统的影响已经严重地影响到人类生存环境与社会经济的可持续发展 ,引起了各国政府、科学家及公众的高度关注。文中从CO2 浓度倍增、温度变化、水分变化、水热与CO2 协同作用、辐射变化、臭氧变化以及人为干扰等气候环境变化对植物光合生理、生长发育、物质分配、水分利用、碳氮代谢等的影响方面阐述了全球变化影响生态系统的过程与机理 ;从地理分布范围、物候、结构与功能、生态系统的稳定性等方面分析了中国植被、森林生态系统、草原生态系统与农田生态系统对全球变化的响应 ;从植被变化引起的动力条件与热力条件的变化及植被固碳潜力的变化探讨了植被对于气候的反馈作用。在此基础上 ,基于当前全球变化研究前沿 ,提出了未来关于陆地生态系统与全球变化相互作用研究需要重视的方面 ,尤其是关于生态系统对全球变化响应的阈值研究应引起高度重视。     

A review on vegetation models and applicability to climate simulations at regional scale

The lack of accurate representations of biospheric components and their biophysical and biogeochemical processes is a great source of uncertainty in current climate models. The interactions between terrestrial ecosystems and the climate include exchanges not only of energy, water and momentum, but also of carbon and nitrogen. Reliable simulations of these interactions are crucial for predicting the potential impacts of future climate change and anthropogenic intervention on terrestrial ecosystems. In this paper, two biogeographical (Neilson’s rule-based model and BIOME), two biogeochemical (BIOME-BGC and PnET-BGC), and three dynamic global vegetation models (Hybrid, LPJ, and MC1) were reviewed and compared in terms of their biophysical and physiological processes. The advantages and limitations of the models were also addressed. Lastly, the applications of the dynamic global vegetation models to regional climate simulations have been discussed.     

A simple carbon cycle representation for economic and policy analyses

Integrated Assessment Models (IAMs) that couple the climate system and the economy require a representation of ocean CO₂ uptake to translate human-produced emissions to atmospheric concentrations and in turn to climate change. The simple linear carbon cycle representations in most IAMs are not however physical at long timescales, since ocean carbonate chemistry makes CO₂ uptake highly nonlinear. No linearized representation can capture the ocean’s dual-mode behavior, with initial rapid uptake and then slow equilibration over ∽10,000 years. In a business-as-usual scenario followed by cessation of emissions, the carbon cycle in the 2007 version of the most widely used IAM, DICE (Dynamic Integrated model of Climate and the Economy), produces errors of ∽2^°C by the year 2300 and ∽6^°C by the year 3500. We suggest here a simple alternative representation that captures the relevant physics and show that it reproduces carbon uptake in several more complex models to within the inter-model spread. The scheme involves little additional complexity over the DICE model, making it a useful tool for economic and policy analyses.     

Climate sensitivity: should the climate tail wag the policy dog?

The small but stubbornly unyielding possibility of a very large long-term response of global temperature to increases in atmospheric carbon dioxide can be termed the fat tail of high climate sensitivity. Recent economic analyses suggest that the fat tail should dominate a rational policy strategy if the damages associated with such high temperatures are large enough. The conclusions of such analyses, however, depend on how economic growth, temperature changes, and climate damages unfold and interact over time. In this paper we focus on the role of two robust physical properties of the climate system: the enormous thermal inertia of the ocean, and the long timescales associated with high climate sensitivity. Economic models that include a climate component, and particularly those that focus on the tails of the probability distributions, should properly represent the physics of this slow response to high climate sensitivity, including the correlated uncertainty between present forcing and climate sensitivity, and the global energetics of the present climate state. If climate sensitivity in fact proves to be high, these considerations prevent the high temperatures in the fat tail from being reached for many centuries. A failure to include these factors risks distorting the resulting economic analyses. For example, we conclude that fat-tail considerations will not strongly influence economic analyses when these analyses follow the common—albeit controversial—practices of assigning large damages only to outcomes with very high temperature changes and of assuming a significant baseline level of economic growth.     

Modeling to evaluate the response of savanna-derived cropland to warming–drying stress and nitrogen fertilizers

Many savannas in West Africa have been converted to croplands and are among the world’s regions most vulnerable to climate change due to deteriorating soil quality. We focused on the savanna-derived cropland in northern Ghana to simulate its sensitivity to projected climate change and nitrogen fertilization scenarios. Here we show that progressive warming–drying stress over the twenty-first century will enhance soil carbon emissions from all kinds of lands of which the natural ecosystems will be more vulnerable to variation in climate variables, particularly in annual precipitation. The carbon emissions from all croplands, however, could be mitigated by applying nitrogen fertilizer at 30–60 kg N ha^???1 year^???1. The uncertainties of soil organic carbon budgets and crop yields depend mainly on the nitrogen fertilization rate during the first 40 years and then are dominated by climate drying stress. The replenishment of soil nutrients, especially of nitrogen through fertilization, could be one of the priority options for policy makers and farm managers as they evaluate mitigation and adaptation strategies of cropping systems and management practices to sustain agriculture and ensure food security under a changing climate.     

美国全球变化研究现状

美国的全球变化研究主要由美国全球变化研究计划（ＵＳＧＣＲＰ）支持,重点资助季节—年际尺度气候变率,十年—百年尺度的气候变化,臭氧、ＵＶ辐射以及大气化学的变化,土地利用以及陆地、海洋生态系统的变化等４个领域。当前,水汽与云仍是全球变化研究中不确定性较大的一个方面,因而受到关注。关于气候变化的信号检测以及成因分析也是一个研究热点。气候模拟研究是全球变化研究的一个主要方法。卫星资料在全球变化研究中的应用取得了大量成果。近期美国在全球变化研究领域的重点是气候模拟,短期气候预测,十年—百年尺度的气候变化,臭氧、ＵＶ辐射以及大气化学的变化,地表以及陆地、海洋生态系统变化,对全球变化的区域尺度估计,卫星资料的应用,气候变化影响的国家级评估等８个方面。     

Modeling responses of the meadow steppe dominated by Leymus chinensis to climate change

Grassland is one of the most widespread vegetation types worldwide and plays a significant role in regional climate and global carbon cycling. Understanding the sensitivity of Chinese grassland ecosystems to climate change and elevated atmospheric CO₂ and the effect of these changes on the grassland ecosystems is a key issue in global carbon cycling. China encompasses vast grassland areas of 354 million ha of 17 major grassland types, according to a national grassland survey. In this study, a process-based terrestrial model the CENTURY model was used to simulate potential changes in net primary productivity (NPP) and soil organic carbon (SOC) of the Leymus chinensis meadow steppe (LCMS) under different scenarios of climatic change and elevated atmospheric CO₂. The LCMS sensitivities, its potential responses to climate change, and the change in capacity of carbon stock and sequestration in the future are evaluated. The results showed that the LCMS NPP and SOC are sensitive to climatic change and elevated CO₂. In the next 100 years, with doubled CO₂ concentration, if temperature increases from 2.7-3.9˚C and precipitation increases by 10% NPP and SOC will increase by 7-21% and 5-6% respectively. However, if temperature increases by 7.5-7.8˚C and precipitation increases by only 10% NPP and SOC would decrease by 24% and 8% respectively. Therefore, changes in the NPP and SOC of the meadow steppe are attributed mainly to the amount of temperature and precipitation change and the atmospheric CO₂ concentration in the future.     

Delayed action and uncertain stabilisation targets. How much will the delay cost?

Despite the growing concern about actual on-going climate change, there is little consensus on the scale and timing of actions needed to stabilise the concentrations of greenhouse gases. Many countries are unwilling to implement mitigation strategies, at least in the short term, and no agreement on an ambitious global stabilisation target has yet been reached. It is thus likely that international climate policies will be characterized by a high degree of uncertainty over the stringency of the climate objective, and that some countries might delay their participation to global action. What additional economic costs will this delay in the adoption of mitigation measures imply? What would the optimal short-term strategy be given the uncertainty surrounding the climate policy to come? Is there a hedging strategy that decision makers can adopt to cope with delayed action and uncertain targets? This paper addresses these questions by quantifying the economic implications of delaying mitigation action, and by computing the optimal abatement strategy in the presence of uncertainty about a global stabilisation target (which will be agreed upon in future climate negotiations). Results point to short-term inaction as the key determinant for the economic costs of ambitious climate policies. They also indicate that there is an effective hedging strategy that could minimise the cost of climate policy uncertainty over the global stabilisation target: a short-term moderate climate policy would be a good strategy to reduce the costs of delayed action and to cope with uncertainty about the outcome of future climate negotiations. By contrast, failing to curb emissions in the short term imposes rapidly increasing additional costs of compliance.     

Impact of climate and socioeconomic changes on fire carbon emissions in the future: Sustainable economic development might decrease future emissions

Fires and their associated carbon and air pollutant emissions have a broad range of environmental and societal impacts, including negative effects on human health, damage to terrestrial ecosystems, and indirect effects that promote climate change. Previous studies investigated future carbon emissions from the perspective of response to climate change and population growth, but the compound effects of other factors like economic development and land use change are not yet well known. We explored fire carbon emissions throughout the 21st century by changing five factors (meteorology, biomass, land use, population density, and gross domestic product [GDP] per capita). Compared to the historical period (2006–2015), global future fire carbon emissions decreased, mainly caused by an increase in GDP per capita, which leads to improvement in fire management and capitalized agriculture. We found that the meteorological factor has a strong individual effect under higher warming cases. Fires in boreal forests were particularly expected to increase because of an increase in fuel dryness. Our research should help climate change researchers consider fire-carbon interactions. Incorporating future spatial changes under diverse scenarios will be helpful to develop national mitigation and adaptation plans.     

Assessing the robustness of adaptation decisions to climate change uncertainties: A case study on water resources management in the East of England

Projections of future climate change are plagued with uncertainties, causing difficulties for planners taking decisions on adaptation measures. This paper presents an assessment framework that allows the identification of adaptation strategies that are robust (i.e. insensitive) to climate change uncertainties. The framework is applied to a case study of water resources management in the East of England, more specifically to the Anglian Water Services’ 25 year Water Resource Plan (WRP). The paper presents a local sensitivity analysis (a ‘one-at-a-time’ experiment) of the various elements of the modelling framework (e.g., emissions of greenhouse gases, climate sensitivity and global climate models) in order to determine whether or not a decision to adapt to climate change is sensitive to uncertainty in those elements.Water resources are found to be sensitive to uncertainties in regional climate response (from general circulation models and dynamical downscaling), in climate sensitivity and in climate impacts. Aerosol forcing and greenhouse gas emissions uncertainties are also important, whereas uncertainties from ocean mixing and the carbon cycle are not. Despite these large uncertainties, Anglian Water Services’ WRP remains robust to the climate change uncertainties sampled because of the adaptation options being considered (e.g. extension of water treatment works), because the climate model used for their planning (HadCM3) predicts drier conditions than other models, and because ‘one-at-a-time’ experiments do not sample the combination of different extremes in the uncertainty range of parameters. This research raises the question of how much certainty is required in climate change projections to justify investment in adaptation measures, and whether such certainty can be delivered.     

Uncertainty in the 2°C warming threshold related to climate sensitivity and climate feedback

Climate sensitivity is an important index that measures the relationship between the increase in greenhouse gases and the magnitude of global warming. Uncertainties in climate change projection and climate modeling are mostly related to the climate sensitivity. The climate sensitivities of coupled climate models determine the magnitudes of the projected global warming. In this paper, the authors thoroughly review the literature on climate sensitivity, and discuss issues related to climate feedback processes and the methods used in estimating the equilibrium climate sensitivity and transient climate response (TCR), including the TCR to cumulative CO₂ emissions. After presenting a summary of the sources that affect the uncertainty of climate sensitivity, the impact of climate sensitivity on climate change projection is discussed by addressing the uncertainties in 2°C warming. Challenges that call for further investigation in the research community, in particular the Chinese community, are discussed.     

The scale and drivers of carbon footprints in households,cities and regions across India

The carbon footprint (CF) has emerged as an important yardstick to understand the total contribution of countries, sectors and individuals to climate change. In contrast to conventional emissions accounting which captures only territorial or local production activities, the CF includes the emissions imposed by consumption across global supply chains for goods and services. Recent interest has grown in the application of CF assessment for municipalities owing to their large contribution to global carbon emissions and the limited coverage of existing data to monitor their climate pledges. By linking household-level consumer surveys to a global supply chain database, spatially-explicit CF assessment is possible at a district and household scale. To date, such technique has exposed otherwise unforeseen differences in consumer carbon footprints in developed countries. Within this study we calculate and compare the household carbon footprints 623 districts in India, based on micro consumption data from 203,313 households and explain their variation by economic, cultural and demographic factors. We show the eradication of extreme poverty does not conflict with ambitious climate change mitigation in India. However, our analysis suggests CF reduction policies within India need to target high-expenditure households which are responsible for nearly seven times the carbon emissions than low-expenditure households (living on $1.9 consumption a day). These vast disparities between the carbon footprint of citizens in India highlights the need to differentiate individual responsibilities for climate change in national and global climate policy.