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Carbon (C) sequestration in forest biomass and soils may help decrease regional C footprints and mitigate future climate change. The efficacy of these practices must be verified by monitoring and by approved calculation methods (i.e., models) to be credible in C markets. Two widely used soil organic matter models - CENTURY and RothC - were used to project changes in SOC pools after clear-cutting disturbance, as well as under a range of future climate and atmospheric carbon dioxide ( CO2) scenarios. Data from the temperate, predominantly deciduous Hubbard Brook Experimental Forest ( HBEF) in New Hampshire, USA, were used to parameterize and validate the models. Clear-cutting simulations demonstrated that both models can effectively simulate soil C dynamics in the northern hardwood forest when adequately parameterized. The minimum postharvest SOC predicted by RothC occurred in postharvest year 14 and was within 1.5% of the observed minimum, which occurred in year 8. CENTURY predicted the postharvest minimum SOC to occur in year 45, at a value 6.9% greater than the observed minimum; the slow response of both models to disturbance suggests that they may overestimate the time required to reach new steady-state conditions. Four climate change scenarios were used to simulate future changes in SOC pools. Climate-change simulations predicted increases in SOC by as much as 7% at the end of this century, partially offsetting future CO2 emissions. This sequestration was the product of enhanced forest productivity, and associated litter input to the soil, due to increased temperature, precipitation and CO2. The simulations also suggested that considerable losses of SOC (8-30%) could occur if forest vegetation at HBEF does not respond to changes in climate and CO2 levels. Therefore, the source/sink behavior of temperate forest soils likely depends on the degree to which forest growth is stimulated by new climate and CO2 conditions. [ABSTRACT FROM AUTHOR]

ABSTRACT The asynchronous regional regression model ( ARRM) is a flexible and computationally efficient statistical model that can downscale station-based or gridded daily values of any variable that can be transformed into an approximately symmetric distribution and for which a large-scale predictor exists. This technique was developed to bridge the gap between large-scale outputs from atmosphere-ocean general circulation models ( AOGCMs) and the fine-scale output required for local and regional climate impact assessments. ARRM uses piecewise regression to quantify the relationship between observed and modelled quantiles and then downscale future projections. Here, we evaluate the performance of three successive versions of the model in downscaling daily minimum and maximum temperature and precipitation for 20 stations in North America from diverse climate zones. Using cross-validation to maximize the independent comparison period, historical downscaled simulations are evaluated relative to observations in terms of three different quantities: the probability distributions, giving a visual image of the skill of each model; root-mean-square errors; and bias in nine quantiles that represent both means and extremes. Successive versions of the model show improved accuracy in simulating extremes, where AOGCMs are often most biased and which are frequently the focus of impact studies. Overall, the quantile regression-based technique is shown to be efficient, robust, and highly generalizable across multiple variables, regions, and climate model inputs. [ABSTRACT FROM AUTHOR]

Dynamic hydrochemical models are useful tools for understanding and predicting the interactive effects of climate change, atmospheric CO2, and atmospheric deposition on the hydrology and water quality of forested watersheds. We used the biogeochemical model, PnET-BGC, to evaluate the effects of potential future changes in temperature, precipitation, solar radiation, and atmospheric CO2 on pools, concentrations, and fluxes of major elements at the Hubbard Brook Experimental Forest in New Hampshire, United States. Future climate projections used to run PnET-BGC were generated specifically for the Hubbard Brook Experimental Forest with a statistical technique that downscales climate output (e.g., air temperature, precipitation, solar radiation) from atmosphere-ocean general circulation models (AOGCMs) to a finer temporal and spatial resolution. These climate projections indicate that over the twenty-first century, average air temperature will increase at the site by 1.7°C to 6.5°C with simultaneous increases in annual average precipitation ranging from 4 to 32 cm above the long-term mean (1970-2000). PnET-BGC simulations under future climate change show a shift in hydrology characterized by later snowpack development, earlier spring discharge (snowmelt), greater évapotranspiration, and a slight increase in annual water yield (associated with C02 effects on vegetation). Model results indicate that under elevated temperature, net soil nitrogen mineralization and nitrification markedly increase, resulting in acidification of soil and stream water, thereby altering the quality of water draining from forested watersheds. Invoking a CO2 fertilization effect on vegetation under climate change substantially mitigates watershed nitrogen loss, highlighting the need for a more thorough understanding of CO2 effects on forest vegetation. [ABSTRACT FROM AUTHOR]

The article presents a correction to the article "Going to the extremes: An intercomparison of model-simulated historical and future changes in extreme events," which was published in the 15 March 2007 issue.

To what extent will increasing High Occupancy Vehicle (HOV) lane-kilometer incentivize carpooling and reduce emissions of air pollutants and greenhouse gases? To answer these questions, we develop a multiple regression model relating HOV lanes and other socioeconomic factors to carpooling propensity in all 50 U.S. states and the District of Columbia, then calculate the extent to which increasing HOV lane-kilometers would lead to reductions in carbon dioxide equivalent (CO 2 e) and major air pollutants across the U.S., by state. Increasing HOV lane extent has the greatest potential to reduce annual CO 2 e in the District of Columbia, followed by Hawaii and New York. The smallest potential is found in states with the lowest population density, led by North Dakota. We then explore the extent to which recommendations made at one level of data aggregation (that of individual states) may be valid for another level, such as individual counties. The only state with sufficient data available to disaggregate the model to the county level is California, where we found a lower potential for state-wide CO 2 e emission reductions under the county-level model as compared to the state-level model (0.69% as compared to 1.08%, under the same hypothetical scenario), albeit with significant differences in emission reduction potential between counties with higher vs. lower population densities. This analysis demonstrates the potential to generate generalizable insight into the magnitude of vehicle emission reductions that might be achieved through expanding HOV lanes, and highlights the importance of data disaggregation in identifying the optimal locations for potential reductions. [ABSTRACT FROM AUTHOR]

Mosquitoes are ecologically and economically important invertebrates that transmit numerous diseases and are an integral part of the food web in many terrestrial and aquatic ecosystems. Despite their importance, there have been no central repositories for occurrence or abundance records of mosquitoes in the United States. To enable research dependent on the geographic distribution and abundance of mosquito species, we built a database of occurrence records collected by many individuals and agencies, primarily in the United States and with emphasis on unpublished data in the Midwest region. This resource is invaluable to researchers studying mosquito ecology, disease vectors and pathways, and insect pests. The database contains 542 144 occurrence records, and each record includes information on sex, life stage, sampling method, presence/absence or abundance, and date and location of sampling. The accessibility of this resource will directly impact management and planning for mosquito-borne diseases with region-specific or species-specific modeling. Because there is little centralized ecological information on mosquitoes, this database is foundational to expanding knowledge of mosquito ecology. [ABSTRACT FROM AUTHOR]

Carbon dioxide emissions from human activities are the primary cause of recent climate change. In the United States, the road transportation sector is one of the largest sources of these emissions. Any policies to reduce emissions must therefore include mitigation strategies for on-road transportation. The aim of this paper is to propose a multi-criteria method, Analytical Hierarchy Process, to rank various on-road emissions mitigation strategies including reduce, avoid, and replace strategies. The method׳s results are obtained based on a survey of transportation and climate science professionals. The Analytical Hierarchy Process was applied to two regional scenarios of a midsize-small city (Lubbock, Texas) and a metropolitan area (Dallas, Texas). To evaluate the effectiveness of these strategies, the Motor Vehicle Emissions Simulator model was run for Dallas. The aim of the model was to estimate the potential carbon dioxide mitigation for a given strategy allocation. Our survey identified no difference between the rankings of reduce, avoid, and replace strategies for our metropolitan and midsize-small city areas. Reduce strategies had the highest preference score of 40% followed by avoid strategies with 36% and replace strategies with 24%. An optimum mixed mitigation scenario would achieve reductions in carbon dioxide emissions of 17% by 2030 from 2010 levels. The contributions of this study are two-fold. First, we evaluate generic scenarios in detail and apply them to a real-world case study. Second, the approach is both simple and generalizable. Applications of this type of platform include ranking transportation strategies on mitigating carbon dioxide emissions, evaluating the strategies, prioritizing budgets, and developing assessments of mitigation potential. [ABSTRACT FROM AUTHOR]

This is the fourth in a series of four articles on historical and projected climate extremes in the United States. Here, we examine the results of historical and future climate model experiments from the phase 5 of the Coupled Model Intercomparison Project (CMIP5) based on work presented at the World Climate Research Programme (WCRP) Workshop on CMIP5 Climate Model Analyses held in March 2012. Our analyses assess the ability of CMIP5 models to capture observed trends, and we also evaluate the projected future changes in extreme events over the contiguous Unites States. Consistent with the previous articles, here we focus on model-simulated historical trends and projections for temperature extremes, heavy precipitation, large-scale drivers of precipitation variability and drought, and extratropical storms. Comparing new CMIP5 model results with earlier CMIP3 simulations shows that in general CMIP5 simulations give similar patterns and magnitudes of future temperature and precipitation extremes in the United States relative to the projections from the earlier phase 3 of the Coupled Model Intercomparison Project (CMIP3) models. Specifically, projections presented here show significant changes in hot and cold temperature extremes, heavy precipitation, droughts, atmospheric patterns such as the North American monsoon and the North Atlantic subtropical high that affect interannual precipitation, and in extratropical storms over the twenty-first century. Most of these trends are consistent with, although in some cases (such as heavy precipitation) underestimate, observed trends. [ABSTRACT FROM AUTHOR]

Future projections of near-surface ozone concentrations depend on the climate/emissions scenario used to drive future simulations, the direct effects of the changing climate on the atmosphere, and the indirect effects of changing temperatures and CO2 levels on biogenic ozone precursor emissions. The authors investigate the influence of these factors on potential future changes in summertime daily 8-h maximum ozone over the United States and China by comparing Model for Ozone and Related Chemical Tracers, version 2.4, (MOZART-2.4) simulations for the period 1996–2000 with 2095–99, using climate projections from NCAR–Department of Energy Parallel Climate Model simulations driven by the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios A1fi (higher) and B1 (lower) emission scenarios, with corresponding changes in biogenic emissions. The effect of projected climate changes alone on surface ozone is generally less than 3 ppb over most regions. Regional ozone increases and decreases are driven mainly by local warming and marine air dilution enhancement, respectively. Changes are approximately the same magnitude under both scenarios, although spatial patterns of responses differ. Projected increases in isoprene emissions (32%–94% over both countries), however, result in significantly greater changes in surface ozone. Increases of 1–15 ppb are found under A1fi and of 0–7 ppb are found under B1. These increases not only raise the frequency of “high ozone days,” but are also projected to occur nearly uniformly across the distribution of daily ozone maxima. Thus, projected future ozone changes appear to be more sensitive to changes in biogenic emissions than to direct climate changes, and the spatial patterns and magnitude of future ozone changes depend strongly on the future emissions scenarios used. [ABSTRACT FROM AUTHOR]

The flora of California, a global biodiversity hotspot, includes 2387 endemic plant taxa. With anticipated climate change, we project that up to 66% will experience .80% reductions in range size within a century. These results are comparable with other studies of fewer species or just samples of a region's endemics. Projected reductions depend on the magnitude of future emissions and on the ability of species to disperse from their current locations. California's varied terrain could cause species to move in very different directions, breaking up present-day floras. However, our projections also identify regions where species undergoing severe range reductions may persist. Protecting these potential future refugia and facilitating species dispersal will be essential to maintain biodiversity in the face of climate change. [ABSTRACT FROM AUTHOR]

The massive deployment of clean energy technologies plays a vital role in the strategy to attain carbon neutrality by 2050 and allow subsequent negative CO2 emissions in order to achieve our climate goals. An emerging challenge, known as ‘From Emissions to Resources,’ highlights the significant increase in demand for critical raw materials (CRMs) in clean energy technologies. Despite the presence of ample geological reserves, ensuring sustainable access to these materials is crucial for the successful transition to clean energy, taking into account the environmental and social impacts. The commentary centers on four renewable energy technologies namely solar photovoltaics, wind turbines, Li-ion batteries, and water electrolysers. Four pathways for mitigation are quantitatively examined to assess their potential in reducing the vulnerability of the CRM supply chain for these four clean energy technologies: (i) Enhancing material efficiency, (ii) employing substitutivity strategies, (iii) exploring recycling prospects, and (iv) promoting relocalisation initiatives. It is important to note that no single mitigation lever can completely eliminate the risk of CRM supply, rather the accelerated adoption of all four levers is necessary to minimize the CRM supply risk to its absolute minimum. Hence, the study underscores the significance of increased research, innovation, and regulatory initiatives, along with raising social awareness, in effectively addressing the challenges faced by the CRM supply chain and contributing to a sustainable energy transition. [ABSTRACT FROM AUTHOR]

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]

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]

1. Streams collect runoff, heat, and sediment from their watersheds, making them highly vulnerable to anthropogenic disturbances such as urbanization and climate change. Forecasting the effects of these disturbances using process-based models is critical to identifying the form and magnitude of likely impacts. Here, we integrate a new biotic model with four previously developed physical models (downscaled climate projections, stream hydrology, geomorphology, and water temperature) to predict how stream fish growth and reproduction will most probably respond to shifts in climate and urbanization over the next several decades. 2. The biotic submodel couples dynamics in fish populations and habitat suitability to predict fish assemblage composition, based on readily available biotic information (preferences for habitat, temperature, and food, and characteristics of spawning) and day-to-day variability in stream conditions. 3. We illustrate the model using Piedmont headwater streams in the Chesapeake Bay watershed of the USA, projecting ten scenarios: Baseline (low urbanization; no on-going construction; and present-day climate); one Urbanization scenario (higher impervious surface, lower forest cover, significant construction activity); four future climate change scenarios [Hadley CM3 and Parallel Climate Models under medium-high (A2) and medium-low (B2) emissions scenarios]; and the same four climate change scenarios plus Urbanization. 4. Urbanization alone depressed growth or reproduction of 8 of 39 species, while climate change alone depressed 22 to 29 species. Almost every recreationally important species (i.e. trouts, basses, sunfishes) and six of the ten currently most common species were predicted to be significantly stressed. The combined effect of climate change and urbanization on adult growth was sometimes large compared to the effect of either stressor alone. Thus, the model predicts considerable change in fish assemblage composition, including loss of diversity. 5. Synthesis and applications. The interaction of climate change and urban growth may entail significant reconfiguring of headwater streams, including a loss of ecosystem structure and services, which will be more costly than climate change alone. On local scales, stakeholders cannot control climate drivers but they can mitigate stream impacts via careful land use. Therefore, to conserve stream ecosystems, we recommend that proactive measures be taken to insure against species loss or severe population declines. Delays will inevitably exacerbate the impacts of both climate change and urbanization on headwater systems. [ABSTRACT FROM AUTHOR]

The use of species distribution as a climate proxy for ecological forecasting is thought to be acceptable for invasive species. Kudzu (Pueraria montana var. lobata) is an important invasive whose northern distribution appears to be limited by winter survival; however, kudzu's cold tolerance thresholds are uncertain. Here, we used biogeographic evidence to hypothesize that exposure to - 20 °C is lethal for kudzu and thus determines its northern distribution limit. We evaluated this hypothesis using survival tests and electrolyte leakage to determine relative conductivity, a measure of cell damage, on 14 populations from eastern North America. Relative conductivity above 36% was lethal. Temperatures causing this damage averaged - 19.6 °C for northern and - 14.4 °C for southern populations, indicating kudzu acclimates to winter cold. To assess this, we measured relative conductivity of above- and belowground stems, and roots collected throughout the winter at a kudzu population in southern Ontario, Canada. Consistent with acclimation, the cold tolerance threshold of aboveground stems at the coldest time of year was - 26 °C, while stems insulated from cold extremes survived to - 17 °C-colder than the survival limits indicated by kudzu's biogeographic distribution. While these results do not rule out alternative cold limitations, they indicate kudzu can survive winters north of its current distribution. For kudzu, biogeography is not a proxy for climatic tolerance and continued northward migration is possible. Efforts to limit its spread are therefore prudent. These results demonstrate that physiological constraints inform predictions of climate-related changes in species distribution and should be considered where possible.

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (eight ensembles of six runs).The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change., (© 2016 by the Ecological Society of America.)

Carbon (C) sequestration in forest biomass and soils may help decrease regional C footprints and mitigate future climate change. The efficacy of these practices must be verified by monitoring and by approved calculation methods (i.e., models) to be credible in C markets. Two widely used soil organic matter models - CENTURY and RothC - were used to project changes in SOC pools after clear-cutting disturbance, as well as under a range of future climate and atmospheric carbon dioxide (CO(2) ) scenarios. Data from the temperate, predominantly deciduous Hubbard Brook Experimental Forest (HBEF) in New Hampshire, USA, were used to parameterize and validate the models. Clear-cutting simulations demonstrated that both models can effectively simulate soil C dynamics in the northern hardwood forest when adequately parameterized. The minimum postharvest SOC predicted by RothC occurred in postharvest year 14 and was within 1.5% of the observed minimum, which occurred in year 8. CENTURY predicted the postharvest minimum SOC to occur in year 45, at a value 6.9% greater than the observed minimum; the slow response of both models to disturbance suggests that they may overestimate the time required to reach new steady-state conditions. Four climate change scenarios were used to simulate future changes in SOC pools. Climate-change simulations predicted increases in SOC by as much as 7% at the end of this century, partially offsetting future CO(2) emissions. This sequestration was the product of enhanced forest productivity, and associated litter input to the soil, due to increased temperature, precipitation and CO(2) . The simulations also suggested that considerable losses of SOC (8-30%) could occur if forest vegetation at HBEF does not respond to changes in climate and CO(2) levels. Therefore, the source/sink behavior of temperate forest soils likely depends on the degree to which forest growth is stimulated by new climate and CO(2) conditions., (© 2013 John Wiley & Sons Ltd.)

The magnitude of future climate change depends substantially on the greenhouse gas emission pathways we choose. Here we explore the implications of the highest and lowest Intergovernmental Panel on Climate Change emissions pathways for climate change and associated impacts in California. Based on climate projections from two state-of-the-art climate models with low and medium sensitivity (Parallel Climate Model and Hadley Centre Climate Model, version 3, respectively), we find that annual temperature increases nearly double from the lower B1 to the higher A1fi emissions scenario before 2100. Three of four simulations also show greater increases in summer temperatures as compared with winter. Extreme heat and the associated impacts on a range of temperature-sensitive sectors are substantially greater under the higher emissions scenario, with some interscenario differences apparent before midcentury. By the end of the century under the B1 scenario, heatwaves and extreme heat in Los Angeles quadruple in frequency while heat-related mortality increases two to three times; alpine/subalpine forests are reduced by 50-75%; and Sierra snowpack is reduced 30-70%. Under A1fi, heatwaves in Los Angeles are six to eight times more frequent, with heat-related excess mortality increasing five to seven times; alpine/subalpine forests are reduced by 75-90%; and snowpack declines 73-90%, with cascading impacts on runoff and streamflow that, combined with projected modest declines in winter precipitation, could fundamentally disrupt California's water rights system. Although interscenario differences in climate impacts and costs of adaptation emerge mainly in the second half of the century, they are strongly dependent on emissions from preceding decades.