Planning Climate Change Mitigation Research Articles

Effects of climate and plant functional types on forest above-ground biomass accumulation

BackgroundForest above-ground biomass (AGB) accumulation is widely considered an important tool for mitigating climate change. However, the general pattern of forest AGB accumulation associated with age and climate gradients across various forest functional types at a global scale have remained unclear. In this study, we compiled a global AGB data set and applied a Bayesian statistical model to reveal the age-related dynamics of forest AGB accumulation, and to quantify the effects of mean annual temperature and annual precipitation on the initial AGB accumulation rate and on the saturated AGB characterizing the limit to AGB accumulation.ResultsThe results of the study suggest that mean annual temperature has a significant positive effect on the initial AGB accumulation rate in needleleaf evergreen forest, and a negative effect in broadleaf deciduous forest; whereas annual precipitation has a positive effect in broadleaf deciduous forest, and negative effect in broadleaf evergreen forest. The positive effect of mean annual temperature on the saturated AGB in broadleaf evergreen forest is greater than in broadleaf deciduous forest; annual precipitation has a greater negative effect on the saturated AGB in deciduous forests than in evergreen forests. Additionally, the difference of AGB accumulation rate across four forest functional types is closely correlated with the forest development stage at a given climate.ConclusionsThe contrasting responses of AGB accumulation rate to mean annual temperature and precipitation across four forest functional types emphasizes the importance of incorporating the complexity of forest types into the models which are used in planning climate change mitigation. This study also highlights the high potential for further AGB growth in existing evergreen forests.

Closing the domain gap: blended synthetic imagery for climate object detection

Abstract Accurate geospatial information about the causes and consequences of climate change, including energy systems infrastructure, is critical to planning climate change mitigation and adaptation strategies. When up-to-date spatial data on infrastructure is lacking, one approach to fill this gap is to learn from overhead imagery using deep-learning-based object detection algorithms. However, the performance of these algorithms can suffer when applied to diverse geographies, which is a common case. We propose a technique to generate realistic synthetic overhead images of an object (e.g., a generator) to enhance the ability of these techniques to transfer across diverse geographic domains. Our technique blends example objects into unlabeled images from the target domain using generative adversarial networks. This requires minimal labeled examples of the target object and is computationally efficient such that it can be used to generate a large corpus of synthetic imagery. We show that including these synthetic images in the training of an object detection model improves its ability to generalize to new domains (measured in terms of average precision) when compared to a baseline model and other relevant domain adaptation techniques.

Climate Change Impact on Forest Cover: A Critical Review

Climate change affects forest cover through increased temperature, change in precipitation, frequent extreme events, and a shifting disturbance regime. In view of understanding the existing studies carried out on climate change impact on forest cover, we reviewed the findings of several researchers to assess the impact of changing climatic parameters on forest cover. The role of deforestation on climate change was analyzed by presenting some of the arguments made by several researchers. Further, the influence of forests in climate change was evaluated by the amount of carbon sequestrated in the forests throughout the world and the effect of climate change on carbon sequestration was also done with the help of review of the literature. Direct, Indirect and compound effects of climate change impact on forest cover were also shortlisted and discussed. Then certain coping mechanisms were also discussed by the authors for the forests whose health was highly affected due to climate change. Several research publications demonstrating the response of forest cover to climate change were critically analyzed. The review revelaed that climatic variations are very important to understand the forest cover changes of an ecosystem in which the climate prevails. It is also understood that the variability of rainfall and temperature were not the main cause for forest cover changes. Climate change impact assessment has significant implications on carbon sequestration greenhouse gas mitigation and adaptation strategies to climate change. Utilising remote sensing data for documenting and identifying forest degradation status is a viable option for planning climate change mitigation in forest regions.

Enriching the Shared Socioeconomic Pathways to co-create consistent multi-sector scenarios for the UK

As the pressure to take action against global warming is growing in urgency, scenarios that incorporate multiple social, economic and environmental drivers become increasingly critical to support governments and other stakeholders in planning climate change mitigation or adaptation actions. This has led to the recent explosion of future scenario analyses at multiple scales, further accelerated since the development of the Intergovernmental Panel on Climate Change (IPCC) research community Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). While RCPs have been widely applied to climate models to produce climate scenarios at multiple scales for investigating climate change impacts, adaptation and vulnerabilities (CCIAV), SSPs are only recently being scaled for different geographical and sectoral applications. This is seen in the UK where significant investment has produced the RCP-based UK Climate Projections (UKCP18), but no equivalent UK version of the SSPs exists. We address this need by developing a set of multi-driver qualitative and quantitative UK-SSPs, following a state-of-the-art scenario methodology that integrates national stakeholder knowledge on locally-relevant drivers and indicators with higher level information from European and global SSPs. This was achieved through an intensive participatory process that facilitated the combination of bottom-up and top-down approaches to develop a set of UK-specific SSPs that are locally comprehensive, yet consistent with the global and European SSPs. The resulting scenarios balance the importance of consistency and legitimacy, demonstrating that divergence is not necessarily the result of inconsistency, nor comes as a choice to contextualise narratives at the appropriate scale.

An assessment of greenhouse gas emissions and material flows caused by the Finnish economy using the ENVIMAT model

An environmentally extended input–output (EE-IO) analysis – environmental impacts of material flows caused by the Finnish economy – was carried out in order to improve data on production and consumption in Finland. The study resulted in the ENVIMAT model, which can be used to analyze the relationship between material flows, environmental impacts and the economy. The model is based on monetary and physical input–output tables and an environmental life-cycle impact assessment. This article summarizes the main methodological aspects and findings regarding the material flows and climate impacts caused by the Finnish economy in 2002 and 2005. The Finnish model has relatively detailed input data with 150 industries and 918 products and the data on imports was assessed according to a mixed approach with the help of life-cycle inventory data. The results of the model showed that the Finnish economy uses imported material resources as much as domestic resources. Life-cycle greenhouse gas (GHG) emissions caused by imports were equivalent to 70–80% of domestic emissions. The GHG emissions embodied in imports (emissions abroad) and exports (emissions within Finland) were of the same magnitude. The analysis showed that the service sector accounted for 44% of GHG emissions caused by the domestic final use of products. Analysis of the results also showed that the indicator of total material requirement (TMR) should not be used for environmental impact comparisons of products and services. In the future, the aim is to use the ENVIMAT model for assessing temporal changes in the economy; for monitoring sustainable development; for planning climate change mitigation; and for identifying important factors in the economy and assessing their impacts.

Uncertainty in predictions of the climate response to rising levels of greenhouse gases

The range of possibilities for future climate evolution needs to be taken into account when planning climate change mitigation and adaptation strategies. This requires ensembles of multi-decadal simulations to assess both chaotic climate variability and model response uncertainty. Statistical estimates of model response uncertainty, based on observations of recent climate change, admit climate sensitivities--defined as the equilibrium response of global mean temperature to doubling levels of atmospheric carbon dioxide--substantially greater than 5 K. But such strong responses are not used in ranges for future climate change because they have not been seen in general circulation models. Here we present results from the 'climateprediction.net' experiment, the first multi-thousand-member grand ensemble of simulations using a general circulation model and thereby explicitly resolving regional details. We find model versions as realistic as other state-of-the-art climate models but with climate sensitivities ranging from less than 2 K to more than 11 K. Models with such extreme sensitivities are critical for the study of the full range of possible responses of the climate system to rising greenhouse gas levels, and for assessing the risks associated with specific targets for stabilizing these levels.