Future climate change and increasing atmospheric CO 2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4°C of global land surface warming (510-758 ppm of CO 2 ), vegetation carbon increases by 52-477 Pg C (224 Pg C mean), mainly due to CO 2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4°C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended.errestrial vegetation is central to many components of the coupled Earth system, in particular the global carbon cycle, biophysical land-atmosphere exchanges, atmospheric chemistry, and the diversity of life with the numerous ecosystem services this engenders. However, vegetation is very sensitive to climate and levels of atmospheric CO 2 , the primary substrate for plant growth. Therefore, it is imperative that we are capable of anticipating the potential responses of global terrestrial vegetation to future changes in climate and atmospheric chemistry. However, a comprehensive, consistent analysis of impacts, taking into account uncertainty in both climate models and impacts models, has so far been lacking. The recent availability of representative concentration pathway (RCP)-driven climate model simulations, with bias-corrected outputs produced within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) (1), allows such an analysis.Vegetation biomass, productivity, and the competitive abilities of different plant types are all influenced by climate and atmospheric CO 2 . Higher temperatures will increase growing season lengths, metabolic rates, and rates of nitrogen mineralization at high latitudes and altitudes, there...
Terrestrial ecosystem and carbon cycle feedbacks will significantly impact future climate, but their responses are highly uncertain. Models and tipping point analyses suggest the tropics and arctic/boreal zone carbon-climate feedbacks could be disproportionately large. In situ observations in those regions are sparse, resulting in high uncertainties in carbon fluxes and fluxes. Key parameters controlling ecosystem carbon responses, such as plant traits, are also sparsely observed in the tropics, with the most diverse biome on the planet treated as a single type in models. We analyzed the spatial distribution of in situ data for carbon fluxes, stocks and plant traits globally and also evaluated the potential of remote sensing to observe these quantities. New satellite data products go beyond indices of greenness and can address spatial sampling gaps for specific ecosystem properties and parameters. Because environmental conditions and access limit in situ observations in tropical and arctic/boreal environments, use of space-based techniques can reduce sampling bias and uncertainty about tipping point feedbacks to climate. To reliably detect change and develop the understanding of ecosystems needed for prediction, significantly, more data are required in critical regions. This need can best be met with a strategic combination of remote and in situ data, with satellite observations providing the dense sampling in space and time required to characterize the heterogeneity of ecosystem structure and function.
The world's ecosystems are losing biodiversity fast. A satellite mission designed to track changes in plant functional diversity around the globe could deepen our understanding of the pace and consequences of this change and how to manage it.The ability to view Earths' vegetation from space is a hallmark of the space age. Yet decades of satellite measurements have provided relatively little insight into the immense diversity of form and function in the plant kingdom in space and time. Humans are rapidly impacting biodiversity around the globe 1,2
SummaryThe first generation of forest free-air CO 2 enrichment (FACE) experiments has successfully provided deeper understanding about how forests respond to an increasing CO 2 concentration in the atmosphere. Located in aggrading stands in the temperate zone, they have provided a strong foundation for testing critical assumptions in terrestrial biosphere models that are being used to project future interactions between forest productivity and the atmosphere, despite the limited inference space of these experiments with regards to the range of global ecosystems. Now, a new generation of FACE experiments in mature forests in different biomes and over a wide range of climate space and biodiversity will significantly expand the inference space. These new experiments are: EucFACE in a mature Eucalyptus stand on highly weathered soil in subtropical Australia; AmazonFACE in a highly diverse, primary rainforest in Brazil; BIFoR-FACE in a 150-yr-old deciduous woodland stand in central England; and SwedFACE proposed in a hemiboreal, Pinus sylvestris stand in Sweden. We now have a unique opportunity to initiate a model-data interaction as an integral part of experimental design and to address a set of cross-site science questions on topics including responses of mature forests; interactions with temperature, water stress, and phosphorus limitation; and the influence of biodiversity.
The impacts of global climate change on different aspects of humanity's diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3°C above the 1980-2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4°C. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty.coinciding pressures | differential climate impacts | ISI-MIP
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