• Energy Research

We use a carbon-cycle data assimilation system to estimate the terrestrial biospheric CO 2 flux until 2090. The terrestrial sink increases rapidly and the increase is stronger in the presence of climate change. Using a linearized model, we calculate the uncertainty in the flux owing to uncertainty in model parameters. The uncertainty is large and is dominated by the impact of soil moisture on heterotrophic respiration. We show that this uncertainty can be greatly reduced by constraining the model parameters with two decades of atmospheric measurements.

Abstract. A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The goal of this study is to identify the current state of carbon observations and needs for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion (by several orders of magnitude) of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over remote areas such as the southern oceans, tropical forests and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in-situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases inter-operable, and on the calibration of each component of the system to agreed-upon international scales.

Abstract This study sheds new light on the variability of wind power across the Australian NEM (National Electricity Market) and in doing so gives an insight on the potential network configuration for a high RE (Renewable Electricity) future. We present idealised cost-minimised simulations for the NEM utilising onshore wind, large-scale solar, pumped hydro and OCGT (open cycle gas turbines) technologies. A model using gridded meteorological data from the regional ACCESS-R (Australian Community Climate and Earth-System Simulator) simulates wind and solar technology output along with generation from OCGT to meet demand in the NEM for the period 2010–2011. A cost for connecting each location to the nearest major load centre is introduced and a base scenario created from an initial connection cost of $1 M/km. A sensitivity study reveals that a cost of $8 M/km results in the contraction of all renewable resources to four major wind installations. Compared to the base scenario the four major wind locations share much of the variability in renewable energy output, demonstrating that the NEM region has four distinct wind regimes. Separated by 1,400 km these four wind installations provide an optimisation-based decorrelation length for the NEM. This information is particularly useful for long-term planners of large-scale energy infrastructure.

AbstractInverse‐estimated net carbon exchange time series spanning two decades for six North American regions are analyzed to examine long‐term trends and relationships to temperature and precipitation variations. Results reveal intensification of carbon uptake in eastern boreal North America (0.1 PgC/decade) and the Midwest United States (0.08 PgC/decade). Seasonal cross‐correlation analysis shows a significant relationship between net carbon exchange and temperature/precipitation anomalies during the western United States growing season with warmer, dryer conditions leading reduced carbon uptake. This relationship is consistent with “global change‐type drought” dynamics which drive increased vegetation mortality, increases in dry woody material, and increased wildfire occurrence. This finding supports the contention that future climate change may increase carbon loss in this region. Similarly, higher temperatures and reduced precipitation are accompanied by decreased net carbon uptake in the Midwestern United States toward the end of the growing season. Additionally, intensified net carbon uptake during the eastern boreal North America growing season is led by increased precipitation anomalies in the previous year, suggesting the influence of “climate memory” carried by regional snowmelt water. The two regions of boreal North America exhibit opposing seasonal carbon‐temperature relationships with the eastern half experiencing a net carbon loss with near coincident increases in temperature and the western half showing increased net carbon uptake. The carbon response in the boreal west region lags the temperature anomalies by roughly 6 months. This opposing carbon‐temperature relationship in boreal North America may be a combination of different dominant vegetation types, the amount and timing of snowfall, and temperature anomaly differences across boreal North America.

AbstractThe climate-carbon cycle feedback is one of the most important climate-amplifying feedbacks of the Earth system, and is quantified as a function of carbon-concentration feedback parameter (β) and carbon-climate feedback parameter (γ). However, the global climate-amplifying effect from this feedback loop (determined by the gain factor, g) has not been quantified from observations. Here we apply a Fourier analysis-based carbon cycle feedback framework to the reconstructed records from 1850 to 2017 and 1000 to 1850 to estimate β and γ. We show that the β-feedback varies by less than 10% with an average of 3.22 ± 0.32 GtC ppm−1 for 1880–2017, whereas the γ-feedback increases from −33 ± 14 GtC K−1 on a decadal scale to −122 ± 60 GtC K−1 on a centennial scale for 1000–1850. Feedback analysis further reveals that the current amplification effect from the carbon cycle feedback is small (g is 0.01 ± 0.05), which is much lower than the estimates by the advanced Earth system models (g is 0.09 ± 0.04 for the historical period and is 0.15 ± 0.08 for the RCP8.5 scenario), implying that the future allowable CO2 emissions could be 9 ± 7% more. Therefore, our findings provide new insights about the strength of climate-carbon cycle feedback and about observational constraints on models for projecting future climate.

The response of the carbon cycle to climate change, including carbon fluxes, is now shown to be the second largest source of uncertainty in projections of temperature. A simplified climate model using temperature records and historical estimates of CO2 concentrations demonstrates that considering these two factors together reduces uncertainty further than treating them as individual parameters.