Institute of Applied Energy

Northern forests are an important carbon sink, but our understanding of the driving factors is limited due to discrepancies between dynamic global vegetation models (DGVMs) and atmospheric inversions. We show that DGVMs simulate a 50% lower sink (1.1 ± 0.5 PgC yr−1 over 2001–2021) across North America, Europe, Russia, and China compared to atmospheric inversions (2.2 ± 0.6 PgC yr−1). We explain why DGVMs underestimate the carbon sink by considering how they represent disturbance processes, specifically the overestimation of fire emissions, and the lack of robust forest demography resulting in lower forest regrowth rates than observed. We reconcile net sink estimates by using alternative disturbance-related fluxes. We estimate carbon uptake through forest regrowth by combining satellite-derived forest age and biomass maps. We calculate a regrowth flux of 1.1 ± 0.1 PgC yr−1, and combine this with satellite-derived estimates of fire emissions (0.4 ± 0.1 PgC yr−1), land-use change emissions from bookkeeping models (0.9 ± 0.2 PgC yr−1), and the DGVM-estimated sink from CO2 fertilisation, nitrogen deposition, and climate change (2.2 ± 0.9 PgC yr−1). The resulting ‘bottom-up’ net flux of 2.1 ± 0.9 PgC yr−1 agrees with atmospheric inversions. The reconciliation holds at regional scales, increasing confidence in our results.

We study Japan’s net-zero emissions target by 2050 in a multi-model framework, focusing on residual emissions and carbon dioxide removal (CDR). Four energy-economic and integrated assessment models show similar but stronger strategies for the net-zero target, compared to the previous, low-carbon policy target (80% emissions reduction). Results indicate that around 90% (inter-model median) of the current emissions are reduced through abatement, including improved energy efficiency and cleaner electricity and fuels. Models deploy new options such as CDR based on carbon capture and storage (CCS) (bioenergy with CCS and direct air carbon dioxide capture and storage) and hydrogen to achieve net zero. The scale of CCS-based CDR deployment reaches an inter-model median of 132Mt-CO2/yr. The median hydrogen share of final energy in 2050 increases from 0.79% to 6.9% between the low-carbon and net-zero scenarios. The CDR sensitivity analysis reveals that limiting the use of CDR significantly increases the mitigation costs for net zero. Achieving Japan’s net-zero goal will require exploring methods to reduce residual emissions, including demand-side solutions, and accelerating responsible CDR policies.

Abstract High-temperature solar thermal energy will be the most promising energy source for hydrogen production by pyrolysis of water. It is well known that such high-temperature energy is currently widely used as solar thermal power generation. High-temperature heat can be obtained from sunlight by concentrating and converting the light coming straight from the sun, which is called direct normal irradiation. In general, the higher the light concentration, the higher the temperature. There are four types of collector technology currently in widespread use, but the parabolic trough and power tower are typical. It is very important to note that the characteristic of the solar thermal power generation is a decoupling of the time zone for obtaining the energy of the sun and the use of the energy. The decoupling can be accomplished by means of thermal energy storage system at a relatively low cost. In the daytime, electricity is generated by using solar thermal energy from the solar collector, and surplus thermal energy is stored in the storage system. And the electricity can be supplied by using the stored thermal energy during nighttime. It is important that the storage system of sufficient capacity enables an around-the-clock generation. In hydrogen production, stable operation is crucial due to the heat supply around the clock. Therefore, it is considered that the technology of solar heat acquisition and its storage can greatly contribute to hydrogen production.KeywordsSolar thermal energyConcentrating solar power (CSP)Solar energy concentrationThermal storage

<strong class="journal-contentHeaderColor">Abstract.</strong> With the focus of climate policy shifting from pledges to implementation, there is an increasing need to track progress on climate change mitigation at country level, especially for the land-use sector. Despite new tools and models offering unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO₂ fluxes between the national greenhouse gas inventories (NGHGIs) used to assess compliance with the Paris Agreement, and the Global Carbon Budget and IPCC assessment reports, both based on global bookkeeping models (BMs). Recent evidence showed that these differences are mainly due to inconsistent definitions of anthropogenic forest CO₂ fluxes. In particular, the part of the land sink that is caused by the indirect effects of human-induced environmental change (e.g., fertilization effect on vegetation growth due to increase atmospheric CO₂ concentration, climate change) on managed lands is treated as non-anthropogenic by BMs, while in most cases is considered anthropogenic in NGHGIs. In addition, countries use a broader definition of managed land than BMs. Building on previous studies, we implement an approach that adds the CO₂ sink due to environmental change from countries&rsquo; managed forest area (estimated by Dynamic Global Vegetation Models, DGVMs) to the original land-use flux from BMs. This sum is expected to be conceptually more comparable to NGHGIs. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of land categories (i.e., forest land, deforestation, organic soils, other land uses) and countries. Our results confirm a large difference in land use CO₂ fluxes between the ensemble mean of the BMs, estimating a source of 4.3 GtCO₂ yr^-1 globally for the period 2001&ndash;2020, and NGHGIs, which estimate a sink of -1.7 GtCO₂ yr^-1. Most of this 6.0 GtCO₂ yr^-1 gap is found on forest land (3.8 GtCO₂ yr^-1), with differences also for deforestation (1.1 GtCO₂ yr^-1), other land uses (1.0 GtCO₂ yr^-1), and to a lesser extent for organic soils (0.1 GtCO₂ yr^-1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (-5.1 GtCO₂ yr^-1) to BMs estimates, the gap between BMs and NGHGIs becomes significantly smaller both globally (residual gap: 0.9 GtCO₂ yr^-1) and in most regions and countries. The remaining differences mostly reflect smaller net emissions from deforestation and agricultural land in the NGHGIs of developing countries than in the BMs. By reconciling most of the differences between NGHGIs and global models (BMs and DGVMs), offering a blueprint for operationalizing future comparisons, and identifying areas to be further investigated, this study represents an important step forward for increasing transparency and confidence in land-use CO₂ flux estimates at the country level. This is crucial to support land-based mitigation investments and assess the countries&rsquo; collective progress under the Paris Agreement&rsquo;s Global Stocktake.

The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO2 and nitrogen deposition driven gains in carbon stocks are partially offset by climate and land-use and land-cover change (LULCC) losses. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO2 (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes. Further analysis of internal plant and soil (individual pools) cycling is needed to reduce uncertainty in the controlling processes behind the global land carbon sink.

The usefulness of global integrated assessment model (IAM) results for policy recommendation in specific regions has not been fully assessed to date. This study presents the variation in results across models for a given region, and what might be behind this variation and how model assumptions and structures drive results. Understanding what drives the differences across model results is important for national policy relevance of global scenarios. We focus on the use of bioenergy in Brazil, a country expected to play an important role in future bioenergy production. We use results of the Stanford University Energy Modeling Forum’s 33rd Study (EMF-33) model comparison exercise to compare and assess projections of Brazil’s bioenergy pathways under climate mitigation scenarios to explore how 10 global IAMs compare to recent trends in the country. We find that, in their current form, global IAMs have limited potential to supply robust insights into regional mitigation strategies. Our results suggest fertile ground for a new research agenda to improve regional representation in global IAMs with improved spatial and technological resolutions.

<strong class="journal-contentHeaderColor">Abstract.</strong> Australia plays an important role in the global terrestrial carbon cycle on inter-annual timescales. While the Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations of net biome production (NBP) and the carbon stored in vegetation between 1901 to 2018 from 13 DGVMs (TRENDY v8 ensemble). We focused our analysis on Australia's short-term (inter-annual) and long-term (decadal to centennial) terrestrial carbon dynamics. The TRENDY models simulated differing magnitudes of NBP on inter-annual timescales, and these differences resulted in significant differences in long-term vegetation carbon accumulation (<span class="inline-formula">−4.7</span> to 9.5 <span class="inline-formula">Pg C</span>). We compared the TRENDY ensemble to several satellite-derived datasets and showed that the spread in the models' simulated carbon storage resulted from varying changes in carbon residence time rather than differences in net carbon uptake. Differences in simulated long-term accumulated NBP between models were mostly due to model responses to<span id="page5640"/> land-use change. The DGVMs also simulated different sensitivities to atmospheric carbon dioxide (<span class="inline-formula">CO₂</span>) concentration, although notably, the models with nutrient cycles did not simulate the smallest NBP response to <span class="inline-formula">CO₂</span>. Our results suggest that a change in the climate forcing did not have a large impact on the carbon cycle on long timescales. However, the inter-annual variability in precipitation drives the year-to-year variability in NBP. We analysed the impact of key modes of climate variability, including the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), on NBP. While the DGVMs agreed on sign of the response of NBP to El Niño and La Niña and to positive and negative IOD events, the magnitude of inter-annual variability in NBP differed strongly between models. In addition, we find that differences in the timing of simulated phenology and fire dynamics are associated with differences in simulated or prescribed vegetation cover and process representation. We further find model disagreement in simulated vegetation carbon, phenology, and apparent carbon residence time, indicating that the models have different types and coverage of vegetation across Australia (whether prescribed or emergent). Our study highlights the need to evaluate parameter assumptions and the key processes that drive vegetation dynamics, such as phenology, mortality, and fire, in an Australian context to reduce uncertainty across models.

<strong class="journal-contentHeaderColor">Abstract.</strong> Satellite data reveal widespread changes in Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as <span class="inline-formula">CO₂</span> fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the <span class="inline-formula">CO₂</span> fertilization effect as the dominant driver in only two biomes, the temperate forests<span id="page4986"/> and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

In the original publication of the article, the incorrect file was published as supplementary material.