Explore the vast range of titles available.

An overview is presented of the progress since 2021 in the construction and scientific programme preparation of the Divertor Tokamak Test (DTT) facility. Licensing for building construction has been granted at the end of 2021. Licensing for Cat. A radiologic source has been also granted in 2022. The construction of the toroidal field magnet system is progressing. The prototype of the 170 GHz gyrotron has been produced and it is now under test on the FALCON facility. The design of the vacuum vessel, the poloidal field coils and the civil infrastructures has been completed. The shape of the first DTT divertor has been agreed with EUROfusion to test different plasma and exhaust scenarios: single null, double null, X-divertor and negative triangularity plasmas. A detailed research plan is being elaborated with the involvement of the EUROfusion laboratories.

The project Land-Use and Climate, Identification of Robust Impacts (LUCID) was conceived to address the robustness of biogeophysical impacts of historical land use–land cover change (LULCC). LUCID used seven atmosphere–land models with a common experimental design to explore those impacts of LULCC that are robust and consistent across the climate models. The biogeophysical impacts of LULCC were also compared to the impact of elevated greenhouse gases and resulting changes in sea surface temperatures and sea ice extent (CO2SST). Focusing the analysis on Eurasia and North America, this study shows that for a number of variables LULCC has an impact of similar magnitude but of an opposite sign, to increased greenhouse gases and warmer oceans. However, the variability among the individual models’ response to LULCC is larger than that found from the increase in CO2SST. The results of the study show that although the dispersion among the models’ response to LULCC is large, there are a number of robust common features shared by all models: the amount of available energy used for turbulent fluxes is consistent between the models and the changes in response to LULCC depend almost linearly on the amount of trees removed. However, less encouraging is the conclusion that there is no consistency among the various models regarding how LULCC affects the partitioning of available energy between latent and sensible heat fluxes at a specific time. The results therefore highlight the urgent need to evaluate land surface models more thoroughly, particularly how they respond to a perturbation in addition to how they simulate an observed average state.

A growing body of literature documents the pressing need to develop soil biogeochemistry models that more accurately reflect contemporary understanding of soil processes and better capture soil carbon (C) responses to environmental perturbations. Models that explicitly represent microbial activity offer inroads to improve representations of soil biogeochemical processes, but have yet to consider relationships between litter quality, functional differences in microbial physiology, and the physical protection of microbial byproducts in forming stable soil organic matter (SOM). To address these limitations, we introduce the MIcrobial-MIneral Carbon Stabilization (MIMICS) model, and evaluate it by comparing site-level soil C projections with observations from a long-term litter decomposition study and soil warming experiment. In MIMICS, the turnover of litter and SOM pools is governed by temperature-sensitive Michaelis–Menten kinetics and the activity of two physiologically distinct microbial functional types. The production of microbial residues through microbial turnover provides inputs to SOM pools that are considered physically or chemically protected. Soil clay content determines the physical protection of SOM in different soil environments. MIMICS adequately simulates the mean rate of leaf litter decomposition observed at temperate and boreal forest sites, and captures observed effects of litter quality on decomposition rates. Moreover, MIMICS better captures the response of SOM pools to experimental warming, with rapid SOM losses but declining temperature sensitivity to long-term warming, compared with a more conventional model structure. MIMICS incorporates current microbial theory to explore the mechanisms by which litter C is converted to stable SOM, and to improve predictions of soil C responses to environmental change.

Over recent decades, the Southern Ocean (SO) has experienced multi‐decadal surface cooling despite global warming. Earlier studies have proposed that recent SO cooling has been caused by the strengthening of surface westerlies associated with a positive trend of the Southern Annular Mode (SAM) forced by ozone depletion. Here we revisit this hypothesis by examining the relationships between the SAM, zonal winds and SO sea‐surface temperature (SST). Applying a low‐frequency component analysis to observations, we show that while positive SAM anomalies can induce SST cooling as previously found, this seasonal‐to‐interannual modulation makes only a small contribution to the observed long‐term SO cooling. Global climate models well capture the observed interannual SAM‐SST relationship, and yet generally fail to simulate the observed multi‐decadal SO cooling. The forced SAM trend in recent decades is thus unlikely the main cause of the observed SO cooling, pointing to a limited role of the Antarctic ozone hole. Plain Language Summary Despite increasing greenhouse gases, the Southern Ocean sea‐surface temperatures have cooled over the recent several decades. The cause of Southern Ocean cooling remains a puzzling feature of recent climate change. Earlier studies have proposed that this multi‐decadal cooling in the Southern Ocean has arisen in part from the strengthening of surface winds associated with a positive trend in a mode of climate variability known as the Southern Annular Mode (SAM). Here we employ a new statistical method to examine this proposed relationship in both observations and climate models. We found that SAM variability only changes Southern Ocean surface temperature on short‐term timescales and makes little contribution to observed long‐term trends. Our results thus suggest the SAM trend, via the strengthening of circumpolar westerlies, is unlikely to be the main cause of the observed long‐term Southern Ocean cooling. Key Points Austral summer Southern Annular Mode (SAM) anomalies affect Southern Ocean (SO) sea‐surface temperature (SST) only on seasonal to interannual timescales Multi‐decadal observed SAM trends make little contribution to observed Southern Ocean SST trends Global climate models (GCMs) capture the observed seasonal SAM‐SST relationship and yet fail to simulate the observed long‐term SO cooling

Seven climate models were used to explore the biogeophysical impacts of human‐induced land cover change (LCC) at regional and global scales. The imposed LCC led to statistically significant decreases in the northern hemisphere summer latent heat flux in three models, and increases in three models. Five models simulated statistically significant cooling in summer in near‐surface temperature over regions of LCC and one simulated warming. There were few significant changes in precipitation. Our results show no common remote impacts of LCC. The lack of consistency among the seven models was due to: 1) the implementation of LCC despite agreed maps of agricultural land, 2) the representation of crop phenology, 3) the parameterisation of albedo, and 4) the representation of evapotranspiration for different land cover types. This study highlights a dilemma: LCC is regionally significant, but it is not feasible to impose a common LCC across multiple models for the next IPCC assessment.

Improved knowledge of the contributing sources of uncertainty in projections of Arctic sea ice over the 21st century is essential for evaluating impacts of a changing Arctic environment. Here, we consider the role of internal variability, model structure and emissions scenario in projections of Arctic sea-ice area (SIA) by using six single model initial-condition large ensembles and a suite of models participating in Phase 5 of the Coupled Model Intercomparison Project. For projections of September Arctic SIA change, internal variability accounts for as much as 40%–60% of the total uncertainty in the next decade, while emissions scenario dominates uncertainty toward the end of the century. Model structure accounts for 60%–70% of the total uncertainty by mid-century and declines to 30% at the end of the 21st century in the summer months. For projections of wintertime Arctic SIA change, internal variability contributes as much as 50%–60% of the total uncertainty in the next decade and impacts total uncertainty at longer lead times when compared to the summertime. In winter, there exists a considerable scenario dependence of model uncertainty with relatively larger model uncertainty under strong forcing compared to weak forcing. At regional scales, the contribution of internal variability can vary widely and strongly depends on the calendar month and region. For wintertime SIA change in the Greenland-Iceland-Norwegian and Barents Seas, internal variability contributes 60%–70% to the total uncertainty over the coming decades and remains important much longer than in other regions. We further find that the relative contribution of internal variability to total uncertainty is state-dependent and increases as sea ice volume declines. These results demonstrate that internal variability is a significant source of uncertainty in projections of Arctic sea ice.

The Arctic sea-ice cover is strongly influenced by internal variability on decadal timescales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea-ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record. The identified patterns account for most of the observed regional sea-ice variability and trends, and they thus help to disentangle the role of forced and internal sea-ice changes over the satellite record. In particular, we identify a mode of decadal ocean–atmosphere–sea-ice variability, characterized by an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30 % of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012 but accounts for at most 10 % of the decline since 1979. For winter sea ice, we find that internal variability has dominated decadal trends in the Bering Sea but has contributed less to trends in the Barents and Kara seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to Arctic sea-ice trends, suggest a lower estimate of the contribution from internal variability than most model-based assessments.

Projecting biogeochemical responses to global environmental change requires multi-scaled perspectives that consider organismal diversity, ecosystem processes, and global fluxes. However, microbes, the drivers of soil organic matter decomposition and stabilization, remain notably absent from models used to project carbon (C) cycle–climate feedbacks. We used a microbial trait-based soil C model with two physiologically distinct microbial communities, and evaluate how this model represents soil C storage and response to perturbations. Drawing from the application of functional traits used to model other ecosystems, we incorporate copiotrophic and oligotrophic microbial functional groups in the MIcrobial-MIneral Carbon Stabilization (MIMICS) model; these functional groups are akin to \"gleaner\" vs. \"opportunist\" plankton in the ocean, or r- vs. K-strategists in plant and animal communities. Here we compare MIMICS to a conventional soil C model, DAYCENT (the daily time-step version of the CENTURY model), in cross-site comparisons of nitrogen (N) enrichment effects on soil C dynamics. MIMICS more accurately simulates C responses to N enrichment; moreover, it raises important hypotheses involving the roles of substrate availability, community-level enzyme induction, and microbial physiological responses in explaining various soil biogeochemical responses to N enrichment. In global-scale analyses, we show that MIMICS projects much slower rates of soil C accumulation than a conventional soil biogeochemistry in response to increasing C inputs with elevated carbon dioxide (CO2) – a finding that would reduce the size of the land C sink estimated by the Earth system. Our findings illustrate that tradeoffs between theory and utility can be overcome to develop soil biogeochemistry models that evaluate and advance our theoretical understanding of microbial dynamics and soil biogeochemical responses to environmental change.

Observations show an increase in the seasonal cycle amplitude of CO 2 in northern latitudes over the past half century. Although multiple drivers contribute, observations and inversion models cannot quantitatively account for the factors contributing to the increased CO 2 amplitude and older versions of Earth System Models (ESMs) do not simulate it. Here we show that several current generation ESMs are closer to the observed CO 2 amplitude and highlight that in the Community Earth System Model (CESM) agricultural nitrogen (N) fertilization increases CO 2 amplitude by 1-3 ppm throughout the Northern Hemisphere and up to 9 ppm in agricultural hotspots. While agricultural N fertilization is the largest contributor to the enhanced amplitude (45%) in Northern Hemisphere land-atmosphere carbon fluxes in CESM, higher CO 2 concentrations and warmer temperatures also contribute, though to a lesser extent (40% and 18% respectively). Our results emphasize the fundamental role of agricultural management in Northern Hemisphere carbon cycle feedbacks and illustrate that agricultural N fertilization should be considered in future carbon cycle simulations. Earth system model simulations illustrate that agricultural nitrogen fertilization significantly enhances the amplitude of land-atmosphere CO 2 exchange, with a magnitude comparable to that of CO 2 fertilization.