A vertical representation of soil carbon in the JULES land surface scheme (vn4.3_permafrost) with a focus on permafrost regions

An improved representation of the carbon cycle in permafrost regions will enable more realistic projections of the future climate–carbon system. Currently JULES (the Joint UK Land Environment Simulator) – the land surface model of the UK Earth System Model (UKESM) – uses the standard four-pool RothC soil carbon model. This paper describes a new version of JULES (vn4.3_permafrost) in which the soil vertical dimension is added to the soil carbon model, with a set of four pools in every soil layer. The respiration rate in each soil layer depends on the temperature and moisture conditions in that layer. Cryoturbation/bioturbation processes, which transfer soil carbon between layers, are represented by diffusive mixing. The litter inputs and the soil respiration are both parametrized to decrease with increasing depth. The model now includes a tracer so that selected soil carbon can be labelled and tracked through a simulation. Simulations show an improvement in the large-scale horizontal and vertical distribution of soil carbon over the standard version of JULES (vn4.3). Like the standard version of JULES, the vertically discretized model is still unable to simulate enough soil carbon in the tundra regions. This is in part because JULES underestimates the plant productivity over the tundra, but also because not all of the processes relevant for the accumulation of permafrost carbon, such as peat development, are included in the model. In comparison with the standard model, the vertically discretized model shows a delay in the onset of soil respiration in the spring, resulting in an increased net uptake of carbon during this time. In order to provide a more suitable representation of permafrost carbon for quantifying the permafrost carbon feedback within UKESM, the deep soil carbon in the permafrost region (below 1 m) was initialized using the observed soil carbon. There is now a slight drift in the soil carbon ( <  0.018 % decade−1), but the change in simulated soil carbon over the 20th century, when there is little climate change, is comparable to the original vertically discretized model and significantly larger than the drift

An improved representation of physical permafrost dynamics in the JULES land-surface model

PublishedJournal Article© Author(s) 2015. It is important to correctly simulate permafrost in global climate models, since the stored carbon represents the source of a potentially important climate feedback. This carbon feedback depends on the physical state of the permafrost. We have therefore included improved physical permafrost processes in JULES (Joint UK Land Environment Simulator), which is the land-surface scheme used in the Hadley Centre climate models. The thermal and hydraulic properties of the soil were modified to account for the presence of organic matter, and the insulating effects of a surface layer of moss were added, allowing for fractional moss cover. These processes are particularly relevant in permafrost zones. We also simulate a higher-resolution soil column and deeper soil, and include an additional thermal column at the base of the soil to represent bedrock. In addition, the snow scheme was improved to allow it to run with arbitrarily thin layers. Point-site simulations at Samoylov Island, Siberia, show that the model is now able to simulate soil temperatures and thaw depth much closer to the observations. The root mean square error for the near-surface soil temperatures reduces by approximately 30%, and the active layer thickness is reduced from being over 1 m too deep to within 0.1 m of the observed active layer thickness. All of the model improvements contribute to improving the simulations, with organic matter having the single greatest impact. A new method is used to estimate active layer depth more accurately using the fraction of unfrozen water. Soil hydrology and snow are investigated further by holding the soil moisture fixed and adjusting the parameters to make the soil moisture and snow density match better with observations. The root mean square error in near-surface soil temperatures is reduced by a further 20% as a result

Green Plants in the Red: A Baseline Global Assessment for the IUCN Sampled Red List Index for Plants

Plants provide fundamental support systems for life on Earth and are the basis for all terrestrial ecosystems; a decline in plant diversity will be detrimental to all other groups of organisms including humans. Decline in plant diversity has been hard to quantify, due to the huge numbers of known and yet to be discovered species and the lack of an adequate baseline assessment of extinction risk against which to track changes. The biodiversity of many remote parts of the world remains poorly known, and the rate of new assessments of extinction risk for individual plant species approximates the rate at which new plant species are described. Thus the question ‘How threatened are plants?’ is still very difficult to answer accurately. While completing assessments for each species of plant remains a distant prospect, by assessing a randomly selected sample of species the Sampled Red List Index for Plants gives, for the first time, an accurate view of how threatened plants are across the world. It represents the first key phase of ongoing efforts to monitor the status of the world’s plants. More than 20% of plant species assessed are threatened with extinction, and the habitat with the most threatened species is overwhelmingly tropical rain forest, where the greatest threat to plants is anthropogenic habitat conversion, for arable and livestock agriculture, and harvesting of natural resources. Gymnosperms (e.g. conifers and cycads) are the most threatened group, while a third of plant species included in this study have yet to receive an assessment or are so poorly known that we cannot yet ascertain whether they are threatened or not. This study provides a baseline assessment from which trends in the status of plant biodiversity can be measured and periodically reassessed

Impact of model developments on present and future simulations of permafrost in a global land-surface model

There is a large amount of organic carbon stored in permafrost in the northern high latitudes, which may become vulnerable to microbial decomposition under future climate warming. In order to estimate this potential carbon–climate feedback it is necessary to correctly simulate the physical dynamics of permafrost within global Earth system models (ESMs) and to determine the rate at which it will thaw. Additional new processes within JULES, the land-surface scheme of the UK ESM (UKESM), include a representation of organic soils, moss and bedrock and a modification to the snow scheme; the sensitivity of permafrost to these new developments is investigated in this study. The impact of a higher vertical soil resolution and deeper soil column is also considered. Evaluation against a large group of sites shows the annual cycle of soil temperatures is approximately 25 % too large in the standard JULES version, but this error is corrected by the model improvements, in particular by deeper soil, organic soils, moss and the modified snow scheme. A comparison with active layer monitoring sites shows that the active layer is on average just over 1 m too deep in the standard model version, and this bias is reduced by 70 cm in the improved version. Increasing the soil vertical resolution allows the full range of active layer depths to be simulated; by contrast, with a poorly resolved soil at least 50 % of the permafrost area has a maximum thaw depth at the centre of the bottom soil layer. Thus all the model modifications are seen to improve the permafrost simulations. Historical permafrost area corresponds fairly well to observations in all simulations, covering an area between 14 and 19 million km2. Simulations under two future climate scenarios show a reduced sensitivity of permafrost degradation to temperature, with the near-surface permafrost loss per degree of warming reduced from 1.5 million km2 °C−1 in the standard version of JULES to between 1.1 and 1.2 million km2 °C−1 in the new model version. However, the near-surface permafrost area is still projected to approximately half by the end of the 21st century under the RCP8.5 scenario

A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models

Most of the world's permafrost is located in the Arctic, where its frozen organic carbon content makes it a potentially important influence on the global climate system. The Arctic climate appears to be changing more rapidly than the lower latitudes, but observational data density in the region is low. Permafrost thaw and carbon release into the atmosphere, as well as snow cover changes, are positive feedback mechanisms that have the potential for climate warming. It is therefore particularly important to understand the links between the energy balance, which can vary rapidly over hourly to annual timescales, and permafrost conditions, which changes slowly on decadal to centennial timescales. This requires long-term observational data such as that available from the Samoylov research site in northern Siberia, where meteorological parameters, energy balance, and subsurface observations have been recorded since 1998. This paper presents the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, processing, and data quality control. Furthermore, we present a merged data set of the parameters, which were measured from 1998 onwards. Additional data include a high-resolution digital terrain model (DTM) obtained from terrestrial lidar laser scanning. Since the data provide observations of temporally variable parameters that influence energy fluxes between permafrost, active-layer soils, and the atmosphere (such as snow depth and soil moisture content), they are suitable for calibrating and quantifying the dynamics of permafrost as a component in earth system models. The data also include soil properties beneath different microtopographic features (a polygon centre, a rim, a slope, and a trough), yielding much-needed information on landscape heterogeneity for use in land surface modelling. For the record from 1998 to 2017, the average mean annual air temperature was −12.3&thinsp;∘C, with mean monthly temperature of the warmest month (July) recorded as 9.5&thinsp;∘C and for the coldest month (February) −32.7&thinsp;∘C. The average annual rainfall was 169&thinsp;mm. The depth of zero annual amplitude is at 20.75&thinsp;m. At this depth, the temperature has increased from −9.1&thinsp;∘C in 2006 to −7.7&thinsp;∘C in 2017. The presented data are freely available through the PANGAEA (https://doi.org/10.1594/PANGAEA.891142) and Zenodo (https://zenodo.org/record/2223709, last access: 6 February 2019) websites.</p

Simulated responses of soil carbon to climate change in CMIP6 Earth system models: the role of false priming

Reliable estimates of soil carbon change are required to determine the carbon budgets consistent with the Paris Agreement climate targets. This study evaluates projections of soil carbon during the 21st century in Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system models (ESMs) under a range of atmospheric composition scenarios. In general, we find a reduced spread of changes in global soil carbon (ΔCs) in CMIP6 compared to the previous CMIP5 model generation. However, similar reductions were not seen in the derived contributions to ΔCs due to both increases in plant net primary productivity (NPP, named ΔCs,NPP) and reductions in the effective soil carbon turnover time (τs, named ΔCs,τ). Instead, we find a strong relationship across the CMIP6 models between these NPP and τs components of ΔCs, with more positive values of ΔCs,NPP being correlated with more negative values of ΔCs,τ. We show that the concept of “false priming” is likely to be contributing to this emergent relationship, which leads to a decrease in the effective soil carbon turnover time as a direct result of NPP increase and occurs when the rate of increase in NPP is relatively fast compared to the slower timescales of a multi-pool soil carbon model. This finding suggests that the structure of soil carbon models within ESMs in CMIP6 has likely contributed towards the reduction in the overall model spread in future soil carbon projections since CMIP5.</p

Simulating carbon accumulation and loss in the central Congo peatlands

Peatlands of the central Congo Basin have accumulated carbon over millennia. They currently store some 29 billion tonnes of carbon in peat. However, our understanding of the controls on peat carbon accumulation and loss and the vulnerability of this stored carbon to climate change is in its infancy. Here we present a new model of tropical peatland development, DigiBog_Congo, that we use to simulate peat carbon accumulation and loss in a rain-fed interfluvial peatland that began forming ~20,000 calendar years Before Present (cal. yr BP, where ‘present’ is 1950 CE). Overall, the simulated age-depth curve is in good agreement with palaeoenvironmental reconstructions derived from a peat core at the same location as our model simulation. We find two key controls on long-term peat accumulation: water at the peat surface (surface wetness) and the very slow anoxic decay of recalcitrant material. Our main simulation shows that between the Late Glacial and early Holocene there were several multidecadal periods where net peat and carbon gain alternated with net loss. Later, a climatic dry phase beginning ~5200 cal. yr BP caused the peatland to become a long-term carbon source from ~3975 to 900 cal. yr BP. Peat as old as ~7000 cal. yr BP was decomposed before the peatland's surface became wetter again, suggesting that changes in rainfall alone were sufficient to cause a catastrophic loss of peat carbon lasting thousands of years. During this time, 6.4 m of the column of peat was lost, resulting in 57% of the simulated carbon stock being released. Our study provides an approach to understanding the future impact of climate change and potential land-use change on this vulnerable store of carbon

Carbon budget for 1.5 and 2oC targets lowered by natural wetland and permafrost feedbacks

Methane emissions from natural wetlands and carbon release from permafrost thaw have a positive feedback on climate, yet are not represented in most state-of-the-art climate models. Furthermore, a fraction of the thawed permafrost carbon is released as methane, enhancing the combined feedback strength. We present simulations with an intermediate complexity climate model which follow prescribed global warming pathways to stabilisation at 1.5°C or 2.0°C above pre-industrial levels by the year 2100, and that incorporates a state-of-the-art global land surface model with updated descriptions of wetland and permafrost carbon release. We demonstrate that the climate feedbacks from those two processes are substantial. Specifically, permissible anthropogenic fossil fuel CO2 emission budgets are reduced by 17-23% (47-56 GtC) for stabilisation at 1.5°C, and 9-13% (52-57 GtC) for 2.0°C stabilisation. In our simulations these feedback processes respond faster at temperatures below 1.5°C, and the differences between the 1.5°C and 2°C targets are disproportionately small. This key finding is due to our interest in transient emission pathways to the year 2100 and does not consider the longer term implications of these feedback processes. We conclude that natural feedback processes from wetlands and permafrost must be considered in assessments of transient emission pathways to limit global warming