Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model

This study discusses the effect of semi-supervised learning in combination with pretrained language models for data-to-text generation. It is not known whether semi-supervised learning is still helpful when a large-scale language model is also supplemented. This study aims to answer this question by comparing a data-to-text system only supplemented with a language model, to two data-to-text systems that are additionally enriched by a data augmentation or a pseudo-labeling semi-supervised learning approach. Results show that semi-supervised learning results in higher scores on diversity metrics. In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension. These results indicate that semi-supervised learning approaches can bolster output quality and diversity, even when a language model is also present.Comment: 22 pages (excluding bibliography and appendix

Robust Ecosystem Demography (RED version 1.0): a parsimonious approach to modelling vegetation dynamics in Earth system models

A significant proportion of the uncertainty in climate projections arises from uncertainty in the representation of land carbon uptake. Dynamic global vegetation models (DGVMs) vary in their representations of regrowth and competition for resources, which results in differing responses to changes in atmospheric CO2 and climate. More advanced cohort-based patch models are now becoming established in the latest DGVMs. These models typically attempt to simulate the size distribution of trees as a function of both tree size (mass or trunk diameter) and age (time since disturbance). This approach can capture the overall impact of stochastic disturbance events on the forest structure and biomass – but at the cost of increasing the number of parameters and ambiguity when updating the probability density function (pdf) in two dimensions. Here we present the Robust Ecosystem Demography (RED), in which the pdf is collapsed onto the single dimension of tree mass. RED is designed to retain the ability of more complex cohort DGVMs to represent forest demography, while also being parameter sparse and analytically solvable for the steady state. The population of each plant functional type (PFT) is partitioned into mass classes with a fixed baseline mortality along with an assumed power-law scaling of growth rate with mass. The analytical equilibrium solutions of RED allow the model to be calibrated against observed forest cover using a single parameter – the ratio of mortality to growth for a tree of a reference mass (μ0). We show that RED can thus be calibrated to the ESA LC_CCI (European Space Agency Land Cover Climate Change Initiative) coverage dataset for nine PFTs. Using net primary productivity and litter outputs from the UK Earth System Model (UKESM), we are able to diagnose the spatially varying disturbance rates consistent with this observed vegetation map. The analytical form for RED circumnavigates the need to spin up the numerical model, making it attractive for application in Earth system models (ESMs). This is especially so given that the model is also highly parameter sparse

Food security outcomes under a changing climate: impacts of mitigation and adaptation on vulnerability to food insecurity

Climate change is a potential threat to achieving food security, particularly in the most food insecure regions. However, interpreting climate change projections to better understand the potential impacts of a changing climate on food security outcomes is challenging. This paper addresses this challenge through presenting a framework that enables rapid country-level assessment of vulnerability to food insecurity under a range of climate change and adaptation investment scenarios. The results show that vulnerability to food insecurity is projected to increase under all emissions scenarios, and the geographic distribution of vulnerability is similar to that of the present-day; parts of sub-Saharan Africa and South Asia are most severely affected. High levels of adaptation act to off-set these increases; however, only the scenario with the highest level of mitigation combined with high levels of adaptation shows improvements in vulnerability compared to the present-day. The results highlight the dual requirement for mitigation and adaptation to avoid the worst impacts of climate change and to make gains in tackling food insecurity. The approach is an update to the existing Hunger and Climate Vulnerability Index methodology to enable future projections, and the framework presented allows rapid updates to the results as and when new information becomes available, such as updated country-level yield data or climate model output. This approach provides a framework for assessing policy-relevant human food security outcomes for use in long-term climate change and food security planning; the results have been made available on an interactive website for policymakers ( www.metoffice.gov.uk/food-insecurity-index )

Nitrogen cycle impacts on CO2 fertilisation and climate forcing of land carbon stores

Anthropogenic fossil fuel burning increases atmospheric carbon dioxide (CO2) concentration, which is adjusting the climate system. The direct impact of rising CO2 levels and climate feedback alters the terrestrial carbon stores. Land stores are presently increasing, offsetting a substantial fraction of CO2 emissions. Less understood is how this human-induced carbon cycle perturbation interacts with other terrestrial biogeochemical cycles. These connections require quantification, as they may eventually suppress land fertilisation, and so fewer emissions are allowed to follow any prescribed future global warming pathway. Using the new Joint UK Land Environment Simulator-CN large-scale land model, which contributed to Coupled Model Intercomparison Project Phase 6 as the land component of the UK Earth System Model v1 climate model, we focus on how the introduction of the simulated terrestrial nitrogen (N) cycle modulates the expected evolution of vegetation and soil carbon pools. We find that the N-cycle suppresses, by approximately one-third, any future gains by the global soil pool when compared to calculations without that cycle. There is also a decrease in the vegetation carbon gain, although this is much smaller. Factorial simulations illustrate that N suppression tracks direct CO2 rise rather than climate change. The finding that this CO2-related effect predominantly influences soil carbon rather than vegetation carbon, we explain by different balances between changing carbon uptake levels and residence times. Finally, we discuss how this new generation of land models may gain further from emerging point knowledge held by the detailed ecological modelling community

Compensatory climate effects link trends in global runoff to rising atmospheric CO2 concentration

River runoff is a key attribute of the land surface, that additionally has a strong influence on society by the provision of freshwater. Yet various environmental factors modify runoff levels, and some trends could be detrimental to humanity. Drivers include elevated CO2 concentration, climate change, aerosols and altered land-use. Additionally, nitrogen deposition and tropospheric ozone changes influence plant functioning, and thus runoff, yet their importance is less understood. All these effects are now included in the JULES-CN model. We first evaluate runoff estimates from this model against 42 large basin scales, and then conduct factorial simulations to investigate these mechanisms individually. We determine how different drivers govern the trends of runoff over three decades for which data is available. Numerical results suggest rising atmospheric CO2 concentration is the most important contributor to the global mean runoff trend, having a significant mean increase of +0.18 ± 0.006 mm yr−2 and due to the overwhelming importance of physiological effects. However, at the local scale, the dominant influence on historical runoff trends is climate in 82% of the global land area. This difference is because climate change impacts, mainly due to precipitation changes, can be positive (38% of global land area) or negative (44% of area), depending on location. For other drivers, land use change leads to increased runoff trends in wet tropical regions and decreased runoff in Southeast China, Central Asia and the eastern USA. Modelling the terrestrial nitrogen cycle in general suppresses runoff decreases induced by the CO2 fertilization effect, highlighting the importance of carbon–nitrogen interactions on ecosystem hydrology. Nitrogen effects do, though, induce decreasing trend components for much of arid Australia and the boreal regions. Ozone influence was mainly smaller than other drivers

The WiggleZ Dark Energy Survey: the transition to large-scale cosmic homogeneity

We have made the largest-volume measurement to date of the transition to large-scale homogeneity in the distribution of galaxies. We use the WiggleZ survey, a spectroscopic survey of over 200,000 blue galaxies in a cosmic volume of ~1 (Gpc/h)^3. A new method of defining the 'homogeneity scale' is presented, which is more robust than methods previously used in the literature, and which can be easily compared between different surveys. Due to the large cosmic depth of WiggleZ (up to z=1) we are able to make the first measurement of the transition to homogeneity over a range of cosmic epochs. The mean number of galaxies N(<r) in spheres of comoving radius r is proportional to r^3 within 1%, or equivalently the fractal dimension of the sample is within 1% of D_2=3, at radii larger than 71 \pm 8 Mpc/h at z~0.2, 70 \pm 5 Mpc/h at z~0.4, 81 \pm 5 Mpc/h at z~0.6, and 75 \pm 4 Mpc/h at z~0.8. We demonstrate the robustness of our results against selection function effects, using a LCDM N-body simulation and a suite of inhomogeneous fractal distributions. The results are in excellent agreement with both the LCDM N-body simulation and an analytical LCDM prediction. We can exclude a fractal distribution with fractal dimension below D_2=2.97 on scales from ~80 Mpc/h up to the largest scales probed by our measurement, ~300 Mpc/h, at 99.99% confidence.Comment: 21 pages, 16 figures, accepted for publication in MNRA

A rapid application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)

Climate policies evolve quickly, and new scenarios designed around these policies are used to illustrate how they impact global mean temperatures using simple climate models (or climate emulators). Simple climate models are extremely efficient although limited to showing only the global picture. Within the Intergovernmental Panel on Climate Change (IPCC) framework, there is a need to understand the regional impacts of scenarios that include the most recent science and policy decisions quickly to support government in negotiations. To address this, we present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), a new flexible probabilistic framework which aims to provide an efficient means to run new scenarios without the significant overheads of larger more complex Earth system models (ESMs). PRIME provides the capability to include the most recent models, science and scenarios to run ensemble simulations on multi-centennial timescales and include analysis of many variables that are relevant and important for impacts assessments. We use a simple climate model to provide the global temperatures to scale the patterns from a large number of the CMIP6 ESMs. These provide the inputs to a weather generator and a land-surface model, which generates an estimate of the land-surface impacts from the emissions scenarios. Here we test PRIME using known scenarios in the form of the Shared Socioeconomic Pathways (SSPs) to demonstrate that PRIME reproduces the climate response to a range of emissions scenarios, as shown in the IPCC reports. We show results for a range of scenarios including the SSP5-8.5 high emissions scenario, which was used to define the patterns; SSP1-2.6, a mitigation scenario with low emissions and SSP5-3.4-OS, an overshoot scenario. PRIME correctly represents the climate response for these known scenarios, which gives us confidence that PRIME will be useful for rapidly providing probabilistic spatially resolved information for novel climate scenarios; substantially reducing the time between the scenarios being released and being used in impacts assessments