Showing papers by "University of Reading published in 2022"

Global Carbon Budget 2021

Abstract: Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1, and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021 suggest a rebound in EFOS relative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

Global Carbon Budget 2022

Abstract: Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Thermal comfort in hospital buildings – A literature review

Abstract: Hospital buildings are required to secure a variety of indoor environments according to the diverse requirements of patients and staff. Among these requirements, thermal comfort is an important design criterion for indoor environmental quality that affects patients' healing processes and the wellbeing of medical staff. The patients’ thermal comfort is given priority due to their medical conditions and impaired immune systems. Thermal comfort and related contexts have been well-covered in many research articles; however, the number of review articles is limited. This article aims to conduct a holistic and critical review of existing studies offering insights on future research trends (180 articles were analyzed). The key research themes are identified using scientometric analysis. Focus is on influencing factors, field-surveys, improving measures and energy saving related to thermal comfort. The primary outcome concludes that ventilation systems play a key role in maintaining acceptable, thermally-comfortable conditions for patients and medical staff. It is also found that acceptable thermal comfort is highly case-dependent and varies substantially based on the health condition of the patient as well as the type and level of staff activities. The measures currently mentioned to minimize energy consumption are also discussed. Some interesting issues, including the inaccuracy arising from the use of predicted mean vote (PMV) and the impact of gender, age, and related factors on thermal comfort, have been noted. This review provides insights into the design and assessment of hospital thermal environments.

How well do the CMIP6 models simulate dust aerosols?

Abstract: Abstract. Mineral dust impacts key processes in the Earth system, including the radiation budget, clouds, and nutrient cycles. We evaluate dust aerosols in 16 models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) against multiple reanalyses and observations. We note that both the reanalyses and observations used here have their limitations and particularly that dust emission and deposition in reanalyses are poorly constrained. Most models, and particularly the multi-model ensemble mean (MEM), capture the spatial patterns and seasonal cycles of global dust processes well. However, large uncertainties and inter-model diversity are found. For example, global dust emissions, primarily driven by model-simulated surface winds, vary by a factor of 5 across models, while the MEM estimate is double the amount in reanalyses. The ranges of CMIP6 model-simulated global dust emission, deposition, burden, and optical depth (DOD) are larger than previous generations of models. Models present considerable disagreement in dust seasonal cycles over North China and North America. Here, DOD values are overestimated by most CMIP6 models, with the MEM estimate 1.2–1.7 times larger compared to satellite and reanalysis datasets. Such overestimates can reach up to a factor of 5 in individual models. Models also fail to reproduce some key features of the regional dust distribution, such as dust accumulation along the southern edge of the Himalayas. Overall, there are still large uncertainties in CMIP6 models' simulated dust processes, which feature inconsistent biases throughout the dust life cycle between models, particularly in the relationship connecting dust mass to DOD. Our results imply that modelled dust processes are becoming more uncertain as models become more sophisticated. More detailed output and dust size-resolved variables in particular, relating to the dust cycle in future intercomparison projects, are needed to enable better constraints of global dust cycles and enable the potential identification of observationally constrained links between dust cycles and optical properties.

Data Assimilation for Chaotic Dynamics

Abstract: Chaos is ubiquitous in physical systems. The associated sensitivity to initial conditions is a significant obstacle in forecasting the weather and other geophysical fluid flows. Data assimilation is the process whereby the uncertainty in initial conditions is reduced by the astute combination of model predictions and real-time data. This chapter reviews recent findings from investigations on the impact of chaos on data assimilation methods: for the Kalman filter and smoother in linear systems, analytic results are derived; for their ensemble-based versions and nonlinear dynamics, numerical results provide insights. The focus is on characterizing the asymptotic statistics of the Bayesian posterior in terms of the dynamical instabilities, differentiating between deterministic and stochastic dynamics. We also present two novel results. Firstly, we study the functioning of the ensemble Kalman filter in the context of a chaotic, coupled, atmosphere-ocean model with a quasi-degenerate spectrum of Lyapunov exponents, showing the importance of having sufficient ensemble members to track all of the near-null modes. Secondly, for the fully non-Gaussian method of the particle filter, numerical experiments are conducted to test whether the curse of dimensionality can be mitigated by discarding observations in the directions of little dynamical growth of uncertainty. The results refute this option, most likely because the particles already embody this information on the chaotic system. The results also suggest that it is the rank of the unstable-neutral subspace of the dynamics, and not that of the observation operator, that determines the required number of particles. We finally discuss how knowledge of the random attractor can play a role in the development of future data assimilation schemes for chaotic multiscale systems with large scale separation.

Amyloid and Hydrogel Formation of a Peptide Sequence from a Coronavirus Spike Protein

Abstract: We demonstrate that a conserved coronavirus spike protein peptide forms amyloid structures, differing from the native helical conformation and not predicted by amyloid aggregation algorithms. We investigate the conformation and aggregation of peptide RSAIEDLLFDKV, which is a sequence common to many animal and human coronavirus spike proteins. This sequence is part of a native α-helical S2 glycoprotein domain, close to and partly spanning the fusion sequence. This peptide aggregates into β-sheet amyloid nanotape structures close to the calculated pI = 4.2, but forms disordered monomers at high and low pH. The β-sheet conformation revealed by FTIR and circular dichroism (CD) spectroscopy leads to peptide nanotape structures, imaged using transmission electron microscopy (TEM) and probed by small-angle X-ray scattering (SAXS). The nanotapes comprise arginine-coated bilayers. A Congo red dye UV–vis assay is used to probe the aggregation of the peptide into amyloid structures, which enabled the determination of a critical aggregation concentration (CAC). This peptide also forms hydrogels under precisely defined conditions of pH and concentration, the rheological properties of which were probed. The observation of amyloid formation by a coronavirus spike has relevance to the stability of the spike protein conformation (or its destabilization via pH change), and the peptide may have potential utility as a functional material. Hydrogels formed by coronavirus peptides may also be of future interest in the development of slow-release systems, among other applications.

Welfare state policies and far right party support: moderating ‘insecurity effects' among different social groups

Abstract: This article examines the interplay between social risks, welfare state policies and far right voting. Distinguishing between compensatory and protective policies and using data from seven waves of...

Global Changes in Water Vapor 1979–2020

Abstract: Global-scale changes in water vapor and responses to surface temperature variability since 1979 are evaluated across a range of satellite and ground-based observations, a reanalysis (ERA5) and coupled and atmosphere-only CMIP6 climate model simulations. Global-mean column integrated water vapor increased by 1%/decade during 1988-2014 in observations and atmosphere-only simulations. However, coupled simulations overestimate water vapor trends and this is partly explained by past studies showing that internal climate variability suppressed observed warming in this period. Decreases in low-altitude tropical water vapor in ERA5 and ground-based observations before around 1993 are considered suspect based on inconsistency with simulations and increased column integrated water vapor in microwave satellite data since 1979. AIRS satellite data does not capture the increased tropospheric water vapor since 2002 shown by other satellite, reanalysis and model products. However, global water vapor responses to interannual surface temperature variability is consistent across datasets with increases of ∼4-5% near the surface and 10-15% at 300 hPa for each 1°C increase in global surface temperature. Global water vapor responses are explained by thermodynamic amplification of upper tropospheric temperature changes and the Clausius Clapeyron temperature dependence of saturation vapor pressure that are dominated by the tropical ocean responses. Upper tropospheric moistening is larger in climate model simulations with greater upper tropospheric warming.

On the corrosion, stress corrosion and cytocompatibility performances of ALD TiO2 and ZrO2 coated magnesium alloys.

Abstract: Magnesium alloys are increasingly studied as materials for temporary implants. However, their high corrosion rate and susceptibility to corrosion-assisted cracking phenomena, such as stress corrosion cracking (SCC), continue to prevent their mainstream use. Recently, coatings have been considered to provide an effective solution to these issues and researchers have focused their attention on Atomic Layer Deposition (ALD). ALD stands out as a coating technology due to the outstanding film conformality and density achievable, and has shown encouraging preliminary results in terms of reduced corrosion rate and reduced SCC susceptibility. Here, we contribute to the ongoing interest in ALD-coated Mg alloys, providing a comprehensive characterisation of the effect of 100 nm thick ALD TiO2 and ZrO2 coatings on the corrosion behaviour and SCC susceptibility of AZ31 alloy. Moreover, we also investigate the effect of these coatings on the induced biological response. Our results suggest that the ALD coatings can improve the corrosion and SCC resistance of the Mg alloy, with the ZrO2 ALD coating showing the best improvements. We suggest that the different corrosion behaviours are the cause of the cytocompatibility results (only the ZrO2 ALD coating was found to meet the demands for cellular applications). Finally, we leverage on considerations about the coatings’ wettability, electrochemical stability and surface integrity to justify the different results.

Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States

Abstract: Abstract. Accurate river streamflow forecasts are a vital tool in the fields of water security, flood preparation and agriculture, as well as in industry more generally. Traditional physics-based models used to produce streamflow forecasts have become increasingly sophisticated, with forecasts improving accordingly. However, the development of such models is often bound by two soft limits: empiricism – many physical relationships are represented empirical formulae; and data sparsity – long time series of observational data are often required for the calibration of these models. Artificial neural networks have previously been shown to be highly effective at simulating non-linear systems where knowledge of the underlying physical relationships is incomplete. However, they also suffer from issues related to data sparsity. Recently, hybrid forecasting systems, which combine the traditional physics-based approach with statistical forecasting techniques, have been investigated for use in hydrological applications. In this study, we test the efficacy of a type of neural network, the long short-term memory (LSTM), at predicting streamflow at 10 river gauge stations across various climatic regions of the western United States. The LSTM is trained on the catchment-mean meteorological and hydrological variables from the ERA5 and Global Flood Awareness System (GloFAS)–ERA5 reanalyses as well as historical streamflow observations. The performance of these hybrid forecasts is evaluated and compared with the performance of both raw and bias-corrected output from the Copernicus Emergency Management Service (CEMS) physics-based GloFAS. Two periods are considered, a testing phase (June 2019 to June 2020), during which the models were fed with ERA5 data to investigate how well they simulated streamflow at the 10 stations, and an operational phase (September 2020 to October 2021), during which the models were fed forecast variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), to investigate how well they could predict streamflow at lead times of up to 10 d. Implications and potential improvements to this work are discussed. In summary, this is the first time an LSTM has been used in a hybrid system to create a medium-range streamflow forecast, and in beating established physics-based models, shows promise for the future of neural networks in hydrological forecasting.

Successful Experiences and Lessons from Conservation Agriculture Worldwide

Abstract: Since 2008/2009, conservation agriculture (CA) cropland area has been expanding globally at an annual rate of more than 10 M ha per year. In 2015/2016, the total CA cropland area was 180.4 M ha, corresponding to 12.5% of global cropland area. In 2018/2019, the total cropland area was 205.4 M ha, corresponding to 14.7% of global cropland area. The spread of CA has been expanding in Asia, Africa, and Europe in recent years because farmers are becoming better organized in working together and networking. More attention and resources are being allocated by stakeholders towards supporting farmers to adopt CA and in generating new knowledge to improve their performance. Globally, expansion of CA remains largely farmer-driven and has become a multi-stakeholder movement comprising formal and informal CA networks at national and international levels involving individuals and institutions in the public, private, and civil sectors. Several lessons from the global spread of CA are elaborated responding to the questions: (i) Why are the three interlinked CA principles universally applicable? (ii) Why does CA work sustainably and optimally? (iii) Why does CA deliver ecosystem services? (iv) Why is CA a valid alternative agricultural paradigm for sustainable development? (v) What are the sufficient conditions for scaling and mainstreaming CA?