Global Imbalances and the Global Saving Glut – A Panel Data Assessment

Since the late 1990s there have been substantial changes in the current account balances of a number of economies, most notably a marked widening in the current account deficit of the United States and increased net lending by many developing nations to developed economies. This paper uses panel data to examine what may have contributed to changes in the current account positions of a wide sample of developing and developed economies. In particular, we aim to assess the ‘global saving glut’ hypothesis that financial crises have contributed to the current account surpluses in developing economies. Overall, we find some support for this argument; there is a significant role for financial crises as well as institutional factors in determining current account balances. However, the model captures the broad trends evident in international capital flows for only some of the major regions in our sample.current accounts; financial crises; capital flows

Partial flux ordering and thermal Majorana metals in (higher-order) spin liquids

In frustrated quantum magnetism, chiral spin liquids are a particularly intriguing subset of quantum spin liquids in which the fractionalized parton degrees of freedom form a Chern insulator. Here we study an exactly solvable spin-3/2 model which harbors not only chiral spin liquids but also spin liquids with higher-order parton band topology -- a trivial band insulator, a Chern insulator with gapless chiral edge modes, and a second-order topological insulator with gapless corner modes. With a focus on the thermodynamic precursors and thermal phase transitions associated with these distinct states, we employ numerically exact quantum Monte Carlo simulations to reveal a number of unconventional phenomena. This includes a heightened thermal stability of the ground state phases, the emergence of a partial flux ordering of the associated $\mathbb{Z}_2$ lattice gauge field, and the formation of a thermal Majorana metal regime extending over a broad temperature range.Comment: 18 page

State of the UK climate 2018

This report provides a summary of the UK weather and climate through the calendar year 2018, alongside the historical context for a number of essential climate variables. This is the fifth in a series of annual “State of the UK climate” publications and an update to the 2017 report (Kendon et al., 2018). It provides an accessible, authoritative and up‐to‐date assessment of UK climate trends, variations and extremes based on the most up to date observational datasets of climate quality. The majority of this report is based on observations of temperature, precipitation, sunshine and wind speed from the UK land weather station network as managed by the Met Office and a number of key partners and co‐operating volunteers. The observations are carefully managed such that they conform to current best practice observational standards as defined by the World Meteorological Organization (WMO). The observations also pass through a range of quality assurance procedures at the Met Office before application for climate monitoring. In addition, time series of near‐coast sea‐surface temperature (SST) and sea‐level rise are also presented. The process for generating national and regional statistics from these observations has been updated since Kendon et al., 2018. This report makes use of a new dataset, HadUK‐Grid, which provides improved quality and traceability for these national statistics along with temperature and rainfall series that extend back into the 19th Century. Differences with previous data are described in the relevant sections and appendices. The report presents summary statistics for year 2018 and the most recent decade (2009–2018) against 1961–1990 and 1981–2010 averages. Year 2009–2018 is a non‐standard reference period, but it provides a 10‐year “snapshot” of the most recent experience of the UK's climate and how that compares to historical records. This means differences between 2009 and 2018 and the baseline reference averages may reflect shorter‐term decadal variations as well as long‐term trends. These data are presented to show what has happened in recent years, not necessarily what is expected to happen in a changing climate. The majority of maps in this report show year 2018 against the 1981–2010 baseline reference averaging period—that is, they are anomaly maps which show the spatial variation in this difference from average. Maps of actual values are in most cases not displayed because these are dominated by the underlying climatology, which for this report is of a lesser interest than the year‐to‐year variability. Throughout the report's text the terms “above normal” and “above average,” etc. refer to the 1981–2010 baseline reference averaging period unless otherwise stated. Values quoted in tables throughout this report are rounded, but where the difference between two such values is quoted in the text (for example, comparing the most recent decade with 1981–2010), this difference is calculated from the original unrounded values

A multi-objective ensemble approach to hydrological modelling in the UK: an application to historic drought reconstruction

Hydrological models can provide estimates of streamflow pre- and post-observations, which enable greater understanding of past hydrological behaviour, and potential futures. In this paper, a new multi-objective calibration method was derived and tested for 303 catchments in the UK, and the calibrations were used to reconstruct river flows back to 1891, in order to provide a much longer view of past hydrological variability, given the brevity of most UK river flow records which began post-1960. A Latin hypercube sample of 500 000 parameterisations for the GR4J model for each catchment were evaluated against six evaluation metrics covering all aspects of the flow regime from high, median, and low flows. The results of the top ranking model parameterisation (LHS1), and also the top 500 (LHS500), for each catchment were used to provide a deterministic result whilst also accounting for parameter uncertainty. The calibrations are generally good at capturing observed flows, with some exceptions in heavily groundwater-dominated catchments, and snowmelt and artificially influenced catchments across the country. Reconstructed flows were appraised over 30-year moving windows and were shown to provide good simulations of flow in the early parts of the record, in cases where observations were available. To consider the utility of the reconstructions for drought simulation, flow data for the 1975–1976 drought event were explored in detail in nine case study catchments. The model's performance in reproducing the drought events was found to vary by catchment, as did the level of uncertainty in the LHS500. The Standardised Streamflow Index (SSI) was used to assess the model simulations' ability to simulate extreme events. The peaks and troughs of the SSI time series were well represented despite slight over- or underestimations of past drought event magnitudes, while the accumulated deficits of the drought events extracted from the SSI time series verified that the model simulations were overall very good at simulating drought events. This paper provides three key contributions: (1) a robust multi-objective model calibration framework for calibrating catchment models for use in both general and extreme hydrology; (2) model calibrations for the 303 UK catchments that could be used in further research, and operational applications such as hydrological forecasting; and (3) ∼ 125 years of spatially and temporally consistent reconstructed flow data that will allow comprehensive quantitative assessments of past UK drought events, as well as long-term analyses of hydrological variability that have not been previously possible, thus enabling water resource managers to better plan for extreme events and build more resilient systems for the future

Meta-learning of Sequential Strategies

In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class. Our goal is to equip the reader with the conceptual foundations of this tool for building new, scalable agents that operate on broad domains. To do so, we present basic algorithmic templates for building near-optimal predictors and reinforcement learners which behave as if they had a probabilistic model that allowed them to efficiently exploit task structure. Furthermore, we recast memory-based meta-learning within a Bayesian framework, showing that the meta-learned strategies are near-optimal because they amortize Bayes-filtered data, where the adaptation is implemented in the memory dynamics as a state-machine of sufficient statistics. Essentially, memory-based meta-learning translates the hard problem of probabilistic sequential inference into a regression problem.Comment: DeepMind Technical Report (15 pages, 6 figures

Risk-based management of invading plant disease

- Effective control of plant disease remains a key challenge. Eradication attempts often involve removal of host plants within a certain radius of detection, targeting asymptomatic infection. Here we develop and test potentially more effective, epidemiologically motivated, control strategies, using a mathematical model previously fitted to the spread of citrus canker in Florida. - We test risk-based control, which preferentially removes hosts expected to cause a high number of infections in the remaining host population. Removals then depend on past patterns of pathogen spread and host removal, which might be nontransparent to affected stakeholders. This motivates a variable radius strategy, which approximates risk-based control via removal radii that vary by location, but which are fixed in advance of any epidemic. - Risk-based control outperforms variable radius control, which in turn outperforms constant radius removal. This result is robust to changes in disease spread parameters and initial patterns of susceptible host plants. However, efficiency degrades if epidemiological parameters are incorrectly characterised. - Risk-based control including additional epidemiology can be used to improve disease management, but it requires good prior knowledge for optimal performance. This focuses attention on gaining maximal information from past epidemics, on understanding model transferability between locations and on adaptive management strategies that change over time.Part of this work was funded by the USDA-APHIS Farm Bill; C.A.G. acknowledges support from USDA-APHIS

HadUK‐Grid—A new UK dataset of gridded climate observations

Abstract HadUK‐Grid is a new dataset of gridded climate observations for the UK produced by the Met Office Hadley Centre for Climate Science and Services. The dataset interpolates in situ observations to a regular grid using methods developed in a previous equivalent dataset that had been made available to users since 2002 through the UK Climate Projections project (UKCIP02, UKCP09). The new dataset differs from the existing one in a number of key respects: higher spatial resolution, longer time series for some variables, improved consistency with regard to the pre‐processing of station observations, the use of publicly‐accessible ancillary data sources, a revised calculation sequence for some variables and improved version control. This makes for a dataset that is more internally consistent, more traceable and more reproducible. The result is a dataset of key UK climate variables of up to 1 km resolution from 1862 for monthly rainfall, 1884 for monthly temperature, 1891 for daily rainfall, 1929 for monthly sunshine and a wider set of variables with start dates from the 1960s to support the need for national climate monitoring and climate research

State of the UK climate 2017

This report provides a summary of the UK weather and climate through the calendar year 2017, alongside the historical context for a number of essential climate variables. This is the fourth in a series of annual “State of the UK climate” publications and an update to the 2016 report (Kendon et al., 2017). It provides an accessible, authoritative and up‐to‐date assessment of UK climate trends, variations and extremes based on the most up to date observational datasets of climate quality. The majority of this report is based on observations of temperature, precipitation, sunshine and wind speed from the UK land weather station network as managed by the Met Office and a number of key partners and co‐operating volunteers. The observations are carefully managed such that they conform to current best practice observational standards as defined by the World Meteorological Organization (WMO). The observations also pass through a range of quality assurance procedures at the Met Office before application for climate monitoring. In addition, time series of near‐coast sea‐surface temperature and sea‐level rise are also presented. The report presents summary statistics for year 2017 and the most recent decade (2008–2017) against 1961–1990 and 1981–2010 averages. 2008–2017 is a non‐standard reference period, but it provides a 10‐year “snapshot” of the most recent experience of the UK’s climate and how that compares to historical records. This means differences between 2008–2017 and the baseline reference averages may reflect shorter‐term decadal variations as well as long‐term trends. These data are presented to show what has happened in recent years, not necessarily what is expected to happen in a changing climate. The majority of maps in this report show year 2017 against the 1981–2010 baseline reference averaging period—i.e., they are anomaly maps which show the spatial variation in this difference from average. Maps of actual values are not displayed because these are dominated by the underlying climatology, which for this report is of a lesser interest than the year‐to‐year variability. Throughout the report’s text the terms “above normal” and “above average” etc. refer to the 1981–2010 baseline reference averaging period unless otherwise stated. Values quoted in tables throughout this report are rounded, but where the difference between two such values is quoted in the text (for example comparing the most recent decade with 1981–2010), this difference is calculated from the original unrounded values