Proliferation of atmospheric datasets can hinder policy making: a data blending technique offers a solution

The proliferation of atmospheric datasets is a key outcome from the continued development and advancement of our collective scientific understanding. Yet often datasets describing ostensibly identical processes or atmospheric variables provide widely varying results. As an example, we analyze several datasets representing rainfall over Nepal. We show that estimates of extreme rainfall are highly variable depending on which dataset you choose to look at. This leads to confusion and inaction from policy-focused decision makers. Scientifically, we should use datasets that sample a range of creation methodologies and prioritize the use of data science techniques that have the flexibility to incorporate these multiple sources of data. We demonstrate the use of a statistically interpretable data blending technique to help discern and communicate a consensus result, rather than imposing a priori judgment on the choice of dataset, for the benefit of policy decision making

A collaborative hackathon to investigate climate change and extreme weather impacts in justice and insurance settings

By bringing together a large group of participants with diverse skillsets, hackathons aim to make good headway into a particular research topic over a short period of time. This collaborative approach supports relationship building, cross team working and the development of technical skills across different areas

Report on the Workshop on Sustainable Software Sustainability 2019 (WOSSS19)

This report is based on the discussions and presentations that took place at the Workshop on Sustainable Software Sustainability (www.software.ac.uk/wosss19) in April 2019 (WOSSS19). It captures the state of the art for a range of Software Sustainability themes that were brought up by the organisers and attendees of the workshop

Probabilistic UK Climate Projections Conditioned on Global Warming Levels

Abstract Probabilistic projections from the UK Climate Projections 2018 are presented for four global warming levels (GWLs) at 1.5, 2, 3, and 4°C above the 1850–1900 baseline. Our results show how uncertainties associated with climate models and four representative concentration pathways (RCP) emission scenarios translate to UK regional scale changes in maximum temperature and precipitation, with data also available for minimum and mean temperatures, humidity and surface net downward shortwave radiation flux. We compare weighting the likelihood of RCPs based on (hypothetical) policy decisions, against our baseline assumption that each RCP is equally likely. Differences between weighted and unweighted GWL distributions are small, particularly in relation to the full breadth of uncertainties that are incorporated into the probabilistic projections. Finally we quantify the relative importance of scenario, model and internal variability on regional projected GWLs and show that uncertainty associated with an uncertain climate response to forcings dominates at all GWLs

Full Waveform Seismic Inversion for the Japan Region

ISSN:1029-7006ISSN:1607-796

Full Seismic Waveform Inversion for the Japanese Islands

ISSN:1029-7006ISSN:1607-796

Exploring inter-basin correlations of tropical cyclones and tropical cyclone losses [Abstract]

Tropical cyclones (TCs) are one of the most costly natural hazards on Earth, and there is a desire to mitigate this risk. It is securely established that TC activity relates to ENSO in all oceanic basins (e.g. N. Atlantic). However, when a recent multi-basin review of correlation coefficients to ENSO was applied to a financial model of losses related to TCs, there appeared to be no significant inter-relationship between the losses between regions (e.g. US, China). It is therefore of interest to examine the chain of environmental and anthropogenic processes from TC genesis to financial loss to examine how correlations degrade. A number of hypotheses are statistically investigated, primarily using Spearman's coefficient and ranks to decouple dependency structures from the marginal distributions, but also Poisson regression.</p

Full-waveform inversion of the Japanese Islands region

We present a full-waveform tomographic model of the crust and upper mantle beneath the Japanese Islands region. This is based on the combination of GPU-accelerated spectral-element wavefield simulations, adjoint techniques, and nonlinear optimization. Our model explains complete seismic waveforms of events not used in the inversion in the period range from 20 to 80 s. Quantitative resolution analysis indicates that resolution lengths within the well-covered areas are around 150 km in the horizontal and around 30 km in the vertical directions. In addition to the high-velocity signatures of known lithospheric slabs in the region, our model reveals a pronounced low-velocity anomaly beneath the volcanic island of Ulleung in the Sea of Japan, reaching −19% around 100 km depth. The Ulleung anomaly originates at or above the Pacific slab, rises vertically upward to the base of the Philippine Sea slab at ∼200 km depth, circumvents it in NW direction, and then significantly strengthens in the uppermost mantle above the Philippine Sea slab. Among the numerous hypotheses for the generation of low-velocity anomalies in subduction systems, those invoking instabilities before or when a slab enters the transition zone seem most likely. The age and fast subduction of the Pacific slab may facilitate the transport of fluids into the transition zone. This may promote the reduction in viscosity and the onset of convective upwelling, aided by ambient mantle flow, such as return flow within the mantle wedge

A new observational analysis of near surface air temperature change since the late 18th century developed for the GloSAT project

The GloSAT project is developing a new observational analysis of global air temperature change over land and ocean since the late 18th century. A new global analysis processing system has been developed that uses a computationally efficient spatial statistical method to estimate air temperature anomaly fields from historical observations. This will be the first presentation of this analysis approach. This method, based on Gaussian Markov Random Fields, jointly estimates temperature anomaly fields over land and ocean based on weather station and ship-based air temperature observations. The increased computational efficiency of the approach compared to conventional kriging-based estimates allows for increased spatial resolution in the analysis. Observational uncertainties are represented within the analysis framework to propagate uncertainty into the output ensemble data set. This accounts for errors arising from uncorrelated effects and structured errors such as residual biases in observations from an individual weather station or ship after correction. Observational error models have been co-developed with project partners providing the input land and marine data products. Initial results from the application of the analysis system to GloSAT air temperature observation data will be demonstrated