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Historical in situ ocean temperature profile measurements are important for a wide range of ocean and climate research activities. A large proportion of the profile observations have been recorded using expendable bathythermographs (XBTs), and required bias corrections for use in climate change studies. It is generally accepted that the bias, and therefore bias correction, depends on the type of XBT used. However, poor historical metadata collection practices mean the XBT probe type information is often missing, for 59% of profiles between 1967 and 2000, limiting the development of reliable bias corrections. We develop a process of estimating missing instrument type metadata (the combination of both model and manufacturer) systematically, constructing a machine learning pipeline based on thorough data exploration to inform these choices. The predicted instrument type, where missing, will facilitate improved XBT bias corrections. The new approach improves the accuracy of the XBT type classification compared to previous approaches from a recall value of 0.75–0.94. We also develop an approach to account for the uncertainty associated with metadata assignments using ensembles of decision trees, which could feed into an ensemble approach to creating ocean temperature datasets. We describe the challenges arising from the nature of the dataset in applying standard machine learning techniques to the problem. We have implemented this in a portable, reproducible way using standard data science tools, with a view to these techniques being applied to other similar problems in climate science.

Uncertainty in numerical weather prediction (NWP) arises due to the initial state not being fully known and physical processes not being perfectly represented within the models. Precipitation is challenging to predict because it is non-linear with complex drivers from the atmosphere and so varies quickly even on a local scale. This means even advanced NWP models struggle to predict precipitation with the correct intensity at the right time or location. This study aims to explore whether machine learning (ML) can rediagnose precipitation rates based on vertical profiles of temperature, humidity and wind, thus replicating the precipitation calculated by cloud and precipitation parametrization schemes that are used in NWP models to represent the unresolved microphysical processes. A small but high-quality dataset comprised of days with widespread precipitation has been curated for developing an initial model, with in depth exploratory data analysis carried out to understand any trends in the model input data and assess the need for feature engineering. Vertical profiles of atmospheric variables (temperature, humidity, wind) taken from 6-hour forecasts of the Met Office Unified Model global ensemble (MOGREPS-G) provide input features for the ML model, and the target variable (or truth) is instantaneous precipitation intensity measured by the UK radar network at a 1km resolution. The two data sources are aligned onto the same grid by calculating the fractions of the MOGREPS-G ~20km cell containing radar precipitation in five precipitation intensity bands, with bounds informed by domain experts.Each MOGREPS-G ensemble member is used to generate a ML prediction of the fractional precipitation coverage that exceeds each intensity threshold, then an ensemble average of these fractions is calculated for each intensity threshold. These values can be considered as ML generated ensemble probabilities. They can then be compared with the true fractional coverage from radar, as well as precipitation probabilities from MOGREPS-G to identify similarities and differences in their behaviour. Explainable AI techniques are applied to better understand the decisions made by the ML model when creating predictions. The aim is to understand the potential of using ML for improving precipitation forecasts, either through complementing NWP outputs with ML outputs, or by using ML as a tool for improving the understanding of the drivers of errors in NWP precipitation forecasts. Initial results look promising and a number of avenues for further development have been identified following consultation with domain experts.

Digital Twins present a new user-centric paradigm for developing and using weather & climate simulations that is currently being widely embraced, for example through large projects such as Destination Earth led by ECMWF. In this project we have taken a smaller scale approach in understanding the opportunities and challenges in translating the Digital Twin concept from the original domain of manufacturing and the built environment to modelling of the earth’s atmosphere.We describe our approach to creating a Digital Twin based on the Met Office’s Atmospheric Dispersion simulation package called NAME. We will discuss the advantages of doing this, such as the ability of nonexpert users to more easily produce scientifically valid simulations of dispersion events, such as industrial fires, and easily obtain results to feed into downstream analysis, for example of health impacts. We will describe the requirements of each of the key components of a digital twin and potential implementation approaches.We will describe how a Digital Twin framework enables multiple models to be joined together to model complex systems as required for atmospheric concentrations around chemical spills or fires modelled by NAME. Overall, we outline a potential project blueprint for future work to improve usability and scientific throughput of existing modelling systems by creating a Digital Twins from core current modelling code and data gathering systems.

Historical ocean temperature measurements are important in studying climate change due to the high proportion of heat absorbed by the ocean. These measurements come from a variety of sources, including Expendable Bathythermographs (XBTs), which are an important source of such data. Their measurements need bias corrections which are dependent on the type of XBT used, but poor metadata collection practices mean the type is often missing, increasing the measurement uncertainty and thus the uncertainty of the downstream dataset. This talk will describe efforts to fill in missing instrument type metadata using machine learning techniques so better bias corrections can be applied and the uncertainty in ocean temperature datasets reduced. I will describe the challenge arising from the nature of the dataset in applying standard ML techniques to the problem. I will also describe how we have used this project to explore the benefits of different platforms for ML and what open reproducible science looks like for Machine Learning projects.

We describe and evaluate historical simulations which use the third Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 (HadGEM3‐GC3.1) and which form part of the UK's contribution to the sixth Coupled Model Intercomparison Project, CMIP6. These simulations, run at two resolutions, respond to historically evolving forcings such as greenhouse gases, aerosols, solar irradiance, volcanic aerosols, land use, and ozone concentrations. We assess the response of the simulations to these historical forcings and compare against the observational record. This includes the evolution of global mean surface temperature, ocean heat content, sea ice extent, ice sheet mass balance, permafrost extent, snow cover, North Atlantic sea surface temperature and circulation, and decadal precipitation. We find that the simulated time evolution of global mean surface temperature broadly follows the observed record but with important quantitative differences which we find are most likely attributable to strong effective radiative forcing from anthropogenic aerosols and a weak pattern of sea surface temperature response in the low to middle latitudes to volcanic eruptions. We also find evidence that anthropogenic aerosol forcings play a role in driving the Atlantic Multidecadal Variability and the Atlantic Meridional Overturning Circulation, which are key features of the North Atlantic ocean. Overall, the model historical simulations show many features in common with the observed record over the period 1850–2014 and so provide a basis for future in‐depth study of recent climate change.

We describe the scientific and technical implementation of two models for a core set of\ud experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6).\ud The models used are the physical atmosphere-land-ocean-sea ice model HadGEM3-GC3.1 and the\ud Earth system model UKESM1 which adds a carbon-nitrogen cycle and atmospheric chemistry to\ud HadGEM3-GC3.1. The model results are constrained by the external boundary conditions (forcing data)\ud and initial conditions.We outline the scientific rationale and assumptions made in specifying these.\ud Notable details of the implementation include an ozone redistribution scheme for prescribed ozone\ud simulations (HadGEM3-GC3.1) to avoid inconsistencies with the model's thermal tropopause, and land use\ud change in dynamic vegetation simulations (UKESM1) whose influence will be subject to potential biases in\ud the simulation of background natural vegetation.We discuss the implications of these decisions for\ud interpretation of the simulation results. These simulations are expensive in terms of human and CPU\ud resources and will underpin many further experiments; we describe some of the technical steps taken to\ud ensure their scientific robustness and reproducibility.

Posttraumatic stress disorder (PTSD) is a common and disabling anxiety disorder that may occur in the aftermath of exposure to potentially traumatic life events. PTSD is moderately heritable, but few specific molecular variants accounting for this heritability have been identified. Genes regulating the hypothalamic-pituitary-adrenal (HPA) axis, such as corticotrophin-releasing hormone type 1 receptor gene (CRHR1), have been implicated in traumatic-stress related phenotypes but have yet to be studied in relation to PTSD. The present study sought to examine the relation between 9 single nucleotide polymorphisms (SNPs) in theCRHR1gene and posttraumatic stress symptoms in a prospective study of pediatric injury patients (n= 103) who were first assessed in the acute aftermath of their injury at the hospital. Results indicated that multiple SNPs were associated with acute symptoms at a univariate level, and after correction for multiple testing, rs12944712 was significantly related to acute PTSD symptoms. Longitudinal latent growth curve analyses suggest that rs12944712 is also related to both acute symptom level and trajectory of symptoms over time. The present study adds support for the role ofCRHR1in the stress response following potentially traumatic event exposure in youth. It should be noted that the sample size in this study was small, and therefore statistical power was low; following, results from this study should be considered preliminary. Although results are not definitive, the findings from this study warrant future replication studies on how variation in this gene relates to response to traumatic event exposure in youth.