Poster Presentations

Abstract: Hazardous wind phenomena associated with severe thunderstorms are a danger not only to nature but also to society (for example air traffic). This poster will deal with the characteristics of dangerous wind phenomena on different scales using the algorithms for the estimation of horizontal wind shear from Doppler radial velocity and detecting these phenomena (such as microbursts, mesocyclones, and gust fronts) from Meteopress weather radar polarimetric data.

Biography: I am a 24-year-old radar meteorologist working at Meteopress since 2021 and a student of physical geography and geoecology at the Faculty of Science of Charles University in Prague. I focus on severe wind phenomena such as tornadoes, downbursts, and derechos and their conditions of formation using proximity soundings, manifestations using radar characteristics, and their impacts. Apart from research on these phenomena, I am interested in the analysis of radar data, and I help my colleagues develop specific meteorological algorithms and software useful for operational meteorology, such as issuing warnings.

Abstract: In 2023, Meteopress developed the mobile C-band polarimetric meteorological radar, MASEC. This radar, working on solid-state technology, stands out for its quick operational readiness, capable of being operational in just under 10 minutes. The entire radar can be easily transported to the desired location using a truck. Another feature is its low energy consumption (around 750 W), making it suitable for areas without electrical grids, powered by batteries or solar panels. Radar was tested in northeastern Czechia in June - August 2023 and in Graz, Austria in September 2023. During the testing period, the radar recorded multiple severe and non-severe events, which will be presented in the form of polarimetric products and a products derived from them.

Biography: I am 24 years old meteorologist working in Meteopress since 2019 with specialization in radar meteorology and severe weather warnings. I come from NW Czechia. I studied at Gymnasium from 2015 to 2018 and physics at Charles University from 2018 - 2020, which I did not finish. Meteorology has been my passion since I was a child and I learned most of it from self learning, attending seminars or presentations and via practice in Meteopress.

Abstract: At approximately midnight on 2 November 2023, a strong tornado affected parts of Jersey in the Channel Islands. The tornado occurred in a thunderstorm that formed close to the cold front of an intense extratropical cyclone, named “Storm Ciarán” by the Met Office, which produced widespread damaging winds over northern France and adjacent areas. The tornadic storm passed within a few kilometres of the Doppler, polarimetric, C-band radar located on Jersey. In this presentation, radar observations of the storm will be explored, making use of Doppler, polarimetric and conventional radar parameters. Evidence of a debris signature in the polarimetric fields and an associated debris ball in the reflectivity field will be presented. These data represent the first documented observations of a polarimetric tornado debris signature in the British Isles. The structure of the tornadic part of the storm will be explored by construction of vertical sections using available plan-position-indicator scans at several elevation angles.

Biography: Matt works in the Nowcasting team at the UK Met Office, developing tools to aid in situational awareness and nowcasting of convection. Matt recently completed a PhD at the University of Leeds, exploring the situations in which cold-frontal tornadoes occur in the UK and Ireland. Latterly, Matt has constructed a climatology of convection associated with flash-flooding in the UK, exploring the typical environments, radar-observed storm morphologies and other characteristics of these events, with a view to advancing understanding and suitable approaches for the nowcasting of these storms in the UK.

Abstract: Precipitation measurements provide historic and near real-time data for Met Services and ground truth references for modelling and forecasting.  Current methods suffer from well-known under-catch problems1.  These are caused by wind effect2 on the gauge, out-splash, evaporation, and internal tipping bucket (‘counting’) errors.  Thereby causing water-balance errors for Hydrology scientists.  Good gauge design and correct siting can minimise these errors but not eliminate them.

Over 10 years of research, into the best aerodynamic shape for a precipitation gauge, was carried out to minimize out-splash and maximize catch3.  Comparison field work1 and Computational Fluid Dynamic4 (CFD) research was undertaken between standard straight-sided, ‘chimney’ shaped, aerodynamic shaped and pit-installed (out of the wind) gauges.  This research demonstrated that it may be possible to quantify under-catch using gauge rim-based wind data, drop-size and drop-type information.  Field comparison between the “new instrument” and pit gauge will be needed.  Once quantified at source, it can then be used to accurately correct live data.

This new instrument uses ultrasonic wind sensors and Doppler-Shift measuring techniques to obtain wind versus rainfall catch data.  Also using optical and/or impact sensing techniques we can measure the individual drop size and count the drops involved in a rain event.  By adding weighing technology to the tipping bucket design and improving calibration methods, we can improve resolution and detect evaporation losses.  Also power efficient and controlled heating to allow the inclusion of solid precipitation measurements.  Then finally use machine learning (ML) techniques to correct the errors.

Therefore, the aim of this project is to design a simple to use intelligent instrument to minimise and possibly eliminate under-catch measurement errors balancing out the water budget.  Allow installation of the instruments at ground and raised levels without increase in errors caused predominately by the wind.  Create near real-time and historic field precipitation data, both corrected and non-corrected to be use by Met Services and Hydrology modelling scientists.

References

Sevruk, B. Methods of correction for systematic error in point precipitation measurement for operational use, World Meteorological Organization - Operational Hydrology, Report No. 21, 1982.

Pollock, M. D., et al. Quantifying and mitigating wind induced undercatch in rainfall measurements, Water Resources Research, 54, 2018.

Strangeways, Ian. Improving precipitation measurement. International Journal of Climatology. 24. 1443 - 1460. 10.1002/joc.1075, 2004.

Colli, M., et al.  A Computational Fluid-Dynamics Assessment of the Improved Performance of Aerodynamic Rain Gauges. Water Resources Research. 54. 10.1002/2017WR020549, 2018.

Biography: I have been involved in meteorology at a professional business level for over 25 years.  I joined Environmental Measurements Ltd (EML) in 1996 after studying electronics at University (BSc/MSc).  I have designed data logging equipment, for use on weather and rain monitoring stations. Installed, maintained, and designed a vast array of hydro-meteorological systems.  I regularly work as a consultant to industry and academia advising how to accurately measure weather and rainfall. In the last 15 years I have become increasingly involved in precipitation instrument development and academic research. I am currently also a part-time PhD student at Newcastle University.

Abstract: The Met Office National Meteorological Archive contains a wealth of historic rainfall observation data in journals and weather logs, extending as far back as the 17th century. In 2020, a concerted effort was made to digitise some of the monthly rainfall data stored in these archives through the Rainfall Rescue project. This project, led by Ed Hawkins with the assistance of around 16,000 volunteers, resulted in over 5 million observations being transcribed, quality controlled and converted into a digital format, which was a remarkable achievement.

A key long-term challenge for the Met Office is how we systematically manage and maximize the use of these new observational datasets and integrate them into our monitoring products alongside existing data sources.  The main objective for this project was to create a more comprehensive monthly rainfall dataset which integrated the digitised Rainfall Rescue data with data derived from MIDAS Open, an open Met Office dataset containing land surface station data back to 1853. An innovative approach was employed to identify the same sites between MIDAS Open and Rainfall Rescue by utilising site metadata and rainfall trend data. A methodology for constructing a new rainfall series for these sites was then developed using this additional source data.

As a result of this initiative, the records of many sites were extended to produce a more complete and robust monthly rainfall series to support climate monitoring and research, with scope to adopt this methodology for other climate variables. I will provide an overview of the newly developed blending method, outline the results, and present the pros and cons of the approach.

Biography: Stephen works as a Scientific Software Engineer at the Met Office. His work includes managing and improving a number of existing UK climate datasets as well as developing new products. These datasets are primarily built on station observations from the Met Office land network sites. Stephen currently maintains the Central England Temperature dataset, the longest continuous monthly temperature series in the world, as well as MIDAS Open, an open dataset of station observations made available publicly via CEDA.

Abstract: The Met Office’s fleet of field service vehicles are fitted with telematics systems, that provide real-time location data and deliver a duty of care for staff working in remote locations. For six vehicles in the fleet, the telematics systems include externally mounted temperature probes, recording near-surface temperatures. Such vehicle-based temperature observations are a potential source of low-cost, high-resolution meteorological information that can increase the spatial coverage of surface observations, particularly as the field service vehicles often visit otherwise data sparse locations. In this study, we review the quality and characteristics of temperature observations from the Met Office vehicles in the UK, to understand their value for use in applications such as numerical weather prediction and nowcasting. We compare the vehicle temperature observations over a 9-month period in 2023 with a range of reference data, including the Met Office UKV 1.5 km and MOGREPS‑UK 2.2 km resolution models, and surface observations from a network of road-side sensors and automatic weather stations. We discuss factors that affect the accuracy of vehicle temperature observations, including ventilation (linked to the vehicle speed), and other influences on temperature such as heating from the engine or the road surface. It is expected that the results will highlight the need for the quality control of vehicle-based temperature observations to enable their utilisation. We also discuss other issues pertinent to the use of vehicle-based observations, including data privacy, and consider the potential for an extensive UK network with observations from other commercial vehicle fleets.

Biography: Gemma Daron is a scientist at the Met Office working in observation research and development. Her role is to support the design of observation networks, by evaluating the quality and reliability of new or opportunistic observations and their potential contribution. She also works to quantify the benefits of existing or new observations as part of cost-benefit analyses. Prior to joining the Met Office in 2022, Gemma worked in the wind energy industry for 12 years, primarily in the field of wind resource assessment. Here, she regularly analysed wind observations and conducted wind flow and energy modelling.

Abstract: Rothamsted Research, Harpenden, has one of the longest continuous sets of daily meteorological recordings in the UK. Rain and wind direction have been measured since 1853, temperature since 1878 and sunshine from 1890. Rothamsted sent returns to the UK Meteorological Office since at least 1878 until automation in 2004. In 2017, Rothamsted was recognised by the World Meteorological Organization (WMO) as a Centennial Observation Station being one of the relatively small number of sites in the UK that has been recording reliable observations continuously for more than 100 years. Weather variables were recorded manually until the end of 2003; an automatic data logger was installed to record the data electronically from 1st January 2004.

We highlight the many varied applications of this long-term weather data for the Rothamsted Long-Term Experiments. These include evaluating variations in biomass and community composition of hay meadow flora, simulation of trends in soil organic carbon of the long-term experiments, the effects of drought stress on crops, whether bioenergy crops are water-limited, forecasting plant diseases and crop pests, investigating the effects of inter-annual variability on crop yield stability and the role of weather types on rainfall chemistry. We also present the application of Rothamsted Long-Term Weather (RLTW) in new projects at Rothamsted including net zero and resilient farming.

As concern about climate change increases these ongoing daily weather data provide the means to assess past trends and predict future impacts on agriculture.

Biography: Sarah has a diverse scientific background, having worked at Rothamsted Research for more than 25 years in agricultural science. She has spent the last 10 years curating and digitising the data from both the Rothamsted Long-Term Experiments and Rothamsted Meteorological Station. The electronic Rothamsted Archive (e-RA), plays host to these data and makes them freely available for users worldwide.

Abstract: The use of satellite data is explored to evaluate high-resolution numerical weather prediction (NWP) models, the latter of which will play a key role in next-generation operational weather forecasts. Urban areas are expected to be one focus for such applications, but this will require new modelling approaches and extensive evaluation.

In this study, we retrieve Landsat land surface temperature (LST) using a new technique and use this to assess 100 m-resolution NWP predictions for London. We demonstrate that the retrieved Landsat LST data are spatially highly correlated with two other LST retrieval methods. We also address the limitations imposed by the restricted viewing angle of the satellite on its ability to view the “complete” surface temperature, and discuss potential ways to improve LST retrievals in urban areas.

The Landsat LST data helps us to enhance the NWP modelling and identify where future model improvements can be made. The extensive LST spatial coverage allows major features to be explored that would not be evident if using analyses only for small areas: notably, spatial patterns visible in the 100 m NWP modelling domain that are not apparent in the Landsat imagery. The resulting investigation identified downscaling soil moisture using soil properties to be the cause of the artefacts. New 100 m model runs have more realistic spatial correlations but a larger mean difference. Correlation of the differences in LST with surface cover suggest that model performance is better for vegetated areas.

Biography: I completed my PhD in meteorology at the University of Reading investigating better ways of representing cloud structure in weather and climate models. After that, I worked at the University of Reading for 12 years in various research areas, including cloud remote sensing, tropical dynamics in climate models, and climate effects on the Indian summer monsoon. During this time, I wrote a general interest book “Introducing Meteorology: a Guide to Weather”. In 2020 I began working for the Met Office, where I am developing an improved representation of the urban surface and its interaction with the boundary layer. 

Abstract: Recent advances have allowed for the development and deployment of lightweight, stratospherically launched micro-dropsondes; designed for release from navigable high-altitude balloons and other high-altitude “pseudo-satellite” systems. Such platforms have great potential to provide critical observations in data-sparse regions, particularly over oceanic and polar regions, provided their data are of sufficient quality. This work examines the quality of observations from the StratoSonde® system, developed by Voltitude Ltd., during a recent field campaign launching from Cabo Verde. The system offers the unique ability to manoeuvre to specific locations and schedule dropsonde deployments to observe developing severe weather systems.

Over a ten-day period, nine dropsonde descents were conducted over the mid-Atlantic, with an additional two descents carried out over the UK in a separate test flight. These descent profiles, recording pressure, temperature, dew point, humidity, and wind speed/direction, were assessed in a multi-model comparison using interpolated profiles from the Met Office's deterministic and ensemble numerical weather prediction models to assess the quality and reliability of the micro-dropsonde data.

Overall, the system performance was shown to be robust, with observations having generally low biases & root mean squared errors compared to all three models. Observations of temperature and winds had the smallest differences with respect to the models, however some greater differences were identified in the relative humidity data in the upper troposphere. Work to address this issue is now ongoing, with further trials – including co-located radiosonde ascents – planned for the near future.

This research contributes valuable insights into the potential for a combined HAB and micro-dropsonde system to deliver atmospheric profiles in remote regions, highlighting their strengths and identifying areas for improvement. The continued refinement of these systems holds promise for enhancing our understanding of atmospheric dynamics in previously under-sampled areas, and in advancing our ability to monitor and forecast potentially severe weather systems.

Biography: Matthew is a Scientist in the Met Office's Observation Network Design team, working to assess the potential of a variety of novel and third-party observations in meeting high priority observational requirements.

Abstract: This study addresses the critical need for precise, localized wind velocity measurement, particularly in the insurance industry, where understanding and managing losses due to natural hazards, such as extreme winds, is paramount. Windstorms, while resulting in relatively few casualties, stand out as the costliest type of natural disaster in north-west Europe. Achieving dense and comprehensive coverage with traditional instrumentation for wind velocity data collection is costly, and has logistical hurdles, posing challenges, especially in densely populated urban areas. To overcome this challenge and improve hazard estimation for industrial and commercial property owners, we propose using the innovative concept of 'Seeing the Wind’ (Cardona et al. 2019). This approach harnesses short video clips of trees as proxy indicators for wind speed, eliminating the need for specialized equipment like anemometers to create high-resolution wind hazard maps. To test this approach in creating a localized (micro-scale) understanding of wind hazards, a pilot study was conducted at a domestic property. This study involved 146 video clips captured on a mobile phone, ranging from 3 to 20 seconds, featuring two trees and two cup anemometers (0.8, 1.6m) each beside a chequered flag. Chequered flags are frequently used in machine vision experiments, and the anemometers allow for a direct comparison with the observed wind speeds. This combination allows for comparison between these methods. Initially focusing on the pear tree, this study aims to systematically evaluate the effectiveness of various methods for wind velocity estimation from the video source, assess the accuracy of the estimates, and explore the potential limitations of this video-based wind velocity estimation method. The findings of this study will pave the way for a larger campus-scale study at Loughborough University, where our wind speed estimates will be integrated with campus weather station data for accurate downscaling. Insurers can utilize the resulting precise wind hazard maps to adjust parameters more closely aligned with the actual risk. Authors: Sai Kulkarni, Dr John Hillier, Dr Sarah Bugby, Dr Tim Marjoribanks, Dr Jonny Higham, Dr Daniel Bannister. 

Biography: Sai Kulkarni is a 1st year PhD student at Loughborough University, UK, supervised by Dr John Hillier, Dr Tim Marjoribanks, Dr Sarah Bugby, Dr Jonny Higham and Dr Daniel Bannister. Sai's PhD is a part of the TECHNGI-Centre of Doctoral Training program in collaboration with Willis Towers Watson (WTW), London. Her research focuses on wind hazard estimation, using machine vision on videos of trees to improve risk mitigation strategies.

Abstract: The atmospheric boundary layer (ABL) in mountainous regions is characterised by a variety of airflows, originating from complex landform forcing, which encompass a range of scales of motion, from synoptic scale flows to very local phenomena, such as the daily-periodic thermally-driven circulations developing over inclines and in the valleys under clear sky and in the absence of major synoptic forcing. These airflows, and turbulence generated therein, affect a variety of processes, including surface-atmosphere exchanges of momentum, energy and mass, and transport across a variety of scales. They may also contribute to the initiation of orographic convection. 
This contribution focuses on the simplest of these flows, namely slope winds, outlines the state of our present understanding, from measurements as well as from numerical model simulations, and highlights still open questions concerning their structure and their representation in terms of similarity.

In particular, the application of classical Monin-Obukhov similarity theory (MOST), original developed for flat horizontal terrain, has been questioned in the literature, both from the theoretical viewpoint, and on the ground of evidence from measurements showing disagreement of observed slope-normal structure of turbulence properties from MOST predictions.

Hence, starting from the same basic grounds on which MOST is built, an alternative theory is proposed for the surface-layer scaling including contributions of along-slope buoyancy force in the momentum equation and of the along-slope advection of warmer/colder air associated with the background stratification, in the energy equation.
It turns out that (1) turbulent surfaces fluxes of momentum and heat are not independent quantities, but rather closely connected, and (2) Obukhov length is still the relevant similarity scale, but the mathematical representation of the slope-normal structure of turbulent properties is quite different from that envisaged by MOST.

Ongoing efforts to investigate these flows under the umbrella of the current research initiative TEAMx - Multi-scale transport and exchange processes in the atmosphere over mountains – programme and experiment (http://www.teamx-programme.org/), in particular under the connected research project “DECIPHER - Disentangling mechanisms controlling atmospheric transport and mixing processes over mountain areas at different space- and timescales”, are also presented.

References

Farina, S., Zardi, D. 2023: Understanding Thermally Driven Slope Winds: Recent Advances and Open Questions. Boundary-Layer Meteorol., 189, 5–52. https://doi.org/10.1007/s10546-023-00821-1 
Farina, S., Marchio, M., Barbano, F., Di Sabatino, S., and D. Zardi, 2023: Characterization of the morning transition over the gentle slope of a semi-isolated massif, J. Appl. Meteor. Climatol. 62, 449–466. https://doi.org/10.1175/JAMC-D-22-0011.1
 

Biography: Dino Zardi is full professor of Atmospheric Physics at the University of Trento (Italy). He got a MSc in Physics cum laude from the University of Bologna (1991) and a PhD in Hydrodynamics from the University of Genova (1995). His research interests focus on boundary layer processes over mountainous terrain and their implications on air quality, agriculture, renewable energy resources, and climate change impacts. He is also Director of the double-degree international MSc programme in Environmental Meteorology, Vice-President of the Italian Association of Atmospheric Sciences and Meteorology (AISAM) and Co-Chief Editor of the Wiley and Royal Meteorological Society journal Meteorological Applications.

Abstract: Research into surface solar ultraviolet (UV) radiation has gained importance due to its link with climate change and epidemiology. However, the understanding of its distribution and atmospheric influences across different climates is limited due to a lack of available measurements. Therefore, this study aimed to establish the UV climatology and dynamics for tropical atmospheres in West Coast Africa, specifically Ghana, by utilizing NASA's Prediction of Worldwide Energy Resources (POWER) Data Access Viewer Enhanced products. The dynamics were examined based on the relationship between UV and station-derived Global Solar Radiation (GSR), total cloud cover (TCC), and atmospheric clarity index (KT) over the past thirty years. The accuracy of NASA's UV data was verified by comparing the UV Fraction derived from NASA and station-derived GSR. Initially, a strong correlation between NASA and ground-based estimated GSR indicated a good representation of the local UV climatology, with the UV Fraction showing a Pearson's correlation (r) of 0.81 - 0.98 ± 0.05. The monthly mean UV radiation ranged from 2.5 - 18.3 ± 0.3 Wm-2, which accounted for approximately 6% of the GSR. The peak season for total cloud cover exhibited daily maximum UV intensities surpassing 60 Wm-2. The lower TCC Savannah region experienced the highest UV levels due to significant attenuation, while the high TCC Forest region displayed a higher UV Fraction during the peak wet monsoon. This suggests a cloud enhancement effect influenced by cloud dynamics and atmosphere-ocean interactions. These dynamics have implications for ecosystem management, public health awareness, and climate impact studies.

Biography: Bio to follow 

Abstract: Dust storm formation in arid areas is a major global environmental problem as dust impacts regions close to the dust sources and can be transported far away for thousands of kilometers. The Middle East and Southwest Asia, which includes countries such as Iran, Iraq, Kuwait, and Saudi Arabia, are commonly affected by Shamal dust storms (mostly in summer) and frontal dust storms (in winter).

We use observations and reanalysis data to describe the formation and evolution of a winter shamal dust storm that occurred in February 2017. It initiated in central western Iraq and dust was transported southeastward towards the Persian Gulf, impacting all the countries in the region up to the Oman Sea.

The SEVIRI Dust RGB product shows dust mobilization at 07 UTC (10 LT) in several point areas west of the Euphrates River and over Mesopotamia, in Iraq. A dense dust plume is then advected in between the Zagros Mountains to the North and East and the high plains of Saudi Arabia to the South and West, reaching the Persian Gulf in the first hours of Febr. 18. Next day, dust spreads throughout the Persian Gulf and surrounding countries. The dust plume is well seen in the true color MODIS imagery of Febr. 17, 18, and 19. It resulted in the widespread reduction of horizontal visibility and impaired air quality, as reported by the region's Synop/METAR surface observations and air quality stations.

The large-scale upper-level processes leading to this event started days before with a strong amplification of an anticyclonic Rossby wave break in the Polar Jet over the North East Atlantic, with large penetration poleward of subtropical air up to Scandinavia and cold air advection equatorward over Iberia on February 11. At a late dissipative stage and downstream displacement, the RWB resulted in a closed ridge over southeastern Europe and a trough downstream over the study area. A strong pressure gradient was established at low levels between high pressures centered over Central Europe extending to the Black Sea and low pressures centered over Afghanistan/Pakistan and extending from the Oman Sea to western India. On Febr. 17, the location of the trough favored descent associated with transverse circulations on the upstream side of the trough over the mountains in eastern Turkey and northern Iraq, reinforcing the northerly winds imposed by the pressure gradient. On Febr. 17, strong downslope winds on the lee (southern) of the mountains in southern Turkey impacted the dust source areas in northern Iraq and Syria. Dust plumes were transported southeastward at heights below 2 km, as shown by CALIPSO profiles in Febr. 18, within the PBL. On Febr. 19, the dust plume was mixed all over the Persian Gulf basin.

Biography: I am Samira Karbasi. I was born in Tehran (Iran) in 1986.
I currently work as a postdoc researcher in the department of Applied Physics at Miguel Hernandez University of Elche in Spain, working as a WRF_Chem expert on multiscale processes related to dust mobilization and transport.
I got a BSc in Physics at Hormozgan University and hold a master and doctorate degree in meteorology in the Azad University of Tehran and Hormozgan university of Bandar Abbas, respectively. Due to my interest in air pollution and atmospheric compounds I wrote my doctoral dissertation on greenhouse gases, which are a major cause of climate change.
 

Abstract: Current state of the art NWP and regional climate models run at km scale or “convection permitting” resolutions. For a number of years now a number of centres have been experimenting with higher resolutions with gridlengths of the order 100m (sometimes referred to as “hectometric” or “Urban-scale” models). It has been shown that higher resolution models give benefits for a variety of meteorological phenomena which would translate into improved forecasts.  Examples include convection, fog and urban effects. In this paper, which is based on a workshop held in Dec 2022, we discuss the challenges which still need to be overcome to make the transition from promising research into useful forecast systems. We summarise the outcomes from the workshop.  As well as over arching issues such as the cost of the models we discuss dynamical cores and physics dynamics coupling, parameterisation issues (including surface), sources of surface data, observations requirements, data assimilation, predictability and postprocessing. 

Biography: Humphrey has a BSc in Physics from the University of Bristol and  PhD in low temperature physics (superconductivity) from the University of Cambridge, Cavendish Laboratory. In his early career he worked in Cambridge on high temperature superconductivity and on the plasma fusion programme at Culham Laboratory. Since joining the Met Office Humphrey has worked on high resolution models, leading a project to develop km scale models during the 2000’s. He now leads the Met Office project to develop Urban-scale models. His main scientific interests are in the areas of convection, urban meteorology and orographic rain. He was a PI on the WesCon/WOEST campaign and the follow on ParaChute project.  

Abstract: Convective-scale ensembles are routinely used in operational centres around the world to produce probabilistic precipitation forecasts, but the added value provided by these models is limited by a lack of spread between members. Currently, it is unclear how much the behaviour of the driving ensemble determines spread towards the convective scale since very few studies have been conducted which compare the evolution of spread between ensembles of differing resolutions. This work focuses on understanding the correlations in spread between a nested convective-scale ensemble and its driving ensemble over the UK and will help researchers understand the extent to which spread characteristics are determined by the specific regional model configuration vs the driving ensemble. 

We have found that correlations are strongest in the first 24 hours of integration, with the nested ensemble typically displaying larger overall spread than the driving ensemble during this time. Further work is being conducted to study the cause of these correlations - for example, is the spread similarity caused by a strong resemblance between the corresponding nested and driving members (i.e., nature over nurture) or has each member evolved the initial state in its own way and the similarity is due to the environment (nurture over nature). It will also be necessary to understand which conditions favour stronger correlations over others, and whether there is leadtime dependence to these correlations. 

Biography: Adam is a third year PhD student whose work focuses on exploiting the operational benefits provided by high-resolution ensembles. While these models offer superior representation of convective events compared to coarser models, their usefulness in probing the uncertainty of these events is limited by a frequent lack of spread between members. Adam’s work is supported by the Met Office Research to Operations team which has identified this spread problem as one of the major issues affecting the guidance produced by these models. 

Abstract: The Wessex Convection Experiment (WesCon) was a UK field campaign conducted during summer 2023 concentrating on understanding dynamical aspects of convection to provide observational data to develop next generation kilometre-scale and urban-scale models. During the campaign, extended evaluation of a variable resolution 300 m Wessex Model (the “WMV”) was conducted, running as an ensemble nested inside the Met Office operational UK 2.2km grid length ensemble (MOGREPS-UK). Results will be presented that show overall, the WMV looks promising for high-impact convective events as it is better able to represent the organisation of convection into lines or larger storms whereas MOGREPS-UK tends to simulate isolated, circular storms. This often leads to more reliable probabilities of heavy rainfall in the WMV ensemble compared to MOGREPS-UK. However, there is still an issue with the WMV producing too many small precipitating showers in situations where there should only be shallow clouds. This is thought to be a result of shallow clouds getting too deep in the model and precipitating erroneously.  

Biography: Kirsty works in the Urban-scale Modelling group at MetOffice@Reading based at the University of Reading. Kirsty's work is focused on the representation of convection in high resolution models (grid lengths in the range 1 km – 100m). The aim of this work is to determine the best configuration for future operational models. An important aspect of this work is validating the Unified Model against observations obtained during field campaigns such as WesCon. Kirsty joined the Met Office in March 2013. Prior to this Kirsty completed her PhD in ocean waves and air-sea interactions at the University of Reading in 2008. Kirsty then went on to do post-doctoral work at the University of Reading, focusing on the initiation of convection over orography in convective-scale ensembles.

Abstract: Ensemble forecasting systems provide useful insight into the uncertainty in the prediction of the atmosphere. However, most analysis considers ensembles in latitude, longitude, and time. Here, the vertical aspects of the spread-skill relation are considered in a convective-scale ensemble via comparisons with radiosonde ascents over a winter season. The specific focus is on the impact of stratifying the spread-skill relation by radiosonde drift. The drift acts as a proxy for the mobility of the atmosphere. The overall spread-skill relation shows the temperature has a better relation than the dewpoint. However, the total variance comparisons between model and observations indicates that the dewpoint is under the climatolgical variance throughout the atmosphere, whilst the temperature is over the climatological variance in the lower atmosphere and under it aloft, suggesting that there could be temperatures that exist outside climatological expectations. This suggests that the model bias is influencing the spread-skill relation. Stratifying these results by the radiosonde drift indicates that the spread-skill relation, and model bias, for both temperature and dewpoint degrades with increased mobility. For the most mobile situations, the ensemble is underspread throughout the atmosphere. These results have implications for ensemble design in terms of the role and influence of the driving ensemble in regional systems as more mobile situations will have a stronger dependence on the lateral boundary conditions. Longer term it may also imply that different strategies are required depending on the mobility of the synoptic conditions. Therefore, it argues for more consideration of “on-demand” ensemble forecasting systems to allow a fairer representation of the uncertainty in different situations.

Biography: David did his undergraduate degree at the University of Reading graduating in 2013. Following his undergraduate David completed his PhD and a short post-doc both at the University of Reading on behaviour of convective-scale ensembles. David then moved to Paris for two years as a post-doc at École Normale Supérieure as part of the Laboratoire de Météorologie Dynamique evaluating extra-tropical cyclones in climate models. In March 2020 David joined the Met Office where he started in Regional Model Evaluation and Development (RMED). Since April 2024 he has a split role between RMED and Weather and Climate Impact and Extremes.

Abstract: A robust approach to model evaluation is essential in supporting the efficient and useful delivery of continuous NWP development cycles. Evaluation underpins the development cycle of our Regional Atmosphere Land (RAL) science configurations used across a range of deterministic and probabilistic applications and underpins the use of RAL science for a variety of research areas in the Met Office.We are developing CSET, an open-source toolkit for evaluation, verification, and investigation of convective- and turbulence-scale numerical models for weather and climate applications, cutting across time and space scales. It will be the core engine of evaluation tools supporting our RAL configurations (UM and LFRic-based), ensuring best practice for verification and evaluation linked to RAL suites.CSET aims to be a community tool to harness the diversity in knowledge across the Met Office, UM partnership, and academia. It provides a home for further development and visibility of existing diagnostics, and we envisage it as the go-to place for researchers to develop and use process-oriented evaluation diagnostics for convective and turbulence-scale modelling systems. In addition, the use of formal software development techniques benefits reproducibility, portability, accessibility, maintainability, and quality assurance.CSET composes small functional units called operators into recipes to produce (new) diagnostics and can run them over case studies or continuous trials to produce a collection of diagnostics displayed on an interactive web page. CSET operators can be generic, like filtering or subtracting, or specific, like calculating the size distribution of storms in a precipitation field. The operators are chained together in a recipe which specifies which operators will be run, in which order and with which parameters.We will outline the main design principles of CSET, introduce how to use and contribute to CSET and current diagnostics available through an extreme weather case study.

Biography: James Frost is a Scientific Software Engineer working at the Met Office in regional model evaluation and development. He is the lead developer of the CSET evaluation tool.

Abstract: A series of 1.5 km simulations, covering a wide range of synoptic types and geographical locations, has been performed. Each simulations is nested within a global forecast and provides a vast amount of data for exciting machine learning applications. The 1.5 km data can be coarse-grained to the scale of a typical global climate model, O(100km). We then use neural networks to predict the profile of coarse-grained cloud fraction, liquid and ice water contents as a function of the coarse-grained temperature, humidity and pressure and some extra information about orography and land-sea fraction. In effect we attempt to replicate the richness of the cloud cover seen in kilometre scale models, but which the coarse models do not know how to predict. Using some newly developed code to couple the neural networks into the Unified Model, we then present a climate simulation where the cloud scheme has been replaced by our machine-learnt emulator. The lessons learnt along the way are highlighted, such as the benefits of using physics-informed cost functions.

Biography: I am a senior lecturer in the Department of Mathematics at the University of Exeter one day per week. The rest of the week, I work at the Met Office. I am part of the Atmospheric Processes and Parametrizations team (APP). We develop the parametrization schemes used in the Unified Model and LFric, which the Met Office uses to model weather and climate. Specifically, I am interested in using machine learning to emulate parametrization schemes, making existing schemes cheaper and making too-expensive schemes affordable. I am also keen to explore emulation as a route towards stochastic physics, all as a way of improving the spread in our ensemble forecasts.
I am also interested in using atmospheric models and observations as a source of data from which to learn better ways of representing physical processes that occur on scales smaller than the model grid-boxes but which are key to realistic weather and climate simulations.
Previously, I worked on cloud-cover parametrizations, and I have an interest in aviation icing, and forecasting surface short-wave radiation for solar panel productivity forecasts.
Before that, I did an MPhys at Warwick University, a PhD in atmospheric sciences at the Meteorology Department at Reading University (slantwise convection and conditional symmetric instability) and a 3-year post-doc also at Reading studying the initiation of summer-time convection in the British Isles.

Abstract: In recent decades forecasts of Tropical Cyclone (TC) tracks have improved significantly. This has enabled earlier warnings, allowing local populations to make better preparations ahead of landfall, and ultimately saving lives. While TC track forecasts have improved dramatically, TC intensity has proved more challenging, and progress has been slower. The resolution of operational numerical weather prediction (NWP) models (10-25km) is not sufficient to capture the mesoscale processes that drive intensification of TCs. A recent example of this was Hurricane Otis which rapidly intensified prior to making landfall as a category 5 storm in Acapulco, Mexico. All global NWP (and ensembles) failed to capture the rapid intensification of Otis, predicting landfall as a weak category 1 hurricane or tropical storm (even at short lead times of 24-48 hours). Failures of NWP forecasts to capture TC intensification reduce the amount of time available to implement readiness and evacuation strategies, leading to more widespread and damaging impacts on vulnerable communities.

As a small group of scientists and software engineers at the Met Office, we have recently been working on using an extreme-gradient boosting (XGBoost) algorithm to bias correct TC intensity forecasts and provide more useful forecast information. We use the International Best Track archive for climate stewardship (IBTracs) database of observed TCs in combination with ERA-5 reanalysis data to train an ML model that we can use to apply a bias correction to TC forecasts. Initial benchmarking of our ML bias corrected output shows a reduction in intensity errors compared with our operational global NWP model. The ML model is trained using physically relevant predictors such as environmental wind shear, sea surface temperatures and environmental moisture. We further explore how best to utilise probabilistic ML techniques to show uncertainty in our tropical cyclone ML predictions.
 

Biography: Richard completed his PhD at the University of East Anglia, focussing on weather and climate in West Antarctica. He joined the Met Office Regional Model Evaluation and Development team in 2020, first focussing on assessing the latest regional model configuration – which will soon become operational. His recent work has utilised high-resolution simulations across very-large tropical domains – assessing the changes in model behaviour we are likely to see as we move to finer resolution. Richard also has a keen interest in tropical cyclones, both through exploiting high-resolution ensembles and utilising machine learning methodologies to improve predictions of tropical cyclone intensity

Abstract: Every year, hailstorms, hurricanes, wildfires, and other hazards — the list is long — wreak havoc on communities, industry, and infrastructure, resulting in fatalities, loss of property, and other socio-economic disruption. Because weather and climate hazards pose a constant threat to business operations worldwide, the management of those risks is a core specialty of WTW, a global advisory company headquartered in London (NASDAQ: WTW). For nearly 20 years, WTW has advanced the study of geophysical risks through a series of innovative partnerships between our firm, universities, government agencies, and the private sector. Owing to the company’s origins in the insurance and reinsurance industries, the WTW Research Network has sponsored an impressive roster of projects directed at those catastrophes known to produce the very highest financial losses. For example, our long-standing collaboration with the University of Exeter has led to groundbreaking advancements in the understanding of European windstorm clustering, wind footprint simulation, and in the impact of climate change on European storm risks. And because so-called ‘secondary perils’ — events that usually cause small- to mid-sized losses — have become more important in recent years, we support several project lines on hailstorms, tornadoes, and fluvial, pluvial, and coastal flooding. Finally, in line with our firm-wide goal to build ‘a smarter way to risk’, our Research Network is working to apply advances in seasonal climate prediction to business, evaluate modelling tools used by insurers and other financial institutions to gauge their exposure to geophysical perils, and build tools and scenarios to help our clients better understand how climate change affects their risk profile. We are grateful to have served as partner to the weather and climate science community since 2007 and are keen to apply the latest findings from the discipline to strengthen the resilience of our clients and society.

Biography: Daniel is the Weather and Climate Risks Research Lead at WTW, enhancing hazard and risk modelling through collaborations with academics and industry scientists. With extensive experience in climate science, he specialises in high-resolution regional climate simulations, focusing on refining weather prediction and mitigating impacts of severe weather, particularly in aviation. Before WTW, Daniel worked on integrating AI with climate science to support sustainable aviation practices. Daniel earned his MSc and PhD in atmospheric sciences from the University of East Anglia, in collaboration with the British Antarctic Survey.

Abstract: The financial and real estate sectors are increasingly interested in understanding their risk to the impacts of climate change. This is due to both increasing regulation from government and regulatory bodies as well as a recognition of the large impacts that climate change can have on profits and business operations. However, these industries are not well placed to generate the necessary climate insights.

Climate X is a private sector provider of climate risk data. We aim to address the gap between academic research and information that is useful for our clients. Using our in-house multidisciplinary team of climate and hazard scientists, we build in-house hazard models coupled with publicly available geospatial and climate model data. We engage with academic research to inform our products, covering hazards from floods and storms to geohazards and wildfires.

This presentation will address how we use publicly available data and build on academic research to provide useful and usable data to our clients. I will cover the specific needs that our clients have, the key requirements that we have identified for our products to be useful to the financial and real estate sector and how we have addressed these. I will finish with takeaways for the academic community to make research more relevant to end users and decision makers.

Biography: Information to follow soon

Abstract: Weather hazards are the leading cause of power outages in the U.S. and a major contributor in Europe. Transmission lines are commonly impacted by wind and winter storms, and substations, which regulate voltage levels across the grid, are susceptible to outages caused by flooding. Recent research has begun to quantify the failure probability of power infrastructure against different weather hazards. Building on these established relationships, we seek to understand how future weather patterns will impact transmission and distribution outages in the United States. We do this by examining the weather patterns that have historically caused large-scale outages and determining how these will evolve under different climate scenarios. Additionally, forecasted outages will be compared to predicted demand to determine if there will be sufficient transmission and distribution capacity. Our results highlight locations particularly susceptible to weather-driven outages, which can help drive resilience planning as U.S. power infrastructure begins to reach the end of its lifespan.

Biography: Information to follow soon

Abstract: Global stilling is the worldwide observed reduction of wind speeds due to climate change. This is highly important as society is increasingly reliant on wind power which could be impacted by significant global stilling. Using the record of wind speed and wind power generation data from the Hazelrigg Meteorological Station and wind turbine, we performed trend analyses and assessed the rate of change. The 10-minute data for Lancaster shows decreasing wind speeds (0.2m s-1) and estimated wind power (20kW) in the last decade, similarly daily data suggests decreasing wind speeds and a total decrease in estimated wind power generation by approximately 350kW between 1985 and 2022. To assess the importance of high-resolution data in wind power analysis we transformed the 10-minute wind speed and daily run of wind data to estimated wind power generation. The opposing patterns shown by the 10-minute and daily data highlight the importance of using high resolution wind speed data for global stilling research due to the large spatial and temporal variability of wind speeds.

Biography: Kathryn Vest is an Environmental Science PhD student at Lancaster University. Kathryn will be presenting work from her undergraduate degree in Environmental Science at Lancaster University. This work used observed wind speed and wind power data from the Hazelrigg weather station to assess how wind speeds are changing. Global stilling was investigated on a local scale for Lancaster and one of the main considerations was the influence of high-resolution data when exploring wind speeds and power.

Abstract: Windstorms are the most damaging natural hazard across western Europe. Risk modellers are limited by the observational data record to only ∼ 60 years of comprehensive reanalysis data that are dominated by considerable inter-annual variability. This makes estimating return periods of rare events difficult and sensitive to the choice of the historical period used. This poster presents a novel statistical method for estimating wind gusts across Europe based on observed windstorm footprints. A good description of extreme wind speeds is obtained by assuming that gust speed peaks over threshold are distributed exponentially, i.e. a generalised Pareto distribution having a zero shape parameter. The North Atlantic Oscillation (NAO) is particularly important for modulating lower return levels, with a less detectable influence on rarer extremes. Our method presents a framework for assessing high-return-period events across a range of hazards without the additional complexities of a full catastrophe model.

Biography: I am a Research Fellow at the University of Exeter and funded by the WTW Research Network. My research focusses on understanding historical variability and future trends of European windstorms using novel statistical techniques. I am also the RMetS Science Engagement Fellow for the Insurance Sector. This role involves driving engagement between academia and the insurance sector, providing resources, understanding collaboration, and increasing awareness of research being undertaken by both sectors.

Abstract: We present results from several case studies exhibiting this behaviour, identifying the sources of the gravity waves observed in simulations. The characteristics of these waves will be compared to those in the idealised model simulations, and gravity-wave parameters will be calculated. Finally, we widen our analysis by examining the broader upstream pattern that contributes to the development of the initial inertial instabilities and explore the different regimes under which these phenomena occur.

References:

[1] Gultepe, I. et al. (2019), "A review of high impact weather for aviation meteorology." Pure and Applied Geophysics, 176, pp.1869–1921.

[2] Williams, J. K. (2014), "Using random forests to diagnose aviation turbulence. " Machine Learning, 95, pp.51-70.

[3] Meneguz, E., Wells, H. and Turp, D. (2016), "An automated system to quantify aircraft encounters with convectively induced turbulence over Europe and the Northeast Atlantic." Journal of Applied Meteorology and Climatology, 55(5), pp.1077–1089.

[4] Thompson, C. F. and Schultz, D. M. (2021), "The release of inertial instability near an idealized zonal jet. " Geophysical Research Letters, 48(14), e2021GL092649.

Biography: Information to follow soon

Abstract: Extreme fluvial floods pose severe socioeconomic and environmental risks across the UK. This paper addresses the critical need to identify the most influential features driving extreme flood events, including atmospheric circulation patterns, and land-surface antecedent conditions, through the integration of datasets from ERA5-Land, CAMELS-GB, and the Met Office Weather Patterns (MO30). Understanding the interplay between atmospheric circulation patterns and antecedent conditions as drivers of flood extremes remains a significant research gap. This paper addresses this gap through employing machine learning techniques (random forest models) to assess the relative importance of daily synoptic scale weather patterns, large scale weather regimes and antecedent land-surface conditions as predictor variables for the target variable of extreme flood magnitudes within the UK's most 'natural' catchments (UKBN2). Findings reveal cyclonic types with deep lows, very windy types, the North Atlantic Oscillation positive phase (NAO+) and southwesterlies as key drivers of the top 1% flood magnitudes. Our analysis also reveals further regional and seasonal variations in the dominance of these drivers. These insights highlight the necessity for further investigation on how driver relationships with extreme floods vary spatially, temporally, and under future climate changes. 
 

Biography: Hello, my name is Emma, and I am a DPhil researcher in Atmospheric Physics at the University of Oxford. I am interested in the atmospheric and land-surface drivers of extreme fluvial catchment floods in the UK. My research takes an integrated, interdisciplinary approach in understanding the relative importance of flood drivers and how this varies across time and space. I primarily use machine learning methods to answer my research questions. Please feel free to reach out to me via email emma.ford@hertford.ox.ac.uk. I look forward to meeting you!

Abstract: It is essential that we work towards better preparation for flooding, as the impacts and risks associated increase with a changing climate. Standard methods for flood risk assessment are typically static, based on flood depths corresponding to return levels. In contrast flood risk changes over time, with the time of day and weather conditions, driving the location and extent of potential debris (e.g. vehicles or trees may cause blockages in culverts) affecting the associated risks. To this end, we aim to provide a platform for dynamic flood risk assessment, to better inform decision making, allowing for improved flood preparation at a local level. With stakeholder collaboration at a local level, a web-platform demonstrator is presented, for the city of Newcastle upon Tyne (U.K.) and the wider catchment, providing interactive visualisations and dynamic flood risk maps.

To achieve this, near real-time updates are incorporated as part of a fully integrated workflow of models, with traditional datasets combined with novel, hidden data. More realistic high-resolution data, citizen science data and novel data sources are combined, making use of data scraping and APIs to obtain additional sensor data. Using machine learning methods, more complex datasets are generated, using artificial intelligence algorithms and object detection to identify potential debris information from satellites, LIDAR point clouds and trash screen images. The model framework involves hyper-resolution hydrodynamic modelling (HIPIMS), with a hydrological catchment model (SHETRAN), working towards a digital twin.

Biography: Amy Green is a Research Associate in the Water Group at Newcastle University, with an interest in radar rainfall estimation, environmental extremes and applied statistics. Her doctoral thesis entitled improving radar rainfall estimation for flood risk using Monte Carlo ensemble simulation was part of the DREAM CDT. She is funded through the IMPETUS4CHANGE project, creating a platform for climate indices, and is improving and updating the Global Sub-Daily Rainfall dataset of quality controlled rain gauge records. She has previously worked on developing a platform for dynamic, hyper-resolution, near-real time flood risk assessment, integrating novel data sources, developing a digitally-enabled environment.

Abstract: Bluetongue is an infectious, non-contagious, vector-borne disease of ruminants, particularly sheep and cattle, caused by the bluetongue virus (BTV). Infection with BTV can cause abortion, stillbirth, birth abnormalities and reduced milk production. Mortality in cattle is usually low, but mortality in sheep can exceed 50%. In addition to the direct effects of bluetongue disease, it is also economically important due to the export restrictions and surveillance measures introduced to limit its spread. BTV is transmitted by various species of Culicoides biting midges, which are active in warm conditions and can carry the disease hundreds of kilometres when blown on the wind.

On 3rd September 2023, BTV was detected on farms in the central Netherlands, and subsequently identified as the BTV-3 the serotype, which had previously never been seen in Europe other than on the Mediterranean islands of Sicily and Sardinia. BTV-3 spread quickly, due to a warm early autumn and the ruminant population being immunologically naïve, so that by the end of October nearly 4000 farms throughout the Netherlands had confirmed BTV-3 cases, with the excess mortality in sheep estimated at over 37,000 animals.

In this presentation we will illustrate the ways in which the NAME atmospheric dispersion model was used to contribute to risk assessments, identifying regions of the UK most at risk from airborne incursions of BTV-3—carrying midges from the Netherlands. This led to the detection of a BTV positive cattle in Kent in early November 2023, the first airborne incursion of BTV to the UK since 2007, and further NAME simulations contributed to the Defra response to BTV cases detected in the UK.

Biography: Will is in the Atmospheric Dispersion & Air Quality team at the Met Office, working on the development and deployment of NAME-based forecasting systems for biological dispersion applications. These typically involve the spread of windborne pests and pathogens of relevance to global and national food security, requiring both close collaboration with domain experts and communication of results directly to government stakeholders. Recent work includes leading on the forecasting of i) desert locust migration in Africa; ii) wheat rust fungal spore spread in South Asia; and iii) risk assessment of airborne incursions of bluetongue disease from the continent to the UK.

Abstract: Identifying large-scale atmospheric patterns that modulate extremes in local-scale variables such as precipitation has the potential to improve long-term climate projections as well as extended-range forecasting skill. We propose a novel machine learning method, RMM-VAE, for identifying probabilistic weather regimes targeted to a local-scale scalar impact variable. Based on a variational autoencoder architecture, this method combines targeted and non-linear dimensionality reduction with probabilistic clustering in a coherent architecture. We apply the new method to identify robust circulation patterns that are predictive of precipitation over Morocco while still capturing the complete phase space of atmospheric dynamics over the Mediterranean. The results are compared to three existing approaches - two established linear methods and another machine learning method. The RMM-VAE method performs well across all different objectives, outperforming linear methods in terms of reconstructing the input space and predicting the target variable, and the other machine learning method in terms of identifying robust and persistent clusters. The results reveal a trade-off between the different objectives of targeted clustering and highlight the benefits of the novel RMM-VAE method in terms of balancing these different objectives for various climate applications.

Biography: Fiona is a PhD student at the University of Reading, working with Prof Marlene Kretschmer and Prof Ted Shepherd on improving seasonal forecasts using causal models of atmospheric teleconnections. Prior to starting her PhD, Fiona worked for three years at a not-for-profit organisation on the alignment of the European financial sectors with climate goals. Fiona co-developed an open-source python package for the comparison and evaluation of statistical bias adjustment methods of climate models and holds a degree in Physics (MSc, University of Edinburgh) as well as Environmental Change and Management (MSc, University of Oxford).

Abstract: In initialised seasonal and decadal prediction systems, retrospective forecasts (“hindcasts”) are routinely used to assess model skill on seasonal or interannual timescales. The frequent initialisation of the hindcasts reduces the development of biases relative to free-running simulations with historical forcing. Here, we apply a recent version of the Met Office’s coupled decadal prediction model to study multidecadal trends over the satellite era. Forty hindcast members are initialised twice annually since 1980 and each run for between 13-66 months. By studying trends between successive hindcast runs, a distribution is built of plausible alternative histories that are consistent with the observed climate state and observed phases of multidecadal variability, while minimising the impact of mean state biases.

A broad survey of circulation trends in the hindcasts is presented from a global perspective, with a focus on changes in temperature and zonal winds in the troposphere. We show that the initialisation largely brings the hindcast trends closer to those of ERA5, as compared to the equivalent free-running model. Nevertheless, although minimised, some persistent model biases remain in the hindcasts. Initialised hindcasts offer a bridge between observations and free-running climate models, and understanding their differences from each will build our understanding of both the observed and modelled climate.

Biography: I am a final year DPhil student in the Atmospheric, Oceanic and Planetary Physics subdepartment of the University of Oxford, supervised by Prof. Tim Woollings and co-supervised by Dr. Nick Dunstone at the Met Office. My research interests are broadly in trends in the large-scale circulation, from the tropics to the poles. 

Abstract: Rainfall events during the January-February dry season over Eastern Africa have significant impact upon society, particularly when they lead to, or exacerbate, ongoing flooding (as in Kenya in 2020 and 2022). Populations across Eastern Africa do not expect rainfall to occur during the January-February dry season, and a lack of preparedness can exacerbate impacts when heavy rainfall does occur. Whilst recent dry season rainfall across Eastern Africa has severely impacted livelihoods and communities, the mechanisms controlling such rainfall are poorly understood, since the majority of previous research has focussed upon the climatological wet seasons. Here, we explore drivers of dry season rainfall over Eastern Africa.

Dry season rainfall over Eastern Africa is found to be linked to an upper-level ridge-trough pattern over the Mediterranean. The presence of a ridge in the central Mediterranean and trough in the Eastern Mediterranean leads to westerly wind anomalies across Central Africa, which enhances moisture transport into Eastern Africa and leads to higher specific humidity and rainfall over the region. Dry season rainfall is further exacerbated by phases 2-4 of the MJO. These findings will improve future forecasts of dry season rainfall over Eastern Africa, which will enhance preparedness for future rainfall events.  Furthermore, climate projections from CMIP5 and CMIP6 models indicate enhanced dry season rainfall over Eastern Africa under future climate change. Improving our understanding of drivers of present-day dry season rainfall will support our understanding of future rainfall changes.

Biography: Caroline is a Lecturer in Climate Change at Cardiff University. Her research is around climate variability and change over Africa, with a specific interest in exploring variability and changes in the seasonal cycle of rainfall over Africa, including recent trends, current variability, and future projected changes. caroline has worked on methodologies for characterising the seasonal cycle of precipitation across Africa and the tropics, and used these methodologies for a range of applications. 

Abstract: The National Meteorological and Hydrological Centers use diagnostic products to operationally monitor the Indian Ocean Dipole (IOD) and issue early warnings on impending extreme climate conditions, e.g. drought. However, such products require decision-making on the part of the service producer, including choosing an appropriate observational or model dataset for the product, a diagnostic that is a good representative of the phenomena, the baseline climatological period, or defining a criterion that needs to be met to identify an IOD event. These choices are sometimes subjective and the scientific rationale behind these subjective choices is often not properly documented. In this paper, we stock-take the current criteria used by multiple operational centers for the monitoring and prediction of the IOD. We find the widely-used Dipole Mode Index (DMI) is sensitive to the choice of SST dataset and time averaging (monthly vs 3-monthly mean DMI) and can lead to marked differences between centers on the current and future state of the IOD. Some regions in Southeast Asia, e.g. the southern Maritime Continent can experience the impact of the IOD on rainfall even when the IOD has not met the current operational threshold for an event. While most models are skillful in capturing the active phase of the IOD, all models have an overactive IOD strength. Calibration of DMI-based monitoring products is therefore recommended for the most skilful and reliable IOD predictions. All models also have low skill for forecasts initialized during January-May, although the skill is sensitive to verifying observations, and using a multi-observational mean dataset can yield better skill scores. Finally, we introduce an objective decision support system to assist climate forecasters in monitoring and predicting of IOD events and issuing timely alerts.

Biography: I am a Lecturer in Meteorological Risks at Institute for Risk and Disaster Reduction, University College London. My current research focuses on weather and climate extremes, with a particular emphasis on hydrometeorological extremes across Asia. I have several years of experience working in operational research, where I led and contributed to the development and delivery of climate services tailored to the users’ needs.

Abstract: The monsoon onset typically starts in southern India by 1 June, taking around 6 weeks to cover the country.  During the monsoon, intraseasonal variations give rise to active and break periods in the rains. Being able to better predict the monsoon onset, its progression, and active and break events would be of great interest to society.  The onset timing is already known to be influenced by tropical intraseasonal variability, but new research has shown that the mid-latitudes exert a powerful control, the full extent of which is not properly quantified or understood.

The MiLCMOP project aims to answer the following: (1) How are the pace and steadiness of monsoon progression affected by interactions with the extratropics? (2) What are the mechanisms of extratropical control on monsoon progression and variability? (3) How do the causal extratropical and tropical drivers of monsoon progression offset or reinforce each other?

Our initial work has tested a new hypothesis that monsoon progression can be described as a “tug-of-war” between tropical and extratropical airmasses.  This “tug-of-war” is unsteady, with a back and forth of the two airmasses before the moist tropical flow takes over for the season.  We demonstrate this for a case study of the 2016 season for India, while also drawing analogies with other monsoon regions, such as for the East Asian monsoon, in which we show the competition between extratropical and tropical flows in establishing the Mei Yu front as it progresses across China.

Current activities revolve around the identification of statistical relationships between monsoon onset and progression and perturbations to the subtropical westerly jet, including blocking anticyclones, meridionally propagating troughs and cyclonic features near the Tibetan Plateau.  Additional focus is also devoted to the relationship between the monsoon advancement and the strength, extent and orientation of the intrusion of mid-tropospheric dry air flowing towards India from westerly and northwesterly quadrants.

Other methods will include use of vorticity budgets and Lagrangian feature tracking in case studies of fast and slow onsets, to suggest the dominant mechanisms by which extratropical drivers affect monsoon onset and progression.  Model experiments will help isolate these mechanisms.  Finally, novel causal inference techniques will help disentangle the effects of extratropical drivers from those in the tropics.

Biography: Andy Turner is a Professor in Monsoon Systems jointly between the University of Reading Department of Meteorology and the National Centre for Atmospheric Science (NCAS). His general interests are in tropical variability and change, including the interaction between monsoon systems and other elements of the climate system. He was founding Co-Chair of the GEWEX/CLIVAR Monsoons Panel, is an Associate Editor of the Quarterly Journal of the Royal Meteorological Society, and a Lead Author of the Working Group I Contribution to the Sixth Assessment Report of the IPCC. Andy is the co-Theme Leader for the Climate & High-Impact Weather theme in NCAS.

Abstract: The onset and progression of Indian summer monsoon exhibit substantial year-to-year variability, affecting the associated precipitation and potentially leading to severe societal impacts. While tropical modes of variability are known factors influencing the evolution of the monsoon, evidence indicates that its unsteady progression can also be accompanied by a change in the strength and reach of a descending mid-tropospheric flow that brings dry air towards the Indian subcontinent from northwestern quadrants. In this work we illustrate our exploration of the link between specific patterns of the upper-level large-scale circulation over Eurasia (e.g., the presence of blocking anticyclones over western Russia) and the onset and progression of the Indian monsoon, highlighting the mediating role of the northwesterly dry air intrusion.

Biography: Ambrogio Volonté is a Senior Research Fellow at NCAS / University of Reading.

He is currently taking part in projects focusing on:

- Dynamics and sea-ice interaction of Arctic summer cyclones (a project that included an aircraft field campaign);

- Dynamical and climatological properties of sting-jet cyclones;

- The role of diabatic processes and air-sea interaction in the dynamics of Mediterranean cyclones;

- Mid-latitude controls on Indian monsoon onset and progression, with particular focus on kinematics and dynamics of the intrusion of mid-latitude dry air.

His research interests go from mesoscale airflows and synoptic-scale extratropical cyclones up to the dynamics of larger systems such as monsoons, as he is interested in process-based and Lagrangian analysis of all sorts of weather features.

Abstract: The Indian monsoon rainfall exhibits large spatial variability associated with orography and surface temperature gradients. The Western Ghats (WG) mountains along the west coast of India is prone to extremely heavy rainfall (e.g., giving rise to the Kerala floods of 2018) and hence it is of immense importance to understand the mechanisms driving different modes and time scales of rainfall variability around this region to improve prospects for prediction. In this study, we use convection-permitting fully-coupled regional simulations of the Met Office model with the latest RAL3 science configuration to investigate the offshore-to-coastal regime transition of monsoon rainfall over the west coast of India, with a focus on the mechanisms driving diurnal variability of orographic precipitation. We also conduct ocean-atmosphere coupled experiments with and without land surface irrigation to explore the impact of irrigation on surface fluxes and orographic precipitation. Our findings reveal that both land-surface initialization and science settings in the model influence diurnal variability of orographic precipitation, with the latter exerting a more significant effect. During the offshore phase, heat and radiation flux diurnal amplitudes intensify over the WG compared to the coastal phase. The simulations indicate a drier mid-troposphere and a moister lower troposphere over the west coast of India during the coastal phase relative to the offshore phase, indicating a strengthening of the mid-tropospheric dry-air intrusion during the coastal phase. Our ongoing investigation aims to elucidate the influence of atmosphere-ocean coupling and irrigation on the mechanisms governing diurnal variability of orographic precipitation and monsoon rainfall modes near the Western Ghats. Future research will further explore the role of irrigation in soil moisture-induced mesoscale circulations and convective initiation.

Biography: Arathy works as a senior scientist at the Met Office in the Momentum® Partnership team. In her current role, she provides scientific and technical support for the use of regional atmosphere and land configurations of the Met Office model at the partner sites. She is a fellow of the Royal Meteorological Society. Her research interest is on high resolution modelling of the monsoon and understanding the physical processes associated with the monsoon. 

Abstract: Equatorially trapped waves, such as Kelvin Waves, Equatorial Rossby Waves and Westward-moving Mixed Rossby-Gravity (WMRG) Waves, play a major role in organising tropical convection on synoptic to sub-seasonal timescales. These waves have the potential to provide an important source of predictability for high impact weather in South East (SE) Asia and the tropics more widely. Global models can adequately predict the evolution of dynamical structure of equatorial waves on time-scales of several days, but they do not predict the relationship between waves and rainfall well. Therefore, hybrid statistical-dynamical forecasting techniques combining model ensemble forecasts of equatorial waves, and large-scale atmospheric conditions, with climatological rainfall statistics are compared with forecasts of rainfall probability taken directly from models over SE Asia. It is hypothesised that forecasts of wave activity may be used to more accurately predict upcoming heavy rainfall events. In tests using the Met Office Global and Regional Forecasting System (MOGREPS) and the Met Office seasonal prediction system (GLOSEA6) the hybrid forecasts outperform model rainfall forecasts in a number of regions. It is also demonstrated that combining forecasts of multiple equatorial wave types into one hybrid forecast can provide an improvement in hybrid forecast skill, relative to a forecast built on a single equatorial wave. However, errors in forecasting equatorial waves diminish the hybrid forecast's skill, with the most significant reduction observed for Kelvin waves, suggesting that a significant improvement in the prediction of the propagation of equatorial waves would have a significant impact on rainfall prediction in the tropics.

Biography: Sam Ferrett is currently a Research Scientist at the University of Reading. She is currently working on the FORecasting high-impact Weather And extreme Rainfall Drivers and dynamics for Southeast Asia (FORWARDS) project as part of WCSSP: SE Asia in partnership with the Met Office. Research interests include tropical climate and weather, uncertainty in future changes in climate and the dynamics and teleconnections of modes of variability (e.g. equatorial waves, El Nino-Southern Oscillation etc.) in the Tropical Pacific.

Abstract: Idealised radiative equilibrium simulations have proved an invaluable tool for studying tropical convection. Using a long-channel configuration (i.e. a narrow yet long domain), simulations can be run with sufficiently high resolution to resolve convective scales over domains that are sufficiently large (in one direction) to resolve the large-scale circulation. These types of simulations are becoming increasingly widely used for studying the coupling between clouds and circulation, which remains a key driver of uncertainty for cloud feedbacks.

In this poster, we describe long-channel radiative convective equilibrium simulations run with the UK Met Office Unified Model. These simulations are run for a variety of fixed sea surface temperature (SST) patterns, including SSTs fixed to a single value and SSTs that vary spatially in an approximation of observed SST gradients.

We detail the extent to which these simulations reproduce the observed large-scale circulation in the tropics and highlight low frequency oscillations that occur when there is an SST gradient both in our simulations and in other models. We investigate the causes and consequences of these oscillations. In the context of these results we present further analysis of the coupling between clouds and circulation in the simulations and how this coupling affects climate sensitivity. 

Biography: I am a postdoctoral researcher at the University of Reading with interests in understanding and improving interactions between clouds, convection, and radiation, in both observations and atmospheric models. 

I am currently working on the CIRCULATES project with Prof. Christopher Holloway. This project is investigating circulation, clouds, and climate sensitivity as part of the wider CloudSense research programme, which aims to reduce uncertainty in climate sensitivity due to clouds. 

Previously, I have studied clouds and convection in satellite observations over Africa and worked on development of the radiation scheme used to calculate atmospheric radiative fluxes in the Met Office Unified Model.

Abstract: Sea Surface Temperature (SST) is an essential climate variable (ECV). Gridded SST datasets are used in many applications including global climate monitoring, evaluation of climate model simulations, providing boundary conditions for reanalysis datasets, and for understanding air-sea interactions. Surface marine observations extend back over 200 years and century-scale historical global datasets typically consist of monthly temperature values; these datasets may not be in-filled to provide complete spatial coverage. Gridded data products that span the period when satellite measurements of SST are available are typically of global extent and are available at much higher resolution.  This poster will present ongoing work on a new gridded SST dataset that bridges the space and time scales between the existing long historical records and the high-resolution records for the past few decades.  

The focus of this poster is an overview and analysis of past El Nino Southern Oscillation (ENSO) events using a new global, in-filled dataset of SST, provided at a sub-monthly, 1 degree resolution dating back to the early twentieth century. The principal source of data used in the construction of the gridded dataset is the International Comprehensive Ocean-Atmosphere Dataset (ICOADS, https://icoads.noaa.gov/), which provides SST observations from a combination of moving and fixed platforms (ships and buoys). The ship data have undergone a new processing procedure, with improved Quality Control (QC) flags, duplicate detection, and improved identification of mis-positioning and mis-dating of observations in some of the data sources. Ongoing work includes improvements in bias estimates by platform and country for the SST measurements. 
Gridded fields have been constructed using modelled ellipses to describe the spatial scales. It provides a unique opportunity to analyse the SST patterns in terms of their variability, spatial extent and persistence, at sub-monthly scales. 

Biography: I work at the National Oceanography Centre in Southampton. My work focuses on historical marine observations, mainly SST and air temperature, and the process of generating in-filled datasets from scattered in-situ data. I am interested in looking at SST datasets and extent of information they can capture. I have previously worked at University of Reading, where I completed my PhD, with both projects using satellite data for industrial thermal plume detection (PhD) and improving cloud cover in coastal regions (post-doc). 

Abstract: Atlantic Multidecadal Variability (AMV) has been associated with climate variations in many regions worldwide. However, the mechanisms driving the development of AMV remain unclear. Modelling studies reveal that global teleconnections from AMV are sensitive to how the tropical branch is represented. Nevertheless, there has been limited attention to understanding how decadal Sea Surface Temperature (SST) anomalies develop in this region. In this study, we present a quantitative examination of the generation of tropical AMV using SST restoring experiments. In contrast to the generally proposed mechanisms such as wind-flux-SST or cloud feedback, our research provides new insights into the dominant and crucial role of upper ocean dynamics, particularly regarding the mixed layer depth. Given the sensitivity of tropical AMV to global implications, accurately simulating upper ocean dynamics in coupled climate models becomes imperative.

Biography: Balaji Senapati is a Postdoctoral Research Scientist at the University of Reading. He did his PhD jointly supervised by Prof. Mihir Kumar Dash and Prof. Swadhin Kumar Behera (APL, JAMSTEC) at IIT Kharagpur, India. Balaji discovered a new ocean-atmosphere coupled wave in the Southern Hemisphere and worked extensively on its generation dynamics and impact during his PhD. Now, he primarily works on the dynamics of the tropical Atlantic Multidecadal Variability.

Abstract: The Po River contributes one-third of the total fresh water into the Adriatic Sea. Fluctuations in the Po's discharge can affect the sea level surface of Venice, 50 km away, as well as regulate the salinity and circulation of the Adriatic Sea. To study the effect of the Po River on the hydrodynamics of the Adriatic Sea, the Regional Ocean Modelling Systems (ROMS) model is used based on two scenarios: one with (WITHPO) and one without the Po (NOPO). The horizontal grid spacing is 2 km with 25 model layers. Hourly ERA5 data is used for atmospheric forcing, and GEBCO data provides the bathymetry. Climatological temperature, salinity, and velocity data is used for the southern open boundary, located in the northern part of the Ionian Sea. In all our analyses, the values WITHPO are subtracted from those NOPO. The surface temperature difference ranges from –2 to 2°C, converging to zero near the bottom. Temperature differences are most noticeable in the northern Adriatic Sea basin in spring and autumn. The WITHPO has colder spring water, but warmer autumn water. Similar patterns are observed in the middle and southern basins.  At the bottom layer, the temperature difference is minimal during the summer. In the southern basin, the temperature difference between 0–200 m (below the surface) is less than 1°C all year round, whereas in the northern basin, it decreases from surface to bottom. Salinity differences range from –1 to –0.35 PSU at the surface, gradually diminishing to –0.2 PSU near the bottom layers in all basins.  The presence of the Po River results in lower sea levels during spring (4–6 cm), contrasting with the rise of 8–10 cm observed in other seasons. Due to the difference in temperature and salinity caused by the WITHPO compared to the NOPO, the temperature and salinity (T–S) graphs differ as the WITHPO reduces salinity. The maximum density, however, is almost the same in both scenarios mainly because higher salinity water is warmer, leading to only small changes in the maximum density. Among the important findings of this study is that the Po River strengthens the southern gyre in winter and autumn but weakens it in spring and summer, resulting in shifting to the core of the southern gyre. This finding complements our previous research on the southern Adriatic gyre where we found that the wind contributes 10 % –14 % to the core displacement, while the influx from the Strait of Otranto contributes 37 %. Moreover, the largest water exchange differences between WITHPO and NOPO occur between the northern and middle basins during summer, totaling approximately –5000 m3/s. June marks the period of greatest temperature difference exchange variations, showing a consistent pattern over time. During summer between middle and southern basin, the temperature flux difference ranges from –0.4 to 1×106 °C m3/s, whereas the exchange of salinity flux displays marked fluctuations, particularly in the daily and weekly time scale, ranging from –2 to 3 ×105 PSU m3/s. Between September and October, the maximum positive flux through the Otranto Strait is 11,000 m3/s and maximum negative flux at –15,000 m3/s from August to October. Consequently, these variations in sea levels, salinity, and temperature in the Adriatic Sea are caused by fluctuations in the Po River discharge and signal the conditions that may exist in the future under reduced inflow from the Po River.

Biography: Javad is a third-year PhD student in atmospheric science at the University of Manchester. His thesis focuses on the circulation of the Mediterranean Sea, utilizing the ROMS model. His background is in physical oceanography, with a focus on geophysical fluid mechanics during his master's studies. He is interested in several topics: ocean-atmosphere interaction, overturning circulation, eddy and gyre dynamics, and the effects of climate change on the ocean.

Abstract: This study represents an attempt to validate WAVEWATCH III® over the coast East African countries. WAVEWATCH III® (Tolman 1997, 1999, 2009) is the third generation wave model used for wave forecasting. One of the greatest challenges face by the Meteorological Departments in the East African countries (Kenya and Tanzania in particular) is to provide accurate and timely marine weather forecast. The Ocean data including observations from Buoys are very scarce over the West Indian Ocean domain. In this research, the satellite altimetry data was used to assess the model to improve the accuracy of the Ocean models used to issue marine forecasts and warning over the region. The WW3 has performed the simulation for different points over South West Indian Ocean domain for the period of one month (June 2014). The statistical results from comparing the altimetry-derived Significant Wave Height (SWH) and those from the Wave model WW3 which is forced by winds input from Global Forecast Systems (GFS) global model shows that the absolute values of mean errors ranged from 0.71 to 3.38 during the period under consideration, the bias values are negative indicating slightly underestimating of modeled wave lengths in comparison with satellite data. Similarly when the WW3 wave model is forced by winds input from European Centre for Medium Range Weather Foresting (ECMWF), shows that the absolute errors are quite small (0.0006 – 0.049) to imply that WW3 gave good forecast if initialized from winds from ECMWF as opposed to GFS, but still slightly underestimating of modeled wave length in comparison with satellite data.

Biography: Mr. Chuki Sangalugembe is the employee of Tanzania Meteorological Authority, currently working at Marine Meteorological Services. Mr. Sangalugembe is the holder of Master of Science in Mathematical modeling, a Postgraduate Diploma in Meteorology and Bachelor of Science (Mathematics and Physics). He is expert in weather and climate modeling and worked at Numerical Weather Prediction section in Tanzania Meteorological Authority for more than fifteen years

Abstract: The UK national science programme CANARI (Climate change in the Arctic-North Atlantic Region and Impacts on the UK) aims to advance understanding of impacts on the UK arising from climate variability and change in the Arctic-North Atlantic region, with a focus on extreme weather and the potential for rapid, disruptive change. One tool that will allow to pursue these aims is a Large Ensemble (or SMILE; Single Model Initial Condition Large Ensemble) that is being produced in CANARI.

The CANARI Large Ensemble uses the Met Office CMIP6 physical climate model (HadGEM3-GC3.1) at N216 atmosphere resolution (about 60 km at midlatitudes) and at 1/4° resolution for the ocean. Forty ensemble members are produced driven by CMIP6 historical and SSP3-7.0 forcings during 1950-2099. This poster provides an overview of the CANARI Large Ensemble, including the status of the production runs and access to the output on JASMIN, and invites discussions about applications of this novel set of community simulations.

Biography: Information to follow soon

Abstract: How can we best apply our science to predicting risks of extreme behaviour in a system as complex as the climate? It would be desirable to be able to represent all of our knowledge about the risks so that it can be applied to enable effective decision-making. Risk assessments often consider only the range of behaviour displayed by climate models, but a substantial part of the risk seems likely to be due to the possibility of the real world veering outside this range. It will be illustrated how implicitly ignoring this component would lead to risks being systematically underestimated, and how multi-model and initial condition large ensembles can be misleading. Recent work on storyline methods has illustrated potential ways to think beyond numerical model simulations, but downplays the quantification of event risks. But since we generally lack clear bounds on how intense extreme events can be, this seems to leave open the question of just how intense should the events that are considered in analyses be. It also does not seem to satisfy decision analyses that seek to quantitatively trade off protection against extremes against other benefits. This presentation considers how we can go beyond counting events in simulations, using tools such as climate models to inform our future projections without being constrained to ignore possible outcomes that they cannot simulate, whilst also retaining as much quantitative knowledge about event risks as possible and acknowledging when ambiguities become very large. Frameworks from philosophy and decision analysis will be surveyed and it will be discussed how these may help to show a way forward in our climate prediction predicament. It will be suggested that climate science should aim to be pluralistic in the knowledge frameworks it considers, to be of use to the broadest possible range of decision making.

Biography: The main focus of my work is understanding the risks posed to society by extreme climate events and how these are being affected by climate change. I am part of the Climate Dynamics group, and this provides a vibrant environment for students and early career researchers.

Abstract: Climate change will increase the frequency and intensity of many extreme weather events at the global scale. This has implications for societal exposure to these hazards and resultant financial losses. One such hazard is that of flood. There is, however, uncertainty in the spatial distribution of changes in river and surface water flood risk, which relates to different projections of climate change’s impact on large scale weather patterns. This uncertainty is typically quantified using an ensemble of climate models and assessing the range of potential hazard intensities for a given climate forcing. An alternative way is to use “physical storylines”, with each storyline representing different plausible future shifts in weather patterns for a given level of climate change.

Here, we present a range of potential physical storylines for flood hazard in Europe based on the output of three climate models, each showing distinct spatial patterns of precipitation and temperature trends. We use these climate model outputs to drive a hydrology model to assess trends in streamflow. The spatial distribution of the climate change signal is extracted through a pattern scaling approach, which scales the precipitation and streamflow changes with changes in global mean surface temperature. This is used to derive climate change adjustments (positive or negative), which are then used to create change factors to adjust the frequency and intensity of a multi-thousand year time series of extreme events. We use these “climate-conditioned event sets” in an industry standard catastrophe model to estimate changes in financial loss from flood events for the different physical storylines. This provides a novel way of exploring uncertainty in our projections of the impact of climate change on flood losses, and useful insights about future surface water and river flooding for the financial, insurance, and development sectors.
 

Biography: Anya joined JBA Risk Management’s Climate Change team in 2022 where she has worked to improve the understanding of changing flood hazard and risk under different climate change scenarios both within the UK and globally. Prior to joining JBA, Anya received a BSc in Geography from Newcastle University and a master’s degree in Earth Sciences from Uppsala University, Sweden. During her studies, Anya focused on changing polar environments and palaeoclimatology. For her master’s dissertation, this included reconstructing past climate in the Channel Islands with a focus on changing circulation patterns during the last glacial period.

Abstract: Interannual to multi-decadal variability in large-scale dynamics such as atmospheric and oceanic circulation results in significant noise and temporary trends in regional climate. Attempting to understand longer term trends as a result of anthropogenic climate change requires disentangling internal variability and climate change signals. One of these climate signals is the Clausius-Clapeyron (CC) scaling in precipitation resulting from temperature increases. In this work, we characterise and constrain variability in sub-seasonal winter rainfall in the UK resulting from synoptic scale-conditions. The UK experiences periods of sustained precipitation in some winters which result in widespread flooding due to extreme accumulation. Using categorised sea-level pressure fields and gridded precipitation between 1900-2020, we simulate ‘expected’ precipitation resulting from North Atlantic synoptic conditions. We find a rising trend since the 1980s in observed monthly accumulation which is not reflected in the simulated precipitation timeseries, indicating that recent wet winters in the UK have been wetter than expected given the synoptic conditions. The rising trend in the residual (observed - simulated) mean monthly precipitation is in line with expected CC scaling rate of ~6-7% per degree warming according to changes in UK annual mean temperature. However, the residual in extreme monthly precipitation has scaled at approximately twice that rate. To better understand differences in changes for average and extreme precipitation accumulation, we explore the influence of dynamical feedbacks which may increase precipitation at higher intensities. We find that residual precipitation is influenced by the persistence of synoptic conditions and exhibits remote teleconnections to sea surface temperature and atmospheric conditions in the tropics and sub-tropics. This work highlights the importance of considering variability in large-scale dynamics when identifying climate change signals and sheds light on influences on sub-seasonal to seasonal winter precipitation in the UK.

Biography: I am a third year PhD student at Newcastle University investigating changes to sub-seasonal to seasonal precipitation events and flooding in the UK.

Abstract: Variations in southern African precipitation have strong effects on local communities, increasing climate-related risks, increasing the severity and intensity of droughts and flooding, and impacting hydroelectric production and natural ecosystems. However, future changes in southern African precipitation are uncertain, with climate models showing a large range of responses from near-future projections (2020-2040) to the end of the 21st century (2080-2100). We assess uncertainty in southern African precipitation change using 5 Ocean-Atmosphere General Circulation single model initial-condition large ensembles (SMILEs; 30 to 50 ensemble members) and four emission scenarios. We show that the main source of uncertainty is the internal climate variability for southern African precipitation across the 21st century. We show that differences between ensemble members in simulating the future changes in the location of the Angola Low and of the large-scale anomalies in atmospheric circulation over the Pacific Ocean (ENSO-related changes) explain a large proportion (~64%) of the precipitation change uncertainty. 

Biography: I am a Senior Research Scientist at the National Centre for Atmospheric Science and the University of Reading. I have experience in tropical climate, particularly in quantifying the effects of external forcing and climate variability on precipitation across Africa. My current work on tropical climate is on understanding uncertainty in simulations of future changes in precipitation and atmospheric circulation, with a focus on West and Southern Africa. I also work on decadal climate variability and predictability, with a particular interest in the North Atlantic. 
 

Abstract: The frequency and intensity of extreme weather events have noticeably risen in recent decades across the globe, especially over the southern Mediterranean region. This trend poses a threat to plant growth, affecting both the physical and metabolic aspects of plants. With the global necessity to double food production by 2050 to meet growing population demands and changing diets, it becomes crucial to understand further how and when significant changes affecting multiple climate-stress indicators may emerge over croplands and some strategic crops for the southern Mediterranean region, such as wheat.

This paper, therefore, aims to identify the spatial distributions and timings of significant positive and negative climate-related stresses affecting croplands and wheatlands. Using 17 bias-corrected climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) under the SSP370 scenario, we examine a series of agronomically-relevant climate indicators, characterising the intensity of heatwave, coldwave, drought, and heavy rainfall, as well as the frequency of such event to combine at the annual scale and during the reproductive phase of winter wheat. Using observed and projected land-use land-cover scenarios, we then quantify the fraction of croplands and wheat-harvested areas that could potentially be affected by positive and negative changes in these climate-stress indicators.

Overall, our analysis revealed predominantly consistent upward trends in heatwave intensity, maximum drought intensity, and the occurrence of compound Dry and Hot (DH) events expected to emerge in the early future (before 2030). Similarly, the number of Wet and Hot (WH) events exhibits an increasing trend, although not as uniform as the indicators above, and is expected to emerge predominantly in the mid-future (before 2050). Conversely, maximum frost intensity, the number of Wet and Cold (WC) and Dry and Cold (DC) events reveal consistent declining trends over the region emerging mostly in the early future (before 2030). 

Biography: I am a PhD student with a strong interest in understanding how climate variability and change affect agriculture sector. My research focuses on assessing the impacts of climate change on agricultural productivity and practices in the southern Mediterranean region, and on developing adaptation strategies to offset its negative impacts.

Through my research, I aim to improve our understanding of the complex interactions between climate, crops, and socio-economic factors, and to identify practical solutions to help farmers and policymakers cope with climate change. By combining cutting-edge modelling techniques with field data and stakeholder engagement, I strive to produce research that is both scientifically rigorous and socially relevant.

I am committed to sharing my research findings and collaborating with other researchers in the field. I believe that interdisciplinary approaches and cross-cultural perspectives are key to addressing the complex challenges of climate change and food security, and I am eager to contribute to this collective effort.

Abstract: Drought events have increased in frequency and severity over recent years and can result in significant economic losses, as well as impacts on both global and regional food security. Drought is a slow onset hazard taking place over months or years. This makes forecasting the propagation of drought from rainfall deficits to impacts upon soil moisture and vegetation health challenging. Drought impacts can depend on societal vulnerability making drought monitoring and forecasting an important task. In Brazil, half of all natural disaster events are drought related. Agricultural impacts can be significant, most severe impacts are historically in the semi-arid northeast. Drought is a significant challenge for farmers across Brazil. The previous La Niña was associated with severe drought in southern Brazil, this had significant impact upon soybean production, affecting food and milk prices as well as harming the country’s agricultural GDP.   

Recent years have seen significant advances in machine learning techniques and the availability of remote sensing data. These advances allow new insights into the propagation of drought and improvements in forecasts and early warning systems. Here we explore methods for forecasting vegetation health and soil moisture using machine learning techniques and the standardized precipitation-evapotranspiration index (SPEI). Models provide estimates of root zone soil moisture and vegetation health for sub-seasonal timescales relevant for agricultural adaptation. Models are trained and evaluated across agricultural regions of major crops, soybean and maize. The study area ranges across contrasting biomes in Brazil, including the semi-arid northeast, south, and major soybean growing region in the central Mato Grosso. This presents a challenge of building a forecasting system that can be accurate across a broad range of environments. The techniques developed as part of this study aim to inform operational drought forecasting at CEMADEN the national centre for monitoring and early warning of natural disasters in Brazil by providing forecasts of future drought in addition to current monitoring information. This will help to improve resilience against agricultural drought in Brazil. 
 

Biography: Joe Gallear is a Postdoctoral researcher at Rothamsted research. Joe's current work consists of using machine learning and satellite data to produce forecasts of drought impacts on vegetation health. This work is in collaboration with the UK Met office and the National Center for Monitoring and Early Warning of Natural Disasters in Brazil (CEMADEN) as part of the wider CSSP Brazil project. Joe completed his PhD at the university of Leeds on using machine learning and process-based crop modelling for regional scale  yield prediction.

Abstract: The Cuvette Centrale swamp forest has the most extensive peatland complex in the tropics, at least 20,000 years old and estimated to contain 29 Gt of Carbon (approximately equivalent to 3 years of global CO2 emissions), but due to its remoteness the extent and depth of the peat was only recently quantified.  The international project CongoPeat has researchers from the UK, the Republic of the Congo and the Democratic Republic of the Congo, working alongside the local people in studying the peatlands to determine how they formed and the possible threats since it is vital that the peat is preserved.  We use the Joint UK Land Environment Simulator (JULES), the land surface component of the UK Earth System model, here setup to form a large amount of peat and to be sensitive to changes in climate.  The long historical simulation, driven by a reconstruction of the past rainfall, shows gains and losses of peat to give a final additional soil depth of 4.27 m and total Carbon amount of 337 Kg C m-2.  It supports the hypothesis that a long period of reduced rainfall a few thousand years ago lead to a notable loss of peat, with JULES losing 2.18 m and 113 Kg C m-2 during this time.  Though JULES was unable to recreate the measured age-depth profile, whereas simpler peat models did, this is only due to its low vertical resolution.  Given the ability of JULES to replicate the observed sensitivity of peatlands to the water table depth, we continued the simulation to 2100 in future projections from 4 global climate models using a high shared socioeconomic pathway (SSP370).  Notable losses in peat occur when rainfall is reduced rather than increased in the future climate, and/or when drainage is introduced to represent disruption of the peatlands (by possible logging, road building or oil prospecting), both of which lower the water table.  With both reduced rainfall and drainage rapid losses of peat are seen, by as much as 0.97 m of additional soil depth and 55 Kg C m-2 of the Carbon amount. This effect is moderated only slightly by the future CO2 fertilization which causes an increase in vegetation productivity and litter and a reduction in evapotranspiration.

Biography: I obtained my PhD in Atmospheric Physics at the University of Manchester and have gone onto postdoctoral positions at a number of Universities, mostly in computer modelling.  Studying air pollution and chemistry, clouds and aerosols, air-sea and air-land interactions, budgets of energy, water and carbon, and vegetation and soil.  At I obtained my PhD in Atmospheric Physics at the University of Manchester and have gone onto postdoctoral positions at a number of Universities, mostly in computer modelling.  Studying air pollution and chemistry, clouds and aerosols, air-sea and air-land interactions, budgets of energy, water and carbon, and vegetation and soil.  At present I’m working for Professor Richard Betts on the CongoPeat, Climate Africa and AmazonFACE projects.

Abstract: Ghana accounts for 2.2% of recorded global malaria cases and demonstrates particularly interesting interannual variability in case numbers over the last few decades. This raises the question: what is driving this variability? Here, we examine the potential role of climate variability in influencing the case numbers. Temperature (max, min, mean), rainfall and humidity data from weather stations in the three established climatic zones in Ghana (coastal, savannah, forest) are examined and compared with a new dataset of regional malaria cases rates from Ghana. At the annual scale, we identify significant relationships between malaria case rates and maximum temperature in the forest and savannah zones, and humidity in the forest and savannah zones. Future work will examine higher spatio-temporal resolution climate data and contextualise these data with information on policy interventions.

Biography: Information to follow soon

Abstract: Tropospheric ozone is the third most important greenhouse gas within the atmosphere and the growth in its concentrations over the industrial period have contributed to the increase in global mean surface temperatures. In addition, ozone at ground-level is a major air pollutant, with elevated concentrations having detrimental long-term effects on human health via respiratory disease. In the troposphere the ozone budget is controlled by chemical production and loss, stratosphere-troposphere exchange of ozone and is removed by deposition at the surface. Ozone concentrations at the surface have increased throughout the 1850 to 2014 period, mainly due to increases in anthropogenic precursor emissions. This increase will have had a large impact on the health of the world population from long-term exposure to ozone concentrations. Here we use results from chemistry-climate models to quantify the impact on surface ozone concentrations and human health over the period 1850 to 2014 in different scenarios that were conducted as part of the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). Sensitivity scenarios were used to explore the impact from fixing different drivers of ozone formation at pre-industrial values. We estimate the change in the relative risk of the mortality burden from long-term exposure to ambient surface ozone concentrations in the different scenarios. We find that the global peak season surface ozone concentrations have increased by 40 to 60% from 1850 to 2014 in three different models, with present day values all being above the WHO air quality guideline value. A coincident increase occurs in the risk of mortality from respiratory disease due to the increase in the long-term exposure to surface ozone concentrations. The increase in surface ozone concentrations and mortality risk is largely driven by increases in anthropogenic NOx and global methane concentrations over the industrial period. Smaller influences on surface ozone concentrations occur from changes in other anthropogenic ozone precursor emissions, anthropogenic aerosols, transport from the stratosphere and historical climate change. These results show the importance of certain drivers in the human health risk from the long-term exposure to air pollution, which can be used to inform future policy directions. 

Biography: Steven joined the Met Office in January 2016 to work on aspects of air quality and climate. Prior to this Steven undertook his PhD at the University of Leeds, investigating the impact of changing anthropogenic emissions on European atmospheric aerosols and climate over the second half of the 20th Century. He has also obtained Masters of Research in environmental science from Lancaster University. Steven has also spent time within the environmental consultancy sector working on local air quality management and also other environmental science issues.
 

Abstract: Aerosols play a significant role in the Earth's radiation budget and the emissions of that fraction originating from human activity (anthropogenic aerosols), increased significantly over the 20th century.
Here we examine how the subpolar gyre in the North Atlantic Ocean responded to these 20th century changes in anthoprogenic aerosol emissions. We use a novel experimental ensemble of climate simulations (the SMURPHS ensemble) consisting of a single GCM (HadGEM3) driven with with set of different estimates of historical Anthropogenic Aerosol emissions.
Here we show that anthropogenic aerosols, whilst cooling on the global scale, drive a warming and salinification of the subpolar gyre, together with an increased AMOC. We describe the structure of this response, and comment on the comparison with the observed evolution of the subpolar gyre.

Biography: Dan Hodson is a research scientist at NCAS, based at the University of Reading. His research interests include North Atlantic ocean and climate interactions, AMOC  and subpolar gyre variability, and North Atlantic predictability. 

Abstract: In July 2021 a cut-off low-pressure system brought extreme precipitation to Western Europe. Record daily rainfall totals led to flooding that caused loss of life and substantial damage to infrastructure.  
We use ensemble boosting to investigate possible alternative storylines of the event, given the observed dynamical situation and current climate. We use the fully-coupled free-running climate model (CESM2), identifying atmospheric flow analogues of the July 2021 event in an initial-condition large ensemble of the present climate. These analogues are re-initialized with slightly perturbed atmospheric initial conditions to generate a set of alternative storylines, dynamically similar to the July 2021 event.  
The set of storylines are used to identify physically plausible worse case scenarios. We do not assess for more intense events, but instead investigate different metrics which could lead to greater impacts to society and ecosystems. We find generated storylines of similar events that persisted longer and covered a larger region – but also show that the observed event was towards the upper end of what is plausible in the current climate. 
Authors - Vikki Thompson1, Dim Coumou2, Erich Fischer3, Urs Beyerle3 
1 Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands.  
2 Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, Netherlands   
3 Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

Biography: Vikki is a scientist at the Royal Netherlands Meteorological Institute and visiting researcher at the Institute of Environmental Studies, VU Amsterdam. Her research focuses on climate extremes, such as heavy rainfall events and heatwaves. She aims to improve understanding of how extreme events are changing and what is driving the changes. 
Vikki has previously worked on extreme heat at the Cabot Institute, University of Bristol; in flood risk at the Scottish Environment Protection Agency; and as a research scientist at the Met Office Hadley Centre. She has a PhD in Meteorology from Reading University.