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When the Lakes Remember: Unravelling the Sudd Floods of 2022

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By Douglas Mulangwa – d.mulangwa@pgr.reading.ac.uk

Between 2019 and 2024, East Africa experienced one of the most persistent high-water periods in modern history: a flood that simply would not recede. Lakes Victoria, Kyoga, and Albert all rose to exceptional levels, and the Sudd Wetland in South Sudan expanded to an unprecedented 163,000 square kilometres in 2022. More than two million people were affected across Uganda and South Sudan as settlements, roads, and farmland remained inundated for months.

At first, 2022 puzzled stakeholders, observers and scientists alike. Rainfall across much of the region was below average that year, yet flooding in the Sudd intensified. This prompted a closer look at the wider hydrological system. Conventional explanations based on local rainfall failed to account for why the water would not recede. The answer, it turned out, lay far upstream and more than a year earlier, hidden within the White Nile’s connected lakes and wetlands.

Figure 1: Map of the White Nile Basin showing delineated sub-catchments, lakes, major  rivers, and the Sudd Wetland extent. Sub-catchments are labelled numerically (1–15) with names listed in the legend. Observation stations (A–F) mark key hydrological data collection  locations used in this study: Lake Victoria (A), Lake Kyoga (B), River Nile at Masindi Port (C), Lake Albert (D), River Nile at Juba (E), and the Sudd Wetland (F). Background river networks and sub-catchment boundaries are derived from the HydroSHED dataset, and wetland extent is based on MODIS flood mask composites. The map is projected in geographic coordinates (EPSG:4326) with a graduated scale bar for accurate distance representation using UTM Zone 36N.

The White Nile: A Basin with Memory

The White Nile forms one of the world’s most complex lake, river, and wetland systems, extending from Lake Victoria through Lakes Kyoga and Albert into the Sudd. Hydrologically, it is a system of connected reservoirs that store, delay, and gradually release floodwaters downstream.

For decades, operational planning assumed that floodwaters take roughly five months to travel from Lake Victoria to the Sudd. That estimate was never actually tested with data; it originated as a rule of thumb based on Lake Victoria annual maxima in May and peak flooding in South Sudan in September/October.

Our recent study challenged that assumption. By combining daily lake-level and discharge data (1950–2024) with CHIRPS rainfall and MODIS flood-extent records (2002–2024), we tracked how flood peaks propagated through the system, segment by segment. Using an automated peak-matching algorithm, we quantified the lag between successive annual maxima peaks in Lake Victoria, Lake Kyoga, Lake Albert, and the Sudd Wetland.

The unprecedented high-water regime of 2019-2024

Figure 2: Lake Victoria water levels (1950–2024) and Sudd Wetland extents (2002–2024), with the 2019–2024 anomalous period shown in dark blue and earlier observations in black. The orange dotted line marks the pre-2019 maximum, while the solid vermillion line denotes the highest peak observed during 2019–2024. The dashed magenta line represents the reconstructed 1878 Lake Victoria peak (1137.3 m a.s.l.) from Nicholson & Yin (2001). The shaded grey band highlights the 2022 flood year, when the Sudd reached its largest extent in the MODIS record.

Between 2019 and 2024, both Lake Victoria and the Sudd reached record levels. Lake Victoria exceeded its historic 1964 peak in 2020, 2021, and 2024, while the Sudd expanded to more than twice its previous maximum extent. Each year from 2019 to 2024 stayed above any pre-2019 record, revealing that this was not a single flood season but a sustained multi-year regime.

The persistence of the 2019–2024 high-water regime mirrors earlier basin-wide episodes, including the 1961–64 and 1870s floods, when elevated lake levels and wetland extents were sustained across multiple years rather than confined to a single rainy season.  However, the 2020s stand out as the most extensive amongst all the episodes since the start of the 20th century. These data confirm that both the headwaters and terminal floodplain remained at record levels for several consecutive years during 2019–2024, highlighting the unprecedented nature of this sustained high-water phase in the modern observational era.

2019–2024: How Multi-Year Rainfall Triggers Propagated a Basin-Wide Flood

The sequence of flood events began with the exceptionally strong positive Indian Ocean Dipole of 2019, which brought extreme rainfall across the Lake Victoria basin. This marked the first in a series of four consecutive anomalous rainfall seasons that sustained elevated inflows into the lake system. The October–December 2019 short rains were among the wettest on record, followed by above-normal rainfall in the March–May 2020 long rains, another wet short-rains season in late 2020, and continued high rainfall through early 2021. Together, these back-to-back wet seasons kept catchments saturated and prevented any significant drawdown of lake levels between seasons. Lake Victoria rose by more than 1.4 metres between September 2019 and May 2020, the highest increase since the 1960s, and remained near the 1960s historical maximum for consecutive years. As that excess water propagated downstream, Lakes Kyoga and Albert filled and stayed high through 2021. Even when regional rainfall weakened in 2022, these upstream lakes continued releasing stored water into the White Nile. The flood peak that reached the Sudd in 2022 corresponded closely to the 2021 Lake Victoria high-water phase.

This sequence shows that the 2022 disaster was not driven by a single rainfall event but by cumulative wetness over multiple seasons. Each lake acted as a slow reservoir that buffered and then released the 2019 to 2021 excess water, resulting in multi-year flooding that persisted long after rainfall had returned to near-normal levels.

Transit Time and Floodwave Propagation

Quantitative tracking showed that it takes an average of 16.8 months for a floodwave to travel from Lake Victoria to the Sudd. The fastest transmission occurs between Victoria and Kyoga (around 4 months), while the slowest and most attenuated segment lies between Albert and the Sudd (around 9 months).

This overturns the long-held assumption of a five-month travel time and reveals a system dominated by floodplain storage and delayed release. The 2019–2021 period showed relatively faster propagation because of high upstream storage, while 2022 exhibited the longest lag as the Sudd absorbed and held vast volumes of water. By establishing this timing empirically, the study offers a more realistic foundation for early-warning systems.

Figure 3: Lake Victoria, Lake Kyoga, and Lake Albert water levels, and Sudd Wetland inundated extent, from 2016 to 2024. Coloured spline curves indicate annual flood-wave trajectories traced from the timing of Lake Victoria annual maxima through the downstream of the White Nile system. Blue shading on the secondary (right) axis shows 180-day rolling rainfall totals over each basin. The panel sequence (Victoria–Kyoga, Kyoga–Albert, Albert–Sudd) highlights the progressive translation of flood waves through the connected lake–river–wetland network.

Wetland Activation and Flood Persistence

Satellite flood-extent maps reveal how the Sudd responded once the inflow arrived. The wetland expanded through multiple activation arms that progressively connected different sub-catchments:

  • 2019: rainfall-fed expansion on the east (Baro–Akobo–Sobat and White Nile sub-basins)
  • 2020–2021: a central-western arm from Bahr el Jebel extending into Bahr el Ghazal and a north-western connection from Bahr el Jebel to Bahr el Arab connected around Bentiu in Unity State.
  • 2022: The two activated arms persisted so the JJAS seasonal rainfall in South Sudan and the inflow from the upstream lakes just compounded the activation leading to the massive flooding in Bentiu, turning the town into an island surrounded by water.

This geometry confirms that the Sudd functions not as a single floodplain but as a network of hydraulically linked basins. Once activated, these wetlands store and recycle water through backwater effects, evaporation, and lateral flow between channels. That internal connectivity explains why flooding persisted long after rainfall declined.

The Bigger Picture

Understanding these long lags is vital for effective flood forecasting and anticipatory humanitarian action. Current early-warning systems in South Sudan and Uganda mainly rely on short-term rainfall forecasts, which cannot capture the multi-season cumulative storage and delayed release that drive multi-year flooding.

By the time floodwaters reach the Sudd Wetland, the hydrological signature of releases from Lake Victoria has been substantially transformed by storage, delay, and attenuation within the intermediate lakes and wetlands. This means that downstream flood conditions are not a direct reflection of upstream releases but the result of cumulative interactions across the basin’s interconnected reservoirs.

The results suggest that antecedent storage conditions in Lakes Victoria, Kyoga, and Albert should be incorporated into regional flood outlooks. When upstream lake levels are exceptionally high, downstream alerts should remain elevated even if rainfall forecasts appear moderate. This approach aligns with impact-based forecasting, where decisions are informed not only by rainfall predictions but also by hydrological memory, system connectivity and potential impact of the floods.

The 2019–2024 high-water regime joins earlier basin-wide flood episodes in the 1870s, 1910s, and 1960s, each linked to multi-year wet phases across the equatorial lakes. The 1961–64 event raised Lake Victoria by about 2.5 metres and reshaped the Nile’s flow for several years. The 1870s flood appears even more extensive, showing that compound, persistent flooding is part of the White Nile’s natural variability.

Climate-change attribution studies indicate that the 2019–2020 rainfall anomaly was intensified by anthropogenic warming, increasing both its magnitude and probability. If such events become more frequent, the basin’s long-memory behaviour could convert short bursts of rainfall into multi-year high-water regimes.

This work reframes how we view the White Nile. It is not a fast, responsive river system but a slow-moving memory corridor in which floodwaves propagate, store, and echo over many months. Recognising this behaviour opens practical opportunities: it enables longer forecast lead times based on upstream indicators, supports coordinated management of lake releases, and strengthens early-action planning for humanitarian agencies across the basin.

It also highlights the need for continued monitoring and data sharing across national borders. Sparse observations remain a major limitation: station gaps, satellite blind spots, and non-public lake-release data all reduce our ability to model the system in real time. Improving this observational backbone is essential if we are to translate scientific insight into effective flood preparedness.

By Douglas Mulangwa (PhD researcher, Department of Meteorology, University of Reading), with contributions from Evet Naturinda, Charles Koboji, Benon T. Zaake, Emily Black, Hannah Cloke, and Elisabeth M. Stephens.

Acknowledgements

This research was conducted under the INFLOW project, funded through the CLARE programme (FCDO and IDRC), with collaboration from the Uganda Ministry of Water and Environment, the South Sudan Ministry of Water Resources and Irrigation, the World Food Programme(WFP), IGAD Climate Prediction and Application Centre  (ICPAC), Médecins Sans Frontières (MSF), the Red Cross Red Crescent Climate Centre, Uganda Red Cross Society (URCS), the South Sudan Red Cross Red Crescent Society (SSRCS) and the Red Cross Red Crescent Climate Centre (RCCC).

Preparing for the assimilation of future ocean-current measurements

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By Laura Risley

Ocean data assimilation (DA) is vital. Firstly, it is essential to improving forecasts of ocean variables. Not only that, the interaction between the ocean and atmosphere is key to numerical weather prediction (NWP) as coupled ocean-atmosphere DA schemes are used operationally.  

At present, observations of the ocean currents are not assimilated operationally. This is all set to change, as satellites are being proposed to measure these ocean currents directly. Unfortunately, the operational DA systems are not yet equipped to handle these observations due to some of the assumptions made about the velocities. In my work, we propose the use of alternative velocity variables to prepare for these future ocean current measurements. These will reduce the number of assumptions made about the velocities and is expected to improve the NWP forecasts.

What is DA? 

DA combines observations and a numerical model to give a best estimate of the state of our system – which we call our analysis. This will lead to a better forecast. To quote my lunchtime seminar ‘Everything is better with DA!’. 

Our model state usually comes from a prior estimate which we refer to as the background. A key component of data assimilation is that the errors present in both sets of data are taken into consideration. These uncertainties are represented by covariance matrices. 

I am particularly interested in variational data assimilation, which formulates the DA problem into a least squares problem. Within variational data assimilation the analysis is performed with a set of variables that differ from the original model variables, called the control variables. After the analysis is found in this new control space, there is a transformation back to the model space. What is the purpose of this transformation? The control variables are chosen such that they can be assumed approximately uncorrelated, reducing the complexity of the data assimilation problem.

Velocity variables in the ocean 

My work is focused on the treatment of the velocities in NEMOVAR. This is the data assimilation software used by the NEMO ocean model, used operationally at the Met Office and ECMWF. In NEMOVAR the velocities are transformed to their unbalanced components, and these are then used as control variables. The unbalanced components of the velocities are highly correlated, therefore contradicting the assumption made about control variables. This would result in suboptimal assimilation of future surface current measurements – therefore we seek alternative velocity control variables. 

The alternative velocity control variables we propose for NEMOVAR are unbalanced streamfunction and velocity potential. This would involve transforming the current control variables, the unbalanced velocities, to these alternative variables using Helmholtz Theorem. This splits a velocity field into its nondivergent (streamfunction) and irrotational (velocity potential) parts. These parts have been suggested by Daley (1993) as more suitable control variables than the velocities themselves. 

Numerical Implications of alternative variables 

We have performed the transformation to these proposed control variables using the shallow water equations (SWEs) on a 𝛽-plane. To do so we discretised the variables on the Arakawa-C grid. The traditional placement of streamfunction on this grid causes issues with the boundary conditions. Therefore, Li et al. (2006) proposed placing streamfunction in the centre of the grid, as shown in Figure 1. This circumvents the need to impose explicit boundary conditions on streamfunction. However, using this grid configuration leads to numerical issues when transforming from the unbalanced velocities to unbalanced streamfunction and velocity potential. We have analysed these theoretically and here we show some numerical results.

Figure 1: The left figure shows the traditional Arakawa-C configuration (Lynch (1989), Watterson (2001)) whereby streamfunction is in the corner of each grid cell. The right figure shows the Arakawa-C configuration proposed by Li et al. (2006) where streamfunction is in the centre of the grid cell. The green shaded region represents land. 

Issue 1: The checkerboard effect 

The transformation from the unbalanced velocities to unbalanced streamfunction and velocity potential involves averaging derivatives, due to the location of streamfunction in the grid cell. This process causes a checkerboard effect – whereby we have numerical noise entering the variable fields due to a loss of information. This is clear to see numerically using the SWEs. We use the shallow water model to generate a velocity field. This is transformed to its unbalanced components and then to unbalanced streamfunction and velocity potential. Using Helmholtz Theorem, the unbalanced velocities are reconstructed. Figure 2 shows the checkboard effect clearly in the velocity error.

Figure 2: The difference between the original ageostrophic velocity increments, calculated using the SWEs, and the reconstructed ageostrophic velocity increments. These are reconstructed using Helmholtz Theorem, from the ageostrophic streamfunction and velocity potential increments. On the left we have the zonal velocity increment error and on the right the meridional velocity increment error. 

Issue 2: Challenges in satisfying the Helmholtz Theorem 

Helmholtz theorem splits the velocity into its nondivergent and irrotational components. We discovered that although streamfunction should be nondivergent and velocity potential should be irrotational, this is not the case at the boundaries, as can be seen in figure 3. This implies the proposed control variables are able to influence each other on the boundary. This would lead to them being strongly coupled and therefore correlated near the boundaries. This directly conflicts the assumption made that our control variables are uncorrelated. 

Figure 3: Issues with Helmholtz Theorem near the boundaries. The left shows the divergence of the velocity field generated by streamfunction. The right shows the vorticity of the velocity field generated by velocity potential. 

Overall, in my work we propose the use of alternative velocity control variables in NEMOVAR, namely unbalanced streamfunction and velocity potential. The use of these variables however leads to several numerical issues that we have identified and discussed. A paper on this work is in preparation, where we discuss some of the potential solutions. Our next work will further this investigation to a more complex domain and assess our proposed control variables in assimilation experiments. 

References: 

Daley, R. (1993) Atmospheric data analysis. No. 2. Cambridge university press. 

Li, Z., Chao, Y. and McWilliams, J. C. (2006) Computation of the streamfunction and velocity potential for limited and irregular domains. Monthly weather review, 134, 3384–3394. 

Lynch, P. (1989) Partitioning the wind in a limited domain. Monthly weather review, 117, 1492–1500. 

Watterson, I. (2001) Decomposition of global ocean currents using a simple iterative method. Journal of Atmospheric and Oceanic Technology, 18, 691–703

Nature vs Nurture in Convective-Scale Ensemble Spread

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By Adam Gainford

Quantifying the uncertainty of upcoming weather is now a common procedure thanks to the widespread use of ensemble forecasting. Unlike deterministic forecasts, which show only a single realisation of the upcoming weather, ensemble forecasts predict a range of possible scenarios given the current knowledge of the atmospheric state. This approach allows forecasters to estimate the likelihood of upcoming weather events by simply looking at the frequency of event occurrence within all ensemble members. Additionally, by sampling a greater range of events, this approach highlights plausible worst-case scenarios, which is of particular interest for forecasts of extreme weather. Understanding the realistic range of outcomes is crucial for forecasters to provide informed guidance, and helps us avoid the kind of costly and embarrassing mistakes that are commonly associated with the forecast of “The Great Storm of 1987”*.

To have trust that our ensembles are providing an appropriate range of outputs, we need some method of verifying ensemble spread. We do this by calculating the spread-skill relationship, which essentially just compares the difference between member values to the skill of the ensemble as a whole. If the spread-skill relationship is appropriate, spread and skill scores should be comparable when averaged over many forecasts. If the ensemble shows a tendency to produce larger spread scores than skill scores, there is too much spread and not enough confidence in the ensemble given its accuracy: i.e., the ensemble is overspread. Conversely, if spread scores are smaller than skill scores, the ensemble is too confident and is underspread. 

Figure 1: Postage stamp plots showing three-hourly precipitation accumulation valid for 2023-07-08 09Z at leadtime T+15 h. There is reasonable spread within both the frontal rain band effecting areas of SW England and Wales, and the convective features ahead of this front.

My PhD work has focussed on understanding the spread-skill relationship in convective-scale ensembles. Unlike medium range ensembles that are used to estimate the uncertainty of synoptic-scale weather at daily-to-weekly leadtimes, convective-scale ensembles quantify the uncertainty of smaller-scale weather at hourly-to-daily leadtimes. To do this, convective-scale ensembles must be run at higher resolutions than medium-range ensembles, with grid spacings smaller than 4 km. These higher resolutions allows the ensemble to explicitly represent convective storms, which has been repeatedly shown to produce more accurate forecasts compared coarser-resolution forecasts that must instead rely on convective parametrizations. However, running models at such high resolutions is too computationally expensive to be done over the entire Earth, so they are typically nested inside a lower-resolution “parent” ensemble which provides initial and boundary conditions. Despite this, researchers often report that convective-scale ensembles are underspread, and the range of outputs is too narrow given the ensemble skill. This is corroborated by operational forecasters, who report that the ensemble members often stay too close to the unperturbed control member. 

To provide the necessary context for understanding the underspread problem, many studies have examined the different sources and behaviours of spread within convective-scale ensembles. In general, spread can be produced through three different mechanisms: firstly, through differences in each member’s initial conditions; secondly, through differences in the lateral boundary conditions provided to each member; and thirdly, through the different internal processes used to evolve the state. This last source is really the combination of many different model-specific factors (e.g., stochastic physics schemes, random parameter schemes etc.), but for our purposes this represents the ways in which the convective-scale ensemble produces its own spread. This contrasts with the other two sources of spread, which are directly linked to the spread of the parent ensemble.  

The evolution of each of these three spread sources is shown in Fig. 2. At the start of a forecast, the ensemble spread is entirely dictated by differences in the initial conditions provided to each ensemble member. As we integrate forward in time, though, this initial information is removed from the domain by the prevailing winds and replaced by information arriving through the boundaries. At the same time, internal model processes start spinning up additional detail within each ensemble member. For a UK-sized domain, it takes roughly 12 hours for the initial information to have fully left the domain, though this is of course highly dependent on the strength of the prevailing winds. After this time, spread in the ensemble is partitioned between internal processes and boundary condition differences.  

Figure 2: Attribution of spread within a convective-scale ensemble by leadtime. 

While the exact partitioning in this schematic shouldn’t be taken too literally, it does highlight the important role that the parent ensemble plays in determining spread in the child ensemble. Most studies which try to improve spread target the child ensemble itself, but this schematic shows that these improvements may have quite a limited impact. After all, if the spread of information arriving from the parent ensemble is not sufficient, this may mask or even overwhelm any improvements introduced to the child ensemble.  

However, there are situations where we might expect internal processes to show a more dominant spread contribution. Forecasts of convective storms, for instance, typically show larger spread than forecasts of other types of weather, and are driven more by local processes than larger-scale, external factors.

This is where our “nature” and “nurture” analogy becomes relevant. Given the similarities of this relationship to the common parent-child theory in behavioural psychology, we thought it would be a fun and useful gimmick to also use this terminology here. So, in the “nature” scenario, each child member shows large similarity to the corresponding parent member, which is due to the dominating influence of genetics (initial and boundary conditions). Conversely, in the “nurture” scenario, spread in the child ensemble is produced more by its response to the environment (internal processes), and as such, we see larger differences between each parent-child pair.  

While the nature and nurture attribution is well understood for most variables, few studies have examined the parent-child relationship for precipitation patterns, which are an important output for guidance production and require the use of neighbourhood-based metrics for robust evaluation. Given that this is already quite a long post, I won’t go into too much detail of our results looking at nature vs nurture for precipitation patterns. Instead, I will give a quick summary of what we found: 

  • Nurture provides a larger than average influence on the spread in two situations: during short leadtimes**, and when forecasting convective events driven by continental plume setups. 
  • In the nurture scenarios, spread is consistently larger in the child ensemble than the parent ensemble. 
  • In contrast to the nurture scenarios, nature provides larger than average spread at medium-to-long leadtimes and under mobile regimes, which is consistent with the boundary arguments mentioned previously. 
  • Spread is very similar between the child and parent ensembles in the nurture scenarios.  

If you would like to read more about this work, we will be submitting a draft to QJRMS very soon.  

To conclude, if we want to improve the spread of precipitation patterns in convective-scale ensembles, we should direct more attention to the role of the driving ensemble. It is clear that the exact nesting configuration used has a strong impact on the quality of the spread. This factor is especially important to consider given recent experiments with hectometric-scale ensembles which are themselves nested within convective-scale ensembles. With multiple layers of nesting, the coupling between each ensemble layer is likely to be complex. Our study provides the foundation for investigating these complex interactions in more detail. 

* This storm was actually well forecast by the Met Office. The infamous Michael Fish weather update in which he said there was no hurricane on the way was referring to a different system which indeed did not impact the UK. Nevertheless, this remains a good example of the importance of accurately predicting (and communicating) extreme weather events.  

** While this appears to be inconsistent with Fig. 2, the ensemble we used does not solely take initial conditions from the driving ensemble. Instead, the ensemble uses a separate, high-resolution data assimilation scheme to the parent ensemble. Each ensemble is produced in a way which makes the influence of the data assimilation more influential to the spread than the initial condition perturbations. 

Starting Your PhD Journey: Tips for Success

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So, you’ve officially embarked on the exciting journey that is a PhD—congrats! You’ve reached a major milestone, and whether you’re feeling excited, overwhelmed, or a mix of both, just know you’ve signed up for an adventure like no other. A PhD is an incredible opportunity to dive headfirst into a subject you’re passionate about, build a toolkit of valuable skills, and—who knows?—maybe even make history in your field.

But let’s be real: it’s not all rainbows and groundbreaking discoveries. The PhD life can be challenging, sometimes feeling like a marathon through an obstacle course. You’ll have moments that test your patience, confidence, and sometimes, your sanity. That’s why here at Social Metwork, we’ve gathered some golden advice from seasoned PhD students to help you navigate these waters. Our goal? To make this transition into PhD life a little smoother, maybe even a little fun.

We’ll break these tips down into three areas: navigating day-to-day life as a PhD student, getting organized like a pro, and growing into the great scholar you’re destined to be. Ready? Let’s dive in!

1. Navigating Day-to-day Life as a PhD Student

Work-life balance

The first year of your PhD can feel overwhelming as you try to juggle research, coursework, and life. One key piece of advice? Don’t overwork yourself. As Laura Risley puts it, “Sometimes if you’re struggling with work, an afternoon off is more useful than staying up late and not taking a break.” It’s easy to get absorbed in your work, but stepping away to recharge can actually help you return with fresh perspectives.

Getting involved in activities outside your PhD is another great way to maintain balance (L. Risley, 2024). Whether it’s exploring more of Reading, participating in a hobby, or just getting outside for some fresh air, your brain will thank you for the break. Remember, “Your PhD is important, but so is your health,” so make sure to take care of yourself and make time for things that bring you joy: exercise, good food, and sleep!

Lastly, don’t underestimate the power of routine. Building a consistent schedule can help bring some stability to PhD life. Most importantly, be kind to yourself. The weight of expectations can be heavy so give yourself permission to not have it all figured out yet. You won’t understand everything right away, and that’s completely normal!

Socialising and Building a Support System

Your cohort is your lifeline. The people you start with are going through the same experiences, and they will be your greatest support system. Whether you’re attending department events, organizing a BBQ, or just grabbing a coffee, socializing with your peers is a great way to get through everything. At the end of the day, we are all in this together! As Rhiannon Biddiscombe wisely says, “Go for coffee with people, go to Sappo, enjoy the pub crawls, waste a night out at PT, take part in the panto, spend time in the department in-person” — so make sure you get involved!

If what you want is to meet new people, you could even help organise social events, like research groups or casual hangouts – feeling connected within your department can make all the difference when you’re having a tough week. And hey, if you’re looking for a fun group activity, “Market House in town has darts boards, ping pong tables, and shuffleboard (you slide little discs to the end of the board, it’s good fun!)”.  

2. Getting Organised Like a Pro

Writing and Coding

Staying organised is critical for both your mental health and your research. Adam Gainford recommends you start by setting up a reference manager early on—trust us, you’ll thank yourself later. And if your research involves coding, learn version control tools like GitHub to keep your projects neat and manageable. As a fellow PhD student says “Keeping organised will help keep your future self sane (and it’s a good skill that will help you with employability and future group projects)”.

A golden rule for writing: write as you go. Don’t wait until the last minute to start putting your thoughts on paper. Whether it’s jotting down a few ideas, outlining a chapter, or even starting a draft, regular writing will save you from stress later on. Remember what Laura always says, “It’s never too early to start writing.”

Time Management

Managing your time as a PhD student is a balancing act. Plans will shift, deadlines will change, and real life will get in the way—it’s all part of the process. Instead of stressing over every slipped deadline, try to “go with the flow”. Your real deadlines are far down the road, and as long as you’re progressing steadily, you’re doing fine.

Being organised also doesn’t have to be complicated. Some find it helpful to create daily, weekly, or even monthly plans. Rhiannon recommends keeping a calendar is a great way to track meetings, seminars, and research group sessions – I myself could not agree more and find time-blocking is a great way to make sure everything gets done. Regarding your inbox, make sure you “stay on top of your emails but don’t look at them constantly. Set aside a few minutes a day to look at emails and sort them into folders, but don’t let them interrupt your work too much!”. Most importantly though, don’t forget to schedule breaks—even just five minutes of stepping away can help you reset (and of course, make sure you have some valuable holiday time off!).

3. Growing into the Scholar You’re Meant to Be

Asking for Help

This journey isn’t something you’re expected to do alone. Don’t be afraid to reach out for help from your friends, supervisors, or other PhD students. Asking questions is a sign of strength, not weakness. What’s great is that everyone has different backgrounds, and more often than not, someone will be able to help you navigate whatever you’re facing (trust me, as a geography graduate my office mates saved my life with atmospheric physics!). Whether you’re stuck on a tricky equation or need clarification on a concept, ask ask ask! 

“You’ve got a whole year to milk the ‘I’m a first year’ excuse, but in all seriousness, its never too late to ask when you’re unsure!” – a fellow PhD student.

Navigating Supervisor Meetings

Your supervisors are there to guide you, but communication is key. Be honest with them, especially when you’re struggling or need more support. If something doesn’t make sense, speak up—don’t nod along and hope for the best, “they should always have your back” (it will also be very embarrassing if you go along with it and are caught out with questions…). 

Also, “If you know some things you want to get out of your PhD, communicate that with your supervisors”. Open communication will help you build a stronger working relationship and ensure you get what you need from the process.

Dealing with Imposter Syndrome

Imposter syndrome can hit hard during a PhD, especially when you’re surrounded by brilliant people doing impressive work. But here’s the thing: don’t compare yourself to others. Everyone’s PhD is different—some projects lend themselves to quick results, while others take longer. Just because someone publishes early doesn’t mean your research is less valuable or that you’re behind – we are all on our own journeys. 

And remember, no one expects you to know everything right away. “There might be a pressure, knowing that you’ve been ‘handpicked’ for a project, that you should know things already; be able to learn things more quickly than you’re managing; be able to immediately understand what your supervisor is talking about when they bring up XYZ concept that they’ve been working on for 20+ years. In reality, no reasonable person expects you to know everything or even much at all yet. You were hand-picked for the project because of your potential to eventually become an independent researcher in your field – A PhD is simply training you for that, so you need to finish the PhD to finish that training.”

If you’d struggling with imposter syndrome, or want to learn about ways to deal with it, I highly recommend attending the imposter syndrome RRDP. 

A Few Final Words of Wisdom

The PhD rollercoaster is full of ups and downs, but remember, you’re doing fine. “If you’re supervisors are happy, then don’t worry! Everything works out in the end, even when it seems to not be working for a while! “– Laura Risley

It’s also super important to enjoy the process. You’ve chosen a topic you’re passionate about, and this is a rare opportunity to fully immerse yourself in it. Take advantage of that! Don’t shy away from opportunities to share your work. Whether it’s giving a talk, presenting a poster (or writing for the Social Metwork blog!!), practice makes perfect when it comes to communicating your research.

Embarking on a PhD is no small feat, but hopefully with these tips, you’ll have the tools to manage the challenges and enjoy the ride. And if all else fails, remember the most important advice of all: “Vote in the Big Biscuit Bracket—it’s the most important part of being a PhD student!”. 

From the department’s PhDs students to you! 

Written by Juan Garcia Valencia 

The Mystery of Coarse Dust Transport in Observations and Models​

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Natalie Ratcliffe – n.ratcliffe@pgr.reading.ac.uk

On Tuesday 23rd April 2024, I presented my PhD work at the lunchtime seminar to the department.  The work I presented incorporated a lot of the work I have achieved during the 3 and a half years of my PhD. This blog post will be a brief overview of the work discussed.

Every year, between 300 and 4000 million tons of mineral dust are lofted from the Earth’s surface (Huneeus et al., 2011; Shao et al., 2011). This dust can travel vast distances, affecting the Earth’s radiative budget, water and carbon cycles, fertilization of land and ocean surfaces, as well as aviation, among other impacts. Observations from recent field campaigns have revealed that we underestimate the amount of coarse particles (>5 um diameter) which are transported long distances (Ryder et al., 2019). Based on our understanding of gravitational settling, some of these particles should not physically be able to travel as far as they do. This results in an underestimation of these particles in climate models, as well as a bias towards modelling finer particles (Kok et al., 2023). Furthermore, fine particles have different impacts on the Earth than coarse particles, for example with the radiative budget at the top of the atmosphere; including more coarse particles in a model reduces the cooling effect that dust has on the Earth.

Thus, my PhD project was born! We wanted to try and peel back the layers of the dusty onion. How are these coarse particles travelling so far?

Comparing a Climate Model and Observations

First, we compared in-situ aircraft observations to a climate model simulation to assess the degree to which the model was struggling to represent coarse particle transport from the Sahara across the Atlantic to the Caribbean. Measuring particles up to 300 um in diameter, the Fennec, AER-D and SALTRACE campaigns provide observations at three stages of transport throughout the lifetime of dust in the atmosphere (near emission, moving over the ocean and at distance from the Sahara; Figure 1). Using these observations, we assess a Met Office Unified Model HadGEM3 configuration. This model has six dust size bins, ranging from 0.063-63.2 um diameter. This is a much larger upper bound than most climate models, which tend to have an upper bound at 10-20 um.

Figure 1: Map showing the location of the flight tracks which were taken when the observations were measured.

We found that the model significantly underestimates the total mass of mineral dust in the atmosphere, as well as the fraction of dust mass made up of coarse particles. This happens at all locations, including at the Sahara: firstly, this suggests that the model is not emitting enough coarse particles to begin with and secondly, the growing model underestimation with distance suggests that the coarse particles are being deposited too quickly. By looking further into the model, we found that the coarsest particles (20-63.2 um) were lost from the atmosphere very quickly, barely surpassing Cape Verde in their westwards transport. Whereas in the observations, these coarsest particles were still present at the Caribbean, representing ~20% of the total dust mass. We also found that the distribution of coarse particles tended to have a stronger dependence on altitude than in the observations, with fewer particles observed at higher altitudes. This work has been written up into a paper which is currently undergoing review, but can be seen in preprint; Ratcliffe et al., (preprint).

Sensitivity Testing of the Model

Now that we have confirmed that the model is struggling to retain coarse particles for long- range transport, we want to work out if any of the model processes involved in transport and deposition could be over- or under-active in coarse particle transport. This involved turning off individual processes one at a time and seeing what impact it has on the dust transport. As we wanted to focus on the impact to coarse particle transport, we needed to start with an improved emission distribution at the Sahara, so we tuned the model to better match the observations from the Fennec campaign.

In our first tests we decided to ‘turn off’ or reduce gravitational settling of dust particles in the model to see what happens if we eliminate the greatest removal mechanism for coarse particles. Figure 2 shows the volume size distribution of these gravitational settling model experiments against the observations. We found that completely removing gravitational settling increased the mass of coarse particles too much, while having little to no effect on the fine particles. We found that to bring the model into better agreement with the observations, sedimentation needs to be reduced by ~50% at the Sahara and more than 80% at the Caribbean.

Figure 2: Mean volume size distribution between 2500-3000 m in the Fennec (red), AER-D (orange) and SALTRACE (yellow) observations, the control mode simulation (black) and the reduced dust sedimentation experiments (blue shades).

We also tested the sensitivity of turbulent mixing, convective mixing and wet deposition on coarse dust transport; however, these experiments did not have as great of an impact on coarse transport as the sedimentation. We found that removing the mixing mechanisms resulted in decreased vertical transport of dust which tended to reduce the horizontal transport. We also carried out an experiment where we doubled the convective mixing, and this did show improved vertical and horizontal transport. Finally, when we removed wet deposition of dust, we found that it had a greater impact on the fine particles, less so on the coarse particles, suggesting that wet deposition is the main removal mechanism for the four finest size bins in the model.

Final Experiment

Now that we know our coarse particles are settling out too quickly and sit a bit too low in the atmosphere, we come to our final set of experiments. Let’s say that our coarse particles in the model and our dust scheme are actually set up perfectly, then could it be the meteorology in the model which is wrong? If the coarse particles were mixed higher up at the Sahara, then would they reach faster horizontal winds to travel further across the Atlantic? To test this theory, I hacked the files which the model uses to start a simulation, and I put all the dust over the Sahara up to the top of the dusty layer (~5 km). We found this increased the lifetime of the coarsest particles so that it took twice as long to lose 50% of the starting mass. This unfortunately only slightly improved transport distance as the particles were still lost relatively quickly. After checking the vertical winds in the model, we found that they were an order of magnitude smaller at the Sahara, Canaries and Cape Verde than the observations made during the field campaigns. This suggests that if the vertical winds were stronger, they could initially raise the dust higher and keep the coarse particles raised higher for longer, extending their atmospheric lifetime.

Summarised Conclusions

To summarise what I’ve found during my PhD:

  1. The model underestimates coarse mass at emission and the underestimation is exacerbated with westwards transport.
  2. Altering the settling velocity of dust in the model brings the model into better agreement with the observations.
    • a. Turbulent mixing, convective mixing and wet deposition have minimal impact on coarse transport.
  3. Lofting the coarse particles higher initially improves transport minimally.
    • a. Vertical winds in the model are an order of magnitude too small.

So what’s next?

If we’ve found that the coarse particles are settling out the atmosphere too quickly (by potentially more than 80%), would that suggest that the deposition equations are wrong and are overestimating particle deposition? So, we change those and everything’s fixed, right? I wish. Unfortunately, the deposition equations are one of the things that we are more scientifically sure of, so our results mean that there’s something happening to the coarse particles that we aren’t modelling which is able to counteract their settling velocity by a very significant amount. Our finding that the vertical winds are too small could be a part of this. Other recent research suggests that processes such as particle asphericity, triboelectrification, vertical mixing and turbulent mixing (has been shown to help in a higher-resolution (not climate) model) in the atmosphere could enhance coarse particle transport.

Huneeus, N., Schulz, M., Balkanski, Y., Griesfeller, J., Prospero, J., Kinne, S., Bauer, S., Boucher, O., Chin, M., Dentener, F., Diehl, T., Easter, R., Fillmore, D., Ghan, S., Ginoux, P., Grini, A., Horowitz, L., Koch, D., Krol, M. C., Landing, W., Liu, X., Mahowald, N., Miller, R., Morcrette, J.-J., Myhre, G., Penner, J., Perlwitz, J., Stier, P., Takemura, T., and Zender, C. S. 2011. Global dust model intercomparison in AeroCom phase I. Atmospheric Chemistry and Physics. 11(15), pp. 7781-7816

Kok, J. F., Storelvmo, T., Karydis, V. A., Adebiyi, A. A., Mahowald, N. M., Evan, A. T., He, C., and Leung, D. M. Jan. 2023. Mineral dust aerosol impacts on global climate and climate change. Nature Reviews Earth Environment 2023, pp. 1–16. url: https://www.nature.com/articles/s43017-022-00379-5

RatcliLe, N. G., Ryder, C. L., Bellouin, N., Woodward, S., Jones, A., Johnson, B., Weinzierl, B., Wieland, L.-M., and Gasteiger, J.: Long range transport of coarse mineral dust: an evaluation of the Met Office Unified Model against aircraft observations, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-806, 2024

Ryder, C. L., Highwood, E. J., Walser, A., Seibert, P., Philipp, A., and Weinzierl, B. 2019. Coarse and giant particles are ubiquitous in Saharan dust export regions and are radiatively significant over the Sahara. Atmospheric Chemistry and Physics. 19(24), pp. 15353–15376

Shao, Y., Wyrwoll, K.-H., Chappell, A., Huang, J., Lin, Z., McTainsh, G. H., Mikami, M., Tanaka, T. Y., Wang, X., and Yoon, S. 2011. Dust cycle: An emerging core theme in Earth system science. Aeolian Research. 2(4), pp. 181–204

The importance of anticyclonic synoptic eddies for atmospheric block persistence and forecasts

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Charlie Suitters – c.c.suitters@pgr.reading.ac.uk

The Beast from the East, the record-breaking winter warmth of February 2020, the Canadian heat dome of 2022…what do these three events have in common? Well, many things I’m sure, but most relevantly for this blog post is that they all coincided with the same phenomenon – atmospheric blocking.

So what exactly is a block? An atmospheric block is a persistent, large-scale, quasi-stationary high-pressure system sometimes found in the mid-latitudes. The prolonged subsidence associated with the high pressure suppresses cloud formation, therefore blocks are often associated with clear, sunny skies, calm winds, and temperature extremes. Their impacts can be diverse, including both extreme heat and extreme cold, drought, poor air quality, and increased energy demand (Kautz et al., 2022). 

Despite the range of hazards that blocking can bring, we still do not fully understand the dynamics that cause a block to start, maintain itself, and decay (Woollings et al., 2018). In reality, many different mechanisms are at play, but the importance of each process can vary between location, season, and individual block events (Miller and Wang, 2022). One process that is known to be important is the interaction between blocks and smaller synoptic-scale transient eddies (Shutts, 1983; Yamazaki and Itoh, 2013). By studying a 43-year climatology of atmospheric blocks and their anticyclonic eddies (both defined by regions of anomalously high 500 hPa geopotential height), I have found that on average, longer blocks absorb more synoptic anticyclones, which “tops up their anticyclonicness” and allows them to persist longer (Fig. 1).

Figure 1: average number of anticyclonic eddies per block for the Euro-Atlantic (left) and North Pacific (right). Block persistence is defined as the quartiles (Q1, Q2, Q3) of all blocks in winter (blue) and summer (red). From Suitters et al. (2023).

It’s great that we now know this relationship, however it would be beneficial to know if these interactions are forecasted well. If they are not, it might explain our shortcomings in predicting the longevity of a block event (Ferranti et al., 2015).  I explore this with a case study from March 2021 using ensemble forecasts from MOGREPS-G. Fortunately, this block in March 2021 was not associated with any severe weather, but it was still not forecasted well. In Figure 2, I show normalised errors in the strength, size, and location of the block, at the time of block onset, for each ensemble member from a range of different initialisation times. In these plots, a negative (positive) value means that the block was forecast to be too weak (strong) or too small (large), and the larger the error in the location, the further away the forecast block was from reality. In general, the onset of this block was forecast to be to be too weak and too small, though there was considerable spread within the ensemble (Fig. 2). Certainty in the forecast was only achieved at relatively small lead times.

Figure 2: Normalised errors in the intensity (left), area (centre), and location of the block’s centre of mass (right), at a validity time of 2021-03-14 12 UTC (the time of onset). Each ensemble member’s error from a particular initialisation time is shown by the grey dots, and the ensemble mean is shown in black. When Z, A, or L are zero, the forecast has a “perfect” replication for this metric of the block (when compared to ERA5 reanalysis).

Now for the interesting bit – what causes the uncertainty in forecasting of the onset this European blocking event? To examine this, I grouped forecast members from an initialisation time of 8 March 2021 according to their ability to replicate the real block: the entire MOGREPS-G mean, members that either have no block or a very small block (Group G), members that perform best (Group H), and members that predict area well, but have the block in the wrong location (Group I). Then, I take the mean geopotential height anomalies () at each time step in each group, and compare these fields between groups to see if I can find a source of forecast error.

This is shown as an animation in Fig. 3. The animation starts at the time of block onset, and goes back in time to selected validity times, as shown at the top of the figure. The domain of the plot also changes in each frame, gradually moving westwards across the Atlantic. By looking at the ERA5 (the “real”) evolution of the block, we see that the onset of the European block was the result of an anticyclonic transient eddy breaking off from an upstream blocking event over North America. However, none of the aforementioned groups of members accurately simulate this vortex shedding from the North American block. In most cases, the eddy leaving the North American block is either too weak or non-existent (as shown by the blue shading, representing that the forecast is much weaker than in ERA5), which resulted in a lack of Eastern Atlantic blocking altogether. Only the group that modelled the block well (Group H) had a sizeable eddy breaking off from the upstream block, but even in this case it was too weak (paler blue shading). Therefore, the uncertain block onset in this case is directly related to the way in which an anticyclonic eddy was forecast to travel (or not) across the Atlantic, from a pre-existing block upstream. This is interesting because the North American block itself was modelled well, yet the eddy that broke off it was not, which was vital for the onset of the Euro-Atlantic block.

To conclude, this is an important finding because it shows the need to accurately model synoptic-scale features in the medium range in order to accurately predict blocking. If these eddies are absent in a forecast, a block might not even form (as I have shown), and therefore potentially hazardous weather conditions would not be forecast until much shorter lead times. My work shows the role of anticyclonic eddies towards the persistence and forecasting of blocks, which until now had not be considered in detail.

References

Kautz, L., Martius, O., Pfahl, S., Pinto, J.G., Ramos, A.M., Sousa, P.M., and Woollings, T., 2022. “Atmospheric blocking and weather extremes over the Euro-Atlantic sector–a review.” Weather and climate dynamics, 3(1), pp305-336.

Miller, D.E. and Wang, Z., 2022. Northern Hemisphere winter blocking: differing onset mechanisms across regions. Journal of the Atmospheric Sciences, 79(5), pp.1291-1309.

Shutts, G.J., 1983. The propagation of eddies in diffluent jetstreams: Eddy vorticity forcing of ‘blocking’ flow fields. Quarterly Journal of the Royal Meteorological Society, 109(462), pp.737-761.

Suitters, C.C., Martínez-Alvarado, O., Hodges, K.I., Schiemann, R.K. and Ackerley, D., 2023. Transient anticyclonic eddies and their relationship to atmospheric block persistence. Weather and Climate Dynamics, 4(3), pp.683-700.

Woollings, T., Barriopedro, D., Methven, J., Son, S.W., Martius, O., Harvey, B., Sillmann, J., Lupo, A.R. and Seneviratne, S., 2018. Blocking and its response to climate change. Current climate change reports, 4, pp.287-300.

Yamazaki, A. and Itoh, H., 2013. Vortex–vortex interactions for the maintenance of blocking. Part I: The selective absorption mechanism and a case study. Journal of the Atmospheric Sciences, 70(3), pp.725-742.

AGU in Sunny San Francisco

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Flynn Ames - f.ames@pgr.reading.ac.uk

For my first (and given carbon budgets, possibly the last) in-person conference of my PhD, I was lucky enough to go to AGU (American Geophysical Union Conference) in December 2023, taking place in San Francisco, California. As my first time in America, there was a lot to be excited about. As my first time presenting at a conference, there was a lot to be nervous about. So what did I discover?

To echo the previous year’s post: AGU is big. I mean really big. I mean seriously (please take me seriously) its huge. The poster hall was the size of an aircraft hangar – poster slots were numbered from 1 to over 3000, with each slot used by a different person for each day. Dozens of talk sessions were held at any time simultaneously across the three separate buildings (that thankfully were very close to each other), commencing anytime from 8am to 6pm, Monday to Friday. I was recommended the AGU app and would (uncharacteristically) do the same as it was very helpful in navigating the sessions. I’d also recommend properly planning what you want to attend in advance of the conference – it is very easy to miss potentially relevant sessions otherwise.

The poster hall from two different angles on Monday Morning (left) and Friday evening (right).

The keynote lectures (one per day) were like something out of Gamescom or E3. They always started with flashy, cinematic vignettes. Hosts and speakers had their own entrance theme song to walk out on stage to, whether that be Katy Perry ‘Fireworks’ or Johnny Cash ‘Ring of Fire’ (and of course, they had the cliche teleprompter from which to read). Some Keynote talks were OK in terms of content, but others were definitely a miss, seemingly prioritising style over substance or referring to subject matter in too abstract a way, so that it was difficult to gauge what the take home message was meant to be. I’d say attend at least one for the experience but skip the rest if they don’t appeal to you.

There were also miscellaneous activities to partake in. Exhibition Hall F was where you could find stalls of many research organisations, along with any American or Chinese university you can name (NASA had a cool one with some great graphics). In that same place you could also get a free massage (in plain sight of everyone else) or a professional headshot (which I tried – they brushed something on my face, I don’t know what it was) or even hang out with the puppies (a stall frequented by a certain Met PhD student). You could say there was something for everyone.

I wasn’t the only one needing rest after a long day of conferencing.

I found poster sessions to be far more useful than talks. Most talks were eight minutes long, with a red light switching on after seven. With these time constraints, presenters are often forced to assume knowledge and cram in content and slides. The presentations can be hard to follow at the best of times, but especially when you yourself are presenting later in the week and all you can do is watch and wait for that red light, knowing that it will be deciding your fate in days to come. In contrast, posters can be taken at one’s own pace – you can ask the presenter to tailor their “spiel” to you, whether that’s giving a higher-level overview (as I asked for 100% of the time) or skipping straight to the details. You get a proper chance to interact and have conversations with those doing work you’re interested in, in contrast to talks where your only hope is to hunt down and corner the presenter in the few microseconds after a session ends.

With that said, there were many great talks. Some of the coolest talks I attended were on existing and future mission concepts to Europa (moon of Jupiter) and Enceladus (moon of Saturn) respectively, which has tangential relevance to my own project (icy moon oceanography – probably best left for a future post). In these talks, they discussed the science of the upcoming Europa Clipper mission, along with a robotic EEL concept (like a robot snake) for traversing within and around the icy crevasses on Enceladus’s surface. It was really cool (and very lucky) getting to interact with people working on Europa Clipper and the current Juno mission orbiting Jupiter. Given the time taken between a mission’s proposal, getting (and sometimes losing) funding, planning, construction, and eventual launch and arrival, many of these scientists had been working on these missions for decades! 

My own talk was scheduled for the final conference day (given the luck with everything else, I won’t complain) at 8:40 am. While seemingly early, I struggled to sleep beyond 3:30am most days anyway owing to jet lag so by 8:40am, stress ensured I was wide awake, alert, and focused. 

The talk was over in a flash – I blinked and it was done (more or less).

The most academically helpful part of the conference was the conversations I had with people about my work after the talk. This was my main take away from AGU – that getting to know people in your field and having in-depth conversations really can’t have been achieved by reading someone’s paper, or even sending an email. Meeting in-person really helps. A poster session can thankfully make this feel very natural (as opposed to just randomly walking up to strangers – not for me…) and is therefore something I recommend taking advantage of. Besides, if they’re presenting a poster, they’re less able to run away, even if they want to.

A quick bullet point list of other things I learned (and didn’t) while at AGU:

Things I learned:

  • Apparently, PhD students having business cards is normal in America? – I got handed one during a dinner and the whole table didn’t understand why I was confused
  • NO BISCUITS DURING COFFEE BREAKS in America – probably because you can’t get biscuits easily in America. Regardless, my stomach deemed this a poor excuse.
  • Food portions are, in general, much bigger – surely to make up for the lack of biscuits during coffee breaks.

Things I didn’t learn:

  • How the automatic flush mechanism worked in the conference venue toilets (I really tried)
  • Given there were dozens of sessions happening simultaneously at the conference, probably many other things.

After AGU finished, I was lucky enough to spend extra time in San Francisco. The city really has a piece of everything: fantastic walks near the Golden Gate and coastal area, the characteristic steep streets and cable cars, lots of great places to eat out (great for vegans/vegetarians too! :)), and they had unexpectedly good street musicians. The weather was very nice for December – around 18 degrees. I even got sunburned on one of the days. Public transport is great in San Francisco and getting around the city was no issue.

Some of the various sights (and only pictures I took) in San Francisco.

But San Francisco also appears to be a city of extremes. There are mansions near the beach in an area that looks like a screenshot from Grand Theft Auto Five. Meanwhile in the city itself, the scale of homelessness is far beyond anything I’ve observed here in the UK. I’d very frequently walk past people with large trolleys containing what appeared to be all their belongings. Nearby the Tenderloin district, pitched tents on the pathways next to roads were common, with people cooking on gas stoves. The line to what appeared to be one soup kitchen stretched outside and round the corner. Drug use was also very noticeable. I frequently spotted people slumped over in wheelchairs, others passed out in a subway station or outside a shop. People pass by as if no-ones there. It’s one thing hearing about these issues, but it is eye-opening to see it.

Overall, attending AGU in San Francisco was an experience I will not forget and certainly a highlight of my PhD so far – I’m very grateful I was able to go! Next year’s AGU will take place in Washington DC from 9th-13th December. Will you be there? Will you be the one to write next years AGU post?  Stay tuned to the Social Metwork (and for the latter, your email inbox) to find out.

Describe your research using the ten-hundred most common words…

Rebecca Couchman-Crook1 Comment

Online comic “xkcd” set a trend for explaining complicated things using only the 1000 most common words when they created this schematic of Saturn-V.  They have subsequently published more on how microwaves, plate tectonics and your computer work, using the same style.

tornado safety
Useful safety advice from xkcd

So we thought we’d jump on the bandwagon in a recent PhD group meeting, and have a go at explaining our research topics using the ten-hundred most common words. You can have a go yourselves, and tweet us with it @SocialMetwork on Twitter. Enjoy!

The Role of the Asian Summer Monsoon in European Summer Climate Variability – Jonathan Beverley

I look at how heavy rain in in-dear in summer makes rain, sun, wind and other things happen in your-up. This happens by big waves high up in the sky moving around the world. We might be able to use this to make a long know-before better and to help people live longer and not lose money.

Contribution of near-infrared bands of greenhouse gases to radiative forcing – Rachael Byrom

I study how the sun’s light warms the sky. This happens when these really tiny things in the air that we can’t see eat the sun’s light which then makes the sky warmer. I use computers to look into how this happens, especially how exactly the really tiny things eat the sun’s light and how this leads to warming. By this I mean, if I add lots of the tiny things to a pretend computer sky, all over the world, then will the sky also warm over all of the world too and by how much will it warm? This might be interesting for people who lead the world so that they can see how much of the really tiny things we should be allowed to put into the sky.

Wind profile effects on gravity wave drag and their impact on the global atmospheric circulation – Holly Turner

I look at waves in the air over high places and how they slow down the wind. When the wind gets faster the higher up you go, it changes how it slows down. I want to use this to make computer wind pictures better.

The pulsatory nature of Bagana volcano, Papua New Guinea – Rebecca Couchman-Crook

To be a doctor, I look at a fire-breathing ground thing with smoke and rocks on a hot place surrounded by water. I look at space pictures to understand the relationships between the air that smells and fire-rock bits in the air, and other stuff. It’s a very angry fire-breathing ground thing and might kill the near-by humans

Surface fluxes, temperatures and boundary layer evolutions in the building grey zone in London – Beth Saunders

I work on numbers which come out of the Met Office’s computer world. These numbers are different to what is seen and felt in real life for cities. True numbers, seen in real life, help to say how hot cities are, and how different the hot city is to areas that aren’t cities, with trees and fields, because of the city’s people, cars and houses. Numbers saying how fast the wind goes, and the wind’s direction, change in cities because of all the areas with tall houses. Finding times where the computer world numbers are bad for cities will help to make the Met Office’s computer give numbers more like the true numbers.

Cloud electrification and lightning in the evolution of convective storms – Ben Courtier

To be a doctor, I look at sudden light shocks from angry water air that happens with noise in the sky and how the angry water air changes before the light shock happens. I do this in order to better guess when the sudden light shock happens.