High-resolution Dispersion Modelling in the Convective Boundary Layer

Lewis Blunn – l.p.blunn@pgr.reading.ac.uk

In this blog I will first give an overview of the representation of pollution dispersion in regional air quality models (AQMs). I will then show that when pollution dispersion simulations in the convective boundary layer (CBL) are run at \mathcal{O}(100 m) horizontal grid length, interesting dynamics emerge that have significant implications for urban air quality. 

Modelling Pollution Dispersion 

AQMs are a critical tool in the management of urban air pollution. They can be used for short-term air quality (AQ) forecasts, and in making planning and policy decisions aimed at abating poor AQ. For accurate AQ prediction the representation of vertical dispersion in the urban boundary layer (BL) is key because it controls the transport of pollution away from the surface. 

Current regional scale Eulerian AQMs are typically run at \mathcal{O}(10 km) horizontal grid length (Baklanov et al., 2014). The UK Met Office’s regional AQM runs at 12 km horizontal grid length (Savage et al., 2013) and its forecasts are used by the Department for Environment Food and Rural Affairs (DEFRA) to provide a daily AQ index across the UK (today’s DEFRA forecast). At such horizontal grid lengths turbulence in the BL is sub-grid.  

Regional AQMs and numerical weather prediction (NWP) models typically parametrise vertical dispersion of pollution in the BL using K-theory and sometimes with an additional non-local component so that 

F=-K_z \frac{\partial{c}}{\partial{z}} +N_l 

where F is the flux of pollution, c is the pollution concentration, K(z) is a turbulent diffusion coefficient and z is the height from the ground. N_l is the non-local term which represents vertical turbulent mixing under convective conditions due to buoyant thermals (Lock et al., 2000; Siebesma et al., 2007).  

K-theory (i.e. N_l=0) parametrisation of turbulent dispersion is consistent mathematically with Fickian diffusion of particles in a fluid. If K(z) is taken as constant and particles are released far from any boundaries (i.e. away from the ground and BL capping inversion), the mean square displacement of pollution particles increases proportional to the time since release. Interestingly, Albert Einstein showed that Brownian motion obeys Fickian diffusion. Therefore, pollution particles in K-theory dispersion parametrisations are analogous to memoryless particles undergoing a random walk. 

It is known however that at short timescales after emission pollution particles do have memory. In the CBL, far from undergoing a random trajectory, pollution particles released in the surface layer initially tend to follow the BL scale overturning eddies. They horizontally converge before being transported to near the top of the BL in updrafts. This results in large pollution concentrations in the upper BL and low concentrations near the surface at times on the order of one CBL eddy turnover period since release (Deardorff, 1972; Willis and Deardorff, 1981). This has important implications for ground level pollution concentration predicted by AQMs (as demonstrated later). 

Pollution dispersion can be thought of as having two different behaviours at short and long times after release. In the short time “ballistic” limit, particles travel at the velocity within the eddy they were released into, and the mean square displacement of pollution particles increases proportional to the time squared. At times greater than the order of one eddy turnover (i.e. the long time “diffusive” limit) dispersion is less efficient, since particles have lost memory of the initial conditions that they were released into and undergo random motion.  For further discussion of atmospheric diffusion and memory effects see this blog (link).

In regional AQMs, the non-local parametrisation component does not capture the ballistic dynamics and K-theory treats dispersion as being “diffusive”. This means that at CBL eddy turnover timescales it is possible that current AQMs have large errors in their predicted concentrations. However, with increases in computing power it is now possible to run NWP for research purposes at \mathcal{O}(100 m) horizontal grid length (e.g. Lean et al., 2019) and routinely at 300 m grid length (Boutle et. al., 2016). At such grid lengths the dominant CBL eddies transporting pollution (and therefore the “ballistic” diffusion) becomes resolved and does not require parametrisation. 

To investigate the differences in pollution dispersion and potential benefits that can be expected when AQMs move to \mathcal{O}(100 m) horizontal grid length, I have run NWP at horizontal grid lengths ranging from 1.5 km (where CBL dispersion is parametrised) to 55 m (where CBL dispersion is mostly resolved). The simulations are unique in that they are the first at such grid lengths to include a passive ground source of scalar representing pollution, in a domain large enough to let dispersion develop for tens of kilometres downstream. 

High-Resolution Modelling Results 

A schematic of the Met Office Unified Model nesting suite used to conduct the simulations is shown in Fig. 1. The UKV (1.5 km horizontal grid length) model was run first and used to pass boundary conditions to the 500 m model, and so on down to the 100 m and 55 m models. A puff release, homogeneous, ground source of passive scalar was included in all models and its horizontal extent covered the area of the 55 m (and 100 m) model domains. The puff releases were conducted on the hour, and at the end of each hour scalar concentration was set to zero. The case study date was 05/05/2016 with clear sky convective conditions.  

Figure 1: Schematic of the Unified Model nesting suite.

Puff Releases  

Figure 2 shows vertical cross-sections of puff released tracer in the UKV and 55 m models at 13-05, 13-20 and 13-55 UTC. At 13-05 UTC the UKV model scalar concentration is very large near the surface and approximately horizontally homogeneous. The 55 m model concentrations however are either much closer to the surface or elevated to great heights within the BL in narrow vertical regions. The heterogeneity in the 55 m model field is due to CBL turbulence being largely resolved in the 55 m model. Shortly after release, most scalar is transported predominantly horizontally rather than vertically, but at localised updrafts scalar is transported rapidly upwards. 

Figure 2: Vertical cross-sections of puff released passive scalar. (a), (b) and (c) are from the UKV model at 13-05, 13-20 and 13-55 UTC respectively. (d), (e) and (f) are from the 55 m model at 13-05, 13-20 and 13-55 UTC respectively. The x-axis is from south (left) to north (right) which is approximately the direction of mean flow. The green line is the BL scheme diagnosed BL height.

By 13-20 UTC it can be seen that the 55 m model has more scalar in the upper BL than lower BL and lowest concentrations within the BL are near the surface. However, the scalar in the UKV model disperses more slowly from the surface. Concentrations remain unrealistically larger in the lower BL than upper BL and are very horizontally homogeneous, since the “ballistic” type dispersion is not represented. By 13-55 UTC the concentration is approximately uniform (or “well mixed”) within the BL in both models and dispersion is tending to the “diffusive” limit. 

It has thus been demonstrated that unless “ballistic” type dispersion is represented in AQMs the evolution of the scalar concentration field will exhibit unphysical behaviour. In reality, pollution emissions are usually continuously released rather than puff released. One could therefore ask the question – when pollution is emitted continuously are the detailed dispersion dynamics important for urban air quality or does the dynamics of particles released at different times cancel out on average?  

Continuous Releases  

To address this question, I included a continuous release, homogeneous, ground source of passive scalar. It was centred on London and had dimensions 50 km by 50 km which is approximately the size of Greater London. Figure 3a shows a schematic of the source.  

The ratio of the 55 m model and UKV model zonally averaged surface concentration with downstream distance from the southern edge of the source is plotted in Fig. 3b. The largest difference in surface concentration between the UKV and 55m model occurs 9 km downstream, with a ratio of 0.61. This is consistent with the distance calculated from the average horizontal velocity in the BL (\approx7 ms-1) and the time at which there was most scalar in the upper BL compared to the lower BL in the puff release simulations (\approx 20 min). The scalar is lofted high into the BL soon after emission, causing reductions in surface concentrations downstream. Beyond 9 km downstream distance a larger proportion of the scalar in the BL has had time to become well-mixed and the ratio increases.  

Figure 3: (a) Schematic of the continuous release source of passive scalar. (b) Ratio of the 55 m model and UKV model zonally averaged surface concentration with downstream distance from the southern edge of the source at 13-00 UTC.

Summary  

By comparing the UKV and 55 m model surface concentrations, it has been demonstrated that “ballistic” type dispersion can influence city scale surface concentrations by up to approximately 40%. It is likely that by either moving to \mathcal{O}(100 m) horizontal grid length or developing turbulence parametrisations that represent “ballistic” type dispersion, that substantial improvements in the predictive capability of AQMs can be made. 

References 

  1. Baklanov, A. et al. (2014) Online coupled regional meteorology chemistry models in Europe: Current status and prospects https://doi.org/10.5194/acp-14-317-2014 
  1. Boutle, I. A. et al. (2016) The London Model: Forecasting fog at 333 m resolution https://doi.org/10.1002/qj.2656 
  1. Deardorff, J. (1972) Numerical Investigation of Neutral and Unstable Planetary Boundary Layers https://doi.org/10.1175/1520-0469(1972)029<0091:NIONAU>2.0.CO;2 
  1. DEFRA – air quality forecast https://uk-air.defra.gov.uk/index.php/air-pollution/research/latest/air-pollution/daqi 
  1. Lean, H. W. et al. (2019) The impact of spin-up and resolution on the representation of a clear convective boundary layer over London in order 100 m grid-length versions of the Met Office Unified Model https://doi.org/10.1002/qj.3519 
  1. Lock, A. P. et al. A New Boundary Layer Mixing Scheme. Part I: Scheme Description and Single-Column Model Tests https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2 
  1. Savage, N. H. et al. (2013) Air quality modelling using the Met Office Unified Model (AQUM OS24-26): model description and initial evaluation https://doi.org/10.5194/gmd-6-353-2013 
  1. Siebesma, A. P. et al. (2007) A Combined Eddy-Diffusivity Mass-Flux Approach for the Convective Boundary Layer https://doi.org/10.1175/JAS3888.1 
  1. Willis. G and J. Deardorff (1981) A laboratory study of dispersion from a source in the middle of the convectively mixed layer https://doi.org/10.1016/0004-6981(81)90001-9 

Weather Variability and its Energy Impacts

James Fallon & Brian Lo –  j.fallon@pgr.reading.ac.ukbrian.lo@pgr.reading.ac.uk 

One in five people still do not have access to modern electricity supplies, and almost half the global population rely on burning wood, charcoal or animal waste for cooking and eating (Energy Progress Report). Having a reliable and affordable source of energy is crucial to human wellbeing: including healthcare, education, cooking, transport and heating. 

Our worldwide transition to renewable energy faces the combined challenge of connecting neglected regions and vulnerable communities to reliable power supplies, and also decarbonising all energy. An assessment on supporting the world’s 7 billion humans to live a high quality of life within planetary boundaries calculated that resource provisioning across sectors including energy must be restructured to enable basic needs to be met at a much lower level of resource use [O’Neill et al. 2018]. 

Adriaan Hilbers recently wrote for the Social Metwork about the renewable energy transition (Why renewables are difficult), and challenges and solutions for modern electricity grids under increased weather exposure. (Make sure to read that first, as it provides an important background for problems associate with meso to synoptic scale variability that we won’t cover here!)  

In this blog post, we highlight the role of climate and weather variability in understanding the risks future electricity networks face. 

Climate & weather variability 

Figure 1 – Stommel diagram of the Earth’s atmosphere 

A Stommel diagram [Stommel, 1963] is used to categorise climate and weather events of different temporal and spatial scales. Logarithmic axes describe time period and size; contours (coloured areas) depict the spectral intensity of variation in sea level. It allows us to identify a variety of dynamical features in the oceans that traverse magnitudes of spatial and temporal scales. Figure 1 is a Stommel diagram adapted to describe the variability of our atmosphere.  

Microscale Smallest scales to describe features generally of the order 2 km or smaller 
Mesoscale Scale for describing atmospheric phenomena having horizontal scales ranging from a few to several hundred kilometres 
Synoptic Largest scale used to describe meteorological phenomena, typically high hundreds or 1000 km or more 

Micro Impacts on Energy 

Microscale weather processes include more predictable phenomena such as heat and moisture flux events, and unpredictable turbulence events. These generally occur at scales much smaller than the grid scale represented in numerical weather prediction models, and instead are represented through parametrisation. The most important microscale weather impacts are for isolated power grids (for example a community reliant on solar power and batteries, off-grid). Microscale weather events can also make reliable supply difficult for grids reliant on a few geographically concentrated renewable energy supplies. 

Extended Range Weather Impacts on Energy 

Across the Stommel diagram, above the synoptic scale are seasonal and intraseasonal cycles, decadal and climate variations. 

Subseasonal-to-Seasonal (S2S) forecasts are an exciting development for decision-makers across a diverse range of sectors – including agriculture, hydrology, the humanitarian sector [White et al. 2017]. In the energy sector, skilful subseasonal energy forecasts are now production ready (S2S4E DST).  Using S2S forecasts can help energy users anticipate electricity demand peaks and troughs, levels of renewable production, and their combined impacts several weeks in advance. Such forecasts will have an increasingly important role as more countries have higher renewable energy penetration (increasing their electricity grid’s weather exposure). 

Decadal Weather Cycle and Climate Impacts on Energy 

Energy system planners and operators are increasingly trying to address risks posed by climate variability, climate change, and climate uncertainty.  

Figure 2 was constructed from the record of Central England temperatures spanning the years of 1659 to 1995 and highlights the modes of variability in our atmosphere on the order of 5 to 50 years. Even without the role of climate change, constraining the boundary conditions of our weather and climate is no small task. The presence of meteorologically impactful climate variability at many different frequencies increases the workload for energy modellers, requiring many decades of climate data in order to understand the true system boundaries. 

Figure 2  – Power spectra of central England from mid 17th century, explaining variability with physical phenomena [Ghil and Lucarini 2020]

When making models of regional, national or continental energy networks, it is now increasingly common for energy modellers to consider several decades of climate data, instead of sampling a small selection of years. Figure 2 shows the different frequencies of climate variability – relying on only a limited few years of data cannot explore the extent of this variability. However significant challenges remain in sampling long-term variability and change in models [Hilbers et al. 2019], and it is the role of weather and climate scientists to communicate the importance of addressing this. 

Important contributions to uncertainty in energy system planning don’t just come from weather and climate. Variability in future energy systems will depend on technological, socioeconomic and political outcomes. Predictions of which future technologies and approaches will be most sustainable and economical are not always clear cut and easy to anticipate. A virtual workshop hosted by Reading’s energy-met group last summer [Bloomfield et al. 2020] facilitated discussions between energy and climate researchers. The workshop identified the need to better understand how contributions of all these different uncertainties propagate through complex modelling chains. 

An Energy-Meteorologist’s Journey through Time and Space 

Research is underway into tackling the uncertainties and understanding of energy risks and impacts across the spectra of spatial and temporal scales. But understanding of energy systems, and successful future planning requires decision-making involving a broad (and perhaps not fully identified) group of important technological and other factors, as well as the weather and climate impacts. It is not enough to consider any one of these alone! It is vital experts across different fields collaborate on working towards what will be best for our future energy grids. 

Tracking SDG7 – The Energy Progress Report https://trackingsdg7.esmap.org 

Why renewables are difficult – Adriaan Hilbers Social Metwork 2021 https://socialmetwork.blog/2021/01/15/why-renewables-are-difficult

O’Neill, D.W., Fanning, A.L., Lamb, W.F. et al. A good life for all within planetary boundaries https://doi.org/10.1038/s41893-018-0021-4 

Stommel, H., 1963. Varieties of oceanographic experience. Science, 139(3555), pp.572-576. https://www.jstor.org/stable/1709894

White et al (2017) Potential applications of subseasonal‐to‐seasonal (S2S) predictions https://doi.org/10.1002/met.1654 

M Ghil, V Lucarini (2020) The physics of climate variability and climate change https://doi.org/10.1103/RevModPhys.92.035002

AP Hilbers, DJ Brayshaw, A Gandy (2019) Importance subsampling: improving power system planning under climate-based uncertainty https://doi.org/10.1016/j.apenergy.2019.04.110 

Bloomfield, H. et al. (2020) The importance of weather and climate to energy systems: a workshop on next generation challenges in energy-climate modelling https://doi.org/10.1175/BAMS-D-20-0256.1 

The role of climate change in the 2003 European and 2010 Russian heatwaves using nudged storylines

Linda van Garderen – linda.vangarderen@hzg.de

During the summer of 2003, Europe experienced two heatwaves with, until then, unprecedented temperatures. The 2003 summer temperature record was shattered in 2010 by the Russian heatwave, which broke even Paleo records. The question remained, if climate change influenced these two events. Many contribution studies based on the likelihood of the dynamical situation were published, providing important input to answering this question. However, the position of low and high-pressure systems and other dynamical aspects of climate change are noisy and uncertain. The storyline method attributes the thermodynamic aspects of climate change (e.g. temperature), which are visible in observations and far more certain. 

Storylines 

All of us regularly think in terms of what if and if only. It is the human way of calculating hypothetic results in case we would have made a different choice. This helps us think in future scenarios, trying to figure out what choice will lead to which consequence. It is a tool to reduce risk by finding a future scenario that seems the best or safest outcome. In the storyline method, we use this exact mind-set. What if there was no climate change, would this heatwave be the same? What if the world was 2°C warmer, what would this heatwave have looked like then? With the help of an atmospheric model we can calculate what a heatwave would have been like in a world without climate change or increased climate change. 

In our study, we have two storylines: 1) the world as we know it that includes a changing climate, which we call the ‘factual’ storyline and 2) a world that could have been without climate change, which we call the ‘counterfactual’ storyline. We simulate the dynamical aspects of the weather extreme exactly the same in both storylines using a spectral nudging technique and compare the differences in temperatures.  To put it more precise, the horizontal wind flow is made up out of vorticity (circular movement) and divergence (spreading out or closing in). We nudge (or push) these two variables in the higher atmosphere to, on large scale, be the same in the factual and counterfactual simulations. 

Figure 1. What if we had another world where climate change did not happen? Would the heatwave have been different? Thinking in counterfactual worlds where we made (or will make) different decisions is a common way of thinking to estimate risk. Now we apply this idea in atmospheric modelling.  

European 2003 and Russian 2010 heatwaves 

Both the European heatwave in 2003 and the Russian heatwave in 2010 were extremes with unprecedented high temperatures for long periods of time. Besides, there had been little rain already from spring  in either case, which reduced the cooling effect from moisty soil to nearly nothing.  In our analysis we averaged the near surface temperatures in both storylines and compared their output to each other as well as the local climatology. Figure 2 shows the results of that averaging for the European heatwave in panel a and the Russian heatwave in panel b. We focus on the orange boxes, where the blue lines (factual storyline) and the red lines (counterfactual storyline) exceed the 5th-95th percentile climatology (green band). This means that during those days the atmosphere near the surface was uncommonly hot (thus a heatwave). The most important result in this graph is that the blue and red lines are separate from each other in the orange boxes. This means that the average temperature of the world with climate change (blue, factual) is higher than in the world without climate change (red, counterfactual).  

“Even though there would have been a heatwave with or without climate change, climate change has made the heat more extreme” 

Figure 2. Daily mean temperature at 2 meters height for (a) European summer 2003 and (b) Russian summer 2010. The orange boxes are the heatwaves, where the temperatures of the factual (blue) and counterfactual (red) are above the green band of 5th – 95th percentile climatology temperatures.  

The difference between these temperatures are not the same everywhere, it strongly depends on where you are in Europe or Russia. Let me explain what I mean with the help of Figure 3 with the difference between factual and counterfactual temperatures (right panels) on a map. In both Europe and Russia, we see that there are local regions with temperature differences of almost 0°C, and we see regions where the differences are almost 2.5°C (for Europe) or even 4°C (for Russia). A person living south from Moscow would therefore not have experienced 33°C but 29°C in a world without climate change. It is easy to imagine that such a temperature difference changes the impacts a heatwave has on e.g. public health and agriculture.  

Figure 3. Upper left: Average Temperature at 2 meter height and Geopotential height over Europe at z500 for 1-15th of August 2003, Lower left: Same as upper left but for 1-15th of Russia August 2010. Upper right: Factual minus Counterfactual average temperature at 2 meter height over Europe for 1-15th of August 2003, Lower right: same as lower left but for 1-15th of Russia August 2010. Stippling indicates robust results (all factuals are > 0.1°C warmer than all counterfactuals) 

 “The 2003 European and 2010 Russian heatwaves could locally have been 2.5°C – 4°C cooler in a world without climate change” 

We can conclude therefore, that with the help of our nudged storyline method, we can study the climate signal in extreme events with larger certainty. 

If you are interested in the elaborate explanation of the method and analysis of the two case studies, please take a look at our paper: 

van Garderen, L., Feser, F., and Shepherd, T. G.: A methodology for attributing the role of climate change in extreme events: a global spectrally nudged storyline, Nat. Hazards Earth Syst. Sci., 21, 171–186, https://doi.org/10.5194/nhess-21-171-2021 , 2021. 

If you have questions or remarks, please contact Linda van Garderen at linda.vangarderen@hzg.de

Main challenges for extreme heat risk communication

Chloe Brimicombe – c.r.brimicombe@pgr.reading.ac.uk, @ChloBrim

For my PhD, I research heatwaves and heat stress, with a focus on the African continent. Here I show what the main challenges are for communicating heatwave impacts inspired by a presentation given by Roop Singh of the Red Cross Climate Center at Understanding Risk Forum 2020.  

There is no universal definition of heatwaves 

Having no agreed definition of a heatwave (also known as extreme heat events) is a huge challenge in communicating risk. However, there is a guideline definition by the World Meteorological Organisation and for the UK an agreed definition as of 2019. In simple terms a heatwave is: 

“A period of above average temperatures of 3 or more days in a region’s warm season (i.e. all year in the tropics and in the summer season elsewhere)”  

We then have heat stress which is an impact of heatwaves, and is the killer aspect of heat. Heat stress is: 

“Build-up of body heat as a result of exertion or external environment”(McGregor, 2018) 

Attention Deficit 

Heatwaves receive low attention in comparison to other natural hazards I.e., Flooding, one of the easiest ways to appreciate this attention deficit is through Google search trends. If we compare ‘heat wave’ to ‘flood’ both designated as disaster search types, you can see that a larger proportion of searches over time are for ‘flood’ in comparison to ‘heat wave’.  

Figure 1: Showing ‘Heat waves’ (blue)  vs ‘Flood’ (red) Disaster Search Types interest over time taken from: https://trends.google.com/trends/explore?date=all&q=%2Fm%2F01qw8g,%2Fm%2F0dbtv 

On average flood has 28% search interest which is over 10 times the amount of interest for heat wave. And this is despite Heatwaves being named the deadliest hydro-meteorological hazard from 2015-2019 by the World Meteorological Organization. Attention is important if someone can remember an event and its impacts easily, they can associate this with the likelihood of it happening. This is known as the availability bias and plays a key role in risk perception. 

Lack of Research and Funding 

One impact of the attention deficit on extreme heat risk, is there is not ample research and funding on the topic – it’s very patchy. Let’s consider a keyword search of academic papers for ‘heatwave*’ and ‘flood*’ from Scopus an academic database.  

Figure 2: Number of ‘heatwave*’ vs number of ‘flood*’ academic papers from Scopus. 

Research on floods is over 100 times bigger in quantity than heatwaves. This is like what we find for google searches and the attention deficit, and reveals a research bias amongst these hydro-meteorological hazards. And is mirrored by what my research finds for the UK, much more research on floods in comparison to heatwaves (https://doi.org/10.1016/j.envsci.2020.10.021). Our paper is the first for the UK to assess the barriers, causes and solutions for providing adequate research and policy for heatwaves. The motivation behind the paper came from an assignment I did during my masters focusing on UK heatwave policy, where I began to realise how little we in the UK are prepared for these events, which links up nicely with my PhD. For more information you can see my article and press release on the same topic. 

Heat is an invisible risk 

Figure 3: Meme that sums up not perceiving heat as a risk, in comparison, to storms and flooding.

Heatwaves are not something we can touch and like Climate Change, they are not ‘lickable’ or visible. This makes it incredibly difficult for us to perceive them as a risk. And this is compounded by the attention deficit; in the UK most people see heatwaves as a ‘BBQ summer’ or an opportunity to go wild swimming or go to the beach.  

And that’s really nice, but someone’s granny could be experiencing hospitalising heat stress in a top floor flat as a result of overheating that could result in their death. Or for example signal failures on your railway line as a result of heat could prevent you from getting into work, meaning you lose out on pay. I even know someone who got air lifted from the Lake District in their youth as a result of heat stress.  

 A quote from a BBC one program on wild weather in 2020 sums up overheating in homes nicely:

“It is illegal to leave your dog in a car to overheat in these temperatures in the UK, why is it legal for people to overheat in homes at these temperatures

For Africa the perception amongst many is ‘Africa is hot’ so heatwaves are not a risk, because they are ‘used to exposure’ to high temperatures. First, not all of Africa is always hot, that is in the same realm of thinking as the lyrics of the 1984 Band Aid Single. Second, there is not a lot of evidence, with many global papers missing out Africa due to a lack of data. But, there is research on heatwaves and we have evidence they do raise death rates in Africa (research mostly for the West Sahel, for example Burkina Faso) amongst other impacts including decreased crop yields.  

What’s the solution? 

Talk about heatwaves and their impacts. This sounds really simple, but I’ve noticed a tendency of a proportion of climate scientists to talk about record breaking temperatures and never mention land heatwaves (For example the Royal Institute Christmas Lectures 2020). Some even make a wild leap from temperature straight to flooding, which is just painful for me as a heatwave researcher. 

Figure 4: A schematic of heatwaves researchers and other climate scientists talking about climate change. 

So let’s start by talking about heatwaves, heat stress and their impacts.  

Air-sea heat fluxes at the oceanic mesoscale: the impact of the ratio of ocean-to-atmosphere grid resolution

Sophia Moreton – s.moreton@pgr.reading.ac.uk

Sea surface temperature (SST) anomalies are vital for regulating the earth’s weather and climate.  The generation and reduction of these SST anomalies are largely determined by air-sea heat fluxes, particularly turbulent heat fluxes (latent and sensible).

The turbulent heat flux feedback (THFF) is a critical parameter, which measures the change in the net air-sea turbulent heat flux in response to a 1 K change in SST. So far in current research, this feedback is well known at large scales, i.e. over the whole ocean basin. However, a quantification of this feedback at much smaller spatial scales (10-100km) over individual mesoscale ocean eddies remains absent.

Why do we care about air-sea feedbacks at the oceanic mesoscale?

Both heat and momentum air-sea exchanges at the mesoscale impact the local and large-scale atmosphere (e.g. shifting storm tracks) and alter the strength of western boundary currents and the large-scale ocean gyre circulation. However, research into this field to date is hindered by the lack of high spatial resolution in observational data at the air-sea interface.

Therefore our study uses three high-resolution configurations from the UK Met Office coupled climate model (HadGEM3-GC3). We provide the first global estimate of turbulent heat flux feedback (α) over individually tracked and composite-averaged coherent mesoscale eddies, which ranges between 35 to 45 Wm-2K-1 depending on eddy amplitude.

Estimates of the turbulent heat flux feedback (THFF) are split, depending if the feedback is calculated using SST on the ocean grid (α0) or after regridding SST to the atmosphere (αA). An example of αA using regridded SST anomalies (SSTA) is given in Fig.1 for large-amplitude eddies in the highest ocean-atmosphere resolution available (a 25km atmosphere coupled to a 1/12° ocean, labelled ‘N512-12’).

Figure 1: A scatter plot of the relationship (THFF, αA) between regridded SST (SSTA) and THF anomalies. αA is the gradient of the linear regression line (black) +/- the 95% confidence interval (shown by the text). The data is from eddy snapshots averaged over 1 year, denoted by ‘< >’. Only large-amplitude eddies in the N512-12 configuration (25km atmosphere – 1/12° ocean) are plotted.

Why is the feedback so sensitive to the ratio of grid resolution?

In high-resolution coupled climate models, the atmospheric resolution is typically coarser than in its ocean component although, to date, a quantification of what the ocean-atmosphere ratio of grid resolution should be remains absent.

We prove increasing the ratio of atmosphere-to-ocean grid resolution in coupled climate models can lead to a large underestimation of turbulent heat flux feedback over mesoscale eddies, by as much as 75% for a 6:1 resolution ratio, as circled in Fig. 2 from a 60km atmosphere coupled to a 1/12° ocean. An underestimation of the feedback is consistent across all eddy amplitudes (A) and all three model configurations shown (Fig. 2); it suggests SST anomalies within these eddies are likely to be not reduced enough by air-sea fluxes of heat, and consequently will remain too large.

The underestimation stems from the calculation of the air-sea heat fluxes in the HadGEM3-GC3.1 model on the coarser atmospheric grid, instead of the finer ocean grid. Many other climate models do the same. At present, for the long spin-ups needed for climate simulations, it is unrealistic to expect the atmospheric resolution to match the very fine (10km) ocean resolution in coupled climate models, i.e. to create a one-to-one grid ratio. Therefore, to minimise this underestimation in the feedback at mesoscales, we advise air-sea heat fluxes should be computed on the finer oceanic grid.

Figure 2: Estimates of the turbulent heat flux feedback (THFF) across different eddy amplitudes (A) for α0 (lighter colours) and αA (darker colours, using regridded SST) for three model configurations: N512-12, N216-12 and N216-025. The ocean and atmosphere resolutions are added in red for each. Increasing the ratio of grid resolution, underestimates the THFF (as α0 differs from αA). The horizontal bars indicate the width of the eddy amplitude bins, and the vertical error bars indicate 95% confidence intervals.

Correctly simulating the air-sea heat flux feedback over mesoscale eddies is fundamental to realistically represent their interaction with the local and large-scale atmosphere and feedback on the ocean, to improve our predictions of the earth’s climate.

For a full analysis of the results, including a decomposition of the turbulent heat flux feedback, the reader is referred to Moreton et al., 2021, Air-Sea Turbulent Heat Flux Feedback over Mesoscale Eddies, GRL (in review).

Manuscript available: https://doi.org/10.1002/essoar.10505981.1

This work lays the foundation for my current work, evaluating how mesoscale air-sea heat fluxes feedback and alter the strength of large-scale ocean gyre circulation, using the MIT general circulation model (MITgcm).

This work is funded by a NERC CASE studentship with the Met Office, UK.

Forecasting space weather using “similar day” approach

Carl Haines – carl.haines@pgr.reading.ac.uk

Space weather is a natural threat that requires good quality forecasting with as much lead time as possible. In this post I outline the simple and understandable analogue ensemble (AnEn) or “similar day” approach to forecasting. I focus mainly on exploring the method itself and, although this work forecasts space weather through a timeseries of ground level observations, AnEn can be applied to many prediction tasks, particularly time series with strong auto-correlation. AnEn has previously been used to predict wind speed [1], temperature [1] and solar wind [2]. The code for AnEn is available at https://github.com/Carl-Haines/AnalogueEnsemble should you wish to try out the method for you own application. 

The idea behind AnEn is to take a set of recent observations, look back in a historic dataset for analogous periods, then take what happened following those analogous periods as the forecast. If multiple analogous periods are used, then an ensemble of forecasts can be created giving a distribution of possible outcomes with probabilistic information. 

Figure 1 – An example of AnEn applied to a space weather event with forecast time t0. The black line shows the observations, the grey line shows the ensemble members, the red line shows the median of the ensemble and the yellow and green lines are reference forecasts. 

Figure 1 is an example of a forecast made using the AnEn method where the forecast is made at t0. The 24-hours of observations (black) prior to tare matched to similar periods in the historic dataset (grey). Here I have chosen to give the most recent observations the most weighting as they hold the most relevant information. The grey analogue lines then flow on after t0 forming the forecast. Combined, these form an ensemble and the median of these is shown in red. The forecast can be chosen to be the median (or any percentile) of the ensemble or a probability of an event occurring can be given by counting how many of the ensemble member do/don’t experience the event.  

Figure 1 also shows two reference forecasts, namely 27-day recurrence and climatology, as benchmarks to beat. 27-day recurrence uses the observation from 27-days ago as the forecast for today. This is reasonable because the Sun rotates every 27-days as seen from earth so broadly speaking the same part of the Sun is emitting the relevant solar wind on timescales larger than 27-days. 

To quantify how well AnEn works as a forecast I ran the forecast on the entire dataset by repeatedly changing the forecast time t0 and applied two metrics, namely mean absolute error (MAE) and skill, to the median of the ensemble members. MAE is the size of the mean difference between the forecast made by AnEn and what was actually observed. The mean of the absolute errors over all the forecasts (taken as median of the ensemble) is taken and we end up with a value for each lead time. Figure 2 shows the MAE for AnEn median and the reference forecasts. We see that AnEn has the smallest (best) MAE at short lead times and outperforms the reference forecasts for all lead times up to a week. 

Figure 2 – The mean absolute error of the AnEn median and reference forecasts.

An error metric such as MAE cannot take into account that certain conditions are inherently more difficult to forecast such as storm times. For this we can use a skill metric defined by  

{\text{Skill} = 1 - \frac{\text{Forecast error}}{\text{Reference error}}}

where in this case we use climatology as the reference forecast. Skill can take any value between -\infty and 1 where a perfect forecast would receive a value of 1 and an unskilful forecast would receive a value of 0. A negative value of skill signifies that the forecast is worse than the reference forecast. 

Figure 3 shows the skill of AnEn and 27-day recurrence with respect to climatology. We see that AnEn is most skilful for short lead times and outperforms 27-day recurrence for all lead times considered.  

Figure 3 – The skill of the AnEn median and 27-day recurrence with respect to climatology.

In summary, the analogue ensemble forecast method matches current conditions with historical events and lifts the previously seen timeseries as the prediction. AnEn seems to perform well for this application and outperforms the reference forecasts of climatology and 27-day recurrence. The code for AnEn is available at https://github.com/Carl-Haines/AnalogueEnsemble

The work presented here makes up a part of a paper that is under review in the journal of Space Weather. 

Here, AnEn has been applied to a dataset from the space weather domain. If you would like to find out more about space weather then take a look at these previous blog posts from Shannon Jones (https://socialmetwork.blog/2018/04/13/the-solar-stormwatch-citizen-science-project/) and I (https://socialmetwork.blog/2019/11/15/the-variation-of-geomagnetic-storm-duration-with-intensity/). 

[1] Delle Monache, L., Eckel, F. A., Rife, D. L., Nagarajan, B., & Searight, K.(2013) Probabilistic Weather Prediction with an Analog Ensemble. doi: 10.1175/mwr-d-12-00281.1 

[2] Owens, M. J., Riley, P., & Horbury, T. S. (2017a). Probabilistic Solar Wind and Ge-704omagnetic Forecasting Using an Analogue Ensemble or “Similar Day” Approach. doi: 10.1007/s11207-017-1090-7 

Why renewables are difficult

Adriaan Hilbers – PhD researcher at Imperial and Reading a.hilbers17@imperial.ac.uk

Adapted from a 2018 blog post: see the original here

Renewable energy represents one of the most promising solutions to climate change since it emits no greenhouse gases. However, it poses some difficulties for power systems. Source: U. Leone

The public have been aware of the importance of reducing carbon emissions since around the 1980’s. Furthermore, renewable technologies such as solar and wind have been around for decades. Under these conditions, it’s surprising that most countries still generate the majority of their electricity from carbon-emitting fossil fuels. Why, after decades of both the problem and a possible solution being known, haven’t renewables taken off yet? This article describes why renewables are “difficult”, and how the world can keep the lights on into the future in a cheap, secure, and sustainable way. 

Until recently, the primary reason was economics. It was impossible to build wind turbines and solar panels cheaply enough to compete with fossil fuel technologies, which have become highly cost effective after more than 100 years of use. Governments were not willing to spend billions to subsidise renewables when electricity could be generated cheaply by other means. Recently, however, improved manufacturing methods, economies of scale and increased competition sent prices plummeting. The price of solar panels has decreased by a factor of 200 in the last 45 years, and wind farms (even offshore) are now cost-effective without subsidy.  

So, is it just a matter of time before fossil fuel electricity disappears? Why are societies still so hesitant to go 100% renewable? To understand why, one needs a quick introduction to power systems: the industries, infrastructures and markets based around electricity. 

At their core, power systems are supply & demand problems. Industries and consumers use electricity provided by generators. One key feature distinguishes power systems from other economic markets: there is very limited means of storing it at large scale (with the notable exception of hydropower, discussed below). For this reason, supply must match demand on a second-by-second basis. 

A still from Drax Electric Insights, where electricity demand and generation levels can be browsed through, both in real time and historically. Source: Drax Electric Insights

(As an aside, in the UK, there is a fantastic website, called Drax Electric Insights, in which the total UK electricity demand, and exactly from which sources it is being generated, can be browsed through in real time as well as historically. Looking through it for a few minutes will give a better feel for how power systems work than any blog post can). 

Before renewables, most electricity came from fossil fuel plants. Fuel (mostly coal or gas) was burnt at different rates and level of electricity supply was directly adjusted to meet demand. This isn’t always easy; for example, the UK’s system operator had to deal with a massive demand spike just after the royal wedding, as millions turned on their kettles at the same time.  

A famous graph showing total UK electricity demand during the 1990 World Cup semi-final against Germany, with spikes at times that viewers turned on their kettles en masse. System operators had to rapidly adjust supply to ensure the lights stayed on. Source: National Grid

With renewables, the single biggest difficulty is that their production levels can’t be controlled. It’s not always windy or sunny, and times of high renewable output do not always align with times of high demand. How does one ensure the lights stay on on a cloudy day or when the wind tails off? 

In most countries, this is not yet a problem since renewable capacity is still small and there’s ample conventional backup capacity. Renewables produce whatever electricity they can, and the rest is picked up by the conventional plants.  

A problem occurs when countries start generating most of their electricity from renewables as this drastically changes the economic outlook of power markets. In a nutshell, building renewable capacity displaces fossil fuel generation, but not generation capacity; all power plants must be kept open for the rare days when there isn’t any wind or sun. Keeping these plants open but using them infrequently is very expensive, and closing them is impossible, unless you want to accept significant risks of blackouts on calm, cloudy days. It’s a perilous choice: higher electricity prices or reduced security of supply, and this problem defines the difficulties of renewable electricity systems. 

Thankfully, there are a few ways that society can generate most of their electricity from renewables while keeping prices low and supply secure. They fall broadly into two categories. 

The first is electricity storage. With grid-scale storage, excess electricity production on windy or sunny days can be stored and used in times when renewable output is low. Besides adding to supply security, this would enhance the economic picture since storage owners buy up electricity when price is low and sell it when price is high, evening out price jumps and allowing a smaller number of conventional plants to run more often. Almost all grid-scale storage currently in existence is hydropower, which countries like Norway use to generate almost all their electricity but requires a mountainous terrain and access to water. The reason other grid-scale storage is rare is economics. Most storage technology (e.g. battery) prices still have to drop significantly before they can be used at large scale. 

Hydropower provides an economical option to store electricity, but requires mountainous terrain. Source: skeeze

A second solution is interconnecting different countries and allowing them to share electricity. When it is wind-free in London, it usually is in Scotland as well, bit it may be windy in Germany or Spain. Transporting electricity around could help alleviate supply insecurity. Many countries are doing just this; the UK, for example, currently has interconnections with France, the Netherlands, Belgium and Ireland, and more are in the pipeline. They may eventually from part of the European Supergrid, where electricity can be transported across Europe to balance out regional renewable supply peaks and troughs. 

The prospect of combining hydropower and interconnections between countries is tempting, since it means countries with lots of wind but little storage capacity, like Germany or Denmark, could “use Norway as a battery” by exporting their excess wind power to Norway in windy periods, which allows dams to accumulate water. In calm spells, hydropower generation levels are increased and excess electricity exported back the other way. Making this work will require significant increases in Norwegian hydropower infrastructure, interconnection lines and international cooperation. 

The batteries in electric cars can be used for grid management provided that owners agree to this. Source: Marilyn Murphy

Another creative solution to the storage problem is to use the batteries in electric cars. Electric car uptake will lead to demand spikes when people return from work and plug them in. An electric car owner can get the option of cheaper electricity if it means her car’s battery is not charged (smart charging), or even emptied (known as vehicle-to-grid), during demand spikes and recharged when demand is lower. Such approaches are currently being trialled in the UK

Current power systems are not yet ready to use renewables for the majority of their electricity supply. However, the immediacy of the climate change danger means business-as-usual is not an option, and a total energy revolution is required. Presently, the most realistic solution is the use of renewables (see a separate blog post on nuclear power here). Nobody knows exactly how the power system of the future will look. But everyone agrees it will be very different. 

A still from an online tutorial on power system models, showing generation from different sources.

Want to know more? For a similar discussion on the merits of nuclear power, see this blog post. To get a feel for how a power system works, see this page. It allows users, inside a cloud (no downloads or installs necessary), to create their own power system for the United Kingdom, and see how electricity is generated from renewable and conventional sources. 

Note: this article was adapted from a 2018 blog post: see the original here

The Greatest Storm – A Virtual Pantomime

Devon Francis d.francis@pgr.reading.ac.uk
Max Coleman m.r.coleman@pgr.reading.ac.uk

Every year the Met-PhDs put on a Christmas pantomime and perform it to the rest of the department. The autumn term always seems to drag: the mornings are dark; the evenings are darker; and no matter how hard you try, the term just feels so busy! So what better way to finish off the term than with department jokes, terrible singing and unnecessary Benny Hill chase scenes?

Met Panto 2020 virtual group photo

And despite of a global pandemic that is in full swing, this year would be no different – the show must go on! On 10th December we premiered the very first virtual Met panto: The Greatest Storm! – A spin-off of the 2017 film ‘The Greatest Showman’. The Greatest Storm follows Professor Sue Gray Barnum (or PG Barnum for short) on her journey to find the greatest storm. On her way she meets her “misfit” team: Helen Dacre, Pete Inness, Tom Frame and Javier Amezcua, and recruits her right-hand man: Philip-Craig Carlyle. Together they develop a new instrument: DOROTHY, the Data recORding unit fOr in-siTu sting jet measurements High in the skY. But with COVID lurking around every corner, will they ever be able to measure the Greatest Storm? (…although it will actually just be the greatest storm on record…)

Panto 2020 poster – designed and created by Meg Stretton

This year, Max and I were persuaded volunteered for the role of panto organisers, with the promise that running the panto would be ‘much easier’ than previous years as everything would be online. This was partly true, though there was still a lot of last-minute tweaking…

We were very fortunate that Kris Boykin brought forward the idea to recreate The Greatest Showman, with a detailed plan for the plot, which fought off the other (very good) competition for plot ideas. This made the script writing relatively pain-free as we filled in the details and decided on which of the staff should be included.

Next was the song writing: in retrospect, the songs we chose were quite difficult to get right, as it was challenging to stay in time when singing for most of them, especially when we had changed the lyrics to include meteorological puns! In a live panto this might not have been so bad, but as everything had to be recorded individually and put together by our audio editing experts Dominic Jones and Beth Saunders, we can only say, Dom, we’re very sorry…  

The next 9 weeks were filled with read throughs, character selection and filming. In a normal year, these weeks would be relatively relaxed, with rehearsals spanning the full 9 weeks, however as we were aware that the video editors Lauren James and Wilson Chan had a lot of work to do in putting all of the scenes together, we tried to film as early as possible to give them more time. Our initial plan was to meet up on a weekend to film the parts in a socially distanced setting, but as the second lockdown was announced, we had to quickly change our plan. Some scenes were filmed individually, but the majority were filmed over Zoom: although this had reduced camera quality, it was much more fun to see each other every week and laugh at everyone’s wacky costumes and improvisation!

The last week leading up to Thursday’s showing (tomorrow as we’re writing this!) was slightly busier, with reviewing footage and making final edits, in the knowledge that in these unprecedented circumstances most of the cast will not have seen a complete run through before the final showing! In the end it all came together with an entirely smooth and seamless virtual viewing experience / it all went horribly wrong and we should never have been entrusted with panto (delete as applicable), which everyone viewing hopefully enjoyed!

Screenshot of scene 2 – the misfits’ entrance.

With that, we’d like to say thank you so much to everyone involved, from script writers, band, editors, cast and everyone that helped both on and off our virtual stage! It has been so lovely to see everyone come together, and although has been a very tiring process, panto 2020 has been a very welcome distraction to the rest of 2020!

This year we did not sell tickets, but instead asked for donations to cover our (reasonably small!) running costs, plus any extra will go to the Reading Meteorology department’s charity: San Francisco Libre Association. If you didn’t donate on the night, but wanted to, here’s a link to our donations page – https://paypal.me/pools/c/8uIzsVEQwB. We were so humbled by everyone that has already donated, both small and large amounts, we really appreciate it!

Thank you to everyone that watched The Greatest Storm on Thursday, we hope you had a fun evening! And we look forward to next year’s panto; who will be next to volunteer for this incredible tradition, with panto 2021…?

The Social Metwork in 2020

James Fallon – j.fallon@pgr.reading.ac.uk
Brian Lo – brian.lo@pgr.reading.ac.uk 

Hello dear readers! Reviewing submissions and discovering the fascinating research that takes place in Reading Meteorology has been an amazing experience, and a personal highlight of the year!

Thank you to everyone who has contributed to the social metwork this year, and especially to those who have been patient whilst myself and Brian have been getting used to our new roles as co-editors. The quality of submissions has been very high, but don’t let that deter you if you haven’t written for the blog before! Writing for the social metwork is not as tricky as you might think – we promise!

At the time of writing, the blog has had over 5550 visitors, and is on track for an all time high by the end of the year. We hope that the social metwork has contributed to lifting spirits and continuing the met department social atmosphere throughout the year. In case you missed any posts, or want a second look at some, here is a list of all the posts from this year:

January
North American weather regimes and the stratospheric polar vortex – Simon Lee
Evaluating ocean eddies in coupled climate simulations on a global scale – Sophia Moreton
The (real) butterfly effect: the impact of resolving the mesoscale range – Tsz Yan Leung

February
Life on Industrial Placement – Holly Turner
An inter-comparison of Arctic synoptic scale storms between four global reanalysis datasets – Alec Vessey
A new, explicit thunderstorm electrification scheme for the Met Office Unified Model – Ben Courtier

March
Relationships in errors between meteorological forecasts and air quality forecasts – Kaja Milczewska
Tips for working from home as a PhD student – Simon Lee

May
Air pollution and COVID-19: is ozone an undercover criminal? – Kaja Milczewska
The philosophy of climate science – Mark Prosser
Explaining complicated things with simple words: Simple writer challenge – Linda Toča

June
Methane’s Shortwave Radiative Forcing – Rachael Byrom

July
How do ocean and atmospheric heat transports affect sea-ice extent? – Jake Aylmer

August
A Journey through Hot British Summers – Simon Lee
Exploring the impact of variable floe size on the Arctic sea ice – Adam Bateson

September
How Important are Post-Tropical Cyclones to European Windstorm Risk? – Elliott Sainsbury
The Scandinavia-Greenland Pattern: something to look out for this winter – Simon Lee

October
My journey to Reading: Going from application to newly minted SCENARIO PhD student – George Gunn
The visual complexity of coronal mass ejections follows the solar cycle – Shannon Jones
Organising a virtual conference – Gwyneth Matthews
Visiting Scientist Week Preview: Laure Zanna – Kaja Milczewska

November
Demonstrating as a PhD student in unprecedented times – Brian Lo
ECMWF/EUMETSAT NWP SAF Workshop on the treatment of random and systematic errors in satellite data assimilation for NWP – Devon Francis
Extra conference funding: how to apply and where to look – Shannon Jones
Youth voices pick up the slack: MOCK COP 26 – James Fallon

Enjoy the panto, have a very merry Christmas, and here’s to 2021!
From your metwork co-editors James & Brian!

Youth voices pick up the slack: MOCK COP 26

James Fallon – j.fallon@pgr.reading.ac.uk

This year’s Conference of the Parties (COP) should have taken place earlier in November, hosted by the UK in Glasgow and in partnership with Italy. Despite many global events successfully moving online this year, from film festivals to large conferences such as the EGU general assembly, the international climate talks were postponed until November 2021.

But young people around the world are more engaged than ever before with the urgent need for international cooperation in the face of the climate emergency. The Fridays for Future (FFF) movement has recorded participation since late 2018 of more than 13,000,000 young people, in 7500 cities from all continents. FFF has adapted to the covid-19 crisis, and on 25th September this year participants from over 150 countries took part both online and in the streets, highlighting the Most Affected People and Areas (MAPA).

Unimpressed by the delay of important climate talks and negotiations, students and youth activists from FFF and a multitude of groups and movements have initiated the MOCK COP26, a 2-week online global conference on climate change that mirrors the real COP.

“My country, the Philippines, is struggling. We don’t want more floods that rise up to 15 feet, winds that peel off roofs in seconds, the rain that drowns our pets and livestock, and storm surges that ravage coastal communities. We don’t want more people to die. We’re still a developing country that contributes so little to global carbon emissions yet we face the worst of its consequences. This is absurd! 

Angelo, Philippines
https://www.mockcop.org/why

Programme

Organisers have chosen five themes to focus on:

  1. Climate education
  2. Climate justice
  3. Climate resilient livelihoods
  4. Health and wellbeing
  5. Nationally Determined Contributions

Full programme here: https://www.mockcop.org/programme

Over a dozen academic support videos break down complicated topics such as “The Kyoto Protocol”, “Agriculture and Agribusiness”, and the “History of Climate Negotiation”. These videos are helping youth delegates and all participants to understand what happens at a COP summit.

Panel sessions have featured United Nations Youth Envoy Jayathma Wickramanayake, 9 year old Climate & Environmental Activist Licypriya Kangujam, and (actual) COP26 president Alok Sharma.

High Level Country Statements

A unique aspect of MOCK COP that I have been excitedly anticipating is the high level country statements; each a 3 minute speech given by youth climate activists representing their nation.

Mock COP26 is not dominated by big polluters as COP26 is. We believe that we need to amplify the people on the frontlines of climate change, which is why we will be aiming to, throughout Mock COP, uplift the voices of those from MAPA (Most Affected People and Areas) countries above those from the Global North. This is why Mock COP26 is special.

Jamie Burrell, UK
https://www.mockcop.org/today

Youth delegates have been encouraged to give speeches in whichever language they are most comfortable talking. At the time of writing, subtitles don’t appear to be fully functioning. However a large number of talks are given in English, and transcripts of all talks have been made available here: https://drive.google.com/drive/folders/1wnQUMt-rcD9XoKtg8YPWba_LZSf16qTD

I highly recommend setting some time aside to give these speeches a listen. Although the total number might put you off, it is very easy to jump in and out of talks. You can find videos embedded below, or on the official youtube channel.

Africa

Pick: Two youth delegates represent Morocco. Whilst Morocco has been ranked a role model for climate action, the reality of the country’s future is alarming. Globally the most affected are the least protected. It’s time for world leaders to protect everyone.

Americas

Pick: The delegate for Suriname explains risks faced as a Small Island Developing State (SIDS) with infrastructure near the coast. Suriname must implement climate adaptation whilst enhancing its legislation in forestry, mining, and agriculture.

Asia

Pick: Indonesia’s delegate opens with the stark warning that the country will lose 1500 of its islands due to rising sea levels by 2050. The high level statement includes calls to incorporate climate education into the national curriculum, and find ways to protect natural habitat. Indonesia has the 2nd biggest rainforest in the world, but currently has no agreed emissions reductions pathway.

Europe

Pick: Ireland’s youth delegates present a necessarily progressive 5 year plan to stick to the EU target of reducing emissions by at least 65% by 2030. The need for much stronger climate education, and providing access to affordable and sustainable energy, are among many other commitments.

Oceania

Pick: The year started with forest fires devastating large swathes of Australia’s natural habitats. Youth delegates want their nation to lead the world as a renewable energy exporter, and an overhaul of media rules to foster new diverse media outlets and prevent monopolies that currently stall climate action.

What is the hoped outcome?

With so many connected issues relating to the climate and ecological emergency, previous COPs have often seen negotiations stall and agreements postponed. The complexity of tackling this crisis is compounded by the vested interests of powerful governments and coal, oil, and gas profiteers.

But youth messages can be heard loud and clear at MOCK COP 26, reflecting the 5 themes of the conference.

We demand concrete action, not mere promises. It’s time for our leaders to wake up, prioritize the realization of the Green Deal, and cut carbon emissions. 

We won’t have more time to alter the effects of the climate crisis if we let this opportunity pass. The clock is ticking. The time for action is NOW. 

In the wake of covid-19 induced economic shocks, policy makers must ensure genuine green recovery that engages with ideas of global climate justice.

Youth delegate panels will continue over the weekend, working towards the creation of a final statement outlining their demands for world leaders. This will be presented to High Level Climate Action Champion for COP26 Nigel Topping, at the closing ceremony (12:00 GMT Tuesday 1st December)