Using deep learning to observe river levels using river cameras

Posted on 15 February, 2021 by danaallen

By: Sarah Dance

In recent times, machine learning is being increasingly used to make sense of digital data. In environmental science, we are only at the beginning of this journey (Blair et al 2021). However, we have already found one useful application, providing us with new observations of river levels.

We have successfully investigated novel deep learning approaches to extract quantitative river level information from CCTV cameras near a river (Vetra-Carvalho et al 2020, Vandaele et al 2020).  These provide a new, inexpensive, source of river-level observations.

Unlike river gauging stations, cameras are used to observe the overall environment instead of directly measuring the water level. The cameras are placed at a distance from the water body to ensure a large field of view, so they have a higher chance of withstanding floods. Many carry back-up batteries so that they can function even if the main power supply is disrupted.

Figure 1: (left) A river camera image. (right) An automated semantic segmentation mask for the same image. Flooded pixels are shown in white and unflooded pixels in black.

Figure 1 shows an example river camera image on the left. On the right we show the results of applying a deep learning technique (automated semantic segmentation using a convolutional neural network).  The deep learning method determines which pixels correspond to flooded areas (white) and unflooded areas (in black). Using this information and some extra information about the heights of the image pixels, we are able to work out the water level from the camera image in an automated way. This method could be used to provide invaluable new source of observations for flood monitoring and forecasting, emergency response and flood risk management.

References

Blair, G.S., Bassett, R., Bastin, L., Beevers, L., Borrajo, M.I., Brown, M., Dance, S.L., Dionescu, A., Edwards, L., Ferrario, M.A. and Fraser, R. et al., 2021: The role of digital technologies in responding to the grand challenges of the natural environment: the Windermere Accord. Patterns, 2(1), 100156. https://doi.org/10.1016/j.patter.2020.100156

Vandaele, R., Dance, S.L. and Ojha, V., 2020: Automated water segmentation and river level detection on camera images using transfer learning. In: 42nd German Conference on Pattern Recognition (DAGM GCPR 2020), 28 Sep – 1 Oct 2020. (In Press)

Vetra-Carvalho, S., Dance, S.L., Mason, D.C., Waller, J.A., Cooper, E.S., Smith, P.J. and Tabeart, J.M., 2020: Collection and extraction of water level information from a digital river camera image dataset, Data in Brief, 33,106338, https://doi.org/10.1016/j.dib.2020.106338.

 

Posted in Climate, Flooding, Machine Learning | Leave a comment

Why do clouds matter when we measure surface temperature from space?

Posted on 8 February, 2021 by danaallen

By: Claire Bulgin

We can use satellites up in space to measure the surface temperature of the Earth over the land and sea.  Satellites have now been making measurements for 40+ years and these data are really helpful for understanding trends in surface temperature as our climate changes.  Measuring surface temperature from space is not without its challenges though, and one of the biggest of these is cloud.

So why do clouds matter?  Basically, they block the view of the Earth’s surface from the satellite.  If we try to measure the surface temperature and there is a cloud in the way, what we really measure is in part the temperature of the cloud.  How much it affects our temperature measurement depends on how transparent it is, and how high up in the atmosphere it is. 

So what do we do?  We really only want to measure the temperature when the sky is clear.  This means that we first screen our data for cloud, and then only use the clear-sky observations.  However, this screening process is not always 100% accurate.  Some clouds are difficult to spot even from space!  Consider cold, white cloud over a cold, bright snow surface as in the example of Figure 1.  This was the winter of 2010 where nearly the whole of the UK was covered in snow in early December.   Some clouds are very difficult to pick out above the snow surface.

 

Figure 1:  Snow and clouds over the UK on 08/12/10 in an image from the MODIS Terra satellite (NASA Earth Observatory, 2010). 

So what do we need to do in those cases where screening is difficult?  We need to understand what impact these clouds could have on our measured surface temperature.  In a recent study, we compared a number of different cloud screening approaches against a cloud screening done manually by an expert.  By looking at the differences between each cloud screening approach and the cloud screening done manually, and how these vary, we can build up a picture of how much getting the cloud screening wrong can introduce uncertainty in our measurement of land surface temperature.

Perhaps not surprisingly, we find that the uncertainty in land surface temperature is higher as the amount of clear-sky in the area we are looking at decreases.  This is shown in Figure 2.   The left hand plot shows that the uncertainty in land surface temperature is on average higher when only 20 % of the sky is cloud-free (2 °C) than when 90 % of the sky is cloud free (0.75 °C). This shows that near cloud edges (where a high fraction of the surface we are looking at is covered by cloud) the uncertainty in our measured surface temperature from cloud screening is higher than in areas with fewer clouds.  The uncertainties are larger at night because cloud screening is more difficult without observations at visible wavelengths.

Figure 2: Left: Uncertainty in measured land surface temperature from clouds as a function of the clear-sky fraction (left).  Right: The number of observations for each clear-sky fraction (Bulgin et al, 2018).

If we choose a consistent percentage of clear-sky pixels from our images, we can also assess how the uncertainty varies as a function of the underlying surface type.  In this study we were able to look at five land surface types: Cropland, evergreen forest, bare-soil, shifting-sand and permanent snow and ice.  We found that for a standardised clear-sky fraction of 74.2 %, uncertainties over snow and ice were largest at 1.95 °C, whilst for cropland they were much smaller, only 0.09  °C.  The other surfaces had uncertainties between these two extremes: 1.2 °C for forest, 0.9 °C for bare soil and 1 °C for shifting sand (Bulgin et al, 2018).

References:

Bulgin, C. E., Merchant, C. J., Ghent, D., Klüser, L., Popp, T., Poulsen, C. and Sogacheva, L. 2018.  Quantifying uncertainty in satellite-retrieved land surface temperature from cloud detection errors. Remote Sensing, 10, 616, doi:10.3390/rs10040616.

NASA Earth Observatory (2010).  Snow in Great Britain and Ireland.  Images courtesy of Jeff Schmaltz, MODIS Rapid Response Team.  Accessed 29/01/21.

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Weather forecasts to save species

Posted on 1 February, 2021 by timpoulter

by Vicky Boult

Extreme weather events impact the lives and livelihoods of people all over the world, a story we hear more and more often as climate change increases the frequency and severity of weather events. But as climate change throws more challenges our way, so science rallies to address them.

Scientific advances in weather forecasting now mean many extreme weather events can be reliably anticipated. Forecasts therefore provide an early warning system, alerting individuals, organisations and governments to an approaching hazard. Early warnings allow for early action. For example, early warnings of flooding give at-risk communities time to evacuate, and early warnings of drought give farmers the opportunity to choose drought-tolerant seeds for planting. Early action means people are better prepared, so that when an extreme weather event hits, the impacts are less damaging.

Traditionally, humanitarian organisations would respond after an extreme weather event, launching an international appeal for funds to support relief efforts. However, major humanitarian actors have come to recognise the benefits of acting early, before an extreme weather event occurs. Better preparation through early action reduces the humanitarian burden, saving lives, infrastructure and livelihoods, whilst also sparing limited funds and resources (because impacts are reduced and there are cost-saving efficiencies in acting early).

The International Federation of Red Cross and Red Crescent Societies, the world’s largest humanitarian network, have called this movement Forecast-based Action. Forecast-based Action is recognised as one of the most important ways to minimise the impacts of climate change on human lives.  

Part of my role within the Department of Meteorology involves working with the Red Cross’ National Societies to develop Forecast-based Action protocols for drought across Africa. The aim is to identify forecasts which reliably anticipate drought and can therefore be used to trigger early actions and reduce drought impacts.

Personally, I find the Forecast-based Action movement incredibly exciting. Not only because Forecast-based Action has the potential to save lives and livelihoods, but also because I believe the Forecast-based Action concept can be applied to reduce the impacts of climate change elsewhere.

Before joining the Meteorology department in early 2019, my studies and research focused on ecology and conservation; during my PhD, I studied the influence of food availability on the movement and abundance of African elephants. Now with both my conservation and my meteorology hats on, I realise there is huge potential to adopt Forecast-based Action in conservation.

Just like people around the world, species too are increasingly at-risk of extreme weather. Drought can lead to food shortages for herbivores, cyclones can destroy precious coral reefs, and bush fires can burn through woodland habitats. Despite the obvious implications of extreme weather events for species survival, research in ecology and conservation has focused more on the gradual effects of climate change on species over much longer timeframes, and relatively little research has focused on the impacts of extreme weather. Whilst a long-term view has highlighted the threats posed by climate change for biodiversity, such warnings are not very actionable and, in the meantime, extreme weather events push species closer to extinction.

What might Forecast-based Action look like in conservation? Here’s one example.

Sea turtles lay their eggs on sandy beaches around the world. Nest temperatures below the sand are important for hatching success; if temperatures get too hot, the developing turtles inside the eggs die. So, if a weather forecasts indicates very high temperatures are expected at a turtle nesting beach, conservationists can take early action to prevent losses. One option is to install temporary structures over nests to provide shade and prevent overheating. Alternatively, nests can be excavated, and eggs artificially incubated at safe temperatures. In this way, forecasts can help to improve turtle hatching success and contribute to the conservation of the species.

Image: Forecast-based Action to improve hatching success in sea turtles. Cartoon adapted from International Federation of Red Cross and Red Crescent Societies media.

Realising Forecast-based Action in conservation will require overcoming several challenges, including deciding when to act on forecasts (even the best forecasts can be wrong) and how to fund early action. But these challenges have already been addressed in the humanitarian sector, and conservation should use lessons learnt there for guidance.

As climate change demands increasing intervention from conservationists to prevent species extinction, I hope that advances in weather forecasting developed in meteorology and innovation in practice established in the humanitarian sector, might inspire and guide more anticipatory action in conservation.

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What’s that data? Why and how the geoscientific community is forging metadata standards.

Posted on 25 January, 2021 by timpoulter

By Sadie Bartholomew

Part I: Why

Earth science is a data-driven endeavour. Advanced simulations of the Earth system, whose configurations are themselves complex controlled data, churn out petabytes of analysis datasets whilst raw data from in-situ and satellite observations stream in continuously. Both upstream sources can then undertake a myriad of different paths in processing and post-processing workflows, becoming derived products or being ingested for reanalysis or as a background state to further model runs downstream.

Figure 1: Weather and climate research involves an abundance of data covering many different variables. Individual keywords from potential variables are indicated in this word cloud (optimised to fit the shape and colour of an image of the Earth) generated (for this post) using the Python library word cloud. Read on to learn precisely what terms and weightings are represented!

For the most part this deluge, which epitomises the various “V” words defining “big data”, comes in the form of array-based data packaged into files in a format called netCDF. A major advantage of netCDF is that it was designed to be self-describing, that is to say each file should outline its own structure and each included array of data should be accompanied by a standalone description of what it represents — by metadata.

An underappreciated subclass of data, metadata is indispensable wherever there is a significant collection of information; imagine trying to locate a book in your favourite library if there were no titles, author names or shelf labels.

Involving a mixture of data drawn from the real atmosphere, surface and ocean as well as varying virtual equivalents, geoscience is more complex than a hypothetical library in data terms. Robust, wide-ranging metadata is therefore essential to making sense of it all.

Take the spatio-temporal (space and time) context of the data arrays, for example. Each represents a snapshot of a location on the real or simulated Earth, a sample across one or more of the latitude, longitude, vertical and time coordinates. What is the coverage of the data across these dimensions and how do the arrays map onto them?

Often the correspondence is not simple. To begin with, data frequently covers intervals, or cells, of space-time rather than discrete points on it. In these cases we need to describe the extent of the cells, the so-called bounds, plus the way in which the data in question represents each full cell (perhaps some average or extremum), the cell-methods.

Moreover the data may not be representative of space-time in a straightforward way, potentially spanning a further axis called climatological time in the case of climatological statistics, or by use of a simplified calendar in lieu of the ordinary (Gregorian) calendar. We need a comprehensive and consistent way to describe all these details.

Another core challenge for geoscientific metadata is identifying the nature of the data itself. Assigning a sensible name to a given dataset may not be too taxing, but with such a diversity of potential variables stemming from the wide variety of physical, biogeochemical and human processes at play, doing so unambiguously is really quite difficult.

NetCDF does not standardise the names of data variables, so without further regulation naming would be subject to variance of interpretation across the membership of a diverse research community, sometimes but not always abiding by other named or informal standards from sub-domains that can be at odds. That scenario would add a significant barrier to data sharing, comparison and archival, not only on a human level, but for the other workhorses involved: the machines crunching all that data. Lack of metadata standardisation would cause relevant software packages to become cluttered with logic to handle the countless interpretations and inconsistencies. It would quickly become unmanageable.

Part II: How

So what solution is there? How can we ensure that, now and sustainably going forwards, anyone is able to describe the data encoded in netCDF in a unambiguous way, with scope to fulfil the potential requirements of researchers from multiple disciplines as well as the strict parsing needs of machines?

Step in the CF-netCDF metadata conventions, more commonly known as the CF Conventions.

A community-led effort that spun up shortly after the millennium and has been gaining traction ever since, the CF Conventions set out to solve challenges such as those presented above by providing a definitive standard for climate (‘C’) and forecast (‘F’) metadata. The fundamental scope is to cover netCDF file metadata, though it has been acknowledged that they have wider application and could be used with other data formats, for example XML.

The conventions are outlined in a definitive and comprehensive document that is updated (in the next tagged version) when there is general agreement from interested parties in the community that a proposed change should be made. In this way it has matured over numerous years and versions, with the latest version 1.8 released in February 2020 and 1.9 due out in 2021.

Another core aspect of the conventions is a table establishing a controlled list of possibilities for the names and associated units of data variables. This is the CF Standard Name Table and it is developed in a similar way, though is versioned separately and on version 76 as of late 2020. The idea is that it should provide any name that researchers could conceivably need to describe the data they own or use. If someone can’t find an appropriate name within the table, they submit an Issue on GitHub to formally propose it as a new name and if accepted will form a CF-compliant entity that can be used by all.

Figure 1 in fact depicts all of the individual words taken from the full set of standard names in the current table (the larger the text, the more the given keyword appears throughout the names).

An illustrative indicator of increased uptake of the CF Conventions is the total number of standard names published over time, as shown in Figure 2. To begin with there were around 700 names, now there are nearly 4500 and counting!

Figure 2: The increase in the number of standard names proposed, accepted and finally added into the CF Standard Name Table since the beginning of the project, up to and including version 75, with an indication of the progression of the table versions.

A further indicator is the popularity of annual workshops dedicated to the CF Conventions. 2020’s ‘CF Workshop’ was due to be held in Santander but due to COVID-19 restrictions was held virtually. Over a hundred people registered from fifteen countries, with at least half attending the sessions across each of three days to participate in ways ranging from learning about the current status and future plans of the conventions to engaging in breakout sessions that made headway towards resolving some weighty open proposals. Much interesting discussion took place, all of which is summarised openly on the website, and I, for one, am looking forward to the next annual meeting.

Many colleagues across the department have been involved or indeed instrumental in the growth of the CF Conventions, including Jonathan Gregory who was one of the five original authors and David Hassell who is the present secretary of the Conventions Committee.

Above all, though, the CF Conventions are run and managed by the geoscience community that they serve. Everyone is encouraged to have their say and get involved, be it by commenting to register thoughts on open proposals for changes to the canonical document or for new standard names, raising their own proposals, or helping to develop tools that facilitate discussion or indeed encourage compliance.

If you’re interested in contributing, please explore the official CF Conventions website or dive into the discussions now contained in an Issue Tracker under the official GitHub organisation ‘cf-convention’.

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All I want for Christmas is some PV maps…

Posted on 21 December, 2020 by timpoulter

By: Ben Harvey

For many years, the department webpages have hosted real-time plots of large-scale atmospheric conditions based on operational ECMWF analysis data. Over the last few months we’ve been revamping them with a new webpage and higher-resolution images. I thought I’d describe some of the new plots here, in case you found yourself with time on your hands over the Christmas break!

First up, here’s the new web link: www.met.reading.ac.uk/~ben/current_weather.

The first set of pages (‘Dynamical Tropopause Maps’) show a suite of variables at tropopause level. More precisely, they show variables interpolated to the height where the potential vorticity (PV) reaches a certain value. This is a good proxy for the tropopause because the PV, which combines information about the stratification and rotation of air parcels, tends to be low in the troposphere and high in the stratosphere (see http://rammb.cira.colostate.edu/wmovl/vrl/tutorials/satmanu-eumetsat/satmanu/basic/parameters/pv.htm  for a brief intro to PV).

But why is this useful? Jet streams and synoptic-scale weather systems are intimately linked to the shape of the tropopause. Figure 1 shows an example from last weekend. On Sunday 13 Dec there was a strong but wavy jet stream crossing right across the North Atlantic (left panel) – a common occurrence this time of year. The tropopause was over 10 km high to the south of the jet but only 5 km high to its north, with the height dropping very rapidly across the jet core (right panel). There’s often incredible structure in the tropopause height field; try clicking through the plots on the webpage for the past few days to explore the range of patterns that occur.

Figure 1: Wind speed (left) and geopotential height (right) at the tropopause from 18Z on 13 December 2020. The annotation highlights the position of the jet stream.

To help visualise what’s going on, Figure 2 shows a cross section through the North Atlantic jet stream taken from an aircraft field campaign back in Autumn 2016. You can see how the tropopause height drops across the jet stream and how the maximum wind speeds tend to lie on the tropopause itself. Figure 1 effectively follows the black tropopause line in Figure 2, and therefore tends to pick out the strongest wind speeds at each location.

Figure 2: A North-south cross section through the jet stream (bold contours) also showing potential vorticity (shading; units: PVU), potential temperature (thin contours) and the tropopause (black line). This example is from an NWP model forecast. The dashed line shows the track of a research flight aimed at measuring the structure of the tropopause in the core of the jet. (Adapted form Harvey et al., 2020)

Perhaps the most striking features in Figure 1 are the large north-south meanders of the jet stream. These are Rossby waves. They occur because the rotation of the Earth, combined with its curvature, inhibits the north/south motion of air parcels on large scales. Instead, they tend to curve back to their original latitude. Figure 3 (left panel) shows the north/south component of the wind (again at tropopause level) and the alternating green-purple pattern highlights these Rossby waves and their evolution.

Figure 3: Meridional wind at the tropopause (left) and low-level equivalent potential temperature (right) at 18Z on 13 December 2020. The right panel also shows surface pressure (grey contours) and a couple of contours of potential temperature on the tropopause (blue and black). The annotations highlight the jet position from Figure 1.

The next set of pages (‘Lower-troposphere Maps’) show surface conditions, including sea-level pressure, lower-tropospheric windspeed, and equivalent potential temperature. These can be compared to the tropopause maps to understand how the large-scale tropopause structures relate to the surface weather we experience, and vice versa. In our example there are two extratropical cyclones developing beneath the meanders of the jet stream (Figure 3, right panel). You may recall, the deeper cyclone located just to the west of the UK dumped quite a bit of rainfall over Reading on Sunday night (13 Dec).

Finally, the ‘Isentropic PV Maps’ show the PV itself, interpolated to potential temperature surfaces. To visualise this, follow one of the potential temperature contours in Figure 2 (e.g. the one at 6 km altitude at the left edge of the plot). Moving northwards, the surface starts in the troposphere but rises and crosses the tropopause into the lower stratosphere and much higher values of PV. Isentropic PV maps provide deep insight into the evolution of the atmosphere because, to a good approximation, both PV and potential temperature are conserved following air parcels. This means PV features on these maps are simply advected by the winds on each surface (see Hoskins et al., 1985, for further details). Any changes in PV following the winds can be pinned down to the presence of either diabatic heating (e.g. phase changes of water in clouds and radiative heating/cooling) or to frictional effects. As such, the non-conservation of PV turns out to also be very useful for understanding the impact of these processes on weather systems and climate more generally. Have a click through the plots from the last 2 weeks and see which PV features you can track through time, and which ones are created or destroyed.

These webpages are still in development and will be added to (when time allows!). Please send any suggestions for improvements/additions to b.j.harvey@reading.ac.uk.

Hoskins, B.J., McIntyre, M.E. and Robertson, A.W., 1985. On the use and significance of isentropic potential vorticity maps. Quarterly Journal of the Royal Meteorological Society, 111(470), pp.877-946.

Harvey, B., Methven, J., Sanchez, C. and Schäfler, A., 2020. Diabatic generation of negative potential vorticity and its impact on the North Atlantic jet stream. Quarterly Journal of the Royal Meteorological Society, 146(728), pp.1477-1497.

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Here comes the rain again…

Posted on 14 December, 2020 by timpoulter

By: Natalie Harvey

British people are well renowned for their obsession for talking about the weather. This is partly because it is a “safe” topic for conversation and partly because it is really fascinating! This is especially true in the UK where our weather is so varied.

Since my son started school in 2017, I have found myself having numerous conversations about the weather and the accuracy of weather forecasts at drop off and pick-up times. These conversations are often critical of the forecasts, which I do my best to dispel in the short time I have. The UK Met Office four-day forecasts are now as accurate as our one-day forecast was 30 years ago, with over 90% of forecasts of next day temperature accurate to within 2 °C [1]. Bauer et al. (2015) [2] give a detailed description of the revolution in numerical weather prediction over the last 40 years.

Another question I hear a lot is “why does it always rain at pick up time?” Now, I live a fair way from my son’s school, so my walk is longer than most and I don’t really feel like it rains that much. However, last Wednesday, at 3pm, it did! I got very soggy. It made me think about the question again as it is something that we can test using rainfall data from the Atmospheric Observatory based on the University’s Whiteknights campus [3] which is a short walk from my son’s school.

Figure 1 – Screenshot from the Met Office weather forecasting app showing rain forecast at 3pm.

In Figure 2a the bars show the fraction of time it rains for rain aggregated over two-hour windows from the last 5 years (2015-2019). Each two-hour window is split into five-minute chunks and each five-minute chunk is counted if at least 0.2mm of rain is recorded. The black bars indicate all days, teal bars indicate summer days (June, July, August) and light blue bars indicate winter days (December, January, February).

Overall, as I thought, it doesn’t rain much per 2-hour window, with each 5-minute chunk being classified as raining just once a month on average. In general, the fraction of time it rains does increase throughout the day, with the signal strongest in the summer months. This is most likely related to the diurnal cycle of convection over land in the summer which brings heavy rain showers. The effect is not very large though, with rainfall during the afternoon around 20% more common than between 8 and 10am, school drop off time.

Figure 2 – (a) Fraction of time rain occurs throughout the day for 2015-2019 in two-hour windows. Black bars indicate all days, teal bars indicate summer days and light blue bars indicate winter days. (b) Mean rainfall per 5-minute window throughout the day when rain is recorded.

Figure 2b shows the mean rainfall (bars) for the times that it is raining in the same 2-hour windows. The grey bars indicate the 5th and 95th percentile of the observed rain at these times. These are included to give an indication of the variability of rainfall and show the highly skewed distribution of rainfall rates. The mean rainfall is reasonably uniform throughout the day with a higher value between 2 and 4pm in the summer months, again this is likely to be related to convective events. During that time window there are some large rainfall events, so it is possible that my fellow parents at the school gate memories are skewed by a few events when they (and their children) got very soggy or maybe it is the rain affecting their mood [4]. This is only a very small-scale study using a small amount of rainfall observations, but it seems to me that it doesn’t always rain at school pick up time but maybe it is best to have your umbrella ready just in case, especially in the summer.

[1] Met Office, 2020 Global accuracy at a local level. Accessed 4 December 2020, https://www.metoffice.gov.uk/about-us/what/accuracy-and-trust/how-accurate-are-our-public-forecasts

[2] Bauer, P., Thorpe, A. & Brunet, G., 2015: The quiet revolution of numerical weather prediction. Nature 525, 47–55. https://doi.org/10.1038/nature14956

[3]Reading University Atmospheric Observatory, 2018: Atmospheric Observatory. Accessed 4 December 2020, https://research.reading.ac.uk/meteorology/atmospheric-observatory/

[4] Flook,J 2019: Can’t stand the rain? How wet weather affects human behaviour. Accessed 4 December 2020, https://www.theguardian.com/science/blog/2019/mar/06/cant-stand-the-rain-how-wet-weather-affects-human-behaviour

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Improving Hydrological Predictions Of Land System Models

Posted on 7 December, 2020 by danaallen

By: Thibault Hallouin

Given the existence of feedbacks between the Earth’s atmosphere and the Earth’s surface, hydrological knowledge (e.g. soil moisture and open water available for evaporation and plant transpiration) is as critical to atmospheric scientists, as meteorological knowledge is to hydrologists. In the context of a changing climate, both communities need to work together, so that we can model the impacts of changes in atmospheric conditions on hydrological conditions and vice versa. This is crucial to answering key societal questions regarding the future availability of water resources, and the intensity of extremes such as floods and droughts.

Historically, land system models were developed as a lower boundary condition to atmospheric models. To this day, this still has implications on how land system models represent hydrological processes. When a land system model is coupled to an atmospheric model, it typically inherits the spatial resolution of the atmospheric model. However, the spatial resolution of any model must be appropriately chosen with respect to the spatial scale of the dynamics being modelled, and the spatial scales of atmospheric and hydrological processes can differ. Land system models typically use a tiling scheme to consider any sub-grid heterogeneity beyond the atmospheric grid resolution: the resolution of the land system is split into a collection of tiles, each considering different functional types (e.g. vegetation, bare soil, urban fabric, ice sheet, etc.). From a hydrological point of view, this approach is insufficient because it does not consider lateral movement of water on or below the surface, and it does not necessarily resolve the hydrological dynamics at their adequate spatial scale.

In order to overcome these limitations, the NERC National Capability 5-year programme Hydro-JULES is developing a framework for models of the terrestrial water cycle (see Figure 1). In collaboration with the UK Centre for Ecology & Hydrology, Bryan Lawrence, Grenville Lister, and I actively contribute to the implementation of this framework, given the expertise of the Department of Meteorology in complex model development and model integration.

Figure 1: The Hydro-JULES framework subdividing the terrestrial water cycle into three inter-connected components.

This framework represents the hydrological processes in the terrestrial water cycle as three interconnected components: surface layer, subsurface, and open water. Each component runs at its own resolution. This supports lateral movement of water and water-borne contaminants through the landscape and allows for the community of hydrological modellers to contribute alternative components that can be compared with existing ones, to improve hydrological predictions.

In collaboration with the UK Met Office, a key objective of Hydro-JULES is to refactor the Joint UK Land Environment Simulator (JULES) so it complies with this new framework. JULES is the land component currently used in the Met Office Unified Model, as in, for example, the UK Earth System Model configuration. JULES is also used on its own to model the land system alone. Therefore, advances in the representation of hydrological processes in JULES will not only benefit the hydrological community but will also benefit atmospheric modellers. Accounting for the lateral movement of water will also benefit ocean and Earth system modellers because water-borne contaminants such as nutrients and sediment are drained to the oceans.

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Does working from home help to reduce climate change?

Posted on 23 November, 2020 by danaallen

By: Helen Dacre

During a recent conversation about working from home during the COVID-19 pandemic a friend asked me ‘Given that I no longer commute to work in my car every day, will that help to reduce climate change?’  The answer to this question is probably yes, due to a reduction in CO2 emissions (ignoring increased heating and electricity use).  But by how much?  This got me thinking about the overall impact of COVID-19 emissions reductions on CO2 concentrations in the atmosphere and therefore on climate change. 

Since the start of 2020, COVID-19 restrictions have significantly reduced power generation, industrial activity and transport volume. Current estimates suggest that in April global CO2 emissions were down by 12-25% compared to the previous year and that over 2020 CO2 emissions may be down by 7-8%.  A decline in annual CO2 emissions of this size would exceed any decline since the end of WWII.  So, can we detect this decline in the atmospheric CO2 concentrations?

Background CO2 measurements are taken at several sites around the UK.  One of these sites is located at Ridge Hill in the West Midlands (O’Doherty et al. 2019). Figure 1(a) (blue line) shows daily CO2 concentrations at Ridge Hill between 1 January 2020 and 31 May 2020.  Interestingly CO2 concentrations start to reduce from April onwards, after the UK went into lockdown (23 March).  However, if we zoom out and look at CO2 concentrations over a longer time period we find that there is a reduction in CO2 concentrations every year in the Spring (figure 1b, blue line).  This is because in the Spring plants begin to photosynthesize and consume CO2 from the atmosphere to use for growth. Therefore, the decrease in CO2 concentrations is not likely to be due to COVID-19 related CO2 emissions reductions.

Given the dramatic reduction in CO2 emissions, why isn’t there an obvious reduction in CO2 concentrations?  There are two explanations for this.  Firstly, the long atmospheric lifetime of CO2 (50-200 years) makes any perturbation in emissions small compared to the reservoir of CO2 currently present in the atmosphere.  This is a bit like tipping a bucket of water every month for the last 100 years into a swimming pool with a pin sized hole in it.  Would you notice a change in the water level if you stopped adding water for a few months? Probably not. Secondly, the large daily and annual variability of CO2 concentrations makes changes in CO2 concentrations difficult to detect. This is similar to trying to detect the change in water level during a gale.   The waves created by the wind make it difficult to measure the water level and detect any changes.

So how long would we need to wait to detect a change in daily CO2 concentrations due to COVID-19 magnitude CO2 emission reductions? To answer this question, I built a simple statistical model to predict CO2 concentrations using only meteorological data.  This model doesn’t capture the decadal-timescale interactions included in complex climate models, but it does allow me to determine how long it would take for COVID-19 magnitude emission reductions to be detected in daily CO2 concentration measurements over short timescales (2-5 years).

Figure 1: (Top) Ridge Hill daily CO2 concentrations from January  2020 – May 2020, observed (blue) and predicted (red). Lockdown on 23 March 2020 (black dashed). (Bottom) Ridge Hill daily, monthly and yearly averaged CO2 concentrations from January 2015 – May 2020, observed (blue, cyan and black respectively), predicted (red, orange and grey respectively).

I used 5 years of meteorological data (2015-2019) plus the date as explanatory variables in my statistical model.  The observed (blue line) and predicted (red line) daily CO2 concentrations are shown in figures 1(a) and (b).  The predicted CO2 concentrations match the observed daily CO2 concentrations pretty well (capturing 76% of the observed variability). The observed 2ppm/year increase (trend) in CO2 concentrations is explained by inclusion of the date in the model.  The observed seasonal cycle in CO2 concentrations (due to photosynthesis) is explained by inclusion of monthly averaged temperature in the model.  Finally, the observed day-to-day variability in CO2 concentrations (due to the weather) is explained by including wind speed, wind direction and boundary layer depth in the model.

Since the model compares reasonably well with the observations, I can perform simple emission scenario simulations with my model by varying the trend whilst maintaining the seasonal and daily variability. Setting the trend to 0ppm/year is equivalent to a net-zero emissions scenario.  Similarly setting the trend to 1.8ppm/year is equivalent to a 10% reduction in CO2 emissions. The difference between CO2 concentrations modelled with the observed 2ppm/year trend and those modelled using a reduced trend can be compared to the daily variability in observed CO2 concentrations. If the difference is larger than the daily variability then we can detect a change in CO2 concentrations due to a change in emissions. For COVID-19 magnitude emissions reductions of 10% this occurs after 36-54 months.  Thus, we would expect to detect a reduction in trend in the daily CO2 concentrations only after 4 years of sustained reduced emissions. Of course, if we average the data further to calculate monthly CO2 concentrations then we smooth out the daily variability.  This means that the variability in observed CO2 concentrations reduces and we can detect a reduction in the monthly CO2 concentration trend earlier, after about 12 months. Therefore, if current global lockdown restrictions continue we might detect a reduction in CO2 trend some time in 2021.

So, does working from home reduce climate change? Unfortunately, while the recent reductions in CO2 emissions are substantial, they do not immediately equate to similar reductions in the trend in atmospheric CO2.  COVID-19 magnitude reductions would only result in significantly reduced daily CO2 trend if they were sustained for many years.  This is bad news for climate change as it means that emissions reduction policies need to be both large and sustained to reverse the upward trend in CO2 concentrations. The current COVID-19 CO2 emissions reductions are similar in magnitude to those stated by the Paris agreement as necessary to keep global temperatures below 2oC. However, the methods employed to control the pandemic are not sustainable long-term. The COVID-19 crisis offers a real wake-up call to highlight the substantial changes in behaviour and infrastructure that are necessary if we are to achieve CO2 reduction targets set out by the Paris agreement.

References:

O’Doherty, S.; Say, D.; Stanley, K. (2019): Deriving Emissions related to Climate Change Network: N20, SF6, CO, H2 and other trace gas species measurements from Ridge Hill Tall Tower, Herefordshire. Centre for Environmental Data Analysis, accessed 1 November 2020. https://catalogue.ceda.ac.uk/uuid/4370dacd17544eb781aa1e51cc4dc633

Posted in Climate modelling, Covid-19, Greenhouse gases, IPCC | Leave a comment

Sources of rainfall over East Asia

Posted on 9 November, 2020 by danaallen

By: Liang Guo

The East Asian monsoon causes intense rainfall over China, Japan and the Koreas every summer, and a cold, dry season every winter. This is driven by thermal and dynamical contrasts between the vast Pacific Ocean and the elevated Tibetan Plateau. Over 80% of East Asian rainfall is carried in from tropical oceans and mid-latitude land and oceans(Guo, Klingaman et al. 2018).

The strength of the monsoon varies each year, with stronger monsoon seasons associated with a higher total rainfall. However, the intensity of individual rainfall events is decided by smaller-scale perturbations in the weather, such as a vortex moving down from Tibet or a tropical cyclone from the Pacific.

As extreme rainfall events are damaging to both people and property, and occurring more often(Chevuturi, Klingaman et al. 2018), we wanted to know how much of the rainfall in East Asia is caused by the mean monsoon flow and how much is caused by perturbations in the weather.

To investigate this, we used a moisture tracking tool: the Water Accounting Model (WAM). This deconvolves atmospheric circulation and moisture using a spatial filter, and assumes that the atmosphere maintains the same hydrological balance at each grid point as occurred in reality (i.e. evaporation and precipitation are unchanged) except over East Asia.Figure 1: Annual cycles of rainfall over five East Asian subregions tracked using decomposed moisture fluxes (coloured lines). Sums of each components (solid grey line) are compared to precipitation of ERA-Interim (dotted grey line).

Figure 1 shows the results of applying this tool to five different regions of East Asia: southeastern China (region 1), Tibetan Plateau (region 2), central eastern China (region 3), northwestern China (region 4) and northeastern China (region 5). This shows the amount of rainfall which occurred due to the mean flow (black); the mean humidity gradient (red); the eddy flow (yellow); the eddy humidity gradient (blue) over the course of a year. The contributions from each of these components added together show the total rainfall for the region (solid grey line). This matches up well to the total precipitation in the ERA-Intermin dataset (dotted grey line), which can be used as a reference.

We found that the mean monsoon flow and mean monsoon gradient are the largest causes of rainfall, particularly in southeastern China and Tibet.

References:

Chevuturi, A., et al. (2018). “Projected Changes in the Asian‐Australian Monsoon Region in 1.5°C and 2.0°C Global‐Warming Scenarios.” Earth’s Future 6(3): 339-358. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017EF000734 

Guo, L., et al. (2018). “The contributions of local and remote atmospheric moisture fluxes to East Asian precipitation and its variability.” Climate Dynamics. https://journals.ametsoc.org/jhm/article/20/4/657/344214/Moisture-Sources-for-East-Asian-Precipitation-Mean

Posted in Climate, Monsoons | Tagged | Leave a comment

Tropical rainfall and sea surface temperature link could improve forecasts

Posted on 2 November, 2020 by danaallen

By: Chris Holloway  and the University of Reading Press Office. 

Tropical rainfall, averaged on seasonal time scales, is influenced far more strongly by nearby sea temperatures in the real world than in almost all climate simulations, scientists have found, paving the way for more accurate global weather forecasts.

A team led by Dr Peter Good at the Met Office and including Dr Chris Holloway at the University of Reading Meteorology Department studied the effect of tropical sea surface temperatures and resulting wind patterns on seasonal rainfall in the region by filtering out other influences, revealing a stronger relationship in the real world than that simulated by 43 of 47 climate models studied.

The study, published in Nature, used new analysis of satellite observations and other meteorological data and also found that important low-altitude wind patterns in the wider tropical region were stronger than most models simulate.  Deficiencies in the simulation of low-altitude cloud cover were highlighted as a potential cause of these discrepancies.

Weather and climate patterns in the tropics are known to have a knock-on effect on weather thousands of miles away. This means that the findings could help improve seasonal weather forecasts for Europe, as well as longer-term climate predictions.

Dr Chris Holloway said: “Global weather and climate models continue to have errors in simulating and predicting tropical rainfall patterns. Limited quality measurements of tropical rainfall and atmospheric circulation have also made it difficult to understand and rectify these errors.

“In our study, we were able to filter out interactions between remote regions to focus on the relationship between sea surface temperatures, rainfall and tropical winds within a particular region, showing us how much the models are underestimating the increase of rainfall that accompanies a warmer sea surface temperature.

“Resolving these discrepancies between models and the real world can make a big difference to how accurately we can predict the weather or how much confidence we have in projections of regional climate change in the future.”

Reference:

Good, P., Chadwick, R., Holloway, C.E., Kennedy, J., Lowe, J. A., Roehrig, R. and Rushley, S. S.  High sensitivity of tropical precipitation to local sea-surface temperature. Nature (2020). https://doi.org/10.1038/s41586-020-2887-3     (free read-only PDF)

 

Posted in Climate, Oceans, Weather forecasting | Tagged | Leave a comment