Weather forecasts to save species

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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All I want for Christmas is some PV maps…

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 Society111(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 Society146(728), pp.1477-1497.

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Here comes the rain again…

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

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 LawrenceGrenville 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?

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 2020https://catalogue.ceda.ac.uk/uuid/4370dacd17544eb781aa1e51cc4dc633

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

Sources of rainfall over East Asia

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, Rainfall | Leave a comment

Tropical rainfall and sea surface temperature link could improve forecasts

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, Rainfall, Weather forecasting | Leave a comment

The Core-cloak Convection Model

By: Jian-Feng Gu

Moist convection plays a fundamental role in large-scale circulations and climate, ranging from cumulus clouds smaller than 100m to organized weather systems of several thousands of kilometers. Limited by their grid spacing, numerical models are not able to fully resolve moist convection across its broad range of scales, therefore, we represent moist convection in numerical models using a simplified process, called parameterization.

Figure 1:. A schematic diagram of bulk mass flux approximation. The left panel shows many individual different clouds. The right panel shows how the clouds are represented using the top-hat assumption.

In current climate models, convection is mostly parameterized using bulk plume models, which estimate the vertical transport of heat, moisture and momentum of a large group of clouds. To understand the general idea, imagine a grid box of around (100×100) km2 – that’s about half the size of Wales. Imagine a large number of different clouds randomly scattered across the domain (see the left panel of figure 1). Representing each cloud individually is very computationally expensive and hence simplification is necessary. The simplest idea to describe the overall behavior of these clouds is to consider them as a single entity, that is, a bulk cloud (see the right panel of figure 1).

A further simplification is to assume that each property of the bulk cloud is the averaged value of all the individual clouds, and that these values are distributed evenly within the bulk cloud. This is called the top-hat assumption (Randall et al. 1992). This means that the overall vertical transport by these clouds can be described as the transport of mean properties by the bulk cloud, which is called the bulk mass flux approximation. It approximates the sub-grid vertical flux of a quantity as being the product of the convective mass flux with the departure from the grid-box average of the transported quantity. However, this approximation can underestimate the vertical fluxes by 30-50% (Yano et al. 2004), depending on the variables considered and the resolution of the model. Therefore, a parameterization of the neglected contributions to the vertical flux is necessary. How might this be achieved without sacrificing computational efficiency?

The vertical flux underestimation arises from the physical assumptions we make in the bulk plume model. In Gu et al. (2020), we show both the mean properties of the clouds, and the departures from these mean properties contribute toward the total vertical flux. By representing many clouds as a single bulk cloud, we are removing differences between individual clouds. By assuming a top-hat distribution, we are neglecting the inhomogeneity within each cloud. These two neglected variabilities are called inter-object and intra-object variability, respectively. As a result, the bulk mass flux approximation underestimates the total vertical transport by moist convection. However, it can be improved by relaxing these assumptions. For example, a spectral model that deals with clouds of different sizes is able to minimize the inter-object variability because it takes into account the differences of mean properties between different types of clouds. But it still underestimates the vertical heat fluxes because of neglecting the intra-object variability.

Figure 2: A schematic of the core-cloak representation of convection. Both updrafts and downdrafts are represented as the combination of a strong core surrounded by a weak cloak.

To improve the representation of both the vertical heat and water fluxes, we proposed the “core-cloak” conceptual model (Figure 2, Gu et al. 2020). In this model, we decompose the flow into different types of drafts depending on the strength of vertical motions. More specifically, we collect the strong updrafts together as the updraft “core” and the weak updrafts together as the updraft “cloak”. This core-cloak structure can also be applied to downdrafts. This flow decomposition partly includes the inter-object and intra-object variability and therefore better represents the vertical heat and water transport.

To evaluate our conceptual model, we performed simulations of shallow convection (dx=25 m, 50 m, 100 m) and deep convection (dx=100 m, 200 m, 400 m), and cloud resolving simulations of organized deep convection (dx=1 km). Our results show that the “core-cloak” conceptual model can significantly improve the representation of vertical heat and moisture fluxes, compared to the bulk mass flux approximation. The improvement can be seen in both shallow and deep convection, and even in organized convection.

We also found that the clouds which have a “core-cloak” structure contribute most of the vertical fluxes. Therefore, the “core-cloak” conceptual model provides simply a possible decomposition of the flow that gives a reasonable and efficient description of turbulent fluxes using a mass flux approximation. Parameterization of this core-cloak model would need careful treatment of exchanges between the different types of drafts. We intend to pursue the practical implications of this conceptual model within the future development of a convection parameterization.

References:

Gu, J.-F., Plant, R. S., Holloway, C. E., Jones, T. R., Stirling, A., Clark, P. A., Woolnough, S. J. and Webb, T. L., 2020: Evaluation of the bulk mass flux formulation using large eddy simulations. J. Atmos. Sci., 76, 2297–2324, doi: https://doi.org/10.1175/JAS-D-19-0224.1

Randall, D. A., Q. Shao, and C.-H. Moeng, 1992: A second-order bulk boundary-layer model. J. Atmos. Sci.49, 1903-1923, doi: https://doi.org/10.1175/1520-0469(1992)049<1903:ASOBBL>2.0.CO;2

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People are in the cities – how could we provide the weather climate information they need?

By: Sue Grimmond

Climate services provide climate information to help individuals and organizations make climate smart decisions. Climate services work by integrating high quality meteorological data (temperature, rainfall, wind, soil moisture and ocean conditions); as well as maps, risk and vulnerability analyses, assessments, and long-term projections and scenarios; with socio-economic variables and non-meteorological data (such as agricultural production, health trends, human settlement in high-risk areas, road and infrastructure maps for the delivery of goods). The data and information collected is transformed into customized products such as projections, trends, economic analyses and services. The aim is to equip decision makers in climate-sensitive sectors with better information to help society adapt to climate variability and change.  At the core are users and their needs.

The demands for such services are wide ranging – for public health; for disaster risk reduction and response; the prediction of energy and water demands or food production; or the operation of climate sensitive infrastructure. These services though need to go further, they need to be developed for a wider range of time scales and conditions (for weather and climate) and to integrate other elements of the environment and human behaviours and responses.

Nowhere is this more important than in cities. Increasingly dense, complex and interdependent urban systems leave cities particularly vulnerable to extreme weather and to changes in climate. Through domino effects, a single extreme event can lead to a wide-scale breakdown of a city’s infrastructure (Figure 1).

Figure 1:  The ‘domino effect’ partially shown for a typhoon or hurricane event, which produces multiple hydro-meteorological hazards (blue) that have immediate effects (green) and follow-on impacts (purple) that can be both short- and long-term. Source: Grimmond et al. 2020)

Integrated Urban Systems (IUS) are needed (Baklanov et al. 2018, Grimmond et al. 2020), yet globally there are few (if any) fully operational systems (e.g. Baklanov et al. 2020). Those systems that do exist often were developed because of a major event (e.g. Olympics; Pan-Am games, Expos) or a significant weather-related disaster (hurricane Sandy. New York City; heat waves in Paris and London in 2003). Recognising this need, the World Meteorological Organization (WMO) is advocating for the development of Integrated Urban Weather, Environment and Climate Services (IUS) for safe, healthy and resilient cities. The concept and methodology for developing these systems was adopted by the 70th WMO Executive Council in 2018 (WMO 2019). The need for Demonstration Cities was adopted by the 71st WMO Executive Council in 2019.

The research community, working collaboratively with others, has an important role to play across the many activities required. This includes contributing to and identifying critical research challenges, developing impact forecasts and warnings, promoting and delivering IUS internationally, and supporting national and local communities in their implementation (Figure 2).

Figure 2: An Integrated Urban Hydrometeorological, Climate and Environmental Service (IUS) System has several components.  Here a generic framework is shown for impact-based prediction systems. Integration may occur in the various boxes with a mature IUS aiming for integration in all components. Source: WMO (2019)

At Reading, we have been working with a range of collaborators and stakeholders to develop UMEP (Urban Multi-scale Environmental Predictor), a city-based climate service tool, that combines models and tools essential for climate simulations (Lindberg et al. 2018). Alongside climate information transformed for the urban context, detailed information about the materials and morphology of the city are integrated through GIS, with information about residents and their behaviour through agent-based models and tools (e.g. Capel- Timms et al. 2020). UMEP has been used to identify heat waves and cold waves; the impact of green infrastructure on runoff; the effects of buildings on human thermal stress; solar energy production; and the impact of human activities on heat emissions. It has been applied in many cities across the world, from London to Shanghai (Google Citations 2020).

UMEP includes tools to enable users to input atmospheric and surface data from multiple sources, to characterise the urban environment, to prepare meteorological data for use in cities, to undertake simulations and consider scenarios, and to compare and visualise different combinations of climate indicators. An open-source tool, UMEP is designed to be easily and widely used. This summer we offered the international urbisphere UMEP workshop, that was planned to be in person in Reading, online.

Much more work is needed in this realm. IUS need to be developed to meet the special needs of cities through a combination of dense observation networks, high-resolution forecasts (weather, climate, air quality, hydrological), multi-hazard early warning systems, disaster management plans and climate services. Such an approach will give cities the tools they need to reduce emissions, build thriving and resilient communities and implement the UN Sustainable Development Goals. This focus on urban environments is particularly important given the large and ever increasing fraction of the world’s population that live in cities (more than 3.5 billion) and the importance of cities, not only regionally but nationally and globally, to the world’s economy.

References: 

Baklanov A, CSB Grimmond, D Carlson, D Terblanche, X Tang, V Bouchet, B Lee, G Langendijk, RK Kolli, A Hovsepyan 2018: From urban meteorology, climate and environment research to Integrated City services, Urban Clim., 23, 330-341,  10.1016/j.uclim.2017.05.004

Baklanov A, B Cárdenas, T Lee, S Leroyer, V Masson, L Molina, T Müller, C Ren, FR Vogel, J Voogt 2019: Integrated urban services: Experience from four cities on different continents, Urban Clim.3210.1016/j.uclim.2020.100610

Capel-Timms I, ST Smith, T Sun, S Grimmond 2020: Dynamic Anthropogenic activities impacting Heat emissions (DASH v1.0): Development and evaluation Geosci. Model Dev. https://doi.org/10.5194/gmd-2020-52

Grimmond S, V Bouchet, L Molina, A Baklanov, J Tan , H Schluenzen, G Mills, B Golding, V Masson, C Ren, J Voogt, S Miao, H Lean, B Heusinkveld, A Hovespyan, G Terrug, P Parrish, P Joe 2020: Integrated Urban Hydrometeorological, Climate and Environmental Services: Concept, Methodology and Key Messages Urban Climate https://doi.org/10.1016/j.uclim.2020.100623

Lindberg F, CSB Grimmond, A Gabey, B Huang, CW Kent, T Sun, NE Theeuwes, L Järvi, H Ward, I Capel-Timms, YY Chang, P Jonsson, N Krave, DW Liu, D Meyer, KFG Olofson, JG Tan, D Wästberg, L Xue, Z Zhang 2018: Urban multiscale environmental predictor (UMEP) – An integrated tool for city-based climate services Environmental Modelling and Software, 99, 70–87 https://doi.org/10.1016/j.envsoft.2017.09.020

WMO 2019: Guidance on Integrated Urban Hydrometeorological, Climate and Environmental Services Volume I: Concept and Methodology WMO-No. 1234 https://library.wmo.int/doc_num.php?explnum_id=9903

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