africa

Cape Verde with a Chance of Dust Storms

natalieratcliffe3Leave a comment

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

My PhD project was could have been done entirely from behind a computer screen, but I ended up in Cape Verde for 3 weeks in June 2022 on a field campaign.

Though the island of Sao Vicente is one of the Cape Verde (= green cape) islands, it wasn’t particularly green…

Working with Dr Franco Marenco from The Cyprus Institute (CyI) and my supervisor at Reading, Dr Claire Ryder, I managed to get some funding to spend 3 weeks in Cape Verde alongside an organised campaign. The ASKOS campaign was created to calibrate and validate aerosol, wind and cloud products from the Aeolus satellite, launched in 2018. They planned on using a combination of ground-based instruments and drones supplied by the Unmanned Systems Research Laboratory (USRL) with CyI to profile dust above Cape Verde to compare with the Aeolus aerosol products.

My PhD project is based around trying to understand how some large dust particles (diameter > 20 um) are travelling much further from the Sahara than expected based on their deposition velocity. One theory about how these particles are transported so far is that they are vertically mixed throughout the depth of the Saharan Air Layer (SAL, dry dusty air layer transported from the Sahara, typically up to ~6 km altitude) during convective mixing in the daytime. At night, with the removal of this convection, these large particles begin to settle through the SAL at a faster rate than other fine particles, before being mixed up again to the top of the SAL during the convective day. This is hypothesised to increase the time taken for the particles to reach the surface, encouraging long-range transport of these coarse particles. We proposed to fly drones with optical particle counters attached up through the SAL during the day and night to see if this theory has any standing.

Before I could go to Cape Verde came all the admin and preamble for going on a field campaign. Before booking flights and accommodation, the wonderfully long health and safety risk assessment form must be completed and approved. Reading through that form really makes it feel like you’re going to face every single threat known to humankind while you’re off campus; hurricanes, volcanoes, Covid-19, getting bitten by ticks (other animals/insects are available), sunburn (to be fair, a very real concern for me) and even getting hacked and bribed. I suppose being prepared for all these eventualities is meant to make it less scary

I had three virtual meetings with everyone involved in the campaign before we travelled, so I had a little bit of an idea what I was supposed to be doing when we were out there. Though to be honest, I still wasn’t entirely sure until a couple of weeks before we left! Claire and I had to introduce our work and what we wanted to achieve from this campaign. I was a little apprehensive as we were going to be requesting to collect data in the very early morning (3-6am ish) meaning we’d have to ask some of the other scientists to be up very early (or late depending on your opinion).

The Wall-e LiDAR. Wall-e was looking at the orientation of the dust particles. eVe was there too but she was basically just an all-white version of Wall-e (disappointing).

Now we get to the fun part where I actually go on the campaign (or on holiday as some people kept insisting. FYI, this was absolutely not the case). Most days would start with a few of us looking at the forecast to work out when we should aim to fly the drones. We would decide on a plan for the day, a suggested plan for the next day, briefly looking at data from the day before and then collating this all into a newsletter which was sent out to everyone on the campaign. These forecasts were useful for those collecting in-situ observations as well as those working on the ground-based remote sensing equipment. It also became very clear in these meetings that each scientist had a preferred forecasting model. We had so many options for forecasts (SKIRON, Met Office, CAMS, IAASARS, ECMWF etc), as well as varying satellite retrievals (EUMETSAT Dust RGB, MODIS NASA AOD, NOAA GOES-East visible images etc) and near-real-time observations from the ground instruments (PollyXT LIDAR, HALO Doppler wind lidar, CIMEL Sunphotometer etc) that there was occasionally some jostling to work out which forecast and measurements to trust and focus our planning based on! I was then able to go to the airport to help the flight team. I would refer to the most recent reading from the lidar and suggest which layers in the dust should be sampled with filters, as well as checking the wind lidar to make sure it wasn’t getting windier.

The USRL team getting ready for launch. The drones were thrown rather than taking off from the ground. The pilot is in the middle; he has a controller and a headset which he can use to pilot the drone.
The drone path, windspeed, ground speed and altitude can be watched from the ground.

Looking back, we should have focused our forecasting on the wind and cloud more than the dust concentration. Initially, we were planning to measure when there was an interesting or high concentration dust event over the island. However, we eventually realised that the wind and cloud cover were the most limiting factors for measuring in terms of the in-situ and ground-based measurements, respectively. This unfortunately meant that, on a few occasions, the flight team were stuck at the airport waiting for the winds to drop before they could launch the drones. Or that the remote sensing teams couldn’t take results at the same time as the drones because there was too much cloud. It was a learning experience for everyone involved!

I’ve taken away four things from this campaign that it seems will probably happen on any field campaign, so take note if you ever get the opportunity!

  • You’ll get to meet some really cool people
  • Probably get food poisoning
  • Your equipment will break at some point
  • And many things will go wrong… It’s an inevitability

Some of the issues we faced were: instruments taking longer to calibrate and setup than expected, helium arriving two weeks late, missing weather balloons, two got covid, five got food poisoning, one drone crash-landed, too windy to fly the drones, not dusty enough, too cloudy for the lidars… It was definitely an exercise in contingency planning. I did say that this was a fun experience and I do mean it! Though there were many tense moments where things went completely opposite to the plan, I got to meet a lot of cool scientists, learn about new instruments, go to Africa for the first time and get hands on with some dust at last!

Feel free to check out this blog post which I wrote for ESA’s Campaign Earth blog page: (https://blogs.esa.int/campaignearth/2022/08/03/delving-deep-into-dusty-skies-on-the-askos-aeolus-field-campaign/).

This blog article is part of the DAZSAL project that is supported by the European Commission under the Horizon 2020 – Research and Innovation Framework Programme, H2020-INFRAIA-2020-1, Grant Agreement number: 101008004, Transnational Access by ATMO-ACCESS.

Main challenges for extreme heat risk communication

Chloe BrimicombeLeave a comment

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

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

There is no universal definition of heatwaves 

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

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

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

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

Attention Deficit 

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

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

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

Lack of Research and Funding 

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

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

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

Heat is an invisible risk 

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

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

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

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

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

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

What’s the solution? 

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

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

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

The onset and end of wet seasons over Africa

Caroline Dunning2 Comments

Email: c.m.dunning@pgr.reading.ac.uk

For many Africans, the timing of the wet season is of crucial importance, especially for those reliant upon subsistence agriculture, who depend on the seasonal rains for crop irrigation. In addition, the wet season recharges lakes, rivers and water storage tanks which constitute the domestic water supply in some areas. The timing of the wet season also affects the availability of energy from hydroelectric schemes, and has impacts upon the prevalence of certain disease carrying vectors, such as mosquitoes.

Climate change is already threatening many vulnerable populations, and changes in the timing or intensity of the wet season, or increasing uncertainty in the timing of the onset, may lead to significant socio-economic impacts. But before we consider future projections or past changes in the seasonality, we need to go back a few steps.

The first step is to find a method for determining when the wet season starts and ends (its ‘onset’ and ‘cessation’). In order to look at large-scale shifts in the timing of the wet season and relate this to wider-scale drivers, this method needs to be applicable across the entirety of continental Africa. Most previous methods for determining the onset focus on the national to regional scale, and are dependent on the exceedance of a certain threshold e.g. the first week with at least 20mm of rainfall, with one rainfall event of more than 10mm, and no dry spell of more than 10 days after the rain event for the next month. While such definitions work well at a national scale they are not applicable at a continental scale where rainfall amounts vary substantially. A threshold suitable for the dry countries at the fringes of the Sahara would not be suitable in the wetter East African highlands.

In addition to a vast range of rainfall amounts, the African continent also spans multiple climatic regimes. The seasonal cycle of precipitation over continental Africa is largely driven by the seasonal progression of the ITCZ and associated rain belts, which follows the maximum incoming solar radiation. In the boreal summer, when the thermal equator sits between the equator and the Tropic of Cancer, the ITCZ sits north of the equator and West Africa and the Sahel experience a wet season. During the boreal autumn the ITCZ moves south, and southern Africa experiences a wet season during the austral summer, followed by the northward return of the ITCZ during the boreal spring. As a consequence of this, central African regions and the Horn of Africa experience two wet seasons per year – one as the ITCZ travels north, and a second as the ITCZ travels south. A method for determining the onset and cessation at the continental scale thus needs to account for regions with multiple wet seasons per year.

In our paper (available here) we propose such a method, based on the method of Liebmann et al (2012). The method has three steps:

  • Firstly, determine the number of seasons experienced per year at the location (or grid point) of interest. This is achieved using harmonic analysis – the amplitude of the first and second harmonic were computed, using the entire timeseries and their ratio compared. If the ratio was greater than 1.0, i.e. the amplitude of the second harmonic was greater than the amplitude of the first harmonic then the grid point was defined as having two wet seasons per year (biannual), if the ratio was less than one then it was defined as having an annual regime. Figure 1 shows the ratio for one African rainfall dataset (TARCATv2). Three regions are identified as biannual regions; the Horn of Africa, an equatorial strip extending from Gabon to Uganda and a small region on the southern West African coastline.

    blog_fig1
    Figure 1: Location of regions with one and two seasons per year, determined using harmonic analysis. Yellow indicates two seasons per year, while pink/purple indicates one season per year. Computed from TARCATv2 data.
  • Secondly the period of the year when the wet season occurs was determined. This was achieved by looking for minima and maxima in the climatological cumulative daily rainfall anomaly to identify one or two seasons.
  • The third and final stage is to calculate the onset and cessation dates for each year. This is done by looking for the minima and maxima in the cumulative daily rainfall anomaly, calculated for each season.

Figure 2 shows the seasonal progression of the onset and cessation, with the patterns observed in agreement with those expected from the driving physical mechanisms, and continuous progression across the annual/biannual boundaries. Over West Africa and the Sahel, Figure 2a-b shows zonally-contiguous progression patterns with onset following the onset of the long rains and moving north, and cessation moving southward, preceding the end of the short rains. Over southern Africa Figure 2c-d shows the onset over southern Africa starting in the north-west and south-east, following the onset of the short rains, reaching the East African coast last, and cessation starting at the Zimbabwe, Mozambique, South Africa border and spreading out radially into the cessation of the long rains.

As well as testing the method for compatibility with known physical drivers of African rainfall, agreement across multiple satellite-based rainfall estimates was also examined. In general, good agreement was found across the datasets, particularly for regions with an annual regime and over the biannual region of East Africa.

blog_fig2
Figure 2: Southward and northward progression of the onset and cessation across the annual/biannual boundaries, computed using GPCP daily rainfall data 1998-2013.

The advantage of having a method that works at the continental scale is the ability to look at the impact of large-scale oscillations on wider-scale variability. One application of this method was to investigate the impact of El Niño upon both the annual rains and short rains (Figure 3). In Figure 3 we see the well-documented dipole in rainfall anomaly, with higher rainfall totals over 0–15°S and the Horn of Africa in El Niño years and the opposite between 15°S and 30°S.  This anomaly is stronger when we use this method compared with using standard meteorological seasons. We can also see that while the lower rainfall to the south is colocated with later onset dates and a consequentially shorter season, the higher rainfall over the Horn of Africa is associated with later cessation of the short rains, with only small differences in onset date.

blog_fig3
Figure 3: a-c) Composite of onset, cessation and wet season rainfall in El Niño years for annual rains and short rains, minus the mean over 1982-2013, computed using CHIRPS data d) Oct-Feb rainfall anomaly in  years (CHIRPS).

In addition to using this method for research purposes, its application within an operational setting is also being explored. Hopefully, the method will be included within the Rainwatch platform, which will be able to provide users with a probabilistic estimate of whether or not the season has started, based on the rainfall experienced so far that year, and historical rainfall data.

For more details, please see the paper detailing this work:

Dunning, C.M., E Black, and R.P. Allan (2016) The onset and cessation of seasonal rainfall over Africa, Journal of Geophysical Research: Atmospheres, 121 11,405-11,424, doi: 10.1002/2016JD025428

References:

Liebmann, B., I. Bladé, G. N. Kiladis, L. M. Carvalho, G. B. Senay, D. Allured, S. Leroux, and C. Funk (2012), Seasonality of African precipitation from 1996 to 2009, J. Clim., 25(12), 4304–4322.