The partitioning of shortwave radiation by vegetation into absorbed, reflected, and transmitted terms is important for most biogeophysical processes including photosynthesis. The most commonly used radiative transfer scheme in climate models does not explicitly account for vegetation architectural effects on shortwave radiation partitioning, and even though detailed 3D radiative transfer schemes have been developed, they are often too computationally expensive and require a large number of parameters.
Using a simple parameterisation, we modified a 1D radiative transfer scheme to simulate the radiative balance consistently with 3D representations. Canopy structure is typically treated via a so called “clumping” factor which acts to reduce the effective leaf area index (LAI) and hence fAPAR (fraction of absorbed photosynthetically radiation, 400-700 nm). Consequently from a production efficiency standpoint it seems intuitive that any consideration of clumping can only lead to reduce GPP (Gross Primary Productivity). We show, to the contrary, that the dominant effect of clumping in more complex models should be to increase photosynthesis on global scales.
The Joint UK Land Environment Simulator (JULES) has recently been modified to include clumping information on a per-plant functional type (PFT) basis (Williams et al., 2017). Here we further modify JULES to read in clumping for each PFT in each grid cell independently. We used a global clumping map derived from MODIS data (He et al., 2012) and ran JULES 4.6 for the year 2008 both with and without clumping using the GL4.0 configuration forced with the WATCH-Forcing-Data-ERA-Interim data set (Weedon et al., 2014). We compare our results against the MTE (Model Tree Ensemble) GPP global data set (Beer et al., 2010).
Fig. 1 shows an almost ubiquitous increase in GPP globally when clumping is included in JULES. In general this improves agreement against the MTE data set (Fig. 2). Spatially the only significant areas where the performance is degraded are some tropical grasslands and savannas (not shown). This is likely due to other model problems, in particular the limited number of PFTs used to represent all vegetation globally. The explanation for the increase in GPP and its spatial pattern is shown in Fig 3. JULES uses a multi-layered canopy scheme coupled to the Farquhar photosynthesis scheme (Farquhar et al., 1980). Changing fAPAR (by including clumping in this case) has largest impacts where GPP is light limited, and this is especially true in tropical forests.
Beer, C. et al. 2010. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science, 329(5993), pp.834-838.
Farquhar, G.D. et al. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta, 149, 78–90.
He, L. et al. 2012. Global clumping index map derived from the MODIS BRDF product. Remote Sensing of Environment, 119, pp.118-130.
Weedon, G. P. et al. 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50, 7505–7514.
Williams, K. et al. 2017. Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska. Geoscientific Model Development, 10(3), pp.1291-1320.
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.
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.
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.
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
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.
When an El Niño is declared, or even forecast, we think back to memorable past El Niños (such as 1997/98), and begin to ask whether we will see the same impacts. Will California receive a lot of rainfall? Will we see droughts in tropical Asia and Australia? Will Peru experience the same devastating floods as in 1997/98, and 1982/83?
El Niño and La Niña, which see changes in the ocean temperatures in the tropical Pacific, are well known to affect weather, and indeed river flow and flooding, around the globe. But how well can we estimate the potential impacts of El Niño and La Niña, and how likely flooding is to occur?
This question is what some of us in the Water@Reading research group at the University of Reading have been looking to answer in our recent publication in Nature Communications. As part of our multi- and inter-disciplinary research, we work closely with the Red Cross / Red Crescent Climate Centre (RCCC), who are working on an initiative called Forecast-based Financing (FbF, Coughlan de Perez et al.). FbF aims to distribute aid (for example providing water purification tablets to prevent spread of disease, or digging trenches to divert flood water) ahead of a flood, based on forecasts. This approach helps to reduce the impact of the flood in the first place, rather than working to undo the damage once the flood has already occurred.
Photo credit: Red Cross / Red Crescent Climate Centre
In Peru, previous strong El Niños in 1982/83 and 1997/98 had resulted in devastating floods in several regions. As such, when forecasts in early 2015 began to indicate a very strong El Niño was developing, the RCCC and forecasters at the Peruvian national hydrological and meteorology agency (SENAMHI) began to look into the likelihood of flooding, and what FbF actions might need to be taken.
Typically, statistical products indicating the historical probability (likelihood [%] based on what happened during past El Niños) of extreme precipitation are used as a proxy for whether a region will experience flooding during an El Niño (or La Niña), such as these maps produced by the IRI (International Research Institute for Climate and Society). You may also have seen maps which circle regions of the globe that will be drier / warmer / wetter / cooler – we’ll come back to these shortly.
These rainfall maps show that Peru, alongside several other regions of the world, is likely to see more rainfall than usual during an El Niño. But does this necessarily mean there will be floods? And what products are out there indicating the effect of El Niño on rivers across the globe?
For organisations working at the global scale, such as the RCCC and other humanitarian aid agencies, global overviews of potential impacts are key in taking decisions on where to focus resources during an El Niño or La Niña. While these maps are useful for looking at the likely changes in precipitation, it has been shown that the link between precipitation and flood magnitude is nonlinear (Stephens et al.), – more rain does not necessarily equal floods – so how does this transfer to the potential for flooding?
The motivation behind this work was to provide similar information, but taking into account the hydrology as well as the meteorology. We wanted to answer the question “what is the probability of flooding during El Niño?” not only for Peru, but for the global river network.
To do this, we have taken the new ECMWF ERA-20CM ensemble model reconstruction of the atmosphere, and run this through a hydrological model to produce the first 20th century global hydrological reconstruction of river flow. Using this new dataset, we have for the first time estimated the historical probability of increased or decreased flood hazard (defined as abnormally high or low river flow) during an El Niño (or La Niña), for the global river network.
The question – “what is the probability of flooding during El Niño?”, however, remains difficult to answer. We now have maps of the probability of abnormally high or low river flow (see Figure 1), and we see clear differences between the hydrological analysis and precipitation. It is also evident that the probabilities themselves are often lower, and much more uncertain, than might be useful – how do you make a decision on whether to provide aid to an area worried about flooding, when the probability of that flooding is 50%?
The likely impacts are much more complex than is often perceived and reported – going back to the afore-mentioned maps that circle regions of the globe and what their impact will be (warmer, drier, wetter?) – these maps portray these impacts as a certainty, not a probability, with the same impacts occurring across huge areas. For example, in Figure 2, we take one of the maps from our results, which indicates the probability of increased or decreased flood hazard in one month during an El Niño, and draw over this these oft-seen circles of potential impacts. In doing this, we remove all information on how likely (or unlikely) the impacts are, smaller scale changes within these circles (in some cases our flood hazard map even indicates a different impact), and a lot of the potential impacts outside of these circles – not to mention the likely impacts can change dramatically from one month to the next. For those organisations that take actions based on such information, it is important to be aware of the uncertainties surrounding the likely impacts of El Niño and La Niña.
“We conclude that while it may seem possible to use historical probabilities to evaluate regions across the globe that are more likely to be at risk of flooding during an El Niño / La Niña, and indeed circle large areas of the globe under one banner of wetter or drier, the reality is much more complex.”
PS. During the winter of 2015/16, our results estimated an ~80% likelihood of increased flood hazard in northern coastal Peru, with only ~10% uncertainty surrounding this. The RCCC took FbF actions to protect thousands of families from potentially devastating floods driven by one of the strongest El Niños on records. While flooding did occur, this was not as severe as expected based on the strength of the El Niño. More recently, during the past few months (January – March 2017), anomalously high sea surface temperatures (SSTs) in the far eastern Pacific (known as a “coastal El Niño” in Peru but not widely acknowledged as an El Niño because central Pacific SSTs are not anomalously warm) have led to devastating flooding in several regions and significant loss of life. And Peru wasn’t the only place that didn’t see the impacts it expected in 2015/16; other regions of the world, such as the US, also saw more rainfall than normal in places that were expected to be drier, and California didn’t receive the deluge they were perhaps hoping for. It’s important to remember that no two El Niños are the same, and El Niño will not be the only influence on the weather around the globe. While El Niño and La Niña can provide some added predictability to the atmosphere, the impacts are far from certain.
From 12th to 16th December 2016, the annual American Geophysical Union (AGU) Fall Meeting took place at the Moscone Centre in San Francisco. AGU remains the largest Earth and Space Science conference in the world with more than 25,000 scientists.
At the 2016 Fall Meeting, I was one of around 8000 students who arrived in San Francisco to present one of the 15,000 posters that would be displayed over the course of the week. While I knew that AGU is one of the largest Earth science conferences, and had indeed spent hours on the plane fine-tuning my schedule to choose which of the ~200 hydrology sessions (let alone the meteorology sessions also related to my work) I would attend, the scope and diversity of the research presented throughout the week really sunk in when I stood on the mezzanine overlooking the poster hall on the first day of the conference.
I was lucky enough to be awarded an AGU student travel grant in order to present my latest PhD research that I’ve been working on at the University of Reading, in collaboration with the European Centre for Medium-Range Weather Forecasts (ECMWF), and funded by NERC as part of the SCENARIO Doctoral Training Partnership. My work maps the historical probability of increased (or decreased) flood hazard across the globe during ENSO (El Niño and La Niña) events, using the first 20th Century ensemble river flow reanalysis, created at ECMWF as part of this work. But more on that another time!
Unlike other conferences I’d presented at, the poster sessions at AGU span half a day – while you are only expected to be there to discuss the work for two hours, it’s inevitable that you get caught up in discussion and I saw many presenters (myself included) who stuck by their poster for the full 4.5 hours! I thoroughly enjoyed my poster session, where several familiar faces dropped by for an update on my work, and others stopped to pose new questions and make a few suggestions for improvements to my maps (wait, why didn’t I think of that?!). As a student presenter, I could also register for the Outstanding Student Poster Award – which means that my poster was anonymously judged, and I will soon be receiving feedback on my poster and presentation – an opportunity I was excited about to make sure I continue to improve the way I communicate my research.
For me, some of the sessions that were highlights of the conference included ‘Global Floods: Forecasting, Monitoring, Risk Assessment and Socioeconomic Response‘, ‘Large-scale Climate Variability and its Impact on Hydrological Systems, Water Resources and Population‘, ‘Forecasting Hydrology at Continental Scale‘, ‘Transforming Hydrologic Prediction and Decision Making: Uncertainty’ and ‘ENSO Dynamics, Observations and Predictability in light of the 2015-2016 El Niño Event‘. With such a range of science being presented, there’s also plenty of opportunity (well, so long as you haven’t double- or triple-booked sessions in your schedule already!) to listen to talks outside of your own field – which is how I ended up in an 8am talk on operational earthquake forecasting and early warning. It was brilliant to learn about forecasting natural hazards outside of hydrology and meteorology!
There was also the social aspect that’s a big part of any conference – networking, networking and more networking! While it can be daunting, particularly at a conference of this size, to find and introduce yourself to scientists in your field whose work you’ve read but you’ve never met, I was pleased to first bump into some friendly faces who in turn introduced me to the new faces. Plus, it’s an AGU tradition that ‘AGU beer’ is served at 3.30pm sharp and the conference centre fills with groups of friends and colleagues in heated debates and discussions about anything from volcanoes to Jupiter’s magnetosphere.
It was impossible not to notice, however, the many more politically-themed conversations than would normally be overheard at such an event, as a result of uncertainty about the future of science in light of the recent US presidential election. While I was in the middle of research discussions at my poster, a ‘Stand up for Science‘ rally took place a few blocks away from the conference centre, where scientists donned lab coats and held signs – “stand up for science”, “ice has no agenda – it just melts” – protesting to raise awareness of the challenges, and to support science. You can read the Guardian article here.
All in all, AGU was a brilliant chance to present and discuss part of my research that I had just finished – it was certainly overwhelming and tough to choose which sessions to stop by (which meant I missed one or two presentations that sounded great), but I would recommend it for showcasing your work (and receiving feedback via the OSPA) and meeting scientists in your field that you wouldn’t normally bump into at conferences in Europe, especially if you can apply for one of AGU’s travel grants to help cover the costs of getting there.
P.S. You can watch presentations from the AGU Fall Meeting 2016 on the website.
Of course, I couldn’t fly all the way out to California and not find time to explore San Francisco a little.
The occurrence of severe convective rainfall is common over the tropical rainforest region. While the basic mechanism of the development of severe convective rainfall over the tropics is well understood in previous studies, the effect of local topography may yield a unique development process.
One part of my PhD project is to look at how local topography modifies severe rainfall events over the western Peninsular Malaysia. This was examined via a case study of severe rainfall that took place on 2nd May 2012. On that day, heavy rainfall caused flash floods and landslides over Klang Valley (red box in Fig. 1). Although the total rainfall on the 2nd May was above the Apr-May average, it was not extremely high.
Fig. 1. The study area, specifically over the western Peninsular Malaysia. The red box is Klang valley area.
Looking at observational data was not enough to understand the processes involved in the development of severe rainfall event on 2nd May 2012 and therefore a simulation study was conducted using the UK Met Office Unified Model (1.5km horizontal resolution).
One theory which could explain the rainfall event on 2nd May 2012 is the influence of a series of rainfall events that developed earlier. There were rainfall events over the Peninsular Malaysia and Sumatra Island in the early evening of 1st May 2012 along the Titiwangsa mountains (Peninsular Malaysia) and Barisan Mountains (Sumatra Island). These rainfall events influenced the development of rainfall over the Malacca Strait overnight. The rainfall event over the strait strengthened by the morning of 2nd May. In the afternoon of 2nd May, the western peninsula had the right atmospheric conditions to develop convective rainfall, and the rainfall over the strait influenced the intensification of rainfall over the western peninsula. Thus, we believe that the local topography has a large impact on the development of the 2nd May rainfall event.
So, how do we test the hypothesis? One way is to perform sensitivity experiments. Four sensitivity experiments were conducted, modifying the orography of both the peninsula and Sumatra, and removing Sumatra altogether (Fig. 2).
Fig. 2. Sensitivity experiments on the local orography and Sumatra Island. Control run on the first panel, flatPM (flat peninsula to sea level), flatSI (flat Sumatra), flatALL(both peninsula and Sumatra are flat), and noSI (Sumatra is removed)
The results show that orography influenced and modified the development of late evening rainfall over both landmasses on both days. On 2nd May, total rainfall in the experiments are as follows:
1. flatPM : Klang valley received less rainfall than control,
2. flatSI : Klang valley received less rainfall than control but more than flatPM,
3. flatALL : Klang valley received more rainfall than control, flatPM and flatSI experiments,
4. noSI : Klang valley received triple the amount of rainfall of the control and other experiments.
These results hint the complex relationship between local topography and rainfall. Moreover, both the peninsula and Sumatra are important for the development of the morning rainfall over the Malacca Strait, regardless of the orographic variability.
Whilst looking at one case study is not enough to draw a general conclusion, this will definitely be a step forward on broadening the information that we already have. A more robust conclusion would require further studies to be taken.
(This PhD project is supervised by Pete Inness and Christopher Holloway, and funded by MARA Malaysia).