The onset and end of wet seasons over Africa


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.

    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.

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.

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


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.

Can we really use El Niño to predict flooding?

R. Emerton, H. Cloke, E. Stephens, E. Zsoter, S. Woolnough, F. Pappenberger (2017). Complex picture for likelihood of ENSO-driven flood hazard. Nature Communications. doi: 10.1038/NCOMMS14796


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.

Figure 1: The probability of increased (blue) or decreased (red) flood hazard during each month of an El Nino. Based on the ensemble mean of the ERA-20CM-R 20th century river flow reconstruction.

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%?

Figure 2: Historical probability of increased / decreased flood hazard map for February, with overlay showing the typical impact map for winter during an El Nino. This highlights the complexity of the link between El Nino and flooding compared to the information usually available.

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.

Presidente Kuczynski recorre zonas afectadas por lluvias e inund
Flooded areas of Trujillo, Peru, March 2017. Photo credit: Presidencia Peru, via Floodlist

Full reference:

R. Emerton, H. Cloke, E. Stephens, E. Zsoter, S. Woolnough, F. Pappenberger (2017). Complex picture for likelihood of ENSO-driven flood hazard. Nature Communications. doi: 10.1038/NCOMMS14796

Press Release:

Understanding our climate with tiny satellites

Gristey, J. J., J. C. Chiu, R. J. Gurney, S.-C. Han, and C. J. Morcrette (2017), Determination of global Earth outgoing radiation at high temporal resolution using a theoretical constellation of satellites, J. Geophys. Res. Atmos., 122, doi:10.1002/2016JD025514.

Email:          Web:

The surface of our planet has warmed at an unprecedented rate since the mid-19th century and there is no sign that the rate of warming is slowing down. The last three decades have all been successively warmer than any preceding decade since 1850, and 16 of the 17 warmest years on record have all occurred since 2001. The latest science now tells us that it is extremely likely that human influence has been the dominant cause of the observed warming1, mainly due to the release of carbon dioxide and other greenhouse gases into our atmosphere. These greenhouse gases trap heat energy that would otherwise escape to space, which disrupts the balance of energy flows at the top of the atmosphere (Fig. 1). The current value of the resulting energy imbalance is approximately 0.6 W m–2, which is more than 17 times larger than all of the energy consumed by humans2! In fact, observing the changes in these energy flows at the top of the atmosphere can help us to gauge how much the Earth is likely to warm in the future and, perhaps more importantly, observations with sufficient spatial coverage, frequency and accuracy can help us to understand the processes that are causing this warming.

Figure 1. The Earth’s top-of-atmosphere energy budget. In equilibrium, the incoming sunlight is balanced by the reflected sunlight and emitted heat energy. Greenhouse gases can reduce the emitted heat energy by trapping heat in the Earth system leading to an energy imbalance at the top of the atmosphere.

Observations of energy flows at the top of the atmosphere have traditionally been made by large and expensive satellites that may be similar in size to a large car3, making it impractical to launch multiple satellites at once. Although such observations have led to many advancements in climate science, the fundamental sampling restrictions from a limited number of satellites makes it impossible to fully resolve the variability in the energy flows at the top of atmosphere. Only recently, due to advancements in small satellite technology and sensor miniaturisation, has a novel, viable and sustainable sampling strategy from a constellation of satellites become possible. Importantly, a constellation of small satellites (Fig. 2a), each the size of a shoe-box (Fig. 2b), could provide both the spatial coverage and frequency of sampling to properly resolve the top of atmosphere energy flows for the first time. Despite the promise of the constellation approach, its scientific potential for measuring energy flows at the top of the atmosphere has not been fully explored.

Figure 2. (a) A constellation of 36 small satellites orbiting the Earth. (b) One of the small “CubeSat” satellites hosting a miniaturised radiation sensor that could be used [edited from earthzine article].
To explore this potential, several experiments have been performed that simulate measurements from the theoretical constellation of satellites shown in Fig 2a. The results show that just 1 hour of measurements can be used to reconstruct accurate global maps of reflected sunlight and emitted heat energy (Fig. 3). These maps are reconstructed using a series of mathematical functions known as “spherical harmonics”, which extract the information from overlapping samples to enhance the spatial resolution by around a factor of 6 when compared with individual measurement footprints. After producing these maps every hour during one day, the uncertainty in the global-average hourly energy flows is 0.16 ± 0.45 W m–2 for reflected sunlight and 0.13 ± 0.15 W m–2 for emitted heat energy. Observations with these uncertainties would be capable of determining the sign of the 0.6 W m–2 energy imbalance directly from space4, even at very short timescales.

Figure 3. (top) “Truth” and (bottom) recovered enhanced-resolution maps of top of atmosphere energy flows for (left) reflected sunlight and (right) emitted heat energy, valid for 00-01 UTC on 29th August 2010.

Also investigated are potential issues that could restrict similar uncertainties being achieved in reality such as instrument calibration and a reduced number of satellites due to limited resources. Not surprisingly, the success of the approach will rely on calibration that ensures low systematic instrument biases, and on a sufficient number of satellites that ensures dense hourly sampling of the globe. Development and demonstration of miniaturised satellites and sensors is currently underway to ensure these criteria are met. Provided good calibration and sufficient satellites, this study demonstrates that the constellation concept would enable an unprecedented sampling capability and has a clear potential for improving observations of Earth’s energy flows.

This work was supported by the NERC SCENARIO DTP grant NE/L002566/1 and co-sponsored by the Met Office.


1 This statement is quoted from the latest Intergovernmental Panel on Climate Change assessment report. Note that these reports are produced approximately every 5 years and the statements concerning human influence on the climate have increased in confidence in every report.

2 Total energy consumed by humans in 2014 was 13805 Mtoe = 160552.15 TWh. This is an average power consumption of 160552.15 TWh  / 8760 hours in a year = 18.33 TW

Rate of energy imbalance per square metre at top of atmosphere is = 0.6 W m–2. Surface area of “top of atmosphere” at 80 km is 4 * pi * ((6371+80)*103 m)2 = 5.23*1014 m2. Rate of energy imbalance for entire Earth = 0.6 W m–2 * 5.23*1014 m2 = 3.14*1014 W = 314 TW

Multiples of energy consumed by humans = 314 TW / 18.33 TW = 17

3 The satellites currently carrying instruments that observe the top of atmosphere energy flows (eg. MeteoSat 8, Aqua) will typically also be hosting a suite of other instruments, which adds to the size of the satellite. However, even the individual instruments are still much larger that the satellite shown in Fig. 2b.

4 Currently, the single most accurate way to determine the top-of-atmosphere energy imbalance is to infer it from changes in ocean heat uptake. The reasoning is that the oceans contain over 90% of the heat capacity of the climate system, so it is assumed on multi-year time scales that excess energy accumulating at the top of the atmosphere goes into heating the oceans. The stated value of 0.6 W m–2 is calculated from a combination of ocean heat uptake and satellite observations.


Allan et al. (2014), Changes in global net radiative imbalance 1985–2012, Geophys. Res. Lett., 41, 5588–5597, doi:10.1002/2014GL060962.

Barnhart et al. (2009), Satellite miniaturization techniques for space sensor networks, Journal of Spacecraft and Rockets46(2), 469–472, doi:10.2514/1.41639.

IPCC (2013), Climate Change 2013: The Physical Science Basis, available online at

NASA (2016), NASA, NOAA Data Show 2016 Warmest Year on Record Globally, available online at

Sandau et al. (2010), Small satellites for global coverage: Potential and limits, ISPRS J. Photogramm., 65, 492–504, doi:10.1016/j.isprsjprs.2010.09.003.

Swartz et al. (2013), Measuring Earth’s Radiation Imbalance with RAVAN: A CubeSat Mission to Measure the Driver of Global Climate Change, available online at

Swartz et al. (2016), The Radiometer Assessment using Vertically Aligned Nanotubes (RAVAN) CubeSat Mission: A Pathfinder for a New Measurement of Earth’s Radiation Budget. Proceedings of the AIAA/USU Conference on Small Satellites, SSC16-XII-03

Understanding the dynamics of cyclone clustering

Priestley, M. D. K., J. G. Pinto, H. F. Dacre, and L. C. Shaffrey (2016), Rossby wave breaking, the upper level jet, and serial clustering of extratropical cyclones in western Europe, Geophys. Res. Lett., 43, doi:10.1002/2016GL071277.


Extratropical cyclones are the number one natural hazard that affects western Europe (Della-Marta, 2010). These cyclones can cause widespread socio-economic damage through extreme wind gusts that can damage property, and also through intense precipitation, which may result in prolonged flood events. For example the intensely stormy winter of 2013/2014 saw 456mm of rain fall in under 90 days across the UK; this broke records nationwide as 175% of the seasonal average fell (Kendon & McCarthy, 2015). One particular storm in this season was cyclone Tini (figure 1), this was a very deep cyclone (minimum pressure – 952 hPa) which brought peak gusts of over 100 mph to the UK. These gusts caused widespread structural damage that resulted in 20,000 homes losing power. These extremes can be considerably worse when multiple extratropical cyclones affect one specific geographical region in a very short space of time. This is known as cyclone clustering. Some of the most damaging clustering events can result in huge insured losses, for example the storms in the winter of 1999/2000 resulted in €16 billion of losses (Swiss Re, 2016); this being more than 10 times the annual average.

Figure 1. A Meteosat visible satellite image at 12 UTC on February 12th 2014 showing cyclone Tini over the UK. Image credit to NEODAAS/University of Dundee.

Up until recently cyclone clustering had been given little attention in terms of scientific research, despite it being a widely accepted phenomenon in the scientific community. With these events being such high risk events it is important to understand the atmospheric dynamics that are associated with these events; and this is exactly what we have been doing recently. In our new study we attempt to characterise cyclone clustering in several different locations and associate each different set of clusters with a different dynamical setup in the upper troposphere. The different locations we focus on are defined by three areas, one encompassing the UK and centred at 55°N. Our other two areas are 10° to the north and south of this (centred at 65°N and 45°N.) The previous study of Pinto et al. (2014) examined several winter seasons and found links between the upper-level jet, Rossby wave breaking (RWB) and the occurrence of clustering. RWB is the meridional overturning of air in the upper troposphere. It is identified using the potential temperature (θ) field on the dynamical tropopause, with a reversal of the normal equator-pole θ gradient representing RWB. This identification method is explained in full in Masato et al. (2013) and also illustrated in figure 2. We have greatly expanded on this analysis to look at all winter clustering events from 1979/1980 to 2014/2015 and their connection with these dynamical features.

Figure 2. Evolution of Rossby waves on the tropopause. RWB occurs when these waves overturn by a significant amount. H: High potential temperature; L: Low potential temperature (Priestley et al., 2017).

We find that when we get clustering it is accompanied with a much stronger jet at 250 hPa than in the climatology, with average speeds peaking at over 50 ms-1 (figures 3a-c). In all cases there is also a much greater presence of RWB in regions not seen from the climatology (Figure 3d). In figure 3a there is more RWB to the south of the jet, in figure 3b there is an increased presence on both the northern and southern flanks, and finally in figure 3c there is much more RWB to the north. The presence of this anomalous RWB transfers momentum into the jet, which acts to strengthen and extend it toward western Europe.

Figure 3. The dynamical setup for clustering occurring at (a) 65°N; (b) 55°N; and (c) 45°N. The climatology is shown in (d). Coloured shading is the average potential temperature on the tropopause, black contours are the average 250 hPa wind speeds and black crosses are where RWB is occurring.

The location of the RWB controls the jet tilt; more RWB to the south of the jet acts to angle it more northwards (figure 3a), there is a southward deflection when there is more RWB to the north of the jet (figure 3c). The presence of RWB on both sides extends it along a more central axis (figure 3b). Therefore the occurrence of RWB in a particular location and the resultant angle of the jet acts to direct cyclones to various parts of western Europe in quick succession.

In our recently published study we go into much more detail regarding the variability associated with these dynamics and also how the jet and RWB interact in time. This can be found at

This work is funded by NERC via the SCENARIO DTP and is also co-sponsored by Aon Benfield.


Della-Marta, P. M., Liniger, M. A., Appenzeller, C., Bresch, D. N., Köllner-Heck, P., & Muccione, V. (2010). Improved estimates of the European winter windstorm climate and the risk of reinsurance loss using climate model data. Journal of Applied Meteorolo

Kendon, M., & McCarthy, M. (2015). The UK’s wet and stormy winter of 2013/2014. Weather, 70(2), 40-47.

Masato, G., Hoskins, B. J., & Woollings, T. (2013). Wave-breaking characteristics of Northern Hemisphere winter blocking: A two-dimensional approach. Journal of Climate, 26(13), 4535-4549.

Pinto, J. G., Gómara, I., Masato, G., Dacre, H. F., Woollings, T., & Caballero, R. (2014). Large‐scale dynamics associated with clustering of extratropical cyclones affecting Western Europe. Journal of Geophysical Research: Atmospheres, 119(24).

Priestley, M. D. K., J. G. Pinto, H. F. Dacre, and L. C. Shaffrey (2017). The role of cyclone clustering during the stormy winter of 2013/2014. Manuscript in preparation.

Swiss Re. (2016). Winter storm clusters in Europe, Swiss Re publishing, Zurich, 16 pp., Accessed 24/11/16.

Geoengineering – how could we detect its cooling effect?

Detecting sulphate aerosol geoengineering with different methods
Lo, Y. T. E. et al. Detecting sulphate aerosol geoengineering with different methods. Sci. Rep. 6, 39169; doi: 10.1038/srep39169 (2016).


Sulphate aerosol injection (SAI) is one of the geoengineering proposals that aim to reduce future surface temperature rise in case ambitious carbon dioxide mitigation targets cannot be met.  Climate model simulations suggest that by injecting 5 teragrams (Tg) of sulphur dioxide gas (SO2) into the stratosphere every year, global surface cooling would be observed within a few years of implementation.  This injection rate is equivalent to 5 million tonnes of SOper year, or one Mount Pinatubo eruption every 4 years (large volcanic eruptions naturally inject SOinto the stratosphere; the Mount Pinatubo eruption in 1991 led to ~0.5 °C global surface cooling in the 2 years that followed (Self et al., 1993)).  However, temperature fluctuations occur naturally in the climate system too.  How could we detect the cooling signal of SAI amidst internal climate variability and temperature changes driven by other external forcings?

The answer to this is optimal fingerprinting (Allen and Stott, 2003), a technique which has been extensively used to detect and attribute climate warming to human activities.  Assuming a scenario (G4, Kravitz et al., 2011) in which 5 Tg yr-1 of SO2 is injected into the stratosphere on top of a mid-range warming scenario called RCP4.5 from 2020-2070, we first estimate the climate system’s internal variability and the temperature ‘fingerprints’ of the geoengineering aerosols and greenhouse gases separately, and then compare observations to these fingerprints using total least squares regression.  Since there are no real-world observations of geoengineering, we cross-compare simulations from different climate models in this research.  This gives us 44 comparisons in total, and the number of years that would be needed to robustly detect the cooling signal of SAI in global-mean near-surface air temperature is estimated for each of them.

Figure 1(a) shows the distribution of the estimated time horizon over which the SAI cooling signal would be detected at the 10% significance level in these 44 comparisons.  In 29 of them, the cooling signal would be detected during the first 10 years of SAI implementation.  This means we would not only be able to separate the cooling effect of SAI from the climate system’s internal variability and temperature changes driven by greenhouse gases, but we would also be able to achieve this early into SAI deployment.

Figure 1: Distribution of the estimated detection horizons of the SAI fingerprint using (a) the conventional two-fingerprint detection method and (b) the new, non-stationary detection method.

The above results are tested by applying a variant of optimal fingerprinting to the same problem.  This new method assumes a non-stationary background climate that is mainly forced by greenhouse gases, and attempts to detect the cooling effect of SAI against the warming background using regression (Bürger and Cubasch, 2015).  Figure 1(b) shows the distribution of the detection horizons estimated by using the new method in the same 44 comparisons: 35 comparisons would require 10 years or fewer for the cooling signal to be robustly detected.  This shows a slight improvement from the results found with the conventional method, but the two distributions are very similar.

To conclude, we would be able to separate and thus, detect the cooling signal of sulphate aerosol geoengineering from internal climate variability and greenhouse gas driven warming in global-mean temperature within 10 years of SAI deployment in a future 5 Tg yr-1 SAI scenario.  This could be achieved with either the conventional optimal fingerprinting method or a new, non-stationary detection method, provided that the climate data are adequately filtered.  Research on the effects of different data filtering techniques on geoengineering detectability is not included in this blog post, please refer to the article cited at the top for more details.

This work has been funded by the University of Reading.  Support has also been provided by the UK Met Office.

Note: So how feasible is a 5 Tg yr-1 SO2 injection scenario?  Robock et al. (2009) estimated the cost of lofting 1 Tg yr-1 SO2 into the stratosphere with existing aircrafts to be several billion U.S. dollars per year. Scaling this to 5 Tg yr-1 is still not a lot compared to the gross world product. There are practical issues to be addressed even if existing aircrafts were to be used for SAI, but the deciding factor of whether to implement sulphate aerosol geoengineering or not would likely be its potential benefits and side effects, both on the climate system and the society. 



Self, Stephen, et al. “The atmospheric impact of the 1991 Mount Pinatubo eruption.” (1993).

Allen, M. R., and P. A. Stott. “Estimating signal amplitudes in optimal fingerprinting, Part I: Theory.” Climate Dynamics 21.5-6 (2003): 477-491.

Kravitz, Ben, et al. “The geoengineering model intercomparison project (GeoMIP).” Atmospheric Science Letters 12.2 (2011): 162-167.

Bürger, Gerd, and Ulrich Cubasch. “The detectability of climate engineering.” Journal of Geophysical Research: Atmospheres 120.22 (2015).

Robock, Alan, et al. “Benefits, risks, and costs of stratospheric geoengineering.” Geophysical Research Letters 36.19 (2009).

From foehn to intense rainfall: the importance of Alps in influencing the regional weather


Figure 1: View from Monte Lema (Italy-Switzerland) looking West. The Lake Maggiore region and the southern Alpine foothills are visible in the foreground whereas Monte Rosa and the Pennine Alps behind them are partially hidden by a characteristic foehn wall.  (A. Volonté, 4 January 2017)

The interaction between atmospheric flow and topography is at the origin of various important weather phenomena, as we have already seen in Carly Wright’s blog post. When a mountain range is particularly high and extended it can even block or deflect weather systems, as it happens with the Alps. For example, in Figure 1 we can see the main Alpine range with its over-4000m-high peaks blocking a cold front coming from the north. The main ridge acts as a wall, enhancing condensation and precipitation processes on the upstream side (stau condition) and leaving clear skies on the downstream lee side, where dry and mild katabatic foehn winds flow. The contrast is striking between sunny weather on Lake Maggiore and snowy conditions over Monte Rosa, just a few miles apart. The same phenomenon is shown in Figure 2 with a satellite image that highlights how a cold front coming from northwest gets blocked by the Alpine barrier. A person enjoying the sunny day in the southern side of the Alps, if unaware of this mechanism, would be very surprised  to know that the current weather is so different on the other side of the range.

Figure 2: Satellite image (MODIS-NASA) over the Alps and Po Valley on 22 October 2014
Figure 3: same as Figure 1 but on 13 December 2016

A comparison with Figure 3 helps to notice that in Figure 2 the shape of the cloud band closely mirrors the mountain range. As an additional remark,  this comparison shows that foehn bring clear skies even in the Po Valley, having blown away the typical mist/fog occurring in the region in Autumn and Winter months in high pressure regimes. The  stau/foehn dynamics is actually very fascinating, and you can read more about it in Elvidge and Renfrew (2015 ) and in Miltenberger et al. (2016), among others. Unfortunately, the interaction of weather systems with the Alps can often trigger very damaging phenomena, like heavy and long-lasting precipitation on one side of the slope, and this is what the rest of this post will be focused on. In fact, the most recent event of this kind just happened at the end of November, with intense and long-lasting rain affecting the southern slope of the Alps  and causing floods particularly in the Piedmont region, in northwestern Italy ( Figure 4).

Figure 4: River Tanaro flooding in the town of Garessio, 24 November 2016 (Piedmont, Italy). Source:
Figure 5: rainfall accumulated between 21 and 26 November 2016 in the Piedmont region. Source: Regional Agency for the protection of the Environment – Piedmont

Figure 5 shows that the accumulated rainfall in the event goes over 300 mm in a large band that follows the shape of the southern Alpine slope in the region (see map of Piedmont, from Google Maps), reaching even 600 mm in a few places. This situation is the result of moist southerly flow being blocked by the Alps and thus causing ascent and consequent precipitation to persist on the same areas for up to five days. It is quite common to see quasi-stationary troughs enter the Mediterranean region during Autumn months causing strong and long-lasting moist flows to move towards the Alps. Hence, it is crucial to understand  where the heaviest precipitation will occur. In other words, will it rain the most on top of the ridge or on the upstream plain? What processes are controlling the location of heavy precipitation with respect to the slope?

The study published by Davolio et al. (2016), available here and originated from my master degree’s thesis, tackles this issue focusing on northeastern Italy. In fact, the analysis includes three case studies in which heavy and long-lasting rain affected the eastern Alps and other three case studies in which intense rainfall was mainly located on the upstream plain. Although all the events showed common large-scale patterns and similar mesoscale settings, characterised by moist southerly low-level flow interacting with the Alps, the rainfall distribution turned out to be very dissimilar. The study highlights that the two precipitation regimes strongly differ in terms of interaction of the flow with the mountain barrier. When the flow is able to go over the Alps the heaviest rain occurs on top of the ridge. When the flow is instead blocked and deflected by the ridge (flow around), creating a so-called barrier wind, intense convection is triggered on the upstream plain (Figure 6) .

Figure 6: Schematic diagram of the key mechanisms governing the two different wind and precipitation patterns over NE Italy. (a) Blocked low-level flow, barrier wind, convergence and deep convection over the plain, upstream the orography. (b) Flow over conditions with orographic lifting and precipitation mainly over the Alps. From Davolio et al. (2016)
Figure 7: cross section going from the Adriatic Sea to the Alps in one of the events simulated. Equivalent potential temperature is shaded, thick black lines indicate clouds while arrows show tangent wind component. See Davolio et al. (2016)

The key mechanism that explains this different evolution is connected to the thermodynamic state of the impinging flow. In fact, when the southerly moist and warm air gets close to the Alpine barrier it is lifted above the colder air already present at the base of the orography. It can be said that the colder air behaves as a first effective mountain for the incoming flow. If this lifting process triggers convection, then the persistence of a blocked-flow condition is highly favoured (see Figure 7). On the contrary, if this initial lifting process does not trigger convection the intense moist flow will eventually be able to go over the ridge, where a more substantial ascent will take place, causing heavy rain on the ridge top. This study also looks at numerical parameters used in more idealised analyses (like in Miglietta and Rotunno (2009)), finding a good agreement with the theory.

To summarise, we can say that the Alpine range is able to significantly modify weather systems when interacting with them. Thus, an in-depth understanding of the processes taking place during the interaction, along with a coherent model is necessary to capture correctly the effects on the local weather, being either a rainfall enhancement, the occurrence of foehn winds or various other phenomena.


Davolio, S., Volonté A., Manzato A., Pucillo A., Cicogna A. and Ferrario M.E. (2016), Mechanisms producing different precipitation patterns over north-eastern Italy: insights from HyMeX-SOP1 and previous events. Q.J.R. Meteorol. Soc., 142 (Suppl 1): 188-205. doi:10.1002/qj.2731

Elvidge A. D., Renfrew, I. A. (2015). The causes of foehn warming in the lee of mountains. Bull. Am. Meteorol. Soc. 97: 455466, doi:10.1175/BAMS-D-14-00194.1.

Miglietta M. and Rotunno R., (2009) Numerical Simulations of Conditionally Unstable Flows over a Mountain Ridge. J. Atmos. Sci., 66, 1865–1885, doi: 10.1175/2009JAS2902.1. 

Miltenberger, A. K., Reynolds, S. and Sprenger, M. (2016), Revisiting the latent heating contribution to foehn warming: Lagrangian analysis of two foehn events over the Swiss Alps. Q.J.R. Meteorol. Soc., 142: 2194–2204. doi:10.1002/qj.2816

What is loss and damage from climate change?

Characterizing loss and damage from climate change
James et al., 2014. Nature Climate Change, 4, 938–939. doi:10.1038/nclimate2411


Under the United Nations Framework Convention on Climate Change (UNFCCC), countries negotiate how to address the impacts of anthropogenic climate change through mitigation and adaptation. Despite these efforts, climate-related events still cause huge impacts across the globe every year. Impacts can be particularly  devastating in developing countries and this is what the relatively new area of ‘loss and damage’ in the negotiations aims to address.

In 2013, the UNFCCC established the Warsaw International Mechanism (WIM) to “address loss and damage associated with impacts of climate change, including extremes events and slow onset events, in developing countries that are particularly vulnerable to the adverse effects of climate change” (UNFCCC, 2013). Two decades of negotiating went into forming this mechanism, since the first calls from small island developing states in the early 1990s to address the effects of sea level rise.

Island states such as Vanuatu in the South Pacific have been requesting support for the impacts of sea level rise since the early 1990s. Source: Meredith James/Flickr/CC BY-NC-ND 2.0

The WIM states it will address the impacts of both extreme events (such as floods and heatwaves) and slow onset events (such as sea level rise). However, as yet, there is no official definition of what loss and damage will actually encompass. In our commentary in Nature Climate Change (James et al., 2014), we considered one aspect of defining loss and damage: whether loss and damage would need to be attributed to anthropogenic climate change. As the text of the WIM describes “loss and damage associated with the impacts of climate change” and the UNFCCC’s definition of climate change is that which is “attributed directly or indirectly to human activity” (UNFCCC, 1992), this could imply that there would need to be proof that impacts from events were caused by anthropogenic climate change.

If this were the case, impacts would first need to be attributed to particular events (e.g. the infrastructure damaged by a particular flood), and then the event would need to be attributed to anthropogenic climate change. For slow-onset events like sea level rise, the science attributing these to anthropogenic climate change is well-established. However for individual events it is much more challenging to say how climate change had an influence. Extreme event attribution can, for some types of events, estimate how anthropogenic climate change affected the probability of the particular event occurring. This generally relies on large ensembles of climate model simulations, which are necessary to estimate the probabilities of such rare events, and studies therefore rely on the ability of the models to represent the processes that produce the extreme event in question. Observations are also necessary to both to validate the model simulations and define the extreme event to be studied, which are not always available, particularly in developing countries. Up to now, studies attributing specific events have been carried out on an ad hoc basis in the aftermath of particularly extreme events, rather than more systematically. They have also mainly focussed on events in developed countries, rather than the developing countries the WIM aims to assist.

Typhoon Haiyan caused devastation in November 2013 as the WIM was being negotiated. It was used as an example of loss and damage, but without any consideration of whether anthropogenic climate change played a role. Is this an important consideration? Source: DFID/Flickr/CC BY 2.0

While the attribution of events to anthropogenic climate change could be relevant to addressing loss and damage, it is controversial in negotiations. This is in part due to its perceived association with compensation claims. However we suggest that, somewhere along the line, the question of causality is likely to come up, to establish just what the loss and damage being addressed is. Attribution may or may not have a role to play here. What is key is that as event attribution science is continuing to develop, scientists and policymakers need to have opportunities for conversations about what information the science can provide and how this could be applied if it was deemed necessary for policy.

Since writing our commentary we have continued to research this science-policy interface. We have investigated what is understood about event attribution science by stakeholders associated with loss and damage negotiations and how they think it could be relevant (Parker et al., 2016). We have also investigated how policymakers and practitioners are defining ‘loss and damage’, as this still has no official definition and there are differing perspectives among those looking to address loss and damage. Our aim is that by better understanding this policy context, the science will be able to develop in ways that are most relevant to the needs of decision makers and, if deemed relevant, ultimately help to address loss and damage in vulnerable regions.

This work forms part of the ACE-Africa project, for more information see 


James, R., Otto, F., Parker, H., Boyd, E., Cornforth, R., Mitchell, D., & Allen, M. (2014). Characterizing loss and damage from climate change. Nature Climate Change, 4, 938-939, doi: 10.1038/nclimate2411.

Parker, H. R. , Boyd, E., Cornforth, R. J., James, R., Otto, F. E. L., & Allen, M. R. (2016). Stakeholder perceptions of event attribution in the loss and damage debate. Climate Policy, doi: 10.1080/14693062.2015.1124750.

UNFCCC (1992). Article 1: Definitions

UNFCCC (2013). Decision 2/CP.19: Warsaw International Mechanism for Loss and Damage Associated with Climate Change Impacts FCCC/CP/2013/10/Add.1