Deficiencies in climate model simulations of the seasonal rains in Africa

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

‘When is the wet season in Britain?’ a new student from Botswana asked me once. ‘Errrr, January-December?’ I replied flippantly. But in Botswana, and across much of Africa they experience one or two well-defined wet seasons per year, when the majority of the annual rainfall occurs. The timing and length of this wet season(s) is of significant societal importance; it replenishes water supplies used for drinking and other domestic purposes, affects the agricultural growing seasons and impacts the lifecycle of a number of vectors associated with the transmission of diseases such as malaria and dengue fever. Delays in the onset, or even failure of the wet season, can lead to reduced yields and potential food insecurity.

Future changes in climate will not be felt solely through changes in mean climate; projected shifts in atmospheric circulation patterns will also alter seasonality. Africa is acutely vulnerable to the effects of climate change so understanding future changes in the seasonal cycle of African precipitation is of utmost importance in establishing appropriate adaptation strategies. In order to produce reliable projections of seasonality, we require models to contain an accurate representation of current seasonality.

In our recent study we use a novel method to diagnose progression of the rainy seasons across continental Africa and identify important deficiencies in the climate simulations (a previous blog post and paper describes this method).

Firstly, when we use the method of Dunning et al. (2016) to identify the wet seasons in satellite-based precipitation estimates, atmosphere-only and coupled climate model simulations we find that the rainy seasons are differentiated more clearly from the dry seasons (shown by larger differences in the average rainfall per rainy day; Figure 1) than when fixed meteorological seasons (OND, MAM etc) are used, as this method accounts for interannual variability in seasonal timing and model timing biases.

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Figure 1. Average rainfall rate (mm day−1) during the wet/dry seasons over the Horn of Africa (a) and the Sahel (b) when defined using meteorological seasons (dashed bars) and dynamically varying seasons (Dunning et al. 2016, solid). See Figure 2 for a map of the regions.

Overall, climate model simulations capture the gross seasonal cycle of African precipitation on a continental scale, and seasonal timing exhibits good agreement with observations, however deficiencies manifest over key regions (Figure 2). The Horn of Africa (Somalia, southern Ethiopia, Kenya) experiences two wet seasons per year; the ‘long rains’ during March-May and the ‘short rains’ during October-November.  Whilst the simulations capture two wet seasons per year, they exhibit significant timing biases, with the long rains around 3 weeks late and the short rains nearly 4 weeks too long on average (Figure 2). Accounting for these biases may be crucial in interpreting the contrasting trend of observed declining rainfall during the ‘long rains’ in recent years and model projections of increasing ‘long rains’ rainfall in the future.

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Figure 2: Multi-model mean onset (open circles) and cessation (filled squares) for observations, atmosphere-only (AMIP) and coupled (CMIP) over selected regions (b). Shaded bars indicate the period of the wet season. For SWAC the mean annual regime onset/cessation in coupled simulations is plotted, along with mean onset/cessation for MIROC4h and BCC-CSM1-1-M (coupled simulations).

The most notable bias affects the southern coastline of West Africa, a region of complex meteorology with growing population and declining air quality. This region experiences the first wet season from April-June and the second wet season from mid-September-October, separated by the ‘Little Dry Season’ (LDS) in July-August. The LDS can be useful for weeding and spraying crops with pesticides between the two wet seasons, but can adversely affect crop yields if it is too long or pronounced. We find that simulations produce an unrealistic single summer wet season, with no mid-summer break in the rains and this is linked with biases in ocean temperature patterns. Given that climate simulations cannot capture the current seasonality, future projections for this region should be treated with caution.

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Figure 3: a) Location of the region that experiences the Little Dry Season (LDS; blue dots) and the SST region of interest (pink box). b) Mean annual cycle of rainfall in observations, atmosphere-only and coupled simulations over LDS region.

This work highlights important deficiencies in the representation of the seasonal cycle of rainfall by climate simulations with implications for the reliability of future climate projections and associated impact assessments, including water availability for hydropower generation, the length of the malaria transmission season and future crop yields.

The full paper can be found here:

Dunning, C.M., Allan, R.P. and Black, E. (2017) Identification of deficiencies in seasonal rainfall simulated by CMIP5 climate models, Environmental Research Letters, 12(11), 114001, doi:10.1088/1748-9326/aa869e

 

The onset and end of wet seasons over Africa

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.

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    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.

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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.

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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.

Robots and COP?

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

Robots aren’t a frequent topic of conversation amongst PhD students in the Met department, but currently everyone seems to be talking about a robot, virtual reality and COP. What’s going on?

COP stands for the Conference of the Parties, and forms the decision making body of the United Nations Framework Convention on Climate Change (UNFCCC). This is the UN body with the responsibility of “stabilizing greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system”. They meet once a year to review and assess the implementation of the UNFCCC and associated agreements and protocols. The 22nd COP will be held in Morocco next week.

So what is significant about this year’s COP? Well 2015 was a landmark year in terms of creation of UN agreements. Firstly, the Sendai Framework for Disaster Risk Reduction aims to substantially reduce disaster risk and losses in lives, livelihoods and health. Secondly, the Sustainable Development Goals, call for action to end poverty, protect the planet and encourage peace and prosperity. Thirdly, and finally the Paris Agreement, when all nations agreed to limit the global temperature rise this century to below 2 degrees Celsius and to try to limit the temperature increase to 1.5 degrees Celsius. Additionally, the agreement aims to strengthen the ability of countries to deal with the impacts of climate change.

We, as PhD students studying meteorology understand that robust evidence underpins the three major global agreements of 2015. The first priority for action under the Sendai Framework is ‘understanding disaster risk’, where science is one of the major contributors. Many of the Sustainable Development Goals, which link strongly with both the Paris Agreement and Sendai Framework , require a strong scientific basis. As early career scientists, appreciating the role of research and importance of our science is crucial.

So where does the robot come into this? Next week, Josh Talib and Caroline Dunning will be attending COP22 in Morocco on behalf of the Walker Institute. Accompanying us will be a robot avatar, which will enable remote participation in COP. Back in Reading, other PhD students will be running a Climate Action Studio, operating the robot and using virtual reality to interact with the COP and conduct interviews with participants. The hope is that, with the help of the robot avatar, we will all be able to engage with the discussions in Marrakech, and gain a greater understanding of climate governance.

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If you are interested in hearing more, or intrigued about how we are using a robot to conduct interviews, we will be posting updates on this blog under the COP22 link (https://thesocialmetwork.wordpress.com/cop22/). We will also be tweeting using the hashtag #COPbot on @SocialMetwork. Thank you to NERC, SCENARIO DTP and the Walker Institute for this incredible opportunity.

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