Concerns about the Kenya/Somalia short rains

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Figure 1. Schematic representation of person years, based on human population from 8000 BC through 2050 AD.

By Chris Funk

Welcome to the first installment of the Climate Hazards Group blog, which we have started to discuss potential climate hazards, current climate extremes, and climate change. I am a federal scientist who has worked for many years with the US Geological Survey’s Center for Earth Resources Observations and Science and the US Agency for International Development’s Famine Early Warning Systems Network (FEWS NET) and collaborating FEWS NET scientists at the Climate Prediction Center, NASA’s Goddard Space Flight Center Hydrology Lab, and the Earth Science Research Laboratory.  I am also an affiliated research professor with the University of California, Santa Barbara Geography Department, and the Research Director for the Climate Hazards Group. We (the CHG) works closely with the NASA SERVIR project, using state-of-the-art satellite observations to improve food security decision making.  In this blog we will analyze and discuss potential climate extremes related to Indo-Pacific sea surface temperatures, focusing on time scales of one to three months and food insecure countries.   We will also leverage new scientific data sets and models to examine potential real world extremes, especially droughts.

Please note, however, that this blog only represents our personal insights and opinions, and not the USGS, FEWS NET, or any of our partner agencies.

The objective of this web space is to monitor climate and put it in historic context. We are living in a unique period of rapid human transition, but also have an unprecedented ability to observe our planet. Since I am a climate watcher, this blog will capture some of that watching and relate what we see to potential food security hazards. Since climate impacts millions of people, my hope is that these posts may be of interest to colleagues in the developing world, FEWS NET, SERVIR, partner agencies like the World Food Programme and other humanitarian relief agencies.

My first observation is that we are living in a time of tremendous change. Think of it this way (Fig. 1). Begin in 1966, and add up all the people living in that year with the number of people living in 1967. Continue adding through to 2055. That’s a measure of ‘human power’ – the total number of person-years people have available to laugh, cook, cry, work and do whatever. Calculating this number we get an estimated total of 631 billion person-years. This staggering amount of time is about 49 times the age of our universe (~13 billion years).

Given such an epic span even I could tell a joke that my teenagers found funny (though of course they wouldn’t laugh).

Now let’s start counting backwards from 1966. We get all the way back to 1000 AD before we accumulate a similar number of person-years. Between 1966 and 2055 as much will happen, in terms of human action, insight, suffering and achievement, as in the 960 years before 1966. So if you feel a little overwhelmed by the world, take heart, the world is probably a little overwhelmed by you as well. Medieval history, the Renaissance, most of the great Chinese empires, the rise of great and powerful Islamic societies, the age of reason, all the wars, the art, the discoveries, and the industrialization between 966 and 1965 – we are likely to achieve that much human activity in our uncertain age of consequence.

Going back another ~631 billion person-years takes us back another two and a half millennia, to around 1500 B.C. Then the next 631 billion years brings us 6,500 years back to about 8000 B.C., the age of the ‘Neolithic’ (new rock) revolution that saw the first continuous human settlements, the advent of farming, and the first domestication of animals. So our current era of the anthropocene will contain about one quarter of human history, expressed in person-years.

I think that we better be paying close attention in this age of compressed activity. Luckily, we have satellites, climate models and networked observations systems. We know that climate change will make our oceans much warmer, and in today’s blog we will examine how warming in the West Pacific and Eastern Indian ocean, combined with La Niña-like cool conditions in the central Pacific, may increase the chances of drought during the upcoming ‘short’ October-November-December (OND) rains in Eastern Kenya and Southern Somalia. More specifically, I am concerned about potential back-to-back droughts in this region in the spring and fall of 2016. We are facing a situation that combines very warm Eastern Indian Ocean/Western Pacific sea surface temperatures with La Niña-like cool  sea surface temperatures in the eastern Pacific: I do some a simple statistical forecast to argue that we need to be concerned about poor East African OND rains.

This part of the world has two rainy seasons each year, one in the spring and one in the fall, and repeated dry seasons can be very hard on the millions of food insecure farmers and pastoralists that live in this region. The spring 2016 March-May rains this year were very low (Fig. 2). This region/season has experienced a substantial decrease in rainfall due to warming in the West Pacific and increases in the West Pacific Warming Mode. A warm West Pacific can contribute to droughts in both the spring and fall.  In the spring we’ve seen a substantial increase in drought frequencies – note in Fig. 2 that ~10 out of the last 16 March-to-May seasons have been dry in comparison to the 1900-1950 mean.

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Figure 2. Eastern Kenya/Southern Somalia standardized rainfall anomalies. Based on CHG CHIRPS and CenTrends data sets.

 

Here I present a very simple statistical forecast of OND Eastern Kenya/Southern Somalia rains based on observed September sea surface temperatures. We begin by taking a look at the correlation between OND rains and September sea surface temperatures (Fig. 3, left). What we see here is that there is a strong negative (<-0.7) correlation with sea surface temperatures over the west Pacific (boxed area with blue shading), and a similar positive (>+0.7) correlation with sea surface temperatures over the equatorial east Pacific (boxed area with red shading). What we see in this figure is that cool La Niña-like conditions are associated with low East African rains, as are warm west Pacific sea surface temperatures.

What concerns me, right now, is that the moderately cool  La Niña conditions will combine with the very warm conditions in the western Pacific to produce a very strong sea surface temperature gradient, which tends to produce east African droughts. The right panel in Fig. 2 shows observed September sea surface temperature anomalies, expressed as standardized deviations. What we see is modest cooling in the central Pacific, and some very warm (>+2 standard deviations) warm conditions in the west Pacific. Note that the Indian Ocean pattern in the right panel of Fig. 3 also resembles the Indian Ocean Dipole — which is also conducive to dry conditions over the Horn of Africa.

Figure 3. Top – correlation between Kenya/S. Somalia OND rainfall and September sea surface temperatures. Bottom – current September sea surface temperatures expressed as standardized anomalies.

We can place these west Pacific sea surface temperatures in historic context by plotting them as a time series (Fig. 4). We have pointed out the very strong climate change component in West Pacific sea surface temperatures in several papers (a,b,c,d,e), and it is very obvious from this figure. There is a very strong warming trend starting around 1970. It is also interesting to note that all of the past four Septembers have been pretty hot. Right now, West Pacific sea surface temperatures are very warm (third warmest on record). These conditions are likely to persist given the strong thermal inertia of the West Pacific and the latest coupled climate model forecasts (not shown).

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Figure 4. West Pacific September sea surface temperature anomalies [deg C].

We next use the observed sea surface temperatures to predict OND East Africa rains using a simple cross-validated regression model. I predicted 1996-2015 Kenya-Somalia OND rains using time series of west Pacific and central Pacific sea surface temperatures. These results are shown in Fig. 5 as blue dots.  While not perfect, this model does a good job of discriminating between most wet and dry seasons. The associated regression coefficients suggest that the influence of the West Pacific is about twice as important as central Pacific. I then used these coefficients to predict 2016 OND Eastern Kenya-Southern Somalia rains (red dot). The very warm West Pacific sea surface temperatures combine with modestly cool central Pacific conditions to produce a forecast for low Eastern Kenya/Southern Somalia rainfall (~-1Z). Note however, that the cross-validated standard error was fairly high (0.8 of a standardized anomaly), so there is a lot of uncertainty in this projection. Below normal rainfall seems likely, however, and this may mean repeated shocks following the poor rainfall performance this spring (Fig. 2).

Figure 5. 1996-2015 cross-validated forecasts of OND Eastern Kenya/Southern Somalia rains (blue circles), together with the 2016 forecast (red circle).

Please bear in mind that this forecast only applies for Eastern Kenya and Southern Somalia. Other nearby regions, like south-eastern Ethiopia or northeastern Tanzania may also be dry, but I did not explicitly evaluate these areas. On the other hand, standardized rainfall maps from past seasons with similar sea surface temperatures showed wide-spread drying  (Figure 6). Note also that current food security situations in Eastern Kenya and Southern Somalia are not at crisis levels. We should be concerned, however, about the possibility of two poor rainy seasons in the spring and fall of 2016.

Figure 6. CHIRPS OND standardized precipitation index maps for similar (analog) seasons.
Figure 6. CHIRPS OND standardized precipitation index maps for similar (analog) seasons.