Video summary
The video addresses the critical vulnerability of West Africa to climate change, particularly regarding rainfed staple crops like millet and sorghum which form the basis of food security in the region. The presenter highlights that while global warming projections show varied changes in annual precipitation across the continent, there is a consistent increase in extreme rainfall events characterized by higher maximum daily precipitation. This shift, combined with rising temperatures, poses a dual threat: it shortens the growing season for crops due to increased heat stress and accelerates crop cycles, thereby reducing biomass production and yield potential. Consequently, even if some areas might see marginal yield increases under warmer conditions, the overall trend points toward significant reductions in agricultural productivity, which is especially detrimental given the region's existing struggles with hunger and livelihood dependence on traditional farming.
To quantify these historical impacts and separate human-induced climate change from natural variability, researchers employed modeling experiments using atmospheric general circulation models to simulate two scenarios: one reflecting actual conditions influenced by human activities and another representing a non-warming counterfactual based on pre-industrial levels. The results of these simulations revealed a stark contrast when compared with observational data; without anthropogenic influences, the simulated temperatures would have remained significantly lower than observed trends. Furthermore, while total rainfall totals showed no significant signal of change due to greenhouse gases in this specific analysis, there were notable differences in very heavy rain events. These findings indicate that human activities have already altered the regional climate, leading to warmer conditions and more intense precipitation extremes that directly threaten crop yields.
The study utilized two distinct crop models, STAR and STICS, to assess how these historical climate changes have affected agricultural output across West Africa. Despite differences in their complexity and processes—such as the inclusion of CO2 fertilization effects and nitrogen dynamics in one model versus water balance and radiation use efficiency in the other—both models produced remarkably similar patterns of yield loss. The geographical maps generated from these simulations show substantial decreases in productivity, particularly in the Sahel and northern parts of West Africa like Niger and Senegal, where estimated yield losses range between 10 to 80 percent for millet and 5 to 50 percent for sorghum. These reductions are attributed primarily to anthropogenic warming causing water deficits and shortened crop durations, suggesting a common underlying mechanism driving these declines regardless of the specific modeling approach used.
In conclusion, the research underscores that climate change is no longer just a future scenario but an observable reality affecting West African agriculture today, with potential yield losses between 6 and 50 percent already evident in historical data. The presenter emphasizes that even under optimistic global warming scenarios failing to stay below 1.5 degrees Celsius, further production losses are expected in the region. This necessitates urgent development of effective adaptation strategies to mitigate the risks to food security. The presentation also touches upon the importance of measuring uncertainty through large ensemble simulations and validating models against observational data, noting that while natural variability complicates attribution studies, the signal of human influence is becoming increasingly clear. Finally, the speaker mentions ongoing efforts within the agricultural community to expand these modeling protocols across more countries in Africa to better understand and address the growing challenges posed by climate change.
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for this very inspiring token I imagine
I'm a researcher in France in south of
France in Mongolia and I will
maybe give some example of what Alex
just shown about climate change impact
about crop on crop productivity and crop
Hill but mainly with the focus in in
West Africa and on rainfed crops it's
quite limited but it could be very
important for food security actually
so Africa is particularly exposed to a
climate probability and climate change
as well you have one sort of the
population facing widespread hunger and
chronic manipulation and you have also
people whose livelihood is heavily
dependent on traditional rainbow culture
so there is a high vulnerability
vulnerability
and climate change could pose an
additional building in achieving food
security goals
in the region
so
um indeed when you have additional
increase in global warming when you have
changes in hot and cold temperatures
and also changes in precipitation you
could have a strong impact on crop yield
and you have here projected changes in
in Africa with an increase of four
degrees of global warming and you have
on the the left annual maximum
temperature and your minimum temperature
and on the right and your total
precipitation and maximum daily
precipitation
and you can see that there are strong
warming in in Africa especially in
Continental Africa and it's it's very
true in the sahelan and you have also
changes in in the rainfall
but these changes are not that uniform
if you look at annual precipitation you
have a part of Africa especially this is
also Africa but also the western part of
West Africa which is a drawing with the
increase of temperature
and uh and some other parts were on the
opposite with a lot of more rainfall in
the future
but you have a common feature in in
Africa which is the increase of the
extreme rain rainfall with the higher
number of maximum daily precipitation
which is uh could also be a problem for
uh for cracking
so this change in climate and especially
the warming as has been shown by uh by
Alex previously could lead to negative
impact on crops especially because of
this higher temperature which could
shorten crop cycle length so with this
index of Growing Degree Days and if you
have some more temperature you have
less time for for crops to to grow and
to uh and to produce biomass and yield
them
and you could also increase water stress
with this higher temperature which is
overall detrimental for Crop Production
and overall we could expect less yield
but also more viable yield under uh and
uh warmer climate so as an example this
study shows the distribution of crop
yields and
present and future climax So based on
cement fiber stimulation and it gives
them
[Music]
the crops we use here are Millet and
sorghums which are the
the the the
main staple food crops in the region and
in West Africa and these are animalies
given from a crop model
so you can see that the distribution is
shifted
in future climate on the lower yard
with also a higher distribution which
means that you have more viable yields
under climate change
so such kind of projection we design a
portal
with the the national Med service in
Senegal to stimulate such kind of a
projection of climate but also
projection of crop here that could be
used for decision making or at least for
for some civilization about connection
impact on cropian so you have here the
the bottle with Israel with the
colleagues in in Senegal
and you have for instance here different
kind of indices we put on the portal
with the temperature with precipitation
but also a projection of crop yield and
you have a revolution of maize under uh
different scenarios of climate change
using three five scenarios and now we
are working on extending the the portal
we sleep six data but also to develop
the portal to other countries and here
you have an example of uh what we are
doing in Burkina Faso as well
so climate change is not uh climate
change and climate warming is that only
in climate models and in future
scenarios but it's also visible in
climate observations you have here an
example of what uh we are in the last
ipcc report in the original fact sheets
in Africa and you have already in the
observation mean temperature and how to
extremes that have energy both natural
viability and you have also uh some
happy changes
some faster changes that we have on the
global average we've also observed the
increase in hot extrins and
and and the rainfall Extreme as well
in this slides you have the upstairs
temperature in the sahelum
um I think 19
since 1950 during the hottest months uh
aprilo junior and in annual mean uh
understanding in the same graph and you
can see that there is a clear increase
of temperature especially during the
hottest month season in April where
there is a plus 1.4 degrees warming
since the 1950s
so it's quite important especially
when you look at the the hottest months
during uh just before the the monsoon
season
so now the question is uh how uh such
changes in terms of temperature has
already affected uh agriculture
and this is not an easy question for at
least two reasons first when you look at
productivity time series there is a lot
of viability and Trends in the crop
politics time series that are not due to
a change in
changing in climate but that could be
due to management change that could be
due to a lot of factors that could
influence crop yield by the end and
second there is also a high natural uh
liability in a historical climate
especially in Africa and sometimes it's
very hard to uh to to attribute the the
changes in terms of uh of
um a non-tropical global warming and as
an example you have here an example of
such viability in Africa with two recent
climate Extremes in Africa which had a
strong impact on food security
and these two events were reported by
the world weather attribution
institution in Madagascar
and for the first event the Madagascar
was facing a several food crisis
exacerbated by exceptionally low levels
of rainfall over 2020 and 2021 but the
attribution analysis based on past data
recline summation concluded that factors
or other than climate change were the
main drivers of this food insecurity and
in fact this higher
high drought belonged to the natural
language of the climate literature
another example
is the increase the rainfall associated
with tropical Cyclones and on the
opposite the theatrician study concluded
that there is a closed link with climate
change in the region so you have a high
viable climate and it's very hard to to
look at the um the the cause and to
attribute it to climate change
so to answer this question and to assess
the historical impacts of human
activities in West Africa we designed
modeling experiments based on two
continents them the first component is
based on the climate model to simulate
historical climatic with and without
autop influence
and a second component based on crop
modeling to assess on topic warming has
already affected crop yields in in West
Africa
so considering climate simulation we use
a global model an atmospheric a general
circulation model caused by SST by C
surface temperature amp and the model
was used for two kinds of simulation
first a factual simulation with actual
conditions that are influenced both a
human activities and natural parsenium
and then an encounter factual climate
simulation with the non-warming
climate with pre-industrial climate
that lacks in fact a human influence on
a global system
so here the the surface temperature and
sea ice were so that we could
remove the influence of
a CO2 of human influence in the
simulation
and by the end we got 60 years of igcn
simulation and with an ensembled member
of 100 simulation
and so this data will interpolated at
the 0.5 resolution
and also buys collected so that we could
use the different dialogues to force
crop models
and this kind of simulation were already
used to assess
exchanges in terms of of temperature a
different regions of the world and
the in a paper made by a shogama and
thalam
and you have here a result with
historical and observation in blackened
and you can see that the simulation of
the model
are quite close when you use the the the
simulation using a orthopic emission
entrepreneurs
and you can also see that uh
distribution has been used for assessing
climate change impacts in a
in the at the global scale so using the
sigma crop model so this study based by
yuzumi Italian
investigated the change in terms of
average yield associated with climate
change of comparison levels in Redlands
you have areas
where climate change them
decreased yield on their historical
in in the past term and on the opposite
you have in green the areas where
climate change increased yields
and you can see clearly that the areas
where the Productions were higher
because of climate change it's very
clear in Northern latitudes in Europe
and in New York in in Russia also in
Canada
but yeah you can see in the tropics in
in West Africa and India you know in
yeah there is a clear decrease of crop
years because of uh of human activities
so what are the impacts of human
activities on West African climate and
so we have a look in the simulation and
we compute several user relevant in this
season that could be very important for
productivity by the end and this this is
a part of a lunar list we established
during meeting with stakeholders in
senegales
and we then compare this in this season
so that our annual mean temperature easy
rainfall events and rainfall intensity
will compare this in this season
uh between the two simulations so with
and without in our gazes and what we
found is that uh this most of these
indices were
significantly different from more
information to another one especially
annual mean temperature and you have
here an example where you show I show
you a Time series of annual surface
temperature in average over West African
in the observation so it's the
absorption from the data from cell from
crew data
and you can see that there is a clear
increase of temperature
with an average temperature of
27 degrees over the last 10 years of the
simulation and you have now in blue the
counter factual simulation
and it is clear from this simulation
that would simulate the the internal
reliability of the the temperature but
we list the increased temperature and
also the the mean temperature just
completely different from what we
observed in the in the in the previous
years
and now it's the factual simulation when
we introduce now the effects of the
anthropic Reynolds gazes in the in the
climate model and you can see that now
we are very close to the observed
temperature and we also have a
very close average of temperature over
the last 10 years of the simulation
so now we uh we have done the the
statement for the same comparison but
for mean temperature total rainfall very
hot taste and very heavy rainfall
and what you can see is that it is very
clear that we have differences in terms
of integration but we have no
differences in terms of total rainfall
which means that we cannot see any
signal of uh increase of greenhouse
gases
in the in the in the in the changes in
terms of total rainfall but we have some
changes in terms of very heavy rains in
the in the model so we have significant
changes in terms of very evarence which
could also have an impact on crop here
so now what are the impacts in terms of
cropianism
so
um
we use the the the the
atmospheric GCM outputs to force uh two
crop models and that our star and sigma
Sarah was developed in a group and sigma
was developed in uh in Tokyo and they
were used to simulate crop yields using
these two simulation the factual and the
control Factor simulation and we have
done one Android simulations for each
so let me briefly introduce you the the
two crop models we use them so we use
them
the Sarah model which has been developed
by ciardan and it combines a water
balance models of simulating what
ordinance salt water availability but
also a carbon acceleration a
partitioning model with radiation use
efficiency with phenology
and this model was used was used several
retirement and it seemed that when you
compare it to a FL captures quite well
and the variability of crop yield in the
in the region
and we also used the
the sigma model which was developed by
narrow in Japan and including much more
processes that uh what we have in the in
the in the sarmadon so you have the this
Growing Degree Days uh uh stimulation
and but you have also a different kind
of uh effect especially the the CO2
effect on the crop here but but also a
lot of different kind of processes that
we don't have such as the the nitrogen
deficit it was and excess water that
have been considered in the volume so we
have first validated the two prop model
compared to the observation
you have iron
the two simulation of millet and saw
women
for the two crop models includes the
Sarah model and the the sigma model and
you can see that you have one model
doing a good job in terms of simulated
the annual Trends so it's the the sigma
module and the another model it's the
star model which is doing a
foreign
so now these maps are the the
geographical impacts of
of crop here associated with historical
climate change connective to a
non-warming counterfactual condition so
when you have a
negative values it indicates that you
have a yield loss due to anthropic
warming
and what you can see is that you have a
high crop in the yellow season
especially in the
in the north of West Africa in the in
the in the sahelan which is uh the
statement in the two crop model so even
if we have two very different components
you you have a kind of exactly the same
relaxable patterns with crop losses due
to uh increase of greenhouse gases and
to a historical climate change
and these losses are estimated between
10 and 80 percent formulate and between
5 and 50 percent of sovereign depending
of the crop model and it could be very
important for some countries for
instance if you look at Niger if you
look at the Senegal we have a very high
losses because of the increase of
greenhouse gases and and its impact on
climate change
so to conclude
I'll show you some examples of the
impact of human activity and Regional
climate so in Africa we have a warmer
climate with more intense rainfall which
could have a negative consequences on
agricultural production and it's already
visible when you you
when we perform the attribution study so
without on topic warming crop field
could have been a higher in West Africa
so between six percent and fifty percent
for trouble and between 11 and 80 for
minutes which is quite high and could
have an impact on food security
and even if we use two different crop
models with two level two levels of
complexity uh we have a kind of a very
similar effect of climate change on
cropianism which suggests that we have a
common mechanism which might explain
this common behavior and we suspect that
it's likely to increase
construction LED water deficits and also
the short-term crop duration induced by
the warming
and since the most optimistic climate
change scenario do not lead to a warming
below 1.5 degrees in Africa so we expect
further Crop Production losses in West
Africa and it's clear that we need to to
think about the most effective
adaptation meters
in the region that will be really
critical
uh so I think uh I'm done and I open uh
for a question if you are thank you very
much
thank you uh Benjamin that was that was
great and we we had a discussion just
yesterday about
the ways that we might assess things
like losses and damages and you know the
the impacts uh attribution detection
attribution world has become much
stronger on the climate side in recent
years uh but getting to the impact side
I think your study with with Toshi is
one of the earlier ones that's really
doing that so uh very exciting to see
any questions in the room
for what we've seen
yes uh let me get the microphone
and and just for Benjamin's sake could
you please uh just say your name in an
institution
um hi Ben I'm Jean or from Australia I
have a couple of questions about your
presentation
um yeah the first the question is is you
mentioned the D100 in Denver members so
I just wondering the in the member is
from the different calendar model or
different initial conditions so I'm not
a canier there the second position is
your work is under which emissions and
the last small question is how much
confidence you can put on your simulated
results
yeah thank you for the question in fact
the the one member the 100 simulations
are captivation of initial conditions
and so we try to look at the the effect
of Eternal variability of the model and
it could be quite high so we need to uh
to uh to to to to assimilate the the
model one right time
but we believe that uh in fact if we
want to really sample the efficiency the
the the the uncertainty may be something
like 20 to 30 simulation would be a
and so the the the question about the
uncertainty of this stimulation I mean
that it uh
it is related to the portal we have done
uh or uh or to the number I give by DNA
I I don't capture all the I want your
question
so it's about uncertainty of the world
study on
um
if I paraphrase it's you know in terms
of how do you measure uncertainty do you
measure it across those hundred
um 100 simulations do you measure it
with other uh uncertainty elements like
the the error bar around
um you know representation of different
processes uh so when we saw those box
and whiskers what was what was the the
variation in there was it must have also
been space
uh let's see I don't know if Benjamin is
Frozen
okay try again
I was offline yeah
so can you can you say it again yeah so
the question is how do you measure
uncertainty is it across the simulations
is it uh do you have any kind of larger
error metrics about how well the model
is performing
um or or factors that might be left out
for example
yes so what we are doing it's uh we are
trying first to validate the model uh
across against observation uh against
yield data and also the we are
validating the the way the the model
represents the relationship between
climate and uh and crop yield in the in
the observation for instance we have
looking at the correlation between
temperature and observed yield the
rainfall and absorb yield and we are
trying to look at how crop models are
doing that and we are confident in the
moments that they could do that
but it's true that there is a high
uncertainty and I think the The agneep
Ensemble simulation uh you have shown
just before uh a very good example of
how we could measure example uh this uh
uncertainty
and I think using a different crop
models it's a way to to sample
uncertainty it's really important to do
that okay yeah so just just to add on to
that within agmap we have taken this
study approach that uh that uh Benjamin
and and his colleague Toshi Izumi from
Japan have pioneered and we are now
bringing it to a larger acting Community
this is kind of what we do which is when
somebody has a nice study we say
wouldn't it be great if all of the
modelers did this because now we could
really understand the model uncertainty
uh component of that so we are actually
at our Global Workshop later this month
uh we are bringing many modelers
together to redo this protocol with more
models and more sites in Africa so that
we can start to factor that in
uh maybe one last question in the room
before I want to make sure we leave
Edmond sometime as well
all right I don't see a burning hand so
uh Benjamin thank you so much I hope you
get to listen also into Edmund's talk
um so our next speaker is Edmund Toten
who is joining us uh virtually