Accounting for El Niño-Southern Oscillation influence becomes urgent for predicting future East African ecosystem responses

1 Department of Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Am Mühlenberg 3, 14476 Potsdam, Germany. 2 DFG Graduate School Shaping the Earth's Surface in a Variable Environment, University of Potsdam, KarlLiebknecht-Str. 24, 14476 Potsdam, Germany. 3 Department of Earth and Environment, Boston University, 675 Commonwealth Avenue, 02215 MA, USA. 4 Biodiversity and Ecological Modelling, Institute of Biology, Freie Universität Berlin, Altensteinstr. 6, 14195 Berlin, Germany. 5 Berlin-Brandenburg, Institute of Advanced Biodiversity Research (BBIB), D-14195 Berlin, Germany. 6 Climate Geochemistry Department, Max-Planck Institute for Chemistry, Hahn-Meitner Weg 1, 55128 Mainz, Germany. 7 International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawai’i at Manoa, Honolulu, 96822 HI, USA. Correspondence to: Istem Fer (fer.istem@gmail.com)


Introduction
The 2010-2011 drought in the Horn of Africa, by some measures the worst drought in 60 years (Nicholson, 2014), is a reminder that rainfall in this politically and socioeconomically vulnerable region can fluctuate dramatically.El Niño Southern Oscillation (ENSO) influence has long been at the center of attention as a driver of this interannual fluctuations in East African rainfall (Indeje et al. 2000, Anyah and Semazzi, 2007, Nicholson, 2015), however, it is still an on-going endeavour to qualify and quantify the future behaviour of ENSO regimes under the predicted future warming (Vecchi & Wittenberg, 2010;Miralles et al., 2014).In this study we aim to identify and quantify the ENSO contribution to the East African rainfall variability in order to increase our understanding on the future response of East African as a result of the exceedingly seasonal precipitation (Bobe, 2006).Agricultural activities also depends on this strong seasonality as it determines the cropping times (Shisanya et al., 2011).Maize, beans, coffee, tea and wheat are among the important agricultural products of East Africa together with fruit products, and grasses for livestock (FAOSTAT, 2016).
An adaptive management of the limited resources will shape the future severity of climate change impacts on food productivity in this rainfall-reliant setup (Thornton et al., 2014).Therefore, a temporally and spatially extensive understanding of how the ecosystem dynamics in the region will respond to changing climate, and of particular concern to East Africa, to the ENSO regimes is needed.
Several studies related the variability in African vegetation to ENSO events (Shisanya et al, 2011;Ivory et al., 2013;Abdi et al., 2016;Detsch et al., 2016).However, the forthcoming of this relationship has been less of a focus, partly due to our imperfect knowledge on the nature of the future ENSO response to changing climate.

ENSO impact on East African vegetation
An opportunity to examine the ENSO -East African vegetation relationship is by means of using predictive tools such as vegetation models which have been successfully applied to determine and forecast regional vegetation dynamics (Moncrieff et al., 2014;Scheiter and Savadogo, 2016) as well as agricultural yields (Waha et al., 2013;Dietrich et al., 2014).In this study, we used the latest climate projections from the Intergovernmental Panel on Climate Change (IPCC) 5 th assessment report for Representative Concentration Pathway (RCP) 8.5 scenario, downscaled by the Coordinated Downscaling Experiment (CORDEX) (Nikulin et al., 2012, Endris et al., 2013) to drive such a processbased dynamic vegetation model, LPJ-GUESS (Lund-Potsdam-Jena general Ecosystem Simulator).To be able to differentiate the vegetation variability due to ENSO, we removed the ENSO signal from the climate data and simulated the vegetation under the historical climate without components related to ENSO teleconnections.Here we aim to look at the ENSO influence on East African vegetation i) under present conditions, ii) under projected future climate, and iii) under a potentially increased ENSO intensity combined with future climate change.Finally, we discuss the effects of ENSO-related vegetation variability on the carbon and hydrological cycles, and its significance for mitigation efforts in the region.

The LPJ-GUESS model
We used the dynamic vegetation model LPJ-GUESS (Lund-Potsdam Jena General Ecosystem Simulator, Smith et al. 2001;Sitch et al. 2003, Gerten et al. 2004), for our study.LPJ-GUESS is a mechanistic model in which ecosystem processes are simulated via explicit equations and is optimised for regional to global applications (Smith et al., 2001;Sitch et al., 2003;Gerten et al., 2004).Vegetation dynamics are simulated as the emergent outcome of growth, reproduction, mortality and competition for resources among woody plant individuals and herbaceous vegetation.
The simulation units in this study are plant functional types (PFTs) distinguished by their growth form, phenology, photosynthetic pathway (C 3 or C 4 ), bioclimatic limits for establishment and survival and, for woody PFTs, allometry and life history strategy.The simulations of this study were carried out in 'cohort mode,' in which, for woody PFTs, cohorts of individuals recruited in the same patch in a given year are represented by a single average individual, and are thus assumed to retain the same size and form as they grow.A sample instruction file used to run LPJ-GUESS in this study with all the parameters listed can be found under github.com/istfer/ENSOpaper/ins. Primary production and plant growth follow the approach of LPJ-DGVM (Sitch et al. 2003).Population dynamics (recruitment and mortality) are influenced by available resources and environmental conditions, and depends on demography and the life history characteristics of each PFT (Hickler et al. 2004).Disturbances such as wildfires are simulated based on temperature, fuel load and moisture availability (Thonicke et al. 2001).Litter arising from phenological turnover, mortality and disturbances enters the soil decomposition cycle.Decomposition rates depend on soil temperature and moisture (Sitch et al. 2003).Soil hydrology follows Gerten et al. (2004).A more detailed description of LPJ-GUESS is available in Smith et al. (2001).We used LPJ-GUESS version 2.1 which includes the PFT set and modifications described in Ahlström et al. (2012).LPJ-GUESS has already been successfully applied and validated to match present-day observed biome distributions of East Africa (Fer et al., 2016).

ENSO
To isolate the ENSO signal contribution to East African precipitation, we conducted an Empirical Orthogonal Teleconnections (EOT) analysis between sea surface temperatures (SSTs) in the tropical pacific ocean and precipitation over East Africa (see section Identifying the ENSO signal).For historical extraction , we use monthly National Oceanic and Atmospheric Administration Extended Reconstructed Sea Surface Temperature (NOAA ERSST) V4 dataset (Huang et al., 2014;Liu et al., 2014), available on 2ºx2º global grids as a predictor field.As the response series, we used monthly Climatic Research Unit Time Series (CRU TS) 3.20 dataset (Harris et al., 2014), available on 0.5ºx0.5ºglobal grids.

LPJ-GUESS datasets
LPJ-GUESS requires monthly climate (temperature, precipitation, cloud cover), atmospheric CO 2 concentration and soil texture as input data.For historical period , we used monthly CRU TS 3.20 climate data.We chose these years for all historical analysis throughout the study as the historical simulations of CORDEX outputs are available for this period.For future projections , we used the outputs from the Coordinated Regional Climate Downscaling Experiment (CORDEX) program for the Africa domain.For the future scenario, we chose high emissions Representative Concentration Pathways (RCP) 8.5 scenario as this is considered as the baseline scenario under the assumption that climate mitigation targets will not be met (Moss et al., 2010;Riahi et al., 2011).This is a reasonable choice given that the observed trends are pointing beyond RCP 8.5 trajectory (Sanford et al., 2014).CORDEX, downscaled global climate models (GCMs) by using regional models, and the outputs are available from ESGF-CoG data portal (https://pcmdi.llnl.gov/search/esgf-llnl/).For East African climate, we took the ensemble mean of 9 downscaled models for future projections of RCP 8.5 scenario: CCCma CanESM2, CERFACS CNRM-CM5, QCCCE CSIRO Mk3-6-0, ICHEC EC-EARTH, IPSL CM5A-MR, MIROC5, MPI ESM-LR, NCC NorESM1-M, NOAA GFDL-ESM2M (Full names of the models are given in the Appendix).Instead of working with individual models we decided to drive our simulations with ensemble means as it has been shown to outperform individual models and show a better agreement with data (Endris et al., 2013).RCP 8.5 compatible atmospheric CO 2 values were also used as provided by NOAA -GISS experiment (Nazarenko et al., 2015).

Bias correction
To eliminate biases originating from using CRU climate dataset for present and model simulations for future, we subtract the 1951-2005 climatology of downscaled GCM ensemble from the 1951-2100 time series of the ensemble and add the anomalies on CRU 1951CRU -2005 climatology.This way we will able to have a meaningful comparison between CRU-driven and GCM-driven vegetation model outputs while keeping the climate variability from the GCM simulations.We should note here, that this would not change the ENSO signal we will retrieve from the GCM outputs (see next section) because we deseason and work with anomalies of the data field for our EOT analysis.

Future Pacific SSTs
For future pacific SSTs, we used outputs from GCM simulations of the same models listed above for RCP 8.5, except ICHEC EC-EARTH which was not available from the data portal at the time.However, these GCM outputs were not downscaled and standardized in terms of spatial resolution (they were all available in monthly time steps in terms of temporal resolution).We created raster files from these outputs and using the "raster-package" (R Core Team, 2015; v2.5-8), we resampled these rasters to brought them to the same spatial resolution as NOAA ERSST V4 dataset, and we took the ensemble mean.

Identifying the ENSO signal
Here we first identify the ENSO signal as a driver for monthly East African precipitation variability over the historical period .To do this we investigate the teleconnectivity between the SSTs in the tropical Pacific Ocean and precipitation over East Africa by using empirical orthogonal teleconnections (EOT).The method is explained by van den Dool et al. (2000) in detail, andAppelhans et al. (2015) implemented the original algorithm in R ('remote' package by Appelhans et al., 2015;R Core Team, 2015).Here, we only briefly present the major steps of the EOT analysis:

Empirical Orthogonal Teleconnections (EOT)
In the EOT analysis, we aim to establish an explanatory relationship between the temporal dynamics of a (predictor) domain and temporal variability of another (response) domain.Such predictor and response domains consist of gridded time series profiles: in this study the gridded monthly SST time series of the tropical pacific as predictor and gridded precipitation time series of East Africa as the response.Then, the first step of EOT analysis is to regress these time series of each predictor domain grid (N p ) against the time series of each response domain grid (N r ) (Appelhans et al., 2015).This will result in a (N p x N r ) number of regression fits after which we can calculate the sum of coefficients of determination per predictor grid (ending up with N p sum of coefficients of determination values).Then, the grid with the highest sum will be identified as the "base point" of the leading mode as it explains the highest portion of the variance in the response domain (Appelhans et al., 2015).The time-series at this base point is referred as the leading teleconnection, or hereafter as the first EOT.

Screening for ENSO signal
We applied the EOT method on de-seasoned and de-noised data fields in order to retrieve a low frequency signal such as ENSO: here we used the SSTs in the tropical pacific ocean as predictor and precipitation over East Africa as response.Then we proceeded to calculate the SSTs modes that are most affecting for East African rainfall variability.We found the 1 st EOT to be the ENSO signal.We compared this EOT with Nino3.4 index to see whether we were able to isolate the ENSO signal.The commented code used for all methods is publicly available on Github (github.com/istfer/ENSOpaper).
Before moving on to identifying future pacific sea surface temperature -East African precipitation interactions, we applied the same extraction to historical GCM outputs (simulations) to see whether we can identify a similar relationship from GCM products.Finally, we prepared the model drivers with the modified ENSO signal we identified from the future simulations (see next section) and ran the model with these datasets (here we focused on precipitation data only, while precipitation varies in these simulations and the others -temperature-were kept as they were in the climate datasets: present -CRU TS 3.2, future -CORDEX ensemble de-biased using CRU as explained above).

Removing and intensifying the ENSO signal
In order to investigate the contribution of the ENSO signal to East African precipitation, we removed the ENSO signal and explored the rainfall pattern with and without ENSO contribution as well as the resulting vegetation changes calculated by LPJ-GUESS.We used the "remote" package which specifically implements the EOT analysis and keeps track of calculated values in a structured workflow: The rainfall we are left with after removing the first EOT mode (which we identified as the ENSO signal) becomes the rainfall behaviour without ENSO contribution (within the 'remote' package, this calculation of the residuals is automatically available after the calculation of the EOT modes).
Therefore, if we take the difference between these residuals and the initial de-seasoned and de-noised data, this will give us the amount that we need to subtract from the raw data field to obtain the rainfall behaviour without ENSO contribution.The steps are explained below as pseudocode: i) Deseason and denoise the response and predictor fields.ii) Conduct Empirical Orthogonal Teleconnection (EOT) analysis: Here the EOT modes object can be thought as a list that stores both the time series of the modes, the reduced fields obtained after the removal of each mode, slopes and intercepts of the fields (for more details see Appelhans et al., 2015).
iii) Calculate the difference (Diff) between the de-seasoned, de-noised data (EA dns ) and the rainfall behaviour without ENSO contribution from the information that is already stored in the resulting EOT modes object (ENSO signal is the first mode, therefore the rainfall behaviour we are left without ENSO will be the EA modes, rr1 where subscript rr1 indicating "response residual" after the removal of the first EOT mode: iv) If we subtract this difference from the initial raw response field (EA r ), we will obtain the East African precipitation without ENSO contribution (EA r, woENSO ): Here EOT modes, eot_1, EOT modes,ri_1 and EOT modes, rs_1 refer to the EOT time series of the 1 st mode (the ENSO signal), intercept of and slope of the response field calculated for the 1 st mode (Appelhans et al., 2015).
vii) Then, it is possible to modify the future ENSO signal (EOT EA r, new = EA r, woENSO + Diff new (8) Here it is noticable that slope(s) and intercept(s) would also have been different if the ENSO signal was changes (EOT modes, eot_m ).However, this simplification is adequate for experiments in this paper.
Moreover, we used the intercept and slope we retrieved from the EOT analysis on observational datasets while re-calculating the new difference (Diff new ) due to intensified ENSO signal.Because the East we were also able to preserve the more accurate patterns in rainfall differences.
viii) Finally, we obtained the modified ENSO signal (EOT modes, eot_m ) in Eq. ( 7) by detrending (fitting a LOWESS smoother and removing it from the signal) and multiplying the ENSO signal we extracted from the future simulations (deseasoned and denoised GCM simulations for East African rainfall -EA dns_ftr -and Tropical Pacific SSTs -PAC dns_ftr -) with a coefficient (k = 3) such that the peaks of the new signal would be as strong as the observed anomalies (± 2.5 °C, Figure 1 and S3).For the code of this step see IdentifyModifyFutureENSO.R script at github.com/istfer/ENSOpaper.

EOT analysis-extracting the ENSO signal
We compared the first EOT mode extracted after de-seasoning and de-noising the fields as explained by Appelhans et al. (2015) to the Nino-3.4index recorded (Figure 1).The high correlation between the two (R = 0.90) confirms that we were able to extract the ENSO signal by conducting the EOT analysis.On the predictor domain (Tropical Pacific SSTs), the Nino-3.4region found to be the area which explains

2 Historical simulations with and without the ENSO signal
After calculating the ENSO signal, we removed the amount due to ENSO from the East African precipitation (CRU precipitation), and simulated East African vegetation using both datasets (CRU normal and CRU without_ENSO ) to see its effect on vegetation.As it can clearly be seen from Figure 1, impact of ENSO signal is not the same everywhere on the East African domain, which means removing ENSO signal would have differential effects on the rainfall amount.Regional maps of rainfall anomalies for the strongest three El Niño (1972,1982,1999) and La Niña (1973,1975,1988)  these parts of East Africa receive up to 200 mm yr -1 more rain other than they would receive, while they receive ~100 mm yr -1 less rain during the La Niña years.The impact is the opposite for western part of Ethiopia : receiving ~200 mm yr -1 less rainfall during El Niño years, while ~100 mm yr -1 more during La Niña years.To provide a closer look to the impacts of ENSO related variability on vegetation, we report the results on vegetation simulations within the two transects where we see the strongest impacts over these two oppositely behaving, coastal and northwestern, regions (Fig. 2).
We drove the dynamic vegetation model once with CRU dataset as is and once with CRU dataset with removed ENSO contribution.Results are reported for the previously mentioned north and south sites in Figure 3 and Table 1.Outputs from the northern-inner part show more variability within the chosen grid-cells for this region.Indeed, this region is on the western edge of Ethiopian Plateau, with a transition of biomes from mountainous forests to woodlands and savannas (Fer et al., 2016).As the rainfall patterns in relation to ENSO signal was the opposite between these regions (Figure 2), we expect to see that the response of these regions to the removal of the ENSO signal to be opposite, and this is indeed what we see in Figure 3: While outputs such as net primary productivity (NPP), net ecosystem exchange (NEE), soil evapotranspiration (EVAP) and surface runoff (RUNOFF) for northern site were less than otherwise they would be for El Nino events, they would be higher La Nina events.
And the opposite behaviour is true for the southern site.ENSO contribution, for the strongest three (1972, 1982, 1997) El Niño and (1973, 1975, 1988)  In order to test whether the difference between the vegetation simulated under climate with ENSO contribution, and the vegetation simulated under climate with removed ENSO contribution, we conducted a paired t-test on the outputs.The results (Table 1) show that except NEE for northern sites, all differences between the vegetation simulated with and without ENSO impact were significant.In summary, ENSO contribution is significantly affecting the East African vegetation and we would expect different vegetation if there were no ENSO events.We conducted the same paired t-test for the north and south sites for the future simulations (Table 1).In the northern site where intensified signal leads to less (more) NPP during El Niño (La Niña) years, the mean difference is -52 (+32.6)gC m -2 yr -1 between the vegetation simulated under future climate with and without intensified ENSO signal.In the southern site where intensified signal leads to more (less) NPP during El Niño (La Niña) years, the mean difference is +49.1 (-101.1)gC m -2 yr -1 between the vegetation simulated under future climate with and without intensified ENSO signal.While the mean differences for NEE were not significant at the northern site, southern site stores 112.7 (173.1)gC m -2 yr -1 more (less) carbon under the intensified ENSO scenario during the El Niño (La Niña) years.
Another noteworthy output is that, the northern site has a lot more runoff during the La Niña years under the intensified ENSO scenario.This is especially clear on Figure 4 where spatial patterns of the differences in the simulated future vegetation under RCP 8.5 scenario with and without intensified ENSO are shown.The opposite behaviour of the northern parts of East Africa under El Niño vs. La Niña conditions can also be observed on NPP and RUNOFF figures, wheres for NEE differences a particular pattern is not emergent.This is mainly because NEE values can themselves be negative (flux to ecosystem) and positive (release to atmosphere).(2025,2026,2077) were calculated by subtracting the GCM-ensemble driven simulations without modification from the GCM-ensemble driven future simulations with intensified ENSO signal.(Right) Same for strong future La Nina events (≤ -1.5°C) (2039,2049,2084,2039,2049,2084).

Identifying and intensifying the ENSO Signal
East African rainfall variability and especially contribution of the ENSO was investigated before (Indeje et al., 2000;Schreck and Semazzi, 2004).Here we used a different method, Empirical Orthogonal Teleconnections (EOT) analysis to quantitatively calculate the ENSO contribution and found the spatial correlation patterns over the East Africa region to be in agreement with previous studies who independently looked at Pacific SST drivers for East African precipitation (Anyah and Semazzi, 2007).
The ENSO signal identified through this method was also showing strong correlation with NOAA Nino3.4 index, which means EOT method was a suitable choice for our analysis.
Using the EOT method, we presented a conservative estimate of ENSO variability in East African rainfall, because we considered the direct Tropical Pacific teleconnection only.However, there are accompanying changes: ENSO events are linked to Indian Ocean Dipole, which more directly influences EA rainfall (Black et al., 2003).It has been suggested that subsequent to ENSO triggering, internal Indian Ocean dynamics could take over.More specifically, East African rainfall increases as the western Indian Ocean gets warmer which is often associated with ENSO forcing.However, warmer western Indian Ocean can weaken the rains when it interacts with southeasterly atmospheric circulations (Schreck and Semazzi, 2004).The exact relationship and discrepancies between IOD and ENSO behaviours are yet to be revealed (Lim et al. 2016).Still, we found that the ENSO-East Africa connection to be robust as previous studies (Indeje et al., 2000;Anyah and Semazzi, 2007) and did not delve into IOD relationship.Also, we were motivated by the previous studies that have identified ENSO influence to be important in dryland vegetation dynamics (Ahlström et al., 2015;Abdi et al., 2016).Hence, we focused on reporting more comparable results with those.Another factor that could affect our estimations is atmospheric latency.In our analysis, we did not consider any time lags for the tropical pacific SST anomalies and East African precipitation teleconnection, but a time lag can be expected due to atmospheric circulation processes, and the influence of SST anomalies might develop instantaneously.Therefore if we account for this time lag, we might explain even more of the rainfall variance.For a more comprehensive study of SST influences on East African rainfall see Appelhans and Nauss (2016).
The EOT method, which is shown here to be effective on the historical observations, produced different East African rainfall variability patterns due to Pacific SSTs when GCM outputs were used.Also the ENSO signal retrieved was much weaker than the one extracted from the observation datasets in terms of both ENSO event strength and the match (correlation) with the Nino 3.However, more formal tests are needed to conclude on this matter, which was beyond the scope of this study.

Present-day simulations
Despite the fact that our estimation of ENSO contribution to the East African interannual rainfall variability was conservative, the precipitation difference between with and without ENSO contribution was equivalent to one or even two rainy months for some of the grid cells.These regions already receive a small amount of rainfall and even minor differences are critical for agricultural food production and the productivity of the natural ecosystem that sustains a large biodiversity.We found up to 0.1 kgC m -2 yr -1 mean difference in NPP in the southern parts of the region solely due to ENSO contribution.
We found that ENSO influence on net ecosystem exchange is also prominent in the semi-arid ecosystems of East Africa.Especially, in southern-coastal parts, ecosystem releases more to the atmosphere during La Nina events whereas it would store more carbon otherwise.This would also have implications on global carbon cycle as it has previously been found that regional response of semi-arid ecosystems, mainly occupying low latitudes, play an important role in determining the trend in CO 2 uptake by terrestrial ecosystems (Ahlström et al. 2015).For instance, La Nina events are associated with large carbon sinks in Australian semi-arid ecosystems due to increased precipitation and 2011 anomaly in global carbon sink was mainly attributed to the response of Australian ecosystems (Poulter et al., 2014).While semi-arid ecosystems of East Africa might play a smaller role than Australian ones (simply due to the difference in the area they cover), it would still influence the magnitude and trend of the global carbon sink by terrestrial ecosystems.Our findings reiterate the importance of considering ENSO contribution in carbon budget calculations for any region that is influenced by ENSO variability.
Here we also reported ENSO influence on surface runoff as excess runoff response causes problems in East Africa.In this region, Rift Valley Fever (RVF) and Malaria outbreaks are threatening the livelihood of the society and these vector-borne diseases are transmitted by mosquitoes who breed in flooded lowlying habitats (Meegan and Bailey, 1989, Kovats et al., 2003, Hope and Thomson, 2008).For example, a major RVF outbreak during late 1997 to early 1998 has been linked to the heavy and prolonged rains that are associated with 1997-98 El Nino event (Trenberth, 1998), in agreement with our results where we found that the southern coastal site experiences higher runoff during El Nino events than otherwise it would do.
Another important ecological factor to be considered for East African vegetation dynamics is fire.The fire occurrence in LPJ-GUESS depends on the atmospheric temperature values, and moisture and litter availability.Therefore, although we did not calibrate LPJ-GUESS fire parameters for East Africa or explicitly changed fire regimes under any of the scenarios, the model simulated the changes in fire behaviour due to different environmental states implicitly.More specifically, for the southern coastal part, a higher mean expected return time of fire was simulated during the El Nino years for simulations with ENSO contribution than without due to higher moisture availability during ENSO years for this region (not shown).For the same site, the opposite was true for La Nina years, and the whole behaviour was reversed for the northern site.A more sophisticated fire -ENSO -vegetation interplay can be further investigated using models that have an individual level representation of fire response such as aDGVM2 (Scheiter, Langan and Higgins, 2013).

Future simulations
For the future simulations we intensified the ENSO signal such that very strong ENSO years can also be experienced as it is the real-world case.Therefore, our 'intensified' version is more likely to be the realistic version, and considering that we are expected to experience even stronger ENSO events in the future than today (Cai et al., 2013) we could have intensified this signal even more.However, our results with even this modest intensification shows the importance of capturing atmosphere-ocean teleconnections in climate simulations for reliable future simulations of the ecosystems.We simulated large differences in future ecosystem responses under our intensified ENSO scenario, as large as the differences we calculated for the present-day with and without ENSO simulations.In other words, if we were to predict vegetation response to future climate change by using GCM outputs as they are, it would be as if simulating the present-day vegetation with climate data without any ENSO contribution.
Apart from the temporal and strength mismatch, the GCM simulations are also producing different spatial patterns for tropical Pacific SST-East African rainfall teleconnection.Therefore, in our modification we chose to correct for this spatial pattern by using the relationships we obtained from the observed datasets as this correction did not influence the temporal behavior and the peakiness of the ENSO signal retrieved from the GCM simulations.As a result, our findings can be compared for present day patterns directly.
Especially the total surface runoff and NPP responses were considerably underestimated.Under the intensified ENSO scenario, an excessive amount of runoff is simulated for the northern parts during La Nina years and for the southern parts during El Nino years, which would exacerbate the disease events in the region.Likewise, the simulated low amounts of runoff for the northern parts during El Nino years indicate drought events in this parts of the region.This effect can also be seen in the simulated NPP responses which reduces considerably for the northern parts during El Nino years.Furthermore, the amounts we calculated here agree well with previous studies showing changes in NPP supply associated with ENSO events in sub-Saharan African drylands (Abdi et al., 2016).

Conclusion
In this study we investigated the relationship between interannual East African rainfall variability and ENSO events using Empirical Orthogonal Teleconnection (EOT) analysis, and found a robust connection from observational datasets in agreement with previous studies.While the strength of this relationship is not homogeneous among the region, and the patterns of vegetation response presented opposite characteristics in the northern and southern areas, ENSO influence on East African vegetation and in return its carbon and hydrological fluxes was apparent.However, we also found that climate simulations are still not good at capturing this pertinent rainfall variability due to ENSO.This implies that the future vegetation would be different from what is simulated under these climate model outputs lacking accurate ENSO contribution, whereas it is important to account for this influence which can bring further environmental stress to East Africa as we show here.Overall, our results highlight that more robust projections on coupled atmosphere-ocean teleconnections can help reducing large uncertainties of the future magnitude and sign of carbon sink provided by terrestrial ecosystems by improving our understanding on the vegetation response.

13
Biogeosciences Discuss., doi:10.5194/bg-2017-49,2017 Manuscript under review for journal Biogeosciences Discussion started: 9 March 2017 c Author(s) 2017.CC-BY 3.0 License.African rainfall patterns explained by Tropical Pacific SSTs in the GCM simulations are different from observations (Figure A1 and A2).By using slopes and intercepts obtained from the observational data

14
BiogeosciencesDiscuss., doi:10.5194/bg-2017Discuss., doi:10.5194/bg--49, 2017     Manuscript under review for journal Biogeosciences Discussion started: 9 March 2017 c Author(s) 2017.CC-BY 3.0 License. the most variance in the response domain (East African precipitation) as expected (FigureA1).The time series of the first EOT explains 0.85% of the rainfall variability over the analyzed period here(1951-  2005).This small amount is not surprising, because East African precipitation follows a strong seasonal pattern following the position of the Intertropical Convergence Zone (ITCZ) within the year.Therefore, seasonality alone explains most of the variability in East African rainfall.In addition, due to the complex topographical setting of the region, local conditions play a major role in the variation of the rainfall.Still, when we de-season and de-noise the raw data fields to identify low-frequency signals such as ENSO, the ENSO signal emerged as the most important teleconnection between tropical pacific SST anomalies and East African precipitation.Having successfully extracted the ENSO signal from the observation datasets, we applied the same procedure with the outputs of the climate models.We used an ensemble of SSTs from 8 GCM outputs as the predictor field and an ensemble of rainfall from 9 GCMs downscaled by CORDEX as the response domain.The comparison between the calculated first EOT time series to the Nino-3.4index observed was much poorer (R=0.19)(Figure1) which indicates that GCMs are not capturing the coupled Pacific SST -East African rainfall teleconnection.Another striking feature that can be observed in Figure1is the smoothness of the time series of the 1 st mode calculated from the EOT analysis on ensembles of climate model outputs when compared to the recorded index and the calculated ENSO signal from the observation datasets.In other words, the ENSO signal retrieved from the EOT analysis on the climate model outputs is nowhere near strong as the others.According to this signal obtained from the simulation datasets, the only ENSO events that happened during 1951-2005 period were in the "weak" category (Figure1).Finally, the calculated patterns were different than the EOT analysis on observed datasets (the corresponding figure is given in the Appenix, FigureA2): The areas where the sum of the coefficients of determination were the highest were again situated around Nino 3.4 region but closer to the Nino 4 region this time (Figure A2 -left panel).Spatially, the north-eastern and central parts of the response domain are the most explained whereas previously it was more centralized around the coastal equatorial parts of the region (Figure A2 -right panel).

Figure 1 .
Figure 1.The comparison between Nino 3.4 Index recorded by NOAA (black line), time series of the 1 st mode obtained from the EOT analysis on observed CRU-NOAA datasets (red), and time series of the 1 st mode obtained from the EOT analysis on ensembles of the climate model simulations (blue).Black dashed line: Zero line.Blue dashed lines ± 1.0° anomaly thresholds for categorizing moderate ENSO events.Red dashed lines: ± 2.0° anomaly thresholds for categorizing very strong ENSO events.16 events between 1951-2005 period are given in Figure 2.Here we show what the rainfall would be if there were not any influence by the Pacific SSTs particularly during these three years.Especially the coastal Kenya and Tanzania experience a strong change in the amount of rainfall they receive: During the El Niño periods,

Figure 3 .
Figure 3. Carbon and water fluxes from north and south transects, simulated under climate with and without La Niña events between 1951-2005 period.Top panel: Net Primary Productivity (NPP).Middle: Net Ecosystem Exchange (NEE).Bottom: Total runoff.Locations of the northern-inner and southern-coastal sites are given in Figure 2. 19 330 Biogeosciences Discuss., doi:10.5194/bg-2017-49,2017 Manuscript under review for journal Biogeosciences Discussion started: 9 March 2017 c Author(s) 2017.CC-BY 3.0 License.

Figure 4 .
Figure 4. Simulated future differences in the NPP, NEE and RUNOFF between with and without intensified ENSO runs.(Left) Mean differences for the strong El Nino years (≥ + 1.5°C) (2025, 2026, 2077) were calculated 4 index.As a preliminary investigation (not shown), we conducted the EOT analysis across mixture of observed-simulated datasets: Pacific SSTs observed (NOAA ERSST) -East African precipitation simulated (CORDEX), and Pacific Biogeosciences Discuss., doi:10.5194/bg-2017-49,2017 Manuscript under review for journal Biogeosciences Discussion started: 9 March 2017 c Author(s) 2017.CC-BY 3.0 License.SSTs simulated (GCMs) -East African precipitation obsrved (CRU).The ENSO signal retrieved from the Pacific SSTs observed -East African precipitation simulated pair was a better match with Nino 3.4 index than the one extracted from the simulated-simulated pair but still worse than the one extracted from observedobserved dataset pair, whereas ENSO signal retrieved from the Pacific SSTs simulated -East African precipitation obsrved pair was not a better match to Nino 3.4 index than the one extracted from the simulated-simulated pair.This quick test indicated that the GCM simulated Tropical Pacific SSTs are the main source of the poor teleconnection identified from the simulated-simulated pair and a dynamic downscaling of the tropical Pacific SSTs might improve the ocean-atmosphere coupled teleconnection.

Table 1 .
Paired t-test results to test whether there is a significant difference in the vegetation simulations that are driven with and without ENSO contributions for the three strongest ENSO events during the historical periodand with and without intensified ENSO signal for the strongest ENSO events during the future period.Grey highlighted cells indicate insignificant differences according to p=0.05 threshold.Significant p-values indicate rejection of the H 0 in favor of the alternative, that is true difference in means is not equal to 0. p: p-value, md: mean of the differences NPP: Net Primary Productivity, NEE: Net Ecosystem Exchange, RUNOFF: Surface runoff).