The consecutive dry days to trigger rainfall over West Africa

Accepted Manuscript The consecutive dry days to trigger rainfall J.H. Lee PII: DOI: Reference:

S0022-1694(16)30354-7 http://dx.doi.org/10.1016/j.jhydrol.2016.06.003 HYDROL 21321

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Journal of Hydrology

Please cite this article as: Lee, J.H., The consecutive dry days to trigger rainfall, Journal of Hydrology (2016), doi: http://dx.doi.org/10.1016/j.jhydrol.2016.06.003

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The consecutive dry days to trigger rainfall

J.H., Lee1,2 1 Politecnico di Milano, Piazza Leonardo Da Vinci, 32, 20133 Milano, Italy 2 CESBIO, 13 avenue du Colonel Roche, UMR 5126, 31401 Toulouse, France

* correspondence: [email protected] Tel: +39-02-2399-6209; Fax: +39-02-2399-6207

Abstract

In order to resolve contradictions in addressing soil moisture-precipitation feedback mechanism and to clarify the impact of antecedent soil moisture on subsequent rainfall evolution, we first validated various data sets (SMOS satellite observations, NOAH land surface model, TRMM rainfall, CMORPH rainfall and HadGEM climate models) with the Analyses Multidisciplinaires de la

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Mousson Africaine (AMMA) field campaign data. The Soil Moisture and Ocean Salinity (SMOS) soil moisture, and Global Land Data Assimilation System (GLDAS)-NOAH land surface model data sets are selected for soil moisture and rain. Based upon the results consistently showing a positive relationship between the consecutive dry days and rainfall, we support a negative feedback often neglected by model process. The merit as an operational framework is that the tendency of consecutive dry days is consistent across various data sets, contrary to the direct comparison of soil moisture state. As this approach uses total rain instead of afternoon rain, this can take into account the day-time effect of temperature on the boundary layer and the morning time effect of buoyancy forces on convective rainfall. In addition, this approach is less sensitive to systematic errors in data sets, as this measures a temporal gradient of soil moisture state. Keywords Soil moisture-precipitation coupling, consecutive dry days, GLDAS, SMOS soil moisture, West Africa, AMMA field campaign

1.

Introduction

The land surface plays an important role in the modulation of the macroscale monsoons (Koster et al., 2004, Xue et al., 2004). Along with East Asia, West Africa is the very region that a large-scale soil moisture distribution governs the monsoon circulation (Douville et al. 2001, Hong & Pan, 2000, Klüpfel et al., 2011, Taylor, 2008). A study on the understanding of land surface – atmosphere feedbacks and the role of land surface in predictability is in line with the Global Land Atmosphere System Study (GLASS) research program under Global Energy and Water Cycle Experiment (GEWEX), which aims at improving the understanding of physical interactions between the land

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surface and the atmosphere, and how their feedbacks influence the subsequent evolution in atmosphere, with a focus for the modelling community. Land surface-precipitation interaction mechanisms are generally organized into three perspectives (Findell & Eltahir, 2003, Guillod et al., 2015): i) Positive feedback - wet soils are more likely to induce precipitation. Steiner et al. (2009) stated that the West African Monsoon (WAM) system belongs to a positive feedback mechanism (often called ‘hot spot’), based upon their land-climate model. In this mechanism, wet soils add more moisture to the near-surface boundary layers, increasing the moist entropy flux, and the amount of Convective Available Potential Energy (CAPE) in a shallower Planetary Boundary Layer (PBL). The latent heat enhanced by wet soils further develops rainfall through a meridional gradient of Moist Static Energy (MSE) and a local evaporation (Douville et al., 2001, Eltahir, 1998, Lee et al., 2012). ii) Negative feedback - dry soils are more likely to trigger precipitation. Taylor et al. (2012) suggested that there is a negative spatial correlation in West Africa between antecedent soil moisture and subsequent afternoon rainfall, based upon satelliteretrieved soil moisture and rainfall data (Koster et al., 2003). They also argued that such a negative feedback was neglected by several climate models including HadGEM. In that analytical frame, dry soils increases the sensible heat flux in the deeper planetary boundary layer, further making air mass buoyant, and increasing convection, and the intensity and occurrence of rain events (Giorgi et al., 1996, Kohler et al., 2010, Suppiah & Hennessy, 1998, Taylor, 2008, Xue et al., 2004). iii) Transitional feedback - precipitation is spatially located in drier soils although precipitation activity has temporally a positive correlation with soil moisture. This perspective suggests that most of the hot spots lie in transition zones (including West Africa) where evaporation is suitably high but still sensitive to soil moisture, and the boundary layer moisture induces moist convection (Koster et al., 2004, Seneviratne et al., 2006). However, there are several arguments about the soil moisture-precipitation feedback mechanisms – e.g. which one of positive, negative or transitional feedbacks is dominant over West Africa. This is

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unsettled, mainly due to the disagreements between the satellite observation and climate model, and uncertainty in data sets. In climate models, the analysis is largely influenced by the convection parameterization scheme, which is usually biased (Guillod et al., 2015, Taylor et al., 2012). In addition, the contribution of soil moisture to monsoons or vertical inhomogeneity is mostly neglected by models (Koster et al., 2004, Lee, 2014, Taylor, 2008). Additionally, there are the satellite retrieval errors associated with soil moisture and precipitation products (Koster et al., 2003, Lee, 2016). Jackson et al. (2010) concluded that all the standard soil moisture products from the National Aeronautics and Space Administration (NASA), Land Parameter Retrieval Model (LPRM) and Japan Aerospace Exploration Agency (JAXA) retrieval algorithms contain large errors and biases. None of them provides reliable soil moisture estimates satisfying all the conditions. West Africa is not an exception. Gruhier et al. (2010) and Dente et al. (2012) also reported on high errors in soil moisture products especially over dry conditions such as West Africa. With respect to precipitation satellite errors, Huffman et al. (2007) previously informed that Tropical Rainfall Measuring Mission (TRMM) 3B42 retrieval algorithm may produce substantial systematic errors, due to a weak physical relationship between infrared brightness temperature and precipitation under certain conditions (Chen et al., 2013, Junzhi et al., 2012, Müller & Thompson, 2013). Due to data vulnerability to systematic errors, the fragmented opinions on the surface-precipitation coupling mechanisms are unavoidable. In order to resolve such a contradiction about a feedback mechanism, we employed the Global Land Data Assimilation System (GLDAS) data reconciling the model estimates with the observations (Zhang et al., 2008). In addition, the new observational dataset from the SMOS satellite designed for retrieving soil moisture for the first time ever in history was used (Kerr et al., 2012). This data set is known to be accurate over West Africa (Lee et al., 2015). Additionally, we also validated various data sets before using them for the assessment of soil moisture and rainfall interactions. The Analyses Multidisciplinaires de la Mousson Africaine (AMMA)–Couplage de l'Atmosphère Tropicale et du Cycle Hydrologique (CATCH) field campaign data was employed for this purpose. AMMA-CATCH

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has been previously established for Global Telecommunication System since 2006. This is a long-term observation system installed at West African regions where operational networks were failing (Cappelaere et al, 2009, Lebel et al. 2011). Several instruments were deployed to monitor ocean, hydrology, aerosols and vegetation as well as atmosphere dynamics. In specific, the up-scaled data from the rain gauge network and soil moisture measurements was used in this study. The objective of this study is to assess various data sets for the soil moisture and rainfall estimates, to clarify the influence of land surface on subsequent evolution of rainfall in West Africa, and to resolve the contradictions that exist in addressing that feedback. The finding of this study is useful for improving the predictability of precipitation, for identifying the process currently neglected by models, as well as for establishing the climate indicator in the Intergovernmental Panel on Climate Change (IPCC)-AR4 model ensemble research (Frich et al., 2002, Tebaldi et al., 2006) for projecting the extreme conditions. This paper is organized as follows. In section 3.1 and 3.2, the soil moisture estimates from SMOS, GLDAS, and climate model and the rainfall estimates from TRMM, Climate Prediction Center Morphing Method (CMORPH), GLDAS, and climate model are validated with field measurements, respectively. In section 3.3, a spatial correlation with total rainfall is assessed with respect to antecedent soil moisture and consecutive dry days. In section 3.4, the same experiment is repeated for afternoon rain only.

2.

Method

2.1. Study domain and AMMA field campaign

The study area is illustrated in Figure 1 – the red line delineates the boundary conditions and blue line

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denotes the actual study domain – that covers the sub-Saharan area between 6.5°-14.5°N and 8.5°W9°E. Usually, there is a meridional moisture gradient between the dry Sahel and vegetated Guinea coast region. There is a latitudinal gradient in terms of vegetation as well as soil texture. The AMMA field campaign system is well established in this region for validating the model simulations and satellite observations. These include rain gauge and soil moisture measurements in Niger (50 km East of Niamey), and Benin (Djougou region). These two local point sites are indicated in red in Figure 1(a). 6 and 8 soil moisture probes are deployed at a depth of 0.05 m for the top layer and 1 m for the root zone layer in Niger and Benin sites, respectively. These measurements are spatially averaged in order to make them representative of satellite data on the 0.25◦ × 0.25◦ pixel (For the detailed soil moisture measurement distributions of each super site, please see www.amma-catch.org). Detailed descriptions of the AMMA field network is found in Cappelaere et al. (2009) for Niger, and Seguis et al. (2011) for Benin. Niger is in sandy and dry bare soils, while Benin is in moderately wet, and sparsely vegetated site with less sand but higher clay factions. Soil property varies with soil texture conditions, as previously discussed by Lee et al. (2014).

2.2. Satellite observations

For the rainfall observations, the TRMM 3B42 data (3 hourly, 0.25, ver.7) achieved from NASA (disc.sci.gsfc.nasa.gov) TRMM multi-satellite precipitation analysis were used (Pellarin et al., 2013, Huffman et al., 2007). The other rainfall data set was the CMORPH version 1.0 (3 hourly, 0.25). This product that combines the passive microwave data with thermal infrared data uses the motion vectors derived from half-hourly interval geostationary satellite IR imagery to complement the precipitation estimates derived from passive microwave data (Joyce et al., 2004). It is also morphed between microwave sensor scanning time by a linear interpolation (Taylor et al., 2012).

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For the soil moisture observations, one day ascending SMOS surface soil moisture data was directly achieved from CATDS (http://www.catds.fr/). The SMOS mission was launched in November 2009. It carries a microwave radiometry at L-band (1.4 GHz, 21 cm) with a higher sensitivity to soil moisture than other higher frequency radiometers, optical sensors, and ASCAT or AMRS-E microwave remote sensors at C-band not penetrating dense vegetation (Kerr et al., 2012). The SMOS L3 product (ver. 2.72, 1 day, 25 km) is a global composite of a swath product. The fundamental retrieval algorithm is based upon a cost minimization that diminishes a mismatch between brightness temperature measurement and simulation. A detailed theoretical description of radiative transfer model and retrieval algorithm was provided by Kerr et al. (2013). A new feature of this version is that a Mironov’s formulation was employed for the calculation of dielectric constant in dry and sandy soils, suggesting a better performance for West Africa (Lee et al., 2015). A Mironov’s formulation suggests a soil dielectric model specific to the frequency of 1.4GHz as functions of temperature and soil texture, while it was known that the previous model formulated as a hyperbolic function of soil moisture does not represent dry and sandy soils very well (Mironov et al., 2013, Wigneron et al., 2011).

2.3. Climate model

In this study, we employed the HadGEM3-RA, a limited-area model of the third Global Atmosphere (GA3) configuration of the Met Office Unified Model (MetUM) – a merged weather and climate prediction system (Moufouma-Okia & Jones, 2014, Walters et al., 2011). It is a regional climate model (RCM) with a fully elastic and non-hydrostatic dynamical core, employing a semi-implicit and semi-Lagrangian scheme (Davies et al., 2005). Prognostic fields are discretized horizontally using the Arakawa staggering C-grid (Arakawa & Lamb, 1977), while the vertical decomposition is constructed through Charney-Philips terrain (Charney & Phillips, 1953).

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The physical formulation includes a deep convection scheme, based on the mass flux CMODS4A scheme (Gregory & Rowntree, 1990), convective available potential energy (CAPE, Frisch & Chappell, 1980, Gregory & Allen, 1991) and convective momentum transport (CMT). Soil– vegetation–atmosphere interactions are calculated with the Joint UK Land Environment Simulator (JULES, Best et al., 2011), which has 4 soil layers (0.1, 0.25, 0.65, 2m). The HadGEM3 model was integrated continuously from 1 December 2008 to September 2010 with a horizontal grid resolution of 0.11° × 0.11°. The 6-hourly atmospheric conditions from the European Centre for Medium range Weather Forecasting (ECMWF) reanalysis dataset ERA-Interim (Dee et al., 2011) were used to drive the model lateral boundary conditions (LBCs), which were updated at every 3 hourly time-step through a linear interpolation.

2.4. Land surface model

Rainfall rate and soil moisture from the Global Land Data Assimilation System (GLDAS, 3 hourly time step, 0.25 degree resolution, Rodell et al., 2004) were also used to assess the land-atmosphere coupling in West Africa. The GLDAS-NOAH data set is the product based upon the National Oceanic and Atmospheric Administration’s NOAH land surface model. The top surface layer of GLDASNOAH soil moisture data is provided at 0.1 m and is known to be in good agreement with in-situ measurements (Dorigo et al., 2012). The observation-based precipitation and downward radiation products and the atmospheric data assimilation systems are employed to force the models (Chen et al., 2013). All the data above are disaggregated to 0.11°. 2.5. Spatial correlation between antecedent soil moisture and subsequent rainfall

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A spatial correlation between antecedent soil moisture and subsequent precipitation was used to assess the impact of soil moisture on rainfall. Two experimental set-ups were compared to each other. One scheme defines the event as the rain at any time if the magnitude exceeds 10 mm per 3 hr at the event time but does not exceed 3 mm per 3 hr at the antecedent time of the event. In the rain events, consecutive dry days are tracked backwards and counted, based upon the signal of dS. dS = soil moisture at time t-1 – soil moisture at time t where, t is the rain event time. If dS is greater than zero, then dry day gets started. It is continued to count the number of days untill the sign of dS become negative. The theoretical rationale for this approach is that the sensible heat under dry conditions is gradually developed to trigger rainfall (Giorgi et al., 1996, Xue et al., 2004). After the summation of several incidences of consecutive dry days, the total number of consecutive dry days accumulated on a given pixel during the experiment period is spatially correlated with time-averaged rain event. To illustrate each variable used for the calculation of a spatial correlation, antecedent soil moisture is indicated by ‘S’, and consecutive dry days are indicated by a solid line in Figure 2. It is also observed that several precipitation events occurred in the morning time (e.g. on 125.5 day, 127.3 day, 132.3 day) and are preceded by low soil moisture contents. The other scheme repeats the same analysis only for afternoon rainfall. Rationale of this approach is that convection sensitivity to land surface is maximized during the afternoon time (Taylor et al., 2012). To be more specific, the antecedent soil moisture data was provided at 6Z, while the rain accumulation between 12 and 21Z is referred to as afternoon rain, where Z is Zulu time.

3. Results

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3.1. Validation of surface soil moisture with AMMA field campaign data

Different surface soil moisture estimates from various sources were compared with AMMA field campaign data. Table 1 shows the time-average error statistics of soil moisture data sets. Each data set is divided into two groups before and after the WAM onset to show the effects of rain on systematic errors. The WAM onset was the 24th of June (refer to Flaounas et al., 2010). Accordingly, the data indicated by ‘pre-onset’ in Table 1 averages the data from the 1 st of April to the WAM onset, while the data indicated by ‘post-onset’ integrates the data from the next day of WAM onset till the end of September in 2010. Sahel site indicates the location at 13.6°N and 2.6°E in the Niger, while Guinea site is at 9.70°N and 1.68°W in the Benin. The RMSEs in Table 1 show that GLDAS data set is the best estimate for Guinea (moderately wet, and vegetated site with precipitation), while the SMOS data set was better for Sahel (dry and sandy soils with no vegetation). This is considered because the SMOS data sets has recently improved the retrieval algorithm over dry and sandy soils (Kerr et al., 2013, Mironov et al., 2013, Wigneron et al., 2011). This tendency became clearer when it comes to correlation index. The correlation of SMOS data set was the best in all conditions with an exception of Guinea during pre-onset in which the GLDAS was the best. In Sahel (Figure 3-(a)), the SMOS data set was the best matched with the field measurements, while there were significant overestimations by GLDAS and HadGEM model. In Guinea (Figure 3-(b)), the GLDAS data set was the best matched with the field measurements, while there were significant overestimations by SMOS observation and HadGEM model. From this result, it was shown that in the case of soil moisture estimation, there is no single best data set to rely on, as the model, and satellite observation datasets all exhibit various errors. In specific, the SMOS data set showed the best correlation in all conditions except rainfall and vegetation conditions. The GLDAS data set showed the best RMSE in all conditions but except dry and sandy soil conditions at Sahel. The poor performance in specific soil condition is likely due to uncertainty

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associated with soil texture, soil organic matter, vegetation effect and vertical inhomogeneity in soil properties, which are oversimplified by the NOAH land surface model (Chen et al., 2013, Lee, 2014, Lee et al., 2014).

3.2. Validation of rainfall with AMMA field campaign data

Table 2 demonstrates the error statistics of rainfall satellite observations such as CMORPH, and TRMM as well as the model estimates such as HadGEM climate model and GLDAS-NOAH land surface model. It was evident that the GLDAS data set is the best estimate in both sites (Dillon et al., 2016). The RMSEs of Table 2 show that the GLDAS data has the lowest RMSEs in all conditions with an exception of the post-onset at Sahel, for which the TRMM observations have the lowest RMSE. This might be because the NOAH model poorly performs in dry and sandy soil conditions (Lee et al., 2014). It is also recognized that the correlation of GLDAS data shows the highest values in all conditions, regardless of rainfall and vegetation as well as soil texture. On the other hand, as shown in Figure 4, other data sets suggested large errors. The TRMM observations significantly overestimated the rain at Sahel in dry and sandy soils (no vegetation, no rain) during pre-onset, while there were another overestimations in the CMORPH observations and HadGEM model. Thus, it was suggested that the GLDAS data is the most appropriate data set for investigating a feedback mechanism in West Africa.

3.3. Spatial correlation between antecedent soil moisture and subsequent rainfall

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Based upon field measurements, the occurence of consecutive dry days is demonstrated in Figure 5 for Sahel and Benin sites, respectively. It was shown that the occurrence and the number of consecutive dry days in Guinea was higher than those of Sahel, as the total rain accumulation during the experimental period in Guinea was higher at 1384 mm than that in Sahel at 365 mm. It was also found that 89.189% of rain events at Guinea occurred under consecutive dry day conditions (dS>0), while 77.777% of rain events was so at Sahel. Based upon the validations in section 3.1, and 3.2, a spatial analysis was also conducted. The SMOS observations and GLDAS-NOAH land surface model data sets were selected for soil moisture, while the GLDAS data was selected for rainfall. From Figure 6, 7 and 8 using those data sets, it was found that a spatial distribution and magnitude of antecedent soil moisture state were divergent by a selection of the data sets, while the spatial distribution of consecutive dry days were consistent across the data sets. To be more specific, a spatial distribution of SMOS antecedent soil moisture (timeaverage throughout experiment period) showed sparsely wet soils at often above 0.4 m3/m3 in Figure 6-(a), while the GLDAS antecedent soil moisture in Figure 7-(a) was mainly concentrated on the Sahel region on the North but generally drier (e.g. the lowest mean value of 0. 1936 m3/m3 in Table 3) in other regions than the SMOS antecedent soil moisture. Contrary to the GLDAS and SMOS antecedent soil moisture, the HadGEM antecedent soil moisture in Figure 8-(a) are mainly concentrated on Guinea on the South. To conclude, three data sets (SMOS, GLDAS, and HadGEM models) were contradictory to each other, if directly comparing them as the absolute values. However, the consecutive dry days showed consistency with rainfall. In detail, they were all commonly high in Sahel regions on the North (12°N to 13.5°N bands), across the data sets (see (b) of Figure 6 to 8). These match the spatial patterns of rainfall. As shown in (c) of Figure 6 to 8, the intensity of rainfall was high in Sahel region on the Northern parts commonly in both GLDAS and HadGEM-modelled rainfall. This is considered because the Inter Tropical Convergence Zone (ITCZ) moved northwards during WAM when the most rain was produced. Additionally, there is the other

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development of rainfall around at 7°N. This is attributed to the development of convection along the Guinean Coast area during pre-onset period of monsoon. The consecutive dry days are also well matched with those spots. More detailed description of precipitation developments are previously described by Flaounas et al. (2006). Taken together, rainfall is more positively correlated with consecutive dry days across data sets than antecedent soil moisture state as the absolute values. To further illustrate this tendency, it is shown in Figure 6-(d) that the GLDAS rainfall has no spatial correlation with SMOS antecedent soil moisture (i.e. 0.02 in Table 3), while they have a positive correlation with SMOS consecutive dry days (i.e. 0.52 in Table 3). Contrary to the SMOS antecedent soil moisture data, it is shown in Figure 7-(d) that the GLDAS rainfall has a positive spatial correlation with the GLDAS antecedent soil moisture (i.e. 0.49 in Table 3). However, Table 3 does not suggest that the HadGEM model could capture a negative feedback because the HadGEM does not model soil moisture very well at Table 1, and we discarded it (Taylor et al., 2012), and because – 0.2 suggests no tendency. Instead, Table 3 more clearly demonstrates that a spatial correlation between rain events and consecutive dry days was commonly positive at above 0.5 across the data sets, although a spatial correlation between rain events and single antecedent soil moisture state (indicated by ‘S’ in Figure 2) was very contradictory to each other and divergent by the data sets – in specific, no relationship for SMOS soil moisture satellite observation, a positive relationship for the GLDAS-NOAH land surface modelled soil moisture, and a negative relationship for the HadGEM climate modelled soil moisture. Thus, it was suggested that there are higher uncertainty and discrepancy associated with a direct comparison between soil moisture state and rainfall, across the data sets. Taken together, the results support a negative feedback. In other words, rainfall is more strongly related to the consecutive dry days rather than the dryness in terms of antecedent soil moisture state itself as the absolute value. With respect to the convection triggering a negative soil moistureprecipitation feedback, it was previously suggested that an increase in sensible heat (or a decrease in

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latent heat) under dry conditions increases the buoyancy forces during the night time, and PBL thickness during the day time, resulting in the development of convective precipitation (Giorgi et al., 1995, Ma et al., 2009, Katul et al., 2012, Kohler et al., 2010). In addition, it was previously discussed why it is more reasonable to read the relative trends of soil moisture are more likely to be a reasonable indicator than an absolute measure of soil moisture state. Wagner (1998) previously stated that a quantitative comparison between the satellite-retrieved soil moisture observations and rainfall is unlikely to be feasible, as the satellite-retrieved soil wetness values are a relative measure of the moisture content relative to hypothetically dry and wet conditions during specified period. Rather, the satellite retrieval algorithm accounts for the heterogeneity of land cover and seasonal vegetation dynamics, as well as roughness, allowing a qualitative comparison with other variables. This is well applicable to this study. Regardless of whether the sensor is active or passive microwave, SMOS soil moisture also needs a re-scaling or bias reduction before comparing them with other data. TRMM has large errors, as shown in Table 2. In addition to this theoretical exposition, the results exhibiting the consistent correlations across the data sets including GLDAS-NOAH land surface model, SMOS satellite observation, and HadGEM climate model further assure the consecutive dry day approach of this study. For those reasons stated above and based upon the error statistics in Table 1 and 2, it was discussed that insufficient evidence exists to conclude a negative or positive relationship of antecedent soil moisture state with subsequent rainfall evolution. This may be in contrast to recently published discussion based upon the ASCAT or AMSR-E satellite soil moisture observations, and the CMORPH or TRMM satellite rainfall observations (Koster et al., 2003, Taylor et al., 2012, Guillod et al., 2015). Contrary to those previous studies, Table 3 illustrates that it is not always the case that a model overestimates the positive feedback mechanism.

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3.4. Afternoon rainfall

In order to conjure the operational merits and limitations of the method suggested by this study, this section provides a comparative study to the consecutive dry day approach. Only afternoon rainfall is considered to repeat the same method. As explained above in Method section, some previous studies considered that the land surface-atmosphere interactions are maximized at day-time (Taylor et al., 2012, Guillod et al., 2015). During the daytime, the land surface is heated, compared with the adjacent water body. Accordingly, the height of the afternoon PBL over land surface dramatically increases, as compared to early morning PBL height or an increase in air temperature (Katul et al., 2012). From the negative correlation in Table S1 (i.e. -0.1268 for SMOS antecedent soil moisture), it was found that our result is consistent with Taylor et al. (2012) and Guillod et al. (2015) that suggested a negative relationship between antecedent soil moisture and afternoon rainfall. However, two arguments may still remain in this approach. First, uncertainty in satellite observations such as CMORPH, and TRMM rainfall is not negligible, as the feedback relationship varied by a selection of data set. Although the same GLDAS rainfall data is used in Table S1, a sign of correlation with GLDAS soil moisture data was contradictory to that of SMOS soil moisture. This uncertainty is not negligible, considering that the GLDAS soil moisture data showed the lower RMSEs at Guinea than SMOS soil moisture data, but they indicated a positive correlation (i.e. 0.37 in Table S1). Another aspect to be reminded is, as discussed above, that the soil moisture satellite retrievals aim for a relative measure, and thus are often required to be rescaled to field measurement unit (Wagner, 1998). This limitation makes it difficult to directly correspond soil moisture state to precipitation, and to conclude the soil moisture-atmosphere feedback, solely based upon satellite observational data. Secondly, this afternoon rainfall scheme may underestimate the contribution from buoyancy forces

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dominant at night or in the morning, but overestimate a change in temperature or heat flux during the day time (Katul et al., 2012). In fact, this factor of buoyancy forces also significantly alters the pressure gradient-induced atmospheric circulation. This interaction is neglected by the afternoon rainfall scheme. Finally, the sign and magnitude for correlations between antecedent soil moisture and subsequent afternoon rainfall evolution appears to be dependent on how to define the rain events in terms of rain threshold. For example, if changing the rain intensity of threshold to define the rain event from 8 mm to 3 mm, then the correlation of antecedent soil moisture state also changed accordingly. This is a difference from the consecutive dry day approach, which showed a consistent signal across the data sets in Table 3. When limiting the HadGEM rainfall to afternoon events, a correlation of HadGEM consecutive dry days much more decreased (-0.38 in Table S1), as compared to that of total rain evants (0.567 in Table 3) or other afternoon event data sets (0.16~0.24 in Table S1). This is considered because the convective precipitation processed by HadGEM climate model is constructed to be over-sensitive to the day-time increase of moisture advection or heat flux, as compared to the satellite observations (Taylor et al., 2012).

4. Conclusions

We shed light on the unresolved issues for the land surface-atmosphere interactions over West Africa. In order to settle contradictions that exist about that feedback mechanism, we assessed various data sets with AMMA field campaign data. Validation showed that there is no single best data set to rely on for soil moisture, although the GLDAS-NOAH land surface model appears the best data set for rainfall, across the land surface conditions. With respect to soil moisture data sets, the GLDASNOAH data set was the best for vegetated and moderately wet soils in Guinea, while the SMOS soil moisture was appropriate for monitoring dry and sandy soils in Sahel. Based upon these data sets

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selected, results showed that the consecutive dry days commonly showed the same positive correlation with rainfall, across the data sets. It is considered because the concept of consecutive dry days estimated by a temporal gradient of soil moisture state is capable of indicating that the sensible heat is sufficiently developed to increase not only the PBL thickness in day time but also buoyancy forces in night time and to induce the convective rainfall as a consequence. Threfore, this study supports a negative feedback which is known as being neglected by current model frame. This approach is less vulnerable to systematic errors in data sets, as this only considers a soil moisture gradient relative to the antecedent estimates. In contrast, a direct comparison between antecedent soil moisture state and subsequent rainfall evolution appears infeasible, exhibiting conflicting relationships, due to systematic errors and biases in each data set.

Acknowledgements

The

Authors

give

special

thanks

to

the

National

Research

Foundaiton

(NRF-

2015R1C1A1A02037224). For SMOS data, we appreciate Centre Aval de Traitement des Données SMOS (CATDS), operated for the Centre National d'Etudes Spatiales (CNES, France) by IFREMER (Brest, France). AMMA data was obtained within the framework of the AMMA project. Based on a French initiative, AMMA has been constructed by an international group and is currently funded by large number of agencies, especially from France, the UK, the US and Africa. It has been the beneficiary of a major financial contribution from the European Community’s Sixth Framework Research Programme. Detailed information on the scientific coordination and funding is available on the AMMA international web site (www.amma-eu.org). The GLDAS data used in this study were acquired as part of the mission of NASA's Earth Science Division and archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC). We also greatly thank

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the International Soil Moisture Network (ISMN) initiated by the Global Energy and Water Cycle Experiment (GEWEX) and the European Space Agency (ESA) for sharing the field measurement data for soil moisture and rainfall over Africa.

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Table

Table 1. Validation of soil moisture with field measurements

Sahel Pre-onset

Guinea

Post-onset

Pre-onset

RMSE (m3/m3) 26

Post-onset

SMOS data

0.0309

0.0967

0.1488

0.1371

HadGEM model

0.0278

0.1175

0.1726

0.2451

GLDAS

0.1661

0.1787

0.0631

0.0487

Correlation ( - ) SMOS data

0.6021

0.7565

0.6430

0.3072

HadGEM model

0.3950

0.1557

0.5638

0.3829

GLDAS

-0.5373

0.1154

-0.5322

0.5693

Table 2. Validation of precipitation with field measurements

Sahel Pre-onset

Guinea

Post-onset

Pre-onset

Post-onset

RMSE (mm/3hr) CMORPH

3.2069

5.3620

3.2069

5.3620

GLDAS

1.0262

2.9392

3.1668

4.4104

TRMM data

2.5326

2.2508

5.0285

4.9731

HadGEM model

1.3068

3.1374

3.3746

4.5179

Correlation ( - ) CMORPH

-0.0135

-0.0109

-0.0135

-0.0109

GLDAS

0.3865

0.1551

0.0291

0.0582

TRMM data

-0.0180

-0.0139

-0.0177

-0.000992

HadGEM model

-0.0199

-0.0251

-0.0413

-0.0726

27

Table 3. Correlation with rain events Data set combination

consecutive dry day

antecedent soil moisture

SMOS soil moisture + GLDAS rain

0.5210

0.0200 (0.2443)

GLDAS soil moisture + GLDAS rain

0.5512

0.4900 (0.1960)

HadGEM soil moisture+ HadGEM rain

0.5667

-0.2032 (0.2642)

The number in the bracket is the spatial mean of time-averaged soil moisture itself from SMOS, GLDAS, and HadGEM (11448 pixels), respectively.

Figures

(a)

28

(b)

(c) Figure 1. Study domain: (a) spatial domain (b) Niger super site (c) Djougou site of Benin

29

Benin 30 Rain Soil moisture

Rain(mm/3hr) or Soil moisture (%)

25

S

20 S 15

S

10

S

5 Number of dry days 0 123

124

125

126

127

128 DoY

129

130

131

132

133

Figure 2. Field measurements for rainfall and surface soil moisture in Benin

0.4 Field measurement SMOS GLDAS HadGEM

0.35

Soil moisture (m 3/m3)

Soil moisture (m 3/m3)

0.3 0.25 0.2 0.15 0.1

0.5

0.4

0.3

0.2

0.1

0.05 0

Field measurement SMOS GLDAS HadGEM

0.6

100

120

140

160

180 DoY

200

220

240

0

260

(a)

100

120

140

160

180 DoY

200

220

240

260

(b)

Figure 3. Validation of surface soil moisture with AMMA field measurements: (a) Sahel (b) Guinea

30

Sahel

Guinea

50

60

Rain gauge CMORPH TRMM GLDAS HadGEM

40 35

Rain guage CMORPH TRMM GLDAS HadGEM

50

Rainfall accumulation (mm) per 3 hr

Rainfall accumulation (mm) per 3 hr

45

30 25 20 15 10

40

30

20

10

5 0

100

120

140

160

180 DoY

200

220

240

0

260

100

120

140

(a)

160

180 DoY

200

220

240

260

(b)

12

12

10

10

8

8

Occurence

Occurence

Figure 4. Validation of rainfall with AMMA rain gauge network: (a) Sahel (b) Guinea

6 4 2 0

6 4 2

0

1 2 3 4 5 Total number of dry days before rain events

0

6

(a)

0

1 2 3 4 5 Total number of dry days before rain events

6

(b)

Figure 5. Consecutive dry days: (a) Sahel (total rain: 365 mm) (b) Guinea (total rain:1384 mm)

31

7

0.45

14

14 0.4

6 13

13 0.35

11

0.25

10

0.2

11

4

10

3

0.15

9

5

12

0.3

Latitude

Latitude

12

9

0.1

2

8

8

1

0.05

7

7 -8

-6

-4

-2

0

2

4

6

-8

8

-6

-4

-2

0

2

4

6

0

8

Longitude

Longitude

(a)

(b)

0.8

14

8 antecedent consecutive soil moisture dry state days consecutive dry days

0.3

0.25

6

0.4

4

0.2

2

3

0.2

Latitude

0.6 3

soil moiture (m /m )

12 11 10

0.15

9

0.1

8

consecutive dry days (day)

13

0.05

7 -8

-6

-4

-2

0

2

4

6

8

0

0

0

0.1

0.2

0.3

Longitude

Rainfall (mm/3hr)

( c)

(d) 3

0.4

0 0.5

3

Figure 6. (a) antecedent SMOS soil moisture (m /m ); (b) the sum of consecutive dry days

(day); (c) time-average GLDAS rain events (mm/3hr); (d) correlation between GLDAS rainfall and SMOS consecutive dry days. (Note: the scale of SMOS consecutive dry days is different from that of GLDAS and HadGEM in Figure 7 and 8).

32

0.45

50

14

14 45

0.4 13

13

40

12

35

0.35 0.3

11

0.25

10

0.2

Latitude

Latitude

12

25 10

0.15

9

30

11

20 9

15

8

10

0.1 8 0.05

5

7

7 -8

-6

-4

-2

0

2

4

6

8

-8

-6

-4

Longitude

-2

0

2

4

6

0

8

Longitude

(a)

(b) 0.8

14

0.3

40 consecutive dry days antecedent soil moisture

0.7

13 0.6

Soil moisture (m /m )

3

30

Latitude

10

0.15

9

0.1

0.5

3

0.2 11

0.4

20

0.3

0.2

8

0.05

consecutive dry days (day)

0.25 12

10

0.1

7 -8

-6

-4

-2

0

2

4

6

8

0

0

0

0.05

0.1

0.15

0.2

0.25

0 0.35

0.3

Rainfall (mm/3hr)

Longitude

( c)

(d) 3

3

Figure 7. (a) antecedent GLDAS soil moisture (m /m ); (b) the sum of consecutive dry days

(day); (c) time-average GLDAS rain events (mm/3hr); (d) correlation between GLDAS rainfall and consecutive dry days

0.45

20

14

14

0.4

18

13

13

16

0.3

12

14

11

0.25

10

0.2

Latitude

Latitude

12

0.35

0.15

9

12

11

10 10 8 9

6

8

4

0.1 8 0.05

2

7

7

-8

-6

-4

-2

0

2

4

6

8

-8

Longitude

-6

-4

-2

0

Longitude

33

2

4

6

8

0

(a)

(b) 0.8 antecedent soil moisture consecutive dry days

0.7

14

0.6

30

Latitude

12

3

3

soil moisture (m /m )

13

0.1

11 10 0.05

9

0.5

0.4

20

0.3

0.2

8

10

consecutive dry days (day)

0.15

40

0.1

7 -8

-6

-4

-2

0

2

4

6

8

0

0

0

0.05

0.1

0.15

0.2

0.25

0.3

0 0.35

Rainfall (mm/3hr)

Longitude

( c)

(d)

Figure 8. (a) antecedent HadGEM soil moisture (m3/m3); (b) the sum of consecutive dry days

(day); (c) time-average HadGEM rain events (mm/3hr); (d) correlation between HadGEM rainfall and consecutive dry days (Note: the scale of HadGEM rainfall is different from that of GLDAS in Figure 6 and 7)

Supplementary Information

Table S1. Correlation with afternoon rain events data sets consecutive dry day antec.soil moisture SMOS soil moisture + GLDAS afternoon rain 0.1682 -0.1268 (0.2470) GLDAS soil moisture + GLDAS afternoon rain 0.2412 0.3755 (0.2001) HadGEM soil moisture+ HadGEM afternoon rain -0.0229 -0.3803 (0.2666) The number in the soil moisture bracket is the spatial mean of time-average antecedent soil moisture.

34

0.45

7

14

14

0.4 6

13

13

0.35

11

0.25

10

0.2 0.15

9

5

12

0.3

Latitude

Latitude

12

11

4

10

3

9

2

0.1 8

8 1

0.05 7

7

-8

-6

-4

-2

0

2

4

6

8

-8

-6

-4

-2

0

2

4

6

8

0

Longitude

Longitude

(a)

(b) 25

14 13 20

Latitude

12 11 15 10 9 10 8 7 -8

-6

-4

-2

0

2

4

6

5

8

Longitude

( c) Figure S1. (a) antecedent SMOS soil moisture (m3/m3); (b) the sum of SMOS consecutive dry

days (day); (c) time-average GLDAS afternoon rain events (mm/3hr) 50

0.45

14

14

45

0.4 13

13

40

12

35

0.35 0.3

11

0.25

10

0.2

Latitude

Latitude

12

0.15

9

30

11

25 10 20 9

15

8

10

0.1 8 0.05

5

7

7 -8

-6

-4

-2

0

2

4

6

8

-8

Longitude

-6

-4

-2

0

Longitude

35

2

4

6

8

0

25 14 13 20

Latitude

12 11 15 10 9 10 8 7 -8

-6

-4

-2

0

2

4

6

5

8

Longitude

Figure S2. (a) antecedent GLDAS soil moisture (m3/m3); (b) the sum of GLDAS consecutive

dry days (day); (c) time-average GLDAS afternoon rain events (mm/3hr)

0.45

50

14

14

0.4

45

13

13

40

0.3

12

35

11

0.25

10

0.2

Latitude

Latitude

12

0.35

0.15

9

30

11

25 10 20 9

15

8

10

0.1 8 0.05

5

7

7

-8

-6

-4

-2

0

2

4

6

8

-8

Longitude

-6

-4

-2

0

2

4

6

8

0

Longitude

(a)

(b)

36

15 14

Latitude

14 13

13

12

12 11

11

10 10 9 9

8

8

7 6

7 -8

-6

-4

-2

0

2

4

6

8

5

Longitude

( c) Figure S3. (a) antecedent HadGEM soil moisture (m3/m3); (b) the sum of HadGEM

consecutive dry days (day); (c) time-average HadGEM afternoon rain events (mm/3hr)

37

Research Highlights



We attempted to resolve contradictions for a land surface-rain feedback machanism



The consecutive dry days are more related to rainfall than soil moisture state



We show a consistent tendency for a negative feedback across the data sets



We also provide validation for climate and land surface models

38