Climate Variability and Prediction over West and Central Africa related to ITCZ Fluctuations.

Climate Variability and Prediction over West and Central Africa related to ITCZ Fluctuations. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, Mkankam Kamga, Mbarga Assomo University of Yaounde I, Douala Meteorological Unit, ASECNA-Cameroon 17 octobre 2016 P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 1 / 61

Overview Aknowledgments 1 Aknowledgments 2 Introduction 3 Configuration 4 Results 5 Conclusion P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 2 / 61

Aknowledgments 1 Aknowledgments 2 Introduction 3 Configuration 4 Results 5 Conclusion P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 3 / 61

Aknowledgments Aklgmt To 12 th SPARC Data Assimilation organising comittee To WMO To my colleagues Special thanks to Errera Quentin P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 4 / 61

Overview Introduction 1 Aknowledgments 2 Introduction 3 Configuration 4 Results 5 Conclusion P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 5 / 61

Introduction 1 Aknowledgments 2 Introduction 3 Configuration 4 Results 5 Conclusion P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 6 / 61

Introduction Problem statement Climate science community is facing new challenges, including the need to provide skilful and reliable regional climate predictions from months to decades ahead. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 7 / 61

Introduction Problem statement Climatic excess and deficit rainfall associated with floods and droughts, greatly impact on socio-economic activities as well as on human livelihoods Particularly in developing countries with agriculture-based economies and vulnerable populations. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 8 / 61

Introduction The West and Central Africa regions have been identified as one of nine "hot spots" of the world for environmental changes by UN Climatic fluctuations, specifically related to the progress of the rainy and dry seasons are associated with the African monsoon. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 9 / 61

Introduction Climatology of Douala 1961 2000 Precipitation (mm/day) 0 10 20 30 40 (b) Figure1 : Rainfall distribution in Douala city from 1961 to 2000 Jan 01 Feb 01 Mar 01 Apr 01 May 01 Jun 01 Jul 01 Aug 01 Sep 01 Oct 01 Nov 01 Dec 01 P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 10 / 61

Introduction Climate change is drastically affecting Africa. Africa is one of regions of the world in which the research in climate modeling need to be expanded and strengthened. The credibility of regional climate simulations over West and Central Africa stands and falls with its ability to reproduce the West African monsoon (WAM). The WAM is hightly correlated with the Inter Tropical Convergence Zone (ITCZ). The WAM is generally characterized by a strong intra-annual as well as inter-annual variability driven by a complex and not yet fully understood features. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 11 / 61

Introduction For a better understanding of the related processes, regional climate models (RCM) are useful tools in this data sparse region. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 12 / 61

Introduction Main objectives 1 Climatologically Determine the mean ITCZ (Inter Tropical Convergence Zone) position centered on Cameroon Republic (10 E-15 E) 2 Rainfall distribution over Cameroon (Rain gauges and TRMM for validation) 3 Numerically reproduce the ITCZ position using WRF-3D Var (Weather Reasearch and Forecasting) 4 From a study case, reproduce climate features over Central and Western Africa P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 13 / 61

Introduction Figure 2 : Study domain and topography P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 14 / 61

Introduction Figure 3 : IR Channel 10.8 EUMETSAT image : 05/09/2016 at 0515UTC P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 15 / 61

Introduction Figure 4 : Map of Cameroon with different climatic areas P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 16 / 61

Data Introduction In situ data EUMETSAT images ERA-I data WRF simulations (GFS and SST data) Assimilated data P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 17 / 61

ITCZ Introduction The ITCZ is characterized as a low pressure zone where the NE and SE trade winds converge at the surface. The ITCZ appears as a band of clouds, usually thunderstorms, that circle the globe near the equator. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 18 / 61

ITCZ Introduction Figure 6 : ITCZ band and trades wind resulting in ITCZ formation (Source : wikipedia) P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 19 / 61

Introduction ITCZ (Inter Tropical Convergence Zone) Variation in the location of the intertropical convergence zone drastically affects rainfall in many equatorial nations, resulting in the wet and dry seasons of the tropics rather than the cold and warm seasons of higher latitudes P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 20 / 61

Overview Configuration 1 Aknowledgments 2 Introduction 3 Configuration 4 Results 5 Conclusion P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 21 / 61

Configuration 1 Aknowledgments 2 Introduction 3 Configuration 4 Results 5 Conclusion P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 22 / 61

WRF Configuration WRF (Weather Research and Forecasting) model is a mesoscale (regional) numerical climate prediction system designed to serve both operational forecasting and atmospheric research needs. The effort to develop WRF has been a collaborative partnership among many USA institutions. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 23 / 61

Configuration Model Configuration Model step WPS WRF Init Assimilated data Validation data Datasets United State Geological Survey 0.5 0.5 GFS, 1 1 SST PREPBUFR, Radiance data Radiance data 0.25 0.25 3B42 TRMM V7 0.75 0.75 ERAI-I P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 24 / 61

Configuration TABLE : Model Configuration. WRF option Run setup WRF Core ARW Resolution 25 Km Sigma Levels 41 Integration time step 150 s Spin-up time 06 hours Microphysics Thompson Long-Wave Radiation RRTMG Short-Wave Radiation RRTMG Land surface model Rapid Update Cycle Planetary boundary Layer (PBL) Mellor Janjic Cumulus Modifed Tiedtke Map projection Lambert Conformal Initial and boundary conditions GFS P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 25 / 61

Overview Results 1 Aknowledgments 2 Introduction 3 Configuration 4 Results 5 Conclusion P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 26 / 61

Results 1 Aknowledgments 2 Introduction 3 Configuration 4 Results 5 Conclusion P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 27 / 61

Results ITCZ Position Latitude 8 10 12 14 16 18 20 AVR1 AVR3 MAY2 JUN1 JUN3 JUL2 AUG1 AUG3 SEP2 OCT1 OCT3 Months Figure 7 : Decadal ITCZ position from 1990 to 2015 centered on Cameroon P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 28 / 61

Results Figure 8 : IR 10.8 EUMETSAT image P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 29 / 61

Results Dispersion 5 10 15 20 Max. Min. Mean Median X1stQu. X3rdQu. AVR1 AVR3 MAY2 JUN1 JUN3 JUL2 AUG1 AUG3 SEP2 OCT1 OCT3 Mois Figure 9 : ITCZ position s dispersion P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 30 / 61

Results Monthly accumulated rainfall Acc. Rainfal (mm/month) 0 500 1000 1500 2011 2012 2013 2014 2015 jan feb mar apr may jun jul aug sep oct nov dec Month Figure 10 : Monthly rainfall distribution in Douala from 2011 to 2015 P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 31 / 61

Results Figure 11 : Monthly rainfall distribution in some stations in Cameroon P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 32 / 61

Results Overall, monthly to annual rainfall distribution is strongly linked to the ITCZ position. When the ITCZ reaches its maximum position (around 20 ) in august-september, the driest region of the country (the soudano-sahel zone) receives its maximum precipitation. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 33 / 61

Results Rainfall distribution is typically modulated by the seasonal ITCZ variability Retreat and on-set of the rainfall is determined by the ITCZ southernmost (minimun value) and northernmost (maximum value) positions When the ITCZ reaches its southernmost position, especially between october and Mars, the whole country is in the dry season. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 34 / 61

Results Rainfall verification Figure 12 : Rainfall verification in some stations in Cameroon : Bias and POD P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 35 / 61

Results Rainfall verification (Rain gauges vs TRMM) Figure 13 : Rainfall verification in some stations in Cameroon : ETS and POFD P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 36 / 61

Results Rainfall thresholds Above, we display the sensibility of categorical indices (BIAS, POD, POFD and ETS) to 21 rainfall thresholds extending from 0.1 mm/day to 35 mm/day that we consider as light to heavy rainfall. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 37 / 61

Results Rainy events are overforecasted by 3B42 with a BIAS greater than 1 Global trend is increasing in BIAS with rainfall threshold About 2% of rainy days are overforecasted for light to moderate precipitations (less than 10 mm/day) in all the stations 3B42 capability worsens in detecting heavy rainfall P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 38 / 61

Results POD is very significant (more than 60%) for light rainfall. When daily precipitation intensity increases from moderate to heavy, only less than 40% of rainfall is detected by 3B42. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 39 / 61

Results 3B42 is more likely to regard no rain day as rain day incorrectly for light rain intensity 3B42 shows a poorer score of POFD for light rain intensity than that for heavy rain intensity 3B42 has a higher probability to correctly identify numerous light rain events with an improved accuracy compared to heavy rain event P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 40 / 61

Results Results from WRF P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 41 / 61

Results Figure 14 : Synthetic map with ITCZ position (red line) as simulated by WRF P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 42 / 61

Results Figure 15 : Simulation of the African Easterly jet and the Tropical Easterly jet P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 43 / 61

Results A wetter monsoon is often related to a stronger TEJ and a northward displacement of the AEJ. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 44 / 61

Results Figure 16 : High altitude wind distribution (monsoon period) P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 45 / 61

Results Figure 17 : Wind vertical profile (monsoon period) P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 46 / 61

Results Figure 18 : Diurnal cycle of MSLP P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 47 / 61

Results Figure 19 : Six-hourly accumulated rainfall (mm/day) initialized at 0000 UTC P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 48 / 61

Results FIGURE : Time-latitude Hovmöller diagrams of 1999 daily precipitation for TRMM (top), ERA-I (middle) and the WRF ensemble mean (ENS, bottom) (C. Klein et al., 2015) P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 49 / 61

Results Figure 20 : Hovmöller diagrams of 6-hourly accumulated rainfall, covering the whole longitudinal band of the study domain and averaged over the region 0 N-20 N P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 50 / 61

Results WRF captures the seasonal cycle, as can be seen from the Hovmöller diagrams for the WRF ensemble mean (ENS) in comparison to TRMM. WRF DA improves the WRF rainfall propagation. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 51 / 61

Results Rainfall verification TABLE : Scores from contingency table for accumulated rainfall Threshold <1 mm Exp Global rate Hit False PSS CNTL 0.75 0.75 0.24 0.51 DA 0.79 0.90 0.37 0.53 P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 52 / 61

Results Rainfall verification TABLE : Scores from contingency table for accumulated rainfall Threshold between [10 ; 25[ mm Exp Global rate Hit False PSS CNTL 0.82 0.29 0.10 0.1 DA 0.83 0.23 0.07 0.15 P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 53 / 61

Results Rainfall verification TABLE : Scores from contingency table for accumulated rainfall Threshold >25 mm Exp Global Rate Hit False PSS CNTL 0.86 0.37 0.083 0.28 DA 0.85 0.29 0.089 0.20 P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 54 / 61

Overview Conclusion 1 Aknowledgments 2 Introduction 3 Configuration 4 Results 5 Conclusion P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 55 / 61

Conclusion 1 Aknowledgments 2 Introduction 3 Configuration 4 Results 5 Conclusion P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 56 / 61

Conclusion ITCZ (Inter Tropical Convergence Zone) fluctuations position has been investigated Link between ITCZ position and rainfall has been established The DA experiment is found to have improved the model results over Western and Central Africa. Consequently, a better simulation of rainfall amount and location is realized The results from this study also suggest that the performance of a regional model in simulating precipitation over Western and Central Africa is very sensitive to the prevailing large-scale circulation. The model biases in large-scale circulation result in considerable differences in the amount of precipitation However, these results are modified when local convection is considered P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 57 / 61

Conclusion Central African rainfall slightly respond to Atlantic ocean SST, compared to West Africa This signal is seasonal and is shifted latitudinally in relation with ITCZ position (Camberlin et al., 2001) From the ITCZ northernmost position, when the south Atlantic ocean is anomalous warm, we have deficit in rainfall in july-september north of 10 N and in october-december south of Cameroon and Gabon (anicot et Fontaine, 1997) South of the ITCZ position inversely is anomalous wet (Mahé, 1993) P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 58 / 61

Future work Conclusion Investigate link between MJO, ITCZ and rainfall inter and intra annual variability P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 59 / 61

References Conclusion Pascal Moudi Igri, S. Roméo Tanessong, D. A. Vondou and F. Mkankam Kamga, (2015) : Added-value of 3DVAR Data Assimilation in the Simulation of Heavy Rainfall events over Western and Central Africa, Pure and Applied Geophysics, 2015, DOI 10.1007/s00024-015-1052-7 Roméo S. Tanessong, Pascal Moudi Igri, Derbetini A. Vondou, and Mkankam Kamga F : Evaluation of Probabilistic Precipitation Forecast Determined from WRF Forecasted Amounts, Theoretical and Applied Climatology, 2013. DOI 10.1007/s00704-013-0965-2 Cornelia, K., H. Dominikus, B. Jan, and K. Harald, 2015 : Variability of west african monsoon patterns generated by a wrf multi-physics ensemble. Clim. Dyn, DOI 10.1007/s00382-015-2505-5. P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 60 / 61

Conclusion Thank you for your attention P. Moudi Igri, Vondou Derbetini, Tanessong Roméo, MkankamASECNA-UYI Kamga, Mbarga Assomo (ASECNA) 17 octobre 2016 61 / 61