Simulation of extreme seasonal climate over South Africa using the high resolution Weather Research and Forecasting (WRF) model

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1 Simulation of extreme seasonal climate over South Africa using the high resolution Weather Research and Forecasting (WRF) model SatyabanB. Ratna 1 Swadhin Behera 1,2, J. Venkata Ratnam 1,2, ThandoNdarana 3, Hannes Rautenbach 4, Keiko Takahashi 1,5 and Toshio Yamagata 1 1 Application Laboratory, JAMSTEC, Yokohama, Japan 2 Research Institute for Global change, JAMSTEC, Yokohama, Japan 3 South African Weather Service, Pretoria, South Africa 4 University of Pretoria, Pretoria, South Africa 5 Earth Simulator Center, JAMSTEC, Yokohama, Japan Chapman Conference: The Agulhas System and its Role in Changing Ocean Circulation, Climate, and Marine Ecosystems 8-12 October, 2012 Cape Town, South Africa

2 Background South Africa receives most of its rainfall during the austral summer season (December-January-February, DJF) except for southwestern region and along the south coast and has large spatio-temporal variability. Performance of summer rainfall is much important for the population of this region due to the agriculture dependence economy. Due to its subtropical location, South Africa comes under the influence of both tropical and midlatitudes dynamics. It also comes under the influence of both Indian ocean and Atlantic ocean processes. This complexity makes difficult for a reliable prediction of summer rainfall on the seasonal and intraseasonal basis.

3 Motivation An alternative to atmospheric general circulation model (AGCM) to produce high resolution simulations is Dynamical downscaling using high resolution regional climate models(rcms) to derive climate information at regional scale (Leung et al., 2003). However, before applying an RCM for a seasonal prediction for a given region, the accuracy of the model to successfully reproduce the observed regional climate characteristics should be assessed. Recently, WRF model has been increasingly used as RCM for downscaling studies over southern Africa (Cretat et al. 2011, 2012; Ratnam et al. 2011; CretatandPohl,2012;Boulardetal.2012;Vigaudetal.2012). These studies are either based on a bit lower resolution and limited to few seasons only. Also, none of the study analyse the rainfall distribution on a subregion scale over different provinces and those studies didn t conclude the possible reason of rainfall bias that arises from different cumulus parameterization schemes.

4 Objective The first objective is to simulate the observed summer rainfall climatology and interannaual variability over the South Africa region using three cumulus parameterization schemes and its validation with available observed data. Second, to verify the sensitivity of cumulus schemes in the model over the different provinces of South Africa. The third objective is to establish the possible bias in the simulation of summer season rainfall and to diagnose further the possible source of the bias in the high-resolution model simulation. Fourth, to asses the air-sea interaction processes of the model during the excess and drought rainfall seasons.

5 Experimental Details Model: WRF v3.4 (Skamarock et al. 2008) Domain: Simulation Period: Convection Microphysics PBL Land Surface Radiation 2-way interactive two nested domain (27 km, 9 km) 00 UTC 01 November 00 UTC 28 February (199/ /11) Model Physics Kain-Fritsch, Betts-Miller-Janjic, Grell-Devenyi-Ens Simple Ice WSM-3 class YSU scheme Noah Dudhia (Short wave ); RRTM (Long wave)

6 Cumulus Parameterization Schemes KF scheme: Kain, 2004; Kain and Fritsch (1993) KF uses the assumption of the removal of convective available potential energy (CAPE) in a grid column within an advective time. A trigger function based on the grid-resolved vertical motion is used to decide the time of activation of the scheme. If the upward motion is sufficiently large to overcome the convective inhibition, the scheme will activate(solongastheunstablelayerisatleastof60-hpadepth). BMJ Scheme: Janjic(1994); Betts,(1986); Betts and Miller(1986) The BMJ scheme includes deep and shallow convection and essentially removes the conditional instability in each grid column based on the principle of relaxing the temperature and moisture profiles toward the reference environmental profile. The scheme is triggered if a parcel when lifted moist adiabatically from the lower troposphere to a level above the cloud base, where it then became warmer than the environment. GDE scheme: Grell and Devenyi(2002) The GD is a cloud ensemble scheme in which effectively multiple cumulus scheme and variants are runwithineachgridboxandthentheresultsareaveragedtogivethefeedbacktothemodel. The schemes are all mass-flux type, but with differing updraft and downdraft entrainment and detrainment parameters, and precipitation efficiencies. An ensemble approach is followed because statistically the ensemble members yield a large spread in the accumulated convective rainfall results.

7 Model Domain Model domain with 27 (D1) and 9 km (D2) resolution. Topography (m) is in shaded. Domain D1 D2 Horizontal resolution 27 km 9 km Grid points (E-W) Grid points (N-S) Topography 10 m 30 s

8 Data The X grid European Centre for Medium-Range Weather (ECMWF) Reanalysis (ERA) Interim data (Simmons et al. 2007; Uppala et al. 2008; Berrisford et al. 2009) The sea surface temperatures of ERA were interpolated to the model grid and used as the slowly varying lower boundary conditions. Surface data at 10-min and 30-second resolutions for the 27-km and 9-km domain respectively are taken from United States Geological Survey (USGS) database, which describes a 24 category land-use index based on climatological averages. The simulated rainfall is compared with African rainfall climatology version 2 (ARC, Novella and Thiaw, 2012) daily precipitation estimates developed by Climate Prediction Centree(CPC) available at 0.1 degree lat/lon resolution.

9 Climatology Mean Rainfall 20 year climatology of mean DJF rainfall (mm/day), East-west gradient rainfall

10 Climatology Mean Rainfall Bias The climatology of simulated mean DJF rainfall bias for KF, BMJ and GDE experiments generated by using the ARC climatology. Spatial Overall MAE RMSE correlation spatial bias KF BMJ GDE

11 Interannual Variability of Rainfall CV (%) Bias RMSE KF BMJ GDE ARC 29.2 Model could simulated the ENSO associated drought/excess years. Limitation in simulating the intensity of extreme(drought/floods) seasons.

12 Model evaluation for the provinces of South Africa The boxes are chosen as representative of the provinces of South Africa for the analysis of area averaged rainfall. [LP-Limpopo; NW-North West; NC-Northern Cape, FS-Free State; GT-Gauteng; ML-Mpumalanga; KN-Kwazulu-Natal; EC- Eastern Cape; WC-Western Cape]

13 Interannual Variability of Rainfall Normalized rainfall anomaly for each of the provinces of South Africa for the 20 JDF season 1991/ /11. LP NW NC FS GT ML KN EC WC ARC KF BMJ GDE Mean CV CC Mean CV CC Mean CV CC Mean CV CC Mean CV CC Mean CV CC Mean CV CC Mean CV CC Mean CV CC

14 Number of rainy days per DJF season Rainy days = rainfall at a grid point > 0.1 mm/day

15 850 hpa Moisture Divergence (*1e-6/s) and Moisture Transport

16 Vertical profile of mean Climatology Bias Specific Humidity (kg/kg) Vertical Velocity (m/s)

17 Rainfall Bias for Excess and Drought Seasons ARC ARC Excess: 1995/1996, 1999/2000, 2005/2006, 2010/2011 Drought: 1991/1992, 1992/1993, 1994/1995, 1997/1998, 2006/2007

18 SST Simulated Latent Heat Flux anomaly

19 10m Wind Anomaly

20 Summary In this study we evaluated the performance of a high resolution (9 km) WRF model being driven by the 0.75 degree ERA-interim reanalysis for a period of 20 DJF seasons(1991/ /11). The model could simulate the climatology and interannual variability realistically with BMJ scheme closer to the observation. Model could simulated the ENSO associated drought and excess season but it has limitation to obtain the observed intensity of rainfall anomaly. KF produces a stronger low level moisture and intense updraft resulting in a large moist bias. Continue..

21 Summary Model could reasonably simulate the air-sea interaction processes during excess and drought seasons. The main source of moisture for the excess seasonal rainfall is the Indian Ocean, as easterly winds enter the continent from the oceanic region adjacent to the southern Africa continent. Stronger heat flux, resulting from the higher ocean temperature and strong surface wind, enhances low level instability. The WRF model accurately simulates the SST induced strong surface winds that lead to higher heat fluxes over the Mozambique Channel during the excess rainfall season.

22 Thank You