Improved rainfall and cloud-radiation interaction with Betts-Miller-Janjic cumulus scheme in the tropics Tieh-Yong KOH 1 and Ricardo M. FONSECA 2 1 Singapore University of Social Sciences, Singapore 2 Lulea University of Technology, Sweden * This work was supported by the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative at Nanyang Technological University, Singapore.
Outline Motivation Model Betts-Miller-Janjic cumulus scheme Precipitating Convective Cloud scheme Summary
Motivation Modelling of rainfall in the global monsoon regions has always been a challenging problem, e.g. Mean biases / too much or too little variability Muted extremes Incorrect monsoon onset / diurnal phase The modelling of clouds poses perhaps a bigger problem, but we know that cloud-radiation interaction plays a role in e.g. global and regional climatology; monsoon variability at different time scales; nocturnal cooling of the boundary layer. We aim to: improve the representation of rainfall by the Betts-Miller-Janjic cumulus scheme develop a precipitating convective cloud scheme to allow cloud-radiation feedback for application in the global tropics.
Model Weather Research and Forecast (WRF) model Downscale CFSR (global reanalysis, 0.5 x 0.5 ) over 27 years (Apr 1988 Mar 2015) Continuous runs of 1 full year (1 Apr 31 Mar) after discarding 1-month spin-up Tropical Channel: Model Terrain (m) Arakawa C-grid: 36 km x 36 km (*); 37 levels; 2-min time-steps; 3 hourly diagnostics * 30 km x 30 km for certain optimization runs used to adapt model schemes Physics Options Parameterization Scheme Reference Cumulus Convection Betts Miller Janjic (BMJ) scheme Precipitating Convective Cloud (PCC) scheme Janjic (1994, MWR; 2000, JAS) Koh et al. (2016, QJRMS) Cloud Microphysics Goddard 6 class (*) Tao et al. (1989) Radiation Rapid Radiative Transfer Model for GCM Applications (RRTMG) Iacono et al. (2008) Surface Layer MM5 Monin Obukhov scheme Monin and Obukhov (1954) Land Surface 4 layer Noah Land Surface Model Chen and Dudhia (2001) Planetary Boundary Layer Yonsei University (YSU) PBL scheme Hong et al. (2006)
Betts-Miller-Janjic cumulus (BMJ) scheme Improvements were made to the deep convection scheme Betts-Miller scheme is an adjustment scheme where the potential temperature and specific humidity in a raining cloud column are damped towards reference profiles that depend on the moist pseudo-adiabat of the boundary-layer air. Janjic modified the BM scheme by introducing a cloud efficiency variable that depends on the ratio of entropy production to enthalpy increment in a raining cloud column. We optimized one of Janjic s parameters, F s, from the default value of 0.9 to the value of 0.6 in order to minimize bias in global tropical deep convective rainfall (Fonseca et al., 2015). This change effectively increases the relative humidity of the reference profile across the deep convective tropics.
JJAS 2008 AVERAGED PRECIPITATION RATE (mmhr -1 ) GLOBAL TROPICS boreal SUMMER TRMM 3B42 GLOBAL TROPICS Optimized result! 0 0.5 1 HORIZONTAL RESOLUTION: 30 km x 30 km (to be comparable to TRMM s grid) WRF: Grid Nudge + Original BMJ WRF: Grid Nudge + BMJ with F S =0.6 Taken from Fonseca et al. (2015) -1 0 1 Shading: BIAS (mmhr -1 ) Contours: NBIAS =0.3 (BIAS/STD, denotes ~5% contribution of BIAS to RMSE)
DJFM 2008/09 AVERAGED PRECIPITATION RATE (mmhr -1 ) GLOBAL TROPICS boreal WINTER TRMM 3B42 GLOBAL TROPICS Test result 0 0.5 1 HORIZONTAL RESOLUTION: 30 km x 30 km (to be comparable to TRMM s grid) WRF: Grid Nudge + Original BMJ WRF: Grid Nudge + BMJ with F S =0.6 Taken from Fonseca et al. (2015) -1 0 1 Shading: BIAS (mmhr -1 ) Contours: NBIAS =0.3 (BIAS/STD, denotes ~5% contribution of BIAS to RMSE)
Pentad variability of rainfall based on TRMM observations (Jan 1998 Dec 2010) cov, var var JJAS 1998-2010 DJFM 1998-2010 var var var Generally good rainfall variability over the oceans across monsoon seasons and intermonsoon periods. AM 1998-2010 Poorer performance over landmass. Taken from Fonseca et al. (2017) ON 1998-2010 GREY : Normalized BIAS μ < 0.5, i.e. BIAS contributes to < 12% of RMSE performance of a random forecast based on climatological variance
Precipitating Convective Cloud (PCC) scheme Adjustment schemes do not estimate clouds because they aim to simulate the end effect of convection on the ambient temperature and humidity, whereas cloud water that is not precipitated re-evaporate and so clouds are transient features of the atmosphere. But cloud-radiation feedback is important to radiative heating/cooling of the surface and atmosphere, especially in the tropics which is in an overall state of radiative-convective equilibrium. We developed a deep convective cloud scheme that estimates the vertical profile of cloud mixing ratio from the surface precipitation generated by an adjustment scheme. (Koh and Fonseca, 2016)
Precipitating Convective Cloud (PCC) scheme Assumptions: Slingo (1987): empirical relation between surface precipitation and the zenith cloud fraction which we take as the peak value in a column. Top-heavy profile of cloud fraction based on the Poisson distribution between the cloud top and cloud base; cloud shape parameter Interpolation of the cloud column mass between the water vapour column mass and surface rainfall mass; cloud mass parameter The cloud column is well-mixed so that the proportion of cloud mixing ratio to water vapour mixing ratio is invariant for cloudy air a column. Average between maximum and minimum overlap of the cloud fractions generated by deep convection and microphysics schemes. The two parameters above were optimized respectively using observations of surface long-wave and short-wave radiative fluxes over tropical oceans. Convective Cloud Composite (> 12 VERTICAL LEVELS) 0 -COORDINATE optimal 1 0 CLOUD FRACTION 0.8
IS THE MODEL S CLOUD COVER REALISTIC? 11 TH SEPT 2008, 08:32UTC MTSAT-IR IR SATELLITE IMAGE 10.3 11.3 m Source: http://ncdc.noaa.gov/gibbs/ (30 km x 30 km model grid, WRF Double Moment 6 class microphysics scheme) 11 TH SEPT 2008, 09UTC 11 TH SEPT 2008, 09UTC CLOUDS (kgm -2 ) Taken from Koh & Fonseca (2016) WRF, No Convective Clouds WRF, with Convective Clouds
IS THE MODEL S CLOUD COVER REALISTIC? 3 RD DEC 2008, 17:45UTC GOES-12 IR SATELLITE IMAGE 10.2 11.2 m Source: http://ncdc.noaa.gov/gibbs/ (30 km x 30 km model grid, WRF Double Moment 6 class microphysics scheme) 3 RD DEC 2008, 18UTC 3 RD DEC 2008, 18UTC CLOUDS (kgm -2 ) Taken from Koh & Fonseca (2016) WRF, NO CONVECTIVE CLOUDS WRF, WITH CONVECTIVE CLOUDS
SURFACE RADIATION FLUXES (positive downwards) We switched the cloud microphysics scheme from WRF Double-moment 6-class (in Koh & Fonseca, 2016) to Goddard 6-class (in Fonseca et al., 2017) to increase the low-cloud cover. April 1988 December 2011 (36km x 36km model grid, Goddard 6 class microphysics scheme) Taken from Fonseca et al. (2017) Short-wave Long-wave Light GREY : Normalized BIAS μ < 0.5, i.e. BIAS contributes to < 12% of RMSE Dark GREY : Uncertainty in observation is large (Appendix A.2 of Fonseca et al., 2017) Bias in short-wave radiation over the oceans is a known elusive problem in the parameterization of shallow cumulus & stratocumulus clouds (Huang et al., 2013). NOCSv2 data: National Oceanography Centre Southampton Version 2 Surface Flux Dataset (monthly mean surface fluxes on a 1 x 1 grid estimated from ship observations);
Summary We succeeded in correcting for the default model rainfall biases: the modified BMJ scheme yields small rainfall biases for the whole tropics in all seasons although only adjusted for boreal summer. WRF model with the above modifications is able to reproduce the monsoon rainfall with good pentad variability over the oceans and is better than random forecast over land. A new precipitating convective cloud (PCC) scheme and the Goddard 6-class microphysics scheme were implemented in WRF, giving more realistic deep convective cloud cover and reducing surface long-wave radiation biases.
Publications 1. Fonseca, R. M., T. Zhang and K. T. Yong (2015), "Improved simulation of precipitation in the tropics using a modified BMJ scheme in the WRF model", Geosci. Model Dev., 8, 2915-2928, doi:10.5194/gmd-8-2915-2015. 2. Koh, T.-Y. and R. Fonseca (2016), "Subgrid-scale cloud-radiation feedback for the Betts-Miller-Janjic convection scheme", Q. J. Royal Meteorol. Soc., 142(695), 989-1006. doi: 10.1002/qj.2702. 3. Fonseca, R.M., Zhang, T. and T.-Y. Koh (2016), "Evaluation of Regional Climate Downscaling over the Maritime Continent" (submitted to Clim. Dyn.)
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