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Promises and Prospects for Predicting the South Asian Monsoon

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Presentation on theme: "Promises and Prospects for Predicting the South Asian Monsoon"— Presentation transcript:

1 Promises and Prospects for Predicting the South Asian Monsoon
Jim Kinter COLA George Mason University Many Thanks To: Rodrigo Bombardi, Paul Dirmeyer, Mike Fennessy, Bohua Huang, Subhadeep Halder, Larry Marx, Ed Schneider, J. Shukla, R. Shukla, Chul-Su Shin, Bohar Singh Special Symposium on the South Asia Monsoon American Meteorological Society Annual Meeting Phoenix, Arizona :: 8 January 2015 Figure credit: National Geographic © 2002

2 South Asian Monsoon One of largest variations of seasonal to interannual climate observed on Earth Affects lives, property and (largely agrarian) economies of countries inhabited by nearly 25% of human population … Current state of the science Rudimentary understanding of monsoon dynamics Extremely limited ability to predict monsoon variations

3 South Asian Monsoon … either by monsoon flood …
One of largest variations seasonal to interannual climate observed on Earth Rudimentary understanding of the monsoon remains Extremely limited ability to predict monsoon variations Affects lives, property and (largely agrarian) economies of countries inhabited by nearly 20% of human population

4 …or by monsoon drought

5 South Asian Monsoon One of largest variations seasonal to interannual climate observed on Earth Affects lives, property and (largely agrarian) economies of countries inhabited by nearly 25% of human population Current state of the science Rudimentary understanding of monsoon dynamics Demonstrable predictability of monsoon seasonal rainfall Limited ability to actually predict monsoon variations

6 National Monsoon Mission of India and COLA Joint Research to Enhance Monsoon Prediction
Ocean-Land-Atmosphere Coupling and Initialization Strategies to Improve CFSv2 and Monsoon Prediction Sponsored by Ministry of Earth Sciences, Government of India Improve the coupled ocean-land-atmosphere model (CFSv2) performance. Improve initialization of ocean and land states in the pre-monsoon season to improve forecasts of onset, monsoon season precipitation. Improve representation of land-atmosphere feedback in monsoon-dominated regions

7 Climate Forecast System version 2 (CFSv2)
Global coupled model: Atmosphere, Ocean, Land Surface, Sea Ice Atmosphere: based on the NCEP Global Forecast System (GFS) used for global numerical weather prediction spectral discretization at T126 resolution (about 100 km grid spacing) 64 levels in the vertical Ocean: Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model version 4 (MOM4) 1/2° horizontal grid spacing; 1/4° meridional grid spacing in the tropics 40 vertical levels Land Surface: Noah (GFS grid) Sea Ice: a modified version of the GFDL Sea Ice Simulator (MOM4 grid)

8 Climate Forecast System version 2 (CFSv2)
Global coupled model: Atmosphere, Ocean, Land Surface, Sea Ice Atmosphere: based on the NCEP Global Forecast System (GFS) used for global numerical weather prediction spectral discretization at T126 resolution (about 100 km grid spacing) 64 levels in the vertical Ocean: Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model version 4 (MOM4) 1/2° horizontal grid spacing; 1/4° meridional grid spacing in the tropics 40 vertical levels Land Surface: Noah (GFS grid) Sea Ice: a modified version of the GFDL Sea Ice Simulator (MOM4 grid) 2011 Operational CFSv2 – plus: Corrected coding irregularity that produces mismatch in air-sea fluxes Corrected low value of sea ice albedo Testing various changes that can improve overall CGCM performance and especially monsoon prediction

9 Role of atmospheric convection

10 Convective Cloud Parameterization:
The Simplified Arakawa-Schubert (SAS) Scheme Deep Convection Trigger Mechanism: Level of free convection (LFC) [for parcel with no sub-cloud level entrainment] being within 150-hPa of convection starting point (ΔP < 150) Source: MetEd

11 Heated Condensation Framework (HCF)
Quantifies how close atmosphere is to moist convection Does not require parcel selection Uses typically measured quantities (needs only q and θ profiles) Is “conserved” diurnally Can be used any time of year or any time of day and interpretation stays the same Evap Sensible (Tawfik and Dirmeyer 2014 GRL)

12 Heated Condensation Framework Convection is initiated when:
Threshold Variables: BCL = Buoyant Condensation Level [m] θBM = Buoyant mixing temperature [K] Convection is initiated when: PBL intersects BCL θ2m reaches θBM New trigger mechanism for deep convection Original SAS criterion (ΔP < 150-hPa) .OR. Condensation due to mixing (HCF) Evap Sensible (Tawfik and Dirmeyer 2014 GRL)

13 Experiments: Seasonal hindcasts Four member per year (1998 – 2010)
OBS Experiments: Seasonal hindcasts Four member per year (1998 – 2010) From April to October starting on April 1,2,3, and 4 With (HCF) and without (CTRL) the new trigger CTL Monthly Accumulated Precip. (mm) Thanks to Rodrigo Bombardi

14 JJAS Precipitation Thanks to Rodrigo Bombardi

15 Small but significant improvement
JJAS Seasonal Precipitation Small but significant improvement of seasonal total Improvement of seasonal variability Mean StDev Thanks to Rodrigo Bombardi

16 Rainy Season Improved seasonal cycle of precipitation
Improved rainy season onset dates. Thanks to Rodrigo Bombardi

17 Mechanisms The new trigger is an alternative condition
Better representation of the background state of convection SAS triggers more frequently Small improvement in the right direction Central India: oN, oE Thanks to Rodrigo Bombardi

18 Land-atmosphere feedbacks

19 Hot Spots The Global Land-Atmosphere Coupling Experiment (GLACE) : A joint GEWEX/CLIVAR modeling study Multi-model results indicate that there are geographic “hot spots” where the atmosphere is responsive to the state of the land surface (soil moisture) = potential predictability. There have been many subsequent studies using models, analyses and observations that corroborate and help explain this result. Koster, et al., 2004: Science, Dirmeyer, et al., 2006: JHM, Guo, et al., 2006: JHM, 611-. Koster, et al., 2006: JHM, 590-.

20 Spatial Variation of Sensitivity
day-1 LHF (Wm-2) Difference in Difference in volumetric surface soil moisture initial state Over the extreme north-west (dry), central (humid) and northeast (wet) India, correlation of LHF with SM is positive, but it strongly depends on the initial soil moisture condition and availability of radiation in June. PLEASE SEE POSTER # 878 BY HALDER ET AL.

21 Ocean initialization

22 Multiple Ocean Initial Conditions (OICs) for CFSv2
Four different ocean data sets ECMWF Combine-NV (NEMO) NCEP CFSR (Climate Forecast System Reanalysis) ECMWF ORA-S3 (Ocean Reanalysis System3) NCEP GODAS (Global Ocean data Assimilation System) Variables Monthly mean potential temperature[°C], salinity[g/kg], u[m/s], v[m/s] Pre-Processing Filling up (extrapolation) the land mass to make up potential gaps due to different land-sea masks between each ocean analysis and MOM4 (Note that zonal mean of each variable is initially assigned to grids over the land as an initial guess.) Period: (Jan. – May)

23 CFSv2 Retrospective Forecasts with 4 OICs
Initial month NEMO (4mem*30yrs) CFSR (4mem*31yrs) ORA-S3 GODAS May (5-month lead) April (6-month lead) March (7-month lead) February (8-month lead) January (9-month lead) 600 runs completed 620 runs completed 620 runs completed 620 runs completed * 4 members : 1st 4 days (00Z) of each month using the atmosphere/land surface conditions from CFSR

24 CFSv2 Prediction Skill of NINO3.4 (1982-2008) vs. OISST
NEMO ORA-S3 CFSR GODAS ES_MEAN JAN ICs FEB ICs MAR ICs

25 CFSv2 Prediction Skill of NINO3.4 (1982-2008) vs. OISST
NEMO ORA-S3 CFSR GODAS ES_MEAN APR ICs MAY ICs

26 CFSv2 Prediction Skill of NINO3.4 (1982-2008) vs. OISST
Multiple Ocean Analyses Ensemble Mean

27 2 Dominant Modes of JJAS mean Precip
corresponds to ENSO onset years corresponds to ENSO decay years (1979 – 2008; linear trend removed)

28 1st EOF Mode CMAP CFSv2 Correlation (1979-2008) lead month NEMO CFSR
ORA-S3 GODAS ES_MEAN

29 2nd EOF Mode CMAP CFSv2 Correlation (1979-2008) lead month NEMO CFSR
ORA-S3 GODAS ES_MEAN

30 Conclusions ROLE OF CONVECTION LAND-ATMOSPHERE FEEDBACKS
HCF - alternative condition to whether or not deep convection should be initiated - was implemented and tested in the Simplified Arakawa-Schubert scheme in the CFSv2 Better representation of background state of convection: increased convection occurrence HCF improves representation of monsoon in CFSv2: Better daily and seasonal precipitation, mean & variability Better seasonal cycle of precipitation, including onset dates LAND-ATMOSPHERE FEEDBACKS Surface fluxes on day 1 strongly correlated with initial surface soil moisture during May and June: terrestrial leg of the coupled feedback pathway is in effect. Relatively weak sensitivity of PBL depth to surface flux variations over eastern part of central India: atmospheric leg not strong in CFSv2. Need better observations to validate. ROLE OF OCEAN ICs Substantial dependence on ocean ICs of skill of monsoon rainfall forecasts Multi-analysis initialization results in superior prediction, on average Two main modes of monsoon variability, associated with onset and decay phases of ENSO, are well reproduced and predicted in CFSv2 with most ocean IC data sets

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