The MJO, via a mix of direct and indirect means, influences global weather. Numerical forecast models have difficulty simulating and forecasting the MJO, especially in its “initiation” phase in the Indian Ocean, due in part to model deficiencies such as incorrectly tuned parameterizations or inadequate subgrid parameterizations. From these facts was born DYNAMO, with its clear focus on three specific hypotheses involving some key processes, two of which involve air-sea interactions. Surface flux, radar, and lidar data collected during the recent field campaign captured frequent episodes in which a “cold pool” of air was laid down on the ocean surface during convective rain events. The dramatic changes in air temperature and winds led to large changes in sensible and latent heat fluxes. Clearly, such events are sub-grid scale to global forecast models. To what extent can stochastic forcing be employed in global models to incorporate the effects of these subgrid events?
We propose to use observations from the DYNAMO IOP, primarily the flux data collected by the R/V Revelle, to diagnose the statistical properties of surface fluxes associated with the deposition of these atmospheric “cold pools”. We further propose to apply LIM techniques to key markers of the MJO over the Indian Ocean, obtained from reanalysis products and GFS ensemble reforecasts, in order to diagnose the quantity, spatial distribution, and time series of stochastic forcing required to maintain the MJO on larger scales. We shall also use LIM results to estimate the “forcing” needed for model dynamics to reproduce the statistical properties of the observed MJO. Having thus established the extent to which forecast errors can be compensated with stochastic forcing, we will use the ship-based flux observations and the estimate of stochastic forcing obtained from the LIM analysis to diagnose the potential contribution of these cold pools to the subgrid (stochastic) forcing during the MJO’s initiation phase.
The proposed research investigates “the behavior and predictability of …atmosphereocean… system interactions giving rise to climate variability … on multiple timescales”* by developing a statistical description of a local process (the formation of atmospheric “cold pools” over the Indian Ocean during convective rain) and then exploring whether/how the process’ effects (altered air-sea fluxes) might be parameterized in a global forecast model. In combining “direct analysis of data collected during the [DYNAMO field] campaign”‡ with analysis of model forecasts of the larger ocean/atmosphere system in which the observed events occurred, it also sets the stage for DYNAMO’s upcoming forecasting component. The research thus contributes to “Improv[ing] Scientific Understanding of the Changing Climate System and its Impacts”† by investigating physically based “advances in climate modeling.”†
* ESS FY2013 Information Sheet, www.cpo.noaa.gov/index.jsp?pg=./opportunities/opp_index.jsp&opp=grants
† NOAA’s Next-Generation Strategic Plan, 2010, www.ppi.noaa.gov/ngsp. “Climate Adaptation and Mitigation”, pp 5-9. 2