Harvesting Climate Model Uncertainty

1. Harvesting Climate Model Uncertainty Results: A climate model is run for 100 years with (EXPT) and 100 years without (CONTROL) the multi-parameter methodology. Figures 1 and 2 are obtained from the analysis of these model results: a) b) c) Figure 1: The regression of the Dec-Jan-Feb mean tropical Pacific SST anomalies on the preceding Jun-Jul-Aug-Sep mean IMR index from a) CONTROL, b) EXPT model simulations and c) OBS. The units are in 0C. Only significant values at 90% confidence according to t-test are shaded. Introduction: Because of the underlying assumption of the parameterization schemes in climate models that sub-grid scale variability is a slave of the grid scale variations (which is not always true), some of the model systematic errors have stubbornly resisted upgrades in resolution and parameterization complexity. These model errors often limit climate predictability. Motivation: Are these systematic model errors arising from poor choice of parameters in the parameterization schemes of climate models? Often these parameters are unobserved quantities but required for closure of the scheme. So it is even difficult to answer what is a good choice for these empirical parameters? It has been shown that much of the perceived model uncertainty stems from the ‘fast physics’ that isolates atmospheric convection as one of the main sources of such uncertainty. Underlying this model disparity is the disproportionate response of the climate simulation to very small changes to these empirical parameters of the scheme. Methodology: In a state-of-the-art climate model (that includes components of atmosphere, biosphere, and ocean) we invoke the atmospheric convection scheme 100 times (EXPT), each time with a unique, infinitesimally, stochastically perturbed value of a chosen parameter (in this case it is the fraction of convective rain evaporated back in to the atmosphere) at every time step of the integration of the climate model. We then conduct a cluster analysis to obtain the most probable heating and moistening profiles of atmospheric convection at the given discrete grid point that is fed back to the climate model from the convection scheme. Potential outcome: The climate simulations are robust (not critically dependent on parameter choices), could reduce erroneous predictability (confident of wrong solutions) of the climate model, get insight on climate sensitivity to model parameters. Figure 2: The climatological mean Jun-Jul-Aug-Sep (JJAS) precipitation (mmday-1) from a) observations, b) EXPT, and c) CONTROL. d) The climatological mean JJAS precipitation difference (mm day-1) between EXPT and CONTROL simulations. Only significant values at 90% confidence interval according to t-test are plotted. The corresponding variance of the mean JJAS precipitation (mm2day-2) from e) observations, f) EXPT simulation, and g) CONTROL simulation. h) The ratio of the variance of the mean JJAS precipitation between EXPT and CONTROL. Only significant values at 90% confidence according to F-test are shaded. a) b) c) d) e) f) g) h) Conclusion: Multi-parameter methodology (EXPT) raises the variance without modifying the mean climate (Fig. 2) by as much (which proves that the model mean climate is robust in the CONTROL and the parameter is indeed perturbed). The variance in EXPT over the Indian monsoon is credible (Fig. 1). This methodology has the advantage of using computing resources comparable to that of the CONTROL model, which is otherwise strained by even moderate increases in the spatial resolution of the model. Future research: It is planned to exploit this methodology for seasonal and decadal prediction problems and to construct robust estimates of past climates. After all, our renditions of past, current, and future climates are all a model estimate whether it is numerical, statistical, or empirical. Observations are sparse, discontinuous and inhomogeneous to completely describe the Earth’s climate system. Therefore the proposed methodology has tremendous scope for further exploration in climate research. Vasubandhu Misra, Department of Earth, Ocean and Atmospheric Science (EOAS) and Center for Ocean-Atmospheric Prediction Studies (COAPS), Florida State University Email: vmisra@fsu.edu; Phone: (850) 644-2814; (850) 645-8859