A requirement for probabilistic climate forecasting is input probability density functions (PDFs) of key parameters related to uncertainties in the forcing and response of climate model simulations. For example, such input PDFs, p(), from Forest et al. (2002) were used in Webster et al. (2003) in their projections to 2100. Here, is a combination of climate sensitivity, rate of deep ocean heat uptake and, the net aerosol forcing. The net aerosol forcing was used to represent the total uncertainty in the climate forcings that were not explicitly included (e.g., solar and volcanic forcings). Here, we present an updated estimate of this joint PDF by (i) including additional forcings, (ii) extending the observational data records (up to 2001), and (iii) improving the climate model resolution (from 7.86o to 4o in latitude). In addition to changes in concentrations of greenhouse gases, sulfate aerosols, and ozone (1979-1995 only) (aka, GSO) for 1860-1995, we include changes in: the stratospheric aerosols from volcanic eruptions (Sato et al., 1993), solar irradiance (Lean, 2000), and land-use vegetation (Ramunkutty Foley, 1999) and apply all forcings (GSOVSV) for 1860-2001. As in previous work (Forest et al., 2002), we compare the modelled climate changes to multiple observational diagnostic datasets and compute goodness-of-fit statistics as derived from climate change detection algorithm. We explicitly vary the climate system properties, , over a wide range to locate regions of parameter space that are inconsistent with the observed record of climate change. Then, based on the goodness-of-fit statistics, for each diagnostic, we estimate the likelihood functions p(i|) where i are individual climate change diagnostics (surface, upper-air, or deep-ocean temperature changes for late 20th century.) The likelihood functions p(i|) are then used to update the posterior PDF, p(|i) according to Bayes theorem where assumed priors are required (see Forest et al., 2002.) The main focus of this talk will be to compare the updated PDFs with the previous results given the updated climate model resolution, longer observational records, and additional climate forcings. We will also compare the climate change detection results for GSO forcings with those for GSOVSV as climate sensitivity and other parameters vary. The individual impacts of the volcanic and solar forcings on the detection results will be explored as well.