Fitting the Viking lander surface pressure cycle with a Mars General Circulation Model

We present a systematic attempt to fit the Viking lander surface pressure cycle using a Mars General Circulation Model, MarsWRF. Following the earlier study by Wood and Paige (1992) using a one-dimensional model, high-precision fitting was achieved by tuning five time-independent parameters: the albedo and emissivity of the seasonal caps of the two hemispheres and the total CO_2 inventory in the atmosphere frost system. We used a linear iterative method to derive the best fit parameters: albedo of the northern cap = 0.795, emissivity of the northern cap = 0.485, albedo of the southern cap = 0.461, emissivity of the southern cap = 0.785, and total CO_2 mass = 2.83 × 10^(16) kg. If these parameters are used in MarsWRF, the smoothed surface pressure residual at the VL1 site is always smaller than several Pascal through a year. As in other similar studies, the best fit parameters do not match well with the current estimation of the seasonal cap radiative properties, suggesting that important physics contributing to the energy balance not explicitly included in MarsWRF have been effectively aliased into the derived parameters. One such effect is likely the variation of thermal conductivity with depth in the regolith due to the presence of water ice. Including such a parameterization in the fitting process improves the reasonableness of the best fit cap properties, mostly improving the emissivities. The conductivities required in the north to provide the best fit are higher than those required in the south. A completely physically reasonable set of fit parameters could still not be attained. Like all prior published GCM simulations, none of the cases considered are capable of predicting a residual southern CO_2 cap

Comparison of stochastic parameterisation approaches in a single-column model

We discuss and test the potential usefulness of single-column models (SCMs) for the testing of stochastic physics schemes that have been proposed for use in general circulation models (GCMs). We argue that although single column tests cannot be definitive in exposing the full behaviour of a stochastic method in the full GCM, and although there are differences between SCM testing of deterministic and stochastic methods, SCM testing remains a useful tool. It is necessary to consider an ensemble of SCM runs produced by the stochastic method. These can be usefully compared to deterministic ensembles describing initial condition uncertainty and also to combinations of these (with structural model changes) into poor man's ensembles. The proposed methodology is demonstrated using an SCM experiment recently developed by the GCSS (GEWEX Cloud System Study) community, simulating transitions between active and suppressed periods of tropical convection

Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics 1.0: A General Circulation Model for Simulating the Climates of Rocky Planets

Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics (ROCKE-3D) is a 3-Dimensional General Circulation Model (GCM) developed at the NASA Goddard Institute for Space Studies for the modeling of atmospheres of Solar System and exoplanetary terrestrial planets. Its parent model, known as ModelE2 (Schmidt et al. 2014), is used to simulate modern and 21st Century Earth and near-term paleo-Earth climates. ROCKE-3D is an ongoing effort to expand the capabilities of ModelE2 to handle a broader range of atmospheric conditions including higher and lower atmospheric pressures, more diverse chemistries and compositions, larger and smaller planet radii and gravity, different rotation rates (slowly rotating to more rapidly rotating than modern Earth, including synchronous rotation), diverse ocean and land distributions and topographies, and potential basic biosphere functions. The first aim of ROCKE-3D is to model planetary atmospheres on terrestrial worlds within the Solar System such as paleo-Earth, modern and paleo-Mars, paleo-Venus, and Saturn's moon Titan. By validating the model for a broad range of temperatures, pressures, and atmospheric constituents we can then expand its capabilities further to those exoplanetary rocky worlds that have been discovered in the past and those to be discovered in the future. We discuss the current and near-future capabilities of ROCKE-3D as a community model for studying planetary and exoplanetary atmospheres.Comment: Revisions since previous draft. Now submitted to Astrophysical Journal Supplement Serie

Learning in a changing environment

Multiple cue probability learning studies have typically focused on stationary environments. We present three experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that participants adapted to these types of change in qualitatively different ways. Also, in contrast to earlier claims that these tasks are learned implicitly, participants showed good insight into what they learned. By fitting formal learning models, we investigated whether participants learned global functional relationships or made localized predictions from similar experienced exemplars. Both a local (the Associative Learning Model) and a global learning model (the novel Bayesian Linear Filter) fitted the data of the first two experiments. However, the results of Experiment 3, which was specifically designed to discriminate between local and global learning models, provided more support for global learning models. Finally, we present a novel model to account for the cue competition effects found in previous research and displayed by some of our participants

Pattern scaling using ClimGen: monthly-resolution future climate scenarios including changes in the variability of precipitation

Development, testing and example applications of the pattern-scaling approach for generating future climate change projections are reported here, with a focus on a particular software application called “ClimGen”. A number of innovations have been implemented, including using exponential and logistic functions of global-mean temperature to represent changes in local precipitation and cloud cover, and interpolation from climate model grids to a finer grid while taking into account land-sea contrasts in the climate change patterns. Of particular significance is a new approach for incorporating changes in the inter-annual variability of monthly precipitation simulated by climate models. This is achieved by diagnosing simulated changes in the shape of the gamma distribution of monthly precipitation totals, applying the pattern-scaling approach to estimate changes in the shape parameter under a future scenario, and then perturbing sequences of observed precipitation anomalies so that their distribution changes according to the projected change in the shape parameter. The approach cannot represent changes to the structure of climate timeseries (e.g. changed autocorrelation or teleconnection patterns) were they to occur, but is shown here to be more successful at representing changes in low precipitation extremes than previous pattern-scaling methods