This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that is implemented in CAM v5.3. We show mean climate and tropical variability results from global simulations. The model has a degradation in precipitation skill but improvements in shortwave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation. We also show estimation of computational expense and sensitivity to number of subcolumns.

Neither the Michaelis-Menten kinetics nor the reverse Michaelis-Menten kinetics is a consistent approximation to the formulation of law of mass action. The ECA kinetics better approximates the exact solution obtained from the law of mass action and the total quasi-steady-state approximation. The ECA kinetics outperformed the MM kinetics and RMM kinetics in predicting the kinetic parametric sensitivity. The ECA kinetics is therefore expected to improve all soil biogeochemical models.

The Ent Terrestrial Biosphere Model is a mixed-canopy dynamic global vegetation model developed specifically for coupling with land surface hydrology and general circulation models. This study describes the leaf phenology submodel implemented in the Ent TBM. We evaluate the performance in reproducing observed leaf seasonal growth as well as water and carbon fluxes for four plant functional types at five Fluxnet sites.

Models of snow on the ground need to represent processes of solar radiation absorption, heat conduction, liquid water movement and compaction in snow and transfers of heat from the atmosphere. There are many such models in use, but their wide range in complexity makes it hard to understand how differences in process representations determine differences in predictions. Processes in the factorial snow model can be switched on or off independently, allowing highly controlled numerical experiments.

We implement a new stomatal conductance scheme within a land surface model coupled to a global climate model. The new model differs from the default in that it allows model parameters to vary by the different plant functional types, derived from global synthesis of observations. We show that the new scheme results in improvements in the model climatology and improves existing biases in warm temperature extremes by up to 10-20% over the boreal forests during summer.

In order to easily enforce solid-wall boundary conditions in the presence of complex coastlines, we propose a new mass and energy conserving Brinkman penalization for the rotating shallow water equations. This penalization does not lead to higher wave speeds in the solid region. The error estimates for the penalization are derived analytically and verified numerically for linearized one-dimensional equations. The penalization is implemented in a conservative dynamically adaptive wavelet method.

Even small errors in observational data can have a profound effect on snowpack simulations, which dilutes our confidence in the results. This paper aims at quantification of the overall contributions of these errors on the uncertainty of snowpack simulations in Arctic environments. The analysis demonstrates that the uncertainties in the data can have a profound impact on glacier mass balance estimates.

Our paper reports on a simple regression-based system for producing probabilistic forecasts of seasonal climate. We discuss the physical motivation behind the statistical relationships underpinning our empirical model and provide a validation of hindcasts produced for the last half century. The generation of probabilistic forecasts on a global scale along with the use of the long-term trend as a source of skill constitutes a novel approach to empirical forecasting of seasonal climate.

This paper documents the NCAR global model topography generation software. The software generates elevation and related data for global atmospheric models based in GTOPO30 or GMTED2010/MODIS source data.

The global simulations with CB05_GE and MOZART-4x predict similar chemical profiles for major gases compared to aircraft measurements, with better agreement for the NOy profile by CB05_GE. The SOA concentrations of SOA at four sites in CONUS and organic carbon over the IMPROVE sites are better predicted by MOZART-4x. The two simulations result in a global average difference of 0.5W m-2 in simulated shortwave cloud radiative forcing, with up to 13.6W m-2 over subtropical regions.

r.randomwalk is a flexible and multi-functional open-source GIS tool for simulating the propagation of mass movements. Mass points are routed from given release pixels through a digital elevation model until a defined break criterion is reached. In contrast to existing tools, r.randomwalk includes functionalities to account for parameter uncertainties, and it offers built-in functions for validation and visualization. We show the key functionalities of r.randomwalk for three test areas.

The IMAGE-Global Nutrient Model (GNM) is used to study the impact of multiple environmental changes on N and P delivery to surface water and transport and in-stream retention in rivers, lakes, wetlands and reservoirs over prolonged time periods. N and P are delivered to water bodies via diffuse sources (agriculture and natural ecosystems) and wastewater. N and P retention in a water body is calculated on the basis of the residence time of the water and nutrient uptake velocity.