Geology topics

T. Robinson

Publications and source records attributed to T. Robinson.

3 recordsLinked to original sources

USGS2018

The GFDL global atmosphere and land model AM4.0/LM4.0: 1. Simulation characteristics with prescribed SSTs

In this two‐part paper, a description is provided of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). This version, with roughly 100 km horizontal resolution and 33 levels in the vertical, contains an aerosol model that generates aerosol fields from emissions and a “light” chemistry mechanism designed to support the aerosol model but with prescribed ozone. In Part 1, the quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode—with prescribed sea surface temperatures (SSTs) and sea‐ice distribution—is described and compared with previous GFDL models and with the CMIP5 archive of AMIP simulations. The model's Cess sensitivity (response in the top‐of‐atmosphere radiative flux to uniform warming of SSTs) and effective radiative forcing are also presented. In Part 2, the model formulation is described more fully and key sensitivities to aspects of the model formulation are discussed, along with the approach to model tuning.

Journal of Advances in Modeling Earth Systems↗

USGS2018

The GFDL global atmosphere and land model AM4.0/LM4.0: 2. Model description, sensitivity studies, and tuning strategies

In Part 2 of this two‐part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part 1. Part 2 provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.

Journal of Advances in Modeling Earth Systems↗

USGS2011

Modelling the distribution of domestic ducks in Monsoon Asia

Domestic ducks are considered to be an important reservoir of highly pathogenic avian influenza (HPAI), as shown by a number of geospatial studies in which they have been identified as a significant risk factor associated with disease presence. Despite their importance in HPAI epidemiology, their large-scale distribution in Monsoon Asia is poorly understood. In this study, we created a spatial database of domestic duck census data in Asia and used it to train statistical distribution models for domestic duck distributions at a spatial resolution of 1 km. The method was based on a modelling framework used by the Food and Agriculture Organisation to produce the Gridded Livestock of the World (GLW) database, and relies on stratified regression models between domestic duck densities and a set of agro-ecological explanatory variables. We evaluated different ways of stratifying the analysis and of combining the prediction to optimize the goodness of fit of the predictions. We found that domestic duck density could be predicted with reasonable accuracy (mean RMSE and correlation coefficient between log-transformed observed and predicted densities being 0.58 and 0.80, respectively), using a stratification based on livestock production systems. We tested the use of artificially degraded data on duck distributions in Thailand and Vietnam as training data, and compared the modelled outputs with the original high-resolution data. This showed, for these two countries at least, that these approaches could be used to accurately disaggregate provincial level (administrative level 1) statistical data to provide high resolution model distributions.

Agriculture, Ecosystems and Environment↗