ORCID Profile
0000-0003-4389-3139
Current Organisation
Independent Researcher
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: American Geophysical Union (AGU)
Date: 11-2020
DOI: 10.1029/2019MS002015
Publisher: American Geophysical Union (AGU)
Date: 19-01-2016
DOI: 10.1002/2015JD024053
Publisher: Springer Science and Business Media LLC
Date: 16-05-2016
DOI: 10.1038/NCLIMATE3029
Publisher: American Meteorological Society
Date: 06-1997
Publisher: American Geophysical Union (AGU)
Date: 02-2019
DOI: 10.1029/2018WR023903
Publisher: American Geophysical Union (AGU)
Date: 11-2019
DOI: 10.1029/2019MS001829
Abstract: We describe the Geophysical Fluid Dynamics Laboratory's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the preindustrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top‐of‐atmosphere fluxes, surface temperature, and precipitation reduced double Intertropical Convergence Zone bias dramatically improved representation of ocean boundary currents a high‐quality simulation of climatological Arctic sea ice extent and its recent decline and excellent simulation of the El Niño‐Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas an inaccurate Antarctic sea ice simulation precipitation and wind composites still affected by the equatorial cold tongue bias muted variability in the Atlantic Meridional Overturning Circulation strong 100 year quasiperiodicity in Southern Ocean ventilation and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.
Publisher: Copernicus GmbH
Date: 14-06-2018
DOI: 10.5194/HESS-22-3311-2018
Abstract: Abstract. The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth system models – a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and computational constraints, these data are underused for global applications. As a proof of concept, this study explores how to effectively and efficiently harness these data in Earth system models over a 1/4∘ (∼ 25 km) grid cell in the western foothills of the Sierra Nevada in central California. First, a novel hierarchical multivariate clustering approach (HMC) is introduced that summarizes the high-dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this clustering approach impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled using HMC. The results over the test domain show that (1) the observed similarity over the landscape makes it possible to converge on the macroscale response of the fully distributed model with around 300 sub-grid land model tiles (2) assembling the sub-grid tile configuration from available environmental data can have a large impact on the macroscale states and fluxes of the water, energy, and carbon cycles for ex le, the defined subsurface connections between the tiles lead to a d ening of macroscale extremes (3) connecting the fine-scale grid to the model tiles via HMC enables circumvention of the classic scale discrepancies between the macroscale and field-scale estimates this has potentially significant implications for the evaluation and application of Earth system models.
Publisher: American Geophysical Union (AGU)
Date: 06-2005
DOI: 10.1029/2004WR003697
No related grants have been discovered for Paul Milly.