Keith Oleson

Project Scientist
TIIMES - CGD
WCAS

Contact Information:
PO Box 3000, Boulder, CO 80307-3000
Office: ML - 208
Telephone: 303-497-1332
Email: oleson@ucar.edu
Home Page

Project Summary:

Click on picture to view the entire figure.

Figure 1. Total water storage anomalies (mm) for U_HYD (CLM3.5) and U_CON (CLM3.0) compared to two sources of GRACE data (Seo and Wilson 2005 (GRACE1) and Chen et al. 2005 (GRACE2)). Model total water storage anomalies are calculated from the sum of snow water, canopy water, total column soil water, and aquifer water. GRACE data were interpolated to the model resolution.

CLM Hydrology

A multi-year project to improve the hydrology of the Community Land Model version 3 (CLM CLM3), the land component of the Community Climate System Model (CCSM), was completed.  CLM3 has energy and water biases resulting from deficiencies in some of its canopy and soil parameterizations related to hydrological processes.   Recent research by the community that utilizes CLM3 and the family of CCSM models indicated several promising approaches to alleviating these biases.  A selected set of these parameterizations was implemented and their effects on the simulated hydrological cycle were analyzed.  The modifications consist of surface datasets based on Moderate Resolution Imaging Spectroradiometer products, new parameterizations for canopy integration, canopy interception, frozen soil, soil water availability, and soil evaporation, a TOPMODEL-based model for surface and sub-surface runoff, a groundwater model for determining water table depth, and the introduction of a factor to simulate nitrogen limitation on plant productivity.  The results from a set of global offline simulations were compared with observed data for runoff, river discharge, soil moisture, and total water storage to assess the performance of the new model (referred to as CLM3.5).  Data from 15 Fluxnet sites were also used to provide a process-level assessment of the modifications.  CLM3.5 exhibits significant improvements in its partitioning of global evapotranspiration (ET) which result in wetter soils, less plant water stress, increased transpiration and photosynthesis, and an improved annual cycle of total water storage.  Phase and amplitude of the runoff annual cycle is generally improved.  Dramatic improvements in vegetation biogeography result when CLM3.5 is coupled to a dynamic global vegetation model.

Click on picture to view the entire figure.

Figure 2. Schematic overview of the modeled urban land-unit. The canyon consists of roof, sunlit and shaded walls of height , and a canyon floor of width divided into pervious and impervious fractions. For each of these surfaces, temperatures (T), sensible (Q_H), latent (Q_E), and storage (Q_S) heat fluxes are simulated. Temperatures for each urban surface include surface temperature (T_U,S) and internal temperatures for 10 layers (T_U,1...10). An internal building temperature (T_tB) is simulated that can be held at prescribed comfort levels, T_iB,min and T_iB,max, thereby simulating heating and/or air conditioning. Hydrology on the roof and canyon floor is simulated, walls are hydrologically inactive. Snowpacks can form on the active surfaces. A certain amount of liquid water is allowed to pond on these surfaces which supports evaporation. Snow melt water or water in excess of the maximum ponding depth runs off (R _roof, R_imprvrd, R_prvrd). The pervious canyon floor has a soil moisture store to support evaporation. Anthropogenic fluxes from traffic (Q_H,traffic) or other sources such as heating and/or air conditioning waste heat (Q_H,waste) can be accommodated. Incident, reflected, and net solar and longwave radiation are calculated for each individual surface but are not shown for clarity.

The new model was approved by the Land Model Working Group and released to the public along with technical documentation and improved atmospheric forcing data in May 2007.  NCAR contributions were led by Keith Oleson, David Lawrence, and Gordon Bonan with additional contributions from Aiguo Dai, Taotao Qian, and Kevin Trenberth, while university collaborators included Robert Dickinson (Georgia Institute of Technology), Zong-Liang Yang and Guo-Yue Niu (University of Texas), Reto Stockli (Colorado State University), and Peter Lawrence (University of Colorado).

Land cover and land use change

Keith Oleson, Gordon Bonan, and Johan Feddema (University of Kansas) continued work on the development and testing of an urban land cover parameterization for CLM (CLMU). The model is designed to be simple enough to be compatible with structural and computational constraints of a land surface model coupled to a global climate model, yet complex enough to explore physically-based processes known to be important in determining urban climatology.  The city representation is based upon the ‘urban canyon’ concept which consists of roofs, sunlit and shaded walls, and canyon floor.  The canyon floor is divided into pervious (e.g., residential lawns, parks) and impervious (e.g., roads, parking lots, sidewalks) fractions.  Trapping of longwave radiation by canyon surfaces and solar radiation absorption and reflection is determined by accounting for multiple reflections.  Separate energy balances and surface temperatures are determined for each canyon facet.  A one-dimensional heat conduction equation is solved numerically for a ten-layer column to determine conduction fluxes into and out of canyon surfaces.  Surface hydrology including snow and runoff is simulated.  Model performance is evaluated against measured fluxes and temperatures from two urban sites in collaboration with Sue Grimmond (King’s College London).  Results indicate the model does a reasonable job of simulating the energy balance of cities.

The robustness of the model was also tested through sensitivity studies and the model’s ability to simulate urban heat islands in different environments was examined. Findings show that heat storage and sensible heat flux are most sensitive to uncertainties in the input parameters within the atmospheric and surface conditions considered.  The sensitivity studies suggest that attention should be paid to not only accurately characterizing the structure of the urban area (e.g., height to width ratio), but also to the input data reflecting the thermal admittance properties of each of the city surfaces. Simulations of the urban heat island show that the urban model is able to capture typical observed characteristics of urban climates qualitatively.  In particular, the model produces a significant heat island that increases with height to width ratio.  In urban areas, daily minimum temperatures increase more than daily maximum temperatures resulting in a reduced diurnal temperature range compared to equivalent rural environments.  The magnitude and timing of the heat island vary tremendously depending on the prevailing meteorological conditions and the characteristics of surrounding rural environments.  The model also correctly increases the Bowen ratio and canopy air temperatures of urban systems as impervious fraction increases.  In general, these findings are in agreement with those observed for real urban ecosystems.  Thus, the model appears to be a useful tool for examining the nature of the urban climate within the framework of global climate models.

Keith Oleson is also participating in a project to compare urban surface energy balance schemes, led by Sue Grimmond (Kings’s College London), Martin Best (UK Met Office), and Janet Barlow (University of Reading).   The purpose of this project is to evaluate the ability of urban models to simulate heat fluxes by performing a multi-step model comparison of urban surface energy balance schemes with observational datasets.  Among the key questions to be answered by this project are:

• What are the main physical processes controlling the urban energy balance which need to be resolved? 

• How complex does a model need to be in order to produce a realistic simulation of urban surface fluxes and temperatures? 

• Which input parameter information is required by an urban model to perform realistically? 

• Are we measuring the correct variables at the correct scales for model evaluation?

Future plans include continued participation in the urban scheme intercomparison project and the implementation of a series of present-day and future scenario climate model experiments using the urban model to address the following questions:

• What are the effects of including urban landcover in a climate model on present day climate?

• What are the characteristics of the urban heat island under various environmental conditions?

• What are the effects of 2100 urban landcover compared to present day urbanization?

• How do the effects of urbanization-induced climate change compare to greenhouse gas-induced climate change?

We will focus on the effects on near-surface air temperature, humidity, and surface hydrology as well as derived quantities such as diurnal temperature range, extremes, and heat indices.

Presentations:

• Judge for Denver Metro Science Fair, K-12, Denver, CO, USA, November 07
• Soil Moisture Variability in CLM3.5, Breckenridge USA, June 2007

TIIMES External Collaborators:

Robert Dickinson, Georgia Institute of Technology
Johannes Feddema, University of Kansas
Sue Grimmond, King's College
Forrest Hoffman, Oak Ridge National Laboratory
Menglin Jin, University of Maryland
Peter Lawrence, University of Colorado
G.-Y. Nui, University of Texas at Austin
Sonia Seneviratne, Swiss Federal Institute of Technology (ETH)
Reto Stockli, Colorado State University
Z.-L. Yang, University of Texas at Austin
Xubin Zeng, University of Arizona

Publications:

Oleson, K. W., G. B. Bonan, J. Feddema, 2007: An urban parameterization for a global climate model. 1. Formulation and evaluation for two cities. J. Appl. Meteor. Climat.. (In Press)

Oleson, K. W., G. B. Bonan, J. Feddema, M. Vertenstein, 2007: An urban parameterization for a global climate model. 2. Sensitivity to input parameters and the simulated urban heat island in offline simulations. J. Appl. Meteor. Climat.. (In Press)

Oleson, K.W., G.-Y. Niu, Z.-L. Yang, D.M. Lawrence, P.E. Thornton, P.J. Lawrence, R. Stockli, R.E. Dickinson, G.B. Bonan, S. Levis, A. Dai, and T. Qian, 2007: Improvements to the Community Land Model and their impact on the hydrological cycle, J. Geophys. Res., submitted.

Stockli, R., D.M. Lawrence, G.-Y. Niu, K.W. Oleson, P.E. Thornton, Z.-L. Yang, G.B. Bonan, A.S. Denning, and S.W. Running, 2007: The use of Fluxnet in the Community Land Model development, J. Geophys. Res., submitted.

Lawrence, D. M., P. E. Thornton, K. W. Oleson, G. B. Bonan, 2007: The partitioning of evapotranspiration into transpiration, soil evaporation, and canopy evaporation in a GCM: Impacts on land-atmosphere interaction. J. Hydrometeorol., 8, 862-880, doi: 10.1175/JHM596.1.

Grimmond, C. S., M. Best, J. Barlow, J.-J. Baik, S. Belcher, M. Bruse, X. Cai, I. Calmet, F. Chen, P. Clarke, A. Dandou, E. Erell, K. Fortuniak, D. Grawe, R. Hamdi, M. Kanda, T. Kawai, H. Kondo, S. Krayenhoff, S. H. Lee, S.-B. Limor, A. Martilli, V. Masson, G. Mills, R. Moriwaki, K. W. Oleson, A. Porson, M. Shiguang, U. Sievers, H. Thompson, M. Tombrou, T. Williamson, 2007: Urban surface energy balance models: model characteristics and methodology for a comparison study, in preparation for COST-728 workshop volume on Model Urbanization Strategy. COST-728 Wkshp on "Model urbanization strategy", Exeter, GB, UKMO, COST-728.

Seneviratne, S. I., R. D. Koster, Z. Guo, P. A. Dirmeyer, E. Kowalczyk, D. Lawrence, P. Liu, C. H. Lu, D. Mocko, K. W. Oleson, D. Verseghy, 2006: Soil moisture memory in AGCM simulations: Analysis of global Land-Atmosphere Coupling Experiments (GLACE) data. J. Hydrometeorol., 7, 1090-1112.

Qian, T., A. Dai, K. E. Trenberth, K. W. Oleson, 2006: Simulation of global land surface conditions from 1948 to 2004. Part I: Forcing data and evaluation. J. Hydrometeorol., 7, 953-975, doi: 10.1175/JHM540.1.