CGD's Dr. Keith Oleson

Abstract

Urbanization, the expansion of built-up areas, is an important yet less studied aspect of land use/cover change in climate science. To date, most global climate models used to evaluate effects of land use/cover change on climate do not include an urban parameterization. Here, we describe the formulation and evaluation of a parameterization of urban areas that is incorporated into the Community Land Model, the land surface component of the Community Climate System Model. 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. Model performance is evaluated against measured fluxes and temperatures from two urban sites. Results indicate the model does a reasonable job of simulating the energy balance of cities.

Figure caption: Schematic overview of the modeled urban land-unit. The canyon consists of roof, sunlit and shaded walls of height H, and a canyon floor of width W 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 u include surface temperature (T_u,s) and internal temperatures for 10 layers (T_u,1...10). An internal building temperature (T_iB) 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.

Support: This research was supported by the Office of Science (BER), U.S. Department of Energy, Cooperative Agreement No. DE-FC02-97ER62402, the National Science Foundation grants ATM-0107404 and ATM-0413540, the National Center for Atmospheric Research Water Cycle Across Scales, Biogeosciences, and Weather and Climate Impacts Assessment Science Initiatives, and the University of Kansas, Center for Research.

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Oleson, K.W., G.B. Bonan, J. Feddema, and 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. Meteorol. Clim., in press.

Abstract

In a companion paper (Oleson et al. 2007), we presented a formulation and evaluation of an urban parameterization designed to represent the urban energy balance in the Community Land Model. Here we test the robustness of the model through sensitivity studies and evaluate the model's ability to simulate urban heat islands in different environments. 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 here. 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.

Figure caption: Annual and seasonal (winter-DJF, spring-MAM, summer-JJA, fall-SON) characteristics of urban and rural air temperature differences. Urban and rural air temperatures, T_urban and T_rural, are from hourly data as described in the text. The lines indicate air temperature differences averaged over all grid cells. The daily maximum (blue line) is T_{urban, max} - T_{rural, max} (with overbar) where T_{urban, max} and T_{rural, max} are the maximum urban and rural air temperature in a given day, and the overbar represents the average over the number of days in a given season. Similarly, the daily minimum (solid black line) is T_{urban, min} - T_{rural, min} (with overbar). The daily average (green line) is T_{urban, avg} - T_{rural, avg} (with overbar) where T_{urban, avg} and T_{rural, avg} are the daily average of the hourly urban and rural air temperatures. The daily average diurnal range (red line) is (T_{urban, max} - T_{urban, min}) - (T_{rural, max} - T_{rural, min}) (with overbar). The dots represent the maximum T_urban - T_ruralat each grid cell for a given height to width ratio, while the long dashed line (average of maximum) represents the average of these at each height to width ratio.

Support: This research was supported by the Office of Science (BER), U.S. Department of Energy, Cooperative Agreement No. DE-FC02-97ER62402, the National Science Foundation grants ATM-0107404 and ATM-0413540, the National Center for Atmospheric Research Water Cycle Across Scales, Biogeosciences, and Weather and Climate Impacts Assessment Science Initiatives, and the University of Kansas, Center for Research.

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

Abstract

Because of a lack of observations, historical simulations of land surface conditions using land surface models are needed for studying variability and changes in the continental water cycle and for providing initial conditions for seasonal climate predictions. Atmospheric forcing datasets are also needed for land surface model development. The quality of atmospheric forcing data greatly affects the ability of land surface models to realistically simulate land surface conditions. Here a carefully constructed global forcing dataset for 1948-2004 with 3-hourly and T62 (~1.875°) resolution is described, and historical simulations using the latest version of the Community Land Model version 3.0 (CLM3) are evaluated using available observations of streamflow, continental freshwater discharge, surface runoff, and soil moisture. The forcing dataset was derived by combining observation-based analyses of monthly precipitation and surface air temperature with intramonthly variations from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis, which is shown to have spurious trends and biases in surface temperature and precipitation. Surface downward solar radiation from the reanalysis was first adjusted for variations and trends using monthly station records of cloud cover anomaly and then for mean biases using satellite observations during recent decades. Surface specific humidity from the reanalysis was adjusted using the adjusted surface air temperature and reanalysis relative humidity. Surface wind speed and air pressure were interpolated directly from the 6-hourly reanalysis data. Sensitivity experiments show that the precipitation adjustment (to the reanalysis data) leads to the largest improvement, while the temperature and radiation adjustments have only small effects. When forced by this dataset, the CLM3 reproduces many aspects of the long-term mean, annual cycle, interannual and decadal variations, and trends of streamflow for many large rivers (e.g., the Orinoco, Changjiang, Mississippi, etc.), although substantial biases exist. The simulated long-term-mean freshwater discharge into the global and individual oceans is comparable to 921 river-based observational estimates. Observed soil moisture variations over Illinois and parts of Eurasia are generally simulated well, with the dominant influence coming from precipitation. The results suggest that the CLM3 simulations are useful for climate change analysis. It is also shown that unrealistically low intensity and high frequency of precipitation, as in most model-simulated precipitation or observed time-averaged fields, result in too much evaporation and too little runoff, which leads to lower than observed river flows. This problem can be reduced by adjusting the precipitation rates using observed-precipitation frequency maps.

Figure caption: Long-term-mean annual freshwater discharge (Sv) into the global oceans smoothed using a 5° lat running mean from observation-based estimates (solid line; from Dai and Trenberth 2002) and the standard CLM3 simulation (dashed line).

Support: This study was supported by NSF Grant ATM-0233568 and NCAR's Water Cycle Across Scales Initiative.

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

Abstract

Soil moisture memory is a key aspect of land-atmosphere interaction and has major implications for seasonal forecasting. Because of a severe lack of soil moisture observations on most continents, existing analyses of global-scale soil moisture memory have relied previously on atmospheric general circulation model (AGCM) experiments, with derived conclusions that are probably model dependent. The present study is the first survey examining and contrasting global-scale (near) monthly soil moisture memory characteristics across a broad range of AGCMs. The investigated simulations, performed with eight different AGCMs, were generated as part of the Global Land-Atmosphere Coupling Experiment. Overall, the AGCMs present relatively similar global patterns of soil moisture memory. Outliers are generally characterized by anomalous water-holding capacity or biases in radiation forcing. Water-holding capacity is highly variable among the analyzed AGCMs and is the main factor responsible for intermodel differences in soil moisture memory. Therefore, further studies on this topic should focus on the accurate characterization of this parameter for present AGCMs. Despite the range in the AGCMs' behavior, the average soil moisture memory characteristics of the models appear realistic when compared to available in situ soil moisture observations. An analysis of the processes controlling soil moisture memory in the AGCMs demonstrates that it is mostly controlled by two effects: evaporation's sensitivity to soil moisture, which increases with decreasing soil moisture content, and runoff's sensitivity to soil moisture, which increases with increasing soil moisture content. Soil moisture memory is highest in regions of medium soil moisture content, where both effects are small.

Figure caption: (top) Mean O(P, S) - O(P, W) of the eight AGCMs investigated. (bottom) Mean O(P, S) - O(P, W) of the eight AGCMs multiplied by the average ?27 values of the models.

Support: This research project was supported by an NCCR-Climate fellowship funded by the Swiss National Science Foundation. The individual AGCM contributions were supported by the participants' home institutions and funding agencies (K. Oleson's contribution was funded by NCAR's Water Cycle Across Scales Initiative).

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Lawrence, D.M., P.E. Thornton, K.W. Oleson, and 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.

Abstract

Although the global partitioning of evapotranspiration (ET) into transpiration, soil evaporation, and canopy evaporation is not well-known, most current land-surface schemes and the few available observations indicate that transpiration is the dominant component on the global scale, followed by soil evaporation and canopy evaporation. The Community Land Model (CLM3), however, does not reflect this global view of ET partitioning with soil evaporation and canopy evaporation far outweighing transpiration. One consequence of this unrealistic ET partitioning in CLM3 is that photosynthesis, which is linked to transpiration through stomatal conductance, is significantly underestimated on a global basis. A number of modifications to CLM3 vegetation and soil hydrology parameterizations are described that improve ET partitioning and reduce an apparent dry soil bias in CLM3. The modifications reduce canopy interception and evaporation, reduce soil moisture stress on transpiration, increase transpiration through a more realistic canopy integration scheme, reduce within canopy soil evaporation, eliminate lateral drainage of soil water, increase infiltration of water into the soil, and increase the vertical redistribution of soil water. The partitioning of ET is improved, with notable increases seen in transpiration (13% to 41% of global ET) and photosynthesis (65 to 148 Pg C yr-1). Soils are wetter and exhibit a far more distinct soil moisture annual cycle and greater interseasonal soil water storage which permits plants to sustain transpiration through the dry season. The broader influences of improved ET partitioning on land-atmosphere interaction are diverse. Stronger transpiration and reduced canopy evaporation yield an extended ET response to rain events and a shift in the precipitation distribution towards more frequent small to medium size rain events. Soil moisture memory timescales decrease particularly at deeper soil levels. Sub-surface soil moisture exerts a slightly greater influence on precipitation. These results indicate that partitioning of ET is an important responsibility for land surface schemes, a responsibility that will gain in relevance as GCMs evolve to incorporate ever more complex treatments of the earth's carbon and hydrologic cycles.

Figure caption: Composites of the model response in terms of ET and 2m air temperature to all rainfall events that are followed by five consecutive rain-free days. Events are identified separately for each grid box located within Amazonia region (10S-0, 50-70W).

Support: This study was supported by funding from the U.S. Department of Energy, Office of Biological and Environmental Research, as part of its Climate Change Prediction Program, cooperative agreement no. DE-FC03-97ER62402/A010 and by NASA Earth Science Enterprise, Terrestrial Ecology Program Grant #W-19,953 to P.E. Thornton. K. Oleson's contribution was funded by NCAR's Water Cycle Across Scales Initiative.