CGD 2008 Profiles in Science: Dr. Gordon Bonan

Summary of achievements

Gordon Bonan

Gordon Bonan's research examines land-atmosphere interactions, especially the ecological, hydrological, and biogeochemical processes by which terrestrial ecosystems affect climate. He studies natural and human changes in land cover and ecosystems functions and their effects on climate, water resources, and biogeochemistry. He develops and uses climate, hydrological, and ecosystem models to study the influence of the biosphere on climate. Publications for 2008 highlight the role of forests as forcings and feedbacks in the climate system, the development of urban land cover parameterizations for climate models, improvements to the hydrologic cycle in the Community Land Model (CLM3.5), and the importance of accurate representation of snow and Arctic vegetation for climate simulation.

Publications

G.B. Bonan. 2008: Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests. Science, 320:1444-1449, doi:10.1126/science.1155121.

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Abstract: The world's forests influence climate through physical, chemical, and biological processes that affect planetary energetics, the hydrologic cycle, and atmospheric composition. These complex and nonlinear forest-atmosphere interactions can dampen or amplify anthropogenic climate change. Tropical, temperate, and boreal reforestation and afforestation attenuate global warming through carbon sequestration. Biogeophysical feedbacks can enhance or diminish this negative climate forcing. Tropical forests mitigate warming through evaporative cooling, but the low albedo of boreal forests is a positive climate forcing. The evaporative effect of temperate forests is unclear. The net climate forcing from these and other processes is not known. Forests are under tremendous pressure from global change. Interdisciplinary science that integrates knowledge of the many interacting climate services of forests with the impacts of global change is necessary to identify and understand as yet unexplored feedbacks in the Earth system and the potential of forests to mitigate climate change.

Figure caption: Biogeochemical (carbon) and biogeophysical (albedo and evapotranspiration) processes by which terrestrial ecosystems affect climate (SOM). (A and B) Geographic extent and total (plant and soil) carbon stock of nonforest (green) and forest (blue) biomes (2). Individual forest biomes are also shown and sum to the forest total. (C) Net ecosystem production (NEP) for tropical, temperate, and boreal forest (47). Individual symbols shown mean NEP for humid evergreen tropical forest, three types of temperate forest, and three types of boreal forest. Vertical bars show NEP averaged across forest types. (D) Satellite-derived direct-beam albedo for snow-covered and snow-free nonforest (green) and forest (blue) biomes (48). Also shown are individual forest biomes. (E) Evapotranspiration normalized by its equilibrium rate in relation to canopy resistance for wheat, corn, temperate deciduous forest, boreal jack pine conifer forest, and oak savanna (49, 50). Shown are individual data points and the mean for each vegetation type.

Bonan, G.B. 2008. Ecological Climatology: Concepts and Applications. 2nd edition. Cambridge University Press, Cambridge. 568 pages.

Figure 2. Ecological Climatology.

Desciption: This book introduces an interdisciplinary framework to understand the interaction between terrestrial ecosystems and climate change. It reviews basic meteorological, hydrological and ecological concepts to examine the physical, chemical and biological processes by which terrestrial ecosystems affect and are affected by climate. The textbook is written for advanced undergraduate and graduate students studying ecology, environmental science, atmospheric science and geography. The central argument is that terrestrial ecosystems become important determinants of climate through their cycling of energy, water, chemical elements and trace gases. This coupling between climate and vegetation is explored at spatial scales from plant cells to global vegetation geography and at timescales of near instantaneous to millennia. The text also considers how human alterations to land become important for climate change. This restructured edition, with updated science and references, chapter summaries and review questions, and over 400 illustrations, including many in colour, serves as an essential student guide.

Oleson, K.W., G.B. Bonan, J. Feddema, M. Vertenstein, and C.S.B. Grimmond, 2008: An urban parameterization for a global climate model. 1. Formulation and evaluation for two cities. J. Appl. Meteorol. Clim., 47, 1038-1060, doi:10.1175/2007JAMC1597.1.

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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.

Oleson, K.W., G.B. Bonan, J. Feddema, and M. Vertenstein, 2008: 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., 47, 1061-1076, doi:10.1175/2007JAMC1598.1.

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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.

Cook, B.I., G.B. Bonan, S. Levis, and H.E. Epstein. 2008: The thermoinsulation effect of snow cover within a climate model. Climate Dynamics, 31:107-124, doi:10.1007/s00382-007-0341-y.

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Abstract: We use a state of the art climate model (CAM3-CLM3) to investigate the sensitivity of surface climate and land surface processes to treatments of snow thermal conductivity. In the first set of experiments, the thermal conductivity of snow at each grid cell is set to that of the underlying soil (SC-SOIL), effectively eliminating any insulation effect. This scenario is compared against a control run (CTRL), where snow thermal conductivity is determined as a prognostic function of snow density. In the second set of experiments, high (SC-HI) and low (SC-LO) thermal conductivity values for snow are prescribed, based on upper and lower observed limits. These two scenarios are used to envelop model sensitivity to the range of realistic observed thermal conductivities. In both sets of experiments, the high conductivity/low insulation cases show increased heat exchange, with anomalous heat fluxes from the soil to the atmosphere during the winter and from the atmosphere to the soil during the summer. The increase in surface heat exchange leads to soil cooling of up to 20 K in the winter, anomalies that persist (though damped) into the summer season. The heat exchange also drives an asymmetric seasonal response in near-surface air temperatures, with boreal winter anomalies of +6 K and boreal summer anomalies of -2 K. On an annual basis there is a net loss of heat from the soil and increases in ground ice, leading to reductions in infiltration, evapotranspiration, and photosynthesis. Our results show land surface processes and the surface climate within CAM3-CLM3 are sensitive to the treatment of snow thermal conductivity.

Figure caption: Differences in 2-meter air temperature (degrees C) for SC-SOIL minus CTRL and SC-HI minus SC-LO, for seasons DJF and JJA. Top row shows results from boreal winter (DJF) comparison, bottom row shows results from boreal summer (JJA) comparison.

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. 2008. Use of FLUXNET in the community land model development. Journal of Geophysical Research-Biogeosciences, doi:10.1029/2007JG000562.

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Abstract: The Community Land Model version 3 (CLM3.0) simulates land-atmosphere exchanges in response to climatic forcings. CLM3.0 has known biases in the surface energy partitioning as a result of deficiencies in its hydrological and biophysical parameterizations. Such models, however, need to be robust for multidecadal global climate simulations. FLUXNET now provides an extensive data source of carbon, water and energy exchanges for investigating land processes, and it encompasses a global range of ecosystem-climate interactions. Data from 15 FLUXNET sites are used to identify and improve model deficiencies. Including a prognostic aquifer, a bare soil evaporation resistance formulation and numerous other changes in the model result in a significantly improved soil hydrology and energy partitioning. Terrestrial water storage increased by up to 300 mm in warm climates and decreased in cold climates. Nitrogen control of photosynthesis is revealed as another missing process in the model. These improvements increase the correlation coefficient of hourly and monthly latent heat fluxes from a range of 0.5-0.6 to the range of 0.7-0.9. RMSE of the simulated sensible heat fluxes decrease by 20-50%. Primary production is overestimated during the wet season in mediterranean and tropical ecosystems. This might be related to missing carbon-nitrogen dynamics as well as to site-specific parameters. The new model (CLM3.5) with an improved terrestrial water cycle should lead to more realistic land-atmosphere exchanges in coupled simulations. FLUXNET is found to be a valuable tool to develop and validate land surface models prior to their application in computationally expensive global simulations.

Figure caption: Performance of four model versions at 15 FLUXNET towers (numbers 1-15). Statistics in the Taylor diagram are derived from hourly simulated and observed LE and H fluxes. Legend: CLM3.0: red asterisks; CLMgw: green crosses; CLMgw_rsoil: cyan diamonds; CLM3.5: violet triangles. In CLM3.0 H is off-scale for the two tropical sites 8 and 9 (and therefore not shown).

Support: We acknowledge the NASA Energy and Water Cycle Study (NEWS) grant NNG06CG42G as the main funding source of this study.

Oleson, K. W., et al., 2008: Improvements to the Community Land Model and their impact on the hydrological cycle. J. Geophys. Res., 113, G01021, doi:10.1029/2007JG000563.

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Abstract: The Community Land Model version 3 (CLM3) is the land component of the Community Climate System Model (CCSM). 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 has indicated several promising approaches to alleviating these biases. This paper describes the implementation of a selected set of these parameterizations and their effects on the simulated hydrological cycle. The modifications consist of surface data sets 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 subsurface 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 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). 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. Lower than observed soil moisture variability in the rooting zone is noted as a remaining deficiency.

Figure caption: Total water storage anomalies (mm) for U_HYD (CLM3.5) and U_CON (CLM3.0) compared to two sources of GRACE data (GRACE1 [Seo and Wilson, 2005] and GRACE2 [Chen et al., 2005]). 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.

Support: This work was supported by the NCAR Water Cycles Across Scales, Biogeosciences, and Weather and Climate Impacts Assessment Science Initiatives.

B. I. Cook, G. B. Bonan, S. Levis, and H. Epstein. 2008: Rapid vegetation responses and feedbacks amplify climate model response to snow cover changes. Climate Dynamics, 30:391-406, doi:10.1007/s00382-007-0296-z.

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Abstract: We investigate the response of a climate system model to two different methods for estimating snow cover fraction. In the control case, snow cover fraction changes gradually with snow depth; in the alternative scenarios (one with prescribed vegetation and one with dynamic vegetation), snow cover fraction initially increases with snow depth almost twice as fast as the control method. In cases where the vegetation was fixed (prescribed), the choice of snow cover parameterization resulted in a limited model response. Increased albedo associated with the high snow caused some moderate localized cooling (3-5°C), mostly at very high latitudes (>70°N) and during the spring season. During the other seasons, however, the cooling was not very extensive. With dynamic vegetation the change is much more dramatic. The initial increases in snow cover fraction with the new parameterization lead to a large-scale southward retreat of boreal vegetation, widespread cooling, and persistent snow cover over much of the boreal region during the boreal summer. Large cold anomalies of up to 15°C cover much of northern Eurasia and North America and the cooling is geographically extensive in the northern hemisphere extratropics, especially during the spring and summer seasons. This study demonstrates the potential for dynamic vegetation within climate models to be quite sensitive to modest forcing. This highlights the importance of dynamic vegetation, both as an amplifier of feedbacks in the climate system and as an essential consideration when implementing adjustments to existing model parameters and algorithms.

Figure caption: Difference in 2-m air temperature for all four seasons between the dynamic vegetation case and control run (DV-Y97 minus CTRL). Insignificant differences have been masked out.

CGD Research Catalog