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CGD 2009 Profiles in Science: Dr. Gordon Bonan

Summary of achievements

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 2009 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

Bonan, G. 2008: CARBON CYCLE Fertilizing change. Nature Geoscience, 1, 645-646, doi:10.1038/ngeo328.

Carbon cycle–climate feedbacks are expected to diminish the size of the terrestrial carbon sink over the next century. Model simulations suggest that nitrogen availability is likely to play a key role in mediating this response.

Jackson, R.B., J.T. Randerson, J.G. Canadell, R.G. Anderson, R. Avissar, D.D. Baldocchi, G.B. Bonan, K. Caldeira, N.s. Diffenbaugh, C.B. Field, B.A. Hungate, E.G. Jobbágy, L.M. Kueppers, M.D. Nosetto and d.E. Pataki. 2008: Protecting climate with forests. Environ. Res. Lett., 044006, doi:10.1088/1748-9326/3/4/044006.



Figure 2: High resolution figure

Abstract: Policies for climate mitigation on land rarely acknowledge biophysical factors, such as reflectivity, evaporation, and surface roughness. Yet such factors can alter temperatures much more than carbon sequestration does, and often in a conflicting way. We outline a framework for examining biophysical factors in mitigation policies and provide some best-practice recommendations based on that framework. Tropical projects—avoided deforestation, forest restoration, and afforestation—provide the greatest climate value, because carbon storage and biophysics align to cool the Earth. In contrast, the climate benefits of carbon storage are often counteracted in boreal and other snow-covered regions, where darker trees trap more heat than snow does. Managers can increase the climate benefit of some forest projects by using more reflective and deciduous species and through urban forestry projects that reduce energy use. Ignoring biophysical interactions could result in millions of dollars being invested in some mitigation projects that provide little climate benefit or, worse, are counter-productive.

Figure caption: Examples of various biophysical factors in a grassland or cropland (A) and forest (B). Because of a grassland or cropland's higher reflectivity (albedo), it typically reflects more sunlight than the forest does, cooling surface air temperatures relatively more. In contrast, the forest often evaporates more water and transmits more heat to the atmosphere (latent and sensible heat, respectively), cooling it locally compared to the grassland or unirrigated cropland. More water vapor in the atmosphere can lead to a greater number and height of clouds as well as to increased convective rainfall. In addition, the forest has a more uneven canopy (surface roughness) that increases mixing and upwelling of air. ((C) and (D)) Comparison of shortwave albedo and surface skin temperature for 215 grassland and forest stands across Argentina and Uruguay. The satellite data were assessed using 180 km × 180 km Landsat images (2000–2005) on seven dates for the Corrientes and Concordia regions of Argentina and three dates for the Rivera region of Uruguay. The Landsat scenes were geometrically and atmospherically corrected and correspond to images 226/80 (path and row) for Corrientes, 225/82 for Concordia, and 223/82 for Rivera. In general, measurements at sites within a region compared adjacent grassland, pine, and eucalypt stands.

Randerson, J.T., F.M. Hoffman, P.E. Thornton, N.M. Mahowald, K. Lindsay, Y-H. Lee, C.D. Nevison, S.C. Doney, G.B. Bonan, R. Stöckli, C. Covey, S.W. Running and I.Y. Fung. 2009: Systematic assessment of terrestrial biogeochemistry in coupled climate–carbon models. Global Change Biology, 044006, doi:10.1111/j.1365-2486.2009.01912.x.



Figure 3: High resolution figure

Abstract: With representation of the global carbon cycle becoming increasingly complex in climate models, it is important to develop ways to quantitatively evaluate model performance against in situ and remote sensing observations. Here we present a systematic framework, the Carbon-LAnd Model Intercomparison Project (C-LAMP), for assessing terrestrial biogeochemistry models coupled to climate models using observations that span a wide range of temporal and spatial scales. As an example of the value of such comparisons, we used this framework to evaluate two biogeochemistry models that are integrated within the Community Climate System Model (CCSM) – Carnegie-Ames-Stanford Approach' (CASA') and carbon–nitrogen (CN). Both models underestimated the magnitude of net carbon uptake during the growing season in temperate and boreal forest ecosystems, based on comparison with atmospheric CO2 measurements and eddy covariance measurements of net ecosystem exchange. Comparison with MODerate Resolution Imaging Spectroradiometer (MODIS) measurements show that this low bias in model fluxes was caused, at least in part, by 1–3 month delays in the timing of maximum leaf area. In the tropics, the models overestimated carbon storage in woody biomass based on comparison with datasets from the Amazon. Reducing this model bias will probably weaken the sensitivity of terrestrial carbon fluxes to both atmospheric CO2 and climate. Global carbon sinks during the 1990s differed by a factor of two (2.4 Pg C yr-1 for CASA' vs. 1.2 Pg C yr-1 for CN), with fluxes from both models compatible with the atmospheric budget given uncertainties in other terms. The models captured some of the timing of interannual global terrestrial carbon exchange during 1988–2004 based on comparison with atmospheric inversion results from TRANSCOM (r=0.66 for CASA' and r=0.73 for CN). Adding (CASA') or improving (CN) the representation of deforestation fires may further increase agreement with the atmospheric record. Information from C-LAMP has enhanced model performance within CCSM and serves as a benchmark for future development. We propose that an open source, community-wide platform for model-data intercomparison is needed to speed model development and to strengthen ties between modeling and measurement communities. Important next steps include the design and analysis of land use change simulations (in both uncoupled and coupled modes), and the entrainment of additional ecological and earth system observations. Model results from C-LAMP are publicly available on the Earth System Grid.

Figure caption: Month of maximum leaf area index from (a) MODIS, (b) CASA', and (c) CN. The observations are from the MOD15A2 collection 4 LAI product from MODIS (Myneni et al., 2002) with additional adjustments to interpolate across periods of cloud contamination as described by Zhao et al. (2005). CASA, Carnegie-Ames-Stanford Approach; CN, carbon–nitrogen; LAI, leaf area index; MODIS, MODerate Resolution Imaging Spectroradiometer.

Pitman, A. J., N. de Noblet-Ducoudre, F. T. Cruz, E. L. Davin, G. B. Bonan, V. Brovkin, M. Claussen, C. Delire, L. Ganzeveld, V. Gayler, B. J. J. M. van den Hurk, P. J. Lawrence, M. K. van der Molen, C. Mueller, C. H. Reick, S. I. Seneviratne, B. Strengers, and A. Voldoire. 2009: Uncertainties in climate responses to past land cover change: First results from the LUCID intercomparison study. Geophys. Res. Lett., doi:10.1029/2009GL039076, in press.



Figure 4: High resolution figure

Abstract: Seven climate models were used to explore the biogeophysical impacts of humaninduced land cover change (LCC) at regional and global scales. The imposed LCC led to statistically significant decreases in the northern hemisphere summer latent heat flux in three models, and increases in three models. Five models simulated statistically significant cooling in summer in near-surface temperature over regions of LCC and one simulated warming. There were few significant changes in precipitation. Our results show no common remote impacts of LCC. The lack of consistency among the seven models was due to: 1) the implementation of LCC despite agreed maps of agricultural land, 2) the representation of crop phenology, 3) the parameterisation of albedo, and 4) the representation of evapotranspiration for different land cover types. This study highlights a dilemma: LCC is regionally significant, but it is not feasible to impose a common LCC across multiple models for the next IPCC assessment.

Figure caption: Extent of land cover change between experiments PD and PDv (PD – PDv) expressed as the difference in crop and pasture cover between the two experiments. Blue colours represent changes that decrease pasture and crop cover while yellows and browns are increases (25%-50% and 50-100% respectively).