Gordon B. Bonan

Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests

The worlds 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.

The Community Climate System Model version 3 (CCSM3)

Abstract The Community Climate System Model version 3 (CCSM3) has recently been developed and released to the climate community. CCSM3 is a coupled climate model with components representing the atmosphere, ocean, sea ice, and land surface connected by a flux coupler. CCSM3 is designed to produce realistic simulations over a wide range of spatial resolutions, enabling inexpensive simulations lasting several millennia or detailed studies of continental-scale dynamics, variability, and climate change. This paper will show results from the configuration used for climate-change simulations with a T85 grid for the atmosphere and land and a grid with approximately 1° resolution for the ocean and sea ice. The new system incorporates several significant improvements in the physical parameterizations. The enhancements in the model physics are designed to reduce or eliminate several systematic biases in the mean climate produced by previous editions of CCSM. These include new treatments of cloud processes, aerosol ...

Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate

Carbon Cycle and Climate Change As climate change accelerates, it is important to know the likely impact of climate change on the carbon cycle (see the Perspective by Reich). Gross primary production (GPP) is a measure of the amount of CO2 removed from the atmosphere every year to fuel photosynthesis. Beer et al. (p. 834, published online 5 July) used a combination of observation and calculation to estimate that the total GPP by terrestrial plants is around 122 billion tons per year; in comparison, burning fossil fuels emits about 7 billion tons annually. Thirty-two percent of this uptake occurs in tropical forests, and precipitation controls carbon uptake in more than 40% of vegetated land. The temperature sensitivity (Q10) of ecosystem respiratory processes is a key determinant of the interaction between climate and the carbon cycle. Mahecha et al. (p. 838, published online 5 July) now show that the Q10 of ecosystem respiration is invariant with respect to mean annual temperature, independent of the analyzed ecosystem type, with a global mean value for Q10 of 1.6. This level of temperature sensitivity suggests a less-pronounced climate sensitivity of the carbon cycle than assumed by recent climate models. A combination of data and models provides an estimate of how much photosynthesis by all the world’s plants occurs each year. Terrestrial gross primary production (GPP) is the largest global CO2 flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 ± 8 petagrams of carbon per year (Pg C year−1) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP’s latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate–carbon cycle process models.

The National Center for Atmospheric Research Community Climate Model: CCM3*

The latest version of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) is described. The changes in both physical and dynamical formulation from CCM2 to CCM3 are presented. The major differences in CCM3 compared to CCM2 include changes to the parameterization of cloud properties, clear sky longwave radiation, deep convection, boundary layer processes, and land surface processes. A brief description of each of these parameterization changes is provided. These modifications to model physics have led to dramatic improvements in the simulated climate of the CCM. In particular, the top of atmosphere cloud radiative forcing is now in good agreement with observations, the Northern Hemisphere winter dynamical simulation has significantly improved, biases in surface land temperatures and precipitation have been substantially reduced, and the implied ocean heat transport is in very good agreement with recent observational estimates. The improvement in implied ocean heat transport is among the more important attributes of the CCM3 since it is used as the atmospheric component of the NCAR Climate System Model. Future improvements to the CCM3 are also discussed.

The common land model

The Common Land Model (CLM) was developed for community use by a grassroots collaboration of scientists who have an interest in making a general land model available for public use and further development. The major model characteristics include enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multilayer parameterization of snow processes; an explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system; a runoff parameterization following the TOPMODEL concept; a canopy photo synthesis-conductance model that describes the simultaneous transfer of CO2 and water vapor into and out of vegetation; and a tiled treatment of the subgrid fraction of energy and water balance. CLM has been extensively evaluated in offline mode and coupling runs with the NCAR Community Climate Model (CCM3). The results of two offline runs, presented as examples, are compared with observations and with the simulation of three other la...

The Importance of Land-Cover Change in Simulating Future Climates

Adding the effects of changes in land cover to the A2 and B1 transient climate simulations described in the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change leads to significantly different regional climates in 2100 as compared with climates resulting from atmospheric SRES forcings alone. Agricultural expansion in the A2 scenario results in significant additional warming over the Amazon and cooling of the upper air column and nearby oceans. These and other influences on the Hadley and monsoon circulations affect extratropical climates. Agricultural expansion in the mid-latitudes produces cooling and decreases in the mean daily temperature range over many areas. The A2 scenario results in more significant change, often of opposite sign, than does the B1 scenario.

The Land Surface Climatology of the Community Land Model Coupled to the NCAR Community Climate Model

The land surface parameterization used with the community climate model (CCM3) and the climate system model (CSM1), the National Center for Atmospheric Research land surface model (NCAR LSM1), has been modified as part of the development of the next version of these climate models. This new model is known as the community land model (CLM2). In CLM2, the surface is represented by five primary subgrid land cover types (glacier, lake, wetland, urban, vegetated) in each grid cell. The vegetated portion of a grid cell is further divided into patches of up to 4 of 16 plant functional types, each with its own leaf and stem area index and canopy height. The relative area of each subgrid unit, the plant functional type, and leaf area index are obtained from 1-km satellite data. The soil texture dataset allows vertical profiles of sand and clay. Most of the physical parameterizations in the model were also updated. Major model differences include: 10 layers for soil temperature and soil water with explicit treatment of liquid water and ice; a multilayer snowpack; runoff based on the TOPMODEL concept; new formulation of ground and vegetation fluxes; and vertical root profiles from a global synthesis of ecological studies. Simulations with CCM3 show significant improvements in surface air temperature, snow cover, and runoff for CLM2 compared to LSM1. CLM2 generally warms surface air temperature in all seasons compared to LSM1, reducing or eliminating many cold biases. Annual precipitation over land is reduced from 2.35 mm day21 in LSM1 to 2.14 mm day21 in CLM2. The hydrologic cycle is also different. Transpiration and ground evaporation are reduced. Leaves and stems evaporate more intercepted water annually in CLM2 than LSM1. Global runoff from land increases from 0.75 mm day21 in LSM1 to 0.84 mm day21 in CLM2. The annual cycle of runoff is greatly improved in CLM2, especially in arctic and boreal regions where the model has low runoff in cold seasons when the soil is frozen and high runoff during the snowmelt season. Most of the differences between CLM2 and LSM1 are attributed to particular parameterizations rather than to different surface datasets. Important processes include: multilayer snow, frozen water, interception, soil water limitation to latent heat, and higher aerodynamic resistances to heat exchange from ground.

GLACE: The Global Land–Atmosphere Coupling Experiment. Part I: Overview

Abstract The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detai...

Carbon-concentration and carbon-climate feedbacks in CMIP5 Earth System Models

AbstractThe magnitude and evolution of parameters that characterize feedbacks in the coupled carbon–climate system are compared across nine Earth system models (ESMs). The analysis is based on results from biogeochemically, radiatively, and fully coupled simulations in which CO2 increases at a rate of 1% yr−1. These simulations are part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CO2 fluxes between the atmosphere and underlying land and ocean respond to changes in atmospheric CO2 concentration and to changes in temperature and other climate variables. The carbon–concentration and carbon–climate feedback parameters characterize the response of the CO2 flux between the atmosphere and the underlying surface to these changes. Feedback parameters are calculated using two different approaches. The two approaches are equivalent and either may be used to calculate the contribution of the feedback terms to diagnosed cumulative emissions. The contribution of carbon–concentration feedback to...

A systems analysis of the global boreal forest

List of contributors 1. Introduction Herman H. Shugart, Rik Leemans and Gordon B. Bonan Part I. Processes in Boreal Forests Gordon B. Bonan: 2. Silvics of the circumpolar boreal forest tree species Nedialko Nikolov and Harry Helmisaari 3. The reproductive process in boreal forest trees John C. Zasada, Terry L. Sharik and Markku Nygren 4. Soil temperature as an ecological factor in boreal forests Gordon B. Bonan 5. Fire as a controlling process in the North American boreal forest Serge Payette 6. The role of forest insects in structuring the boreal landscape C. S. Holling Part II. Patterns in Space and Time in Boreal Forests Herman H. Shugart: 7. The transition between boreal forest and tundra Luc Sirois 8. The southern boreal-northern hardwood forest border John Pastor and David J. Mladenoff 9. Transitions between boreal forest and wetland F. Z. Glebov and M. D. Korzukhin 10. Remote sensing technology for forest ecosystem analysis K. Jon Ranson and Darrel L. Williams 11. The nature and distribution of past, present and future boreal forests: lessons for a research and modeling agenda Allen M. Solomon Part III. Computer Models for Synthesis of Pattern and Process in the Boreal Forest Rik Leemans: 12. Individual-tree-based models of forest dynamics and their application in global change research Herman H. Shugart and I. Colin Prentice 13. Population-level models of forest dynamics M. D. Korzukhin and M. Ya. Antonovski 14. A spatial model of long-term forest fibre dynamics and its applications to forests in western Siberia M. Ya. Antonovski, M. T. Ter-Mikaelian and V. V. Furyaev 15. A simulation analysis of environmental factors and ecological processes in North American boreal forests Gordon B. Bonan 16. The biological component of the simulation model for boreal forest dynamics Rik Leemans 17. Role of stand simulation in modeling forest response to environmental change and management interventions Peter Duinker, Ola Sallnas and Sten Nilsson 18. Concluding comments Herman H. Shugart, Rik Leemans and Gordon B. Bonan References Index.