CGD's Terrestrial Sciences Section

Summary

The goal of the Terrestrial Sciences Section (TSS) is to increase scientific understanding of land-atmosphere interactions, in particular surface forcing of climate, through model development, application, and observational analyses and to represent that understanding in climate models. Research in TSS spans a broad knowledge of the relationships among the biosphere, hydrosphere, cryosphere, and atmosphere. Scientists in TSS develop and use appropriate multi-scale models, remote sensing, advanced analytical techniques, and observations to study the role of the terrestrial biosphere in the climate system. Topics of study include the regulation of planetary energetics, planetary ecology, and planetary metabolism through exchanges of energy, momentum, and materials (e.g., water, carbon, mineral aerosols) with the atmosphere and ocean and the response of the climate system to changes in land cover and land use.

Scientists in TSS are involved in developing the land model used in the Community Atmosphere Model (CAM) and the Community Climate System Model (CCSM). This model, the Community Land Model (CLM), includes biogeophysics and hydrology, the traditional physical core components of land models, and is being further developed to include river routing, biogeochemistry (carbon, nitrogen, mineral aerosols, biogenic volatile organic compounds, water isotopes), and vegetation dynamics. Scientists in TSS actively participate in the CCSM Land Model Working Group and the CCSM Biogeochemistry Working Group, providing strong input to model development and implementing and testing model parameterizations. Model development is based on process studies of the relevant physical, chemical, and biological mechanisms and the numerical modeling techniques required to represent these mechanisms. TSS scientists compare model output with observed atmospheric, ecological, and hydrological data to validate and improve the model on a wide range of spatial and temporal scales. TSS provides a focal point for CGD and university ecological and hydrological research and serves as a resource to these communities in their use of CCSM.

CLM hydrology

TSS scientists completed a project to improve the hydrology of the Community Land Model version 3 (CLM3), 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 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 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.

The new model was approved by the CCSM Land Model Working Group and released to the public along with technical documentation and improved atmospheric forcing data in May, 2007. TSS contributions were led by Keith Oleson, David Lawrence, and Gordon Bonan 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). Please see figure 1 for related information.

CLM/CCSM biogeochemistry

TSS scientists conducted several projects to implement biogeochemistry in CLM and CCSM. This research broadly addresses how biogeochemical coupling of carbon, nitrogen, and iron cycles affects climate, air quality, radiative forcing, and ecosystem function on regional to global scales. It involves two specific research agendas related to the terrestrial carbon cycle and mineral aerosols.

Carbon-nitrogen interactions in CLM

Peter Thornton, in collaboration with the CCSM Biogeochemistry Working Group, recently completed a model development project to bring together parallel efforts in the climate modeling and ecological modeling communities, integrating a detailed ecological treatment of carbon-nitrogen cycle interactions (from the Biome-BGC model) with the CLM. The resulting model, CLM-CN, has now been tested and its performance documented both in offline and fully-coupled configurations. A critical application of the model has been to study the influence of carbon-nitrogen cycle coupling on present-day and potential future climate-carbon cycle feedbacks.

A preliminary test of the newly completed CLM-CN was to drive the model with reanalysis surface weather, to document its present-day predictions for carbon fluxes and stocks and to evaluate the influence of changes in atmospheric CO₂ concentration and changes in the rate of mineral nitrogen deposition on carbon, nitrogen, water, and energy cycles. This study found that the introduction of carbon-nitrogen coupling significantly altered the model response to increasing CO₂, and the sensitivities of net land carbon flux to variation in temperature and precipitation. The study suggested that C-N coupling would have an important impact on the magnitude and possibly the sign of climate-carbon cycle feedbacks when exercised in the fully coupled Community Climate System Model (CCSM).

Thornton and collaborators have recently completed a series of fully-coupled simulations, using CLM-CN as well as a new ocean ecosystem model as components of CCSM, testing the sensitivity of global climate-carbon cycle feedbacks to carbon-nitrogen cycle coupling in the land model. Please see figure 2 for related information.

Aerosols

Research by Natalie Mahowald and collaborators focused on better understanding what forces desert dust changes. One study is based on station data, looking at fluctuations in visibility at stations located in desert regions globally over the period 1974-2003. This study found that the spatial distribution of low visibility events suggests that dust is impacted by human land use, but the temporal variability may be more closely tied to meteorological factors. In addition, in a study based on the Coupled Model Intercomparison Project, she showed that deserts will increase in size going into the future if carbon dioxide fertilization is not included, but that deserts will decrease in size in the future if carbon dioxide fertilization is included. Thus, better understanding how carbon dioxide impacts desert species is important to understanding human impacts on desert dust.

The impact of dynamic vegetation on dust and Sahel precipitation

Andrea Sealy (ASP/CGD) and Natalie Mahowald (CGD/TIIMES, currently at Cornell University)

I am working with Natalie Mahowald to examine the interaction of vegetation dynamics with desert dust and Sahel precipitation using the Community Atmosphere Model (CAM). The configuration of the model simulations that we are analyzing differ as to whether the runs include dust radiative forcing and feedback (both shortwave and longwave), the SST forcing (whether observed sea surface temperature/AMIP or interactive SST from the Slab Ocean Model/SOM) and if coupled with the Dynamic Global Vegetation Model (DGVM). Comparison of CAM/DGVM to uncoupled CAM simulations suggests that there is a response of precipitation and dust to dynamic vegetation. The interaction with and response of dust and precipitation to dynamic vegetation also appears to vary with observed SST forcing versus interactive SST.

Preliminary results indicate that for runs forced with SOM SST and coupled with DGVM (both with and without dust radiative forcing and feedback), the June-July-August-September (JJAS) precipitation over the Sahel is on average higher than those runs using the default vegetation (Figure 3, left).

However for the cases forced with observed SST the coupling with DGVM produces more complex results. The JJAS precipitation is lower for the DGVM cases over sub-Saharan Africa, but in the coastal regions closer to the Gulf of Guinea, the JJAS precipitation is higher (Figure 3, right).

Land cover and land use change

A major research focus for TSS is natural and human-mediated changes in land cover and ecosystem functions and their effects on climate, water resources, and biogeochemistry. The industrial age and growing human population has produced large changes in land surface characteristics, particularly deforestation, cultivation of cropland, and urbanization. Change in land cover from human uses of land is increasingly being recognized as an important forcing of climate. The influence of historical land cover change on climate needs to be considered as a climate forcing in additional to traditional forcings such as greenhouse gases, aerosols, solar variability, and ozone. Future projected land cover changes due to human land uses are also likely to alter climate, especially in the tropics, subtropics, and semiarid regions.

Scientists in TSS have initiated studies of the climate forcing associated with land use. This work has the goal of documenting (a) how changes in land use and land cover have altered present-day climate and are likely to alter future climate and (b) the importance of the land use and land cover change forcing relative to other IPCC SRES forcings. This work involves developing parameterizations of urban land cover, agroecosystems, and soil degradation for use with the CLM. It also involves development of historical and future datasets of land cover change.

Land use forcing of climate

Gordon Bonan, Sam Levis, and Peter Lawrence (University of Colorado) participated in the LUCID (Land-Use and Climate, IDentification of robust impacts) project under the auspices of IGBP-iLEAPS and GEWEX-GLASS. Accomplishments to date include: development of historical land cover change datasets for 1870 and for potential natural vegetation; and climate model simulations following the LUCID protocol. Please see figure 4 for related information.

Dynamic vegetation

In addition to human-mediated land use changes, large-scale changes in the geographic distribution of vegetation as a result of past and future climate changes feed back to alter climate. These feedbacks are especially important in arid landscapes, where the albedo contrast between vegetation and soil is large, and in arctic landscapes, where trees and shrubs mask the high albedo of snow.

Sam Levis and Gordon Bonan examined the importance of snow albedo for climate sensitivity. They showed with the Community Atmosphere Model (CAM) that a simulation with higher(lower) albedo at 1xCO₂ due to more(less) snow cover is more(less) sensitive to increased CO₂. As a corollary, they showed with output from 15 CMIP3 models that the wide variation in snow-albedo feedbacks and climate sensitivities among the models correlates well with variations in continental middle to high latitude present-day springtime albedo.

In a related project, Sam Levis, Gordon Bonan, and Ben Cook (University of Virginia) showed that climate sensitivity to different snow fraction parameterizations is emphasized by the presence of dynamic vegetation in a model. Please see figure 5 for related information.

Sam Levis, Gordon Bonan, and Peter Thornton have initiated a project to merge the carbon and nitrogen cycling capability of the CLM-CN model with a previously developed global dynamic vegetation model (CLM-DGVM).

Agricultural models

Sam Levis in collaboration with PhD students B. Sacks (UW-Madison), N. Buenning (CU-Boulder), and B. Cook (University of Virginia) developed a parameterization of cropland irrigation and evaluated its climate significance in the CCSM

Urbanization

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 (CLM-U). 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 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 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.

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? Please see figures 6 and 7 for related information.

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