Showing papers by "Gordon B. Bonan published in 2016"

Managing uncertainty in soil carbon feedbacks to climate change

Abstract: Climate change may accelerate decomposition of soil carbon leading to a reinforcing cycle of further warming and soil carbon loss. This Review considers the uncertainties and modelling challenges involved in projecting soil responses to warming.

On the development of a coupled regional climate–vegetation model RCM–CLM–CN–DV and its validation in Tropical Africa

Abstract: This paper presents a regional climate system model RCM–CLM–CN–DV and its validation over Tropical Africa. The model development involves the initial coupling between the ICTP regional climate model RegCM4.3.4 (RCM) and the Community Land Model version 4 (CLM4) including models of carbon–nitrogen dynamics (CN) and vegetation dynamics (DV), and further improvements of the models. Model improvements derive from the new parameterization from CLM4.5 that addresses the well documented overestimation of gross primary production (GPP), a refinement of stress deciduous phenology scheme in CN that addresses a spurious LAI fluctuation for drought-deciduous plants, and the incorporation of a survival rule into the DV model to prevent tropical broadleaf evergreens trees from growing in areas with a prolonged drought season. The impact of the modifications on model results is documented based on numerical experiments using various subcomponents of the model. The performance of the coupled model is then validated against observational data based on three configurations with increasing capacity: RCM–CLM with prescribed leaf area index and fractional coverage of different plant functional types (PFTs); RCM–CLM–CN with prescribed PFTs coverage but prognostic plant phenology; RCM–CLM–CN–DV in which both the plant phenology and PFTs coverage are simulated by the model. Results from these three models are compared against the FLUXNET up-scaled GPP and ET data, LAI and PFT coverages from remote sensing data including MODIS and GIMMS, University of Delaware precipitation and temperature data, and surface radiation data from MVIRI and SRB. Our results indicate that the models perform well in reproducing the physical climate and surface radiative budgets in the domain of interest. However, PFTs coverage is significantly underestimated by the model over arid and semi-arid regions of Tropical Africa, caused by an underestimation of LAI in these regions by the CN model that gets exacerbated through vegetation dynamics in RCM–CLM–CN–DV.

Forests, Climate, and Public Policy: A 500-Year Interdisciplinary Odyssey

Abstract: Forests regulate climate at local, regional, and global scales through exchanges of momentum, energy, moisture, and chemicals with the atmosphere. The notion that forests affect climate is not new. A vigorous debate about deforestation, land use, and climate change occurred during the colonial settlement of North America and continued through the 1800s, but the arguments of conservationists and foresters for forest–climate influences were dismissed by meteorologists. Modern climate science shows that forests warm climate annually by decreasing surface albedo, cool climate through surface roughness and evapotranspiration and by storing carbon, and have additional effects through atmospheric chemistry. Land use is a key aspect of climate policy, but we lack comprehensive policy recommendations. Like our predecessors, we are seeking a deeper understanding of Earth's climate, its ecosystems, and our uses of those ecosystems, and just as importantly we are still searching for the right interdisciplinary framew...

Evaluating the Climate Effects of Reforestation in New England Using a Weather Research and Forecasting (WRF) Model Multiphysics Ensemble

Abstract: The New England region of the northeastern United States has a land use history characterized by forest clearing for agriculture and other uses during European colonization and subsequent reforestation following widespread farm abandonment. Despite these broad changes, the potential influence on local and regional climate has received relatively little attention. This study investigated wintertime (December through March) climate impacts of reforestation in New England using a high-resolution (4 km) multiphysics ensemble of the Weather Research and Forecasting Model. In general, the conversion from mid-1800s cropland/grassland to forest led to warming, but results were sensitive to physics parameterizations. The 2-m maximum temperature (T2max) was most sensitive to choice of land surface model, 2-m minimum temperature (T2min) was sensitive to radiation scheme, and all ensemble members simulated precipitation poorly. Reforestation experiments suggest that conversion of mid-1800s cropland/grassland ...

Optimizing Available Network Resources to Address Questions in Environmental Biogeochemistry

Abstract: An increasing number of network observatories have been established globally to collect long-term biogeochemical data at multiple spatial and temporal scales. Although many outstanding questions in biogeochemistry would benefit from network science, the ability of the earthand environmental-sciences community to conduct synthesis studies within and across networks is limited and seldom done satisfactorily. We identify the ideal characteristics of networks, common problems with using data, and key improvements to strengthen intraand internetwork compatibility. We suggest that targeted improvements to existing networks should include promoting standardization in data collection, developing incentives to promote rapid data release to the public, and increasing the ability of investigators to conduct their own studies across sites. Internetwork efforts should include identifying a standard measurement suite—we propose profiles of plant canopy and soil properties— and an online, searchable data portal that connects network, investigator-led, and citizen-science projects.

The soil and plant biogeochemistry sampling design for The National Ecological Observatory Network

Abstract: Human impacts on biogeochemical cycles are evident around the world, from changes to forest structure and function due to atmospheric deposition, to eutrophication of surface waters from agricultural effluent, and increasing concentrations of carbon dioxide (CO2) in the atmosphere. The National Ecological Observatory Network (NEON) will contribute to understanding human effects on biogeochemical cycles from local to continental scales. The broad NEON biogeochemistry measurement design focuses on measuring atmospheric deposition of reactive mineral compounds and CO2 fluxes, ecosystem carbon (C) and nutrient stocks, and surface water chemistry across 20 eco-climatic domains within the United States for 30 yr. Herein, we present the rationale and plan for the ground-based measurements of C and nutrients in soils and plants based on overarching or “high-level” requirements agreed upon by the National Science Foundation and NEON. The resulting design incorporates early recommendations by expert review teams, as well as recent input from the larger natural sciences community that went into the formation and interpretation of the requirements, respectively. NEON's efforts will focus on a suite of data streams that will enable end-users to study and predict changes to biogeochemical cycling and transfers within and across air, land, and water systems at regional to continental scales. At each NEON site, there will be an initial, one-time effort to survey soil properties to 1 m (including soil texture, bulk density, pH, baseline chemistry) and vegetation community structure and diversity. A sampling program will follow, focused on capturing long-term trends in soil C, nitrogen (N), and sulfur stocks, isotopic composition (of C and N), soil N transformation rates, phosphorus pools, and plant tissue chemistry and isotopic composition (of C and N). To this end, NEON will conduct extensive measurements of soils and plants within stratified random plots distributed across each site. The resulting data will be a new resource for members of the scientific community interested in addressing questions about long-term changes in continental-scale biogeochemical cycles, and is predicted to inspire further process-based research.

Evaluating the climate effects of mid-1800s deforestation in New England, USA, using a Weather, Research, and Forecasting (WRF) Model Multi-Physics Ensemble

Abstract: The New England region of the northeastern United States has a land use history characterized by forest clearing for agriculture and other uses during European colonization and subsequent reforestation following widespread farm abandonment. Despite these broad changes, the potential influence on local and regional climate has received relatively little attention. This study investigated wintertime (December through March) climate impacts of reforestation in New England using a high-resolution (4 km) multiphysics ensemble of the Weather Research and Forecasting Model. In general, the conversion from mid-1800s cropland/grassland to forest led to warming, but results were sensitive to physics parameterizations. The 2-m maximum temperature (T2max) was most sensitive to choice of land surface model, 2-m minimum temperature (T2min) was sensitive to radiation scheme, and all ensemble members simulated precipitation poorly. Reforestation experiments suggest that conversion of mid-1800s cropland/grassland to present-day forest warmed T2max 10.5 to 13K, with weaker warming during a warm, dry winter compared to a cold, snowy winter.Warmer T2max over forests was primarily the result of increased absorbed shortwave radiation and increased sensible heat flux compared to cropland/grassland. At night, T2min warmed 10.2 to 11.5K where deciduous broadleaf forest replaced cropland/grassland, a result of decreased ground heat flux. By contrast, T2min of evergreen needleleaf forest cooled –0.5 to –2.1K, primarily owing to increased ground heat flux and decreased sensible heat flux.

Nonlinear Interactions between Climate and Atmospheric Carbon Dioxide Drivers of Terrestrial and Marine Carbon Cycle Changes from 1850 to 2300

Abstract: Quantifying feedbacks between the global carbon cycle and Earth's climate system is important for predicting future atmospheric CO2 levels and informing carbon management and energy policies. We applied a feedback analysis framework to three sets of Historical (1850-2005), Representative Concentration Pathway 8.5 (2006-2100), and its extension (2101-2300) simulations from the Community Earth System Model version 1.0 (CESM1(BGC)) to quantify drivers of terrestrial and ocean responses of carbon uptake. In the biogeochemically coupled simulation (BGC), the effects of CO2 fertilization and nitrogen deposition influenced marine and terrestrial carbon cycling. In the radiatively coupled simulation (RAD), the effects of rising temperature and circulation changes due to radiative forcing from CO2, other greenhouse gases, and aerosols were the sole drivers of carbon cycle changes. In the third, fully coupled simulation (FC), both the biogeochemical and radiative coupling effects acted simultaneously. We found that climate-carbon sensitivities derived from RAD simulations produced a net ocean carbon storage climate sensitivity that was weaker and a net land carbon storage climate sensitivity that was stronger than those diagnosed from the FC and BGC simulations. For the ocean, this nonlinearity was associated with warming-induced weakening of ocean circulation and mixing that limited exchange of dissolved inorganic carbon between surface and deeper water masses. For the land, this nonlinearity was associated with strong gains in gross primary production in the FC simulation, driven by enhancements in the hydrological cycle and increased nutrient availability. We developed and applied a nonlinearity metric to rank model responses and driver variables. The climate-carbon cycle feedback gain at 2300 was 42% higher when estimated from climate-carbon sensitivities derived from the difference between FC and BGC than when derived from RAD. These differences are important to quantify and understand because different model intercomparison efforts have used different approaches to compute feedbacks, complicating intercomparison of ESMs over time. Underestimating the climate-carbon cycle feedback gain would result in allowable emissions estimates that would be too low to meet climate change targets. We further explored the degree to which these nonlinearities affect climate-carbon cycle feedback gain estimates in CMIP5 models at year 2100.