Drought effects on large fire activity in Canadian and Alaskan forests

Fire is the dominant disturbance in forest ecosystems across Canada and Alaska, and has important implications for forest ecosystems, terrestrial carbon dioxide emissions and the forestry industry. Large fire activity had increased in Canadian and Alaskan forests during the last four decades of the 20th century. Here we combined the Palmer Drought Severity Index and historical large fire databases to demonstrate that Canada and Alaska forest regions experienced summer drying over this time period, and drought during the fire season significantly affected forest fire activity in these regions. Climatic warming, positive geopotential height anomalies and ocean circulation patterns were spatially and temporally convolved in causing drought conditions, which in turn enhanced fuel flammability and thereby indirectly affected fire activity. Future fire regimes will likely depend on drought patterns under global climate change scenarios

Modeling The Influences Of Climate Change, Permafrost Dynamics, And Fire Disturbance On Carbon Dynamics Of High -Latitude Ecosystems

Thesis (Ph.D.) University of Alaska Fairbanks, 2001A Soil Thermal Model (STM) with the capability to operate with a 0.5-day internal time step and to be driven with monthly input data was developed for applications with large-scale ecosystem models. The use of monthly climate inputs to drive the STM resulted in an error of less than 1�C in the upper organic soil layer and in an accurate simulation of seasonal active layer dynamics. Uncertainty analyses identified that soil temperature estimates of the upper organic layer were most sensitive to variability in parameters that described snow thermal conductivity, moss thickness, and moss thermal conductivity. The STM was coupled to the Terrestrial Ecosystem Model (TEM), and the performance of the STM-TEM was verified for the simulation of soil temperatures in applications to black spruce, white spruce, aspen, and tundra sites. A 1�C error in the temperature of the upper organic soil layer had little influence on the carbon dynamics simulated for a black spruce site. Application of the model across the range of black spruce ecosystems in North America demonstrated that the STM-TEM has the capability to operate over temporal and spatial domains that consider substantial variations in surface climate. To consider how fire disturbance interacts with climate change and permafrost dynamics, the STM was updated to more fully evaluate how these factors influence ecosystem dynamics during stand development. The ability of the model to simulate seasonal patterns of soil temperature, gross primary production, and ecosystem respiration, and the age-dependent pattern of above-ground vegetation carbon storage was verified. The model was applied to a post-fire chronosequence in interior Alaska and was validated with estimates of soil temperature, soil respiration, and soil carbon storage that were based on measurements of these variables in 1997. Sensitivity analyses indicate that the growth of moss, changes in the depth of the organic layer, and nitrogen fixation should be represented in models that simulate the effects of fire disturbance in boreal forests. Furthermore, the sensitivity analyses revealed that soil drainage and fire severity should be considered in spatial application of these models to simulate carbon dynamics at landscape to regional scales

An Efficient Method of Estimating Downward Solar Radiation Based on the MODIS Observations for the Use of Land Surface Modeling

Solar radiation is a critical variable in global change sciences. While most of the current global datasets provide only the total downward solar radiation, we aim to develop a method to estimate the downward global land surface solar radiation and its partitioned direct and diffuse components, which provide the necessary key meteorological inputs for most land surface models. We developed a simple satellite-based computing scheme to enable fast and reliable estimation of these variables. The global Moderate Resolution Imaging Spectroradiometer (MODIS) products at 1° spatial resolution for the period 2003–2011 were used as the forcing data. Evaluations at Baseline Surface Radiation Network (BSRN) sites show good agreement between the estimated radiation and ground-based observations. At all the 48 BSRN sites, the RMSE between the observations and estimations are 34.59, 41.98 and 28.06 W∙m−2 for total, direct and diffuse solar radiation, respectively. Our estimations tend to slightly overestimate the total and diffuse but underestimate the direct solar radiation. The errors may be related to the simple model structure and error of the input data. Our estimation is also comparable to the Clouds and Earth’s Radiant Energy System (CERES) data while shows notable improvement over the widely used National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data. Using our MODIS-based datasets of total solar radiation and its partitioned components to drive land surface models should improve simulations of global dynamics of water, carbon and climate

Focus on the impact of climate change on wetland ecosystem and carbon dynamics

The renewed growth in atmospheric methane (CH4)since 2007 after a decade of stabilization has drawn much attention to its causes and future trends. Wetlands are the single largest source of atmospheric CH4. Understanding wetland ecosystems and carbon dynamics is critical to the estimation of global CH4 and carbon budgets. After approximately 7 years of CH4 related research following the renewed growth in atmospheric CH4, Environmental Research Letters launched a special issue of research letters on wetland ecosystems and carbon dynamics in 2014. This special issue highlights recent developments in terrestrial ecosystem models and field measurements of carbon fluxes across different types of wetland ecosystems. The 14 research letters emphasize the importance of wetland ecosystems in the global CO2 and CH4 budget

A Model Intercomparison Analysis for Controls on C Accumulation in North American Peatlands

Peatland biogeochemical processes have not been adequately represented in existing earth system models, which might have biased the quantification of Arctic carbon-climate feedbacks. We revise the Peatland Terrestrial Ecosystem Model (PTEM) by incorporating additional peatland biogeochemical processes. The revised PTEM is evaluated by comparing with Holocene Peatland Model (HPM) in simulating peat physical and biogeochemical dynamics in three North American peatlands: a permafrost-free fen site, a permafrost-free bog site and a permafrost bog site. Peatland carbon dynamics are simulated from peat initiation to 1990 and then to year 2300. Model responses to the changes in temperature and precipitation are analyzed to identify key processes affecting peatland carbon accumulation rates. We find that the net C balance is sensitive to water table depth and nutrient availability. Future simulations to year 2300 are conducted with both models under RCP 2.6, RCP 4.5, and RCP 8.5. PTEM predicts these peatlands to be C sources or weaker C sinks when insufficient precipitation suppresses soil moisture and thereby net N mineralization and net primary production, while HPM predicts the same when drier climate leads to increasing water table depth. Our results highlight the importance of water balance and C-N feedback on peatland C dynamics. With a warmer climate, these peatlands could become a weaker C sink or a source under drier conditions, otherwise a larger C sink if wetter. Improved understanding to peatland processes can help future quantification of peatland C dynamics in the boreal and Arctic regions

Spatial scale-dependent land–atmospheric methane exchanges in the northern high latitudes from 1993 to 2004

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Biogeosciences 11 (2014): 1693-1704, doi:10.5194/bg-11-1693-2014.Effects of various spatial scales of water table dynamics on land–atmospheric methane (CH4) exchanges have not yet been assessed for large regions. Here we used a coupled hydrology–biogeochemistry model to quantify daily CH4 exchanges over the pan-Arctic from 1993 to 2004 at two spatial scales of 100 km and 5 km. The effects of sub-grid spatial variability of the water table depth (WTD) on CH4 emissions were examined with a TOPMODEL-based parameterization scheme for the northern high latitudes. We found that both WTD and CH4 emissions are better simulated at a 5 km spatial resolution. By considering the spatial heterogeneity of WTD, net regional CH4 emissions at a 5 km resolution are 38.1–55.4 Tg CH4 yr−1 from 1993 to 2004, which are on average 42% larger than those simulated at a 100 km resolution using a grid-cell-mean WTD scheme. The difference in annual CH4 emissions is attributed to the increased emitting area and enhanced flux density with finer resolution for WTD. Further, the inclusion of sub-grid WTD spatial heterogeneity also influences the inter-annual variability of CH4 emissions. Soil temperature plays an important role in the 100 km estimates, while the 5 km estimates are mainly influenced by WTD. This study suggests that previous macro-scale biogeochemical models using a grid-cell-mean WTD scheme might have underestimated the regional CH4 emissions. The spatial scale-dependent effects of WTD should be considered in future quantification of regional CH4 emissions.The research is funded by a DOE SciDAC project and an Abrupt Climate Change project. This study is also supported through projects funded by the NASA Land Use and Land Cover Change program (NASA-NNX09AI26G), Department of Energy (DE-FG02-08ER64599), the NSF Division of Information & Intelligent Systems (NSF-1028291), and the NSF Carbon and Water in the Earth Program (NSF-0630319). This research is also in part supported by the Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy under Contract No. DE-AC02-05CH11231 as part of their Earth System Modeling Program

A large-scale methane model by incorporating the surface water transport

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Biogeosciences 121 (2016): 1657–1674, doi:10.1002/2016JG003321.The effect of surface water movement on methane emissions is not explicitly considered in most of the current methane models. In this study, a surface water routing was coupled into our previously developed large-scale methane model. The revised methane model was then used to simulate global methane emissions during 2006–2010. From our simulations, the global mean annual maximum inundation extent is 10.6 ± 1.9 km2 and the methane emission is 297 ± 11 Tg C/yr in the study period. In comparison to the currently used TOPMODEL-based approach, we found that the incorporation of surface water routing leads to 24.7% increase in the annual maximum inundation extent and 30.8% increase in the methane emissions at the global scale for the study period, respectively. The effect of surface water transport on methane emissions varies in different regions: (1) the largest difference occurs in flat and moist regions, such as Eastern China; (2) high-latitude regions, hot spots in methane emissions, show a small increase in both inundation extent and methane emissions with the consideration of surface water movement; and (3) in arid regions, the new model yields significantly larger maximum flooded areas and a relatively small increase in the methane emissions. Although surface water is a small component in the terrestrial water balance, it plays an important role in determining inundation extent and methane emissions, especially in flat regions. This study indicates that future quantification of methane emissions shall consider the effects of surface water transport.The finacial support for this work is from the Open Fund of State Key Laboratory of Remote Sensing Science of China (OFSLRSS201501); 2 Supported by the Fundamental Research Funds for the Central Universities (20720160109).2016-12-2