peerreviewed_2013.bib

@article{Hoffman_LandEco_2013,
  author = {Forrest M. Hoffman and Jitendra Kumar and Richard T. Mills and William
	W. Hargrove},
  title = {Representativeness-Based Sampling Network Design for the {St}ate
	of {A}laska},
  journal = {Landscape Ecology},
  year = {2013},
  volume = {28},
  pages = {1567-1586},
  number = {8},
  abstract = {Resource and logistical constraints limit the frequency and extent
	of environmental observa- tions, particularly in the Arctic, necessitating
	the development of a systematic sampling strategy to maximize coverage
	and objectively represent envi- ronmental variability at desired
	scales. A quantitative methodology for stratifying sampling domains,
	informing site selection, and determining the repre- sentativeness
	of measurement sites and networks is described here. Multivariate
	spatiotemporal clustering was applied to down-scaled general circulation
	model results and data for the State of Alaska at 4 km2 resolution
	to define multiple sets of ecoregions across two decadal time periods.
	Maps of ecoregions for the present (2000–2009) and future (2090–2099)
	were produced, showing how combinations of 37 character- istics are
	distributed and how they may shift in the future. Representative
	sampling locations are identified on present and future ecoregion
	maps. A representa- tiveness metric was developed, and representativeness
	maps for eight candidate sampling locations were produced. This metric
	was used to characterize the environmental similarity of each site.
	This analysis provides model-inspired insights into optimal sampling
	strategies, offers a framework for up-scaling measure- ments, and
	provides a down-scaling approach for integration of models and measurements.
	These tech- niques can be applied at different spatial and temporal
	scales to meet the needs of individual measurement 
	
	 campaigns.},
  doi = {10.1007/s10980-013-9902-0},
  file = {pubs/Hoffman_LandEco_2013.pdf},
  owner = {jkumar},
  timestamp = {2012.07.31}
}

@article{Mills_DMESS_2013,
  author = {Richard Tran Mills and Jitendra Kumar and Forrest Hoffman and William
	Hargrove and Joseph P. Spruce and Steve P. Norman},
  title = {Identification and Visualization of Dominant Patterns and Anomalies
	in Remotely Sensed Vegetation Phenology Using a Parallel Tool for
	Principal Components Analysis},
  journal = {Proceedings of the 2013 International Conference on Computational
	Science},
  year = {2013},
  doi = {10.1016/j.procs.2013.05.411},
  file = {pubs/Mills_ICCS_2013.pdf},
  owner = {jkumar},
  timestamp = {2013.04.01}
}

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@inproceedings{Christie_9th-SOFORGIS_20131210,
  author = {William M. Christie and William W. Hargrove and Steven P. Norman and Joseph P. Spruce and Jitendra Kumar and Forrest Hoffman and Sean W. Schroeder},
  title = {\textit{ForWarn} Forest Change Detection System Provides a Weekly Snapshot of {US} Forest Contribtions to Aid Forest Managers},
  booktitle = {Proceedings of the 9$^\textnormal{th}$ Southern Forestry and Natural Resource Management GIS Conference},
  dates = {8--10 December 2013},
  location = {Athens, Georgia, USA},
  day = 10,
  month = dec,
  year = 2013
}

@article{Keppel-Aleks_JClim_20130701,
  author = {Gretchen Keppel-Aleks and James T. Randerson and Keith Lindsay and Britton B. Stephens and J. Keith Moore and Scott C. Doney and Peter E. Thornton and Natalie M. Mahowald and Forrest M. Hoffman and Colm Sweeney and Pieter P. Tans and Paul O. Wennberg and Steven C. Wofsy},
  title = {Atmospheric Carbon Dioxide Variability in the {C}ommunity {E}arth {S}ystem {M}odel: Evaluation and Transient Dynamics during the Twentieth and Twenty-First Centuries},
  journal = jclim,
  volume = 26,
  number = 13,
  pages = {4447--4475},
  doi = {10.1175/JCLI-D-12-00589.1},
  day = 1,
  month = jul,
  year = 2013,
  abstract = {Changes in atmospheric CO$_2$ variability during the twenty-first century may provide insight about ecosystem responses to climate change and have implications for the design of carbon monitoring programs. This paper describes changes in the three-dimensional structure of atmospheric CO$_2$ for several representative concentration pathways (RCPs 4.5 and 8.5) using the Community Earth System Model--Biogeochemistry (CESM1-BGC). CO$_2$ simulated for the historical period was first compared to surface, aircraft, and column observations. In a second step, the evolution of spatial and temporal gradients during the twenty-first century was examined. The mean annual cycle in atmospheric CO$_2$ was underestimated for the historical period throughout the Northern Hemisphere, suggesting that the growing season net flux in the Community Land Model (the land component of CESM) was too weak. Consistent with weak summer drawdown in Northern Hemisphere high latitudes, simulated CO$_2$ showed correspondingly weak north--south and vertical gradients during the summer. In the simulations of the twenty-first century, CESM predicted increases in the mean annual cycle of atmospheric CO$_2$ and larger horizontal gradients. Not only did the mean north--south gradient increase due to fossil fuel emissions, but east--west contrasts in CO$_2$ also strengthened because of changing patterns in fossil fuel emissions and terrestrial carbon exchange. In the RCP8.5 simulation, where CO$_2$ increased to 1150~ppm by 2100, the CESM predicted increases in interannual variability in the Northern Hemisphere midlatitudes of up to 60\% relative to present variability for time series filtered with a 2--10-yr bandpass. Such an increase in variability may impact detection of changing surface fluxes from atmospheric observations.}
}

@article{Mao_RemoteSens_20130321,
  author = {Jiafu Mao and Xiaoying Shi and Peter E. Thornton and Forrest M. Hoffman and Zaichun Zhu and Ranga B. Myneni},
  title = {Global Latitudinal-Asymmetric Vegetation Growth Trends and Their Driving Mechanisms: 1982--2009},
  journal = remotesens,
  volume = 5,
  number = 3,
  pages = {1484--1497},
  doi = {10.3390/rs5031484},
  day = 21,
  month = mar,
  year = 2013,
  abstract = {Using a recent Leaf Area Index (LAI) dataset and the Community Land Model version 4 (CLM4), we investigated percent changes and controlling factors of global vegetation growth for the period 1982 to 2009. Over that 28-year period, both the remote-sensing estimate and model simulation show a significant increasing trend in annual vegetation growth. Latitudinal asymmetry appeared in both products, with small increases in the Southern Hemisphere (SH) and larger increases at high latitudes in the Northern Hemisphere (NH). The south-to-north asymmetric land surface warming was assessed to be the principal driver of this latitudinal asymmetry of LAI trend. Heterogeneous precipitation functioned to decrease this latitudinal LAI gradient, and considerably regulated the local LAI change. A series of factorial experiments were specially-designed to isolate and quantify contributions to LAI trend from different external forcings such as climate variation, CO$_2$, nitrogen deposition and land use and land cover change. The climate-only simulation confirms that climate change, particularly the asymmetry of land temperature variation, can explain the latitudinal pattern of LAI change. CO$_2$ fertilization during the last three decades was simulated to be the dominant cause for the enhanced vegetation growth. Our study, though limited by observational and modeling uncertainties, adds further insight into vegetation growth trends and environmental correlations. These validation exercises also provide new quantitative and objective metrics for evaluation of land ecosystem process models at multiple spatio-temporal scales.}
}

@article{Todd-Brown_Biogeosci_20130313,
  author = {K. E. O. Todd-Brown and J. T. Randerson and W. M. Post and F. M. Hoffman and C. Tarnocai and E. A. G. Schuur and S. D. Allison},
  title = {Causes of Variation in Soil Carbon Simulations from {CMIP5} {E}arth System Models and Comparison with Observations},
  journal = biogeosci,
  volume = 10,
  number = 3,
  pages = {1717--1736},
  doi = {10.5194/bg-10-1717-2013},
  day = 13,
  month = mar,
  year = 2013,
  abstract = {Stocks of soil organic carbon represent a large component of the carbon cycle that may participate in climate change feedbacks, particularly on decadal and centennial timescales. For Earth system models (ESMs), the ability to accurately represent the global distribution of existing soil carbon stocks is a prerequisite for accurately predicting future carbon--climate feedbacks. We compared soil carbon simulations from 11 model centers to empirical data from the Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). Model estimates of global soil carbon stocks ranged from 510 to 3040~Pg\,C, compared to an estimate of 1260~Pg\,C (with a 95\% confidence interval of 890--1660~Pg\,C) from the HWSD. Model simulations for the high northern latitudes fell between 60 and 820~Pg\,C, compared to 500~Pg\,C (with a 95\% confidence interval of 380--620~Pg\,C) for the NCSCD and 290~Pg\,C for the HWSD. Global soil carbon varied 5.9 fold across models in response to a 2.6-fold variation in global net primary productivity (NPP) and a 3.6-fold variation in global soil carbon turnover times. Model--data agreement was moderate at the biome level ($R^2$ values ranged from 0.38 to 0.97 with a mean of 0.75); however, the spatial distribution of soil carbon simulated by the ESMs at the 1$^\circ$ scale was not well correlated with the HWSD (Pearson correlation coefficients less than 0.4 and root mean square errors from 9.4 to 20.8~kg\,C\,m$^{-2}$). In northern latitudes where the two data sets overlapped, agreement between the HWSD and the NCSCD was poor (Pearson correlation coefficient 0.33), indicating uncertainty in empirical estimates of soil carbon. We found that a reduced complexity model dependent on NPP and soil temperature explained much of the 1$^\circ$ spatial variation in soil carbon within most ESMs ($R^2$ values between 0.62 and 0.93 for 9 of 11 model centers). However, the same reduced complexity model only explained 10\% of the spatial variation in HWSD soil carbon when driven by observations of NPP and temperature, implying that other drivers or processes may be more important in explaining observed soil carbon distributions. The reduced complexity model also showed that differences in simulated soil carbon across ESMs were driven by differences in simulated NPP and the parameterization of soil heterotrophic respiration (inter-model $R^2 = 0.93$), not by structural differences between the models. Overall, our results suggest that despite fair global-scale agreement with observational data and moderate agreement at the biome scale, most ESMs cannot reproduce grid-scale variation in soil carbon and may be missing key processes. Future work should focus on improving the simulation of driving variables for soil carbon stocks and modifying model structures to include additional processes.}
}