peerreviewed_2012.bib

@article{Kumar_JHydro_2012,
  author = {Jitendra Kumar and E. D. Brill and G. Mahinthakumar and S. Ranji
	Ranjithan},
  title = {Contaminant source characterization in water distribution systems
	using filtered sensor data},
  journal = {Journal of Hydroinformatics},
  year = {2012},
  volume = {Vol 14 No 3},
  pages = {585--602},
  file = {pubs/Kumar_JHydro_2012.pdf},
  doi = {10.2166/hydro.2012.073},
  file = {pubs/Kumar_JHydro_2012.pdf},
  owner = {jkumar},
  timestamp = {2010.08.21}
}

@article{Kumar2012a,
  author = {Jitendra Kumar and Bj{\o}rn-Gustaf J. Brooks and Peter E. Thornton
	and Michael C. Dietze},
  title = {Sub-daily Statistical Downscaling of Meteorological Variables Using
	Neural Networks},
  journal = {Procedia Computer Science},
  year = {2012},
  volume = {9},
  pages = {887 - 896},
  number = {0},
  note = {Proceedings of the International Conference on Computational Science,
	ICCS 2012},
  abstract = {A new open source neural network temporal downscaling model is described
	and tested using CRU-NCEP reanal ysis and CCSM3 climate model output.
	We downscaled multiple meteorological variables in tandem from monthly
	to sub-daily time steps while also retaining consistent correlations
	between variables. We found that our feed forward, error backpropagation
	approach produced synthetic 6 hourly meteorology with biases no greater
	than 0.6% across all variables and variance that was accurate within
	1% for all variables except atmospheric pressure, wind speed, and
	precipitation. Correlations between downscaled output and the expected
	(original) monthly means exceeded 0.99 for all variables, which indicates
	that this approach would work well for generating atmospheric forcing
	data consistent with mass and energy conserved GCM output. Our neural
	network approach performed well for variables that had correlations
	to other variables of about 0.3 and better and its skill was increased
	by downscaling multiple correlated variables together. Poor replication
	of precipitation intensity however required further post-processing
	in order to obtain the expected probability distribution. The concurrence
	of precipitation events with expected changes in sub ordinate variables
	(e.g., less incident shortwave radiation during precipitation events)
	were nearly as consistent in the downscaled data as in the training
	data with probabilities that differed by no more than 6%. Our downscaling
	approach requires training data at the target time step and relies
	on a weak assumption that climate variability in the extrapolated
	data is similar to variability in the training data.},
  doi = {10.1016/j.procs.2012.04.095},
  file = {pubs/Kumar_ICCS_2012.pdf},
  issn = {1877-0509},
  keywords = {statistical downscaling},
  owner = {jkumar},
  timestamp = {2012.06.13},
  url = {http://www.sciencedirect.com/science/article/pii/S1877050912002165}
}

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@article{Bauerle_PNAS_20120529,
  author = {William L. Bauerle and Ram Oren and Danielle A. Way and Song S. Qian and Paul C. Stoy and Peter E. Thornton and Joseph D. Bowden and Forrest M. Hoffman and Robert F. Reynolds},
  title = {Photoperiodic Regulation of the Seasonal Pattern of Photosynthetic Capacity and the Implications for Carbon Cycling},
  journal = pnas,
  volume = 109,
  number = 22,
  pages = {8612--8617},
  doi = {10.1073/pnas.1119131109},
  day = 29,
  month = may,
  year = 2012,
  abstract = {Although temperature is an important driver of seasonal changes in photosynthetic physiology, photoperiod also regulates leaf activity. Climate change will extend growing seasons if temperature cues predominate, but photoperiod-controlled species will show limited responsiveness to warming. We show that photoperiod explains more seasonal variation in photosynthetic activity across 23 tree species than temperature. Although leaves remain green, photosynthetic capacity peaks just after summer solstice and declines with decreasing photoperiod, before air temperatures peak. In support of these findings, saplings grown at constant temperature but exposed to an extended photoperiod maintained high photosynthetic capacity, but photosynthetic activity declined in saplings experiencing a naturally shortening photoperiod; leaves remained equally green in both treatments. Incorporating a photoperiodic correction of photosynthetic physiology into a global-scale terrestrial carbon-cycle model significantly improves predictions of seasonal atmospheric CO$_2$ cycling, demonstrating the benefit of such a function in coupled climate system models. Accounting for photoperiod-induced seasonality in photosynthetic parameters reduces modeled global gross primary production 2.5\% ($\sim$4~PgC~y$^{-1}$), resulting in a >3\% ($\sim$2~PgC~y$^{-1}$) decrease of net primary production. Such a correction is also needed in models estimating current carbon uptake based on remotely sensed greenness. Photoperiod-associated declines in photosynthetic capacity could limit autumn carbon gain in forests, even if warming delays leaf senescence.}
}

@article{Huntzinger_EcolModel_20120510,
  author = {D. N. Huntzinger and W. M. Post and Y. Wei and A. M. Michalak and T. O. West and A. R. Jacobson and I. T. Baker and J. M. Chen and K. J. Davis and D. J. Hayes and F. M. Hoffman and A. K. Jain and S. Liu and A. D. McGuire and R. P. Neilson and Chris Potter and B. Poulter and David Price and B. M. Raczka and H. Q. Tian and P. Thornton and E. Tomelleri and N. Viovy and J. Xiao and W. Yuan and N. Zeng and M. Zhao and R. Cook},
  title = {{N}orth {A}merican {C}arbon {P}rogram ({NACP}) Regional Interim Synthesis: Terrestrial Biospheric Model Intercomparison},
  journal = ecolmodel,
  volume = 232,
  number = 0,
  pages = {144--157},
  doi = {10.1016/j.ecolmodel.2012.02.004},
  day = 10,
  month = may,
  year = 2012,
  abstract = {Understanding of carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding to much larger terrestrial regions. Although models vary in their specific goals and approaches, their central role within carbon cycle science is to provide a better understanding of the mechanisms currently controlling carbon exchange. Recently, the North American Carbon Program (NACP) organized several interim-synthesis activities to evaluate and inter-compare models and observations at local to continental scales for the years 2000--2005. Here, we compare the results from the TBMs collected as part of the regional and continental interim-synthesis (RCIS) activities. The primary objective of this work is to synthesize and compare the 19 participating TBMs to assess current understanding of the terrestrial carbon cycle in North America. Thus, the RCIS focuses on model simulations available from analyses that have been completed by ongoing NACP projects and other recently published studies. The TBM flux estimates are compared and evaluated over different spatial ($1^\circ \times 1^\circ$ and spatially aggregated to different regions) and temporal (monthly and annually) scales. The range in model estimates of net ecosystem productivity (NEP) for North America is much narrower than estimates of productivity or respiration, with estimates of NEP varying between $-$0.7 and 2.2~PgC~yr$^{-1}$, while gross primary productivity and heterotrophic respiration vary between 12.2 and 32.9~PgC~yr$^{-1}$ and 5.6 and 13.2~PgC~yr$^{-1}$, respectively. The range in estimates from the models appears to be driven by a combination of factors, including the representation of photosynthesis, the source and of environmental driver data and the temporal variability of those data, as well as whether nutrient limitation is considered in soil carbon decomposition. The disagreement in current estimates of carbon flux across North America, including whether North America is a net biospheric carbon source or sink, highlights the need for further analysis through the use of model runs following a common simulation protocol, in order to isolate the influences of model formulation, structure, and assumptions on flux estimates.}
}

@article{Luo_Biogeosci_20121009,
  author = {Y. Q. Luo and J. T. Randerson and G. Abramowitz and C. Bacour and E. Blyth and N. Carvalhais and P. Ciais and D. Dalmonech and J. B. Fisher and R. Fisher and P. Friedlingstein and K. Hibbard and F. Hoffman and D. Huntzinger and C. D. Jones and C. Koven and D. Lawrence and D. J. Li and M. Mahecha and S. L. Niu and R. Norby and S. L. Piao and X. Qi and P. Peylin and I. C. Prentice and W. Riley and M. Reichstein and C. Schwalm and Y. P. Wang and J. Y. Xia and S. Zaehle and X. H. Zhou},
  title = {A Framework for Benchmarking Land Models},
  journal = biogeosci,
  volume = 9,
  number = 10,
  pages = {3857--3874},
  doi = {10.5194/bg-9-3857-2012},
  day = 9,
  month = oct,
  year = 2012,
  abstract = {Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data--model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.}
}