Preface: Impacts of extreme climate events and disturbances on carbon dynamics

The impacts of extreme climate events and disturbances (ECE&D) on the carbon cycle have received growing attention in recent years. This special issue showcases a collection of recent advances in understanding the impacts of ECE&D on carbon cycling. Notable advances include quantifying how harvesting activities impact forest structure, carbon pool dynamics, and recovery processes; observed drastic increases of the concentrations of dissolved organic carbon and dissolved methane in thermokarst lakes in western Siberia during a summer warming event; disentangling the roles of herbivores and fire on forest carbon dioxide flux; direct and indirect impacts of fire on the global carbon balance; and improved atmospheric inversion of regional carbon sources and sinks by incorporating disturbances. Combined, studies herein indicate several major research needs. First, disturbances and extreme events can interact with one another, and it is important to understand their overall impacts and also disentangle their effects on the carbon cycle. Second, current ecosystem models are not skillful enough to correctly simulate the underlying processes and impacts of ECE&D (e.g., tree mortality and carbon consequences). Third, benchmark data characterizing the timing, location, type, and magnitude of disturbances must be systematically created to improve our ability to quantify carbon dynamics over large areas. Finally, improving the representation of ECE&D in regional climate/earth system models and accounting for the resulting feedbacks to climate are essential for understanding the interactions between climate and ecosystem dynamics

Robust observations of land-to-atmosphere feedbacks using the information flows of FLUXNET

Feedbacks between atmospheric processes like precipitation and land surface fluxes including evapotranspiration are difficult to observe, but critical for understanding the role of the land surface in the Earth System. To quantify global surface-atmosphere feedbacks we use results of a process network (PN) applied to 251 eddy covariance sites from the LaThuile database to train a neural network across the global terrestrial surface. There is a strong land–atmosphere coupling between latent (LE) and sensible heat flux (H) and precipitation (P) during summer months in temperate regions, and between H and P during winter, whereas tropical rainforests show little coupling seasonality. Savanna, shrubland, and other semi-arid ecosystems exhibit strong responses in their coupling behavior based on water availability. Feedback couplings from surface fluxes to P peaks at aridity (P/potential evapotranspiration ETp) values near unity, whereas coupling with respect to clouds, inferred from reduced global radiation, increases as P/ETp approaches zero. Spatial patterns in feedback coupling strength are related to climatic zone and biome type. Information flow statistics highlight hotspots of (1) persistent land–atmosphere coupling in sub-Saharan Africa, (2) boreal summer coupling in the central and southwestern US, Brazil, and the Congo basin and (3) in the southern Andes, South Africa and Australia during austral summer. Our data-driven approach to quantifying land atmosphere coupling strength that leverages the global FLUXNET database and information flow statistics provides a basis for verification of feedback interactions in general circulation models and for predicting locations where land cover change will feedback to climate or weather

Drought supersedes warming in determining volatile and tissue defenses of piñon pine (Pinus edulis)

Trees are suffering mortality across the globe as a result of drought, warming, and biotic attacks. The combined effects of warming and drought on in situ tree chemical defenses against herbivory have not been studied to date. To address this, we transplanted mature pinon pine trees-a well-studied species that has undergone extensive drought and herbivore-related mortality-within their native woodland habitat and also to a hotter-drier habitat and measured monoterpene emissions and concentrations across the growing season. We hypothesized that greater needle temperatures in the hotter-drier site would increase monoterpene emission rates and consequently lower needle monoterpene concentrations, and that this temperature effect would dominate the seasonal pattern of monoterpene concentrations regardless of drought. In support of our hypothesis, needle monoterpene concentrations were lower across all seasons in trees transplanted to the hotter-drier site. Contrary to our hypothesis, basal emission rates (emission rates normalized to 30 degrees C and a radiative flux of 1000 mu mol m(-2) s(-1)) did not differ between sites. This is because an increase in emissions at the hotter-drier site from a 1.5 degrees C average temperature increase was offset by decreased emissions from greater plant water stress. High emission rates were frequently observed during June, which were not related to plant physiological or environmental factors but did not occur below pre-dawn leaf water potentials of -2 MPa, the approximate zero carbon assimilation point in pinon pine. Emission rates were also not under environmental or plant physiological control when pre-dawn leaf water potential was less than -2 MPa. Our results suggest that drought may override the effects of temperature on monoterpene emissions and tissue concentrations, and that the influence of drought may occur through metabolic processes sensitive to the overall needle carbon balance.National Science Foundation, Division of Atmospheric and Geospace Sciences [0919189]; USDA National Institute of Food and Agriculture Hatch project [MONB00389, 228396]; National Science Foundation, Division of Integrative Organismal Systems [1755346]; National Science Foundation Division of Environmental Biology [1552976]; Department of the Energy National Institute for Climate Change Research (Western Region) [DE-FCO2-O6ER64159]; National Science Foundation Macrosystems Biology [EF-1340624, EF-1550756]; Critical Zone Observatories [EAR-1331408]; DIRENet [DEB-0443526]; Biosphere 2 through the Philecology Foundation (Fort Worth, TX); US Environmental Protection Agency (STAR Fellowship Assistance Agreement) [FP-91717801-0]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Energy, water, and carbon fluxes in a loblolly pine stand: Results from uniform and gappy canopy models with comparisons to eddy flux data

1] This study investigates the impacts of canopy structure specification on modeling net radiation (R n), latent heat flux (LE) and net photosynthesis (A n) by coupling two contrasting radiation transfer models with a two-leaf photosynthesis model for a maturing loblolly pine stand near Durham, North Carolina, USA. The first radiation transfer model is based on a uniform canopy representation (UCR) that assumes leaves are randomly distributed within the canopy, and the second radiation transfer model is based on a gappy canopy representation (GCR) in which leaves are clumped into individual crowns, thereby forming gaps between the crowns. To isolate the effects of canopy structure on model results, we used identical model parameters taken from the literature for both models. Canopy structure has great impact on energy distribution between the canopy and the forest floor. Comparing the model results, UCR produced lower R n , higher LE and higher A n than GCR. UCR intercepted more shortwave radiation inside the canopy, thus producing less radiation absorption on the forest floor and in turn lower R n . There is a higher degree of nonlinearity between A n estimated by UCR and by GCR than for LE. Most of the difference for LE and A n between UCR and GCR occurred around noon, when gaps between crowns can be seen from the direction of the incident sunbeam. Comparing with eddy-covariance measurements in the same loblolly pine stand from May to September 2001, based on several measures GCR provided more accurate estimates for R n , LE and A n than UCR. The improvements when using GCR were much clearer when comparing the daytime trend of LE and A n for the growing season. Sensitivity analysis showed that UCR produces higher LE and A n estimates than GCR for canopy cover ranging from 0.2 to 0.8. There is a high degree of nonlinearity in the relationship between UCR estimates for A n and those of GCR, particularly when canopy cover is low, and suggests that simple scaling of UCR parameters cannot compensate for differences between the two models. LE from UCR and GCR is also nonlinearly related when canopy cover is low, but the nonlinearity quickly disappears as canopy cover increases, such that LE from UCR and GCR are linearly related and the relationship becomes stronger as canopy cover increases. These results suggest the uniform canopy assumption can lead to underestimation of R n , and overestimation of LE and A n . Given the potential in mapping regional scale forest canopy structure with high spatial resolution optical and Lidar remote sensing plotforms, it is possible to use GCR for up-scaling ecosystem processes from flux tower measurements to heterogeneous landscapes, provided the heterogeneity is not too extreme to modify the flow dynamics., Energy, water, and carbon fluxes in a loblolly pine stand: Results from uniform and gappy canopy models with comparisons to eddy flux data, J. Geophys. Res., 114, G04021, doi:10.1029/2009JG000951

Characterizing the diurnal patterns of errors in the prediction of evapotranspiration by several land‐surface models: An NACP analysis

Land‐surface models use different formulations of stomatal conductance and plant hydraulics, and it is unclear which type of model best matches the observed surface‐atmosphere water flux. We use the North American Carbon Program data set of latent heat flux (LE) measurements from 25 sites and predictions from 9 models to evaluate models' ability to resolve subdaily dynamics of transpiration. Despite overall good forecast at the seasonal scale, the models have difficulty resolving the dynamics of intradaily hysteresis. The majority of models tend to underestimate LE in the prenoon hours and overestimate in the evening. We hypothesize that this is a result of unresolved afternoon stomatal closure due to hydrodynamic stresses. Although no model or stomata parameterization was consistently best or worst in terms of ability to predict LE, errors in model‐simulated LE were consistently largest and most variable when soil moisture was moderate and vapor pressure deficit was moderate to limiting. Nearly all models demonstrate a tendency to underestimate the degree of maximum hysteresis which, across all sites studied, is most pronounced during moisture‐limited conditions. These diurnal error patterns are consistent with models' diminished ability to accurately simulate the natural hysteresis of transpiration. We propose that the lack of representation of plant hydrodynamics is, in part, responsible for these error patterns. Key Points Land‐surface models produce subdaily patterns of latent heat flux error Error patterns are characterized by the stomatal conductance formulation used Current models lack a mechanism to simulate hysteretic transpirationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108341/1/jgrg20246.pd

Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter

Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Applications 20 (2010): 1285–1301, doi:10.1890/09-0876.1.Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter performance, but it did not improve performance when used individually. The EnKF estimates of leaf area followed the expected springtime canopy phenology. However, there were also diel fluctuations in the leaf-area estimates; these are a clear indication of a model deficiency possibly related to vapor pressure effects on canopy conductance.This material is based upon work supported by the U.S. National Science Foundation under grants OPP-0352897, DEB-0423385, DEB-0439620, DEB-0444592, and OPP- 0632139

Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes

Reviews and syntheses : Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities

Evaporation (E) and transpiration (T) respond differently to ongoing changes in climate, atmospheric composition, and land use. It is difficult to partition ecosystem-scale evapotranspiration (ET) measurements into E and T, which makes it difficult to validate satellite data and land surface models. Here, we review current progress in partitioning E and T and provide a prospectus for how to improve theory and observations going forward. Recent advancements in analytical techniques create new opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully tested. For example, many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency exhibit optimal responses to atmospheric vapor pressure deficit (D). We use observations from 240 eddy covariance flux towers to demonstrate that optimal ecosystem response to D is a reasonable assumption, in agreement with recent studies, but more analysis is necessary to determine the conditions for which this assumption holds. Another critical assumption for many partitioning approaches is that ET can be approximated as T during ideal transpiring conditions, which has been challenged by observational studies. We demonstrate that T can exceed 95% of ET from certain ecosystems, but other ecosystems do not appear to reach this value, which suggests that this assumption is ecosystem-dependent with implications for partitioning. It is important to further improve approaches for partitioning E and T, yet few multi-method comparisons have been undertaken to date. Advances in our understanding of carbon-water coupling at the stomatal, leaf, and canopy level open new perspectives on how to quantify T via its strong coupling with photosynthesis. Photosynthesis can be constrained at the ecosystem and global scales with emerging data sources including solar-induced fluorescence, carbonyl sulfide flux measurements, thermography, and more. Such comparisons would improve our mechanistic understanding of ecosystem water fluxes and provide the observations necessary to validate remote sensing algorithms and land surface models to understand the changing global water cycle.Peer reviewe