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Is Arctic Greening Consistent with the Ecology of Tundra? Lessons from an Ecologically Informed Mass Balance Model

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Is Arctic Greening Consistent with the Ecology of Tundra? Lessons from an Ecologically Informed Mass Balance Model Environmental Research Letters LETTER • OPEN ACCESS Is arctic greening consistent with the ecology of tundra? Lessons from an ecologically informed mass balance model To cite this article: A V Rocha et al 2018 Environ. Res. Lett. 13 125007 View the article online for updates and enhancements. This content was downloaded from IP address 129.74.231.133 on 02/01/2019 at 15:50 Environ. Res. Lett. 13 (2018) 125007 https://doi.org/10.1088/1748-9326/aaeb50 LETTER Is arctic greening consistent with the ecology of tundra? Lessons OPEN ACCESS from an ecologically informed mass balance model RECEIVED 9 August 2018 A V Rocha1 , B Blakely1, Y Jiang2, K S Wright1 and S R Curasi1 REVISED 1 University of Notre Dame, Department of Biological Sciences & the Environmental Change Initiative, Notre Dame, IN, 46556, United 23 October 2018 States of America ACCEPTED FOR PUBLICATION 2 Oregon State University, Forest Ecosystems and Society, Corvallis, Oregon, 97331, United States of America 25 October 2018 PUBLISHED E-mail: [email protected] 7 December 2018 Keywords: NDVI, arctic greening/browning, disturbance, arctic ecology and biogeochemistry Original content from this work may be used under the terms of the Creative Abstract Commons Attribution 3.0 licence. Climate change has been implicated in the widespread ‘greening’ of the arctic in recent decades. Any further distribution of However, differences in arctic greening patterns among satellite platforms and recent reports of this work must maintain attribution to the decreased rate of greening or of browning have made attributing arctic greening trends to a warming author(s) and the title of climate challenging. Here, we compared MODIS greening trends to those predicted by the coupled the work, journal citation and DOI. carbon and nitrogen model (CCaN); a mass balance carbon and nitrogen model that was driven by MODIS surface temperature and climate. CCaN was parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite based data. We found that, at long temporal and large spatial scales, MODIS greening trends were consistent with ecological and biogeochemical data from arctic tundra. However, at smaller spatial scales, observations and CCaN greening trends differed in the location, extent, and magnitude of greening. CCaN was unable to capture the high rates of MODIS greening in northern wetlands, and the patchy MODIS browning in the southern portion of the North Slope. This model-data disagreement points to disturbance and its legacy impacts on land cover as an important mechanism for understanding greening trends on the North Slope of Alaska. 1. Introduction De Jong et al 2011,Wanget al 2012).Hence,inorderto better understand the observed greening response, it Satellite based observations of the normalized difference must be determined whether the greening signal is vegetation index (NDVI) have illustrated a ‘greening of expected given aprioriknowledge of the ecophysiology the arctic’. Because NDVI is a proxy for leaf area, several and biogeography of arctic vegetation. have hypothesized that these greening trends result from Due to low diversity and low temperatures, pan- increased vegetation productivity as a result of the rapidly arctic CO2 fluxes are thought to be functionally con- warming temperatures (Walker et al 2003,Goetzet al vergent and can be reasonably modeled with a single 2005, Beck and Goetz 2011,Forkelet al 2016). However, response to light, temperature, and canopy leaf area attributing greening trends to increased vegetation (Street et al 2006; Shaver et al 2007, 2013; Loranty et al productivity is not straightforward (Alcaraz-Segura et al 2011; Street et al 2012; Mbufong et al 2014). This func- 2010). Satellites differ in the magnitude and spatial extent tional convergence is thought to arise from strong of greening, while some regions have exhibited insignif- nitrogen (N) limitation of arctic vegetation, which icant greening trends or declines (i.e. ‘browning’) even as controls both allocation to LAI and leaf photo- the arctic continues to warm (Stow et al 2007,Bhattet al synthetic rates (Williams and Rastetter 1999, Shaver 2013,JuandMasek2016). Moreover, changes in et al 2013). Low mineralization, deposition, and leach- background reflectance or satellite performance (e.g. ing rates cause a tight recycling of N among biomass, orbitalorsensordrift) may confound such trends soil organic matter, and available N pools (Shaver et al resulting in a spurious greening signal without an 1992, Stieglitz et al 2006). Consequently, arctic green- increase in leaf area index (LAI)(Rocha and Shaver 2009, ing is dependent on the tight coupling between C © 2018 The Author(s). Published by IOP Publishing Ltd Environ. Res. Lett. 13 (2018) 125007 and N cycles. This conceptual framework was imple- region comprises nine out of fifteen arctic vegetation mented into a parsimonious mass balance model of community types as defined in Raynolds et al (2006), coupled C and N cycles (e.g. the coupled carbon and and included erect-shrub tundra in the south, acidic nitrogen (CCaN) model)(Wright and Rocha 2018). and non-acidic graminoid tundra in the foothills of CCaN was parameterized with model-data fusion, the North Slope, and wetlands in the north. The North where prior observed ecological information (i.e. eddy Slope also is a region where satellite based NDVI covariance, NDVI, and vegetation and soil data from trends have diverged across satellite platforms, indi- the Arctic Long Term Ecological Research (LTER) cating a need for further validation of arctic greening program) provided realistic biogeochemical con- trends (Stow et al 2007, Ju and Masek 2016). straints on model parameters and predictions. CCaN performed well at capturing the seasonal and spatial 2.2. CCaN model and parameterization variability in NDVI across arctic systems on the North Details on CCaN formulation and parameterization ( ) Slope of Alaska Wright and Rocha 2018 . Conse- are described in Wright and Rocha (2018). Briefly, quently, it was hypothesized that CCaN along with a CCaN couples C and N cycles through the linear model-data fusion approach would provide insight relationship between LAI and foliar N (Williams and into arctic greening trends. Rastetter, 1999), a shared litter-fall rate for C and N, The modern landscape temperature sensitivity of and plant N uptake limited by root biomass. The arctic productivity may place a strong constraint on model is driven with daily light and temperature, and arctic greening trends. Temperature and NDVI on the predicted daily net ecosystem exchange of CO2 (NEE), North Slope of Alaska are tightly linked and follow a LAI, and NDVI using the functional convergence general north to south gradient of increasing temper- model of Shaver et al (2007, 2013) and Loranty et al ( ature and NDVI Raynolds et al 2008; Wright and (2011). NDVI was obtained by inverting the exponen- ) Rocha 2018 . Such sensitivity is likely related to tial relationship between ground based LAI and NDVI the long-term equilibrium response of arctic bio- observations (Shaver et al 2007, Rocha and Shaver geochemistry to temperature; providing an upper 2009). CCaN was constrained with field measured C limit to the expected shifts in ecosystem productivity and N cycling processes and their uncertainties, and with warmer temperatures. Here model-data fusion was parameterized with model-data fusion (Richard- was used with CCaN and latitudinal MODIS NDVI son et al 2010, Keenan et al 2012). observations on the North Slope to constrain the The functional convergence model of Shaver et al temperature sensitivity of LAI under tightly coupled C (2007, 2013) and Loranty et al (2011) hypothesizes that and N cycles. Model-data fusion is an iterative techni- NEE in all arctic tundra communities responds simi- que used to find the best set of parameters that fita larly to temperature, light, LAI, and NDVI throughout proposed model to data with quantified uncertainties time. In accordance with this hypothesis, CCaN was (Keenan et al 2012). Parameters were constrained originally parameterized with NEE and NDVI obser- within a given range, given a priori information vations from a single site (Wright and Rocha 2018). from historical LTER measurements (Wright and With this parameterization, CCaN captured a major- Rocha 2018). Model-data fusion provided a posteriori ity of NDVI observations over 8 years at a single site statistical likelihood parameter distribution based on and NDVI observations across a latitudinal gradient. model structure, data uncertainty, and parameter con- The gradient was comprised of 32 tundra sites along a straints. We hypothesize that such constraints will North–South transect that included all major vegeta- allow for attribution of the observed greening trends tion communities found on the North Slope of Alaska from 2000–2015 to increased temperatures in the (Wright and Rocha 2018). However, this original region. parameterization underestimated NDVI at sites with high LAI (Wright and Rocha 2018). Consequently, 2. Methods for this study we reparameterized CCaN using the MODIS NDVI from the 32 tundra sites previously 2.1. Region of interest mentioned, with a focus on constraining the temper- Analyses focused on the foothills and coastal plains of ature sensitivity of the proportion of N in leaves, which the North Slope of Alaska, but excluded the Brooks ultimately determined LAI and NDVI. Range mountains (figure 1). The region of interest CCaN
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