Ecosystem function in complex mountain terrain: Combining models and long‐term observations to advance process‐based understanding

Wieder, William R.; Knowles, John F.; Blanken, Peter D.; Swenson, Sean; Suding, Katharine N.

doi:10.1002/2016jg003704

Cited by 22 publications

(27 citation statements)

References 103 publications

(114 reference statements)

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“…Additionally, the EVI trends during the 2012 drought indicate earlier senescence and a lower peak EVI despite significant monsoon precipitation. These observations support emerging work on the importance of snowpack amount and timing over summer precipitation in sustaining plants through times of increased water demand (Berkelhammer et al, 2020;Knowles et al, 2018;Sloat et al, 2015;Wieder et al, 2017).…”

Section: Soil Respiration Patterns Under Drought Conditionssupporting

confidence: 80%

Soil Respiration Response to Rainfall Modulated by Plant Phenology in a Montane Meadow, East River, Colorado, USA

et al. 2020

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Soil respiration is a primary component of the terrestrial carbon cycle. However, predicting the response of soil respiration to climate change remains a challenge due to the complex interactions between environmental drivers, especially plant phenology, temperature, and soil moisture. In this study, we use a 1-D diffusion-reaction model to calculate depth-resolved CO 2 production rates from soil CO 2 concentrations and surface efflux observations in a subalpine meadow in the East River watershed, CO. Modeled rates are compared to in situ soil temperature and moisture conditions and MODIS satellite enhanced vegetation index (EVI) representing plant phenology across three hydrologically distinct growing seasons from 2016-2018. While soil respiration correlated with temperature on diel timescales (p < 0.05), seasonal variability was dominated by soil moisture and plant phenology (p < 0.05). We observed significant respiration increases in response to precipitation events; however, magnitude and duration were significantly higher in 2017 than 2016 despite similar wetting characteristics. Based on MODIS EVI, we suggest that the respiration response to rainfall is controlled by plant phenology, which in turn reflects the capacity of plants to respond to precipitation via increased photosynthesis and autotrophic respiration, behavior that is not captured in typical soil respiration pulse models. Projected changes in montane climate such as earlier snowmelt and prolonged fore-summer drought may decrease soil respiration fluxes by decreasing the overlap between peak productivity and the summer monsoon. Finally, we observed significant late season CO 2 fluxes from the deep subsoil (>165 cm) that support growing evidence for the importance of subsoil processes in driving integrated respiration fluxes.

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Section: Soil Respiration Patterns Under Drought Conditionssupporting

confidence: 80%

Soil Respiration Response to Rainfall Modulated by Plant Phenology in a Montane Meadow, East River, Colorado, USA

et al. 2020

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“…As there is no communication between grid cells in CLM, the model will perform exactly as it will in a global run, but at a fraction of the computational cost. Although CLM is normally run globally, using the model in single point mode is common for development and testing (e.g., Bonan et al, ; Duarte et al, ; Mao et al, ; Wieder et al, ). A multi‐instance CLM version 4.5 with active biogeochemistry and prognostic leaf area case (an ICLM45BGC component set—see technical description in Oleson et al, ) with 80 ensemble members was spun up by simply cycling through the 12 years of CAM output for 1,000 years using 20th century atmospheric CO 2 concentrations and nitrogen deposition rates.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a Data Assimilation System for Land Surface Models Using CLM4.5

Fox

Hoar

Anderson

et al. 2018

J Adv Model Earth Syst

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The magnitude and persistence of land carbon (C) pools influence long-term climate feedbacks. Interactive ecological processes influence land C pools and our understanding of these processes is imperfect so land surface models have errors and biases when compared to each other and to real observations. Here we implement an Ensemble Adjustment Kalman Filter (EAKF), a sequential state data assimilation technique to reduce these errors and biases. We implement the EAKF using the Data Assimilation Research Testbed coupled with the Community Land Model (CLM 4.5 in CESM 1.2). We assimilated simulated and real satellite observations for a site in central New Mexico, United States. A series of observing system simulation experiments allowed assessment of the data assimilation system without model error. This showed that assimilating biomass and leaf area index observations decreased model error in C dynamics forecasts (29% using biomass observations and 40% using leaf area index observations) and that assimilation in combination shows greater improvement (51% using both observation streams). Assimilating real observations highlighted likely model structural errors and we implemented an adaptive model-variance-inflation technique to allow the model to track the observations. Monthly and longer model forecasts using real observations were improved relative to forecasts without data assimilation. The reliable forecast lead-time varied by model pool and is dependent on how tightly the C pool is coupled to meteorologically driven processes. The EAKF and similar state data assimilation techniques could reduce errors in projections of the land C sink and provide more robust forecasts of C pools and land-atmosphere exchanges. Plain Language Summary The amount of carbon stored in vegetation and soils is an importantcontrol on how much carbon dioxide is in the atmosphere, and that influences future climate. Land surface models are used to simulate where this carbon is, but they are imperfect and there are often differences between model predictions and observations of the carbon stores. Here we describe a system that combines model predictions and observations and updates the modeled carbon stores so they are closer to the observations, considering uncertainty in both the model and the observations. We test our system at a location in New Mexico, United States, where we use observations from satellites of the amount of leaves on the vegetation and the amount of carbon stored in the vegetation. When we combine these observations with our land surface model there are large changes in the predicted amounts of stored carbon and the times of the year when the vegetation has the most leaves. These changes persist in the model after we stop updating it with observations, improving the model forecast.FOX ET AL.

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“…Recent initiatives address the missing integration of below-ground biota in terrestrial Earth system science and models (Key to Soil Organic Matter Dynamics and Modelling -KEYSOM-BIOLINK project; Filser et al, 2016), and the provision of substantial datasets on soil biota and biodiversity (global soil biodiversity database; Ramirez et al, 2015). Some models are being developed that are capable of estimating the role of biotic activity in soil formation, decomposition-mineralization processes, and predicting the carbon and nutrient cycles in specific soil types (Komarov et al, 2017;Wieder et al, 2017). Nevertheless, joining discipline-specific data with the largely site-tocatchment-based but discipline-specific modelling expertise of the CZO and LTER communities would lay the ground for new findings.…”

Section: Open Issues and Implicationsmentioning

confidence: 99%

Steering operational synergies in terrestrial observation networks: opportunity for advancing Earth system dynamics modelling

Baatz

Sullivan

et al. 2018

Earth Syst. Dynam.

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Abstract. Advancing our understanding of Earth system dynamics (ESD) depends on the development of models and other analytical tools that apply physical, biological, and chemical data. This ambition to increase understanding and develop models of ESD based on site observations was the stimulus for creating the networks of Long-Term Ecological Research (LTER), Critical Zone Observatories (CZOs), and others. We organized a survey, the results of which identified pressing gaps in data availability from these networks, in particular for the future development and evaluation of models that represent ESD processes, and provide insights for improvement in both data collection and model integration. From this survey overview of data applications in the context of LTER and CZO research, we identified three challenges: (1) widen application of terrestrial observation network data in Earth system modelling, (2) develop integrated Earth system models that incorporate process representation and data of multiple disciplines, and (3) identify complementarity in measured variables and spatial extent, and promoting synergies in the existing observational networks. These challenges lead to perspectives and recommendations for an improved dialogue between the observation networks and the ESD modelling community, including co-location of sites in the existing networks and further formalizing these recommendations among these communities. Developing these synergies will enable cross-site and cross-network comparison and synthesis studies, which will help produce insights around organizing principles, classifications, and general rules of coupling processes with environmental conditions.

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Ecosystem function in complex mountain terrain: Combining models and long‐term observations to advance process‐based understanding

Cited by 22 publications

References 103 publications

Soil Respiration Response to Rainfall Modulated by Plant Phenology in a Montane Meadow, East River, Colorado, USA

Soil Respiration Response to Rainfall Modulated by Plant Phenology in a Montane Meadow, East River, Colorado, USA

Evaluation of a Data Assimilation System for Land Surface Models Using CLM4.5

Steering operational synergies in terrestrial observation networks: opportunity for advancing Earth system dynamics modelling

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