Community Earth System Model Simulations Reveal the Relative Importance of Afforestation and Forest Management to Surface Temperature in Eastern North America

Ahlswede, B.; Thomas, R. Quinn

doi:10.3390/f8120499

Cited by 10 publications

(13 citation statements)

References 22 publications

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“…We find the magnitude of the simulated surface energy flux changes to be on the order approaching that which may be associated with anthropogenic land use or land cover changes (Alkama & Cescatti, 2016;Ahlswede & Thomas, 2017;Bright et al, 2017) or vegetation "greening" in the same region (Forzieri et al, 2017), reinforcing the notion that forestry is an often overlooked driver of regional energy and moisture budgets in climate modeling studies (Nabel et al, 2019;Naudts et al, 2016;Pongratz et al, 2018;Shevliakova et al, 2009;Yue et al, 2017).…”

Section: 1029/2019jd032092supporting

confidence: 59%

“…The annual ΔLST was lower in the deciduous conversion scenario than in the fully developed conversion scenario irrespective of the decrease in LAI. The study by Ahlswede and Thomas (2017) in eastern North America highlights that Δα alone can contribute to ΔLST by changing the intensity of forest management 10.1029/2019JD032092 Journal of Geophysical Research: Atmospheres through its effect on LAI. Seasonally, ΔλTr and ΔH dominate ΔLST in the undeveloped and fully developed conversion scenarios, respectively, whereas Δα dominates in summer and ΔG in winter for the deciduous conversion scenario.…”

Section: 1029/2019jd032092mentioning

confidence: 99%

“…Although latest developments in modeling, e.g., the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) in Community Land Model version 5.0 (CLM 5.0; Fisher et al, 2015;Lawrence et al, 2019), can represent forest age classes, many LSMs (e.g., Community Land Model version 4.5 [CLM 4.5]) lack the representation of secondary forests (different development classes analogous to forest management) while calculating the local/regional surface energy budgets; these calculations can be improved by representing the "managed forests" properly in these models (Sato et al, 2015). Recent regional modeling studies (Ahlswede & Thomas, 2017;Luyssaert et al, 2018;Naudts et al, 2016) suggest that management-induced changes to forest structure can be important for the regional surface energy balance because forest management changes the forest structure, which affects the energy and water vapor exchanges with the overlying atmosphere. Further, Schultz et al (2016) modified the default configuration of CLM 4.5 so that each PFT is assigned its own soil column and found that the magnitude and patterns of simulated surface air temperature between grass and tree PFT agree closely with the observations than in PFTs' shared soil column setting.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Regional Surface Energy Responses to Forest Structural Change in Nordic Fennoscandia

et al. 2020

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In a climate model, surface energy and water fluxes of the vegetated ecosystem largely depend on important structural attributes like leaf area index and canopy height. For forests, management can greatly alter these attributes with resulting consequences for the surface albedo, surface roughness, and evapotranspiration. The sensitivity of surface energy and water budgets to alterations in forest structure is relatively unknown in boreal regions, particularly in Nordic Fennoscandia (Norway, Sweden, and Finland), where the forest management footprint is large. Here we perform offline simulations to quantify the sensitivity of surface heat and moisture fluxes to changes in forest composition and structure across daily, seasonal, and annual time scales. For the region on average, it is found that broadleaved deciduous forests cool the surface by 0.16 K annually and 0.3 K in the growing season owed to higher year‐round albedo and lower Bowen ratio, yet in some locations the local cooling can be as much as 2.4 K and 3.0 K, respectively. Moreover, fully developed forests cool the surface by 0.04 K annually in our domain owed to higher evapotranspiration, reaching up to 0.4 K locally in some locations, whereas undeveloped forests warm annually by 0.14 K owed to much lower evapotranspiration reaching up to 0.8 K for some locations. If regional forests are ever to be managed for the local climate regulation services that they provide, our results are an important first step illuminating the potential adverse impacts or benefits across space and time.

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Section: 1029/2019jd032092supporting

confidence: 59%

Section: 1029/2019jd032092mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantifying Regional Surface Energy Responses to Forest Structural Change in Nordic Fennoscandia

et al. 2020

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“…Across much of the world, human impacts are an important component of the land surface [1]. Growth and loss of urban populations [2], changes in certain land areas and habitability [3], and changing agricultural and forestry practices [4,5] have all changed the ways in which energy and matter move through and among landscapes. In an Earth system modeling context, human impacts are mostly represented as changes in the footprint of urban areas and the parameters of those urban areas, changes in the extent and types of agricultural practices, changes in nitrogen deposition, and changes in global atmospheric CO 2 concentration [6].…”

Section: Introductionmentioning

confidence: 99%

Challenging a Global Land Surface Model in a Local Socio-Environmental System

Dahlin

Akanga

Lombardozzi

et al. 2020

Land

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Land surface models (LSMs) predict how terrestrial fluxes of carbon, water, and energy change with abiotic drivers to inform the other components of Earth system models. Here, we focus on a single human-dominated watershed in southwestern Michigan, USA. We compare multiple processes in a commonly used LSM, the Community Land Model (CLM), to observational data at the single grid cell scale. For model inputs, we show correlations (Pearson’s R) ranging from 0.46 to 0.81 for annual temperature and precipitation, but a substantial mismatch between land cover distributions and their changes over time, with CLM correctly representing total agricultural area, but assuming large areas of natural grasslands where forests grow in reality. For CLM processes (outputs), seasonal changes in leaf area index (LAI; phenology) do not track satellite estimates well, and peak LAI in CLM is nearly double the satellite record (5.1 versus 2.8). Estimates of greenness and productivity, however, are more similar between CLM and observations. Summer soil moisture tracks in timing but not magnitude. Land surface reflectance (albedo) shows significant positive correlations in the winter, but not in the summer. Looking forward, key areas for model improvement include land cover distribution estimates, phenology algorithms, summertime radiative transfer modelling, and plant stress responses.

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“…The aridity also plays a key role, giving forests a net effect of slightly warming arid zones, due to the overwhelming impact of enhanced net radiation; in contrast a net cooling effect would result from afforestation or reforestation of humid zones due to enhanced latent heat flux (Huang et al 2018). Climatic impacts of forest also depend on the tree species, in particular their specific albedo and evapotranspiration (Naudts et al 2016, Ahlswede andThomas, 2017). Through changes in albedo and in convective heat exchanges with the lower troposphere, forest management may impact the surface and planetary boundary layer temperature by the same magnitude as that from land-use changes (Bright et al 2012, Ahlswede and Thomas 2017.…”

mentioning

confidence: 99%

Energy, water and carbon exchanges in managed forest ecosystems: description, sensitivity analysis and evaluation of the INRAE GO+ model, version 3.0

Moreaux¹,

Martel²,

Bosc³

et al. 2020

Preprint

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Abstract. The mechanistic model GO+ describes the functioning and growth of managed forests based upon biophysical and biogeochemical processes. The biophysical and biogeochemical processes included are modelled using standard formulations of radiative transfer, convective heat exchange, evapotranspiration, photosynthesis, respiration, plant phenology, growth and mortality, biomass nutrient content, and soil carbon dynamics. The forest ecosystem is modelled as three layers, namely the tree overstorey, understorey and soil. The vegetation layers include stems, branches and foliage and are partitioned dynamically between sunlit and shaded fractions. The soil carbon sub-model is an adaption of the Roth-C model to simulate the impact of forest operations. The model runs at an hourly time-step. It represents a forest stand covering typically 1 ha and can be straightforwardly up-scaled across gridded data at regional, country or continental levels. GO+ accounts for both the immediate and long-term impacts of forest operations on energy, water and carbon exchanges within the soil-vegetation-atmosphere continuum. It includes exhaustive and versatile descriptions of management operations (soil preparation, regeneration, vegetation control, selective thinning, clear-cutting, coppicing, etc.), thus permitting the effects of a wide variety of forest management strategies to be estimated: from close-to-nature to intensive. This paper examines the sensitivity of the model to its main parameters and estimates how errors in parameter values are propagated into the predicted values of its main output variables. We show how the model performs when compared with observations such as time series of forest-atmosphere exchanges of energy, water and CO2 monitored over Douglas fir, European beech and pine forests of different ages as well as long-term series of tree growth, soil water and soil carbon data recorded at continuously monitored forests plots. We also illustrate the capacity of the GO+ model to simulate the provision of key ecosystem services, such as the long-term storage of carbon in biomass and soil under various management and climate scenarios.

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Community Earth System Model Simulations Reveal the Relative Importance of Afforestation and Forest Management to Surface Temperature in Eastern North America

Cited by 10 publications

References 22 publications

Quantifying Regional Surface Energy Responses to Forest Structural Change in Nordic Fennoscandia

Quantifying Regional Surface Energy Responses to Forest Structural Change in Nordic Fennoscandia

Challenging a Global Land Surface Model in a Local Socio-Environmental System

Energy, water and carbon exchanges in managed forest ecosystems: description, sensitivity analysis and evaluation of the INRAE GO+ model, version 3.0

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