scholarly journals New Insights into Terrestrial Ecosystems Through Reanalysis

Eos ◽  
2021 ◽  
Vol 102 ◽  
Author(s):  
Roland Baatz ◽  
Harrie-Jan Hendricks-Franssen ◽  
Harry Vereecken
Keyword(s):  
Carbon Cycle ◽  
Land Surface ◽  
Terrestrial Ecosystems ◽  
Reanalysis Data ◽  
Hydrologic Cycle

Reanalysis data, already used to understand terrestrial processes on the physical land surface, the carbon cycle, and the hydrologic cycle, is now being applied to terrestrial ecosystems.

MODELLING THE TERRESTRIAL NITROGEN AND PHOSPHORUS CYCLE IN THE UVic ESCM 

2021 ◽  
Author(s):  
Makcim De Sisto ◽  
Andrew MacDougall
Keyword(s):  
Carbon Cycle ◽  
Nutrient Limitation ◽  
Nitrogen Cycle ◽  
Land Surface ◽  
Regular Structure ◽  
Terrestrial Ecosystems ◽  
Phosphorus Cycle ◽  
Nitrogen And Phosphorus ◽  
N Cycle ◽  
C:P Ratio

<div> <div> <div> <p>The role of nitrogen and phosphorus in terrestrial ecosystems has been shown to be critical in the regulation of the terrestrial carbon cycle. Therefore, the implementation of nutrient limitation in Earth System Models should be considered in order to have a more accurate representation of carbon fluxes, vegetation distribution and the response of the biosphere to climate change. Previous attempts to introduce the terrestrial nitrogen cycle and nutrient limitation in the UVic ESCM resulted in an incomplete project that was not added to the regular structure of the model. Here, we intend to improve the current state of the terrestrial nitrogen cycle and to develop a new terrestrial phosphorus cycle that will be coupled to the carbon cycle. The most prominent changes in the N cycle are the enforcement of N mass conservation and the merge with a deep land surface and a new wetland model. The N and P cycles estimates the fluxes between three organic pools: litter, soil and vegetation compartments (leaf, root and wood), two N pools (NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>) and one inorganic P pool. The basic structure of the N cycle was left in place, it estimates the inputs via biological nitrogen fixation and outputs via leaching, furthermore, with the merger with the new wetland model denitrification was added to the N loss of the system. The P cycle accounts the inputs from estimations of rock weathering and losses from occlusion and leaching. Both cycles regulate the vegetation system in 2 ways: (1) by controlling vegetation biomass if nutrient is limiting, reducing the amount of carbon in the plant compartments until the C:N or C:P ratio is met and (2) directly regulating the primary productivity by taking into account the relationship between leaf N and P and the maximum carboxylation rate (Vcmax). We aim to improve projections of the future CO<sub>2</sub> fertilization feedback, and thus carbon budgets and ZEC.</p> </div> </div> </div>

scholarly journals GlobSim (v1.0): deriving meteorological time series for point locations from multiple global reanalyses

2019 ◽  
Vol 12 (11) ◽  
pp. 4661-4679 ◽  
Author(s):  
Bin Cao ◽  
Xiaojing Quan ◽  
Nicholas Brown ◽  
Emilie Stewart-Jones ◽  
Stephan Gruber
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Ground Surface ◽  
Reanalysis Data ◽  
Meteorological Variables ◽  
Ensemble Simulations ◽  
Driving Time ◽  
Wide Range ◽  
Global Coverage ◽  
Uncertain Input

Abstract. Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses – currently ERA5, ERA-Interim, JRA-55 and MERRA-2 – to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for 10 terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulations and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data.

A novel application of an adaptive surrogate-based modeling optimization (ASMO) for the Community Territory System Model (CTSM) in Alaska

2021 ◽  
Author(s):  
Yifan Cheng ◽  
Andrew Newman ◽  
Sean Swenson ◽  
David Lawrence ◽  
Anthony Craig ◽  
...  
Keyword(s):  
High Resolution ◽  
River Basin ◽  
Land Surface ◽  
Computational Cost ◽  
Reanalysis Data ◽  
Freshwater Ecosystems ◽  
System Model ◽  
Surface Model ◽  
Yukon River ◽  
Yukon River Basin

<p>Climate-induced changes in snow cover, river flow, and freshwater ecosystems will greatly affect the indigenous groups in the Alaska and Yukon River Basin. To support policy-making on climate adaptation and mitigation for these underrepresented groups, an ongoing interdisciplinary effort is being made to combine Indigenous Knowledge with western science (https://www.colorado.edu/research/arctic-rivers/).</p><p>A foundational component of this project is a high fidelity representation of the aforementioned land surface processes. To this end, we aim to obtain a set of reliable high-resolution parameters for the Community Territory System Model (CTSM) for the continental scale domain of Alaska and the entire Yukon River Basin, which will be used in climate change simulations. CTSM is a complex, physically based state-of-the-science land surface model that includes complex vegetation and canopy representation, a multi-layer snow model, as well as hydrology and frozen soil physics necessary for the representation of streamflow and permafrost. Two modifications to the default CTSM configuration were made. First, we used CTSM that is implemented with hillslope hydrology to better capture the fine-scale hydrologic spatial heterogeneity in complex terrain. Second, we updated the input soil textures and organic carbon in CTSM using the high-resolution SoilGrid dataset.</p><p>In this study, we performed a multi-objective optimization on snow and streamflow metrics using an adaptive surrogate-based modeling optimization (ASMO). ASMO permits optimization of complex land-surface models over large domains through the use of surrogate models to minimize the computational cost of running the full model for every parameter combination. We ran CTSM at a spatial resolution of 1/24<sup>th</sup> degree and a temporal resolution of one hour using the ERA5 reanalysis data as the meteorological forcings. The ERA5 reanalysis data were bias-corrected to account for the orographic effects. We will discuss the ASMO-CTSM coupling workflow, performance characteristics of the optimization (e.g., computational cost, iterations), and comparisons of the default configuration and optimized model performance.</p>

scholarly journals Advantages of using a fast urban canopy model as compared to a full mesoscale model to simulate the urban heat island of Barcelona

2016 ◽  
Author(s):  
M. García-Díez ◽  
D. Lauwaet ◽  
H. Hooyberghs ◽  
J. Ballester ◽  
K. De Ridder ◽  
...  
Keyword(s):  
Simple Model ◽  
Land Surface ◽  
Climate Model ◽  
Mesoscale Model ◽  
Urban Climate ◽  
Urban Canopy ◽  
Forecast Model ◽  
Reanalysis Data ◽  
Urban Canopy Model ◽  
Canopy Model

Abstract. As most of the population lives in urban environments, the simulation of the urban climate has become a key problem in the framework of the climate change impact assessment. However, the high computational power required by these simulations is a severe limitation. Here we present a study on the performance of a Urban Climate Model (UrbClim), designed to be several orders of magnitude faster than a full-fledge mesoscale model. The simulations are validated with station data and with land surface temperature observations retrieved by satellites. To explore the advantages of using a simple model like UrbClim, the results are compared with a simulation carried out with a state-of-the-art mesoscale model, the Weather Research and Forecasting model, using an Urban Canopy model. The effect of using different driving data is explored too, by using both relatively low resolution reanalysis data (70 km) and a higher resolution forecast model (15 km). The results show that, generally, the performance of the simple model is comparable to or better than the mesoscale model. The exception are the winds and the day-to-day correlation in the reanalysis driven run, but these problems disappear when taking the boundary conditions from the higher resolution forecast model.

scholarly journals Constraining a land surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System

2016 ◽  
Author(s):  
G. J. Schürmann ◽  
T. Kaminski ◽  
C. Köstler ◽  
N. Carvalhais ◽  
M. Voßbeck ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Land Surface ◽  
Data Stream ◽  
Atmospheric Co2 ◽  
Surface Model ◽  
Terrestrial Biosphere ◽  
Multiple Observations ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We describe the Max Planck Institute Carbon Cycle Data Assimilation System (MPI-CCDAS) built around the tangent-linear version of the land surface scheme of the MPI-Earth System Model v1 (JSBACH). The simulated terrestrial biosphere processes (phenology and carbon balance) were constrained by observations of the fraction of photosynthetically active radiation (TIP-FAPAR product) and by observations of atmospheric CO2 at a global set of monitoring stations for the years 2005–2009. The system successfully, and computationally efficiently, improved average foliar area and northern extra-tropical seasonality of foliar area when constrained by TIP-FAPAR. Global net and gross carbon fluxes were improved when constrained by atmospheric CO2, although the system tended to underestimate tropical productivity. Assimilating both data streams jointly allowed the MPI-CCDAS to match both observations (TIP-FAPAR and atmospheric CO2) equally well as the single data stream assimilation cases, therefore overall increasing the appropriateness of the resultant parameter values and biosphere dynamics. Our study thus highlights the role of the TIP-FAPAR product in stabilising the underdetermined atmospheric inversion problem and demonstrates the value of multiple-data stream assimilation for the simulation of terrestrial biosphere dynamics. The constraint on regional gross and net CO2 flux patterns is limited through the parametrisation of the biosphere model. We expect improvement on that aspect through a refined initialisation strategy and inclusion of further biosphere observations as constraints.

scholarly journals Dynamical prediction of terrestrial ecosystems and the global carbon cycle: A 25-year hindcast experiment

2008 ◽  
Vol 22 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Ning Zeng ◽  
Jin-Ho Yoon ◽  
Augustin Vintzileos ◽  
G. James Collatz ◽  
Eugenia Kalnay ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Terrestrial Ecosystems ◽  
Global Carbon Cycle ◽  
Global Carbon

scholarly journals ORCHILEAK (revision 3875): a new model branch to simulate carbon transfers along the terrestrial–aquatic continuum of the Amazon basin

2017 ◽  
Vol 10 (10) ◽  
pp. 3821-3859 ◽  
Author(s):  
Ronny Lauerwald ◽  
Pierre Regnier ◽  
Marta Camino-Serrano ◽  
Bertrand Guenet ◽  
Matthieu Guimberteau ◽  
...  
Keyword(s):  
Land Surface ◽  
Water Surface ◽  
Amazon Basin ◽  
River System ◽  
Terrestrial Ecosystems ◽  
Plant Litter ◽  
Mean Values ◽  
New Model ◽  
Canopy Soil ◽  
C Cycle

Abstract. Lateral transfer of carbon (C) from terrestrial ecosystems into the inland water network is an important component of the global C cycle, which sustains a large aquatic CO2 evasion flux fuelled by the decomposition of allochthonous C inputs. Globally, estimates of the total C exports through the terrestrial–aquatic interface range from 1.5 to 2.7 Pg C yr−1 (Cole et al., 2007; Battin et al., 2009; Tranvik et al., 2009), i.e. of the order of 2–5 % of the terrestrial NPP. Earth system models (ESMs) of the climate system ignore these lateral transfers of C, and thus likely overestimate the terrestrial C sink. In this study, we present the implementation of fluvial transport of dissolved organic carbon (DOC) and CO2 into ORCHIDEE (Organising Carbon and Hydrology in Dynamic Ecosystems), the land surface scheme of the Institut Pierre-Simon Laplace ESM. This new model branch, called ORCHILEAK, represents DOC production from canopy and soils, DOC and CO2 leaching from soils to streams, DOC decomposition, and CO2 evasion to the atmosphere during its lateral transport in rivers, as well as exchange with the soil carbon and litter stocks on floodplains and in swamps. We parameterized and validated ORCHILEAK for the Amazon basin, the world's largest river system with regard to discharge and one of the most productive ecosystems in the world. With ORCHILEAK, we are able to reproduce observed terrestrial and aquatic fluxes of DOC and CO2 in the Amazon basin, both in terms of mean values and seasonality. In addition, we are able to resolve the spatio-temporal variability in C fluxes along the canopy–soil–water continuum at high resolution (1°, daily) and to quantify the different terrestrial contributions to the aquatic C fluxes. We simulate that more than two-thirds of the Amazon's fluvial DOC export are contributed by the decomposition of submerged litter. Throughfall DOC fluxes from canopy to ground are about as high as the total DOC inputs to inland waters. The latter, however, are mainly sustained by litter decomposition. Decomposition of DOC and submerged plant litter contributes slightly more than half of the CO2 evasion from the water surface, while the remainder is contributed by soil respiration. Total CO2 evasion from the water surface equals about 5 % of the terrestrial NPP. Our results highlight that ORCHILEAK is well suited to simulate carbon transfers along the terrestrial–aquatic continuum of tropical forests. It also opens the perspective that provided parameterization, calibration and validation is performed for other biomes, the new model branch could improve the quantification of the global terrestrial C sink and help better constrain carbon cycle–climate feedbacks in future projections.

scholarly journals Impacts of temperature extremes on European vegetation during the growing season

2017 ◽  
Author(s):  
Lukas Baumbach ◽  
Jonatan F. Siegmund ◽  
Magdalena Mittermeier ◽  
Reik V. Donner
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Extreme Temperature ◽  
Terrestrial Ecosystems ◽  
Growing Season ◽  
Temperature Extremes ◽  
Minor Effect ◽  
European Continent

Abstract. Temperature is a key factor controlling plant growth and vitality in the temperate climates of the mid-latitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.

scholarly journals The impact of structural error on parameter constraint in a climate model

2016 ◽  
Vol 7 (4) ◽  
pp. 917-935 ◽  
Author(s):  
Doug McNeall ◽  
Jonny Williams ◽  
Ben Booth ◽  
Richard Betts ◽  
Peter Challenor ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Parameter Space ◽  
Land Surface ◽  
History Matching ◽  
Climate Model ◽  
The Other ◽  
Future Climate Change ◽  
Amazon Forest ◽  
Structural Error ◽  
Rule Out

Abstract. Uncertainty in the simulation of the carbon cycle contributes significantly to uncertainty in the projections of future climate change. We use observations of forest fraction to constrain carbon cycle and land surface input parameters of the global climate model FAMOUS, in the presence of an uncertain structural error. Using an ensemble of climate model runs to build a computationally cheap statistical proxy (emulator) of the climate model, we use history matching to rule out input parameter settings where the corresponding climate model output is judged sufficiently different from observations, even allowing for uncertainty. Regions of parameter space where FAMOUS best simulates the Amazon forest fraction are incompatible with the regions where FAMOUS best simulates other forests, indicating a structural error in the model. We use the emulator to simulate the forest fraction at the best set of parameters implied by matching the model to the Amazon, Central African, South East Asian, and North American forests in turn. We can find parameters that lead to a realistic forest fraction in the Amazon, but that using the Amazon alone to tune the simulator would result in a significant overestimate of forest fraction in the other forests. Conversely, using the other forests to tune the simulator leads to a larger underestimate of the Amazon forest fraction. We use sensitivity analysis to find the parameters which have the most impact on simulator output and perform a history-matching exercise using credible estimates for simulator discrepancy and observational uncertainty terms. We are unable to constrain the parameters individually, but we rule out just under half of joint parameter space as being incompatible with forest observations. We discuss the possible sources of the discrepancy in the simulated Amazon, including missing processes in the land surface component and a bias in the climatology of the Amazon.

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