Multi-scale Synthesis And Terrestrial Model Intercomparison Project Research Articles

The Terrestrial Biosphere Model Farm.

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ‐GUESS, ORCHIDEE, SiB‐3, and SiB‐CASA. All models were wrapped in a software framework driven with common forcing data, spin‐up, and run protocols specified by the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open‐source, cloud‐based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

Impact of Prior Terrestrial Carbon Fluxes on Simulations of Atmospheric CO2 Concentrations

AbstractTerrestrial ecosystems have a significant role in shaping the distribution of atmospheric CO2, but it is uncertain how much they affect CO2 concentrations. We assessed the impact of terrestrial fluxes derived from different biosphere models in the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) on atmospheric CO2 estimations based on a global chemical transport model (GEOS‐Chem) when these fluxes were applied as prior information. We examined the spatiotemporal uncertainty in terrestrial flux estimations from 15 MsTMIP terrestrial fluxes. We found high uncertainties in the terrestrial fluxes for temperate North America, tropical and temperate South America, southern Africa, Europe and tropical Asia. Sensitivity simulations showed that the annual mean CO2 concentration changed by 6.0–8.0 ppmv with the spread of the terrestrial fluxes. The interannual trend in the terrestrial fluxes could significantly affect the simulated trend of atmospheric CO2 concentrations. As a result of the spread in the prior terrestrial fluxes, large differences in the daily mean CO2 anomalies changed with an interquartile range of −1.0 to +1.0 ppmv and the magnitude of change in the sub‐daily CO2 concentrations was in the range 4.0–6.0 ppmv for China, the USA and Europe. Our results suggest an urgent need to increase the reliability of terrestrial flux estimates in CO2 simulations. Surface seasonal CO2 concentrations were simulated to change by ±2.0 ppmv in most regions of the world due to the differences in the ensemble mean fluxes, reflecting the impact of the uncertainties in the existing optimized terrestrial fluxes on CO2 simulations.

Divergence in land surface modeling: linking spread to structure

Divergence in land carbon cycle simulation is persistent and widespread. Regardless of model intercomparison project, results from individual models diverge significantly from each other and, in consequence, from reference datasets. Here we link model spread to structure using a 15-member ensemble of land surface models from the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as a test case. Our analysis uses functional benchmarks and model structure as predicted by model skill in a machine learning framework to isolate discrete aspects of model structure associated with divergence. We also quantify how initial conditions prejudice present-day model outcomes after centennial-scale transient simulations. Overall, the functional benchmark and machine learning exercises emphasize the importance of ecosystem structure in correctly simulating carbon and water cycling, highlight uncertainties in the structure of carbon pools, and advise against hard parametric limits on ecosystem function. We also find that initial conditions explain 90% of variation in global satellite-era values—initial conditions largely predetermine transient endpoints, historical environmental change notwithstanding. As MsTMIP prescribes forcing data and spin-up protocol, the range in initial conditions and high levels of predetermination are also structural. Our results suggest that methodological tools linking divergence to discrete aspects of model structure would complement current community best practices in model development.

Maximum carbon uptake rate dominates the interannual variability of global net ecosystem exchange

Terrestrial ecosystems contribute most of the interannual variability (IAV) in atmospheric carbon dioxide (CO2 ) concentrations, but processes driving the IAV of net ecosystem CO2 exchange (NEE) remain elusive. For a predictive understanding of the global C cycle, it is imperative to identify indicators associated with ecological processes that determine the IAV of NEE. Here, we decompose the annual NEE of global terrestrial ecosystems into their phenological and physiological components, namely maximum carbon uptake (MCU) and release (MCR), the carbon uptake period (CUP), and two parameters, α and β, that describe the ratio between actual versus hypothetical maximum C sink and source, respectively. Using long-term observed NEE from 66 eddy covariance sites and global products derived from FLUXNET observations, we found that the IAV of NEE is determined predominately by MCU at the global scale, which explains 48% of the IAV of NEE on average while α, CUP, β, and MCR explain 14%, 25%, 2%, and 8%, respectively. These patterns differ in water-limited ecosystems versus temperature- and radiation-limited ecosystems; 31% of the IAV of NEE is determined by the IAV of CUP in water-limited ecosystems, and 60% of the IAV of NEE is determined by the IAV of MCU in temperature- and radiation-limited ecosystems. The Lund-Potsdam-Jena (LPJ) model and the Multi-scale Synthesis and Terrestrial Model Inter-comparison Project (MsTMIP) models underestimate the contribution of MCU to the IAV of NEE by about 18% on average, and overestimate the contribution of CUP by about 25%. This study provides a new perspective on the proximate causes of the IAV of NEE, which suggest that capturing the variability of MCU is critical for modeling the IAV of NEE across most of the global land surface.

Land carbon models underestimate the severity and duration of drought\u2019s impact on plant productivity

The ability to accurately predict ecosystem drought response and recovery is necessary to produce reliable forecasts of land carbon uptake and future climate. Using a suite of models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we assessed modeled net primary productivity (NPP) response to, and recovery from, drought events against a benchmark derived from tree ring observations between 1948 and 2008 across forested regions of the US and Europe. We find short lag times (0–6 months) between climate anomalies and modeled NPP response. Although models accurately simulate the direction of drought legacy effects (i.e. NPP decreases), projected effects are approximately four times shorter and four times weaker than observations suggest. This discrepancy between observed and simulated vegetation recovery from drought reveals a potential critical model deficiency. Since productivity is a crucial component of the land carbon balance, models that underestimate drought recovery time could overestimate predictions of future land carbon sink strength and, consequently, underestimate forecasts of atmospheric CO2.

Contribution of environmental forcings to US runoff changes for the period 1950–2010

Runoff in the United States is changing, and this study finds that the measured change is dependent on the geographic region and varies seasonally. Specifically, observed annual total runoff had an insignificant increasing trend in the US between 1950 and 2010, but this insignificance was due to regional heterogeneity with both significant and insignificant increases in the eastern, northern, and southern US, and a greater significant decrease in the western US. Trends for seasonal mean runoff also differed across regions. By region, the season with the largest observed trend was autumn for the east (positive), spring for the north (positive), winter for the south (positive), winter for the west (negative), and autumn for the US as a whole (positive). Based on the detection and attribution analysis using gridded WaterWatch runoff observations along with semi-factorial land surface model simulations from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we found that while the roles of CO2 concentration, nitrogen deposition, and land use and land cover were inconsistent regionally and seasonally, the effect of climatic variations was detected for all regions and seasons, and the change in runoff could be attributed to climate change in summer and autumn in the south and in autumn in the west. We also found that the climate-only and historical transient simulations consistently underestimated the runoff trends, possibly due to precipitation bias in the MsTMIP driver or within the models themselves.

Sources of Uncertainty in Modeled Land Carbon Storage within and across Three MIPs: Diagnosis with Three New Techniques

Terrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land–Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.

Technical note: 3-hourly temporal downscaling of monthly global terrestrial biosphere model net ecosystem exchange

Abstract. The land surface provides a boundary condition to atmospheric forward and flux inversion models. These models require prior estimates of CO2 fluxes at relatively high temporal resolutions (e.g., 3-hourly) because of the high frequency of atmospheric mixing and wind heterogeneity. However, land surface model CO2 fluxes are often provided at monthly time steps, typically because the land surface modeling community focuses more on time steps associated with plant phenology (e.g., seasonal) than on sub-daily phenomena. Here, we describe a new dataset created from 15 global land surface models and 4 ensemble products in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), temporally downscaled from monthly to 3-hourly output. We provide 3-hourly output for each individual model over 7 years (2004–2010), as well as an ensemble mean, a weighted ensemble mean, and the multi-model standard deviation. Output is provided in three different spatial resolutions for user preferences: 0.5° × 0.5°, 2.0° × 2.5°, and 4.0° × 5.0° (latitude × longitude). These data are publicly available from doi:10.3334/ORNLDAAC/1315.

An analysis of global terrestrial carbon, water and energy dynamics using the carbon–nitrogen coupled CLASS-CTEMN+ model

A carbon–nitrogen (C–N) coupled terrestrial ecosystem model, CLASS-CTEMN+, was used to simulate global terrestrial carbon, water, and energy cycles at 0.5-degree spatial resolution from 1901 to 2010. Forcing and initializing data sets from the North American Carbon Program (NACP)—Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) were used. Two versions of the model (the C–N coupled and the C-only version) were compared. The C–N (C) version of the model simulated global annual total gross ecosystem productivity (GEP) of 122.7 (128.2) PgCyr−1, ecosystem respiration (Re) of 119.1 (122.4) PgCyr−1, net ecosystem productivity (NEP) of 3.64 (5.82) PgCyr−1, net primary productivity (NPP) of 62.7 (67.4) PgCyr−1, latent heat (LE) of 146.2 (167.4) ZJyr−1, sensible heat (H) of 194.0 (162) ZJyr−1, soil organic carbon (SOC) of 1230 (1328) PgC, and total vegetation biomass (Tvg) of 608 (629) PgC, over the 1980–2010 period were comparable to scaled observations and modelling studies in the literature. Our results suggest that altough inclusion of N cycling has not had a large effect on the simulated total global terrestrial ecosystem C budget over this period; however the temporal evolution and spatial distribution of the simulated C budget revealed important influences of the N cycle. Analysis of long-term annual variations in response to evolving climate, CO2 concentration levels, and N deposition over the 1901–2010 period showed largely similar increasing trends in global GEP, Re, NEP and NPP until about 1970, after which the rate of increase was suppressed in the C–N coupled version of the model relative to the C-only version. Spatial plots showed that GEP and Re were reduced in the C–N coupled version of the model over the temperate and boreal regions and in high latitudes, where N limitation is well documented. The strongest response in NEP was in the boreal forests of North America, Northern Europe and Northwestern Asia, which showed decreases in response to the inclusion of a N cycle. Commensurate with the reductions in GEP and NEP, leaf area index (LAI) and total vegetation biomass (Tvg) also showed decreases in the boreal region, while SOC showed wider decreases in temperate, boreal and northern regions.This study helps to identify and diagnose the impact of N controls on the simulated spatiotemporal patterns of terrestrial C sinks and sources, and to evaluate and quantify the variability of simulated N cycle effects on different plant functional types (PFTs). These results have important implications for future climate change scenarios because the response to increasing CO2, N deposition, and climate patterns shows diverging model behavior as compared with the C-only version, when a N cycle is incorporated in the model.

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901–2005

AbstractDespite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature‐based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr−1, respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time‐varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr−1 during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.

Surface Solar Radiation in North America: A Comparison of Observations, Reanalyses, Satellite, and Derived Products*

Abstract Observations of daily surface solar or shortwave radiation data from over 4000 stations have been gathered, covering much of the continental United States as well as portions of Alberta and British Columbia, Canada. The quantity of data increases almost linearly from 1998, when only several hundred stations had data. A quality-control procedure utilizing threshold values along with computing the clear-sky radiation envelope for individual stations was implemented to both screen bad data and rescue informative data. Over two-thirds of the observations are seen as acceptable. There are 15 different surface solar radiation products assessed relative to observations, including reanalyses [Twentieth-Century Reanalysis (20CR), CFS Reanalysis and Reforecast (CFSRR), ERA-Interim, Japanese 55-year Reanalysis Project (JRA-55), MERRA, NARR, and NCEP–NCAR Reanalysis 1 (NCEP-1)], derived products [observations from the CRU and NCEP-1 (CRU–NCEP); Daily Surface Weather and Climatological Summaries (Daymet); Global Land Data Assimilation System, version 1 (GLDAS-1); Global Soil Wetness Project Phase 3 (GSWP3); Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP); and phase 2 of the North American Land Data Assimilation System (NLDAS-2)], and two satellite products (CERES and GOES). All except the CERES product have daily or finer temporal resolution. The RMSE of spatial biases is greater than 18 W m−2 for 13 of the 15 products over the summer season (June–August). None of the daily resolution products fulfill all three desirable criteria of low (<5%) annual or seasonal bias, high correlation with observed cloudiness, and correct distribution of clear-sky radiation. Some products display vestiges of underlying algorithm issues [e.g., from the Mountain Microclimate Simulation Model, version 4.3 (MTCLIM 4.3)] or bias-correction methods. A new bias-correction method is introduced that preserves clear-sky radiation values and better replicates cloudiness statistics. The current quantity of data over the continental United States suggests that a solar radiation product based on, or enhanced with, observations is feasible.

Toward “optimal” integration of terrestrial biosphere models

AbstractMultimodel ensembles (MME) are commonplace in Earth system modeling. Here we perform MME integration using a 10‐member ensemble of terrestrial biosphere models (TBMs) from the Multiscale synthesis and Terrestrial Model Intercomparison Project (MsTMIP). We contrast optimal (skill based for present‐day carbon cycling) versus naïve (“one model‐one vote”) integration. MsTMIP optimal and naïve mean land sink strength estimates (−1.16 versus −1.15 Pg C per annum respectively) are statistically indistinguishable. This holds also for grid cell values and extends to gross uptake, biomass, and net ecosystem productivity. TBM skill is similarly indistinguishable. The added complexity of skill‐based integration does not materially change MME values. This suggests that carbon metabolism has predictability limits and/or that all models and references are misspecified. Resolving this issue requires addressing specific uncertainty types (initial conditions, structure, and references) and a change in model development paradigms currently dominant in the TBM community.

The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project – Part 2: Environmental driver data

Abstract. Ecosystems are important and dynamic components of the global carbon cycle, and terrestrial biospheric models (TBMs) are crucial tools in further understanding of how terrestrial carbon is stored and exchanged with the atmosphere across a variety of spatial and temporal scales. Improving TBM skills, and quantifying and reducing their estimation uncertainties, pose significant challenges. The Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal multi-scale and multi-model intercomparison effort set up to tackle these challenges. The MsTMIP protocol prescribes standardized environmental driver data that are shared among model teams to facilitate model–model and model–observation comparisons. This paper describes the global and North American environmental driver data sets prepared for the MsTMIP activity to both support their use in MsTMIP and make these data, along with the processes used in selecting/processing these data, accessible to a broader audience. Based on project needs and lessons learned from past model intercomparison activities, we compiled climate, atmospheric CO2 concentrations, nitrogen deposition, land use and land cover change (LULCC), C3 / C4 grasses fractions, major crops, phenology and soil data into a standard format for global (0.5° × 0.5° resolution) and regional (North American: 0.25° × 0.25° resolution) simulations. In order to meet the needs of MsTMIP, improvements were made to several of the original environmental data sets, by improving the quality, and/or changing their spatial and temporal coverage, and resolution. The resulting standardized model driver data sets are being used by over 20 different models participating in MsTMIP. The data are archived at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov) to provide long-term data management and distribution.

Impact of large‐scale climate extremes on biospheric carbon fluxes: An intercomparison based on MsTMIP data

AbstractUnderstanding the role of climate extremes and their impact on the carbon (C) cycle is increasingly a focus of Earth system science. Climate extremes such as droughts, heat waves, or heavy precipitation events can cause substantial changes in terrestrial C fluxes. On the other hand, extreme changes in C fluxes are often, but not always, driven by extreme climate conditions. Here we present an analysis of how extremes in temperature and precipitation, and extreme changes in terrestrial C fluxes are related to each other in 10 state‐of‐the‐art terrestrial carbon models, all driven by the same climate forcing. We use model outputs from the North American Carbon Program Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). A global‐scale analysis shows that both droughts and heat waves translate into anomalous net releases of CO2 from the land surface via different mechanisms: Droughts largely decrease gross primary production (GPP) and to a lower extent total respiration (TR), while heat waves slightly decrease GPP but increase TR. Cold and wet periods have a smaller opposite effect. Analyzing extremes in C fluxes reveals that extreme changes in GPP and TR are often caused by strong shifts in water availability, but for extremes in TR shifts in temperature are also important. Extremes in net CO2 exchange are equally strongly driven by deviations in temperature and precipitation. Models mostly agree on the sign of the C flux response to climate extremes, but model spread is large. In tropical forests, C cycle extremes are driven by water availability, whereas in boreal forests temperature plays a more important role. Models are particularly uncertain about the C flux response to extreme heat in boreal forests.

The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project – Part 1: Overview and experimental design

Abstract. Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding of land–atmosphere carbon exchange to larger regions. The North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. Here we provide an overview of the MsTMIP effort and describe how the MsTMIP experimental design enables the assessment and quantification of TBM structural uncertainty. Model structure refers to the types of processes considered (e.g., nutrient cycling, disturbance, lateral transport of carbon), and how these processes are represented (e.g., photosynthetic formulation, temperature sensitivity, respiration) in the models. By prescribing a common experimental protocol with standard spin-up procedures and driver data sets, we isolate any biases and variability in TBM estimates of regional and global carbon budgets resulting from differences in the models themselves (i.e., model structure) and model-specific parameter values. An initial intercomparison of model structural differences is represented using hierarchical cluster diagrams (a.k.a. dendrograms), which highlight similarities and differences in how models account for carbon cycle, vegetation, energy, and nitrogen cycle dynamics. We show that, despite the standardized protocol used to derive initial conditions, models show a high degree of variation for GPP, total living biomass, and total soil carbon, underscoring the influence of differences in model structure and parameterization on model estimates.