Christopher R. Schwalm

A model-data intercomparison of CO2 exchange across North America: Results from the North American Carbon Program site synthesis

[1]xa0Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site-years, 10 biomes, and includes two large-scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models ability to simulate the seasonal cycle of CO2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model-data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.

Climate and vegetation controls on the surface water balance: Synthesis of evapotranspiration measured across a global network of flux towers

[1]xa0The Budyko framework elegantly reduces the complex spatial patterns of actual evapotranspiration and runoff to a general function of two variables: mean annual precipitation (MAP) and net radiation. While the methodology has first-order skill, departures from a globally averaged curve can be significant and may be usefully attributed to additional controls such as vegetation type. This paper explores the magnitude of such departures as detected from flux tower measurements of ecosystem-scale evapotranspiration, and investigates their attribution to site characteristics (biome, seasonal rainfall distribution, and frozen precipitation). The global synthesis (based on 167 sites with 764 tower-years) shows smooth transition from water-limited to energy-limited control, broadly consistent with catchment-scale relations and explaining 62% of the across site variation in evaporative index (the fraction of MAP consumed by evapotranspiration). Climate and vegetation types act as additional controls, combining to explain an additional 13% of the variation in evaporative index. Warm temperate winter wet sites (Mediterranean) exhibit a reduced evaporative index, 9% lower than the average value expected based on dryness index, implying elevated runoff. Seasonal hydrologic surplus explains a small but significant fraction of variance in departures of evaporative index from that expected for a given dryness index. Surprisingly, grasslands on average have a higher evaporative index than forested landscapes, with 9% more annual precipitation consumed by annual evapotranspiration compared to forests. In sum, the simple framework of supply- or demand-limited evapotranspiration is supported by global FLUXNET observations but climate type and vegetation type are seen to exert sizeable additional controls.

Compensatory water effects link yearly global land CO2 sink changes to temperature.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales. Here we use empirical models based on eddy covariance data and process-based models to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance. Our study indicates that spatial climate covariation drives the global carbon cycle response.

Climate change and site: relevant mechanisms and modeling techniques ☆

Forest growth modeling is moving away from description and toward explanation. The acceptance of global warming and effects related to climate change has reinforced this evolution. In the recent past, there have been several reviews of modeling techniques that have addressed, among other things, model structure and hierarchies within models. We argue that models seeking to adequately address climate change must include a specific suite of site characteristics. These range from primary effects of climate change (temperature, CO2, and O3 increase) to secondary effects (increase in soil temperature, microbial activity, and changes in precipitation patterns) and tertiary effects (changes in tree phenology and photosynthesis). This paper (i) compares 12 existing individual tree growth simulators designed to address climate change or related effects, (ii) proposes a set of site-related mechanisms and entities to be included in any modeling framework to address climate change, and (iii) suggests appropriate lines of research to attain the goal of a model driven by climate and able to be initialized with readily available metrics.

Carbon consequences of global hydrologic change, 1948–2009

[1]xa0Eddy covariance data (FLUXNET) provide key insights into how carbon and water fluxes covary with climate and ecosystem states. Here we merge FLUXNET data with reanalyzed evaporative fraction and dynamic land cover to create monthly global carbon flux anomalies attributable to hydrologic change from 1948 to 2009. Changes in land cover had a relative influence of <1% with an absolute effect less than uncertainty. The lack of trend globally in Net Ecosystem Productivity (NEP) attributable to hydroclimatic change masked positive trends in North America and Australia and negative trends in Africa and Asia. This spatial pattern coincided with geographic variation in hydroclimate excluding the temperature-limited high latitudes. Global NEP anomalies due to hydroclimatic variability ranged from −2.1 to +2.3 Pg C yr−1 relative to a global average sink of +2.8 Pg C yr−1. Trends in hydroclimate-induced NEP anomalies exceeded the background mean sink in many regions.

Ranking drivers of global carbon and energy fluxes over land

The accurate estimation of carbon and heat fluxes at global scale is paramount for future policy decisions in the context of global climate change. This paper analyzes the relative relevance of potential remote sensing and meteorological drivers of global carbon and energy fluxes over land. The study is done in an indirect way via upscaling both Gross Primary Production (GPP) and latent energy (LE) using Gaussian Process regression (GPR). In summary, GPR is successfully compared to multivariate linear regression (RMSE gain of +4.17% in GPP and +7.63% in LE) and kernel ridge regression (+2.91% in GPP and +3.07% in LE). The best GP models are then studied in terms of explanatory power based on the analysis of the lengthscales of the anisotropic covariance function, sensitivity maps of the predictive mean, and the robustness to distortions in the input variables. It is concluded that GPP is predominantly mediated by several vegetation indices and land surface temperature (LST), while LE is mostly driven by LST, global radiation and vegetation indices.