Land surface conductance linked to precipitation: Co-evolution of vegetation and climate in Earth system models.

Vegetation and precipitation are known to fundamentally influence each other. However, this interdependence is not fully represented in climate models because the characteristics of land surface (canopy) conductance to water vapor and CO2 are determined independently of precipitation. Working within a coupled atmosphere and land modelling framework (CAM6/CLM5; coupled Community Atmosphere Model v6/Community Land Model v5), we have developed a new theoretical approach to characterizing land surface conductance by explicitly linking its dynamic properties to local precipitation, a robust proxy for moisture available to vegetation. This will enable regional surface conductance characteristics to shift fluidly with climate change in simulations, consistent with general principles of co-evolution of vegetation and climate. Testing within the CAM6/CLM5 framework shows that climate simulations incorporating the new theory outperform current default configurations across several error metrics for core output variables when measured against observational data. In climate simulations for the end of this century the new, adaptive stomatal conductance scheme provides a revised prognosis for average and extreme temperatures over several large regions, with increased primary productivity through central and east Asia, and higher rainfall through North Africa and the Middle East. The new projections also reveal more frequent heatwaves than originally estimated for the south-eastern US and sub-Saharan Africa but less frequent heatwaves across east Europe and northeast Asia. These developments have implications for evaluating food security and risks from extreme temperatures in areas that are vulnerable to climate change.

Increasing the spatial and temporal impact of ecological research: A roadmap for integrating a novel terrestrial process into an Earth system model.

Terrestrial ecosystems regulate Earth's climate through water, energy, and biogeochemical transformations. Despite a key role in regulating the Earth system, terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change. Ecological insights can improve ESM realism and reduce process uncertainty, while ESMs offer ecologists an opportunity to broadly test ecological theory and increase the impact of their work by scaling concepts through time and space. Despite this mutualism, meaningfully integrating the two remains a persistent challenge, in part because of logistical obstacles in translating processes into mathematical formulas and identifying ways to integrate new theories and code into large, complex model structures. To help overcome this interdisciplinary challenge, we present a framework consisting of a series of interconnected stages for integrating a new ecological process or insight into an ESM. First, we highlight the multiple ways that ecological observations and modeling iteratively strengthen one another, dispelling the illusion that the ecologist's role ends with initial provision of data. Second, we show that many valuable insights, products, and theoretical developments are produced through sustained interdisciplinary collaborations between empiricists and modelers, regardless of eventual inclusion of a process in an ESM. Finally, we provide concrete actions and resources to facilitate learning and collaboration at every stage of data-model integration. This framework will create synergies that will transform our understanding of ecology within the Earth system, ultimately improving our understanding of global environmental change, and broadening the impact of ecological research.

Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions.

Land models are often used to simulate terrestrial responses to future environmental changes, but these models are not commonly evaluated with data from experimental manipulations. Results from experimental manipulations can identify and evaluate model assumptions that are consistent with appropriate ecosystem responses to future environmental change. We conducted simulations using three coupled carbon-nitrogen versions of the Community Land Model (CLM, versions 4, 4.5, and-the newly developed-5), and compared the simulated response to nitrogen (N) and atmospheric carbon dioxide (CO2) enrichment with meta-analyses of observations from similar experimental manipulations. In control simulations, successive versions of CLM showed a poleward increase in gross primary productivity and an overall bias reduction, compared to FLUXNET-MTE observations. Simulations with N and CO2 enrichment demonstrate that CLM transitioned from a model that exhibited strong nitrogen limitation of the terrestrial carbon cycle (CLM4) to a model that showed greater responsiveness to elevated concentrations of CO2 in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO2 enrichment than previous versions of the model. These simulations also exposed shortcomings in structural assumptions and parameterizations. Specifically, no version of CLM captures changes in plant physiology, allocation, and nutrient uptake that are likely important aspects of terrestrial ecosystems' responses to environmental change. These highlight priority areas that should be addressed in future model developments. Moving forward, incorporating results from experimental manipulations into model benchmarking tools that are used to evaluate model performance will help increase confidence in terrestrial carbon cycle projections.

Comparing optimal and empirical stomatal conductance models for application in Earth system models.

Earth system models (ESMs) rely on the calculation of canopy conductance in land surface models (LSMs) to quantify the partitioning of land surface energy, water, and CO2 fluxes. This is achieved by scaling stomatal conductance, gw , determined from physiological models developed for leaves. Traditionally, models for gw have been semi-empirical, combining physiological functions with empirically determined calibration constants. More recently, optimization theory has been applied to model gw in LSMs under the premise that it has a stronger grounding in physiological theory and might ultimately lead to improved predictive accuracy. However, this premise has not been thoroughly tested. Using original field data from contrasting forest systems, we compare a widely used empirical type and a more recently developed optimization-type gw model, termed BB and MED, respectively. Overall, we find no difference between the two models when used to simulate gw from photosynthesis data, or leaf gas exchange from a coupled photosynthesis-conductance model, or gross primary productivity and evapotranspiration for a FLUXNET tower site with the CLM5 community LSM. Field measurements reveal that the key fitted parameters for BB and MED, g1B and g1M, exhibit strong species specificity in magnitude and sensitivity to CO2 , and CLM5 simulations reveal that failure to include this sensitivity can result in significant overestimates of evapotranspiration for high-CO2 scenarios. Further, we show that g1B and g1M can be determined from mean ci /ca (ratio of leaf intercellular to ambient CO2 concentration). Applying this relationship with ci /ca values derived from a leaf δ13 C database, we obtain a global distribution of g1B and g1M , and these values correlate significantly with mean annual precipitation. This provides a new methodology for global parameterization of the BB and MED models in LSMs, tied directly to leaf physiology but unconstrained by spatial boundaries separating designated biomes or plant functional types.

Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models.

Many global change stresses on terrestrial and marine ecosystems affect not only ecosystem services that are essential to humankind, but also the trajectory of future climate by altering energy and mass exchanges with the atmosphere. Earth system models, which simulate terrestrial and marine ecosystems and biogeochemical cycles, offer a common framework for ecological research related to climate processes; analyses of vulnerability, impacts, and adaptation; and climate change mitigation. They provide an opportunity to move beyond physical descriptors of atmospheric and oceanic states to societally relevant quantities such as wildfire risk, habitat loss, water availability, and crop, fishery, and timber yields. To achieve this, the science of climate prediction must be extended to a more multifaceted Earth system prediction that includes the biosphere and its resources.

Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models.

Emerging insights into factors responsible for soil organic matter stabilization and decomposition are being applied in a variety of contexts, but new tools are needed to facilitate the understanding, evaluation, and improvement of soil biogeochemical theory and models at regional to global scales. To isolate the effects of model structural uncertainty on the global distribution of soil carbon stocks and turnover times we developed a soil biogeochemical testbed that forces three different soil models with consistent climate and plant productivity inputs. The models tested here include a first-order, microbial implicit approach (CASA-CNP), and two recently developed microbially explicit models that can be run at global scales (MIMICS and CORPSE). When forced with common environmental drivers, the soil models generated similar estimates of initial soil carbon stocks (roughly 1,400 Pg C globally, 0-100 cm), but each model shows a different functional relationship between mean annual temperature and inferred turnover times. Subsequently, the models made divergent projections about the fate of these soil carbon stocks over the 20th century, with models either gaining or losing over 20 Pg C globally between 1901 and 2010. Single-forcing experiments with changed inputs, temperature, and moisture suggest that uncertainty associated with freeze-thaw processes as well as soil textural effects on soil carbon stabilization were larger than direct temperature uncertainties among models. Finally, the models generated distinct projections about the timing and magnitude of seasonal heterotrophic respiration rates, again reflecting structural uncertainties that were related to environmental sensitivities and assumptions about physicochemical stabilization of soil organic matter. By providing a computationally tractable and numerically consistent framework to evaluate models we aim to better understand uncertainties among models and generate insights about factors regulating the turnover of soil organic matter.

Evaluating litter decomposition in earth system models with long-term litterbag experiments: an example using the Community Land Model version 4 (CLM4).

Decomposition is a large term in the global carbon budget, but models of the earth system that simulate carbon cycle-climate feedbacks are largely untested with respect to litter decomposition. We tested the litter decomposition parameterization of the community land model version 4 (CLM4), the terrestrial component of the community earth system model, with data from the long-term intersite decomposition experiment team (LIDET). The LIDET dataset is a 10-year study of litter decomposition at multiple sites across North America and Central America. We performed 10-year litter decomposition simulations comparable with LIDET for 9 litter types and 20 sites in tundra, grassland, and boreal, conifer, deciduous, and tropical forest biomes using the LIDET-provided climatic decomposition index to constrain temperature and moisture effects on decomposition. We performed additional simulations with DAYCENT, a version of the CENTURY model, to ask how well an established ecosystem model matches the observations. The results show large discrepancy between the laboratory microcosm studies used to parameterize the CLM4 litter decomposition and the LIDET field study. Simulated carbon loss is more rapid than the observations across all sites, and nitrogen immobilization is biased high. Closer agreement with the observations requires much lower decomposition rates, obtained with the assumption that soil mineral nitrogen severely limits decomposition. DAYCENT better replicates the observations, for both carbon mass remaining and nitrogen, independent of nitrogen limitation. CLM4 has low soil carbon in global earth system simulations. These results suggest that this bias arises, in part, from too rapid litter decomposition. More broadly, the terrestrial biogeochemistry of earth system models must be critically tested with observations, and the consequences of particular model choices must be documented. Long-term litter decomposition experiments such as LIDET provide a real-world process-oriented benchmark to evaluate models.

Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate.

Terrestrial gross primary production (GPP) is the largest global CO(2) flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 +/- 8 petagrams of carbon per year (Pg C year(-1)) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP's latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate-carbon cycle process models.

Changes in Arctic vegetation amplify high-latitude warming through the greenhouse effect.

Arctic climate is projected to change dramatically in the next 100 years and increases in temperature will likely lead to changes in the distribution and makeup of the Arctic biosphere. A largely deciduous ecosystem has been suggested as a possible landscape for future Arctic vegetation and is seen in paleo-records of warm times in the past. Here we use a global climate model with an interactive terrestrial biosphere to investigate the effects of adding deciduous trees on bare ground at high northern latitudes. We find that the top-of-atmosphere radiative imbalance from enhanced transpiration (associated with the expanded forest cover) is up to 1.5 times larger than the forcing due to albedo change from the forest. Furthermore, the greenhouse warming by additional water vapor melts sea-ice and triggers a positive feedback through changes in ocean albedo and evaporation. Land surface albedo change is considered to be the dominant mechanism by which trees directly modify climate at high-latitudes, but our findings suggest an additional mechanism through transpiration of water vapor and feedbacks from the ocean and sea-ice.

Forests and climate change: forcings, feedbacks, and the climate benefits of forests.

The world's forests influence climate through physical, chemical, and biological processes that affect planetary energetics, the hydrologic cycle, and atmospheric composition. These complex and nonlinear forest-atmosphere interactions can dampen or amplify anthropogenic climate change. Tropical, temperate, and boreal reforestation and afforestation attenuate global warming through carbon sequestration. Biogeophysical feedbacks can enhance or diminish this negative climate forcing. Tropical forests mitigate warming through evaporative cooling, but the low albedo of boreal forests is a positive climate forcing. The evaporative effect of temperate forests is unclear. The net climate forcing from these and other processes is not known. Forests are under tremendous pressure from global change. Interdisciplinary science that integrates knowledge of the many interacting climate services of forests with the impacts of global change is necessary to identify and understand as yet unexplored feedbacks in the Earth system and the potential of forests to mitigate climate change.

The importance of land-cover change in simulating future climates.

Adding the effects of changes in land cover to the A2 and B1 transient climate simulations described in the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change leads to significantly different regional climates in 2100 as compared with climates resulting from atmospheric SRES forcings alone. Agricultural expansion in the A2 scenario results in significant additional warming over the Amazon and cooling of the upper air column and nearby oceans. These and other influences on the Hadley and monsoon circulations affect extratropical climates. Agricultural expansion in the mid-latitudes produces cooling and decreases in the mean daily temperature range over many areas. The A2 scenario results in more significant change, often of opposite sign, than does the B1 scenario.