Land surface models (LSMs) are a vital tool for understanding, projecting, and predicting the dynamics of the land surface and its role within the Earth system, under global change. Driven by the ...need to address a set of key questions, LSMs have grown in complexity from simplified representations of land surface biophysics to encompass a broad set of interrelated processes spanning the disciplines of biophysics, biogeochemistry, hydrology, ecosystem ecology, community ecology, human management, and societal impacts. This vast scope and complexity, while warranted by the problems LSMs are designed to solve, has led to enormous challenges in understanding and attributing differences between LSM predictions. Meanwhile, the wide range of spatial scales that govern land surface heterogeneity, and the broad spectrum of timescales in land surface dynamics, create challenges in tractably representing processes in LSMs. We identify three “grand challenges” in the development and use of LSMs, based around these issues: managing process complexity, representing land surface heterogeneity, and understanding parametric dynamics across the broad set of problems asked of LSMs in a changing world. In this review, we discuss progress that has been made, as well as promising directions forward, for each of these challenges.
Plain Language Summary
Land surface models (LSMs) are the part of climate models that simulate processes happening at the Earth's surface. These include reflection of the sunlight, evaporation from ecosystems, and the amount of carbon from human emissions that the land takes up. LSMs also need to simulate how human management of the land surface changes the climate both directly (e.g., via the effect on evaporation) and in the long term (via changing the amount of carbon stored in wood and soil). Not surprisingly, trying to make a single mathematical representation of all of these different parts of the Earth system is difficult. Here we discuss themes that repeatedly affect all teams developing LSMs: how to manage the increasing number of complicated model components, how to represent the high degree of variability of the land surface, and how to predict how the properties of the surface (particularly those of plant communities) will change. These are large problems, with no obvious easy solutions. We hope to spark discussion and investment into their resolution, concomitant with the increasing importance of LSMs as our best tools for translating possible trajectories of climate change into impacts on humans, ecosystems, food and water supplies, and river systems.
Key Points
Land surface models have grown in complexity, and new methods of managing this complexity are required for scientific understanding
New methods are also needed to represent, classify, and benchmark models across the multidimensional heterogeneity of the land surface
A further challenge is to constrain model parameters in ways that are consistent with allowing long‐term ecological dynamics to occur
Climate change is expected to drive increased tree mortality through drought, heat stress, and insect attacks, with manifold impacts on forest ecosystems. Yet, climate-induced tree mortality and ...biotic disturbance agents are largely absent from process-based ecosystem models. Using data sets from the western USA and associated studies, we present a framework for determining the relative contribution of drought stress, insect attack, and their interactions, which is critical for modeling mortality in future climates. We outline a simple approach that identifies the mechanisms associated with two guilds of insects – bark beetles and defoliators – which are responsible for substantial tree mortality. We then discuss cross-biome patterns of insect-driven tree mortality and draw upon available evidence contrasting the prevalence of insect outbreaks in temperate and tropical regions. We conclude with an overview of tools and promising avenues to address major challenges. Ultimately, a multitrophic approach that captures tree physiology, insect populations, and tree–insect interactions will better inform projections of forest ecosystem responses to climate change.
Climate-driven vegetation mortality is occurring globally and is predicted to increase in the near future. The expected climate feedbacks of regional-scale mortality events have intensified the need ...to improve the simple mortality algorithms used for future predictions, but uncertainty regarding mortality processes precludes mechanistic modeling. By integrating new evidence from a wide range of fields, we conclude that hydraulic function and carbohydrate and defense metabolism have numerous potential failure points, and that these processes are strongly interdependent, both with each other and with destructive pathogen and insect populations. Crucially, most of these mechanisms and their interdependencies are likely to become amplified under a warmer, drier climate. Here, we outline the observations and experiments needed to test this interdependence and to improve simulations of this emergent global phenomenon.
Earth System Models typically use static responses to temperature to calculate photosynthesis and respiration, but experimental evidence suggests that many plants acclimate to prevailing ...temperatures. We incorporated representations of photosynthetic and leaf respiratory temperature acclimation into the Community Land Model, the terrestrial component of the Community Earth System Model. These processes increased terrestrial carbon pools by 20 Pg C (22%) at the end of the 21st century under a business‐as‐usual (Representative Concentration Pathway 8.5) climate scenario. Including the less certain estimates of stem and root respiration acclimation increased terrestrial carbon pools by an additional 17 Pg C (~40% overall increase). High latitudes gained the most carbon with acclimation, and tropical carbon pools increased least. However, results from both of these regions remain uncertain; few relevant data exist for tropical and boreal plants or for extreme temperatures. Constraining these uncertainties will produce more realistic estimates of land carbon feedbacks throughout the 21st century.
Key Points
Most global land surface models do not account for plant temperature acclimation
With acclimation, future carbon gain was larger in high latitudes and smaller in the tropics
Studies determining acclimation in tropical and boreal plants are needed
The Community Land Model version 4 overestimates gross primary production (GPP) compared with estimates from FLUXNET eddy covariance towers. The revised model of Bonan et al. (2011) is consistent ...with FLUXNET, but values for the leaf‐level photosynthetic parameterVcmaxthat yield realistic GPP at the canopy‐scale are lower than observed in the global synthesis of Kattge et al. (2009), except for tropical broadleaf evergreen trees. We investigate this discrepancy betweenVcmaxand canopy fluxes. A multilayer model with explicit calculation of light absorption and photosynthesis for sunlit and shaded leaves at depths in the canopy gives insight to the scale mismatch between leaf and canopy. We evaluate the model with light‐response curves at individual FLUXNET towers and with empirically upscaled annual GPP. Biases in the multilayer canopy with observedVcmaxare similar, or improved, compared with the standard two‐leaf canopy and its lowVcmax, though the Amazon is an exception. The difference relates to light absorption by shaded leaves in the two‐leaf canopy, and resulting higher photosynthesis when the canopy scaling parameterKn is low, but observationally constrained. Larger Kndecreases shaded leaf photosynthesis and reduces the difference between the two‐leaf and multilayer canopies. The low modelVcmaxis diagnosed from nitrogen reduction of GPP in simulations with carbon‐nitrogen biogeochemistry. Our results show that the imposed nitrogen reduction compensates for deficiency in the two‐leaf canopy that produces high GPP. Leaf trait databases (Vcmax), within‐canopy profiles of photosynthetic capacity (Kn), tower fluxes, and empirically upscaled fields provide important complementary information for model evaluation.
Key Points
CLM overestimates gross primary production
Shaded leaf photosynthesis erroneously contributes to high GPP
Multi‐scale leaf, canopy, and global data are needed for model evaluation
Large-scale afforestation programmes are generally presented as effective ways of increasing the terrestrial carbon sink while preserving water availability and biodiversity. Yet, a meta-analysis of ...both numerical and observational studies suggests that further research is needed to support this view. The use of inappropriate concepts (e.g., the biotic pump theory), the poor simulation of key processes (e.g., tree mortality, water use efficiency), and the limited model ability to capture recent observed trends (e.g., increasing water vapour deficit, terrestrial carbon uptake) should all draw our attention to the limitations of available theories and Earth System Models. Observations, either based on remote sensing or on early afforestation initiatives, also suggest potential trade-offs between terrestrial carbon uptake and water availability. There is thus a need to better monitor and physically understand the observed fluctuations of the terrestrial water and carbon cycles to promote suitable nature-based mitigation pathways depending on pre-existing vegetation, scale, as well as baseline and future climates.
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•Indiscriminate tree planting may be both inefficient and harmful to water resources.•Inappropriate concepts or insufficiently evaluated Earth System Models may provide misleading policy recommendations.•Observations suggest potential trade-offs between terrestrial carbon uptake and water availability.•Further research efforts are needed to support more effective and sustainable tree restoration policies.
The nitrogen cycle and its effect on carbon uptake in the
terrestrial biosphere is a recent progression in earth system models. As
with any new component of a model, it is important to understand the
...behaviour, strengths, and limitations of the various process
representations. Here we assess and compare five land surface models with
nitrogen cycles that are used as the terrestrial components of some of the
earth system models in CMIP6. The land surface models were run offline with
a common spin-up and forcing protocol. We use a historical control
simulation and two perturbations to assess the model nitrogen-related
performances: a simulation with atmospheric carbon dioxide increased by 200 ppm and one with nitrogen deposition increased by 50 kgN ha−1 yr−1. There is generally greater variability in productivity response
between models to increased nitrogen than to carbon dioxide. Across the five
models the response to carbon dioxide globally was 5 % to 20 % and the
response to nitrogen was 2 % to 24 %. The models are not evenly distributed
within the ensemble range, with two of the models having low productivity
response to nitrogen and another one with low response to elevated atmospheric
carbon dioxide, compared to the other models. In all five models individual
grid cells tend to exhibit bimodality, with either a strong response to
increased nitrogen or atmospheric carbon dioxide but rarely to both to an
equal extent. However, this local effect does not scale to either the
regional or global level. The global and tropical responses are generally
more accurately modelled than boreal, tundra, or other high-latitude areas
compared to observations. These results are due to divergent choices in the
representation of key nitrogen cycle processes. They show the need for more
observational studies to enhance understanding of nitrogen cycle processes,
especially nitrogen-use efficiency and biological nitrogen fixation.
We have observed that the dry-season length (DSL) has increased over southern Amazonia since 1979, primarily owing to a delay of its ending dates (dry-season end, DSE), and is accompanied by a ...prolonged fire season. A poleward shift of the subtropical jet over South America and an increase of local convective inhibition energy in austral winter (June–August) seem to cause the delay of the DSE in austral spring (September–November). These changes cannot be simply linked to the variability of the tropical Pacific and Atlantic Oceans. Although they show some resemblance to the effects of anthropogenic forcings reported in the literature, we cannot attribute them to this cause because of inadequate representation of these processes in the global climate models that were presented in the Intergovernmental Panel on Climate Change's Fifth Assessment Report. These models significantly underestimate the variability of the DSE and DSL and their controlling processes. Such biases imply that the future change of the DSE and DSL may be underestimated by the climate projections provided by the Intergovernmental Panel on Climate Change's Fifth Assessment Report models. Although it is not clear whether the observed increase of the DSL will continue in the future, were it to continue at half the rate of that observed, the long DSL and fire season that contributed to the 2005 drought would become the new norm by the late 21st century. The large uncertainty shown in this study highlights the need for a focused effort to better understand and simulate these changes over southern Amazonia.
Forest ecosystem models based on heuristic water stress functions poorly predict tropical forest response to drought partly because they do not capture the diversity of hydraulic traits (including ...variation in tree size) observed in tropical forests. We developed a continuous porous media approach to modeling plant hydraulics in which all parameters of the constitutive equations are biologically interpretable and measurable plant hydraulic traits (e.g., turgor loss point πtlp, bulk elastic modulus ε, hydraulic capacitance Cft, xylem hydraulic conductivity ks,max, water potential at 50 % loss of conductivity for both xylem (P50,x) and stomata (P50,gs), and the leaf : sapwood area ratio Al : As). We embedded this plant hydraulics model within a trait forest simulator (TFS) that models light environments of individual trees and their upper boundary conditions (transpiration), as well as providing a means for parameterizing variation in hydraulic traits among individuals. We synthesized literature and existing databases to parameterize all hydraulic traits as a function of stem and leaf traits, including wood density (WD), leaf mass per area (LMA), and photosynthetic capacity (Amax), and evaluated the coupled model (called TFS v.1-Hydro) predictions, against observed diurnal and seasonal variability in stem and leaf water potential as well as stand-scaled sap flux. Our hydraulic trait synthesis revealed coordination among leaf and xylem hydraulic traits and statistically significant relationships of most hydraulic traits with more easily measured plant traits. Using the most informative empirical trait–trait relationships derived from this synthesis, TFS v.1-Hydro successfully captured individual variation in leaf and stem water potential due to increasing tree size and light environment, with model representation of hydraulic architecture and plant traits exerting primary and secondary controls, respectively, on the fidelity of model predictions. The plant hydraulics model made substantial improvements to simulations of total ecosystem transpiration. Remaining uncertainties and limitations of the trait paradigm for plant hydraulics modeling are highlighted.
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