Land contributions to natural CO2 variability on time scales of centuries

The present paper addresses the origin of natural variability arising internally from the climate system of the global carbon cycle at centennial time scales. The investigation is based on the Max Planck Institute for Meteorology, Coupled Model Intercomparison Project Phase 5 (MPI-MCMIP5) preindustrial control simulations with the MPI Earth System Model in low resolution (MPI-ESM-LR) supplemented by additional simulations conducted for further analysis. The simulations show a distinct low-frequency component in the global terrestrial carbon content that induces atmospheric CO2 variations on centennial time scales of up to 3 ppm. The main drivers for these variations are low-frequency fluctuations in net primary production (NPP) of the land biosphere. The signal arises from small regions scattered across the whole globe with a pronounced source in North America. The main reason for the global NPP fluctuations is found in climatic changes leading to long-term variations in leaf area index, which largely determines the strength of photosynthetic carbon assimilation. The underlying climatic changes encompass several spatial diverse climatic alterations. For the particular case of North America, the carbon storage changes are (besides NPP) also dependent on soil respiration. This second mechanism is strongly connected to low-frequency variations in incoming shortwave radiation at the surface. ©2013. American Geophysical Union. All Rights Reserved

Palaeo plant diversity in subtropical Africa – ecological assessment of a conceptual model of climate–vegetation interaction

We here critically re-assess a conceptual model dealing with the potential effect of plant diversity on climate–vegetation feedback, and provide an improved version adjusted to plant types that prevailed during the African Humid Period (AHP). Our work contributes to the understanding of the timing and abruptness of vegetation decline at the end of the AHP, investigated by various working groups during the past two decades using a wide range of model and palaeoproxy reconstruction approaches. While some studies indicated an abrupt collapse of vegetation at the end of the AHP, others suggested a gradual decline. Claussen et al. (2013) introduced a new aspect in the discussion, proposing that plant diversity in terms of moisture requirements could affect the strength of climate–vegetation feedback. In a conceptual model study, the authors illustrated that high plant diversity could stabilize an ecosystem, whereas a reduction in plant diversity might allow for an abrupt regime shift under gradually changing environmental conditions. Based on recently published pollen data and the current state of ecological literature, we evaluate the representation of climate–vegetation feedback in this conceptual approach, and put the suggested conclusions into an ecological context. In principle, the original model reproduces the main features of different plant types interacting together with climate although vegetation determinants other than precipitation are neglected. However, the model cannot capture the diversity of AHP vegetation. Especially tropical gallery forest taxa, indirectly linked to local precipitation, are not appropriately represented. In order to fill the gaps in the description of plant types regarding AHP diversity, we modify the original model in four main aspects. First, the growth ranges in terms of moisture requirements are extended by upper limits to represent full environmental envelopes. Second, data-based AHP plant types replace the hypothetical plant types. Third, the tropical gallery forest type follows the gradual insolation forcing with a linear approximation because it relies more on large scale climate than on regional precipitation amounts. Fourth, we replace the dimensionless vegetation cover fractions with individual effective leaf areas to capture different contributions to climate–vegetation feedback. These adjustments allow for the consideration of a broader spectrum of plant types, plant-climate feedbacks, and implicitly for plant-plant interactions. With the consideration of full environmental envelopes and the prescribed retreat of the tropical gallery forest type we can simulate a diverse mosaic-like environment as it was reconstructed from pollen. Transient simulations of this diverse environment support the buffering effect of high functional diversity on ecosystem performance and precipitation, concluded by Claussen et al. (2013) from the simple approach. Sensitivity studies with different combinations of plant types highlight the importance of plant composition on system stability, and the stabilizing or destabilizing potential a single functional type may inherit. In a broader view, the adjusted model provides a useful tool to study the roles of real plant types in an ecosystem and their combined climate–vegetation feedback under changing precipitation regimes

Robust identification of local biogeophysical effects of land-cover change in a global climate model

Land-cover change (LCC) happens locally. However, in almost all simulation studies assessing biogeophysical climate effects of LCC, local effects (due to alterations in a model grid box) are mingled with nonlocal effects (due to changes in wide-ranging climate circulation). This study presents a method to robustly identify local effects by changing land surface properties in selected “LCC boxes” (where local plus nonlocal effects are present), while leaving others unchanged (where only nonlocal effects are present). While this study focuses on the climate effects of LCC, the method presented here is applicable to any land surface process that is acting locally but is capable of influencing wide-ranging climate when applied on a larger scale. Concerning LCC, the method is more widely applicable than methods used in earlier studies. The study illustrates the possibility of validating simulated local effects by comparison to observations on a global scale and contrasts the underlying mechanisms of local and nonlocal effects. In the MPI-ESM, the change in background climate induced by extensive deforestation is not strong enough to influence the local effects substantially, at least as long as sea surface temperatures are not affected. Accordingly, the local effects within a grid box are largely independent of the number of LCC boxes in the isolation approach

Why does the locally induced temperature response to land cover change differ across scenarios

Land cover change (LCC) affects temperature locally. The underlying biogeophysical effects are influenced not only by land use (location and extent) but also by natural biogeographic shifts and background climate. We examine the contributions of these three factors to surface temperature changes upon LCC and compare them across Coupled Model Intercomparison Project phase 5 (CMIP5) scenarios. To this end, we perform global deforestation simulations with an Earth system model to deduce locally induced changes in surface temperature for historical and projected forest cover changes. We find that the dominant factors differ between historical and future scenarios: the local temperature response is historically dominated by the factor land use change, but the two other factors become just as important in scenarios of future land use and climate. An additional factor contributing to differences across scenarios is the dependence on the extent of forests before LCC happens: For most locations, the temperature response is strongest when starting deforestation from low forest cover fractions

Identification of linear response functions from arbitrary perturbation experiments in the presence of noise - Part II. Application to the land carbon cycle in the MPI Earth System Model

The response function identification method introduced in the first part of this study is applied here to investigate the land carbon cycle in the Max Planck Institute for Meteorology Earth System Model. We identify from standard C4MIP 1 % experiments the linear response functions that generalize the land carbon sensitivities β and γ. The identification of these generalized sensitivities is shown to be robust by demonstrating their predictive power when applied to experiments not used for their identification. The linear regime for which the generalized framework is valid is estimated, and approaches to improve the quality of the results are proposed. For the generalized γ sensitivity, the response is found to be linear for temperature perturbations until at least 6 K. When this sensitivity is identified from a 2×CO2 experiment instead of the 1 % experiment, its predictive power improves, indicating an enhancement in the quality of the identification. For the generalized β sensitivity, the linear regime is found to extend up to CO2 perturbations of 100 ppm. We find that nonlinearities in the β response arise mainly from the nonlinear relationship between net primary production and CO2. By taking as forcing the resulting net primary production instead of CO2, the response is approximately linear until CO2 perturbations of about 850 ppm. Taking net primary production as forcing also substantially improves the spectral resolution of the generalized β sensitivity. For the best recovery of this sensitivity, we find a spectrum of internal timescales with two peaks, at 4 and 100 years. Robustness of this result is demonstrated by two independent tests. We find that the two-peak spectrum can be explained by the different characteristic timescales of functionally different elements of the land carbon cycle. The peak at 4 years results from the collective response of carbon pools whose dynamics is governed by fast processes, namely pools representing living vegetation tissues (leaves, fine roots, sugars, and starches) and associated litter. The peak at 100 years results from the collective response of pools whose dynamics is determined by slow processes, namely the pools that represent the wood in stem and coarse roots, the associated litter, and the soil carbon (humus). Analysis of the response functions that characterize these two groups of pools shows that the pools with fast dynamics dominate the land carbon response only for times below 2 years. For times above 25 years the response is completely determined by the pools with slow dynamics. From 100 years onwards only the humus pool contributes to the land carbon respons

Identification of linear response functions from arbitrary perturbation experiments in the presence of noise - Part I. Method development and toy model demonstration

Existent methods to identify linear response functions from data require tailored perturbation experiments, e.g., impulse or step experiments, and if the system is noisy, these experiments need to be repeated several times to obtain good statistics. In contrast, for the method developed here, data from only a single perturbation experiment at arbitrary perturbation are sufficient if in addition data from an unperturbed (control) experiment are available. To identify the linear response function for this ill-posed problem, we invoke regularization theory. The main novelty of our method lies in the determination of the level of background noise needed for a proper estimation of the regularization parameter: this is achieved by comparing the frequency spectrum of the perturbation experiment with that of the additional control experiment. The resulting noise-level estimate can be further improved for linear response functions known to be monotonic. The robustness of our method and its advantages are investigated by means of a toy model. We discuss in detail the dependence of the identified response function on the quality of the data (signal-to-noise ratio) and on possible nonlinear contributions to the response. The method development presented here prepares in particular for the identification of carbon cycle response functions in Part 2 of this study (Torres Mendonça et al., 2021a). However, the core of our method, namely our new approach to obtaining the noise level for a proper estimation of the regularization parameter, may find applications in also solving other types of linear ill-posed problems

Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change

Anthropogenic land cover change (ALCC) is one of the few climate forcings with still unknown sign of their climate response. Major uncertainty results from the often counteracting temperature responses to biogeochemical as compared to biogeophysical effects. Here, we separate the strength of these two effects for ALCC during the last millennium. We add unprecedented detail by (i) using a coupled atmosphere/ocean general circulation model (GCM), and (ii) applying a high-detail reconstruction of historical ALCC. We find that biogeophysical effects have a slight cooling influence on global mean temperature (-0.03 K in the 20th century), while biogeochemical effects lead to strong warming (0.16-0.18 K). During the industrial era, both effects cause significant changes in certain regions; only few regions, however, experience biogeophysical cooling strong enough to dominate the overall temperature response. This study therefore suggests that the climate response to historical ALCC, both globally and in most regions, is dominated by the rise in CO2 caused by ALCC emissions

Two drastically different climate states on an Earth-like terra-planet

We study an Earth-like terra-planet (water-limited terrestrial planet) with an overland recycling mechanism bringing fresh water back from the high latitudes to the low latitudes. By performing model simulations for such a planet we find two drastically different climate states for the same set of boundary conditions and parameter values: a cold and wet (CW) state with dominant low-latitude precipitation and a hot and dry (HD) state with only high-latitude precipitation. We notice that for perpetual equinox conditions, both climate states are stable below a certain threshold value of background soil albedo while above the threshold only the CW state is stable. Starting from the HD state and increasing background soil albedo above the threshold causes an abrupt shift from the HD state to the CW state resulting in a sudden cooling of about 35 °C globally, which is of the order of the temperature difference between present day and the Snowball Earth state. When albedo starting from the CW state is reduced down to zero the terra-planet does not shift back to the HD state (no closed hysteresis). This is due to the high cloud cover in the CW state hiding the surface from solar irradiation so that surface albedo has only a minor effect on the top of the atmosphere radiation balance. Additional simulations with present-day Earth's obliquity all lead to the CW state, suggesting a similar abrupt transition from the HD state to the CW state when increasing obliquity from zero. Our study also has implications for the habitability of Earth-like terra-planets. At the inner edge of the habitable zone, the higher cloud cover in the CW state cools the planet and may prevent the onset of a runaway greenhouse state. At the outer edge, the resupply of water at low latitudes stabilizes the greenhouse effect and keeps the planet in the HD state and may prevent water from getting trapped at high latitudes in frozen form. Overall, the existence of bistability in the presence of an overland recycling mechanism hints at the possibility of a wider habitable zone for Earth-like terra-planets at low obliquities