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

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

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

Contribution of anthropogenic land cover change emissions to preindustrial atmospheric CO2

Based on a recent reconstruction of anthropogenic land cover change (ALCC), we derive the associated CO2 emissions since 800 AD by two independent methods: a bookkeeping approach and a process model. The results are compared with the pre-industrial development of atmospheric CO2 known from antarctic ice cores. Our results show that pre-industrial CO2 emissions from ALCC have been relevant for the pre-industrial carbon cycle, although before 1750 AD their trace in atmospheric CO2 is obscured by other processes of similar magnitude. After 1750 AD, the situation is different: the steep increase in atmospheric CO2 until 1850 AD-this is before fossil fuel emissions rose to significant values-is to a substantial part explained by growing emissions from ALCC. © 2010 The Authors Tellus B © 2010 International Meteorological Institute in Stockholm

Reforestation in a high-CO2 world - Higher mitigation potential than expected, lower adaptation potential than hoped for

We assess the potential and possible consequences for the global climate of a strong reforestation scenario for this century. We perform model experiments using the Max Planck Institute Earth System Model (MPI-ESM), forced by fossil-fuel CO2 emissions according to the high-emission scenario Representative Concentration Pathway (RCP) 8.5, but using land use transitions according to RCP4.5, which assumes strong reforestation. Thereby, we isolate the land use change effects of the RCPs from those of other anthropogenic forcings. We find that by 2100 atmospheric CO2 is reduced by 85 ppm in the reforestation model experiment compared to the reference RCP8.5 model experiment. This reduction is higher than previous estimates and is due to increased forest cover in combination with climate and CO2 feedbacks. We find that reforestation leads to global annual mean temperatures being lower by 0.27 K in 2100. We find large annual mean warming reductions in sparsely populated areas, whereas reductions in temperature extremes are also large in densely populated areas

Past land use decisions have increased mitigation potential of reforestation

Anthropogenic land cover change (ALCC) influences global mean temperatures via counteracting effects: CO2 emissions contribute to global warming, while biogeophysical effects, in particular the increase in surface albedo, often impose a cooling influence. Previous studies of idealized, large-scale deforestation found that albedo cooling dominates over CO 2 warming in boreal regions, indicating that boreal reforestation is not an effective mitigation tool. Here we show the importance of past land use decisions in influencing the mitigation potential of reforestation on these lands. In our simulations, CO2 warming dominates over albedo cooling because past land use decisions resulted in the use of the most productive land with larger carbon stocks and less snow than on average. As a result past land use decisions extended CO2 dominance to most agriculturally important regions in the world, suggesting that in most places reversion of past land cover change could contribute to climate change mitigation. While the relative magnitude of CO2 and albedo effects remains uncertain, the historical land use pattern is found to be biased towards stronger CO2 and weaker albedo effects as compared to idealized large-scale deforestation. Copyright 2011 by the American Geophysical Union

Landschaft pflügt das Klima um

Seit der Erfindung von Ackerbau und Viehzucht wandelt der Mensch natürliche Vegetation in Acker- und Weideland um. Die Pflanzengemeinschaften der Kontinente bestimmen jedoch unser Klima auf vielfältige Weise mit. Der Mensch hat also möglicherweise schon Klimaveränderungen verursacht, lange bevor er begann, massiv Öl und Kohle zu verbrennen. Wissenschaftler am Max-Planck-Institut für Meteorologie in Hamburg haben die Ausbreitung der Landwirtschaft im letzten Jahrtausend untersucht. Dabei zeigt sich, dass der Mensch insbesondere das regionale Klima schon vor Beginn der Industrialisierung stark beeinflusst hat