Environmental Science Land Use, Land Cover and Land Management Change: Definitions, Scenarios, and Role in the Climate System
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  • LAST REVIEWED: 10 June 2020
  • LAST MODIFIED: 25 September 2018
  • DOI: 10.1093/obo/9780199363445-0103

Introduction

Using land resources in fulfilment of their needs, humans have either altered land surface properties (land cover changes) or modified characteristics of the existing land cover (land management changes). Nowadays, over 70 percent of the world’s land surface is under direct human influence. Examples of land cover changes include deforestation for cropland expansion, urban growth, or polder drainage. In some cases these land cover changes may induce harmful effects, such as biodiversity losses or increased landslide susceptibility. Human land-use activities have also resulted in large changes to the biogeochemical and biogeophysical properties of the Earth surface, with profound implications for the climate system. Since the start of the industrial revolution, land use changes have been estimated to contribute to about 26 percent of all human-induced emission (145 GtC +/- 50 GtC during the period 1870–2015). Today this contribution has decreased to 9 percent of all anthropogenic emissions (1.0 GtC/yr during the period 2006–2015). As a consequence, it is estimated that 40 percent +/- 16 percent of present-day radiative forcing can be attributed to LCMC. In addition to this biogeochemical effect, land use and land cover changes also affect the climate by modifying the biogeophysical properties of the land surface (e.g., albedo, evapotranspiration, and roughness). While the overall biogeophysical effect of LCMC highly depends on the geographical context, land use and land cover changes may locally affect surface temperature in similar proportions to other climate forcings. Besides land cover changes, whose climatic consequences have been much studied in recent decades, land management changes have recently been advanced as another important human influence on the climate system, with an aggregated impact on surface temperature of similar magnitude as (and sometimes larger than) land cover changes. Given the important effects of land use on climate, and given its substantial historic contribution to global warming, land surface management may be used as a tool to mitigate climate change and adapt to its impacts. Recent evidence suggests that various forms of land-based mitigation may be required for reaching the targets fixed by the Paris Climate Agreement. The way in which humans manage the land surface is therefore inevitably connected to sustainable development and human and societal health.

General Overview: Definitions

As the terminology used in this field of research is regularly a cause of confusion, this overview will provide a few relevant definitions and examples (based on Intergovernmental Panel on Climate Change 2000): Land cover refers to the physical elements covering the land surface and to their physical properties. Examples of land cover types are trees, crops, grasses, lakes, and cities. Land use refers to the total of arrangements, activities, and inputs undertaken in a certain land cover type (a set of human actions). The term land use is also used in the sense of the social and economic purposes for which land is managed (e.g., grazing, timber extraction, and conservation). It thus denotes how people utilize the land. Examples of land use are agriculture, recreational, transport, and residential. Finally, land management refers to controlling the characteristics of a given land cover without changing the type of land cover. Land management practices aim at the conservation or intensification of existing land use. Examples of land management are forestry, irrigation, tillage, and increased harvest rates through application of fertilizers and pesticides. One single land cover type may thus encompass several land uses (e.g., a forest may be used for recreational, biological conservation, and wood production purposes) and may be managed in several ways (parts of the forest may undergo forestry, other parts not). Land use change refers to a change in the use or management of land by humans, which may lead to a change in land cover. Examples of land cover change are deforestation, reservoir installation, urbanization, or polder drainage, whereas examples of land management changes are irrigation expansion, or the introduction of conservation agriculture. Many acronyms have been introduced in the past to denote the processes described above, such as land use change (LUC, see Le Quéré, et al. 2018); land cover change (LCC, see Luyssaert, et al. 2014); land management change (LMC, see Luyssaert, et al. 2014); anthropogenic land cover change (ALCC, see Davin, et al. 2007 and Pongratz, et al. 2010; land use, land-use change, and forestry (LULUCF, see Intergovernmental Panel on Climate Change 2014); or agriculture, forestry and other land use (AFOLU, see Intergovernmental Panel on Climate Change 2014). In this article, the term land cover and land management change (LCMC) will be used consistent with Bright, et al. 2017. Although land cover changes may also result from natural processes, the focus in LCMC is on the consequence of human activities, either directly (e.g., agricultural expansion) or indirectly (e.g., species migration because of anthropogenic climate change). LCMC has manifold consequences, and may induce harmful effects in some cases, such as biodiversity loss, analyzed in Newbold, et al. 2015 and Newbold, et al. 2016; and an increased susceptibility for erosion, flash floods, and landslides, described in Jacobs, et al. 2016a and Jacobs, et al. 2016b. The focus of this article will be on the climatic effects of anthropogenic LCMC.

Reference Works

There exist several excellent reference works on physical climate science (e.g., Hartmann 1994, Wallace and Hobbs 2006) that describe the importance of the land surface as a component of the climate system. More specialized is Bonan 2008: it introduces all the necessary tools to understand the two-way interactions between terrestrial ecosystems and climate. Finally, the Intergovernmental Panel on Climate Change (IPCC) 2013 synthesizes and assesses the literature related to climate change. Land-climate interactions, and the Climatic Impacts of LCMC, are part of this assessment.

  • Bonan, G. 2008. Ecological climatology. Cambridge, UK: Cambridge Univ. Press.

    DOI: 10.1017/CBO9780511805530Save Citation »Export Citation »

    This book describes all the major processes through which vegetation interacts with the climate. The author of the book has made a tremendous contribution implementing such processes into climate models.

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  • Hartmann, D. L. 1994. Global physical climatology. San Diego, CA: Academic Press.

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    Similar to Wallace and Hobbs, this book provides a general overview of atmospheric and climate science.

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  • IPCC. 2013. Climate change 2013: The physical science basis. Contribution of Working Group 1 to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by T. F. Stocker, D. Qin, G. -K. Plattner, et al. Cambridge, UK and New York: Cambridge Univ. Press.

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    The consecutive assessment reports by the Intergovernmental Panel on Climate Change (IPCC) provide a unique and comprehensive overview of the state of knowledge in climate science. The technical summary, the summary for policymakers, and the headline statement represent condensed versions of this vast report.

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  • Wallace, J. M., and P. V. Hobbs. 2006. Atmospheric science: An introductory survey. San Diego, CA: Academic Press.

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    An excellent introduction to the physical science basis of atmospheric and climate science.

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Journals

The climatic effects of land cover have been reported in a large number of international peer-reviewed journals, including for instance Science, Nature, Nature Geoscience, Nature Climate Change, Nature Communications, Proceedings of the National Academy of Sciences, Journal of Climate, Geophysical Research Letters, Environmental Research Letters, Journal of Geophysical Research and Geoscientific Model Development.

Reconstructions, Observations, and Scenarios of Land Cover and Management Change (LCMC)

Holocene LCMC

A first key data source for inferring Holocene LCMC is the HYDE3.2 dataset presented in Klein Goldewijk 2017. The dataset provides high-resolution (5 arc minute) maps of cropland (irrigated and rainfed), rice (irrigated and rain), and grazing land (pasture, rangeland, converted and non-converted natural rangeland). While this database is essentially describing agricultural expansion, modelers also use this dataset to infer maps of historical deforestation. A second group of European and global land cover reconstructions emerged from the Past Global Changes (PAGES) program; see Kaplan, et al. 2009; Kaplan, et al. 2011; Kaplan, et al. 2017; and Gaillard, et al. 2010. Among many applications, these reconstructions have been used to estimate the impact of deforestation on the global carbon cycle during the Holocene; see Boyle, et al. 2011 and Kaplan, et al. 2012. A third reconstruction of cropland area for the last millennium was developed specifically for use in earth system models in Pongratz, et al. 2008. Note that the scale of cropland expansion is still debated, since, for example, total amount of agricultural area in HYDE3.2 is substantially smaller compared to the estimate in Kaplan, et al. 2011, a difference which can largely be explained by differences in methodology, as explained in Klein Goldewijk 2017.

Industrial Period LCMC

Since the start of the Industrial Revolution, deforestation has occurred mainly at mid-latitudes; see Boisier, et al. 2013. While deforestation for agriculture, infrastructure, and urban use represents the main form of LCMC, other types also occurred in this historical period. Notably both irrigation extent and irrigation amounts grew rapidly throughout the 20th century, with estimated total volumes increasing from approximately 500 km3 yr-1 at the start of the 20th century to about 2600 km3 yr-1 by the year 2000, as overviewed in Shiklomanov 2000 and Siebert, et al. 2015. Although representing only ~2 percent of the global land surface according to Siebert, et al. 2005, irrigated lands account for over 40 percent of the global food production and irrigation thus constitutes an essential aspect of food security (Bonfils and Lobell 2007; Thiery, et al. 2017). Finally, other types of land management emerged and expanded throughout the 20th century, such as forestry, presented in McGrath, et al. 2015, and no-till farming and conservation agriculture, as seen in Prestele, et al. 2018, but in many cases global datasets are currently still lacking (see Erb, et al. 2017).

Recent LCMC

With the advent of satellite remote sensing and high-performance computing facilities, Donchyts, et al. 2016 shows it is now possible to assess LCMC at high spatiotemporal resolution, and to process the data on the fly on new Cloud platforms. While presenting a new, high-resolution dataset of forest cover change, Hansen, et al. 2013 highlights that historical mid-latitude deforestation, which has dominated the LCMC signal throughout most of the industrial period, has mostly stabilized since 2000. Indeed, forest transition theory suggests that industrialization, urbanization, and cropland abandonment may, under some contingent local socioeconomic and ecological boundary conditions, even lead to a reversal of deforestation and to forest gain (Meyfroidt and Lambin 2011). In contrast, in recent years most of the deforestation has occurred in the tropics. While deforestation rates are decreasing in Brazil, this benefit is offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. The second largest contribution of forest loss, both in absolute and relative terms, arises from boreal deforestation, and is caused primarily by fire and forestry according to Hansen, et al. 2013. Again LCMC is not limited to deforestation. For instance, Donchyts, et al. 2016 showed that between 1985 and 2015, an area of about 173,000 km² was converted to land (e.g., due to polder drainage and the desiccation of the Aral Sea and Lake Chad; see Lauwaet, et al. 2012), while 115,000 km² has been converted into water (e.g., due to dam construction). Furthermore, while irrigation extent has stabilized in several regions of the world (Bonfils and Lobell 2007), conservation agriculture expanded from 45 Mha in 1999 to 157 Mha in 2013; see Derpsch, et al. 2010; Kassam, et al. 2015; and Prestele, et al. 2018.

Projected Future LCMC

While future LCMC is subject to large uncertainties, incorporating these scenarios into climate models represents an important step toward improving the realism of future climate projections. To this end, several efforts have been made to construct scenarios of future land cover and land management scenarios. Notably, the Land Use Harmonization project (LUH) has been preparing harmonized sets of land-use data or the Coupled Model Intercomparison Project Phases Five (LUH1 > CMIP5; see Hurtt, et al. 2011) and Six (LUH2 > CMIP6; see Lawrence, et al. 2016). LUH thereby essentially streamlines different data sources of land cover (e.g., HYDE for the historical period and output from Integrated Assessment Models [IAMs] for the future period) into one consistent dataset which can be incorporated into earth system models (ESMs). Future land cover scenarios greatly depend on the followed shared socioeconomic pathway (SSP). Yet some general findings may be discerned. For instance, Foley, et al. 2011 notes that in the future irrigation activities are expected to expand and intensify to meet growing demands for food, fiber, and energy. However, projected climate change caused by both anthropogenic greenhouse gas emissions and land-use activities is likely to constrain this evolution. Also urban land is projected to continue expanding, and population growth combined with city development may possibly lead to a tripling of urban land area by 2030 compared to approximately the year 2000; see Seto, et al. 2012.

  • Foley, J. A., N. Ramankutty, K. A. Brauman, et al. 2011. Solutions for a cultivated planet. Nature 478.7369: 337–342.

    DOI: 10.1038/nature10452Save Citation »Export Citation »

    Seminal study demonstrating that it is possible to drastically improve food production while at the same time reducing the environmental footprint of agriculture.

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  • Hurtt, G. C., L. P. Chini, S. Frolking, et al. 2011. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change 109.1–2: 117–161.

    DOI: 10.1007/s10584-011-0153-2Save Citation »Export Citation »

    In this paper the first generation of harmonized land use scenarios (LUH1) is presented. This data has been used by earth system modeling teams across the world to prepare simulations for the Coupled Model Intercomparison Project Phase Five (CMIP5), thereby supporting the IPCC’s fifth assessment report.

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  • Lawrence, D. M., G. C. Hurtt, A. Arneth, et al. 2016. The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: Rationale and experimental design. Geoscientific Model Development 9.9: 2973–2988.

    DOI: 10.5194/gmd-9-2973-2016Save Citation »Export Citation »

    In this paper the second generation of harmonized land-use scenarios (LUH2) is presented, which will be used in Coupled Model Intercomparison Project Phase Six (CMIP6) and in dedicated experiments from the Land Use Model Intercomparison Project (LUMIP) to assess the impacts of LCMC on climate.

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  • Seto, K. C., B. Güneralp, and L. R. Hutyra. 2012. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences 109.40: 16083–16088.

    DOI: 10.1073/pnas.1211658109Save Citation »Export Citation »

    Presentation of future global urbanization scenarios, showing a potential tripling of urban land cover by 2030 relative to 2000, with notable hotspots in the West Africa, the Western Ghats, Sri Lanka, and the African Great Lakes region. These changes may have important consequences for the regional climate (see Thiery, et al. 2015 and Thiery, et al. 2016, cited under Importance of Land-Climate Interactions).

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Climatic Impacts of LCMC

The conversion of natural ecosystems into human-managed systems (for food, timber, and habitat) is one of the most profound transformations of the environment induced by human activities. Luyssaert, et al. 2014 assesses that the extent of this change is already of global importance, with more than 70 percent of all terrestrial ecosystems currently under direct human management, including agricultural land, managed forest, and human settlements, and that it is expected to further increase in future decades. In addition to having major consequences for biodiversity and natural hazards, LCMC also has important implications for climate. Since the start of the industrial period, LCMC is responsible for roughly one-third of the anthropogenic emissions of carbon dioxide (mainly due to deforestation), thus substantially contributing to recent global warming (IPCC 2013). This effect is referred to as the Biogeochemical Impacts on Climate of LCMC. In addition to biogeochemical effects, Bonan 2008 describes how LCMC also affects climate by modifying the biogeophysical properties of the land surface such as surface albedo, evapotranspiration, and surface roughness. This effect is referred to as the Biogeophysical Impacts on Climate of LCMC. To better understand the role of LCMC in the climate system, its biogeochemical and biogeophysical impacts need to be compared against other historical climate forcers (see Vavrus, et al. 2008; Dermody, et al. 2012; Cook, et al. 2012).

Importance of Land-Climate Interactions

Seneviratne, et al. 2010 notes that the land surface is a key component of the climate system. The exchange of carbon, water, and energy between the land and the atmosphere highly depends on the land properties, such as vegetation characteristics or the soil moisture state (Zscheischler, et al. 2015). This is particularly relevant for the occurrence of climate extremes, such as heat waves, droughts, or intense precipitation. For instance, a soil moisture deficit is an important precursor for heat waves—see Hirschi, et al. 2011; Mueller and Seneviratne 2012; and Miralles, et al. 2014—while the conservative water use of trees may mitigate extreme heat waves over forests relative to grasslands (see Teuling, et al. 2010). Thiery, et al. 2015 and Thiery, et al. 2016 highlight that land processes also strongly affect the hydrological cycle as, for instance, illustrated by the impact of lakes on regional climate.

  • Hirschi, M., S. I. Seneviratne, V. Alexandrov, et al. 2011. Observational evidence for soil-moisture impact on hot extremes in southeastern Europe. Nature Geoscience 4.1: 17–21.

    DOI: 10.1038/ngeo1032Save Citation »Export Citation »

    In this study the authors use quantile regression to show a strong relationship between soil moisture deficits and the occurrence of hot temperature extremes in southeastern Europe.

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  • Miralles, D. G., A. J. Teuling, C. C. Van Heerwaarden, and J. V. G. de Arellano. 2014. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nature Geoscience 7.5: 345–349.

    DOI: 10.1038/ngeo2141Save Citation »Export Citation »

    Local-scale boundary layer dynamics and soil desiccation reinforce heat waves initially triggered by large-scale heat advection, as shown in this study.

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  • Mueller, B., and S. I. Seneviratne. 2012. Hot days induced by precipitation deficits at the global scale. Proceedings of the National Academy of Sciences 109.31: 12398–12403.

    DOI: 10.1073/pnas.1204330109Save Citation »Export Citation »

    Study extending the findings of Hirschi, et al. 2011 to a global scale, confirming that there exists a strong relationship between the number of hot days and precipitation deficits over wide areas of the world.

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  • Seneviratne, S. I., T. Corti, E. L. Davin, et al. 2010. Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews 99.3–4: 125–161.

    DOI: 10.1016/j.earscirev.2010.02.004Save Citation »Export Citation »

    Seminal study providing a comprehensive overview of the interactions between soil moisture conditions and the climate system, with a particular focus on evapotranspiration, temperature, and precipitation.

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  • Teuling, A. J., S. I. Seneviratne, R. Stöckli, et al. 2010. Contrasting response of European forest and grassland energy exchange to heatwaves. Nature Geoscience 3.10: 722–727.

    DOI: 10.1038/ngeo950Save Citation »Export Citation »

    It is debated whether forests or grasslands respond more strongly to heat waves. Analyzing FLUXNET data, this study contributes to this debate by showing that the conservative water use of forests leads to enhanced temperatures in the short term, but mitigates the effects of the most intense heat waves.

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  • Thiery, W., E. L. Davin, H. J. Panitz, M. Demuzere, S. Lhermitte, and N. Van Lipzig. 2015. The impact of the African Great Lakes on the regional climate. Journal of Climate 28.10: 4061–4085.

    DOI: 10.1175/JCLI-D-14-00565.1Save Citation »Export Citation »

    Surface-climate interactions are crucial for understanding precipitation and temperature patterns in the region of the African Great Lakes. The African Great Lakes nearly double precipitation amounts over their surface, and exert a profound influence on atmospheric dynamics through contrasting surface temperature and evapotranspiration patterns.

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  • Thiery, W., E. L. Davin, S. I. Seneviratne, K. Bedka, S. Lhermitte, and N. P. van Lipzig. 2016. Hazardous thunderstorm intensification over Lake Victoria. Nature Communications 7:12786.

    DOI: 10.1038/ncomms12786Save Citation »Export Citation »

    Future changes in lake-climate interactions render Lake Victoria a hotspot of climate change, in particular regarding extreme precipitation intensification.

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  • Zscheischler, J., R. Orth, and S. I. Seneviratne. 2015. A submonthly database for detecting changes in vegetation‐atmosphere coupling. Geophysical Research Letters 42.22: 9816–9824.

    DOI: 10.1002/2015GL066563Save Citation »Export Citation »

    In this study the authors present a new index characterizing the coupling between the state of vegetation on the one hand and near-surface air temperature on the other hand.

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Methods for Assessing LCMC Impacts on Climate

Model Experiments

Observational records such as historical temperature measurements do not allow for a direct estimation of LCMC influence on temperature (or other variables), because this effect is intertwined with that of other climate drivers. For this reason, most of the current knowledge regarding the climatic consequences of LCMC relies on climate-modeling studies. The first international initiative to assess the effects of LCMC on climate was the Land-Use and Climate, Identification of robust impacts (LUCID; see Pitman, et al. 2009) model intercomparison project. Results of this project highlighted that while LCMC from preindustrial times until today likely had a small impact on global temperature, it may have locally affected temperature in similar proportion as CO2 increase over the same period did (de Noblet-Ducoudré, et al. 2012). LUCID was succeeded by the Land Use Model Intercomparison Project (LUMIP), a global climate model intercomparison providing a protocol for inclusion of LCMC (Lawrence, et al. 2016). LUMIP is endorsed by the Coupled Model Intercomparison Project Phase 6 (CMIP6) and thereby directly contributes to the regular scientific assessments by the Intergovernmental Panel on Climate Change (IPCC). Three approaches may be used to assess the effects of LCMC in models, referred to as the reconstruction method, the factorial experiment, and the tile analysis method (See Meier, et al. 2018 for a detailed discussion). In the reconstruction method, an LCMC signal is extracted through the comparison of neighboring model grid cells that differ in land cover or LCMC rates. This method, also referred to as the window-searching strategy, has already been applied in a model context as in Kumar, et al. 2013, as well as to observational data in Li, et al. 2015. Secondly, in the factorial experiment approach two model simulations are compared that differ in terms of their land cover; see de Noblet-Ducoudré, et al. 2012; Akkermans, et al. 2014; He, et al. 2014; Thiery, et al. 2017; Hirsch, et al. 2018. In the final approach, climate variables are compared over tiles representing different vegetation types within the same grid cell. While the results of this method are qualitatively similar compared to the other methods, they show a stronger LCMC signal (Malyshev, et al. 2015; Thiery, et al. 2017; Hirsch, et al. 2018; Meier, et al. 2018). Important advantages of this method are that it requires less model simulations compared to the factorial approach and a less complicated statistical treatment of the model output compared to the reconstruction method.

  • Akkermans, T., W. Thiery, and N. P. van Lipzig. 2014. The regional climate impact of a realistic future deforestation scenario in the Congo Basin. Journal of Climate 27.7: 2714–2734.

    DOI: 10.1175/JCLI-D-13-00361.1Save Citation »Export Citation »

    High-resolution regional climate model experiment comparing the influence of a realistic forest removal scenario to a middle-of-the-road climate projection. The results indicate that deforestation contributes to an additional warming of about 50 percent of the magnitude of greenhouse gas–induced warming in the Congo Basin.

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  • de Noblet-Ducoudré, N., J. P. Boisier, A. Pitman, et al. 2012. Determining robust impacts of land-use-induced land cover changes on surface climate over North America and Eurasia: Results from the first set of LUCID experiments. Journal of Climate 25.9: 3261–3281.

    DOI: 10.1175/JCLI-D-11-00338.1Save Citation »Export Citation »

    This study builds further upon the initial analyses of LUCID data presented in Pitman, et al. 2009. The results highlight that the biogeophysical LCMC signal may be of similar magnitude (and opposite sign) compared to greenhouse warming, but also highlight that intermodel uncertainties are large.

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  • He, F., S. J. Vavrus, J. E. Kutzbach, W. F. Ruddiman, J. O. Kaplan, and K. M. Krumhardt. 2014. Simulating global and local surface temperature changes due to Holocene anthropogenic land cover change. Geophysical Research Letters 41.2: 623–631.

    DOI: 10.1002/2013GL058085Save Citation »Export Citation »

    This study applies a global climate model to compare the biogeochemical and biogeophysical impacts of Holocene land cover change on climate.

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  • Hirsch, A., R. Prestele, E. Davin, S. Seneviratne, W. Thiery, and P. Verburg. 2018. Modelled biophysical impacts of conservation agriculture on local climates. Global Change Biology 1–17.

    DOI: 10.1111/gcb.14362Save Citation »Export Citation »

    This study presents a set of Global Climate Model experiments to test the influence of conservation agriculture on temperature extremes.

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  • Kumar, S., P. A. Dirmeyer, V. Merwade, T. DelSole, J. M. Adams, and D. Niyogi. 2013. Land use/cover change impacts in CMIP5 climate simulations: A new methodology and 21st century challenges. Journal of Geophysical Research: Atmospheres 118.12: 6337–6353.

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    First study to apply the reconstruction method to multimodel climate simulations. While uncertainties are large, all model simulations indicate an LCMC-induced summertime warming in North America and Eurasia under RCP8.5.

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  • Lawrence, D. M., G. C. Hurtt, K. V. Calvin, et al. 2016. The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: Rationale and experimental design. Geoscientific Model Development 9.9: 2973–2998.

    DOI: 10.5194/gmd-9-2973-2016Save Citation »Export Citation »

    Protocol description for the Land Use Model Intercomparison Project (LUMIP) simulations that will contribute to the Coupled Model Intercomparison Project Phase 6 (CMIP6) and thereby also to upcoming assessments by the IPCC.

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  • Li, Y., M. Zhao, S. Motesharrei, Q. Mu, E. Kalnay, and S. Li. 2015. Local cooling and warming effects of forests based on satellite observations. Nature Communications 6:6603.

    DOI: 10.1038/ncomms7603Save Citation »Export Citation »

    In this paper, Yan Li and colleagues derive the local biogeophysical temperature influence of LCMC from satellite data, showing that tropical forests cool the local climate throughout the year, whereas temperate forests cool during summer and warm during winter.

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  • Malyshev, S., E. Shevliakova, R. J. Stouffer, and S. W. Pacala. 2015. Contrasting local versus regional effects of land-use-change-induced heterogeneity on historical climate: Analysis with the GFDL Earth System Model. Journal of Climate 28.13: 5448–5469.

    DOI: 10.1175/JCLI-D-14-00586.1Save Citation »Export Citation »

    First study to analyze subgrid-scale model output to assess the influence of LCMC on local climate conditions.

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  • Meier, R., E. L. Davin, Q. Lejeune, et al. 2018. Evaluating and improving the community land model’s sensitivity to land cover. Biogeosciences 15.15: 4731–4757.

    DOI: 10.5194/bg-15-4731-2018Save Citation »Export Citation »

    Building further on the work of Malyshev, et al. 2015, this study assesses the effects of deforestation on a surface’s albedo, land-surface temperature, and evapotranspiration using the tile analysis method.

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  • Pitman, A. J., N. de Noblet‐Ducoudré, F. T. Cruz, et al. 2009. Uncertainties in climate responses to past land cover change: First results from the LUCID intercomparison study. Geophysical Research Letters 36.14.

    DOI: 10.1029/2009GL039076Save Citation »Export Citation »

    The first time that the climatic effects of LCMC were assessed in a multimodel framework was the Land-Use and Climate, Identification of robust impacts (LUCID). This paper presents the initial analyses performed on the LUCID data archive.

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  • Thiery, W., E. L. Davin, D. M. Lawrence, A. L. Hirsch, M. Hauser, and S. I. Seneviratne. 2017. Present‐day irrigation mitigates heat extremes. Journal of Geophysical Research: Atmospheres 122.3: 1403–1422.

    DOI: 10.1002/2016JD025740Save Citation »Export Citation »

    Study presenting a factorial model experiment to assess the effects of irrigation on mean and extreme temperatures.

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Surface Energy Balance Decomposition

The surface energy balance decomposition method in Juang, et al. 2007 represents a powerful tool to assess the impacts of LCMC on individual surface energy balance components. When applying this method, the net impact on surface temperature arising from a given land cover or land management change is split into direct contributions of modified biogeophysical processes (such as surface reflection and evapotranspiration) and indirect contributions due to atmospheric feedbacks (such as cloud-radiative feedbacks). As such, several studies have employed this method to better understand the processes underlying the biogeophysical effects of LCMC. These studies include Akkermans, et al. 2014; Luyssaert, et al. 2014; Thiery, et al. 2015; Thiery, et al. 2017; Vanden Broucke, et al. 2015; Hirsch, et al. 2017; Hirsch, et al. 2018; and Meier, et al. 2018.

  • Akkermans, T., W. Thiery, and N. P. van Lipzig. 2014. The regional climate impact of a realistic future deforestation scenario in the Congo Basin. Journal of Climate 27.7: 2714–2734.

    DOI: 10.1175/JCLI-D-13-00361.1Save Citation »Export Citation »

    Applying the method to the output of a regional climate model, the authors attribute deforestation-induced warming in the Congo Basin to a reduction in evapotranspiration, and show that this effect is partially mitigated by increased albedo and enhanced sensible heat loss to the atmosphere.

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  • Hirsch, A. L., M. Wilhelm, E. L. Davin, W. Thiery, and S. I. Seneviratne. 2017. Can climate‐effective land management reduce regional warming? Journal of Geophysical Research: Atmospheres 122.4: 2269–2288.

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    This study applies the surface energy balance decomposition method to output from a global earth system model to better understand the influence of crop albedo management on the future local climate.

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  • Hirsch, A. L., R. Prestele, E. L. Davin, S. I. Seneviratne, W. Thiery, and P. Verburg. 2018. Modelled biophysical impacts of conservation agriculture on local climates. Global Change Biology 1–17.

    DOI: 10.1111/gcb.14362Save Citation »Export Citation »

    This study applies the method to output from a global earth system model to better understand the influence of conservation agriculture on the present-day local climate.

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  • Juang, J.- Y., G. Katul, M. Siqueira, P. Stoy, and K. Novick. 2007. Separating the effects of albedo from eco‐physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophysical Research Letters 34.21.

    DOI: 10.1029/2007GL031296Save Citation »Export Citation »

    First study to introduce the surface energy balance decomposition method through an application to three adjacent ecosystems in the southeastern United States.

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  • Luyssaert, S., M. Jammet, P. C. Stoy, et al. 2014. Land management and land-cover change have impacts of similar magnitude on surface temperature. Nature Climate Change 4.5: 389–393.

    DOI: 10.1038/nclimate2196Save Citation »Export Citation »

    This study shows that in mid-latitudes, land management changes lead to potential cooling from enhanced albedo, but that this effect is offset by warming from reduced upward sensible heat loss.

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  • Meier, R., E. L. Davin, Q. Lejeune, et al. 2018. Evaluating and improving the community land model’s sensitivity to land cover. Biogeosciences 15.15: 4731–4757.

    DOI: 10.5194/bg-15-4731-2018Save Citation »Export Citation »

    Meier and colleagues for the first time apply the SEB decomposition method to subgrid-scale output to assess the local influence of deforestation as simulated by the Community Land Model, and use this analysis to suggest pathways for improving this model.

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  • Thiery, W., E. L. Davin, H. J. Panitz, M. Demuzere, S. Lhermitte, and N. van Lipzig. 2015. The impact of the African Great Lakes on the regional climate. Journal of Climate 28.10: 4061–4085.

    DOI: 10.1175/JCLI-D-14-00565.1Save Citation »Export Citation »

    By applying this method to high-resolution output from a regional climate model, this study highlights the strong diurnal cycle of the impact of the African Great Lakes on the surface energy balance, and through that a profound influence on atmospheric circulation and precipitation in the African Great Lakes region.

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  • Thiery, W., E. L. Davin, D. M. Lawrence, A. L. Hirsch, M. Hauser, and S. I. Seneviratne. 2017. Present‐day irrigation mitigates heat extremes. Journal of Geophysical Research: Atmospheres 122.3: 1403–1422.

    DOI: 10.1002/2016JD025740Save Citation »Export Citation »

    Running the surface energy decomposition method on output from a global earth system model, this study formally attributes the cooling effects from irrigation to an increase in evaporative fraction over irrigated cropland compared to rainfed cropland.

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  • Vanden Broucke, S., S. Luyssaert, E. L. Davin, I. Janssens, and N. van Lipzig. 2015. New insights in the capability of climate models to simulate the impact of LUC based on temperature decomposition of paired site observations. Journal of Geophysical Research: Atmospheres 120.11: 5417–5436.

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    Analyzing paired eddy covariance flux tower data and regional climate model output, Vanden Broucke and colleagues show that deforestation leads to nighttime cooling in mid-latitudes, and that a state-of-the-art climate model is not able to capture this effect caused by a reduction in downwelling longwave radiation.

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Observational Studies

While most evidence on LCMC impacts on climate is informed by climate models, recent years have seen the emergence of new analysis strategies providing observation-based evidence of the local influence of LCMC. In situ measurements and more recently remote sensing data are becoming increasingly available for a range of ecosystems and make it possible to examine local differences in surface energy balance and temperature between different ecosystem types; see Jackson, et al. 2008; Lee, et al. 2011; Mildrexler, et al. 2011; Boisier, et al. 2013; Zhao and Jackson 2014; Li, et al. 2015; Alkama and Cescatti 2016; Bright, et al. 2017; Chen, et al. 2018; and Teuling, et al. 2017.

Biogeochemical Impacts on Climate

Prior to the onset of the industrial revolution, LCMC constituted the single most important source of anthropogenic CO2 emissions. In this context, LCMC has been suggested to have influenced climate long before the start of the industrial period, notably by advocates of the “Early Anthropogenic Hypothesis” or “Ruddiman hypothesis” (Ruddiman 2003; Ruddiman 2017; Ruddiman, et al. 2016). While this proposed effect marks the start of the Anthropocene to some (Nevle, et al. 2011; Lewis and Maslin 2015), its validity has been questioned by several scholars (e.g. Joos, et al. 2004; Berger 2006; Pongratz, et al. 2009). More recently, emissions from fossil fuel burning first appeared toward the end of the 19th century and then rapidly increased after the Second World War. In contrast, emissions from LCMC have remained more or less constant over time and are even gradually decreasing since 2000; see Le Quéré, et al. 2016. As a consequence, the contribution of LCMC to total anthropogenic emissions decreased over time from 77 percent in 1870 to 9 percent during the last decade (1.0 GtC/yr during the period 2006–2015). Accumulated over the period 1870 to 2015, LCMC accounted for 26 percent of all emissions (145 GtC +/- 50 GtC). To facilitate the comparison of different possible drivers of climate change (e.g., emissions from fossil fuel burning or LCMC or volcanic eruptions), climate drivers are generally expressed in terms of their radiative forcing at the top of the atmosphere. Recently Ward, et al. 2014 estimated that 40% +/- 16% of present-day radiative forcing can be attributed to LCMC. The positive radiative forcing from LCMC in turn contributes to global warming, which has been estimated to amount to up to +0.16 to +0.18 K for near-surface air temperature according to a modeling study (see Pongratz, et al. 2010).

Biogeophysical Impacts on Climate

From a global perspective, the anthropogenic increase in CO2 and other trace gases has been by far the main driver of recent climate change (IPCC 2013). At the regional scale, however, other forcings may play a more important role. In particular, LCMC can impact local climate conditions in various ways by altering “biogeophysical” land properties such as albedo, evapotranspiration, and surface aerodynamic properties. Although the quantification of these effects is still subject to particularly large uncertainties, there is growing evidence that LCMC may locally affect surface temperature in similar proportions as other climate forcings, notably in areas strongly affected by deforestation or other land management changes (see Pongratz, et al. 2010; Akkermans, et al. 2014; Strandberg, et al. 2014; Smith, et al. 2016; and Thiery, et al. 2017) Climate models have consistently shown that the overall biogeophysical effect of LCMC highly depends on the geographical context. Deforestation, for instance, may lead to cooling or warming depending on whether it takes place at high latitudes or in the tropics (Claussen, et al. 2001); this striking contrast being due to the dominance of radiative processes (e.g., albedo) in boreal regions and of nonradiative processes (evapotranspiration and surface roughness) in the tropics (Davin and de Noblet-Ducoudré 2010). At high latitudes LCMC typically induces a cooling as surface albedo increases after deforestation: while forests mask the snow, nonforest vegetation is brighter and may be covered by snow during certain periods of the year. In the tropics, in contrast, a reduction in roughness, leaf area, and rooting depth following deforestation reduces evapotranspiration more strongly than in the extra-tropics. The resulting loss of evaporative cooling may thereby even lead to a local warming (e.g., Claussen, et al. 2001; Akkermans, et al. 2014; Lejeune, et al. 2015). In temperate regions, Lejeune, et al. 2017 notes that the biogeophysical impacts of LCMC remain particularly uncertain, in part because of the complex interweaving between radiative and nonradiative processes (Davin and de Noblet-Ducoudré 2010). Despite the important biogeophysical impacts of LCMC on the regional climate, the effects are still largely ignored in current regional climate models and resulting regional or national climate change assessments. Furthermore, biogeophysical effects of LCMC are not accounted for in national and international climate protocols, certainly in part due to the lack of scientific consensus and appropriate biogeophysical metrics; notably, the iconic figure of the IPCC report which compares the magnitude of various climate forcings since preindustrial times only includes an estimate of the albedo-induced radiative forcing from land-use change (IPCC 2013).

Importance of Land Management

Besides land cover changes, whose climatic consequences have been much studied in recent decades, land management changes have recently been advanced as another important human influence on the climate system. Right now, there is growing evidence from Lobell, et al. 2006; Luyssaert, et al. 2014; Davin, et al. 2014; Mueller, et al. 2016; Hirsch, et al. 2017; Hirsch, et al. 2018; Thiery, et al. 2017; and Erb, et al. 2018 that the biogeophysical and biogeochemical impacts of land management changes may be as important for climate as those arising from vegetation conversions. Some land management practices were also identified as having the potential to either mitigate or aggravate climate extremes. For instance, Davin, et al. 2014 and Hirsch, et al. 2018 showed the direct effect of suppressed tillage on mid-latitude heat wave intensity. Likewise, Hirsch, et al. 2017 advocates that the use of brighter crops may reduce summertime maximum daily temperatures. Irrigation is the land management practice with perhaps the largest effect on climate, as detailed in Sacks, et al. 2009. The biogeophysical impacts of irrigation have been investigated in several regional climate modeling studies (e.g., Kueppers, et al. 2007). Although using different irrigation implementations for different regions of the world, all studies report a cooling of the near-surface air in response to irrigation. Estimates of global mean temperature change caused by irrigation remain uncertain, but global climate model studies, including Lobell, et al. 2006; Sacks, et al. 2009; Guimberteau, et al. 2012; Cook, et al. 2015; and Thiery, et al. 2017, generally suggest a small cooling. This large uncertainty is mainly due to limitations in the applied methods, in particular the lack of realism in either the land surface model or the amount of irrigation water applied. In contrast, irrigation clearly has a strong cooling effect on hot extremes according to Thiery, et al. 2017, and impacts regional circulation and precipitation patterns, such as a delayed onset of the Indian monsoon (Guimberteau, et al. 2012; De Vrese, et al. 2016; Thiery, et al. 2017).

  • Cook, B. I., S. P. Shukla, M. J. Puma, and L. S. Nazarenko. 2015. Irrigation as an historical climate forcing. Climate Dynamics 44.5–6: 1715–1730.

    DOI: 10.1007/s00382-014-2204-7Save Citation »Export Citation »

    One of the first studies to employ transient ensemble simulations to quantify the effect of irrigation on the surface energy balance, temperatures, and precipitation at the global scale.

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  • Davin, E. L., S. I. Seneviratne, P. Ciais, A. Olioso, and T. Wang. 2014. Preferential cooling of hot extremes from cropland albedo management. Proceedings of the National Academy of Sciences 111.27: 9757–9761.

    DOI: 10.1073/pnas.1317323111Save Citation »Export Citation »

    Study showing the direct effect of suppressed tillage on European heat wave intensity. The biogeophysical impact of no-till farming was found to be most pronounced during summertime hot extremes, when the enhanced surface albedo reduces the net radiative energy input to the surface and thereby also its temperature.

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  • De Vrese, P., S. Hagemann, and M. Claussen. 2016. Asian irrigation, African rain: Remote impacts of irrigation. Geophysical Research Letters 43.8: 3737–3745.

    DOI: 10.1002/2016GL068146Save Citation »Export Citation »

    Using a global earth system model, this study investigates the remote influences of irrigation on atmospheric dynamics and precipitation.

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  • Erb, K. H., T. Kastner, C. Plutzar, A. L. S. Bais, et al. 2018. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553.7686: 73–76.

    DOI: 10.1038/nature25138Save Citation »Export Citation »

    Study demonstrating the importance of land management for global biomass stocks.

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  • Guimberteau, M., K. Laval, A. Perrier, and J. Polcher. 2012. Global effect of irrigation and its impact on the onset of the Indian summer monsoon. Climate Dynamics 39.6: 1329–1348.

    DOI: 10.1007/s00382-011-1252-5Save Citation »Export Citation »

    Comprehensive study of the influence of irrigation on temperature and Indian monsoon dynamics.

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  • Hirsch, A. L., M. Wilhelm, E. L. Davin, W. Thiery, and S. I. Seneviratne. 2017. Can climate‐effective land management reduce regional warming? Journal of Geophysical Research: Atmospheres 122.4: 2269–2288.

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    This study quantifies the potential effects of crop management options (albedo change and irrigation) in future climate projections.

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  • Hirsch, A. L., R. Prestele, E. L. Davin, S. I. Seneviratne, W. Thiery, and P. Verburg. 2018. Modelled biophysical impacts of conservation agriculture on local climates. Global Change Biology 1–17.

    DOI: 10.1111/gcb.14362Save Citation »Export Citation »

    This study uncovers the climatic effects of conservation agriculture with particular attention to temperature extremes.

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  • Kueppers, L. M., M. A. Snyder, and L. C. Sloan. 2007. Irrigation cooling effect: Regional climate forcing by land‐use change. Geophysical Research Letters 34.3.

    DOI: 10.1029/2006GL028679Save Citation »Export Citation »

    Regional climate model experiment showing that irrigation has a substantial cooling influence on climate over California.

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  • Lobell, D. B., G. Bala, and P. B. Duffy. 2006. Biogeophysical impacts of cropland management changes on climate. Geophysical Research Letters 33.6.

    DOI: 10.1029/2005GL025492Save Citation »Export Citation »

    Study comparing the effects of different land management techniques on the modeled global climate.

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  • Luyssaert, S., M. Jammet, P. C. Stoy, et al. 2014. Land management and land-cover change have impacts of similar magnitude on surface temperature. Nature Climate Change 4.5: 389–393.

    DOI: 10.1038/nclimate2196Save Citation »Export Citation »

    In this study the authors advocate that land management changes have a similar biogeophysical impact on climate compared to land cover changes, based on analyses of eddy covariance flux towers and satellite observations.

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  • Mueller, N. D., E. E. Butler, K. A. McKinnon, et al. 2016. Cooling of US Midwest summer temperature extremes from cropland intensification. Nature Climate Change 6.3: 317–322.

    DOI: 10.1038/nclimate2825Save Citation »Export Citation »

    Observational study linking cropland intensification to cooling patterns over the US Midwest.

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  • Sacks, W. J., B. I. Cook, N. Buenning, S. Levis, and J. H. Helkowski. 2009. Effects of global irrigation on the near-surface climate. Climate Dynamics 33.2–3: 159–175.

    DOI: 10.1007/s00382-008-0445-zSave Citation »Export Citation »

    Pioneering study regarding the implementation of a physically sound irrigation parameterization in a global climate model.

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  • Thiery, W., E. L. Davin, D. M. Lawrence, A. L. Hirsch, M. Hauser, and S. I. Seneviratne. 2017. Present‐day irrigation mitigates heat extremes. Journal of Geophysical Research: Atmospheres 122.3: 1403–1422.

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    Study showing that the cooling effect of irrigation on hot extremes is of similar magnitude compared to the greenhouse gas warming over some regions, and uncovering the causes behind the strong and robust influence of irrigation on temperature extremes.

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Land-Based Climate Change Adaptation and Mitigation

Given the large influences on climate, land management has been advanced as a possible way to mitigate and adapt to climate change and its local impacts. Carbon sequestration through reforestation and afforestation, as well as the use of bioenergy, either from crops or wood, are an essential element of ambitious mitigation scenarios that aim to reduce CO2 emissions in coming decades (e.g. van Vuuren, et al. 2011; Smith, et al. 2016; Boysen, et al. 2017; Griscom, et al. 2017; Popp, et al. 2017; Seneviratne, et al. 2018). In addition, biogeophysical effects of LCMC also have important implications in terms of regional climate change adaptation potentials (e.g. increasing surface albedo and evaporative fraction) which have the potential to mute temperature extremes (Hirsch, et al. 2017; Thiery, et al. 2017). Yet Naudts, et al. 2016 shows challenges remain huge, as 250 years of forest management in Europe did not mitigate climate warming. Climate change mitigation and adaptation through afforestation, forest management, and other LCMCs therefore essentially needs to be considered with the recognition that not all practices have beneficial effects. Moreover, it remains an open question whether land-based mitigation and adaptation may not induce negative consequences in other sectors, for instance threatening food security and biodiversity in the case of biofuels (Boysen, et al. 2017; Schleussner, et al. 2018) or water availability in the case of irrigation (Thiery, et al. 2017).

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