Editing Test:SRCCL/Chapter-1 (section)

== 1.2.2.2 Nature and scope of uncertainties related to land use ==

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Identification and communication of uncertainties is crucial to support decision making towards sustainable land management. Providing a robust, and comprehensive understanding of uncertainties in observations, models and scenarios is a fundamental first step in the IPCC confidence framework (see above). This will remain a challenge in future, but some important progress has been made over recent years.

Uncertainties in observations

The detection of changes in vegetation cover and structural properties underpins the assessment of land-use change, degradation and desertification. It is continuously improving by enhanced Earth observation capacity (Hansen et al. 2013 <sup>[[#fn:r394|394]]</sup> ; He et al. 2018 <sup>[[#fn:r395|395]]</sup> ; Ardö et al. 2018 <sup>[[#fn:r396|396]]</sup> ; Spennemann et al. 2018 <sup>[[#fn:r397|397]]</sup> ) (see also Table SM.1.1 in Supplementary Material). Likewise, the picture of how soil organic carbon, and GHG and water fluxes, respond to land-use change and land management continues to improve through advances in methodologies and sensors (Kostyanovsky et al. 2018 <sup>[[#fn:r398|398]]</sup> ; Brümmer et al. 2017 <sup>[[#fn:r399|399]]</sup> ; Iwata et al. 2017 <sup>[[#fn:r400|400]]</sup> ; Valayamkunnath et al. 2018 <sup>[[#fn:r401|401]]</sup> ). In both cases, the relative shortness of the record, data gaps, data treatment algorithms and – for remote sensing – differences in the definitions of major vegetation-cover classes limit the detection of trends (Alexander et al. 2016a <sup>[[#fn:r402|402]]</sup> ; Chen et al. 2014 <sup>[[#fn:r403|403]]</sup> ; Yu et al. 2014 <sup>[[#fn:r404|404]]</sup> ; Lacaze et al. 2015 <sup>[[#fn:r405|405]]</sup> ; Song 2018 <sup>[[#fn:r406|406]]</sup> ; Peterson et al. 2017 <sup>[[#fn:r407|407]]</sup> ). In many developing countries, the cost of satellite remote sensing remains a challenge, although technological advances are starting to overcome this problem (Santilli et al. 2018 <sup>[[#fn:r408|408]]</sup> ), while ground-based observations networks are often not available.

Integration of multiple data sources in model and data assimilation schemes reduces uncertainties (Li et al. 2017 <sup>[[#fn:r409|409]]</sup> ; Clark et al. 2017 <sup>[[#fn:r410|410]]</sup> ; Lees et al. 2018 <sup>[[#fn:r411|411]]</sup> ), which might be important for the advancement of early warning systems. Early warning systems are a key feature of short-term (i.e. seasonal) decision-support systems and are becoming increasingly important for sustainable land management and food security (Shtienberg 2013 <sup>[[#fn:r412|412]]</sup> ; Jarroudi et al. 2015 <sup>[[#fn:r413|413]]</sup> ) (Sections 6.2.3 and 7.4.3). Early warning systems can help to optimise fertiliser and water use, aid disease suppression, and/or increase the economic benefit by enabling strategic farming decisions on when and what to plant (Caffi et al. 2012 <sup>[[#fn:r414|414]]</sup> ; Watmuff et al. 2013 <sup>[[#fn:r415|415]]</sup> ; Jarroudi et al. 2015 <sup>[[#fn:r416|416]]</sup> ; Chipanshi et al. 2015 <sup>[[#fn:r417|417]]</sup> ). Their suitability depends on the capability of the methods to accurately predict crop or pest developments, which in turn depends on expert agricultural knowledge, and the accuracy of the weather data used to run phenological models (Caffi et al. 2012 <sup>[[#fn:r418|418]]</sup> ; Shtienberg 2013 <sup>[[#fn:r419|419]]</sup> ).

Uncertainties in models

Model intercomparison is a widely used approach to quantify some sources of uncertainty in climate change, land-use change and ecosystem modelling, often associated with the calculation of model-ensemble medians or means (see e.g., Sections 2.2 and 5.2). Even models of broadly similar structure differ in their projected outcome for the same input, as seen for instance in the spread in climate change projections from Earth System Models (ESMs) to similar future anthropogenic GHG emissions (Parker 2013 <sup>[[#fn:r932|932]]</sup> ; Stocker et al. 2013a <sup>[[#fn:r933|933]]</sup> ). These uncertainties arise, for instance, from different parameter values, different processes represented in models, or how these processes are mathematically described. If the outputs of ESM simulations are used as input to impact models, these uncertainties can propagate to projected impacts (Ahlstrom et al. 2013 <sup>[[#fn:r420|420]]</sup> ).

Thus, the increased quantification of model performance in benchmarking exercises (the repeated confrontation of models with observations to establish a track-record of model developments and performance) is an important development to support the design and the interpretation of the outcomes of model ensemble studies (Randerson et al. 2009 <sup>[[#fn:r421|421]]</sup> ; Luo et al. 2012 <sup>[[#fn:r422|422]]</sup> ; Kelley et al. 2013 <sup>[[#fn:r423|423]]</sup> ). Since observational datasets in themselves are uncertain, benchmarking benefits from transparent information on the observations that are used, and the inclusion of multiple, regularly updated data sources (Luo et al. 2012 <sup>[[#fn:r424|424]]</sup> ; Kelley et al. 2013 <sup>[[#fn:r425|425]]</sup> ). Improved benchmarking approaches and the associated scoring of models may support weighted model means contingent on model performance. This could be an important step forward when calculating ensemble means across a range of models (Buisson et al. 2009 <sup>[[#fn:r426|426]]</sup> ; Parker 2013 <sup>[[#fn:r427|427]]</sup> ; Prestele et al. 2016 <sup>[[#fn:r428|428]]</sup> ).

Uncertainties arising from unknown futures

Large differences exist in projections of future land-cover change, both between and within scenario projections (Fuchs et al. 2015 <sup>[[#fn:r429|429]]</sup> ; Eitelberg et al. 2016 <sup>[[#fn:r430|430]]</sup> ; Popp et al. 2016 <sup>[[#fn:r431|431]]</sup> ; Krause et al. 2017 <sup>[[#fn:r432|432]]</sup> ; Alexander et al. 2016a <sup>[[#fn:r433|433]]</sup> ). These differences reflect the uncertainties associated with baseline data, thematic classifications, different model structures and model parameter estimation (Alexander et al. 2017a <sup>[[#fn:r434|434]]</sup> ; Prestele et al. 2016 <sup>[[#fn:r435|435]]</sup> ; Cross-Chapter Box 1 in Chapter 1). Likewise, projections of future land-use change are also highly uncertain, reflecting – among other factors – the absence of important crop, pasture and management processes in Integrated Assessment Models (Rose 2014 <sup>[[#fn:r436|436]]</sup> ) (Cross-Chapter Box 1 in Chapter 1 ) and in models of the terrestrial carbon cycle (Arneth et al. 2017 <sup>[[#fn:r437|437]]</sup> ). These processes have been shown to have large impacts on carbon stock changes (Arneth et al. 2017 <sup>[[#fn:r438|438]]</sup> ). Common scenario frameworks are used to capture the range of future uncertainties in scenarios. The most commonly used recent framework in climate change studies is based on the Representative Concentration Pathways (RCPs) and the Shared Socio-economic Pathways (SSPs) (Popp et al. 2016 <sup>[[#fn:r439|439]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r440|440]]</sup> ). The RCPs prescribe levels of radiative forcing (W m <sup>–2</sup> ) arising from different atmospheric concentrations of GHGs that lead to different levels of climate change. For example, RCP2.6 (2.6 W m <sup>–2</sup> ) is projected to lead to global mean temperature changes of about 0.9°C–2.3°C, and RCP8.5 (8.5 W m <sup>–2</sup> ) to global mean temperature changes of about 3.2°C–5.4°C (van Vuuren et al. 2014 <sup>[[#fn:r441|441]]</sup> ).

The SSPs describe alternative trajectories of future socio-economic development with a focus on challenges to climate mitigation and challenges to climate adaptation (O’Neill et al. 2014 <sup>[[#fn:r442|442]]</sup> ). SSP1 represents a sustainable and cooperative society with a low-carbon economy and high capacity to adapt to climate change. SSP3 has social inequality that entrenches reliance on fossil fuels and limits adaptive capacity. SSP4 has large differences in income within and across world regions; it facilitates low-carbon economies in places, but limits adaptive capacity everywhere. SSP5 is a technologically advanced world with a strong economy that is heavily dependent on fossil fuels, but with high adaptive capacity. SSP2 is an intermediate case between SSP1 and SSP3 (O’Neill et al. 2014 <sup>[[#fn:r443|443]]</sup> ). The SSPs are commonly used with models to project future land-use change (Cross-Chapter Box 1 in Chapter 1).

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