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Land cover and land management changes (LCLMCs) play an important role in achieving low-end warming scenarios through land-based mitigation. However, their effects on moisture fluxes and recycling remain uncertain, although they have important implications for the future viability of such strategies. Here, we analyse the impact of idealized LCLMC scenarios on atmospheric moisture transport in three different Earth system model (ESMs): the Community Earth System Model (CESM), the Max Planck Institute Earth System Model (MPI-ESM), and the European Consortium Earth System Model (EC-EARTH). The LCLMC scenarios comprise of a full cropland world, a fully afforested world, and a cropland world with unlimited irrigation expansion. The effects of these LCLMC in the different ESMs are analysed for precipitation, evaporation, and vertically integrated moisture flux convergence to understand the LCLMC-induced changes in the atmospheric moisture cycle. Then, a moisture tracking algorithm is applied to assess the effects of LCLMC on moisture recycling at the local (grid cell level) and the global scale (continental moisture recycling). By applying a moisture tracking algorithm on fully coupled ESM simulations we are able to quantify the complete effects of LCLMC on moisture recycling (including circulation changes), which are generally not considered in moisture recycling studies. Our results indicate that cropland expansion is generally causing a drying and reduced local moisture recycling, while afforestation and irrigation expansion generally cause wetting and increased local moisture recycling. However, the strength of this effect varies across ESMs and shows a large dependency on the dominant driver. Some ESMs show a dominance of large-scale atmospheric circulation changes while other ESMs show a dominance of local to regional changes in the atmospheric water cycle only within the vicinity of the LCLMC. Overall, these results corroborate that LCLMC can induce substantial effects on the atmospheric water cycle and moisture recycling, both through local effects and changes in atmospheric circulation. However, more research is needed to constrain the uncertainty of these effects within ESMs to better inform future land-based mitigation strategies. [ABSTRACT FROM AUTHOR]

Land cover and land management changes (LCLMCs) play an important role in achieving low-end warming scenarios through land-based mitigation. However, their effects on moisture fluxes and recycling remain uncertain although they have important implications for the future viability of such strategies. Here, we analyse the impact of idealised LCLMC scenarios on atmospheric moisture transport in three different ESMs: the Community Earth System Model (CESM), the Max Planck Institute Earth System Model (MPI-ESM) and the European Consortium Earth System Model (EC-EARTH). The LCLMC scenarios comprise of a full cropland world, a fully afforested world, and a cropland world with unlimited irrigation expansion. The effects of these LCLMCs in the different ESMs are analysed for precipitation, evaporation and vertically integrated moisture flux convergence to understand the LCLMC-induced changes in the atmospheric moisture cycle. Then, a moisture tracking algorithm is applied to assess the effects of LCLMCs on moisture recycling at the local (grid cell level) and the global scale (continental moisture recycling). Our results indicate that LCLMCs are generally inducing consistent effects on moisture fluxes over land in all ESMs. Cropland expansion causes drying and reduced local moisture recycling in all ESMs, while afforestation and irrigation expansion generally cause wetting and increased local moisture recycling. However, the strength of this influence varies in time and space and across the ESMs and shows a strong dependency on the dominant driver: Some ESMs show a dominance of large scale atmospheric circulation changes while other ESMs show a dominance of local to regional changes in the atmospheric water cycle only within the vicinity of the LCLMC. Overall, these results corroborate that LCLMCs can induce large effects on the atmospheric water cycle and moisture recycling, but more research is needed to constrain the uncertainty of these effects within ESMs and better evaluate land-based mitigation strategies. [ABSTRACT FROM AUTHOR]

Land cover changes have been proposed to play a significant role, alongside emission reductions, in achieving the temperature goals agreed upon under the Paris Agreement. Such changes carry both global implications, pertaining to the biogeochemical effects of land cover change and thus the global carbon budget, and regional or local implications, pertaining to the biogeophysical effects arising within the immediate area of land cover change. Biogeophysical effects of land cover change are of high relevance to national policy and decision makers, and accounting for them is essential for effective deployment of land cover practices that optimise between global and regional impacts. To this end, Earth system model (ESM) outputs that isolate the biogeophysical responses of climate to land cover changes are key in informing impact assessments and supporting scenario development exercises. However, generating multiple such ESM outputs in a manner that allows comprehensive exploration of all plausible land cover scenarios is computationally untenable. This study proposes a framework to explore in an agile manner the local biogeophysical responses of climate under customised tree cover change scenarios by means of a computationally inexpensive emulator, the Tree cover change clIMate Biophysical responses EmulatoR (TIMBER) v0.1. The emulator is novel in that it solely represents the biogeophysical responses of climate to tree cover changes, and it can be used as either a standalone device or as a supplement to existing climate model emulators that represent the climate responses from greenhouse gas (GHG) or global mean temperature (GMT) forcings. We start off by modelling local minimum, mean, and maximum surface temperature responses to tree cover changes by means of a month- and Earth system model (ESM)-specific generalised additive model (GAM) trained over the whole globe; 2 m air temperature responses are then diagnosed from the modelled minimum and maximum surface temperature responses using observationally derived relationships. Such a two-step procedure accounts for the different physical representations of surface temperature responses to tree cover changes under different ESMs whilst respecting a definition of 2 m air temperature that is more consistent across ESMs and with observational datasets. In exploring new tree cover change scenarios, we employ a parametric bootstrap sampling method to generate multiple possible temperature responses, such that the parametric uncertainty within the GAM is also quantified. The output of the final emulator is demonstrated for the Shared Socioeconomic Pathway (SSP) 1-2.6 and 3-7.0 scenarios. Relevant temperature responses are identified as those displaying a clear signal in relation to their surrounding parametric uncertainty, calculated as the signal-to-noise ratio between the sample set mean and sample set variability. The emulator framework developed in this study thus provides a first step towards bridging the information gap surrounding biogeophysical implications of land cover changes, allowing for smarter land use decision making. [ABSTRACT FROM AUTHOR]

Land cover and land management change (LCLMC) has been highlighted for its critical role in mitigation scenarios, both in terms of global mitigation and local adaptation. Yet, the climate effect of individual LCLMC options, their dependence on the background climate and the local vs. non-local responses are still poorly understood across different Earth system models (ESMs). Here we simulate the climatic effects of LCLMC using three state-of-the-art ESMs, including the Community Earth System Model (CESM), the Max Planck Institute for Meteorology Earth System Model (MPI-ESM) and the European Consortium Earth System Model (EC-EARTH). We assess the LCLMC effects using the following four idealized experiments: (i) a fully afforested world, (ii) a world fully covered by cropland, (ii) a fully afforested world with extensive wood harvesting and (iv) a full-cropland world with extensive irrigation. In these idealized sensitivity experiments, performed under present-day climate conditions, the effects of the different LCLMC strategies represent an upper bound for the potential of global mitigation and local adaptation. To disentangle the local and non-local effects from the LCLMC, a checkerboard-like LCLMC perturbation, i.e. alternating grid boxes with and without LCLMC, is applied. The local effects of deforestation on surface temperature are largely consistent across the ESMs and the observations, with a cooling in boreal latitudes and a warming in the tropics. However, the energy balance components driving the change in surface temperature show less consistency across the ESMs and the observations. Additionally, some biases exist in specific ESMs, such as a strong albedo response in CESM mid-latitudes and a soil-thawing-driven warming in boreal latitudes in EC-EARTH. The non-local effects on surface temperature are broadly consistent across ESMs for afforestation, though larger model uncertainty exists for cropland expansion. Irrigation clearly induces a cooling effect; however, the ESMs disagree whether these are mainly local or non-local effects. Wood harvesting is found to have no discernible biogeophysical effects on climate. Overall, our results underline the potential of ensemble simulations to inform decision making regarding future climate consequences of land-based mitigation and adaptation strategies. [ABSTRACT FROM AUTHOR]

Land cover and land management change (LCLMC) has been highlighted for its critical role in mitigation scenarios, both in terms of global mitigation and local adaptation. Yet, the climate effect of individual LCLMC options, their dependence on the background climate and the local vs. non-local responses are still poorly understood across different Earth System Models (ESMs). Here we simulate the climatic effects of LCLMC using three state-of-the-art ESMs, including the Community Earth System Model (CESM), the Max Planck Institute for Meteorology Earth System Model (MPI-ESM) and the European Consortium Earth System Model (EC-EARTH). We assess the LCLMC effects using four idealized experiments: (i) a fully afforested world, (ii) a world fully covered by cropland, (ii) a fully afforested world with extensive wood harvesting, and (iv) a full cropland world with extensive irrigation. In these idealized sensitivity experiments, performed under present-day climate conditions, the effects of the different LCLMC strategies represent an upper bound for the potential of global mitigation and local adaptation. To disentangle the local and non-local effects from the LCLMC, a checkerboard-like LCLMC perturbation, i.e., alternating grid boxes with and without LCLMC, is applied. The local effects of deforestation on surface temperature are largely consistent across the ESMs and the observations, with a cooling in boreal latitudes and a warming in the tropics. However, the energy balance components driving the change in surface temperature show less consistency across the ESMs and the observations. Additionally, some biases exist in specific ESMs, such as a strong albedo response in CESM mid-latitudes and a soil thawing driven warming in boreal latitudes in EC-EARTH. The non-local effects on surface temperature are broadly consistent across ESMs for afforestation, though larger model uncertainty exists for cropland expansion. Irrigation clearly induces a cooling effect, however; the ESMs disagree whether these are mainly local or non-local effects. Wood harvesting is found to have no discernible biogeophysical effects on climate. Our results overall underline the potential of ensemble simulations to inform decision making regarding future climate consequences of land-based mitigation and adaptation strategies. [ABSTRACT FROM AUTHOR]

Society is set to experience significant land cover changes in order to achieve the temperature goals agreed upon under the Paris Agreement. Such changes carry both global implications, pertaining to the biogeochemical effects of land cover change and thus the global carbon budget, and regional/local implications, pertaining to the biogeophysical effects arising within the immediate area of land cover change. Biogeophysical effects of land cover change are of high relevance to national policy- and decision- makers and their accountance is essential towards effective deployment of land cover practices that optimises between global and regional impacts. To this end, ESM outputs that isolate the biogeophysical responses of climate to land cover changes are key in informing impact assessments and supporting scenario development exercises. Generating multiple such ESM outputs, in a manner that allows comprehensive exploration of all plausible land cover scenarios however, is computationally untenable. This study proposes a framework to agilely explore the local biogeophysical responses of climate under different land cover scenarios by means of a computationally inexpensive emulator. The emulator is novel in that it solely represents the land cover forced, biogeophysical responses of climate, and can be used as either a standalone device or supplementary to existing climate model emulators that represent greenhouse gas (GHG)- or Global Mean Temperature (GMT)- forced climate responses. We start off by modelling local minimum, mean and maximum surface temperature responses to tree cover changes by means of a month- and Earth System Model (ESM)- specific Generalised Additive Model (GAM) trained over the whole globe. 2-m air temperature responses are then diagnosed from the modelled minimum and maximum surface temperature responses using observationally derived relationships. Such a two-step procedure accounts for the different physical representations of surface temperature responses to tree cover changes under different ESMs, whilst respecting a definition of 2-m air temperature that is more consistent across ESMs and with observational datasets. In exploring new tree cover change scenarios, we employ a parametric bootstrap sampling method to generate multiple possible temperature responses, such that the uncertainty within the GAM's derived shape of the response is also quantified. The output of the final emulator is demonstrated for the SSP 1-2.6 and 3-7.0 scenarios. Relevant temperature responses are identified as those displaying a clear signal in relation to the surrounding uncertainty in shape of derived response, calculated as the 'signal-to-noise' ratio between the sample set mean and sample set variability. The emulator framework developed in this study thus provides a first step towards bridging the information-gap surrounding biogeophysical implications of land cover changes, allowing for smarter land-use decision making. [ABSTRACT FROM AUTHOR]

The carbon flux due to land-use and land-cover change (net LULCC flux) historically contributed to a large fraction of anthropogenic carbon emissions while at the same time being associated with large uncertainties. This study aims to compare the contribution of several sensitivities underlying the net LULCC flux by assessing their relative importance in a bookkeeping model (BLUE) based on a LULCC dataset including uncertainty estimates (the LUH2 dataset). The sensitivity experiments build upon the approach of Hurtt et al. (2011) and compare the impacts of LULCC uncertainty (a high, baseline and low landuse estimate), the starting time of the bookkeeping model simulation (850, 1700 and 1850), net area transitions versus gross area transitions (shifting cultivation) and neglecting wood harvest on estimates of the net LULCC flux. Additional factorial experiments isolate the impact of uncertainty from initial conditions and transitions on the net LULCC flux. Finally, historical simulations are extended with future land-use scenarios to assess the impact of past LULCC uncertainty in future projections. Over the period 1850–2014, baseline and low LULCC scenarios produce a comparable cumulative net LULCC flux while the high LULCC estimate initially produces a larger net LULCC flux which decreases towards the end of the period and even becomes smaller than in the baseline estimate. LULCC uncertainty leads to slightly higher sensitivity in the cumulative net LULCC flux (up to 22 %, reference are the baseline simulations) compared to the starting year of a model simulation (up to 15 %). The contribution from neglecting wood harvest activities (up to 28 % cumulative net LULCC flux) is larger than from LULCC uncertainty and the implementation of land-cover transitions (gross or net transitions) exhibits the smallest sensitivity (up to 13 %). At the end of the historical LULCC dataset in 2014, the LULCC uncertainty retains some impact on the net LULCC flux (±0.15 PgC yr-1 at an estimate of 1.7 PgC yr-1). Of the past uncertainties in LULCC, a small impact persists in 2099, mainly due to uncertainty of harvest remaining in 2014. However, compared to the uncertainty range of the LULCC flux estimated today, the estimates in 2099 appear to be indistinguishable. These results, albeit from a single model, are important for CMIP6 as they compare the relative importance of starting year, uncertainty of LULCC, applying gross transitions and wood harvest on the net LULCC flux. For the cumulative net LULCC flux over the industrial period the uncertainty of LULCC is as relevant as applying wood harvest and gross transitions. However, LULCC uncertainty matters less (by about a factor three) than the other two factors for the net LULCC flux in 2014 and historical LULCC uncertainty is negligible for estimates of future scenarios. [ABSTRACT FROM AUTHOR]