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Author
Date
2021Type
- Doctoral Thesis
ETH Bibliography
yes
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Abstract
Humans currently utilize 69-76 % of the ice-free land surface. The associated Land Use and Land Cover Change (LULCC) affects the local, regional, and global climate. The climate impact of LULCC comprises the release or sequestration of greenhouse gases, the biogeochemical effects, and the alteration of the local energy and water redistribution at the land surface, the biogeophysical effects. Observations and models often disagree on the size and even the sign of biogeophysical effects from LULCC, even though they indicate that those effects are relevant for the local and regional climate. Most scenarios that confine global warming levels to below 2 °C incorporate substantial alterations of human land use, often to sequester greenhouse gases. It is therefore crucial to obtain a thorough understanding of the biogeophysical effects of LULCC and reconcile them in models with observations. Clearing of natural forests for agricultural food production has been a widespread LULCC in the past. This trend is now reverted in some of the developed countries. In addition, re- or afforestation is frequently proposed as a tool to mitigate anthropogenic greenhouse gas emissions. This thesis therefore investigates the biogeophysical effects of re- or afforesting grassland and cropland, which is subsequently called forestation. To this aim, I employ both climate model simulations and analysis of observational data. In particular, I evaluate and undertake targeted improvements to reconcile the local biogeophysical effect of forestation in the Community Land Model (CLM) with observations. Further, I investigate whether forestation might affect precipitation in Europe employing observational data sets. In Chapter 2, I confront the local biogeophysical sensitivity of CLM to forestation with various observational constraints. It appears that CLM agrees reasonably with observations regarding the sensitivity of albedo, daily mean Land Surface Temperature (LST), and daily maximum LST. Nonetheless, the albedo decrease following forestation is more pronounced in CLM compared to remote sensing observations. The daily maximum LST is distinctly lower in forests than over grassland/cropland both in observations and in CLM with the exception of winters at higher latitudes. However, CLM exhibits a slight positive bias in the daily maximum LST difference of forest minus grassland/cropland. The latter bias appears to be linked to a pronounced underestimation of the increase in EvapoTranspiration (ET) following forestation compared to various observational constraints, which do however exhibit a substantial spread themselves. Subsequently, I propose various modifications of the model to improve its ET sensitivity to forestation, which also reduce the positive bias in the effect of forestation on daily maximum LST. The simulated daily minimum LST difference between forest and grassland/cropland by and large resembles the sensitivity of daily maximum LST, although somewhat weaker, while remote sensing observations indicate that the daily minimum LST of forests is often higher than the one of grassland/cropland. Overall, this study indicates that CLM can represent some aspects of the local biogeophysical sensitivity to forestation well, while further model development is required for other aspects to reconcile CLM with observations. In the next chapter, I investigate whether the lack of Biomass Heat Storage (BHS) in CLM is responsible for the identified biases in the sensitivity of daily maximum and minimum LST to forestation. The cooling of daily maximum temperatures is marginal, as most of the energy uptake by the vegetation biomass is compensated by a reduction of the turbulent heat fluxes. On the other hand, this process results in a pronounced warming of nighttime temperatures in forests, because the stable structure of the surface layer at night inhibits the compensation of the energy release from the vegetation by the sensible heat flux. The resultant nighttime warming frequently exceeds 2 °C in forests, while BHS appears negligible for grassland and cropland, due their comparably small amount of biomass. Given this diurnal asymmetry, BHS warms daily mean temperatures in forested regions. CLM overestimates the diurnal temperature range in forests compared to remote sensing observations, which is improved substantially after including BHS in the model. Finally, I show that the inclusion of BHS alleviates the apparent deficiency of CLM related to the impact of forestation on the daily minimum LST, which emerged in Chapter 2. In summary, BHS strongly modulates nighttime temperatures in forests and is also relevant for the daily mean temperature, while its impact on daytime temperatures is only marginal. In Chapter 4, I estimate alterations of precipitation from foresting agricultural land in Europe, a biogeophysical effect that has been largely disregarded in observational studies previously. This is done in two (almost independent) approaches: Firstly, I identify suitable site pairs in two rain gauge data collections that differ by at least 20 % in the agricultural land and forest fractions. Secondly, I model the climatology of a state-of-the-art spatially-continuous precipitation data set with Generalized Additive Models (GAMs) to link precipitation to land cover. In both approaches, forestation is estimated to increase precipitation locally, in particular during the winter months. The structure of the GAMs further allows to estimate precipitation changes downwind of the forestation locations. During winter, downwind precipitation increases in the southern and western parts of Europe, while the signal is near-neutral to negative in central and northern Europe. During summer, I find a downwind increase in precipitation due to forestation. The combined local and downwind effect from a realistic reforestation scenario are estimated to compensate a substantial fraction of the reduction in summertime precipitation, which is expected under RCP4.5 by the end of this century in an ensemble of regional climate models. While this study implies that forestation results in relevant alterations of precipitation in Europe, I would also like to highlight that this study is novel to the field and therefore more uncertain than the previous ones. Forestation results in biogeophysical effects that are relevant for both the local and regional climate. Such effects should be considered before utilizing forestation as a tool to mitigate greenhouse gas emissions. Yet, many aspects regarding the biogeophysical effects of LULCC in observations and models are still uncertain or unknown. In this thesis, I demonstrate that BHS is relevant for the local climate in forests and should consequently be included in the next generation of earth system models that are used to assess the climate impact of LULCC. Further, I provide observational evidence of changes in precipitation following forestation in Europe. LULCC induces therefore not only temperature alterations, but also relevant modifications of the hydrological cycle, which need to be considered when assessing the climatic consequences of LULCC. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000484696Publication status
publishedExternal links
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Contributors
Examiner: Seneviratne, Sonia I.
Examiner: Davin, Edouard Léopold
Examiner: Luyssaert, Sebastiaan
Publisher
ETH ZurichSubject
Biogeophysical effects of forestation; Land cover change; Land surface modeling; Biomass heat storage; HydrologyOrganisational unit
03778 - Seneviratne, Sonia / Seneviratne, Sonia
Funding
172715 - CLimate IMPacts of Utilizing Land in Switzerland and Europe (CLIMPULSE) (SNF)
Related publications and datasets
Is supplemented by: https://doi.org/10.3929/ethz-b-000448232
Has part: https://doi.org/10.5194/bg-15-4731-2018
Has part: https://doi.org/10.3929/ethz-b-000368521
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