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Forests influence climate through biogeochemical and biogeophysical processes. Biogeochemical processes include greenhouse gas (GHG) exchange as well as emissions of other chemical compounds such as biogenic volatile organic compounds, which can act as aerosol precursors. The biogeophysical effect, on the other hand, refer to the alteration of land properties such as albedo, evapotranspiration and surface roughness. The climate impacts of land use activities such as forestry are routinely monitored in terms of GHG emissions under the United Nations Framework Convention on Climate Change. The associated biogeophysical impacts, however, are not accounted for as part of this framework despite the growing awareness that these effects matter regionally and should therefore be considered in the decision-making process. In this report, we synthetizes the current state of knowledge concerning the biogeophysical effect of forestry activities with a special focus on Switzerland. Beside reviewing the existing literature we also present new results for Switzerland based on observation-driven estimates as well as process-based modelling.

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.

Climate model biases in the representation of albedo variations between land cover classes contribute to uncertainties on the climate impact of land cover changes since pre-industrial times, especially on the associated radiative forcing. Recent publications of new observation-based datasets offer opportunities to investigate these biases and their impact on historical surface albedo changes in simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Conducting such an assessment is, however, complicated by the non-availability of albedo values for specific land cover classes in CMIP and the limited number of simulations isolating the land use forcing. In this study, we demonstrate the suitability of a new methodology to extract the albedo of trees and crops–grasses in standard climate model simulations. We then apply it to historical runs from 17 CMIP5 models and compare the obtained results to satellite-derived reference data. This allows us to identify substantial biases in the representation of the albedo of trees and crops–grasses as well as the surface albedo change due to the transition between these two land cover classes in the analysed models. Additionally, we reconstruct the local surface albedo changes induced by historical conversions between trees and crops–grasses for 15 CMIP5 models. This allows us to derive estimates of the albedo-induced radiative forcing from land cover changes since pre-industrial times. We find a multi-model range from 0 to −0.17 W m−2, with a mean value of −0.07 W m−2. Constraining the surface albedo response to transitions between trees and crops–grasses from the models with satellite-derived data leads to a revised multi-model mean estimate of −0.09 W m−2 but an increase in the multi-model range. However, after excluding one model with unrealistic conversion rates from trees to crops–grasses the remaining individual model results vary between −0.03 and −0.11 W m−2. These numbers are at the lower end of the range provided by the IPCC AR5 (−0.15±0.10 W m−2). The approach described in this study can be applied to other model simulations, such as those from CMIP6, especially as the evaluation diagnostic described here has been included in the ESMValTool v2.0. ISSN:2190-4987 ISSN:2190-4979

Observational evidence suggests that compared to non-forested areas, forests have a cooling effect on daytime land surface temperature (LST) and a warming effect on nighttime LST in many regions of the world, thus implying that forests dampen the diurnal temperature range. This feature is not captured by current climate models. Using the Community Land Model 5.0 (CLM5.0), we show that this diurnal behavior can be captured when accounting for biomass heat storage (BHS). The nighttime release of energy absorbed by the vegetation biomass during the day increases both nighttime LST and ambient air temperature in forested regions by more than 1 K. The daytime cooling is weaker than the nighttime warming effect, because the energy uptake by the biomass is compensated by a reduction in the turbulent heat fluxes during day. This diurnal asymmetry of the temperature response to BHS leads to a warming of daily mean temperatures, which is amplified during boreal summer warm extremes. Compared to MODIS, CLM5.0 overestimates the diurnal LST range over forested areas. The inclusion of BHS reduces this bias due to its dampening effect on diurnal LST variations. Further, BHS attenuates the negative bias in the nighttime LST difference of forest minus grassland and cropland, when compared to MODIS observations. These results indicate that it is essential to consider BHS when examining the influence of forests on diurnal temperature variations. BHS should thus be included in land surface models used to assess the climatic consequences of land use changes such as deforestation or afforestation., Environmental Research Letters, 14 (8), ISSN:1748-9326, ISSN:1748-9318

Modeling studies have shown the importance of biogeophysical effects of deforestation on local climate conditions but have also highlighted the lack of agreement across different models. Recently, remote-sensing observations have been used to assess the contrast in albedo, evapotranspiration (ET), and land surface temperature (LST) between forest and nearby open land on a global scale. These observations provide an unprecedented opportunity to evaluate the ability of land surface models to simulate the biogeophysical effects of forests. Here, we evaluate the representation of the difference of forest minus open land (i.e., grassland and cropland) in albedo, ET, and LST in the Community Land Model version 4.5 (CLM4.5) using various remote-sensing and in situ data sources. To extract the local sensitivity to land cover, we analyze plant functional type level output from global CLM4.5 simulations, using a model configuration that attributes a separate soil column to each plant functional type. Using the separated soil column configuration, CLM4.5 is able to realistically reproduce the biogeophysical contrast between forest and open land in terms of albedo, daily mean LST, and daily maximum LST, while the effect on daily minimum LST is not well captured by the model. Furthermore, we identify that the ET contrast between forests and open land is underestimated in CLM4.5 compared to observation-based products and even reversed in sign for some regions, even when considering uncertainties in these products. We then show that these biases can be partly alleviated by modifying several model parameters, such as the root distribution, the formulation of plant water uptake, the light limitation of photosynthesis, and the maximum rate of carboxylation. Furthermore, the ET contrast between forest and open land needs to be better constrained by observations to foster convergence amongst different land surface models on the biogeophysical effects of forests. Overall, this study demonstrates the potential of comparing subgrid model output to local observations to improve current land surface models' ability to simulate land cover change effects, which is a promising approach to reduce uncertainties in future assessments of land use impacts on climate., Biogeosciences, 15 (15), ISSN:1726-4170

Climate model biases in the representation of albedo variations between land cover classes contribute to uncertainties on the climate impact of land cover changes since pre-industrial times, especially on the associated radiative forcing. Recent publications of new observation-based datasets offer opportunities to investigate these biases and their impact on historical surface albedo changes in simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Conducting such an assessment is, however, complicated by the non-availability of albedo values for specific land cover classes in CMIP and the limited number of simulations isolating the land use forcing. In this study, we demonstrate the suitability of a new methodology to extract the albedo of trees and crops–grasses in standard climate model simulations. We then apply it to historical runs from 17 CMIP5 models and compare the obtained results to satellite-derived reference data. This allows us to identify substantial biases in the representation of the albedo of trees and crops–grasses as well as the surface albedo change due to the transition between these two land cover classes in the analysed models. Additionally, we reconstruct the local surface albedo changes induced by historical conversions between trees and crops–grasses for 15 CMIP5 models. This allows us to derive estimates of the albedo-induced radiative forcing from land cover changes since pre-industrial times. We find a multi-model range from 0 to −0.17 W m−2, with a mean value of −0.07 W m−2. Constraining the surface albedo response to transitions between trees and crops–grasses from the models with satellite-derived data leads to a revised multi-model mean estimate of −0.09 W m−2 but an increase in the multi-model range. However, after excluding one model with unrealistic conversion rates from trees to crops–grasses the remaining individual model results vary between −0.03 and −0.11 W m−2. These numbers are at the lower end of the range provided by the IPCC AR5 (−0.15±0.10 W m−2). The approach described in this study can be applied to other model simulations, such as those from CMIP6, especially as the evaluation diagnostic described here has been included in the ESMValTool v2.0., Earth System Dynamics, 11 (4), ISSN:2190-4987, ISSN:2190-4979

Land-cover changes can affect the climate by altering the water and energy balance of the land surface. Numerous modelling studies have indicated that alterations at the land surface can result in considerable changes in precipitation. Yet land-cover-induced precipitation changes remain largely unconstrained by observations. Here we use an observation-based continental-scale statistical model to show that forestation of rain-fed agricultural land in Europe triggers substantial changes in precipitation. Locally, we find an increase in precipitation following forestation, in particular in winter, which is supported by a paired rain-gauge analysis. In addition, forests are estimated to increase downwind precipitation in most regions during summer. By contrast, the downwind effect in winter is positive in coastal areas but near neutral and negative in Continental and Northern Europe, respectively. The combined local and non-local effects of a realistic reforestation scenario, constrained by sustainability safeguards, are estimated to increase summer precipitation by 7.6 ± 6.7% on average over Europe (0.13 ± 0.11 mm d–1), potentially offsetting a substantial part of the projected precipitation decrease from climate change. We therefore conclude that land-cover-induced alterations of precipitation should be considered when developing land management strategies for climate change adaptation and mitigation., Nature Geoscience, 14, ISSN:1752-0908, ISSN:1752-0894

Forests influence climate through a myriad of chemical, physical and biological processes and are an essential lever in the efforts to counter climate change. The majority of studies investigating potential climate benefits from forests have focused on forest area changes, while changes to forest management, in particular those affecting species composition, have received much less attention. Using a statistical model based on remote sensing observations over Europe, we show that broad-leaved tree species locally reduce land surface temperatures in summer compared to needle-leaved species. The summer mean cooling effect related to an increase in broad-leaved tree fraction of 80% is relatively modest (~ 0.3–0.75 K), but is amplified during exceptionally warm periods. The reduction of daily maximum temperatures during the hottest days reaches up to 1.8 K in the Atlantic region and up to 1.5 K in Continental and Mediterranean regions. Hot temperature extremes adversely affect humans and ecosystems and are expected to become more frequent in a future climate. Thus, forest management strategies aiming to increase the fraction of broad-leaved species could help to reduce some of the adverse local impacts caused by hot temperature extremes. However, the overall benefits and trade-offs related to an increase in the broad-leaved tree fraction in European forests needs to be further investigated and assessed carefully when adapting forest management strategies., Scientific Reports, 10, ISSN:2045-2322

Urban trees influence temperatures in cities. However, their effectiveness at mitigating urban heat in different climatic contexts and in comparison to treeless urban green spaces has not yet been sufficiently explored. Here, we use high-resolution satellite land surface temperatures (LSTs) and land-cover data from 293 European cities to infer the potential of urban trees to reduce LSTs. We show that urban trees exhibit lower temperatures than urban fabric across most European cities in summer and during hot extremes. Compared to continuous urban fabric, LSTs observed for urban trees are on average 0-4 K lower in Southern European regions and 8-12 K lower in Central Europe. Treeless urban green spaces are overall less effective in reducing LSTs, and their cooling effect is approximately 2-4 times lower than the cooling induced by urban trees. By revealing continental-scale patterns in the effect of trees and treeless green spaces on urban LST our results highlight the importance of considering and further investigating the climate-dependent effectiveness of heat mitigation measures in cities., Nature Communications, 12 (1), ISSN:2041-1723