ALBERT

Description: Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model–data integration approaches can guide the future development of global process-oriented vegetation-fire models.

Description: This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates – internally consistently – the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at https://gitlab.pik-potsdam.de/lpjml/LPJmL, we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process.

Description: Ecosystem service (ES) models can only inform policy design adequately if they incorporate ecological processes. We used the Lund-Potsdam-Jena managed Land (LPJmL) model, to address following questions for Mexico, Bolivia and Brazilian Amazon: (i) How different are C stocks and C sequestration quantifications under standard (when soil and litter C and heterotrophic respiration are not considered) and comprehensive (including all C stock and heterotrophic respiration) approach? and (ii) How does the valuation of C stock and C sequestration differ in national payments for ES and global C funds or markets when comparing both approach? We found that up to 65% of C stocks have not been taken into account by neglecting to include C stored in soil and litter, resulting in gross underpayments (up to 500 times lower). Since emissions from heterotrophic respiration of organic material offset a large proportion of C gained through growth of living matter, we found that markets and decision-makers are inadvertently overestimating up to 100 times C sequestrated. New approaches for modelling C services relevant ecological process-based can help accounting for C in soil, litter and heterotrophic respiration and become important for the operationalization of agreements on climate change mitigation following the COP21 in 2015.

Description: Fluxes of organic and inorganic carbon within the Amazon basin are considerably controlled by annual flooding, which triggers the export of terrigenous organic material to the river and ultimately to the Atlantic Ocean. The amount of carbon imported to the river and the further conversion, transport and export of it depend on temperature, atmospheric CO2, terrestrial productivity and carbon storage, as well as discharge. Both terrestrial productivity and discharge are influenced by climate and land use change. The coupled LPJmL and RivCM model system (Langerwisch et al., 2016) has been applied to assess the combined impacts of climate and land use change on the Amazon riverine carbon dynamics. Vegetation dynamics (in LPJmL) as well as export and conversion of terrigenous carbon to and within the river (RivCM) are included. The model system has been applied for the years 1901 to 2099 under two deforestation scenarios and with climate forcing of three SRES emission scenarios, each for five climate models. We find that high deforestation (business-as-usual scenario) will strongly decrease (locally by up to 90 %) riverine particulate and dissolved organic carbon amount until the end of the current century. At the same time, increase in discharge leaves net carbon transport during the first decades of the century roughly unchanged only if a sufficient area is still forested. After 2050 the amount of transported carbon will decrease drastically. In contrast to that, increased temperature and atmospheric CO2 concentration determine the amount of riverine inorganic carbon stored in the Amazon basin. Higher atmospheric CO2 concentrations increase riverine inorganic carbon amount by up to 20 % (SRES A2). The changes in riverine carbon fluxes have direct effects on carbon export, either to the atmosphere via outgassing or to the Atlantic Ocean via discharge. The outgassed carbon will increase slightly in the Amazon basin, but can be regionally reduced by up to 60 % due to deforestation. The discharge of organic carbon to the ocean will be reduced by about 40 % under the most severe deforestation and climate change scenario. These changes would have local and regional consequences on the carbon balance and habitat characteristics in the Amazon basin itself as well as in the adjacent Atlantic Ocean.

Description: Comparing model output and observed data is an important step for assessing model performance and quality of simulation results. However, such comparisons are often hampered by differences in spatial scales between local point observations and large-scale simulations of grid cells or pixels. In this study, we propose a generic approach for a pixel-to-point comparison and provide statistical measures accounting for the uncertainty resulting from landscape variability and measurement errors in ecosystem variables. The basic concept of our approach is to determine the statistical properties of small-scale (within-pixel) variability and observational errors, and to use this information to correct for their effect when large-scale area averages (pixel) are compared to small-scale point estimates. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity (woody net primary productivity, NPP) and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest, a region with the typical problem of low data availability, potential scale mismatch and thus high model uncertainty. We find that the DGVMs under- and overestimate aboveground biomass by 25 % and up to 60 %, respectively. Our comparison metrics provide a quantitative measure for model–data agreement and show moderate to good agreement with the region-wide spatial biomass pattern detected by plot observations. However, all four DGVMs overestimate woody productivity and underestimate residence time of woody biomass even when accounting for the large uncertainty range of the observational data. This is because DGVMs do not represent the relation between productivity and residence time of woody biomass correctly. Thus, the DGVMs may simulate the correct large-scale patterns of biomass but for the wrong reasons. We conclude that more information about the underlying processes driving biomass distribution are necessary to improve DGVMs. Our approach provides robust statistical measures for any pixel-to-point comparison, which is applicable for evaluation of models and remote-sensing products.

Description: Climate extremes have the potential to cause extreme responses of terrestrial ecosystem functioning. However, it is neither straightforward to quantify and predict extreme ecosystem responses, nor to attribute these responses to specific climate drivers. Here, we construct a factorial experiment based on a large ensemble of process-oriented ecosystem model simulations driven by a regional climate model (12 500 model years in 1985–2010) in six European regions. Our aims are to (1) attribute changes in the intensity and frequency of simulated ecosystem productivity extremes (EPEs) to recent changes in climate extremes, CO2 concentration, and land use, and to (2) assess the effect of timing and seasonal interaction on the intensity of EPEs. Evaluating the ensemble simulations reveals that (1) recent trends in EPEs are seasonally contrasting: spring EPEs show consistent trends towards increased carbon uptake, while trends in summer EPEs are predominantly negative in net ecosystem productivity (i.e. higher net carbon release under drought and heat in summer) and close-to-neutral in gross productivity. While changes in climate and its extremes (mainly warming) and changes in CO2 increase spring productivity, changes in climate extremes decrease summer productivity neutralizing positive effects of CO2. Furthermore, we find that (2) drought or heat wave induced carbon losses in summer (i.e. negative EPEs) can be partly compensated by a higher uptake in the preceding spring in temperate regions. Conversely, however, carry-over effects from spring to summer that arise from depleted soil moisture exacerbate the carbon losses caused by climate extremes in summer, and are thus undoing spring compensatory effects. While the spring-compensation effect is increasing over time, the carry-over effect shows no trend between 1985–2010. The ensemble ecosystem model simulations provide a process-based interpretation and generalization for spring-summer interacting carbon cycle effects caused by climate extremes (i.e. compensatory and carry-over effects). In summary, the ensemble ecosystem modelling approach presented in this paper offers a novel route to scrutinize ecosystem responses to changing climate extremes in a probabilistic framework, and to pinpoint the underlying eco-physiological mechanisms.

Description: This paper shows recent progress in our understanding of climate variability and trends in the Amazon region, and how these interact with land use change. The review includes an overview of up-to-date information on climate and hydrological variability, and on warming trends in Amazonia, which reached 0.6–0.7°C over the last 40 years, with 2016 as the warmest year since at least 1950 (0.9°C + 0.3°C). We focus on local and remote drivers of climate variability and change. We review the impacts of these drivers on the length of dry season, the role of the forest in climate and carbon cycles, the resilience of the forest, the risk of fires and biomass burning, and the potential “die back” of the Amazon forests if surpassing a “tipping point”. The role of the Amazon in moisture recycling and transport is also investigated, and a review of model development for climate change projections in the region is included. In sum, future sustainability of the Amazonian forests and its many services requires management strategies that consider the likelihood of multi-year droughts superimposed on a continued warming trend. Science has assembled enough knowledge to underline the global and regional importance of an intact Amazon region that can support policymaking and to keep this sensitive ecosystem functioning. This major challenge requires substantial resources and strategic cross-national planning, and a unique blend of expertise and capacities established in Amazon countries and from international collaboration. This also highlights the role of deforestation control in support of policy for mitigation options as established in the Paris Agreement of 2015.

Description: Irrigated rice croplands are among the world's most important agro-ecosystems. They provide food for more than 3.5 billion people and a range of other ecosystem services (ESS). However, the sustainability of rice agro-ecosystems is threatened by continuing climate and land-use changes. To estimate their combined effects on a bundle of ESS, we applied the vegetation and hydrology model LPJmL to seven study areas in the Philippines and Vietnam. We quantified future changes in the provision of four essential ESS (carbon storage, carbon sequestration, provision of irrigation water and rice production) under two climate scenarios (until 2100) and three site-specific land-use scenarios (until 2030), and examined the synergies and trade-offs in ESS responses to these drivers. Our results show that not all services can be provided in the same amounts in the future. In the Philippines and Vietnam the projections estimated a decrease in rice yields (by approximately 30%) and in carbon storage (by 15%) and sequestration (by 12%) towards the end of the century under the current land-use pattern. In contrast, the amount of available irrigation water was projected to increase in all scenarios by 10%–20%. However, the results also indicate that land-use change may partially offset the negative climate impacts in regions where cropland expansion is possible, although only at the expense of natural vegetation. When analysing the interactions between ESS, we found consistent synergies between rice production and carbon storage and trade-offs between carbon storage and provision of irrigation water under most scenarios. Our results show that not only the effects of climate and land-use change alone but also the interaction between ESS have to be considered to allow sustainable management of rice agro-ecosystems under global change.

Description: Biological responses to climate change have been widely documented across taxa and regions, but it remains unclear whether species are maintaining a good match between phenotype and environment, i.e. whether observed trait changes are adaptive. Here we reviewed 10,090 abstracts and extracted data from 71 studies reported in 58 relevant publications, to assess quantitatively whether phenotypic trait changes associated with climate change are adaptive in animals. A meta-analysis focussing on birds, the taxon best represented in our dataset, suggests that global warming has not systematically affected morphological traits, but has advanced phenological traits. We demonstrate that these advances are adaptive for some species, but imperfect as evidenced by the observed consistent selection for earlier timing. Application of a theoretical model indicates that the evolutionary load imposed by incomplete adaptive responses to ongoing climate change may already be threatening the persistence of species.