High-resolution global population projections dataset developed with CMIP6 RCP and SSP scenarios for year 2010-2100.

We present a novel, global 30 arc seconds (∼1 km at the equator) population projection dataset covering each year from 2010 to 2100 that is consistent with both country level population and gridded urban fractions from the Coupled Model Intercomparison Project 6 (CMIP6). While IPCC population projections until 2100 are available at country level for Socio-Economic Pathways (SSPs), land cover (including the urban fraction) is only available for Representative Concentration Pathways (RCPs). To perform simulations of e.g., future supply and demand for agricultural products, fine scale projections of population density are needed for combinations of SSPs and RCPs. Therefore, we generated a 30 arc seconds dataset consistent with both SSPs and RCPs within the framework of the IPCC. This data set is useful in applications where spatially explicit projections of aspects of global change are investigated at a fine spatial scale. For example, if a link function between night-time lights and population density is found based on current satellite images and recent population density data, a projection of night-time light lights can be generated by using this link function with our projected population density. Such a projection can for example be used to evaluate the potential for future light pollution.

Approaching the potential of model-data comparisons of global land carbon storage.

Carbon storage dynamics in vegetation and soil are determined by the balance of carbon influx and turnover. Estimates of these opposing fluxes differ markedly among different empirical datasets and models leading to uncertainty and divergent trends. To trace the origin of such discrepancies through time and across major biomes and climatic regions, we used a model-data fusion framework. The framework emulates carbon cycling and its component processes in a global dynamic ecosystem model, LPJ-GUESS, and preserves the model-simulated pools and fluxes in space and time. Thus, it allows us to replace simulated carbon influx and turnover with estimates derived from empirical data, bringing together the strength of the model in representing processes, with the richness of observational data informing the estimations. The resulting vegetation and soil carbon storage and global land carbon fluxes were compared to independent empirical datasets. Results show model-data agreement comparable to, or even better than, the agreement between independent empirical datasets. This suggests that only marginal improvement in land carbon cycle simulations can be gained from comparisons of models with current-generation datasets on vegetation and soil carbon. Consequently, we recommend that model skill should be assessed relative to reference data uncertainty in future model evaluation studies.

Implications of accounting for management intensity on carbon and nitrogen balances of European grasslands.

European managed grasslands are amongst the most productive in the world. Besides temperature and the amount and timing of precipitation, grass production is also highly controlled by applications of nitrogen fertilizers and land management to sustain a high productivity. Since management characteristics of pastures vary greatly across Europe, land-use intensity and their projections are critical input variables in earth system modeling when examining and predicting the effects of increasingly intensified agricultural and livestock systems on the environment. In this study, we aim to improve the representation of pastures in the dynamic global vegetation model LPJ-GUESS. This is done by incorporating daily carbon allocation for grasses as a foundation to further implement daily land management routines and land-use intensity data into the model to discriminate between intensively and extensively used regions. We further compare our new simulations with leaf area index observations, reported regional grassland productivity, and simulations conducted with the vegetation model ORCHIDEE-GM. Additionally, we analyze the implications of including pasture fertilization and daily management compared to the standard version of LPJ-GUESS. Our results demonstrate that grassland productivity cannot be adequately captured without including land-use intensity data in form of nitrogen applications. Using this type of information improved spatial patterns of grassland productivity significantly compared to standard LPJ-GUESS. In general, simulations for net primary productivity, net ecosystem carbon balance and nitrogen leaching were considerably increased in the extended version. Finally, the adapted version of LPJ-GUESS, driven with projections of climate and land-use intensity, simulated an increase in potential grassland productivity until 2050 for several agro-climatic regions, most notably for the Mediterranean North, the Mediterranean South, the Atlantic Central and the Atlantic South.

Remotely sensed soil moisture to estimate savannah NDVI.

Satellite derived normalized difference vegetation index (NDVI) is a common data source for monitoring regional and global ecosystem properties. In dry lands it has contributed to estimation of inter-annual and seasonal vegetation dynamics and phenology. However, due to the spectral properties of NDVI it can be affected by clouds which can introduce missing data in the time series. Remotely sensed soil moisture has in contrast to NDVI the benefit of being unaffected by clouds due to the measurements being made in the microwave domain. There is therefore a potential in combining the remotely sensed NDVI with remotely sensed soil moisture to enhance the quality and estimate the missing data. We present a step towards the usage of remotely sensed soil moisture for estimation of savannah NDVI. This was done by evaluating the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture and three of its individual products with respect to their relative performance. The individual products are from the advance scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), and the Land Parameter Retrieval Model-Advanced Microwave Scanning Radiometer-Earth Observing System (LPRM-AMSR-E). Each dataset was used to simulate NDVI, which was subsequently compared to remotely sensed NDVI from MODIS. Differences in their ability to estimate NDVI indicated that, on average, CCI soil moisture differs from its individual products by showing a higher average correlation with measured NDVI. Overall NDVI modelled from CCI soil moisture gave an average correlation of 0.81 to remotely sensed NDVI which indicates its potential to be used to estimate seasonal variations in savannah NDVI. Our result shows promise for further development in using CCI soil moisture to estimate NDVI. The modelled NDVI can potentially be used together with other remotely sensed vegetation datasets to enhance the phenological information that can be acquired, thereby, improving the estimates of savannah vegetation phenology.

The climate, the fuel and the land use: Long-term regional variability of biomass burning in boreal forests.

The influence of different drivers on changes in North American and European boreal forests biomass burning (BB) during the Holocene was investigated based on the following hypotheses: land use was important only in the southernmost regions, while elsewhere climate was the main driver modulated by changes in fuel type. BB was reconstructed by means of 88 sedimentary charcoal records divided into six different site clusters. A statistical approach was used to explore the relative contribution of (a) pollen-based mean July/summer temperature and mean annual precipitation reconstructions, (b) an independent model-based scenario of past land use (LU), and (c) pollen-based reconstructions of plant functional types (PFTs) on BB. Our hypotheses were tested with: (a) a west-east northern boreal sector with changing climatic conditions and a homogeneous vegetation, and (b) a north-south European boreal sector characterized by gradual variation in both climate and vegetation composition. The processes driving BB in boreal forests varied from one region to another during the Holocene. However, general trends in boreal biomass burning were primarily controlled by changes in climate (mean annual precipitation in Alaska, northern Quebec, and northern Fennoscandia, and mean July/summer temperature in central Canada and central Fennoscandia) and, secondarily, by fuel composition (BB positively correlated with the presence of boreal needleleaf evergreen trees in Alaska and in central and southern Fennoscandia). Land use played only a marginal role. A modification towards less flammable tree species (by promoting deciduous stands over fire-prone conifers) could contribute to reduce circumboreal wildfire risk in future warmer periods.

Effect of climate dataset selection on simulations of terrestrial GPP: Highest uncertainty for tropical regions.

Biogeochemical models use meteorological forcing data derived with different approaches (e.g. based on interpolation or reanalysis of observation data or a hybrid hereof) to simulate ecosystem processes such as gross primary productivity (GPP). This study assesses the impact of different widely used climate datasets on simulated gross primary productivity and evaluates the suitability of them for reproducing the global and regional carbon cycle as mapped from independent GPP data. We simulate GPP with the biogeochemical model LPJ-GUESS using six historical climate datasets (CRU, CRUNCEP, ECMWF, NCEP, PRINCETON, and WFDEI). The simulated GPP is evaluated using an observation-based GPP product derived from eddy covariance measurements in combination with remotely sensed data. Our results show that all datasets tested produce relatively similar GPP simulations at a global scale, corresponding fairly well to the observation-based data with a difference between simulations and observations ranging from -50 to 60 g m-2 yr-1. However, all simulations also show a strong underestimation of GPP (ranging from -533 to -870 g m-2 yr-1) and low temporal agreement (r < 0.4) with observations over tropical areas. As the shortwave radiation for tropical areas was found to have the highest uncertainty in the analyzed historical climate datasets, we test whether simulation results could be improved by a correction of the tested shortwave radiation for tropical areas using a new radiation product from the International Satellite Cloud Climatology Project (ISCCP). A large improvement (up to 48%) in simulated GPP magnitude was observed with bias corrected shortwave radiation, as well as an increase in spatio-temporal agreement between the simulated GPP and observation-based GPP. This study conducts a spatial inter-comparison and quantification of the performances of climate datasets and can thereby facilitate the selection of climate forcing data over any given study area for modelling purposes.

Earlier occurrence and increased explanatory power of climate for the first incidence of potato late blight caused by Phytophthora infestans in Fennoscandia.

BACKGROUND: Late blight (caused by Phytophthora infestans) is a devastating potato disease that has been found to occur earlier in the season over the last decades in Fennoscandia. Up until now the reasons for this change have not been investigated. Possible explanations for this change are climate alterations, changes in potato production or changes in pathogen biology, such as increased fitness or changes in gene flow within P. infestans populations. The first incidence of late blight is of high economic importance since fungicidal applications should be typically applied two weeks before the first signs of late blight and are repeated on average once a week. METHODS: We use field observations of first incidence of late blight in experimental potato fields from five sites in Sweden and Finland covering a total of 30 years and investigate whether the earlier incidence of late blight can be related to the climate. RESULTS: We linked the field data to meteorological data and found that the previous assumption, used in common late blight models, that the disease only develops at relative humidity levels above 90% had to be rejected. Rather than the typically assumed threshold relationship between late blight disease development and relative humidity we found a linear relationship. Our model furthermore showed two distinct responses of late blight to climate. At the beginning of the observation time (in Sweden until the early 90s and in Finland until the 2000s) the link between climate and first incidence was very weak. However, for the remainder of the time period the link was highly significant, indicating a change in the biological properties of the pathogen which could for example be a change in the dominating reproduction mode or a physiological change in the response of the pathogen to climate. CONCLUSIONS: The study shows that models used in decision support systems need to be checked and re-parametrized regularly to be able to capture changes in pathogen biology. While this study was performed with data from Fennoscandia this new pathogen biology and late blight might spread to (or already be present at) other parts of the world as well. The strong link between climate and first incidence together with the presented model offers a tool to assess late blight incidence in future climates.

High-resolution African population projections from radiative forcing and socio-economic models, 2000 to 2100.

For its fifth assessment report, the Intergovernmental Panel on Climate Change divided future scenario projections (2005-2100) into two groups: Socio-Economic Pathways (SSPs) and Representative Concentration Pathways (RCPs). Each SSP has country-level urban and rural population projections, while the RCPs are based on radiative forcing caused by greenhouse gases, aerosols and associated land-use change. In order for these projections to be applicable in earth system models, SSP and RCP population projections must be at the same spatial scale. Thus, a gridded population dataset that takes into account both RCP-based urban fractions and SSP-based population projection is needed. To support this need, an annual (2000-2100) high resolution (approximately 1km at the equator) gridded population dataset conforming to both RCPs (urban land use) and SSPs (population) country level scenario data were created.

Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model.

Savannah regions are predicted to undergo changes in precipitation patterns according to current climate change projections. This change will affect leaf phenology, which controls net primary productivity. It is of importance to study this since savannahs play an important role in the global carbon cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r2 of 0.6 and root mean squared error of 0.1. We therefore expect similar average results when applying the model to other savannah areas and further expect that it could be used to estimate the productivity of savannah regions.

Leaf traits capture the effects of land use changes and climate on litter decomposability of grasslands across Europe.

Land use and climate changes induce shifts in plant functional diversity and community structure, thereby modifying ecosystem processes. This is particularly true for litter decomposition, an essential process in the biogeochemical cycles of carbon and nutrients. In this study, we asked whether changes in functional traits of living leaves in response to changes in land use and climate were related to rates of litter potential decomposition, hereafter denoted litter decomposability, across a range of 10 contrasting sites. To disentangle the different control factors on litter decomposition, we conducted a microcosm experiment to determine the decomposability under standard conditions of litters collected in herbaceous communities from Europe and Israel. We tested how environmental factors (disturbance and climate) affected functional traits of living leaves and how these traits then modified litter quality and subsequent litter decomposability. Litter decomposability appeared proximately linked to initial litter quality, with particularly clear negative correlations with lignin-dependent indices (litter lignin concentr tion, lignin:nitrogen ratio, and fiber component). Litter quality was directly related to community-weighted mean traits. Lignin-dependent indices of litter quality were positively correlated with community-weighted mean leaf dry matter content (LDMC), and negatively correlated with community-weighted mean leaf nitrogen concentration (LNC). Consequently, litter decomposability was correlated negatively with community-weighted mean LDMC, and positively with community-weighted mean LNC. Environmental factors (disturbance and climate) influenced community-weighted mean traits. Plant communities experiencing less frequent or less intense disturbance exhibited higher community-weighted mean LDMC, and therefore higher litter lignin content and slower litter decomposability. LDMC therefore appears as a powerful marker of both changes in land use and of the pace of nutrient cycling across 10 contrasting sites.

Assessing the effects of land-use change on plant traits, communities and ecosystem functioning in grasslands: a standardized methodology and lessons from an application to 11 European sites.

BACKGROUND AND AIMS: A standardized methodology to assess the impacts of land-use changes on vegetation and ecosystem functioning is presented. It assumes that species traits are central to these impacts, and is designed to be applicable in different historical, climatic contexts and local settings. Preliminary results are presented to show its applicability. METHODS: Eleven sites, representative of various types of land-use changes occurring in marginal agro-ecosystems across Europe and Israel, were selected. Climatic data were obtained at the site level; soil data, disturbance and nutrition indices were described at the plot level within sites. Sixteen traits describing plant stature, leaf characteristics and reproductive phase were recorded on the most abundant species of each treatment. These data were combined with species abundance to calculate trait values weighed by the abundance of species in the communities. The ecosystem properties selected were components of above-ground net primary productivity and decomposition of litter. KEY RESULTS: The wide variety of land-use systems that characterize marginal landscapes across Europe was reflected by the different disturbance indices, and were also reflected in soil and/or nutrient availability gradients. The trait toolkit allowed us to describe adequately the functional response of vegetation to land-use changes, but we suggest that some traits (vegetative plant height, stem dry matter content) should be omitted in studies involving mainly herbaceous species. Using the example of the relationship between leaf dry matter content and above-ground dead material, we demonstrate how the data collected may be used to analyse direct effects of climate and land use on ecosystem properties vs. indirect effects via changes in plant traits. CONCLUSIONS: This work shows the applicability of a set of protocols that can be widely applied to assess the impacts of global change drivers on species, communities and ecosystems.