• Energy Research

AbstractAimEarth observation‐based estimates of land–atmosphere exchange of carbon are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. Temperature, soil water content and gross primary production are the main drivers of ecosystem respiration (Reco), and the main aims of this study are to develop an Reco model driven by long‐term global‐scale Earth observations and to study Reco spatiotemporal dynamics 1982–2015.LocationGlobal scale.Time Period1982–2015.Major Taxa StudiedTerrestrial ecosystems.MethodsWe parameterized and applied a global Reco model for 1982–2015 using novel Earth observation‐based data. We studied the relationships between Reco measured at field sites globally and land surface temperature, gross primary production and soil water content. Trends 1982–2015 were quantified, and the contributions from terrestrial regions to the spatiotemporal variability were evaluated.ResultsThe Reco model (LGS‐Reco) captured the between‐site and intra‐ and interannual variability in field‐observed Reco and soil respiration well in comparison with other Earth observation‐based products. The global annual Reco was on average 105.6 ± 2.3 Pg C for 1982–2015, which is close to 105 Pg C according to residuals of the carbon exchange processes within the global carbon budgets. The trend in global terrestrial Reco 1982–2015 was 0.19 ± 0.02 Pg C y−1, with the strongest positive trends found in cropland areas, whereas negative trends were primarily observed for savannah/shrublands of Southern Africa and South America. Trends were especially strong during the eighties and nineties, but substantially smaller 1998–2015.Main ConclusionsThe LGS‐Reco model revealed a substantial increase in global Reco during recent decades. However, the growth rates of global Reco were slower during 1998–2015, partially explaining the reduced growth rates of atmospheric CO2 during this period. The LGR‐Reco product may be an essential source for studying carbon sources and sinks and functioning of the Earth system.

Semi-arid regions play an increasingly important role as a sink within the global carbon (C) cycle and is the main biome driving inter-annual variability in carbon dioxide (CO2) uptake by terrestrial ecosystems. This indicates the need for detailed studies of spatiotemporal variability in C cycling for semi-arid ecosystems. We have synthesized data on the land-atmosphere exchange of CO2 measured with the eddy covariance technique from the six existing sites across the Sahel, one of the largest semi-arid regions in the world. The overall aim of the study is to analyse and quantify the spatiotemporal variability in these fluxes and to analyse to which degree spatiotemporal variation can be explained by hydrological, climatic, edaphic and vegetation variables. All ecosystems were C sinks (average ± total error -162 ± 48 g C m-2 y-1), but were smaller when strongly impacted by anthropogenic influences. Spatial and inter-annual variability in the C flux processes indicated a strong resilience to dry conditions, and were correlated with phenological metrics. Gross primary productivity (GPP) was the most important flux process affecting the sink strength, and diurnal variability in GPP was regulated by incoming radiation, whereas seasonal dynamics was closely coupled with phenology, and soil water content. Diurnal variability in ecosystem respiration was regulated by GPP, whereas seasonal variability was strongly coupled to phenology and GPP. A budget for the entire Sahel indicated a strong C sink mitigating the global anthropogenic C emissions. Global circulation models project an increase in temperature, whereas rainfall is projected to decrease for western Sahel and increase for the eastern part, indicating that the C sink will possibly decrease and increase for the western and eastern Sahel, respectively.

AbstractEarth observation‐based estimates of global gross primary production (GPP) are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. In this study, we attempt an ecosystem‐level physiological approach of estimating GPP using an asymptotic light response function (LRF) between GPP and incoming photosynthetically active radiation (PAR) that better represents the response observed at high spatiotemporal resolutions than the conventional light use efficiency approach. Modelled GPP is thereafter constrained with meteorological and hydrological variables. The variability in field‐observed GPP, net primary productivity and solar‐induced fluorescence was better or equally well captured by our LRF‐based GPP when compared with six state‐of‐the‐art Earth observation‐based GPP products. Over the period 1982–2015, the LRF‐based average annual global terrestrial GPP budget was 121.8 ± 3.5 Pg C, with a detrended inter‐annual variability of 0.74 ± 0.13 Pg C. The strongest inter‐annual variability was observed in semi‐arid regions, but croplands in China and India also showed strong inter‐annual variations. The trend in global terrestrial GPP during 1982–2015 was 0.27 ± 0.02 Pg C year−1, and was generally larger in the northern than the southern hemisphere. Most positive GPP trends were seen in areas with croplands whereas negative trends were observed for large non‐cropped parts of the tropics. Trends were strong during the eighties and nineties but levelled off around year 2000. Other GPP products either showed no trends or continuous increase throughout the study period. This study benchmarks a first global Earth observation‐based model using an asymptotic light response function, improving simulations of GPP, and reveals a stagnation in the global GPP after the year 2000.

AbstractIncreasing aridity is one major consequence of ongoing global climate change and is expected to cause widespread changes in key ecosystem attributes, functions, and dynamics. This is especially the case in naturally vulnerable ecosystems, such as drylands. While we have an overall understanding of past aridity trends, the linkage between temporal dynamics in aridity and dryland ecosystem responses remain largely unknown. Here, we examined recent trends in aridity over the past two decades within global drylands as a basis for exploring the response of ecosystem state variables associated with land and atmosphere processes (e.g., vegetation cover, vegetation functioning, soil water availability, land cover, burned area, and vapor‐pressure deficit) to these trends. We identified five clusters, characterizing spatiotemporal patterns in aridity between 2000 and 2020. Overall, we observe that 44.5% of all areas are getting dryer, 31.6% getting wetter, and 23.8% have no trends in aridity. Our results show strongest correlations between trends in ecosystem state variables and aridity in clusters with increasing aridity, which matches expectations of systemic acclimatization of the ecosystem to a reduction in water availability/water stress. Trends in vegetation (expressed by leaf area index [LAI]) are affected differently by potential driving factors (e.g., environmental, and climatic factors, soil properties, and population density) in areas experiencing water‐related stress as compared to areas not exposed to water‐related stress. Canopy height for example, has a positive impact on trends in LAI when the system is stressed but does not impact the trends in non‐stressed systems. Conversely, opposite relationships were found for soil parameters such as root‐zone water storage capacity and organic carbon density. How potential driving factors impact dryland vegetation differently depending on water‐related stress (or no stress) is important, for example within management strategies to maintain and restore dryland vegetation.

In many domains, large amounts of time series data are being collected and analyzed in a semi-automatic manner. A prominent approach is the seasonal and trend decomposition using locally estimated scatterplot smoothing (STL) technique, which has been applied extensively in the past. However, STL quickly becomes computationally very expensive when applied to large data sets. In this work, we propose the first parallel implementation for the STL decomposition approach, which is tailored to the specific needs of graphics processing units (GPU). Our experimental evaluation on two global-scale case studies in temperature and vegetation trend analysis exhibits at least three-to-four orders of magnitude speed-up, demonstrating the effectiveness of the overall approach and the immense potential of the implementation in spatio-temporal data analyses. The source code is publicly available at https://github.com/diku-dk/hastl. An artifact that allows the experimental results to be reproduced is available at https://sid.erda.dk/sharelink/hOUrqJJ FfA.

Non-linear trend detection in Earth observation time series has become a standard method to characterize changes in terrestrial ecosystems. However, results are largely dependent on the quality and consistency of the input data, and only few studies have addressed the impact of data artifacts on the interpretation of detected abrupt changes. Here we study non-linear dynamics and turning points (TPs) of temperate grasslands in East Eurasia using two independent state-of-the-art satellite NDVI datasets (CGLS v3 and MODIS C6) and explore the impact of water availability on observed vegetation changes during 2001-2019. By applying the Break For Additive Season and Trend (BFAST01) method, we conducted a classification typology based on vegetation dynamics which was spatially consistent between the datasets for 40.86 % (459,669 km2) of the study area. When considering also the timing of the TPs, 27.09 % of the pixels showed consistent results between datasets, suggesting that careful interpretation was needed for most of the areas of detected vegetation dynamics when applying BFAST to a single dataset. Notably, for these areas showing identical typology we found that interrupted decreases in vegetation productivity were dominant in the transition zone between desert and steppes. Here, a strong link with changes in water availability was found for >80 % of the area, indicating that increasing drought stress had regulated vegetation productivity in recent years. This study shows the necessity of a cautious interpretation of the results when conducting advanced characterization of vegetation response to climate variability, but at the same time also the opportunities of going beyond the use of single dataset in advanced time-series approaches to better understanding dryland vegetation dynamics for improved anthropogenic interventions to combat vegetation productivity decrease.

The Sahel has been the object of intensive international research since the drought of the early 1970s. A considerable part of the research has focused on environmental change in general and land degradation, land cover change and climate change in particular. Rich and diverse insights from many different scientific disciplines about these three domains have been put forward. One intriguing feature is that an agreement on the overall trends of environmental change does not appear to emerge: questions such as whether the Sahel is greening, cropland is encroaching on rangelands, drought persists remain contested in the scientific literature, and arguments are supported by contrasting empirical evidence. The paper explores the generic reasons behind this situation in a systematic manner. We distinguish between divergences in interpretations emerging from (1) conceptualizations, definitions and choice of indicators, (2) biases, for example, related to selection of study sites, methodological choices, measurement accuracy, perceptions among interlocutors, and selection of temporal and spatial scales of analysis. The analysis of the root causes for different interpretations suggests that differences in findings could often be considered as complementary insights rather than mutually exclusive. This will have implications for the ways in which scientific results can be expected to support regional environmental policies and contribute to knowledge production.