Vegetation anomalies caused by antecedent precipitation in most of the world

Quantifying environmental controls on vegetation is critical to predict the net effect of climate change on global ecosystems and the subsequent feedback on climate. Following a non-linear Granger causality framework based on a random forest predictive model, we exploit the current wealth of multi-decadal satellite data records to uncover the main drivers of monthly vegetation variability at the global scale. Results indicate that water availability is the most dominant factor driving vegetation globally: about 61% of the vegetated surface was primarily water-limited during 1981-2010. This included semiarid climates but also transitional ecoregions. Intraannually, temperature controls Northern Hemisphere deciduous forests during the growing season, while antecedent precipitation largely dominates vegetation dynamics during the senescence period. The uncovered dependency of global vegetation on water availability is substantially larger than previously reported. This is owed to the ability of the framework to (1) disentangle the co-linearities between radiation/temperature and precipitation, and (2) quantify non-linear impacts of climate on vegetation. Our results reveal a prolonged effect of precipitation anomalies in dry regions: due to the long memory of soil moisture and the cumulative, nonlinear, response of vegetation, water-limited regions show sensitivity to the values of precipitation occurring three months earlier. Meanwhile, the impacts of temperature and radiation anomalies are more immediate and dissipate shortly, pointing to a higher resilience of vegetation to these anomalies. Despite being infrequent by definition, hydro-climatic extremes are responsible for up to 10% of the vegetation variability during the 1981-2010 period in certain areas, particularly in water-limited ecosystems. Our approach is a first step towards a quantitative comparison of the resistance and resilience signature of different ecosystems, and can be used to benchmark Earth system models in their representations of past vegetation sensitivity to changes in climate

A non-linear Granger-causality framework to investigate climate-vegetation dynamics

Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate-vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate-vegetation dynamics

Terrestrial evaporation response to modes of climate variability

Large-scale modes of climate variability (or teleconnection patterns), such as the El Nino Southern Oscillation and the North Atlantic Oscillation, affect local weather worldwide. However, the response of terrestrial water and energy fluxes to these modes of variability is still poorly understood. Here, we analyse the response of evaporation to 16 teleconnection patterns, using a simple supervised learning framework and global observation-based datasets of evaporation and its key climatic drivers. Our results show that the month-to-month variability in terrestrial evaporation is strongly affected by (coupled) oscillations in sea-surface temperature and air pressure: in specific hotspot regions, up to 40% of the evaporation dynamics can be explained by climate indices describing the fundamental modes of climate variability. While the El Nino Southern Oscillation affects the dynamics in land evaporation worldwide, other phenomena such as the East Pacific-North Pacific teleconnection pattern are more dominant at regional scales. Most modes of climate variability affect terrestrial evaporation by inducing changes in the atmospheric demand for water. However, anomalies in precipitation associated to particular teleconnections are crucial for the evaporation in water-limited regimes, as well as in forested regions where interception loss forms a substantial fraction of total evaporation. Our results highlight the need to consider the concurrent impact of these teleconnections to accurately predict the fate of the terrestrial branch of the hydrological cycle, and provide observational evidence to help improve the representation of surface fluxes in Earth system models

Contribution of water-limited ecoregions to their own supply of rainfall

The occurrence of wet and dry growing seasons in water-limited regions remains poorly understood, partly due to the complex role that these regions play in the genesis of their own rainfall. This limits the predictability of global carbon and water budgets, and hinders the regional management of naturalresources. Using novel satellite observations and atmospheric trajectory modelling, we unravel the origin and immediate drivers of growing-season precipitation, and the extent to which ecoregions themselves contribute to their own supply of rainfall. Results show that persistent anomalies in growing-season precipitation—and subsequent biomass anomalies—are caused by a complex interplay of land and ocean evaporation, air circulation and local atmospheric stability changes. For regions such as the Kalahari and Australia, the volumes of moisture recycling decline in dry years, providing a positive feedback that intensifies dry conditions. However, recycling ratios increase up to40%, pointing to the crucial role of these regions in generating their own supply of rainfall; transpiration in periods of water stress allows vegetation to partly offset the decrease in regional precipitation. Findings highlight the need to adequately represent vegetation–atmosphere feedbacks in models to predict biomass changes and to simulate the fate of water-limited regions in our warming climate

A carbon sink-driven approach to estimate gross primary production from microwave satellite observations

Global estimation of Gross Primary Production (GPP) - the uptake of atmospheric carbon dioxide by plants through photosynthesis - is commonly based on optical satellite remote sensing data. This presents a source-driven approach since it uses the amount of absorbed light, the main driver of photosynthesis, as a proxy for GPP. Vegetation Optical Depth (VOD) estimates obtained from microwave sensors provide an alternative and independent data source to estimate GPP on a global scale, which may complement existing GPP products. Recent studies have shown that VOD is related to aboveground biomass, and that both VOD and temporal changes in VOD relate to GPP. In this study, we build upon this concept and propose a model for estimating GPP from VOD. Since the model is driven by vegetation biomass, as observed through VOD, it presents a carbon sink-driven approach to quantify GPP and, therefore, is conceptually different from common source-driven approaches. The model developed in this study uses single frequencies from active or passive microwave VOD retrievals from C-, X- and Ku-band (Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E)) to estimate GPP at the global scale. We assessed the ability for temporal and spatial extrapolation of the model using global GPP from FLUXCOM and in situ GPP from FLUXNET. We further performed upscaling of in situ GPP based on different VOD data sets and compared these estimates with the FLUXCOM and MODerate-resolution Imaging Spectroradiometer (MODIS) GPP products. Our results show that the model developed for individual grid cells using VOD and change in VOD as input performs well in predicting temporal patterns in GPP for all VOD data sets. For spatial extrapolation of the model, however, additional input variables are needed to represent the spatial variability of the VOD-GPP relationship due to differences in vegetation type. As additional input variable, we included the grid cell median VOD (as a proxy for vegetation cover), which increased the model performance during cross validation. Mean annual GPP obtained for AMSR-E X-band data tends to overestimate mean annual GPP for FLUXCOM and MODIS but shows comparable latitudinal patterns. Overall, our findings demonstrate the potential of VOD for estimating GPP. The sink-driven approach provides additional information about GPP independent of optical data, which may contribute to our knowledge about the carbon source-sink balance in different ecosystems

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as "open-loop" models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byrans Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E(SWI), SMOSSWI, AMSR2(SWI), and ASCAT(SWI), with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50% of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six openloop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by C0 :12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by C0:06, suggesting that data assimilation yields significant benefits at the global scale

Assessing the relationship between microwave vegetation optical depth and gross primary production

At the global scale, the uptake of atmospheric carbon dioxide by terrestrial ecosystems through photosynthesis is commonly estimated through vegetation indices or biophysical properties derived from optical remote sensing data. Microwave observations of vegetated areas are sensitive to different components of the vegetation layer than observations in the optical domain and may therefore provide complementary information on the vegetation state, which may be used in the estimation of Gross Primary Production (GPP). However, the relation between GPP and Vegetation Optical Depth (VOD), a biophysical quantity derived from microwave observations, is not yet known. This study aims to explore the relationship between VOD and GPP. VOD data were taken from different frequencies (L-, C-, and X-band) and from both active and passive microwave sensors, including the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS) mission, the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E) and a merged VOD data set from various passive microwave sensors. VOD data were compared against FLUXCOM GPP and Solar-Induced chlorophyll Fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). FLUXCOM GPP estimates are based on the upscaling of flux tower GPP observations using optical satellite data, while SIF observations present a measure of photosynthetic activity and are often used as a proxy for GPP. For relating VOD to GPP, three variables were analyzed: original VOD time series, temporal changes in VOD (ΔVOD), and positive changes in VOD (ΔVOD≥0). Results show widespread positive correlations between VOD and GPP with some negative correlations mainly occurring in dry and wet regions for active and passive VOD, respectively. Correlations between VOD and GPP were similar or higher than between VOD and SIF. When comparing the three variables for relating VOD to GPP, correlations with GPP were higher for the original VOD time series than for ΔVOD or ΔVOD≥0 in case of sparsely to moderately vegetated areas and evergreen forests, while the opposite was true for deciduous forests. Results suggest that original VOD time series should be used jointly with changes in VOD for the estimation of GPP across biomes, which may further benefit from combining active and passive VOD data

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Drought, mutualism breakdown, and landscape-scale degradation of seagrass beds

In many marine ecosystems, biodiversity critically depends on foundation species such as corals and seagrasses that engage in mutualistic interactions [1-3]. Concerns grow that environmental disruption of marine mutualisms exacerbates ecosystem degradation, with breakdown of the obligate coral mutualism ("coral bleaching") being an iconic example [2, 4, 5]. However, as these mutualisms are mostly facultative rather than obligate, it remains unclear whether mutualism breakdown is a common risk in marine ecosystems, and thus a potential accelerator of ecosystem degradation. Here, we provide evidence that drought triggered landscape-scale seagrass degradation and show the consequent failure of a facultative mutualistic feedback between seagrass and sulfide-consuming lucinid bivalves that in turn appeared to exacerbate the observed collapse. Local climate and remote sensing analyses revealed seagrass collapse after a summer with intense low-tide drought stress. Potential analysis-a novel approach to detect feedback-mediated state shifts-revealed two attractors (healthy and degraded states) during the collapse, suggesting that the drought disrupted internal feedbacks to cause abrupt, patch-wise degradation. Field measurements comparing degraded patches that were healthy before the collapse with patches that remained healthy demonstrated that bivalves declined dramatically in degrading patches with associated high sediment sulfide concentrations, confirming the breakdown of the mutualistic seagrass-lucinid feedback. Our findings indicate that drought triggered mutualism breakdown, resulting in toxic sulfide concentrations that aggravated seagrass degradation. We conclude that external disturbances can cause sudden breakdown of facultative marine mutualistic feedbacks. As this may amplify ecosystem degradation, we suggest including mutualisms in marine conservation and restoration approaches

Attributable mortality of antibiotic resistance in Gram-negative infections in the Netherlands: a parallel matched cohort study

Objectives: Antibiotic resistance in Gram-negative bacteria has been associated with increased mortality. This was demonstrated mostly for third-generation cephalosporin-resistant (3GC-R) Enterobacterales bacteraemia in international studies. Yet, the burden of resistance specifically in the Netherlands and created by all types of Gram-negative infection has not been quantified. We therefore investigated the attributable mortality of antibiotic resistance in Gram-negative infections in the Netherlands. Methods: In eight hospitals, a sample of Gram-negative infections was identified between 2013 and 2016, and separated into resistant and susceptible infection cohorts. Both cohorts were matched 1:1 to non-infected control patients on hospital, length of stay at infection onset, and age. In this parallel matched cohort set-up, 30-day mortality was compared between infected and non-infected patients. The impact of resistance was then assessed by dividing the two separate risk ratios (RRs) for mortality attributable to Gram-negative infection. Results: We identified 1954 Gram-negative infections, of which 1190 (61%) involved Escherichia coli, 210 (11%) Pseudomonas aeruginosa, and 758 (39%) bacteraemia. Resistant Gram-negatives caused 243 infections (12%; 189 (78%) 3GC-R Enterobacterales, nine (4%) multidrug-resistant P. aeruginosa, no carbapenemase-producing Enterobacterales). Subsequently, we matched 1941 non-infected controls. After adjustment, point estimates for RRs comparing mortality between infections and controls were similarly higher than 1 in case of resistant infections and susceptible infections (1.42 (95% confidence interval 0.66–3.09) and 1.32 (1.06–1.65), respectively). By dividing these, the RR reflecting attributable mortality of resistance was calculated as 1.08 (0.48–2.41). Conclusions: In the Netherlands, antibiotic resistance did not increase 30-day mortality in Gram-negative infections