Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land)

AbstractGlobal surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS; SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TU-Wien-WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010–2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space–time analysis using Hovmöller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R>0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and south-eastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRA-Land SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records

“Hot Hand” on Strike: Bowling Data Indicates Correlation to Recent Past Results, Not Causality

Recently, the “hot hand” phenomenon regained interest due to the availability and accessibility of large scale data sets from the world of sports. In support of common wisdom and in contrast to the original conclusions of the seminal paper about this phenomenon by Gilovich, Vallone and Tversky in 1985, solid evidences were supplied in favor of the existence of this phenomenon in different kinds of data. This came after almost three decades of ongoing debates whether the “hot hand” phenomenon in sport is real or just a mis-perception of human subjects of completely random patterns present in reality. However, although this phenomenon was shown to exist in different sports data including basketball free throws and bowling strike rates, a somehow deeper question remained unanswered: are these non random patterns results of causal, short term, feedback mechanisms or simply time fluctuations of athletes performance. In this paper, we analyze large amounts of data from the Professional Bowling Association(PBA). We studied the results of the top 100 players in terms of the number of available records (summed into more than 450,000 frames). By using permutation approach and dividing the analysis into different aggregation levels we were able to supply evidence for the existence of the “hot hand” phenomenon in the data, in agreement with previous studies. Moreover, by using this approach, we were able to demonstrate that there are, indeed, significant fluctuations from game to game for the same player but there is no clustering of successes (strikes) and failures (non strikes) within each game. Thus we were lead to the conclusion that bowling results show correlation to recent past results but they are not influenced by them in a causal manner

The Hot (Invisible?) Hand: Can Time Sequence Patterns of Success/Failure in Sports Be Modeled as Repeated Random Independent Trials?

The long lasting debate initiated by Gilovich, Vallone and Tversky in is revisited: does a “hot hand” phenomenon exist in sports? Hereby we come back to one of the cases analyzed by the original study, but with a much larger data set: all free throws taken during five regular seasons () of the National Basketball Association (NBA). Evidence supporting the existence of the “hot hand” phenomenon is provided. However, while statistical traces of this phenomenon are observed in the data, an open question still remains: are these non random patterns a result of “success breeds success” and “failure breeds failure” mechanisms or simply “better” and “worse” periods? Although free throws data is not adequate to answer this question in a definite way, we speculate based on it, that the latter is the dominant cause behind the appearance of the “hot hand” phenomenon in the data

Protocol of the baseline assessment for the Environments for Healthy Living (EHL) Wales cohort study

Background Health is a result of influences operating at multiple levels. For example, inadequate housing, poor educational attainment, and reduced access to health care are clustered together, and are all associated with reduced health. Policies which try to change individual people's behaviour have limited effect when people have little control over their environment. However, structural environmental change and an understanding of the way that influences interact with each other, has the potential to facilitate healthy choices irrespective of personal resources. The aim of Environments for Healthy Living (EHL) is to investigate the impact of gestational and postnatal environments on health, and to examine where structural change can be brought about to optimise health outcomes. The baseline assessment will focus on birth outcomes and maternal and infant health. Methods/Design EHL is a longitudinal birth cohort study. We aim to recruit 1000 pregnant women in the period April 2010 to March 2013. We will examine the impact of the gestational environment (maternal health) and the postnatal environment (housing and neighbourhood conditions) on subsequent health outcomes for the infants born to these women. Data collection will commence during the participants' pregnancy, from approximately 20 weeks gestation. Participants will complete a questionnaire, undergo anthropometric measurements, wear an accelerometer, compile a food diary, and have environmental measures taken within their home. They will also be asked to consent to having a sample of umbilical cord blood taken following delivery of their baby. These data will be complemented by routinely collected electronic data such as health records from GP surgeries, hospital admissions, and child health and development records. Thereafter, participants will be visited annually for follow-up of subsequent exposures and child health outcomes. Discussion The baseline assessment of EHL will provide information concerning the impact of gestational and postnatal environments on birth outcomes and maternal and infant health. The findings can be used to inform the development of complex interventions targeted at structural, environmental factors, intended to reduce ill-health. Long-term follow-up of the cohort will focus on relationships between environmental exposures and the later development of adverse health outcomes, including obesity and diabetes

Molecular Insights into Reprogramming-Initiation Events Mediated by the OSKM Gene Regulatory Network

Somatic cells can be reprogrammed to induced pluripotent stem cells by over-expression of OCT4, SOX2, KLF4 and c-MYC (OSKM). With the aim of unveiling the early mechanisms underlying the induction of pluripotency, we have analyzed transcriptional profiles at 24, 48 and 72 hours post-transduction of OSKM into human foreskin fibroblasts. Experiments confirmed that upon viral transduction, the immediate response is innate immunity, which induces free radical generation, oxidative DNA damage, p53 activation, senescence, and apoptosis, ultimately leading to a reduction in the reprogramming efficiency. Conversely, nucleofection of OSKM plasmids does not elicit the same cellular stress, suggesting viral response as an early reprogramming roadblock. Additional initiation events include the activation of surface markers associated with pluripotency and the suppression of epithelial-to-mesenchymal transition. Furthermore, reconstruction of an OSKM interaction network highlights intermediate path nodes as candidates for improvement intervention. Overall, the results suggest three strategies to improve reprogramming efficiency employing: 1) anti-inflammatory modulation of innate immune response, 2) pre-selection of cells expressing pluripotency-associated surface antigens, 3) activation of specific interaction paths that amplify the pluripotency signal

Current and emerging developments in subseasonal to decadal prediction

Weather and climate variations of subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus remains broadly similar (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correct, calibration and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Prograame (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis

Heatwave Characteristics in the Recent Climate and at Different Global Warming Levels: A Multimodel Analysis at the Global Scale

Abstract The representation of heatwaves (HWs) in the Coupled Model Intercomparison Project phase 6 (CMIP6) models is analyzed. This study (a) evaluates the performance of CMIP6 simulations against global reanalysis and observations regarding time‐ and intensity‐related criteria and (b) investigates how HWs are projected to change at different global warming levels (GWLs). During 1979–2014, the dispersion of the models is comparable to the observational uncertainty for the time indices (duration, frequency, number of events). It is of the order of one event per year, 1 day for the duration of the events and 2 days for the frequency, with tendencies for over‐ or underestimation, depending on the reference data set and the region considered. For the HW magnitude, the models' dispersion can reach 15°C for a given region and is significantly higher than the observational uncertainty. The mean intensity of HWs tends to be overestimated, which is partly attributed to overly pronounced drying of the soil during HW events. The contribution of the soil moisture anomaly to the temperature anomaly during recent specific HWs is shown to reach up to 30% of the signal. For a given GWL, intensification of HW occurrence, spatial extension, and duration is detected worldwide, but it is modulated at the regional scale and strongly model dependent. For time‐related indices, tropical regions and the Arabian Peninsula will be most impacted, but the maximum temperature will strongly increase in mid‐latitude regions. Time–space analyses of the evolution of HW properties for a given GWL are discussed

The Role of Irrigation Expansion on Historical Climate Change: Insights From CMIP6

International audienceApproximately 70% of the world's total land surface is under human management (Luyssaert et al., 2014), which is altering Earth's landscape. Human-water interactions (Oki, 1999), in particular, combined with increasing population growth (Vörösmarty et al., 2000), are expected to further exacerbate hydrologic extremes such as floods and droughts (Milly et al., 2002). Societies are increasingly intervening in the hydrological cycle, for instance, through irrigating cultivated areas, which is likely to expand in the future to meet the growing demand for food and energy (Foley et al., 2011). According to Puy (2018), irrigated areas have increased proportionally faster than the growth of the world population. Crops grown in irrigated areas account for over 40% of global food production (Abatzoglou et al., 2013). Irrigated areas represent ∼90% of consumptive water use globally (Rost Abstract To produce food for a growing world population, irrigated areas have increased from approximately 0.63 million km 2 of land in 1900 to 3.1 million km 2 of land in 2005. Despite this massive expansion, irrigation is still overlooked in most state-of-the-art Earth system models (ESMs) involved in the Coupled Model Intercomparison Project phase 6 (CMIP6). To our knowledge, only three CMIP6 models represent irrigation activities: CESM2, GISS-E2-1-G, and NorESM2-LM. Here, we investigate the role of irrigation on historical climate at global and regional scales by analyzing trends of key surface climate variables in CMIP6 simulations during 1900-2014. The three models including irrigation show distinct behavior from the 15 models without irrigation over intensively irrigated areas (i.e., >50% of grid cell area is equipped by irrigation): both annual (months that correspond to monthly air temperature higher than 274 K) mean latent heat flux (LHF) and soil moisture increase over time, in contrast to the models without irrigation that show no trend or even a negative trend. The positive LHF trend over intensively irrigated areas in the irrigation-on models is consistent with three satellite-based LHF products. While annual (considering the warmest month in a year) warming trends are found in these regions for most of the no-irrigation models, the increase in LHF induces a cooling trend for the models with irrigation, which was not confirmed by observational air temperature data sets. These findings, supported by satellite data, indicate the importance of improved representation of land management in the next generation of ESM

Satellite-based soil moisture provides missing link between summertime precipitation and surface temperature biases in CMIP5 simulations over conterminous United States

International audiencePast studies have shown that climate simulations have substantial warm and dry biases during the summer in the conterminous United States (CONUS), particularly in the central Great Plains (CGP). These biases have critical implications for the interpretation of climate change projections, but the complex overlap of multiple land-atmosphere feedback processes make them difficult to explain (and therefore correct). Even though surface soil moisture (SM) is often cited as a key control variable in these processes, there are still knowledge gaps about its specific role. Here, we use recently developed remotely sensed SM products to analyse the link between spatial patterns of summertime SM, precipitation and air temperature biases over CONUS in 20 different CMIP5 simulations. We identify three main types of bias combinations: (i) a dry/warm bias over the CGP region, with a significant inter-model correlation between SM and air temperature biases (R = −0.65), (ii) a wet/cold bias in NW CONUS, and (iii) a dry/cold bias in SW CONUS. Combined with irrigation patterns, these results suggest that land-atmosphere feedbacks over the CGP are not only local but have a regional dimension, and demonstrate the added-value of large-scale SM observations for resolving the full feed-back loop between precipitation and temperature