The breastfeeding experiences of mothers with COVID-19 infection in a selected hospital, South India

Breastfeeding is the natural way of feeding a new-born. The COVID-19 scenario makes breastfeeding more challenging than usual periods. This study aimed to describe the breastfeeding experiences of mothers with COVID-19 infection. Qualitative data were collected from thirteen COVID-19-infected immediate postnatal mothers through google form questionnaires and in-depth interviews. The COVID-19 infected mothers faced difficulties in feeding their babies. Some of the mothers couldn’t be fed colostrum. Severe stress, lack of family and professional support, COVID-19 infection, and inadequate breastmilk production are the hindering factors affecting the breastfeeding habit of mothers. The main breastfeeding problems are apprehension of spreading the infection to the baby, physical separation due to COVID-19 infection, difficulty in latching, and reluctance to breastfeed after formula feeding. Even though there are lots of problems in breastfeeding among mothers and babies, breastfeeding must be initiated and maintained for the benefit of the mother and baby

Prior biosphere model impact on global terrestrial CO2 fluxes estimated from OCO-2 retrievals

This study assesses the impact of different state of the art global biospheric CO2 flux models, when applied as prior information, on inverse model &ldquo;top-down&rdquo; estimates of terrestrial CO2 fluxes obtained when assimilating Orbiting Carbon Observatory 2 (OCO-2) observations. This is done with a series of observing system simulation experiments (OSSEs) using synthetic CO2 column-average dry air mole fraction (XCO2) retrievals sampled at the OCO-2 satellite spatiotemporal frequency. The OSSEs utilized a 4-D variational (4D-Var) assimilation system with the GEOS-Chem global chemical transport model (CTM) to estimate CO2 net ecosystem exchange (NEE) fluxes using synthetic OCO-2 observations. The impact of biosphere models in inverse model estimates of NEE is quantified by conducting OSSEs using the NASA-CASA, CASA-GFED, SiB-4, and LPJ models as prior estimates and using NEE from the multi-model ensemble mean of the Multiscale Synthesis and Terrestrial Model Intercomparison Project as the &ldquo;truth&rdquo;. Results show that the assimilation of simulated XCO2 retrievals at OCO-2 observing modes over land results in posterior NEE estimates which generally reproduce &ldquo;true&rdquo; NEE globally and over terrestrial TransCom-3 regions that are well-sampled. However, we find larger spread among posterior NEE estimates, when using different prior NEE fluxes, in regions and seasons that have limited OCO-2 observational coverage and a large range in &ldquo;bottom-up&rdquo; NEE fluxes. Seasonally averaged posterior NEE estimates had standard deviations (SD) of &sim;10&thinsp;% to &sim;50&thinsp;% of the multi-model-mean NEE for different TransCom-3 land regions with significant NEE fluxes (regions/seasons with a NEE flux &ge;0.5&thinsp;PgC&thinsp;yr&minus;1). On a global average, the seasonally averaged residual impact of the prior model NEE assumption on the posterior NEE spread is &sim;10&thinsp;%&ndash;20&thinsp;% of the posterior NEE mean. Additional OCO-2 OSSE simulations demonstrate that posterior NEE estimates are also sensitive to the assumed prior NEE flux uncertainty statistics, with spread in posterior NEE estimates similar to those when using variable prior model NEE fluxes. In fact, the sensitivity of posterior NEE estimates to prior error statistics was larger than prior flux values in some regions/times in the tropics and Southern Hemisphere where sufficient OCO-2 data were available and large differences between the prior and truth were evident. Overall, even with the availability of spatiotemporally dense OCO-2 data, noticeable residual differences (up to &sim;20&thinsp;%&ndash;30&thinsp;% globally and 50&thinsp;% regionally) in posterior NEE flux estimates remain that were caused by the choice of prior model flux values and the specification of prior flux uncertainties.</p

National CO2 budgets (2015–2020) inferred from atmospheric CO2 observations in support of the Global Stocktake

Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries’ carbon budgets. These estimates are based on "top-down" NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements, or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with "bottom-up" estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1° × 1° gridded dataset and as a country-level dataset. Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 PgCO2 yr-1 (0.90–1.25 PgC yr-1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems

Model preemption based on dynamic analysis of simulation data to accelerate traffic light timing optimisation

Since simulation-based optimisation typically requires large numbers of runs to identify sufficiently good solutions, the costs in terms of time and hardware can be enormous. To avoid unnecessary simulation runs, surrogate models can be applied, which estimate the simulation output under a given parameter combination. Model preemption is a related technique that dynamically analyses the simulation state at runtime to identify runs unlikely to result in a high-quality solution and terminates such runs early. However, existing work on model preemption relies on model-specific termination rules. In this paper, we describe an architecture for simulation-based optimisation using model preemption based on estimations of the simulation output. In a case study, the approach is applied to the optimisation of traffic light timings in a traffic simulation. We show that within a given time and hardware budget, model preemption enables the identification of higher-quality solutions than those found through traditional simulation-based optimisation.National Research Foundation (NRF)Accepted versionThis work was financially supported by the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) programme

OCO‐2 Satellite‐Imposed Constraints on Terrestrial Biospheric CO 2 Fluxes Over South Asia

International audienceThe spatiotemporal variability of terrestrial biospheric carbon dioxide (CO2) fluxes over South Asia has large uncertainty. The Orbiting Carbon Observatory 2 (OCO-2) satellite provides much-needed retrievals of column-average CO2 on a global-scale, with the highest sensitivity to surface CO2 fluxes and spatiotemporal resolution available to-date. This study conducted global inverse model simulations, assimilating in situ (IS) data and OCO-2 retrievals, to assess optimized CO2 net ecosystem exchange (NEE) fluxes for South Asia. Annual Net Biome Exchange (NBE = NEE + biomass burning) fluxes over South Asia were estimated to be near neutral (0.04 ± 0.14 PgC yr−1) using both IS and OCO-2 observations. The most robust result found by assimilating OCO-2 observations was the constraint imposed on the seasonal cycle of NBE fluxes. The amplitude of the seasonal cycle of NEE was found to be larger than previously assumed. The OCO-2 inversion led to an NBE seasonal amplitude of 0.34 PgC month−1, which was larger compared to IS constrained NBE (0.19 PgC month−1) and MsTMIP ensemble mean NEE (0.16 PgC month−1). Moreover, OCO-2 data imposed a phase shift in the NBE seasonal cycle predicted by the prior model. The larger magnitude of NEE seasonality, and phase shift, simulated when assimilating OCO-2 observations are in general agreement with previous studies assimilating regional aircraft observations in addition to global IS observations. This result suggests that OCO-2 provides valuable data that allows for the estimate of NBE on a regional scale in a similar manner as regional in situ aircraft networks

Constraining China’s land carbon sink from emerging satellite CO 2 observations: progress and challenges

International audienc

Improved Constraints on the Recent Terrestrial Carbon Sink Over China by Assimilating OCO‐2 XCO₂ Retrievals

The magnitude and distribution of China's terrestrial carbon sink remain uncertain due to insufficient observational constraints; satellite column-average dry-air mole fraction carbon dioxide (XCO 2) retrievals may fill some of this gap. Here, we estimate China's carbon sink using atmospheric inversions of the Orbiting Carbon Observatory 2 (OCO-2) XCO 2 retrievals within different platforms, including the Global Carbon Assimilation System (GCAS) v2, the Copernicus Atmosphere Monitoring Service, and the OCO-2 Model Inter-comparison Project (MIP). We find that they consistently place the largest net biome production (NBP) in the south on an annual basis compared to the northeast and other main agricultural areas during peak growing season, coinciding well with the distribution of forests and crops, respectively. Moreover, the mean seasonal cycle amplitude of NBP in OCO-2 inversions is obviously larger than that of biosphere model simulations and slightly greater than surface CO 2 inversions. More importantly, the mean seasonal cycle of the OCO-2 inversions is well constrained in the temperate, tropical, and subtropical monsoon climate zones, with better inter-model consistency at a sub-regional scale compared to in situ inversions and biosphere model simulations. In addition, the OCO-2 inversions estimate the mean annual NBP in China for 2015-2019 to be between 0.34 (GCASv2) and 0.47 ± 0.16 PgC/yr (median ± std; OCO-2 v10 MIP), and indicate the impacts of climate extremes (e.g., the 2019 drought) on the interannual variations of NBP. Our results suggest that assimilating OCO-2 XCO 2 retrievals is crucial for improving our understanding of China's terrestrial carbon sink regime

Evaluating Global Atmospheric Inversions of Terrestrial Net Ecosystem Exchange CO ₂ Over North America on Seasonal and Sub‐Continental Scales

International audienceGlobal atmospheric inversions are increasingly being used to estimate regional-scale net ecosystem exchange (NEE) of CO 2 (e.g.

Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2 (OCO-2) version 9 and in situ data and comparison to OCO-2 version 7

International audienceThe Orbiting Carbon Observatory 2 (OCO-2) satellite has been providing information to estimate carbon dioxide (CO 2) fluxes at global and regional scales since 2014 through the combination of CO 2 retrievals with top-down atmospheric inversion methods. Column average CO 2 dry-air mole fraction retrievals have been constantly improved. A bias correction has been applied in the OCO-2 version 9 retrievals compared to the previous OCO-2 version 7r improving data accuracy and coverage. We study an ensemble of 10 atmospheric inversions all characterized by different transport models, data assimilation algorithms, and prior fluxes using first OCO-2 v7 in 2015-2016 and then OCO-2 version 9 land observations for the longer period 2015-2018. Inversions assimilating in situ (IS) measurements have also been used to provide a baseline against which the satellite-driven results are compared. The time series at different scales (going from global to regional scales) of the models emissions are analyzed and compared to each experiment using either OCO-2 or IS data. We then evaluate the inversion ensemble based on the dataset from the Total Carbon Column Observing Network (TCCON), aircraft, and in situ observations, all independent from assimilated data. While we find a similar constraint of global total carbon emissions between the ensemble spread using IS and both OCO-2 retrievals, differences between the two retrieval versions appear over regional scales and particularly in tropical Africa. A difference in the carbon budget between v7 and v9 is found over this region, which seems to show the impact Published by Copernicus Publications on behalf of the European Geosciences Union. 1098 H. Peiro et al.: Four years of global carbon cycle observed from OCO-2 version 9 and in situ data of corrections applied in retrievals. However, the lack of data in the tropics limits our conclusions, and the estimation of carbon emissions over tropical Africa require further analysis