ALBERT

Description: Cloud radar reflectivity profiles can be an important measurement for the investigation of cloud vertical structure (CVS). However, extracting intended meteorological cloud content from the measurement often demands an effective technique or algorithm that can reduce error and observational uncertainties in the recorded data. In this work, a technique is proposed to identify and separate cloud and non-hydrometeor echoes using the radar Doppler spectral moments profile measurements. The point and volume target-based theoretical radar sensitivity curves are used for removing the receiver noise floor and identified radar echoes are scrutinized according to the signal decorrelation period. Here, it is hypothesized that cloud echoes are observed to be temporally more coherent and homogenous and have a longer correlation period than biota. That can be checked statistically using ∼ 4 s sliding mean and standard deviation value of reflectivity profiles. The above step helps in screen out clouds critically by filtering out the biota. The final important step strives for the retrieval of cloud height. The proposed algorithm potentially identifies cloud height solely through the systematic characterization of Z variability using the local atmospheric vertical structure knowledge besides to the theoretical, statistical and echo tracing tools. Thus, characterization of high-resolution cloud radar reflectivity profile measurements has been done with the theoretical echo sensitivity curves and observed echo statistics for the true cloud height tracking (TEST). TEST showed superior performance in screening out clouds and filtering out isolated insects. TEST constrained with polarimetric measurements was found to be more promising under high-density biota whereas TEST combined with linear depolarization ratio and spectral width perform potentially to filter out biota within the highly turbulent shallow cumulus clouds in the convective boundary layer (CBL). This TEST technique is promisingly simple in realization but powerful in performance due to the flexibility in constraining, identifying and filtering out the biota and screening out the true cloud content, especially the CBL clouds. Therefore, the TEST algorithm is superior for screening out the low-level clouds that are strongly linked to the rainmaking mechanism associated with the Indian Summer Monsoon region's CVS.

Description: Methane (CH4) is one of the most important short-lived climate forcers for its critical roles in greenhouse warming and air pollution chemistry in the troposphere, and the water vapor budget in the stratosphere. It is estimated that up to about 8 % of global CH4 emissions occur from South Asia, covering less than 1 % of the global land. With the availability of satellite observations from space, variability in CH4 has been captured for most parts of the global land with major emissions, which were otherwise not covered by the surface observation network. The satellite observation of the columnar dry-air mole fractions of methane (XCH4) is an integrated measure of CH4 densities at all altitudes from the surface to the top of the atmosphere. Here, we present an analysis of XCH4 variability over different parts of India and the surrounding cleaner oceanic regions as measured by the Greenhouse gases Observation SATellite (GOSAT) and simulated by an atmospheric chemistry-transport model (ACTM). Distinct seasonal variations of XCH4 have been observed over the northern (north of 15° N) and southern (south of 15° N) parts of India, corresponding to the peak during the southwestern monsoon (July–September) and early autumn (October–December) seasons, respectively. Analysis of the transport, emission, and chemistry contributions to XCH4 using ACTM suggests that a distinct XCH4 seasonal cycle over northern and southern regions of India is governed by both the heterogeneous distributions of surface emissions and a contribution of the partial CH4 column in the upper troposphere. Over most of the northern Indian Gangetic Plain regions, up to 40 % of the peak-to-trough amplitude during the southwestern (SW) monsoon season is attributed to the lower troposphere ( ∼  1000–600 hPa), and  ∼  40 % to uplifted high-CH4 air masses in the upper troposphere ( ∼  600–200 hPa). In contrast, the XCH4 seasonal enhancement over semi-arid western India is attributed mainly ( ∼  70 %) to the upper troposphere. The lower tropospheric region contributes up to 60 % in the XCH4 seasonal enhancement over the Southern Peninsula and oceanic region. These differences arise due to the complex atmospheric transport mechanisms caused by the seasonally varying monsoon. The CH4 enriched air mass is uplifted from a high-emission region of the Gangetic Plain by the SW monsoon circulation and deep cumulus convection and then confined by anticyclonic wind in the upper tropospheric heights ( ∼  200 hPa). The anticyclonic confinement of surface emission over a wider South Asia region leads to a strong contribution of the upper troposphere in the formation of the XCH4 peak over northern India, including the semi-arid regions with extremely low CH4 emissions. Based on this analysis, we suggest that a link between surface emissions and higher levels of XCH4 is not always valid over Asian monsoon regions, although there is often a fair correlation between surface emissions and XCH4. The overall validity of ACTM simulation for capturing GOSAT observed seasonal and spatial XCH4 variability will allow us to perform inverse modeling of XCH4 emissions in the future using XCH4 data.

Description: We estimate the uncertainty of CO2 flux estimates in atmospheric inversions stemming from differences between different global transport models. Using a set of observing system simulation experiments (OSSEs), we estimate this uncertainty as represented by the spread between five different state-of-the-art global transport models (ACTM, LMDZ, GEOS-Chem, PCTM and TM5), for both traditional in situ CO2 inversions and inversions of XCO2 estimates from the Orbiting Carbon Observatory 2 (OCO-2). We find that, in the absence of relative biases between in situ CO2 and OCO-2 XCO2, OCO-2 estimates of terrestrial flux for TRANSCOM-scale land regions can be more robust to transport model differences than corresponding in situ CO2 inversions. This is due to a combination of the increased spatial coverage of OCO-2 samples and the total column nature of OCO-2 estimates. We separate the two effects by constructing hypothetical in situ networks with the coverage of OCO-2 but with only near-surface samples. We also find that the transport-driven uncertainty in fluxes is comparable between well-sampled northern temperate regions and poorly sampled tropical regions. Furthermore, we find that spatiotemporal differences in sampling, such as between OCO-2 land and ocean soundings, coupled with imperfect transport, can produce differences in flux estimates that are larger than flux uncertainties due to transport model differences. This highlights the need for sampling with as complete a spatial and temporal coverage as possible (e.g., using both land and ocean retrievals together for OCO-2) to minimize the impact of selective sampling. Finally, our annual and monthly estimates of transport-driven uncertainties can be used to evaluate the robustness of conclusions drawn from real OCO-2 and in situ CO2 inversions.

Description: There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

Description: Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH4) budget over 2000–2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000–2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000–2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008–2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16–32] Tg CH4 yr−1 higher methane emissions over the period 2008–2012 compared to 2002–2006. This emission increase mostly originated from the tropics, with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seem to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002–2006 and 2008–2012 differs from one atmospheric inversion study to another. However, all top-down studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the Emission Database for Global Atmospheric Research (EDGARv4.2) inventory, which should be revised to smaller values in a near future. We apply isotopic signatures to the emission changes estimated for individual studies based on five emission sectors and find that for six individual top-down studies (out of eight) the average isotopic signature of the emission changes is not consistent with the observed change in atmospheric 13CH4. However, the partitioning in emission change derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. In addition, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. In most of the top-down studies included here, OH concentrations are considered constant over the years (seasonal variations but without any inter-annual variability). As a result, the methane loss (in particular through OH oxidation) varies mainly through the change in methane concentrations and not its oxidants. For these reasons, changes in the methane loss could not be properly investigated in this study, although it may play a significant role in the recent atmospheric methane changes as briefly discussed at the end of the paper.

Description: The aim of this paper is to present an overview of inverse modeling methods that have been developed over the years for estimating the global sources and sinks of CH4. It provides insight into how techniques and estimates have evolved over time and what the remaining shortcomings are. As such, it serves a didactical purpose of introducing apprentices to the field, but it also takes stock of developments so far and reflects on promising new directions. The main focus is on methodological aspects that are particularly relevant for CH4, such as its atmospheric oxidation, the use of methane isotopologues, and specific challenges in atmospheric transport modeling of CH4. The use of satellite retrievals receives special attention as it is an active field of methodological development, with special requirements on the sampling of the model and the treatment of data uncertainty. Regional scale flux estimation and attribution is still a grand challenge, which calls for new methods capable of combining information from multiple data streams of different measured parameters. A process model representation of sources and sinks in atmospheric transport inversion schemes allows the integrated use of such data. These new developments are needed not only to improve our understanding of the main processes driving the observed global trend but also to support international efforts to reduce greenhouse gas emissions.

Description: Measurements of methane have been made from various observational platforms in the atmosphere. In this article, we have compared four high precision balloon-borne measurements from Hyderabad (17.5°N), India in the period of 1987 and 1998 with a part of HALOE/UARS global observations available since 1991. All the balloon measurements correspond to boreal spring (March and April) but belong to different years. A comparison shows fairly good agreement with each other. The gradient in CH4 profiles in the troposphere is controlled by the variation in vertical transport. The strongest effect of dynamical influence on methane vertical profiles is shown to be resulting from the dynamical quasi-biennial oscillation in the stratosphere, and that has been consistently derived from both the measurement techniques and chemistry-transport model simulations. It is observed that the QBO signal in CH4 anomaly exhibits interhemispheric asymmetry caused by the tropics to midlatitude circulation in the stratosphere. A mechanism is suggested to explain how and to what extent the methane vertical profiles over Hyderabad and higher latitudes could be modulated by the prevailing QBO winds in the tropics. We have also discussed how the same mechanism would affect ozone distribution in the stratosphere quite differently.

Description: We present the mixing ratio, δ13C and δD of atmospheric CH4 using commercial aircraft in the upper troposphere (UT) over the Western Pacific for the period December 2005–September 2010. The observed results were compared with those obtained using commercial container ships in the lower troposphere (LT) over the same region. In the Northern Hemisphere (NH), the UT CH4 mixing ratio shows high values in the boreal summer–autumn, when the LT CH4 mixing ratio reaches a seasonal minimum. From tagged tracer experiments made using an atmospheric chemistry transport model, we found that such high CH4 values are due to rapid transport of air masses influenced by CH4 sources in South Asia and East Asia. The observed isotopic ratio data imply that these areas have CH4 sources with relatively low δ13C and δD signatures such as biogenic sources. Latitudinal distributions of the annual average UT and LT CH4 mixing ratio intersect each other in the tropics; the mixing ratio value is lower in the UT than in the LT in the NH and the situation is reversed in the Southern Hemisphere (SH), due mainly to the NH air intrusion into the SH through the UT. Such intersection of the latitudinal distributions is observable in δD but not in δ13C, implying an additional contribution from reaction of CH4 with active chlorine in the marine boundary layer. δ13C and δD show low values in the NH and high values in the SH both in the UT and in the LT. We also observed an increase in the CH4 mixing ratio and decreases in δ13C and δ

Description: Atmospheric column-averaged mole fractions of carbon dioxide (XCO2) at 53 locations around the world were derived from aircraft measurements covering the altitude range of about 1–10 km. We used CO2 vertical profile measurements from three major carbon cycle programs, a global climatological data set of air number density profiles and tropopause height for calculating XCO2 for the period of 2007–2009. Vertical profiles of the CO2 mixing ratio are complemented by tall tower data up to 400 m from the earth's surface and by simulated profiles in the stratosphere from a chemistry-transport model. The amplitude of the seasonal cycle of calculated XCO2 values shows clear latitudinal dependence, and the amplitude decreases from about 10 ppm at high latitudes in the Northern Hemisphere to at most 2 ppm in the tropics and the Southern Hemisphere. The uncertainties of XCO2 were estimated from assumptions about CO2 profiles for each flight. Typically, uncertainties were less than 1 ppm; thus, this data set is within the level of uncertainty needed for primary validation of XCO2 measurements by the Greenhouse gases Observing SATellite (GOSAT) and by future satellite missions for monitoring greenhouse gases.

Description: We computed high-resolution (1º latitude x  1º longitude) seasonal and annual nitrous oxide (N2O) concentration fields for the Arabian Sea surface layer using a database containing more than 2400 values measured between December 1977 and July 1997. N2O concentrations are highest during the southwest (SW) monsoon along the southern Indian continental shelf. Annual emissions range from 0.33 to 0.70 Tg N2O and are dominated by fluxes from coastal regions during the SW and northeast monsoons. Our revised estimate for the annual N2O flux from the Arabian Sea is much more tightly constrained than the previous consensus derived using averaged in-situ data from a smaller number of studies. However, the tendency to focus on measurements in locally restricted features in combination with insufficient seasonal data coverage leads to considerable uncertainties of the concentration fields and thus in the flux estimates, especially in the coastal zones of the northern and eastern Arabian Sea. The overall mean relative error of the annual N2O emissions from the Arabian Sea was estimated to be at least 65%.