Retrieval of XCO2 from simulated Orbiting Carbon Observatory measurements using the fast linearized R-2OS radiative transfer model

In a recent paper, we introduced a novel technique to compute the polarization in a vertically inhomogeneous, scattering-absorbing medium using a two orders of scattering (2OS) radiative transfer (RT) model. The 2OS computation is an order of magnitude faster than a full multiple scattering scalar calculation and can be implemented as an auxiliary code to compute polarization in operational retrieval algorithms. In this paper, we employ the 2OS model for polarization in conjunction with a scalar RT model (Radiant) to simulate backscatter measurements in near infrared (NIR) spectral regions by space-based instruments such as the Orbiting Carbon Observatory (OCO). Computations are performed for six different sites and two seasons, representing a variety of viewing geometries, surface and aerosol types. The aerosol extinction (at 13000 cm^−1) was varied from 0 to 0.3. The radiance errors using the Radiant/2OS (R-2OS) RT model are an order of magnitude (or more) smaller than errors arising from the use of the scalar model alone. In addition, we perform a linear error analysis study to show that the errors in the retrieved column-averaged dry air mole fraction of CO2 (XCO2) using the R-2OS model are much lower than the “measurement” noise and smoothing errors appearing in the inverse model. On the other hand, we show that use of the scalar model alone induces X CO2 errors that could dominate the retrieval error budget

A new space-borne perspective of crop productivity variations over the US Corn Belt

Remotely-sensed solar-induced chlorophyll fluorescence (SIF) provides a means to assess vegetation productivity in a more direct way than via the greenness of leaves. SIF is produced by plants alongside photosynthesis so it is generally thought to provide a more direct probe of plant status. We analyze inter-annual variations of SIF over the US Corn Belt using a seven-year time series (2010–2016) retrieved from measurements of short-wave IR radiation collected by the Japanese Greenhouse gases Observing SATellite (GOSAT). Using survey data and annual reports from the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), we relate anomalies in the GOSAT SIF time series to meteorological and climatic events that affected planting or growing seasons. The events described in the USDA annual reports are confirmed using remote sensing-based data such as land surface temperature, precipitation, water storage anomalies and soil moisture. These datasets were carefully collocated with the GOSAT footprints on a sub-pixel basis to remove any effect that could occur due to different sampling. We find that cumulative SIF, integrated from April to June, tracks the planting progress established in the first half of the planting season (Pearson correlation r > 0.89). Similarly, we show that crop yields for corn (maize) and soybeans are equally well correlated to the integrated SIF from July to October (r > 0.86). Our results for SIF are consistent with reflectance-based vegetation indices, that have a longer established history of crop monitoring. Despite GOSAT’s sparse sampling, we were able to show the potential for using satellite-based SIF to study agriculturally-managed vegetation

An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data

Emissions of methane (CH4) from tropical ecosystems, and how they respond to changes in climate, represent one of the biggest uncertainties associated with the global CH4 budget. Historically, this has been due to the dearth of pan-tropical in situ measurements, which is particularly acute in Africa. By virtue of their superior spatial coverage, satellite observations of atmospheric CH4 columns can help to narrow down some of the uncertainties in the tropical CH4 emission budget. We use proxy column retrievals of atmospheric CH4 (XCH4) from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the nested version of the GEOS-Chem atmospheric chemistry and transport model (0.5 ∘ ×0.625 ∘ ) to infer emissions from tropical Africa between 2010 and 2016. Proxy retrievals of XCH4 are less sensitive to scattering due to clouds and aerosol than full physics retrievals, but the method assumes that the global distribution of carbon dioxide (CO2) is known. We explore the sensitivity of inferred a posteriori emissions to this source of systematic error by using two different XCH4 data products that are determined using different model CO2 fields. We infer monthly emissions from GOSAT XCH4 data using a hierarchical Bayesian framework, allowing us to report seasonal cycles and trends in annual mean values. We find mean tropical African emissions between 2010 and 2016 range from 76 (74–78) to 80 (78–82) Tg yr−1, depending on the proxy XCH4 data used, with larger differences in Northern Hemisphere Africa than Southern Hemisphere Africa. We find a robust positive linear trend in tropical African CH4 emissions for our 7-year study period, with values of 1.5 (1.1–1.9) Tg yr−1 or 2.1 (1.7–2.5) Tg yr−1, depending on the CO2 data product used in the proxy retrieval. This linear emissions trend accounts for around a third of the global emissions growth rate during this period. A substantial portion of this increase is due to a short-term increase in emissions of 3 Tg yr−1 between 2011 and 2015 from the Sudd in South Sudan. Using satellite land surface temperature anomalies and altimetry data, we find this increase in CH4 emissions is consistent with an increase in wetland extent due to increased inflow from the White Nile, although the data indicate that the Sudd was anomalously dry at the start of our inversion period. We find a strong seasonality in emissions across Northern Hemisphere Africa, with the timing of the seasonal emissions peak coincident with the seasonal peak in ground water storage. In contrast, we find that a posteriori CH4 emissions from the wetland area of the Congo Basin are approximately constant throughout the year, consistent with less temporal variability in wetland extent, and significantly smaller than a priori estimates

Seasonal and Inter-annual Variation of Evapotranspiration in Amazonia Based on Precipitation, River Discharge and Gravity Anomaly Data

We analyzed seasonal and spatial variations of evapotranspiration (ET) for five Amazon sub-basins and their response to the 2015/16 El Nino episode using a recently developed water-budget approach. ET varied typically between similar to 7 and 10 cm/month with exception of the Xingu basin for which it varied between 10 and 15 cm/month. Outstanding features of ET seasonality are (i) generally weak seasonality, (ii) two ET peaks for the two very wet catchments Solimoes and Negro, with one occurring during the wet season and one during the drier season, and (iii) a steady increase of ET during the second half of the dry season for the three drier catchments (Madeira, Tapajos, Xingu). Peak ET occurs during the first half of the wet season consistent with leaf flush occurring before the onset of the wet season. With regards to inter-annual variation, we found firstly that for the Solimoes and Madeira catchments the period with large positive wet season anomalies (2012-2015) is associated with negative ET anomalies, and negative SIF (solar induced fluorescence) anomalies. Furthermore, we found negative ET of several cm/months and SIF (up to 50%) anomalies for most of the Amazon basin during the 2015/16 El Nino event suggesting down-regulation of productivity as a main factor of positive carbon flux anomalies during anomalously hot and dry conditions. These results are of interest in view of predicted warmer and more erratic future climate conditions.Peer reviewe

Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground‐based TCCON data and model calculations

We report new short-wave infrared (SWIR) column retrievals of atmospheric methane (X_(CH4)) from the Japanese Greenhouse Gases Observing SATellite (GOSAT) and compare observed spatial and temporal variations with correlative ground-based measurements from the Total Carbon Column Observing Network (TCCON) and with the global 3-D GEOS-Chem chemistry transport model. GOSAT X_(CH4) retrievals are compared with daily TCCON observations at six sites between April 2009 and July 2010 (Bialystok, Park Falls, Lamont, Orleans, Darwin and Wollongong). GOSAT reproduces the site-dependent seasonal cycles as observed by TCCON with correlations typically between 0.5 and 0.7 with an estimated single-sounding precision between 0.4–0.8%. We find a latitudinal-dependent difference between the X_(CH4) retrievals from GOSAT and TCCON which ranges from 17.9 ppb at the most northerly site (Bialystok) to −14.6 ppb at the site with the lowest latitude (Darwin). We estimate that the mean smoothing error difference included in the GOSAT to TCCON comparisons can account for 15.7 to 17.4 ppb for the northerly sites and for 1.1 ppb at the lowest latitude site. The GOSAT X_(CH4) retrievals agree well with the GEOS-Chem model on annual (August 2009 – July 2010) and monthly timescales, capturing over 80% of the zonal variability. Differences between model and observed X_(CH4) are found over key source regions such as Southeast Asia and central Africa which will be further investigated using a formal inverse model analysis

Advancing Scientific Understanding of the Global Methane Budget in Support of the Paris Agreement

The 2015 Paris Agreement of the United Nations Framework Convention on Climate Change aims to keep global average temperature increases well below 2 °C of preindustrial levels in the Year 2100. Vital to its success is achieving a decrease in the abundance of atmospheric methane (CH4), the second most important anthropogenic greenhouse gas. If this reduction is to be achieved, individual nations must make and meet reduction goals in their nationally determined contributions, with regular and independently verifiable global stock taking. Targets for the Paris Agreement have been set, and now the capability must follow to determine whether CH4 reductions are actually occurring. At present, however, there are significant limitations in the ability of scientists to quantify CH4 emissions accurately at global and national scales and to diagnose what mechanisms have altered trends in atmospheric mole fractions in the past decades. For example, in 2007, mole fractions suddenly started rising globally after a decade of almost no growth. More than a decade later, scientists are still debating the mechanisms behind this increase. This study reviews the main approaches and limitations in our current capability to diagnose the drivers of changes in atmospheric CH4 and, crucially, proposes ways to improve this capability in the coming decade. Recommendations include the following: (i) improvements to process‐based models of the main sectors of CH4 emissions—proposed developments call for the expansion of tropical wetland flux measurements, bridging remote sensing products for improved measurement of wetland area and dynamics, expanding measurements of fossil fuel emissions at the facility and regional levels, expanding country‐ specific data on the composition of waste sent to landfill and the types of wastewater treatment systems implemented, characterizing and representing temporal profiles of crop growing seasons, implementing parameters related to ruminant emissions such as animal feed, and improving the detection of small fires associated with agriculture and deforestation; (ii) improvements to measurements of CH4 mole fraction and its isotopic variations—developments include greater vertical profiling at background sites, expanding networks of dense urban measurements with a greater focus on relatively poor countries, improving the precision of isotopic ratio measurements of 13CH4, CH3D, 14CH4, and clumped isotopes, creating isotopic reference materials for international‐scale development, and expanding spatial and temporal characterization of isotopic source signatures; and (iii) improvements to inverse modeling systems to derive emissions from atmospheric measurements—advances are proposed in the areas of hydroxyl radical quantification, in systematic uncertainty quantification through validation of chemical transport models, in the use of source tracers for estimating sector‐level emissions, and in the development of time and spaceresolved national inventories. These and other recommendations are proposed for the major areas of CH4 science with the aim of improving capability in the coming decade to quantify atmospheric CH4 budgets on the scales necessary for the success of climate policies. Plain Language Summary Methane is the second largest contributor to climate warming from human activities since preindustrial times. Reducing human‐made emissions by half is a major component of the 2015 Paris Agreement target to keep global temperature increases well below 2 °C. In parallel to the methane emission reductions pledged by individual nations, new capabilities are needed to determine independently whether these reductions are actually occurring and whether methane concentrations in the atmosphere are changing for reasons that are clearly understood. At present significant challenges limit the ability of scientists to identify the mechanisms causing changes in atmospheric methane. This study reviews current and emerging tools in methane science and proposes major advances needed in the coming decade to achieve this crucial capability. We recommend further developing the models that simulate the processes behind methane emissions, improving atmospheric measurements of methane and its major carbon and hydrogen isotopes, and advancing abilities to infer the rates of methane being emitted and removed from the atmosphere from these measurements. The improvements described here will play a major role in assessing emissions commitments as more cities, states, and countries report methane emission inventories and commit to specific emission reduction targets. </div

Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations

Wetlands are the largest natural source of methane. The ability to model the emissions of methane from natural wetlands accurately is critical to our understanding of the global methane budget and how it may change under future climate scenarios. The simulation of wetland methane emissions involves a complicated system of meteorological drivers coupled to hydrological and biogeochemical processes. The Joint UK Land Environment Simulator (JULES) is a process-based land surface model that underpins the UK Earth System Model (UKESM) and is capable of generating estimates of wetland methane emissions. In this study, we use GOSAT satellite observations of atmospheric methane along with the TOMCAT global 3-D chemistry transport model to evaluate the performance of JULES in reproducing the seasonal cycle of methane over a wide range of tropical wetlands. By using an ensemble of JULES simulations with differing input data and process configurations, we investigate the relative importance of the meteorological driving data, the vegetation, the temperature dependency of wetland methane production and the wetland extent. We find that JULES typically performs well in replicating the observed methane seasonal cycle. We calculate correlation coefficients to the observed seasonal cycle of between 0.58 and 0.88 for most regions; however, the seasonal cycle amplitude is typically underestimated (by between 1.8 and 19.5 ppb). This level of performance is comparable to that typically provided by state-of-the-art data-driven wetland CH4 emission inventories. The meteorological driving data are found to be the most significant factor in determining the ensemble performance, with temperature dependency and vegetation having moderate effects. We find that neither wetland extent configuration outperforms the other, but this does lead to poor performance in some regions. We focus in detail on three African wetland regions (Sudd, Southern Africa and Congo) where we find the performance of JULES to be poor and explore the reasons for this in detail. We find that neither wetland extent configuration used is sufficient in representing the wetland distribution in these regions (underestimating the wetland seasonal cycle amplitude by 11.1, 19.5 and 10.1 ppb respectively, with correlation coefficients of 0.23, 0.01 and 0.31). We employ the Catchment-based Macro-scale Floodplain (CaMa-Flood) model to explicitly represent river and floodplain water dynamics and find that these JULES-CaMa-Flood simulations are capable of providing a wetland extent that is more consistent with observations in this regions, highlighting this as an important area for future model development.</p