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The new single-pixel TROPESS (TRopospheric Ozone and its Precursors from Earth System Sounding) profile retrievals of carbon monoxide (CO) from the Cross-track Infrared Sounder (CrIS) are evaluated using vertical profiles of in situ observations from the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) aircraft program and from the Atmospheric Tomography Mission (ATom) campaigns. The TROPESS optimal estimation retrievals are produced using the MUSES (MUlti-SpEctra, MUlti-SpEcies, MUlti-Sensors) algorithm, which has heritage from retrieval algorithms developed for the EOS/Aura Tropospheric Emission Spectrometer (TES). TROPESS products provide retrieval diagnostics and error covariance matrices that propagate instrument noise as well as the uncertainties from sequential retrievals of parameters such as temperature and water vapor that are required to estimate the carbon monoxide profiles. The validation approach used here evaluates biases in column and profile values as well as the validity of the retrieval error estimates using the mean and variance of the compared satellite and aircraft observations. CrIS–NOAA GML comparisons had biases of 0.6 % for partial column average volume mixing ratios (VMRs) and (2.3, 0.9, -4.5) % for VMRs at (750, 511, 287) hPa vertical levels, respectively, with standard deviations from 9 % to 14 %. CrIS–ATom comparisons had biases of -0.04 % for partial column and (2.2, 0.5, -3.0) % for (750, 511, 287) hPa vertical levels, respectively, with standard deviations from 6 % to 10 %. The reported observational errors for TROPESS/CrIS CO profiles have the expected behavior with respect to the vertical pattern in standard deviation of the comparisons. These comparison results give us confidence in the use of TROPESS/CrIS CO profiles and error characterization for continuing the multi-decadal record of satellite CO observations. [ABSTRACT FROM AUTHOR]

We present an overview of an optimal estimation algorithm to retrieve peroxyacetyl nitrate (PAN) from single-field-of-view Level 1B radiances measured by the Cross-Track Infrared Sounder (CrIS). CrIS PAN retrievals show peak sensitivity in the mid-troposphere, with degrees of freedom for signal less than or equal to 1.0. We show comparisons with two sets of aircraft measurements from the Atmospheric Tomography Mission (ATom), the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) and the Georgia Tech chemical ionization mass spectrometer (GT-CIMS). We find a systematic difference between the two aircraft datasets, with vertically averaged mid-tropospheric values from the GT-CIMS around 14 % lower than equivalent values from PANTHER. However, the two sets of aircraft measurements are strongly correlated (R2 value of 0.92) and do provide a consistent view of the large-scale variation of PAN. We demonstrate that the retrievals of PAN from CrIS show skill in measurement of these large-scale PAN distributions in the remote mid-troposphere compared to the retrieval prior. The standard deviation of individual CrIS–aircraft differences is 0.08 ppbv, which we take as an estimate of the uncertainty of the CrIS mid-tropospheric PAN for a single satellite field of view. The standard deviation of the CrIS–aircraft comparisons for averaged CrIS retrievals (median of 20 satellite coincidences with each aircraft profile) is lower at 0.05 ppbv. This would suggest that the retrieval error is reduced with averaging, although not with the square root of the number of observations. We find a negative bias of the order of 0.1 ppbv in the CrIS PAN results with respect to the aircraft measurements. This bias shows a dependence on column water vapor. We provide a water-vapor-dependent bias correction for use with the CrIS PAN data. [ABSTRACT FROM AUTHOR]

Single-footprint retrievals of carbon monoxide from the Atmospheric Infrared Sounder (AIRS) are evaluated using aircraft in situ observations. The aircraft data are from the HIAPER Pole-to-Pole Observations (HIPPO, 2009–2011), the first three Atmospheric Tomography Mission (ATom, 2016–2017) campaigns, and the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) Global Greenhouse Gas Reference Network aircraft program in years 2006–2017. The retrievals are obtained using an optimal estimation approach within the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES) algorithm. Retrieval biases and estimated errors are evaluated across a range of latitudes from the subpolar to tropical regions over both ocean and land points. AIRS MUSES CO profiles were compared with HIPPO, ATom, and NOAA GML aircraft observations with a coincidence of 9 h and 50 km to estimate retrieval biases and standard deviations. Comparisons were done for different pressure levels and column averages, latitudes, day, night, land, and ocean observations. We found mean biases of +6.6±4.6 %, +0.6±3.2 %, and -6.1±3.0 % for three representative pressure levels of 750, 510, and 287 hPa, as well as column average mean biases of 1.4±3.6 %. The mean standard deviations for the three representative pressure levels were 15 %, 11 %, and 12 %, and the column average standard deviation was 9 %. Observation errors (theoretical errors) from the retrievals were found to be broadly consistent in magnitude with those estimated empirically from ensembles of satellite aircraft comparisons, but the low values for these observation errors require further investigation. The GML aircraft program comparisons generally had higher standard deviations and biases than the HIPPO and ATom comparisons. Since the GML aircraft flights do not go as high as the HIPPO and ATom flights, results from these GML comparisons are more sensitive to the choice of method for extrapolation of the aircraft profile above the uppermost measurement altitude. The AIRS retrieval performance shows little sensitivity to surface type (land or ocean) or day or night but some sensitivity to latitude. Comparisons to the NOAA GML set spanning the years 2006–2017 show that the AIRS retrievals are able to capture the distinct seasonal cycles but show a high bias of ∼20% in the lower troposphere during the summer when observed CO mixing ratios are at annual minimum values. The retrieval bias drift was examined over the same years 2006–2017 and found to be small at <0.5%. [ABSTRACT FROM AUTHOR]

It is unclear to what extent seasonal water stress impacts on plant productivity over Amazonia. Using new Greenhouse gases Observing SATellite (GOSAT) satellite measurements of sun-induced chlorophyll fluorescence, we show that midday fluorescence varies with water availability, both of which decrease in the dry season over Amazonian regions with substantial dry season length, suggesting a parallel decrease in gross primary production (GPP). Using additional SeaWinds Scatterometer onboard QuikSCAT satellite measurements of canopy water content, we found a concomitant decrease in daily storage of canopy water content within branches and leaves during the dry season, supporting our conclusion. A large part (r[sup 2] = 0.75) of the variance in observed monthly midday fluorescence from GOSAT is explained by water stress over moderately stressed evergreen forests over Amazonia, which is reproduced by model simulations that include a full physiological representation of photosynthesis and fluorescence. The strong relationship between GOSAT and model fluorescence (r[sup 2] = 0.79) was obtained using a fixed leaf area index, indicating that GPP changes are more related to environmental conditions than chlorophyll contents. When the dry season extended to drought in 2010 over Amazonia, midday basin-wide GPP was reduced by 15 per cent compared with 2009. [ABSTRACT FROM AUTHOR]

Atmospheric methane concentrations rose rapidly over the past decade and surged in 2020-2022 but the causes have been unclear. We find from inverse analysis of GOSAT satellite observations that emissions from the wet tropics drove the 2010-2019 increase and the subsequent 2020-2022 surge, while emissions from northern mid-latitudes decreased. The 2020-2022 surge is principally contributed by emissions in Equatorial Asia (43%) and Africa (30%). Wetlands are the major drivers of the 2020-2022 emission increases in Africa and Equatorial Asia because of tropical inundation associated with La Niña conditions, consistent with trends in the GRACE terrestrial water storage data. In contrast, emissions from major anthropogenic emitters such as the United States, Russia, and China are relatively flat over 2010-2022. Concentrations of tropospheric OH (the main methane sink) show no long-term trend over 2010-2022 but a decrease over 2020-2022 that contributed to the methane surge., Competing Interests: Competing interests statement:The authors declare no competing interest.

Carbon dioxide and methane emissions are the two primary anthropogenic climate-forcing agents and an important source of uncertainty in the global carbon budget. Uncertainties are further magnified when emissions occur at fine spatial scales (<1 km), making attribution challenging. We present the first observations from NASA's Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer showing quantification and attribution of fine-scale methane (0.3 to 73 tonnes CH 4 hour -1 ) and carbon dioxide sources (1571 to 3511 tonnes CO 2 hour -1 ) spanning the oil and gas, waste, and energy sectors. For selected countries observed during the first 30 days of EMIT operations, methane emissions varied at a regional scale, with the largest total emissions observed for Turkmenistan (731 ± 148 tonnes CH 4 hour -1 ). These results highlight the contributions of current and planned point source imagers in closing global carbon budgets.

Atmospheric humidity and soil moisture in the Amazon forest are tightly coupled to the region's water balance, or the difference between two moisture fluxes, evapotranspiration minus precipitation (ET-P). However, large and poorly characterized uncertainties in both fluxes, and in their difference, make it challenging to evaluate spatiotemporal variations of water balance and its dependence on ET or P. Here, we show that satellite observations of the HDO/H 2 O ratio of water vapor are sensitive to spatiotemporal variations of ET-P over the Amazon. When calibrated by basin-scale and mass-balance estimates of ET-P derived from terrestrial water storage and river discharge measurements, the isotopic data demonstrate that rainfall controls wet Amazon water balance variability, but ET becomes important in regulating water balance and its variability in the dry Amazon. Changes in the drivers of ET, such as above ground biomass, could therefore have a larger impact on soil moisture and humidity in the dry (southern and eastern) Amazon relative to the wet Amazon., (© 2022. The Author(s).)

Although it is well established that transpiration contributes much of the water for rainfall over Amazonia, it remains unclear whether transpiration helps to drive or merely responds to the seasonal cycle of rainfall. Here, we use multiple independent satellite datasets to show that rainforest transpiration enables an increase of shallow convection that moistens and destabilizes the atmosphere during the initial stages of the dry-to-wet season transition. This shallow convection moisture pump (SCMP) preconditions the atmosphere at the regional scale for a rapid increase in rain-bearing deep convection, which in turn drives moisture convergence and wet season onset 2-3 mo before the arrival of the Intertropical Convergence Zone (ITCZ). Aerosols produced by late dry season biomass burning may alter the efficiency of the SCMP. Our results highlight the mechanisms by which interactions among land surface processes, atmospheric convection, and biomass burning may alter the timing of wet season onset and provide a mechanistic framework for understanding how deforestation extends the dry season and enhances regional vulnerability to drought., Competing Interests: The authors declare no conflict of interest.