Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde.

The solar absorption spectrometry in the infrared spectral region, using high-resolution Fourier transform infrared (FTIR) spectrometer, has been established as a powerful tool in atmospheric science. These observations cannot be performed continuously, for example, clouds prevent observations. On the other hand, chemical transport models give continuously data. Their results depend on the knowledge of emission inventories, the chemistry involved, and the meteorological fields, yielding to potential biases between measurements and simulations. In our study we concentrated on Formaldehyde (HCHO) and used machine learning approach to fill the gap between the observations, performed on an irregular time scale and having their measurement lacks, and model data, giving continuous data, but having potential variable biases. The proposed machine learning approach is based on the Light Gradient Boosting Machine (LightGBM) algorithm and created by using GEOS-Chem simulations, meteorological fields, emission inventory, and is referred to as the GEOS-Chem-LightGBM model. The results of established GEOS-Chem-LightGBM model have generated consistent HCHO predictions with the ground-based FTIR and satellite (OMI and TROPOMI) observations. In order to understand the GEOS-Chem model to measurement discrepancy, we have investigated the contribution of each input variable to GEOS-Chem-LightGBM model HCHO predictions through the SHapely Additive exPlanations (SHAP) approach. We found that the GEOS-Chem model underestimates the sensitivities of HCHO total column to most photochemical variables, contributing to lower amplitudes of diurnal cycle and seasonal cycle by the GEOS-Chem model. By correcting the model-to-measurement discrepancy, the sensitivities of HCHO total column to all variables by the GEOS-Chem-LightGBM became to be in good agreement with the FTIR observations. As a result, GEOS-Chem-LightGBM model has significantly improved the performance of HCHO predictions compared to the GEOS-Chem alone. The proposed GEOS-Chem-LightGBM model can be extendible to other atmospheric constituents obtained by various measurement techniques and platforms, and is expected to have wide applications.

COVID-19 Crisis Reduces Free Tropospheric Ozone Across the Northern Hemisphere.

Throughout spring and summer 2020, ozone stations in the northern extratropics recorded unusually low ozone in the free troposphere. From April to August, and from 1 to 8 kilometers altitude, ozone was on average 7% (≈4 nmol/mol) below the 2000-2020 climatological mean. Such low ozone, over several months, and at so many stations, has not been observed in any previous year since at least 2000. Atmospheric composition analyses from the Copernicus Atmosphere Monitoring Service and simulations from the NASA GMI model indicate that the large 2020 springtime ozone depletion in the Arctic stratosphere contributed less than one-quarter of the observed tropospheric anomaly. The observed anomaly is consistent with recent chemistry-climate model simulations, which assume emissions reductions similar to those caused by the COVID-19 crisis. COVID-19 related emissions reductions appear to be the major cause for the observed reduced free tropospheric ozone in 2020.

FTIR time series of stratospheric NO2 over Hefei, China, and comparisons with OMI and GEOS-Chem model data.

We present the trend and seasonal variability of stratospheric NO2 column for the first time over the polluted atmosphere at Hefei, China, retrieved using Fourier transform infrared spectroscopy (FTIR) between 2015 and 2018. The FTIR observed stratospheric NO2 columns over Hefei show a peak in June and reach a minimum in January. The mean stratospheric NO2 column concentration in June is (3.49 ± 0.25) × 1015 molecules*cm-2, and is 39.20% ± 8.95% higher than that in January with a mean value of (2.51 ± 0.21) × 1015 molecules*cm-2. We find a negative trend of (-0.34 ± 0.05) %/yr in the FTIR observations of stratospheric NO2 column. The FTIR data are compared to the satellite OMI observations to assess the new data set quality and also applied to evaluate the GEOS-Chem model simulations. We find in general the OMI observations and GEOS-Chem model results are in good agreement with the coincident FTIR data, and they all show similar seasonal cycles with strong correlation coefficients of 0.84-0.86. The annual average OMI minus FTIR difference is (1.48 ± 5.33) × 1014 molecules*cm-2 (4.82% ± 17.37%), and average GEOS-Chem minus FTIR difference is (2.36 ± 2.33) × 1014 molecules*cm -2 (7.66% ± 7.49%). Their maximum differences occur in April and May with mean differences of 12-16%. We also found negative trends in the stratospheric NO2 column over Hefei for 2015-2018 with both OMI observations (-0.91 ± 0.09%/yr) and GEOS-Chem model results (-0.31 ± 0.05%/yr), demonstrating some consistency among them.

Simultaneous retrieval of atmospheric CO2 and light path modification from space-based spectroscopic observations of greenhouse gases: methodology and application to GOSAT measurements over TCCON sites.

This paper presents an improved photon path length probability density function method that permits simultaneous retrievals of column-average greenhouse gas mole fractions and light path modifications through the atmosphere when processing high-resolution radiance spectra acquired from space. We primarily describe the methodology and retrieval setup and then apply them to the processing of spectra measured by the Greenhouse gases Observing SATellite (GOSAT). We have demonstrated substantial improvements of the data processing with simultaneous carbon dioxide and light path retrievals and reasonable agreement of the satellite-based retrievals against ground-based Fourier transform spectrometer measurements provided by the Total Carbon Column Observing Network (TCCON).

The total carbon column observing network.

A global network of ground-based Fourier transform spectrometers has been founded to remotely measure column abundances of CO(2), CO, CH(4), N(2)O and other molecules that absorb in the near-infrared. These measurements are directly comparable with the near-infrared total column measurements from space-based instruments. With stringent requirements on the instrumentation, acquisition procedures, data processing and calibration, the Total Carbon Column Observing Network (TCCON) achieves an accuracy and precision in total column measurements that is unprecedented for remote-sensing observations (better than 0.25% for CO(2)). This has enabled carbon-cycle science investigations using the TCCON dataset, and allows the TCCON to provide a link between satellite measurements and the extensive ground-based in situ network.

Dynamic processes governing lower-tropospheric HDO/H2O ratios as observed from space and ground.

The hydrological cycle and its response to environmental variability such as temperature changes is of prime importance for climate reconstruction and prediction. We retrieved deuterated water/water (HDO/H2O) abundances using spaceborne absorption spectroscopy, providing an almost global perspective on the near-surface distribution of water vapor isotopologs. We observed an unexpectedly high HDO/H2O seasonality in the inner Sahel region, pointing to a strong isotopic depletion in the subsiding branch of the Hadley circulation and its misrepresentation in general circulation models. An extension of the analysis at high latitudes using ground-based observations of deltaD and a model study shows that dynamic processes can entirely compensate for temperature effects on the isotopic composition of precipitation.