Contrasting the direct radiative effect and direct radiative forcing of aerosols

The direct radiative effect (DRE) of aerosols, which is the instantaneous radiative impact of all atmospheric particles on the Earth's energy balance, is sometimes confused with the direct radiative forcing (DRF), which is the change in DRE from pre-industrial to present-day (not including climate feedbacks). In this study we couple a global chemical transport model (GEOS-Chem) with a radiative transfer model (RRTMG) to contrast these concepts. We estimate a global mean all-sky aerosol DRF of −0.36 Wm[superscript −2] and a DRE of −1.83 Wm[superscript −2] for 2010. Therefore, natural sources of aerosol (here including fire) affect the global energy balance over four times more than do present-day anthropogenic aerosols. If global anthropogenic emissions of aerosols and their precursors continue to decline as projected in recent scenarios due to effective pollution emission controls, the DRF will shrink (−0.22 Wm[superscript −2] for 2100). Secondary metrics, like DRE, that quantify temporal changes in both natural and anthropogenic aerosol burdens are therefore needed to quantify the total effect of aerosols on climate.United States. Environmental Protection Agency (EPA STAR Program)Massachusetts Institute of Technology (Charles E. Reed Faculty Initiative Fund)United States. Environmental Protection Agency (grant/cooperative agreement (RD-83503301)

Long-term stability of TES satellite radiance measurements

The utilization of Tropospheric Emission Spectrometer (TES) Level 2 (L2) retrieval products for the purpose of assessing long term changes in atmospheric trace gas composition requires knowledge of the overall radiometric stability of the Level 1B (L1B) radiances. The purpose of this study is to evaluate the stability of the radiometric calibration of the TES instrument by analyzing the difference between measured and calculated brightness temperatures in selected window regions of the spectrum. The Global Modeling and Assimilation Office (GMAO) profiles for temperature and water vapor and the Real-Time Global Sea Surface Temperature (RTGSST) are used as input to the Optimal Spectral Sampling (OSS) radiative transfer model to calculate the simulated spectra. The TES reference measurements selected cover a 4-year period of time from mid 2005 through mid 2009 with the selection criteria being; observation latitudes greater than −30° and less than 30°, over ocean, Global Survey mode (nadir view) and retrieved cloud optical depth of less than or equal to 0.01. The TES cloud optical depth retrievals are used only for screening purposes and no effects of clouds on the radiances are included in the forward model. This initial screening results in over 55 000 potential reference spectra spanning the four year period. Presented is a trend analysis of the time series of the residuals (observation minus calculations) in the TES 2B1, 1B2, 2A1, and 1A1 bands, with the standard deviation of the residuals being approximately equal to 0.6 K for bands 2B1, 1B2, 2A1, and 0.9 K for band 1A1. The analysis demonstrates that the trend in the residuals is not significantly different from zero over the 4-year period. This is one method used to demonstrate that the relative radiometric calibration is stable over time, which is very important for any longer term analysis of TES retrieved products (L2), particularly well-mixed species such as carbon dioxide and methane

Thin liquid water clouds: their importance and our challenge

Many clouds important to the Earth’s energy balance contain small amounts of liquid water, yet despite many improvements, large differences in retrievals of their liquid water amount and particle size still must be resolved

HCOOH measurements from space: TES retrieval algorithm and observed global distribution

Presented is a detailed description of the TES (Tropospheric Emission Spectrometer)-Aura satellite formic acid (HCOOH) retrieval algorithm and initial results quantifying the global distribution of tropospheric HCOOH. The retrieval strategy, including the optimal estimation methodology, spectral microwindows, a priori constraints, and initial guess information, are provided. A comprehensive error and sensitivity analysis is performed in order to characterize the retrieval performance, degrees of freedom for signal, vertical resolution, and limits of detection. These results show that the TES HCOOH retrievals (i) typically provide at best 1.0 pieces of information; (ii) have the most vertical sensitivity in the range from 900 to 600 hPa with ~ 2 km vertical resolution; (iii) require at least 0.5 ppbv (parts per billion by volume) of HCOOH for detection if thermal contrast is greater than 5 K, and higher concentrations as thermal contrast decreases; and (iv) based on an ensemble of simulated retrievals, are unbiased with a standard deviation of ±0.4 ppbv. The relative spatial distribution of tropospheric HCOOH derived from TES and its associated seasonality are broadly correlated with predictions from a state-of-the-science chemical transport model (GEOS-Chem CTM). However, TES HCOOH is generally higher than is predicted by GEOS-Chem, and this is in agreement with recent work pointing to a large missing source of atmospheric HCOOH. The model bias is especially pronounced in summertime and over biomass burning regions, implicating biogenic emissions and fires as key sources of the missing atmospheric HCOOH in the model

Tropospheric methanol observations from space: retrieval evaluation and constraints on the seasonality of biogenic emissions

Methanol retrievals from nadir-viewing space-based sensors offer powerful new information for quantifying methanol emissions on a global scale. Here we apply an ensemble of aircraft observations over North America to evaluate new methanol measurements from the Tropospheric Emission Spectrometer (TES) on the Aura satellite, and combine the TES data with observations from the Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp-A satellite to investigate the seasonality of methanol emissions from northern midlatitude ecosystems. Using the GEOS-Chem chemical transport model as an intercomparison platform, we find that the TES retrieval performs well when the degrees of freedom for signal (DOFS) are above 0.5, in which case the model : TES regressions are generally consistent with the model : aircraft comparisons. Including retrievals with DOFS below 0.5 degrades the comparisons, as these are excessively influenced by the a priori. The comparisons suggest DOFS > 0.5 as a minimum threshold for interpreting retrievals of trace gases with a weak tropospheric signal. We analyze one full year of satellite observations and find that GEOS-Chem, driven with MEGANv2.1 biogenic emissions, underestimates observed methanol concentrations throughout the midlatitudes in springtime, with the timing of the seasonal peak in model emissions 1-2 months too late. We attribute this discrepancy to an underestimate of emissions from new leaves in MEGAN, and apply the satellite data to better quantify the seasonal change in methanol emissions for midlatitude ecosystems. The derived parameters (relative emission factors of 11.0, 1.0, 0.05 and 8.6 for new, growing, mature, and old leaves, respectively, plus a leaf area index activity factor of 0.75 for expanding canopies with leaf area index < 2.0) provide a more realistic simulation of seasonal methanol concentrations in midlatitudes on the basis of IASI, TES, and ground-based measurements

Satellite Monitoring Over the Canadian Oil Sands: Highlights from Aura OMI and TES

Satellite remote sensing provides a unique perspective for air quality monitoring in and around the Canadian Oil Sands as a result of its spatial and temporal coverage. Presented are Aura satellite observations of key pollutants including nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ammonia (NH3), methanol (CH3OH), and formic acid (HCOOH) over the Canadian Oil Sands. Some of the highlights include: (i) the evolution of NO2 and SO2 from the Ozone Monitoring Instrument (OMI), including comparisons with other nearby sources, (ii) two years of ammonia, carbon monoxide, methanol, and formic acid observations from 240 km North-South Tropospheric Emission Spectrometer (TES) transects through the oils sands, and (iii) preliminary insights into emissions derived from these observations

Validation of MUSES NH₃ observations from AIRS and CrIS against aircraft measurements from DISCOVER-AQ and a surface network in the Magic Valley

Ammonia is a significant precursor of PM2.5 particles and thus contributes to poor air quality in many regions. Furthermore, ammonia concentrations are rising due to the increase of large-scale, intensive agricultural activities, which are often accompanied by greater use of fertilizers and concentrated animal feedlots. Ammonia is highly reactive and thus highly variable and difficult to measure. Satellite-based instruments, such as the Atmospheric Infrared Sounder (AIRS) and the Cross-Track Infrared Sounder (CrIS), have been shown to provide much greater temporal and spatial coverage of ammonia distribution and variability than is possible with in situ networks or aircraft campaigns, but the validation of these data is limited. Here we evaluate MUSES (multi-spectra, multi-species, multi-sensors) ammonia retrievals from AIRS and CrIS against ammonia measurements from aircraft in the California Central Valley and in the Colorado Front Range. These are small datasets taken over high-source regions under very different conditions: winter in California and summer in Colorado. Direct comparisons of the surface values of the retrieved profiles are biased very low in California (∼ 40 ppbv) and slightly high in Colorado (∼ 4 ppbv). This bias appears to be primarily due to smoothing error, since applying the instrument operator effectively reduces the bias to zero; even after the smoothing error is accounted for, the average uncertainty at the surface is in the 20 %–30 % range. We also compare 3 years of CrIS ammonia against an in situ network in the Magic Valley in Idaho We show that CrIS ammonia captures both the seasonal signal and the spatial variability in the Magic Valley, although it is biased low here also. In summary, this analysis substantially adds to the validation record but also points to the need for more validation under many different conditions and at higher altitudes.</p

Inverse modeling of NH3 sources using CrIS remote sensing measurements

Spatiotemporal uncertainty in emissions in the US hinders prediction of environmental effects of atmospheric . We conducted 4D-Var inversions using CrIS remote-sensing observations and GEOS-Chem to estimate monthly emissions over the contiguous US at the 0.25&deg;&times; 0.3125&deg; resolution in 2014, finding they are 33% higher than the prior emissions which likely underestimated most agricultural emissions, especially intense springtime fertilizer and livestock sources over the Central US. However, decreases were found in the Central Valley, southern Minnesota, northern Iowa and southeastern North Carolina during warm months. These updates increased the correlation coefficient between modeled monthly mean and surface observations from 0.53 to 0.84, and reduced the normalized mean bias of annual mean simulated and wet by a factor of 1.3 to 12.7. Our satellite-based inversion approach thus holds promise for improving estimates of and reactive nitrogen deposition throughout the world where measurements are scarce.</p

Optically Thin Liquid Water Clouds: Their Importance and Our Challenge

Many of the clouds important to the Earth's energy balance, from the tropics to the Arctic, are optically thin and contain liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP) when the liquid water path is small (i.e., < g/sq m) and, thus, the radiative properties of these clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earth's energy balance, and explain the difficulties in observing them. In particular, because these clouds are optically thin, potentially mixed-phase, and often (i.e., have large 3-D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison included eighteen different algorithms to evaluate their retrieved LWP, optical depth, and effective radii. Surprisingly, evaluation of the simplest case, a single-layer overcast cloud, revealed that huge discrepancies exist among the various techniques, even among different algorithms that are in the same general classification. This suggests that, despite considerable advances that have occurred in the field, much more work must be done, and we discuss potential avenues for future work