Goddard Earth Observing System Version Research Articles

Surface Radiative Forcing as a Climate-Change Indicator in North India due to the Combined Effects of Dust and Biomass Burning

This study estimates the radiative forcing by biomass burning and dust aerosols over the Indian subcontinent, with emphasis on the Indo-Gangetic Plains (IGP) during the period from January 2021 to April 2021, based on multiple satellite and reanalysis datasets. In this respect, we used retrievals from the Moderate Resolution Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) system, as well as reanalysis data from the Goddard Earth Observing System, version 5 (GEOS-5), the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), the Copernicus Atmosphere Monitoring Service (CAMS), and ERA-Interim. According to the MERRA-2 and the CAMS, the highest black carbon (BC) concentrations in January 2021 were 7–8 µg m−3, which were significantly lower than measurements performed in main cities along the IGP, such as Patiala, Delhi, and Kanpur. The meteorological data analysis accompanied by the CALIPSO lidar measurements showed that the vertical distribution of total attenuated backscatter (TAB) could reach altitudes of up to ~4–5 km and could be transported over the central Himalayan region. The spatial-averaged daily aerosol radiative forcing (ARF) values over the Indian subcontinent from January 2021 to April 2021 were found to range from −51.40 to −6.08 W m−2 (mean of −22.02 ± 9.19 W m−2), while on a monthly basis, the ARF values varied widely, from −146.24 to −1.63 W m−2 (mean of −45.56 ± 22.85 W m−2) over different parts of the study region. Furthermore, the spatial-averaged daily BC radiative forcing ranged from −2.23 to −0.35 (−1.01 ± 0.40 W m−2), while it varied from −15.29 to −0.31 W m−2 (−2.46 ± 2.32 W m−2) over different regions of southern Asia, indicating a rather small contribution to the total aerosol radiative effect and a large presence of highly scattering aerosols. Our findings highlight the importance of growing biomass burning, in light of recent climate change and the rapid decline in air quality over North India and the Indian Ocean.

The Potential of Monitoring Carbon Dioxide Emission in a Geostationary View with the GIIRS Meteorological Hyperspectral Infrared Sounder

With the help of various polar-orbiting environment observing platforms, the atmospheric concentration of carbon dioxide (CO2) has been well established on a global scale. However, the spatial and temporal pattern of the CO2 emission and its flux dependence on daily human activity processes are not yet well understood. One of the limiting factors could be attributed to the low revisit time frequency of the polar orbiting satellites. With high revisiting frequency and CO2-sensitive spectrum, the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Chinese FY-4A and FY-4B satellites have the potential to measure the CO2 concentration at a higher temporal frequency than polar-orbiting satellites. To provide a prototypical demonstration on the CO2 monitoring capability using GIIRS observations, a hybrid-3D variational data assimilation system is established in this research and a one-month-long experiment is conducted. The evaluations against the Goddard Earth Observing System version 5 (GEOS-5) analysis field and Orbiting Carbon Observatory -2/-3 (OCO-2/-3) CO2 retrieval products reveal that assimilating GIIRS observations can reduce the first guess’s CO2 concentration mean bias and standard deviation, especially over the lower troposphere (975–750 hPa) and improve the diurnal variation of near surface CO2 concentration.

Automatic variable selection in ecological niche modeling: A case study using Cassin's Sparrow (Peucaea cassinii).

MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max’s Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin’s Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim’s Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species’ bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA’s Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.

Analysis of the MODIS above-cloud aerosol retrieval algorithm using MCARS

Abstract. The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of atmospheric column inclusive of clouds, aerosols, and a variety of gases and land–ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass-burning aerosols overlying marine boundary layer clouds in the southeastern Atlantic Ocean. The algorithm's ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies in an area where current clear-sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. The purpose of this study is to develop a set of constraints a model developer might use during assimilation of MOD06ACAERO data. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules were used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30 %, optical thickness of the underlying cloud layer was greater than 4, and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE=0.107. When only near-nadir pixels were considered (view zenith angle within ±20∘) the agreement with source data further improved (0.977, 0.051, and 0.096 respectively). Algorithm closure was examined using a single case out of the five used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as an ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with an offset of −0.007 and RMSE of 0.097 at a pixel uncertainty level of less than 40 %, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.

Application of Topological Data Analysis to Multi-Resolution Matching of Aerosol Optical Depth Maps

Topological data analysis (TDA) combines concepts from algebraic topology, machine learning, statistics, and data science which allow us to study data in terms of their latent shape properties. Despite the use of TDA in a broad range of applications, from neuroscience to power systems to finance, the utility of TDA in Earth science applications is yet untapped. The current study aims to offer a new approach for analyzing multi-resolution Earth science datasets using the concept of data shape and associated intrinsic topological data characteristics. In particular, we develop a new topological approach to quantitatively compare two maps of geophysical variables at different spatial resolutions. We illustrate the proposed methodology by applying TDA to aerosol optical depth (AOD) datasets from the Goddard Earth Observing System, Version 5 (GEOS-5) model over the Middle East. Our results show that, contrary to the existing approaches, TDA allows for systematic and reliable comparison of spatial patterns from different observational and model datasets without regridding the datasets into common grids.

Determining cloud thermodynamic phase from the polarized Micro Pulse Lidar

Abstract. A method to distinguish cloud thermodynamic phase from polarized Micro Pulse Lidar (MPL) measurements is described. The method employs a simple enumerative approach to classify cloud layers as either liquid water, ice water, or mixed-phase clouds based on the linear volume depolarization ratio and cloud top temperatures derived from Goddard Earth Observing System, version 5 (GEOS-5), assimilated data. Two years of cloud retrievals from the Micro Pulse Lidar Network (MPLNET) site in Greenbelt, MD, are used to evaluate the performance of the algorithm. The fraction of supercooled liquid water in the mixed-phase temperature regime (−37–0 ∘C) calculated using MPLNET data is compared to similar calculations made using the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, with reasonable consistency.

Stratospheric impact on the Northern Hemisphere winter and spring ozone interannual variability in the troposphere

Abstract. In this study we use ozone and stratospheric ozone tracer simulations from the high-resolution (0.5∘×0.5∘) Goddard Earth Observing System, Version 5 (GEOS-5), in a replay mode to study the impact of stratospheric ozone on tropospheric ozone interannual variability (IAV). We use these simulations in conjunction with ozonesonde measurements from 1990 to 2016 during the winter and spring seasons. The simulations include a stratospheric ozone tracer (StratO3) to aid in the evaluation of the impact of stratospheric ozone IAV on the IAV of tropospheric ozone at different altitudes and locations. The model is in good agreement with the observed interannual variation in tropospheric ozone, except for the post-Pinatubo period (1992–1994) over the region of North America. Ozonesonde data show a negative ozone anomaly in 1992–1994 following the Pinatubo eruption, with recovery thereafter. The simulated anomaly is only half the magnitude of that observed. Our analysis suggests that the simulated stratosphere–troposphere exchange (STE) flux deduced from the analysis might be too strong over the North American (50–70∘ N) region after the Mt. Pinatubo eruption in the early 1990s, masking the impact of lower stratospheric ozone concentration on tropospheric ozone. European ozonesonde measurements show a similar but weaker ozone depletion after the Mt. Pinatubo eruption, which is fully reproduced by the model. Analysis based on the stratospheric ozone tracer identifies differences in strength and vertical extent of stratospheric ozone impact on the tropospheric ozone interannual variation (IAV) between North America and Europe. Over North American stations, the StratO3 IAV has a significant impact on tropospheric ozone from the upper to lower troposphere and explains about 60 % and 66 % of the simulated ozone IAV at 400 hPa and ∼11 % and 34 % at 700 hPa in winter and spring, respectively. Over European stations, the influence is limited to the middle to upper troposphere and becomes much smaller at 700 hPa. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), assimilated fields exhibit strong longitudinal variations over Northern Hemisphere (NH) mid-high latitudes, with lower tropopause height and lower geopotential height over North America than over Europe. These variations associated with the relevant variations in the location of tropospheric jet flows are responsible for the longitudinal differences in the stratospheric ozone impact, with stronger effects over North America than over Europe.

How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth

Abstract. Many applications of geophysical data – whether from surface observations, satellite retrievals, or model simulations – rely on aggregates produced at coarser spatial (e.g. degrees) and/or temporal (e.g. daily and monthly) resolution than the highest available from the technique. Almost all of these aggregates report the arithmetic mean and standard deviation as summary statistics, which are what data users employ in their analyses. These statistics are most meaningful for normally distributed data; however, for some quantities, such as aerosol optical depth (AOD), it is well-known that distributions are on large scales closer to log-normal, for which a geometric mean and standard deviation would be more appropriate. This study presents a method of assessing whether a given sample of data is more consistent with an underlying normal or log-normal distribution, using the Shapiro–Wilk test, and tests AOD frequency distributions on spatial scales of 1∘ and daily, monthly, and seasonal temporal scales. A broadly consistent picture is observed using Aerosol Robotic Network (AERONET), Multiangle Imaging SpectroRadiometer (MISR), Moderate Resolution Imagining Spectroradiometer (MODIS), and Goddard Earth Observing System Version 5 Nature Run (G5NR) data. These data sets are complementary: AERONET has the highest AOD accuracy but is sparse, and MISR and MODIS represent different satellite retrieval techniques and sampling. As a model simulation, G5NR is spatiotemporally complete. As timescales increase from days to months to seasons, data become increasingly more consistent with log-normal than normal distributions, and the differences between arithmetic- and geometric-mean AOD become larger, with geometric mean becoming systematically smaller. Assuming normality systematically overstates both the typical level of AOD and its variability. There is considerable regional heterogeneity in the results: in low-AOD regions such as the open ocean and mountains, often the AOD difference is small enough (<0.01) to be unimportant for many applications, especially on daily timescales. However, in continental outflow regions and near source regions over land, and on monthly or seasonal timescales, the difference is frequently larger than the Global Climate Observation System (GCOS) goal uncertainty in a climate data record (the larger of 0.03 or 10 %). This is important because it shows that the sensitivity to an averaging method can and often does introduce systematic effects larger than the total goal GCOS uncertainty. Using three well-studied AERONET sites, the magnitude of estimated AOD trends is shown to be sensitive to the choice of arithmetic vs. geometric means, although the signs are consistent. The main recommendations from the study are that (1) the distribution of a geophysical quantity should be analysed in order to assess how best to aggregate it, (2) ideally AOD aggregates such as satellite level 3 products (but also ground-based data and model simulations) should report a geometric-mean or median AOD rather than (or in addition to) arithmetic-mean AOD, and (3) as this is unlikely in the short term due to the computational burden involved, users can calculate geometric-mean monthly aggregates from widely available daily mean data as a stopgap, as daily aggregates are less sensitive to the choice of aggregation scheme than those for monthly or seasonal aggregates. Furthermore, distribution shapes can have implications for the validity of statistical metrics often used for comparison and evaluation of data sets. The methodology is not restricted to AOD and can be applied to other quantities.

Structural changes and variability of the ITCZ induced by radiation\u2013cloud\u2013convection\u2013circulation interactions: inferences from the Goddard Multi-scale Modeling Framework (GMMF) experiments

In this paper, we have investigated the impact of radiation–cloud–convection–circulation interaction (RC3I) on structural changes and variability of the Inter-tropical Convergence Zone (ITCZ) using the Goddard Multi-scale Modeling Framework, where cloud processes are super-parameterized, i.e., explicitly resolved with 2-D cloud resolving models embedded in each coarse grid of the host Goddard Earth Observing System-Version 5 global climate model. Experiments have been conducted under prescribed sea surface temperature conditions for 10 years (2007–2016), with and without cloud radiation feedback in the atmosphere, respectively. Diagnostic analyses separately for January and July show that RC3I leads to an enhanced and expanded Hadley Circulation characterized by (1) a quasi-uniform warming and moistening of the tropical atmosphere and a sharpening of the ITCZ with enhanced deep convection, more intense precipitation and higher clouds, (2) extended drying of the tropical marginal convective zones, and extratropical mid- to lower troposphere, and (3) a cooling of the polar regions, with increased baroclinicity and midlatitude storm track activities. Computations based on the zonal mean thermodynamic energy balance equation show that the radiative warming and cooling are strongly balanced by local adiabatic processes associated with changes in large-scale vertical motions, as well as horizontal atmospheric heat transport. In the tropics, enhanced short-wave absorption and longwave water vapor greenhouse effects by high clouds play key roles in providing strong positive feedback to the tropospheric warming. In the extratropics, increased atmospheric heat transport associated with changes in the Hadley circulation is balanced by strong longwave cooling above, and warming below due to increased high clouds. We also find a strong positive correlation between daily and pentad heavy rain in the ITCZ core, and expansion of the drier zones coupled to a contraction of the highly convective zones in the ITCZ, indicating a strong tendency RC3I-induced convective aggregation in tropical clouds i.e., wet-regions-get-wetter and contracted, and dry-areas-get-drier and expanded.

Improved representation of the diurnal variation of warm season precipitation by an atmospheric general circulation model at a 10\xa0km horizontal resolution

This study investigates the diurnal variation of the warm season precipitation simulated by the Goddard Earth Observing System version 5 atmospheric general circulation model for 2 years (2005–2006) at a horizontal resolution of 10 km. The simulation was validated with the satellite-derived Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation data and the Modern-Era Retrospective analysis for Research and Applications atmospheric reanalysis for atmospheric winds and moisture. The simulation is compared with the coarse-resolution run in 50 km to examine the impacts driven by resolution change. Overall, the 10 km model tends to reproduce the important features of the observed diurnal variation, such as the amplitude and phase at which precipitation peaks in the evening on land and in the morning over the ocean, despite an excessive amplitude bias over land. The model also reproduces the realistic propagation patterns of precipitation in the vicinity of ocean coasts and major mountains. The regional characteristics of the diurnal precipitation over two regions, the Bay of Bengal and the Great Plains in North America, are examined in detail, where the observed diurnal cycle exhibits a systematic transition in the peak phase due to the development and propagation of regional-scale convective systems. The model is able to reproduce this pattern as well as the diurnal variation of low-level wind and moisture convergence; however, it is less effective at representing the nocturnal peak of precipitation over the Great Plains. The model results suggest that increasing the horizontal resolution of the model to 10 km substantially improves the representation of the diurnal precipitation cycle. However, intrinsic model deficiencies in topographical precipitation and the accurate representation of mesoscale convective systems remain a challenge.

Temporal sampling error analysis of the Earth’s outgoing radiation from a Moon-based platform

ABSTRACT This study investigates the temporal sampling error on the Earth’s outgoing radiation, with a potential Earth observation platform, the Moon-based platform. To simulate the Earth’s outgoing radiation viewed from a Moon-based platform, we used the datasets of the NASA’s Goddard Earth Observing System Version 5 (GEOS-5) systems as the truth. The analysis is proposed by sampling the simulated time series. The sampling uncertainty associated with a given sampling interval is measured by computing the Root-Mean-Square-Error (RMSE) of the original and subsampled time series. The effects of different sampling intervals are evaluated by maximum bias. The effects of different sampling time are estimated by comparing correlations between the subsampled time series of different start time at specific temporal interval and the origin. The results show that the temporal sampling errors have the characteristics of the periodical uncertainties and bias, and 4-h sampling interval is a turning point. The sampling interval larger than 4 h will result in large uncertainties and bias.

A new global anthropogenic SO<sub>2</sub> emission inventory for the last decade: a mosaic of satellite-derived and bottom-up emissions

Abstract. Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor have been used to detect emissions from large point sources. Emissions from over 400 sources have been quantified individually based on OMI observations, accounting for about a half of total reported anthropogenic SO2 emissions. Here we report a newly developed emission inventory, OMI-HTAP, by combining these OMI-based emission estimates and the conventional bottom-up inventory, HTAP, for smaller sources that OMI is not able to detect. OMI-HTAP includes emissions from OMI-detected sources that are not captured in previous leading bottom-up inventories, enabling more accurate emission estimates for regions with such missing sources. In addition, our approach offers the possibility of rapid updates to emissions from large point sources that can be detected by satellites. Our methodology applied to OMI-HTAP can also be used to merge improved satellite-derived estimates with other multi-year bottom-up inventories, which may further improve the accuracy of the emission trends. OMI-HTAP SO2 emissions estimates for Persian Gulf, Mexico, and Russia are 59 %, 65 %, and 56 % larger than HTAP estimates in 2010, respectively. We have evaluated the OMI-HTAP inventory by performing simulations with the Goddard Earth Observing System version 5 (GEOS-5) model. The GEOS-5 simulated SO2 concentrations driven by both HTAP and OMI-HTAP were compared against in situ measurements. We focus for the validation on 2010 for which HTAP is most valid and for which a relatively large number of in situ measurements are available. Results show that the OMI-HTAP inventory improves the agreement between the model and observations, in particular over the US, with the normalized mean bias decreasing from 0.41 (HTAP) to −0.03 (OMI-HTAP) for 2010. Simulations with the OMI-HTAP inventory capture the worldwide major trends of large anthropogenic SO2 emissions that are observed with OMI. Correlation coefficients of the observed and modeled surface SO2 in 2014 increase from 0.16 (HTAP) to 0.59 (OMI-HTAP) and the normalized mean bias dropped from 0.29 (HTAP) to 0.05 (OMI-HTAP), when we updated 2010 HTAP emissions with 2014 OMI-HTAP emissions in the model.

Link Between Arctic Tropospheric BrO Explosion Observed From Space and Sea‐Salt Aerosols From Blowing Snow Investigated Using Ozone Monitoring Instrument BrO Data and GEOS‐5 Data Assimilation System

AbstractBromine radicals (Br + BrO) are important atmospheric species owing to their ability to catalytically destroy ozone as well as their potential impacts on the oxidative pathways of many trace gases, including dimethylsulfide and mercury. Using space‐based observations of BrO, recent studies have reported rapid enhancements of tropospheric BrO over large areas (so called “BrO explosions”) connected to near‐surface ozone depletion occurring in polar spring. However, the source(s) of reactive bromine and mechanism(s) that initiate these BrO explosions are uncertain. In this study, we investigate the relationships between Arctic BrO explosions and two of the proposed sources of reactive bromine: sea‐salt aerosol (SSA) generated from blowing snow and first‐year (seasonal) sea ice. We use tropospheric column BrO derived from the Ozone Monitoring Instrument (OMI) in conjunction with the Goddard Earth Observing System Version 5 (GEOS‐5) data assimilation system provided by National Aeronautics and Space Administration Global Modeling and Assimilation Office. Case studies demonstrate a strong association between the temporal and spatial extent of OMI‐observed BrO explosions and the GEOS‐5 simulated blowing snow‐generated SSA during Arctic spring. Furthermore, the frequency of BrO explosion events observed over the 11‐year record of OMI exhibits significant correlation with a time series of the simulated SSA emission flux in the Arctic and little to no correlation with a time series of satellite‐based first‐year sea ice area. Therefore, we conclude that SSA generated by blowing snow is an important factor in the formation of the BrO explosion observed from space during Arctic spring.

The impact of SST-forced and unforced teleconnections on 2015/16 El Niño winter precipitation over the western United States.

The factors impacting western U.S. winter precipitation during the 2015/16 El Niño are investigated using the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data, and simulations with the Goddard Earth Observing System version 5 (GEOS-5) atmospheric general circulation model forced with specified sea surface temperatures (SSTs). Results reveal that the simulated response to the tropical Pacific SST associated with the 2015/16 El Niño was to produce wetter than normal conditions over much of the west coast including California - a result at odds with the negative precipitation anomalies observed over much of the Southwestern U.S. It is shown that two factors acted to partly counter the canonical ENSO response in that region. First, a potentially predictable but modest response to the unusually strong and persistent warm SST in the northeastern Pacific decreased precipitation in the Southwestern U.S. by increasing sea level pressure, driving anticyclonic circulation and atmospheric descent, and reducing moisture transport into that region. Second, large-scale unforced (by SST) components of atmospheric variability (consisting of the leading modes of unpredictable intra-ensemble variability) resembling the positive phase of the North Atlantic Oscillation and Arctic Oscillation are found to be an important contributor to the drying over the western U.S. While a statistical reconstruction of the precipitation from our simulations that account for internal atmospheric variability does much to close the gap between the ensemble mean and observed precipitation in the Southwestern U.S., some differences remain, indicating that model error is also playing a role.

Global and regional evaluation of a global model simulated AODs with AERONET and MODIS observations

ABSTRACTAerosol optical properties vary widely globally due to their short residence time and the large variability in their composition and size resulting from varying sources. In this study, we make use of the three‐dimensional global atmospheric chemistry transport model MOZART (Model for OZone And Related chemical Tracers), to simulate Aerosol Optical Depths (AODs) and compare with observations from MODIS (Moderate Resolution Imaging Spectroradiometer, level‐3) on board the Terra satellite and AERONET (AErosol RObotic NETwork level‐2), at regional and global scale for the period 2000–2007. The AOD values at a reference wavelength 550 nm are reported for all the data sets. Model and observations show intense aerosol burden over the parts of the world where developing countries are located. On a regional scale, varying features are noted for different regions depending upon topography, regional emissions and weather conditions. MOZART captures the general features observed in AERONET and MODIS fairly well; however, in biomass burning dominated regions (Central South America, South Africa, and North Australia) it underestimates the observed AOD. In industrial regions (North Europe and Mediterranean) MOZART overestimates the observations and over dust dominant regions (Saharan Africa, Middle East, and East Asia), the model AODs are comparable to AERONET and MODIS observations. In general, MOZART simulated AOD show similar seasonal and regional variability as observed in AERONET and MODIS. Our results are similar to the most commonly used models, i.e. GOCART (the GOddard Chemistry, Aerosol, Radiation, and Transport) and GEOS‐4 (Goddard Earth Observing System‐version 4).

The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies.

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is NASA's latest reanalysis for the satellite era (1980 onward) using the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model. MERRA-2 provides several improvements over its predecessor (MERRA-1), including aerosol assimilation for the entire period. MERRA-2 assimilates bias-corrected aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer and the Advanced Very High Resolution Radiometer instruments. Additionally, MERRA-2 assimilates (non bias corrected) AOD from the Multiangle Imaging SpectroRadiometer over bright surfaces and AOD from Aerosol Robotic Network sunphotometer stations. This paper, the second of a pair, summarizes the efforts to assess the quality of the MERRA-2 aerosol products. First, MERRA-2 aerosols are evaluated using independent observations. It is shown that the MERRA-2 absorption aerosol optical depth (AAOD) and ultraviolet aerosol index (AI) compare well with Ozone Monitoring Instrument observations. Next, aerosol vertical structure and surface fine particulate matter (PM2.5) are evaluated using available satellite, aircraft, and ground-based observations. While MERRA-2 generally compares well to these observations, the assimilation cannot correct for all deficiencies in the model (e.g., missing emissions). Such deficiencies can explain many of the biases with observations. Finally, a focus is placed on several major aerosol events to illustrate successes and weaknesses of the AOD assimilation: the Mount Pinatubo eruption, a Saharan dust transport episode, the California Rim Fire, and an extreme pollution event over China. The article concludes with a summary that points to best practices for using the MERRA-2 aerosol reanalysis in future studies.

The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) updates NASA's previous satellite era (1980 - onward) reanalysis system to include additional observations and improvements to the Goddard Earth Observing System, Version 5 (GEOS-5) Earth system model. As a major step towards a full Integrated Earth Systems Analysis (IESA), in addition to meteorological observations, MERRA-2 now includes assimilation of aerosol optical depth (AOD) from various ground- and space-based remote sensing platforms. Here, in the first of a pair of studies, we document the MERRA-2 aerosol assimilation, including a description of the prognostic model (GEOS-5 coupled to the GOCART aerosol module), aerosol emissions, and the quality control of ingested observations. We provide initial validation and evaluation of the analyzed AOD fields using independent observations from ground, aircraft, and shipborne instruments. We demonstrate the positive impact of the AOD assimilation on simulated aerosols by comparing MERRA-2 aerosol fields to an identical control simulation that does not include AOD assimilation. Having shown the AOD evaluation, we take a first look at aerosol-climate interactions by examining the shortwave, clear-sky aerosol direct radiative effect. In our companion paper, we evaluate and validate available MERRA-2 aerosol properties not directly impacted by the AOD assimilation (e.g. aerosol vertical distribution and absorption). Importantly, while highlighting the skill of the MERRA-2 aerosol assimilation products, both studies point out caveats that must be considered when using this new reanalysis product for future studies of aerosols and their interactions with weather and climate.

Assimilation for skin SST in the NASA GEOS atmospheric data assimilation system.

The present article describes the sea surface temperature (SST) developments implemented in the Goddard Earth Observing System, Version 5 (GEOS-5) Atmospheric Data Assimilation System (ADAS). These are enhancements that contribute to the development of an atmosphere-ocean coupled data assimilation system using GEOS. In the current quasi-operational GEOS-ADAS, the SST is a boundary condition prescribed based on the OSTIA product, therefore SST and skin SST (Ts) are identical. This work modifies the GEOS-ADAS Ts by modeling and assimilating near sea surface sensitive satellite infrared (IR) observations. The atmosphere-ocean interface layer of the GEOS atmospheric general circulation model (AGCM) is updated to include near surface diurnal warming and cool-skin effects. The GEOS analysis system is also updated to directly assimilate SST-relevant Advanced Very High Resolution Radiometer (AVHRR) radiance observations. Data assimilation experiments designed to evaluate the Ts modification in GEOS-ADAS show improvements in the assimilation of radiance observations that extends beyond the thermal IR bands of AVHRR. In particular, many channels of hyperspectral sensors, such as those of the Atmospheric Infrared Sounder (AIRS), and Infrared Atmospheric Sounding Interferometer (IASI) are also better assimilated. We also obtained improved fit to withheld, in-situ buoy measurement of near-surface SST. Evaluation of forecast skill scores show marginal to neutral benefit from the modified Ts.

Large-scale Controls on Atlantic Tropical Cyclone Activity on Seasonal Time Scales.

Interannual variations in seasonal tropical cyclone (TC) activity (e.g., genesis frequency and location, track pattern, and landfall) over the Atlantic are explored by employing observationally-constrained simulations with the NASA Goddard Earth Observing System version (GEOS-5) atmospheric general circulation model. The climate modes investigated are El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Atlantic Meridional Mode (AMM). The results show that the NAO and AMM can strongly modify and even oppose the well-known ENSO impacts, like in 2005, when a strong positive AMM (associated with warm SSTs and a negative SLP anomaly over the western tropical Atlantic), led to a very active TC season with enhanced TC genesis over the Caribbean Sea and a number of landfalls over North America, under a neutral ENSO condition. On the other end, the weak TC activity during 2013 (characterized by weak negative Niño index) appears caused by a NAO-induced positive SLP anomaly with enhanced vertical wind shear over the tropical North Atlantic. During 2010, the combined impact of the three modes produced positive SST anomalies across the entire low- latitudinal Atlantic and a weaker subtropical high, leading to more early recurvers and thus fewer landfalls despite enhanced TC genesis. The study provides evidence that TC number and track are very sensitive to the relative phases and intensities of these three modes, and not just to ENSO alone. Examination of seasonal predictability reveals that predictive skill of the three modes is limited over tropics to sub-tropics, with the AMM having the highest predictability over the North Atlantic, followed by ENSO and NAO.

Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Description and Usage

AbstractA modular extensible framework for conducting observing system simulation experiments (OSSEs) has been developed with the goals of 1) supporting decision-makers with quantitative assessments of proposed observing systems investments, 2) supporting readiness for new sensors, 3) enhancing collaboration across the community by making the most up-to-date OSSE components accessible, and 4) advancing the theory and practical application of OSSEs. This first implementation, the Community Global OSSE Package (CGOP), is for short- to medium-range global numerical weather prediction applications. The CGOP is based on a new mesoscale global nature run produced by NASA using the 7-km cubed sphere version of the Goddard Earth Observing System, version 5 (GEOS-5), atmospheric general circulation model and the January 2015 operational version of the NOAA global data assimilation (DA) system. CGOP includes procedures to simulate the full suite of observing systems used operationally in the global DA system, including conventional in situ, satellite-based radiance, and radio occultation observations. The methodology of adding a new proposed observation type is documented and illustrated with examples of current interest. The CGOP is designed to evolve, both to improve its realism and to keep pace with the advance of operational systems.