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NASA NTRS2017

Classifying Multi-Model Wheat Yield Impact Response Surfaces Showing Sensitivity to Temperature and Precipitation Change

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (minus 2 to plus 9 degrees Centigrade) and precipitation (minus 50 to plus 50 percent). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

Classification; Climate change; Crop model; Ensemb↗
NASA NTRS2002

The Surface Energy Budget and Precipitation Efficiency for Convective Systems During TOGA, COARE, GATE, SCSMEX and ARM: Cloud-Resolving Model Simulations

A two-dimensional version of the Goddard Cumulus Ensemble (GCE) Model is used to simulate convective systems that developed in various geographic locations. Observed large-scale advective tendencies for potential temperature, water vapor mixing ratio, and horizontal momentum derived from field campaigns are used as the main forcing. By examining the surface energy budgets, the model results show that the two largest terms are net condensation (heating/drying) and imposed large-scale forcing (cooling/moistening) for tropical oceanic cases. These two terms arc opposite in sign, however. The contributions by net radiation and latent heat flux to the net condensation vary in these tropical cases, however. For cloud systems that developed over the South China Sea and eastern Atlantic, net radiation (cooling) accounts for about 20% or more of the net condensation. However, short-wave heating and long-wave cooling are in balance with each other for cloud systems over the West Pacific region such that the net radiation is very small. This is due to the thick anvil clouds simulated in the cloud systems over the Pacific region. Large-scale cooling exceeds large-scale moistening in the Pacific and Atlantic cases. For cloud systems over the South China Sea, however, there is more large-scale moistening than cooling even though the cloud systems developed in a very moist environment. though For three cloud systems that developed over a mid-latitude continent, the net radiation and sensible and latent heat fluxes play a much more important role. This means the accurate measurement of surface fluxes and radiation is crucial for simulating these mid-latitude cases.

Tao, W.-K.↗
NASA NTRS2024

An Extensible Perturbed Parameter Ensemble for the Community Atmosphere Model Version 6

This paper documents the methodology and preliminary results from a Perturbed Parameter Ensemble (PPE) technique, where multiple parameters are varied simultaneously and the parameter values are determined with Latin hypercube sampling. This is done with the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community Earth System Model version 2 (CESM2). We apply the PPE method to CESM2-CAM6 to understand climate sensitivity to atmospheric physics parameters. The initial simulations vary 45 parameters in the microphysics, convection, turbulence and aerosol schemes with 263 ensemble members. These atmospheric parameters are typically the most uncertain in many climate models. Control simulations are analyzed and targeted simulations to understand climate forcing due to aerosols and fast climate feedbacks. The use of various emulators is explored in the multi- dimensional space mapping input parameters to output metrics. Parameter impacts on various model outputs, such as radiation, cloud and aerosol properties are evaluated. Machine learning is also used to probe optimal parameter values against observations. Our findings show that using PPE is a valuable tool for climate uncertainty analysis. Furthermore, by varying many parameters simultaneously, we find that many different combinations of parameter values can produce results consistent with observations, and thus careful analysis of tuning is important. The CESM2-CAM6 PPE is publicly available, and extensible to other configurations to address questions of other model processes in the atmosphere and other model components (e.g. coupling to the land surface).

Machine learning↗
NASA NTRS2020

El Niño Related Tropical Land Surface Water and Energy Response in MERRA-2

Though El Niño events each have distinct evolutionary character they typically provide systematic large scale forcing for warming and increased drought frequency across the tropical continents. We assess this response in the MERRA-2 reanalysis and in a ten-member model AMIP ensemble. The lagged response (3-4 months) of mean tropical land temperature to El Niño warming in the Pacific Ocean is well represented. MERRA-2 reproduces the patterns of precipitation in the tropical regions, while the AMIP ensemble reproduces some regional responses better than others. Model skill is dependent on event forcing strength and temporal proximity to the peak of the sea surface warming. A composite approach centered on maximum Niño3.4 SSTs and lag relationships to energy fluxes and transports is used to identify mechanisms supporting tropical land warming. The composite necessarily moderates weather scale variability of the individual events, while retaining the systematic features across all events. We find that that reduced continental upward motions lead to reduced cloudiness and more shortwave radiation at the surface, as well as reduced precipitation. The increased shortwave heating at the land surface, along with reduced soil moisture leads to warmer surface temperature, more sensible heating and warming of the lower troposphere. While the composite provides a broad picture of the mechanisms governing the hydrologic response to El Niño forcing, the regional and temporal response in any given event can be quite variable. The 2015-16 El Niño, one of the strongest events, demonstrates some of the forced response noted in the composite, but with shifts in the evolution that depart from the composite, demonstrating the limitations of the composite and individuality of El Niño.

Michael G Bosilovich↗
NASA NTRS2004

Multivariate Error Covariance Estimates by Monte-Carlo Simulation for Assimilation Studies in the Pacific Ocean

One of the most difficult aspects of ocean state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model-observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross-covariances between different model variables used. Here a comparison is made between a univariate Optimal Interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature. In the UOI case only temperature is updated using a Gaussian covariance function and in the MvOI salinity, zonal and meridional velocities as well as temperature, are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimation of the model error statistics is made by Monte-Carlo techniques from an ensemble of model integrations. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross-covariances between the fields of different physical variables constituting the model state vector, at the same time incorporating the model's dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere-Ocean array have been assimilated in this study. In order to investigate the efficacy of the multivariate scheme two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity and temperature. For reference, a third control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when multivariate correction is used, as evident from the analyses of the rms differences of these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating the water masses with properties close to the observed, while the UOI failed to maintain the temperature and salinity structure.

Borovikov, Anna↗
NASA NTRS2007

Evaluating Model Performance of an Ensemble-based Chemical Data Assimilation System During INTEX-B Field Mission

We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSFINCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (-140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrate the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements. The work described above also brought to light several short-comings of the data assimilation approach for CO profiles. Because of the limited vertical resolution of the measurement, the retrievals at different altitudes are correlated which can lead to problems with numerical error and overall efficiency. This has resulted in a manuscript that is about to be submitted to JGR:

Arellano, A. F., Jr.↗
NASA NTRS2002

Cloud-Resolving Model Simulations of LBA Convective Systems: Easterly and Westerly Regimes

The 3D Goddard Cumulus Ensemble (GCE) model was used to simulate convection that occurred during the TRMM LBA field experiment in Brazil. Convection in this region can be categorized into two different regimes. Low-level easterly flow results in moderate to high CAPE and a drier environment. Convection is more intense like that seen over continents. Low-level westerly flow results in low CAPE and a moist environment. Convection is weaker and more widespread characteristic of oceanic or monsoon-like systems. The GCE model has been used to study both regimes in order to provide cloud data sets that are representative of both environments in support of TRMM rainfall and heating algorithm development. Two different case are presented: Jan 26,1999, an easterly regime case, and Feb 23,1999, a westerly regime case. The Jan 26 case is an organized squall line and is initialized with a standard cold pool. The sensitivity to mid-level sounding moisture and wind shear will also be shown. The Feb 23 case is less-organized with only transient lines and is initialized with either warm bubbles or prescribed surface fluxes. Heating profiles, rainfall statistics and storm characteristics are compared and validated for the two cases against observations collected during the experiment.

Lang, Stephen E.↗
NASA NTRS2002

Mesoscale Convective Systems During SCSMEX: Simulations with a Regional Climate Model and a Cloud-Resolving Model

The South China Sea Monsoon Experiment (SCSMEX) was conducted in May-June 1998. One of its major objectives is to better understand the key physical processes for the onset and evolution of the summer monsoon over Southeast Asia and southern China. Multiple observation platforms (e.g., upper-air soundings, Doppler radar, ships, wind profilers, radiometers, etc.) during SCSMEX provided a first attempt at investigating the detailed characteristics of convection and circulation changes associated with monsoons over the South China Sea region. SCSMEX also provided precipitation derived from atmospheric budgets and comparison to those obtained from the Tropical Rainfall Measuring Mission (TRMM). In this paper, a regional scale model (with grid size of 20 km) and Goddard Cumulus Ensemble (GCE) model (with 1 km grid size) are used to perform multi-day integration to understand the precipitation processes associated with the summer monsoon over Southeast Asia and southern China. The regional climate model is used to understand the soil-precipitation interaction and feedback associated with a flood event that occurred in and around China's Yantz River during SCSMEX Sensitivity tests on various land surface models, sea surface temperature (SST) variations, and cloud processes are performed to understand the precipitation processes associated with the onset of the monsoon over the S. China Sea during SCSMEX. These tests have indicated that the land surface model has a major impact on the circulation over the S. China Sea. Cloud processes can effect the precipitation pattern while SST variation can effect the precipitation amounts over both land and ocean. The exact location (region) of the flooding can be effected by the soil-rainfall feedback. The GCE-model results captured many observed precipitation characteristics because it used a fine grid size. For example, the model simulated rainfall temporal variation compared quite well to the sounding-estimated rainfall. The results show there are more latent heat fluxes prior to the onset of the monsoon. However, more rainfall was simulated after the onset of the monsoon. This modeling study indicates the latent heat fluxes (or evaporation) have more of an impact on precipitation processes and rainfall in the regional climate model simulations than in the cloud-resolving model simulations. Research is underway to determine if the difference in the grid sizes or the moist processes used in these two models is responsible for the differing influence of surface fluxes an precipitation processes.

Tao, W.-K.↗
NASA NTRS2012

Global Warming Induced Changes in Rainfall Characteristics in IPCC AR5 Models

Changes in rainfall characteristic induced by global warming are examined from outputs of IPCC AR5 models. Different scenarios of climate warming including a high emissions scenario (RCP 8.5), a medium mitigation scenario (RCP 4.5), and 1% per year CO2 increase are compared to 20th century simulations (historical). Results show that even though the spatial distribution of monthly rainfall anomalies vary greatly among models, the ensemble mean from a sizable sample (about 10) of AR5 models show a robust signal attributable to GHG warming featuring a shift in the global rainfall probability distribution function (PDF) with significant increase (>100%) in very heavy rain, reduction (10-20% ) in moderate rain and increase in light to very light rains. Changes in extreme rainfall as a function of seasons and latitudes are also examined, and are similar to the non-seasonal stratified data, but with more specific spatial dependence. These results are consistent from TRMM and GPCP rainfall observations suggesting that extreme rainfall events are occurring more frequently with wet areas getting wetter and dry-area-getting drier in a GHG induced warmer climate.

Lau, William K. M.↗
NASA NTRS2021

Dynamic Ensemble Prediction of Cognitive Performance in Space

Astronauts are exposed to a unique set of stressors in spaceflight. Microgravity, isolation, confinement, and environmental and operational hazards: all of these can impact sleep, vigilant attention, and alertness, which are critical to mission success. In this paper, we seek to understand the most important predictors of alertness over the course of a space mission, using self-reported, cognitive, and environmental data collected from 24 astronauts on 6-month missions to the International Space Station (ISS). Alertness was repeatedly and objectively assessed on the ISS with a brief 3-minute Psychomotor Vigilance Test (PVT) that is highly sensitive to sleep deprivation. To relate PVT performance to time-varying and sparsely-measured environmental, operational, and psychological covariates, we propose a n ensemble prediction model comprising of linear mixed effects regression, random forest, and functional concurrent regression models. An extensive cross-validation procedure reveals that this ensemble outperforms any one of its components alone. We also discover that a participant’s past performance, reported fatigue and stress, and temperature and radiation exposure were among the most important variables associated with alertness. This method is broadly applicable to environmental studies where the main goal is accurate, individualized prediction involving a mixture of person-level traits and irregularly measured time series.

Danni Tu↗
NASA NTRS2008

3D Air Quality and the Clean Air Interstate Rule: Lagrangian Sampling of CMAQ Model Results to Aid Regional Accountability Metrics

The Clean Air Interstate Rule (CAIR) is expected to reduce transport of air pollutants (e.g. fine sulfate particles) in nonattainment areas in the Eastern United States. CAIR highlights the need for an integrated air quality observational and modeling system to understand sulfate as it moves in multiple dimensions, both spatially and temporally. Here, we demonstrate how results from an air quality model can be combined with a 3d monitoring network to provide decision makers with a tool to help quantify the impact of CAIR reductions in SO2 emissions on regional transport contributions to sulfate concentrations at surface monitors in the Baltimore, MD area, and help improve decision making for strategic implementation plans (SIPs). We sample results from the Community Multiscale Air Quality (CMAQ) model using ensemble back trajectories computed with the NASA Langley Research Center trajectory model to provide Lagrangian time series and vertical profile information, that can be compared with NASA satellite (MODIS), EPA surface, and lidar measurements. Results are used to assess the regional transport contribution to surface SO4 measurements in the Baltimore MSA, and to characterize the dominant source regions for low, medium, and high SO4 episodes.

Fairlie, T. D.↗
NASA NTRS2012

The Impact of Aerosols on Cloud and Precipitation Processes: Cloud-Resolving Model Simulations

Recently, a detailed spectral-bin microphysical scheme was implemented into the Goddard Cumulus Ensemble (GCE) model. Atmospheric aerosols are also described using number density size-distribution functions. A spectral-bin microphysical model is very expensive from a computational point of view and has only been implemented into the 2D version of the GCE at the present time. The model is tested by studying the evolution of deep tropical clouds in the west Pacific warm pool region and summertime convection over a mid-latitude continent with different concentrations of CCN: a low clean concentration and a high dirty concentration. The impact of atmospheric aerosol concentration on cloud and precipitation will be investigated.

cloud-resolving model↗
NASA NTRS2001

Forced and Free Intra-Seasonal Variability Over the South Asian Monsoon Region Simulated by 10 AGCMs

This study examines intra-seasonal (20-70 day) variability in the South Asian monsoon region during 1997/98 in ensembles of 10 simulations with 10 different atmospheric general circulation models. The 10 ensemble members for each model are forced with the same observed weekly sea surface temperature (SST) but differ from each other in that they are started from different initial atmospheric conditions. The results show considerable differences between the models in the simulated 20-70 day variability, ranging from much weaker to much stronger than the observed. A key result is that the models do produce, to varying degrees, a response to the imposed weekly SST. The forced variability tends to be largest in the Indian and western Pacific Oceans where, for some models, it accounts for more than 1/4 of the 20-70 day intra-seasonal variability in the upper level velocity potential during these two years. A case study of a strong observed MJO (intraseasonal oscillation) event shows that the models produce an ensemble mean eastward propagating signal in the tropical precipitation field over the Indian Ocean and western Pacific, similar to that found in the observations. The associated forced 200 mb velocity potential anomalies are strongly phase locked with the precipitation anomalies, propagating slowly to the east (about 5 m/s) with a local zonal wave number two pattern that is generally consistent with the developing observed MJO. The simulated and observed events are, however, approximately in quadrature, with the simulated response 2 leading by 5-10 days. The phase lag occurs because, in the observations, the positive SST anomalies develop upstream of the main convective center in the subsidence region of the MJO, while in the simulations, the forced component is in phase with the SST. For all the models examined here, the intraseasonal variability is dominated by the free (intra-ensemble) component. The results of our case study show that the free variability has a predominately zonal wave number one pattern, and has propagation speeds (10 - 15 m/s) that are more typical of observed MJO behavior away from the convectively active regions. The free variability appears to be synchronized with the forced response, at least, during the strong event examined here. The results of this study support the idea that coupling with SSTs plays an important, though probably not dominant, role in the MJO. The magnitude of the atmospheric response to the SST appears to be in the range of 15% - 30% of the 20-70 day variability over much of the tropical eastern Indian and western Pacific Oceans. The results also highlight the need to use caution when interpreting atmospheric model simulations in which the prescribed SST resolve MJO time scales.

Wu, Man Li C.↗
NASA NTRS2013

Generalized Ocean Color Inversion Model for Retrieving Marine Inherent Optical Properties

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future ensemble applications.

Werdell, P. Jeremy↗
NASA NTRS2008

Application of an Ensemble Smoother to Precipitation Assimilation

Assimilation of precipitation in a global modeling system poses a special challenge in that the observation operators for precipitation processes are highly nonlinear. In the variational approach, substantial development work and model simplifications are required to include precipitation-related physical processes in the tangent linear model and its adjoint. An ensemble based data assimilation algorithm "Maximum Likelihood Ensemble Smoother (MLES)" has been developed to explore the ensemble representation of the precipitation observation operator with nonlinear convection and large-scale moist physics. An ensemble assimilation system based on the NASA GEOS-5 GCM has been constructed to assimilate satellite precipitation data within the MLES framework. The configuration of the smoother takes the time dimension into account for the relationship between state variables and observable rainfall. The full nonlinear forward model ensembles are used to represent components involving the observation operator and its transpose. Several assimilation experiments using satellite precipitation observations have been carried out to investigate the effectiveness of the ensemble representation of the nonlinear observation operator and the data impact of assimilating rain retrievals from the TMI and SSM/I sensors. Preliminary results show that this ensemble assimilation approach is capable of extracting information from nonlinear observations to improve the analysis and forecast if ensemble size is adequate, and a suitable localization scheme is applied. In addition to a dynamically consistent precipitation analysis, the assimilation system produces a statistical estimate of the analysis uncertainty.

Zhang, Sara↗
NASA NTRS2013

An Ensemble Recentering Kalman Filter with an Application to Argo Temperature Data Assimilation into the NASA GEOS-5 Coupled Model

A two-step ensemble recentering Kalman filter (ERKF) analysis scheme is introduced. The algorithm consists of a recentering step followed by an ensemble Kalman filter (EnKF) analysis step. The recentering step is formulated such as to adjust the prior distribution of an ensemble of model states so that the deviations of individual samples from the sample mean are unchanged but the original sample mean is shifted to the prior position of the most likely particle, where the likelihood of each particle is measured in terms of closeness to a chosen subset of the observations. The computational cost of the ERKF is essentially the same as that of a same size EnKF. The ERKF is applied to the assimilation of Argo temperature profiles into the OGCM component of an ensemble of NASA GEOS-5 coupled models. Unassimilated Argo salt data are used for validation. A surprisingly small number (16) of model trajectories is sufficient to significantly improve model estimates of salinity over estimates from an ensemble run without assimilation. The two-step algorithm also performs better than the EnKF although its performance is degraded in poorly observed regions.

Particle Filter↗
NASA NTRS2021

Improved Representation of the Global Dust Cycle Using Observational Constraints on Dust Properties and Abundance

Even though desert dust is the most abundant aerosol by mass in Earth’s atmosphere, atmospheric models 25 struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused byfundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, 30 extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of two relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global 35 basis, we find that the emission flux of dust with geometric diameter up to 20 μm (PM20) is approximately 5,000 Tg/year, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded data sets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this data set is more accurate than current 40 model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.

aerosols, dust↗