The next generation of Earth System models promisesunprecedented predictive power through the application of improvedphysical representations, data collection, and high-performancecomputing. A key component to the accuracy, efficiency, and robustnessof the Earth System simulations is the time integration ofdifferential equations describing the physical processes. Manyexisting Earth System models are simulated using low-order,constant-stepsize time-integration methods with no error control,opening them up to being inaccurate, inefficient, or require aninfeasible amount of manual tweaking when run over multipleheterogeneous domains or scales. We have implemented the variable-stepize, variable-order differentialequation solver SUNDIALS as the time integrator within the Structurefor Unifying Multiple Modelling Alternatives (SUMMA) modelframework. The model equations in SUMMA were modified and augmented toexpress conservation of mass and enthalpy. Water and energy balanceerrors were tracked and kept below a strict tolerance. The resultingSUMMA-SUNDIALS software was successfully run in a fully automatedfashion to simulate hydrological processes on the North Americancontinent, sub-divided into over 500,000 catchments. We compared the performance of SUMMA-SUNDIALS with a version (calledSUMMA-BE) that used the backward Euler method with a fixed stepsize asthe time-integration method. We find that SUMMA-BE required two ordersof magnitude more CPU time to produce solutions of comparable accuracyto SUMMA-SUNDIALS. Solutions obtained with SUMMA-BE in a similar orshorter amount of CPU time than SUMMA-SUNDIALS often contained largediscrepancies. We conclude that sufficient accuracy, efficiency, and robustness ofnext-generation Earth System model simulations can realistically onlybe obtained through the use of adaptive solvers. Furthermore, wesuggest simulations produced with low-order, constant-stepsizesolvers deserve more scrutiny in terms of their accuracy.

Local acceleration driven by whistler mode chorus waves is fundamentally important for the acceleration of seed electrons in the outer radiation belt to relativistic energies. This mechanism depends strongly on substorm activity and on the source and seed electron populations injected by substorms into the inner magnetosphere. In this work, a selection of geospace disturbance events, emerging from single and isolated interplanetary drivers, is divided into two groups, one resulting in enhancement and one in depletion of the average relativistic electron Phase Space Density (PSD). Because substorm activity does not always coincide with or depend on magnetic storm occurrence, we have not limited our study to storms, but have included also non-storm events that are able to cause enhancements and depletions of the relativistic electrons in the outer radiation belt. We investigate solar wind and geomagnetic parameters, wave activity and the seed electron PSD in the outer Van Allen radiation belt, looking for the occurrence of characteristic patterns, by performing a Superposed Epoch Analysis (SEA). Our study indicates the importance of substorm-associated enhancements of seed electrons, along with prolonged, intense ULF and VLF wave activity and an earthward displaced plasmapause, as conditions leading to substantial enhancements of relativistic electrons in the outer Van Allen belt. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870437 for the SafeSpace project.

The Arctic Boreal Vulnerability Experiment (ABoVE) is a NASA Terrestrial Ecology field campaign comprised of researchers from many disciplines and institutions who use field-based, remote sensing, and modeling approaches to understand the vulnerability and resilience of Arctic ecosystems and people in western North America. One goal of ABoVE is to provide the scientific basis for informed decision-making to guide societal responses at local to international levels, which requires knowledge of the identities and data needs of ABoVE’s partners and collaborators. The research presented here sought to identify relevant existing and new communities, organizations and institutions for ABoVE products while simultaneously assessing their research needs that future ABoVE activities can meet. We report on the results of an online survey sent to all ABoVE participants, defined broadly as an individual who engages with ABoVE, to identify their most relevant research themes and activities while simultaneously asking participants to identify potential new partners and collaborators for ABoVE engagement. These results are compared with data about who has downloaded ABoVE data products from the ORNL DAAC (the designated data archive for most ABoVE data products). The analysis reveals the current relevancy of ABoVE research themes and activities to participants, identification of additional organizations for ABoVE engagement, and potential gaps between data product access, usage, and engagement with ABoVE. Results indicate opportunities to tailor to the needs of current participants and focused outreach for newly identified groups.

The martian surface is predominantly covered by FeO-rich basalts and their alteration products. Several samples, either analyzed in situ by rovers or recovered as meteorites, might represent primitive (i.e. near-primary) basaltic melts that can shed light on the mineralogy, the bulk composition, and the temperature of their mantle sources. We recently developed a new melting model, called MAGMARS, that can predict the melt compositions of FeO-rich mantles and the martian mantle in particular (Collinet et al., submitted to JGR:P). It represents a more accurate alternative to pMELTS (Ghiorso et al., 2002, G3), which systematically overestimates the FeO and MgO content of martian melts and underestimates the SiO2 content (by up to 8 wt.%). MAGMARS can simulate near-fractional and batch melting of various mantle compositions. For example, MAGMARS can produce melts identical to the Adirondack-class basalts by near-fractional melting, between 2.3 and 1.7 GPa, of a depleted mantle with a potential temperature (Tp) of 1390°C (~7 wt.% melt fraction). For this study, MAGMARS is applied to all other martian basalts from which the primary melt compositions can be inferred in order to constrain their mantle sources: the Columbia hills basalts, igneous rocks from Gale crater, shergottites, nakhlites and Northwest Africa (NWA) 7034/7533. We find that a few basaltic clasts in the pre-Noachian polymict regolith breccia NWA 7034/7533 are the only samples with bulk compositions that could represent melts derived from a primitive mantle. The Columbia hills basalts (Gusev crater), alkali-rich rocks from Gale crater, nakhlites and enriched shergottites are most easily reproduced by melting depleted mantle reservoirs that were re-fertilized to different degrees in alkalis by fluids or melts (i.e. metasomatized sources). Most martian basalts, with the exception of depleted shergottites, can be produced from martian mantle reservoirs with Mg# comprised between 75 and 81. From this sample set, the melting conditions of the martian mantle seem to remain relatively stable through time (Tp = 1400 ± 100 ºC and P = 2 ± 0.5 GPa) but the depleted nature of all mantle sources sampled after the pre-Noachian points towards an early crust-mantle differentiation.

To make timely decisions for weather -and climate-related disasters and vulnerabilities, decision makers need current information that can be readily shared and communicated to stakeholders. To date, geospatial data is distributed using monolithic storage architectures and formats best suited for traditional research applications. Thus, everyday decision-makers face significant “barriers to entry” when trying to access, explore, and modify vast historical archives and real time data feeds. To address this need, we are developing a server architecture for rapidly creating and publishing data products. Privileged users can rapidly create or change products by operating on another product or combining multiple disparate data sources together. Users can then consume these new products using OGC-compliant WMS/WCS clients such as ArcGIS, QGIS, or Leaflet. This enables decision makers to effectively communicate with stakeholders using customized maps. Moreover, this capability enables products to be rapidly updated in cases where timely information is important. Our server architecture is containerized, making it easy to deploy on various architectures including serverless cloud resources. It is implemented in Python, leverages the plug-and-play data wrangling capabilities of the PODPAC library, and uses a custom library for serving OGC-compliant data. The result is an easy-to-use architecture for rapidly publishing custom geospatial products that exploit vast earth science data resources. We will demonstrate our server capabilities by showing how privileged users can build a set of products that are computed on-demand starting from a fresh server. Using Jupyter Lab notebooks, we will create products that modify single data sources as well as products that combine multiple disparate sources. We will then show how users can consume these products using OGC-compliant clients. Next, we will detail our cloud-based, serverless deployment of this technology using Amazon Web Services. Finally, we will discuss the advantages of our approach along with any caveats. Enabling everyday decision makers to rapidly create and share geospatial data will revolutionize their productivity and effectiveness for assessing and remediating weather- and climate-related vulnerabilities and disasters.

During the second half of 2019, a series of recurring, moderate geomagnetic storms (Dst ≈ - 70 nT) emerged after a sequence of high-speed solar wind streams (Vsw ≥ 600 km/s) impacted the magnetosphere. During one of these storms, intense substorm activity was also recorded (SML ≈ - 2000 nT on August 31 and September 1), as well as a longer-lasting solar wind pressure pulse. We investigate this series of events, using particle measurements from three missions that recorded significant enhancements of relativistic electron fluxes: the Van Allen Probes, Arase and Galileo 207 & 215 satellites. We use both the flux intensity and the phase space density (PSD) of electrons, along with interplanetary parameters and information on ultra-low frequency (ULF) and chorus wave activity for a detailed analysis of this event. Our study demonstrates the importance of substorm injections, even during moderate or weak geomagnetic storms. The presence of seed electrons at L* = 4-5, in addition to intense ULF and chorus wave activity, seems to result in very efficient electron acceleration to relativistic and ultra-relativistic energies. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870437 for the SafeSpace project.

Tropical peatlands are estimated to hold carbon stocks of 70 Pg C or more as partly-decomposed organic matter, or peat. Peat may accumulate over thousands of years into gently mounded deposits called peat domes with a relief of several meters over distances of kilometers. The curved shape of peat domes accounts for much of the carbon storage in these landscapes, but their subtle topographic signal is difficult to measure. As many of the world’s tropical peatlands are remote and inaccessible, spaceborne laser altimetry data from missions such as NASA’s Global Ecosystem Dynamics Investigation (GEDI) and the Advanced Topographic Laser Altimeter System (ATLAS) instrument on the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory could help to describe these deposits. However, for better and for worse, tropical peatlands may also support forests with high above-ground biomass—averages of over 200 Mg C / ha have been reported—which increases their carbon stocks but further complicates determination of their surface topography using laser altimetry. In this work, we evaluate retrieval of ground elevations and canopy metrics derived from GEDI waveform data, as well as single-photon data from ATLAS, with reference to an airborne laser scanning dataset covering an area of over 100 km^2 in the Belait District of Brunei Darussalam. We find that despite infrequent ground retrievals, with regularization these spaceborne platforms can provide useful data for tropical peatland surface altimetry.

Climatic changes with warmer temperatures in mid-latitudes require the need to improve the simplified vegetation scheme of the regional climate model COSMO-CLM, which is not capable of modelling complex processes depending on temperature, water availability and day length. Thus, we have implemented the physically based Ball-Berry approach coupled with photosynthesis processes based on Farquhar and Collatz models for C3 and C4 plants in COSMO-CLM (v 5.16). The implementation of the new algorithms includes the replacement of the “one-big leaf” by a “two-big leaf” approach. We performed single column simulations with COSMO-CLM over three observational sites with C3 grass plants in Germany for the period from 1999 to 2015 (Parc, Linden and Lindenberg domain, Fig.1). Hereby, we tested three alternative formulations of the new algorithms. The first formulation (COSMO_v3.5) is based on the Community Land Model (CLM v3.5) algorithms for stomatal resistance, which depend on leaf photosynthesis, CO2 partial and vapor pressure and minimum stomatal conductance. The second one is COSMO_v4.5, which is based on the phenology algorithms of CLM v4.5 including the soil water stress function. The third one is similar to COSMO_v4.5 but with additional equations for dry leaf calculations (COSMO_v4.5e). The results revealed major differences in the annual cycle of stomatal resistance compared to the control simulation (COSMO_orig) with the original algorithm (Fig. 1). The biggest changes are from October to April when stomata are closed. The summer values of experiments are closer to measured values, than COSMO_orig. Further, changes in the stomatal resistance algorithms improve the accuracy of calculated transpiration rate and total evapotranspiration. The results indicate that changes in stomatal resistance and photosynthesis algorithms can improve the accuracy of other parameters of the COSMO-CLM model by comparing them with FLUXNET data and meteorological observations at the sites, and GLEAM datasets. Figure 1: The stomatal resistance based on COSMO-CLM experiments (a - annual cycle; b - daily values from 01.06.2011 to 15.09.2011) for: I – Parc domain, II – Linden domain, III – Lindenberg domain. This research was funded by the German Research Foundation (DFG) through grant number 401857120

Providing environmental flows is challenging in the middle portion of the Rio Grande/Bravo Basin (between Elephant Butte Reservoir in New Mexico and Presidio, Texas) where water demand has continued to increase over time despite limited river water and dropping groundwater levels. Riparian ecosystems in this agriculture-dominated desert environment will likely become more vulnerable as competition over scarce water increases in the face of growing demand and dwindling supply. Little to no water is allocated to riparian ecosystems unless water or water rights are purchased or transferred to sustain those systems. Ongoing debates about providing environmental flows for riparian forest galleries in this water-scarce region have not been backed by quantitative modeling results of potential impacts on surface water and groundwater availability. We quantify water requirements to provide a menu of options for environmental flow allocation to establish cottonwood forest galleries. We apply hydrologic modeling under a projected warm-dry future to determine the frequency of river water availability for providing minimum environmental flows, and to evaluate water budget tradeoffs associated with environmental flow allocations in this region. Results inform water resources management decisions that support riparian habitats.

The state of the practice for large area land cover mapping is based on the application of supervised classifiers to multi-temporal multi-spectral optical wavelength data. Deep learning approaches have been developed but are typically applied to individual images with less research on their application to satellite time series. The recent availability of 30 m Landsat analysis ready data (ARD) has significantly increased the ease of using Landsat data. Irregular gaps in the Landsat image time series reduce the easy application of deep learning to time series. This study proposes a novel solution based on a two dimensional (2-D) array (one spectral dimension and one temporal dimension) derived at each ARD pixel time series and using the 2-D convolutional neural network (CNN) deep learning algorithm. Classification results are presented for all of the Conterminous United States (CONUS) considering a year of Landsat 5 and 7 data. The 30 m USGS National Land Cover Database (NLCD) (15 classes after filtering) and USDA Crop Data Layer (CDL) (22 classes after filtering) products are filtered conservatively across the CONUS and sampled to construct >3.31 and >0.48 million training pixels respectively defined with reliable accuracy and reduced spatial autocorrelation. The CNN and a conventional Random Forest (RF) classifier are trained using 10%, 50% and 90% of the training samples, and used to classify the remaining samples. Classification experiments are undertaken independently using the NLCD and CDL training data. Different CNN structures with different learnable coefficients are used and the accuracy results compared with conventional RF results. The main findings were (1) application of the CNN with different structures provided only about 1% accuracy difference, the optimal CNN structure was dependent on the number of training samples, and increasing the number of CNN learnable coefficients beyond the number of training samples was not helpful; (2) although the CNN training time was up to two orders of magnitude slower than the RF, the classification time was an order of magnitude faster, (3) the CNN provided 2-5% higher accuracy than the RF which is notable given the large number of classes and that the overall classification accuracies were >80%.

Satellite-based land surface phenology information (e.g. time-series spectral metrics and seasonal composites) can be used to model tropical tree canopy cover (TCC) but is limited by missing observations caused by clouds and cloud shadows and the sensor failure. Gap-filling (i.e. reconstruction of missing data in images) provides a key to solving the limitation, but its effectiveness and necessity for a given application, e.g. TCC modelling have been rarely explained. The main goal of this study was to examine the effectiveness and necessity of gap-filling, applied to the TCC modelling using Landsat time series. We chose Missing Observation Prediction Based on Spectral-Temporal Metrics (MOPSTM) method for its simple tuning, fast computation, and good performance in Landsat missing data reconstruction. MOPSTM models the relationship between valid observations in time series and spectral-temporal metrics in the k-Nearest Neighbor regression to predict the missing observations. We provided a quantitative comparison of TCC modelling using predictor variables (e.g. seasonal composite, spectral-temporal metrics, and harmonic regression coefficients) derived from Landsat time series that included gap-filling versus those that did not include gap-filling. With 1-year Landsat 8 Surface Reflectance time series acquired from a tropical area in Taita Hills, Kenya throughout 2015 and a reference TCC map scaled in 0 – 100 derived from the Airborne Laser Scanning (ALS) data between 2014 and 2015, we applied random forest regression to model TCC using the predictor variables. The results indicated that TCC modelling using gap-filled predictors yielded smaller root mean square error than that using the non-gap-filled predictors, which proved the effectiveness of gap-filling in tropical TCC modelling. The effects of gap-filling might be reduced when the predictor variables were of high quality, e.g. median composite derived from the time series where sufficient observations exist. We concluded that gap-filling has a positive effect on the accuracy of TCC modelling.

This study investigates the characteristics and climate impacts of the quasi-biweekly oscillation (QBWO) over the western North Pacific (WNP) in boreal winter based on observational and reanalysis data and numerical experiments with a simplified model. The wintertime convection over the WNP is dominated by significant biweekly variability with a 10-20-day period, which explains about 66% of the intraseasonal variability. Its leading mode on the biweekly timescale is a northwestward-propagating convection dipole over the WNP, which oscillates over a period of about 12 days. When the convection-active center of this QBWO is located to the east of the Philippines, it can generate an anticyclonic vorticity source to the south of Japan via inducing upper-tropospheric divergence and excite a Rossby wave train propagating towards North America along the Pacific rim. The resultant lower-tropospheric circulation facilitates cold advection and leads to cold anomalies over central North America in the following week. This result highlights a cause-effect relationship between the WNP convection and the North American climate on the quasi-biweekly timescale and may provide some prediction potential for the North American climate.

Based on the observational dataset SA-OBS and model outputs from the Community Earth System Model Large Ensemble project, this study investigates heatwaves in Southeast Asia in the current and future warmer climate. A heatwave is detected when the daily maximum temperature exceeds the 90th percentile threshold at each grid for at least three consecutive days. Three characteristics describing the frequency, duration, and amplitude of heatwaves are examined, including the sum of heatwave days per year (HWF) satisfying the heatwave definition, the length of the longest yearly heatwave event (HWD), and the hottest amplitude of the hottest yearly heatwave event (HWA). Results indicate that increased global warming is associated with substantial changes in heatwave characteristics over Southeast Asia, with more frequent heatwaves, longer heatwave duration, and higher extreme temperatures. The increase in HWA has a linear growth against global warming levels with distinct regional differences between the Maritime Continent and the Indochina Peninsula due to their different heat content of lower atmospheric boundaries. In contrast, those in HWF and HWD have nonlinear growth characteristics. The projected warmer future tends to be associated with a higher risk ratio value with the occurrence of rarer extreme heatwaves relative to the current climate. These results reiterate the potential risks of extreme regional heatwaves if global warming is unrestricted.

High-resolution topographic data are used in geomorphic and hydrologic research for many purposes, including topographic change detection, development of computational meshes for hydraulic models, characterizing channel and hillslope geometry, measuring vegetation structure and density. These data can be collected in a variety of ways, ranging from manual surveying with a Total Station or GPS system, airborne LiDAR, terrestrial laser scanning (TLS), and Structure-from-Motion (SfM) photogrammetry using images collected from drones or pole-mounted cameras. These methods can be very time consuming to collect, and the equipment they require can be very costly. With the release of the 2020 iPad Pro and iPhone 12 Pro, Apple added a LiDAR sensor to their devices, enabling them to be used as hand-held 3D scanners. This new technology has the potential to enable very rapid collection of high-resolution topographic data at low cost. Here, we investigate how well iPad-based LiDAR characterizes topography and topographic change in hillslope and fluvial environments. A 2020 iPad Pro using two apps (3D Scanner and Polycam) was used to collect topographic data over areas ranging from about 100 – 600 m2. These same areas were scanned with a Topcon GLS-2000 TLS system, and aerial imagery were collected with a UAV and processed with Agisoft Metashape to create SfM point clouds. Ground-based targets visible in the datasets were surveyed with an RTK-GNSS system and used to register and scale the datasets. The datasets were aligned using the ICP algorithm in CloudCompare, and cross-sections and topographic differences were extracted from each dataset and compared. Our analysis indicates that transects collected with the iPad LiDAR have mean absolute differences with TLS and SfM data within 3 cm, making these data comparable to other high-resolution topographic data collection methods.

Regional crop production estimates are important in both public and private sectors to ensure the adequacy of a food supply and aid policymakers and farmers in managing harvest, storage, import/export, transportation, and anticipate market fluctuations. Food security will be progressively challenged by population growth and climate change. Thus, the prediction of accurate regional crop yield is essential for national food security and the sustainable development of the Indian agriculture sector. In this study, we have selected Punjab, the highest wheat yielding state in India. The district-wise wheat yield data were available for the year 2000 – 2019. We have used several covariates for crop health viz. normalized difference vegetation index (NDVI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR); meteorological indicators viz. land surface temperature (LST), and evapotranspiration (ET); and surface characteristics viz. protrusion coefficient (PC). These indicators were generated at 250 m spatial resolution from the MODIS data using Google Earth Engine. The whole data was divided into two groups for training (2000 – 2009, 2011, 2013, 2014, 2016 - 2019) and testing (2010, 2012, 2015), which were randomly selected. This study uses the random forest (RF) regression method to create a wheat yield prediction model. We created several combinations of covariates and found that fAPAR and ET are highly correlated with NDVI and do not have much influence on the model’s prediction accuracy. Hence, only four out of six covariates were selected for final training. The coefficient of determination between district-level yield vs. (NDVI/LAI/PC/LST) was 0.37/0.31/0.15/0.13 respectively. We used randomized search cross-validation as well as grid search cross-validation for hyper-parameter tuning. Furthermore, we used mean absolute error (MAE) and accuracy as quality metrics. The MAE for training was 0.1870 t/Ha with 95.81% accuracy, whereas the MAE on test data was obtained as 0.4293 t/Ha with 90.02% accuracy. The results of this study are within acceptable error limits of the published research articles. Overall, this study demonstrates that covariates derived from coarse resolution satellite data can predict district-level crop yield with reasonable accuracy.

A key benefit to the scientific community of a pathfinder to 542 AU is calibration data for an array of instruments on a flagship probe to interstellar space. There are fundamental processes and parameters of the near interstellar medium, whose estimated range of values could be greatly narrowed by in-situ sampling from a fast and small mission. By selecting an angle relative to the sun, plane of the ecliptic and a scientifically interesting target (such as Trappist-1), it is possible to perform initial optical measurements from the Solar Gravitational Lens (SGL) focal region on the same pathfinder. Doing so provides a basic set of data for larger follow-on missions to observe that (and other) solar systems in greater detail. By combining the datasets from 2 solar cycles (22 years) of space weather monitoring satellites, Voyager 1 and other deep space probes, the Practical Interplanetary Propulsion (PIP) Study constructed a radial profile for the solar wind ranging from 1 AU through the foreshock at 83 AU, to a notional heliopause at 123 AU, and the near interstellar medium out to 1,800 AU. The resulting matrix of plasma parameters was applied to a trajectory model “seed code,” to test flight paths for future probes. This paper presents an example pathfinder, consisting of a cubesat bus equipped with a Wind Rider propulsion system and radioisotope power system (RPS). A brief description of those subsystems and how they interact with the solar wind or interstellar medium is included. Trajectory simulation results estimate the trip time from 1 to 542 AU near the plane of the ecliptic takes 6.9 years. Adding a compact imaging instrument enables the probe to sample data from the vantage point of the Trappist-1 SGL, as well as PickUp Ions (PUI) for a 1 year science campaign. Total pathfinder mission time after launch is less than 8 years. A set of policy-making recommendations for enabling such small precursor-type missions is provided in the conclusions, as well as ways to extend the mission to communicate from 1,000 AU to 1,800 AU. Alternatively, a method to gradually decelerate to a near stop at the end of the mission, using the Wind Rider to drag against the interstellar plasma, is also included.

Earth System Models (ESMs) traditionally operate at large horizontal resolutions, on the order of 100 km, which can obscure the effects of smaller scale heterogeneity. When examining land surface states and fluxes in ESMs, one common approach to mitigate this issue is to divide the sub-grid land surface into distinct homogeneous clusters and then resolve the water, energy, and biogeochemical processes on each cluster or tile. The literature, as well as work in the Coupling of Land and Atmospheric Subgrid Parameterizations (CLASP) project, indicates that surface heterogeneity has important implications for atmospheric processes as well. Previous work using large-eddy simulation (LES) shows that spatial variability in surface heating can produce significant secondary circulations closely related to the type and scale of heterogeneity that are not captured by single column models. This presentation aims to address this persistent weakness by using a clustering or tiling approach, similar to that used with land surface processes, for the atmosphere. To accomplish this task, we run the Cloud Layers Unified By Binomials (CLUBB) single column model, a sub-grid turbulence and cloud parameterization scheme, over a 100 km box centered at the Southern Great Plains site in Oklahoma for a variety of surface and atmospheric conditions. The model is run independently over multiple surface clusters defined by surface sensible heat fluxes in the given domain. Results indicate that significant differences exist for some cases between the single column and multicolumn cases for liquid water path (LWP) as well as the turbulent kinetic energy (TKE) budget, and that results converge on consistent results with a fairly low number of clusters (i.e., atmospheric columns). We follow this up with a connected multicolumn setup where each column is dynamically connected with the other columns throughout the run to qualitatively capture the circulations observed in the LES output. The existing results show promise for capturing the effects of subgrid scale surface flux heterogeneity on the lower atmosphere in ESMs with the application of a multicolumn CLUBB setup.

There is a need to better characterize aerosol absorption due to its remarkable radiative effects on climate. In particular, we need to understand the separation of total absorption between the two main components, the black carbon (BC) and brown carbon (BrC), especially in places where the anthropic influence is little, such as in pristine regions like Central Amazonia. The mechanisms that control the formation and evolution of BC and BrC in tropical forests remain unclear. In this study, we have performed detailed measurements at the Amazon Tall Tower Observatory (ATTO) tower on aerosols collected in Nuclepore filters and analyzed them with high-resolution optical spectrometers with a wide spectral range (300 to 2500 nm). Thus, we determined the absorption characteristics of BrC as a function of wavelength. The results show that BrC absorption is spectrally significant below 660 nm and is maximum at wavelengths close to 370 nm. Combining the measured spectral dependency with MIE modeling of the BC contribution, we determined that the BrC accounts for 14.8% of the total absorption. A similar fraction of BrC to total absorption was obtained through a similar analysis of in-situ measurements. Elemental chemical analysis of the filters, cluster, and factor analysis shows that the BrC is associated with airborne dust. The different methods to quantify BC and BrC are consistent and show similar results. This study will allow the quantification of the role of BrC and BC in aerosol absorption of radiation in Amazonia.