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Observational needs for estimating Alaskan soil carbon stocks under current and future climate

Abstract Representing land surface spatial heterogeneity when designing observation networks is a critical scientific challenge. Here we present a geospatial approach that utilizes the multivariate spatial heterogeneity of soil‐forming factors—namely, climate, topography, land cover types, and surficial geology—to identify observation sites to improve soil organic carbon (SOC) stock estimates across the State of Alaska, USA. Standard deviations in existing SOC samples indicated that 657, 870, and 906 randomly distributed pedons would be required to quantify the average SOC stocks for 0–1 m, 0–2 m, and whole‐profile depths, respectively, at a confidence interval of 5 kg C m −2 . Using the spatial correlation range of existing SOC samples, we identified that 309, 446, and 484 new observation sites are needed to estimate current SOC stocks to 1 m, 2 m, and whole‐profile depths, respectively. We also investigated whether the identified sites might change under future climate by using eight decadal (2020–2099) projections of precipitation, temperature, and length of growing season for three representative concentration pathway (RCP 4.5, 6.0, and 8.5) scenarios of the Intergovernmental Panel on Climate Change. These analyses determined that 12 to 41 additional sites (906 + 12 to 41; depending upon the emission scenarios) would be needed to capture the impact of future climate on Alaskan whole‐profile SOC stocks by 2100. The identified observation sites represent spatially distributed locations across Alaska that captures the multivariate heterogeneity of soil‐forming factors under current and future climatic conditions. This information is needed for designing monitoring networks and benchmarking of Earth system model results.

54 ENVIRONMENTAL SCIENCES↗

Climate Conundrum: A Wet or Dry European and Northern African Climate During the Middle Miocene

Abstract End of 21st‐century hydroclimate projections suggest an expansion of subtropical dry zones, with Mediterranean and Sahel regions becoming much drier. However, paleobotanical assemblage evidence from the middle Miocene (17‐12 Ma), suggests both regions were instead humid environments. Here we show that by modifying regional sea surface temperatures (SST) in an Earth System Model (CESM1.2) simulation of the middle Miocene, the increased ocean evaporation and integrated water vapor flux overrides any drying effects associated with warming‐induced land‐surface evaporation driven by atmospheric CO 2 concentrations. These modifications markedly reduce the bias in the model‐data comparison for this period. A vegetation model (BIOME4) forced with simulated climatologies predicts both regions were dominated by mixed forest, which is largely consistent with the paleobotanical record. This study unveils the potential for wetter subtropical Mediterranean climates associated with warming, presenting an alternative scenario from future drying projections with localized SST warming governing regional climate change.

Acosta, R. P.↗

Retrieval of vertical moisture profiles with microwave radiometry

A statistical correlation technique is applied to the retrieval of vertical moisture profiles from downlooking radiometric measurements of atmospheric radiation at microwave wavelengths. Only an optimum subset of available radiometer channels is selected for estimating water vapor at specific pressure levels. To test its validity the algorithm was applied, in a numerical experiment, to 50 independent tropical radiosondes over a sea surface. It was also used to retrieve continuous sequences of atmospheric moisture profiles from a set of data obtained with a 4-channel microwave radiometer carried aboard an aircraft over a land surface.

Kakar, R. K.↗

Vegetation stress as a feedback mechanism in midlatitude drought

An atmospheric general circulation model with land surface properties represented by the Simplified Simple Biosphere Model is used to investigate the effect of soil moisture and vegetation stress on drought in the mid-latitudes. An idealized land-sea distribution with simple topography is used to remove as many external sources of climate variation as possible. The land consists of a single, flat, rectangular continent covered with prairie vegetation and centered on 44 deg N of an aqua planet. A control integration of 4 years is performed, and several sets of seasonal anomaly integrations are made to test the sensitivity of seasonal climate to low initial (1 April) soil moisture and dormant vegetation like what would occur during a severe drought. It is found that the inclusion of dormant vegetation during the spring and early summer greatly reduces evapotranspiration by eliminating transpiration. This affects local climate more strongly as summer progresses. Low initial soil moisture, combined with dormant vegetation, leads to a severe drought. The reduction in precipitation is much greater in magnitude than that due to low soil moisture alone, and greater than the sum of the effects computed separately. Although the short-term drought is more severe, the dormancy of the vegetation prevents further depletion of moisture in the root zone of the soil, so soil moisture begins to rebound toward the middle of summer.

Dirmeyer, Paul A.↗

Seasonal-to-Interannual Precipitation Variability and Predictability in a Coupled Land-Atmosphere System

In an earlier GCM study, we showed that interactive land surface processes generally contribute more to continental precipitation variance than do variable sea surface temperatures (SSTs). A new study extends this result through an analysis of 16-member ensembles of multi-decade GCM simulations. We can now show that in many regions, although land processes determine the amplitude of the interannual precipitation anomalies, variable SSTs nevertheless control their timing. The GCM data can be processed into indices that describe geographical variations in (1) the potential for seasonal-to-interannual prediction, and (2) the extent to which the predictability relies on the proper representation of land-atmosphere feedback.

Koster, Randal D.↗

A Coupled fcGCM-GCE Modeling System: A 3D Cloud Resolving Model and a Regional Scale Model

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a super-parameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and ore sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM), and it has started production runs with two years results (1998 and 1999). Also, at Goddard, we have implemented several Goddard microphysical schemes (21CE, several 31CE), Goddard radiation (including explicity calculated cloud optical properties), and Goddard Land Information (LIS, that includes the CLM and NOAH land surface models) into a next generation regional scale model, WRF. In this talk, I will present: (1) A Brief review on GCE model and its applications on precipitation processes (microphysical and land processes), (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), (3) A discussion on the Goddard WRF version (its developments and applications), and (4) The characteristics of the four-dimensional cloud data sets (or cloud library) stored at Goddard.

Tao, Wei-Kuo↗

A Coupled GCM-Cloud Resolving Modeling System, and A Regional Scale Model to Study Precipitation Processes

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a super-parameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM), and it has started production runs with two years results (1998 and 1999). Also, at Goddard, we have implemented several Goddard microphysical schemes (21CE, several 31CE), Goddard radiation (including explicitly calculated cloud optical properties), and Goddard Land Information (LIS, that includes the CLM and NOAH land surface models) into a next generation regional scale model, WRF. In this talk, I will present: (1) A brief review on GCE model and its applications on precipitation processes (microphysical and land processes), (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), and (3) A discussion on the Goddard WRF version (its developments and applications).

Tao, Wei-Kuo↗

A Coupled GCM-Cloud Resolving Modeling System, and a Regional Scale Model to Study Precipitation Processes

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a superparameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM), and it has started production runs with two years results (1998 and 1999). Also, at Goddard, we have implemented several Goddard microphysical schemes (2ICE, several 31CE), Goddard radiation (including explicitly calculated cloud optical properties), and Goddard Land Information (LIS, that includes the CLM and NOAH land surface models) into a next generatio11 regional scale model, WRF. In this talk, I will present: (1) A brief review on GCE model and its applications on precipitation processes (microphysical and land processes), (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), and (3) A discussion on the Goddard WRF version (its developments and applications).

Tao, Wei-Kuo↗

Combining Modis and Quikscat Data to Delineate Surface and Near-Surface Melt on the Greenland Ice Sheet

Over the last two decades, increasing melt has been measured on the Greenland Ice Sheet, along with mass loss as determined from satellite data, Monitoring the state of the Greenland Ice Sheet becomes critical especially because it is actively losing mass, and the ice sheet has a sea-level rise potential of 7 in. However measurement of the extent of surface melt varies depending on the sensor used, whether it is passive or active microwave or visible or thermal infrared. We have used remote-sensing data products to study surface and near-surface melt characteristics of the Greenland Ice Sheet. We present a blended MODIS-QS melt daily product for 2007 [1]. The products, including Moderate Resolution Imaging Spectroradiometer (MODIS) daily land-surface temperature (LST) and a special daily melt product derived from the QuikSCAT (QS) scatterometer [2,3] show consistency in delineating the melt boundaries on a daily basis in the 2007 melt season [I], though some differences are identified. An assessment of maximum melt area for the 2007 melt shows that the QSCAT product detects a greater amount of melt (862,769 square kilometers) than is detected by the MODIS LST product (766,184 square kilometers). The discrepancy is largely because the QS product can detect both surface and near-surface melt and the QS product captures melt if it occurred anytime during the day while the MODIS product is obtained from a point in time on a given day. However on a daily bases, other factors influence the measurement of melt extent. In this work we employ the digital-elevation model of Bamber et al. [4] along with the National Centers for Environmental Prediction (NCEP) data to study some areas and time periods in detail during the 2007 melt season. We focus on times in which the QS and MODIS LST products do not agree exactly. We use NCEP and elevation data to analyze the atmospheric factors forcing the melt process, to gain an improved understanding of the conditions that lead to melt and melt persistence, and our ability to capture surface melt accurately using MODIS and QS data.

Hall, Dorothy K.↗

Recommended Crew Systems Capabilities for a 30 to 90 Day Four-Crew Lunar Surface Mission

For any given surface mission duration, there exists a set of surface capabilities necessary to sustain a four-person crew for the length of time the crew is on the surface. Identifying these capabilities is important to mission planners and spacecraft design engineers responsible for lunar surface habitation elements, who need to know which systems and capabilities must be prioritized for the vehicles they are designing. However, simply identifying the surface capabilities does not necessarily equal a viable surface configuration. These capabilities may or may not satisfy stakeholder expectations or even permit completion of mission objectives. Lunar lander cargo mass constraints are a powerful forcing function that requires significant efforts to reduce the mass of all surface elements, including those supporting crew habitation. The required capabilities do increase as a function of mission duration, but it is a nonlinear growth. There are key points in mission duration, beyond which certain additional capabilities are needed. This forms bands of capability between each point. Within a given band, the same functional capabilities are needed, and the only scaling is that associated with daily consumables. The next higher band requires additional capabilities. This paper will focus on those capabilities needed for missions of 30-90 days. It should be noted that there are some uncertainties interpreting NASA requirements that require capabilities after a certain number of days, as typically this refers to number of days in space, not number of days on the lunar surface. Depending on transportation architecture, the number of days between Earth launch and lunar surface landing may vary, therefore impacting when certain required capabilities are implemented. However, activities to prepare humans for missions to Mars and commercial interest in lunar development may drive progressive increases in surface duration. Missions up to 30 days are expected to follow the initial 6.5-day surface missions and there may be commercial interest in extending to even greater durations. This paper will examine the potential case of a 30-90-day surface mission duration with respect to the systems necessary to support crew living and working on the lunar surface.

Habitat↗

Improving Surface Soil Moisture Estimates in West Africa Through GRACE Data Assimilation

Recent annual trends of precipitation and terrestrial water storage (TWS) in West Africa have been increasing over the past decade. Despite a significant impact of soil moisture on the TWS in West Africa, there is little research on the recent spatial and temporal behaviors of surface soil moisture (SSM) along with the hydrological trends and variability in West Africa. In this study, we assimilate TWS estimates from the Gravity Recovery and Climate Experiment (GRACE) mission into the Catchment Land Surface Model (CLSM) and evaluate its impacts on SSM simulations for the years, 2002–2017. The evaluation is performed using reference datasets: the African Monsoon Multidisciplinary Analysis (AMMA) in situ soil moisture observations, three currently available microwave satellite SSM observations from the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS), and the Soil Moisture Active Passive (SMAP) satellites and also the triple collocation analysis (TCA). Overall, modeled SSM shows good agreement with the reference datasets in terms of the anomaly SSM correlations. However, both modeled and ASCAT SSM are limited in their representation of the drying rates, as observed by ground observations, SMOS and SMAP estimates. Further, GRACE data assimilation results in improved SSM simulations in the humid regions with large annual TWS variability. This study demonstrates the utility of land data assimilation to inform hydrological conditions in West Africa, where soil moisture monitoring is necessary for water resource and livestock management.

West Africa↗

Satellite Beach Energy - Restructuring the Energy Balance in Satellite Beach, Florida, by Quantifying Solar Energy Production Potential using NASA POWER Data Products and LiDAR

The City of Satellite Beach, Florida, has committed to supplying 100% of its energy use from renewable energy, primarily solar, by the year 2050. The team created a methodology for estimating rooftop solar power potential using a high-resolution Light Detection and Ranging (LiDAR) dataset and the NASA Prediction of Worldwide Energy Resources (POWER) dataset to assist Satellite Beach in reaching their solar renewable energy goals. The POWER dataset provides information on direct and diffuse solar irradiation on horizontal surfaces, surface albedo, and effects of local meteorology, such as clouds. The team integrated the solar irradiance data with the LiDAR data to model slope, aspect, and shadowing in the 7 km2 study area to find suitable roof segments for solar panel installation and estimate the solar potential of each segment. This process was supplemented by an analysis of land surface temperature and urban greenness measured through the Normalized Difference Vegetation Index (NDVI) from Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) observations. These metrics serve to target areas for cooling initiatives aimed at reducing Satellite Beach’s overall energy consumption. The team found the total rooftop solar potential throughout the city to be 221,919,330KWh per year with an average annual rooftop photovoltaic, or PV, potential of 55,647KWh per building. As such, the average building could generate over five times the annual energy needs for an average household if PV panels were installed on all viable areas of its roof.

Spencer Nelson↗

High–Resolution Maps of Near–Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

Permafrost soils are a critical component of the global carbon cycle and are locally important because they regulate the hydrologic flux from uplands to rivers. Furthermore, degradation of permafrost soils causes land surface subsidence, damaging infrastructure that is crucial for local communities. Regional and hemispherical maps of permafrost are too coarse to resolve distributions at a scale relevant to assessments of infrastructure stability or to illuminate geomorphic impacts of permafrost thaw. Here we train machine learning models to generate meter–scale maps of near–surface permafrost for three watersheds in the discontinuous permafrost region. The models were trained using ground truth determinations of near–surface permafrost presence from measurements of soil temperature and electrical resistivity. We trained three classifiers: extremely randomized trees (ERTr), support vector machines (SVM), and an artificial neural network (ANN). Model uncertainty was determined using k–fold cross validation, and the modeled extents of near–surface permafrost were compared to the observed extents at each site. At–a–site near–surface permafrost distributions predicted by the ERTr produced the highest accuracy (70%–90%). However, the transferability of the ERTr to the sites outside of the training data set was poor, with accuracies ranging from 50% to 77%. The SVM and ANN models had lower accuracies for at–a–site prediction (70%–83%), yet they had greater accuracy when transferred to the non–training site (62%–78%). These models demonstrate the potential for integrating high–resolution spatial data and machine learning models to develop maps of near–surface permafrost extent at resolutions fine enough to assess infrastructure vulnerability and landscape morphology influenced by permafrost thaw.

54 ENVIRONMENTAL SCIENCES↗

Surface reflectance retrieval from satellite and aircraft sensors - Results of sensors and algorithm comparisons during FIFE

Visible to shortwave infrared radiometric data collected by a number of remote sensing instruments on aircraft and satellite platforms were compared over common areas in the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site on August 4, 1989, to assess their radiometric consistency and the adequacy of atmospheric correction algorithms. The instruments in the study included the Landsat 5 Thematic Mapper (TM), the SPOT 1 high-resolution visible (HRV) 1 sensor, the NS001 Thematic Mapper simulator, and the modular multispectral radiometers (MMRs). Atmospheric correction routines analyzed were an algorithm developed for FIFE, LOWTRAN 7, and 5S. A comparison between corresponding bands of the SPOT 1 HRV 1 and the Landsat 5 TM sensors indicated that the two instruments were radiometrically consistent to within about 5 percent. Retrieved surface reflectance factors using the FIFE algorithm over one site under clear atmospheric conditions indicated a capability to determine near-nadir surface reflectance factors to within about 0.01 at a reflectance of 0.06 in the visible (0.4-0.7 microns) and about 0.30 in the near infrared (0.7-1.2 microns) for all but the NS001 sensor. All three atmospheric correction procedures produced absolute reflectances to within 0.005 in the visible and near infrared. In the shortwave infrared (1.2-2.5 microns) region the three algorithms differed in the retrieved surface reflectances primarily owing to differences in predicted gaseous absorption. Although uncertainties in the measured surface reflectance in the shortwave infrared precluded definitive results, the 5S code appeared to predict gaseous transmission marginally more accurately than LOWTRAN 7.

Markham, B. L.↗

Wildfires Temperature Estimation by Complementary Use of Hyperspectral PRISMA and Thermal (ECOSTRESS & L8)

This paper deals with detection and temperature analysis and of wildfires using PRISMA imagery. Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 400–2,500 nm and an average spectral resolution less than 12 nm and a spatial resolution of 30 m/pixel. This study focuses on the wildfire temperature estimation over the Bootleg Fire, US 2021. The analysis starts by considering the Hyperspectral Fire Detection Index (HFDI) which is used to analyze the informative content of the images, along with the analysis of some specific visible, near-infrared and shortwave-infrared bands. This first analysis is used as input to perform a temperature estimation of the areas with active wildfire. Surface temperature is retrieved using PRISMA radiance and a linear mixing model based on two background components (vegetation and burn scar) and two active fire components. PRISMA temperatures are compared with LST (Land Surface Temperature) products from NASA's ECOSTRESS and Landsat 8 which imaged the Bootleg Fire before and after PRISMA. A critical discussion of the results obtained with PRISMA is presented, followed by the advantages and limitation of the proposed approach.

54 ENVIRONMENTAL SCIENCES↗

Contrasting Trends and Drivers of Global Surface and Canopy Urban Heat Islands

A comprehensive comparison of the trends and drivers of global surface and canopy urban heat islands (termed / s and / c trends, respectively) is critical for better designing urban heat mitigation strategies. However, such a global comparison remains largely absent. Using spatially continuous land surface temperatures and surface air temperatures (2003–2020), here we find that the magnitude of the global mean / s trend (0.19 ± 0.006°C/decade, mean ± SE) for 5,643 cities worldwide is nearly six-times the corresponding / c trend (0.03 ± 0.002°C/decade) during the day, while the former (0.06 ± 0.004°C/decade) is double the latter (0.03 ± 0.002°C/decade) at night. Variable importance scores indicate that global daytime / s trend is slightly more controlled by surface property, while background climate plays a more dominant role in regulating global daytime / c trend. At night, both global / s and / c trends are mainly controlled by background climate.

54 ENVIRONMENTAL SCIENCES↗

2nd Annual Report: Tropical Forest Response To A Drier Future: Turnover Times Of Soil Organic Matter, Roots, Respired CO 2 , And CH 4 Across Moisture Gradients In Time And Space

Moisture may be more important than temperature in driving soil carbon storage and emissions in the tropics, while the role of moisture on soil carbon dynamics is underrepresented in current land surface models. Data on belowground carbon cycling in the tropics is sparse, making extrapolation from field experiments to the tropics as a whole uncertain and limiting our ability to test and improve land model performance. The overall goal of my Early Career research is to constrain belowground carbon turnover times for tropical forests across a broad range in moisture regimes. Focusing on the Neo-tropics where climate projections indicate a drier future (Figure 1), my group is using 14 C analysis and modeling to address two major objectives: 1. Quantify age and belowground carbon turnover times across tropical forests spanning a moisture gradient from wetlands to dry forests and test the following hypotheses: Hypothesis 1: Moisture is the dominant regional-scale climate driver for belowground carbon turnover times across large precipitation gradients in tropical forests. Hypothesis 2: In seasonally dry forests, the age of respired CO 2 , emitted methane, and DOC shift to reflect new photosynthates during the wet season. Hypothesis 3: With decreasing precipitation, roots become distributed more deeply in the soil profile and their relative importance to deep soil carbon cycles increases. 2. Identify specific areas for focused model improvement and data needs through site-specific modeldata comparison and belowground carbon modeling for tropical forests. In addition, smaller collaborative focus studies will allow us to assess the role of other drivers for soil carbon storage and turnover in tropical forests, such as soil type, parent materials and mineralogy, landuse, and soil biology.

54 ENVIRONMENTAL SCIENCES↗