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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation

Abstract. Atmospheric aerosol has substantial impacts on climate, air quality and biogeochemical cycles, and its concentrations are highly variable in space and time. A key variability to evaluate within models that simulate aerosol is the vertical distribution, which influences atmospheric heating profiles and aerosol–cloud interactions, to help constrain aerosol residence time and to better represent the magnitude of simulated impacts. To ensure a consistent comparison between modeled and observed vertical distribution of aerosol, we implemented an aerosol lidar simulator within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package version 2 (COSPv2). We assessed the attenuated total backscattered (ATB) signal and the backscatter ratios (SRs) at 532 nm in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1). The simulator performs the computations at the same vertical resolution as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), making use of aerosol optics from the E3SMv1 model as inputs and assuming that aerosol is uniformly distributed horizontally within each model grid box. The simulator applies a cloud masking and an aerosol detection threshold to obtain the ATB and SR profiles that would be observed above clouds by CALIOP with its aerosol detection capability. Our analysis shows that the aerosol distribution simulated at a seasonal timescale is generally in good agreement with observations. Over the Southern Ocean, however, the model does not produce the SR maximum as observed in the real world. Comparison between clear-sky and all-sky SRs shows little differences, indicating that the cloud screening by potentially incorrect model clouds does not affect the mean aerosol signal averaged over a season. This indicates that the differences between observed and simulated SR values are due not to sampling errors, but to deficiencies in the representation of aerosol in models. Finally, we highlight the need for future applications of lidar observations at multiple wavelengths to provide insights into aerosol properties and distribution and their representation in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Representing plant diversity in land models: An evolutionary approach to make “Functional Types” more functional

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of "Plant Functional Types" (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. Here we review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages ("Lineage Functional Types") will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2013

Simulations of the Mid-Pliocene Warm Period Using Two Versions of the NASA-GISS ModelE2-R Coupled Model

The mid-Pliocene Warm Period (mPWP) bears many similarities to aspects of future global warming as projected by the Intergovernmental Panel on Climate Change (IPCC, 2007). Both marine and terrestrial data point to high-latitude temperature amplification, including large decreases in sea ice and land ice, as well as expansion of warmer climate biomes into higher latitudes. Here we present our most recent simulations of the mid-Pliocene climate using the CMIP5 version of the NASAGISS Earth System Model (ModelE2-R). We describe the substantial impact associated with a recent correction made in the implementation of the Gent-McWilliams ocean mixing scheme (GM), which has a large effect on the simulation of ocean surface temperatures, particularly in the North Atlantic Ocean. The effect of this correction on the Pliocene climate results would not have been easily determined from examining its impact on the preindustrial runs alone, a useful demonstration of how the consequences of code improvements as seen in modern climate control runs do not necessarily portend the impacts in extreme climates.Both the GM-corrected and GM-uncorrected simulations were contributed to the Pliocene Model Intercomparison Project (PlioMIP) Experiment 2. Many findings presented here corroborate results from other PlioMIP multi-model ensemble papers, but we also emphasize features in the ModelE2-R simulations that are unlike the ensemble means. The corrected version yields results that more closely resemble the ocean core data as well as the PRISM3D reconstructions of the mid-Pliocene, especially the dramatic warming in the North Atlantic and Greenland-Iceland-Norwegian Sea, which in the new simulation appears to be far more realistic than previously found with older versions of the GISS model. Our belief is that continued development of key physical routines in the atmospheric model, along with higher resolution and recent corrections to mixing parameterisations in the ocean model, have led to an Earth System Model that will produce more accurate projections of future climate.

climate change↗
NASA NTRS2016

Collaborations and Partnerships in NASA’s Earth Science Data Systems

NASA has been collecting Earth observation data from spaceborne instruments since 1960. Today, there are tens of satellites orbiting the Earth and collecting frequent global observations for the benefit of mankind. Collaboration between NASA and organizations in the US and other countries has been extremely important in maintaining the Earth observation capabilities as well as collecting, organizing and managing the data. These collaborations have occurred in the form of: 1. NASA’s developing and launching spacecraft and instruments for operation by other agencies; 2. Instruments from collaborating organizations being flown on NASA satellites; and 3. Instruments from NASA being flown on satellites from collaborating organizations. In addition, there are collaborations such as joint science teams, data exchanges, and participation in international organizations to promote interoperability of various data systems. The purpose of this paper is to describe some of the Earth science data-related collaborative efforts in which NASA participates, and highlight a few results relevant to Earth system science research obtained through such collaborations.

Ramapriyan, Hampapuram K.↗
DOE OSTI2020

Relative Impact of Sea Ice and Temperature Changes on Arctic Marine Production

We use a modern Earth system model to approximate the relative importance of ice versus temperature on Arctic marine ecosystem dynamics. We show that while the model adequately simulates ice volume, water temperature, air-sea CO 2 flux, and annual primary production in the Arctic, itunderestimates upper water column nitrate across the region. This nitrate bias is likely responsible for the apparent underestimation of ice algae production. Despite this shortcoming, the model appears to be a useful tool for exploring the impacts of environmental change on phytoplankton production and carbon dynamics over the Arctic Ocean. Our experiments indicate that under a warmer climate scenario, the percentage of ocean warming that could be apportioned to a reduction in ice area ranged from 11% to 100%, while decreasing ice area could account for 22–100% of the increase in annual ocean primary production. The change to CO2 air-sea flux in response to ice and temperature changes averaged an Arctic-wide 5.5 Tg C yr –1 (3.5%) increase, into the ocean. This increased carbon sink may be short-lived, as ice cover continues to decrease and the ocean warms. The change in carbon fixation from phytoplankton in response to increased temperatures and reduced ice was generally more than a magnitude larger than the changes to CO 2 flux, highlighting the importance of fully considering changes to the marine ecosystem when assessing Arctic carbon cycle dynamics. Our work demonstrates the importance of ice dynamics in controlling ocean warming and production and thus the need for well-behaved ice and BGC models within Earth system models if we hope to accurately predict Arctic changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Nitrogen and phosphorus cycling in an ombrotrophic peatland: a benchmark for assessing change

Aims Slow decomposition and isolation from groundwater mean that ombrotrophic peatlands store a large amount of soil carbon (C) but have low availability of nitrogen (N) and phosphorus (P). To better understand the role these limiting nutrients play in determining the C balance of peatland ecosystems, we compile comprehensive N and P budgets for a forested bog in northern Minnesota, USA. Methods N and P within plants, soils, and water are quantified based on field measurements. The resulting empirical dataset are then compared to modern-day, site-level simulations from the peatland land surface version of the Energy Exascale Earth System Model (ELM-SPRUCE).

Salmon, Verity G.↗
DOE OSTI2023

Peatland hydrology across scale: a probabilistic framework for confronting variability, heterogeneity, and uncertainty (Final Report)

Our project aimed to guide the representations of peatland hydrology and water-carbon feedbacks within Earth System Models, by improving process understanding and developing parsimonious models that elucidate the effects of hydroclimatic variability and spatial heterogeneity. Our research questions centered on the three key drivers of peatland hydrology: (1) seasonal and interannual hydroclimatic fluctuations, (2) spatial heterogeneity of peatland microtopography, and (3) hydrological connectivity across landscape units within a peatland watershed. Our analyses and model testing used existing long-term datasets within the Marcell Experimental Forest (MEF), with new data collected whenever necessary. To date, we have demonstrated the strong influence of water table elevations on the temperature sensitivity of CH 4 emissions, using a newly developed eddy covariance dataset spanning eleven years at MEF. Specifically, higher water tables dampen the increase in CH 4 emissions in the spring as well as their decrease in the fall, resulting in hysteresis. These results imply that any hydroclimatological changes in peatlands that shift seasonal water availability from winter to summer will increase annual CH 4 emissions, even if soil temperature remains unchanged. To further investigate water table elevation change within the peatland microtopography, we installed automated water table gauges across four bog-forest transects (the “lagg”) in two peatland watersheds at MEF, which are hotspots of intense biogeochemical activity. These measurements will give us new information about the extent of lagg expansion and contraction during snowmelt and high intensity rainfall events, as well as the directionality of water and nutrient flow into the bog from the surrounding forests. Finally, we investigated the hydrological connectivity across the peatland watershed complex during early spring – a critical period where water table is elevated from snowmelt – using extended hydrological records from MEF (e.g., snow and frost depths, water table elevations, streamflow). The importance of thermal and wintertime processes within the Energy Exascale Earth System Model (E3SM) was determined through sensitivity analyses. Results from data analyses show that (i) streamflow has decreased over decades likely due to increased evapotranspiration rates (despite no detectable trends in precipitation), (ii) spring streamflow generation is controlled by a sequence of fill-and-spill pathways from the snowpack to the stream through peatland water stores, and (iii) frost depth is a key explanatory variable for the timing and magnitude of streamflow. These results suggest that frost plays an important role in connecting surface water storage in peatlands to stream outlets, and that the hydrological connectivity across the peatland watershed complex can mediate the sensitivity of hydrological responses to climate variations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

SWOT: The Surface Water and Ocean Topography Mission. Wide- Swath Altimetric Elevation on Earth

The elevation of the surface of the ocean and freshwater bodies on land holds key information on many important processes of the Earth System. The elevation of the ocean surface, called ocean surface topography, has been measured by conventional nadirlooking radar altimeter for the past two decades. The data collected have been used for the study of large-scale circulation and sea level change. However, the spatial resolution of the observations has limited the study to scales larger than about 200 km, leaving the smaller scales containing substantial kinetic energy of ocean circulation that is responsible for the flux of heat, dissolved gas and nutrients between the upper and the deep ocean. This flux is important to the understanding of the ocean's role in regulatingfuture climate change.The elevation of the water bodies on land is a key parameter required for the computation of storage and discharge of freshwater in rivers, lakes, and wetlands. Globally, the spatial and temporal variability of water storage and discharge is poorly known due to the lack of well-sampled observations. In situ networks measuring river flows are declining worldwide due to economic and political reasons. Conventional altimeter observations suffers from the complexity of multiple peaks caused by the reflections from water, vegetation canopy and rough topography, resulting in much less valid data over land than over the ocean. Another major limitation is the large inter track distance preventing good coverage of rivers and other water bodies.This document provides descriptions of a new measurement technique using radar interferometry to obtain wide-swath measurement of water elevation at high resolution over both the ocean and land. Making this type of measurement, which addresses the shortcomings of conventional altimetry in both oceanographic and hydrologic applications, is the objective of a mission concept called Surface Water and Ocean Topography (SWOT), which was recommended by the National Research Council's first decadal survey of NASA's Earth science program. This document provides wide-ranging examples of research opportunities in oceanography and land hydrology that would be enabled by the new type of measurement. Additional applications in many other branches of Earth System science ranging from ocean bathymetry to sea ice dynamics are also discussed. Many of the technical issues in making the measurement are discussed as well. Also presented is a preliminary design of the SWOT Mission concept, which is being jointly developed by NASA and CNES, with contributions from the Canadian Space Agency.

altimetry↗
DOE OSTI2023

Projecting Future Energy Production from Operating Wind Farms in North America. Part II: Statistical Downscaling

Abstract Capacity factors (CFs) derived from daily expected power at 22 operating wind farms in different regions of North America are used as predictands to train statistical downscaling algorithms using output from ERA5. The statistical downscaling models are then used to make CF projections for a suite of CMIP6 Earth System Models (ESMs). Downscaling is performed using a hybrid statistical approach that employs synoptic types derived using k -means clustering applied to sea level pressure fields with variance corrections applied as a function of the pressure gradient intensity. ESMs exhibit marked variability in terms of the skill with which the frequency of synoptic types and pressure gradients are reproduced relative to ERA5, and that differential skill is used to infer differential credibility in the associated CF projections. Projections of median annual mean CF [P50(CF)] in each 20-yr period from 1980 to 2099 show evidence of declines at most wind farms except in parts of the southern Great Plains, although the magnitude of the changes is strongly dependent on the ESM. For example, P50(CF) in 2080–99 deviate from those in 1980–99 by from −3.1 to +0.2 percentage points in the Northeast. The largest-magnitude declines in P50(CF) ranging from −3.9 to −2 percentage points are projected for the southern West Coast. CF trends exhibit marked seasonality and are strongly linked to changes in the relative intensity of future synoptic patterns, with much less impact from shifts in the occurrence of synoptic types over time. Internal climate modes continue to play a significant role in inducing interannual variability in wind power production, even under high radiative forcing scenarios. Significance Statement We describe how future climate changes may affect wind resources and wind power generation. Near-term changes in projected wind power electricity generation potential at operating wind farms over North America are small, but by the end of the current century electricity production is projected to decrease in many areas but may increase in parts of the southern Great Plains. The amount of change in projected wind power production is a strong function of the Earth system model that is downscaled and also depends on the continued presence of internally forced climate variability. An additional dependence on the amount of greenhouse gas–induced global warming indicates the transition of the energy sector to low-carbon sources may assist in maintaining the abundant U.S. wind resource.

Meteorology & Atmospheric Sciences↗
DOE OSTI2025

Transformation rate maps of dissolved organic carbon in the contiguous US

Riverine dissolved organic carbon (DOC) plays a vital role in regional and global carbon cycles. However, the processes of DOC conversion from soil organic carbon (SOC) and leaching into rivers are insufficiently understood, inconsistently represented, and poorly parameterized, particularly in land surface and Earth system models. As a first attempt to fill this gap, we propose a generic formula that directly connects SOC concentration with DOC concentration in headwater streams, where a single parameter, the transformation rate from SOC in the soil to DOC leaching flux (P r ), accounts for the overall processes governing SOC conversion to DOC and leaching from soils (along with runoff) into headwater streams. We then derive high-resolution P r maps over the contiguous US (CONUS) using SOC data from two different sources: the Harmonized World Soil Database v1.2 (HWSD) and SoilGrids 2.0. Both maps are developed following the same five major steps: (1) selecting independent catchments where observed riverine DOC data are available with reasonable quality; (2) estimating catchment-average SOC for the independent catchments; (3) estimating the P r values for these catchments based on the generic formula and catchment-average SOC; (4) developing a predictive model of P r with machine learning (ML) techniques and catchment-scale climate, hydrology, geology, and other attributes; and (5) deriving a national map of P r based on the ML model. For evaluation, we compare the DOC concentration derived using the P r map and the observed DOC concentration values at evaluation catchments. The resulting mean absolute scaled error and coefficient of determination are 0.73 and 0.47 for the HWSD-based model and 0.58 and 0.72 for the SoilGrids-based model, respectively, suggesting the effectiveness of the overall methodology. Efforts to constrain uncertainty and evaluate sensitivity of P r to different factors are discussed. To illustrate the use of such maps, we derive a riverine DOC concentration reanalysis dataset over CONUS. The two P r maps, robustly derived and empirically validated, lay a critical cornerstone for better simulating the terrestrial carbon cycle in land surface and Earth system models. Our findings not only set a foundation for improving our predictive understanding of the terrestrial carbon cycle at the regional and global scales, but also hold promises for informing policy decisions related to decarbonization and climate change mitigation. The data presented in this study are publicly available at https://doi.org/10.5281/zenodo.14563816 (Li et al., 2024).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Apperception of Clouds in AIRS Data

Our capacity to simulate the radiative characteristics of the Earth system has advanced greatly over the past decade. However, new space based measurements show that idealized simulations might not adequately represent the complexity of nature. For example, AIRS simulated multi-layer cloud clearing research provides an excellent groundwork for early Atmospheric Infra-Red Sounder (AIRS) operational cloud clearing and atmospheric profile retrieval. However, it doesn't reflect the complicated reality of clouds over land and coastal areas. Thus far, operational AIRS/AMSU (Advanced Microwave Sounding Unit) cloud clearing is not only of low yield but also of unsatisfying quality. This is not an argument for avoiding this challenging task, rather a powerful argument for exploring other synergistic approaches, and for adapting these strategies toward improving both indirect and direct use of cloudy infrared sounding data. Ample evidence is shown in this paper that the indirect use of cloudy sounding data by way of cloud clearing is sub-optimal for data assimilation. Improvements are needed in quality control, retrieval yield, and overall cloud clearing retrieval performance. For example, cloud clearing over land, especially over the desert surface, has led to much degraded retrieval quality and often a very low yield of quality controlled cloud cleared radiances. If these indirect cloud cleared radiances are instead to be directly assimilated into NWP models, great caution must be used. Our limited and preliminary cloud clearing results from AIRS/AMSU (with the use of MODIS data) and an AIRS/MODIS synergistic approach have, however, shown that higher spatial resolution multispectral imagery data can provide much needed quality control of the AIRS/AMSU cloud clearing retrieval. When AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) are used synergistically, a higher spatial resolution over difficult terrain (especially desert areas) can be achieved and with a much improved accuracy. Preliminary statistical analyses of cloud cleared radiances derived from (1) operational AIRS/AMSU, (2) operational AIRS/AMSU plus the use of MODIS data as quality control, and (3) AIRS/MODIS synergistic single channel and two field of views cloud clearing are Our capacity to simulate the radiative characteristics of the Earth system has

Huang, Hung-Lung↗
DOE OSTI2021

Identifying the sources of uncertainty in climate model simulations of solar radiation modification with the G6sulfur and G6solar Geoengineering Model Intercomparison Project (GeoMIP) simulations

We present here results from the Geoengineering Model Intercomparison Project (GeoMIP) simulations for the experiments G6sulfur and G6solar for six Earth system models participating in the Climate Model Intercomparison Project (CMIP) Phase 6. The aim of the experiments is to reduce the warming that results from a high-tier emission scenario (Shared Socioeconomic Pathways SSP5-8.5) to that resulting from a medium-tier emission scenario (SSP2-4.5). These simulations aim to analyze the response of climate models to a reduction in incoming surface radiation as a means to reduce global surface temperatures, and they do so either by simulating a stratospheric sulfate aerosol layer or, in a more idealized way, through a uniform reduction in the solar constant in the model. We find that over the final two decades of this century there are considerable inter-model spreads in the needed injection amounts of sulfate (29 ± 9 Tg-SO 2 /yr between 2081 and 2100), in the latitudinal distribution of the aerosol cloud and in the stratospheric temperature changes resulting from the added aerosol layer. Even in the simpler G6solar experiment, there is a spread in the needed solar dimming to achieve the same global temperature target (1.91 ± 0.44 %). The analyzed models already show significant differences in the response to the increasing CO 2 concentrations for global mean temperatures and global mean precipitation (2.05 K ± 0.42 K and 2.28 ± 0.80 %, respectively, for SSP5-8.5 minus SSP2-4.5 averaged over 2081–2100). With aerosol injection, the differences in how the aerosols spread further change some of the underlying uncertainties, such as the global mean precipitation response (–3.79 ± 0.76 % for G6sulfur compared to –2.07 ± 0.40 % for G6solar against SSP2-4.5 between 2081 and 2100). These differences in the behavior of the aerosols also result in a larger uncertainty in the regional surface temperature response among models in the case of the G6sulfur simulations, suggesting the need to devise various, more specific experiments to single out and resolve particular sources of uncertainty. The spread in the modeled response suggests that a degree of caution is necessary when using these results for assessing specific impacts of geoengineering in various aspects of the Earth system. However, all models agree that compared to a scenario with unmitigated warming, stratospheric aerosol geoengineering has the potential to both globally and locally reduce the increase in surface temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6

The Observations for Model Intercomparison Project (Obs4MIPs) was initiated in 2010 to facilitate the use of observations in climate model evaluation and research, with a particular target being the Coupled Model Intercomparison Project (CMIP), a major initiative of the World Climate Research Programme (WCRP). To this end, Obs4MIPs (1) targets observed variables that can be compared to CMIP model variables; (2) utilizes dataset formatting specifications and metadata requirements closely aligned with CMIP model output; (3) provides brief technical documentation for each dataset, designed for nonexperts and tailored towards relevance for model evaluation, including information on uncertainty, dataset merits, and limitations; and (4) disseminates the data through the Earth System Grid Federation (ESGF) platforms, making the observations searchable and accessible via the same portals as the model output. Taken together, these characteristics of the organization and structure of obs4MIPs should entice a more diverse community of researchers to engage in the comparison of model output with observations and to contribute to a more comprehensive evaluation of the climate models. At present, the number of obs4MIPs datasets has grown to about 80; many are undergoing updates, with another 20 or so in preparation, and more than 100 are proposed and under consideration. A partial list of current global satellite-based datasets includes humidity and temperature profiles; a wide range of cloud and aerosol observations; ocean surface wind, temperature, height, and sea ice fraction; surface and top-of-atmosphere longwave and shortwave radiation; and ozone (O 3 ), methane (CH 4 ), and carbon dioxide (CO 2 ) products. A partial list of proposed products expected to be useful in analyzing CMIP6 results includes the following: alternative products for the above quantities, additional products for ocean surface flux and chlorophyll products, a number of vegetation products (e.g., FAPAR, LAI, burned area fraction), ice sheet mass and height, carbon monoxide (CO), and nitrogen dioxide (NO 2 ). While most existing obs4MIPs datasets consist of monthly-mean gridded data over the global domain, products with higher time resolution (e.g., daily) and/or regional products are now receiving more attention. Along with an increasing number of datasets, obs4MIPs has implemented a number of capability upgrades including (1) an updated obs4MIPs data specifications document that provides additional search facets and generally improves congruence with CMIP6 specifications for model datasets, (2) a set of six easily understood indicators that help guide users as to a dataset's maturity and suitability for application, and (3) an option to supply supplemental information about a dataset beyond what can be found in the standard metadata. With the maturation of the obs4MIPs framework, the dataset inclusion process, and the dataset formatting guidelines and resources, the scope of the observations being considered is expected to grow to include gridded in situ datasets as well as datasets with a regional focus, and the ultimate intent is to judiciously expand this scope to any observation dataset that has applicability for evaluation of the types of Earth system models used in CMIP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau

With the highest albedo of the land surface, snow plays a vital role in Earth's surface energy budget and water cycle. Snow albedo is primarily controlled by snow grain properties (e.g., size and shape) and light-absorbing particles (LAPs) such as black carbon (BC) and dust. The mixing state of LAPs in snow also has impacts on LAP-induced snow albedo reduction and surface radiative forcing (RF). However, most land surface models assume that snow grain shape is spherical and LAPs are externally mixed with the snow grains. This study improves the snow radiative transfer model in the Energy Exascale Earth System Model version 2.0 (E3SM v2.0) Land Model (ELM v2.0) by considering non-spherical snow grain shapes (i.e., spheroid, hexagonal plate, and Koch snowflake) and internal mixing of dust–snow, and it systematically evaluates the impacts on the surface energy budget and water cycle over the Tibetan Plateau (TP). A series of ELM simulations with different treatments of snow grain shape, mixing state of BC–snow and dust–snow, and sub-grid topographic effects (TOP) on solar radiation are performed. Compared with two remote sensing snow products derived from the Moderate Resolution Imaging Spectroradiometer, the control ELM simulation (ELM_Control) with the default configurations of spherical snow grain shape, internal mixing of BC–snow, external mixing of dust–snow, and without TOP as well as the ELM simulation with new model features (ELM_New) can both capture the overall snow distribution reasonably. Additionally, ELM_New overall shows smaller biases in snow cover fraction than ELM_Control in spring when snowmelt is important for water management. The estimated LAP-induced RF in ELM_New ranges from 0 to 19.3 W m -2 with the area-weighted average value of 1.5 W m -2 that is comparable to the reported values in existing studies. The Koch snowflake shape, among other non-spherical shapes, shows the largest difference from the spherical shape in spring when snow processes related to the surface energy budget and water cycle have high importance. The impacts of the mixing state of LAP in snow are smaller than the shape effects and depend on snow grain shape. Compared to external mixing, internal mixing of LAP–snow can lead to larger snow albedo reduction and snowmelt, which further affect the surface energy budget and water cycle. The individual contributions of non-spherical snow shape, mixing state of LAP–snow, and local topography impacts on the snow and surface fluxes have different signs and magnitudes, and their combined effects may be negative or positive due to complex and nonlinear interactions among the factors. Overall, the changes in net solar radiation in spring due to individual and combined effects range from -28.6 to 16.9 W m -2 and -29.7 to 12.2 W m -2 , respectively. This study advances understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offers guidance for improving snow simulations and RF estimates in Earth system models under climate change.

58 GEOSCIENCES↗
DOE OSTI2020

Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysis of Biosphere–Atmosphere Interactions

The National Ecological Observatory Network (NEON) is a continental-scale observatory with sites across the US collecting standardized ecological observations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision data in near-real time to the NEON Data Portal and to High Performance Computing (HPC) systems, running ensembles of Earth system models (ESMs) that assimilate the data. For the first time gridded EC data products and response functions promise to offset pervasive observational biases through evaluating, benchmarking, optimizing parameters, and training new machine learning parameterizations within ESMs all at the same model-grid scale. Leveraging open-source software for EC data analysis, we are already building software infrastructure for integration of near-real time data streams into the International Land Model Benchmarking (ILAMB) package for use by the wider research community. We will present a perspective on the design and integration of end-to-end infrastructure for data acquisition, edge computing, HPC simulation, analysis, and validation, where Artificial Intelligence (AI) approaches are used throughout the distributed workflow to improve accuracy and computational performance.

Durden, David J.↗
DOE OSTI2024

Winter precipitation predictability in Central Southwest Asia and its representation in seasonal forecast systems

In Central Southwest Asia (CSWA; 22°N to 40°N and 30°E to 70°E), winter (November to February) precipitation contributes up to 70% of the annual mean, but substantial interannual variations exist. Dynamical models exhibit subpar predictability in this region, but the limits of their skills are not well established. Here, we identify the tropical and extratropical forcings that explain ~75% of area-averaged seasonal variability in CSWA winter precipitation. Tropical forcing comes from the indirect El Niño-Southern Oscillation (ENSO) pathway, the leading mode of tropical Indian Ocean precipitation variability. This mode is coupled with ENSO-related Pacific Ocean sea surface temperature variability. A direct ENSO influence on CSWA does not extend beyond its Indian Ocean connection. Extratropical forcing comes from a large-scale mode of internal atmospheric variability. The spatial structure, variability of tropical forcing, and teleconnection with CSWA winter precipitation are skillfully depicted in two seasonal forecasting systems: the fifth-generation seasonal forecasting system (SEAS5) and Seamless System for Prediction and Earth System Research (SPEAR). Extratropical forcing’s spatial structure is also produced skillfully in the two modelling systems; however, the representation of its interannual variability and teleconnection requires improvement. While SEAS5 displays skill in representing extratropical forcing influence on CSWA winter precipitation and marginal skill in reproducing interannual variability, SPEAR has negligible ability in both areas. Consequently, these models have limited predictive skills over CSWA in winter. While improvements in representing extratropical forcing may be inherently limited as it arises from internal atmospheric variability, further research is needed to establish its predictability limits fully.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Strong Red Noise Ocean Forcing on Atlantic Multidecadal Variability Assessed from Surface Heat Flux: Theory and Application

The role of ocean forcing on Atlantic multidecadal variability (AMV) is assessed from the (downward) heat flux–SST relation in the framework of a new stochastic climate theory forced by red noise ocean forcing. Previous studies suggested that atmospheric forcing drives SST variability from monthly to interannual time scales, with a positive heat flux–SST correlation, while heat flux induced by ocean processes can drive SST variability at decadal and longer time scales, with a negative heat flux–SST correlation. Here, first, we develop a theory to show how the sign of heat flux–SST correlation is affected by atmospheric and oceanic forcing with time scale. In particular, a red noise ocean forcing is necessary for the sign reversal of heat flux–SST correlation. Furthermore, this sign reversal can be detected equivalently in three approaches: the low-pass correlation at lag zero, the unfiltered correlation at long (heat flux) lead, and the real part of the heat flux–SST coherence. Second, we develop a new scheme in combination with the theory to assess the magnitude and time scale of the red noise ocean forcing for AMV in the GFDL SPEAR model (Seamless System for Prediction and Earth System Research) and observations. In both the model and observations, the ocean forcing on AMV is in general comparable with the atmospheric forcing, with a 90% probability greater than the atmospheric forcing in observations. In contrast to the white noise atmospheric forcing, the ocean forcing has a persistence time comparable or longer than a year, much longer than the SST persistence of ~3 months. This slow ocean forcing is associated implicitly with slow subsurface ocean dynamics.

54 ENVIRONMENTAL SCIENCES↗