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NASA NTRS2023

Arctic Warming in Response to Regional Aerosol Emissions Reductions

This study examines the Arctic surface air temperature response to regional aerosol emissions reductions using three fully coupled chemistry–climate models: National Center for Atmospheric Research-Community Earth System Model version 1, Geophysical Fluid Dynamics Laboratory-Coupled Climate Model version 3 (GFDL-CM3) and Goddard Institute for Space Studies-ModelE version 2. Each of these models was used to perform a series of aerosol perturbation experiments, in which emissions of different aerosol types (sulfate, black carbon (BC), and organic carbon) in different northern mid-latitude source regions, and of biomass burning aerosol over South America and Africa, were substantially reduced or eliminated. We find that the Arctic warms in nearly every experiment, the only exceptions being the U.S. and Europe BC experiments in GFDL-CM3 in which there is a weak and insignificant cooling. The Arctic warming is generally larger than the global mean warming (i.e. Arctic amplification occurs), particularly during non-summer months. The models agree that changes in the poleward atmospheric moisture transport are the most important factor explaining the spread in Arctic warming across experiments: the largest warming tends to coincide with the largest increases in moisture transport into the Arctic. In contrast, there is an inconsistent relationship (correlation) across experiments between the local radiative forcing over the Arctic and the simulated Arctic warming, with this relationship being positive in one model (GFDL-CM3) and negative in the other two. Our results thus highlight the prominent role of poleward energy transport in driving Arctic warming and amplification, and suggest that the relative importance of poleward energy transport and local forcing/feedbacks is likely to be model dependent.

Arctic↗

DOE OSTI2014

Microbial dormancy improves development and experimental validation of ecosystem model

Climate feedbacks from soils can result from environmental change followed by response of plant and microbial communities, and/or associated changes in nutrient cycling. Explicit consideration of microbial life history traits and functions may be necessary to predict climate feedbacks due to changes in the physiology and community composition of microbes and their associated effect on carbon cycling. Here, we enhanced the Microbial-Enzyme-mediated Decomposition (MEND) model by incorporating microbial dormancy and the ability to track multiple isotopes of carbon. We tested two versions of MEND, i.e., MEND with dormancy and MEND without dormancy, against long-term (270 d) lab incubations of four soils with isotopically-labeled substrates. MEND without dormancy adequately fitted multiple observations (total and 14C respiration, and dissolved organic carbon), but at the cost of significantly underestimating the total microbial biomass. The MEND with dormancy improved estimates of microbial biomass by 20 71% over the MEND without dormancy. We observed large differences for two fitted model parameters, the specific maintenance and growth rates for active microbes, depending on whether dormancy was considered. Together our model extrapolations of the incubation study show that long-term soil incubations with observations in multiple carbon pools are necessary to estimate both decomposition and microbial parameters. These efforts should provide essential support to future field- and global-scale simulations and enable more confident predictions of feedbacks between environmental change and carbon cycling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Modeling Uncertainty in Climate Change: A Multi-Model Comparison

The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity and estimates the pdfs of key output variables, including CO 2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insight on tail events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Crop growth and irrigation interact to influence surface fluxes in a regional climate-cropland model (WRF3.3-CLM4crop)

Here, we coupled Version 4.0 of the Community Land Model that includes crop growth and management (CLM4crop) into the Weather Research and Forecasting (WRF) model Version 3.3 to better represent interactions between climate and agriculture. We evaluated the performance of the coupled model (WRF3.3-CLM4crop) by comparing simulated crop growth and surface climate to multiple observational datasets across the continental United States. The results showed that although the model with dynamic crop growth overestimated leaf area index (LAI) and growing season length, interannual variability in peak LAI was improved relative to a model with prescribed crop LAI and growth period, which has no environmental sensitivity. Adding irrigation largely improved daily minimum temperature but the RMSE is still higher over irrigated land than non-irrigated land. Improvements in climate variables were limited by an overall model dry bias. However, with addition of an irrigation scheme, soil moisture and surface energy flux partitioning were largely improved at irrigated sites. Irrigation effects were sensitive to crop growth: the case with prescribed crop growth underestimated irrigation water use and effects on temperature and overestimated soil evaporation relative to the case with dynamic crop growth in moderately irrigated regions. We conclude that studies examining irrigation effects on weather and climate using coupled climate–land surface models should include dynamic crop growth and realistic irrigation schemes to better capture land surface effects in agricultural regions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2015

The GEWEX Process Evaluation Study: GEWEX-PROES

Progress in realistically simulating the energy and water cycles in weather and climate models has been slower than desirable. Climate models submitted to the Coupled Model Intercomparison Project Phase-5 (CMIP-5), while more comprehensive than their predecessors, have shown little to no improvement in their biases in simulating key features of the climate system. As a result, uncertainties in global climate and hydrological sensitivities, and in the simulation of regional climate change, have not been reduced significantly. It is time to make use of these opportunities to significantly advance our understanding of key energy and water cycle processes at a wide range of space and time scales, and to provide a more insightful evaluation of the representation of these processes within models. This will require new ways of both analyzing the observations and diagnosing model behavior. The keys to success will be in skillfully combining different data sets and exploring relationships between them, as well as in the ability of models to reproduce those relationships correctly. In response to this challenge, a new GEWEX-wide activity, the GEWEX Process Evaluation Study, will take advantage of the opportunities that the combination of many of the existing data sets provide.

climate models↗

DOE OSTI2016

Final DOE-ASR Report for the Project “Advancing our Understanding and the Remote Sensing of Ice Clouds”

This project has evolved during its execution, and what follows are the key project findings. This project has arguably provided the first global view of how cirrus cloud (defined as having cloud base temperature T < 235 K) nucleation physics (evaluated through satellite retrievals of ice particle number concentration Ni, effective diameter De and ice water content IWC) evolves with the seasons for a given temperature, latitude zone and surface type (e.g. ocean vs. land), based on a new satellite remote sensing method developed for this project. The retrieval method is unique in that it is very sensitive to the small ice crystals that govern the number concentration Ni, allowing Ni to be retrieved. The method currently samples single-layer cirrus clouds having visible optical depth ranging from about 0.3 to 3.0, using co-located observations from the Infrared Imaging Radiometer (IIR) and from the CALIOP (Cloud and Aerosol Lidar with Orthogonal Polarization) lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) polar orbiting satellite, employing IIR channels at 10.6 μm and 12.05 μm. Retrievals of Ni are primarily used to estimate the cirrus cloud formation mechanism; that is, either homo- or heterogeneous ice nucleation (henceforth hom and het). This is possible since, in general, hom produces more than an order of magnitude more ice crystals than does het. Thus the retrievals provide insight on how these mechanisms change with the seasons for a given latitude zone or region, based on the years 2008 and 2013. Using a conservative criterion for hom cirrus, on average, the sampled cirrus clouds formed through hom occur about 43% of the time in the Arctic and 50% of the time in the Antarctic, and during winter at mid-latitudes in the Northern Hemisphere, hom cirrus occur 37% of the time. Elsewhere (and during other seasons in the Northern Hemisphere mid-latitudes), this hom cirrus fraction is lower, and it is lowest in the tropics. Thus, the microphysical properties of cirrus clouds in the Polar Regions are much different than they are in the tropics; something unknown prior to this study. Moreover, the frequency of cirrus cloud occurrence in the Polar Regions varies strongly with season, peaking during winter in the Arctic and during spring in the Antarctic. Considering these seasonal changes in microphysics and inferred cloud coverage, this leads us to speculate that the buildup of Arctic cirrus during winter may significantly contribute to tropospheric heating in that region, possibly affecting winter jet-stream dynamics and mid-latitude weather patterns through the thermal-wind balance relationship. This cirrus cloud research provides essential guidance for realistically representing cirrus clouds in climate models; guidance previously unavailable. For example, mid-latitude hom cirrus were widespread during winter over or nearby mountainous terrain, evidently due to mountain-induced waves that produce strong updrafts at cirrus cloud levels. The treatment of turbulent mountain stress and gravity waves will likely need to be improved in climate models in order to adequately represent cirrus clouds outside the tropics. Another goal of this project was to develop a ground-based 94-GHz radar retrieval for winter snowstorms, based on (1) an improved analytical framework describing the interaction of radiation from radar with snowfall and (2) the development of a steady-state snow growth model that predicts the height-evolution of the ice particle size distribution through ice particle growth by vapor diffusion, aggregation and riming (i.e. the growth of snow through collisions with supercooled cloud droplets). Although activities (1) and (2) were completed, there was insufficient time to test and finalize the radar retrieval scheme. However, activity (2) provided a new method for relating ice particle mass “m” and projected area “A” to the ice particle maximum dimension “D”. The ice cloud microphysical processes (which determine ice cloud radiative properties) in climate models are parameterized in terms of these m-D and A-D relationships. By improving these relationships, the ice cloud radiative properties in Community Atmosphere Model version 5, or CAM5 (an atmosphere global climate model, or GCM) were improved. Student funding from the University of Nevada, Reno, was combined with funds from this project to conduct some basic research on the mechanism of the North American monsoon, or NAM. Federal research on the NAM has dwindled since 2006, but atmospheric soundings taken during research vessel cruises in the Gulf of California (GC) during the North American Monsoon Experiment (NAME) were used to reveal a likely mechanism that explains the relationship between an intrusion of tropical warm water into the GC during late spring-early summer and the onset of relatively heavy NAM rainfall in northwest Mexico and the southwestern United States. These soundings, combined with reanalysis data, satellite sea surface temperatures and satellite measurements of outgoing longwave radiation were used to develop and provide evidence for a planetary-scale NAM mechanism. As far as we know, no other physical explanation has been offered for the spring-summer evolution of the NAM system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Evaluation of native Earth system model output with ESMValTool v2.6.0

Earth system models (ESMs) are state-of-the-art climate models that allow numerical simulations of the past, present-day, and future climate. To extend our understanding of the Earth system and improve climate change projections, the complexity of ESMs heavily increased over the last decades. As a consequence, the amount and volume of data provided by ESMs has increased considerably. Innovative tools for a comprehensive model evaluation and analysis are required to assess the performance of these increasingly complex ESMs against observations or reanalyses. One of these tools is the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of ESMs. Input data for ESMValTool needs to be formatted according to the CMOR (Climate Model Output Rewriter) standard, a process that is usually referred to as “CMORization”. While this is a quasi-standard for large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP), this complicates the application of ESMValTool to non-CMOR-compliant climate model output. In this paper, we describe an extension of ESMValTool introduced in v2.6.0 that allows seamless reading and processing of “native” climate model output, i.e., operational output produced by running the climate model through the standard workflow of the corresponding modeling institute. This is achieved by an extension of ESMValTool's preprocessing pipeline that performs a CMOR-like reformatting of the native model output during runtime. Thus, the rich collection of diagnostics provided by ESMValTool is now fully available for these models. For models that use unstructured grids, a further preprocessing step required to apply many common diagnostics is regridding to a regular latitude–longitude grid. Extensions to ESMValTool's regridding functions described here allow for more flexible interpolation schemes that can be used on unstructured grids. Currently, ESMValTool supports nearest-neighbor, bilinear, and first-order conservative regridding from unstructured grids to regular grids. Example applications of this new native model support are the evaluation of new model setups against predecessor versions, assessing of the performance of different simulations against observations, CMORization of native model data for contributions to model intercomparison projects, and monitoring of running climate model simulations. For the latter, new general-purpose diagnostics have been added to ESMValTool that are able to plot a wide range of variable types. Currently, five climate models are supported: CESM2 (experimental; at the moment, only surface variables are available), EC-Earth3, EMAC, ICON, and IPSL-CM6. As the framework for the CMOR-like reformatting of native model output described here is implemented in a general way, support for other climate models can be easily added.

58 GEOSCIENCES↗

NASA NTRS2010

Constraints and Opportunities in GCM Model Development

Over the past 30 years climate models have evolved from relatively simple representations of a few atmospheric processes to complex multi-disciplinary system models which incorporate physics from bottom of the ocean to the mesopause and are used for seasonal to multi-million year timescales. Computer infrastructure over that period has gone from punchcard mainframes to modern parallel clusters. Constraints of working within an ever evolving research code mean that most software changes must be incremental so as not to disrupt scientific throughput. Unfortunately, programming methodologies have generally not kept pace with these challenges, and existing implementations now present a heavy and growing burden on further model development as well as limiting flexibility and reliability. Opportunely, advances in software engineering from other disciplines (e.g. the commercial software industry) as well as new generations of powerful development tools can be incorporated by the model developers to incrementally and systematically improve underlying implementations and reverse the long term trend of increasing development overhead. However, these methodologies cannot be applied blindly, but rather must be carefully tailored to the unique characteristics of scientific software development. We will discuss the need for close integration of software engineers and climate scientists to find the optimal processes for climate modeling.

Schmidt, Gavin↗

DOE OSTI2015

Final Report Collaborative Project. Improving the Representation of Coastal and Estuarine Processes in Earth System Models

This project aimed to improve long term global climate simulations by resolving and enhancing the representation of the processes involved in the cycling of freshwater through estuaries and coastal regions. This was a collaborative multi-institution project consisting of physical oceanographers, climate model developers, and computational scientists. It specifically targeted the DOE objectives of advancing simulation and predictive capability of climate models through improvements in resolution and physical process representation. The main computational objectives were: 1. To develop computationally efficient, but physically based, parameterizations of estuary and continental shelf mixing processes for use in an Earth System Model (CESM). 2. To develop a two-way nested regional modeling framework in order to dynamically downscale the climate response of particular coastal ocean regions and to upscale the impact of the regional coastal processes to the global climate in an Earth System Model (CESM). 3. To develop computational infrastructure to enhance the efficiency of data transfer between specific sources and destinations, i.e., a point-to-point communication capability, (used in objective 1) within POP, the ocean component of CESM.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Optical Properties and Aging of Light Absorbing Secondary Organic Aerosol

The light-absorbing organic aerosol (OA), commonly referred to as “brown carbon (BrC)”, has attracted considerable attention in recent years because of its potential to affect atmospheric radiation balance, especially in the ultraviolet region and thus impact photochemical processes. A growing amount of data has indicated that BrC is prevalent in the atmosphere, which has motivated numerous laboratory and field studies; however, our understanding of the relationship between the chemical composition and optical properties of BrC remains limited. We conducted chamber experiments to investigate the effect of various VOC precursors, NOx concentrations, photolysis time and relative humidity (RH) on the light absorption of selected secondary organic aerosols (SOA). Light absorption of chamber generated SOA samples, especially aromatic SOA, was found to increase with NOx concentration, at moderate RH, and for the shortest photolysis aging times. The highest mass absorption coefficients (MAC) value is observed from toluene SOA products formed under high NOx conditions at moderate RH, in which nitro-aromatics were previously identified as the major light absorbing compounds. BrC light absorption is observed to decrease with photolysis time, correlated with a decline of the organonitrate fraction of SOA. SOA formed from mixtures of aromatics and isoprene absorb less visible and UV light than SOA formed from aromatic precursors alone on a mass basis. However, the mixed-SOA absorption was underestimated when optical properties were predicted using a two-product SOA formation model, as done in many current climate models. Further investigation, including analysis on detailed mechanisms, are required to explain the discrepancy.

Brown Carbon↗

DOE OSTI2016

An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models

This is the second paper of a two-part series on introducing an experimental seasonal hydrological forecasting system over the Yellow River basin in northern China. While the natural hydrological predictability in terms of initial hydrological conditions (ICs) is investigated in a companion paper, the added value from eight North American Multimodel Ensemble (NMME) climate forecast models with a grand ensemble of 99 members is assessed in this paper, with an implicit consideration of human-induced uncertainty in the hydrological models through a post-processing procedure. The forecast skill in terms of anomaly correlation (AC) for 2 m air temperature and precipitation does not necessarily decrease over leads but is dependent on the target month due to a strong seasonality for the climate over the Yellow River basin. As there is more diversity in the model performance for the temperature forecasts than the precipitation forecasts, the grand NMME ensemble mean forecast has consistently higher skill than the best single model up to 6 months for the temperature but up to 2 months for the precipitation. The NMME climate predictions are downscaled to drive the variable infiltration capacity (VIC) land surface hydrological model and a global routing model regionalized over the Yellow River basin to produce forecasts of soil moisture, runoff and streamflow. And the NMME/VIC forecasts are compared with the Ensemble Streamflow Prediction method (ESP/VIC) through 6-month hindcast experiments for each calendar month during 1982–2010. As verified by the VIC offline simulations, the NMME/VIC is comparable to the ESP/VIC for the soil moisture forecasts, and the former has higher skill than the latter only for the forecasts at long leads and for those initialized in the rainy season. The forecast skill for runoff is lower for both forecast approaches, but the added value from NMME/VIC is more obvious, with an increase of the average AC by 0.08–0.2. To compare with the observed streamflow, both the hindcasts from NMME/VIC and ESP/VIC are post-processed through a linear regression model fitted by using VIC offline-simulated streamflow. The post-processed NMME/VIC reduces the root mean squared error (RMSE) from the post-processed ESP/VIC by 5–15 %. And the reduction occurs mostly during the transition from wet to dry seasons. As a result, with the consideration of the uncertainty in the hydrological models, the added value from climate forecast models is decreased especially at short leads, suggesting the necessity of improving the large-scale hydrological models in human-intervened river basins.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2002

Software Engineering Support of the Third Round of Scientific Grand Challenge Investigations: An Earth Modeling System Software Framework Strawman Design that Integrates Cactus and UCLA/UCB Distributed Data Broker

One of the most significant challenges in large-scale climate modeling, as well as in high-performance computing in other scientific fields, is that of effectively integrating many software models from multiple contributors. A software framework facilitates the integration task. both in the development and runtime stages of the simulation. Effective software frameworks reduce the programming burden for the investigators, freeing them to focus more on the science and less on the parallel communication implementation, while maintaining high performance across numerous supercomputer and workstation architectures. This document proposes a strawman framework design for the climate community based on the integration of Cactus, from the relativistic physics community, and UCLA/UCB Distributed Data Broker (DDB) from the climate community. This design is the result of an extensive survey of climate models and frameworks in the climate community as well as frameworks from many other scientific communities. The design addresses fundamental development and runtime needs using Cactus, a framework with interfaces for FORTRAN and C-based languages, and high-performance model communication needs using DDB. This document also specifically explores object-oriented design issues in the context of climate modeling as well as climate modeling issues in terms of object-oriented design.

Talbot, Bryan↗

DOE OSTI2020

Improving collaborations between empiricists and modelers to advance grassland community dynamics in ecosystem models

Climate change, increasing atmospheric CO 2 , and land use change have altered biogeochemical and hydrologic cycles world-wide, with grassland systems being particularly vulnerable to resulting vegetation shifts (Komatsu et al., 2019). Therefore, incorporating plant community dynamics into ecosystem models is critical for accurate forecasting of ecosystem responses to global change (Levine, 2016). Process-based ecosystem models, which simulate the biogeochemical transfers of mass and energy among biota, the subsurface, and atmosphere, require representation of dynamic composition of organisms within ecosystems. For example, these models simulate leaf and plant-level characteristics, such as electron transport rate and allometry of carbon (C) allocation, to predict how net primary productivity and other ecosystem processes respond to abiotic drivers. These models are particularly useful in scaling from organismal to ecosystem levels but are still underdeveloped in their ability to capture community change, especially in grassland ecosystems. To represent compositional changes, these models must simulate competition, mortality, establishment, and reproduction of plant populations within communities. Yet, current ecosystem modeling approaches to forecast plant community change have derived from studies of forested systems and are either too coarse to capture fine-scale community dynamics (e.g. dynamic global vegetation models (DGVMs)) or too complex to be used at large spatial scales (e.g. forest gap models).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2018

Quantifying the Effect of Mixing on the Mean Age of Air in CCMVal-2 and CCMI-1 Models

The stratospheric age of air (AoA) is a useful measure of the overall capabilities of a general circulation model (GCM) to simulate stratospheric transport. Previous studies have reported a large spread in the simulation of AoA by GCMs and coupled chemistry-climate models (CCMs). Compared to observational estimates, simulated AoA is mostly too low. Here we attempt to untangle the processes that lead to the AoA differences between the models and between models and observations. AoA is influenced by both mean transport by the residual circulation and two-way mixing; we quantify the effects of these processes using data from the CCM inter-comparison projects CCMVal-2 (Chemistry-Climate Model Validation Activity 2) and CCMI-1 (Chemistry-Climate Model Initiative, phase 1). Transport along the residual circulation is measured by the residual circulation transit time (RCTT). We interpret the difference between AoA and RCTT as additional aging by mixing. Aging by mixing thus includes mixing on both the resolved and subgrid scale. We find that the spread in AoA between the models is primarily caused by differences in the effects of mixing and only to some extent by differences in residual circulation strength. These effects are quantified by the mixing efficiency, a measure of the relative increase in AoA by mixing. The mixing efficiency varies strongly between the models from 0.24 to 1.02. We show that the mixing efficiency is not only controlled by horizontal mixing, but by vertical mixing and vertical diffusion as well. Possible causes for the differences in the models' mixing efficiencies are discussed. Differences in subgrid-scale mixing (including differences in advection schemes and model resolutions) likely contribute to the differences in mixing efficiency. However, differences in the relative contribution of resolved versus parameterized wave forcing do not appear to be related to differences in mixing efficiency or AoA.

CCMI↗

DOE OSTI2021

Attribution of the Australian bushfire risk to anthropogenic climate change

Abstract. Disastrous bushfires during the last months of 2019 and January 2020 affected Australia, raising the question to what extent the risk of these fires was exacerbated by anthropogenic climate change. To answer the question for southeastern Australia, where fires were particularly severe, affecting people and ecosystems, we use a physically based index of fire weather, the Fire Weather Index; long-term observations of heat and drought; and 11 large ensembles of state-of-the-art climate models. We find large trends in the Fire Weather Index in the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) since 1979 and a smaller but significant increase by at least 30 % in the models. Therefore, we find that climate change has induced a higher weather-induced risk of such an extreme fire season. This trend is mainly driven by the increase of temperature extremes. In agreement with previous analyses we find that heat extremes have become more likely by at least a factor of 2 due to the long-term warming trend. However, current climate models overestimate variability and tend to underestimate the long-term trend in these extremes, so the true change in the likelihood of extreme heat could be larger, suggesting that the attribution of the increased fire weather risk is a conservative estimate. We do not find an attributable trend in either extreme annual drought or the driest month of the fire season, September–February. The observations, however, show a weak drying trend in the annual mean. For the 2019/20 season more than half of the July–December drought was driven by record excursions of the Indian Ocean Dipole and Southern Annular Mode, factors which are included in the analysis here. The study reveals the complexity of the 2019/20 bushfire event, with some but not all drivers showing an imprint of anthropogenic climate change. Finally, the study concludes with a qualitative review of various vulnerability and exposure factors that each play a role, along with the hazard in increasing or decreasing the overall impact of the bushfires.

van Oldenborgh, Geert Jan (ORCID:0000000268989535)↗

DOE OSTI2017

Projected precipitation changes within the Great Lakes and Western Lake Erie Basin: a multi‐model analysis of intensity and seasonality

ABSTRACT The Great Lakes region encompasses the largest freshwater lake network in the world and supports a diverse network of agriculture, transportation, and tourism. Recently, Lake Erie has experienced increased hypoxia events, which have been attributed to agricultural practices and changes in run‐off. Here we examine the projected changes in extreme precipitation events to address concerns regarding regional agriculture, surface run‐off, and subsequent water quality. Precipitation projections within the overall Great Lakes Basin and the Western Lake Erie Basin subregion are examined using climate model simulations of varying spatial resolutions to understand historical precipitation and projected future precipitation. We develop three model ensembles for the historical period (1980–1999) and the mid‐century (2041–2060) that cover a range of spatial resolutions and future emissions scenarios, including: (1) 12 global model members from the fifth Climate Model Intercomparison Project ( CMIP5 ) using Representative Concentration Pathway ( RCP ) 8.5, (2) ten regional climate model ( RCM ) members from the North American Regional Climate Change Assessment Program driven by CMIP3 global models using the A2 emissions scenario, and (3) two high resolution RCM simulations ( RCM4 ) driven by CMIP5 global models using the RCP 8.5 scenario. For the historical period, all model ensembles overestimate winter and spring precipitation, and many of the models simulate a summer drying that is not observed. At mid‐century, most of the models predict a 10–20% increase in precipitation depending on the time of year. Daily probability distribution functions from three model ensembles reveal spring seasonal increases in high precipitation event probabilities when compared to the historical period, suggesting an increase in the frequency of high intensity precipitation at mid‐century. Overall, the presence of lakes or higher spatial resolution does not ensure improved representation of historical processes, and more complex interactions between large‐scale dynamics, local feedbacks, and physical parameterizations drive the model spread.

Basile, Samantha J.↗

DOE OSTI2015

Final Report for “Simulating the Arctic Winter Longwave Indirect Effects. A New Parameterization for Frost Flower Aerosol Salt Emissions” (DESC0006679) for 9/15/2011 through 9/14/2015

Description of the Project: This project has improved the aerosol formulation in a global climate model by using innovative new field and laboratory observations to develop and implement a novel wind-driven sea ice aerosol flux parameterization. This work fills a critical gap in the understanding of clouds, aerosol, and radiation in polar regions by addressing one of the largest missing particle sources in aerosol-climate modeling. Recent measurements of Arctic organic and inorganic aerosol indicate that the largest source of natural aerosol during the Arctic winter is emitted from crystal structures, known as frost flowers, formed on a newly frozen sea ice surface [Shaw et al., 2010]. We have implemented the new parameterization in an updated climate model making it the first capable of investigating how polar natural aerosol-cloud indirect effects relate to this important and previously unrecognized sea ice source. The parameterization is constrained by Arctic ARM in situ cloud and radiation data. The modified climate model has been used to quantify the potential pan-Arctic radiative forcing and aerosol indirect effects due to this missing source. This research supported the work of one postdoc (Li Xu) for two years and contributed to the training and research of an undergraduate student. This research allowed us to establish a collaboration between SIO and PNNL in order to contribute the frost flower parameterization to the new ACME model. One peer-reviewed publications has already resulted from this work, and a manuscript for a second publication has been completed. Additional publications from the PNNL collaboration are expected to follow.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Improving tide-estuary representation in MPAS-Ocean (Final Report)

The overarching goal of this research is to produce a skill-assessed tide-estuary representation, including BGC (biogeochemistry) components of the ecosystem, into the MPAS-O global climate modeling system. Specifically, in the original proposal we proposed one-way nesting, into global MPAS-O, of a high-resolution model for TES (tidal and estuary systems) including the coupling of the BGC components. We have completed the proposed tasks during the past 4 years. Specifically, after extensive consultation with the MPAS-O group during the first year, we have decided to go beyond the original proposal and to two-way nest SCHISM into MPAS-O. As a result, SCHISM-CoSiNE (with CoSiNE being the BGC model) is now fully integrated into MPAS-O as a subroutine (more precisely, an analysis member) and can be invoked as MPAS-O is running with two-way exchange of information between the TES and global models (with proper spatial interpolation done on the fly). As originally proposed, we have also developed the new finite-volume (FV) model MPAS-OI (aka ELCIRC-sub) for TES. We have conducted several tests using the coupled MPAS-SCHISM- CoSiNE model and also MPAS-OI for Columbia River, San Francisco Bay and Chesapeake Bay. Four journal publications have been produced so far as the result of the project and 1 is under review (cf. Publication section). With these achievements, we will be ready to conduct fully coupled simulations of TES and global ocean anywhere, as soon as some missing capabilities in MPAS-O (e.g. tides, improved ability to handle the boundary conditions, and streamlining of the global BGC model) are worked out. The significance of the outcome of this project cannot be overstated. It marks the first time, as far as we know, that a global climate model is tightly coupled to a bona fide nearshore and terrestrial model (SCHISM system); in fact SCHISM is the first bona fide compound flooding model that seamlessly covers the coastal ocean, estuaries, rivers and watersheds (Ye et al. 2020; Zhang et al. 2020). The coupled model will reduce the errors and uncertainties of the MPAS-O global climate model system. It also complements and extends on-going E3SM project, as the latter seeks in the long-term to include components required to simulate impacts of sea-level change and storm surge on coastal regions, including wave models and focusing resolution in coastal and storm-track regions, as an important component of the Cryosphere-ocean system. Note that a spectral wave model (the unstructured-grid version of Wave Watch III, aka Wind Wave Model) is already incorporated inside the SCHISM system and is therefore directly accessible in the coupled system.

58 GEOSCIENCES↗