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DOE OSTI2016

Final Technical Report for "Reducing tropical precipitation biases in CESM"

In state-of-the-art climate models, each cloud type is treated using its own separate cloud parameterization and its own separate microphysics parameterization. This use of separate schemes for separate cloud regimes is undesirable because it is theoretically unfounded, it hampers interpretation of results, and it leads to the temptation to overtune parameters. In this grant, we have created a climate model that contains a unified cloud parameterization (“CLUBB”) and a unified microphysics parameterization (“MG2”). In this model, all cloud types --- including marine stratocumulus, shallow cumulus, and deep cumulus --- are represented with a single equation set. This model improves the representation of convection in the Tropics. The model has been compared with ARM observations. The chief benefit of the project is to provide a climate model that is based on a more theoretically rigorous formulation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

A Radiative Transfer Model for Climate Calculations

This paper describes a radiative transfer model developed to accurately predict the atmospheric radiant flux in both the infrared and the solar spectrum with a minimum of computational effort. We use a newly developed k-distribution model for both the thermal and solar parts of the spectrum. We employ a generalized two-stream approximation for the scattering by aerosol and clouds. To assess the accuracy of the model, the results are compared to other more detailed models for several standard cases in the solar and thermal spectrum. We perform several calculations focussing primarily on the question of absorption of solar radiation by gases and aerosols. We estimate the accuracy of the k-distribution to be approx. 1 W/sq m for the gaseous absorption in the solar spectrum. We estimate the accuracy of the two-stream method to be 3-12 W/sq m for the downward solar flux and 1-5 W/sq m for the upward solar flux at the top of atmosphere depending on the optical depth of the aerosol layer. We also show that the effect of ignoring aerosol absorption on the downward solar flux at the surface is 50 W/sq m for the TARFOX aerosol for an optical depth of 0.5 and 150 W/sq m for a highly absorbing mineral aerosol. Thus, we conclude that the uncertainty introduced by the aerosol solar radiative properties (and merely assuming some "representative" model) can be considerably larger than the error introduced by the use of a two-stream method.

Bergstrom, Robert W.↗

DOE OSTI2016

The role of Atlantic Multi-decadal Oscillation in the global mean temperature variability

We simulated the global mean 1900–2015 warming by 42 Coupled Models Inter-comparison Project, phase 5 (CMIP5) climate models varies between 0.58 and 1.70 °C. The observed warming according to the NASA GISS temperature analysis is 0.95 °C with a 1200 km smoothing radius, or 0.86 °C with a 250 km smoothing radius. The projection of the future 2015–2100 global warming under a moderate increase of anthropogenic radiative forcing (RCP4.5 scenario) by individual models is between 0.7 and 2.3 °C. The CMIP5 climate models agree that the future climate will be warmer; however, there is little consensus as to how large the warming will be (reflected by an uncertainty of over a factor of three). Moreover, a parsimonious statistical regression model with just three explanatory variables [anthropogenic radiative forcing due to greenhouse gases and aerosols (GHGA), solar variability, and the Atlantic Multi-decadal Oscillation (AMO) index] accounts for over 95 % of the observed 1900–2015 temperature variance. This statistical regression model reproduces very accurately the past warming (0.96 °C compared to the observed 0.95 °C) and projects the future 2015–2100 warming to be around 0.95 °C (with the IPCC 2013 suggested RCP4.5 radiative forcing and an assumed cyclic AMO behavior). The AMO contribution to the 1970–2005 warming was between 0.13 and 0.20 °C (depending on which AMO index is used) compared to the GHGA contribution of 0.49–0.58 °C. During the twenty-first century AMO cycle the AMO contribution is projected to remain the same (0.13–0.20 °C), while the GHGA contribution is expected to decrease to 0.21–0.25 °C due to the levelling off of the GHGA radiative forcing that is assumed according to the RCP4.5 scenario. Therefore, the anthropogenic contribution and natural variability are expected to contribute about equally to the anticipated global warming during the second half of the twenty-first century for the RCP4.5 trajectory.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2021

Improved Representation of Atmospheric Dynamics in CMIP6 Models Removes Climate Sensitivity Dependence on Hadley Cell Climatological Extent

The persistent inter-model spread in the response of global mean surface temperature to increased CO. (known as the “Equilibrium Climate Sensitivity”, or “ECS”) is a crucial problem across model generations. This work examines the influence of the models’ present-day atmospheric circulation climatologies, and the accompanying climatological cloud radiative effects, in explaining that spread. We analyze the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and _nd that they simulate a more poleward, and thus more realistic, edge of the Hadley Circulation (HC) in the SouthernHemisphere than the CMIP5 models, although the climatological shortwave cloud radiative effects are similar in the two generations of models. A few CMIP5 models with extreme equatorward biases in the HC edge exhibited high ECS due to strong Southern midlatitude shortwave cloud radiative warming in response to climate change, suggesting an ECS dependence on HC position. We find that such constraint no longer holds for the CMIP6 models, due to absence of models with extreme HC climatologies. In spite of this, however, the CMIP6 models show an increased spread in ECS, with more models in the high ECS range. Additionally, an improved representation of the climatological jet dynamics does not lead to a new emergent constraint in the CMIP6 models either.

Equilibrium Climate Sensitivity↗

DOE OSTI2017

Centralized Activities in Support of the Radiative Forcing Model Intercomparison Project

A new paradigm in benchmark absorption-scattering radiative transfer is presented that enables both the globally-averaged and spatially-resolved testing of climate model radiation parameterizations using a novel benchmark computational strategy to uncover persistent sources of biases in the aerosol Instantaneous Radiative Effect (IRE). A proof-of-concept is demonstrated with the GFDL AM4 and CESM 1.2.2 climate models. Instead of prescribing atmospheric conditions and aerosols, as in prior intercomparisons, snapshots of the atmospheric state and aerosol optical properties from the participating models are used as inputs to a highly accurate radiation solver to uncover relevant biases unique to each climate model. These diagnostic results show that the models’ aerosol IRE bias is of the same magnitude as the persistent intermodel spread (~1 W/m2), and varies spatially and with intrinsic aerosol optical properties. The findings underscore the significance of native model error analysis and its ability to diagnose global biases, confirming its fundamental value for the Radiative Forcing Model Intercomparison Project (RFMIP).

58 GEOSCIENCES↗

DOE OSTI2023

Implicit learning of convective organization explains precipitation stochasticity

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ~ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ~ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Regional Climate Zone Modeling of a Commercial Absorption Heat Pump Hot Water Heater Part 1: Southern and South Central Climate Zones

Commercial hot water heating accounts for approximately 0.78 Quads of primary energy use with 0.44 Quads of this amount from natural gas fired heaters. An ammonia-water based commercial absorption system, if fully deployed, could achieve a high level of savings, much higher than would be possible by conversion to the high efficiency nonheat-pump gas fired alternatives. In comparison with air source electric heat pumps, the absorption system is able to maintain higher coefficients of performance in colder climates. The ammonia-water system also has the advantage of zero Ozone Depletion Potential and low Global Warming Potential. A thermodynamic model of a single effect ammonia-water absorption system for commercial space and water heating was developed, and its performance was investigated for a range of ambient and return water temperatures. This allowed for the development of a performance map which was then used in a building energy modeling software. Modeling of two commercial water heating systems was performed; one using an absorption heat pump and another using a condensing gas storage system. The energy and financial savings were investigated for a range of locations and climate zones in the southern and south central United States. A follow up paper will analyze northern and north/central regions. Results showed that the system using an absorption heat pump offers significant savings.

Geoghegan, Patrick J↗

NASA NTRS2018

Synergistic and Collaborative Development Strategies for FV3 Powered Next Generation Unified Global Modeling System

The GFDL (Geophysical Fluid Dynamics Laboratory, NOAA - National Oceanic and Atmospheric Administration) Finite-Volume Cubed-Sphere Dynamical Core (FV3) is a scalable and flexible dynamical core capable of both hydrostatic and non-hydrostatic atmospheric simulations. FV3 has been chosen as the dynamical core for the Next Generation Global Prediction System project (NGGPS), designed to upgrade the current NCEP (National Centers for Environmental Prediction, NOAA) operational Global Forecast System (GFS) to run as a unified, fully-coupled system in NOAA's Environmental Modeling System infrastructure. FV3 dynamic core has a long history of serving as the main engine for global atmospheric models at various Government and Academic Research Laboratories including NOAA: GFDL Climate Modeling Suite (AM4 (Atmosphere Modeling 4), CM4 (Climate Model 4) ESM4 (Earth System Model 4), Hiram (HIgh Resolution Atmospheric Model)); NASA: GMAO (Global Modeling and Assimilation Office) Goddard Earth Observing System Model (GEOS); and NCAR (National Center for Atmospheric Research) Community Earth System Model (CESM). The three primary stakeholders in FV3 (GFDL, GMAO, EMC (Environmental Modeling Center - NOAA)) have embarked on synergistic and collaborative strategies with focus on the advancement of non-hydrostratic dynamic core, physics, chemistry, and data assimilation efforts, leveraging collective strengths of each of the partnering agencies. This talk presents the ongoing plans for model advancements at GFDL, GMAO and EMC, with special emphasis on contributions towards developing community based unified global modeling system for operational and research applications at respective organizations. Future plans include extending the collaborations to the development of earth system components including GMAO's aerosol chemistry models (GOCART/MAM (Goddard Chemistry Aerosol Radiation and Transport / Modal Aerosol Model)), Land Information System (LIS), and advanced data assimilation techniques; and GFDL's Modular Ocean Model (MOM) and Sea Ice Simulator (SIS) for transition to operations at NCEP.

Putman, William↗

DOE OSTI2023

CMIP6 skill at predicting interannual to multi-decadal summer monsoon precipitation variability

Monsoons affect the economy, agriculture, and human health of two thirds of the world’s population. Therefore, predicting variations in monsoon precipitation is societally important. We explore the ability of climate models from the sixth phase of the Climate Model Intercomparison Project to predict summer monsoon precipitation variability by using hindcasts from the Decadal Climate Prediction Project (Component A). The multi-model ensemble-mean shows significant skill at predicting summer monsoon precipitation from one year to 6–9 years ahead. However, this skill is dependent on the model, monsoon domain, and lead-time. In general, the skill of the multi-model ensemble-mean prediction is low in year 1 but increases for longer-lead times and is largely consistent with externally forced changes. The best captured region is northern Africa for the 2–5 and 6–9 year forecast lead times. In contrast, there is no significant skill using the ensemble-mean over East and South Asia and, furthermore, there is significant spread in skill among models for these domains. By sub-sampling the ensemble we show that the difference in skill between models is tied to the simulation of the externally forced response over East and South Asia, with models with a more skilful forced response capable of better predictions. A further contribution is from skilful prediction of Pacific Ocean temperatures for the South Asian summer monsoon at longer lead-times. Therefore, these results indicate that predictions of the East and South Asian monsoons could be significantly improved.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

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.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

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

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Role of Ocean and Atmosphere Variability in Scale-Dependent Thermodynamic Air-Sea Interactions

This study investigates the influence of oceanic and atmospheric processes in extratropical thermodynamic air-sea interactions resolved by satellite observations (OBS) and by two climate model simulations run with eddy-resolving high-resolution (HR) and eddy-parameterized low-resolution (LR) ocean components. Here, in this work, spectral methods are used to characterize the sea surface temperature (SST) and turbulent heat flux (THF) variability and co-variability over scales between 50 and 10,000 km and 60 days to 80 years in the Pacific Ocean. The relative roles of the ocean and atmosphere are interpreted using a stochastic upper-ocean temperature evolution model forced by noise terms representing intrinsic variability in each medium, defined using climate model data to produce realistic rather than white spectral power density distributions. The analysis of all datasets shows that the atmosphere dominates the SST and THF variability over zonal wavelengths larger than ~2,000–2,500 km. In HR and OBS, ocean processes dominate the variability of both quantities at scales smaller than the atmospheric first internal Rossby radius of deformation (R 1 , ~600–2,000 km) due to a substantial ocean forcing coinciding with a weaker atmospheric modulation of THF (and consequently of SST) than at larger scales. The ocean forcing also induces oscillations in SST and THF with periods ranging from intraseasonal to multidecadal, reflecting a red spectrum response to ocean forcing similar to that driven by atmospheric forcing. Such features are virtually absent in LR due to a weaker ocean forcing relative to HR.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1979

Application of a coupled aerosol formation: Radiative transfer model to climatic studies of aerosols

A sophisticated one dimensional physical-chemical model of the formation and evolution of stratospheric aerosols was used to predict the size and number concentration of the stratospheric aerosols as functions of time and altitude following: a large volcanic eruption; increased addition of carbonyl sulfide (OCS) or sulfur dioxide (SO2) to the troposphere; increased supersonic aircraft (SST) flights in the stratosphere; and, large numbers of space shuttle (SS) flights through the stratosphere. A radiative-convective one dimensional climate sensitivity study, using the results of the aerosol formation model, was performed to assess the ground level climatic significance of these perturbations to the stratospheric aerosol layer. Volcanic eruptions and large OCS or SO2 increases could cause significant climatic changes. Currently projected SS launches and moderate fleets of SST's are unlikely to upset the stratospheric aerosol layer enough to significantly impact climate.

Toon, O. B.↗

DOE OSTI2019

Future climate emulations using quantile regressions on large ensembles

The study of climate change and its impacts depends on generating projections of future temperature and other climate variables. For detailed studies, these projections usually require some combination of numerical simulation and observations, given that simulations of even the current climate do not perfectly reproduce local conditions. We present a methodology for generating future climate projections that takes advantage of the emergence of climate model ensembles, whose large amounts of data allow for detailed modeling of the probability distribution of temperature or other climate variables. The procedure gives us estimated changes in model distributions that are then applied to observations to yield projections that preserve the spatiotemporal dependence in the observations. We use quantile regression to estimate a discrete set of quantiles of daily temperature as a function of seasonality and long-term change, with smooth spline functions of season, long-term trends, and their interactions used as basis functions for the quantile regression. A particular innovation is that more extreme quantiles are modeled as exceedances above less extreme quantiles in a nested fashion, so that the complexity of the model for exceedances decreases the further out into the tail of the distribution one goes. We apply this method to two large ensembles of model runs using the same forcing scenario, both based on versions of the Community Earth System Model (CESM), run at different resolutions. The approach generates observation-based future simulations with no processing or modeling of the observed climate needed other than a simple linear rescaling. The resulting quantile maps illuminate substantial differences between the climate model ensembles, including differences in warming in the Pacific Northwest that are particularly large in the lower quantiles during winter. We show how the availability of two ensembles allows the efficacy of the method to be tested with a “perfect model” approach, in which we estimate transformations using the lower-resolution ensemble and then apply the estimated transformations to single runs from the high-resolution ensemble. Finally, we describe and implement a simple method for adjusting a transformation estimated from a large ensemble of one climate model using only a single run of a second, but hopefully more realistic, climate model.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Toward the credibility of Northeast United States summer precipitation projections in CMIP5 and NARCCAP simulations

Abstract Precipitation projections for the northeast United States and nearby Canada (Northeast) are examined for 15 Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) models. A process‐based evaluation of atmospheric circulation features associated with wet Northeast summers is performed to examine whether credibility can be differentiated within the multimodel ensemble. Based on these evaluations, and an analysis of the interannual statistical properties of area‐averaged precipitation, model subsets were formed. Multimodel precipitation projections from each subset were compared to the multimodel projection from all of the models. Higher‐resolution North American Regional Climate Change Assessment Program (NARCCAP) regional climate models (RCMs) were subjected to a similar evaluation, grouping into subsets, and examination of future projections. CMIP5 models adequately simulate most large‐scale circulation features associated with wet Northeast summers, though all have errors in simulating observed sea level pressure and moisture divergence anomalies in the western tropical Atlantic/Gulf of Mexico. Relevant large‐scale processes simulated by the RCMs resemble those of their driving global climate models (GCMs), which are not always realistic. Future RCM studies could benefit from a process analysis of potential driving GCMs prior to dynamical downscaling. No CMIP5 or NARCCAP models were identified as clearly more credible, but six GCMs and four RCMs performed consistently better. Among the “Better” models, there is no consistency in the direction of future summer precipitation change. CMIP5 projections suggest that the Northeast precipitation response depends on the dynamics of the North Atlantic anticyclone and associated circulation and moisture convergence patterns, which vary among “Better” models. Even when model credibility cannot be clearly differentiated, examination of simulated processes provides important insights into their evolution under greenhouse warming.

Thibeault, Jeanne M.↗

DOE OSTI2018

Anthropogenic influences on major tropical cyclone events

There is no consensus on whether climate change has yet affected the statistics of tropical cyclones, owing to their large natural variability and the limited period of consistent observations. In addition, projections of future tropical cyclone activity are uncertain, because they often rely on coarse-resolution climate models that parameterize convection and hence have difficulty in directly representing tropical cyclones. Here we used convection-permitting regional climate model simulations to investigate whether and how recent destructive tropical cyclones would change if these events had occurred in pre-industrial and in future climates. We found that, relative to pre-industrial conditions, climate change so far has enhanced the average and extreme rainfall of hurricanes Katrina, Irma and Maria, but did not change tropical cyclone wind-speed intensity. In addition, future anthropogenic warming would robustly increase the wind speed and rainfall of 11 of 13 intense tropical cyclones (of 15 events sampled globally). Additional regional climate model simulations suggest that convective parameterization introduces minimal uncertainty into the sign of projected changes in tropical cyclone intensity and rainfall, which allows us to have confidence in projections from global models with parameterized convection and resolution fine enough to include tropical cyclones.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Testing NeuralGCM's capability to simulate future heatwaves based on the 2021 Pacific Northwest heatwave event

AI-based weather and climate models are emerging as accurate and computationally efficient tools. Beyond weather forecasting, they also show promise to accelerate storyline analyses. We evaluate NeuralGCM’s ability to simulate an extreme heatwave against the Energy Exascale Earth System Model (E3SM), a physics-based climate model. NeuralGCM accurately replicates the targeted event, and generates stable and realistic mid-century projections. However, due to the absence of land feedbacks, NeuralGCM underestimates the projected warming amplitude compared to physics-based model references.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Priorities, opportunities, and challenges for integrating microorganisms into Earth system models for climate change prediction

ABSTRACT Climate change jeopardizes human health, global biodiversity, and sustainability of the biosphere. To make reliable predictions about climate change, scientists use Earth system models (ESMs) that integrate physical, chemical, and biological processes occurring on land, the oceans, and the atmosphere. Although critical for catalyzing coupled biogeochemical processes, microorganisms have traditionally been left out of ESMs. Here, we generate a “top 10” list of priorities, opportunities, and challenges for the explicit integration of microorganisms into ESMs. We discuss the need for coarse-graining microbial information into functionally relevant categories, as well as the capacity for microorganisms to rapidly evolve in response to climate-change drivers. Microbiologists are uniquely positioned to collect novel and valuable information necessary for next-generation ESMs, but this requires data harmonization and transdisciplinary collaboration to effectively guide adaptation strategies and mitigation policy.

Microbiology↗