News & Media

Latest Stories

538 results found
Filtered by Advanced Hydrocarbon Conversion, Earth System Science, Ecosystem Science, Emergency Response, Grid Cybersecurity, Materials Science, Materials in Extreme Environments, Neutrino Physics, Nuclear & Particle Physics, Precision Materials by Design, Secure & Adaptive Systems, Solar Energy, and Transmission

Probing Uncertainties in Modeling Deciduous Forests

road through a forest with leafy trees on either side

A research team tested the ability of a vegetation model to represent carbon cycling and community succession over 100 years in a deciduous forest in the Upper Midwest. They found that accurately representing both processes is challenging.

Photo: Aaron Burden | Unsplash

Forest composition and carbon cycling are difficult to model simultaneously

September 18, 2020
September 18, 2020
Highlight

The Science

Regrowing forests drive landscape carbon and nutrient cycling over decades, but whether vegetation models can reproduce these long-term patterns of forest succession is uncertain. A team of researchers led by scientists from the U.S. Department of Energy’s Pacific Northwest National Laboratory, simulated carbon cycling and community composition during 100 years of forest regrowth following disturbance. They examined which processes and parameters are most important to accurately model forest succession in the Upper Midwest region of the United States, along with the relative importance of model structure versus parameters. The researchers found that parameter uncertainty is far more important than structural uncertainty, and that simulating both productivity and plant community composition accurately remains a challenge. These results have implications for robustly simulating future climate change effects with Earth system models.

The Impact

Understanding the influences behind 20th- and 21st-century forest growth and the ability to predict its future evolution, is essential to shaping global policy around climate, biodiversity, and natural resource management. The ability of even state-of-the-art dynamic vegetation models to simulate successional change and forest regrowth is uncertain, and thus the results of this study provide important bounds on our predictive ability for Upper Midwest forests, as well as the ecological implications of these uncertainties.

Summary

Vegetation models capture researchers’ understanding of forest function, but whether models can reproduce multidecadal patterns of forest succession is highly uncertain. This research team tested the accuracy and precision with which a vegetation model can simulate carbon cycling and community composition during 100 years of forest regrowth. To do this, they ran ensembles of an ecosystem demography model with different representations of processes important to competition for light. Then, the researchers compared the magnitude of structural and parameter uncertainty. They also assessed which submodel-parameter combinations best reproduced observed carbon fluxes and community composition. On average, the simulations underestimated observed forest production and leaf area after 100 years and predicted complete dominance by a single plant functional type. Parameter uncertainty was large; the two parameters that consistently contributed most to uncertainty were plant-soil water conductance and growth respiration—both empirical coefficients that cannot be observed. The team concluded that parameter uncertainty is more important than structural uncertainty, at least for this model in Upper Midwest forests, and simulating both productivity and plant community composition accurately without physically unrealistic parameters remains a challenge for demographic vegetation models.

PNNL Contact

Ben Bond-Lamberty, Pacific Northwest National Laboratory, bondlamberty@pnnl.gov  

Funding

This project was supported by the National Science Foundation. Cyberinfrastructure capabilities were provided by the Pacific Northwest National Laboratory.

A. Shiklomanov, B. Bond-Lamberty, J. Atkins, and C. Gough, “Structure and parameter uncertainty in centennial projections of forest community structure and carbon cycling.” Global Change Biology in press (2020). [DOI: 10.1111/gcb.15164]

SEPTEMBER 17, 2020
Web Feature

Not Your Average Refinery

In a new review, PNNL researchers outline how to convert stranded biomass to sustainable fuel using electrochemical reduction reactions in mini-refineries powered by renewable energy.

Madden-Julian Oscillation Events Ride a Monsoonal Seesaw

Madden-Julian Oscillation Events Ride a Monsoonal Seesaw

Researchers discovered the strength of Madden-Julian Oscillation events is related to their migration across the moisture convergence flux controlled by monsoons.

Photo: Tim Mossholder, Unsplash

The slow eastward movement of monsoon moisture convergence controls the strength of the events.  

September 1, 2020
September 1, 2020
Highlight

The Science

Madden-Julian Oscillation (MJO) events are characterized by eastward-traveling anomalies of convection and rainfall lasting between 30–60 days over the Indian and Pacific oceans. Some MJO events strengthen to produce more rain as they cross the Indo-Pacific Maritime Continent (MC), while others weaken or remain relatively unaffected. Understanding the processes responsible for these variations has been difficult. One hypothesis about the variations of MJO strengths is that they are related to variations of atmospheric moisture but quantifying the moisture over the ocean is challenging because of a lack of reliable observations. Scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) and the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory developed a method to infer the moisture transport from precipitation. Using this method, researchers showed that variability in the strength of MJO events is modulated by the slow eastward migration of moisture convergence associated with seasonal monsoon changes.

The Impact

MJO events have been linked to extreme events such as tropical cyclones and atmospheric rivers, so understanding the variability of MJO strength is a key element in international efforts to improve sub-seasonal to seasonal prediction. This study provides a framework for understanding the cross-scale interactions between intra-seasonal (MJO) and seasonal (monsoon) processes leading to the apparent spread in the strength of MJO events in the MC region, suggesting that future improvements in predicting the monsoons may improve prediction of the MJO and its global consequence.

Summary

To understand the factors controlling the variability in the strength of MJO events as they propagate across the MC region, researchers developed a method to calculate the zonal and meridional components of the MJO events’ moisture supply directly from observed precipitation. The research team showed that seasonal monsoon-related anomalies of zonal moisture flux convergence regulate the strength of MJO events as manifested in their precipitation. The monsoons start in the central equatorial Indian Ocean during the Asian summer monsoon season between June and August, migrate eastward, and end over the eastern boundary of the MC region at the end of the Australian monsoon season between December and February. Hence, depending on the season, MJO events may encounter a favorable environment of abundant moisture due to moisture convergence induced by the monsoons at different longitudes. In February, March, and April, MJO events tend to start weak over the eastern Indian Ocean, where moisture is diverged away by the Asian winter monsoon. The events strengthen as they propagate to the western Pacific, where there is moisture convergence induced by the Australian monsoon. In contrast, in May, June, and July, MJO events are most likely to weaken as they propagate from the region of anomalous moisture convergence to that of moisture divergence. While MJO events are usually strong at either the eastern Indian Ocean or the western Pacific, peak MJO strength is also observed over the MC region, particularly during strong Australian monsoon months that strengthen the moisture convergence over the MC region.

PNNL Contacts

L. Ruby Leung, Pacific Northwest National Laboratory, Ruby.Leung@pnnl.gov

Jerome Fast, Pacific Northwest National Laboratory, Jerome.Fast@pnnl.gov

Samson Hagos, Pacific Northwest National Laboratory, samson.hagos@pnnl.gov

Funding

This work is supported by the NOAA Oceanic and Atmospheric Research, Climate Program Office (CPO), as well as the U.S. Department of Energy Office of Science Biological and Environmental Research as part of the Atmospheric System Research program and the Regional and Global Model Analysis, Earth and Environmental System Modeling program.

Hagos S, C Zhang, LR Leung, CD Burleyson, and K Balaguru. 2019. “A zonal migration of monsoon moisture flux convergence and the strength of MaddenJulian Oscillation events.” Geophysical Research Letters, 46:8554–8562, https://doi.org/10.1029/2019GL083468.

Polar Imbalance: Understanding Differences in How the Arctic and Antarctic Respond to Increased Greenhouse Gases

iceberg

Polar temperatures might be expected to respond similarly to changing atmospheric conditions, but the Arctic’s response to increased carbon dioxide levels has been much more dramatic than what has been seen in the Antarctic.

August 31, 2020
August 31, 2020
Highlight

The Science

Polar climates might be expected to respond similarly to changing atmospheric conditions, but observations show the Arctic has warmed more rapidly in response to increased carbon dioxide levels than the Antarctic. One explanation for the Antarctic response has been the uptake—or absorption—of atmospheric heating by the Southern Ocean. Researchers at the U.S. Department of Energy’s Pacific Northwest National Laboratory showed that the different responses between the Arctic and Antarctic also arose because the ocean triggered environmental feedbacks that caused additional warming over the Arctic—not from differences in how the subpolar oceans responded to warming from greenhouse gases.

The Impact

This study provides an alternative explanation for why the Arctic warms more than the Antarctic. Researchers found stronger and more destabilizing feedbacks occurring over the Arctic than over the Antarctic. These destabilizing feedbacks increase the top-of-atmosphere energy imbalance, so the Earth must warm even more to reach equilibrium. Future studies could consider why the Arctic and Antarctic experience different feedbacks, not necessarily differences in the ocean response to warming.

Summary

Greenhouse gas emissions from human activities affect the planet globally, but nowhere more so than over the Arctic—a phenomenon known as polar amplification. Surprisingly, the response over the Antarctic is much more muted than over the Arctic. Previous research attributed this difference to the large amount of heat being absorbed into the deep Southern Ocean, cooling the Southern Hemisphere. Researchers analyzed model results and found that the ocean heat uptake might not be the sole cause. They then designed experiments to test that idea.

Results showed that the weaker response over the Antarctic was partly due to weaker intrinsic sensitivity to both greenhouse gas forcing and ocean heat transport and uptake. The study indicated that the Arctic had greater local climate sensitivity (i.e., a greater surface temperature response) to doubling of carbon dioxide from preindustrial conditions. In addition to warming, carbon dioxide causes changes in how the ocean takes up heat from the atmosphere. Results showed that those changes in the ocean only served to increase local sensitivity over the Arctic and do not produce that same impact over the Antarctic. Even climate models with similar amounts of heat absorbed into the deep ocean in both hemispheres warmed more over the Arctic than over the Antarctic. Similar increases in winter heat transport to the polar oceans in both hemispheres triggered stronger and more destabilizing feedbacks over the Arctic than over the Antarctic. The most important feedbacks included the ice-albedo (reflectivity that speeds up ice melt) and lapse rate (atmospheric temperature change with increase in altitude) feedbacks. Therefore, greater warming can be expected over the Arctic than over the Antarctic. This holds true even if changes in ocean heat transport or uptake are similar in both hemispheres.

PNNL Contacts

Philip Rasch, Pacific Northwest National Laboratory, Philip.Rasch@pnnl.gov

Hansi A. Singh, Pacific Northwest National Laboratory, hansi.singh@pnnl.gov

Funding

H.A.S. was funded through the Linus Pauling Distinguished Postdoctoral Fellowship, a Laboratory Directed Research and Development Program of Pacific Northwest National Laboratory. O.A.S. and P.J.R. were funded through High-Latitude Application and Testing of Earth System Models (HiLAT), a project of the U.S. Department of Energy Office of Science, Biological and Environmental Research as part of its Regional and Global Model Analysis, Earth and Environmental System Modeling program.

Singh HA, OA Garuba, and PJ Rasch. 2018. “How Asymmetries Between Arctic and Antarctic Climate Sensitivity Are Modified by the Ocean.” Geophysical Research Letters 45(23):13,031–13,040, https://doi.org/10.1029/2018GL079023.

Sunny Tropical Islands Contribute to Strengthening of Madden-Julian Oscillation Events

an aerial view of green fields with sun, clouds and palm trees in the background

A new study reveals the amount of sunlight that Indonesian islands receive affects the strength of the Madden-Julian Oscillation. 

Image courtesy of Joe Yates on Unsplash

The Madden-Julian Oscillation strength over the Indo-Pacific Maritime Continent islands is related to the semi-annual variability of sunlight the region receives

August 31, 2020
August 31, 2020
Highlight

The Science                                

The Madden-Julian Oscillation (MJO) is a pulse of clouds and precipitation that travels eastward across the tropics every month or two, on average. Predicting whether and how this oscillation travels over the Maritime Continent islands—including Sumatra, Borneo, New Guinea, and the Philippines—is challenging because the factors that influence it in this region are not well understood. Now scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory and the National Oceanic and Atmospheric Administration’s Pacific Marine Environmental Laboratory have analyzed observations and model simulations. They discovered that the semi-annual variation in the amount of sunlight the region receives plays a role in enhancing MJO precipitation over the Maritime Continent islands.

The Impact

Limitations in understanding and simulating the variability of the MJO propagation across the Indo-Pacific Maritime Continent have been an impediment to MJO prediction. The MJO can influence hurricanes and atmospheric rivers, so challenges in its prediction can have implications for global predictions of extreme weather events. This study examines the roles of the semi-annual variation of solar radiation and soil moisture on the MJO propagation across the Maritime Continent islands. The relationship between the effects of the annual monsoonal cycle and semi-annual variability due to sunlight exposure provides important insight for interpreting variations in the predictability of MJO events.

Summary

Researchers investigated the seasonality of the interaction of the MJO with the Maritime Continent. Using observations, they showed that the MJO amplitude and precipitation over the Maritime Continent islands exhibit semi-annual variability. The MJO signal appeared to strengthen during March and September when sunlight, or solar insolation, is strongest over this region.

Next, the researchers performed a series of regional convection permitting simulations for the November 2014 MJO event as a case study under various insolation and soil moisture conditions. Insolation and soil moisture are two factors that influence convection over land. Results showed that increased insolation increases precipitation, including precipitation associated with MJO moisture convergence over the Maritime Continent region. Using a moisture budget analysis that isolated MJO and non‐MJO signals, along with additional idealized simulations, the MJO response to high insolation is demonstrated to be related to an increase in the basic state atmospheric moisture due to the high insolation over the Maritime Continent region.

PNNL Contact

L. Ruby Leung, Pacific Northwest National Laboratory, ruby.leung@pnnl.gov

Funding

This work is supported by National Oceanic and Atmospheric Administration Oceanic and Atmospheric Research, Program Climate Program Office, as well as U.S. Department of Energy Office of Science, Biological and Environmental Research as part of the Atmospheric Systems Research Program and Global and Regional Model program area. Computing resources for the model simulations are provided by the National Energy Research Scientific Computing Center. 

S. Hagos, et al., “Impacts of Insolation and Soil Moisture on Seasonality of Interactions Between the Madden-Julian Oscillation and Maritime Continent.” Journal of Geophysical Research 125, e2020JD032382 (2020). [DOI: 10.1029/2020JD032382]

Wildfires Trigger Violent Storms with Large Hail and Lightning

illustration of how wildfires influence storms

The heat released from fires plays a predominant role in triggering a strong storm, while aerosols play a significant role in enhancing storm intensity, hail, and lightning at the mature stage. 

Illustration: PNNL

Research reveals how heat and aerosols from wildfires initiate and invigorate severe storms

August 31, 2020
August 31, 2020
Highlight

The Science

Wildfires emit tremendous amounts of gases, aerosols, and sensible heat, which impact environmental temperature and severe convective storms. However, weather forecasting and climate models lack the capability to account for the impact of sensible heat on meteorology and associated severe storms. In addition, a quantitative understanding is needed for how the heat and aerosol effects of wildfires influence severe storm characteristics such as intensity, hail, and lightning. Researchers at the U.S. Department of Energy’s Pacific Northwest National Laboratory led a study to model heat’s impact and apply this to understand how wildfires contribute to storm severity. They found the heat released from fires plays a predominant role in triggering a strong storm, while aerosols also play a significant role in enhancing storm intensity, hail, and lightning after the storm is initiated.

The Impact

The model developed in this study enables scientists to study wildfire impacts on environmental thermodynamics and forecast pyrocumulonimbus storm severity. Quantifying the respective heat and contribution of aerosols from wildfires that invigorate pyro-convection and produce hail and lightning provides significant scientific guidance to identify hazardous weather threats and overall impact of wildfires on weather and climate.

Summary

In addition to forming atmospheric particles (aerosols) with global impact on clouds, precipitation, and radiation, wildfire activity can significantly influence environmental thermodynamics. Researchers developed a model that accounts for the impact of heat flux from wildfires and is computationally efficient. Using the new model to explore a pyrocumulonimbus event associated with the Texas Mallard Fire on May 11-12, 2018, researchers used comparisons and observations to investigate the effects of both heat flux and aerosol emissions from the wildfire and predict radar reflectivity, precipitation, hailstone size, and lightning. The analysis showed that heat flux and aerosol emissions from the wildfire increased low-level temperatures and mid-level thermal buoyancy significantly, causing stronger upward motion that lifted more supercooled water to higher levels. The increase in available supercooled water for hail growth and invigorated updrafts led to larger hail size and enhanced lightning. Overall, the effect of heat flux on storm intensity was more significant than that of aerosol emissions. However, after the storm was initiated, aerosols were shown to greatly enhance storm intensity and the production of hailstones and lightning.

PNNL Contact

Jiwen Fan, Pacific Northwest National Laboratory, jiwen.fan@pnnl.gov

Funding

This study is supported by the U.S. Department of Energy Office of Science Early Career Award Program. PNNL is operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under contract DEAC0576RL01830. Zhanqing Li acknowledges the support of NASA grant (NNX16AN61G).

Zhang, Y., Fan, J., Logan, T., Li, Z., and Homeyer, C. R. (2019). “Wildfire impact on environmental thermodynamics and severe convective storms.” Geophysical Research Letters 46:16, (2019) [DOI: 10.1029/2019GL084534].  

Evaluating the Future Evolution of Virtual Water Trade

various types of produce in blue plastic bins

Water embedded in traded goods such as agricultural crops is called virtual water. Changing socioeconomic and climate conditions will drive changes in the trading of agricultural goods across international borders.

Image courtesy of ready made from Pexels

Water trading is projected to at least triple by the end of the century

August 31, 2020
August 31, 2020
Highlight

The Science

Water-stressed regions rely heavily on the import of water-intensive goods such as crops from other regions. The amount of water embedded in these traded goods is known as virtual water. Changing socioeconomic and climate conditions will drive changes in the trading of agricultural goods across international borders. Now a research team including scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory has predicted future changes in virtual water trading. They combined future socioeconomic and climate conditions in a complex model that accounts for the relationships between energy, water, land, climate, and human activities. Their analysis reveals that the volume of water embedded in internationally traded agricultural goods will at least triple current values by the end of the century, because of future population dynamics and changes in water stress.

The Impact

Previous research has focused on reconstructing historical virtual water trade estimates to understand previous evolution, yet future estimations remain nearly nonexistent. This is often due to an inability to model future agricultural trade driven by the interconnection between socioeconomics and water. However, the Global Change Analysis Model (GCAM) can capture such conditions in the modeling of future scenarios. Researchers used this model to analyze the future social dependency of water embedded in agricultural crop trading across international borders. The results highlight areas that may become increasingly reliant upon importing water-intensive crops because domestic water supply may not allow for adequate growth.

Summary

Evolving socioeconomic and climate conditions will cause alterations in trade dependencies and water availability resulting in changes in virtual water trading. While virtual water has received increasing interest in the scientific community, comprehensive future projections of virtual water remain absent. Researchers used the GCAM model to show, for the first time, changes during the 21st century in the amount of various water types embedded in traded agriculture necessary to meet international demands. The model allows for the distinction between and quantification of renewable surface water and groundwater recharge, nonrenewable groundwater, and soil moisture embedded in these traded goods.

They found that future interregional virtual water trading of renewable water sources may triple by 2100, while nonrenewable groundwater trading may at least double. Basins in North America, as well as the La Plata and Nile River basins, will contribute extensively to virtual water exports, while much of Africa, India, and the Middle East will rely heavily on virtual water imports by the end of the century.

PNNL Contact

Mohamad Hejazi, Pacific Northwest National Laboratory, Mohamad.Hejazi@pnnl.gov

Funding

This research was supported by the U.S. Department of Energy Office of Science as part of research in the MultiSector Dynamics, Earth and Environmental System Modeling Program.

N.T. Graham, et al., “Future changes in the trading of virtual water.” Nature Communications 11, 3632 (2020). [DOI: 10.1038/s41467-020-17400-4]  

 

Better Accuracy for Simulations of Noisy Phenomena

computer screen with code on it and red and purple lights in the background representing randomness

Researchers have derived a generalized Itô correction to accurately represent stochastic phenomena.

Fabian Grohs, Unsplash

A generalized method is developed to address fast-evolving phenomena with a wide range of atmospheric characteristics

August 31, 2020
August 31, 2020
Highlight

The Science

Fast evolving physical phenomena can be described by mathematical equations containing noisy processes. Care is needed when numerically solving such equations, as naïve application of traditional methods can lead to large errors. A correction term, known as the Itô correction in the theory of stochastic differential equations, can be added to traditional methods to address the challenge. While the original Itô correction was designed to handle white noise, a recent study by a team of Pacific Northwest National Laboratory researchers proposed a generalization that can be applied to noisy processes of a wide range of characteristics.

The Impact

The generalized Itô correction can be used to improve the quality of simulations containing noisy terms that state-of-the-art weather and climate models have started to include in their equations to help improve the simulation’s statistical properties and to facilitate the quantification of uncertainties. It is also applicable to a wide range of equations beyond weather and climate modeling.

Summary

Both fast-evolving and inherently random physical phenomena can appear noisy in numerical simulations. Numerical methods originally developed for deterministic and smooth phenomena can produce large errors when applied to noisy processes and can even lead to qualitatively different results. The concept of Itô correction, widely known as part of the theory of stochastic differential equations, can help address the challenge, but the classical Itô correction is only applicable to white noise. In this study, a generalized formulation of the Itô correction is derived for noise of any color, making it applicable to processes with memory and more suitable for many applications in weather, climate, and Earth system modeling. The generalized Itô correction is particularly helpful for the development of state-of-the-art weather and climate models, as noisy terms describing small-scale phenomena are being introduced to these models as part of the so-called stochastic parameterizations. The generalized Itô correction can help improve solution accuracy without requiring a complete redesign of the time stepping methods in the original model codes. While this study was motivated by needs in atmosphere modeling, the formulation of the new Itô correction is general and applicable to a broad range of stochastic model equations.

PNNL Contact

Hui Wan, Pacific Northwest National Laboratory, Hui.Wan@pnnl.gov

Funding

This work was supported by the U.S. Department of Energy’s (DOE) Scientific Discovery through Advanced Computing (SciDAC) program. Panos Stinis, Huan Lei, and Jing Li were supported by DOE’s Office of Advanced Scientific Computing Research. Hui Wan was supported by DOE’s Office of Biological and Environmental Research.

10.1175/MWR-D-19-0178.1

P. Stinis, H. Lei, J. Li, and H. Wan, “Improving solution accuracy and convergence for stochastic physics parameterizations with colored noise.” Monthly Weather Review, 148, 2251-2263 (2020). [DOI:10.1175/MWR-D-19-0178.1]

May 6, 2020
May 6, 2020

A New Perspective on Shallow Convective Clouds

satellite image of clouds over the central United States

Researchers used satellite images, such as this NASA MODIS image of clouds on August 30, 2016 over the Central U.S. during the HI-SCALE campaign, to validate models simulating how cloud populations are influenced across space by various factors.

Image courtesy of NASA

Field campaign data and high-resolution modeling are helping to understand the processes that affect organization of convective cloud populations

August 31, 2020
August 31, 2020
Highlight

The Science

Shallow clouds occur over many areas of the world and are an important component of Earth’s atmospheric radiation budget. Models, called Large Eddy Simulations (LES), that can represent turbulent motions near the surface as well as the size of small clouds have been used extensively to better understand processes that control the life cycle of shallow convective clouds. However, because LES models can take days to weeks to run on a supercomputer, these models usually simulate clouds over relatively small areas (less than 50 km wide) and use simplified assumptions of the environmental meteorological conditions so that the predicted clouds are nearly uniformly distributed. Researchers at the U.S. Department of Energy’s Pacific Northwest National Laboratory used a more realistic modeling approach to represent observed complex cloud distributions over Oklahoma on a day during the U.S. Department of Energy’s recent Atmospheric Radiation Measurement (ARM) Holistic Interactions of Shallow Clouds, Aerosols, and Land Ecosystems (HI-SCALE) atmospheric sampling campaign. Researchers compared the model predictions with satellite images and collected extensive measurements near the ARM Southern Great Plains (SGP) site.

The Impact

The high-resolution model experiments show that the convective cloud populations observed on one day of ARM’s HI-SCALE field campaign were influenced by two factors: (1) in the morning, the distribution of drier and wetter soil moisture drove the initial formation of shallow convection, and (2) the land–atmosphere coupling associated with these soil moisture gradients enabled some shallow clouds to grow deeper, precipitating convection during the early afternoon and producing cold pools that further disrupted the spatial distribution and evolution of clouds. The strength of this land–atmosphere–cloud coupling is expected to vary depending on the ambient wind speed. This suggests that weather and climate model predictions can be improved by including the effects of cold pools and small-scale variations in surface properties, such as land use and soil moisture.

Summary

The SGP is one of several land–atmosphere “hotspots” around the world that influence the life cycle of shallow convection. The measurements collected at the ARM SGP site make this region unique in terms of providing information needed to better understand how coupling of the land–surface interface influences the development of cloud populations. By integrating measurements from this site, special aircraft measurements collected during HI-SCALE, and satellite images with higher-resolution modeling, researchers were able to deduce two superimposed processes that contribute to the organization of convective clouds:

  • The first factor is soil moisture variability and, although to a lesser degree, cooler lake temperatures, which control cumulus formation early in the day. Regions with drier soil moisture had warmer boundary layers and higher boundary-layer depths had more vigorous shallow cumulus formation earlier in the day than regions with wetter soil moisture. The boundaries of drier and wetter soil regions favor the transition of shallow cumulus to deeper, precipitating convection.
  • The second factor is cold pools that form during the early afternoon. There are many precipitating convective cells that last only a few minutes and only perturb the local cloud fields. Fewer, longer-lasting, and stronger convective cells produce cold pools that are more persistent and expand over large areas that are tens of kilometers wide. The cold pools suppress turbulence and cloud formation so that clear skies are present over relatively large areas. The cloud distributions forced by soil moisture variations are thus perturbed by cold pools that expand and overlap each other.

These processes are likely responsible for the complex observed population of convective clouds. In addition, a cloud tracking algorithm that can quantify the lifetime and size of the clouds in the model showed that more complex soil moisture distributions led to larger clouds with longer lifetimes.

PNNL Contact

Jerome Fast, Pacific Northwest National Laboratory, jerome.fast@pnnl.gov

Funding

The U.S. Department of Energy (DOE) Office of Science, Biological and Environmental Research supported this research as part of the Atmospheric System Research (ASR) program. Observations collected during HI-SCALE were supported by the Atmospheric Radiation Measurement (ARM) Climate Research Facility and the Environmental Molecular Sciences Laboratory (EMSL), both DOE Office of Science user facilities sponsored by the Office of Biological and Environmental Research.

J.D. Fast, L.K. Berg, Z. Feng, F. Mei, R. Newsom, K. Sakaguchi, and H. Xiao, “The impact of variable land-atmosphere coupling on convective cloud populations observed during the 2016 HI-SCALE field campaign.” Journal of Advances in Modeling Earth Systems 11, (2019). [DOI: 10.1029/2019MS001727].

New Modeling Improves Precipitation Simulation in Mountains High and Low

orange puffs of clouds above a mountain

Research developed simple methods for downscaling precipitation using topographic features to capture how mountains and valleys influence precipitation. These methods could be used to refine precipitation predictions in Earth systems models.

Photo by Marc Thunis on Unsplash

Downscaling methods improve representation of terrain effects on precipitation for earth system models

August 31, 2020
August 31, 2020
Highlight

The Science

Earth system models describe interactions between land, freshwater, and the atmosphere. These models cover the Earth’s surface with a mesh to simulate processes, such as clouds, precipitation, water flow, and air movement at each grid point on the mesh. Because computing resources are limited, the mesh is generally too coarse to capture complex terrain characteristics, such as valleys and mountains. However, these features influence precipitation, which has subsequent impacts on runoff, snowpack, vegetation, and other land surface processes. Now, scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory have developed simple methods that use topographic features and airflow characteristics to describe precipitation spatial variation at a resolution smaller than the mesh. These methods could be implemented in Earth system models, such as the Energy Exascale Earth System Models (E3SM).

The Impact

About 60 to 90% of the world’s freshwater supply originates from mountains. However, global models of Earth systems poorly represent the water cycle in mountain regions. This study developed and compared simple methods, based in topography and airflow characteristics, to represent precipitation on a smaller scale and account for terrain effects. The minimal computational cost of these methods means they could enhance the ability of Earth system models to better simulate freshwater supply without increasing the computational requirements, with downstream implications on water, energy, and food systems.

Summary

Terrain influences precipitation through topographic features, such as height and slope, as well as atmospheric processes, such as winds and stability of air mass. This study explored various simple, but physically based downscaling methods of precipitation to represent the effect of complex terrain that is not explicitly resolved in Earth system models. Researchers evaluated and compared the performance of these methods based on their ability to capture the observed spatial pattern of precipitation as depicted in the high-resolution (4 km) Precipitationelevation Regressions on Independent Slopes Model dataset over the conterminous United States. Two of the four methods tested performed well, as measured by multiple metrics including model errors and sensitivity to model grid sizes. These methods are the Elevation Range with Maximum elevation Method (ERMM), which utilizes elevation measurements of the subgrid landscape within a model grid cell, and the Froude Number Method (FNM), which accounts for mountains blocking airflow. By accounting for blocking of airflow, the FNM method performs slightly better than the ERMM method in mountainous regions consistently across multiple grid sizes. Both the ERMM and FNM methods have minimal computational and input data requirements, making them useful additions to Earth system models. They have been implemented in E3SM for downscaling precipitation from the atmosphere model grid to the smaller subgrid units of the land model to improve modeling of land surface processes in mountain regions.

PNNL Contacts

L. Ruby Leung, Pacific Northwest National Laboratory, Ruby.Leung@pnnl.gov

Teklu K. Tesfa, Pacific Northwest National Laboratory, teklu.tesfa@pnnl.gov

Funding

The U.S. Department of Energy Office of Science, Biological and Environmental Research supported this research as part of the Earth System Model Development program area through the Climate Model Development and Validation (CMDV) project facilitating model development collaboration between the Next Generation Ecosystem Experiments (NGEE-Tropics and NGEE-Arctic) and the Energy Exascale Earth System Model (E3SM) projects and the E3SM project.

T. K. Tesfa, L. R. Leung, and S. J. Ghan. “Exploring TopographyBased Methods for Downscaling Subgrid Precipitation for Use in Earth System Models.” Journal of Geophysical Research: Atmospheres125, e2019JD031456 (2020). [DOI: 10.1029/2019JD031456]

How Have Local Radiative Feedbacks Contributed to Polar Amplification since 1980?

white ice sheet bordering a blue sea

The Antarctic region is one of the three polar regions on Earth where the surface temperature has risen more than the global mean increase. This phenomenon is commonly known as polar amplification. 

Image courtesy of Matt Palmer on Unsplash

Models have good agreement on common and important feedbacks, while disagreement on cloud feedback calls for further investigation

August 31, 2020
August 31, 2020
Highlight

The Science                                

The global mean temperature at Earth's surface has risen rapidly since 1980. Three of the planet’s polar regions—the Arctic, Antarctic, and Tibetan Plateau—have warmed more than the global mean, a phenomenon commonly known as polar amplification. Radiative feedbacks such as surface albedo feedback, temperature feedback, and cloud feedback contribute to polar amplification. Scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory led a study to quantify these feedbacks using historical short-term climate simulations. These simulations can reproduce the observed warming and polar amplification.

The Impact

This research is the first systematic quantification of individual radiative feedbacks over the “three poles” based on historical short-term simulations from multiple state-of-the-art climate models. The global mean net feedback in historical years 1980–2017 is estimated to be negative; this means that the net feedback decreases the historical warming. The magnitude of the net feedback is stronger than that estimated from long‐term experiments, with rapid warming driven by quadrupled carbon dioxide levels. This large magnitude is primarily due to a near‐zero global‐mean cloud feedback in recent decades. All models agree that the temperature lapse rate feedback is the largest contributor to polar amplification.

Summary

Previously, the team identified that incomplete knowledge of the evolving effective radiative forcing due to changes in greenhouse gases, aerosols, and land conditions can produce uncertainty in quantifying the feedbacks based on the historical shortterm climate simulations. This study extends that work to examine historical radiative feedbacks by analyzing a unique set of atmospheric general circulation model (AGCM) simulations. This includes simulations from the Atmospheric Model Intercomparison Project within CMIP Phase 6 (AMIP6) with known effective radiative forcing for 1980–2014 and a specifically designed CAM5 simulation with zero effective radiative forcing for 1980–2017.

The historical global mean net feedback estimated from the AGCM simulations is around −2 W m-2 K-1, which is about twice the magnitude estimated from dozens of longterm warming experiments driven by quadrupled levels of atmospheric carbon dioxide. This difference is mainly caused by a nearzero net cloud feedback for the historical time period in short-term simulations. The team also showed that the temperature lapse rate feedback for 1980–2017/2014 is the largest contributor to the amplified temperature change over the three poles, followed by surface albedo feedback and Planck feedback deviation from its global mean. Interestingly, except for a higher surface albedo feedback in the Antarctic region, all other feedbacks are similar between the Arctic and Antarctic. The largest disagreement between the CAM5 and the AMIP6 model results is in both shortwave and longwave cloud feedbacks that differ in sign as well as magnitude. This result calls for further investigation into why this uncertainty in global and regional cloud feedback exists in climate models. 

PNNL Contact 

Hailong Wang, Pacific Northwest National Laboratory, hailong.wang@pnnl.gov

Funding

This research has been supported by the U.S. Department of Energy Office of Science, Biological and Environmental Research, Regional and Global Model Analysis program as part of the HiLAT-RASM project.

R. Zhang, et al., “Assessing global and local radiative feedbacks based on AGCM simulations for 1980–2014/2017.” Geophysical Research Letters 47, e2020GL088063 (2020). [DOI: 10.1029/2020GL088063]

 

An Extended Radar Relative Calibration Technique for Research Radars

weather monitoring equipment at a remote site

From October 2018 through April 2019, the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign gathered data to help researchers better understand the convective cloud life cycle over the Sierras de Córdoba mountain range in Argentina.

Image courtesy of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility

The technique can be used to monitor and track radar calibration at remote field sites, including higher-frequency radars and range-height indicator scans

August 31, 2020
August 31, 2020
Highlight

The Science                                

Weather radars need routine calibration to maintain quality performance. However, for radars located in remote regions with limited on-site monitoring, this calibration is challenging and sometimes impossible. To mitigate this, researchers at the U.S. Department of Energy’s Pacific Northwest National Laboratory enhanced an existing method for radar calibration so it could be used at remote sites. This method uses the stability of radar returns from ground clutter to estimate and track radar calibration. The researchers validated the use of this extended relative calibration adjustment technique for both research radars and radars of higher frequency (C-, X-, and Ka-band).

The Impact

Previous uses of this calibration technique were limited to lower radar frequencies and a single operational mode. Now researchers expanded the technique to be used with more radar frequencies and other scan types. This extension is particularly relevant to the ARM program given its fleet of high frequency radars. The extension of this calibration technique makes it easier to monitor and correct calibration drifts, both during the operational period and a posteriori with historical datasets. The open-source code that accompanies the release of this publication has already been used in multiple field campaigns for the U.S. Department of Energy Atmospheric Radiation Measurement program, including the calibrated radar dataset for the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina.

Summary

The researchers recognized a need for monitoring and tracking radar calibration, especially for ARM’s research-grade weather radars located at remote sites. Their work extends the relative calibration adjustment technique for calibration of weather radars to higher-frequency radars (including cloud radars) as well as range-height indicator scans.

The relative calibration adjustment technique uses the statistics of the ground clutter surrounding the radar as a monitoring source for the stability of calibration. At higher frequencies, the properties of clutter can be much more variable. This work introduces an extended clutter selection procedure that incorporates the temporal stability of clutter and helps to improve the technique’s operational stability for relatively higher-frequency radars. The researchers also extended this technique to utilize range-height scans from radars where the elevation is varied rather than the azimuth. Research radars often utilize range-height scans to examine the vertical structure of clouds. The researchers applied the newly extended technique, called extended relative calibration adjustment or eRCA, to four DOE ARM weather radars ranging in frequency from C- to Ka-band. Cross comparisons of three co-located radars with frequencies C, X, and Ka at the ARM CACTI site show that the technique can determine changes in calibration with high accuracy. Using an X-band radar at the ARM Eastern North Atlantic site, they showed how the technique can be modified to be more resilient to clutter fields that show increased variability, such as sea clutter in this case. The results show that this technique is also promising for a posteriori data calibration and monitoring.

PNNL Contact

Joseph C. Hardin, Pacific Northwest National Laboratory, joseph.hardin@pnnl.gov

Funding

This work was funded by the U.S. Department of Energy’s Atmospheric Radiation Measurement program and the Atmospheric Systems Research ICLASS Science Focus Area.

A. Hunzinger, et al., “An extended radar relative calibration adjustment (eRCA) technique for higher-frequency radars and range-height indicator (RHI) scans.” Atmospheric Measurement Techniques 13, 3147-3166 (2020). [DOI: 10.5194/amt-13-3147-2020]

Rainfall Characteristics over the Central U.S. Influence Agriculture and Flood Risk

gray storm cloud above a field

Variation in rainfall intensity and frequency between mesoscale convective systems and other warm-season rainfall in the central United States could influence the region’s future agricultural productivity and flooding risk.

Photo by Bernard Schmidt on Unsplash

Mesoscale convective systems produce more intense rainfall than warm-season rain in this region

August 31, 2020
August 31, 2020
Highlight

The Science

Mesoscale convective systems (MCS), a form of organized deep convection that produces severe weather conditions, significantly affect the central United States. As MCSs account for half of the warm-season rainfall in this region, understanding different characteristics of MCS and non-MCS rainfall, as well as their historical trends, is fundamental to exploring how these storms may change in the future. Scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory studied the rainfall characteristics of MCS and non-MCS storms east of the Rocky Mountains. They found that MCS rainfall is approximately seven times more intense than non-MCS rainfall, but it happens less frequently in time and space. They also identified opposite trends in MCS and non-MCS rainfall in the past two decades, with increased frequency and duration of MCS storms and reduced non-MCS rainfall area.

The Impact

Precipitation characteristics are intrinsically connected with the local hydrologic responses and agricultural productivity. Frequent light-to-moderate rainfall can benefit vegetation while infrequent, heavy rainfall may produce floods. The large disparities in intensity and frequency identified between MCS and non-MCS storms suggest differences in their impacts on surface hydrology and land-atmosphere interactions. Increases in heavy rainfall associated with MCSs could increase flood risk in the central U.S. and impact drought prone ecosystems in the future.

Summary

In this study, researchers applied an algorithm to hourly rainfall and radar data to detect and track MCSs. They compared rainfall intensity, area, and frequency of MCS and non-MCS storms. The team found the intensity of MCS rainfall is about seven times greater than non-MCS storms; however, it occurs less often in time and space. They also investigated the historical trends in the characteristics of MCS and non-MCS rainfall between 1997 and 2018. MCS rainfall increased during this time period due to an increase in frequency and a longer duration per MCS storm. In contrast, non-MCS rainfall decreased mainly due to a reduction in rainfall area. Altogether, the total wet days have decreased by about 12 percent in the Northern Great Plains, while dry intervals between events have become longer and more variable. The differences in rainfall characteristics have important implications for local responses of ecohydrological systems, which would be the focus of this team’s future work.

PNNL Contact

L. Ruby Leung, Pacific Northwest National Laboratory, Ruby.Leung@pnnl.gov

Funding

The U.S. Department of Energy Office of Science, Biological and Environmental Research supported this research as part of the Regional and Global Climate Modeling program area.

10.1029/2019GL086783

H. Hu, L.R. Leung, and Z. Feng, “Observed WarmSeason Characteristics of MCS and NonMCS rainfall and Their Recent Changes in the Central United States.” Geophysical Research Letters 47, e2019GL086783 (2020). [DOI: 10.1029/2019GL086783].

The Gathering Storm: Clouds Determine Drought or Drowning

satellite view of earth from space

Scientists are looking at how warming temperatures may affect global circulation and weather patterns that originate in the Intertropical Convergence Zone around Earth’s equatorial regions. A detailed modeling study shows changes to cloud systems lead to larger and drier dry regions and narrower and wetter wet regions.

Atmospheric processes near the equator drive changes in global circulation patterns that shrink wet regions and expand dry regions

August 28, 2020
August 28, 2020
Highlight

The Science

Earth’s trade winds meet in a trough of low pressure near the equator in an area that scientists refer to as the Intertropical Convergence Zone (ITCZ). Heat and moisture generated by deep, or convective, cloud systems in the ITCZ drive atmospheric circulation around the globe. Scientists from University of Maryland, NASA Goddard Space Flight Center, and the U.S. Department of Energy’s Pacific Northwest National Laboratory conducted a detailed modeling study to investigate how interactions between large scale atmospheric circulation, clouds, and Earth’s incoming and outgoing energy modulate the ITCZ and circulation on global scales. Results showed that radiation-cloud-convection-circulation interactions, or RC3I, increased the heating contrasts between wet and dry regions, leading to larger and drier dry regions and narrower and wetter wet regions, and that subsequent adjustments by the atmosphere influence general circulation patterns.

The Impact

RC3I plays fundamental roles in the Earth’s response to changes in the global energy balance, but the mechanisms for the response are not well understood. This modeling study isolated the impacts of RC3I on global circulation patterns and showed that RC3I increases the heating contrasts between wet and dry regions, and that atmospheric adjustments to this change influence general circulation patterns. The results lay a foundation for evaluating how warming temperatures may influence global circulation through RC3I, with implications for projecting future changes in the ITCZ and precipitation worldwide.

Summary

Researchers explored how radiation-cloud-convection-circulation interactions (RC3I) affect the ITCZ and circulation at the global scale. The team used a global climate model coupled to embedded cloud resolving sub-models (i.e., super-parameterization), to conduct 10-year simulation experiments with and without cloud-radiation feedback and using observed sea surface temperature. Cloud-radiation feedback induced anomalies of the atmospheric energy balances due to changes in atmospheric heating (shortwave, longwave radiation, and latent heating), adiabatic processes, and heat transport. Several key results surfaced.

First, RC3I leads to warmer and moister tropics, with deeper convection, intensified precipitation in the ITCZ core and a narrowing of the ITCZ ascent region. These changes were amplified by increased heating of the tropical troposphere due to increased trapping of longwave radiation by enhanced deep clouds and water vapor, as well as increased shortwave absorption by high clouds. Second, models showed that with RC3I, the subtropical dry zone becomes drier and expanded, leading to increased longwave cooling above clouds and increased warming below clouds. Third, researchers found that RC3I leads to an increased tropics-to-pole tropospheric thermal gradient along with a poleward shift of storm tracks, increased poleward and upward heat transport, and enhanced longwave cooling to space in the extratropics. Finally, enhanced convective aggregation in the ITCZ, i.e., clustering of deep convective into smaller areas, coupled to expanding drier and less cloudy areas in marginal convective zones under RC3I led to a new balance between increase latent heating and more efficient cooling by longwave radiation to space.

The RC3I induced changes are consistent with, and provide a deeper understanding of, the roles of cloud radiation feedback in the “Deep Tropical Squeeze” (DTS) reported in numerous observational and climate model projection studies. This phenomenon refers to a sharpening of the ITCZ, with an expansion of the subsiding branch of the Hadley circulation and a poleward shift of storm tracks under greenhouse warming.

PNNL Contact

L. Ruby Leung, Pacific Northwest National Laboratory, Ruby.Leung@pnnl.gov  

Funding

This work was supported jointly by the NASA Precipitation Measuring Mission (PMM) Grant NNX16AE45G to the University of Maryland, and by the Department of Energy, Office of Science, Biological and Environmental Research through the Regional and Global Model Analysis program area. The Pacific Northwest National Laboratory is operated for the Department of Energy, by Battelle Memorial Institute under contract DE-AC05-76RL01830. Partial support was also provided by the NASA Modeling, Analysis, and Prediction (MAP).

W.K.M. Lau, K.-M. Kim, J.-D. Chern, W.K. Tao, and L.R. Leung, “Structural Changes and Variability of the ITCZ Induced by Radiation-Cloud-Convection-Circulation Interactions: Inferences from the Goddard Multi-scale Modeling Framework (GMMF) Experiments.” Climate Dynamics 46, 211-229 (2020). [DOI: 10.1007/s00382-019-05000-y]

The Intrinsic Skew Towards Cooling of Earth’s Climate

a blue-green iceberg on a dark beach

Analysis of nonlinear response reveals an intrinsic skew of Earth’s climate system towards cooling.

Photo by Pavel Brodsky on Unsplash

A new study uncovers a recurrent global cooling pattern that’s the result of ice-related feedbacks

August 28, 2020
August 28, 2020
Highlight

The Science

The climate-sensitivity and feedback community has been overwhelmingly focused on the linear aspect of the climate change response. Meanwhile, the nonlinear aspect of climate change has rarely been examined. In a study led by scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory, researchers discovered an intriguing nonlinear skew toward the cooling climate system in a large suite of Green’s function experiments. In these numerical experiments using a climate model, the climate system is perturbed by an ocean heat flux anomaly prescribed one location at a time for all oceans of the globe. The nonlinear response is found to be characteristic of a polar-amplified global cooling, with maximum cooling along the ice margins in both the Northern and Southern Hemispheres. This nonlinearity can be attributed to sea-ice processes removing sea ice in the climate model by either allowing the sea water to continue to cool without freezing or by melting all the sea ice with a large amount of CO2 that warms the planet significantly and eliminates the nonlinear response.

The Impact

Nonlinear global cooling occurring in all 99 forcing cases with ocean heat flux prescribed at different locations points to a behavior intrinsic to the climate system. This result can have important bearing on geoengineering, in that a lesser energy perturbation is needed to cool the climate than to warm it. This work also points to a new frontier for understanding the coupled climate system from the perspective of a nonlinear dynamical system.

Summary

Researchers examined the surface temperature (TS) response to pairs of oceanic forcings of equal amplitude but the opposite sign in a large set of local q-flux perturbation experiments with Community Atmosphere Model 5 coupled to a slab ocean. They found strong asymmetry in TS responses to the heating and cooling forcings, indicating a strong nonlinearity intrinsic to the climate system being examined. Regardless of where the symmetric forcing is placed, the cooling response to the negative forcing always exceeds the warming response to the positive forcing. In our current climate, this implies an intrinsic global inclination towards cooling. Thus, the ongoing global warming induced by increasing greenhouse gases may have already been alleviated by the asymmetric component of the response.

The common asymmetry in TS response peaks in high latitudes, especially along sea ice edges, with notable seasonal dependence. Decomposition into different radiative feedbacks through a radiative kernel indicates that the asymmetry in the cooling response is enhanced largely through lapse rate and albedo feedbacks. Further process interference experiments disabling the seasonal cycle and/or sea ice reveal that the asymmetry originates ultimately from the presence of the sea ice component and is further amplified by the seasonal cycle. The fact that a pair of opposite tropical q-flux forcings can excite very similar asymmetric response as a pair placed at 55°S strongly suggests the asymmetric response is a manifestation of an internal mode of the climate model system.

PNNL Contacts

L. Ruby Leung, Pacific Northwest National Laboratory, Ruby.Leung@pnnl.gov 

Jian Lu, Pacific Northwest National Laboratory, Jian.Lu@pnnl.gov

Funding

The U.S. Department of Energy’s Office of Science, Biological and Environmental Research, supported this study as part of the Regional and Global Climate Modeling program. F.L. was supported by the China Scholarship Council and National Science Foundation of China (41906002 and 91858210). Y.H. was supported by the Discovery Program of the Sciences and Engineering Council of Canada (RGPIN-2019-04511).

F. Liu, J. Lu, Y. Huang, L.R. Leung, B.E. Harrop, and Y. Luo, “Sensitivity of Surface Temperature to Oceanic Forcing via q-Flux Green’s Function Experiments. Part III: Asymmetric Response to Warming and Cooling.” Journal of Climate 33, 1283-1297 (2020). [DOI: 10.1175/JCLI-D-19-0131.1]

Improving Assessments of Reduced-Precision Calculations

black computer screen with vertical lines of green numbers

Identifying and reducing unnecessary precisions in weather and climate simulations can lead to substantial savings in computing resources, subsequently enabling otherwise unaffordable simulations and capabilities.

Photo by Markus Spiske on Unsplash

A study introduces a correctness-assessment method that inexpensively ensures the quality of climate simulations

August 28, 2020
August 28, 2020
Highlight

The Science

Complex and computationally expensive climate simulation systems (computer codes) such as the Energy Exascale Earth System Model can benefit from reducing the precision of floating-point calculations. Ideally, that improves computational performance without reducing the fidelity of a model―though these codes might need to be adjusted at various places to accommodate the precision change. A recent paper introduces a new way to assess reduced-precision calculations that is objective, easy to implement, and computationally efficient. It can quickly identify problematic code pieces and accurately evaluates a computed solution by using a simple, quantitative error metric that is based on time-step convergence.

The Impact

Identifying and reducing unnecessary precisions in weather and climate simulations can lead to substantial savings in computing resources and can subsequently enable otherwise unaffordable simulations and capabilities. The new correctness-assessment method helps to ensure the quality of the simulations despite the reduction in cost.

Summary

Computers use strings of zeros and ones to represent numbers. The length of such strings determines the precision of a calculation. It has been a common practice for numerical weather and climate models to use 64bit strings. However, researchers have started evaluating the feasibility of using shorter strings to save computing resources. A key question is whether the resulting lower precision degrades the quality of the model results.

A study by researchers at Pacific Northwest National Laboratory and Lawrence Livermore National Laboratory demonstrates an objective test method for assessing the correctness of reducedprecision calculations. The method pivots on comparing the precision error with the numerical error caused by finite time resolution.

The test method can detect the dominant role of precision error using a small amount of model output data from a set of five-day simulations. That makes the method much more computationally efficient compared to the traditional way of assessing climate model results, which uses a large number of statistics from multiple years of simulations.

The researchers tested the effectiveness of the new method by using the atmosphere component of the Energy Exascale Earth System Model. They say the same method will be applicable to other models that solve time-evolution equations of an underlying physical system.

PNNL Contact

Hui Wan, Pacific Northwest National Laboratory, Hui.Wan@pnnl.gov

Funding

This work was supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research and Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program. Computing resources were provided by National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility operated under Contract DEAC0205CH11231.

S. Zhang, H. Wan, P. J. Rasch, B. Singh, V. E. Larson, and C. S. Woodward, “An Objective and Efficient Method for Assessing the Impact of Reduced‐Precision Calculations on Solution Correctness.” Journal of Advances in Modeling Earth Systems 11, 3131–3147 (2019). [DOI: 10.1029/2019MS001817]

Atmospheric Rivers Trigger Heavy Snowmelt in the Western United States

view through a car windshield of a rainy drive on a dirt mountain road

In the western United States, mountain precipitation usually correlates with snowpack accumulation, but snowmelt during precipitation can happen at higher elevations, particularly during atmospheric river events.

Researchers quantified the impacts of precipitation-snowpack interactions on runoff in mountainous areas

August 28, 2020
August 28, 2020
Highlight

The Science

In mountainous regions, both precipitation and snowpack control runoff patterns that are integral to water supply and flood risk. Previous studies examined the roles of precipitation and snowpack individually—but not together. Scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory found that interactions between snowpack and precipitation were critical to runoff in the high-elevation mountains of the western United States. The team also found that precipitation induced by atmospheric rivers (ARs)—long, narrow bands of intense atmospheric moisture transport across the subtropics—caused more snowmelt than non-AR precipitation. ARs that made landfall were responsible for up to 10‒20% of significant snowmelt events in the region, though they accounted for only 2% of the precipitation events involving snowpack (melt or accumulation).

The Impact

By quantifying the contribution of snowpack to runoff and extreme flooding in mountainous regions in the western United States, researchers provided a unified view of the interactions between snowpack and precipitation. Their analysis revealed regions within the western United States where current water resources management practice can benefit from incorporating snowpack information and underscored the unique footprint of ARs in snowpack and snowmelt. Researchers can apply the framework developed in this study to other mountainous regions where snowpack affects land-surface hydrologic processes.

Summary

Precipitation directly contributes to runoff by pushing water through soil, and it can also modulate runoff through its impacts on snow accumulation or melt. To quantify the interaction between snowpack and precipitation across the western United States, researchers used a decades-long, high-resolution regional hydroclimate simulation at 6-kilometer grid spacing. The team compared the magnitude of snowpack and precipitation, which are two components of the surface water budgets, in all precipitation events over existing snowpack. Researchers then classified the precipitation events into four categories based on the roles of snowpack in runoff: light snowmelt, heavy snowmelt, light snowpack accumulation, and heavy snowpack accumulation. This framework showed that precipitation in mountainous regions was mostly correlated with snowpack accumulation. Snowmelt during precipitation was rare and limited to high-elevation areas, but such events were responsible for a considerable amount of runoff and were critical to flooding. Researchers also found that ARs drove significant snowmelt along with heavy precipitation, creating a hydrologic condition more conducive to flooding.

The findings highlight the importance of snowpack response to AR-induced precipitation in high-elevation areas. Given that current water management practice does not sufficiently consider snowpack dynamics, AR-induced snowmelt events pose greater challenges to infrastructure safety. Because mountain snowpack is projected to change under warming, the roles of snowpack in land-surface hydrologic processes warrant further investigation.

PNNL Contacts

L. Ruby Leung, Pacific Northwest National Laboratory, Ruby.Leung@pnnl.gov

Xiaodong Chen, Pacific Northwest National Laboratory, xiaodong.chen@pnnl.gov

Funding

This study was supported by the U.S. Department of Energy (DOE) Office of Science Biological and Environmental Research as part of the Regional and Global Model Analysis and MultiSector Dynamics programs. The regional climate simulation used in this study was supported by the Strategic Environmental Research and Development Program (SERDP) under Contract RC2546 by the U.S. Department of Defense. This research used computational resources from PNNL Institutional Computing (PIC) and the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science user facility.

X. Chen, Z. Duan, L.R. Leung, and M. Wigmosta, “A Framework to Delineate Precipitation‐Runoff Regimes: Precipitation Versus Snowpack in the Western United States.” Geophysical Research Letters 46, 13044‒13053 (2019). [DOI: 10.1029/2019GL085184]

Detecting Foresight in the Water Release Decisions of Dams Throughout the U.S.

a tall concrete wall holding back a reservoir of water

Predicting the flow of water into reservoirs helps water managers mitigate flooding and drought by using dams, such as Arizona’s Glen Canyon Dam.

Study infers that 80% of large U.S. dams respond to inflow forecasts by releasing water

August 28, 2020
August 28, 2020
Highlight

The Science

Predicting the volume of water that will arrive in a reservoir, known as inflow forecasting, helps water storage operations make water available for agriculture, municipalities, industry, mining activities, and energy throughout dry seasons and during severe drought. However, the science community lacks a systematic understanding of concepts, such as typical forecast lead times adopted and times of year when forecasting is deemed most important. Researchers at the U.S. Department of Energy’s Pacific Northwest National Laboratory are addressing this knowledge gap by studying the nuances in how humans regulate river flows and water availability at hundreds of dams across the United States. This research develops a new, data-driven approach to determine how forecasts inform water release decisions. The study finds inflow forecasts with a lead time greater than one week influence decision making at up to eighty percent of large dams in the United States.

The Impact

This research provides the first set of national-scale estimates of the contribution inflow forecasts make on the seasonally varying release and storage operations of a large sample of dams. These new data will improve estimation of climate impacts on water availability, strengthening the ability to evaluate effects of drought on water-dependent sectors, including agricultural regions and power grids.

Summary

Representing the operation of water infrastructure is critical for understanding the interactions and coevolution of water, energy, and land systems, but data describing how and when forecasts are applied in practice remain undeveloped. This lack of knowledge may prevent hydrological modelers from developing accurate reservoir release schemes for large-scale, distributed hydrology models used to assess drought and seasonal water vulnerabilities of large regions of the United States. The team collected records of inflow, storage, and release for 316 dams across the United States and developed a nuanced reservoir release scheme capable of representing the operator response to prevailing flood or drought conditions. The team then developed horizon, a new tool to parse these records for evidence of foresight in decision making. They found that forecasts influence decision-making across most large dams; this information could be used to better manage water infrastructure management by improving simulations of reservoirs facing drought. This work advances state-of-the-art, large-scale hydrological modeling away from generalized water storage operations to incorporate data-driven release rules tailored to individual dams.

PNNL Contact

Jennie Rice, Pacific Northwest National Laboratory, Jennie.Rice@pnnl.gov

Funding

This research was supported by the Office of Science of the U.S. Department of Energy through the Integrated Multisector, Multiscale Modeling Scientific Focus Area.

S. Turner, W. Xu, and N. Voisin, “Inferred inflow forecast horizons guiding reservoir release decisions across the United States.” Hydrology and Earth System Sciences 24, 1275–1291 (2020). [DOI: 10.5194/hess-24-1275-2020].

A Century of Data Is Not Enough to Establish Reliable Drought Thresholds

cracked soil with water in the background

Statistical analyses of hydrologic measurements establish a threshold for declaring drought in a given region.

Image courtesy of redcharlie on Unsplash

Historical records are commonly one-tenth the length needed to reliably project long-term hydrologic drought conditions

August 28, 2020
August 28, 2020
Highlight

The Science                                        

To project long-term future hydrologic drought conditions, drought researchers customarily use statistics collected over a historical reference period of 100 years or less to establish a threshold for declaring when a region is—or is not—experiencing a drought. This study demonstrates the statistical uncertainty in estimating a threshold from these small datasets is sufficient to compromise many types of drought analysis. The study develops formulae for calculating the statistical uncertainties caused by limited record lengths and for estimating the record length needed to achieve a specified level of threshold accuracy. Results show that hydrologic datasets of 100 years are approximately one-tenth the length needed to achieve the level of drought threshold reliability required for many applications.

The Impact

Researchers and practitioners in the hydrology community are generally aware of the effects of limited datasets on estimating thresholds, but issues surrounding threshold uncertainty still are not well-quantified or acknowledged in the scientific literature. This study raises the hydrology community’s awareness of this issue by assessing threshold uncertainties, identifying record lengths required to achieve high reliability, and summarizing options for augmenting the historical record to increase threshold reliability.

Summary

To evaluate long-term changes in future hydrologic drought conditions, drought researchers customarily use hydrologic data collected over a historical reference period to establish a threshold for declaring when a region is experiencing a drought. The length of the historical record is often no longer than 50 to 100 years. This study shows the statistical uncertainty in threshold estimates resulting from these limited datasets is sufficient to compromise many types of drought analysis. The researchers provide formulae for calculating the statistical uncertainties caused by limited record lengths and for estimating the record length needed to achieve a specified level of accuracy in a given drought analysis. Results show that datasets of 100 years (or less) are approximately one-tenth (or less) the length needed to achieve the level of reliability required for many drought applications. Despite the widespread use of thresholds in drought research, this threshold uncertainty issue has not previously been acknowledged in the hydrology literature. Finally, the study summarizes options for augmenting the historical record to improve threshold accuracy.

PNNL Contact

Mohamad Hejazi, Pacific Northwest National Laboratory, Mohamad.Hejazi@pnnl.gov

Funding

This research was supported by the U.S. Department of Energy, Office of Science, as part of research in MultiSector Dynamics, Earth and Environmental System Modeling Program.

R. Link, et al., “100 Years of Data is not Enough to Establish Reliable Drought Thresholds.” Journal of Hydrology 7, 100052 (2020). [DOI: 10.1016/j.hydroa.2020.100052]