RAL

A Beginners Introduction to the Analog Ensemble Technique

A Beginners Introduction to the Analog Ensemble Technique

LAURA CLEMENTE-HARDING | THE PENNSYLVANIA STATE UNIVERSITY, UNIVERSITY PARK, PA, AND THE ENGINEER RESEARCH AND DEVELOPMENT CENTER, ALEXANDRIA, VA

Warmer temperatures cause decline in key runoff measure

BOULDER, Colo. — Since the mid-1980s, the percentage of precipitation that becomes streamflow in the Upper Rio Grande watershed has fallen more steeply than at any point in at least 445 years, according to a new study led by the National Center for Atmospheric Research (NCAR).While this decline was driven in part by the transition from an unusually wet period to an unusually dry period, rising temperatures deepened the trend, the researchers said.The study paints a detailed picture of how temperature has affected the runoff ratio — the amount of snow and rain that actually makes it into the river — over time, and the findings could help improve water supply forecasts for the Rio Grande, which is a source of water for an estimated 5 million people.The study results also suggest that runoff ratios in the Upper Rio Grande and other neighboring snow-fed watersheds, such as the Colorado River Basin, could decline further as the climate continues to warm.Sandhill cranes in the San Luis Valley of Colorado. The mountains ringing the valley form the headwaters of the Rio Grande River, which flows south into New Mexico and along the border between Texas and Mexico. (Photo courtesy of the National Park Service.)"The most important variable for predicting streamflow is how much it has rained or snowed," said NCAR scientist Flavio Lehner, lead author of the study. "But when we looked back hundreds of years, we found that temperature has also had an important influence  — which is not currently factored into water supply forecasts. We believe that incorporating temperature in future forecasts will increase their accuracy, not only in general but also in the face of climate change."The study, published in the journal Geophysical Research Letters, was funded by the Bureau of Reclamation, Army Corps of Engineers, National Oceanic and Atmospheric Administration (NOAA), and National Science Foundation, which is NCAR's sponsor.Co-authors of the paper are Eugene Wahl, of NOAA; Andrew Wood, of NCAR; and Douglas Blatchford and Dagmar Llewellyn, both of the Bureau of Reclamation.Over-predicting water supplyBorn in the Rocky Mountains of southern Colorado, the Rio Grande cuts south across New Mexico before hooking east and forming the border between Texas and Mexico. Snow piles up on the peaks surrounding the headwaters throughout the winter, and in spring the snowpack begins to melt and feed the river.The resulting streamflow is used both by farmers and cities, including Albuquerque, New Mexico, and El Paso, Texas, and water users depend on the annual water supply forecasts to determine who gets how much of the river. The forecast is also used to determine whether additional water needs to be imported from the San Juan River, on the other side of the Continental Divide, or pumped from groundwater.Current operational streamflow forecasts depend on estimates of the amount of snow and rain that have fallen in the basin, and they assume that a particular amount of precipitation and snowpack will always yield a particular amount of streamflow.In recent years, those forecasts have tended to over-predict how much water will be available, leading to over-allocation of the river. In an effort to understand this changing dynamic, Lehner and his colleagues investigated how the relationship between precipitation and streamflow, known as the runoff ratio, has evolved over time.Precipitation vs. streamflow: Tree rings tell a new storyThe scientists used tree ring-derived streamflow data from outside of the Upper Rio Grande basin to reconstruct estimates of precipitation within the watershed stretching back to 1571. Then they combined this information with a separate streamflow reconstruction within the basin for the same period. Because these two reconstructions were independent, it allowed the research team to also estimate runoff ratio for each year: the higher the ratio, the greater the share of precipitation that was actually converted into streamflow."For the first time, we were able to take these two quantities and use them to reconstruct runoff ratios over the past 445 years," Wahl said.They found that the runoff ratio varies significantly from year to year and even decade to decade. The biggest factor associated with this variation was precipitation. When it snows less over the mountains in the headwaters of the Rio Grande, not only is less water available to become streamflow, but the runoff ratio also decreases. In other words, a smaller percentage of the snowpack becomes streamflow during drier years.But the scientists also found that another factor affected the runoff ratio: temperature. Over the last few centuries, the runoff ratio was reduced when temperatures were warmer. And the influence of temperature strengthened during drier years: When the snowpack was shallow, warm temperatures reduced the runoff ratio more than when the snowpack was deep, further exacerbating drought conditions. The low runoff ratios seen in dry years were two and a half to three times more likely when temperatures were also warmer."The effect of temperature on runoff ratio is relatively small compared to precipitation," Lehner said. "But because its greatest impact is when conditions are dry, a warmer year can make an already bad situation much worse."A number of factors may explain the influence of temperature on runoff ratio. When it's warmer, plants take up more water from the soil and more water can evaporate directly into the air. Additionally, warmer temperatures can lead snow to melt earlier in the season, when the days are shorter and the angle of the sun is lower. This causes the snow to melt more slowly, allowing the meltwater to linger in the soil and giving plants added opportunity to use it.The extensive reconstruction of historical runoff ratio in the Upper Rio Grande also revealed that the decline in runoff ratio over the last three decades is unprecedented in the historical record. The 1980s were an unusually wet period for the Upper Rio Grande, while the 2000s and 2010s have been unusually dry. Pair that with an increase in temperatures over the same period, and the decline in runoff ratio between 1986 and 2015 was unlike any other stretch of that length in the last 445 years.The graph shows changes to runoff ratio in the Upper Rio Grande over time. (Image courtesy Flavio Lehner, NCAR.) Upgrading the old approachesThis new understanding of how temperature influences runoff ratio could help improve water supply forecasts, which do not currently consider whether the upcoming months are expected to be hotter or cooler than average. The authors are now assessing the value of incorporating seasonal temperature forecasts into water supply forecasts to account for these temperature influences. The study complements a multi-year NCAR project funded by the Bureau of Reclamation and the Army Corps of Engineers that is evaluating prospects for enhancing seasonal streamflow forecasts for reservoir management.“Forecast users and stakeholders are increasingly raising questions about the reliability of forecasting techniques if climate is changing our hydrology," said Wood, who led the effort. "This study helps us think about ways to upgrade one of our oldest approaches — statistical water supply forecasting — to respond to recent trends in temperature. Our current challenge is to find ways to make sure the lessons of this work can benefit operational streamflow forecasts.” Because the existing forecasting models were calibrated on conditions in the late 1980s and 1990s, it's not surprising that they over-predicted streamflow in the drier period since 2000, Lehner said."These statistical models often assume that the climate is stable," Lehner said. "It's an assumption that sometimes works, but statistical forecasting techniques will struggle with any strong changes in hydroclimatology from decade to decade, such as the one we have just experienced."Lehner is a Postdoc Applying Climate Expertise (PACE) fellow, which is part of the Cooperative Programs for the Advancement of Earth System Science (CPAESS). CPAESS is a community program of the University Corporation for Atmospheric Research (UCAR).About the articleTitle: Assessing recent declines in Upper Rio Grande River runoff efficiency from a paleoclimate perspectiveAuthors: Flavio Lehner, Eugene R. Wahl, Andrew W. Wood, Douglas B. Blatchford, and Dagmar LlewellynJournal: Geophysical Research Letters, DOI: 10.1002/2017GL073253Writer:Laura Snider, Senior Science Writer and Public Information Officer

NCAR to develop advanced wind and solar energy forecasting system for Kuwait

BOULDER, Colo. — Expanding its work in renewable energy, the National Center for Atmospheric Research (NCAR) is launching a three-year project to develop specialized forecasts for a major wind and solar energy facility in Kuwait."We're putting our expertise and technology to work around the world," said NCAR Senior Scientist Sue Ellen Haupt, the principal investigator on the project. "This landmark project meets our mission of science in service to society."The $5.1 million project will focus on developing a system to provide detailed forecasts of wind and solar irradiance at Kuwait's planned 2-gigawatt Shagaya renewable energy plant. After NCAR develops the system, the technology will be transferred to the Kuwait Institute for Scientific Research (KISR) for day-to-day operations.Salem Al-Hajraf of KISR and Antonio J. Busalacchi of UCAR shake hands over an agreement to create a renewable energy forecasting system. Behind them are NCAR scientists (left to right): Gerry Wiener, Branko Kosovic, Sue Ellen Haupt, and William Mahoney. (©UCAR. Photo by Carlye Calvin. This image is freely available for media & nonprofit use.)The forecasts will help Kuwait reach its goal of generating 15 percent of its energy from renewable sources by 2030. With the ability to anticipate the amount of electricity that sun and wind will produce hours to days in advance, energy operators will be able to power up or down traditional plants as needed to meet demand."This technology will provide us with important benefits," said Salem Al-Hajraf, manager of KISR's Renewable Energy Program. "We are providing green energy to the grid using abundant sources of energy, which are sun and wind."Reducing renewable energy costsWhen electric utilities integrate power from intermittent sources such as wind or solar into the grid, they temporarily reduce or shut off traditional sources such as oil or natural gas. But if weather conditions fail to come together as expected, the utility may not be able to power up traditional plants in time to meet their customer needs.To help utility managers anticipate renewable wind energy more reliably, NCAR has designed and is constantly improving a wind energy prediction system for Xcel Energy that has saved tens of millions of dollars for the utility’s customers in Colorado and nearby states. The specialized system relies on a suite of tools, including highly detailed observations of atmospheric conditions, advanced computer modeling, and artificial intelligence techniques that enable Xcel Energy to issue high-resolution forecasts for wind farm sites.With funding from the U.S. Department of Energy, NCAR has also led a national team of scientists who have developed a cutting-edge forecasting system with the potential to save the solar energy industry hundreds of millions of dollars in the United States alone through improved forecasts. The new Sun4CastTM system, unveiled last year, greatly improves predictions of clouds and other atmospheric conditions that influence the amount of energy generated by solar arrays.Adapting to new conditionsIn Kuwait, the NCAR team will build on these technologies to develop both wind and solar energy forecasts. The scientists will customize the system to predict dust storms that can blot out sunlight and damage wind turbines. They will also incorporate the influence of nearby mountain ranges and the Persian Gulf on local weather patterns."This is a great opportunity to do research into dust and other particulates, which we haven't previously needed to focus on to this extent for wind and solar energy prediction," Haupt said. "This kind of work will pay multiple dividends for energy forecasting as well as better understanding and predicting of weather in certain desert environments."Haupt and her team will collaborate with researchers at Pennsylvania State University and Solar Consulting Services in Florida, as well as with KISR."This is an exciting international partnership that will both generate significant economic benefits and advance our understanding of the atmosphere," said Antonio J. Busalacchi, president of the University Corporation for Atmospheric Research. "In addition to reducing energy costs for our partners in Kuwait, the knowledge that we gain will help us further improve weather prediction skills here in the United States."The University Corporation for Atmospheric Research is a nonprofit consortium of 110 North American colleges and universities that manages the National Center for Atmospheric Research under sponsorship by the National Science Foundation.KISR leads and partners internationally to develop, deploy, and exploit the best science, technology, knowledge, and innovation for public and private sector clients, for the benefit of Kuwait and others facing similar challenges and opportunities. Sun4Cast is a trademark of the University Corporation for Atmospheric Research.

Drones need aviation forecasts, too

UAS Weather ForumWhat: An opportunity for stakeholders from the UAS community -- including manufacturers, operators, regulators, and researchers -- to come together to discuss weather effects on drones and the support needed to mitigate those impacts.When: 9 a.m. - 12 p.m., Monday, May 8, 2017Where: XPONENTIAL, Kay Bailey Hutchison Convention Center, Dallas Click here for more information.April 13, 2017 | The possible future uses for drones are spectacularly diverse. Unmanned aircraft systems (UAS) could make door-to-door deliveries, search for a lost hiker, survey agricultural crops, inspect infrastructure, or collect scientific data from difficult-to-reach places, among other things. Already Amazon is experimenting with drone delivery of packages, for example, and BNSF Railway is testing the use of drones to inspect hundreds of miles of railroad tracks.Yet the ultimate success of efforts like these may hinge on a good weather forecast. The National Center for Atmospheric Research (NCAR), long a trusted provider of critical weather information to the aviation industry, is beginning to lend its expertise to the UAS community as well.Staff in NCAR's Research Applications Laboratory are already working with NASA to provide low-level turbulence forecasts for NASA's project to create a UAS Traffic Management (UTM) system, which would be similar to the air traffic control system for crewed airplanes. And in May, the NCAR team is hosting a UAS Weather Forum in Dallas. The forum will be co-located with XPONENTIAL, a conference on "all things unmanned" that is organized by the Association of Unmanned Vehicle Systems International."As the aircraft get smaller and smaller, the challenges of providing the needed weather information increase," said NCAR scientist Matthias Steiner, deputy director of RAL's Aviation Applications Program. "These small UAS's are more sensitive to winds, temperature, turbulence, precipitation — essentially the full range of weather — than larger planes flying at higher altitudes."NASA engineers prepare to launch a remotely piloted aircraft during practice runs for an Unmanned Aircraft Systems Traffic Management test. (Image courtesy NASA.)Weather impacts on dronesDrones, at least the small ones allowed under current Federal Aviation Administration rules, fly in the lowest few hundred feet of the atmosphere, where weather can be highly dynamic and less predictable.This layer of the atmosphere is heavily affected by land surface and topography. Consider, for example, wind as it blows through a city. The buildings force the wind to speed through "urban canyons" and swirl into tight eddies behind structures. Uneven heating — the sunny side of the street warming more than the shady side, for example — can create circulating downdrafts and updrafts.Piloting a drone through a built-up area could be tricky without a detailed understanding of the local atmospheric circulation patterns. And even with that information, it's important to understand how different drones will be affected. The tinier and lighter the drone, the more vulnerable it is to the vagaries of the weather, just as a small Cessna is more vulnerable to turbulence than a giant 747. And the type of drone, such as a fixed wing or a quadcopter, matters as well because each has a different ability to respond.The concern is not just crashing on the ground; severe weather conditions could also lead to a collision in the sky. NASA's UTM project is exploring the possibility of managing a high volume of drones by essentially assigning individual UAS's to a lane of airspace. But weather will affect the ability of a drone to stay in its lane. An abrupt updraft, for instance, could force a drone that is supposed to fly at a lower altitude into the higher-altitude lane assigned to another UAS (or a crewed aircraft in mixed airspace), increasing the possibility of a collision between the two.Weather can have less obvious impacts on drone operation as well. Extremely cold weather, headwinds, or turbulence that requires a lot of flight control adjustments could drain the aircraft's battery more quickly, reducing its range and, potentially, its ability to return home. Facilitating a community dialogueThese kinds of weather challenges would likely not surprise a seasoned aviator. But many of the organizations interested in using drones today come to the UAS community from the technology side, not the aviation side, and may lack a full understanding of the impacts that atmospheric conditions can have on flight.This is where NCAR has expertise to offer. For decades, NCAR has been providing the aviation industry with the tools they need to increase flight safety, including wind shear alerts, turbulence forecasts, and information on inflight icing potential.In an effort to stay on top of the latest weather challenges facing the aviation industry, NCAR launched the Friends and Partners in Aviation Weather Forum in 1997. The meeting, now held twice yearly, is an opportunity for stakeholders from the operational, regulatory, and research sectors to come together."We created these meetings as a means of fostering dialogue," Steiner said. "We want to know: 'What are your operational sensitivities? How can we help you?' Now we are emulating these forums with the UAS community. "The UAS Weather Forum at the XSPONENTIAL conference on May 8 is the first effort at starting a similar meeting—and fostering the dialogue needed to advance drone safety, even in the face of challenging weather conditions."We want drone operators to know NCAR is a partner that can help them address their weather impacts," Steiner said.Writer/contact: Laura Snider, Senior Science Writer and Public Information Officer

Congressional briefing on wildland fires

WASHINGTON, D.C. — Scientists and fire experts are making landmark progress in developing new tools to improve the management and prediction of wildland fires, a panel of experts said at a congressional briefing today. The developments offer the potential of better protecting vulnerable residents and property from these extreme events, as well as reducing their costs. The briefing, sponsored by the University Corporation for Atmospheric Research (UCAR), highlighted the development of new observing tools and advanced computer models to better understand wildland fires. "We're at a turning point where new technologies and advances in basic research are enabling us to tackle a major real-world problem," said UCAR President Antonio J. Busalacchi. "Federal and state agencies, firefighters, and scientists are all working together to develop a new generation of tools that will keep firefighters safer, reduce the costs of these massive conflagrations, and better safeguard lives and property."Bureau of Land Management firefighter near Burns, Oregon, in September 2011. (Photo by Dave Toney, BLM Oregon.)UCAR is a consortium of 110 universities that manages the National Center for Atmospheric Research (NCAR) on behalf of the National Science Foundation. NCAR's wildland fire research includes working with Colorado on an advanced prediction system.Toll of wildland fires The costs of forest, grass, and other types of wildland fires are increasing dramatically. In 2016 alone, more than 67,000 wildfires consumed 5.5 million acres across the nation. The U.S. Forest Service spends more than $2.5 billion annually on fire management, an increase of more than 60 percent over the last decade. The total losses can run many times higher: Last year's Chimney Tops 2 fire in Gatlinburg, Tennessee, left 14 people dead and destroyed more than 2,400 structures at a cost of $500 million. "The money spent by the federal government on suppressing the fires is only a fraction of the overall costs, such as the destruction of houses and other property," said Michael Gollner, assistant professor at the University of Maryland's Department of Fire Protection Engineering. "There are more large-scale fires than there used to be, and those are the most dangerous blazes that are particularly expensive and destructive." Donald Falk, assistant professor of the University of Arizona's School of Natural Resources and the Environment, warned that decades of fire suppression coupled with drier and warmer temperatures in some regions will lead to longer fire seasons and more major fires. "The problem is not going away," he said. "It's going to get bigger, and we're going to have to live with it without breaking the bank." Wildland fires are extremely difficult to predict because they are influenced by local topography and vegetation, as well as by atmospheric conditions that, in turn, are affected by a blaze's heat and smoke. To better anticipate fire risk as well as predict a fire once it has started, scientists are harnessing new technologies. These include specialized satellite instruments and unmanned aerial vehicles to observe the blazes, as well as specialized computer models that incorporate weather-fire interactions, the density and condition of vegetation, landscape features such as elevation and topography, and the physics of fires. The researchers are working with federal and state agencies, emergency managers, and firefighters to adapt the new capabilities for real-time decision support. "Practitioners and scientists are bringing their expertise and knowledge to the table in order to create new evolutions of technology that will result in safer and more effective firefighting, enhance how we predict events and their potential impacts, and better plan for ways to prevent those wildfires we consider harmful," said Todd Richardson, state fire management officer of the Bureau of Land Management's Colorado office. "Having better guidance prior to planning your fire operations can provide critical information to the tactical operations and fire management," said William Mahoney, interim director of NCAR's Research Applications Laboratory. "Taking advantage of these important data sources and integrating these research areas provides tremendous opportunities to advance wildland fire management." The event is the latest in a series of UCAR congressional briefings that draw on expertise from the university consortium and public-private partnerships to provide insights into critical topics in the Earth system sciences. Past briefings have focused on predicting space weather, aviation weather safety, the state of the Arctic, hurricane prediction, potential impacts of El Niño, and new advances in water forecasting.

From GOES-16 to the world

March 6, 2017 | As atmospheric scientists around the world look forward to seeing extraordinarily detailed images from the new GOES-16 satellite, the University Corporation for Atmospheric Research (UCAR) and National Center for Atmospheric Research (NCAR) are preparing for central roles in disseminating the satellite's data.The first of a series of next-generation National Oceanic and Atmospheric Administration (NOAA) satellites, GOES-16 was launched in November and is expected to become fully operational late this year. It will immediately improve weather forecasts with its rapid, high-resolution views of hurricanes, thunderstorms, and other severe events, as well as provide a breakthrough lightning mapping system and more detailed monitoring of geomagnetic disturbances caused by the Sun."Scientists are rightfully excited because this is a revolutionary system," said Mohan Ramamurthy, director of UCAR's Unidata Program. "It's going to truly transform weather forecasting and research."GOES-16 captured this view of the mid-Atlantic and New England states on Jan. 15. (Image by National Oceanic and Atmospheric Administration.) Data from GOES-16 will be transmitted to a new downlink facility at the NCAR Mesa Lab. Unidata, which provides data, software tools, and support to enhance Earth system science education and research, will then make that data widely available.  As the only open-access and free source of GOES data in real time, Unidata's services have become indispensable to scientists as well as to operational forecasters in regions that lack their own downlink facilities, such as parts of Latin America.In addition, NCAR's Earth Observing Laboratory (EOL) will produce customized data products from GOES-16 to support field campaigns. EOL currently uses observations from GOES satellites and other sources to help scientists make critical decisions as they're taking measurements in the field.More data than everFor years, NCAR and UCAR have provided real-time data from a series of NOAA satellites known as GOES (Geostationary Operational Environmental Satellite). These satellites, which provide views of the Americas and adjoining ocean regions, are part of a global network of satellites whose observations are shared by forecasters and researchers worldwide.But the advantages of GOES-16 also create new challenges. The satellite has three times as many spectral channels as its predecessors, each with four times more resolution. It can scan the entire Western Hemisphere every 15 minutes and simultaneously generate images of severe weather every 30-60 seconds. All this data will amount to about 1 terabyte per day, more than 100 times the amount of data produced by an existing GOES satellite. And even more data can be expected when NOAA launches additional advanced GOES satellites in coming years.Thanks to a NOAA grant, UCAR and NCAR have installed a direct broadcast receiving station to receive the data, as well as the computers and electronics needed to process and transmit it. In addition to Unidata and EOL, NCAR's Research Applications Laboratory helps operate the downlink facilities for existing GOES satellites and relies on satellite data for the development of specialized forecasting products.The volume of information means that Unidata will continue to move toward making data available in the cloud. It will store GOES-16 data for about 10 days and is in discussions with Amazon over long-term storage options.EOL will customize GOES-16 observations for worldwide field projects, which advance understanding of Earth system science, including weather, climate, and air quality. Such projects deploy teams of scientists with aircraft, ships, ground-based instruments, and other tools. They rely on detailed forecasts and real-time updates about evolving atmospheric conditions."The data from GOES 16 will provide invaluable information for flight planning and decision making during field projects," said EOL director Vanda Grubišić. "This will enable scientists to gather additional observations, further advancing our understanding of the atmosphere and related aspects of the Earth system."EOL will also include the GOES data in their field catalog, along with measurements from field campaigns and other observations. This catalog is widely used by scientists when analyzing results from past campaigns or planning new ones.Other scientists say they are looking forward to the new capabilities that GOES-16 offers."The observations collected by the Geostationary Lightning Mapper on GOES-16 have the potential to help advance our understanding of hurricanes and their intensity changes," said Kristen Corboseiero, a professor in the Department of Atmospheric and Environmental Sciences at the University of Albany-SUNY. "Being able to access this data through Unidata will streamline and expedite our research."In Costa Rica, agencies are planning to use the GOES-16 data from Unidata for weather forecasting and research. In addition, the data will help with monitoring water levels for hydropower to avoid possible power cuts during the dry season, as well as for observing volcanic ash that can affect aviation and farming near San Jose."Several institutions will be using the new GOES-16 data in ways that will help safeguard society from potential natural disasters as well as avoiding energy shortages," said Marcial Garbanzo Salas, an atmospheric sciences professor at the Universidad de Costa Rica (University of Costa Rica). "This is extremely important to us, and we're very pleased that Unidata will be making it available."Writer/contact:David Hosansky, Media Relations ManagerFunder:National Oceanic and Atmospheric Administration

Slower snowmelt in a warming world

BOULDER, Colo. — As the world warms, mountain snowpack will not only melt earlier, it will also melt more slowly, according to a new study by scientists at the National Center for Atmospheric Research (NCAR).The counterintuitive finding, published today in the journal Nature Climate Change, could have widespread implications for water supplies, ecosystem health, and flood risk."When snowmelt shifts earlier in the year, the snow is no longer melting under the high sun angles of late spring and early summer," said NCAR postdoctoral researcher Keith Musselman, lead author of the paper. "The Sun just isn't providing enough energy at that time of year to drive high snowmelt rates."Snowpack in the Colorado Rockies as seen from the NSF/NCAR C-130 research aircraft. (©UCAR. Photo by Carlye Calvin. This image is freely available for media & nonprofit use.)The study was funded by the National Science Foundation, NCAR's sponsor.The findings could explain recent research that suggests the average streamflow in watersheds encompassing snowy mountains may decline as the climate warms — even if the total amount of precipitation in the watershed remains unchanged. That's because the snowmelt rate can directly affect streamflow. When snowpack melts more slowly, the resulting water lingers in the soil, giving plants more opportunity to take up the moisture. Water absorbed by plants is water that doesn't make it into the stream, potentially reducing flows.Musselman first became interested in how snowmelt rates might change in the future when he was doing research in the Sierra Nevada. He noticed that shallower, lower-elevation snowpack melted earlier and more slowly than thicker, higher-elevation snowpack. The snow at cooler, higher elevations tended to stick around until early summer — when the Sun was relatively high in the sky and the days had grown longer — so when it finally started to melt, the melt was rapid.Musselman wondered if the same phenomenon would unfold in a future climate, when warmer temperatures are expected to transform higher-elevation snowpack into something that looks much more like today's lower-elevation snowpack. If so, the result would be more snow melting slowly and less snow melting quickly. To investigate the question, Musselman first confirmed what he'd noticed in the Sierra by analyzing a decade's worth of snowpack observations from 979 stations in the United States and Canada. He and his co-authors — NCAR scientists Martyn Clark, Changhai Liu, Kyoko Ikeda, and Roy Rasmussen — then simulated snowpack over the same decade using the NCAR-based Weather Research and Forecasting (WRF) model.Once they determined that the output from WRF tracked with the observations, they used simulations from the model to investigate how snowmelt rates might change in North America around the end of the century if climate change continues unabated."We found a decrease in the total volume of meltwater — which makes sense given that we expect there to be less snow overall in the future," Musselman said. "But even with this decrease, we found an increase in the amount of water produced at low melt rates and, on the flip side, a decrease in the amount of water produced at high melt rates."While the study did not investigate the range of implications that could come from the findings, Musselman said the impacts could be far-reaching. For example, a reduction in high melt rates could mean fewer spring floods, which could lower the risk of infrastructure damage but also negatively affect riparian ecosystems. Changes in the timing and amount of snowmelt runoff could also cause warmer stream temperatures, which would affect trout and other fish species, and the expected decrease in streamflow could cause shortages in urban water supplies."We hope this study motivates scientists from many other disciplines to dig into our research so we can better understand the vast implications of this projected shift in hydrologic patterns," Musselman said.About the articleTitle: Slower snowmelt in a warmer worldAuthors: Keith N. Musselman, Martyn P. Clark, Changhai Liu, Kyoko Ikeda, and Roy RasmussenJournal: Nature Climate Change, DOI: 10.1038/nclimate3225WriterLaura Snider, Senior Science Writer and Public Information OfficerFunderNational Science Foundation

High-resolution regional modeling (no supercomputer needed)

Annual precipitation over Colorado as modeled by the low-resolution, global Community Earth System Model (top) compared to the high-resolution, regional Weather Research and Forecasting model (below). (Images courtesy Ethan Gutmann, NCAR.) February 13, 2017 | In global climate models, the hulking, jagged Rocky Mountains are often reduced to smooth, blurry bumps. It's a practical reality that these models, which depict the entire planet, typically need to be run at a relatively low resolution due to constraints on supercomputing resources. But the result, a virtual morphing of peaks into hills, affects the ability of climate models to accurately project how precipitation in mountainous regions may change in the future — information that is critically important to water managers.To address the problem, hydrologists have typically relied on two methods to "downscale" climate model data to make them more useful. The first, which uses statistical techniques, is fast and doesn't require a supercomputer, but it makes many unrealistic assumptions. The second, which uses a high-resolution weather model like the Weather Research and Forecasting model (WRF), is much more realistic but requires vast amounts of computing resources.Now hydrologists at the National Center for Atmospheric Research (NCAR) are developing an in-between option: The Intermediate Complexity Atmospheric Research Model (ICAR) gives researchers increased accuracy using only a tiny fraction of the computing resources."ICAR is about 80 percent as accurate as WRF in the mountainous areas we studied," said NCAR scientist Ethan Gutmann, who is leading the development of ICAR. "But it only uses 1 percent of the computing resources. I can run it on my laptop."Drier mountains, wetter plainsHow much precipitation falls in the mountains — and when — is vitally important for communities in the American West and elsewhere that rely on snowpack to act as a frozen reservoir of sorts. Water managers in these areas are extremely interested in how a changing climate might affect snowfall and temperature, and therefore snowpack, in these regions.But since global climate models with low resolution are not able to accurately represent the complex topography of mountain ranges, they are unsuited for answering these questions.For example, as air flows into Colorado from the west, the Rocky Mountains force that air to rise, cooling it and causing moisture to condense and fall to the ground as snow or rain. Once these air masses clear the mountains, they are drier than they otherwise would have been, so there is less moisture available to fall across Colorado's eastern plains.Low-resolution climate models are not able to capture this mechanism — the lifting of air over the mountains — and so in Colorado, for example, they often simulate mountains that are drier than they should be and plains that are wetter. For a regional water manger, these small shifts could mean the difference between full reservoirs and water shortages."Climate models are useful for predicting large-scale circulation patterns around the whole globe, not for predicting precipitation in the mountains or in your backyard," Gutmann said.Precipitation in millimeters over Colorado between Oct. 1 and May 1 as simulated by the Weather Research and Forecasting model (WRF), the Intermediate Complexity Atmospheric Research model (ICAR), and the observation-based Parameter-Elevation Regressions on Independent Slopes Model. (Images courtesy Ethan Gutmann.)A modeling middle groundA simple statistical fix for these known problems may include adjusting precipitation data to dry out areas known to be too wet and moisten areas known to be too dry. The problem is that these statistical downscaling adjustments don't capture the physical mechanisms responsible for the errors. This means that any impact of a warming climate on the mechanisms themselves would not be accurately portrayed using a statistical technique.That's why using a model like WRF to dynamically downscale the climate data produces more reliable results — the model is actually solving the complex mathematical equations that describe the dynamics of the atmosphere. But all those incredibly detailed calculations also take an incredible amount of computing.A few years ago, Gutmann began to wonder if there was a middle ground. Could he make a model that would solve the equations for just a small portion of the atmospheric dynamics that are important to hydrologists — in this case, the lifting of air masses over the mountains — but not others that are less relevant?"I was studying statistical downscaling techniques, which are widely used in hydrology, and I thought, 'We should be able to do better than this,'" he said. "'We know what happens when you lift air up over a mountain range, so why don’t we just do that?'"Gutmann wrote the original code for the model that would become ICAR in just a few months, but he spent the next four years refining it, a process that's still ongoing.100 times as fastLast year, Gutmann and his colleagues — Martyn Clark and Roy Rasmussen, also of NCAR; Idar Barstad, of Uni Research Computing in Bergen, Norway; and Jeffrey Arnold, of the U.S. Army Corps of Engineers — published a study comparing simulations of Colorado created by ICAR and WRF against observations.The authors found that ICAR and WRF results were generally in good agreement with the observations, especially in the mountains and during the winter. One of ICAR's weaknesses, however, is in simulating storms that build over the plains in the summertime. Unlike WRF, which actually allows storms to form and build in the model, ICAR estimates the number of storms likely to form, given the atmospheric conditions, a method called parameterization.Even so, ICAR, which is freely available to anyone who wants to use it, is already being run by teams in Norway, Austria, France, Chile, and New Zealand."ICAR is not perfect; it's a simple model," Gutmann said. "But in the mountains, ICAR can get you 80 to 90 percent of the way there at 100 times the speed of WRF. And if you choose to simplify some of the physics in ICAR, you can get it close to 1,000 times faster."About the articleTitle: The Intermediate Complexity Atmospheric Research Model (ICAR)Authors: Ethan Gutmann, Idar Barstad, Martyn Clark, Jeffrey Arnold, and Roy RasmussenJournal: Journal of Hydrometeorology, DOI: 10.1175/JHM-D-15-0155.1Funders:U.S. Army Corps of EngineersU.S. Bureau of ReclamationCollaborators:Uni Research Computing in NorwayU.S. Army Corps of EngineersWriter/contact: Laura Snider, Senior Science Writer

Scientists take to the skies to test cloud seeding

February 7, 2017 | Does cloud seeding successfully increase snowfall? This winter, scientists with the National Center for Atmospheric Research (NCAR) are taking part in a field project in Idaho that will help answer the question.The project, called SNOWIE (Seeded and Natural Orographic Wintertime Clouds — the Idaho Experiment), is taking place from Jan. 7 to March 17 in the Payette Basin region north of Boise. A public-private partnership, SNOWIE is led by scientists at the University of Wyoming and other universities in collaboration with NCAR, with funding from the National Science Foundation (which is NCAR's sponsor) and the Idaho Power Company.The research team is using airborne and ground-based radars, high-resolution snow gauges, and computer modeling to gain insights into what happens after clouds are seeded with silver iodide. Snow from winter storms develops when ice crystals form on dust and other particles known as "ice nuclei." In cloud seeding, silver iodide is used to make artificial nuclei to encourage snowflakes to form.Silver iodide is released during the SNOWIE field project in such a way that it disperses downwind to the east, with its highest concentrations forming a zigzag pattern (shown in red). This allows scientists to fly a research aircraft from west to east through both seeded and unseeded regions and compare differences in ice crystal formation. (Image by Lulin Xue, ©UCAR. This image is freely available for media & nonprofit use.)NCAR scientists are focusing much of their work on observations taken by a University of Wyoming King Air plane that is flying though plumes of silver iodide released by a seeding aircraft. The silver iodide disperses downwind in a zigzag pattern, enabling the King Air to intercept it multiple times. The scientists will compare the formation of ice crystals in regions of clouds that are seeded with those that are not.The results can also be used to improve the NCAR-based Weather Research and Forecasting model (WRF), especially its simulation of cloud microphysics related to cloud seeding.Although scientists think that cloud seeding and other types of weather modification can increase precipitation in certain circumstances, the effects are difficult to quantify."NCAR's role in these weather modification experiments is to provide an unbiased viewpoint," said NCAR scientist Sarah Tessendorf, a principal investigator on SNOWIE. "The project uses observations and computer models to determine what is happening during a cloud seeding program and whether it is effective as a water augmentation tool."For more about the project, see the NSF news release.Writer/contactDavid Hosansky, Manager of Media RelationsFundersNational Science FoundationIdaho Power CompanyPartnersUniversity of WyomingIdaho Power CompanyUniversity of Colorado BoulderUniversity of Illinois at Urbana-ChampaignBoise State UniversityCenter for Severe Weather ResearchWeather Modification, Inc.

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