scientificamerican--2020-12-17--Jupiter and Saturn's Great Conjunction Is the Best in 800 Years--Here's How to See It

This holiday season , the most special thing to see in the sky won ’ t be flying reindeer pulling a sleigh , but rather a rare celestial rendezvous—a cosmic gift of sorts , many lifetimes in the making .
On December 21 , Jupiter and Saturn will meet in a “ great conjunction , ” the closest they could be seen in the sky together for nearly 800 years .
An astronomical conjunction occurs when any two heavenly bodies appear to pass or meet each other as seen from Earth .
To make one “ great , ” though @ @ @ @ @ @ @ system ’ s two largest planets .
The orbits of Jupiter and Saturn align to allow the giant worlds to seemingly convene roughly every 20 years .
However , some great conjunctions are , well , greater than others .
The slightly oval shape of Jupiter and Saturn ’ s orbits , and how inclined each orbit is with respect to the sun ’ s equator , causes the planets ’ closeness in the sky to fluctuate across their cyclic conjunctions .
During some great conjunctions , the two worlds appear to come @ @ @ @ @ @ @ other ; during others , they seem to approach no nearer than arm ’ s length .
( Of course , the planets are never actually close at all ; during their December 21 encounter , they will still be separated by more than 730 million kilometers . )
For the last great conjunction , on May 28 , 2000 , the apparent distance between Jupiter and Saturn in the sky was 68.9 arc minutes , or more than twice the diameter of the full moon .
In contrast , with 2020 ’ @ @ @ @ @ @ @ solstice , the shortest day of the year in the northern hemisphere and the longest in the southern—the gas giants will appear separated by just 6.1 arc minutes .
That is roughly the thickness of a dime held at arm ’ s length .
“ If you have a telescope , you ’ ll be able to see both the rings of Saturn and the Galilean moons of Jupiter close together at the same moment , ” says astronomer Jackie Faherty at the American Museum of Natural History in New York .
In @ @ @ @ @ @ @ this year ’ s astronomical spectacle all the more poetic : The last time Jupiter and Saturn appeared so close was July 16 , 1623 , back when Galileo was still alive , a little more than a decade after he first used a telescope to discover Jupiter ’ s four largest moons that now collectively bear his name .
The odds are low , however , that Galileo or anyone else on Earth managed to witness that great conjunction , which was virtually impossible to see because of its apparent position near @ @ @ @ @ @ @ to appear as close and as visible as the upcoming one occurred on March 4 , 1226 .
“ For perspective , Genghis Khan was still roaming Asia then , ” says astronomer Patrick Hartigan at Rice University in Houston .
You can see the upcoming great conjunction in detail with binoculars and telescopes , “ but the best part about it is we ’ ll be able to watch it with the naked eye , ” Faherty says .
Find a spot where you can watch the sunset with a clear horizon @ @ @ @ @ @ @ trees or buildings .
In the hour or so after nightfall , first Jupiter will appear in the western sky , and then Saturn , both shining dots distinguishable from the stars by the fact they do not twinkle .
“ They will likely be visible even with light pollution—Jupiter is pretty bright , ” Hartigan says .
Although the great conjunction will arrive on December 21 , “ you should be watching Jupiter and Saturn draw close every night until then , ” Faherty recommends .
Otherwise , “ it ’ d @ @ @ @ @ @ @ a show without seeing all the episodes before it to get you caught up on what ’ s going on .
By watching them get closer and closer , you can get a sense how celestial mechanics works in the nighttime sky.
” Great conjunctions have at times drawn scientists to speculate over their possible links with major events .
For instance , Johannes Kepler investigated whether the Star of Bethlehem , which in the nativity story of the Gospel of Matthew guided the Three Wise Men to Christ ’ s birth , @ @ @ @ @ @ @ one did occur in 7 B.C .
“ Often astronomers like to look through stories from ancient times and see if there might be an astronomical phenomenon behind something captivating that people saw , ” Faherty says .
( Hartigan notes that ancient great conjunction was not an especially close or remarkable one . )
After this great conjunction ends , stargazers need not wait centuries for the next close one .
Another rendezvous where the giant planets are separated by just six arc minutes will arrive on March 15 , 2080 , @ @ @ @ @ @ @ who goes out and sees this great conjunction now can potentially see the next close one in 2080 , ” he says .
“ It ’ d be a nice connection between generations , one that makes you think about all those who have seen these conjunctions in the past—and those who will glimpse it in the future.
” All in all , the great conjunction is a reminder of how one can find solace in the constancy of heavenly cycles over the millennia given the inconstancy of modern times , Faherty says @ @ @ @ @ @ @ things that happen over the small given amount of time that a human life exists under , but astronomy encompasses a timeframe so much more than that , ” she notes .
“ In the face of everything that is going on , you can find perspective in astronomical timeframes .
”

This holiday season, the most special thing to see in the sky won’t be flying reindeer pulling a sleigh, but rather a rare celestial rendezvous—a cosmic gift of sorts, many lifetimes in the making. On December 21, Jupiter and Saturn will meet in a “great conjunction,” the closest they could be seen in the sky together for nearly 800 years.
An astronomical conjunction occurs when any two heavenly bodies appear to pass or meet each other as seen from Earth. To make one “great,” though, requires an encounter between our solar system’s two largest planets. The orbits of Jupiter and Saturn align to allow the giant worlds to seemingly convene roughly every 20 years.
However, some great conjunctions are, well, greater than others. The slightly oval shape of Jupiter and Saturn’s orbits, and how inclined each orbit is with respect to the sun’s equator, causes the planets’ closeness in the sky to fluctuate across their cyclic conjunctions. During some great conjunctions, the two worlds appear to come so close as to practically hug each other; during others, they seem to approach no nearer than arm’s length. (Of course, the planets are never actually close at all; during their December 21 encounter, they will still be separated by more than 730 million kilometers.)
For the last great conjunction, on May 28, 2000, the apparent distance between Jupiter and Saturn in the sky was 68.9 arc minutes, or more than twice the diameter of the full moon. In contrast, with 2020’s great conjunction—which coincides with the December solstice, the shortest day of the year in the northern hemisphere and the longest in the southern—the gas giants will appear separated by just 6.1 arc minutes. That is roughly the thickness of a dime held at arm’s length.
“If you have a telescope, you’ll be able to see both the rings of Saturn and the Galilean moons of Jupiter close together at the same moment,” says astronomer Jackie Faherty at the American Museum of Natural History in New York.
In a way, that particular detail makes this year’s astronomical spectacle all the more poetic: The last time Jupiter and Saturn appeared so close was July 16, 1623, back when Galileo was still alive, a little more than a decade after he first used a telescope to discover Jupiter’s four largest moons that now collectively bear his name. The odds are low, however, that Galileo or anyone else on Earth managed to witness that great conjunction, which was virtually impossible to see because of its apparent position near the sun. The last great conjunction to appear as close and as visible as the upcoming one occurred on March 4, 1226. “For perspective, Genghis Khan was still roaming Asia then,” says astronomer Patrick Hartigan at Rice University in Houston.
You can see the upcoming great conjunction in detail with binoculars and telescopes, “but the best part about it is we’ll be able to watch it with the naked eye,” Faherty says. Find a spot where you can watch the sunset with a clear horizon in front of you, free of trees or buildings. In the hour or so after nightfall, first Jupiter will appear in the western sky, and then Saturn, both shining dots distinguishable from the stars by the fact they do not twinkle. “They will likely be visible even with light pollution—Jupiter is pretty bright,” Hartigan says.
Although the great conjunction will arrive on December 21, “you should be watching Jupiter and Saturn draw close every night until then,” Faherty recommends. Otherwise, “it’d be like tuning into the finale of a show without seeing all the episodes before it to get you caught up on what’s going on. By watching them get closer and closer, you can get a sense how celestial mechanics works in the nighttime sky.”
Great conjunctions have at times drawn scientists to speculate over their possible links with major events. For instance, Johannes Kepler investigated whether the Star of Bethlehem, which in the nativity story of the Gospel of Matthew guided the Three Wise Men to Christ’s birth, was a great conjunction, calculating that one did occur in 7 B.C. “Often astronomers like to look through stories from ancient times and see if there might be an astronomical phenomenon behind something captivating that people saw,” Faherty says. (Hartigan notes that ancient great conjunction was not an especially close or remarkable one.)
After this great conjunction ends, stargazers need not wait centuries for the next close one. Another rendezvous where the giant planets are separated by just six arc minutes will arrive on March 15, 2080, Hartigan says. “A young person who goes out and sees this great conjunction now can potentially see the next close one in 2080,” he says. “It’d be a nice connection between generations, one that makes you think about all those who have seen these conjunctions in the past—and those who will glimpse it in the future.”
All in all, the great conjunction is a reminder of how one can find solace in the constancy of heavenly cycles over the millennia given the inconstancy of modern times, Faherty says. “We get caught up in things that happen over the small given amount of time that a human life exists under, but astronomy encompasses a timeframe so much more than that,” she notes. “In the face of everything that is going on, you can find perspective in astronomical timeframes.”


scientificamerican--2020-12-17--Gravitational Waves Probe Exotic Matter inside Neutron Stars

A mystery lurks inside the corpses of dead stars .
Neutron stars , formed when certain types of stars die in supernova explosions , are the densest form of matter in the universe ; black holes are the only thing denser , but they have so fully escaped the bounds of normal physics that they are not matter anymore .
The atoms in neutron stars have been squeezed so tightly by gravity that they have broken down , the protons and electrons inside them smushing together to create neutrons , leaving objects the size of small cities that contain masses @ @ @ @ @ @ @ percent of the mass of a neutron star is pure neutrons , but physicists wonder what happens at the very center , where the density peaks .
Do neutrons break down further into their constituent quarks and gluons ?
Do some of the quarks transform from their normal “ up ” and “ down ” flavors to become weirder and heavier “ strange quarks ” not found in ordinary matter ?
Do the particles form an extreme state of matter called a superfluid that sloshes with no viscosity , never slowing down ?
@ @ @ @ @ @ @ understanding the inner workings of these bizarre bodies by studying the light and gravitational waves that result when two neutron stars slam into each other and become a black hole .
Gravitational waves are folds in spacetime carved out when large masses move around .
Scientists only gained the ability to detect gravitational waves in 2015 , and have spotted just a handful of events involving neutron stars so far ( the others have been collisions of black holes ) .
But studying the properties of these waves—their frequency and how they change @ @ @ @ @ @ @ the objects that created them .
Physicists seek accurate measurements of neutron stars ’ masses and radii , which would help reveal their “ equation of state ” —the relationship between pressure and density within these stars .
Knowing the neutron star equation of state would in turn indicate what kind of matter hides inside them .
In a new study , an international team of researchers combined gravitational wave measurements from two neutron star collisions , as well as the light signals that arrived along with one of them ( the other @ @ @ @ @ @ @ neutron star masses and radii from watching rapidly spinning neutron stars called pulsars .
“ The big advantage is it ’ s a very coherent picture , ” says study member Tim Dietrich of the University of Potsdam in Germany , who co-authored a paper reporting the results published today in Science .
“ We combine all the things we know currently , including gravitational waves and electromagnetic waves , information from single neutron stars , and theoretical computations from nuclear physics.
” The equation of state they derived predicted that a neutron @ @ @ @ @ @ @ would have a radius of about 11.75 kilometers , plus or minus .81 to .86 km .
That ’ s a bit more than half the length of Manhattan .
“ The size of the neutron star directly depends on the behavior of matter inside the core , so this gives us a better understanding about the properties of the neutron star material , ” Dietrich says .
For instance , if neutrons remain intact in the core of these stars , they would push out against the outer layers , potentially leading @ @ @ @ @ @ @ , on the other hand , the neutrons break down into a soup of quarks , the core would be squishier and the whole star would sink in a bit , resulting in a smaller radius .
The new measurement is in general agreement with earlier studies that have looked at gravitational wave data and other ways to measure neutron star size .
“ This paper is a nice joint reanalysis of previous studies , and does n't change the overall impression that has been in place for the last few years , @ @ @ @ @ @ @ is about 11 to 13 km , ” says Mark Alford , a physicist at Washington University in St. Louis .
Anna Watts , an astrophysicist at the University of Amsterdam , says this type of combined analysis “ is clearly the way forward , ” but that none of the measurements “ are yet good enough to really pin down the nature of dense matter.
” The field will need to wait for future data to really understand what ’ s happening inside neutron stars .
“ I think it ’ s @ @ @ @ @ @ @ physicist James Lattimer of Stony Brook University , who was not involved in the research .
He cautions that in modeling how well different possible equations of state fit the data , the team may have mistakenly eliminated too many equations that produce neutron stars with large radii , however .
“ I think they ’ ve underestimated their uncertainty .
But in some sense , it ’ s a matter of opinion and how much faith you place in different statistical methods.
” Besides revealing secrets of neutron stars , the study @ @ @ @ @ @ @ constant , which reflects the expansion rate of the universe .
To derive the constant , the scientists used the amplitude of the gravitational waves coming from one of the collisions to estimate how far away the crash occurred .
They then compared their distance measurement with the known speed of the collision ’ s host galaxy , which was measured by looking at the galaxy ’ s redshift—how much its light has slid toward the red end of the spectrum .
The Hubble constant they found , 66.2 kilometers per second per @ @ @ @ @ @ @ decide between the competing measurements that already exist , but adds another data point to the hotly contested question of how fast the cosmos is growing .
The scientists hope to apply the same type of analysis to future neutron star collisions that appear .
“ We made this first step and now we ’ ll push forward , ” says team member Sarah Antier of the University of Paris , an astronomer who searches for light signals accompanying gravitational wave events .
“ My task is to connect different observatories to provide @ @ @ @ @ @ @ when gravitational wave detectors find a new signal .
Physicists are biding their time until the next generation of gravitational-wave detectors , such as the Cosmic Explorer in the U.S. and the Einstein Telescope in Europe , come online in the 2030s .
These machines should be much more sensitive , allowing them to capture many more signals from more events and offering higher precision data .
Future projects such as the Enhanced X-ray Timing and Polarimetry Mission ( eXTP ) and the Athena X-ray Observatory should also gather more accurate measurements of @ @ @ @ @ @ @ in the short time since gravitational wave data became available , the future promises to greatly expand our knowledge of extreme matter under intense pressure .
“ The last four years have been remarkable , ” Lattimer says .
“ It shows the potential that we are going to be getting in the future .
We should have many more measurements from gravitational wave events , and as we add each new event the results are going to converge .
”

A mystery lurks inside the corpses of dead stars. Neutron stars, formed when certain types of stars die in supernova explosions, are the densest form of matter in the universe; black holes are the only thing denser, but they have so fully escaped the bounds of normal physics that they are not matter anymore. The atoms in neutron stars have been squeezed so tightly by gravity that they have broken down, the protons and electrons inside them smushing together to create neutrons, leaving objects the size of small cities that contain masses larger than the sun. Some 95 percent of the mass of a neutron star is pure neutrons, but physicists wonder what happens at the very center, where the density peaks. Do neutrons break down further into their constituent quarks and gluons? Do some of the quarks transform from their normal “up” and “down” flavors to become weirder and heavier “strange quarks” not found in ordinary matter? Do the particles form an extreme state of matter called a superfluid that sloshes with no viscosity, never slowing down?
Scientists have come a step closer to understanding the inner workings of these bizarre bodies by studying the light and gravitational waves that result when two neutron stars slam into each other and become a black hole. Gravitational waves are folds in spacetime carved out when large masses move around. Scientists only gained the ability to detect gravitational waves in 2015, and have spotted just a handful of events involving neutron stars so far (the others have been collisions of black holes). But studying the properties of these waves—their frequency and how they change over time—can tell scientists a lot about the objects that created them. Physicists seek accurate measurements of neutron stars’ masses and radii, which would help reveal their “equation of state”—the relationship between pressure and density within these stars. Knowing the neutron star equation of state would in turn indicate what kind of matter hides inside them.
In a new study, an international team of researchers combined gravitational wave measurements from two neutron star collisions, as well as the light signals that arrived along with one of them (the other was dark), with estimates of neutron star masses and radii from watching rapidly spinning neutron stars called pulsars. “The big advantage is it’s a very coherent picture,” says study member Tim Dietrich of the University of Potsdam in Germany, who co-authored a paper reporting the results published today in Science. “We combine all the things we know currently, including gravitational waves and electromagnetic waves, information from single neutron stars, and theoretical computations from nuclear physics.” The equation of state they derived predicted that a neutron star containing the mass of 1.4 suns would have a radius of about 11.75 kilometers, plus or minus .81 to .86 km. That’s a bit more than half the length of Manhattan. “The size of the neutron star directly depends on the behavior of matter inside the core, so this gives us a better understanding about the properties of the neutron star material,” Dietrich says.
For instance, if neutrons remain intact in the core of these stars, they would push out against the outer layers, potentially leading to a slightly larger radius. If, on the other hand, the neutrons break down into a soup of quarks, the core would be squishier and the whole star would sink in a bit, resulting in a smaller radius.
The new measurement is in general agreement with earlier studies that have looked at gravitational wave data and other ways to measure neutron star size. “This paper is a nice joint reanalysis of previous studies, and doesn't change the overall impression that has been in place for the last few years, that the radius of a neutron star is about 11 to 13 km,” says Mark Alford, a physicist at Washington University in St. Louis. Anna Watts, an astrophysicist at the University of Amsterdam, says this type of combined analysis “is clearly the way forward,” but that none of the measurements “are yet good enough to really pin down the nature of dense matter.” The field will need to wait for future data to really understand what’s happening inside neutron stars.
“I think it’s a very nice analysis,” says physicist James Lattimer of Stony Brook University, who was not involved in the research. He cautions that in modeling how well different possible equations of state fit the data, the team may have mistakenly eliminated too many equations that produce neutron stars with large radii, however. “I think they’ve underestimated their uncertainty. But in some sense, it’s a matter of opinion and how much faith you place in different statistical methods.”
Besides revealing secrets of neutron stars, the study also produced a measurement of the Hubble constant, which reflects the expansion rate of the universe. To derive the constant, the scientists used the amplitude of the gravitational waves coming from one of the collisions to estimate how far away the crash occurred. They then compared their distance measurement with the known speed of the collision’s host galaxy, which was measured by looking at the galaxy’s redshift—how much its light has slid toward the red end of the spectrum. The Hubble constant they found, 66.2 kilometers per second per megaparsec, is not precise enough to decide between the competing measurements that already exist, but adds another data point to the hotly contested question of how fast the cosmos is growing.
The scientists hope to apply the same type of analysis to future neutron star collisions that appear. “We made this first step and now we’ll push forward,” says team member Sarah Antier of the University of Paris, an astronomer who searches for light signals accompanying gravitational wave events. “My task is to connect different observatories to provide a network to make immediate observations” when gravitational wave detectors find a new signal.
Physicists are biding their time until the next generation of gravitational-wave detectors, such as the Cosmic Explorer in the U.S. and the Einstein Telescope in Europe, come online in the 2030s. These machines should be much more sensitive, allowing them to capture many more signals from more events and offering higher precision data. Future projects such as the Enhanced X-ray Timing and Polarimetry Mission (eXTP) and the Athena X-ray Observatory should also gather more accurate measurements of pulsars.
Scientists have learned so much in the short time since gravitational wave data became available, the future promises to greatly expand our knowledge of extreme matter under intense pressure. “The last four years have been remarkable,” Lattimer says. “It shows the potential that we are going to be getting in the future. We should have many more measurements from gravitational wave events, and as we add each new event the results are going to converge.”


scientificamerican--2020-12-17--Pandemic Leads Geoengineering Experiment to Move from U.S. to Sweden

Harvard University researchers announced yesterday that they are seeking to move their first stratospheric test of a balloon that could one day be used in experiments to shade the Earth from the heating effects of greenhouse gases to Sweden .
Frank Keutsch , the principal investigator of the program , said the test is being moved out of the United States in part because of the coronavirus pandemic .
An alternative facility in the United States couldn ’ t be found to host the balloon launch scheduled for early summer .
The Swedish Space Corp. ’ s center in Kiruna @ @ @ @ @ @ @ a land-based descent , so the balloon and its propeller-driven gondola could be reused , Keutsch explained .
He added that working in Sweden would help increase international collaboration , “ which is important to us.
” The first test of the project , called the Stratospheric Controlled Perturbation Experiment , is meant to see how well the balloon will operate in the thinning atmosphere at a height of 12.4 miles with an equipment payload of 1,323 pounds .
If it works , then a following test would use the propellers to slowly @ @ @ @ @ @ @ calcium carbonate , a common form of mineral dust , into the atmosphere .
The idea is to use about 4 pounds of the dust to create a test “ beaker , ” or a cloud that ’ s about 0.62 mile long and 328 feet in diameter .
After that , the balloon could loiter for as long as 24 hours , using instruments to test how the particles interact with each other and if they can limit solar radiation , thus reducing the amount being reflecting back into the atmosphere to @ @ @ @ @ @ @ The geoengineering program is meant to prevent heat from building up in the atmosphere .
Later tests would also analyze whether dispersing substances from the balloon could harm the protective ozone layer that keeps the more harmful rays of sunlight from reaching the Earth .
Keutsch , who is a professor of chemistry and chemical biology , stressed that the tests will have to be approved by an outside advisory committee established by the dean of Harvard ’ s School of Engineering and Applied Sciences .
David Keith , a Harvard physicist who @ @ @ @ @ @ @ a press release yesterday that new models showed that the aerosol would likely reduce ozone in the “ beaker ” by less than 3 % .
Keith explained in a video released earlier this month that the point of the atmospheric test was to provide real data to make sure the models are correct .
“ The big question is how could we be wrong ?
” he added .
Such geoengineering processes might be needed on a large scale even if global leaders are able to reduce greenhouse gas emissions to net @ @ @ @ @ @ @ Future generations , he noted , will have to deal with the effects of emissions from prior years that will linger in the atmosphere for decades or longer .
“ The seas will still be rising , and people will still be dying from heat waves , ” he said .
Reprinted from Climatewire with permission from E & E News .
E & E provides daily coverage of essential energy and environmental news at www.eenews.net .

Harvard University researchers announced yesterday that they are seeking to move their first stratospheric test of a balloon that could one day be used in experiments to shade the Earth from the heating effects of greenhouse gases to Sweden.
Frank Keutsch, the principal investigator of the program, said the test is being moved out of the United States in part because of the coronavirus pandemic. An alternative facility in the United States couldn’t be found to host the balloon launch scheduled for early summer.
The Swedish Space Corp.’s center in Kiruna, Sweden, offered the possibility of a land-based descent, so the balloon and its propeller-driven gondola could be reused, Keutsch explained. He added that working in Sweden would help increase international collaboration, “which is important to us.”
The first test of the project, called the Stratospheric Controlled Perturbation Experiment, is meant to see how well the balloon will operate in the thinning atmosphere at a height of 12.4 miles with an equipment payload of 1,323 pounds.
If it works, then a following test would use the propellers to slowly scatter and mix a small amount of calcium carbonate, a common form of mineral dust, into the atmosphere. The idea is to use about 4 pounds of the dust to create a test “beaker,” or a cloud that’s about 0.62 mile long and 328 feet in diameter.
After that, the balloon could loiter for as long as 24 hours, using instruments to test how the particles interact with each other and if they can limit solar radiation, thus reducing the amount being reflecting back into the atmosphere to be trapped there by former emissions. The geoengineering program is meant to prevent heat from building up in the atmosphere.
Later tests would also analyze whether dispersing substances from the balloon could harm the protective ozone layer that keeps the more harmful rays of sunlight from reaching the Earth.
Keutsch, who is a professor of chemistry and chemical biology, stressed that the tests will have to be approved by an outside advisory committee established by the dean of Harvard’s School of Engineering and Applied Sciences.
David Keith, a Harvard physicist who first proposed the experiment, explained in a press release yesterday that new models showed that the aerosol would likely reduce ozone in the “beaker” by less than 3%.
Keith explained in a video released earlier this month that the point of the atmospheric test was to provide real data to make sure the models are correct. “The big question is how could we be wrong?” he added.
Such geoengineering processes might be needed on a large scale even if global leaders are able to reduce greenhouse gas emissions to net zero by 2060, Keith said.
Future generations, he noted, will have to deal with the effects of emissions from prior years that will linger in the atmosphere for decades or longer.
“The seas will still be rising, and people will still be dying from heat waves,” he said.
Reprinted from Climatewire with permission from E&E News. E&E provides daily coverage of essential energy and environmental news at www.eenews.net.


scientificamerican--2020-12-17--A Smile at a Wedding, a Cheer at a Soccer Game Are Alike the World Over

In the 19th century , French clinician Guillaume-Benjamin-Amand Duchenne posited that humans universally use their facial muscles to make at least 60 discrete expressions , each reflecting one of 60 specific emotions .
Charles Darwin , who greeted that number with some skepticism , himself was invested in exploring the universality of facial expressions as evidence of humanity ’ s common evolutionary history .
He ended up writing a book about human expressions , leaning heavily toward the idea that at least some were common across all cultures .
Since these early forays in the field , debate has run @ @ @ @ @ @ @ are common to all of us , and if so , how many there are .
Duchenne settled on 60 , whereas psychologist Paul Ekman beginning in the 1970s famously characterized six ( disgust , sadness , happiness , fear , anger , surprise ) in a construct that has held sway for decades .
A new study published December 16 in Nature takes things a step further and lands on yet another tally of universal facial expressions , this time based on millions of moving images instead of the small number of @ @ @ @ @ @ @ Using human ratings of 186,744 videos showing people reacting in different situations , the authors trained a neural network to label facial expressions from a list of emotion labels , such as awe , confusion and anger .
With this training , the neural network assessed six million videos originating from 144 countries and consistently linked similar facial expressions to similar social contexts across 12 regions of the globe .
For example , a facial reaction labeled “ triumph ” was often associated with sporting events regardless of geographical region , suggesting a @ @ @ @ @ @ @ the results imply that how we move our faces in certain situations may be common across cultures , they do not address whether these expressions accurately signal the internal experience of an emotion .
A number of factors could have affected the results : the limitations of machine learning , the use of only English-speaking raters in India for training the algorithm , and the potential for some misinterpretations of the findings raise concerns .
Using video and considering context is “ absolutely a step forward ” for the field , says Lisa @ @ @ @ @ @ @ at Northeastern University , who wrote an accompanying commentary on the study .
“ The question that they ’ re asking strikes right at the heart of the nature of emotion , ” she remarks , but there ’ s a risk that such information might be used to judge people , which “ would be premature because there are multiple ways to interpret these results.
” Study first author Alan Cowen , a researcher at the University of California , Berkeley , and a visiting faculty researcher at Google , agrees , @ @ @ @ @ @ @ studying the physiology of emotion has just begun .
“ It ’ s early days for sure , and this is pretty nascent , ” he says .
“ We ’ re just focused on whether and how machine learning can help researchers answer important questions about human emotions.
” The machine doing the learning in this case was a deep neural network , which takes input , such as a video clip , and parses it through many layers to predict what the input material contains .
In this case , the @ @ @ @ @ @ @ in videos and labeled the facial expressions in different social situations .
But first , it had to learn to apply various labels that human raters associated with specific facial configurations .
To teach the network , Cowen and colleagues needed a large repository of videos rated by human viewers .
A team of English-speaking raters in India completed this task , making 273,599 ratings of 186,744 YouTube video clips lasting one to three seconds each .
The research team used the results to train the neural network to classify patterns of facial @ @ @ @ @ @ @ , such as pain , doubt , or surprise .
The scientists then had another neural network analyze visual cues in three million videos from 144 countries to assign a social context to each video , from weddings to weightlifting to witnessing fireworks , ultimately characterizing 653 contexts .
They then tested the facial expression network on these three million videos , assessing how consistently it assigned specific facial expression labels in similar social contexts , such as “ joy ” when seeing a toy .
The results showed a similar pattern of @ @ @ @ @ @ @ .
For example , regardless of region , the neural network tended to associate facial expressions labeled “ amusement ” most often with contexts labeled “ practical joke , ” and the “ pain ” expression label was consistently associated with uncomfortable contexts such as weightlifting .
To rule out an influence of faces in those three million videos on the assignment of social context , the researchers had another network assign context using only the words in labels and descriptions accompanying videos .
That network took on another three million videos and @ @ @ @ @ @ @ facial expression network applied the 16 labels to these videos , there was a similar but slightly weaker consistency between the label for a facial expression and the context assigned the video .
Cowen said that this outcome was expected because context from language is a lot less accurate than context assignments based on video , “ which starts to illustrate what happens when you rely too much on language.
” When they compared results across regions , Cowen and colleagues found some instances of greater shared similarities in geographically adjacent regions , @ @ @ @ @ @ @ Africa was most similar to the nearby Middle East in its expression–context associations , and less similar to the farther away India .
Yet on average , each individual region was similar to the average across all 12 regions , Cowen says , often more so than to any of its direct neighbors .
The caveat is that these facial expressions don ’ t provide a readout of emotion or intention , asserts Jeffrey Cohn , professor of psychology at the University of Pittsburgh , who also peer-reviewed the paper .
“ They @ @ @ @ @ @ @ that ’ s a long way from making inferences about the meaning of any particular expression .
There is no one-to-one mapping between facial expression and emotion.
” Cowen confirmed that “ we don ’ t know what somebody ’ s feeling based on their facial muscle movements , and we ’ re not claiming to infer that.
” For example , “ the same facial expression used in sporting events in one culture versus another might be associated with a more positive or more negative emotional experience.
” Machine learning is a @ @ @ @ @ @ @ it has to be used with caution .
“ You have to be careful not to encode the beliefs of the human raters in these models and where beliefs will influence training , ” she says .
“ No matter how fancy schmancy the modeling is , it ’ s not going to protect you from the weaknesses that creep in when there ’ s human inference involved.
” She notes , for example , that the human raters of the videos used to train the facial expression algorithm were all from the @ @ @ @ @ @ @ using a list of labels that themselves are emotion words , such as “ anger , ” rather than descriptions , such as “ scowl.
” Casey Fiesler , assistant professor of information science at the University of Colorado , Boulder , who was not involved in the work , expressed concern about the possibility of bias from applying the four categories of race that authors used to evaluate an influence of that factor .
“ There ’ s a huge body of literature that speaks to , for example , implicit bias @ @ @ @ @ @ @ different races , ” she pointed out .
Used in the wrong ways , assumptions about the universality of facial expression can cause harm to people who are marginalized or vulnerable economically , says Nazanin Andalibi , assistant professor in the school of information science at the University of Michigan .
As an example from earlier facial recognition applications , she says : “ No matter how much someone smiles , some algorithms continue to associate negative emotions with Black faces , so there are a lot of individual-level harms.
” The technology @ @ @ @ @ @ @ says , such as recognizing facial expression cues in people at risk for suicide .
The relevance of context in this work is a major step in that direction , he said .
“ I am not saying we can go out on the street and detect whether someone is depressed , but within a specific context like a clinical interview , we could measure severity of depression.
” Efforts to use the technology moving forward—for suicide prevention or other uses—require attention to the various real-world pitfalls that are possible when an algorithm @ @ @ @ @ @ @ Machine-learning techniques are cool and really useful , but they are not a magic bullet , ” says Barrett .
“ This is not just an esoteric debate within the walls of the academy or the walls of Google .
”

In the 19th century, French clinician Guillaume-Benjamin-Amand Duchenne posited that humans universally use their facial muscles to make at least 60 discrete expressions, each reflecting one of 60 specific emotions. Charles Darwin, who greeted that number with some skepticism, was invested in exploring the universality of facial expressions as evidence of humanity’s common evolutionary history. He ended up writing a book about human expressions, leaning heavily toward the idea that at least some were common across all cultures.
Since these early forays in the field, debate has run hot over whether some faces we make are common to all of us and, if so, how many there are. Duchenne settled on 60, whereas beginning in the 1970s, psychologist Paul Ekman famously characterized six (disgust, sadness, happiness, fear, anger, surprise) in a construct that has held sway for decades.
A new study published December on 16 in Nature takes things a step further and lands on yet another tally of universal facial expressions, this time based on millions of moving images instead of the small number of still photographs employed in earlier studies. Using human ratings of 186,744 videos showing people reacting in different situations, the authors trained a neural network to label facial expressions from a list of emotion labels, such as awe, confusion and anger. With this training, the neural network assessed six million videos originating from 144 countries and consistently linked similar facial expressions to similar social contexts across 12 regions of the globe. For example, a facial reaction labeled “triumph” was often associated with sporting events regardless of geographical region, suggesting a universal response in that context.
Although the results imply that how we move our faces in certain situations may be common across cultures, they do not address whether these expressions accurately signal the internal experience of an emotion. A number of factors could have affected the results: the limitations of machine learning, the use of only English-speaking raters in India for training the algorithm and the potential for some misinterpretations of the findings raise concerns.
Using video and considering context is “absolutely a step forward” for the field, says Lisa Feldman Barrett, a professor and psychologist at Northeastern University, who wrote an accompanying commentary on the study. “The question that they’re asking strikes right at the heart of the nature of emotion,” she remarks. But there’s a risk that such information might be used to judge people, which “would be premature because there are multiple ways to interpret these results.”
The study’s first author Alan Cowen, a researcher at the University of California, Berkeley, and a visiting faculty researcher at Google, agrees, saying the use of machine learning for studying the physiology of emotion has just begun. “It’s early days for sure, and this is pretty nascent,” he says. “We’re just focused on whether and how machine learning can help researchers answer important questions about human emotions.”
The machine doing the learning in this case was a deep neural network, which takes input, such as a video clip, and parses it through many layers to predict what the input material contains. In this case, the neural network tracked the movement of faces in videos and labeled the facial expressions in different social situations. But first, it had to learn to apply various labels that human raters associated with specific facial configurations.
To teach the network, Cowen and his colleagues needed a large repository of videos rated by human viewers. A team of English-speaking raters in India completed this task, making 273,599 ratings of 186,744 YouTube video clips lasting one to three seconds each. The research team used the results to train the neural network to classify patterns of facial movement with one of 16 emotion-related labels, such as pain, doubt or surprise.
The scientists then had another neural network analyze visual cues in three million videos from 144 countries to assign a social context to each video, from weddings to weight lifting to witnessing fireworks, ultimately characterizing 653 contexts.
They then tested the facial expression network on these three million videos, assessing how consistently it assigned specific facial expression labels in similar social contexts, such as “joy” when seeing a toy. The results showed a similar pattern of associations across 12 regions around the globe. For example, regardless of region, the neural network tended to associate facial expressions labeled “amusement” most often with contexts labeled “practical joke,” and the “pain” expression label was consistently associated with uncomfortable contexts such as weight lifting.
To rule out an influence of faces in those three million videos on the assignment of social context, the researchers had another network assign context using only the words in labels and descriptions accompanying videos. That network took on another three million videos and assigned 1,953 social contexts. When the facial expression network applied the 16 labels to these videos, there was a similar but slightly weaker consistency between the label for a facial expression and the context assigned the video. Cowen said that this outcome was expected because context from language is a lot less accurate than context assignments based on video, “which starts to illustrate what happens when you rely too much on language.”
When they compared results across regions, Cowen and his colleagues found some instances of greater shared similarities in geographically adjacent regions, though there was some regional variation. Africa was most similar to the nearby Middle East in its expression-context associations and less similar to the farther away India.
Yet on average, each individual region was similar to the average across all 12 regions, Cowen says—often more so than to any of its direct neighbors.
The caveat is that these facial expressions do not provide a readout of emotion or intention, asserts Jeffrey Cohn, a professor of psychology at the University of Pittsburgh, who also peer-reviewed the paper. “They’re related in context but that’s a long way from making inferences about the meaning of any particular expression. There is no one-to-one mapping between facial expression and emotion.”
Cowen confirmed that “we don’t know what somebody’s feeling based on their facial muscle movements, and we’re not claiming to infer that.” For example, “the same facial expression used in sporting events in one culture versus another might be associated with a more positive or more negative emotional experience.”
Machine learning is a powerful technique, Barrett acknowledges, but it has to be used with caution. “You have to be careful not to encode the beliefs of the human raters in these models and where beliefs will influence training,” she says. “No matter how fancy schmancy the modeling is, it’s not going to protect you from the weaknesses that creep in when there’s human inference involved.” She notes, for example, that the human raters of the videos used to train the facial expression algorithm were all from the same culture and region and constrained to using a list of labels that themselves are emotion words, such as “anger,” rather than descriptions, such as “scowl.”
Casey Fiesler, an assistant professor of information science at the University of Colorado Boulder, who was not involved in the work, expressed concern about the possibility of bias from applying the four categories of race that authors used to evaluate an influence of that factor. “There’s a huge body of literature that speaks to, for example, implicit bias in judging facial expressions of people of different races,” she pointed out.
Used in the wrong ways, assumptions about the universality of facial expression can cause harm to people who are marginalized or vulnerable economically, says Nazanin Andalibi, assistant professor in the school of information science at the University of Michigan. As an example from earlier facial-recognition applications, she says, “no matter how much someone smiles, some algorithms continue to associate negative emotions with Black faces, so there are a lot of individual-level harms.”
The technology also offers some potential benefits, Cohn says, such as recognizing facial expression cues in people at risk for suicide. The relevance of context in this work is a major step in that direction, he adds. “I am not saying we can go out on the street and detect whether someone is depressed,” Cohn says, “but within a specific context like a clinical interview, we could measure severity of depression.”
Efforts to use the technology moving forward—for suicide prevention or other uses—require attention to the various real-world pitfalls that are possible when an algorithm makes a judgment about people. “Machine-learning techniques are cool and really useful, but they are not a magic bullet,” Barrett says. “This is not just an esoteric debate within the walls of the academy or the walls of Google.”


scientificamerican--2020-12-17--Biden Should Restore the Role of Science in the U.S. Government

During the 2010 BP Deepwater Horizon oil spill disaster , I flew on Air Force Two with Vice President Joe Biden to the Gulf of Mexico to join him in meetings with fishing communities to hear their concerns and share what the federal government was doing .
Although I had been in high-level group meetings with the VP , it was the first time he and I had an extended one-on-one exchange .
Our plane conversation covered a range of topics and apparently intrigued him enough that , as we landed , he asked me to please join him in @ @ @ @ @ @ @ us could continue talking about what scientists had learned about the spill and what the National Oceanic and Atmospheric Administration was doing to help .
As he peppered me with questions , it was abundantly clear that he respects science and looks to scientists to help understand complex issues and chart a smart path forward .
It was equally clear from his subsequent remarks to fishing communities that he was unusually adept at integrating scientific knowledge into an empathetic message with honesty , substance and sensitivity .
In his meetings and speech , @ @ @ @ @ @ @ relevant to people ’ s lives .
Ten years later his presidential campaign pledged to restore the place of science in government and to heed science in tackling urgent problems .
In his acceptance speech on November 7 , 2020 , he said , “ America has called upon us to marshal the forces of decency , the forces of fairness , to marshal the forces of science and the forces of hope in the great battles of our time.
” These battles include the pandemic , economic recovery , health care , @ @ @ @ @ @ @ change .
Now that Biden is on the eve of his presidency , what specific actions should he take to restore the scientific expertise needed for our collective good ?
A comprehensive list would take too many pages , so I have bundled my priorities into four categories , drawing on my four years as Undersecretary of Commerce for Oceans and Atmosphere and administrator of NOAA and my two years as the first U.S. Science Envoy for the Ocean with the U.S. State Department .
1 .
Make science and scientists prominent .
@ @ @ @ @ @ @ mechanisms to ensure science is part of every major decision .
If the president takes these steps early on , that tone will be emulated across the executive branch .
President-elect Barack Obama announced his “ Science Team ” in mid-December of 2008 , shortly after announcing his selections for cabinet-level positions .
Elevating science so early on made an unmistakable statement about his priorities .
Most modern presidents have had a science adviser , but announcing a Science Team was new and noteworthy .
I was fortunate to be an inaugural member @ @ @ @ @ @ @ sent a clear signal to other political appointees : these folks matter , give them access , listen to them .
Biden should name a Science Team and make his science adviser a cabinet member .
Doing so would make an unambiguous declaration of the importance of science to this administration .
Any major special team—for example , a new White House Climate Team—should have a visible lead scientist .
A scientist need not be in charge of the team ( although that is fine , too ! )
, but having a @ @ @ @ @ @ @ that science is at the table will help inform key decisions .
In the Obama administration , numerous interagency teams were created in response to various needs .
During the BP Deepwater Horizon disaster , the national incident commander who led the interagency group of mostly cabinet-level principals routinely began each daily meeting by asking me to recap the latest relevant scientific information .
Placing science at the top of the daily agenda made it visible , present and respected .
Announcements of major decisions or actions should include summaries of the relevant @ @ @ @ @ @ @ in the decisions taken .
For example , when NOAA and the National Weather Service changed the language used to issue extreme weather watches and warnings , we made it clear that we did so because social science findings told us that the previous language was insufficient in communicating when there was a life-threatening situation that warranted immediate action , such as an imminent tornado .
2 .
Restore and strengthen the conditions that enable science to thrive and inform decisions .
The president should issue scientific integrity ( SI ) guidance that @ @ @ @ @ @ @ agencies to create or strengthen SI policies within a specified time frame .
A policy should forbid anyone in the executive branch from cherry-picking , manipulating , suppressing or distorting scientific information .
Agency scientists should be free to speak to the media about their scientific findings ( as distinct from opining on agency policies ) without having to go through a gatekeeper or be scripted with politically constrained talking points .
SI policies must apply to everyone , career and political employees .
NOAA ’ s already strong SI policy , for @ @ @ @ @ @ @ violations of that policy by political appointees who tried to provide cover for President Donald Trump during the so-called SharpieGate scandal , when Trump showed a hand-altered map to support a false claim he had made about Hurricane Dorian ’ s predicted path .
NOAA ’ s policy had a mechanism for investigating violations , but there were no real consequences for serious violations .
An SI policy should be a living document , part of the active culture of an agency , owned and embraced by employees .
Because SI policies are @ @ @ @ @ @ @ scientific enterprise within the federal government , basic principles should be enshrined in law , not just in agency policy .
Some members of Congress agree and are keen to act .
Biden should work with them to pass a robust SI law .
External advisory committees bring outside expertise to agencies , but many were eliminated or weakened under the previous administration .
Biden should reverse Trump ’ s executive orders on Federal Advisory Committees and empower agencies to take full advantage of the scientists who voluntarily share their expertise to complement @ @ @ @ @ @ @ on agency decisions .
During my time at NOAA , I was constantly impressed with the knowledge , dedication and integrity of its scientists .
Today , however , the scientific workforce across the federal government has been reduced and demoralized .
Reconstituting that capacity with diverse , stellar , early-career scientists should be initiated immediately and will bring decades-long benefit to the nation .
Agencies should ensure that there are vibrant career paths for scientists and that scientists have a strong voice within the agency .
At NOAA , we created clear @ @ @ @ @ @ @ and annual all-scientists meetings to empower scientists and enable them to deliver what the nation needed .
Persistent national and global challenges require periodic , refreshed syntheses of relevant scientific information .
Biden should ensure that the next National Climate Assessment , which is just getting underway , is well led and free from political interference .
In addition , he should consider a similar interagency mechanism that engages outside scientists for a periodic National Assessment for the Ocean , for Biodiversity , for Environmental Justice and for Health .
3 .
Modernize @ @ @ @ @ @ @ .
Today ’ s problems require more holistic approaches , drawing on complex adaptive systems thinking and integrated socioeconomic approaches .
Solving many problems requires actions across multiple agencies , but the mechanisms are often ineffective .
Climate change is an obvious example , which is why President-elect Biden is creating a cross-agency team anchored in the White House .
Obama created a National Ocean Council to coordinate the 26 federal agencies and offices that relate to the ocean and help implement a National Ocean Policy .
Trump upended the policy and weakened @ @ @ @ @ @ @ emerging scientific information about powerful opportunities for healthy ocean ecosystems to help address climate change , economic recovery , biodiversity protection , coastal resilience and equity , and he should empower a new National Ocean Policy and Council to harness the power of the ocean .
4 .
Depoliticize science—it is not partisan .
A persistent , smart effort is needed to share the awe and demonstrate ( not just assert ) the importance and relevance of science .
I believe that a major reason Americans across the political spectrum reacted negatively to @ @ @ @ @ @ @ it is to have accurate lifesaving information , free from political interference .
They appreciate the fact that hurricane forecasts are based on science , and they want to know what the experts think is likely to happen .
A vibrant democracy requires informed citizens .
Decision makers will typically consider a range of factors—values , economics , politics , and opinions of peers or family .
But science should also be at the table .
Opportunities to participate in science can help demystify science and make it more accessible .
Seeing how @ @ @ @ @ @ @ help the nation move beyond polarization of science .
Messages and actions from the country ’ s leader matter .
In short , Mr. President , on my imaginary flight with you on Air Force One today , I ask you to please tap , elevate and empower scientists to deliver knowledge and solutions , create the conditions to protect the integrity of science and enable it to thrive , embrace and use science , even when it is inconvenient , and explain why and how science matters .
The nation and the @ @ @ @ @

During the 2010 BP Deepwater Horizon oil spill disaster, I flew on Air Force Two with Vice President Joe Biden to the Gulf of Mexico to join him in meetings with fishing communities to hear their concerns and share what the federal government was doing. Although I had been in high-level group meetings with the VP, it was the first time he and I had an extended one-on-one exchange. Our plane conversation covered a range of topics and apparently intrigued him enough that, as we landed, he asked me to please join him in his VPOTUS car so the two of us could continue talking about what scientists had learned about the spill and what the National Oceanic and Atmospheric Administration was doing to help.
As he peppered me with questions, it was abundantly clear that he respects science and looks to scientists to help understand complex issues and chart a smart path forward. It was equally clear from his subsequent remarks to fishing communities that he was unusually adept at integrating scientific knowledge into an empathetic message with honesty, substance and sensitivity. In his meetings and speech, the point was how to make science relevant to people's lives.
Ten years later his presidential campaign pledged to restore the place of science in government and to heed science in tackling urgent problems. In his acceptance speech on November 7, 2020, he said, “America has called upon us to marshal the forces of decency, the forces of fairness, to marshal the forces of science and the forces of hope in the great battles of our time.” These battles include the pandemic, economic recovery, health care, racial justice, systemic racism and climate change.
Now that Biden is on the eve of his presidency, what specific actions should he take to restore the scientific expertise needed for our collective good? A comprehensive list would take too many pages, so I have bundled my priorities into four categories, drawing on my four years as Undersecretary of Commerce for Oceans and Atmosphere and administrator of NOAA and my two years as the first U.S. Science Envoy for the Ocean with the U.S. State Department.
1. Make science and scientists prominent. Show they are respected. Create transparent mechanisms to ensure science is part of every major decision. If the president takes these steps early on, that tone will be emulated across the executive branch.
President-elect Barack Obama announced his “Science Team” in mid-December of 2008, shortly after announcing his selections for cabinet-level positions. Elevating science so early on made an unmistakable statement about his priorities. Most modern presidents have had a science adviser, but announcing a Science Team was new and noteworthy. I was fortunate to be an inaugural member of that team. That early visibility sent a clear signal to other political appointees: these folks matter, give them access, listen to them. Biden should name a Science Team and make his science adviser a cabinet member. Doing so would make an unambiguous declaration of the importance of science to this administration.
Any major special team—for example, a new White House Climate Team—should have a visible lead scientist. A scientist need not be in charge of the team (although that is fine, too!), but having a scientist vested with the responsibility of ensuring that science is at the table will help inform key decisions. In the Obama administration, numerous interagency teams were created in response to various needs. During the BP Deepwater Horizon disaster, the national incident commander who led the interagency group of mostly cabinet-level principals routinely began each daily meeting by asking me to recap the latest relevant scientific information. Placing science at the top of the daily agenda made it visible, present and respected.
Announcements of major decisions or actions should include summaries of the relevant science. This should help instill confidence in the decisions taken. For example, when NOAA and the National Weather Service changed the language used to issue extreme weather watches and warnings, we made it clear that we did so because social science findings told us that the previous language was insufficient in communicating when there was a life-threatening situation that warranted immediate action, such as an imminent tornado.
2. Restore and strengthen the conditions that enable science to thrive and inform decisions.
The president should issue scientific integrity (SI) guidance that applies across the executive branch and direct agencies to create or strengthen SI policies within a specified time frame. A policy should forbid anyone in the executive branch from cherry-picking, manipulating, suppressing or distorting scientific information. Agency scientists should be free to speak to the media about their scientific findings (as distinct from opining on agency policies) without having to go through a gatekeeper or be scripted with politically constrained talking points. SI policies must apply to everyone, career and political employees. NOAA's already strong SI policy, for example, should be updated based on violations of that policy by political appointees who tried to provide cover for President Donald Trump during the so-called SharpieGate scandal, when Trump showed a hand-altered map to support a false claim he had made about Hurricane Dorian's predicted path. NOAA's policy had a mechanism for investigating violations, but there were no real consequences for serious violations. An SI policy should be a living document, part of the active culture of an agency, owned and embraced by employees.
Because SI policies are the bedrock of a healthy, thriving scientific enterprise within the federal government, basic principles should be enshrined in law, not just in agency policy. Some members of Congress agree and are keen to act. Biden should work with them to pass a robust SI law.
External advisory committees bring outside expertise to agencies, but many were eliminated or weakened under the previous administration. Biden should reverse Trump's executive orders on Federal Advisory Committees and empower agencies to take full advantage of the scientists who voluntarily share their expertise to complement agency knowledge or act as a check on agency decisions.
During my time at NOAA, I was constantly impressed with the knowledge, dedication and integrity of its scientists. Today, however, the scientific workforce across the federal government has been reduced and demoralized. Reconstituting that capacity with diverse, stellar, early-career scientists should be initiated immediately and will bring decades-long benefit to the nation. Agencies should ensure that there are vibrant career paths for scientists and that scientists have a strong voice within the agency. At NOAA, we created clear science tracks, a science council, and annual all-scientists meetings to empower scientists and enable them to deliver what the nation needed.
Persistent national and global challenges require periodic, refreshed syntheses of relevant scientific information. Biden should ensure that the next National Climate Assessment, which is just getting underway, is well led and free from political interference. In addition, he should consider a similar interagency mechanism that engages outside scientists for a periodic National Assessment for the Ocean, for Biodiversity, for Environmental Justice and for Health.
3. Modernize the use of science across the agencies. Today's problems require more holistic approaches, drawing on complex adaptive systems thinking and integrated socioeconomic approaches. Solving many problems requires actions across multiple agencies, but the mechanisms are often ineffective. Climate change is an obvious example, which is why President-elect Biden is creating a cross-agency team anchored in the White House. Obama created a National Ocean Council to coordinate the 26 federal agencies and offices that relate to the ocean and help implement a National Ocean Policy. Trump upended the policy and weakened the council. Biden should listen to emerging scientific information about powerful opportunities for healthy ocean ecosystems to help address climate change, economic recovery, biodiversity protection, coastal resilience and equity, and he should empower a new National Ocean Policy and Council to harness the power of the ocean.
4. Depoliticize science— it is not partisan. A persistent, smart effort is needed to share the awe and demonstrate (not just assert) the importance and relevance of science.
I believe that a major reason Americans across the political spectrum reacted negatively to SharpieGate was that they understood how important it is to have accurate lifesaving information, free from political interference. They appreciate the fact that hurricane forecasts are based on science, and they want to know what the experts think is likely to happen.
A vibrant democracy requires informed citizens. Decision makers will typically consider a range of factors—values, economics, politics, and opinions of peers or family. But science should also be at the table. Opportunities to participate in science can help demystify science and make it more accessible. Seeing how it is used in making decisions can help the nation move beyond polarization of science. Messages and actions from the country's leader matter.
In short, Mr. President, on my imaginary flight with you on Air Force One today, I ask you to please tap, elevate and empower scientists to deliver knowledge and solutions, create the conditions to protect the integrity of science and enable it to thrive, embrace and use science, even when it is inconvenient, and explain why and how science matters. The nation and the world will thank you.