# data[].attributes.attachment_link.description
OCEARCH is a data-centric organization built to help scientists collect previously unattainable data in the ocean. Our mission is to accelerate the ocean's return to abundance through fearless innovations in scientific research, education, outreach and policy.

# data[].attributes.attachment_link.title
Billions of Birds Migrate. Where Do They Go?

# data[].attributes.attachment_link.description
Migratory birds have made their thousand-mile flights for millennia, but we are just now learning to map their mesmerizing journeys.

# data[].attributes.headline
Bird Migration Across the Western Hemisphere

# data[].attributes.subject
Bird Migration Across the Western Hemisphere

# data[].attributes.attachment_link.readable_text
For the first time, scientists at the Cornell Lab of Ornithology have documented migratory movements of bird populations spanning the entire year for 118 species throughout the Western Hemisphere. The study finds broad similarity in the routes used by specific groups of species-vividly demonstrated by animated maps showing patterns of movement across the annual cycle. The results of these analyses were published today in the Proceedings of the Royal Society B.
Each dot represents a single bird species; the location represents the average of the population for each day of the year (see paper for a more precise explanation of the "average location"). Here's a key to which species is which.
"We used millions of observations from the eBird citizen-science database," says lead author Frank La Sorte, a research associate at the Cornell Lab. "After tracing the migration routes of all these species and comparing them, we concluded that a combination of geographic features and broad-scale atmospheric conditions influence the choice of routes used during spring and fall migration."

La Sorte says a key finding of the study is that bird species that head out over the Atlantic Ocean during fall migration to spend winter in the Caribbean and South America follow a clockwise loop and take a path farther inland on their return journey in the spring. Species that follow this broad pattern include Bobolinks, Yellow and Black-billed cuckoos, Connecticut and Cape May warblers, Bicknell's Thrush, and shorebirds, such as the American Golden Plover.

"These looped pathways help the birds take advantage of conditions in the atmosphere," explains La Sorte. "Weaker headwinds and a push from the northeast trade winds as they move farther south make the fall journey a bit easier. The birds take this shorter, more direct route despite the dangers of flying over open-ocean."

The study finds the spring migration path follows a more roundabout route but the birds move faster thanks to the presence of strong tailwinds as they head north to their breeding grounds.

For species that do not fly over the open ocean, the study finds that many use the same migration routes in the spring and fall. Geographic features shaping this pattern include mountain chains or isthmuses that funnel migrants along narrow routes.

"It's an exciting new area of research," says La Sorte. "By using eBird data and other forms of migration tracking information, we're getting a more detailed picture than ever before about where and when birds migrate. That's the kind of information we need to make smart conservation decisions for species that live in vastly different regions during the year. Citizen science makes it possible to do this for populations across an entire hemisphere."

# data[].attributes.attachment_link.title
Mesmerizing Migration: Watch 118 Bird Species Migrate Across a Map of the Western Hemisphere

# data[].attributes.attachment_link.description
For the first time, scientists at the Cornell Lab of Ornithology have documented migratory movements of bird populations spanning the entire year for 118 species throughout the Western Hemisphere. The study finds broad similarity in the routes used by specific groups of species-vividly demonstrated

# data[].attributes.attachment_link.provider_name
Pew Research Center's Global Attitudes Project

# data[].attributes.attachment_link.title
Origins and Destinations of the World's Migrants, 1990-2017

# data[].attributes.attachment_link.description
Explore origin and destination totals of migrants from 233 countries between 1990 and 2017.

# data[].attributes.attachment_link.description
Explore the popularity of some of the world's favourite foods on Instagram. Discover Instagram's capital of curry, which cities are big on burgers, and where pulled pork is most prolific.

# data[].attributes.attachment_link.readable_text
The Refugee Project is a learning experience created to increase awareness about the plight of millions of people just like you around the world who have had no choice but to flee their homes.

The Refugee Project was created by Hyperakt and Ekene Ijeoma. Special thanks to the UNHCR for providing access to over 50 years of refugee migration data, their complete digital archive of published refugee stories and original photography.

# data[].attributes.attachment_link.description
The Refugee Project is a learning experience created to increase awareness about the plight of millions of people just like you around the world who have had no choice but to flee their homes. The Refugee Project was created by Hyperakt and Ekene Ijeoma.

# data[].attributes.attachment_link.title
Migrations in Motion - The Nature Conservancy

# data[].attributes.attachment_link.description
Data visualization showing projected direction 2900 species need to move to track climate change

# data[].attributes.attachment_link.readable_text
View Methodology

The methodology behind these projections is described in full in Hsiang, Kopp, Jina, Rising et al., 2017. The climate projection methodology is described in full in Rasmussen et al. (2016). All daily projections from this analysis are freely available online here.
Climate Projections
The climate projections show on this map are based on Representative Concentration Pathway 2.6, 4.5, and 8.5 (van Vuuren et al., 2012) experiments run by global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) exercise (Taylor et al., 2012). In particular, we used downscaled CMIP5 climate projections prepared by the US Bureau of Reclamation (Brekke et al., 2013). This dataset is bias-corrected and downscaled using the Bias-Correction Spatial Disaggregation (BCSD) method (Thrasher et al., 2012).

CMIP5 projections do not inherently constitute a probability distribution; rather, they are an ensemble of opportunity, composed of runs conducted by climate modeling teams participating on a voluntary basis and running models that roughly represent 'best-estimate' projections of climate behavior. To produce a probabilistic ensemble, we used the Surrogate Model/Mixed Ensemble (SMME) method of Rasmussen et al. (2016). This method weights projections by comparing their global mean surface temperature projections to those of a probabilistic simple climate model, in this case (as in Rasmussen et al., 2016) the MAGICC6 model (Meinshausen et al., 2011). The target global mean temperature distributions for 2080-2099 used were identical to those of Rasmussen et al. (2016). As in that paper, potential temperature outcomes produced the probabilistic simple climate model but not represented within the downscaled CMIP5 dataset were represented by 'model surrogates', produced using linear pattern scaling, with residuals added to represent high-frequency variability and non-linearities.

The gridded projections were aggregated to regional estimates by first transforming the daily min, average, or maximum temperature at the grid scale, then aggregating to regions using a weighted average. Annual and seasonal average temperatures are weighted using the shares of each region's land area within each grid cell; estimates of days above 95F and below 32F are weighted using the shares of each region's population within each grid cell.

Hsiang, Kopp, Jina, Rising, et al., 2017: Estimating Economic Damage from Climate Change in the United States. Science.

Meinshausen, M., S. C. B. Raper, and T. M. L. Wigley, 2011: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6-Part 1: Model description and calibration. Atmospheric Chemistry and Physics, 11, 1417-1456, doi:10.5194/acp-11-1417-2011.

Rasmussen, D. J, Meinshausen M. and Kopp, R.E., 2016: Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis. Journal of Applied Meteorology and Climatology 55, 2301-2322. doi:10.1175/JAMC-D-15-0302.1.

Brekke, L., B. L. Thrasher, E. P. Maurer, and T. Pruitt (2013), Downscaled CMIP3 and CMIP5 Climate Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with Preceding Information, and Summary of User Needs, 116 pp., U.S. Department of the Interior, Bureau of Reclamation, Technical Service Center, Denver, Colorado.

van Vuuren, D. P., and Coauthors, 2011: The representative concentration pathways: An overview. Climatic Change, 109, 5- 31, doi:10.1007/s10584-011-0148-z.

Download Data

View Methodology

The climate projection methodology is described in full in Rasmussen et al. (2016). These projections are based on Representative Concentration Pathway 4.5 and 8.5 (van Vuuren et al., 2012) experiments run by global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) exercise (Taylor et al., 2012). In particular, we used the NASA Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS). This dataset is bias-corrected and downscaled using the Bias-Correction Spatial Disaggregation (BCSD) method (Thrasher et al., 2012).

CMIP5 projections do not inherently constitute a probability distribution; rather, they are an ensemble of opportunity, composed of runs conducted by climate modeling teams participating on a voluntary basis and running models that roughly represent 'best-estimate' projections of climate behavior. To produce a probabilistic ensemble, we used the Surrogate Model/Mixed Ensemble (SMME) method of Rasmussen et al. (2016). This method weights projections by comparing their global mean surface temperature projections to those of a probabilistic simple climate model, in this case (as in Rasmussen et al., 2016) the MAGICC6 model (Meinshausen et al., 2011). The target global mean temperature distributions for 2080-2099 used were identical to those of Rasmussen et al. (2016). As in that paper, potential temperature outcomes produced the probabilistic simple climate model but not represented within the NEX-GDDP dataset were represented by 'model surrogates', produced using linear pattern scaling, with residuals added to represent high-frequency variability and non-linearities.

The gridded projections were aggregated to regional estimates by first transforming the daily min, average, or maximum temperature at the grid scale, then aggregating to regions using a weighted average. All variables are weighted using the shares of each region's land area within each grid cell.

Meinshausen, M., S. C. B. Raper, and T. M. L. Wigley, 2011: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6-Part 1: Model description and calibration. Atmospheric Chemistry and Physics, 11, 1417-1456, doi:10.5194/acp-11-1417-2011.

Rasmussen, D. J, Meinshausen M. and Kopp, R.E., 2016: Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis. Journal of Applied Meteorology and Climatology 55, 2301-2322. doi:10.1175/JAMC-D-15-0302.1.

Thrasher, B., Maurer, E. P., McKellar, C., & Duffy, P. B., 2012: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrology and Earth System Sciences, 16(9), 3309-3314. doi:10.5194/hess-16-3309-2012.

van Vuuren, D. P., and Coauthors, 2011: The representative concentration pathways: An overview. Climatic Change, 109, 5- 31, doi:10.1007/s10584-011-0148-z.

# data[].attributes.attachment_link.description
View Methodology The methodology behind these projections is described in full in Hsiang, Kopp, Jina, Rising et al., 2017. The climate projection methodology is described in full in Rasmussen et al. (2016). All daily projections from this analysis are freely available online here.

# data[].attributes.attachment_link.description
Climate change is among the biggest challenges humans have ever faced. From coastal cities to America's heartland, climate change impacts us all.

# data[].attributes.attachment_link.description
Drag and drop countries around the map to compare their relative size. Is Greenland really as big as all of Africa? You may be surprised at what you find! A great tool for educators.

# data[].attributes.attachment_link.description
Trending celebrity and entertainment news