Is there more or less water vapor in the Arctic atmosphere?
About this map
Is the Arctic getting more humid over time, releasing more water vapor into the atmosphere? This interactive map shows how the amount of water vapor in the Arctic atmosphere each month compares to the long-term average. Each monthly map shows where water vapor is above or below the average for that month over the period of 1979 to 2015. The corresponding bar graph shows how much the overall water vapor for that month departs from the long-term average.
Color key and bar graph
When you select a month on the dropdown selector and a year on the lower-left slider, the map will show water vapor anomalies.
- Areas with higher-than-average amounts of water vapor will appear in shades of purple, and areas with lower-than-average amounts of water vapor will appear in shades of green.
- Areas with water vapor at or near the long-term monthly average are white or nearly white.
- The greater the temperature departure from average, the darker the color.
The bar graph at the bottom indicates the water vapor anomaly (departure from the long-term average) for the entire Arctic—the whole region from 60°N of the equator to the North Pole. Green bars indicate negative anomalies (years in which water vapor is less than the long-term average) and purple bars show positive anomalies (years in which water vapor is greater than the long-term average). The bars show every year in the time series for the selected month, and the bar that correlates with the map on display is highlighted in light gray.
How to change the display
- To change the month displayed on both the map and the graph, use the month dropdown selector in the bar graph box (lower right).
- To change the year displayed, move the slider in the year box (lower left).
- To animate the time series for the selected month, click the play arrow (lower left). The animation will display maps for the selected month for all years in the time series.
Why water vapor matters
Water vapor is water in gaseous form, and an everyday term for the amount of water vapor in the atmosphere is humidity. Water evaporates into the atmosphere from oceans, lakes, rivers, and vegetation; it exists in the atmosphere as vapor then returns to the planet's surface in the form of precipitation. Water vapor has important implications for climate, weather, and the water cycle, and it affects temperature and precipitation.
As water vapor travels into, through, and out of the atmosphere, it moves moisture from one place to another, moving vertically in the water column but also laterally over oceans and landmasses. Water vapor also transports energy; moist air moves heat more effectively than dry air. Water vapor therefore plays a crucial role in carrying heat from the tropics to high latitudes.
Water vapor is a greenhouse gas, meaning it is a gas in Earth’s atmosphere that traps heat. Like carbon dioxide and methane, water vapor is composed of complex molecules that absorb some of the heat radiated from Earth’s surface and re-radiate that energy back to the planet. In fact, water vapor is the most abundant greenhouse gas in Earth’s atmosphere. But unlike carbon dioxide and methane, water vapor is a short-lived greenhouse gas because it soon precipitates out of the atmosphere in the form of rain, snow, or hail.
In general, a warmer atmosphere can hold more water vapor. Typically cold air limits the amount of water vapor the Arctic atmosphere can hold. When it is not covered by ice, however, the Arctic Ocean is a potentially significant source of water vapor. Winds that blow across the ice-free Arctic Ocean carry humid air. When those winds reach coasts, or the edges of glaciers, ice sheets, or sea ice, the winds bring precipitation, usually snow. Snow can help keep the region cooler because it reflects more sunlight, but water vapor is still a greenhouse gas. More water vapor in the atmosphere has a warming effect.
Across the globe, the relationship between global warming and worldwide humidity varies by region. Scientists are still learning why warmer conditions can cause droughts in some land areas, drying the air overhead, while in other places, warmer conditions can cause widespread increases in water vapor over the ocean, including the Arctic Ocean. A 2020 study found that understanding water vapor is crucial for projecting global warming trends. Reducing the uncertainty about water vapor can reduce the uncertainty about future climate change, so scientists place a high priority on understanding the relationship between warmth and water vapor.
What the data show
This interactive map shows the satellite record of atmospheric water vapor dating back to 1980, and how conditions compare to the long-term average.
From one year to the next, for any month, the locations of positive and negative anomalies vary considerably. An area that experiences below-average water vapor one year may see above-average water vapor 12 months later. In all months, however, negative anomalies are more common early in the time series, and positive anomalies are more common late in the time series. Anomalies—both negative ones early in the record and positive ones late in the record—are greatest in summertime and smallest in wintertime.
By viewing different months and years, you can use this map to examine changes in water vapor over time. Try using these maps to answer questions such as:
- In months that display large anomalies in the bar graph, are the anomalies driven primarily by widespread above- or below-average water vapor conditions across the Arctic? Or are they driven by intense departures from average water vapor conditions in one or two regions?
- Are positive water vapor anomalies more likely to occur over land or sea? What about negative anomalies?
- Can you spot geographical areas of recurring anomalies?
By comparing this map to other maps in Satellite Observations of Arctic Change, you can see whether anomalies in water vapor correlate with anomalies in near-surface air temperature, sea ice concentration, or snow cover duration.
These water vapor maps have been assembled by combining weather-forecast models with observations of atmospheric pressure, air temperature, humidity, and wind speed. The observations come from multiple sources: satellites, aircraft, balloon-borne instruments and weather stations.
The data shown here are from the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis project. To cover gaps in regions where surface observations are sparse, which has historically been true of the Arctic, reanalysis “predicts” the weather of the past. Observations of surface air pressure, air temperature, humidity and wind speed are blended with short-term forecasts from weather forecast models to provide the best estimate of atmospheric and surface conditions given available data.
Estimates of water vapor from reanalyses are not as reliable as, for example, surface pressure. All reanalysis products tend to have biases as compared to direct observations, especially in winter. Wintertime reanalyses may fail to capture low-level temperature inversions, and overestimate both temperatures and water vapor near the surface. Nevertheless, both reanalyses and observations generally show positive trends in atmospheric water vapor, though with varying levels of statistical significance.
Explore the source data for this map:
Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 instM_2d_int_Nx: 2d,Monthly mean,Instantaneous,Single-Level,Assimilation,Vertically Integrated Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [December 11, 2017], https://doi.org/10.5067/KVTU1A8BWFSJ
Data processing steps
To create this map, NSIDC took the following steps:
- Download data from GSFC
- Original data is received in a .25 x .33 degree latitude/longitude grid and is resampled using a nearest neighbor algorithm to a ~5 km polar stereo grid on on EPSG:3413.
- Use the ‘tqv’ variable in the dataset for the total column water vapor
- Create monthly average CSV files, i.e., for each month:
- Mask out data south of 60 degrees North
- Round to three decimals
- Compute the 1979-2015 climatological mean
- Compute anomaly by subtracting climatological mean
- Generate monthly climatology gridded datasets
- Calculate the mean grid for each month across the years 1979-2015
- Generate anomaly images for every year/month in the full timeseries by subtracting the monthly climatological mean grid (previous step) from each month’s grid.