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Data Enhanced Investigations for Climate Change Education (DICCE) Giovanni Help Page

DICCE-G Help Page, providing links to parameter definitions, examples of basic Giovanni procedures, and usage tips

Table of Contents

The name of each data parameter below is linked to its corresponding explanatory section.   Note that each section has a corresponding guide to interpreting trends in the data that may be discerned by time-series analysis.

 

Return to DICCE-G Basic Monthly Data Portal

Return to DICCE-G Basic Daily Data Portal

 

Physical Ocean

 

Ocean Biosphere

 

Physical Atmosphere

 

Atmospheric Gases

DICCE-G Daily only:

 

Precipitation

 

Energy

 
Physical Land
 
 
Land Biosphere 

 

Physical Ocean: 

Euphotic Depth,  Fraction of Sea Ice, Sea Surface Temperature


 Discussion of trends in the data:  Physical Ocean Trend Guide

Euphotic Depth

Definition:  The euphotic depth is the depth in a body of water at which light intensity is reduced to 1% of the value at the surface. Euphotic depth is an indicator of how readily light penetrates in the water column; light will penetrate to greater depth if the water is clear than if the water is turbid.
 
Measurement: Euphotic depth is calculated directly from satellite radiometric measurements of the ocean surface.  The current observational frequency is about once every other day.  Higher resolution ocean data, compared to other data parameters, will make Giovanni operations somewhat slower.
 
Why is it important, and what do trends mean?  Euphotic depth is a measure of the clarity (i.e., transparency) of ocean and lake waters. The clearer the water, the greater the numeric value of euphotic depth (i.e., the deeper you can see below the surface). For example, in an estuary that is turbid (i.e., one that has a lot of matter suspended or dissolved in the water), the euphotic depth may be only a few meters below the surface, but it may be 100 meters below the surface in the open ocean. Suspended matter in the water may be:
  • Sediments
  • Dead organic material
  • Microscopic plants called phytoplankton
Rivers carry a lot of suspended matter (and nutrients which feed phytoplankton) into the ocean, so an influx of river water will reduce the euphotic depth. Increasing trends in euphotic depth usually means that either less sediments or dead organic material are suspended in the water, or less microscopic plants called phytoplankton are growing in the water. However, decreasing trends in euphotic depth may be evidence that more of these substances are present. The presence of more phytoplankton growing in the water could be an effect of more of their nutrients reaching the ocean. 
 
Example. An example of a large body of water that has experienced a loss of euphotic depth is Lake Tahoe, on the border of California and Nevada. This lake, famous in the past for its very clear water, has been experiencing a trend of decreasing euphotic depth over several decades. This trend is probably due to the likelihood that increasing numbers of phytoplankton nutrients from human development projects around the lake are being transported into the lake through rainwater runoff and these nutrients are being consumed by increasing numbers of phytoplankton. The result is murkier water near the lake surface and lower readings of euphotic depth. (A phytoplankton nutrient is any substance that phytoplankton need in order to exist and grow. When in water, phytoplankton nutrients are in a dissolved state).
 
For more information about interpreting trends in euphotic depth, see the Physical Ocean Trend Guide.
 
 
 
Definition:  Sea ice is frozen seawater floating on the surface of the ocean.  Though the Fraction of Sea Ice data parameter does not distinguish between them, sea ice takes several forms:  frazil, grease ice, nilas, congelation ice, pancake ice, sheet ice, first-year ice, and multi-year ice.  See the end of this section for short definitions of each kind of sea ice.  
 
Measurement:  In DICCE-Giovanni, Fraction of Sea Ice is simply the fraction of a global data grid square covered by sea ice.  Thus, a value of 1.0 indicates 100% coverage of a grid square by sea ice, and a value of 0.5 indicates that 50% of the grid square is covered with sea ice. Fraction of Sea Ice is a variable from the Modern Era Retrospective-analysis for Research and Analysis (MERRA).  MERRA is a data assimilation system which utilizes satellite observational data acquired from 1979 to present to generate numerous environmental variables over this period.  
 
A data assimilation system (DAS) uses a weather model to maintain data continuity over time. When meteorologists forecast the weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements. In the field, snow mass would be determined by collecting and weighing the amount of snow on a square meter of ground.
 
Why is it important, and what do trends mean
In both hemispheres, sea ice forms in the polar oceans during winter as the surface freezes, and melts during summer. Thus, the maximum extent of sea ice coverage occurs near the end of winter and the minimum extent of sea ice occurs near the end of summer.  During winter in the Arctic, the ice grows from the edge of the sea ice pack to extend further south.   Different seas (such as the Bering Sea and Barents Sea) around the Arctic Ocean have different amounts of sea ice coverage due to ocean circulation (current) patterns.            
 
Recent observations in the Arctic Ocean have shown a marked decrease in the minimum (summer) sea ice extent, which is indicative of the overall warming trend in the Arctic region, both on land and in the Arctic Ocean.  Models of climate change indicate that the polar regions are likely to warm faster than the entire globe. There is not, however, as clear a trend in the maximum sea ice extent in the Arctic.  As long as the air temperature goes below the freezing point of seawater, sea ice will form, and Fraction of Sea Ice does not indicate the thickness (the sea ice volume) of the ice covering the sea surface.  Sea ice volume cannot be measured directly by remote sensing, but remote sensing observations can provide input to models of sea ice volume.  These models indicate a marked decrease in Arctic sea ice volume over past decades.
 
The situation in the Southern Ocean around Antarctica is more complex.  Satellite observations indicate a slight increasing trend in the sea ice maximum extent in winter.  There is no observable trend in the minimum sea ice extent, though it has become more variable in recent years.  In the Antarctic, sea ice growth extends out from the continent (northward) as winter progresses.  In the summer, sea ice in the Southern Ocean melts back to nearly the coast or ice shelf edges.
 
Several processes are affecting the maximum sea ice extent around Antarctica.  Wind speeds over the Southern Ocean are increasing, most likely due to a colder stratosphere induced by stratospheric ozone depletion and stratospheric cooling due to greenhouse gas trapping of heat in the troposphere.  Higher wind speeds may push the ice together, forming thicker ridges of multi-year ice on the coast.  This thicker multi-year ice may allow the seasonal sea ice to freeze faster and extend slightly further each year.  Also, Antarctica’s continental ice sheets are melting due to warmer ocean surface temperatures, which is making the surface ocean water fresher (less salty), and this less salty water freezes at a slightly higher temperature.  Also, warmer surface ocean waters are causing more snow to fall over the oceans, and on the surface of sea ice.  The snow insulates the surface of the sea ice and may allow it to freeze faster.
 
Sea ice is highly reflective, and thus entering solar radiation will be reflected back into space because of the high albedo of sea ice.  If sea ice is not present, solar radiation can enter the surface ocean waters, where it can be absorbed and warm the surface waters.   Sea ice is an important factor in polar ecosystems, because several polar fauna spend part or all of their life cycles on it (or under it).  The underside of sea ice can also host a rich amount of phytoplankton, and this provides food for organisms higher on the food chain in the polar oceans.  One of the important organisms in the Antarctic Ocean is krill, a form of zooplankton, which is a major food source for whales and penguins.
 
Types of sea ice:
 
·         Frazil: small crystals of ice suspended in the water; 
·         Grease ice: ice that forms at the surface just as the water freezes; 
·         Nilas, sheets of thin ice formed from grease ice; 
·         Congelation ice, thicker sheets with a smooth bottom surface; 
·         Pancake ice, thicker ice that forms edges which eventually merge, forming rafts and ridges;
·         Sheet ice, continuous sheets formed from merged pancake ice; 
·         First-year ice, the ice formed from open ocean water during winter;  and
·         Multi-year ice, ice that did not melt over the summer and remained to refreeze and become thicker during subsequent winters. 
 
For more information on interpreting trends in Fraction of Sea Ice data, see the Physical Ocean Trend Guide
 
 
Also see the section “Climate Trends and Global Warming.”
 

Sea Surface Temperature

Definition: Sea surface temperature (SST) is the water temperature close to the ocean surface.
 
Measurement: Sea surface temperature data are acquired by satellite remote sensing using microwave (infrared) wavelengths.  The brightness (i.e. radiative intensity) of a surface in microwave wavelengths is directly related to the temperature of the surface, and microwave brightness can be converted to temperature. The current satellite observational frequency is about once a day, and both daytime and nighttime observations are acquired. SST data from other sources is collected several times a day using sensors on multiple satellites. Higher resolution ocean data, compared to other data parameters, will make Giovanni operations somewhat slower.
 
 
Animated depiction of MODIS scanning while in orbit:

 
 
Extended definition: The ways these data are collected vary depending on the instruments used. The SST data product in DICCE-G measures the SST at night by remote sensing to a depth 1 millimeter (0.04 in) below the ocean surface. Other ways scientists measure SST include placing thermometers in the water on buoys or ships. Depending on other factors such as time of day and weather, scientists place these thermometers anywhere between 1 millimeter and 20 meters (i.e., 70 ft) below the surface. 
 
An instrument on National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites (with a history going back to the TIROS and Nimbus satellite missions), called the Advanced Very High Resolution Radiometer (AVHRR), measures microwave brightness at the wavelength of 11µm.  This is the 'traditional' wavelength for SST measurements, due to the long data record from this instrument. However, MODIS also measures microwave radiation at 4µm for SST, which may be a better wavelength to use for improved accuracy.  Thus, SST measured at both wavelengths is available in DICCE-Giovanni.
 
Why is it important, and what do trends mean? In many regions, SST varies over a considerable range of temperatures due to many processes, such as a change in current speed or the frequency of events like El Niño. Hence, it may be difficult to detect trends. However, a trend of increasingly higher SST over time would indicate warming ocean waters. In contrast however, a trend of decreasing SST over time in certain regions could indicate enhanced upwelling of greater amounts of cold deep water rising to the surface. This upwelling could be a result of warmer air temperatures heating up the water at the surface. (Upwelling of cold water occurs primarily adjacent to wind-driven ocean currents, such as the along the North American Pacific coast.) Some research has indicated that the strength of ocean currents may increase or decrease with global warming. [See the section “Climate Trends and Global Warming”].
 
For more information on interpreting trends in sea surface temperature, see the Physical Ocean Trend Guide.


 

Ocean Biosphere

Chlorophyll a


 Discussion of trends in the data:   Ocean Biosphere Trend Guide

Chlorophyll a  from SeaWiFS and MODIS

DefinitionChlorophyll a  is the most common photosynthetic pigment found in plants; for this oceanographic parameter, this means the chlorophyll found in the cells of phytoplankton. Chlorophyll a is responsible for the green color of many plants.
 
Measurement: Chlorophyll a concentration is calculated using remotely sensed observations of the ocean surface with visible wavelength data. The observational frequency is about once every other day. Higher resolution ocean data, compared to other data parameters, will make Giovanni operations somewhat slower.
 
Data product note:   There are two chlorophyll a data products available, from two instruments, the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite.  The SeaWiFS mission lasted from September 1997 to December 2010, with some missing months late in the mission.  The MODIS-Aqua mission began in June 2002 and continues into 2012.   Attempting to create averaged lat-lon maps of these data outside of the mission time ranges will fail, and the two data products cannot be averaged together.   Time-series with both data products can be plotted, however.   Data processing of the two data products intends to make them match as closely as possible.
 
Why is it important, and what do trends mean? The concentration of chlorophyll a in the ocean indicates the presence and concentration of phytoplankton, and is related to oceanic primary production (the creation of organic carbon by plants via photosynthesis). Trends over time of chlorophyll a may be related to changes in ocean conditions, such as changing SST, increasing nutrients, changes in seasonal timing, changes in river flow delivering water to the oceans, or changes in ocean currents. Because both human activities and natural processes can influence such factors as the concentration of nutrients in a river or the volume of water flowing in a river, not all trends in chlorophyll a are directly connected to climate change, though they likely indicate an environmental change. [See the section “Climate Trends and Global Warming”].
 

For more information on interpreting trends in chlorophyll a concentration, see the Ocean Biosphere Trend Guide.
 

 

The Physical Atmosphere

Cloud Fraction, CO2 Fraction, Relative Humidity, Temperature Profile

 Discussion of trends in the data:  Physical Atmosphere Trend Guide

Cloud Fraction (Day and Night), Terra and Aqua

Definition: The fraction of the sky that is covered by clouds; also referred to as Cloud Amount or Cloud Cover. In a satellite image, Cloud Fraction is the number of cloudy pixels divided by the total number of pixels.
 
Measurement:   Cloud fraction is calculated from microwave wavelength measurements (called “brightness temperatures”) by satellite sensors. Observational frequency is about twice a day.  Cloud fraction is unitless
 
Why is it important, and what do trends mean? Cloud fraction is indicative of weather patterns and the movement of air in the atmosphere. Climate is determined by the average weather conditions in a region, and one of these basic conditions is the amount of the sky that is covered by clouds. (For example, polar regions are much cloudier than desert regions, in general.)   Trends over time in cloud fraction can indicate changes in weather patterns related to precipitation and storms, and may also indicate changes in atmospheric temperature and humidity, all of which are affected by the average temperature of the atmosphere over a specific region of the Earth. [See the section “Climate Trends and Global Warming”].
 
 For more information on interpreting trends in cloud fraction data, see the Physical Atmosphere Trend Guide.

CO2 Fraction

Definition: Carbon dioxide (CO2) fraction indicates the amount of CO2 in the atmosphere by volume. It is usually expressed in units of parts per million (ppm), i.e., it is the number of molecules of CO2 represented in a million atmospheric gas (air) molecules. Currently, the average atmospheric CO2 fraction is about 380 ppm (380/1,000,000 or .00038), and it varies around the world by about ± 5 ppm.
 
Measurement: CO2 fraction is calculated by observing the absorption of energy by CO2 molecules for a specific wavelength of radiation. Observational frequency is about twice a day.
 
Why is it important, and what do trends mean? CO2 is probably the most well-known greenhouse gas because changes in CO2 concentration directly affect the amount of radiation absorbed in the atmosphere, and this influences the temperature of the Earth. CO2 is a greenhouse gas. Hence a trend over time in CO2 fraction thus indicates a likely change in the amount of re-radiated energy (in the form of heat) that is being reflected back to the Earth's surface and hence maintained in the Earth system rather than escaping back into space.
 
CO2 fraction varies from region to region due to fluxes into and out of the atmosphere and to its rapid mixing with other atmospheric gases. It is constantly being released into the atmosphere, taken out of the atmosphere, mixed with other atmospheric gases, and dispersed in different directions within the atmosphere by winds. In a region where human activities are emitting a large amount of CO2 into the atmosphere from sources such as motor vehicles and power plants, some of that emitted carbon will be absorbed into carbon "sinks" (also known as reservoirs) such as plants and large bodies of water, or carried to other places, just as water spilled onto the land from a pipe is rapidly mixed with other surface elements and dispersed. While globally the amount of COin the atmosphere is increasing (along with air temperatures). Yet the amount of CO2 present in the atmosphere over any particular region during any particular time period will both decrease and increase variably. Accompanying this variability are regionally varying greenhouse impacts of atmospheric CO2 on air temperatures.
 
CO2 in the atmosphere is constantly in a state of flux. For example, after it is emitted into the atmosphere (for example, from human sources such as power plants and motor vehicles), weather systems scatter it over wide geographical areas.In the process, some of it gets absorbed in carbon reservoirs such as the oceans, plants (via photosynthesis), and other carbon sinks. 
 
Basic information about greenhouse gases and the greenhouse effect.
Greenhouse gases are certain gases in the atmosphere which allow shorter-wavelength light energy from the Sun to pass through the atmosphere toward Earth's surface and then back out of the atmosphere. These gases also trap some of the Sun's energy in the form of longer-wavelength heat energy that results when shortwave energy is absorbed and then emitted as heat from the Earth's surface.  This trapped heat can then be re-radiated back toward Earth by the greenhouse gases.

The primary greenhouse gases in the atmosphere are water and CO2. Without these heat-trapping gases, Earth would be too cold for life to survive. The amount of re-radiated heat trapped by the atmosphere depends on the total concentration of greenhouse gases in the atmosphere. As the concentration of these gases increases, more heat is trapped and the temperature of Earth’s atmosphere rises. The warming that results when heat is trapped near Earth’s surface by greenhouse gases is known as the greenhouse effect.
 
 For more information on interpreting trends in CO2 data, see the Atmospheric Gases Trend Guide.

 

Relative Humidity

Definition: The relative humidity is a measure of the amount of water vapor in the air (at a specific temperature) compared to the maximum amount of water vapor air could hold at that temperature, and is given as a percentage value (i.e. it is unitless). Relative humidity depends on the temperature of the air, as warm air can hold more moisture than cold air. A relative humidity of 100% indicates that the air is holding all the water it can at the current temperature, and any additional moisture at that point will result in condensation (that temperature would be called the dew point).
 
Measurement:   Relative humidity is based on measurements of radiation absorption in microwave (infrared) wavelengths, which indicate the concentration of water vapor in the atmosphere. Observational frequency is about twice a day.  Many Earth observation satellites can take microwave measurements for both day and night, providing different information about parameters that can vary daily.  (See the note below about "ascending and descending nodes").
 
The instrument that acquires the relative humidity data is called a sounder. It acquires the data at 12 levels of the atmosphere, from the top to the bottom. Taken together, the 12 levels make up a data profile.
 
Why is it important, and what do trends mean? Because relative humidity is directly related to atmospheric temperature, a trend in relative humidity is probably indicative of a trend in atmospheric temperature. This could be a regional condition, due to changes in weather patterns, or a global condition, due to changes in global temperature (note that changing weather patterns can be due to changes in global temperature, too).   Different air masses can have different levels of relative humidity as well as different temperatures, so a trend can also indicate changes in storm patterns and the atmospheric transport of moisture.  [See the section “Climate Trends and Global Warming”].
 
 For more information on interpreting trends in relative humidity data, see the Physical Atmosphere Trend Guide.

Temperature Profile

Definition: The temperature of the atmosphere, measured from the surface of the Earth to the top of the stratosphere.
 
Measurement: Atmospheric temperature utilizes microwave and infrared (IR) wavelength bands on satellite sensors.  Atmospheric gases, notably oxygen and water vapor, have distinct spectra in the IR and microwave regions that  are temperature-dependent.  Analysis of these spectra, which are acquired by the satellite sensor, allows calculation of the atmospheric temperature.   Observational frequency is about twice a day. Many Earth observation satellites can take microwave measurements for both day and night, providing different information about parameters that can vary daily.  (See the note below about "ascending and descending nodes").
 
The instrument that acquires the atmospheric temperature data is called a sounder. It acquires the data at 24 levels of the atmosphere, from the top to the bottom. Taken together, the 24 levels make up a data profile.
 
Why is it important, and what do trends mean? Earth’s atmosphere has a general pattern of being warmer at the surface and colder at higher altitudes (think of high mountain peaks that are always covered with snow).   The temperature profile will vary with the weather and the characteristic of different air masses at different heights in the atmosphere.   Trends over time in atmospheric temperature profiles can indicate how much infrared radiation is being absorbed in the atmosphere rather than returning to space. The stratosphere will actually cool if this radiation, emanating from Earth's surface, gets trapped before reaching the stratosphere by greenhouse gases in the troposphere.   Hence, cooling of the stratospheric part of the temperature profile is indicative of increased warming potential in the tropospheric part of the temperature profile. [See the section “Climate Trends and Global Warming”].
 

 For more information on interpreting trends in temperature profile data, see the Physical Atmosphere Trend Guide.

 

Atmospheric  Gases

Aerosol Optical Depth, Deep Blue AOD, Total Column Ozone

  

Discussion of trends in the data:  Atmospheric Gases Trend Guide

Aerosol Optical Depth (MODIS-Terra)

Technical Definition: The degree to which aerosols (very small particles suspended in the atmosphere, such as dust or soot) prevent the transmission of light. The aerosol optical depth (AOD) or optical thickness (τ) is defined as the integrated extinction coefficient over a vertical column of unit cross section, where extinction refers to the intensity of light. AOD is a dimensionless quantity, expressing the negative logarithm of the fraction of radiation (e.g., light) that is not scattered or absorbed on a path.
 
Measurement:  
Aerosol optical depth (AOD) is calculated by measuring light absorption at specific wavelengths of the visible spectrum. For the most widely used AOD data product, the absorption at 550 nanometers is the preferred wavelength for measurement. (In the visible spectrum, humans perceive a light wavelength measuring 550 nanometers as a shade of green.) The measurement is taken by the satellite about once every other day. The satellites provide more continuous coverage nearer to the poles but there are more gaps in coverage the nearer the satellite is to the equator.    (See the animation under "Sea Surface Temperature").

Extended definition: AOD is a measure of the clarity (i.e., transparency) of the air in the atmosphere.  The more aerosols that are suspended in the atmosphere, the higher the value of AOD.   High AOD indicates a significant amount of absorption and scattering of radiation (i.e., light).  Low AOD indicates clearer air with fewer aerosols and increased transmission of radiation.   Increasing aerosol concentrations can thus affect global temperature and the radiation balance of the globe by reducing the amount of radiation reaching the Earth’s surface, and that reduction can result in lower air temperatures.   
 
Why is it important, and what do trends mean? AOD is important because aerosols absorb and scatter light, and can reduce the amount of light reaching the Earth’s surface. [See the section “Climate Trends and Global Warming”].
 
Example: Trends in AOD can be related to increasing or decreasing dust storms, wildfires, volcanic activity, and pollution caused by humans. High AOD could correspond to lower temperatures, and vice versa.
 
For more information on interpreting trends in AOD data, see the Atmospheric Gases Trend Guide.

Deep Blue AOD (MODIS-Terra for Monthly data, MODIS-Terra and MODIS-Aqua for Daily data)

DefinitionDeep Blue AOD is the same variable as AOD, but uses different wavelengths closer to the "blue" end of the visible spectrum to calculate the surface reflectivity so that the AOD values can be calculated over bright surfaces, particularly desert regions.  (The basic idea is that desert regions are "darker" in the bluer part of the spectrum, so the aerosol absorption will be easier to detect.) Deep Blue AOD is provided by the MODIS-Aqua satellite mission because the data extend over a longer time period.  The standard AOD algorithm for MODIS data does not work over spectrally bright (reflective) regions, hence the need for Deep Blue AOD data.  Exactly similar to AOD, it is a dimensionless parameter.
 
Deep Blue AOD is not available for MODIS-Terra after December 2007, due to unavailability of the required polarization corrections to the Level 1B data.  Level 1B is an intermediate data processing step where calibrations and corrections are applied to the raw data collected by the sensor.
 
Just as for 'standard' AOD, Deep Blue AOD is a measure of the clarity (i.e., transparency) of the air in the atmosphere.  The more aerosols that are suspended in the atmosphere, the higher the value of AOD.  
 
 Why is it important, and what do trends mean? Increasing or decreasing trends in Deep Blue AOD are likely related to the occurrence of dust storms in a given region.
 
For more information on interpreting trends in AOD data, see the Atmospheric Gases Trend Guide.
 

 

Definition:  Mass concentration is the mass of aerosol in the atmospheric column, from the surface to the top of the atmosphere.  The Mass Concentration (Land) data product is a measure of the aerosol optical depth over land surfaces.
 
Measurement: The satellite measures mass concentration in units of micrograms per cubic centimeter. The ”QA-w” in the name refers to Quality Assurance-weighting.  Data processing for certain global data products (including these) use indicators of data accuracy and reliability to select only data that can be averaged to produce a high-quality global data set. The need to take extra measures to assure the quality of the satellite-observed data varies depending on the data’s characteristics. For example, cloud cover does not need as rigorous a quality assurance test as does aerosol optical depth, because dust particles are more varied in content and hence harder to optically measure than are the contents of clouds.
 
MODIS mission scientists determine the quality of their data and filter out data that have poor quality. There are several reasons why the data may have poor quality; for example, thin cirrus clouds can interfere with the abilities of the instruments on the satellites to remotely sense the Earth’s surface.  This is why for the aerosol data from the MODIS instrument, scientists and technicians have instituted a quality assurance (QA) process, as explained in this paraphrased text from a published description of algorithms used to produce the MODIS aerosol data products. 
 
The MODIS products are assigned a QA “confidence’ flag” (QAC) that represents an overall assessment of the quality of the data based on the individual QA flags. Each QA flag indicates whether or not a certain condition affected its quality. A QAC value can be 3, 2, 1, or 0. A value of 3 represents the best quality and 0 represents the worst.  The QA confidence (QAC) flag serves another purpose as well. All MODIS-atmosphere products are averaged globally, on a 1° x 1° degree grid, on daily, weekly, and monthly time scales. These gridded products are known as the Level 3 (L3) products. The QAC flag is used for weighting data products at a spatial resolution of 10 km onto the 1° grid. Those retrievals with QAC = 3 are assigned higher weights than those with QAC = 2 or QAC = 1. Retrievals that receive a rating of 0 are not included in the 1° averages. A higher weight means greater confidence that the data are accurate. Note that DICCE-G uses only Level 3 data products from MODIS.
 
 
Why is it important, and what do trends mean? Because mass concentration is calculated from aerosol optical depth, it is an indication of the presence and concentration of the same constituents (dust, smoke, and pollution particles) that affect the value of aerosol optical depth.  The value of mass concentration quantifies the dimensionless value of aerosol optical depth, and thus can be used to provide an improved impression of how much aerosol is in the atmosphere, altering the reflection, absorption, and attenuation of incoming sunlight.   Note that the Mass Concentration (Land) parameter is not based on the Deep Blue AOD algorithm. In the varied and complex ways that aerosols interact with water vapor and other gases and influence cloud structures, scientists believe that aerosols contribute to both greater warming and greater cooling of the atmosphere. Most believe at this juncture that there is more of a cooling effect than a warming effect.   Many aerosols, particularly very small particles, can be trapped in the lungs and thus can negatively impact human health by causing respiratory problems.
 
For more information on interpreting trends in Mass Concentration data, see the Atmospheric Gases Trend Guide.
 
 
Definition: Mass concentration is the mass of aerosol in the atmospheric column, from the surface to the top of the atmosphere, in units of micrograms per cubic centimeter.  Mass Concentration (Ocean) is calculated from the aerosol optical depth over the ocean.
 
'QA-w' refers to Quality Assurance-weighting (see above for more information).  Data processing for certain global data products (including these) uses indicators of data accuracy and reliability to select only data that can be averaged to produce a high-quality global data set .
 
Why is it important, and what do trends mean?  Because mass concentration is calculated from aerosol optical depth, it is an indication of the presence and concentration of the same constituents (dust, smoke, and pollution particles) that affect the value of aerosol optical depth.  The value of mass concentration quantifies the dimensionless value of aerosol optical depth, and thus can be used to provide an improved impression of how much aerosol is in the atmosphere, altering the reflection, absorption, and attenuation of incoming sunlight. In the varied and complex ways that aerosols interact with water vapor and other gases and influence cloud structures, scientists believe that aerosols contribute to both greater warming and greater cooling of the atmosphere. Most believe at this juncture that there is more of a cooling effect than a warming effect, yet all would agree that aerosols negatively impact human health by causing respiratory problems.
 
For more information on interpreting trends in Mass Concentration data, see the Atmospheric Gases Trend Guide.
 

Total Column Ozone 

Definition: The total amount of ozone that is found in a column of air above the earth from the surface to the top of the atmosphere. The majority of this amount is typically found in the stratosphere. Total column ozone is expressed in Dobson Units (DU). A Dobson Unit refers to the measurement of ozone concentration by a Dobson spectrometer. At standard temperature and pressure conditions, 1 Dobson unit would be a layer of ozone molecules 1/100 of a millimeter thick.
 
Measurement: For both ground-based and satellite sensors, the measurement of ozone concentration is based on the absorption of radiation at specific wavelengths by ozone molecules. The observational frequency is about twice per day.
 
Why is it important, and what do trends mean? Ozone occurs in the stratosphere and the troposphere, which is below the stratosphere all the way to the Earth's surface. In the stratosphere, the ozone absorbs ultraviolet light from the Sun, preventing it from reaching the Earth’s surface. This can be thought of as “good” ozone. Until very recently, people were emitting chemicals called chlorofluorocarbons (CFCs) into the atmosphere that they were using as coolants and cleaners in refrigerators and other machines. These CFC emissions caused depletion of the stratospheric ozone, leaving actual holes (strongly reduced concentrations) in the stratosphere under certain conditions. These holes have caused greater amounts of harmful solar radiation to get to the Earth surface, which increases the risk of skin cancer. Recently, the Montreal Protocol banned CFCs and the ozone holes are slowly being filled in again as ozone concentrations recover. This ozone hole phenomenon is important for understanding the effects of good ozone but has nothing to do with the bad effects of ozone as greenhouse gases, which are explained below. 
 
Yet, in addition to producing tropospheric ozone through these photochemical reactions, sunlight also breaks it down. The amount of it in the troposphere also decreases when it is taken in by various ozone sinks, such as plants (http://www.ghgonline.org/otherstropozone.htm). Tropospheric ozone also can decrease the amounts of methane and other greenhouse gases because it reacts with water vapor to form hydroxyl (OH) radicals, which oxidize the methane.
  
The Total Column Ozone data product is more suited to examine stratospheric ozone concentrations than lower atmosphere (tropospheric) ozone concentrations. Land surface altitude can also affect the total column ozone value, because the length of the atmospheric column is decreased over high altitude regions. Other satellite instruments are better for the measurement of tropospheric ozone concentrations. 
 
For more information on interpreting trends in total column ozone data, see the Atmospheric Gases Trend Guide.
 

Methane, CH4

Definition: Methane is a highly flammable and highly potent, yet short-lived, greenhouse gas. Much of the fossil fuel we call natural gas consists of methane. As a byproduct of combustion, concentrations of methane in the atmosphere may be the result of large quantities of fossil fuel combustion or biomass burning at the Earth's surface.  Methane is also the product of the breakdown of organic matter by certain types of bacteria that exist in certain sediment environments found in wetlands and on the ocean floor.
 
Methane also exists in solid form. These solids, known as clathrates, exist on the ocean floor and in cold polar waters. Warming water can cause these clathrates to release methane in its gaseous state.
 
The volume mixing ratio for CH4 is the ratio of the density of CH4 in a particular volume of air to the total density of CH4 in the atmosphere. The density is the number of molecules for a particular unit of volume.
 
Measurement: Satellite instruments measure quantities of methane in the atmosphere by detecting how much radiation is being absorbed by methane molecules at wavelengths in the infrared/microwave (IR/MW) spectral range. The satellite is especially sensitive to detecting methane in the mid-troposphere. It was designed this way to make it well suited for global daily observations, as methane concentrations nearer to the surface are much more variable.  

Why is it important, and what do trends mean? Methane in the atmosphere is an indicator of human activities and can be an effect of climate change.  Because it is released into the air from combustion, it is likely to be mainly concentrated where either humans are burning fossil fuels, or where biomass is burning, due (for example) to forest fires or to farmers clearing fields through controlled burnings.
 
The satellite that remotely senses the methane data product is set up to observe it in the mid- to upper troposphere, where seasonal weather processes may have a greater effect on the size of methane concentrations than surface processes. In other words, a large concentration of methane detected in a certain column of the atmosphere may have been brought there by weather fronts, rather than from combustion events at the bottom of the column.
 
Methane concentrations may be related to climate change in several ways. Warming wetlands, estuaries (salt marshes), and permafrost will release methane to the atmosphere, as will the melting of seafloor methane deposits if cold deep waters get warmer. Also, biomass burning, which releases methane into the atmosphere, may be a result of droughts that arise from the decreased precipitation that is predicted to occur as a result of increasingly warm temperatures in many regions of the world. Yet it should be noted that even though atmospheric methane concentrations have generally been increasing, there have been recent periods when the concentrations did not increase for several years.
 
 For more information on interpreting trends in methane data, see the Atmospheric Gases Trend Guide.
 

Carbon monoxide, CO

Definition:  Carbon monoxide (CO) is a noxious yet odorless gas that, like methane, is a byproduct of combustion. Thus, large concentrations of CO in the atmosphere can be evidence of fossil fuel or biomass burning at the surface. The volume mixing ratio (VMR) for CO is the ratio of the number density of CO to the total number density of the atmosphere (density is the number of molecules per unit volume). CO absorbs infrared radiation just like carbon dioxide, water vapor, methane, and other gases that are called greenhouse gases, but because CO molecules are short-lived in the atmosphere, they are not considered to have an influence on global warming.
 
Measurement:  Satellite instruments measure quantities of carbon monoxide in the atmosphere by detecting how much radiation is being absorbed by carbon monoxide molecules at  wavelengths in the infrared/microwave (IR/MW) spectral range. The satellite that remotely senses this data product, AIRS, measures the VMR of carbon monoxide in several atmospheric layers. 
 
Why is it important, and what do trends mean?  Unlike methane, which is another fairly direct measurement of the environmental influence of combustion, carbon monoxide has a relatively short atmospheric lifetime, so it does not have a large influence on climate. Hence,  it is not as helpful as other some other trace gases in predicting climate change because climate change occurs over a much longer time period than does the existence of any specific concentration of carbon monoxide. Yet, because high concentrations of carbon monoxide indicate surface events such as heavy fossil fuel consumption, forest fires, or agricultural burning, its presence constitutes evidence of such events. Also, an increased frequency of forest fires may be a result of droughts; the droughts may be a result of less precipitation; and the decreased precipitation may be the result of warming air temperatures and a general drying trend in warm regions of the Earth's land surface.
 
For more information on interpreting trends in carbon monoxide data, see the Atmospheric Gases Trend Guide.

Nitrogen dioxide, NO2

Definition: Nitrogen dioxide is a trace gas that is produced through both human and natural events. The nitrogen dioxide data product is a measure of the number of molecules of NO2 in a vertical column of the atmosphere stretching from the Earth's surface to the top of the troposphere. Nitrogen dioxide is formed when combustion takes place, because the burning process creates nitrogen dioxide from the massive quantities of naturally produced nitrogen that are already in the air. NOis even formed by lightning. Hence, high concentrations of nitrogen dioxide may result from lightning storms, heavy fossil fuel consumption, forest fires, or crop burning.
 
Measurement:  Satellite instruments measure quantities of nitrogen dioxide in the atmosphere by detecting how much radiation is being absorbed by nitrogen dioxide molecules at wavelengths in the ultraviolet/visible (UV/VIS) spectral range.
 
Why is it important, and what do trends mean?  Though relatively short-lived in the atmosphere, and not in itself considered at this juncture a greenhouse gas, nitrogen dioxide makes an indirect contribution to global warming because it is a constituent in a series of chemical reactions that, in the presence of sunlight, convert two human-generated air pollutants known as volatile organic compounds and nitrogen oxide into tropospheric ozone, which is a greenhouse gas.
 
A high concentration of nitrogen dioxide may also be a sign of warming in another indirect way:  forest fires which contribute to increased concentrations of NO2 may be a result of droughts, the droughts may be a result of less precipitation, and the decreased precipitation may be the result of warming air temperatures from climate change.
 
For more information on interpreting trends in nitrogen dioxide data, see the Atmospheric Gases Trend Guide.
 

Sulfur dioxide, SO2

Definition: Sulfur dioxide is an atmospheric gas that is released by volcanic eruptions as well as by human sources such as fossil fuel power plants. The sulfur dioxide data product is a measure of how much sulfur dioxide (SO2) there is in a vertical column of the atmosphere stretching from the Earth's surface to the top of the troposphere . Like ozone, SO2 concentrations are estimates of the density of SO2 in a particular column of the atmosphere.
 
Measurement:  The column density of sulfur dioxide is measured in Dobson Units, where 1 Dobson Unit (i.e., DU) = 2.69 ∙1016 molecules per centimeter squared (i.e., cm2).Satellite instruments measure quantities of atmospheric sulfur dioxide by detecting how much radiation is being absorbed by sulfur dioxide molecules at wavelengths that are in the ultraviolet/visible (UV/VIS) range of the color spectrum.
 
Why is it important, and what do trends mean?  As a type of aerosol, sulfur dioxide can contribute to both atmospheric cooling and warming, yet is widely assumed at this juncture to have more of a cooling effect (for more on this see the Enhanced Help entries for Mass Concentration). While most eruptions do not release an amount of sulfur dioxide that will significantly influence the Earth's climate, very large eruptions may do so. Evidence indicates that large amounts of sulfur dioxide aerosols in the upper atmosphere, particularly the stratosphere, reflect incoming sunlight, at least partially, and this reflectance helps to cool the temperature of the Earth. Even the largest individual eruptions will rarely influence Earth's climate this way for longer than a decade, yet when sustained over long periods of time, volcanic activity is believed to be a cause of major climate changes in Earth's history, partly due to the cooling effects of sulfur dioxide. As with other aerosols, concentrations of sulfur dioxide in the atmosphere can threaten human health due to its effect on the respiratory system. It also contributes to acid rain, which threatens aquatic life in lakes and other bodies of water.
 
For more information on interpreting trends in sulfur dioxide data, see the Atmospheric Gases Trend Guide.
 

Precipitation

Click for:

Discussion of trends in the data:  Precipitation Trend Guide
 
Definition: Any of all of the forms of water particles, whether liquid or solid, that fall from the atmosphere, some of which reach the ground and some of which evaporate before reaching the ground. Forms of precipitation are: rain, drizzle, snow, snow grains, snow pellets, diamond dust, hail, and ice pellets. The amount of precipitation that accumulates on the ground is expressed in units of depth (such as inches or millimeters). The rate or intensity of precipitation is commonly expressed in units of depth or mass per unit time and area (for example, inches or millimeters or rainfall per day over a square meter area).   Snowfall accumulation is a measure of how deep the water would be if the snow melted, and snowfall depth is a measure of how deep the snow is on the ground in the area of snow accumulation before the snow melts.
 
Measurement: The detection of precipitation by satellite remote sensing is based on the absorption of radiation at specific wavelengths in the microwave radar range of the electromagnetic spectrum. The observational frequency is about once a day. In contrast, weather stations on the ground simply measure precipitation by directly measuring how much rainwater falls into a calibrated collection vessel. Snow measurements on the ground are accomplished by different methods, which will be discussed for snow variables.
 
Why is it important, and what do trends mean?   Precipitation is important because it provides water for natural ecosystems, and is required by plants for growth. Precipitation is also important as a source of freshwater to freshwater lakes, aquifers, and rivers.   Humans activities need precipitation for their drinking water, sanitation and household use, and for crop irrigation.  
 
Trends in precipitation can be important indicators of climate change. Increased precipitation and also increased numbers of severe storm events are generally linked to increasing relative humidity.   Decreased precipitation is generally associated with increasing surface temperatures, which can cause (1) evaporation of light rain before it reaches the ground, (2) evaporation of soil moisture (which provides a source of water vapor to produce rain), or (3) changes in weather patterns that cause air masses with different water vapor content to be transported over a given region.  [See the section “Climate Trends and Global Warming”].


Precipitation data sets in DICCE-G Basic: 

Accumulated Precipitation

The Global Precipitation Climatology Project (GPCP) is a data set of the amount of accumulated precipitation.   It is created using a combination of measurement techniques, both satellite remote sensing and rain gauges at ground stations. 
 
The GPCP is an element of the Global Energy and Water Cycle Experiment (GEWEX), part of the World Climate Research program (WCRP). It was established by the WCRP in 1986 with the initial goal of providing monthly mean precipitation data on a 2.5°× 2.5° latitude-longitude grid. The GPCP has accomplished this by merging infrared and microwave satellite estimates of precipitation with more than 6,000 rain gauge data stations. Infrared precipitation estimates are obtained from GOES (United States), GMS (Japan) and Meteosat (European Community) geostationary satellites and National Oceanic and Atmospheric Administration (NOAA) operational polar orbiting satellites. Microwave estimates are obtained from the U.S. Defense Meteorological Satellite Program (DMSP) satellites using the Special Sensor Microwave Imager (SSM/I). These data sets are used to validate general circulation and climate models, study the global hydrological cycle, and diagnose the variability of the global climate system. Data sets have been expanded so that in addition to the monthly mean product available in DICCE-Giovanni, the GPCP now has a 2.5°×2.5° degree pentad (i.e., 5-day) data set starting in 1979 and a 1°×1° daily data set starting in 1997.
 
The data represent the amount of precipitation that has accumulated as water on an area of land in a particular time period.

 

Notes: 
 ̶  Climatology (as it appears in the name of this data set) refers to a compilation of weather conditions over time; it can also mean meteorological conditions averaged over an extended period of time.
 ̶  A geographical degree is a square area of Earth's surface that is bounded by 1° of latitude and 1° of longitude. At any place on Earth, 1 degree of longitude is always a length of 111 kilometers. However, due to the fact that the Earth's circumference is different depending on how distance from the equator, a latitudinal degree is on average also 111 km but is slightly larger at the polar latitudes than at the equatorial latitudes.
 
For more information on interpreting trends in GPCP precipitation data, see the Precipitation Trend Guide.
 

Observed Ground Station Precipitation

This data set is the Willmott-Matsuura ground station- (rain gauge) based precipitation data set. It is a measure of how much precipitation fell at individual ground stations over a given period of time. Global Historical Climatology Network (GHCN version 2) and Legates and Willmott's (1990a and b) station records of monthly and annual mean air temperature (T) and total precipitation (P) were used to produce these data. The time period evaluated was 1950 through 1999. The previous version (3.01) only extended through 1996 and years with any missing monthly values were treated as missing. All available monthly values were taken into account in this version. The total number of GHCN stations used was 7,280 for air temperature, and 20,599 for precipitation. However, the actual number of GHCN stations available for each month varies from about 1,260 to 5,860 for air temperature and from about 1,870 to 16,360 for precipitation.  The number of stations (and oceanic grid nodes) taken from the Legates and Willmott archive was 24,941 for air temperature, and 26,858 for precipitation, respectively.   http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_clim2.html
 
Notes:   In the compilation of this data set, the number of available stations with acceptable data quality is different every month. So each month of data likely had a different number of weather stations contributing to the averaged data values for that month. 
 
For more information on interpreting trends in the Willmott-Matsuura precipitation data, see the Precipitation Trend Guide.
 
For more information on the data set, go to the online document Welcome to Willmott, Matsuura, and Collaborators' Global Climate Resource Pages. This site requires a brief registration to view the content.

GLDAS Rainfall Rate

This precipitation data product is a model output variable from the Global Land Data Assimilation System, and is given in units of kilograms per square meter per second (kg/m2/s). It represents the average rate at which rain fell over a given area in a given month or set of months.
 
A data assimilation system (DAS) uses a weather model to maintain data continuity over time. When meteorologists forecast the weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements.
 
For more information on interpreting trends in GLDAS rainfall rate data, see the Precipitation Trend Guide
 

Energy

Net Longwave Radiation, Net Shortwave Radiation, Photosynthetically Available Radiation

Discussion of trends in the data:  Energy and Radiation Trend Guide

Net Longwave Radiation

Definition: Net longwave radiation is a measure of the difference between outgoing longwave radiation radiating upward from the earth surface and atmospheric longwave counter-radiation radiating downward toward the earth surface. Thus, net longwave radiation can be expressed by:

       net longwave radiation (W/m2) = incident longwave counter-radiation (W/m2) - outgoing longwave radiation (W/m2
 
 

Measurement: Net longwave radiation data is a model output variable from the Global Land Data Assimilation System. A data assimilation system uses a weather model to maintain data continuity over time. When meteorologists forecast the weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements.
 
Why is it important, and what do trends mean?
Some of the energy that is absorbed into the land and water on the Earth's surface  is re-radiated from the Earth surface as terrestrial longwave radiation). The amount of energy emitted is primarily dependent on the temperature of the surface. The hotter the surface the more radiant energy it will emit. When re-radiated from the Earth surface , some of the radiation is scattered by the atmosphere downward, which is the longwave atmospheric counter-radiation). Most will re-radiate back into the atmosphere.  [ Note that because of the way that net longwave radiation is calculated, higher negative values indicate hotter surfaces, as more longwave radiation is being radiated from those surfaces. ]
 
Certain gases of the atmosphere are relatively good absorbers of longwave radiation, driving the greenhouse effect.
Trends in net longwave radiation therefore indicate a change in the energy balance of a certain region.  A decreasing trend could be evidence of greater amounts of absorption of this longwave radiation being absorbed into in our atmosphere rather than returning to space and that could be a sign of more greenhouse gases in the atmosphere. An increasing trend value could be evidence of lesser amounts being absorbed into our atmosphere, and that could be the product of fewer greenhouse gases such as carbon dioxide or water vapor in the atmosphere. [See the section “Climate Trends and Global Warming”].
 
For more information on interpreting trends in net longwave radiation data, see the Energy and Radiation Trend Guide.
 

Net Shortwave Radiation

DefinitionNet shortwave radiation is a measure of the difference between incoming solar shortwave radiation and outgoing shortwave radiation from the Earth’s surface (outgoing shortwave radiation is primarily due to reflection, rather than the result of the absorption into the land or water on the Earth's surface of radiation and the subsequent re-radiation back out into the air that takes the form of longwave radiation). Net shortwave radiation can be expressed by the amount of incident solar shortwave radiation absorbed on the earth surface per unit of area:

          net shortwave radiation (W/m^2) = {direct shortwave radiation (W/m^2)
       + diffused shortwave radiation (W/m^2)} (1 – surface albedo)

Expanded definition:

Put more simply, Net SW = (Incoming SW) (1-albedo)

So, if the albedo (reflectivity) is high (as it is for snow) then the "1-albedo" term is low, so Net SW will be low. Correspondingly, if albedo is low (as it is for grass or trees or dirt), then "1-albedo" is high, and Net SW is high.

Therefore, net shortwave radiation is roughly the total amount of shortwave radiation absorbed by the land, For highly reflective areas, like snow or sand, less shortwave radiation is absorbed than for less reflective areas, like areas with vegetative cover.

SummaryNet shortwave radiation is low on surfaces that are highly reflective (i.e., with high albedo) and high for surfaces with low reflectivity (i.e., low albedo). Net Shortwave Radiation is expressed this way because this is the radiation that gets absorbed by the land surface, and is then converted to heat and re-radiated out in all directions as longwave radiation. It is this heat, as longwave radiation, that gets increasingly trapped by greenhouse gases in the atmosphere as greenhouse gas concentrations rise.

Diagram of net shortwave radiation difference over low and high albedo areas 

 
 
Measurement: Net shortwave radiation data is a model output variable from the Global Land Data Assimilation System.  
 
A data assimilation system (DAS) uses a weather model to maintain data continuity over time. When meteorologists forecast the weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements.
 
 
Why is it important, and what do trends mean?  
Shortwave radiation from the Sun penetrates through space to the outer edge of the atmosphere unimpeded by the vacuum of outer space. Once solar radiation begins to penetrate through the atmosphere this amount begins to decrease due to absorption and reflection in the atmosphere.  About 30% of the available solar radiation at the top of the atmosphere (TOA) is reflected or scattered back to space by particulates and clouds before it reaches the ground.  Because the gases of the atmosphere are relatively poor absorbers of solar radiation, only about 20% of the TOA radiation is absorbed in the atmosphere. The remaining solar radiation, about 50% of the TOA radiation, makes its way to the surface as direct and diffuse solar radiation.Direct solar radiation).  In equation form: (S) is shortwave radiation able to penetrate through the atmosphere without having been affected by constituents of the atmosphere in any way. Diffuse radiation (D) is shortwave radiation that has been scattered by gases in the atmosphere. Scattering is a process whereby a beam of radiation is broken down into many weaker rays redirected in other directions. Together, direct and diffuse shortwave radiation accounts for the total incoming solar radiation or insolation(K
K↓ = S+D 
A portion of the incoming solar radiation is absorbed by the surface and a portion is also reflected away. The proportion of light reflected from a surface is the albedo (a). Albedo values range from 0 for no reflection to 1 for complete reflection of light striking the surface. Albedo can be expressed as a percentage (albedo multiplied by 100) that for some is easier to understand. For instance, grass has an albedo of about .23. This means that of the incoming solar radiation that strikes the grass, 23% of it is reflected away. On the other hand, highly reflective surfaces like snow have an albedo upwards of .87, or 87% of sunlight is reflected away.
 
Trends in net shortwave radiation therefore indicate changes in the processes that absorb, reflect, or scatter incoming solar radiation.   Such processes include reflection or scattering by clouds and atmospheric aerosols. Decreased albedo, which could be caused by a decrease in reflective ground cover (notably snow or ice) will also affect net shortwave radiation by decreasing how much of it is reflected off of the surface. [See the section “Climate Trends and Global Warming”].
 
For more information on interpreting trends in net shortwave radiation data, see the Energy and Radiation Trend Guide.
 

Photosynthetically Available Radiation (PAR)

DefinitionPhotosynthetically Available Radiation (PAR) may also be called Photosynthetically Active Radiation. This data product is the number of photons impinging upon a square meter per second in the visible wavelength range (i.e., 400 to 700 nanometers). It indicates the total energy available to plants for photosynthesis, and is thus a key parameter for biological and ecological studies. (http://www.hobilabs.com/cms/index.cfm/37/1288/1301/1407/3241.htm)
  
Measurement: PAR data are acquired by directly measuring the intensity of radiation at specific visible wavelengths and summing these measurements over the visible wavelength range.
 
Why is it important, and what do trends mean? Because PAR is the solar radiation that is used by plants for photosynthesis, changes in PAR will affect the growth of plants on land and in the oceans. In other words, more PAR leads to greater amounts of production of new plant matter during times of the year when production is possible (such as the spring and summer in the temperate areas of the Northern Hemisphere where deciduous trees grow.) For example, in the ocean the increase of PAR that occurs in the spring contributes to rapid growth of phytoplankton blooms. On land in the spring and summer, deciduous trees produce leaves which enable growth.
 
PAR is affected by any process that absorbs, reflects, or scatters light in the atmosphere (or reduces it in ocean waters). Therefore, trends showing changes in PAR levels could be evidence of changes in the amount of cloud cover or aerosols (dust and polluting gases) in the atmosphere. For example, cloud cover generally increases over tropical areas in the summer and this reduces PAR in these regions. If a trend of decreasing PAR is observed over the course of several years, this may be due to increased cloud cover, which may in turn be caused by an increased capacity of a warmer atmosphere to hold greater amounts of moisture. For related information, see the entry for Relative Humidity.   [See the section “Climate Trends and Global Warming”].
 
For more information on interpreting trends in PAR data, see the Energy and Radiation Trend Guide.
 

Physical Land

GLDAS Soil Moisture
 
Surface temperature:
GLDAS Average Surface Temperature, MODIS Land Surface Temperature, GLDAS Near Surface Air Temperature, MERRA Surface Skin Temperature
 
Near Surface Wind Magnitude, Snowfall Rate, Snow Occurrence Frequency, Snow Depth, Fractional Snow Cover, Snow Mass

 
Definition: 
     Generally, soil moisture is the water that is held in the spaces between soil particles. Surface soil moisture is the water that is in the upper 10 cm of soil, whereas root zone soil moisture is the water that is available to plants, which is generally considered to be in the upper 200 cm of soil.  (Adapted from “Soil Moisture”, http://wwwghcc.msfc.nasa.gov/landprocess/lp_home.html ).  Surface soil moisture usually refers to soil moisture in the top 10 centimeters of soil, while root zone soil moisture refers to soil moisture in the top 200 cm of soil.
 
Measurement: 
     Soil moisture can be measured in the field by different methods.  The most accurate method, gravimetry, is to obtain a sample of soil, weigh it, heat it to drive off all of the water, and then weigh the sample again.  The difference between the original weight and the dry weight of the soil is the water content, which can be converted to soil moisture units of grams per cubic centimeter of soil (g/cc) or kilograms per cubic meter (kg/m3).    There are several different types of sensors that can directly measure soil moisture on the ground, including frequency domain sensors, neutron moisture gauges, time domain transmissometers, and time domain reflectometers.  Soil moisture sensors can measure the soil properties that are different for wet and dry soils, such as electrical conductivity or resistance, neutron scattering, or heat dissipation.  A soil moisture probe frequently uses multiple soil moisture sensors to give the most accurate reading.  
      Soil moisture is measured by remote sensing using microwave radiation, because wet soil and dry soil have a very distinct contrast in their dielectric properties.  The dielectric constant of water is 80 and the dielectric constant of dry soil is less than 5.  This means that there will be a large contrast in the emission of microwave radiation from wet and dry soil (emissivity), which can be measured by a satellite sensor.  One important factor in measuring soil moisture from space is that a sufficiently large area must be observed to provide a large enough signal to be detected.  (Dielectric materials are materials that are poor conductors of electricity.)
 
Why is it important, and what do trends mean?
     Soil moisture is important for a number of reasons.  Likely the most significant reason is that the moisture content of soil is vital for plant growth, hence important in agriculture for the growth of crops.   Soil moisture is also a fundamental factor affecting soil erosion, potential for landslides, and amount or runoff that can be expected from rainfall or snow melt.
      Soil moisture is also an important determinant of weather phenomena.  More water will evaporate from wet soils as they get warmer (especially when warmed by sunlight), and this evaporated water can then form clouds which produce precipitation.   Dry soil also tends to be lighter in color than wet soil, so drier soils tend reflect more solar radiation whereas wetter soils absorb more of it, warming the soil and surface temperature.  
     Trends in soil moisture are usually related to the amount of precipitation received in a particular region.   More rainfall will generally result in wetter soils, and less rainfall will generally result in drier soils.  Note, however, that the impacts of soil moisture can vary quite a bit over an area, depending on types of soil and topography. For example, soils on a slope will drain and dry out faster than soils in flat areas and soil full of sand and gravel absorbs more water than soil full of clay. (http://www.shodor.org/Master/environmental/water/runoff/RunoffApplication.html)
     Other factors that can affect soil moisture trends are surface temperature, as warmer soils will dry out faster than cooler soils, and sun exposure, as soil exposed to more sunlight will dry out faster than soil exposed to less.   Thus, for example, if the amount of rain falling over a certain area remains about the same over a series of days or months, yet the amount of soil moisture is persistently decreasing, the change might be from persistently warmer temperatures, which could be causing more evaporation. Conversely, if the rainfall is consistent yet the soil moisture is persistently decreasing, the change might be from persistently greater cloud coverage, which would lead to less solar radiation reaching the ground and less evaporation of the surface water.
 
Example: 
     Soil moisture can be used to estimate the intensity of a drought, because as a drought worsens, the depth of dry soil will increase.    The National Oceanic and Atmospheric Administration uses the soil moisture anomaly (the amount of soil moisture more or less than the average soil moisture for an area in a given month or season) in the top 1 meter of the soil as an indicator of how severe a drought is or how likely a flood might be. For example, in January 2014, much of California was experiencing the effects of a severe drought, and much of the Midwest was experiencing moderate drought.  The image below displays the soil moisture anomaly for January 31, 2014, indicating those areas. It also shows where snow in the unusually cold winter had increased soil moisture levels (for example, in Montana, the Dakotas, New York, and the New England states).
 
Soil moisture anomaly, January 31, 2014
 
 
 

 
Surface Temperature: (general definition, and types of data sets)
Definition:Atmospheric temperature is, in literal terms, a measure of the average kinetic energy of air molecules in the atmosphere, either over land or over water. (Kinetic energy is the energy of motion). Atmospheric temperature is expressed in units of Kelvin or degrees Celsius.
 
There are several types of data products available in Giovanni that express atmospheric temperatures. Near surface air temperature data express the temperature of the air over land.  In the field, these surface temperatures would be measured by thermometers placed approximately 2 meters above the ground. 
 
In contrast, the data product called average surface temperature presents the actual temperature on the surfaces themselves, on both land and ocean. There are, for land surfaces only, also surface "skin" temperatures. Giovanni surface skin temperature data are calculated from measurements of microwave radiation at the land surface taken by satellite. 
 
Note that because of their definitions and measurement methods, surface air temperatures and surface skin temperatures can be slightly different. However, they are related sufficiently so that either can be used to examine temperature trends over time.

 

Why is it important, and what do trends mean? Temperature is a fundamental variable in climate, affected by all of the absorptive and reflective properties of the Earth’s atmosphere, oceans, and land surface. The surface temperature is related to the amount of solar radiation a region receives, which is in turn dependent on both astronomical factors (which determine the seasons and longer climate cycles) and weather (clouds, atmospheric air movements, evaporation and precipitation, etc.) The surface temperature is also related to the average global temperature, which is determined by the Earth’s radiative balance, as set by the absorptive and reflective properties of the atmosphere. A changing average global temperature may influence local weather processes in ways that make regional temperature trends more pronounced than average global temperature trends.

 

 

Example: Scientists have strong evidence that global warming and its effects are more pronounced in the world's polar regions than in the temperate zones. This is due to many factors such as increased absorption of heat into the polar lands and oceans. A major cause of this increased absorption of heat is increased melting of the ice and snow that had been reflecting much of the solar radiation back up into the atmosphere through a process known as albedo. [See the section “Climate Trends and Global Warming”]. 

 

GLDAS Average Surface Temperature

This is a model-generated surface temperature calculated by the Global Land Data Assimilation System.  GLDAS products are only generated over land. Average surface temperature is also called surface skin temperature. http://www.asprs.org/a/publications/proceedings/portland08/0020.pdf 
 
 
A data assimilation system (DAS) uses a weather model to maintain data continuity over time. When meteorologists forecast the
weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements. In the field, the average surface temperature or surface temperature is measured with an infrared radiation measuring device directed at the surface of the Earth on bare ground.
 
For more information about interpreting trends in average surface temperature, see the Physical Land Trend Guide.

MODIS Land Surface Temperature (daytime and nighttime, Terra and Aqua)

Measurement: The MODIS and surface temperature product is calculated from infrared radiation data measured by the MODIS satellite instruments, based on measured radiation intensity in specific bands. (Details: http://modis-land.gsfc.nasa.gov/temp.htm '5.6 km' indicates the size of the individual data pixels (the spatial resolution) is 5.6 x 5.6 kilometers.
 
For more information about interpreting trends in land surface temperature, see the Physical Land Trend Guide. 
 

GLDAS Near Surface Air Temperature

This GLDAS model output variable expresses the air surface temperature above the Earth’s surface.  GLDAS products are only generated over land. 
 
A data assimilation system (DAS) uses a weather model to maintain data continuity over time. When meteorologists forecast the weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements.
 
For more information about interpreting trends in near surface air temperature, see the Physical Land Trend Guide. 

MERRA Surface Skin Temperature

The Modern Era Retrospective-analysis for Research and Analysis (MERRA) is a data assimilation system which utilizes satellite observational data acquired from 1979 to present to generate numerous environmental variables over this period. MERRA surface skin temperature is similar to the GLDAS average surface temperature, although MERRA surface skin temperature is calculated over both land and oceans.
 
A data assimilation system (DAS) uses a weather model to maintain data continuity over time. When meteorologists forecast the weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements. In the field, the temperature directly above the surface of the Earth is determined by measuring the radiative emission (infrared) from the surface with an infrared radiation measuring device.
 
For more information about interpreting trends in surface skin temperature, see the Physical Land Trend Guide.
   

Near surface wind magnitude

DefinitionNear surface wind magnitude is a measure of the strength of the horizontal movement of the air near the Earth’s surface. The MERRA definition is wind speed at 10 meters above the ground. Near surface wind magnitude is directionless, indicating only the strength of the wind.
 
Measurement: Near surface wind magnitude is a model output variable from GLDAS. A data assimilation system (DAS) uses a weather model to maintain data continuity over time. When meteorologists forecast the weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements. In the field, near surface wind magnitude is measured with an anemometer.
 
Why is it important, and what do trends mean? The speed (or strength) of the wind is an important climate variable. At any particular time and place, wind always blows in a certain direction. As it moves along its path, wind changes speed and the variances in its speed are primarily a product of differences in the amounts of air pressure at whatever surfaces it encounters along its path. Variances in wind speed affect other variables in the atmosphere and at the Earth's surface including levels of evaporation, precipitation, and temperature. Changes in wind speed indicate changes in weather patterns, and climate change if they persist over long periods of time. Larger differences in pressure or temperature along a path of wind would be apparent as fast-moving strong-blowing wind whereas smaller differences in pressure or temperature would be apparent as slower weaker-moving wind. Depending on regional characteristics, a climate change-related trend in changing wind speeds may be in either direction (faster or slower). [See the section “Climate Trends and Global Warming”].
 
Example. Some say that climate change may bring about more extreme weather. Some regions will become wetter and others will become drier. Applying what we know about the scientific relationship between air pressure levels and weather, it is fair to say that the air pressure in the increasingly wetter regions will be increasingly low for longer periods of time whereas the air pressure in increasingly dry regions will become increasingly high. If this proves to be the case and these regions share the same wind paths, these climate changes would also be characterized by increasing wind speeds.
 
For more information about interpreting trends in near surface wind magnitude, see the Physical Land Trend Guide.

 

Snowfall Rate

DefinitionSnowfall Rate is a measure of the intensity of snowfall. It is measured by calculating how much snow falls to the earth surface per unit area per unit of time. The units are kilograms per square meter per second (kg/m2/s). 
 
Measurement: Snowfall rate is a model output variable from GLDAS. A data assimilation system (DAS) uses a weather model to maintain data continuity over time. When meteorologists forecast the weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements. In the field, snowfall rate is measured by weighing the amount of snow that falls on a measured area over a specific period of time.
 
Why is it important, and what do trends mean? Snowfall rate is a weather variable related to the temperature of the atmosphere and the water vapor content of the atmosphere. Trends of changing snowfall rates would likely indicate an increasing or decreasing frequency of snowfall events, which may be affected by trends of changing atmospheric temperatures or water vapor content.  [See the section “Climate Trends and Global Warming”].
 
Example: Lower snowfall rates that persist over many winters in a certain region could be evidence of region's climate warming because the warmer air could hold more water vapor. Furthermore, whenever the air got cold and moist enough to become saturated and release precipitation, the precipitation would hence be more likely to fall to the ground as rain rather than snow .
 
For more information about interpreting trends in snowfall rate, see the Physical Land Trend Guide.

Snow Occurrence Frequency

DefinitionThe monthly snow occurrence frequency is a measure of what percentage of a certain geographical area is covered by snow. The geographical area is a 1-degree grid cell, and the occurrence frequency is calculated as an average of all daily fractions of snow for a given month.   The grid used is roughly 24 x 24 kilometers in area, and each of these 1 degree grid cells has about 20 sub-grid cells. Daily snow cover for the 1 degree grid cell is calculated by determining how many sub-grid cells are covered with snow each day; if 10 are covered, then the fraction would be 50%.   Averaging these daily values gives the monthly snow occurrence frequency.
 
Measurement: Snow occurrence frequency is an output from the National Oceanic and Atmospheric Administration (NOAA) Interactive Multisensor Snow and Ice Mapping System (IMS). The input data for the IMS are acquired by a wide variety of satellite imagery, as well as mapped products derived from satellite imagery and surface observations.
 
Why is it important, and what do trends mean? Snow cover is an important variable influenced by weather, particularly the temperature and the water content of the atmosphere. Trends in snow occurrence frequency can indicate either more or less snowfall in a given region, or more or less snow melt in a given region. [See the section “Climate Trends and Global Warming”].
 
Example. A trend of persistently small snow occurrence frequency fractions over the course of many winters could indicate that less snow is accumulating and is melting faster. Both of these trends could signify warming climate. Data showing decreased snow fraction is also a sign of less albedo (i.e., less reflecting of solar radiation off of the surface and more absorption of it into the ground).
 
For more information about interpreting trends in snow occurrence frequency, see the Physical Land Trend Guide.
 

Snow depth

Definition: The actual depth of snow on the ground, measured in units of length (inches or centimeters).
 
Measurement: Snow depth is a model output variable from MERRA. A data assimilation system (DAS) uses a weather model to maintain data continuity over time. When meteorologists forecast the weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements. In the field, snow depth would be determined by measuring the depth of snow with an accurately calibrated ruler that can be inserted into the snow pack on flat ground with little influence of wind.
  
Why is it important, and what do trends mean? The depth of the snow is determined by how much snow has fallen and what is its  consistency. The same amount of snow is likely to be deeper when it is light and fluffy than when it is full of water because wet snow takes up a smaller amount of space. Hence, data about snow depth are difficult to interpret for meaningfulness without knowing also about how thick and wet the snow is, how much of it has fallen over a particular period of time, and what the air temperatures were during and after the snowfalls. These additional data would be helpful in interpreting whether deep snow means lots of snow or just thick dense snowfall filled with lots of water. Decreased snow depth could be evidence of warming temperatures if the snow depth is shallower due to greater water content in the snow, since snow falling in a relatively warm below-freezing temperatures is likely to be warmer than snow falling in relatively cold below-freezing temperatures. Conversely, decreased snow depth could also be evidence of warming temperatures if the warmer temperatures are leading to less snowfall overall. A sign of this would be snow content that remains as dry and fluffy as it was in prior winters yet less deep. [See the section “Climate Trends and Global Warming”].
 
For more information about interpreting trends in snow depth, see the Physical Land Trend Guide.
 

Fractional snow cover 

Definition: The standard MODIS snow product is a daily fractional snow map, generated from the daily 500 meter resolution MODIS snow product (acquired using the 500-meter resolution bands) by collecting the observations and calculating the fraction of snow observations (pixels) mapped into a 0.25 degree grid cell. Each 0.25 degree grid cell will have about 80 pixels, each providing a percentage of snow cover. Average fractional snow cover is calculated by summing the 0-100% snow values; the 0% fraction snow class is included in the sum, then dividing by the count of observations included in the sum.  Fractional snow cover is more quantitative than snow occurrence frequency, which only indicates whether or not snow was present.  As it is expressed as a percentage, fractional snow cover is unitless.
 
Measurement: The fractional snow cover variable is a model output variable from MERRA. The actual remote-sensing measurement technique which provides data for the model is based on the definition described above. A data assimilation system (DAS) uses a weather model to maintain data continuity over time. When meteorologists forecast the weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements.
 
Why is it important, and what do trends mean? Like snow occurrence frequency, fractional snow cover indicates how much of a given area was covered by snow in a given time period.   Because snow cover is influenced by both the amount of snow delivered to an area by snowfall events and the average temperature of a region, trends in fractional snow cover can indicate both the influence of changing weather patterns and changes in the average temperature of a region. [See the section “Climate Trends and Global Warming”].
 
For more information about interpreting trends in fractional snow cover, see the Physical Land Trend Guide. 
 

Snow mass

Definition: The mass of snow, in kilograms, on a square meter of land surface. This is a more direct measure of the amount of water contained in snow than snow depth or snow occurrence frequency.
 
Measurement: Snow mass is a model output variable from MERRA. 
A data assimilation system (DAS) uses a weather model to maintain data continuity over time. When meteorologists forecast the weather they need assimilated weather models to fill in the gaps in real observed data that they use to make their weather forecasts. The gaps exist because of missing data or bad data quality. To ensure that the model mixes well with the real data and hence most accurately predicts the weather, the data assimilation system uses the observed data to make the output from the model consistent with that data, which ensures that the model accurately produces environmental variables. Thus, most output variables from a weather DAS are based on data acquired by remote sensing or ground station measurements. In the field, snow mass would be determined by collecting and weighing the amount of snow on a square meter of ground.
 
Why is it important, and what do trends mean? Snow mass provides the actual mass of snow – and hence the water content – of snow in a given region. Unlike snow depth, which is influenced by the characteristics of snow, snow mass is more directly related to how much water the snow contains, which is important for the hydrology of a region, particularly if that region relies on snowmelt for a substantial part of its water supply. Snow mass is directly related to how much snow falls on a given region, and also the regional temperature, so as is true for other snow variables, trends in snow mass can indicate changes in regional temperature or the water content of the atmosphere.   [See the section “Climate Trends and Global Warming”].
 
For more information about interpreting trends in snow mass, see the Physical Land Trend Guide.
 


Land Biosphere

Normalized Difference Vegetation Index, Leaf Area Index


 Discussion of trends in the data:   Land Biosphere Trend Guide

Normalized Difference Vegetation Index (NDVI)

Definition: The Normalized Difference Vegetation Index (NDVI) is calculated from measurements of spectral reflectance in the near infrared (NIR) and visible (VIS) portions of the electromagnetic spectrum.   NIR and VIS spectral reflectances are calculated from the bands or wavelengths of the observing instruments. NDVI is calculated as:
 

NDVI =  (NIR - VIS) / (NIR + VIS)

 
These spectral reflectances are themselves ratios of the reflected over the incoming radiation in each spectral band individually – hence they take on values between 0.0 and 1.0. By design, the NDVI itself thus varies between -1.0 and +1.0. (Note that land cover values of NDVI will range from 0 to +1.0; clouds and water have negative NDVI values).   Note that the NDVI 5.6 km data parameter is the same quantity as the NDVI parameter, at a higher spatial resolution.  As NDVI is a ratio, it is a unitless parameter.
 
Extended definition: NDVI indicates the “greenness” of the surface without specifying what it is about that surface that makes it appear green. Nevertheless, greenness is almost always signifies the presence of green plants.
 
Measurement:   NDVI is calculated from MODIS radiation data acquired in specific wavelength bands in the visible and near infrared parts of the electromagnetic spectrum. '5.6 km' indicates the size of the individual data pixels (the spatial resolution) is 5.6 x 5.6 kilometers. '5.6 km' indicates the size of the individual data pixels (the spatial resolution) is 5.6 x 5.6 kilometers.
 
Why is it important, and what do trends mean? For areas that have seasonal differences in greenness, i.e., changes in the growing vegetation cover, NDVI will show large changes between the seasons. NDVI is also useful for indicating the influence of precipitation, either higher or lower than average, which will affect NDVI by either increasing or decreasing the greenness of a region (due to enhanced or diminished growth of vegetation). NDVI anomalies, i.e., the difference between NDVI at a given time from the average NDVI for a region, can thus indicate drought conditions or higher-than-average precipitation conditions. Abrupt changes in NDVI may indicate places where people are people are destroying forests or the spatial growth of urban areas.  [See the section “Climate Trends and Global Warming”].
 
For more information on interpreting trends in NDVI data, see the Land Biosphere Trend Guide.

 

Enhanced Vegetation Index (EVI)

Definition:  A vegetation index is a measurement of the "greenness" of the Earth's land surface, with increasing greenness indicating increase ground coverage by growing vegetation. The Enhanced Vegetation Index (EVI) was developed to strengthen the detection of vegetation, with greater sensitivity in high biomass regions.  An article at the NASA Earth Observatory, Measuring Vegetation (NDVI & EVI), states: "...it [EVI] corrects for some distortions in the reflected light caused by the particles in the air as well as the ground cover below the vegetation. The EVI data product also does not become saturated* as easily as the NDVI when viewing rainforests and other areas of the Earth with large amounts of chlorophyll."  Thus, compared to NDVI, the EVI detects small vegetation cover differences better in areas that are heavily vegetated.
 
*saturated means that the value of the index is at the highest value possible, which for NDVI and EVI is a value of 1.0 (i.e. 100%).
 
Measurement:   EVI is calculated from MODIS radiation data acquired in specific wavelength bands in the visible and near infrared parts of the electromagnetic spectrum.  '5.6 km' indicates the size of the individual data pixels (the spatial resolution) is 5.6 x 5.6 kilometers. EVI is also a unitless parameter.
 
Why is it important, and what do trends mean?  EVI is similar to NDVI with regard to the surface conditions it represents and the information that it conveys.  Therefore, for areas that have seasonal differences, i.e., changes in the growing vegetation cover, EVI, like NDVI, will show large changes between the seasons.  EVI is also useful for indicating the influence of precipitation, either higher or lower than average, which will affect the parameter by either increasing or decreasing the greenness of a region (due to enhanced or diminished growth of vegetation).  EVI anomalies, i.e., the difference between EVI at a given time from the average EVI for a region, can thus indicate drought conditions or higher-than-average precipitation conditions. Abrupt changes in EVI may indicate places where people are people are destroying forests or the spatial growth of urban areas.

 

For more information on interpreting trends in EVI data, see the Land Biosphere Trend Guide.
 
 
 


 
Many satellites that orbit the Earth are in orbits that are called polar orbits - that means that they go over both of Earth's polar regions, the Arctic and Antarctic.   Satellites with this kind of orbit have an ascending node and a descending node of the orbit.  The ascending node is when the satellite is traveling south to north over the Earth's surface.  The descending node is when the satellite is traveling from north to south over the Earth's surface.
 
The AIRS instrument that provides relative humidity and temperature data is on the NASA Aqua satellite, which has its descending node on the sunlit (day) side of the Earth, and its ascending node on the dark (night) side of the Earth.  
 
Below is a table that shows for parameters that have ascending and descending node data, which of these is on the day side and which of these is on the night side of the Earth.  For the data in the DICCE Daily portal, both the Aqua and Aura satellites are in ascending node orbits, so all the ascending node data products are acquired on the daylight side, and the descending node data products are acquired on the nighttime side.
 
Parameter Node Day Side/Night Side
 CH4 (methane) Ascending Day
 CH4 (methane) Descending Night
 CO (carbon monoxide) Ascending Day
CO (carbon monoxide) Descending Night
Outgoing longwave radiation flux Ascending Day
Outgoing longwave radiation flux Descending Night
Relative humidity Ascending Day
Relative humidity Descending Night
Surface air temperature Ascending Day
Surface air temperature Descending Night
Temperature profile Ascending Day
Temperature profile Descending Night
Total column ozone Ascending Day
Total column ozone Descending Night
 
 

 



Additional information:  'Dimensionless' or 'unitless' parameters

A “dimensionless” data parameter is one that does not express real units of measurement. It is a relative quantity based on a mathematical operation that was conducted on real observations. Dimensionless data parameters may, for example, be percentages, ratios, or negative natural logarithms. In DICCE G, the real observations are either from remote sensing by NASA satellites or data assimilation models

 

 

 

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