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Projects > M - O > MERRA-2 Climatology

Preferred term

MERRA-2 Climatology  

Definition

  • The Modern Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) contains a wealth of information that can be used for weather and climate studies. By combining the assimilation of observations with a frozen version of the Goddard Earth Observing System (GEOS), a global analysis is produced at an hourly temporal resolution spanning from January 1980 through present (Gelaro et al., 2017). It can be difficult to parse through a multidecadal dataset such as MERRA-2 to evaluate the interannual variability of weather that occurs on a daily timescale, let alone determine the occurrence of an extreme weather event. Furthermore, it was recognized that standard metrics were needed to evaluate climate change among climate models and international research efforts. As a result of these concerns, the Expert Team on Climate Change Detection and Indices (ETCCDI) developed a set of indices that represent the frequency and intensity of extreme weather events using a daily time series of 2-m air temperature (T2m) and precipitation (Alexander et al., 2016). These indices were used as a basis to comprise a list of fields that represent daily extreme temperature and precipitation events, heatwaves, multi-day precipitation, as well monthly percentile statistics from the MERRA-2 dataset. Also included in this data product is a climatological long term mean and standard deviation representing the interannual variability on a monthly timescale. (en)

Broader concept

Change note

  • 2020-08-07 08:18:29.0 [tstevens] Insert Concept add broader relation (MERRA-2 Climatology [7f951de2-9ebf-4143-ac9d-eb12676cae86,560586] - M - O [a31c2828-9b6d-44e9-b6ad-7ae81030f322,548709]);
  • 2020-08-07 08:19:15.0 [tstevens] insert AltLabel (id: null category: primary text: MERRA-2 Climate Statistics and Climatological Long Term Mean language code: en);
  • 2020-08-07 14:04:18.0 [tstevens] insert Definition (id: null text: TheModernEraRetrospectiveanalysisforResearchandApplications,Version2(MERRA-2)containsawealthofinformationthatcanbeusedforweatherandclimatestudies.BycombiningtheassimilationofobservationswithafrozenversionoftheGoddardEarthObservingSystem(GEOS),aglobalanalysisisproducedatanhourlytemporalresolutionspanningfromJanuary1980throughpresent(Gelaroetal.,2017).ItcanbedifficulttoparsethroughamultidecadaldatasetsuchasMERRA-2toevaluatetheinterannualvariabilityofweatherthatoccursonadailytimescale,letalonedeterminetheoccurrenceofanextremeweatherevent.Furthermore,itwasrecognizedthatstandardmetricswereneededtoevaluateclimatechangeamongclimatemodelsandinternationalresearchefforts.Asaresultoftheseconcerns,theExpertTeamonClimateChangeDetectionandIndices(ETCCDI)developedasetofindicesthatrepresentthefrequencyandintensityofextremeweathereventsusingadailytimeseriesof2-mairtemperature(T2m)andprecipitation(Alexanderetal.,2016).Theseindiceswereusedasabasistocomprisealistoffieldsthatrepresentdailyextremetemperatureandprecipitationevents,heatwaves,multi-dayprecipitation,aswellmonthlypercentilestatisticsfromtheMERRA-2dataset.Also included in this data product is a climatological long term mean and standard deviation representing the interannual variability on a monthly timescale. language code: en);
  • 2020-08-07 14:06:30.0 [tstevens] update Definition (The Modern Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) contains a wealth of information that can be used for weather and climate studies. By combining the assimilation of observations with a frozen version of the Goddard Earth Observing System (GEOS), a global analysis is produced at an hourly temporal resolution spanning from January 1980 through present (Gelaro et al., 2017). It can be difficult to parse through a multidecadal dataset such as MERRA-2 to evaluate the interannual variability of weather that occurs on a daily timescale, let alone determine the occurrence of an extreme weather event. Furthermore, it was recognized that standard metrics were needed to evaluate climate change among climate models and international research efforts. As a result of these concerns, the Expert Team on Climate Change Detection and Indices (ETCCDI) developed a set of indices that represent the frequency and intensity of extreme weather events using a daily time series of 2-m air temperature (T2m) and precipitation (Alexander et al., 2016). These indices were used as a basis to comprise a list of fields that represent daily extreme temperature and precipitation events, heatwaves, multi-day precipitation, as well monthly percentile statistics from the MERRA-2 dataset. Also included in this data product is a climatological long term mean and standard deviation representing the interannual variability on a monthly timescale.);

URI

https://gcmd.earthdata.nasa.gov/kms/concept/7f951de2-9ebf-4143-ac9d-eb12676cae86

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