Terrestrial satellite records for climate studies: how long is long enough? A test case for the Sahel

Satellite-based observations provide a unique data record to study the Earth system. Recent efforts of the space agencies to reprocess the archives of satellite observations aim to provide Essential Climate Variable (ECV) data records for manifold applications in climate sciences. Varying lengths of a data record or gaps in a data time series are likely to affect the analysis results obtained from long-term satellite data records. The present paper provides a systematic assessment of the impact of variations in the observational record of terrestrial ECVs for selected climate applications like trend detection and the analysis of relationships between different ECVs. As an example, the Sahelian drought and the subsequent recovery in precipitation and vegetation will be analyzed in detail using observations of precipitation, surface albedo, vegetation index, as well as ocean indices. The paper provides a different perspective on the robustness of long-term satellite observations than previous studies. It shows in particular that the long-term significant trends in precipitation and vegetation dynamics are rather sensitive to the investigation period chosen and that small data gaps can already have a considerable influence on the analysis results. It is therefore a plea for continuous climate observations from space.

[1]  Bruce A. Wielicki,et al.  Satellite Instrument Calibration for Measuring Global Climate Change: Report of a Workshop , 2004 .

[2]  Axel Andersson,et al.  The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3 , 2010 .

[3]  F. Joseph Turk,et al.  Precipitation from Space: Advancing Earth System Science , 2013 .

[4]  Rob J Hyndman,et al.  Phenological change detection while accounting for abrupt and gradual trends in satellite image time series , 2010 .

[5]  L. Leung,et al.  Evaluating regional cloud-permitting simulations of the WRF model for the Tropical Warm Pool International Cloud Experiment (TWP-ICE, Darwin 2006) , 2009 .

[6]  J. Janowiak,et al.  The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present) , 2003 .

[7]  Benjamin Smith,et al.  Precipitation controls Sahel greening trend , 2005 .

[8]  John F. B. Mitchell,et al.  THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research , 2007 .

[9]  Mark A. Bourassa,et al.  Globally Gridded Satellite Observations for Climate Studies , 2011 .

[10]  Jörg Trentmann,et al.  Remote sensing of solar surface radiation for climate monitoring — the CM-SAF retrieval in international comparison , 2012 .

[11]  J. Charney Dynamics of deserts and drought in the Sahel , 1975 .

[12]  Yi Y. Liu,et al.  Evaluating global trends (1988–2010) in harmonized multi‐satellite surface soil moisture , 2012 .

[13]  K. Wolter,et al.  Measuring the strength of ENSO events: How does 1997/98 rank? , 1998 .

[14]  R. Roebeling,et al.  Operational climate monitoring from space: the EUMETSAT Satellite Application Facility on Climate Monitoring (CM-SAF) , 2008 .

[15]  Alexander Loew,et al.  Evaluation of vegetation cover and land‐surface albedo in MPI‐ESM CMIP5 simulations , 2013 .

[16]  Michel M. Verstraete,et al.  A climate model-based review of drought in the Sahel: Desertification, the re-greening and climate change , 2008 .

[17]  Rasmus Fensholt,et al.  Analysis of trends in the Sahelian `rain-use efficiency' using GIMMS NDVI, RFE and GPCP rainfall data , 2011 .

[18]  K. Beurs,et al.  The response of African land surface phenology to large scale climate oscillations , 2010 .

[19]  Yi Y. Liu,et al.  Trend-preserving blending of passive and active microwave soil moisture retrievals , 2012 .

[20]  Alexander Loew,et al.  Combined evaluation of MPI‐ESM land surface water and energy fluxes , 2012 .

[21]  J. Wallace,et al.  A Pacific Interdecadal Climate Oscillation with Impacts on Salmon Production , 1997 .

[22]  R. Jeu,et al.  The European heat wave 2003: early indicators from multisensoral microwave remote sensing? , 2009 .

[23]  C. Thorncroft,et al.  African Monsoon Multidisciplinary Analysis: An International Research Project and Field Campaign , 2006 .

[24]  Ning Zeng,et al.  Expansion of the world's deserts due to vegetation‐albedo feedback under global warming , 2009 .

[25]  C. Tucker,et al.  Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003 , 2005 .

[26]  Yves M. Govaerts,et al.  Towards Multidecadal Consistent Meteosat Surface Albedo Time Series , 2010, Remote. Sens..

[27]  V. Brovkin,et al.  The effect of a dynamic background albedo scheme on Sahel/Sahara precipitation during the mid-Holocene , 2010 .

[28]  R. Fensholt,et al.  Evaluation of Earth Observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series , 2012 .

[29]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[30]  S. Goward,et al.  Objective assessment of the NOAA global vegetation index data product , 1993 .

[31]  S. Bruin,et al.  Trend changes in global greening and browning: contribution of short‐term trends to longer‐term change , 2012 .

[32]  D. Diner,et al.  Surface albedo retrieval from Meteosat: 1. Theory , 2000 .

[33]  Tim R. McVicar,et al.  Global evaluation of four AVHRR-NDVI data sets: Intercomparison and assessment against Landsat imagery , 2011 .

[34]  Frank Paul,et al.  Alpine glaciers to disappear within decades? , 2006 .

[35]  P. Sen Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[36]  J. Otterman,et al.  Baring High-Albedo Soils by Overgrazing: A Hypothesized Desertification Mechanism , 1974, Science.

[37]  A. Hall,et al.  Using the current seasonal cycle to constrain snow albedo feedback in future climate change , 2006 .

[38]  F. Chauvin,et al.  Interannual and decadal SST‐forced responses of the West African monsoon , 2011 .

[39]  Rasmus Fensholt,et al.  Analysis of teleconnections between AVHRR-based sea surface temperature and vegetation productivity in the semi-arid Sahel , 2011 .

[40]  A. Cazenave,et al.  The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables , 2013 .

[41]  H. Theil A Rank-Invariant Method of Linear and Polynomial Regression Analysis , 1992 .

[42]  Benjamin F. Zaitchik,et al.  Europe's 2003 heat wave: a satellite view of impacts and land–atmosphere feedbacks , 2006 .

[43]  Charles Doutriaux,et al.  Performance metrics for climate models , 2008 .

[44]  Rasmus Fensholt,et al.  Greenness in semi-arid areas across the globe 1981–2007 — an Earth Observing Satellite based analysis of trends and drivers , 2012 .

[45]  J. Ardö,et al.  A recent greening of the Sahel—trends, patterns and potential causes , 2005 .