Trends in extremes of 35 years of remotely sensed soil moisture Wouter Dorigo Stefan Schlaffer, Bernhard Bauer-Marschallinger, Diego Miralles, Wolfgang Wagner Vienna University of Technology Projected water cycle acceleration •  •  „Wet get wetter, dry get drier“ Higher variability -> more extreme events • 17 Global Climate Models •  A2 scenario (T increase of 3.0°C to 4.8°C in 2100 wrt 1980-‐2000) • SMpples: 90% of models agree • Color: 66% agree •  Is acceleration already visible in observations? [IPCC/SREX, 2012] Trends in CCI soil moisture (1988-2010) [Dorigo et al., 2012, GRL] [Dorigo et al., 2014, BAMS] §  Can we see trends also in soil moisture extremes? Microwave missions for soil moisture Resolution: ~50 km Revisit time: 1-3 days -> CCI soil moisture product •  Version 02.0 (July 2014) •  0.25° resolution •  Daily product •  Period 1978-2013 [Dorigo, W.A., unpublished] CCI Merging methodology in a nut shell 1) Scaling of the passive and acFve microwave products 1. Individual radiometer products based on VUA NASA method 2. Individual scaBerometer products on TU Wien method 3. Scaling and merging passive products to climatology AMSR-‐E 4. Scaling and merging acFve products to climatology ASCAT [Liu et al. 2011 & Liu et al. 2012] CCI merging methodology in a nut shell 2) Rescaling of the products to common global reference 3. Scaling and merging passive products to climatology AMSR-‐E 4. Scaling and merging acFve products to climatology ASCAT 5. Rescale acFve and passive to GLDAS-‐ NOAH reference [Liu et al. 2011 & Liu et al. 2012] Merging methodology in a nut shell [Liu et al. 2011 & Liu et al. 2012] 5. Rescale acFve and passive to GLDAS-‐ NOAH reference 6. Test sensiFvity to vegetaFon density 3) Quality assessment 7. Blend rescaled acFve and passive datasets 4) Merging at the global scale Merging active and passive observations •  •  •  •  For areas with moderate to dense vegetaMon we use acMve For (semi-‐)arid areas we use passive In “transiMon zones” we use both by averaging Ranking maps are dynamic over Mme, depending on available sensors Revisit time is variable over time [Dorigo W.A. et al., in rev., RSE] How do we express extremes? •  Block maxima, i.e. yearly extremes •  Precipitation •  Annual maximum precipitaMon •  Annual minimum precipitaMon is always 0 •  Soil moisture •  QuanMles (5% for dry, 95% for wet) •  CCI data set has data gaps -‐> use of ERA-‐Land for consistency checks •  Period 1991-‐2010 Generalised Extreme Value Distribution §  Block maxima can be described by: §  Trend can be incorporated: Surface temperature anomalies §  Parameters are fitted by minimum least squares m Sensitivity of extremes % change K-‐1,of 5% quanMle, GEV % change K-‐1,of 95% quanMle, GEV CCI soil moisture CCI soil moisture ERA-‐Land ERA-‐Land Effect of block size? monthly pentad daily 5% quanMle of ERA-‐Land 95% quanMle of ERA-‐Land Do we see changes in variability? CCI soil moisture ERA-‐Land Number of consecutive dry/wet days CWD CDD ERA-‐Land GPCP Consistency between soil moisture and P? Trends in maxima 1991-‐2010 CCI soil moisture ERA-‐Land GPCP Conclusions and outlook •  Trends in extremes can be detected in EO soil moisture •  SpaMal paierns are consistent for reanalysis and CCI SM products, but less so for precipitaMon •  Regional hotspots seem to be Australia, Sahel, Southern America, and Sahara/Arabian peninsula. Outlook: •  •  •  •  Independent data is needed to address diﬀerences Repeat analyses with new CCI soil moisture dataset (1978-‐2013) Beier metrics to describe soil moisture extremes? What is inﬂuence of climate dynamics. Data can be downloaded from: hBp://www.esa-‐soilmoisture-‐cci.org/ Data can be downloaded from: hBp://www.esa-‐soilmoisture-‐cci.org/ Thank you Composition per time period §  Sensors used per period and latitude ET Assimilation in hydrological model 30N–90N! Evaluation ECV_SM v0.1 §  Comparison with reanalysis soil moisture (ERA-Land, MERRA-Land) Spearman correlaMon [Albergel et al., 2013, RSE, JHM, Dorigo et al., 2012, GRL] Evaluation ECV_SM v0.1 §  Validation against in-situ data from the ISMN §  Slight degradation in latest period: §  §  §  Changed composition of stations Resampling of ASCAT (NN) Mismatch between nominal time stamp (0:00 UTC) and actual overpass time [Dorigo et al., 2014, RSE] Availability active and passive merged data AcMve Passive Combined Metadata Original retrieval Mmestamp Satellite mode Used sensor per retrieval Day/night ﬂag Quality ﬂag Frequency band Some applications (based on ECV_SM v0.1) §  Linking soil moisture to the carbon cycle Muñoz et al. (2014), Austral Ecology Comparison of the leading Empirical Orthogonal FuncMon (EOF) of six updated tree-‐ring chronologies of Araucaria with (A) regional satellite-‐observed summer (Dec–Feb) soil moisture and (B) correlaMon ﬁeld between this EOF and summer soil moisture variability across southern South America from 1979 to 2000. Some applications (based on ECV_SM v0.1) §  Coupling SM to climate modes (CEOF analysis) Can we predict soil moisture patterns? SSM anomalies 1997-‐98 (El Nino) CEOF SSM Forecast CCI SSM Anomaly observed MAM 1998 JJA 1998