1. Global cloud observations based on three decades of AVHRR measurements Martin Stengel, Rainer Hollmann, Karl-Göran Karlsson, Jan Fokke Meirink ISCCP at 30, City College of New York, NY, April 2013

2. 2 Outline AVHRR-based datasets (including CM SAF CLARA-A1) Evaluating the accuracy of AVHRR cloud retrievals Sampling issue COSP type satellite simulator Aerosols and clouds Summary

3. CLARA-A1 NWC SAF Polar Processing System (PPS) software package for CFC, CTH, CTP, CTT (Dybbroe et al., 2005a, 2005b) Cloud Physical Properties (CPP) software for COT, LWP, IWP, CPH (Roebeling et al., 2006) AVHRR-GAC of all NOAAs and MetOp, 1982 – 2009, global coverage on 0.25° lat/lon VIS: Recalibrated visible reflectances provided by NOAA (Heidinger et al., 2010). IR: unchanged (only onboard BB calibration) Daily and monthly means, 1d/2d histograms CM SAF cLoud, Albedo and RAdiation dataset, AVHRR-based, v1 (Karlsson et al, 2013) 3

4. AVHRR based cloud climatologies Advantages: Global coverage with similar viewing geometries (Tropics tendentially with larger SZAs) 5 spectral channels back to 1982 (this is at the cost of spatiotemporal coverage) Disadvantages Need for inter-calibration of VIS channels Sparse temporal sampling (i.e. in the 80’s and early 90’s) Satellite drifting Example datasets: Patmos-X v6 (AVHRR Pathfinder Atmospheres - Extended version 6), Heidinger et al. (2009, 2012) CLARA-A1 (CMSAF cLoud, Albedo and Radiation – AVHRR based version 1), Karlsson et al. (2013) Cloud CCI (Climate Change Initiative – on ECV clouds, ORAC alg.) 4 Foster and Heidinger (2012)

5. 5 Accuracy of AVHRR-based cloud properties Validating AVHRR-based cloud retrievals (of existing or planned datasets) in a common framework CM SAF: EUMETSAT Satellite Application Facility on Climate Monitoring; CPP (Cloud Physical Properties; Roebeling et al., 2006) algorithm developed at KNMI and PPS (Polar Platform System; Dybbroe et al., 2005a; Dybbroe et al. 2005b) developed at SMHI ORAC: (Oxford RAL Retrieval of Aerosol and Cloud) algorithm (Poulsen et al., 2010 and Watts et al., 1998) developed at Oxford University and Rutherford Appleton Laboratory (RAL) CLAVR-X: Cloud from AVHRR Extended processing scheme hosted at NOAA at University of Wisconsin (Pavolonis et al.,2005; Walther et al., 2012) AVHRR/NOAA18MODIS/AQUA (1.6mic) MODIS/AQUA (3.7mic) CalipsoCMa, CTH, CPH AMSR-ELWP DARDARIWP

6. Cloud detection & height assignment 6 Sensitivity of clouds detection to cloud optical depth Sensitivities of cloud height assignment to cloud optical depth We start to systematically miss clouds with COT<0.3 for passive imagers (Karlsson et al., 2013) These are global values; there is certainly a scene dependence (vs. CALISPO)

7. Detecting thermodynamic phase Phase validation against CALIPSO, Liquid cloud occurrence 7 IR vs. VIS/NIR information gives different phase retrievals due to different penetration depths CALIPSO phase vs. AVHRR CTT shows interesting features

8. Integrated quantities - Cloud water paths Liquid water path against AMSR-E Ice water path against DARDAR (Delanoë and Hogan (2008, 2010)) also see Eliasson et al. 2013 (JGR) All results in Stengel et al. (2013) Recurring validation/evaluation efforts of this kind helps us to identify strengths and weakness of these datasets and helps us to understand which application are meaningful

9. Comparing different datasets 9 CM SAF CLARA-A1 PATMOS-x ISCCP

10. CLARA-A1 sampling issue Significant diurnal cycle in cloud properties create trends when satellites drift (or jumps at transitions of different satellites) Correcting for imperfect sampling of diurnal cycle (e.g. Devasthale et al, 2012; Foster and Heidinger, 2012) For potential climate model evaluation satellite simulators will solve this problem in this context 10 Foster and Heidinger (2012)

11. CLARA-A1 long-term stability Cloud fraction vs. SYNOP (which reliably reported over the full period) 11 (GAC ALL = CLARA-A1) SYNOP cloud fraction seems to be stable over this period Decrease in CLARA-A1 also visible in afternoon satellite time series

12. COSP type CLARA simulator Currently being developed in CM SAF, including specific retrieval considerations CTP-COT 2D histograms CLARA vs. EC-EARTH (AMIP, SST prescr.) - JAN 1999 12 CLARA-A1 ECEARTH CLARA simulated Courtesy of Joseph Sedlar (Temporal sampling of AVHRR not included so far)

13. Can we detect impact of aerosols? 13 Is there any detectable signal of liquid cloud occurrences for different cloud top temperature? ( PATMOS-x version 6, 2 years of NOAA15, NOAA18, MetOp-A data, daylight, ocean only) -20°C Liquid cloud fraction -25°C -30°C Zonal average of liquid cloud fraction for different cloud top temperature Global liquid cloud occurrence relative to all clouds Very preliminary results! more aerosol less aerosol

14. 14 Summary CM SAF has recently jointed the international effort of generating AVHRR- based long-term cloud property dataset (CLARA-A1) Taking AVHRR-only gives the advantage of enhanced spectral information at the cost of temporal coverage Intercomparison study done, revisiting the accuracy of AVHRR-based retrieval schemes Differences among the results of the retrieval schemes are partly large (might partly be large than the difference to products of other sensors) Outcomes of this has been fed back to retrieval developers, already initialized further developments (will always be a never ending process) Cloud cover stability (Trend in CLARA-A1 CFC not confirmed by SYNOP) CLARA-A1 simulator results Quality of AVHRR-based retrievals have improve and enable new applications (E.g. looking at the aerosol impact?) Uncertainty estimates? Few schemes provide this pixel-based information (but based on different sources. How do we include this information into higher level products (monthly means/histograms)?

15. 15 Thank you

16. 16 Choi, Y.-S. et al., 2010: Space observations of cold-cloud phase change, PNAS, vol. 107, issue 25, pp. 11211- 11216 Devasthale, A. et al. 2012: Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function, Atmos. Meas. Tech., 5, 267-273 Doutriaux-Boucher, M. and J. Quaas, 2004: Evaluation of cloud thermodynamic phase parametrizations in the LMDZ GCM by using POLDER satellite data, Geophys. Res. Lett., 31, L06126, doi:10.1029/2003GL019095. Dybbroe, A. et al., 2005a: NWCSAF AVHRR cloud detection and analysis using dynamic thresholds and radiative transfer modeling - Part I: Algorithm description, J. Appl. Meteor, 44, pp. 39-54. Dybbroe, A. et al., 2005b: NWCSAF AVHRR cloud detection and analysis using dynamic thresholds and radiative transfer modeling - Part II: Tuning and validation, J. Appl. Meteor, 44, 55-71. Foster M.J. and A. Heidinger, 2012: PATMOS-x: Results from a Diurnally-Corrected Thirty-Year Satellite Cloud Climatology, J. of Climate, 26, 414-425 Heidinger, A.K. et al., 2010: Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record. Int. J. Rem. Sens., 31(24), 6493-6517 Karlsson, K.-G. et al. 2012: CLARA - The CMSAF cloud and radiation dataset from 28 years of global AVHRR data (in preparation). Mittaz, P.D. and R. Harris, 2009: A Physical Method for the Calibration of the AVHRR/3 Thermal IR Channels 1: The Prelaunch Calibration Data. J. Atmos. Ocean. Tech., 26, 996-1019, doi: 10.1175/2008JTECHO636.1 Roebeling, R.A. et al., 2006, Cloud property retrievals for climate monitoring: implications of differences between SEVIRI on METEOSAT-8 and AVHRR on NOAA-17, J. Geophys. Res., 111 SATBD1, 2009: Algorithm Theoretical Basis Document - Cloud Fraction, Cloud Type and Cloud Top Parameter Retrieval from SEVIRI, reference no.: SAF/CM/DWD/ATBD/ CFC_CTH_CTO_SEVIRI, Version: 1.0, 10 September 2009, available at www.cmsaf.eu Schulz, J., et al., 2009: Operational climate monitoring from space: the EUMETSAT Satellite Application Facility on Climate Monitoring (CM-SAF), Atmos. Chem. Phys., 9, 1687-1709 References