1. JCSDA Summer Colloquium Erica Dolinar 4 August 2015

2. Quick Background B.S. Meteorology from Millersville University (2007 - 2011) M.S. Atmospheric Science from the University of North Dakota (2012 - 2014) Ph.D. Atmospheric Science from the University of North Dakota (2014 – present) 2

3. Evaluation of CMIP5 simulated clouds and TOA radiation budgets using NASA satellite observations Master’s thesis Large degree of uncertainty in global climate models (GCMs) can be attributed to the representation of clouds (IPCC AR5) The Coupled Model Intercomparison Project Phase 5 (CMIP5) has been implemented to understand the discrepancies between similarly forced models Many improvements have been made in CMIP5, in comparison to its predecessor CMIP3 – Simulated clouds and their feedbacks are still problematic in GCMs The purpose of this study is to report on the outstanding issues regarding the simulation of clouds and how they impact the global Earth radiation budget in the recent climate Dolinar et al. (2014) Evaluation of CMIP5 simulated clouds and TOA radiation budgets using NASA satellite observations. Clim. Dyn., 44:2229-2247. doi:10.1007/s00382-014-2158-9. 3

4. The global cloud fraction (CF) and cloud water path (CWP) is under predicted by 28 GCMs by nearly 7% and 16 g/m 2, respectively – Large multimodel spread in the Artic (~60%, a need for better cloud parameterizations and satellite observations in this region) Clouds induce a TOA net cooling effect of -21 W/m 2 – This value is too strong in the model simulations (stronger cooling) Several areas consistently show large model bias – Southern Ocean, the East Indies, South America, the Indian Ocean, the Tibetan Plateau, and the Saharan Desert An error analysis disseminates the total error of the simulated cloud radiative effect (CRE) into three parts for different dynamic regimes – Sensitivity to CF/CWP, bias in CF/CWP, and the co-variations General Conclusions 4

5. Evaluation and Intercomparison of Clouds, Precipitation, and Radiation Budgets in Recent Reanalyses using Satellite-Surface Observations Reanalyses are used for a variety of different applications – Examine forecast skill, investigate extreme weather events and climate, provide forcing data to number different models (single column, cloud resolving, radiative transfer, etc.) A reasonable estimate of reanalyzed clouds, radiation budgets, and precipitation (and their errors) is important to the study of climate change Satellite and ground-based observations are a great tool to assess the strengths and weaknesses in reanalyses – Report on the effects of data assimilation, changes in observing system, and model parameterizations Dolinar et al. (2015) Evaluation and Intercomparison of Clouds, Precipitation, and Radiation Budgets in Recent Reanalyses using Satellite-Surface Observations. Clim. Dyn., doi:10.1007/s00382-015-2693-z 5

6. Distribution of Clouds and Precipitation 6 The Net CRE is stronger than in the observations, just as in the GCMs (not shown)

7. Reanalyzed Vertical Motion Field Different distributions of the vertical velocity field among the reanalyses Ascent: < -25 hPa/day – Convective-type clouds – CF overpredicted (except CFSR) – PR overpredicted – Net CRE variety of results Descent: > 25 hPa/day – Stratiform-type clouds – CF underpredicted – PR overpredicted – Net CRE variety of results 7

8. Precipitation Rate Observed/reanalyzed Gaussian distribution of PR in the ascent regime Observed lognormal distribution of PR in the descent regime – Different distributions provided by the reanalyses 8

9. 9 DescentAscent Comparison/Validation at ARM sites

10. Calculating clear-sky atmospheric heating rates using the 1D Fu-Liou RTM PhD work CERES surface and TOA fluxes are calculated using a modified Fu-Liou RTM – Forward model with inputs from the MERRA reanalysis – Inputs: temperature, water vapor, and ozone profiles Surface albedo and skin temperature CSZA and Sun-Earth distance Aerosols* This study focuses on generating a satellite/surface-based data set of input parameters for clear-sky cases and constraining our calculated surface and TOA radiation fluxes with observed values – Clouds will be added once we are confident in our clear-sky calculations (within the observational uncertainty, ~5 W/m 2 ) – Perform calculations at several ARM sites (SGP, ENA, NSA, TWP-C2) 10 Under support from the NASA Earth and Space Science Fellowship Program 2014-2015

11. Datasets (Satellite) Input – Microwave Limb Sounder (MLS) on the Aura satellite Level-2 temperature, humidity, and ozone Water vapor provided by J. Jiang from JPL – AIRS Ozone Verification – CERES Ed3A 3-hour average fluxes Used to constrain the calculated fluxes 11

12. Datasets (Surface-based) Atmospheric Radiation Measurement (ARM) Program – Input Merged soundings (combination of radiosonde, surface- based instruments, and ECMWF model output) Surface albedo and skin temperature Aerosols (not yet implemented) – Verification Surface fluxes 12

13. Identifying clear-sky cases Aura satellite overpasses within ~60 km of ARM SGP site (will eventually perform this same study at three other ARM sites) Confirm clear-sky with ARM cloud radar/lidar observations during the 3 hours surrounding the overpass time Confirm availability of all other data sets near overpass time 13 Overpass at 8:38 UTC

14. Generated Profiles for 35 Clear-sky Cases 14 MLS ARM AIRS

15. Preliminary Results 15 SURFACE TOA

16. Questions? 16