The importance of upper tropospheric water and cloud for climate change Richard P. Allan Department of Meteorology/NCAS Climate, University of Reading ~ sgs02rpa

Wild et al. (2012) Clim. DynamicsWild et al. (2012) Clim. Dynamics. See also: Trenberth et al. (2009) BAMSTrenberth et al. (2009) BAMS Earth’s Global Annual Average Energy Balance

Water Vapour amplifies climate change  CO 2  Greenhouse effect  Net Heating  Temperature  Water vapour But what about cloud?

From Ed Hawkins, University of Reading How much will the planet warm?

Current changes in global water cycle Adapted from: Allan et al. (2013) Surv. Geophys Allan et al. (2013) Surv. Geophys Co-variation: dW/dTs ~ 7%/ o C ~1%/decade trend

Is simulated upper tropospheric water realistic? See also John and Soden (2007) GRLJohn and Soden (2007) GRL Pierce et al. (2006) GRL

Observed and simulated clouds from space e.g. Allan et al. (2007) QJRMSAllan et al. (2007) QJRMS

Evaluating model cloud convective processes Model (12km resolution)GERB satellite data Pearson et al. (2010, 2014) QJRMS

Are the most important deep convective processes well represented by models? Radiative Exchanges Exchanges of energy and moisture From Figure 4.6 of thesis, after The Comet Program

Guiding Questions: Will clouds amplify or reduce climate change in response to greenhouse gas increases? How realistic are simulated moist processes & what are implications for their global water cycle Novelty: Multiple New Satellite Datasets (active/passive) New methods to focus on the processes contributing to deep convective systems to aid model improvement “Evaluating Tropical Upper-tropospheric Water in Climate Models Using Satellite Data” by Marston S. Johnston

Linking systematic biases in water vapour & precipitation in NWP & climate models PRECIPITATION Model – OBSWATER VAPOUR model – OBS Liu et al. (2013) JAMC

Improving cloud simulations GEWEX Cloud Systems Study (GCSS) and Cloud Forcing Model Inter- comparison Project (CFMIP) are examples of major international collaboration programmes Data from satellite, field experiments and Cloud Resolving Models used to evaluate performance of GCM parametrizations CloudSat vertical structure Model Radar Reflectivity  Altitude 

Ongoing work and future plans Correct for scan-dependent biases Evaluate climate model processes using the generated data sets Correct for time-dependent biases Extend the comparisons to other CMIP5 models and ERA-Interim

Diurnal cycle of precipitation ① Spin-up in P & water vapour in W. Pacific & Atlantic ② poor diurnal cycle over land (i) timing (ii) amplitude ① ② Liu et al. (2013) JAMC ①