Climate & Trends in the TTL: Science Questions Part I Climate & Trends TTL Workshop October 18, 2012 Takuji Sugidachi, Takatoshi Sakazaki, Wiwiek Setyawati, Sasha Glanville, Nawo Eguchi, and Marta Abalos

Radiation Large uncertainties in TTL radiation budgets 1.What is the effect of clouds on heating rates? 2. How will they change with anthropogenic emissions? (need of model improvement)

Clouds Key role of clouds in climate. Microphysical cloud processes still not well understood 3. How can we improve our model parameterizations of clouds? (Need of in situ observations). 4. What is the role of deep convection on large scale transport? Cloud resolving models. 5. What are the impacts of thin cirrus on chemistry and transport (water vapor)? How can we measure them?

Water Vapor Water vapor in the TTL very important for surface climate 6. What controls cold point temperature long term variability and trends? (in turn controls stratospheric WV (Boulder long record –2001 drop?) HALOE MLS radiosondes GPS

Waves and Transport 7. How well do models represent waves? (need to compare with observations) 8. Is there observational evidence of changes in wave propagation and transport as predicted by models? Waves drive global stratospheric circulation

How do we address these questions? Models Reanalyses Observations – Satellite – Ground-based – Aircraft How good are our current tools and how can we improve them?

Models Can models reproduce TTL… Climatology? Interannual variability? Why is the QBO so sensitive in models? very stable in observations Trends?

Reanalyses Need to do intercomparisons and validations of reanalyses (e.g. S-RIP) Additional observations notably improve reanalyses

Future Observations Need of long-record measurements for climate importance of maintained observations over time and accurate, easily accesible metadata and quality information for future data analysis Satellite very important, but need of ground-based measurements for calibration and validation

Key Points and Conclusions Long record data crucial to understand past variability, present climate and future changes in models Satellite data gives us global view Ground based measurements key for validation Understanding small spatial and temporal scale processes is important also for large scale and climate (parameterization of processes in CCMs and validation of satellite, models and assimilation datasets)

Thank you.

Climate & Trends in the TTL: Campaign Logistics & Mapping Part II Climate & Trends TTL Workshop October 18, 2012 Takuji Sugidachi, Takatoshi Sakazaki, Wiwiek Setyawati, Sasha Glanville, Nawo Eguchi, and Marta Abalos

Connecting Science Questions with Projects Good to have in-situ measurements link and validate satellites, models, and reanalyses TIME CONSISTENCY: Need continued satellite measurements with high accuracy SPATIAL COVERAGE: Need vertical/horizontal measurements throughout the tropics (to understand transport)

Satellite measurements (TTL and/or Stratosphere) present(2012) HALOE SMR SBUV(/2) MLS/UARS SABER MLS HIRDLS AMSU CALIPSO, Cloudsat SSU, MSU GPS-RO (CHAMP, COSMIC) Ozone Water vapor Temperature Cloud ACE-FTS GOMOS SCIAMACHY MIPAS SAGE I SAGE III SAGE II SMILES 2010 : non-sun-synchronous Bias between different measurements (due to instrumental bias, diurnal cycle) needs to be considered when merging data sets (e.g., HALOE and MLS). TRMM AIRS Geostationary (MTSAT,GOES)

GRUAN, 15 sites, (H2O,….), since 2011 SHADOZ, 11 sites, (O3), since 1998 NOAA, 3 sites, (H2O), since 1980’s Ticosonde, 1 site, (H2O, O3), since 2005 SOWER, 3 sites, (H2O,) since 1998 Sites (Balloon-born measurements) MOZAIC IAGOS (CARIBIC)

Synergize GRUAN + SHADOZ offers wider spatial and longer temporal coverage MISSING DATA: observation gaps in South America, Central Africa, and Atlantic/Indian Oceans Data Center (SPARC) needs to gather multiple data sets (with different variables) to have a comprehensive, easy- access stock of data Key Point: maintain long records of data – We have many satellites measuring different variables (especially since 2000); keep them robust

Thank you.