Isotopic constraints on moist processes over the tropics in NASA GISS ModelE2 Robert Field, Daehyun Kim, Gavin Schmidt, John Worden, Allegra LeGrande,

Slides:



Advertisements
Similar presentations
Recent Evidence for Reduced Climate Sensitivity Roy W. Spencer, Ph.D Principal Research Scientist The University of Alabama In Huntsville March 4, 2008.
Advertisements

Mapping hydrogen isotope ratios of water vapor with satellite-based measurements David Noone Dept. Atmospheric and Oceanic Sciences and Cooperative Institute.
Process-oriented MJO Simulation Diagnostic: Moisture Sensitivity of Simulated Convection Daehyun Kim 1, Prince Xavier 2, Eric Maloney 3, Matthew Wheeler.
(Mt/Ag/EnSc/EnSt 404/504 - Global Change) Climate Models (from IPCC WG-I, Chapter 8) Climate Models Primary Source: IPCC WG-I Chapter 8 - Climate Models.
N emissions and the changing landscape of air quality Rob Pinder US EPA Office of Research and Development Atmospheric Modeling & Analysis Division.
CO budget and variability over the U.S. using the WRF-Chem regional model Anne Boynard, Gabriele Pfister, David Edwards National Center for Atmospheric.
UNSTABLE, DRI and Water Cycling Ronald Stewart McGill University.
Estimating uncertainty of reanalyses using energy budget diagnostics Michael Mayer Michael Mayer - April 2012.
Dynamical Downscaling of CCSM Using WRF Yang Gao 1, Joshua S. Fu 1, Yun-Fat Lam 1, John Drake 1, Kate Evans 2 1 University of Tennessee, USA 2 Oak Ridge.
Tropical Water Vapor and Cloud Feedbacks in CCSM3.5: A Preliminary Evaluation D.-Z. Sun and T. Zhang University of Colorado & National Oceanic & Atmospheric.
Comparisons of TES v002 Nadir Ozone with GEOS-Chem by Ray Nassar & Jennifer Logan Thanks to: Lin Zhang, Inna Megretskaia, Bob Yantosca, Phillipe LeSager,
Assimilation of EOS-Aura Data in GEOS-5: Evaluation of ozone in the Upper Troposphere - Lower Stratosphere K. Wargan, S. Pawson, M. Olsen, J. Witte, A.
Understanding MJO dynamics and model bias in DYNAMO hindcasts Eric D. Maloney, Colorado State University Contributors: Walter Hannah, Emily Riley, Adam.
Assessing the Lightning NO x Parameterization in GEOS-Chem with HNO 3 Columns from IASI Matthew Cooper 1 Randall Martin 1,2, Catherine Wespes 3, Pierre-Francois.
Water and Methane in the Upper Troposphere and Stratosphere based on ACE-FTS Measurements Acknowledgements: The Canadian Space Agency (CSA) is the primary.
The use of HDO observations for understanding processes controlling the water vapor feedback David Noone Dept. Atmospheric and Oceanic Sciences and Cooperative.
Synoptic variability of cloud and TOA radiative flux diurnal cycles Patrick Taylor NASA Langley Research Center Climate Science Branch
MJO simulations under a dry environment Marcela Ulate M Advisor: Chidong Zhang (… in a Nudging World)
Predicted and Observed Histograms of Free Tropospheric Water vapor Steven Sherwood, Yale University Robert Kursinski, JPL William Read, JPL (Also thks.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
Investigation of Atmospheric Recycling Rate from Observation and Model James Trammell 1, Xun Jiang 1, Liming Li 2, Maochang Liang 3, Jing Zhou 4, and Yuk.
The Relation Between SST, Clouds, Precipitation and Wave Structures Across the Equatorial Pacific Anita D. Rapp and Chris Kummerow 14 July 2008 AMSR Science.
3. Products of the EPS for three-month outlook 1) Outline of the EPS 2) Examples of products 3) Performance of the system.
Atmospheric Hydrological Cycle in the Tropics in Twentieth Century Coupled Climate Simulations Hailan Wang and William Lau Laboratory for Atmospheres,
Trends in Tropical Water Vapor ( ): Satellite and GCM Comparison Satellite Observed ---- Model Simulated __ Held and Soden 2006: Robust Responses.
Trends in Tropical Water Vapor ( ): Satellite and GCM Comparison Satellite Observed ---- Model Simulated __ Held and Soden 2006: Robust Responses.
Hou/JTST NASA GEOS-3/TRMM Re-Analysis: Capturing Observed Rainfall Variability in Global Analysis Arthur Hou NASA Goddard Space Flight Center 2.
Impact of the changes of prescribed fire emissions on regional air quality from 2002 to 2050 in the southeastern United States Tao Zeng 1,3, Yuhang Wang.
Climatic implications of changes in O 3 Loretta J. Mickley, Daniel J. Jacob Harvard University David Rind Goddard Institute for Space Studies How well.
Using CO observations from space to track long-range transport of pollution Daniel J. Jacob with Patrick Kim, Peter Zoogman, Helen Wang and funding from.
The tropics in a changing climate Chia Chou Research Center for Environmental Changes Academia Sinica October 19, 2010 NCU.
Convective Transport of Carbon Monoxide: An intercomparison of remote sensing observations and cloud-modeling simulations 1. Introduction The pollution.
NAME SWG th Annual NOAA Climate Diagnostics and Prediction Workshop State College, Pennsylvania Oct. 28, 2005.
Climate models 101 for air quality Anand Gnanadesikan Department of Earth and Planetary Sciences Johns Hopkins University GAIA Conference on Climate Change.
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
Tropical Convection and MJO
Transport Working Group
The impacts of dynamics and biomass burning on tropical tropospheric Ozone inferred from TES and GEOS-Chem model Junhua Liu
Tropical Convective Transport and TTL Structure in the UM global model
Sensitivity of precipitation extremes to ENSO variability
Alfredo Ruiz-Barradas, and Sumant Nigam
CHAPTER 3 LESSON 2 SYSTEM INTERACTIONS.
Weather Chapter 16 Notes.
Earth Science SAGE Workbook SAGE Review
The Water Cycle MYP 1.
Alfredo Ruiz-Barradas Sumant Nigam
The Water Cycle.
The Water Cycle Daily Starter Get Your Textbook (Open to Page 23-25)
Aim: How is Earth’s supply of water being continuously recycled?
Chemistry, Aerosols, and Climate: Tropospheric Unified Simulation (CACTUS) Objective: to improve understanding of interactions between atmospheric chemistry,
Aura Science Team meeting
Investigation of Atmospheric Recycling Rate from Observation and Model PI: Xun Jiang1; Co-I: Yuk L. Yung2 1 Department of Earth & Atmospheric Sciences,
Earth’s Atmosphere.
Water in the Hydrosphere
The Water Cycle.
The Water Cycle.
Robert Wood University of Washington
The Water Cycle.
The Water Cycle.
The Water Cycle.
CHAPTER 3 LESSON 2 SYSTEM INTERACTIONS.
The Water Cycle.
Water never leaves the Earth
Water never leaves the Earth
Water never leaves the Earth
The Water Cycle By Christine Ward.
Water Cycle Model Sign with group members
Climate sensitivity of the CCM3 to horizontal resolution and interannual variability of simulated tropical cyclones J. Tsutsui, K. Nishizawa,H. Kitabata,
The Water Cycle.
The Water Cycle By Christine Ward.
Presentation transcript:

Isotopic constraints on moist processes over the tropics in NASA GISS ModelE2 Robert Field, Daehyun Kim, Gavin Schmidt, John Worden, Allegra LeGrande, Max Kelley JPL

Motivation Climate projections with ModelE form a major part of the GISS contribution to IPCC. Are measurements of the isotopic composition of water vapor useful for model evaluation? Can we use them to identify compensation errors in the parameterizations? What physical processes might they be constraining, especially those that are hard to measure?

Moisture in lower free troposphere ERA-I ModelE

Model performance across different metrics Pattern correlation OceanLand

Aura TES HDO/H 2 O Simulator Varying TES retrieval quality and vertical sensitivity must be taken into account. See: Field, R.D., C. Risi, G.A. Schmidt, J. Worden, A. Voulgarakis, A.N. LeGrande, A.H. Sobel, R.J. Healy, A Tropospheric Emission Spectrometer HDO/H 2 O retrieval simulator for climate models, Atmospheric Chemistry and Physics, 12, doi: /acp , , Retrieval quality (%) Height of peak HDO retrieval sensitivity (hPa)

Effects of TES operator on modeled δD fields Raw model δD (‰) Change to raw model δD after applying ‘standard’ TES operator. Only works for prescribed meteorology. Change to raw model δD after applying new TES operator. Works for arbitrary, free-running model configurations.

δD over lower free troposphere TES ModelE with operator

Pattern correlation Model performance across different metrics OceanLand

Model performance across different metrics Pattern correlation OceanLand

q as a constraint on convective recycling Pattern correlation Convective recycling ratio OceanLand

δD as a constraint on convective recycling Pattern correlation Convective recycling ratio OceanLand

Key points Isotopic measurements provide a stronger constraint than moisture amount or precipitation, especially over the ocean. They can provide guidance on the fidelity of hard- to-measure processes within the model. Next up: – Mechanistic constraints on model variability: MJO & ENSO – Land surface fluxes: evaporation / transpiration partitioning – Observational error

Convective moisture recycling Ratio of re-evaporated to total convective condensate Not well constrained

q and δD over lower free troposphere HDO concentrations are expressed relative to an ocean water standard, in units of permil (‰). q ERA-I δD TES

Capturing variation in vertical sensitivity More complicated categorizations Obs.

GPCP ModelE Precipitation

TES vs. ModelE δD δD Raw - δD TES δD TES δD RetrAK - δD TES δD CatAK - δD TES

Stable isotopes (isotopologues) of water 16 O 1H1H 1H1H 99.73% 18 O 1H1H 1H1H 0.20% 16 O 2H2H 1H1H 0.03% The heavy isotopes of water evaporate less readily and condense preferentially. There are now sufficient HDO measurements in the troposphere against which to evaluate models. We are currently working with HDO/H2O retrievals from the Tropospheric Emission Spectrometer (TES).