ISCCP at 30, April 2013 Backup Slides. ISCCP at 30, April 2013 NVAP-M Climate Monthly Average TPW Animation Less data before 1993.

Slides:



Advertisements
Similar presentations
The Effects of El Niño-Southern Oscillation on Lightning Variability over the United States McArthur “Mack” Jones Jr. 1, Jeffrey M. Forbes 1, Ronald L.
Advertisements

TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
TOA radiative flux diurnal cycle variability Patrick Taylor NASA Langley Research Center Climate Science Branch NEWS PI Meeting.
1 The Recycling Rate of Atmospheric Moisture Liming Li, Moustafa Chahine, Edward Olsen, Eric Fetzer, Luke Chen, Xun Jiang, and Yuk Yung NASA Sounding Science.
Institute of Meteorology and Water Management „Satellite Remote Sensing as a tool for monitoring of climate and environment Piotr Struzik Satellite Research.
THE GLOBAL ATMOSPHERIC HYDROLOGICAL CYCLE: Past, Present and Future (What do we really know and how do we know it?) Phil Arkin, Cooperative Institute for.
Climate Variability and Change: Introduction Image from NASA’s Terra satellite Temperature anomalies for July 2010.
Extreme Events and Climate Variability. Issues: Scientists are telling us that global warming means more extreme weather. Every year we seem to experience.
Reflected Solar Radiative Kernels And Applications Zhonghai Jin Constantine Loukachine Bruce Wielicki Xu Liu SSAI, Inc. / NASA Langley research Center.
(Mt/Ag/EnSc/EnSt 404/504 - Global Change) Observed Surface & Atmosphere (from IPCC WG-I, Chapter 3) Observed Changes in Surface and Atmosphere Climate.
SIO 210: ENSO conclusion Dec. 2, 2004 Interannual variability (end of this lecture + next) –Tropical Pacific: El Nino/Southern Oscillation –Southern Ocean.
Interannual and Regional Variability of Southern Ocean Snow on Sea Ice Thorsten Markus and Donald J. Cavalieri Goal: To investigate the regional and interannual.
First results from the inter-comparison Maarit Lockhoff, Marc Schröder.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Long-Term Upper Air Temperature.
December 2002 Section 2 Past Changes in Climate. Global surface temperatures are rising Relative to average temperature.
Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1,
ISCCP at 30, April 2013 Concurrent Study of a) 22 – year reanalysis and extension of global water vapor over both land and ocean (NVAP–M) and b) the matching.
By Anthony R. Lupo Department of Soil, Environmental, and Atmospheric Science 302 E ABNR Building University of Missouri Columbia, MO
The La Niña Influence on Central Alabama Rainfall Patterns.
Slide 1 Sakari Uppala and Dick Dee European Centre for Medium-Range Weather Forecasts ECMWF reanalysis: present and future.
Synoptic variability of cloud and TOA radiative flux diurnal cycles Patrick Taylor NASA Langley Research Center Climate Science Branch
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 POES Microwave Products Presented.
Initial Trends in Cloud Amount from the AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew K Heidinger, Michael J Pavolonis**, Aleksandar.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
1 AISC Workshop May 16-18, 2006 Lesson Learned from CCSP 1.1: Temperature Trends in the Atmosphere Lesson Learned from CCSP 1.1 Temperature Trends in the.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Infrared Temperature and.
USE OF AIRS/AMSU DATA FOR WEATHER AND CLIMATE RESEARCH Joel Susskind University of Maryland May 12, 2005.
Thomas R. Karl Director, National Climatic Data Center, NOAA Editor, Journal of Climate, Climatic Change & IPCC Climate Monitoring Panel Paul D. Try, Moderator.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 The Influences of Changes.
A New Inter-Comparison of Three Global Monthly SSM/I Precipitation Datasets Matt Sapiano, Phil Arkin and Tom Smith Earth Systems Science Interdisciplinary.
Trends & Variability of Liquid Water Clouds from Eighteen Years of Microwave Satellite Data: Initial Results 6 July 2006 Chris O’Dell & Ralf Bennartz University.
Instrumental Surface Temperature Record Current Weather Data Sources Land vs. Ocean Patterns Instrument Siting Concerns Return Exam II For Next Class:
Trends in Tropical Water Vapor ( ): Satellite and GCM Comparison Satellite Observed ---- Model Simulated __ Held and Soden 2006: Robust Responses.
Introduction Conclusion and Future Work An Antarctic Cloud Mass Transport Climatology J. A. Staude, C. R. Stearns, M. A. Lazzara, L. M. Keller, and S.
July 2006GEWEX Cloud Assessment1 Assessment of cloud properties from Satellite Data: ISCCP,TOVS Path-B, UW HIRS Claudia Stubenrauch CNRS/IPSL - Laboratoire.
Investigations of Artifacts in the ISCCP Datasets William B. Rossow July 2006.
ISCCP Calibration 25 th Anniversary Symposium July 23, 2008 NASA GISS Christopher L. Bishop Columbia University New York, New York.
Global Space-based Inter- Calibration System (GSICS) Progress Report Mitch Goldberg, NOAA/NESDIS GSICS Executive Panel chair.
CE 401 Climate Change Science and Engineering evolution of climate change since the industrial revolution 9 February 2012
Layered Water Vapor Quick Guide by NASA / SPoRT and CIRA Why is the Layered Water Vapor Product important? Water vapor is essential for creating clouds,
ISCCP SO FAR (at 30) GOALS ►Facilitate "climate" research ►Determine cloud effects on radiation exchanges ►Determine cloud role in global water cycle ▬
Point Comparison in the Arctic (Barrow N, 156.6W ) Part I - Assessing Satellite (and surface) Capabilities for Determining Cloud Fraction, Cloud.
Radio Occultation. Temperature [C] at 100 mb (16km) Evolving COSMIC Constellation.
Validation of Satellite-derived Clear-sky Atmospheric Temperature Inversions in the Arctic Yinghui Liu 1, Jeffrey R. Key 2, Axel Schweiger 3, Jennifer.
Winter Outlook for the Pacific Northwest: Winter 06/07 14 November 2006 Kirby Cook. NOAA/National Weather Service Acknowledgement: Climate Prediction Center.
Interannual Variability and Decadal Change of Solar Reflectance Spectra Zhonghai Jin Costy Loukachine Bruce Wielicki (NASA Langley research Center / SSAI,
“CLIMATE IS WHAT WE EXPECT, AND WEATHER IS WHAT WE GET” ~ MARK TWAIN.
PRELIMINARY VALIDATION OF IAPP MOISTURE RETRIEVALS USING DOE ARM MEASUREMENTS Wayne Feltz, Thomas Achtor, Jun Li and Harold Woolf Cooperative Institute.
MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.
An Improved Microwave Satellite Data Set for Hydrological and Meteorological Applications Wenze Yang 1, Huan Meng 2, and Ralph Ferraro 2 1. UMD/ESSIC/CICS,
What is the Difference Between Weather and Climate?
Heating of Earth's climate continues in the 2000s based upon satellite data and ocean observations Richard P. Allan 1, N. Loeb 2, J. Lyman 3, G. Johnson.
1 Changes in global net radiative imbalance Richard P. Allan, Chunlei Liu (University of Reading/NCAS Climate); Norman Loeb (NASA Langley); Matt.
Decadal Variability in the Southern Hemisphere Xiaojun Yuan 1 and Emmi Yonekura 2 1 Lamont-Doherty Earth Observatory Columbia University 2 Department Environment.
© University of Reading 2011www.reading.ac. uk Tracking Earth’s Energy since 2000 Richard Allan University of Reading/NCAS climate Collaborators: Norman.
Instrumental Surface Temperature Record
Cal/Val Activities at CIRA
El Niño and La Niña.
Atmosphere & Weather Review
Observing Climate Variability and Change
NASA Satellite Images for 2007
Changes in the Free Atmosphere
Instrumental Surface Temperature Record
El Niño-Southern Oscillation
Project Title: The Sensitivity of the Global Water and Energy Cycles:
Satellite Foundational Course for JPSS (SatFC-J)
Predictive Modeling of Temperature and Precipitation Over Arizona
Instrumental Surface Temperature Record
Global Observational Network and Data Sharing
Climate data and models What they show us and how they can be used in planning BY Henri TONNANG.
Presentation transcript:

ISCCP at 30, April 2013 Backup Slides

ISCCP at 30, April 2013 NVAP-M Climate Monthly Average TPW Animation Less data before 1993

ISCCP at 30, April Monthly Mean TPW (mm) from NVAP-M Climate

ISCCP at 30, April 2013 Correlation Coefficient Correlation of ISCCP total cloud and NVAP-M total precipitable water vapor monthly anomalies ( ) NVAP-M Ocean (SSM/I only) NVAP-M Climate (SSM/I, AIRS, HIRS, Sondes) Blue areas indicate cloud amount decreases as TPW increases

ISCCP at 30, April NVAP-M Ocean Trend NVAP-M Trenberth et al Trend (mm / decade) Trend (% / decade) Preliminary - Significance testing in progress

ISCCP at 30, April 2013 Trend (mm / decade): No cloud consideration NVAP-M-Ocean NVAP-M-Climate Preliminary - Significance testing in progress

ISCCP at 30, April 2013 Trend (mm / decade): ISCCP Monthly Mean Cloud 50 – 100% only NVAP-M-Ocean NVAP-M-Climate Preliminary - Significance testing in progress

ISCCP at 30, April 2013 Trend (mm / decade): ISCCP Monthly Mean Cloud 0 – 50% only NVAP-M-Ocean NVAP-M-Climate Preliminary - Significance testing in progress

ISCCP at 30, April 2013 Dataset Description (3 of 3) Surface – 700 mb 300 mb – 100 mb500 mb – 300 mb 700 mb – 500 mb mm mm 0 0 Layered Precipitable Water (HIRS + Radiosonde) September 10, 2004

ISCCP at 30, April 2013 At this time, we can neither prove nor disprove a robust trend in the global water vapor data.

ISCCP at 30, April Future Connections with GEWEX ESA Globvapour project ( completed initial phase (SSM/I+MERIS, GOME/SCIAMACHY/GOME-2 total column water vapor products). NVAP-M being submitted to the GEWEX water vapour and temperature assessment in collaboration with GlobVapour. NVAP-M ended in data year 2009, more production years possible, to include SSM/IS sensor. Fetzer et al. related MEaSUREs dataset “A Multi-sensor Water Water Vapor Climate Data Record Using Cloud Classification”; uses CloudSat / CALIPSO to examine cloud cover effects on retrievals and sampling. Consideration of alternative data fusion techniques (e.g kriging).

ISCCP at 30, April NVAP-M Ocean Trend NVAP-M Trenberth et al Trend (mm / decade) Trend (% / decade) Preliminary - Significance testing in progress

ISCCP at 30, April 2013 Dataset Description (3 of 3) Surface – 700 mb 300 mb – 100 mb500 mb – 300 mb 700 mb – 500 mb mm mm 0 0 Layered Precipitable Water (HIRS + Radiosonde) September 10, 2004

ISCCP at 30, April Statement on Using Existing NVAP Dataset (1988 – 2001) for Trends (Tom Vonder Haar and the NVAP production team, July 2010) This statement summarizes our thoughts in regard to the frequently asked question “What is the trend in global water vapor from the NVAP (NASA Water Vapor Dataset)?”. While other datasets (radiosonde, microwave ocean-only) have been used for trend studies (e.g. see IPCC AR4), NVAP is unique in that it covers global land and ocean by combining a variety of input sources. The NVAP dataset (available at the NASA Langley DAAC Data Center) has been used in hundreds of studies of water vapor and has proven to be valuable for daily to interannual variability studies (monsoon, ENSO, MJO etc.). Like many related climate datasets (precipitation, clouds), NVAP was originally designed for weather and process studies and not to detect climate trends. There are several natural events and especially data and algorithmic time-dependent biases that cause us to conclude that the extant NVAP dataset is not currently suitable for detecting trends in total precipitable water (TPW) or layered water vapor on decadal scales. These include: Several changes in the NOAA TIROS Operational Vertical Sounder (TOVS) retrievals during the 1990’s. And lack of any instrument-to- instrument calibration when the dataset was produced. TOVS data provides much of the information over land. Changes in the microwave ocean algorithm and supporting data (sea ice, sea surface temperature), and lack of any intercalibration of the Special Sensor Microwave / Imager (SSM/I) instruments onboard six different satellites. Radiance intercalibration of this important dataset is just beginning to appear in Production of NVAP in four steps during the 1990’s, with new instruments as they became available. Large natural geophysical events occurring during the time period (1987 ENSO and transition to 1988 La Nina at the beginning of the record; Pinatubo eruption in 1991, large El Nino. Whether or not one uses these events in a trend study can impact the slope of the trend line. The NVAP dataset now available to the public has never been reanalyzed. A reanalysis effort should be a natural part of a climate dataset, as the first trend studies often uncover previously unknown errors in the data. At this time, we cannot prove or disprove a robust trend due to atmospheric changes with NVAP, as we stated in our 2005 paper “Water Vapor Trends and Variability from the Global NVAP Dataset” at the 16th AMS Symposium on Global Change and Climate Variations. Using lessons learned from the existing NVAP data and knowledge including the factors listed above, a reanalysis effort is now underway to produce and extend the NVAP water vapor record. This effort is supported by the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program ( The new dataset covering 20+ years will be available to the public in 2012 or Updates on the status and availability of this data will be posted at the NVAP-MEaSUREs project website (

ISCCP at 30, April 2013 The effects of changing land / ocean coverage through time must be investigated for robust trend analysis. Monthly mean area of total coverage for all instruments used in NVAP-M Climate. HIRS and AIRS coverage for ocean / land / total indicated by dotted / dashed / solid lines. land ocean Wentz et al. (2007) trend: mm/decade from SSM/I (ocean-only; 50°N to 50°S).

ISCCP at 30, April The challenge of creating a multisensor climate record.

ISCCP at 30, April 2013 TPW over the Nino 3.4 region (5°N to 5°S) Reflects seasonal migration of the ITCZ with ENSO impact.