Transcript Climate Variability NOAA Research Overview: Drought in Great Plains, ca. 1934

NOAA Research Overview:
Climate Variability
California floods during 1998 El Nino
Drought in Great Plains, ca. 1934
Climate variability research is
critical to NOAA’s mission:
“To understand and predict changes in the
Earth’s environment and conserve and manage
coastal and marine resources …”
And, specifically, Strategic Goal 2:
“Understand climate variability and change to
enhance society’s ability to respond”.
CV: Overall Strategy
Four components:
Monitor and Observe
Understand and Describe
Assess and Predict
Engage, Advise, and Inform
For the success of the overall program, it is vital that
these components be linked together.
Measures of Success
Understand and describe:
Increased number of new research findings
and progress toward their implementation into
NOAA operations.
Decreased degree of uncertainty of climate
system processes.
U.S. Climate Change Science Plan
Chapter 4: Climate Variability and Change
Major Research Questions:
1. To what extent can uncertainties due to climate
feedbacks be reduced?
2. What are limits of climate predictability, and how can
climate predictions and climate change projections
be further improved?
3. What is the likelihood of abrupt climate changes?
4. How do extreme events respond to climate variability
and change?
5. How can information on climate variability and
change be most efficiently developed and
communicated to serve societal needs?
Research Priorities
NOAA CV programs emphasize priority areas
described in the CCSP and NOAA SP.
ü
Increase understanding of climate feedbacks (CLIVAR;
NOAA labs; SEARCH).
2. Clarify limits to climate predictability; improve climate
predictions (CLIVAR; CDC; other NOAA labs).
3. Increase understanding mechanisms for abrupt climate
change (CLIVAR ATL, SEARCH).
4. Response of extreme events to climate variability and
change (Weather-Climate Connection; CDC, ETL, AOML).
5.
Develop climate information to serve societal needs (CDC;
overall program, especially where linked to RISAs, CDEP,
NWS, and IRI).
CV Resource Allocation (FY03 est.)
(in $M)
CLIVAR
20.6*
SEARCH
2.0
WX-CLIM
.9
AOML
4.2
CDC
2.5
ETL
.2
FSL
.2
PMEL
2.4
ENSO obs (PMEL) 4.7
35.3
* CLIVAR amount includes $6.1 M for sustained ocean obs., and OGPsponsored research funding for CLIVAR Pacific, CLIVAR Atlantic,
and CLIVAR Pan-American Climate Studies (PACS).
NOAA’s global and regional observing systems are
crucial in supporting monitoring, interpretations,
and predictions of climate variability.
Total Climate Variability contributions to the Climate Observation
Program are $13.1M (CLIVAR Obs $5.1M, OAR Labs $8M).
NOAA Base Components:
Climate Variability Research
 Office of Global Programs
•
Climate Variability and Predictability (CLIVAR)
- CLIVAR Atlantic
- CLIVAR Pacific
- CLIVAR Pan-American Climate Studies (PACS)
 Climate Observations and Services
Program (COSP)
•
•
Study of Environmental Arctic Change (SEARCH)
Weather-Climate Connection
 NOAA Research labs
NOAA/OGP CLIVAR Program
CLIVAR Atlantic
CLIVAR Pacific
CLIVAR Pan-American Climate Studies (PACS)
Climate Variability and Predictability
Overall Objectives:


Develop an understanding of of natural climate variations and
their global and regional manifestations.
Assess predictability of these climate modes through
observational and modeling studies.
Foci:
•
•
El Niño - Southern Oscillation (ENSO), Pacific Decadal Variability
(PDV), Arctic Oscillation (AO), North Atlantic Oscillation (NAO),
Tropical Atlantic Variability, and North American monsoon
system.
Abrupt climate change (Atlantic thermohaline circulation).
Method:
•
•
•
Sponsor PI research/field experiments in key regions: CLIVARPacific, CLIVAR-Atlantic, CLIVAR-PACS.
Support interagency national and international programs.
Implement Climate Model Process Teams (CPTs) to develop and
improve climate model representations of physical processes.
CLIVAR Atlantic aims to
describe changes in, and
assess predictability of,
three major climate
phenomena
• Tropical Atlantic Variability (TAV)
• North Atlantic Oscillation (NAO)
• Meridional Overturning
Circulation (MOC)
Figure courtesy of Science (2002)
CLIVAR Atlantic research examples
• Coupled modeling - TAV, NAO
– P. Chang and R. Saravanan
– S.-P. Xie
– J. Marshall
• Coupled modeling - tropical teleconnections
– M. Hoerling and J. Hurrell
• Ocean modeling - TAV (including
subtropical cells), MOC
– G. Halliwell and R. Weisberg
• Atmospheric analysis - NAO
– J. Hurrell et al.
– R. Miller et al.
– M. Baldwin and T. Dunkerton
• Data set development and analysis - TAV,
MOC, NAO
– P. Niiler
– L. Yu and R. Weller
CLIVAR Pacific Objectives
• Improve understanding of Pacific basin-scale atmosphere-ocean
variability, its predictability on seasonal and longer timescales, and
anthropogenic impacts. Particular foci include ENSO and Pacific
Decadal Variability. This requires further comprehensive analysis,
testing and improvement of coupled models.
• Document time-varying T, S, currents in the upper ocean at 300 km, 10
day resolution over the entire basin north of 40o S for a 15 year period,
with higher resolution in boundary currents and near the equator.
Apply ODA to provide a three-dimensional time-dependent analysis
based on this data.
• Document time-varying vertical and lateral fluxes and air-sea exchange
of heat, fresh water, and momentum over the corresponding period.
• Improve physical parameterizations in OGCMs, AGCMs and NWP models
via process studies and via ODA, which as a by-product identifies
systematic errors in the atmospheric forcing of the ocean or the
assimilating ocean model.
PACS Science Objectives
PACS seeks to extend the scope and improve the skill of climate prediction
over the Americas on subseasonal to interdecadal time scales with an
emphasis on summer precipitation. Specific objectives:
• Improve understanding and provide more realistic simulations of coupled
ocean-atmosphere-land processes, with emphasis on:
– the response of planetary-scale atmospheric circulation and
precipitation patterns to potentially predictable surface boundary
conditions
– the mechanisms that couple climate variability over ocean and land
– the seasonally varying climatological mean state of the ocean,
atmosphere, and land surface
– The effects of land surface processes and orography on the variability
of seasonal rainfall
• Determine the predictability of warm-season precipitation anomalies over
the Americas on seasonal and longer time scales.
• Advance the development of the climate observing and prediction system
for seasonal and longer time scales.
PACS Foci
PACS focuses on the phenomena that are crucial for
organizing seasonal rainfall patterns:
- the oceanic ITCZs
- the continental scale monsoon systems,
- the tropical and extratropical storm tracks
NAME
EPIC
MESA
For implementation, three regional process study domains
are defined:
- Eastern Pacific (EPIC and VEPIC)
- North American Monsoon System (NAME)
- South American Monsoon System (MESA)
PACS Achievements
•
Established enhanced pibal upper air
sounding network and auxiliary
raingauge networks to augment
existing networks over Pan-America
for studying low-level flow and
precipitation
•
Enhanced observing system in eastern
Pacific with extension of 95W TAO
line and stratus mooring
•
Initiated the PIRATA array of moored
buoys in the tropical Atlantic
PACS Deliverables
• Measured improvements in coupled climate models capability
to predict North and South American climate variability
months to seasons in advance
• Infrastructure to monitor and predict the North and South
American monsoon systems
• More comprehensive understanding of Pan-American summer
climate variability and predictability
• Contributions to assessments of climate variability and longterm climate change for regions within North and South
America
• Strengthened multinational scientific collaboration across PanAmerica
CLIVAR milestones/deliverables
•
Improved climate predictions for global climate variability on
S/I and longer time scales.
•
Contributions to the development of the sustained global
climate observing system, esp. ocean observations.
•
Data sets from process field campaigns.
•
Improved physical understanding of climate feedbacks.
•
Assessments of predictability of climate modes.
•
Accelerated improvements in modeling of physical processes
through the CPTs. Initiate a CPT focusing on deep atmospheric
convection (Q4, OGP).
•
Conduct the South American Low Level Jet Experiment (Q2,
OGP)
•
Enhance observations in Mexico and the southwest U.S. for
NAME experiment (Q4, OGP)
Study of Environmental Arctic Change
(SEARCH)
Objectives:
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Identify causes for observed multi-decadal trends of interrelated
changes in the Arctic (atmosphere, ice, ocean, land).
Clarify potential for feedbacks (albedo, fresh water export,
release of carbon from permafrost/methane hydrates)
Determine implications for abrupt changes.
Assess impacts to ecosystem and society.
Foci:
•
•
•
•
Interannual to decadal time scales.
Arctic/subarctic ocean fluxes; relationship to
thermohaline variability.
Expand on limited observations to track key variables;
incorporate into models.
Method:
•
•
•
Implement and sustain environmental observations.
Data analysis and process research.
Coordinated with other agencies in SEARCH, as well as larger national
and international programs in Arctic research.
Examples: SEARCH Products
5) Weaker Beaufort High
(anticyclone) decreases ice
convergence resulting in more open
water lower albedo, more summer
heat input, and more melting.
Combined with possible fresh water
from Russian shelves, this freshens
upper Beaufort Sea.
4) Spin up of AO imposes cyclonic
component on the Ocean
circulation shifting front and
T ranspolar Drift. Surface salinity of
Makarov Basin increases because
of frontal shift. Russian shelf water
goes to Beaufort?
EOF Sea Level Pressure from Thompson and Wallace, 1998
3) Advection of
warm air
increases SAT
and permafrost
thawing in
Russian Arctic
and Alaska.
2) Warmer
conditions over
Greenland Sea
allows warmer
Atlantic Water into
the Arctic Ocean
6) Increased freshwater
export increases
stratification in the
Greenland Sea, preventing
deep convection
1) Spin up of AO brings warm air to
the Greenland Sea and Russian
Arctic
Atmosphere-Ocean-Land
Surface Interactions and
Feedbacks over Arctic
Prototype Observing Array
SEARCH deliverables
•
Temperature, radiation and ice data to support analyses
of ice/albedo feedback, ocean thermohaline circulation,
Arctic shipping routes, marine mammal management.
•
Atmospheric data to enhance model physics and improve
prediction of Arctic Oscillation, US temperature and
hydrologic forecasts
•
Long-term data to detect decadal changes, demonstrate
links to mid-latitudes
Weather-Climate Connection
Objectives:
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Improve understanding and predictions of links between
climate variations and high impact weather phenomena
Improve regional observing capabilities
Develop stronger link between climate research and user
needs
Infuse new science and technology into NOAA operations
Foci:
•
•
Improve predictions on weekly to seasonal time scales.
Initial focus on tropical-midlatitude interactions over the
Pacific and their regional impacts on U.S.
Method:
•
•
Observational, diagnostic, and modeling studies at
regional scales to assess predictability and realize the
potential for operational prediction.
Research coordinated with services (NWS) and end users.
Weather-climate Connection
40” of rain/
7 days
MJO
Pineapple
Express
Where will storm track be for the next few weeks?
When will an arctic outbreak affect the east coast?
When will the rain (drought, heat wave, etc.) end?
How will a climate shift affect the weather in a
particular region?
Week 2 reliability and skill score derived
from an Ensemble MRF Reforecast Experiment (CDC)
(23 years of training data, cross validated)
Bottom line: Big gains can be made in forecast skill by statistically correcting
forecasts from a “frozen” model. Validations over last two years indicate that skills
of U.S. T, p week two (8-14 day) forecasts derived by this method are superior to
official 6-10 day forecasts.
How many years of training data are needed?
Results suggest that most of the gains can be achieved by conducting
reforecasts for 5-10 years, with forecasts run every 4-5 days.
Weather-Climate Connection:
Milestones/Deliverables
•
Improved regional forecast capabilities of U.S.
temperatures and precipitation from a week to a season.
•
Climate prediction capabilities for high-impact events,
including droughts and major floods.
•
Enhanced data sets and analyses to identify and interpret
weather-climate connections between the tropics and midlatitudes.
•
Develop modified and improved practices for biweekly
and/or monthly U.S. temperature and precipitation
outlooks (Q2; CPC/CDC).
NOAA Research
Objectives:
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Carry out long-term research central to NOAA’s mission
Provide sustained support for NOAA climate observations
and services (e.g., NWS Climate Prediction Center)
Deliver products for decision support
Regular and timely provision of climate obs. and predictions
Foci:
•
•
•
Develop national capabilities to describe, interpret, and
predict climate variations, emphasizing major climate
phenomena such as ENSO, droughts, and floods.
Provide and interpret ocean data
Develop capabilities to monitor and predict the ocean
environment on time scales from days to decades.
Research Example: AOML
Relation between decadal modes (NAO)
and hurricane frequency
PMEL: Recent Accomplishments
• Monitor and observe: The TAO array has provided accurate, high resolution, real-time
•
•
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data for tracking the evolution of the 2002-2003 El Niño.
Understand and describe: TAO data and related shipboard measurements have supported
approximately 50 refereed journal publications per year on climate variability and change.
Assess and predict: The TAO array is the backbone of the ENSO observing system
providing real-time data essential for accurate analyses and forecasts of evolving climatic
conditions in the tropical Pacific. TAO data were fundamental to the successful NCEP
forecast in January 2002 that an El Niño was developing.
Engage, advise, inform: a) PMEL scientists serve on many national and international
committees promoting awareness of climate science and NOAA’s climate mission. c) TAO
web pages, providing valuable information to the general public, educators, government
policy makers and private businesses, received nearly 25 million hits in the past year.
Test of Bridge Hypothesis in GFDL Model (CDC,
GFDL) - obs. vs. model correlations - 1950-99
Observed SSTs (FMA)
correlated with Niño
Index (NDJ)
Mixed Layer Model
correlations - SSTs (FMA)
specified in Niño region,
MLM elsewhere
The Perfect Ocean for Drought (CDC, CPC)
Observed Temperature and
Precipitation anomalies
(June 1998 - May 2002)
Model-simulated Temperature
and Precipitation Anomalies
given SSTs over this period
NOAA Research:
FY03 Milestones/Deliverables
•
Determine the origins and assess the predictability of the
1998-2002 U. S. drought, leading to improved drought
forecasts (Q2, CDC)
•
Continue internationally coordinated studies to determine
the role of the Tropical Atlantic on global climate (Q3,
AOML)
•
Develop a website dedicated to ongoing, real-time
predictions of tropical convection associated with the MJO
(Q3, CDC).
•
Provide data for operational forecasting and analyses of
the 2002-2003 El Niño and for comparisons with previous
events (Q4, PMEL).