Kevin E Trenberth NCAR Kevin E Trenberth NCAR Observations of climate change Help!
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Transcript of Kevin E Trenberth NCAR Kevin E Trenberth NCAR Observations of climate change Help!
Kevin E TrenberthNCAR
Kevin E TrenberthNCAR
Observations of climate changeObservations of climate change
Help!
Global Warming is unequivocal
Global Warming is unequivocal
Since 1970, rise in: Decrease in: Global surface temperatures NH Snow extent Tropospheric temperatures Arctic sea ice Global SSTs, ocean Ts Glaciers Global sea level Cold temperatures Water vapor Rainfall intensity Precipitation extratropics Hurricane intensity Drought Extreme high temperatures Heat waves
Since 1970, rise in: Decrease in: Global surface temperatures NH Snow extent Tropospheric temperatures Arctic sea ice Global SSTs, ocean Ts Glaciers Global sea level Cold temperatures Water vapor Rainfall intensity Precipitation extratropics Hurricane intensity Drought Extreme high temperatures Heat waves
IPCC 2007
The climate is changing. We can and should take mitigating
actions that will slow and eventually stop climate change.
Meanwhile we must adapt to climate change.
But adapt to what?We do not have predictions.We do not have adequate reliable
observations.We do not have the needed
information system!
The climate is changing. We can and should take mitigating
actions that will slow and eventually stop climate change.
Meanwhile we must adapt to climate change.
But adapt to what?We do not have predictions.We do not have adequate reliable
observations.We do not have the needed
information system!
Global mean temperatures are rising faster with timeGlobal mean temperatures are rising faster with time
150 0.0450.012100 0.0740.018
50 0.1280.026 25 0.1770.052
Warmest 12 years:1998,2005,2003,2002,2004,200
6, 2001,1997,1995,1999,1990,200
0
Period Rate
Years /decade
IPCC 2007
Extreme Heat WaveSummer 2003Europe30,000 deaths
Extreme Heat WaveSummer 2003Europe30,000 deaths
Heat waves are increasing: an exampleHeat waves are increasing: an example
Trend plus variability?Trend plus variability?
IPCC 2007
Surface Temperature1901-2005
Surface Temperature1901-2005
It has not warmed uniformly:More warming over landWhy no warming over SE USA?Or N Atlantic
It has not warmed uniformly:More warming over landWhy no warming over SE USA?Or N Atlantic
IPCC 2007
The most important spatial pattern (top) of the monthly Palmer Drought Severity Index (PDSI) for 1900 to 2002.
The time series (below) accounts for most of the trend in PDSI.
Drought is increasing most placesDrought is increasing most places
Mainly decrease in rain over land in tropics and
subtropics, but enhanced by increased atmospheric demand
with warming
IPCC 2007
Extremes of temperature are changing!
Observed trends (days) per decade for 1951 to 2003:
5th or 95th percentiles
From Alexander et al. (2006)
Extremes of temperature are changing!
Observed trends (days) per decade for 1951 to 2003:
5th or 95th percentiles
From Alexander et al. (2006)
IPCC 2007
Absence of warming by day coincides with wetter and cloudier conditions
Drought
Increases in rainfall and cloud counter warmingIncreases in rainfall and cloud counter warming
Trend in Warm Days 1951-2003
IPCC 2007
Regional climate change
Hypothesis: It is impossible to address regional climate change without fully addressing how patterns of climate variability (modes) change, and thus how:
ENSO: El Niño Southern Oscillation
NAO/NAM: North Atlantic Oscillation/Northern Annular Mode
SAM: Southern Annular Mode
PDO: Pacific Decadal Oscillation
AMO: Atlantic Multidecadal Oscillation
change!
Regional climate change
Hypothesis: It is impossible to address regional climate change without fully addressing how patterns of climate variability (modes) change, and thus how:
ENSO: El Niño Southern Oscillation
NAO/NAM: North Atlantic Oscillation/Northern Annular Mode
SAM: Southern Annular Mode
PDO: Pacific Decadal Oscillation
AMO: Atlantic Multidecadal Oscillation
change!
El Niño - Southern Oscillation
SLP Surface temperature
PrecipitationIPCC 2007
Cooler
Wetter
Pacific Decadal OscillationSST pattern (above) and time series (lower right) of 1st EOF of N Pacific SSTs.NPI index of Aleutian LowIndian Ocean SST (tropics)
1976/77 climate shift1976/77 climate shiftIPCC 2007
Many observed climate anomalies can be simulated
in models with specified SSTs
Many observed climate anomalies can be simulated
in models with specified SSTs• Sahel drought: Hurrell et al 2004, Giannini et al 2003,
Hoerling,
• US Dust Bowl: Schubert et al. 2004, Seager et al. 2005
• Drought (US, Europe, Asia): Hoerling and Kumar 2003
But we can not (yet) simulate the observed SSTs.
• Sahel drought: Hurrell et al 2004, Giannini et al 2003, Hoerling,
• US Dust Bowl: Schubert et al. 2004, Seager et al. 2005
• Drought (US, Europe, Asia): Hoerling and Kumar 2003
But we can not (yet) simulate the observed SSTs.
Global increases in SST are not uniform. Why?
Coupling with atmosphere Tropical Indian Ocean has
warmed to be competitive as warmest part of global ocean.
Tropical Pacific gets relief owing to ENSO?
Deeper mixing in Atlantic, THC.
This pattern is NOT well simulated by coupled models!
Relates to ocean uptake of heat, heat content & transport.
Global increases in SST are not uniform. Why?
Coupling with atmosphere Tropical Indian Ocean has
warmed to be competitive as warmest part of global ocean.
Tropical Pacific gets relief owing to ENSO?
Deeper mixing in Atlantic, THC.
This pattern is NOT well simulated by coupled models!
Relates to ocean uptake of heat, heat content & transport.
IPCC 2007
IPCC experience on observations
IPCC experience on observations
Sorting out the climate signal from the noise in inadequate observations from a changing observing system is an ongoing continual challenge
Space-based observations are a particular challenge
Sorting out the climate signal from the noise in inadequate observations from a changing observing system is an ongoing continual challenge
Space-based observations are a particular challenge
Annual anomalies of maximum and minimum temperatures and diurnal temperature range (DTR) (°C) averaged for the 71% of global land areas for 1950 to 2004. DTR 1979-2004
Annual anomalies of maximum and minimum temperatures and diurnal temperature range (DTR) (°C) averaged for the 71% of global land areas for 1950 to 2004. DTR 1979-2004
Issues:1. Missing data and treatment2. Quality control3. Max and Min T much more
sensitive to inhomogeneities4. Urban heat island5. Need to continue to pressure
countries to provide high frequency data (hourly and daily)
Temperatures IPCC 2007
IPCC 2007
Precipitation: not a continuous variablePrecipitation: not a continuous variable
Large differences in amounts. Inability to analyze characteristics: intensity, frequency, duration, type, as well as amount. Need hourly data!
Tropical rainfall 30N-30S
Land Total
Ocean
Tropical rainfall 30N-30S
Land Total
Ocean
Land: systematic offset 3%Ocean: no relationshipTotal: dominated by ocean
Land: systematic offset 3%Ocean: no relationshipTotal: dominated by ocean
Issues:
Need much more complete and better data on all hydrological variables set in a holistic framework:
Precipitation: hourly (intensity, frequency, duration, type, amount); streamflow, runoff, evaporation, drought indices, soil moisture (incl ice), snow cover depth…
N. Atlantic hurricane record best after 1944 with aircraft surveillance.
Global number and percentage of intense hurricanes is increasing
North Atlantic hurricanes have increased with SSTsNorth Atlantic hurricanes have increased with SSTs
SST(1944-2005)
Marked increase after
1994
Some issues:
Partial reprocessing of ISCCP data has occurred for tropical storms (Kossin)
Records are far from homogeneous, even for satellite era
Records/practices are not comparable in different regions, even now.
We desperately need an internationally coordinated reprocessing of all satellite data for hurricanes, to get many parameters of interest, such as size, intensity, rainfall, integrated variables (0-100 km; 0-400 km) etc.
Ivan 2004Ivan 2004
Main IssuesMain Issues• The in situ data are not global and have
problems• Satellites drift in orbit and instruments
degrade: the data generally do not provide a climate record. They could.
• The satellite record is in jeopardy, especially from demanifesting several climate instruments from NPOESS.
• A baseline transfer standard is essential: in situ super sites (reference radiosonde plus network).
• Regional climate requires attention to modes of variability and model initialization
• The in situ data are not global and have problems
• Satellites drift in orbit and instruments degrade: the data generally do not provide a climate record. They could.
• The satellite record is in jeopardy, especially from demanifesting several climate instruments from NPOESS.
• A baseline transfer standard is essential: in situ super sites (reference radiosonde plus network).
• Regional climate requires attention to modes of variability and model initialization
Why do we need an integrated Earth System
Analysis?
Why do we need an integrated Earth System
Analysis?• We have a lot of observations: from satellites
and other remote sensing.• The volumes are huge• We use but a small fraction• Most are not climate quality• Inconsistencies exist across variables• They do not make a climate observing
system • Reprocessing and reanalysis must be part of
system
• We have a lot of observations: from satellites and other remote sensing.
• The volumes are huge• We use but a small fraction• Most are not climate quality• Inconsistencies exist across variables• They do not make a climate observing
system • Reprocessing and reanalysis must be part of
systemGoal: Climate Data RecordsGoal: Climate Data Records
1. There is a need to better come to grips with the continually changing observing system.
2. There is no baseline network to anchor the analyses or space observations.
The radiosonde network is not it!3. The challenge is to improve continuity and be
able to relate a current set of observations to those taken 20 years ago (or in the future).
4. There is a need for more attention to data synthesis, reprocessing, analysis and re-analysis of existing data sets; and
5. There must be a baseline set of measurements:
Sparse network (30-40) of “reference sondes” for satellite calibration and climate monitoring, UT water vapor; co-located with regular sonde sites to replace them at appropriate times; integrated with ozone sondes and/or GAW and BSRN = GRUAN? GPS Radio Occultation.
1. There is a need to better come to grips with the continually changing observing system.
2. There is no baseline network to anchor the analyses or space observations.
The radiosonde network is not it!3. The challenge is to improve continuity and be
able to relate a current set of observations to those taken 20 years ago (or in the future).
4. There is a need for more attention to data synthesis, reprocessing, analysis and re-analysis of existing data sets; and
5. There must be a baseline set of measurements:
Sparse network (30-40) of “reference sondes” for satellite calibration and climate monitoring, UT water vapor; co-located with regular sonde sites to replace them at appropriate times; integrated with ozone sondes and/or GAW and BSRN = GRUAN? GPS Radio Occultation.
The challenge is to better determine:
1) how the climate system is changing2) how the forcings are changing3) how these relate to each other (incl.
feedbacks)4) attribution of anomalies to causes 5) what they mean for the immediate and more
distant future (assessment)6) Validate and improve models7) seamless predictions on multiple time scales 8) how to use this information for informed
planning and decision making
9) how to manage the data and reanalyze it routinely
10)how to disseminate products around the world
11)how to interact with users and stakeholders and add regional value
From Trenberth et al 2002
The challenge is to better determine:
1) how the climate system is changing2) how the forcings are changing3) how these relate to each other (incl.
feedbacks)4) attribution of anomalies to causes 5) what they mean for the immediate and more
distant future (assessment)6) Validate and improve models7) seamless predictions on multiple time scales 8) how to use this information for informed
planning and decision making
9) how to manage the data and reanalyze it routinely
10)how to disseminate products around the world
11)how to interact with users and stakeholders and add regional value
From Trenberth et al 2002