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