Climate Modeling Univ. of Alaska-Fairbanks Summer School

July 16, 2012

Joe Barsugli

CIRES, University of Colorado, affiliated with NOAA/ESRL/PSD and Western Water Assessment

The Basic Narrative of Climate Change

Energy Balance

Water vapor

Temperature

We are changing the Carbon Cycle

Which causes an imbalance in the amount of energy reaching the Earth

Which warms the planet

Which changes the water cycle

Which amplifies the climate change, mainly because of increased water vapor

Because the Energy Balance and the Water Cycle involve the winds and ocean currents -- they are changed too, potentially changing all aspects of climate, including clouds.

Eventually feeding back on the carbon cycle

GoalsClimate Science

• What is the basic narrative of climate change science? Emissions Scenarios

• What is an emissions scenario?• Which emissions scenarios are used by climate modelers?• What are the main factors causing the Earth’s energy imbalance?• What are the important similarities and differences among the scenarios?

Climate Models• What is in a climate model?• What does the hydrologic component of a climate model look like?• What are the basic methodologies of making climate model projections?• What are the known biases in climate model simulations?• What will the future of climate modeling bring?

Emissions from Fossil Fuel + Cement

Data Source: G. Marland, T.A. Boden, R.J. Andres, and J. Gregg at CDIAC; Slide from global Carbon Project ; NOAA ESRL;

1850 1870 1890 1910 1930 1950 1970 1990 2010

CO2 emissions accumulation of CO2 in atmospherePre-Industrial: 270 ppm 2012: 393 ppm

TOTAL ~9 Gt C/yr

Warming of about 0.8C (1.5F) since pre-industrial climate

Climate Change: The Usual Graphics

IPCC “SRES” Emissions Scenarios

• IPCC Special Report on Emissions Scenarios http://www.ipcc.ch/ipccreports/sres/emission/index.htm

SRES scenarios…• Were developed because previous IPCC assessments did not make a

clear distinction between uncertainty among climate model forecasts for a given scenario and uncertainty in the emissions trajectory itself.

• “…are alternative images of how the future might unfold…”

• Differ in their assumptions regarding “demographic development, socio-economic development, and technological change.”

• Are given memorable names like “B1”, “A1B”, and “A2”

SRES (2000) CO2 Emissions Scenarios

IPCC 5th assessment scenarios will probably include some scenarios with higher emissions.

Emissions Scenarios: Resulting CO2 Concentrations

Fossil Fuel Emissions: Actual vs. IPCC Scenarios

Raupach et al 2007, PNAS (updated); Slide from global Carbon Project

RCPs, the new SRES scenarios

• Stands for “Representative Concentration Pathways”• Derived from emissions scenarios much like SRES concentrations

were… so that Earth System Models and “classic” Atmosphere Ocean GCMs could run comparable scenarios.

• Are named by the approximate radiative forcing strength at the year 2100, in W/m^2.

• Policy (mitigation) assumption are made (SRES did not make any policy assumptions)

Adapted from vanVuuren et al, 2011

For comparison: Doubling CO2 ~ +4 W/m^2Average insolation at top of the atmosphere ~ 340 W/m^2

Radiative Forcing: Imbalance in longwave + solar radiation caused by changes in greenhouse gas and aerosols relative to pre-industrial conditions.

RCP8.5

RCP4.5

RCP6.0

RCP2.6

RCPs are the new Emissions Scenarios

RCP <-> SRES

vanVuuren et al, 2011

For reference.. Doubling CO2 ~ +4 W/m^2Average insolation ~ 340 W/m^2

Imbalance in radiation (solar + longwave (infrared) caused by changes in GHG relative to pre-industrial conditions

Note: different baselines used)

Which affects the Earth’s Energy Balance …

Radiative Forcing at Present (year 2005)

Source: IPCC AR4 WG1 SPM

Source: IPCC AR4 WG1 SPM

Global Mean Surface Air Temperature Projections (relative to 1980-1999)

2040 2070

Bottom Line on Emissions Scenarios

•Scenarios are not forecasts, but can be useful planning tools. •There are no probabilities associated with the occurrence of these scenarios.•The emissions scenarios provide standard parameters for climate modelers to make intercomparison of their projections easier. • Scenarios B1, A1B, and A2 have become de facto “low”, “medium” and “high” emissions scenarios for the CMIP3/AR4 models based on year 2100 cumulative emissions, and were chosen by climate modeling centers to intensively study.•CMIP5 RCP’s are in many ways comparable to the SRES scenarios.•RCPs 4.5 6.0 and 8.5 will probably be taken as the new “low” medium” and “high” scenarios. •Earth System Models introduce another level of complexity and uncertainty. Radiative forcing in an Earth System model under a RCP emissions may differ from an AOGCM using the concentrations from the RCP

Bottom Line on (Global Mean) Climate Projections

• The global mean climate responses to the different SRES scenarios start to diverge after about 2030. Before that, climate change is similar in all scenarios. By 2040 the scenarios have diverged somewhat. By 2070 B1 has appreciably diverged from the others. By 2100, all three scenarios are distinct. However the full estimate of uncertainty from all sources still has a large degree of overlap in year 2100.

• For a given climate model, the temperature and precipitation changes in the different scenarios differ mainly in magnitude and timing.

• For a given scenario, there is significant variation in temperature and precipitation changes among the different climate model simulations due to differences in model formulation.

• CMIP5 – more divergence among radiative forcing, esp if you consider RCP2.6 and RCP8.5. In the latter half of the century there will be more divergence among simulations because of carbon-cycle feedbacks.

The projected climate is contingent on the choice of emissions.

Emissions scenario is not a big deal for regional climate out to mid-century.

Emissions scenario becomes a big deal by the end of the century.

Choice (or not) of Emissions Scenarios

emissions emissions

modelmodel

Global

Fraction of uncertainty due to various sources Hawkins and Sutton, 2009

Regional (UK)

variability

Review and QuestionsClimate Science

• What is the basic narrative of climate change science? • How are we changing the carbon cycle?

Emissions Scenarios• What is an emissions scenario? • Which emissions scenarios are used by climate modelers? • What are the main factors causing the Earth’s energy imbalance? • What are the important similarities and differences among the SRES

scenarios? Climate Models

• What is in a climate model?• What does the hydrologic component of a climate model look like?• What is the basic methodology of making climate model projections?• What are the known biases in climate model simulations?• Why is downscaling necessary?• How do climate models differ in their projections for Western/Central US.• What will the future of climate modeling bring?

Breakout Exercise 1: Why use climate models to inform

local decisions on adaptation?

Break into groups, including instructors….

Your goal is to explain to a stakeholder why climate models are used to inform the future.

OCEAN

ATMOSPHERE

SEA ICE

Earth System Model (ESM)

Atmosphere-Ocean GCM Bio

geo

chem

ical

Mod

el

(incl

. C

arb

on C

ycle

)

OCEAN

ATMOSPHERICCHEMISTRY

DYNAMICVEGETATION

LANDLAND SURFACE

(Physical)

Components of Climate Models

• AOGCM – Atmosphere/Ocean General Circulation Model• Earth System Models• Problems with biases being passed from one component to another• IPCC Fifth Assessment is looking at both ESM and Atmosphere-Ocean

GCMs

Numerical Solution: Timesteps and Gridboxes

wind

Typical grid size (resolution):Horizontal ~ 60-180 mi (100-300 km)Vertical ~30 layers of varying depth

Typical timestep: 5 min - 20 min.

Solved at discrete time intervals:

NCAR CCSM 3.0 T85 (1.4ox1.4o)

WRF Regional Climate Model at 48km resolution

Model Topography: GCM vs. RCM Need for Downscaling!

feet

feet

Some CMIP5 Atmosphere Model Resolutions

AOGCM:

NCAR CCSM4 (1.25 lon x 0.9 lat)MIROC4H (0.56X0.56; T213)MIROC5 (1.4X1.4) HadCM3(N48) (3.75 x 2.5)MRI CGCM3 (1.1X1.1)CNRM CM5 (1.4 X 1.4) (TL127) CANCM4 (2.8 X 2.8;T63)

ESM:HadGEM-CC (1.875 x 1.25) GFDL ESM2M (2.5 x 2.0)MIROC-ESM-CHEM (2.8X2.8)

Compare this to CMIP3, where the median model resolution was around 3-degrees, the highest (one) was at ~1 degree, and the next highest was NCAR CCSM3 was at 1.4 degrees. The coarsest were 4x5 degrees.

Technical note: Take model resolutions with a small grain of salt. Resolution is not directly comparable between models with different numerical schemes.

• Most of the physical process are at scales smaller than the grid spacing– Need to represent the cumulative efect of sub-gridscale

processes on the grid in terms of gridscale parameters.– e.g. convective precipitation = f(T, moisture, winds)

• Atmosphere– clouds:

• precipitation & radiation – boundary layers

• Surface fluxes, turbulent energy• Ocean

– Mixing by eddies– Vertical mixing in upper ocean– Flow over sills => deep water formation

• Based on theory and observations• Parameters “tuned” (usually globally) to get reasonable climate

Numerical Solution: Parameterization

Hydrologic Component of Land Surface Model

NCAR CCSM3

Biophysical Component of Land Surface Model

NCAR CCSM3

GFDL CM2.0

Hydrologic Component of Land Surface Model

• Each land surface model has a different formulation -- some simple, some sophisticated.

• In even the most sophisticated global model, many (most?) topographic effects are lacking.

• Sub-grid variations in land surface characteristics only partially represented.

• Small basins not accounted for in routing models. • Land surface models in climate models have not (generally) been

calibrated for specific regions.

Hydrology in Climate Models

If the climate models have hydrology component in their land surface model, why do we need to run the output through another hydrology model?

Emissions Scenario (B1, A1B, A2; RCPs)

GHG Concentrations (IAM)

Climate Model (AOGCM)

NCAR CCSM

Run1 … Run 4

NCAR CESMUKMO-HadCM3

Different numbers of runs for each model and scenario….

Earth System Model

HadGEM

Climate Prediction vs. Projection

Globally averaged annual mean surface temperature anomaly (relative to 1979–2001). Smith et al. 2007)

Climate Projections (IPCC AR4) • estimate the forced signal due to GHG and other radiative forcing agents. • are obtained from free-running GCM simulations that are not periodically

restarted from observed conditions. • compare the climate “with” and “without” GHG emissions, etc..

Climate (decadal) Prediction (to be Included in IPCC AR5)• Starts from the observed conditions (a process called data assimilation- very

difficult for the ocean!)• Attempts to predict the actual trajectory of the decadal variations in climate • It is an open question how much skill there is on decadal time scales.

Blue curve: free-running model

Red blurs: ensemble of predictions for three starting dates.

Black curve: observed

20th Century Simulations

Observations

Climate Model Projections

Bias-corrected Projections

BiasCorrection

In many IPCC figures, a simple bias correction is (implicitly) applied:

• Temperatures are shown relative to a reference climatology for each model.

• Precipitation is shown as a percent change from a reference climatology for each model.

Modeling Methodology: Bias Correction is needed!

Which spatial features of the climate do we expect the 20th century model simulations to reproduce, and which can’t they reproduce, even in theory?

Resolution and Climate Model Evaluation

CAN’T REPRODUCE EXACTLY

• The climate statistics at a given observational site

• The average climate in mountainous regions (snow may be badly misrepresented due to elevation effects)

• The different climates in Denver, South Park, and Grand Junction

SHOULD REPRODUCE

• The average regional climate for homogeneous regions.

• The “large-scale” climate trends in mountainous regions

• The qualitative changes in climate for broad regions as one crosses the Rockies.

Which temporal features of the climate do we expect the 20th century model simulations to reproduce, and which can’t they reproduce, even in theory?

CAN’T REPRODUCE

• The weather on May 4th, 1953, or any other given day.

• The 1997-8 El Nino or other specific El Nino/La Nina Events

• A specific warm decade or cold decade if it was due to internal modes of variability

SHOULD REPRODUCE

• The climatological averages and other statistics for the observed record.

• The statistics of El Nino/La Nina and other tropical disturbances such as the Madden-Julian Oscillation.

• The statistics of internal decadal and multi-decadal climate fluctuations. Decadal or multi-decadal changes forced by volcanic, solar, and GHG variations.

Chaos and Climate Model Evaluation

Precipitation and Temperature Bias

Percent precipitation error (a,b) and absolute temperature error (c,d) of the ensemble average of 18 models in relation to the University of Delaware climate data (1979- 99). Panels on the left show the mean error for May to October; panels on the right, November to April. (McAfee, Russell, and Webb, 2012 )

. Köppen classification maps produced using (a) University of Delaware temperature and precipitation (1979-99), (b) the ensemble of 18 models (1979-99), using (c) adjusted 2079-99 ensemble climate and (d) unadjusted 2079-99 ensemble projections. See Table 1 for Köppen type descriptions. (McAfee, Russell and Webb, 2012)

Koppen Classification bias (as a proxy for ecosystem types)

OBS Model

ModelProjection

Bias-AdjustedProjection

Colorado R.~72 BCM/yr(obs ~ 19)

Model Biases: Streamflow

River routing model output: NCAR CCSM4.0 20th Century AverageOnly cells with > 5MAF/year are shown.

???

Green R. ?

WETCOLD

Model Biases: The Regional Picture

Source: IPCC WG1 Ch. 11, 11 Supp. And Climate Change in Colorado

ENSO Spectra in CMIP3 GCMs

NCAR CCSM has 2-year ENSO

GFDL model ENSO3x too strong

HADCM3 is close

Gray band is obs.uncertainty.

Individual Model Projections: Temperature

Source: IPCC WG1 11 Supp.

Individual Model Projections: Precipitation

Source: IPCC WG1 Ch. 11 Supp.

1990

1995

2001

2007

Trends in Global Climate Modeling

Increased Resolution; but computational cost increases rapidly! We won’t be able to directly resolve cloud-scale processes (1km) on a global scale for long climate runs for quite a while.

Increased Complexity -- more components of the Climate System (more sources of uncertainty!) Chemistry is expensive!

Trends in Global Climate Modeling

• Neither of these guarantee that the model simulations will improve! We also need better scientific understanding…

• Some personal opinions…– ENSO/Tropical Ocean changes -- I am optimistic based on

increasing knowledge of oceanic processes .– MJO -- I am less optimistic, based on progress so far. – PDO and other decadal variability … problematic!– Mean climate biases over land -- increased resolution, better land

surface models will help, but the remote influence of oceanic biases may remain.

Why use climate models – followup exercies

• Take the reasons we all came up with and write a 1 paragraph statement about why(or why not) climate models are a useful source of information for adapation studies. [ we will leave the questions about downscaling for another day!]

End

Why Use Climate Models?

To create plausible, physically based scenarios, reflecting the current state of scientific understanding, to inform planning for the future.