Anthropogenic Land Cover Change Experiments in the CCSM

Participants

NCAR University of KansasGordon Bonan Johannes Feddema Linda Mearns Trish JacksonKeith Oleson Pei-Ling LinJerry Meehl John BauerWarren WashingtonDoug NychkaLawrence Buja

This research is supported by the Office of Science (BER), U.S. Department of Energy, Cooperative Agreement No. DE-FC02-97ER62402, by the National Science Foundation grant numbers ATM-0107404, and ATM-0413540, the NCAR Weather and Climate Impact Assessment Science Initiative, and the University of Kansas, Center for Research.

Overview:

1. How are the experiments set up and developed?a. Equilibrium vs transient experimentsb. Uncertainty about land cover and its impactsc. Multiple land cover forcings (e.g. agriculture vs grazing)

2. Dealing with multiple climate forcings a. Land cover change alongside other forcingb. Statistical Significance in this framework

3. Separating out signals and feedbacks between forcingsa. Complex and non-linear responses to the same forcingb. Optimizing experimental design

1. How are the experiments set up and developed?a. Equilibrium vs transient experimentsb. Uncertainty about land cover and its impactsc. Multiple land cover forcings (e.g. agriculture vs grazing)

2. Dealing with multiple climate forcings a. Land cover change alongside other forcingb. Statistical Significance in this framework

3. Separating out signals and feedbacks between forcingsa. Complex and non-linear responses to the same forcingb. Optimizing experimental design

Equilibrium Experiments:1. Hold all conditions equal and allow the model to run to equilibrium 2. Compare a control and experiment where one or more boundary conditions are

changed3. Typically compare 10-50 year time slices after equilibrium is reached

Transient Experiments:1. Starting from some equilibrium state the model runs through time as forcings

change (e.g. increasing CO2 through time)2. Compare a control and experiment integrated over one or more time periods during

the simulation3. Model usually does not reach equilibrium so equivalent time slices of 10-30 years

are compared

1. How are the experiments set up and developed?a. Equilibrium vs transient experimentsb. Uncertainty about land cover and its impactsc. Multiple land cover forcings (e.g. agriculture vs. grazing)

2. Dealing with multiple climate forcings a. Land cover change alongside other forcingb. Statistical Significance in this framework

3. Separating out signals and feedbacks between forcingsa. Complex and non-linear responses to the same forcingb. Optimizing experimental design

PCM Uncertainty/Historical Equilibrium Land Cover Simulations

PCM Uncertainty/Historical Equilibrium Land Cover Simulations

PRESENT DAY UNCERTAINTY• Arctic – albedo• Amazon – latent heat flux• Australia – albedo

HISTORICAL CHANGEClimate difference from land cover classification is as large as the climate difference from land cover change• Primarily shift to agriculture

Question: How do we deal with input uncertainty?

AgreementGLC2000

IGBPMODIS

No Ag1 product

All products2 products

Comparison of Agriculture land classes from 3 satellite products10 degree tile over East Africa

1. How are the experiments set up and developed?a. Equilibrium vs transient experimentsb. Uncertainty about land cover and its impactsc. Multiple land cover forcings (e.g. agriculture vs. grazing)

2. Dealing with multiple climate forcings a. Land cover change alongside other forcingb. Statistical Significance in this framework

3. Separating out signals and feedbacks between forcingsa. Complex and non-linear responses to the same forcingb. Optimizing experimental design

Question: How do to isolate the impacts of multiple forcings?

1. How are the experiments set up and developed?a. Equilibrium vs transient experimentsb. Uncertainty about land cover and its impactsc. Multiple land cover forcings (e.g. agriculture vs grazing)

2. Dealing with multiple climate forcings a. Land cover change alongside other forcingb. Statistical Significance in this framework

3. Separating out signals and feedbacks between forcingsa. Complex and non-linear responses to the same forcingb. Optimizing experimental design

Halocarbons

N2O

CH4

CO2

0

1

2

3

-1

-2

Stratosphericozone

Troposphericozone

Sulfate

Fossil fuel burning

BiomassBurning

MineralDust

Aerosolindirect effect

Land use(albedo)

SolarBlack carbon

Organic carbon

Aerosols

Rad

iativ

e F

orci

ng (

Wm

-2)

War

min

gC

oolin

g

Global Mean Radiative Forcing In 2000 Relative To 1750

High Medium Medium Low Very Low

Very Low

Very Low

Very Low

Very Low

Very Low

Level Of Scientific Understanding(IPCC, 2001)

IPCC and human impacts

IMAGE 2.2 - 1970 Land Cover

IMAGE 2.2 Land Cover Types

0 - Ocean

1 - Agriculture

2 - Extensive grassland

3 - C plantation - NU

4 - Regrowth (abandon)

5 - Regrowth (timber)

6 - Ice

7 - Tundra

8 - Wooded Tundra

9 - Boreal Forest

10 - Cool Conifer

11 - Temperate Mixed Forest

12 - Temperate Decid Forest

13 - Warm Mixed Forest

14 - Grass/Steppe

15 - Desert

16 - Scrubland

17 - Savanna

18 - Tropical Woodland

19 - Tropical Forest

No Data

By 2100, expansion of agricultural land in North America, South America, Africa, and Southeast Asia

Question:What is the land use forcing relative to other natural and anthropogenic forcings?

IMAGE 2.2 - A2: 2100 Land Cover

IMAGE 2.2 Land Cover Types

0 - Ocean

1 - Agriculture

2 - Extensive grassland

3 - C plantation - NU

4 - Regrowth (abandon)

5 - Regrowth (timber)

6 - Ice

7 - Tundra

8 - Wooded Tundra

9 - Boreal Forest

10 - Cool Conifer

11 - Temperate Mixed Forest

12 - Temperate Decid Forest

13 - Warm Mixed Forest

14 - Grass/Steppe

15 - Desert

16 - Scrubland

17 - Savanna

18 - Tropical Woodland

19 - Tropical Forest

No Data

The A2 Scenario:The A2 storyline and scenario family describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities. Fertility patterns across regions converge very slowly, which results in continuously increasing global population. Economic development is primarily regionally oriented and per capita economic growth and technological change are more fragmented and slower than in other storylines.

2100

1970

PCM Future SRES A2 Transient Simulations

Future IPCC SRES Scenarios for PCM

PCM Future SRES A2 Transient Simulations

Projected change by 2100 – Annual Average TemperatureGHG only

LC contribution(GHG+LC) – GHG only

GHG + LC

* Note Shift in Divergent Scale

B1 A2

2050

2100

Question: How to best identify land cover impacts in a multi-forcing run?

Relative impact of land cover forcing compared to GHG effectsOn average LULC contributes 11% of 2100 forcing compared to GHG-only forcing.However, this is highly regional and offsetting with respect to global average temperature

PCM Future SRES A2 Transient Simulations

Question: What is a good measure to compare different forcings? (radiative forcing) Given that we have spatial and temporal results that can be offsetting.

B1 A2

2050

2100

Question: How to best isolate direct impacts from teleconnections?

1. How are the experiments set up and developed?a. Equilibrium vs transient experimentsb. Uncertainty about land cover and its impactsc. Multiple land cover forcings (e.g. agriculture vs grazing)

2. Dealing with multiple climate forcings a. Land cover change alongside other forcingb. Statistical Significance in this framework

3. Separating out signals and feedbacks between forcingsa. Complex and non-linear responses to the same forcingb. Optimizing experimental design

Change in temperature

Shading = standard t test 0.95 confidence levelContour = bootstrap 0.95 confidence level

Annual

PCM Historical Comparison

JJA

DJF

Bootstrap confidence test shows strongsummer hemisphere signal in sub-tropics

Many of the areas are over land cover change locations

Question: How to best /most efficiently evaluate confidence?

1. How are the experiments set up and developed?a. Equilibrium vs transient experimentsb. Uncertainty about land cover and its impactsc. Multiple land cover forcings (e.g. agriculture vs grazing)

2. Dealing with multiple climate forcings a. Land cover change alongside other forcingb. Statistical Significance in this framework

3. Separating out signals and feedbacks between forcingsa. Complex and non-linear responses to the same forcingb. Optimizing experimental design

Seasonal Change inAlbedo

Seasonal Change inNet Radiation

PCM Present Day ComparisonImage - LSM

Strong winter/spring albedo changein the Northern Hemispheretranslates to spring/summer

net radiation change due to solarseasonality

Question: How to best detect seasonally varying responses?

Albedo

PCM Historical Comparison

DJF JJACloud cover change

Incident radiation

Albedo changes, but cloud cover

also plays a major role

Local feedbacks or changes in circulation?

Question: How to identify feedbacks, and can we have confidence in these signals?

Future Scenario:All grid cells that have been converted from tropical rain forest to agricultural change

The Amazon response is very different from SE Asia response in part because of the

large scale circulation conditions

Question: How to best detect spatial

variability in specific responses?

Variability in Simulated Heat Island caused by Climate

and Rural Environment •Atmospheric forcing from CAM (offline model)

•Default city with H/W=0.5,…,3.0

•Rural environment from CLM Surface Data

The urban model has very distinctly different responses depending on

weather conditions and on surrounding vegetation types

Question: How to organize output to best analyze the variability in responses?

1. How are the experiments set up and developed?a. Equilibrium vs transient experimentsb. Uncertainty about land cover and its impactsc. Multiple land cover forcings (e.g. agriculture vs grazing)

2. Dealing with multiple climate forcings a. Land cover change alongside other forcingb. Statistical Significance in this framework

3. Separating out signals and feedbacks between forcingsa. Complex and non-linear responses to the same forcingb. Optimizing experimental design

Currently simulations are run independently for all possible forcings then in combination.

Question: Knowing there are non linear feedbacks, is there a way to reduce the number of runs with combinations of experiments to:

a) Extract the individual climate impacts of each forcingb) Understand the non linear interactions between the forcings