Anthropogenic Land Cover Change Experiments in the CCSM Participants NCAR University of Kansas Gordon BonanJohannes Feddema Linda MearnsTrish Jackson Keith OlesonPei-Ling Lin Jerry MeehlJohn Bauer Warren Washington Doug Nychka Lawrence 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 , and ATM , 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 experiments b.Uncertainty about land cover and its impacts c.Multiple land cover forcings (e.g. agriculture vs grazing) 2.Dealing with multiple climate forcings a.Land cover change alongside other forcing b.Statistical Significance in this framework 3.Separating out signals and feedbacks between forcings a.Complex and non-linear responses to the same forcing b.Optimizing experimental design

1.How are the experiments set up and developed? a.Equilibrium vs transient experiments b.Uncertainty about land cover and its impacts c.Multiple land cover forcings (e.g. agriculture vs grazing) 2.Dealing with multiple climate forcings a.Land cover change alongside other forcing b.Statistical Significance in this framework 3.Separating out signals and feedbacks between forcings a.Complex and non-linear responses to the same forcing b.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 changed 3.Typically compare 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 CO 2 through time) 2.Compare a control and experiment integrated over one or more time periods during the simulation 3.Model usually does not reach equilibrium so equivalent time slices of years are compared

1.How are the experiments set up and developed? a.Equilibrium vs transient experiments b.Uncertainty about land cover and its impacts c.Multiple land cover forcings (e.g. agriculture vs. grazing) 2.Dealing with multiple climate forcings a.Land cover change alongside other forcing b.Statistical Significance in this framework 3.Separating out signals and feedbacks between forcings a.Complex and non-linear responses to the same forcing b.Optimizing experimental design

PCM Uncertainty/Historical Equilibrium Land Cover Simulations

PRESENT DAY UNCERTAINTY Arctic – albedo Amazon – latent heat flux Australia – albedo HISTORICAL CHANGE Climate 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?

Agreement GLC2000 IGBPMODIS No Ag 1 product All products 2 products Comparison of Agriculture land classes from 3 satellite products 10 degree tile over East Africa

1.How are the experiments set up and developed? a.Equilibrium vs transient experiments b.Uncertainty about land cover and its impacts c.Multiple land cover forcings (e.g. agriculture vs. grazing) 2.Dealing with multiple climate forcings a.Land cover change alongside other forcing b.Statistical Significance in this framework 3.Separating out signals and feedbacks between forcings a.Complex and non-linear responses to the same forcing b.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 experiments b.Uncertainty about land cover and its impacts c.Multiple land cover forcings (e.g. agriculture vs grazing) 2.Dealing with multiple climate forcings a.Land cover change alongside other forcing b.Statistical Significance in this framework 3.Separating out signals and feedbacks between forcings a.Complex and non-linear responses to the same forcing b.Optimizing experimental design

Halocarbons N2ON2O CH 4 CO Stratospheric ozone Tropospheric ozone Sulfate Fossil fuel burning Biomass Burning Mineral Dust Aerosol indirect effect Land use (albedo) Solar Black carbon Organic carbon Aerosols Radiative Forcing (W  m -2 ) Warming Cooling Global Mean Radiative Forcing In 2000 Relative To 1750 HighMedium LowVery Low Very Low Very Low Very Low Very Low Very Low Level Of Scientific Understanding (IPCC, 2001) IPCC and human impacts

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? 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 PCM Future SRES A2 Transient Simulations

Future IPCC SRES Scenarios for PCM

PCM Future SRES A2 Transient Simulations Projected change by 2100 – Annual Average Temperature GHG only LC contribution (GHG+LC) – GHG only GHG + LC * Note Shift in Divergent Scale

B1A Question: How to best identify land cover impacts in a multi-forcing run?

Relative impact of land cover forcing compared to GHG effects On 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.

B1A Question: How to best isolate direct impacts from teleconnections?

1.How are the experiments set up and developed? a.Equilibrium vs transient experiments b.Uncertainty about land cover and its impacts c.Multiple land cover forcings (e.g. agriculture vs grazing) 2.Dealing with multiple climate forcings a.Land cover change alongside other forcing b.Statistical Significance in this framework 3.Separating out signals and feedbacks between forcings a.Complex and non-linear responses to the same forcing b.Optimizing experimental design

Change in temperature Shading = standard t test 0.95 confidence level Contour = bootstrap 0.95 confidence level Annual PCM Historical Comparison JJA DJF Bootstrap confidence test shows strong summer 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 experiments b.Uncertainty about land cover and its impacts c.Multiple land cover forcings (e.g. agriculture vs grazing) 2.Dealing with multiple climate forcings a.Land cover change alongside other forcing b.Statistical Significance in this framework 3.Separating out signals and feedbacks between forcings a.Complex and non-linear responses to the same forcing b.Optimizing experimental design

Seasonal Change in Albedo Seasonal Change in Net Radiation PCM Present Day Comparison Image - LSM Strong winter/spring albedo change in the Northern Hemisphere translates to spring/summer net radiation change due to solar seasonality Question: How to best detect seasonally varying responses?

Albedo PCM Historical Comparison DJFJJA Cloud 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 experiments b.Uncertainty about land cover and its impacts c.Multiple land cover forcings (e.g. agriculture vs grazing) 2.Dealing with multiple climate forcings a.Land cover change alongside other forcing b.Statistical Significance in this framework 3.Separating out signals and feedbacks between forcings a.Complex and non-linear responses to the same forcing b.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 forcing b)Understand the non linear interactions between the forcings