Climate modeling, INPE's projections for the 21st century, and the distribution of Brazilian biomes

National Institute for Space Research INPE Earth System Science Center CCST Climate modeling, INPE's projections for the 21st century, and the distribution of Brazilian biomes Gilvan Sampaio gilvan.sampaio@inpe.br Workshop Dimensions US-BIOTA São Paulo A multidisciplinary framework for biodiversity prediction in the Brazilian Atlantic forest hotspot February 10th 2014 FAPESP Rua Pio XI, 1500, Alto da Lapa, São Paulo, SP

What are the likely biome changes in Tropical South America due to a suite of environmental drivers of change? Environmental Drivers of Change Climate Change: CO 2,temperature, rainfall Land Cover Change: Deforestation, Forest Degradation Primary Drivers Droughts Forest Fires Secondary Drivers Short term (interannual to interdecadal) Long term (interdecadal to centennial) Secondarization Ecosystem Responses Changes in Species Composition Savannization/ forest dieback Tree Mortality x Tree Growth Savannization in this context is a statement on regional climate change and not intended to describe complex ecological processes of vegetation substitution.

How well can CMIP5 models simulate the precipitation over tropical South America?

CMIP5 - Bias in model precip (1971-2000 model-cru) Kay et al., 2013

CMIP5 - Temperature Anomalies RCP 4.5 2015-2034

CMIP5 - Temperature Anomalies RCP 8.5 2015-2034 Source: Sampaio et al., 2013, in preparation

CMIP5 - Temperature Anomalies RCP 4.5 2040-2059

CMIP5 - Temperature Anomalies RCP 8.5 2040-2059

Temperature change 2071-2100 Kay et al., 2013

CMIP5 - Precipitation Anomalies RCP 8.5 2015-2034 Source: Sampaio et al., 2013, in preparation

CMIP5 - Precipitation Anomalies RCP 8.5 2040-2059

Indicator of CMIP5 model consensus in precipitation changes 2071-2100 wetter conditions in DJF Brown colours indicate model agreement for a drying signal and greens for a wetting signal. Kay et al., 2013

CMIP5 - BESM RCPs Scenarios Surface Temperature over Brazil RCP 8.5: 0.235 C/decade RCP 4.5: 0.134 C/decade Fonte: Nobre et al (2013)

INPE-Eta model RCP4.5 Lyra and Chou, 2013

Source: Sillmann et al., 2013

LAND USE AND COVER CHANGE

(Foley et al., 2003)

EFFECTS OF LARGE SCALE DEFORESTTION Numerical simulations of deforestation Increase in surface temperature: 0.3 C to 3.0 C Decrease in evapotranspiration: 15% to 30% Decrease in precipitation: 5% to 20% Increases the length of the dry season Sources: Lean e Warrilow-1989; Nobre, et al.-1991; Henderson-Sellers et al.-1993; Lean et al.-1993, Sud et al.- 1996, Lean et al.-1996, Manzi e Planton-1996, Rocha et al.-1996, Hahmann e Dickinson.-1997, Costa e Foley- 2000, Rocha-2001, Werth e Avissar-2002, Voldoire e Royer-2004 e Correia-2005, Sampaio et al., 2007, Costa et al., 2007.

Effect of regional deforestation Enhance local circulation Increase rainfall amounts Different impact on cloudiness in the dry and wet seasons

Biomes of tropical South America and precipitation seasonality Biomes of South America Tropical Forest Shrubland Tropical Forest-Savanna Boundary Number of consecutive months with less than 50 mm rainfall Savanna Sombroek 2001, Ambio Annual Rainfall The importance of rainfall seasonality (short dry season) for maintaining tropical forests all over Amazonia Terrestrial biosphere models are competent at predicting plant and ecosystem carbon fluxes under the present climate, but still require substantial development for predicting the consequences of severe drought scenarios - Powell et al, 2013.

Biomes for South America. Forest Is the current Climate- Vegetation equilibrium in Amazonia the only stable equilibrium possible? After Olsen et al. (2001).

Biome-Climate Equilibrium variabilidade Regime 2 Regime 1

Tipping points of the Earth System Application to Amazonia Tropical forest Savanna state triggered by climate change or deforestation Tipping points: temperature, rainfall and deforestation area Stability of savanna enhanced by increased droughts and fires Cardoso and Borma, 2010

Biome changing bi-stability in the Amazon (a) First State - Biome-climate equilibrium starting from forest land cover as initial condition for the Dynamic Vegetation Model. These results are similiar to current natural vegetation. (b) Second State - Biomeclimate equilibrium starting from desert land cover as Initial Condition for the Dynamic Vegetation Model Savannization of Amazonia and desertification in NE Brazil Oyama and Nobre, 2003

What does it take to tip the equilibrium between the two stable states??

Climate Change Consequences on the Biome distribution in tropical South America Projected distribution of natural biomes in South America for 2090-2099 from 15 AOGCMs for the A2 emissions scenarios. Savanas in the Amazon and Semi-Desert in the Northeast of Brazil Savannization in this context is a statement on regional climate change and not intended to Salazar et al., 2007 describe complex ecological processes of vegetation substitution.

Climate change How does the forest respond to increased atmospheric CO 2? Only Climate CO 2 760 ppm ½ CO 2 fertilization effect Taking into account the potential positive effect of CO 2 on forest resilience Lapola et al. GBC, 2009

Preliminary attempt at determining quantitative tipping points for collapse of the Amazon forests Tentatively, thresholds for the maintenance of the rainforests are: T Global Warming < 3 C (3.5 C in the Amazon) Total Deforested Area < 40% Forest fires decrease even further the resilence of tropical forests in the Amazon The role of CO 2 fertilization is unkown and could increase resilience

Ecosystems of Amazonia - environmental drivers of change Complex Earth System Models are needed to study all these interacting and simultaneous drivers LUCC Fire Climate Change Climate Extremes

Canavesi et al. 2012 Projeto PROVEG Vieira et al., 2013

Percentage of deforestation Deforestation maps in scenario C INPE s Brazilian Scenarios + AMAZALERT Project Aguiar et al. (2013, submitted) http://www.eu-amazalert.org/home

Impacts of deforestation, climate change and fire on the future biomes distribution and climate in Amazonia

Projected distribution of natural biomes in South America where more than 66,7% of the models used (>=6 models) coincide for 2050 from 9 Earth System Models for the RCP 2.6, 4.5 and 8.5 emission scenarios Deforestation = 20% or 40% or 50% + Fire effect Sampaio et al., 2014, in preparation Tropical Seasonal Forest Savanna Tropical Evergreen Forest Savanna/Seasonal forest replaces Forest

Brazilian Earth System Model - BESM BESM is a fully-coupled, global climate model that provides state-of-the-art computer simulations of the Earth s past, present, and future climate states. BESM is an evolution of previous versions of the Center for Weather Forecasting and Climate Studies (CPTEC) coupled ocean-atmosphere model. The creation of the Brazilian Earth System Model is a goal of various projects, among them the National Institute of Science and Technology on Climate Change (INCT-MC) and projects such as the FAPESP-Global Climate Change Program, which represents a large collaboration between INPE and various other national and foreign institutions and universities.

Brazilian Earth System Model - BESM ATMOS CHEMISTRY (Max Planck) CO 2 Trace Gases Particles Heat H 2 O CO 2 ATMOSPHERE (INPE/CPTEC) 2080-2099 A1B 2080-2099 A1B INLAND Hydrology Land Use FMS COUPLER OCEAN (NOAA/GFDL MOM4) RIVER ICE Fire RIVERS BioChemistry Predictability

INLAND Integrated Land Surface Model We developed our integrated model on the top of the Integrated Biosphere Simulator (IBIS) v. 2.6 INLAND is a submodel of the Brazilian Earth System Model (BESM). Represent processes that are important to us (South America) and may be considered secondary in other models Collaboration with advanced climate change centers abroad INLAND: land surface processes parameterization INLAND is a community model. 40

INLAND Processes represented Fluxes of radiation, energy and mass IBIS 2.6/INLAND 1 Complete terrestrial carbon cycle IBIS 2.6/INLAND 1 Phenology and vegetation dynamics IBIS 2.6/INLAND 1 Recovery of abandoned lands IBIS 2.6/INLAND 1 Croplands representation Agro-IBIS/INLAND 2 River discharge and seasonally flooded areas THMB 2/INLAND 3 Specific representation of South American ecosystems INLAND 2 Fires (ignition, combustion, spreading, emissions) Anthropogenic land use (deforestation) Soil fertility (P) Continental ice sheets INLAND 2 INLAND 2 INLAND 2 INLAND 2 Subgrid tiling INLAND 2 Additional processes to be discussed INLAND 3

CMIP5-INLAND Results

Vegetation Type 2050: RCP4.5, LandUSE OFF, FIRE - OFF CCSM4 CSIRO-Mk-3-6-0 GFDL-ESM2M GISS-E2-R HadGEM2-ES IPSL-CM5A-LR MIROC5 MRI-CGCM3 NorESM1-M 1-Tropical evergreen 2-Tropical deciduous 3-Temp. evergreen broadleaf 4-Temp. evergreen conifer 5-Temp. deciduous 6-Boreal evergreen 7-Boreal deciduous 8-Mixed forest 9- Savanna 10-Grass. Steppe 11-Dense shrubland 12-Open shrubland 13-Tundra 14-Desert 15-Polar desert / rock / ice

Vegetation Type 2050: RCP4.5, LandUSE ON, FIRE - ON CCSM4 CSIRO-Mk-3-6-0 GFDL-ESM2M GISS-E2-R HadGEM2-ES IPSL-CM5A-LR MIROC5 MRI-CGCM3 NorESM1-M 1-Tropical evergreen 2-Tropical deciduous 3-Temp. evergreen broadleaf 4-Temp. evergreen conifer 5-Temp. deciduous 6-Boreal evergreen 7-Boreal deciduous 8-Mixed forest 9- Savanna 10-Grass. Steppe 11-Dense shrubland 12-Open shrubland 13-Tundra 14-Desert 15-Polar desert / rock / ice

Biomass 2050: RCP4.5, LandUSE OFF, FIRE - OFF CCSM4 CSIRO-Mk-3-6-0 GFDL-ESM2M GISS-E2-R HadGEM2-ES IPSL-CM5A-LR MIROC5 MRI-CGCM3 NorESM1-M

Biomass 2050: RCP4.5, LandUSE ON, FIRE - ON CCSM4 CSIRO-Mk-3-6-0 GFDL-ESM2M GISS-E2-R HadGEM2-ES IPSL-CM5A-LR MIROC5 MRI-CGCM3 NorESM1-M

BESM-INLAND Results

No fire and LU Temperature RCP4.5-2050 + fire and LU

No fire and LU Precipitation RCP4.5-2050 + fire and LU

Dry season length RCP4.5-2050 No fire and LU + fire and LU

Biomass RCP4.5-2050 No fire and LU + fire and LU reduction of upper-canopy biomass More grasses, less trees

In Summary Climate change will very likely affect most biomes in South America during this century, but the intensity of the change is uncertain, in part because of differences in rainfall projections. Drivers of change: global warming, deforestation, fire, droughts There are many uncertainties associated with the long-term effect of CO 2, SST, land use changes, aerosols, etc The role of CO 2 fertilization is unkown and could increase resilience We need to calibrate the INLAND model for Brazilian Atlantic forest How to combine predictions of biodiversity with climate projections? There are many issues and improvements to do!

Muito Obrigado!