Work Package 3 “Uncertainties in the projections by coupled models” MetOffice (UK), INPE (BR), IPSL (FR), VU (NL), FAN (BO)

CMIP5 - Bias in model precip ( model-CRU) Kay et al., 2013 CMIP5 models exhibit strong regional disagreement with one another and with observations

SEASONAL CYCLE OF PRECIPITATION (mm/day) FROM CRU (BLACK LINE) AND CMIP5 MODELS (COLORED LINES) FOR YEARS 1961–90 AMAZONIA Best performance: ACCESS1-0, CCSM4, HadGEM-ES, NorESM1 Worst performance: CSIRO and MIROC-ESM, GFDL Source: Alves et al., 2012

CMIP5 - INTERANNUAL VARIABILITY OF PRECIPITATION Amazon basin Source: Alves et al., 2012

Annual mean PRECIPITATION change (RCP8.5: )

Annual PRECIPITATION anomaly

Brown colours indicate model agreement for a drying signal and greens for a wetting signal.

Temperature Anomalies (C) –

Annual mean TEMPERATURE change (RCP8.5: )

Temperature change

Annual TEMPERATURE anomaly

J. P. Boisier

The timescales of forest response to climate change: mortality processes such as drought or fire – how the models simulate that?

J. P. Boisier

Modelling drivers of change: Global change: Different ‘pathways’ allow alternative scenarios of emissions to be explored, most recently through CMIP5 (Coupled Model Intercomparison Project phase 5). However, owing to great uncertainty in the terrestrial carbon cycle feedback, there is likewise great uncertainty in the transformation of emissions into atmospheric concentrations, something that has not received much attention to date. Carbon dioxide (CO 2 ): CO 2 fertilization confers significant benefits to Amazon forest carbon uptake and increased carbon storage in the models, but this process is a key uncertainty marked by a lack of understanding of how this operates in tropical vegetation and in conjunction with other nutrient and radiation availability. Temperature: Temperature increase is a common feature of climate projections, and considered alone has a negative effect on forest health. However, poorly-represented temperature dependency of respiration and photosynthesis is likely to make most if not all models too sensitive to high temperatures. Ongoing observational work, including through AMAZALERT, should help to develop better model representation of this process. Drought and dry season characteristics: Droughts such as 2005 and 2010 as well as imposed drought experiments have demonstrated that the forest is sensitive to these conditions. The mechanisms of response to drought appear to be different than in current models, which is in part due to missing processes such as direct drought- and fire-driven mortality.

“There is not really a way of testing the ability of climate models to predict the response to the kind of forcing expected for coming decades (increased GHGs, etc.), because such conditions have not been seen before, at least not within the instrumental record.” R. Betts “We have to assume that other aspects of model behavior (e.g. Teleconnections to SSTs on seasonal timescales) give us an indication of model skill. By some such measures, the old Hadley model (which gave the huge dieback) was the best of its generation - but gave an outlier response in future.” R. Betts There are missing or partially represented processes in models (like fire, drought mortality, nutrient limitations), and hence uncertainty is wider than that encompassed by CMIP and other ensembles. Furthermore, there is inaccurate representation of other processes such as drought phenology and the onset of the wet season, as well as a widespread dry bias in the ensemble model climatology, which requires further model development to address. There are many uncertainties associated with the long-term effect of CO2, SST, land use changes, aerosols, etc… “Uncertainties in the projections by coupled models”

There is uncertainty in the magnitude and even the direction of change in annual rainfall. The tendency of the GCMs towards a strengthened Amazon dry season. The 'diagnostic' projections and the observed past trends indicate that the model democracy approach (ensemble mean) would likely underestimate the amplitude of the projected Amazon dry season lengthening, which may have implications for forest viability. Further analyses are needed to shed light on the spatial detail of constrained projections to evaluate whether the regions affected are vulnerable or not. Land use: Land use (LU) and climate change interaction is still poorly understood, but improved scenarios of land use provide the opportunity to investigate the combined effects. Fire: Fire is a critical process that is still missing in most complex models, in particular so-called land-use fires that involve the combination of a climate-induced high fire risk, forest fragmentation and human drivers of deforestation and pasture formation. Recent improvements to INPE’s INLAND model include a new scheme for estimating the impacts of fires on vegetation dynamics. It estimates that the impacts of climate change in Amazonia increase when effects of land use changes and fire are considered. “Uncertainties in the projections by coupled models”

Additional WP3 experiments: Met Office: we can continue the existing experiments to the end of the century with rcp8.5 climate and LuccME Scenario C 2050 LU within the Amazon basin IPSL: existing transient simulations (reported in D3.4) can be extended beyond 2050 with LuccME Scenario C 2050 LU INPE: similar experiments as Met Office planned/in progress with BESM. In addition, we will run offline Inland model forced with BESM, Met Office and IPSL models: rcp8.5 climate + LuccME LU + (optional) fire.