1. How are Land Properties in a Climate Model Coupled through the Boundary Layer to Affect the Amazon Hydrological Cycle? Robert Earl Dickinson, Georgia Institute of Technology LBA III Scientific Conference, 28 July S3

2. Simplest recipe- past approaches to Amazon climate dependence on surface conditions Use a climate model to characterize control climate Impose some scenario of land use change – e.g. total replacement of Amazon forest with grassland Examine simulated climate change that results Establish attribution by “one-factor at a time” sensitivity studies.

3. Generic Conclusions for Past Studies Evapotranspiration decreased by deforestation – responding to decreased surface radiation Driver: higher albedo – less solar – shorter vegetation – more LW loss; negative feedbacks in terms of fewer low clouds. In most models precipitation reduced. Problematic to establish: role of changing soil properties, connections to and feedbacks from runoff.

4. Limitations and criticisms of simplest approach From a probabilistic viewpoint, how much more information (bits useful to a policy maker) have we gained considering? Inadequate connection between scenarios assumed and actual history of land use changes Inadequate rigor in evaluation of results from standard science practice reduces confidence: –need to establish levels of natural variability, to judge results relative to such; –models have provided inadequate control simulations. –need consistent results between different models

5. More attention to physical processes? Better quantification of local changes that occur –Has been substantially accomplished by LBA and earlier field programs, e.g ABRACOS. Better description of the mechanisms relating land use/cover change to precipitation. -- relate changes of surface fluxes to changes in atmospheric hydrological cycle. -- how does modification of boundary layer modify characteristics of precipitation?

6. Fine – how do we do this? Perhaps a good starting point is to look at how climate model makes convective precipitation. Produce boundary layer from adiabatic mixing from surface sensible heat fluxes – this can act to dry the BL by entrainment – whether this functions properly generally not validated. Moisture produced from surface makes clouds at LCL – but realistically – these are necessarily on a subgrid scale Deep convection can be triggered by large scale conditions – but how correlated with actual occurrence of convection questioned – ARM data

7. Conclusion: basis on which climate models determine precipitation from changing surface conditions too oversimplified and unvalidated. What can be concluded? Deforestation likely a major driver of Amazon hydrological change but that we need more robust theoretical tools to quantify impact on precipitation and determine future climate trends.

8. Problem is with model parameterizations – so what is a parameterization? Climate model has too many degrees of freedom for all to be computed from basic continuum description so: need to describe by sound statistical theories scales from 0.1 mm to 100 km or so (below handled by well established gas kinetic theory – above by model direct simulation). Statistical theories are not easy to do and need much numerical experimentation re the processes. Now done by two implausibly simple sets of rules call turbulence and moist convection parameterization.

9. So what get left out? The subgrid processes occur in spatially correlated structures which plausibly can couple to land surface spatial structures – Pielke and Avissar have been saying this for over a decade – their programs have demonstrated interesting effects but still not close to seeing how to fix this in climate models. Spatial structures interact with each other and produce patterning – for example at times organized structures of BL clouds. Local attractors – like potential wells and repulsions – convection cell in place discourages other from occurring.

10. Directions Needed? More realistic formulations of parameterizations (statistical theories) of convection allowing for dominant spatial scales of interaction (progress slow). More realistic formulation of subgrid spatial variabilities in climate models (also by statistical theory: –Report on CLM3- reformulation in CCSM3

15. Some Conclusions – re Amazon Frequency of tropical rainfall decreased by order of magnitude (intensities so increase) Water evaporated by canopy reduces by factor to two – transpiration like increases Some increase of runoff – especially in dry season Some increase of surface radiation – overall somewhat warmer and drier – more sensible Average of P and E change little

16. So What? Changes time scales of soil moisture variability and coupling to ET. Carbon assimilation driven by transpiration so want to get rid of such serious bias. Provides a framework for systematic addition of other important land subgrid processes – and their coupling to precipitation