4. Testing the LAI model

1. (a1, b1, c1) LAI from MODIS Terra satellite Monthly maximum surface solar radiation (a0, b0, c0) Testing current understanding of Amazon phenology using a Monte Carlo Markov Chain algorithm Silvia Caldararu (First year PhD) Paul Palmer, Drew Purves 1. Introduction Forests play an important role in the global carbon cycle through photosynthesis and respiration, and through the emission of hydrocarbons. To quantify forest carbon budgets and understand how they will respond to changing environmental conditions, we need to understand the factors that determine the temporal variations of vegetation. Ground-based data are sparse in time and space, making it difficult to relate these data to larger spatial and temporal scales. 4. Testing the LAI model To accurately fit a model to a large data set, as in the case of the global-scale space-borne LAI data, there is a need for an efficient algorithm. We use the Metropolis algorithm, also known as simulated annealing, which uses a Monte Carlo Markov Chain (MCMC) approach to explore model parameter space. How does it work? Satellite observations of vegetation optical properties allow us to observe vegetation cover at global scales with sub-1km resolution. These data are useful to fill in gaps left by ground-based data, particularly over remote tropical regions. Leaf area index (LAI), the ratio of one-sided leaf area to the underlying ground area, can be calculated using a radiative algorithm based on the fraction of light at different wavelengths absorbed and reflected by vegetation. Step 1: Choose a starting point Step 2: Make the jump Step 3: Calculate the likelihood L for both points. Step 4: Take the decision If L1>L0: accept the new parameters If L1<L0:: 2. The Amazon Our initial focus is on the Amazon rain forest, one of the largest tropical forests in the world, where our knowledge of phenology is incomplete due to its large biodiversity (see Figure). Accept the new parameters with probability P Reject the new parameters with probability (1-P) The Amazon exhibits two seasons: 1) a dry season (July to November) and 2) a wet season (December-May); while May-June are transition months. Longer dry seasons occur over Eastern Amazonia. Satellite observations reveal large, as yet unexplained, seasonal swings in LAI over the Amazon rainforest (see Figure). Step 5: Go back to step 2. After convergence, the resulting posterior distribution can be averaged to give the desired parameter values. • 5. Future work • Can we accurately reproduce observed space-based LAI data over the Amazon using a relatively simple model description of driving environmental factors? • How will vegetation respond to possible changes in climate such as the Amazon forest die-back as predicted by the Hadley Centre climate model? • Does the phenology over other tropical rainforests behave in the same way, e.g., can the Amazon model reproduce phenology over African rainforests? • What are the implications of our new phenology model for understanding the size and location of reactive and unreactive carbon fluxes? Myneni et al, PNAS, 2006 3. Modelling approach The observed variation in LAI can be described as the difference between the number of leaf layers lost and the number added: Leaf gain and loss for any vegetation type will be limited by a number of environmental factors, but mainly temperature, sunlight, water availability and soil fertility. The deep root system of the Amazonian rainforest gives plants access to the deeper soil layers, which are not water depleted during the dry season. We have developed a simple phenology model that can be fitted to available data to test hypotheses about environmental factors.