Strategies for adaptation to climate change in Timor Leste: the importance of climate thresholds
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The document outlines the aims of a talk which are to: 1) Describe past climate in Timor Leste including rainfall and temperature trends and seasonality. 2) Comment on historical climate variability and food production in Timor Leste. 3) Discuss the utility of assessing past adaptation for future climate planning. The talk will analyze temperature and rainfall data from Timor Leste to describe historical climate trends, seasons, and variability and their impacts on food production.
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Strategies for adaptation to climate change in Timor Leste: the importance of climate thresholds
- 2. Aim of talk 1. Describe past climate in Timor Leste (rainfall & temperature); 2. Summarise changes in long- term trend and seasonality; 3. Comment on historical climate variability and food production in Timor Leste. 4. Discuss utility of assessing Photo: Kirsten Abernethy past (autonomous) adaptation for future planning.
- 3. • •
- 4. Temperature ~ location + trend + season + random component
- 5. Seasonal change in Rainfall at Dili Airport East Timor Photo: Jamie Oliver
- 12. • • • • • •
Notas del editor
- Hello, My name is Doug Beare. I only recently joined WorldFish. I am responsible for managing our engagement with CCAFs (Climate Change and Food Security). My background is in fisheries management and more in examining how climate variability has and is impacting all types of marine biota.
- This year I became involved in an ADB funded project (PL=Sarah Park) to examine climate change adaptation options for farmers and fishers in TL and SI (2013). In particular we recognised the importance of examining past changes in climate and how communities autonomously responded. The aim of the talk is to go through points 1-4.
- To that end we started gathering data for East Timor. Some data came from CSIRO and some from the (US) National Center for Atmospheric Research (http://ncar.ucar.edu/). Data for the marine EEZ of TL came from ICOADs (also hosted at NCAR). There are a lot of free data on the web. Many other potential sources. Situation for land is complex but ICOADs is unrivalled in spatio-temporal extent for the seas. Data back to the 18th century etc. etc. The main point is that there are a lot of free data out there!
- Climate can be measured/assessed using a large range of variables. Here, however, we focus on rainfall and temperature (air temperature and sea surface temperature). Clearly rainfall and temperature depend on location, season (could be measured in weeks, months, quarters) and the year. The year ‘effect’ is really the ‘long-term trend effect’. We take a ‘classical’ time-series analytical view of the data which involves ‘decomposing them’ into long-term trend, seasonal and random components. It should not be forgotten that the main effects can also ‘interact’ with one another: ie seasonal patterns can vary between locations and years …. Our data analysis concentrated on exposing these factors.
- Our data analyses focused on exposing these factors. Rainfall key findings are that the dry season has got drier and that it starts earlier. The wet season has gotten shorter but on average the rain that does fall is more intense/heavier.
- What is immediately obvious is the dramatic increase in trend. The blue line is a linear model through the data. The war period is
- ICOADS data for the EEZ of East Timor
- This is a 3D plot of sea surface temperature in Timor Leste. Change in seasonality is profound. The dry season (on land) is dryer and this corresponds to sst which has gotten shorter and warmer. Similary during the the wet season the sea is now a lot warmer than it used to be.Mention El Nino/La Nina Oscillation.
- Big deal! Strong +ve correlation. Both SST and rice production are going up. But what’s really happening ? Need to remove theTrend from the rice production first. And then see if changes in SST relate to changes in Rice production given year (ie. factors due toPopulation growth etc. are removed).
- Analysis of Variance TableModel 1: quant ~ 1Model 2: quant ~ yearModel 3: quant ~ year + sstModel 4: quant ~ year + sst + whRes.Df RSS Df Sum of Sq F Pr(>F) 1 49 2.6975e+10 2 48 7.7649e+09 1 1.9210e+10 130.7404 4.844e-15 ***3 47 7.1224e+09 1 6.4251e+08 4.3728 0.04207 * 4 46 6.7588e+09 1 3.6358e+08 2.4745 0.12256 ---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1