Overview of climate variability and climate change in GMS


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Overview of climate variability and climate change in GMS

  1. 1. Overview of climate variability and climate change Eitzinger Anton, Giang Linh, Lefroy Rod Laderach Peter, Carmona Stephania Overview of climate variability and likely climate change impacts on agriculture across the Greater Mekong Sub-region (GMS) 10 – 11 March, 2014, Hanoi, Vietnam
  2. 2. Climate science … many questions and uncertain answers! 1. What is the evidence and observed changes in the climate system and how reliable are climate models and scenarios? 2. How to use climate models & future predictions for Agriculture and modeling? 3. How can we adapt agriculture systems to unknown future conditions?
  3. 3. The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, sea level has risen, and the concentrations of greenhouse gases have increased. IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  4. 4. Each of the last three decades has been successively warmer at the Earth’s surface than any preceding decade since 1850. In the Northern Hemisphere, 1983–2012 was likely the warmest 30-year period of the last 1400 years (medium confidence). Ocean warming dominates the increase in energy stored in the climate system, accounting for more than 90% of the energy accumulated between 1971 and 2010 (high confidence). It is virtually certain that the upper ocean (0−700 m) warmed from 1971 to 2010. IPCC AR5 report – observed changes in the climate system Over the last two decades, the Greenland and Antarctic ice sheets have been losing mass, glaciers have continued to shrink almost worldwide, and Arctic sea ice and Northern Hemisphere spring snow cover have continued to decrease in extent (high confidence) The rate of sea level rise since the mid-19th century has been larger than the mean rate during the previous two millennia (high confidence). Over the period 1901–2010, global mean sea level rose by 0.19 [0.17 to 0.21] m. The atmospheric concentrations of carbon dioxide (CO2), methane, and nitrous oxide have increased to levels unprecedented in at least the last 800,000 years. CO2 concentrations have increased by 40% since pre-industrial times, primarily from fossil fuel emissions and secondarily from net land use change emissions. The ocean has absorbed about 30% of the emitted anthropogenic carbon dioxide, causing ocean acidification. IPCC, 2013
  5. 5. Drivers of Climate Change Total radiative forcing is positive, and has led to an uptake of energy by the climate system. The largest contribution to total radiative forcing is caused by the increase in the atmospheric concentration of CO2 since 1750. Climate models have improved since the AR4. Models reproduce observed continental-scale surface temperature patterns and trends over many decades, including the more rapid warming since the mid-20th century and the cooling immediately following large volcanic eruptions. (very high confidence). This evidence for human influence has grown since AR4. It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century. Changes in the global water cycle in response to the warming over the 21st century will not be uniform. The contrast in precipitation between wet and dry regions and between wet and dry seasons will increase, although there may be regional exceptions. IPCC, 2013
  6. 6. IPCC Global-scale assessment of recent observed changes, human contribution to the changes, and projected further changes IPCC, 2013
  7. 7. Observed ocean and surface temperature anomaly • Annual average • Decadal average • Contribution to change IPCC, 2013
  8. 8. Representative Concentration Pathways (RCPs) … former Emission Scenarios (SRES) concentrations of the full suite of greenhouse gases and aerosols and chemically active gases, as well as land use/land cover RCP 8.5 (high emissions) RCP 6.0 RCP 4.5 RCP 2.6 (low emissions) IPCC, 2013
  9. 9. Climate Models & Future predictions IPCC, 2013
  10. 10. AR 5 projected regional changes: Southeast Asia “Reduced precipitation in Indonesia during Jul- Oct. due to the pattern of Indian Ocean warming; increased rainfall extremes of landfall cyclones on the coasts of the South China Sea, Gulf of Thailand, and Andaman Sea.” IPCC, 2013
  11. 11. How to use climate models & future predictions for Agriculture and modeling?
  12. 12. To know the uncertainty of the data is important! We don’t know… What are the conditions in 30, 50, 100 years? The different emission scenarios are not important ... by 2030 the difference between the concentration pathways is minimal. Understand variability and precise forecasting is important! 2030 For agriculture: IPCC, 2013
  13. 13. Climate variability • There is still uncertainty on climate models when it comes to variability • Historical observations of weather and climate can help to understand better variability • We need a better forecasting for Agriculture
  14. 14. Historical climate in GMS Presentation: Linh Giang
  15. 15. • Recent studies show the emergence of general trends in the climate of the GMS. • Average daily temperatures across Southeast Asia have increased • Precipitation patterns are quite complex across Southeast Asia. • In the Greater Mekong region from 1961 to 1998, although the number of extreme rainfall events decreased, the amount of rain falling during these events increased (Manton et al 2001). OVERVIEW
  16. 16. CRU TS 3.10.01 The CRU TS 3.10.01 Climate dataset has been produced by the Climatic Research Unit (CRU) of University of East Anglia. The database comprises 5583 station records of which 4842 have enough data for the 1961-1990 period to calculate estimate the average temperatures for this period.
  17. 17. Climate grids are constructed for nine climate variables for the period 1901-2009 - Temperature, - Diurnal temperature range, - Daily minimum temperature, - Maximum temperatures, - Precipitation, - Wet-day frequency, - Frost-day frequency, - Vapor pressure, and - Cloud cover. CRU TS 3.10.01
  18. 18. 842 points in GMS were collected from CRU TS 3.10.01 which covers from 1901 to 2009, globally at 0.5 degree spatial resolution on land area, including: • Precipitation • Mean temperature • Minimum temperature • Maximum temperature
  19. 19. • Mean temperature increased by between 1.8 ˚C and 2 ˚C. • Maximum temperature rose by between 1.7˚C and 2.2˚C. • Minimum temperature grew by between 1.6˚C and 2.2˚C.
  20. 20. The region has seen more hot days and warm nights and fewer cool days and nights.
  21. 21. • Total annual rainfall will increase by 5-25% across the northern part of the Mekong region in the next few decades. • Heavier storms during the wet season will account for the regional increase because drier dry seasons are predicted (TKK & SEA START RC 2009).
  22. 22. The trends in rainfall had the range of highly variable
  23. 23. Conclusion • In spite of a few station in South-East Asia , CRU data is useful to get overview of the climate change in the long time, • The highest temperature in research area concentrate in the south, and recorded the significant increase in South of Cambodia and South-East of Thailand, • The shoreline area receive the large amount of precipitation (Especially in Middle of Vietnam, Myanmar, and Thailand)
  24. 24. http://ccafs.cgiar.org
  25. 25. Our focus • Providing information and climate data for Agriculture • Climate Change impact assessment – For Food security & cash crops, entire value chains • Vulnerability of communities – Perception of risks, adaptive capacity, gender differences • Social & economic constraints for adaptation • Adaptation & mitigation strategies • Cost & benefit of strategies • Supply chain inclusive adaptation framework • Work for/with national policy institutions! • Mitigation through carbon insetting • Triple-win of adaptation, mitigation and food security … whilst conserving biodiversity • Bring to implementation of CSA (climate smart agriculture) practices
  26. 26. http://ccafs-climate.org
  27. 27. Statistical downscaling of climate models • Use anomalies and discard baselines in GCMs – Climate baseline: WorldClim – Used in the majority of studies – Takes original GCM timeseries – Calculates averages over a baseline and future periods (i.e. 2020s, 2050s) – Compute anomalies – Spline interpolation of anomalies – Sum anomalies to WorldClim
  28. 28. http://www.worldclim.org Worldclim stations worldwide 47,554 precipitation 24,542 tmean 14,835 tmax y tmin Sources: •GHCN •FAOCLIM •WMO •CIAT •R-Hydronet •Redes nacionales for GMS
  29. 29. • Bio1 = Annual mean temperature • Bio2 = Mean diurnal range (Mean of monthly (max temp - min temp)) • Bio3 = Isothermality (Bio2/Bio7) (* 100) • Bio4 = Temperature seasonality (standard deviation *100) • Bio5 = Maximum temperature of warmest month • Bio6 = Minimum temperature of coldest month • Bio7 = Temperature Annual Range (Bio5 – Bi06) • Bio8 = Mean Temperature of Wettest Quarter • Bio9 = Mean Temperature of Driest Quarter • Bio10 = Mean Temperature of Warmest Quarter • Bio11 = Mean Temperature of Coldest Quarter • Bio12 = Annual Precipitation • Bio13 = Precipitation of Wettest Month • Bio14 = Precipitation of Driest Month • Bio15 = Precipitation Seasonality (Coefficient of Variation) • Bio16 = Precipitation of Wettest Quarter • Bio17 = Precipitation of Driest Quarter • Bio18 = Precipitation of Warmest Quarter • Bio19 = Precipitation of Coldest Quarter Changes from 24 climate models using climate clusters for GMS * X current annual mean temperature, X current annual rainfall, source http://worldclim.org x x x x x x x x x x
  30. 30. B 31 Examples of DAPA research
  31. 31. Impacts on coffee production in Nicaragua Coffee under pressure CUP Nicaragua, Country • Increasing Temperatures during dry season • Excess precipitation in wet season, long dry season • Increasing precipitation seasonality Nicaragua, above 1000m • Increasing Temperatures during dry season • Current annual precipitation optimal • Stronger droughts negative • At high elevations increasing max temperatures positive
  32. 32. Specific vulnerability profiles of farmers in Nicaragua Coffee under pressure CUP Matagalpa is characterized by high exposure (coffee suitability decreases drastically) high sensitivity (high variability in yields) and low adaptive capacity (poor access to credit, poor knowledge on pest and disease management and low diversification). The adaptation strategy focuses on diversification, capacity building, strengthening of the organizations and on the enforcement of environmental laws and development policies for the coffee sector.
  33. 33. 34 Breedings requirements to make coffee production in Nicaragua climate resilient
  34. 34. Simulación del "Yield" presente siembra: Primera (en mayo), 2 suelos genéricos, 2 fertilizantes, promedio de 30 años de clima (worldclim), sin riego. Rendimientos esperados en grandes partes entre 500 - 700 kg/ha Simulación del futuro 2020 Con predicciones del clima (GCM) a la década 2020 (promedio entre 2010- 2039). Reducción del rendimiento en las zonas del corredor seco de Nicaragua y Honduras. Cambio hasta -75% de rendimiento esperado (sin estrategias) en 2020 Zonas de alta producción Identificación de Zonas focales para estrategias de adaptación con intensificación (Adaptation Spots), diversificación (Hot Spots) y conservación (Pressure Spots) Posibles estrategias de adaptación: Germoplasma con más resistencia al calor y sequía, riego con sistemas de cosecha de agua durante la época de alta lluvia, diversificación a otros sistemas de producción, recuperación de suelos degradados, ... Bean systems in Central America Project: Tortillas on the Roaster:
  35. 35. Deforestation Risk! Less areas available for agriculture Diversification options in mid- altitudes Climate change impact assessment for Haiti New areas that need strategic intensification
  36. 36. Transformative adaptation of current production systems to climate resilient systems in the future When, where, how and with whom? How can we adapt agriculture systems to unknown future conditions?
  37. 37. POLICY constraints SUPPLY CHAIN constraints IPCC GCM scenario outputs (AR5) Changed situation Accelerated adoption of climate resilient practices Current situation Production & social constraints to dealing with climate change & variability Baseline domains Future scenarios Experimental Application domains Benefit analysis Science Policy Improved climate data Land health indicators (CIAT soils) Community Participatory workshops Surveys Trials / Farm visits National Station data (Historical) Weather prognostics (seasonal) Integrated crop-modelling Agronomic management (CIAT Agro biodiversity) CSA practices CSA Analogues Best practice Triple-win Eco- systems Exposure Sensitivity Adaptive Capacity The local perspective Community participation Trade- offs Land use change market Vulnerability Food security smallholder global gender Policy briefs Scientific publication Manuals site-specific CSA practices Policy networking BEHAVIOURIAL constraints (culture, social)
  38. 38. Change in climate-suitability Losses gains