[1] Emissions of CO 2 and other GHGs are increasing

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1 The science and policy of climate change is based on four major pieces of evidence [1] Emissions of CO 2 and other GHGs are increasing Global emissions (millions of metric tons o carbon) Solid Liquid Gas Cement Gas flaring Total Year After Marland, Andres and Boden (1994) A Global CO 2 emissions database. In T.A., D.P. Kaiser, R.J. Sepanski, and F.W. Stoss (eds.) Trends 93 - a compendium of data on global change. (ESD publication ; no CDIAC USDE

2 [2] Atmospheric concentrations of CO 2 and other GHGs are increasing Monthly Atmospheric CO2 concentration (Mauna Loa, ppmbv) Year After Conway, Tans and Waterman (1994) Atmospheric CO 2 records from Mauna Loa air sampling network, Hawaii. In Boden, T.A., D.P. Kaiser, R.J. Sepanski, and F.W. Stoss (eds.) Trends 93 - a compendium of data on global change. (ESD publication ; no CDIAC USDE.

3 [3] Global temperatures are on the increase Annual temperature anomalies expressed in degrees C relative to the reference period, ENSO corrected *Northern hemisphere *Southern hemisphere *Global After Jones, Wigley and Briffa (1994) Global and hemispheric temperature anomalies. In Boden, T.A., D.P. Kaiser, R.J. Sepanski, and F.W. Stoss (eds.) Trends 93 - a compendium of data on global change. (ESD publication ; no CDIAC USDE.

4 [4] Atmospheric theory and General Circulation Models (GCMs) indicate that, if CO 2 continues to increase, so will temperature. This will affect other climatic variables in complex ways Output of the HADSUL experiment, ground temperature for 186, 196 and 26. After Hadley Centre 1999 ( and climate impacts LINK project (

5 How a general circulation model sees the Earth. (a) spatially crude - the Hadley centre model is one of the best and has only 7 cells for the whole of Colombia (b) averaged - earth surface properties such as altitude, vegetation are averaged over hundreds of square kilometres (c) simplified - the complexity of atmospheric, terrestrial, biospheric and oceanic processes is highly simplified Even at this level of abstraction, resolving complex atmospheric dynamics over the whole earth with a GCM requires the most powerful computers available and is still computationally limited.

6 GFDL grid for Colombia Nevertheless, GCMs are the best tool that we have for understanding the impact of atmospheric change on future climates. Some of the better GCMs are the following: ECHAM (Hamburg) Model (Germany) HADCM2 (UK) GFDL (USA) These models perform well at simulating past and present patterns of climate at the global scale. They produce scenarios not predictions of future change. All GCMs are of much less use at the regional and local scales because of their coarse grid size so their results must be downscaled. Longitude

7 HADCM2 grid for Colombia Less coarse than GFDL CIAT rainfall stations CIAT temperature stations IDEAM Cauca stations Latitude Longitude

8 ECHAM grid for Colombia CIAT rainfall stations CIAT temperature stations IDEAM Cauca stations Tambito site

9 The grid-box output of GCMs must be downscaled if the scenarios are to be meaningful. Downscaling involves : (a) choosing the variables of interest (in this case temperature and rainfall), (b) extracting data for the relevant grid cells, (c) calibrating monthly GCM output against long term station data for overlapping periods, (d) making the necessary grid box altitude corrections, (e) using statistical downscaling techniques to generate daily, hourly or sub-hourly scenarios. The biggest problem is lack of high quality long-term instrumental datasets. If not downscaled, GCMs produce quite different results (with respect to each other and to instrumental records)[i] Some of this discrepency can be removed by correcting for the altitudinal differences between the model grid boxes[ii] (HADCM2 altitude = 1527m, ECHAM altitude =198m, GFDL altitude =55m) Annual average temperature (C) [i] Annual Average temperature (C) - corrected to sea level [ii] Average of colombia W(24)deg C HCGS1 Average of Col SW(2) GFDLGS deg C Average of Col SW (22) C ECHAMGS deg C Average of apto GL Temp (C) Year Average of colombia W(24)deg C HCGS1 Average of Col SW(2) GFDLGS deg C Average of Col SW (22) C ECHAMGS deg C Average of apto GL Temp (C) Year Temperature for SW Colombia grid boxes and instrumental record Cauca

10 3 In addition to discrepancies in the annual averages or annual totals, GCM results may also show discrepancies in seasonality [i] with respect to each other and to instrumental records. This is not surprising given the crude topography and large grid cells of GCMs. Downscaling must therefore be carried out on a monthly basis with independent calibration for each month so that the seasonality of the region is accounted for.[ii] Mean temperature (C) Month Total Average of colombia W(24)deg C HCGS1 Total Average of Col SW(2) GFDLGS deg C Total Average of Col SW (22) C ECHAMGS deg C Total Average of apto GL Temp (C) [i] Misrepresentation of temperature seasonality for Cauca 3 25 Total Average of apto GL Temp (C) Total Average of ECHAMgs downscaled Total Average of GFDLgs downscaled Total Average of HADCM2gs downscaled Total Average of apto GL Temp (C) Average monthly temperature ( Month [ii] Seasonality corrected in downscaling process

11 7 Average of colombia W(24)mm/month HCGS1 Average of Col SW(2) mm/month GFDLGS 6 Rainfall (mm/month) Average of Col SW (22) mm/month ECHAMGS Average of Precip(mm/month) Apto GL Valencia Mean Rainfall (mm/month) Total Average of colombia W(24)mm/month HCGS1 Total Average of Col SW(2) mm/month GFDLGS Total Average of Col SW (22) mm/month ECHAMGS Total Average of Precip(mm/month) Apto GL Valencia Year Month [i] Rainfall GCMs tend to be much further in error for rainfall (totals[i] and seasonality [ii]) because it is a much more complex variable to model and is spatially highly variable. Downscaling procedures can resolve 5 many of the discrepancies.[iii] [ii] [iii] Average rainfall (mm/month) Total Average of Precip(mm/month) Apto GL Valencia Total Average of ECHAMgs downscaled to Apto GL Total Average of GFDLgs downscaled to Apto GL Total Average of HADCM2gs downscaled to Apto GL Month

12 When properly downscaled to an individual station or a group of stations representing a region, GCM results can provide useful scenarios for potential climate change in an area. This figure shows temperature change for HADCM2, ECHAM and GFDL cells covering south west Colombia for a transient 2*CO 2 + sulphate aerosols experiment. The results are downscaled to the instrumental record for Apto. GL Valencia. All models show a relatively stable temperature (with some inter-annual variability) to, the early 198 s followed by a shallow increase becoming steeper from 24 Annual Average Temperature (Downscaled) Average of colombia W(24)deg C HCGS1 Average of Col SW(2) GFDLGS deg C Average of Col SW (22) C ECHAMGS deg C Average of apto GL Temp (C) All models show increases in temperature whilst the limited field data is stationary. GFDL (177-25) +.5 C/yr +1.4 C HADCM2 ( ) +.2 C/yr +4.8 C ECHAM (186-25) +.11 C /yr +2.1 C Apto GL (meas ) + C/yr C Year

13 35 3 Average of colombia W(24)mm/month HCGS1 Average of Col SW(2) mm/month GFDLGS Average of Col SW (22) mm/month ECHAMGS Average of Precip(mm/month) Apto GL Valencia Downscaled rainfall (mm/month) Year The downscaled rainfall pattern is more complex. All models show increases in rainfall over the simulation whilst the field data indicates a much larger measured increase. GFDL (177-25) +.63 mm/yr +28 mm overall increase HADCM2 ( ) +.81 mm/yr +194 mm overall increase ECHAM (186-25) mm/yr +335 mm overall increase Apto GL (meas ) mm/yr +178 mm overall increase

14 Climatic Regionalisation Climatic trends for a single station are not a great deal of use for decision making and strategic planning. Regional climatic trends are required for this. Month;y minimum temperature (C) y = -.55x R 2 =.9626 Regionalisation of climatic variables requires a detailed knowledge of the spatial variability of climate based on observed data Altitude (m) Temperature vs. altitude. CIAT climate database, all Colombian stations. 3 Fortunately, in Colombia, the most important climate parameters (temperature and rainfall) vary consistently with altitude - though rainfall is a more complex function of topography over large areas - need to work on mesoscale effects. Number of rain days y =.23x R 2 =.258 Regionalisation is therefore simply a matter of applying altitudinal relationships across DEMs Altitude (m) Raindays vs. altitude. CIAT climate database, all Colombian stations.

15 TAMBITO ECHAM TAMBITO ECHAM TAMBITO ECHAM 2-21 TAMBITO ECHAM 25-26

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17 TAMBITO ECHAM TAMBITO ECHAM TAMBITO ECHAM 2-21 TAMBITO ECHAM 25-26

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19 TAMBITO ECHAM TAMBITO ECHAM TAMBITO ECHAM 2-21 TAMBITO ECHAM 25-26

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21 Impacts of climate change I (a) Patterns of weather: seasonality Seasonality is measured here as the coefficient of variation (σ/x) of monthly values within a year. Rainfall - no trend in rainfall seasonality for Cauca for any of the GCMs. Though there is strong inter-annual and decadal variability in rainfall and temperature seasonality. Temperature - again strong interannual and decadal variability but no long term trend. There are strong differences between models but they all show no long term trend in seasonality for rainfall or temperature. Coeeficient of variation for monthly rainfall Year CoV ECHAM CoV GFDL CovHADCM2 Rainfall seasonality for Tambito catchments, ECHAM, GFDL and HADCM2 Coefficient of variation for monthly temperatures (C) CoV ECHAM CoV GFDL CovHADCM2 Temperature seasonality for Tambito catchments, ECHAM, GFDL and HADCM Year

22 Impacts of climate change II (a) Patterns of weather: storminess/droughtiness Droughtiness measured here as the rainfall for the driest month. All models show an increase in rainfall for the driest month (a decrease in the intensity of drought) throughout the simulation periods. Interannual and decadal variability is very high with single years showing strong positive and negative anomalies. Storminess measured here as the rainfall for the wettest month. All models show an increase in storminess which is particularly strong from 2 onwards. Again a few single years show very strong positive anomalies. Minimum monthly rainfall (mm) Droughtiness for Tambito catchments, ECHAM, GFDL and HADCM2 Maximum monthly rainfall (mm) HADCM2gs and ECHAMgs Min of ECHAMgs downscaled Min of GFDLgs downscaled Min of HADCM2gs downscaled Year Max of ECHAMgs downscaled 1991 Max of HADCM2gs downscaled Max of GFDLgs downscaled Year Maximum monthly rainfall (mm) GFDLgs Storminess for Tambito catchments, ECHAM, GFDL and HADCM2

23 Impacts of climate change III (b) Water balance Calculated as 1 year mean rainfallpotential evaporation f(rn,t) integrated over the whole Tambito DEM for ECHAM. Though mean catchment air temperature shows a significant increase the effect on catchment potential evaporation is small and the change in the water balance is controlled much more by the increasing rainfall. The change in water input to the catchment (2144 Ha) from is +8.51Mm 3 water (+397m 3 /Ha/yr) Catchment mean Rainfall, Evap and Water Balance (mm) Rainfall-Evap (mm) Rainfall(mm) Potential Evaporation (mm) Temperature (C) 194 Water balance for the Tambito catchments - ECHAM 2*CO 2GS 195 Year Catchment Mean Temperature (C) Year Temperature(ºC) Catchment Ep(mm/yr) Catchment Rf(mm/yr) Water balance(mm/yr) Some of these impacts may be nullified in TMCF where climate change could produce a reduction occult precipitation.

24 Impacts of climate change IV (c) Species distributions Whilst the effect of temperature on hydrology is limited, the effect of temperature change on ecological processes may be much greater. In spatially heterogeneous TMEs the distribution of species can be strongly controlled by the distribution of climatic normals. Climatic change can produce changes in competitive advantage and thus in teh distribution of species. (d) Forest productivity and dynamics Changes in temperature can lead to changes in the rate of photosynthesis and respiration which can have direct impacts on forest productivity and carbon sequestration. These direct effects are compounded by effects mediated through hydrological change and subtle changes in cloud cover, solar radiation and other climate variables. (e) Agricultural productivity. Climate change may impact on crop production both directly as above and indirectly through enhanced land degradation, waterlogging, or soil erosion. In this case it is important to understand the interaction of climatic variability and change and human decisions on land use and land use change. (f) Land and economic degradation The increased storminess indicated by the scenarios may lead to increased flooding, erosion, crop failure and infrastructural damage, all of which can lead to degradation of land and economy. These topics are the subject of ongoing research.