Vulnerability and Adaptation Assessment Handson Training Workshop Climate
Vulnerability and Adaptation Assessment Hands-on Training Workshop Climate Change Scenarios 1 A. 1
Outline n n n Brief review of climate change Why we use scenarios Review of options n n n Incremental Analogue Models n n n GCMs Use of GCMs Downscaling options n n Statistical downscaling RCMs 2
Outline n n n (continued) Techniques for understanding the range of regional climate change What to use under what conditions Obtaining data 3
Brief Primer on Regional Climate Change Temperatures over most land areas are likely to rise n n Other factors, e. g. , land use change, may also be important Warmer temperatures mean increases in heat waves and evaporation Global-mean sea level rise: 0. 1 to 0. 9 m n Modified by local subsidence/uplift Precipitation will change; increase globally n n Local changes uncertain: critical uncertainty Increase in storm intensity in some regions 4
Normalized Annual-Mean Temperature Changes in CMIP 2 Greenhouse Warming Experiments 5
Normalized Annual-Mean Precipitation Changes in CMIP 2 Greenhouse Warming Experiments -6 -9 0 -3 6 3 9 % 6
Why Use Climate Change Scenarios? n n We are unsure exactly how regional climate will change Scenarios are plausible combinations of variables consistent with what we know about human-induced climate change One can think of them as the prediction of a model, contingent upon the GHG emissions scenario Since estimates of regional change by models differ substantially, an individual model estimate should be treated more as a scenario 7
What Are Reasonable Scenarios? n Scenarios should be: n n Consistent with our understanding of the anthropogenic effects on climate Internally consistent n n e. g. , clouds, temperature, precipitation Scenarios are a communication tool about what is known and not known about climate change n Should reflect plausible range for key variables 8
Scenarios for Impacts Analysis n n Need to be at a scale necessary for analysis Spatial n n e. g. , to watershed or farm level Temporal n n n Monthly Daily Sub-daily 9
Options for Creating Scenarios n n Past climates: analogues Spatial analogues Arbitrary changes; incremental Climate models 10
Past Climates n Options n n n Instrumental record Paleoclimate reconstructions Instrumental record n Pros n n n Can provide daily data Includes past extreme events Cons n n Range of change in past climate is limited Data can be limited 11
Past Climates n Paleoclimate reconstructions n n From tree rings, boreholes, ice cores, etc. Can give annual, sometimes seasonal, climate Can go back hundreds of years Pro n n (continued) Wider range of climates Cons n n Incomplete data Uncertainties about values 12
Mann et al. Reconstruction of N. Hemisphere Temperatures 13
Spatial Analogues 14
Spatial Analogues n Advantage n n (continued) Communication tool: perhaps easier to understand Disadvantages n n Require using a model result to choose the spatial analogue region Do not capture changes in variability 15
Arbitrary/Incremental Scenarios n n Assume uniform annual or seasonal changes across a region e. g. , +2°C or +4°C for temperature +/-10% or 20% change in precipitation Can also make assumptions about changes in variability and extremes 16
Arbitrary/Incremental Scenarios (continued) n Pros n n n Easy to use Can simulate a wide range of conditions Cons n n Assuming a uniform change over the year or across a region may fail to capture important seasonal or spatial details Combinations of changes in climate for different variables can be physically implausible 17
Climate Models n n Models are mathematical representations of the climate system They can be run with different forcings, e. g. , higher GHG concentrations n Models are the only way to capture the complexities of increased GHG concentrations 18
General Circulation Models n Pros n n n Can represent the spatial details of future climate conditions for all variables Can maintain internal consistency Cons n n Relatively low spatial resolution May not accurately represent climate parameters 19
Example of GCM Output 20
Downscaling from GCMs n n Downscaling is a way to obtain higher spatial resolution output based on GCMs Options include: n n n Combine low-resolution monthly GCM output with highresolution observations Use statistical downscaling n Easier to apply n Assumes fixed relationships across spatial scales Use regional climate models (RCMs) n High resolution n Capture more complexity n Limited applications n Computationally very demanding 21
Combine Monthly GCM Output with Observations n n An approach that has been used in many studies Typically, one adds the (low resolution) average monthly change from a GCM to an observed (high resolution) present-day “baseline” climate n 30 year averages should be used, if possible n n e. g. , 1961 -1990 or 1971 -2000 Make sure the baseline from the GCM (i. e. , the period from which changes are measured) is consistent with the choice of observational baseline 22
Combining Monthly GCMs and Observations n n This method can provide daily data at the resolution of weather observation stations Assumes uniform changes within a GCM grid box and over a month n No spatial or daily/weekly variability 23
How Many GCM Grid Boxes Should Be Used n Using the single grid box that includes the area being examined would be ideal, but n n There can be model noise at the scale of single grid boxes Many scientists do not think single box results are reliable Hewitson (2003) recommends using 9 grid boxes: the grid being examined plus the 8 surrounding grid boxes Need to consider the total area covered by all those grid boxes. Does it include topography or climates not similar to the area being studied? 24
Statistical Downscaling n Statistical downscaling is a mathematical procedure that relates changes at the large spatial scale that GCMs simulate to a much finer scale n For example, a statistical relationship can be created between variables simulated by GCMs such as air, sea surface temperature, and precipitation at the GCM scale (predictors) with temperature and precipitation at a particular location (predictands) 25
Statistical Downscaling n Is most appropriate for n n n Subgrid scales (small islands, point processes, etc. ) Complex/heterogeneous environments Extreme events Exotic predictands Transient change/ensembles Is not appropriate for n n n (continued) Data-poor regions Where relationships between predictors and predictands may change Statistical downscaling is much easier to apply than regional climate modeling 26
Statistical Downscaling n n n (continued) Statistical downscaling assumes that the relationship between the predictors and the predictands remains the same Those relationships could change In such cases, using regional climate models may be more appropriate 27
Statistical Downscaling Model (SDSM) Currently, only feasible based on outputs from a few GCMs n 28
Global Data to Use in Downscaling with SDSM n Canadian site n Go to scenarios, then SDSM Only has Had. CM 3 n. Get output for individual grid n 29
Regional Climate Models (RCMs) n These are high resolution models that are “nested” within GCMs n A common grid resolution is 50 km n n n Some are higher resolution RCMs are run with boundary conditions from GCMs They give much higher resolution output than CCMs n Hence, much greater sensitivity to smaller scale factors such as mountains, lakes 30
RCM Limitations n n n Can correct for some, but not all, errors in GCMs Typically applied to one GCM or only a few GCMs In many applications, just run for a simulated decade, e. g. , 2040 s Still need to parameterize many processes May need further downscaling for some applications 31
GCM vs. RCM Resolution Precipitation RCM GCM Temperature 32
Extreme Precipitation (JJA) RCM Observation GCM 33
By Now You May Be Confused n So many choices, what to do? n First, let’s remember the basics n Scenarios are essentially educational tools to help: n n n See ranges of potential climate change Provide tools for better understanding the sensitivities of affected systems So, we need to select scenarios that enable us to meet these goals 34
Tools for Assessing Regional Model Output n n n It is useful first to compare results from a number of GCMs that might be used to drive an RCM Normalized GCM results allow comparison of the relative regional changes Can analyze the degree to which models agree about change in direction and relative magnitude n A measure of GCM uncertainty 35
Tools for Assessing Regional Model Output (continued) n n Agreement between GCMs does not necessarily mean that they are all correct – they may all be repeating the same mistakes Still, GCMs are the primary tool for estimating the range of future possibilities 36
Normalizing GCM Output n Expresses regional change relative to an increase of 1°C in mean global temperature n n This is a way to avoid high sensitivity models dominating results It allows us to compare GCM output based on relative regional change Normalized temperature change = ΔTRGCM/ΔTGMTGCM Normalized precipitation change = ΔPRGCM/ΔTGMTGCM 37
Pattern Scaling n n Is a technique for estimating change in regional climate using normalized patterns of change and changes in GMT Pattern scaled temperature change: n n ΔTRΔGMT = (ΔTRGCM/ΔTGMTGCM) x ΔGMT Pattern scaled precipitation n ΔPRΔGMT = (ΔPRGCM/ΔTGMTGCM) x ΔGMT 38
Tools to Survey GCM Results n n n Finnish report: “Future climate. . . ” COSMIC MAGICC/SCENGEN 39
Finnish Publication n Shows regional output on temperature and precipitation for a number of models n n For three time slices over 21 st century Uses some scaling Useful as a look-up to see degree of model agreement or disagreement MAGICC/SCENGEN and COSMIC provide more flexibility to users 40
Finnish Environment Example 41
COSMIC n n n Developed by M. Schlesinger, R. Mendelsohn, and L. Williams Can choose from 28 emission scenarios Select individual GCM model n n Select country n n Results scaled Area or population weighted Yields annual change in GHGs, SO 2, SLR, and temperature 42
COSMIC Output n n Will give global changes in CO 2, SO 4, temperature, and sea level rise Will also give month-by-month temperature and precipitation at the country level Easy to use and obtain data Analyst should not use raw output, but compute changes in temperature and precipitation 43
COSMIC Limitations n Since results are scaled, change will be smooth and will not reflect interannual variability n n GCMs tend to be older than in SCENGEN n n Since results are smoothed, it is sufficient to use a single year output as representative of average climate change Are 2 x CO 2; not transients Does not have mapping capabilities of SCENGEN 44
MAGICC/SCENGEN MAGICC is a simple model of global T and SLR n. Used in IPCC TAR n. SCENGEN uses pattern scaling for 17 GCMs n. Yield n n n Model by model changes Mean change Intermodel SD Interannual variability changes Current and future climate on 5 x 5°grid 45
Using MAGICC/SCENGEN 46
MAGICC: Selecting Scenarios 47
MAGICC: Selecting Scenarios (continued) 48
MAGICC: Selecting Forcings 49
MAGICC: Displaying Results 50
MAGICC: Displaying Results (continued) 51
Running SCENGEN 52
Running SCENGEN (continued) 53
SCENGEN: Analysis 54
SCENGEN: Model Selection 55
SCENGEN: Area of Analysis 56
SCENGEN: Select Variable 57
SCENGEN: Scenario 58
SCENGEN: Map Results 59
SCENGEN: Quantitative Results INTER-MOD S. D. : AREA AVERAGE = 5. 186 % (FOR NORMALIZED GHG DATA) INTER-MOD SNR : AREA AVERAGE = -. 067 (FOR NORMALIZED GHG DATA) PROB OF INCREASE : AREA AVERAGE = . 473 (FOR NORMALIZED GHG DATA) GHG ONLY : AREA AVERAGE = -. 411 % (FOR SCALED DATA) AEROSOL ONLY : AREA AVERAGE = -. 277 % (FOR SCALED DATA) GHG AND AEROSOL : AREA AVERAGE = -. 687 % (FOR SCALED DATA) *** SCALED AREA AVERAGE RESULTS FOR INDIVIDUAL MODELS *** (AEROSOLS INCLUDED) MODEL = BMRCD 2 : AREA AVE = 2. 404 (%) MODEL = CCC 1 D 2 : AREA AVE = -5. 384 (%) MODEL = CCSRD 2 : AREA AVE = 6. 250 (%) MODEL = CERFD 2 : AREA AVE = -2. 094 (%) MODEL = CSI 2 D 2 : AREA AVE = 6. 058 (%) MODEL = CSM_D 2 : AREA AVE = 1. 245 (%) MODEL = ECH 3 D 2 : AREA AVE = . 151 (%) MODEL = ECH 4 D 2 : AREA AVE = -1. 133 (%) MODEL = GFDLD 2 : AREA AVE = 1. 298 (%) MODEL = GISSD 2 : AREA AVE = -3. 874 (%) MODEL = HAD 2 D 2 : AREA AVE = -5. 442 (%) MODEL = HAD 3 D 2 : AREA AVE = -. 459 (%) MODEL = IAP_D 2 : AREA AVE = -. 088 (%) MODEL = LMD_D 2 : AREA AVE = -6. 548 (%) MODEL = MRI_D 2 : AREA AVE = . 065 (%) MODEL = PCM_D 2 : AREA AVE = -3. 451 (%) MODEL = MODBAR : AREA AVE = -. 687 (%) 60
SCENGEN: Global Analysis 61
SCENGEN: Error Analysis 62
SCENGEN Error Analysis (continued) UNWEIGHTED STATISTICS MODEL CORREL RMSE MEAN DIFF NUM PTS mm/day BMRCTR . 632 1. 312 1. 026 20 CCC 1 TR . 572 1. 160 -. 207 20 CCSRTR . 587 . 989 . 322 20 CERFTR . 634 1. 421 -1. 167 20 CSI 2 TR . 553 1. 112 -. 306 20 CSM_TR . 801 1. 044 -. 785 20 ECH 3 TR . 174 1. 501 -. 649 20 ECH 4 TR . 767 1. 121 -. 881 20 GFDLTR . 719 . 954 -. 553 20 GISSTR . 688 . 799 . 123 20 HAD 2 TR . 920 . 743 -. 598 20 HAD 3 TR . 923 . 974 -. 883 20 IAP_TR . 599 1. 408 -. 734 20 LMD_TR . 432 2. 977 -2. 103 20 MRI_TR . 216 2. 895 -2. 026 20 PCM_TR . 740 1. 372 -1. 041 20 MODBAR . 813 . 879 -. 654 20 63
How to Select Scenarios n n Use MAGICC/SCENGEN, COSMIC, or the Finnish study to assess the range of temperature or precipitation changes Models can be selected based on n How well they simulate current climate n n SCENGEN has a routine How well they representing a broad range of conditions 64
How to Select Scenarios n n (continued) One can use results from actual GCM data or scaled data Can include other sources for scenarios, e. g. , arbitrary, analogue 65
Selecting GCMs n Some factors to consider in selecting GCMs n Age of the model run n n Model resolution n n More recent runs tend to be better, but there are exceptions (see comments on slide 54) Higher resolution tends to be better Model accuracy in simulating current climate n MAGICC/SCENGEN has a routine 66
What to Use under What Conditions? n n n Nothing wrong with using combinations of different sources for creating scenarios, e. g. , models and arbitrary scenarios The climate models tend to be better for longer run analyses, e. g. , beyond several decades (beyond 2050) Climate analogues tend to be better for near term, e. g. , within several decades (20102030) 67
Scenarios for Extreme Events n n Difficult to obtain from any of these sources Options n n Use long historical or paleoclimate records Incrementally change historical extremes n n Try to be consistent with transient GCMs These methods are primarily useful for sensitivity studies 68
Data Sources Climate Models and Observations 1 A. 69
Some Climate Data Sources n n IPCC Data Distribution Centre http: //ipcc-ddc. cru. uea. ac. uk/ Program for Climate Model Diagnosis and Intercomparison http: //www-pcmdi. llnl. gov/ 70
IPCC Data Distribution Center n n The IPCC Data Distribution Centre is probably the best site for public-access climate model data Observed climate data 1901 -1990 n n n Gridded to 0. 5 x 0. 5° 10 and 30 year means http: //ipcc-ddc. cru. uea. ac. uk/ 71
IPCC Data Distribution Center n GCM data from n n n n (continued) CCC (Canada) CSIRO (Australia) ECHAM 4 (Germany) GFDL-R 30 (U. S. ) Had. CM 3 (UK) NIES (Japan) Can obtain actual (not scaled) GCM output 72
IPCC Data Distribution Center n (continued) Contains monthly-mean data from GCMs on n n n n Mean temperature (°C) Maximum temperature (°C) Minimum temperature (°C) Precipitation (mm/day) Vapor pressure (h. Pa) Cloud cover (%) Wind speed (m/s) Soil moisture 73
Example Data from DDC – Temperature 74
Example Data – Precipitation 75
PCMDI Has GCM Output 76
Observational Record n n National meteorological offices MARA/ARMA has 1951 -1995 monthly temperature and precipitation n Developed by Climatic Research Unit, University of East Anglia, UK 3 minute scale http: //www. mara. org. za/ 77
African Weather Data Sites 78
Final Thoughts n n Remember that individual scenarios are not predictions of future regional climate change If used properly, they can help us understand portray n n n What is known about how regional climates may change Uncertainties about regional climate change The potential consequences 79
Uses n If assessing vulnerability, scenarios ought to reflect a wide, but realistic range of climate change n n Serves education purpose If examining adaptation, it is important to reflect a wide range of climate change n If the selected uncertainty range is too narrow, this could lead to ill-informed decisions 80
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