Climate Change Scenarios Dr. Elaine Barrow Canadian Climate Impacts Scenarios (CCIS) Project

Climate Change Scenarios Dr. Elaine Barrow Canadian Climate Impacts Scenarios (CCIS) Project

What is a scenario? a coherent, internally consistent and plausible description of a possible future state of the world [Parry & Carter, 1998]

Scenario Terms Emissions scenarios Projections Climate Change Scenarios Climate Scenarios a plausible future climate that has been constructed for explicit use in investigating the potential consequences of anthropogenic climate change [IPCC TAR, 2001]

Why do we need scenarios? To provide data for VIA assessment studies To act as an awareness-raising devices To aid strategic planning and/or policy formation To scope the range of plausible futures To structure our knowledge (or ignorance) of the future To explore the implications of decisions

What are the challenges of developing climate change scenarios? simple to obtain, interpret and apply provide sufficient information for VIA assessments physically plausible and spatially compatible consistent with the broad range of global warming projections reflect the potential range of future regional climate change, i.e., be representative of the range of uncertainty in projections

What sort of information does the VIA community require? Typically: Daily, monthly or seasonal temporal resolution Site, regional to continental scales Single and/or multiple climate variables

Scenario Needs 1. Which climate variables? 2. How many scenarios? 3. Local data for case studies/sites, or national/regional coverage? 4. Spatial resolution - 300km, 100km, 50km, 10km, 1km? Can this choice be justified? 5. Changes in average climate, and/or changes in variability? 6. Daily or monthly changes?

Three ways... C O M P L E X I T Y Incremental (arbitrary, synthetic) scenarios Analogue scenarios Scenarios from global climate models (GCMs)

Incremental Scenarios Mean annual temperature ( C) 12 10 8 6 4 Climate scenario Observed time series T=2 C Climate change scenario 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 Year Can provide valuable information about: sensitivity thresholds or discontinuities of response tolerable climate change

ADVANTAGES: simple to construct and apply, allow relative sensitivity of impacts sectors/models to be explored DISADVANTAGES: arbitrary (and unrealistic) changes, may be inconsistent with uncertainty range Yield change (t/ha) of Valencia orange in response to changing temperature and CO 2 concentration [Source: Rosenzweig et al. (1996)]

Analogue Scenarios Identification of recorded climate regimes which may resemble the future climate in a given region Assumption: climate will respond in the same way to a unit change in forcing despite its source and even if boundary conditions differ Spatial Temporal

Spatial Analogues [Source: Parry & Carter, 1988] Identify regions which today have a climate analogous to that anticipated in the study region in the future Approach restricted by frequent lack of correspondence between other non-climatic features of the two regions Causes of the analogue climate likely different from the causes of future climate change

Temporal Analogues: Palaeoclimatic Use information from the geological record - fossils, sedimentary deposits - to reconstruct past climates mid-holocene, 5-6k BP, 1 C warmer last (Eemian) interglacial, 125k BP, approx. 2 C warmer Pliocene, 3-4m BP, 3-4 C warmer IPCC, 1990

Palaeoclimatic Analogues changes in the past unlikely to have been caused by increased GHG concentrations data and resolution generally insufficient, i.e., extremely unlikely to get daily resolution and individual site information uncertainty about the quality of palaeoclimatic reconstructions higher resolution (and most recent) data generally lie at the low end of the range of anticipated future climatic warming

Instrumental Analogues Past periods of observed global- or hemisphericscale warmth used as an analogue for the future Difference =0.4 C Northern Hemisphere temperature record Lough et al., 1983

Instrumental Analogues The 1930s in the North American Great Plains have frequently been used as an analogue for the future. State Winter (DJF) Spring (MAM) Summer (JJA) Autumn (SON) Annual Missouri 1951-1980 0.3 12.7 29.4 13.9 12.8 1930s +1.0 0.0 +1.1 +0.6 +0.7 Iowa 1951-1980 -5.7 8.9 22.3 10.6 9.0 1930s +1.1 +0.3 +1.2 +0.6 +0.8 Nebraska 1951-1980 -3.6 8.9 22.6 10.5 9.6 1930s +0.6 +0.7 +1.6 +1.0 +1.0 Kansas 1951-1980 0.1 12.1 25.2 13.6 12.8 1930s +0.9 +0.6 +1.3 +1.0 +0.9 Mean temperature ( C) State Winter (DJF) Spring (MAM) Summer (JJA) Autumn (SON) Annual Missouri 1951-1980 51 100 97 82 989 1930s +16-23 -21-1 -28 Iowa 1951-1980 26 79 106 61 815 1930s +6-53 -28 +16-60 Nebraska 1951-1980 14 60 79 35 566 1930s +4-23 -54-21 -93 Kansas 1951-1980 19 67 88 54 684 1930s 0-19 -59-24 -102 Precipitation (mm) Differences between 1931-1940 average and 1951-1980 average in the MINK states (Easterling et al., 1992)

Instrumental Analogues Palmer Drought Severity Index (PSDI) for the US Corn Belt, 1930-1980. [Source: Rosenberg et al., 1993]

Instrumental Analogues Rice-growing areas in Japan 0.4 C warmer than base Base, 1951-1980 Warm decade, 1921-1930 Climate scenario

Instrumental Analogues ADVANTAGES data available on a daily and local scale scenario changes in climate actually observed and so are internally consistent and physically plausible DISADVANTAGES climate anomalies during the past century have been fairly minor cf. anticipated future changes anomalies probably associated with naturallyoccurring changes in atmospheric circulation rather than changes in GHG concentrations

Scenarios from GCMs GCMs are the only credible tools currently available for simulating the physical processes that determine global climate... [IPCC] [Source: David Viner, UK Climate Impacts LINK Project]

GCM evolution 1980s late 1980s early 1990s EQUILIBRIUM EXPERIMENTS TRANSIENT EXPERIMENTS COLD START WARM START

Global-mean temperature change ( C) wrt 1961-1990 0.6 0.5 0.4 0.3 0.2 0.1 0.0-0.1-0.2-0.3-0.4-0.5-0.6 Warm start GCMs Projection of future climate (model output) 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Anthropogenic Natural All Forcings Only [Source: IPCC TAR]

IPCC Special Report on Emissions Scenarios (2000)

Emissions scenarios

Climate change scenario 1.4-5.8 C

Which GCM(s)? Vintage Resolution Validity Representativeness of results [Source: Smith and Hulme, 1998]

Spatial Scale of Scenarios Effect of scenario resolution on impact outcome [Source: IPCC, WGI, Chapter 13]

IPCC-TGCIA Criteria fully-coupled ocean-atmosphere GCMs documented in the peer reviewed literature performed a multi-century control run participated in CMIP2

GCMs meeting IPCC-TGCIA criteria Canadian Centre for Climate Modelling and Analysis (CGCM1, CGCM2) Hadley Centre for Climate Prediction and Research (HadCM2, HadCM3) Australian Commonwealth Scientific and Industrial Research Organisation (CSIROMk2b) German Climate Research Centre (ECHAM4) Geophysical Fluid Dynamics Laboratory (GFDL-R15, GFDL-R30) Japanese Centre for Climate Research Studies (CCSR/NIES) US National Centre for Atmospheric Research (NCAR-PCM)

But GCMs are not accurate, so we cannot use their output directly... Global mean temperature ( C) t 1 Climate change experiment Time t 2 t 1 is typically 1961-1990 t 2 is a future time period, e.g., 2040-2069, representing the 2050s T=t 2 -t 1 Climate change scenario Some models exhibit large inter-decadal variability, so average over 30 years to capture longer-term trend.

Mean temperature change ( C) Climate change scenario 6 5 4 3 2 1 Mean temperature ( C) 25 20 15 10 5 0-5 -10 Climate change scenario 0 Temperature J F change M ( C) A M J J A S O N D -15-20 Month J F M A M J J A S O N D Month Climate scenarios

Cooler, wetter Scatter Plots Warmer, wetter Cooler, drier Warmer, drier

Natural climate variability 40 Precipitation change (%) 30 20 10 0-10 -20-2 -1 0 1 2 3 4 5 6 7 8 Mean tem perature change ( C)

Key Points Scenarios should: be internally consistent, physically plausible and spatially compatible address the range of uncertainty provide, as far as is possible, climate information at the scales required by the VIA community