Uncertainty from above: can it be reduced?

Krishna AchutaRaoIndian Institute of Technology Delhi

Outline

• Uncertainty from “above” – How have scientists reported it?– Spatial & temporal scales

• Can we identify and quantify the various sources of uncertainty

• Can we reduce or constrain uncertainty?• Some inferences

Singh, R., and K. M. AchutaRao “Quantifying and Constraining Structural Uncertainty in Future Climate Projections over India”, GC43C-1218, American Geophysical Union Fall Meeting 2015. San Francisco, CA USA 14-18 December 2015.

(IPCC AR5 Working Group – I, FAQ 12.1, Figure 1) Global mean temperature change averaged across all Coupled Model Intercomparison Project Phase 5 (CMIP5) models (relative to 1986–2005) for the four Representative Concentration Pathway (RCP) scenarios: RCP2.6 (dark blue), RCP4.5 (light blue), RCP6.0 (orange) and RCP8.5 (red); 32, 42, 25 and 39 models were used respectively for these 4 scenarios. Likely ranges for global temperature change by the end of the 21st century are indicated by vertical bars. Note that these ranges apply to the difference between two 20-year means, 2081–2100 relative to 1986–2005, which accounts for the bars being centred at a smaller value than the end point of the annual trajectories. For the highest (RCP8.5) and lowest (RCP2.6) scenario, illustrative maps of surface temperature change at the end of the 21st century (2081–2100 relative to 1986–2005) are shown for two CMIP5 models. These models are chosen to show a rather broad range of response, but this particular set is not representative of any measure of model response uncertainty.

Figure 7. CMIP5 model ensemble mean precipitation change (%) projected for 2030s (2021–2050), 2060s (2046–2075)and 2080s (2070–2099) relative to the pre-industrial period (1880s, i.e. over 1861–1900).

Chaturvedi et al., Current Science 2012

Chaturvedi et al., Current Science 2012

Figure 3. CMIP5 model-based time series of temperature and precipitation anomalies (historical and projections) from1861 to 2099 relative to the 1961–1990 baseline for the RCP scenarios. Shaded area represents the range of changes projected by the 18 models for each year

IPCC, 2013: Annex I: Atlas of Global and Regional Climate Projections [van Oldenborgh, G.J., M. Collins, J. Arblaster, J.H. Christensen, J. Marotzke, S.B. Power, M. Rummukainen and T. Zhou (eds.)]

Reflexive Uncertainty

“Reflexive: Scenario uncertainty” This is linked to the fact that the 21st Century GHG emission trajectory is unknown and involves unpredictable climate–society feedbacks as well as non-climate related (e.g., political and economic) factors (also not predictable).

Epistemic Uncertainty

“Epistemic: of or relating to knowledge or knowing” This is related to the lack of knowledge in the model representation of physical and dynamical processes including missing processes and/or interactions within climate models. Also called structural uncertainty or model uncertainty/error. Reduction of this uncertainty is a key long-term objective.

Aleatoric Uncertainty“Aleatoric: characterized by chance or indeterminate elements” This is related to the fact that the climate (defined as a long-term mean) is partially chaotic due to unpredictable internal variability.

What do the uncertainties look like?

Global mean temperature

East Asia decadal mean JJA rainfall

Figure 2 (Adapted from IPCC AR5 Working Group – I, Figure 11.8) Sources of uncertainty in climate projections as a function of lead time based on an analysis of CMIP5 results. (Top). The fraction of variance explained by each source of uncertainty for East Asian (5°N to 45°N, 67.5°E to 130°E) decadal mean boreal summer (June to August) precipitation (Bottom).

Also see: Hawkins & Sutton (2011)

Summer Temperature Change Over India

Aleatoric and epistemic uncertainty in projected summer (June-July-August) temperature change under RCP8.5. The aleatoric (top row) and epistemic (bottom row) uncertainties are shown for 4 different 20-year periods into the future from left to right: 2020-2039 (left most column), 2040-2059, 2060-2079 and 2080-2099.

Focusing on temperature change in the Central North East India region

Parthasarathy et al 1994: All-India Monthly and seasonal rainfall series 1871-1993. Theor. Appl. Climatol, 49, 217-224

Winter Rainfall Change Over India

Winter rainfall change over Peninsular India (RCP4.5 and RCP8.5 scenarios)

RCP 4.5 RCP 8.5

Where Aleatoric Uncertainty Exceeds Epistemic Uncertainty

• We may not be able to reduce the overall uncertainty any further

• Since aleatoric uncertainty does not grow over time, – we can use current information on variability to

inform future uncertainty. – past knowledge of coping with variability may play

an important part in adaptation strategies.

Where Epistemic Uncertainty Exceeds Aleatoric, can we Constrain it?

• Model democracy• How to weight models?

– “Good” models get higher weights– What does a “good model” mean?– How does one compare models to observations?

• Our research indicates that model weighting schemes do not constrain epistemic uncertainty over India

• Observational uncertainty matters more

Collins, AchutaRao, Ashok, Mitra, Prakash, Srivastava, Turner, Observational challenges in evaluating climate models, Nature Climate Change, 2013

Observational Uncertainty

• How good are our observations?

• Fundamental barrier to reducing model uncertainty

Thank you