Update from Reto and Erich

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1 Update from Reto and Erich From theory and models to observations Erich Fischer and Reto Knutti ETH Zurich, Switzerland

2 Schematic illustrating precipitation changes Where does it come from? After CCSP (2008) and IPCC AR5 Chapter 1

3 It started at the end of the Little Ice Age Rudolf Clausius ( ) Benoît Paul Émile Clapeyron ( )

4 Simple model predictions first MRI-GCM-1 A different kind of model: Resolution of 4 x 5 5 vertical layers 10-day (!) analysis period No Alps Consistent with Hansen et al. (1988), Wetherald and Manabe (1988) but inconsistent with mixed results from Wilson and Mitchell (1987) 1xCO 2 2xCO 2 Noda and Tokioka (1989)

5 More heavy rainfall days across the globe Results still hold «classic scientific dilemma whether it is appropriate to 'go public' with results in which we have limited confidence» Gordon et al (1992), see also Whetton et al. (1993) and Fowler and Hennessy (1995) explained by Allen and Ingram (2002), Trenberth et al. (2003)

6 First poor observational evidence Some (poor) evidence over US and Japan IPCC SAR (1995) found no clear large-scale pattern of heavy rainfall intensification in observations Iwashima and Yamamoto (1993) see also Karl et al. (1995)

7 Building confidence From theory -> simple models -> observations/high resolution models Today s predictions broadly consistent with those made decades ago Question about dynamic vs. thermodynamic contribution remains open Confidence results from the combination of model, observations and particularly our process understanding

8 Difficult to illustrate and communicate Westra et al Donat et al. 2013

9 Observed daily percentiles in Europe All-day percentiles based on daily gridded EOBS data Based on Fischer and Knutti 2015, Nature CC

10 More very wet days Based on Fischer and Knutti 2015, Nature CC

11 Observational-based schematic An observation-based schematic useful for outreach?c

12 Remarkably consistent with CC-scaling

13 Models consistently at the low end

14 EURO-CORDEX even lower?

15 The higher resolved the lower the change?

16 The higher resolved the lower the change?

17 Good agreement at 2 C when regridded

18 Consistent with cloud-resolving models First results suggest that changes at daily scale are consistent Convection-resolving (2km) Conv. parameterized (12km) Summer (JJA) Winter (DJF) Parameterized (12km) Resolved (1.5km) Ban et al., 2015, GRL Chan et al., 2014, ERL

19 What fraction all globally occurring heavy precipitation or hot extremes is attributable to warming?

20 Pre-industrial precipitation series Percentiles defined on all days in pre-industrial control simulations th percentile Fischer and Knutti 2015, Nature CC

21 The more extreme the greater the increase PR = P 2 C P pre ind Fischer and Knutti 2015, Nature CC

22 Probability ratio at 2 C warming Multi-model mean exceedance of pre-industrial 99% quantile of daily precipitation Fischer and Knutti 2015, Nature CC

23 The more extreme the greater the increase PR = P 2 C P pre ind Fischer and Knutti 2015, Nature CC

24 Global land-only probability ratio times more heavy precipitation days across the globe Fischer and Knutti 2015, Nature CC

Global land-only probability ratio 1.4-2.

25 Global land-only probability ratio times more heavy precipitation days across the globe Fischer and Knutti 2015, Nature CC

26 Non-linear increase with warming Fischer and Knutti 2015, Nature CC

$The attributable fraction of occurrence FAR = 1- P Pre ind P 0. 85 C Observed +0.$

27 The attributable fraction of occurrence FAR = 1- P Pre ind P C Observed C 18% [10-28%] of all globally occurring heavy precipitation days are attributable to warming

28 Rapid increase in hot extremes 2 C 1.5 C Twice as many warm extremes at 2 C than 1.5 C warming Observed

29 Explained by uniform distribution shift FAR = FAR = C shift Probability ratio

30 Model spread due to climatology Model differences due to scale and shape of pre-industrial temperature distribution

31 Same shift different probability ratio Low probability ratio High probability ratio

32 Explained by uniform distribution shift FAR = FAR = ERA interim shifted by 2 C Probability ratio

33 Conclusions At continental to global scale probability ratio estimates for moderate thresholds are consistent with simple mean warming and CC-scaling Probability ratio is highly sensitive to scale and shape of climatological distribution -> potential for observational constraints Understanding potential dynamic changes remains important in particularly for regional to local changes

Climate Extremes Research: Recent Findings and New Direc8ons

Climate Extremes Research: Recent Findings and New Direc8ons Kenneth Kunkel NOAA Cooperative Institute for Climate and Satellites North Carolina State University and National Climatic Data Center h#p://assessment.globalchange.gov