Climate Change Adaptation for ports and navigation infrastructure

Climate Change Adaptation for ports and navigation infrastructure The application of climate projections and observations to address climate risks in ports Iñigo Losada Research Director IHCantabria Universidad de Cantabria

Why do we need climate projections? What are the relevant time scales, timelines and spatial resolution to define climatic drivers in climate risks in ports? How can we downscale global climate models to the geographic resolutions needed to address climate risks in ports? Dealing with uncertainties in risk assessment: SLR Conclusion

Risk assessment framework in ports Core framing in terms of risk Climate change, variability and extremes plus multi-stressor setting

Materiality of risk Anything that contributes to impact the port capacity to get the investment payback affecting either Capital costs Operating efficiency Maintaining costs

Ports value chain Breakwaters Cargo handling Inner transport Navigation Berthing-Dock: Operations Storage External connections

RELATIVE SEA LEVEL RISE= REGIONAL SLR + subsidence/uplift Flooding: Combined effects!!! Waves Wind Atmospheric pressure Astronomical tide Mean Sea Level (Subsidence/uplift)

Regional Spatial scales Global Local High Resolution

HOW TO ADDRESS IMPACTS AT DIFFERENT SCALES RS Regional Scale 10-100km H E V Univariate indices for each hazard at regional scale Macro indices for each hazard and sector Macro indices for each hazard and sector Analytical formulation involving H, E and V for each hazard and sector Statistical approach HR- LD High Resolution Low Definition 1Km Analytical formulation to downscale dynamics 00 Univariate indices for each hazard Macro indices for each hazard and sector Macro indices for each hazard and sector Analytical formulation involving H, E and V for each hazard and sector Statistical approach HR- HD High Resolution High Definition 10-100m Multivariate indices for each hazard Hydrodynamic models, for waves and sea 00 level Morphodynamic model Flooding model HR MDT Spatial distribution of vulnerability Numerical computation of damages Multirun simulation

Increasing uncertainty Adaptation 1 PORT LIFE CYCLE AND FUTURE CLIMATE 2 3 4

When does my timeline begin? Today? Is the port constructed with current standards? What is the current resilience to climate change? What has been the role of maintenance strategy on current resilience? When does my timeline end? Life cycles of investments <20 years. If so, differences between internal climate variability and climate change signal may be difficult to find and models providing projections may not provide statistically significant information > 30-50 years. Projections are to be used

Internal variability due to NAO, ENSO.. Interdecadal variations, seasonality, etc.

Extend trends with advanced statistical models incorporating internal variability modes Use projections based on an ensemble of GCM models Observations 1850 Long-term historical global reanalysis 1950 2016 2100 Long-term projections for different climate change scenarios 1850 1950 2016 2100 Analyze long-term trends and internal variability Near-term 2016-2035 Mid-term 2046-2065 Long-term 2081-2100

GLOBAL CLIMATE MODELS 42 GCMs from CMIP5 Simulations for several scenarios 137 historical 56 RCP2.6 98 RCP4.5 72 RCP8.5 Variable: Daily sea Level Pressure fields (SLP)

DYNAMIC DOWNSCALING Nesting of GCM models with regional/local models to represent physical processes explicitly at much higher spatial resolution STATISTICAL DOWNSCALING Based on statistical/empirical relationships between large-scale climate variables (predictors) and regional/local variables (predictand) without modelling of the physical processes Sea Level Pressure in the North Atlantic Wave height at a given port

Dynamic Downscaling Global DOWNSCALING Local Regional

Statistical downscaling approach based on weather types ATMOSPHERIC CIRCULATION (predictor X: SLP) MULTIVARIATE WAVE CLIMATE (predictand Y, H, T, Dir) Regional atmospheric climatology (X) Local wave climatology (Y)

Wave climate projections FRAMEWORK Perez, J., Menendez, M., Camus, P., Mendez, F.J., Losada, I.J. Statistical multi-model climate projections of surface ocean waves in Europe (2015). Ocean Modelling, DOI: 10.1016/j.ocemod.2015.06.001

Statistical downscaling approach based on weather types Historical Info ATMOSPHERIC DATA (PREDICTOR) WAVE DATA (PREDICTAND) X= SLP Y= {Hs,Tm,Dir,..} Calibration Statistical downscaling PREDICTOR PRE-PROCESS (Spatial domain, temporal lag, gradients,..) WEATHER TYPES (K-means clustering) p 1 p 3 p 2 p 4 WT i p i (WT i ) ASSOCIATED SEA-STATES f(y)= p i f i (Y) Validation Wave Hindcast Wave Forecast Wave Projections NEW PROBABILITIES p i (WT i ) p 1 p 3 P 2 P 4 INFERRED SEA-STATES f (Y)= p i f i (Y) Camus et al., 2014

Perez et al., 2014 GLOBAL CLIMATE MODELS (GCM)

Wave climate projections MEAN WAVE PERIOD PROJECTIONS Changes in T02 with respect to the control period (1975-2004)

Wave climate projections WAVE ENERGY FLUX PROJECTIONS Changes in energy flux with respect to the control period (1975-2004)

Validation SD applied to SS Validation of SD for storm surge levels in Cuxhaven. Monthly 95% percentile

High resolution projections of storm surge and wave heights along a coastal stretch (200 km) of the north coast of Spain for application in port risk assessment in the Region of Asturias. Results show representative statistics for the differences between the historical control period and 2071-2099 under and RCP8.5. Boxplots show the uncertainties (min, max, mean, 25% and 75% percentiles) associated to the 40 GCMs used in the statistical projections

STATISTICAL DOWNSCALING With limitations, but probably the most appropriate approach for risk assessment in ports requiring high spatial resolution information by mid and the end of the century

GMSL It is virtually certain that globally averaged sea level has risen over the 20th century, with a very likely mean rate between 1900 and 2010 of 1.7 [1.5 to 1.9] mm yr 1 and 3.2 [2.8 and 3.6] mm yr 1 between 1993 and 2010

Representative Concentration Pathways: Greenhouse concentration trajectories RCPs, RCP2.6, RCP4.5, RCP6, and RCP8.5, are named after a possible range of radiative forcing values in the year 2100 relative to pre-industrial values (+2.6, +4.5, +6.0 and +8.5 W/m 2, respectively)

Are these projections the values to be used in ports risk management? Most authoritative source of information for coastal risk management Widely used Likely (66-100% probability) (results of models + calibrated uncertainty language-consensus by IPCC authors) Central values and the 5-95% range of what?

Are these the values to be used in port risk management? Not to be used exclusively because: Global vs. Local (regional + local spatial variations: meteooceanographic factors, gravitational effects due to ice-melting and local uplift/subsidence) WGI design: aiming at reducing uncertainties not risks Process-based models including laws of physics + parameterizations Include ensembles of different models and treat spread of projections as a normal distribution, taking 5th and 90th percentiles of the Gaussian distribution as upper and lower boundaries

We are improving our modelling capacities reducing uncertainties

RSLR RCP4.5 (Slangen et al. 2014)

Other approches: semi-empirical,physical constraints on ice-sheet dynamics, paleo records,.. ALL are included but not considered into scenarios due to less confidence ALL OF THEM GIVE HIGHER PROJECTED VALUES!!! IPCC AR5 2014 Rahmstorf (2010). Science

Are these the values to be used in port risk management? Not to be used exclusively because: IPCC projections are focused on the central distribution rather than on the high risk tail of GMSL (where is the other 33%) RISK MANAGEMENT requires an analysis of decisions against all available knowledge, including all uncertainties and ambiguities among available approaches and expert opinions. Can the problem be bounded? Upper bound Least upper bound (worst case scenario) High End (++) scenarios (Hinkel et al. 2015) impossible scenario cannot be established Plausible but very unlikely (no probability assigned)

What matters is disclosing the wide range of variability at the appropriate resolution

Conclusions Risk assessment in ports requires high resolution, long-terms time series of the relevant parameters representing the drivers for a given impact A comprehensive analysis should include the combination of climate data from instrumental observations and numerical reanalysis and projections. The analysis of trends and internal variability modes in the geography of interest is the first step to understand direction and magnitude of changes Statistical downscaling is an appropriate technique to obtain the required high resolution information to assess climate risks in ports in the long-term For near term analysis < 20 years, numerical projections may not be able to catch the contribution of climate change signal to internal variability

Conclusions RISK MANAGEMENT requires an analysis of decisions against all available knowledge, including all uncertainties and ambiguities among available approaches and expert opinions.