Nathanael Melia | Ed Hawkins | Keith Haines

Department of Meteorology

Predicting the Opening of Arctic Sea RoutesA53C-0389 Improved Arctic Sea Ice Thickness Projections Using Bias Corrected CMIP5 Simulations

Method developmentSIT is a particularly challenging variable and using traditional bias correction techniques results in unwanted behaviour (Fig 3) In refining our methods we created the Mean And VaRIance Correction (MAVRIC)

MAVRIC validationWe test MAVRIC using a data denial method where using the CSIRO-Mk360 GCM (CSIRO) we calibrate CSIRO with MAVRIC using the first 20 years of PIOMAS observations to test how MAVRIC predicts the following 15 years of observations It is clear from the validation bean plots (Fig 4 right) that the distribution of CSIRO post MAVRIC matches PIOMAS closer than raw CSIRO

Introduction

Arctic sea ice thickness

bull To represent observations of SIT we use the coupled ice-ocean model PIOMAS (Fig 1) which is forced with reanalysis

bull Global climate models (GCMs) all show distinct spatial and temporal biases This makes their use for impact-based climate change studies problematic

Spatial perspective

Sources of projection uncertaintyUncertainty in climate projections can be partitioned into three distinct sources

1 Model Uncertainty model biases

2 Internal Variability natural fluctuations

3 Scenario Uncertainty unknown future emissions

Improved Arctic sea ice thickness projections

bull In

bull Summary

Figure 1 September 1979ndash2014 mean SIT and standard deviation (SD) from the PIOMAS reanalysis SD is calculated after removing the linear trend

Projections of Arctic sea ice thickness (SIT) have the potential

to inform stakeholders about accessibility to the region but

are currently rather uncertain We present a new method to

constrain global climate model simulations of SIT to narrow

projection uncertainty via a statistical bias-correction

technique

bull Post MAVRIC the SIT distributions and variability have been successfully corrected

bull MAVRIC retains (though adjusts) an ensemblersquos internal variability and the GCMrsquos ensemble spread

Figure 2 Mean September SIT for each of the six GCMs considered averaged over the period 1979ndash2014

Figure 3 Performance of different SIT BCs for one particular month at a hypotheticalgrid point in a lsquotoyrsquo model Mean SD (detrended) and trend legend statistics arecalculated over the observation period (1979ndash2014) lsquoIce-freersquo is defined as the firstoccurrence of any ensemble member below 015 m The ice-free ensemble range isshown ie the year of the first ensemble member to be ice-free to the last ensemblemember to be ice-free The black line represents lsquoobservationsrsquo the blue and red linesrepresent high and low ice models respectively The thin coloured lines representensemble members and the thick lines represent the ensemble mean

Figure 4 September SIT at three grid point locations in the Arctic from PIOMAS(black) and CSIRO-Mk360 historical (1979ndash2005) and RCP85 (2006ndash2014) rawoutput (grey) and post-MAVRIC (green) The raw CSIRO ensembles (grey) are bias-corrected via the MAVRIC using the PIOMAS observations (black) over the calibrationwindow producing the MAVRIC ensembles (green) for the validation window Beanplots (right) show the distribution of the SIT for the validation period The smallhorizontal lines show every SIT value the frequency of which is illustrated by thewidth of the shaded region The thick horizontal line depicts the mean

Figure 5 CSIRO-Mk360 and HadGEM2-ES September 1979ndash2014 ensemble meanSIT and SD (detrended) The raw columns are the model solutions as found in theCMIP5 archive The corrected columns show the distribution after the MAVRIC hasbeen applied PIOMAS SIT fields are shown in Fig 1

Figure 6 The evolution of the sources of September SIT uncertainty in the CMIP5sub-set with lead time Year zero is the MAVRIC window mid-point (1997) and theemission scenarios (RCPs) start in 2006 Panel (a) shows the change in magnitudeof the different sources of uncertainty The uncertainty shown is the median SITvariance and hence the lines scale additively The dashed lines are for the rawmodel output and solid lines are for post-MAVRIC Contributions of modeluncertainty internal variability and scenario uncertainty as a fraction of totaluncertainty are shown for the raw output (b) and post-MAVRIC (c)

Citationbull Melia N et al Improved Arctic sea ice thickness projections using bias corrected

CMIP5 simulations The Cryosphere doi 105194tc-9-2237-2015 2015

bull Dataset available for download httpdxdoiorg101786419479

Contact informationbull Department of Meteorology University of Reading RG6 6AH UK

bull Email nmeliapgrreadingacuk

bull Web wwwmetreadingacuk~sq011930home

bull -

Reduced spread in projectionsBy considering sea ice volume (SIV) we show that MAVRIC has reduced both the magnitude of SIV and the spread in future projections This has the effect in reducing the range in likely ice-free dates and reducing the median date to 2052 in RCP85 10 years earlier than without the correction

Figure 7 CMIP5 subset sea ice volume (SIV) projections and first ice-freeconditions Panels (a b) show the projected SIV from all six models (18 ensemblemembers total) in both the raw and corrected GCMs (11-year running mean) andshaded regions are the 16thndash84th percentiles Panel (c) shows the number ofensemble members having passed the ice-free threshold Panel (d) shows thestatistics of (c) with the whiskers representing the range (1st and 18th ensemblemember ice-free) the box capturing the 16thndash84th percentiles and the bold lineshowing the median (9th ensemble member) Ice-free is defined as the first year thepan-Arctic SIV dips below 1 times 10ଷଷ for a particular ensemble memberVolume (SIV) is calculated using a constant 50 SIC throughout

Summarybull GCM simulations of SIT exhibit a wide range of biases

bull The MAVRIC can successfully correct the mean and variance in simulated SIT to a reference data set such as PIOMAS

bull The MAVRIC significantly reduces the total uncertainty in SIT projections by reducing the model uncertainty

bull Projections of SIV have been reduced and are now potentially less uncertain with earlier ice-free dates