Pattern scaling using ClimGen

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  • Pa#ern scaling using ClimGen: User needs

    Changing precipita0on variability Interac0on between global & regional responses

    Tim Osborn & Craig Wallace Clima&c Research Unit, University of East Anglia

    April 2014

    Pa:ern scaling, climate model emulators & their applica&on to the new scenario process

    NCAR, Boulder, Colorado

    Work supported by TOPDAD & HELIX EU projects

  • Pattern scaling: meeting user needs

    Key requirements: Explore spread (uncertainty?) of climate projec0ons

    Pre-CMIP3, CMIP3, CMIP5 mul0-model, QUMP perturbed parameters Generate projec0ons for un-simulated scenarios User needs: Iden0cal formats for all scenarios (& observa0ons) Flexible temporal, seasonal and geographic windowing/averaging

  • Pattern scaling: meeting user needs

    Example na0onal average summer T & P changes Pink = CMIP3 distribu:on Open symbols = CMIP3 models

    Key requirements: Explore spread (uncertainty?) of climate projec0ons

    Pre-CMIP3, CMIP3, CMIP5 mul0-model, QUMP perturbed parameters Generate projec0ons for un-simulated scenarios

    Natural variability

    T = 0.5, 1.5, 3

    For global warming T = 3 K (left panel) or 0.5, 1.5 and 3 K (right panel)

    Based on Osborn et al. (under review) Climatic Change

  • Pattern scaling: meeting user needs

    Example na0onal average summer T & P changes Pink = CMIP3 distribu:on Open symbols = CMIP3 models Brown = CMIP5 distribu:on Solid symbols = CMIP5 models

    Key requirements: Explore spread (uncertainty?) of climate projec0ons

    Pre-CMIP3, CMIP3, CMIP5 mul0-model, QUMP perturbed parameters Generate projec0ons for un-simulated scenarios

    Natural variability

    T = 0.5, 1.5, 3

    For global warming T = 0.5, 1.5 and 3 K (right panel)

    Based on Osborn et al. (under review) Climatic Change

  • Pattern scaling: meeting user needs

    Key requirements: Explore spread (uncertainty?) of climate projec0ons

    Pre-CMIP3, CMIP3, CMIP5 mul0-model, QUMP perturbed parameters Generate projec0ons for un-simulated scenarios

    Natural variability

    T = 0.5, 1.5, 3

    Example na0onal average summer T & P changes Pink = CMIP3 distribu:on Open symbols = CMIP3 models Brown = CMIP5 distribu:on Solid symbols = CMIP5 models Blue = QUMP distribu:on Black le#ers = QUMP models

    For global warming T = 0.5, 1.5 and 3 K (right panel)

    Based on Osborn et al. (under review) Climatic Change

  • Pattern scaling: meeting user needs

    Mul0ple climate variables (all monthly means, mostly land-only): Near-surface temperature (mean, min, max, DTR) Precipita0on & wet-day frequency Cloud-cover (can es0mate sunshine hours or radia0on variables) Vapour pressure (can es0mate other humidity variables) SST is currently the only variable provided over the oceans

    User needs: more derived variables, extreme events & variability Hea0ng & cooling degree days (HDD & CDD) Poten0al evapotranspira0on (PET, e.g. from Penman-Mon0eth) Drought indicators (e.g. Standardised Precipita0on-Evapotranspira0on

    Index, SPEI)

    How to deal with climate (and weather) variability?

  • Climate variability in pattern scaling: (1) use observations

    Sample from observed variability: Realis0c for present-day But doesnt change when the mean climate changes

    Design sampling to allow the separa0on of climate change and natural variability effects

    Use mul0ple 0me-shided sequences instead of single observed sequence

  • Climate variability in pattern scaling: (1) use observations

    Sample from observed variability: Realis0c for present-day But doesnt change when the mean climate changes

    Design sampling to allow the separa0on of climate change and natural variability effects

    Use mul0ple 0me-shided sequences instead of single observed sequence

  • Climate variability in pattern scaling: (1) use observations

    Or generate slices represen0ng climate+variability for specific amounts of T

    Fig. S3 of Osborn et al. (under review) Climatic Change

  • Climate variability in pattern scaling: (2) perturb observations

    Pahern-scale higher moments (e.g. standard devia0on, skew) We divide GCM monthly precipita0on 0meseries by low-pass filter Represent the high-frequency devia0ons with a gamma distribu0on Scale changes in gamma shape parameter with T

    Fig. 1 of Osborn et al. (under review) Climatic Change

    Rel

    ativ

    e ch

    ange

    in

  • Climate variability in pattern scaling: (2) perturb observations

    Example applica0on SE England grid cell, HadCM3 GCM, July precipita0on For T = 3C, pahern-scaling gives 45% reduc0on in mean precipita0on But also 62% reduc0on in gamma shape param. of monthly precipita0on

    Fig. 1 of Osborn et al. (under review) Climatic Change

    Observed sequence

    Sequence x 0.55 Sequence x 0.55

    Sequence x 0.55 & perturbed to have 62% lower shape

  • Is there agreement in GCM-simulated changes of variability?

    Mul0-model mean of 22 CMIP3 GCMs Normalised change in gamma shape of July precipita0on

    Units: % change / K

    Fig. 1 of Osborn et al. (under review) Climatic Change

  • Is there agreement in GCM-simulated changes of variability?

    Mul0-model mean of 20 CMIP5 GCMs Normalised change in gamma shape of July precipita0on

    Units: % change / K

    Based on Osborn et al. (under review) Climatic Change

  • Is there agreement in GCM-simulated changes of variability?

    Mul0-model agreement of 22 CMIP3 GCMs Frac0on of models showing increased gamma shape of July precipita0on

    Units: fraction

    Based on Osborn et al. (under review) Climatic Change

  • Is there agreement in GCM-simulated changes of variability?

    Mul0-model agreement of 20 CMIP5 GCMs Frac0on of models showing increased gamma shape of July precipita0on

    Units: fraction

    Based on Osborn et al. (under review) Climatic Change

  • Transform observed rainfall series by factors given by range of T from 0 to 6K Count frequency of short droughts in each transformed series Estimate uncertainty

    UK drought frequency vs.

    global T

    Does pattern-scaling emulate GCM/RCM behaviour?

    HadCM3 GCM HadRM3 RCM

  • Can we treat global and regional changes independently? Separa0on into global T & regional paherns is convenient Especially for the treatment of uncertain0es

  • Can we treat global and regional changes independently? Separa0on into global T & regional paherns is convenient Especially for the treatment of uncertain0es

    Simple example: Estimating conditional PDFs of UK drought frequency, using HadRM3 RCM pattern-scaling results and the Wigley & Raper (2001) PDFs of T

  • Simple example: Estimating conditional PDFs of UK drought frequency, using HadRM3 RCM pattern-scaling results and the Wigley & Raper (2001) PDFs of T

    Can we treat global and regional changes independently? Separa0on into global T & regional paherns is convenient Especially for the treatment of uncertain0es

  • Estimating conditional PDFs of UK drought frequency

    Can we treat global and regional changes independently? Separa0on into global T & regional paherns is convenient Especially for the treatment of uncertain0es

  • Can we treat global and regional changes independently? Separa0on into global T & regional paherns is convenient Especially for the treatment of uncertain0es

    But can I combine T derived from a par0cular climate sensi0vity with any of the GCM paherns?

    Or are the normalised change paherns of high sensi0vity GCMs systema0cally different from those of low sensi0vity GCMs?

  • Rank correla0on between temperature and ECS for CMIP3

    Are the normalised change patterns of high sensitivity GCMs systematically different from those of low sensitivity GCMs?

    Osborn et al. (in preparation) Rank correlation for 22 GCMs

    >80% significant correlations shown

  • Rank correla0on between temperature and ECS for QUMP

    Are the normalised change patterns of high sensitivity GCMs systematically different from those of low sensitivity GCMs?

    Osborn et al. (in preparation) Rank correlation for 17 GCMs

    >80% significant correlations shown

  • Rank correla0on between temperature and ECS for CMIP3, CMIP5 & QUMP

    Are the normalised change patterns of high sensitivity GCMs systematically different from those of low sensitivity GCMs?

    Osborn et al. (in preparation) Rank correlation for 52 GCMs

    >80% significant correlations shown

  • Conclusions: meeting user needs with pattern scaling

    Exploring the uncertainty of climate projec0ons: Given wide mul0-model ensemble ranges, sufficient to approximately

    emulate plume of future regional changes Increasing demand for emula0on to include variability & represent extremes: Need to treat variability with care, sufficient sampling etc. Can pahern-scale higher order parameters (e.g. standard devia0on,

    skew) and perturb observed variability accordingly More complicated changes (e.g. shid in ENSO behaviour) cannot,

    however, be captured Systema0c differences between normalised paherns from low and high sensi0vity models complicates the separate treatment of uncertainty in global T and regional climate change

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