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Interannual Variability in Surface Climate from CMIP3 Models and Its Implications for Future Warming

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serina-dillon
15 diapositives · 296 Views · Nov 30, 2014

This research explores the ability of CMIP3 models to represent interannual variability in surface temperature, precipitation, and sea level pressure, focusing on potential problem regions. It examines differences in variability representation across various geographic areas, including tropics and extratropics. The study evaluates if scenario ranges for future warming can be constrained by models performing well in present-day variability. Key findings indicate significant regional discrepancies in model performance, highlighting challenges in using selected models to predict future temperature changes effectively.

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Interannual Variability in Surface Climate from CMIP3 Models and Its Implications for Future Warming
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  1. Present-day interannual variability of surface climate in CMIP3 models and its relation to future warming* Simon C. Scherrer Federal of Office of Meteorology and Climatology MeteoSwiss 15 July 2010 11th International Meeting on Statistical Climatology, Edinburgh UK* partly based on a publication in the International Journal of Climatology (doi:10.1002/joc.2170)

  2. Outline • Can CMIP3 models adequately represent interannual variability in surface temp, precip, SLP? Problem regions? • Are there differences in variability representation for large areas (tropics, extratropics, land, sea)? • Can future temperature changes be constrained using only the „good“ models based on present day variability skill?

  3. PrecipGPCP 1979-99 Observed interannual variabilityspread: standard deviation of seasonal averages DJF JJA 2m TempERA-40 1958-99 dotted: precip < 1mm/season K (top)mm/day (bottom) large small signal signal time time

  4. Simple variability metrics: VI • Variability index (VI) cf. Gleckler et al. (2008) is used to analyze variability performance for each model, variable and grid-point • “multi-model mean” index defined as the average over all model run’s #1 for 21 GCMs of the CMIP3 archive What is a good VI? s: standard deviation estimate x, y: grid point coordinate var: variable (e.g. temperature) ref: reference data set (e.g. ERA-40) VI-range: 0 (perfect) to 

  5. Precip dotted: precip < 1mm/season Multi-model mean variability index2m Temp (ERA-40, 1958-1999) and Precip (GPCP, 1979-1999) DJF JJA 2m Temp 0 [-29;+41] [-38;+61] [-48;+93] [-62;+161] [-71;+245] % good bad

  6. 2m Temp Multi-model mean variability indexsea level pressure (ERA-40, 1958-1999) DJF JJA SLP 0 [-29;+41] [-38;+61] [-48;+93] [-62;+161] [-71;+245] %

  7. Outline • Can CMIP3 models adequately represent interannual variability in surface temp, precip, SLP? Problem regions? • Are there differences in variability representation for large areas (tropics, extratropics, land, sea)? • Can scenario range be constrained using only information of the „good“ models using present day variability skill?

  8. tropics worse tropics worse Variability index for 2m Temptropics vs extratropics  good models in tropics are in general not also good models in extratropics

  9. sea worse sea worse Variability index for 2m Templand vs sea DJF JJA  good models over land are not necessarily also good models over sea

  10. Outline • Can CMIP3 models adequately represent interannual variability in surface temp, precip, SLP? Problem regions? • Are there differences in variability representation for large areas (tropics, extratropics, land, sea)? • Can scenario range be constrained using only information of the „good“ models using present day variability skill?

  11. Variability index vs. temperature changeTROPICS, dT 2080-2099 DJF JJA 3.8 3.9 dT [K] 1.5 1.5 good VI bad good VI bad no constraining potential

  12. Variability index vs. temperature change EXTRA-TROPICS, dT 2080-2099 DJF JJA 5.7 4.8 dT [K] 2.3 1.8 good VI bad good VI bad no constraining potential

  13. Variability index vs. temperature change LAND, dT 2080-2099 DJF JJA 5.3 5.0 4.8 dT [K] 2.5 2.0 good VI bad good VI bad no real constraining potential

  14. Variability index vs. temperature change SEA, dT 2080-2099 DJF JJA 3.7 3.7 dT [K] 1.5 1.4 good VI bad good VI bad no constraining potential

  15. Conclusions • Can CMIP3 models adequately represent interannual variability in surface temp, precipitation, sea level pressure? Problem regions?T/SLP pretty much, Precip hardly. Problems with sea ice boundary, ENSO, Central Africa, monsoon regions • Are there differences in variability representation for large areas (tropics, extratropics, land, sea)?Yes. Surface variability better on land than on sea and in the extra-tropics than in the tropics, but: “good” models in tropics (over sea) are not necessarily also “good” in extratropics (over land). • Can scenario range of dT on large scale be constrained using only information of models that represent present day variability skill well?Rather not. Weak negative relation between good IAV representation and more warming not enough to do constraining. • Model combination: Equal model weighting safest and most transparent. “Omitting” really bad models may be option.