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Assessing reliability of regional climate projections: the case of Indian monsoon.

Ramesh KV, Goswami P - Sci Rep (2014)

Bottom Line: An important question is the degree of progress made since the earlier IPCC simulations (CMIP3) to the latest, recently completed CMIP5.While the scope has increased in CMIP5, there is essentially no improvement in skill in projections since CMIP3 in terms of reliability (confidence).Analysis of climate indices shows that in both CMIP5 and CMIP3 certain common processes at large and regional scales as well as slow timescales are associated with successful simulation of trend and mean.

View Article: PubMed Central - PubMed

Affiliation: CSIR Centre for Mathematical Modelling and Computer Simulation, Wind Tunnel Road, Bangalore-560037, Karnataka, India.

ABSTRACT
Projections of climate change are emerging to play major roles in many applications. However, assessing reliability of climate change projections, especially at regional scales, remains a major challenge. An important question is the degree of progress made since the earlier IPCC simulations (CMIP3) to the latest, recently completed CMIP5. We consider the continental Indian monsoon as an example and apply a hierarchical approach for assessing reliability, using the accuracy in simulating the historical trend as the primary criterion. While the scope has increased in CMIP5, there is essentially no improvement in skill in projections since CMIP3 in terms of reliability (confidence). Thus, it may be necessary to consider acceptable models for specific assessment rather than simple ensemble. Analysis of climate indices shows that in both CMIP5 and CMIP3 certain common processes at large and regional scales as well as slow timescales are associated with successful simulation of trend and mean.

No MeSH data available.


Correlation coefficients between CIM rainfall and (a) large scale climate indicators (ENSO – Purple, land-ocean thermal gradient: LETG-SA - green) and (b) regional scale indicators (IOD – Purple and land-ocean thermal gradient (LETG-IND, green) for CMIP3 (top panels) and CMIP5 (bottom panels); the corresponding observed correlation coefficients from multiple observations for the period (1951–2005) are shown in the middle panel.The symbol• indicates significant (P < 0.2) negative trend, while * indicates simulations that reproduce current mean annual and seasonal rainfall in the acceptable uncertainty band.
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f4: Correlation coefficients between CIM rainfall and (a) large scale climate indicators (ENSO – Purple, land-ocean thermal gradient: LETG-SA - green) and (b) regional scale indicators (IOD – Purple and land-ocean thermal gradient (LETG-IND, green) for CMIP3 (top panels) and CMIP5 (bottom panels); the corresponding observed correlation coefficients from multiple observations for the period (1951–2005) are shown in the middle panel.The symbol• indicates significant (P < 0.2) negative trend, while * indicates simulations that reproduce current mean annual and seasonal rainfall in the acceptable uncertainty band.

Mentions: Comparison of the large-scale indices (ENSO index and the LETG-SA) from the simulations (Fig. 4a, brown and green bars, respectively) with the corresponding observed values (Fig. 4a, middle panel) shows that while many (16) of the CMIP3 simulations reproduce observed negative CC (Fig. 3a, top panel) at 99% significant level, only a few (10) CMIP5 simulations show this observed characteristic (Fig. 4a, bottom panel). In particular, the CMIP3 ensemble shows a negative ENSO-CIM index at more than 99% significance level; for the CMIP5 ensemble this significance is ~95% (Fig. 4a, thick brown bars). With respect to LETG-SA, the CMIP3 ensemble produces a positive CC as observed, but with low significance; for CMIP5 ensemble, this CC is zero (Fig. 4a, thick green bars). Further, only CMIP3 ensemble shows LETG-SA index of the same sign and significance as that of the observed (Fig. 4a). Thus the CMIP5 simulations have in general poorer quality than the CMIP3 simulations in reproducing the observed association between large-scale processes and CIM. Similar conclusions also hold for distribution of simulations for the regional process (Fig. 4b). In terms of the IOD index, eight of the CMIP3 models simulate values similar to the observed and the negative trend in the seasonal rainfall; five CMIP5 simulations satisfy these twin criteria (Table S2 and S3).


Assessing reliability of regional climate projections: the case of Indian monsoon.

Ramesh KV, Goswami P - Sci Rep (2014)

Correlation coefficients between CIM rainfall and (a) large scale climate indicators (ENSO – Purple, land-ocean thermal gradient: LETG-SA - green) and (b) regional scale indicators (IOD – Purple and land-ocean thermal gradient (LETG-IND, green) for CMIP3 (top panels) and CMIP5 (bottom panels); the corresponding observed correlation coefficients from multiple observations for the period (1951–2005) are shown in the middle panel.The symbol• indicates significant (P < 0.2) negative trend, while * indicates simulations that reproduce current mean annual and seasonal rainfall in the acceptable uncertainty band.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3921638&req=5

f4: Correlation coefficients between CIM rainfall and (a) large scale climate indicators (ENSO – Purple, land-ocean thermal gradient: LETG-SA - green) and (b) regional scale indicators (IOD – Purple and land-ocean thermal gradient (LETG-IND, green) for CMIP3 (top panels) and CMIP5 (bottom panels); the corresponding observed correlation coefficients from multiple observations for the period (1951–2005) are shown in the middle panel.The symbol• indicates significant (P < 0.2) negative trend, while * indicates simulations that reproduce current mean annual and seasonal rainfall in the acceptable uncertainty band.
Mentions: Comparison of the large-scale indices (ENSO index and the LETG-SA) from the simulations (Fig. 4a, brown and green bars, respectively) with the corresponding observed values (Fig. 4a, middle panel) shows that while many (16) of the CMIP3 simulations reproduce observed negative CC (Fig. 3a, top panel) at 99% significant level, only a few (10) CMIP5 simulations show this observed characteristic (Fig. 4a, bottom panel). In particular, the CMIP3 ensemble shows a negative ENSO-CIM index at more than 99% significance level; for the CMIP5 ensemble this significance is ~95% (Fig. 4a, thick brown bars). With respect to LETG-SA, the CMIP3 ensemble produces a positive CC as observed, but with low significance; for CMIP5 ensemble, this CC is zero (Fig. 4a, thick green bars). Further, only CMIP3 ensemble shows LETG-SA index of the same sign and significance as that of the observed (Fig. 4a). Thus the CMIP5 simulations have in general poorer quality than the CMIP3 simulations in reproducing the observed association between large-scale processes and CIM. Similar conclusions also hold for distribution of simulations for the regional process (Fig. 4b). In terms of the IOD index, eight of the CMIP3 models simulate values similar to the observed and the negative trend in the seasonal rainfall; five CMIP5 simulations satisfy these twin criteria (Table S2 and S3).

Bottom Line: An important question is the degree of progress made since the earlier IPCC simulations (CMIP3) to the latest, recently completed CMIP5.While the scope has increased in CMIP5, there is essentially no improvement in skill in projections since CMIP3 in terms of reliability (confidence).Analysis of climate indices shows that in both CMIP5 and CMIP3 certain common processes at large and regional scales as well as slow timescales are associated with successful simulation of trend and mean.

View Article: PubMed Central - PubMed

Affiliation: CSIR Centre for Mathematical Modelling and Computer Simulation, Wind Tunnel Road, Bangalore-560037, Karnataka, India.

ABSTRACT
Projections of climate change are emerging to play major roles in many applications. However, assessing reliability of climate change projections, especially at regional scales, remains a major challenge. An important question is the degree of progress made since the earlier IPCC simulations (CMIP3) to the latest, recently completed CMIP5. We consider the continental Indian monsoon as an example and apply a hierarchical approach for assessing reliability, using the accuracy in simulating the historical trend as the primary criterion. While the scope has increased in CMIP5, there is essentially no improvement in skill in projections since CMIP3 in terms of reliability (confidence). Thus, it may be necessary to consider acceptable models for specific assessment rather than simple ensemble. Analysis of climate indices shows that in both CMIP5 and CMIP3 certain common processes at large and regional scales as well as slow timescales are associated with successful simulation of trend and mean.

No MeSH data available.