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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.


Trends in the seasonal (June-September) and the annual rainfall over continental India (CIM: D2: 70-85E, 10-30N) and continental India plus ocean (D5: 60-95E, 10S-30N) from CMIP5 (left panels), CMIP3 (right panels) climate model simulations compared with the trends in the multiple observations (middle panel) for the period (1951–2005); the trends are expressed as % of respective standard deviation for the period.For the observations the trend in the different composites (highlighted, yellow) represent average of all (green), high significance (P < 0.01, orange) and low significance (P < 0.05, purple) ensembles. The percentage of models that simulated significant negative trend as against the total number of significant trends (positive and negative) is shown in each panel; the numbers in the brackets show the % of the total simulations with negative trend (as observed) in the respective case. The blue, red and black lines indicate, respectively, significant (P < 0.2) positive trend, significant (P < 0.2) negative trend and insignificant (P > 0.2) trends. The dash line indicates the observed composite trend and the grey band shows the dispersion (1 σ) in the observed trends.
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f2: Trends in the seasonal (June-September) and the annual rainfall over continental India (CIM: D2: 70-85E, 10-30N) and continental India plus ocean (D5: 60-95E, 10S-30N) from CMIP5 (left panels), CMIP3 (right panels) climate model simulations compared with the trends in the multiple observations (middle panel) for the period (1951–2005); the trends are expressed as % of respective standard deviation for the period.For the observations the trend in the different composites (highlighted, yellow) represent average of all (green), high significance (P < 0.01, orange) and low significance (P < 0.05, purple) ensembles. The percentage of models that simulated significant negative trend as against the total number of significant trends (positive and negative) is shown in each panel; the numbers in the brackets show the % of the total simulations with negative trend (as observed) in the respective case. The blue, red and black lines indicate, respectively, significant (P < 0.2) positive trend, significant (P < 0.2) negative trend and insignificant (P > 0.2) trends. The dash line indicates the observed composite trend and the grey band shows the dispersion (1 σ) in the observed trends.

Mentions: Where σ is defined as the dispersion in observation is defined as the difference between maximum and minimum value divided by 2. A comparison of the trends of the continental India (CIM: D3, 70-85E, 5-30N, Fig. 2a) and CIM plus ocean (D5:60-94E, 10S-30N, Fig. 2b) for the period 1951–2005 shows that all the seven observations show significant negative trends for CIM seasonal rainfall (Fig. 2a, middle panels) but positive trends for the larger domains (Fig. 2b, middle panels). Thus the decreasing trend in the seasonal rainfall is highly regional effect, as also earlier noted15. These negative trends are well captured by some of the simulations in both the CMIP5 (Fig. 2a, left panel) and CMIP3 (Fig. 2a, right panels). The symbols A-U/a-x used to represent the individual CMIP3/CMIP5 climate model simulations as described in Table S1. However, while all-member ensemble shows show insignificant negative trends as observed, the CMIP3 ensembles show opposite trends (Fig. 2a right panels), essentially due to a few simulations with large positive trends. In terms of the larger domain, both CMIP5 and CMIP3 ensembles show positive trends as observed, but not significant as the observed trends. For the CMIP5 models, however, Fig. 2a (top left) shows that, out of the 21 models considered, only nine reproduce the negative trend as observed; only five of these trends (Fig. 2a, top left panel) are of statistical significance (P < 0.2) and comparable to the observed trend (Fig. 2a, top middle, P < 0.05). In particular, none of the CMIP5 ensembles (thick arrows, Fig. 2a) satisfies the criterion of even negative trend. The result is no different for the annual rainfall; thus, the lack of skill for CIM rainfall cannot be attributed to shifts in the seasonal rainfall in the simulations (Fig. 2a, bottom left).


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

Ramesh KV, Goswami P - Sci Rep (2014)

Trends in the seasonal (June-September) and the annual rainfall over continental India (CIM: D2: 70-85E, 10-30N) and continental India plus ocean (D5: 60-95E, 10S-30N) from CMIP5 (left panels), CMIP3 (right panels) climate model simulations compared with the trends in the multiple observations (middle panel) for the period (1951–2005); the trends are expressed as % of respective standard deviation for the period.For the observations the trend in the different composites (highlighted, yellow) represent average of all (green), high significance (P < 0.01, orange) and low significance (P < 0.05, purple) ensembles. The percentage of models that simulated significant negative trend as against the total number of significant trends (positive and negative) is shown in each panel; the numbers in the brackets show the % of the total simulations with negative trend (as observed) in the respective case. The blue, red and black lines indicate, respectively, significant (P < 0.2) positive trend, significant (P < 0.2) negative trend and insignificant (P > 0.2) trends. The dash line indicates the observed composite trend and the grey band shows the dispersion (1 σ) in the observed trends.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: Trends in the seasonal (June-September) and the annual rainfall over continental India (CIM: D2: 70-85E, 10-30N) and continental India plus ocean (D5: 60-95E, 10S-30N) from CMIP5 (left panels), CMIP3 (right panels) climate model simulations compared with the trends in the multiple observations (middle panel) for the period (1951–2005); the trends are expressed as % of respective standard deviation for the period.For the observations the trend in the different composites (highlighted, yellow) represent average of all (green), high significance (P < 0.01, orange) and low significance (P < 0.05, purple) ensembles. The percentage of models that simulated significant negative trend as against the total number of significant trends (positive and negative) is shown in each panel; the numbers in the brackets show the % of the total simulations with negative trend (as observed) in the respective case. The blue, red and black lines indicate, respectively, significant (P < 0.2) positive trend, significant (P < 0.2) negative trend and insignificant (P > 0.2) trends. The dash line indicates the observed composite trend and the grey band shows the dispersion (1 σ) in the observed trends.
Mentions: Where σ is defined as the dispersion in observation is defined as the difference between maximum and minimum value divided by 2. A comparison of the trends of the continental India (CIM: D3, 70-85E, 5-30N, Fig. 2a) and CIM plus ocean (D5:60-94E, 10S-30N, Fig. 2b) for the period 1951–2005 shows that all the seven observations show significant negative trends for CIM seasonal rainfall (Fig. 2a, middle panels) but positive trends for the larger domains (Fig. 2b, middle panels). Thus the decreasing trend in the seasonal rainfall is highly regional effect, as also earlier noted15. These negative trends are well captured by some of the simulations in both the CMIP5 (Fig. 2a, left panel) and CMIP3 (Fig. 2a, right panels). The symbols A-U/a-x used to represent the individual CMIP3/CMIP5 climate model simulations as described in Table S1. However, while all-member ensemble shows show insignificant negative trends as observed, the CMIP3 ensembles show opposite trends (Fig. 2a right panels), essentially due to a few simulations with large positive trends. In terms of the larger domain, both CMIP5 and CMIP3 ensembles show positive trends as observed, but not significant as the observed trends. For the CMIP5 models, however, Fig. 2a (top left) shows that, out of the 21 models considered, only nine reproduce the negative trend as observed; only five of these trends (Fig. 2a, top left panel) are of statistical significance (P < 0.2) and comparable to the observed trend (Fig. 2a, top middle, P < 0.05). In particular, none of the CMIP5 ensembles (thick arrows, Fig. 2a) satisfies the criterion of even negative trend. The result is no different for the annual rainfall; thus, the lack of skill for CIM rainfall cannot be attributed to shifts in the seasonal rainfall in the simulations (Fig. 2a, bottom left).

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.