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An objective tropical Atlantic sea surface temperature gradient index for studies of south Amazon dry-season climate variability and change.

Good P, Lowe JA, Collins M, Moufouma-Okia W - Philos. Trans. R. Soc. Lond., B, Biol. Sci. (2008)

Bottom Line: We examine the index in 36 different coupled atmosphere-ocean model projections of climate change under a simple compound 1% increase in CO2.Furthermore, the magnitude of the trend relationship is consistent with the inter-annual variability relationship found in the AGCM simulations.This suggests that the index would be of use in quantifying uncertainties in climate change in the region.

View Article: PubMed Central - PubMed

Affiliation: Met Office Hadley Centre, FitzRoy Road, Exeter, Devon EX1 3PB, UK. peter.good@metoffice.gov.uk

ABSTRACT
Future changes in meridional sea surface temperature (SST) gradients in the tropical Atlantic could influence Amazon dry-season precipitation by shifting the patterns of moisture convergence and vertical motion. Unlike for the El Niño-Southern Oscillation, there are no standard indices for quantifying this gradient. Here we describe a method for identifying the SST gradient that is most closely associated with June-August precipitation over the south Amazon. We use an ensemble of atmospheric general circulation model (AGCM) integrations forced by observed SST from 1949 to 2005. A large number of tropical Atlantic SST gradient indices are generated randomly and temporal correlations are examined between these indices and June-August precipitation averaged over the Amazon Basin south of the equator. The indices correlating most strongly with June-August southern Amazon precipitation form a cluster of near-meridional orientation centred near the equator. The location of the southern component of the gradient is particularly well defined in a region off the Brazilian tropical coast, consistent with known physical mechanisms. The chosen index appears to capture much of the Atlantic SST influence on simulated southern Amazon dry-season precipitation, and is significantly correlated with observed southern Amazon precipitation. We examine the index in 36 different coupled atmosphere-ocean model projections of climate change under a simple compound 1% increase in CO2. Within the large spread of responses, we find a relationship between the projected trend in the index and the Amazon dry-season precipitation trends. Furthermore, the magnitude of the trend relationship is consistent with the inter-annual variability relationship found in the AGCM simulations. This suggests that the index would be of use in quantifying uncertainties in climate change in the region.

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Choosing the northern box. Correlation coefficient for JJA between the gridpoint SST anomaly with respect to SST averaged over southern box, and southern Amazon precipitation. The northern and southern boxes of the chosen index are marked.
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fig3: Choosing the northern box. Correlation coefficient for JJA between the gridpoint SST anomaly with respect to SST averaged over southern box, and southern Amazon precipitation. The northern and southern boxes of the chosen index are marked.

Mentions: Having chosen the southern box of our index, the next step is to pick the northern box. At each gridpoint and for each year, the SST anomaly is calculated with respect to SST averaged over the southern box. Figure 3 shows the correlation coefficients between these anomalies and JJA south Amazon precipitation. If this correlation is large at a given gridpoint, this means that the difference between local SST and SST averaged over the southern box is strongly associated with JJA south Amazon precipitation. In other words, that gridpoint is a candidate for part of the north box of our index, given the prior choice of the southern box. (The weak positive correlations found almost everywhere appear by construction.) The strongest correlations form a near-zonal band in the north tropical Atlantic between 5 and 25° N. The strongest correlations are near the upwelling region off West Africa (roughly corresponding to the longitude of maximum SST variability), consistent with the gradient search results in figure 2. However, the westward decrease in correlation coefficient is not significant, and a larger region for area averaging might be more robust when applied to other contexts (e.g. to coupled climate models). Thus, we select a box extending across most of the basin (15–70° W, 5–25° N; figure 3). The northern box centres of the 2000 indices most strongly correlated with south Amazon precipitation all fall within or near to our chosen northern box (not shown), again suggesting that it is a robust choice. Notably, the region of the highest correlations does not quite correspond with those in figure 2, being more zonal, and further south near the east coast. This illustrates the point that patterns of SST anomalies may be hard to interpret in terms of anomalous SST gradients. The chosen index is the difference (north–south box averages), henceforth labelled TAGjja (Tropical Atlantic SST Gradient for JJA).


An objective tropical Atlantic sea surface temperature gradient index for studies of south Amazon dry-season climate variability and change.

Good P, Lowe JA, Collins M, Moufouma-Okia W - Philos. Trans. R. Soc. Lond., B, Biol. Sci. (2008)

Choosing the northern box. Correlation coefficient for JJA between the gridpoint SST anomaly with respect to SST averaged over southern box, and southern Amazon precipitation. The northern and southern boxes of the chosen index are marked.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2373890&req=5

fig3: Choosing the northern box. Correlation coefficient for JJA between the gridpoint SST anomaly with respect to SST averaged over southern box, and southern Amazon precipitation. The northern and southern boxes of the chosen index are marked.
Mentions: Having chosen the southern box of our index, the next step is to pick the northern box. At each gridpoint and for each year, the SST anomaly is calculated with respect to SST averaged over the southern box. Figure 3 shows the correlation coefficients between these anomalies and JJA south Amazon precipitation. If this correlation is large at a given gridpoint, this means that the difference between local SST and SST averaged over the southern box is strongly associated with JJA south Amazon precipitation. In other words, that gridpoint is a candidate for part of the north box of our index, given the prior choice of the southern box. (The weak positive correlations found almost everywhere appear by construction.) The strongest correlations form a near-zonal band in the north tropical Atlantic between 5 and 25° N. The strongest correlations are near the upwelling region off West Africa (roughly corresponding to the longitude of maximum SST variability), consistent with the gradient search results in figure 2. However, the westward decrease in correlation coefficient is not significant, and a larger region for area averaging might be more robust when applied to other contexts (e.g. to coupled climate models). Thus, we select a box extending across most of the basin (15–70° W, 5–25° N; figure 3). The northern box centres of the 2000 indices most strongly correlated with south Amazon precipitation all fall within or near to our chosen northern box (not shown), again suggesting that it is a robust choice. Notably, the region of the highest correlations does not quite correspond with those in figure 2, being more zonal, and further south near the east coast. This illustrates the point that patterns of SST anomalies may be hard to interpret in terms of anomalous SST gradients. The chosen index is the difference (north–south box averages), henceforth labelled TAGjja (Tropical Atlantic SST Gradient for JJA).

Bottom Line: We examine the index in 36 different coupled atmosphere-ocean model projections of climate change under a simple compound 1% increase in CO2.Furthermore, the magnitude of the trend relationship is consistent with the inter-annual variability relationship found in the AGCM simulations.This suggests that the index would be of use in quantifying uncertainties in climate change in the region.

View Article: PubMed Central - PubMed

Affiliation: Met Office Hadley Centre, FitzRoy Road, Exeter, Devon EX1 3PB, UK. peter.good@metoffice.gov.uk

ABSTRACT
Future changes in meridional sea surface temperature (SST) gradients in the tropical Atlantic could influence Amazon dry-season precipitation by shifting the patterns of moisture convergence and vertical motion. Unlike for the El Niño-Southern Oscillation, there are no standard indices for quantifying this gradient. Here we describe a method for identifying the SST gradient that is most closely associated with June-August precipitation over the south Amazon. We use an ensemble of atmospheric general circulation model (AGCM) integrations forced by observed SST from 1949 to 2005. A large number of tropical Atlantic SST gradient indices are generated randomly and temporal correlations are examined between these indices and June-August precipitation averaged over the Amazon Basin south of the equator. The indices correlating most strongly with June-August southern Amazon precipitation form a cluster of near-meridional orientation centred near the equator. The location of the southern component of the gradient is particularly well defined in a region off the Brazilian tropical coast, consistent with known physical mechanisms. The chosen index appears to capture much of the Atlantic SST influence on simulated southern Amazon dry-season precipitation, and is significantly correlated with observed southern Amazon precipitation. We examine the index in 36 different coupled atmosphere-ocean model projections of climate change under a simple compound 1% increase in CO2. Within the large spread of responses, we find a relationship between the projected trend in the index and the Amazon dry-season precipitation trends. Furthermore, the magnitude of the trend relationship is consistent with the inter-annual variability relationship found in the AGCM simulations. This suggests that the index would be of use in quantifying uncertainties in climate change in the region.

Show MeSH
Related in: MedlinePlus