Figure 2 shows the trends for the 12 rainfall indices. Figure 2 shows there to be large regions of spatial coherence in the sign of the trends, as well as general agreement between the indices. Note that in all indices except the maximum consecutive dry days CDD , an increase corresponds to wetter conditions.
The stations in northern Brazil show no consistent sign. In all indices, the stations with a significant trend are located mainly in regions I and II. Punta Arenas also shows a significant trend in many indices.
The positive trends in southern Brazil, Uruguay, and northeast Argentina agree with previous findings by Barros et al. There are several notable differences in the patterns of change between the indices and the general observations mentioned above. For Paraguay, although the sign of the trends of most of the indices agrees with those of the surrounding regions, the trends in CDD and the maximum consecutive wet days CWD are of the opposite sign. This is also the case for the single Bolivian station, which shows an increase in all the indices some significantly , except CWD.
Several stations show a significant trend in the extremes but not in the total, for example, in Peru and Ecuador for R10 mm, but the reverse is also the case. The two percentile exceedence indices, R95p and R99p, show similar patterns of trends to the total rainfall, although there are less significant changes in the area of increase in the central eastern region region I. In this region more stations show a significant increase in the rainfall exceeding the 99th percentile than the 95th, including a significant decrease in R99p at a station in western Uruguay that showed a nonsignificant increase in total rainfall.
The two indices of maximum event intensity, RX1day and RX5day, show a less defined regional signal due to lower spatial coherence. No stations in region I show a significant trend and the trends here are of mixed sign. There are more stations showing a significant change for the 99th percentile than for the 95th, particularly in region I. One of the most consistent signals in Fig.
In all indices except CDD, these stations have showed a decrease, which in most cases is significant. These findings are consistent with previously discussed studies noting a rainfall decrease in this region Minetti ; Minetti et al. With many stations showing significant trends in some of the indices, when averaged over larger areas the trends are significant in some regions.
So as to weight each station equally we first normalized each station's time series to zero mean and unit variance. This figure shows a significant trend in the southeast quadrant covering southern Brazil, Paraguay, and Uruguay. For R99p Fig. Other than the southeast region, which has a significant trend in 6 of the 12 indices, the only other significant regional trend is for SDII in the northeast quadrant.
The high spatial coherence of the sign of the trends of the rainfall indices suggests that there could be changes to the large-scale forcing of the rainfall. This section examines possible changes in forcing. The choice of predictors to examine is governed to a large extent by the time period over which the indices are calculated as well as the size of the region under examination.
Although the rainfall indices are calculated annually, most of the indices are dominated by the rainfall during the wettest time of the year. However the diverse nature of the station network, which includes stations from the Tropics to the midhigh latitudes, means that the wet season occurs at different times of the year between stations.
Therefore our potential predictor should be fairly constant throughout the year. The large area under consideration also dictates that our forcing variable should also be large scale. The most obvious candidate is the large-scale, slowly evolving SST anomalies. The canonical patterns and coefficients were calculated using a singular value decomposition SVD of the cross-covariance matrix of the principal components PCs of the two fields being examined. This is numerically more stable than the more common method of working with the joint variance—covariance matrix Press et al.
Bretherton et al. This was objectively decided by a Monte Carlo process, whereby PC analyses were carried out using data randomly resampled in time from the stations Preisendorfer et al. In each of the analyses, 54 station annual series of length 41 yr were generated with similar statistical properties to the original data but with random interstation correlations. Then, for each randomization, the eigenvalues were calculated.
This methodology is the same as that employed by Haylock and Goodess The rainfall indices were used at the 54 station locations. Although this will give slightly more weight toward regions with higher station density southern Brazil , it was decided that this was better than either interpolating the stations to a regular grid or thinning the station network. Gridding the 54 stations over the entire continent, even with a very coarse grid spacing, would leave large areas without stations and many grid cells with only one station, thereby negating the point of gridding.
Also, with only 54 stations in the network, discarding stations to thin the network to more regular spacing would remove valuable regional information. There is a chance that the CCA methodology, which optimizes correlation between linear combinations of the two fields, might be biased toward years with unusually high or low values in the possibly non-Gaussian distributed indices.
We therefore performed a CCA using the rank of the indices rather than the indices themselves to reduce the effect of outliers with almost identical results. Since it is easier to interpret the results using the raw indices we have just presented these results. Table 3 shows statistics of the CCA for each index. The table includes the number of coupled patterns, the canonical correlations, the proportion of variance explained by each canonical coefficient for both the SST and rainfall indices, the correlation of the canonical coefficients with the SOI, and the significance of the trend of the indices coefficients.
The objectively determined number of PCs of the indices varied from 2 to 4 PCs. Table 3 shows that the canonical coefficients accounted for between While this at first seems low, it is an important conclusion that continental-scale variability is still an important factor in interannual variability of extreme rainfall. Since the CCA truncates the number of components of SST from the initial seven, the proportion of variance explained by the SST canonical coefficients varied for each index, between Generally one would expect variables with a higher spatial coherence to yield significant PCs explaining a higher proportion of the variance.
For example, although the SST observations were over a much larger area than the indices, the SST yielded significant PCs accounting for a larger proportion of total variance than any of the rainfall indices. These three indices have at least three significant PCs. The indices with the lowest number of significant PCs and proportion of variance are the two indices reflecting the proportion of total rainfall falling on extreme days R95pTOT These three indices all have two significant PCs.
The maximum canonical correlation varies between the indices from 0. An examination of the canonical correlations in Table 3 shows no clear relationship between the correlation and the proportion of variance explained by the associated coefficients. Figure 5 shows the factor loadings of the canonical coefficients for the SST and indices for these three cases. The loadings represent the correlations between the canonical coefficients and the SST or indices.
The familiar high loadings in the tropical eastern Pacific and loadings of opposite sign in the southeast Pacific are indicative of ENSO, however the latter are weaker and displaced farther south than observed in other ENSO studies. Also the moderate loadings to the east of the continent as well as those of opposite sign in the South Atlantic are less similar to ENSO. Table 3 shows that the canonical coefficients for these three SST patterns all have a correlation with the annual SOI above 0.
It is interesting to note that these four indices reflect rainfall in a single spell of days whereas all the other indices are indicative of the extremes over the entire year.
Therefore one would expect that these four indices would be more sensitive to single anomalous weather patterns possibly not caused by the larger-scale forcing. The figure also shows that there has been a trend to a more negative SOI and a corresponding trend in the indices, leading to the conclusion that changes in ENSO have contributed to the observed trend in the indices.
Since the trend in the rainfall coefficients is negative, the contribution to the trend of the indices from this pattern is opposite to the sign of the loadings in Fig. The mechanisms for the observed ENSO-related pattern of rainfall changes have been discussed in several of the papers mentioned in the introduction. Both of these patterns are reproduced in our CCA results in Fig. To quantify whether any of the other CCA patterns have contributed to the trends in the indices, we calculated the trends in the canonical coefficients Table 3.
Since the units of the normalized canonical coefficients are dimensionless, we have just shown the significance of the trends as given by the probability of the null hypothesis no trend being true using a nonparametric Kendall Tau test on the coefficients. Probabilities less than 0. Therefore the patterns in Fig. This pattern of loadings for the rainfall index shows large negative loadings for the stations in the southwest and generally positive loadings elsewhere. Several other stations farther north have negative loadings, but these are of much smaller magnitude.
Therefore this pattern represents behavior in the south that is out of phase with the other stations. Interestingly the loading over the southernmost station, Punta Arenas, is small, indicating that this mode is not important for variability at this station. Figure 8 shows the corresponding canonical coefficients with a strong positive trend in both series, leading to drier conditions in southern Chile and wetter conditions in the rest of the region.
The SST pattern in Fig. Since the canonical coefficients have a strong positive trend, the areas of positive negative SST loading are also areas of warming cooling with regard to the contribution from this pattern. These composites are shown in Figs. The most important features with regard to rainfall over the region are the significant change to higher pressures in the south of the continent and the change to lower pressures in the northeast. In the south the increase in pressure corresponds to a weakening of the trough in Fig. This significant increase in MSLP over the south of the continent suggests that the northward cyclone propagation through South America may be affected, thus confining the rain-bearing systems to higher latitudes.
This is supported in Fig. Recently, Pezza and Ambrizzi indicated that the total number of Southern Hemisphere cyclones during the austral winter season had declined from to On the other hand, they also showed that the number of intense cyclones had increased. Our results suggest that an increase in baroclinic activity at higher latitudes could lead to the confinement of the transient systems to the south, thereby affecting the meridional displacement of polar air masses to South America. The possibility that the climatological decrease in the number of cyclones over the Southern Hemisphere is linked to climate change has been suggested by several previous studies e.
This regional pattern is consistent with a hemispheric-scale pattern of increases in pressure in middle latitudes and decreases at high latitudes associated with a trend in the Southern Annular Mode SAM; Thompson et al. Modeling and other studies have linked the trend in the SAM and southward shift in the storm track to a climate response to decreases in stratospheric ozone Gillett and Thompson ; Sexton ; Thompson and Solomon or as a response to increasing greenhouse gases Cai et al.
In Fig. Since the loadings for this station are small for this canonical pattern Fig. Also this station is located almost directly on the zero contour in Fig. The significant decrease in pressure in northeast Brazil related to this pattern is probably related to the warming in the central Atlantic Fig. The rainfall stations in this region all have a positive loading, corresponding to wetter conditions due to the significant trend in the canonical coefficients.
The two large-scale changes discussed so far, the changes in ENSO and the weakening of the continental trough, account for most of the regional coupled patterns identified by the CCA for all the indices. Other patterns listed in Table 3 that are not related to one of these two changes either account for a small proportion of total variance of the indices or have a small canonical correlation reflecting a less significant statistical relationship between the SST and rainfall indices.
It must be mentioned however that a low proportion of total variance might still indicate a pattern that is important for a particular area, but when considered over the entire region under study is less important. When performing an analysis like CCA that optimizes linear correlation, there is a chance that any trends in the two datasets will unduly influence the results, leading to coupled patterns that have similar trends but low correlation in interannual variability. Figures 6 and 8 show that this is not the case, with the series having strong similarity in the interannual variability as well as the trends.
Still, we checked the results by performing a CCA using detrended data with very similar results. Careful quality control, to the degree that was done in section 2 , was not possible with such a large set. We therefore first removed all days with rainfall above 1 mm that appeared after a missing observation, as this rainfall was likely to be an accumulated value.
We then examined the time series of the annual total rainfall for each station and compared this with the average of the 10 nearest stations. In this comparison we calculated the ratio of the candidate station to the reference series average of neighbors and removed stations that had either a significant trend in this ratio or a significant jump as detected by our homogeneity test Easterling and Peterson A trend in this ratio series could indicate a gradual change over time in the exposure of the rain gauge, for example, the growth of surrounding trees, while a jump could indicate a change in the station location, instrumentation, or observer practice.
To grid the indices we used natural neighbor interpolation Sibson , a weighted average method that, for each grid point, weights each neighboring station depending on its relative area by constructing a Voronoi diagram.
All stations in this region receive most of their rainfall in the months November—February. We therefore calculated the annual indices over the months July—June rather than January—December. This was so as not to split the rainy season, but it also enabled us to determine if the results of sections 3 and 4 were sensitive to the months used.
Figure 10 shows the sign of the trend of the indices as well as those grid points with a significant trend as measured by Kendall's Tau Kendall There is a general tendency to wetter conditions in the southwest of the region and drier conditions in the northeast, as typified by PRCPTOT. Grid points in both the region of increase and decrease show significant trends in some of the indices.
These results mostly agree with the station analysis Fig. This is unusual since all the other indices show that total and extreme rainfall has increased in this region.
This suggests that there has been a larger increase in the number of days with low rainfall than high rainfall, thus leading to a decline in the average rainfall on wet days. This is supported by the observed increase in the maximum number of consecutive wet days and a decrease in consecutive dry days. The number of significant PCs of the indices used in the CCA was different than for the stations, as determined by our objective methodology section 4. This was within one of the number chosen for the stations with the exception of SDII, for which five were selected, compared with two for the stations.
In most cases, the proportion of variance explained by the significant PCs was higher than for the stations, in the range of One would expect this to be higher than for the analysis of the stations as the gridded data represent area-averaged rainfall over a smaller region, which we would expect to be more spatially coherent. The results of the CCA are shown in Table 4. The canonical patterns e. As with the station analysis section 4 , this implies that changes in ENSO have contributed to the observed trends in the indices.
Correlations with the SOI greater than 0. The leading canonical patterns for the remaining six indices have strong correlations between the SST component and the SOI, however, the correlations with the rainfall coefficients are lower. This, together with a lower canonical correlation, implies a weaker relationship between SST and these indices.
For all the coupled SST—indices patterns, the only pattern with a significant trend in the rainfall canonical coefficient that does not have a strong correlation with the Southern Oscillation index SOI is the second pattern of SDII. The canonical patterns for this component are shown in Fig. Although the canonical coefficients have a significant positive trend over the period —, Fig.
Interestingly, the highest magnitude rainfall loadings are the negative loadings over the region in southern Brazil that has seen the previously noted decrease in SDII that was not shown in the station analysis. The associated SST pattern shows positive loadings in midlatitudes that are out of phase with the negative loadings at low latitudes. With canonical correlations for this pattern less than 0. Therefore concluding possible dynamical relationships between the SST and rainfall patterns is difficult.
A composite of MSLP depending on the sign of the rainfall canonical coefficient shows no significant difference over the entire continent. The above discussion explains the important patterns in Table 4. We have used the same SST region in both the continental station analysis section 4 and the eastern Brazil gridded analysis.
For the Brazilian analysis we also tried a CCA with SSTs over just the surrounding Atlantic Ocean to see if more localized SST—rainfall relationships were revealed that were being obscured when using the larger region. This resulted in coupled patterns that explained less rainfall variance than for the larger SST region with lower canonical correlations implying a weaker SST—rainfall relationship.
This confirms that the Pacific, in particular ENSO, is very important in modulating rainfall in eastern Brazil, as has been revealed by many of the studies mentioned in the introduction. This study presented results from a weeklong workshop attended by 28 scientists from southern South America. The regional analysis, covering a large part of continental South America, was made possible through the workshop format.
It showed that valuable results of changes in climate extremes could be obtained using a consistent methodology, but without individual countries having to surrender potentially commercially valuable daily data. This paper presents results on trends and variability of 12 annual rainfall indices, covering changes in both the entire distribution as well as its wet and dry extremes. Fifty-four stations were deemed to be of sufficiently high quality and to have sufficient observations to be used to assess changes for the period — To investigate the possible causes of these spatially coherent trends, we performed a CCA of each of the indices with annually averaged SST observations.
This revealed two large-scale coupled patterns that we proposed have contributed to the rainfall changes.
Extreme South book. Read 8 reviews from the world's largest community for readers. In years of polar exploration no-one had ever walked from the edge. . In the footsteps of Scott and Amundsen, two Aussies, Cas and Jonesy, set out to conquer the last great wilderness on earth. This is their story of tenacity.
Through various previously documented mechanisms, such as increased subsidence and a northward-shifted ITCZ over northeast Brazil and the Amazon Basin and a southeast shift in the SACZ, this was shown to have partly caused the observed trend in the rainfall indices. A separate CCA mode, uncorrelated with ENSO, suggested that this was caused by a general weakening of the continental trough at higher latitudes, causing a southward shift in the storm tracks during the period. Both of these large-scale SST signals were represented by canonical coefficients with statistically significant trends, which have contributed to the observed significant changes in the rainfall indices.
A similar analysis was carried out for eastern Brazil using gridded indices calculated from stations from the GHCN database. While the workshop provided the mechanism to produce a regional study, it also provided the means to build a strong scientific network in the region. These published results, while presenting a valid and statistically sound picture, still only represent a small selection of stations from a vast region.
We hope that this will only improve with time. Participation at the workshop by J. Abreu de Sousa, G. Miranda, L. Molion, E. Ramirez, J. Barbosa de Brito, R. Jaildo dos Anjos, P. Meira, and G. Ontaneda was greatly appreciated. Support for the workshop was provided by the U. Location of 54 rainfall stations.
Sign of the linear trend in rainfall indices as measured by Kendall's Tau. An increase is shown by a plus symbol, a decrease by a circle. Normalized number of days above 20 mm averaged across all stations in the four quadrants of the continent. Normalized 99th percentile of wet-day rainfall averaged across all stations in the four quadrants of the continent. For the indices, a circle indicates a negative loading and a plus symbol is positive.
The size of the symbol is proportional to the magnitude, with the maximum symbol size given in the scale at the top right of each frame. Same as in Fig. Sign of the linear trend in gridded rainfall indices as measured by Kendall's Tau. Table 1. Rainfall indices with their definition and units.
RR is the daily rainfall rate. All indices are calculated annually from January to December. Table 2. Location, elevation, and period of data availability for 54 stations. Table 3. Totals are indicated in italics. Table 4. Same as in Table 3 , except for gridded indices. Next Article. Previous Article. Haylock a x. Search for articles by this author. Data availability and quality. Linear trends. Links with SST. Detailed analysis for eastern Brazil. Acknowledgments Participation at the workshop by J. View larger version 41K Fig. View larger version 47K Fig.
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Stalnaker, Robert. Giusti, Giuliana. PRT a to Trump Trump. Arrives at our Sydney warehouse in weeks and once received will be despatched with online tracking. Therefore, its presence makes the insertion of a redundant.
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Such a great read, it's amazing what these Aussie boys achieved with their perseverance, friendship, impeccable planning skills and their love for adventure. Very well written with a healthy amount of history from past Antarctic expeditions woven throughout. Would recommend! Nov 03, John rated it it was amazing. This was an excellent, frank account of the extreme journey Cas and Jonsey took to the South Pole and back. Written with honesty and frankness, it speaks of how far people can go in pushing themeselves to the limit.
Recommended for a read on a warm summers day, not as I did on a cold night while camping. Aug 05, Lee rated it did not like it. I appreciate the honesty of what they felt whilst undertaking this incredible journey. However I just couldn't seem to like the author, to me he came across as self centred and to put it bluntly a bit of a dick. View 1 comment. Mar 08, Wendy rated it really liked it Shelves: adventure. Cas and Jonesy take you along on the extreme highs and lows of their inspiring polar expedition. In addition to a suspenseful read - I didn't have a sense of whether they'd make it or not and didn't know - the appendix is an invaluable expedition planning reference.
Feb 13, Anne Hayes rated it really liked it. A gripping and informative account of a ground breaking expedition to the South Pole. The narrative flows and I found it hard to put down.
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