Variabilidad decadal y multidecadal del clima:

Presentación del tema: "Variabilidad decadal y multidecadal del clima:"— Transcripción de la presentación:

1 Variabilidad decadal y multidecadal del clima:
Curso de Posgrado: Fundamentos de la Variabilidad Climática Global y en Sudamérica. Clase 7 Variabilidad decadal y multidecadal del clima: Modos dominantes: PDO, AMO Variabilidad decadal en Sudamérica Cambio Climático: Alteraciones de la circulación general Alteraciones del clima regional en Sudamérica Carolina Vera CIMA-DCAO, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires CONICET-UMI3351/CNRS

2 Pacific decadal Oscillation (PDO)

3 Pacific decadal Oscillation (PDO)
The Pacific decadal variability (PDV) and its possible impacts on regional and global climate has been the subject of much research. Century long reconstructions of sea surface temperature (SST) and sea level pressure (SLP) suggest that several ‘‘regime shifts’’ associated with PDV have occurred during the 20th century particularly around 1925, 1947, 1976, and perhaps recently in the mid 1990s. It is widely accepted that PDV influences climate anomalies over North and South America, Australia and Europe as well as the El Niño and Southern Oscillation (ENSO) period and strength [e.g. Latif and Barnett, 1994; Mantua et al., 1997; Power et al., 1999]. Cold PDO regimes prevailed from and again from , while warm PDO regimes dominated from and from 1977 through the mid-1990's.)

4 PDO signal over precipitation in South America
PDO (+) PDO (-) ENSO (+) ENSO (-) Kayano and Andreoli (2005)

5 Atlantic Multidecadal Oscillation (AMO)
Regression of global SST anomalies onto detrended AMO index

6 AMO influence

7 How stable are these relationships?
OND Precipitation Interannual Variability in Southeastern South America (SESA) Positive OND precipitation anomalies in SESA -ENSO warm events -SAM negative phase ( ) SESA How stable are these relationships?

8 Decadal variations on ENSO and SAM relationship
Correlations between SAM and EN3.4 indexes *Correlations significant at 90% **Correlations significant at 95% Correlation between both indexes over the entire period is negligible. Significant correlation values of negative sign are only found over the last decades, being the maximum ones those obtained for the last 22 years considered. On the other hand, both SAM and ENSO had independent activity during the first decades of the period under study. (Silvestri and Vera (2009, J. Climate)

9 ENSO and Precipitation anomalies in SESA
Correlations between EN3.4 and PP indexes *Correlations significant at 90% **Correlations significant at 95% The temporal stability of the relationship between ENSO and precipitation anomalies over SESA for OND has been explored. The correlation between the PPindex and EN34index is significantly positive not only over the entire period but also for most of the sub-periods considered. This result confirms that in general the ENSO influence on precipitation anomalies over SESA is relatively stable. Accordingly, Garreaud and Battisti (1999) show that interannual and interdecadal variability of the circulation anomalies in the SH associated with ENSO exhibit similar spatial signatures in the SLP, low-level winds and temperature. Furthermore, previous works have identified interdecadal variation associated to ENSO (e.g. Setoh et al. 1999). Nevertheless, the analysis of the influence of such variations onto the climate variability in South America is beyond the scope of this study. (Silvestri and Vera (2009, J. Climate)

10 SAM and Precipitation anomalies in SESA
Correlations between SAM and PP indexes *Correlations significant at 90% **Correlations significant at 95% (ENSO signal was previously linearly removed from both PP index and SAM index) The temporal stability of the relationship between SAM and precipitation variability over SESA was also explored. The ENSO signal was linearly removed from PPindex and SAMindex (by a linear regression based on EN34index) before the computation of the correlations. Results show that SAM and precipitation anomalies in SESA are independent when the entire period is considered, in agreement with Gillett et al. (2006). On the other hand, significant positivecorrelations are found in the first decades (0.37 for ) while significant negative correlations characterize the last decades (-0.64 for ). The analysis of Table 1 shows a change in the relationship between SAM and both ENSO and precipitation anomalies over SESA from the first decades ( ) to the last decades ( ) of the period considered. The issue about whether such change is also noticeable in the climate variability over the entire SH or not, was further explored (Silvestri and Vera (2009, J. Climate)

11 Changes in SAM signal on SH Climate
The maps clearly depict the changes in the sign and intensity of the SAM influence onto precipitation anomalies in SESA between both sub-periods described in the previous section. The correlation between SAM and precipitation anomalies is also significantly negative (positive) in ( ) at few stations located in the southern sectors of both Africa and Australia. Although, correlation values are no significant at most of the stations for both periods, limiting considerably the determination of a consistent SAM signal over those two regions. The analysis of the precipitation anomalies over the Antarctic region was not included due to the poor quality of the data available, especially for the periods before In agreement, Bromwich et al. (2000) exploring the interdecadal changes of ENSO signal in Antarctic precipitation, found serious deficiencies in the representation of the Antarctic net precipitation from the reanalysis datasets. Figures 1c-f show in general for both sub-periods, the circulation anomaly pattern typically associated with SAM and characterized by negative correlations at polar latitudes and positive ones at middle latitudes. However, a poleward migration of the correlation centers located at middle latitudes particularly over South America and Australia is noticeable between both sub-periods. Similar spatial behavior of the SLP anomalies have been associated with an observed trend of the SAM towards a more positive phase (Marshall et al. 2006, and references therein). Nevertheless, considering that the anomalies used here have been previously detrended, the results confirm that changes in the SLP anomaly gradient between middle and high latitudes can also be associated with natural low-frequency variability of the climate system. A more detailed analysis at regional scales shows that most of stations located in Australia and New Zealand sector are significant correlated with SAM in but only few of them show significant correlation in On the other hand, no significant changes are evident in the SAM signature onto the SLP anomalies in the vicinity of Africa. Changes in the spatial structure of the annular correlation center over the Antarctica are discernible between both sub-periods from the reanalysis but they cannot be confirmed from in-situ observations. Such changes could be associated to the low quality of the reanalyzed fields over Antarctica. Marshall (2003), among others, shows that the quality of the NCEP-NCAR reanalysis in the pre-satellite data is principally poor at high southern latitudes. Correlation maps between SAM and Z500 are shown in Figs. 1g-h and in general agree with the features found for SLP. That confirms the equivalent barotropic structure of the SAM-related circulation anomaly pattern, described in previous publications (e.g., Thompson and Wallace 2000). Correlation maps for Z500 show during (Fig. 1h), evidences at middle latitudes of a zonal wavenumber-4-like pattern that is absent in the corresponding map for (Fig. 1g). That feature is partially discernible in the correlation map for SLP for the same sub-period (Fig. 1f). However, due to the lack of enough upper-air stations along the SH over the entire period, it is not possible to verify whether the wavenumber-4-like pattern was present in the pre-satellite period or not. Previous works like Gupta and England (2006), among others, have described the SAM influence onto the surface temperature anomalies in the SH, that seems to be in general characterized by a SAM positive phase related with negative temperature anomalies over the Antarctic continent, and positive anomalies over the Antarctic Peninsula. Correlation maps between the SAMindex and surface temperature anomalies for the two sub-periods under study are shown in Figs. 1i-j. Negative significant correlations are observed in New Zealand in , but they are not significant in In addition, one of the most noticeable changes takes place in Australia. In fact, significant positive correlations cover most of the southern Australian territory between 1958 and 1979, but the pattern changes considerably between 1983 and 2004, when only four stations show significant correlation values of negative sign. Over the Antarctica continent excepting the peninsula, negative correlations are observed in both sub-periods although they are more significant in than in In the vicinity of the Antarctic Peninsula, the correlations change notably between both periods. Correlations are significantly negative during , while they are significant and with positive sign in only one station during In addition, significant positive correlation values covering most of the Patagonia region are clearly identifiable during It is suggested that the anticyclonic circulation anomaly observed over the southern continent and the adjacent Atlantic during that period (Fig. 2b) might promote positive temperature anomaly advection into the region. Correlations of the SAMindex with (a-b) in-situ precipitation, (c-d) in-situ SLP, (e-f) reanalyzed SLP, (g-h) reanalyzed Z500, and (i-j) in-situ surface temperature. (Silvestri and Vera (2009)

12 SAM signal on South America Climate
In the vicinity of South America, the development of a large positive correlation center over the southwestern Atlantic Ocean is particularly evident in (Fig. 1f), that is weaker and located northeastward in (Fig. 1e). The analysis of the corresponding low-level wind anomalies confirms that during (Fig. 2a) positive SAM phases were associated with anomalous southward wind anomalies over SESA induced by the anticyclonic circulation anomaly center located in the southwestern subtropical Atlantic. Such circulation enhances moisture advection and promotes increase of precipitation over SESA, resulting in the positive significant correlation between PPindex and SAM depicted in Table 1c for that sub-period. During , the anticyclonic anomaly associated with SAM positive phase is located further south and covering most of the southern tip of South America and the adjacent Atlantic. This circulation anomaly pattern is associated to weakened moisture convergence (Fig. 2b) and decreased precipitation over SESA (Fig. 1b), which justifies the negative significant correlation between PPindex and SAM displayed in Table 1c. In addition, Figure 2b shows that the anticyclonic anomaly pattern is related with negative correlations between precipitation anomalies over the southernmost region of South America (usually named as Patagonia region) and the SAMindex (Fig. 1b). Under normal conditions, the Andes Mountains extended along the western coast, force the ascent of the westerly flow causing abundant cloudiness and precipitation in the surroundings of southern Andes (e.g., Schwerdtfeger, 1976). In that sense, the anomalous anticyclonic circulation depicted in Fig. 2b, weakens the eastward flow, promotes subsidence conditions over the area, and thus inhibits precipitation (Fig. 1b). Correlations SAM index-SLP and regressions SAM index-WIND850. Areas where correlations are statistically significant at the 90% (95%) of a T-Student test are shaded in light (dark) grey. (Silvestri and Vera (2009)

13 EXTREMOS DIARIOS DE PRECIPITACION EN LA ARGENTINA Y SU ASOCIACIÓN CON LA TEMPERATURA SUPERFICIAL DE LOS OCÉANOS TROPICALES El análisis se llevó a cabo utilizando la base de datos de temperatura superficial del mar Kaplan SST V2 (de aquí en más TSM) de la NOAA y datos de precipitación diaria de pluviómetros del Servicio Meteorológico Nacional (SMN). La media mensual de la intensidad diaria de precipitación extrema (IP75) se definió como el cociente entre el acumulado mensual extremo y el número de días de precipitación extrema. Se considera la precipitación extrema diaria cuando supera el percentil 75 diario medio, que fue calculado en el período por Robledo y Penalba (2008). Se aplicó la metodología de descomposición en valores singulares (DVS) a TSM y el índice IP75. (Robledo, Vera, Penalba (2012)

14 Regresión de anomalía TSM (panel superior) y de anomalía de IP75 (panel inferior izquierdo) con el Modo SVD 1 Figura de SST: correlación de SST SVD times series modo 1 y la anomalía de SST. Los valores sombreados son significativos al 1% o más. Rojo (azul) correlación positiva (negativa). Figura de argentina: correlación de DIER75 SVD times series modo 1 y la anomalía de DIER75 para cada estación. Los puntos llenos son significativos al 1% o más. Rojo (azul) correlación positiva (negativa). (Robledo, Vera, Penalba (2012)

15 Regresión de anomalía TSM (panel superior) y de anomalía de IP75 (panel inferior izquierdo) con el Modo SVD 3 Figura de SST: correlación de SST SVD times series modo 3 y la anomalía de SST. Los valores sombreados son significativos al 1% o más. Rojo (azul) correlación positiva (negativa). Figura de argentina: correlación de DIER75 SVD times series modo 3 y la anomalía de DIER75 para cada estación. Los puntos llenos son significativos al 1% o más. Rojo (azul) correlación positiva (negativa).

16 OBSERVED AND PROJECTED LONG-TERM TRENDS IN SOUTH AMERICA

17 OBSERVED LONG-TERM TRENDS (1901-2005)
Annual surface temperature Annual precipitation IPCC/AR4, 2007

18 OBSERVED LONG-TERM TRENDS (1951-2003) Extreme events
Increase of the maximum intensity of summer rainfalls (RX5day en mm/decade) Increment of extreme warm nights (Tn90p in days/decade) Alexander et al. (2006)

19 Temperature and precipitation changes from Multi-model ensemble simulations ( )-( ). SRES A1B The annual mean warming is likely to be similar to the global mean warming in southern South America but larger than the global mean warming in the rest of the area. Annual precipitation is likely to decrease in most of Central America and in the southern Andes, although changes in atmospheric circulation may induce large local variability in precipitation response in mountainous areas. Winter precipitation in Tierra del Fuego and summer precipitation in south-eastern South America is likely to increase. It is uncertain how annual and seasonal mean rainfall will change over northern South America, including the Amazon forest. However, there is qualitative consistency among the simulations in some areas (rainfall increasing in Ecuador and northern Peru, and decreasing at the northern tip of the continent and in southern northeast Brazil). Figure Temperature and precipitation changes over Central and South America from the MMD-A1B simulations. Top row: Annual mean, DJF and JJA temperature change between 1980 to 1999 and 2080 to 2099, averaged over 21 models. Middle row: same as top, but for fractional change in precipitation. Bottom row: number of models out of 21 that project increases in precipitation. El calentamiento sobre varias regiones es mayor que el calentamiento medio anual global debido a menos disponibilidad de agua por enfriamiento evaporativo y a un menor inercia termica comparada con los oceanos El calentamiento generalmente aumenta la variabilidad espacial de la precipitacion, contribuyendo a una reduccion de la precipitacion en los subtropicos y a un aumento en las latitudes mas altas y en parte de los tropicos. La ubicación precisa del limite entre ambas regiones de crecimiento y reducción robusto permanece incierta y es donde las proyecciones de los AOGCM difieren. La expansión hacia los polos de las altas subtropicales, combinada con la tendencia generalizada de reducción de la precipitación subtropical , crea especialmente proyecciones robustas de una disminución de la precipitación en la región hacia el polo de los subtropicos, en particular en las areas adyacentes a las altas subtropicales. Hay una tendencia a que las circulaciones monzónicas resulten en un aumento de la precipitación debido a una intensificación de las convergencias de humedad, a pesar de la tendencia hacia un debilitamiento de los flujos monzónicos mismos. Aunque, diferentes aspectos de la respuesta climática de los trópicos permanece incierta. Un panorama mas claro de los aspectos regionales ha emergido gracias al aumento de la resolución de los modelo, mejoras en la simulacion de los procesos de importancia climatica regional y al conjunto expandido de simulaciones disponibles. Se ha avanzado en desarrollar información probabilistica regional pero todavia estos metodos estan en una fase exploratoria. Ha habido poco desarrollo en extender estas tecnicas probabilisticas a información regional downscaled. Los metodos de downscaling han madurado desde TAR, aunque en pocas regiones ha habido esfuerzos de experimentos de downscaling multi-modelos.

20 Precipitation changes as depicted by the WCRP/CMIP3 multi-model ensemble (SRESA1B)
( )-( ) (IPCC, 2007)

21 IPCC-AR4 Projections of Climate Change
The WCRP CMIP3 dataset provided higher confidence in projected patterns of warming and other regional-scale features, including changes in wind patterns, precipitation, and some aspects of extremes and of ice. Projected warming in the 21st century shows scenario-independent geographical patterns similar to those observed over the past several decades. Warming is expected to be greatest over land and at most high northern latitudes, and least over the Southern Ocean and parts of the North Atlantic ocean. There is an improving understanding of projected patterns of precipitation. Increases in the amount of precipitation are projected in high-latitudes, while decreases are projected in most subtropical land regions excepting some regions as Southeastern South America. In addition, extra-tropical storm tracks are projected to move poleward, with consequent changes in wind, precipitation, and temperature patterns. (J m²) Relative changes in precipitation , sea-level pressure and storm tracks between and based on SRESA1B scenario

22 Projections of Hadley cell changes
A robust poleward expansion of the Hadley cell is projected in a global warming. It is mostly due to an increase in the subtropical static stability, which pushes poleward the baroclinically unstable zone and hence the poleward edge of the Hadley cell.  In addition, the extratropical dynamics has to be considered to achieve a fuller understanding of the HC expansion under global warming.

23 Contributions of the different climate forcings to SH circulation changes
Arblaster and Meehl (2006)

24 Impact of Antarctic Ozone Depletion and Recovery on Southern Hemisphere Precipitation
Multimodel-mean trends of precipitation in DJF. Trends are calculated by differencing 1961–70 from 1990–99 averages and 1990–99 from 2056–65 averages for twentieth- and twenty-first-century trends, respectively. (left to right) Multimodel-mean trends are shown for models with and without ozone depletion in the twentieth century and for models with and without ozone recovery in the twenty-first century, respectively. Purish and Soon 2012

25 Impact of Antarctic Ozone Depletion and Recovery on Southern Hemisphere circulation
Perlwitz, 2008

26 Impact of Antarctic Ozone Depletion and Recovery on Southern Hemisphere circulation
Perlwitz 2011

27 Precipitation changes as depicted by the WCRP/CMIP3 multi-model ensemble (SRESA1B)
( )-( ) (IPCC, 2007)

28 → Asymmetric signal Full future SST change (from WCRP/CMIP3 models)
Experiments of 30-member ensemble of DJF simulations forced by climatological mean SST + SST CHANGE in the XXI century SST change Zonal Mean Full - Zonal Mean SST change → Asymmetric signal Junquas et al. (2012b)

29 RAINFALL CHANGE IN SOUTH AMERICA
Forcing by SST change Zonal Mean RAINFALL CHANGE IN SOUTH AMERICA (mm/day) Zonally asymmetric SST change A B Junquas et al. (2012b)

30 Humidity flux vertically integrated between 1000 and 300 hPa
Vertical section of the humidity flux at 22°S Junquas et al. (2012b)

31 Teleconexiones que explican la influencia del cambio en las temperaturas de la superficie de los océanos Indico-Pacífico Oeste entre fines y principios del siglo XXI sobre los cambios en las precipitaciones en SESA Junquas et al. (2012)