Thielke Anja (Orcid ID: 0000-0002-2441-9397)
Observed and simulated Indian Ocean Dipole activity since the mid-19th century and its relation to East
African short rains
IOD activity since 19th Century
Anja Thielke* and Thomas Mölg1
Climate System Research Group, Institute of Geography, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
*Corresponding author
E-mail: [email protected]; Phone: +49 9131 85 22643
1TM acknowledges support by the German Research Foundation (DFG), Grants No. MO 2869/1-1 and MO 2869/3-1.
Abstract
The coupled atmosphere-ocean phenomenon in the Indian Ocean, known as the Indian Ocean Dipole (IOD) mode, shows various influences on regional tropical climates. East Africa (EA) experiences enhanced precipitation during October-December (the annual “short rains”), and therefore it was hypothesized that a shift from wetter to drier conditions in EA after 1880 documented by proxy data (e.g., lake levels, glacier size) was favored by a decline in positive IOD frequency. However, this proposed century-scale pattern of the IOD emerged from single data sets, while the ensemble perspective from the CMIP5 global climate model (GCM) archive is still missing. Based on rigorous test criteria that capture crucial IOD and EA climate characteristics, the present study identifies 15 runs from the CMIP5 data for the ensemble analyses. A key result of the test is that the ensemble is dominated by the GISS E2 H model (13
Keywords: Indian Ocean Dipole (IOD), East African short rains, CMIP5, longterm variability 1. Introduction
The Indian Ocean Dipole (IOD) mode is the dominant mechanism of climate variability in the Indian Ocean (IO) with a large area of influence (Saji et al. 1999, Webster et al. 1999, Murtugudde & Busalacchi 1999, Annamalai et al. 2003). A positive phase of IOD is normally characterized by anomalous cooling (warming) in the eastern (western) equatorial IO and equatorial easterly wind anomalies in the lower troposphere. The wind anomalies are accompanied by enhanced (suppressed) convection in the west (east) (Gadgil et al. 2004, Hastenrath 2001). A negative IOD event is indicated by intensification of the normal conditions, i.e. positive (negative) sea surface temperature anomaly (SSTA) and enhanced (suppressed) convection in the eastern (western) equatorial IO. IOD events develop mostly in boreal summer and peak during September and October before returning to the mean state in subsequent months; this sequence is called the seasonal phase-locking of the IOD.
IOD event causes enhanced convection in the western Indian Ocean and, therefore, influences precipitation dynamics in EA. In particular, EA experiences anomalously high precipitation in the October-December (OND) short rain season during positive IOD events (Black 2005, Clark et al. 2000, 2003). Enhanced rainfall in the EA region coincides with increased moisture convergence due to strong westerlies in central Africa and easterlies from the equatorial IO as a response to the warm western pool of the positive IOD (Ummenhofer et al. 2009). Since the variability of EA rainfall has a huge impact on the livelihood of millions of people who depend on agriculture and regional fisheries, it is essential to improve our understanding of the variability triggers (Vinayachandran et al. 2009).
Beyond the interannual scale, it has been hypothesized that EA climate change is linked to IOD changes (Marchant et al. 2006). Several proxy archives (lake levels, lake sediments, glaciers) indicate a sudden shift from wetter to drier conditions during 1880 - 1900 and the continuation of the drier climate into the 20th century (e.g. Hastenrath 2001). The preceding wet period likely lasted for at least a few decades
(Verschuren 1999, Verschuren et al. 2000, Nicholson & Yin 2001, Stager et al. 2005). The late 19th century shift manifested in the drop of lake levels (Nicholson & Yin 2001) and in the onset of glacier retreat in EA (e.g. Hastenrath 2001, Mölg et al. 2003). However, a comprehensive assessment of the IOD influence on the EA short rains from a century-scale perspective is still missing. The observed link between IOD, zonal wind and OND rainfall in EA, together with recent drought years in EA (Roxy et al. 2015), thus evokes one important question (e.g. Mölg et al. 2006): Did the frequency of IOD events change since the late 19th century, and how did this change impact the regional precipitation characteristics?
ensemble analysis has been made. Hence, the primary goals are to evaluate the representation of IOD states in the Coupled Model Intercomparison Project phase 5 (CMIP5) models and to document the long-term temporal and spatial IOD patterns in the models. We investigate these patterns with a special focus on regional implications for the precipitation climate in EA during the short-rain season.
2. Data and Methods
interpolation was employed for SST and the zonal wind component data sets; first-order-conservative remapping is employed for the precipitation data set (Jones 1999).
To identify IOD events we calculated the September-November (SON) Dipole Mode Index (DMI) (Saji et al. 1999). The DMI was defined as the difference in SSTA between the area-averaged western (50°-70°E; 10°N-10°S) and area-averaged eastern (90°-110°E; 0°-10°S) pole of the IO. An IOD event was expected to occur when the amplitude of the DMI was higher than one standard deviation (std) in the boreal autumn September to November (SON) (Vinayachandran et al. 2009). To quantify the atmospheric manifestation of the IOD, we calculated a zonal wind index (ZWI) (Hastenrath 2001, Vuille et al. 2005). The ZWI was defined as the area-averaged zonal wind component at 850 hPa over the central IO (60°-90°E; 5°N-5°S). During a positive IOD event, the easterly wind anomalies over the tropical equatorial IO are described by a negative ZWI.
Additionally, we calculated area-averaged OND precipitation in the EA domain (25°-40°E; 10°N-15°S). To evaluate the models' capability to represent IOD states and their impact on EA climate we defined three criteria. As a validation period we chose 1948 to 2005 because the NCEP/NCAR reanalysis only starts in 1948. The first criterion was also used as the principal check of the models' ability to simulate the large-scale linkages of the IOD state. Only models succeeding in criterion 1 are used with several realisations (if available) in the further criteria tests.
The first criterion addressed the ability to represent relevant teleconnections of the IOD (Mölg et al. 2006). To reveal their strength we calculated linear correlations between three key variables (SON DMI, SON ZWI and OND precipitation). These correlations were calculated for each model run as well as for the
mean of the observations were considered for further analysis. The third criterion was the representation of the EA precipitation climatology (Yang et al. 2014). We calculated the correlation between the
simulated and observed mean annual cycle in precipitation to evaluate modeled rainfall seasonality. In addition, we determined the root-mean-square-difference (RMSD) between these cycles as a measure of absolute differences. Only models that showed significant correlations and a RMSD lower than the observed monthly standard deviation succeeded in this test. For the final analysis we only kept models that satisfied all three criteria. The ensemble mean was calculated from these remaining model runs. In further analyses with the selected GCMs, we assessed the strength of the zonal equatorial circulation in the lower troposphere by two indices: the mean sea level pressure (MSLP) index and the mean 500 hPa vertical velocity anomaly (ω500) index(Plesca et al. 2018). The MSLP index is the difference between the area-averaged eastern equatorial (70°-110°E; 5°N-5°S) and the western equatorial (30°-70°E; 5°N-5°S) SON MSLP anomaly. The ω500 index is the mean SON 500 hPa vertical velocity anomaly difference between the eastern (50°-80°E; 5°N-5°S) and western (30°-50°E; 5°N-5°S) IO (Hastenrath & Lamb 2004). Anomalies were calculated with respect to the long-term mean (1860 - 2005). Both the MSLP index and the ω500 index are positive in case of a positive IOD event.
3. Results and Discussion 3.1. Model evaluation
With regard to the teleconnection criterion, all related variables (SON DMI, SON ZWI and OND
precipitation) have to be significantly correlated (Figure 1). These correlations express the interlinkage of Indian Ocean SON SST (SON DMI), lower troposphere SON zonal wind (SON ZWI) and EA OND
(9 out of 64 model runs) and therefore fail the criterion. Overall, 55 out of 64 model runs (Table 1) pass the first criterion.
The strength of the positive dipole mode events is evaluated using the dipole mode magnitude criterion (Figure 2). Only model runs within the one standard deviation range of the mean of the observations pass this criterion. The observations are indicated by the thick black lines in Figure 2. The eastern pole SSTA patterns' (Figure 2 a) deviation from the observations is more pronounced than the one for the western pole (Figure 2 b). 26 out of 64 model runs are able to capture the eastern pole SSTA pattern in comparison with the observations; 57 out of 64 model runs are able to capture the western pole SSTA pattern within the defined threshold. Overall, 20 out of 64 model runs passed the second criterion (Figure 3).
The EA precipitation variability was assessed using the precipitation climatology criterion (Figure 4). Like in the first criterion, only statistically significant model runs whose RMSD is lower than the observed monthly standard deviation are considered. 49 out of 64 model runs passed this criterion. For the further analysis only model runs that fulfilled all three criteria are used (15 out of 64). The ensemble mean and variability of these 15 selected model runs (Table 2) are thereafter used to investigate the long-term evolution of temporal and spatial characteristics of positive IOD events since the mid-19th century with a focus on EA precipitation. It is important to note that only realisations of GISS E2 H and just two
realisations of HadCM3 were able to pass all three criteria, and therefore the subsequent results are dominated by the GISS E2 H.
3.2. IOD activity since 1860
from the early 20th century onwards, and from the mid-20th century onwards the observations and simulations converge. The deviation of the observations from the simulations in the early years is very likely due to the high uncertainty of the observations in the 19th century (Liu et al. 2015). However, the good agreement over time between observations and simulations corroborate the ensemble mean of the simulations as an appropriate data set for further analysis.
It is apparent that the number of positive IOD events in the late 20th century is higher than in previous periods (e.g. Ajayamohan & Rao 2008). This increased IOD activity is promoted by the fast-warming west pole of the IO (Roxy et al. 2014, Ritika et al. 2015). To assess the basic cause of the increase in positive IOD events over time, internally or externally forced, the long-term linear trend of the SST is removed before calculating the DMI in Figure 5 b. This removal is achieved by calculating SSTA based on a moving centered 30-year base period updated every year (following the National Oceanic and Atmospheric Administration (NOAA)). Comparing the number of events based on detrended SSTA with the number of events based on the SSTA with trend suggests that the trend in positive IOD events in the recent decades is most likely not internally forced. As expected, the number of IOD events doesn’t increase as much in the case of Figure 5b as in the case of Figure 5a from the late 19th century to the late 20th century. In particular, the constantly high number of events (6) in recent decades in model and observations (Figure 5 a) is not maintained in the detrended data (Figure 5 b). Therefore, to not dismiss the external signal of the IOD, the SSTA without detrending has been used for further analysis.
SSTA dipole in the recent period goes along with a basin-wide warming in the SON season. However, the strong zonal gradient in SSTs along the equator during positive IOD events is retained.
In case of the 850 hPa wind field in the IO, a slight decline in the late 20th century in the strength of easterly winds is noted especially in the region of the equatorial western IO warm pole. Betts & Ridgway (1989) proposed that in a warmer climate, tropical convection may decrease and Vecchi & Soden (2007) have found that the equatorial zonal overturning of air is weakening under global warming. This is an important aspect, since EA is strongly dependent on convection over the tropical western IO as a driver of precipitation. Thus, the change in the strength of the zonal circulation and possible effects on EA climate will be further investigated in section 3.3.
For the EA precipitation we focus on the OND season because the IOD strongly influences the short rain season of East Africa, which coincides closely with the peak of the IOD in SON (Black 2005, Ummenhofer et al. 2009). It is apparent that the spatial pattern of the OND precipitation associated with the positive IOD is similar in both periods (statistically significant at p < 0.05 based on a Kolmogorov-Smirnov test). There are local maxima near the coast in the southeast of EA, more precisely east of 35° E and south of the equator, and west of Lake Victoria close to the equator (Figure 6 a/b). However, the total amount of precipitation is higher in the first period than in the second period (Figure 6 c), which is confirmed by a statistical significance test (t-test) at the 1% level. Thus, although there has been an increase in positive IOD events in recent decades (frequency) the amount of precipitation (intensity) has decreased. To investigate this decline in OND precipitation during positive IOD events we have a look at the thermodynamic and dynamic contributions (e.g. Park and Chiang, 2010) to EA precipitation in the following section.
As a result of higher SSTs and air temperatures during the late 20th century, the thermodynamic
atmosphere-ocean processes are affected. Variability of the short rain season of East Africa are potentially influenced by the associated changes in the thermodynamic contributions to precipitation. One possible cause for a change in tropical precipitation is, therefore, a change in the local convection properties, which can be diagnosed by the outgoing longwave radiation (OLR). Figure 7 shows the change in the thermodynamic contributors: OLR and evaporation as well as the resulting precipitation and convective precipitation during IOD events between the former period (1860-1909) and the recent period (1956-2005). An increase in OLR is evident in most of the southern EA domain in the late 20th century, which implies suppressed convection in the later period. This development agrees with a decrease in convective precipitation all over southern EA; the said OLR-precipitation relationship also holds for both the western and eastern pole of the IO.
Conversely to the higher air temperatures under global warming that allow the atmosphere to store more water in the late 20th century (not shown), there is less precipitation over EA in recent decades compared to the second half of the 19th century (Figure 7). Although there is more moisture in a warmer climate, higher air temperatures may also result in a higher threshold for precipitation events to occur (Johnson and Xie, 2010). It is evident in the GCM data (Figure 7) that there has been less evaporation of surface moisture in EA in the 20th century than in the 19th century period. As argued by Trenberth (1999), local evaporation is one source of regional moisture recycling (or local precipitation) in the atmosphere. Thus another possible cause of decreased precipitation in the short rain season of East Africa in recent decades during positive IOD events is the decline in local evaporation and, more-over, the higher water vapor storage capacity of the atmosphere. This decline in evaporation most probably maintains a feedback with the decline in precipitation.
1956-2005 is shown in Figure 8. It is evident that in the area of tropical convection in the western IO (between 40°-50°E) the easterly wind component is weaker in the second half of the 20th century than in the 19th century. The prominent mean easterly flow during IOD events south of the equator coincides with westerly wind anomalies in recent decades, resulting in the weaker zonal flow toward the EA coast. Additionally, we investigate the strength of the three-dimensional large-scale circulation along the equatorial region (Figure 9) by means of the indices described in Section 2. These indices are useful diagnostics for the estimation of the intensity of the zonal Walker cell (Plesca et al. 2018). Negative indices in our case imply enhanced easterlies at 850 hPa and ascent of air in the mid troposphere (50°-80°E; 5°N-5°S), while positive indices stand for attenuated easterlies and weaker ascent of air, during positive IOD states. It is apparent that in the first period (1860-1909) both indices are mostly negative while in the second period (1956-2005) the indices are positive. Thus, our GCM data suggest that the inverse zonal circulation that is established during positive IOD events has weakened over time. A weakened zonal circulation entails less moisture advection from the oceans, less upwards moisture transport and therefore could be a possible cause of the decreased precipitation in EA from a large-scale viewpoint. This result from our carefully selected GCM ensemble is in agreement with the work of Vecchi & Soden (2007), who showed that the tropical circulation is prone to weaken under global warming.
4. Conclusions
With the background of the observed climate shift from wet to dry conditions in EA during the late 19th century, the IOD activity since the mid-19th century has been investigated using a model-based approach. Out of the available CMIP5 data, 15 runs showed a satisfactory simulation of (a) the relevant
previous results based on proxy and observational data (e.g. Black et al. 2003, Abram et al. 2008), our 15-member ensemble underlines that there has been an evident increase in IOD activity in the late 20th century; other comparable changes in the frequency of positive IODs since 1860 are not evident. However, a striking result is that the intensity of positive IOD events for the African branch has decreased over time. This IOD property appears as significantly lower OND precipitation in EA during the recent decades with respect to 1860-1909. Thermodynamic factors are identified as important contributions to this change on the regional scale, in particular reductions in convective activity and surface evaporation. A consistent influence from the large-scale tropical circulation is the finding of a weaker zonal circulation cell along the equatorial Indian Ocean in recent decades; the manifestations in the model ensemble are weaker
easterlies in the lower troposphere and reduced upward motion in the mid troposphere over the western Indian Ocean (Figure 6-8). This result agrees with the framework of a weakening tropical circulation under global warming (Vecchi & Soden 2007).
Regarding the century-scale influences of the IOD on EA moisture climate, our results do not confirm earlier hypotheses that the frequency of positive IOD events dropped significantly during the late 19th century and early 20th century. Our results do indicate, however, that a consistent contribution from the IOD to the moister EA climate in the mid to late 19th century (e.g. Hastenrath 2001) came in the form of higher OND precipitation amounts in positive IOD years. The intensity aspect of the IOD, therefore, seems to overshadow the frequency aspect in the specific case of regional climate implications for EA.
Acknowledgements
CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. All plots were generated by the NCAR Command Language (Version 6.4.0) [Software]. (2017). Boulder, Colorado:
UCAR/NCAR/CISL/TDD. http://dx.doi.org/10.5065/D6WD3XH5. The authors would like to thank the editor, Dr Radan Huth, and two anonymous reviewers whose suggestions led to a substantially improved
manuscript.
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Captions
Figure 1: Teleconnection criterion: Correlation of SON DMI vs. SON ZWI vs. OND EA precipitation. Dark
grey squares are statistically not significant model runs. Light grey dots show statistically significant (p < 0.05) model runs. Black dots show the three observations. Models have to pass the statistical significance threshold for both correlations (X and Y axes, respectively).
Figure 2: IOD magnitude criterion: Frequency distribution of (a) eastern (90°-110°E; 0°-10°S) and (b)
Figure 3: Same as Figure 2 but only with the 15 selected ensemble members.
Figure 4: EA precipitation climatology criterion: Correlation of observation and simulations (precipitation
variability) vs. root mean square difference of simulations - observation (absolute precipitation). The threshold (horizontal line) is the standard deviation of the observation. Dark grey squares are statistically not significant model runs. Light grey dots show statistically significant model runs (p < 0.05).
Figure 5: Number of positive IOD events per running 30-year interval from SSTA (a) with trend and (b)
without trend. The thick red line is the mean of the observations, the thick black line shows mean of the simulations; the light gray lines show each model run individually. The dark gray envelope illustrates the standard deviation of the model ensemble.
Figure 6: Composite maps of positive IOD years in the (a) second half of the 19thcentury, (b) the second half of the 20th century and (c) the difference “b - a”. Shown are the SON SSTA field (red-blue colors), SON 850 hPa wind field (vector) and the OND precipitation (green-violet); the differences between the two periods are statistically significant at p < 0.05 for all shown variables based on a Kolmogorov-Smirnov test. The rectangles identify (from west to east) the EA domain (25°-40°E; 10°N-15°S), the western (50°-70°E; 10°N-10°S) and eastern (90°-110°E; 0°-10°S) IO. Note the different wind vector reference size in Panel C.
Figure 7: Composite maps of the change (1956 - 2005 minus 1860 - 1909 composites for positive IOD
events during OND) in outgoing longwave radiation (OLR), precipitation (PR), convective precipitation (PRC) and evaporation (EV). PR, PRC and EV are given in mm month-1, OLR is given in W m-2; the
differences between the two periods are statistically significant at p < 0.05 for all shown variables based on a Kolmogorov-Smirnov test. The rectangles identify (from west to east) the EA domain (25°-40°E; 10°N-15°S), the western (50°- 70°E; 10°N - 10°S) and eastern (90°-110°E; 0°-10°S) IO pole.
Figure 8: SON difference in 850 hPa zonal wind (m s-1) between 1956-2005 and 1860-1909 for positive IOD
Figure 9: Time series of the 30-year running average SON MSLP (grey) and SON ω500(black) index from
1875 to 1991.
Anja Thielke* and Thomas Mölg
Observed and simulated Indian Ocean Dipole activity since the mid 19
thcentury and its relation to
East African short rains
Table 2: Selected model runs.
Model name realization
GISS E2 H r1i1p1
