Contribution of water-limited ecoregions to their own supply of rainfall

Our analysis relies on the multi-decadal record of VOD recently developed by Liu et al (2011) and updated by Liu et al (2015), which is based on a wide range of passive microwave satellite observations. This data set is used to identify global water-limited ecoregions—for which our atmospheric vapour trajectories model will be run afterwards—and to quantify the changes in the state of vegetation between wet and dry years. VOD is a close proxy for the water content in vegetation, including both leaf and woody components, and it is strongly linked to aboveground biomass density and vegetation activity (Andela et al 2013, Liu et al 2015). Apart from being more easily interpretable than traditional greenness indices, it is also insensitive to sun-sensor geometry and has a minimal sensitivity to atmospheric conditions (Liu et al 2011). The VOD by Liu et al (2011) is based on the application of the land parameter retrieval model (Owe et al 2008) to passive microwave observations from the scanning multichannel microwave radiometer, the special sensor microwave imager (SSM/I), the tropical rainfall measuring mission microwave imager (TMI) and the advanced microwave scanning radiometer-Earth observing system (AMSR-E).

To complement the analysis, we use normalized difference vegetation index (NDVI) data coming from the global inventory monitoring and modeling system (GIMMS) third generation (3g) data set (Tucker et al 2005), which is based on optical data from the advanced very high resolution radiometer (AVHRR). In addition, precipitation, evaporation and atmospheric stability data are used to investigate the differences in the origin and volumes of precipitation between wet and dry years. Precipitation observations are obtained from the Climate Research Unit (CRU) 3.10 gauge-based product (Harris et al 2013). Land evaporation data (including transpiration) are taken from the satellite observation-based global evaporation Amsterdam model (GLEAM, Miralles et al 2011), and ocean evaporation from the satellite observation-based OAFlux data set (Yu 2007). Background atmospheric stability is assessed using monthly estimates vertical wind velocity (ω) at 500 hPa and convective available potential energy (CAPE), both derived from the ERA-Interim reanalysis (Dee et al 2011). All data sets are re-gridded to a common 0.25° spatial resolution and aggregated to monthly temporal resolutions. Monthly anomalies are then calculated by subtracting the corresponding month-of-the-year mean considering the multi-annual (1980–2011) record.

The Lagrangian particle dispersion model FLEXPART v9.0 (Stohl et al 1998, Seibert and Frank 2004) is used to trace water vapour trajectories and quantify moisture sources. FLEXPART is here constrained by wind and specific humidity data from ERA-Interim, as in recent applications of the model dedicated to investigate water vapour trajectories (Drumond et al 2014, Nieto et al 2014, Pampuch et al 2016). The atmosphere is divided into two million air particles that are moved by 3D winds. The model calculates increases and decreases in moisture along a given trajectory based on differentials of specific humidity in time, and records these changes in specific humidity and the 3D coordinates of all particles every 6 h. By summing all the increases and decreases of moisture over a given grid cell, the difference between evaporation and precipitation (E – P) can be obtained, which is then integrated over the average atmospheric residence time. A review of the advantages, limitations and uncertainties of different moisture transport models, including FLEXPART, can be found in Gimeno et al (2012).

Based on the VOD data and precipitation observations from CRU, figure 1(c) delineates the ten major water-dependent ecoregions on Earth—here, the concept of 'ecoregion' by Olson and Dinerstein (2001) is loosely used to refer to large (>100 000 km²) contiguous geographical areas of similar climate and vegetation conditions. The identification of these water-limited ecoregions is based on clustering neighbouring pixels following two inclusive criteria: (a) the expected timing of the annual peak in VOD, as revealed from the monthly climatology calculated based on 1980–2011, should be similar (i.e. ±1 month) for all pixels in the area (see figure 1(a)), and (b) the Pearson's correlation between the annual value of VOD at that seasonal peak, and the cumulative precipitation during the antecedent three months should be statistically significant (p < 0.01) and larger than 0.5 (see figure 1(b)). Hereafter, we refer to these three months prior to the seasonal peak in VOD as 'growing season'. Correlations to precipitation were calculated by considering other antecedent periods ranging from 1 to 10 months, and the final choice of three months was adopted for it yields the maximum average correlations globally (not shown). We note that this definition of a constant-length growing season differs from others found in literature, yet it facilitates the study of atmospheric vapour trajectories (see section 3.2). We note as well, that the inclusion of water-limited ecoregions in figure 1(c) is not aiming to be exhaustive, and that several other areas that fulfil the two above-mentioned criteria are not further analysed with FLEXPART due to their smaller size, their less clustered geographical distribution, or their proximity to larger water-limited ecoregions; examples are the Russian steppe or Southeastern Australia (see figure 1(b)).

Zoom In Zoom Out Reset image size

Overall, we identify ten major ecoregions, from West to East (figure 1(c)): (1) Chihuahuan Desert, (2) Pampas, (3) Caatinga, (4) West Sudanian savanna, (5) Kalahari Desert, (6) East Sudanian savanna, (7) Serengeti bushland, (8) Mongolian steppe, (9) Central Australia and (10) Northern Australia. The general characteristics of these ecoregions are summarised in supplementary table A1.

The precipitation origin is calculated following three sequential steps. First, from each water-stressed ecoregion in figure 1(c), the FLEXPART model is run backward (Nieto et al 2014) to simulate the origin of the air particles entering the ecoregion's atmosphere during the growing season. As mentioned above, the length of the growing season is considered constant and equal to the three months prior to each regions' seasonal peak in VOD (see supplementary table A1). The common time length for all ecoregions allows their results to be inter-comparable, and by considering a short enough period, we circumvent the confounding effect of the seasonal cycle of atmospheric circulation. Backward runs are analysed separately for the five years of higher and lower peak in vegetation water content (i.e. VOD) during the 1980–2011 period, hereafter referred to as 'wet years' and 'dry years' (respectively). This allows us to identify inter-annual anomalies in the trajectories of air particles entering the ecoregion, and in the gain of water vapour through these trajectories. An optimal lifetime of vapour in the atmosphere needs to be considered in the calculation of back-trajectories with FLEXPART; for each region, this lifetime (in number of days) is optimised by executing the model sequentially for a range of possible lifetime values, and then selecting the number of days that minimises the absolute differences between the precipitation simulated by FLEXPART and the CRU observations for each ecoregion (see supplementary table A1 for the resulting lifetimes).

Second, once the vapour source region has been identified for each ecoregion, FLEXPART is run forward from these source regions. Only the fraction of them contributing to 90% of the water vapour entering the ecoregions is considered, in order to exclude remote source regions with very small moisture contributions (Drumond et al 2014). This forward run yields the volumes of rain falling in each ecoregion during the corresponding growing season. Therefore, while the backward run only identifies the origin of the moisture entering the ecoregion, the forward run reveals whether the moisture gained over that trajectory does in fact precipitate in the ecoregion, which would finally depend on atmospheric stability. This forward run is done for wet and dry years independently, and separately for: (a) the ocean pixels within the source region, (b) the land pixels within the source region but outside the ecoregion, and (c) the ecoregion itself. This enables us to discern whether precipitation falling in the ecoregion during the growing season is of oceanic or terrestrial origin, and if the latter, whether or not it originates from the ecoregion itself (see figure 2).

Zoom In Zoom Out Reset image size

Third, the volumes of precipitation simulated by the forward runs of FLEXPART are scaled to match monthly CRU observations, while maintaining the FLEXPART-derived ratios of ocean and terrestrial origin. The resulting volumes of precipitation from land and oceanic origin are spatially distributed across the pixels in the source region using the results from the backward runs. This allows us to spatially map the origin of the precipitation volumes falling into each ecoregion, showing the specific contribution of each source pixel to these volumes (see e.g. figure 3). Finally, precipitation recycling ratios for each water-stressed ecoregion are calculated as the ratio of the precipitation generated within the ecoregion, over the total input of precipitation. This ratio is then multiplied by 0.9 to account for the fact that the source region used in forward simulations includes only 90% of the particles bringing moisture into the ecoregion, and that the ecoregion is always contained within that 90%. These recycling ratios are then averaged for wet and dry years separately, and for each corresponding ecoregion, as shown in figure 2.

Zoom In Zoom Out Reset image size

Based on the VOD data and CRU precipitation, figure 1(c) delineates the ten major global water-limited ecoregions on Earth. For all these regions the seasonal peak in VOD is significantly (p < 0.01) and positively correlated (R ≥ 0.5) to the cumulative precipitation occurring in the prior three months (figure 1(b)). Independently of the magnitude of precipitation anomalies, comparatively high VOD values are found in the more tropical Caatinga and Serengeti, and relatively low VOD values are found in the more arid East Sudanian savanna and Central Australia (figure 2), which is in agreement with the results by Liu et al (2013). Most regions experience pronounced differences in VOD between wet and dry years, especially the Kalahari, Serengeti, East Sudanian savanna and Northern Australia, likely due to the strong differences in precipitation volumes between wet and dry years in these regions (figure 2) and a potentially high sensitivity of vegetation to those differences. Analogous inter-region and inter-period variability is found when using vegetation greenness (NDVI) instead of VOD (figure 2). The precipitation during the growing season is markedly lower for dry years in all ecoregions, but the response of vegetation is not equal for all of them: figure 2 reveals important inter-region differences that may reflect the presence of vegetation species with different sensitivities to rainfall scarcity, and the existence of environmental or climatic controls other than the availability of water. As an example, although VOD values are similar in the Pampas grasslands and Northern Australian woodlands, the observed input of rainfall in the latter is more than seven times larger, both during wet and dry years.

Results in figure 2 indicate that the fraction of precipitation coming from oceanic and continental origin varies markedly from ecoregion to ecoregion. While the Pampas and Caatinga receive most of the precipitation from the Pacific and Atlantic oceans (respectively), others, such as the East Sudanian savanna and the Mongolian steppe, receive almost their entire supply from continental areas. These findings agree with previous studies pointing to central Asia and Sahel as two of the world's regions with largest dependency on continental-origin rainfall (Zeng et al 1999, Dirmeyer et al 2014). We note as well that in the case of the Pampas and Mongolian steppe, part of the input of moisture during the growing season may come through melting of snow and not from precipitation directly (Shinoda et al 2010, Havrylenko et al 2016), and that in regions such as Caatinga, a small fraction of the water supply may also come from irrigation (Siebert et al 2005). Figure 2 also shows that for each given ecoregion, the partitioning of precipitation between continental and oceanic origin remains similar during wet and dry years. This is in clear contrast to what could be expected if ocean evaporation dynamics alone were responsible for driving the anomalously wet and dry years, and suggests that land surface conditions help intensify wet and dry spells.

The strength of land–atmospheric coupling and the importance of regional evaporation for precipitation are often diagnosed by means of the recycling ratio (Eltahir and Bras 1996, Trenberth 1999), i.e. the volume of precipitation originated from regional evaporation over the total input of precipitation. Recycling ratios are usually larger for areas of high evaporation and low air advection, and tend to increase during convective seasons (Trenberth 1999). Precipitation recycling has been studied based on observational data (Eltahir and Bras 1996, Trenberth 1999), transport models (Numaguti 1999, Dirmeyer and Brubaker 2007) and isotopes (Wright et al 2001), but comparisons between different studies are problematic due to the dependency on the scale of the study region (with recycling being 100% when considering the world as a whole, and 0% when considering a point domain). Based on our approach, we show that mean recycling ratios for the growing season vary substantially amongst the different ecoregions: from 3% recycling in the Pampas during wet years, up to 34% in the Kalahari Desert during dry years (figure 2). This range reflects to some extent the differences in area covered by each ecoregion but, undoubtedly, also the different role each ecoregion plays on its own climate (see section 5).

The location of the main sources of moisture to each ecoregion (see contours in figure 3) is in agreement with the expectations based on prevailing winds, and is in line with previous analyses of precipitation origin (Dirmeyer et al 2009, Keys et al 2012, Dirmeyer et al 2014, Gimeno 2014). As mentioned above, the relative contributions of oceanic- and continental-origin precipitation remain generally similar during wet and dry years (figure 2). However, when zooming into the specific source areas, we do observe local differences between wet and dry periods; red-coloured areas in figure 3 are responsible for an anomalously low contribution to the corresponding ecoregion's precipitation during the dry growing seasons, i.e. those are the areas from which the observed deficits in precipitation come from. We show that for ecoregions such as the Chihuahuan Desert—which harvests most of its rainfall from the surrounding land regions and from the Gulf of Mexico (Dirmeyer et al 2014)—the contribution of precipitation in dry years is homogeneously decreased across the entire source area. But for other ecoregions, restrictions in the supply of moisture in dry years come from very specific locations. In the case of Caatinga—which is watered from the equatorial Atlantic (Gimeno et al 2010a)—the difference mainly comes from a narrow offshore band south of the equator. For the Kalahari Desert and Central Australia—which are regions largely fed by rainfall of continental origin (Gimeno et al 2010a) (figure 2)—restrictions in the supply of moisture come from land areas surrounding the ecoregions, or even including them (figure 3).

The observed inter-annual anomalies in precipitation volumes (figure 2) and origin (figure 3) can be a consequence of any of the following three mechanisms, or combinations of them: (a) a persistent anomaly in wind speed and/or direction that leads to a change in the location of the source area (e.g., in the case of dry years, part of the moisture that is normally brought into the ecoregion is steered towards other regions); (b) a positive or negative anomaly in the volume of moisture generated in the source area; (c) a particularly high (or low) atmospheric stability over the ecoregion (see e.g. Dirmeyer et al 2014). These mechanisms are schematically represented in figure 4(a). As might be expected, any large-scale condition affecting these three proximate mechanisms will also impact the occurrence and variability of precipitation, including modes of internal climate variability such as ocean–atmospheric oscillations (Trenberth et al 2003, Schubert et al 2016).

Zoom In Zoom Out Reset image size

General patterns of atmospheric circulation typically show little inter-annual variability, since they are largely dominated by the persistent mode of prevailing easterlies, westerlies and trade winds (Gimeno et al 2010a, Keys et al 2014). As a consequence, the location of the upwind source areas is rather stable for most regions; only the Kalahari Desert and the Australian ecoregions show notable differences between dry and wet growing seasons in the areas the air comes from (figures 4(b), supplementary figure A1). Meanwhile, during the dry growing seasons, the average evaporation in the source area of most ecoregions is anomalously low according to the satellite-based evaporation retrievals (Yu 2007, Miralles et al 2011), in particular for the Pampas, Caatinga, Kalahari Desert and the Australian ecoregions (figure 4(c)). We also see declines in the ecoregions' evaporation during dry years; this is mainly the case for the Chihuahuan and Kalahari deserts, Australia and the Mongolian steppe (figure 4(c)). This finding agrees with the declining volumes of recycled rainfall found in our FLEXPART experiments (figure 2), and further supports the existence of a positive feedback during dry times: growing-season rainfall scarcity and subsequent vegetation water stress lead to declines in soil evaporation and transpiration, which further reduce precipitation supply. As expected, this reduction is stronger in ecoregions with a larger dependency on precipitation recycling (figure 2). This is exemplified in figure 5, which shows the temporal evolution of water fluxes during the driest and wettest growing seasons on record for the Kalahari and Northern Australia, two of the ecoregions with highest recycling ratios. For both regions, the driest years—which were reported as extraordinary drought events in both cases (White et al 2004, Masih et al 2014)—show a consistent decline in growing-season (December–February) precipitation and evaporation, and a reduction in their seasonal VOD and NDVI peaks (March). The observed reduction in ecoregions' evaporation in dry years coincides with a decline in the simulated contribution of the ecoregions to their own precipitation supply, even if their recycling ratios are higher during dry years (see also figure 2). For both ecoregions, the vast majority of growing-season evaporation comes from transpiration: 78% and 73% for the Kalahari, and 66% and 71% for Northern Australia, for the driest and wettest year (respectively).

Zoom In Zoom Out Reset image size

Regardless of the fact that upwind evaporation and atmospheric circulation are defining the volume of atmospheric moisture present over an ecoregion, what finally controls whether that moisture precipitates is the instability of the atmosphere, i.e. the tendency to encourage the condensation and precipitation of that moisture. Figure 4(d) uses reanalysis data (Dee et al 2011) of 500 hPa vertical wind velocity anomalies (ω') to investigate mean synoptic stability—see e.g. Pampuch et al (2016), Gimeno et al (2010b)—and anomalies in CAPE to investigate mean convective stability—see e.g. Taylor and Ellis (2006), Johnson and Xie (2010). Overall, synoptic stability anomalies appear consistently relevant across all ecoregions, with the exception of the West Sudanian savanna. Mean stable conditions during the growing season are typical of years with limited input of precipitation and a reduced peak in vegetation biomass; anomalously unstable conditions are typical of years with a strong seasonal peak in biomass (figure 4(d)). This tendency occurs despite the fact that soil dryness may instigate convective instability through the warming of the lower troposphere (Taylor et al 2012, Guillod et al 2015), which agrees with the mean positive CAPE anomalies in regions such as the Sudanian savanna and Central Australia during dry years (figure 4(d)). In the examples of Kalahari and Northern Australia (figure 5), stable (unstable) synoptic conditions are found during the driest (wettest) growing season on record, while the effects of drier conditions on the average convection are only observed in the case of Northern Australia.