1. Introduction
The Plurinational State of Bolivia is facing numerous climate-related threats. In the Andes, rapidly retreating glaciers affect the supply of drinking water, agricultural production, and the provision of energy from hydropower (Bradley et al. 2006). In the lowlands, a reduction of rainfall as is projected by some general circulation models (GCMs) may lead to the partial loss of the Amazon forest (Rammig et al. 2010). Droughts and floods associated with El Niño–Southern Oscillation (ENSO) events affect thousands of people and lead to economic losses equivalent to millions of U.S. dollars (UNDP 2011). As a developing country with one-third of the labor force working in the agricultural sector (http://www.ine.gob.bo), Bolivia is considered to be extremely vulnerable to climate change (World Bank 2010).
Given this exposure to climate-related threats, a regional analysis of climate change projections is of great interest. The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) documented climate change projections from multiple GCMs belonging to the third phase of the Coupled Model Intercomparison Project (CMIP3) for the South American continent (Christensen et al. 2007). AR4 found that GCMs had a cold bias in the Andes and a slightly warm bias in the tropical lowlands. Averaged over tropical South America (12°N–20°S and 82°–34°W, hereinafter AMZ), this bias was estimated with an annual median of 0.6°C. Climate change projections in the same region showed a median increase in annual temperatures of 3.3°C and an interquartile range of 2.6°–3.7°C under the intermediate emission scenario “A1B.” For precipitation, the same report found a wet bias in the Andes and a dry bias in the tropical lowlands, estimated with an annual median of −8% for the AMZ region (Christensen et al. 2007, their Fig. S11.26 and Table S11.1). This dry bias was likely due to an underestimation as well as an extension too far southward of the intertropical convergence zone. In most of tropical South America, GCMs did not agree on the directional change of annual rainfall, resulting in a median change of 0% and an interquartile range from −3% to +6% in the AMZ region. This lack of agreement was confirmed by numerous other studies that focused on the Amazon region (e.g., Li et al. 2006; Jupp et al. 2010). Seasonal changes were more certain, with more rainfall from January until March and less rainfall from July until September in major parts of the Amazon, including Bolivia’s northern lowlands (Vera et al. 2006). In the Bolivian Altiplano, multiple GCMs revealed a warm and wet bias (Seth et al. 2010). Temperature projections under the high-emission scenario “A2” ranged from +5° to +6°C (monthly medians), and rainfall projections tended toward less annual rainfall, with decreases from May to September and increases during March and April.
Numerous attempts have been made to dynamically downscale global climate change scenarios for tropical South America (e.g., Soares and Marengo 2009; Urrutia and Vuille 2009; Marengo et al. 2010). Projections include temperature increases from 6° to 8°C, with higher values during June–August (JJA), enhanced moisture transport from the Amazon to the La Plata basin that leads to rainfall deficiency in the former and rainfall excess in the latter, and a more intense hydrological cycle with more rainfall in December–February (DJF) and less rainfall in JJA in the Bolivian lowlands. Projections are based on very few lateral boundary conditions [mainly the Hadley Centre Atmosphere Model, version 3 (HadAM3)], however, limiting their robustness in a region with little agreement among GCMs on the directional change of rainfall. Other efforts include the La Plata Basin Regional Hydroclimate Project (CLARIS LPB; http://www.claris-eu.org), which aims at predicting regional climate change impacts on the La Plata basin using several regional climate models in conjunction with three lateral boundary conditions (Boulanger et al. 2010). To our knowledge though, climate change scenarios related to CLARIS have so far only been published for southern South America (Nunez et al. 2009).
Previous analyses of climate change scenarios mentioned above are of mainly subcontinental scale and include very few Bolivian stations for model evaluation, making it difficult to apply this information to Bolivia. Also, an evaluation of the most recent generation of GCMs (CMIP5; see section 2) is still missing. The coarse spatial resolution of GCMs limits the validity of model results in the Andes. Given the potential disagreement among models on directional changes of precipitation, however, a comprehensive evaluation of GCMs is a necessary precursor to any downscaling effort. We therefore analyzed projections from 35 different GCMs, including models from CMIP3 and CMIP5, as well as five emission scenarios. In doing so, we assessed for the Bolivian case 1) how well GCMs reproduce historical climate patterns, 2) what changes in climate means and variability may be expected, and 3) to what extent there are differences between the CMIP3 and CMIP5 ensembles. Our results may provide input for climate change impact assessments, exploring the probability of climate-related threats such as water scarcity or Amazon dieback in Bolivia.
The following sections describe our study area, methods, results, and discussion. The method section outlines the data and emission scenarios, as well as approaches related to model validation, ensemble weighting, and multimodel agreement. Model skills in reproducing historical climate patterns and future projections of temperature, precipitation, and shortwave (SW) radiation are presented in the results section. Our discussion interprets the results in the context of existing literature and elaborates on the principal findings.
2. Study area
Bolivia is a tropical country measuring more than 106 km2. With its main altitudinal divisions being lowlands (<800 m MSL), Andean slopes (800–3200 m MSL), and highlands (Altiplano; >3200–6500 m MSL), Bolivia’s climate varies with increasing altitude from tropical to cold desert climate, with annual mean surface air temperatures ranging from 0° to 30°C (Fig. 1). Rainfall ranges from <300 to >3000 mm yr−1 and varies from high to low from the northern Andean slopes, northern lowlands, southern lowlands, and southern Andean slopes to the Altiplano, with a north–south division roughly at 18°S. Much higher values (>5000 mm yr−1) along the northern Andean slopes are documented by SENAMHI (2009) from stations not included in this research. The austral summer (DJF) and winter (JJA) coincide with the wet and dry seasons, respectively. Incoming SW radiation ranges from 160 to 260 W m−2, with higher values in the Altiplano and southern lowlands. Seasonal differences in temperature, precipitation, and SW radiation vary among regions, with the largest seasonal differences being 8°C in the Andes, 180 mm month−1 in the northern lowlands, and 112 W m−2 in the southern lowlands, respectively. To account for the spatial gradients in climate, we stratified Bolivia into three regions [northern lowlands (NLL), southern lowlands (SLL), and Andes (AND); see Fig. 1c], that roughly characterize a warm and humid climate, a warm and dry climate, and a cold and dry climate, respectively. Given the coarse spatial resolution of GCMs, Andean slopes were merged partly with the lowlands regions and partly with the Andes region.
(a) Location of Bolivia, (b) surface elevation (m MSL), (c) the three regions studied in this work, (d) annual mean air temperature (°C) from 1961 to 1990, (e) annually accumulated precipitation (mm yr−1) from 1961 to 1990, and (f) mean SW radiation (W m−2) from 1979 to 1990. Black dots denote locations of meteorological stations.
Citation: Journal of Applied Meteorology and Climatology 52, 6; 10.1175/JAMC-D-12-0224.1
Climate patterns in Bolivia are shaped by the following synoptic scale systems: In austral summer (DJF), a low pressure system called the thermal chaco low intensifies at 25°S, enhancing the easterly trade winds to transport moisture from the northern tropical Atlantic Ocean into the continent. Deflected by the Andes, moisture is transported southward from the Amazon River region to the subtropical plains of southeastern South America by a low-level jet (LLJ) located at ~925–850 hPa (~1 km MSL) and less than 100 km east of the Andean slopes (Marengo et al. 2004). This pattern leads to enhanced precipitation with a southeastward extension toward the Atlantic Ocean, referred to as the South Atlantic convergence zone (SACZ). At the same time, the release of condensational heat over the Amazon and Andean slopes leads to the formation of the upper-level Bolivian high pressure system at 200 hPa (~12 km MSL) (Lenters and Cook 1997), which further enhances moisture advection from the Amazon to the Bolivian lowlands and highlands (Vuille 1999). An upper-tropospheric trough near the coast of Northeast Brazil forms as a response to the rising air motion over the continent (Silva and Kousky 2012). The chaco low, Bolivian high, LLJ, SACZ, and upper-tropospheric trough form the main components of the South American monsoon system (Zhou and Lau 1998), affecting rainfall in DJF. In austral winter (JJA), the chaco low and the SACZ dissipate, leading to less moisture transport from the north. Cold fronts from the southern polar regions penetrate into the Bolivian lowlands, leading to low temperatures and to limited precipitation when colliding with warm tropical air masses (Garreaud 2000; Ronchail and Gallaire 2006; SENAMHI 2009). The Bolivian high dissipates and westerly winds prevail in Bolivia, preventing moisture transport from the lowlands to the Andes in JJA (Vuille 1999).
Sources of climate variability in Bolivia include 1) the Pacific decadal oscillation (PDO), 2) ENSO, and 3) the Antarctic Oscillation (Garreaud et al. 2009; Seiler et al. 2013). Climate variability may lead to extreme events, including droughts and floods. Droughts mainly occur in the southern lowlands and in many regions in the Altiplano from June to August (CONARADE 2010), whereas floods happen from January to March mainly in the savannas of the northern lowlands (Bourrel et al. 2009) but also in the catchment areas of Lake Titicaca and Poopó (UNDP 2011) in the Andes.
Meteorological observations reveal that Bolivia’s climate is currently warming at a rate of 0.1°C (10 yr)−1, with larger increases in the Andes and during the dry season (Seiler et al. 2013). Rainfall increased from 1965 to 1984 (12% in DJF and 18% in JJA) and decreased from 1985 to 2004 (−4% in DJF and −10% in JJA), roughly following the pattern of the PDO.
3. Methods
a. Data
1) Observations
Meteorological observations consisted of surface air temperature, precipitation, and incoming shortwave radiation at the surface. Temperature and precipitation measurements were provided by the Bolivian National Service of Meteorology and Hydrology (SENAMHI) and were homogenized by Seiler et al. (2013). Meteorological stations with sufficient data for the period 1961–90 were selected from this homogenized dataset, resulting in 25 stations with temperature measurements and 59 stations with precipitation measurements (Figs. 1d,e). Stations contained at least 50% and, on average, 77% and 83% of daily temperature and precipitation measurements, respectively. There were four stations with slightly less than 50% of data in regions with low station density. Daily data were converted to monthly data if no more than 3 days were missing in a month.
Monthly mean SW radiation was obtained for the period 1979–90 from the Modern-Era Retrospective Analysis for Research and Applications (MERRA), a National Aeronautics and Space Administration (NASA) reanalysis for the satellite era using the Goddard Earth Observing System Data Assimilation System, version 5 (GEOS-5; Rienecker et al. 2011). The data belong to a collection of datasets (“Obs4MIPs”) that has been organized according to the CMIP5 model output requirements and made available on the Earth System Grid gateway (http://pcmdi3.llnl.gov).
2) General circulation models
We used a total of 35 different GCMs, with 23 GCMs corresponding to CMIP3 and 12 GCMs corresponding to CMIP5. CMIP3 GCMs contributed to AR4 (Pachauri and Reisinger 2007), and CMIP5 data will contribute to the IPCC’s Fifth Assessment Report (AR5), expected for publication by 2014. Changes from CMIP3 to CMIP5 GCMs include changes in emission scenarios (see section 3b), improved model physics, finer spatial resolution, and additional processes related to the oceanic and terrestrial carbon cycle, aerosols, atmospheric chemistry, and ice sheets. All GCMs were validated against observational data for the period 1961–90, and future climates were evaluated for the period 2070–99. The number of GCMs for future climates depended on their availability at the time of writing and therefore varied among scenarios, with 21, 22, 18, 12, and 11 GCMs for IPCC Special Report on Emissions Scenarios (SRES) B1, A1B, A2, RCP4.5, and RCP8.5, respectively (Table 1). To make a more valid comparison between CMIP3 and CMIP5 projections, we also used a subsample consisting of 12 CMIP3 GCMs, including only predecessors of the 12 CMIP5 GCMs (viz., B, D, G, J, M, N, O, P, R, S, V, and W; see Table 1).
Overview of the GCMs used in this study for the validation period of 1961–90 and for emissions scenarios SRES B1, A1B, A2, RCP4.5, and 85. Not all GCMs were available for all scenarios. GCMs available at the time of writing and used in this study are denoted with an “x.”
b. Emission scenarios
Our analysis covered three SRES emission scenarios for the CMIP3 GCMs (Nakicenovic et al. 2000) and two representative concentration pathway (RCP) emission scenarios for the CMIP5 GCMs (Moss et al. 2010). The SRES scenarios are based on socioeconomic storylines and differ in the resulting atmospheric carbon dioxide (CO2)-equivalent concentrations, which include the net effect of all anthropogenic forcing agents. We used SRES scenarios B1, A1B, and A2 with the respective atmospheric CO2-equivalent concentrations of 600, 850, and 1250 ppm by the year 2100. For comparison, CO2-equivalent concentration for the year 2005 is estimated to be 375 ppm (Pachauri and Reisinger 2007).
RCPs project future radiative forcings without defining new socioeconomic scenarios. RCPs are compatible with the full range of stabilization, mitigation, and reference emissions scenarios available in the current scientific literature with an adequate separation of the radiative forcing pathways. There are four RCPs in total (RCP2.6, -4.5, -6.0, and -8.5), each named after their radiative forcing reached by 2100. We used RCP4.5 and RCP8.5, corresponding to radiative forcings of 4.5 and 8.5 W m−2 by 2100, respectively. The resulting CO2-equivalent concentrations in the year 2100 for RCP4.5 and RCP8.5 are 650 and 1370 ppm, respectively (Moss et al. 2010).
c. Validation
We quantified the ability of each GCM to reproduce historical temperature, precipitation, and SW radiation patterns for Bolivia. For this purpose we compared modeled with observed monthly values averaged over 1961–90 and plotted the correlation coefficient, centered root-mean-square error (RMSE), and standard deviation of each GCM in a Taylor diagram (Taylor 2001). Altitudinal differences between grid cells and meteorological stations may lead to large differences in temperature. To correct for this orographic effect, we calculated local lapse rates for each individual GCM (3.1°C km−1 on average) and used these lapse rates in conjunction with the models’ surface elevation to bring modeled temperatures to the altitude of the meteorological stations. In addition to Taylor diagrams, we used box-plot diagrams to assess whether errors were random or systematic.
d. Ensemble weighting
Weights were used to develop box plots of likely ranges of climate change by calculating the weighted 5th, 17th, 50th, 83rd, and 95th percentiles of projected yearly and monthly changes for each region. The IPCC considers changes to be likely if their probability of occurrence is estimated at 66% (Mastrandrea et al. 2010), corresponding to the range enclosed by the 17th and 83rd percentiles. Changes in the mean were tested for significance using a weighted t test with a probability level of 95%.
e. Multimodel agreement
Last, we plotted for each grid cell the number of GCMs with significant changes in interannual variability and significant increases and decreases in precipitation and SW radiation.
4. Results
a. Validation and ensemble weighting
GCMs revealed an overall cold bias with RMSE ranging from 3° to 7°C and high correlations ranging from 0.85 to 0.95 (Fig. 2). Most GCMs overestimated spatial and/or temporal variability, with higher standard deviations (5°–13°C) relative to the observed overall standard deviation of 7°C.
(left) Taylor and (right) box-plot diagrams of (a),(b) monthly mean air temperature, (c),(d) monthly precipitation, and (e),(f) monthly SW radiation. Black uppercase and blue lowercase letters correspond to GCMs from CMIP3 and CMIP5, respectively. Horizontal bars in the box plots present the median, the boxes give the interquartile ranges, and the whiskers show the minimum and maximum values. White boxes correspond to CMIP3, and gray-shaded boxes show CMIP5 GCMs.
Citation: Journal of Applied Meteorology and Climatology 52, 6; 10.1175/JAMC-D-12-0224.1
For rainfall, GCMs revealed an overall wet bias with RMSE ranging from 40 to 140 mm month−1. Correlations strongly varied among GCMs, with coefficients ranging from 0.3 to 0.9. Most GCMs modeled higher standard deviations (60–160 mm month−1) relative to the observed overall standard deviation of 75 mm month−1. For SW radiation, GCMs revealed an overall positive SW radiation bias with RMSE ranging from 15 to 40 W m−2. As for rainfall, correlations strongly varied among GCMs, with coefficients ranging from 0.5 to 0.96. Most GCMs had higher standard deviations (30–50 W m−2) relative to the observed overall standard deviation of 35 W m−2.
GCMs differed most with respect to precipitation, differed by less for radiation, and differed least for temperature, but none of the GCMs performed best in all three variables (Fig. 3). The highest and lowest final weights differed by a factor of 9. The 10 best GCMs consisted of 7 CMIP3 and 3 CMIP5 GCMs, with the Japanese CMIP3 model MRI-CGCM2.3.2 having achieved the highest weight. Some GCMs improved from CMIP3 to CMIP5 (e.g., MIROC-ESM), some hardly changed (e.g., MPI-ESM-LR), and others worsened (e.g., CanESM2) in reproducing Bolivia’s historical climate. The mean weights of CMIP3 and CMIP5 were about equal (0.029 and 0.028, respectively), while the mean weight of the 12 GCMs from the CMIP3 subsample had a slightly higher weight of 0.035. Hence, the skill to reproduce the historical climate of Bolivia has on average not improved from CMIP3 to CMIP5.
GCM scores for reproducing temperature (dark gray), precipitation (light gray), and SW radiation (white), along with the corresponding final weights (black).
Citation: Journal of Applied Meteorology and Climatology 52, 6; 10.1175/JAMC-D-12-0224.1
b. Temperature projections
Changes in temperature were statistically significant throughout Bolivia and coincided with changes in CO2 concentrations, with increasing values from B1, RCP4.5, A1B, A2, to RCP8.5. Weighted median changes of annual mean temperature were 2.6°, 2.9°, 3.8°, 4.4°, and 5.6°C for emission scenarios B1, RCP4.5, A1B, A2, and RCP8.5, respectively, with slightly higher medians in the Andes (Fig. 4). Changes in monthly temperatures were generally higher during the late dry season (August–November) and were less high during the wet season (December–February) (Fig. 5). Monthly weighted median changes ranged from 2° (SRES B1) to 8°C (RCP8.5) in the northern lowlands and from 2° (SRES B1) to 6°C (RCP8.5) in both southern lowlands and Andes. Weighting GCMs had no major impact on the resulting temperature scenarios.
Changes in annual mean temperature (°C) of the weighted ensemble comparing 1961–90 with 2070–99 for NLL, SLL, and AND under emission scenarios SRES B1, A1B, A2, RCP4.5, and RCP8.5. The central line within each box represents the weighted median value of the model ensemble. The top and bottom of each box show the weighted 83th and 17th percentiles, enclosing 66% of the data, and the top and bottom of each whisker display the weighted 95th and 5th percentiles, respectively. Gray boxes denote statistically significant changes with a 95% probability (t test).
Citation: Journal of Applied Meteorology and Climatology 52, 6; 10.1175/JAMC-D-12-0224.1
Changes in monthly mean temperatures (°C) from 1961–90 to 2070–99 for emission scenarios SRES B1, A1B, and A2, as well as RCP4.5 and RCP8.5. Also given are observed mean temperatures (OBS) from 1961 to 1990 for each region.
Citation: Journal of Applied Meteorology and Climatology 52, 6; 10.1175/JAMC-D-12-0224.1
c. Precipitation projections
1) Annual precipitation
A count of the number of GCMs that agree on statistically significant changes in annual precipitation revealed that most GCMs projected no significant changes under SRES B1, and the number of GCMs with significant increases and decreases was about equal (not shown). Under SRES A1B and A2, about one-half of the GCMs projected significant changes, but also here there was no clear preference for either increases or decreases. This result also applied to the reduced SRES sample of 12 GCMs, which only included predecessors of the CMIP5 GCMs used in this study (Figs. 6a,d). Under RCP4.5, most GCMs projected no significant changes; among the remaining GCMs, however, there were more models with statistically significant decreases than increases in both the northern and southern lowlands (Figs. 6b,e). The periods of comparison are 1961–90 and 2070–99. Under RCP8.5, more than one-half of the GCMs (up to 7 of 11 GCMs) projected significant decreases in the lowlands, with mean changes of up to −15% (Fig. 6i). This tendency for less rainfall was not just restricted to the Bolivian lowlands, but was also present in large parts of the Amazon basin, with mean decreases of up to −20% at the equator (Figs. 6g–i). Most GCMs from both ensembles did not project significant changes in the interannual variability of annual precipitation (not shown).
Number of GCMs per grid cell agreeing on significant (a)–(c) increases and (d)–(f) decreases in annually accumulated precipitation. (g)–(i) Mean changes (%) of annually accumulated precipitation (not weighted), and (j)–(l) number of GCMs per grid cell agreeing on significant changes in interannual variance in August. The results apply to SRES emission scenarios (left) A1B, (center) RCP4.5, and (right) RCP8.5. The CMIP3 ensemble was reduced to 12 GCMs, using models with corresponding versions in CMIP5 (B, D, G, J, M, N, O, P, R, S, V, and W). The periods of comparison are 1961–90 and 2070–99. Significance was tested with a t test with a 95% probability.
Citation: Journal of Applied Meteorology and Climatology 52, 6; 10.1175/JAMC-D-12-0224.1
The decrease of annual rainfall projected by the CMIP5 ensemble in the Bolivian lowlands was largest for RCP8.5 (−9% median and −17% 5th percentile; see Fig. 7). In the Andes, the CMIP3 ensemble tended toward slightly less annual rainfall (−3% median and −7% 5th percentile) while the CMIP5 ensemble tended toward slightly more annual rainfall (+3% median and +5% 83rd percentile). The projections of the individual GCMs varied so much, however, that changes of the weighted ensembles were not statistically significant. Weighting GCMs had no major impact on the projected changes.
As in Fig. 4, but for relative changes in annually accumulated precipitation (%).
Citation: Journal of Applied Meteorology and Climatology 52, 6; 10.1175/JAMC-D-12-0224.1
In summary, changes in annual rainfall remained uncertain in the lowlands for CMIP3 while CMIP5 GCMs were more inclined to project decreases (−9%) there, as well as in most of the Amazon basin. In the Andes, CMIP3 GCMs tended toward slightly less annual rainfall (−3%) while CMIP5 tended toward slightly more annual rainfall (+3%).
2) Monthly precipitation
Counting the number of GCMs agreeing on statistically significant changes in monthly precipitation revealed that both ensembles were clearly more inclined to project less rainfall from July to November in the lowlands, as well as in most of the Amazon. This was most evident in October under RCP8.5 with 6–10 of 11 GCMs projecting statistically significant decreases throughout most of the Amazon basin and no model projecting significant increases (not shown). In Bolivia the decrease in monthly precipitation was strongest in the northern lowlands with changes of the median by −29 mm month−1 (−19%) in November under RCP8.5 (Fig. 8). This decrease was accompanied by significant changes in interannual variability, projected by more than one-half of the GCMs from both ensembles, mainly during JJA (up to 10 of 12 GCMs under RCP8.5; see Figs. 6j–l). During the wet months of January–March, CMIP3 GCMs projected mainly an increase in rainfall in the lowlands (up to 8 of 18 GCMs under SRES A2), as well as in most of the Amazon basin (not shown). In Bolivia this increase was biggest under SRES A2 (+15 mm month−1 and +8%) and was absent for the CMIP5 ensemble (Fig. 8). In the Andes, the CMIP3 ensemble tended toward less rainfall (−11 mm month−1 and −9%) while the CMIP5 ensemble tended toward more rainfall (+24 mm month−1 and +20%) in parts of the wet season. For annual totals, the projections of the individual GCMs varied so much that changes in the weighted ensembles were not statistically significant. Weighting GCMs had no major impact on the changes projected. In summary, both ensembles agreed on less rainfall (−19%) in the lowlands during drier months (JJA and SON), with significant changes in interannual rainfall variability, but disagreed on changes during wetter months (JFM). In the Andes, CMIP3 GCMs tended toward less monthly rainfall (−9%), while CMIP5 tended toward more (+20%) monthly rainfall during parts of the wet season.
As in Fig. 5, but for changes in monthly precipitation (mm month−1).
Citation: Journal of Applied Meteorology and Climatology 52, 6; 10.1175/JAMC-D-12-0224.1
d. Shortwave radiation projections
Under all scenarios, at least one-half of the GCMs predicted a significant increase and only a very few predicted a decrease in annual SW radiation in the northern lowlands (not shown). This signal became stronger from the SRES to the RCP scenarios, with the latter predicting more annual SW radiation in the southern lowlands as well. In the Andes, no clear signal was visible under the SRES scenarios while under the RCP scenarios most GCMs predicted a significant increase.
The medians of the regional changes in annual SW radiation of the weighted ensemble were positive across all regions and scenarios, with 66% of the ensemble predicting an increase under emission scenarios SRES B1, RCP45, and RCP85 (Fig. 9). Weighted median changes in the annual mean SW radiation ranged from 1% to 3%, with likely changes as big as +11%.
As in Fig. 4, but for relative changes in annually accumulated SW radiation (%).
Citation: Journal of Applied Meteorology and Climatology 52, 6; 10.1175/JAMC-D-12-0224.1
Projected changes in monthly SW radiation showed the biggest increases for scenarios and seasons with decreases in rainfall. Increases in the median were as large as 20 W m−2 (9%) in September in the northern lowlands, 9 W m−2 (3%) in February in the southern lowlands, and 13 W m−2 (5%) in December in the Andes (Fig. 10). Despite the overall trend for more radiation, changes were not statistically significant.
As in Fig. 5, but for changes in monthly SW radiation (W m−2).
Citation: Journal of Applied Meteorology and Climatology 52, 6; 10.1175/JAMC-D-12-0224.1
5. Discussion
We validated 35 GCMs against observed surface air temperature, precipitation, and incoming SW radiation and analyzed climate change projections from five emission scenarios, distinguishing among three climatologically contrasting regions in Bolivia. GCMs revealed an overall cold, wet, and positive SW radiation bias and showed no substantial improvement from the CMIP3 to the CMIP5 ensemble for the Bolivian case. Models projected an increase in temperature (2.5°–5.9°C) and SW radiation (1%–5%), with seasonal and regional differences. In the lowlands, changes in annual rainfall remained uncertain for CMIP3 while CMIP5 GCMs were more inclined to project decreases (−9%). This result also applied to most of the Amazon basin, suggesting a higher risk of partial biomass loss for the CMIP5 ensemble results. Both ensembles agreed on less rainfall (−19%) during drier months (JJA and SON), with significant changes in interannual rainfall variability, but disagreed on changes during wetter months (JFM). In the Andes, CMIP3 GCMs tended toward less rainfall (−9%) while CMIP5 tended toward more (+20%) rainfall during parts of the wet season.
Our approach included the following five limitations:
We are aware that climate change projections have only limited validity in mountainous regions because of the coarse spatial resolution of GCMs. High-resolution regional climate modeling would therefore be more appropriate in the Andes. Forcing a regional climate model with multiple lateral boundary conditions, however, is very resource intensive, leading to very few model runs being available currently (e.g., Soares and Marengo 2009; Urrutia and Vuille 2009; Marengo et al. 2010). Most of these regional climate change scenarios are based on very few lateral boundary conditions (mainly HadAM3), limiting their robustness in a region with little agreement among GCMs on the directional change of rainfall. We therefore consider our approach to be a necessary precursor to future downscaling efforts in the region.
The stratification of Bolivia into three regions (northern lowlands, southern lowlands, and Andes) corresponded to the coarse spatial resolution of most GCMs but strongly simplified the true heterogeneity of the country. The merger of parts of the Andean slopes with the Altiplano into one region neglected the contrasting precipitation regimes caused by the steep orography of the Andes. Given the coarse resolution, however, such processes were hardly reproduced, making our stratification reasonable.
The final weights assigned to each GCM were to some extent subjective. We combined the weights related to temperature, precipitation, and SW radiation by forming the product, whereas a sum of the weights would have been an equally valid option. We preferred the product over the sum because it led to stronger differences among weights. Because weighting the ensembles had no major impact, however, uncertainties related to choice of method may be neglected.
The significance of monthly changes was tested with the Welch t test for weighted samples, despite the fact that for some cases the data were not normally distributed. We therefore tested significance for critical months with the nonparametric Mann–Whitney–Wilcoxon test, which did not lead to very different results. We were unfortunately not able to implement this test for the weighted ensemble but only for the unweighted ensemble, and therefore we adhered to the t test for weighted samples.
The observation period of SW radiation lasted only from 1979 to 1990. We assumed that this period was sufficiently long for obtaining mean values representative for the complete period 1961–90.
Our results confirm numerous findings from Christensen et al. (2007), Soares and Marengo (2009), Urrutia and Vuille (2009), and Marengo et al. (2010), including 1) a cold and wet bias for the Andean region, 2) stronger temperature increases during the austral winter, 3) great uncertainty associated with the directional change of annual rainfall, and 4) a tendency for an intensification of the hydrological cycle for CMIP3 GCMs, with more rainfall during the wet season. For SW radiation, our positive bias agreed with the positive bias of 6 W m−2 for the global average found for multiple CMIP3 GCMs, which was most likely determined by processes in the cloud-free atmosphere rather than by an anomalous absorption through clouds (Wild 2008).
Differences between projections from the CMIP3 and CMIP5 ensembles may emerge from differences in the emission scenarios, resolution, or processes. Given the lack of a common scenario, both ensembles cannot be compared directly. To make a comparison nevertheless, Knutti and Sedláček (2013) calibrated the simple Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC) to 19 CMIP3 models and ran it for the RCP scenarios. On a global scale, CMIP5 projections seem to be largely consistent with CMIP3 projections. The same study, however, reveals different rainfall projections for both ensembles in the Amazon region, suggesting that differences are not due to differences in the scenarios alone but also to the addition of new processes. Additional analysis will be required to determine which processes are responsible for differences in rainfall projections.
The projected changes may heavily affect human and natural systems in Bolivia. Climate change impact studies are required to further assess the potential implications for different sectors under different scenarios, however. Such studies could address potential risks for drinking water supply from glaciers, hydropower, agricultural production, and ecosystem stability. Given the large uncertainty of rainfall projections, it will be essential to incorporate a wide range of climate models in these studies. Furthermore, it is recommended to try to identify the factors that lead to a decrease in rainfall as well as changes in interannual rainfall variability during JJA in the Amazon. This process would require a detailed analysis of how well GCMs reproduce the synoptic-scale systems of South America and of how these systems change under scenarios of climate change.
To conclude, we hope that this research has contributed to a better understanding of climate change projections in Bolivia and has provided a basis for a discussion on climate change impacts and adaptation. Our findings may provide inputs to further assess how resilient human and natural systems are under different climate change scenarios.
Acknowledgments
This research was supported by the Departmental Pilot Program of Adaptation to Climate Change (PDACC) as well as the Raising the Alert about Critical Feedbacks between Climate and Land Use Change in Amazonia project (AMAZALERT). PDACC is carried out by the Fundación Amigos de la Naturaleza (FAN) as well as the departmental government of Santa Cruz and is funded by the embassy of the Netherlands. AMAZALERT is jointly funded by the European Seventh Framework Programme and national organizations. We thank the Bolivian SENAMHI for the provision of the meteorological data. MERRA data used in this study were provided by the Global Modeling and Assimilation Office at the NASA Goddard Space Flight Center. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for 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 leads development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We are grateful for the comments from three anonymous reviewers who have helped us to improve the quality of this paper.
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