Changing climate in the Bolivian Altiplano: CMIP3 projections for temperature and precipitation extremes

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1 Click Here for Full Article JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115,, doi: /2009jd012718, 2010 Changing climate in the Bolivian Altiplano: CMIP3 projections for temperature and precipitation extremes J. M. Thibeault, 1 A. Seth, 1 and M. Garcia 2 Received 23 June 2009; revised 25 November 2009; accepted 7 December 2009; published 22 April [1] Rural agriculture in the Bolivian Altiplano is vulnerable to climate related shocks including drought, frost, and flooding. We examine multimodel, multiscenario projections of eight precipitation and temperature extreme indices for the Altiplano and compute temperature indices for La Paz/Alto, covering Significant increasing trends in observed warm nights and warm spells are consistent with increasing temperatures in the tropical Andes. The increase in observed frost days is not simulated by the models in the 20th century, and projections of warm nights, frost days, and heat waves are consistent with projected annual cycle temperature increases; PDFs are outside their 20th century ranges by Projected increases in precipitation extremes share the same sign as observed trends at Patacamaya and are consistent with annual cycle projections indicating a later rainy season characterized by less frequent, more intense precipitation. Patacamaya precipitation indices show shifts in observed distributions not seen in the models until , implying that precipitation changes may occur earlier than projected. The observed increase in frost days can be understood within the context of precipitation changes and an increase in radiative cooling. Model warm/wet biases suggest that a decrease in frost days may not occur as early or be as large as projected. Nevertheless, consistencies between simulated and observed extremes, other than frost days, suggest the directions of projected changes are reliable. These results are a first step toward providing the critical information necessary to reduce threats to food security and water resources in the Altiplano from changing climate. Citation: Thibeault, J. M., A. Seth, and M. Garcia (2010), Changing climate in the Bolivian Altiplano: CMIP3 projections for temperature and precipitation extremes, J. Geophys. Res., 115,, doi: /2009jd Introduction [2] The Altiplano is a high plateau located in the central Andes of South America. Traditional methods of rainfed agriculture are practiced by approximately fifty percent of the rural population [Garcia et al., 2007]. More than 60% of annual precipitation occurs during the months of December through February and is associated with the southwest margin of the South American Monsoon (SAM) [Garreaud et al., 2003; Garreaud and Aceituno, 2001]. Crop production in the Altiplano is particularly vulnerable to climate related shocks including drought, flooding, frost and pests [Gilles and Valdivia, 2009]. Information about future changes in extreme precipitation and temperature is being sought by government and NGOs in the Altiplano for the purpose of developing strategies to reduce future climaterelated risks to agriculture. This research examines climate 1 Department of Geography, University of Connecticut, Storrs, Connecticut, USA. 2 Instituto de Investigaciones Agropecuarias y de Recursos Naturales, Universidad Mayor de San Andres, La Paz, Bolivia. Copyright 2010 by the American Geophysical Union /10/2009JD projections for the Altiplano region with a focus on the evolution of precipitation and temperature related extremes, and discusses their consistency with the projected changes in the annual cycle. [3] Analyses of 21st century climate projections for the Altiplano indicate that changes in mean temperature and precipitation are likely [Urrutia and Vuille, 2009; Seth et al., 2010; Bradley et al., 2006]. Mean temperature changes at high elevations in the Andes are expected to be greater than those in lower elevations [Bradley et al., 2006] and research efforts are ongoing to understand the effects of increasing temperatures on tropical Andean glaciers [e.g., Vuille et al., 2008; Juen et al., 2007; Bradley et al., 2006; Francou et al., 2003; Thompson et al., 2003; Vuille et al., 2003]. While projected changes in annual mean precipitation appear to be small, Seth et al. [2010] have noted a shift in the annual cycle of precipitation in the Altiplano toward reduced early season rains (October December) and increased peak season rains (January March). This shift is apparently related to similar changes documented in the larger scale SAM [Seth et al., 2009]. [4] Temperature increases are likely to have a substantial impact on agriculture in tropical regions. Battisti and Naylor 1of18

2 [2009] point out that projected mean growing season temperatures through much of the tropics will likely be outside the range of extremes experienced in the 20th century. While the need for climate projection information in the Altiplano is clear, and such information is essential to the development of local and national climate change adaptation policies [Giorgi et al., 2008], two challenges in this region include a lack of observations and climate model resolution. [5] Analyses of observed changes in temperature and precipitation related extremes require daily data sets of sufficient quality and length [e.g., Easterling et al., 1999; Alexander et al., 2006]. Observed changes in the frequency and intensity of extreme temperature [Vincent et al., 2005] and precipitation [Haylock et al., 2006] events have been documented for South America; only one station in the Altiplano (Patacamaya, Bolivia) had daily data that met the quality and length criteria to be included in these studies. While analyses of global observations show trends in extreme temperatures consistent with warming [Frich et al., 2002; Alexander et al., 2006], no significant trends in temperature extremes were identified at Patacamaya by Vincent et al. [2005]. Consistent with increasing precipitation intensity observed in many regions [e.g., Frich et al., 2002; Groisman et al., 2005; Alexander et al., 2006], significant increasing trends were identified in several extreme precipitation indices at Patacamaya by Haylock et al. [2006]. This study examines daily data for La Paz/Alto, Bolivia (located in El Alto at the airport, hereafter La Paz), covering , providing a new set of extreme temperature indices (as defined by Alexander et al. [2006]) for the Altiplano. In the present analysis, precipitation indices from Patacamaya and temperature indices computed for La Paz are compared with model simulations for the 20th century. [6] At current horizontal resolutions, coupled global climate models are not capable of representing complex eventdriven climatic extremes which occur rarely, but research suggests that such models can simulate the more simple and frequently occurring statistics related to high and low daily temperature and precipitation [Easterling et al., 2000; Meehl et al., 2000, 2005]. Simulated temperature and precipitation extremes have been analyzed by Tebaldi et al. [2006] for 20th century historical simulations and 21st century projections using output from the World Climate Research Program (WCRP) Coupled Model Intercomparison Project version 3 (CMIP3) [Meehl et al., 2007b]. Simulated historical trends in temperature extremes are qualitatively consistent with observed global trends and are considered to be reliable [Tebaldi et al., 2006]. Projected temperature extremes are consistent with what would be expected in a warmer world (e.g., fewer frost days, more warm nights, increasing heat waves). Projected precipitation extremes suggest greater precipitation intensity, but the CMIP3 models disagree about regional patterns of change. Previous studies have shown that models have less skill at simulating precipitation extremes regionally [Kiktev et al., 2003, 2007; Kharin et al., 2007] and identification of trends can be somewhat dependent on the index being examined [Meehl et al., 2007a; Sillmann and Roeckner, 2008; Alexander and Arblaster, 2009]. [7] Frost days, which are a threat to agriculture in the Altiplano, were not analyzed for tropical regions by Tebaldi et al. [2006]. In the Altiplano, increases in consecutive dry days, warm nights, heat waves, and the extreme temperature range are expected by However, climate change projections for the next two or three decades are more relevant to decision makers and planners who are developing adaptation strategies now. [8] The analysis performed here specifically for the Altiplano involves the added challenge of a relatively small, high elevation region ( m). The CMIP3 models tend to produce excess precipitation near the Andes [Vera et al., 2006; Seth et al., 2010], but most are able to realistically simulate the phase of the annual cycle in the core region of the SAM [Bombardi and Carvalho, 2009]. Being relatively flat, the Altiplano is coarsely represented in the mediumand higher resolution global models employed here. In addition, consistencies between the large scale projections for the SAM [Seth et al., 2009] and Altiplano [Seth et al., 2010] projections suggest that the current models may be able to provide qualitative information about the direction of future changes in climate extremes for the Altiplano, while the tools needed to assess regional scale climate change continue to improve. [9] This research explores the evolution of extreme temperature and precipitation indices for the Bolivian Altiplano in the 20th century and Special Report on Emissions Scenarios (SRES) B1, A1B, and A2 scenarios (B1, A1B, and A2, hereafter) using nine CMIP3 models. Ten extreme climate indices (defined by Frich et al. [2002]) based on simulated daily temperature and precipitation are available for the CMIP3 models. This study examines eight of these ten indices that are relevant for the Altiplano, focusing on time evolution of the indices and changes in probability density functions (PDFs) for the middle of the 21st century ( ), an appropriate time frame for adaptation, as well as the end of the century ( ). To better understand the model projections, this study makes qualitative comparisons between simulated Altiplano extremes and observed precipitation extremes at Patacamaya and observed temperature extremes that we calculate for La Paz, Bolivia. Though climate models cannot simulate extremes as they occur in nature, if there are consistencies between simulated and observed extremes (e.g., the directions of trends), greater confidence can be placed in the projections. The Altiplano spans an area from 13 S 25 S and 65 W 70 W; however, the southern region is arid and it is the northern Lake Titicaca region which lies on the southern margin of the SAM, where conditions are more favorable for agriculture [Garcia et al., 2007]. [10] The remainder of the paper is structured as follows. Section 2 describes the data sets and methods used in the study. The results follow in section 3. We begin with observed extremes indices for comparison with the modeled indices: (1) temperature indices for La Paz and (2) PDFs of precipitation indices from Patacamaya. Our analysis of the simulated extremes indices follows, including comparison with projected changes in the annual cycle. The results are followed by a discussion in section 4, which is followed by our conclusions. 2. Data and Methods [11] Eight simulated annual indices are analyzed (defined in Table 1); four indices derived from daily minimum and/or 2of18

3 Table 1. Modeled Extreme Indices Used in This Study and Their Definitions a Index Name Definition Unit Extreme temperature range xtemp range Difference between the highest and lowest temperature K observations within a given year Frost days frost days Total number of days with minimum temperature <0 C days Heat wave duration index heat waves Max. period of at least 5 d when T max >5 C above the days daily T max average Warm nights warm nights Percent of time in a year when T min >90th percentile % of minimum temperature for a particular calendar date Consecutive dry days dry days Maximum number of consecutive dry days (R day <1 mm) days 5 day precipitation 5 day precip Maximum 5 day precipitation total mm Precipitation >95th percentile precip >95th Fraction of total annual precipitation from events >the % th percentile Precipitation intensity precip intensity Annual total precipitation divided by the number of days with precip. 1 mmd 1 mm d 1 a From Frich et al. [2002] and Tebaldi et al. [2006]. maximum temperature: extreme temperature range, frost days, heat wave duration index, and warm nights, and four from daily precipitation: consecutive dry days, 5 day precipitation, precipitation >95th percentile, and precipitation intensity. Hereafter, all discussion of the simulated indices will use the names listed in Table 1. [12] Simulations from nine CMIP3 global coupled climate models are analyzed. Extreme indices are not available for all models in all scenarios (see Table 2). 20th century historical simulations and 21st century B1, A1B, and A2 emissions scenarios are examined using a single realization from each model. Intermodel variability is larger by an order of magnitude than the internal variability of the models. By using only one realization from each model, we are sampling the larger uncertainty. Pierce et al. [2009] have demonstrated that incorporating more models into the multimodel average actually improves model skill more quickly than including a larger number of realizations of the same model in the multimodel average. In cases where more than one realization was available, we chose the run at random. [13] This research focuses on the northern Altiplano (Figure 1). All calculations for the models are averaged for the area 16 S 19 S by 67 W 70 W. Model selection was based on resolution and the availability of extremes indices. Eight models are used for the B1 scenario, nine for A1B, and seven for A2. Medium and high resolution models were carefully examined to determine how well they represent the northern Altiplano. The area defined by 16 S 19 S/ 67 W 70 W is above 2000 m for all grid points in all models selected with one exception: one grid point in the CCSM3 model has an elevation of 1828 m. Because the sensitivity of the area averaged surface temperature annual cycle to the inclusion of this grid point was very small compared with the warm bias, it was retained in the analysis. Being relatively flat, the models do represent the northern Altiplano, but several are lower than observed and none represent the highest peaks in the region. Extending the region to 15 S 20 S by 65 W 70 W includes more grid points from elevations below 2000 m, especially in the higher resolution models, increasing warm and wet biases. Sensitivity of the results to area definition will be explored in the discussion. [14] Observed extreme indices from La Paz (16.50 S/ W, 4061 m) and Patacamaya (17.20 S/67.92 W, 3789 m) are used to validate model simulations of the indices and identify consistencies in the directions of trends. Table 2. CMIP3 Coupled Ocean Atmosphere Models Used in This Study Modeling Center Model Name Atmosphere Resolution a Ocean Resolution b National Center for Atmospheric Research CCSM3 c Meteo France, Centre National de Recherches Meteorologiques CNRM CM U.S. Department of Commerce/NOAA/Geophysical Fluid GFDL CM Dynamics Laboratory U.S. Department of Commerce/NOAA/Geophysical Fluid GFDL CM Dynamics Laboratory Center for Climate System Research (University of Tokyo), MIROC3.2 MedRes National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC) Center for Climate System Research (University of Tokyo), MIROC3.2 HiRes c National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC) National Center for Atmospheric Research PCM Institut Pierre Simon Laplace IPSL CM Meteorological Research Institute MRI CGCM2.3.2 d a Longitude by latitude in degrees. b Number of grids in longitude and latitude. c Annual extreme indices not provided for A2 scenario. d Annual extreme indices not provided for B1 scenario. 3of18

4 Figure 1. The northern Altiplano: 16 S 19 S and 67 W 70 W (shaded). The comparison of areally averaged data from the models with station (point) data can be problematic [Chen and Knutson, 2008], but our main intention is to make qualitative comparisons regarding the signs of observed and simulated trends. [15] Temperature extreme indices are calculated for La Paz using the U.S. National Climatic Data Center s (NCDC) Global Surface Summary of the Day (GSOD), and cover the period The definitions of observed warm nights and warm spells differ from the model definitions (see Table 3). After the modeling centers submitted extreme indices to the CMIP3 archive, several statistical problems were discovered in indices calculated using the original definitions of Frich et al. [2002] [Alexander et al., 2006]. Modeled indices could not be recalculated using the new definitions because the original daily model output is not available. Percentiles for observed warm nights and warm spells now use the bootstrapping method of Zhang et al. [2005] to address problems related to inhomogeneities that exist at base period boundaries [Alexander et al., 2006]. In Australia, trends in warm nights and heat waves calculated using the definition of Frich et al. [2002] were about twice as large as trends in warm nights and warm spells calculated using the definition of Alexander et al. [2006] [Alexander and Arblaster, 2009]. Many temperature data for La Paz have a precision of 1 C, especially in the earlier portion of the record. Low precision in the temperature data, as is the case for La Paz, is known to produce a bias in the calculation of percentile based temperature indices [Zhang et al., 2009]. Therefore it was necessary to artificially restore the data precision, as described by Zhang et al. [2009], before calculating warm spells and warm nights for La Paz. These issues make quantitative comparisons between the observed and modeled indices impossible, and our discussion will emphasize the qualitative aspects of indicated changes. Hereafter, all discussion of La Paz temperature indices will use the names listed in Table 3. [16] Precipitation extreme indices for Patacamaya ( ) were provided by the CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) Climate Extreme Indices data set available at their Web site: The definitions for observed 5 day precip, precip >95th, and precip intensity are the same as those for the models. The definition for observed dry days is the same as for the models except that a spell can continue across calendar years [Alexander and Arblaster, 2009]. Because the calendar year changes during the Altiplano wet season, the difference in definition is not likely to present a problem in analysis of dry days in the Altiplano. [17] The time evolution of changes in simulated annual extreme indices is evaluated by calculating multimodel average time series for each scenario covering the period Time series for were produced by appending data from 21st century scenarios to data from the 20th century experiments for each model. The time series of each index for each model were standardized, adjusting for any absolute differences in the models and anomalies were calculated with respect to the base period of Anomalies were standardized by the standard deviation of the detrended base period and the time series for each model in each scenario were then centered by removing the average before calculating the multimodel averages. Each multimodel average time series is smoothed by a 10 year running average, indicating the direction of change. The significance of trends in the multimodel averages are evaluated at the 90% confidence level using the nonparametric Mann Kendall trend test, which has been used extensively to evaluate trends in environmental time series [Mann, 1945; Hipel and McLeod, 1994]. As a measure of intermodel variability, the width of one standard deviation of the ensemble mean is shown with each time series. PDFs of multimodel averages of the eight selected extreme indices are plotted for two periods in the 20th century ( and ) and two periods in the 21st century (2020 Table 3. Trends in Annual Temperature Extreme Indices at La Paz, Bolivia, for a Index Name Definition t p Value Xtemp range same as models Frost days same as models Warm nights T min >90th percentile, but percentile calculation uses bootstrapping method of [Zhang et al., 2005] (see text) Warm spells maximum period >5 consecutive days with T max >90th percentile of daily T max for base period. Percentiles are calculated using bootstrapping method of [Zhang et al., 2005] and spells continue across calendar years a Boldface indicates significance at the 90% confidence level based on the Mann Kendall trend test. Indices were derived from NCDC GSOD data. 4of18

5 2049 and ) to identify shifts in the distributions for each scenario. Kolmogorov Smirnov tests were performed on the modeled indices to determine whether the middle and late 21st century samples are significantly different from [18] Trends in La Paz temperature indices were identified and tested for statistical significance at the 90% confidence level using the Mann Kendall trend test. Relative frequency histograms and PDFs of xtemp range, frost days, warm spells, and warm nights were produced for comparison with simulated indices. Precipitation indices at La Paz could not be calculated for most years because many precipitation data are missing. To explore the precipitation indices for Patacamaya beyond what was presented by Haylock et al. [2006], we plot PDFs of dry days, 5 day precip, precip >95th, and precip intensity for and Kolmogorov Smirnov tests were used to determine whether these samples are significantly different from each other. [19] Examination of simulated extreme indices appears to provide insight into the projected shift in the annual cycle of precipitation identified by Seth et al. [2010]. However, their selection of models was not constrained by the availability of extremes data. For consistency, this study recalculates changes in the annual cycles of temperature and precipitation using the same models employed in the extremes analysis. The changes in each scenario are analyzed by calculating the differences between the middle ( ) or late 21st century ( ) and late 20th century ( ) climate and then standardizing by each model s late 20th century standard deviation. The multimodel statistics for present day and future climate are represented by box plots, which provide information on variability among the models. Differences in 21st century minus multimodel mean monthly, seasonal (SON and JFMA), and annual precipitation were tested for their significance. Each sample was first tested for normality using the Shapiro Wilk test. F tests were then used to compare the variances between normally distributed 21st and 20th century samples. Twosample t tests (Wilcoxon tests) were performed for normally (not normally) distributed samples to identify significant differences in multimodel mean precipitation at the 90% confidence level. 3. Results [20] First, we present our results for the Altiplano observed extreme indices: temperature indices that we calculate for La Paz, and PDFs for Patacamaya precipitation indices. We then present results for the multimodel simulated extremes, including comparison with observed extremes. The simulated extreme indices are then compared to projections for the annual cycle Observed Extreme Indices [21] We begin with La Paz temperature indices, which provided annual indices for 94% of the period Trends are shown in Table 3 along with index definitions. Positive trends exist in all four indices: xtemp range, frost days, warm nights, and warm spells. Trends in warm nights and warm spells are significant. Zhang et al. [2009] have demonstrated that after artificially restoring low precision temperature data, exceedance rates have long term means and trends similar to those from data that are originally of the same precision as the artificially restored data. This provides a measure of confidence in our results for La Paz warm nights and warm spells, which were calculated with precision adjusted data. [22] PDFs of precipitation extreme indices at Patacamaya are shown for two periods ( and ) in Figure 2. The PDFs of dry days (Figure 2a) and precip >95th (Figure 2c) suggest positive shifts in , but they are not significantly different from the samples according to Kolmogorov Smirnov tests. The distributions of 5 day precip (Figure 2b) show no clear differences between and The distribution of precip intensity (Figure 2d) is more variable compared to ; it has longer tails at both ends, with a larger increase at the high end. The time periods selected for comparison coincide with a cold and warm phase of the Pacific Decadal Oscillation (PDO), which entered a warm phase in the late 1970s [Mantua et al., 1997]. Decadal variability may also be an important factor in explaining the differences in rainfall extremes at Patacamaya for the two periods. [23] To summarize, increasing trends were found in all four temperature indices at La Paz. Increases in warm nights and warm spells are significant. Frost days and warm nights, both based on daily minimum temperatures, exhibit simultaneously increasing trends. This result is somewhat unexpected, but can be understood within the context of precipitation changes. Trends in extreme precipitation indices at Patacamaya suggest that rainfall events may be occurring less frequently in the Altiplano. If this is the case in La Paz, there may be an increase in the number of clear nights and radiation frosts, offering a possible explanation for the increase in frost days. PDFs of the precipitation indices suggest shifts in distributions: positive shifts in dry days and precip >95th, higher variability in precip intensity with larger changes at the high end of the distribution. Kolmogorov Smirnov tests did not reveal any statistically significant differences in the distributions of precipitation indices in relative to Simulated Temperature Related Extreme Indices [24] This section presents the multimodel results for temperature related extreme indices. The results are discussed by index and include analysis of the time evolution of the indices over , and examination of PDFs for , , , and Observed temperature extremes from La Paz are compared to 20th century simulated extremes to identify qualitative consistencies. Time series of multimodel average temperature indices are shown in Figure 3, relative frequency histograms and PDFs of temperature extremes at La Paz are shown in Figure 4, and PDFs of multimodel temperature extremes are shown in Figure 5. Standardized changes in the annual cycle of temperature are shown in Figure 6. [25] Xtemp range is projected to increase by the end of the 21st century in all three scenarios, sharing the same sign as the trend observed at La Paz (Figure 3a). The largest increase shown by the 10 year running average is approximately 1.0 standard deviation (hereafter, SD) in the A2 scenario; the increase is about 0.7 SD in the B1 and A1B scenarios. The range of observed xtemp range at La Paz (about 10 C) is 5of18

6 Figure 2. PDFs of precipitation extreme indices for Patacamaya, Bolivia. Two periods are shown for the 20th century: (solid line) and (dashed line). Data are from the ETCCDI Climate Extreme Indices data set available at more than twice as large as the simulated 20th century xtemp range (about 4 5 C for each group of models) (Figures 4a, 5a, 5b, and 5c). PDFs of multimodel average xtemp range exhibit shifts toward higher values by the middle and late 21st century in each scenario (Figures 5a, 5b, and 5c). However, in the B1 scenario, low values of xtemp range are still close to low values for the 20th century. The lowest values of xtemp range in the A1B and A2 scenarios are approximately 1 2 C higher than the lowest 20th century values. [26] Preliminary analysis revealed that the IPSL CM4 model is not simulating frost days for the Altiplano in most years. It also overestimates Altiplano temperature by approximately 8 C throughout the year, which is approximately 4 C greater than the next warmest models. IPSL CM4 was excluded from the frost days analysis because it would not provide any useful information about changes in frost days for the Altiplano. Despite its poor simulation of frost days in the Altiplano, the IPSL CM4 model is employed in the multimodel averages of the remaining seven indices in order to include the maximum number of models possible. [27] Frost days decrease over in all three scenarios (Figure 3b). From approximately , each scenario shows a decrease in frost days of about SD. This is in contrast to the increase in frost days observed at La Paz, and is discussed below. From 2000 to 2099, each scenario shows a decrease in frost days of approximately 2.0 SD. Multimodel averages of frost days are low compared to the number of frost days that occur annually in La Paz, consistent with warm biases in several models (Figures 4b, 5d, 5e, and 5f). In each scenario, distributions of frost days (Figures 5d, 5e, and 5f) show a steady shift toward lower values from the middle 20th century to the late 21st century, with the largest reduction in frost days occurring in the A2 scenario. By the late century, the distributions of frost days do not share any values with those from the 20th century in the A1B and A2 scenarios, suggesting that the highest number of annual frost days in the late 21st century will be less than the lowest number of annual frost days in the 20th century. [28] Heat waves (Figure 3c) increase sharply after approximately 2040 in all three scenarios, but appear to increase more slowly in the B1 scenario after By 2099, heat waves increase by about 3.0 SD in the A2 and A1B scenarios and by about 2.0 SD in the B1 scenario. The PDFs of multimodel averages of heat waves (Figures 5g, 5h, and 5i) show increases in range and variability from the 20th century to the middle century ( ). By the late century ( ), all three scenarios show dramatic 6of18

7 Figure 3. Simulated time series of temperature extreme indices for for the Altiplano. The 21st century simulations were appended to 20th century simulations. The resulting time series have been standardized for each model, then averaged to provide a multimodel time series for each scenario. Thick lines show 10 year running averages. Shading represents the width of 1 standard deviation of the ensemble mean. The 21st century scenarios are shown for the B1 (blue, eight models), A1B (green, nine models), and A2 (red, seven models) scenarios. Note that each scenario uses a different subset of models, resulting in differences in the 20th century portions of the time series. changes in heat waves relative to the middle century with large shifts toward higher values and greater variability. It should be noted that heat waves, as defined by Frich et al. [2002], is not a statistically robust index because it uses a fixed threshold of 5 C above the daily T max average in its definition. This is problematic in some regions (e.g., the tropics) where there is little variability in daily temperatures [Kiktev et al., 2003; Alexander et al., 2006]. Warm spell duration at La Paz is calculated from a percentile based threshold and is not directly comparable to modeled heat waves (Figures 4c, 5g, 5h, and 5i). [29] Warm nights increase in all three scenarios by about 1.0 SD from (Figure 3d). Though they are not directly comparable, trends in modeled warm nights and observed warm nights at La Paz share the same sign. From 2000 to 2099, warm nights increase dramatically in each scenario: an additional 5.0 SD in the A2 and A1B scenarios, and 4.0 SD in the B1 scenario. The multimodel averages of warm nights in the 20th century are consistent with those observed at La Paz (Figures 4d, 5j, 5k, and 5l). The vertical line in Figure 4d indicates the base period average, which is close to 10%. PDFs of the multimodel averages for the 20th century indicate a shift toward more warm nights and higher variability in relative to (Figures 5j, 5k, and 5l). By the middle century ( ), there is a shift toward more warm nights with increased variability in all three scenarios. By the late century ( ), the PDFs show larger shifts toward more warm nights, not sharing any values with the 20th century distributions in all three scenarios. [30] Seth et al. [2010] have previously shown that Altiplano temperature is expected to increase throughout the annual cycle by the end of the century. For consistency, we have recalculated the changes using the models and area definition employed in the extremes analysis. Our results affirm earlier results for the late century and also show results for the middle century ( ). [31] The multimodel medians and means increase by approximately 2.0 SD across all scenarios by (Figures 6a, 6c, and 6e). By , multimodel statistics show that there is less agreement among the models in the magnitude of the temperature increase, but multimodel means and medians suggest an increase of SD throughout the year across all scenarios (Figures 6b, 6d, and 6f). [32] In summary, projections of frost days, heat waves, and warm nights are consistent with the projected warming. With the exception of xtemp range, intermodel variability is smaller than the projected changes. Mann Kendall trend tests reveal that increasing trends in warm nights, heat waves, and xtemp range are significant (all p values <0.000). Trends in simulated warm nights and xtemp range share the same sign as those observed at La Paz. The negative trends in frost days are also significant (all p values 7of18

8 Figure 4. Extreme temperature indices for La Paz, Bolivia ( ). The base period is Relative frequency histograms (shaded) and PDFs (black lines) are shown. Vertical line in Figure 4d indicates base period average of warm nights. <0.000). The decrease in simulated frost days is inconsistent with the increasing trend at La Paz. However, the increase at La Paz can be understood within the context of observed and projected precipitation changes: in the early stages of warming, an increase in clear nights may lead to an increase in radiation frosts. The models at present are not able to simulate this observed increase in frost days. The difference in projected and observed trends in frost days will need to be evaluated with high resolution models. One possible explanation for the increase in xtemp range, which is based on only two temperatures out of the year, is that very low minimum temperatures will still occur while maximum temperatures increase. The PDFs of frost days and warm nights show values that are completely outside the range of 20th century values. Kolmogorov Smirnov tests revealed that the distributions of frost days, heat waves, and warm nights for the middle and late century are significantly different from distributions in all scenarios (all p values <0.000). The distribution of xtemp range is significantly different from in all scenarios by the late century (all p values <0.05). The result for heat waves should be taken with caution because the model definition is not statistically robust, especially in regions like the tropics where there is little variability in daily maximum temperatures. The term heat waves, as defined here for the model simulations, is usually more applicable in warm regions and cities where larger populations are vulnerable to the effects of extremely high temperatures. In this sense, an increase in heat waves may be less of a health concern for 8of18

9 Figure 5. PDFs of multimodel average extreme temperature indices for the Altiplano; 20th century simulations ( and ) and 21st century projections ( and ). (a,d,g,j)b1models(blue),(b,e,h,k)a1bmodels(green),and(c,f,i,l)a2models(red).for consistency, and PDFs use the same models as 21st century PDFs. 9of18

10 Figure 6. Standardized monthly temperature differences (21st century minus ) for the middle ( ) and late 21st century ( ): (a) B1 midcentury, (b) B1 late century, (c) A1B midcentury, (d) A1B late century, (e) A2 midcentury, and (f) A2 late century. Box plots show multimodel statistics: the interquartile range (IQR, shaded) and multimodel median values (horizontal black lines); whiskers represent the furthest model value within 1.5 times the IQR, and outliers are shown as open circles. Circled cross symbols indicate the multimodel average. society in the Altiplano, but rather a concern for the effects of such temperatures on water availability and agriculture Simulated Precipitation Related Extreme Indices [33] This section presents the multimodel results for precipitation related extreme indices. 20th century simulations are compared to observed precipitation extremes from Patacamaya. Time series of multimodel average precipitation indices are shown in Figure 7, PDFs of precipitation extremes at Patacamaya are shown in Figure 2, and PDFs of multimodel temperature extremes are shown in Figure 8. Standardized changes in the annual cycle of precipitation are shown in Figure 9. [34] Dry days increase in each scenario from the first decade of the 21st century to about 2040, sharing the same sign as the trend at Patacamaya (Figure 7a). From 2040, dry days in the B1 scenario remain steady throughout the rest of the century with a total increase of about 0.5 SD. Dry days in the A1B scenario increase until reaching a peak at approximately 2060, and then change little from 2070 onward, reaching a total increase of about 0.5 SD. The A2 scenario shows a steady increase throughout the century, increasing by approximately 1.0 SD by Maximum values of the 20th century multimodel averages of dry days are low compared to the maximum values of dry days at Patacamaya, which can be greater than days 10 of 18

11 Figure 7. As in Figure 3 but for precipitation extreme indices. (Figures 2a, 8a, 8b, and 8c). This discrepancy is likely due to excess precipitation produced by the models because they do not represent the highest altitudes of the Andes, but may also be explained by the comparison of a single station with an areal average. PDFs of multimodel averages of dry days show little change during the 20th century (Figures 8a, 8b, and 8c). By the middle century, all three scenarios show a shift toward longer periods of dry days. By the end of the century, dry days increase in all three scenarios relative to the middle 21st century. [35] Five day precip shows little change over the 20th century (Figure 7b). From the early 21st century to about 2020, 5 day precip increases in each scenario, again sharing the same sign as the trend at Patacamaya. It then remains steady until and then increases further in each scenario. By 2100, the changes in each scenario are less than 1.0 SD, with the smallest change in the B1 scenario. Minimum and median values of 20th century multimodel averages of 5 day precip are high compared to the observed minimum and median values at Patacamaya (Figures 2b, 8d, 8e, and 8f), consistent with the excess moisture simulated by the models. Multimodel average PDFs of 5 day precip in the 20th century are similar in and (Figures 8d, 8e, and 8f). Each scenario shows a shift toward higher 5 day precip by the middle century that continues into the late century, but many of the values for the 20th century overlap with 21st century values, suggesting that the changes may be smaller relative to the other precipitation indices. [36] Precip >95th shows little change over the 20th century (Figure 7c). By 2100, it increases by approximately 0.8 SD for the B1 scenario and 1.0 SD for the A1B and A2 scenarios. The positive trend in precip >95th shares the same sign as the observed trend at Patacamaya. Minimum values of precip >95th in the 20th century multimodel averages are high compared to minimum values of precip >95th at Patacamya, which can be as low as 0.0% in some years (Figures 2c, 8g, 8h, and 8i). PDFs of simulated precip >95th for the 20th century are similar (Figures 8g, 8h, and 8i). By , precip >95th shows a shift toward higher values in each scenario, but it also becomes more variable. This shift continues into , with the largest variability in the A1B scenario. [37] Precip intensity increases from the late 20th century onward, sharing the same sign as the observed trend at Patacamaya (Figure 7d). By 2100, the increase is approximately 1.0 SD in all three scenarios. Minimum values of precip intensity in 20th century multimodel averages are high compared to those at Patacamaya, again consistent with excess moisture in the models. The multimodel average is less variable than observed, with a range of 2 mm/d compared to 4 5 mm/d at Patacamaya (Figures 2d, 8j, 8k, and 8l). The multimodel PDFs of precip intensity (Figures 8j, 8k, and 8l) exhibit shifts toward higher precipitation intensity in the 21st century, but many values overlap with 20th century values, indicating relatively small changes. [38] Seth et al. [2010] have previously identified a shift in the annual cycle of precipitation for the Altiplano. For consistency, we have recalculated the changes using the models and area employed in the extremes analysis. By the middle century, multimodel means and medians show small changes in the Altiplano annual cycle of precipitation (Figures 9a, 9c, and 9e). Early season precipitation (SON) changes are very small, but multimodel means and medians suggest a tendency toward higher precipitation during the rainy season (January April), which is statistically 11 of 18

12 Figure 8. As in Figure 5 but for precipitation extreme indices. 12 of 18

13 Figure 9. As in Figure 6 but for standardized precipitation differences, including annual and seasonal (SON, JFMA) multimodel averages. significant at the 90% level (+4.2%) in the B1 scenario (Table 4). By the late century, the rainy season changes in the multimodel means and medians are larger in the B1 and A1B scenarios (Figures 9b, 9d, and 9f). In the A2 scenario, rainy season multimodel mean and median values for are similar to middle century values. Variability among the models increases in all three scenarios by late century. Early rainy season multimodel means suggest a decrease in precipitation, but multimodel medians show small changes in all scenarios. Late century changes in the multimodel seasonal means are statistically significant in all three scenarios: increased precipitation during the rainy season (+3 6%) and decreased in the early season ( 3.5 to 7%). These results suggest that by the late century, the rainy season may be shorter and more intense and the dry season may extend longer into what is now the early rainy season. [39] Note that precipitation decreases during the dry season (May August) are not very meaningful for the Altiplano because precipitation is already very low during these months, less than 0.5 mm/d. Projected changes in total annual precipitation are generally small and not significant. [40] Mann Kendall tests confirm that increasing trends in dry days, 5 day precip, precip >95th, and precip intensity in the modeled precipitation indices are significant (all p values <0.000). The positive trends in modeled precipitation indices share the same sign as trends calculated for Patacamaya by Haylock et al. [2006], but intermodel variability is relatively large compared to the projected changes. Modeled precipitation indices have values that are consistent with excess moisture in the models; dry days are too low and the other indices have minimum and median values that are too high. PDFs of simulated precipitation indices suggest that increases will be small; many 21st century values overlap with 20th century values. Kolmogorov Smirnov tests show that middle and late century distributions of dry days, precip >95th, and precip intensity are significantly different from 13 of 18

14 Table 4. Multimodel Projected Precipitation Changes for the B1, A1B, and A2 Scenarios for the Middle ( ) and Late ( ) 21st Century for the Altiplano a B1 A1B A2 Midcentury Late Century Midcentury Late Century Midcentury Late Century Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec JFMA SON Annual a Differences are shown in percent change. Boldface indicates statistical significance at the 90% confidence level distributions (all p values <0.05). Distributions of 5 day precip are significantly different from distributions in all scenarios by the late century (all p values <0.000). The expected increases in dry days, 5 day precip, precip >95th, and precip intensity are consistent with annual cycle projections for a drier or weaker early rainy season and shorter more intense peak rainy season. 4. Discussion [41] Our results indicate positive trends in observed temperature indices at La Paz: xtemp range, frost days, warm nights, and warm spells, of which warm nights and warm spells are significant. Haylock et al. [2006] identified increasing trends in precipitation indices at Patacamaya: dry days, 5 day precip, precip >95th, precip intensity, of which precip >95th was significant. With the exception of frost days, the observed trends agree in direction with the projected trends. In this section we will discuss the observed versus projected trends, and begin with a sensitivity analysis. [42] Because the resolution of the CMIP3 models presents a challenge in our analysis for the Altiplano, we examine the sensitivity of our results to area definition. Individual model grid points do represent the northern Altiplano with elevations between approximately 2000 m and 4000 m within 16 S 19 S and 67 W 70 W (defined as the northern Altiplano, hereafter, study region ). Because this region is small, we reexamine frost days for a larger region encompassing 15 S 20 S by 65 W 70 W (hereafter, extended region ). [43] PDFs of frost days illustrate the sensitivity of our results to area definition (Figure 10). The extended region includes more grid points from lower elevations, increasing the warm and wet biases in the results. Simulated frost days are sensitive to the area selected, but the direction of change is not. The results continue to indicate that the number of frost days is expected to decrease through the 21st century with late century values largely outside the range of 20th century frost days. Standardized time series show that the decrease in frost days in the extended region (not shown) is approximately 3.0 SD (covering ) compared to approximately 4.0 SD in the study region. Standardized time series of dry days and precip intensity in the extended region show similar changes to those for the study region. Their PDFs (not shown) indicate a larger wet bias in comparison with the study region. Frost days seem to be the most sensitive index to area selection. Similarities in the standardized time series of dry days and precip intensity in the extended region and study region provide added confidence that the extremes projections presented above are reliable, indicating the direction of future changes. [44] The observed temperature indices at La Paz (section 3.1), were shown to have increasing trends in warm nights and warm spells, which are consistent with increasing temperature trends identified in the tropical Andes by a number of studies [see Vuille et al., 2008]. However, unexpectedly, frost days and warm nights both exhibit increasing trends. An additional index calculation for cold nights, defined as the number of days within a year in which T min <10th percentile also shows an increasing trend at La Paz. Though this trend is not statistically significant, it provides further evidence that the frequencies of both cold and warm nights are increasing at La Paz. Vincent et al. [2005] also identified positive trends in cold nights and warm nights at Patacamaya covering that were not statistically significant. The increasing trends in cold nights and warm nights at Patacamaya provide evidence that the simultaneous increases and decreases in minimum temperatures found at La Paz may not be an isolated occurrence. [45] We found that these observed increasing trends in frost days and cold nights at La Paz are not simulated by the models in the 20th century. In the early stages of warming, there may be more frequent radiation frosts resulting from clear nights due to precipitation changes. However, frosts are likely to decrease as warming continues. Because the 20th century simulated frost days are fewer than those observed at La Paz, the reduction in frost days may not be as large or occur as soon as the models project. A reduction in frost days may be beneficial to agriculture in the Altiplano if water supplies are adequate. However, the expected higher temperatures may limit the potential benefits from a reduction in frost days unless there is an increase in precipitation large enough to offset increased evaporation. The projected 14 of 18