Annex Document

Climate and Climate Change in Ecuador: An Overview

Erin Burke, Department of Earth and Space Sciences Naomi Goldenson, Department of Atmospheric Sciences Twila Moon, Department of Earth and Space Sciences

Stephen Po-Chedley, Department of Atmospheric Sciences

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I. Historical Climate Background ...........................................................................3

A. Climate in the Tropics .....................................................................................3 1. Seasonal Cycle near the Equator ................................................................3 2. El Niño Southern Oscillation (ENSO) .........................................................3

i. Predicting ENSO ..........................................................................................4 B. Climate in Ecuador ............................................................................................5

1. Overview of Ecuador’s Climatology ..........................................................5 i. Temperature in Ecuador ...........................................................................6 ii. Precipitation in Ecuador ........................................................................ 10 iii. ENSO in Ecuador .................................................................................... 11

2. Regional Climates within Ecuador.......................................................... 18 C. Changing Glaciers .......................................................................................... 20

1. Andean Glaciers .......................................................................................... 20 2. Ecuador’s Glaciers ...................................................................................... 21

i. Glaciers and Water Supply..................................................................... 25 II. Understanding Climate Models and Uncertainty ....................................... 26

A. General Circulation Models (GCMs) ........................................................... 26 3. Climate Model Predictive Skill ................................................................. 27

i. Climate Model Predictive Skill for Temperature .............................. 27 ii. Climate Model Predictive Skill for Precipitation ............................. 30

B. Downscaling Climate Models for Regional Climates ............................. 32 1. Dynamical Downscaling ............................................................................ 33 2. Statistical Downscaling ............................................................................. 33 3. Glacier Models Coupled with GCMs ....................................................... 33

III. Future Climate Predictions for Ecuador ...................................................... 35 A. Future Temperature and Variability ......................................................... 35

1. Predicting Future Temperature ............................................................... 35 2. The Future of Frost in Ecuador ............................................................... 41

B. Future Precipitation and Variability .......................................................... 42 1. Model Skill in Replicating Past Precipitation ....................................... 42 2. Changes in Precipitation due to Climate Change ............................... 44

C. Historic Temperature and Precipitation Predictability from ENSO ... 47 IV. References .......................................................................................................... 54

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I. Historical Climate BackgroundA. Climate in the Tropics

1. Seasonal Cycle near the Equator Ecuador’s equatorial position dictates the basic climate of the country, though the varied topography does create different regional climatology (see Section IB). With fairly constant daily solar radiation, there is generally no temperature seasonality in tropical nations (located roughly between 30ºN and 30ºS, though regional variation in temperature may result (especially from ocean influences). Instead of seasonal shifts, large temperature changes may be observed in the diurnal cycle. High-altitude regions of Ecuador may see differences in day and night temperatures that exceed 20°C. Precipitation in Ecuador, as with other tropical nations, is much more varied than temperature both regionally and seasonally. We see pronounced regional differences within our study group, the high Andes. Eastern slope precipitation is impacted by perennially wet easterly trade winds that travel from the Amazon basin and tropical Atlantic. Slopes in the northwest are influenced by the Intertropical Convergence Zone, which brings moisture from the eastern Pacific. This moisture is forced upward by increasing elevations, creating precipitation. In contrast, southwestern slopes are primarily influenced by the Humboldt Current, which brings air north along South America’s western coast. This is a dry, cool airmass, which keeps the southwestern slopes especially dry (except during El Niño events). As a result of these difference precipitation sources, the east side of the Andes has lower maximum annual precipitation (400-800 m) than the west slope (2000-2500 m) [Buytaert et al., 2006].

2. El Niño Southern Oscillation (ENSO) The Southern Oscillation is a global atmospheric oscillation that directly links to El Niño and La Niña, two climate states that affect ocean and atmosphere behavior in the Pacific. El Niño Southern Oscillation (ENSO) is characterized by two prominent phases of ocean temperature anomalies. Warm events, or El Niño events, are associated with sea surface temperature (SST) anomalies more than 0.5°C above normal across the central tropical Pacific, while La Niña is associated with anomalies 0.5°C below normal. During El Niño, warm waters along the coasts of Peru and Ecuador interrupt the upwelling of deep, cold ocean water. In general, El Niño events increase the likelihood of the following conditions:

• Western Pacific drought • Increased precipitation for the equatorial coast of South America

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• Increased storm and hurricane activity in the central Pacific The El Niño events of 1982-1983 and 1997-1998 were particularly strong in regards to impacts for Ecuador. La Niña conditions have occurred less often over the past 35 years than El Niño conditions (Figure 1), and generally do not have as significant effect for equatorial South America (though effects elsewhere may be notable).

Figure 1: Multivariate ENSO index shows El Niño (red) and La Niña (blue) phases. Six variables are used to determine the index: sea level pressure, east-west and north-south components of the surface wind, sea surface temperature, surface air temperature, and total amount of cloudiness. [Source: Climate Diagnostics Center, NOAA/CIRES, http://www.esrl.noaa.gov/psd]

The range of impacts on Ecuador and the Ecuadorian Andes from ENSO is not well-understood. One recent study suggests that the influence of ENSO on the Ecuadorian Andes is limited, with significant precipitation influence ending at the western edge of the mountains (Figure 2) [Rossel and Cadier, 2009]. On the other hand, researchers examining glacier behavior on Antisana Volcano in the northern Ecuadorian Andes found a connection between El Niño /La Niña phases and glacier mass balance (see section IC) [Francou, et al., 2004]. Discrepancies such as these point toward the wide range of scientific opinion in regards to ENSO and its Ecuadorian impacts. Nevertheless, ENSO is likely to remain an important factor in Ecuador’s climate as the global climate changes.

i. Predicting ENSO Through the use of both coupled ocean/atmosphere models and statistical models, ENSO conditions are predictable on a 3-12 month timescale. A comprehensive list of ENSO forecast sources is available from the U.S. National Oceanic and Atmospheric Administration (NOAA) (via http://www.pmel.noaa.gov/tao/elnino/forecasts.html), with various forecasts available in Spanish and English.

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Figure 2: Boundaries of strong (>40%) significant (>20%) ENSO influences on annual rainfalls in northwestern South America [Rossel and Cadier, 2009].

B. Climate in Ecuador

1. Overview of Ecuador’s Climatology To understand the implications of climate change on Ecuador, it is useful to have an understanding of both current and historical climate trends. We will focus on measures of temperature and precipitation, followed by some discussion of the role of ENSO. Gridded temperature and precipitation data were made available by Willmott, Matsuura, and collaborators at the University of Delaware

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(hereafter referred to as UDel data)1. These gridded data were produced by spatially interpolating temperature and precipitation observations collected by several sources from land weather station across the globe23 We extract temperature and precipitation data from the UDel data set over a grid box which corresponds to the common grid box for model output used in Section III (Figure 3).

i. Temperature in Ecuador The UDel data is only available over land, whereas the model outputs may incorporate some fraction of output over the ocean. This potential source of bias will be discussed in Section IIA. Maps of the average temperature and precipitation over the whole time series are shown in Figure 4 and Figure 5.

Figure 3: The grid box used to extract UDel data (only sub-grid boxes over land were used).

1 The data is provided in ASCII format from the University of Delaware: http://climate.geog.udel.edu/~climate/. We were provided with a gridded netCDF format version by University of Washington graduate student Rob Nicholas. 2 Version 2.01 of the 1900 – 2008 temperature product; Matsuura and Willmott, 2009. 3 Version 2.01 of the 1900 – 2008 precipitation product; Matsuura and Willmott, 2009.

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Figure 4: Temperature from the UDel dataset averaged over the entire time series (1900-2008). Grey line delineates the coast, and dark black contours delineates topography. Blue stars show the locations of selected weather stations. Scale in oC.

Figure 5. Precipitation from the UDel dataset, as in Figure 4. Scale in mm/day.

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Figure 6: Monthly temperature data (UDel), showing a linear warming trend over the last century of about 0.1o/decade.

A warming trend emerges from the background variability in the temperature data for the time series (1900-2008) (Figure 6). After subtracting the trend, we can look at the probability distribution of temperature anomalies about the mean (Figure 7). This will serve as our base distribution to compare future temperature predictions in Section III. It is also instructive to see the temperature probability distribution for each season (Figure 8). The seasonal cycle of temperature is plotted in Figure 9. As mentioned in section IA, the diurnal (day-night) temperature range is bigger than the difference in mean temperature across the seasons.

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Figure 7: The frequency of months at, above, or below the mean temperature (normalized to 0oC to emphasize deviation from the mean) for the grid box.

Figure 8: As in Figure 7, divided up by season. Note how the distribution differs for each season.

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Figure 9: Historical seasonal cycle of monthly mean temperature, which varies by only a few degrees due to proximity to the equator.

ii. Precipitation in Ecuador The precipitation time series for 1900-2008 is plotted in Figure 10 and the seasonal cycle is displayed by Figures 11 and 12. The variation between seasons is much more substantial in precipitation than temperature.

Figure 10: The historical precipitation record (UDel) for 1900-2008 shows no clear trend.

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Figure 11: The seasonal cycle of precipitation for the entire grid box. The wettest month is March and the driest is August.

Figure 12: Pie chart showing how precipitation is distributed by season.

iii. ENSO in Ecuador ENSO is a major source of natural variability in Ecuador. The extent to which ENSO is responsible for temperature and precipitation extremes will be investigated in this section. We correlate indices that measure the strength of ENSO with the historical temperature and precipitation record. The ENSO indices that we use are sea surface temperature (SST)

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anomalies, based on the regions of the tropical Pacific shown in Figure 13, and accessed from the National Weather Service4.

Figure 13 The sea surface temperature indices of ENSO that we consider are based on the areas of ocean shown in the map. Niño1+2 is best correlated with precipitation in Ecuador. (http://www.bom.gov.au/climate/enso/indices/oceanic-indices-map.gif)

It results that the SST indices for the combined region of Niño1+2 correlate the best with precipitation anomalies, unsurprising since the Niño1+2 regions are just off the coast of Ecuador. The correlations are significant (to 95% confidence) in the months in which precipitation is greatest (November-May) and the correlation is strongest (and not well correlated or significant in the other months – June to October). The SST index that best predicts ENSO, however, is Niño 3.4. Because Niño 3.4 is removed from the Ecuadorian coast, it is clear that Ecuadorian precipitation and temperature anomalies are not influencing the SST index, so it better isolates the extent to which anomalies in Ecuador are related to ENSO. The results of the correlations with each of the SST indices are presented in Table 1. The time series plots of Niño 3.4 anomalies and monthly temperature anomalies are shown in Figure 14Error! Reference source not found.. Precipitation anomalies are compared with Niño 3.4 in Figure 15. The best-correlated month (April), which is also one of the rainiest months, is shown by itself in Figure 16Error! Reference source not found.. This suggests that based on historical data, and baring any drastic change in the state of ENSO in the future, indices of the strength of ENSO do a good job of indicating when precipitation in the rainy months is particularly high.

4 http//www.cpc.ncep.noaa.gov/data/indices/

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Table 1: Correlations of SST index anomalies with monthly precipitation anomalies from UDel data. Correlations that are significant to the 95% level are in bold.

Month

Niño 1+2 Niño 3 Niño 4 Niño 3.4

Jan 0.2748 0.1403 -0.0890 0.0301 Feb 0.4134 0.2448 -0.0470 0.1188 Mar 0.6011 0.4780 0.0284 0.2919 Apr 0.7385 0.6460 0.2683 0.4978 May 0.5039 0.3657 0.1741 0.3055 Jun 0.2530 0.1462 0.0471 0.0525 Jul 0.0351 0.0182 -0.0510 -0.0438 Aug -0.1180 -0.0734 -0.0519 -0.0782 Sep -0.0144 -0.0899 -0.0822 -0.0934 Oct -0.0132 -0.0153 -0.0702 -0.0516 Nov 0.5156 0.4685 0.1914 0.3735 Dec 0.6389 0.5402 0.2409 0.4320

Figure 14: Time series of the relative NINO 3.4 anomaly and the relative surface temperature anomaly averaged over all of Ecuador.

Figure 15: Time series of the relative NINO 3.4 anomaly and the relative precipitation anomaly averaged over all of Ecuador.

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Figure 16: Time series of precipitation anomalies from UDel data – April only, compared with SST anomalies of Niño1+2 indices.

The monthly correlations in Table 1, are illustrated graphically in Figure 17, for the Niño3.4 index, which best represents ENSO. Also shown are the monthly correlations of ENSO with temperature, which are notably stronger (Figure 18). These correlations are for the entire box, covering most of Ecuador, from which we extracted data.

Figure 17: Bar graph showing the correlation coefficient between the NINO 3.4 index anomaly and the precipitation anomaly averaged over all of Ecuador. Values that are statistically significant to 95% are denoted with * and values that are statistically significant to 99% are denoted with **.

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Figure 18: Bar graph showing the correlation coefficient between the NINO 3.4 index anomaly and the temperature anomaly averaged over all of Ecuador. Values that are statistically significant to 95% are denoted with * and values that are statistically significant to 99% are denoted with **.

The discussion in Section IA2 indicates that the influence of ENSO should be looked at spatially (Figure 2). Below we compare the correlation between ENSO, precipitation, and temperature for the coast and for the sierra. For precipitation (Figure 19 and Figure 21) the correlations improve slightly for the coast and deteriorate for the sierra region. For temperature (Figure 20 and Figure 22) the correlations do not change (r<0.05) for the coast and decrease for the sierra. The monthly correlations for ENSO and precipitation and ENSO and temperature are shown in Figures 19-20 for Ecuador’s coast.

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Figure 19: Bar graph showing the correlation coefficient between the NINO 3.4 index anomaly and the precipitation anomaly averaged over the coastal region. Values that are statistically significant to 95% are denoted with * and values that are statistically significant to 99% are denoted with **.

Figure 20: Bar graph showing the correlation coefficient between the NINO 3.4 index anomaly and the temperature anomaly averaged over the coastal region. Values that are statistically significant to 95% are denoted with * and values that are statistically significant to 99% are denoted with **.

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The monthly correlations for ENSO and precipitation and ENSO and temperature are shown in Figures 21-22 for the Sierra.

Figure 21. Bar graph showing the correlation coefficient between the NINO 3.4 index anomaly and the precipitation anomaly averaged over the sierra region. Values that are statistically significant to 95% are denoted with * and values that are statistically significant to 99% are denoted with **.

Figure 22. Bar graph showing the correlation coefficient between the NINO 3.4 index anomaly and the temperature anomaly averaged over the sierra region. Values that are statistically significant to 95% are denoted with * and values that are statistically significant to 99% are denoted with **.

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2. Regional Climates within Ecuador The climate of Ecuador varies regionally due to altitudinal and coastal effects. The mainland of Ecuador can be divided into three climatic regimes, including the Amazon Rainforest (east of the Andes Mountains), the cordillera and highlands of the Andes (running North-South along the center of the country), and the Pacific Coast (west of the Andes Mountains) (Figure 23). One generalization that can be made of equatorial climates is that they do not have a seasonal cycle akin to higher latitudes. Instead, the tropical climate of Ecuador can be characterized by a wet season and a dry season, though each season varies by geographical location. Four weather stations have been picked out as representations of the different climates in Ecuador (Figure 24): Quito, in the north, represents the sierra of the Andes; Puyo, in the southeast, is representative of the climate in the Amazon Rainforest; Guayaquil, in the southwest, represents coastal climate; and San Cristóbal is an island in the Galapagos Archipelago (not a focus of this work).

Figure 23. Map of Ecuador. (Geographic Guide)

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Figure 24. Mean annual temperature cycle for Quito (Sierra), Guayaquil (Coast), Puyo (Amazon), and San Cristóbal (Pacific Island) prior to 2000. [Bermeo et al., 2000]

Figure 25. Time series is an example of the diurnal temperature cycle in Quito and Guayaquil, Ecuador (from April 23 through April 24, 2010). Relative to the annual cycle, the changes in temperature in the diurnal cycle are much larger. (Weather Underground).

The mean monthly temperature in all regions is relatively constant compared to the diurnal cycle (Figures 24, 25), differing by only a few degrees throughout the year. Even though some regions are cooler (e.g. the highlands around Quito) and others warmer (e.g. the coast near Guayaquil), the seasonal differences at each location are not large. The annual precipitation cycle, on the other hand, varies more distinctly both temporally and spatially (Figure 26).

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Figure 26. Mean annual precipitation cycle for Quito (Sierra), Quayquil (Coast), Puyo (Amazon), and San Cristóbal (Pacific Island) prior to 2000. [Bermeo et al., 2000] The precipitation cycle for different regions of Ecuador is identified in Table 2

Table 2. Rainy seasons and peak rainfall months for different regions of Ecuador. [Bermeo et al. 2000].

Region Rainy Season Peak Dry Season Coastal December - May February-March June - December Sierra October - May October, April June - September Amazon Throughout NA NA

C. Changing Glaciers

1. Andean Glaciers The Andes hold more than 99% of all tropical glaciers, with Peru holding ~70% of those and the other significant glaciers locations being primarily in Ecuador and Bolivia. With icemelting throughout the year, communities throughout the Andes rely on glaciers for some or all of their seasonal water supplies (especially during dry seasons). The future of Andean glaciers, however, is not bright. Evidence for past, present, and future loss of glaciers throughout the Andes is present through direct observations of mass balance [e.g., Francou et al., 2003; Soruco, et al., 2009] and indirect indications from studies examining related factors,

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Figure 26. Figure demonstrates the topography and rainfall throughout Ecuador. There is relatively little precipitation along the Sierra and southern coast, but more rainfall in the Amazon and along the northern coast. (Ecominga, 2010).

such as atmospheric warming [Bradley et al., 2009]. While many elements can influence glacier retreat (including slope aspect, humidity, precipitation, temperature, and wind patterns) an examination of studies throughout Peru, Bolivia, and Ecuador indicate a consistent regional retreat of glaciers during the 20th century and into the 21st (Figure 28) [Vuille, et al., 2008]. Climate projections do not provide any indication that this trend will stop over the next several decades.

2. Ecuador’s Glaciers Roughly speaking, Ecuador’s mountains and, thus, its glaciers, lie within two mountain ranges, the Cordillera Occidental (more western peaks) and the Cordillera Oriental (more eastern peaks, bordering the Amazon). Glaciers in the Cordillera Oriental are found at lower elevation because of the high peaks on which they sit and the high precipitation brought in from the Amazon basin and tropical Atlantic. Ecuador’s glaciers have already seen important changes that are linked to climate change. Cotacachi, Corazon, and Sincholagua peaks, in the Cordillera Occidental, completely lost their permanent ice in the last 10-15 years, while the higher peaks of the Cordillera Oriental (Chimborazi, Cayambe, Antisana, and Cotopaxi) are continuing to experience ice retreat (Figure 29).

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Figure 27: Change in length and surface area of 10 tropical Andean glaciers from Ecuador (Antisana 15a and 15b), Peru (Yanamarey, Broggi, Pastoruri, Uruashraju, Gajap) and Bolivia (Zongo, Charquini, Chacaltaya) between 1930 and 2005. [Vuille, et al., 2008]

Figure 28: Satellite image showing Quito (red star) and Antisana Volcano, Cayambe Volcano, and Cotopaxi Volcano (blue circles). [Image source: Google Earth]

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Ecuador’s glaciers are uniquely sensitive to climate because of their equatorial geography. With no seasonal temperature cycle, there are no distinct seasons for accumulation or ablation of ice - gain and loss of ice can happen at any time of year. Thus, the 0°C isotherm, which determines where precipitation falls as snow (above the isotherm) or rain (below the isotherm), is constantly moving and is particularly sensitive to temperature change [Favier et al., 2004]. The temperature increases that Ecuador has seen over the last several decades and the many El Niño events during the last half-century have both acted to decrease ice mass throughout Ecuador. The best-studied glacier in Ecuador is the Antisana Glacier 15 (Figure 30), sitting at 4800-5760 m elevation on Antisana Volcano located 50 km southeast of Quito. Antisana Glacier 15, with an area of roughly 1 km2 is a reasonable representative for other small glaciers throughout the Andes. The surface area and volume of Antisana Glacier 15 had declined by more than 30% since 1956, with an increasing rate of loss in the most recent decades (with 8% of area lost during 1996-98) [Francou et al., 2000]. Photogrammetry research on Cotopaxi Volcano also showed a 30% area loss for the volcano ice cap between 1976 and 1997 [Jordan et al., 2005]. These results from Ecuador are in agreement with glacier studies throughout the Andes showing an acceleration of glacier ice loss since the 1970s, as outlined in the above section.

Figure 29 The two glaciers of Antisana Glacier 15, with location of the main equipment installed on or near the glacier. Inset shows location of Antisana within Ecuador. Dashed line separates glacier tongues 15a and 15b and solid triangle shows Antisana Volcano summit (5760m). [Francou, et al., 2004]

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Research on Antisana Glacier 15 suggests that ENSO does have a significant affect on glacier mass balance, with La Niña events favoring a positive or neutral glacier mass balance and El Niño conditions favoring a negative mass balance (Figure 31) [Vuille et al., 2003; Francou et al., 2004]. The different climate effects of these ENSO phases helps to explain the impact ENSO plays in glacier mass balance:

El Niño o With El Niño events, the Andes are likely to experience warm

and dry anomalies § Higher air temperatures favor rainfall over snowfall § Glacier albedo (the amount of radiation reflected) is

decreased and the ice absorbs more radiation § Generally low wind speeds, which encourage melting § Less cloud cover, which allows for higher incoming

radiation

La Niña o The Andes experience cold and wet weather during La Niña

events. § Cold temperatures encourage snowfall over rainfall § Increased snowfall helps to maintain high albedo,

reflecting more radiation § Generally higher wind speed that help reduce melting

Thus, the hydrologic and temperature effects of ENSO act to increase or decrease glacier mass balance. Antisana glacier mass balance changes are also most variable during February-May, which is consistent with the delayed ENSO signal that is recorded in the Andes [Francou et al., 2004].

Figure 30 Antizana mass balance anomalies (mm water equivalent) in the ablation zone stratified by ENSO events. Individual monthly measurements (small circles), the mean (large circles), and +/- 1 standard deviation are indicated (vertical bars) for El Niño (solid circles) and La Niña events (open circles). [Francou, et al., 2004]

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Given Antisana’s glacier sensitivity to El Niño , tropical Pacific sea surface temperature is clearly one of the more important variables for determining future glacier mass balance. If temperature and precipitation remain linked to sea surface temperature, this provides further reason to expect a continued decline in glacier mass: all climate models examined by Paeth et al. [2008] predict eastern tropical Pacific warming on the order of 5 °C by 2100.

i. Glaciers and Water Supply Water supply in several areas of Ecuador is linked to glacial runoff. Unfortunately, sorting out a precise impact of glacial melt on water supply in Ecuador is difficult because there is little current understanding of the connection between water output from glaciers, regional hydrology, and water supply for the end-user. For example, estimates of water use from Quito are inconsistent, and it is not clear what portions come from ice melt versus other precipitation or runoff. A brief survey of sources produces the following array of estimates: - “Roughly 50% of Quito’s water comes from Antisana glacier.”

[Wehner, 2002] - “ "In 20 to 30 years we will have a problem with the potable water

supply," says Bolivar Caceres, a glaciologist with the hydrology and meteorology institute. As the glaciers recede, he says, there will be less water for Quito, where 70 percent of the water comes from surrounding ice caps.“ [Bartolone, 2006]

- About 10 per cent of the water supply to Quito, the capital of Ecuador, is also estimated to come from surrounding ice caps.” [Painter, 2007]

Without data for glacier-sourced water usage, it will be difficult for communities of all sizes in Ecuador to plan for future water security. In the case of Quito, the majority of the city’s water supply comes from the paramo, with three water supply locations that all pull from glaciated peaks: the Cunuyacta river in the Cayambe-Coca reserve, reservoirs on the slopes of the Antisana Volcano and the Rio Pita from Cotopaxi and Sincholagua volcanoes [Buytaert et al., 2006]. Unfortunately, science does not currently understand how glacier loss and climate change will affect the hydrology of the paramo. Ecuador is also lacking the monitoring tools needed to correctly assess changes in glacial water discharge. Sorting out the issue of glacier water supply is a clear need in light of the significant changes expected for Ecuador’s glaciers and deserving of increased scientific research.

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II. Understanding Climate Models and Uncertainty

A. General Circulation Models (GCMs) Climate scientists use General Circulation Models (GCMs) to predict future climate trends. The system is forced by the fluid dynamics of large-scale atmospheric and oceanic motions, and parameterizations of the radiation balance. The latest models use parameterizations of many other aspects of the Earth system, from clouds to sea ice to the land surface. In the IPCC Forth Assessment Report (AR4) there are 25 different models from 18 different groups worldwide (see Figure 31). These model output data are archived by the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset.

Figure 31 Models included in the IPCC AR4, and their IDs with the Coupled Model Intercomparison Project.

The GCMs simulate atmospheric dynamics and other processes by dividing the globe into a finite number of discrete grid boxes horizontally and vertically, where each box interacts with its neighbors. The computational resources that are available limit the size of the grid. The AR4 IPCC models use grid boxes of order 5 degrees in size. One of the improvements for the Fifth Assessment Report will be a finer-resolution grid. One of the side effects of a large grid box in a GCM is that topography is not properly resolved. This means that the true effects of

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the topography on atmospheric circulation are not accounted for. Additionally, the output does not have the resolution to distinguish temperature differences between the mountains and the coast.

3. Climate Model Predictive Skill Climate model predictive skill for future climates depends on the area over which the projection is being made and the variable in question. Global circulation models can replicate historical temperature trends, but precipitation is not as easily represented. Even though climate models are able to replicate historical climates, there are still large uncertainties in predicting future climate conditions both because the future anthropogenic influence is unknown and because it is unclear how large climate feedbacks will be (a climate feedback is something that enhances or diminishes an effect of climate change).

i. Climate Model Predictive Skill for Temperature Global climate models have been able to reproduce historical 20th century temperature trends when known climate forcings or influences are taken into account (Figure 32). While this report does not attempt to demonstrate good agreement between historical Ecuadorian temperature trends and climate model predictions, the mean model bias for surface-air temperature in this region of South America is typically less than 2oC on land (Figure 33). Temperature biases may be a result of issues such as ocean contamination in land dominated grid boxes or altitudinal effects (flattening a grid box when there is topography). Despite temperature biases, the temperature trend (temperature change over time) is expected to be representative of future climates, even if the predicted trend is not well constrained (Figure 34).

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Figure 32. Comparison of observed continental- and global-scale changes in surface temperature with results simulated by climate models using natural and anthropogenic forcings. Decadal averages of observations are shown for the period 1906 to 2005 (black line) plotted against the centre of the decade and relative to the corresponding average for 1901–1950. Lines are dashed where spatial coverage is less than 50%. Blue shaded bands show the 5–95% range for 19 simulations from five climate models using only the natural forcings due to solar activity and volcanoes. Red shaded bands show the 5–95% range for 58 simulations from 14 climate models using both natural and anthropogenic forcings. [IPCC, 2007]

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Figure 33. MMD ensemble annual mean surface air temperatures in South America compared with observations. a) observations from the HadCRUT2v data set (Jones et al., 2001); b) mean of the 21 MMD models; c) difference between the multi-model mean and the Had- CRUT2v data [IPCC, 2007].

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Figure 34. Temperature anomalies with respect to 1901 to 1950 for the Amazon region of South America for 1906 to 2005 (black line) and as simulated (red envelope) by MMD models incorporating known forcings; and as projected for 2001 to 2100 by MMD models for the A1B scenario (orange envelope). The bars at the end of the orange envelope represent the range of projected changes for 2091 to 2100 for the B1 scenario (blue), the A1B scenario (orange) and the A2 scenario (red). The black line is dashed where observations are present for less than 50% of the area in the decade concerned [IPCC, 2007].

ii. Climate Model Predictive Skill for Precipitation Although GCMs are able to represent temperature trends, climate models tend to have much larger biases for precipitation (Figure 35). While temperature biases can be several degrees, it is not uncommon for precipitation biases to be off by a factor of two (in other words, climate models for Ecuador can double precipitation). Section B1 discusses the specific challenges in replicating precipitation in Ecuador.

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Figure 35. MMD ensemble annual mean precipitaition in South America compared with observations. a) Observations (CMAP) are an update of Xie and Arkin (1997). Units mm/day); b) mean of the 21 MMD models; c) difference between the multi-model mean and the CMAP data [IPCC, 2007].

Although climate models often misrepresent the seasonal cycle for precipitation and the amount of rainfall, the majority of climate models used in the IPCC Assessment (>75%) predict an increase in rainfall for Ecuador (Figure 36). Even though there is general agreement on the sign of the precipitation change in future climates, much work needs to be done to determine the magnitude of the change and to downscale the precipitation response regionally.

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Figure 36. Temperature and precipitation changes over Central and South America from the MMD-A1B simulations. Top row: Annual mean, DJF and JJA temperature change between 1980 to 1999 and 2080 to 2099, averaged over 21 models. Middle row: same as top, but for fractional change in precipitation. Bottom row: number of models out of 21 that project increases in precipitation [IPCC, 2007].

B. Downscaling Climate Models for Regional Climates For reasons described in the previous section, general circulation models best describe fluid dynamics of the atmosphere at scales that are continental in scale. Such global models generally have spatial resolution of a few hundred kilometers, requiring sub-scale processes to be parameterized. It is common sentiment in the climate science community to doubt regional-scale climate simulations by GCMs because of these parameterizations [Widmann et al, 2003; Wilby & Wigley, 1997]. Often, smaller-scale processes are also the least understood, such as cloud and water vapor feedbacks [Wilby & Wigley, 1997]. Downscaling techniques are commonly employed as a means to analyze climate change experiments and seasonal forecasts at a higher resolution than GCM output. Two downscaling techniques have emerged in the past couple of decades: dynamical and statistical downscaling. In the following sections, we’ll explore the benefits, downsides, and applications of both approaches.

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1. Dynamical Downscaling Dynamical downscaling derives smaller-scale information by using regional climate models (RCMs) driven by boundary conditions prescribed by a GCM [UNFCCC, 2010]. This process assumes that regional climates are largely a function of the large-scale atmospheric state [Giorgi et al, 2001]. Sea surface temperatures (SSTs) and lateral boundary conditions (e.g. surface pressures, atmospheric winds, temperatures, humidities) are saved from GCM integrations and then used to drive RCMs at smaller resolutions (50-100 km) [Murphy, 1999]. Different research groups have made use of dynamical downscaling [Giorgi et al. 1993; Giorgi et al. 1994; Jones et al. 1995, 1997; Hirakuchi and Giorgi, 1995; Giorgi and Marinucci, 1996] for different parts of the world. In these studies, distributions of temperature and precipitation contain a significant signal at smaller scales than the original GCM resolution, but the large-scale circulation follows that of the driving model. These results suggest that the RCM acts as a physically based interpolator of the GCM output [Murphy, 1999].

2. Statistical Downscaling Statistical downscaling derives statistical relationships between small-scale information (e.g. point station data) and larger scale variables derived from a GCM using one of a variety of methods [UNFCCC, 2010]. This approach assumes that the atmospheric circulation produced by a GCM is more likely to be reliable than the distribution of climate fields at the surface, due to the parameterizations of small-scale physical processes (radiative transfer, cloud formation, turbulence) mentioned earlier [Murphy, 1999]. Statistical downscaling can be performed with different methods that can each be approached with varying degrees of complexity. Regression methods involve establishing relationships between sub-grid scale parameters and grid-scale [Wilby & Wigley, 1997] predictor variables. Weather pattern based methods involve linking observational station data to a given weather classification scheme through statistical relationships. These weather based methods are appealing due to their strong foundation on physical relationships between small-scale weather and large-scale circulation patterns.

3. Glacier Models Coupled with GCMs It is clear that modeling a sub-grid cell size physical process with climate data projected at a much larger grid scale imparts some level of uncertainty. Glaciers are much smaller than typical GCM-model output, and as a result are difficult to model under future global warming scenarios. Because of this size discrepancy, modeling future glacier response to climate generally involves running a glacier model with downscaled GCM output (Reichert et al, 2002). Simulating glacier behavior can be done with a range of complexities that incorporate a

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range of details about climate interactions and glacier dynamics. These models can range from the very simple (e.g. Roe & O’Neal, 2009; Oerlemans, 2005) to the very complicated (e.g. GLIMMER, SICOPOLIS). Modeling tropical glaciers is different than simulating those found in the midlatitudes. Glaciers found in the tropics exist because temperature decreases with altitude; they are often located at such high elevations that do not experience summertime air temperatures above 0°C. It is important to remember that it is heat, not air temperature, that causes melting. Models that simulate midlatitude glaciers can often use temperature as a perfectly adequate proxy for heat. However, since limited portions of tropical glaciers extend to low altitudes with temperatures above 0°C, the energy balance must be incorporated. For the portion of the glacier above the 0° isotherm, terms in the energy budget start to become major influences (Figure 38). These terms are sensitive to air temperature, atmospheric humidity, cloudiness, and wind; and affect the amount of solar radiation available for ablation. Depending on these conditions, the glacier surface can warm to 0°C and melt (with a smaller amount of ice sublimating). Clearly, it is difficult to project the dependencies of the glacier system response to changes in climate while accounting for every process in a model. As mentioned earlier horizontal temperature variations in the tropics are small above a short layer near the Earth’s surface, due to the weak influence of the Coriolis. Other meteorological variables (e.g. precipitation, humidity, cloudiness) don’t exhibit such a horizontal homogeneity as air temperature. Because of this, a widespread, synchronous retreat of tropical glaciers gives reason to believe temperature is playing a direct role.

Figure 38: A schematic of the energy budget for glaciers. While many of these terms are not discussed, the take-home message is the complexity of the picture (http://geosci.uchicago.edu/~rtp1/glaciers/EnergyBudget.html)

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III. Future Climate Predictions for Ecuador

A. Future Temperature and Variability

1. Predicting Future Temperature To determine temperature trends in Ecuador, historical data was constrained for the land surface falling between 5o S and the equator and 77.5o W and 82.5o W to fit within a grid box that was interpolated to be common from all 19 of the climate models used in this report. This historical data contain 100 surface points per one model grid box, with 69 of these points being land points (sea surface points are omitted). The 69 historical land grid boxes were averaged together to form a historical grid box that could be compared to the model grid box during the 1980 – 2000 period. For future scenarios, the climate models (See Table 3 for GCMs used in this report) were run off each model’s native grid spacing, but constrained to the grid boxes from 5o S to the equator and 77.5o W to 82.5o W. The future scenarios are average monthly values for the periods between 2040 to 2060 and 2080 to 2100 for 19 global climate models. This method was used both for precipitation and temperature. It should be noted that while the historical data is for surface points only, the global climate model data includes sea surface points, which may add biases for temperature and precipitation. In cases where distinctions were made between the coast and the Andes, the coast was defined as areas west of 79o W longitude and the sierra was defined as 77o W to 77.5o W longitude.

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Table 3 The IPCC model output used in the analysis of future trends.

Model Originating Group(s) Country Abbreviation 1 Bjerknes Centre for Climate Research Norway bccr_bcm2_0 2 Canadian Centre for Climate Modeling

& Analysis Canada cccma_cgcm3_1

3 Météo-France / Centre National de Recherches Météorologiques

France cnrm_cm3

4 CSIRO Atmospheric Research Australia csiro_mk3_0 5 CSIRO Atmospheric Research Australia csiro_mk3_5 6 US Dept. of Commerce / NOAA /

Geophysical Fluid Dynamics Laboratory US gfdl_cm2_0

7 US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics Laboratory

US gfdl_cm2_1

8 NASA / Goddard Institute for Space Studies

US giss_model_e_r

9 Instituto Nazionale di Geofisica e Vulcanologia

Italy ingv_echam4

10 Institute for Numerical Mathematics Russia inmcm3_0 11 Institut Pierre Simon Laplace France ipsl_cm4 12 Center for Climate System Research

(The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC)

Japan miroc3_2_medres

13 Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA, and Model and Data group

Germany/Korea

miub_echo_g

14 Max Planck Institute for Meteorology Germany mpi_echam5 15 Meteorological Research Institute Japan mri_cgcm2_3_2a 16 National Center for Atmospheric

Research US ncar_ccsm3_0

17 National Center for Atmospheric Research

US ncar_pcm1

18 Hadley Centre for Climate Prediction and Research / Met Office

UK ukmo_hadcm3

19 Hadley Centre for Climate Prediction and Research / Met Office

UK ukmo_hadgem1

Combining the temperature trend predictions from 19 different models, we assume that the temporal and spatial variability of temperature will remain the same for future scenarios (following Battisti and Naylor’s assumptions regarding temperature variability). The climate model data used in this report comes from 19 climate models (see Table 3) and the A2 scenario. To determine the future warming for Ecuador, the warming trend was calculated from the difference between the average of the historical

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scenario for each climate model (1980 – 2000) and the future scenario for each model (2030 – 2050 and 2080 – 2100) (Figure 39 and Figure ).

Figure 39:. Linear trend for our Ecuador GCM grid cell during the period from 1990 – 2040 for each model used in this report. The mean trend is 0.23oC decade-1.

Figure 40: Linear trend for our Ecuador GCM grid cell during the period from 1990 – 2090 for each model used in this report. The mean trend is 0.31oC decade-1.

The probability distribution function from data (1900 – 2008) was then

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shifted by the calculated model temperature change (for both 2030 – 2050 and 2080 – 2100). All 19 model probability distribution functions were averaged to produce future probability distribution functions (Figure and Figure ). The total warming from all models were averaged together and this value was added to the historical temperature data for each spatial point (ignoring lapse rate changes) (Figure 37 and Figure 38).

Figure 41: Probability distribution functions for monthly historical de-trended Ecuadorian temperatures (1900 – 2008) about a mean value of 22.8oC and the shifted probability distribution function for the future climate (2030 – 2050).

Figure 42. Probability distribution functions for monthly historical de-trended Ecuadorian temperatures (1900 – 2008, blue) about a mean value of 22.8oC and the shifted probability distribution function for the future climate (2080 – 2100, red).

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Figure 37. Historical spatial temperature distribution (1980 – 2000 – left figure) and the shifted spatial temperature distribution with the mean temperature increase from model output added to each grid box (1.13oC) for 2030 – 2050 (right figure).

Figure 38. Historical spatial temperature distribution (1980 – 2000 – left figure) and the shifted spatial temperature distribution with the mean temperature increase from model output added to each grid box (3.13oC) for 2080 – 2100 (right figure).

Similarly, this approach was taken for each seasonal cycle. In this case the seasons were averaged (December – February, March – May, June – August, September – November) and the probability distribution function was calculated for the historical data for each season. The model calculated change for each season was then used to shift the probability distribution function. The shifted probability distribution functions were then averaged together to form the future seasonal scenarios (Figure 39 and Figure 40).

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Figure 39. Probability distribution functions for monthly historical de-trended Ecuadorian temperatures for each season (1900 – 2008, blue) and the shifted probability distribution function for the future climate (2020 – 2050, red).

Figure 40. Probability distribution functions for monthly historical de-trended Ecuadorian temperatures for each season (1900 – 2008, blue) and the shifted probability distribution function for the future climate (2080 – 2100, red).

Future temperature trends are summarized in along with historical temperature trends for reference.

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Table 4 Historic and future temperature trends for Ecuador.

Period Ecuador Average Coast Sierra

1900-1950 0.18oC decade-1 0.18oC decade-1 0.18oC decade-1

1950-2008 0.08oC decade-1 0.04oC decade-1 0.10oC decade-1

1900-2008 0.10oC decade-1 0.09oC decade-1 0.10oC decade-1

1990-2040 0.22oC decade-1 (Projected)

1990-2090 0.31oC decade-1 (Projected) Note in Table 4 that temperature trends are typically greater in the sierra than along the coast (historically). This phenomenon of greater warming at altitude is predicted into the future.

Figure 47. Global warming in the American Cordillera. Projected changes in mean annual free-air temperatures between (1990 to 1999) and (2090 to 2099) along a transect from Alaska (68°N) to southern Chile (50°S), following the axis of the American Cordillera mountain chain. Results are the mean of eight different general circulation models used in the 4th assessment of the Intergovernmental Panel on Climate Change (IPCC) (15), using CO2 levels from scenario A2 in (16). Black triangles denote the highest mountains at each latitude; areas blocked in white have no data (surface or below in the models). [Bradley et al, 2006].

2. The Future of Frost in Ecuador Unfortunately, the instances of frost were not taken directly from general circulation model output for this report and the future of frost incidences in Ecuador is uncertain. Radiative cooling at night causes the majority of frost in the Andes (Trognitz) and in some studies frost in the Andes was associated with freezing air temperatures only 22% of the time [Villegas, 1991]. Since direct station data was not analyzed on a daily basis, frost trends are not directly measured in this report.

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For radiative cooling to occur to this degree, clear-sky conditions must exist. Again, this report did not look directly at the cloud-fraction from climate model predictions, but there is a useful analog between precipitation and cloud cover. Richards and Arkin (1980) showed that for large-scale (several degrees latitude) gridboxes precipitation and cloud-cover are well correlated (r>0.8 in many cases). With our expectation for increased rainfall throughout the year (Section III B), this suggests that there will be increased cloudiness and more insolation to reduce the number of frost days. Further, correlating temperature anomalies and precipitation anomalies since 1950 show statistically significant and positive correlations for April, May, June, and July (p<0.05). This is further anecdotal evidence that less frost should be expected in the future, since precipitation is expected to increase. Importantly, though, monthly temperature anomalies are not a good representation of the daily diurnal cycle, especially on regional scales. Contradicting this semi-empirical estimate of future frost in Ecuador is a study of the Montaro Basin in the central Andes of Peru. In this region of Peru (~10o – 13o S) frost days have been increasing with a trend of 2.8 – 14.8 frost days decade-1 depending on location (p<0.05). While the Montaro Basin is more than 5o south of Ecuador, the study demonstrates that increasing temperature does not mean a decrease in the number of frost days. Frost is an important variable for many crops and work should be done to understand past and future trends for frost days in the highlands of Ecuador.

B. Future Precipitation and Variability

1. Model Skill in Replicating Past Precipitation Model biases in precipitation can be quite large. Although some climate models over or under predict temperature by several degrees (<1% deviation from the mean temperature), precipitation in some models can over or under predict monthly precipitation by as much as a factor of four. Unlike temperature, the direction of the precipitation trend is not robust across all models and the seasonal cycle of precipitation is often not well represented. Analyses of precipitation was initiated by plotting the mean model precipitation for each month for 1950 to 2000 on the same axes as the historical data for that period (Figure 41).

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Figure 41. Figure shows the average annual precipitation cycle for both historical data and each model over the period from 1950 – 2000.

We then calculated the root-mean-square (rms) error and the correlation value for the historical data and each set of model data, eliminating models with an rms error value above 3 and a correlation below 0.7. Our rms error criterion eliminates models that over or underestimate precipitation, while our correlation cut-off eliminates models that are phase-shifted from historical data. Three models met these threshold values (ECHAM5/MPI-OM, MRI-CGCM2.3.2, and UKMO-HadCM3) (Figure 42).

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Figure 42. Figure shows the average annual precipitation cycle for both historical data and the best fit models (r > 0.7, rms error < 3) over the period from 1950 – 2000.

2. Changes in Precipitation due to Climate Change Using only the three models selected in the previous section, we plot probability distribution functions for precipitation based on predicted shifts between 1990 and 2040 and between 1990 and 2090. In 2040, the magnitude of the increase in mean precipitation will be much smaller than the range in precipitation due to inter-annual variability (Figure ). By 2090, the predicted increase in precipitation is more substantial (Figure ).

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Figure 50: Probability distribution functions for monthly historical Ecuadorian precipitation (1900 – 2008) and the shifted probability distribution function for the future climate (average for 2030 – 2050).

Figure 51: Probability distribution functions for monthly historical Ecuadorian precipitation (1900 – 2008) and the shifted probability distribution function for the future climate (average for 2080-2100).

As with the annual trend, the seasonal shifts in precipitation expected by 2040 are small compared to the inter-annual variability. However, shifts of up to 16% (for Dec-Feb) are not insignificant. There is not a notable shift in the seasonal cycle. By 2090, precipitation is predicted to increase substantially more during Dec-May, than the other months. June-August is the season where-in differences between the models are the most clear:

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one model predicts a doubling of precipitation, while the other two do not. Such discrepancies serve as a reminder not to over-interpret these results.

Figure 52: As in Figure , by season.

Figure 53: As in Figure , by season.

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In summary, the predicted shifts in temperature and precipitation by 2040 and 2090 are compiled in Table 5. Table 5 Average temperature and precipitation shifts by seasonal and annual average.

Temperature increase 2040-1990

Temperature increase 2090-1990

Precipitation increase by 2040 compared to 1990

Precipitation increase by 2090 compared to 1990

December – February

1.1oC 3.0oC 16.4% 49.0%

March – May

1.1oC 3.1oC 9.1% 32.2%

June – August

1.2oC 3.3oC 4.9% 49.6%

September - November

1.1oC 3.2oC 3.9% 22.4%

Annual 1.1oC 3.1oC 9.3% 35.2%

C. Historic Temperature and Precipitation Predictability from ENSO From the discussion regarding correlations between the large-scale ENSO indices with temperature and precipitation (Section IB1iii), we expect some effect of El Niño (positive ENSO indices) and La Niña (negative ENSO indices) on those climate variables in Ecuador. In this section, we will express projected future increases in temperature and precipitation in terms of the strength of the El Niño event that would be required to produce those values in the current climate. Using linear relationships between temperature and precipitation anomalies and ENSO anomaly, we can express the future climate’s temperature and precipitation changes in terms of ENSO anomalies. This relationship can then be used to compare the future climate to past El Niño events that have impacts that are well understood. Specifically, we begin by expressing the 2040 and 2090 temperature increases in terms of ENSO anomalies (Figure 43 and Figure 44).

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Figure 43. Figure shows the 2040 temperature increases expressed in terms of an ENSO anomaly (NINO 3.4) for each month. The error is shaded red and is derived from the fit error between historical temperature anomalies and ENSO anomalies. The standard deviations for the mean ENSO state are also plotted for reference.

Figure 44. Figure shows the 2090 temperature increases expressed in terms of an ENSO anomaly (NINO 3.4) for each month. The error is shaded red and is derived from the fit error between historical temperature anomalies and ENSO anomalies. The standard deviations for the mean ENSO state are also plotted for reference.

From Figure 43 and Figure 44, temperature is expected to be similar to a +1σ anomaly in ENSO by 2040 and greater than a +2σ anomaly by 2090 (in terms of temperature only). Future expectations for precipitation can

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similarly be explored in terms of ENSO state (Figure 45 and Figure 46).

Figure 45 Figure shows the 2040 precipitation changes expressed in terms of an ENSO anomaly (NINO 3.4) for each month. The error is shaded red and is derived from the fit error between historical precipitation anomalies and ENSO anomalies. The standard deviations for the mean ENSO state are also plotted for reference. Only months denoted with * have a statistically significant relationship between ENSO anomaly and precipitation anomaly (p<0.05).

Figure 46: Figure shows the 2090 precipitation changes expressed in terms of an ENSO anomaly (NINO 3.4) for each month. The error is shaded red and is derived from the fit error between historical precipitation anomalies and ENSO anomalies. The standard deviations for the mean ENSO state are also plotted for reference. Only months denoted with * have a statistically significant relationship between ENSO anomaly and precipitation anomaly (p<0.05).

For precipitation, the increases in precipitation are not as dramatic as the increases in temperature when expressed in terms of ENSO anomalies.

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Further, the correlations are not high enough to make a confident comparison between future climate and ENSO anomaly for every month. With this in mind, it is possible to look at the temperature, precipitation, and ENSO index anomalies for some of the larger El Niño events (ie 1982-1983 and 1997-1998, which were 1.5σ to 3σ above the average ENSO state). Even though projections for precipitation increases are large in some months (as much as 163% for 2040 and 486% for 2090 for June and July) peak rains (in March) are only expected to increase by 14% in 2040 and 30% in 2090. While this is substantial, past El Niño events have doubled the cumulative March rainfall. In Figure 47 and Figure 48 past El Niño events are compared with the predicted precipitation in the future.

Figure 47. Plot shows the historical precipitation for two large ENSO anomalies, the mean historical precipitation cycle, and the predicted precipitation for the 2040 climate.

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Figure 48. Plot shows the historical precipitation for two large ENSO anomalies, the mean historical precipitation cycle, and the predicted precipitation for the 2090 climate. Similarly, the 1982-1983 and 1997-1998 El Niño events can be compared with the future in terms of temperature (Figure and Figure 49). Unlike precipitation, the future temperature change is expected to be larger than the changes associated with large El Niño events.

Figure 60: Plot shows the historical temperature for two large ENSO anomalies, the mean historical temperature cycle, and the predicted temperature for the 2040 climate.

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Figure 49:. Plot shows the historical temperature for two large ENSO anomalies, the mean historical temperature cycle, and the predicted temperature for the 2090 climate.

As a final comparison, the actual ENSO indices for the 1997-1998 and 1982-1983 El Niño are compared with expectations for future precipitation and temperature (expressed in terms of ENSO anomalies).

Figure 50: Future precipitation changes expressed in terms of ENSO anomalies (months that have a statistically significant correlation between precipitation anomaly and ENSO anomaly are denoted with *). The 1982-1983 and 1997-1998 ENSO anomalies are more than one standard deviation above the mean ENSO state, but the future climate is typically not expected to change by this magnitude. Error bars have been omitted, but are available in Figure 45 and Figure 46.

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Figure 51. Future temperature changes expressed in terms of ENSO anomalies (months that have a statistically significant correlation between precipitation anomaly and ENSO anomaly are denoted with *). By 2040 the temperature changes are comparable to a one standard deviation increase in ENSO state and by 2090 the typical climate (in terms of temperature) will be greater than the large El Niño events of 1982-1983 and 1997-1998. Error bars have been omitted, but are available in Figure 43 and Figure 44.

To summarize the analogy between El Niño and the future climate of Ecuador:

• By 2040 the temperature change will approach that experienced during El Niño years (temperature change will be larger than the temperature increase experienced for a +1σ increase in ENSO).

• By 2090 the temperature will be greater than the temperature experienced during large El Niño events such as the 1982-1983 El Niño and the 1997-1998 El Niño.

• Precipitation changes are not expected to be negligible, but are not as large as strong El Niño years.

• The relationship between ENSO and precipitation only exists in the rainy seasons March – May and November – December. During other months, the relationship between ENSO and precipitation anomalies is not clear.

• Even though precipitation changes are not of the same magnitude as strong El Niño years, the changes are still large relative to the natural variability for most months (an average shift of +0.5σ by 2040).

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