Downloaded 09/26/21 06:54 AM UTC 106 JOURNAL of HYDROMETEOROLOGY VOLUME 11 Evaporation, with the Aim of Identifying the Types of TABLE 1

FEBRUARY 2010 J O H N S O N A N D S H A R M A 105

A Comparison of Australian Open Water Body Evaporation Trends for Current and Future Climates Estimated from Class A Evaporation Pans and General Circulation Models

FIONA JOHNSON AND ASHISH SHARMA University of New South Wales, Sydney, New South Wales, Australia

(Manuscript received 3 February 2009, in ﬁnal form 11 July 2009)

ABSTRACT

Trends of decreasing pan evaporation around the world have renewed interest in evaporation and its be- havior in a warming world. Observed pan evaporation around Australia has been modeled to attribute changes in its constituent variables. It is found that wind speed decreases have generally led to decreases in pan evaporation. Trends were also calculated from reanalysis and general circulation model (GCM) outputs. The reanalysis reflected the general pattern and magnitude of the observed station trends across Australia. However, unlike the station trends, the reanalysis trends are mainly driven by vapor pressure deficit changes than wind speed changes. Some of the GCMs modeled the trends well, but most showed an average positive trend for Australia. Half the GCMs analyzed show increasing wind speed trends, and most show larger changes in vapor pressure deficit than would be expected based on the station data. Future changes to open water body evaporation have also been assessed using projections for two emission scenarios. Averaged across Australia, the models show a 5% increase in open water body evaporation by 2070 compared to 1990 levels. There is considerable variability in the model projections, particularly for the aerodynamic component of evaporation. Assumptions of increases in evaporation in a warming world need to be considered in light of the variability in the parameters that affect evaporation.

1. Introduction in the last 40–50 years (e.g., Brutsaert 2006; Roderick and Farquhar 2004; Lawrimore and Peterson 2000). Loss of water through evaporation is a major consid- The pan evaporation trends prompt several questions; eration in the design and management of water supply ﬁrst, can we model the twentieth-century trends in reservoirs. Because of the large surface area of most Australia using either observed climatological data or water supply reservoirs, there is little that can be done to outputs from general circulation models (GCMs) and in reduce these losses, which makes it even more important doing so understand the drivers of the trends? And to accurately predict evaporation losses from a system. second, if pan evaporation in the current climate has Concerns about the impacts of climate change on the been decreasing even with increasing temperatures, security of water supplies have led to renewed interest in what will happen in the future? evaporation processes. In this paper, we analyze observed pan evaporation It has generally been expected that potential evapo- records in Australia at 27 locations, with a view to un- ration will increase in the future because of increasing derstanding what has led to the trends and evaluate the temperatures from global warming and an intensifying projections of these trends for future climates. Trends hydrologic cycle (Huntington 2006). However, despite in- are analyzed using observed climatological data as increasing temperatures over recent decades, pan evapora- puts to the PenPan model of a class A evaporation pan tion has been decreasing in many places around the world (Rotstayn et al. 2006). After establishing that the model can reproduce the observed trends adequately, we see if the GCMs and a reanalysis dataset also can reproduce Corresponding author address: Ashish Sharma, School of Civil and Environmental Engineering, University of New South Wales, the observed pan evaporation trends using the model. Sydney, NSW 2052, Australia. We analyze why they get the trends right or wrong E-mail: [email protected] and then examine future estimates of open water body

DOI: 10.1175/2009JHM1158.1

Ó 2010 American Meteorological Society Unauthenticated | Downloaded 09/26/21 06:54 AM UTC 106 JOURNAL OF HYDROMETEOROLOGY VOLUME 11 evaporation, with the aim of identifying the types of TABLE 1. Annual pan evaporation trends. uncertainties inherent in any such projections. Annual trend (mm yr21), Location period of analysis, and reference 2. Pan evaporation trends Australia 22.9 6 1.7, 1970–2002, Roderick and Farquhar (2004) Evaporation is difficult to measure directly, and there- Australia 22.5 6 5.1, 1970–2005,* fore many theoretical and empirical approaches have Jovanovic et al. (2007) been developed. Some of the commonly used tech- Australia 20.7 6 1.6, 1970–2004, niques include the Penman equation (Penman 1948), the Kirono and Jones (2007) Australia 22.0, 1975–2004, Priestly–Taylor equation (Priestley and Taylor 1972), Roderick et al. (2007) and energy balance methods. The eddy correlation or New Zealand 22.1, varying periods, covariance method estimates evaporation directly by Roderick and Farquhar (2005) measuring water vapor fluxes. However, the method is China 22.9, 1955–2000, generally only used in experimental settings because of Liu et al. (2004) USA 22–0.7, 1948–98, the cost of the instrumentation (Brutsaert 2005). Lawrimore and Peterson (2000) In contrast to the eddy correlation method, evapora- USSR 24–0.1, 1950–90, tion pans are a common method of measuring apparent After Golubev et al. (2001) potential evaporation because they are simple to use and * As discussed in section 2, the trend and uncertainty from affordable (Roderick et al. 2009a). It is assumed the ob- Jovanovic et al. (2007) is based on Australian average evapora- served pan evaporation is proportional to the potential tion rather than the average of trends from individual stations; evaporation, with the proportionality constant referred to the uncertainty is therefore not comparable to other studies as the pan coefficient. For open water bodies, as long as which have used the latter methodology. the body is not so deep that heat storage becomes significant, monthly estimates of potential evaporation using pans with a pan coefficient applied ‘‘should be within trends, such that when all the individual station trends 610% in most climates’’ (Shuttleworth 1993). Because are averaged at a regional and/or national scale, the the focus of this paper is on pan and open water body overall trend is negative (i.e., one of decreasing pan evaporation, for simplicity we use the term ‘‘evapora- evaporation). tion’’ throughout, rather than also using ‘‘evapotranspi- Looking at Australia specifically, all four previous ration.’’ It is important to note that in some situations, studies, as shown in Table 1, have found that the pan transpiration will contribute significantly to the total evaporation averaged over the country has decreased, water transfer to the atmosphere. although the size and significance of the trends was dif- As mentioned earlier, many studies completed in the ferent in each study. The significance of the average last 10 years have shown trends of decreasing pan decreasing trends across the country depends on the evaporation. A summary of some of the worldwide period and method of trend analysis. Roderick and trends is presented in Table 1. Lawrimore and Peterson Farquhar (2004) found a statistically significant de- (2000) related average regional warm-season pan evap- creasing trend of approximately 3 mm yr22 for the pe- oration trends to warm-season precipitation trends. Pan riods 1970–2002 and 1975–2002; however, when the evaporation trends were found to range from approxi- latter analysis was extended to 1975–2004 (Roderick mately 21.5 to 22mmyr22 over much of the United et al. 2007), the trend, although still negative, was not States, with the exception of the southeast, which re- significant at the 90% level—a result confirmed by the corded increases of approximately 0.6 mm yr22. Golubev work of Kirono and Jones (2007). Jovanovic et al. (2007) et al. (2001) examined trends in the former Union of found that the negative trend of 22.5 mm yr22 for av- Soviet Socialist Republics (USSR) as well as the conti- erage Australian annual evaporation for 1970 to 2005 nental United States. For the USSR, decreasing pan is not significant, despite individual stations and some evaporation was found in all locations, except for geographic regions showing decreases significant at the a small, not significant, increase in Central Asia and 95% level. It is important to note that the uncertainty Kazakhstan. Liu et al. (2004) found that the averaged limits from their study are not directly comparable to the pan evaporation for China decreased by 2.9 mm yr22, other studies listed earlier, as they are based on the trend with individual regions recording decreases of up to of a time series of Australian average evaporation, rather 9mmyr22. In summary, although some individual sta- than the average of trends from individual stations. tions have recorded increasing pan evaporation, the In Brutsaert and Parlange (1998), the pan evaporation majority of stations have generally shown decreasing trends are explained with reference to the complementary

Unauthenticated | Downloaded 09/26/21 06:54 AM UTC FEBRUARY 2010 J O H N S O N A N D S H A R M A 107 principle of evaporation, which was ﬁrst described by from after 2000. They ﬁnd that the brightening contin- Bouchet (1963). The complementary principle can be ued over much of Europe, the United States, and East summarized by (1), which shows that the apparent po- Asia, with a leveling off of brightening in Japan and tential evaporation recorded by the evaporation pan Antarctica. Liu et al. (2004) attribute the pan evapora-

EPAN, and actual evaporation EA can be related to the tion trends in China to decreasing solar radiation, which, potential evaporation EPO, the evaporation rate over in the absence of cloud cover changes, is believed to be a large uniform land surface when water is not limited caused by increasing aerosol concentrations. Wild et al. (Kahler and Brutsaert 2006). It is important to note that (2009) found that dimming continues in China into the the complementary principle applies at the landscape or twenty-ﬁrst century. Roderick et al. (2007) found that regional scale, rather than the point scale of pan evapo- decreases in solar radiation in northern Australia con- ration considered thus far. The constants b and CP,the tributed to the pan evaporation decreases. It has been pan coefﬁcient, account for the additional energy an hypothesized that these solar radiation decreases are evaporation pan receives compared to its surrounding due to increased cloudiness and rainfall resulting from environment, changes to monsoonal winds caused by Asian anthropogenic aerosols (Rotstayn et al. 2007).

(1 1 b)EPO 5 bEA 1 CPEPAN. (1) Recent studies in Australia (Rayner 2007; Roderick et al. 2007) have also found that wind speed changes It was argued by Brutsaert and Parlange (1998) that have contributed to decreases in pan evaporation. Po- the decreases in pan evaporation at the station level are tential reasons for changes in wind run are unclear, and a consequence of increases in EA over larger regions. they may be due to large-scale climatological changes or Golubev et al. (2001) compared actual evaporation and alterations to the environment surrounding a particular pan evaporation and also found that actual evaporation evaporation pan (Rayner 2007). McVicar et al. (2008) had increased despite decreasing pan evaporation for found decreasing wind run trends for the period 1975– regions with a high index of dryness (ratio of potential 2006 over almost 90% of Australia, concluding that this evaporation to precipitation). Increases in actual evap- high degree of spatial coherence indicates regional pro- oration will occur in moisture-limited locations that cesses dominate the changes. experience increasing precipitation or increased mois- There are numerous studies evaluating the perfor- ture supply, for example, expansion of irrigation regions. mance of GCMs in reproducing the observed climate of Chattopadhyay and Hulme (1997) used stepwise re- the twentieth century—for example, Meehl et al. (2007), gression to analyze historic pan evaporation trends in Murphy et al. (2004), and Perkins et al. 2007—particularly India, finding that increases in relative humidity were with respect to temperature trends. In addition, the In- the most strongly associated with the decreasing pan tergovernmental Panel on Climate Change’s (IPCC) as- evaporation trends. Roderick et al. (2009b) suggest that sessment reports (e.g., Meehl et al. 2007) provide detailed this may be due to widespread irrigation, leading to in- information regarding expected temperature increases creased actual evaporation from the landscape and via resulting from different greenhouse gas emission sce- the complementary principle and decreased pan evap- narios. For Australia specifically, the best estimates of oration. For other regions, in the absence of changes to temperature increases by 2070 are 1.58–2.58C for the moisture supply, which will change EA and hence EPAN, Special Report on Emissions Scenarios (SRES) B1 emis- we must look at alternative reasons for the pan evapo- sion scenario and 28–48C in the A2 scenario (CSIRO and ration trends. BOM 2007). However, only a few studies have examined Solar radiation changes have been examined as future projections of evaporation using the outputs of a possible driver of the pan evaporation trends. Solar GCMs. McKenney and Rosenberg (1993) compared eight radiation changes may be due to cloud cover changes or alternative methods of estimating potential evaporation changes to the concentrations of aerosols in the atmo- for five sites in the central United States. They noted that sphere. Norris and Wild (2007) found that cloud cover many previous studies had only considered temperature- changes could not fully account for trends in solar ra- based estimates of evaporation, and they point out that diation in the Northern Hemisphere between 1965 and changes in other climatic variables may have important 2004. Instead, the authors offer as the most likely ex- impacts on evaporation in the future but conclude that planation that changes in the concentrations of anthro- the future estimates are sensitive to both the GCM and pogenic aerosols led to a period of ‘‘global dimming’’ evaporation equation used. Chattopadhyay and Hulme during the 1970s to the mid-1980s, followed by a period (1997) used six GCMs to provide estimates of percent- of ‘‘solar brightening’’ from the mid-1980s. Wild et al. age changes in potential evaporation per degree Celsius (2009) extend previous studies of global dimming to data of warming. Projected potential evaporation increases in

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India in the future result from decreased relative hu- The original Penman wind function f(u), with wind midity and increases in solar radiation (from decreas- speed (u) in units of meters per second, has been used ing cloudiness). The Commonwealth Scientiﬁc Industrial as recommended by Brutsaert (1982) and is shown in Research Organisation (CSIRO) and the Australian Eq. (3), with constants based on metric units for the Bureau of Meteorology (BOM; CSIRO and BOM 2007) variables (Shuttleworth 1993): assess future potential evapotranspiration across Aus- 6.43 tralia using 14 GCMs and six emission scenarios. The 5 3 1 f (u) 6 (1 0.536u). (3) potential evapotranspiration is estimated using the com- 1 3 10 Ly plementary relationship areal evaporation (CRAE) model (Morton 1983). The best estimates for potential evapo- The Penman equation cannot be used to directly es- transpiration increases in 2070 are 6% in the south and timate the evaporation from a class A pan, as we need to west of Australia and 10% in the north and east. account for the additional energy available at the pan We now model the observed pan evaporation trends due to the exposed base and sides. Several variations of for Australia using the PenPan model. We then use the the Penman equation have been developed to model PenPan model with the outputs from reanalysis data and evaporation pans (e.g., Thom et al. 1981; Linacre 1994; a suite of GCMs to ascertain if they correctly represent Rotstayn et al. 2006). The PenPan model (Rotstayn the observed pan evaporation trends. Lastly, we use et al. 2006) has been used in the present study, as it has GCM projections to estimate open water body evapo- previously demonstrated model Australian class A pans ration for three periods in the future: 2030, 2050, and successfully (Roderick et al. 2007). The equation for the 2070. PenPan model is shown in (4):

D RNPAN ag 3. Pan and open water body evaporation estimates EPAN 5 1 f PAN(u)(es À ea), (4) D1ag rLy D1ag We now present details of the equations used for the analysis of pan evaporation and open water body evap- where EPAN is the modeled pan evaporation, a is a con- oration. Data sources for the station data and GCMs are stant, taken to be 2.4, which accounts for the additional then described. energy exchanges due to the walls of the pan, RNPAN is the net radiation at the pan, the calculation of which is a. Methodology described below and other variables are as defined in 1) OPEN WATER BODY AND PAN EVAPORATION Eq. (2). We reduce the predicted EPAN by scaling the EQUATIONS model outputs for each location by a constant factor to account for the presence of bird guards on the pans. The Penman combination approach is often used for Through field trials, it has been found that bird guards estimating open water body evaporation, and Shuttleworth reduce the evaporation from a pan by approximately 7% (1993) recommends it for this purpose. The Penman (van Dijk 1985), and hence the estimated monthly equation for open water body evaporation (Brutsaert evaporation has been reduced by 7% for all locations 1982) is shown in Eq. (2): and time steps. For the PenPan model, the wind function is modified D Q g E 5 N 1 f (u)(e À e ), (2) based on the measurements of a pan by Thom et al. W D1g r D1g s a Ly (1981), with wind speed (u) in units of meters per second, as shown in Eq. (5), and the constant of 86 400 to 21 where EW is open water body evaporation (mm day ), given the evaporation in (4) in millimeters per day: D is the gradient of the saturated vapor pressure function 21 21 (Pa K ), g is the psychometric constant (Pa K ), r is f (u) 5 1.39 3 10À8(1 1 1.35u) 3 86 400. (5) 23 PAN the density of water (kg m ), Ly is the latent heat of 21 vaporization (J kg ), es is the saturated vapor pressure In summary, the PenPan equation differs from the (kPa), and ea is the actual vapor pressure (kPa). Here, Penman equation in the calculation of net radiation, in- 22 QN is the available energy flux (W m ). For calcula- cluding differences in the water surface albedo, the tions at daily or longer periods, the ground conductance adopted wind function, and the inclusion of the constant a, of heat from an open water body is relatively small and to account for additional local energy supplied to the pan. can be neglected (Shuttleworth 1993), and we can ap- The PenPan model is used to calculate pan evapora- proximate the available energy as the net (all wave) tion at station locations around Australia to attribute radiation RN. the pan evaporation trends to changes in other climatic

Unauthenticated | Downloaded 09/26/21 06:54 AM UTC FEBRUARY 2010 J O H N S O N A N D S H A R M A 109 variables. We have also calculated pan evaporation us- upward longwave radiation and RSP. We now outline the ing the PenPan model using reanalysis and GCM model process followed for the calculations of these quantities. outputs to analyze the strengths and weakness of these Solar radiation measurements were only available for datasets in reproducing the observed pan evaporation 15 of the 27 stations and generally for relatively limited trends for the twentieth century. For the future, we are durations. To increase the length of the time series an- interested in open water body evaporation estimates alyzed, an empirical relationship between solar radia- and thus the Penman equation is used. tion and sunshine duration developed by Prescott (1940), To assist in the analysis of the results presented in shown in (10), was used to estimate solar radiation for sections 4 and 5, we will consider the contribution of the missing periods at stations with RS measurements and different drivers of evaporation to the observed trends for stations with no RS measurements: and future projected changes. It is common to separate n evaporation into the component driven by solar radiation R 5 R a 1 b , (10) and a second aerodynamic component, driven by wind S A N speed and vapor pressure deficit, which defines the drying power of the air (Brutsaert 2005). We define these com- where RA is the top of the atmosphere radiation, n and N ponents as follows in Eqs. (6) and (7) for the Penman are the actual and maximum sunshine hours for the lo- equation and the PenPan model, respectively, where EWR cation and time of year, and a and b are constants de- and EWA refer to the open water evaporation radiation pendent on location and season, which take values of 0.25 and aerodynamic contributions, respectively, and EPANR and 0.5, respectively, in the absence of local calibration and EPANA are used to denote the pan evaporation ra- data. The implications of this empirical approach on the diation and aerodynamic contributions, respectively: results are discussed in section 4. The pan solar radiation (RSP) for the stations and GCMs was calculated using the R D n procedures outlined in Rotstayn et al. (2006). EWR 5 , D1g rLy For both the station data and GCM calculations, g R E 5 f (u)(e À e ), and (6) upward longwave radiation ( lu) has been calculated WA D1g s a assuming a blackbody radiating at the water surface temperature (assumed to be the air temperature at 2 m). D R NPAN Downward clear sky longwave radiation (Rldc) was cal- EPANR 5 , D1ag rLy culated for station locations using Eq. (11) from Brutsaert ag (1982), where the atmospheric emissivity « is a function E 5 f (u)(e À e ). (7) ac PANA D1ag PAN s a of the actual vapor pressure, s is the Stefan Boltzmann

constant, and Ta is the taken to be the air temperature in 2) RADIATION CALCULATIONS degrees Celsius, in the absence of data on the water surface temperature: We require net (all wave) radiation to estimate both the open water body evaporation and pan evaporation. 4 This is calculated for open water bodies as shown in (8), Rldc 5 eacs(Ta 1 237.2) . (11) where RS is the solar radiation, a is the albedo of the Net longwave radiation was found according to Eq. (12) water surface, taken to be 0.06 (Brutsaert 1982), and Rnl is the net longwave radiation: (Brutsaert 1982), where a is taken to be 0.2 and n and N are as deﬁned for Eq. (10): 5 a 1 RN RS(1 À ) Rnl. (8) hi n R 5 (R À R ) a 1 (1 À a) . (12) For the model of the class A evaporation pan, we need nl ldc lu N to estimate RNPAN as shown in Eq. (9), where RSP is the solar radiation received by the pan and ap is the albedo b. Data sources of the pan, assumed to be 0.14 (Rotstayn et al. 2006): 1) STATION DATA R 5 R (1 À a ) 1 R . (9) NPAN SP p nl Observed pan evaporation data were sourced from For the station data, only some locations have mea- the BOM high-quality database of monthly class A pan surements of RS. We need to calculate RS for the re- evaporation data (Jovanovic et al. 2007). These data have maining stations and Rnl and RSP for all the stations. For been corrected for known inhomogeneities in the records, the GCMs and reanalysis, we have RS and downward such as the introduction of bird guards, station relo- longwave radiation outputs, so we only need to calculate cations, and changes in station exposure or observation

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TABLE 2. GCM skill scores for Australia. N/A indicates ‘‘not obtained from the World Climate Research Programme’s available.’’ (WCRP’s) Coupled Model Intercomparison Project Skill score Skill score Average skill phase 3 (CMIP3) multimodel dataset. The available Model 1* 21 score Rank models were ranked by averaging the skill scores of their BCCR 0.59 0.79 0.69 3** performance over Australia from Perkins et al. (2007) CCCMA T47 0.52 0.71 0.61 10 and CSIRO and BOM (2007). The top five ranked CNRM 0.54 n/a 0.54 11 models, as indicated in Table 2, were adopted for the CSIRO Mk3.5 0.61 0.8 0.70 2** analyses. We have also analyzed the trends in the Na- GISS-AOM 0.56 0.75 0.66 7 tional Centers for Environmental Prediction–National IAP 0.64 0.73 0.68 4** INMCM 0.63 N/A 0.63 9 Center for Atmospheric Research (NCEP–NCAR) re- IPSL 0.51 0.78 0.64 8 analysis data (Kalnay et al. 1996) to provide a comparison MIROC-m 0.61 0.83 0.72 1** to the GCMs. It is acknowledged that there are de- MRI 0.60 0.76 0.68 5** ficiencies in the NCEP reanalysis data product, especially NCAR CCSM 0.68 0.67 0.67 6 for surface variables. These are related to the assimilation * Skill score 1 from CSIRO and Bureau of Meteorology (2007). of observations with the model-derived data (Reichler 1 Skill score 2 from Perkins et al. (2007). and Kim 2008), particularly for areas with sparse obser- ** Models used for analyses. vations. However, it provides an observationally derived comparison to the GCM outputs at a similar resolution to methodology. Data required for the PenPan estimates of the GCMs, whereas station-to-grid comparisons are dif- evaporation include mean temperature, vapor pressure ficult. Monthly climate variables used from each of the deficit, wind speed, and net radiation. Daily mean tem- GCMs and the NCEP reanalysis to calculate pan perature data were sourced from the BOM high-quality and open water body evaporation are mean temperature, database of daily mean temperature data. Forty-one zonal and meridional wind speed, specific humidity, stations had both high-quality pan evaporation and downward solar and longwave radiation, and surface temperature datasets. In the absence of published high- pressure. quality datasets for the remaining variables (relative Two climate scenarios were used to estimate open wa- humidity, wind speed, and net radiation), data were ter body evaporation in the future. The scenarios adopted sourced from the BOM (product IDCJHCO2.200706) for analysis were A2 (high emissions) and B1 (stabiliza- and the MetAccess National Weather database, which is tion at 550 ppm; Nakicénovic´ et al. 2000). Table 3 sum- a database of daily historical meteorological data de- marizes the salient features of each of the models used in veloped by CSIRO (Horizon Agriculture Pty Ltd 2006). the analyses (CSIRO and BOM 2007). For each model Daily data from each of the data sources were collated and scenario, open water body evaporation was estimated and checked for inconsistencies and gaps. Gaps in the for three decades in the future—2030s, 2050s and 2070s— records were filled with monthly average values calculated whereas pan evaporation trends were calculated using for each station for each variable. Stations with fewer than the twentieth-century simulations from each model. 10 consecutive years of all variables required for the Three important steps in the analysis of GCM outputs Penman estimates were eliminated from the analysis. The are interpolation, grid cell averaging, and bias correc- remaining27stationswereusedfortheanalysis. tion. For the data processing, the observed dataset has been taken from the NCEP–NCAR reanalysis data 2) GCM AND REANALYSIS DATA (Kalnay et al. 1996). The interpolation, averaging, and GCM outputs for the trend analysis and estimates of bias correction have been undertaken for each of the open water body evaporation in future climates were input variables to the evaporation calculations.

TABLE 3. GCM properties.

SRES Number of available Horizontal Model ID Full model name Organization scenarios model realizations resolution (km) BCCR bccr_bccm2_0 BCCR, Norway A2, B1 1 175 CSIRO csiro_mk3_5 CSIRO, Australia A2, B1 1 175 IAP iap_fgoals1_o_g LASG/Institute of Atmospheric Physics, China B1 3 300 MIROC miroc3_2_medres Model for Interdisciplinary Research on climate, A2, B1 3 250 Centre for Climate Research, Japan MRI mri_cgcm2_3_2a Meteorological Research Institute, Japan A2, B1 5 250

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For the pixel-based bias correction discussed later, we require the GCM output and the NCEP reanalysis data to be on the same grid. Because the reanalysis data have been compared to all five selected GCMs, its grid size of 2.5832.58 has been adopted as the constant, and all GCM outputs have been interpolated to this grid, using bilinear interpolation. The gridcell averaging has been carried out by calculating the value at each grid cell as the mean of the nine closest grid cells by Euclidean distance. This was undertaken because Wilby and Wigley (2000) found that the highest correlations of a variable with its predictor variables did not necessarily occur within the same grid cells. By averaging over a number of grid cells, we can allow for this result. Bias correction recognizes that the GCM outputs for the current climate may have biases when compared to observed data, either ground-based FIG. 1. Estimated (EPAN) and observed (EOBS) monthly pan data or reanalysis data. The correction process adjusts the evaporation data. Correlation coefficient is 0.96. Dashed line shows one-to-one relationship. Solid lines shows the least squares fit to GCM outputs to match the mean and variance of the data; equation of least squares fit is y 5 15.24 1 0.90x with R2 5 recorded historical data. The bias correction is then ap- 0.92. Standard errors of the fitted regression line are 0.68 and 0.003 plied to future projections, assuming that the bias will not for the intercept and slope, respectively. change over time. For each variable and grid cell, the bias correction was undertaken by subtracting the monthly The trends in the radiative (EPANR) and aerodynamic modeled mean and dividing by the modeled standard (EPANA) components of the estimates at each station deviation. The variables are then rescaled by the ob- are also listed. Figure 2 presents a comparison of the served (in this case NCEP reanalysis) monthly means and direction of the observed (EOBS) and modeled (EPAN) standard deviations for 1961–90 for each variable. pan evaporation trends across Australia, whereas Fig. 3 shows scatterplots of the trends and their components at all stations. 4. Observed and modeled pan evaporation The majority of observed pan evaporation trends are negative across Australia, particularly for coastal a. Observed pan evaporation trends stations. The PenPan trends are generally in the same Figure 1 shows the modeled monthly evaporation direction as the observed trends, although on average compared to observations at each of the 27 stations, they are smaller in magnitude. The average observed along with the line of best fit to the data. The correlation trend, with standard error, across Australia is 20.5 6 between the observations and modeled values is very 3.1 mm yr22, and the modeled trend is 20.4 6 high, although there is some bias in the relationship for 2.7 mm yr22. The correlation between the modeled and large evaporation totals. The root-mean-square error observed trends at the 27 stations is large (r 5 0.78). To of the predictions is 29 mm month21. We calculated evaluate the uncertainty of the trend estimates, we have modeled (EPAN) and observed (EOBS) pan evaporation constructed confidence limits based on the assumption of trends for each of the stations for the period 1975–99. no observed trend at each station. The 95th percentile This period was chosen so that the trends can be com- confidence limit is estimated as the 95th percentile trend pared to those calculated from the GCM twentieth- from a set of randomized samples that are recreated from century outputs. Trends in evaporation were calculated the original historic data by randomly bootstrapping the for each month using linear regression and then summed observations (Sharma 2000). These results are presented to give an annual trend (Roderick et al. 2007). This was in Table 4. It can be seen that the trends at 22 of the sta- undertaken so that in the case of missing values in the tions lie outside the 95% confidence limits from the his- historic data, the other data available for the year could toric resampling and are therefore considered significant. still be used in the trend calculations. The analysis pe- We can see from Table 4 and from Fig. 3c that the riod of the trends varies between stations as a result of trends in the radiative component (EPANR)ofthePenPan data availability, with the years analyzed for each station estimates are generally quite small and are weakly cor- listed in Table 4. related with the observed pan evaporation trends. The The modeled and observed pan evaporation trends at correlation between the modeled trends and the advec- the 27 stations across Australia are presented in Table 4. tion component (EPANA) of the trends is large (r 5 0.78).

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22 TABLE 4. Trend comparisons (mm yr ) for all stations. The asterisk denotes that p values for the signiﬁcance of the observed trends have been estimated using randomized samples of the observed data to construct 95th percentile conﬁdence limits.

Station number BOM station name Record length EOBS p* EPAN EPANA EPANR Stn002012 Halls Creek Airport 1976–91 28.9 0 25.9 22.8 3.0 Stn003003 Broome Airport 1986–99 230.5 0 247.5 240.6 26.9 Stn004032 Port Hedland Airport 1975–91 10.5 0.004 12.1 5.1 7.0 Stn008051 Geraldton Airport 1975–92 210.1 0 219.3 216.3 23.0 Stn009021 Perth Airport 1981–99 13.9 0 13.2 8.2 5.1 Stn009741 Albany Airport 1975–99 1.5 0.154 1.6 2.2 20.6 Stn012038 Kalgoorlie–Boulder Airport 1979–91 213.8 0 29.3 24.9 24.4 Stn013017 Giles Meteorological Ofﬁce (MO) 1975–99 12.6 0 13.8 15.4 21.6 Stn014015 Darwin Airport 1977–99 218.9 0 213.7 210.0 23.7 Stn015135 Tennant Creek Airport 1975–99 12.9 0.001 9.9 9.2 0.6 Stn015590 Alice Springs Airport 1981–99 47.1 0 7.8 7.1 0.7 Stn016001 Woomera Aerodrome 1980–99 13.1 0 7.4 10.7 23.3 Stn018012 Ceduna Airport Meteorological Ofﬁce (AMO) 1975–99 21.6 0.256 2.9 4.5 21.6 Stn026021 Mount Gambier Aero 1975–99 27.3 0 27.6 27.1 20.5 Stn031011 Cairns Aero 1975–99 0.5 0.432 6.3 4.1 2.2 Stn032040 Townsville Aero 1981–99 215.0 0 5.1 23.6 8.7 Stn036031 Longreach Aero 1975–91 13.7 0.006 15.8 6.5 9.3 Stn039083 Rockhampton Aero 1975–88 4.6 0.063 14.1 9.5 4.6 Stn048027 Cobar MO 1978–99 225.7 0 212.9 211.9 21.1 Stn059040 Coffs Harbour MO 1975–99 214.7 0 212.0 211.6 20.4 Stn061078 Williamtown Royal Australian Air Force (RAAF) 1975–99 26.5 0.004 27.8 23.9 23.9 Stn061089 Scone Soil Conservation Station (SCS) 1975–99 25.4 0.02 1.9 2.3 20.4 Stn070014 Canberra Airport 1978–99 29.6 0.001 25.7 24.0 21.7 Stn082039 Rutherglen Research 1975–98 29.4 0 28.9 25.4 23.4 Stn085072 East Sale Airport 1975–99 24.6 0.005 29.8 28.8 20.9 Stn091104 Launceston Airport comparison 1975–99 22.6 0.049 1.0 20.3 1.3 Stn094069 Grove (comparison) 1975–99 1.8 0.087 4.6 3.6 1.0

If we look at the meteorological variables involved, then et al. (2008) suggest that the primary driver of wind the highest correlation between the modeled observed speed decreases in Australia is the poleward expansion trends is with wind speed trend (r 5 0.63). The net ra- of the Hadley cell (Lu et al. 2007; Seidel et al. 2008). diation shows a very weak correlation with the observed Shuttleworth et al. (2009) also show that large-scale trends (r 5 0.22). Further investigation is required of the changes in wind speed are the primary reason for de- causes of the trends in the different climatic variables creasing pan evaporation in Australia. The implications contributing to the changes to evaporation. However, of localized land use changes on evaporation trends are this is outside the scope of the current study. McVicar also discussed in McVicar et al. (2008) and Rayner

FIG. 2. Pan (EOBS) and PenPan (EPAN) evaporation trends across Australia. Positive trends are shown as a cross and negative trends as a triangle.

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in the sunshine duration measurements. However, an investigation into the sunshine duration–radiation relationship [Eq. (10)] indicated an overall good match to Prescott’s (1940) coefﬁcients when estimated over the entire historical record, with deviations when the estimation was based over shorter periods or smaller regions (results available on request). There may be some sites though where the relationship has changed over time, and for these locations we will not accurately

represent the EPANR trends. However, we think that by estimating the EPANR directly for each station, we provide a comparison to the Roderick et al. (2007) meth-

odology that calculated the EPANR trends as the residual of the observed EPAN trend and EPANA trend for 34 of the 41 sites analyzed. With both methods, it is found that

the EPANA trends contribute the most to the total EPAN trends. The trend analysis supports the findings of two other recent attribution studies on pan evaporation trends in Australia. Rayner (2007) found that solar radiation made the smallest contribution to pan evaporation trends. Roderick et al. (2007) found that changes in the aerodynamic component, and specifically the wind speed changes, were responsible for most of the trends in the pan evaporation data. Kirono et al. (2009) found a correlation of 0.51 between the modeled and observed monthly pan trends, with an average 60% of sites agreeing with the direction of the trends for a particular month. On the basis of the PenPan modeling, we find a correlation of 0.71 between the observed and modeled monthly trends, and an average of greater than 80% of sites agreeing in the directions of the monthly trends. The improvement in the results using the PenPan model compared to Morton’s point potential evaporation as in Kirono et al. (2009) may be because it explicitly includes wind speed, which is shown to be an important component of the Australian pan evaporation trends. Morton’s point potential evaporation does not use wind speed, instead it includes a vapor transfer coefficient. It is important to note that the trends recorded and modeled for the pans will be larger than those for a hy- FIG. 3. Comparison of EPAN and EOBS pan evaporation trends pothetical open water body in the same location. Szilagyi direction and magnitude for (a) EPAN vs EOBS trends, (b) EPANA vs (2007) and Kahler and Brutsaert (2006) show that in a E trends, and (c) E vs E trends. OBS PANR OBS water-limited environment, the change in pan evaporation will be larger than the corresponding change in (2007); however, neither of them concluded that this was actual evaporation because 1) there is additional energy an important driver overall. available to the pan through the base and sides of the It is acknowledged that the radiation component of pan and 2) the pan is subject to local advection. For the the calculation is based on solar radiation estimated stations analyzed for this paper, trends calculated using from sunshine duration, rather than actual measure- the Penman equation were on average 65% of those ments. If changes to aerosols (either anthropogenic or calculated for the class A pans, as expected from Szilagyi natural) have led to changes in radiative energy at the (2007) and Kahler and Brutsaert (2006). Considering earth’s surface, then these changes may not be reflected the monthly evaporation totals, rather than trends, we

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22 TABLE 5. Mean Australian EPAN and component trends for 1975–99 (mm yr ) with 95% conﬁdence intervals estimated from NCEP reanalysis and GCM outputs.

Model EPAN trend EPANR trend EPANA trend U* D* T* NCEP 23.2 6 0.6 20.8 6 0.2 22.4 6 0.7 0.8 6 0.6 23.1 6 0.3 0.0 6 0.01 BCCR 1.1 6 0.5 20.1 6 0.1 1.2 6 0.4 1.5 6 0.3 20.4 6 0.2 0.1 6 0.01 CSIRO 21.9 6 0.5 0.6 6 0.1 22.5 6 0.5 23.0 6 0.4 0.3 6 0.2 0.1 6 0.01 IAP.01 20.1 6 0.3 20.3 6 0.1 0.2 6 0.2 1.5 6 0.2 21.6 6 0.2 0.0 6 0.01 IAP.02 9.3 6 0.7 2.0 6 0.1 7.3 6 0.6 2.4 6 0.3 4.6 6 0.3 0.1 6 0.01 IAP.03 2.3 6 0.3 0.9 6 0.1 1.4 6 0.3 0.0 6 0.3 1.5 6 0.2 0.1 6 0.01 MIROC.01 0.5 6 0.6 0.6 6 0.2 20.2 6 0.4 20.5 6 0.6 0.1 6 0.5 0.1 6 0.03 MIROC.02 22.9 6 0.7 20.5 6 0.1 22.5 6 0.7 1.2 6 0.5 23.6 6 0.4 0.0 6 0.01 MIROC.03 5.0 6 1.3 0.9 6 0.2 4.2 6 1.2 1.2 6 0.4 2.5 6 0.9 0.1 6 0.02 MRI.01 22.3 6 0.3 0.0 6 0.1 22.3 6 0.2 21.2 6 0.3 21.5 6 0.2 0.0 6 0.01 MRI.02 20.4 6 0.8 0.9 6 0.2 21.3 6 0.7 20.7 6 0.4 20.7 6 0.4 0.1 6 0.01 MRI.03 1.2 6 0.5 1.0 6 0.1 0.3 6 0.5 20.1 6 0.4 0.2 6 0.3 0.1 6 0.01 MRI.04 3.7 6 0.4 1.3 6 0.1 2.4 6 0.4 21.5 6 0.3 3.7 6 0.2 0.2 6 0.02 MRI.05 2.7 6 0.5 0.8 6 0.1 1.9 6 0.5 0.7 6 0.3 0.9 6 0.3 0.2 6 0.02

found that the PenPan monthly evaporation estimates trends in the aerodynamic and radiative components of were on average 16% higher than the Penman estimates. evaporation compared to the trend in total evaporation. This is equivalent to an average pan coefﬁcient of 0.86, We can see the strong correlations between aerodynamic although values for individual locations range from 0.78 trends and total evaporation trends. This agrees well to 0.93. with the ﬁndings for the station-based pan evaporation estimates. The magnitude of the reanalysis E trend b. GCM and reanalysis representation of observed PANA is similar to that found for the pan data. For the GCMs pan evaporation trends the results are varied with only a few of the models

Moving now to the reanalysis and GCM trend calcu- showing the expected negative trend in EPANA. lations, average trends across Australia for 1975 to 1999 Although the contribution of the radiative component from all the GCMs analyzed are presented in the first to the overall evaporation trend is small, we have exam- column of Table 5. The results in the other columns of ined the trends in the radiation outputs from the rean- Table 5 are discussed in the following paragraphs. The alysis and GCMs. Trends for 1975–99 are not significant estimated NCEP pan evaporation trends are negative at the 90% level at the majority of locations for any of the over much of Australia. Overall, the NCEP data reflects GCMs, for either shortwave or longwave radiation. Av- the average trend from the observed pan data well. On eraged over all the GCMs analyzed, the shortwave radi- the other hand, the average pan evaporation trend is ation trend is 20.02% and the longwave radiation trend is negative for only 5 out of 13 GCMs. Figure 4 shows 20.07%. For the reanalysis data, the radiation trends are the trends across Australia from the NCEP data and significant over much of southern Australia. The average Bjerknes Centre for Climate Research–Bergen Climate shortwave trend is 20.08%, with longwave radiation Model version 2 (BCCR-BCM2.0) and CSIRO Mark decreasing by approximately 0.2% yr21. version 3.5 (Mk3.5) model outputs. We can see that To understand the drivers in the large changes in the there is significant spatial variations in the trends across aerodynamic components of evaporation, particularly in Australia as well as differences in the predictions from the reanalysis data, we use the methodology of Roderick the two GCMs. Cells where trends are significant at the et al. (2007) to estimate the contribution of the changes 5% level are marked. Spatially, the trends at the ma- from wind speed, vapor pressure deficit, and temperature jority of cells from the GCM predictions are not sig- according to Eq. (13), where U*, D*, and T* represent nificant, mainly as a result of the large interannual the changes in EPANA due to wind speed, vapor pressure variability in the predictions. deficit and temperature, respectively. The values of U*,

Splitting the pan evaporation trends into EPANR and D*, and T* are presented in Table 5 for each model, EPANA, we ﬁnd that as for the station-level analysis, dE ›E du ›E dD trends are primarily generated by the aerodynamic PANA ’ PANA 1 PANA component. Columns 2 and 3 of Table 5 list the contri- dt ›u dt ›D dt bution from the two components, respectively. Figure 5 ›E ds dT 1 PANA 5 U* 1 D* 1 T*. (13) shows two scatterplots with the correlations between ›s dT dt

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wind speed trends. By way of comparison, Roderick

et al. (2007) found that the average EPANA trend across Australia was 22.6 mm yr22, with U* equal to 22.7 mm yr22 and D* and T* making negligible contribu-

tions to the total EPANA trend based on the analysis of data from 41 meteorological stations. Recent analysis by McVicar et al. (2008) highlighted that the NCEP reanalysis fails to capture recorded wind run trends across Australia. The vapor pressure deﬁcit effects (D*) on the other hand are generally negative with an average of 23.1 mm yr22, and it is this component that contributes

to the negative EPANA trend across Australia. Thus, the apparently good agreement between the observed pan trends and reanalysis occurs for different reasons. For the GCMs, the CSIRO Mk3.5 model does the best in capturing the observed split between U*, D*, and T* with the vapor pressure deﬁcit and temperature components close to zero, and the wind speed trend leading to

the overall EPANA trend. Figure 6 shows the spatial pattern of the NCEP and CSIRO EPANA component trends. The EPANA trend for the remaining models is primarily due to the trends in vapor pressure deﬁcit unlike the station data. Future work will investigate why the CSIRO model performs better in this respect than the other GCMs considered. Temperature changes, from all of the GCMs and the reanalysis, do not contribute strongly to the estimated evaporation trends, in agreement with Roderick et al.’s (2007) ﬁndings for the station data.

5. Future evaporation projections We have used the GCMs to assess trends in pan evaporation across Australia for the latter part of the twentieth century. But of more interest is what the projected changes in evaporation for the future will be. As explained in the methodology, for this analysis we have considered open water body evaporation projections for the future, calculated using the Penman equation. In the previous section, we showed that there are considerable uncertainties in the way that the GCMs represent the observed trends in evaporation. This may raise questions as to the appropriateness of examining future changes in evaporation using these same models.

22 However, we argue that given the importance of evap- FIG. 4. Comparison of EPAN trends (mm yr ) for 1975–99 from (a) NCEP, (b) CSIRO, and (c) BCCR. Cells with trends signiﬁcant oration in the hydrologic cycle, it is important to look at at the 5% level are marked with an asterisk. Results are based on the future changes and to understand where the sensi- bias-corrected GCM outputs. tivities in the projections occur. This way, when current trends as modeled by the GCMs are understood better, we can use the future sensitivities to assess their impact We ﬁrst look at the NCEP reanalysis results, which on the future projections. showed decreasing EPANA as expected. The average of Tables 6 and 7 summarize the average percentage U* across Australia is 0.8 mm yr22 and looking at in- changes across Australia for each GCM, with the en- dividual grid cells, 68% of the land cells have increasing semble means presented for the GCMs that have

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FIG. 5. Scatterplots for NCEP reanalysis and GCMs comparing total EPAN trend to (a) EPANA and (b) EPANR. Individual points represent the results at each grid cell. multiple ensemble runs available. The percentage change nario, as also evident from Table 7. Evaporation is has been calculated compared to the average for 1990– projected to increase in the southwest of Australia in all 2000 from each of the models. The mean percentage cases, with even the minimum projections from the change in open water body evaporation across Australia multiple GCMs being positive. For the rest of Australia, was found to be 2.2% for 2030, 3.9% for 2050, and 6.8% we see in Fig. 7a that the minimum change in 2030 is for 2070 in SRES A2 and 1.7%, 3.9%, and 4.8% for 2030, actually a decrease in evaporation compared to 1990 2050, and 2070, respectively, for the SRES B1 scenario. values. Decreasing evaporation in 2030 is projected for The CSIRO GCM projects the largest increases for all at least some grid cells by all three of the MIROC en- three periods, with the MIROC models projecting the semble runs and four of the ﬁve MRI ensemble runs. smallest increases. The results presented here differ from the evapora- Tables 6 and 7 also show the contributions to the total tion projections in CSIRO and BOM (2007), in which all changes from the aerodynamic and radiative compo- models project either no change or increases in potential nents. The radiative and aerodynamic contributions to evapotranspiration. The differences between the two the increases vary between models. In some cases, for results are likely resulting from the analysis method example, CSIRO, the aerodynamic component con- used. As previously noted, the CSIRO and BOM (2007) tributes approximately 60% of the increase for all three report used the CRAE model (Morton 1983) to calcu- time windows considered. Other models indicate that late the changes in potential evapotranspiration. The radiation increases will contribute more to increasing CRAE model does not use wind speed in calculating open water body evaporation. For the SRES B1 sce- evaporation. nario (Table 7), the models are more consistent in at- To further examine the variability in the outputs from tributing increases in open water body evaporation to the different GCMs evident in Tables 6 and 7, we ex- aerodynamic component increases. amine the convergence of the model projections us- Figure 7 shows the spatial variation of the projected ing the methodology of Johnson and Sharma (2009). In changes in total open water body evaporation over time. Fig. 8 we present plots showing the spatial variation of We show the mean, minimum, and maximum percent- the coefﬁcient of variation (CV) of the model projections age changes projected at each grid cell from all the of EW, EWR, and EWA. The rationale behind assessing GCMs for 2030, 2050, and 2070 for the SRES A2 emis- model agreement using CV values is that the ensemble sions scenario. Spatially, the patterns of change are mean can be expected to give the best projection of the generally consistent for the minimums, mean, and likely state of the climate in the future (Johnson and maximums for all three periods. The spatial patterns are Sharma 2009). We therefore examine the spread of the similar for SRES B1 (not shown here), with generally models at each grid cell around the ensemble mean for smaller increases projected than for the SRES A2 sce- that location; in places where the GCMs all lead to

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22 FIG. 6. Pan evaporation (EPANA) component trends (mm yr ) for NCEP reanalysis for (a) U*, (b) D*, and (c) T* and for CSIRO Mk3.5 for (d) U*, (e) D*, and (f) T*. Cells with trends signiﬁcant at the 5% level are marked with an asterisk. Note the large areas in the

U* component from NCEP reanalysis that show positive contributions to the EPANA trends as compared to the CSIRO results, which are negative over all of Australia. In contrast, the D* trends are negative for the reanalysis and significant at most locations, whereas for the CSIRO Mk3.5 D* trends are not significant at any grid cell. Station level data has been previously shown (Roderick et al. 2007) to generally have negative U* trends and minimal trends in D* and T*, which agrees with the CSIRO outputs shown here. similar projections, the CV values are small, with larger nent shows good model convergence and no strong values resulting when there is less agreement between spatial trends in convergence. Interestingly, the model the models. The left column in Fig. 8 shows the con- convergence does not change significantly over time for vergence in EW over time, with slightly higher CV values either of the components or the total evaporation esti- occurring in the eastern part of Australia and no strong mates. Similar analysis for the variables making up the variations with time in the spatial patterns. The middle aerodynamic components (results available on request) column of Fig. 8 shows that divergence between the indicate that vapor pressure deficits contribute to the models is primarily driven by the estimates of the variability of EWA in inland areas and eastern Australia, aerodynamic component of the evaporation, with rela- whereas wind speed projections show the greatest di- tively large CV values occurring over much of eastern vergence between the models in the southern parts of half of the continent. In contrast, the radiative compo- the continent.

TABLE 6. Future climate projections of evaporation for SRES A2 scenario: mean percentage change with 95% conﬁdence limits and percentage contribution from radiative and aerodynamic components, where dA/dE 1 dR/dE 5 100%. The asterisk denotes the average of ensemble runs.

2030 2050 2070 Model dE/EdA/dE dR/dE dE/EdA/dE dR/dE dE/EdA/dE dR/dE BCCR 2.6 6 0.3 37.3 62.7 2.9 6 0.5 59.9 40.1 4.9 6 0.5 38 62 CSIRO 5.2 6 0.2 66.7 33.3 5.7 6 0.3 64.8 35.2 8.6 6 0.4 61.9 38.1 MIROC* 0.9 6 0.3 51.3 48.7 2.5 6 0.3 7.6 92.4 5.9 6 0.4 21.7 78.3 MRI* 2.3 6 0.1 64 36 4.6 6 0.2 64.5 35.5 7.4 6 0.2 63.3 36.7

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TABLE 7. Same as Table 6 but for SRES B1 scenario.

2030 2050 2070 Model dE/EdA/dE dR/dE dE/EdA/dE dR/dE dE/EdA/dE dR/dE BCCR 0.4 6 0.3 60.5 39.5 2.8 6 0.5 64.9 35.1 2.9 6 0.5 44.6 55.4 CSIRO 3.7 6 0.2 65.2 34.8 7.2 6 0.2 67.1 32.9 8.1 6 0.3 67.2 32.8 IAP* 1.7 6 0.2 61.2 38.8 2.8 6 0.2 48.4 51.6 5.2 6 0.1 55.6 44.4 MIROC* 1.1 6 0.2 18.6 81.4 3.2 6 0.2 36.3 63.7 2 6 0.3 17.2 82.8 MRI* 1.6 6 0.1 65.7 34.3 3.7 6 0.1 61.6 38.4 5.8 6 0.1 65 35

6. Conclusions evaporation trends across Australia, in general agreement with the ﬁndings of Rayner (2007) and Roderick We investigated pan evaporation trends for the period et al. (2007) . The PenPan model was then applied to 1975–99 using the PenPan model and found that wind the model outputs from the NCEP reanalysis and ﬁve speed changes are the primary driver of decreasing pan GCMs for the same period. We found that the EPAN

FIG. 7. (a),(d),(g) Minimum, (b),(e),(h) mean, and (c),(f),(i) maximum projected percentage changes in EW compared to 1990 values from all GCMs for the SRES A2 in (a)–(c) 2030, (d)–(f) 2050, and (g)–(i) 2070. Changes are the largest in the southwest of Australia and for later periods. Some models show minor decreases in EW in (a) 2030 and (d) 2050.

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FIG. 8. GCM agreement in future projections of (a),(d),(g) total open water body evaporation, (b),(e),(h) aerodynamic, and (c),(f),(i) radiative components of evaporation. Model agreement is assessed using the CV of the set of model projections at each grid cell for (a)–(c) 2030, (d)–(f) 2050 and (g)–(i) 2070. The CV values are presented as percentages to improve the clarity of the figures. Darker shading indicates areas where the CV is larger—that is, there is a larger spread in the model projections for the future. trends from the reanalysis reflected the general pattern station level D* components. It is important to note that and magnitude of the observed station trends across these conclusions relate specifically to class A pans, Australia. Some of the GCMs modeled the trends well rather than open water or landscape evaporation. but most showed an average positive pan evaporation The average increase in open water body evaporation in trend for Australia. Splitting the total trend into aero- 2070 is approximately 7% for the SRES A2 scenario and dynamic and radiative components confirmed that, as 5% for the SRES B1 scenario using the outputs of all five for the station data, trends are primarily driven by the GCMs considered. In the 2030s and 2050s, the predictions aerodynamic component. However, further analysis are more variable, and in some cases the predicted de- demonstrated that the reanalysis trends occur mainly creases in the aerodynamic component of evaporation because of vapor deficit changes, rather than wind speed completely offset increases in the radiative component. trends, as was found to be the case for the station data. We therefore conclude that, as for the current climate, Half the GCMs considered show increasing wind speed evaporation changes for the future are not straightforward trends and most show larger changes in EPANA due to and no speculation should be made. Commonly held be- vapor pressure deficit than we would expect given the liefs of increasing potential evaporation increases because

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