Climate Change Projections of Potential Evapotranspiration for the North American Monsoon Region

doi:10.21203/rs.3.rs-4009798/v1

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Climate Change Projections of Potential Evapotranspiration for the North American Monsoon Region

https://doi.org/10.21203/rs.3.rs-4009798/v1

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published 14 Jun, 2024

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We assessed future projected changes in terrestrial evaporative demand by calculating Potential Evapotranspiration (PET) for the North American Monsoon region at the Southwest U.S. and Mexico. The PET projections were calculated using the daily Penman-Monteith equation and the terrestrial meteorological variables needed for the equation (i.e. minimum and maximum daily temperature, specific humidity, wind speed, incoming shortwave radiation, and pressure) were available from the North American – CORDEX initiative. We used six dynamically downscaled projections of three CMIP5 GCMs forced with RCP8.5 emission scenarios (i.e. HadGEM2-ES, MPI-ESM-LR, and GFDL-ESM2M), each was dynamically downscaled to ~ 25 km by two RCMs (i.e. WRF and regCM4). All terrestrial annual PET projections showed a statistically significant increase when comparing 1986–2005 to 2020–2039 and 2040–2059. The regional spatial average of the six climate models projected an increase in the annual PET of about + 4% and + 8% for 2020–2039 and 2040–2059, respectively. The projected average 20-year annual changes over the study area range respectively for the two projection periods were + 1.4%-+8.7% and + 3%-+14.2%. The projected annual PET increase trends are consistent across the entire region and for the 6 climate models. Higher annual changes are projected in the northeast part of the region, while smaller changes are projected along the pacific coast. The main drivers for the increase are the projected warming and increase in the vapor pressure deficit. The projected changes in PET, which represent the changes in the atmospheric evaporative demand, are substantial and likely to impact vegetation and the hydrometeorological regime in the area.

Climate change

Potential Evapotranspiration

North American Monsoon

NA-CORDEX

Penman-Monteith

Climate change impacts on rainfall and temperature patterns and the effect of these changes in the North American Monsoon (NAM) region (Southwest U.S. and Mexico) is an active research topic (a few examples: Wang et al., 2021; Pascale et al., 2017 and 2019; Hernandez and Chen, 2022; Shamir et al., 2015 and 2021). While the effects on precipitation is well studied, a less studied topic, which is the focus of this study, is the impact of climate change on the atmospheric evaporative demand (AED) in the NAM region. The AED represents the upper limit of actual evapotranspiration when water availability is unlimited. Changes in AED impact the hydrological cycle to alter water resources availability, soil moisture conditions, runoff production from rainfall events, water storage in surface and subsurface reservoirs, vegetative land cover, and drought severity. AED is linked to the NAM dynamics by influencing the interplay between enhancing tropospheric stability that suppress convection to increase evaporation that in turn increases moisture for convective activity (e.g., Pascale et al., 2019). Moreover, evapotranspiration in the NAM region is a major source of moisture for convective precipitation events (e.g., Hu and Dominguez, 2015; Feng and Houser, 2015).

Projecting changes in future AED is a challenging task because it depends on changes of various near-surface atmospheric variables that represent the radiative and aerodynamic state of the atmosphere. The radiative state represents the energy available to vaporize water and it is estimated by temperature and net radiation. The aerodynamic state represents that capacity of the air to store and remove water and estimated by wind and vapor pressure deficit. A common method to assess the AED is by estimating the Potential Evapotranspiration (PET), which is the evaporation that would occur when sufficient water source at the land surface is available.

A review of 55 worldwide studies of observed pan evaporation datasets pointed to declining evaporation since the second half of the 20th century (McVicar et al. 2012). This historical declining trend, as measured by pan evaporation, was also shown in several studies from the Southwest U.S and Mexico (Ruiz-Alvarez et al., 2019; Magallanes Quintanar et al., 2019; Breña-Naranjo et al., 2016; Blanco-Macías et al., 2011; Groisman et al. 2004). This reduction in evaporation demand may be seen as counterintuitive to the concurrent observed warming trend (Cuervo-Robayo et al., 2020; Magallanes Quintanar et al., 2019; Shamir et al., 2021). The causes for the decline in pan evaporation are yet inconclusive and various explanations have been provided, such as an increase in cloudiness that in conjunction of aerosols presence causing a reduction in shortwave radiation (Roderick et al. 2009; Stanhill and Cohen, 2001), changes in the aerodynamic component such as reduced wind speeds (Groisman et al., 2004; Pryor et al., 2009; Roderick et al., 2009) and decreased vapor pressure deficit (Hobbins et al., 2004); and other factors such as declines in El Nino Southern Oscillation and cyclical sunspot activity (Magallanes Quintanar et al., 2019; Blanco-Macías et al., 2011).

Another possible reason for the observed historical decline may be attributed to the deficiency of pan evaporation to represent AED in water-limited environments. This is because in these water-limited regions the unused energy at the land surface increase the sensible heat flux from the ground, a process that is not being accounted for in pan evaporation (Brutsaert and Parlange, 1998).

Notwithstanding the observed historical declining trend, using output from Global Climate Models (GCM), Cook et al., (2014) projected globally widespread increases in PET. The projected future PET increase was attributed to projected increases in surface net radiation and vapor pressure deficit. The highest PET increases are projected for the mid-latitudes of the Northern Hemisphere and in western North America, Europe, and southeast China. Other GCM studies for the conterminous U.S. also projected PET increases (Dewes et al., 2017; Ficklin et al., 2016). In Mexico, Martinez-Sifuentes et al., (2023) projected increase PET for northern part of the state of Durango in north-central Mexico, using a PET equation that considers only air surface temperature. Mundo-Molina (2015) estimated an increase in annual PET that can reach 8% in Northern Mexico for a climate change scenario of temperature increase by 3^°C over the entire country. Since the NAM region is dominated by regional (mesoscale) processes, its representation by relatively coarse GCMs is challenging (e.g. Castro et al. 2012 and 2017; Bukovsky et al. 2013 and; 2015; Geil et al. 2013). Dynamical downscaling with regional climate models (RCMs) for longer-term climate simulations has shown skill in capturing climate variability, depending on the region of study and storm types (Prein et al., 2013; Qing and Wang, 2021). For future climate projections, RCMs with dynamical downscaling generally improve the representation of mean precipitation changes and convective precipitation (e.g. Chang et al. 2015, Kendon et al. 2017), as compared to the GCM projections.

In this study we assess the projected changes in annual and monthly PET in the NAM region using terrestrial meteorological variables from dynamically downscaled simulations of six Coupled Model Intercomparison Project Phase 5 (CMIP5) Representative Concentration Pathways 8.5 (RCP8.5) GCMs as input to the daily Penman-Monteith equation.

PET Equation.

To estimate PET we used the FAO-56 reference crop PET equation that is often named the standardized reference evapotranspiration equation (Allen et al., 1998). In contrary to ET that represents an upward water flux from soil, free water, and vegetation, PET indicates the demand of water from the atmosphere and is calculated using terrestrial meteorological variables that represent the radiative and aerodynamic state of the atmosphere. The FAO-56 reference crop equation was developed to estimate evapotranspiration from a well-watered vegetated surface using the Penman-Monteith equation for a reference crop with a height of 0.12 m, surface resistance of 70 s/m^− 1, and albedo of 0.23. The FAO-56 equation for daily PET in millimeters is written as follows:

$$PET= \frac{0.408{\Delta }\left({R}_{n}-G\right)+\gamma \frac{900}{{T}_{a}+273}{u}_{2}VPD }{{\Delta }+\gamma (1+0.34{u}_{2})},$$

where ${R}_{N}$ is net daily radiation at the vegetated surface (MJ m^− 2 d^− 1) calculated as the difference between incoming net shortwave radiation and outgoing net longwave radiation (as described in Allen et al., 1998), G is heat flux into the ground (MJ m^− 2 d^− 1), which is assumed negligible at the daily time scale, ${\Delta }$ is the slope of the vapor pressure as a function of temperature curve (kPa ^oC⁻¹), $\gamma$ is the psychrometric constant (kPa ^°C^− 1), T_a is mean daily air temperature (^°C), VPD is vapor pressure deficit (kPa), which is calculated as the difference between the saturated and actual vapor pressure, and u₂ is average daily wind speed at 2m above ground elevation (m s^− 1). The daily near-surface (i.e., 2-meter elevation above ground) atmospheric variables that are required as input for the equation are temperature, incoming short wave radiation, specific humidity, wind speed, and atmospheric pressure. The implementation of this equation followed the procedure outlined by Allen et al., (1998).

Climate Model Projections

Dynamically Downscaled climate projections of the CMIP5 GCMs are available from the North America Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) program (https://na-cordex.org/; Mearns et al. 2017), an initiative sponsored by the World Climate Research Program to provide regional climate downscaling data for regional climate change adaptation and impact assessment. Although the Intergovernmental Panel on Climate Change (IPCC) has already published the Sixth Assessment Report (AR6) in 2021, as of November 2023, dynamically downscaled projections of the GCM simulations that supported the AR6 are not readily available for the study region. We retrieved six dynamically downscaled projections from the NA-CORDEX of GCMs that were forced by the Representative Concentration Pathway (RCP) 8.5 greenhouse gas and aerosol emission scenario. This set of six projections consists of three GCMs that were downscaled to a 25 km horizontal grid resolution by two different Regional Climate Models (RCMs). The GCMs are the HadGEM2-ES (Global Environmental Model, Version 2 from the United Kingdom Meteorological Office, the Hadley Centre); the MPI-ESM-LR (Earth System Model) running on the low resolution (LR) grid from the Max Planck Institute for Meteorology; and the GFDL-ESM2M from the NOAA Geophysical Fluid Dynamics Laboratory, U.S. using the Earth System Model version 2. Each of these three GCMs were downscaled for the domain of the NA-CORDEX program using two different RCMs. The first RCM is the Advanced Research version of the Weather Research and Forecasting (WRF) model (Version 3.4, Skamarock et al. 2005) that is supported by the National Center for Atmospheric Research, which is responsible for WRF development, management and user support. The configuration and parameterization of the WRF model is described in Chang et al. (2015) and Castro et al. (2017). The second RCM, RegCM4 is supported by the Regional Climate Research Network, a network of scientists coordinated by the Earth System Physics section of the Abdus Salam International Centre for Theoretical Physics (ICTP http://users.ictp.it/RegCNET/). Both RCMs the WRF and RegCM4 are open development community models. The NA-CORDEX experimental design can be found in Mearns et al. 2017 and Bukovsky and Mearns 2020.

The three GCMs were selected to be dynamically downscaled because of their representation of a range of North America climate sensitivities (Sheffield et al., 2013a, Sheffield et al., 2013b). They were also found to represent well the global air temperature, atmospheric pressure, wind, and solar radiation patterns as well as the Western US regional temperature, precipitation, sea level pressure, and El Niño/Southern Oscillation variability (Cayan & Tyree, 2015). In addition, the selected GCMs were found to represent the large-scale synoptic features of the NAM (Geil et al., 2013). These GCMs were previously used in numerous climate impact assessments for the region (e.g., Gupta et al., 2023; Bearup and Gangopadhyay, (2021); Tapia et al., (2020); Shamir and Halper, (2019); Shamir et al., (2019); Shamir et al., (2015).

In this study we used these six climate projections to assess two future horizon periods (i.e., 2020–2039 and 2040–2059). These two projected future periods were compared to the simulated historical period of 1986–2005.

Historical Data

We used data from TerraClimate to calculate the climatological PET for the NAM region (Fig. 1). TerraClimate is a ~ 4km (1/24th degree) monthly climatological dataset of global terrestrial surface meteorological fluxes (i.e., maximum and minimum temperature, vapor pressure, precipitation accumulation, downward surface shortwave radiation, wind-speed) with data updated annually and available since 1958 (Abatzoglou et al., 2018). We note that, as far as we know, this is the only observation-based climatological datasets that include all the variables needed as input for the FAO-56 equation. In Fig. 1 the PET annual climatologies were calculated using Eq. 1 with the 1986–2005 monthly climatological variables as input. The 20-year average annual values range from 750 to 2436 mm/year with a regional average of 1456 mm/year. The spatial distribution shows the highest annual PET in southeastern California and western Arizona, as well as another high-evaporation region that is centered at the southeast border of New Mexico with a clear decline in the northward and eastward directions. To evaluate the annual variability of the PET estimates for the analysis period, in Fig. 2 the spatial distribution of the maximum, minimum, and the difference between these are shown. The regional average of the annual PET difference is 266 mm/year with lowest and highest differences being 69 and 397 mm/year, respectively. It can be seen that for most of Mexico the PET difference between the maximum and the minimum annual PET did not exceed 200 mm/year, while higher ranges are found north of the U.S.-Mexico border with the highest differences seen in Kansas and Northern Nevada. Although this distinct differences in the PET ranges between the two countries may be attributed to climatological differences, it may also be because of the differences between the two countries’ datasets that were used to derive the climatological dataset.

Although the TerraClimate dataset is used in this study as the quantitative ground truth measure, we would like to recognize its sources of epistemic uncertainties. The main sources of uncertainties in the PET estimates are the large differences in number of observation stations among the various meteorological variables, the variety of sources of data with differences in quality and reporting times, and the uncertainties associated with the selection of the interpolating and extrapolating procedures. In addition, the standardized reference evapotranspiration equation used herein is a nonlinear equation and therefore the use of monthly average variables to calculate daily PET will not necessarily yield the average monthly PET values as calculated daily. The 20-year average annual climatologies of the TerraClimate meteorological variables for the 1986–2005 period that were used in Eq. 1 are shown in Fig. 3.

PET Projections

Quantitative historical PET simulations by climate models are often inaccurate because of biases in the meteorological variables, and consequently the PET quantitative projections of the future period are highly uncertain (e.g. Shi et al. 2020; Donohue et al. 2010). Thus, in this analysis, rather than looking at quantitative PET projections, we assessed the percent changes from the historical to the future periods of interest. The relative changes were calculated as the ratio between the difference of the future and historical periods and the historical period as expressed in the equation below:

$$\delta PET\left(dur\right)= \frac{{PET\left(dur\right)}_{future }- {PET\left(dur\right)}_{historical}}{{PET\left(dur\right)}_{historical}} \times 100$$

where $\delta$ is the calculated change in percent (%), dur indicates the duration of the analysis of change (e.g. monthly, seasonal, annual), and the future and historical subscripts refer to the periods of the climate model simulations.

In the analysis presented in the upper panels of Fig. 4, the 20-year arithmetic average of the annual changes are shown for the two future projection periods (2020–2039 and 2040–2059). The average annual changes and other statistical indices were calculated for each grid cell from a statistical sample that contains 2,400 estimates of percent annual change. The 2,400 annual PET changes were calculated for each of the six climate models for 400 combinations (20 historical years x 20 projection years). It is seen in Fig. 4 that PET is projected to increase across the entire North American Monsoon region. This PET increase is projected to amplify during 2040–2059. The regions that are projected to have higher changes are the high elevation mountain ranges along the Sierra Madre Occidental and Sierra Madre del Sur and also the Sierra Madre Oriental, while smaller changes are projected in the Central Mexican Plateau and along the Pacific coast.

In the lower panels of Fig. 4 the number of models (out of the six climate model projections used in this study) that projected future changes significantly different from the simulated historical period (1985–2005). The statistical significance test is calculated from the 2-sample non-parametric Kolmogorov-Smirnov (KS) hypothesis test with α assigned to 0.05. We note that the KS significant test results were comparable to results obtained from Student’s t-test. For the entire region of analysis, at least three of the projections showed that the projected PET was significantly different than the distribution of the simulated historical period. It is also seen that for the further away future period (2040–2059), the annual PET projections, in most of the region, were found to be significantly different than the historical period by the six climate models.

It appears that the area with largest agreement among the models on the projected future being significantly different that the historical period is the longitudinal band that includes the Sierra Madre Occidental and the Sierra Madre Del Sur. In general, the 2020–2039 projections of PET annual changes have an overall west-to-east pattern. In the western coastal region, the annual PET changes are projected to be less than 5%, while farther east towards the Sierra Madre Occidental the projected changes are about 7–8%. The changes are projected to decrease moving eastward towards the Mexican Plateau and to increase again at the Sierra Madre Oriental. A similar pattern is shown for the 2040–2059 projections but with larger projected annual changes that exceed 15%.

In Table 1 the annual PET regional statistics of the six climate models are summarized. It can be seen that the average projected change for 2020–2039 and 2040–2059 over the entire study area are 4.0% and 7.0%, respectively. It is interesting to note that although the signal shows projected increase in annual PET overall, an average of only 77% and 87% of the statistical samples show positive changes for 2020–2039 and 2040–2059, respectively.

Table 1

Areal average of PET changes statistics
	2020–2039	2040–2059
Average	4.0%	7.0%
Median	3.6%	6.5%
95th percentile	14.9%	18.5%
Minimum 20-year average	1.4%	3.0%
Maximum 20-year average	8.7%	14.2%
5th percentile	-5.4%	-2.6%
Standard deviation	6.2%	6.5%
Positive change	77%	87%

To represent the spatial variability of the spread in the distribution of projected annual changes in PET, Fig. 5 shows the ranges between the 95th and 5th percentiles of the PET annual changes for the two projection periods. It is seen that that although there a clear increase in annual PET changes from the near future to the far future, the spatial distribution of the PET annual change ranges are almost similar between the two projection periods. An interesting pattern seen in this plot is the generally increasing gradient from west-to-east and south-to-north. The largest ranges of annual changes, which are larger than 55%, are seen in the northeast while smaller ranges (< 10%) are seen for example along the Pacific coast. These differences of ranges can be attributed to greater inter-annual variability of the annual PET, which we attempted to capture in Fig. 2. However, these ranges may also be attributed to the uncertainty that stems from the differences among the climate projections, which is explored in the following analysis.

In Fig. 6 we show scatter plots of all the climate models grid cells projected average changes in the annual PET as a function of the (a) grid cells elevation and (b) the average 6-model simulated historical average annual PET (mm/year). It is seen that the projected annual PET changes is positively correlated with the grid cell elevation (Correlation coefficients (R) of 0.62 and 0.75 for the near and far future, respectively). On the other hand the projected anuual change of PET is negatively correlated with the historical simulations of the annual PET (R -0.46 and − 0.59 for the near and far future, respectively). These two factors (elevation and historical simulations) although exhibiting substantial correlations are, as seen by the large scatter in Fig. 6, are limited predictors of the projected annual change. It is also observed the GCMs that were downscaled by the RegCM showed considerable higher correlation values than the GCMs that were downscaled by the WRF.

While in the upper panels of Fig. 4 we show the average of the 6 climate models, in Figs. 7 and 8 the projected annual changes indicated by the individual models are shown for 2020–2039 and 2040–2059, respectively. All six models projected an overall positive changes in annual PET over most of the region. It can also be seen that within individual model, the annual PET increases from the early to mid 21st century (Figs. 7 and 8, respectively). However, the models do not always agree among themselves on the spatial pattern of the annual PET changes.

In general, it can be seen that the GCMs that were downscaled with the RegCM model show PET annual changes that are overall larger than the GCMs that were downscaled with the WRF model. It can also be seen, for instance, that while the GFDL-RegCM and MPI-WRF showed the lowest changes in southern Texas, for the same region the MPI-RegCM, Had-RegCM and GFDL-WRF showed their largest projected changes. Another example of a notable difference is seen between MPI-WRF and GFDL-WRF in the eastern region. There are noticeable differences among the GCMs and their representation of the regional atmosphere and surface meteorological variables. In this study we assumed that the 6 climate models are equally skillful in their PET simulations. Thus although all models agree that PET is projected to increase, the spatial distribution of the changes is rather uncertain.

In order to assess the main causes for the projected annual PET increase, we analyzed the changes in meteorological variables considered in the PET equation (Fig. 9). As expected, both minimum and maximum temperature are showing significant projected warming. The projected annual changes in wind speed and surface radiation were found not to be significantly different than during the historical period. Another flux that shows a clear signal of significant projected increase is the specific humidity, which is associated with temperature because warmer air can hold more water vapor. Increases in specific humidity for a given temperature decreases the Vapor Pressure Deficit (VPD), which as can be seen in Eq. 1, decreases the daily PET. The VPD (i.e. the difference between the actual specific humidity and the specific humidity at saturation) indicates the maximum amount of water that can be evaporated. The sensitivity of VPD to changes in temperature and specific humidity as calculated with the Clausius–Clapeyron equation is demonstrated in Fig. 10. It can be seen in this figure that for a given temperature, an increase in the actual specific humidity, as expected, decreases the VPD. However, increases in temperature for a given specific humidity increase the VPD, which in turn would increase the PET.

Monthly Changes

The regional interannual average of monthly PET as calculated using the TerraClimate variables is shown in Fig. 11. In Figs. 12 and 13 the average projected percent changes of monthly PET are shown for 2020–2039 and 2040–2059, respectively. Table 2 provides the estimated regional average monthly PET and the regional projected changes for the two projection periods. It is interesting to note that PET is projected to increase for all months, except for a projected PET decrease in November-December for the 2020–2039 period. We note that the largest projected increases are expected during March-April and the lowest are in November-December. Evaluating the spatial extent of the projected monthly PET changes it is seen that for the near future some negative changes are projected during September-January mainly at the northern portion of the region (Fig. 12). For the far future (Fig. 13) the projected negative changes in PET are seen only for November – December and in higher latitudes.

Table 2

Areal average monthly PET and projected changes for the two projection periods.
	TerraClimate 1986–2005 (mm/month)	Projected Change 2020–2039	Projected Change 2040–2059
Jan	71	5.8%	7.9%
Feb	91	5.7%	12.6%
Mar	119	7.8%	13.9%
Apr	145	9.4%	12.6%
May	161	7.9%	9.5%
Jun	167	4.7%	7.3%
Jul	166	3.8%	6.2%
Aug	156	2.6%	5.2%
Sep	129	2.3%	5.3%
Oct	107	1.0%	6.2%
Nov	83	-2.0%	3.8%
Dec	68	-1.9%	5.8%
Annual	1483(mm/year)	3.9%	8.0%

In this study we projected the future changes in annual and monthly PET using terrestrial meteorological variables from six dynamically downscaled climate models as input to the daily standardized Penman-Montieth equation. In the following we discuss the potential caveats and sources of uncertainties in our analysis. First we note that the study relied on six CMIP5 RCP8.5 dynamically downscaled projections from the NA-CORDEX. Although CMIP6 GCMs are already available since 2019, at the time of writing, the NA-CORDEX are the only available community standardized dynamically downscaled regional climate model simulations for the study region. Although the analysis is limited to RCP 8.5 emission scenario and the estimated uncertainty is based on analysis of 6 ensemble members, as far as we know, these results reflects the most recently available state-of-the-art projections.

As discussed in the Results section, in some cases the six climate projections disagree on the spatial variability of the projected changes. Despite these differences, overall the models are highly cross-correlated, as can be seen in Table 3. In this table the correlation coefficient of the average areal PET historical period ranges from 0.62 to 0.97. We note that lower correlation coefficients are obtained when the cross-correlation is derived between GCMs that were downscaled by WRF and RegCM, while when the same RCM is used the cross-correlation coefficients are greater than 0.95.

Table 3

Correlation coefficients of the average areal PET of the historical period (1985–2005) among the six climate models.
	Had-WRF	GFDL-WRF	MPI-RegCM	Had-RegCM	GFDL-RegCM
MPI-WRF	0.96	0.95	0.68	0.70	0.62
Had-WRF		0.97	0.70	0.74	0.66
GFDL-WRF			0.69	0.74	0.70
MPI-RegCM				0.97	0.95
Had-RegCM					0.97

The PET estimates in this study are based on projected changes in terrestrial meteorological variables. These estimates neglect to consider the physiological adaption of vegetation to increased concentrations of atmospheric carbon dioxide. Studies have shown that various plant species have adapted to reduce stomatal conductance to decrease transpiration when atmospheric evaporative demand is high (e.g., Vicente-Serrano et al., 2022). However, this adaptation is highly dependent on the vegetation type and on the ecosystems’ response to elevated CO2.

Notwithstanding the potential limitations described above, we believe that the approach used in this study points to a clear trend in projected monthly and annual PET changes, and that the use of six projections provides uncertainty bounds that can be used for decision making and risk analysis studies.

In this study we assessed the climate change impacts on PET in the Southwest US and Mexico. To assess these changes we used an ensemble of daily terrestrial meteorological variables from six CMIP5 RCP8.5 dynamically downscaled climate projections that followed the standards set by the NA-CORDEX initiative. These climate projections are from three GCMs that were found to represent the study region well (HadGEM2-ES, MPI-ESM-LR and GFDL-ESM2M) and each was downscaled by two regional (mesoscale) models (WRF and RegCM). The terrestrial meteorological variables from these six models, which include minimum and maximum temperature, wind speed, specific humidity, pressure, precipitation, and incoming short-wave radiation, were used as input to the FAO-56 reference crop daily PET equation. The projected future changes in the annual and monthly PET were assessed by comparing the historical period (1986–2005) of the climate models’ simulations to simulations of two future projection periods (2020–2039 and 2040–2059). As a quantitative PET reference for the historical period (1986–2005), we used the 4km-resolution monthly climatological variables available from the TerraClimate dataset.

Our study provides a statistical distribution for each grid cell of the dynamically downscaled models that represents the projected changes in the annual and monthly PET for the two projection periods. Although some of the ensemble members projected a slight decline in annual PET, in general over the entire region the average 20-year annual change is + 4% and + 7% for 2020–2039 and 2040–2059 (Table 1), respectively. The projected average 20-year annual changes over the study area range respectively for the two projection periods were + 1.4%-+8.7% and + 3%-+14.2%. In general, the higher values of annual changes were found in the northeast part of the region, while smaller changes are projected along the pacific coast. The projected annual PET increase trends are consistent across the entire region and for the 6 climate models. The main drivers for the increase are the projected warming and increase in the vapor pressure deficit.

The projected changes in PET, which represent the changes in the atmospheric evaporative demand, are substantial and likely to impact vegetation and the hydrometeorological regime in the area. Our analysis provides a probabilistic characterization of the PET changes that describe their interannual and spatial variability in addition to the associated uncertainties. These probabilistic indices can be used in local climate change risk analysis studies that assess the impacts of various agricultural practices and water resources management strategies.

Author Contribution

E.S wrote the main manuscript text and created the figures. L.M-F collected and assembled the datasets for the analysis All authors reviewed the manuscript and provided constructive comments.

Acknowledgements

The project is funded by the Frasnillo Plc as part of grant titled ‘Ensemble-based Climate Assessments for Fresnillo Mining Sites in Mexico’. Spatial thanks are extended to our collaborators from Fresnillo Mr. Exequiel Rolon and Ms. Alicia del Carmen Sanchez Rangel.

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Climate Change Projections of Potential Evapotranspiration for the North American Monsoon Region

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