1. Introduction
The 100th meridian was conceptualized by the nineteenth-century explorer, scientist, and director of the U.S. Geological Survey and Bureau of Ethnology John Wesley Powell as the clearly demarcated divide between America’s arid west and humid east (Powell 1879, 1890). In Part I of this two-part paper, we showed the validity of this conceptual divide in terms of a sharp zonal gradient in aridity across the Great Plains, elucidated the physical processes in the atmosphere that establish the aridity gradient, and examined how it is expressed in terms of land surface hydrology and vegetation and, finally, how it is realized in terms of the agricultural economy. It was found that, on all scores, the 100th meridian does indeed represent a divide in the physical character and social and economic structure of the central United States. Powell used the 100th meridian to argue that plans for settlement and development of the arid land west of the meridian should be different to those applied to the East and be very conscious of the constraints imposed by aridity and the need for irrigation in the presence of limited water availability. While his ideas were largely ignored (Stegner 1954), apparently it is nonetheless the case that environmental conditions did influence the land development and use over the period since Powell.
The 100th meridian as the arid–humid divide was a description of the late-nineteenth-century climate and landscape that has remained valid at least into the beginning of this century. But now climate change caused by rising greenhouse gases from fossil fuel burning is advancing. Based on the most recent climate model projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and analyzed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5), many workers have reported that North America will see, over the coming decades, a marked transition in hydroclimate. Precipitation is expected to decline in southwestern North America but increase in the northeast, and temperature will rise everywhere (Seager et al. 2013; Maloney et al. 2014; Seager et al. 2014). Cook et al. (2015) used the CMIP5 model ensemble to show that these changes combine to cause a quite alarming increase in aridity in the plains as measured either by the Palmer drought severity index (utilizing the Penman–Monteith formulation for potential evapotranspiration) or in the actual modeled soil moisture. Under such climate change we would expect the modeled arid–humid divide to move or the “effective 100th meridian” to advance eastward. Given that the aridity gradient is expressed in the agricultural economy, this could necessitate farms to adapt to new environmental conditions, by consolidation and changes in crops grown, for example, or risk becoming unprofitable. Disruption could be minimized if the climate changes and agricultural economic implications were anticipated in advance.
To project the future we need models. These can range greatly in complexity, but for future climate change the best guidance comes from the coupled atmosphere–ocean–land–sea ice models of the CMIP5. Here in Part II, we will use the CMIP5 models to compute projections of the change in aridity and aridity gradient for the next century. However, we should never use these models blindly, and instead we should be fully aware of model biases and limitations. Often a “bias correction” is needed and one will be applied here to estimate the future aridity index. We will then use simple relations between aspects of the agricultural economy and the aridity index to suggest the implications of the changes in climate. The work suggests that the 100th meridian in effect moves steadily east. The physical reasons for why this is so will be determined. The implications are that, all else being equal (which it will not be), cultivation of wheat and rangelands will expand east at the expense of corn and that farm size will need to increase to reflect the new climate and land productivity.
2. Observational and climate model data
To compare the models against observations, we use data from the National Land Data Assimilation System 2 (NLDAS-2). The following text follows that in Part I. NLDAS-2 is a land surface model based on land surface hydrology models driven by atmospheric data (Xia et al. 2012a,b) available online (at http://ldas.gsfc.nasa.gov/index.php; last accessed September 2017). Land surface models are physical models of the upper part of the land surface that solve equations for transfer of heat and moisture between the surface and the deeper layers and that also contain a representation of vegetation and interactions between it, the atmosphere, and the soil below. The land surface models are forced by imposed air temperature, humidity, winds, surface radiation, and other quantities. In this case atmospheric data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) reanalysis (Mesinger et al. 2006) are used in combination with precipitation data developed by the PRISM Climate Group at Oregon State University [details of which can be found at http://www.prism.oregonstate.edu and in Daly et al. (2000)]. The data period covers 1979 to 2015, and the spatial resolution is ⅛° in latitude and longitude. The atmospheric data were used by NLDAS-2 to force three different land surface models, Mosaic, VIC, and Noah, but, for brevity, as in Part I, we only present results using the Noah model. For farm economy we examined type of agricultural use, crops grown, and farm numbers and size using the USDA/National Agriculture Statistics Service 2012 Census of Agriculture data at the county level (www.agcensus.usda.gov/Publications/2012/Online_Resources/Ag_Census_Web_Maps/Overview; last accessed 30 September 2017). For nonfederal rangelands, defined as grasslands that provide forage for grazing animals, we used data from the USDA Natural Resources Conservation Service (available at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/technical/nra/nri/?cid=stelprdb1253602; last accessed 30 September 2017). This provides rangeland area in hectares, which we convert to the percent of rangeland for each U.S. county.
To analyze precipitation, evapotranspiration, and atmospheric moisture transports, we make use of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim for the 1979 to 2015 period (Dee et al. 2011). ERA-Interim assimilates multiple sources of atmospheric and surface data into a recent version of ECMWF’s weather forecast model using up-to-date data assimilation methods. Notably, it assimilates satellite-derived, moisture-sensitive irradiances and pays close attention to representing the hydrological cycle. The moisture transports within the ERA-Interim will be compared to those from the climate models.
We make use of all the models that participated in CMIP5 that provide all the data needed. The need for 6-hourly data to evaluate moisture transports limits the number of models to 18. We use one run from each of the models. The models and some of their details are given in Table 1. To compare model simulations to the observed state, we analyze the “historical” coupled simulations forced by known and estimated changes in radiative forcing, land use, etc., for 1979 to 2005 and the RCP8.5 business-as-usual projections of the 2006 to 2100 period forced by estimates of changes in climate forcing, assuming no effort is made to reduce greenhouse gas emissions. This scenario is chosen because of the current lack of any enforceable and strong international agreement to combat climate change. All model data were regridded to a common 
CMIP5 models used in this study, their ensemble size, institution, and horizontal and vertical resolution.
3. Methodology for projecting future aridity and impact on agricultural economy
3.1. Future AI projections
3.2. Evaluating plausible impacts of future aridity on the agricultural economy
In Part I, we showed using maps that there was a notable west–east gradient in farm size and rangeland coverage, at all latitudes, and percent of cropland under wheat or corn, in the northern plains, that was associated with the aridity gradient. To examine how changes in AI will impact the agricultural economy we will first use the historical data to examine farm size, rangeland coverage, and percent of crops under corn as a function of the AI. This will be done separately for the southern, central, and northern plains, with the expectation that we will see different relationships. We will then fit polynomials to the data distributions. We will then use the future bias-corrected AI projections and the polynomial relations developed on observed historical data to project future farm size, rangeland coverage, and percent of crops under corn. For the historical relations it is reasonable to suppose that they reflect an adjustment of the farm economy to prevailing environmental conditions. The projections therefore simply assume that the future farm economy will undergo a similar adjustment to changing environmental conditions and ignore other changes (technological change, economic adjustment, subsidies, etc.) that could also be influential. Our goal here is to provide a simple illustration of plausible change and not a prediction of actual expected change, which will additionally be affected by other factors such as changes in technology and plant water-use efficiency caused by 
4. Results
4.1. The 100th meridian in state-of-the-art climate models
We begin by examining how well the current generation of climate models (those participating in CMIP5) simulate P, PET, and the AI. Figure 1 shows these by season for the multimodel average across the CMIP5 models, and Figure 2 shows annual-mean values and, for comparison, those from NLDAS-2. The precipitation distribution, with the wettest conditions in the Pacific Northwest and southeast in winter and spring and drier conditions in the southwest and interior west year-round, is qualitatively correct. Also qualitatively correct is the more zonal pattern of PET with declining values from south to north and the development of high PET regions in the southwest and southern plains in summer. The model AI shows vast areas of highly positive values in the northwest and east during fall and winter separated by a central region of lower values that spreads into the southwest and widespread values less than 1 in the spring and summer with minimum values in the southwest.
Parameters P, PET, and AI by season for (top to bottom) October–December (OND), January–March (JFM), April–June (AMJ), and July–September (JAS) for the average across the CMIP5 models for the historical period. Units for P and PET are mm day−1.
Citation: Earth Interactions 22, 5; 10.1175/EI-D-17-0012.1
Annual-mean P, PET, and AI (top and third rows) and the AI that results from zonal variations in (left) VPD alone (second and bottom rows) and (right) P alone (second and bottom rows) for the (top) CMIP5 multimodel average and (bottom) NLDAS-2 evaluated for the same 1979 to 2014 period. Units for P and PET are mm day−1.
Citation: Earth Interactions 22, 5; 10.1175/EI-D-17-0012.1
Comparing the annual-mean 
Next, as in Part I, we recompute for each model AI while setting all quantities except 1) VPD and 2) P to their zonal-mean values for the season and year and then average across the models. This allows determination of the causes of zonal variations in AI within the model ensemble mean. Figure 2 shows the AI that derives from zonal variations in VPD alone 
To examine the relations between model 
(columns left to right) Annual-mean P, E, and P − E for (top) NLDAS-2, (middle) ERA-Interim, and the (bottom) CMIP5 multimodel mean. For ERA-Interim and CMIP5, the vertically integrated moisture fluxes are also shown as vectors. All results are for 1979 to 2015. The units for P, E, P − E are mm day−1, and the moisture fluxes are kg−1 m−2 s−1 with reference vectors at bottom right of panels
Citation: Earth Interactions 22, 5; 10.1175/EI-D-17-0012.1
4.2. Bias-corrected model projections of changes in aridity
In Figure 4, we show maps of the bias-corrected AI averaged over 2021–40, 2041–60, 2061–80, and 2081–99 as well as the differences between these periods and the 1979–2015 NLDAS-2 
(top) Annual-mean, bias-corrected AI as computed from the CMIP5 model ensemble for four two-decade periods of the current century shown as absolute values and (bottom) difference relative to the 1979–2015 climatological values. In the difference maps, stippling is added where more than three-quarters of the models agree on the sign of the change and agree with the ensemble-mean sign change.
Citation: Earth Interactions 22, 5; 10.1175/EI-D-17-0012.1
What are the main causes of the change in AI? To examine this we recompute the bias-corrected AI holding, for each model, the PET fixed at the climatological 1979–2015 value but allowing P to change to create 
As in Figure 4, but only allowing P to change, keeping PET at the 1979–2015 climatological values.
Citation: Earth Interactions 22, 5; 10.1175/EI-D-17-0012.1
As in Figure 4, but only allowing PET to change, keeping P at the 1979–2015 climatological values.
Citation: Earth Interactions 22, 5; 10.1175/EI-D-17-0012.1
The changes in P alone tend to reduce AI in the southern part of North America, most strongly in the Gulf states, and increase AI across northern North America, most strongly in the Northeast. There is considerable model agreement on these changes. These patterns are somewhat different to changes in P alone (see Seager et al. 2014) because of the seasonal weighting by PET, which provides preferential weighting to the cooler, low PET, seasons. The P-induced changes in AI only modestly weaken the AI gradient across the southern plains. In contrast, the PET-induced changes in AI cause a notable decline everywhere in Mexico and the United States and also a weakening of the west–east gradient as AI declines more east of the 100th meridian than west (again with considerable model agreement). This is because a given change in PET will yield a larger change in AI in the east, where the historical PET is smaller and the historical AI is larger than in the west. The models agree everywhere that climate change will increase PET and tend to reduce AI. This is no doubt because of the dominant influence of rising temperature and vapor pressure deficit on PET (Cook et al. 2014).
To look at the temporal evolution of the aridity gradient we average the AI over latitude for the northern (
The NLDAS-2 AI for 1979–2015 followed by, for 2016–2099, the bias-corrected AI as computed from the CMIP5 model ensemble and averaged over the (top) northern, (middle) central, and (bottom) southern plains as a function of time (vertical axis) and longitude (horizontal axis).
Citation: Earth Interactions 22, 5; 10.1175/EI-D-17-0012.1
4.3. Potential influences of aridity change on the farm economy
In Part I, we showed maps that demonstrated how farm size, number of farms, rangeland coverage, and percent of cropland used to grow wheat or corn have evolved to reflect the sharp west–east aridity gradient across the plains, albeit with notable exceptions (e.g., Nebraska where irrigation from the Ogallala Aquifer allows cultivation of corn in a dry state). Here, we present the same data in a different way by plotting in Figure 8, for the northern, central, and southern plains, the farm size, percent of the county under rangeland, and percent of cropland under corn as a function of the NLDAS-2 AI. Each data point is the average across latitude and longitude for a 1° longitudinal bin.
(left)The farm size in acres, (center) percent of cropped land under corn, and (right) average percent of counties under rangeland plotted against the 1979–2015 AI from NLDAS-2 for the (top) northern, (middle) central, and (bottom) southern plains. The values corresponding to the dots are the average for 1° longitude bins. The black lines are best-fitting, third-order polynomial curves.
Citation: Earth Interactions 22, 5; 10.1175/EI-D-17-0012.1
To assess how the projected changes in AI might impact the farm economy, we will use statistical relations described by the third-order polynomial curves that best fit the observed farm variable–AI relations. These curve fits are also shown in Figure 8 for the three farm quantities and three regions of the plains. Consistent with the maps shown in Part I, at all latitudes there is a decrease in farm size as AI increases. This is the reflection in farm–AI space of the decrease in farm size from west to east across the plains, as expected in terms of the area of operation required to achieve profitability in the face of lower aridity and increased land productivity. In the southern and central plains, there is little relation between the percent of cropped land under corn and AI. This reflects the small area under corn in the south and the “Nebraska exception”—high percentage of corn cropping in an arid area caused by accessible groundwater and irrigation—in the central plains. However, in the northern plains there is a clear increase of the percent of cropped land under corn as AI increases, reflecting the transition from wheat cultivation in the northwest plains to corn cultivation in the northeast plains (see Part I).
Across the plains the percent of counties under rangeland decreases from west to east, reflecting the opportunity for more intensive use of land in the more humid eastern plains.
The bias-corrected projections of AI can then be used to infer from the curves in Figure 8 the projected future changes in farm size and percent of cropped land under corn. That is, we first develop the current functional relations: 
(left) The farm size in acres, (middle) projected percent of cropped land under corn, and (right) percent of land under rangeland plotted against longitude for the (top) northern, (middle) central, and (bottom) southern plains and for 1979–2005 (blue) and projected 2030–50 (black) and 2070–90 (red). The projections use the best-fit curves to the historical relation between farm variables and AI and the bias-corrected AI from CMIP5 models.
Citation: Earth Interactions 22, 5; 10.1175/EI-D-17-0012.1
As expected for the southern and central plains the changes in AI do not lead to projected changes in the percent of cropped land under corn because of the absence of dependence of the latter on AI or longitude in the historical period. In the northern plains, where the U.S. Corn Belt encroaches in the east and corn can reach over 50% of cropped land, declining AI leads to little change in the percent of corn in the near future but a decline of up to a few percent later in the century. Although not shown, the same methodology projects that wheat cropping would expand in the cropland vacated by corn. In all regions of the plains, and primarily east of the 100th meridian, the naive model suggests an increase in the percent of land under rangeland as aridity increases.
5. Discussion and conclusions
The 100th meridian was shown in Part I to be a stark divider between a more arid west and more humid east that is expressed well in the natural landscape and also in the farm economy. While the plains have experienced a never-ending variation between times of drought and times of pluvial (Cook et al. 2007; Stahle et al. 2007; Seager et al. 2005; Herweijer et al. 2006; Schubert et al. 2004; Forman et al. 2001) in which the aridity gradient would have changed in intensity and location, it appears that agriculture has evolved in a way that the size of farms, and how the farm land is used, responds to the mean aridity gradient. However, in recent decades, human-induced climate change has been added onto this natural variability. The United States has broadly warmed (apart from the southeast; Capparelli et al. 2013), while radiatively forced precipitation changes have been modest to date (with a slight reduction in the southwest; Seager and Hoerling 2014). Consequently, we argue that the farm economy has come into rough statistical equilibrium with the spatially varying aridity intensity of the plains and the oscillations between drier and wetter years. This reasoning also implies that the farm economy will evolve as the climate conditions of the plains respond to rising greenhouse gases. To determine what might happen requires projections by models of future conditions. This in turn requires an assessment of how well models can simulate the aridity intensities of the plains. In this paper we assess the ability of the current generation of state-of-the-art climate models that participated in CMIP5 to simulate North American climate, focusing on the plains, and also develop statistical relations between aspects of the farm economy and aridity. Finally, we develop bias-corrected projections of aridity and use these to develop simple projections of the farm economy variables over the current century. The conclusions are as follows:
Climate models generally fail to correctly simulate the amplitude and spatial variation of aridity, measured by
To preserve the gradient in the projections of AI we developed a bias-corrected method that altered the observations-based AI from NLDAS-2 by the fractional difference between the CMIP5 multimodel means for the future and the 1979–2015 historical period. The bias-corrected projections show an overall decrease in AI (increase in aridity) across Mexico and the United States.
When the model projections of future AI are decomposed into contributions from changes in P and PET it is shown that the change in P causes increased aridity across southern North America and decreased aridity to the north but that rising PET (caused by atmospheric warming) causes increasing aridity everywhere.
Because of the greater AI and smaller PET in the east than the west, the projected cross-continent increases in PET cause aridity to increase by more in the east than the west. This reduces the strength of the west–east aridity gradient across the central part of the continent. It also means that the effective 100th meridian—that defined by an aridity level as opposed to a specific longitude—moves progressively east as the century progresses.
Aspects of the farm economy well reflect the west–east aridity gradient. Across the plains, farm size increases as aridity intensifies (to the west). In the northern plains, as aridity declines from west to east, cropped land is increasingly used to grow corn instead of wheat. Given the reasonable assumption that the farm economy evolved over the past century and more to adjust to the environmental conditions, it is expected that it will evolve further in coming decades as those environmental conditions progressively change toward heightened aridity. A simple projection of the current farm economy–AI relations into the future predicts that farm size will need to increase across the plains, but especially in the south, and that in the northern plains there will be a shift toward wheat cultivation and away from corn. Rangeland would expand to the east. The changes in farm size and percent of cropland under wheat or corn are coupled. The value of production and profit per acre are higher for corn than for wheat (see USDA data at https://www.ers.usda.gov/data-products/commodity-costs-and-returns/), and hence a shift from corn to wheat will require an increase in farm size to maintain farm profitability. Across the plains but primarily east of the 100th meridian the projections suggest increased percent of land under rangeland. The current west–east gradients in farm size, choice of crop, and rangeland already reflect environmental reality, and so the projected environmental-driven changes make sense, all else being equal.
There are many caveats that should be attached to this work and its conclusions. First, we have shown that state-of-the-art models simulate the aridity gradient across North America poorly. Hence, even though we have applied a bias correction to the projections to remove the mean state bias, it can be questioned whether these models should be trusted to properly simulate the changes. In response we would say that the model error that leads to an incorrect mean state aridity arises from the precipitation simulation, while the projected aridity change arises most strongly from the temperature and vapor pressure deficit change, which we suspect is more faithfully simulated. Second, while current Earth system models (the subset of all climate models that simulate, in varying degrees of complexity, vegetation and carbon dynamics) predict widespread declines in soil moisture and increases in continental aridity, they also simulate increases in net primary productivity (Scheff et al. 2017; Mankin et al. 2017). This is because, within the models, the beneficial effects on photosynthesis and water-use efficiency of increased 
Over the past century the adjustment of the farm economy to aridity in the plains was not steady but instead occurred in jumps. The Dust Bowl drought of the 1930s made evident that many farms were too small to ensure profitability in the face of adverse environmental conditions and to effectively practice erosion control (Hansen and Libecap 2004). Recovery from the drought led to a permanent increase in farm size and a permanent change in many aspects of the agricultural economy (Hornbeck 2009). The steady increase in aridity over the current century shown here is also artificial. It is only that steady because we have averaged across many different climate models in order to identify the common change caused by rising greenhouse gases. In the real world, the response to radiative forcing will occur against the background of natural variability on interannual to decadal time scales. Variability plus forced change can combine to create abrupt shifts in aridity or temporally mask an ongoing forced aridification. Advances in decadal prediction of the natural variability of oceans and their influence on North American hydroclimate (Seager and Ting 2017) and projection of forced change might allow anticipation of how the natural environment of the plains will alter over the coming decades. This information may be able to inform policy that can aid adaptation to changing conditions and avoid the negative effects of surprises followed by crises and social and economic disruption.
Acknowledgments
This work was supported by NSF Awards AGS-1243204 and AGS-1401400. We thank Ben Cook, Jason Smerdon, Jack Scheff, Adam Schempp, William DeBuys, Gidon Eshel, and personnel in the agricultural extension offices for North Dakota, South Dakota, and Kansas for useful conversations and the Lamont Summer Undergraduate Intern program for hosting Nathan Lis and Jamie Feldman during summer 2016. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. LDEO Contribution Number 8182.
REFERENCES
Allen, C. D., D. D. Breshears, and N. G. McDowell, 2015: On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere, 6, 1–55, https://doi.org/10.1890/ES15-00203.1.
Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration—Guidelines for computing crop water requirements—FAO Irrigation and drainage. Food and Agricultural Organization of the United Nations Irrigation and Drainage Paper 56, 333 pp.
Capparelli, V., C. Franzke, A. Vecchio, M. P. Freeman, N. W. Watkins, and V. Carbone, 2013: A spatiotemporal analysis of U.S. station temperature trends over the last century. J. Geophys. Res. Atmos., 118, 7427–7434, https://doi.org/10.1002/jgrd.50551.
Cook, B. I., J. E. Smerdon, R. Seager, and S. Coats, 2014: Global warming and 21st century drying. Climate Dyn., 43, 2607–2627, https://doi.org/10.1007/s00382-014-2075-y.
Cook, B. I., T. R. Ault, and J. E. Smerdon, 2015: Unprecedented 21st century drought risk in the American Southwest and central plains. Sci. Adv., 1, e1400082, https://doi.org/10.1126/sciadv.1400082.
Cook, B. I., E. R. Cook, J. E. Smerdon, R. Seager, A. P. Williams, S. Coats, D. W. Stahle, and J. Villaneuva Diaz, 2016: North American megadroughts in the Common Era: Reconstructions and simulations. Wiley Interdiscip. Rev.: Climate Change, 7, 411–432, https://doi.org/10.1002/wcc.394.
Cook, E. R., R. Seager, M. A. Cane, and D. W. Stahle, 2007: North American droughts: Reconstructions, causes, and consequences. Earth-Sci. Rev., 81, 93–134, https://doi.org/10.1016/j.earscirev.2006.12.002.
Daly, C., W. P. Gibson, G. H. Taylor, G. L. Johnson, and P. Pasteris, 2000: High-quality spatial climate data sets for the United States and beyond. Trans. Amer. Soc. Agric. Biol. Eng., 43, 1957–1962, https://doi.org/10.13031/2013.3101.
Dee, D., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828.
Forman, S., R. Oglesby, and R. S. Webb, 2001: Temporal and spatial patterns of Holocene dune activity on the Great Plains of North America: Megadroughts and climate links. Global Planet. Change, 29, 1–29, https://doi.org/10.1016/S0921-8181(00)00092-8.
Gaughan, J. B., T. L. Mader, S. M. Holt, M. L. Sullivan, and G. L. Hahn, 2010: Assessing the heat tolerance of 17 beef cattle genotypes. Int. J. Biometeor., 54, 617–627, https://doi.org/10.1007/s00484-009-0233-4.
Hansen, Z. K., and G. D. Libecap, 2004: Small farms, externalities, and the Dust Bowl of the 1930s. J. Political Econ., 112, 665–694, https://doi.org/10.1086/383102.
Herweijer, C., R. Seager, and E. R. Cook, 2006: North American droughts of the mid to late nineteenth century: History, simulation and implications for mediaeval drought. Holocene, 16, 159–171, https://doi.org/10.1191/0959683606hl917rp.
Hornbeck, R., 2009: The enduring impact of the American Dust Bowl: Short- and long-run adjustments to environmental catastrophe. National Bureau of Economic Research Tech. Rep. Working Paper 15605, 40 pp.
Malcolm, S., E. Marshall, M. Aillery, P. Heisey, M. Livingston, and K. Day-Rubenstein, 2012: Agricultural adaptation to a changing climate: Economic and environmental implications vary by U.S. region. U.S. Department of Agriculture Economic Research Rep. ERR-136, 84 pp.
Maloney, E. D., and Coauthors, 2014: North American climate in CMIP5 experiments: Part III: Assessment of 21st century projections. J. Climate, 27, 2230–2270, https://doi.org/10.1175/JCLI-D-13-00273.1.
Mankin, J. S., J. E. Smerdon, B. I. Cook, A. P. Williams, and R. Seager, 2017: The curious case of projected twenty-first-century drying but greening in the American West. J. Climate, 30, 8689–8710, https://doi.org/10.1175/JCLI-D-17-0213.1.
Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360, https://doi.org/10.1175/BAMS-87-3-343.
Powell, J. W., 1879: Report on the lands of the arid regions of the United States, with a more detailed account of the lands of Utah. Government Printing Office, 207 pp.
Powell, J. W., 1890: The irrigable lands of the arid region. Cent. Mag., 39, 766–776.
Scheff, J., R. Seager, H. Liu, and S. Coats, 2017: Are glacials dry? Consequences for paleoclimatology and for greenhouse warming. J. Climate, 30, 6593–6609, https://doi.org/10.1175/JCLI-D-16-0854.1.
Schubert, S. D., M. J. Suarez, P. J. Pegion, R. D. Koster, and J. T. Bacmeister, 2004: Causes of long-term drought in the U.S. Great Plains. J. Climate, 17, 485–503, https://doi.org/10.1175/1520-0442(2004)017<0485:COLDIT>2.0.CO;2.
Seager, R., and N. Henderson, 2013: Diagnostic computation of moisture budgets in the ERA-Interim reanalysis with reference to analysis of CMIP-archived atmospheric model data. J. Climate, 26, 7876–7901, https://doi.org/10.1175/JCLI-D-13-00018.1.
Seager, R., and M. P. Hoerling, 2014: Atmosphere and ocean origins of North American drought. J. Climate, 27, 4581–4606, https://doi.org/10.1175/JCLI-D-13-00329.1.
Seager, R., and M. Ting, 2017: Decadal drought variability over North America: Mechanisms and predictability. Curr. Climate Change Rep., 3, 141–149, https://doi.org/10.1007/s40641-017-0062-1.
Seager, R., Y. Kushnir, C. Herweijer, N. Naik, and J. Velez, 2005: Modeling of tropical forcing of persistent droughts and pluvials over western North America: 1856–2000. J. Climate, 18, 4065–4088, https://doi.org/10.1175/JCLI3522.1.
Seager, R., M. Ting, C. Li, N. Naik, B. Cook, J. Nakamura, and H. Liu, 2013: Projections of declining surface water availability for the southwestern U.S. Nat. Climate Change, 3, 482–486, https://doi.org/10.1038/nclimate1787.
Seager, R., D. Neelin, I. Simpson, H. Liu, N. Henderson, T. Shaw, Y. Kushnir, and M. Ting, 2014: Dynamical and thermodynamical causes of large-scale changes in the hydrological cycle over North America in response to global warming. J. Climate, 27, 7921–7948, https://doi.org/10.1175/JCLI-D-14-00153.1.
Sheffield, J., and Coauthors, 2013: North American climate in CMIP5 experiments. Part I: Evaluation of historical simulations of continental and regional climatology. J. Climate, 26, 9209–9245, https://doi.org/10.1175/JCLI-D-12-00592.1.
Stahle, D. W., F. K. Fye, E. R. Cook, and R. D. Griffin, 2007: Tree-ring reconstructed megadroughts over North America since A.D. 1300. Climatic Change, 83, 133–149, https://doi.org/10.1007/s10584-006-9171-x.
Stegner, W., 1954: Beyond the Hundredth Meridian. Penguin Books, 496 pp.
Xia, Y., and Coauthors, 2012a: Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. J. Geophys. Res., 117, D03109, https://doi.org/10.1029/2011JD016048.
Xia, Y., and Coauthors, 2012b: Continental-scale water and energy flux analysis and validation for North American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow. J. Geophys. Res., 117, D03110, doi:10.1029/2011JD016051.
Zhu, Z., and Coauthors, 2016: Greening of the Earth and its drivers. Nat. Climate Change, 6, 791–795, https://doi.org/10.1038/nclimate3004.
We also evaluated the variability of annual-mean AI values. Relative to NLDAS-2, the average of variability across the models reasonably reproduces interannual variability of AI in the plains but underestimates it in the northern plains. The models do not project a notable change in AI variability over the current century.
