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Jonghun Kam and Justin Sheffield

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This study evaluates wintertime drought and pluvial risk over California through a Bayesian analysis of the upper and lower quartile of PRISM-based precipitation from 1901 to 2015. Risk is evaluated for different time windows to estimate the impact of interannual and decadal-to-multidecadal Pacific and Atlantic variability [positive and negative phases of ENSO, Pacific decadal oscillation (PDO), and Atlantic multidecadal oscillation (AMO)]. The impact of increasing trends in global sea surface temperature (SST) on drought and pluvial risk is also examined with idealized experimental runs from three climate models [GFDL Atmospheric Model version 2.1 (AM2.1), CCM3, and GFS]. The results show that the influence of oceanic conditions on drought risk in California is significant but has changed with higher risk in the last half century, especially in Southern California. The influence of oceanic conditions on pluvial risk has also been significant, especially during the warm phase of the Pacific Ocean, but increases over the last century are small compared to drought. Results from the idealized climate model experiments show that natural variability likely played a major role in the observed changes in risk, with the global SST increasing trend possibly tempering the increases regionally but not significantly over California. Despite evolving preferential oceanic conditions for a pluvial event during the 2015/16 winter (positive phase of ENSO and PDO), California received an 11% winter precipitation surplus, which was not sufficient for drought recovery. The seasonal and longer-term outlook for negative phases of ENSO and PDO implies that drought risk will be elevated in Southern California for the next decade.

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Alexis Berg and Justin Sheffield

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Evapotranspiration (ET) is a key process affecting terrestrial hydroclimate, as it modulates the land surface carbon, energy, and water budgets. Evapotranspiration mainly consists of the sum of three components: plant transpiration, soil evaporation, and canopy interception. Here we investigate how the partitioning of ET into these three main components is represented in CMIP5 model simulations of present and future climate. A large spread exists between models in the simulated mean present-day partitioning; even the ranking of the different components in the global mean differs between models. Differences in the simulation of the vegetation leaf area index appear to be an important cause of this spread. Although ET partitioning is not accurately known globally, existing global estimates suggest that CMIP5 models generally underestimate the relative contribution of transpiration. Differences in ET partitioning lead to differences in climate characteristics over land, such as land–atmosphere fluxes and near-surface air temperature. On the other hand, CMIP5 models simulate robust patterns of future changes in ET partitioning under global warming, notably a marked contrast between decreased transpiration and increased soil evaporation in the tropics, whereas transpiration and evaporation both increase at higher latitudes and both decrease in the dry subtropics. Idealized CMIP5 simulations from a subset of models show that the decrease in transpiration in the tropics largely reflects the stomatal closure effect of increased atmospheric CO2 on plants (despite increased vegetation from CO2 fertilization), whereas changes at higher latitudes are dominated by radiative CO2 effects, with warming and increased precipitation leading to vegetation increase and simultaneous (absolute) increases in all three ET components.

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Alexis Berg and Justin Sheffield

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Soil moisture–atmosphere coupling is a key process underlying climate variability and change over land. The control of soil moisture (SM) on evapotranspiration (ET) is a necessary condition for soil moisture to feed back onto surface climate. Here we investigate how this control manifests itself across simulations from the CMIP5 ensemble, using correlation analysis focusing on the interannual (summertime) time scale. Analysis of CMIP5 historical simulations indicates significant model diversity in SM–ET coupling in terms of patterns and magnitude. We investigate the relationship of this spread with differences in background simulated climate. Mean precipitation is found to be an important driver of model spread in SM–ET coupling but does not explain all of the differences, presumably because of model differences in the treatment of land hydrology. Compared to observations, some land regions appear consistently biased dry and thus likely overly soil moisture–limited. Because of ET feedbacks on air temperature, differences in SM–ET coupling induce model uncertainties across the CMIP5 ensemble in mean surface temperature and variability. We explore the relationships between model uncertainties in SM–ET coupling and climate projections. In particular over mid-to-high-latitude continental regions of the Northern Hemisphere but also in parts of the tropics, models that are more soil moisture–limited in the present tend to warm more in future projections, because they project less increase in ET and (in midlatitudes) greater increase in incoming solar radiation. Soil moisture–atmosphere processes thus contribute to the relationship observed across models between summertime present-day simulated climate and future warming projections over land.

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Justin Sheffield and Eric F. Wood

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Global and regional trends in drought for 1950–2000 are analyzed using a soil moisture–based drought index over global terrestrial areas, excluding Greenland and Antarctica. The soil moisture fields are derived from a simulation of the terrestrial hydrologic cycle driven by a hybrid reanalysis–observation forcing dataset. Drought is described in terms of various statistics that summarize drought duration, intensity, and severity. There is an overall small wetting trend in global soil moisture, forced by increasing precipitation, which is weighted by positive soil moisture trends over the Western Hemisphere and especially in North America. Regional variation is nevertheless apparent, and significant drying over West Africa, as driven by decreasing Sahel precipitation, stands out. Elsewhere, Europe appears to have not experienced significant changes in soil moisture, a trait shared by Southeast and southern Asia. Trends in drought duration, intensity, and severity are predominantly decreasing, but statistically significant changes are limited in areal extent, of the order of 1.0%–7.0% globally, depending on the variable and drought threshold, and are generally less than 10% of continental areas. Concurrent changes in drought spatial extent are evident, with a global decreasing trend of between −0.021% and −0.035% yr−1. Regionally, drought spatial extent over Africa has increased and is dominated by large increases over West Africa. Northern and East Asia show positive trends, and central Asia and the Tibetan Plateau show decreasing trends. In South Asia all trends are insignificant. Drought extent over Australia has decreased. Over the Americas, trends are uniformly negative and mostly significant.

Within the long-term trends there are considerable interannual and decadal variations in soil moisture and drought characteristics for most regions, which impact the robustness of the trends. Analysis of detrended and smoothed soil moisture time series reveals that the leading modes of variability are associated with sea surface temperatures, primarily in the equatorial Pacific and secondarily in the North Atlantic. Despite the overall wetting trend there is a switch since the 1970s to a drying trend, globally and in many regions, especially in high northern latitudes. This is shown to be caused, in part, by concurrent increasing temperatures. Although drought is driven primarily by variability in precipitation, projected continuation of temperature increases during the twenty-first century indicate the potential for enhanced drought occurrence.

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Justin Sheffield, Gopi Goteti, and Eric F. Wood

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Understanding the variability of the terrestrial hydrologic cycle is central to determining the potential for extreme events and susceptibility to future change. In the absence of long-term, large-scale observations of the components of the hydrologic cycle, modeling can provide consistent fields of land surface fluxes and states. This paper describes the creation of a global, 50-yr, 3-hourly, 1.0° dataset of meteorological forcings that can be used to drive models of land surface hydrology. The dataset is constructed by combining a suite of global observation-based datasets with the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. Known biases in the reanalysis precipitation and near-surface meteorology have been shown to exert an erroneous effect on modeled land surface water and energy budgets and are thus corrected using observation-based datasets of precipitation, air temperature, and radiation. Corrections are also made to the rain day statistics of the reanalysis precipitation, which have been found to exhibit a spurious wavelike pattern in high-latitude wintertime. Wind-induced undercatch of solid precipitation is removed using the results from the World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison. Precipitation is disaggregated in space to 1.0° by statistical downscaling using relationships developed with the Global Precipitation Climatology Project (GPCP) daily product. Disaggregation in time from daily to 3 hourly is accomplished similarly, using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly real-time dataset. Other meteorological variables (downward short- and longwave radiation, specific humidity, surface air pressure, and wind speed) are downscaled in space while accounting for changes in elevation. The dataset is evaluated against the bias-corrected forcing dataset of the second Global Soil Wetness Project (GSWP2). The final product provides a long-term, globally consistent dataset of near-surface meteorological variables that can be used to drive models of the terrestrial hydrologic and ecological processes for the study of seasonal and interannual variability and for the evaluation of coupled models and other land surface prediction schemes.

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Aihui Wang, Dennis P. Lettenmaier, and Justin Sheffield

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Four physically based land surface hydrology models driven by a common observation-based 3-hourly meteorological dataset were used to simulate soil moisture over China for the period 1950–2006. Monthly values of total column soil moisture from the simulations were converted to percentiles and an ensemble method was applied to combine all model simulations into a multimodel ensemble from which agricultural drought severities and durations were estimated. A cluster analysis method and severity–area–duration (SAD) algorithm were applied to the soil moisture data to characterize drought spatial and temporal variability. For drought areas greater than 150 000 km2 and durations longer than 3 months, a total of 76 droughts were identified during the 1950–2006 period. The duration of 50 of these droughts was less than 6 months. The five most prominent droughts, in terms of spatial extent and then duration, were identified. Of these, the drought of 1997–2003 was the most severe, accounting for the majority of the severity–area–duration envelope of events with areas smaller than 5 million km2. The 1997–2003 drought was also pervasive in terms of both severity and spatial extent. It was also found that soil moisture in north central and northeastern China had significant downward trends, whereas most of Xinjiang, the Tibetan Plateau, and small areas of Yunnan province had significant upward trends. Regions with downward trends were larger than those with upward trends (37% versus 26% of the land area), implying that over the period of analysis, the country has become slightly drier in terms of soil moisture. Trends in drought severity, duration, and frequency suggest that soil moisture droughts have become more severe, prolonged, and frequent during the past 57 yr, especially for northeastern and central China, suggesting an increasing susceptibility to agricultural drought.

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Justin Sheffield, Ben Livneh, and Eric F. Wood

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The North American Regional Reanalysis (NARR) is a state-of-the-art land–atmosphere reanalysis product that provides improved representation of the terrestrial hydrologic cycle compared to previous global reanalyses, having the potential to provide an enhanced picture of hydrologic extremes such as floods and droughts and their driving mechanisms. This is partly because of the novel assimilation of observed precipitation, state-of-the-art land surface scheme, and higher spatial resolution. NARR is evaluated in terms of the terrestrial water budget and its depiction of drought at monthly to annual time scales against two offline land surface model [Noah v2.7.1 and Variable Infiltration Capacity (VIC)] simulations and observation-based runoff estimates over the continental United States for 1979–2003. An earlier version of the Noah model forms the land component of NARR and so the offline simulation provides an opportunity to diagnose NARR land surface variables independently of atmospheric feedbacks. The VIC model has been calibrated against measured streamflow and so provides a reasonable estimate of large-scale evapotranspiration. Despite similar precipitation, there are large differences in the partitioning of precipitation into evapotranspiration and runoff. Relative to VIC, NARR and Noah annual evapotranspiration is biased high by 28% and 24%, respectively, and the runoff ratios are 50% and 40% lower. This is confirmed by comparison with observation-based runoff estimates from 1130 small, relatively unmanaged basins across the continental United States. The overestimation of evapotranspiration by NARR is largely attributed to the evapotranspiration component of the Noah model, whereas other factors such as atmospheric forcings or biases induced by precipitation assimilation into NARR play only a minor role. A combination of differences in the parameterization of evapotranspiration and in particular low stomatal resistance values in NARR, the seasonality of vegetation characteristics, the near-surface radiation and meteorology, and the representation of soil moisture dynamics, including high infiltration rates and the relative coupling of soil moisture with baseflow in NARR, are responsible for the differences in the water budgets. Large-scale drought as quantified by soil moisture percentiles covaries closely over the continental United States between the three datasets, despite large differences in the seasonal water budgets. However, there are large regional differences, especially in the eastern United States where the VIC model shows higher variability in drought dynamics. This is mostly due to increased frequency of completely dry conditions in NARR that result from differences in soil depth, higher evapotranspiration, early snowmelt, and early peak runoff. In the western United States, differences in the precipitation forcing contribute to large discrepancies between NARR and Noah/VIC simulations in the representation of the early 2000s drought.

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Julio E. Herrera-Estrada and Justin Sheffield

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Droughts and heat waves have important impacts on multiple sectors including water resources, agriculture, electricity generation, and public health, so it is important to understand how they will be affected by climate change. However, there is large uncertainty in the projected changes of these extreme events from climate models. In this study, historical biases in models are compared against their future projections to understand and attempt to constrain these uncertainties. Biases in precipitation, near-surface air temperature, evapotranspiration, and a land–atmospheric coupling metric are calculated for 24 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) against 2 models from phase 2 of the North American Land Data Assimilation System (NLDAS-2) as reference for 1979–2005. These biases are highly correlated across variables, with some models being hotter and drier and others wetter and cooler. Models that overestimate summer precipitation project larger increases in precipitation, evapotranspiration, and land–atmospheric coupling over important agricultural regions by the end of the twenty-first century (2070–99) under RCP8.5, although the percentage variance explained is low. Changes in the characteristics of droughts and heat waves are calculated and linked to historical biases in precipitation and temperature. A method to constrain uncertainty by ranking models based on historical performance is discussed but the rankings differ widely depending on the variable considered. Despite the large uncertainty that remains in the magnitude of the changes, there is consensus among models that droughts and heat waves will increase in multiple regions in the United States by the end of the twenty-first century unless climate mitigation actions are taken.

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Tara J. Troy, Justin Sheffield, and Eric F. Wood

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Northern Eurasia has experienced significant change in its hydrology during the past century. Much of the literature has focused on documenting and understanding the trends rather than documenting the uncertainty that exists in current estimates of the mean hydroclimatology. This study quantifies the terrestrial water budget with reanalysis, hydrologic modeling, remote sensing, and in situ observations and shows there is significant uncertainty in the estimates of precipitation, evapotranspiration, runoff, and terrestrial water storage changes. The spread among the various datasets highlights the scientific community's inability to accurately characterize the hydroclimatology of this region, which is problematic because much attention has focused on hydrologic trends using these datasets. The largest relative differences among estimates exist in the terrestrial storage change, which also is the least studied variable. Seasonally, the spread in estimates relative to the mean is largest in winter, when uncertainty in cold-season processes and measurements causes large differences in the estimates. A methodology is developed that takes advantage of multiple sources of data and observed discharge to improve estimates of precipitation, evapotranspiration, and storage changes. The method also provides a framework to evaluate the errors in datasets for variables that have no large-scale in situ measurements, such as evapotranspiration.

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Justin Sheffield, Alan D. Ziegler, Eric F. Wood, and Yangbo Chen

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A spurious wavelike pattern in the monthly rain day statistics exists within the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis precipitation product. The anomaly, which is an artifact of the parameterization of moisture diffusion, occurs during the winter months in the Northern and Southern Hemisphere high latitudes. The anomaly is corrected by using monthly statistics from three different global precipitation products from 1) the University of Washington (UW), 2) the Global Precipitation Climate Project (GPCP), and 3) the Climatic Research Unit (CRU), resulting in three slightly different corrected precipitation products. The correction methodology, however, compromises spatial consistency (e.g., storm tracking) on a daily time scale. The effect that the precipitation correction has on the reanalysis-derived global land surface water budgets is investigated by forcing the Variable Infiltration Capacity (VIC) land surface model with all four datasets (i.e., the original reanalysis product and the three corrected datasets). The main components of the land surface water budget cycle are not affected substantially; however, the increased spatial variability in precipitation is reflected in the evaporation and runoff components but reduced in the case of soil moisture. Furthermore, the partitioning of precipitation into canopy evaporation and throughfall is sensitive to the rain day statistics of the correcting dataset, especially in the Tropics, and this has implications for the required accuracy of the correcting dataset. The output fields from these long-term land surface simulations provide a global, consistent dataset of water and energy states and fluxes that can be used for model intercomparisons, studies of annual and seasonal climate variability, and comparisons with current versions of numerical weather prediction models.

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