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Xiao-Wei Quan, Martin P. Hoerling, Judith Perlwitz, Henry F. Diaz, and Taiyi Xu

Abstract

Diagnosing the sensitivity of the tropical belt provides one framework for understanding how global precipitation patterns may change in a warming world. This paper seeks to understand boreal winter rates of subtropical dry zone expansion since 1979, and explores physical mechanisms. Various reanalysis estimates based on the latitude where zonal mean precipitation P exceeds evaporation E and the zero crossing latitude for the zonal mean meridional streamfunction () yield tropical width expansion rates in each hemisphere ranging from near zero to over 1° latitude decade−1. Comparisons with 30-yr trends computed from unforced climate model simulations indicate that the range among reanalyses is nearly an order of magnitude greater than the standard deviation of internal climate variability. Furthermore, comparisons with forced climate models indicate that this range is an order of magnitude greater than the forced change signal since 1979. Rapid widening rates during 1979–2009 derived from some reanalyses are thus viewed to be unreliable.

The intercomparison of models and reanalyses supports the prevailing view of a tropical widening, but the forced component of tropical widening has likely been only about 0.1°–0.2° latitude decade−1, considerably less than has generally been assumed based on inferences drawn from observations and reanalyses. Climate model diagnosis indicates that the principal mechanism for forced tropical widening since 1979 has been atmospheric sensitivity to warming oceans. The magnitude of this widening and its potential detectability has been greater in the Southern Hemisphere than in the Northern Hemisphere during boreal winter, in part owing to Antarctic stratospheric ozone depletion.

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Xiao-Wei Quan, Martin P. Hoerling, Judith Perlwitz, and Henry F. Diaz

Abstract

The tropical belt is expected to expand in response to global warming, although most of the observed tropical widening since 1980, especially in the Northern Hemisphere, is believed to have mainly originated from natural variability. The view is of a small global warming signal relative to natural variability. Here we focus on the question whether and, if so when, the anthropogenic signal of tropical widening will become detectable. Analysis of two large ensemble climate simulations reveals that the forced signal of tropical width is strongly constrained by the forced signal of global mean temperature. Under a representative concentration pathway 8.5 (RCP8.5) emissions scenario, the aggregate of the two models indicates a regression of about 0.5° lat °C−1 during 1980–2080. The models also reveal that interannual variability in tropical width, a measure of noise used herein, is insensitive to global warming. Reanalysis data are therefore used to constrain the interannual variability, whose magnitude is estimated to be 1.1° latitude. Defining the time of emergence (ToE) for tropical width change as the first year (post-1980) when the forced signal exceeds the magnitude of interannual variability, the multimodel simulations of CMIP5 are used to estimate ToE and its confidence interval. The aforementioned strong constraint between the signal of tropical width change and global mean temperature change motivates using CMIP5-simulated global mean temperature changes to infer ToE. Our best estimate for the probable year for ToE, under an RCP8.5 emissions scenario, is 2058 with 10th–90th percentile confidence of 2047–68. Various sources of uncertainty in estimating the ToE are discussed.

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Martin Hoerling, Jon Eischeid, Judith Perlwitz, Xiao-Wei Quan, Klaus Wolter, and Linyin Cheng

Abstract

Time series of U.S. daily heavy precipitation (95th percentile) are analyzed to determine factors responsible for regionality and seasonality in their 1979–2013 trends. For annual conditions, contiguous U.S. trends have been characterized by increases in precipitation associated with heavy daily events across the northern United States and decreases across the southern United States. Diagnosis of climate simulations (CCSM4 and CAM4) reveals that the evolution of observed sea surface temperatures (SSTs) was a more important factor influencing these trends than boundary condition changes linked to external radiative forcing alone. Since 1979, the latter induces widespread, but mostly weak, increases in precipitation associated with heavy daily events. The former induces a meridional pattern of northern U.S. increases and southern U.S. decreases as observed, the magnitude of which closely aligns with observed changes, especially over the south and far west. Analysis of model ensemble spread reveals that appreciable 35-yr trends in heavy daily precipitation can occur in the absence of forcing, thereby limiting detection of the weak anthropogenic influence at regional scales.

Analysis of the seasonality in heavy daily precipitation trends supports physical arguments that their changes during 1979–2013 have been intimately linked to internal decadal ocean variability and less so to human-induced climate change. Most of the southern U.S. decrease has occurred during the cold season that has been dynamically driven by an atmospheric circulation reminiscent of teleconnections linked to cold tropical eastern Pacific SSTs. Most of the northeastern U.S. increase has been a warm season phenomenon, the immediate cause for which remains unresolved.

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Andrew Hoell, Judith Perlwitz, Candida Dewes, Klaus Wolter, Imtiaz Rangwala, Xiao-Wei Quan, and Jon Eischeid
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Linyin Cheng, Martin Hoerling, Amir AghaKouchak, Ben Livneh, Xiao-Wei Quan, and Jon Eischeid

Abstract

The current California drought has cast a heavy burden on statewide agriculture and water resources, further exacerbated by concurrent extreme high temperatures. Furthermore, industrial-era global radiative forcing brings into question the role of long-term climate change with regard to California drought. How has human-induced climate change affected California drought risk? Here, observations and model experimentation are applied to characterize this drought employing metrics that synthesize drought duration, cumulative precipitation deficit, and soil moisture depletion. The model simulations show that increases in radiative forcing since the late nineteenth century induce both increased annual precipitation and increased surface temperature over California, consistent with prior model studies and with observed long-term change. As a result, there is no material difference in the frequency of droughts defined using bivariate indicators of precipitation and near-surface (10 cm) soil moisture, because shallow soil moisture responds most sensitively to increased evaporation driven by warming, which compensates the increase in the precipitation. However, when using soil moisture within a deep root zone layer (1 m) as covariate, droughts become less frequent because deep soil moisture responds most sensitively to increased precipitation. The results illustrate the different land surface responses to anthropogenic forcing that are relevant for near-surface moisture exchange and for root zone moisture availability. The latter is especially relevant for agricultural impacts as the deep layer dictates moisture availability for plants, trees, and many crops. The results thus indicate that the net effect of climate change has made agricultural drought less likely and that the current severe impacts of drought on California’s agriculture have not been substantially caused by long-term climate changes.

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Martin Hoerling, Lesley Smith, Xiao-Wei Quan, Jon Eischeid, Joseph Barsugli, and Henry F. Diaz

Abstract

Observed United States trends in the annual maximum 1-day precipitation (RX1day) over the last century consist of 15%–25% increases over the eastern United States (East) and 10% decreases over the far western United States (West). This heterogeneous trend pattern departs from comparatively uniform observed increases in precipitable water over the contiguous United States. Here we use an event attribution framework involving parallel sets of global atmospheric model experiments with and without climate change drivers to explain this spatially diverse pattern of extreme daily precipitation trends. We find that RX1day events in our model ensembles respond to observed historical climate change forcing differently across the United States with 5%–10% intensity increases over the East but no appreciable change over the West. This spatially diverse forced signal is broadly similar among three models used, and is positively correlated with the observed trend pattern. Our analysis of model and observations indicates the lack of appreciable RX1day signals over the West is likely due to dynamical effects of climate change forcing—via a wintertime atmospheric circulation anomaly that suppresses vertical motion over the West—largely cancelling thermodynamic effects of increased water vapor availability. The large magnitude of eastern U.S. RX1day increases is unlikely a symptom of a regional heightened sensitivity to climate change forcing. Instead, our ensemble simulations reveal considerable variability in RX1day trend magnitudes arising from internal atmospheric processes alone, and we argue that the remarkable observed increases over the East has most likely resulted from a superposition of strong internal variability with a moderate climate change signal. Implications for future changes in U.S. extreme daily precipitation are discussed.

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Andrew Hoell, Martin Hoerling, Jon Eischeid, Xiao-Wei Quan, and Brant Liebmann

Abstract

Two theories for observed East Africa drying trends during March–May 1979–2013 are reconciled. Both hypothesize that variations in tropical sea surface temperatures (SSTs) caused East Africa drying. The first invokes a mainly human cause resulting from sensitivity to secular warming of Indo–western Pacific SSTs. The second invokes a mainly natural cause resulting from sensitivity to a strong articulation of ENSO-like Pacific decadal variability involving warming of the western Pacific and cooling of the central Pacific. Historical atmospheric model simulations indicate that observed SST variations contributed significantly to the East Africa drying trend during March–May 1979–2013. By contrast, historical coupled model simulations suggest that external radiative forcing alone, including the ocean’s response to that forcing, did not contribute significantly to East Africa drying. Recognizing that the observed SST variations involved a commingling of natural and anthropogenic effects, this study diagnosed how East African rainfall sensitivity was conditionally dependent on the interplay of those factors. East African rainfall trends in historical coupled models were intercompared between two composites of ENSO-like decadal variability, one operating in the early twentieth century before appreciable global warming and the other in the early twenty-first century of strong global warming. The authors find the coaction of global warming with ENSO-like decadal variability can significantly enhance 35-yr East Africa drying trends relative to when the natural mode of ocean variability acts alone. A human-induced change via its interplay with an extreme articulation of natural variability may thus have been key to Africa drying; however, these results are speculative owing to differences among two independent suites of coupled model ensembles.

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Xiao-Wei Quan, Martin P. Hoerling, Bradfield Lyon, Arun Kumar, Michael A. Bell, Michael K. Tippett, and Hui Wang

Abstract

The prospects for U.S. seasonal drought prediction are assessed by diagnosing simulation and hindcast skill of drought indicators during 1982–2008. The 6-month standardized precipitation index is used as the primary drought indicator. The skill of unconditioned, persistence forecasts serves as the baseline against which the performance of dynamical methods is evaluated. Predictions conditioned on the state of global sea surface temperatures (SST) are assessed using atmospheric climate simulations conducted in which observed SSTs are specified. Predictions conditioned on the initial states of atmosphere, land surfaces, and oceans are next analyzed using coupled climate-model experiments. The persistence of the drought indicator yields considerable seasonal skill, with a region’s annual cycle of precipitation driving a strong seasonality in baseline skill. The unconditioned forecast skill for drought is greatest during a region’s climatological dry season and is least during a wet season. Dynamical models forced by observed global SSTs yield increased skill relative to this baseline, with improvements realized during the cold season over regions where precipitation is sensitive to El Niño–Southern Oscillation. Fully coupled initialized model hindcasts yield little additional skill relative to the uninitialized SST-forced simulations. In particular, neither of these dynamical seasonal forecasts materially increases summer skill for the drought indicator over the Great Plains, a consequence of small SST sensitivity of that region’s summer rainfall and the small impact of antecedent soil moisture conditions, on average, upon the summer rainfall. The fully initialized predictions for monthly forecasts appreciably improve on the seasonal skill, however, especially during winter and spring over the northern Great Plains.

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Xiao-Wei Quan, Martin Hoerling, Lesley Smith, Judith Perlwitz, Tao Zhang, Andrew Hoell, Klaus Wolter, and Jon Eischeid
Open access
Bradfield Lyon, Michael A. Bell, Michael K. Tippett, Arun Kumar, Martin P. Hoerling, Xiao-Wei Quan, and Hui Wang

Abstract

The inherent persistence characteristics of various drought indicators are quantified to extract predictive information that can improve drought early warning. Predictive skill is evaluated as a function of the seasonal cycle for regions within North America. The study serves to establish a set of baseline probabilities for drought across multiple indicators amenable to direct comparison with drought indicator forecast probabilities obtained when incorporating dynamical climate model forecasts. The emphasis is on the standardized precipitation index (SPI), but the method can easily be applied to any other meteorological drought indicator, and some additional examples are provided. Monte Carlo resampling of observational data generates two sets of synthetic time series of monthly precipitation that include, and exclude, the annual cycle while removing serial correlation. For the case of no seasonality, the autocorrelation (AC) of the SPI (and seasonal precipitation percentiles, moving monthly averages of precipitation) decays linearly with increasing lag. It is shown that seasonality in the variance of accumulated precipitation serves to enhance or diminish the persistence characteristics (AC) of the SPI and related drought indicators, and the seasonal cycle can thereby provide an appreciable source of drought predictability at regional scales. The AC is used to obtain a parametric probability density function of the future state of the SPI that is based solely on its inherent persistence characteristics. In addition, a method is presented for determining the optimal persistence of the SPI for the case of no serial correlation in precipitation (again, the baseline case). The optimized, baseline probabilities are being incorporated into Internet-based tools for the display of current and forecast drought conditions in near–real time.

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