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Paul A. Dirmeyer and James L. Kinter III

Abstract

The characteristics of situations of extremely high rainfall over the midwestern region of the United States during late spring and summer are investigated from the perspective of the regional water cycle using observations and observationally based analyses. The period of May–July has the greatest mean rainfall rates of the year and higher interannual variability than the periods either before or after. This is also a critical time of year for water resources and cultivation schedules in this agriculturally important region. Large-scale floods during this time of year are usually characterized by an enhanced source of moisture evaporating from low latitudes, specifically the Caribbean Sea. This is part of a fetch of moisture that extends from the Caribbean northward along the coast of Central America, over the Yucatan Peninsula, along the east coast of Mexico and the western Gulf of Mexico, and over Texas, where it links into the Great Plains low-level jet. In fact, heavy rainfall over much of the eastern half of the United States is associated with above-average Caribbean moisture supply. There is also indication of an enhanced source of moisture from the subtropical Pacific during Midwest flood events. Drought events appear to have a different spatial pattern of water cycle variables and circulation anomalies, and are not simply equal and opposite manifestations of flood events. While not a dominant source of moisture even during extreme events, the Caribbean region seems to be part of an important link for remote moisture, supplying floods over the Midwest.

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Renguang Wu and James L. Kinter III

Abstract

The impacts of droughts depend on how long droughts persist and the reasons why droughts extend to different time scales may be different. The present study distinguishes the time scale of droughts based on the standardized precipitation index and analyzes the relationship of boreal summer U.S. droughts with sea surface temperature (SST) and soil moisture. It is found that the roles of remote SST forcing and local soil moisture differ significantly for long-term and short-term droughts in the U.S. Great Plains and Southwest. For short-term droughts (≤3 months), simultaneous remote SST forcing plays an important role with an additional contribution from soil moisture. For medium-term and long-term droughts (≥6 months), both simultaneous and antecedent SST forcing contribute to droughts, and the soil moisture is important for the persistence of droughts through a positive feedback to precipitation. The antecedent remote SST forcing contributes to droughts through soil moisture and evaporation changes. The tropical Pacific SST is the dominant remote forcing for U.S. droughts. The most notable impacts of the tropical Pacific SST are found in the Southwest with extensions to the Great Plains. Tropical Indian Ocean SST forcing has a notable influence on medium-term and long-term U.S. droughts. The relationships between tropical Indian and Pacific Ocean SST and boreal summer U.S. droughts have undergone obvious long-term changes, especially for the Great Plains droughts.

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Sanjiv Kumar, Venkatesh Merwade, James L. Kinter III, and Dev Niyogi

Abstract

The authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This study is focused on continental areas (60°S–60°N) during 1930–2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble–mean global land–average temperature trend (0.07°C decade−1) captures the corresponding observed trend well (0.08°C decade−1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models’ relative performances are different for temperature and precipitation trends. The models capture the long-term persistence in temperature reasonably well. The areal coverage of observed long-term persistence in precipitation is 60% less (32% of land area) than that of temperature (78%). The models have limited capability to capture the long-term persistence in precipitation. Most climate models underestimate the spatial variability in temperature trends. The multimodel ensemble–average trend generally provides a conservative estimate of local/regional trends. The results of this study are generally not biased by the choice of observation datasets used, including Climatic Research Unit Time Series 3.1; temperature data from Hadley Centre/Climatic Research Unit, version 4; and precipitation data from Global Historical Climatology Network, version 2.

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Tuantuan Zhang, Bohua Huang, Song Yang, and James L. Kinter III

Abstract

The predictable patterns and intraensemble variability of monthly 850-hPa zonal wind over the tropical Indo-Pacific region are investigated using 7-month hindcasts for 1983–2009 from Project Minerva. When applied to the ensemble hindcasts initialized on 1 May and 1 November, a maximum signal-to-noise empirical orthogonal function analysis identifies the patterns of high predictability as the hindcasts progress. For both initial months, the most predictable patterns are associated with El Niño–Southern Oscillation (ENSO). The second predictable patterns with May initialization reflect the anomalous evolution of the western North Pacific (WNP) monsoon, characterized by a northward shift of the WNP anomalous anticyclone/cyclone in summer and a southward shift in fall. The intraensemble variability shows a strong seasonality that affects different predictable patterns in different seasons. For May initialization, the dominant patterns of the ensemble spread bear some resemblance to the predictable WNP patterns in summer and ENSO patterns in fall, which reflect the noise-induced differences in the evolution of the predictable signals among ensemble members. On the other hand, the noise patterns with November initialization are dominated by the northern extratropical atmospheric perturbations from winter to early spring, which expand southward through the coupled footprinting mechanism to perturb the ENSO evolution in different ensemble members. In comparison, the extratropical perturbations in the Southern Hemisphere, most significant in early months with May-initialized predictions, are less effective in affecting the tropical circulation.

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Rodrigo J. Bombardi, James L. Kinter III, and Oliver W. Frauenfeld

Abstract

The Rainy and Dry Seasons (RADS) dataset, a new compilation of precipitation statistics available to the public, is described. The dataset contains the dates of onset and demise of the rainy season (one date per year), the duration of the rainy and dry seasons, and the accumulated precipitation during the rainy and dry seasons. The methodology for detecting the characteristics of the rainy season is based solely on precipitation data. RADS was developed from multiple global gridded daily precipitation datasets [Tropical Rainfall Measuring Mission (TRMM), 1998–2015; Climate Prediction Center Unified Gauge-Based Analysis of Global Daily Precipitation (CPC_UNI), 1979–present; and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), 1980–present] and therefore shares the spatial resolution, temporal range, and limitations of the original precipitation datasets. This is the first free public dataset of the characteristics of the rainy and dry seasons created using a consistent methodology across the globe, including all major monsoonal regions. We expect that the RADS dataset will contribute to our understanding of the sources of variability of the timing of rainy seasons (on local to regional scales) and monsoons (on large scales) and their impacts on water resource management and other aspects of geosciences and human activities.

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Edwin K. Schneider, Michael J. Fennessy, and James L. Kinter III

Abstract

Changes in the atmospheric response to SST variability in the decade 2065–75 are estimated from time-slice-like experiments using the NCAR Community Atmosphere Model, version 3 (CAM3) AGCM forced by specified SST and external forcing. The current climate is simulated using observed monthly SST and external forcing for 1951–2000. The change in mean SST for the future is represented by the difference between the 2065–75 and 1965–75 decadal mean SST climatologies from coupled model twentieth-century/future climate simulations of the response to external forcing. The change in external forcing is similarly specified as the change of the external forcing concurrent with the SST change. These seasonally varying changes in SST and external forcing are added to the 50-year sequence of 1951–2000 observed SST and external forcings to produce the specified future climate forcings for the AGCM.

Changes in the December through February mean ENSO teleconnections are evaluated from the difference between ensemble means from future and current climate time slice simulations. The ENSO teleconnections are strengthened and displaced westward in the time slice simulations, which is not in agreement with CGCM projections. These changes are associated with increased precipitation/atmospheric heating anomalies due to the warmer tropical SST. The quasigeostrophic stationary wave activity flux indicates that the dominant cause of the changes is a southward shift in a midlatitude central Pacific wave activity source rather than changes in the basic-state stationary wave dispersion properties.

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Benjamin A. Cash, Xavier Rodó, and James L. Kinter III

Abstract

Recent studies arising from both statistical analysis and dynamical disease models indicate that there is a link between incidence of cholera, a paradigmatic waterborne bacterial disease (WBD) endemic to Bangladesh, and the El Niño–Southern Oscillation (ENSO). However, a physical mechanism explaining this relationship has not yet been established. A regionally coupled, or “pacemaker,” configuration of the Center for Ocean–Land–Atmosphere Studies atmospheric general circulation model is used to investigate links between sea surface temperature in the central and eastern tropical Pacific and the regional climate of Bangladesh. It is found that enhanced precipitation tends to follow winter El Niño events in both the model and observations, providing a plausible physical mechanism by which ENSO could influence cholera in Bangladesh.

The enhanced precipitation in the model arises from a modification of the summer monsoon circulation over India and Bangladesh. Westerly wind anomalies over land to the west of Bangladesh lead to increased convergence in the zonal wind field and hence increased moisture convergence and rainfall. This change in circulation results from the tropics-wide warming in the model following a winter El Niño event. These results suggest that improved forecasting of cholera incidence may be possible through the use of climate predictions.

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Jieshun Zhu, Bohua Huang, Arun Kumar, and James L. Kinter III

Abstract

Seasonality of sea surface temperature (SST) predictions in the tropical Indian Ocean (TIO) was investigated using hindcasts (1982–2009) made with the NCEP Climate Forecast System version 2 (CFSv2). CFSv2 produced useful predictions of the TIO SST with lead times up to several months. A substantial component of this skill was attributable to signals other than the Indian Ocean dipole (IOD). The prediction skill of the IOD index, defined as the difference between the SST anomaly (SSTA) averaged over 10°S–0°, 90°–110°E and 10°S–10°N, 50°–70°E, had strong seasonality, with high scores in the boreal autumn. In spite of skill in predicting its two poles with longer leads, CFSv2 did not have skill significantly better than persistence in predicting IOD. This was partly because the seasonal nature of IOD intrinsically limits its predictability.

The seasonality of the predictable patterns of the TIO SST was further explored by applying the maximum signal-to-noise (MSN) empirical orthogonal function (EOF) method to the predicted SSTA in March and October, respectively. The most predictable pattern in spring was the TIO basin warming, which is closely associated with El Niño. The basin mode, including its associated atmospheric anomalies, can be predicted at least 9 months ahead, even though some biases were evident. On the other hand, the most predictable pattern in fall was characterized by the IOD mode. This mode and its associated atmospheric variations can be skillfully predicted only 1–2 seasons ahead. Statistically, the predictable IOD mode coexists with El Niño; however, the 1994 event in a non-ENSO year (at least not a canonical ENSO year) can also be predicted at least 3 months ahead by CFSv2.

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Yoshiaki Miyamoto, Masaki Satoh, Hirofumi Tomita, Kazuyoshi Oouchi, Yohei Yamada, Chihiro Kodama, and James Kinter III

Abstract

The degree of gradient wind balance was investigated in a number of tropical cyclones (TCs) simulated under realistic environments. The results of global-scale numerical simulations without cumulus parameterization were used, with a horizontal mesh size of 7 km. On average, azimuthally averaged maximum tangential velocities at 850 (925) hPa in the simulated TCs were 0.72% (1.95%) faster than gradient wind–balanced tangential velocity (GWV) during quasi-steady periods. Of the simulated TCs, 75% satisfied the gradient wind balance at the radius of maximum wind speed (RMW) at 850 and at 925 hPa to within about 4.0%. These results were qualitatively similar to those obtained during the intensification phase. In contrast, averages of the maximum and minimum deviations from the GWV, in all the azimuths at the RMW, achieved up to 40% of the maximum tangential velocity. Azimuthally averaged tangential velocities exceeded the GWV (i.e., supergradient) inside the RMW in the lower troposphere, whereas the velocities were close to or slightly slower than GWV (i.e., subgradient) in the other regions. The tangential velocities at 925 hPa were faster (slower) in the right-hand (left hand) side of the TC motion. When the tangential velocities at the RMW were supergradient, the primary circulation tended to decay rapidly in the vertical direction and slowly in the radial direction, and the eyewall updraft and the RMW were at larger radii. Statistical analyses revealed that the TC with supergradient wind at the RMW at 850 hPa was characterized by stronger intensity, larger RMW, more axisymmetric structure, and an intensity stronger than potential intensity.

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Julia V. Manganello, Benjamin A. Cash, Erik T. Swenson, and James L. Kinter III

Abstract

Tropical cyclone (TC) landfalls over the U.S. mid-Atlantic region, which include the so-called Sandy-like, or westward-curving, tracks, are among the most infrequent landfalls along the U.S. East Coast. However, when these events do occur, the resulting economic and societal consequences can be devastating. A recent example is Hurricane Sandy in 2012. Multimodel ensemble seasonal hindcasts conducted with a high-atmospheric-resolution coupled prediction system based on the ECMWF operational model (Project Minerva) are used here to compile the statistics of these rare events. Minerva hindcasts are found to exhibit skill in reproducing climatological characteristics of the mid-Atlantic TC landfalls particularly at the highest atmospheric horizontal spectral resolution of T1279 (16-km grid spacing). Historical forecasts are further interrogated to identify regional and large-scale environmental conditions associated with these rare TC tracks to better quantify their predictability on synoptic time scales, and their dependence on model resolution. Evolution of the large-scale atmospheric flow patterns leading to mid-Atlantic TC landfalls is analyzed using local finite-amplitude wave activity (LWA). We have identified large-amplitude quasi-stationary features in the LWA and sea surface temperature (SST) anomaly distributions that persist up to about a week leading to these land-falling events. A statistical model utilizing indices based on the LWA and SST anomalies as predictors is developed that exhibits skill (mostly at T1279) in predicting mid-Atlantic TC landfalls several days in advance. Implications of these results for longer time-scale predictions of mid-Atlantic TC landfalls including climate change projections are discussed.

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