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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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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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Rachel Gaal
and
James L. Kinter III

Abstract

Mesoscale convective systems (MCS) are known to develop under ideal conditions of temperature and humidity profiles and large-scale dynamic forcing. Recent work, however, has shown that summer MCS events can occur under weak synoptic forcing or even unfavorable large-scale environments. When baroclinic forcing is weak, convection may be triggered by anomalous conditions at the land surface. This work evaluates land surface conditions for summer MCS events forming in the U.S. Great Plains using an MCS database covering the contiguous United States east of the Rocky Mountains, in boreal summers 2004–16. After isolating MCS cases where synoptic-scale influences are not the main driver of development (i.e., only non-squall-line storms), antecedent soil moisture conditions are evaluated over two domain sizes (1.25° and 5° squares) centered on the mean position of the storm initiation. A negative correlation between soil moisture and MCS initiation is identified for the smaller domain, indicating that MCS events tend to be initiated over patches of anomalously dry soils of ∼100-km scale, but not significantly so. For the larger domain, soil moisture heterogeneity, with anomalously dry soils (anomalously wet soils) located southwest (northeast) of the initiation point, is associated with MCS initiation. This finding is similar to previous results in the Sahel and Europe that suggest that induced meso-β circulations from surface heterogeneity can drive convection initiation.

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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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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.

Open access
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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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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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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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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V. Krishnamurthy
,
Cristiana Stan
,
David A. Randall
,
Ravi P. Shukla
, and
James L. Kinter III

Abstract

The simulation of the South Asian monsoon by a coupled ocean–atmosphere model with an embedded cloud-resolving model is analyzed on intraseasonal and interannual time scales. The daily modes of variability in the superparameterized Community Climate System Model, version 3 (SP-CCSM), are compared with those in observation, the superparameterized Community Atmospheric Model, version 3 (SP-CAM3), and the control simulation of CCSM (CT-CCSM) with conventional parameterization of convection. The CT-CCSM fails to simulate the observed intraseasonal oscillations but is able to generate the atmospheric El Niño–Southern Oscillation (ENSO) mode, although with regular biennial variability. The dominant modes of variability extracted from daily anomalies of outgoing longwave radiation, precipitation, and low-level horizontal wind in SP-CCSM consist of two intraseasonal oscillations and two seasonally persisting modes, in good agreement with observation. The most significant observed features of the intraseasonal oscillations correctly simulated by the SP-CCSM are the northward propagation of convection, precipitation, and circulation as well as the eastward and westward propagations. The observed spatial structure and the periods of the oscillations are also well captured by the SP-CCSM, although with lesser magnitude. The SP-CCSM is able to simulate the chaotic variability and spatial structure of the seasonally persisting atmospheric ENSO mode, while the evidence for the Indian Ocean dipole mode is inconclusive. The SP-CAM3 simulates two intraseasonal oscillations and the atmospheric ENSO mode. However, the intraseasonal oscillations in SP-CAM3 do not show northward propagation while their periods and spatial structures are not comparable to observation. The results of this study indicate the necessity of coupled models with sufficiently realistic cloud parameterizations.

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