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Porathur V. Joseph
,
Jon K. Eischeid
, and
Robert J. Pyle

Abstract

The long-term mean date of the monsoon onset over Kerala (MOK) varies between 30 May and 2 June according to different estimates, with a standard deviation of 8–9 days. The earliest date of MOK, and the most delayed one, during the last 100 years differ by 46 days (7 May and 22 June, respectively). MOK switches on a spatially large and intense convective heat source over south Asia, lasting from June to September, whose moisture supply is made available through the cross-equatorial low-level jet stream.

Superposed epoch analysis of 10 years of outgoing longwave radiation (OLR) data shows that MOK is a significant stage in the evolution of the OLR field in the tropics of the eastern hemisphere. At the time of MOK there is increased convection in a band about 5–10 degrees wide meridionally, extending from the south Arabian Sea to south China, and convection is suppressed all around, particularly in the western Pacific Ocean. In 1983 when MOK was delayed by about 3 pentads, OLR data showed that the boreal spring-to-summer migration of the equatorial convective cloudiness maximum (ECCM), both westward and northward, was also delayed. The delayed MOK is accompanied by delays in the northwestward movement of ECCM and is confirmed by an analysis of long-term data of southwest Pacific tropical cyclones.

Of the 22 years between 1870–1989 when MOK was delayed by 8 days or more, 16 casts were associated with a moderate or strong El Niño. Of the 13 strong El Niños during the same period, 9 were associated with moderate-to-large delays in MOK. Delays preferentially occurred in the year +1 of an El Niño, where year 0 is the growing phase of the El Niño in sea surface temperature (SST).

Analysis of the SST field has shown that delayed MOK is associated with warm SST anomalies at and south of the equator in the Indian and Pacific oceans and cold SST anomalies in the tropical and subtropical oceans to the north during the season prior to the monsoon onset (i.e., March to May). It is hypothesized that such SST anomalies over the Indian and Pacific oceans (generally found associated with El Niño, either in year 0 or year +1 or in both) cause the interannual variability of the MOK through their action in affecting the timing of the northwestward movement of the ECCM.

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Klaus Wolter
,
Martin Hoerling
,
Jon K. Eischeid
, and
Dave Allured
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Klaus Wolter
,
Jon K. Eischeid
,
Linyin Cheng
, and
Martin Hoerling
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Martin P. Hoerling
,
Jon K. Eischeid
,
Henry F. Diaz
,
Balaji Rajagopolan
, and
Eric Kuhn

Abstract

Of concern to Colorado River management, as operating guidelines post-2026 are being considered, is whether water resource recovery from low flows during 2000–2020 is possible. Here we analyze new simulations from the sixth generation of the Coupled Model Intercomparison Project (CMIP6) to determine plausible climate impacts on Colorado River flows for 2026–2050 when revised guidelines would operate. We constrain projected flows for Lee Ferry, the gauge through which 85% of the river flow passes, using its estimated sensitivity to meteorological variability together with CMIP6 projected precipitation and temperature changes. The critical importance of precipitation, especially its natural variability, is emphasized. Model projections indicate increased precipitation in the Upper Colorado River basin due to climate change, which alone increases river flows 5%–7% (relative to a 2000–2020 climatology). Depending on the river’s temperature sensitivity, this wet signal compensates some, if not all, of the depleting effects from basin warming. Considerable internal decadal precipitation variability (~5% of the climatological mean) is demonstrated, driving a greater range of plausible Colorado River flow changes for 2026–2050 than previously surmised from treatment of temperature impacts alone: the overall precipitation-induced Lee Ferry flow changes span −25% to +40% contrasting with a −30% to −5% range from expected warming effects only. Consequently, extreme low and high flows are more likely. Lee Ferry flow projections, conditioned on initial drought states akin to 2000–2020, reveal substantial recovery odds for water resources, albeit with elevated risks of even further flow declines than in recent decades.

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Jon K. Eischeid
,
C. Bruce Baker
,
Thomas R. Karl
, and
Henry F. Diaz

Abstract

One of the major concerns with detecting global climate change is the quality of the data. Climate data are extremely sensitive to errant values and outliers. Prior to analysis of these time series, it is important to remove outliers in a methodical manner.

This study provides statistically derived bounds for the uncertainty associated with surface temperature and precipitation measurements and yields a baseline dataset for validation of climate models as well as for a variety of other climatological uses. A two-step procedure using objective analysis was used to identify outliers. The first step was a temporal check that determines if a particular monthly value is consistent with other monthly values for the same station. The second step utilizes six different spatial interpolation techniques to estimate each monthly time series. Each of the methods is ranked according to its respective correlation coefficients with the actual time series, and the technique with the highest correlation coefficient is chosen as the best estimator. For both temperature and precipitation, a multiple regression scheme was found to be the best estimator for the majority of records. Results from the two steps are merged, and a combined set of quality control flags are generated.

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Mauro Di Luzio
,
Gregory L. Johnson
,
Christopher Daly
,
Jon K. Eischeid
, and
Jeffrey G. Arnold

Abstract

This paper presents and evaluates a method for the construction of long-range and wide-area temporal spatial datasets of daily precipitation and temperature (maximum and minimum). This method combines the interpolation of daily ratios/fractions derived from ground-based meteorological station records and respective fields of monthly estimates. Data sources for the described implementation over the conterminous United States (CONUS) are two independent and quality-controlled inputs: 1) an enhanced compilation of daily observations derived from the National Climatic Data Center digital archives and 2) the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) maps. The results of this study show that this nonconventional interpolation preserves the spatial and temporal distribution of both the PRISM maps (monthly, topography-sensitive patterns) and the original daily observations. Statistics of a preliminary point comparison with the observed values at high-quality and independent reference sites show a reasonable agreement and a noticeable improvement over the nearest station method in orographically sensitive areas. The implemented datasets provide daily precipitation and temperature values at 2.5-min (around 4 km) resolution for 1960–2001. Combining seamless spatial and temporal coverage and topographic sensitivity characteristics, the datasets offer the potential for supporting current and future regional and historical hydrologic assessments over the CONUS.

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Jon K. Eischeid
,
Phil A. Pasteris
,
Henry F. Diaz
,
Marc S. Plantico
, and
Neal J. Lott

Abstract

The development of serially complete (no missing values) daily maximum–minimum temperatures and total precipitation time series over the western United States is documented. Several estimation techniques based on spatial objective analysis schemes are used to estimate daily values, with the &ldquost” estimate chosen as a missing value replacement. The development of a continuous and complete daily dataset will be useful in a variety of meteorological and hydrological research applications.

The spatial interpolation schemes are evaluated separately by interpolation method and calendar month. Cross validation of the results indicates a distinct seasonality to the efficiency (error) of the estimates, although no systematic bias in the estimation procedures was found. The resulting number of serially complete daily time series for the western United States (all states west of the Mississippi River) includes 2034 maximum–minimum temperature stations and 2962 total daily precipitation locations.

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Henry F. Diaz
,
Eugene R. Wahl
,
Eduardo Zorita
,
Thomas W. Giambelluca
, and
Jon K. Eischeid

Abstract

Few if any high-resolution (annually resolved) paleoclimate records are available for the Hawaiian Islands prior to ~1850 CE, after which some instrumental records start to become available. This paper shows how atmospheric teleconnection patterns between North America and the northeastern North Pacific (NNP) allow for reconstruction of Hawaiian Islands rainfall using remote proxy information from North America. Based on a newly available precipitation dataset for the state of Hawaii and observed and reconstructed December–February (DJF) sea level pressures (SLPs) in the North Pacific Ocean, the authors make use of a strong relationship between winter SLP variability in the northeast Pacific and corresponding DJF Hawaii rainfall variations to reconstruct and evaluate that season’s rainfall over the period 1500–2012 CE. A general drying trend, though with substantial decadal and longer-term variability, is evident, particularly during the last ~160 years. Hawaiian Islands rainfall exhibits strong modulation by El Niño–Southern Oscillation (ENSO), as well as in relation to Pacific decadal oscillation (PDO)-like variability. For significant periods of time, the reconstructed large-scale changes in the North Pacific SLP field described here and by construction the long-term decline in Hawaiian winter rainfall are broadly consistent with long-term changes in tropical Pacific sea surface temperature (SST) based on ENSO reconstructions documented in several other studies, particularly over the last two centuries. Also noted are some rather large multidecadal fluctuations in rainfall (and hence in NNP SLP) in the eighteenth century of undetermined provenance.

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Brant Liebmann
,
Martin P. Hoerling
,
Chris Funk
,
Ileana Bladé
,
Randall M. Dole
,
Dave Allured
,
Xiaowei Quan
,
Philip Pegion
, and
Jon K. Eischeid

Abstract

Observations and sea surface temperature (SST)-forced ECHAM5 simulations are examined to study the seasonal cycle of eastern Africa rainfall and its SST sensitivity during 1979–2012, focusing on interannual variability and trends. The eastern Horn is drier than the rest of equatorial Africa, with two distinct wet seasons, and whereas the October–December wet season has become wetter, the March–May season has become drier.

The climatological rainfall in simulations driven by observed SSTs captures this bimodal regime. The simulated trends also qualitatively reproduce the opposite-sign changes in the two rainy seasons, suggesting that SST forcing has played an important role in the observed changes. The consistency between the sign of 1979–2012 trends and interannual SST–precipitation correlations is exploited to identify the most likely locations of SST forcing of precipitation trends in the model, and conceivably also in nature. Results indicate that the observed March–May drying since 1979 is due to sensitivity to an increased zonal gradient in SST between Indonesia and the central Pacific. In contrast, the October–December precipitation increase is mostly due to western Indian Ocean warming.

The recent upward trend in the October–December wet season is rather weak, however, and its statistical significance is compromised by strong year-to-year fluctuations. October–December eastern Horn rain variability is strongly associated with El Niño–Southern Oscillation and Indian Ocean dipole phenomena on interannual scales, in both model and observations. The interannual October–December correlation between the ensemble-average and observed Horn rainfall 0.87. By comparison, interannual March–May Horn precipitation is only weakly constrained by SST anomalies.

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Martin P. Hoerling
,
Jon K. Eischeid
,
Xiao-Wei Quan
,
Henry F. Diaz
,
Robert S. Webb
,
Randall M. Dole
, and
David R. Easterling

Abstract

How Great Plains climate will respond under global warming continues to be a key unresolved question. There has been, for instance, considerable speculation that the Great Plains is embarking upon a period of increasing drought frequency and intensity that will lead to a semipermanent Dust Bowl in the coming decades. This view draws on a single line of inference of how climate change may affect surface water balance based on sensitivity of the Palmer drought severity index (PDSI). A different view foresees a more modest climate change impact on Great Plains surface moisture balances. This draws on direct lines of analysis using land surface models to predict runoff and soil moisture, the results of which do not reveal an ominous fate for the Great Plains. The authors’ study presents a parallel diagnosis of projected changes in drought as inferred from PDSI and soil moisture indicators in order to understand causes for such a disparity and to shed light on the uncertainties. PDSI is shown to be an excellent proxy indicator for Great Plains soil moisture in the twentieth century; however, its suitability breaks down in the twenty-first century, with the PDSI severely overstating surface water imbalances and implied agricultural stresses. Several lines of evidence and physical considerations indicate that simplifying assumptions regarding temperature effects on water balances, especially concerning evapotranspiration in Palmer’s formulation, compromise its suitability as drought indicator in a warming climate. The authors conclude that projections of acute and chronic PDSI decline in the twenty-first century are likely an exaggerated indicator for future Great Plains drought severity.

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