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Interannual variation in precipitation totals is a critical factor governing the year-to-year availability of water resources, yet the connection between interannual precipitation variability and underlying event- and season-scale...
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Interannual variation in precipitation totals is a critical factor governing the year-to-year availability of water resources, yet the connection between interannual precipitation variability and underlying event- and season-scale precipitation variability remains unclear. In this study, tropical and midlatitude precipitation characteristics derived from extensive station records and high-frequency satellite observations were analyzed to attribute the fraction of interannual variability arising as a result of individual variability in precipitation event intensity, frequency, and seasonality, as well as the cross-correlation between these factors at the global scale. This analysis demonstrates that variability in the length of the wet season is the most important factor globally, causing 52% of the total interannual variability, while variation in the intensity of individual rainfall events contributes 31% and variability in interstorm wait times contributes only 17%. Spatial patterns in the contribution of each of these intra-annual rainfall characteristics are informative, with regions such as Indonesia and southwestern North America primarily influenced by seasonality, while regions such as the eastern United States, central Africa, and the upper Amazon basin are strongly influenced by storm intensity and frequency. A robust cross-correlation between climate characteristics is identified in the equatorial Pacific, revealing an increased interannual variability over what is expected based on the variability of individual events. This decomposition of interannual variability identifies those regions where accurate representation of daily and seasonal rainfall statistics is necessary to understand and correctly model rainfall variability at longer time scales.
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Factors governing spatiotemporal variations of the daily outgoing longwave radiation (OLR) are studied using 35-yr (1979-2013) data records by employing multiple linear regression, wavelet transforms, and bandpass filtering method...
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Factors governing spatiotemporal variations of the daily outgoing longwave radiation (OLR) are studied using 35-yr (1979-2013) data records by employing multiple linear regression, wavelet transforms, and bandpass filtering methods. From the regression coefficients of nine predictors and the explained variances, we found that the largest contributions to OLR variability are associated with the Madden-Julian oscillation and El Nino-Southern Oscillation (ENSO). The ENSO signatures on OLR show dipole patterns over the Maritime Continent (MC) and Pacific regions with an extension to the Atlantic. Subsequently, the third significant contribution of the Indian Ocean dipole is confined to the Indian Ocean and Africa. Then, the solar cycle and stratospheric aerosols show mainly negative correlations, while a positive linear trend is observed mainly in the Northern Hemisphere. Lastly, factors associated with the stratospheric quasi-biennial oscillation (QBO) are the least significant contributor to OLR. In terms of oscillatory signals, time-longitude variations of the annual cycle (AC) show pairs of contrasting phases that characterize monsoon systems, in which the MC and Pacific regions are found to be in the same phase group. The most consistent AC signals are found to correspond with North and South American monsoons that respectively exhibit weakening and strengthening trends. Wavelet spectra and filtered OLR signals in intraseasonal oscillation, QBO, and ENSO frequency bands show an interdependent relationship that largely varies with time scale and longitudes.
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This paper examines the effects of climatic and non-climatic factors on the mean and variance of corn, soybean and winter wheat yield in southwestern Ontario, Canada over a period of 26 years. Average crop yields increase at a dec...
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This paper examines the effects of climatic and non-climatic factors on the mean and variance of corn, soybean and winter wheat yield in southwestern Ontario, Canada over a period of 26 years. Average crop yields increase at a decreasing rate with the quantity of inputs used, and decrease with the area planted to the crop. Climate variables have a major impact on mean yield with the length of the growing season being the primary determinant across all three crops. Increases in the variability of temperature and precipitation decrease mean yield and increase its variance. Yield variance is poorly explained by both seasonal and monthly climate variable models. Projections of future climate change suggest that average crop yield will increase with warmer temperatures and a longer growing season which is only partially offset by forecast increases in the variability of temperature and rainfall. The projections would also depend on future technological developments, which have generated significant increases in yield over time despite changing annual weather conditions.
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Over the past century, and especially after the 1970s, rainfall observations show an increase (decrease) of the wet summer (winter) season rainfall over northwest (southwest) Western Australia. The rainfall in central west Western...
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Over the past century, and especially after the 1970s, rainfall observations show an increase (decrease) of the wet summer (winter) season rainfall over northwest (southwest) Western Australia. The rainfall in central west Western Australia (CWWA), however, has exhibited comparatively much weaker coastal trends, but a more prominent inland increase during the wet summer season. Analysis of seasonally averaged rainfall data from a group of stations, representative of both the coastal and inland regions of CWWA, revealed that rainfall trends during the 1958-2010 period in the wet months of November-April were primarily associated with El Nino-Southern Oscillation (ENSO), and with the southern annular mode (SAM) farther inland. During the wet months of May-October, the Indian Ocean dipole (IOD) showed the most robust relationships. Those results hold when the effects of ENSO or IOD are excluded, and were confirmed using a principal component analysis of sea surface temperature (SST) anomalies, rainfall wavelet analyses, and point-by-point correlations of rainfall with global SST anomaly fields. Although speculative, given their long-term averages, reanalysis data suggest that from 1958 to 2010 the increase in CWWA inland rainfall largely is attributable to an increasing cyclonic anomaly trend over CWWA, bringing onshore moist tropical flow to the Pilbara coast. During May-October, the flow anomaly exhibits a transition from an onshore to offshore flow regime in the 2001-10 decade, which is consistent with the observed weaker drying trend during this period.Digital Object Identifier http://dx.doi.org/10.1175/JCLI-D-12-00129.1
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The leading pattern of precipitation for the Indian Ocean, one of the most intense areas of rainfall on the globe, is calculated for November-April 1979-2008. The associated regional circulation and thermodynamic forcing of precip...
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The leading pattern of precipitation for the Indian Ocean, one of the most intense areas of rainfall on the globe, is calculated for November-April 1979-2008. The associated regional circulation and thermodynamic forcing of precipitation over Asia are examined at both intraseasonal and interannual time scales. The leading pattern is determined using both empirical orthogonal function analysis of monthly precipitation data and a closely related index of daily outgoing longwave radiation filtered into intraseasonal (33-105 days) and interannual (greater than 105 days) components. The leading pattern has a maximum in the tropical eastern Indian Ocean, and is closely associated with the Madden-Julian oscillation at intraseasonal time scales and related to the El Nino-Southern Oscillation at interannual time scales. Both time scales are associated with baroclinic Gill-Matsuno-like circulation responses extending over southern Asia, but the interannual component also has a strong equivalent barotropic circulation. Thermodynamically, both time scales are associated with cold temperature advection and subsidence over southwest Asia, with advection of the mean temperature by the anomalous wind more important at lower and midlevels and advection of the anomalous temperature by the mean wind more important at upper levels. For individual months, the intraseasonal variability can overwhelm the interannual variability. Enhanced Indian Ocean convection persisted for almost the entire 2007/08 season in association with severe drought over southwest Asia, but a strong intraseasonal signal in January 2008 reversed the pattern, resulting in damaging floods in the midst of drought.
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In designing studies and developing plans for analyses, we must consider which tests are appropriate for the types of variables we are using. Here I describe the types of variables available to us, and I briefly consider the appro...
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In designing studies and developing plans for analyses, we must consider which tests are appropriate for the types of variables we are using. Here I describe the types of variables available to us, and I briefly consider the appropriate tools to use in their analysis.
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Direct observations indicate a southeastward expansion of the South Pacific convergence zone (SPCZ) fresh pool and a freshening trend since the 1970s. Understanding decadal and longer-term variability of the SPCZ fresh pool and of...
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Direct observations indicate a southeastward expansion of the South Pacific convergence zone (SPCZ) fresh pool and a freshening trend since the 1970s. Understanding decadal and longer-term variability of the SPCZ fresh pool and of the salinity front located at its southeastern margin has been limited by the scarcity of instrumental sea surface salinity (SSS) measurements. This study uses coral O-18 as a proxy for SSS to extend the salinity record back to the 1880s, from three different locations across the SSS front: Fiji, Tonga, and Rarotonga (FTR region). High percentages of observed SSS variance are explained by multicoral O-18 mean composite at each site. At the interannual time scale, the salinity front displacement over the last 200 years follows the El Nino-Southern Oscillation (ENSO) index. The different El Nino flavors are observable in the amplitude of the salinity front interannual displacement. However, no significant changes in either the frequency or the amplitude of its displacements were observed. At longer time scales, the timing and magnitude of the freshening trend vary among sites. The earliest freshening onset of about -0.06 psu decade(-1) is detected in Fiji (around 1865), then Rarotonga (around 1939), and Tonga (around 1982). The role of atmospheric freshwater fluxes on SSS variability is evaluated by comparing coral SSS to historical precipitation data. The results suggest that, despite the known influence of the interdecadal Pacific oscillation (IPO) negative phases on increasing atmospheric freshwater fluxes and lowering SSS in the FTR region, ocean dynamics has a dominant influence at decadal time scale and in the onset of freshening trends.
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In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from...
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In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.
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The annual mean latitude at which tropical cyclones (TCs) reach their lifetime maximum intensity (LMI) over the western North Pacific Ocean basin has shifted northward since the early 1980s, and it is suggested that the shift is d...
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The annual mean latitude at which tropical cyclones (TCs) reach their lifetime maximum intensity (LMI) over the western North Pacific Ocean basin has shifted northward since the early 1980s, and it is suggested that the shift is due to the northward migration of the mean TC formation location. In this study, the TC intensity is simulated with an intensity model to assess the historical records of TC intensity. During the period 1980-2015, the simulated poleward trend in the mean latitude of LMI is 0.44 degrees (10 yr)(-1), which agrees well with the one [0.48 degrees (10 yr)(-1)] derived from the Joint Typhoon Warning Center (JTWC) dataset. This suggests that the observed poleward trend in the mean latitude of LMI is physically consistent with changes in the large-scale ocean-atmosphere environment and TC track. This study also demonstrates that the temporal change in the environmental parameters (sea surface temperature, outflow temperature, vertical wind shear, and ocean mixed layer depth) has little influence on the observed shift of the mean LMI latitude. The poleward migration of the mean LMI latitude is mainly due to the TC track shift, which results primarily from the change in the large-scale steering flow.
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Changes in precipitation variability can have large societal consequences, whether at the short time scales of flash floods or the longer time scales of multiyear droughts. Recent studies have suggested that in future climate proj...
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Changes in precipitation variability can have large societal consequences, whether at the short time scales of flash floods or the longer time scales of multiyear droughts. Recent studies have suggested that in future climate projections, precipitation variability rises more steeply than does its mean, leading to concerns about societal impacts. This work evaluates changes in mean precipitation over a broad range of spatial and temporal scales using a range of models from high-resolution regional simulations to millennial-scale global simulations. Results show that changes depend on the scale of aggregation and involve strong regional differences. On local scales that resolve individual rainfall events (hours and tens of kilometers), changes in precipitation distributions are complex and variances rise substantially more than means, as is required given the well-known disproportionate rise in precipitation intensity. On scales that aggregate across many events, distributional changes become simpler and variability changes smaller. At regional scale, future precipitation distributions can be largely reproduced by a simple transformation of present-day precipitation involving a multiplicative shift and a small additive term. The "extra" broadening is negatively correlated with changes in mean precipitation: in strongly "wetting" areas, distributions broaden less than expected from a simple multiplicative mean change; in "drying" areas, distributions narrow less. Precipitation variability changes are therefore of especial concern in the subtropics, which tend to dry under climate change. Outside the tropics, variability changes are similar on time scales from days to decades (i.e., show little frequency dependence). This behavior is highly robust across models, suggesting it may stem from some fundamental constraint.
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