The Identity and Distribution of Species of Phytoseius Ribaga (Acarina: Phytoseiidae) in Canada,

1965 ◽  
Vol 97 (9) ◽  
pp. 897-909 ◽  
Author(s):  
D. A. Chant
Keyword(s):  
The Arctic ◽  
Deciduous Trees ◽  
The Tropics

AbstractMites of the genus Phytoseius Ribaga largely inhabit plants and are at least partly predacious, feeding on tetranychid, eriophyid, and other mites. They probably also feed on pollen, honeydew, and plant juices, as do other phytoseiids that have been studied (Chant 1959; Dosse 1961; McMurty and Scriven 1964). They are not usually found in soil or humus but occur on many kinds of low growing plants as well as coniferous and deciduous trees. They have been collected on all continents and from the arctic to the tropics.

scholarly journals Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations

2012 ◽  
Vol 12 (4) ◽  
pp. 1785-1810 ◽  
Author(s):  
Y. Qian ◽  
C. N. Long ◽  
H. Wang ◽  
J. M. Comstock ◽  
S. A. McFarlane ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Probability Distribution ◽  
Vertical Structure ◽  
Cloud Amount ◽  
Cloud Fraction ◽  
Department Of Energy ◽  
The Arctic ◽  
Radiative Fluxes ◽  
The Tropics ◽  
Ipcc Ar4

Abstract. Cloud Fraction (CF) is the dominant modulator of radiative fluxes. In this study, we evaluate CF simulated in the IPCC AR4 GCMs against ARM long-term ground-based measurements, with a focus on the vertical structure, total amount of cloud and its effect on cloud shortwave transmissivity. Comparisons are performed for three climate regimes as represented by the Department of Energy Atmospheric Radiation Measurement (ARM) sites: Southern Great Plains (SGP), Manus, Papua New Guinea and North Slope of Alaska (NSA). Our intercomparisons of three independent measurements of CF or sky-cover reveal that the relative differences are usually less than 10% (5%) for multi-year monthly (annual) mean values, while daily differences are quite significant. The total sky imager (TSI) produces smaller total cloud fraction (TCF) compared to a radar/lidar dataset for highly cloudy days (CF > 0.8), but produces a larger TCF value than the radar/lidar for less cloudy conditions (CF < 0.3). The compensating errors in lower and higher CF days result in small biases of TCF between the vertically pointing radar/lidar dataset and the hemispheric TSI measurements as multi-year data is averaged. The unique radar/lidar CF measurements enable us to evaluate seasonal variation of cloud vertical structures in the GCMs. Both inter-model deviation and model bias against observation are investigated in this study. Another unique aspect of this study is that we use simultaneous measurements of CF and surface radiative fluxes to diagnose potential discrepancies among the GCMs in representing other cloud optical properties than TCF. The results show that the model-observation and inter-model deviations have similar magnitudes for the TCF and the normalized cloud effect, and these deviations are larger than those in surface downward solar radiation and cloud transmissivity. This implies that other dimensions of cloud in addition to cloud amount, such as cloud optical thickness and/or cloud height, have a similar magnitude of disparity as TCF within the GCMs, and suggests that the better agreement among GCMs in solar radiative fluxes could be a result of compensating effects from errors in cloud vertical structure, overlap assumption, cloud optical depth and/or cloud fraction. The internal variability of CF simulated in ensemble runs with the same model is minimal. Similar deviation patterns between inter-model and model-measurement comparisons suggest that the climate models tend to generate larger biases against observations for those variables with larger inter-model deviation. The GCM performance in simulating the probability distribution, transmissivity and vertical profiles of cloud are comprehensively evaluated over the three ARM sites. The GCMs perform better at SGP than at the other two sites in simulating the seasonal variation and probability distribution of TCF. However, the models remarkably underpredict the TCF at SGP and cloud transmissivity is less susceptible to the change of TCF than observed. In the tropics, most of the GCMs tend to underpredict CF and fail to capture the seasonal variation of CF at middle and low levels. The high-level CF is much larger in the GCMs than the observations and the inter-model variability of CF also reaches a maximum at high levels in the tropics, indicating discrepancies in the representation of ice cloud associated with convection in the models. While the GCMs generally capture the maximum CF in the boundary layer and vertical variability, the inter-model deviation is largest near the surface over the Arctic.

Potential predictability of Southwest US rainfall : role of tropical and high-latitude variability

2021 ◽  
pp. 1-45
Keyword(s):  
Time Scale ◽  
Climate Model ◽  
Southern Oscillation ◽  
Seasonal Prediction ◽  
Seasonal Rainfall ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Potential Predictability ◽  
Subseasonal Variability ◽  
The Tropics

Abstract This study explores the potential predictability of Southwest US (SWUS) precipitation for the November-March season in a set of numerical experiments performed with the Whole Atmospheric Community Climate Model. In addition to the prescription of observed sea surface temperature and sea ice concentration, observed variability from the MERRA-2 reanalysis is prescribed in the tropics and/or the Arctic through nudging of wind and temperature. These experiments reveal how a perfect prediction of tropical and/or Arctic variability in the model would impact the prediction of seasonal rainfall over the SWUS, at various time scales. Imposing tropical variability improves the representation of the observed North Pacific atmospheric circulation, and the associated SWUS seasonal precipitation. This is also the case at the subseasonal time scale due to the inclusion of the Madden-Julian Oscillation (MJO) in the model. When additional nudging is applied in the Arctic, the model skill improves even further, suggesting that improving seasonal predictions in high latitudes may also benefit prediction of SWUS precipitation. An interesting finding of our study is that subseasonal variability represents a source of noise (i.e., limited predictability) for the seasonal time scale. This is because when prescribed in the model, subseasonal variability, mostly the MJO, weakens the El Niño Southern Oscillation (ENSO) teleconnection with SWUS precipitation. Such knowledge may benefit S2S and seasonal prediction as it shows that depending on the amount of subseasonal activity in the tropics on a given year, better skill may be achieved in predicting subseasonal rather than seasonal rainfall anomalies, and conversely.

scholarly journals Tropical and Midlatitude Impact on Seasonal Polar Predictability in the Community Earth System Model

2019 ◽  
Vol 32 (18) ◽  
pp. 5997-6014 ◽  
Author(s):  
Edward Blanchard-Wrigglesworth ◽  
Qinghua Ding
Keyword(s):  
Forecast Skill ◽  
Specific Humidity ◽  
The Arctic ◽  
The North ◽  
Perfect Model ◽  
Community Earth System Model ◽  
Free Running ◽  
Skill Improvement ◽  
The Tropics ◽  
The Impact

Abstract The impact on seasonal polar predictability from improved tropical and midlatitude forecasts is explored using a perfect-model experiment and applying a nudging approach in a GCM. We run three sets of 7-month long forecasts: a standard free-running forecast and two nudged forecasts in which atmospheric winds, temperature, and specific humidity (U, V, T, Q) are nudged toward one of the forecast runs from the free ensemble. The two nudged forecasts apply the nudging over different domains: the tropics (30°S–30°N) and the tropics and midlatitudes (55°S–55°N). We find that the tropics have modest impact on forecast skill in the Arctic or Antarctica both for sea ice and the atmosphere that is mainly confined to the North Pacific and Bellingshausen–Amundsen–Ross Seas, whereas the midlatitudes greatly improve Arctic winter and Antarctic year-round forecast skill. Arctic summer forecast skill from May initialization is not strongly improved in the nudged forecasts relative to the free forecast and is thus mostly a “local” problem. In the atmosphere, forecast skill improvement from midlatitude nudging tends to be largest in the polar stratospheres and decreases toward the surface.

scholarly journals Natural Gas Prices and the Extreme Winters of 2011/12 and 2013/14: Causes, Indicators, and Interactions

2015 ◽  
Vol 96 (11) ◽  
pp. 1879-1894 ◽  
Author(s):  
Carl J. Schreck ◽  
Stephen Bennett ◽  
Jason M. Cordeira ◽  
Jake Crouch ◽  
Jenny Dissen ◽  
...  
Keyword(s):  
United States ◽  
Natural Gas ◽  
The United States ◽  
The Arctic ◽  
Cool Season ◽  
Natural Gas Markets ◽  
Global Recession ◽  
Warm Anomaly ◽  
Natural Gas Prices ◽  
The Tropics

Abstract Day-to-day volatility in natural gas markets is driven largely by variability in heating demand, which is in turn dominated by cool-season temperature anomalies over the northeastern quadrant of the United States (“Midwest–East”). Energy traders rely on temperature forecasts at horizons of 2–4 weeks to anticipate those fluctuations in demand. Forecasts from dynamical models are widely available, so the markets react quickly to changes in the model predictions. Traders often work with meteorologists who leverage teleconnections from the tropics and the Arctic to improve upon the model forecasts. This study demonstrates how natural gas prices react to Midwest–East temperatures using the anomalous winters of 2011/12 and 2013/14. These examples also illustrate how energy meteorologists use teleconnections from the Arctic and the tropics to forecast heating demand. Winter 2011/12 was exceptionally warm, consistent with the positive Arctic Oscillation (AO). March 2012 was a fitting exclamation point on the winter as it featured the largest warm anomaly for the United States above the twentieth-century climatology of any month since 1895. The resulting lack of heating demand led to record surpluses of natural gas storage and spurred prices downward to an 11-yr low in April 2012. In sharp contrast, winter 2013/14 was unusually cold. An anomalous Alaskan ridge led to cold air being transported from Siberia into the United States, despite the AO generally being positive. The ensuing swell in heating demand exhausted the surplus natural gas inventory, and prices rose to their highest levels since the beginning of the global recession in 2008.

scholarly journals Intercomparison of temperature trends in IPCC CMIP5 simulations with observations, reanalyses and CMIP3 models

2013 ◽  
Vol 6 (5) ◽  
pp. 1705-1714 ◽  
Author(s):  
J. Xu ◽  
L. Zhao ◽  
Keyword(s):  
Coupled Model ◽  
Temperature Trend ◽  
The Arctic ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Model Simulations ◽  
Temperature Trends ◽  
Intercomparison Project ◽  
Stratospheric Cooling ◽  
The Tropics

Abstract. On the basis of the fifth Coupled Model Intercomparison Project (CMIP5) and the climate model simulations covering 1979 through 2005, the temperature trends and their uncertainties have been examined to note the similarities or differences compared to the radiosonde observations, reanalyses and the third Coupled Model Intercomparison Project (CMIP3) simulations. The results show noticeable discrepancies for the estimated temperature trends in the four data groups (radiosonde, reanalysis, CMIP3 and CMIP5), although similarities can be observed. Compared to the CMIP3 model simulations, the simulations in some of the CMIP5 models were improved. The CMIP5 models displayed a negative temperature trend in the stratosphere closer to the strong negative trend seen in the observations. However, the positive tropospheric trend in the tropics is overestimated by the CMIP5 models relative to CMIP3 models. While some of the models produce temperature trend patterns more highly correlated with the observed patterns in CMIP5, the other models (such as CCSM4 and IPSL_CM5A-LR) exhibit the reverse tendency. The CMIP5 temperature trend uncertainty was significantly reduced in most areas, especially in the Arctic and Antarctic stratosphere, compared to the CMIP3 simulations. Similar to the CMIP3, the CMIP5 simulations overestimated the tropospheric warming in the tropics and Southern Hemisphere and underestimated the stratospheric cooling. The crossover point where tropospheric warming changes into stratospheric cooling occurred near 100 hPa in the tropics, which is higher than in the radiosonde and reanalysis data. The result is likely related to the overestimation of convective activity over the tropical areas in both the CMIP3 and CMIP5 models. Generally, for the temperature trend estimates associated with the numerical models including the reanalyses and global climate models, the uncertainty in the stratosphere is much larger than that in the troposphere, and the uncertainty in the Antarctic is the largest. In addition, note that the reanalyses show the largest uncertainty in the lower tropical stratosphere, and the CMIP3 simulations show the largest uncertainty in both the south and north polar regions.

scholarly journals Cloud Effects on the Meridional Atmospheric Energy Budget Estimated from Clouds and the Earth’s Radiant Energy System (CERES) Data

2008 ◽  
Vol 21 (17) ◽  
pp. 4223-4241 ◽  
Author(s):  
Seiji Kato ◽  
Fred G. Rose ◽  
David A. Rutan ◽  
Thomas P. Charlock
Keyword(s):  
Energy Transport ◽  
Radiant Energy ◽  
Energy System ◽  
The Arctic ◽  
Polar Regions ◽  
Radiative Effect ◽  
Cloud Radiative Effect ◽  
Cloud Effect ◽  
The Difference ◽  
The Tropics

Abstract The zonal mean atmospheric cloud radiative effect, defined as the difference between the top-of-the-atmosphere (TOA) and surface cloud radiative effects, is estimated from 3 yr of Clouds and the Earth’s Radiant Energy System (CERES) data. The zonal mean shortwave effect is small, though it tends to be positive (warming). This indicates that clouds increase shortwave absorption in the atmosphere, especially in midlatitudes. The zonal mean atmospheric cloud radiative effect is, however, dominated by the longwave effect. The zonal mean longwave effect is positive in the tropics and decreases with latitude to negative values (cooling) in polar regions. The meridional gradient of the cloud effect between midlatitude and polar regions exists even when uncertainties in the cloud effect on the surface enthalpy flux and in the modeled irradiances are taken into account. This indicates that clouds increase the rate of generation of the mean zonal available potential energy. Because the atmospheric cooling effect in polar regions is predominately caused by low-level clouds, which tend to be stationary, it is postulated here that the meridional and vertical gradients of the cloud effect increase the rate of meridional energy transport by the dynamics of the atmosphere from the midlatitudes to the polar region, especially in fall and winter. Clouds then warm the surface in the polar regions except in the Arctic in summer. Clouds, therefore, contribute toward increasing the rate of meridional energy transport from the midlatitudes to the polar regions through the atmosphere.

scholarly journals Interaction between the MJO and Polar Circulations

2013 ◽  
Vol 26 (11) ◽  
pp. 3562-3574 ◽  
Author(s):  
Maria Flatau ◽  
Young-Joon Kim
Keyword(s):  
Indian Ocean ◽  
Southern Hemisphere ◽  
Warm Season ◽  
Cold Season ◽  
The Arctic ◽  
Boreal Winter ◽  
Annular Modes ◽  
The Indian Ocean ◽  
The Arctic Oscillation ◽  
The Tropics

Abstract A tropical–polar connection and its seasonal dependence are examined using the real-time multivariate Madden–Julian oscillation (MJO) (RMM) index and daily indices for the annular modes, the Arctic Oscillation (AO) and the Antarctic Oscillation (AAO). On the intraseasonal time scale, the MJO appears to force the annular modes in both hemispheres. On this scale, during the cold season, the convection in the Indian Ocean precedes the increase of the AO/AAO. Interestingly, during the boreal winter (Southern Hemisphere warm season), strong MJOs in the Indian Ocean are related to a decrease of the AAO index, and AO/AAO tendencies are out of phase. On the longer time scales, a persistent AO/AAO anomaly appears to influence the convection in the tropical belt and impact the distribution of MJO-preferred phases. It is shown that this may be a result of the sea surface temperature (SST) changes related to a persistent AO, with cooling over the Indian Ocean and warming over Indonesia. In the Southern Hemisphere, the SST anomalies are to some extent also related to a persistent AAO pattern, but this relationship is much weaker and appears only during the Southern Hemisphere cold season. On the basis of these results, a mechanism involving the air–sea interaction in the tropics is suggested as a possible link between persistent AO and convective activity in the Indian Ocean and western Pacific.

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