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Jeffrey H. Yin and Grant W. Branstator

Abstract

A conceptual framework is developed for quantifying the relationship between low-frequency variability and extreme events. In this framework, variability is decomposed into low-frequency and synoptic components using complementary 10-day low-pass and high-pass filters, and a distinction is made between two ways that low-frequency variability influences extremes: the additive effect, which neglects the dependence of synoptic variability on the low-frequency state, and the multiplicative effect, which is due to the dependence of synoptic variability on the low-frequency state. The influence of various factors on the relationship between low-frequency variability and extreme events is decomposed and quantified by generating a series of simple synthetic datasets based on different assumptions about low-frequency and synoptic variability and their relationship.

These techniques are used to study the relationship between low-frequency variability and extreme westerly wind events in three datasets, an 1158-yr GCM simulation and two reanalysis datasets, with similar results for all three. Geographical variations in the low-frequency–extreme relationship are only partially explained by geographical variations in the low-frequency–synoptic variance ratio; the non-Gaussianity of low-frequency and synoptic variability and the relationship between synoptic variance and the low-frequency state are also found to be important. The simple synthetic datasets that include these factors provide good estimates of the magnitude and probability of extremes. Implications for predictability and applications to more complex low-frequency–extreme relationships are discussed.

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Jeffrey H. Yin and David S. Battisti

Abstract

Theoretical and modeling studies of nongeostrophic effects in baroclinic waves predict that baroclinic waves should tilt poleward with height, with a larger tilt in total meridional wind than in geostrophic quantities. Regression analysis of NCEP–NCAR reanalysis 6-hourly data demonstrates that observed baroclinic waves do indeed tilt poleward with height, although the observed tilt is smaller than predicted by previous studies. The meridional ageostrophic wind enhances the poleward tilt of meridional wind perturbations, despite being smaller in amplitude than the meridional geostrophic wind by a factor of 5.

An improved estimate of the structure of the meridional ageostrophic wind in baroclinic waves is calculated assuming force balance. Several important terms in this estimate have been left out of previous estimates of the meridional ageostrophic wind. Three terms in the improved estimate produce nearly all of the poleward tilt of the meridional wind: 1) the advection of geostrophic zonal wind perturbations by the mean zonal wind, 2) the convergence of the eddy momentum flux, and 3) the effect of friction.

The poleward tilt with height of baroclinic waves explains why upper-level storm tracks tend to occur poleward of near-surface baroclinic regions, and may play a role in the midwinter suppression of the Pacific storm track.

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Jeffrey H. Yin and David S. Battisti

Abstract

Prescribed SST experiments are performed using the National Center for Atmospheric Research’s Community Climate Model version 3 general circulation model to isolate the contribution of the tropical SSTs reconstructed by the Climate, Long-range Investigation, Mapping and Prediction study (CLIMAP) to the modeled global atmospheric circulation anomalies at the Last Glacial Maximum (LGM). The changes in tropical SST patterns cause changes in tropical convection that force large (>300 m in 500-mb geopotential height) changes in Northern Hemisphere wintertime circulation. These midlatitude circulation changes occur despite the small (1°C) change in the mean tropical SST between the present and the CLIMAP reconstruction. In fact, the midlatitude circulation changes due to the difference in the tropical SST pattern between the present and the CLIMAP reconstruction are greater than the circulation changes due to a uniform tropical SST cooling of 3°C or those due to the presence of the LGM ice sheets. The circulation anomalies due to the change in tropical SST patterns result in a wintertime warming (cooling) of 8°C (8°C) over the Laurentide (Fennoscandian) ice sheet and a decrease (increase) in annual mass balance of over 1000 mm yr−1 (800 mm yr−1) along the southern margin of the ice sheet. These results demonstrate that detailed knowledge of tropical SST patterns is needed in order to produce reliable simulations of LGM climate. In the appendix, it is shown that the lion’s share of the midlatitude circulation changes are due to SST gradients in the northern Tropics, and that the physics involved in the teleconnection between tropical SST forcing and midlatitude circulation changes is rich and highly nonlinear.

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Li Fang, Xiwu Zhan, Jifu Yin, Jicheng Liu, Mitchell Schull, Jeffrey P. Walker, Jun Wen, Michael H. Cosh, Tarendra Lakhankar, Chandra Holifield Collins, David D. Bosch, and Patrick J. Starks

Abstract

In the past decade, a variety of algorithms have been introduced to downscale passive microwave soil moisture observations. Some exploit the soil moisture information from optical/thermal sensing of land surface temperature (LST) and vegetation dynamics while others use active microwave (radar) observations. In this study, downscaled soil moisture data at 9- or 1-km resolution from several algorithms are intercompared against in situ soil moisture measurements to determine their reliability in an operational system. The finescale satellite data used here for downscaling the coarse-scale SMAP data are observations of LST from the Geostationary Operational Environmental Satellite (GOES) and vegetation index (VI) from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) for the warm seasons in 2015 and 2016. Three recently developed downscaling algorithms are evaluated and compared: a simple regression algorithm based on 9-km thermal inertial data, a data mining approach called regression tree based on 9- and 1-km LST and VI, and the NASA SMAP enhanced 9-km soil moisture product algorithm. Seven sets of in situ soil moisture data from intensive networks were used for validation, including 1) the CREST-SMART network in Millbrook, New York; 2) Walnut Gulch Watershed in Arizona; 3) Little Washita Watershed in Oklahoma; 4) Fort Cobb Reservoir Experimental Watersheds in Oklahoma; 5) Little River Watershed in Georgia; 6) the Tibetan Plateau network in China, and 7) the OzNet in Australia. Soil moisture measurements of the in situ networks were upscaled to the corresponding SMAP reference pixels at 9 km and used to assess the accuracy of downscaled products at a 9-km scale. Results revealed that the downscaled 9-km soil moisture products generally outperform the 36-km product for most in situ datasets. The linear regression algorithm using the thermal sensing based evaporative stress index (ESI) had the best agreement with the in situ measurements from networks in the contiguous United States according to the site-by-site comparison. In addition, the inertial thermal linear regression method demonstrated the lowest unbiased RMSE when comparing to the matched-up in situ datasets as well. In general, this method is promising for operational generation of fine-resolution soil moisture data product.

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