In this study, we investigate links between Arctic sea ice loss and the variability of 2-m temperatures over a 6-month period (November–April) over two domains centered over northern Eurasia and northern North America. Based on data from the Climate Forecast System Reanalysis (CFSR), there has been an increase (a decrease) in recent seasonal temperature variability over Eurasia (North America), which can be attributed to cooling (warming) during the winter months. Decreases in the intraseasonal variability of temperature anomalies, however, are noted in both regions for the November–April period. This study investigates the role of different forcings on the changes seen in the reanalysis product using Atmospheric Model Intercomparison Project simulations forced with repeating sea surface temperature, sea ice, and carbon dioxide concentration relative to climatologies from two different base periods, 1981–90 and 2005–14. The seasonal temperature and intraseasonal anomaly variabilities are examined, and we find that only the simulations with reduction in sea ice (2005–14 base-period sea ice concentration) produce significant decreases in intraseasonal temperature anomaly variability over these regions, agreeing with the CFSR analysis. Runs that reduce sea ice also result in a significant decrease in the frequency and magnitude of extreme warm and cold temperature anomalies. It is proposed that the weakened latitudinal temperature gradient, resulting from decreased sea ice, leads to reduced meridional temperature advection variability, which in turn contributes to the reduction in the variability of temperature anomalies.

Conflicting results have been presented regarding the link between Arctic sea-ice loss and midlatitude cooling, particularly over Eurasia. This study analyzes uncoupled (atmosphere-only) and coupled (ocean–atmosphere) simulations by the Climate Forecast System, version 2 (CFSv2), to examine this linkage during the Northern Hemisphere winter, focusing on the simulation of the observed surface cooling trend over Eurasia during the last three decades. The uncoupled simulations are Atmospheric Model Intercomparison Project (AMIP) runs forced with mean seasonal cycles of sea surface temperature (SST) and sea ice, using combinations of SST and sea ice from different time periods to assess the role that each plays individually, and to assess the role of atmospheric internal variability. Coupled runs are used to further investigate the role of internal variability via the analysis of initialized predictions and the evolution of the forecast with lead time. The AMIP simulations show a mean warming response over Eurasia due to SST changes, but little response to changes in sea ice. Individual runs simulate cooler periods over Eurasia, and this is shown to be concurrent with a stronger Siberian high and warming over Greenland. No substantial differences in the variability of Eurasian surface temperatures are found between the different model configurations. In the coupled runs, the region of significant warming over Eurasia is small at short leads, but increases at longer leads. It is concluded that, although the models have some capability in highlighting the temperature variability over Eurasia, the observed cooling may still be a consequence of internal variability.

The capability of a numerical model to simulate the statistical characteristics of the summer sea ice date of retreat (DOR) and the winter date of advance (DOA) is investigated using sea ice concentration output from the Climate Forecast System Version 2 model (CFSv2). Two model configurations are tested, the operational setting (CFSv2CFSR) which uses initial data from the Climate Forecast System Reanalysis, and a modified version (CFSv2PIOMp) which ingests sea ice thickness initialization data from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) and includes physics modifications for a more realistic representation of heat fluxes at the sea ice top and bottom. First, a method to define DOR and DOA is presented. Then, DOR and DOA are determined from the model simulations and observational sea ice concentration from the National Aeronautics and Space Administration (NASA). Means, trends, and detrended standard deviations of DOR and DOA are compared, along with DOR/DOA rates in the Arctic Ocean. It is found that the statistics are generally similar between the model and observations, although some regional biases exist. In addition, regions of new ice retreat in recent years are represented well in CFSv2PIOMp over the Arctic Ocean, in terms of both spatial extent and timing. Overall, CFSv2PIOMp shows a reduction in error throughout the Arctic. Based on results, it is concluded that the model produces a reasonable representation of the climatology and variability statistics of DOR and DOA in most regions. This assessment serves as a prerequisite for future predictability experiments.

The impacts of model physics and initial sea ice thickness on seasonal forecasts of surface energy budget and air temperature in the Arctic during summer were investigated based on Climate Forecast System, version 2 (CFSv2), simulations. The model physics changes include the enabling of a marine stratus cloud scheme and the removal of the artificial upper limit on the bottom heat flux from ocean to sea ice. The impact of initial sea ice thickness was examined by initializing the model with relatively realistic sea ice thickness generated by the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Model outputs were compared to that from a control run that did not impose physics changes and used Climate Forecast System Reanalysis (CFSR) sea ice thickness. After applying the physics modification to either sea ice thickness initialization, the simulated total cloud cover more closely resembled the observed monthly variations of total cloud cover except for the midsummer reduction. Over the Chukchi–Bering Seas, the model physics modification reduced the seasonal forecast bias in surface air temperature by 24%. However, the use of initial PIOMAS sea ice thickness alone worsened the surface air temperature predictions. The experiment with physics modifications and initial PIOMAS sea ice thickness achieves the best surface air temperature improvement over the Chukchi–Bering Seas where the area-weighted forecast bias was reduced by 71% from 1.05 K down to −0.3 K compared with the control run. This study supports other results that surface temperatures and sea ice characteristics are highly sensitive to the Arctic cloud and radiation formulations in models and need priority in model formulation and validation.

AbstractBecause sea ice thickness is known to influence future patterns of sea ice concentration, it is likely that an improved initialization of sea ice thickness in a coupled ocean–atmosphere model would improve Arctic sea ice cover forecasts. Here, two sea ice thickness datasets as possible candidates for forecast initialization were investigated: the Climate Forecast System Reanalysis (CFSR) and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Using Ice, Cloud, and Land Elevation Satellite (ICESat) data, it was shown that the PIOMAS dataset had a more realistic representation of sea ice thickness than CFSR. Subsequently, both March CFSR and PIOMAS sea ice thicknesses were used to initialize hindcasts using the Climate Forecast System, version 2 (CFSv2), model. A second set of model runs was also done in which the original model physics were modified to more physically reasonable settings—namely, increasing the number of marine stratus clouds in the Arctic region and enabling realist...

The 2013–2014 California drought was initiated by an anomalous high-amplitude ridge system. The anomalous ridge was investigated by Wang et al. (2014) using reanalysis data and the Community Earth System Model (CESM). Analysis from observational data (NCEP/NCAR Reanalysis) showed that the ridge emerged from continual sources of Rossby wave energy in the western North Pacific starting in late summer and subsequently intensified into winter (Fig. 1). The ridge generated a surge of wave energy downwind and deepened further the trough over the northeast U.S., forming a dipole. The wavelength of this dipole is rather large (at zonal wave numbers 1-2 scale) surpassing the common short-wave patterns (zonal wave #4-6) accompanying heat waves and stormy weather (Chang and Wallace, 1987; Screen and Simmonds, 2014; Teng et al., 2013; Wang et al., 2013a). This long-wave feature of the dipole suggests a closer link with SST teleconnection from the tropical Pacific (Wallace and Gutzler, 1981), rather than with the higherlatitude forcing that tends to produce shorter waves (Branstator, 2002). Furthermore, the study of Wang et al. (2014) indicated that the dipole and associated circulation pattern is not linked directly with either El Niño– Southern Oscillation (ENSO) or Pacific Decadal Oscillation. Instead, it is correlated with a type of ENSO precursor, one that has a predominant signal in the Western North Pacific (WNP) and this dynamical linkage has intensified in recent decades (Wang et al., Fig. 1 NDJ 2013-14 anomalies of geopotential height (red contours) and OLR (color shadings) overlaid with the wave activity fluxes (vectors) for a threemonth period from June-August to November-January as indicated, computed from the levels of (a) 850 hPa and (b) 250 hPa. Contour interval is 25 m for 850 hPa and 50 m for 250 hPa; the zero and the first positive and negative contours are omitted. SCIENCE AND TECHNOLOGY INFUSION CLIMATE BULLETIN 38 2013b). The variance of the dipole has increased, while the connection between the dipole and ENSO precursor has become stronger since the 1970s; this is attributed to increased greenhouse gas loading as simulated by the CESM (Fig. 2). As further verification, a recent study (Wang et al., 2015) using 17 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) found that most models capture the midlatitude circulation dipole, its association with El Niño precursor, and its intensification under anthropogenic greenhouse gas forcing (Fig. 3). Therefore, there is a traceable anthropogenic warming footprint in the enormous intensity of the anomalous ridge during winter 2013–2014 and the associated drought. References Branstator, G., 2002: Circumglobal teleconnections, the jet stream waveguide, and the North Atlantic Oscillation. J. Climate, 15, 1893-1910. Chang, F.-C., and J. M. Wallace, 1987: Meteorological conditions during heat waves and droughts in the United States Great Plains. Mon. Wea. Rev., 115, 12531269. Screen, J. A., and I. Simmonds, 2014: Amplified mid-latitude planetary waves favour particular regional weather extremes. Nature Clim. Change, 4, 704– 709, doi:10.1038/nclimate2271. Teng, H., G. Branstator, H. Wang, G. A. Meehl, and W. M. Washington, 2013: Probability of US heat waves affected by a subseasonal planetary wave pattern. Nature Geoscience, 6, 1056-1061, doi:10.1038/ngeo1988. Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784-812. Fig. 2 (a) Running variance of the dipole index (x 10 m) with a 30-year window plotted at the end year beginning in 1900, derived from the 20CR (black), CESM1-GHG run (red) and CESM1-NAT run (blue). (b) Sliding correlations between the dipole index and Niño4 (Y+1) within a 30-year window corresponding to (a). The dashed line indicates significance at the 99% level. Fig. 3 Moving variance of the dipole index (see text) over a 30-year running window, plotted at the end year, for the ensemble of different experiments as indicated by the legend. Error bars indicate model spread from all members and all models. The dipole index was normalized (between -2 and 2) prior to computing the variance.

This study investigates the influences of soil moisture and vegetation on 30 h convective precipitation forecasts using the Weather Research and Forecasting model over the United States Great Plains with explicit treatment of convection. North American Regional Reanalysis (NARR) data were used as initial and boundary conditions. We also used an adjusted soil moisture (uniformly adding 0.10 m(3)/m(3) over all soil layers based on NARR biases) to determine whether using a simple observationally based adjustment of soil moisture forcing would provide more accurate simulations and how the soil moisture addition would impact meteorological parameters for different vegetation types. Current and extreme (forest and barren) land covers were examined. Compared to the current vegetation cover, the complete removal of vegetation produced substantially less precipitation, while conversion to forest led to small differences in precipitation. Adding 0.10 m(3)/m(3) to the soil moisture with the current vegetation cover lowered the near surface temperature and increased the humidity to a similar degree as using a fully forested domain with no soil moisture adjustment. However, these temperature and humidity effects on convective available potential energy and moist enthalpy nearly canceled each other out, resulting in a limited precipitation response. Although no substantial changes in precipitation forecasts were found using the adjusted soil moisture, the similarity found between temperature and humidity forecasts using the increased soil moisture and those with a forested domain highlights the sensitivity of the model to soil moisture changes, reinforcing the need for accurate soil moisture initialization in numerical weather forecasting models.

The European Space Agency launched the Soil Moisture Ocean Salinity (SMOS) satellite in November 2009. Using SMOS soil moisture retrievals for 2010 processed using algorithm V4.00, we evaluated SMOS retrievals by comparing them to in situ soil moisture observations for the top 5 cm at several stations in the Great Plains of the U.S. A major issue with comparing the satellite data with in situ data is that a SMOS footprint is about 40 km across and we compare to point observations. To address this issue, we chose locations in Oklahoma that have 10 to 25 different in situ observations within each SMOS footprint. The SMOS retrievals have a dry bias when compared to the average of all in situ stations in a footprint. Large differences exist between the in situ observations, even for probes only a few meters apart. Observations from different sensors within a SMOS footprint differ from each other by a larger amount than they differ from the SMOS retrieval. Removing the mean and normalizing the data bring the in situ observations into better agreement with each other and with SMOS but there are still substantial differences. Agricultural Research Service Micronet regions in Oklahoma had highly varying values of soil moisture despite being in close proximity to one another, but when averaged and compared to SMOS they had less of a bias than the other regions. Further north in the Great Plains, SMOS retrievals of top 5 cm soil moisture from descending orbits were consistently about 5% by volume wetter than ascending retrievals.