Journal of Climate

Extensive summer sea ice loss has occurred within the Beaufort, Chukchi, East Siberian, and Laptev Seas over the last decade. Associated anomalies in sensible and latent heat fluxes in autumn have increased Arctic atmospheric precipitable water and air temperatures, with the potential to impact autumn and winter cyclone activity. To examine if a connection exists between recent Arctic sea ice loss and cyclone activity, several cyclone metrics from 60° to 90°N are analyzed. Results show that following years with less September sea ice, there is a subsequent increase in moisture availability, regional baroclinicity, and changes in vertical stability that favor cyclogenesis. However, tracking of individual cyclones indicates no coherent increase in cyclone frequency or intensity associated with sea ice loss. Furthermore, no robust northward progression of extreme cyclones is observed.

The arid and semiarid region in central Asia is sensitive and vulnerable to climate variations. However, the sparse and highly unevenly distributed meteorological stations in the region provide limited data for understanding of the region’s climate variations. In this study, the near-surface air temperature change in central Asia from 1979 to 2011 was examined using observations from 81 meteorological stations, three local observation validated reanalysis datasets of relatively high spatial resolutions, and the Climate Research Unit (CRU) dataset. Major results suggested that the three reanalysis datasets match well with most of the local climate records, especially in the low-lying plain areas. The consensus of the multiple datasets showed significant regional surface air temperature increases of 0.36°–0.42°C decade−1 in the past 33 years. No significant contributions from declining irrigation and urbanization to temperature change were found. The rate is larger in recent years than in the early years in the study period. Additionally, unlike in many regions in the world, the temperature in winter showed no increase in central Asia in the last three decades, a noticeable departure from the global trend in the twentieth century. The largest increase in surface temperature was occurring in the spring season. Analyses further showed a warming center in the middle of the central Asian states and weakened temperature variability along the northwest–southeast temperature gradient from the northern Kazakhstan to southern Xinjiang. The reanalysis datasets also showed significant negative correlations between temperature increase rate and elevation in this complex terrain region.

This study presents climate change results from the North American Regional Climate Change Assessment Program (NARCCAP) suite of dynamically downscaled simulations for the North American monsoon system in the southwestern United States and northwestern Mexico. The focus is on changes in precipitation and the processes driving the projected changes from the regional climate simulations and their driving coupled atmosphere–ocean global climate models. The effect of known biases on the projections is also examined. Overall, there is strong ensemble agreement for a large decrease in precipitation during the monsoon season; however, this agreement and the magnitude of the ensemble-mean change is likely deceiving, as the greatest decreases are produced by the simulations that are the most biased in the baseline/current climate. Furthermore, some of the greatest decreases in precipitation are being driven by changes in processes/phenomena that are less credible (e.g., changes in El Niño–Southern Oscillation, when it is initially not simulated well). In other simulations, the processes driving the precipitation change may be plausible, but other biases (e.g., biases in low-level moisture or precipitation intensity) appear to be affecting the magnitude of the projected changes. The most and least credible simulations are clearly identified, while the other simulations are mixed in their abilities to produce projections of value.

The spatiotemporal variability and three-dimensional structures of mesoscale convective systems (MCSs) east of the U.S. Rocky Mountains and their large-scale environments are characterized across all seasons using 13 years of high-resolution radar and satellite observations. Long-lived and intense MCSs account for over 50% of warm season precipitation in the Great Plains and over 40% of cold season precipitation in the southeast. The Great Plains has the strongest MCS seasonal cycle peaking in May–June, whereas in the U.S. southeast MCSs occur year-round. Distinctly different large-scale environments across the seasons have significant impacts on the structure of MCSs. Spring and fall MCSs commonly initiate under strong baroclinic forcing and favorable thermodynamic environments. MCS genesis frequently occurs in the Great Plains near sunset, although convection is not always surface based. Spring MCSs feature both large and deep convection, with a large stratiform rain area and high volume of rainfall. In contrast, summer MCSs often initiate under weak baroclinic forcing, featuring a high pressure ridge with weak low-level convergence acting on the warm, humid air associated with the low-level jet. MCS genesis concentrates east of the Rocky Mountain Front Range and near the southeast coast in the afternoon. The strongest MCS diurnal cycle amplitude extends from the foothills of the Rocky Mountains to the Great Plains. Summer MCSs have the largest and deepest convective features, the smallest stratiform rain area, and the lowest rainfall volume. Last, winter MCSs are characterized by the strongest baroclinic forcing and the largest MCS precipitation features over the southeast. Implications of the findings for climate modeling are discussed.

Precipitation extremes have a widespread impact on societies and ecosystems; it is therefore important to understand current and future patterns of extreme precipitation. Here, a set of new global coupled climate models with varying atmospheric resolution has been used to investigate the ability of these models to reproduce observed patterns of precipitation extremes and to investigate changes in these extremes in response to increased atmospheric CO2 concentrations. The atmospheric resolution was increased from 2° × 2° grid cells (typical resolution in the CMIP5 archive) to 0.25° × 0.25° (tropical cyclone permitting). Analysis has been confined to the contiguous United States (CONUS). It is shown that, for these models, integrating at higher atmospheric resolution improves all aspects of simulated extreme precipitation: spatial patterns, intensities, and seasonal timing. In response to 2 × CO2 concentrations, all models show a mean intensification of precipitation rates during extreme events of approximately 3%–4% K−1. However, projected regional patterns of changes in extremes are dependent on model resolution. For example, the highest-resolution models show increased precipitation rates during extreme events in the hurricane season in the U.S. Southeast; this increase is not found in the low-resolution model. These results emphasize that, for the study of extreme precipitation there is a minimum model resolution that is needed to capture the weather phenomena generating the extremes. Finally, the observed record and historical model experiments were used to investigate changes in the recent past. In part because of large intrinsic variability, no evidence was found for changes in extreme precipitation attributable to climate change in the available observed record.

The Northern Aerosol Regional Climate Model (NARCM) was used to construct a 44-yr climatology of spring Asian dust aerosol emission, column loading, deposition, trans-Pacific transport routes, and budgets during 1960–2003. Comparisons with available ground dust observations and Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) measurements verified that NARCM captured most of the climatological characteristics of the spatial and temporal distributions, as well as the interannual and daily variations of Asian dust aerosol during those 44 yr. Results demonstrated again that the deserts in Mongolia and in western and northern China (mainly the Taklimakan and Badain Juran, respectively) were the major sources of Asian dust aerosol in East Asia. The dust storms in spring occurred most frequently from early April to early May with a daily averaged dust emission (diameter d < 41 μm) of 1.58 Mt in April and 1.36 Mt in May. Asian dust aerosol contributed most of the dust aerosol loading in the troposphere over the midlatitude regions from East Asia to western North America during springtime. Climatologically, dry deposition was a dominant dust removal process near the source areas, while the removal of dust particles by precipitation was the major process over the trans-Pacific transport pathway (where wet deposition exceeded dry deposition up to a factor of 20). The regional transport of Asian dust aerosol over the Asian subcontinent was entrained to an elevation of <3 km. The frontal cyclone in Mongolia and northern China uplifted dust aerosol in the free troposphere for trans-Pacific transport. Trans-Pacific dust transport peaked between 3 and 10 km in the troposphere along a zonal transport axis around 40°N. Based on the 44-yr-averaged dust budgets for the modeling domain from East Asia to western North America, it was estimated that of the average spring dust aerosol (diameter d < 41 μm) emission of ∼120 Mt from Asian source regions, about 51% was redeposited onto the source regions, 21% was deposited onto nondesert regions within the Asian subcontinent, and 26% was exported from the Asian subcontinent to the Pacific Ocean. In total, 16% of Asian dust aerosol emission was deposited into the North Pacific, while ∼3% of Asian dust aerosol was carried to the North American continent via trans-Pacific transport.

A Markov random field (MRF) statistical model is introduced, developed, and validated for detecting the east Pacific intertropical convergence zone in instantaneous satellite data from May through October. The MRF statistical model uses satellite data at a given location as well as information from its neighboring points (in time and space) to decide whether the given point is classified as ITCZ or non-ITCZ. Two different labels of ITCZ occurrence are produced. IR-only labels result from running the model with 3-hourly infrared data available for a 30-yr period, 1980–2009. All-data labels result from running the model with additional satellite data (visible and total precipitable water), available from 1995 to 2008. IR-only labels detect less area of ITCZ than all-data labels, especially where the ITCZ is shallower. Yet, qualitatively, the results for the two sets of labels are similar.

The seasonal distribution of the ITCZ through the summer half year is presented, showing typical location and extent. The ITCZ is mostly confined to the eastern Pacific in May, and becomes more zonally distributed toward September and October each year. Northward and westward shifts in the location of the ITCZ occur in line with the seasonal cycle and warm sea surface temperatures. The ITCZ is quite variable on interannual time scales and highly correlated with ENSO variability. When the ENSO signal was removed from labels, interannual variability remained high. The resulting IR-only labels, representing the longer time series, showed no evidence of a trend in location nor evidence of a trend in area for the 30-yr period.

Diurnal sea surface warming affects the fluxes of latent heat, sensible heat, and upwelling longwave radiation. Diurnal warming most typically reaches maximum values of 3°C, although very localized events may reach 7°–8°C. An analysis of multiple years of diurnal warming over the global ice-free oceans indicates that heat fluxes determined by using the predawn sea surface temperature can differ by more than 100% in localized regions over those in which the sea surface temperature is allowed to fluctuate on a diurnal basis. A comparison of flux climatologies produced by these two analyses demonstrates that significant portions of the tropical oceans experience differences on a yearly average of up to 10 W m−2. Regions with the highest climatological differences include the Arabian Sea and the Bay of Bengal, as well as the equatorial western and eastern Pacific Ocean, the Gulf of Mexico, and the western coasts of Central America and North Africa. Globally the difference is on average 4.45 W m−2. The difference in the evaporation rate globally is on the order of 4% of the total ocean–atmosphere evaporation. Although the instantaneous, year-to-year, and seasonal fluctuations in various locations can be substantial, the global average differs by less than 0.1 W m−2 throughout the entire 10-yr time period. A global heat budget that uses atmospheric datasets containing diurnal variability but a sea surface temperature that has removed this signal may be underestimating the flux to the atmosphere by a fairly constant value.

The climate of the northeast Atlantic region comprises substantial decadal variability in storminess. It also exhibits strong inter- and intra-annual variability in extreme high and low wind speed episodes. Here the authors quantify and discuss causes of the variability seen in the U.K. wind climate over the recent period 1980–2010. Variations in U.K. hourly mean (HM) wind speeds, in daily maximum gust speeds and in associated wind direction measurements, made at standard 10-m height and recorded across a network of 40 stations, are considered. The Weibull distribution is shown to generally provide a good fit to the hourly wind data, albeit with the shape parameter k spatially varying from 1.4 to 2.1, highlighting that the commonly assumed k = 2 Rayleigh distribution is not universal. It is found that the 10th and 50th percentile HM wind speeds have declined significantly over this specific period, while still incorporating a peak in the early 1990s. The authors' analyses place the particularly “low wind” year of 2010 into longer-term context and their findings are compared with other recent international studies. Wind variability is also quantified and discussed in terms of variations in the exceedance of key wind speed thresholds of relevance to the insurance and wind energy industries. Associated interannual variability in energy density and potential wind power output of the order of ±20% around the mean is revealed. While 40% of network average winds are in the southwest quadrant, 51% of energy in the wind is associated with this sector. The findings are discussed in the context of current existing challenges to improve predictability in the Euro-Atlantic sector over all time scales.

The physical climate formulation and simulation characteristics of two new global coupled carbon–climate Earth System Models, ESM2M and ESM2G, are described. These models demonstrate similar climate fidelity as the Geophysical Fluid Dynamics Laboratory’s previous Climate Model version 2.1 (CM2.1) while incorporating explicit and consistent carbon dynamics. The two models differ exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4p1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. Differences in the ocean mean state include the thermocline depth being relatively deep in ESM2M and relatively shallow in ESM2G compared to observations. The crucial role of ocean dynamics on climate variability is highlighted in El Niño–Southern Oscillation being overly strong in ESM2M and overly weak in ESM2G relative to observations. Thus, while ESM2G might better represent climate changes relating to total heat content variability given its lack of long-term drift, gyre circulation, and ventilation in the North Pacific, tropical Atlantic, and Indian Oceans, and depth structure in the overturning and abyssal flows, ESM2M might better represent climate changes relating to surface circulation given its superior surface temperature, salinity, and height patterns, tropical Pacific circulation and variability, and Southern Ocean dynamics. The overall assessment is that neither model is fundamentally superior to the other, and that both models achieve sufficient fidelity to allow meaningful climate and earth system modeling applications. This affords the ability to assess the role of ocean configuration on earth system interactions in the context of two state-of-the-art coupled carbon–climate models.