The observed climatic controls on springtime and summertime Saudi Arabian dust activities during 1975-2012 are analyzed, leading to development of a seasonal dust prediction model. According to empirical orthogonal function analysis, dust storm frequency exhibits a dominantly homogeneous pattern across Saudi Arabia, with distinct interannual and decadal variability. The previously identified positive trend in remotely sensed aerosol optical depth since 2000 is shown to be a segment of the decadal oscillation in dust activity, according to long-duration station record. Regression and correlation analyses reveal that the interannual variability in Saudi Arabian dust storm frequency is regulated by springtime rainfall across the Arabian Peninsula and summertime Shamal wind intensity. The key drivers of Saudi Arabian dust storm variability are identified. Winter-to-spring La Niña enhances subsequent spring dust activity by decreasing rainfall across the country's primary dust source region, the Rub' al Khali Desert. A relatively cool tropical Indian Ocean favors frequent summer dust storms by producing an anomalously anticyclonic circulation over the central Arabian Peninsula, which enhances the Shamal wind. Decadal variability in Saudi Arabian dust storm frequency is associated with North African rainfall and Sahel vegetation, which regulate African dust emissions and transport to Saudi Arabia. Mediterranean sea surface temperatures (SSTs) also regulate decadal dust variability, likely through their influence on Sahel rainfall and Shamal intensity. Using antecedent-accumulated rainfall over the Arabian Peninsula and North Africa, and Mediterranean SSTs, as low-frequency predictors, and tropical eastern Pacific and tropical Indian Ocean SSTs as high-frequency predictors, Saudi Arabia's seasonal dust activity is well predicted.

This study tests the hypothesis that Arctic amplification (AA) of global warming remotely affects midlatitudes by promoting a weaker, wavier atmospheric circulation conducive to extreme weather. The investigation is based on the late twenty-first century over greater North America (20°–90°N, 50°–160°W) using 40 simulations from the Community Earth System Model Large Ensemble, spanning 1920–2100. AA is found to promote regionally varying ridging aloft (500 hPa) with strong seasonal differences reflecting the location of the strongest surface thermal forcing. During winter, maximum increases in future geopotential heights are centered over the Arctic Ocean, in conjunction with sea ice loss, but minimum height increases (troughing) occur to the south, over the continental United States. During summer the location of maximum height inflation shifts equatorward, forming an annular band across mid-to-high latitudes of the entire Northern Hemisphere. This band spans the continents, whose enhanced surface heating is aided by antecedent snow-cover loss and reduced terrestrial heat capacity. Through the thermal wind relationship, midtropospheric winds weaken on the equatorward flank of both seasonal ridging anomalies—mainly over Canada during winter and even more over the continental United States during summer—but strengthen elsewhere to form a dipole anomaly pattern in each season. Changes in circulation waviness, expressed as sinuosity, are inversely correlated with changes in zonal wind speed at nearly all latitudes, both in the projections and as observed during recent decades. Over the central United States during summer, the weaker and wavier flow promotes drying and enhanced heating, thus favoring more intense summer weather.

Anthropogenic climate change is altering ecological and human systems globally, including in United States (US) national parks, which conserve unique biodiversity and resources. Yet, the magnitude and spatial patterns of climate change across all the parks have been unknown. Here, in the first spatial analysis of historical and projected temperature and precipitation across all 417 US national parks, we show that climate change exposes the national park area more than the US as a whole. This occurs because extensive parts of the national park area are in the Arctic, at high elevations, or in the arid southwestern US. Between 1895 and 2010, mean annual temperature of the national park area increased 1.0°C±0.2°C century −1 (mean±standard error), double the US rate. Temperature has increased most in Alaska and its extensive national parks. Annual precipitation of the national park area declined significantly on 12% of national park area, compared to 3% of the US. Higher temperatures due to climate change have coincided with low precipitation in the southwestern US, intensifying droughts in the region. Physical and ecological changes have been detected and attributed mainly to anthropogenic climate change in areas of significant temperature increases in US national parks. From 2000 to 2100, under the highest emissions scenario (representative concentration pathway [RCP] 8.5), park temperatures would increase 3°C-9°C, with climate velocities outpacing dispersal capabilities of many plant and animal species. Even under the scenario of reduced emissions (RCP2.6), temperature increases could exceed 2°C for 58% of national park area, compared to 22% of the US. Nevertheless, greenhouse gas emissions reductions could reduce projected temperature increases in national parks by one-half to two-thirds.

Classic, model-based theory of land-atmosphere interactions across the Sahel promote positive vegetation-rainfall feedbacks dominated by surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback nor its underlying albedo mechanism has been convincingly demonstrated using observational data. Here, we present observational evidence for the region’s proposed positive vegetation-rainfall feedback on the seasonal to interannual time scale, and find that it is associated with a moisture recycling mechanism, rather than the classic albedo-based mechanism. Positive anomalies of remotely sensed vegetation greenness across the Sahel during the late and post-monsoon periods favor enhanced evapotranspiration, precipitable water, convective activity and rainfall, indicative of amplified moisture recycling. The identified modest low-level cooling and anomalous atmospheric subsidence in response to positive vegetation greenness anomalies are counter to the responses expected through the classic vegetation-albedo feedback mechanism. The observational analysis further reveals enhanced dust emissions in response to diminished Sahel vegetation growth, potentially contributing to the positive vegetation-rainfall feedback.

The seasonal impacts of the dominant sea surface temperature (SST) modes to North American climate are assessed comprehensively in observations using the multivariate generalized equilibrium feedback assessment (GEFA) method. The GEFA method is first validated before applying it to observations. Impacts of each individual SST mode are quantified and the associated mechanisms are discussed. Four critical SST modes for North American climate are found: the ENSO mode, Indian Ocean Basin (IOB) mode, North Pacific first empirical orthogonal function (EOF) mode, and tropical Atlantic second EOF mode. The impacts of the ENSO mode are consistent with previous studies qualitatively, while the impact strength is further quantified here. The IOB mode has a strong influence on surface air temperature across North America, and it is demonstrated for the first time that its impact strength might even exceed that of ENSO during both winter and summer. The IOB mode also affects the year-round precipitation. A deeper understanding of the impact of North Pacific SSTs on wintertime surface air temperature is achieved: namely, positive SST anomalies in the Kuroshio Extension region correspond to colder (warmer) air in western (eastern) North America. The tropical Atlantic has a more significant influence on North American precipitation than does the extratropical Atlantic, with colder than normal tropical North Atlantic SSTs supporting wetter conditions across much of the United States, especially during autumn. Because of the linearity of GEFA, the total impacts of multiple SST modes can be obtained by the linear combination of each individual mode's impact. The GEFA method is a potentially powerful tool for seasonal climate prediction.