Summer Cloud Cover Research Articles

Arctic September Sea Ice Concentration Biases in CMIP6 Models and Their Relationships with Other Model Variables

Abstract The models that participated in the Coupled Model Intercomparison Project (CMIP) exhibit large biases in Arctic sea ice climatology that seem related to biases in seasonal atmospheric and oceanic circulations. Using historical runs of 34 CMIP6 models from 1979 to 2014, we investigate the links between the climatological sea ice concentration (SIC) biases in September and atmospheric and oceanic model climatologies. The main intermodel spread of September SIC is well described by two leading EOFs, which together explain ∼65% of its variance. The first EOF represents an underestimation or overestimation of SIC in the whole Arctic, while the second EOF describes opposite SIC biases in the Atlantic and Pacific sectors. Regression analysis indicates that the two SIC modes are closely related to departures from the multimodel mean of Arctic surface heat fluxes during summer, primarily shortwave and longwave radiation, with incoming Atlantic Water playing a role in the Atlantic sector. Local and global links with summer cloud cover, low-level humidity, upper or lower troposphere temperature/circulation, and oceanic variables are also found. As illustrated for three climate models, the local relationships with the SIC biases are mostly similar in the Arctic across the models but show varying degrees of Atlantic inflow influence. On a global scale, a strong influence of the summer atmospheric circulation on September SIC is suggested for one of the three models, while the atmospheric influence is primarily via thermodynamics in the other two. Clear links to the North Atlantic oceanic circulation are seen in one of the models.

Differential growth rate, water use efficiency and climate sensitivity between males and females of Ilex aquifolium in north-western Spain.

Dioecious plant species, i.e., those in which male and female functions are housed in different individuals, are particularly vulnerable to global environmental changes. For long-lived plant species, such as trees, long-term studies are imperative to understand how growth patterns and their sensitivity to climate variability differentially affect the sexes. Here, we explore long-term intersexual differences in wood traits, namely radial growth rates, water use efficiency quantified as stable carbon isotope abundance of wood cellulose, and their climate sensitivity in Ilex aquifolium trees growing in a natural population in NW Spain. We found that sex differences in secondary growth rates were variable over time, with males outperforming females in both radial growth rates and water use efficiency in recent decades. Summer water stress significantly reduced the growth of female trees in the following growing season, while the growth of male trees was primarily favoured by cloudy and rainy conditions the previous fall and winter combined with low cloud cover and warm conditions in summer. Sex-dependent lagged correlations between radial growth and water availability were found, with a strong association between tree growth and cumulative water availability in females at 30 months and in males at 10 months. Overall, our results point to greater vulnerability of female tress to increasing drought, which could lead to sex-ratio biases threatening population viability in the future.

Interannual Variation in Top-of-Atmosphere Upward Shortwave Flux over the Arctic Related to Sea Ice, Snow Cover, and Land Cloud Cover in Spring and Summer

Abstract We investigated the interannual variations in the annual mean and seasonal cycle of upward shortwave radiation at the top of the atmosphere (TOA SW↑) over the Arctic using the Clouds and the Earth’s Radiant Energy System (CERES) observation data during 2001–20. The annual mean TOA SW↑ over the Arctic showed a decreasing trend from 2001 to 2012 (−2.5 W m−2 decade−1) and had a large interannual variability after 2012. The standard deviation of detrended TOA SW↑ increased from 0.4 W m−2 in 2001–12 to 1.1 W m−2 in 2012–20. Over land, TOA SW↑ variation was related to snow cover in May; snow cover, cloud fraction, and cloud optical depth (COD) in June; and cloud fraction and COD in July. Over ocean, TOA SW↑ variation in June and July was linked to sea ice cover. TOA SW↑ variation over ocean in June and July after 2012 was highly related to the North Atlantic Oscillation (NAO). This study suggests that changes in the large annual mean TOA SW↑ variability after 2012 are explained by the timing of land snow and sea ice melt in spring and summer and cloud variability over land in summer.

Simulating the Effects of Regional Forest Cover and Windthrow‐Induced Cover Changes on Mid‐Latitude Boundary‐Layer Clouds

AbstractEvidence has been provided that land‐cover changes such as deforestation can have an impact on cloudiness and precipitation. However, conflicting results have been obtained at different scales and places, highlighting our poor understanding of the physical processes involved. Here we focus on mesoscale summer cloudiness in a temperate region, as influenced by a large forest massif (the Landes forest in France). Our study is based on an up‐to‐date atmosphere‐surface mesoscale model (Meso‐NH coupled with SURFEX). Based on observational data, we first optimize the model configuration for our purpose, and show that with a 500 m horizontal resolution we can successfully simulate the higher summer cloud cover observed over the forest, compared to its surroundings. Second, we investigate the physical processes leading to cloud formation in a representative case study. Based on a comparative analysis of diagnostics and budgets over forest and non‐forest areas, we find that the larger sensible heat flux over the forest and its higher roughness are the main drivers of cloudiness, enhancing vertical velocity and boundary‐layer mixing. Third, we simulate the impact of the 2009 Klaus storm that led to the loss of about one third of the trees. Considering 15 representative convective summer days, we show that the model simulates well the resulting decrease in summer cloudiness that was reported in a previous study based on satellite observations. As a complementary tool, the mesoscale simulations allow to quantify the impacts of the Klaus storm windthrow on the diurnal cycle of the boundary layer.

Urban effects on local cloud patterns

Urbanization extensively modifies surface roughness and properties, impacting regional climate and hydrological cycles. Urban effects on temperature and precipitation have drawn considerable attention. These associated physical processes are also closely linked to clouds' formation and dynamics. Cloud is one of the critical components in regulating urban hydrometeorological cycles but remains less understood in urban-atmospheric systems. We analyzed satellite-derived cloud patterns spanning two decades over 447 US cities and quantified the urban-influenced cloud patterns diurnally and seasonally. The systematic assessment suggests that most cities experience enhanced daytime cloud cover in both summer and winter; nocturnal cloud enhancement prevails in summer by 5.8%, while there is modest cloud suppression in winter nights. Statistically linking the cloud patterns with city properties, geographic locations, and climate backgrounds, we found that larger city size and stronger surface heating are primarily responsible for summer local cloud enhancement diurnally. Moisture and energy background control the urban cloud cover anomalies seasonally. Under strong mesoscale circulations induced by terrains and land-water contrasts, urban clouds exhibit considerable nighttime enhancement during warm seasons, which is relevant to strong urban surface heating interacting with these circulations, but other local and climate impacts remain complicated and inconclusive. Our research unveils extensive urban influences on local cloud patterns, but the effects are diverse depending on time, location, and city properties. The comprehensive observational study on urban-cloud interactions calls for more in-depth research on urban cloud life cycles and their radiative and hydrologic implications under the urban warming context.

Impact of stratospheric aerosol intervention geoengineering on surface air temperature in China: a surface energy budget perspective

Abstract. Stratospheric aerosol intervention (SAI) geoengineering is a proposed scheme to counteract anthropogenic global warming, but the climate response to SAI, with great regional disparities, remains uncertain. In this study, we use Geoengineering Model Intercomparison Project G4 experiment simulations from six models that counteract anthropogenic forcing under medium–low emissions (Representative Concentration Pathway 4.5 – RCP4.5) by injecting a certain amount of SO2 into the stratosphere every year to investigate the surface air temperature response to SAI geoengineering over China. We have found that SAI has led to surface cooling over China during the last 40 years of injection simulation (2030–2069), which varies among models, regions, and seasons. Decreased tropospheric temperature and water vapor and increased stratospheric aerosols induce robust decreases in downward clear-sky longwave and shortwave radiation fluxes at the surface, respectively, dominating the temperature change over China. Changes in cloud effective forcing and surface albedo feedback also relate to the temperature response but with large spatial and seasonal variations. We find that the increased summer cloud cover and winter surface albedo lead to strong cooling, while the decreased summer cloud cover and winter surface albedo lead to weak cooling or even insignificant warming for the certain subregions and models. Our results suggest that cloud and land surface processes in models dominate the spatial pattern of SAI-induced surface air temperature change over China.

Contrasting impacts of forests on cloud cover based on satellite observations

Forests play a pivotal role in regulating climate and sustaining the hydrological cycle. The biophysical impacts of forests on clouds, however, remain unclear. Here, we use satellite data to show that forests in different regions have opposite effects on summer cloud cover. We find enhanced clouds over most temperate and boreal forests but inhibited clouds over Amazon, Central Africa, and Southeast US. The spatial variation in the sign of cloud effects is driven by sensible heating, where cloud enhancement is more likely to occur over forests with larger sensible heat, and cloud inhibition over forests with smaller sensible heat. Ongoing forest cover loss has led to cloud increase over forest loss hotspots in the Amazon (+0.78%), Indonesia (+1.19%), and Southeast US (+ 0.09%), but cloud reduction in East Siberia (-0.20%) from 2002-2018. Our data-driven assessment improves mechanistic understanding of forest-cloud interactions, which remain uncertain in Earth system models.

Intraseasonal and synoptic climate variability of surface solar radiation over South‐West Indian Ocean: Regional climate modelling

AbstractThe abundant surface solar radiation (SSR) over South‐West Indian Ocean (SWIO) presents significant temporal variability. To characterize this temporal variability is important for the application of solar energy, such as photovoltaic industry. This article studied the intraseasonal and synoptic climate variability of SSR by regional climate modelling over SWIO region. The regional climate model, RegCM4's skill is first evaluated through analysing the seasonal mean SSR with the precipitation, near surface temperature and total cloud cover in austral summer and winter. The basic validation of those simulated parameters with the reference data showed model's performance on SSR. The austral summer (November–February) 1999–2008 was chosen to search the Madden–Julian Oscillation patterns and tropical temperate troughs which are the major expression of intraseasonal and synoptic climate variability. The circulation, moisture fluxes, and radiation fluxes have been checked at the beginning for RegCM4's input dataset (ERA‐Interim) to find the signals. Then, the output simulation results were taking into account to see if the model can reproduce the intraseasonal and synoptic climate variability or not. SSR from SARAH‐E (CM SAF@5 km) as the reference dataset in the end has been used to validate the simulated patterns, which showed that the eastward SSR anomalies propagation and negative SSR anomalies bands can be observed in RegCM4 and the according satellite dataset. These results identified and explained SSR's intraseasonal and synoptic climate variability over SWIO region, which provide a way through RegCM to perform SSR's evaluation and prediction.

Influence of Eurasian spring snowmelt on surface air temperature in late spring and early summer

AbstractThe effect of Eurasian spring snowmelt on surface air temperature (SAT) in late spring (April–May) and early summer (June–July) and the relevant physical mechanisms during 1981–2016 are investigated. Results show that the first mode of the inter-annual Eurasian spring snowmelt represents an east–west dipole anomaly pattern, with an intense center over Siberia and another moderate center around eastern Europe. The European spring snowmelt shows an insignificant relation to the local SAT, whereas the Siberian spring snowmelt has a significant impact on the SAT in late spring and early summer. More Siberian spring snowmelt contributes to higher SAT in late spring and lower SAT in early summer via different mechanisms. In late spring, increased Siberian spring snowmelt corresponds to less local surface albedo and cloud cover, leading to the surface absorbing more shortwave radiation and thereby higher SAT. The sub-surface and deep soil moisture anomalies generated from Siberian spring snowmelt can persist into early summer. Excessive Siberian spring snowmelt corresponds to positive soil moisture anomalies, contributing to decreased sensible heat and increased cloud cover in early summer. Increased cloud cover leads to the surface receiving less shortwave radiation. Thus, lower SAT appears over Siberia in early summer due to reduced sensible heat and shortwave radiation. However, the simulation of Eurasian spring snowmelt variability and its influences on SAT via the snow hydrological effect is still a challenge for the climate models that participated in the Coupled Model Intercomparison Project phase 6.

Cloud Cover Feedback Moderates Fennoscandian Summer Temperature Changes Over the Past 1,000 Years

AbstractNorthern Fennoscandia has experienced little summer warming over recent decades, in stark contrast to the hemispheric trend, which is strongly linked to greenhouse gas emissions. A likely explanation is the feedback between cloud cover and temperature. We establish the long‐ and short‐term relationships between summer cloud cover and temperature over Northern Fennoscandia, by analyzing meteorological and proxy climate data. We identify opposing feedbacks operating at different timescales. At short timescales, dominated by internal variability, the cloud cover‐temperature feedback is negative; summers with increased cloud cover are cooler and sunny summers are warmer. However, over longer timescales, at which forced climate changes operate, this feedback is positive, rising temperatures causing increased regional cloud cover and vice versa. This has occurred both during warm (Medieval Climate Anomaly and at present) and cool (Little Ice Age) periods. This two‐way feedback relationship therefore moderates Northern Fennoscandian temperatures during both warm and cool hemispheric periods.

Evaluation of Upper Indus Near-Surface Climate Representation by WRF in the High Asia Refined Analysis

AbstractData paucity is a severe barrier to the characterization of Himalayan near-surface climates. Regional climate modeling can help to fill this gap, but the resulting data products need critical evaluation before use. This study aims to extend the appraisal of one such dataset, the High Asia Refined Analysis (HAR). Focusing on the upper Indus basin (UIB), the climatologies of variables needed for process-based hydrological and cryospheric modeling are evaluated, leading to three main conclusions. First, precipitation in the 10-km HAR product shows reasonable correspondence with most in situ measurements. It is also generally consistent with observed runoff, while additionally reproducing the UIB’s strong vertical precipitation gradients. Second, the HAR shows seasonally varying bias patterns. A cold bias in temperature peaks in spring but reduces in summer, at which time the high bias in relative humidity diminishes. These patterns are concurrent with summer overestimation (underestimation) of incoming shortwave (longwave) radiation. Finally, these seasonally varying biases are partly related to deficiencies in cloud, snow, and albedo representations. In particular, insufficient cloud cover in summer leads to the overestimation of incoming shortwave radiation. This contributes to the reduced cold bias in summer by enhancing surface warming. A persistent high bias in albedo also plays a critical role, particularly by suppressing surface heating in spring. Improving representations of cloud, snow cover, and albedo, and thus their coupling with seasonal climate transitions, would therefore help build upon the considerable potential shown by the HAR to fill a vital data gap in this immensely important basin.

Spatiotemporal Distributions of Cloud Parameters and the Temperature Response Over the Mongolian Plateau During 2006–2015 Based on MODIS Data

The Mongolian Plateau (MP) has important influences on regional and global climate change. The spatiotemporal variations in the cloud cover and cloud optical thickness of total, high, middle, and low clouds over the MP during the daytime from 2006 to 2015 are analyzed using MODIS level 2 atmospheric data. Results show that the annual average total cloud cover over the MP decreases from the forest area in the northeast to the desert area in the southwest. The total cloud cover over the MP is obviously higher in summer than in other seasons, in which high clouds have a largest proportion, with the substantial total cloud cover changes. The spatial distributions of the high, middle, and low cloud covers over the MP are highly variable. The cooling effect of the cloud net radiative forcing is greater during the daytime in summer than in other seasons, which is likely associated with thick cloud optical thickness or large cloud cover in summer. Combined with the analyses of relationships among cloud cover, cloud optical thickness, cloud radiative forcing, and air temperature, the results show that significantly negative correlations exist between cloud optical thickness and cloud radiative forcing, and between total cloud cover and air temperature in the MP. The decrease in air temperature in summer over the MP during daytime confirm that the increase in the daytime total cloud cover strengthen the cooling effects of clouds and decrease the air temperature, especially in the high-value area with cloud cover distribution over the northeast MP.

Sensible heating as a potential mechanism for enhanced cloud formation over temperate forest

It has been recently shown for two forests in France (Les Landes and Sologne) that summer cloud cover over the forest is increased relative to its surroundings. This study aims to contribute to the elucidation of the physical mechanisms responsible for this increased cloud cover, focusing on surface flux partitioning. This was done by performing a case study for a heatwave day on which enhanced cloud cover over the forest of Les Landes was observed. Two numerical experiments (large‐eddy simulations) with a homogeneous forest cover were performed, one in which the sensible heat flux was increased by approximately 5% of the total available energy and another one in which the same amount of energy was added to the latent heat flux. The addition of energy to the sensible heat flux led to a stronger increase in cloud cover than the same addition to the latent heat flux. The mean relative humidity at the boundary layer top showed only small differences, indicating it was not a sufficient indicator for cloud formation in this case. Important information, which immediately underlines the need for large‐eddy simulations, is contained in modifications of the shape of the probability density functions of temperature and humidity. With enhanced sensible heating, the higher peak values of relative humidity contribute to an increased cloud cover. A crucial reason for the differences in cloud cover between the experiments is conjectured to be a decrease in the required amount of energy for air parcels to reach the lifting condensation level, indirectly caused by the boundary layer and near‐surface warming associated with the stronger sensible heat flux. As forests in the region do have a higher sensible heat flux than their surroundings, we highlight one potential mechanism for enhanced cloud cover.

How Well Are Clouds Simulated over Greenland in Climate Models? Consequences for the Surface Cloud Radiative Effect over the Ice Sheet

AbstractUsing lidar and radiative flux observations from space and ground, and a lidar simulator, we evaluate clouds simulated by climate models over the Greenland ice sheet, including predicted cloud cover, cloud fraction profile, cloud opacity, and surface cloud radiative effects. The representation of clouds over Greenland is a central concern for the models because clouds impact ice sheet surface melt. We find that over Greenland, most of the models have insufficient cloud cover during summer. In addition, all models create too few nonopaque, liquid-containing clouds optically thin enough to let direct solar radiation reach the surface (−1% to −3.5% at the ground level). Some models create too few opaque clouds. In most climate models, the cloud properties biases identified over all Greenland also apply at Summit, Greenland, proving the value of the ground observatory in model evaluation. At Summit, climate models underestimate cloud radiative effect (CRE) at the surface, especially in summer. The primary driver of the summer CRE biases compared to observations is the underestimation of the cloud cover in summer (−46% to −21%), which leads to an underestimated longwave radiative warming effect (CRELW= −35.7 to −13.6 W m−2compared to the ground observations) and an underestimated shortwave cooling effect (CRESW= +1.5 to +10.5 W m−2compared to the ground observations). Overall, the simulated clouds do not radiatively warm the surface as much as observed.

Simulating Arctic 2-m air temperature and its linear trends using the HIRHAM5 regional climate model

Air temperature at 2-m (T2) in the Arctic represents its local climate. Its quantification is one of the major criteria to evaluate the performance of numerical models in reflecting the complex physical and dynamical processes associated with the surface energy balance. This study uses HIRHAM5 regional climate model to simulate the Arctic climate during 1979–2014. Evaluations with Arctic station observations reveal that HIRHAM5 can generally reproduce the temporal and spatial variation of the T2, although a systematic cold bias of ca. −2 °C exists in all seasons. The overestimated surface albedo in spring and autumn, and the underestimated downward solar radiation associated with the cloud cover in summer are the main causes of the cold biases in each respective season. The model also simulates the Arctic warming well (with linear trends of 0.40 °C decade−1 for the annual mean T2), although the magnitude is less than that from ERA-Interim (0.55 °C decade−1) and station observations (0.60 °C decade−1). In addition, strong decadal variability is clear in the T2 trends calculated using an 11-year moving windows, especially in winter and spring, which is mainly associated with the variability of the Arctic/North Atlantic Oscillations.

Analyzing of Cloud Macroscopic Characteristics in the Shigatse Area of the Tibetan Plateau Using the Total-Sky Images

AbstractThe macroscopic characteristics of clouds in the Tibetan Plateau are crucial to understanding the local climatic conditions and their impact on the global climate and water vapor cycle. In this study, the variations of cloud cover and cloud types are analyzed by using total-sky images of two consecutive years in Shigatse, Tibetan Plateau. The results show that the cloud cover in Shigatse presents a distinct seasonal difference that is characterized by low cloud cover in autumn and winter and high cloud cover in summer and spring. July is the month with the largest cloud coverage, and its average cloud cover exceeds 75%. The probability of clouds in the sky is the lowest in November, with an average cloud cover of less than 20%. The diurnal variations of cloud cover in different months also have considerable differences. Specifically, cloud cover is higher in the afternoon than that in the morning in most months, whereas the cloud cover throughout the day varies little from July to September. The dominant cloud types in different months are also not the same. The proportion of clear sky is large in autumn and winter. Stratiform cloud occupies the highest percentage in March, April, July, and August. The probability of emergence of cirrus is highest in May and June. The Shigatse region has clear rainy and dry seasons, and correlation analysis between precipitation and clouds shows that the largest cumulative precipitation, the highest cloud cover, and the highest proportion of stratiform clouds occur simultaneously in July.

Twenty-five years of cloud base height measurements by ceilometer in Ny-Ålesund, Svalbard

Abstract. Clouds are a key factor for the Arctic amplification of global warming, but their actual appearance and distribution are still afflicted by large uncertainty. On the Arctic-wide scale, large discrepancies are found between the various reanalyses and satellite products, respectively. Although ground-based observations by remote sensing are limited to point measurements, they have the advantage of obtaining extended time series of vertically resolved cloud properties. Here, we present a 25-year data record of cloud base height measured by ceilometer at the Ny-Ålesund, Svalbard, Arctic site. We explain the composition of the three sub-periods with different instrumentation contributing to the data set, and show examples of potential application areas. Linked to cyclonic activity, the cloud base height provides essential information for the interpretation of the surface radiation balance and contributes to the understanding of meteorological processes. Furthermore, it is a useful auxiliary component for the analysis of advanced technologies that provide insight into cloud microphysical properties, like the cloud radar. The long-term time series also allows derivation of an annual cycle of the cloud occurrence frequency, revealing the more frequent cloud cover in summer and the lowest cloud cover amount in April. However, as the use of different ceilometer instruments over the years potentially imposed inhomogeneities onto the data record, any long-term trend analysis should be avoided. The Ny-Ålesund cloud base height data from August 1992 to July 2017 are provided in a high temporal resolution of 5 min (1 min) before (after) July 1998, respectively, at the PANGAEA repository (https://doi.org/10.1594/PANGAEA.880300).

Decreasing cloud cover drives the recent mass loss on the Greenland Ice Sheet.

The Greenland Ice Sheet (GrIS) has been losing mass at an accelerating rate since the mid-1990s. This has been due to both increased ice discharge into the ocean and melting at the surface, with the latter being the dominant contribution. This change in state has been attributed to rising temperatures and a decrease in surface albedo. We show, using satellite data and climate model output, that the abrupt reduction in surface mass balance since about 1995 can be attributed largely to a coincident trend of decreasing summer cloud cover enhancing the melt-albedo feedback. Satellite observations show that, from 1995 to 2009, summer cloud cover decreased by 0.9 ± 0.3% per year. Model output indicates that the GrIS summer melt increases by 27 ± 13 gigatons (Gt) per percent reduction in summer cloud cover, principally because of the impact of increased shortwave radiation over the low albedo ablation zone. The observed reduction in cloud cover is strongly correlated with a state shift in the North Atlantic Oscillation promoting anticyclonic conditions in summer and suggests that the enhanced surface mass loss from the GrIS is driven by synoptic-scale changes in Arctic-wide atmospheric circulation.

新疆阿勒泰地区低云量的气候变化及其与降水量的关系

选取1961~2010年新疆阿勒泰地区7站月平均低云量，月降水量，利用变差分析、Mann-Kendall、滑动t检验法、相关分析等方法来分析该地区低云量的变化及其与降水量的关系。结果表明：1) 大部分站低云量除冬季外，年及其它季节呈明显增多趋势，以1.1%~1.7%/10a的速率显著增加；2) 大部分站年、春、夏季低云量突变趋势显著，突变的时间为70年代中后期至80年代前期，冬季突变趋势不显著；3) 大部分站年低云量与春、夏、秋季低云量有很好的相关性，尤其和夏季的相关性最好。除冬季低云量外，春、夏、秋季低云量有较好的持续性。 By selecting average low cloud cover and monthly precipitation during 1961-2010 of 7 stations in Aletai Area of XinJiang Province, and using variation analysis, Mann-Kendall, sliding t-test method, correlation analysis and so on, the change of low cloud cover and its relationship with precipitation in Aletai Area of Xinjiang Province were analyzed. Our results showed that: 1) for most stations, low cloud cover significantly increased annually and seasonally except winter at the rate of 1.1% - 1.7%/decade; 2) for most stations, the mutation trend of low cloud cover in years, spring and summer was obvious and it occurred from the late 1970s to the early 1980s, while the trend was not significant in winter; 3) the annual low cloud cover of most stations had a good correlation with that of spring, summer and autumn, especially summer. In addition, in most stations, low cloud cover in spring, summer and autumn had good continuity.

Recent Declines in Warming and Vegetation Greening Trends over Pan-Arctic Tundra

Vegetation productivity trends for the Arctic tundra are updated for the 1982–2011 period and examined in the context of land surface temperatures and coastal sea ice. Understanding mechanistic links between vegetation and climate parameters contributes to model advancements that are necessary for improving climate projections. This study employs remote sensing data: Global Inventory Modeling and Mapping Studies (GIMMS) Maximum Normalized Difference Vegetation Index (MaxNDVI), Special Sensor Microwave Imager (SSM/I) sea-ice concentrations, and Advanced Very High Resolution Radiometer (AVHRR) radiometric surface temperatures. Spring sea ice is declining everywhere except in the Bering Sea, while summer open water area is increasing throughout the Arctic. Summer Warmth Index (SWI—sum of degree months above freezing) trends from 1982 to 2011 are positive around Beringia but are negative over Eurasia from the Barents to the Laptev Seas and in parts of northern Canada. Eastern North America continues to show increased summer warmth and a corresponding steady increase in MaxNDVI. Positive MaxNDVI trends from 1982 to 2011 are generally weaker compared to trends from 1982–2008. So to better understand the changing trends, break points in the time series were quantified using the Breakfit algorithm. The most notable break points identify declines in SWI since 2003 in Eurasia and 1998 in Western North America. The Time Integrated NDVI (TI-NDVI, sum of the biweekly growing season values of MaxNDVI) has declined since 2005 in Eurasia, consistent with SWI declines. Summer (June–August) sea level pressure (slp) averages from 1999–2011 were compared to those from 1982–1998 to reveal higher slp over Greenland and the western Arctic and generally lower pressure over the continental Arctic in the recent period. This suggests that the large-scale circulation is likely a key contributor to the cooler temperatures over Eurasia through increased summer cloud cover and warming in Eastern North America from more cloud-free skies.