Hemispheric transport of air pollutants can have a significant impact on regional air quality, as well as on the effect of air pollutants on regional climate. An accurate representation of hemispheric transport in regional chemical transport models (CTMs) depends on the specification of the lateral boundary conditions (LBCs). This study focuses on the methodology for evaluating LBCs of two moderately long-lived trace gases, carbon monoxide (CO) and ozone (O-3), for the European model domain and over a 7-year period, 2006-2012. The method is based on combining the use of satellite observations at the lateral boundary with the use of both satellite and in situ ground observations within the model domain. The LBCs are generated by the global European Monitoring and Evaluation Programme Meteorological Synthesizing Centre - West (EMEP MSC-W) model; they are evaluated at the lateral boundaries by comparison with satellite observations of the Terra-MOPITT (Measurements Of Pollution In The Troposphere) sensor (CO) and the Aura-OMI (Ozone Monitoring Instrument) sensor (O-3). The LBCs from the global model lie well within the satellite uncertainties for both CO and O-3. The biases increase below 700 hPa for both species. However, the satellite retrievals below this height are strongly influenced by the a priori data; hence, they are less reliable than at, e.g. 500 hPa. CO is, on average, underestimated by the global model, while O-3 tends to be overestimated during winter, and underestimated during summer. A regional CTM is run with (a) the validated monthly climatological LBCs from the global model; (b) dynamical LBCs from the global model; and (c) constant LBCs based on in situ ground observations near the domain boundary. The results are validated against independent satellite retrievals from the Aqua-AIRS (Atmospheric InfraRed Sounder) sensor at 500 hPa, and against in situ ground observations from the Global Atmospheric Watch (GAW) network. It is found that (i) the use of LBCs from the global model gives reliable in-domain results for O-3 and CO at 500 hPa. Taking AIRS retrievals as a reference, the use of these LBCs substantially improves spatial pattern correlations in the free troposphere as compared to results obtained with fixed LBCs based on ground observations. Also, the magnitude of the bias is reduced by the new LBCs for both trace gases. This demonstrates that the validation methodology based on using satellite observations at the domain boundary is sufficiently robust in the free troposphere. (ii) The impact of the LBCs on ground concentrations is significant only at locations in close proximity to the domain boundary. As the satellite data near the ground mainly reflect the a priori estimate used in the retrieval procedure, they are of little use for evaluating the effect of LBCs on ground concentrations. Rather, the evaluation of ground-level concentrations needs to rely on in situ ground observations. (iii) The improvements of dynamic over climatological LBCs become most apparent when using accumulated ozone over threshold 40 ppb (AOT40) as a metric. Also, when focusing on ground observations taken near the inflow boundary of the model domain, one finds that the use of dynamical LBCs yields a more accurate representation of the seasonal variation, as well as of the variability of the trace gas concentrations on shorter timescales.
The impact of very deep convection on the water budget and thermal structure of the tropical tropopause layer is still not well quantified, not least because of limitations imposed by the available observation techniques. Here, we present detailed analysis of the climatology of the cloud top brightness temperatures as indicators of deep convection during the Indian summer monsoon, and the variations therein due to active and break periods. We make use of the recently newly processed data from the Advanced Very High Resolution Radiometer (AVHRR) at a nominal spatial resolution of 4 km. Using temperature thresholds from the Atmospheric Infrared Sounder (AIRS), the AVHRR brightness temperatures are converted to climatological mean (2003-2008) maps of cloud amounts at 200, 150 and 100 hPa. Further, we relate the brightness temperatures to the level of zero radiative heating, which may allow a coarse identification of convective detrainment that will subsequently ascend into the stratosphere. The AVHRR data for the period 1982-2006 are used to document the differences in deep convection between active and break conditions of the monsoon. The analysis of AVHRR data is complemented with cloud top pressure and optical depth statistics (for the period 2003-2008) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua satellite. Generally, the two sensors provide a very similar description of deep convective clouds. Our analysis shows that most of the deep convection occurs over the Bay of Bengal and central northeast India. Very deep convection over the Tibetan plateau is comparatively weak, and may play only a secondary role in troposphere-to-stratosphere transport. The deep convection over the Indian monsoon region is most frequent in July/August, but the very highest convection (coldest tops, penetrating well into the TTL) occurs in May/June. Large variability in convection reaching the TTL is due to monsoon break/active periods. During the monsoon break period, deep convection reaching the TTL is almost entirely absent in the western part of the study area (i.e. 60 E-75 E), while the distribution over the Bay of Bengal and the Tibetan Plateau is less affected. Although the active conditions occur less frequently than the break conditions, they may have a larger bearing on the composition of the TTL within the monsoonal anticyclone, and tracer transport into the stratosphere because of deep convection occurring over anthropogenically more polluted regions.
A daytime climatological spatio-temporal distribution of high opaque ice cloud (HOIC) classes over the Indian subcontinent (0-40 degrees N, 60 degrees E-100 degrees E) is presented using 25-year data from the Advanced Very High Resolution Radiometers (AVHRRs) for the summer monsoon months. The HOICs are important for regional radiative balance, precipitation and troposphere-stratosphere exchange. In this study, HOICs are sub-divided into three classes based on their cloud top brightness temperatures (BT). Class I represents very deep convection (BT < 220 K). Class II represents deep convection (220 K <=BT < 233 K) and Class III background convection (233 K <=BT < 253 K). Apart from presenting finest spatial resolution (0.1x0.1 degrees) and long-term climatology of such cloud classes from AVHRRs to date, this study for the first time illustrates on (1) how these three cloud classes are climatologically distributed during monsoon months, and (2) how their distribution changes during active and break monsoon conditions. It is also investigated that how many deep convective clouds reach the tropopause layer during individual monsoon months. It is seen that Class I and Class II clouds dominate the Indian subcontinent during monsoon. The movement of monsoon over continent is very well reflected in these cloud classes. During monsoon breaks strong suppression of convective activity is observed over the Arabian Sea and the western coast of India. On the other hand, the presence of such convective activity is crucial for active monsoon conditions and all-India rainfall. It is found that a significant fraction of HOICs (3-5%) reach the tropopause layer over the Bay of Bengal during June and over the north and northeast India during July and August. Many cases are observed when clouds penetrate the tropopause layer and reach the lower stratosphere. Such cases mostly occur during June compared to the other months.
Data from the National Oceanic and Atmospheric Administration (NOAA) satellites' Advanced Very High Resolution Radiometers (AVHRRs) represent the longest record (more than 25 years) of continuously available satellite-based thermal measurements, and have well-chosen spatial and spectral resolutions. As a consequence, these data are used extensively to develop cloud climatologies. However, for such applications, accurate calibration and intercalibration of both solar and thermal channels of the AVHRRs is necessary so as to homogenize the data obtained from the different AVHRR sensors. AVHRR thermal channels 4 and 5 are routinely used in threshold-based hierarchical decision-tree cloud detection and classification algorithms, and therefore an evaluation of the stability of these channels at low temperatures is important. In this letter, the AVHRR channel 4 and 5 brightness temperatures (BTs) are compared at five stations in Antarctica. The data for the period of June, July and August (the coldest months of every year and with minimal atmospheric influence) from 1982 to 2006 were used for the evaluations. The calibration and intercalibration of the thermal channels are found to be very robust. The root mean square errors (RMSEs) range from 2.2 to 3.4K and the correlation coefficients from 0.84 to 0.95. No apparent artefacts or artificial jumps in the BTs are visible in the data series after changes of sensors. The BTs from the thermal channels of the AVHRRs can be used for preparing cloud climatologies, as their intercalibration is found to be consistent across different afternoon satellites.
The Advanced Very High Resolution Radiometer (AVHRR) instruments onboard the series of National Oceanic and Atmospheric Administration (NOAA) satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of the sensors onboard. Depending upon the amplitude of the diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to estimate an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis (REOF) and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in subtracting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and rigorous testing thereof applying final orbital drift corrections.
Using measurements from the national network of 12 weather radar stations for the 11-year period 2000-2010, we investigate the large-scale spatio-temporal variability of precipitation over Sweden. These statistics provide useful information to evaluate regional climate models as well as for hydrology and energy applications. A strict quality control is applied to filter out noise and artifacts from the radar data. We focus on investigating four distinct aspects: the diurnal cycle of precipitation and its seasonality, the dominant timescale (diurnal versus seasonal) of variability, precipitation response to different wind directions, and the correlation of precipitation events with the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO). When classified based on their intensity, moderate-to high-intensity events (precipitation >0.34 mm/3 h) peak distinctly during late afternoon over the majority of radar stations in summer and during late night or early morning in winter. Precipitation variability is highest over the southwestern parts of Sweden. It is shown that the high-intensity events (precipitation >1.7 mm/3 h) are positively correlated with NAO and AO (esp. over northern Sweden), while the low intensity events are negatively correlated (esp. over southeastern parts). It is further observed that southeasterly winds often lead to intense precipitation events over central and northern Sweden, while southwesterly winds contribute most to the total accumulated precipitation for all radar stations. Apart from its operational applications, the present study demonstrates the potential of the weather radar data set for studying climatic features of precipitation over Sweden.
The record sea ice minimum (SIM) extents observed during the summers of 2007 and 2012 in the Arctic are stark evidence of accelerated sea ice loss during the last decade. Improving our understanding of the Arctic atmosphere and accurate quantification of its characteristics becomes ever more crucial, not least to improve predictions of such extreme events in the future. In this context, the Atmospheric Infrared Sounder (AIRS) instrument onboard NASA's Aqua satellite provides crucial insights due to its ability to provide 3-D information on atmospheric thermodynamics. Here, we facilitate comparisons in the evolution of the thermodynamic state of the Arctic atmosphere during these two SIM events using a decade-long AIRS observational record (2003-2012). It is shown that the meteorological conditions during 2012 were not extreme, but three factors of preconditioning from winter through early summer played an important role in accelerating sea ice melt. First, the marginal sea ice zones along the central Eurasian and North Atlantic sectors remained warm throughout winter and early spring in 2012 preventing thicker ice build-up. Second, the circulation pattern favoured efficient sea ice transport out of the Arctic in the Atlantic sector during late spring and early summer in 2012 compared to 2007. Third, additional warming over the Canadian archipelago and southeast Beaufort Sea from May onward further contributed to accelerated sea ice melt. All these factors may have lead the already thin and declining sea ice cover to pass below the previous sea ice extent minimum of 2007. In sharp contrast to 2007, negative surface temperature anomalies and increased cloudiness were observed over the East Siberian and Chukchi seas in the summer of 2012. The results suggest that satellite-based monitoring of atmospheric preconditioning could be a critical source of information in predicting extreme sea ice melting events in the Arctic.
An accurate characterization of the vertical structure of the Arctic atmosphere is useful in climate change and attribution studies as well as for the climate modelling community to improve projections of future climate over this highly sensitive region. Here, we investigate one of the dominant features of the vertical structure of the Arctic atmosphere, i.e. water-vapour inversions, using eight years of Atmospheric Infrared Sounder data (2002-2010) and radiosounding profiles released from the two Arctic locations (North Slope of Alaska at Barrow and during SHEBA). We quantify the characteristics of clear-sky water vapour inversions in terms of their frequency of occurrence, strength and height covering the entire Arctic for the first time. We found that the frequency of occurrence of water-vapour inversions is highest during winter and lowest during summer. The inversion strength is, however, higher during summer. The observed peaks in the median inversion-layer heights are higher during the winter half of the year, at around 850 hPa over most of the Arctic Ocean, Siberia and the Canadian Archipelago, while being around 925 hPa during most of the summer half of the year over the Arctic Ocean. The radiosounding profiles agree with the frequency, location and strength of water-vapour inversions in the Pacific sector of the Arctic. In addition, the radiosoundings indicate that multiple inversions are the norm with relatively few cases without inversions. The amount of precipitable water within the water-vapour inversion structures is estimated and we find a distinct, two-mode contribution to the total column precipitable water. These results suggest that water-vapour inversions are a significant source to the column thermodynamics, especially during the colder winter and spring seasons. We argue that these inversions are a robust metric to test the reproducibility of thermodynamics within climate models. An accurate statistical representation of water-vapour inversions in models would mean that the large-scale coupling of moisture transport, precipitation, temperature and water-vapour vertical structure and radiation are essentially captured well in such models.
Simulating the radiative impacts of aerosols located above liquid water clouds presents a significant challenge. In particular, absorbing aerosols, such as smoke, may have significant impact in such situations and even change the sign of net radiative forcing. It is not possible to reliably obtain information on such overlap events from existing passive satellite sensors. However, the CALIOP instrument onboard NASA's CALIPSO satellite allows us to examine these events with unprecedented accuracy. Using four years of collocated CALIPSO 5 km Aerosol and Cloud Layer Version 3 Products (June 2006 May 2010), we quantify, for the first time, the characteristics of overlapping aerosol and water cloud layers globally. We investigate seasonal variability in these characteristics over six latitude bands to understand the hemispheric differences when all aerosol types are included in the analysis (the AAO case). We also investigate frequency of smoke aerosol-cloud overlap (the SAO case). Globally, the frequency is highest during the JJA months in the AAO case, while for the SAO case, it is highest in the SON months. The seasonal mean overlap frequency can regionally exceed 20% in the AAO case and 10% in the SAO case. In about 5-10% cases the vertical distance between aerosol and cloud layers is less than 100 m, while about in 45-60% cases it less than a kilometer in the annual means for different latitudinal bands. In about 70-80% cases, aerosol layers are less than a kilometer thick, while in about 18-22% cases they are 1-2 km thick. The frequency of aerosol layers 2-3 km thick is about 4-5% in the tropical belts during overlap events. Over the regions where high aerosol loadings are present, the overlap frequency can be up to 50% higher when quality criteria on aerosol/cloud feature detection are relaxed. Over the polar regions, more than 50% of the overlapping aerosol layers have optical thickness less than 0.02, but the contribution from the relatively optically thicker aerosol layers increases towards the equatorial regions in both hemispheres. The results suggest that the frequency of occurrence of overlap events is far from being negligible globally.
Temperature inversions influence the local air quality at smaller scales and the pollution transport at larger spatio-temporal scales and are one of the most commonly observed meteorological phenomena over Scandinavia (54 degrees N-70 degrees N, 0-30 degrees E) during winter. Here, apart from presenting key statistics on temperature inversions, a large-scale co-variation of inversion strength and carbon monoxide (CO), an ideal pollution tracer, is further quantified at six vertical levels in the free troposphere during three distinct meteorological regimes that are identified based on inversion strength. Collocated temperature and CO profiles from Atmospheric Infrared Sounder (AIRS) are used for this purpose. Higher values of CO (up to 15%) are observed over Scandinavia during weakly stable regimes at all vertical levels studied, whereas lower CO values (up to 10%) are observed when inversions become stronger and elevated. The observed systematic co-variation between CO and inversion strength in three meteorological regimes is most likely explained by the efficacy of long-range transport to influence tropospheric composition over Scandinavia. We argue that this large-scale co-variation of temperature inversions and CO would be a robust metric to test coupling of large-scale meteorology and chemistry in transport models. (C) 2012 Elsevier Ltd. All rights reserved.
The main purpose of this study is to underline the sensitivity of cloud liquid water content (LWC) estimates purely to 1) the shape of computationally simplified temperature-dependent thermodynamic phase and 2) the range of subzero temperatures covered to partition total cloud condensate into liquid and ice fractions. Linear, quadratic, or sigmoid-shaped functions for subfreezing temperatures (down to -20 degrees or -40 degrees C) are often used in climate models and reanalysis datasets for partitioning total condensate. The global vertical profiles of clouds obtained from CloudSat for the 4-yr period June 2006-May 2010 are used for sensitivity analysis and the quantitative estimates of sensitivities based on these realistic cloud profiles are provided. It is found that three cloud regimes in particular-convective clouds in the tropics, low-level clouds in the northern high latitudes, and middle-level clouds over the midlatitudes and Southern Ocean-are most sensitive to assumptions on thermodynamic phase. In these clouds, the LWC estimates based purely on quadratic or sigmoid-shaped functions with a temperature range down to -20 degrees C can differ by up to 20%-40% over the tropics (in seasonal means). 10%-30% over the midlatitudes, and up to 50% over high latitudes compared to a linear assumption. When the temperature range is extended down to -40 degrees C. LWC estimates in the sigmoid case can be much higher than the above values over high-latitude regions compared to the commonly used case with quadratic dependency down to -20 C. This sensitivity study emphasizes the need to critically investigate radiative impacts of cloud thermodynamic phase assumptions in simplified climate models and reanalysis datasets.
The main purpose of this study is to investigate the influence of the Arctic Oscillation (AO), the dominant mode of natural variability over the northerly high latitudes, on the spatial (horizontal and vertical) distribution of clouds in the Arctic. To that end, we use a suite of sensors on-board NASA's A-Train satellites that provide accurate observations of the distribution of clouds along with information on atmospheric thermodynamics. Data from three independent sensors are used (AQUA-AIRS, CALIOP-CALIPSO and CPR-CloudSat) covering two time periods (winter half years, November through March, of 2002-2011 and 2006-2011, respectively) along with data from the ERA-Interim reanalysis. We show that the zonal vertical distribution of cloud fraction anomalies averaged over 67-82 degrees N to a first approximation follows a dipole structure (referred to as "Greenland cloud dipole anomaly", GCDA), such that during the positive phase of the AO, positive and negative cloud anomalies are observed eastwards and westward of Greenland respectively, while the opposite is true for the negative phase of AO. By investigating the concurrent meteorological conditions (temperature, humidity and winds), we show that differences in the meridional energy and moisture transport during the positive and negative phases of the AO and the associated thermodynamics are responsible for the conditions that are conducive for the formation of this dipole structure. All three satellite sensors broadly observe this large-scale GCDA despite differences in their sensitivities, spatio-temporal and vertical resolutions, and the available lengths of data records, indicating the robustness of the results. The present study also provides a compelling case to carry out process-based evaluation of global and regional climate models.
Clouds play a crucial role in the Arctic climate system. Therefore, it is essential to accurately and reliably quantify and understand cloud properties over the Arctic. It is also important to monitor and attribute changes in Arctic clouds. Here, we exploit the capability of the CALIPSO-CALIOP instrument and provide comprehensive statistics of tropospheric thin clouds, otherwise extremely difficult to monitor from passive satellite sensors. We use 4 yr of data (June 2006-May 2010) over the circumpolar Arctic, here defined as 67-82 degrees N, and characterize probability density functions of cloud base and top heights, geometrical thickness and zonal distribution of such cloud layers, separately for water and ice phases, and discuss seasonal variability of these properties. When computed for the entire study area, probability density functions of cloud base and top heights and geometrical thickness peak at 200-400, 1000-2000 and 400-800 m, respectively, for thin water clouds, while for ice clouds they peak at 6-8, 7-9 and 400-1000 m, respectively. In general, liquid clouds were often identified below 2 km during all seasons, whereas ice clouds were sensed throughout the majority of the upper troposphere and also, but to a smaller extent, below 2 km for all seasons.
Influx of aerosols from the mid-latitudes has a wide range of impacts on the Arctic atmosphere. In this study, the capability of the CALIPSO-CALIOP instrument to provide accurate observations of aerosol layers is exploited to characterize their vertical distribution, probability density functions (PDFs) of aerosol layer thickness, base and top heights, and optical depths over the Arctic for the 4-yr period from June 2006 to May 2010. It is shown that the bulk of aerosols, from about 65% in winter to 45% in summer, are confined below the lowermost kilometer of the troposphere. In the middle troposphere (3-5 km), spring and autumn seasons show slightly higher aerosol amounts compared to other two seasons. The relative vertical distribution of aerosols shows that clean continental aerosol is the largest contributor in all seasons except in summer, when layers of polluted continental aerosols are almost as large. In winter and spring, polluted continental aerosols are the second largest contributor to the total number of observed aerosol layers, whereas clean marine aerosol is the second largest contributor in summer and autumn. The PDFs of the geometrical thickness of the observed aerosol layers peak about 400-700 m. Polluted continental and smoke aerosols, which are associated with the intrusions from mid-latitudes, have much broader distributions of optical and geometrical thicknesses, suggesting that they appear more often optically thicker and higher up in the troposphere.
Temperature inversions are one of the dominant features of the Arctic atmosphere and play a crucial role in various processes by controlling the transfer of mass and moisture fluxes through the lower troposphere. It is therefore essential that they are accurately quantified, monitored and simulated as realistically as possible over the Arctic regions. In the present study, the characteristics of inversions in terms of frequency and strength are quantified for the entire Arctic Ocean for summer and winter seasons of 2003 to 2008 using the AIRS data for the clear-sky conditions. The probability density functions (PDFs) of the inversion strength are also presented for every summer and winter month. Our analysis shows that although the inversion frequency along the coastal regions of Arctic decreases from June to August, inversions are still seen in almost each profile retrieved over the inner Arctic region. In winter, inversions are ubiquitous and are also present in every profile analysed over the inner Arctic region. When averaged over the entire study area (70 degrees N-90 degrees N), the inversion frequency in summer ranges from 69 to 86% for the ascending passes and 72-86% for the descending passes. For winter, the frequency values are 88-91% for the ascending passes and 89-92% for the descending passes of AIRS/AQUA. The PDFs of inversion strength for the summer months are narrow and right-skewed (or positively skewed), while in winter, they are much broader. In summer months, the mean values of inversion strength for the entire study area range from 2.5 to 3.9 K, while in winter, they range from 7.8 to 8.9 K. The standard deviation of the inversion strength is double in winter compared to summer. The inversions in the summer months of 2007 were very strong compared to other years. The warming in the troposphere of about 1.5-3.0K vertically extending up to 400 hPa was observed in the summer months of 2007.
Clouds forming during the summer monsoon over the Indian subcontinent affect its evolution through their radiative impact as well as the release of latent heat. While the latter is previously studied to some extent, comparatively little is known about the radiative impact of different cloud types and the vertical structure of their radiative heating/cooling effects. Therefore, the main aim of this study is to partly fill this knowledge gap by investigating and documenting the vertical distributions of the different cloud types associated with the Indian monsoon and their radiative heating/cooling using the active radar and lidar sensors on-board CloudSat and CALIPSO. The intraseasonal evolution of clouds from May to October is also investigated to understand pre-to-post monsoon transitioning of their radiative heating/cooling effects. The vertical structure of cloud radiative heating (CRH) follows the northward migration and retreat of the monsoon from May to October. Throughout this time period, stratiform clouds radiatively warm the middle troposphere and cool the upper troposphere by more than +/- 0.2 K day(-1) (after weighing by cloud fraction), with the largest impacts observed in June, July and August. During these months, the fraction of high thin cloud remains high in the tropical tropopause layer (TTL). Deep convective towers cause considerable radiative warming in the middle and upper troposphere, but strongly cool the base and inside of the TTL. This cooling is stronger during active (-1.23 K day(-1)) monsoon periods compared to break periods (-0.36 K day(-1)). The contrasting radiative warming effect of high clouds in the TTL is twice as largeduring active periods than in break periods. These results highlight the increasing importance of CRH with altitude, especially in the TTL. Stratiform (made up of alto- and nimbostratus clouds) and deep convection clouds radiatively cool the surface by approximately -100 and -400Wm(-2) respectively while warming the atmosphere radiatively by about 40 to 150Wm(-2). While the cooling at the surface induced by deep convection and stratiform clouds is largest during active periods of monsoon, the importance of stratiform clouds further increases during break periods. The contrasting CREs (cloud radiative effects) in the atmosphere and at surface, and during active and break periods, should have direct implications for the monsoonal circulation.
Understanding the coupling of clouds to large-scale circulation is one of the grand challenges for the global climate research community. In this context, realistically modelling the vertical structure of cloud radiative heating (CRH) and/or cooling in Earth system models is a key premise to understand this coupling. Here, we evaluate CRH in two versions of the European Community Earth System Model (EC-Earth) using retrievals derived from the combined radar and lidar data from the CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites. One model version is also used with two different horizontal resolutions. Our study evaluates large-scale intraseasonal variability in the vertical structure of CRH and cloud properties and investigates the changes in CRH during different phases of the El Nino-Southern Oscillation (ENSO), a process that dominates the interannual climate variability in the tropics. EC-Earth generally captures both the intraseasonal and meridional pattern of variability in CRH over the convectively active and stratocumulus regions and the CRH during the positive and negative phases of ENSO. However, two key differences between model simulations and satellite retrievals emerge. First, the magnitude of CRH, in the upper troposphere, over the convectively active zones is up to twice as large in the models compared to the satellite data. Further dissection of net CRH into its shortwave and longwave components reveals noticeable differences in their vertical structure. The shortwave component of the radiative heating is overestimated by all model versions in the lower-most troposphere and underestimated in the middle troposphere. These over- and underestimates of shortwave heating are partly compensated by an overestimate of longwave cooling in the lowermost troposphere and heating in the middle troposphere. The biases in CRH can be traced back to disagreement in cloud amount and cloud water content. There is no noticeable improvement of CRH by increasing the horizontal resolution in the model alone. Our findings highlight the importance of evaluating models with satellite observations that resolve the vertical structure of clouds and cloud properties.
Two alternative methods for probabilistic cloud masking of images from the Advanced Very High Resolution Radiometer (AVHRR) sensor have been examined. Both methods are based on Bayesian theory and were trained using data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. Results were evaluated by comparing to independent CALIPSO-CALIOP observations and to a one-year ground-based cloud dataset composed from five different remote sensing systems over the observation site in Cabauw in the Netherlands. In addition, results were compared to two different cloud masks; one derived from the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor and one from the Climate Monitoring Satellite Application Facility Clouds (CMSAF), Albedo and Radiation dataset from AVHRR data (CLARA-A1). It was demonstrated that the probabilistic methods compare well with the referenced satellite datasets and for daytime conditions they provide even better performance than the reference methods. Among the two probabilistic approaches, it was found that the formulation based on a Naive Bayesian formulation (denoted PPS-Prob Naive) performed clearly superior to the formulation based on a linear summation of conditional cloud probabilities (denoted PPS-Prob SPARC) for daytime conditions. For the study based on the observations over the Cabauw site, the overall daytime Kuipers Skill Score for PPS-Prob Naive was 0.84, for PPS-Prob SPARC 0.79, for CLARA-A1 0.74 and for SEVIRI 0.66. Corresponding results for night-time conditions were less favourable for the probabilistic formulations (Kuipers Skill Score 0.74 for PPS-Prob Naive, 0.68 for PPS-Prob SPARC, 0.80 for CLARA-A1 and 0.79 for SEVIRI) but still relatively close to the reference dataset The Cabauw distribution of cloudiness occurrences in different octa categories was reproduced very closely by all methods, including the probabilistic formulations. Results based on Cabauw observations were also largely in good agreement with results deduced from comparisons with the CALIPSO-CALIOP cloud mask. The PPS-Prob Naive approach will be implemented in an upcoming version of the Polar Platform System (PPS) cloud software issued by the EUMETSAT Nowcasting Satellite Application Facility (NWC SAF). It will also be used in the second release of the CMSAF CLARA cloud climate data record based on historic AVHRR GAC data (to be denoted CIARA-A2). (C) 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-SA license
Spatial and temporal variations of summer sea ice albedo over the Arctic are analyzed using an ensemble of historical CMIP5 model simulations. The results are compared to the CLARA-SAL product that is based on long-term satellite observations. The summer sea ice albedo varies substantially among CMIP5 models, and many models show large biases compared to the CLARA-SAL product. Single summer months show an extreme spread of ice albedo among models; July values vary between 0.3 and 0.7 for individual models. The CMIP5 ensemble mean, however, agrees relatively well in the central Arctic but shows too high ice albedo near the ice edges and coasts. In most models, the ice albedo is spatially too uniformly distributed. The summer-to-summer variations seem to be underestimated in many global models, and almost no model is able to reproduce the temporal evolution of ice albedo throughout the summer fully. While the satellite observations indicate the lowest ice albedos during August, the models show minimum values in July and substantially higher values in August. Instead, the June values are often lower in the models than in the satellite observations. This is probably due to too high surface temperatures in June, leading to an early start of the melt season and too cold temperatures in August causing an earlier refreezing in the models. The summer sea ice albedo in the CMIP5 models is strongly governed by surface temperature and snow conditions, particularly during the period of melt onset in early summer and refreezing in late summer. The summer surface net solar radiation of the ice-covered Arctic areas is highly related to the ice albedo in the CMIP5 models. However, the impact of the ice albedo on the sea ice conditions in the CMIP5 models is not clearly visible. This indicates the importance of other Arctic and large-scale processes for the sea ice conditions.
The objective of this paper is to assess the accuracy of the Semi-Analytical CloUd Retrieval Algorithm (SACURA) that retrieves cloud-top heights (CTHs) using hyperspectral SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard Environmental Satellite measurements for overcast single-layer cloud fields. Intercomparisons with ground-based 35-GHz millimeter wave cloud radar CTHs were performed for 14 dates during 2003-2007 at the US. Atmospheric Radiation Measurement (ARM) program Southern Great Plains site (36.6 degrees N, 97.5 degrees W). In addition, for some of these dates, European Space Agency MEdium Resolution Imaging Spectrometer (MERIS) and the NASAL-TERRA Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-top pressure retrievals were also collected, transformed into CTHs using nearby ARM radiosonde profiles, and compared with the SACURA SCIAMACHY and radar retrievals. The accuracy of the SACURA-SCIAMACHY CTH retrievals is better than 0.34 km for low-level clouds and 2.22 km for high-level clouds with an underestimate in CTH on average for all clouds. The average bias in SCIAMACHY CTHs was about 0.07 km for low clouds and about 0.5 km for high-level clouds. Both MODIS and MERIS slightly overestimated the CTHs of low-level clouds by MO m, with an uncertainty better than 1 km. However, although MODIS accuracy for high-level clouds is close to SCIAMACHY, MERIS CTHs were significantly underestimated for these fairly optically thick cases.
We characterize the climatological features of the double inter-tropical convergence zones (DITCZs) over the western Indian Ocean during November-December by a synergistic analysis of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS III) data (1988-2005) and the National Aeronautics and Space Administration's (NASA's) A-Train data (2002-2009). We investigate rainfall, freshwater flux and cloud liquid water, cloud fraction and relative humidity over the DITCZs. In addition, the daily rainfall data from the Global Precipitation Climatology Project (GPCP) are used to document the DITCZs during the El Nino southern oscillation (ENSO) events. An analysis of the GPCP data shows that the DITCZs are clearly discernible during strong ENSO events (1997, 2002 and 2006), in sharp contrast to the DITCZs in the eastern Pacific Ocean, where they are absent during ENSOs. Further, these convergence zones on either side of the equator are of short duration, approximately 3-6 pentads during November and December. All satellite sensor data sets consistently capture the major features of DITCZs. As an accurate simulation of DITCZs in coupled global climate models remains a challenge, the results from the present study would provide a platform for evaluating these models.
Accurate snowfall estimates are important for both weather and climate applications. Ground-based weather radars and space-based satellite sensors are often used as viable alternatives to rain gauges to estimate precipitation in this context. In particular, the Cloud Profiling Radar (CPR) on board CloudSat is proving to be a useful tool to map snowfall globally, in part due to its high sensitivity to light precipitation and its ability to provide near-global vertical structure. CloudSat snowfall estimates play a particularly important role in the high-latitude regions as other ground-based observations become sparse and passive satellite sensors suffer from inherent limitations. In this paper, snowfall estimates from two observing systems-Swerad, the Swedish national weather radar network, and CloudSat - are compared. Swerad offers a well-calibrated data set of precipitation rates with high spatial and temporal resolution, at very high latitudes. The measurements are anchored to rain gauges and provide valuable insights into the usefulness of CloudSat CPR's snowfall estimates in the polar regions. In total, 7 : 2 x 10(5) matchups of CloudSat and Swerad observations from 2008 through 2010 were intercompared, covering all but the summer months (June to September). The intercomparison shows encouraging agreement between the two observing systems despite their different sensitivities and user applications. The best agreement is observed when CloudSat passes close to a Swerad station (46-82 km), where the observational conditions for both systems are comparable. Larger disagreements outside this range suggest that both platforms have difficulty with shallow snow but for different reasons. The correlation between Swerad and CloudSat degrades with increasing distance from the nearest Swerad station, as Swerad's sensitivity decreases as a function of distance. Swerad also tends to overshoot low-level precipitating systems further away from the station, leading to an underestimation of snowfall rate and occasionally to missing precipitation altogether. Several statistical metrics-including the probability of detection, false alarm rate, hit rate, and Pierce's skill score - are calculated. The sensitivity of these metrics to the snowfall rate, as well as to the distance from the nearest radar station, are summarised. This highlights the strengths and the limitations of both observing systems at the lower and upper ends of the snowfall distributions as well as the range of uncertainties that can be expected from these systems in high-latitude regions.
Analyzing temporal series of satellite data for regional scale studies demand high accuracy in calibration and precise geo-rectification at higher spatial resolution. The Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration (NOAA) series of satellites provide daily observations for the last 30 years at a nominal resolution of 1.1 km at nadir. However, complexities due to on-board malfunctions and orbital drifts with the earlier missions hinder the usage of these images at their original resolution. In this study, we developed a new method using multiple open source tools which can read level 1B radiances, apply solar and thermal calibration to the channels, remove bow-tie effects on wider zenith angles, correct for clock drifts on earlier images and perform precise geo-rectification by automated generation and filtering of ground control points using a feature matching technique. The entire workflow is reproducible and extendable to any other geographical location. We developed a time series of brightness temperature maps from AVHRR local area coverage images covering the sub alpine lakes of Northern Italy at 1 km resolution (1986-2014; 28 years). For the validation of derived brightness temperatures, we extracted Lake Surface Water Temperature (LSWT) for Lake Garda in Northern Italy and performed inter-platform (NOAA-x vs. NOAA-y) and cross-platform (NOAA-x vs. MODIS/ATSR/AATSR) comparisons. The MAE calculated over available same day observations between the pairs-NOAA-12/14, NOAA-17/18 and NOAA-18/19 are 1.18 K, 0.67 K, 0.35 K, respectively. Similarly, for cross-platform pairs, the MAE varied between 0.5 to 1.5 K. The validation of LSWT from various NOAA instruments with in-situ data shows high accuracy with mean R-2 and RMSE of 0.97 and 0.91 K respectively.
Monthly clear-sky anomalies of atmospheric temperature and water vapor over the East Siberian and Laptev Sea regions of the Arctic for 2003-2010 are examined here. This region experiences significant interannual variations in sea ice concentration and is also where ice loss was most apparent in the record year 2007. Clear-sky thermodynamic profiles come from the Atmospheric Infrared Sounder (AIRS) sensor onboard the Aqua satellite. Associated longwave (LW) and shortwave (SW) radiation-flux anomalies are estimated through radiative transfer modeling. Anomalies of temperature (+/- 10 K) and water vapor (+/- 1 g kg(-1)) often positively covary, resulting in distinct signatures in the clear-sky downwelling LW (LWD) anomalies, occasionally larger than +/- 10 W m(-2) around the 2003-2010 climatology. Estimates of mean greenhouse anomalies indicate a shift from negative to positive anomalies midway through the 8-year record. Sensitivity tests suggest that temperature anomalies are the strongest contributor to both LWD and greenhouse anomalies, relative to water-vapor anomalies; monthly averaging of column precipitable water yields relatively small anomalies (order 1 mm) that produce a linear response in greenhouse anomalies. Finally the clear-sky contribution to 2007 monthly ice thickness is estimated. Anomalous clear-sky radiation retards the total 2007 ice thickness by 0.3 m (15-30% of ice-thickness climatology), and anomalous LW radiation is most important for preconditioning the ice during the months prior to, and after, the summer melt season. A highly sensitive interaction between cloud fraction, surface albedo and LWD anomalies is found, and we develop a metric for determining clear-sky anomalous ice melt potential.
Among various factors that influence the long-range transport of pollutants in the free troposphere (FT), the prevailing atmospheric weather states probably play the most important role in governing characteristics and efficacy of such transport. The weather states, such as a particular wind pattern, cyclonic or anticyclonic conditions, and their degree of persistency determine the spatio-temporal distribution and the final fate of the pollutants. This is especially true in the case of Nordic countries, where baroclinic disturbances and associated weather fronts primarily regulate local meteorology, in contrast to the lower latitudes where a convective paradigm plays a similarly important role. Furthermore, the long-range transport of pollutants in the FT has significant contribution to the total column burden over the Nordic countries. However, there is insufficient knowledge on the large-scale co-variability of pollutants in the FT and atmospheric weather states based solely on observational data over this region. The present study attempts to quantify and understand this statistical co-variability while providing relevant meteorological background. To that end, we select eight weather states that predominantly occur over the Nordic countries and three periods of their persistency (3 days, 5 days, and 7 days), thus providing in total 24 cases to investigate sensitivity of free tropospheric carbon monoxide, an ideal tracer for studying pollutant transport, to these selected weather states. The eight states include four dominant wind directions (namely, NW, NE, SE and SW), cyclonic and anticyclonic conditions, and the enhanced positive and negative phases of the North Atlantic Oscillation (NAO). For our sensitivity analysis, we use recently released Version 6 retrievals of CO at 500 hPa from the Atmospheric Infrared Sounder (AIRS) onboard Aqua satellite covering the 11-year period from September 2002 through August 2013 and winds from the ECMWF's ERA-Interim project to classify weather states for the same 11-year period. We show that, among the various weather states studied here, southeasterly winds lead to highest observed CO anomalies (up to +8%) over the Nordic countries while transporting pollution from the central and eastern parts of Europe. The second (up to +4%) and third highest (up to +2.5%) CO anomalies are observed when winds are northwesterly (facilitating inter-continental transport from polluted North American regions) and during the enhanced positive phase of the NAO respectively. Higher than normal CO anomalies are observed during anticyclonic conditions (up to +1%) compared to cyclonic conditions. The cleanest conditions are observed when winds are northeasterly and during the enhanced negative phases of the NAO, when relatively clean Arctic air masses are transported over the Nordic regions in the both cases. In the case of nearly all weather states, the CO anomalies consistently continue to increase or decrease as the degree of persistency of a weather state is increased. The results of this sensitivity study further provide an observational basis for the process-oriented evaluation of chemistry transport models, especially with regard to the representation of large-scale coupling of chemistry and local weather states and its role in the long-range transport of pollutants in such models.
One of the key knowledge gaps when estimating aerosol forcing and their role in air quality is our limited understanding of their vertical distribution. As an active lidar in space, the CALIOP-CALIPSO is helping to close this gap. The descending orbital track of CALIPSO follows elongated semi-major axis of Sweden, slicing its atmosphere every 2-3 d, thus providing a unique opportunity to characterise aerosols and their verticality in all seasons irrespective of solar conditions. This favourable orbital configuration of CALIPSO over Sweden is exploited in the present study. Using five years of night-time aerosol observations (2006-2011), we investigated the vertical distribution of aerosols. The role of temperature inversions and winds in governing this distribution is additionally investigated using collocated AIRS-Aqua and ERA-Interim Reanalysis data. It is found that the majority of aerosols (up to 70%) are located within 1 km above the surface in the lowermost troposphere, irrespective of the season. In summer, convection and stronger mixing lift aerosols to slightly higher levels, but their noticeable presence in the upper free troposphere is observed in the winter half of the year, when the boundary layer is decoupled due to strong temperature inversions separating local sources from the transport component. When southerly winds prevail, two or more aerosol layers are most frequent over southern Sweden and the polluted air masses have higher AOD values. The depolarisation ratio and integrated attenuated backscatter of these aerosol layers are also higher. About 30-50% of all aerosol layers are located below the level where temperature inversions peak. On the other hand, relatively cleaner conditions are observed when the winds have a northerly component.
To reduce uncertainties and hence to obtain a better estimate of aerosol (direct and indirect) radiative forcing, next generation climate models aim for a tighter coupling between chemistry transport models and regional climate models and a better representation of aerosol-cloud interactions. In this study, this coupling is done by first forcing the Rossby Center regional climate model (RCA4) with ERA-Interim lateral boundaries and sea surface temperature (SST) using the standard cloud droplet number concentration (CDNC) formulation (hereafter, referred to as the 'stand-alone RCA4 version' or 'CTRL' simulation). In the stand-alone RCA4 version, CDNCs are constants distinguishing only between land and ocean surface. The meteorology from this simulation is then used to drive the chemistry transport model, Multiple-scale Atmospheric Transport and Chemistry (MATCH), which is coupled online with the aerosol dynamics model, Sectional Aerosol module for Large Scale Applications (SALSA). CDNC fields obtained from MATCH-SALSA are then fed back into a new RCA4 simulation. In this new simulation (referred to as 'MOD' simulation), all parameters remain the same as in the first run except for the CDNCs provided by MATCH-SALSA. Simulations are carried out with this model setup for the period 2005-2012 over Europe, and the differences in cloud microphysical properties and radiative fluxes as a result of local CDNC changes and possible model responses are analysed. Our study shows substantial improvements in cloud microphysical properties with the input of the MATCH-SALSA derived 3-D CDNCs compared to the stand-alone RCA4 version. This model setup improves the spatial, seasonal and vertical distribution of CDNCs with a higher concentration observed over central Europe during boreal summer (JJA) and over eastern Europe and Russia during winter (DJF). Realistic cloud droplet radii (CD radii) values have been simulated with the maxima reaching 13 mu m, whereas in the stand-alone version the values reached only 5 mu m. A substantial improvement in the distribution of the cloud liquid-water paths (CLWP) was observed when compared to the satellite retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the boreal summer months. The median and standard deviation values from the 'MOD' simulation are closer to observations than those obtained using the stand-alone RCA4 version. These changes resulted in a significant decrease in the total annual mean net fluxes at the top of the atmosphere (TOA) by -5 W m(-2) over the domain selected in the study. The TOA net fluxes from the 'MOD' simulation show a better agreement with the retrievals from the Clouds and the Earth's Radiant Energy System (CERES) instrument. The aerosol indirect effects are estimated in the 'MOD' simulation in comparison to the pre-industrial aerosol emissions (1900). Our simulations estimated the domain averaged annual mean total radiative forcing of -0.64 W m(-2) with a larger contribution from the first indirect aerosol effect (-0.57 W m(-2)) than from the second indirect aerosol effect (-0.14 W m(-2)).