Downwelling Longwave Research Articles

Evaluation of surface shortwave and longwave downwelling radiations over the global tropical oceans

In the present study, daily downwelling shortwave (QS) and longwave radiation (QL) data from one satellite and two hybrid products have been evaluated using Global Tropical Moored Buoy Array during 2001–2009 in the tropical oceans. Daily satellite data are used from the Clouds and Earth’s Radiant Energy System (CERES) program. Data are obtained using Moderate Resolution Imaging Spectroradiometer (MODIS) (CM) aboard the Terra and Aqua satellites. Coordinated Ocean Research Experiments (CORE-II) and Tropical Flux data (TropFlux) are the other two hybrid products used in this study. The analysis shows that majority of QS observations as well as derived products lie in 200–300 Wm−2 range in all the three tropical oceans. Both QS and QL in all products overestimated the majority of the observations. Yet, they underestimated the lower (0–100 Wm−2) values in QS and higher (300–440 Wm−2) values in QL. Majority of the QL observations lie within 390–420 Wm−2 range, and CM slightly overestimated this observed distribution in the Pacific and the Atlantic Oceans. But, majority of the observations in the Indian Ocean lie within 420–450 Wm−2 range. This implies that the tropical Indian Ocean receives 30 Wm−2 more energy as compared to the tropical Pacific and the Atlantic in the form of downwelling longwave radiation. Daily observed QS shows dominant seasonal cycle over the central, the eastern Pacific and the eastern Atlantic. On the other hand, the western Pacific, the central Atlantic and the Indian Oceans show intraseasonal variations. All products show this variation with high root-mean-square error (RMSE) values (QS and QL) over the Indian Ocean than in the Pacific and the Atlantic Oceans. Downwelling radiation from CORE-II shows highest RMSE (for both QS and QL) with least correlation coefficient (CC), and TropFlux has lowest RMSE and highest CC among all products in all three tropical oceans. CM has intermediate values of standard deviation, CC and RMSE. These results are not seasonally dependent, since the seasonal statistics are consistent with seasonal changes. Assuming that the SST is only driven by the downwelling shortwave and longwave fluxes, the errors associated with monthly SST can be as large as 0.2–0.3 (0.1–0.2) °C associated with errors in QS (QL). Both QS and QL in CORE-II have lower spatial variability as compared to other datasets. QL in the tropical oceans shows seasonal spatial variability determined by intertropical convergence zone positions. This variability does not change significantly over the Pacific and the Atlantic Oceans. The summer and winter monsoon patterns in the Indian Ocean guide the QL variability. Opposite to QS, higher QL values have lower variability. Thus, this study aims at finding better radiation dataset to use in the numerical models and deduce that satellite data could be an alternative to existing reanalysis products.

Near-Surface Biases in ERA5 Over the Canadian Prairies

We quantify the biases in the diurnal cycle of air temperature in ERA5, using hourly climate station data for four stations in Saskatchewan, Canada. Compared with ERA-Interim, the biases in ERA5 have been greatly reduced, and show no differences with snow cover. We compute fits to the ERA5 mean air temperature biases based on ERA5 effective cloud albedo. They can be used to improve the ERA5 diurnal cycle of air temperature for modeling agricultural processes. Diurnally, ERA5 has a negative wind speed bias, which increases quasi-linearly with wind speed, and is greater in the daytime than at night. We evaluate ERA5 precipitation against the original climate station precipitation data, and a second generation adjusted precipitation dataset by Mekis and Vincent [2011]. For the warm season, ERA5 has a high bias of 8±9% above the Mekis dataset. ERA5 is -22±7% below the Mekis estimate in winter, suggesting that their correction with snow may be too large. It is likely that the ERA5 precipitation bias is small, which is encouraging for agricultural modelling. Data from a BSRN site near Regina shows that the biases in the downwelling shortwave and longwave radiation estimates in ERA5 are small, and have changed little from ERA-Interim. We showed that the annual cycle of the Saskatchewan surface energy and water budgets in ERA5 are realistic. In particular the damping of extremes in summer precipitation by the extraction of soil water is comparable in ERA5 to our earlier observational estimate based on gravity satellite data.

Regional Arctic Amplification by a Fast Atmospheric Response to Anthropogenic Sulfate Aerosol Forcing in China

AbstractIt is known that an increase of water vapor over the Arctic is one of most plausible causes driving Arctic amplification. However, debate continues with regard to the explanation of the underlying mechanisms driving the increase of moisture over the Arctic region in the observations. Here, we used the Community Atmosphere Model with prescribed sea surface temperature along with reanalysis datasets to examine the role of fast atmospheric responses to the increase of anthropogenic sulfate aerosol concentrations in China. We found that it plays an additive role in moisture transport from the midlatitudes, resulting in warming of the Arctic region, especially around the Barents–Kara Seas. Specifically, sulfate aerosol forcing in China reduces the meridional temperature gradient and leads to the increase of moisture transport into the Arctic by altering atmospheric circulation. The resulting increase of moisture then leads to surface warming through the enhancement of the downwelling longwave radiation. This implies that Arctic warming around the Barents–Kara Seas has been accelerated, at least in part, by a fast atmospheric response to anthropogenic sulfate aerosol emissions in China in the recent past.

MicroclimUS: hourly estimates of historical microclimates for the United States of America with example applications.

Microclimatic data are required for many problems in pure and applied ecology. This data includes aboveground convective and radiative conditions as well as soil temperature and moisture. In cold regions, the connection between above- and belowground microclimates via snow cover is also critically important. Here I describe a data set of hourly microclimates for the continental United States, simulated from the years 1979 to 2017 across a grid of 2,287 locations approximately 60km apart. The data were generated with the NicheMapR microclimate model, driven by 0.04° gridded daily meteorological forcing data (air temperature, wind speed, humidity, solar radiation, air pressure, and rainfall). The aboveground microclimate variables include horizontal plane solar radiation, solar zenith angle, sky temperature (from which downwelling longwave radiation can be computed), air temperature, relative humidity, and wind speed at 1 and 200cm height, and snow depth. The belowground variables include soil temperature, pore humidity, soil moisture, and soil water potential for 0, 2.5, 5, 10, 15, 20, 30, 50, 100, and 200cm belowground. The computations are for four shade levels (0%, 50%, 75%, and 90%). The predictions are validated against detailed soil temperature, soil moisture, and snow observations and show enhanced performance over existing microclimatic data for the United States. The data set can be used for a wide variety of applications, including the computation of heat and water budgets of organisms, the potential for vegetation growth, and the computation of stress and growth indices. The use of daily forcing data also allows assessments of the consequences of extreme events including heat waves and drought. Example applications are provided for computing plant growth potential, lizard egg development and body temperature, and mammalian energy and water requirements. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper.

Multi-scale structure of tropical rainfall response to SST fluctuations

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 78:165-178 (2019) - DOI: https://doi.org/10.3354/cr01566 Multi-scale structure of tropical rainfall response to SST fluctuations Miguel Nogueira* Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisbon, Portugal *Corresponding author: mdnogueira@fc.ul.pt ABSTRACT: The sensitivity of tropical rainfall to sea surface temperature (SST) fluctuations was investigated using a multi-scale analysis framework, covering time scales between a few days and 1 decade. The analysis includes an intercomparison between a satellite-based observational dataset and two twentieth century general circulation model (GCM)-based products: the ERA-20CM ensemble of atmospheric simulations and the ERA-20C reanalysis. The results provide robust evidence for the physical linear response of regional rainfall to SST fluctuations at multi-year time scales. All datasets showed tight correlations between rainfall and local SST, the Niño 3.4 index and local surface downwelling longwave radiation (DLR) over wide tropical regions. Additionally, the rainfall-DLR correlation held under a clear sky approximation. These 3 correlations are tightly related since the Niño 3.4 index and clear sky DLR can be computed from SST alone. The latter requires a robust correlation between local precipitable water vapor (W) and SST (i.e. Clausius-Clapeyron relationship), which was confirmed at multi-year scales over large portions of the tropics. All datasets also showed a robust transition in all these correlations at faster time scales (below ~1 yr), suggesting a change in the physical mechanisms controlling rainfall (and W) variability. Based on the linear sensitivities, 3 simplified models were proposed to reconstruct 3-yearly tropical rainfall time series at 5° resolution, forced only by ERA-20CM’s prescribed SST fields. The simplified linear response models were shown to reproduce observations with similar accuracy to GCM-based products, despite having a (easily correctable) systematic bias. The good agreement between all datasets, including the simplified model, provides confidence to the representation of the tropical rainfall response to SST fluctuations in GCM-based and satellite-based products, at least at multi-year time scales. KEY WORDS: Tropical Rainfall · Multiscale Analysis · Rainfall sensitivity · Statistical modelling Full text in pdf format PreviousNextCite this article as: Nogueira M (2019) Multi-scale structure of tropical rainfall response to SST fluctuations. Clim Res 78:165-178. https://doi.org/10.3354/cr01566 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 78, No. 2. Online publication date: August 08, 2019 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2019 Inter-Research.

Connecting Direct Effects of CO2 Radiative Forcing to Ocean Heat Uptake and Circulation

AbstractThe ocean's response to direct atmospheric effects of increased carbon dioxide's (CO2) radiative forcing is examined. These direct effects are defined as the climate changes that result from forcing on a fast time scale of about a year, independent of the slower surface warming that the forcing also provokes. To evaluate how these direct effects impact ocean heat uptake and circulation, output of atmospheric general circulation model (GCM) simulations are used to force an ocean GCM with comprehensive boundary conditions. Perturbation simulations with the prescribed response to a quadrupling of atmospheric CO2 include altered surface winds, freshwater fluxes, downwelling shortwave radiation, and downwelling longwave cloud radiative effect. The perturbation simulations show that the intensification and poleward shift of surface winds, particularly in the Southern Ocean, strengthen the shallow overturning circulation in the tropical Pacific and deep overturning in the Atlantic. This, in turn, has a cooling effect on the global ocean at shallow depths. A two‐layer energy balance model, designed to capture transient global mean climate change, is adapted to account for the altered ocean heat uptake from direct effects. The direct change in global mean ocean heat uptake is a decrease of about 0.3 W/m2 for quadrupling of CO2, offsetting about 5% of the surface longwave forcing.

LASG Global AGCM with a Two-moment Cloud Microphysics Scheme: Energy Balance and Cloud Radiative Forcing Characteristics

Cloud dominates influence factors of atmospheric radiation, while aerosol-cloud interactions are of vital importance in its spatiotemporal distribution. In this study, a two-moment (mass and number) cloud microphysics scheme, which significantly improved the treatment of the coupled processes of aerosols and clouds, was incorporated into version 1.1 of the IAP/LASG global Finite-volume Atmospheric Model (FAMIL1.1). For illustrative purposes, the characteristics of the energy balance and cloud radiative forcing (CRF) in an AMIP-type simulation with prescribed aerosols were compared with those in observational/reanalysis data. Even within the constraints of the prescribed aerosol mass, the model simulated global mean energy balance at the top of the atmosphere (TOA) and at the Earth’s surface, as well as their seasonal variation, are in good agreement with the observational data. The maximum deviation terms lie in the surface downwelling longwave radiation and surface latent heat flux, which are 3.5 W m-2 (1%) and 3 W m-2 (3.5%), individually. The spatial correlations of the annual TOA net radiation flux and the net CRF between simulation and observation were around 0.97 and 0.90, respectively. A major weakness is that FAMIL1.1 predicts more liquid water content and less ice water content over most oceans. Detailed comparisons are presented for a number of regions, with a focus on the Asian monsoon region (AMR). The results indicate that FAMIL1.1 well reproduces the summer-winter contrast for both the geographical distribution of the longwave CRF and shortwave CRF over the AMR. Finally, the model bias and possible solutions, as well as further works to develop FAMIL1.1 are discussed.

The multi-scale structure of atmospheric energetic constraints on globally averaged precipitation

Abstract. This study presents a multi-scale analysis of cross-correlations based on Haar fluctuations of globally averaged anomalies of precipitation (P), precipitable water vapor (PWV), surface temperature (T), and atmospheric radiative fluxes. The results revealed an emergent transition between weak correlations at sub-yearly timescales (down to ∼5 days) to strong correlations at timescales larger than about ∼1–2 years (up to ∼1 decade). At multiyear timescales, (i) Clausius–Clapeyron becomes the dominant control of PWV (ρPWV,T≈0.9), (ii) surface temperature averaged over global land and over global ocean (sea surface temperature, SST) become strongly correlated (ρTland,SST∼0.6); (iii) globally averaged precipitation variability is dominated by energetic constraints, specifically the surface downwelling longwave radiative flux (DLR) (ρP,DLR≈-0.8) displayed stronger correlations than the direct response to T fluctuations, and (iv) cloud effects are negligible for the energetic constraints in (iii), which are dominated by clear-sky DLR. At sub-yearly timescales, all correlations underlying these four results decrease abruptly towards negligible values. Such a transition has important implications for understanding and quantifying the climate sensitivity of the global hydrological cycle. The validity of the derived correlation structure is demonstrated by reconstructing global precipitation time series at 2-year resolution, relying on the emergent strong correlations (P vs. clear-sky DLR). Such a simple linear sensitivity model was able to reproduce observed P anomaly time series with similar accuracy to an (uncoupled) atmospheric model (ERA-20CM) and two climate reanalysis (ERA-20C and 20CR). The linear sensitivity breaks down at sub-yearly timescales, whereby the underlying correlations become negligible. Finally, the relevance of the multi-scale framework and its potential for stochastic downscaling applications are demonstrated by deriving accurate monthly P probability density functions (PDFs) from the reconstructed 2-year P time series based on scale-invariant arguments alone. The derived monthly PDFs outperform the statistics simulated by ERA-20C, 20CR, and ERA-20CM in reproducing observations.

Anisotropic corrections for the downwelling radiative heat transfer flux from various types of aerosols

Comprehensive Monte Carlo simulations are used to correct deviations in the atmospheric downwelling longwave (DLW) radiative flux calculated by isotropic scattering assumptions. The widely used δ-M approximation is validated for low to medium values of aerosol loading. For very high aerosol loading conditions, the δ-M approximation incurs an error. Here we propose scaling corrections for extreme loading conditions routinely found in selected urban areas in Asia, but also in other continental and coastal areas susceptible to large-scale wildfire pollution (Western USA) or dust storms (Mediterranean regions and Northern Africa). The scaling rules are expressed as functions of the normalized aerosol optical depth t∗ and the scattering asymmetry factor eg. An exponential relationship between the DLW deviation that assumes isotropic scattering and t∗ is found, and the corresponding fitting coefficients are correlated for different types of aerosols (sample internal mixing, urban, continental and marine aerosols). The δ-M approximation is sufficiently accurate when aerosol optical depths (AOD) at the ground level are smaller than 0.5. For AOD beyond this threshold, the proposed scaling rule corrections should be used for estimation of downwelling thermal radiative fluxes. The effects of moisture content on aerosol composition and on DLW radiative fluxes are also investigated for all conditions of interest.

Does Surface Temperature Respond to or Determine Downwelling Longwave Radiation?

AbstractDownward longwave radiation (DLR) is often assumed to be an independent forcing on the surface energy budget in analyses of Arctic warming and land‐atmosphere interaction. We use radiative kernels to show that the DLR response to forcing is largely determined by surface temperature perturbations. We develop a method by which vertically integrated versions of the radiative kernels are combined with surface temperature and specific humidity to estimate the surface DLR response to greenhouse forcing. Through a decomposition of the DLR response, we estimate that changes in surface temperature produce at least 63% of the clear‐sky DLR response in greenhouse forcing, while the changes associated with clouds account for only 11% of the full‐sky DLR response. Our results suggest that surface DLR is tightly coupled to surface temperature; therefore, it cannot be considered an independent component of the surface energy budget.

Analysis of the temporal–spatial changes in surface radiation budget over the Antarctic sea ice region

The surface radiation budget (SRB) of the Antarctic sea ice region (ASIR) is important for the climate change in Antarctica, which partly explains the global warming. Long-term (1982–2015) satellite-derived (EUMETSAT Satellite Application Facility on Climate Monitoring, CLARA-A2) and reanalysis (European Centre for Medium-Range Weather Forecasts, ERA-Interim) datasets provided high quality and global coverage surface radiation products, which enabled us to quantify the SRB of the ASIR and widen our understanding of the polar climate. The downwelling longwave (LW) and shortwave (SW) radiation from the two datasets were validated at two Baseline Surface Radiation Network Stations (i.e., Neumayer and Syowa). Cloud LW emission dominated over cloud SW reflection and absorption due to high-albedo over the sea ice surface, thereby leading to a positive all-wave cloud radiative forcing (CRF) within 0–100 W m−2. This result indicated a warming effect on the surface of the ASIR, except during the austral summer. Compared with the all sky, substantial solar radiation was absorbed by the surface which under the clear sky will result in the increasing of temperature and sea ice melting during the austral summer; hence, the SW CRF had a major influence on all-wave CRF and caused more than −150 W m−2 of cooling near the edge of the sea ice. The summer averages of all-wave CRF over the ASIR surface were − 43.02 and − 35.74 W m−2 for the ERA-Interim and CLARA-A2 datasets, respectively. Contrary to the effect of CRF, the surface net LW radiation (LWnet) exhibited a cooling effect over the ASIR surface throughout the year and peaks at −47.21 W m−2 for CLARA-A2 dataset in December. Meanwhile, a warming effect was caused by the surface net SW radiation (SWnet) peaks at 101.52 and 104.09 W m−2 for the ERA-Interim and CLARA-A2 datasets, respectively. In summer, the warming effect caused by the surface net all-wave radiation in East Antarctica was deeper than that in West Antarctica. The time series and trend analysis showed that the surface net radiation and CRF over the entire ASIR basically exhibited a decreasing trend. The descending speed of SWnet (−3.652 W m−2 decade−1) about five times faster than that of LWnet (−0.722 W m−2 decade−1). Although a significant trough appeared in 1993/1994 of the time series curves of All-wave CRF, it had a deeply negative trend with about −3.443 W m−2 decade−1. Detailed analysis on the five longitudinal sectors around Antarctica (the Weddell Sea, the Indian Ocean, the Pacific Ocean, the Ross Sea, and the Bellingshausen-Amundsen Sea) can further help us understand the SRB of the ASIR.

Revisiting the Linkages between the Variability of Atmospheric Circulations and Arctic Melt-Season Sea Ice Cover at Multiple Time Scales

Abstract The sharp decline of Arctic sea ice in recent decades has captured the attention of the climate science community. A majority of climate analyses performed to date have used monthly or seasonal data. Here, however, we analyze daily sea ice data for 1979–2016 using the self-organizing map (SOM) method to further examine and quantify the contributions of atmospheric circulation changes to the melt-season Arctic sea ice variability. Our results reveal two main variability modes: the Pacific sector mode and the Barents and Kara Seas mode, which together explain about two-thirds of the melt-season Arctic sea ice variability and more than 40% of its trend for the study period. The change in the frequencies of the two modes appears to be associated with the phase shift of the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO). The PDO and AMO trigger anomalous atmospheric circulations, in particular, the Greenland high and the North Atlantic Oscillation and anomalous warm and cold air advections into the Arctic Ocean. The changes in surface air temperature, lower-atmosphere moisture, and downwelling longwave radiation associated with the advection are consistent with the melt-season sea ice anomalies observed in various regions of the Arctic Ocean. These results help better understand the predictability of Arctic sea ice on multiple (synoptic, intraseasonal, and interannual) time scales.

Meteorological Drivers and Large-Scale Climate Forcing of West Antarctic Surface Melt

Understanding the drivers of surface melting in West Antarctica is crucial for understanding future ice loss and global sea level rise. This study identifies atmospheric drivers of surface melt on West Antarctic ice shelves and ice sheet margins and relationships with tropical Pacific and high-latitude climate forcing using multidecadal reanalysis and satellite datasets. Physical drivers of ice melt are diagnosed by comparing satellite-observed melt patterns to anomalies of reanalysis near-surface air temperature, winds, and satellite-derived cloud cover, radiative fluxes, and sea ice concentration based on an Antarctic summer synoptic climatology spanning 1979–2017. Summer warming in West Antarctica is favored by Amundsen Sea (AS) blocking activity and a negative phase of the southern annular mode (SAM), which both correlate with El Niño conditions in the tropical Pacific Ocean. Extensive melt events on the Ross–Amundsen sector of the West Antarctic Ice Sheet (WAIS) are linked to persistent, intense AS blocking anticyclones, which force intrusions of marine air over the ice sheet. Surface melting is primarily driven by enhanced downwelling longwave radiation from clouds and a warm, moist atmosphere and by turbulent mixing of sensible heat to the surface by föhn winds. Since the late 1990s, concurrent with ocean-driven WAIS mass loss, summer surface melt occurrence has increased from the Amundsen Sea Embayment to the eastern Ross Ice Shelf. We link this change to increasing anticyclonic advection of marine air into West Antarctica, amplified by increasing air–sea fluxes associated with declining sea ice concentration in the coastal Ross–Amundsen Seas.

Geographical and Seasonal Variability of Cloud‐Radiative Feedbacks on Precipitation

AbstractWe have used observations to study the temporal covariability of precipitation and atmospheric radiative cooling (ARC, defined as positive when the atmosphere is radiatively cooled) on seasonal and longer time scales. Clouds act to decrease the globally averaged ARC, but their radiative effect on the ARC varies with latitude. Clouds decrease the ARC in the tropics, mainly by reducing the outgoing longwave radiation, but they increase the ARC in higher latitudes, primarily by increasing the downwelling longwave radiation at the surface. The temporal correlation of the zonally averaged precipitation rate and the zonally averaged ARC is about −0.7 in the tropics and +0.5 in higher latitudes, and it changes sign almost discontinuously toward the poles at approximately 30°N and 30°S. This suggests that changes in the ARC feed back negatively on precipitating tropical systems and positively on precipitating systems at higher latitudes.

Using CALIOP to estimate cloud-field base height and its uncertainty: the Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm and dataset

Abstract. A technique is presented that uses attenuated backscatter profiles from the CALIOP satellite lidar to estimate cloud base heights of lower-troposphere liquid clouds (cloud base height below approximately 3 km). Even when clouds are thick enough to attenuate the lidar beam (optical thickness τ≳5), the technique provides cloud base heights by treating the cloud base height of nearby thinner clouds as representative of the surrounding cloud field. Using ground-based ceilometer data, uncertainty estimates for the cloud base height product at retrieval resolution are derived as a function of various properties of the CALIOP lidar profiles. Evaluation of the predicted cloud base heights and their predicted uncertainty using a second statistically independent ceilometer dataset shows that cloud base heights and uncertainties are biased by less than 10 %. Geographic distributions of cloud base height and its uncertainty are presented. In some regions, the uncertainty is found to be substantially smaller than the 480 m uncertainty assumed in the A-Train surface downwelling longwave estimate, potentially permitting the most uncertain of the radiative fluxes in the climate system to be better constrained. The cloud base dataset is available at https://doi.org/10.1594/WDCC/CBASE.

Seasonal climatic effects and feedbacks of anthropogenic heat release due to global energy consumption with CAM5

Anthropogenic heat release (AHR) is the heat generated in global energy consumption, which has not been considered in global climate models generally. The global high-resolution AHR from 1992 to 2013, which is estimated by using the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) satellite data, is implemented into the Community Atmosphere Model version 5 (CAM5). The seasonal climatic effects and possible feedbacks of AHR are examined in this study. The modeling results show that AHR increases the global annual mean surface temperature and land surface temperature by 0.02 ± 0.01 K (1σ uncertainty) and 0.05 ± 0.02 K (1σ uncertainty), respectively. The global climatic effect of AHR varies with season: with a stronger climatic effect in the boreal winter leading to global mean land surface temperature increases by 0.10 ± 0.01 K (1σ uncertainty). In the selected regions (40°N–60°N, 0°E–45°E) of Central and Western Europe the average surface temperature increases by 0.46 K in the boreal summer, and in the selected regions (45°N–75°N, 30°E–140°E) of northern Eurasia the average surface temperature increases by 0.83 K in the boreal winter. AHR changes the height and thermodynamic structure of the global planetary boundary layer, as well as the stability of the lower troposphere, which affects the global atmospheric circulation and low cloud fraction. In addition, at the surface both the shortwave radiation flux in the boreal summer and the down-welling longwave flux in the boreal winter change significantly, as a result of the change in low clouds caused by the effect of AHR. This study suggests a possible new mechanism of AHR effect on global climate through changing the global low-cloud fraction, which is crucial for global energy balance, by modifying the thermodynamic structure and stability of the lower troposphere. Thus this study improves our understanding of the global climate change caused by human activities.

Climatic Responses to Future Trans-Arctic Shipping.

As global temperatures increase, sea ice loss will increasingly enable commercial shipping traffic to cross the Arctic Ocean, where the ships' gas and particulate emissions may have strong regional effects. Here we investigate impacts of shipping emissions on Arctic climate using a fully coupled Earth system model (CESM 1.2.2) and a suite of newly developed projections of 21st‐century trans‐Arctic shipping emissions. We find that trans‐Arctic shipping will reduce Arctic warming by nearly 1 °C by 2099, due to sulfate‐driven liquid water cloud formation. Cloud fraction and liquid water path exhibit significant positive trends, cooling the lower atmosphere and surface. Positive feedbacks from sea ice growth‐induced albedo increases and decreased downwelling longwave radiation due to reduced water vapor content amplify the cooling relative to the shipping‐free Arctic. Our findings thus point to the complexity in Arctic climate responses to increased shipping traffic, justifying further study and policy considerations as trade routes open.

Correction of CCI cloud data over the Swiss Alps using ground-based radiation measurements

Abstract. The validation of long-term cloud data sets retrieved from satellites is challenging due to their worldwide coverage going back as far as the 1980s. A trustworthy reference cannot be found easily at every location and every time. Mountainous regions present a particular problem since ground-based measurements are sparse. Moreover, as retrievals from passive satellite radiometers are difficult in winter due to the presence of snow on the ground, it is particularly important to develop new ways to evaluate and to correct satellite data sets over elevated areas. In winter for ground levels above 1000 m (a.s.l.) in Switzerland, the cloud occurrence of the newly released cloud property data sets of the ESA Climate Change Initiative Cloud_cci Project (Advanced Very High Resolution Radiometer afternoon series (AVHRR-PM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua series) is 132 to 217 % that of surface synoptic (SYNOP) observations, corresponding to a rate of false cloud detections between 24 and 54 %. Furthermore, the overestimations increase with the altitude of the sites and are associated with particular retrieved cloud properties. In this study, a novel post-processing approach is proposed to reduce the amount of false cloud detections in the satellite data sets. A combination of ground-based downwelling longwave and shortwave radiation and temperature measurements is used to provide independent validation of the cloud cover over 41 locations in Switzerland. An agreement of 85 % is obtained when the cloud cover is compared to surface synoptic observations (90 % within ± 1 okta difference). The validation data are then co-located with the satellite observations, and a decision tree model is trained to automatically detect the overestimations in the satellite cloud masks. Cross-validated results show that 62±13 % of these overestimations can be identified by the model, reducing the systematic error in the satellite data sets from 14.4±15.5 % to 4.3±2.8 %. The amount of errors is lower, and, importantly, their distribution is more homogeneous as well. These corrections happen at the cost of a global increase of 7±2 % of missed clouds. Using this model, it is possible to significantly improve the cloud detection reliability in elevated areas in the Cloud_cci AVHRR-PM and MODIS-Aqua products.

The unprecedented 2016-17 Arctic sea ice growth season: the crucial role of atmospheric rivers and longwave fluxes.

The 2016-17 Arctic sea ice growth season (October-March) exhibited the lowest end-of-season sea ice volume and extent of any year since 1979. An analysis of MERRA2 atmospheric reanalysis data and CERES radiative flux data reveals that a record warm and moist Arctic atmosphere supported the reduced sea ice growth through two pathways. First, numerous regional episodes of increased atmospheric temperature and moisture, transported from lower latitudes, increased the cumulative energy input from downwelling longwave surface fluxes. Second, in those same episodes, the efficiency that the atmosphere cooled radiatively to space was reduced, increasing the amount of energy retained in the Arctic atmosphere and reradiated back toward the surface. Overall, the Arctic radiative cooling efficiency shows a decreasing trend since 2000. The results presented highlight the increasing importance of atmospheric forcing on sea ice variability demonstrating that episodic Arctic atmospheric rivers, regions of elevated poleward water vapor transport, and the subsequent surface energy budget response is a critical mechanism actively contributing to the evolution of Arctic sea ice.

Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset

Abstract. Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds). Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (Calton et al., 2016) rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (Stackhouse Jr. et al., 2011) data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016). This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.