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South and East Asian anthropogenic aerosols mostly reside in an air mass extending from the Indian Ocean to the North Pacific. Yet the surface temperature effects of Asian aerosols spread across the whole globe. Here, we remove Asian anthropogenic aerosols from two independent climate models (ECHAM6.1 and NorESM1) using the same representation of aerosols via MACv2-SP (a simple plume implementation of the second version of the Max Planck Institute Aerosol Climatology). We then robustly decompose the global distribution of surface temperature responses into contributions from atmospheric energy flux changes. We find that the horizontal atmospheric energy transport strongly moderates the surface temperature response over the regions where Asian aerosols reside. Atmospheric energy transport and changes in clear-sky longwave radiation redistribute the temperature effects efficiently across the Northern Hemisphere and to a lesser extent also over the Southern Hemisphere. The model-mean global surface temperature response to Asian anthropogenic aerosol removal is 0.26±0.04 ∘C (0.22±0.03 for ECHAM6.1 and 0.30±0.03 ∘C for NorESM1) of warming. Model-to-model differences in global surface temperature response mainly arise from differences in longwave cloud (0.01±0.01 for ECHAM6.1 and 0.05±0.01 ∘C for NorESM1) and shortwave cloud (0.03±0.03 for ECHAM6.1 and 0.07±0.02 ∘C for NorESM1) responses. The differences in cloud responses between the models also dominate the differences in regional temperature responses. In both models, the northern-hemispheric surface warming amplifies towards the Arctic, where the total temperature response is highly seasonal and weakest during the Arctic summer. We estimate that under a strong Asian aerosol mitigation policy tied with strong climate mitigation (Shared Socioeconomic Pathway 1-1.9) the Asian aerosol reductions can add around 8 years' worth of current-day global warming during the next few decades.

The number of cloud droplets formed at the cloud base depends both on the properties of aerosol particles and the updraft velocity of an air parcel at the cloud base. As the spatial scale of updrafts is too small to be resolved in global atmospheric models, the updraft velocity is commonly parameterised based on the available turbulent kinetic energy. Here we present alternative methods through parameterising updraft velocity based on high-resolution large eddy simulation (LES) runs in the case of marine stratocumulus clouds. First we use our simulations to assess the accuracy of a simple linear parametrisation where the updraft velocity depends only on cloud top radiative cooling. In addition, we present two different machine learning methods (Gaussian process emulation and random forest) that account for different boundary layer conditions and cloud properties. We conclude that both machine learning parameterisations reproduce the LES-based updraft velocities at about the same accuracy, while the simple approach employing radiative cooling only produce on average lower coefficient of determination and higher root mean square error values. Finally, we apply these machine learning methods to find the key parameters affecting cloud base updraft velocities.

Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. Monitoring snow water equivalent (SWE) at continental scale is only possible from satellites, yet substantial uncertainties have been reported in NH SWE estimates. A recent bias-correction method significantly reduces the uncertainty of NH SWE estimation, which enables a more reliable analysis of the climate models' ability to describe the snow cover. We have intercompared the CMIP6 (Coupled Model Intercomparison Project Phase 6) and satellite-based NH SWE estimates north of 40° N for the period 1982–2014, and analyzed with a regression approach whether temperature (T) and precipitation (P) could explain the differences in SWE. We analyzed separately SWE in winter and SWE change rate in spring. The SnowCCI SWE data are based on satellite passive microwave radiometer data and in situ data. The analysis shows that CMIP6 models tend to overestimate SWE, however, large variability exists between models. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. This is in line with the expectation that even too cold temperatures cannot cause too high SWE without precipitation. T contributes to SWE biases mainly in regions, where T is close to 0 °C in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. Furthermore, it is obvious from the results that biases in T or P can not explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as deficiencies in model parameterizations and possibly biases in the observational datasets, also contribute to SWE discrepancies. In particular, linear regression suggests that when the biases in T and P are eliminated, the models generally overestimate the snowmelt rate in spring.

Significant discrepancies remain in estimates of climate impacts of anthropogenic aerosols between different general circulation models (GCMs). Here, we demonstrate that eliminating differences in model aerosol or radiative forcing fields results in close agreement in simulated globally averaged temperature and precipitation responses in the studied GCMs. However, it does not erase the differences in regional responses. We carry out experiments of equilibrium climate response to modern-day anthropogenic aerosols using an identical representation of anthropogenic aerosol optical properties and the first indirect effect of aerosols, MACv2-SP (a simple plume implementation of the second version of the Max Planck Institute Aerosol CLimatology), in two independent climate models (NorESM, Norwegian Earth System Model, and ECHAM6). We find consistent global average temperature responses of −0.48 (±0.02) and −0.50 (±0.03) K and precipitation responses of −1.69 (±0.04) % and −1.79 (±0.05) % in NorESM1 and ECHAM6, respectively, compared to modern-day equilibrium climate without anthropogenic aerosols. However, significant differences remain between the two GCMs' regional temperature responses around the Arctic circle and the Equator and precipitation responses in the tropics. The scatter in the simulated globally averaged responses is small in magnitude when compared against literature data from modern GCMs using model intrinsic aerosols but same aerosol emissions −(0.5–1.1) K and −(1.5–3.1) % for temperature and precipitation, respectively). The Pearson correlation of regional temperature (precipitation) response in these literature model experiments with intrinsic aerosols is 0.79 (0.34). The corresponding correlation coefficient for NorESM1 and ECHAM6 runs with identical aerosols is 0.78 (0.41). The lack of improvement in correlation coefficients between models with identical aerosols and models with intrinsic aerosols implies that the spatial distribution of regional climate responses is not improved via homogenizing the aerosol descriptions in the models. Rather, differences in the atmospheric dynamic and snow/sea ice cover responses dominate the differences in regional climate responses. Hence, even if we would have perfect aerosol descriptions inside the global climate models, uncertainty arising from the differences in circulation responses between the models would likely still result in a significant uncertainty in regional climate responses.

This study assesses the change in anthropogenic aerosol forcing from the mid-1970s to the mid-2000s. Both decades had similar global-mean anthropogenic aerosol optical depths but substantially different global distributions. For both years, we quantify (i) the forcing spread due to model-internal variability and (ii) the forcing spread among models. Our assessment is based on new ensembles of atmosphere-only simulations with five state-of-the-art Earth system models. Four of these models will be used in the sixth Coupled Model Intercomparison Project (CMIP6; Eyring et al., 2016). Here, the complexity of the anthropogenic aerosol has been reduced in the participating models. In all our simulations, we prescribe the same patterns of the anthropogenic aerosol optical properties and associated effects on the cloud droplet number concentration. We calculate the instantaneous radiative forcing (RF) and the effective radiative forcing (ERF). Their difference defines the net contribution from rapid adjustments. Our simulations show a model spread in ERF from −0.4 to −0.9 W m−2. The standard deviation in annual ERF is 0.3 W m−2, based on 180 individual estimates from each participating model. This result implies that identifying the model spread in ERF due to systematic differences requires averaging over a sufficiently large number of years. Moreover, we find almost identical ERFs for the mid-1970s and mid-2000s for individual models, although there are major model differences in natural aerosols and clouds. The model-ensemble mean ERF is −0.54 W m−2 for the pre-industrial era to the mid-1970s and −0.59 W m−2 for the pre-industrial era to the mid-2000s. Our result suggests that comparing ERF changes between two observable periods rather than absolute magnitudes relative to a poorly constrained pre-industrial state might provide a better test for a model's ability to represent transient climate changes.

To support the planning of future solar energy production in India, forthcoming changes in incoming surface solar radiation and the main physical factors contributing to the change were inferred from simulations performed with 27 global CMIP5 climate models. According to the multimodel-mean response, radiation diminishes by 0.5%–4% by the period 2030–59 (relative to 1971–2000), in tandem with strengthening aerosol and water vapor dimming. The largest reduction is anticipated for northern India. The evolution of incident radiation in the mid- and late twenty-first century depends substantially on the emission scenario. According to the representative concentration pathways RCP2.6 and RCP4.5, solar radiation would gradually recover close to the level that prevailed in the late twentieth century. This results from the peaking of aerosol loading before midcentury while the water vapor content continuously increases somewhat. Conversely, under RCP8.5, incident radiation would still decline, although more slowly than during the early century. This coincides with a substantial increase in atmospheric water vapor content and a modest decrease in aerosol forcing. In cloud forcing, multimodel-mean changes are minor, but divergence among the model simulations is substantial. Moreover, cloud forcing proved to be the factor that correlates most strongly with intermodel differences in the solar radiation response. Multimodel-mean changes in solar radiation are small and would not crucially affect the conditions of solar energy production. Nevertheless, some individual models simulate far more substantial reductions of up to ~10%.

We investigate how a regionally confined radiative forcing of South and East Asian aerosols translate into local and remote surface temperature responses across the globe. To do so, we carry out equilibrium climate simulations with and without modern day South and East Asian anthropogenic aerosols in two climate models with independent development histories (ECHAM6.1 and NorESM1). We run the models with the same anthropogenic aerosol representations via MACv2-SP (a simple plume implementation of the 2nd version of the Max Planck Institute Aerosol Climatology). This leads to a near identical change in instantaneous direct and indirect aerosol forcing due to removal of Asian aerosols in the two models. We then robustly decompose and compare the energetic pathways that give rise to the global and regional surface temperature effects in the models by a novel temperature response decomposition method, which translated the changes in atmospheric and surface energy fluxes into surface temperature responses by using a concept of planetary emissivity. We find that the removal of South and East Asian anthropogenic aerosols leads to strong local warming response from increased clear-sky shortwave radiation over the region, combined with opposing warming and cooling responses due to changes in cloud longwave and shortwave radiation. However, the local warming response is strongly modulated by the changes in horizontal atmospheric energy transport. Atmospheric energy transport and changes in clear-sky longwave radiation redistribute the surface temperature responses efficiently across the Northern hemisphere, and to a lesser extent also over the Southern hemisphere. The model-mean global surface temperature response to Asian anthropogenic aerosol removal is 0.26±0.04 °C (0.22±0.03 for ECHAM6.1 and 0.30±0.03 °C for NorESM1) of warming. Model-to-model differences in global surface temperature response mainly arise from differences in longwave cloud (0.01±0.01 for ECHAM6.1 and 0.05±0.01 °C for NorESM1) and shortwave cloud (0.03±0.03 for ECHAM6.1 and 0.07±0.02 °C for NorESM1) responses. The differences in cloud responses between the models also dominate the differences in regional temperature responses. In both models, the Northern hemispheric surface warming amplifies towards the Arctic, where the total temperature response is highly seasonal and modulated by seasonal changes in oceanic heat exchange and clear-sky longwave radiation.We estimate that under a strong Asian aerosol mitigation policy tied with strong greenhouse gas mitigation (Shared Socioeconomic Pathway 1-1.9) the Asian aerosol reductions can add around 8 years’ worth of current day global warming during the next few decades.

The development of robust emission metrics to guide climate policy is more complicated for short-lived climate forcers like black carbon (BC) than for long-lived greenhouse gases like CO2. The challenge is that for short-lived climate forcers, the atmospheric concentrations, the radiative forcing (RF), and ultimately, effects on climate, depend on the location and timing of the emissions. In the present work, the impact of emission location and season on the RF resulting from emissions of BC is studied using the NorESM1 climate model. NorESM1 is run in a configuration in which the distribution of aerosols is simulated using a state-of-the-art aerosol scheme, but the interactive aerosols are not allowed to influence the simulated meteorological conditions. Consequently, the patterns of weather are repeated identically irrespective of the assumed aerosol emissions. This allows for an essentially noise-free evaluation of the radiative forcing associated with changes in aerosol emissions, irrespective of the magnitude and spatiotemporal extent of the emission changes.We employ the model to systematically evaluate the radiative forcing efficiency (i.e., global-mean RF divided by the emissions) of BC emissions, for various assumptions about the latitude, longitude and season of the emissions. The BC direct effect and the effect of BC on snow albedo are considered. Preliminary results from tests focusing on BC emissions in the subarctic region (60-70°N) indicate the RF efficiency depends strongly both on the timing and longitude of the emissions. The RF efficiency of emissions in spring and summer is much larger than that of emissions in fall and winter, mainly due to the stronger insolation. Furthermore, emissions in the Siberian and North American sectors have higher RF efficiency than emissions in the Atlantic and European sectors. This is largely because emissions from subarctic Siberia and North America preferentially increase the atmospheric BC burden and BC deposition in regions with seasonal snow cover persisting into late spring / early summer. This acts to increase both the BC direct RF and the RF due to BC in snow. Furthermore, long atmospheric residence times act to increase the direct RF associated with Siberian BC emissions in summer.An implication is that the use of large-scale mean (e.g., subarctic average) emission metrics may mispresent the role of BC emissions from smaller regions like individual countries.

Projections for near-surface soil moisture content in Europe for the 21st century were derived from simulations performed with 26 CMIP5 global climate models (GCMs). Two Representative Concentration Pathways, RCP4.5 and RCP8.5, were considered. Unlike in previous research in general, projections were calculated separately for all four calendar seasons. To make the moisture contents simulated by the various GCMs commensurate, the moisture data were normalized by the corresponding local maxima found in the output of each individual GCM. A majority of the GCMs proved to perform satisfactorily in simulating the geographical distribution of recent soil moisture in the warm season, the spatial correlation with an satellite-derived estimate varying between 0.4 and 0.8. In southern Europe, long-term mean soil moisture is projected to decline substantially in all seasons. In summer and autumn, pronounced soil drying also afflicts western and central Europe. In northern Europe, drying mainly occurs in spring, in correspondence with an earlier melt of snow and soil frost. The spatial pattern of drying is qualitatively similar for both RCP scenarios, but weaker in magnitude under RCP4.5. In general, those GCMs that simulate the largest decreases in precipitation and increases in temperature and solar radiation tend to produce the most severe soil drying. Concurrently with the reduction of time-mean soil moisture, episodes with an anomalously low soil moisture, occurring once in 10 years in the recent past simulations, become far more common. In southern Europe by the late 21st century under RCP8.5, such events would be experienced about every second year.

The timing of springtime end of snowmelt (snow-off date) in northern Eurasia in version 5.4 of the ECHAM5 atmospheric general circulation model (GCM) is evaluated through comparison with a snow-off date data set based on space-borne microwave radiometer measurements and with Russian snow course data. ECHAM5 reproduces well the observed gross geographical pattern of snow-off dates, with earliest snow-off (in March) in the Baltic region and latest snow-off (in June) in the Taymyr Peninsula and in northeastern parts of the Russian Far East. The primary biases are (1) a delayed snow-off in southeastern Siberia (associated with too low springtime temperature and too high surface albedo, in part due to insufficient shielding by canopy); and (2) an early bias in the western and northern parts of northern Eurasia. Several sensitivity experiments were conducted, where biases in simulated atmospheric circulation were corrected through nudging and/or the treatment of surface albedo was modified. While this alleviated some of the model biases in snow-off dates, 2 m temperature and surface albedo, especially the early bias in snow-off in the western parts of northern Eurasia proved very robust and was actually larger in the nudged runs. A key issue underlying the snow-off biases in ECHAM5 is that snowmelt occurs at too low temperatures. Very likely, this is related to the treatment of the surface energy budget. On one hand, the surface temperature Ts is not computed separately for the snow-covered and snow-free parts of the grid cells, which prevents Ts from rising above 0 °C before all snow has vanished. Consequently, too much of the surface net radiation is consumed in melting snow and too little in heating the air. On the other hand, ECHAM5 does not include a canopy layer. Thus, while the albedo reduction due to canopy is accounted for, the shielding of snow on ground by the overlying canopy is not considered, which leaves too much solar radiation available for melting snow.

The global potential for collecting usable water from dew on an artificial collector sheet was investigated by utilizing 34 years of meteorological reanalysis data as input to a dew formation model. Continental dew formation was found to be frequent and common, but daily yields were mostly below 0.1 mm. Nevertheless, some water-stressed areas such as parts of the coastal regions of northern Africa and the Arabian Peninsula show potential for large-scale dew harvesting, as the yearly yield may reach up to 100 L m−2 for a commonly used polyethylene foil. Statistically significant trends were found in the data, indicating overall changes in dew yields of between ±10% over the investigated time period.

The impacts of representing cloud microphysical processes in a stochastic subcolumn framework are investigated, with emphasis on estimating the aerosol indirect effect. It is shown that subgrid treatment of cloud activation and autoconversion of cloud water to rain reduce the impact of anthropogenic aerosols on cloud properties and thus reduce the global mean aerosol indirect effect by 19%, from −1.59 to −1.28 W m−2. This difference is partly related to differences in the model basic state; in particular, the liquid water path (LWP) is smaller and the shortwave cloud radiative forcing weaker when autoconversion is computed separately for each subcolumn. However, when the model is retuned so that the differences in the basic state LWP and radiation balance are largely eliminated, the global-mean aerosol indirect effect is still 14% smaller (i.e. −1.37 W m−2) than for the model version without subgrid treatment of cloud activation and autoconversion. The results show the importance of considering subgrid variability in the treatment of autoconversion. Representation of several processes in a self-consistent subgrid framework is emphasized. This paper provides evidence that omitting subgrid variability in cloud microphysics contributes to the apparently chronic overestimation of the aerosol indirect effect by climate models, as compared to satellite-based estimates.

Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77–0.78 in the visible region) than in the spherical case ( ≈ 0.89). Therefore, for the same effective snow grain size (or equivalently, the same specific projected area), the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02–0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. −0.22 W m−2. Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2 m air temperature in NONSPH is 1.17 K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain size increased by ca. 70 %, the climatic differences to the SPH experiment become very small. Finally, the impact of assumed snow grain shape on the radiative effects of absorbing aerosols in snow is discussed.

This paper provides an overview of the HLRADIA shortwave (SW) and longwave (LW) broadband radiation schemes used in the HIRLAM numerical weather prediction (NWP) model and available in the HARMONIE-AROME mesoscale NWP model. The advantage of broadband, over spectral, schemes is that they can be called more frequently within the model, without compromising on computational efficiency. In mesoscale models fast interactions between clouds and radiation and the surface and radiation can be of greater importance than accounting for the spectral details of clear-sky radiation; thus calling the routines more frequently can be of greater benefit than the deterioration due to loss of spectral details. Fast but physically based radiation parametrizations are expected to be valuable for high-resolution ensemble forecasting, because as well as the speed of their execution, they may provide realistic physical perturbations. Results from single-column diagnostic experiments based on CIRC benchmark cases and an evaluation of 10 years of radiation output from the FMI operational archive of HIRLAM forecasts indicate that HLRADIA performs sufficiently well with respect to the clear-sky downwelling SW and longwave LW fluxes at the surface. In general, HLRADIA tends to overestimate surface fluxes, with the exception of LW fluxes under cold and dry conditions. The most obvious overestimation of the surface SW flux was seen in the cloudy cases in the 10-year comparison; this bias may be related to using a cloud inhomogeneity correction, which was too large. According to the CIRC comparisons, the outgoing LW and SW fluxes at the top of atmosphere are mostly overestimated by HLRADIA and the net LW flux is underestimated above clouds. The absorption of SW radiation by the atmosphere seems to be underestimated and LW absorption seems to be overestimated. Despite these issues, the overall results are satisfying and work on the improvement of HLRADIA for the use in HARMONIE-AROME NWP system is ongoing. In a HARMONIE-AROME 3-D forecast experiment we have shown that the frequency of the call for the radiation parametrization and choice of the parametrization scheme makes a difference to the surface radiation fluxes and changes the spatial distribution of the vertically integrated cloud cover and precipitation.

Climate–aerosol interactions in India are studied by employing the global climate–aerosol model ECHAM5-HAM and the GAINS inventory for anthropogenic aerosol emissions. Model validation is done for black carbon surface concentrations in Mukteshwar and for features of the monsoon circulation. Seasonal cycles and spatial distributions of radiative forcing and the temperature and rainfall responses are presented for different model setups. While total aerosol radiative forcing is strongest in the summer, anthropogenic forcing is considerably stronger in winter than in summer. Local seasonal temperature anomalies caused by aerosols are mostly negative with some exceptions, e.g., parts of northern India in March–May. Rainfall increases due to the elevated heat pump (EHP) mechanism and decreases due to solar dimming mechanisms (SDMs) and the relative strengths of these effects during different seasons and for different model setups are studied. Aerosol light absorption does increase rainfall in northern India, but effects due to solar dimming and circulation work to cancel the increase. The total aerosol effect on rainfall is negative for northern India in the months of June–August, but during March–May the effect is positive for most model setups. These differences between responses in different seasons might help converge the ongoing debate on the EHPs and SDMs. Due to the complexity of the problem and known or potential sources for error and bias, the results should be interpreted cautiously as they are completely dependent on how realistic the model is. Aerosol–rainfall correlations and anticorrelations are shown not to be a reliable sole argument for deducing causality.

Increases in Asian aerosol emissions have been suggested as one possible reason for the hiatus in global temperature increase during the past 15 years. We study the effect of sulphur and black carbon (BC) emission changes between 1996 and 2010 on the global energy balance. We find that the increased Asian emissions have had very little regional or global effects, while the emission reductions in Europe and the U.S. have caused a positive radiative forcing. In our simulations, the global-mean aerosol direct radiative effect changes by 0.06 W/m2 during 1996 to 2010, while the effective radiative forcing (ERF) is 0.42 W/m2. The rather large ERF arises mainly from changes in cloudiness, especially in Europe. In Asia, the BC warming due to sunlight absorption has largely offset the cooling caused by sulphate aerosols. Asian BC concentrations have increased by a nearly constant fraction at all altitudes, and thus, they warm the atmosphere also in cloudy conditions.

Using the long annual mean temperature time series provided by millennium Earth System Model simulations and a method of discrete Fourier transform with varying starting point and length of time window together with averaging, we get good fits to power laws between two characteristic oscillatory timescales of the model climate: multidecadal (50–80 years) and El Nino (3–6 years) timescales. For global mean temperature, we fit β ∼ 0.35 in a relation S(f) ∼ f−β in a simulation without external climate forcing and β over 0.7 in a simulation with external forcing included. The power law is found both with and without external forcing despite the forcings, e.g. the volcanic forcing, not showing similar behaviour, indicating a nonlinear temperature response to time-varying forcing. We also fit a power law with β ∼ 8 to the narrow frequency range between El Nino frequencies (up to 1/(3.2 years)) and the Nyquist frequency (1/(2 years)). Also, monthly mean temperature time series are considered and a decent power-law fit for frequencies above 1/year is obtained. Regional variability in best-fit β is explored, and the impact of choosing the frequency range on the result is illustrated. When all resolved frequencies are used, land areas seem to have lower βs than ocean areas on average, but when fits are restricted to frequencies below 1/(6 years), this difference disappears, while regional differences still remain. Results compare well with measurements both for global mean temperature and for the central England temperature record.

There has been growing interest in the vertical structure of the recent Arctic warming. We investigated temperatures at the surface, 925, 700, 500 and 300 hPa levels in the Arctic (north of 70° N) using observations and four reanalyses: ERA-Interim, CFSR, MERRA and NCEP II. For the period 1979–2011, the layers at 500 hPa and below show a warming trend in all seasons in all the chosen reanalyses and observations. Restricting the analysis to the 1998–2011 period, however, all the reanalyses show a cooling trend in the Arctic-mean 500 hPa temperature in autumn, and this also applies to both observations and the reanalyses when restricting the analysis to the locations with available IGRA radiosoundings. During this period, the surface observations mainly representing land areas surrounding the Arctic Ocean reveal no summertime trend, in contrast with the reanalyses whether restricted to the locations of the available surface observations or not. In evaluating the reanalyses with observations, we find that the reanalyses agree better with each other at the available IGRA sounding locations than for the Arctic average, perhaps because the sounding observations were assimilated into reanalyses. Conversely, using the reanalysis data only from locations matching available surface (air) temperature observations does not improve the agreement between the reanalyses. At 925 hPa, CFSR deviates from the other three reanalyses, especially in summer after 2000, and it also deviates more from the IGRA radiosoundings than the other reanalyses do. The CFSR error in summer T925 is due mainly to underestimations in the Canadian-Atlantic sector between 120° W and 0°. The other reanalyses also have negative biases in this longitude band.

Future seasonal changes in surface incident solar radiation and relative humidity (RH) over Europe and adjacent ocean areas were assessed based on phase 3 of the Coupled Model Intercomparison Project (CMIP3) model ensemble. Under the A1B scenario, by 2070–99, summertime solar radiation is projected to increase by 5%–10% in central and southern Europe. In winter, radiation decreases in most of northern and eastern Europe by 5%–15%. RH drops in summer in the southern European inland by 8%–12%, whereas in winter a small increase of 2%–3% is projected for northeastern Europe. In spring, the change is an intermediate between those in the extreme seasons, while in autumn the patterns resemble summer. Over the northern Atlantic Ocean, RH increases in all seasons by 1%–2%. The intermodel agreement on the sign of all these shifts is good, and the patterns recur in the responses to the A2 and B1 scenarios. Substantial changes are already simulated to occur before the midcentury, for example, in summer RH decreases by more than 5% in the inner Balkan Peninsula. Projected changes in these two variables agree well and are also mainly consistent with precipitation responses both in the multimodel mean and in individual models. According to all indicators, southern European summers become more arid, while winters, in the north particularly, become moister and darker. The increasing radiation and declining RH exacerbate summertime drought in southern Europe, whereas excessive humidity in the north may, for example, inflict moisture damages in constructions.

We have measured spectral albedo, as well as ancillary parameters, of seasonal European Arctic snow at Sodankylä, Finland (67°22' N, 26°39' E). The springtime intensive melt period was observed during the Snow Reflectance Transition Experiment (SNORTEX) in April 2009. The upwelling and downwelling spectral irradiance, measured at 290–550 nm with a double monochromator spectroradiometer, revealed albedo values of ~0.5–0.7 for the ultraviolet and visible range, both under clear sky and variable cloudiness. During the most intensive snowmelt period of four days, albedo decreased from 0.65 to 0.45 at 330 nm, and from 0.72 to 0.53 at 450 nm. In the literature, the UV and VIS albedo for clean snow are ~0.97–0.99, consistent with the extremely small absorption coefficient of ice in this spectral region. Our low albedo values were supported by two independent simultaneous broadband albedo measurements, and simulated albedo data. We explain the low albedo values to be due to (i) large snow grain sizes up to ~3 mm in diameter; (ii) meltwater surrounding the grains and increasing the effective grain size; (iii) absorption caused by impurities in the snow, with concentration of elemental carbon (black carbon) in snow of 87 ppb, and organic carbon 2894 ppb, at the time of albedo measurements. The high concentrations of carbon, detected by the thermal–optical method, were due to air masses originating from the Kola Peninsula, Russia, where mining and refining industries are located.

A new method for parameterizing the subgrid variations of vertical velocity and cloud droplet number concentration (CDNC) is presented for general circulation models (GCMs). These parameterizations build on top of existing parameterizations that create stochastic subgrid cloud columns inside the GCM grid cells, which can be employed by the Monte Carlo independent column approximation approach for radiative transfer. The new model version adds a description for vertical velocity in individual subgrid columns, which can be used to compute cloud activation and the subgrid distribution of the number of cloud droplets explicitly. Autoconversion is also treated explicitly in the subcolumn space. This provides a consistent way of simulating the cloud radiative effects with two-moment cloud microphysical properties defined at subgrid scale. The primary impact of the new parameterizations is to decrease the CDNC over polluted continents, while over the oceans the impact is smaller. Moreover, the lower CDNC induces a stronger autoconversion of cloud water to rain. The strongest reduction in CDNC and cloud water content over the continental areas promotes weaker shortwave cloud radiative effects (SW CREs) even after retuning the model. However, compared to the reference simulation, a slightly stronger SW CRE is seen e.g. over mid-latitude oceans, where CDNC remains similar to the reference simulation, and the in-cloud liquid water content is slightly increased after retuning the model.

We study climate effects of mid- and high-latitude volcanic eruptions seen in an ensemble of six 1200-year simulations with the COSMOS Earth System Model. 18 eruptions are studied, of which 7 happen in January, 8 in July and 3 in September. The average aerosol optical depth (AOD) over latitudes north of 30° N reached after the eruptions ranges from 0.1030 to 0.2941. The largest maximum monthly effects on clear-sky shortwave radiation occur for the January eruptions (in the following summer), but for the averaged quantity over the 21 months following the eruptions, the summer eruptions cause a larger effect. Largest zonal average radiative forcings are 5–6 W m‐2 (cooling). Average maximum perturbation of northern hemisphere monthly mean temperature is − 0.19 K and is on average − 0.095 K during the 21 months following an eruption. The maximum decrease in northern hemisphere temperature is not that sensitive to the time of the eruption but depends more on the size of the eruption. July and September eruptions, however, cause a larger average effect on northern hemisphere mean temperature for the 21 months following the eruptions. The precipitation anomaly is on average − 0.0058 mm/day during the 21 months following an eruption, and though the signal is weak compared to climate variability, our large sample allows us to assign 90% significance to the decrease in precipitation. Effects on atmospheric carbon dioxide and the resulting feedbacks are small.

We examine an oscillation of global mean temperature with a period of about two thirds of a century. We find evidence for the oscillation both in the instrumental temperature record and in an Earth System Model millennium simulation without external forcing. There is also evidence for the oscillation in the Central England Temperature record, the longest instrumental record available. Our method is based on a discrete Fourier transform with varying starting point and length of time window. This method allows us to make a quantitative estimate of the contribution of an oscillation to global mean temperature, to track the phase evolution of the oscillation and to compare measurement and model results. The multidecadal oscillation could provide part of the explanation both for near-constant global mean temperatures in recent years despite warming by rising concentrations of greenhouse gases and for declining global mean temperature in the 1950s and 1960s alongside with the explanation of aerosol cooling. Quantitative estimates of the contribution of the oscillation to global mean temperature vary between ±(0.03–0.17) K. For the instrumental temperature record, our results indicate an amplitude of 0.03 K presently if the IPCC model average represents the effect of external forcings well, and (0.08–0.17) K when using simple linear and quadratic fits for detrending. For the millennium simulation, the amplitude of the oscillation is (0.05–0.06) K, but could be underestimated as compared to reality if external forcing acts to globally synchronize multidecadal variability. The role of the Atlantic Multidecadal Oscillation (AMO) in the model is discussed. The AMO has a spatial temperature distribution similar to earlier literature results and is more correlated with the global oscillation when external forcing is included.

An evaluation of a statistical cloud scheme taking into account subgrid-scale variability for water vapour and cloud condensate in the ECHAM5 general circulation model of the atmosphere is presented. Three-dimensional modelled water vapour, cloud liquid water and cloud ice were distributed stochastically into subcolumns of each grid box and vertically integrated to total water path (TWP). Thus the lower atmosphere is emphasized in the evaluation of TWP due to its exponential profile. The edited model dataset was compared with the globally analyzed distribution of TWP measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument. The results show that the mean TWP and mean cloud cover are on average relatively well simulated. However, large deficiencies are revealed by the evaluation of both variance and skewness of the probability density function (PDF). Systematically negative deviations of variance are found for almost all regions of the globe. Skewness of the TWP is overestimated in the Tropics and underestimated at high latitudes. Moreover, sensitivity experiments were performed to reveal the deficiencies in the parametrization leading to the observed deviations of variance and skewness of TWP. It was found that the positive bias in skewness in the Tropics can be reduced by modifying the influence of convection on the PDF. Copyright © 2011 Royal Meteorological Society

A climate-aerosol model is employed to study spatial and temporal variability of aerosol properties over India and China for recent (year 2006) and future conditions (year 2020) under different emission pathways. We present results for aerosol mass concentration in different size classes and optical properties for the five different aerosol species treated by the model. Aerosol mass concentration and optical depth have significant contributions from both anthropogenic and natural aerosols. Different species have maxima in different regions, with the highest anthropogenic aerosol concentrations found in Kolkata and elsewhere in the Ganges basin in India and on the northern part of the east coast and in the Sichuan basin in China. In India natural aerosols have a maximum in the summer due to higher wind speeds and anthropogenic aerosols have a maximum in the winter due to less efficient wet removal. Surface concentrations are also higher in winter due to the additional reason of lower average boundary layer height. In China seasonal cycles are weaker with natural aerosols having a maximum in the spring and sulfate contribution to the aerosol optical depth (AOD) being higher in the latter half of the year. MODIS AOD spatial distributions are reproduced well by the model, except for the Ganges valley with high absorption and for the Thar desert with high dust concentrations. Seasonal cycles compare well qualitatively with MODIS results. The larger AOD in China during the latter half of the year in the year 2006 simulation as compared to the MODIS data can be traced back to sulfate contribution with some contribution also from natural aerosols.

The impact of modifying two physical parametrizations in the ECHAM5 atmospheric general circulation model (GCM) is reported. First, a diagnostic (relative humidity-based) cloud fraction scheme is replaced by one based on a prognostic description of the subgrid-scale distribution of total water content (the Tompkins scheme). Second, the subgrid-scale information provided by the Tompkins scheme is introduced into radiation calculations using the Monte Carlo Independent Column Approximation (McICA). Experiments are carried out in three model configurations: (1) ECHAM5 with prescribed distributions of sea-surface temperature and sea ice; (2) ECHAM5 coupled to a mixed-layer ocean model; and (3) ECHAM5 coupled to the MPIOM ocean GCM. The primary direct impact of replacing the RH-based cloud fraction scheme by the Tompkins scheme is an increase in very low cloudiness, mainly at mid and high latitudes, along with a reduction in mid-level cloudiness. The most notable effect of using McICA is a strengthening of the negative short-wave cloud radiative effect, without substantial effects on cloudiness. However, when compared to observational data, all model versions perform in essence equally well. For all of them, cloud field statistical properties show substantial differences from the International Satellite Cloud Climatology Project data; in particular, there is a general lack of low- and mid-level clouds with low optical depth. The differences in temperature, precipitation and sea-level pressure between the model versions are rather small. However, in spite of similar performance for present climate, the different model versions show marked differences in their response to increased atmospheric CO2. Copyright © 2010 Royal Meteorological Society

The Monte Carlo Independent Column Approximation (McICA) computes domain-average, broadband radiative flux profiles within conventional global climate models (GCMs). While McICA is unbiased with respect to the full ICA, it generates, as a by-product, random noise. If this by-product leads to statistically significant impacts on GCM simulations, it could limit the usefulness of McICA. This paper assesses the impact of McICA's random noise on six GCMs. To this end, the GCMs performed ensembles of 14-day long simulations for various renditions of McICA, each with differing amounts of random noise. As seen in the past, low-cloud fraction and surface temperature were affected most by noise. However, all GCM simulations using operationally viable renditions of McICA showed no statistically significant impacts, even for precipitation - a highly intermittent variable that one might expect to be sensitive to random fluctuations. Two GCMs showed statistically significant responses using an academic version of McICA that generates overly large sampling noise. Time series analyses of high-resolution (i.e. typically 2-hourly) data revealed that fluctuations associated with most variables and GCMs are immune to McICA noise. Moreover, the nature of these fluctuations can vary substantially among GCMs and most often they overwhelm any noise impacts. Overall, the results presented here corroborate a range of previous studies done on one GCM at a time: random noise produced by recommended versions of McICA has statistically insignificant effects on GCM simulations. Copyright c � 2008 Royal Meteorological Society and Her Majesty in Right of Canada.

The Monte Carlo independent-column approximation (McICA), as a method for computing domain-average radiative fluxes, allows a flexible treatment of unresolved cloud structure, and is unbiased with respect to the full independent-column approximation, but its flux estimates contain conditional random noise. In our previous study with the ECHAM5 atmospheric general-circulation model with prescribed sea-surface temperatures (SSTs), McICA noise caused a slight reduction in low-cloud fraction. Here, we first demonstrate that this feature originates from an immediate, nonlinear response of precipitation formation to McICA's random errors in radiative-heating rates, and is subsequently amplified by a radiative feedback. We then study the long-term impacts of McICA noise on climate in ECHAM5 simulations employing a mixed-layer ocean model. The use of interactive rather than prescribed SST somewhat amplifies the reduction in low-cloud fraction, which contributes to an overall warming of simulated climate with increasing McICA noise. For a typical implementation of McICA, the global-mean 2 m air temperature is 0.33 K higher than for a low-noise reference simulation. A 0.1 µm systematic increase in cloud-droplet effective radius re causes a similar global-mean warming (0.31 K), with generally similar spatial patterns. However, in the eastern tropical Pacific, McICA noise has locally larger effects than the uniform perturbation in re. It is concluded that the climatic impacts of McICA noise mainly represent a straightforward response to the systematic perturbation in the surface and top-of-atmosphere energy budget related to the initial reduction in low cloudiness. Copyright © 2008 Royal Meteorological Society

The Monte Carlo Independent Column Approximation (McICA) method for computing domain-average radiative fluxes allows a flexible treatment of unresolved cloud structure, and it is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. Here, tests of McICA in the ECHAM5 atmospheric GCM are reported. ECHAM5 provides an interesting test bed for McICA because it carries prognostic variables for the subgrid-scale probability distribution of total water content, which allows us to determine subgrid-scale cloud variability directly from the resolved-scale model variables. Three experiments with differing levels of radiative noise, each consisting of ten 6-yr runs, are performed to estimate the impact of McICA noise on simulated climate. In an experiment that attempted to deliberately maximize McICA noise, a systematic reduction in low cloud fraction occurred. For a more reasonable implementation of McICA, the impact of noise is very small, although statistically discernible. In terms of the impacts of noise, McICA appears to be a viable approach for use in ECHAM5. However, to improve the simulation of cloud radiative effects, realistic representation of both unresolved and resolved cloud structures is needed, which remains a challenging problem. Comparison of ECHAM5 data with a global cloud system–resolving model dataset and with International Satellite Cloud Climatology Project data suggested two problems related to unresolved cloud structures. First, ECHAM5 appears to underestimate subgrid-scale cloud variability. This problem seems partly related to the use of the beta distribution scheme for total water content in ECHAM5: in its current form, the scheme is unable to generate highly inhomogeneous clouds (relative standard deviation of condensate amount >1). Second, it appears that in ECHAM5, overcast cloud layers occur too frequently and partially cloudy layers too rarely. This problem is not unique to the beta distribution scheme; in fact, it is more pronounced when using an alternative, relative humidity–based cloud fraction scheme.

The Independent Column Approximation (ICA), a stochastic subgrid-scale cloudy column generator, and a dataset produced by an array of two-dimensional cloud system-resolving models are used to estimate radiative sensitivities and uncertainties for parameters that describe the macrostructure of layered cloud at scales unresolved by conventional global climate models (GCMs). Three cloud structural parameters are considered: vertical overlap of fractional cloud, vertical overlap of cloud condensate, and horizontal variability of condensate. Radiative sensitivity is the derivative of top of atmosphere (TOA) fluxes with respect to a cloud structural parameter. Radiative uncertainty is defined as the product of radiative sensitivity and corresponding uncertainty in structural parameter. It is argued that radiative sensitivities and uncertainties can help direct and prioritize development of subgrid-scale parametrizations. Using estimates of sensitivities and uncertainties for structural parameters, it is shown that uncertainties in TOA short-wave fluxes for cloud fraction overlap and horizontal variability are of similar magnitude averaged over the globe but display distinct spatial differences. Corresponding values for overlap of condensate are smaller and thus less important. For long-wave radiation, cloud horizontal variability is generally the most important of the three parameters considered here, but sensitivities, and thus uncertainties, are smaller than those for short-wave radiation. It is estimated that uncertainties associated with overlap of fractional cloud and horizontal variability are approximately as large as those associated with effective size of cloud particles. These results reinforce prior claims that GCM parametrizations for cloud overlap and horizontal variability should be addressed together, especially at solar wavelengths. These results are echoed in the appendix which examines TOA forcings that would arise in a GCM if the maximum-random overlap of homogeneous clouds model was replaced by a model that addresses the cloud parameters mentioned above. Copyright © 2005 Royal Meteorological Society.

To use the Monte Carlo Independent Column Approximation method for computing domain-average radiative fluxes in large-scale atmospheric models (LSAMs), a method is needed for generating cloudy subcolumns within LSAM columns. Here, a stochastic cloud generator is introduced to produce the subcolumns. The generator creates a cloud field on a column-by-column basis using information about layer cloud fraction, vertical overlap of cloud fraction and cloud condensate for adjacent layers, and density functions describing horizontal variations in cloud water content. The performance of the generator is assessed using a single day's worth of data from an LSAM simulation that employed a low-resolution two-dimensional cloud-resolving model (CRM) within each LSAM column (a total of ∼59 000 cloudy domains). Statistical characteristics of generated cloud fields are compared against original CRM data, and radiative-transfer biases associated with the generator are evaluated. When the generator is initialized to the greatest extent possible with information obtained from the CRM fields, overall biases are small. For example, global-mean total cloud fraction exhibits a bias of −0.004, as compared with −0.024 for maximum-random overlap (MRO) and 0.047 for random overlap. Biases in radiative fluxes and heating rates are in general ¼ to ½ those for MRO with horizontally homogeneous clouds. Copyright © 2004 Royal Meteorological Society

Output from a global climate model (GCM) that employed a low-resolution two-dimensional cloud-system-resolving model (CSRM) in each column is used to assess the radiative impact of neglecting subgrid-scale horizontal variations in cloud-droplet effective radius re. For this diagnostic study, only liquid-phase variations in re are addressed; the ice-cloud particle distributions are assumed to be constant. For reference calculations, values of re in the CSRM cells are computed assuming that the droplet-number concentration Ncld and the effective variance of droplet-size distribution are constant in a GCM cell. The independent-column approximation is used to produce flux profiles for each GCM column. Three alternative methods of setting horizontally-invariant re are examined, each of which resemble how re is set in one-dimensional radiative-transfer models. Relative to the reference calculations, the other methods lead to positive spurious radiative forcings at the surface and at the top of the atmosphere. These stem from overestimation of optical-depth variability and, thus, reduced short-wave albedo of clouds. Globally averaged, these forcings range from 1 W m−2 to 3 W m−2, with zonal-mean biases reaching almost 15 W m−2. The most severe biases arise from use of constant values of re over the land and the ocean. In addition, radiative effects due to unacknowledged uncertainty in Ncld (or re) are assessed. It is shown that ad hoc, but not outlandish, estimates of unbiased uncertainty in Ncld impart biases on estimates of the earth's solar-radiation budget (tantamount to a spurious radiative forcing). These arise through the chain of nonlinear relations that link Ncld to solar radiative transfer. Copyright © 2004 Royal Meteorological Society.

The primary purpose of this study is to assess the performance of 1D solar radiative transfer codes that are used currently both for research and in weather and climate models. Emphasis is on interpretation and handling of unresolved clouds. Answers are sought to the following questions: (i) How well do 1D solar codes interpret and handle columns of information pertaining to partly cloudy atmospheres? (ii) Regardless of the adequacy of their assumptions about unresolved clouds, do 1D solar codes perform as intended? One clear-sky and two plane-parallel, homogeneous (PPH) overcast cloud cases serve to elucidate 1D model differences due to varying treatments of gaseous transmittances, cloud optical properties, and basic radiative transfer. The remaining four cases involve 3D distributions of cloud water and water vapor as simulated by cloud-resolving models. Results for 25 1D codes, which included two line-by-line (LBL) models (clear and overcast only) and four 3D Monte Carlo (MC) photon transport ...

Effects of horizontal variations in liquid-water path (LWP) and droplet effective radius (re) on solar radiative-transfer properties of stratiform clouds are addressed. To this end, a broadband Monte Carlo solar radiation code and in situ aircraft observations of cloud properties collected in the Radiation, Aerosol and Cloud Experiment are used. The experiments confirm that the radiative impact of cloud horizontal inhomogeneity is dominated by variations in LWP, which make cloud short-wave albedo smaller than that for a corresponding plane-parallel horizontally homogeneous cloud. When horizontal variations in re are accounted for, however, the impact due to horizontal variations in LWP is usually reduced. When re and LWP are highly correlated, as is typically the case, the impact on albedo due to LWP variations alone can be reduced easily by 30% when variations in re are included. Uncertainty factors involved in the calculations are also discussed. Copyright © 2003 Royal Meteorological Society.

Two-stream approximations (TSAs) are employed for multiple-scattering calculations in most solar radiation schemes used in atmospheric general-circulation models (GCMs). Two issues related to TSAs are addressed. First, a simple extension of the two-stream formalism is presented that allows different parametrizations for different factors, such as water clouds, ice clouds and aerosols. A ‘tuned two-stream approximation’ that uses this extension is then developed. Second, the accuracy of the tuned TSA and selected other TSAs is quantified using a global GCM-generated dataset (nearly 25000 atmospheric columns with clouds, aerosols and absorbing gases). In the tests with the GCM dataset, the Practical Improved Flux Method (PIFM) provided slightly better results than the delta-Eddington approximation. Two recent modifications of delta-Eddington yielded no, or only slight, improvement. For PIFM, the root-mean-square (r.m.s.) errors in top-of-the-atmosphere (TOA) and surface net short-wave fluxes and in total column absorption were 2.22, 3.09 and 2.98 W m−2, as compared with delta-16-stream discrete-ordinate method results. For the tuned TSA, which was developed using the present dataset, the TOA and surface r.m.s. errors were 33% and 48% smaller than for PIFM, with substantially larger improvements at low solar elevations. The robustness of these results is tested in several sensitivity experiments in which the properties of the GCM dataset are modified systematically. While typical delta-TSAs performed, overall, fairly well, they all had a negative bias in atmospheric absorption and a positive bias in surface net short-wave flux of ≈1 W m−2 in the global mean (including, also, night grid points). Furthermore, the absorption errors depended substantially on solar elevation. The reasons for these problems are discussed. The tests with GCM data also confirmed that the delta-four-stream method is considerably more accurate than the TSAs, especially so for atmospheric absorption. Copyright © 2002 Royal Meteorological Society

The global potential for collecting usable water from dew on an artificial collector sheet was investigated by utilising 34 years of meteorological reanalysis data as input to a dew formation model. Dew formation was found to be frequent and common, but daily yields were mostly below 0.1 mm. Nevertheless, some water-stressed areas such as the coastal regions of northern Africa and the Arabian Peninsula show potential for large-scale dew harvesting, as the yearly yield can reach up to 100 L m−2 for a commonly used polyethylene foil. Statistically significant trends were found in the data, indicating overall changes in dew yields between ±15% over the investigated time period.

The timing of springtime end of snow melt (snow-off date) in Northern Eurasia in version 5.4 of the ECHAM5 atmospheric GCM is evaluated through comparison with a snow-off date dataset based on space-borne microwave radiometer measurements and with Russian snow course data. ECHAM5 reproduces well the observed gross geographical pattern of snow-off dates, with earliest snow-off (in March) in the Baltic region and latest snow-off (in June) in the Taymyr Peninsula and in northeastern parts of the Russian Far East. The primary biases are (1) a delayed snow-off in southeastern Siberia (associated with too low springtime temperature and too high surface albedo, in part due to insufficient shielding by canopy); and (2) an early bias in the western and northern parts of Northern Eurasia. Several sensitivity experiments were conducted, where biases in simulated atmospheric circulation were corrected through nudging and/or the treatment of surface albedo was modified. While this alleviated some of the model biases in snow-off dates, 2 m temperature and surface albedo, especially the early bias in snow-off in the western parts of the Northern Eurasia proved very robust and was actually larger in the nudged runs. A key issue underlying the snow-off biases in ECHAM5 is that snow melt occurs at too low temperatures. Very likely, this is related to the treatment of the surface energy budget. On one hand, the surface temperature Ts is not computed separately for the snow-covered and snow-free parts of the grid cells, which prevents Ts from rising above 0 °C before all snow has vanished. Consequently, too much (too little) of the surface net radiation is consumed in melting snow (heating the air). On the other hand, ECHAM5 does not include a canopy layer. Thus, while the albedo reduction due to canopy is accounted for, the shielding of snow on ground by the overlying canopy is not considered, which leaves too much solar radiation available for melting snow.

The representation of the effect of tropical deep convective (DC) systems on upper-tropospheric moist processes and outgoing longwave radiation is evaluated in the EC-Earth3, ECHAM6, and CAM5 (Community Atmosphere Model) climate models using satellite-retrieved data. A composite technique is applied to thousands of deep convective systems that are identified using local rain rate maxima in order to focus on the temporal evolution of the deep convective processes in the model and satellite-retrieved data. The models tend to over-predict the occurrence of rain rates that are less than ≈ 3 mm h−1 compared to Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA). While the diurnal distribution of oceanic rain rate maxima in the models is similar to the satellite-retrieved data, the land-based maxima are out of phase. Despite having a larger climatological mean upper-tropospheric relative humidity, models closely capture the satellite-derived moistening of the upper troposphere following the peak rain rate in the deep convective systems. Simulated cloud fractions near the tropopause are larger than in the satellite data, but the ice water contents are smaller compared with the satellite-retrieved ice data. The models capture the evolution of ocean-based deep convective systems fairly well, but the land-based systems show significant discrepancies. Over land, the diurnal cycle of rain is too intense, with deep convective systems occurring at the same position on subsequent days, while the satellite-retrieved data vary more in timing and geographical location. Finally, simulated outgoing longwave radiation anomalies associated with deep convection are in reasonable agreement with the satellite data, as well as with each other. Given the fact that there are strong disagreements with, for example, cloud ice water content, and cloud fraction, between the models, this study supports the hypothesis that such agreement with satellite-retrieved data is achieved in the three models due to different representations of deep convection processes and compensating errors.

This paper presents a comparison of several longwave (LW) downwelling radiative flux parameterizations with hourly averaged pointwise surface-radiation observations made at Sodankyla, Finland, in 1997 and 1999. Both clear and cloudy nighttime conditions are considered. The comparisons covered a 2-m temperature range from +11°C all the way to −49°C. The clear-sky comparisons included eight simple LW parameterizations, which mainly use screen-level input data, and four radiation schemes from numerical weather prediction (NWP) models: the former European Centre for Medium-Range Weather Forecast (ECMWF) scheme, the Deutscher Wetterdienst (DWD) scheme, the High Resolution Limited Area Model (HIRLAM) scheme, and the new ECMWF LW scheme (Rapid Radiative Transfer Model, RRTM). Atmospheric-sounding profiles were used as input for the NWP schemes. For the cases with clouds, three simple cloud-correction methods were tested. Almost all LW schemes usually underestimated the downwelling clear-sky flux, particularly, in cold (surface inversion) conditions. Overall, the RRTM scheme performed best. The simple cloud-correction methods turned out to be useful in the LW region. Finally, some new simple parameterization formulas were developed using the present data.

The effect of vertical resolution on cloudy-sky radiation calculations is investigated through idealized, single-column experiments and extensive tests with a high vertical resolution (100 layers) general circulation model (GCM)-generated data set. As examples of GCM-type radiation codes, the European Centre for Medium-Range Weather Forecasts (ECMWF) and Deutscher Wetterdienst (DWD) schemes are considered. The basic assumption in the tests is that vertical discretization distorts the profiles of temperature, absorbing gases, liquid water, and ice only by removing the subgrid-scale details. On the whole, cloudy-sky radiation calculations appear significantly more sensitive to vertical resolution than clear-sky calculations, owing to the following reasons: (1) Both in the longwave and in the shortwave, a critical factor is how the assumed subgrid-scale horizontal distribution of cloud water changes with vertical resolution. This issue also depends on the cloud overlap assumptions, random overlap tending to lead to increasing cloud cover and cloud forcing with improving resolution. (2) Application of maximum-random overlap to effective cloud fractions in the ECMWF longwave scheme leads, in particular, to severe underestimation of cloud forcing at the top of the atmosphere at high resolution. (3) The assumption (in the ECMWF scheme) that cloud layers emit upward (downward) at the exact layer top (bottom) temperature tends to lead to overestimated longwave cloud forcing at coarse resolution; the same also occurs if (4) cloud top (bottom) heights are overestimated (underestimated) at coarse resolution. (5) Cloud optics parameterizations may be dependent on vertical resolution (this affects, to some extent, the shortwave results of both schemes considered). On the positive side, both the longwave and shortwave results of the DWD scheme depend little on vertical resolution if vertical discretization does not distort the cloud properties.

Atmospheric aerosol particles influence the climate system directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. Apart from black carbon aerosol, aerosols cause a negative radiative forcing at the top of the atmosphere and substantially mitigate the warming caused by greenhouse gases. In the future, tightening of controls on anthropogenic aerosol and precursor vapour emissions to achieve higher air quality may weaken this beneficial effect. Natural aerosols, too, might affect future warming. Here we analyse long-term observations of concentrations and compositions of aerosol particles and their biogenic precursor vapours in continental mid- and high-latitude environments. We use measurements of particle number size distribution together with boundary layer heights derived from reanalysis data to show that the boundary layer burden of cloud condensation nuclei increases exponentially with temperature. Our results confirm a negative feedback mechanism between the continental biosphere, aerosols and climate: aerosol cooling effects are strengthened by rising biogenic organic vapour emissions in response to warming, which in turn enhance condensation on particles and their growth to the size of cloud condensation nuclei. This natural growth mechanism produces roughly 50% of particles at the size of cloud condensation nuclei across Europe. We conclude that biosphere-atmosphere interactions are crucial for aerosol climate effects and can significantly influence the effects of anthropogenic aerosol emission controls, both on climate and air quality. © 2013 Macmillan Publishers Limited. All rights reserved.

Clouds cool Earth's climate by reflecting 20% of the incoming solar energy, while also trapping part of the outgoing radiation. The effect of human activities on clouds is poorly understood, but the present-day anthropogenic cooling via changes of cloud albedo and lifetime could be of the same order as warming from anthropogenic addition in CO2. Soluble trace gases can increase water condensation to particles, possibly leading to activation of smaller aerosols and more numerous cloud droplets. We have studied the effect of nitric acid on the aerosol indirect effect with the global aerosol-climate model ECHAM5.5-HAM2. Including the nitric acid effect in the model increases cloud droplet number concentrations globally by 7%. The nitric acid contribution to the present-day cloud albedo effect was found to be −0.32 W m−2 and to the total indirect effect −0.46 W m−2. The contribution to the cloud albedo effect is shown to increase to −0.37 W m−2 by the year 2100, if considering only the reductions in available cloud condensation nuclei. Overall, the effect of nitric acid can play a large part in aerosol cooling during the following decades with decreasing SO2 emissions and increasing NOx and greenhouse gases.

[1] For the period of 1990 to 2000, atmospheric particulate mass concentrations have decreased in Central Europe. Simultaneously, the amount of shortwave radiation reaching the ground increased during clear sky conditions. The aerosol indirect effect has not been seen as clearly, as the radiation reaching the ground during overcast conditions has not increased as much as might be expected. Here we show that this may be caused by the condensation kinetics of water during cloud droplet formation. The decrease in the particulate mass led to a clear decrease in the number concentration of cloud condensation nuclei (CCN). However, in urban areas a relatively larger decrease in the number of particles in the upper end of the accumulation mode has led to slower condensation of water. As a result, a higher maximum supersaturation is reached during the cloud droplet formation. This compensates for the effect of decreased CCN concentrations. For example in Erfurt between 1991 and 1996, the aerosol properties changed so that aerosol optical depth decreased by 58% and CCN concentration decreased by 25 to 50%. These led to a 4 to 12% reduction in cloud droplet number concentration (CDNC) and less than a 2 Wm � 2 increase in shortwave radiation during overcast conditions. These results demonstrate that locally the aerosol direct effect can be much larger than the aerosol indirect effect. Furthermore, even though AOD appears to be a valid proxy for CCN, the correlation between AOD and CDNC is not straightforward and thus AOD cannot be used as a proxy for CDNC.

[1] There is a debate on whether the snow/ice change feedback or poleward energy transport from lower latitudes generates the observed Arctic warming amplification. There is another possibility that remotely induced warming in the Arctic can be amplified by snow/ice feedbacks. We demonstrate that this mechanism plays an important role in two independent climate models: CAM3 and ECHAM5. We also show with these two models that the June-August temperature structure in the vertical is a good indicator of how much the climate forcing from lower latitudes contributes to Arctic warming. Compared with the June-August 3D temperature trend in ERA Interim reanalysis, the CMIP3 models simulate warming at higher levels, suggesting that the models over-simulate the role of poleward energy transport in Arctic warming. This finding has implications for climate feedback and aerosol forcing.

Thum, T., et al. (2011), 'Soil carbon model alternatives for ECHAM5/JSBACH climate model: Evaluation and impacts on global carbon cycle estimates', Journal of Geophysical Research, Vol. 116, G02028, published 29 June 2011. The version of record is available at doi:10.1029/2010JG001612 © 2011 American Geophysical Union.

[1] A simple parameterization to derive the broadband effective albedo over highly reflecting surfaces under overcast conditions is presented. High spatial variability in the surface albedo affects the downwelling solar irradiance in neighboring regions via the multiple reflections of light between the surface and the cloud base. The effective albedo is defined as the albedo of a homogeneous surface that would result in the same downwelling irradiance as observed at the observation point in the presence of a heterogeneous surface. The proposed method parameterizes the effective albedo using the cloud base height and a surface albedo map as inputs. The parameterization is based on the spatial distribution of surface reflections contributing to the downwelling irradiance at the observation site, which is approximated with a gamma distribution. The parameterization was validated against reference values of effective albedo derived from three-dimensional backward Monte Carlo and one-dimensional DISORT radiative transfer calculations for four idealized surface albedo maps and various specifications of cloud properties. It gave values of effective albedo very close to the reference calculations, performing substantially better than any other approach tested, also when applied to the retrieval of cloud optical depth. The method can be implemented into one-dimensional radiative transfer models or used to interpret broadband irradiance measurements in Polar coastal regions, in the marginal sea ice zones, or in patchy terrain with forests and snow-covered fields.

Mie theory is used for calculation of the long-wave (5–200μm) optical properties of non-precipitatingand precipitating water clouds, and of rain. The optical parameters (mass extinctionand absorption coefficients, coalbedo and asymmetry parameter) are then parameterized assimple functions of cloud drop effective radius, cloud water amount and rain rate, for use ingeneral circulation and weather forecast models. Broadband and some wideband parameterizationsare given and tested both in two-stream and emissivity applications. A simple relationbetween the effective radius and cloud water content is also suggested for maritime and continentalair masses. DOI: 10.1034/j.1600-0870.1998.00001.x

The role of aerosols remains a major uncertainty for climate and climate change. For the direct radiative forcing by mineral aerosols, the uncertainty in the refractive index m has been regarded as the most important error source, while the impact of aerosol non-sphericity has been considered a minor issue and is neglected in climate models. Here, the errors caused by the spherical particle approximation (SPA) are evaluated by comparing radiative fluxes based on (i) Mie simulations and (ii) laboratory measurements of aerosol optical properties. Furthermore, they are contrasted with the errors related to the uncertainty in the refractive index. These two error sources are found to be of comparable magnitude, although they are strongly dependent on optical depth, surface albedo, and particle size. Thus, our results provide evidence that, contrary to common beliefs, the use of spherical model particles in radiative transfer simulations is probably among the major sources of error in quantifying the climate forcing effect of mineral aerosols. This stems from misrepresentation of the scattering phase function and the asymmetry parameter. Aerosol single-scattering computations based on non-spherical model particles are expected to reduce the shape-related errors and thus significantly improve the accuracy of radiative forcing simulations. Copyright © 2007 Royal Meteorological Society