Journal of Geophysical Research D: Atmospheres

Global Navigation Satellite System (GNSS)‐based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth's atmosphere for weather and climate applications. Above about 30 km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium‐range Weather Forecasts (ECMWF) short‐range forecast and analysis fields, together with averaged‐observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method's performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrieved refractivity profiles and atmospheric (temperature) profiles benefit by improved error characteristics, especially above about 30 km. Based on these encouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records.

The radiative effects of Saharan dust aerosols are investigated in the NASA GEOS‐5 atmospheric general circulation model. A sectional aerosol microphysics model (CARMA) is run online in GEOS‐5. CARMA treats the dust aerosol lifecycle, and its tracers are radiatively coupled to GEOS‐5. A series of AMIP‐style simulations are performed, in which input dust optical properties (particle shape and refractive index) are varied. Simulated dust distributions for summertime Saharan dust compare well to observations, with best results found when the most absorbing dust optical properties are assumed. Dust absorption leads to a strengthening of the summertime Hadley cell circulation, increased dust lofting to higher altitudes, and a strengthening of the African easterly jet, resulting in increased dust atmospheric lifetime and farther northward and westward transport. We find a positive feedback of dust radiative forcing on emissions, in contrast with previous studies, which we attribute to our having a relatively strong longwave forcing caused by our simulating larger effective particle sizes. This longwave forcing reduces the magnitude of midday net surface cooling relative to other studies, and leads to a nighttime warming that results in higher nighttime wind speeds and dust emissions. The radiative effects of dust particle shape have only minor impact on transport and emissions, with small (~5%) impact on top of atmosphere shortwave forcing, in line with previous studies, but relatively more pronounced effects on shortwave atmospheric heating and surface forcing (~20% increase in atmospheric forcing for spheroids). Shape effects on longwave heating terms are of order ~10%.

We characterized the chemical composition and optical properties of particulate matter (PM) emitted by a marine diesel engine operated on heavy fuel oil (HFO), marine gas oil (MGO), and diesel fuel (DF). For all three fuels, ∼80% of submicron PM was organic (and sulfate, for HFO at higher engine loads). Emission factors varied only slightly with engine load. Refractory black carbon (rBC) particles were not thickly coated for any fuel; rBC was therefore externally mixed from organic and sulfate PM. For MGO and DF PM, rBC particles were lognormally distributed in size (mode at d_rBC≈120 nm). For HFO, much larger rBC particles were present. Combining the rBC mass concentrations with in situ absorption measurements yielded an rBC mass absorption coefficient MAC_BC,780nm of 7.8 ± 1.8 m²/g at 780 nm for all three fuels. Using positive deviations of the absorption Ångström exponent (AAE) from unity to define brown carbon (brC), we found that brC absorption was negligible for MGO or DF PM (AAE(370,880 nm)≈1.0 ± 0.1) but typically 50% of total 370‐nm absorption for HFO PM. Even at 590 nm, ∼20  of the total absorption was due to brC. Using absorption at 880 nm as a reference for BC absorption and normalizing to organic PM mass, we obtained a MAC_OM,370nm of 0.4 m²/g at typical operating conditions. Furthermore, we calculated an imaginary refractive index of (0.045 ± 0.025)(λ/370nm)⁻³ for HFO PM at 370 nm>λ > 660 nm, more than twofold greater than previous recommendations. Climate models should account for this substantial brC absorption in HFO PM.

The absorption Ångström exponent (AAE) describes the spectral dependence of light absorption by aerosols. AAE is typically used to differentiate between different aerosol types for example., black carbon, brown carbon, and dust particles. In this study, the variation of AAE was investigated mainly in fresh aerosol emissions from different fuel and combustion types, including emissions from ships, buses, coal‐fired power plants, and residential wood burning. The results were assembled to provide a compendium of AAE values from different emission sources. A dual‐spot aethalometer (AE33) was used in all measurements to obtain the light absorption coefficients at seven wavelengths (370–950 nm). AAE_470/950 varied greatly between the different emission sources, ranging from −0.2 ± 0.7 to 3.0 ± 0.8. The correlation between the AAE_470/950 and AAE_370‐950 results was good (R² = 0.95) and the mean bias error between these was 0.02. In the ship engine exhaust emissions, the highest AAE_470/950 values (up to 2.0 ± 0.1) were observed when high sulfur content heavy fuel oil was used, whereas low sulfur content fuels had the lowest AAE_470/950 (0.9–1.1). In the diesel bus exhaust emissions, AAE_470/950 increased in the order of acceleration (0.8 ± 0.1), deceleration (1.1 ± 0.1), and steady driving (1.2 ± 0.1). In the coal‐fired power plant emissions, the variation of AAE_470/950 was substantial (from −0.1 ± 2.1 to 0.9 ± 1.6) due to the differences in the fuels and flue gas cleaning conditions. Fresh wood‐burning derived aerosols had AAE_470/950 from 1.1 ± 0.1 (modern masonry heater) to 1.4 ± 0.1 (pellet boiler), lower than typically associated with wood burning, while the burn cycle phase affected AAE variation.

Black carbon (BC) in snow/ice induces enhanced snow and glacier melting. As over 60% of atmospheric BC is emitted from anthropogenic sources, which directly impacts the distribution and concentration of BC in snow/ice, it is essential to assess the origin of anthropogenic BC transported to the Tibetan Plateau (TP) where there are few direct emissions attributable to local human activities. In this study, we used a regional climate‐atmospheric chemistry model and a set of BC scenarios for quantitative evaluation of the impact of anthropogenic BC from various sources and its climate effects over the TP in 2013. The results showed that the model performed well in terms of climatology, aerosol optical properties, and near‐surface concentrations, which indicates that this modeling framework is appropriate to characterize anthropogenic BC source‐receptor relationships over the TP. The simulated surface concentration associated with the anthropogenic sources showed seasonal differences. In the monsoon season, the contribution of anthropogenic BC was less than in the nonmonsoon season. In the nonmonsoon season, westerly winds prevailed and transported BC from central Asia and north India to the western TP. In the monsoon season, BC aerosol was transported to the middle‐upper troposphere over the Indo‐Gangetic Plain and crossed the Himalayas via southwesterly winds. The majority of anthropogenic BC over the TP was transported from South Asia, which contributed to 40%–80% (mean of 61.3%) of surface BC in the nonmonsoon season, and 10%–50% (mean of 19.4%) in the monsoon season. For the northeastern TP, anthropogenic BC from eastern China accounted for less than 10% of the total in the nonmonsoon season but can be up to 50% in the monsoon season. Averaged over the TP, the eastern China anthropogenic sources accounted for 6.2% and 8.4% of surface BC in the nonmonsoon and monsoon seasons, respectively. The anthropogenic BC induced negative radiative forcing and cooling effects at the near surface over the TP.

An evaluation‐classification‐downscaling‐based climate projection (ECDoCP) framework is developed to fill a methodological gap of general circulation models (GCMs)‐driven statistical‐downscaling‐based climate projections. ECDoCP includes four interconnected modules: GCM evaluation, climate classification, statistical downscaling, and climate projection. Monthly averages of daily minimum (Tmin) and maximum (Tmax) temperature and daily cumulative precipitation (Prec) over the Athabasca River Basin (ARB) at a 10 km resolution in the 21st century under four Representative Concentration Pathways (RCPs) are projected through ECDoCP. At the octodecadal scale, temperature and precipitation would increase; after bias correction, temperature would increase with a decreased increment, while precipitation would increase only under RCP 8.5. Interannual variability of climate anomalies would increase from RCPs 4.5, 2.6, 6.0 to 8.5 for temperature and from RCPs 2.6, 4.5, 6.0 to 8.5 for precipitation. Bidecadal averaged climate anomalies would decrease from December‐January‐February (DJF), March‐April‐May (MAM), September‐October‐November (SON) to June‐July‐August (JJA) for Tmin, from DJF, SON, MAM to JJA for Tmax, and from JJA, MAM, SON to DJF for Prec. Climate projection uncertainties would decrease in May to September for temperature and in November to April for precipitation. Spatial climatic variability would not obviously change with RCPs; climatic anomalies are highly correlated with climate‐variable magnitudes. Climate anomalies would decrease from upstream to downstream for temperature, and precipitation would follow an opposite pattern. The north end and the other zones would have colder and warmer days, respectively; precipitation would decrease in the upstream and increase in the remaining region. Climate changes might lead to issues, e.g., accelerated glacier/snow melting, deserving attentions of researchers and the public.

Coastal safety may be influenced by climate change, as changes in extreme surge levels and wave extremes may increase the vulnerability of dunes and other coastal defenses. In the North Sea, an area already prone to severe flooding, these high surge levels and waves are generated by low atmospheric pressure and severe wind speeds during storm events. As a result of the geometry of the North Sea, not only the maximum wind speed is relevant, but also wind direction. Climate change could change maximum wind conditions, with potentially negative effects for coastal safety. Here, we use an ensemble of 12 Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Models (GCMs) and diagnose the effect of two climate scenarios (rcp4.5 and rcp8.5) on annual maximum wind speed, wind speeds with lower return frequencies, and the direction of these annual maximum wind speeds. The 12 selected CMIP5 models do not project changes in annual maximum wind speed and in wind speeds with lower return frequencies; however, we do find an indication that the annual extreme wind events are coming more often from western directions. Our results are in line with the studies based on CMIP3 models and do not confirm the statement based on some reanalysis studies that there is a climate‐change‐related upward trend in storminess in the North Sea area.

El Niño–Southern Oscillation is the strongest interannual variability in the tropical oceans and the major source of global climate predictability. In this work, we examine the evolution of oceanic and atmospheric anomalies in the tropical Pacific during 2020/2021 La Niña and compare it with the historical strong La Niña events since 1982, identify the contributions of different time scale components, and assess the predictions and the impact on extra‐tropical climate. 2020/2021 La Niña emerged in August 2020 and dissipated in May 2021. 2020/2021 La Niña was uniquely preceded by a borderline El Niño instead of an El Niño and a weak equatorial‐heat discharge process. That resulted in the weakest event among the strong La Niñas since 1982, although there were strong upwelling Kelvin wave activities. Moreover, compared with other strong La Niña events, the surface easterly wind anomalies and the warm pool extended further eastward in 2020/2021 La Niña, linking to a relatively weaker dipole‐like pattern of the subsurface ocean temperature anomalies. The strength of all the strong La Niña events is determined by the in‐phase amplification of all time scale variations. Their decay in the boreal spring and early summer is mainly controlled by the intra seasonal‐inter seasonal variation. 2020/2021 La Niña was successfully predicted, however, the North American climate anomalies did not match the typical La Niña response, leading to low prediction skill in the extra‐tropics during its mature phase.

This study investigates the dynamics that led to the repeated cold surges over midlatitude Eurasia, exceptionally warm conditions and sea ice loss over the Arctic, and the unseasonable weakening of the stratospheric polar vortex in autumn and early winter 2016–2017. We use ERA‐Interim reanalysis data and COBE sea ice and sea surface temperature observational data to trace the dynamical pathways that caused these extreme phenomena. Following abnormally low sea ice conditions in early autumn over the Pacific sector of the Arctic basin, blocking anticyclones became dominant over Eurasia throughout autumn. Ural blocking (UB) activity was four times above climatological levels and organized in several successive events. UB episodes played a key role in the unprecedented sea ice loss observed in late autumn 2016 over the Barents‐Kara Seas and the weakening of the stratospheric vortex. Each blocking induced circulation anomalies that resulted in cold air advection to its south and warm advection to its north. The near‐surface warming anomalies over the Arctic and cooling anomalies over midlatitude Eurasia varied in phase with the life cycles of UB episodes. The sea ice cover minimum over the Barents‐Kara Seas in 2016 was not observed in late summer but rather in mid‐November and December shortly after the two strongest UB episodes. Each UB episode drove intense upward flux of wave activity that resulted in unseasonable weakening of the stratospheric vortex in November. The surface impact of this weakening can be linked to the migration of blocking activity and cold spells toward Europe in early winter 2017.

The large systematic biases in coupled models impact seasonal prediction results. With a motivation to reduce the influence of coupled‐model biases on seasonal predictions, the singular value decomposition method was applied in our study to improve the ability to predict flood season precipitation. Based on the coupled climate model, CAS‐ESM‐C, we conducted ensemble seasonal prediction experiments from 1982 to 2018, with initial conditions provided by the assimilation system. The prediction system was integrated from March to August of each year with a focus on the June to August precipitation in China. The results showed that the prediction skills for anomalous summer precipitation were very low without bias corrections. However, the system effectively predicted the interannual variabilities in large‐scale atmospheric circulation systems that were associated with anomalous summer precipitation. We used the singular value decomposition method to reduce pattern‐dependent precipitation errors by replacing prediction patterns with observation patterns, and the predictive skill for precipitation dramatically improved. The results demonstrated that this correction method is a viable tool to reduce systematic biases in coupled model predictions.