Abstract

The lower reach of the Yangtze River basin (LYRB) is located at the central region of the mei-yu and baiu front, which represents the subtropical East Asian (EA) summer monsoon. Based on the newly released daily rainfall data, two dominant intraseasonal variation (ISV) modes are identified over the LYRB during boreal summer (May–August), with spectral peaks occurring on day 15 (the biweekly mode) and day 24 (the 21–30-day mode). These two modes have comparable intensities, and together they account for above about 57% of the total intraseasonal variance. Both ISV modes exhibit baroclinic structures over the LYRB at their extreme phases.

However, the genesis and evolutions associated with the two modes are different. Considering the genesis of their extreme wet phases over the LYRB, the biweekly mode is initiated by a midlatitude jet stream vorticity anomaly moving southeastward, while the 21–30-day mode is primarily associated with a low-level westward propagation of an anticyclonic anomaly from 145° to 120°E, which reflects the westward extension of the western North Pacific subtropical high (WNPSH). The development of the biweekly mode at LYRB is enhanced by the northwestward movement of a low-level anticyclonic anomaly from the Philippine Sea to the south of Taiwan, which is a result of the enhancement of the WNPSH resulting from its merger with a transient midlatitude high. In contrast, the development of the 21–30-day mode is enhanced by an upper-level trough anomaly moving from Lake Baikal to far east Russia. These two ISV periodicities are also found to be embedded in their corresponding source regions.

The new knowledge on the sources and evolutions of the two major LYRB ISV modes provides empirical predictors for the intraseasonal variation in the subtropical EA summer monsoon.

Abstract

Boreal summer extratropical intraseasonal oscillation (EISO) is crucial in modulating regional subseasonal variation and particularly causing extreme meteorological events, but it has yet to be well clarified and operationally monitored. This study first objectively sorts out three dominant EISOs trapped along two extratropical westerly jet streams over Eurasia, and then proposes the corresponding real-time metrics. The three dominant EISOs are (i) an 8–25-day eastward-propagating wave along the subtropical westerly jet (EISO-SJE) initiating at the exit of the North America–North Atlantic jet and strengthening over the Black Sea–Caspian Sea–arid central Asia region; (ii) a 10–30-day eastward-traveling wave along the polar front jet (EISO-PJE), starting near Scandinavia and enhancing from the East European Plain to the West Siberian Plain and then decaying over the Okhotsk region; (iii) a 10–40-day westward-migrating wave along the polar front jet (EISO-PJW), which enhances near the Ural Mountains and weakens over Scandinavia. The real-time metrics then, following the three EISOs, have been constructed, and they are able to capture the spatiotemporal features of three EISOs in application. Moreover, the close linkages between these EISOs and the regional extremes/the blocking occurrence have been clearly demonstrated, confirming the importance of real-time EISO metrics. Together with tropical intraseasonal oscillation, this study provides the subseasonal-to-seasonal (S2S) community with a well-portrayed unified picture of extratropical intraseasonal waves and the real-time metrics for monitoring boreal summer intraseasonal signals over Eurasia and facilitate subseasonal predictions.

Abstract

Realistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day⁻¹, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.

Abstract

Some climate datasets are incomplete at certain places and times. A novel technique called the point estimation model of Biased Sentinel Hospitals-based Area Disease Estimation (P-BSHADE) is introduced to interpolate missing data in temperature datasets. Effectiveness of the technique was empirically evaluated in terms of an annual temperature dataset from 1950 to 2000 in China. The P-BSHADE technique uses a weighted summation of observed stations to derive unbiased and minimum error variance estimates of missing data. Both the ratio and covariance between stations were used in calculation of these weights. In this way, interpolation of missing data in the temperature dataset was improved, and best linear unbiased estimates (BLUE) were obtained. Using the same dataset, performance of P-BSHADE was compared against three estimators: kriging, inverse distance weighting (IDW), and spatial regression test (SRT). Kriging and IDW assume a homogeneous stochastic field, which may not be the case. SRT employs spatiotemporal data and has the potential to consider temperature nonhomogeneity caused by topographic differences, but has no objective function for the BLUE. Instead, P-BSHADE takes into account geographic spatial autocorrelation and nonhomogeneity, and maximizes an objective function for the BLUE of the target station. In addition to the theoretical advantages of P-BSHADE over the three other methods, case studies for an annual Chinese temperature dataset demonstrate its empirical superiority, except for the SRT from 1950 to 1970.

Abstract

Spring persistent rainfall (SPR) over southern China has great impact on its society and economics. A remarkable feature of the SPR is high frequency. However, SPR frequency obviously decreases over the period of 1997–2011. In this study, the possible causes have been investigated from the perspective of the individual and concurrent effects of the East Asian subtropical jet (EASJ) and East Asian polar front jet (EAPJ). A close relationship is detected between SPR frequency and EASJ intensity (but not EAPJ intensity). Associated with strong EASJ, abundant water vapor is transported to southern China by the southwesterly flow, which may trigger the SPR. Additionally, frequencies of both strong EASJ and weak EAPJ events are positively correlated with SPR frequency. Further investigation of the concurrent effect indicates a significant positive correlation between the frequencies of SPR and the strong EASJ–weak EAPJ configuration. Associated with this configuration, southwesterly flow strengthens in the lower troposphere, while northerly wind weakens in the upper troposphere. This provides a dynamic and moist condition, as enhanced ascending motion and intensified convergence of abundant water vapor over southern China, which favors the SPR. All analyses suggest that the EASJ may play a dominant role in the SPR occurrence and that the EAPJ may play a modulation role. Finally, a possible mechanism maintaining the strong EASJ–weak EAPJ configuration is proposed. Significant cooling over the northeastern Tibetan Plateau may induce a cyclone anomaly in the upper troposphere, which could result in an accelerating EASJ and a decelerating EAPJ.

Abstract

This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.

Abstract

Two aspects of Beijing cloudiness are studied: its relationship to other climate parameters during the period 1951–1990 and the reconstruction of proxy values between 1875 and 1950. For the recent period, cloudiness varies with no apparent trend and is highly correlated with the total number of rain days (r=0.77) and total sunshine duration (r=0.72). Good correlation is also found with maximum surface air temperature, surface relative humidity, and total precipitation. While the correlation between cloudiness and solar radiation was large prior to 1976, the coefficient for the period 1976–1990 is much smaller. This decrease can be attributed to a negative trend in solar radiation, which is consistent with an observed decrease in visibility. Variations in Beijing cloudiness are closely related to those found over most of northern China, while little similarity is found with locations south of 35°N.

The large correlation between annual cloudiness and the total number of rain days between 1951 and 1990 was used in conjunction with the observed rain day record for the period 1875–1950 to construct a proxy cloudiness record for Beijing for the period 1875–1950. Comparisons between proxy cloudiness and available observations of surface air temperature and relative humidity reveal that the relationships are consistent with those found when observed cloudiness is compared with observed temperature and humidity data. On the century time scale, there is no clear trend in percent cloudiness. However, on the decadal time scale, there is a negative trend in cloudiness during the period 1880–1930 followed by a period of relatively constant values between 1940 and 1975.

Abstract

Fire-emitted aerosols play an important role in influencing Earth’s climate, directly by scattering and absorbing radiation and indirectly by influencing cloud microphysics. The quantification of fire–aerosol interactions, however, remains challenging and subject to uncertainties in emissions, plume parameterizations, and aerosol properties. Here we optimized fire-associated aerosol emissions in the Energy Exascale Earth System Model (E3SM) using the Global Fire Emissions Database (GFED) and AERONET aerosol optical depth (AOD) observations during 1997–2016. We distributed fire emissions vertically using smoke plume heights from Multiangle Imaging SpectroRadiometer (MISR) satellite observations. From the optimization, we estimate that global fires emit 45.5 Tg yr⁻¹ of primary particulate organic matter and 3.9 Tg yr⁻¹ of black carbon. We then performed two climate simulations with and without the optimized fire emissions. We find that fire aerosols significantly increase global AOD by 14% ± 7% and contribute to a reduction in net shortwave radiation at the surface (−2.3 ± 0.5 W m⁻²). Together, fire-induced direct and indirect aerosol effects cause annual mean global land surface air temperature to decrease by 0.17° ± 0.15°C, relative humidity to increase by 0.4% ± 0.3%, and diffuse light fraction to increase by 0.5% ± 0.3%. In response, GPP declines by 2.8 Pg C yr⁻¹ as a result of large positive drivers (decreases in temperature and increases in humidity and diffuse light), nearly cancelling out large negative drivers (decreases in shortwave radiation and soil moisture). Our analysis highlights the importance of fire aerosols in modifying surface climate and photosynthesis across the tropics.

Abstract

Two decades of high-resolution satellite observations and climate modeling studies have indicated strong ocean–atmosphere coupled feedback mediated by ocean mesoscale processes, including semipermanent and meandrous SST fronts, mesoscale eddies, and filaments. The air–sea exchanges in latent heat, sensible heat, momentum, and carbon dioxide associated with this so-called mesoscale air–sea interaction are robust near the major western boundary currents, Southern Ocean fronts, and equatorial and coastal upwelling zones, but they are also ubiquitous over the global oceans wherever ocean mesoscale processes are active. Current theories, informed by rapidly advancing observational and modeling capabilities, have established the importance of mesoscale and frontal-scale air–sea interaction processes for understanding large-scale ocean circulation, biogeochemistry, and weather and climate variability. However, numerous challenges remain to accurately diagnose, observe, and simulate mesoscale air–sea interaction to quantify its impacts on large-scale processes. This article provides a comprehensive review of key aspects pertinent to mesoscale air–sea interaction, synthesizes current understanding with remaining gaps and uncertainties, and provides recommendations on theoretical, observational, and modeling strategies for future air–sea interaction research.