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A sea ice model is an important component of an Earth system model, which is an essential tool for the study of sea ice, including its internal processes, interactions with other components, and projected future changes. This paper evaluates a simulation of sea ice by the Chinese Academy of Sciences Earth System Model version 2 (CAS-ESM 2.0), focusing on a historical simulation in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with the observations, CAS-ESM 2.0 reproduces reasonable seasonal cycle features and the climatological spatial distribution of Arctic and Antarctic sea ice, including sea ice extent (SIE), sea ice concentration, and sea ice thickness and motion. However, the SIE in CAS-ESM 2.0 is too large in winter and too low in summer in both hemispheres, indicating higher seasonal variations of the model relative to observations. Further sea ice mass budget diagnostics show that basal growth contributes most to ice increase in both hemispheres, basal melt and top melt make a comparable contribution to Arctic ice decrease, and basal melt plays a dominant role in Antarctic ice loss. This, combined with surface air temperature (SAT) and sea surface temperature (SST) biases, suggests that the excess of sea ice simulated in wintertime in both hemispheres and the lower SIE simulated in the Antarctic summer are mainly attributable to the bias in SST, whereas the lower SIE simulated in the Arctic summer is probably due to the combined effects of both the SST and SAT biases. [ABSTRACT FROM AUTHOR]

This study investigates the contribution of different CH4 sources to the seasonal cycle of δ 13 C during 2000–2012 by using the TM5 atmospheric transport model, including spatially varying information on isotopic signatures. The TM5 model is able to produce the background seasonality of δ 13 C, but the discrepancies compared to the observations arise from incomplete representation of the emissions and their source-specific signatures. Seasonal cycles of δ 13 C are found to be an inverse of CH4 cycles in general, but the anti-correlations between CH4 and δ 13 C are imperfect and experience a large variation ( p = −0.35 to −0.91) north of 30° S. We found that wetland emissions are an important driver in the δ 13 C seasonal cycle in the Northern Hemisphere and Tropics, and in the Southern Hemisphere Tropics, emissions from fires contribute to the enrichment of δ 13 C in July–October. The comparisons to the observations from 18 stations globally showed that the seasonal cycle of EFMM emissions in the EDGAR v5.0 inventory is more realistic than in v4.3.2. At northern stations (north of 55° N), modeled δ 13 C amplitudes are generally smaller by 12–68%, mainly because the model could not reproduce the strong depletion in autumn. This indicates that the CH4 emission magnitude and seasonal cycle of wetlands may need to be revised. In addition, results from stations in northern latitudes (19–40° N) indicate that the proportion of biogenic to fossil-based emissions may need to be revised, such that a larger portion of fossil-based emissions is needed during summer. [ABSTRACT FROM AUTHOR]

Precipitation time series exhibit complex fluctuations and statistical changes. Existing research stops short of a simple and feasible model for precipitation forecasting. In this article, the authors investigate and forecast precipitation variations in South Korea from 1973 to 2021 using cyclostationary empirical orthogonal function (CSEOF) and regression methods. First, empirical orthogonal function (EOF) and CSEOF analyses are used to examine the periodic changes in the precipitation data. Then, the autoregressive integrated moving average (ARIMA) method is applied to the principal component (PC) time series derived from the EOF and CSEOF precipitation analyses. The fifteen leading EOF and CSEOF modes and their corresponding PC time series clearly reflect the spatial distribution and temporal evolution characteristics of the precipitation data. Based on the PC forecasts of the EOF and CSEOF models, the EOF–ARIMA composite model and CSEOF–ARIMA composite model are used to obtain quantitative precipitation forecasts. The comparison results show that both composite models have good performance and similar accuracy. However, the performance of the CSEOF–ARIMA model is better than that of the EOF–ARIMA model under various measurements. Therefore, the CSEOF–ARIMA composite forecast model can be considered an efficient and feasible technology representing an analytical approach for precipitation forecasting in South Korea. [ABSTRACT FROM AUTHOR]

A sea ice model is an important component of an Earth system model, which is an essential tool for the study of sea ice, including its internal processes, interactions with other components, and projected future changes. This paper evaluates a simulation of sea ice by the Chinese Academy of Sciences Earth System Model version 2 (CAS-ESM 2.0), focusing on a historical simulation in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with the observations, CAS-ESM 2.0 reproduces reasonable seasonal cycle features and the climatological spatial distribution of Arctic and Antarctic sea ice, including sea ice extent (SIE), sea ice concentration, and sea ice thickness and motion. However, the SIE in CAS-ESM 2.0 is too large in winter and too low in summer in both hemispheres, indicating higher seasonal variations of the model relative to observations. Further sea ice mass budget diagnostics show that basal growth contributes most to ice increase in both hemispheres, basal melt and top melt make a comparable contribution to Arctic ice decrease, and basal melt plays a dominant role in Antarctic ice loss. This, combined with surface air temperature (SAT) and sea surface temperature (SST) biases, suggests that the excess of sea ice simulated in wintertime in both hemispheres and the lower SIE simulated in the Antarctic summer are mainly attributable to the bias in SST, whereas the lower SIE simulated in the Arctic summer is probably due to the combined effects of both the SST and SAT biases.

This study investigates the contribution of different CH4 sources to the seasonal cycle of δ13C during 2000–2012 by using the TM5 atmospheric transport model, including spatially varying information on isotopic signatures. The TM5 model is able to produce the background seasonality of δ13C, but the discrepancies compared to the observations arise from incomplete representation of the emissions and their source-specific signatures. Seasonal cycles of δ13C are found to be an inverse of CH4 cycles in general, but the anti-correlations between CH4 and δ13C are imperfect and experience a large variation (p=−0.35 to −0.91) north of 30° S. We found that wetland emissions are an important driver in the δ13C seasonal cycle in the Northern Hemisphere and Tropics, and in the Southern Hemisphere Tropics, emissions from fires contribute to the enrichment of δ13C in July–October. The comparisons to the observations from 18 stations globally showed that the seasonal cycle of EFMM emissions in the EDGAR v5.0 inventory is more realistic than in v4.3.2. At northern stations (north of 55° N), modeled δ13C amplitudes are generally smaller by 12–68%, mainly because the model could not reproduce the strong depletion in autumn. This indicates that the CH4 emission magnitude and seasonal cycle of wetlands may need to be revised. In addition, results from stations in northern latitudes (19–40° N) indicate that the proportion of biogenic to fossil-based emissions may need to be revised, such that a larger portion of fossil-based emissions is needed during summer.

Precipitation time series exhibit complex fluctuations and statistical changes. Existing research stops short of a simple and feasible model for precipitation forecasting. In this article, the authors investigate and forecast precipitation variations in South Korea from 1973 to 2021 using cyclostationary empirical orthogonal function (CSEOF) and regression methods. First, empirical orthogonal function (EOF) and CSEOF analyses are used to examine the periodic changes in the precipitation data. Then, the autoregressive integrated moving average (ARIMA) method is applied to the principal component (PC) time series derived from the EOF and CSEOF precipitation analyses. The fifteen leading EOF and CSEOF modes and their corresponding PC time series clearly reflect the spatial distribution and temporal evolution characteristics of the precipitation data. Based on the PC forecasts of the EOF and CSEOF models, the EOF–ARIMA composite model and CSEOF–ARIMA composite model are used to obtain quantitative precipitation forecasts. The comparison results show that both composite models have good performance and similar accuracy. However, the performance of the CSEOF–ARIMA model is better than that of the EOF–ARIMA model under various measurements. Therefore, the CSEOF–ARIMA composite forecast model can be considered an efficient and feasible technology representing an analytical approach for precipitation forecasting in South Korea.

Air pollution is an important public health issue. High levels of carbon monoxide in the atmosphere are hazardous to human health. Studies regarding the concentration of this and other gases in the atmosphere allow political actions to manage and reduce the emission of pollutants. In this context, this paper studied the annual, seasonal, weekly and daily variations of carbon monoxide (CO) concentration for the Metropolitan Region of São Paulo (MRSP).We studied three sites in the MRSP, two of them are located in areas under the influence of heavy vehicle traffic (Osasco and Congonhas) and the third one in a city park (Ibirapuera Park). The results showed high influence of gasoline vehicles on CO emission. In the annual scale, CO concentration decreased due to improvements in emission technology, despite the increasing number of vehicles. CO emission showed a seasonal, weekly and diurnal cycle associated to meteorological conditions and emission patterns. The highest values of mean concentration were observed in June/July for Osasco (2.20 ppm), Congonhas (2.04 ppm) and Ibirapuera (1.04 ppm), during the morning, due to weak dispersion of the polluting gases and higher emission from the rush hours. [ABSTRACT FROM AUTHOR]

Long-term surface ozone observations at 25 National Park Service sites across the United States were analyzed for processes on varying time scales using a time scale decomposition technique, the Ensemble Empirical Mode Decomposition (EEMD). Time scales of interest include the seasonal cycle, large-scale climate oscillations, and long-term (>10 years) trends. Emission reductions were found to have a greater impact on sites that are nearest major urban areas. Multidecadal trends in surface ozone were increasing at a rate of 0.07 to 0.37 ppbv year−1 before 2004 and decreasing at a rate of −0.08 to −0.60 ppbv year−1 after 2004 for sites in the East, Southern California, and Northwestern Washington. Sites in the Intermountain West did not experience a reversal of trends from positive to negative until the mid- to late 2000s. The magnitude of the annual amplitude (=annual maximum–minimum) decreased at eight sites, two in the West, two in the Intermountain West, and four in the East, by 5–20 ppbv and significantly increased at three sites; one in Alaska, one in the West, and one in the Intermountain West, by 3–4 ppbv. Stronger decreases in the annual amplitude occurred at a greater proportion of sites in the East (4/6 sites) than in the West/Intermountain West (4/19 sites). The date of annual maximums and/or minimums has changed at 12 sites, occurring 10–60 days earlier in the year. There appeared to be a link between the timing of the annual maximum and the decrease in the annual amplitude, which was hypothesized to be related to a decrease in ozone titration resulting from NOx emission reductions. Furthermore, it was found that a phase shift of the Pacific Decadal Oscillation (PDO), from positive to negative, in 1998–1999 resulted in increased occurrences of La Niña-like conditions. This shift had the effect of directing more polluted air masses from East Asia to higher latitudes over the North American continent. The change in the Pacific Decadal Oscillation (PDO)/El Niño Southern Oscillation (ENSO) regime influenced surface ozone at an Alaskan site over its nearly 30-year data record.

Precipitation has been considered to be a critical water source for both human livelihoods and ecosystems in Central Asia. Using observational data and gridded datasets, we studied the regional and seasonal differences of precipitation climate characteristics and variations in precipitation over Central Asia. Using observational data obtained from the China Meteorological Administration, Global Historical Climatology Network (V3.02), we divided Central Asia into four subregions (North, Center, Southwest, and Southeast) based on the differences in seasonal cycles of precipitation. 'Single peaks' were detected as types of seasonal cycles over the North and the Southeast, while 'two peaks' was the type that occurred in the Southwest. For the Center, the zone of transition between the North and the Southwest, each monthly precipitation value was higher than the Southwest's and less than the North's. GPCC (R² of 0.89, RMSE of 64.5 mm/year) was proven to be the most suitable dataset of the four datasets (CRU, GPCC, MERRA, and TRMM) to describe precipitation in Central Asia, based on validation against observational data, and used to detect the spatial and temporal trend of precipitation in Central Asia and four subregions during 1960-2013. No significant trends were observed for annual precipitation in Central Asia, while precipitation in winter displayed a significant increase (0.11 mm/year). Additionally, significantly increasing trends (0.16, 0.27, 0.13, and 0.13 mm/year) were detected in spring, summer, autumn, and winter over the Southeast during 1960-2013. [ABSTRACT FROM AUTHOR]

Long-term surface ozone observations at 25 National Park Service sites across the United States were analyzed for processes on varying time scales using a time scale decomposition technique, the Ensemble Empirical Mode Decomposition (EEMD). Time scales of interest include the seasonal cycle, large-scale climate oscillations, and long-term (>10 years) trends. Emission reductions were found to have a greater impact on sites that are nearest major urban areas. Multidecadal trends in surface ozone were increasing at a rate of 0.07 to 0.37 ppbv year−1 before 2004 and decreasing at a rate of −0.08 to −0.60 ppbv year−1 after 2004 for sites in the East, Southern California, and Northwestern Washington. Sites in the Intermountain West did not experience a reversal of trends from positive to negative until the mid- to late 2000s. The magnitude of the annual amplitude (=annual maximum–minimum) decreased at eight sites, two in the West, two in the Intermountain West, and four in the East, by 5–20 ppbv and significantly increased at three sites; one in Alaska, one in the West, and one in the Intermountain West, by 3–4 ppbv. Stronger decreases in the annual amplitude occurred at a greater proportion of sites in the East (4/6 sites) than in the West/Intermountain West (4/19 sites). The date of annual maximums and/or minimums has changed at 12 sites, occurring 10–60 days earlier in the year. There appeared to be a link between the timing of the annual maximum and the decrease in the annual amplitude, which was hypothesized to be related to a decrease in ozone titration resulting from NOx emission reductions. Furthermore, it was found that a phase shift of the Pacific Decadal Oscillation (PDO), from positive to negative, in 1998–1999 resulted in increased occurrences of La Niña-like conditions. This shift had the effect of directing more polluted air masses from East Asia to higher latitudes over the North American continent. The change in the Pacific Decadal Oscillation (PDO)/El Niño Southern Oscillation (ENSO) regime influenced surface ozone at an Alaskan site over its nearly 30-year data record. [ABSTRACT FROM AUTHOR]