2022 Vol. 39, No. 10
Display Method:
2022, 39(10): 1-2.
Abstract:
2022, 39(10): 1591-1597.
doi: 10.1007/s00376-022-2087-1
Abstract:
Seasonal minimum Antarctic sea ice extent (SIE) in 2022 hit a new record low since recordkeeping began in 1978 of 1.9 million km2 on 25 February, 0.17 million km2 lower than the previous record low set in 2017. Significant negative anomalies in the Bellingshausen/Amundsen Seas, the Weddell Sea, and the western Indian Ocean sector led to the new record minimum. The sea ice budget analysis presented here shows that thermodynamic processes dominate sea ice loss in summer through enhanced poleward heat transport and albedo–temperature feedback. In spring, both dynamic and thermodynamic processes contribute to negative sea ice anomalies. Specifically, dynamic ice loss dominates in the Amundsen Sea as evidenced by sea ice thickness (SIT) change, while positive surface heat fluxes contribute most to sea ice melt in the Weddell Sea.
Seasonal minimum Antarctic sea ice extent (SIE) in 2022 hit a new record low since recordkeeping began in 1978 of 1.9 million km2 on 25 February, 0.17 million km2 lower than the previous record low set in 2017. Significant negative anomalies in the Bellingshausen/Amundsen Seas, the Weddell Sea, and the western Indian Ocean sector led to the new record minimum. The sea ice budget analysis presented here shows that thermodynamic processes dominate sea ice loss in summer through enhanced poleward heat transport and albedo–temperature feedback. In spring, both dynamic and thermodynamic processes contribute to negative sea ice anomalies. Specifically, dynamic ice loss dominates in the Amundsen Sea as evidenced by sea ice thickness (SIT) change, while positive surface heat fluxes contribute most to sea ice melt in the Weddell Sea.
2022, 39(10): 1598-1607.
doi: 10.1007/s00376-022-2063-9
Abstract:
The year 2021 was recorded as the 6th warmest since 1880. In addition to large-scale warming, 2021 will be remembered for its unprecedented climate extremes. Here, a review of selected high-impact climate extremes in 2021, with a focus on China, along with an extension to extreme events in North America and Europe is presented. Nine extreme events that occurred in 2021 in China are highlighted, including a rapid transition from cold to warm extremes and sandstorms in spring, consecutive drought in South China and severe thunderstorms in eastern China in the first half of the year, extremely heavy rainfall over Henan Province and Hubei Province during summer, as well as heatwaves, persistent heavy rainfall, and a cold surge during fall. Potential links of extremes in China to four global-scale climate extremes and the underlying physical mechanisms are discussed here, providing insights to understand climate extremes from a global perspective. This serves as a reference for climate event attribution, process understanding, and high-resolution modeling of extreme events.
The year 2021 was recorded as the 6th warmest since 1880. In addition to large-scale warming, 2021 will be remembered for its unprecedented climate extremes. Here, a review of selected high-impact climate extremes in 2021, with a focus on China, along with an extension to extreme events in North America and Europe is presented. Nine extreme events that occurred in 2021 in China are highlighted, including a rapid transition from cold to warm extremes and sandstorms in spring, consecutive drought in South China and severe thunderstorms in eastern China in the first half of the year, extremely heavy rainfall over Henan Province and Hubei Province during summer, as well as heatwaves, persistent heavy rainfall, and a cold surge during fall. Potential links of extremes in China to four global-scale climate extremes and the underlying physical mechanisms are discussed here, providing insights to understand climate extremes from a global perspective. This serves as a reference for climate event attribution, process understanding, and high-resolution modeling of extreme events.
2022, 39(10): 1608-1622.
doi: 10.1007/s00376-022-1463-1
Abstract:
The attainment of suitable ambient air quality standards is a matter of great concern for successfully hosting the XXIV Olympic Winter Games (OWG). Transport patterns and potential sources of pollutants in Zhangjiakou (ZJK) were investigated using pollutant monitoring datasets and a dispersion model. The PM2.5 concentration during February in ZJK has increased slightly (28%) from 2018 to 2021, mostly owing to the shift of main potential source regions of west-central Inner Mongolia and Mongolian areas (2015–18) to the North China Plain and northern Shanxi Province (NCPS) after 2018. Using CO as an indicator, the relative contributions of the different regions to the receptor site (ZJK) were evaluated based on the source-receptor-relationship method (SRR) and an emission inventory. We found that the relative contribution of pollutants from NCPS increased from 33% to 68% during 2019–21. Central Inner Mongolia (CIM) also has an important impact on ZJK under unfavorable weather conditions. This study demonstrated that the effect of pollution control measures in the NCPS and CIM should be strengthened to ensure that the air quality meets the standard during the XXIV OWG.
The attainment of suitable ambient air quality standards is a matter of great concern for successfully hosting the XXIV Olympic Winter Games (OWG). Transport patterns and potential sources of pollutants in Zhangjiakou (ZJK) were investigated using pollutant monitoring datasets and a dispersion model. The PM2.5 concentration during February in ZJK has increased slightly (28%) from 2018 to 2021, mostly owing to the shift of main potential source regions of west-central Inner Mongolia and Mongolian areas (2015–18) to the North China Plain and northern Shanxi Province (NCPS) after 2018. Using CO as an indicator, the relative contributions of the different regions to the receptor site (ZJK) were evaluated based on the source-receptor-relationship method (SRR) and an emission inventory. We found that the relative contribution of pollutants from NCPS increased from 33% to 68% during 2019–21. Central Inner Mongolia (CIM) also has an important impact on ZJK under unfavorable weather conditions. This study demonstrated that the effect of pollution control measures in the NCPS and CIM should be strengthened to ensure that the air quality meets the standard during the XXIV OWG.
2022, 39(10): 1623-1636.
doi: 10.1007/s00376-022-1393-y
Abstract:
It is still not well understood if subseasonal variability of the local PM2.5 in the Beijing-Tianjin-Hebei (BTH) region is affected by the stratospheric state. Using PM2.5 observations and the ERA5 reanalysis, the evolution of the air quality in BTH during the January 2021 sudden stratospheric warming (SSW) is explored. The subseasonal variability of the PM2.5 concentration after the SSW onset is evidently enhanced. Stratospheric circumpolar easterly anomalies lasted for 53 days during the January–February 2021 SSW with two evident stratospheric pulses arriving at the ground. During the tropospheric wave weakening period and the intermittent period of dormant stratospheric pulses, the East Asian winter monsoon weakened, anomalous temperature inversion developed in the lower troposphere, anomalous surface southerlies prevailed, atmospheric moisture increased, and the boundary layer top height lowered, all of which favor the accumulation of pollutant particulates, leading to two periods of pollution processes in the BTH region. In the phase of strengthened East Asian winter monsoon around the very beginning of the SSW and another two periods when stratospheric pulses had reached the near surface, opposite-signed circulation patterns and meteorological conditions were observed, which helped to dilute and diffuse air pollutants in the BTH region. As a result, the air quality was excellent during the two periods when the stratospheric pulse had reached the near surface. The increased subseasonal variation of the regional pollutant particulates after the SSW onset highlights the important role of the stratosphere in the regional environment and provides implications for the environmental prediction.
It is still not well understood if subseasonal variability of the local PM2.5 in the Beijing-Tianjin-Hebei (BTH) region is affected by the stratospheric state. Using PM2.5 observations and the ERA5 reanalysis, the evolution of the air quality in BTH during the January 2021 sudden stratospheric warming (SSW) is explored. The subseasonal variability of the PM2.5 concentration after the SSW onset is evidently enhanced. Stratospheric circumpolar easterly anomalies lasted for 53 days during the January–February 2021 SSW with two evident stratospheric pulses arriving at the ground. During the tropospheric wave weakening period and the intermittent period of dormant stratospheric pulses, the East Asian winter monsoon weakened, anomalous temperature inversion developed in the lower troposphere, anomalous surface southerlies prevailed, atmospheric moisture increased, and the boundary layer top height lowered, all of which favor the accumulation of pollutant particulates, leading to two periods of pollution processes in the BTH region. In the phase of strengthened East Asian winter monsoon around the very beginning of the SSW and another two periods when stratospheric pulses had reached the near surface, opposite-signed circulation patterns and meteorological conditions were observed, which helped to dilute and diffuse air pollutants in the BTH region. As a result, the air quality was excellent during the two periods when the stratospheric pulse had reached the near surface. The increased subseasonal variation of the regional pollutant particulates after the SSW onset highlights the important role of the stratosphere in the regional environment and provides implications for the environmental prediction.
2022, 39(10): 1637-1649.
doi: 10.1007/s00376-022-1456-0
Abstract:
An extreme drought appeared in South China from October 2020 to March 2021. During that time, sea surface temperatures exhibited an unprecedented warm center over the northwest Pacific (NWP) and a cold center over the tropical eastern Pacific (La Niña). This study demonstrates the combined effects of an exceptionally warm NWP and a moderate La Niña are closely linked to the anomalous drought in South China. The sea surface temperature anomaly in these two regions induced a steeper horizontal geopotential height gradient over South China. As a result, anomalous northeasterly winds prevailed over South China, altering water vapor transport and moisture convergence. A simplified atmospheric general circulation model also verifies the influence of the NWP warm anomaly on South China precipitation. This study points out that the sea surface temperature variation in the NWP was important to the occurrence of extreme drought in South China from October 2020 to March 2021.
An extreme drought appeared in South China from October 2020 to March 2021. During that time, sea surface temperatures exhibited an unprecedented warm center over the northwest Pacific (NWP) and a cold center over the tropical eastern Pacific (La Niña). This study demonstrates the combined effects of an exceptionally warm NWP and a moderate La Niña are closely linked to the anomalous drought in South China. The sea surface temperature anomaly in these two regions induced a steeper horizontal geopotential height gradient over South China. As a result, anomalous northeasterly winds prevailed over South China, altering water vapor transport and moisture convergence. A simplified atmospheric general circulation model also verifies the influence of the NWP warm anomaly on South China precipitation. This study points out that the sea surface temperature variation in the NWP was important to the occurrence of extreme drought in South China from October 2020 to March 2021.
2022, 39(10): 1650-1672.
doi: 10.1007/s00376-022-1381-2
Abstract:
This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5 (CMIP5) and 33 models from the CMIP phase 6 (CMIP6) to determine the climatological and seasonal variation of ocean salinity from the surface to 2000 m. The general pattern of the ocean salinity climatology can be simulated by both the CMIP5 and CMIP6 models from the surface to 2000-m depth. However, this study shows an increased fresh bias in the surface and subsurface salinity in the CMIP6 multimodel mean, with a global average of −0.44 g kg−1 for the sea surface salinity (SSS) and −0.26 g kg−1 for the 0–1000-m averaged salinity (S1000) compared with the CMIP5 multimodel mean (−0.25 g kg−1 for the SSS and −0.07 g kg−1 for the S1000). In terms of the seasonal variation, both CMIP6 and CMIP5 models show positive (negative) anomalies in the first (second) half of the year in the global average SSS and S1000. The model-simulated variation in SSS is consistent with the observations, but not for S1000, suggesting a substantial uncertainty in simulating and understanding the seasonal variation in subsurface salinity. The CMIP5 and CMIP6 models overestimate the magnitude of the seasonal variation of the SSS in the tropics in the region 20°S–20°N but underestimate the magnitude of the seasonal change in S1000 in the Atlantic and Indian oceans. These assessments show new features of the model errors in simulating ocean salinity and support further studies of the global hydrological cycle.
This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5 (CMIP5) and 33 models from the CMIP phase 6 (CMIP6) to determine the climatological and seasonal variation of ocean salinity from the surface to 2000 m. The general pattern of the ocean salinity climatology can be simulated by both the CMIP5 and CMIP6 models from the surface to 2000-m depth. However, this study shows an increased fresh bias in the surface and subsurface salinity in the CMIP6 multimodel mean, with a global average of −0.44 g kg−1 for the sea surface salinity (SSS) and −0.26 g kg−1 for the 0–1000-m averaged salinity (S1000) compared with the CMIP5 multimodel mean (−0.25 g kg−1 for the SSS and −0.07 g kg−1 for the S1000). In terms of the seasonal variation, both CMIP6 and CMIP5 models show positive (negative) anomalies in the first (second) half of the year in the global average SSS and S1000. The model-simulated variation in SSS is consistent with the observations, but not for S1000, suggesting a substantial uncertainty in simulating and understanding the seasonal variation in subsurface salinity. The CMIP5 and CMIP6 models overestimate the magnitude of the seasonal variation of the SSS in the tropics in the region 20°S–20°N but underestimate the magnitude of the seasonal change in S1000 in the Atlantic and Indian oceans. These assessments show new features of the model errors in simulating ocean salinity and support further studies of the global hydrological cycle.
2022, 39(10): 1673-1692.
doi: 10.1007/s00376-022-1351-8
Abstract:
The middle and lower Yangtze River basin (MLYRB) suffered persistent heavy rainfall in summer 2020, with nearly continuous rainfall for about six consecutive weeks. How the likelihood of persistent heavy rainfall resembling that which occurred over the MLYRB in summer 2020 (hereafter 2020PHR-like event) would change under global warming is investigated. An index that reflects maximum accumulated precipitation during a consecutive five-week period in summer (Rx35day) is introduced. This accumulated precipitation index in summer 2020 is 60% stronger than the climatology, and a statistical analysis further shows that the 2020 event is a 1-in-70-year event. The model projection results derived from the 50-member ensemble of CanESM2 and the multimodel ensemble (MME) of the CMIP5 and CMIP6 models show that the occurrence probability of the 2020PHR-like event will dramatically increase under global warming. Based on the Kolmogorov–Smirnoff test, one-third of the CMIP5 and CMIP6 models that have reasonable performance in reproducing the 2020PHR-like event in their historical simulations are selected for the future projection study. The CMIP5 and CMIP6 MME results show that the occurrence probability of the 2020PHR-like event under the present-day climate will be double under lower-emission scenarios (CMIP5 RCP4.5, CMIP6 SSP1-2.6, and SSP2-4.5) and 3–5 times greater under higher-emission scenarios (3.0 times for CMIP5 RCP8.5, 2.9 times for CMIP6 SSP3-7.0, and 4.8 times for CMIP6 SSP5-8.5). The inter-model spread of the probability change is small, lending confidence to the projection results. The results provide a scientific reference for mitigation of and adaptation to future climate change.
The middle and lower Yangtze River basin (MLYRB) suffered persistent heavy rainfall in summer 2020, with nearly continuous rainfall for about six consecutive weeks. How the likelihood of persistent heavy rainfall resembling that which occurred over the MLYRB in summer 2020 (hereafter 2020PHR-like event) would change under global warming is investigated. An index that reflects maximum accumulated precipitation during a consecutive five-week period in summer (Rx35day) is introduced. This accumulated precipitation index in summer 2020 is 60% stronger than the climatology, and a statistical analysis further shows that the 2020 event is a 1-in-70-year event. The model projection results derived from the 50-member ensemble of CanESM2 and the multimodel ensemble (MME) of the CMIP5 and CMIP6 models show that the occurrence probability of the 2020PHR-like event will dramatically increase under global warming. Based on the Kolmogorov–Smirnoff test, one-third of the CMIP5 and CMIP6 models that have reasonable performance in reproducing the 2020PHR-like event in their historical simulations are selected for the future projection study. The CMIP5 and CMIP6 MME results show that the occurrence probability of the 2020PHR-like event under the present-day climate will be double under lower-emission scenarios (CMIP5 RCP4.5, CMIP6 SSP1-2.6, and SSP2-4.5) and 3–5 times greater under higher-emission scenarios (3.0 times for CMIP5 RCP8.5, 2.9 times for CMIP6 SSP3-7.0, and 4.8 times for CMIP6 SSP5-8.5). The inter-model spread of the probability change is small, lending confidence to the projection results. The results provide a scientific reference for mitigation of and adaptation to future climate change.
2022, 39(10): 1693-1708.
doi: 10.1007/s00376-022-1367-0
Abstract:
Precipitation isotope ratios (O and H) record the history of water phase transitions and fractionation processes during moisture transport and rainfall formation. Here, we evaluated the isotopic composition of precipitation over the central-southeastern region of Brazil at different timescales. Monthly isotopic compositions were associated with classical effects (rainfall amount, seasonality, and continentality), demonstrating the importance of vapor recirculation processes and different regional atmospheric systems (South American Convergence Zone-SACZ and Cold Fronts-CF). While moisture recycling and regional atmospheric processes may also be observed on a daily timescale, classical effects such as the amount effect were not strongly correlated (δ18O-precipitation rate r ≤ –0.37). Daily variability revealed specific climatic features, such as δ18O depleted values (~ –6‰ to –8‰) during the wet season were associated with strong convective activity and large moisture availability. Daily isotopic analysis revealed the role of different moisture sources and transport effects. Isotope ratios combined with d-excess explain how atmospheric recirculation processes interact with convective activity during rainfall formation processes. Our findings provide a new understanding of rainfall sampling timescales and highlight the importance of water isotopes to decipher key hydrometeorological processes in a complex spatial and temporal context in central-southeastern Brazil.
Precipitation isotope ratios (O and H) record the history of water phase transitions and fractionation processes during moisture transport and rainfall formation. Here, we evaluated the isotopic composition of precipitation over the central-southeastern region of Brazil at different timescales. Monthly isotopic compositions were associated with classical effects (rainfall amount, seasonality, and continentality), demonstrating the importance of vapor recirculation processes and different regional atmospheric systems (South American Convergence Zone-SACZ and Cold Fronts-CF). While moisture recycling and regional atmospheric processes may also be observed on a daily timescale, classical effects such as the amount effect were not strongly correlated (δ18O-precipitation rate r ≤ –0.37). Daily variability revealed specific climatic features, such as δ18O depleted values (~ –6‰ to –8‰) during the wet season were associated with strong convective activity and large moisture availability. Daily isotopic analysis revealed the role of different moisture sources and transport effects. Isotope ratios combined with d-excess explain how atmospheric recirculation processes interact with convective activity during rainfall formation processes. Our findings provide a new understanding of rainfall sampling timescales and highlight the importance of water isotopes to decipher key hydrometeorological processes in a complex spatial and temporal context in central-southeastern Brazil.
2022, 39(10): 1709-1720.
doi: 10.1007/s00376-022-1346-5
Abstract:
China national air quality monitoring network has become the core data source for air quality assessment and management in China. However, during network construction, the significant change in numbers of monitoring sites with time is easily ignored, which brings uncertainty to air quality assessments. This study aims to analyze the impact of change in numbers of stations on national and regional air quality assessments in China during 2013–18. The results indicate that the change in numbers of stations has different impacts on fine particulate matter (PM2.5) and ozone concentration assessments. The increasing number of sites makes the estimated national and regional PM2.5 concentration slightly lower by 0.6−2.2 µg m−3 and 1.4−6.0 µg m−3 respectively from 2013 to 2018. The main reason is that over time, the monitoring network expands from the urban centers to the suburban areas with low population densities and pollutant emissions. For ozone, the increasing number of stations affects the long-term trends of the estimated concentration, especially the national trends, which changed from a slight upward trend to a downward trend in 2014−15. Besides, the impact of the increasing number of sites on ozone assessment exhibits a seasonal difference at the 0.05 significance level in that the added sites make the estimated concentration higher in winter and lower in summer. These results suggest that the change in numbers of monitoring sites is an important uncertainty factor in national and regional air quality assessments, that needs to be considered in long-term concentration assessment, trend analysis, and trend driving force analysis.
China national air quality monitoring network has become the core data source for air quality assessment and management in China. However, during network construction, the significant change in numbers of monitoring sites with time is easily ignored, which brings uncertainty to air quality assessments. This study aims to analyze the impact of change in numbers of stations on national and regional air quality assessments in China during 2013–18. The results indicate that the change in numbers of stations has different impacts on fine particulate matter (PM2.5) and ozone concentration assessments. The increasing number of sites makes the estimated national and regional PM2.5 concentration slightly lower by 0.6−2.2 µg m−3 and 1.4−6.0 µg m−3 respectively from 2013 to 2018. The main reason is that over time, the monitoring network expands from the urban centers to the suburban areas with low population densities and pollutant emissions. For ozone, the increasing number of stations affects the long-term trends of the estimated concentration, especially the national trends, which changed from a slight upward trend to a downward trend in 2014−15. Besides, the impact of the increasing number of sites on ozone assessment exhibits a seasonal difference at the 0.05 significance level in that the added sites make the estimated concentration higher in winter and lower in summer. These results suggest that the change in numbers of monitoring sites is an important uncertainty factor in national and regional air quality assessments, that needs to be considered in long-term concentration assessment, trend analysis, and trend driving force analysis.
2022, 39(10): 1721-1733.
doi: 10.1007/s00376-022-1343-8
Abstract:
We propose a novel machine learning approach to reconstruct meshless surface wind speed fields, i.e., to reconstruct the surface wind speed at any location, based on meteorological background fields and geographical information. The random forest method is selected to develop the machine learning data reconstruction model (MLDRM-RF) for wind speeds over Beijing from 2015–19. We use temporal, geospatial attribute and meteorological background field features as inputs. The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance. The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error (RMSE) of the reconstructed wind speed field across Beijing. The average RMSE is 1.09 m s−1, considerably smaller than the result (1.29 m s−1) obtained with inverse distance weighting (IDW) interpolation. Finally, we extract the important feature permutations by the method of mean decrease in impurity (MDI) and discuss the reasonableness of the model prediction results. MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions. Such a model is needed in many wind applications, such as wind energy and aviation safety assessments.
We propose a novel machine learning approach to reconstruct meshless surface wind speed fields, i.e., to reconstruct the surface wind speed at any location, based on meteorological background fields and geographical information. The random forest method is selected to develop the machine learning data reconstruction model (MLDRM-RF) for wind speeds over Beijing from 2015–19. We use temporal, geospatial attribute and meteorological background field features as inputs. The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance. The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error (RMSE) of the reconstructed wind speed field across Beijing. The average RMSE is 1.09 m s−1, considerably smaller than the result (1.29 m s−1) obtained with inverse distance weighting (IDW) interpolation. Finally, we extract the important feature permutations by the method of mean decrease in impurity (MDI) and discuss the reasonableness of the model prediction results. MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions. Such a model is needed in many wind applications, such as wind energy and aviation safety assessments.
2022, 39(10): 1734-1745.
doi: 10.1007/s00376-022-2008-3
Abstract:
To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia (CA), six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and their changes in the near-term future (2031–50) are assessed relative to the reference period (1986–2005). The quantile mapping (QM) method is applied to correct the model data before calculating the indicators. Results show the QM method largely reduces the biases in all the indicators. Growing season length (GSL, day), summer days (SU, day), warm spell duration index (WSDI, day), and tropical nights (TR, day) are projected to significantly increase over CA, and frost days (FD, day) are projected to decrease. However, changes in biologically effective degree days (BEDD, °C) are spatially heterogeneous. The high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.
To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia (CA), six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and their changes in the near-term future (2031–50) are assessed relative to the reference period (1986–2005). The quantile mapping (QM) method is applied to correct the model data before calculating the indicators. Results show the QM method largely reduces the biases in all the indicators. Growing season length (GSL, day), summer days (SU, day), warm spell duration index (WSDI, day), and tropical nights (TR, day) are projected to significantly increase over CA, and frost days (FD, day) are projected to decrease. However, changes in biologically effective degree days (BEDD, °C) are spatially heterogeneous. The high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.
2022, 39(10): 1746-1765.
doi: 10.1007/s00376-022-1439-1
Abstract:
A super-large ensemble simulation dataset with 110 members has been produced by the fully coupled model FGOALS-g3 developed by researchers at the Institute of Atmospheric Physics, Chinese Academy of Sciences. This is the first dataset of large ensemble simulations with a climate system model developed by a Chinese modeling center. The simulation has the largest realizations up to now worldwide in terms of single-model initial-condition large ensembles. Each member includes a historical experiment (1850–2014) and an experiment (2015–99) under the very high greenhouse gas emissions Shared Socioeconomic Pathway scenario (SSP5-8.5). The dataset includes monthly and daily temperature, precipitation, and other variables, requiring storage of 275 TB. Additionally, the surface air temperature (SAT) and land precipitation simulated by the FGOALS-g3 super-large ensemble have been validated and projected. The ensemble can capture the response of SAT and land precipitation to external forcings well, and the internal variabilities can be quantified. The availability of more than 100 realizations will help researchers to study rare events and improve the understanding of the impact of internal variability on forced climate changes.
A super-large ensemble simulation dataset with 110 members has been produced by the fully coupled model FGOALS-g3 developed by researchers at the Institute of Atmospheric Physics, Chinese Academy of Sciences. This is the first dataset of large ensemble simulations with a climate system model developed by a Chinese modeling center. The simulation has the largest realizations up to now worldwide in terms of single-model initial-condition large ensembles. Each member includes a historical experiment (1850–2014) and an experiment (2015–99) under the very high greenhouse gas emissions Shared Socioeconomic Pathway scenario (SSP5-8.5). The dataset includes monthly and daily temperature, precipitation, and other variables, requiring storage of 275 TB. Additionally, the surface air temperature (SAT) and land precipitation simulated by the FGOALS-g3 super-large ensemble have been validated and projected. The ensemble can capture the response of SAT and land precipitation to external forcings well, and the internal variabilities can be quantified. The availability of more than 100 realizations will help researchers to study rare events and improve the understanding of the impact of internal variability on forced climate changes.
2022, 39(10): 1766-1776.
doi: 10.1007/s00376-022-1428-4
Abstract:
In the spring of 2021, southwestern China (SWC) experienced extreme drought, accompanied by the highest seasonal-mean temperature record since 1961. This drought event occurred in the decaying phase of a La Niña event with negative geopotential height anomalies over the Philippine Sea, which is distinct from the historical perspective. Historically, spring drought over SWC is often linked to El Niño and strong western North Pacific subtropical high. Here, we show that the extreme drought in the spring of 2021 may be mainly driven by the atmospheric internal variability and amplified by the warming trend. Specifically, the evaporation increase due to the high temperature accounts for about 30% of drought severity, with the contributions of its linear trend portion being nearly 20% and the interannual variability portion being about 10%. Since the sea surface temperature forcing from the tropical central and eastern Pacific played a minor role in the occurrence of drought, it is a challenge for a climate model to capture the 2021 SWC drought beyond one-month lead times.
In the spring of 2021, southwestern China (SWC) experienced extreme drought, accompanied by the highest seasonal-mean temperature record since 1961. This drought event occurred in the decaying phase of a La Niña event with negative geopotential height anomalies over the Philippine Sea, which is distinct from the historical perspective. Historically, spring drought over SWC is often linked to El Niño and strong western North Pacific subtropical high. Here, we show that the extreme drought in the spring of 2021 may be mainly driven by the atmospheric internal variability and amplified by the warming trend. Specifically, the evaporation increase due to the high temperature accounts for about 30% of drought severity, with the contributions of its linear trend portion being nearly 20% and the interannual variability portion being about 10%. Since the sea surface temperature forcing from the tropical central and eastern Pacific played a minor role in the occurrence of drought, it is a challenge for a climate model to capture the 2021 SWC drought beyond one-month lead times.
2022, 39(10): 1777-1786.
doi: 10.1007/s00376-022-1326-9
Abstract:
An atmosphere-only model system for making seasonal prediction and projecting future intensities of landfalling tropical cyclones (TCs) along the South China coast is upgraded by including ocean and wave models. A total of 642 TCs have been re-simulated using the new system to produce a climatology of TC intensity in the South China Sea. Detailed comparisons of the simulations from the atmosphere-only and the fully coupled systems reveal that the inclusion of the additional ocean and wave models enable differential sea surface temperature responses to various TC characteristics such as translational speed and size. In particular, interaction with the ocean does not necessarily imply a weakening of the TC, with the coastal bathymetry possibly playing a role in causing a near-shore intensification of the TC. These results suggest that to simulate the evolution of TC structure more accurately, it is essential to use an air-sea coupled model instead of an atmosphere-only model.
An atmosphere-only model system for making seasonal prediction and projecting future intensities of landfalling tropical cyclones (TCs) along the South China coast is upgraded by including ocean and wave models. A total of 642 TCs have been re-simulated using the new system to produce a climatology of TC intensity in the South China Sea. Detailed comparisons of the simulations from the atmosphere-only and the fully coupled systems reveal that the inclusion of the additional ocean and wave models enable differential sea surface temperature responses to various TC characteristics such as translational speed and size. In particular, interaction with the ocean does not necessarily imply a weakening of the TC, with the coastal bathymetry possibly playing a role in causing a near-shore intensification of the TC. These results suggest that to simulate the evolution of TC structure more accurately, it is essential to use an air-sea coupled model instead of an atmosphere-only model.
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