2023 Vol. 40, No. 11
Display Method:
2023, 40(11): 1-1.
Abstract:
2023, 40(11): 1941-1951.
doi: 10.1007/s00376-023-3171-x
Abstract:
Extreme weather events and their consequential impacts have been a key feature of the climate in recent years in many parts of the world, with many partly attributed to ongoing global-scale warming. The past year, 2022, has been no exception, with further records being broken. The year was marked by unprecedented heatwaves and droughts with highly unusual spatial extent, duration and intensity, with one measure indicating an aggregated and overall intensity of extreme heat events worldwide not seen since at least 1950. The extreme drought measured by surface soil moisture covered 47.3% of global land areas in 2022, which was the second most widespread year since 1980. Here, we examine notable events of the year in five major regions of the world: China’s Yangtze River region, western Europe, the western U.S., the Horn of Africa and central South America. For each event, we review the potential roles of circulation, oceanic forcing (especially the “triple-dip” La Niña) and anthropogenic climate change, with an aim of understanding the extreme events in 2022 from a global perspective. This will serve as a reference for mechanism understanding, prediction and attribution of extreme events.
Extreme weather events and their consequential impacts have been a key feature of the climate in recent years in many parts of the world, with many partly attributed to ongoing global-scale warming. The past year, 2022, has been no exception, with further records being broken. The year was marked by unprecedented heatwaves and droughts with highly unusual spatial extent, duration and intensity, with one measure indicating an aggregated and overall intensity of extreme heat events worldwide not seen since at least 1950. The extreme drought measured by surface soil moisture covered 47.3% of global land areas in 2022, which was the second most widespread year since 1980. Here, we examine notable events of the year in five major regions of the world: China’s Yangtze River region, western Europe, the western U.S., the Horn of Africa and central South America. For each event, we review the potential roles of circulation, oceanic forcing (especially the “triple-dip” La Niña) and anthropogenic climate change, with an aim of understanding the extreme events in 2022 from a global perspective. This will serve as a reference for mechanism understanding, prediction and attribution of extreme events.
2023, 40(11): 1952-1967.
doi: 10.1007/s00376-023-2347-8
Abstract:
HadISDH.extremes is an annually updated global gridded monthly monitoring product of wet and dry bulb temperature–based extremes indices, from January 1973 to December 2022. Data quality, including spatial and temporal stability, is a key focus. The hourly data are quality controlled. Homogeneity is assessed on monthly means and used to score each gridbox according to its homogeneity rather than to apply adjustments. This enables user-specific screening for temporal stability and avoids errors from inferring adjustments from monthly means for the daily maximum values. For general use, a score (HQ Flag) of 0 to 6 is recommended. A range of indices are presented, aligning with existing standardised indices. Uniquely, provision of both wet and dry bulb indices allows exploration of heat event character — whether it is a “humid and hot”, “dry and hot” or “humid and warm” event. It is designed for analysis of long-term trends in regional features. HadISDH.extremes can be used to study local events, but given the greater vulnerability to errors of maximum compared to mean values, cross-validation with independent information is advised.
HadISDH.extremes is an annually updated global gridded monthly monitoring product of wet and dry bulb temperature–based extremes indices, from January 1973 to December 2022. Data quality, including spatial and temporal stability, is a key focus. The hourly data are quality controlled. Homogeneity is assessed on monthly means and used to score each gridbox according to its homogeneity rather than to apply adjustments. This enables user-specific screening for temporal stability and avoids errors from inferring adjustments from monthly means for the daily maximum values. For general use, a score (HQ Flag) of 0 to 6 is recommended. A range of indices are presented, aligning with existing standardised indices. Uniquely, provision of both wet and dry bulb indices allows exploration of heat event character — whether it is a “humid and hot”, “dry and hot” or “humid and warm” event. It is designed for analysis of long-term trends in regional features. HadISDH.extremes can be used to study local events, but given the greater vulnerability to errors of maximum compared to mean values, cross-validation with independent information is advised.
2023, 40(11): 1968-1985.
doi: 10.1007/s00376-023-2348-7
Abstract:
Heat events may be humid or dry. While several indices incorporate humidity, such combined indices obscure identification and exploration of heat events by their different humidity characteristics. The new HadISDH.extremes global gridded monitoring product uniquely provides a range of wet and dry bulb temperature extremes indices. Analysis of this new data product demonstrates its value as a tool for quantifying exposure to humid verses dry heat events. It also enables exploration into “stealth heat events”, where humidity is high, perhaps enough to affect productivity and health, while temperature remains moderate. Such events may not typically be identified as “heat events” by temperature-focused heat indices. Over 1973–2022, the peak magnitude of humid extremes (maximum daily wet bulb temperature over a month; TwX) for the global annual mean increased significantly at 0.13 ± 0.04°C (10 yr)−1, which is slightly slower than the global annual mean Tw increase of 0.22± 0.04°C (10 yr)−1. The frequency of moderate humid extreme events per year (90th percentile daily maxima wet bulb temperature exceedance; TwX90p) also increased significantly at 4.61 ± 1.07 d yr−1 (10 yr)−1. These rates were slower than for temperature extremes, TX and TX90p, which respectively increased significantly at 0.27 ± 0.04°C (10 yr)−1 and 5.53 ± 0.72 d yr−1 (10 yr)−1. Similarly, for the UK/Europe focus region, JJA-mean TwX increased significantly, again at a slower rate than for TX and mean Tw. HadISDH.extremes shows some evidence of “stealth heat events” occurring where humidity is high but temperature remains more moderate.
Heat events may be humid or dry. While several indices incorporate humidity, such combined indices obscure identification and exploration of heat events by their different humidity characteristics. The new HadISDH.extremes global gridded monitoring product uniquely provides a range of wet and dry bulb temperature extremes indices. Analysis of this new data product demonstrates its value as a tool for quantifying exposure to humid verses dry heat events. It also enables exploration into “stealth heat events”, where humidity is high, perhaps enough to affect productivity and health, while temperature remains moderate. Such events may not typically be identified as “heat events” by temperature-focused heat indices. Over 1973–2022, the peak magnitude of humid extremes (maximum daily wet bulb temperature over a month; TwX) for the global annual mean increased significantly at 0.13 ± 0.04°C (10 yr)−1, which is slightly slower than the global annual mean Tw increase of 0.22± 0.04°C (10 yr)−1. The frequency of moderate humid extreme events per year (90th percentile daily maxima wet bulb temperature exceedance; TwX90p) also increased significantly at 4.61 ± 1.07 d yr−1 (10 yr)−1. These rates were slower than for temperature extremes, TX and TX90p, which respectively increased significantly at 0.27 ± 0.04°C (10 yr)−1 and 5.53 ± 0.72 d yr−1 (10 yr)−1. Similarly, for the UK/Europe focus region, JJA-mean TwX increased significantly, again at a slower rate than for TX and mean Tw. HadISDH.extremes shows some evidence of “stealth heat events” occurring where humidity is high but temperature remains more moderate.
2023, 40(11): 1986-1997.
doi: 10.1007/s00376-022-2079-1
Abstract:
The prediction of summer precipitation over the Yangtze River basin (YRB) has long been challenging, especially during June–July (JJ), when the mei-yu generally occurs. This study explores the potential signal for the YRB precipitation in JJ and reveals that the Tibetan Plateau tropospheric temperature (TPTT) in the middle and upper levels during the preceding December–January (DJ) is significantly correlated with JJ YRB precipitation. The close connection between the DJ TPTT anomaly with JJ YRB precipitation may be due to the joint modulation of the DJ ENSO and spring TP soil temperatures. The lagged response to an anomalously cold TPTT during the preceding DJ is a TPTT that is still anomalously cold during the following JJ. The lower TPTT can lead to an anomalous anticyclone to the east of Lake Baikal, an anomalous cyclone at the middle latitudes of East Asia, and an anomalous anticyclone over the western North Pacific. Meanwhile, the East Asian westerly jet shifts southward in response to the meridional thermal gradient caused by the colder troposphere extending from the TP to the east of Lake Baikal. The above-mentioned circulation anomalies constitute the positive anomaly of the East Asia-Pacific pattern, known to be conducive to more precipitation over the YRB. Since the DJ TPTT contains both the land (TP soil temperature) and ocean (ENSO) signals, it has a closer relationship with the JJ precipitation over the YRB than the DJ ENSO alone. Therefore, the preceding DJ TPTT can be considered an alternative predictor of the JJ YRB precipitation.
The prediction of summer precipitation over the Yangtze River basin (YRB) has long been challenging, especially during June–July (JJ), when the mei-yu generally occurs. This study explores the potential signal for the YRB precipitation in JJ and reveals that the Tibetan Plateau tropospheric temperature (TPTT) in the middle and upper levels during the preceding December–January (DJ) is significantly correlated with JJ YRB precipitation. The close connection between the DJ TPTT anomaly with JJ YRB precipitation may be due to the joint modulation of the DJ ENSO and spring TP soil temperatures. The lagged response to an anomalously cold TPTT during the preceding DJ is a TPTT that is still anomalously cold during the following JJ. The lower TPTT can lead to an anomalous anticyclone to the east of Lake Baikal, an anomalous cyclone at the middle latitudes of East Asia, and an anomalous anticyclone over the western North Pacific. Meanwhile, the East Asian westerly jet shifts southward in response to the meridional thermal gradient caused by the colder troposphere extending from the TP to the east of Lake Baikal. The above-mentioned circulation anomalies constitute the positive anomaly of the East Asia-Pacific pattern, known to be conducive to more precipitation over the YRB. Since the DJ TPTT contains both the land (TP soil temperature) and ocean (ENSO) signals, it has a closer relationship with the JJ precipitation over the YRB than the DJ ENSO alone. Therefore, the preceding DJ TPTT can be considered an alternative predictor of the JJ YRB precipitation.
2023, 40(11): 1998-2012.
doi: 10.1007/s00376-023-2288-2
Abstract:
The dynamical prediction of the Asian-Australian monsoon (AAM) has been an important and long-standing issue in climate science. In this study, the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide. Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences, especially for the second mode. The first AAM mode, from various seasonal sequences, coincides with the El Niño phase transition in the eastern-central Pacific. The second mode, initialized from boreal summer and autumn, leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring. Our findings hint that ENSO, as an early signal, is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes. Still, the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features. The multimodel ensemble (MME) mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features. The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes. The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.
The dynamical prediction of the Asian-Australian monsoon (AAM) has been an important and long-standing issue in climate science. In this study, the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide. Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences, especially for the second mode. The first AAM mode, from various seasonal sequences, coincides with the El Niño phase transition in the eastern-central Pacific. The second mode, initialized from boreal summer and autumn, leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring. Our findings hint that ENSO, as an early signal, is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes. Still, the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features. The multimodel ensemble (MME) mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features. The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes. The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.
2023, 40(11): 2013-2028.
doi: 10.1007/s00376-023-2258-8
Abstract:
The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon (EAWM), as well as the associated atmospheric circulation, was investigated using the hindcast data from Global Seasonal Forecast System version 5 (GloSea5), with a focus on the evolution of model bias among different forecast lead times. While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well, systematic biases exist, including a cold bias for most of China’s mainland, especially for North and Northeast China. GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead, which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation. GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO) on the EAWM, especially for the western North Pacific anticyclone (WNPAC). Compared with the North Pacific and North America, the representation of circulation anomalies over Eurasia is poor, especially for sea level pressure (SLP), which limits the prediction skill for surface air temperature over East Asia. The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.
The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon (EAWM), as well as the associated atmospheric circulation, was investigated using the hindcast data from Global Seasonal Forecast System version 5 (GloSea5), with a focus on the evolution of model bias among different forecast lead times. While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well, systematic biases exist, including a cold bias for most of China’s mainland, especially for North and Northeast China. GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead, which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation. GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO) on the EAWM, especially for the western North Pacific anticyclone (WNPAC). Compared with the North Pacific and North America, the representation of circulation anomalies over Eurasia is poor, especially for sea level pressure (SLP), which limits the prediction skill for surface air temperature over East Asia. The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.
2023, 40(11): 2029-2042.
doi: 10.1007/s00376-023-2297-1
Abstract:
The East Asian Summer Monsoon (EASM) provides the majority of annual rainfall to countries in East Asia. Although state-of-the-art models broadly project increased EASM rainfall, the spread of projections is large and simulations of present-day rainfall show significant climatological biases. Systematic evapotranspiration biases occur locally over East Asia, and globally over land, in simulations both with and without a coupled ocean. This study explores the relationship between evapotranspiration and EASM precipitation biases. First, idealized model simulations are presented in which the parameterization of land evaporation is modified, while sea surface temperature is fixed. The results suggest a feedback whereby excessive evapotranspiration over East Asia results in cooling of land, a weakened monsoon low, and a shift of rainfall from the Philippine Sea to China, further fueling evapotranspiration. Cross-model regressions against evapotranspiration over China indicate a similar pattern of behavior in Atmospheric Model Intercomparison Project (AMIP) simulations. Possible causes of this pattern are investigated. The feedback is not explained by an overly intense global hydrological cycle or by differences in radiative processes. Analysis of land-only simulations indicates that evapotranspiration biases are present even when models are forced with prescribed rainfall. These are strengthened when coupled to the atmosphere, suggesting a role for land-model errors in driving atmospheric biases. Coupled atmosphere–ocean models are shown to have similar evapotranspiration biases to those in AMIP over China, but different precipitation biases, including a northward shift in the ITCZ over the Pacific and Atlantic Oceans.
The East Asian Summer Monsoon (EASM) provides the majority of annual rainfall to countries in East Asia. Although state-of-the-art models broadly project increased EASM rainfall, the spread of projections is large and simulations of present-day rainfall show significant climatological biases. Systematic evapotranspiration biases occur locally over East Asia, and globally over land, in simulations both with and without a coupled ocean. This study explores the relationship between evapotranspiration and EASM precipitation biases. First, idealized model simulations are presented in which the parameterization of land evaporation is modified, while sea surface temperature is fixed. The results suggest a feedback whereby excessive evapotranspiration over East Asia results in cooling of land, a weakened monsoon low, and a shift of rainfall from the Philippine Sea to China, further fueling evapotranspiration. Cross-model regressions against evapotranspiration over China indicate a similar pattern of behavior in Atmospheric Model Intercomparison Project (AMIP) simulations. Possible causes of this pattern are investigated. The feedback is not explained by an overly intense global hydrological cycle or by differences in radiative processes. Analysis of land-only simulations indicates that evapotranspiration biases are present even when models are forced with prescribed rainfall. These are strengthened when coupled to the atmosphere, suggesting a role for land-model errors in driving atmospheric biases. Coupled atmosphere–ocean models are shown to have similar evapotranspiration biases to those in AMIP over China, but different precipitation biases, including a northward shift in the ITCZ over the Pacific and Atlantic Oceans.
2023, 40(11): 2043-2062.
doi: 10.1007/s00376-023-2298-0
Abstract:
Nested simulations of a downslope windstorm over Cangshan mountain, Yunnan, China, have been used to demonstrate a method of topographic smoothing that preserves a relatively large amount of terrain detail compared to typical smoothing procedures required for models with terrain-following grids to run stably. The simulations were carried out using the Met Office Unified Model (MetUM) to investigate downslope winds. The smoothing method seamlessly blends two terrain datasets to which uniform smoothing has been applied — one with a minimum of smoothing, the other smoothed more heavily to remove gradients that would cause model instabilities. The latter dataset dominates the blend where the steepest slopes exist, but this is localised and recedes outside these areas. As a result, increased detail is starkly apparent in depictions of flow simulated using the blend, compared to one using the default approach. This includes qualitative flow details that were absent in the latter, such as narrow shooting flows emerging from roughly 1−2 km wide leeside channels. Flow separation is more common due to steeper lee slopes. The use of targeted smoothing also results in increased lee side temporal variability at a given point during the windstorm, including over flat areas. Low-/high-pass filtering of the wind perturbation field reveals that relative spatial variability above 30 km in scale (reflecting the background flow) is similar whether or not targeting is used. Beneath this scale, when smoothing is targeted, relative flow variability decreases at the larger scales,and increases at lower scales. This seems linked to fast smaller scale flows disturbing more coherent flows (notably an along-valley current over Erhai Lake). Spatial variability of winds in the model is unsurprisingly weaker at key times than is observed across a local network sampling mesoscale variation, but results are compromised due to relatively few observation locations sampling the windstorm. Only when targeted smoothing is applied does the model capture the downslope windstorm's extension over the city of Dali at the mountain's foot, and the peak mean absolute wind.
Nested simulations of a downslope windstorm over Cangshan mountain, Yunnan, China, have been used to demonstrate a method of topographic smoothing that preserves a relatively large amount of terrain detail compared to typical smoothing procedures required for models with terrain-following grids to run stably. The simulations were carried out using the Met Office Unified Model (MetUM) to investigate downslope winds. The smoothing method seamlessly blends two terrain datasets to which uniform smoothing has been applied — one with a minimum of smoothing, the other smoothed more heavily to remove gradients that would cause model instabilities. The latter dataset dominates the blend where the steepest slopes exist, but this is localised and recedes outside these areas. As a result, increased detail is starkly apparent in depictions of flow simulated using the blend, compared to one using the default approach. This includes qualitative flow details that were absent in the latter, such as narrow shooting flows emerging from roughly 1−2 km wide leeside channels. Flow separation is more common due to steeper lee slopes. The use of targeted smoothing also results in increased lee side temporal variability at a given point during the windstorm, including over flat areas. Low-/high-pass filtering of the wind perturbation field reveals that relative spatial variability above 30 km in scale (reflecting the background flow) is similar whether or not targeting is used. Beneath this scale, when smoothing is targeted, relative flow variability decreases at the larger scales,and increases at lower scales. This seems linked to fast smaller scale flows disturbing more coherent flows (notably an along-valley current over Erhai Lake). Spatial variability of winds in the model is unsurprisingly weaker at key times than is observed across a local network sampling mesoscale variation, but results are compromised due to relatively few observation locations sampling the windstorm. Only when targeted smoothing is applied does the model capture the downslope windstorm's extension over the city of Dali at the mountain's foot, and the peak mean absolute wind.
2023, 40(11): 2063-2081.
doi: 10.1007/s00376-023-2296-2
Abstract:
Thermal processes on the Tibetan Plateau (TP) influence atmospheric conditions on regional and global scales. Given this, previous work has shown that soil moisture−driven surface flux variations feed back onto the atmosphere. Whilst soil moisture is a source of atmospheric predictability, no study has evaluated soil moisture−atmosphere coupling on the TP in general circulation models (GCMs). In this study, we use several analysis techniques to assess soil moisture−atmosphere coupling in CMIP6 simulations including: instantaneous coupling indices; analysis of flux and atmospheric behaviour during dry spells; and a quantification of the preference for convection over drier soils. Through these metrics we partition feedbacks into their atmospheric and terrestrial components. Consistent with previous global studies, we conclude substantial inter-model differences in the representation of soil moisture−atmosphere coupling, and that most models underestimate such feedbacks. Focusing on dry spell analysis, most models underestimate increased sensible heat during periods of rainfall deficiency. For example, the model-mean bias in anomalous sensible heat flux is 10 W m−2 (≈25%) smaller compared to observations. Deficient dry-spell sensible heat fluxes lead to a weaker atmospheric response. We also find that most GCMs fail to capture the negative feedback between soil moisture and deep convection. The poor simulation of feedbacks in CMIP6 experiments suggests that forecast models also struggle to exploit soil moisture−driven predictability. To improve the representation of land−atmosphere feedbacks requires developments in not only atmospheric modelling, but also surface processes, as we find weak relationships between rainfall biases and coupling indexes.
Thermal processes on the Tibetan Plateau (TP) influence atmospheric conditions on regional and global scales. Given this, previous work has shown that soil moisture−driven surface flux variations feed back onto the atmosphere. Whilst soil moisture is a source of atmospheric predictability, no study has evaluated soil moisture−atmosphere coupling on the TP in general circulation models (GCMs). In this study, we use several analysis techniques to assess soil moisture−atmosphere coupling in CMIP6 simulations including: instantaneous coupling indices; analysis of flux and atmospheric behaviour during dry spells; and a quantification of the preference for convection over drier soils. Through these metrics we partition feedbacks into their atmospheric and terrestrial components. Consistent with previous global studies, we conclude substantial inter-model differences in the representation of soil moisture−atmosphere coupling, and that most models underestimate such feedbacks. Focusing on dry spell analysis, most models underestimate increased sensible heat during periods of rainfall deficiency. For example, the model-mean bias in anomalous sensible heat flux is 10 W m−2 (≈25%) smaller compared to observations. Deficient dry-spell sensible heat fluxes lead to a weaker atmospheric response. We also find that most GCMs fail to capture the negative feedback between soil moisture and deep convection. The poor simulation of feedbacks in CMIP6 experiments suggests that forecast models also struggle to exploit soil moisture−driven predictability. To improve the representation of land−atmosphere feedbacks requires developments in not only atmospheric modelling, but also surface processes, as we find weak relationships between rainfall biases and coupling indexes.
2023, 40(11): 2082-2091.
doi: 10.1007/s00376-023-2251-2
Abstract:
Variability in the East Asian summer monsoon (EASM) brings the risk of heavy flooding or drought to the Yangtze River basin, with potentially devastating impacts. Early forecasts of the likelihood of enhanced or reduced monsoon rainfall can enable better management of water and hydropower resources by decision-makers, supporting livelihoods and major economic and population centres across eastern China. This paper demonstrates that the EASM is predictable in a dynamical forecast model from the preceding November, and that this allows skilful forecasts of summer mean rainfall in the Yangtze River basin at a lead time of six months. The skill for May–June–July rainfall is of a similar magnitude to seasonal forecasts initialised in spring, although the skill in June–July–August is much weaker and not consistently significant. However, there is some evidence for enhanced skill following El Niño events. The potential for decadal-scale variability in forecast skill is also examined, although we find no evidence for significant variation.
Variability in the East Asian summer monsoon (EASM) brings the risk of heavy flooding or drought to the Yangtze River basin, with potentially devastating impacts. Early forecasts of the likelihood of enhanced or reduced monsoon rainfall can enable better management of water and hydropower resources by decision-makers, supporting livelihoods and major economic and population centres across eastern China. This paper demonstrates that the EASM is predictable in a dynamical forecast model from the preceding November, and that this allows skilful forecasts of summer mean rainfall in the Yangtze River basin at a lead time of six months. The skill for May–June–July rainfall is of a similar magnitude to seasonal forecasts initialised in spring, although the skill in June–July–August is much weaker and not consistently significant. However, there is some evidence for enhanced skill following El Niño events. The potential for decadal-scale variability in forecast skill is also examined, although we find no evidence for significant variation.
2023, 40(11): 2092-2107.
doi: 10.1007/s00376-023-2342-0
Abstract:
The spatial distribution of summer precipitation anomalies over eastern China often shows a dipole pattern, with anti-phased precipitation anomalies between southern China and northern China, known as the “southern flooding and northern drought” (SF-ND) pattern. In 2015, China experienced heavy rainfall in the south and the worst drought since 1979 in the north, which caused huge social and economic losses. Using reanalysis data, the atmospheric circulation anomalies and possible mechanisms related to the summer precipitation anomalies in 2015 were examined. The results showed that both El Niño and certain atmospheric teleconnections, including the Pacific Japan/East Asia Pacific (PJ/EAP), Eurasia pattern (EU), British–Baikal Corridor pattern (BBC), and Silk Road mode (SR), contributed to the dipole pattern of precipitation anomalies. The combination of these factors caused a southwards shift of the western Pacific subtropical high (WPSH) and a weakening of the East Asian summer monsoon. Consequently, it was difficult for the monsoon front and associated rain band to migrate northwards, which meant that less precipitation occurred in northern China while more precipitation occurred in southern China. This resulted in the SF-ND event. Moreover, further analysis revealed that global sea surface temperature anomalies (SSTAs) or sea-ice anomalies were key to stimulating these atmospheric teleconnections.
The spatial distribution of summer precipitation anomalies over eastern China often shows a dipole pattern, with anti-phased precipitation anomalies between southern China and northern China, known as the “southern flooding and northern drought” (SF-ND) pattern. In 2015, China experienced heavy rainfall in the south and the worst drought since 1979 in the north, which caused huge social and economic losses. Using reanalysis data, the atmospheric circulation anomalies and possible mechanisms related to the summer precipitation anomalies in 2015 were examined. The results showed that both El Niño and certain atmospheric teleconnections, including the Pacific Japan/East Asia Pacific (PJ/EAP), Eurasia pattern (EU), British–Baikal Corridor pattern (BBC), and Silk Road mode (SR), contributed to the dipole pattern of precipitation anomalies. The combination of these factors caused a southwards shift of the western Pacific subtropical high (WPSH) and a weakening of the East Asian summer monsoon. Consequently, it was difficult for the monsoon front and associated rain band to migrate northwards, which meant that less precipitation occurred in northern China while more precipitation occurred in southern China. This resulted in the SF-ND event. Moreover, further analysis revealed that global sea surface temperature anomalies (SSTAs) or sea-ice anomalies were key to stimulating these atmospheric teleconnections.
2023, 40(11): 2108-2126.
doi: 10.1007/s00376-023-2294-4
Abstract:
This paper provides a systematic evaluation of the ability of 12 Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate the spatial inhomogeneity of the atmospheric carbon dioxide (CO2) concentration. The multi-model ensemble mean (MME) can reasonably simulate the increasing trend of CO2 concentration from 1850 to 2014, compared with the observation data from the Scripps CO2 Program and CMIP6 prescribed data, and improves upon the CMIP5 MME CO2 concentration (which is overestimated after 1950). The growth rate of CO2 concentration in the northern hemisphere (NH) is higher than that in the southern hemisphere (SH), with the highest growth rate in the mid-latitudes of the NH. The MME can also reasonably simulate the seasonal amplitude of CO2 concentration, which is larger in the NH than in the SH and grows in amplitude after the 1950s (especially in the NH). Although the results of the MME are reasonable, there is a large spread among ESMs, and the difference between the ESMs increases with time. The MME results show that regions with relatively large CO2 concentrations (such as northern Russia, eastern China, Southeast Asia, the eastern United States, northern South America, and southern Africa) have greater seasonal variability and also exhibit a larger inter-model spread. Compared with CMIP5, the CMIP6 MME simulates an average spatial distribution of CO2 concentration that is much closer to the site observations, but the CMIP6-inter-model spread is larger. The inter-model differences of the annual means and seasonal cycles of atmospheric CO2 concentration are both attributed to the differences in natural sources and sinks of CO2 between the simulations.
This paper provides a systematic evaluation of the ability of 12 Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate the spatial inhomogeneity of the atmospheric carbon dioxide (CO2) concentration. The multi-model ensemble mean (MME) can reasonably simulate the increasing trend of CO2 concentration from 1850 to 2014, compared with the observation data from the Scripps CO2 Program and CMIP6 prescribed data, and improves upon the CMIP5 MME CO2 concentration (which is overestimated after 1950). The growth rate of CO2 concentration in the northern hemisphere (NH) is higher than that in the southern hemisphere (SH), with the highest growth rate in the mid-latitudes of the NH. The MME can also reasonably simulate the seasonal amplitude of CO2 concentration, which is larger in the NH than in the SH and grows in amplitude after the 1950s (especially in the NH). Although the results of the MME are reasonable, there is a large spread among ESMs, and the difference between the ESMs increases with time. The MME results show that regions with relatively large CO2 concentrations (such as northern Russia, eastern China, Southeast Asia, the eastern United States, northern South America, and southern Africa) have greater seasonal variability and also exhibit a larger inter-model spread. Compared with CMIP5, the CMIP6 MME simulates an average spatial distribution of CO2 concentration that is much closer to the site observations, but the CMIP6-inter-model spread is larger. The inter-model differences of the annual means and seasonal cycles of atmospheric CO2 concentration are both attributed to the differences in natural sources and sinks of CO2 between the simulations.
2023, 40(11): 2127-2134.
doi: 10.1007/s00376-022-2197-9
Abstract:
Northeast Asian cut-off lows are crucial cyclonic systems that can bring temperature and precipitation extremes over large areas. Skillful subseasonal forecasting of Northeast Asian cut-off lows is of great importance. Using two dynamical forecasting systems, one from the Beijing Climate Center (BCC-CSM2-HR) and the other from the Met Office (GloSea5), this study assesses simulation ability and subseasonal prediction skill for early-summer Northeast Asian cut-off lows. Both models are shown to have good ability in representing the spatial structure of cut-off lows, but they underestimate the intensity. The skillful prediction time scales for cut-off low intensity are about 10.2 days for BCC-CSM2-HR and 11.4 days for GloSea5 in advance. Further examination shows that both models can essentially capture the initial Rossby wave train,rapid growth and decay processes responsible for the evolution of cut-off lows, but the models show weaker amplitudes for the three-stage processes. The underestimated simulated strength of both the Eurasian midlatitude and East Asian subtropical jets may lead to the weaker local eddy-mean flow interaction responsible for the cut-off low evolution.
Northeast Asian cut-off lows are crucial cyclonic systems that can bring temperature and precipitation extremes over large areas. Skillful subseasonal forecasting of Northeast Asian cut-off lows is of great importance. Using two dynamical forecasting systems, one from the Beijing Climate Center (BCC-CSM2-HR) and the other from the Met Office (GloSea5), this study assesses simulation ability and subseasonal prediction skill for early-summer Northeast Asian cut-off lows. Both models are shown to have good ability in representing the spatial structure of cut-off lows, but they underestimate the intensity. The skillful prediction time scales for cut-off low intensity are about 10.2 days for BCC-CSM2-HR and 11.4 days for GloSea5 in advance. Further examination shows that both models can essentially capture the initial Rossby wave train,rapid growth and decay processes responsible for the evolution of cut-off lows, but the models show weaker amplitudes for the three-stage processes. The underestimated simulated strength of both the Eurasian midlatitude and East Asian subtropical jets may lead to the weaker local eddy-mean flow interaction responsible for the cut-off low evolution.
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