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- Author or Editor: Cheng Qian x
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Abstract
North China has undergone a severe drying trend since the 1950s, but whether this trend is natural variability or anthropogenic change remains unknown due to the short data length. This study extends the analysis of dry–wet changes in north China to 1900–2010 on the basis of self-calibrated Palmer drought severity index (PDSI) data. The ensemble empirical mode decomposition method is used to detect multidecadal variability. A transition from significant wetting to significant drying is detected around 1959/60. Approximately 70% of the drying trend during 1960–90 originates from 50–70-yr multidecadal variability related to Pacific decadal oscillation (PDO) phase changes. The PDSI in north China is significantly negatively correlated with the PDO index, particularly at the 50–70-yr time scale, and is also stable during 1900–2010. Composite differences between two positive PDO phases (1922–45 and 1977–2002) and one negative PDO phase (1946–76) for summer exhibit an anomalous Pacific–Japan/East Asian–Pacific patternlike teleconnection, which may develop locally in response to the PDO-associated warm sea surface temperature anomalies in the tropical Indo-Pacific Ocean and meridionally extends from the tropical western Pacific to north China along the East Asian coast. North China is dominated by an anomalous high pressure system at mid–low levels and an anticyclone at 850 hPa, which are favorable for dry conditions. In addition, a weakened land–sea thermal contrast in East Asia from a negative to a positive PDO phase also plays a role in the dry conditions in north China by weakening the East Asian summer monsoon.
Abstract
North China has undergone a severe drying trend since the 1950s, but whether this trend is natural variability or anthropogenic change remains unknown due to the short data length. This study extends the analysis of dry–wet changes in north China to 1900–2010 on the basis of self-calibrated Palmer drought severity index (PDSI) data. The ensemble empirical mode decomposition method is used to detect multidecadal variability. A transition from significant wetting to significant drying is detected around 1959/60. Approximately 70% of the drying trend during 1960–90 originates from 50–70-yr multidecadal variability related to Pacific decadal oscillation (PDO) phase changes. The PDSI in north China is significantly negatively correlated with the PDO index, particularly at the 50–70-yr time scale, and is also stable during 1900–2010. Composite differences between two positive PDO phases (1922–45 and 1977–2002) and one negative PDO phase (1946–76) for summer exhibit an anomalous Pacific–Japan/East Asian–Pacific patternlike teleconnection, which may develop locally in response to the PDO-associated warm sea surface temperature anomalies in the tropical Indo-Pacific Ocean and meridionally extends from the tropical western Pacific to north China along the East Asian coast. North China is dominated by an anomalous high pressure system at mid–low levels and an anticyclone at 850 hPa, which are favorable for dry conditions. In addition, a weakened land–sea thermal contrast in East Asia from a negative to a positive PDO phase also plays a role in the dry conditions in north China by weakening the East Asian summer monsoon.
Abstract
The annual cycle is the largest variability for many climate variables outside the tropics. Whether human activities have affected the annual cycle at the regional scale is unclear. In this study, long-term changes in the amplitude of surface air temperature annual cycle in the observations are compared with those simulated by the climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Different spatial domains ranging from hemispheric to subcontinental scales in mid- to high-latitude land areas for the period 1950–2005 are considered. Both the optimal fingerprinting and a nonoptimal detection and attribution technique are used. The results show that the space–time pattern of model-simulated responses to the combined effect of anthropogenic and natural forcings is consistent with the observed changes. In particular, models capture not only the decrease in the temperature seasonality in the northern high latitudes and East Asia, but also the increase in the Mediterranean region. A human influence on the weakening in the temperature seasonality in the Northern Hemisphere is detected, particularly in the high latitudes (50°–70°N) where the influence of the anthropogenic forcing can be separated from that of the natural forcing.
Abstract
The annual cycle is the largest variability for many climate variables outside the tropics. Whether human activities have affected the annual cycle at the regional scale is unclear. In this study, long-term changes in the amplitude of surface air temperature annual cycle in the observations are compared with those simulated by the climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Different spatial domains ranging from hemispheric to subcontinental scales in mid- to high-latitude land areas for the period 1950–2005 are considered. Both the optimal fingerprinting and a nonoptimal detection and attribution technique are used. The results show that the space–time pattern of model-simulated responses to the combined effect of anthropogenic and natural forcings is consistent with the observed changes. In particular, models capture not only the decrease in the temperature seasonality in the northern high latitudes and East Asia, but also the increase in the Mediterranean region. A human influence on the weakening in the temperature seasonality in the Northern Hemisphere is detected, particularly in the high latitudes (50°–70°N) where the influence of the anthropogenic forcing can be separated from that of the natural forcing.
Abstract
Temperature seasonality, the difference between summer and winter temperatures in mid–high latitudes, is an important component of the climate. Whether humans have had detectable influences on changing surface temperature seasonality at scales smaller than the subcontinental scale, where humans are directly impacted, is not clear. In this study, the first detection and attribution analysis of changes in temperature seasonality in China has been carried out. Detection and attribution of both summer and winter temperatures were also conducted, with careful consideration of observational uncertainty and the inconsistency between observation and model simulations induced by the long coastline and country border in China. The results show that the response to external forcings is robustly detectable in the spatiotemporal pattern of weakening seasonality and in that of warming winter temperature, although models may have underestimated the observed changes. The response to external forcings is detectable and consistent with the observed change in summer temperature averaged over China. Human influences are detectable in changes in seasonality and summer and winter temperatures, most robustly in winter, and these influences can be separated from those of natural forcing when averaged over China. The recent increase in summer temperature was found to be due to external forcings, and the warming hiatus in winter temperature from 1998 to 2013 was due to a statistically significant cooling trend induced by internal variability. These results will give insights into the understanding of the warming hiatus in China, as well as the hot summers and cold winters in recent years.
Abstract
Temperature seasonality, the difference between summer and winter temperatures in mid–high latitudes, is an important component of the climate. Whether humans have had detectable influences on changing surface temperature seasonality at scales smaller than the subcontinental scale, where humans are directly impacted, is not clear. In this study, the first detection and attribution analysis of changes in temperature seasonality in China has been carried out. Detection and attribution of both summer and winter temperatures were also conducted, with careful consideration of observational uncertainty and the inconsistency between observation and model simulations induced by the long coastline and country border in China. The results show that the response to external forcings is robustly detectable in the spatiotemporal pattern of weakening seasonality and in that of warming winter temperature, although models may have underestimated the observed changes. The response to external forcings is detectable and consistent with the observed change in summer temperature averaged over China. Human influences are detectable in changes in seasonality and summer and winter temperatures, most robustly in winter, and these influences can be separated from those of natural forcing when averaged over China. The recent increase in summer temperature was found to be due to external forcings, and the warming hiatus in winter temperature from 1998 to 2013 was due to a statistically significant cooling trend induced by internal variability. These results will give insights into the understanding of the warming hiatus in China, as well as the hot summers and cold winters in recent years.
Abstract
Climate change is not only reflected in the changes in annual means of climate variables but also in the changes in their annual cycles (seasonality), especially in the regions outside the tropics. In this study, the ensemble empirical mode decomposition (EEMD) method is applied to investigate the nonlinear trend in the amplitude of the annual cycle (which contributes 96% of the total variance) of China’s daily mean surface air temperature for the period 1961–2007. The results show that the variation and change in the amplitude are significant, with a peak-to-peak annual amplitude variation of 13% (1.8°C) of its mean amplitude and a significant linear decrease in amplitude by 4.6% (0.63°C) for this period. Also identified is a multidecadal change in amplitude from significant decreasing (−1.7% decade−1 or −0.23°C decade−1) to significant increasing (2.2% decade−1 or 0.29°C decade−1) occurring around 1993 that overlaps the systematic linear trend. This multidecadal change can be mainly attributed to the change in surface solar radiation, from dimming to brightening, rather than to a warming trend or an enhanced greenhouse effect. The study further proposes that the combined effect of the global dimming–brightening transition and a gradual increase in greenhouse warming has led to a perceived warming trend that is much larger in winter than in summer and to a perceived accelerated warming in the annual mean since the early 1990s in China. It also notes that the deseasonalization method (considering either the conventional repetitive climatological annual cycle or the time-varying annual cycle) can also affect trend estimation.
Abstract
Climate change is not only reflected in the changes in annual means of climate variables but also in the changes in their annual cycles (seasonality), especially in the regions outside the tropics. In this study, the ensemble empirical mode decomposition (EEMD) method is applied to investigate the nonlinear trend in the amplitude of the annual cycle (which contributes 96% of the total variance) of China’s daily mean surface air temperature for the period 1961–2007. The results show that the variation and change in the amplitude are significant, with a peak-to-peak annual amplitude variation of 13% (1.8°C) of its mean amplitude and a significant linear decrease in amplitude by 4.6% (0.63°C) for this period. Also identified is a multidecadal change in amplitude from significant decreasing (−1.7% decade−1 or −0.23°C decade−1) to significant increasing (2.2% decade−1 or 0.29°C decade−1) occurring around 1993 that overlaps the systematic linear trend. This multidecadal change can be mainly attributed to the change in surface solar radiation, from dimming to brightening, rather than to a warming trend or an enhanced greenhouse effect. The study further proposes that the combined effect of the global dimming–brightening transition and a gradual increase in greenhouse warming has led to a perceived warming trend that is much larger in winter than in summer and to a perceived accelerated warming in the annual mean since the early 1990s in China. It also notes that the deseasonalization method (considering either the conventional repetitive climatological annual cycle or the time-varying annual cycle) can also affect trend estimation.
Stratospheric Temperature Climate Data Record from Merged SSU and AMSU-A ObservationsAbstract
Observations from the Stratospheric Sounding Unit (SSU) on board historical NOAA polar-orbiting satellites have played a vital role in investigations of long-term trends and variability in the middle- and upper-stratospheric temperatures during 1979–2006. The successor to SSU is the Advanced Microwave Sounding Unit-A (AMSU-A) starting from 1998 until the present. Unfortunately, the two observations came from different sets of atmospheric layers, and the SSU weighting functions varied with time and location, posing a challenge to merge them with sufficient accuracy for development of an extended SSU climate data record. This study proposes a variational approach for the merging problem, matching in both temperatures and weighting functions. The approach yields zero means with a small standard deviation and a negligible drift over time in the temperature differences between SSU and its extension to AMSU-A. These features made the approach appealing for reliable detection of long-term climate trends. The approach also matches weighting functions with high accuracy for SSU channels 1 and 2 and reasonable accuracy for channel 3. The total decreases in global mean temperatures found from the merged dataset were from 1.8 K in the middle stratosphere to 2.4 K in the upper stratosphere during 1979–2015. These temperature drops were associated with two segments of piecewise linear cooling trends, with those during the first period (1979–97) being much larger than those of the second period (1998–2015). These differences in temperature trends corresponded well to changes of the atmospheric ozone amount from depletion to recovery during the respective time periods, showing the influence of human decisions on climate change.
Abstract
Observations from the Stratospheric Sounding Unit (SSU) on board historical NOAA polar-orbiting satellites have played a vital role in investigations of long-term trends and variability in the middle- and upper-stratospheric temperatures during 1979–2006. The successor to SSU is the Advanced Microwave Sounding Unit-A (AMSU-A) starting from 1998 until the present. Unfortunately, the two observations came from different sets of atmospheric layers, and the SSU weighting functions varied with time and location, posing a challenge to merge them with sufficient accuracy for development of an extended SSU climate data record. This study proposes a variational approach for the merging problem, matching in both temperatures and weighting functions. The approach yields zero means with a small standard deviation and a negligible drift over time in the temperature differences between SSU and its extension to AMSU-A. These features made the approach appealing for reliable detection of long-term climate trends. The approach also matches weighting functions with high accuracy for SSU channels 1 and 2 and reasonable accuracy for channel 3. The total decreases in global mean temperatures found from the merged dataset were from 1.8 K in the middle stratosphere to 2.4 K in the upper stratosphere during 1979–2015. These temperature drops were associated with two segments of piecewise linear cooling trends, with those during the first period (1979–97) being much larger than those of the second period (1998–2015). These differences in temperature trends corresponded well to changes of the atmospheric ozone amount from depletion to recovery during the respective time periods, showing the influence of human decisions on climate change.
Abstract
The Gaussian assumption has been widely used without testing in many previous studies on climate variability and change that have used traditional statistical methods to estimate linear trends, diagnose physical mechanisms, or construct statistical prediction/downscaling models. In this study, the authors carefully test the normality of two hot extreme indices in Macao, China, during the last 100 years based on consecutive daily temperature observational data and find that the occurrences of both hot day and hot night indices are non-Gaussian. Simple least squares fitting is shown to overestimate the linear trend when the Gaussian assumption is violated. Two approaches are further proposed to statistically predict non-Gaussian temperature extremes: one uses a multiple linear regression model after transforming the non-Gaussian predictant to a quasi-Gaussian variable and uses Pearson’s correlation test to identify potential predictors, and the other uses a generalized linear model when the transformation is difficult and uses a nonparametric Spearman’s correlation test to identify potential predictors. The annual occurrences of hot days and hot nights in Macao are used as examples of these two approaches, respectively. The physical mechanisms for these two hot extremes in Macao are also investigated, and the results show that both are related to the interannual and interdecadal variability of a coupled El Niño–Southern Oscillation (ENSO)–East Asian summer monsoon system. Finally, the authors caution other researchers to test the assumed distribution of climate extremes and to apply appropriate statistical approaches.
Abstract
The Gaussian assumption has been widely used without testing in many previous studies on climate variability and change that have used traditional statistical methods to estimate linear trends, diagnose physical mechanisms, or construct statistical prediction/downscaling models. In this study, the authors carefully test the normality of two hot extreme indices in Macao, China, during the last 100 years based on consecutive daily temperature observational data and find that the occurrences of both hot day and hot night indices are non-Gaussian. Simple least squares fitting is shown to overestimate the linear trend when the Gaussian assumption is violated. Two approaches are further proposed to statistically predict non-Gaussian temperature extremes: one uses a multiple linear regression model after transforming the non-Gaussian predictant to a quasi-Gaussian variable and uses Pearson’s correlation test to identify potential predictors, and the other uses a generalized linear model when the transformation is difficult and uses a nonparametric Spearman’s correlation test to identify potential predictors. The annual occurrences of hot days and hot nights in Macao are used as examples of these two approaches, respectively. The physical mechanisms for these two hot extremes in Macao are also investigated, and the results show that both are related to the interannual and interdecadal variability of a coupled El Niño–Southern Oscillation (ENSO)–East Asian summer monsoon system. Finally, the authors caution other researchers to test the assumed distribution of climate extremes and to apply appropriate statistical approaches.
Abstract
In recognizing the importance of Stratospheric Sounding Unit (SSU) onboard historical NOAA polar-orbiting satellites in assessment of long-term stratospheric temperature changes and limitations in previous available SSU datasets, this study constructs a fully documented, publicly accessible, and well-merged SSU time series for climate change investigations. Focusing on methodologies, this study describes the details of data processing and bias corrections in the SSU observations for generating consistent stratospheric temperature data records, including 1) removal of the instrument gas leak effect in its CO2 cell; 2) correction of the atmospheric CO2 increase effect; 3) adjustment for different observation viewing angles; 4) removal of diurnal sampling biases due to satellite orbital drift; and 5) statistical merging of SSU observations from different satellites. After reprocessing, the stratospheric temperature records are composed of nadirlike, gridded brightness temperatures that correspond to identical weighting functions and a fixed local observation time. The 27-yr reprocessed SSU data record comprises global monthly and pentad layer temperatures, with grid resolution of 2.5° latitude by 2.5° longitude, of the midstratosphere (TMS), upper stratosphere (TUS), and top stratosphere (TTS), which correspond to the three SSU channel observations. For 1979–2006, the global mean trends for TMS, TUS, and TTS, are respectively −1.236 ± 0.131, −0.926 ± 0.139, and −1.006 ± 0.194 K decade−1. Spatial trend pattern analyses indicated that this cooling occurred globally with larger cooling over the tropical stratosphere.
Abstract
In recognizing the importance of Stratospheric Sounding Unit (SSU) onboard historical NOAA polar-orbiting satellites in assessment of long-term stratospheric temperature changes and limitations in previous available SSU datasets, this study constructs a fully documented, publicly accessible, and well-merged SSU time series for climate change investigations. Focusing on methodologies, this study describes the details of data processing and bias corrections in the SSU observations for generating consistent stratospheric temperature data records, including 1) removal of the instrument gas leak effect in its CO2 cell; 2) correction of the atmospheric CO2 increase effect; 3) adjustment for different observation viewing angles; 4) removal of diurnal sampling biases due to satellite orbital drift; and 5) statistical merging of SSU observations from different satellites. After reprocessing, the stratospheric temperature records are composed of nadirlike, gridded brightness temperatures that correspond to identical weighting functions and a fixed local observation time. The 27-yr reprocessed SSU data record comprises global monthly and pentad layer temperatures, with grid resolution of 2.5° latitude by 2.5° longitude, of the midstratosphere (TMS), upper stratosphere (TUS), and top stratosphere (TTS), which correspond to the three SSU channel observations. For 1979–2006, the global mean trends for TMS, TUS, and TTS, are respectively −1.236 ± 0.131, −0.926 ± 0.139, and −1.006 ± 0.194 K decade−1. Spatial trend pattern analyses indicated that this cooling occurred globally with larger cooling over the tropical stratosphere.
Abstract
This study investigates changes in the frequency of ENSO, especially the prolonged 1990–95 El Niño event, in the context of secular changes in the annual cycle, ENSO interannual variability, and background mean state of the tropical eastern Pacific sea surface temperature (SST). The ensemble empirical mode decomposition (EEMD) method is applied to isolate those components from the Niño-3 SST index for the period 1880–2008. It is shown that the annual cycle [referred to as a refined modulated annual cycle (MAC)] has strong interannual modulation and secular change in both amplitude and phase: a clear transition from increasing to decreasing amplitude around 1947/48, with both linear trends before and after this turning point statistically significant and the amplitude decreasing by 14% since then, and a significant phase delay trend for the period 1881–1938, but hardly any thereafter. A clear transition from significant deceasing to increasing by about 30% in the amplitude of the ENSO interannual variability around 1937 is also found. When El Niño events are represented as the collective interannual variability, their frequency is found to be almost equivalent to that of La Niña events after 1976. A method for conducting synthetic experiments based on time series analysis further reveals that the apparent prolonged 1990–95 El Niño event was not caused solely by ENSO interannual variability. Rather, the 1991/92 warm period is attributable to an interannual variation superimposed by change in the background mean state; the 1993 warm period is attributable to change in the mean state; and the 1994/95 warm period is attributable to a residual annual cycle, which cannot be fully excluded by a 30-yr mean annual cycle approach. The impact that changing base periods has on the classification of ENSO events and possible solutions is also discussed.
Abstract
This study investigates changes in the frequency of ENSO, especially the prolonged 1990–95 El Niño event, in the context of secular changes in the annual cycle, ENSO interannual variability, and background mean state of the tropical eastern Pacific sea surface temperature (SST). The ensemble empirical mode decomposition (EEMD) method is applied to isolate those components from the Niño-3 SST index for the period 1880–2008. It is shown that the annual cycle [referred to as a refined modulated annual cycle (MAC)] has strong interannual modulation and secular change in both amplitude and phase: a clear transition from increasing to decreasing amplitude around 1947/48, with both linear trends before and after this turning point statistically significant and the amplitude decreasing by 14% since then, and a significant phase delay trend for the period 1881–1938, but hardly any thereafter. A clear transition from significant deceasing to increasing by about 30% in the amplitude of the ENSO interannual variability around 1937 is also found. When El Niño events are represented as the collective interannual variability, their frequency is found to be almost equivalent to that of La Niña events after 1976. A method for conducting synthetic experiments based on time series analysis further reveals that the apparent prolonged 1990–95 El Niño event was not caused solely by ENSO interannual variability. Rather, the 1991/92 warm period is attributable to an interannual variation superimposed by change in the background mean state; the 1993 warm period is attributable to change in the mean state; and the 1994/95 warm period is attributable to a residual annual cycle, which cannot be fully excluded by a 30-yr mean annual cycle approach. The impact that changing base periods has on the classification of ENSO events and possible solutions is also discussed.
Abstract
An automated synoptic weather typing and stepwise cumulative logit/nonlinear regression analyses were employed to simulate the occurrence and quantity of daily rainfall events. The synoptic weather typing was developed using principal component analysis, an average linkage clustering procedure, and discriminant function analysis to identify the weather types most likely to be associated with daily rainfall events for the four selected river basins in Ontario. Within-weather-type daily rainfall simulation models comprise a two-step process: (i) cumulative logit regression to predict the occurrence of daily rainfall events, and (ii) using probability of the logit regression, a nonlinear regression procedure to simulate daily rainfall quantities. The rainfall simulation models were validated using an independent dataset, and the results showed that the models were successful at replicating the occurrence and quantity of daily rainfall events. For example, the relative operating characteristics score is greater than 0.97 for rainfall events with daily rainfall ≥10 or ≥25 mm, for both model development and validation. For evaluation of daily rainfall quantity simulation models, four correctness classifications of excellent, good, fair, and poor were defined, based on the difference between daily rainfall observations and model simulations. Across four selected river basins, the percentage of excellent and good simulations for model development ranged from 62% to 84% (of 20 individuals, 16 cases ≥ 70%, 7 cases ≥ 80%); the corresponding percentage for model validation ranged from 50% to 76% (of 20 individuals, 15 cases ≥ 60%, 6 cases ≥ 70%).
Abstract
An automated synoptic weather typing and stepwise cumulative logit/nonlinear regression analyses were employed to simulate the occurrence and quantity of daily rainfall events. The synoptic weather typing was developed using principal component analysis, an average linkage clustering procedure, and discriminant function analysis to identify the weather types most likely to be associated with daily rainfall events for the four selected river basins in Ontario. Within-weather-type daily rainfall simulation models comprise a two-step process: (i) cumulative logit regression to predict the occurrence of daily rainfall events, and (ii) using probability of the logit regression, a nonlinear regression procedure to simulate daily rainfall quantities. The rainfall simulation models were validated using an independent dataset, and the results showed that the models were successful at replicating the occurrence and quantity of daily rainfall events. For example, the relative operating characteristics score is greater than 0.97 for rainfall events with daily rainfall ≥10 or ≥25 mm, for both model development and validation. For evaluation of daily rainfall quantity simulation models, four correctness classifications of excellent, good, fair, and poor were defined, based on the difference between daily rainfall observations and model simulations. Across four selected river basins, the percentage of excellent and good simulations for model development ranged from 62% to 84% (of 20 individuals, 16 cases ≥ 70%, 7 cases ≥ 80%); the corresponding percentage for model validation ranged from 50% to 76% (of 20 individuals, 15 cases ≥ 60%, 6 cases ≥ 70%).
