Abstract
This study aims to comprehend the propagation of meteorological drought [expressed by the standardized precipitation evapotranspiration index (SPEI)] into hydrological drought [expressed by the standardized runoff index (SRI)] using the combined application of principal component analysis (PCA) and wavelet analysis for a period of 39 years (1980–2018) in the Indus basin, Pakistan. PCA was used to calculate principal components of precipitation, temperature, and streamflow, which were used to systematically propagate drought from one catchment to another, resulting in a catchment-scale drought assessment. The systematic propagation of drought was useful in capturing the effects of local climate variability in the 27 catchments of the Indus basin. Wavelet analyses are used to calculate the variability of SPEI/SRI and propagation (analyzed with the wavelet coherence) from SPEI to SRI. The propagation time from SPEI to SRI was cross correlated. SPEI/SRI time series showed extreme/severe droughts in 16 out of the 39 years, where relatively weak apparent wet and drought events are observed at short periods (1 month) and apparent at longer periods (6 and 12 months). Propagation from SPEI to SRI is catchment specific, with most catchments showing transition in early years (1997–2003). Propagation rate is higher in the upper Indus basin (UIB) and lower Indus basin (LIB) than in the middle Indus basin (MIB), suggesting that climate plays an important role in drought development and propagation. Results also showed a shorter and longer propagation time in the UIB and LIB, respectively. This study has helped us understand the behavior of droughts at catchment scale and will therefore help in the development of drought mitigation plans in Pakistan and similar regions around the world.
Abstract
This study aims to comprehend the propagation of meteorological drought [expressed by the standardized precipitation evapotranspiration index (SPEI)] into hydrological drought [expressed by the standardized runoff index (SRI)] using the combined application of principal component analysis (PCA) and wavelet analysis for a period of 39 years (1980–2018) in the Indus basin, Pakistan. PCA was used to calculate principal components of precipitation, temperature, and streamflow, which were used to systematically propagate drought from one catchment to another, resulting in a catchment-scale drought assessment. The systematic propagation of drought was useful in capturing the effects of local climate variability in the 27 catchments of the Indus basin. Wavelet analyses are used to calculate the variability of SPEI/SRI and propagation (analyzed with the wavelet coherence) from SPEI to SRI. The propagation time from SPEI to SRI was cross correlated. SPEI/SRI time series showed extreme/severe droughts in 16 out of the 39 years, where relatively weak apparent wet and drought events are observed at short periods (1 month) and apparent at longer periods (6 and 12 months). Propagation from SPEI to SRI is catchment specific, with most catchments showing transition in early years (1997–2003). Propagation rate is higher in the upper Indus basin (UIB) and lower Indus basin (LIB) than in the middle Indus basin (MIB), suggesting that climate plays an important role in drought development and propagation. Results also showed a shorter and longer propagation time in the UIB and LIB, respectively. This study has helped us understand the behavior of droughts at catchment scale and will therefore help in the development of drought mitigation plans in Pakistan and similar regions around the world.
Abstract
Much of Siberia experienced exceptional warmth during the spring of 2020, which followed an unusually warm winter over the same region. Here, we investigate the drivers of the spring warmth from the perspective of atmospheric dynamics and remote influences, focusing on monthly timescale features of the event. We find that the warm anomalies were associated with separate quasi-stationary Rossby wave trains emanating from the North Atlantic in April and May. The wave trains are shown to be extreme manifestations of the dominant modes of spring subseasonal meridional wind variability over the Northern Hemisphere. Using a large ensemble of simulations from NASA’s GEOS atmospheric model, in which the model is constrained to remain close to observations over selected regions, we further elucidate the remote drivers of the unusual spring temperatures in Siberia. In both April and May, the wave trains were likely forced from an upstream region including eastern North America and the western North Atlantic. Analysis with a stationary wave model shows that transient vorticity flux forcing over and downwind of the North Atlantic, which is strongly related to storm activity caused by internal variability, is key to generating the wave trains, suggesting limited subseasonal predictability of the Rossby waves and hence the exceptional Siberian warmth. Our observational and model analysis also suggests that anomalous tropical atmospheric heating contributed to the unusual warmth in Siberia through a teleconnection involving upper-troposphere dynamics and the mean meridional circulation. This tropical-extratropical teleconnection offers a possible physical mechanism by which anthropogenic climate change influenced the extreme Siberian warmth.
Abstract
Much of Siberia experienced exceptional warmth during the spring of 2020, which followed an unusually warm winter over the same region. Here, we investigate the drivers of the spring warmth from the perspective of atmospheric dynamics and remote influences, focusing on monthly timescale features of the event. We find that the warm anomalies were associated with separate quasi-stationary Rossby wave trains emanating from the North Atlantic in April and May. The wave trains are shown to be extreme manifestations of the dominant modes of spring subseasonal meridional wind variability over the Northern Hemisphere. Using a large ensemble of simulations from NASA’s GEOS atmospheric model, in which the model is constrained to remain close to observations over selected regions, we further elucidate the remote drivers of the unusual spring temperatures in Siberia. In both April and May, the wave trains were likely forced from an upstream region including eastern North America and the western North Atlantic. Analysis with a stationary wave model shows that transient vorticity flux forcing over and downwind of the North Atlantic, which is strongly related to storm activity caused by internal variability, is key to generating the wave trains, suggesting limited subseasonal predictability of the Rossby waves and hence the exceptional Siberian warmth. Our observational and model analysis also suggests that anomalous tropical atmospheric heating contributed to the unusual warmth in Siberia through a teleconnection involving upper-troposphere dynamics and the mean meridional circulation. This tropical-extratropical teleconnection offers a possible physical mechanism by which anthropogenic climate change influenced the extreme Siberian warmth.
Abstract
This study investigates the tropical and extratropical circulation anomalies that directly affect the summer rainfall over the Yangtze River basin (YRB). In the lower troposphere, the tropical circulation anomalies that enhance the YRB rainfall manifest as an anticyclonic anomaly over the tropical western North Pacific (WNP) and the extratropical circulation anomalies are characterized by northeasterly anomalies to the north of the YRB. It is found that the heavier the YRB rainfall, the more necessary the cooperation between the tropical WNP anticyclonic anomaly and the mid-latitude northeasterly anomalies, and compared to the tropical WNP anticyclonic anomaly, the mid-latitude northeasterly anomalies can more efficiently induce the YRB rainfall. Further results indicate that the tropical WNP anticyclonic anomaly exhibits notable quasi-biweekly feature and provides a favorable background for the enhanced YRB rainfall. By contrast, the northeasterly anomalies are dominated by synoptic variability. Furthermore, there are significant precursor signals for the lower-tropospheric northeasterly anomalies. These signals manifest as the eastward propagation of two wave trains in the upper troposphere: a mid-latitude one and a high-latitude one, which tend to be independent. The mid-latitude one originates around the Mediterranean Sea and propagates eastward along the Asian westerly jet. The high-latitude one propagates over the high-latitude Eurasian continent, from Europe eastward to Lake Baikal and then southeastward to East Asia.
Abstract
This study investigates the tropical and extratropical circulation anomalies that directly affect the summer rainfall over the Yangtze River basin (YRB). In the lower troposphere, the tropical circulation anomalies that enhance the YRB rainfall manifest as an anticyclonic anomaly over the tropical western North Pacific (WNP) and the extratropical circulation anomalies are characterized by northeasterly anomalies to the north of the YRB. It is found that the heavier the YRB rainfall, the more necessary the cooperation between the tropical WNP anticyclonic anomaly and the mid-latitude northeasterly anomalies, and compared to the tropical WNP anticyclonic anomaly, the mid-latitude northeasterly anomalies can more efficiently induce the YRB rainfall. Further results indicate that the tropical WNP anticyclonic anomaly exhibits notable quasi-biweekly feature and provides a favorable background for the enhanced YRB rainfall. By contrast, the northeasterly anomalies are dominated by synoptic variability. Furthermore, there are significant precursor signals for the lower-tropospheric northeasterly anomalies. These signals manifest as the eastward propagation of two wave trains in the upper troposphere: a mid-latitude one and a high-latitude one, which tend to be independent. The mid-latitude one originates around the Mediterranean Sea and propagates eastward along the Asian westerly jet. The high-latitude one propagates over the high-latitude Eurasian continent, from Europe eastward to Lake Baikal and then southeastward to East Asia.
Abstract
The Maritime Continent (MC), located in the heart of the Indo-Pacific warm pool, plays an important role in the global climate. However, the future MC climate is largely unknown, in particular the ENSO-rainfall teleconnection. ENSO induces a zonal dipole pattern of rainfall variability across the Indo-Pacific Ocean, i.e., positive variability in the Tropical Pacific and negative variability towards the MC. Here new CMIP6 models robustly project that, for both land and sea rainfall, the negative ENSO teleconnection over the MC (drier/wetter during El Niño/La Niña) could intensify significantly under the SSP585 warming scenario. Strengthened teleconnection may cause enhanced droughts and flooding, leading to agricultural impacts and altering rainfall predictability over the region. Models also project that the Indo-Pacific rainfall center and the zero-crossing of dipole-like rainfall variability both shift eastward, which adjustments are more notable during boreal summer than winter. All these projections are robustly supported by the model agreement and scale up with the warming trend.
Abstract
The Maritime Continent (MC), located in the heart of the Indo-Pacific warm pool, plays an important role in the global climate. However, the future MC climate is largely unknown, in particular the ENSO-rainfall teleconnection. ENSO induces a zonal dipole pattern of rainfall variability across the Indo-Pacific Ocean, i.e., positive variability in the Tropical Pacific and negative variability towards the MC. Here new CMIP6 models robustly project that, for both land and sea rainfall, the negative ENSO teleconnection over the MC (drier/wetter during El Niño/La Niña) could intensify significantly under the SSP585 warming scenario. Strengthened teleconnection may cause enhanced droughts and flooding, leading to agricultural impacts and altering rainfall predictability over the region. Models also project that the Indo-Pacific rainfall center and the zero-crossing of dipole-like rainfall variability both shift eastward, which adjustments are more notable during boreal summer than winter. All these projections are robustly supported by the model agreement and scale up with the warming trend.
Abstract
The annual mean net surface heat fluxes (NSHFs) from the ocean to the atmosphere generated by historical forcing simulations using the UK HadGEM3-GC3.1 coupled climate model are shown to be relatively independent of resolution, for model horizontal grid spacings between 1° and 1/12°, and to agree well with those based on the DEEP-C (Diagnosing Earth’s Energy Pathways in the Climate system) analyses. Interpretations of the geographical patterns of the NSHFs are suggested that use basic ideas extracted from the theory of the ventilated thermocline and planetary geostrophic layer models. As a step toward investigation of the validity of the assumptions underlying the interpretations, we examine the contributions to the rate of change of the active tracers from the main terms in their prognostic equations as a function of the active tracer and latitude. We find that, consistent with our assumptions, the main contributions from vertical diffusion occur in “near surface” layers. We also find that, except at high latitudes, the sum of the NSHF and vertical diffusion is mainly balanced by time-mean advection of potential temperature. A corresponding statement holds for potential density but not salinity. We also show that the heat input by latitude bands is dominated by the NSHFs, the time-mean advection, and the equatorial Pacific. It is usually assumed that global integrals of tracer tendencies due to advection as a function of the tracer should be identically zero. We show that non-negligible contributions to them arise from net freshwater surface fluxes.
Abstract
The annual mean net surface heat fluxes (NSHFs) from the ocean to the atmosphere generated by historical forcing simulations using the UK HadGEM3-GC3.1 coupled climate model are shown to be relatively independent of resolution, for model horizontal grid spacings between 1° and 1/12°, and to agree well with those based on the DEEP-C (Diagnosing Earth’s Energy Pathways in the Climate system) analyses. Interpretations of the geographical patterns of the NSHFs are suggested that use basic ideas extracted from the theory of the ventilated thermocline and planetary geostrophic layer models. As a step toward investigation of the validity of the assumptions underlying the interpretations, we examine the contributions to the rate of change of the active tracers from the main terms in their prognostic equations as a function of the active tracer and latitude. We find that, consistent with our assumptions, the main contributions from vertical diffusion occur in “near surface” layers. We also find that, except at high latitudes, the sum of the NSHF and vertical diffusion is mainly balanced by time-mean advection of potential temperature. A corresponding statement holds for potential density but not salinity. We also show that the heat input by latitude bands is dominated by the NSHFs, the time-mean advection, and the equatorial Pacific. It is usually assumed that global integrals of tracer tendencies due to advection as a function of the tracer should be identically zero. We show that non-negligible contributions to them arise from net freshwater surface fluxes.
A Neural Network–Based Cloud Mask for PREFIRE and Evaluation with Simulated ObservationsAbstract
The Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) will fill a gap in our understanding of polar processes and the polar climate by offering widespread, spectrally resolved measurements through the far-infrared (FIR) with two identical CubeSat spacecraft. While the polar regions are typically difficult for skillful cloud identification due to cold surface temperatures, the reflection by bright surfaces, and frequent temperature inversions, the inclusion of the FIR may offer increased spectral sensitivity, allowing for the detection of even thin ice clouds. This study assesses the potential skill, as well as limitations, of a neural network (NN)-based cloud mask using simulated spectra mimicking what the PREFIRE mission will capture. Analysis focuses on the polar regions. Clouds are found to be detected approximately 90% of time using the derived neural network. The NN’s assigned confidence for whether a scene is “clear” or “cloudy” proves to be a skillful way in which quality flags can be attached to predictions. Clouds with higher cloud-top heights are typically more easily detected. Low-altitude clouds over polar surfaces, which are the most difficult for the NN to detect, are still detected over 80% of the time. The FIR portion of the spectrum is found to increase the detection of clear scenes and increase mid- to high-altitude cloud detection. Cloud detection skill improves through the use of the overlapping fields of view produced by the PREFIRE instrument’s sampling strategy. Overlapping fields of view increase accuracy relative to the baseline NN while simultaneously predicting on a sub-FOV scale.
Significance Statement
Clouds play an important role in defining the Arctic and Antarctic climates. The purpose of this study is to explore the potential of never-before systematically measured radiative properties of the atmosphere to aid in the detection of polar clouds, which are traditionally difficult to detect. Satellite measurements of emitted radiation at wavelengths longer than 15 μm, combined with complex machine learning methods, may allow us to better understand the occurrence of various cloud types at both poles. The occurrence of these clouds can determine whether the surface warms or cools, influencing surface temperatures and the rate at which ice melts or refreezes. Understanding the frequencies of these various clouds is increasingly important within the context of our rapidly changing climate.
Abstract
The Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) will fill a gap in our understanding of polar processes and the polar climate by offering widespread, spectrally resolved measurements through the far-infrared (FIR) with two identical CubeSat spacecraft. While the polar regions are typically difficult for skillful cloud identification due to cold surface temperatures, the reflection by bright surfaces, and frequent temperature inversions, the inclusion of the FIR may offer increased spectral sensitivity, allowing for the detection of even thin ice clouds. This study assesses the potential skill, as well as limitations, of a neural network (NN)-based cloud mask using simulated spectra mimicking what the PREFIRE mission will capture. Analysis focuses on the polar regions. Clouds are found to be detected approximately 90% of time using the derived neural network. The NN’s assigned confidence for whether a scene is “clear” or “cloudy” proves to be a skillful way in which quality flags can be attached to predictions. Clouds with higher cloud-top heights are typically more easily detected. Low-altitude clouds over polar surfaces, which are the most difficult for the NN to detect, are still detected over 80% of the time. The FIR portion of the spectrum is found to increase the detection of clear scenes and increase mid- to high-altitude cloud detection. Cloud detection skill improves through the use of the overlapping fields of view produced by the PREFIRE instrument’s sampling strategy. Overlapping fields of view increase accuracy relative to the baseline NN while simultaneously predicting on a sub-FOV scale.
Significance Statement
Clouds play an important role in defining the Arctic and Antarctic climates. The purpose of this study is to explore the potential of never-before systematically measured radiative properties of the atmosphere to aid in the detection of polar clouds, which are traditionally difficult to detect. Satellite measurements of emitted radiation at wavelengths longer than 15 μm, combined with complex machine learning methods, may allow us to better understand the occurrence of various cloud types at both poles. The occurrence of these clouds can determine whether the surface warms or cools, influencing surface temperatures and the rate at which ice melts or refreezes. Understanding the frequencies of these various clouds is increasingly important within the context of our rapidly changing climate.
A Precise Zenith Hydrostatic Delay Calibration Model in China Based on the Nonlinear Least Square MethodAbstract
Zenith hydrostatic delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since the Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work first estimated the Saastamoinen model using the integrated ZHD as reference values obtained from radiosonde data collected at 73 stations in China from 2012 to 2016. Then, the residuals between the reference values and the Saastamoinen modeled ZHDs were calculated, and the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semiannual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least squares estimation method was introduced to establish an improved ZHD model—China Revised Zenith Hydrostatic Delay (CRZHD)—adapted for China. The accuracy of the CRZHD model was assessed using radiosonde data and International GNSS Service (IGS) data in 2017; the radiosonde data results show that the CRZHD model is superior to the Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS and RMSE are decreased by 2.7 and 1.5 (URUM), 5.9 and 5.3 (BJFS), and 9.6 and 8.8 mm (TCMS), respectively. The performance of the CRZHD model retrieving PWV was discussed using radiosonde data in 2017. It is shown that the CRZHD model retrieving PWV (CRZHD-PWV) outperforms the Saastamoinen model (SAAS-PWV), in which the precision is improved by 44.4%. The BIAS ranged from −1 to 1 mm and RMSE ranged from 0 to 2 mm in CRZHD-PWV account for 89.0% and 95.9%, while SAAS-PWV account for 46.6% and 58.9%.
Significance Statement
Zenith hydrostatic delay (ZHD) is one of the most important parameters in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology, which can be derived from a precise ZHD model due to its stability. This research established an improved ZHD model for China to obtain accurate ZHD, which is a prerequisite for pinpoint precipitable water vapor (PWV) retrieval. And the PWV value is beneficial to analyze the change in precipitation in some regions, forecast the short-term rainfall, and monitor the climate.
Abstract
Zenith hydrostatic delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since the Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work first estimated the Saastamoinen model using the integrated ZHD as reference values obtained from radiosonde data collected at 73 stations in China from 2012 to 2016. Then, the residuals between the reference values and the Saastamoinen modeled ZHDs were calculated, and the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semiannual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least squares estimation method was introduced to establish an improved ZHD model—China Revised Zenith Hydrostatic Delay (CRZHD)—adapted for China. The accuracy of the CRZHD model was assessed using radiosonde data and International GNSS Service (IGS) data in 2017; the radiosonde data results show that the CRZHD model is superior to the Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS and RMSE are decreased by 2.7 and 1.5 (URUM), 5.9 and 5.3 (BJFS), and 9.6 and 8.8 mm (TCMS), respectively. The performance of the CRZHD model retrieving PWV was discussed using radiosonde data in 2017. It is shown that the CRZHD model retrieving PWV (CRZHD-PWV) outperforms the Saastamoinen model (SAAS-PWV), in which the precision is improved by 44.4%. The BIAS ranged from −1 to 1 mm and RMSE ranged from 0 to 2 mm in CRZHD-PWV account for 89.0% and 95.9%, while SAAS-PWV account for 46.6% and 58.9%.
Significance Statement
Zenith hydrostatic delay (ZHD) is one of the most important parameters in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology, which can be derived from a precise ZHD model due to its stability. This research established an improved ZHD model for China to obtain accurate ZHD, which is a prerequisite for pinpoint precipitable water vapor (PWV) retrieval. And the PWV value is beneficial to analyze the change in precipitation in some regions, forecast the short-term rainfall, and monitor the climate.
