Abstract
The tropical cloud forest ecosystem in western equatorial Africa (WEA) is known to be sensitive to the presence of an extensive and persistent low-level stratiform cloud deck during the long dry season from June to September (JJAS). Here, we present a new climatology of the diurnal cycle of the low-level cloud cover from surface synoptic stations over WEA during JJAS 1971–2019. For the period JJAS 2008–19, we also utilized estimates of cloudiness from four satellite products, namely, the Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting (SAFNWC) cloud classification, the Day and Night Microphysical Schemes (DMS/NMS), and cross sections from CALIPSO and CloudSat (2B-GEOPROF-lidar). A comparison with surface stations reveals that the NMS at night together with SAFNWC at daytime yield the smallest biases. The climatological analysis reveals that low-level clouds persist during the day over the coastal plains and windward side of the low mountain ranges. Conversely, on their leeward sides, i.e., over the plateaus, a decrease of the low-level cloud frequency is observed in the afternoon, together with a change from stratocumulus to cumulus. At night, the low-level cloud deck reforms over this region with the largest cloud occurrence frequencies in the morning. Vertical profiles from 2B-GEOPROF-lidar reveal cloud tops below 3000 m even at daytime. The station data and the suitable satellite products form the basis to better understand the physical processes controlling the clouds and to evaluate cloudiness from reanalyses and models.
Abstract
The tropical cloud forest ecosystem in western equatorial Africa (WEA) is known to be sensitive to the presence of an extensive and persistent low-level stratiform cloud deck during the long dry season from June to September (JJAS). Here, we present a new climatology of the diurnal cycle of the low-level cloud cover from surface synoptic stations over WEA during JJAS 1971–2019. For the period JJAS 2008–19, we also utilized estimates of cloudiness from four satellite products, namely, the Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting (SAFNWC) cloud classification, the Day and Night Microphysical Schemes (DMS/NMS), and cross sections from CALIPSO and CloudSat (2B-GEOPROF-lidar). A comparison with surface stations reveals that the NMS at night together with SAFNWC at daytime yield the smallest biases. The climatological analysis reveals that low-level clouds persist during the day over the coastal plains and windward side of the low mountain ranges. Conversely, on their leeward sides, i.e., over the plateaus, a decrease of the low-level cloud frequency is observed in the afternoon, together with a change from stratocumulus to cumulus. At night, the low-level cloud deck reforms over this region with the largest cloud occurrence frequencies in the morning. Vertical profiles from 2B-GEOPROF-lidar reveal cloud tops below 3000 m even at daytime. The station data and the suitable satellite products form the basis to better understand the physical processes controlling the clouds and to evaluate cloudiness from reanalyses and models.
Abstract
Some of the largest climatic changes in the Arctic have been observed in Alaska and the surrounding marginal seas. Near-surface air temperature (T2m), precipitation (P), snowfall, and sea ice changes have been previously documented, often in disparate studies. Here, we provide an updated, long-term trend analysis (1957–2021; n = 65 years) of such parameters in ERA5, NOAA U.S. Climate Gridded Dataset (NClimGrid), NOAA National Centers for Environmental Information (NCEI) Alaska climate division, and composite sea ice products preceding the upcoming Fifth National Climate Assessment (NCA5) and other near-future climate reports. In the past half century, annual T2m has broadly increased across Alaska, and during winter, spring, and autumn on the North Slope and North Panhandle (T2m > 0.50°C decade−1). Precipitation has also increased across climate divisions and appears strongly interrelated with temperature–sea ice feedbacks on the North Slope, specifically with increased (decreased) open water (sea ice extent). Snowfall equivalent (SFE) has decreased in autumn and spring, perhaps aligned with a regime transition of snow to rain, while winter SFE has broadly increased across the state. Sea ice decline and melt-season lengthening also have a pronounced signal around Alaska, with the largest trends in these parameters found in the Beaufort Sea. Alaska’s climatic changes are also placed in context against regional and contiguous U.S. air temperature trends and show ∼50% greater warming in Alaska relative to the lower-48 states. Alaska T2m increases also exceed those of any contiguous U.S. subregion, positioning Alaska at the forefront of U.S. climate warming.
Significance Statement
This study produces an updated, long-term trend analysis (1957–2021) of key Alaska climate parameters, including air temperature, precipitation (including snowfall equivalent), and sea ice, to inform upcoming climate assessment reports, including the Fifth National Climate Assessment (NCA5) scheduled for publication in 2023. Key findings include widespread annual and seasonal warming with increased precipitation across much of the state. Winter snowfall has broadly increased, but spring and autumn snowfalls have decreased as rainfall increased. Autumn warming and precipitation increases over the North Slope, in particular, appear related to decreased sea ice coverage in the Beaufort Sea and Chukchi Seas. These trends may result from interrelated processes that accelerate Alaska climate changes relative to those of the contiguous United States.
Abstract
Some of the largest climatic changes in the Arctic have been observed in Alaska and the surrounding marginal seas. Near-surface air temperature (T2m), precipitation (P), snowfall, and sea ice changes have been previously documented, often in disparate studies. Here, we provide an updated, long-term trend analysis (1957–2021; n = 65 years) of such parameters in ERA5, NOAA U.S. Climate Gridded Dataset (NClimGrid), NOAA National Centers for Environmental Information (NCEI) Alaska climate division, and composite sea ice products preceding the upcoming Fifth National Climate Assessment (NCA5) and other near-future climate reports. In the past half century, annual T2m has broadly increased across Alaska, and during winter, spring, and autumn on the North Slope and North Panhandle (T2m > 0.50°C decade−1). Precipitation has also increased across climate divisions and appears strongly interrelated with temperature–sea ice feedbacks on the North Slope, specifically with increased (decreased) open water (sea ice extent). Snowfall equivalent (SFE) has decreased in autumn and spring, perhaps aligned with a regime transition of snow to rain, while winter SFE has broadly increased across the state. Sea ice decline and melt-season lengthening also have a pronounced signal around Alaska, with the largest trends in these parameters found in the Beaufort Sea. Alaska’s climatic changes are also placed in context against regional and contiguous U.S. air temperature trends and show ∼50% greater warming in Alaska relative to the lower-48 states. Alaska T2m increases also exceed those of any contiguous U.S. subregion, positioning Alaska at the forefront of U.S. climate warming.
Significance Statement
This study produces an updated, long-term trend analysis (1957–2021) of key Alaska climate parameters, including air temperature, precipitation (including snowfall equivalent), and sea ice, to inform upcoming climate assessment reports, including the Fifth National Climate Assessment (NCA5) scheduled for publication in 2023. Key findings include widespread annual and seasonal warming with increased precipitation across much of the state. Winter snowfall has broadly increased, but spring and autumn snowfalls have decreased as rainfall increased. Autumn warming and precipitation increases over the North Slope, in particular, appear related to decreased sea ice coverage in the Beaufort Sea and Chukchi Seas. These trends may result from interrelated processes that accelerate Alaska climate changes relative to those of the contiguous United States.
Abstract
The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-yr period versus 19 high-quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to time scale, meteorological season, PMW source, QI, and land surface type. Results indicate that 1) the cold season’s (November–April) larger relative bias can be mitigated via backward morphing; 2) IMERG 6-h precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index − 1 (FBI-1); 3) the performance of five PMW sources is affected by the season to different degrees; 4) in terms of some metrics, skills do not always enhance with increasing QI; 5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products.
Significance Statement
The purpose of the study was to assess the performance of the gridded precipitation product from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 6 over the Great Lakes region of North America. The assessment performs a statistical comparison of precipitation amounts from IMERG versus surface stations as a function of time scale, season, precipitation event threshold, and input source among satellites. Interpretation of the results identifies shortcomings in the IMERG algorithms, particularly in extreme precipitation events and over ice-covered surfaces. The results also describe spatial variability in the IMERG data quality due to the complex geography of the study area and offer a clear threshold in the Quality Index (QI) flag for optimal application of the precipitation products.
Abstract
The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-yr period versus 19 high-quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to time scale, meteorological season, PMW source, QI, and land surface type. Results indicate that 1) the cold season’s (November–April) larger relative bias can be mitigated via backward morphing; 2) IMERG 6-h precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index − 1 (FBI-1); 3) the performance of five PMW sources is affected by the season to different degrees; 4) in terms of some metrics, skills do not always enhance with increasing QI; 5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products.
Significance Statement
The purpose of the study was to assess the performance of the gridded precipitation product from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 6 over the Great Lakes region of North America. The assessment performs a statistical comparison of precipitation amounts from IMERG versus surface stations as a function of time scale, season, precipitation event threshold, and input source among satellites. Interpretation of the results identifies shortcomings in the IMERG algorithms, particularly in extreme precipitation events and over ice-covered surfaces. The results also describe spatial variability in the IMERG data quality due to the complex geography of the study area and offer a clear threshold in the Quality Index (QI) flag for optimal application of the precipitation products.
Abstract
Extreme near-surface wind speeds in cities can have major societal impacts but are not well represented in climate models. Despite this, large-scale dynamics in the free troposphere, which models resolve better, could provide reliable constraints on local extreme winds. This study identifies synoptic circulations associated with midlatitude extreme wind events and assesses how resolution affects their representation in analysis products and a climate model framework. Composites of reanalysis (ERA5) sea level pressure and upper-tropospheric winds during observed extreme wind events reveal distinct circulation structures for each quadrant of the surface-wind rose. Enhanced resolution of the analysis product (ERA5 versus the higher-resolution ECMWF Operational Analysis) reduced wind speed biases but has little impact on capturing occurrences of wind extremes seen in station observations. Composite circulations for surface wind extremes in a climate model (CESM) skillfully reproduce circulations found in reanalysis. Regional refinement of CESM over a region centered on southern Ontario, Canada, using variable resolution (VR-CESM) improves representation of surface ageostrophic circulations and the strength of vertical coupling between upper-level and near-surface winds. We thus can distinguish situations for which regional refinement (dynamical downscaling) is necessary for realistic representation of the large-scale atmospheric circulations associated with extreme winds, from situations where the coarse resolution of standard GCMs is sufficient.
Significance Statement
In this study we identify the large-scale atmospheric circulation patterns that drive extreme wind speeds in Canadian cities, and how well numerical climate models, which are used for producing climate change projections, represent these circulation patterns. Climate models do not simulate local winds as accurately as larger-scale phenomena, so this work can help identify useful information that models contain regarding extreme winds. For cities in eastern Canada, a benchmark model generally performs well, but a model with refined spatial resolution over southern Ontario improves agreement with patterns for observed extreme winds in that region.
Abstract
Extreme near-surface wind speeds in cities can have major societal impacts but are not well represented in climate models. Despite this, large-scale dynamics in the free troposphere, which models resolve better, could provide reliable constraints on local extreme winds. This study identifies synoptic circulations associated with midlatitude extreme wind events and assesses how resolution affects their representation in analysis products and a climate model framework. Composites of reanalysis (ERA5) sea level pressure and upper-tropospheric winds during observed extreme wind events reveal distinct circulation structures for each quadrant of the surface-wind rose. Enhanced resolution of the analysis product (ERA5 versus the higher-resolution ECMWF Operational Analysis) reduced wind speed biases but has little impact on capturing occurrences of wind extremes seen in station observations. Composite circulations for surface wind extremes in a climate model (CESM) skillfully reproduce circulations found in reanalysis. Regional refinement of CESM over a region centered on southern Ontario, Canada, using variable resolution (VR-CESM) improves representation of surface ageostrophic circulations and the strength of vertical coupling between upper-level and near-surface winds. We thus can distinguish situations for which regional refinement (dynamical downscaling) is necessary for realistic representation of the large-scale atmospheric circulations associated with extreme winds, from situations where the coarse resolution of standard GCMs is sufficient.
Significance Statement
In this study we identify the large-scale atmospheric circulation patterns that drive extreme wind speeds in Canadian cities, and how well numerical climate models, which are used for producing climate change projections, represent these circulation patterns. Climate models do not simulate local winds as accurately as larger-scale phenomena, so this work can help identify useful information that models contain regarding extreme winds. For cities in eastern Canada, a benchmark model generally performs well, but a model with refined spatial resolution over southern Ontario improves agreement with patterns for observed extreme winds in that region.
Abstract
Central Europe has experienced a sequence of unprecedented summer droughts since 2015, which had considerable effects on the functioning and productivity of natural and agricultural systems. Placing these recent extremes in a long-term context of natural climate variability is, however, constrained by the limited length of observational records. Here, we use tree-ring stable oxygen and carbon isotopes to develop annually resolved reconstructions of growing season temperature and summer moisture variability for central Europe during the past 2000 years. Both records are independently interpolated across the southern Czech Republic and northeastern Austria to produce explicit estimates of the optimum agroclimatic zones, based on modern references of climatic forcing. Historical documentation of agricultural productivity and climate variability since 1090 CE provides strong quantitative verification of our new reconstructions. Our isotope records not only contain clear expressions of the medieval (920–1000 CE) and Renaissance (early sixteenth century) droughts, but also the relative influence of temperature and moisture on hydroclimatic conditions during the first millennium (including previously reported pluvials during the early third, fifth, and seventh centuries of the Common Era). We conclude that Czech agricultural production has experienced significant extremes over the past 2000 years, which includes periods for which there are no modern analogs.
Significance Statement
As temperatures increase, droughts are becoming a growing concern for European agriculture. Our study allows recent extremes to be contextualized and helps to better the understanding of potential drivers. Stable carbon and oxygen isotopes in oak tree rings were analyzed to reconstruct year-to-year and longer-term changes in both temperature and moisture over central Europe and the past 2000 years. We combine these proxy-based climate reconstructions to model how well crops were growing in the past. The early fifth and the early sixteenth centuries of the Common Era were most likely characterized by extreme conditions beyond what has been experienced in recent decades. Our reconstructions of natural variability might be used as a baseline in projections of future conditions.
Abstract
Central Europe has experienced a sequence of unprecedented summer droughts since 2015, which had considerable effects on the functioning and productivity of natural and agricultural systems. Placing these recent extremes in a long-term context of natural climate variability is, however, constrained by the limited length of observational records. Here, we use tree-ring stable oxygen and carbon isotopes to develop annually resolved reconstructions of growing season temperature and summer moisture variability for central Europe during the past 2000 years. Both records are independently interpolated across the southern Czech Republic and northeastern Austria to produce explicit estimates of the optimum agroclimatic zones, based on modern references of climatic forcing. Historical documentation of agricultural productivity and climate variability since 1090 CE provides strong quantitative verification of our new reconstructions. Our isotope records not only contain clear expressions of the medieval (920–1000 CE) and Renaissance (early sixteenth century) droughts, but also the relative influence of temperature and moisture on hydroclimatic conditions during the first millennium (including previously reported pluvials during the early third, fifth, and seventh centuries of the Common Era). We conclude that Czech agricultural production has experienced significant extremes over the past 2000 years, which includes periods for which there are no modern analogs.
Significance Statement
As temperatures increase, droughts are becoming a growing concern for European agriculture. Our study allows recent extremes to be contextualized and helps to better the understanding of potential drivers. Stable carbon and oxygen isotopes in oak tree rings were analyzed to reconstruct year-to-year and longer-term changes in both temperature and moisture over central Europe and the past 2000 years. We combine these proxy-based climate reconstructions to model how well crops were growing in the past. The early fifth and the early sixteenth centuries of the Common Era were most likely characterized by extreme conditions beyond what has been experienced in recent decades. Our reconstructions of natural variability might be used as a baseline in projections of future conditions.
Abstract
A method is presented to obtain the climatology of extreme wind speeds coincident with the occurrence of rain. The simultaneous occurrence of wind and rain can force water through building wall components such as windows, resulting in building damage and insured loss. To quantify this hazard, extreme value distributions are fit to peak 3-second wind speed data recorded during 1-minute intervals with specific reported rain intensities. This improves upon previous attempts to quantify the wind-driven rain hazard, that computed wind speed and rainfall intensity probabilities independently and used hourly data which cannot assure the simultaneous occurrence of peak wind which represents only a several-second interval within the hour and rain which is accumulated over the entire hour.
The method is applied across the southeastern U.S., where the wind-driven rain hazard is most pronounced. For the lowest rainfall intensities, the computed wind speed extremes agree with published values that ignore rainfall occurrence. Such correspondence is desirable for aligning the rain-intensity-dependent windspeed return periods with established extreme wind statistics. Maximum 50-year return period wind speeds in conjunction with rainfall intensities ≥ 0.254 mm min−1 exceed 45 ms−1 in a swath from Oklahoma to the Gulf Coast and at stations along the immediate Atlantic Coast. For rainfall intensities >2.54 mm min−1 maximum, 50-year return period wind speeds decrease to 35 ms−1 but occur over a similar area. The methodology is also applied to stations outside the Southeast to demonstrate its applicability for incorporating the wind-driven rain hazard in U.S. building standards.
Abstract
A method is presented to obtain the climatology of extreme wind speeds coincident with the occurrence of rain. The simultaneous occurrence of wind and rain can force water through building wall components such as windows, resulting in building damage and insured loss. To quantify this hazard, extreme value distributions are fit to peak 3-second wind speed data recorded during 1-minute intervals with specific reported rain intensities. This improves upon previous attempts to quantify the wind-driven rain hazard, that computed wind speed and rainfall intensity probabilities independently and used hourly data which cannot assure the simultaneous occurrence of peak wind which represents only a several-second interval within the hour and rain which is accumulated over the entire hour.
The method is applied across the southeastern U.S., where the wind-driven rain hazard is most pronounced. For the lowest rainfall intensities, the computed wind speed extremes agree with published values that ignore rainfall occurrence. Such correspondence is desirable for aligning the rain-intensity-dependent windspeed return periods with established extreme wind statistics. Maximum 50-year return period wind speeds in conjunction with rainfall intensities ≥ 0.254 mm min−1 exceed 45 ms−1 in a swath from Oklahoma to the Gulf Coast and at stations along the immediate Atlantic Coast. For rainfall intensities >2.54 mm min−1 maximum, 50-year return period wind speeds decrease to 35 ms−1 but occur over a similar area. The methodology is also applied to stations outside the Southeast to demonstrate its applicability for incorporating the wind-driven rain hazard in U.S. building standards.
Abstract
El Niño Southern Oscillation (ENSO) has a profound influence on the occurrence of extreme precipitation events at local and regional scales in the present-day climate, and thus it is important to understand how that influence may change under future global warming. We consider this question using the large ensemble simulations of CESM2, which simulates ENSO well historically. CESM2 projects that the influence of ENSO on extreme precipitation will strengthen further under the SSP3-7.0 scenario in most regions whose extreme precipitation regimes are strongly affected by ENSO in the boreal cold season. Extreme precipitation in the boreal cold season that exceeds historical thresholds is projected to become more common throughout the ENSO cycle. The difference in the intensity of extreme precipitation events that occur under El Niño and La Niña conditions will increase, resulting in “more extreme and more variable hydroclimate extremes”. We also consider the processes that affect the future intensity of extreme precipitation and how it varies with the ENSO cycle by partitioning changes into thermodynamic and dynamic components. The thermodynamic component, which reflects increases in atmospheric moisture content, results in a relatively uniform intensification of ENSO-driven extreme precipitation variation. In contrast, the dynamic component, which reflects changes in vertical motion, produces strong regional difference in the response to forcing. In some regions, this component amplifies the thermodynamic-induced changes, while in others, it offsets them or even results in reduction in extreme precipitation variation.
Abstract
El Niño Southern Oscillation (ENSO) has a profound influence on the occurrence of extreme precipitation events at local and regional scales in the present-day climate, and thus it is important to understand how that influence may change under future global warming. We consider this question using the large ensemble simulations of CESM2, which simulates ENSO well historically. CESM2 projects that the influence of ENSO on extreme precipitation will strengthen further under the SSP3-7.0 scenario in most regions whose extreme precipitation regimes are strongly affected by ENSO in the boreal cold season. Extreme precipitation in the boreal cold season that exceeds historical thresholds is projected to become more common throughout the ENSO cycle. The difference in the intensity of extreme precipitation events that occur under El Niño and La Niña conditions will increase, resulting in “more extreme and more variable hydroclimate extremes”. We also consider the processes that affect the future intensity of extreme precipitation and how it varies with the ENSO cycle by partitioning changes into thermodynamic and dynamic components. The thermodynamic component, which reflects increases in atmospheric moisture content, results in a relatively uniform intensification of ENSO-driven extreme precipitation variation. In contrast, the dynamic component, which reflects changes in vertical motion, produces strong regional difference in the response to forcing. In some regions, this component amplifies the thermodynamic-induced changes, while in others, it offsets them or even results in reduction in extreme precipitation variation.
Abstract
Since the 1950s, precipitation has been measured at national weather stations in China using national standard precipitation gauges. Gauges without a wind fence can significantly underestimate precipitation amounts, while this undercatch bias is closely related to surface wind speed and precipitation type. The observed surface wind speed across China has substantially declined during the past decades. Therefore, this study investigated the wind-induced error of the observed precipitation and its impact on regional and national mean trends in precipitation over China due to the reduction in surface wind speed. It was found that the wind-induced error for the mean annual precipitation nationwide was 29.28 mm yr−1, accounting for 3.92% of total precipitation amount. The variation of precipitation at the regional scale was large but the trends were both positive and negative, approximately cancelling at the national level and resulting in a small national mean trend. The raw observation data showed that the national mean precipitation increased at a rate of 1.85 mm yr−1 (10 a)−1 from 1960 to 2018, which was reduced to 0.33 mm yr−1 (10 a)−1 after correction, demonstrating that the correction of wind-induced error had an important impact on the trend of annual precipitation. Meanwhile, the reduction of surface wind speed was consistent at both the regional and national levels. On average, the wind-induced errors decreased at rates of −1.52, −1.34, and −0.14 mm yr−1 (10 a)−1 for total precipitation, rainfall, and snowfall, respectively. It illustrates that the decreases of the wind-induced error result in the increasing precipitation of raw observation.
Abstract
Since the 1950s, precipitation has been measured at national weather stations in China using national standard precipitation gauges. Gauges without a wind fence can significantly underestimate precipitation amounts, while this undercatch bias is closely related to surface wind speed and precipitation type. The observed surface wind speed across China has substantially declined during the past decades. Therefore, this study investigated the wind-induced error of the observed precipitation and its impact on regional and national mean trends in precipitation over China due to the reduction in surface wind speed. It was found that the wind-induced error for the mean annual precipitation nationwide was 29.28 mm yr−1, accounting for 3.92% of total precipitation amount. The variation of precipitation at the regional scale was large but the trends were both positive and negative, approximately cancelling at the national level and resulting in a small national mean trend. The raw observation data showed that the national mean precipitation increased at a rate of 1.85 mm yr−1 (10 a)−1 from 1960 to 2018, which was reduced to 0.33 mm yr−1 (10 a)−1 after correction, demonstrating that the correction of wind-induced error had an important impact on the trend of annual precipitation. Meanwhile, the reduction of surface wind speed was consistent at both the regional and national levels. On average, the wind-induced errors decreased at rates of −1.52, −1.34, and −0.14 mm yr−1 (10 a)−1 for total precipitation, rainfall, and snowfall, respectively. It illustrates that the decreases of the wind-induced error result in the increasing precipitation of raw observation.
Abstract
Mesoscale eddies are ubiquitous features of the global ocean circulation. Traditionally, anticyclonic eddies are thought to be associated with positive temperature anomalies while cyclonic eddies are associated with negative temperature anomalies. However, our recent study found that about one-fifth of the eddies identified from global satellite observations are cold-core anticyclonic eddies (CAEs) and warm-core cyclonic eddies (WCEs). Here we show that in the tropical oceans where the probabilities of CAEs and WCEs are high, there are significantly more CAEs and WCEs in summer than in winter. We conduct a suite of idealized numerical model experiments initialized with composite eddy structures obtained from Argo profiles as well as a heat budget analysis. The results highlight the key role of relative wind-stress-induced Ekman pumping, surface mixed layer depth, and vertical entrainment in the formation and seasonal cycle of these unconventional eddies. The relative wind stress is found to be particularly effective in converting conventional eddies into CAEs or WCEs when the surface mixed layer is shallow. The abundance of CAEs and WCEs in the global ocean calls for further research on this topic.
Abstract
Mesoscale eddies are ubiquitous features of the global ocean circulation. Traditionally, anticyclonic eddies are thought to be associated with positive temperature anomalies while cyclonic eddies are associated with negative temperature anomalies. However, our recent study found that about one-fifth of the eddies identified from global satellite observations are cold-core anticyclonic eddies (CAEs) and warm-core cyclonic eddies (WCEs). Here we show that in the tropical oceans where the probabilities of CAEs and WCEs are high, there are significantly more CAEs and WCEs in summer than in winter. We conduct a suite of idealized numerical model experiments initialized with composite eddy structures obtained from Argo profiles as well as a heat budget analysis. The results highlight the key role of relative wind-stress-induced Ekman pumping, surface mixed layer depth, and vertical entrainment in the formation and seasonal cycle of these unconventional eddies. The relative wind stress is found to be particularly effective in converting conventional eddies into CAEs or WCEs when the surface mixed layer is shallow. The abundance of CAEs and WCEs in the global ocean calls for further research on this topic.
Abstract
IMERG provides state-of-the-art satellite-based precipitation estimates that combine observations from multiple satellite platforms. This study evaluates IMERG products by examining hydrologic simulations of streamflow at a range of spatial scales. The main objective of this study is to assess the predictive utility of the near-real-time product (IMERG-Early). The assessment also includes the IMERG-Final product that is not available in real time. The authors used MRMS precipitation estimates and USGS streamflow observation data as references for the precipitation and streamflow evaluations during a 5-yr period (2016–20). The precipitation evaluation results show that IMERG-Early yields significant overestimations, particularly during warm months, with higher variability in its conditional distributions, whereas the performance of IMERG-Final seems unbiased. The authors performed hydrologic simulations using the Iowa Flood Center’s Hillslope Link Model with three precipitation forcing products, i.e., MRMS, IMERG-Early, and IMERG-Final. The simulation results reveal that IMERG-Early leads to high hit and false alarm rates due to its overestimation in precipitation and has almost no skill, as measured by the overall performance metric Kling–Gupta efficiency (KGE), in streamflow prediction regarding basin scales ranging from 10 to 30 000 km2. This indicates that the product requires a bias correction before it is useful for real-time flood prediction. The streamflow prediction performance of IMERG-Final seems comparable to that of MRMS at spatial scales greater than 100 km2. This scale limitation is attributable to the IMERG’s product spatial resolution that is inadequate to capture the small-scale variability of precipitation.
Abstract
IMERG provides state-of-the-art satellite-based precipitation estimates that combine observations from multiple satellite platforms. This study evaluates IMERG products by examining hydrologic simulations of streamflow at a range of spatial scales. The main objective of this study is to assess the predictive utility of the near-real-time product (IMERG-Early). The assessment also includes the IMERG-Final product that is not available in real time. The authors used MRMS precipitation estimates and USGS streamflow observation data as references for the precipitation and streamflow evaluations during a 5-yr period (2016–20). The precipitation evaluation results show that IMERG-Early yields significant overestimations, particularly during warm months, with higher variability in its conditional distributions, whereas the performance of IMERG-Final seems unbiased. The authors performed hydrologic simulations using the Iowa Flood Center’s Hillslope Link Model with three precipitation forcing products, i.e., MRMS, IMERG-Early, and IMERG-Final. The simulation results reveal that IMERG-Early leads to high hit and false alarm rates due to its overestimation in precipitation and has almost no skill, as measured by the overall performance metric Kling–Gupta efficiency (KGE), in streamflow prediction regarding basin scales ranging from 10 to 30 000 km2. This indicates that the product requires a bias correction before it is useful for real-time flood prediction. The streamflow prediction performance of IMERG-Final seems comparable to that of MRMS at spatial scales greater than 100 km2. This scale limitation is attributable to the IMERG’s product spatial resolution that is inadequate to capture the small-scale variability of precipitation.
