Abstract
To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warnings rely heavily on forecasts issued by underlying prediction systems. When deciding which prediction system(s) to utilize when constructing warnings, it is important to compare systems in their ability to forecast the occurrence and severity of high-impact weather events. However, evaluating forecasts for particular outcomes is known to be a challenging task. This is exacerbated further by the fact that high-impact weather often manifests as a result of several confounding features, a realization that has led to considerable research on so-called compound weather events. Both univariate and multivariate methods are therefore required to evaluate forecasts for high-impact weather. In this paper, we discuss weighted verification tools, which allow particular outcomes to be emphasized during forecast evaluation. We review and compare different approaches to construct weighted scoring rules, both in a univariate and multivariate setting, and we leverage existing results on weighted scores to introduce conditional probability integral transform (PIT) histograms, allowing forecast calibration to be assessed conditionally on particular outcomes having occurred. To illustrate the practical benefit afforded by these weighted verification tools, they are employed in a case study to evaluate probabilistic forecasts for extreme heat events issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss).
Abstract
To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warnings rely heavily on forecasts issued by underlying prediction systems. When deciding which prediction system(s) to utilize when constructing warnings, it is important to compare systems in their ability to forecast the occurrence and severity of high-impact weather events. However, evaluating forecasts for particular outcomes is known to be a challenging task. This is exacerbated further by the fact that high-impact weather often manifests as a result of several confounding features, a realization that has led to considerable research on so-called compound weather events. Both univariate and multivariate methods are therefore required to evaluate forecasts for high-impact weather. In this paper, we discuss weighted verification tools, which allow particular outcomes to be emphasized during forecast evaluation. We review and compare different approaches to construct weighted scoring rules, both in a univariate and multivariate setting, and we leverage existing results on weighted scores to introduce conditional probability integral transform (PIT) histograms, allowing forecast calibration to be assessed conditionally on particular outcomes having occurred. To illustrate the practical benefit afforded by these weighted verification tools, they are employed in a case study to evaluate probabilistic forecasts for extreme heat events issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss).
Abstract
Submesoscale baroclinic instabilities have been shown to restratify the surface mixed layer and to seasonally energize submesoscale turbulence in the upper ocean. But do these instabilities also affect the large-scale circulation and stratification of the upper thermocline? This question is addressed for the North Atlantic Subtropical Mode Water region with a series of numerical simulations at varying horizontal grid spacings (16, 8, 4, and 2 km). These simulations are realistically forced and integrated long enough for the thermocline to adjust to the presence or absence of submesoscales. Linear stability analysis indicates that a 2-km grid spacing is sufficient to resolve the most unstable mode of the wintertime mixed layer instability. As the resolution is increased, spectral slopes of horizontal kinetic energy flatten and vertical velocities increase in magnitude, consistent with previous regional and short-time simulations. The equilibrium stratification of the thermocline changes drastically as the grid spacing is refined from 16 to 8 km and mesoscale eddies are fully resolved. The thermocline stratification remains largely unchanged, however, between the 8-, 4-, and 2-km runs. This robustness is argued to arise from a mesoscale constraint on the buoyancy variance budget. Once mesoscale processes are resolved, the rate of mesoscale variance production is largely fixed. This constrains the variance destruction by submesoscale vertical buoyancy fluxes, which thus remain invariant across resolutions. The bulk impact of mixed layer instabilities on upper-ocean stratification in the Subtropical Mode Water region through an enhanced vertical buoyancy flux is therefore captured at 8-km grid spacing, even though the instabilities are severely underresolved.
Abstract
Submesoscale baroclinic instabilities have been shown to restratify the surface mixed layer and to seasonally energize submesoscale turbulence in the upper ocean. But do these instabilities also affect the large-scale circulation and stratification of the upper thermocline? This question is addressed for the North Atlantic Subtropical Mode Water region with a series of numerical simulations at varying horizontal grid spacings (16, 8, 4, and 2 km). These simulations are realistically forced and integrated long enough for the thermocline to adjust to the presence or absence of submesoscales. Linear stability analysis indicates that a 2-km grid spacing is sufficient to resolve the most unstable mode of the wintertime mixed layer instability. As the resolution is increased, spectral slopes of horizontal kinetic energy flatten and vertical velocities increase in magnitude, consistent with previous regional and short-time simulations. The equilibrium stratification of the thermocline changes drastically as the grid spacing is refined from 16 to 8 km and mesoscale eddies are fully resolved. The thermocline stratification remains largely unchanged, however, between the 8-, 4-, and 2-km runs. This robustness is argued to arise from a mesoscale constraint on the buoyancy variance budget. Once mesoscale processes are resolved, the rate of mesoscale variance production is largely fixed. This constrains the variance destruction by submesoscale vertical buoyancy fluxes, which thus remain invariant across resolutions. The bulk impact of mixed layer instabilities on upper-ocean stratification in the Subtropical Mode Water region through an enhanced vertical buoyancy flux is therefore captured at 8-km grid spacing, even though the instabilities are severely underresolved.
Abstract
The Greenland Sea produces a significant portion of the dense water from the Nordic Seas that supplies the lower limb of the Atlantic Meridional Overturning Circulation. Here, we use a continuous 10-year hydrographic record from moored profilers to examine dense-water formation in the central Greenland Sea between 1999 and 2009. Of primary importance for dense-water formation is air-sea heat exchange, and 60–80% of the heat lost to the atmosphere during winter occurs during intense, short-lived events called cold-air outbreaks (CAOs). The long duration and high temporal resolution of the moored record has for the first time facilitated a statistical quantification of the direct impact of CAOs on the wintertime mixed layer in the Greenland Sea. The mixed-layer development can be divided into two phases: a cooling phase and a deepening phase. During the cooling phase (typically between November and January), CAOs cooled the mixed layer by up to 0.08 K per day, depending on the intensity of the events, while the mixed-layer depth remained nearly constant. Later in winter (February–April), heat fluxes during CAOs primarily led to mixed-layer deepening, of up to 38 m per day. Considerable variability was observed in the mixed-layer response, indicating that lateral fluxes of heat and salt were also important. The magnitude and vertical distributions of these fluxes were quantified, and idealized mixed-layer simulations suggest that their combined effect is a reduction in the mixed-layer depth at the end of winter of up to several hundred meters.
Abstract
The Greenland Sea produces a significant portion of the dense water from the Nordic Seas that supplies the lower limb of the Atlantic Meridional Overturning Circulation. Here, we use a continuous 10-year hydrographic record from moored profilers to examine dense-water formation in the central Greenland Sea between 1999 and 2009. Of primary importance for dense-water formation is air-sea heat exchange, and 60–80% of the heat lost to the atmosphere during winter occurs during intense, short-lived events called cold-air outbreaks (CAOs). The long duration and high temporal resolution of the moored record has for the first time facilitated a statistical quantification of the direct impact of CAOs on the wintertime mixed layer in the Greenland Sea. The mixed-layer development can be divided into two phases: a cooling phase and a deepening phase. During the cooling phase (typically between November and January), CAOs cooled the mixed layer by up to 0.08 K per day, depending on the intensity of the events, while the mixed-layer depth remained nearly constant. Later in winter (February–April), heat fluxes during CAOs primarily led to mixed-layer deepening, of up to 38 m per day. Considerable variability was observed in the mixed-layer response, indicating that lateral fluxes of heat and salt were also important. The magnitude and vertical distributions of these fluxes were quantified, and idealized mixed-layer simulations suggest that their combined effect is a reduction in the mixed-layer depth at the end of winter of up to several hundred meters.
Abstract
In addition to their well-known seasonal cycle, eastern boundary upwelling systems (EBUS) undergo modulation on shorter synoptic to intraseasonal time scales. Energetic intensifications and relaxations of upwelling-favorable winds with 5–10-day typical time scales can impact the EBUS dynamics and biogeochemical functioning. In this work the dynamical effects of wind-forced synoptic fluctuations on the South Senegalese Upwelling Sector (SSUS) are characterized. The region geomorphology is unique with its wide continental shelf and a major coastline discontinuity at its northern edge. The ocean response to synoptic events is explored using a modeling framework that involves applying idealized synoptic wind intensification or relaxation to a five-member climatological SSUS ensemble run. Model evaluation against sparse midshelf in situ observations indicates qualitative agreement in terms of synoptic variability of temperature, stratification, and ocean currents, despite a moderate but systematic bias in current intensity. Modeled synoptic wind and heat flux fluctuations produce clear modulations of all dynamical variables with robust SSUS-scale and mesoscale spatial patterns. A mixed layer heat budget analysis is performed over the continental shelf to uncover the dominant processes involved in SSUS synoptic variability. Modulations of horizontal advection and atmospheric forcing are the leading-order drivers of heat changes during either wind intensification or relaxation while vertical dynamics is of primary importance only in a very localized area. Also, modest asymmetries in the oceanic responses to upwelling intensification and relaxation are only identified for meridional velocities. This brings partial support to the hypothesis that synoptic variability has a modest net effect on the climatological state and functioning of upwelling systems dynamics.
Abstract
In addition to their well-known seasonal cycle, eastern boundary upwelling systems (EBUS) undergo modulation on shorter synoptic to intraseasonal time scales. Energetic intensifications and relaxations of upwelling-favorable winds with 5–10-day typical time scales can impact the EBUS dynamics and biogeochemical functioning. In this work the dynamical effects of wind-forced synoptic fluctuations on the South Senegalese Upwelling Sector (SSUS) are characterized. The region geomorphology is unique with its wide continental shelf and a major coastline discontinuity at its northern edge. The ocean response to synoptic events is explored using a modeling framework that involves applying idealized synoptic wind intensification or relaxation to a five-member climatological SSUS ensemble run. Model evaluation against sparse midshelf in situ observations indicates qualitative agreement in terms of synoptic variability of temperature, stratification, and ocean currents, despite a moderate but systematic bias in current intensity. Modeled synoptic wind and heat flux fluctuations produce clear modulations of all dynamical variables with robust SSUS-scale and mesoscale spatial patterns. A mixed layer heat budget analysis is performed over the continental shelf to uncover the dominant processes involved in SSUS synoptic variability. Modulations of horizontal advection and atmospheric forcing are the leading-order drivers of heat changes during either wind intensification or relaxation while vertical dynamics is of primary importance only in a very localized area. Also, modest asymmetries in the oceanic responses to upwelling intensification and relaxation are only identified for meridional velocities. This brings partial support to the hypothesis that synoptic variability has a modest net effect on the climatological state and functioning of upwelling systems dynamics.
Abstract
The Tigris–Euphrates dryland river basin has experienced a declining trend in terrestrial water storage (TWS) from April 2002 to June 2017. Using satellite observations and a process-based land surface model, we find that climate variations and direct human interventions explain ∼61% (−0.57 mm month−1) and ∼39% (−0.36 mm month−1) of the negative trend, respectively. We further disaggregate the effects of climate variations and find that interannual climate variability contributes substantially (−0.27 mm month−1) to the negative TWS trend, slightly greater than the decadal climate change (−0.25 mm month−1). Interannual climate variability affects TWS mainly through the nonlinear relationship between monthly TWS dynamics and aridity. Slow recovery of TWS during short wetting periods does not compensate for rapid depletion of TWS through transpiration during prolonged drying periods. Despite enhanced water stress, the dryland ecosystems show slightly enhanced resilience to water stress through greater partitioning of evapotranspiration into transpiration and weak surface “greening” effects. However, the dryland ecosystems are vulnerable to drought impacts. The basin shows straining ecosystem functioning after experiencing a severe drought event. In addition, after the onset of the drought, the dryland ecosystem becomes more sensitive to variations in climate conditions.
Significance Statement
The purpose of the research is to better understand climate impacts on terrestrial water storage over dryland regions with declining water storage. In our study, we disaggregate three components of climate impacts, namely, decadal climate change, interannual variability, and intra-annual variability. We then use observational datasets and a process-based model to quantify their individual effects on water storage. We find that interannual variability is the most significant climatic contributor to the declining water storage, mainly caused by prolonged drought periods and corresponding quick drying rates due to plant transpiration. We also find that the dryland ecosystem is sensitive and vulnerable to severe drought events. This study is important because 1) it provides a framework to investigate climate impacts on water fluxes and storages, 2) it highlights the importance of vegetation dynamics on dryland hydrology, and 3) it emphasizes the negative impacts of extreme hydroclimatological events on ecosystem functioning.
Abstract
The Tigris–Euphrates dryland river basin has experienced a declining trend in terrestrial water storage (TWS) from April 2002 to June 2017. Using satellite observations and a process-based land surface model, we find that climate variations and direct human interventions explain ∼61% (−0.57 mm month−1) and ∼39% (−0.36 mm month−1) of the negative trend, respectively. We further disaggregate the effects of climate variations and find that interannual climate variability contributes substantially (−0.27 mm month−1) to the negative TWS trend, slightly greater than the decadal climate change (−0.25 mm month−1). Interannual climate variability affects TWS mainly through the nonlinear relationship between monthly TWS dynamics and aridity. Slow recovery of TWS during short wetting periods does not compensate for rapid depletion of TWS through transpiration during prolonged drying periods. Despite enhanced water stress, the dryland ecosystems show slightly enhanced resilience to water stress through greater partitioning of evapotranspiration into transpiration and weak surface “greening” effects. However, the dryland ecosystems are vulnerable to drought impacts. The basin shows straining ecosystem functioning after experiencing a severe drought event. In addition, after the onset of the drought, the dryland ecosystem becomes more sensitive to variations in climate conditions.
Significance Statement
The purpose of the research is to better understand climate impacts on terrestrial water storage over dryland regions with declining water storage. In our study, we disaggregate three components of climate impacts, namely, decadal climate change, interannual variability, and intra-annual variability. We then use observational datasets and a process-based model to quantify their individual effects on water storage. We find that interannual variability is the most significant climatic contributor to the declining water storage, mainly caused by prolonged drought periods and corresponding quick drying rates due to plant transpiration. We also find that the dryland ecosystem is sensitive and vulnerable to severe drought events. This study is important because 1) it provides a framework to investigate climate impacts on water fluxes and storages, 2) it highlights the importance of vegetation dynamics on dryland hydrology, and 3) it emphasizes the negative impacts of extreme hydroclimatological events on ecosystem functioning.
Abstract
This study analyzes the potential of deep learning using probabilistic artificial neural networks (ANNs) for postprocessing ensemble precipitation forecasts at four observation locations. We split the precipitation forecast problem into two tasks: estimating the probability of precipitation and predicting the hourly precipitation. We then compare the performance with classical statistical postprocessing (logistical regression and GLM). ANNs show a higher performance at three of the four stations for estimating the probability of precipitation and at all stations for predicting the hourly precipitation. Further, two more general ANN models are trained using the merged data from all four stations. These general ANNs exhibit an increase in performance compared to the station-specific ANNs at most stations. However, they show a significant decay in performance at one of the stations at estimating the hourly precipitation. The general models seem capable of learning meaningful interactions in the data and generalizing these to improve the performance at other sites, which also causes the loss of local information at one station. Thus, this study indicates the potential of deep learning in weather forecasting workflows.
Abstract
This study analyzes the potential of deep learning using probabilistic artificial neural networks (ANNs) for postprocessing ensemble precipitation forecasts at four observation locations. We split the precipitation forecast problem into two tasks: estimating the probability of precipitation and predicting the hourly precipitation. We then compare the performance with classical statistical postprocessing (logistical regression and GLM). ANNs show a higher performance at three of the four stations for estimating the probability of precipitation and at all stations for predicting the hourly precipitation. Further, two more general ANN models are trained using the merged data from all four stations. These general ANNs exhibit an increase in performance compared to the station-specific ANNs at most stations. However, they show a significant decay in performance at one of the stations at estimating the hourly precipitation. The general models seem capable of learning meaningful interactions in the data and generalizing these to improve the performance at other sites, which also causes the loss of local information at one station. Thus, this study indicates the potential of deep learning in weather forecasting workflows.
Abstract
Supertyphoon rainstorms are apposite examples to evaluate the utility of multisource precipitation products in monitoring and forecasting short-duration heavy rainfall and the resulting intense floods. In this study, the record-breaking floods induced by Typhoon Lekima in Jiao River, China, were retrospectively forecasted. The Xinanjiang (XAJ) model was calibrated based on parameter regionalization derived from SOM+k-means clustering. Via XAJ, the performance of the currently prevailing atmosphere reanalysis (CLDASv2 and CMA-CMORP), quantitative precipitation estimation (QPE) (IMERG-ER and PERSIANN-CCS), and quantitative precipitation forecasts (QPFs) (GRAPES_MESO, ECMWF, and GFS) in monitoring and forecasting Lekima rainfall and flood was comprehensively evaluated. A three-component blended ensemble was proposed, by blending QPE nowcasts with the weighted ensemble of QPFs through a transition of the regional GRAPES_MESO, and compared with two conventional two-component blending methods. The results indicated that the parameter regionalization enabled an explicit consideration of the spatial heterogeneity of basin attributes as well as meteorology, resulting in a minimum NSE of 0.81. CLDASv2 and CMA-CMORPH provided superior spatiotemporal accuracy with a structural similarity index up to 0.75 and NSE > 0.9 for the flood simulation. PERSIANN-CCS rainfall and the driven flood were seriously underestimated by 70% and 80%, respectively. The real-time application of QPFs during the Lekima flood provided encouraging results with a lead time of 40 h. The three-component blended ensemble method resulted in more stable and accurate flood forecasts, especially for the flood peak on 9 August, which was improved by 80%. Our results are expected to present support for real-time flood preparation and mitigation with practical significance.
Abstract
Supertyphoon rainstorms are apposite examples to evaluate the utility of multisource precipitation products in monitoring and forecasting short-duration heavy rainfall and the resulting intense floods. In this study, the record-breaking floods induced by Typhoon Lekima in Jiao River, China, were retrospectively forecasted. The Xinanjiang (XAJ) model was calibrated based on parameter regionalization derived from SOM+k-means clustering. Via XAJ, the performance of the currently prevailing atmosphere reanalysis (CLDASv2 and CMA-CMORP), quantitative precipitation estimation (QPE) (IMERG-ER and PERSIANN-CCS), and quantitative precipitation forecasts (QPFs) (GRAPES_MESO, ECMWF, and GFS) in monitoring and forecasting Lekima rainfall and flood was comprehensively evaluated. A three-component blended ensemble was proposed, by blending QPE nowcasts with the weighted ensemble of QPFs through a transition of the regional GRAPES_MESO, and compared with two conventional two-component blending methods. The results indicated that the parameter regionalization enabled an explicit consideration of the spatial heterogeneity of basin attributes as well as meteorology, resulting in a minimum NSE of 0.81. CLDASv2 and CMA-CMORPH provided superior spatiotemporal accuracy with a structural similarity index up to 0.75 and NSE > 0.9 for the flood simulation. PERSIANN-CCS rainfall and the driven flood were seriously underestimated by 70% and 80%, respectively. The real-time application of QPFs during the Lekima flood provided encouraging results with a lead time of 40 h. The three-component blended ensemble method resulted in more stable and accurate flood forecasts, especially for the flood peak on 9 August, which was improved by 80%. Our results are expected to present support for real-time flood preparation and mitigation with practical significance.
Scripps Argo Trajectory-Based Velocity Product: Global Estimates of Absolute Velocity Derived from Core, Biogeochemical, and Deep Argo Float Trajectories at Parking DepthAbstract
Global estimates of absolute velocities can be derived from Argo float trajectories during drift at parking depth. A new velocity dataset developed and maintained at Scripps Institution of Oceanography is presented based on all Core, Biogeochemical, and Deep Argo float trajectories collected between 2001 and 2020. Discrepancies between velocity estimates from the Scripps dataset and other existing products including YoMaHa and ANDRO are associated with quality control criteria, as well as selected parking depth and cycle time. In the Scripps product, over 1.3 million velocity estimates are used to reconstruct a time-mean velocity field for the 800–1200 dbar layer at 1° horizontal resolution. This dataset provides a benchmark to evaluate the veracity of the BRAN2020 reanalysis in representing the observed variability of absolute velocities and offers a compelling opportunity for improved characterization and representation in forecast and reanalysis systems.
Significance Statement
The aim of this study is to provide observation-based estimates of the large-scale, subsurface ocean circulation. We exploit the drift of autonomous profiling floats to carefully isolate the inferred circulation at the parking depth, and combine observations from over 11 000 floats, sampling between 2001 and 2020, to deliver a new dataset with unprecedented accuracy. The new estimates of subsurface currents are suitable for assessing global models, reanalyses, and forecasts, and for constraining ocean circulation in data-assimilating models.
Abstract
Global estimates of absolute velocities can be derived from Argo float trajectories during drift at parking depth. A new velocity dataset developed and maintained at Scripps Institution of Oceanography is presented based on all Core, Biogeochemical, and Deep Argo float trajectories collected between 2001 and 2020. Discrepancies between velocity estimates from the Scripps dataset and other existing products including YoMaHa and ANDRO are associated with quality control criteria, as well as selected parking depth and cycle time. In the Scripps product, over 1.3 million velocity estimates are used to reconstruct a time-mean velocity field for the 800–1200 dbar layer at 1° horizontal resolution. This dataset provides a benchmark to evaluate the veracity of the BRAN2020 reanalysis in representing the observed variability of absolute velocities and offers a compelling opportunity for improved characterization and representation in forecast and reanalysis systems.
Significance Statement
The aim of this study is to provide observation-based estimates of the large-scale, subsurface ocean circulation. We exploit the drift of autonomous profiling floats to carefully isolate the inferred circulation at the parking depth, and combine observations from over 11 000 floats, sampling between 2001 and 2020, to deliver a new dataset with unprecedented accuracy. The new estimates of subsurface currents are suitable for assessing global models, reanalyses, and forecasts, and for constraining ocean circulation in data-assimilating models.
Abstract
Eddies in the northwestern tropical Atlantic Ocean play a crucial role in transporting the South Atlantic Upper Ocean Water to the North Atlantic and connect the Atlantic and the Caribbean Sea. Although surface characteristics of those eddies have been well studied, their vertical structures and governing mechanisms are much less known. Here, using a time-dependent energetics framework based on the multiscale window transform, we examine the seasonal variability of the eddy kinetic energy (EKE) in the northwestern tropical Atlantic. Both altimeter-based data and ocean reanalyses show a substantial EKE seasonal cycle in the North Brazil Current Retroflection (NBCR) region that is mostly trapped in the upper 200 m. In the most energetic NBCR region, the EKE reaches its minimum in April–June and maximum in July–September. By analyzing six ocean reanalysis products, we find that barotropic instability is the controlling mechanism for the seasonal eddy variability in the NBCR region. Nonlocal processes, including advection and pressure work, play opposite roles in the EKE seasonal cycle. In the eastern part of the NBCR region, the EKE seasonal evolution is similar to the NBCR region. However, it is the nonlocal processes that control the EKE seasonality. In the western part of the NBCR region, the EKE magnitude is one order of magnitude smaller than in the NBCR region and shows a different seasonal cycle, which peaks in March and reaches its minimum in October–November. Our results highlight the complex mechanisms governing eddy variability in the northwestern tropical Atlantic and provide insights into their potential changes with changing background conditions.
Abstract
Eddies in the northwestern tropical Atlantic Ocean play a crucial role in transporting the South Atlantic Upper Ocean Water to the North Atlantic and connect the Atlantic and the Caribbean Sea. Although surface characteristics of those eddies have been well studied, their vertical structures and governing mechanisms are much less known. Here, using a time-dependent energetics framework based on the multiscale window transform, we examine the seasonal variability of the eddy kinetic energy (EKE) in the northwestern tropical Atlantic. Both altimeter-based data and ocean reanalyses show a substantial EKE seasonal cycle in the North Brazil Current Retroflection (NBCR) region that is mostly trapped in the upper 200 m. In the most energetic NBCR region, the EKE reaches its minimum in April–June and maximum in July–September. By analyzing six ocean reanalysis products, we find that barotropic instability is the controlling mechanism for the seasonal eddy variability in the NBCR region. Nonlocal processes, including advection and pressure work, play opposite roles in the EKE seasonal cycle. In the eastern part of the NBCR region, the EKE seasonal evolution is similar to the NBCR region. However, it is the nonlocal processes that control the EKE seasonality. In the western part of the NBCR region, the EKE magnitude is one order of magnitude smaller than in the NBCR region and shows a different seasonal cycle, which peaks in March and reaches its minimum in October–November. Our results highlight the complex mechanisms governing eddy variability in the northwestern tropical Atlantic and provide insights into their potential changes with changing background 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 2,000 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 16th 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 3rd, 5th, and 7th centuries CE). We conclude, Czech agricultural production has experienced significant extremes over the past 2,000 years, which includes periods for which there are no modern analogues.
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 2,000 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 16th 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 3rd, 5th, and 7th centuries CE). We conclude, Czech agricultural production has experienced significant extremes over the past 2,000 years, which includes periods for which there are no modern analogues.
