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-year period versus nineteen 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 timescale, meteorological season, PMW source, QI, and land surface type. Results indicate that: (1) the cold season’s ( Nov - Apr ) larger relative bias can be mitigated via backward morphing; (2) IMERG 6-hour 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 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.
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-year period versus nineteen 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 timescale, meteorological season, PMW source, QI, and land surface type. Results indicate that: (1) the cold season’s ( Nov - Apr ) larger relative bias can be mitigated via backward morphing; (2) IMERG 6-hour 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 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.
Abstract
The role of time-dependent freezing of ice nucleating particles (INPs) is evaluated with the ‘Aerosol-Cloud’ (AC) model in: 1) deep convection observed over Oklahoma during the Midlatitude Continental Convective Cloud Experiment (MC3E), 2) orographic clouds observed over North California during the Atmospheric Radiation Measurement (ARM) Cloud Aerosol Precipitation Experiment (ACAPEX), and 3) supercooled, stratiform clouds over the UK, observed during the Aerosol Properties, Processes And Influences on the Earth’s climate (APPRAISE) campaign. AC uses the dynamical core of the WRF model and has hybrid bin/bulk microphysics and a 3D mesoscale domain. AC is validated against coincident aircraft, ground-based and satellite observations for all three cases. Filtered concentrations of ice (> 0.1 to 0.2 mm) agree with those observed at all sampled levels.
AC forms ice heterogeneously through condensation, contact, deposition, and immersion freezing. AC predicts the INP activity of various types of aerosol particles with an empirical parameterization (EP), which follows a singular approach (no time dependence). Here, the EP is modified to represent time-dependent INP activity by a purely empirical approach, using our published laboratory observations of time-dependent INP activity.
In all simulated clouds, the inclusion of time dependence increases the predicted INP activity of mineral dust particles by 0.5 to 1 order of magnitude. However, there is little impact on the cloud glaciation because the total ice is mostly (80-90%) from secondary ice production (SIP) at levels warmer than about −36°C. The Hallett-Mossop process and fragmentation in ice-ice collisions together initiate about 70% of the total ice, whereas fragmentation during both raindrop freezing and sublimation contributes < 10%. Overall, total ice concentrations and SIP are unaffected by time-dependent INP activity.
Abstract
The role of time-dependent freezing of ice nucleating particles (INPs) is evaluated with the ‘Aerosol-Cloud’ (AC) model in: 1) deep convection observed over Oklahoma during the Midlatitude Continental Convective Cloud Experiment (MC3E), 2) orographic clouds observed over North California during the Atmospheric Radiation Measurement (ARM) Cloud Aerosol Precipitation Experiment (ACAPEX), and 3) supercooled, stratiform clouds over the UK, observed during the Aerosol Properties, Processes And Influences on the Earth’s climate (APPRAISE) campaign. AC uses the dynamical core of the WRF model and has hybrid bin/bulk microphysics and a 3D mesoscale domain. AC is validated against coincident aircraft, ground-based and satellite observations for all three cases. Filtered concentrations of ice (> 0.1 to 0.2 mm) agree with those observed at all sampled levels.
AC forms ice heterogeneously through condensation, contact, deposition, and immersion freezing. AC predicts the INP activity of various types of aerosol particles with an empirical parameterization (EP), which follows a singular approach (no time dependence). Here, the EP is modified to represent time-dependent INP activity by a purely empirical approach, using our published laboratory observations of time-dependent INP activity.
In all simulated clouds, the inclusion of time dependence increases the predicted INP activity of mineral dust particles by 0.5 to 1 order of magnitude. However, there is little impact on the cloud glaciation because the total ice is mostly (80-90%) from secondary ice production (SIP) at levels warmer than about −36°C. The Hallett-Mossop process and fragmentation in ice-ice collisions together initiate about 70% of the total ice, whereas fragmentation during both raindrop freezing and sublimation contributes < 10%. Overall, total ice concentrations and SIP are unaffected by time-dependent INP activity.
Abstract
Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an initial step toward untangling sources of uncertainty in convective weather regimes, we evaluate a novel Bayesian data assimilation methodology based on particle filtering within a WRF ensemble analysis and forecasting system. Unlike most geophysical DA methods, the particle filter (PF) represents prior and posterior error distributions non-parametrically rather than assuming a Gaussian distribution and can accept any type of likelihood function. This approach is known to reduce bias introduced by Gaussian approximations in low dimensional and idealized contexts. The form of PF used in this research adopts a dimension-reduction strategy, making it affordable for typical weather applications. The present study examines posterior ensemble members and forecasts for select severe weather events between 2019 — 2020, comparing results from the PF with those from an Ensemble Kalman Filter (EnKF). We find that assimilating with a PF produces posterior quantities for microphysical variables that are more consistent with model climatology than comparable quantities from an EnKF, which we attribute to a reduction in DA bias. These differences are significant enough to impact the dynamic evolution of convective systems via cold pool strength and propagation, with impacts to forecast verification scores depending on the particular microphysics scheme. Our findings have broad implications for future approaches to the selection of physical parameterization schemes and parameter estimation within pre-existing data assimilation frameworks.
Abstract
Obtaining a faithful probabilistic depiction of moist convection is complicated by unknown errors in subgrid-scale physical parameterization schemes, invalid assumptions made by data assimilation (DA) techniques, and high system dimensionality. As an initial step toward untangling sources of uncertainty in convective weather regimes, we evaluate a novel Bayesian data assimilation methodology based on particle filtering within a WRF ensemble analysis and forecasting system. Unlike most geophysical DA methods, the particle filter (PF) represents prior and posterior error distributions non-parametrically rather than assuming a Gaussian distribution and can accept any type of likelihood function. This approach is known to reduce bias introduced by Gaussian approximations in low dimensional and idealized contexts. The form of PF used in this research adopts a dimension-reduction strategy, making it affordable for typical weather applications. The present study examines posterior ensemble members and forecasts for select severe weather events between 2019 — 2020, comparing results from the PF with those from an Ensemble Kalman Filter (EnKF). We find that assimilating with a PF produces posterior quantities for microphysical variables that are more consistent with model climatology than comparable quantities from an EnKF, which we attribute to a reduction in DA bias. These differences are significant enough to impact the dynamic evolution of convective systems via cold pool strength and propagation, with impacts to forecast verification scores depending on the particular microphysics scheme. Our findings have broad implications for future approaches to the selection of physical parameterization schemes and parameter estimation within pre-existing data assimilation frameworks.
Abstract
A Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) follow-on constellation, COSMIC-2, was successfully launched into equatorial orbit on June 24, 2019. With an increased signal-to-noise ratio due to improved receivers and digital beam-steering antennas, COSMIC-2 is producing about 5,000 high-quality radio-occultation (RO) profiles daily over the tropics and subtropics. The initial evaluation of the impact of assimilating COSMIC-2 into NOAA’s Global Forecast System (GFS) showed mixed results, and adjustments to quality control procedures and observation error characteristics had to be made prior to the assimilation of this dataset in the operational configuration in May 2020. Additional changes in the GFS that followed this initial operational implementation resulted in a larger percentage of rejection (~ 90 %) of all RO observations, including COSMIC-2, in the mid-lower troposphere. Since then, two software upgrades directly related to the assimilation of RO bending angle observations were developed. These improvements aimed at optimizing the utilization of COSMIC-2 and other RO observations to improve global weather analyses and forecasts. The first upgrade was implemented operationally in September 2021 and the second one in November 2022. This study describes both RO software upgrades and evaluates the impact of COSMIC-2 with this most recently improved configuration. Specifically, we show that the assimilation of COSMIC-2 observations has a significant impact in improving temperature and winds in the tropics, though benefits also extend to the extra-tropical latitudes.
Abstract
A Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) follow-on constellation, COSMIC-2, was successfully launched into equatorial orbit on June 24, 2019. With an increased signal-to-noise ratio due to improved receivers and digital beam-steering antennas, COSMIC-2 is producing about 5,000 high-quality radio-occultation (RO) profiles daily over the tropics and subtropics. The initial evaluation of the impact of assimilating COSMIC-2 into NOAA’s Global Forecast System (GFS) showed mixed results, and adjustments to quality control procedures and observation error characteristics had to be made prior to the assimilation of this dataset in the operational configuration in May 2020. Additional changes in the GFS that followed this initial operational implementation resulted in a larger percentage of rejection (~ 90 %) of all RO observations, including COSMIC-2, in the mid-lower troposphere. Since then, two software upgrades directly related to the assimilation of RO bending angle observations were developed. These improvements aimed at optimizing the utilization of COSMIC-2 and other RO observations to improve global weather analyses and forecasts. The first upgrade was implemented operationally in September 2021 and the second one in November 2022. This study describes both RO software upgrades and evaluates the impact of COSMIC-2 with this most recently improved configuration. Specifically, we show that the assimilation of COSMIC-2 observations has a significant impact in improving temperature and winds in the tropics, though benefits also extend to the extra-tropical latitudes.
Abstract
The Arctic sea ice decline and associated change in maritime accessibility have created a pressing need for sea ice thickness (SIT) predictions. This study developed a linear Markov model for the seasonal prediction of model assimilated SIT. It tested the performance of physically relevant predictors by a series of sensitivity tests. As measured by the anomaly correlation coefficient (ACC) and root mean square error (RMSE), the SIT prediction skill was evaluated in different Arctic regions and across all seasons. The results show that SIT prediction has better skill in the cold season than in the warm season. The model performs best in the Arctic basin up to 12 months in advance with ACCs of 0.7 to 0.8. Linear trend contributions to model skill increase with lead months. Although monthly SIT trends contribute largely to the model skill, the model remains skillful up to 2-month leads with ACCs of 0.6 for detrended SIT predictions in many Arctic regions. In addition, the Markov model's skill generally outperforms an anomaly persistence forecast even after all trends were removed. It also shows that, apart from SIT itself, upper ocean heat content (OHC) generally contributes more to SIT prediction skill than other variables. SIC is a relatively less sensitive predictor for SIT prediction skill than OHC. Moreover, the Markov model can capture the melt-to-growth season reemergence of SIT predictability and does not show a spring predictability barrier, which has previously been observed in regional dynamical model forecasts of September sea ice area, suggesting that the Markov model is an effective tool for SIT seasonal predictions.
Abstract
The Arctic sea ice decline and associated change in maritime accessibility have created a pressing need for sea ice thickness (SIT) predictions. This study developed a linear Markov model for the seasonal prediction of model assimilated SIT. It tested the performance of physically relevant predictors by a series of sensitivity tests. As measured by the anomaly correlation coefficient (ACC) and root mean square error (RMSE), the SIT prediction skill was evaluated in different Arctic regions and across all seasons. The results show that SIT prediction has better skill in the cold season than in the warm season. The model performs best in the Arctic basin up to 12 months in advance with ACCs of 0.7 to 0.8. Linear trend contributions to model skill increase with lead months. Although monthly SIT trends contribute largely to the model skill, the model remains skillful up to 2-month leads with ACCs of 0.6 for detrended SIT predictions in many Arctic regions. In addition, the Markov model's skill generally outperforms an anomaly persistence forecast even after all trends were removed. It also shows that, apart from SIT itself, upper ocean heat content (OHC) generally contributes more to SIT prediction skill than other variables. SIC is a relatively less sensitive predictor for SIT prediction skill than OHC. Moreover, the Markov model can capture the melt-to-growth season reemergence of SIT predictability and does not show a spring predictability barrier, which has previously been observed in regional dynamical model forecasts of September sea ice area, suggesting that the Markov model is an effective tool for SIT seasonal predictions.
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 reveals 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 centred 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.
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 reveals 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 centred 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.
Abstract
The Congo Basin is severely understudied compared to other tropical regions; this is partly due to the lack of meteorological stations and the ubiquitous cloudiness hampering the use of remote-sensing products. Clustering of small-scale agricultural deforestation events within the Basin may result in deforestation on scales that are atmospherically important. This study uses 500 m MODIS data and the Global Forest Change dataset (GFC) to detect deforestation at a monthly and sub-km scale and to quantify how deforestation impacts vegetation proxies (VPs) within the Basin, the timescales over which these changes persist, and how they’re affected by the deforestation driver.
Missing MODIS data has meant that a new method, based on two-date image differencing, was developed to detect deforestation at a monthly scale. Evaluation against the yearly GFC data shows that the highest detection rate was 79% for clearing sizes larger than 500 m2. Recovery to pre-deforestation levels occurred faster than expected; analysis of post-deforestation evolution of the VPs found 66% of locations recovered within a year. Separation by land-cover type also showed unexpected regrowth as over 50% of rural complex and plantation land recovered within a year. The fallow period in the study region was typically short; by the 6th year after the initial deforestation event, ~88% of the locations underwent a further considerable drop. These results show the importance of fine spatial and temporal information to assess Congo Basin deforestation and highlight the large differences in the impacts of land-use change compared to other rainforests.
Abstract
The Congo Basin is severely understudied compared to other tropical regions; this is partly due to the lack of meteorological stations and the ubiquitous cloudiness hampering the use of remote-sensing products. Clustering of small-scale agricultural deforestation events within the Basin may result in deforestation on scales that are atmospherically important. This study uses 500 m MODIS data and the Global Forest Change dataset (GFC) to detect deforestation at a monthly and sub-km scale and to quantify how deforestation impacts vegetation proxies (VPs) within the Basin, the timescales over which these changes persist, and how they’re affected by the deforestation driver.
Missing MODIS data has meant that a new method, based on two-date image differencing, was developed to detect deforestation at a monthly scale. Evaluation against the yearly GFC data shows that the highest detection rate was 79% for clearing sizes larger than 500 m2. Recovery to pre-deforestation levels occurred faster than expected; analysis of post-deforestation evolution of the VPs found 66% of locations recovered within a year. Separation by land-cover type also showed unexpected regrowth as over 50% of rural complex and plantation land recovered within a year. The fallow period in the study region was typically short; by the 6th year after the initial deforestation event, ~88% of the locations underwent a further considerable drop. These results show the importance of fine spatial and temporal information to assess Congo Basin deforestation and highlight the large differences in the impacts of land-use change compared to other rainforests.
Abstract
Previous studies show that some soil moisture products have a good agreement with in situ measurements on the Tibetan Plateau (TP). However, the soil moisture response to precipitation variability in different products is yet to be assessed. In this study, we focus on the soil moisture response to precipitation variability across weekly to decadal time scales in satellite observations and reanalyses. The response of soil moisture to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. Using June 2009 as an example, weekly mean anomalous soil moisture varies by up to 25% between products. Across decadal time scales, soil moisture trends vary spatially and across different products. In light of the soil moisture response to precipitation at different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s (ESA) Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Even products that strongly agree with in situ observations on daily time scales, such as the Global Land Data Assimilation System (GLDAS), show inconsistent soil moisture responses to decadal precipitation trends. European Centre for Medium-Range Weather Forecasts (ECWMF) reanalysis products have a relatively poor agreement with in situ observations compared to satellite observations and land-only reanalysis datasets. Unsurprisingly, products which show a consistent soil moisture response to precipitation variability are those mostly aligned to observations or describe the physical relationship between soil moisture and precipitation well.
Significance Statement
We focus on soil moisture responses to precipitation across weekly to decadal time scales by using multiple satellite observations and reanalysis products. Several soil moisture products illustrate good consistency with in situ measurements in different biomes on the Tibetan Plateau, while the response to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. The response of soil moisture to decadal trends in boreal summer precipitation varies spatially and temporally across products. Based on the assessments of the soil moisture response to precipitation variability across different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Reanalysis products from ECWMF show inconsistent soil moisture responses to precipitation. The results highlight the importance of using multiple soil moisture products to understand the surface response to precipitation variability and to inform developments in soil moisture modeling and satellite retrievals.
Abstract
Previous studies show that some soil moisture products have a good agreement with in situ measurements on the Tibetan Plateau (TP). However, the soil moisture response to precipitation variability in different products is yet to be assessed. In this study, we focus on the soil moisture response to precipitation variability across weekly to decadal time scales in satellite observations and reanalyses. The response of soil moisture to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. Using June 2009 as an example, weekly mean anomalous soil moisture varies by up to 25% between products. Across decadal time scales, soil moisture trends vary spatially and across different products. In light of the soil moisture response to precipitation at different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s (ESA) Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Even products that strongly agree with in situ observations on daily time scales, such as the Global Land Data Assimilation System (GLDAS), show inconsistent soil moisture responses to decadal precipitation trends. European Centre for Medium-Range Weather Forecasts (ECWMF) reanalysis products have a relatively poor agreement with in situ observations compared to satellite observations and land-only reanalysis datasets. Unsurprisingly, products which show a consistent soil moisture response to precipitation variability are those mostly aligned to observations or describe the physical relationship between soil moisture and precipitation well.
Significance Statement
We focus on soil moisture responses to precipitation across weekly to decadal time scales by using multiple satellite observations and reanalysis products. Several soil moisture products illustrate good consistency with in situ measurements in different biomes on the Tibetan Plateau, while the response to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. The response of soil moisture to decadal trends in boreal summer precipitation varies spatially and temporally across products. Based on the assessments of the soil moisture response to precipitation variability across different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Reanalysis products from ECWMF show inconsistent soil moisture responses to precipitation. The results highlight the importance of using multiple soil moisture products to understand the surface response to precipitation variability and to inform developments in soil moisture modeling and satellite retrievals.
Abstract
The North Pacific storm-track activity is suppressed substantially under the excessively strong westerlies to form a distinct minimum in midwinter, which seems inconsistent with linear baroclinic instability theory. This “midwinter minimum” of the storm-track activity has been intensively investigated for decades as a test case for storm-track dynamics. However, the mechanisms controlling it are yet to be fully unveiled and are still under debate. Here we investigate the detailed seasonal evolution of the climatological density of surface migratory anticyclones over the North Pacific, in comparison with its counterpart for cyclones, based on a Lagrangian tracking algorithm. We demonstrate that the frequency of surface cyclones over the North Pacific maximizes in midwinter, whereas that of anticyclones exhibits a distinct midwinter minimum under the upstream influence, especially from the Japan Sea region. In midwinter, it is only on such a rare occasion that prominent weakening of the East Asian winter monsoon allows a migratory surface anticyclone to form over the Japan Sea, despite the unfavorable climatological-mean conditions due to persistent monsoonal cold-air outbreaks and the excessively strong upper-tropospheric westerlies. The midwinter minimum of the North Pacific anticyclone density suggests that anticyclones are likely the key to understanding the midwinter minimum of the North Pacific storm-track activity as measured by Eulerian eddy statistics.
Abstract
The North Pacific storm-track activity is suppressed substantially under the excessively strong westerlies to form a distinct minimum in midwinter, which seems inconsistent with linear baroclinic instability theory. This “midwinter minimum” of the storm-track activity has been intensively investigated for decades as a test case for storm-track dynamics. However, the mechanisms controlling it are yet to be fully unveiled and are still under debate. Here we investigate the detailed seasonal evolution of the climatological density of surface migratory anticyclones over the North Pacific, in comparison with its counterpart for cyclones, based on a Lagrangian tracking algorithm. We demonstrate that the frequency of surface cyclones over the North Pacific maximizes in midwinter, whereas that of anticyclones exhibits a distinct midwinter minimum under the upstream influence, especially from the Japan Sea region. In midwinter, it is only on such a rare occasion that prominent weakening of the East Asian winter monsoon allows a migratory surface anticyclone to form over the Japan Sea, despite the unfavorable climatological-mean conditions due to persistent monsoonal cold-air outbreaks and the excessively strong upper-tropospheric westerlies. The midwinter minimum of the North Pacific anticyclone density suggests that anticyclones are likely the key to understanding the midwinter minimum of the North Pacific storm-track activity as measured by Eulerian eddy statistics.
Abstract
Sustainable development is a challenging field of research, colored by the paradoxes of modernity and development, and the tradeoffs involved in balancing the ‘sustainable’ and ‘development’ side of the various sustainable development goals. We must take these overarching challenges into account when entering a more specific discussion of what a concept of sustainable climate change adaptation may entail. This article reviews the history of this concept, including insights provided by the 10 recent publications in a special collection of WCAS on the topic of sustainable climate change adaptation. This collection reflects on why and how the term sustainable development should be included in our understandings of and efforts towards climate change adaptation and proposes a preliminary framework for distinguishing between conventional and sustainable adaptation.
Abstract
Sustainable development is a challenging field of research, colored by the paradoxes of modernity and development, and the tradeoffs involved in balancing the ‘sustainable’ and ‘development’ side of the various sustainable development goals. We must take these overarching challenges into account when entering a more specific discussion of what a concept of sustainable climate change adaptation may entail. This article reviews the history of this concept, including insights provided by the 10 recent publications in a special collection of WCAS on the topic of sustainable climate change adaptation. This collection reflects on why and how the term sustainable development should be included in our understandings of and efforts towards climate change adaptation and proposes a preliminary framework for distinguishing between conventional and sustainable adaptation.
